diff --git a/.gitignore b/.gitignore
index f7e73f2..8bccdca 100644
--- a/.gitignore
+++ b/.gitignore
@@ -131,8 +131,13 @@ dmypy.json
 # custom repo information
 westerschelde/
 etna_*/
-herstappe/
-averbode/
 draft/
 other/
 20200302_preps_gig_python.md
+2020-09-17_Sentinel_2_L1C_B02.tiff
+2020-09-17_Sentinel_2_L1C_B03.tiff
+argo_float_4901412.npz
+sst.mnmean.v4.nc
+DHMVIIDSMRAS5m_k30/
+*/data/raw/
+zwalmbeek/
diff --git a/README.md b/README.md
index 92ddc37..a45d8cd 100644
--- a/README.md
+++ b/README.md
@@ -1,2 +1,35 @@
-# DS-python-geospatial
-Python for GIS and Geoscience - specialist course Doctoral schools of Ghent University
+# Python for GIS and Geoscience
+
+## Introduction
+
+An important aspect of daily work in geographic information science and earth sciences is the handling of potentially large amounts of data. Reading in spatial data, exploring the data, creating visualisations and preparing the data for further analysis may become tedious tasks. Hence, increasing efficiency and reproducibility in this process without the need of a GUI interface is beneficial for many scientists. The usage of high-level scripting languages such as R and Python are increasingly popular for these tasks thanks to the development of GIS oriented packages.
+
+This course trains students to use Python effectively to do these tasks, with a focus on geospatial data. It covers both vector and raster data. The course focuses on introducing the main Python packages for handling such data (GeoPandas, Numpy and Rasterio, Xarray) and how to use those packages for importing, exploring, visualizing and manipulating geospatial data. It is the aim to give the students an understanding of the data structures used in Python to represent geospatial data (geospatial dataframes, (multi-dimensional) arrays and composite netCDF-like multi-dimensional datasets), while also providing pointers to the broader ecosystem of Python packages for GIS and geosciences.
+
+The course has been developed as a specialist course for the Doctoral schools of Ghent University, but can be taught to others upon request.
+
+## Aim & scope
+
+This course targets researchers that want to enhance their general data manipulation and analysis skills in Python specifically for handling geospatial data.
+
+The course does not aim to provide a course in specific spatial analysis and statistics, cartography, remote sensing, OGC web services, ... or general Geographical Information Management (GIS). It aims to provide researchers the means to effectively tackle commonly encountered spatial data handling tasks in order to increase the overall efficiency of the research. The course does not tackle desktop GIS Python extensions such as arcpy or pyqgis.
+
+## Getting started
+
+The course uses Python 3, data analysis packages such as Pandas, Numpy and Matplotlib and geospatial packages such as GeoPandas and Xarray. To install the required libraries, we recommend Anaconda or miniconda or another Python distribution that includes the scientific libraries (this recommendation applies to all platforms, so for both Window, Linux and Mac).
+
+For detailed instructions to get started on your local machine , see the [setup instructions](./docs/setup.md).
+
+In case you do not want to install everything and just want to try out the course material, use the environment setup by Binder [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/plovercode/DS-python-geospatial/HEAD?urlpath=lab/tree/notebooks) and open de notebooks rightaway.
+
+
+## Contributing
+
+Found any typo or have a suggestion, see [how to contribute](./CONTRIBUTING.md).
+
+
+## Meta
+Authors: Joris Van den Bossche, Stijn Van Hoey
+
+<img src="img/logo_flanders+richtingmorgen.png" width="79%">
+<img src="img/doctoralschoolsprofiel_hq_rgb_web.png" width="20%">
diff --git a/_config.yml b/_config.yml
deleted file mode 100644
index 631e815..0000000
--- a/_config.yml
+++ /dev/null
@@ -1,5 +0,0 @@
-title: Python for GIS and Geoscience
-logo: 
-description: Specialist course Doctoral schools of Ghent University
-show_downloads: true
-theme: jekyll-theme-minimal
diff --git a/_solved/00-jupyter_introduction.ipynb b/_solved/00-jupyter_introduction.ipynb
new file mode 100644
index 0000000..b841fb6
--- /dev/null
+++ b/_solved/00-jupyter_introduction.ipynb
@@ -0,0 +1,584 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<p><font size=\"6\"><b>Jupyter notebook INTRODUCTION </b></font></p>\n",
+    "\n",
+    "\n",
+    "> *DS Python for GIS and Geoscience*  \n",
+    "> *October, 2025*\n",
+    ">\n",
+    "> *© 2025, Joris Van den Bossche and Stijn Van Hoey  (<mailto:jorisvandenbossche@gmail.com>, <mailto:stijnvanhoey@gmail.com>). Licensed under [CC BY 4.0 Creative Commons](http://creativecommons.org/licenses/by/4.0/)*\n",
+    "\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<big><center>To run a cell: push the start triangle in the menu or type **SHIFT + ENTER/RETURN** <br>\n",
+    "![](../img/notebook/shiftenter.jpg)\n",
+    "</big></center>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Notebook cell types"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We will work in **Jupyter notebooks** during this course. A notebook is a collection of `cells`, that can contain different content:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Code"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Code cell, then we are using python\n",
+    "print('Hello DS')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "DS = 10\n",
+    "print(DS + 5) # Yes, we advise to use Python 3 (!)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Writing code is what you will do most during this course!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Markdown"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Text cells, using Markdown syntax. With the syntax, you can make text **bold** or *italic*, amongst many other things..."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* list\n",
+    "* with\n",
+    "* items\n",
+    "\n",
+    "[Link to interesting resources](https://www.youtube.com/watch?v=z9Uz1icjwrM) or images: ![images](https://listame.files.wordpress.com/2012/02/bender-1.jpg)\n",
+    "\n",
+    "> Blockquotes if you like them\n",
+    "> This line is part of the same blockquote."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Mathematical formulas can also be incorporated (LaTeX it is...)\n",
+    "$$\\frac{dBZV}{dt}=BZV_{in} - k_1 .BZV$$\n",
+    "$$\\frac{dOZ}{dt}=k_2 .(OZ_{sat}-OZ) - k_1 .BZV$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Or tables:\n",
+    "\n",
+    "course | points\n",
+    " --- | --- \n",
+    " Math | 8\n",
+    " Chemistry | 4\n",
+    "\n",
+    "or tables with Latex..\n",
+    "\n",
+    " Symbool | verklaring\n",
+    " --- | --- \n",
+    " $BZV_{(t=0)}$      | initiële biochemische zuurstofvraag (7.33 mg.l-1)\n",
+    " $OZ_{(t=0)}$\t    | initiële opgeloste zuurstof (8.5 mg.l-1)\n",
+    " $BZV_{in}$\t\t  | input BZV(1 mg.l-1.min-1)\n",
+    " $OZ_{sat}$\t\t  | saturatieconcentratie opgeloste zuurstof (11 mg.l-1)\n",
+    " $k_1$\t\t      | bacteriële degradatiesnelheid (0.3 min-1)\n",
+    " $k_2$\t\t      | reäeratieconstante (0.4 min-1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Code can also be incorporated, but than just to illustrate:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "```python\n",
+    "BOT = 12\n",
+    "print(BOT)\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "See also: https://github.com/adam-p/markdown-here/wiki/Markdown-Cheatsheet"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## HTML"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can also use HTML commands, just check this cell:\n",
+    "<h3> html-adapted titel with &#60;h3&#62; </h3> <p></p>\n",
+    "<b> Bold text &#60;b&#62; </b> of <i>or italic &#60;i&#62; </i>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Headings of different sizes: section\n",
+    "### subsection\n",
+    "#### subsubsection"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Raw Text"
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "metadata": {},
+   "source": [
+    "Cfr. any text editor"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Notebook handling ESSENTIALS"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Execution: SHIFT + ENTER\n",
+    "\n",
+    "**REMEMBER**: To run a cell: <strike>push the start triangle in the menu or</strike> type **SHIFT + ENTER**\n",
+    "    \n",
+    "![](../img/notebook/shiftenter.jpg)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Completion: TAB\n",
+    "![](../../img/notebook/tabbutton.jpg)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* The **TAB** button is essential: It provides you all **possible actions** you can do after loading in a library *AND* it is used for **automatic autocompletion**:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "os.mkdir"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "my_very_long_variable_name = 3"
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "metadata": {},
+   "source": [
+    "my_ + TAB"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Help: SHIFT + TAB\n",
+    "![](../../img/notebook/shift-tab.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* The  **SHIFT-TAB** combination is ultra essential to get information/help about the current operation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "round(3.2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "os.mkdir"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# An alternative is to put a question mark behind the command\n",
+    "os.mkdir?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-success\">\n",
+    "    <b>EXERCISE</b>: What happens if you put two question marks behind the command?\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import glob\n",
+    "glob.glob??"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## *edit* mode vs *command* mode\n",
+    "\n",
+    "* *edit* mode means you're editing a cell, i.e. with your cursor inside a cell to type content -->  <font color=\"blue\">blue colored border</font>\n",
+    "* *command* mode means you're NOT editing(!), i.e. NOT with your cursor inside a cell to type content --> selected cell has a grey border\n",
+    "\n",
+    "To start editing, click inside a cell or \n",
+    "<img src=\"../img/notebook/enterbutton.png\" alt=\"Key enter\" style=\"width:150px\">\n",
+    "\n",
+    "To stop editing,\n",
+    "<img src=\"../img/notebook/keyescape.png\" alt=\"Key A\" style=\"width:150px\">"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## new cell A-bove\n",
+    "<img src=\"../img/notebook/keya.png\" alt=\"Key A\" style=\"width:150px\">\n",
+    "\n",
+    "Create a new cell above with the key A... when in *command* mode"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## new cell B-elow\n",
+    "<img src=\"../img/notebook/keyb.png\" alt=\"Key B\" style=\"width:150px\">\n",
+    "\n",
+    "Create a new cell below with the key B... when in *command* mode"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## CTRL + SHIFT + C"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Just do it!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Trouble..."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-danger\">\n",
+    "\n",
+    "**NOTE**: When you're stuck, or things are crashing: \n",
+    "\n",
+    "* first try **Kernel** > **Interrupt** -> your cell should stop running\n",
+    "* if no succes -> **Kernel** > **Restart** -> restart your notebook\n",
+    "\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* **Stackoverflow** is really, really, really nice!\n",
+    "\n",
+    "  http://stackoverflow.com/questions/tagged/python"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* Google search is with you!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<big><center>**REMEMBER**: To run a cell: <strike>push the start triangle in the menu or</strike> type **SHIFT + ENTER**\n",
+    "    \n",
+    "![](../img/notebook/shiftenter.jpg)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# some MAGIC..."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## `%psearch`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%psearch os.*dir"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## `%%timeit`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%timeit\n",
+    "\n",
+    "mylist = range(1000)\n",
+    "for i in mylist:\n",
+    "    i = i**2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%timeit\n",
+    "\n",
+    "np.arange(1000)**2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## `%whos`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%whos"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## `%lsmagic`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%lsmagic"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Let's get started!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from IPython.display import FileLink, FileLinks"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "FileLinks('.', recursive=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "jupytext": {
+   "default_lexer": "ipython3"
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.11"
+  },
+  "nav_menu": {},
+  "toc": {
+   "navigate_menu": true,
+   "number_sections": true,
+   "sideBar": true,
+   "threshold": 6,
+   "toc_cell": false,
+   "toc_section_display": "block",
+   "toc_window_display": true
+  },
+  "toc_position": {
+   "height": "860px",
+   "left": "0px",
+   "right": "1568px",
+   "top": "106px",
+   "width": "301px"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/_solved/01-introduction-tabular-data.ipynb b/_solved/01-introduction-tabular-data.ipynb
index 67fb546..26ddd74 100644
--- a/_solved/01-introduction-tabular-data.ipynb
+++ b/_solved/01-introduction-tabular-data.ipynb
@@ -2,21 +2,23 @@
  "cells": [
   {
    "cell_type": "markdown",
+   "id": "edceaf1b",
    "metadata": {},
    "source": [
     "<p><font size=\"6\"><b> Introduction to working with pandas and tabular data</b></font></p>\n",
     "\n",
     "\n",
     "> *DS Python for GIS and Geoscience*  \n",
-    "> *October, 2020*\n",
+    "> *October, 2025*\n",
     ">\n",
-    "> *© 2020, Joris Van den Bossche and Stijn Van Hoey  (<mailto:jorisvandenbossche@gmail.com>, <mailto:stijnvanhoey@gmail.com>). Licensed under [CC BY 4.0 Creative Commons](http://creativecommons.org/licenses/by/4.0/)*\n",
+    "> *© 2025, Joris Van den Bossche and Stijn Van Hoey  (<mailto:jorisvandenbossche@gmail.com>, <mailto:stijnvanhoey@gmail.com>). Licensed under [CC BY 4.0 Creative Commons](http://creativecommons.org/licenses/by/4.0/)*\n",
     "\n",
     "---"
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "d93bb4dc",
    "metadata": {},
    "source": [
     "## Working with tabular data using Pandas"
@@ -24,6 +26,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "ebdb30ec",
    "metadata": {},
    "source": [
     "To load the pandas package and start working with it, we first import the package. The community agreed alias for pandas is `pd`,  which we will also use here:"
@@ -31,7 +34,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
+   "id": "2de0f6b4",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -40,6 +44,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "fb180e5d",
    "metadata": {},
    "source": [
     "Let's start with importing some actual data. Pandas provides functions for many different formats, but here we will read a simple CSV file with information about the countries of the world:"
@@ -47,7 +52,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
+   "id": "a983927b",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -56,159 +62,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>iso_a3</th>\n",
-       "      <th>name</th>\n",
-       "      <th>pop_est</th>\n",
-       "      <th>gdp_md_est</th>\n",
-       "      <th>continent</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>FJI</td>\n",
-       "      <td>Fiji</td>\n",
-       "      <td>920938</td>\n",
-       "      <td>8374.0</td>\n",
-       "      <td>Oceania</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>TZA</td>\n",
-       "      <td>Tanzania</td>\n",
-       "      <td>53950935</td>\n",
-       "      <td>150600.0</td>\n",
-       "      <td>Africa</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>ESH</td>\n",
-       "      <td>W. Sahara</td>\n",
-       "      <td>603253</td>\n",
-       "      <td>906.5</td>\n",
-       "      <td>Africa</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>CAN</td>\n",
-       "      <td>Canada</td>\n",
-       "      <td>35623680</td>\n",
-       "      <td>1674000.0</td>\n",
-       "      <td>North America</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>USA</td>\n",
-       "      <td>United States of America</td>\n",
-       "      <td>326625791</td>\n",
-       "      <td>18560000.0</td>\n",
-       "      <td>North America</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>170</th>\n",
-       "      <td>SRB</td>\n",
-       "      <td>Serbia</td>\n",
-       "      <td>7111024</td>\n",
-       "      <td>101800.0</td>\n",
-       "      <td>Europe</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>171</th>\n",
-       "      <td>MNE</td>\n",
-       "      <td>Montenegro</td>\n",
-       "      <td>642550</td>\n",
-       "      <td>10610.0</td>\n",
-       "      <td>Europe</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>172</th>\n",
-       "      <td>-99</td>\n",
-       "      <td>Kosovo</td>\n",
-       "      <td>1895250</td>\n",
-       "      <td>18490.0</td>\n",
-       "      <td>Europe</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>173</th>\n",
-       "      <td>TTO</td>\n",
-       "      <td>Trinidad and Tobago</td>\n",
-       "      <td>1218208</td>\n",
-       "      <td>43570.0</td>\n",
-       "      <td>North America</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>174</th>\n",
-       "      <td>SSD</td>\n",
-       "      <td>S. Sudan</td>\n",
-       "      <td>13026129</td>\n",
-       "      <td>20880.0</td>\n",
-       "      <td>Africa</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>175 rows × 5 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "    iso_a3                      name    pop_est  gdp_md_est      continent\n",
-       "0      FJI                      Fiji     920938      8374.0        Oceania\n",
-       "1      TZA                  Tanzania   53950935    150600.0         Africa\n",
-       "2      ESH                 W. Sahara     603253       906.5         Africa\n",
-       "3      CAN                    Canada   35623680   1674000.0  North America\n",
-       "4      USA  United States of America  326625791  18560000.0  North America\n",
-       "..     ...                       ...        ...         ...            ...\n",
-       "170    SRB                    Serbia    7111024    101800.0         Europe\n",
-       "171    MNE                Montenegro     642550     10610.0         Europe\n",
-       "172    -99                    Kosovo    1895250     18490.0         Europe\n",
-       "173    TTO       Trinidad and Tobago    1218208     43570.0  North America\n",
-       "174    SSD                  S. Sudan   13026129     20880.0         Africa\n",
-       "\n",
-       "[175 rows x 5 columns]"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "id": "e2fcb925",
+   "metadata": {},
+   "outputs": [],
    "source": [
     "countries"
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "8a8ba82f",
    "metadata": {},
    "source": [
     "The object created here is a **DataFrame**:"
@@ -216,26 +80,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "pandas.core.frame.DataFrame"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "id": "0cc8e201",
+   "metadata": {},
+   "outputs": [],
    "source": [
     "type(countries)"
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "5d6c6d8b",
    "metadata": {},
    "source": [
     "A `DataFrame` is a 2-dimensional, **tablular data structure** comprised of rows and columns. It is similar to a spreadsheet, a database (SQL) table or the data.frame in R.\n",
@@ -245,6 +100,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "8773d8a5",
    "metadata": {},
    "source": [
     "A DataFrame can store data of different types (including characters, integers, floating point values, categorical data and more) in columns. In pandas, we can check the data types of the columns with the `dtypes` attribute:"
@@ -252,31 +108,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "iso_a3         object\n",
-       "name           object\n",
-       "pop_est         int64\n",
-       "gdp_md_est    float64\n",
-       "continent      object\n",
-       "dtype: object"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "id": "0a103cee",
+   "metadata": {},
+   "outputs": [],
    "source": [
     "countries.dtypes"
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "0243387e",
    "metadata": {},
    "source": [
     "### Each column in a `DataFrame` is a `Series`\n",
@@ -288,51 +130,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0         920938\n",
-       "1       53950935\n",
-       "2         603253\n",
-       "3       35623680\n",
-       "4      326625791\n",
-       "         ...    \n",
-       "170      7111024\n",
-       "171       642550\n",
-       "172      1895250\n",
-       "173      1218208\n",
-       "174     13026129\n",
-       "Name: pop_est, Length: 175, dtype: int64"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "id": "91ee1fb0",
+   "metadata": {},
+   "outputs": [],
    "source": [
     "countries['pop_est']"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "pandas.core.series.Series"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "id": "d2084877",
+   "metadata": {},
+   "outputs": [],
    "source": [
     "s = countries['pop_est']\n",
     "type(s)"
@@ -340,219 +151,39 @@
   },
   {
    "cell_type": "markdown",
+   "id": "8b08091e",
    "metadata": {},
    "source": [
     "### Pandas objects have attributes and methods\n",
     "\n",
-    "Pandas provides a lot of functionalities for the DataFrame and Series. The `.dtypes` shown above is an *attribute* of the DataFrame. In addition, there are also functions that can be called on a DataFrame or Series, i.e. *methods*. As methods are functions, do not forget to use parentheses `()`.\n",
+    "Pandas provides a lot of functionalities for the DataFrame and Series. The `.dtypes` shown above is an *attribute* of the DataFrame. In addition, there are also functions that can be called on a DataFrame or Series, i.e. *methods*. As methods are functions, do not forget to use parentheses `()` to call them.\n",
     "\n",
     "A few examples that can help exploring the data:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "run_control": {
-     "frozen": false,
-     "read_only": false
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "\n",
-       "    .dataframe tbody tr th {\n",
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-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>iso_a3</th>\n",
-       "      <th>name</th>\n",
-       "      <th>pop_est</th>\n",
-       "      <th>gdp_md_est</th>\n",
-       "      <th>continent</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>FJI</td>\n",
-       "      <td>Fiji</td>\n",
-       "      <td>920938</td>\n",
-       "      <td>8374.0</td>\n",
-       "      <td>Oceania</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>TZA</td>\n",
-       "      <td>Tanzania</td>\n",
-       "      <td>53950935</td>\n",
-       "      <td>150600.0</td>\n",
-       "      <td>Africa</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>ESH</td>\n",
-       "      <td>W. Sahara</td>\n",
-       "      <td>603253</td>\n",
-       "      <td>906.5</td>\n",
-       "      <td>Africa</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>CAN</td>\n",
-       "      <td>Canada</td>\n",
-       "      <td>35623680</td>\n",
-       "      <td>1674000.0</td>\n",
-       "      <td>North America</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>USA</td>\n",
-       "      <td>United States of America</td>\n",
-       "      <td>326625791</td>\n",
-       "      <td>18560000.0</td>\n",
-       "      <td>North America</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "  iso_a3                      name    pop_est  gdp_md_est      continent\n",
-       "0    FJI                      Fiji     920938      8374.0        Oceania\n",
-       "1    TZA                  Tanzania   53950935    150600.0         Africa\n",
-       "2    ESH                 W. Sahara     603253       906.5         Africa\n",
-       "3    CAN                    Canada   35623680   1674000.0  North America\n",
-       "4    USA  United States of America  326625791  18560000.0  North America"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "id": "932715b3",
+   "metadata": {},
+   "outputs": [],
    "source": [
     "countries.head() # Top rows"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "run_control": {
-     "frozen": false,
-     "read_only": false
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
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-       "\n",
-       "    .dataframe tbody tr th {\n",
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-       "\n",
-       "    .dataframe thead th {\n",
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>iso_a3</th>\n",
-       "      <th>name</th>\n",
-       "      <th>pop_est</th>\n",
-       "      <th>gdp_md_est</th>\n",
-       "      <th>continent</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>170</th>\n",
-       "      <td>SRB</td>\n",
-       "      <td>Serbia</td>\n",
-       "      <td>7111024</td>\n",
-       "      <td>101800.0</td>\n",
-       "      <td>Europe</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>171</th>\n",
-       "      <td>MNE</td>\n",
-       "      <td>Montenegro</td>\n",
-       "      <td>642550</td>\n",
-       "      <td>10610.0</td>\n",
-       "      <td>Europe</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>172</th>\n",
-       "      <td>-99</td>\n",
-       "      <td>Kosovo</td>\n",
-       "      <td>1895250</td>\n",
-       "      <td>18490.0</td>\n",
-       "      <td>Europe</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>173</th>\n",
-       "      <td>TTO</td>\n",
-       "      <td>Trinidad and Tobago</td>\n",
-       "      <td>1218208</td>\n",
-       "      <td>43570.0</td>\n",
-       "      <td>North America</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>174</th>\n",
-       "      <td>SSD</td>\n",
-       "      <td>S. Sudan</td>\n",
-       "      <td>13026129</td>\n",
-       "      <td>20880.0</td>\n",
-       "      <td>Africa</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "    iso_a3                 name   pop_est  gdp_md_est      continent\n",
-       "170    SRB               Serbia   7111024    101800.0         Europe\n",
-       "171    MNE           Montenegro    642550     10610.0         Europe\n",
-       "172    -99               Kosovo   1895250     18490.0         Europe\n",
-       "173    TTO  Trinidad and Tobago   1218208     43570.0  North America\n",
-       "174    SSD             S. Sudan  13026129     20880.0         Africa"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "id": "75f9ef89",
+   "metadata": {},
+   "outputs": [],
    "source": [
     "countries.tail() # Bottom rows"
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "89feaca7",
    "metadata": {},
    "source": [
     "The ``describe`` method computes summary statistics for each column:"
@@ -560,39 +191,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "run_control": {
-     "frozen": false,
-     "read_only": false
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "count    1.750000e+02\n",
-       "mean     4.218906e+07\n",
-       "std      1.467367e+08\n",
-       "min      2.931000e+03\n",
-       "25%      3.449254e+06\n",
-       "50%      1.024807e+07\n",
-       "75%      2.956658e+07\n",
-       "max      1.379303e+09\n",
-       "Name: pop_est, dtype: float64"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "id": "6e2c4031",
+   "metadata": {},
+   "outputs": [],
    "source": [
     "countries['pop_est'].describe()"
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "b9e42167",
    "metadata": {},
    "source": [
     "**Sort**ing your data **by** a specific column is another important first-check:"
@@ -600,164 +209,34 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "run_control": {
-     "frozen": false,
-     "read_only": false
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>iso_a3</th>\n",
-       "      <th>name</th>\n",
-       "      <th>pop_est</th>\n",
-       "      <th>gdp_md_est</th>\n",
-       "      <th>continent</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>20</th>\n",
-       "      <td>FLK</td>\n",
-       "      <td>Falkland Is.</td>\n",
-       "      <td>2931</td>\n",
-       "      <td>281.8</td>\n",
-       "      <td>South America</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>22</th>\n",
-       "      <td>GRL</td>\n",
-       "      <td>Greenland</td>\n",
-       "      <td>57713</td>\n",
-       "      <td>2173.0</td>\n",
-       "      <td>North America</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>158</th>\n",
-       "      <td>-99</td>\n",
-       "      <td>N. Cyprus</td>\n",
-       "      <td>265100</td>\n",
-       "      <td>3600.0</td>\n",
-       "      <td>Asia</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>133</th>\n",
-       "      <td>NCL</td>\n",
-       "      <td>New Caledonia</td>\n",
-       "      <td>279070</td>\n",
-       "      <td>10770.0</td>\n",
-       "      <td>Oceania</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>88</th>\n",
-       "      <td>VUT</td>\n",
-       "      <td>Vanuatu</td>\n",
-       "      <td>282814</td>\n",
-       "      <td>723.0</td>\n",
-       "      <td>Oceania</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28</th>\n",
-       "      <td>BRA</td>\n",
-       "      <td>Brazil</td>\n",
-       "      <td>207353391</td>\n",
-       "      <td>3081000.0</td>\n",
-       "      <td>South America</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>IDN</td>\n",
-       "      <td>Indonesia</td>\n",
-       "      <td>260580739</td>\n",
-       "      <td>3028000.0</td>\n",
-       "      <td>Asia</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>USA</td>\n",
-       "      <td>United States of America</td>\n",
-       "      <td>326625791</td>\n",
-       "      <td>18560000.0</td>\n",
-       "      <td>North America</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>97</th>\n",
-       "      <td>IND</td>\n",
-       "      <td>India</td>\n",
-       "      <td>1281935911</td>\n",
-       "      <td>8721000.0</td>\n",
-       "      <td>Asia</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>138</th>\n",
-       "      <td>CHN</td>\n",
-       "      <td>China</td>\n",
-       "      <td>1379302771</td>\n",
-       "      <td>21140000.0</td>\n",
-       "      <td>Asia</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>175 rows × 5 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "    iso_a3                      name     pop_est  gdp_md_est      continent\n",
-       "20     FLK              Falkland Is.        2931       281.8  South America\n",
-       "22     GRL                 Greenland       57713      2173.0  North America\n",
-       "158    -99                 N. Cyprus      265100      3600.0           Asia\n",
-       "133    NCL             New Caledonia      279070     10770.0        Oceania\n",
-       "88     VUT                   Vanuatu      282814       723.0        Oceania\n",
-       "..     ...                       ...         ...         ...            ...\n",
-       "28     BRA                    Brazil   207353391   3081000.0  South America\n",
-       "8      IDN                 Indonesia   260580739   3028000.0           Asia\n",
-       "4      USA  United States of America   326625791  18560000.0  North America\n",
-       "97     IND                     India  1281935911   8721000.0           Asia\n",
-       "138    CHN                     China  1379302771  21140000.0           Asia\n",
-       "\n",
-       "[175 rows x 5 columns]"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "id": "37e4591f",
+   "metadata": {},
+   "outputs": [],
    "source": [
     "countries.sort_values(by='pop_est')"
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "3b971568",
+   "metadata": {},
+   "source": [
+    "<div style=\"border: 5px solid #3776ab; border-radius: 2px; padding: 2em;\">\n",
+    "\n",
+    "## Python recap\n",
+    "\n",
+    "Python objects have **attributes** and **methods**:\n",
+    "\n",
+    "* Attribute: `obj.attribute` (no parentheses!) -> property of the object (pandas examples: `dtypes`, `columns`, `shape`, ..)\n",
+    "* Method: `obj.method()` (function call with parentheses) -> action (pandas examples: `mean()`, `sort_values()`, ...)\n",
+    "\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "19889faf",
    "metadata": {},
    "source": [
     "## Basic operations on Series and DataFrames"
@@ -765,13 +244,15 @@
   },
   {
    "cell_type": "markdown",
+   "id": "75e0a020",
    "metadata": {},
    "source": [
-    "### Elementwise-operations "
+    "### Elementwise-operations"
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "f20b1b98",
    "metadata": {},
    "source": [
     "The typical arithmetic (+, -, \\*, /) and comparison (==, >, <, ...) operations work *element-wise*.\n",
@@ -781,25 +262,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0       920938\n",
-       "1     53950935\n",
-       "2       603253\n",
-       "3     35623680\n",
-       "4    326625791\n",
-       "Name: pop_est, dtype: int64"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "id": "dedd8497",
+   "metadata": {},
+   "outputs": [],
    "source": [
     "population = countries['pop_est'].head()\n",
     "population"
@@ -807,61 +273,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {
-    "run_control": {
-     "frozen": false,
-     "read_only": false
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0       920.938\n",
-       "1     53950.935\n",
-       "2       603.253\n",
-       "3     35623.680\n",
-       "4    326625.791\n",
-       "Name: pop_est, dtype: float64"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "id": "f6ad75e4",
+   "metadata": {},
+   "outputs": [],
    "source": [
     "population / 1000"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0    False\n",
-       "1     True\n",
-       "2    False\n",
-       "3     True\n",
-       "4     True\n",
-       "Name: pop_est, dtype: bool"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "id": "e76f9989",
+   "metadata": {},
+   "outputs": [],
    "source": [
     "population > 1_000_000"
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "724590ec",
    "metadata": {},
    "source": [
     "With two Series objects:"
@@ -869,37 +301,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0      0.009093\n",
-       "1      0.002791\n",
-       "2      0.001503\n",
-       "3      0.046991\n",
-       "4      0.056823\n",
-       "         ...   \n",
-       "170    0.014316\n",
-       "171    0.016512\n",
-       "172    0.009756\n",
-       "173    0.035766\n",
-       "174    0.001603\n",
-       "Length: 175, dtype: float64"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "id": "3a6bdc55",
+   "metadata": {},
+   "outputs": [],
    "source": [
     "countries['gdp_md_est'] / countries['pop_est']"
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "173ce59c",
    "metadata": {},
    "source": [
     "### Aggregations (reductions)"
@@ -907,13 +319,15 @@
   },
   {
    "cell_type": "markdown",
+   "id": "8d120745",
    "metadata": {},
    "source": [
-    "Pandas provides a large set of **summary** functions that operate on different kinds of pandas objects (DataFrames, Series, Index) and produce single value. When applied to a DataFrame, the result is returned as a pandas Series (one value for each column). "
+    "Pandas provides a large set of **summary** functions that operate on different kinds of pandas objects (DataFrames, Series, Index) and produce a single value. When applied to a DataFrame, the result is returned as a pandas Series (one value for each column)."
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "ea50c351",
    "metadata": {},
    "source": [
     "The average population number:"
@@ -921,31 +335,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {
-    "run_control": {
-     "frozen": false,
-     "read_only": false
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "83544919.4"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "id": "2e967cb7",
+   "metadata": {},
+   "outputs": [],
    "source": [
     "population.mean()"
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "6e133940",
    "metadata": {},
    "source": [
     "The maximum GDP:"
@@ -953,31 +353,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {
-    "run_control": {
-     "frozen": false,
-     "read_only": false
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "21140000.0"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "id": "f024df06",
+   "metadata": {},
+   "outputs": [],
    "source": [
     "countries['gdp_md_est'].max()"
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "766e35b8",
    "metadata": {},
    "source": [
     "For dataframes, only the numeric columns are included in the result:"
@@ -985,33 +371,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {
-    "run_control": {
-     "frozen": false,
-     "read_only": false
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "pop_est       10248069.0\n",
-       "gdp_md_est       85160.0\n",
-       "dtype: float64"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "id": "04b4e05c",
+   "metadata": {},
+   "outputs": [],
    "source": [
-    "countries.median()"
+    "countries.median(numeric_only=True)"
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "874cdb01",
    "metadata": {},
    "source": [
     "### Adding new columns\n",
@@ -1023,7 +393,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": null,
+   "id": "7e68c4ca",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1032,115 +403,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>iso_a3</th>\n",
-       "      <th>name</th>\n",
-       "      <th>pop_est</th>\n",
-       "      <th>gdp_md_est</th>\n",
-       "      <th>continent</th>\n",
-       "      <th>gdp_capita</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>FJI</td>\n",
-       "      <td>Fiji</td>\n",
-       "      <td>920938</td>\n",
-       "      <td>8374.0</td>\n",
-       "      <td>Oceania</td>\n",
-       "      <td>0.009093</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>TZA</td>\n",
-       "      <td>Tanzania</td>\n",
-       "      <td>53950935</td>\n",
-       "      <td>150600.0</td>\n",
-       "      <td>Africa</td>\n",
-       "      <td>0.002791</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>ESH</td>\n",
-       "      <td>W. Sahara</td>\n",
-       "      <td>603253</td>\n",
-       "      <td>906.5</td>\n",
-       "      <td>Africa</td>\n",
-       "      <td>0.001503</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>CAN</td>\n",
-       "      <td>Canada</td>\n",
-       "      <td>35623680</td>\n",
-       "      <td>1674000.0</td>\n",
-       "      <td>North America</td>\n",
-       "      <td>0.046991</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>USA</td>\n",
-       "      <td>United States of America</td>\n",
-       "      <td>326625791</td>\n",
-       "      <td>18560000.0</td>\n",
-       "      <td>North America</td>\n",
-       "      <td>0.056823</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "  iso_a3                      name    pop_est  gdp_md_est      continent  \\\n",
-       "0    FJI                      Fiji     920938      8374.0        Oceania   \n",
-       "1    TZA                  Tanzania   53950935    150600.0         Africa   \n",
-       "2    ESH                 W. Sahara     603253       906.5         Africa   \n",
-       "3    CAN                    Canada   35623680   1674000.0  North America   \n",
-       "4    USA  United States of America  326625791  18560000.0  North America   \n",
-       "\n",
-       "   gdp_capita  \n",
-       "0    0.009093  \n",
-       "1    0.002791  \n",
-       "2    0.001503  \n",
-       "3    0.046991  \n",
-       "4    0.056823  "
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "id": "65913a92",
+   "metadata": {},
+   "outputs": [],
    "source": [
     "countries.head()"
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "132a2771",
    "metadata": {},
    "source": [
     "## Indexing: selecting a subset of the data"
@@ -1148,6 +421,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "e6e629bb",
    "metadata": {},
    "source": [
     "### Subset variables (columns)"
@@ -1155,6 +429,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "e13082c8",
    "metadata": {},
    "source": [
     "For a DataFrame, basic indexing selects the columns (cfr. the dictionaries of pure python)\n",
@@ -1164,42 +439,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {
-    "run_control": {
-     "frozen": false,
-     "read_only": false
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0         920938\n",
-       "1       53950935\n",
-       "2         603253\n",
-       "3       35623680\n",
-       "4      326625791\n",
-       "         ...    \n",
-       "170      7111024\n",
-       "171       642550\n",
-       "172      1895250\n",
-       "173      1218208\n",
-       "174     13026129\n",
-       "Name: pop_est, Length: 175, dtype: int64"
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "id": "c799e9ef",
+   "metadata": {},
+   "outputs": [],
    "source": [
     "countries['pop_est'] # single []"
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "d1f92bd3",
    "metadata": {},
    "source": [
     "Remember that the same syntax can also be used to *add* a new columns: `df['new'] = ...`.\n",
@@ -1209,128 +459,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {
-    "run_control": {
-     "frozen": false,
-     "read_only": false
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>name</th>\n",
-       "      <th>pop_est</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Fiji</td>\n",
-       "      <td>920938</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Tanzania</td>\n",
-       "      <td>53950935</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>W. Sahara</td>\n",
-       "      <td>603253</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Canada</td>\n",
-       "      <td>35623680</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>United States of America</td>\n",
-       "      <td>326625791</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>170</th>\n",
-       "      <td>Serbia</td>\n",
-       "      <td>7111024</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>171</th>\n",
-       "      <td>Montenegro</td>\n",
-       "      <td>642550</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>172</th>\n",
-       "      <td>Kosovo</td>\n",
-       "      <td>1895250</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>173</th>\n",
-       "      <td>Trinidad and Tobago</td>\n",
-       "      <td>1218208</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>174</th>\n",
-       "      <td>S. Sudan</td>\n",
-       "      <td>13026129</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>175 rows × 2 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                         name    pop_est\n",
-       "0                        Fiji     920938\n",
-       "1                    Tanzania   53950935\n",
-       "2                   W. Sahara     603253\n",
-       "3                      Canada   35623680\n",
-       "4    United States of America  326625791\n",
-       "..                        ...        ...\n",
-       "170                    Serbia    7111024\n",
-       "171                Montenegro     642550\n",
-       "172                    Kosovo    1895250\n",
-       "173       Trinidad and Tobago    1218208\n",
-       "174                  S. Sudan   13026129\n",
-       "\n",
-       "[175 rows x 2 columns]"
-      ]
-     },
-     "execution_count": 22,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "id": "2ca18f9d",
+   "metadata": {},
+   "outputs": [],
    "source": [
     "countries[['name', 'pop_est']] # double [[]]"
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "161d2f8f",
    "metadata": {},
    "source": [
     "### Subset observations (rows)"
@@ -1338,6 +477,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "df93af63",
    "metadata": {},
    "source": [
     "Using `[]`, slicing or boolean indexing accesses the **rows**:"
@@ -1345,6 +485,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "5264cfab",
    "metadata": {},
    "source": [
     "### Slicing"
@@ -1352,98 +493,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>iso_a3</th>\n",
-       "      <th>name</th>\n",
-       "      <th>pop_est</th>\n",
-       "      <th>gdp_md_est</th>\n",
-       "      <th>continent</th>\n",
-       "      <th>gdp_capita</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>FJI</td>\n",
-       "      <td>Fiji</td>\n",
-       "      <td>920938</td>\n",
-       "      <td>8374.0</td>\n",
-       "      <td>Oceania</td>\n",
-       "      <td>0.009093</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>TZA</td>\n",
-       "      <td>Tanzania</td>\n",
-       "      <td>53950935</td>\n",
-       "      <td>150600.0</td>\n",
-       "      <td>Africa</td>\n",
-       "      <td>0.002791</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>ESH</td>\n",
-       "      <td>W. Sahara</td>\n",
-       "      <td>603253</td>\n",
-       "      <td>906.5</td>\n",
-       "      <td>Africa</td>\n",
-       "      <td>0.001503</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>CAN</td>\n",
-       "      <td>Canada</td>\n",
-       "      <td>35623680</td>\n",
-       "      <td>1674000.0</td>\n",
-       "      <td>North America</td>\n",
-       "      <td>0.046991</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "  iso_a3       name   pop_est  gdp_md_est      continent  gdp_capita\n",
-       "0    FJI       Fiji    920938      8374.0        Oceania    0.009093\n",
-       "1    TZA   Tanzania  53950935    150600.0         Africa    0.002791\n",
-       "2    ESH  W. Sahara    603253       906.5         Africa    0.001503\n",
-       "3    CAN     Canada  35623680   1674000.0  North America    0.046991"
-      ]
-     },
-     "execution_count": 23,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "id": "6625555a",
+   "metadata": {},
+   "outputs": [],
    "source": [
     "countries[0:4]"
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "2e3aeca6",
    "metadata": {},
    "source": [
     "### Boolean indexing (filtering)"
@@ -1451,118 +511,20 @@
   },
   {
    "cell_type": "markdown",
+   "id": "5b7ba9ce",
    "metadata": {},
    "source": [
-    "Often, you want to select rows based on a certain condition. This can be done with *'boolean indexing'* (like a where clause in SQL). \n",
+    "Often, you want to select rows based on a certain condition. This can be done with *'boolean indexing'* (like a WHERE clause in SQL).\n",
     "\n",
     "The indexer (or boolean mask) should be 1-dimensional and the same length as the thing being indexed."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>iso_a3</th>\n",
-       "      <th>name</th>\n",
-       "      <th>pop_est</th>\n",
-       "      <th>gdp_md_est</th>\n",
-       "      <th>continent</th>\n",
-       "      <th>gdp_capita</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>FJI</td>\n",
-       "      <td>Fiji</td>\n",
-       "      <td>920938</td>\n",
-       "      <td>8374.0</td>\n",
-       "      <td>Oceania</td>\n",
-       "      <td>0.009093</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>TZA</td>\n",
-       "      <td>Tanzania</td>\n",
-       "      <td>53950935</td>\n",
-       "      <td>150600.0</td>\n",
-       "      <td>Africa</td>\n",
-       "      <td>0.002791</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>ESH</td>\n",
-       "      <td>W. Sahara</td>\n",
-       "      <td>603253</td>\n",
-       "      <td>906.5</td>\n",
-       "      <td>Africa</td>\n",
-       "      <td>0.001503</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>CAN</td>\n",
-       "      <td>Canada</td>\n",
-       "      <td>35623680</td>\n",
-       "      <td>1674000.0</td>\n",
-       "      <td>North America</td>\n",
-       "      <td>0.046991</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>USA</td>\n",
-       "      <td>United States of America</td>\n",
-       "      <td>326625791</td>\n",
-       "      <td>18560000.0</td>\n",
-       "      <td>North America</td>\n",
-       "      <td>0.056823</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "  iso_a3                      name    pop_est  gdp_md_est      continent  \\\n",
-       "0    FJI                      Fiji     920938      8374.0        Oceania   \n",
-       "1    TZA                  Tanzania   53950935    150600.0         Africa   \n",
-       "2    ESH                 W. Sahara     603253       906.5         Africa   \n",
-       "3    CAN                    Canada   35623680   1674000.0  North America   \n",
-       "4    USA  United States of America  326625791  18560000.0  North America   \n",
-       "\n",
-       "   gdp_capita  \n",
-       "0    0.009093  \n",
-       "1    0.002791  \n",
-       "2    0.001503  \n",
-       "3    0.046991  \n",
-       "4    0.056823  "
-      ]
-     },
-     "execution_count": 24,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "id": "aeef0c9c",
+   "metadata": {},
+   "outputs": [],
    "source": [
     "# taking the first 5 rows to illustrate\n",
     "df = countries.head()\n",
@@ -1571,30 +533,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {
-    "run_control": {
-     "frozen": false,
-     "read_only": false
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0    False\n",
-       "1     True\n",
-       "2    False\n",
-       "3     True\n",
-       "4     True\n",
-       "Name: pop_est, dtype: bool"
-      ]
-     },
-     "execution_count": 25,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "id": "1b197017",
+   "metadata": {},
+   "outputs": [],
    "source": [
     "mask = df['pop_est'] > 1_000_000\n",
     "mask"
@@ -1602,174 +544,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>iso_a3</th>\n",
-       "      <th>name</th>\n",
-       "      <th>pop_est</th>\n",
-       "      <th>gdp_md_est</th>\n",
-       "      <th>continent</th>\n",
-       "      <th>gdp_capita</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>TZA</td>\n",
-       "      <td>Tanzania</td>\n",
-       "      <td>53950935</td>\n",
-       "      <td>150600.0</td>\n",
-       "      <td>Africa</td>\n",
-       "      <td>0.002791</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>CAN</td>\n",
-       "      <td>Canada</td>\n",
-       "      <td>35623680</td>\n",
-       "      <td>1674000.0</td>\n",
-       "      <td>North America</td>\n",
-       "      <td>0.046991</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>USA</td>\n",
-       "      <td>United States of America</td>\n",
-       "      <td>326625791</td>\n",
-       "      <td>18560000.0</td>\n",
-       "      <td>North America</td>\n",
-       "      <td>0.056823</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "  iso_a3                      name    pop_est  gdp_md_est      continent  \\\n",
-       "1    TZA                  Tanzania   53950935    150600.0         Africa   \n",
-       "3    CAN                    Canada   35623680   1674000.0  North America   \n",
-       "4    USA  United States of America  326625791  18560000.0  North America   \n",
-       "\n",
-       "   gdp_capita  \n",
-       "1    0.002791  \n",
-       "3    0.046991  \n",
-       "4    0.056823  "
-      ]
-     },
-     "execution_count": 26,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "id": "1ba591b2",
+   "metadata": {},
+   "outputs": [],
    "source": [
     "df[mask]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>iso_a3</th>\n",
-       "      <th>name</th>\n",
-       "      <th>pop_est</th>\n",
-       "      <th>gdp_md_est</th>\n",
-       "      <th>continent</th>\n",
-       "      <th>gdp_capita</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>TZA</td>\n",
-       "      <td>Tanzania</td>\n",
-       "      <td>53950935</td>\n",
-       "      <td>150600.0</td>\n",
-       "      <td>Africa</td>\n",
-       "      <td>0.002791</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>CAN</td>\n",
-       "      <td>Canada</td>\n",
-       "      <td>35623680</td>\n",
-       "      <td>1674000.0</td>\n",
-       "      <td>North America</td>\n",
-       "      <td>0.046991</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>USA</td>\n",
-       "      <td>United States of America</td>\n",
-       "      <td>326625791</td>\n",
-       "      <td>18560000.0</td>\n",
-       "      <td>North America</td>\n",
-       "      <td>0.056823</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "  iso_a3                      name    pop_est  gdp_md_est      continent  \\\n",
-       "1    TZA                  Tanzania   53950935    150600.0         Africa   \n",
-       "3    CAN                    Canada   35623680   1674000.0  North America   \n",
-       "4    USA  United States of America  326625791  18560000.0  North America   \n",
-       "\n",
-       "   gdp_capita  \n",
-       "1    0.002791  \n",
-       "3    0.046991  \n",
-       "4    0.056823  "
-      ]
-     },
-     "execution_count": 27,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "id": "0cfc3c25",
+   "metadata": {},
+   "outputs": [],
    "source": [
     "# or in one go\n",
     "df[df['pop_est'] > 1_000_000]"
@@ -1777,6 +565,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "8f518f67",
    "metadata": {},
    "source": [
     "With the full dataset:"
@@ -1784,215 +573,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>iso_a3</th>\n",
-       "      <th>name</th>\n",
-       "      <th>pop_est</th>\n",
-       "      <th>gdp_md_est</th>\n",
-       "      <th>continent</th>\n",
-       "      <th>gdp_capita</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>USA</td>\n",
-       "      <td>United States of America</td>\n",
-       "      <td>326625791</td>\n",
-       "      <td>18560000.0</td>\n",
-       "      <td>North America</td>\n",
-       "      <td>0.056823</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>97</th>\n",
-       "      <td>IND</td>\n",
-       "      <td>India</td>\n",
-       "      <td>1281935911</td>\n",
-       "      <td>8721000.0</td>\n",
-       "      <td>Asia</td>\n",
-       "      <td>0.006803</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>138</th>\n",
-       "      <td>CHN</td>\n",
-       "      <td>China</td>\n",
-       "      <td>1379302771</td>\n",
-       "      <td>21140000.0</td>\n",
-       "      <td>Asia</td>\n",
-       "      <td>0.015327</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "    iso_a3                      name     pop_est  gdp_md_est      continent  \\\n",
-       "4      USA  United States of America   326625791  18560000.0  North America   \n",
-       "97     IND                     India  1281935911   8721000.0           Asia   \n",
-       "138    CHN                     China  1379302771  21140000.0           Asia   \n",
-       "\n",
-       "     gdp_capita  \n",
-       "4      0.056823  \n",
-       "97     0.006803  \n",
-       "138    0.015327  "
-      ]
-     },
-     "execution_count": 28,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "id": "d700f288",
+   "metadata": {},
+   "outputs": [],
    "source": [
     "countries[countries['gdp_md_est'] > 5_000_000]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>iso_a3</th>\n",
-       "      <th>name</th>\n",
-       "      <th>pop_est</th>\n",
-       "      <th>gdp_md_est</th>\n",
-       "      <th>continent</th>\n",
-       "      <th>gdp_capita</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>FJI</td>\n",
-       "      <td>Fiji</td>\n",
-       "      <td>920938</td>\n",
-       "      <td>8374.0</td>\n",
-       "      <td>Oceania</td>\n",
-       "      <td>0.009093</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>PNG</td>\n",
-       "      <td>Papua New Guinea</td>\n",
-       "      <td>6909701</td>\n",
-       "      <td>28020.0</td>\n",
-       "      <td>Oceania</td>\n",
-       "      <td>0.004055</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>88</th>\n",
-       "      <td>VUT</td>\n",
-       "      <td>Vanuatu</td>\n",
-       "      <td>282814</td>\n",
-       "      <td>723.0</td>\n",
-       "      <td>Oceania</td>\n",
-       "      <td>0.002556</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>133</th>\n",
-       "      <td>NCL</td>\n",
-       "      <td>New Caledonia</td>\n",
-       "      <td>279070</td>\n",
-       "      <td>10770.0</td>\n",
-       "      <td>Oceania</td>\n",
-       "      <td>0.038592</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>134</th>\n",
-       "      <td>SLB</td>\n",
-       "      <td>Solomon Is.</td>\n",
-       "      <td>647581</td>\n",
-       "      <td>1198.0</td>\n",
-       "      <td>Oceania</td>\n",
-       "      <td>0.001850</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>135</th>\n",
-       "      <td>NZL</td>\n",
-       "      <td>New Zealand</td>\n",
-       "      <td>4510327</td>\n",
-       "      <td>174800.0</td>\n",
-       "      <td>Oceania</td>\n",
-       "      <td>0.038756</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>136</th>\n",
-       "      <td>AUS</td>\n",
-       "      <td>Australia</td>\n",
-       "      <td>23232413</td>\n",
-       "      <td>1189000.0</td>\n",
-       "      <td>Oceania</td>\n",
-       "      <td>0.051178</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "    iso_a3              name   pop_est  gdp_md_est continent  gdp_capita\n",
-       "0      FJI              Fiji    920938      8374.0   Oceania    0.009093\n",
-       "7      PNG  Papua New Guinea   6909701     28020.0   Oceania    0.004055\n",
-       "88     VUT           Vanuatu    282814       723.0   Oceania    0.002556\n",
-       "133    NCL     New Caledonia    279070     10770.0   Oceania    0.038592\n",
-       "134    SLB       Solomon Is.    647581      1198.0   Oceania    0.001850\n",
-       "135    NZL       New Zealand   4510327    174800.0   Oceania    0.038756\n",
-       "136    AUS         Australia  23232413   1189000.0   Oceania    0.051178"
-      ]
-     },
-     "execution_count": 29,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "id": "7b56e218",
+   "metadata": {},
+   "outputs": [],
    "source": [
     "countries[countries['continent'] == \"Oceania\"]"
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "521c959f",
    "metadata": {},
    "source": [
     "An overview of the possible comparison operations:\n",
@@ -2004,7 +605,7 @@
     "\\>       | Greater than\n",
     "\\>=       | Greater than or equal\n",
     "<       | Lesser than\n",
-    "!=       | Lesser than or equal\n",
+    "<=       | Lesser than or equal\n",
     "\n",
     "and to combine multiple conditions:\n",
     "\n",
@@ -2016,6 +617,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "495be455",
    "metadata": {},
    "source": [
     "<div class=\"alert alert-info\" style=\"font-size:120%\">\n",
@@ -2032,6 +634,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "1154d8f2",
    "metadata": {},
    "source": [
     "## Let's practice! (part I)\n",
@@ -2041,11 +644,12 @@
     "Here, we start with the following datasets:\n",
     "\n",
     "- Information about the administrative districts of Paris: `paris_districts.csv`\n",
-    "- Real-time (at the moment I downloaded them ..) information about the public bicycle sharing system in Paris (vélib, https://opendata.paris.fr/explore/dataset/stations-velib-disponibilites-en-temps-reel/information/): `paris_bike_stations.csv`\n"
+    "- Real-time (at the moment I downloaded them ..) information about the public bicycle sharing system in Paris (vélib, https://opendata.paris.fr/explore/dataset/stations-velib-disponibilites-en-temps-reel/information/): `paris_bike_stations.csv`"
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "90fd2f8e",
    "metadata": {},
    "source": [
     "<div class=\"alert alert-success\">\n",
@@ -2067,8 +671,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
+   "execution_count": null,
+   "id": "54df6e11",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
    "outputs": [],
    "source": [
     "districts = pd.read_csv(\"data/paris_districts.csv\")"
@@ -2076,262 +685,42 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>id</th>\n",
-       "      <th>district_name</th>\n",
-       "      <th>arrondissement</th>\n",
-       "      <th>population</th>\n",
-       "      <th>area</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1</td>\n",
-       "      <td>St-Germain-l'Auxerrois</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1672</td>\n",
-       "      <td>8.690007e+05</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>2</td>\n",
-       "      <td>Halles</td>\n",
-       "      <td>1</td>\n",
-       "      <td>8984</td>\n",
-       "      <td>4.124585e+05</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>3</td>\n",
-       "      <td>Palais-Royal</td>\n",
-       "      <td>1</td>\n",
-       "      <td>3195</td>\n",
-       "      <td>2.736968e+05</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>4</td>\n",
-       "      <td>Place-Vendôme</td>\n",
-       "      <td>1</td>\n",
-       "      <td>3044</td>\n",
-       "      <td>2.694568e+05</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>5</td>\n",
-       "      <td>Gaillon</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1345</td>\n",
-       "      <td>1.880122e+05</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>75</th>\n",
-       "      <td>76</td>\n",
-       "      <td>Combat</td>\n",
-       "      <td>19</td>\n",
-       "      <td>38988</td>\n",
-       "      <td>1.294988e+06</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>76</th>\n",
-       "      <td>77</td>\n",
-       "      <td>Belleville</td>\n",
-       "      <td>20</td>\n",
-       "      <td>35773</td>\n",
-       "      <td>8.065686e+05</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>77</th>\n",
-       "      <td>78</td>\n",
-       "      <td>Saint-Fargeau</td>\n",
-       "      <td>20</td>\n",
-       "      <td>42087</td>\n",
-       "      <td>1.486971e+06</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>78</th>\n",
-       "      <td>79</td>\n",
-       "      <td>Père-Lachaise</td>\n",
-       "      <td>20</td>\n",
-       "      <td>42332</td>\n",
-       "      <td>1.599002e+06</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>79</th>\n",
-       "      <td>80</td>\n",
-       "      <td>Charonne</td>\n",
-       "      <td>20</td>\n",
-       "      <td>62901</td>\n",
-       "      <td>2.090904e+06</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>80 rows × 5 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "    id           district_name  arrondissement  population          area\n",
-       "0    1  St-Germain-l'Auxerrois               1        1672  8.690007e+05\n",
-       "1    2                  Halles               1        8984  4.124585e+05\n",
-       "2    3            Palais-Royal               1        3195  2.736968e+05\n",
-       "3    4           Place-Vendôme               1        3044  2.694568e+05\n",
-       "4    5                 Gaillon               2        1345  1.880122e+05\n",
-       "..  ..                     ...             ...         ...           ...\n",
-       "75  76                  Combat              19       38988  1.294988e+06\n",
-       "76  77              Belleville              20       35773  8.065686e+05\n",
-       "77  78           Saint-Fargeau              20       42087  1.486971e+06\n",
-       "78  79           Père-Lachaise              20       42332  1.599002e+06\n",
-       "79  80                Charonne              20       62901  2.090904e+06\n",
-       "\n",
-       "[80 rows x 5 columns]"
-      ]
-     },
-     "execution_count": 31,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "id": "b46e32b1",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
    "source": [
     "districts"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>id</th>\n",
-       "      <th>district_name</th>\n",
-       "      <th>arrondissement</th>\n",
-       "      <th>population</th>\n",
-       "      <th>area</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1</td>\n",
-       "      <td>St-Germain-l'Auxerrois</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1672</td>\n",
-       "      <td>869000.664583</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>2</td>\n",
-       "      <td>Halles</td>\n",
-       "      <td>1</td>\n",
-       "      <td>8984</td>\n",
-       "      <td>412458.496338</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>3</td>\n",
-       "      <td>Palais-Royal</td>\n",
-       "      <td>1</td>\n",
-       "      <td>3195</td>\n",
-       "      <td>273696.793306</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>4</td>\n",
-       "      <td>Place-Vendôme</td>\n",
-       "      <td>1</td>\n",
-       "      <td>3044</td>\n",
-       "      <td>269456.780605</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>5</td>\n",
-       "      <td>Gaillon</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1345</td>\n",
-       "      <td>188012.203855</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   id           district_name  arrondissement  population           area\n",
-       "0   1  St-Germain-l'Auxerrois               1        1672  869000.664583\n",
-       "1   2                  Halles               1        8984  412458.496338\n",
-       "2   3            Palais-Royal               1        3195  273696.793306\n",
-       "3   4           Place-Vendôme               1        3044  269456.780605\n",
-       "4   5                 Gaillon               2        1345  188012.203855"
-      ]
-     },
-     "execution_count": 32,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "id": "9893d670",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
    "source": [
     "districts.head()"
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "3a4dfeb9",
    "metadata": {},
    "source": [
     "<div class=\"alert alert-success\">\n",
     "\n",
     "**EXERCISE**:\n",
     "\n",
-    "* What is the average popolation number of the districts?\n",
+    "* What is the average population number of the districts?\n",
     "* And what is the median?\n",
     "\n",
     "<details>\n",
@@ -2347,77 +736,49 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0      1672\n",
-       "1      8984\n",
-       "2      3195\n",
-       "3      3044\n",
-       "4      1345\n",
-       "      ...  \n",
-       "75    38988\n",
-       "76    35773\n",
-       "77    42087\n",
-       "78    42332\n",
-       "79    62901\n",
-       "Name: population, Length: 80, dtype: int64"
-      ]
-     },
-     "execution_count": 33,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "id": "ed02425b",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
    "source": [
     "districts['population']"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "26573.1375"
-      ]
-     },
-     "execution_count": 34,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "id": "9bd61a2e",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
    "source": [
     "districts['population'].mean()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "21418.5"
-      ]
-     },
-     "execution_count": 35,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "id": "723d10e1",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
    "source": [
     "districts['population'].median()"
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "a5098bf3",
    "metadata": {},
    "source": [
     "<div class=\"alert alert-success\">\n",
@@ -2442,77 +803,49 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "7205014.420161385"
-      ]
-     },
-     "execution_count": 36,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "id": "9cef74ee",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
    "source": [
     "districts['area'].max()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "7.205014420161385"
-      ]
-     },
-     "execution_count": 37,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "id": "bac960b5",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
    "source": [
     "districts['area'].max() / 1000**2  # or / 10**6"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0     0.869001\n",
-       "1     0.412458\n",
-       "2     0.273697\n",
-       "3     0.269457\n",
-       "4     0.188012\n",
-       "        ...   \n",
-       "75    1.294988\n",
-       "76    0.806569\n",
-       "77    1.486971\n",
-       "78    1.599002\n",
-       "79    2.090904\n",
-       "Name: area, Length: 80, dtype: float64"
-      ]
-     },
-     "execution_count": 38,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "id": "fc050db0",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
    "source": [
     "districts['area'] / 1000**2"
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "c9e59ff8",
    "metadata": {},
    "source": [
     "<div class=\"alert alert-success\">\n",
@@ -2534,31 +867,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0      1924.049161\n",
-       "1     21781.585492\n",
-       "2     11673.501766\n",
-       "3     11296.802378\n",
-       "4      7153.790937\n",
-       "          ...     \n",
-       "75    30106.845200\n",
-       "76    44352.083591\n",
-       "77    28303.838410\n",
-       "78    26474.013610\n",
-       "79    30083.160559\n",
-       "Length: 80, dtype: float64"
-      ]
-     },
-     "execution_count": 39,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "id": "851c95e1",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
    "source": [
     "population_density = districts[\"population\"] / (districts[\"area\"] / 1000**2)\n",
     "population_density"
@@ -2566,26 +882,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "45472.80292754569"
-      ]
-     },
-     "execution_count": 40,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "id": "a9ae6dec",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
    "source": [
     "population_density.max()"
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "e944bd4a",
    "metadata": {},
    "source": [
     "<div class=\"alert alert-success\">\n",
@@ -2606,109 +917,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>id</th>\n",
-       "      <th>district_name</th>\n",
-       "      <th>arrondissement</th>\n",
-       "      <th>population</th>\n",
-       "      <th>area</th>\n",
-       "      <th>population_density</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1</td>\n",
-       "      <td>St-Germain-l'Auxerrois</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1672</td>\n",
-       "      <td>869000.664583</td>\n",
-       "      <td>1924.049161</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>2</td>\n",
-       "      <td>Halles</td>\n",
-       "      <td>1</td>\n",
-       "      <td>8984</td>\n",
-       "      <td>412458.496338</td>\n",
-       "      <td>21781.585492</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>3</td>\n",
-       "      <td>Palais-Royal</td>\n",
-       "      <td>1</td>\n",
-       "      <td>3195</td>\n",
-       "      <td>273696.793306</td>\n",
-       "      <td>11673.501766</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>4</td>\n",
-       "      <td>Place-Vendôme</td>\n",
-       "      <td>1</td>\n",
-       "      <td>3044</td>\n",
-       "      <td>269456.780605</td>\n",
-       "      <td>11296.802378</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>5</td>\n",
-       "      <td>Gaillon</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1345</td>\n",
-       "      <td>188012.203855</td>\n",
-       "      <td>7153.790937</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   id           district_name  arrondissement  population           area  \\\n",
-       "0   1  St-Germain-l'Auxerrois               1        1672  869000.664583   \n",
-       "1   2                  Halles               1        8984  412458.496338   \n",
-       "2   3            Palais-Royal               1        3195  273696.793306   \n",
-       "3   4           Place-Vendôme               1        3044  269456.780605   \n",
-       "4   5                 Gaillon               2        1345  188012.203855   \n",
-       "\n",
-       "   population_density  \n",
-       "0         1924.049161  \n",
-       "1        21781.585492  \n",
-       "2        11673.501766  \n",
-       "3        11296.802378  \n",
-       "4         7153.790937  "
-      ]
-     },
-     "execution_count": 41,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "id": "521d4731",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
    "source": [
     "districts['population_density'] = districts[\"population\"] / (districts[\"area\"] / 1000**2)\n",
     "districts.head()"
@@ -2716,6 +932,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "92dc8b47",
    "metadata": {},
    "source": [
     "<div class=\"alert alert-success\">\n",
@@ -2737,191 +954,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>id</th>\n",
-       "      <th>district_name</th>\n",
-       "      <th>arrondissement</th>\n",
-       "      <th>population</th>\n",
-       "      <th>area</th>\n",
-       "      <th>population_density</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>56</th>\n",
-       "      <td>57</td>\n",
-       "      <td>Saint-Lambert</td>\n",
-       "      <td>15</td>\n",
-       "      <td>82032</td>\n",
-       "      <td>2.829202e+06</td>\n",
-       "      <td>28994.752912</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>49</th>\n",
-       "      <td>50</td>\n",
-       "      <td>Gare</td>\n",
-       "      <td>13</td>\n",
-       "      <td>69008</td>\n",
-       "      <td>3.044178e+06</td>\n",
-       "      <td>22668.848864</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>60</th>\n",
-       "      <td>61</td>\n",
-       "      <td>Auteuil</td>\n",
-       "      <td>16</td>\n",
-       "      <td>67967</td>\n",
-       "      <td>6.383888e+06</td>\n",
-       "      <td>10646.647042</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>68</th>\n",
-       "      <td>69</td>\n",
-       "      <td>Grandes-Carrières</td>\n",
-       "      <td>18</td>\n",
-       "      <td>67152</td>\n",
-       "      <td>1.905566e+06</td>\n",
-       "      <td>35239.925867</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>69</th>\n",
-       "      <td>70</td>\n",
-       "      <td>Clignancourt</td>\n",
-       "      <td>18</td>\n",
-       "      <td>64868</td>\n",
-       "      <td>1.653715e+06</td>\n",
-       "      <td>39225.618453</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>3</td>\n",
-       "      <td>Palais-Royal</td>\n",
-       "      <td>1</td>\n",
-       "      <td>3195</td>\n",
-       "      <td>2.736968e+05</td>\n",
-       "      <td>11673.501766</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>4</td>\n",
-       "      <td>Place-Vendôme</td>\n",
-       "      <td>1</td>\n",
-       "      <td>3044</td>\n",
-       "      <td>2.694568e+05</td>\n",
-       "      <td>11296.802378</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>6</td>\n",
-       "      <td>Vivienne</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2917</td>\n",
-       "      <td>2.435508e+05</td>\n",
-       "      <td>11976.968878</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1</td>\n",
-       "      <td>St-Germain-l'Auxerrois</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1672</td>\n",
-       "      <td>8.690007e+05</td>\n",
-       "      <td>1924.049161</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>5</td>\n",
-       "      <td>Gaillon</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1345</td>\n",
-       "      <td>1.880122e+05</td>\n",
-       "      <td>7153.790937</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>80 rows × 6 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "    id           district_name  arrondissement  population          area  \\\n",
-       "56  57           Saint-Lambert              15       82032  2.829202e+06   \n",
-       "49  50                    Gare              13       69008  3.044178e+06   \n",
-       "60  61                 Auteuil              16       67967  6.383888e+06   \n",
-       "68  69       Grandes-Carrières              18       67152  1.905566e+06   \n",
-       "69  70            Clignancourt              18       64868  1.653715e+06   \n",
-       "..  ..                     ...             ...         ...           ...   \n",
-       "2    3            Palais-Royal               1        3195  2.736968e+05   \n",
-       "3    4           Place-Vendôme               1        3044  2.694568e+05   \n",
-       "5    6                Vivienne               2        2917  2.435508e+05   \n",
-       "0    1  St-Germain-l'Auxerrois               1        1672  8.690007e+05   \n",
-       "4    5                 Gaillon               2        1345  1.880122e+05   \n",
-       "\n",
-       "    population_density  \n",
-       "56        28994.752912  \n",
-       "49        22668.848864  \n",
-       "60        10646.647042  \n",
-       "68        35239.925867  \n",
-       "69        39225.618453  \n",
-       "..                 ...  \n",
-       "2         11673.501766  \n",
-       "3         11296.802378  \n",
-       "5         11976.968878  \n",
-       "0          1924.049161  \n",
-       "4          7153.790937  \n",
-       "\n",
-       "[80 rows x 6 columns]"
-      ]
-     },
-     "execution_count": 42,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "id": "726585c0",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
    "source": [
     "districts.sort_values(by=\"population\", ascending=False)"
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "da37b2f8",
    "metadata": {},
    "source": [
     "<div class=\"alert alert-success\">\n",
     "\n",
     "**EXERCISE**:\n",
     "\n",
-    "* Select all rows for the districts of the 1st arrondissemnt.\n",
+    "* Select all rows for the districts of the 3rd arrondissement.\n",
     "* For this subset, what is the total population of the arrondissement?\n",
     "    \n",
     "</div>"
@@ -2929,98 +983,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>id</th>\n",
-       "      <th>district_name</th>\n",
-       "      <th>arrondissement</th>\n",
-       "      <th>population</th>\n",
-       "      <th>area</th>\n",
-       "      <th>population_density</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>9</td>\n",
-       "      <td>Arts-et-Metiers</td>\n",
-       "      <td>3</td>\n",
-       "      <td>9560</td>\n",
-       "      <td>318087.740461</td>\n",
-       "      <td>30054.600615</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>10</td>\n",
-       "      <td>Enfants-Rouges</td>\n",
-       "      <td>3</td>\n",
-       "      <td>8562</td>\n",
-       "      <td>271750.323943</td>\n",
-       "      <td>31506.862166</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>11</td>\n",
-       "      <td>Archives</td>\n",
-       "      <td>3</td>\n",
-       "      <td>8609</td>\n",
-       "      <td>367728.386875</td>\n",
-       "      <td>23411.301132</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11</th>\n",
-       "      <td>12</td>\n",
-       "      <td>Sainte-Avoie</td>\n",
-       "      <td>3</td>\n",
-       "      <td>7501</td>\n",
-       "      <td>213316.351515</td>\n",
-       "      <td>35163.736613</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "    id    district_name  arrondissement  population           area  \\\n",
-       "8    9  Arts-et-Metiers               3        9560  318087.740461   \n",
-       "9   10   Enfants-Rouges               3        8562  271750.323943   \n",
-       "10  11         Archives               3        8609  367728.386875   \n",
-       "11  12     Sainte-Avoie               3        7501  213316.351515   \n",
-       "\n",
-       "    population_density  \n",
-       "8         30054.600615  \n",
-       "9         31506.862166  \n",
-       "10        23411.301132  \n",
-       "11        35163.736613  "
-      ]
-     },
-     "execution_count": 43,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "id": "f8b95875",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
    "source": [
     "arr3 = districts[districts[\"arrondissement\"] == 3]\n",
     "arr3"
@@ -3028,221 +998,56 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "34232"
-      ]
-     },
-     "execution_count": 44,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "id": "5b54d98c",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
    "source": [
     "arr3[\"population\"].sum()"
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "5c3c1523",
    "metadata": {},
    "source": [
     "<div class=\"alert alert-success\">\n",
     "\n",
     "**EXERCISE**:\n",
     "\n",
-    "* Select the districts with a population of more than 50.000 inhabitans.\n",
+    "* Select the districts with a population of more than 50.000 inhabitants.\n",
     "\n",
     "</div>"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>id</th>\n",
-       "      <th>district_name</th>\n",
-       "      <th>arrondissement</th>\n",
-       "      <th>population</th>\n",
-       "      <th>area</th>\n",
-       "      <th>population_density</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>45</th>\n",
-       "      <td>46</td>\n",
-       "      <td>Picpus</td>\n",
-       "      <td>12</td>\n",
-       "      <td>62947</td>\n",
-       "      <td>7.205014e+06</td>\n",
-       "      <td>8736.554340</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>49</th>\n",
-       "      <td>50</td>\n",
-       "      <td>Gare</td>\n",
-       "      <td>13</td>\n",
-       "      <td>69008</td>\n",
-       "      <td>3.044178e+06</td>\n",
-       "      <td>22668.848864</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>50</th>\n",
-       "      <td>51</td>\n",
-       "      <td>Maison-Blanche</td>\n",
-       "      <td>13</td>\n",
-       "      <td>64797</td>\n",
-       "      <td>2.231506e+06</td>\n",
-       "      <td>29037.343809</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>55</th>\n",
-       "      <td>56</td>\n",
-       "      <td>Plaisance</td>\n",
-       "      <td>14</td>\n",
-       "      <td>57229</td>\n",
-       "      <td>1.784948e+06</td>\n",
-       "      <td>32062.000823</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>56</th>\n",
-       "      <td>57</td>\n",
-       "      <td>Saint-Lambert</td>\n",
-       "      <td>15</td>\n",
-       "      <td>82032</td>\n",
-       "      <td>2.829202e+06</td>\n",
-       "      <td>28994.752912</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>60</th>\n",
-       "      <td>61</td>\n",
-       "      <td>Auteuil</td>\n",
-       "      <td>16</td>\n",
-       "      <td>67967</td>\n",
-       "      <td>6.383888e+06</td>\n",
-       "      <td>10646.647042</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>68</th>\n",
-       "      <td>69</td>\n",
-       "      <td>Grandes-Carrières</td>\n",
-       "      <td>18</td>\n",
-       "      <td>67152</td>\n",
-       "      <td>1.905566e+06</td>\n",
-       "      <td>35239.925867</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>69</th>\n",
-       "      <td>70</td>\n",
-       "      <td>Clignancourt</td>\n",
-       "      <td>18</td>\n",
-       "      <td>64868</td>\n",
-       "      <td>1.653715e+06</td>\n",
-       "      <td>39225.618453</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>72</th>\n",
-       "      <td>73</td>\n",
-       "      <td>Villette</td>\n",
-       "      <td>19</td>\n",
-       "      <td>53650</td>\n",
-       "      <td>1.285705e+06</td>\n",
-       "      <td>41728.078744</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>74</th>\n",
-       "      <td>75</td>\n",
-       "      <td>Amérique</td>\n",
-       "      <td>19</td>\n",
-       "      <td>55365</td>\n",
-       "      <td>1.835720e+06</td>\n",
-       "      <td>30159.823779</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>79</th>\n",
-       "      <td>80</td>\n",
-       "      <td>Charonne</td>\n",
-       "      <td>20</td>\n",
-       "      <td>62901</td>\n",
-       "      <td>2.090904e+06</td>\n",
-       "      <td>30083.160559</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "    id      district_name  arrondissement  population          area  \\\n",
-       "45  46             Picpus              12       62947  7.205014e+06   \n",
-       "49  50               Gare              13       69008  3.044178e+06   \n",
-       "50  51     Maison-Blanche              13       64797  2.231506e+06   \n",
-       "55  56          Plaisance              14       57229  1.784948e+06   \n",
-       "56  57      Saint-Lambert              15       82032  2.829202e+06   \n",
-       "60  61            Auteuil              16       67967  6.383888e+06   \n",
-       "68  69  Grandes-Carrières              18       67152  1.905566e+06   \n",
-       "69  70       Clignancourt              18       64868  1.653715e+06   \n",
-       "72  73           Villette              19       53650  1.285705e+06   \n",
-       "74  75           Amérique              19       55365  1.835720e+06   \n",
-       "79  80           Charonne              20       62901  2.090904e+06   \n",
-       "\n",
-       "    population_density  \n",
-       "45         8736.554340  \n",
-       "49        22668.848864  \n",
-       "50        29037.343809  \n",
-       "55        32062.000823  \n",
-       "56        28994.752912  \n",
-       "60        10646.647042  \n",
-       "68        35239.925867  \n",
-       "69        39225.618453  \n",
-       "72        41728.078744  \n",
-       "74        30159.823779  \n",
-       "79        30083.160559  "
-      ]
-     },
-     "execution_count": 45,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "id": "1efa8314",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
    "source": [
     "districts[districts['population'] > 50000]"
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "515bc15d",
    "metadata": {},
    "source": [
     "<div class=\"alert alert-success\">\n",
     "\n",
     "**EXERCISE**:\n",
     "\n",
-    "* How many districts are this that have a population of more than 50.000 inhabitans?\n",
+    "* How many districts have a population of more than 50.000 inhabitants?\n",
     "\n",
     "<details>\n",
     "  <summary>Hints</summary>\n",
@@ -3256,20 +1061,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "11"
-      ]
-     },
-     "execution_count": 46,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "id": "19d263a0",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
    "source": [
     "subset = districts[districts['population'] > 50000]\n",
     "len(subset)"
@@ -3277,46 +1076,35 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(11, 6)"
-      ]
-     },
-     "execution_count": 47,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "id": "7a3141d6",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
    "source": [
     "subset.shape"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "11"
-      ]
-     },
-     "execution_count": 48,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "id": "8a30d981",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
    "source": [
     "(districts['population'] > 50000).sum()"
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "44a2903c",
    "metadata": {},
    "source": [
     "## Plotting: visual exploration of data"
@@ -3324,6 +1112,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "3c17f0c6",
    "metadata": {},
    "source": [
     "The **`plot`** method can be used to quickly visualize the data in different ways:"
@@ -3331,94 +1120,43 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
-   "metadata": {
-    "run_control": {
-     "frozen": false,
-     "read_only": false
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<AxesSubplot:>"
-      ]
-     },
-     "execution_count": 49,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "countries['pop_est'].plot()"
+   "execution_count": null,
+   "id": "7772d637",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "countries['pop_est'].plot();"
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "7f42df91",
    "metadata": {},
    "source": [
-    "The default is a *line* plot. However, for this dataset, that's a not very useful plot type. With the `kind` keyword, you can specify other plot types:"
+    "The default is a *line* plot. However, for this dataset, that's a not very useful plot type. With the `.plot.<kind>` accessor (or the `kind` keyword), you can specify other plot types:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
-   "metadata": {
-    "run_control": {
-     "frozen": false,
-     "read_only": false
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<AxesSubplot:ylabel='Frequency'>"
-      ]
-     },
-     "execution_count": 50,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "countries['pop_est'].plot(kind='hist')"
+   "execution_count": null,
+   "id": "b567739a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "countries['pop_est'].plot.hist()  # or: .plot(kind='hist')"
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "a750bfc1",
    "metadata": {},
    "source": [
-    "This is still not the best plot, but now you can see this is due to a few countries with a very large population."
+    "This is still not the best plot. This is due to a few countries with a very large population."
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "24abb880",
    "metadata": {},
    "source": [
     "### A `matplotlib` primer\n",
@@ -3434,6 +1172,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "77d11b9c",
    "metadata": {},
    "source": [
     "Matplotlib comes with a convenience sub-package called ``pyplot`` which, for consistency with the wider matplotlib community, should always be imported as ``plt``:"
@@ -3441,7 +1180,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": null,
+   "id": "78c4d72e",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3450,6 +1190,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "a13a07e5",
    "metadata": {},
    "source": [
     "The object returned by the pandas `plot()` method is called an **Axes**"
@@ -3457,48 +1198,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": null,
+   "id": "3b8d31ca",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "ax = countries['pop_est'].plot(kind='hist')"
+    "ax = countries['pop_est'].plot.hist()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "matplotlib.axes._subplots.AxesSubplot"
-      ]
-     },
-     "execution_count": 53,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "id": "c7cd9fe9",
+   "metadata": {},
+   "outputs": [],
    "source": [
     "type(ax)"
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "1689986f",
    "metadata": {},
    "source": [
     "The Axes represents the \"data space\" of a typical plot: where data is being plotted, and typically having an x and y *axis*. The Axes is part of a **Figure** (in the above, the Figure has one Axes, or subplot, but you can also create a Figure with multiple Axes or subplots). \n",
@@ -3508,28 +1228,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": null,
+   "id": "bcc89b8b",
+   "metadata": {},
+   "outputs": [],
    "source": [
     "fig, axs = plt.subplots()  # ncols="
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "132be32a",
    "metadata": {},
    "source": [
     "and then specify the Axes to plot on with pandas:"
@@ -3537,39 +1246,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<AxesSubplot:ylabel='Frequency'>"
-      ]
-     },
-     "execution_count": 55,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": null,
+   "id": "23889ae8",
+   "metadata": {},
+   "outputs": [],
    "source": [
     "fig, axs = plt.subplots()\n",
-    "countries['pop_est'].plot(kind=\"hist\", ax=axs)"
+    "countries['pop_est'].plot.hist(ax=axs)"
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "2f8d97aa",
    "metadata": {},
    "source": [
     "The Axes object can then be used for further customization:"
@@ -3577,35 +1265,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(0.0, 500000000.0)"
-      ]
-     },
-     "execution_count": 56,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": null,
+   "id": "a46197ea",
+   "metadata": {},
+   "outputs": [],
    "source": [
     "fig, axs = plt.subplots()\n",
-    "countries['pop_est'].plot(kind=\"hist\", ax=axs, bins=50)\n",
+    "countries['pop_est'].plot.hist(ax=axs, bins=50)\n",
     "axs.set_title(\"Countries of the world\")\n",
     "axs.set_xlabel(\"Population\")\n",
     "axs.set_xlim(0, 5e8)"
@@ -3613,6 +1279,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "22a58dcb",
    "metadata": {},
    "source": [
     "An example of another package built on top of matplotlib is **`seaborn`** (https://seaborn.pydata.org/). It provides a high-level interface for a set of statistical graphics.\n",
@@ -3622,7 +1289,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
+   "execution_count": null,
+   "id": "db436501",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3631,32 +1299,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 58,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<AxesSubplot:xlabel='gdp_capita', ylabel='continent'>"
-      ]
-     },
-     "execution_count": 58,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 576x576 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": null,
+   "id": "10bb43df",
+   "metadata": {},
+   "outputs": [],
    "source": [
     "fig, ax = plt.subplots(figsize=(8, 8))\n",
     "seaborn.boxplot(y=\"continent\", x=\"gdp_capita\", color=\"C0\", data=countries, ax=ax)  # violinplot"
@@ -3664,13 +1310,15 @@
   },
   {
    "cell_type": "markdown",
+   "id": "a7178dfe",
    "metadata": {},
    "source": [
-    "This is only an ultra short intro to matplotlib. In the course materials, you can find another notebook with some more details ([visualization_01_matplotlib.ipynb](visualization_01_matplotlib.ipynb#An-small-cheat-sheet-reference-for-some-common-elements))."
+    "This is only an ultra short intro to matplotlib. In the course materials, you can find another notebook with some more details ([visualization-01-matplotlib.ipynb](visualization-01-matplotlib.ipynb#An-small-cheat-sheet-reference-for-some-common-elements))."
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "512961a9",
    "metadata": {},
    "source": [
     "<div class=\"alert alert-info\" style=\"font-size:18px\">\n",
@@ -3679,7 +1327,7 @@
     "\n",
     "Galleries are great to get inspiration, see the plot you want, and check the code how it is created:\n",
     "    \n",
-    "* [matplotlib gallery](http://matplotlib.org/gallery.html\") is an important resource to start from\n",
+    "* [matplotlib gallery](https://matplotlib.org/stable/gallery/) is an important resource to start from\n",
     "* [seaborn gallery](https://seaborn.pydata.org/examples/index.html)\n",
     "* The Python Graph Gallery (https://python-graph-gallery.com/)\n",
     "\n",
@@ -3688,6 +1336,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "b848eca8",
    "metadata": {},
    "source": [
     "## Aggregating statistics grouped by category\n",
@@ -3697,6 +1346,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "7f4d2824",
    "metadata": {},
    "source": [
     "Assume we want to know the total population number of a certain continent. Using the boolean indexing and aggregation we learned above, we can do:"
@@ -3704,7 +1354,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 59,
+   "execution_count": null,
+   "id": "123152df",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3713,26 +1364,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 60,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "1219176238"
-      ]
-     },
-     "execution_count": 60,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "id": "67392102",
+   "metadata": {},
+   "outputs": [],
    "source": [
     "africa['pop_est'].sum()"
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "648ec166",
    "metadata": {},
    "source": [
     "To calculate this for all continents, we could repeat the above for each continent. However, this quickly becomes tedious, and can directly be performed by pandas:"
@@ -3740,33 +1382,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 61,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "continent\n",
-       "Africa           1219176238\n",
-       "Asia             4389144868\n",
-       "Europe            746398461\n",
-       "North America     573042112\n",
-       "Oceania            36782844\n",
-       "South America     418540749\n",
-       "Name: pop_est, dtype: int64"
-      ]
-     },
-     "execution_count": 61,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "id": "decddd9d",
+   "metadata": {},
+   "outputs": [],
    "source": [
     "countries.groupby('continent')['pop_est'].sum()"
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "56ca34b8",
    "metadata": {},
    "source": [
     "Calculating a given statistic (e.g. sum of the population) for each category in a column (e.g. the different continents) is a common pattern. The `groupby()` method is used to support this type of operations. More general, this fits in the more general split-apply-combine pattern:\n",
@@ -3780,38 +1406,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 62,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<AxesSubplot:ylabel='continent'>"
-      ]
-     },
-     "execution_count": 62,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "countries.groupby('continent')['pop_est'].sum().plot(kind=\"barh\")"
+   "execution_count": null,
+   "id": "0f7ae4f9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "countries.groupby('continent')['pop_est'].sum().plot.barh()  # or plot(kind=\"barh\")"
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "a95e39ca",
    "metadata": {},
    "source": [
     "## Let's practice! (part II)"
@@ -3819,7 +1424,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 63,
+   "execution_count": null,
+   "id": "8caca287",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3829,211 +1435,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 64,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>id</th>\n",
-       "      <th>district_name</th>\n",
-       "      <th>arrondissement</th>\n",
-       "      <th>population</th>\n",
-       "      <th>area</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1</td>\n",
-       "      <td>St-Germain-l'Auxerrois</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1672</td>\n",
-       "      <td>869000.664583</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>2</td>\n",
-       "      <td>Halles</td>\n",
-       "      <td>1</td>\n",
-       "      <td>8984</td>\n",
-       "      <td>412458.496338</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>3</td>\n",
-       "      <td>Palais-Royal</td>\n",
-       "      <td>1</td>\n",
-       "      <td>3195</td>\n",
-       "      <td>273696.793306</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>4</td>\n",
-       "      <td>Place-Vendôme</td>\n",
-       "      <td>1</td>\n",
-       "      <td>3044</td>\n",
-       "      <td>269456.780605</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>5</td>\n",
-       "      <td>Gaillon</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1345</td>\n",
-       "      <td>188012.203855</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   id           district_name  arrondissement  population           area\n",
-       "0   1  St-Germain-l'Auxerrois               1        1672  869000.664583\n",
-       "1   2                  Halles               1        8984  412458.496338\n",
-       "2   3            Palais-Royal               1        3195  273696.793306\n",
-       "3   4           Place-Vendôme               1        3044  269456.780605\n",
-       "4   5                 Gaillon               2        1345  188012.203855"
-      ]
-     },
-     "execution_count": 64,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "id": "c705486d",
+   "metadata": {},
+   "outputs": [],
    "source": [
     "districts.head()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 65,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>name</th>\n",
-       "      <th>bike_stands</th>\n",
-       "      <th>available_bikes</th>\n",
-       "      <th>lat</th>\n",
-       "      <th>lon</th>\n",
-       "      <th>arrondissement</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>14002 - RASPAIL QUINET</td>\n",
-       "      <td>44</td>\n",
-       "      <td>4</td>\n",
-       "      <td>48.839199</td>\n",
-       "      <td>2.329555</td>\n",
-       "      <td>14.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>20503 - COURS DE VINCENNES PYRÉNÉES</td>\n",
-       "      <td>21</td>\n",
-       "      <td>3</td>\n",
-       "      <td>48.847725</td>\n",
-       "      <td>2.405907</td>\n",
-       "      <td>20.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>20011 - PYRÉNÉES-DAGORNO</td>\n",
-       "      <td>21</td>\n",
-       "      <td>0</td>\n",
-       "      <td>48.855501</td>\n",
-       "      <td>2.405169</td>\n",
-       "      <td>20.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>31008 - VINCENNES (MONTREUIL)</td>\n",
-       "      <td>56</td>\n",
-       "      <td>0</td>\n",
-       "      <td>48.857702</td>\n",
-       "      <td>2.437369</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>43006 - MINIMES (VINCENNES)</td>\n",
-       "      <td>28</td>\n",
-       "      <td>27</td>\n",
-       "      <td>48.841487</td>\n",
-       "      <td>2.430793</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                  name  bike_stands  available_bikes  \\\n",
-       "0               14002 - RASPAIL QUINET           44                4   \n",
-       "1  20503 - COURS DE VINCENNES PYRÉNÉES           21                3   \n",
-       "2             20011 - PYRÉNÉES-DAGORNO           21                0   \n",
-       "3        31008 - VINCENNES (MONTREUIL)           56                0   \n",
-       "4          43006 - MINIMES (VINCENNES)           28               27   \n",
-       "\n",
-       "         lat       lon  arrondissement  \n",
-       "0  48.839199  2.329555            14.0  \n",
-       "1  48.847725  2.405907            20.0  \n",
-       "2  48.855501  2.405169            20.0  \n",
-       "3  48.857702  2.437369             NaN  \n",
-       "4  48.841487  2.430793             NaN  "
-      ]
-     },
-     "execution_count": 65,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "id": "a0f53ece",
+   "metadata": {},
+   "outputs": [],
    "source": [
     "stations.head()"
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "e229a0f9",
    "metadata": {},
    "source": [
     "<div class=\"alert alert-success\">\n",
@@ -4046,152 +1468,88 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 66,
+   "execution_count": null,
+   "id": "556ba277",
    "metadata": {
-    "clear_cell": true,
-    "run_control": {
-     "frozen": false,
-     "read_only": false
-    }
+    "tags": [
+     "nbtutor-solution"
+    ]
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<AxesSubplot:>"
-      ]
-     },
-     "execution_count": 66,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "districts['population'].hist()  # or .plot(kind=\"hist\")"
+   "outputs": [],
+   "source": [
+    "districts['population'].plot.hist()  # or .plot(kind=\"hist\")"
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "0ffbdbbd",
    "metadata": {},
    "source": [
     "<div class=\"alert alert-success\">\n",
     "\n",
     "<b>EXERCISE</b>:\n",
     "\n",
-    "* Using groupby(), calculate the total population of each of the arrondissments.\n",
+    "* Using groupby(), calculate the total population of each of the arrondissements.\n",
     "\n",
     "</div>"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 67,
+   "execution_count": null,
+   "id": "4d24b6a6",
    "metadata": {
-    "clear_cell": true,
-    "run_control": {
-     "frozen": false,
-     "read_only": false
-    }
+    "tags": [
+     "nbtutor-solution"
+    ]
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "arrondissement\n",
-       "1      16895\n",
-       "2      19640\n",
-       "3      34232\n",
-       "4      30671\n",
-       "5      58841\n",
-       "6      44903\n",
-       "7      56988\n",
-       "8      39303\n",
-       "9      55783\n",
-       "10     89685\n",
-       "11    149166\n",
-       "12    136662\n",
-       "13    171577\n",
-       "14    132822\n",
-       "15    225467\n",
-       "16    161817\n",
-       "17    161138\n",
-       "18    184581\n",
-       "19    172587\n",
-       "20    183093\n",
-       "Name: population, dtype: int64"
-      ]
-     },
-     "execution_count": 67,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "districts.groupby('arrondissement')['population'].sum()"
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "ef016c60",
    "metadata": {},
    "source": [
     "<div class=\"alert alert-success\">\n",
     "\n",
     "<b>EXERCISE</b>:\n",
     "\n",
-    "* Using the bike stations dataset (`stations`), make a figure with a histogram of the total bike stands and available bikes.\n",
+    "* Using the bike stations dataset (`stations`), make a figure with a histogram of both the total bike stands and available bikes.\n",
+    "\n",
+    "<details>\n",
+    "<summary>Hints</summary>\n",
+    "\n",
+    "* You can plot both histograms on top of each other using transparancy (`alpha` keyword) to see both.\n",
+    "* Use the `plt.subplots()` function to create a figure and axes object. You can then use this axes object to pass to `.plot.hist(..)` to plot two histograms on the same axes.\n",
+    "</details>\n",
     "\n",
     "</div>"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 68,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.legend.Legend at 0x7ff46bc73c70>"
-      ]
-     },
-     "execution_count": 68,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": null,
+   "id": "1cc89119",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
    "source": [
     "fig, ax = plt.subplots()\n",
-    "stations[\"bike_stands\"].hist(ax=ax, alpha=.5, label=\"Total bike stands\")\n",
-    "stations[\"available_bikes\"].hist(ax=ax, alpha=.5, label=\"Available bikes\")\n",
-    "ax.legend()"
+    "stations[\"bike_stands\"].plot.hist(ax=ax, alpha=.5, label=\"Total bike stands\")\n",
+    "stations[\"available_bikes\"].plot.hist(ax=ax, alpha=.5, label=\"Available bikes\")\n",
+    "ax.legend()\n",
+    "ax.grid()"
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "57a486ad",
    "metadata": {},
    "source": [
     "<div class=\"alert alert-success\">\n",
@@ -4205,8 +1563,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 69,
-   "metadata": {},
+   "execution_count": null,
+   "id": "f4172121",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
    "outputs": [],
    "source": [
     "import seaborn"
@@ -4214,47 +1577,31 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 70,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<AxesSubplot:xlabel='arrondissement', ylabel='bike_stands'>"
-      ]
-     },
-     "execution_count": 70,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": null,
+   "id": "4e10223b",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
    "source": [
     "seaborn.boxplot(x=\"arrondissement\", y=\"bike_stands\", data=stations)"
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "8124483d",
    "metadata": {},
    "source": [
     "## Other useful things to know!\n",
     "\n",
-    "This notebook is only a very short intro. The pandas library provides a lot more functionality for working with tabular data, which we won't be able to cover all in this course."
+    "This notebook is only an intro. The pandas library provides a lot more functionality for working with tabular data, which we won't be cover in this course."
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "5e3b784a",
    "metadata": {},
    "source": [
     "### Counting values"
@@ -4262,6 +1609,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "6000cab9",
    "metadata": {},
    "source": [
     "Do you want to know what the unique values are of a column, and how many times each value occurs? Use the `value_counts()` method:"
@@ -4269,32 +1617,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 71,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Africa           51\n",
-       "Asia             47\n",
-       "Europe           39\n",
-       "North America    18\n",
-       "South America    13\n",
-       "Oceania           7\n",
-       "Name: continent, dtype: int64"
-      ]
-     },
-     "execution_count": 71,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "id": "19c8cbd5",
+   "metadata": {},
+   "outputs": [],
    "source": [
     "countries['continent'].value_counts()"
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "2def17b3",
    "metadata": {},
    "source": [
     "### Selecting a specific value of the DataFrame"
@@ -4302,6 +1635,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "809f2c7a",
    "metadata": {},
    "source": [
     "To access a specific value of a DataFrame, we can use the `.loc` method passing the (row label, column name):"
@@ -4309,26 +1643,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 72,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'Fiji'"
-      ]
-     },
-     "execution_count": 72,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "id": "cd1291c4",
+   "metadata": {},
+   "outputs": [],
    "source": [
     "countries.loc[0, \"name\"]"
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "6e994287",
    "metadata": {},
    "source": [
     "### Merging two dataframes"
@@ -4336,14 +1661,16 @@
   },
   {
    "cell_type": "markdown",
+   "id": "5a07b612",
    "metadata": {},
    "source": [
-    "Pandas provides several ways to combine different DataFrames. If there is a common column on which you want to match both DataFrames, we can use the `pd.merge()` function: "
+    "Pandas provides several ways to combine different DataFrames. If there is a common column on which you want to match both DataFrames, we can use the `pd.merge()` function:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 73,
+   "execution_count": null,
+   "id": "d6d515df",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -4352,551 +1679,67 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 74,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>iso_a3</th>\n",
-       "      <th>name</th>\n",
-       "      <th>population</th>\n",
-       "      <th>latitude</th>\n",
-       "      <th>longitude</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>VAT</td>\n",
-       "      <td>Vatican City</td>\n",
-       "      <td>832</td>\n",
-       "      <td>41.900012</td>\n",
-       "      <td>12.447808</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>SMR</td>\n",
-       "      <td>San Marino</td>\n",
-       "      <td>29579</td>\n",
-       "      <td>43.917150</td>\n",
-       "      <td>12.466670</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>LIE</td>\n",
-       "      <td>Vaduz</td>\n",
-       "      <td>36281</td>\n",
-       "      <td>47.133724</td>\n",
-       "      <td>9.516669</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>SWZ</td>\n",
-       "      <td>Lobamba</td>\n",
-       "      <td>9782</td>\n",
-       "      <td>-26.466667</td>\n",
-       "      <td>31.199997</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>LUX</td>\n",
-       "      <td>Luxembourg</td>\n",
-       "      <td>107260</td>\n",
-       "      <td>49.611660</td>\n",
-       "      <td>6.130003</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "  iso_a3          name  population   latitude  longitude\n",
-       "0    VAT  Vatican City         832  41.900012  12.447808\n",
-       "1    SMR    San Marino       29579  43.917150  12.466670\n",
-       "2    LIE         Vaduz       36281  47.133724   9.516669\n",
-       "3    SWZ       Lobamba        9782 -26.466667  31.199997\n",
-       "4    LUX    Luxembourg      107260  49.611660   6.130003"
-      ]
-     },
-     "execution_count": 74,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "id": "e3aa23d6",
+   "metadata": {},
+   "outputs": [],
    "source": [
     "cities.head()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 75,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>iso_a3</th>\n",
-       "      <th>name</th>\n",
-       "      <th>pop_est</th>\n",
-       "      <th>gdp_md_est</th>\n",
-       "      <th>continent</th>\n",
-       "      <th>gdp_capita</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>FJI</td>\n",
-       "      <td>Fiji</td>\n",
-       "      <td>920938</td>\n",
-       "      <td>8374.0</td>\n",
-       "      <td>Oceania</td>\n",
-       "      <td>0.009093</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>TZA</td>\n",
-       "      <td>Tanzania</td>\n",
-       "      <td>53950935</td>\n",
-       "      <td>150600.0</td>\n",
-       "      <td>Africa</td>\n",
-       "      <td>0.002791</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>ESH</td>\n",
-       "      <td>W. Sahara</td>\n",
-       "      <td>603253</td>\n",
-       "      <td>906.5</td>\n",
-       "      <td>Africa</td>\n",
-       "      <td>0.001503</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>CAN</td>\n",
-       "      <td>Canada</td>\n",
-       "      <td>35623680</td>\n",
-       "      <td>1674000.0</td>\n",
-       "      <td>North America</td>\n",
-       "      <td>0.046991</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>USA</td>\n",
-       "      <td>United States of America</td>\n",
-       "      <td>326625791</td>\n",
-       "      <td>18560000.0</td>\n",
-       "      <td>North America</td>\n",
-       "      <td>0.056823</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "  iso_a3                      name    pop_est  gdp_md_est      continent  \\\n",
-       "0    FJI                      Fiji     920938      8374.0        Oceania   \n",
-       "1    TZA                  Tanzania   53950935    150600.0         Africa   \n",
-       "2    ESH                 W. Sahara     603253       906.5         Africa   \n",
-       "3    CAN                    Canada   35623680   1674000.0  North America   \n",
-       "4    USA  United States of America  326625791  18560000.0  North America   \n",
-       "\n",
-       "   gdp_capita  \n",
-       "0    0.009093  \n",
-       "1    0.002791  \n",
-       "2    0.001503  \n",
-       "3    0.046991  \n",
-       "4    0.056823  "
-      ]
-     },
-     "execution_count": 75,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "id": "18d769c9",
+   "metadata": {},
+   "outputs": [],
    "source": [
     "countries.head()"
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "e7419654",
    "metadata": {},
    "source": [
-    "Both DataFrames have the `'iso_a3'` column with a 3-character code of the country. Based on this, we can add information about the country to the cities dataset: "
+    "Both DataFrames have the `'iso_a3'` column with a 3-character code of the country. Based on this, we can add information about the country to the cities dataset:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 76,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>iso_a3</th>\n",
-       "      <th>name_x</th>\n",
-       "      <th>population</th>\n",
-       "      <th>latitude</th>\n",
-       "      <th>longitude</th>\n",
-       "      <th>name_y</th>\n",
-       "      <th>pop_est</th>\n",
-       "      <th>gdp_md_est</th>\n",
-       "      <th>continent</th>\n",
-       "      <th>gdp_capita</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>SWZ</td>\n",
-       "      <td>Lobamba</td>\n",
-       "      <td>9782</td>\n",
-       "      <td>-26.466667</td>\n",
-       "      <td>31.199997</td>\n",
-       "      <td>eSwatini</td>\n",
-       "      <td>1467152</td>\n",
-       "      <td>11060.0</td>\n",
-       "      <td>Africa</td>\n",
-       "      <td>0.007538</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>SWZ</td>\n",
-       "      <td>Mbabane</td>\n",
-       "      <td>90138</td>\n",
-       "      <td>-26.316651</td>\n",
-       "      <td>31.133335</td>\n",
-       "      <td>eSwatini</td>\n",
-       "      <td>1467152</td>\n",
-       "      <td>11060.0</td>\n",
-       "      <td>Africa</td>\n",
-       "      <td>0.007538</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>LUX</td>\n",
-       "      <td>Luxembourg</td>\n",
-       "      <td>107260</td>\n",
-       "      <td>49.611660</td>\n",
-       "      <td>6.130003</td>\n",
-       "      <td>Luxembourg</td>\n",
-       "      <td>594130</td>\n",
-       "      <td>58740.0</td>\n",
-       "      <td>Europe</td>\n",
-       "      <td>0.098867</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>TTO</td>\n",
-       "      <td>Port-of-Spain</td>\n",
-       "      <td>294934</td>\n",
-       "      <td>10.651997</td>\n",
-       "      <td>-61.517031</td>\n",
-       "      <td>Trinidad and Tobago</td>\n",
-       "      <td>1218208</td>\n",
-       "      <td>43570.0</td>\n",
-       "      <td>North America</td>\n",
-       "      <td>0.035766</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>RWA</td>\n",
-       "      <td>Kigali</td>\n",
-       "      <td>860000</td>\n",
-       "      <td>-1.953590</td>\n",
-       "      <td>30.060532</td>\n",
-       "      <td>Rwanda</td>\n",
-       "      <td>11901484</td>\n",
-       "      <td>21970.0</td>\n",
-       "      <td>Africa</td>\n",
-       "      <td>0.001846</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>204</th>\n",
-       "      <td>ITA</td>\n",
-       "      <td>Rome</td>\n",
-       "      <td>3339000</td>\n",
-       "      <td>41.895956</td>\n",
-       "      <td>12.483258</td>\n",
-       "      <td>Italy</td>\n",
-       "      <td>62137802</td>\n",
-       "      <td>2221000.0</td>\n",
-       "      <td>Europe</td>\n",
-       "      <td>0.035743</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>205</th>\n",
-       "      <td>KEN</td>\n",
-       "      <td>Nairobi</td>\n",
-       "      <td>3010000</td>\n",
-       "      <td>-1.283347</td>\n",
-       "      <td>36.816657</td>\n",
-       "      <td>Kenya</td>\n",
-       "      <td>47615739</td>\n",
-       "      <td>152700.0</td>\n",
-       "      <td>Africa</td>\n",
-       "      <td>0.003207</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>206</th>\n",
-       "      <td>IDN</td>\n",
-       "      <td>Jakarta</td>\n",
-       "      <td>9125000</td>\n",
-       "      <td>-6.174418</td>\n",
-       "      <td>106.829438</td>\n",
-       "      <td>Indonesia</td>\n",
-       "      <td>260580739</td>\n",
-       "      <td>3028000.0</td>\n",
-       "      <td>Asia</td>\n",
-       "      <td>0.011620</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>207</th>\n",
-       "      <td>COL</td>\n",
-       "      <td>Bogota</td>\n",
-       "      <td>7772000</td>\n",
-       "      <td>4.596424</td>\n",
-       "      <td>-74.083344</td>\n",
-       "      <td>Colombia</td>\n",
-       "      <td>47698524</td>\n",
-       "      <td>688000.0</td>\n",
-       "      <td>South America</td>\n",
-       "      <td>0.014424</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>208</th>\n",
-       "      <td>EGY</td>\n",
-       "      <td>Cairo</td>\n",
-       "      <td>11893000</td>\n",
-       "      <td>30.049960</td>\n",
-       "      <td>31.249968</td>\n",
-       "      <td>Egypt</td>\n",
-       "      <td>97041072</td>\n",
-       "      <td>1105000.0</td>\n",
-       "      <td>Africa</td>\n",
-       "      <td>0.011387</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>209 rows × 10 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "    iso_a3         name_x  population   latitude   longitude  \\\n",
-       "0      SWZ        Lobamba        9782 -26.466667   31.199997   \n",
-       "1      SWZ        Mbabane       90138 -26.316651   31.133335   \n",
-       "2      LUX     Luxembourg      107260  49.611660    6.130003   \n",
-       "3      TTO  Port-of-Spain      294934  10.651997  -61.517031   \n",
-       "4      RWA         Kigali      860000  -1.953590   30.060532   \n",
-       "..     ...            ...         ...        ...         ...   \n",
-       "204    ITA           Rome     3339000  41.895956   12.483258   \n",
-       "205    KEN        Nairobi     3010000  -1.283347   36.816657   \n",
-       "206    IDN        Jakarta     9125000  -6.174418  106.829438   \n",
-       "207    COL         Bogota     7772000   4.596424  -74.083344   \n",
-       "208    EGY          Cairo    11893000  30.049960   31.249968   \n",
-       "\n",
-       "                  name_y    pop_est  gdp_md_est      continent  gdp_capita  \n",
-       "0               eSwatini    1467152     11060.0         Africa    0.007538  \n",
-       "1               eSwatini    1467152     11060.0         Africa    0.007538  \n",
-       "2             Luxembourg     594130     58740.0         Europe    0.098867  \n",
-       "3    Trinidad and Tobago    1218208     43570.0  North America    0.035766  \n",
-       "4                 Rwanda   11901484     21970.0         Africa    0.001846  \n",
-       "..                   ...        ...         ...            ...         ...  \n",
-       "204                Italy   62137802   2221000.0         Europe    0.035743  \n",
-       "205                Kenya   47615739    152700.0         Africa    0.003207  \n",
-       "206            Indonesia  260580739   3028000.0           Asia    0.011620  \n",
-       "207             Colombia   47698524    688000.0  South America    0.014424  \n",
-       "208                Egypt   97041072   1105000.0         Africa    0.011387  \n",
-       "\n",
-       "[209 rows x 10 columns]"
-      ]
-     },
-     "execution_count": 76,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "id": "b08032c8",
+   "metadata": {},
+   "outputs": [],
    "source": [
     "pd.merge(cities, countries, on=\"iso_a3\")"
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "9b05d64a",
    "metadata": {},
    "source": [
     "## Towards geospatial data\n",
     "\n",
-    "The datasets used in this notebook are actually containing spatial information: data about areas (countries, districts) or point locations (cities, bike stations). But the data itself didn't always explicitly include the the spatial component. For example, we don't know the exact extent of the countries with the dataset used here. \n",
+    "The datasets used in this notebook contain spatial information: data about areas (countries, districts) or point locations (cities, bike stations). But the data itself didn't always explicitly include the the spatial component. For example, we don't know the exact extent of the countries with the dataset used here.\n",
     "\n",
     "With point locations, such as the cities DataFrame, the location is included as two columns:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 77,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>iso_a3</th>\n",
-       "      <th>name</th>\n",
-       "      <th>population</th>\n",
-       "      <th>latitude</th>\n",
-       "      <th>longitude</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>VAT</td>\n",
-       "      <td>Vatican City</td>\n",
-       "      <td>832</td>\n",
-       "      <td>41.900012</td>\n",
-       "      <td>12.447808</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>SMR</td>\n",
-       "      <td>San Marino</td>\n",
-       "      <td>29579</td>\n",
-       "      <td>43.917150</td>\n",
-       "      <td>12.466670</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>LIE</td>\n",
-       "      <td>Vaduz</td>\n",
-       "      <td>36281</td>\n",
-       "      <td>47.133724</td>\n",
-       "      <td>9.516669</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>SWZ</td>\n",
-       "      <td>Lobamba</td>\n",
-       "      <td>9782</td>\n",
-       "      <td>-26.466667</td>\n",
-       "      <td>31.199997</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>LUX</td>\n",
-       "      <td>Luxembourg</td>\n",
-       "      <td>107260</td>\n",
-       "      <td>49.611660</td>\n",
-       "      <td>6.130003</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "  iso_a3          name  population   latitude  longitude\n",
-       "0    VAT  Vatican City         832  41.900012  12.447808\n",
-       "1    SMR    San Marino       29579  43.917150  12.466670\n",
-       "2    LIE         Vaduz       36281  47.133724   9.516669\n",
-       "3    SWZ       Lobamba        9782 -26.466667  31.199997\n",
-       "4    LUX    Luxembourg      107260  49.611660   6.130003"
-      ]
-     },
-     "execution_count": 77,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "id": "66335f4e",
+   "metadata": {},
+   "outputs": [],
    "source": [
     "cities.head()"
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "01f5b54e",
    "metadata": {},
    "source": [
     "Which allows us to, for example, plot the locations manually:"
@@ -4904,38 +1747,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 78,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<AxesSubplot:xlabel='longitude', ylabel='latitude'>"
-      ]
-     },
-     "execution_count": 78,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "cities.plot(x=\"longitude\", y=\"latitude\", kind=\"scatter\")"
+   "execution_count": null,
+   "id": "300fe025",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cities.plot.scatter(x=\"longitude\", y=\"latitude\")"
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "c9243515",
    "metadata": {},
    "source": [
     "However, this doesn't enable to *easily* work with those locations and do spatial analyses on them. For that, we are going to introduce a new package: **`geopandas`**.\n",
@@ -4945,7 +1767,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 79,
+   "execution_count": null,
+   "id": "185fd8f7",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -4954,7 +1777,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 80,
+   "execution_count": null,
+   "id": "c35ee0e5",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -4963,200 +1787,30 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 81,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>iso_a3</th>\n",
-       "      <th>name</th>\n",
-       "      <th>population</th>\n",
-       "      <th>latitude</th>\n",
-       "      <th>longitude</th>\n",
-       "      <th>geometry</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>VAT</td>\n",
-       "      <td>Vatican City</td>\n",
-       "      <td>832</td>\n",
-       "      <td>41.900012</td>\n",
-       "      <td>12.447808</td>\n",
-       "      <td>POINT (12.44781 41.90001)</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>SMR</td>\n",
-       "      <td>San Marino</td>\n",
-       "      <td>29579</td>\n",
-       "      <td>43.917150</td>\n",
-       "      <td>12.466670</td>\n",
-       "      <td>POINT (12.46667 43.91715)</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>LIE</td>\n",
-       "      <td>Vaduz</td>\n",
-       "      <td>36281</td>\n",
-       "      <td>47.133724</td>\n",
-       "      <td>9.516669</td>\n",
-       "      <td>POINT (9.51667 47.13372)</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>SWZ</td>\n",
-       "      <td>Lobamba</td>\n",
-       "      <td>9782</td>\n",
-       "      <td>-26.466667</td>\n",
-       "      <td>31.199997</td>\n",
-       "      <td>POINT (31.20000 -26.46667)</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>LUX</td>\n",
-       "      <td>Luxembourg</td>\n",
-       "      <td>107260</td>\n",
-       "      <td>49.611660</td>\n",
-       "      <td>6.130003</td>\n",
-       "      <td>POINT (6.13000 49.61166)</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>238</th>\n",
-       "      <td>BRA</td>\n",
-       "      <td>Rio de Janeiro</td>\n",
-       "      <td>11748000</td>\n",
-       "      <td>-22.925023</td>\n",
-       "      <td>-43.225021</td>\n",
-       "      <td>POINT (-43.22502 -22.92502)</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>239</th>\n",
-       "      <td>BRA</td>\n",
-       "      <td>São Paulo</td>\n",
-       "      <td>18845000</td>\n",
-       "      <td>-23.558680</td>\n",
-       "      <td>-46.625020</td>\n",
-       "      <td>POINT (-46.62502 -23.55868)</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>240</th>\n",
-       "      <td>AUS</td>\n",
-       "      <td>Sydney</td>\n",
-       "      <td>4630000</td>\n",
-       "      <td>-33.920011</td>\n",
-       "      <td>151.185180</td>\n",
-       "      <td>POINT (151.18518 -33.92001)</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>241</th>\n",
-       "      <td>SGP</td>\n",
-       "      <td>Singapore</td>\n",
-       "      <td>5183700</td>\n",
-       "      <td>1.293033</td>\n",
-       "      <td>103.855821</td>\n",
-       "      <td>POINT (103.85582 1.29303)</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>242</th>\n",
-       "      <td>HKG</td>\n",
-       "      <td>Hong Kong</td>\n",
-       "      <td>7206000</td>\n",
-       "      <td>22.304981</td>\n",
-       "      <td>114.185009</td>\n",
-       "      <td>POINT (114.18501 22.30498)</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>243 rows × 6 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "    iso_a3            name  population   latitude   longitude  \\\n",
-       "0      VAT    Vatican City         832  41.900012   12.447808   \n",
-       "1      SMR      San Marino       29579  43.917150   12.466670   \n",
-       "2      LIE           Vaduz       36281  47.133724    9.516669   \n",
-       "3      SWZ         Lobamba        9782 -26.466667   31.199997   \n",
-       "4      LUX      Luxembourg      107260  49.611660    6.130003   \n",
-       "..     ...             ...         ...        ...         ...   \n",
-       "238    BRA  Rio de Janeiro    11748000 -22.925023  -43.225021   \n",
-       "239    BRA       São Paulo    18845000 -23.558680  -46.625020   \n",
-       "240    AUS          Sydney     4630000 -33.920011  151.185180   \n",
-       "241    SGP       Singapore     5183700   1.293033  103.855821   \n",
-       "242    HKG       Hong Kong     7206000  22.304981  114.185009   \n",
-       "\n",
-       "                        geometry  \n",
-       "0      POINT (12.44781 41.90001)  \n",
-       "1      POINT (12.46667 43.91715)  \n",
-       "2       POINT (9.51667 47.13372)  \n",
-       "3     POINT (31.20000 -26.46667)  \n",
-       "4       POINT (6.13000 49.61166)  \n",
-       "..                           ...  \n",
-       "238  POINT (-43.22502 -22.92502)  \n",
-       "239  POINT (-46.62502 -23.55868)  \n",
-       "240  POINT (151.18518 -33.92001)  \n",
-       "241    POINT (103.85582 1.29303)  \n",
-       "242   POINT (114.18501 22.30498)  \n",
-       "\n",
-       "[243 rows x 6 columns]"
-      ]
-     },
-     "execution_count": 81,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "id": "f24bee75",
+   "metadata": {},
+   "outputs": [],
    "source": [
     "cities_geo"
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "7cbac42e",
    "metadata": {},
    "source": [
     "Now, we have a single column with the location information (the \"geometry\" column). This is the topic of the next notebook."
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
+  "jupytext": {
+   "default_lexer": "ipython3",
+   "formats": "ipynb,md:myst"
+  },
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -5170,9 +1824,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.5"
+   "version": "3.12.11"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 4
+ "nbformat_minor": 5
 }
diff --git a/_solved/02-introduction-geospatial-data.ipynb b/_solved/02-introduction-geospatial-data.ipynb
index 4cd386a..60cad7f 100644
--- a/_solved/02-introduction-geospatial-data.ipynb
+++ b/_solved/02-introduction-geospatial-data.ipynb
@@ -2,25 +2,34 @@
  "cells": [
   {
    "cell_type": "markdown",
+   "id": "60ebdf8b",
    "metadata": {},
    "source": [
-    "# Introduction to geospatial vector data in Python"
+    "<p><font size=\"6\"><b> Introduction to geospatial vector data in Python</b></font></p>\n",
+    "\n",
+    "\n",
+    "> *DS Python for GIS and Geoscience*  \n",
+    "> *October, 2025*\n",
+    ">\n",
+    "> *© 2025, Joris Van den Bossche and Stijn Van Hoey  (<mailto:jorisvandenbossche@gmail.com>, <mailto:stijnvanhoey@gmail.com>). Licensed under [CC BY 4.0 Creative Commons](http://creativecommons.org/licenses/by/4.0/)*\n",
+    "\n",
+    "---"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
+   "id": "4cb8b8e1",
    "metadata": {},
    "outputs": [],
    "source": [
-    "%matplotlib inline\n",
-    "\n",
     "import pandas as pd\n",
     "import geopandas"
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "b59f6882",
    "metadata": {},
    "source": [
     "## Importing geospatial data"
@@ -28,180 +37,73 @@
   },
   {
    "cell_type": "markdown",
+   "id": "5c35bcd4",
    "metadata": {},
    "source": [
     "Geospatial data is often available from specific GIS file formats or data stores, like ESRI shapefiles, GeoJSON files, geopackage files, PostGIS (PostgreSQL) database, ...\n",
     "\n",
-    "We can use the GeoPandas library to read many of those GIS file formats (relying on the `fiona` library under the hood, which is an interface to GDAL/OGR), using the `geopandas.read_file` function.\n",
+    "We can use the GeoPandas library to read many of those GIS file formats (relying on the `pyogrio` library under the hood, which is an interface to GDAL/OGR), using the `geopandas.read_file` function.\n",
     "\n",
     "For example, let's start by reading a shapefile with all the countries of the world (adapted from http://www.naturalearthdata.com/downloads/110m-cultural-vectors/110m-admin-0-countries/, zip file is available in the `/data` directory), and inspect the data:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
+   "id": "3c51c022",
    "metadata": {},
    "outputs": [],
    "source": [
-    "countries = geopandas.read_file(\"zip://./data/ne_110m_admin_0_countries.zip\")\n",
+    "countries = geopandas.read_file(\"data/ne_110m_admin_0_countries.zip\")\n",
     "# or if the archive is unpacked:\n",
     "# countries = geopandas.read_file(\"data/ne_110m_admin_0_countries/ne_110m_admin_0_countries.shp\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
+   "id": "4a08e7c6",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>iso_a3</th>\n",
-       "      <th>name</th>\n",
-       "      <th>continent</th>\n",
-       "      <th>pop_est</th>\n",
-       "      <th>gdp_md_est</th>\n",
-       "      <th>geometry</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>AFG</td>\n",
-       "      <td>Afghanistan</td>\n",
-       "      <td>Asia</td>\n",
-       "      <td>34124811.0</td>\n",
-       "      <td>64080.0</td>\n",
-       "      <td>POLYGON ((61.21082 35.65007, 62.23065 35.27066...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>AGO</td>\n",
-       "      <td>Angola</td>\n",
-       "      <td>Africa</td>\n",
-       "      <td>29310273.0</td>\n",
-       "      <td>189000.0</td>\n",
-       "      <td>MULTIPOLYGON (((23.90415 -11.72228, 24.07991 -...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>ALB</td>\n",
-       "      <td>Albania</td>\n",
-       "      <td>Europe</td>\n",
-       "      <td>3047987.0</td>\n",
-       "      <td>33900.0</td>\n",
-       "      <td>POLYGON ((21.02004 40.84273, 20.99999 40.58000...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>ARE</td>\n",
-       "      <td>United Arab Emirates</td>\n",
-       "      <td>Asia</td>\n",
-       "      <td>6072475.0</td>\n",
-       "      <td>667200.0</td>\n",
-       "      <td>POLYGON ((51.57952 24.24550, 51.75744 24.29407...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>ARG</td>\n",
-       "      <td>Argentina</td>\n",
-       "      <td>South America</td>\n",
-       "      <td>44293293.0</td>\n",
-       "      <td>879400.0</td>\n",
-       "      <td>MULTIPOLYGON (((-66.95992 -54.89681, -67.56244...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "  iso_a3                  name      continent     pop_est  gdp_md_est  \\\n",
-       "0    AFG           Afghanistan           Asia  34124811.0     64080.0   \n",
-       "1    AGO                Angola         Africa  29310273.0    189000.0   \n",
-       "2    ALB               Albania         Europe   3047987.0     33900.0   \n",
-       "3    ARE  United Arab Emirates           Asia   6072475.0    667200.0   \n",
-       "4    ARG             Argentina  South America  44293293.0    879400.0   \n",
-       "\n",
-       "                                            geometry  \n",
-       "0  POLYGON ((61.21082 35.65007, 62.23065 35.27066...  \n",
-       "1  MULTIPOLYGON (((23.90415 -11.72228, 24.07991 -...  \n",
-       "2  POLYGON ((21.02004 40.84273, 20.99999 40.58000...  \n",
-       "3  POLYGON ((51.57952 24.24550, 51.75744 24.29407...  \n",
-       "4  MULTIPOLYGON (((-66.95992 -54.89681, -67.56244...  "
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "countries.head()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
+   "id": "ccf47b00",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<AxesSubplot:>"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "countries.plot()"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6c0ac4fa",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "countries.explore()"
+   ]
+  },
   {
    "cell_type": "markdown",
+   "id": "69191cf2",
    "metadata": {},
    "source": [
-    "What can we observe:\n",
+    "What do we observe:\n",
     "\n",
     "- Using `.head()` we can see the first rows of the dataset, just like we can do with Pandas.\n",
-    "- There is a 'geometry' column and the different countries are represented as polygons\n",
-    "- We can use the `.plot()` method to quickly get a *basic* visualization of the data"
+    "- There is a `geometry` column and the different countries are represented as polygons\n",
+    "- We can use the `.plot()` (matplotlib) or `explore()` (Folium / Leaflet.js) method to quickly get a *basic* visualization of the data"
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "508d3f1d",
    "metadata": {},
    "source": [
     "## What's a GeoDataFrame?\n",
@@ -211,26 +113,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
+   "id": "ea4363f9",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "geopandas.geodataframe.GeoDataFrame"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "type(countries)"
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "7c1f1160",
    "metadata": {},
    "source": [
     "A GeoDataFrame contains a tabular, geospatial dataset:\n",
@@ -241,131 +134,62 @@
     "Such a `GeoDataFrame` is just like a pandas `DataFrame`, but with some additional functionality for working with geospatial data:\n",
     "\n",
     "* A `.geometry` attribute that always returns the column with the geometry information (returning a GeoSeries). The column name itself does not necessarily need to be 'geometry', but it will always be accessible as the `.geometry` attribute.\n",
-    "* It has some extra methods for working with spatial data (area, distance, buffer, intersection, ...), which we will see in later notebooks"
+    "* It has some extra methods for working with spatial data (area, distance, buffer, intersection, ...), which we will learn in later notebooks"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
+   "id": "9c3f719b",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0      POLYGON ((61.21082 35.65007, 62.23065 35.27066...\n",
-       "1      MULTIPOLYGON (((23.90415 -11.72228, 24.07991 -...\n",
-       "2      POLYGON ((21.02004 40.84273, 20.99999 40.58000...\n",
-       "3      POLYGON ((51.57952 24.24550, 51.75744 24.29407...\n",
-       "4      MULTIPOLYGON (((-66.95992 -54.89681, -67.56244...\n",
-       "                             ...                        \n",
-       "172    MULTIPOLYGON (((167.84488 -16.46633, 167.51518...\n",
-       "173    POLYGON ((52.00001 19.00000, 52.78218 17.34974...\n",
-       "174    POLYGON ((19.89577 -24.76779, 20.16573 -24.917...\n",
-       "175    POLYGON ((23.21505 -17.52312, 22.56248 -16.898...\n",
-       "176    POLYGON ((29.43219 -22.09131, 28.79466 -21.639...\n",
-       "Name: geometry, Length: 177, dtype: geometry"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "countries.geometry"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
+   "id": "34dbbaba",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "geopandas.geoseries.GeoSeries"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "type(countries.geometry)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
+   "id": "f8af4ea8",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "<ipython-input-7-68baff36b7de>:1: UserWarning: Geometry is in a geographic CRS. Results from 'area' are likely incorrect. Use 'GeoSeries.to_crs()' to re-project geometries to a projected CRS before this operation.\n",
-      "\n",
-      "  countries.geometry.area\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "0       63.593500\n",
-       "1      103.599439\n",
-       "2        3.185163\n",
-       "3        7.095047\n",
-       "4      278.923392\n",
-       "          ...    \n",
-       "172      0.631326\n",
-       "173     38.475618\n",
-       "174    112.718524\n",
-       "175     62.789498\n",
-       "176     32.280371\n",
-       "Length: 177, dtype: float64"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "countries.geometry.area"
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "33b554b4",
    "metadata": {},
    "source": [
-    "**It's still a DataFrame**, so we have all the pandas functionality available to use on the geospatial dataset, and to do data manipulations with the attributes and geometry information together.\n",
+    "**It's still a DataFrame**, so we have all the Pandas functionality available to use on the geospatial dataset, and to do data manipulations with the attributes and geometry information together.\n",
     "\n",
     "For example, we can calculate average population number over all countries (by accessing the 'pop_est' column, and calling the `mean` method on it):"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
+   "id": "373067e4",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "41712369.84180791"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "countries['pop_est'].mean()"
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "c7042158",
    "metadata": {},
    "source": [
     "Or, we can use boolean filtering to select a subset of the dataframe based on a condition:"
@@ -373,7 +197,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
+   "id": "f22cd53f",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -382,38 +207,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
+   "id": "6e56375b",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<AxesSubplot:>"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "africa.plot()"
+   "outputs": [],
+   "source": [
+    "africa.plot();"
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "7a0ea171",
    "metadata": {},
    "source": [
     "<div class=\"alert alert-info\" style=\"font-size:120%\">\n",
@@ -430,6 +234,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "03114e5e",
    "metadata": {},
    "source": [
     "## Geometries: Points, Linestrings and Polygons\n",
@@ -447,6 +252,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "4719339c",
    "metadata": {},
    "source": [
     "For the example we have seen up to now, the individual geometry objects are Polygons:"
@@ -454,23 +260,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
+   "id": "995a0901",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "POLYGON ((21.0200403174764 40.84272695572588, 20.99998986174722 40.58000397395401, 20.67499677906363 40.43499990494303, 20.61500044117275 40.11000682225935, 20.15001590341052 39.62499766698397, 19.98000044117015 39.69499339452341, 19.96000166187321 39.91500580500605, 19.40608198413673 40.25077342382247, 19.31905887215714 40.72723012955356, 19.40354983895429 41.40956574153546, 19.54002729663711 41.71998607031276, 19.37176883309496 41.87754751237065, 19.37176816334725 41.8775506797835, 19.30448611825079 42.19574514420782, 19.73805138517963 42.68824738216557, 19.80161339689869 42.50009349219084, 20.07070000000004 42.58863000000008, 20.28375451018189 42.32025950781508, 20.52295000000004 42.21787000000006, 20.59024654668023 41.85540891928363, 20.59024743010491 41.85540416113361, 20.4631750830992 41.51508901627534, 20.60518191903736 41.08622630468523, 21.0200403174764 40.84272695572588))\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "print(countries.geometry[2])"
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "65ddf8cf",
    "metadata": {},
    "source": [
     "Let's import some other datasets with different types of geometry objects.\n",
@@ -480,32 +280,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
+   "id": "c3295faf",
    "metadata": {},
    "outputs": [],
    "source": [
-    "cities = geopandas.read_file(\"zip://./data/ne_110m_populated_places.zip\")"
+    "cities = geopandas.read_file(\"data/ne_110m_populated_places.zip\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
+   "id": "a5045be0",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "POINT (12.45338654497177 41.90328217996012)\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "print(cities.geometry[0])"
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "b47470e3",
    "metadata": {},
    "source": [
     "And a dataset of rivers in the world (from http://www.naturalearthdata.com/downloads/50m-physical-vectors/50m-rivers-lake-centerlines/, zip file is available in the `/data` directory) where each river is a (multi-)line:"
@@ -513,32 +308,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
+   "id": "1c533d17",
    "metadata": {},
    "outputs": [],
    "source": [
-    "rivers = geopandas.read_file(\"zip://./data/ne_50m_rivers_lake_centerlines.zip\")"
+    "rivers = geopandas.read_file(\"data/ne_50m_rivers_lake_centerlines.zip\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": null,
+   "id": "84582bad",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "LINESTRING (51.9371337598152 55.70106609892139, 51.88086646731369 55.68625891701544, 51.82031249962222 55.69745514553858, 51.7476018274624 55.69366250841807, 51.6628417966117 55.60817291874525, 51.57871093775964 55.59943268477065, 51.51342773400279 55.58312409100404, 51.50854492161091 55.52948639548083, 51.48583984403365 55.49640534033426, 51.36914062543957 55.46796295772435, 51.21306254869774 55.50264985760492, 51.13452148447897 55.48273346527725, 51.07934570274205 55.46759674659262, 50.98022460947817 55.46637604371949, 50.83445217522774 55.45630956063775, 50.6883789060617 55.42011139502489, 50.4118652342932 55.40119049644431, 50.07802734358711 55.38112213757665, 49.82216796867687 55.33466217681809, 49.53222656260584 55.260614325191, 49.38232421848795 55.17182037990665, 49.24808475131027 55.11301870345045)\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "print(rivers.geometry[0])"
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "6c0e225e",
    "metadata": {},
    "source": [
     "### The `shapely` library\n",
@@ -548,26 +338,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": null,
+   "id": "1d1a86ed",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "shapely.geometry.polygon.Polygon"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "type(countries.geometry[0])"
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "7ac32d9c",
    "metadata": {},
    "source": [
     "To construct one ourselves:"
@@ -575,16 +356,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": null,
+   "id": "0af29507",
    "metadata": {},
    "outputs": [],
    "source": [
-    "from shapely.geometry import Point, Polygon, LineString"
+    "from shapely import Point, Polygon, LineString"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": null,
+   "id": "10a5ecb1",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -593,24 +376,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": null,
+   "id": "4f197ebd",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "POINT (0 0)\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "print(p)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": null,
+   "id": "98d6ede1",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -619,46 +396,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": null,
+   "id": "2d6f595f",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.5"
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "polygon.area"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": null,
+   "id": "9d4185e1",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "1.4142135623730951"
-      ]
-     },
-     "execution_count": 22,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "polygon.distance(p)"
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "aa0ae3df",
    "metadata": {},
    "source": [
     "<div class=\"alert alert-info\" style=\"font-size:120%\">\n",
@@ -677,6 +435,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "0315991d",
    "metadata": {},
    "source": [
     "## Plotting our different layers together"
@@ -684,34 +443,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": null,
+   "id": "d7e672f4",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[(-20.0, 60.0), (-40.0, 40.0)]"
-      ]
-     },
-     "execution_count": 23,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1080x720 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "ax = countries.plot(edgecolor='k', facecolor='none', figsize=(15, 10))\n",
+   "outputs": [],
+   "source": [
+    "# import matplotlib.pyplot as plt\n",
+    "# fig, ax = plt.subplots(figsize=(10, 8))\n",
+    "ax = countries.plot(edgecolor='k', facecolor='none', figsize=(10, 8))\n",
     "rivers.plot(ax=ax)\n",
     "cities.plot(ax=ax, color='red')\n",
     "ax.set(xlim=(-20, 60), ylim=(-40, 40))"
@@ -719,13 +458,15 @@
   },
   {
    "cell_type": "markdown",
+   "id": "747aeba8",
    "metadata": {},
    "source": [
-    "See the [04-more-on-visualization.ipynb](04-more-on-visualization.ipynb) notebook for more details on visualizing geospatial datasets."
+    "See the [visualization-02-geopandas.ipynb](visualization-02-geopandas.ipynb) notebook for more details on visualizing geospatial datasets."
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "d049bee7",
    "metadata": {},
    "source": [
     "## Let's practice!\n",
@@ -744,6 +485,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "7e43492e",
    "metadata": {},
    "source": [
     "<div class=\"alert alert-success\">\n",
@@ -753,13 +495,15 @@
     "We will start with exploring the bicycle station dataset (available as a GeoPackage file: `data/paris_bike_stations_mercator.gpkg`)\n",
     "    \n",
     "* Read the stations datasets into a GeoDataFrame called `stations`.\n",
-    "* Check the type of the returned object (with `type(..)`)\n",
-    "* Check the first rows of the dataframes. What kind of geometries dooes this datasets contain?\n",
-    "* How many features are there in the dataset? (hint: use the `.shape` attribute)\n",
+    "* Check the type of the returned object\n",
+    "* Check the first rows of the dataframes. What kind of geometries does this datasets contain?\n",
+    "* How many features are there in the dataset? \n",
     "    \n",
     "<details><summary>Hints</summary>\n",
     "\n",
-    "* The geopandas.read_file() function can read different geospatial file formats. You pass the file name as first argument.\n",
+    "* Use `type(..)` to check any Python object type\n",
+    "* The `geopandas.read_file()` function can read different geospatial file formats. You pass the file name as first argument.\n",
+    "* Use the `.shape` attribute to get the number of features\n",
     "\n",
     "</details>\n",
     "    \n",
@@ -769,9 +513,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": null,
+   "id": "3f339748",
    "metadata": {
-    "clear_cell": true
+    "tags": [
+     "nbtutor-solution"
+    ]
    },
    "outputs": [],
    "source": [
@@ -780,149 +527,49 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": null,
+   "id": "fd87d352",
    "metadata": {
-    "clear_cell": true
+    "tags": [
+     "nbtutor-solution"
+    ]
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "geopandas.geodataframe.GeoDataFrame"
-      ]
-     },
-     "execution_count": 25,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "type(stations)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": null,
+   "id": "6f46af55",
    "metadata": {
-    "clear_cell": true
+    "tags": [
+     "nbtutor-solution"
+    ]
    },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>name</th>\n",
-       "      <th>bike_stands</th>\n",
-       "      <th>available_bikes</th>\n",
-       "      <th>geometry</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>14002 - RASPAIL QUINET</td>\n",
-       "      <td>44</td>\n",
-       "      <td>4</td>\n",
-       "      <td>POINT (259324.887 6247620.771)</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>20503 - COURS DE VINCENNES PYRÉNÉES</td>\n",
-       "      <td>21</td>\n",
-       "      <td>3</td>\n",
-       "      <td>POINT (267824.377 6249062.894)</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>20011 - PYRÉNÉES-DAGORNO</td>\n",
-       "      <td>21</td>\n",
-       "      <td>0</td>\n",
-       "      <td>POINT (267742.135 6250378.469)</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>31008 - VINCENNES (MONTREUIL)</td>\n",
-       "      <td>56</td>\n",
-       "      <td>0</td>\n",
-       "      <td>POINT (271326.638 6250750.824)</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>43006 - MINIMES (VINCENNES)</td>\n",
-       "      <td>28</td>\n",
-       "      <td>27</td>\n",
-       "      <td>POINT (270594.689 6248007.705)</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                  name  bike_stands  available_bikes  \\\n",
-       "0               14002 - RASPAIL QUINET           44                4   \n",
-       "1  20503 - COURS DE VINCENNES PYRÉNÉES           21                3   \n",
-       "2             20011 - PYRÉNÉES-DAGORNO           21                0   \n",
-       "3        31008 - VINCENNES (MONTREUIL)           56                0   \n",
-       "4          43006 - MINIMES (VINCENNES)           28               27   \n",
-       "\n",
-       "                         geometry  \n",
-       "0  POINT (259324.887 6247620.771)  \n",
-       "1  POINT (267824.377 6249062.894)  \n",
-       "2  POINT (267742.135 6250378.469)  \n",
-       "3  POINT (271326.638 6250750.824)  \n",
-       "4  POINT (270594.689 6248007.705)  "
-      ]
-     },
-     "execution_count": 26,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "stations.head()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": null,
+   "id": "ca3f144f",
    "metadata": {
-    "clear_cell": true
+    "tags": [
+     "nbtutor-solution"
+    ]
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(1226, 4)"
-      ]
-     },
-     "execution_count": 27,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "stations.shape"
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "ba47565a",
    "metadata": {},
    "source": [
     "<div class=\"alert alert-success\">\n",
@@ -930,56 +577,35 @@
     "**EXERCISE**:\n",
     "\n",
     "* Make a quick plot of the `stations` dataset.\n",
-    "* Make the plot a bit larger byt setting the figure size to (12, 6) (hint: the `plot` method accepts a `figsize` keyword).\n",
+    "* Make the plot a bit larger by setting the figure size to (12, 6) (hint: the `plot` method accepts a `figsize` keyword).\n",
     " \n",
     "</div>"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": null,
+   "id": "9bb788bd",
    "metadata": {
-    "clear_cell": true
+    "tags": [
+     "nbtutor-solution"
+    ]
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<AxesSubplot:>"
-      ]
-     },
-     "execution_count": 28,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 864x432 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "stations.plot(figsize=(12,6))"
+   "outputs": [],
+   "source": [
+    "stations.plot(figsize=(12,6))  # or .explore()"
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "d3a51c45",
    "metadata": {},
    "source": [
-    "A plot with just some points can be hard to interpret without any spatial context. Therefore, in the next exercise we will learn how to add a background map.\n",
+    "A plot with just some points can be hard to interpret without any spatial context. We have seen that we can use the `explore()` method to easily get an interactive figure that by default includes a background map. But also for the static matplotlib-based plot, it can be useful to add such a base map, and that's what we will learn in the next excercise.\n",
     "\n",
     "We are going to make use of the [contextily](https://github.com/darribas/contextily) package. The `add_basemap()` function of this package makes it easy to add a background web map to our plot. We begin by plotting our data first, and then pass the matplotlib axes object (returned by dataframe's `plot()` method) to the `add_basemap()` function. `contextily` will then download the web tiles needed for the geographical extent of your plot.\n",
     "\n",
     "\n",
-    "\n",
-    "\n",
     "<div class=\"alert alert-success\">\n",
     "\n",
     "**EXERCISE**:\n",
@@ -994,9 +620,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": null,
+   "id": "ca984bfe",
    "metadata": {
-    "clear_cell": true
+    "tags": [
+     "nbtutor-solution"
+    ]
    },
    "outputs": [],
    "source": [
@@ -1005,24 +634,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": null,
+   "id": "f98afb71",
    "metadata": {
-    "clear_cell": true
+    "tags": [
+     "nbtutor-solution"
+    ]
    },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 864x432 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "ax = stations.plot(figsize=(12,6), markersize=5)\n",
     "contextily.add_basemap(ax)"
@@ -1030,6 +649,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "1295be7c",
    "metadata": {},
    "source": [
     "<div class=\"alert alert-success\">\n",
@@ -1051,40 +671,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": null,
+   "id": "57c87858",
    "metadata": {
-    "clear_cell": true
+    "tags": [
+     "nbtutor-solution"
+    ]
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<AxesSubplot:>"
-      ]
-     },
-     "execution_count": 31,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "stations['bike_stands'].hist()"
+   "outputs": [],
+   "source": [
+    "stations['bike_stands'].plot.hist()"
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "e233a8e5",
    "metadata": {},
    "source": [
     "<div class=\"alert alert-success\">\n",
@@ -1094,7 +695,7 @@
     "Let's now visualize where the available bikes are actually stationed:\n",
     "    \n",
     "* Make a plot of the `stations` dataset (also with a (12, 6) figsize).\n",
-    "* Use the `'available_bikes'` colums to determine the color of the points. For this, use the `column=` keyword.\n",
+    "* Use the `'available_bikes'` columns to determine the color of the points. For this, use the `column=` keyword.\n",
     "* Use the `legend=True` keyword to show a color bar.\n",
     " \n",
     "</div>"
@@ -1102,40 +703,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": null,
+   "id": "7040b996",
    "metadata": {
-    "clear_cell": true
+    "tags": [
+     "nbtutor-solution"
+    ]
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<AxesSubplot:>"
-      ]
-     },
-     "execution_count": 32,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 864x432 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "stations.plot(figsize=(12, 6), column='available_bikes', legend=True)"
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "f74d86fd",
    "metadata": {},
    "source": [
     "<div class=\"alert alert-success\">\n",
@@ -1154,9 +736,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": null,
+   "id": "317c8ff3",
    "metadata": {
-    "clear_cell": true
+    "tags": [
+     "nbtutor-solution"
+    ]
    },
    "outputs": [],
    "source": [
@@ -1165,161 +750,49 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": null,
+   "id": "d310726c",
    "metadata": {
-    "clear_cell": true
+    "tags": [
+     "nbtutor-solution"
+    ]
    },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>id</th>\n",
-       "      <th>district_name</th>\n",
-       "      <th>population</th>\n",
-       "      <th>geometry</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1</td>\n",
-       "      <td>St-Germain-l'Auxerrois</td>\n",
-       "      <td>1672</td>\n",
-       "      <td>POLYGON ((451922.133 5411438.484, 451922.080 5...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>2</td>\n",
-       "      <td>Halles</td>\n",
-       "      <td>8984</td>\n",
-       "      <td>POLYGON ((452278.419 5412160.893, 452192.407 5...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>3</td>\n",
-       "      <td>Palais-Royal</td>\n",
-       "      <td>3195</td>\n",
-       "      <td>POLYGON ((451553.806 5412340.522, 451528.058 5...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>4</td>\n",
-       "      <td>Place-Vendôme</td>\n",
-       "      <td>3044</td>\n",
-       "      <td>POLYGON ((451004.908 5412654.095, 450960.640 5...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>5</td>\n",
-       "      <td>Gaillon</td>\n",
-       "      <td>1345</td>\n",
-       "      <td>POLYGON ((451328.752 5412991.278, 451294.721 5...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   id           district_name  population  \\\n",
-       "0   1  St-Germain-l'Auxerrois        1672   \n",
-       "1   2                  Halles        8984   \n",
-       "2   3            Palais-Royal        3195   \n",
-       "3   4           Place-Vendôme        3044   \n",
-       "4   5                 Gaillon        1345   \n",
-       "\n",
-       "                                            geometry  \n",
-       "0  POLYGON ((451922.133 5411438.484, 451922.080 5...  \n",
-       "1  POLYGON ((452278.419 5412160.893, 452192.407 5...  \n",
-       "2  POLYGON ((451553.806 5412340.522, 451528.058 5...  \n",
-       "3  POLYGON ((451004.908 5412654.095, 450960.640 5...  \n",
-       "4  POLYGON ((451328.752 5412991.278, 451294.721 5...  "
-      ]
-     },
-     "execution_count": 34,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "districts.head()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": null,
+   "id": "a4f4d738",
    "metadata": {
-    "clear_cell": true
+    "tags": [
+     "nbtutor-solution"
+    ]
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(80, 4)"
-      ]
-     },
-     "execution_count": 35,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "districts.shape"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": null,
+   "id": "6212655a",
    "metadata": {
-    "clear_cell": true
+    "tags": [
+     "nbtutor-solution"
+    ]
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<AxesSubplot:>"
-      ]
-     },
-     "execution_count": 36,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 864x432 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "districts.plot(figsize=(12, 6))"
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "cae2de14",
    "metadata": {},
    "source": [
     "<div class=\"alert alert-success\">\n",
@@ -1334,7 +807,7 @@
     "\n",
     "<details><summary>Hints</summary>\n",
     "\n",
-    "* Adding a column can be done by assing values to a column using the same square brackets syntax: `df['new_col'] = values`\n",
+    "* Adding a column can be done by assigning values to a column using the same square brackets syntax: `df['new_col'] = values`\n",
     "* To sort the rows of a DataFrame, use the `sort_values()` method, specifying the colum to sort on with the `by='col_name'` keyword. Check the help of this method to see how to sort ascending or descending.\n",
     "\n",
     "</details>\n",
@@ -1344,42 +817,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": null,
+   "id": "d00905ab",
    "metadata": {
-    "clear_cell": true
+    "tags": [
+     "nbtutor-solution"
+    ]
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0     8.685379e+05\n",
-       "1     4.122371e+05\n",
-       "2     2.735494e+05\n",
-       "3     2.693111e+05\n",
-       "4     1.879097e+05\n",
-       "          ...     \n",
-       "75    1.294254e+06\n",
-       "76    8.061191e+05\n",
-       "77    1.486139e+06\n",
-       "78    1.598127e+06\n",
-       "79    2.089783e+06\n",
-       "Length: 80, dtype: float64"
-      ]
-     },
-     "execution_count": 37,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "districts.geometry.area"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": null,
+   "id": "fcac6e86",
    "metadata": {
-    "clear_cell": true
+    "tags": [
+     "nbtutor-solution"
+    ]
    },
    "outputs": [],
    "source": [
@@ -1389,174 +846,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": null,
+   "id": "2b0f6b0c",
    "metadata": {
-    "clear_cell": true
+    "tags": [
+     "nbtutor-solution"
+    ]
    },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>id</th>\n",
-       "      <th>district_name</th>\n",
-       "      <th>population</th>\n",
-       "      <th>geometry</th>\n",
-       "      <th>area</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>45</th>\n",
-       "      <td>46</td>\n",
-       "      <td>Picpus</td>\n",
-       "      <td>62947</td>\n",
-       "      <td>POLYGON ((456790.759 5408686.978, 456841.941 5...</td>\n",
-       "      <td>7.201383</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>60</th>\n",
-       "      <td>61</td>\n",
-       "      <td>Auteuil</td>\n",
-       "      <td>67967</td>\n",
-       "      <td>POLYGON ((444930.499 5411923.067, 444957.444 5...</td>\n",
-       "      <td>6.380679</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>44</th>\n",
-       "      <td>45</td>\n",
-       "      <td>Bel-Air</td>\n",
-       "      <td>33976</td>\n",
-       "      <td>POLYGON ((456987.121 5409120.599, 456996.502 5...</td>\n",
-       "      <td>5.967841</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>61</th>\n",
-       "      <td>62</td>\n",
-       "      <td>Muette</td>\n",
-       "      <td>45214</td>\n",
-       "      <td>POLYGON ((444686.860 5413985.234, 445358.893 5...</td>\n",
-       "      <td>5.475037</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>62</th>\n",
-       "      <td>63</td>\n",
-       "      <td>Porte-Dauphine</td>\n",
-       "      <td>27423</td>\n",
-       "      <td>POLYGON ((446548.869 5414236.010, 447025.036 5...</td>\n",
-       "      <td>3.085061</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>10</td>\n",
-       "      <td>Enfants-Rouges</td>\n",
-       "      <td>8562</td>\n",
-       "      <td>POLYGON ((453580.220 5412266.849, 453591.609 5...</td>\n",
-       "      <td>0.271603</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>4</td>\n",
-       "      <td>Place-Vendôme</td>\n",
-       "      <td>3044</td>\n",
-       "      <td>POLYGON ((451004.908 5412654.095, 450960.640 5...</td>\n",
-       "      <td>0.269311</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>6</td>\n",
-       "      <td>Vivienne</td>\n",
-       "      <td>2917</td>\n",
-       "      <td>POLYGON ((451686.936 5412747.032, 451682.879 5...</td>\n",
-       "      <td>0.243418</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11</th>\n",
-       "      <td>12</td>\n",
-       "      <td>Sainte-Avoie</td>\n",
-       "      <td>7501</td>\n",
-       "      <td>POLYGON ((452928.277 5412227.550, 452830.786 5...</td>\n",
-       "      <td>0.213201</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>5</td>\n",
-       "      <td>Gaillon</td>\n",
-       "      <td>1345</td>\n",
-       "      <td>POLYGON ((451328.752 5412991.278, 451294.721 5...</td>\n",
-       "      <td>0.187910</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>80 rows × 5 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "    id   district_name  population  \\\n",
-       "45  46          Picpus       62947   \n",
-       "60  61         Auteuil       67967   \n",
-       "44  45         Bel-Air       33976   \n",
-       "61  62          Muette       45214   \n",
-       "62  63  Porte-Dauphine       27423   \n",
-       "..  ..             ...         ...   \n",
-       "9   10  Enfants-Rouges        8562   \n",
-       "3    4   Place-Vendôme        3044   \n",
-       "5    6        Vivienne        2917   \n",
-       "11  12    Sainte-Avoie        7501   \n",
-       "4    5         Gaillon        1345   \n",
-       "\n",
-       "                                             geometry      area  \n",
-       "45  POLYGON ((456790.759 5408686.978, 456841.941 5...  7.201383  \n",
-       "60  POLYGON ((444930.499 5411923.067, 444957.444 5...  6.380679  \n",
-       "44  POLYGON ((456987.121 5409120.599, 456996.502 5...  5.967841  \n",
-       "61  POLYGON ((444686.860 5413985.234, 445358.893 5...  5.475037  \n",
-       "62  POLYGON ((446548.869 5414236.010, 447025.036 5...  3.085061  \n",
-       "..                                                ...       ...  \n",
-       "9   POLYGON ((453580.220 5412266.849, 453591.609 5...  0.271603  \n",
-       "3   POLYGON ((451004.908 5412654.095, 450960.640 5...  0.269311  \n",
-       "5   POLYGON ((451686.936 5412747.032, 451682.879 5...  0.243418  \n",
-       "11  POLYGON ((452928.277 5412227.550, 452830.786 5...  0.213201  \n",
-       "4   POLYGON ((451328.752 5412991.278, 451294.721 5...  0.187910  \n",
-       "\n",
-       "[80 rows x 5 columns]"
-      ]
-     },
-     "execution_count": 39,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "districts.sort_values(by='area', ascending=False)"
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "c2dbb64f",
    "metadata": {},
    "source": [
     "<div class=\"alert alert-success\">\n",
@@ -1572,10 +876,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": null,
+   "id": "709ad19d",
    "metadata": {
-    "clear_cell": true,
-    "scrolled": true
+    "tags": [
+     "nbtutor-solution"
+    ]
    },
    "outputs": [],
    "source": [
@@ -1585,34 +891,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": null,
+   "id": "246781fc",
    "metadata": {
-    "clear_cell": true
+    "tags": [
+     "nbtutor-solution"
+    ]
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<AxesSubplot:>"
-      ]
-     },
-     "execution_count": 41,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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bZi7qzePubiSkzeV8DbveRCh2LidzGw0TKZ8jdbkT3y95J7h9Ux39l/NfhivbHDvWx9SUh5oaVVdUoViJHNcRLUqy+Rd/DPgurxVCBUBK+SJLFzP9MPDXUspw4jhVcE6hSPDSWD+P/PgLK4rQBXpmfYw5y2m01ORhZ0tj1RuYCqcu5FLltvJyYjI3/UMF4ZSdYvEs1C9dFiEYrYxhtxeHxTsTNE3y5JO5uYBQKG4WJAJNpn9bqyQrRCXwMyHESSHEB65/UAjRBLwd+GwKa28C7hVCHBVCHBJC3JbCuQrFTUkoHuMvjj/De575Fs5g8lncE/4Q54YMrLc1r35wDugoqUBm3PF9ZQSgl905m1/TSlL+C2Iit0k4lUYzce3mSPR58omzKmlJoVDcQLJC9G4p5T7gTcBHhBDX1+L4J+DjiwqdJoMBqAAOAn/IfDHVGyS9EOIDQogTQogTU1NTKUyvUKwtLs05efQnX+ILXcuGYa+INxLjUE+EzfYNWd7Z6lSZctRhaBE7SqoJxVOP40wWTZanfE6E3CWK3VZWy/ThCULBPGSA5QGn08OJE/2F3oZCUdTE0aV9W6sktXMp5VjipxP4PnD7dYccAL4phBhgvqXTp4UQb1tl2hHge3KeY4AGVC+x9ueklAeklAdqagrnelQocoUmJV/oOs5bf/Ilul2ZXWyVmY0UovaPFKnX4EyVGlNuP2ijWooZ84Cf3HSyemCDhYlj48RiN5cF8cknzhZ6CwpF0SIBTerSvq1VVk1WEkLYAZ2U0pu4/zDwF4uPkVK2Lzr+i8DjUsofrDL1D4AHgReEEJsAE5A7c4dCUYQ4Az7++ys/4aXxzCxFOgEPtlcxHh+g25+bGMpl1wamwuM5X8es85DLvk2h6A3Xwasyp2X/f200aPzKW79F8CEvPSfezktPlzM6UthEtGxx7txQobegUBQxgvgtWL4pmaz5OuD7Ca+5Afi6lPIpIcSHAKSUK8aFCiG+ATwAVAshRoA/lVJ+HvgC8IVEWacI8D4pb4aqeQpFcjw12M0nDj+JK5KZmNleXUp5uZu+0KUs7Sw1NjgqiGi5voaUhGO5LeDvCtdAiq722ViAMmFCy+JH17vvDRKXQ5hssP2+L7DtXpjsez3Hn9vDiaMeNG3tfky63UFcrgDl5bZCb0WhKDoWLKK3GqsKUSnlFWD3EuNLClAp5a9d9/svLXNcBPjVpHapUNxEeCNh/vz4M3yn73xW5hvzBqmrtGRlrnRosFoYz3GL+RqTg7DWm9M1pgN2ILXuSlJAud3CrC+YtX3s3zRwze9CQP3GZ/j5jc/wyPtKCLjb8c824nVV454pxTVjwTWrp6/HTyBQ/PGkAwNT7Nlz89RIVSgUmaE6KykUeeT45DC/98rjjPiy16JyLhzlhR54uKOD7sDq5Z6yjUGX+1CAVqs952sMedOzRJRmUYgadBKH7RTxZYyeRrOXstpzlNXeWArJN7OJb3/65+m/knqr0nwyODithKhCsQy3omv+1rMBKxQFIBKP83fnnuPvzr2QVRG6gCbhqcsuWvRbMIjcZ7C/hsQVzX0J4ApjbpN2wrF1XJhxpXWu1WbM2j7esDdCXKa3D0fVZd778c9w7+tST7rKJ4MDKhVAoVgKKcUtmay0dneuUKwRej3TvOu5/+Szl17l5NwgB+tzV+vz0OAM5lA7ZUZHztZYTL25BG8s+8L6evRL9sTIHoOuO9M+12TOnvC/e3tmXZQMpgBveM8/8Mu/GcZgKM6P98HBmUJvQaEoWm7FFp9rd+cKRZEjpeQrPSd49On/R+fca12Hjs8N0FpSnrN1L0y5mXBWsM5an7M1Fmh15F7wCiAYy122tZRwZDz9GFthyp4rra6yM+M5hICtd3+Zj/zJBSoqrVnYVXYZGlIWUYVC8RpKiCoUOcAXDfOxw9/jz08/RTgeu+YxIUBn1LAasufSvZ4Jf4gjfZLNto05WwPAkWOXOUCLtYyYzF4y0PUEI9vpc6cfV6llqd/8xoY4cbKXkFXd+gof+v9+QMemsqzNmQ1mZ/14PLl7PhWKtYoENETat7WKEqIKRZbp9UzzC898gadGupY9Zjgwy9bqqpzuI6JpPHXZTYt+C2ZdbkRvMJ5blzlAs3XeWinITexr79yezCbI0uf/m27LfpKRrWyIX/6Dfy26uNGhIeWeVyhuRCjXvEKhyIwnhi/yC898gT7v6l+051wjHGzIfW/4Q4MzhD1NNFqy25nMpjcyHc4spjEZLvm8nPfvwG7am5P5r7gzE+mayI5FdMu6vqzMcz0GY4g3vOcf+MX3xdHpisNqohKWFIobma8jKtK+rVWUEFUoskBM0/irM8/w24e/hz+WfNvH47MDHKhtyuHO5ul3+Tl+RccWW0fW5uwoKUeS++LqM5EAnlgYZG7Ey6Ans/JT2fgPWIwaRlPu2l8KATsf+AIf/PgADocpZ+sky8Rk7hPcFIq1iOo1r1AoUmYq6OO9h77K5y8fSflcIeCcZ4h9NY05j/CJaBpPXnaxzlqXlfkqs5gtvhpGocMfHc76vLF4ObPhTIVo5lL0kf0RNBnIeJ7VaNz0Mz70Jy9iseQuPjkZpqaKu9apQqHIH6qgvUKRIu5IkB73FD2eKS67p/jpyCWcIV/a82lIznmHuKOhjSPjuW1jCdA5bGDnugYGg5n1h9dk/vqft9nKkSm230wGocs8aSaeBdf8HVvGMp4jWcrqzvPYb27ji/9amrc1r2dmLn+vHYVirSBZ2y72dFFCVKFYhVGfhy90HeeyaxqTEQ45c9O96PjsALfXtXFsMrdidCYU4dU+HQ92bKDbn15cok4IpsL5E0/1ZiNa9nUoehHGYTThiyYfTnE9IuPvDUl1xelluynlgg37v8V9D/0+Lz6bP0Eokay7pwr9HXr67blPclMo1iLaLeioVkJUoViBp4Yu8/FXn8QdmXffCiRba2u57Ml+NyEh4LR7kF1VTZybmVj9hAyIaBpPdbt5eOMWekPdKbuXOxwVROVUjnZ3I3Z9EG8OhChApSVDIZqhBeOh3VHiMn+ifoHX/eLnuHL5A4wM51aMOhot1L+pnOGaOc5ERkCCOWBASonIXMUrFDcNUkL8FrSI3nrSW6FIglAsyp8c+RkfeuH7V0UozLtOvME4Rl1u4iMlkivBCTaUVuZk/msR/Kx3hlq5KeXyTvVWc472tDSalrs2oiXGzJ5LXYZfHG/Yl/twjKUwmHy8+8PPZ8GieyNCD21vqqHxjyoZ/qU5jpRdYTQyd/XxsBbDFc19TKxCsdZQWfMKhYLLrine+sSX+erl00s+Puz1sKdiXc7WD2kxvPiptdpztsZijozOog+0UmKwJX2OXuSvILlFpycQG83Z/OEMfeK6DNLMdDqNqvKjGa2fCeUNp9m2I3sF76u3lrDhI/XE/0BwYusg52IjxOXS/9+JoMqcVygUyjWvUFxFSsk3es7y58efvaEb0vUcGxulvaqSAV9uYt1mI37W2SqxR00plYNKl85pN23RWmqrXUyFXascLZmL5r5+6ALttgokuevg5IpEMzo/k1ylN+6NEs9RWapk2XPnJJ3n029xarTraX1LDbPr/VxaqCubxL90Muhma1lj2usqFDcb88lKt559UAlRhQJwh0N84vCTPDl0Oanj4xJkzIAOgZajWprDgVm2VjZwcWp6WatSNhlw+/FFHGxtNjMSvFFobravZyg0SqnBgC82mPP9LFBm0GWnWOcShKIbmQ5mZt0VWvoW0YNb8xdnuxztu59Cr38H8XjyYl8iabq9AtPdJi4axjgSvwLh1NadCKkSTgrF9cTXcKvOdLn1pLdCcR0nnCO8+fH/TFqELjAXClJlya37vMszzr66hpyusZjpYJhTA4INtmtDDzZZNvNUt4eYr4mOkty2Jr0eQw4/l4c8mXdrEhkkUTVUpfaaywVm2xS79ibXAtRWbaLjffU4Pmnj3D1jnBADBOLpWex90czqtyoUNxu57qwkhLAIIY4JIc4KITqFEH+eGP8zIcSoEOJM4vbmRed8UgjRK4ToFkK8cdH4fiHE+cRj/ywSmYdCCLMQ4luJ8aNCiLbV9qWEqOKWJa5p/Mu5V3n3T7/OqD8160y93YHdKpjKoH5ospx2DXGwPvetQBfwR+M83xNis309AB2WLfy0dxYQXJ7xMjqX348NfQ6XO+fM/EIiHk4vbKCqRKKJrozXzwZtHctnzkskrQ9Vs+4Pqxl/n5fDVVcYDGfeK14lzCsUeScMPCil3A3sAR4RQhxMPPaPUso9idsTAEKIbcBjwHbgEeDTQoiF7M7PAB8AOhK3RxLj7wfmpJQbgX8E/ma1TSkhqrglmQh4+ZWnv8Xfn3kpZbd3e1k5miHMeDB/rsXjcwPsq8lfPF1cwlPdHqrjm/hZ77Wi4/BoEKPIT2ceo9Bj0+VG7IdjzZyfcWU8j9eTok86wSMHApDD2NdUqKi9MU61vNXGxg/Wo/+EkZO7hzgdHyIqs1dDS9yCLkiFYmXmY0TTva2GnGfhA9WYuK30Bfgo8E0pZVhK2Q/0ArcLIRqAUinlYSmlBL4MvG3ROV9K3P8O8JBYpU6bEqKKW45xv4e3PfFljkwOpXzutqpqZuNuZsP5LT0jBFz0jbK5oiafq3J8fO6G0WBMo9rYmpcdPFTtwBPJTQ/2/rm7M59EwvhsenU4d6/PrLNVNimvnn8v6M2C9Y/WUfuJcvp+YYYj9is4IyqWU6HIFxoi7RtQLYQ4sej2gevnF0LohRBnACfwtJRyoWzHR4UQ54QQXxBCVCTGmoDFvZVHEmNNifvXj19zjpQyBriBFeO5VLKS4pbCGwnz6899h4lA6la2WquN/qCTaC5a/CRBTMaZis7RbC9lJMVQgmzjDZZDDlvNt9vKqTKZ8EZeycn8sXgVT/RnPk+jsSStWEeHVaOi9GReuymtRLjCwPrfrqPbMsGxWD/kvlCDQqG4jiwUtJ+WUh5YeQ0ZB/YIIcqB7wshdjDvZv9L5q2jfwn8PfAbsKTbQq4wziqPLYkSoopbhpim8dEXf8ilufQylZ3BAAcbmzkxm7+M8evxxELYLCbKTJZrCu3nm1PjMTblKGxVL3SsM50nLnP3950c/zncWbD01ers+Eh9n3/wjjHiMndF+pMhrJXTFXgTL85U0eWZA0M/rFy1LKso17xCcSP5Kt8kpXQJIV4AHpFS/t3CuBDiP4DHE7+OAIszV5uBscR48xLji88ZEUIYgDJgxTqHyjWvuCWQUvI/j/6MQ2OZmcGOjI1yoCo/bunlmAh5aCy15ay7UzIMeMJUGetyMrdJ6HMqQr2hPTw5kB2Lsk1LPVZ2Z1uchpqfZGX9dIhodl50/SafuPQ2/r1fNy9CFQrFTY8QoiZhCUUIYQVeD1xKxHwu8HbgQuL+j4DHEpnw7cwnJR2TUo4DXiHEwUT853uBHy46532J++8EnkvEkS6Lsogqbgn+vfMY3+jJRqyh4OjoKLvrG7jgKlyMX4/PycbKWmb8YaaC/oLsQa81ANkvbL+1pGL1g9JEk4LH+3YjyU5XH0M01Wt5yUd+/jCyAL5vTcKl4Dv55kglDdYKQvHCtBZdQPWZVyiuZb6gfU7fFw3AlxKZ7zrg21LKx4UQXxFC7GHehT4AfBBAStkphPg2cJF5f8lHEq59gA8DXwSswJOJG8Dnga8IIXqZt4Q+ttqmlBBV3PT8ZOASf33qhazNJxH0zrhpLCljLFC4NoW9PielBgu7quo5NzOR9/V7pg2U5UAz1plceHKk0+JaLZfmsvecpZpE/t9+zg36pVvH5pLxyN18e3wvlzxzgB9fLEq9pYyJUOFev4FYetUGFIqbGS2HIStSynPADcWTpZTvWeGcTwGfWmL8BLBjifEQ8K5U9qVc84qbmpPOEX7v5cdXPzBFfNEoJSL53uy5whML0ekb4Y76Zgwiv2/nExN+bPob63BmGvs3GSnP6PyVkNKU3fliyWcbravR2LPph6sfmEU8sfV8a/Jj/EV3W0KEzhOMR7DojXl/zSzmuYniqKGqUBQLuS5oX6woIaq4aRn0zvFbz3+PSI6y3DtnpthTmb9C88shBJx0DbChqow6myNv68YllOlbrhmrM+6m07mFct0+ak1Ny5y5MqfcTizGu7KxxRvQZHacQBa9gUZ7CSazgaaqMuorSqgps1NZYqPMZsFhMWE1GTAuqsb/h++8gCbzU+0gojl4bu4D/M/ue3jBuXSewIB/ml0V65Z8LB/0eCfp8eTfkq9QKIoL5ZpX3JTMhYL82jP/xWw4sz7iKyM4OzHFpupaLnsKmwEN0OebwmEws6e6gTPT+YlfnfI6wDx/v8awh//qm6+p+aOBCGBhU9lOdlfrCMpBPDFX0vM+O+3k/qq70eLH0GQ0a/vVpHnFx60GI1VmGyVGExa9EaPQIyTEY5JQJIY3FGEuEMIbDjNBkAmGYLXmTBIseh3/eHwr7RWbaS4NU+cIUmHx4TC5MRvmQPYgyc7f6Ymt53PDb6LP51r12FOzg+wob+KCazQra6fK46Nn+b3S+oKsrVAUI/nKmi8mlBBV3HSE4jE+8ML36PfmPhs4qmmMugM02Erz2mlpOXyxMOe9w+ysauL8TPYTia7n1ZEgj2ypIRJdx3ev3BhveNkd4LIboJIHG9dTaZ/EGU5G9AjOuT3sdOghSwJNh4FSYwm7Kxsw6fTopI54TCO4IDD9QXyRCGP4gZUTwFIKPxAQ0jTOOw2cdwKYgBKg9uohj+58mMc2f5G45kr9D1vEaORe/vXKZlzR5OcZ8E1Tay7BGU6vMH8mPDF6jt/Z8gZ0BQwRUCiKhjXuYk8XJUQVNxWalPzRK09w3Jm/jGB3OEyJsYRSowVPGsXNs40QMBaeodpiZzqU/Yx6g9CxxVFDecCC2QTf7w0ixGpJL4LnxtyAmdtr99Be5mU83LfiGbeVW/Gn+f/UCxO15lYq9AZKRAAHY9i0QTQ5xF8MtBPVlm6tWYjals0VJtylg4TFLgy8mPY85/zv5j/6TcRkal4AXyxMraWUmbCP+Mp1p7OOM+Thp2MXeFPTrryuq1AUI5LcJisVK0qIKm4qzk6P86OB/CdBjPi8bK2sIhBzEpOF7x/ujoXYVFLKbDiAtnIJt6QoMZrZbKnGOKNjuHOOcZ+LcWDnwzWIlMqZCo45vRxzwtbyXeyuiTIR7ka7rud6vdlBMHoqpT06DHU0mKqo1c1SoZ1DLxJCd1G/D50Ah8nIXKg4MrZtJh27dswwGfHx7GwFbyq3oqUoJONSz9NzH+CHox7S7V1/xTfF/so2Ts4OpHV+Jvyv8z9iR3kz6+yVeV9boSg2lEVUoVjj9HlmCrZ21+wM++uaOOseXv3gPHDZ6+T2+jaOjKdnHV5nK2MdZURGYgxemqE/Pn3jQQZWad62PF0uP10uWGffyl0NOmZi3US0+bpNe0oF3sjKbX50GKk1t1Jn1FEje7FzYv6B5RrQJbCb9MwV3nANSB65I8ZgZD5h56IvREx7A49W9xCLJ3cxFdbK+dr4ezg+u2LjkqQ4OTvAtrJGLrrHVj84i/hiYf7w1Lf48l2/hUmvvpIUilsN9a5X3FT0ugonRAFOTk5wZ2MrxwvYBnQxJ2YH2FrRQFcSbU0NQsdmRw0VAQvTPT6mRn10r9K+UuokZFiUYNgf4lu9UGlq54FmGxVmN4Ho80sea9dXU2+upVbnplKexcCVlIWww1S4jlSLeftBG4ORc9eMXQ6E+frkBn6xenUhOhvdwWeH7mU4kLkIXWAkMEuV2cFM2Je1OZOhyz3G3118ij/e+Za8rqtQFBML5ZtuNZQQVdxUXPFk70s5XQ6PjXJ74zpOzRaBZVTAnOZdtjd9mdHCJks1hmnB8MU5JnwuUimoI/WZC9EFZiMxvnfFAwgs+gc5WGNke3mILSUTVBsi1NCPXTuNEKRthQVwpN6VM+u8fqeDIbl0Yfuotvof1xN8C5/tryYQz26CnCcaoqOkjtmwL8/RovCtwaO4on7+eMfPU24qfI1ehaIQ3IpCVKUqKm4qCumafw3BqfFJtpYVR1ma6bCPtvLSq7+32Mq5y9rCvukGjM9LrjwxxeVjToK+1LPTNZEbuRKKwwsTUf7tkp4KDdrlMzhkH9noClmS3Zr2KbO7xYbXfj7t80/4fpV/6C0jEM9eWavF9Hgn2VfZlpO5V+OnYxf4hUP/wouT3QVZX6EoJAstPlVBe4VijRLV4gx6XIXeBgAxKemf9dBiz13f9GQoMZjZYq/DETFyHy1s7q3E/9Mg3T+bZLBrlkzzmDRd7u1mP57IrqAvNeWmwUEyNJWbqFjXR0wuH/+60n90JHw//9mf/X1dz6nZQbaUNuR+oSWYDvv42PGv8udnf4CvCKpQKBT5REOkfVurKNe84qZhyOsqioz1BXzRKI6giUqzjdlwIKdrGYSOZms5VdgxBw1EZuJMDweYdQYYx8c4uYn5i4vc/7+/Myr43ZZKhMxO2IXDuHISVK6wmnTs2TnDRGTl52K5rxNXbDP/PrgJjdy+lgAkkomgmwqTjblI7tdbiu8Nn+TIdB9/uecdHKhqL8geFApF7lFCVLEily+NcebEAB2b69m4uYGSUmuht7Qsfe7Cx4dez0TAz57auqwK0XpLKXV6B/aImbhL4hkPMTHsYy4WZo5ryxLlui5mPM1yQamtoWM0to9m/TNZmc9iKIRFVPKmO7SrGfLp8N3Jh5kO5+817ooG2FxaXzAhCjAWdPGbh/+Te2o7eLB+Kw/UbaXSvForK4VijSJvzRhRJUQVyxLwh/nvH/wi4fBrFqSGpgo6tjSwcXM9HZsb6NjSSGlZcYjTHvcS5YUKzKaKSkZDmYkHh8HMZlMteqeO0YsefO4IA6xWQD4/xPIgRAFenm3ksZrszDUXMgG5bP16I4/efmOGfCr4442cyEKJplTp9kwUrL7oAhLJS87LvOS8zF/yI/ZVtvJg/TYebNhKg7W8YPtSKLKNyppXKK7j1Re7rxGhAOOjc4yPzvHisxevjtU1lNOxpeHqbdOWBkrL8p/12uMqPiEajMVIp3thtdnOBlFNbFjjyoU5emPFZ+2F/FhEATY5shdacGIyv6Hx921xMCKWzpBPlp/OvAPIfcvapTg9O8imkjoue3PfMnY1NCQnZgc4MTvA/7n4BNvKGnmwfisP1W9jfUnt6hMoFEWOEqIKxSKe/9mFpI6bHHcxOe7i5edfq31YV192VZhu3Dz/s7wity61XncxZMxfy7DXw7qSUirNrOqeX2ctpzlWgbcvzFC3i0sUn7C+nqjMj5t7q60no5JNC0yFWhlyZ7/t6XJsrrcSq7iYWsOj676HhsKv4+nJwohQmBd/02EfZUYr7mh+LcmrcdE9xkX3GP/a/Szvar2NP9n51kJvSaFQpIgSooolcc35OXls5V7gKzE54WZyws3LL1y6OlZTVzovTjcvWE8bqajMjjjVpKSvCGqILsVKYnSjrZraUAnTlwJMDvq4iBMoTM/zdMiHEG2wSKyyMytznZq9JyvzJEO5TU/LxhFmo6m1E1383MekjS8Pbwa8Wd5d8uiFQJOSVnsV51zpdenKB98dPMGvtt9JmyNLMRwKRZ5ZKN90q6GEqGJJXnquCy2e3dI8U5MepiY9vHrotRqB1bWl17j0N25uoLLKkdK8UkqO9g4QjOWmrmI2WBCjNRYdtTo7ldNWpo67cY/5MO0oxTO7NsvURFYoQ5Qt3t0UId0e6tdzeqoFVukWlQ10Ah44EGQ4jeSixbVSX3C9h9Fg4UQowAZHHZe9E7hchUtaSgYNyb91P8vf7n+s0FtRKNJGKiGqUMzz/NPJueUzZdrpYdrp4fCLr4nTqpqSRVbT+VtVdckN58ZicV589iLf/9ZRui+Ocd/99UzfZuBiYPV2lvnErNezzVpL6YSe8WMzzLrczC5KNuq+MEZDUzmBSpiYLazoSJWIlnuL6N6y7Fm6e10m8iFE33anmcFwT5pnz38RTUX38/2R/IURLIfVUAStqJLkZ+Od/IZ7jK1ljYXeikKRFmu5Hmi6KCGquAHnhJsLZ4YKtv7MlJeZKS9HXr58dayy2nGNOB3qn+aH3znOtPO1FocThyaQh+C+1zXg3K/jUqBwMZZGnY69lgbMI5Kx49NMB5wrRnxOjLqw2oxs21zLxWFn3vaZKWEt9xbRJvN4VuJDAcbS6B6VKg9udzAYzyw5KS71fHX0IBqu7GwqAwxCX+gtpMS/XnqGf7vjvYXehkKRMlKVb1oeIcQA80FKcSAmpTywzHG3AUeAd0spv5MY+wLwFsAppdyxxDl/APwtUCOlLP7sjFuAF57JTjxeNpmd9nF0uoejr6xsZRLAxPPjyOfhgTc0Mr4bugP5T2K6zdTI8NfGUzonGIjSf3qc2w6s49TQOPEkeo4XEokknKM2k4tXsdObtdk84dzv11I5hjuS2Ryven6Ny15XlvaUGdE8WL2zybHpfk5PD7G3uqXQW1EoFEmQSh2T10kp96wgQvXA3wA/ve6hLwKPLHPOOuANQOHMb4obSDZbvpgRwNjTY2h/N8bPeVvZ6KjK29pGnQ7vIc/qBy6BADpPDLPRXkZteWqxsvnGaNJny1C5LLtLBUJmp2ZqTNMTiObWgru31c5kJLMyR3OROM9MFo+zaiayNsJFqoyl7CzZgBa18zfnnkNm2r9WoSgAUoq0b2uVbBbU+xjwXeAav6KU8kVguSCvfwT+iKw53hSZMtQ/Rd/l9Lu/FBsCiHYFcf/IzUF3I5sc1Tlf84C5EddYZrF9w/0zaONBGipLs7Sr7GOx5N5le191RqbFa3DFGllfayOXHzfr16WWIb8UYanRUQ66IogVM+sMjAVchd7GskgJG+2NdFjbGPVEOOocwRMNcXJmhEMT6Vf9UCgKw3zWfLq3tUqyQlQCPxNCnBRCfOD6B4UQTcDbgc8mu7AQ4q3AqJTybLLnKHJPvpKU8ol3wotAcOXkFHM/cnH7bAPbSnJT4kUvBP6X0rOGXk/AH8Hq1bCaizNZxGRJ32onkgw72F2avQ5SJyL3EavvZcveWV53h56H95VxYEMp5dbsWB/LbAZGY9kRP1PRAe6uK3zCTbWlpGitBBvtjTSaGjk3PcPZ2XG063b6DxeeR1NWUcUa41a0iCb7CXy3lHJMCFELPC2EuJSwdC7wT8DHpZRxIVb/ZwghbMD/AB5O4tgPAB8AaGlRMT8L/ONf/hCPO8gbH93LbXduRG/I3DolpeT5nxVffGimzIy6MG4vJRrTEAgGzkzDGTiwo57wBsn5LHaMOWBtZGw4exbliXE3m7Y3cHay+BKYjObkHSq3TdRiezaIZ8JHwBsmEoriKLdSVleCpcaCvtJIrFwgNBBDUSJzYUwVZmwbZpDWOoTM/Dl6fq4MmCEQD9MXHJ4ftENpB2w0V1CpqyQaNDMxHaNn0k8sxfJl92wxMyGzF4M6GTnHtrJtXHQXrqOR3WAu2NpLUWaw0WqrZzYU4dz0ytUxulyTPDlykZ9btz1Pu1MoMkO1+FwBKeVY4qdTCPF94HZgsRA9AHwzIUKrgTcLIWJSyh8sM+UGoB04mzinGTglhLhdSnnNt7iU8nPA5wAOHDigLm+BV57v4qkfzmflvvrCJSqrHbzhLXt441v30tSSfizk5a4xxkaKsyh8RkioryhheOpa69rQhRm4APu31RHpIGNBqhOC8KvZL7fT0znO3tvWcbp/LOtzZ4LRvPrFT1PAwdanTfQd7r/hMZ8riM8VhO4lTkzwV/tv44y7DZtBY12ZpKlUo8ERp9QcYy5oIBgTWA0ShMQf0RGOCcLxxC0mCMchFIOqcjtj1ctbK53hOZwLLTSrobXGQKOlBqtWQjRsYHImRteoF7msu1yi2ccge5EEICRG4zDlxnJc0cLU8HQUiRBtsdZg15Vwdmacce9o0uf904VDPNy0BaNubWX+KxS3EqsKUSGEHdBJKb2J+w8Df7H4GCll+6Ljvwg8voIIRUp5HqhddM4AcEBlza+Oa9bH//3fP75mbHbax7e++DLf+uLL7NzXyhvfupd7X78Ni8WU0tzP3QRJSstRYbUwzNJu3uGLs3Axc0F6wNrIeH9u4mtHupyUVVlw+4un8L3BdKNF1BAXtPtLaZy2YboUZfDIKH2R9BKEjKUmeoPzz1kgpqN7Brpn9EDqoQrrtxgZS2EbURljMDgOJCofVMGWWgtNpnosERMDE0EujMdYqPm5r9WOM5K97P4FgpqHHZXVvFwgo2gglk1lnRpCCraUrsMf1rg0OwWkHvIy4Jvl+wPn+MX1e7O/QYUi28j5uOdbjWQsonXA9xOWSwPwdSnlU0KIDwFIKVeMCxVCfAN4AKgWQowAfyql/HxGu75FkVLyT5/6Me655a0j508Ncv7UIJ/+2yd54I07eOTRfWza1shqIRPxuMahIizblC3MSbg7rgrSrbXENuk4601eVAogdiR3Viu/L8yWDdWc8hdHIpnJqKdGGtk/VUO504B+LIZv0IdzYJZYzJeVMhjx92/A585Oj3WvYRYyTJj3x0NcDg7wq616WurO8Ma9lfhDdzIwa8ZQ6mQ88zylJXFGr3B37W5ecSZvCcwGVr2JHk9+FbCUUGlysM5aR7/HzUln5us/O3ZZCVHFmkEVtF8CKeUVYPcS40sKUCnlr133+y8lsUbbasco4JmfnOXwoRX8mIsI+MM88b2TPPG9k7RvrOXht+7loTfvoqx86d7u504PMjvty+Z2iwothULmw11z0AV7N9fCFj2nvavXA91vb2SyL7df2l1nR9m8p57ukfx2jtLrBOuqyqgyWzD4Y/iGPTh7pjFN6pg9P7xsSYxMMJWbueRxZWk2yVQkO4LWbtDji3UBENRmmdBmmDI7IUcidIHp6Hk6SjbT483fc19nKWXAnzsnlZRQYy6l2lSOSWfCH4ky4ncz6gky6sleT/uLruK4eFMoVkOS2xafQggL82GVZub133eklH8qhKgEvgW0AQPAL0op5xLnfBJ4P/N15H9bSvnTxPh+5stzWoEngN+RUkohhBn4MrAfmGG+rvzASvsqnmJ1ihVxTrj49N8+mda5/b1O/v0ffsoX/uUZDt6/mUce3cfe29ej17/mWn3hJnbLA/ic/pQ9uqPdc9ANezqqEduMKwpSeTz3LnMB6L25KS7eVFlKvc2GIaohfTGi/jAhd5jgTADfXBCfnOP6yxSzPbXQj1SovrMZTWYn3raqxExYy04S0f01DmIyoTqlDochd/+DxUihUWKZwBG04YvlWPUurHlDvrzkg60aD1SMYdf5EWhIBN54KVNRB86IBWfEyFhIx1BQMBTUY9eXUGq0Y9KZQIr5zjFI4ppkwDvHkDvIELm9gJsIepkLB6gw23K6jkKxBggDD0opfUIII/CyEOJJ4B3As1LKvxZCfAL4BPBxIcQ24DFgO9AIPCOE2CSljAOfYT6R/AjzQvQR4EnmReuclHKjEOIx5uvLv3ulTSkhugaIxzX+/s9/SMCf2RdQNBrnpWcu8tIzF6mpK+Xht+7l4Z/fw8SYixefu5il3RYnMyNziPXWtOJvxnrc0AO7N1Sj32HilPfapKH99gac3fnJah/om2Lvbc2c7k+ta9Ny6HU6djXWMP5kH4Ph1HzXuez8pK23QzA7QrS+ypy1RpmVpnFcC5pWaJQaXuS+qk2Mhmvp8+U2mcwXn2NvdQ0vTeRHiMYXvVnarPCPW3up5JUbjivTQ7MesFw7/rLnrfzeeT1QmESrxVxyT3JnbfvqByoUBSW39UDlfJeHBZuCMXGTwKPMh1ACfAl4Afh4YvybUsow0C+E6AVuT+T1lEopDwMIIb4MvI15Ifoo8GeJub4D/KsQQsgVOkwoIboG+Py/PM2Z4zdmHWfC1KSHr/3HIb72H4eobSxDFnk7yUyJhePUlpcwOZd+l5jxPjf0wa72Kky7LJz0jiIBcTr3/csX03NqjJ176jk/mLolSa/Tsb62gkqdgehEgPHzEwwfS8+57vXlThCNGSMQzM5c5aW6rAjRdrsVV/T8DeNh7TLVxss0Vm/DHa2l2ztBWMtNks9k5DIHa3ZzZCq38aJ3V8KD1VOU6IPUmby0GY5Aih2u7nA8RbnpQ7gi+RHOK3HJ5VRCVLEmyHWyUqIL5klgI/BvUsqjQog6KeX4/PpyPFGqE6CJeYvnAiOJsWji/vXjC+cMJ+aKCSHcQBWwbJyPEqJFzlM/OMV3v3o4p2s4x9xs2dVM18XiKg+Ubaoc1oyE6AIT/R7o97C9pZLG7WV0XxzOwu6SR4tr9J8eZ+fehlXF6FLCcy44SzYiJqecuWn9aKmx0Zm1+FCwWCVomc9ze5XAv4LROBK/iFV3kb1lZkz6nUyFbVz2jRGXWVh8Ea54J232DQz4Z7I6L4BVB5/eMcl6w1Nc04EqjS9Hoy7C62pNfH+k8EK0S8WJKtYIGcaIVgshTiz6/XOJEpiL5pdxYI8Qopz5RPQdK8y31GbkCuMrnbMsSogWMedODvAvf/14Xta6dG6EnftbOX82v6Iqn9hEdmsJOoe8uMeDbI1LjOtruDA0jZYny7KmSUYuTNLUXsro9GtlbXIpPK8nHIpgMhuIpOjSX42qg43IGyJS00fotYyFqB7Q5OWkjpWECcdPUGqAOyoqkOzgss/PTMSV2SYSaMSots/gDJkIxLNned1dKvi7TYcxaZeyNmedOUtm7Qy56CpcUwCFIlmkzFiITkspDyS3lnQJIV5gPrZzUgjRkLCGNvBaq/YRYN2i05qBscR48xLji88ZEUIYgDKWb/MOZLfXvCKLjI3M8hd/9C1isexaU1ai88wQGzfX5229vBPMrmACCEdjiO219H3lFZpH59jdVnNNElguCYfjWLySMruFA0317NOVUHvey9zjffT9qJuhY8NEg7kLG2hrr8m6CAWItWU5qURkfnFwV00ZwXjqcj4u59DkS2ywneLOyqbVT0gST2yaAzWlGc0hkBwoE3y0LcaXd4/zzx3fzqoIBSgxFN4aCtDnmSYcz/5rVaFYSwghahKWUIQQVuD1wCXgR8D7Eoe9D/hh4v6PgMeEEGYhRDvQARxLuPG9QoiDYr425HuvO2dhrncCz60UHwrKIlqU+LxB/vT3vo7XnV9rgoxLxvqnaGisYHwsFza0whKazU3SxOmxabbtb2Xi5CAzvU4a2muovnsTF0ZmiMVzdyFhNxtpihsInZ5kIJB/16Pdmnph+WQY0RWHeFlMm83NXAaaXgjQ5EvcU3UHr87MoGUhVmAiconbqnZzfCbZeFHJwzWCN9e46bAO4eASLK5MkANjfoPJCTRkf+IUiUmNXs802ytu4gttxU1Bjlt8NgBfSsSJ6oBvSykfF0IcBr4thHg/MAS8C0BK2SmE+DZwkflKzB9JuPYBPsxr5ZueTNwAPg98JZHYNMt81v2KKCFaZAQDYf7097/BUH9hmkwFA1EckSg2m4lAoHBdVXLB7LAbGnPzko/ubUKeHEQAc/1TzPVPUddSRc19W+gcnSUay37ZJX84iheNuoZyhvry34s+4Mn+hZK1zkGvN7WkmFxTaTTijp7LylxR7Sj3VO/m8EyIaBb60gfkJRqsTYwHl+46ZEDyy82Sh6vGaTacRcjE6yRPuYmbrJ0UgxCF+XqiSogqip1cJitJKc8BN3R3kFLOAA8tc86ngE8tMX4CuCG+VEoZIiFkk0W55ouI8ZFZ/uhDX+LC6Wz0pUmfqQkPLW3VBd1DLgh4QpQ7rDmZu2dilvVv3nnNmGtohp6vvkL15Qn2ttZgMma/33X3xCyXYgHW391OSVlu/rYlkZLJkeyXsq88mAPRkqFr/t5aK1qmbZkWEY6f5UBlTVbmihKmrTSK/rrOabeXCz6/08kz+x/n/bVfZp3+6ddEaB6pNfRhKpI+75dUnKhiDSClSPu2VlEW0QITj8U5fbyfZ584y0vPXCQazU3B8lTpPjfCtr0tXDyfvQ4nxUBtmR2XLzchD6NVZvQmA/Hreqt7RufwfPUVqhrKqX9wGxcnXITT7L++JEIw6fUTymE5peupqHLgHs5+1na0xQr+a7PxzXo94Xj674sbC7OndrbdMIgny6G28fjL7K94gJNzmV90zkSHuat2F4cnR/lAm8Zbqjqxy1PzDxa4KttAZA8RrTg+03o9+e1IplCkimRtC8p0UUK0AEgp6e4c5fmnzvPCzy7gms1O4e5sM3h5gsoqB7MzN0/rT4chN3GNABNzPm5/1356v3Z0ycc94y48X3uV8roymh7aTudk9gRpi9lGVw7jUa+npiY3QnREF8JiMLCxrIKKiJHgaJDxSzNYHSZqt1UiqwyMRH0Me5Z2RS9JBhbR7aUleKLZ7zomBOjlC9xZeQ+HZzMrm2bVCd5T182ftwyjjx8ruPhczAlvUgm8eWEuUhwZ/AqF4lqUEM0jHneAZ584x5PfP8ngleK/Og/4I9Q0lON2BYjnUeTkEkM0t39HZzhIRU0Jganla2z6Jt10f/1VympKaH7DTrqm3ARDmZncRJ4voi2m7H10mCxG1q2voaTWwcCIjnh3hJnYOItlrt8Vov/V1wTbphobzbfV4bHHGfV4GPcuf7GUiS7bVR7Dm6Nk6/kEppdps92VVrKZHskHGq3caz2OXjqJi0b8mIDiie0+5qqCrPW1ygxvtPiS4BSK6ymi68i8oYRojlmwfj7+neO88LMLRCPF4aZKlsFeJ9v3tnDhJnHRR1y57QnvD0XY9vM76fvCq6sfO+Wl++uv4qhysPWNO7k05SGQpiC97PVhc5gJ5Mk9f334QSosCE9biQWPO8TQwBS9PU42VjsY7UwuSc87FSAajnN6cl6cNpTYaaoqQxOSIbebaf/iCgnpXXwYhSCS5XJGS9FsjTOQUkEHyS/XOXhzyRmM2tDVby69NobZdIBwZPXXXj6IaQaOzxSPt8cbye17X6HImMzriK5JlBDNEaFQhEM/vcCPv3Ocnq7s9AUvFJ2nh9h5oI3zZwqbRJUNXOOe+fK6OaQvGEToBTKe3LVtYMZH99cPY6+0s+XhXXTPevEHU7NqeYJh9u9ppuvlvnS2nDKuFMI1TBYj6zbUYnOYrxGe16O3pPZx5DW+JoadXj9O77zokUBzeQkNFSVEpYZOpFeK7N6aUsJabrpHLSamncGm30AgvrpQ2lNi4XfqBrBpzy2pr03RM8QM24nHOnOw09S4FLoLfyy/7W9XwhsNI6VE5Nt9oFCkwi1oElVCNMuMDs3w+HdP8LMfn8bnuXmuwM+fGGDzzma6u9Z2G9C5cS+WmnJC2UwWuo5Zb5C9D25l8OmLKZ0XmPXT/c3DWMqtbH5kN5fn/PgCyVk4BRKRp65Oer2OyZHlxZ3JbGDdhlqsJRa8C8Lz8uoZy8KYQhEPAVfcruUeYtzlZdw1LyIf3R5jr7UKpJ0+3zSeaHLmx0bbDK48eLklYbaW1HLStfyF3habmd+s89MsforQln/tCiGxalMEdI1oWmHfqxf8m5cclxI2l9YRjEUZDma/8sJyaEj8sQgOozlvayoUitVRQjSL/NdXXuH//d+nC72NnCCA8cEZHA4LPt/aFdgCqKsoYXAytwX74+2VaZ8bcgXp/uYRzCUW9r15Dz1uP17/8oK0rMTCdkcJZ169kvaaqVDfUMZ492tizmo30dBajc1hwesNMdw/vaTFczXiKRiqGjZWcjaSZMKS0JgI9wNgNwo2lLQiZAl9vincy4jSOosZdyR/VkWb/gpLfRzfV2bl3dXTVMnnEUmGGOjwYtU14pf2awvW55kd9kvAtqu/SwlbSxsIhDQ6J2fZXFGNlPmNb/ZGQ0qIKooa5ZpXpM1Q/xRf+vRzhd5GTvF6gmzdvY6Lncl2cilOys1mBnO8RpdzltoqB8EMKg6EvSG6v7UgSHfT4w7cIEg3NFUSeKmbM6NzdNzRQcRsYTDHzRAqKuw49rZgKLMyHYwwPOWircrB+SOZCeFYChZde5sDZpMTovHFNUCFZCI8AIDNKFjvaEFQQp9vBnf0NdF2d7WJiJa/BL2INkS7/S76/RMIJL9QY+fNZf3YtQtpuer02hhWwxaC0TPk29c3F6vHHaslHLNyf3UrrjB4wmEmA37OT7722uyem2Z/QzPn3PmLP/dGw0VSXl+hWJpcFrQvVpQQzQLxuMY//OUPi6YGaC65eHZ4zdcXNedBX0RjGk2v30rvt45nPNe8ID2KucTC/jft4bI3gNcXYt+6ai5/+wgypiGA3qM9SKDj9o1ErTYGclCZYeud6znVP4FEgCch3ISO02PT7NrXSvep9CV+KJp8uMSETL4UT4RlXPFCMhGZ36/NKFhf0oaUdnq8k5h0V4jkuVBEizXKw6VW7rWfx6gNpptjdRVj/BKa6c68JS9Nhtv514HH+PHQ4rjalcMDRt1+DEJPTObns1NlziuKGYmyiCrS5PH/Ok7XubUrzFJBMF/sfufeFnoujRNalOVtMOiIx7Wiv6KL+fJT3mbakd23V9gb4tK35y2km7c00v31GwWGAHqP9SKB7W/aT+eF7MYJCuaLLl+PRNA552bTtkauXExvTV+SCVpldXZ65pIPrQhqScSECnnVfb/B3sqAdwsbSmrxxbuIZ6EV58pI7nA0c9B8GL02nLEAXYwpegrNdDvRyLHsTXodk+F2vjPxNr7SJwnFU0vumgj4ONiwjpPugdxs7jrcKnNeUcxIQAlRRapMjM3xhX97ptDbyCtaXOPCiQFMJj1trdXoDTrcs36mp7yYTHo2715X1Bn2fqcfTLlf58rkHBs7aplNI15yJcLeECPHV3aDC0AGsvulq9MLhmeXFxqxuORKJETz+hpGUrTGGswGZt3JJRHVbK2kx5tcJQqBxB9PqTYSoVgVh6ZHAQsVxgM81Gii3jpLKD5IWEuhkH4S7LLVc4/NgyX+clYF6AJCCCzRLjTDFuKx7JSikhIuBe7iicm7eGZcx3ggAKQv1s9NOSm323AlmUSWCd6oEqIKRbGhhGgGSCn517/+CaFg8ZQoySeRSJzBvtdElgCikTgXTgzQtqkuJ67hbDA9NIeuw46WB9Nt+daGrAvRZLl4qJPNd2/BVGJDxuPEdXoudY2nbbHetGsdZ8dX7qYUisSZtOrp2N1Mz9nkvQTrdjbROedO6tiQRUKShre2BktKLT71GDk/99rrdi4a5zuDQcAKbGZbmZXt5YIaixeNUfyx1PuXG9FxX1kzW43jmOKHIcdeaSFiWLVZAvpmtHj6npvxyAZ+OvUIPxop4YrHB2RH1AViUXZYmzgdHcjKfCsRjN+an9WKtUOxexRzgRKiGfDSsxc5/mpvobdRdOj0Orye4m2nF49p1FU6GJ/JfY1IUePI+RrLrg1cfuVaK1jd+joqNjXR3TWBlmK5p9l4cjGc3mCE84Ewmw62Y9dg8OLYqoX2jVU2SEKISuCKz5XUPgC2bRarRCleS5Whg/Px5cS24KI7xEU3zH90tlJn2ci+SiPN9hBm/RTe2CCaXPr/ZEDHmyqa2aC7iE47lHMBuhgdXmyiDr+oQMrkwxo8sUpemPt5fjzazPGphecn+y1/T0yO0V5VyVAgt+WcjDp9TudXKDJGCVFFsgQDYT77908VehtFybY96zh/drjQ21iRSps1L0LUm0JpzHwweWWSySuT1LTVULOjha6ucbQkCu937GzigtOV/EJCcHlsXlQYaqxs3t2MIRijv3OUSPhaoSaBkRVc/otp2lzFmWByllMA4ZhNyWvsDKb2kTgZivLk2MICVZhENfuqrGwsiVNunsEdna8lu8/ewL2WYfTaoZy44JNBp01iNawnEA0Ay18YRDUjRz1v4ifj23l2LEBEiwPJ/8/TQZMSQ8yc83JOZp36ylMUM0IlKymS5/vfO0FtUwVuV4DYLZAtnyz2EjN9Pam7K/ONVeTHMjLu8aODJdJ7CsvUwBRTA1NUrauifkcbXV0TxOPLKySvLv3L9Fhc0jk8X7anbnsdbXYrsZnA1aSmps119M4mZ2WzrbMnZTldIELylnmHrpJj7sxeuxEpOTId4Mg0gINf3/AA763qpFx7tWACdDGG+BWspp0EIydZbHqREi4G7uHJyTt5YkQyGw6RdPxDluh2TbOjtp5u30TO1jDplUVUUeQoi6giGcbGXXzp668SjcYprXewpaGS8YFpZpz5/eAuRuoayukr0tjQawjmrrPSYtz+EG3rKvEM56+DTCrMDM8wMzxDZVMFDbvWc+nSBLHYtYqpY2cTFyZdWVmvvqGcM53zcYrVu+ppLStBX2amtyu52MUZXWrld/xa8m5ki2hltXJDyaIXgtuqm/jJ+Cg7zA08aMvKtFnBGOtEMx0kHDnMaHgLTzrfwI9H7Qx6fZCCcM8FMpZboWhSFlGFouhQ78o0+My/P3u1ZqjHF+Z0zzgCyZZ9LXjGPTjHXYXdYAEp5tjQxQSm89dxpnJbY9EK0QVmR+eYHT1JRUMFTXvXc6l7kmhk/jWerfACs8nAwPBr8ZfTniDTniByGFrXVVFebmNgeAa3d+nXkLXERM9s8v9Hh1VHIMmMeSnhsic7sY/byuqICT8XPPOVDf53v44HdpSjk66szJ8pIVlKb6SE/7j8J/x40MW8CSb7cZ/p0DnrZHNNDVf8ubmYNeqKLFZGoViMvDXriKp3ZYqcOTvIK6/03DAuEXT1O3HGIuy4rb0AOysOnBMeaupKC72NVZkdzm3M22IM9SV5WytT5sbnuPDESexeLzt2NLJ+Qw0lPi8NtZk/p5s31eNa4kJFAEPDM5w7P4zPE2TLhjq2bWrAaLjWOla7oTylSgdN1cnX6Ko2tjMRzLw008GadQyFhhlb1EM9rMHl6G0Zz50JcamnX97GTyMP8kVPJS96+jGbijOD3CItOZvbrFe2F0WRIzO4rVGUEE2Rb3zzyIqPR2Map3vH2bBvHRarMU+7Kh4EUFdXVuhtrErIF6aixJqXtQLmtReX5na66XzyJHJojO7vHWH6q8+xw65PW5DqdIKRidWztTVN0n15gq6uMcx6Hbu2NrG+tRqAwTNO9tbWJ71mTVXyH2/haEXSxy7HhpIqLnr7l3zsi+PlGc+fKlLCpNzCS7HX8yXfJp50j9MXuHw1q7+57AgOYx4K6qbI2ekJWmyVOZlbueYVxY/I4LY2UUI0BcbHXRw/sfQXzfVc6p+ivK2K8gp7jndVfFw8O0RDY+Zf7LmmtjQ/z82Ef22EK1zP+u1N9J1IWP+lvCpIdzoM1NekZuXdsrmBmdnUwiH8gQjnL4zQf2WKukoH+3avw6Ml3xWrtDQ5E4EBM+dcmdV6Nev0CF0ITS6dkfSKK0ZAbMlojWTxynpOag/yzcABvuv2cd53acmORwb9GI9tKc649jJdbsqeKYuoQlF8KCGaAqdOp9ZHe3jChVZuobmtOkc7Kk5kXGK1FP8HvsOQH4v1tNtP832b8rJWNokuFTMpJZe+e5jZbzzPToeRuurkBKnXn1nxc+eUl6ANelyzbGqoZl9LI1bjyq8xky25NSuNHYQyLHS+r7qBidDKFt9D3q0ZrbESQVnOBe1+fhC6l6+4DRz1XGYuunr2ebntBd7UWp6zfaXLaec4jZbyrM9rUnVEFcWOcs0rVuLM2dSEKMCMy8+IP8D2/a052FHx0n95kh271xV6Gyuii+Svns7lahMdv3IHQr823Ccdu1oYODuw7OMyLrn03VeZ++a8IF3JQtrWWs3QSGbJWjabieNiGgRcmp7mxPgYcYNkT0sDW+prljwnakgu5nM8w7y1HRX1nHev3HIV4LMjAkn2wkHC2Lkk7+Hx8P180V3Ki54+xkLJeWwWs7XuB3SUFVc4jQRqjNnfkxKiiqLnFhSixW+2KhKklJw+k7oQhfm40TNXJtm6r4XJvim87rXpqk2Vnguj1NSWMuVMLQnEYjFSUeXAZDIQj2u45vz4vNnvER2ey9/zEIrEOBpx0/a+O9Ce6sI3lr9kqXTwTazcynOBBUGKEOx8+x1M2+2MX/d8W22ZW55b99TTp13bXz4Ui3NqfH6sttxOW3k5o3Mext1edDqYia/ubi/V13LEM4UA9lSWMRoI4QwlXyKqxGjGHUtOZM9GJSPa7azTHUp6/sVoUodTbGFEq2c0EmU8PIwmB9KaazE6EeZNG04wem4bgVjxJDCdcU5QW+ZgKpJ6Rr9Rp2N7ZQ1mvf5qgXyBqiOqKHIkcAtmzSshmiTDw7PMphjjdj1d/U7Kyqw0lFgYH0m+zd5aJRKO4bCbSaYQS01dKfUN5cxOehgdnmViUUtICVRU2KiqK8NqNxPXNDzuABNjrhtqXqaCa8wDucmJWJYBp4ud7TVFLUS37Guj85nTqZ0kJZe+dwSEYMfb72AmIUgrK+xcujy++vkrYDToOWWYWbElptPvx+n3g4RNDVU0VhqJsrp1sM5Szztag7jjQ/hiQ3SYDdxj3sT5WehZpZyTQehocVgZDCQfX/pNZy1/mHy+FSFZRh+7GYzoGQuPEtE8QObZ/ddjMl7h3Zs38J+dxeMki0mNZkt10kLUqNOxo7IGswF6fBNc9g/dcIyKEVUUO6rXvGJZ0rWGXo/bG8RaXYLJZCASyU9R9UIy0DPJjv2tXFim5Wd1bQk11SVcOj/C9KgLuDH3TwCuuQCuuWtrQgq9oLGhnPIqB3qjnlAoyrTTw1ySFwxupw9bYwWBUH6tQHpT8b7tdHodU32j6U+QSGpCCHY8ejvWlkqOnc/sAm791jpeiCdZV1LA5ekZLk+DZWA9d++0UVIzw0hkAHmd70oAgfhlPLHXLgpiMsZo6CKVNnh75XrG/A6OTs2xVEbqbTUNnHen5gr/0VSU321owShvFEmLkRIuczcve2cIa6u7/bNBleNpXr/uMZ4ZLp6L5DPOCYxWPVG59FWIQLK7uh6LEXq9E3QvIT4Xo7LmFYriQ70rkyRbQhRgYtrL3j3ruHAs9XiutciVrjEqKu3XCESdXrB9xzq6zw8zk6Z1UMYl4yNzN1iXHaUW6hrKsTnMaBI87iCT464bhL8A6spL6J/IT7H5UocFh9WE3a7RsmsdOr0OoRcInUDodQidDnQgdPPj6MT8TyFg4adOIHUCo9Chm5kPV7h6BS1fCxPSNHn1p0QiNYm2cItrxDVJaZmFmcEpJhcVmd+yt5XzPzuV+R8rJd0/ODrvsn/b7cyUOhhLMURjAX8NK7VGX5ZQRPLsST9gobFqJwe26QnZhpmJzrcb3WBvZjLct+z5Y6EroIc3tdSC1sjzYy5C2rwFfldF6iJ0HsHx4C7usiwvmDyykZcimxgM9qYxf2bsqv8hfe630O/JvtU1HaosNqbl8u/Pg/VNnE3heVAxooqiR1lEFUuhaZIzZ1e+0k6VntEZTGYDkfDNbxUNBqKsa6+lotKOw2FBi2uMDc9y4eRATtbze0Jc8VybMSx0gvr6MiqqHRhNBkLhKDNTXkrMuamjaNDr2Nhag8FqwBkKMOb2MKmFmYyH6TMDe5ItT7N0FPqGqgrCzy4vopJhwe659a4tuMZmmB6fY+R8li+OpKT7++kLUrvNxJnwdMbbGJuJ8qOXokA1eza0sr49hFWf3D5mI07AyV0NDmy6DRxzhpiLpr+nzwybubPDgODa935cGjkj7+Wkd5CYzL8IBdDpgvz8xrP8+9mNhOMrxELkieaSEqa9SwvRJlsJl7xLe1qWIxSPYjOYs7E1hSI3qBhRxVJ0d4/jyXLrSl8gwp6dTXSeyJ6ltZi53JmBuzcLSE0yOeZicsx1zXjkSIy2PTUMTGbHHdlUW0ZFjYOumWlOu5zgWvWUtIgtU68yHS5dmO+vvuW2DlxXRpkbz4FrNk1Bum5zDb1kVuPzWgRn+gIMTOg5cM9ASmf64z788bNsrbJg12/iRaePdIpID4Q0ZthHNceujo2whxcDelzRyynPl21Mxss8tnk9X7pY6J2AftkqE5Iah4HL3tQu5GcjASVEFUWNuAUtosUTmV7EPH+oKyfzXhqZpba+uMqm3Gr4ZgJEjk+wvj6zrKXGmjI2b6unN+rm+PgYvkjyhdfTQZeDLhqXzo0y4YMtbz7I+ts6sj4/cFWQTn/5OXZadDSu0qkpWJ6bbdy/W09MpueNCGkhZqLneKSxEr1I73k47W8EwE8NT0ce4keuaVzRybTmygXVjqe4K8P3RDY463TSaqu6YfyOuiYue1evk3o9c+EMa3UpFLkkk9JNSQhYIcQ6IcTzQoguIUSnEOJ3EuN/JoQYFUKcSdzevOicTwoheoUQ3UKINy4a3y+EOJ947J+FmP8wFEKYhRDfSowfFUK0rbYvJURXQdMkh168lJO5Q+EolFuwWIuvzd6tRNATJnhkjA0NqX/xGvQ6du9oZkDzcGZiEtIUJqliy2H2b+/FcYamImx8w+1sumd7bha5TpAu1TpUpxN0xlw5Wd5UkbmFfjjYxcM1DdhE6h26Ov0mzmqv4+uecnoC3RnvJZvUmuvZXdrKQ83nC70VgrEo464Qe8paEAmXZZXFRn9gLK35XJHA6gcpFDcvMeC/Sym3AgeBjwghtiUe+0cp5Z7E7QmAxGOPAduBR4BPCyEWAq0/A3wA6EjcHkmMvx+Yk1JuBP4R+JvVNqWE6CqcONnP1FTu2uCNTrpp2FyHXq+eikIS8kXwvzrGxoYbrS/L0VJfQUVLCUfGRonn2Z1iFrl/vQz0THJlxEfL/fvY/vo9iFyI7IQgnfnyc+y0XtvLXq/X0WCwZX3Jrc1mpqKZC9FyQxXfOwLhoQaqRGoXMYfdglc8PUS14qgpbNZZ6LCvZ6ejDAfn8EWOU2s9RLmp8G5sfyzC8fFxqnUV7C5tZn2ZHV8sjew1wBVVQlRRzIj5GNF0b6sgpRyXUp5K3PcCXUDTCqc8CnxTShmWUvYDvcDtQogGoFRKeVhKKYEvA29bdM6XEve/AzwkVvnyUOpnFX704yxkEK/C5cEpNuxdh0536wUpFxNhfwTPyyNsalxdjO7Y1MiVmIchd2Gyiw35awrF2OAM3b1zNNy5i51vPIAhF+WnEmWfZr78HDttehpqSpFSMhHPvnCoNFQjBx/EHuxIu2afURi53LMBVzDOiMfLaG8JzWJd0vONhcIYRPa6LKWDDj2b7BvY6aig2ThKPHYEf7Tn6uN6EedgfeGF6AJjfi86nUanZyTtOWbDqRfHVyjySmau+WohxIlFtw8st0zCZb4XOJoY+qgQ4pwQ4gtCiIrEWBOwOCNwJDHWlLh//fg150gpY4AbWPFLVQnRFZid83PkaGaZycnS2TfJxn0t6NZIC8iblWgwiuvFYTY3VS97zI5NDZyYHiOq5VENXochlv+IdueYi67uKbb94n1s+uCDGCyZd0y6ASnp/u4RZr7yHFsdOkqyHLaiA7rGZnmxc5YnnrfT8+rdGEYfwBpOrR2twbuPc6OvWeXmQiGOX4gQutJKnWsHG6M7aBGtmFlu/wKrvi79PyQDdOjZ7NjIZluMWOww/mg3cpluATsri6fEXJnJxKSWWRztVDh33i2FIitkJkSnpZQHFt0+t9QSQggH8F3gd6WUHubd7BuAPcA48PcLhy6zw+XGVzpnWZQQXYHnn794tR5jPujsm6R9dzNGk6p1V0iioRhzh4bYsoQY3bq+jlMzE8gcJAulgogURgSXVdk5a41xLBxEfPB+On79PoQhB69XKen+xmGMf/gs9zsN1Nqy46LfXl/HXOA1d/i428+zZ1089WwVQ8fuw+Hev6pVs0G3kx+fWbrlrCsU4szoFC9dnuLohRBDFyuxjG+mNbCLDXIz1aL6tfmvGh3yg14Y2OzYyCZbjGj0VcJJNAlodrxYNLU3dzaVZOxanwopIaq4tRFCGJkXoV+TUn4PQEo5KaWMSyk14D+A2xOHjwCLr9KbgbHEePMS49ecI4QwAGXAisW6lRBdgaef6cz7mpf6p2jYUo/NXjwusVuRaCjGzAuDbG2uuTq2YV01nd7pvMeDLoUIF6bGo/3OFtzR+U5UU24/x2UU00dfx4ZfvisndZhlNM7AP7+I449f4L45EzUZCtKVSqP0T3v4yStxRk7cj8O3e0lBWm1s5NuvJi/MNCnpn3VxpH+Sly+6OH9BT7Cvhdq5HYTC+clKNwoTWx0b6LBGiEZfJZJslyrAqPNxe11JDneXHHvrqzjvybzUnRKiiqInt1nzAvg80CWl/IdF4w2LDns7cCFx/0fAY4lM+Hbmk5KOSSnHAa8Q4mBizvcCP1x0zvsS998JPJeII10WVUd0GYaGZ7jck3p5kGzQNzxD87pyzFN+5mZUTFOhiIXjTD83wLYH2/DFYvRHPYTjhXPHLyYeyG9bUoAtD2ziZd+NXbAmXX4mTbDp42+GZ7pwnsy+OzceijH49y9QYjWy9SP30NWoZyqQmnWszGyha3x1EXZlys2VKcHm+gfYuMWJzzZfUNOis3DsfBPhWGaludzhMGfHpthWZ6Yhh955m95Ou62eaOwC4Wj6cZV7qid5ebxwF8ZlZhNuMbP6gUkwFSqOjlEKxZJIcl3Q/m7gPcB5IcSZxNgfA78khNiT2MEA8EEAKWWnEOLbwEXmM+4/IuXVfrsfBr4IWIEnEzeYF7pfEUL0Mm8JfWy1TSkhugxHjhSms8kCI5NuykuttJTVMHQleQuGIrvEInG0zlmcW0z4w/kXf8sR9+W2Tun11DRXcFwXgBV0+OXJOXS7Gtj10DaiR3oZebE76wEM8WCUwb97/qogvdigYzqYXOb5puoqTgwmny3fPeGie8LEzuYHWdcxQiBUwZXppV3y6TA4Z8iJEC0xlNJmrSIYPUMwkvlFQbvjJQSvL1DnQcnWBhtdy3RXShVn2IuUMjcVIBSKLJDLgvZSypdZOobziRXO+RTwqSXGTwA7lhgPAe9KZV9JueaFEAOJwqVnhBAnVjjuNiFEXAjxzkVjXxBCOIUQF6479m+FEJcSWVrfF0KUp7LxXHP8ROGD9F2eIENeP9v3txZ6K7csVruZ2S1W5sLZEyDZIOpJr3xN2uypI5xEcpYmJWecs3Sur6TiDx5h02/cj6ks+9nhC4K09E9e5D63mWrrKmtImPKmV8z8/Mgs45MbmfFkN6azazK73zg15jp2layjVj+AP3IUTWbnNWI1jrOjKv+NN0w6Hfe119LlTd+aez2heDTt0k8KRV7IoWu+WEklRvR1iUKnB5Z6MFHk9G+An1730Bd5rdDpYp4GdkgpdwGXgU+msJecEgpFOXc+tR7GuSIa0zhzZZK2Pc2qC1Oe0ekFJfc2MFiE3VgirvwJ4y0PdNDlS92lOTDt5pgWwfvYbWz4wzfTcE9H1j8r48Eog3/7PKX/30vc77YsK0jbKysYnHWltUZ7QwXHZkc4NTPGjobaDHZ7LXNBiUVXnuEskhZrKzsd1ZRwHl/kJJL0OkatxP6a/IYI1dltbGs2c9aTfYOAcs8rFMVFNpOVPsZ8JtY1jaGllC+yRMaUlPJniRpTAEe4NgOroJw9N0Q0WphkkOXoGZxmMhZh2+3tVNXkNnnAYjXlfI21QNtD6zkTyEHf9SwQnM1PYe7KulJO6jMTvcFwlHNzbnruaYL/7w20/cmbaXzDtqyK0rg/wsDfPjcvSD03CtLqNJOcjHodflsUTUo0Ken2TtFRnb0kIwPpClvJelsbO+wlGLTj+KO5bQy/qexYTudfzM7aKswlXvr9uQlJUiWcFIriItkYUQn8TAghgX+/vjaVEKKJ+UyrB4Hb0tjHbwDfWuqBREHWDwC0tLSkMXXqHD9+JS/rpEosLjnbM45OwOb9LegjGgM9k4RD6cUu2uxmqutKsZda0Zv1hGMac94AE1MevLEoVe2VlNjNxOMSicRsNOCwmIj4wvR1jaVdDHwtsPm+9Twfmi70NpbEoNMR9obzUkDKclsTwSUSlFJlU1sdJ2cm8IUjTALsKKfqjofYoDOjnRph9MnzyCyUI4j7Iwz8n+cotZvY/pF76KwTuEIhLk+m91xu31LH0enXXMPheJzxqJfmshJG3JkLmmisMmVzwAbbeixijGDsGPnqE1RmukiL436GfLm0jEruaa2n03cFLYelKZzKIqooYnIZI1qsJCtE75ZSjgkhaoGnhRCXEpbOBf4J+LiUMp5qELgQ4n8wn431taUeT4jezwEcOHAgL0/RyVMD+VgmbTQJXVfmDc/GEiPrOmootZqRMY1IKEokHEOLa+h0AoNRj9FsQG/QozPoiAOBUIQpVwC3J4B7zgtz13+hzj+HM64AM66lv+pq11fRVFlCX+cYwUB+E2dyTcftDVxZF7i2n0QR4bCY8iJCN925nlezIEIBQuEoG6sq6Z15zTky4w8yQxA6HNz+c+8i6vXD8/2MP5m5dW+xIN3z52/iWV/q1rWO5qprROgCnkgYi81AfYmdiTTjThdwBxxYHMkdu9G+HpMcJhQ/Qr4bgwoBd9TFGcqRDjXpdNzeWsF5T+4biCghqihqcps1X5QkJUSllGOJn04hxPeZL3a6WIgeAL6ZEKHVwJuFEDEp5Q9WmlcI8T7gLcBDq9WZyhdzc34Gh7JTKiQfRGMaV0bSzCjNIHPUOevHOevHUmpm+44mJgdnmJ5c+x/wje0V9D46gMVqpE3fxMBA8fWmtpty0NHoOkor7HQ64pClQgF9w9Nsaq0BKZd83fX6XUwEfejurOCeHQ/S/7fPZUVsx/0RpnSpx0xaTQamjYFl/35nwE+dzc6Gqgr6ZtIP3xh3m2lfRYhusK3HLEYIxY5QyJS5g3U/whP5BZ4Z8RLP4sd1hcVMe50hK3VCk2EyqFzziiJljScdpcuqTiEhhF0IUbJwH3iY14qdAiClbJdStkkp25hvcv/fkhChjwAfB94qpSyab/vzF7KXoXkrEApHOdMzzkQkzLbb2wu9nYzQ6QSRXw4SNkZwx/zo9ozQ3p6djj7ZxGbIvRAtu/u1wvXZwquPLSlCOxqqmfDPm9o0KXnREaL5r9+MyEK7W8eGGi54XSmf19FRzURgZfPfZMBPf3CO21obkWl+e/RNL1ccX7LBtp5tdjMyfoRQrPCfS1bDBI+2/xt/dfBF3rnBnpXXYVtZCdVVUfp8mbXuTAVlEVUoiotkopPqgJeFEGeBY8BPpJRPCSE+JIT40GonCyG+ARwGNgshRoQQ70889K9ACfOu/jNCiM+m+TdklfMXitQfW+RIBLriMGqnTcddDYyVvebC9cQCyF3DbNhgL+CubsSqy21DtE0H13M6Sy75BXZuaqR/zrXkY+NzHtaVXFsR4nDYRdnfvRlTZWYXAlXv3oOW4utye3stx6eTqzcakxpHnSNsa66hviT118klp4ZY9DEsEGy0b2C73ZIQoMX3eeQwDvBg02f51B0/5H2bDdjTFKS766qIWGbzLgyVEFUUNbdg+aZVXfNSyivA7iXGlxSOUspfu+73X1rmuI3JbTG/FEvZprWG2WTgysXxQm8jI3x33eiy88WDOHYMsVG00ttbHF2uLDJ3QrS00k5nSfZc8gAmg57BwPJf/r5QhA22aoa914rfc5452v7kPkr/4zSertQtZi2/cZBTjgikGMLsNUVINQuoc8aJ1WDgttZGzo5MEEmyA1ckDlZ9LWFtmo32NnRaH+HY4bzHgKaDWT/DHXX/yZaKbXyu8wH6vcm6vCV3r6unK3glqy7+ZFFCVFHM3IrJSqrX/CL8/jB9fc7VD1TcwJbWGgL+tVsounljFf1VY0s+5osHiWwfoKOjOCyjhmjuPqly4ZLfuqkep3/lpB79MgH6A14Po7++nfo3bE5pTVOVnRPrTfgjqf0tuzvq6XGlFyMejMU46hzBVmrkttZGrMbkckErRTsdlhDR6KuE4/lzUWeLMtNFfmfX17i3oXyVIyV766vZ22LnQqCvICIUYCbsI6YVV3k+heIqt6BFVAnRRfQPTKFpa/jZLCCRWJzmtupCbyNtHPcbl258liAQDxHeNsDmLUmmOOcQXTg3X6Ib9rdy2ptdl3yJzcyFmdUz1uUKBsS5YIizD9SmJEY73niA/aN2DpgTCVJJIJGMapknssyGghx1jqC3CW5ra6TEbFrx+Gg0SETLTgvLQmHSu3n3xn/hvZsNCCQNDhv76qu5u6WO+9vruLu9gtZ6yZXoAFf8hb3Y15DMhIvDu6FQKFSv+WsYHl7bXwaFpKvfCVLSur2BSquZ4T4n7rmiyUFbke1vaub0pq5VjwvEw8jN/WwR7VzqKtwXmfRnv3MOgLPOBIHsWkPXt9dwdGz1eMtYbGVxHY7FGXxjC2Vnxwg5VxaLEphEx0TPFPTAgaZyTJtLOBqeJL7CxUZHYxWdaZR5Wg5PJMzRyREO6prQTBKjTc941MeAz33NRY/TV0q7JWvLFgy9sPJAcxeYd/HDUSd9UbIa4pFNJkMe6qyqU52iCLkFbWFKiC5ibKw4u+isGYRgcGyOQUAnYMPuZmx6PQPdE0Xrtt/6UBOn7+9a0Rq6mGA8DB1X2CrW03WxMGI05s7+/3LTwfW8GshuK9OKEitnJpOLGw4n0cls0u+n4RP3E/z9x1d8uja+fjfdi8Tq2KgLRl1srymhfEcFr0QmlhSktgoTZLmHwQZrJee7Jq4Za7BZaamtAAsMB70MzEVoX7vOBIy6JtC34Qz1EYtdpMXmQccWtCL+RlVxoopiREgVI3rL4/UVskrfzYUmoWdomrP9k4QcRrbd3k51XWmht3UNW+5t5NwbulN+FwS1CN6NV9i2vTBtUCNz2U1l0ekEQxXZ/yhY11JJOMmkHX8ouYyiM95Z2v7owRWPka31S45PT3npfX6I/cMONpqvtYYZ9Tq63NlvKVkTst4w5gqEODcwzrlL48wN+hi9snba6UoJRl09FuMBjMa7iYgdjEW8jAXPE0tU4TPqRjhYU9zKelIJUUWxIkX6tzWKEqKLCNxkHYKKhXAkxtlErdGO/a20bEi3v3b26Li9gc439yB16V1+hrQIng19bNuRfxERnMqu5bLjzvWMBLMbRmGzGDnnTD7xxhtI/iLwcEmEutcvHS+64eE9XB5cOdloZGiW8AszPEg9xoRtdUtbDb5odt//bZZyzvdPrHrc2cEwOnGjYC0GBCYshi1YjHciDHfgo5WxSIDh4GXGgudxR4eWPG932bk87zQ1lEVUUbSoZKVbm2CSVhlFekgEF69M0jfjpmVXEw3rKguyj/W767j89ivE9clZ65YjpEVwr+9lx878ilH/KjGSqeKrWDmZJh02ttQQWiXuczGeYJjlSrtfT0zTuPJw8w1i1F5TxmRJcs9FPK5x6eUhdvVb2WIuJ2zKfgJYQzjZKgsCvWzO+vrpICWY9R2YEtbOqZid4dAYw8FOnKEuQvHkwpes+ovcWV2T492mjzOohKhCUSwoIboIU5LlVhQZIgR9wzOMBYNsv70de0n+MjVaNlfT/4tDRPXZSfgJa1Hm2nvZuSs7YQc7muvY39pEdcnShdzLrGZkPHuXvjq9jp5g9mNdAyL1/2+5NfnXwUwwyOn7a2j94zfMe6WAxkfvxOVOLWxhfNSF7/kpmrr1mLJVn1XC3bZ1nB9Y3Rq6QCRSOC+BlGAytGMy3k1QbGQkPMlowtopSV+g76s4lZU2rblAWUQVxcpCnGg6t7WKUl6LuO/ezQwOTnOlP/uxYoobicUlZ3rGsTlMbNlSj4jGGRucyVm2fUNbBRO/OkHYmF3Ld1iLMtN2mZ2ig/Nn07dW7myuo+fyJFrCUFti0lPmsOKwm7GYjRhMeoxCYHyblbgvQtQdJjgbwD/pJewLp/Wlv25zHSfj2bUG1lWW0Ol0knQGWIJSk5WZYPIu+pim8ZLZz/Z/+DlaO4OcGUgv00jTJN0vDrOvoZTAQQvndOnVEV3gNkcjZzqXrkm7HHPeKiqrMlo2ZYy6RnT6NtyxWaZDY0Bmf/f1mHWXubtmLy9PZTkDLAsoIaooWtawoEwXJUQXcf99W7j/vi30D0zxwgtdPP9CFyOjKpM+1wRCEc73JqxHUrJhVxMOg54rXeOEgtmp/1LdUIr71+bwmXPTsyaixZhu6WGX6ODcmdTF6PbmOnp7nFdFKEA4Esc568M5u4zFsgwoM0O7GZNRT5nNQonZhE2vxyx0OH/QRdizsrCzNpdBJLtfynUNpQyOpW5lNabZutSpRRjeAdv2tzF7aBLfdHrP8eS4B/EDD4/cvY5DdZME07AGGoWOmZHUY3hHZ+x5EaJ6XQ1Gw0a8MT9TkQHgQk7X2112klemWovuu3Uy5EFKiRDFarNV3JKscctmuighugTtbTW0/1oNv/a+e+npneSFF7p47oUunE51FZ1zEm57AHOpmc07moh6QlzpTt7NeT2VtQ4iH/DjsmU3tvJ6ojLG1Loe9ohNnDmd/Gtla2Mt/b1TxDNwuUeicabcfqZ4TQQ1hFYX8TNmUm6BuRIWk4GL0/m1gLVWl3N0fJRXQmOY9+vZ72hj7kUnvqnULetSQvfLw+ysLSF+p52ThtS8I7dbmjjjTs0aCnB5zMzOTSmflhRGXTN6QwvemG9efEY6c7PQEpj1vdxbc4AXp4rLyxTWYniiQcpMS4fAKBSK/KGE6AoIIdjUUc+mjnp+8/0PcLFrlOdf6OKFQ5eYm8tu5rLiRsKRGBd6J0BKaqvszM2k/j8vq7QhPxhm2u7K/gaXICpjTDZ3s0ds5syp1cXo1sYahq5ME4tlljh1PQ6riXhkZYue2Wrksi+74nzT+lqOJVk7FKC5opR6m4MrrjnSNU45g6+9LsLxOK+6xzDt03PQ3Er/jwfTcnVNOb3wQy+v31HP8KYo3cK16jlbrNVcSPOCqWtYRzDcTCC0Dp0uREXJ6ZVPkAaspl1IBBBFyiigockAOmFFiHLiGPFEp5iKTEAkt5bPldhVfoyXp9YXXV3RyZBHCVFF8VFcb5O8oIRokuh0gh3bm9mxvZn/9qGHOHtuiOee7+Kll7vxelX90ZwiBKVN5UQjcXwp/K9LyqwYP6wxVpLfjllRGWeiqZu9us2cPrG8GN3SUMNw/wzRLItQgFKrmdXSherX1zCa5X7fE+HlrZDlNgtmo4FGhwNzXIdnzMfoiVl8zCJ0guqInbKaWk4Fpogl+WlcV+Kgf851QzhqJB7nxcA4W97ZgPmYn9nB9LwZfRcm4AK8cV8j3W0BBsTSwt2uMxIZixLT0nsurZZS/vjbbwTAoNPxsdc30lL3kyWPNeiq8ctapoK9y8zmBtL3IGQbk+4K99bexiFnccWKTgY9bCpduuasQlEwbkEhqrLm00Cv17Fvbxt/8Ptv4jvf+hj/+3+9k9c/tB2rNftlcBTz9A7PYKy109BckdTxNrsJ+4d1jJUVxiUYk3HGG7rZd9vS2fSbG6oZGZglEs2+CAVwGI2rHmOyZ/f1untrE0Ou13rV15Y72L2lnvs2NdPmMsN5H+FTLvpfHOHSK0OM9c9e1Y9Sk/RcmmDgpTHaLut5nWig2rB6Fn1zZdmKOVGX5ma4vC1G+5tbU82duobLp8bQ/8DNm0YbaJDXlmUSErbHaph0p199oKH6tddJTNP4v083cKH/PSx1nSD0m/FER9JeqxDsLDuKvsjiMVXCkqIYuRWz5pUQzRCjUc/BOzbyx5/4eb777Y/xp//zbdx372aMxmSrIiqSxTnrxxmL0bG9acXjzBYjFf/NzFBlYa1CMRlntP4S+++4tovPpvpqxgbniCTR1jJdrPrVX386c/YcIm2NlXS6ptnWXsOu22spu0swvG2CY5W9ePx+XCmEVXi8ITpPDFPXJ2gyrVyL0xVZ3UIejMZ4MTKO+RfqqNuefkaQpkkuHRnB8SM/j0w14kg4lA7qm+gcSr54/1K4wtf+HZqUfP5lE//5/O8x4nwELfElo9fVMBG8lNFahcCkG+C+2jyXBVgFJUQViuJAueaziMVivJp57/eHeeXVHp5/oYsTJ/uJJ9nqULEyoXCUrrFp9tzWzoXj/Tc8bjTpqf+wnZ6a4QLs7kbiUmO4posDB7dy4oibjvoqxofmCK8Sv5kp58ansL+lHavJiMVowGzQY9LpMAkdegl6KdGZTHAl/VJZFpOB9vWVGKp1OEu8uMQcM+LGMIioJ72arVPTXmpwMNduIKDdOEeV3cblmZmkLZ29rjnu2NtEXasV25TGwMlxpJa6GSEWi9P94jAdFTZs91Th1TLL9qpwWOiZWrp00vlRD+dHm2iv+h3euHOCLa0TaKwSP1qk7Cg9zIvOTcSzHA6SLqrNp0JRHCghmiPsdjMPv2EHD79hB25PkJde7ub557s4c3ZwSXebInkkgtO94+y6rY1LJwau/j/1eh3rPlTOpYaBgu7vejQ0hqq7eODh7Zx5wp1zEQrz2d++UATfCt3CqsvsSNLzWJfYzFjvM3BEf+PFwA1k4JGdmvZxW1MTh/QTXJ/N1FZdgXMieSFdZbPS1TtBOGGJrr63jA5rOZ7LLqauuFLem2sugOvHAdbft46+lM9+jba6SqbGVs6075/x8dkXHJRYtnFPxx42t57C6jibdoJXITDqhri/9k6emyyOWFFlEVUUJbegPlCu+TxQVmrlLW/ew9//7S/x7W98lI98+CG2bW0s9LbWPOd6J9i0vw2dXqDTCdo/WMWl5oFCb2tJNDQumM6z8xf0pFkuM+tMu/2sqy1b/cDr2La9jvADYXr0zqSO1xkyU0tdZ0d5QNfA9Vdw3mg4pXk2llZeFaEA074Ah6fG6KwIoN1fTuOb17Hh/nUYTKmF1cRHMqtNu5S1dzm8oQhPnvfxT49v4r+e/Q2Ght5FLFJcLu+V2F76CgZRHG+AyZB79YMUinySQXzoWo4RVRbRPFNV5eAX3nEbv/CO25iYcPHcC10882wnA2l2hbnVudA3wdY969Dvi3C+rafQ21mVrngfB35pAye+ESbNBOusUlNZwogzuS/k2ioHtn1GDpuupLSGazjzJgIXT4xwcGMtnnq4GJylzGKheya198zo+PJ/57QvwLRv3rpafoedbdZKRo+MEfSs7nYP+MKQXA7dDdjMRrrTzCbvnfLSOwUG3eu5c0MJO9t7Ka04hE5XvN9IRt0IG0vu5JInv5UslmJS9ZtXFCPF+/bNGcVxaXqLUl9fzi8/dif/79/fz4c/+GCht7NmiTdZmIzbMci1cV11Kd7HgV82FYVl1B1YPdnHaNCx684GBu+Y5qwptWxti96IJwX3+Upc6XUy/bKT+2N1bKupJhUdv6mqiilXclntrkCIV2fGmNxmYP2b2rBXrpy9r8vgidzQUJV2yacFYprGSz1uPv2zGj7/k/dwsfs9+L170OLF+X5osxdHPIE3FiIQS82qrlDkHJnBbRWEEOuEEM8LIbqEEJ1CiN9JjFcKIZ4WQvQkflYsOueTQoheIUS3EOKNi8b3CyHOJx77Z5FoUyaEMAshvpUYPyqEaFttX8X5SXWLodMJ3vXO2/H5Qnzla68Wejtriq13tnLINwE+2BLfhndbNxFR/F8ul2JXOPDL6zn5jQhZbvWeElfGZ3HYTPgCS1v+1jWU49sd5CX9cjUrV6bKaCNGdp+PrrOjVFfUpXROhdHCYIrrhKIxjjjHMHcY2FvZhvPoJL7ZG627fl8Gf1+GYQvXM+UL8oNTwKkOzIYtdNQ6qHKAzRzHbolQYvNit85gsYxiMI+g06WXSJYJtZbCW0MXcIY8tDlqCr0NhSJfxID/LqU8JYQoAU4KIZ4Gfg14Vkr510KITwCfAD4uhNgGPAZsBxqBZ4QQm6SUceAzwAeAI8ATwCPAk8D7gTkp5UYhxGPA3wDvXmlTSogWEe99zz2cvzDCmbNDhd7KmmDzniYOBcevJrFcGp5lY3wT+h19BHXZscLlkkuxK+z/pXZOfiNaMDGqSUlbUzUXem5Mltl1oIEjNVeI6tK32FmFEW+WhWhjQzknw9M3JC+txNQKhfZXIxxbLEhbmTrmxDvzmiD1+UKUGRy4Y6llz+t1gp7ppbPls0E4FufC2PXhCGbmv08a0Ynbaa9ycKcvTGXLFLb1F9FV5L4+aamhD1if83WSYVIJUUURIchtrKeUchwYT9z3CiG6gCbgUeCBxGFfAl4APp4Y/6aUMgz0CyF6gduFEANAqZTyMIAQ4svA25gXoo8Cf5aY6zvAvwohhJTLp2kXgXNQsYBer+N/fPKtVJSrtnOr0bqhhiO62RvESO/YHLqz7dg1R4F2lhpRc5TN7yhBpy+ku/LazweHzcSGN1TwUl1vRiIUwJyDa13HxpKURCjMC+5MmRek44xsFGx4pA2T9bWmARstqSd9bWyoxhfOrPRTJmhSMuoK8uL3ZvjhP+n4xm/v4Kf//Re4/IVfwfXKW4hPbEbmII7ZKPopNa7erCAfqDhRRdGRQ9f8YhIu873AUaAuIVIXxGpt4rAmYHEtxJHEWFPi/vXj15wjpYwx3+ptxYxKZREtMqqqHPzxJ9/KH33im6rM0zJU15TQWxUmukxXooFJN82nWijdO4pHX7yZse32Bs5O+wjFXex8RzM93/OjxfP/pF+ZmEUIgZSS1uYKpnZ5OCWyYxkzZzlu12w2cEKmntxTZ7EzjisrewjHYhyeGqP+YAkNYzrGu6exe0TKl/U2mxEK7KWutdsQzF39fdrpZ9rph+cB2nGUbGH9dgeNWwKUbhxA33QenSEzd74QsNHh4NRc4VsjT6jMeUUxkXn2e7UQ4sSi3z8npfzc9QcJIRzAd4HflVJ6xPIX9ks9sFzVv4Wdr/TYkighWoTs39fGr/zyXXxVxYvegM1mwr/JzFxg6Z7fC4xMe6g/3kDFbXrm9MUTk7bAOmstF2dChOLzX+rngyPseEczfT/wE4/mV4z6ghE6mquwVRk52thPVGQvTsAQz67TZcP2ega01DtmeT3ZFz0THh9z5Xr2Hmxi4Mwklp0GQlpy/zsJDLoLL4IqDOYV5bnPG+bckTDnjgDUYTI1sX5rKc1bo1RsGsHYcg5hTr5r1gLNthin5lY/Ltcoi6jiJmNaSnlgpQOEEEbmRejXpJTfSwxPCiEapJTjQogGYKE23wiwbtHpzcBYYrx5ifHF54wIIQxAGatccivXfJHyvvfcw84dzasfeAuh0wnKDlQzuIoIXWDC5WP2aDXVseKKAWuyVtPniuOPRa8ZvxAcYcPbbBhM+X1bmgx6zJt1vNzUm1URCqCPZTfkYKg0dUFp0OnoH89NLGY4FudobIrmexrZZa1M+rzWmnKc3tQFXLaxxVOrmRqJxLh0dpZnvunlv/6ijG9+8D6O/O9fYvT77ybYeT8ykFwdq3JT4UU4qKL2iiIkt1nzAvg80CWl/IdFD/0IeF/i/vuAHy4afyyRCd8OdADHEu57rxDiYGLO9153zsJc7wSeWyk+FJRFtGjR63X87d88xosvdfP4T85w7nxxtKwsJJvuauFFb2rWsGlPgPjhMpru1DNpKGzveYB6SyXDbvAsU4z9QnCUbY82MfjDINHI0qEHlbUWGjbbufDSNCKTtkVAS0M5gR1BDuuS6JCUBrosCtG29moOR1wpnyelzGnNVinh1blx7mhpgiS1ZXW5gys+V+42lSTGYGbWdy0u6euao68LwArcQXNbGS1b9VRvnMPaPIiuqh+d+dpqA/6YHUiunFYusRlMhd6CQnEtuXWI3Q28BzgvhDiTGPtj4K+Bbwsh3g8MAe8CkFJ2CiG+DVxkPuP+I4mMeYAPA19k/o3/ZOIG80L3K4nEplnms+5XRAnRIsZkMvD6h7bz+oe2Mzg4zeNPnOHppy/g8RY+tirfbN7VyIu+8ZSTVADm/CFir9hpv6uZcWPus4KXo95SyZhHx2xk5efvYnCUrY82MvTD0FUxai81suFAGd4qH5eDE0wA+97RxvnvzaUlRm0WIxv3VXHUcYWYyJ1KE9HVj0l6rhYzpDFfXMr5Kkk5jng4OjTKnpYGzg6Nr7rUTKg4qjpo7uwnS40MuBkZWPitBWihospGbZOFykYorQ8RbSmjxWRiMuYhnEJnqWyzzp68FVuhyAc5zpp/meWbLj+0zDmfAj61xPgJYMcS4yESQjZZlBBdI7S2VvORD7+e33r/A7z4Ujc//slpzp8vnKjKJw2N5ZwwuSGevnXNGwzT97KJTXe3MGLKf3msOnMFE149s5Hkugx1BcfY+mgD4dMGSvcILoRHOCGnYNHppyID7H1HG53fnwOZ3P9Gr9OxfVctF2vHeEWkVxs0JbKkc0pLLRyPJtdS9HpsRiMxGc3QdpwcZ4bGuXt7G69c7F/2OalwWOibLo645ZAz865XyTA3E2BuJgDn5n/vaDAQOzNMJVC6voSSrWWY2q3Iej3BMg2XKcRkzENQy+KVzBK02NZOe1TFLcItmKSshOgaY7GVdGBwmsd/coann7mA9ya1kup0Al+7iWAg8y9MfzjCpZcNbLurnSFLblzRS1FrrmDSZ2Q6xVqWXcFx2AKs8NSejgyw9x2tdH7PtaIYFQJ27Kinv36KV3R5EKAJtEh2PlWbd1ehrQ9QGnVwcmA8pXMD0ShbG6u4Mpa7mp0ABr2ObZvreW68nzu2NnHm4o21WSXQ2lDF1MhoTveSLJ6hwsRIuqfm47wF4L3ixXvlxrjvcqClo5TSHeUY11uI1+rwlUSZ1QdxRr3Es1BbqllZRBWKgqOE6BqmrbWaj/631/Nb77+fQy9285MnznD+ws1lJd18xzoOBSazNl8wEuPcSzr23b2ZK7burM27HDXmcqb8qYvQVDgdHmTPO1rp+r77hrqPOj1svauKYGOAyfgs4978JonIcHaEaH+th97QLHdY7GmdLxy5TQCrq7BjqDRyfGJeYB6dGOX2zU2c7X5NjOr1gm3t9ZwoEhFaYjYR9iSX+JdtKjbWMTvhRsaXF5MC8PZ48PZ4bhivNwhKN5Zi7yjB1GJF1ukJlWm4zWEmNS/+eHJNFFrtyiKqKCLSqAd6M6CE6E2A2Wzk4Tfs4OE37LhqJf3Z0+fxZdJ6sAhoaCznlXB67tiViMY1jr3k5+CdO+gtvZD1+ReoNpUx4zcxlYd4wDPhQXa/vYVL3/cgNbDY9Gy+t5wBu5PTkT7wg0mn57aaZo5P5U8IxYKZW63WtVRwxDD/OhiIz3Dbhga6hmfwRZL3+496vcsWv8uU7evruByYxTd3bfLNyekxtrfW0D04hc1spLGhnNOjqVlzc0mt1U6M3FqJl6P70gRb793C5UNdpFMwWcYk7ktu3JduvLAqBRqa7JRuLcW8wQ71BiIVEo8ljFP6cMfmvSs2vYlKU3oXNgpFrshljGixooToTcZiK+lLL19mcGiGYDBCKBjhQucoQ8OF+eJJFSEgtN5CzJ+bGDYp4fCrLnZv3EuwfTDrtUYrjCV4QjacofxZnM6Gh9j1jnWYIiYu6UY4Hpu5JkYzosU55xrkYG0rJ6fGiOahY0I8mHk5KOsW69X7k1EPk3jY2dbEwIAXfyS5GEJ3KESlxYQ/lL3kHL1OsHNLA0cnlhb2cSm5EnaxdV0NnniUS5NTWVs7G5TqTQWtp9/VNcb2122l67mLWb1AEEBg1E9g1A/PXPuYHaissVC+vZyW2xpZoZC3QlEYlBBV3CyYzUZe/9D2a8Y++jtfKdBuUmfL7S0c8mfPJb8cZ3unMA+Wsm9PC/0V54nrMhdOZUY7oUgJY4H8x9+dCyXKfK1giDw9N0hHeS3OQJjpcG5rWeotmbnEDQYdp8tufB2cD45irNOz37qOrv5ZAtHVBWmJzZw1IVpdZsdWY15WhC7gi0QYNwbQwjmsH5UmtpgodGMnOjvH2Pngdjqf68xLMhlAeCrE5AsTdFjr8rSiQpE8t6JFVBW0v0VwOj1cvFgcsWmrUVNbypFo/qxH4WiMw8en0Z/cREu4PaO57HoL8Wg5w/7iLpTd53Oi00e4vaYZkcNL8LldYdClP/+G7bW4dEtna0WJczw4QGWLnoZSx6pz6XTZkTpb22rx2qL0zCbnXZj0+ygrt2A2pFY8PtfoA8Uhjs93jrL9we15NwQ1tq39+FBN05gcmcUz5ycWzW4zCoUiXyiL6C3CoZcuFXoLSSM2Owj78h9CMDrjZfR52LtpL57WKyn3qdcLHSW6ei64U++FXgjc0SBnXQNsLK/CIiycn5sg21GUvcFp7nlbOz3fSy820te6+pfrUGSO0koLu0vrOTcyuaygia+QGJMMOh3s2tLE0YnUEwJ752bZ09TA+cHCN1VYQJvLfg3RdDnfOcquhGU0XzS2FVfHtXRwzfj4tXv/19XfjSYDVocZm92M1W7G6rAsum/mDe+8nR23rS/gjhWrcgtaRJUQvUU4dGhtCNGtB9ZxyJf9BKVUOH15CttAJXv2ttJbfgGZZMH39dYNHHYWTzJKsgz550X/9so6pKbnoiu7//9TpSM0Nzpwj6UW71taauGkJbn/pyce4rQYoHa9g3ZdLSOTXsa91yYPOWxmJufS6+ZTWWqlpM6alghd4IxznPtaW3hlsDi6pAUmC99idDHnOkfZed8WLr6Yn8+qhpa1bxH1uq5NhIxGYkRnY3hml35u73z9DfXHFcXELZo1r1zztwCTTjcXu26saVhsVNeUcKzgUWvzBCJRXj06i/X0Vpojrasev61k45oUoYvp9U7S5x9jV1U1m0urszZvIB7F9nO2lM9r/v/bO/PwuK7ycL9nVmlmtO+7ZEmWLHm3LHmJsydOAtlIQhISSCAQEgIkpaUtlJa9TWjLkv5KaAIpgRZSCgXCloXsqx073mXLkq1930cazUijmfP7Y0aJbGuZfUbSeZ/nPpo5995zzj26c+53v/Mt69Nw+Wkw1eccZ8/kaTqT+qksT2JTYTYlacmsy80kPS0eGcAsX1GUwYTFzcnB4LT028y51L/eQV1KTlD1BMum1Ey2d8aRqTVGtR9zEUnnoZLK6P4fQkF+SQaJKb57/ielL27CoogeIshtqaI0oiuAV14Jf7zMUKCptGAfjw1BdIbWvlFaX4AtazYxWNCITXOuRi0/PoM3e6OrxQ0lDVbP8vHG9FzGp6Zpsgb/Pzno6GTbxYU0v+D7ODVnBWFnK6De4V0G98rAtQnFpBRaKLYkMzhgo61v4fSoQsDGNXm8FYQWdIbt5lyO7/e8DDYc7mHt2gyOjkbWi740OZmsegfd/1sPwHCcnpy8FLp7IhtbdiGGOiNjkpNXnEFCsn+hm6Ymnej0WjSa6OhvxkYnMCfEndH+U0+8hnXYd8325z7wMGlZieSXZlFYmkl+aSYFpVmsqsolKVUJqYrooATRFcBLS2BZfs3W6C/JL8T+4/0kNGeydlMxTYnH3nVt1AsdWlcGpWYXLunG6XYz5XbhcrvJNScyNDVGq21phMw6m+OjHsFpc3oeQ/ZJWmwjQdV3umSIOIuOyfHFc4vnF6awRxfa+8GlddE7Pk7vuOdlorAkidy4BLp6Rug962GebIkjJdccEiEUCdb29+p3ud0MnByhaFUirRFwass0m6gcMtL2WCOzdfaTDic4nMTH6bE7wptK0xcSEuPoauyJiGZn9YYCv44/Vd/JQ5/9KVVbSnjgoZvD1KuF+YePPsapY51kF6SSU5RObnE6TQGkeR7stTLYa+XQG41nlOeVZFC1pZiqLSWs2VJMQWlm1ITuFc0KXJpXgugyZ2BgjOMnYntZPiMzkb3u2BfWxhxTvPnmFKtyqkmoGqFL30GpaRUvds4djaBjYhSQlFgyyIg30TTWy9j00kvFemqsj9yGHOp0aXTlDdEuhgOqp29qnO03FHH6icXDcplmxQ4NFePuMxM8tI2O0jY6CgJyihKI1+nQa7ToNRrGnVOcGAiN09kWSxanGs68ZptjClO3gZT0OIanwnNPmPR6ajQpdP/iJO0T03MKeL2dw1SsL+B4U69HBRxF8nJTaGyMjDNXxYZCn45zu9389j9f5fFv/Z7pKRftp/rYddUGtlxQGeYenktX6wDOqWnaT/XRfir0L+2dzf10Nvfz3C/fBsCSFM+azcWs2VRM+fp8ytcVKK1pBFiJ4ZuUILrMmVoKIT0qzDG3JL8Qp7tHED1wft1WXhw5ucjRgubxIZrHh9BrNFSnFCBxcXy0a8m8+K7uL+B40xBgRdMg2F5VRmv2AF1ixO+69rjbWL8+i+7D85+r1Wo4NEfs0GBZKLlA91iYEg9IcHbN7Z3ePzxOiSmNCcMUk+7QhVLSCEFdYibW37fQ3rO43XLD4XbW163i8NHohnfTR/AXsXrj4oLoUL+Vb//Vz9l/lmnTk9//c8QFUZvVPq8DUrgYH7Xz9ovHefvF4++WZealsHp94buCafm6AiyJoX9pXNEslQdDCFGC6DLHbI49h4TZxIKXfCCUZKVwlJ6F4safg9Pt5uCgRzudHZ/MuGucSffiy9TRZKu7lIN739NWu6XkyLEetMcFO6rKaM7qo1v4vrzsRmLd5kQcAznPO1Lpmkxe1ITes3x4yo7FmMDEZOSWoWssWTQ1zC9UN3cOsrE8mz2TPSHRSJYmJ5P6lpWeI/6FQap/u5niyhxaWqO3MjHYFpm2dXotpVV5ix73zJN7zhFCAY7uPU3T0Q7K1uaHo3tz0tkSGyHh+jqH6esc5rU/HXq3LLc4nfJ1BVRtKabukmqy8lOj2MNlwAoURJUByDLHEsOCaHKyif0BLvNGk81rszld1EEvgdv39djHWJO0+MMwmmw0FnHgmbkfgC635PDRHiZehB19ZWTg+5LdafsgZddnz7vfXhK+QOs5fngYB42EqXm0obOpb+zhvMTghBqdRsOuuEzkf55m4Ij/L3Zut2TK6ghZ0H9/SUo20dMcGeetVWtyMRj1Cx4jpeTo26fn3f/LR18MdbcWpKs1NgTRuehqGeDl3x3gka/8mjt3fYN7dn+Lxx/6PUffPo1regmsyCmijhJElzlarYa4uIUn3WhhWZ/C+HT0nSR8JckUR2VdMnstjUz7GFt0IbptkctD7y9l8Zk0/HFsQa9y8DjeHD7cw/RLGnYOlJEmfRP09ls6Sc47N6STxWxkf3z4wmDFx0VuEajGkkVbt28vWoeOdrA9OTegdkqSkth0XNDx03rkdOD3ZU/HEGvXBNaHYMnNSY5Y+JnV6xdfln/tT4d559X5o428+oeDNB0NgSObj3THiEbUF1pP9vC/P3iBz3/w//Ghuq/wyFf+j9YI2f4ueaTHRjTQbanikyAqhGgRQhwRQhwUQuxb4LitQgiXEOLGWWWPCyH6hBBHzzo2VQjxnBCi0fs3JfDLUCyExRIX7S6cQ8XGPN4ZWzqTa2VhOnL9BEdF6JaMOyZGqUqKzoN/ITIMCYy8JJie8l2ocU67OXSwB/mKjp1D5STLheOG2l1O4q6KOyeuZ8H6NJwhEPLnw2aPkCmEj9rQ2Rw73MmmZP/yn+9IykL70xYGDofGprbhYCuZmQkhqcsftK7Iac58sQ8dHVo48YHbLXnwMz9lYjwyzoexrBFdCOuQjaeeeI17Lv8WX/vk40og9QUZxLZE8UcjepGUcqOUsmaunUIILfAQ8MxZu34MXDHHKX8LPC+lLAee935XhIFYsxNNTIznsCG2c7HPoNNo2Lw5i6M5zQwRemeByenYmj3iNHoSjiUzOjS5+MFzMOV0ceidbvSvGjhvpIwEOf9L0CFHF6WXnCl4deSG1yFjaMy/7E6B4o82dAYpoa2+n8qExTP+mA16zrcl0f3DY7gmQyfEOadcJMXpPZ2JIAMRFLR88ZhPyVhcGO9s6eeh+/8rIsJoV0tkY86GgzefPcqnrvwXfvC1X2O3BTa/rASURjQ4PgP8CjjDQElK+QrMmS7nWuAJ7+cngOtC2BfFLGLNTjR1YzqjztjJcz0fSaY4Curi2GtoClvaihOjvRRbQpfFKFiqxgtpbwreZMA+Nc3BfT2YXjex01qGWRrmPK6xeBCjxbNcnpuXzAlteAWSMXsEHoABaENnmHK6sJ4eo8A0vyBUmpzM6j2TtP/xVKA9XJBTx7up8sGZJ1SkpJrpi5CjkjkhjvxVi+eYT89K8qm+vS/U88B13+Ub9/6YHz34O/74szc58PpJejuGcLlCp9nvWkJL8wvhdnnCYX3m6m/TelJpR+dkBWpEfTWYksCzQggJ/IeU8tHZO4UQecD1wMXAVh/rzJJSdgNIKbuFEJlzHSSEuBu4G6Cw0LfYb4oziSWNaNmabN6wRj9moS/krDdyVIbbDkxg0caTpI8n2WCKevD7UKdYtDmmOLS3h0RTAhvXpfCOpRW7eM8ueGDKxrpbc9C86Ea3xhD2PHWWOANWe3hfghbzlF+MsYlJsnr1pKbHMTR5prZte1IW/T85wUgItaBz0dXUi9lswGYL/wtjTnYS1uORCR21en2hT0Hay9YVkJmbQl/X4lrt+eJ66gxasvJSyS1OJ6cwjZwiz9/c4nSy8tMwGN97/LpcbjQaMefvzzbmYGRwYVOBpUZncz9/eePDfP2Ju1mzqTja3VFEGV8F0Z1Syi6vsPicEOKEV9M5w3eBv5FSukL9IPMKvY8C1NTULGGZP3rEio2o0aijOXkKHLEvhFYVZ3CY+b1mQ8mhwS60Gg1aptFrtDjdkbOXW5OURbJRx56+LtxIplPCI3iMTUxyaE8PqeYkitcls9/cgkN47DWPOLqpeV8Rb4Yii9EiJFviwiuIBqENnU3v0DhFcanY45zYXS7MBj2bR0x0/K9/YZkCxToyQfWWYo6cCJ/j2AyaCMY69jWjklar4cOfu4J//aufB9zW9JTr3SDxZyOEID0nySOYFqXjcrl55Q+HyClMJafQK7gWp5NTmI4zguHGIoltzMGX7niUbz15n0/htFYKS3mJPVB8EkSllF3ev31CiF8DtcBsQbQGeNIrhKYDVwkhpqWUv1mg2l4hRI5XG5rDWUv6itARKxrRkq15vGJbGssxYzljEVvqcOPJ4NJrH6cus4B3hloi0m52fCI22UX/hJ2NGQWcGnEQZwbPhYfnZWHU5uDQWz2kW1IoXJfIPlMLUyJygog5zBEk5sqiFCitXUOsXZXFUPIUiS8N0dEYfqFwNscPtJJTmkl310hY2+lrjtzU70/sz0s+UMOLv31nQe/5QJFS0t81Qn/XCIffes/EoqWhh5aGpTFHhoKJMQf/8NHH+O5vHiAjJzna3Yk+S3yJPVAWXaMQQpiFEAkzn4HLgTM84KWUJVLKYillMfBL4FOLCKEATwF3eD/fAfzWv64rfMViib4gmpufwusTS2OCrShIp1lGxzng2FAvCbrwa7BNWgOpJicTbo/zTpu9nQzzJK2TDRSXhz+N38i4ncNv9pK1N5UdE6VESgms1YZPG18cl4S9LbSOKydO91I4aGC4MfKZx9xuSYJeG9Y20tITGOiMXCzh8nW+55gXQnDlrdvC2BsFwFCflW/e+2OcU7Gd3CNirEAbUV+clbKA14QQh4C9wB+klE8LIe4RQtyz2MlCiJ8DbwIVQogOIcRd3l0PApcJIRqBy7zfFWEgOWnhUDrhRghwlBhxLZEfiiY3ehPi+PQUZQnzB3sPBQKoTk2kb/JMYXtk2so0LrLLwyt8zGZ4zM7hN3rpe8bGBbZVZInwhg7SaUMfOrnSlMa26SxGDo7S1T8a8vr3dfRS9v41Ia/XF5rquygrndN8PyRkZyZELH5oYqqZzDz/ogTWXLgGY/zcjnaK0NFwqI2ffufpaHdDESUWXZqXUp4GNsxR/oN5jr/zrO+3znPcIHCJT71UBMXll63lJz99HdtEdEJmVC6hNJ65qQkcIfTpJf3hnYEO8hNS6JwIj6ZoW2Y+jRMn5t0/nRLZnNYAI+MORvZ3oxGC7WVFOLKdHKAr5BYCurgQVShhoyUT3YCbphMDhFvXv2+gn6r1OXQfjuzyPIBjZAKBRIZDZIyguXj52ny/nfHi4g1sPm81bz53dPGDFUHxq8de4vIba8kP44tPrCNYmTaiKrPSCiApycSHbt0elbbT0i3sk0sjjefq/DRMq4now3EupqVEI/VoReh/nptS8zhpm18IBeiY7kYbOaXoGbil5HhjH82vDlNxLINd9lXkC99C6fhUf5B+H0JCrSWH9dYUWvb30RSh+Jcut6Q/x4jeHPksaV1tg1SHyZmkvr6LYj+Wy4OhbG1g7az2Ie6oInjcLjc/+fafot2N6KOW5hXLlQ9cv4XMjMSIt6tdk4jNFbten+kJJraszyZju5b6/BZO0BXtLgFwemyQDSmhfQCusqTR7Ty1aOQsu3uK0qrw24kuRu/QOEfe7mb85SnWn8zhfPsqCglcKNUIwamOwF6K9ELDDnMeFQMJnNzX7Xew+lDQMzxGzu7yiLcL0NnUh9kc+iVqKWFUgtEU/uXv8vW+OyqdcZ4fDk6K4Hj1j4doONQW7W5EFSFlwNtSRQmiKwSjUc/HPnp+RNtcU1fI0fHoxsWcC51Ww7rSLCrqkuit6mWPqZFuORLtbp3D3r62kKUATTGYELohpqVv9q/pJbE1NbT1jHD47W60B7QUJyZRl1VAkSUZf9QAFQmZjE74F1rJpNGxy5RHQWcc9fs76RkMPth/MLzT0UvJBaURb3d02EZBVlJYMi4N9I9Ruj38AnZ5gBrRsnVKEI0k/+9Lv8Q1HblIGjFFMNrQpSuHKkF0JXHpJdWUlfmXyzpQcvKSecMZW2npzEYDW9ZnE1fr5EBGE8dEBzKGQ5pKBKdGh8mJD25pWq/RUpioY3TadyHKnhB6p5tQYDEbabMN8/ZAK+32IQoSEqnLyqckIYXFZmKL2/doBMk6I+fH5ZHarOHIO50MWSeC7HnocFRbsBREPh/8ySMdrA+TUHa8oYdkH7MZBUJiqpmM3OSAzk1KtQR8rsJ/mo528KvHXop2N5YtQojHhRB9Qoijs8q+IoToFEIc9G5Xzdr3BSFEkxCiQQixe1b5FiHEEe++h4XXAFsIYRRC/I+3fI8QonixPilBdAWh0QjuufuisLej02kYLzbglKFLcRcMeq2GLeuzmd40zh5TIyMydoSKxRifnsI26abYvHj+8fnYkp5Bp8M/k4N2Zw/GUDn2hJD4+DP9KzsnRnl7oI3WiUHyExKoyypgVWIqcwmlrZ2LC9eZhnjON+QRd9LF4YOdjIc5C5M/CAGV5+Xx6ngHrutyo6IAOba3OSxe9NNOFznV4dM8rl5fEFTWsLySxdOCKkLHf333GTrmyFa1EohArvkfA1fMUf4dKeVG7/ZHACFEFXALUO095/tCiBkPgkfwZL0s924zdd4FDEspy4DvAA8t1iEliK4wNm8qpq42vEt7ZXUFnJqIDY3amqJ0Umo17DE1YiM6UQOCZXBqgo7xcdYk5vh9bl1GAU0TTX6f55Quyqqjbyd6Nob4+QN9dE1YeXuglRbbAHmWBGoyc6nNzKcqJZP1ydkMjM0f4zPfmMAuTS7uY5McPtyJI8ZiGiYkxpG7I4O3BjypMI+MDlB4e2XE+yGlpK9lgPS00N8b9Se6SC9IDXm9AOvqgpvzsgsCfxFU+I9zappv//WTK3OJPsxL896smL4GJr4WeFJKOSmlbAaagFpvEqJEKeWbUkoJ/AS4btY5T3g//xK4RCzyFqgE0RXI3R+/EI0mPNquotIMXouBwPXpiWaq61I5kt1M1xLx2l8Iu2uaY8N9bPTDgak6OZvTC4RpWoykGHQW1hp8u2+77VbeGexg32AbJ6w9HB3rIm21YO26JGrXZbKpPIOCjAQqElLYKXOwHR7jSH0X067Y0OLPJr84hclyLccGzzR1eU03SNr6yGvqxq12ksPgXOR2SZKzkkNeL8D6urKgzs8pUoJopDn+Tgv//b1no92NiBOkRjRdCLFv1na3H01/Wghx2Lt0PxNwNw/OiGfY4S3L834+u/yMc6SU08AosOAPSAmiK5CSkgyuuHxdyOs1GHR0ZbmI5qNcp9WweX02w1UDHBKtUQ/FFEpcEt7u72BzSvGix+bGJzHq7kAGEZRu3Bx7AryMC/x6BidtHLV2s9fawoGpVtpNvSTkaTjW0B0OH5yQULkll5NmK322c2O7Ot1u+nYloDVGPtbW6RPdlBSnh7xeTULos4rFW4yUB2nbqpbmo8OT//5nDr3ZGO1uRJbgNKIDUsqaWdujPrb6CFAKbAS6gX/1ls/1BJULlC90zrwoQXSFcucdu4gLcd7tkto8Ou2RD4Y+m6KtJvaaGpkktpZWQ4dgT38765OL5o0zatYZSYp34HAHl26ydbKH7TfHk5YZO5llJvWh/b8edHSSlBb+lKr+otEKVu/K5S1bF1Ou+Zcnm8dGybizIoI9ew+dO/TS+/H6LnLKQmuDWl1TglYXnLC+rrYUrU49LiONlJIffPU3yFh9U1wmSCl7pZQuKaUbeAyo9e7qAGaHm8gHurzl+XOUn3GOEEIHJLGIKYD6Za1Q0tMTuOmGrSGrr2hVOq/ZIp/1ZTY5KQk0yOj2IVLsH+hglTkLs854RrkA1qRY6J8KTdisencj2ova2X5NQkw4L9m0oc3lPi3d5NZFPr7uQmRkWsioS2Vvv28OZq/Ze8m5KPJ2FKeOd1EchiVrc15o7UQ3bAtuWR4gMcXM1gujk2Z1pdPS0M3Jw9HNdhcxgliWDyYjk9fmc4brgRmP+qeAW7ye8CV4nJL2Sim7gTEhxDav/edHgN/OOucO7+cbgRfkIm8SShBdwdz8wTpSkoPPQ6/Xa+nLJjwpAP1gwGqjZrScIhH6JcNYpH6kjySdhXTje44j2zLzaLG3hrSdSbeTeuNx8j4wyubzk4hmwLoRjT3kddZretAboj8VCgFV2/Joy7DTMOTfi8SRchcJReELfzQfRk3ox63hRA+5IQwztz5EMUovDeGLu8I/JmMoekXYCbOzkhDi58CbQIUQokMIcRfwLW8opsPARcBfAEgpjwG/AOqBp4H7pJQzSzT3Aj/E48B0CphJi/UjIE0I0QR8Dvjbxfq0aK55xfLFZDLy6A8+RsPJbk6d6qOxqZdTp/vo7h7xq57S2nxemcNBSasRFKQm0zY4gjsCSytOl5uDJzz92FBSykD2MJ3SV+fApUnr+AhpRhOF5lTS4+I5aWtYNHNSoAw6rQxmHWPDbbmM7bNwuiHyZhj9cjzkdY46HWzfWczxF6MTLkYCFRuy6Y2z88ZIZ0B1jDgcjF+dgfaxcVyTkfM0bjzaQd7qbDo7Q2tPbM5Lhabe4OtJiKO0Ovj0pFOTTrpDmM7VlBTPxGjoX6pCiQTKt65CoxGc3HMqqn2JNxsXP2gZEIlc81LKW+co/tECx38T+OYc5fuAtXOUO4Cb/OmTEkRXOGlpFnZsL2fHLK2BzTbJ6eY+mpr6OHWql6ZTfTS39ON0nvuAKyhKe9dLXiMEa/IyqS3Np7a0gC0leZiMBtoGRvjw9/+HwfHIxe881tyHrlVDbVU5xy3tjBHaJd1YYnByghxTFl2TTWETQmfTMtVF8ZZKVq9Kp+etcazDkRnbZEs8VsIj/LYnjSCRiAhq9SVQuSGbnjg7e0d7CDa6WKN1mG0fr6D/3+sjujahd7nRaATuENqMnjjRTX5ZFt1BCqNra0vRagPX2jqnpnn2f/fy8//3HIM9oQlJZ0qMZ8IcT0JOChkZCcQbdDhtkwx3DdPXNhh9/0ohqNhWyuiUi4a2IUwmA0npCYwORC+rWGKKOWptK8KPEkQV52A2G1m3toB1s1LiuVxu2toHOXWqj1On+2hq6qWldYDkzel8uGA1W0vz2VKST0L8uW+uhenJ/OCu67nzB/+LbTJySyzTbjf1DQPkbElmjOiHlAoXaUYzU5puphdwagklmcYUXm3vxy3BtEnPRvI4+Wo/087wxktITY4nXFmoO+wjbN1aSOPbodN6zYuAivUeAXRPCATQ2bw11suFt1TQ8WRD6CpdhLZTfazdWsLhev+SJiyGJT94reiG7YHZh7qmXbzwm/389/eepbcjdKsqEiiuWcWRY52MDE8wMnzmy3lccQY5OclY4g1I5zTWvlF6TvfjmkMJEGrizEZKNhfTN2qn/vR7v4OJiSmK1hYw+lJ92PswF5bEeDLzUhY/cLmwAh2zlCCq8AmtVkNJcQYlxRlcekm13+evycvk4Tuu5p4f/QZnhAQmo15H3pY4Gli+DkwGjZaiJOieipy2It6diVt6HlQT007eoIWcixNZNZBKw/7+c7SKa3Zm4hx303jo3H3+YEoMr/f+eMEkvB3GBgRUbsyhSz/BHmtoBdDZvBY3RN3OfHpf71j84BBx9O1mVm8s5GRj8MvpM5w43k1BWRZdAQqjOoOWmgv9C/rvcrl59Q8H+a/vPkNnc+hTFK+9pJrDR+c3v3DYnTSfPrNdbUYS2TlJJCeZ0LjdTAzZ6GvuY2KBBA0+IwQlGwowplpoOtXP0ZNzj/Xx411U1Kyiad/p4Nv0k9UbCoPKirXUCPfSfCyiBFFFxKgrK+SfbtnN53/2x7C/9Bl1Woq2xFNPaLU0scbWzDROOyIXZy/TmMLr7edqDbvtVrrNVqquykIch85mKwAZuWb26dpxJrnJ3Z1IsSOVln1DTNicfretNYX3YXTc1su6ilzaG0ZCWq8QULExh069jbes3RBm08Bpt5ujVZKy3hRGmyIXC7a3uZ+UVDPDQ6Exn4g3GbCkJQSkFTUYdfzDo3dRUOqb05PDPsWff/k2v/rhS/S0hSbixNmUbSlZUAidD5fLTWfHMJ0ds/6XpjjSizJIT7Ng0AomxxwMtA0y3Du66KueKSme/MpcdOY42rtGaOq2Qrd10X50jdqjskS/84rQx7yOWfxwOlpOKEFUEVGu2FBB55CV7/zptbC1oddqKK6xcIzIaYSiwY6sPE47IrtcZpKZuOT8y9f1472IAqitLKT7TSumjQac454l+y67lS6sxNfqWG/MZbTeQU+b7w+1aWP4Nen6Kg2EaFVbCKjYlEO71sZbY+EXQGczOumgf3cyCYMTTA5HJrXt2KidTJMhaGFUp9dSVZFNy/5mTjYH5kB22/272bhj4WV5KSWn6jt5+akDPPvLvVhDJEDP2RYwEeIIAwP9Ywz0z/79CBLKs8nKTMJk1OFyTDHcPYJtZILssiyMifEMjdrpaB/ieIv/wvaY1U5ZZS6jr5+M2PJxamYiF19fE5G2YgURe8ndwo5YSoFia2pq5L59+6LdDUUQvFR/ii/+zzNY7eF5OOq0GsprEjkilnfcuXUpWQy4GyO6jpNpTOGddg0uH5uM1+qwuxYOQF9rKUB3UEN9Zy+LeVoVXJjIMRFeMwutEBQdTGOoJ3DHOo0GKjbl0qYZo3Mseg4eAOuS0nE80oicjtzTLT0rEZdRz+Cg/xEOqqpy6TvexXDPSND9iLcYKavOJ39VBmnZScSbjExPuxgZGKezuZ+GQ22MBtDHQMhfk0vrUOScNcPJ+uo8jr1wLCJtfemRO9l5xfqItCWE2C+ljKrUa0ktkOsveyDg89/8xV9F/RoCQWlEFRFBSsljL77Nvz3zeshepksyUugeGcPh9Ag7Oo2G1TVJHBbhcmmJDXLiE7GJ9ogbEy2mDT2bxYRQAINNS0NjDwXpCWRlJnG8uw/b1NzL9v0i/EKdS0oyt1oY+p3/QoNGK6jYlEMzVt4cjw2TkCOjA+y4u5Le70fOk36g10paZiLp6RYGBvwT9CanphkbCo1waB+f5MieUxyJcughgKScFFgmgujhY51UbS+j4c2msLWh0Wq45x+ui5gQGlMsHd1gyIh+FGfFsmfSOc3f/vxpHn46dEJoXkoiT9z7QX73+Tt536ZKdBoNlTXJy14IjdfqyUiYxO6ObAzCNEMir3eE1nYuXquj7TWPR3LPwBiH6jvQTEhqCnLJTz0z25FOq6EvDDFE5+Kwq5N4i+/v6FqdYM3WXPQbTbw53kXPeGT66StvjPdScJt/TjvBMthnRdomycz0L2vVqaY+CutK0eqDS8kZa4w7/LeJjmVOtg9TsiE82byKVmfzb0/9BVd/5Lyw1B/rRCOzUrRRgqgirIzY7HzsP37JHw6eCFmdJoOef7vzWlItJnKSE3jo1iv56V/chExbrvnl32NTZjL9U5EPvJ4scpgOsRnPRn0uduuZ4bwck04Oneikt32UqvR01uVnocFj9xspld6Ey0nJzsXTTOr0Gqpq89BuMPHmWBe9tsgH+PeVV3QDEU8DOjwwzvToBFlZ/gmjjSd7KawtIz1/+YTsGYhiDM5wMO100TFipzAEyQJmc9G1m/nur+9nVVVo610ySDz2t4FuSxS1NK8IKz988W0OtYXOrk8IeOjWK1mdc2Yaz3VZuTyZ+TF+33GUbx99nm774l6gS43tWXk02yMfyy9BZ+L1EGfOARjZv/BSZWOrJ4xNdoqZ/PwU7HoHVmdkHG+ajAMIDcg5TCv1eg3lm3I4OT3MG9bAMiFFGreUHCx1UtGVxkhDeLzC52JkyEaShKzsRHp7fP9NNnk95ZMrczGb40hMMKIHhjqG6GrqjX7Qdz8wJcZjjfEsSoHgcDjp0Qjy1+TScTx4U5SrP7yTe7/6gRUVqmkulrJmM1CURlQRVvS60C6x3X/FTi6qLp1znxCCqwvW8cfL7uP+qguJ1+pD2nY0WZOUSetk6LTK/pBrKGAyxLFfN5hzGWjzbQm7f9jGgSMdaHCxNSOfIkv4NWV9k+OU1575smMwaKiqy8O11sgb1k4GJpaWzd/Y1BS9lyRiTIlsusTRYRuTQzZyspP8PndkeILOjiGOH+/m8PFuOsYmydxUTOWO8iVjSleyuSTaXQgb9okp+idd5FVkB1XP+m2lfPLL1694IXSlogRRRVgxG0MnDL5vUyV3Xbh10ePidXrurTyfpy+/j6vy/Q++H2tkxllw6bqRRD6uh1Gj5+2u0GuX9QH4j2hdGg4Mt9FhH6Q6NZMNqblh0YwZNTq2ZhRgL3JgStC/K4BOVxt5Y7STIfvS1W61j1vR3l4Cmsg+8K3DE9gHx8nJTQ66rp6uEY6d6mfVeRWYk03Bdy6MGOL0tHRFLpZrNJiwTTI0DbnlgQmjOoOW+x+8OahUrMsKGcS2RFH/eUVYMRlCkw1nXUE2X7vxMr/emLPiE/l27Q18f9vNZMRZQtKPSBOv1ZObOM2YKzoOMCXxxQyHOC3rKlMqLQf8Xx7WCo92XQhosPZwzNpBjsVCbUYBCfrgtXwWnYG6jAIS4rQcGG7l+HgPuVdZcC0DAXQ2h0f7yf7Emoi3ax2ZYKLPSl4IhFGApsZeTCWZmBLiQlJfOCjfVrYsl+XPZnzcwQiQU5rp97mX31RHblH64geuAATKWUmhCDmh0IhmJVl4+I6rMeoDM2m+OLeCP1z6KW4q3hR0XyKJADZkJNI7Fbq0if6gFRoO9YT+IZozkBBQqk+tPPecPoeVd4ZbcYupgJftU40m6jIL0Ghd7B9uZWTqvWs+ON5Oamp4U4tGg9fsveTfWB7xdsdG7Yz1WskPkSNST9cIWRuKEDGoTdMbdbR2j0a7GxFjzOrAqtWSVZLh13mX37j4KteKIRhHpSXsrBR7v17FssJkDO4hHqfX8fAd15CRGJxGM9EQx9c3X82Pz/sw+abkoOqKFDuyc2l1RD638wzlpjJ6JkIriKYbzTS9HpjXv2EB38pJt+vdZfs1KelsSM1Fs4iwmx2fQG1GAXb3BPuHWplwzR1iJz5jeaY6ed0yQua23Ii3O261M9o1QkGIhNGmxl5Wn1+JiLC5wWKs3l7O6MjSsiMOFuuoHZtBT6aPGk5LUjzl6wvC3CtFrKMEUUVYMRkC14jGG/R87yNXU53vW75oX9iWWcJTl97DHWV1Me15W5uRxyn78ai1n6gz80Z76LU5lZPpuKcDe3OPcy9+LwkBjWN9HLN2kGmOpzajgCTDmUu3RZYUtmTkMjjl0aY653KNn0X9WDcVyyiU0AxOt5sT6zUkFPnvRBQstjEHI52hE0br67so21WJzhAbgWB0Bh1tPStHGzqb0ZEJ7PFG0gsWD4FWubEITYhTny511NK8QhFiAtWIpprj+c9P3sjOiuLQdggw6Qx8Yf1ufn7hxyhL8G8ZKRJUJWfS6TyxWMbLsJKuzWfMGdq4rBadgdZXhgI+3xdBdDYDk+O8M9zKpHSwJT2P2owC1qdl0WEf5NBwB9KPmVufvrwCks8w5LAzenU6enPkI0zYxmeE0cUFFl84caKb9HWFZBSmhaS+YFi9vZyR4ZWlDZ3NyLCNKUv8orFgKzZGNrbtkkA5KykUocUUgI1oQVoS/3XfLawtCC4kyGJsTM3n/y7+BJ+qPB+diI2fQk58IpOaDtxR8JB/tw9x6bzc7nsqT1/ZoMnBYQtcoDNPBeaQNC3dHBpp553hVupHuwMS8E+M9VBZsPy0ogCnrSMk3Rl5e1HwCKPDnUOUlKRz4WXVfPWfb2bnBYFngersGGJEo6Fy5+qgn8tF6/KpuriagjW5fi37aw1aOvqWXxxjfxkesjGdZCE1J3neYyo2FkWuQ0uElagRjY11DMWyxeynRrQqL5NH7rqeNEtkwrIYtDo+W3Uhu/PW8Hf7f8fRkejlCDfrDGRYHPQ7o+tl63Sk4JaBay7nQi809Lwe3MPZOK6DKAY/0KZOQbskYimeIshbY71c+IFyOv6vMaLtpmUmcNWNtVxxQw1p3nSg23dV8MYrDTz80B8YGvQ/WoTD7uRYUx+VF6yh+0gb40OBZbzSpVg4csyTsMCQn0Z6egJ6vRaNRqDTatDptOi0AqZdjHSP0HmyBwFUbCvnSENPQG0uNwYHxkjPSCBFwnDPyDn7K8KUJnTJIgH3EpYoA0QJooqw4o8gunN1Ed++/f2Y4yLvpVyRlMWTF36MJ5re4uH6l5h0RzZdqADWpVtodTRHtN2zqTSX8szp0AqhAJvi8mge6A+qjqlRF4RXSb4gDeO9VBcXcawlcpmJIsmeZCtr16QxfDz817eupoRrbt3G9ovWoJsjr/yO8ytYu6GAb/7drziwL7DfxIkT3SSmJ1K5Jo/jr5/06/UhvSCVk7OEyanJaboWyS6WvamY1Hg9Hf3LK51nsAz0j5GZlUiicxrrrBeLlPQEElPMUeyZIlZQgqgirKSY4/mv+25myulCqxXoNFq0GoFOq0Xn/TvzPSPBjC6KYVh0Gg13rd7BpbmVfOmd3/H2QGvE2t6RnctpR/SckwAyjSm82hqeh6jjSPCpOZ2O0GZ3CgRX4gQetcXy04rap6fpvziJ+DYrziBMKOYjLt7AJVdv5OpbtlFcvrgDYmKSiW9850P809//itdeCiyrmHXUzrFRO8XbysFqo73etxWPrPJseo/5tzrS0zWC0oPOTV+vlbziTKYcThw2z1yQEaJ4ssuOlacQVYKoIvxsLIp8iJhgKLKk8sSuj/CL5v3889E/Y5sObUD3s6lJz+WU/XhUnZM0aJiwpTLhGgl53SWmVLobg7eZm56OfhilJls/60qKONK8PLWiLWOj1N1ZzsC/14dM1M4vTufqW7Zx6TWbMPsZfF6v1/KFr9/Alz//JPveCiAdl5eWZo82vnxXBcI2SdM7LfNenykpntbOkYDbUsxNZ8cQ5ZuLOf3aSZCShBjPihUtlrKtZ6DEhoeGQhFjaITgllU1/OHST3FxzuqwtVOemE7v9MmoCqEA5aZyjg2OhKXuPHtiSOrRxEicSEeCjeWsttgz1kvBhwN3GAIwGHVsv2gN//joR3nsqQe49rbtfguhM+j1Wr70zRspWx28XUbjyV5Odo6QUp1P1SXVlG4uxpQYD3j+owVVuSSV56y4+J+RovFkL9UXVQEw7Yz+CkdMsgID2iuNqEKxANmmRL6//RY6bCP8seMov28/yklrYAHZzybdaEbo+5h2RXdCLjLl8OeW0HvJzzByPDBnkbOJFUG02TbAhtJiDp0K35hFm5dFPxdcU0bHU00+nxNvMnDjR3exeXsZZVW56APMhDYXJrORr3/7Vh74+OP0hiA+5+DAOIMDXnvFeCNxKRbcbknL4AQMKiE0nBw+1knFlhJsY45odyUmWYkaUSWIKhQ+kG9O5u6K87i74jwarX38of0of+g4RrttYQeG+TBqdBQkS3omo+vYYNDoaBnQ45bhMT/IiUukp9kaUErPs5Ex9MY/bhpjudqKgufKXksdZcdlxXQ917Lo8Vt2lvPZf7iWrNzwhbhKS0/gG9/5EPff9SMmJkJ7vzocyzNObKzSNzGF7BnhO998irExBx+5+0KKV/mfp16xPFBL8wqFn5QnZvJA9cU8e/mn+Z8LP8ZHSuvIMPoXV2hLZgo9k91h6qHvFBvLaBsLjcZyzvplSkiEUACnM/o2ojO0TgyysTz2kiGEkmm3mz35E2Sdlz/vMZbEeP7qmzfyjUfuCKsQOkNRSQZf/MYNMaMdVwTG8JCN5IpcnvnjIV576QRf+etfRLtLsUEwwexj5z3db5QgqlAEiBCCDan5fHHDbl666gH+87zb+WDxZpIN8QuetyMrj2aH70ue4SInLp0XW8O7vOxqC10YrKmp2LIpG40bRSzl2d8HJl0uDlZMk77lXC/38y5by2O/fYBLr9mEiKCRc+2Och74wvsj1p4iPLQ09+N2eX4/m7aWRLk3sYEAhJQBb0sVtTSvUIQArdCwPXMV2zNX8fcbr+St/mb+1FHPn7tOYHW+Zwu1ITWb0476qDsnAYyPJzEtR8JWv1lnoPVw6GKSaqMY2msu2ieG2bS6hHdOBhcfNdaxOZ2c2BJH+YQnxmhKuoVPf+kadl5SHbU+XXH1JqRb8t0Hf7+UfTQUXpQgOovYWfiJGLE1sysUywC9RsuurDL+ccs1vHbV53hk+y1cU7CONUmZjMqWmBBCK81lHA2Tl/wM5cZ03NOhkxJMUciHvhiDhmFWwirxiMNB6wVmrry9jkd/80BUhdAZrrx2M3/3jRsxGJU+ZamzYXNxtLuwYhBCPC6E6BNCHJ1VliqEeE4I0ej9mzJr3xeEEE1CiAYhxO5Z5VuEEEe8+x4W3mURIYRRCPE/3vI9QojixfqkBFGFIowYtDouylnNt7Zez5MX3sUXq2/h/Iz16DXRe3gm6y280eF/6kR/sYyFNkOWwRR7AkenfYRNy9xWFCAlPp6HrtrN/X9zDQlJC5ueRJLzL6niez/8GAVF6dHuiiJAcvNTSVIxRd8lAkvzPwauOKvsb4HnpZTlwPPe7wghqoBbgGrvOd8XQsykQnsEuBso924zdd4FDEspy4DvAA8t1iEliCoUEcKo1XN+5nq+uu4O/u+8r/DXlTezOaU8ZM48vmLRmalKS0QfZlXeWLM9pPVp42NT9dhvGFrWWtF12Vn89iO3saukONpdmZPS8my+/8Qn+Og9FymBZglSWZ0X7S7EDhFwVpJSvgKcbTN1LfCE9/MTwHWzyp+UUk5KKZuBJqBWCJEDJEop35SecCY/Oeucmbp+CVwiFjEijz0Vg0KxArDo4rkyt5Yrc2sZmBzlxd6DPN97gIax9rC33WHvZbU5niSDgQFH8Kk35yJBb6SrcYSQhjcyxqYxYJd9lM2rS9jXsLxsRXMSEvjszu1cX12FThPbOgtjnJ5b79zFjbft4OihNg6/00JTQw9dHcOMDNtwTbuIizeQkmbBaNThdkvsE1P09Y5iD3EoKIV/KEF0NkEHpk8XQuyb9f1RKeWjPpyXJaXsBpBSdgshZmJp5QFvzTquw1vm9H4+u3zmnHZvXdNCiFEgDZjXM1YJogpFlEk3JnFT4QXcVHgBbbY+Xug9wAt9B2ifCL1gk6S3YHbn8tzp8KaoLDWk0SlD56gE4NLFpiAK0KMbRCvAFbtd9JmU+Hg+ta2OD21cj1G3tB4Rer2WTTUlbKrxzflFSkl7ywB732jihWeP0tQQ/ZBqK4l4k4Grrtsc7W7EFEEGtB+QUtaEqCswtyZhvgDKMz1faN+c+DTLCCFagDHABUzPd6FCiK14pOebpZS/9JZdAXwP0AI/lFI+6C3fCPwAiAOmgU9JKff60h+FYrlSaM7kzlW7uaPkchrHO3mx9yAv9h2k1xFY4PzZVJhL2dMxwehU+POkh9o+FGBaF1vhm2bT47CyqbKEfceXrlbUbDBwV80WPrZ1CxZD6P9/sYgQgsKSDApLMrjxtu00nujm+acPs++tU3S0DeJ2S+9xYLbEYbbEkZAQhzkhjvh4AxqtYNrpYnzMwUD/GP29o8qL3w/sE1NLOv7lMqJXCJHj1YbmADPpAzuAglnH5QNd3vL8Ocpnn9MhhNABSZxrCnAG/rzuXiSlnFe16jVgfQh45qyyfwcu83bubSHEU1LKeuBbwFellH8SQlzl/X6hH/1RKJYtQghWJ+SzOiGfu0vfR721lRd6D/Jy3yEGp6x+1ZVmSETrzOLZ06HVUC7EWGto7UMBNDK2DTG7NQPoNIJp99J6shq0Wm7buIF7ttWSZlrZNpbllTmUV+YA4HK5cTicaDQCo1HvUxB9h8NJy6k+6o+0887e0xzY14wzxuLfxhJlq7NV1IOzic6bzFPAHcCD3r+/nVX+MyHEt4FcPE5Je6WULiHEmBBiG7AH+Ajwb2fV9SZwI/CCXCQtXijvgM8AvwK2ziqrBZqklKcBhBBP4jFkrcfzHpToPS6J96RphUIxCyEE1UnFVCcV86nyazgycprnew/wSt9hrNML58WuNJfzeruVcWfkhFCTTk/3yeDzgZ+NYUwH/iWwiii9jjE2Vazi7eN9ix8cA2iE4Ia11Xxmx3ZyExOi3Z2YQ6vVYDYb/TonLk5PZXUeldV5fOCWbdhsk7z03FF+84u9tJ5eutrycFF3Xnm0uxBbSBBhjiMqhPg5HqVfuhCiA/gyHgH0F0KIu4A24CYAKeUxIcQv8Mhs08B9UsqZN6t78XjgxwN/8m4APwJ+KoRowqMJvWWxPvkqiErgWSGEBP7jbONXIUQecD1wMWcKou8arXrpAOq8nx8AnhFC/Ase7/0dczUshLgbT4gACgsLfeyuQrE80QoNG1PK2JhSxv2rP8C+oZO80HuA1waOYne953iUZUzFYU/hmQhqQWcoNabR4x4Jeb0TA07ICXm1IaVT9C8JregVq8v53K6drEpNjXZXljVms5H3XbeFK6/ZzMvPH+PRh59jsH8s2t2KGWp3KEH0HMKsEZVS3jrPrkvmOf6bwDfnKN8HrJ2j3IFXkPUVXwXRnVLKLq8n1XNCiBPeEAAzfBf4G6+6dvZ5Cxmt3gv8hZTyV0KID+KRoi8952CP0PsoQE1NTWzP7gpFBNFptGxLX8O29DU4XFO8NXicF3oPMOSY4s+nh5gKgzDoC0n2OHrCUG97+whxa3U4ROjShoaavskxNleuYm99bGpFzysu4q92ncfa7HNTdirCh0Yj2L6rgh/9+/PR7krMsKosi4oq5TGv8FEQlVJ2ef/2CSF+jWfJfbYgWgM86RVC04GrhBDTzG/oCh4bgvu9n/8X+GGA16BQrHjitAYuzNzAhZkbsE45uCjtJL9rref13mZcEbY5cnSEJxzO5NQ0a+157DO1hqX+UNFGL3qdFud07OTqy0tM5KErd7OtsGDxgxVhQa/XsmZtPn09oTdbWYpcdd1mn+xuVxwrUN22qCAqhDADGinlmPfz5cDXZh8jpSyZdfyPgd9LKX/j9ZgqF0KUAJ14bAU+5D20C7gAeAnPkn5j0FejUChINMRxw6r13LBqPYMOG39qP8HvW+t5u7897HOcXmjoOjEStvrlKQHrwlZ9SBiYtLGlYhV7jsWGVlQrBN+7+n1szI1xu4Zljlar4a+/fB32iUn2vtEU7e5EFYNRx8W7Y/yHHCX8yJC0bPBFI5oF/Nqr7dQBP5NSPi2EuAdASvmD+U70BjP9NB5Pei3wuJTymHf3J4DveYVVB147UIVCETrS4szcXr6F28u30D1h5Q9tx/ldaz1HhsITL3GVKY2hyfDZwDWdGqB8TSaNutgQ8ubjtLsHo17PpDO6HtMaIfj8+buUEBoj6PVavvSPN/F3f/HfHDnQFu3uRI3zLlyDJSEu2t2ITZQgei5ej/cNc5TPKYBKKe886/sfgT/OcdxrwBZfO6pQKIIjx5TIxyvr+HhlHS1jQ/y+tZ7ftdbTaJ03KpvfpE2ZGCK8zhippyywui+kSZtCzfDUhEcrejQ6ArNWCK6rruKeulpKUlOi0gfF3MTF6fnav9zKr372JnFxen64Au1Gr7hmY7S7EJtIIHYseiKGCuClUKxAihNS+fTa87iveicnRvr5fVs9v2s9RoctOPs1d2/4Z9ETx/vYlVTOq1mNMS2MnpruJt5gxD4VOecqvUbDDWur+WRdLQXJSRFrV+EfZrORD3/8Aj5714+i3ZWIk5OXwvpNxdHuhiKGiO0EwgqFIqwIIViTksnnN1zIy1d/il9ddgd3rq4hPc4cUH29jf4F2w+Uw291c95QWUTaCpQRp50NFZEJj2TUafnwpo08/4m7+Mbuy5QQugQQQvDlBz9IVk5ytLsSUXa/f6NyUpoHgUTIwLelitKIKhQKwPNg3JSex6b0PP5u06W81dfK71rrebq9AavTsej5uXGJjA9NLnpcqDjyag8brsznkLEjYm36S4OzC5MhjokwaUXj9To+tGEDH99aQ4YlsJcHRfRIz0zkwYdv5y/v+TFDg+PR7k7Y0WgEl7/vHEs/xWyWsEAZKEojqlAozkGr0bAzu4QH697Hnus/y6O7buT9hVXEa/XznpOnSZx3X7iw73Ohl9qIt+srY04H6ytDb6NpNhi4p66Wl+7+OF+46AIlhC5h8gpS+aeHbychMT7aXQk7NdtKSc+M/DyxpJAy8G2JojSiCoViQYxaHZfmr+bS/NXYnFP8ubORp1qP8Wr3aablezahRmvkBcLe/jFqrMW8mXQq4m37yonJLixxJsYdzqDrSooz8tEtW/jw5o0kxSmv4+VCSWkm199cy08eeznaXQkrl1yxPtpdiG2Us5JCoVAsjFlv4Nriaq4trmZ4coKn2xt4qvUYe/vaGG+L3LL8bNy62J65x6cnqVmdy1uHA881nmYy8bGazdy2aSMWgyGEvVPECqPDExiMOsorchgestHTPYzbtXS1XHNRvUElVFCcixJEFQpFQKQYTdxatolbyzbRPWHl+ZdP8uzkCRpaIhuyqC8uMg5SwXB8spNEkxnrhH9a0SyLmU/UbuXm9euI189vFqFY+pRV5vDITVspKEoHYMxq509PHeBn//kqE7bovOSFktQ0CxlqWX5RlrLTUaAoQVShUARNjimR26+s4fYra2jrGeaFvSd5bk8DTe2hi1E6Fwa9llYxFPD5Jq0eh2sad5hzTtmmp6gpy+etw74J6XmJiXyybis3rK3GqFPT9Epg9/s3nvE9ITGeD96+g1VlmXzxgZ9Fp1MhJDnFjNPpwmBQ9/OCKEFUoVAogqMwO4U7r6njzmvqaOka4s4v/zf2yeDtI+ciNyeJfk1gsU/XJufyr7Uf4I2+03z14Dk5N0JOvaNjUa1oYXIS926r47qqNei1seuEpYgcU1PRzc4VCvR6Lbuv3oher+7phVnaTkeBogRRhUIRNqw2R9iEUICEjMDsJe8q38791Rdj0GgpsqTSY7fyHw2vhbh3ZzLhml8rWpqWyr11tbx/TSU6jQpmoniPocFx9HotSSlmUlI9m8Go58SxTgb6Yt8sJb8wjS9+/QOUVag0s4q5UYKoQqEIG0+/fjys9bsT/dMeZBgtPFhzLTuzSs8of6DqInrtY/ym7VAou3cOZ2tFqzIzuWdbLVesLkcjVJBvxblccfVG3nfdZsRZ94eUktNNvex5rZGD+5vpbB9i0uFEq9W8u2lmf9aId8umnS5stkkmbA5stsmwOUXtfv9GPvW5K4g3KQc7n5AojahCoVCECpfbzQv7GsPahlPn+7LlBVll/OOWa0mbI2uUEIKvb34/A45xXusLXyioGa0oI0Y+WbuV84qLzhEwFIrZ6HRzL2cLISgtz6a0PJsPfXRXwPVLKentHuXkiS6OHmzjpeeOMTJsC7g+AJPJwP1/+34uunxtUPWsSGI7CEhYUIKoQqEIC1qNhl/988eoP93D0VPdHGnsorN/lKHRCay2xTM1+cK0ZnFBVK/R8vm1l/Lh0toFhT69Rsv3tt3ER155gmMj3SHp39lcllvJXeU72JiWH5b6FQp/EUKQnZtMdm4y519cxSc/ezn7957iz386zBuvNDA16V9WsNodZdz3l1eSkxf6RA4rAeU1r1AoFCHEHG9ga3UhW6sLzyh3TrsYGbMzNDrBkNXm/evdvJ+HvZ9HxiZwueeenKe0CwuiJZY0/rX2A1Ql+2afZtYZ+I8dt3LLS4/TMTHi0zmLoRMa3l+wjk+s3kFpYkZI6lQowoVWp6F2Rzm1O8qx2SY5cbSD7q4RejqHGRm2YR21Yx2dYHRkAuuonalJJ263RErJVddt4b6/vEJp+YNBCaIKhUIRfvQ6LRkpFjJSLIse63ZLrDYH/cPjHGzo4PVDzbxzvJ1Jp4sp4UKDmDP80o1Fm/jiht2YdP7Zp6XHWXhs523c+vLjjEzZ/Tp3NvFaPR8s2cydZdvIMSUFXI9CES3MZiNb6koXP1ChCAIliCoUiphGoxEkJ8STnBBPeWEGN122iSHrBI/93xu8oD3Bs7s/w6TLSfP4IKfHBjg9NsgF2WVcmV8dcJslCWk8tuNDPNt1Ao0QaIVAILyfNWhmfRfe/RqhQeMtM2i0XJxTQYrRFMKRUCgUyxoJzLP6s5xRgqhCoVhypCaa+Js7L+UD1vWYdQbyzckhX/Zel5rHutS8kNapUCgU86PiiCoUCsWSojwxM9pdUCgUitChBFGFQqFQKBQKRVRYgYKoSuGhUCgUCoVCoYgKSiOqUCgUCoVCEW2Us5JCoVAoFAqFIjpIkCsvtZISRBUKhUKhUChiAWUjqlAoFAqFQqFQRAYliCoUCoVCoVBEmxkb0UA3HxBCtAghjgghDgoh9nnLUoUQzwkhGr1/U2Yd/wUhRJMQokEIsXtW+RZvPU1CiIdFEHldlSCqUCgUCoVCEQtIGfjmOxdJKTdKKWu83/8WeF5KWQ487/2OEKIKuAWoBq4Avi+E0HrPeQS4Gyj3blcEeslKEFUoFAqFQqGIBSIjiJ7NtcAT3s9PANfNKn9SSjkppWwGmoBaIUQOkCilfFNKKYGfzDrHb5QgqlAoFAqFQhF1ghBCPYJouhBi36zt7rkb4VkhxP5Z+7OklN0A3r8zKevygPZZ53Z4y/K8n88uDwjlNa9QKBQKhUKx9BmYtdw+HzullF1CiEzgOSHEiQWOncvuUy5QHhBKEFUoFAqFQqGINhJwhzeOqJSyy/u3Twjxa6AW6BVC5Egpu73L7n3ewzuAglmn5wNd3vL8OcoDYkkJovv37x8QQrQuclg6MBCJ/iwB1FiciRqP91BjcSZqPM5Ejcd7qLE4k+U6HkXR7gAQ1jiiQggzoJFSjnk/Xw58DXgKuAN40Pv3t95TngJ+JoT4NpCLxylpr5TSJYQYE0JsA/YAHwH+LdB+LSlBVEqZsdgxQoh9PqimVwRqLM5Ejcd7qLE4EzUeZ6LG4z3UWJyJGo8wE96A9lnAr72RlnTAz6SUTwsh3gZ+IYS4C2gDbvJ0RR4TQvwCqAemgfuklC5vXfcCPwbigT95t4BYUoKoQqFQKBQKxfLE93igAdUu5Wlgwxzlg8Al85zzTeCbc5TvA9aGol/Ka16hUCgUCoVCERWWo0b00Wh3IIZQY3EmajzeQ43FmajxOBM1Hu+hxuJM1HiECwlShtdZKRYRMrz2CAqFQqFQKBSKRUjSZcjtidcFfP4zwz/cvxTtd5ejRlShUCgUCoVi6bEClYMxZSMqhNAKIQ4IIX5/VvlfCSGkECLd+/0yb1aAI96/F8869iUhRIMQ4qB3y/SWG4UQ/yOEaBJC7BFCFM865w4hRKN3uyNCl7sgfoxFsRDCPut6fzDr2C3eMWoSQjwsvK5yS20swK/xuG3WWBwUQriFEBu9+5bFvQHnjocQ4itCiM5Z13bVrGO/4L22BiHE7lnly+L+8HUsVsK8AX6Nx4qcOxYYj2U/d5w9Ft6yz3iv7ZgQ4luzypf1vKGIHWJNI3o/cBxInCkQQhQAl+EJKTDDAHC1NzvAWuAZzkwvdZvXo2s2dwHDUsoyIcQtwEPAzUKIVODLQA2ecLL7hRBPSSmHQ3xt/uLrWACcklJunKOOR4C7gbeAPwJX4AmxsNTGAnwcDynlfwP/7d2/DvitlPLgrHqWw70Bc4wH8B0p5b/MPkgIUQXcAlTjiQP3ZyHEam8IjuVyf/g0FqyMeQN8Hw9YoXMHc4zHCpk7zhgLIcRFePKJr5dSTs4SsFfCvBF7SBn2gPaxSMxoRIUQ+cD7gB+etes7wF8zK32UlPLATHYA4BgQJ4QwLtLEtcAT3s+/BC7xvsntBp6TUg55fxjP4flhRQ1/xmKBOnKARCnlm9JjCPwT4Drv7iUzFhDUeNwK/NyHJpbLeMzFtcCTUspJKWUz0ATULpf7w5+xWO7zBvh9b8xXx7K4NyCo8Vh2c8c8Y3Ev8KCUchI82Xa85ct63ohpgss1vySJGUEU+C4eoeLd1wEhxDVAp5Ty0ALn3QAcmPkheflP7/LJ388sG+DRfLQDSCmngVEgbXa5lw7O1JJEg+/i31iUeJdbXhZC7PKW5eG5lhlmX9dSGgsI/N64mXMfJkv93oA5xsPLp4UQh4UQjwshUrxl813Dcrk/vovvYzGb5ThvgP/jseLmDi+L3R/Lce74LueOxWpgl3cp/WUhxFZv+XKfN2IW6XYHvC1VYkIQFUK8H+iTUu6fVWYC/g74hwXOq8aj/v/krOLbpJTrgF3e7cMzh89RhVygPCoEMBbdQKGUchPwOTzpuBJZ+LqWxFhAUPdGHTAhpTw6q3hJ3xsw93h4eQQoBTbiuSf+deaUOapZ7NqWxHgEMBYz5y27eQMCGo8VN3d4Wez+WHZzxwJjoQNSgG3A5/Fk1xEs43kjtglCG6o0okGzE7hGCNECPAlcDPwUKAEOecvzgXeEENnw7jLDr4GPSClPzVQkpez0/h0DfgbUend1AAXec3VAEjA0u9xLPtBF9PBrLLxLJ4MA3knmFJ633A7vcTPMvq6lMhYQwL3h5RbO0mgsg3sD5hgPIcR/SSl7pZQu6QlC9xhzXJuXmWtYDveHv2OxnOcN8HM8VuLcsdj94WU5zh1zjgWefv6f9LAXj7Y0neU9byhiDSllTG3AhcDv5yhvAdK9n5OBQ8ANZx2jm3WMHo+dyj3e7/cBP/B+vgX4hfdzKtCM560wxfs5Ndrj4MdYZABa7+dVQOdM/4G38bzpCjzG5Fct1bHwdTy83zV4Jr9Vy/XeOHs8gJxZ5X+Bx74LPM4GhwAjHuH99Kz7ZdncHz6ORTIrYN7wYzxW5Nwx33h4vy/7ueOssbgH+Jr382o8S+iCFTJvxNqWKFLl5XG3B7wB+6J9DYFsseY17yufBsqAvxdC/L237HLABjwjhNADWuDPeN54AX4E/FQI0YTnLe0WACnlkBDi63h+XOD5UQ5F5jJCwvnA14QQ04ALzwQ50/97gR8D8XgmjD95y5frWMxwPtAhPXl1ZzCyvO+NbwlPqBmJRzD/JICU8pgQ4hdAPTAN3Cc9nq+wfO+POceClTtvzDceK3XumG88YOXNHY8DjwshjgJTwB1SSgmsxHkjNlCZlRQKhUKhUCgUkSZRkya36XYvfuA8POf8ucqspFAoFAqFQqEIAClXpEY0VpyVFAqFQqFQKBQrDKURVSgUCoVCoYgBpHvlmUsqQVShUCgUCoUiFliBS/PKWUmhUCgUCoUiygghnsYTxzVQBqSUSy59qhJEFQqFQqFQKBRRQTkrKRQKhUKhUCiighJEFQqFQqFQKBRRQQmiCoVCoVAoFIqooARRhUKhUCgUCkVUUIKoQqFQKBQKhSIq/H94tjE6yIFJKAAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<Figure size 864x432 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Make a plot of the districts colored by the population density\n",
     "districts.plot(column='population_density', figsize=(12, 6), legend=True)"
@@ -1620,34 +906,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": null,
+   "id": "643af16c",
    "metadata": {
-    "clear_cell": true
+    "tags": [
+     "nbtutor-solution"
+    ]
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<AxesSubplot:>"
-      ]
-     },
-     "execution_count": 42,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 864x432 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# As comparison, the misleading plot when not turning the population number into a density\n",
     "districts.plot(column='population', figsize=(12, 6), legend=True)"
@@ -1655,6 +921,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "ba22cba9",
    "metadata": {},
    "source": [
     "---\n",
@@ -1664,33 +931,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Note on `fiona`\n",
-    "\n",
-    "Under the hood, GeoPandas uses the [Fiona library](http://toblerity.org/fiona/) (pythonic interface to GDAL/OGR) to read and write data. GeoPandas provides a more user-friendly wrapper, which is sufficient for most use cases. But sometimes you want more control, and in that case, to read a file with fiona you can do the following:\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import fiona\n",
-    "from shapely.geometry import shape\n",
-    "\n",
-    "with fiona.Env():\n",
-    "    with fiona.open(\"zip://./data/ne_110m_admin_0_countries.zip\") as collection:\n",
-    "        for feature in collection:\n",
-    "            # ... do something with geometry\n",
-    "            geom = shape(feature['geometry'])\n",
-    "            # ... do something with properties\n",
-    "            print(feature['properties']['name'])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
+   "id": "00092e1d",
    "metadata": {},
    "source": [
     "### Constructing a GeoDataFrame manually"
@@ -1699,6 +940,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
+   "id": "dbbca0cf",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1710,6 +952,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "9ddbb420",
    "metadata": {},
    "source": [
     "### Creating a GeoDataFrame from an existing dataframe\n",
@@ -1720,6 +963,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
+   "id": "1182f116",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1733,6 +977,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
+   "id": "69eb3c12",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1743,6 +988,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
+   "id": "b63b1508",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1751,22 +997,19 @@
   },
   {
    "cell_type": "markdown",
+   "id": "ea7a3211",
    "metadata": {},
    "source": [
-    "See http://geopandas.readthedocs.io/en/latest/gallery/create_geopandas_from_pandas.html for full example"
+    "See https://geopandas.org/en/latest/gallery/create_geopandas_from_pandas.html for full example"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
+  "jupytext": {
+   "default_lexer": "ipython3"
+  },
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -1780,9 +1023,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.5"
+   "version": "3.12.11"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 4
+ "nbformat_minor": 5
 }
diff --git a/_solved/03-coordinate-reference-systems.ipynb b/_solved/03-coordinate-reference-systems.ipynb
index e8c0366..7b54be0 100644
--- a/_solved/03-coordinate-reference-systems.ipynb
+++ b/_solved/03-coordinate-reference-systems.ipynb
@@ -2,36 +2,46 @@
  "cells": [
   {
    "cell_type": "markdown",
+   "id": "fb148624",
    "metadata": {},
    "source": [
-    "# Coordinate reference systems"
+    "<p><font size=\"6\"><b>Coordinate reference systems</b></font></p>\n",
+    "\n",
+    "\n",
+    "> *DS Python for GIS and Geoscience*  \n",
+    "> *October, 2025*\n",
+    ">\n",
+    "> *© 2025, Joris Van den Bossche and Stijn Van Hoey  (<mailto:jorisvandenbossche@gmail.com>, <mailto:stijnvanhoey@gmail.com>). Licensed under [CC BY 4.0 Creative Commons](http://creativecommons.org/licenses/by/4.0/)*\n",
+    "\n",
+    "---"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
+   "id": "9f6f168a",
    "metadata": {},
    "outputs": [],
    "source": [
-    "%matplotlib inline\n",
-    "\n",
     "import pandas as pd\n",
     "import geopandas"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
+   "id": "8b49d24c",
    "metadata": {},
    "outputs": [],
    "source": [
-    "countries = geopandas.read_file(\"zip://./data/ne_110m_admin_0_countries.zip\")\n",
-    "cities = geopandas.read_file(\"zip://./data/ne_110m_populated_places.zip\")\n",
-    "rivers = geopandas.read_file(\"zip://./data/ne_50m_rivers_lake_centerlines.zip\")"
+    "countries = geopandas.read_file(\"data/ne_110m_admin_0_countries.zip\")\n",
+    "cities = geopandas.read_file(\"data/ne_110m_populated_places.zip\")\n",
+    "rivers = geopandas.read_file(\"data/ne_50m_rivers_lake_centerlines.zip\")"
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "0e2b3c1c",
    "metadata": {},
    "source": [
     "## Coordinate reference systems\n",
@@ -40,11 +50,12 @@
     "\n",
     "> The **Coordinate Reference System (CRS)** relates the coordinates to a specific location on earth.\n",
     "\n",
-    "For a nice in-depth explanation, see https://docs.qgis.org/2.8/en/docs/gentle_gis_introduction/coordinate_reference_systems.html"
+    "For an in-depth explanation, see https://docs.qgis.org/2.8/en/docs/gentle_gis_introduction/coordinate_reference_systems.html"
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "525679f2",
    "metadata": {},
    "source": [
     "### Geographic coordinates\n",
@@ -66,6 +77,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "cb62ebd7",
    "metadata": {},
    "source": [
     "### Projected coordinates\n",
@@ -79,7 +91,7 @@
     "\n",
     "We project the surface of the earth onto a 2D plane so we can express locations in cartesian x and y coordinates, on a flat surface. In this plane, we then typically work with a length unit such as meters instead of degrees, which makes the analysis more convenient and effective.\n",
     "\n",
-    "However, there is an important remark: the 3 dimensional earth can never be represented perfectly on a 2 dimensional map, so projections inevitably introduce distortions. To minimise such errors, there are different approaches to project, each with specific advantages and disadvantages.\n",
+    "However, there is an important remark: the 3 dimensional earth can never be represented perfectly on a 2 dimensional map, so projections inevitably introduce distortions. To minimize such errors, there are different approaches to project, each with specific advantages and disadvantages.\n",
     "\n",
     "Some projection systems will try to preserve the area size of geometries, such as the Albers Equal Area projection. Other projection systems try to preserve angles, such as the Mercator projection, but will see big distortions in the area. Every projection system will always have some distortion of area, angle or distance.\n",
     "\n",
@@ -94,6 +106,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "9bf3f673",
    "metadata": {},
    "source": [
     "**Projected size vs actual size (Mercator projection)**:\n",
@@ -103,6 +116,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "8f0dac79",
    "metadata": {},
    "source": [
     "## Coordinate Reference Systems in Python / GeoPandas"
@@ -110,6 +124,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "0d95b51c",
    "metadata": {},
    "source": [
     "A GeoDataFrame or GeoSeries has a `.crs` attribute which holds (optionally) a description of the coordinate reference system of the geometries:"
@@ -117,36 +132,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
+   "id": "3fbe677b",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<Geographic 2D CRS: EPSG:4326>\n",
-       "Name: WGS 84\n",
-       "Axis Info [ellipsoidal]:\n",
-       "- Lat[north]: Geodetic latitude (degree)\n",
-       "- Lon[east]: Geodetic longitude (degree)\n",
-       "Area of Use:\n",
-       "- name: World\n",
-       "- bounds: (-180.0, -90.0, 180.0, 90.0)\n",
-       "Datum: World Geodetic System 1984\n",
-       "- Ellipsoid: WGS 84\n",
-       "- Prime Meridian: Greenwich"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "countries.crs"
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "04f95c5c",
    "metadata": {},
    "source": [
     "For the `countries` dataframe, it indicates that it uses the EPSG 4326 / WGS84 lon/lat reference system, which is one of the most used for geographic coordinates.\n",
@@ -157,38 +153,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
+   "id": "736cc1b5",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<AxesSubplot:>"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "countries.plot()"
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "ec639d0c",
    "metadata": {},
    "source": [
     "The `.crs` attribute returns a `pyproj.CRS` object. To specify a CRS, we typically use some string representation:\n",
@@ -216,6 +191,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "7bdfb050",
    "metadata": {},
    "source": [
     "### Transforming to another CRS\n",
@@ -227,7 +203,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
+   "id": "75acd02f",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -237,47 +214,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
+   "id": "cc49ad60",
    "metadata": {},
    "outputs": [],
    "source": [
-    "countries_mercator = countries.to_crs(epsg=3395)  # or .to_crs({'init': 'epsg:3395'})"
+    "countries_mercator = countries.to_crs(epsg=3395)  # or .to_crs(\"EPSG:3395\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
+   "id": "326bc22b",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<AxesSubplot:>"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "countries_mercator.plot()"
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "2242f826",
    "metadata": {},
    "source": [
     "Note the different scale of x and y."
@@ -285,6 +242,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "97870295",
    "metadata": {},
    "source": [
     "### Why using a different CRS?\n",
@@ -302,19 +260,20 @@
     "\n",
     "- Mapping (distortion of shape and distances)\n",
     "\n",
-    "- Distance / area based calculations -> ensure you use an appropriate projected coordinate system expressed in a meaningful unit such as metres or feet (not degrees).\n",
+    "- Distance / area based calculations -> ensure you use an appropriate projected coordinate system expressed in a meaningful unit such as meters or feet (not degrees).\n",
     "\n",
     "<div class=\"alert alert-info\" style=\"font-size:120%\">\n",
     "\n",
     "**ATTENTION:**\n",
     "\n",
-    "All the calculations that happen in geopandas and shapely assume that your data is in a 2D cartesian plane, and thus the result of those calculations will only be correct if your data is properly projected.\n",
+    "All the calculations that happen in GeoPandas and Shapely assume that your data is in a 2D cartesian plane, and thus the result of those calculations will only be correct if your data is properly projected.\n",
     "\n",
     "</div>"
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "5db42e9b",
    "metadata": {},
    "source": [
     "## Let's practice!\n",
@@ -326,6 +285,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "8504045d",
    "metadata": {},
    "source": [
     "Going back to the Paris districts dataset, this is now provided as a GeoJSON file (`\"data/paris_districts.geojson\"`) in geographic coordinates.\n",
@@ -338,7 +298,7 @@
     "\n",
     "* Read the districts datasets (`\"data/paris_districts.geojson\"`) into a GeoDataFrame called `districts`.\n",
     "* Look at the CRS attribute of the GeoDataFrame. Do you recognize the EPSG number?\n",
-    "* Make a simple plot of the `districts` dataset.\n",
+    "* Make a plot of the `districts` dataset.\n",
     "* Calculate the area of all districts.\n",
     "* Convert the `districts` to a projected CRS (using the `EPSG:2154` for France). Call the new dataset `districts_RGF93`.\n",
     "* Make a similar plot of `districts_RGF93`.\n",
@@ -347,9 +307,9 @@
     "    \n",
     "<details><summary>Hints</summary>\n",
     "\n",
-    "* The CRS information is stored in the `crs` attribute of a GeoDataFrame.\n",
+    "* The CRS information is stored in the `.crs` attribute of a GeoDataFrame.\n",
     "* Making a simple plot of a GeoDataFrame can be done with the `.plot()` method.\n",
-    "* Converting to a different CRS can be done with the `to_crs()` method, and the CRS can be specified as an EPSG number using the `epsg` keyword.\n",
+    "* Converting to a different CRS can be done with the `.to_crs()` method, and the CRS can be specified as an EPSG number using the `epsg` keyword.\n",
     "\n",
     "</details>\n",
     "\n",
@@ -358,9 +318,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
+   "id": "47b85c58",
    "metadata": {
-    "clear_cell": true
+    "tags": [
+     "nbtutor-solution"
+    ]
    },
    "outputs": [],
    "source": [
@@ -370,32 +333,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
+   "id": "a7f886e3",
    "metadata": {
-    "clear_cell": true
+    "tags": [
+     "nbtutor-solution"
+    ]
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<Geographic 2D CRS: EPSG:4326>\n",
-       "Name: WGS 84\n",
-       "Axis Info [ellipsoidal]:\n",
-       "- Lat[north]: Geodetic latitude (degree)\n",
-       "- Lon[east]: Geodetic longitude (degree)\n",
-       "Area of Use:\n",
-       "- name: World\n",
-       "- bounds: (-180.0, -90.0, 180.0, 90.0)\n",
-       "Datum: World Geodetic System 1984\n",
-       "- Ellipsoid: WGS 84\n",
-       "- Prime Meridian: Greenwich"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Check the CRS information\n",
     "districts.crs"
@@ -403,99 +348,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
+   "id": "31a9176b",
    "metadata": {
-    "clear_cell": true
+    "tags": [
+     "nbtutor-solution"
+    ]
    },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>id</th>\n",
-       "      <th>district_name</th>\n",
-       "      <th>population</th>\n",
-       "      <th>geometry</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1</td>\n",
-       "      <td>St-Germain-l'Auxerrois</td>\n",
-       "      <td>1672</td>\n",
-       "      <td>POLYGON ((2.34459 48.85405, 2.34459 48.85405, ...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>2</td>\n",
-       "      <td>Halles</td>\n",
-       "      <td>8984</td>\n",
-       "      <td>POLYGON ((2.34937 48.86058, 2.34822 48.85852, ...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>3</td>\n",
-       "      <td>Palais-Royal</td>\n",
-       "      <td>3195</td>\n",
-       "      <td>POLYGON ((2.33947 48.86214, 2.33912 48.86148, ...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>4</td>\n",
-       "      <td>Place-Vendôme</td>\n",
-       "      <td>3044</td>\n",
-       "      <td>POLYGON ((2.33194 48.86491, 2.33135 48.86399, ...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>5</td>\n",
-       "      <td>Gaillon</td>\n",
-       "      <td>1345</td>\n",
-       "      <td>POLYGON ((2.33632 48.86797, 2.33587 48.86700, ...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   id           district_name  population  \\\n",
-       "0   1  St-Germain-l'Auxerrois        1672   \n",
-       "1   2                  Halles        8984   \n",
-       "2   3            Palais-Royal        3195   \n",
-       "3   4           Place-Vendôme        3044   \n",
-       "4   5                 Gaillon        1345   \n",
-       "\n",
-       "                                            geometry  \n",
-       "0  POLYGON ((2.34459 48.85405, 2.34459 48.85405, ...  \n",
-       "1  POLYGON ((2.34937 48.86058, 2.34822 48.85852, ...  \n",
-       "2  POLYGON ((2.33947 48.86214, 2.33912 48.86148, ...  \n",
-       "3  POLYGON ((2.33194 48.86491, 2.33135 48.86399, ...  \n",
-       "4  POLYGON ((2.33632 48.86797, 2.33587 48.86700, ...  "
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Show the first rows of the GeoDataFrame\n",
     "districts.head()"
@@ -503,34 +363,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
+   "id": "c423340e",
    "metadata": {
-    "clear_cell": true
+    "tags": [
+     "nbtutor-solution"
+    ]
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<AxesSubplot:>"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Plot the districts dataset\n",
     "districts.plot()"
@@ -538,42 +378,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
+   "id": "9971503b",
    "metadata": {
-    "clear_cell": true
+    "tags": [
+     "nbtutor-solution"
+    ]
    },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "<ipython-input-12-62d43396fdd2>:2: UserWarning: Geometry is in a geographic CRS. Results from 'area' are likely incorrect. Use 'GeoSeries.to_crs()' to re-project geometries to a projected CRS before this operation.\n",
-      "\n",
-      "  districts.geometry.area\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "0     0.000107\n",
-       "1     0.000051\n",
-       "2     0.000034\n",
-       "3     0.000033\n",
-       "4     0.000023\n",
-       "        ...   \n",
-       "75    0.000159\n",
-       "76    0.000099\n",
-       "77    0.000182\n",
-       "78    0.000196\n",
-       "79    0.000256\n",
-       "Length: 80, dtype: float64"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Calculate the area of all districts\n",
     "districts.geometry.area"
@@ -581,9 +393,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
+   "id": "5109ab88",
    "metadata": {
-    "clear_cell": true
+    "tags": [
+     "nbtutor-solution"
+    ]
    },
    "outputs": [],
    "source": [
@@ -593,34 +408,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": null,
+   "id": "9b7eb762",
    "metadata": {
-    "clear_cell": true
+    "tags": [
+     "nbtutor-solution"
+    ]
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<AxesSubplot:>"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Plot the districts dataset again\n",
     "districts_RGF93.plot()"
@@ -628,33 +423,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": null,
+   "id": "3a2b00db",
    "metadata": {
-    "clear_cell": true
+    "tags": [
+     "nbtutor-solution"
+    ]
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0     8.690007e+05\n",
-       "1     4.124585e+05\n",
-       "2     2.736968e+05\n",
-       "3     2.694568e+05\n",
-       "4     1.880122e+05\n",
-       "          ...     \n",
-       "75    1.294988e+06\n",
-       "76    8.065686e+05\n",
-       "77    1.486971e+06\n",
-       "78    1.599002e+06\n",
-       "79    2.090904e+06\n",
-       "Length: 80, dtype: float64"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Calculate the area of all districts (the result is now expressed in m²)\n",
     "districts_RGF93.geometry.area"
@@ -662,13 +438,14 @@
   },
   {
    "cell_type": "markdown",
+   "id": "1e28cd04",
    "metadata": {},
    "source": [
     "<div class=\"alert alert-success\">\n",
     "\n",
     "**EXERCISE:**\n",
     "\n",
-    "In the first notebook, we did an exercise on plotting the bike stations locations in Paris and adding a background map to it using the `contextily` package.\n",
+    "In the previous notebook, we did an exercise on plotting the bike stations locations in Paris and adding a background map to it using the `contextily` package.\n",
     "\n",
     "Currently, `contextily` assumes that your data is in the Web Mercator projection, the system used by most web tile services. And in that first exercise, we provided the data in the appropriate CRS so you didn't need to care about this aspect.\n",
     "\n",
@@ -682,7 +459,7 @@
     "<details><summary>Hints</summary>\n",
     "\n",
     "* Making a simple plot of a GeoDataFrame can be done with the `.plot()` method. This returns a matplotlib axes object.\n",
-    "* The marker size can be specified with the `markersize` keyword if the `plot()` method.\n",
+    "* The marker size can be specified with the `markersize` keyword if the `.plot()` method.\n",
     "* To add a background map, use the `contextily.add_basemap()` function. It takes the matplotlib `ax` to which to add a map as the first argument.\n",
     "\n",
     "</details>\n",
@@ -692,99 +469,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": null,
+   "id": "8a26c794",
    "metadata": {
-    "clear_cell": true
+    "tags": [
+     "nbtutor-solution"
+    ]
    },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "      <th>name</th>\n",
-       "      <th>bike_stands</th>\n",
-       "      <th>available_bikes</th>\n",
-       "      <th>geometry</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
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-       "  </tbody>\n",
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-       "</div>"
-      ],
-      "text/plain": [
-       "                                  name  bike_stands  available_bikes  \\\n",
-       "0               14002 - RASPAIL QUINET           44                4   \n",
-       "1  20503 - COURS DE VINCENNES PYRÉNÉES           21                3   \n",
-       "2             20011 - PYRÉNÉES-DAGORNO           21                0   \n",
-       "3        31008 - VINCENNES (MONTREUIL)           56                0   \n",
-       "4          43006 - MINIMES (VINCENNES)           28               27   \n",
-       "\n",
-       "                   geometry  \n",
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-       "4  POINT (2.43079 48.84149)  "
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "stations = geopandas.read_file(\"data/paris_bike_stations.geojson\")\n",
     "stations.head()"
@@ -792,143 +484,44 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": null,
+   "id": "00d60562",
    "metadata": {
-    "clear_cell": true
+    "tags": [
+     "nbtutor-solution"
+    ]
    },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
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-       "\n",
-       "    .dataframe thead th {\n",
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-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>name</th>\n",
-       "      <th>bike_stands</th>\n",
-       "      <th>available_bikes</th>\n",
-       "      <th>geometry</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
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-       "      <td>POINT (2.40591 48.84772)</td>\n",
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-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>20011 - PYRÉNÉES-DAGORNO</td>\n",
-       "      <td>21</td>\n",
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-       "      <td>POINT (2.40517 48.85550)</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>31008 - VINCENNES (MONTREUIL)</td>\n",
-       "      <td>56</td>\n",
-       "      <td>0</td>\n",
-       "      <td>POINT (2.43737 48.85770)</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>43006 - MINIMES (VINCENNES)</td>\n",
-       "      <td>28</td>\n",
-       "      <td>27</td>\n",
-       "      <td>POINT (2.43079 48.84149)</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                  name  bike_stands  available_bikes  \\\n",
-       "0               14002 - RASPAIL QUINET           44                4   \n",
-       "1  20503 - COURS DE VINCENNES PYRÉNÉES           21                3   \n",
-       "2             20011 - PYRÉNÉES-DAGORNO           21                0   \n",
-       "3        31008 - VINCENNES (MONTREUIL)           56                0   \n",
-       "4          43006 - MINIMES (VINCENNES)           28               27   \n",
-       "\n",
-       "                   geometry  \n",
-       "0  POINT (2.32956 48.83920)  \n",
-       "1  POINT (2.40591 48.84772)  \n",
-       "2  POINT (2.40517 48.85550)  \n",
-       "3  POINT (2.43737 48.85770)  \n",
-       "4  POINT (2.43079 48.84149)  "
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Convert to the Web Mercator projection\n",
-    "stations_webmercator = stations.to_crs(epsg=3857)\n",
+    "stations_webmercator = stations.to_crs(\"EPSG:3857\")\n",
     "stations.head()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": null,
+   "id": "5d5a4488",
    "metadata": {
-    "clear_cell": true
+    "tags": [
+     "nbtutor-solution"
+    ]
    },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Plot the stations with a background map\n",
     "import contextily\n",
     "ax = stations_webmercator.plot(markersize=5)\n",
     "contextily.add_basemap(ax)"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
+  "jupytext": {
+   "default_lexer": "ipython3"
+  },
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -942,9 +535,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.5"
+   "version": "3.12.11"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 4
+ "nbformat_minor": 5
 }
diff --git a/_solved/04-spatial-relationships-joins.ipynb b/_solved/04-spatial-relationships-joins.ipynb
index f935ed4..de11c00 100644
--- a/_solved/04-spatial-relationships-joins.ipynb
+++ b/_solved/04-spatial-relationships-joins.ipynb
@@ -2,36 +2,46 @@
  "cells": [
   {
    "cell_type": "markdown",
+   "id": "6ec40f36",
    "metadata": {},
    "source": [
-    "# Spatial relationships and joins"
+    "<p><font size=\"6\"><b>Spatial relationships and joins</b></font></p>\n",
+    "\n",
+    "\n",
+    "> *DS Python for GIS and Geoscience*  \n",
+    "> *October, 2025*\n",
+    ">\n",
+    "> *© 2025, Joris Van den Bossche and Stijn Van Hoey  (<mailto:jorisvandenbossche@gmail.com>, <mailto:stijnvanhoey@gmail.com>). Licensed under [CC BY 4.0 Creative Commons](http://creativecommons.org/licenses/by/4.0/)*\n",
+    "\n",
+    "---"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
+   "id": "91f5eb4a",
    "metadata": {},
    "outputs": [],
    "source": [
-    "%matplotlib inline\n",
-    "\n",
     "import pandas as pd\n",
     "import geopandas"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
+   "id": "030222e2",
    "metadata": {},
    "outputs": [],
    "source": [
-    "countries = geopandas.read_file(\"zip://./data/ne_110m_admin_0_countries.zip\")\n",
-    "cities = geopandas.read_file(\"zip://./data/ne_110m_populated_places.zip\")\n",
-    "rivers = geopandas.read_file(\"zip://./data/ne_50m_rivers_lake_centerlines.zip\")"
+    "countries = geopandas.read_file(\"data/ne_110m_admin_0_countries.zip\")\n",
+    "cities = geopandas.read_file(\"data/ne_110m_populated_places.zip\")\n",
+    "rivers = geopandas.read_file(\"data/ne_50m_rivers_lake_centerlines.zip\")"
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "82e05049",
    "metadata": {},
    "source": [
     "## Spatial relationships\n",
@@ -46,6 +56,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "77bb5a86",
    "metadata": {},
    "source": [
     "### Relationships between individual objects"
@@ -53,6 +64,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "4ec1f00e",
    "metadata": {},
    "source": [
     "Let's first create some small toy spatial objects:\n",
@@ -62,7 +74,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
+   "id": "e943800a",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -71,6 +84,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "173a2f18",
    "metadata": {},
    "source": [
     "Two points:"
@@ -78,7 +92,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
+   "id": "2bb3cf6b",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -88,6 +103,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "5a8b6eab",
    "metadata": {},
    "source": [
     "And a linestring:"
@@ -95,16 +111,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
+   "id": "8752404b",
    "metadata": {},
    "outputs": [],
    "source": [
-    "from shapely.geometry import LineString\n",
+    "from shapely import LineString\n",
     "line = LineString([paris, brussels])"
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "f027692e",
    "metadata": {},
    "source": [
     "Let's visualize those 4 geometry objects together (I only put them in a GeoSeries to easily display them together with the geopandas `.plot()` method):"
@@ -112,38 +130,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<AxesSubplot:>"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": null,
+   "id": "421d8325",
+   "metadata": {},
+   "outputs": [],
    "source": [
     "geopandas.GeoSeries([belgium, paris, brussels, line]).plot(cmap='tab10')"
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "6fe336aa",
    "metadata": {},
    "source": [
     "You can recognize the abstract shape of Belgium.\n",
@@ -153,26 +150,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "id": "cd6864f7",
+   "metadata": {},
+   "outputs": [],
    "source": [
     "brussels.within(belgium)"
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "646b1c29",
    "metadata": {},
    "source": [
     "And using the reverse, Belgium contains Brussels:"
@@ -180,26 +168,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "id": "b1962d41",
+   "metadata": {},
+   "outputs": [],
    "source": [
     "belgium.contains(brussels)"
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "5de0cdad",
    "metadata": {},
    "source": [
     "On the other hand, Paris is not located in Belgium:"
@@ -207,46 +186,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "False"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "id": "dd091664",
+   "metadata": {},
+   "outputs": [],
    "source": [
     "belgium.contains(paris)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "False"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "id": "4a2c48f8",
+   "metadata": {},
+   "outputs": [],
    "source": [
     "paris.within(belgium)"
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "544ca814",
    "metadata": {},
    "source": [
     "The straight line we draw from Paris to Brussels is not fully located within Belgium, but it does intersect with it:"
@@ -254,46 +214,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "False"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "id": "b4d8af11",
+   "metadata": {},
+   "outputs": [],
    "source": [
     "belgium.contains(line)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "id": "4a28b3d1",
+   "metadata": {},
+   "outputs": [],
    "source": [
     "line.intersects(belgium)"
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "31e3c4ba",
    "metadata": {},
    "source": [
     "### Spatial relationships with GeoDataFrames\n",
@@ -305,37 +246,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0      False\n",
-       "1      False\n",
-       "2      False\n",
-       "3      False\n",
-       "4      False\n",
-       "       ...  \n",
-       "172    False\n",
-       "173    False\n",
-       "174    False\n",
-       "175    False\n",
-       "176    False\n",
-       "Length: 177, dtype: bool"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "id": "669486fb",
+   "metadata": {},
+   "outputs": [],
    "source": [
     "countries.contains(paris)"
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "06eb23d2",
    "metadata": {},
    "source": [
     "Because the above gives us a boolean result, we can use that to filter the dataframe:"
@@ -343,71 +264,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>iso_a3</th>\n",
-       "      <th>name</th>\n",
-       "      <th>continent</th>\n",
-       "      <th>pop_est</th>\n",
-       "      <th>gdp_md_est</th>\n",
-       "      <th>geometry</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>55</th>\n",
-       "      <td>FRA</td>\n",
-       "      <td>France</td>\n",
-       "      <td>Europe</td>\n",
-       "      <td>67106161.0</td>\n",
-       "      <td>2699000.0</td>\n",
-       "      <td>MULTIPOLYGON (((2.51357 51.14851, 2.65842 50.7...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   iso_a3    name continent     pop_est  gdp_md_est  \\\n",
-       "55    FRA  France    Europe  67106161.0   2699000.0   \n",
-       "\n",
-       "                                             geometry  \n",
-       "55  MULTIPOLYGON (((2.51357 51.14851, 2.65842 50.7...  "
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "id": "a0ea2703",
+   "metadata": {},
+   "outputs": [],
    "source": [
     "countries[countries.contains(paris)]"
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "4ece2631",
    "metadata": {},
    "source": [
     "And indeed, France is the only country in the world in which Paris is located."
@@ -415,6 +282,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "0446872f",
    "metadata": {},
    "source": [
     "Another example, extracting the linestring of the Amazon river in South America, we can query through which countries the river flows:"
@@ -422,7 +290,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": null,
+   "id": "0dbe59fc",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -431,93 +300,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>iso_a3</th>\n",
-       "      <th>name</th>\n",
-       "      <th>continent</th>\n",
-       "      <th>pop_est</th>\n",
-       "      <th>gdp_md_est</th>\n",
-       "      <th>geometry</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>22</th>\n",
-       "      <td>BRA</td>\n",
-       "      <td>Brazil</td>\n",
-       "      <td>South America</td>\n",
-       "      <td>207353391.0</td>\n",
-       "      <td>3081000.0</td>\n",
-       "      <td>POLYGON ((-57.62513 -30.21629, -56.29090 -28.8...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>35</th>\n",
-       "      <td>COL</td>\n",
-       "      <td>Colombia</td>\n",
-       "      <td>South America</td>\n",
-       "      <td>47698524.0</td>\n",
-       "      <td>688000.0</td>\n",
-       "      <td>POLYGON ((-66.87633 1.25336, -67.06505 1.13011...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>124</th>\n",
-       "      <td>PER</td>\n",
-       "      <td>Peru</td>\n",
-       "      <td>South America</td>\n",
-       "      <td>31036656.0</td>\n",
-       "      <td>410400.0</td>\n",
-       "      <td>POLYGON ((-69.52968 -10.95173, -68.66508 -12.5...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "    iso_a3      name      continent      pop_est  gdp_md_est  \\\n",
-       "22     BRA    Brazil  South America  207353391.0   3081000.0   \n",
-       "35     COL  Colombia  South America   47698524.0    688000.0   \n",
-       "124    PER      Peru  South America   31036656.0    410400.0   \n",
-       "\n",
-       "                                              geometry  \n",
-       "22   POLYGON ((-57.62513 -30.21629, -56.29090 -28.8...  \n",
-       "35   POLYGON ((-66.87633 1.25336, -67.06505 1.13011...  \n",
-       "124  POLYGON ((-69.52968 -10.95173, -68.66508 -12.5...  "
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "id": "85a70d36",
+   "metadata": {},
+   "outputs": [],
    "source": [
     "countries[countries.crosses(amazon)]  # or .intersects"
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "b1ca2507",
    "metadata": {},
    "source": [
     "<div class=\"alert alert-info\" style=\"font-size:120%\">\n",
@@ -535,6 +328,7 @@
     "* `touches`\n",
     "* `within`\n",
     "* `covers`\n",
+    "* `covered_by`\n",
     "\n",
     "\n",
     "See https://shapely.readthedocs.io/en/stable/manual.html#predicates-and-relationships for an overview of those methods.\n",
@@ -546,16 +340,18 @@
   },
   {
    "cell_type": "markdown",
+   "id": "c12ebdf9",
    "metadata": {},
    "source": [
     "## Let's practice!\n",
     "\n",
-    "We will again use the Paris datasets to do some exercises. Let's start importing them again, and directly converting both to the local projected CRS:"
+    "We will again use the Paris datasets to exercise. Let's start importing them again, and directly converting both to the local projected CRS:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": null,
+   "id": "54bf64ff",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -565,6 +361,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "76901eee",
    "metadata": {},
    "source": [
     "<div class=\"alert alert-success\">\n",
@@ -573,7 +370,7 @@
     "\n",
     "The Eiffel Tower is an iron lattice tower built in the 19th century, and is probably the most iconic view of Paris.\n",
     "\n",
-    "The location of the Eiffel Tower is: x of 648237.3 and y of 6862271.9.\n",
+    "The location of the Eiffel Tower is: x of 648237.3 and y of 6862271.9\n",
     "\n",
     "* Create a Shapely point object with the coordinates of the Eiffel Tower and assign it to a variable called `eiffel_tower`. Print the result.\n",
     "* Check if the Eiffel Tower is located within the Montparnasse district (provided).\n",
@@ -596,19 +393,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": null,
+   "id": "17abfdd8",
    "metadata": {},
    "outputs": [],
    "source": [
     "# Import the Point geometry\n",
-    "from shapely.geometry import Point"
+    "from shapely import Point"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": null,
+   "id": "8f0ef088",
    "metadata": {
-    "clear_cell": true
+    "tags": [
+     "nbtutor-solution"
+    ]
    },
    "outputs": [],
    "source": [
@@ -618,19 +419,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": null,
+   "id": "88ee3bf9",
    "metadata": {
-    "clear_cell": true
+    "tags": [
+     "nbtutor-solution"
+    ]
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "POINT (648237.3 6862271.9)\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Print the result\n",
     "print(eiffel_tower)"
@@ -638,30 +434,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": null,
+   "id": "7764a34e",
    "metadata": {},
    "outputs": [],
    "source": [
     "# Accessing the Montparnasse geometry (Polygon)\n",
-    "district_montparnasse = districts.loc[52, 'geometry']\n",
-    "bike_station = stations.loc[293, 'geometry']"
+    "district_montparnasse = districts.loc[districts['district_name'] == 'Montparnasse', 'geometry'].item()\n",
+    "bike_station = stations.loc[stations['name'] == '14033 - DAGUERRE GASSENDI', 'geometry'].item()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": null,
+   "id": "a4026fb9",
    "metadata": {
-    "clear_cell": true
+    "tags": [
+     "nbtutor-solution"
+    ]
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "False\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Is the Eiffel Tower located within the Montparnasse district?\n",
     "print(eiffel_tower.within(district_montparnasse))"
@@ -669,19 +461,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": null,
+   "id": "ab518184",
    "metadata": {
-    "clear_cell": true
+    "tags": [
+     "nbtutor-solution"
+    ]
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "True\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Does the Montparnasse district contains the bike station?\n",
     "print(district_montparnasse.contains(bike_station))"
@@ -689,19 +476,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": null,
+   "id": "47b390af",
    "metadata": {
-    "clear_cell": true
+    "tags": [
+     "nbtutor-solution"
+    ]
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "3540.1534488921966\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# The distance between the Eiffel Tower and the bike station?\n",
     "print(eiffel_tower.distance(bike_station))"
@@ -709,6 +491,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "6fe734dc",
    "metadata": {},
    "source": [
     "<div class=\"alert alert-success\">\n",
@@ -733,7 +516,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": null,
+   "id": "4c2f0cd4",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -743,33 +527,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": null,
+   "id": "e22452ad",
    "metadata": {
-    "clear_cell": true
+    "tags": [
+     "nbtutor-solution"
+    ]
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0     False\n",
-       "1     False\n",
-       "2     False\n",
-       "3     False\n",
-       "4     False\n",
-       "      ...  \n",
-       "75    False\n",
-       "76    False\n",
-       "77    False\n",
-       "78    False\n",
-       "79    False\n",
-       "Length: 80, dtype: bool"
-      ]
-     },
-     "execution_count": 26,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Create a boolean Series\n",
     "mask = districts.contains(eiffel_tower)\n",
@@ -778,63 +543,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": null,
+   "id": "312ce404",
    "metadata": {
-    "clear_cell": true
+    "tags": [
+     "nbtutor-solution"
+    ]
    },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>id</th>\n",
-       "      <th>district_name</th>\n",
-       "      <th>population</th>\n",
-       "      <th>geometry</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>27</th>\n",
-       "      <td>28</td>\n",
-       "      <td>Gros-Caillou</td>\n",
-       "      <td>25156</td>\n",
-       "      <td>POLYGON ((649336.752 6861767.761, 649110.815 6...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "    id district_name  population  \\\n",
-       "27  28  Gros-Caillou       25156   \n",
-       "\n",
-       "                                             geometry  \n",
-       "27  POLYGON ((649336.752 6861767.761, 649110.815 6...  "
-      ]
-     },
-     "execution_count": 27,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Filter the districts with the boolean mask\n",
     "districts[mask]"
@@ -842,6 +558,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "4143054a",
    "metadata": {},
    "source": [
     "<div class=\"alert alert-success\">\n",
@@ -858,8 +575,8 @@
     "\n",
     "<details><summary>Hints</summary>\n",
     "\n",
-    "* The `distance()` method of a GeoDataFrame works element-wise: it calculates the distance between each geometry in the GeoDataFrame and the geometry passed to the method.\n",
-    "* A Series has a `min()` method to calculate the minimum value.\n",
+    "* The `.distance()` method of a GeoDataFrame works element-wise: it calculates the distance between each geometry in the GeoDataFrame and the geometry passed to the method.\n",
+    "* A Series has a `.min()` method to calculate the minimum value.\n",
     "* To create a boolean mask based on a condition, we can do e.g. `s < 100`.\n",
     "\n",
     "</details>\n",
@@ -869,9 +586,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": null,
+   "id": "9b5d2f80",
    "metadata": {
-    "clear_cell": true
+    "tags": [
+     "nbtutor-solution"
+    ]
    },
    "outputs": [],
    "source": [
@@ -881,22 +601,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": null,
+   "id": "8e62a28d",
    "metadata": {
-    "clear_cell": true
+    "tags": [
+     "nbtutor-solution"
+    ]
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "232.34672323449053"
-      ]
-     },
-     "execution_count": 29,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# The distance to the closest station\n",
     "dist_eiffel.min()"
@@ -904,45 +616,52 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": null,
+   "id": "33632bc2",
    "metadata": {
-    "clear_cell": true
+    "tags": [
+     "nbtutor-solution"
+    ]
    },
    "outputs": [],
    "source": [
-    "# Filter the restaurants for closer than 1 km\n",
+    "# Filter the bike stations closer than 1 km\n",
     "stations_eiffel = stations[dist_eiffel < 1000]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Make a plot of the close-by restaurants\n",
-    "ax = stations_eiffel.to_crs(epsg=3857).plot()\n",
-    "geopandas.GeoSeries([eiffel_tower], crs='EPSG:2154').to_crs(epsg=3857).plot(ax=ax, color='red')\n",
+   "execution_count": null,
+   "id": "0e0dbe53",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Make a plot of the close-by bike stations using matplotlib and contextily\n",
+    "import matplotlib.pyplot as plt\n",
     "import contextily\n",
-    "contextily.add_basemap(ax)\n",
+    "\n",
+    "fig, ax = plt.subplots(figsize=(8, 8))\n",
+    "stations_eiffel.plot(ax=ax)\n",
+    "geopandas.GeoSeries([eiffel_tower], crs='EPSG:2154').plot(ax=ax, color='red')\n",
+    "contextily.add_basemap(ax, crs=stations_eiffel.crs)\n",
     "ax.set_axis_off()"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "de64844f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Make a plot of the close-by bike stations using matplotlib and contextily\n",
+    "m = stations_eiffel.explore(marker_kwds=dict(radius=5))\n",
+    "geopandas.GeoSeries([eiffel_tower], crs='EPSG:2154').explore(m=m, color='red', marker_kwds=dict(radius=5))"
+   ]
+  },
   {
    "cell_type": "markdown",
+   "id": "4ae35c11",
    "metadata": {},
    "source": [
     "---"
@@ -950,6 +669,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "67ce618b",
    "metadata": {},
    "source": [
     "## Spatial joins"
@@ -957,11 +677,8 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
+   "id": "ae1c467f",
+   "metadata": {},
    "source": [
     "In the previous section of this notebook, we could use the spatial relationship methods to check in which country a certain city was located. But what if we wanted to perform this same operation for every city and country? For example, we might want to know for each city in which country it is located.  \n",
     "\n",
@@ -975,6 +692,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "9115eeb7",
    "metadata": {},
    "source": [
     "### Recap - joining dataframes\n",
@@ -984,14 +702,16 @@
   },
   {
    "cell_type": "markdown",
+   "id": "64ecdd74",
    "metadata": {},
    "source": [
-    "To illustrate the concept of joining the information of two dataframes with pandas, let's take a small subset of our `cities` and `countries` datasets: "
+    "To illustrate the concept of joining the information of two dataframes with pandas, let's take a small subset of our `cities` and `countries` datasets:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
+   "id": "0089f168",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1001,161 +721,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>name</th>\n",
-       "      <th>geometry</th>\n",
-       "      <th>iso_a3</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>26</th>\n",
-       "      <td>Bern</td>\n",
-       "      <td>POINT (7.46698 46.91668)</td>\n",
-       "      <td>CHE</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>170</th>\n",
-       "      <td>Brussels</td>\n",
-       "      <td>POINT (4.33137 50.83526)</td>\n",
-       "      <td>BEL</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>219</th>\n",
-       "      <td>London</td>\n",
-       "      <td>POINT (-0.11867 51.50194)</td>\n",
-       "      <td>GBR</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>235</th>\n",
-       "      <td>Paris</td>\n",
-       "      <td>POINT (2.33139 48.86864)</td>\n",
-       "      <td>FRA</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "         name                   geometry iso_a3\n",
-       "26       Bern   POINT (7.46698 46.91668)    CHE\n",
-       "170  Brussels   POINT (4.33137 50.83526)    BEL\n",
-       "219    London  POINT (-0.11867 51.50194)    GBR\n",
-       "235     Paris   POINT (2.33139 48.86864)    FRA"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "id": "df2b8a1e",
+   "metadata": {},
+   "outputs": [],
    "source": [
     "cities2"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>iso_a3</th>\n",
-       "      <th>name</th>\n",
-       "      <th>continent</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>AFG</td>\n",
-       "      <td>Afghanistan</td>\n",
-       "      <td>Asia</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>AGO</td>\n",
-       "      <td>Angola</td>\n",
-       "      <td>Africa</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>ALB</td>\n",
-       "      <td>Albania</td>\n",
-       "      <td>Europe</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>ARE</td>\n",
-       "      <td>United Arab Emirates</td>\n",
-       "      <td>Asia</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>ARG</td>\n",
-       "      <td>Argentina</td>\n",
-       "      <td>South America</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "  iso_a3                  name      continent\n",
-       "0    AFG           Afghanistan           Asia\n",
-       "1    AGO                Angola         Africa\n",
-       "2    ALB               Albania         Europe\n",
-       "3    ARE  United Arab Emirates           Asia\n",
-       "4    ARG             Argentina  South America"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "id": "ea259c55",
+   "metadata": {},
+   "outputs": [],
    "source": [
     "countries2 = countries[['iso_a3', 'name', 'continent']]\n",
     "countries2.head()"
@@ -1163,6 +742,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "db42db7d",
    "metadata": {},
    "source": [
     "We added a 'iso_a3' column to the `cities` dataset, indicating a code of the country of the city. This country code is also present in the `countries` dataset, which allows us to merge those two dataframes based on the common column.\n",
@@ -1172,100 +752,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>name_x</th>\n",
-       "      <th>geometry</th>\n",
-       "      <th>iso_a3</th>\n",
-       "      <th>name_y</th>\n",
-       "      <th>continent</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Bern</td>\n",
-       "      <td>POINT (7.46698 46.91668)</td>\n",
-       "      <td>CHE</td>\n",
-       "      <td>Switzerland</td>\n",
-       "      <td>Europe</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Brussels</td>\n",
-       "      <td>POINT (4.33137 50.83526)</td>\n",
-       "      <td>BEL</td>\n",
-       "      <td>Belgium</td>\n",
-       "      <td>Europe</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>London</td>\n",
-       "      <td>POINT (-0.11867 51.50194)</td>\n",
-       "      <td>GBR</td>\n",
-       "      <td>United Kingdom</td>\n",
-       "      <td>Europe</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Paris</td>\n",
-       "      <td>POINT (2.33139 48.86864)</td>\n",
-       "      <td>FRA</td>\n",
-       "      <td>France</td>\n",
-       "      <td>Europe</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "     name_x                   geometry iso_a3          name_y continent\n",
-       "0      Bern   POINT (7.46698 46.91668)    CHE     Switzerland    Europe\n",
-       "1  Brussels   POINT (4.33137 50.83526)    BEL         Belgium    Europe\n",
-       "2    London  POINT (-0.11867 51.50194)    GBR  United Kingdom    Europe\n",
-       "3     Paris   POINT (2.33139 48.86864)    FRA          France    Europe"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "id": "d42d1dfa",
+   "metadata": {},
+   "outputs": [],
    "source": [
     "cities2.merge(countries2, on='iso_a3')"
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "2f7b8b2f",
    "metadata": {},
    "source": [
-    "**But**, for this illustrative example, we added the common column manually, it is not present in the original dataset. However, we can still know how to join those two datasets based on their spatial coordinates."
+    "**But** for this illustrative example we added the common column manually, it is not present in the original dataset. However, we can still know how to join those two datasets based on their spatial coordinates."
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "99b140bb",
    "metadata": {},
    "source": [
     "### Recap - spatial relationships between objects\n",
@@ -1279,46 +784,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
+   "id": "42d1d955",
    "metadata": {},
    "outputs": [],
    "source": [
-    "france = countries.loc[countries['name'] == 'France', 'geometry'].squeeze()"
+    "france = countries.loc[countries['name'] == 'France', 'geometry'].item()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0      False\n",
-       "1      False\n",
-       "2      False\n",
-       "3      False\n",
-       "4      False\n",
-       "       ...  \n",
-       "238    False\n",
-       "239    False\n",
-       "240    False\n",
-       "241    False\n",
-       "242    False\n",
-       "Length: 243, dtype: bool"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "id": "82e25307",
+   "metadata": {},
+   "outputs": [],
    "source": [
     "cities.within(france)"
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "16806fc5",
    "metadata": {},
    "source": [
     "The above gives us a boolean series, indicating for each point in our `cities` dataframe whether it is located within the area of France or not.  \n",
@@ -1327,78 +813,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>name</th>\n",
-       "      <th>geometry</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>Monaco</td>\n",
-       "      <td>POINT (7.40691 43.73965)</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13</th>\n",
-       "      <td>Andorra</td>\n",
-       "      <td>POINT (1.51649 42.50000)</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>186</th>\n",
-       "      <td>Geneva</td>\n",
-       "      <td>POINT (6.14003 46.21001)</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>235</th>\n",
-       "      <td>Paris</td>\n",
-       "      <td>POINT (2.33139 48.86864)</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        name                  geometry\n",
-       "10    Monaco  POINT (7.40691 43.73965)\n",
-       "13   Andorra  POINT (1.51649 42.50000)\n",
-       "186   Geneva  POINT (6.14003 46.21001)\n",
-       "235    Paris  POINT (2.33139 48.86864)"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "id": "50f09e4a",
+   "metadata": {},
+   "outputs": [],
    "source": [
     "cities[cities.within(france)]"
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "e311e469",
    "metadata": {},
    "source": [
     "We could now repeat the above analysis for each of the countries, and add a column to the `cities` dataframe indicating this country. However, that would be tedious to do manually, and is also exactly what the spatial join operation provides us.\n",
@@ -1408,16 +833,13 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
+   "id": "0ca11f96",
+   "metadata": {},
    "source": [
     "## Spatial join operation\n",
     "\n",
     "<div class=\"alert alert-info\" style=\"font-size:120%\">\n",
-    "    \n",
+    "\n",
     "**SPATIAL JOIN** = *transferring attributes from one layer to another based on their spatial relationship* <br>\n",
     "\n",
     "\n",
@@ -1425,7 +847,7 @@
     "\n",
     "* The GeoDataFrame to which we want add information\n",
     "* The GeoDataFrame that contains the information we want to add\n",
-    "* The spatial relationship we want to use to match both datasets ('intersects', 'contains', 'within')\n",
+    "* The spatial relationship (\"predicate\") we want to use to match both datasets ('intersects', 'contains', 'within')\n",
     "* The type of join: left or inner join\n",
     "\n",
     "\n",
@@ -1436,11 +858,8 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "-"
-    }
-   },
+   "id": "6117d1c5",
+   "metadata": {},
    "source": [
     "In this case, we want to join the `cities` dataframe with the information of the `countries` dataframe, based on the spatial relationship between both datasets.\n",
     "\n",
@@ -1449,243 +868,47 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
+   "id": "9ed6570a",
    "metadata": {},
    "outputs": [],
    "source": [
-    "joined = geopandas.sjoin(cities, countries, op='within', how='left')"
+    "joined = geopandas.sjoin(cities, countries, predicate='within', how='left')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>name_left</th>\n",
-       "      <th>geometry</th>\n",
-       "      <th>index_right</th>\n",
-       "      <th>iso_a3</th>\n",
-       "      <th>name_right</th>\n",
-       "      <th>continent</th>\n",
-       "      <th>pop_est</th>\n",
-       "      <th>gdp_md_est</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Vatican City</td>\n",
-       "      <td>POINT (12.45339 41.90328)</td>\n",
-       "      <td>79.0</td>\n",
-       "      <td>ITA</td>\n",
-       "      <td>Italy</td>\n",
-       "      <td>Europe</td>\n",
-       "      <td>6.213780e+07</td>\n",
-       "      <td>2221000.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>San Marino</td>\n",
-       "      <td>POINT (12.44177 43.93610)</td>\n",
-       "      <td>79.0</td>\n",
-       "      <td>ITA</td>\n",
-       "      <td>Italy</td>\n",
-       "      <td>Europe</td>\n",
-       "      <td>6.213780e+07</td>\n",
-       "      <td>2221000.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Vaduz</td>\n",
-       "      <td>POINT (9.51667 47.13372)</td>\n",
-       "      <td>9.0</td>\n",
-       "      <td>AUT</td>\n",
-       "      <td>Austria</td>\n",
-       "      <td>Europe</td>\n",
-       "      <td>8.754413e+06</td>\n",
-       "      <td>416600.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Lobamba</td>\n",
-       "      <td>POINT (31.20000 -26.46667)</td>\n",
-       "      <td>152.0</td>\n",
-       "      <td>SWZ</td>\n",
-       "      <td>Swaziland</td>\n",
-       "      <td>Africa</td>\n",
-       "      <td>1.467152e+06</td>\n",
-       "      <td>11060.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Luxembourg</td>\n",
-       "      <td>POINT (6.13000 49.61166)</td>\n",
-       "      <td>97.0</td>\n",
-       "      <td>LUX</td>\n",
-       "      <td>Luxembourg</td>\n",
-       "      <td>Europe</td>\n",
-       "      <td>5.941300e+05</td>\n",
-       "      <td>58740.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>238</th>\n",
-       "      <td>Rio de Janeiro</td>\n",
-       "      <td>POINT (-43.22697 -22.92308)</td>\n",
-       "      <td>22.0</td>\n",
-       "      <td>BRA</td>\n",
-       "      <td>Brazil</td>\n",
-       "      <td>South America</td>\n",
-       "      <td>2.073534e+08</td>\n",
-       "      <td>3081000.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>239</th>\n",
-       "      <td>São Paulo</td>\n",
-       "      <td>POINT (-46.62697 -23.55673)</td>\n",
-       "      <td>22.0</td>\n",
-       "      <td>BRA</td>\n",
-       "      <td>Brazil</td>\n",
-       "      <td>South America</td>\n",
-       "      <td>2.073534e+08</td>\n",
-       "      <td>3081000.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>240</th>\n",
-       "      <td>Sydney</td>\n",
-       "      <td>POINT (151.18323 -33.91807)</td>\n",
-       "      <td>8.0</td>\n",
-       "      <td>AUS</td>\n",
-       "      <td>Australia</td>\n",
-       "      <td>Oceania</td>\n",
-       "      <td>2.323241e+07</td>\n",
-       "      <td>1189000.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>241</th>\n",
-       "      <td>Singapore</td>\n",
-       "      <td>POINT (103.85387 1.29498)</td>\n",
-       "      <td>111.0</td>\n",
-       "      <td>MYS</td>\n",
-       "      <td>Malaysia</td>\n",
-       "      <td>Asia</td>\n",
-       "      <td>3.138199e+07</td>\n",
-       "      <td>863000.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>242</th>\n",
-       "      <td>Hong Kong</td>\n",
-       "      <td>POINT (114.18306 22.30693)</td>\n",
-       "      <td>30.0</td>\n",
-       "      <td>CHN</td>\n",
-       "      <td>China</td>\n",
-       "      <td>Asia</td>\n",
-       "      <td>1.379303e+09</td>\n",
-       "      <td>21140000.0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>243 rows × 8 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "          name_left                     geometry  index_right iso_a3  \\\n",
-       "0      Vatican City    POINT (12.45339 41.90328)         79.0    ITA   \n",
-       "1        San Marino    POINT (12.44177 43.93610)         79.0    ITA   \n",
-       "2             Vaduz     POINT (9.51667 47.13372)          9.0    AUT   \n",
-       "3           Lobamba   POINT (31.20000 -26.46667)        152.0    SWZ   \n",
-       "4        Luxembourg     POINT (6.13000 49.61166)         97.0    LUX   \n",
-       "..              ...                          ...          ...    ...   \n",
-       "238  Rio de Janeiro  POINT (-43.22697 -22.92308)         22.0    BRA   \n",
-       "239       São Paulo  POINT (-46.62697 -23.55673)         22.0    BRA   \n",
-       "240          Sydney  POINT (151.18323 -33.91807)          8.0    AUS   \n",
-       "241       Singapore    POINT (103.85387 1.29498)        111.0    MYS   \n",
-       "242       Hong Kong   POINT (114.18306 22.30693)         30.0    CHN   \n",
-       "\n",
-       "     name_right      continent       pop_est  gdp_md_est  \n",
-       "0         Italy         Europe  6.213780e+07   2221000.0  \n",
-       "1         Italy         Europe  6.213780e+07   2221000.0  \n",
-       "2       Austria         Europe  8.754413e+06    416600.0  \n",
-       "3     Swaziland         Africa  1.467152e+06     11060.0  \n",
-       "4    Luxembourg         Europe  5.941300e+05     58740.0  \n",
-       "..          ...            ...           ...         ...  \n",
-       "238      Brazil  South America  2.073534e+08   3081000.0  \n",
-       "239      Brazil  South America  2.073534e+08   3081000.0  \n",
-       "240   Australia        Oceania  2.323241e+07   1189000.0  \n",
-       "241    Malaysia           Asia  3.138199e+07    863000.0  \n",
-       "242       China           Asia  1.379303e+09  21140000.0  \n",
-       "\n",
-       "[243 rows x 8 columns]"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "id": "b0684ca5",
+   "metadata": {},
+   "outputs": [],
    "source": [
     "joined"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Asia             59\n",
-       "Africa           57\n",
-       "Europe           46\n",
-       "North America    26\n",
-       "South America    14\n",
-       "Oceania           8\n",
-       "Name: continent, dtype: int64"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "id": "7a1f5964",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "joined[joined[\"name_right\"] == \"France\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2e574f9b",
+   "metadata": {},
+   "outputs": [],
    "source": [
     "joined['continent'].value_counts()"
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "a5aff0ca",
    "metadata": {},
    "source": [
     "## Let's practice!\n",
@@ -1695,7 +918,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
+   "id": "7756034f",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1705,6 +929,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "0b340745",
    "metadata": {},
    "source": [
     "<div class=\"alert alert-success\">\n",
@@ -1724,135 +949,42 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
+   "id": "fbdbc1c7",
    "metadata": {
-    "clear_cell": true
+    "tags": [
+     "nbtutor-solution"
+    ]
    },
    "outputs": [],
    "source": [
-    "joined = geopandas.sjoin(stations, districts[['district_name', 'geometry']], op='within')"
+    "joined = geopandas.sjoin(stations, districts[['district_name', 'geometry']], predicate='within')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": null,
+   "id": "8656ae41",
    "metadata": {
-    "clear_cell": true
+    "tags": [
+     "nbtutor-solution"
+    ]
    },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "      <th>district_name</th>\n",
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-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>14002 - RASPAIL QUINET</td>\n",
-       "      <td>44</td>\n",
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-       "      <td>52</td>\n",
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-       "    <tr>\n",
-       "      <th>143</th>\n",
-       "      <td>14112 - FAUBOURG SAINT JACQUES CASSINI</td>\n",
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-       "    <tr>\n",
-       "      <th>293</th>\n",
-       "      <td>14033 - DAGUERRE GASSENDI</td>\n",
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-       "      <td>POINT (650694.991 6859723.873)</td>\n",
-       "      <td>52</td>\n",
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-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>346</th>\n",
-       "      <td>14006 - SAINT JACQUES TOMBE ISSOIRE</td>\n",
-       "      <td>22</td>\n",
-       "      <td>0</td>\n",
-       "      <td>POINT (651327.035 6859441.637)</td>\n",
-       "      <td>52</td>\n",
-       "      <td>Montparnasse</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>429</th>\n",
-       "      <td>14111 - DENFERT-ROCHEREAU CASSINI</td>\n",
-       "      <td>24</td>\n",
-       "      <td>8</td>\n",
-       "      <td>POINT (651261.351 6859926.893)</td>\n",
-       "      <td>52</td>\n",
-       "      <td>Montparnasse</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                       name  bike_stands  available_bikes  \\\n",
-       "0                    14002 - RASPAIL QUINET           44                4   \n",
-       "143  14112 - FAUBOURG SAINT JACQUES CASSINI           16                0   \n",
-       "293               14033 - DAGUERRE GASSENDI           38                1   \n",
-       "346     14006 - SAINT JACQUES TOMBE ISSOIRE           22                0   \n",
-       "429       14111 - DENFERT-ROCHEREAU CASSINI           24                8   \n",
-       "\n",
-       "                           geometry  index_right district_name  \n",
-       "0    POINT (650791.111 6860114.328)           52  Montparnasse  \n",
-       "143  POINT (651406.382 6859738.689)           52  Montparnasse  \n",
-       "293  POINT (650694.991 6859723.873)           52  Montparnasse  \n",
-       "346  POINT (651327.035 6859441.637)           52  Montparnasse  \n",
-       "429  POINT (651261.351 6859926.893)           52  Montparnasse  "
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "joined.head()"
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "6e5a00cb",
    "metadata": {},
    "source": [
     "<div class=\"alert alert-success\">\n",
     "\n",
     "**EXERCISE: Map of tree density by district (I)**\n",
     "\n",
-    "Using a dataset of all trees in public spaces in Paris, the goal is to make a map of the tree density by district. For this, we first need to find out how many trees each district contains, which we will do in this exercise. In the following exercise, we will then use this result to calculate the density and create a map.\n",
+    "Using a dataset of all trees in public spaces in Paris, the goal is to make a map of the tree density by district. We first need to find out how many trees each district contains, which we will do in this exercise. In the following exercise, we will use this result to calculate the density and create a map.\n",
     "\n",
     "To obtain the tree count by district, we first need to know in which district each tree is located, which we can do with a spatial join. Then, using the result of the spatial join, we will calculate the number of trees located in each district using the pandas 'group-by' functionality.\n",
     "\n",
@@ -1872,9 +1004,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": null,
+   "id": "bcc8a5e9",
    "metadata": {
-    "clear_cell": true
+    "tags": [
+     "nbtutor-solution"
+    ]
    },
    "outputs": [],
    "source": [
@@ -1885,86 +1020,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": null,
+   "id": "1ac4e1a9",
    "metadata": {
-    "clear_cell": true
+    "tags": [
+     "nbtutor-solution"
+    ]
    },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
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-       "      <th>species</th>\n",
-       "      <th>location_type</th>\n",
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-       "    <tr>\n",
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-       "    <tr>\n",
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-       "    </tr>\n",
-       "    <tr>\n",
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-       "      <td>Marronnier</td>\n",
-       "      <td>Alignement</td>\n",
-       "      <td>POINT (451779.708 5409292.071)</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Sophora</td>\n",
-       "      <td>Alignement</td>\n",
-       "      <td>POINT (447041.361 5409756.712)</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "      species location_type                        geometry\n",
-       "0  Marronnier    Alignement  POINT (455834.122 5410780.606)\n",
-       "1  Marronnier    Alignement  POINT (446546.284 5412574.697)\n",
-       "2  Marronnier    Alignement  POINT (449768.283 5409876.557)\n",
-       "3  Marronnier    Alignement  POINT (451779.708 5409292.071)\n",
-       "4     Sophora    Alignement  POINT (447041.361 5409756.712)"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# The trees dataset with point locations of trees\n",
     "trees.head()"
@@ -1972,140 +1035,38 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": null,
+   "id": "5fb48650",
    "metadata": {
-    "clear_cell": true
+    "tags": [
+     "nbtutor-solution"
+    ]
    },
-   "outputs": [
-    {
-     "data": {
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-       "      <td>36476</td>\n",
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-       "      <td>43</td>\n",
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-       "      <td>Sainte-Marguerite</td>\n",
-       "      <td>36476</td>\n",
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-       "    <tr>\n",
-       "      <th>142</th>\n",
-       "      <td>Platane</td>\n",
-       "      <td>Alignement</td>\n",
-       "      <td>POINT (455704.681 5410991.067)</td>\n",
-       "      <td>43</td>\n",
-       "      <td>44</td>\n",
-       "      <td>Sainte-Marguerite</td>\n",
-       "      <td>36476</td>\n",
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-       "    <tr>\n",
-       "      <th>402</th>\n",
-       "      <td>Cedrele</td>\n",
-       "      <td>Alignement</td>\n",
-       "      <td>POINT (455538.223 5411112.314)</td>\n",
-       "      <td>43</td>\n",
-       "      <td>44</td>\n",
-       "      <td>Sainte-Marguerite</td>\n",
-       "      <td>36476</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>428</th>\n",
-       "      <td>Micocoulier</td>\n",
-       "      <td>Alignement</td>\n",
-       "      <td>POINT (455487.563 5411285.863)</td>\n",
-       "      <td>43</td>\n",
-       "      <td>44</td>\n",
-       "      <td>Sainte-Marguerite</td>\n",
-       "      <td>36476</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "         species location_type                        geometry  index_right  \\\n",
-       "0     Marronnier    Alignement  POINT (455834.122 5410780.606)           43   \n",
-       "130  Micocoulier    Alignement  POINT (455458.848 5411310.443)           43   \n",
-       "142      Platane    Alignement  POINT (455704.681 5410991.067)           43   \n",
-       "402      Cedrele    Alignement  POINT (455538.223 5411112.314)           43   \n",
-       "428  Micocoulier    Alignement  POINT (455487.563 5411285.863)           43   \n",
-       "\n",
-       "     id      district_name  population  \n",
-       "0    44  Sainte-Marguerite       36476  \n",
-       "130  44  Sainte-Marguerite       36476  \n",
-       "142  44  Sainte-Marguerite       36476  \n",
-       "402  44  Sainte-Marguerite       36476  \n",
-       "428  44  Sainte-Marguerite       36476  "
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Spatial join of the trees and districts datasets\n",
-    "joined = geopandas.sjoin(trees, districts, op='within')\n",
+    "joined = geopandas.sjoin(trees, districts, predicate='within')\n",
     "joined.head()"
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "288272ba",
    "metadata": {},
    "source": [
     "<div class=\"alert alert-success\">\n",
     "\n",
     "**EXERCISE: Map of tree density by district (II)**\n",
     "\n",
-    "- Calculate the number of trees located in each district: group the `joined` DataFrame by the `'district_name'` column, and calculate the size of each group. Call the resulting Series `trees_by_district`.  <br>\n",
-    "  We then convert `trees_by_district` to a DataFrame for the next exercise.\n",
+    "Calculate the number of trees located in each district: group the `joined` DataFrame by the `'district_name'` column, and calculate the size of each group. Call the resulting Series `trees_by_district`.  <br>\n",
+    "\n",
+    "We then convert `trees_by_district` to a DataFrame for the next exercise.\n",
     "\n",
     "<details><summary>Hints</summary>\n",
     "\n",
     "- The general group-by syntax in pandas is: `df.groupby('key').aggregation_method()`, substituting 'key' and 'aggregation_method' with the appropriate column name and method. \n",
     "- To know the size of groups, we can use the `.size()` method.\n",
     "\n",
-    "\n",
     "</details>\n",
     "\n",
     "</div>"
@@ -2113,9 +1074,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": null,
+   "id": "f3650cad",
    "metadata": {
-    "clear_cell": true
+    "tags": [
+     "nbtutor-solution"
+    ]
    },
    "outputs": [],
    "source": [
@@ -2125,10 +1089,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {
-    "clear_cell": true
-   },
+   "execution_count": null,
+   "id": "2170f77a",
+   "metadata": {},
    "outputs": [],
    "source": [
     "# Convert the series to a DataFrame and specify column name\n",
@@ -2137,79 +1100,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {
-    "clear_cell": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
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-       "\n",
-       "    .dataframe tbody tr th {\n",
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-       "\n",
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>n_trees</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>district_name</th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>Amérique</th>\n",
-       "      <td>183</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Archives</th>\n",
-       "      <td>8</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Arsenal</th>\n",
-       "      <td>60</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Arts-et-Metiers</th>\n",
-       "      <td>20</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Auteuil</th>\n",
-       "      <td>392</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                 n_trees\n",
-       "district_name           \n",
-       "Amérique             183\n",
-       "Archives               8\n",
-       "Arsenal               60\n",
-       "Arts-et-Metiers       20\n",
-       "Auteuil              392"
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "id": "7fcdcc21",
+   "metadata": {},
+   "outputs": [],
    "source": [
     "# Inspect the result\n",
     "trees_by_district.head()"
@@ -2217,6 +1111,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "63c3beea",
    "metadata": {},
    "source": [
     "<div class=\"alert alert-success\">\n",
@@ -2227,7 +1122,7 @@
     "\n",
     "For this, we first need to merge the number of trees in each district we calculated in the previous step (`trees_by_district`) back to the districts dataset. We will use the [`pd.merge()`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.merge.html) function to join two dataframes based on a common column.\n",
     "\n",
-    "Since not all districts have the same size, it is a fairer comparison to visualize the tree density: the number of trees relative to the area.\n",
+    "Since not all districts have the same size, we should compare the tree density for the visualisation: the number of trees relative to the area.\n",
     "\n",
     "- Use the `pd.merge()` function to merge `districts` and `trees_by_district` dataframes on the `'district_name'` column. Call the result `districts_trees`.\n",
     "- Add a column `'n_trees_per_area'` to the `districts_trees` dataframe, based on the `'n_trees'` column divided by the area.\n",
@@ -2251,105 +1146,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": null,
+   "id": "4feedaa2",
    "metadata": {
-    "clear_cell": true
+    "tags": [
+     "nbtutor-solution"
+    ]
    },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
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-       "\n",
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-       "      <th></th>\n",
-       "      <th>id</th>\n",
-       "      <th>district_name</th>\n",
-       "      <th>population</th>\n",
-       "      <th>geometry</th>\n",
-       "      <th>n_trees</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1</td>\n",
-       "      <td>St-Germain-l'Auxerrois</td>\n",
-       "      <td>1672</td>\n",
-       "      <td>POLYGON ((451922.133 5411438.484, 451922.080 5...</td>\n",
-       "      <td>40</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>2</td>\n",
-       "      <td>Halles</td>\n",
-       "      <td>8984</td>\n",
-       "      <td>POLYGON ((452278.419 5412160.893, 452192.407 5...</td>\n",
-       "      <td>40</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>3</td>\n",
-       "      <td>Palais-Royal</td>\n",
-       "      <td>3195</td>\n",
-       "      <td>POLYGON ((451553.806 5412340.522, 451528.058 5...</td>\n",
-       "      <td>4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>4</td>\n",
-       "      <td>Place-Vendôme</td>\n",
-       "      <td>3044</td>\n",
-       "      <td>POLYGON ((451004.908 5412654.095, 450960.640 5...</td>\n",
-       "      <td>7</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>5</td>\n",
-       "      <td>Gaillon</td>\n",
-       "      <td>1345</td>\n",
-       "      <td>POLYGON ((451328.752 5412991.278, 451294.721 5...</td>\n",
-       "      <td>7</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   id           district_name  population  \\\n",
-       "0   1  St-Germain-l'Auxerrois        1672   \n",
-       "1   2                  Halles        8984   \n",
-       "2   3            Palais-Royal        3195   \n",
-       "3   4           Place-Vendôme        3044   \n",
-       "4   5                 Gaillon        1345   \n",
-       "\n",
-       "                                            geometry  n_trees  \n",
-       "0  POLYGON ((451922.133 5411438.484, 451922.080 5...       40  \n",
-       "1  POLYGON ((452278.419 5412160.893, 452192.407 5...       40  \n",
-       "2  POLYGON ((451553.806 5412340.522, 451528.058 5...        4  \n",
-       "3  POLYGON ((451004.908 5412654.095, 450960.640 5...        7  \n",
-       "4  POLYGON ((451328.752 5412991.278, 451294.721 5...        7  "
-      ]
-     },
-     "execution_count": 22,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Merge the 'districts' and 'trees_by_district' dataframes\n",
     "districts_trees = pd.merge(districts, trees_by_district, on='district_name')\n",
@@ -2358,9 +1162,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": null,
+   "id": "7152250d",
    "metadata": {
-    "clear_cell": true
+    "tags": [
+     "nbtutor-solution"
+    ]
    },
    "outputs": [],
    "source": [
@@ -2370,24 +1177,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": null,
+   "id": "53d6038c",
    "metadata": {
-    "clear_cell": true
+    "tags": [
+     "nbtutor-solution"
+    ]
    },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 864x432 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Make of map of the districts colored by 'n_trees_per_area'\n",
     "ax = districts_trees.plot(column='n_trees_per_area', figsize=(12, 6))\n",
@@ -2397,14 +1194,18 @@
   {
    "cell_type": "code",
    "execution_count": null,
+   "id": "3a9d5170",
    "metadata": {},
    "outputs": [],
    "source": []
   }
  ],
  "metadata": {
+  "jupytext": {
+   "default_lexer": "ipython3"
+  },
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -2418,9 +1219,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.5"
+   "version": "3.12.11"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 4
+ "nbformat_minor": 5
 }
diff --git a/_solved/05-spatial-operations-overlays.ipynb b/_solved/05-spatial-operations-overlays.ipynb
index 1fc9754..294a564 100644
--- a/_solved/05-spatial-operations-overlays.ipynb
+++ b/_solved/05-spatial-operations-overlays.ipynb
@@ -2,16 +2,32 @@
  "cells": [
   {
    "cell_type": "markdown",
+   "id": "b24c03ef",
    "metadata": {},
    "source": [
-    "# Spatial operations and overlays: creating new geometries\n",
+    "<p><font size=\"6\"><b>Spatial operations and overlays: creating new geometries</b></font></p>\n",
     "\n",
+    "\n",
+    "> *DS Python for GIS and Geoscience*  \n",
+    "> *October, 2025*\n",
+    ">\n",
+    "> *© 2025, Joris Van den Bossche and Stijn Van Hoey  (<mailto:jorisvandenbossche@gmail.com>, <mailto:stijnvanhoey@gmail.com>). Licensed under [CC BY 4.0 Creative Commons](http://creativecommons.org/licenses/by/4.0/)*\n",
+    "\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2245b3f3",
+   "metadata": {},
+   "source": [
     "In the previous notebook we have seen how to identify and use the spatial relationships between geometries. In this notebook, we will see how to create new geometries based on those relationships."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
+   "id": "8fb8f8cd",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -22,18 +38,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
+   "id": "bcded20e",
    "metadata": {},
    "outputs": [],
    "source": [
-    "countries = geopandas.read_file(\"zip://./data/ne_110m_admin_0_countries.zip\")\n",
-    "cities = geopandas.read_file(\"zip://./data/ne_110m_populated_places.zip\")\n",
-    "rivers = geopandas.read_file(\"zip://./data/ne_50m_rivers_lake_centerlines.zip\")"
+    "countries = geopandas.read_file(\"data/ne_110m_admin_0_countries.zip\")\n",
+    "cities = geopandas.read_file(\"data/ne_110m_populated_places.zip\")\n",
+    "rivers = geopandas.read_file(\"data/ne_50m_rivers_lake_centerlines.zip\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
+   "id": "68f625b2",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -44,6 +62,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "b7a42c28",
    "metadata": {},
    "source": [
     "## Spatial operations\n",
@@ -78,6 +97,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "2ac691cd",
    "metadata": {},
    "source": [
     "For example, using the toy data from above, let's construct a buffer around Brussels (which returns a Polygon):"
@@ -85,38 +105,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<AxesSubplot:>"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": null,
+   "id": "73a6711e",
+   "metadata": {},
+   "outputs": [],
    "source": [
     "geopandas.GeoSeries([belgium, brussels.buffer(1)]).plot(alpha=0.5, cmap='tab10')"
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "fd7d7159",
    "metadata": {},
    "source": [
     "and now take the intersection, union or difference of those two polygons:"
@@ -124,75 +123,37 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
+   "id": "401744bc",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/svg+xml": [
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-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "brussels.buffer(1).intersection(belgium)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": null,
+   "id": "4519bf91",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
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-      "text/plain": [
-       "<shapely.geometry.polygon.Polygon at 0x7f952b59d790>"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "brussels.buffer(1).union(belgium)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
+   "id": "02cbfaf6",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/svg+xml": [
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-      ],
-      "text/plain": [
-       "<shapely.geometry.multipolygon.MultiPolygon at 0x7f952b48a400>"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "brussels.buffer(1).difference(belgium)"
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "109cc881",
    "metadata": {},
    "source": [
     "### Spatial operations with GeoPandas"
@@ -200,17 +161,18 @@
   },
   {
    "cell_type": "markdown",
+   "id": "bad10271",
    "metadata": {},
    "source": [
-    "Above we showed how to create a new geometry based on two individual shapely geometries. \n",
-    "The same operations can be extended to GeoPandas. Given a GeoDataFrame, we can calculate the intersection, union or difference of each of the geometries with another geometry.\n",
+    "Above we showed how to create a new geometry based on two individual shapely geometries. The same operations can be extended to GeoPandas. Given a GeoDataFrame, we can calculate the intersection, union or difference of each of the geometries with another geometry.\n",
     "\n",
-    "Let's look at an example with a subset of the countries. We have a GeoDataFrame with the country polygons of Africa, and  now consider a rectangular polygon, representing an area around the equator:\n"
+    "Let's look at an example with a subset of the countries. We have a GeoDataFrame with the country polygons of Africa, and  now consider a rectangular polygon, representing an area around the equator:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
+   "id": "1462dffb",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -219,42 +181,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": null,
+   "id": "5b596b4c",
    "metadata": {},
    "outputs": [],
    "source": [
-    "from shapely.geometry import LineString\n",
+    "from shapely import LineString\n",
     "box = LineString([(-10, 0), (50, 0)]).buffer(10, cap_style=3)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<AxesSubplot:>"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x432 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": null,
+   "id": "bf41ef29",
+   "metadata": {},
+   "outputs": [],
    "source": [
     "fig, ax = plt.subplots(figsize=(6, 6))\n",
     "africa.plot(ax=ax, facecolor='none', edgecolor='k')\n",
@@ -263,6 +204,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "246079f7",
    "metadata": {},
    "source": [
     "The intersection method of the GeoDataFrame will now calculate the intersection with the rectangle for each of the geometries of the africa GeoDataFrame element-wise. Note that for many of the countries, those that do not overlap with the rectangle, this will be an empty geometry:"
@@ -270,25 +212,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
+   "id": "8ff545ab",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "1     MULTIPOLYGON (((13.22332 -10.00000, 13.12099 -...\n",
-       "11    POLYGON ((29.34000 -4.49998, 29.27638 -3.29391...\n",
-       "13    POLYGON ((2.69170 6.25882, 1.86524 6.14216, 1....\n",
-       "14    POLYGON ((-2.89227 10.00000, -2.82750 9.64246,...\n",
-       "25                                        POLYGON EMPTY\n",
-       "dtype: geometry"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "africa_intersection = africa.intersection(box)\n",
     "africa_intersection.head()"
@@ -296,6 +223,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "92761ae8",
    "metadata": {},
    "source": [
     "What is returned is a new GeoSeries of the same length as the original dataframe, containing one row per country, but now containing only the intersection. In this example, the last element shown is an empty polygon, as that country was not overlapping with the box."
@@ -303,53 +231,31 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<AxesSubplot:>"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# remove the empty polygons before plotting\n",
-    "africa_intersection = africa_intersection[~africa_intersection.is_empty]\n",
+   "execution_count": null,
+   "id": "0562f5e9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
     "# plot the intersection\n",
     "africa_intersection.plot()"
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "2a797b7d",
    "metadata": {},
    "source": [
-    "# Unary union and dissolve\n",
+    "## Unary union and dissolve\n",
     "\n",
-    "Another useful method is the `unary_union` attribute, which converts the set of geometry objects in a GeoDataFrame into a single geometry object by taking the union of all those geometries.\n",
+    "Another useful method is `union_all()` (in older GeoPandas versions this is available as the `unary_union` attribute), which converts the set of geometry objects in a GeoDataFrame into a single geometry object by taking the union of all those geometries.\n",
     "\n",
     "For example, we can construct a single Shapely geometry object for the Africa continent:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
+   "id": "27802095",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -358,55 +264,37 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
+   "id": "64bf79e6",
    "metadata": {},
    "outputs": [],
    "source": [
-    "africa = africa_countries.unary_union"
+    "africa = africa_countries.union_all()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": null,
+   "id": "269f065f",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/svg+xml": [
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-      ],
-      "text/plain": [
-       "<shapely.geometry.multipolygon.MultiPolygon at 0x7f9521391550>"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "africa"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": null,
+   "id": "f62cf729",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "MULTIPOLYGON (((32.83012047702888 -26.7421916643362, 32.58026492689768 -27.47015756603182, 32.46213260267845 -28.30101124442056, 32.20338870619304 -28.75240488049007, 31.52100141777888 -29.25738697684626, 31.325561150851 -29.40197763439891, 30.90176272962535 -29.90995696382804, 30.62281334811382 -30.42377573010613, 30.05571618014278 -31.14026946383296, 28.92555260591954 -32.1720411109725, 28.2197558936771 -32.77195281344886, 27.46460818859597 -33.2269637997788, 26.41945234549283 -33.61495045342619, 25.90966434093349 -33.6670402971764, 25.7806282895007 -33.94464609144834, 25.17286176931597 -33.79685149509358, 24.67785322439212 -33.98717579522455, 23.59404340993464 -33.79447437920815, 22.98818891774474 -33.91643075941698, 22.57415734222224 -33.86408253350531, 21.54279910654103 -34.25883879978294, 20.689052768647 -34.41717538832523, 20.07126102059763 -34.79513681410799, 19.61640506356457 -34.81916635512371, 19.19327843595872 -34.46259897230979, 18.85531456876987 -34.44430551527847, 18.424\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "print(str(africa)[:1000])"
+    "print(str(africa)[:100])"
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "9cf02954",
    "metadata": {},
    "source": [
     "Alternatively, you might want to take the unary union of a set of geometries but *grouped* by one of the attributes of the GeoDataFrame (so basically doing \"groupby\" + \"unary_union\"). For this operation, GeoPandas provides the `dissolve()` method:"
@@ -414,7 +302,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": null,
+   "id": "13c9fde0",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -423,167 +312,34 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>geometry</th>\n",
-       "      <th>iso_a3</th>\n",
-       "      <th>name</th>\n",
-       "      <th>pop_est</th>\n",
-       "      <th>gdp_md_est</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>continent</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>Africa</th>\n",
-       "      <td>MULTIPOLYGON (((32.830 -26.742, 32.580 -27.470...</td>\n",
-       "      <td>AGO</td>\n",
-       "      <td>Angola</td>\n",
-       "      <td>29310273.0</td>\n",
-       "      <td>189000.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Antarctica</th>\n",
-       "      <td>MULTIPOLYGON (((-159.208 -79.497, -161.128 -79...</td>\n",
-       "      <td>ATA</td>\n",
-       "      <td>Antarctica</td>\n",
-       "      <td>4050.0</td>\n",
-       "      <td>810.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Asia</th>\n",
-       "      <td>MULTIPOLYGON (((120.716 -10.240, 120.295 -10.2...</td>\n",
-       "      <td>AFG</td>\n",
-       "      <td>Afghanistan</td>\n",
-       "      <td>34124811.0</td>\n",
-       "      <td>64080.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Europe</th>\n",
-       "      <td>MULTIPOLYGON (((-51.658 4.156, -52.249 3.241, ...</td>\n",
-       "      <td>ALB</td>\n",
-       "      <td>Albania</td>\n",
-       "      <td>3047987.0</td>\n",
-       "      <td>33900.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>North America</th>\n",
-       "      <td>MULTIPOLYGON (((-61.680 10.760, -61.105 10.890...</td>\n",
-       "      <td>BHS</td>\n",
-       "      <td>Bahamas</td>\n",
-       "      <td>329988.0</td>\n",
-       "      <td>9066.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Oceania</th>\n",
-       "      <td>MULTIPOLYGON (((173.020 -40.919, 173.247 -41.3...</td>\n",
-       "      <td>AUS</td>\n",
-       "      <td>Australia</td>\n",
-       "      <td>23232413.0</td>\n",
-       "      <td>1189000.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Seven seas (open ocean)</th>\n",
-       "      <td>POLYGON ((68.935 -48.625, 69.580 -48.940, 70.5...</td>\n",
-       "      <td>ATF</td>\n",
-       "      <td>Fr. S. Antarctic Lands</td>\n",
-       "      <td>140.0</td>\n",
-       "      <td>16.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>South America</th>\n",
-       "      <td>MULTIPOLYGON (((-66.960 -54.897, -67.291 -55.3...</td>\n",
-       "      <td>ARG</td>\n",
-       "      <td>Argentina</td>\n",
-       "      <td>44293293.0</td>\n",
-       "      <td>879400.0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                                                  geometry  \\\n",
-       "continent                                                                    \n",
-       "Africa                   MULTIPOLYGON (((32.830 -26.742, 32.580 -27.470...   \n",
-       "Antarctica               MULTIPOLYGON (((-159.208 -79.497, -161.128 -79...   \n",
-       "Asia                     MULTIPOLYGON (((120.716 -10.240, 120.295 -10.2...   \n",
-       "Europe                   MULTIPOLYGON (((-51.658 4.156, -52.249 3.241, ...   \n",
-       "North America            MULTIPOLYGON (((-61.680 10.760, -61.105 10.890...   \n",
-       "Oceania                  MULTIPOLYGON (((173.020 -40.919, 173.247 -41.3...   \n",
-       "Seven seas (open ocean)  POLYGON ((68.935 -48.625, 69.580 -48.940, 70.5...   \n",
-       "South America            MULTIPOLYGON (((-66.960 -54.897, -67.291 -55.3...   \n",
-       "\n",
-       "                        iso_a3                    name     pop_est  gdp_md_est  \n",
-       "continent                                                                       \n",
-       "Africa                     AGO                  Angola  29310273.0    189000.0  \n",
-       "Antarctica                 ATA              Antarctica      4050.0       810.0  \n",
-       "Asia                       AFG             Afghanistan  34124811.0     64080.0  \n",
-       "Europe                     ALB                 Albania   3047987.0     33900.0  \n",
-       "North America              BHS                 Bahamas    329988.0      9066.0  \n",
-       "Oceania                    AUS               Australia  23232413.0   1189000.0  \n",
-       "Seven seas (open ocean)    ATF  Fr. S. Antarctic Lands       140.0        16.0  \n",
-       "South America              ARG               Argentina  44293293.0    879400.0  "
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "id": "03a499d5",
+   "metadata": {},
+   "outputs": [],
    "source": [
     "continents"
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "76a174f0",
    "metadata": {},
    "source": [
     "<div class=\"alert alert-info\" style=\"font-size:120%\">\n",
     "\n",
     "**REMEMBER**:\n",
     "\n",
-    "GeoPandas (and Shapely for the individual objects) provides a whole lot of basic methods to analyse the geospatial data (distance, length, centroid, boundary, convex_hull, simplify, transform, ....), much more than the few that we can touch in this tutorial.\n",
+    "GeoPandas (and Shapely for the individual objects) provide a whole lot of basic methods to analyze the geospatial data (distance, length, centroid, boundary, convex_hull, simplify, transform, ....), much more than what we can touch in this tutorial.\n",
     "\n",
+    "An overview of all methods provided by GeoPandas can be found here: https://geopandas.org/en/latest/docs/reference.html\n",
     "\n",
-    "* An overview of all methods provided by GeoPandas can be found here: https://geopandas.readthedocs.io/en/latest/docs/reference.html\n",
     "\n",
-    "\n",
-    "</div>\n",
-    "\n"
+    "</div>"
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "e1b4fcd4",
    "metadata": {},
    "source": [
     "## Let's practice!"
@@ -591,27 +347,31 @@
   },
   {
    "cell_type": "markdown",
+   "id": "addc8e3b",
    "metadata": {},
    "source": [
     "<div class=\"alert alert-success\">\n",
     "\n",
     "**EXERCISE: What are the districts close to the Seine?**\n",
     "\n",
-    "Below, the coordinates for the Seine river in the neighbourhood of Paris are provided as a GeoJSON-like feature dictionary (created at http://geojson.io). \n",
+    "Below, the coordinates for the Seine river in the neighborhood of Paris are provided as a GeoJSON-like feature dictionary (created at http://geojson.io). \n",
     "\n",
     "Based on this `seine` object, we want to know which districts are located close (maximum 150 m) to the Seine. \n",
     "\n",
-    "* Create a buffer of 150 m around the Seine.</li>\n",
-    "* Check which districts intersect with this buffered object.</li>\n",
-    "* Make a visualization of the districts indicating which districts are located close to the Seine.</li>\n",
+    "* Create a buffer of 150 m around the Seine.\n",
+    "* Check which districts intersect with this buffered object.\n",
+    "* Make a visualization of the districts indicating which districts are located close to the Seine.\n",
     " \n",
     "</div>"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
+   "execution_count": null,
+   "id": "e4e8a1ff",
+   "metadata": {
+    "clear_cell": false
+   },
    "outputs": [],
    "source": [
     "districts = geopandas.read_file(\"data/paris_districts.geojson\").to_crs(epsg=2154)"
@@ -619,8 +379,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
+   "execution_count": null,
+   "id": "90b165c2",
+   "metadata": {
+    "clear_cell": false
+   },
    "outputs": [],
    "source": [
     "# created a line with http://geojson.io\n",
@@ -630,7 +393,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": null,
+   "id": "8ab944e5",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -640,32 +404,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<AxesSubplot:>"
-      ]
-     },
-     "execution_count": 22,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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jo+NQoxeVSoXGxkZcu3YN3d3d8PT0RHBwMHx8fGBvb89xtISQdxUlfYS8w8rlSvz2cDayahRYEOWG3y+NhIO1BddhEQNqa2vDkSNHYG1tDXt7e4jFYgiFQpiZmcHMzAxCoRBCoVAvyVZJSQmcnZ3h5eWl82Prw2CVUV8EAgHEYjEUCgVcXHR34cXc3Jw31TWhUGhySd+LxGIx/P394e/vD41Gg5aWFmRmZuLGjRuQyWRYsmQJrQEkhBgcJX2EvIO0Wha77j3BHy4VQWRuhv/6IBaLYty5DosYWH19PY4dOwZvb294enoafPzKykp4eHgYfNzRGlyjp09isRgdHR06TfqEQiF6e3t1dryxMDMzg1ar5ToMgxEKhXB1dYWrqysAoKysDD/++CPef/99vVWMCSHkVbibJ0MI4URNmwprv3+A/3W2ABMDnHD511Mo4XsHlZeX4/DhwwgKCuIk4QOeJp1cjT1a+p5eamtri46ODp0e0xSnVBqrwMBAeHp6Yv/+/aiqquI6HELIO4QqfYS8I1iWxaG0Gvz+bAEYhsEfVkbj/fGe1GXuHZSTk4Nr164hOjoadnZ2nMSg1Wohl8vh6+vLyfijYYhKnz6SPgsLC94kfYMNe95l7u7uEIlEOH78OGbPno3IyEiuQyKEvAMo6SPkHdDUqcbvjuXgRrEcEwMc8YeV0fC0F3MdFjGwvr4+PHr0COnp6YiPj4dYzN17oKmpCVZWVpBIJJzFMFKDzWz0yc7ODk1NTTo9Jp/W0ZlK986xcnBwQFxcHK5evYqOjg5MnDiRLsARQvSKkj5CTBjLsjidXY//cSofvZoB/PPiCKxL8oFAQCcX7xKlUons7GxkZGTAysoKCQkJnLeRr6ioMKr1fAAwMDCg9xNzR0dHlJaW6vSYfOreSUnff7OxsUFiYiIeP36Mjo4OvPfeexAK6bSMEKIf9OlCiIlqVfbiH07m4UJeI+K87fCfq8bBz8ma67CIAbW0tCAjIwMFBQWwt7dHbGwsp9W9Z1VXV8Pf35/rMEZM30mfvb09urq6dHpMc3NzaDQanR5ztAQCAViW5ToM3rCwsMCECROQnZ2NY8eOYfHixbCysuI6LEKICaKkjxATdCm/EX9/PBddag3+dl4otkz2hxlV994JLMuipqYGaWlpqK6uhpubG5KTk2Fubs51aM9pamrCtGnTuA5jRAQCgd47T0qlUqhUKp0e09zcnDfTOwFQpe8FZmZmiIuLQ1FREQ4cOIDly5dzttaWEGK6KOkjxIR0qPrxz2fycTyzDhHuEuzfMg4hrrZch0UMYGBgACUlJXj48CGUSiU8PT2RkpLC6Wbmr9PR0YH+/n6dbktgCIaoUolEIgwMDOh0g3YLCwv09PTo5FhjRZW+1wsNDcWTJ0+wb98+rFixYmibB0II0QVK+ggxETdL5Pjd0RzIlb341cwg/MWMQJib8e+En+iWWq1GXl4eHj16BDMzM/j4+EAmk3Ed1huVlpbCw8ODlwnpmxhqPZqFhYVOkz6+Vfoo6Xs9X19fWFlZ4eDBg1iwYAGCgoK4DokQYiIo6SPEyCl7NfjX84XY/7AaQc42+G59PKI8pVyHRfSso6MDmZmZyMzMhFQqRVhYmNF0wqyurja6Ji6AYaZ3Av9dmdPV95PW9BkXFxcXiEQinD17FlOnTkVcXBzXIRFCTAAlfYQYsYcVrfiro9mobe/Bz6b44y9nB0Nkrt+W8oRbDQ0NSE9PR2lpKZydnZGQkACRSMR1WCPS0NCA2NhYrsMYMYZhDFLpE4lEOl3XZ2Fhwaukj9b0vZ1UKkVCQgLu3LmDjo4OTJ061egq44QQfqGkjxAjpO4fwL9fKsbOu5XwdhDjyM+SEe/rwHVYRE+0Wi0qKyvx8OFDyOVyuLu7Y9KkSUbZ3l2lUg2tOTQ2DMMYpNJnaWmp86SPL9M7qdI3fCKRCImJicjMzERHRwfmz58PCwsLrsMihBgp4ztjIOQdl1WjwG8OZ6FC3o31yT7423mhEFvQj7IpYlkWRUVFuH37Nvr7++Ht7Y3g4GCj3sS5srISLi4uvOsm+iKNRoPDhw9DqVRCq9VCq9WipaUFM2fO1PvYuk76zM3NebVPH1+qjsZAKBRi/PjxyM7ORmpqKubMmcN1SIQQI0VnioQYiT6NFl9eK8U3N8vhYmuJvZsSkRLkxHVYRE9qa2uRmpqKjo4OhISEwMHBNCq5FRUVRlHly8nJQWdnJ2bNmgWhUAgzMzMIhUI4Oen/Z04sFkOpVOrseCKRiDeVPj6tLzQWAoEAkZGRuH//PqZOnWp007kJIfxASR8hRqCwoRO/OZyNwoZOvD/eE/+4KBwSEb8rJWR02tvbcevWLVRWVsLf3x/h4eFch6RTDQ0NRrE/X3p6OiZOnIjAwECDj21nZ4f29nadHY9P0zuFQiFvYjEm5ubmkEgkKC4uRkxMDNfhEEKMECV9hPCYZkCLbbcq8MXVEkitLPD9+njMCjeuvc3I8PT09ODhw4fIzMwcWrNnao0bNBoN2tra4OXlxXUob1RTU4Oenh5ERERwMr6LiwvKysp0djxLS0veJFpCoZA3U02NjaenJzIyMijpI4SMCiV9hPBUWbMSvz2SjewaBRZGu+H3SyJhb02L+E3NwMAAsrOzcffuXUgkEiQlJZlss4aqqirY2dnBysqK61De6N69e4iJiYGZGTedcF1dXdHW1qaz4/Gpe6eZmZlBmuGYIicnJ5SUlKChoQFubm5ch0MIMTKU9BHCM1otix/uPcEfLhbBysIM//VBLBbFuHMdFtGDsrIyfPvtt9BoNFi2bBlsbGy4Dkmvnjx5wvv9+To7O1FVVYX58+dzFoNUKkV/f7/ONmgXiUTQarUYGBjgLJEdZGZmxpsE1Bg5OzsjJyeHkj5CyIiZ1twhQoxcTZsKH3z3AL8/W4CUQCdc/ssplPCZoKamJhw4cADnz5/HuHHjIJfLkZ6eznVYeldXV8f7Ji73799HUFAQbG1tOY1DJBKhq6tLZ8cTCAS8mFZpbm5Olb4x8PLyQkFBAXp7e7kOhRBiZKjSRwgPsCyLg2k1+JezBRAwDP59ZTRWjvc06tb85GWdnZ24c+cOSkpK4OPjg4kTJ4JhGLi5uWHfvn3o7OzEggULOK/G6EtzczN8fHy4DuO1NBoN8vLysHr1aq5DgZWVFbq6unTWLdTc3Bz9/f2cT621srKCXC5Hf38/77ft4CNLS0vY2NigpKQEUVFRXIdDCDEiVOkjhGONHWps/CENf3c8F+O87XDxL6fg/XgvSvhMSH9/P+7evYsffvgBCoUCycnJ8Pb2Hvoe29nZYdOmTWhubsahQ4dM8iq+XC6HUCjk9dYTmZmZcHR05EU1UiwW67TSJxQKefG+ioqKgkgkwsWLF7kOxWh5e3vj8ePHXIdBCDEylPQRwhGWZXEysw7v/fEmHlW24fdLIvDjJ4nwsON3kwsyMs3Nzfjxxx9RWFiI+Ph4hISEQCh8eZKFSCTCxo0bIRAIsGfPHp2e8PNBeXk53N35PVU5PT0dEyZM4DoMAE+TPl1u0M6XpA8AVq1ahYqKCjx69IjrUIySk5MTFAoFGhsbuQ6FEGJEKOkjhAMtyl58tvcxfn0oC8Eutrjwq8lYl+wLgYCqe6ZCq9UiPT0d+/fvh5OTE2JjY9+6qbJQKMSaNWvg4eGBXbt2oampyUDR6l9tbS2vm7iUl5dDo9EgLCyM61AAANbW1jrdoN3MzIw32zaIxWKsWrUKt27dQkNDA9fhGCUXFxfk5uZyHQYhxIhQ0keIgV3Ma8ScP97C9aJm/P38UBz6WTJ8nay5DovoUGdnJ44ePYpHjx5hwoQJI0525s+fj9jYWOzbtw+VlZV6itKwGhoa4O3tzXUYr3X//n3ExsbyZm9EiUSi06TP3NycN5U+AHBzc0N4eDhu3rzJdShGydvbG/n5+bxozkMIMQ78+O1GyDugQ9WPvzyUhU/3ZsDNToSzv0zB1ikBMKPqnkkpLi7Grl27wLIsEhIS3lrde52UlBTMmTMHx44dM/or+iqVCj09Pbyd3tnS0oLGxkaMHz+e61D0xtzcnHcJwqxZs9DW1oYbN25wHYrRsbS0hLW1NUpKSrgOhRBiJKh7JyEGcKO4Gb87loNWZR9+PSsIv5geCHMzuuZiSnp7e5Gamori4mJERkbCzs5uzMeMiIiAjY0Njh07ho6ODqSkpIw9UA6Ul5fD1dWVt11J7969i4iICM47Wz6rp6dHJ3v0DbKwsOBd0mdhYQE7Ozvk5+dj2rRpXIdjdDw9PZGZmYnIyEiuQyGEGAE66yREj5S9Gvzd8Vxs/CENUitznPj5JPx6VjAlfCamvr4eu3fvRmNjI5KSknSS8A3y8fHB+vXr8fjxY5w7d84o9zh78uQJb6t8arUaxcXFSExM5DqU56jVap0mfXyr9Gk0Ghw6dAgqlQobNmzgOhyjJJPJ0NbWhubmZq5DIYQYATrzJERPHlS0Yu4Xt3AwrRo/m+qP03+RgihPKddhER3SarW4d+8eDh06BA8PD0RFRb2yM+dYOTk5YfPmzairq8Phw4d5tTZrOPi8nq+wsBDu7u6820qir69v1FODX4VPSZ9arcaPP/6I/v5+rFu3DjY2NlyHZJQYhoGzszPy8vK4DoUQYgQo6SNEx9T9A/hfZwqwZvsDCAUMjn6ajL+bFwaROT+ntpHRUSgUOHDgAHJycpCYmAhXV1e9jicWi7F69WoUFRXh2rVreh1LlzQaDdrb2+Hl5cV1KK9UU1PDyyqkWq3WadInFovR09Ojs+ONlkajwQ8//ACJRIIPPvhAp9XMd5G3tzdyc3N5k9ATQviL1vQRokOZ1e347ZFsVMi7sSHZB7+bFwqxBf2YmZr8/HxcvXoVHh4eCA0N1ft4gxXFBw8eIDY2FlOmTNH7mLpSVVUFOzs7Xq2Xe1ZzczOCg4O5DuMl/f39Ok2IJBIJqqurdXa80bpy5QrKysrg6emJ5uZmuLm5cR2SUROJRLCyskJlZSVCQkK4DocQwmN0NkqIDvRptPjTtRJ8c6McblIr7NuciEmBTlyHRXSsp6cH165dQ0VFBWJiYmBra6v3McvLy3H58mVYWFhg9erVvK2YvU5lZSUvK2nA02S6ra0Nnp6eXIfyEqVSCYlEorPj6XoLiNFQq9XIz8/HJ598gqqqKuzbtw+Ojo6Ii4tDZGQkbxv98J1UKkVjYyMlfYSQN6Kkj5AxKqjvxG8OZ6GosQur4j3xDwvDIRGZcx0W0bGqqiqcP38eNjY2SE5O1vt+bh0dHTh//jyampowefJkXu0hNxL19fWIjo7mOoxXampqgpWVFe/WlGm1WnR1dcHJSXcXjqRSKbq7u3V2vNG4ceMGfH19ERERgYiICLz33nvIysrCo0ePkJqaisjISIwfPx729vacxmlsbG1tqZkLIeStKOkjZJQ0A1p8e7Mcf7pWCjuxBXZsiMfMMBeuwyI6ptFocOfOHWRlZSEsLEynJ+Kvk5OTg1OnTmHChAlYunQpb6dGDkdzczN8fHy4DuOVnjx5wsvphW1tbbC0tNTp910qlXJe6SstLcXChQuH/i0UChEfH4/4+HjU1dXh4cOH+O677+Dt7Y2wsDDY29vDzs4ONjY2RnnBw1AkEgkqKyu5DoMQwnOU9BEyCmXNSvz2SDayaxRYFOOO/7U4AvbWFlyHRXSsubkZ586dw8DAAJKSkmBubpgKrlQqBcMwEIvFRp3wNTU1wdzcnLeVm/r6er034BmN5uZmnf+ficVisCyL3t5eTpqnyOVy9PX1vfYCgIeHB5YvXw61Wo1Hjx4hOzsbSqUS3d3d6O/vh62tLWxtbSGRSGBjYwOpVDr0x8XF5Z1OCq2srNDb26vz5j+EENNCSR8hI6DVsvjh3hP84WIRxBZm+PPaWCyM5ud6JTJ6Wq0Wjx8/xp07d+Dn52fwNV8+Pj74+c9/joMHD6KxsRFLliwxyi6HFRUV8PDw4DqM15LL5YiLi+M6jJe0tLToJVG2srJCZ2cnZDKZzo/9Nrm5uQgMDHxrciYSiTBlypTnmhWp1WooFAooFAq0t7dDoVDgyZMn6O7uRmdnJxwcHLBs2TKDrLHlK5FIBIVCwcuLGIQQfqCkj5BhqmlT4a+OZONhZRtmhbngX5dHwtmWrqqamubmZly7dg0KhQLx8fGcVdocHBywefNmHD16FLt27cL777/Pu73k3qa6upq3Uzv7+vqgUCh42WSmrq4Ofn5+Oj+ulZUVurq6OEn6ysvLMXHixFE9VyQSwdXV9ZUJjVarxYULF7Bz504sWbIEvr6+Y4zUOIlEIrS3t1PSRwh5rXd3PgQhw8SyLPY/rMacL26hoL4T//F+DL5bP54SPhNUWFiIf/mXf8HDhw8xYcIEzqdWWlhYYO3atQgODsauXbtQVlbGaTwj1dTUxMvOmMDTxMre3p53FVS1Wo2amhqEh4fr/NiDlT5DU6lUaG9vR2BgoM6PLRAIsGDBAkyfPh1HjhzBgwcPdD6GMRCLxWhpaeE6DEIIjw0r6WMYxo5hmKMMwxQxDFPIMEzyC/dLGYY5wzBMNsMw+QzDfDyc5zIM8znDMMU/PecPuntZhOhGY4caG35Iw9+fyEWctz0u/eUUrBzvCYZhuA6N6JBarcbZs2dx5coVLFiwAF1dXbhx4wbXYQ2ZPn065s+fj5MnT+LevXtchzMsSqUSarWal41SgKfdWPlYFcnLy4Obm5tOt2sYZG1tzUkzl/z8fHh6euo1wY6Ojsb69euRnp6Oo0ePore3V29j8ZGtrS2ampq4DoMQwmPDrfT9CcBFlmVDAcQAKHzh/l8AKGBZNgbANAD/yTCMxZueyzDMdABLAESzLBsB4D/G8kII0bWq1m4s+PI20irb8PulkfhxUwLc7Yy3qQZ5terqavzwww9oa2tDUlISnJycsHbtWmRmZiIrK4vr8IaEhoZiw4YNyMjIwOnTpzEwMMB1SG9UUVEBV1dX3u691tjYyMupnfn5+YiIiNDLsa2traFSqfRy7DcpKytDQECA3sdxcXHB5s2b0dfXh127dr1TlS9bW9t36vUSQkburUkfwzASAFMA7AAAlmX7WJZVvPAwFoAt87T8YQOgDYDmLc/9DMC/sSzb+9N9tMkM4Y2Onn58sisNAyyLM59PwrokH6rumRiNRoMbN27g2LFj8Pf3R0RExFCCYmdnh9WrV+P8+fOorq7mONL/JpPJsGXLFrS2tuLHH39EV1cX1yG91pMnT3iZVA1qbm7mXXxKpRJNTU0ICwvTy/G52KBdq9WitrYWQUFBBhlPJBJh7dq1CA0Nxe7du1FUVGSQcbkmFouhVCrR19fHdSiEEJ4aTqXPH4AcwA8Mw2QyDPM9wzDWLzzmzwDCANQDyAXwK5ZltW95bjCAyQzDPGQY5ibDMBNeNTjDMFsZhklnGCZdLpeP4iUSMjL9A1r8xf7HqG5T4duPxiPQ+d3tCGeq2trasHfvXpSVlSE5OfmVe+95eHjAz88P169f5yDC1xOJRNiwYQNkMhl++OEHXiWlz2psbIS3tzfXYbySSqVCT08P76Z3ZmZmwtfXV29rSblI+qqqqmBra2vwJkRTp07FkiVLcO7cOV5N1dYXhmGGOngSQsirDCfpEwKIA/ANy7KxALoB/O0Lj5kDIAuAO4BxAP78U5XvTc8VArAHkATgrwEcZl5RSmFZdjvLsvEsy8Zz0XGMvFtYlsX/PJ2P26Ut+N/LopDk78h1SETHSktL8eOPP0IqlSI2NvaNe+8tW7YM3d3duHPnjgEjfLvB5hWTJ0/GoUOHeNe8QqPRoLW1FV5eXlyH8kqVlZVwdnbm3dTTwsJCvU3tBJ5eMDD0WrfCwkLOOrgGBgbi448/RkFBAQ4fPmzy6/ysrKzQ3t7OdRiEEJ4aTtJXC6CWZdmHP/37KJ4mcs/6GMBx9qkyAJUAQt/y3NpnnvMIgBbAy5fbCTGgXfeeYN/Danw6NQCr4vl5wkpGR6vV4tatWzh79iwiIyOHVYUSiURYvXo1Hjx4gJKSEgNEOTKxsbG8bF5RVVUFBwcHzrufvk51dTXvqnwtLS3o6upCaGio3sYQCoXQarV6O/6rVFdXG2xq56sMbn2i0Wiwe/duk06KrKysaF0fIeS13pr0sSzbCKCGYZiQn26aCaDghYdV/3Q7GIZxARACoOItzz0JYMZPzwkGYAGAPq0IZ1KLmvH7swWYE+GCv5kT8vYnEKOhUqlw9OhR5OfnIzk5GXZ2dsN+rpOTExYtWoSzZ8/y8oRqsHmFRqPBjh070NDQwHVIvF/P19jYCJFIhKysLKSmpvJiSlxWVhYCAgL0Wn0UCAQGTfo6OjrQ1dWllz0HR2Jw65OAgADs3r0bFRUVnMajLxKJBM3N1B6BEPJqw+3e+TmAfQzD5ODp9M1/ZRjmU4ZhPv3p/t8DmMgwTC6AawB+x7Jsy+ue+9PtOwH4MwyTB+AggA0sy7JjfUGEjEZRYyc+P5CJcHcJ/rh6HAQCatpiKhoaGrB792709/djwoQJb5zO+TohISGIj4/H4cOHOWl5/zYikQhr1qxBbGws9u3bh0ePHnEaT11dHW+ndgLAwMAA8vLykJubi4cPH6KtrY3rkNDc3Kz3NZCGrvQNTu3kyzTamTNnYvbs2Thx4gTvpkTrgq2tLSV9hJDXEg7nQSzLZgGIf+Hmb5+5vx7AeyN4LliW7QPw0TDjJERv5F292LQrHdaWZvh+/QSILYb1Y0GMQFZWFm7cuIHg4GC4uLiM6VhTpkyBQqHA0aNH8eGHH44qedS35ORk+Pr64tixY3jy5AkWLVrEyRRLQyQwY7F582YAT9ce/sd//Ac8PDw4jghob2+HvtetGzrpKysrQ0gIv2ZNREREwMnJCYcOHYJcLsf8+fN5k5SOlVgsRldXF7RaLQSC4V7TJ4S8K+hTgbzT1P0D2PpjOtq6+7BjwwS4SkVch0R0oL+/HxcuXMCtW7cwfvz4MSd8gxYuXAihUIgTJ04YfG3UcLm5uWHr1q0QCAT4/vvvDd7ds6mpCUKh0ODdGkejrq4OUqlUr5uGD4dWq0VnZyecnZ31Oo6ZmZnB9nfUaDSor6/nXdIH/PeU6JaWFt5vfTISAoEA5ubm6O7u5joUQggPUdJH3lksy+JvjuYgs1qBP64eh0gPKdchER1QKBTYt28fGhoakJSUBLFYrLNjCwQCrFq1Cm1tbbhy5QpvGqe8yMLCAitXrkRUVBT27t1r0AS1srKS1+v5nsWXhi5yuRxisVjvyachk76KigpIpVJIJBKDjDdSYrF4aOuT3bt3o6enh+uQdIKSPkLI61DSR95Zf7pWitPZ9fjd3FDMjeT+xI+MjUajQVlZGfbs2QNbW1vExMToZdrWYFOIrKws/Od//if4vH9obGwsBAIB7t27Z7Axq6ur4enpabDxxqK+vh5ubm5chwGlUqnTixOvY2ZmZrALAEVFRQgICDDIWKM1uPWJq6srzpw5w3U4OmFhYcHLdceEEO5R0kfeSaey6vDF1VKsHO+JT6f6cx0OGQGtVou2tjaUl5cjLS0NZ86cwY4dO/DFF1/gwoULCA8Ph6+vr15jkEgk+M1vfoOUlBQcOnSItydZUqkUmzdvRmZmJk6fPm2QKk9TUxOv1/M9q7W1lRdVSYlEApVKpfdxLCwsoNFo9D4OANTU1CAwMNAgY43V4sWL0dTUZPTNXbRaLdrb23nRwZcQwj/UsYK8cx5Xt+Ovj+Ygwc8B/7osCgxDnTr5rKamBk1NTWhubkZ+fj5qa2sRGBgIkUgEkUgEiUQCf39/2NjYGLR5gbm5OaZMmYLOzk4cOXIEa9eu5Xxt2Ks4OTlhy5Yt2L9/P/bt24fly5fDxsZGL2N1dnZCrVbzonr2Nn19fejq6tLZes+xsLW1Ncj0QkM1cpHL5VCr1bzu4PqswenQ+/btg4+Pj1G8f5/V1taGhw8foqioCAA43yKDEMJPVOkj75TadhW27kmHm1SEbz8aDwsh/QjwVV9fHy5cuIATJ06goKAAfX19iIiIgFwuh7OzM+Li4hAeHg5PT09IJBLOutXNnz8f5ubmOHXqFG+bu4hEImzcuBFSqRS7du1CU1OTXsaprKyEm5ubUXRDrKmpgZ2dHS8SdZHoaQMpfa8R7evrM8j3JjMzE4GBgUbxPhjk5uaGSZMm4cSJE7xdq/ssjUaDx48f44cffsDOnTuh0Wjw/vvvY9WqVbCwsOA6PEIID9EZL3lndKn7sWlXOno1WuzYMAEO1vSLka+ampqwZ88eNDU1ISkpCREREfDy8oKLiwtmzZqFixcv8ubE7MXmLnwlEAiwZMkSjBs3Dnv37h2qCujSkydPeLH9wXDU1NTwoonLIEtLS713kdRqtQZJxEpLSxEREaH3cXQtOTkZdnZ2OH/+PNehvFZDQwNOnjyJL774AtnZ2YiJicHnn3+ORYsWwdPTE2KxGB0dHVyHSQjhIUr6yDthQMvilwcyUSZX4psPxyPQWT/T28jYsCyLjIwMHDhwAC4uLoiKinrpJDUmJgZ2dnZITU3lKMqXDTZ3KSws5P26oJSUFCxZsgRnz57FnTt3dHrshoYGo5nS19DQwKtpfFZWVnpfG9rf36/3inhDQwP6+/vh72+ca6WXL1+O6upqPH78mOtQhqjVaty9exfffvstDh06NNR59OOPP0ZcXNxz1WorKytK+gghr0Rr+sg74X+fK0RqsRz/uiwKKUFOXIdDXkGlUuHChQtobGxEfHz8GzcVX7x4MbZv347w8HDeNA2RSCT44IMPsHfvXtjZ2SE0NJTrkF4rMDAQGzduxIEDB6BQKDB//vwxJwNqtRodHR1Gk/S1tLRg8uTJXIcxRCgUoq+vT69j9Pf3QyjU76/9zMxMBAcHG+3m4CKRCEuXLsXRo0cRFRUFc3NzzmIpKytDRkYGqqqq4O3tjcmTJyM0NPSN1VpLS0uo1WoMDAwY1fRaQoj+GeenMiEjsPdBFXbercSmFD+sTeRHgkCeV11djR9++AFqtRqJiYlvTPiApwnW5MmTceHCBYPtOzYcLi4uWLJkCc6dO4fa2lquw3kjJycnbNq0CfX19Th69OiYp8tWVVXBycmJF2vk3katVkOlUvGq0qfRaIbW9umLIRKB0tJSREZG6nUMffPx8YFEIkFJSYnBx+7o6MDly5fx5Zdf4vLly3Bzc8Onn36KNWvWICIi4q3fP4ZhaK8+QsgrUdJHTNqd0hb80+l8zAh1xt/PD+M6HPKCgYEB3L59G8eOHUNgYCBCQ0OH3U01MTER5ubmuHXrlp6jHJnAwEBMmTIFx44dQ3t7O9fhvJFYLMbGjRvR19eHAwcOjGnbgCdPnvBi+4PhqK6uhqOjI68qIb29vW+92DFWGo1GrxW4qqoqmJmZ8ab6PhYhISEoLCw0yFgajQY5OTnYvXs3tm/fjp6eHixatAg///nPMWXKlBFvcE979RFCXoWSPmKyypq78Nm+DAQ52+DLD2JhJqCtGfiko6MDBw8eREFBAZKSkuDkNPJpt0uXLkVGRobeulGO1oQJExAREYHDhw8bpBX/WAyuR7Szs8Pu3btHnag2NjYaTROX6upqXlX5gKfVR0MkffpMdAsKCni/IftwjRs3DhUVFXptGNXU1ISzZ8/iiy++QFpaGsLDw/HLX/4SS5YsGdO2C5T0EUJehZI+YpLauvvwya50WArN8P2GeNhY0vJVPikuLsbu3bthYWGB+Pj4UbcYd3BwQEJCAs6ePcu77RJmzZoFR0dHHDt2jFdTUF9FIBBg6dKlCA4Oxp49e4a1ufOjR4+ea6Yjl8uNZj1fR0cH7O3tuQ5jSGVlJaytrfW2f+Igfa/pq6mpMZmkTyKRQCaT6bza19fXh0ePHuG7777D3r17YWZmhnXr1mHTpk2YMGGCTqZHm5ubU9JHCHkJnQkTk9OrGcCnP2agsVONQ1uT4Gkv5jok8pO+vj6kpqaisLAQ0dHRkEqlYz5mSkoKioqKcP/+fUyaNEkHUerO0qVLsWfPHpw7dw6LFy/mOpy3mjlzJmxtbbF//34sWbIEgYGBr3zcrVu38PjxY1hYWEChUGDSpEkQCAS8SqTepL+/n1d7meXm5iI4OFjv4/T29uqtMYlarYZCoYCPj49ejs+FsLAwFBQUYNy4cWM+VlVVFdLT01FRUQE3NzckJiYiLCxML98PkUiEzs5OnR+XEGLcKOkjJoVlWfz98Tw8etKG//ogFrHexnES+i6Qy+U4c+YMgKf7Yemq4jDY8bCtrU0nx9MloVCItWvX4vvvv8etW7cwZcoUrkN6q4SEBEgkEpw4cQLvvfceYmJinrs/NTUV2dnZWL9+PcRiMfbv34/vvvvOqNZxaTQaTrsyvqiqqgpLlizR+zg9PT16a7RTUlICV1dXo2jkM1zjxo3DrVu3oFQqR1WFValUSEtLQ35+PjQaDSIjI7F582a9XxwxMzPj/ewCQojhUdJHTMo3N8tx7HEt/nJWMBbFGEdTCVPHsiyys7ORmpoKf39/eHp66uzYnZ2d2Lt3Lzw9PbFgwQKdHVeXRCIR1q5di127dsHOzg7R0dFch/RWoaGhsLa2xpEjR9Dd3Y2JEycCAK5evYqCggJs3LgRdnZ2AID169fju+++G3YDHj7o7+/nTdLX1NSEgYEBnf5cvE5nZydsbW31cuyysjKjSvyHQyQSwd7eHvX19SOqxBYXFyMjIwO1tbXw9fXFrFmzEBgYaLTbWBBCTAMlfcRkXMxrwB8uFmNxjDt+OfPV09KIYfX09ODSpUuora3F+PHjYW1trbNjt7S0YN++fQgLC8N7772ns+Pqg4ODA95//30cOnQItra2Y2rSYCheXl5Yt24d9u3bB6VSCZZlUVJSgg0bNjw3LVcoFGLp0qU4fPgwh9GOjEaj4c30ztzcXAQEBBgkIWhra9Nbh9W6ujrEx8fr5dhckkqlw5pFoFAo8PDhQxQWFkIkEiEyMhKLFy/W+zrNV2FZ1uBjEkL4j5I+YhJyazvw60NZiPO2wx9WRhtV1cFU1dbW4syZM7C1tUViYqJOT2prampw5MgRJCQkICUlRWfH1ScvLy/MmzcPJ06cwLp16yCTybgO6a1kMhk++eQT7Ny5E729vfjss89e2T7ezc0NLMuioaGBd10xX5SRkYHGxkbeJH3FxcWYM2eOQcZqa2vTyfq0FykUCvT29hqkWmloDg4OaG1tfeV9Go0Gubm5yMrKQmtrK4KCgrB8+XJeVDypqkgIeRElfcToNXaosXlPGhytLbFtXTxE5vzZe+tdVVRUhAsXLiAsLEznyU1JSQnOnDmDmTNn6uUEVp8iIiLQ2tqKQ4cOYePGjZxUAUZKIpHg5z//OTQaDcTi1zdF8vb2RnFxMa+TvsrKSty4cQOLFi3ixfYSNTU10Gg08Pf3N8h4HR0do9oa5W2Kiorg7e1tkomGTCZDXl7ec7c1NDTg0aNHKC0thaOjI2JiYhAREWFS6xkJIaaHkj5i1FR9GmzanYbu3gEc+ywRMlv6pcu1uro6XLx4EbGxsTpfP5SZmYnr169j0aJFBul2qA9TpkyBQqHAkSNH8NFHH/FmbdmbWFhYvLUyFhISgkePHmHatGmGCWqENBoNzpw5g9mzZ/NmXeXDhw8RERFhkGRpcIru4FpMXaqoqDCZrRpe5OzsjPb2dqjVamRmZiI3NxdKpRIRERFYt24dXFxcuA7xJTS9kxDyKqZ3WY68M7RaFr8+mIXChk7819pYhLjqp0EBGb62tjYcP34cYWFhOk/4cnNzceHCBaxatcpoE75BCxcuhLm5OU6cOMG7/QVHKygoCHK5nHf7g9XU1OD27ds4d+4crxrp1NTUoKqqCklJSQYZr76+Ho6Ojjo/bmdnJ+rq6hAUFKTzY/NBV1cXKisr8eWXX6KyshKTJk3Cr371K8yZM4eXCR8hhLwOVfqI0fq/l4pwuaAJ/3NROKaHOHMdzjtPpVLh2LFj8PLy0ssUMplMBqFQiK6uLp0f29AEAgFWrVqFnTt34sqVKwZb06VPFhYWcHFxQWlpKWJjY7kOB8DT6tb+/fsRHBwMtVqN2bNncx3SkIsXL2LSpEkGm+Lb1NQEBwcHnR/30qVLCA8P18uxudLZ2Ym0tDQUFBSAYRjMmDEDkyZN0kuVlBBCDIWSPmKUDqfVYNvNCqxL8sGGib5ch/PO02g0OHnyJMRiMby8vPQyhqurK1asWIETJ07AysrKKDpgvomFhQXWrl2LnTt3wt7eHgkJCVyHNGaBgYEoLy/nTdJXWloKDw8PLFu2jOtQnpOeng6WZTFhwgSDjdna2qrz/eGqqqpQU1ODTz/9VKfH5UpRURHS0tLQ0NCAwMBAzJ8/H/7+/kbZGMwYYyaE6BclfcTo3C9vxd+fyMXkICf806Jw+uXGMZZlcfHiRahUKr2f7AcEBAx1wPzggw943TRkOCQSCdasWYO9e/fCzs7O6KetRkRE4P79+xgYGICZGfcNlbq6uvS2L91oqdVq3Lp1C8uWLTPo/5FKpYKPj4/OjqfVanHhwgWkpKS8scGPMaiursaVK1egVqsxfvx4rFy5ElZWVlyHRQghOkVr+ohRqWzpxmf7MuDrZI0/r42D0Izewly7ffs2qqurERMTY5DxIiIiMGnSJBw5coR368dGw9XVFYsXL8aZM2dQUlLCdThjYmdnBysrK1RVVXEdCoCnFyR6e3u5DuM5Fy9ehJ+fn8Er1Uql8rn9Fcfq9u3bsLS0NOq9+To7O3H06FF88cUXEIvF+PTTT5GUlGT0CR81ciGEvAqdMROj0aHqx6ZdaWAA7NwwAVIr/nc9NHU5OTnIzMxEXFycQdu1JyYmwt/fH8ePH8fAwIDBxtWX4OBgLFy4EGfOnMHVq1eNurmLj48PiouLuQ4DWq0W2dnZiIuL4zqUITU1NSgvL+dkbWF3d7fO1qS1tbUhLS0N8+fPN8ptGjQaDa5evYpt27bBysoKW7ZsQX19Pdrb27kOjRBC9Mb4Pq3JO6l/QIvP9mWgtr0H29fHw9vRuKcTmYInT57g2rVriIuL42Tbgfnz50Oj0eDixYsGH1sfQkJCsHnzZlRWVmLPnj3o7OzkOqRRiYiIQGVlJddhIDc3F1ZWVggMDOQ6lCFFRUUIDQ3lZH9GtVoNa2trnRzr9OnTiIuLM8ruldnZ2fjzn/+MpqYmrF+/HgsWLEBYWBji4+Nx9OhRaDQarkMkhBC9oKSP8B7Lsvgfp/Jxr7wV/2d5FCb4mk6XOGMll8tx6tQpREVFcbaeRyAQYM2aNSgtLUVGRgYnMeiaVCrFpk2b4Orqip07d6KoqIjrkEbMx8cHKpUKLS0tnMbR0tKi0+mMujAwMMDJBt5arRZarVYnYz9+/BgqlQqTJ0/WQWSGU1NTg++++w737t3D3Llz8eGHHz6XtE6ePBm2trY4f/48h1HqhlQq5c0Ua0IIf1DSR3hvx51KHHhUjV9MD8CK8Z5ch/PO6+rqwtGjRxEQEKDzboAjJRaLMW/ePFy6dIl3a7dGSyAQYO7cuZg3bx7Onz+Py5cvG9UUVoFAAA8PjzeuT1QoFHrfemPSpEmora1Ffn6+XscZCY1Gw8l0yL6+PgiFY+/bplKpcP36dcybN4+T6v5odHR04MiRIzhy5AiioqKwdetWhIaGvvQ4gUCAJUuWoLKyklfvmdFwdHREe3s7WltbuQ6FEMIjlPQRXrta0IT/fb4Q8yJd8dvZIVyH887r6+vD8ePH4ejoyJvOmT4+PjA3N0dmZibXoejU4HTPmpoa7Nmzx6jWG4WGhqKiouKV91VWVmLHjh345ptvsH//fuTk5OglYReJRFiyZAkuXryIuro6nR9/NLRarU6Sr5Hq7e3VybhnzpxBSEiIUWyX0tfXhytXruC7776DjY3NUJOWN3VMtbGxwaJFi3DhwgUoFArDBatjDMPAycnJKGcKEEL0hzGmLk/x8fFseno612EQAymo78TKb+8h0NkGh7Ymw8qC+xbw7zKtVosTJ06gq6sLUVFRXIfznKamJvz4449YuHDhK6/iGzOtVotr164hNzcXc+bMQUREBNchvZVarcaXX36JX/3qV89NKSwrK8OpU6cwd+5cBAQEIDMzEwUFBWhvb0dQUBCioqLg6+ur02pYWloa7t69iw8//BAymUxnxx2N48ePw9nZGSkpKTo9rkqlQldXFzo6OqBUKtHZ2Ymuri50d3eju7sbra2tqK2tRUBAAAQCARiGgUAggJmZGQQCwdDXDMM8d9vg7WZmZuju7kZJSQnmz58PR0dH2NnZwdbWlpPpqm9TV1eHI0eOwNXVFTNnzhzx9z01NRUlJSXYsmWLUTaqAZ52Ji0qKsKnn35K2xoR8g5hGCaDZdlXtlWmffoILzV3qrF5dxqkVub4fn08JXw8cP36dcjlcl62aHdxccGyZctw8uRJ2NrawsPDg+uQdEYgEGD27Nnw8/PD6dOnUVVVhTlz5vBiH7zXEYlEcHR0RGlpKSIjIwE8bWJy9uxZLFq0CCEhT6v2ycnJSE5ORltbG9LT03Hu3DkAQFhYGGJiYnSSpE2YMAG9vb3Ys2cPpk2bhvHjx4/5mKPFsuywkwitVovu7m50dHSgs7MTSqUS3d3dUCqV6OnpGUrouru7wTAMRCIRxGIxxGIxbGxshn4OpFIpJBIJzMzMoNVqMTAwAJZlMTAwAI1GM3Tbs38Gb9NqtdBoNBgYGIBQKERgYCBKS0uRnZ2N7u5u9PT0wMLCAra2tkNjDv7t6+vLSZKdnZ2NK1euYPr06aP+Xk+dOhU1NTW4cOECFixYoOMIDUMikWBgYAD19fUm9XlICBk9qvQR3lH3D2D1tvsobVbiyKfJiHDnVzOGd1F6ejru37+PhIQETqanDVdaWhru3buHDRs26Kw9PZ8olUocOXIEVlZWWLNmDdfhvNH169fR0dGBZcuWITs7G5cvX8ayZcve2k2zqqoKjx8/RllZGVxcXDBx4kSddOAsLy/HgQMH8Omnn8LJyWnMxxuNw4cPw9fXFzExMejq6kJnZ+dQVa6rqwtKpRJKpXKoSmdubj6UyFlbW0MkEsHW1nboj1QqhZ2dHUQiESevB3i6Zk6hUAz9PZigVldXw8PDAx988IHBqmWXLl1CYWEhli9fDm9v7zEdq7OzEzt37sTcuXONdvZAaWkpnJycMGvWLK5DIYQYCFX6iNHQaln89nA2cuo6sO2j8ZTw8UBJSQnu3LmDCRMm8DrhA55WdRQKBQ4fPowNGzbwcurZWNjY2GDdunXYvn077t27h4kTJ3Id0isNDAzAysoKV65cQXNzM3p7e7Fy5cphrQXz8fGBj48PNBoN0tLScP78eVhZWSExMRERERGjrnBWV1fDz8/PoAmfQqFAc3MzmpubIZfL8eDBAzx69AjXrl2DSCSCtbX1UGXOxsYGfn5+kEqlsLe3h1QqhYWFhcFiHS2pVPrKLql79+5FTk4OoqKiEB0drdcY+vr6cPjwYfT09GDjxo06ueAjkUiwcOFCnDx5Em5ubrzrBDscXl5eSE9Px7Rp03j/2U0I0T+q9BFe+X+Xi/Hl9TL8/fxQbJ0SwHU477yGhgYcPHgQ48aNg0Qi4TqcYTt8+DA0Gg1Wr17N62mQo9XU1IQ9e/ZgzZo18PLy4jocAEBPTw9KS0tRVlaGyspKNDY2wsXFBYsWLUJQUNCoqz1arRa5ubl4+PAh+vr6EB8fj9jY2BEl9Gq1Gn/+85+xZs0aeHrqpwNwV1cXSktLUVdXh9bWVsjlcjAMA6lUCkdHR7i4uKC+vh7d3d3YuHGjXmLgi8E1cX5+fujp6cGSJUv0NpZCocD+/fvh7OyMRYsW6fxCz5UrV1BVVYVPPvnEKNf3paWlITExEbGxsVyHQggxAKr0EaNwMrMOX14vw+p4L2yZ7M91OO88tVqNY8eOITQ01KgSPgBYvnw5du3ahYsXLxrtmpw3cXFxwfTp03Hy5Els3rwZVlZWnMTR1taGoqIilJWVobGxETKZDAEBAZgyZQqOHTuGOXPmwNfXd0xjCAQCxMTEICYmBiUlJbh37x7u3buH2NhYJCYmDmufyBs3bsDX11enCZ9Wq0V1dfVQktve3g5XV1d4eXkhICBgaD3ds0pKSnD37l2dxcBHubm5yM7Oxvr166HVarF792709PTo5T1aVVWFY8eOITY2FtOnT9f58QFgxowZ+PHHH3H58mXMnTtXL2PoU3h4OG7evAkPDw84OztzHQ4hhEOU9BFeSH/Shr85moMkfwf8fmkkdRvjgZaWFgiFQqM8URAKhVi7di127NiBO3fu6LxbIh/Ex8fjyZMnOHPmDFatWmWQMbVaLWpra1FSUoKysjJ0dXXB09MTkZGRWLVq1XNryzQajc73cgsODkZwcDDq6upw8+ZNfP3114iIiEBSUtJr94xUKpXIzc3VSXVNqVSitLQU5eXlqKqqgoWFBby9vTFlyhQEBAS8dQqdpaUl+vr6xhwHXzU0NODixYtYuXIlHBwcAAC+vr44ceIEVqxYodMqXFpaGm7duoU5c+YMNQvSBzMzMyxduhQ7d+6Ev78/goOD9TaWPlhbWyMgIACnTp3C+vXrTW7KOyFk+CjpI5yraVPhZz9mwMPeCt9+NB4WQuObQmOKFAoFpw0ixkosFuODDz7Anj17IJVKebfNhC4sXrwY27dvx6NHj5CQkKCXMXp7e1FWVoaysjKUl5fD3NwcPj4+mDVrFvz9/V875W2w46M+eHh4YO3atWhra8OtW7fw/fffIyAgAMnJyS/tH3nt2jWEhoaOqpOkVqtFfX39UDVPLpfDxcUFgYGBmDFjxlBiM1wikchkk77Ozk4cOnQIM2bMeG7t5pIlS3DkyBFs374d8+bNG3NTHq1WiwsXLqC8vByrV6/W23TdZ9nZ2WH+/Pk4e/YsNm/ebHQzH9zd3dHW1oYrV65g4cKFXIdDCOEIJX2EU53qfnyyKw0aLYsdG+JhJ+Z/44J3RUtLC2fTBnXFyckJK1aswJEjRyCVSsfc0Y9vLCwssGLFCuzbtw9eXl4vJTyj1d7ejuLiYpSXl6Ourg6Ojo7w9fXFhx9+CBcXl2EdQ6PR6L0RiYODA5YuXQqVSoXbt29j3759cHd3R1JSEvz9/dHe3j6039pwDa5NHKzmMQwDb29vJCQkICgoaEyvycrKSi8b0XNNo9Hg4MGDCAsLe2mbBKFQiA8++AC5ubk4efIkZs2ahXHjxo1qHLVajUOHDkGr1WLjxo0GTb5CQ0NRVVWFo0eP4pNPPjHYuLoSHh6OBw8eICsra9T//4QQ40ZJH+GMZkCLz/dnorKlG3s+SYC/zIbrkMgzWlpaYGtry3UYY+bj44P33nsPx48fx0cffcRZu359cXNzw8SJE3Hq1Cl8/PHHo5q+Nbg+raSkBBUVFUPTNkNCQrBs2bJhrZt7kT6md76OWCzGnDlzMH36dDx48ABnzpyBUChEd3c3bG1t8baGZQ0NDSgtLUVFRQWamprg7OwMPz8/TJw4cdhJ7nCYaqXv+PHjkEgkmD179msfExUVhc7OTlRUVIwq6WhsbMThw4fh4+OD+fPnG+y99axZs2Zh9+7duHr1qtFtgyAQCDBu3DjcuHEDLi4uOrtARAgxHpT0Ec78/mwBbpbI8W/LozAx0LROxE2BXC5HREQE12HoRHR09NBWDhs3bhxVEsNnEydOREVFBS5fvoxFixYN6zkvdtu0sLCAr6/vW6dtDtfAwIDBT8wtLCwwZcoUpKSkoKamBg8fPkRXVxd27NgBoVAId3f3oRPevr4+lJeX48mTJ2BZFl5eXoiLi0NwcLDepjUPVgl7e3tNZm1Vamoq2tvbsXHjxre+Z0b7nsrJycGlS5cwdepUvU1jHo7B9X27du2Cn58fAgKMq8O0WCxGcHAwDh48iMmTJyMuLs4oO5ISQkaHkj7Cid33nmD3/SpsmeyHNQmmNeXOFPT396O7u9ukkqMpU6agra0Nhw8fxocffshJpUCfli9fju3btyMnJ+e1+6K1tLSgpKQE5eXlaGhoeK7bpi4roFqtFlqtlrPERiAQDO33N0gul6OyshLZ2dk4e/YsQkND4evri9WrV8PV1dVgsVlaWppM0vdsp87hvJ7RNOi6cOECiouL8f7774+5E6wuODg4YM6cOTh9+jS2bNkCGxvjmqHi4uICiUSCR48eobS0FHPnzn1tEyRCiGmhpI8Y3I3iZvzzmXzMCnPB384L4zoc8gqDTVxMrYvq4sWLsWfPHpw5cwbLly/nOhydEovFWLJkCY4fPw5PT084ODhAq9WiqqpqaOqiUqkc6rb5/vvv662i1dfXx7vNoGUyGWQyGeLi4vD//t//w4oVK3SyifdICYVC9PT0GF0zkBc1NDTg0qVLWLFixYgb2gzX+fPnUVdXh08++YRX/18RERF48uQJjh49apR7LlpZWSExMREVFRXYs2cPpk2bhujoaJP7vCeEPI9fv5WJyStp6sLn+zMR4irBn9aMg5mAfsnwkbF37nwdgUCANWvWYOfOnUa5LudtXF1d4erqiu3btyM4OBiVlZUQiUTw8fHB7Nmz4efnZ5DpXH19fTAzM9P7OKMxOM2zuLgYiYmJBh9/sNJnzJRKJQ4ePIjp06c/16lTl7Kzs1FeXo4NGzbwKuEb9N5772HXrl24ceMGpk2bxnU4o+Lv7w9nZ2fcvn0bpaWlmDNnjkms4yaEvBolfcRgWpW9+GRXGkQWZtixIR7WlvT246u2tjaTTPqAp800PvzwQ+zcuRP29vYvdRs0BgMDA2hpaUFTUxOamprQ0tKClpYWKJVKSCQSWFlZwcHBQefTNoerr6+P19Nnw8PDkZeXx0nSZ25uDpVKZfBxdUWj0WD//v2v7NQ5HG9rqgM8nYZ89epVrFixgpcJH/D0+7hs2TLs3r0bfn5+z00lNiY2NjZISkpCeXk5fvjhB8yaNQvh4eFch0UI0QM66yYGoe4fwNYfMyDv6sXhnyXD3c64twIwdabSufN1pFIp1qxZg/3790MqlY557zB96urqQkNDA5qbmyGXy9HS0oK2tjZYWlrC3t4ejo6OCAkJwfTp0+Hs7MyLxgz9/f28m975rPDwcFy9ehUqlcrg61aNvdJ38uRJ2NjY4L333hvV8982hVChUODAgQOYMGECL9bwvYmTkxNmz56NEydOYOvWrUa7BpphGAQGBsLZ2RlXr15FaWkpZs+ebbSvhxDyavz9rUxMBsuy+LvjucioasdXa+MQ42XHdUivpNFoMDAwYBINFsZKLpfDy8uL6zD0ys3NDQsWLMDp06dHtP+cvvT29j5XuRtM8DQaDezs7ODk5AQXFxdER0fDw8OD15VYPq7pe5ZIJIKLiwuKi4sRGxtr0LGFQqHRbttw69YtNDc34+OPP9bbxYWzZ88iKCgIU6ZM0cvxdS06OhpVVVVDW8IYM4lEguTkZJSUlGDnzp2IjY1FQEAAXFxcaL0fISaAv7+Vicn4KrUMJzLr8FfvBWNBND/3BiouLsb169fR3d0NiUQCDw8PuLu7QyaTwcnJidcn2Lqm1WrR2tqKsDDTb7ITGhqKtLQ0XLlyxaAnbEqlcmhPuJaWFrS2tg5NzbSzs4NMJkNsbCw8PDz01iRDn/he6QOA4OBgTpI+c3NzDAwMGHRMXSguLkZ6ejrWrVsHKyv9zNQYrGivWLFCL8fXl8H1fXfu3EFKSgrX4YyJQCBAaGgourq68OTJEzx+/BhmZmYICwtDYGAgPDw8eDGbgBAycvz+rUyM3rmcBvzH5RIsi/XAL6bzcwpdZ2cnzp07h5iYGEilUiiVSigUCuTk5KCnpwcqlQrW1tZwd3eHh4fHUBdAU00EOzs7YW5uzvuT9rHSaDQ4fvw4VCoV3n//fb2P19TUhMLCQpSVlaGtrQ0ymQzOzs4ICgrCtGnTIJPJTOb/XKPR8P61REVF4c6dOwbfPkGtVvP+/+ZFLS0tOHPmDBYvXgyZTKa3cXJychAaGqq3pFJfLC0tsXTpUuzduxc+Pj4mMUvC1tYWoaGhAJ5epGpoaEBhYSE0Gg1CQkIQHBwMLy8vo3svE/Iuo59WojdZNQr85nAW4n3s8W8rong7PaSkpAQODg5D7dttbW1ha2s79IubZVl0d3dDoVAgNzcXKpVqKBF8tiIok8mM7mTlVRQKxTsxxfX8+fOQy+XYvHmzXl6vVqtFdXU1iouLUVZWht7eXvj6+iIlJQWBgYEmfbJkDJU+kUgElmXR399v0Pd7Y2PjqNfDcUGtVuPgwYNISkpCcHCwXscpLCzEwoUL9TaGPrm4uGD69Ok4fvw4fvazn5nURUEbGxsEBQUBePp9qq+vx6VLl9DT04PAwECEhITAx8fnnfi9QYgx4/dvZWK06hQ92Lw7Hc4SS2xbNx6WQn62bweebjDs4eHx2vsZhoGNjQ1sbGzg6ek5dPtgRTAvL2+oImhlZTVUEXR2doaTk5PRLYY35c6dz5o6dSp2796Nu3fvYsaMGTo5Zn9/P8rLy1FcXIzy8nKYm5vDz88PCxcuNNrufqPR39/P2y0bBpWXl8Pe3t6gm2vL5XKYmZnptVqma4cPH4aHh4fepy1eunQJnp6evG6q9DZxcXFD6/vWrl3LdTh6IRKJ4O/vD39/f/T19aGxsRE3btyAUqmEj48PQkND4efnZ3S/9wh5F1DSR3RO2avBpl1p6O0fwP4tiXC04e/Vv9bWVnR0dCA6OnrEz31VItjd3Y329nYUFBQgPT19KBF0c3N7LhG0trbW5cvQqebmZpPu3DlIKpViw4YN2L17NwCMOvHr6elBSUkJiouLUVVVBalUCn9/f140h+GKMUzvLC4uNnh3yLKyMri7uxt0zLG4cOEC+vv7dVp9e9WWDVVVVSgrK8PWrVt1Ng5X5s+fjx07duDevXuYOHEi1+HolYWFBby9veHt7Q2NRoPm5mbcu3cPd+7cwaZNm3j/GUDIu4Z+IolODWhZ/PpgJkqbldi5cQKCXfidPBQXF8PR0VFnx7O2toa1tfVziaBKpUJ7ezuKioqQkZEBlUoFS0vLoamhg4mgISsObyKXy+Hq6sp1GAbxbOKn1WqHvVl7V1cXioqKUFpaitraWshkMgQHB2POnDmQSqV6jpr/jKHSV1NTg/nz5xt0zCdPnsDf39+gY45WZmYmSkpK8PHHH+t0z8UXp/lrNBqcPXsWU6dONYmLTZaWlli2bBn27dsHPz8/uLnxs3mZrgmFQri7u8Pd3R0ZGRnIy8vDuHHjuA6LEPIMSvqITv3bhUJcLWzG75dEYGowv6cwsSyLvLw8vU8nEovFEIvFz00hValUUCgUKCkpQWZmJlQqFczNzZ9LBGUymcETQZZl0dLSMrR+413wYsXvdYlfW1sbMjIyUFFRgY6ODri5uSEkJARLly6lqUwv4PuaPoVCgZ6eHnh7ext03MbGRp1NJdanuro6XLt2DatWrdL75ug3btyAra0t4uPj9TqOIfT396OgoAAMw8DX1xdHjx7Fli1b3onp8s8KCgrCnTt3EB4eDgsLC67DIYT8hL+/lYnROfCoGt/drsTGib5Yl+zLdThv1dTUhN7eXk4qM4OJ4LNTvXp6eqBQKFBaWoqsrKyhRHDw6ulgIqjPq+FKpRIAdHpl3xgMJn579uwB8N+JX0NDA4qKilBWVgaFQgFHR0c0NDRgypQpRnHyzhW+T+8sLCyEt7e3QauRLS0t0Gq1vJ/yq1QqceTIEUyfPl3vSXFTUxOysrKwceNGvY5jKJWVlbhy5QpcXV0xMDAAa2trqFSqdy7pk0gkEIlEyMrKQkJCAtfhEEJ+wt/fysSo3CtrwT+ezMPUYBn+YYFx7O9WVFSk06mdY2VlZTW0/m+QWq1Ge3s7ysrKkJ2dDZVKBaFQOLRG0MXFBU5OTmO+Gq/RaFBcXIwff/wR7e3tiI6OHupm+q6QSqVYt24dtm3bhqqqKvT09KCvrw++vr6YMmUKAgICIBQK0dbWhoMHD6KlpQVLliyhjnWv0N/fz+sLB5WVlQavZldUVDw37ZuPNBoNDhw4gJCQEIwfP17nx39xPd/p06eRkJAAJycnnY/FhY6ODnh6emLNmjVch8K5kJAQPHjwAFFRUSbR1ZoQU0BJHxmzCrkSn+7NgL/MGv+1NhZCM/5v3KrVapGXl4eoqCiuQ3kjkUgENze3lxJBhUKBiooK5OXlobu7GwKB4JWJ4Nu2yeju7kZOTg4yMjJgYWGBhIQEZGVl4bvvvkN4eDgmT56s9+ldXBsYGHiu46alpSWEQiEWLVr0yo6bDg4O2Lx5M06cOIGdO3di+fLlvK/eGFp/fz+vmxW1trZi6tSpBh1TLpfzPrk5e/YsrKysDLKlxL179wAAkyZN0vtYhtLR0cGbtdlcE4vFkEgkSEtLw5QpU7gOhxACSvrIGLV39+GTXWkwNxNgx4YJkIj4e3X/WXV1dUNbMRgbkUgEV1fX55qtDCaClZWVyMvLg0qlAsMwQ1NDBxNBqVQKhmEgl8vx+PFjFBQUwN7eHlFRUUP/Fz4+Pujo6EBqaiq2bduGyMhIpKSkmESThUG9vb0oLS1FcXExKisrYW1tjYCAAKxZs2ZYTWwsLCywevVq3LlzB3v37sW8efMQHh5ugMiNg0aj4W0jF7VaDZVKZfBmRV1dXby+OHDv3j3U1tbik08+0fv3rqOjA/fv38fq1at5+z4Zja6uLjg7O3MdBm8EBQUhLS0NcXFxRvm7lhBTQ0kfGbU+jRaf7ctAvUKNA1sT4eVgPM0sCgsLeX/VfSRelQj29vZCoVCgqqoKBQUFUKlUYFkWUqkUHR0dcHV1RXJy8iun4UmlUixduhQKhQLXr1/Ht99+i+joaEyaNMnof3mfOHECpaWlcHR0RFBQEGbOnDnqqawpKSlwc3PDqVOnUF9fjxkzZkAg4H+lW980Gg1vp3fW1dXBwcHB4MlGd3c3b392lEol7t69i/Xr1xukKdGZM2cQERHB++muI9XZ2YmQkBCuw+ANkUgEJycnPHz4EDNnzuQ6HELeeZT0kVFhWRb/cDIXDyra8MXqcRjv48B1SMOm0WhQWFhoEt3i3sTS0hIuLi7PVRd6e3vR3d2NyMjIYSUndnZ2WL58Odra2p5L/iZOnMjbE9g3KSoqQm1tLX7+85/rLP6AgABs2rQJhw4dQn19PRYvXvzOrYd8Ed+TPi6qMf39/bxt6PHw4UP4+/sbpBJZXl4Oa2trvP/++3ofy9BaWlqMah9GQ/Dx8cGZM2cQFhZG/zeEcIwuSZNR2X6rAofTa/HLGYFYGuvx9ifwSHV1NSwtLXl7AqZPlpaWcHBwGHE1ysHBAStXrsTGjRuhUCiwbds2nDt3Dt3d3XqKVD/u3r2L5ORknSesUqkUmzdvhkwmw86dO1FUVKTT4xsbPk/vbGxs5GQfyr6+Pt42tMjPz0dcXJzex1EqlWhtbcXixYtNrgGSQqEAy7Lv/AWfQQqFAufPn8fXX3+N5uZmPHjwgOuQCHnnUaWPjNil/Eb828UiLIh2w69nBXMdzogVFBRAJuP3HoJ85eTkhFWrVkEul+Pf//3f8fjxYyQnJ2PixIm836uupKQE3d3deju5FQgEmDdvHgICAnD27FmUl5fjvffe423FS580Gg1v9+eSy+VISkoy+Lj9/f28THSKi4thYWEBPz8/vY/l7e0NGxsbXjf5Ga3GxkbY29tzHQbnysrK8ODBA9TX1yMsLAzr1q2Do6Mj7t27h5aWFpNaVkGIsaFKHxmRvLoO/PpgFqI97fCf78dAIHhzd0i+6evrQ2lp6XMbpZORk8lkiI2NRUJCAuRyOb7++mtcvnx5aJ8/Prp79y6SkpL0vuYuODgYW7duhVwux5EjR6DVavU6Hh/xdZ8+jUbDWUOVjo6Ot3bT5UJ6ejqio6MNMlZISAiSkpJw8uRJDAwMGGRMQ2loaODVFkCG1NfXh/v37+Orr77CxYsX4evri88//xyLFi2Ci4sLhEIhPD09cfv2ba5DJeSdRkkfGbamTjU2706Hvdgc360fD5E5P6dvvclgp8Z3sfqiaw4OT9dxfvDBB9iwYQM6OzvxzTff4OLFi+js7OQ4uueVlZWho6PDIFPYAMDGxgYfffQRurq6cO3aNYOMySdarZaXlb66ujpIpVKDV9zKy8vR09ODb7/9Fl999RUOHz6M+/fvo6GhwaBxvKizsxN1dXUYN26cwcacMWMGWJY1uZ8LuVz+zq1ZU6vVuHLlCr788kuUl5dj5syZ+PnPf46UlJSXpjL7+vqiqqqK8/c8Ie8y/l2KJbzU0zeAzbvT0aXux9HPJsLZdmTr4ViW5cVV7vz8fF63TTcmMpkMxcXFQ1+vXLkSbW1tuHHjBrZt24bw8HBMmjSJF2tc7t27hwkTJhi0+iQUCrFmzRrs2LEDLi4uBqum8IEuG7lotdqhbRZ6enrQ09MDW1tb2Nvbj3hdbm1tLSdNXLKzszFv3jxMnToVdXV1qK2tRV1dHdLT09HT0wOZTAYXFxd4eXnBz8/PYE2S0tLSEBgYaNCp2QKBACtXrsT3338Pb29vhIaGGmxsfVIqlbz4rDMEjUaDBw8e4NGjR/Dw8MC6deve+ntVIBDAx8cHt27dwurVqw0UKSHkWZT0kbfSaln85nAW8uo78P36eIS5jWyz7t7eXuzYsQNWVlaIi4tDSEgIJ01UVCoVnjx5YlKbAXPJxcUF9+/ff+42BwcHLF++HAqFAjdu3MB3332H0NBQpKSkcLbepbKyEq2trVi7dq3Bx5ZKpVixYgWOHj0KmUwGNzc3g8fAhYGBgTEnfZmZmbh//z4UCgWEQiEsLS1haWkJCwsL9PT0oKurC2ZmZrC2toaNjQ1sbGwgkUhgZ2cHMzMz1NXVoaWlBe3t7bCwsIBYLEZFRQVmzJiho1c5PBqNBuXl5fj4449hZmYGb29veHt7D93f2dmJmpoa1NXVITMzExcuXIClpSVkMhnc3d3h4+MDLy8vvVywyM/Px8KFC3V+3LeRSCRYsGABzp07B1dXV5NIllQqFSSSkf1uNCZarRaFhYXIy8tDXV0dnJycsHLlyufey2/j5eWFe/fuobq6ekTPI4ToBiV95K3+80oxLuQ14h8WhGFm2MirZDdu3IBYLIabmxvS09Nx/fp1BAcHIyoqCl5eXgarAJaXl0MikfByrZExcnFxQWdnJ/r7+186wbezs8PSpUvR2dmJGzdu4Pvvv0dwcDAmTZpk8IX8d+/exfjx4zn7vvv4+GDixIk4duwYtmzZwstmHro2lkYuDQ0NOHv2LLRaLaZNm4aQkJBXdgLVarVQKpVQKBRQKBTo7OxEZ2cnKioqoNFoIJPJMGHCBDg7O0OtVqOzsxNKpRLXrl1Dfn4+7O3tYWtrC5FIBLFYDLFYPPS1ubk5ent70dPTg4GBAQiFQgiFQmi1WgwMDECj0Qx9rdVqn/v6xduamppgbW392ve9RCJBREQEIiIihl5XU1MTamtrUV9fj8uXL6O9vR12dnZwdnaGp6cnfHx8xjxjoaSkBEKhEP7+/mM6zmiFhISgoqICJ0+exLp163jb7XW4uru7TSJ5fZFKpcL9+/eRk5MDa2trhIeHY9q0aaN6/zEMAz8/P9y4cQPr1q3jxewfQt4ldPZL3uhYRi2+Si3HBwne2JQy8u5uFRUVKCwsRHJyMoRCIZycnNDf34/a2lqcOXMGZmZmiI2NRVhYmN6vkubl5b0zlRZDEAqFsLa2RktLy2v/XyUSCRYvXgylUokbN27ghx9+QGBgICZPnmyQ5K+6uhrNzc1YtWqV3sd6k+TkZJSXl+PGjRuYM2cOp7EYwmCiNBJqtRqXL19GaWkpkpKSkJSU9MZEQCAQQCKRQCKRDLtqkJmZidjYWAQEBEAul0OhUEClUqG5uRlqtRq9vb3o7e0dSlotLS3BMMxzyZxQKIS5uTkEAsEb/5iZmUEgEKC2thYpKSnD/n8QCARwc3N77meqt7d3qBpYUVGBe/fuQa1WQyKRQCqVwtfXF8HBwSPqSvzo0SPOpxzPmTMHO3bsQGpqKmbNmsVpLGNRW1sLkUhkUtsANTU14e7duygvL4efnx9WrFihk+qcu7s7qqqqUF5ejsDAQB1ESggZLkr6yGs9qmzD3x7PwcQAR/yvJREjviqnUqlw4cIFREREPHcCaG5uDj8/P/j5+aGjowOFhYW4e/cuvL29ERMTAz8/P51XZTo7O9HQ0EC/ZHTMzs4Ocrn8rcm0jY0NFi5ciGnTpuHWrVsGS/7u3r2L2NhYXjQVWbRoEbZv347o6GiTv/gw3ErfYDfd4uJiVFZWws/PD1u2bNHbBaDGxkbMnTsXTk5OBvkeKJVKZGdnY8KECWM6jqWlJQIDA5/7/BpMVltaWlBVVYVHjx6BYRi4u7vD398fAQEBr51S3dnZifr6eixfvnxMcY2VQCDA+++/j++//x6+vr5G+/lcVlYGLy8vrsPQiaKiIjx48ACtra2IiorC5s2bdT41PzAwEDdv3oS/v7/euykTQv4bJX3klapau/GzH9PhZS/GNx+Oh7nZyD+Yr127BqlUOtTl8VWkUimioqKg1WpRX1+P69evo7e3F9HR0YiIiNDZfnplZWWwt7enXzA6Zmdnh9bW1mE/3sbGBvPnz8e0adOeq/ylpKTofO/EwWYZK1as0OlxR0sqlSI5ORnnz5/Hxx9/bLLvRY1GAwAvXbjp6OhAbW0tGhoa0NzcjNbWViiVSri6usLX1xeJiYl620pFrVbj6NGjsLCwMOj04sePH8PPz08vm7KLxWL4+vrC19cX8fHxAJ4mtRUVFUhNTcXBgwfh5+cHDw8P+Pv7Izg4eKgS9fDhQ4M3cHkdOzs7zJ8/H2fPnsWmTZtga2vLdUgjVllZiYSEBK7DGLW+vj6kpaXh8ePHEAqFGD9+PGJiYvQ2FV0mk6GyshLl5eUICgrSyxiEkJdR0kde0tHTj092pYEFsGPjBEjFI2/IUFRUhMrKSiQnJw/r8QKBAJ6envD09IRKpUJ1dTWysrLg4OCA2NhYBAUFjWnqTG5u7jvXTtsQZDIZ6uvrR/w8sVg8lPzdvHkTu3btgr+/P1JSUnTWXZVPVb5BEydORF5eHh49esTJBuH6NjAwgJKSEnR1deHRo0eor69Ha2srFAoFtFotZDIZnJ2dERQUhMmTJ8PZ2dkgaxyLi4vR09Nj8KYlVVVViIyMNNh4rq6ucHV1RWlpKaZOnQpnZ2dUVlYiKysLe/bsgZeXFzw8PFBZWYkPP/zQYHG9TXh4OCoqKnDq1CmsXbvWqC6IKJVKyOVyo0teNBoN6urqkJubi6KiIri5uWHOnDkIDAw0yP+/g4MD6urqjO7/jRBjRkkfeU7/gBZ/sf8xqttU+HFTIvycrEd8jK6uLly+fBmRkZGj+uUhFosRGhqKkJAQtLS04MGDB7h69SpCQkIQHR0NDw+PEU01bWtrQ3t7+1CjBKI7rq6uyMnJGfXzxWLxUCv7W7duYc+ePfDx8cHkyZPHNP2uoaEBNTU1WLJkyaiPoQ8CgQCLFi3CwYMHER4ebtTd/rRaLeRyOerq6lBfX4+mpia0tLSgoaEBtra2ePLkCWQyGYKDg+Hm5sZZ91bgaROnkJAQgzctUSgUcHV1NeiYSqUSTU1NWLVqFaysrODr64vOzk40NjZizpw5aGxsRFJSEu8ugs2dOxfff/89bt++jalTp3IdzrANvs/5dHHpRX19fWhoaBhqDiSXy9HZ2QmJRAIvL69hbbmga3Z2dqO6YEgIGT1K+sgQlmXxP0/n43ZpC/6wMhpJ/o6jOsalS5fg7OwMqVQ6pngYhoFMJoNMJkNfXx9qa2tx8uRJWFhYIDY2FqGhocOaClRSUgIHBwfqFKYHbm5uQ1WcsVwdFovFmDt3LqZNm4bbt29j37598PLyQkpKyqim/N29exdRUVG8bKzg4eGBkJAQXLlyhTdTT1+nv78f3d3dUCqVUCqVUKlUkMvlaGpqQlNTE4RCIWQyGVxdXZGSkgIfHx/s3bsXKSkpvNp/ra6ubmgKpCEplco3Tm/Xh7S0tJemlObm5sLX1xehoaG8+r48SygUYuXKldi9ezd8fHzg6+vLdUjD0tfXp5fpu2PR2NiIkpISNDQ0oLW1FV1dXUPdXz08PBAfHw93d3dOOwlLpVIUFBTwZg9fQt4FlPSRIbvuPcG+h9X42VR/rIof3aL03NzcoSvJumRhYQF/f3/4+/tDoVAgPz8fd+7cgY+PD2JiYuDr6/vaTn+5ubmctSU3dSKRCObm5lAoFDo5uRWJRJg9ezYmT56MO3fu4MCBA/D09ERKSgo8PT2HdYympiZUVlbi888/H3M8+jJjxgz8+c9/Rltbm8GTghd1dXWhpqZmqCLe3t6Orq4uqFQq9Pf3w9LScqgzoZWVFezt7REbGwsfH59XViodHBzQ0tLCwSt5tY6ODqjVar2tF3wdhUIx1AHUkAoLC1/qhFlWVgZbW1v09vbyessQJycnzJgxA6dPn8bmzZt5seZwOLRa7StvV6vVuHPnDqqqqtDa2oqBgQGwLAuWZQFgaP9IGxsb2NraQiwWw9bW9rl9Jwe3FVGr1ZDL5WhtbUVbWxsUCgU6OjrQ398PjUYDjUaDgYGBoc65g+s9k5OT4eLiwrvv+2AH3I6ODpPc6oIQPqKkjwAAUoua8fuzBXgv3AW/mzO6K8EKhQKpqakYN26cXq/c2dnZwc7ODgMDA2hoaMDly5cxMDCAqKgoREZGwtHxvyuUTU1N6OnpoV8qeiSVStHc3KzT5EUkEmHWrFlISUnBvXv3cPDgQbi7u2Py5Mlv7ZJ39+5dREZG8rLKN0gsFiMyMhJXrlzB6tWrOYujsrISJ06cgL29/VDTJV9fXzg5OQ2dfI6Uk5MT2tra9BDt6JSWlsLDw8Pg+8C1tLQY/HOnpqYG/f39L3XBnDt3Lq5cuYKvvvoKCQkJSExMfGlvTb6IjY1FZWUlTp8+jTVr1nAdzlsJBIJXJn3l5eU4c+YM3N3dkZycDG9vb5ibm0MoFA79fnxxn8nBSnpVVRW6u7uhUqmgUqnQ0tICe3t7ODg4wM7ODlKpFC4uLggJCYFYLIZQKISFhQXMzMxgYWEBGxsbo1gXaWVlhdbWVvr9TIiBDCvpYxjGDsD3ACIBsAA+YVn2/jP3SwHsBeD90zH/g2XZH4b53L8C8O8AZCzL8ufy8DukqLETnx/IRJibBF+sGQeBYOQJ28DAAC5cuAB3d/dRnSiOhpmZ2VDzl+7ublRXVyMzMxNOTk4YN24cOjs78fjxY513hSTPs7e311tlRyQSYcaMGUhJScHdu3dx5MgRuLi4YNKkSa+c/tXS0oLy8nL84he/0Es8ujRr1ix88803SE9PN+jUw4GBAdTX16O0tBQZGRmYP3++Tte7uri4oKysTGfHG6snT57oZH+xkeIi6cvIyEBYWNhLJ/wuLi746KOPUFNTg2vXriEjIwPJyckYP348LzdFX7hwIb777js8ePCA9w2PXkz6BvebLCsrw6xZs964F+Jw95k8efIkRCIR5s6dq7O4+cDa2hpyuRwBAQFch0LIO2G4lb4/AbjIsuxKhmEsALw45+IXAApYll3EMIwMQDHDMPtYlu1703MZhvECMBtA9ZhfCRkVeVcvNu1Kh7WlGXZsmACxxeiKv9evX0dXVxfGjx+v4wiHx9raGmFhYWBZFs3Nzbh//z7KyspQU1ODn//855zE9K5wcnLS+3Q+CwsLTJ8+HZMmTcL9+/dx/PhxyGQypKSkwM/Pb+hxd+/eRVhYmFFMC7OwsMDKlStx4MABCIVCjBs3Ti/jDCZ5lZWVQ9tYiEQiuLu746OPPtL5fnVubm68qvTV1dUNu4uwLrW3t495XfNIaDQalJaWYv369a99jJeXFzZu3Ijy8nJcv359aIP2yMhIzqcZP8vCwgIrVqzA3r17hzqO8lFVVRWePHmC+vp6XLhwAUqlElVVVfD19cXmzZt11qhpwoQJOHr0KN577z2jqOANl1QqRUNDA9dhEPLOeOsZPsMwEgBTAGwEgJ8Sub4XHsYCsGWezlmwAdAGQDOM5/4RwN8AODWWF0FGR90/gK0/pqO1uxdHfjYRrtLRTYd7/PgxCgsLkZiYqOMIR45hGLi4uMDFxQVxcXE4duwYTp8+zekUOlNnyMqOhYUFpk6dikmTJuHBgwc4efIkHB0dMXHiRDg6OqKkpASfffaZQWLRBQ8PD3zwwQc4cOAABgYGdHLR5E1JXmhoKBYuXKjXrqESiQQsyw51B+SSQqFAf3+/QTZif1FHR4dBNxvPzc2Fk5PTsLowBgQEICAgACUlJcjKysLDhw/h4OCAsLAwREZGcv59A552Bp48eTJOnTqFTZs28WpNWlVVFS5cuACNRgM7Ozt4enrC3NwcHh4emDJlis47YXp4eEAoFKKiosJoN7B/FalUiry8PK7DIOSdMZyyjj8AOYAfGIaJAZAB4Fcsy3Y/85g/AzgNoB6ALYDVLMtqGYZ57XMZhlkMoI5l2ew3rf9iGGYrgK0AOJmiY6pYlsXfHM1BZrUC334UhyjP0V2RfvLkCW7evIkJEya8tBkzHyxatAjbt2/Ho0ePjHrzXD5zdXUd0QbtuiAUCpGSkoKkpCSkpaXh3LlzaGpqgpubm8GmF+uKh4cHPvzwQxw4cABmZmYjrvhxneS9yuA6T66Th5KSEnh6enJSHens7DTo9M6cnBxERUWN6DnBwcEIDg6GRqNBYWEh8vLycOfOHbi6uiIqKgrh4eGcJluJiYmorKzEuXPnsHz5cs7ieNa1a9eQlZWFyZMnIz4+3mDvraCgIBQWFppU0icWi9HT04Oenh7edUAlxBQN5yxdCCAOwOcsyz5kGOZPAP4WwD8+85g5ALIAzAAQAOAKwzC3X/dchmH+D4D/D8B7bxucZdntALYDQHx8PDvcF0be7E/XSnE6ux5/MzcEcyNHdxW8paUFp0+fRlRUFG8/sAenCe3btw9ubm5vbQJCRk4qlXJW2REKhUhOTkZiYiK+++47lJSUYOfOnZg4cSJvW9O/ipubG95//30cOnQILi4ub6xMabVa1NfX48mTJ6iurn4uyQsODuYkyXuRnZ0dWlpaOD9Brays5OxiYU9Pz7C2lNEFhUKB5ubmUTc+EQqFiIqKQlRUFNRqNfLy8pCVlYXr168jNDQU8fHxBt/HbdDSpUuxbds2ZGRkcLZ8AABUKhUOHz4MjUaDjz/+2ODTYWUyGYqKigw6piGIxWK0trYOuzszIWT0hpP01QKoZVn24U//PoqnSd+zPgbwb+zTPsRlDMNUAgjF07V6r3puAAA/AINVPk8AjxmGSWBZtnEsL4i83amsOnxxtRQr4jzx2dTRLaBWqVQ4ceIEfHx8ON10eTjc3NwwY8YMnDhxAh9//LHBTsTeJXZ2dpxWdgQCATZu3IhvvvkGdnZ2uHLlCu7cucO7/eLexMvLCxMnTsSJEyeem872bJJXU1OD2tpaiEQiuLm5ITg4GAsWLDDo2rHh6Onp4cV0vIaGBkyZMoWz8Q3VJOXhw4cIDg7Wyf+5SCRCfHw84uPjIZfL8eDBA/z4449wdHREQkKCTpv+DDeewbWv7u7unEzVLSgowOXLlxEYGIg5c+Zw0vnUysoKvb29Bh9X30QiEeRyOSV9hBjAW5M+lmUbGYapYRgmhGXZYgAzARS88LDqn26/zTCMC4AQABUsy7a86rksy+YCcB58MsMwTwDEU/dO/Xtc3Y6/PpqDBF8H/OvyyFFtraDRaHD69GlYW1sbzQd1XFwcGhsbcejQIaxbt44XJ6SmRCqVorW1ldPKjqWlJSZPnoyHDx/i5z//ObKzs3HlyhXcvn0bEydOfGVXQ76ZOHEiqqurcfHiRSQkJCA9PR0lJSUwNzeHu7s7goKCMH/+fN4lec9Sq9VoampCUFAQp3G0tLRgYGCAkyQBeJqsG+r9VlxcjMWLF+v8uDKZDIsWLcK8efOQm5uLy5cvo7m5GdOnT9f5WG/i4eGBSZMmvXRBRB+USiXq6upQX1+PxsZG1NfXQywWY/bs2QZPeJ9lqkmfRCJBU1MT12EQ8k4Y7iKszwHs+6n7ZgWAjxmG+RQAWJb9FsDvAexiGCYXAAPgd88kcC89V5cvgAxfbbsKW/ekw1UiwrfrxsNSOLqr0NeuXUNXVxfi4uJ0HKF+zZ8/H/v378epU6ewcuVK3icAxsTR0ZEXG3LHxsbi8ePHyMjIQEJCAsaNG4fs7GzcuHEDd+7cQXJyMiIjI3n7vddoNPDx8cHRo0dRWFiImJgYbNy48bm9J/kuKyvLoFu3vE5paSmn07lZltXrfqWDysrKhjbj1hehUIjY2Fj4+flh79696O7uxvz58w36c5ScnIzy8nJcvnwZixYt0tlx5XI5SkpKUF1djcbGRvT398PJyQkymQz+/v6YMWMGZ1NbnyWVSqFUKrkOQ+ekUimqqqq4DoOQd8Kwkj6WZbMAvLiR1LfP3F+P16zPe81zX3yM73DiIKPXpe7Hpl3p6NVocXBrPBysLUZ1nMHKg7E2RVm5ciV27tyJa9euYfbs2VyHYzJcXFyQmZnJdRgQCASYNWsWTp48iejoaIhEIsTGxiI2NhY5OTm4e/cu7t27h+TkZERFRfEq+VMoFNixYwesrKywdu1aREdH83YD7Teprq7mfC0f8LTJlL+/P2fjGyrpy8jIQGRkpN7HAZ5O4/7kk0/w448/4ujRo1i2bJlB36PLly/Htm3bkJ+fP+qqm0ajQUVFBQoLC1FVVQWNRgNvb28EBARgxowZkMlkvPpcGGRnZ4eBgQFedMXVJYlEgvb2dmg0Gl42gyPElPDvk43o3ICWxS8PZKJMrsTXH8Yh0Hl0a9ry8/Nx+/ZtxMXFGe2Hs4WFBdauXYvc3Fzk5uZyHY7JcHV1RXt7O9dhAAD8/Pzg7u6OGzduPHd7dHQ0PvvsM0yePBn379/Htm3bkJmZiYGBAW4CfYGNjQ0iIyPR3d2N2tpao72q39bWxovKSENDg16rX2/Dsqzekwe1Wo2qqiq97fH4KmKxGB9//DHUajUOHDiAnp4eg469YMECXLp0CZ2dncN+nkajQXZ2Ng4cOIA//vGPSE1NhbW1NZYuXYpf//rXWLlyJRISEuDi4sLLhG+Qg4MD6uvruQ5DpwQCASwtLXm1tychpoq/n25EZ/73uUKkFsvxz4sjMDlINqpjZGVl4fLlyxg/fjxEotHt58cXEokEy5Ytw+XLl3kxJdEUODo6QqVS8WbNycyZM5GXlweFQvHSfREREfj0008xdepUPHr0CN988w0yMjI4T/6EQiHmzJmDX/ziFzAzM8P333+Pc+fOoauri9O4RkKr1UKhUHCe9MnlcggEAk7jMETSl5WVBU9PT4NXfiwsLPDRRx9BJBJh165dBk38goODERISglOnTkGr1b7xsZWVlThy5Aj++Mc/4vHjx0Obpv/sZz/DrFmz4O3tzesk70VOTk5obDS9XndWVlYG3/aHkHeR8XzakVHZ+6AKO+9W4pNJfvgoyWfEz1epVLh48SJu3ryJ+Ph4WFtb6yFKw/Pz88O4ceNw6tQpzk/2TYFAIIBEIkFzczPXoQB4enIUGRmJS5cuvfYx4eHh+NnPfobZs2fj8ePH+Prrr/Ho0SPO3w9isRgLFy7E1q1b0dvbi23btqGkpITTmIZLLpfDysoKYrGY0zgG9+fjkiGSvtzcXERHR+t1jNcRCARYunTp0Jo4Q5ozZw66u7tx7969l+5Tq9W4c+cOvvrqK5w9exYymQybN2/Gxx9/jOTkZN53m34TqVRqVBeBhsvGxoaauRBiAJT0mbA7pS34p9P5mBHqjP9vQdiwn8eyLGpra3H58mVs374dzc3NSEpK4vxETtdmzpwJhmFw7do1rkMxCVKplFeV06lTp6Kuru6tTQJCQkKwZcsWzJkzB7m5ufjqq6/w4MED9Pf3GyjSV5NKpZg/fz4AGM02I/X19ZDJRjebQJeqqqo4259vkFar1euaPrlcjs7OToSFDf+zXdcsLCywcuVKXLlyxaDT5YVCIVasWIEHDx6gtrYWwNPpvMePH8eXX36JmpoazJ49G7/4xS8wbdo0o070nlVbWwsPDw+uw9A5qVRKSR8hBmCcC7PIW5U1d+GzfRkIcrbBlx/Ewkzw9pMPuVyO4uJi5ObmQqvVwsnJCQkJCSa9vcGKFSvw3XffISwsjDZuHyN7e3teJX1WVlaYOHEiLl26hK1bt7718cHBwQgODkZ5eTlu3ryJhw8fIjExEePHj+esocrNmzfh7e3N2bYDI9XY2AgnJyeuw0BDQ8NLjZp6e3tRU1MztN+hSqWCp6cnfHx84O/vr/Mpkvqu9KWlpSE0NNRgewG+Tnh4OKytrXHkyBF0d3cjKSnJIOPKZDJMnDgR27Ztg7u7O3p7exEZGYktW7aYTJL3LK1Wi8bGRixcuJDrUHTOysoK1dXVXIdBiMmjpM8EtXX34ZNd6bAUCvD9hnjYWL7+29zV1YXi4mJkZ2dDqVTC0dER4eHhnLdbNxSpVIrJkyfjzJkzet//ydS5uLigrKyM6zCeM2HCBGRmZiI7OxsxMTHDek5AQAACAgJQWVmJmzdv4sGDB0hISMD48eMN+v5QqVTIycnB+vXrDTbmWLW2thqsk+TrNDU1wczMDBKJBGVlZUNJXnNzM6RSKTw8PJCQkAAbGxtUVFQgOzsbly9fho2NDby8vIaSwLF+Buoz6dNqtSgqKsKqVav0cvyR8vHxwYYNG7Bv3z4olUrMmjVL72Pm5OQgIyMDra2tcHZ2xueff855AqxPNTU1sLa2NsmE1tzcnDfrwQkxZZT0mZhezQA+/TEDjZ1qHNyaBE/7l6dkDgwMoKqqCllZWaiqqoK9vT28vb3h4ODAQcTcS0xMRHl5OS5evIglS5ZwHY7RcnFxwaNHj7gO4zlmZmaYPn06Ll68iIiIiBF1nfXz84Ofnx+qqqpw48YNPHr0CPHx8ZgwYYJBkr+amhqIRCJeTJccrtbWVs6ap/T396OqqgqXLl1CW1sbvvjiC0ilUri7uyMhIQEBAQEvNaHy8Xm6zlmr1aKmpgalpaXIyMjAxYsXIZFIIJVKERMTAz8/P1hZWY0oHpZl9ZaElJSUwNramvN1i8+SyWT45JNPsH37dsTExOjtfVtSUoLU1FSwLIsZM2Zgw4YN2L59O6qrq+Hn56eXMfmguLiY8ynL+kJJHyGGQUmfCWFZFn9/PA+PnrThyw9iEef9/BXBjo4O5OXlITMzE2ZmZnBzc8OkSZNM+urocA3u//To0SOj3YOQay4uLujo6MDAwACv3lOhoaFIT0/HrVu3MGPGjBE/f7CKUVNTM5T8TZgwQe9Tn0NCQpCamor09HSjeE+q1WqoVCo4OzsbZLzBJK+6uhrV1dVoamqCVCpFTU0Npk6diunTpw+707BAIICPj89QEqjRaJCXl4fDhw9DpVLh9OnTcHJygre3N7y9veHn5/fG7/1gV0l9VfoyMzNHvU+dPkkkEgQFBSEjIwNz587V6bFrampw5coVKJVKTJo0CbGxsUP/vzNnzsSFCxdMerZGTU2NwabOGtrg97G/v98o9yYlxFhQ0mdCvrlZjmOPa/HrWUFYHOMO4GkiWF1djcePH6OyshJOTk6Iiop6Z6ZvDpdIJMKqVauwd+9eyGQyk75irC8WFhYQi8VoaWnhvGX/i2bOnIl9+/ZhypQpo95j0svLC+vWrUNNTQ1u3ryJtLQ0jB8/HklJSXo70Zw3bx6OHTuGyMhI3jdSamhogIODg94S/tclee7u7hg/fjyCgoIgEonwxz/+EYmJiWPaWkYoFKK/vx9RUVH44IMP0NfXhydPnqCsrAy3bt3CqVOn4OzsDE9PT/j5+cHLy+u594BGo9FbwqdWq1FTU8PbtV1JSUnYu3cvZs6cqZMTeLlcjitXrqCxsREJCQlITEx86bixsbHIz89HamqqzpNNPujr60NLSwv8/f25DkVvhEIh+vr6KOkjRI8o6TMRF/Ma8IeLxVgc445fzQxCT08PCgsLkZ6ejv7+fri5uSElJYVXFRi+cXNzw/z583HixAl8+OGHvEtcjIFEIuFl0ufm5gZHR0cUFBSMucW9l5cXPvroI9TV1Q1V4uLi4pCUlDTiKYBv4+PjAy8vL6SmpmLBggU6Pbau6bpz5+uSPDc3N4wfPx4BAQGvTITVarVOtpaprq4eau5kYWEx1OhncIyKigqUl5fj6tWr6OjogJubG7y8vODr6wtnZ2e9JX0ZGRnw9vbmbUdXFxcXSKVS5Ofnj2nT+L6+Ply5cgWFhYWIi4vDsmXL3vjztXjxYmzfvh2BgYEIDAwc9bh8VFFRAScnJ51/vvCJUChEb2+vyWwLRQgfUdJnAnJrO/DrQ1mI9bbDX01xxeXLl1FQUACpVIqAgADY2dlxHaLRiIiIgFKpxMGDB/Hxxx8bfNNjY8e3Dp7PioyMRFZWls72NfPw8MBHH32EhoYGpKam4uuvv0ZsbKzOtzeZM2cOtm3bhri4OF538WxqahpT587+/v6h7pq1tbVoaGgYVpL3LJVKBYFAoJPKa319PSZMmPDK+0QiEcLDwxEeHg7gaRJYUlKCiooKnD9/Hp2dnairq0NaWhrCwsJ0OrMiPz8fkyZN0tnx9GHcuHHIysoaddKXk5OD69evw9XVFVu3bh3W57BEIsH8+fNx+vRpbNy40aTWqJeVlZl8d2kzMzP09fVxHQYhJo2SPiPX2KHG5t1pkFgI8J5VJU4cTYerqyuSkpJgYWHBdXhGKTExEe3t7Th69Cg2bNhA1dERkMlkvN1vKTIyEqmpqejs7NRpMu/m5oa1a9eisbFxKPkbN24ckpKSdHKyX19fD4Zh0NbWxuukr7W1dSgJGo7BJK+qqgo1NTVobGyERCKBm5sbYmNjsXLlyhEnz11dXTqpFCiVSvT09Ax7TzSRSITo6OihCwpNTU344osvUFRUhNTUVLi6uiI0NHTMnZEbGhqgVCoRGho66mMYwrhx45CamoqmpqZhV/21Wi3y8vJw//59aLVazJ07d8SvMzw8HE1NTThy5Ag2btxoMuv76uvrMW3atLc+Tq1WIyMjA/n5+Zg3b55RJYoCgYCSPkL0jJI+I6bq02DN1zfQruzHOvdm+Li4wTkqVK8bAr8r3nvvPezatQupqakGaT9uKlxcXFBQUMB1GK9kZWWFgIAApKenj6qhy9u4urrigw8+gFwux/Xr1/Htt98iOjoaEydOHNWJvlarxeXLl1FUVISlS5fyfsqaQqGAu7v7a+/v7+9HbW0tnjx5gurqajQ2NsLW1hYeHh4YN24cAgMDx1wh7erq0skUuIqKCri5uY36go+1tTUcHBywbt06qNVqFBQUoKCgADdu3ICrqytCQkIQHh4+4imaaWlpCA8P5/2FKKFQiJCQEGRmZr51jV1fXx/S0tLw+PFjWFhYICEhAdHR0aN+jdOnT0dzczNOnDiBVatW6XWvREMxNzdHT0/Pa+9Xq9W4fv068vPz4enpCQ8PD1y8eBFbtmwxYJRjQ5U+QvSPkj4jpdWy2PTdbVR1aPCbCdaY6DuO65BMikAgwPLly/H9998jKiqKd2vU+MrV1RXt7e1ch/FaMTExuHTpkl6SvkEymQyrV69GS0sLbty4gW+//RZRUVGYNGnSsJM/pVKJ/fv3o6ysDAsWLOB9YyGFQgGGYZ6roKpUKjx58gSFhYXo6OiAXC6HjY0NPDw8EBsbq5Mk70VdXV06OWZZWdmYtkN4toOtSCRCXFwc4uLioFarUVhYiIKCAty8eXMoAQwLC3tr9Vmr1aKkpATr1q0bdVyGlJiY+MaGLiqVCrdv30Zubi5cXV0xb948nV3YWLFiBb7//ntcvXoV7733nk6OyaWwsLDn9hrVarVoa2tDc3Mz6uvrkZ2dDR8fH2zcuBEymQwDAwPYtm3biPYn5dpgIxdCiP5Q0mek/uVMLu7XqPBBiAUm+kq5Dsck2dnZYfz48Th//jw2bNhgEleM9c3GxgYCgQAKhYKXa0n9/f2H9qkcbM+vL05OTli5cuVQ8vfNN98MVf7eVOGprq7GyZMn4e3tjUmTJuHevXvIzc1FUlISYmNjeVnlqaurg0QiQX5+Pqqrq1FbW4vW1lbIZDJoNBrI5XJs3rwZrq6ueo1DqVTqrIlLYmLiqJ+vVqtfmeiIRCLExsYiNjb2pQTQ2dkZISEhiIiIeGUCmJ+fDzs7O6O5AOXi4gI7O7uXGrq0tLTg9u3bKCsrQ0BAAD788EOdT1sWCoVYu3YtduzYAWdn5zE1lOFKX18fenp60NPTA6lUiuLiYnz77bfo7++HUqmEpaUlHBwc4OjoiPfff/+5PfzMzMwwY8YMXLp0acT7k3KFpncSon/8/yQgLzmcVoOd92uQ4NCLFVHGcQJgrKZOnYrS0lLcunVrWGsqyH938ORj0icQCBAeHo7Hjx/rPekbNJj8tba2IjU19bnK34vJ34MHD3Dnzh3MmDEDcXFxAJ42FyouLsatW7dw//59JCYmYvz48bxI/pqampCRkYHMzEx0dnbCzMwMnp6emD59Onx9fYdONi9cuIDTp09j3bp1eu1AqFQqx1zpq6urg0AgGPZ6vtfF8bYtI55NAPv6+lBYWIj8/Hzcvn0bzs7OCAsLQ0RExFB1OCsrC5GRkaOOiQvPNnSpqqrC7du30djYiMjISGzevBn29vZvP8goSSQSrFy5EocOHYKDg4PRbGze1NSECxcuoL6+HpaWlrC0tIRYLEZsbCw8PT3h5eUFJyent65XDA0NRUZGxqj3JzU0oVD4ximshJCxo6TPyNwvb8XfHc+Bt4UKfznVh9bv6ZlAIMDKlSuxc+dOuLu7D7VsJ69nZ2eHlpYW3q5BGzduHH744QdoNBqDXgF3dHTEypUr0dbW9lzyN3HiRFhaWuLMmTNobGzEunXrXqrmhISEICQkBCUlJbh9+zYePHiAhIQEjB8/ntN9rYqKipCTk4PVq1fDx8fntbHMmzcPR48exZEjR/DBBx/oLWaVSjXmqlFOTg4CAgLGdIzu7u4RVRwtLCwQExODmJiYoQQwNzcXN2/ehIeHB/z9/VFXV4cVK1aMKS5Di4mJwYULF/DNN9+gr68P48ePx4oVKwy29YCXlxdmz56NEydOYMOGDby8EDVIpVLh8uXLKC8vR3x8PD788MMx/5zMmjULe/bsQXx8PO87UZubm0OtVnMdBiEmjearGZHKlm58ujcdUrM+/DZJCgsh91f63wUODg5YvHgxTp8+jYyMDGi1Wq5D4jWZTAa5XM51GK/l5OQEJycn5ObmcjK+g4MDVqxYgc2bN0OlUmHbtm3Ytm0bBgYGsHnz5jdO3wsODsamTZuwYMECFBYW4uuvv0ZnZ6cBo3+et7c3JBIJAgMD33qCunz5cgDAiRMn9PYz1N3dPeaOqVVVVQgJCRnTMcbSUGYwAfzoo4/wy1/+EkFBQbh06RLq6upw7tw5FBUVYWBgYEzxGUJbWxsOHz4MhmEQHh6Ov/iLv0BKSorB95qLiYlBaGgojhw5gt7eXoOOPRxqtRpXrlzBV199BYZhsGXLFkydOlUnF0ZcXFwQGhqKq1ev6iBS/aKkjxD9o0qfkehQ9WPTrjRoNBps9FHC08U4pqqYiuDgYLz//vs4d+4c7t+/j+joaLi6ukIsFv//7L13eFtXmqf5AkRgBBOYo5hEMYpikEjlnCXLysEKtuVyuTzT1TO9s9vb07OzsxN6u3q7q7rLOcm2opVzsAJFUVSgKOYgRjGJopgTCJAEsH/IZMm2AgNAgNR9n4ePLeLecz4QF/ee3/kSvb29PHnyhCdPntDd3Y1EIsHd3Z3Y2NhxUzJ8KLi6ulJdXW1qM15KZGQkOTk5xMTEmMwGR0dH3nzzTVpbW3n8+PGQytMHBgaiUCjYs2ePSZsZD2URLRaL2bRpE3v27OHcuXOsWLHC4PaoVKoRNS1vbW2ls7NzxIVzOjs7DSJuLC0tSUhIID09nVWrVtHR0cH169c5e/YsISEhREZG4u/vP+J5DIlKpeLq1asUFhYyefJk1q5da/L74OLFi9m/fz8nT55kw4YNJrUFoKKigry8POrq6mhubiYgIIDt27cbJV9z7ty5fPbZZ9TV1Zl1y5dXVSgVEBAYOYLoGwNodXp+uy+DqmYVaxyqSYoy3UL1dcbPz48PPviA4uJi8vLyePDgAT09PVhYWAwUWPDw8KC3t5eSkhLu37/Ppk2bRtSweizi5uZGc3Ozqc14KZGRkVy9ehWVSmXwCpJDxcHBYVhhZ87OzshkMkpKSkzWt+3u3bsDuYeDQSaTsW3bNr7++muuXbvG3LlzDWaLTqejo6NjRHliubm5TJgwYcT5kt3d3Tg7O49ojH6qq6vp6+tj8uTJiMViZs+eTUNDAxkZGZw6dQq9Xo+vry8+Pj74+/ub7H6jUqm4fv06eXl5BAUFGT1nb6isW7duoKKnKdrw9Oe/FhcXI5VKmThxInPmzMHHx8eo3k9bW1umTp3K+fPnefvtt402z0iRSqW0tbWZ2gwBgXGNIPrGALfKmkgra2KxspX5Ef5johLXeCYkJOSVuX2JiYlcu3aN48ePj6leSYbAyckJtVpNd3f3qIdyDRa5XI6zszM1NTVjNk9TLBazePFizp49S11dHY6Ojuh0Otra2vDw8DC6EKypqaGxsZEtW7YM6Txra2u2bdvGN998Q29vL/PnzzdIUZqKigrs7OxGlLf18OFDgxRLUalUBisUlJ6eTnh4+M+qB7u4uLBkyRKWLFlCXV0dJSUlFBUVkZycjEQiwdPTc6Doh6enp1GL/tTW1nLr1i0qKioIDAwcaBtgbshkMrZu3crXX3+No6MjsbGxRp+zs7OTzMxMCgoK6OzsJDQ0lLVr14560/SpU6eSlZVFQUEBYWFhozr3YBHCOwUEjI+gHsYAlwvrkYphsqLbLB+mAs9n7ty53Lt3j/b2drNPojckYrEYhUJBQ0ODWVfMc3V1pba2dsyKPnhaoc/KyoqMjIyBqpPW1tbk5eVx48YNkpKSmDRpklHajfSHOQ9nE8rBwYG3336bI0eOsGfPHhYtWjTihXBhYeGIwzKbmppG3FZCp9Px6NEj5s+fP6JxAPr6+igrK2PXrl0vPMbDw+NnYXsNDQ2UlZVRVVVFRkYG3d3duLu7DwhBPz8/g2zG5Ofnk5aWRldXF1FRUSxatMjs73P29vZs2rSJ/fv3Y29vb5RiUz09PeTn55Obm8vjx4/x9/dn9uzZBAcHm6zirlQqZc6cOVy5coXQ0FCzbD8kFKUTEDA+gugzc/R6PRfzHuEj7SQ6wjx36ASeT2NjI4DZeruMib29PY2NjWYt+tzc3CgvLze1GSPGz8/vV14lnU5HdnY2ycnJpKamkpiYSEREhMEWe83NzTx8+JCVK1cOewx7e3veeecdkpOTOXr0KBKJhNDQUCIjI4eV21RVVTWiRtyPHz8GGHHeU2lpKXZ2dgbJz8rNzR0oPDRYXFxccHFxYdq0acBTb1N5eflAy4QTJ07g4OCAh4cHPj4++Pj4DGkzMTc3l5SUFCwsLJg6dSpRUVFm0T5ksHh4eLBixQpOnjzJli1bDJbnVlpaSlZWFhUVFbi4uBAeHs769evN5v4fERHBvXv3SE1NZdasWaY251dotdoxdR0JCIxFBNFn5hTWtVPX3sN6f7nJk+EFhsaPP/5IdHS0SUvqmwonJ6cB0WuuuLu7k56ebmozjIJYLB7oAZeTk8PNmze5efMmiYmJREZGjnhxdePGDSZNmvTKXnSDYc6cOcyZM4eKigqys7PZu3cvtra2TJo0icjIyEHlhbW2tqJSqUbk6SsoKBixpxAgLy+P4ODgEY8DT9tHREZGjmgMW1tboqKiiIqKAp56D2traykvL6egoIDk5GT0ej2enp54enri4+ODl5fXr543zc3NnDlzhvb2dubOnWs0D/JoMHHiRFpbWzl8+DA7d+4ctoeyvr6e9PR0SktLkclkhIWFMX/+fLPKZXyWxYsXs2/fPqKiosyufYVOpxNEn4CAkRFEn5lz6EYBAIsizLfqlsCvqaiooL6+njfeeMPUppgENzc3Hjx4YGozXoqbmxudnZ309PQgk8lMbY7R6F/w5+fnk5qaOiD+oqOjh7XIam5upri4mN/85jcGtXPChAlMmDABnU5HSUkJ2dnZ3LlzB6VSyaRJkwgLC3vh4jwnJ2fEBVgqKysNkuf15MkTg1SFbWtr48mTJ2zatGnEYz2LRCL5lXe4sbGRhw8fUlVVRWFhIW1tbYSFhbF8+XJ0Oh3JyckDTdY3btw4LjYgp06dSktLC4cOHWL79u2Dfk/t7e1kZmZSVFREV1eXyfL0hoOHhweTJk0iOTnZ7J5NOp3utdwgFRAYTQTRZ8b09vZy8l45SokF2k4LsBLy+cYCOp2O8+fPM3PmTLMJ7Rlt3NzcuHXrlqnNeClyuRyFQkFdXZ3Bim6YM+Hh4YSHh1NUVMSNGzdIS0tj2rRpxMTEDEks9Xv5jJW/JRaLB5rR9/X1UVBQwKVLlzh69CixsbFEREQwceLEny0QS0pKmDp16rDnVKvVPHnyxCAeur6+PoNsIqSnpxMUFDQqAqs/hDQuLg54Wohm3759fPzxx2i1WpydndmxY8e4yylfsmQJhw4d4ujRo2zcuPGF34Oenh7y8vLIzc2lvr4ef39/Zs6cycSJE8ecd2r69Ol88cUXZpdrLoR3CggYH0H0mTGXbtyh1cKBGHE1e/acwMPDg5iYGEJDQ4WboxlTUVGBWCwelepw5oqbmxvt7e1m/yB3c3OjpqbmtRB9/YSGhhIaGsqDBw+4ceMGt27dYurUqcTGxr7yszKWl+9FSCQSoqKiyMrKIjExEZFIxK1bt7h48SIhISFER0ejVCppamoakWArKyvD1dXVIJs0arV6RL0C+yksLGT58uUjHmc49PT0YGlpyb179/Dz8+O9994btxtYa9euHegd+WyOar/HOSsri8rKStzd3QkPD2fDhg1j+m/h4OBASEgIN27cMNn19Tx0Op1QmVxAwMgI3zAzpa2tjWO3iwE33po/GY8V0dy/f58bN25w+fJloqKiiI2NNaudOoGntLS0mF2+xGgjkUiwtramsbHRKA2HDYWrq+tAAY/XjX5vWmlpKdevX+f27dskJCQQGxv7wjCrmzdvGtXL9zzq6up48uQJW7ZsQSKRkJSURGNjI/fu3eP48eN0dnYikUjo6uoatlfswYMHBsnn6+npoaenB1tb2xGNU1FRgUgkIiAgYMQ2DYW+vr6BUM6oqCj++Mc/cuLECS5dusTq1atH1ZbRQiKRsGXLFr766itSUlIIDg7m3r17lJSUYG1tTVhYGIsWLTLbPL3hMH36dL755hvmzp1r8j6l/Wi1WkH0CQgYGeEbZqZcv36dWpxxtbbAVyFBJBIxbdo0pk2bRmVlJbdv3+bTTz8lMDCQefPmjasH0lhHoVDQ1dVlajNMjqOjI/X19WYt+tzd3cnLyzO1GSYlKCiIoKAgysrKSElJ4c6dO8THxxMbG/szEdXS0kJRUdGoefn6uXPnDuHh4T9bECqVyoE+dfn5+WRmZvLVV1+hVCoJDw8nIiJiSIvZmpoaEhISRmxrc3MzCoVixAVOMjIyCA8PH7E9Q6GgoIAff/zxV6GcS5cu5YsvvkCj0YyLXL7n0dfXh6+vL9988w2TJk0iKiqKjRs34uXlZWrTjIJSqWTChAmkpKSwZMkSU5sDPK1ULog+AQHjInzDzJDq6mqKyx5S1hnIwgD5r/rX9Cfhq1Qqrl+/zldffUVCQgLTp08361C61wVPT0+amprMPrTR2Dg4ONDU1GRqM16Kl5cXra2t6HS6MVuJ0FAEBgYSGBhIZWUlycnJ3L17l7i4OOLj45HL5Vy8eBGpVDpiL9ZQqK+vp6SkhN/+9rcvPKY/V7Gvr4/8/HxycnJITk7G39+f8PDwV4bDNzQ00NfXh6en54jtbWpqGrEXtKenh/LycoP0+RsMarWaw4cP09raysKFC3/VvNvBwQFXV1dycnKIj48fFZtGg/48vZycHJ48eUJgYCATJ05k3rx5A7mN45mZM2fy/fffM2fOHINU4R0pr/vzUkBgNBBEn5mh1+u5cuUKPc6B9NZDvOeLd1atra1ZunQpU6ZM4dSpUxQXF/Pmm2/i5OQ0ihYL/BJbW1sUCgUlJSWEhoaa2hyT4eLiwqNHj0xtxkuxsrLCxsaG+vp6g/XrGuv4+fmxY8cOKisruX79Onfv3mXKlCkUFxdjaWnJZ599RmJiIlFRUUYXyrdv3yYiImJQQlMikRAdHU10dDQqlYrMzEzS0tK4cOECoaGhREVFPbfCYnFx8ZD64L2M1tbWEefz5eTk4O7uPmrRG0eOHMHa2poNGza80JM3efJk7t27Ny5EX1lZGZmZmVRUVODm5kZUVBTh4eHI5XLu3r1LcXHxayH63Nzc8PHx4ebNm6O2wfAyhJw+AQHj83pvbZshVVVVdHZ2UqqywloqIszl1VXg3Nzc2L17NxMmTOCbb76hqKhoFCwVeBmRkZHjtgfcYHF1daW5udnUZrwSV1dXqqurTW2G2eHn58f27dtZv3496enp2NnZ8Z/+039i9uzZ3Llzh08//ZSsrCx0Op1R5m9vb+fBgwdMnz59yOdaW1szffp0du/ezbZt2xCLxRw9epSPP/6YlJQUWlpaBo4NCAigvb2d1NTUEdvc3NyMvb39iMYwRG++wXLnzh3a29tZsWLFS0M3w8PDaWlpob6+flTsMjQNDQ2cP3+eP/3pT1y8eBGlUsm7777L9u3bmTJlysB7j4iIoKam5mfXx3hm+vTpZGVl0dPTY2pThD59AgKjgLCtYmbcuXMHL28fvsjUEOMuRyIWvfqkn5g/fz4+Pj6cOnWK1tZWpk2bZkRLBV7G1KlTuXv3LqWlpQQFBZnaHJPg4eFBc3Oz2YdOurm5mb1H0pQ4OzsjFovZvHkzAGFhYYSFhVFQUEBKSgq3bt0yiufv7t27BAQEjDhc0s3NjaVLl7J06VJKS0u5f/8+t2/fxt3dnYiICMLDw5k+fTp37txh1qxZI5qrtbV1RP3ampubaWpq+lWIpTFobGwkJSWFzZs3vzJXTyKREBISQmZmptnkgL2Kfm9vfn4+HR0dhISEsGbNGnx9fV94jrW1NTExMZw4cYLt27ePexHi7e2Nu7s7t2/fHvG1P1K0Wq3Qp09AwMgIos+MaGlpoba2FtdJU2nTtLw0tPNFhISEsGPHDvbv309rayuLFi0y6wX3eEUikTBz5kyuXLky4qbRYxVra2ssLCxobW0165Bjd3d3SktLTW2G2XLz5s2BxeGz9Iu//Pz8gdYPSUlJREZGjvieo9FoyM7OZtu2bSMa55f0F63p6ekhJyeH7OxsfvzxRyorK1m2bNmINyg6OjpGdK2np6cTEhJi9IIpOp2OY8eOER8fj7e396DOSUhIYN++fSxcuNBs72f9fR2zs7Opq6vD39+f6dOnD6nN0fz58/nmm2+4cuUKixYtMrLFpicpKYmTJ0+SlJRk0vBKIbxTQMD4CGrAjKitrcXe3p6Mxz1YiCDGfXgPfhcXF9555x0ePnzIkSNH0Gg0BrZUYDDExcUhEom4c+eOqU0xGQ4ODjx58sTUZrwUT09PmpubUavVpjbF7Ojo6CArK+ulOT/h4eG8//77zJo1i7S0ND7//HNycnJGFPaZmZmJUqk0WuVXmUxGXFwcu3btIj4+HltbWyorK/nzn//M5cuXaWhoGNa4nZ2dI8oPLCwsJDo6etjnD5Zr164hlUqZOXPmoM9xd3fHzs6OwsJCI1o2dHQ6HcXFxRw5coQ//vGPZGRkEBwczIcffsiGDRsIDw8fkkgVi8WsX7+evLw8iouLjWi5eTBhwgQcHR25d++eSe0QRJ+AgPERvmFmREVFBU5OTqQXaJiklGErG74mt7W15e233+bIkSN89913rFmzxmDFCgQGz8qVK9m/fz+TJk16LdtqODo6mn0FT4VCQXBwMEePHmXjxo3CwuMZUlJSCAwMHNS9o7+KZm5uLqmpqaSlpZGYmDhkz59OpyMzM5O5c+eOxPRBodVqKSgo4J133mHixIlUV1eTkZHBt99+i7OzM+Hh4URGRg6qGXdzc/NAf8rhUFpaikwmw9/ff1jnD5ba2lqysrLYsWPHkD12kZGRZGdnExERYSTrBk9FRQXZ2dmUlZVha2vLxIkTmTNnjkGecwqFguXLl3Pu3Dnc3d3HfT/cxMRELly4QEJCgskig/R6vdl6kAUExguCp89M0Ov1PHz4EJ21E9XtfcMK7fwlMpmMLVu2EBQUxLfffktOTo4BLBUYCh4eHoSFhXH27FlTm2ISXFxcaGxsNLUZr2TZsmWIRCI++uij12J3fzC0trZSUFAw5Mp+kZGR/Pa3v2X69OmkpaXx2WefkZ2dPWjPX2lpKX19faNS+TY3NxepVMrEiRMB8PHx4Y033uD3v/89sbGxFBUV8W//9m8cPnyY4uLil76HR48ejUhwZGRkGF1M9fX1ceLECWbOnDksW2NiYqitrTVZoZPa2lrOnDnDH//4R86ePYutrS3btm3jN7/5jcEEXz8TJ05k4sSJnDhxwmjFisyFkJAQrK2tycrKMpkNer1eSEUREDAywpa2mdDU1IReryf3J6dInAFEXz9z584lICCAY8eOUVFRwZIlS8Ztk11zZOHChXz++efcvn37tSuu4+rqSllZmanNeCVyuZwtW7aQl5fH+fPnyczMZPny5aPak87cuH79OhMnThx2NcrIyEgiIyMHcv76PX+vKviSnp7OlClThmv2oNHpdNy+ffu5IY4SiYSoqCiioqLo7OwkIyODy5cvc+7cOcLCwoiOjv5V6Onjx4+HLTrUajWVlZUsXbp0WOcPlgsXLuDo6DjsRvSWlpb4+/uTlZU1Kp5YtVpNcXExpaWl1NbWotPpmDhxImvXrh1RwZzBsnjxYr788kuSk5OZN2+e0eczJYmJiSQnJxv1u6fT6ejs7ESv1wMM/Lf/94KnT0DAuAiiz0yora3Fzs6OS480+CgkuNsa9qPx8/Pjt7/9LceOHeOLL75gxYoVRg8jEniKRCJhzZo17N+/n6CgIIPsRmu1Wmpra6mrq6O1tZXu7m76+voQiUTIZDIUCgVubm64u7vj4OAw8jcxTNzd3cdE24Z+IiIiCAwM5PLly3zxxRf89V//talNMgkNDQ0UFxe/tCn6YOkP+8zPzyc1NZVbt24xbdo0oqKifrXIq6+vp66ujvXr14943ldRUFCAVqslPDz8pcfZ2toye/ZsZs+eTW1tLffu3eP777/H0dGR8PBwoqKisLa2pq6u7pVjvYisrCy8vLxQKBRUV1eTnZ1NRUUF1tbWJCQkGKSFw4MHDyguLubdd98d0Tjx8fGcOXOG2bNnG9wz09PTQ0VFBSUlJdTU1NDW1oa7uzt+fn7ExcXh7e09qt4gsVjMhg0b+PLLL/H19R3XlZgnTZpEamoqubm5Bm0ZotPpKC8vJycnh4qKip8VSxKJRIhEooHjRmOzR0DgdUYQfWZCRUUFlgonCop7eGOijVHmsLS0ZMuWLWRmZnLkyBFmzZo17B1fgaHh4eFBdHQ0Z86cYfv27cNauGi1Wh48eEBeXh6VlZVYWlri4uKCo6MjLi4uSKVSdDod3d3dNDU1UVJSQmtrK3q9HqVSiaurKy4uLri6uuLh4TEq3l4HBwe0Wi2dnZ1jxmtmZWXFsmXL+Id/+AdTm2Iyrl+/TmRkpEE/s37xV1BQ8DPPX3R09ID4S09PJzQ0FJns1f1JR8rt27dJSkoa0jleXl54eXnR19dHYWEh2dnZpKSk4O/vT0VFBYsXLx6WLffu3UMqlfKv//qvwNNwu5UrV9Lc3ExaWtqAByY+Pn5Yf5uKigpOnz7NqlWrRpyfNmHCBADKy8sNIoLa2trIycmhtLSU+vp6lEolvr6+LFiwAB8fH5NHpTg4OLB06VLOnj3L22+/jZ2dnUntMRZisZipU6eSlpZmENFXVlZGdnY2Dx8+xNrampCQELZt2/bC4kyZmZlGK9wkICDwFEH0mQE6nY7Kykp6PaLR6bsMks/3MmJiYvD09GTfvn3Y2tqOSk8oAZg3bx6ff/459+7dG5LY1mg0pKenc+/ePaysrAgLC2Px4sWDDrtra2ujtraWx48fU15eTnp6Ou3t7djZ2aFUKnFxcRnwCjo5ORl8J12hUNDQ0DBmRB+AhYUFIpGInp6eUREgg0Gr16Pq1aPq1dHVo6erV4eqV09Xz0//7dXR9dO/u3467unrT1/ztZewItiGeC85FqIX9/+sra3l4cOHfPjhh0Z5H/2tHoqKikhJSSEtLY2kpCRCQkIoKirinXfeMcq8z1JbW0t7e/uwK2VKJJKB8NX+8M+CggL2799PaGgo0dHReHh4DGqshoYGqqqqmDNnDhERET/zZvn7+zNlyhRKS0u5c+cOt27dIiIigqSkpEGLN7VazenTp1m4cCEhISHDer+/JDw8nOzs7BGJvr6+Pi5evEhBQQETJkwgNjaW4ODgQRXNGW3Cw8MpKyvj5MmTbNmyZdzmnkVFRZGamsqDBw8G8lyHQllZGbm5uZSXl2NpaUlISAhbt24dlJgTCrkICBgfQfSZAY2NjYjFYjIbtNjLxQQ5Gb9BqZubG6tWreLUqVP4+PiM291Lc0IsFrNy5UoOHjxISEjIoMIuMzIyuHHjBs7OzqxZswY/P78hz2tvb4+9vf3PxL1Op6O+vn4gRDQjI4OmpiZ6e3tRKpUD5fJdXV1xd3cfdkVCeLpT3tDQMOAhGCtIpVKDir4+nf4v4myQoq2rV4/qp3939+lfOYe1RIS1TISNVIyNVITS2gJfexGWEhGZdT384VYrrjYWLA+2Zr6/FVbSXy9er1+/zpQpU7C0tDTI+34RoaGhhIaGUlRUxI0bNzh79iwKhWJUKiXm5uYSHBxskMX7s+GfdXV1pKens3//fhQKxUD458s2PNLT05k1axbLli174TH9/QXr6uq4e/cun376KRMmTCApKQkvL6+X2nfkyBH8/PwM2goiLi6OTz/9FJVKNax7Q0VFBWfOnMHFxYXdu3ebNAR9sCxbtowvv/ySGzduMHv2bFObYxTEYjGTJ0/m/v37gxZ9fX19nDt3jtLS0gGht3nz5kFvevSj0+kE0ScgYGQE0WcG1NbWYmVrR2a5hmlelohfsgtvSIKCgggICODq1ausXr16VOZ83fHy8mLSpElcuHCBTZs2vfC45uZmzpw5Q0dHBytXriQwMNCgdojFYjw8PH71YFapVNTU1PD48WOqqqrIysqitbUVKysrXFxcBsSgu7s7SqVyUA9pZ2dns2/b8DwkEgm9vb3odDq0Wi3qXi1dPVo61Fo6e7Q/iTPdMyJNPyDOVH3Q3QfdWujuE6HWiujVv/x7LUKPTKRDLtIiE2mR0YcULY704UofUos+JLpepPQi0fch1fdioe3BQteDRN+LWKtBr9Oh++mnr68PrVb7NLxWq2WCVoezYgKtkhi+ydJyKK+T+QFWLAuyxtXm6aOgoqKCx48fs27dutH4EwN/EX+ZmZncunWLTz75hMTERCZPnmyURaBWq6WoqIiNGzcafGwPDw9WrVqFTqejsLCQzMxMUlNT8fX1JTIy8ldNwnU6HUVFRWzYsGHQ469evZr29nbu3r3L4cOHsbGxITY2lsmTJ/9KxKamptLV1WXwHEmFQoGnpyfZ2dkkJiYO+jy1Ws2FCxcoLy9n3rx5TJ482aB2GROJRMK6dev49ttv8fX1HXObWIOlra0NJyenQR9/5coVWlpahiX0nkXw9AkIGB9B9JkBFRUVtEqcUfXqjR7a+UsWLVrERx99RGNjo9DHb5RYsGABn376KTk5OURFRf3stcbGRu7fv09ubi4RERFs2bJlVPvG9edePBsGptPpaGho4NGjRzx+/Jjs7GyuXbuGRqPByclpIF+wXwz+0qvh6upKXl7eqL2HwVDb0cfjTi1PWtp51NBCY7uKDnUfXb161FoRPXoxXYpZpJ99jNaiBa1Yhl788s9BpNcj0fci0fUioRfpT8LMnj5cfhJxMnTIRFrkIi1ysRa56Om/H5YUYmclIzAgAIlEgkgkQiwWY2FhMfAjFosRiURYWMiwsLBCLBY/9xixWIxUKh34r0QiGfhpa2sjJSWFB40aun2mcbZEz9liFVO95awMseF2SgoJCQkmCWmNiYkhJiaGBw8ekJKSMlDwJSYmxqCLwbKyMiwtLUe0QH0VYrF4IIdRpVKRmZnJzZs3uXDhApMmTSIqKgpvb2+Ki4uxsbHB29t7SOMrFAoWLFjA3LlzycvL4969e9y4cYOEhATi4+ORSCQ0NjZy+/Zttm7dapS8uJiYmIG8zMGQnZ3NlStX8PX1Zffu3WMyukSpVLJgwQJOnz7N22+/PaZC1gdLTU0NixYtGtSxjx8/Ji8vj127dg1JKD4PvV4/UNRFQEDAOAiiz8TodDqqqqootY5EJlYT5Ta6iy1ra2smTZrErVu3WLly5ajO/boik8lYtmwZJ0+epKOjg/Lycpqbm+nr60Ov1xMQEMCOHTvMRoSLxWLc3Nx+lZehVqsHcgUfP35Mfn4+LS0tyOXyn4WIisVis+nV16vV8tXtOn58ZAH0LzAUgAIL9FhZ6bGSiHCUifGRW2Art8BWJsZGKsZa+lPYpEyE9U/hkzay/t+LkFuIhr1oOX9eglQqZcGCBYZ6q8/F1taWzZs309DQwNWrV1E8akIbMIPsx67cqtFgJ4ljgrc/Wp0eC7FpFmD9/dH6xV9/qxNDib/CwkKCg4MNYOngsLa2Zvr06UyfPp36+nrS09P54YcfsLGxoaura0TFtCwsLIiOjiY6OprS0lLS0tK4desWkydPpry8nNjYWKOJ20mTJnHx4kVqampeKlobGho4c+YM3d3drFq1asxXwIyOjh4IT31ZtMZYpKOjg7a2tkG1w9DpdJw8eZLExMQRCz54ei1rNJoRjyMgIPBiBNFnYurr67GwkHD/cS+RbnIsJaOfID5jxgw+//xz5s+fP6LcLYHBExQURGhoKA8ePBgorGNpaYmdnd2YKRJgaWlJYGDgr0JPGxsbefToEXV1deTn5w9UEj1//rzR+5C9iJaWFlLSszn5xIk2uSvBkiaWT7Rngrc7Nj/lwMksTLfLbGFhQV9f36jN5+LiwsaNG2lsbCQ5ORnZwytUSzzp9knkT+kd7MvvYlmQDQsmWGEjM831aAzxp9VqKSkp4e233zawtYPDzc2NFStWoNPpyMnJ4eDBgwZryN6f91ddXc2NGzfIzc01aoN7sVhMaGgo2dnZzxV9PT09XL16ldzcXOLj45k+fTpSqfHz1UeDFStW8Pnnn5OamsqMGTNMbY7BKC8vx83NbVDRJampqUgkEoP1npVIJKjVaoOMJSAg8HwE0WdiiouL6bVW8qRWy5uTjNOq4VU4ODjg4+PD9evXTbYofx1Zvny5qU0wCv1evmdDV9vb29m7dy+nT59m+fLloyZs6+rquHnzJpmPunnoMRutlZTfxSqY5+8+KvMPFqlUSldX16jPq1QqWbduHc3NzZw+fZr68kNIfGN4Io3gu5wOfsjvZN4EK5YHWxu8d+hg+aX4u3XrFomJicMSfxUVFdja2hrEMzESxGIxXV1dTJ482eBFTHx8fNiyZQuzZs3i1KlTVFVVsW7dOqOE7CYkJLBnzx6WLFnys8+ioKCAS5cu4e7uzjvvvGPyv7ehkUgkrF+/nj179uDn5zcqjeJHAysrK5qammhvb39pQaXm5mbu3r3L1q1bDXYvt7CwEESfgICRGRsuhXGKXq8nPz+fRyJnAOI8TNePaPny5RQUFFBcXGwyGwTGLwqFgp07d1JbW8uZM2fQ6XRGna+hoYGDBw+yd99+imXBPPBYiFJhzT8uUDLP3/y82Vqt1qRFDJycnNixYwcf/Pa3RDmDS+4+5vbcIcKhj0tlKj4838g/3Gwhv6EHvf7VVUSNwcSJE9m9ezeLFi0iMzOTjz/+mHv37qHVagc9RklJCb6+vka0cvDk5ub+KqfXkHh7e/POO+8gkUj48ssvaW1tNfgcSqUSuVzOo0ePAGhtbWXv3r1cuXKFRYsWsWnTpnEn+PpxcXFh7ty5nDx5ckjXoDkTEhJCWFgYBw8efKkAO3nyJJMnTzZo6LBMJjPJxpeAwOuEIPpMSF1dHTqdjuxGPcFOUhytTLfoUygULF++nLNnz9LS0mIyOwTGL9bW1uzcuZO6ujpOnjxpFOHX29vL5cuX+fbbb7F0dKN9yg4yNG7M9rPk/53vhK+9eYaXdXd3m0V/MltbWxYsWMAHH3xAtK8TVjmHmdN5lTmuGooae/gvyc38p8tNXK/spldnWvG3ePFiMjMz+eSTTwYt/iorK4fVf8zQ1NXVoVKpjG6LXC5n/fr1TJo0ic8//5z8/HyDz+Ho6Eh9fT1Xrlzhyy+/xM3Njffee2/c938tLi6mqKiIkpISMjMzTW2OwVi0aBHOzs58/fXXNDQ0/Or1e/fu0d3dbfC2FYLoExAwPoLoMyFFRUXIFEpKmnuJG+Wqnc8jNDSUyMhIfvjhByGhWsAoWFpasmPHDhoaGjhx4oRBhV9VVRVfffUVNTU1JK3ZxWlNOKWtffwuXsG/S3AwSb7sYNFoNGaVT2tlZcXMmTP53e9+R0xoAPqcUyQ8Ps0K9w40Wj3/ereND842cLSwkw6Ncb22LyIkJITdu3ezcOHCAfGXkZHxQvHX0tJCR0eHWZTaT09PJywsbFS8uyKRiFmzZiGXy9m/fz/Z2dkGHd/KyopTp05RW1vLW2+9xcKFC41SLdQcUKvV3Lx5k48++ohLly4REBDAqlWrKC8vN7VpBkMsFrNmzRqioqLYs2cPx48fp7m5GXgapp+cnMyyZcsMnp8pl8tRqVQGHVNAQODnCDl9JqK3t5e8vDzalBFAt0lDO59lwYIFNDc3c/jwYTZv3iz0zREwOJaWlmzfvp3vv/+eY8eOsWbNmhFdZ1qtlqtXr5Kdnc30GTOpsgvjn+934Wlnwf8129lsvXvPolarzcLT90ukUinTpk0jPj6e3Nxc7ty5ykStlukRsyjSe7A/r5MjhZ3M8bNiRYgNXnaj/0h5Xs5fUlIS0dHRP7uumpqa6Ovro7q6Gj8/v1G3sx+dTkdxcTFvvfXWqM15/vx5FAoFb7zxBsePH6e1tdVgnhqlUomvry/bt283yHjmSGVlJRkZGZSVleHl5cX8+fMJCQlBLBbT1tbG559/Tm9v77gpVCMWi5kxYwZRUVHcvHmTr7/+Gl9fX7q7u5k0aRL+/v4Gn1Mmk9HW1mbwcQUEBP6C+W59j3NKSkqwtLQku0GHi7UYP3vz0d9vvvkmfX19HD9+3Oi5VwKvJ5aWlrz11lu0trZy9OjRYefEtLS0sGfPHqqrq9mw/V0uaoL5oaCLWX6W/L8Lxobgg6fhnebk6fslFhYWTJ48md27dzN3zhxUZenYZ37PW8oqpnvLufawm39/oZH/mdpCTr3GJHl//WGfCxYsID09nU8++YT79+8PXFtBQUE4OjoaJbdtKNy5c4eWlhYaGxtHpWJrVlYWpaWlbNy4ET8/P7Zt20Z+fj6nTp0yyP3d19d33OS09dPT08P9+/c5cOAA//RP/8SZM2ewtbXlnXfeYcuWLYSGhg4UMLG3t8fOzo6ysjITW214FAoFS5cu5YMPPqCjo4OKigqjtZWRy+VCeKeAgJExH6XxmpGRkYGrhxc5tzQsCLA2q6akEomETZs28e2333L27NlRrbYo8PrQ7/Hbu3cvR48eZe3atUPy+NXX13PgwAEiIiJwi5rJ/327DVWfjt/FKZjrb2VW36lXodFoxkSjZ7FYTFhYGGFhYZSWlnLr1i00T26wOXwyHe5TuFrVy/+d0oKfvYQVwdbM9LVCOsqtMEJDQwkNDaWoqOhnnj8fHx86OjoIDw8fVXt+SXFxMWFhYdy6dYtz584RGBhIdHT0r1qfGIK6ujquXLnChg0bBjYVlEol27dv5/Dhw3z//fdMnjwZf39/7O3thzWHm5sbra2t6HS6Mf+c6OnpITU1lfv37+Pq6kpQUBALFizAxcXlpecFBARQUlJi1BYZpkQsFtPe3s62bduMFrpraWkpiD4BASMjiD4T0NjYSFNTE532E+jRtZlFPt8v6c+92rNnjyD8BIyGTCZj27Zt7Nu3j8OHD7N+/fpBCb/6+noOHjxIfMJUau0j+fR6y5gK5/wl5hre+TL6+8LV1NSQkpJC+vn/yuqFi5FHJXGxso+P7rWzN7eTJUHWLA6wwt5ydEPFfyn+Tp8+jVKpNOl9rLOzk/r6ej788EOsra1paGggNzeXCxcu0NvbS1BQEDExMXh5eY14ru7ubo4fPz4geJ/F1taWLVu2cOvWLXJzc7l06RISiQRXV1c8PT3x9/fHx8dnUP3arK2tkUgktLW14ejoOGK7TcGzYs/Ly4tNmza9tOH8L4mKiuLgwYPjQvg+j7NnzxIQEEBQUJDR5pBIJOh0Onp6eozSXkRAQEAQfSahqKgIpVLJtboerCUiwlzM8wb3bO7ViRMnWL16tZDjJ2BwZDIZb731Fnv37uXQoUOsX7/+pbkxLS0tHDp0iNDJCfzYO5Hcgk5m+VryXqwCKzMu1vIidDrdmPH0PQ9vb290Oh2JiYloe9Tknf6SFTFTcEicyoWKHg7ld3KssJNZflasCLYedVHeL/4yMjJIS0vj888/Z8aMGQZrij4U0tPTmTBhwoDXzcXFhXnz5jFv3jxqamrIy8vjyJEjiMViQkJCmDZt2rA9cKdOncLV1ZXExMTnvi6Xy5kzZw7w9BpsaGigurqauro6Ll26REtLCw4ODri7u+Pt7U1AQMAL2y/Y2dnR1NQ05kSfWq0mLS1t2GKvH3d3d8RiMTU1NWbTEsRQFBcXU11dzXvvvWf0uWQyGSqVShB9AgJGQhB9o4xOpyMnJ4dJYWFkXOsmxkOOVGy+YWjW1tbs2LFjwBOzZs2acVuZTcB0SCQStm3bxv79+/nhhx/YsGHDc4VfZ2cnBw4cwD44nh/aA1H19PDbOAXzx1g457Oo1WqkUumY9RDcvn2b9vZ23nvvPSQSCXV1dSQnJ5Od/RkzYmPZMi+BSw97SK7s5kpFN9FuMlYE2zDZXYZ4FD+z2NhYYmNjycnJ4fr169y6dYuZM2eOakheQUEBCxcufO5r3t7eeHt7s2jRIh4+fEheXh5ffPEFYWFhzJkzZ0g5n6mpqTQ1NfHuu+8O6nixWIybmxtubm4Dv9NoNFRXV1NTU0NxcTHXr19HJBLh4uKCl5fXQFNymUyGg4MDTU1NRvUEGRK1Wk1qaipZWVl4e3sPW+w9i7+/P0VFReNK9PX09HD+/HkWLFgwKjnHUqmUrq4uHBwcjD6XgMDriCD6Rpn+JraPe+S0arqIN8PQzl/SH+p58OBBDhw4wMaNG8dcKJqA+SORSNiyZQsHDx4c8Pg9u8Gg0Wg4eOgQTe4JXOv0xt1OxH+Z5YzfGAznfBaVSjVmq/7V19eTmprKtm3bBkIBPTw82Lx5M9XV1aSkpJCRkUFCQgKblk7j8kM150tV/I/UFrztLFgeYsNsPyvko5j3FxUVRVRUFPfv3+fHH3/k1q1bTJ8+nZCQEKPOW11dTV9f3yuFkVgsJiAggICAABobG0lJSeGTTz4hKiqKmTNnYmlp+dLzKyoquHPnDjt27BiRx0Qulw+E8PbT7w2sra3l8uXLA97Ajo6OMXENq1Qqbty4QW5uLj4+PmzevNkgobQA4eHhXLx4kUWLFhlkPHPgwoULuLu7j5pXvN/TJyAgYBxEpqiyNlzi4uL09+7dM7UZI+LixYu0trZyp9uNEw+6+GaVK7aysbHDr9PpOH78OE1NTWzatAmFQmFqkwTGIX19ffzwww/09fWxceNG5HI5vb29fHvoOPdlkdSLHMd0OOcvqamp4dSpU3zwwQemNmVIaLVavv76ayZOnMisWbNeeNzDhw9JTk6mq6uLJUuW4DshgLRqNaeLu6ho7cNOJmJxoDVLAq1xtBrd8HGdTse9e/e4ffs2dnZ2RhV/J06cwNraeliioL6+nuvXr1NVVUVsbCzTp09/rqDr6Ojg66+/Zu7cuURFRRnC7JfS7w28d+/egLfXHGltbeX69es8ePCAwMBAkpKS8PDwMOgcOp2Of/7nf2bHjh2vLPwyFigrK+PkyZPs3r0bOzu7UZkzLy+P6OhoJk+ePCrzCQiMR0QiUYZer4973mtjf8U0hujt7aWoqAhvb2/uPdIwSSkbM4IPnu5Ar127Fh8fH7799lvq6+tNbZLAOKS/eqxUKuXgwYN0d3fz2fGrXJUm0GzhyG/jFPz7BPtxIfhg7Hr6rl69ioWFxUsFHzwNe9u5cyeJiYmcPn2aoz8cIsaxlz8scOa/zXEiVCnjaGEX759t4N/utlLR2jtK7+DpPS0hIYEPP/yQ8PBwLly4wDfffENpaalB5+nr66OkpISYmJhhne/m5saGDRvYtGkTdXV1/PnPfyY1NfVnLR90Oh0nTpwgICBgVAQf/MUbOHv2bHp6ekZlzqFQV1fHoUOH+OKLL5BIJLz77rusXbvW4IIPnl5Lfn5+PHjwwOBjjzZqtZpTp06xYMGCURN88DSqqL29fdTmExB43RDCO0eR2tpa5HI5rX0Sqtr72Bk9ejdTQ7J06VJu3brF3r17Wb58+bgtUy1gOsRiMRs3buTQ4cP8b99coU4Zi4edBX+T5Ii/w9gTSC9jLFburKioICcnZ9A5YwBTpkxh0qRJJCcn8/nnnxMeHk5SUhL/x3RH6jr7OFei4mpFN8mVasJdZKwMsSbWQz4qeX/94i8uLo579+5x7tw57OzsmDlzpkHy1PLz81EqlSP2AHl7e7NlyxYqKiq4ceMGGRkZJCUlERsbS3JyMiqVis2bN4/Y3qHi4uJCe3u72TQor6ysJDk5mcbGRmJiYli+fPmoFEqaOHEiGRkZzJgxw+hzGZNjx46N6uZBP4LoExAwLoLoG0UePnyIvb099+o0AGMin+9FJCYm4uzszKlTp3jy5Mkrd/sFBIZKR4+e27Ip1LnYkegp4XcJTlhJx4d371nUavWYqlan0Wg4e/Ys8+bNG3JlSSsrK5YuXUpcXBxpaWl89tlneHh4EB8fzzsxoWwKt+XHim7Ol3TxDzdb8bC1YHmwNXP9rbAcBc/uL8Xf2bNnUSgUIxZ/WVlZBs2LmjBhAhMmTKC0tJTr169z8uRJxGIx/+E//IdBtVkwNBKJBGtra5qbm39WDGa0efDgAWlpabS1tT3NI920aVQLj4WGhnLhwgU6OzvHbDXetLQ0WltbWbt27ajPbWlpSXNz86jPKyDwuiCIvlGkpKSEwMBA9mdp8FZIcLcd23/+kJAQdu7cyaFDh3j8+DErV64ccx4LAfOkuVvLf7jwmM5ea3aES1kZ5jRmq3O+irEm+q5du4aDg8OwQxXhqWdo9erVdHd3k5WVRXJyMhcvXiQ8PJx5CQmsCHbhTq2a08Uqvszs4EBeJwsDrFkWZI2ztfHz/gwp/trb26mvr2fDhg0GtzMoKAgrKys+/vhjRCIR7e3tJqt86ODgQGNj46iLvr6+PjIzM0lPT0ev1xMfH090dLRJqkzLZDLc3d0pKioiLu65KTVmi06n49q1a2RlZbFlyxaT/P2srKwET5+AgBEZ26pjDNHW1kZnZycSKzsKGp6waqKNqU0yCEqlkt27d3Pq1Cm++OILFi1aJIR7CowIrU7P/7peT1ePjt9H6pkR5mxqk4xKd3f3mNksqaqqIi8vz2AFO6ysrEhMTCQxMZGKigoyMzP59NNP8fb2Ji4ujn+YH8KDph5OF3dx6kEXp4u7SPS2ZGWIDUFOxg8jfFb83b17l7Nnz2Jvb8+MGTMGLf7u3bs3IM4MTV9fHydPnmT16tVYWlpy5MgR3nrrLZMUElEoFKPqpenvsZednY2DgwNz5swhNDTU5K1PQkJCKC0tHVOiT6VS8cMPP6DT6dixYwdKpdIkdlhaWtLR0WGSuQUEXgcE0TdKVFdXY29vT+ZjDVr92A7t/CUymYx169ZRUFDAhQsXyMnJYeHChWOuUa+AefBNRiPlHWLe8GhjRtgkU5tjdDQazZj4rmi1Ws6dO8fMmTONUrm3P2Sxs7OTrKwsfvzxR77//numTZvG1ilT2B7lwrlSFZfLu0mtVhPqLGVliA3xXnIsjOwFFovFTJs27Weev8GKv8LCQhYvXmwUu3788UcUCgUJCQnA0z6W+/bt4+233x716spKpZKWlhajz9Pe3k5KSgqFhYX4+vqydu1as+qNFxERwY0bN9BoNGOip21dXR0//PADAQEBLFu2DAuL0a2g+ywWFhaIxWLUavUrW5MICAgMHUH0jRKlpaU4OztztUaDvVw8KrvUo01YWBgBAQFcvXqVL7/8krCwMJKSksx2QavT6Uy+Kyzwc25XdXL+oZYwWTNvzQoztTmjwlgp5JKSkoJMJmPq1KlGncfW1pYZM2bg6enJ0aNHAfjhhx8QiUQEBATw99GRlPQpOVuq4g+3WnG1sWB5kDXzJlhhbeScT4lEMiTxV1lZiVarJSAgwOC2VFZWUlBQwDvvvDPwu6SkJDo7O9m7dy9vv/32qC6cXV1dKSsrM9r4ra2tXLt2jdLSUkJDQ9m5c6dZtkawtbXFwcGBsrIywsLM+x52//59rl69ypw5c8zGM9nfq08QfQIChkcQfaOAVqvl4cOHTImL5/69NqZ5WRp9Z9pUWFpasmzZMqZPn05ycjJffvklgYGBxMXFmXQ3trOzk4aGBurr66mvr+fx48c0NTXh4uKCt7c3zs7OSCQSent70Wq1A+9FoVDg5eU1JhblY53HHT388XYLDiINf79i/Hv4+lGr1VhbW5vajJdSX19PRkYGb7/99qjNmZubS0xMDAsWLECn01FTU0NhYSE/njtNX18fC/38EU+YTHq7Hd9kd3Aov5N5E6xYHmyNq41xH23PE3/Py/nLyMggLCzM4JtLPT09nDp1irlz5/4qh2/RokWoVCr27dvHjh07Rq2wi6urq1E8fY2NjSQnJ1NRUUFERAS/+c1vzL5HbEBAACUlJWYr+nQ6HSdPnqSmpoZNmzbh7e1tapMGkEqldHV14eTkZGpTBATGHYLoGwUeP36MTCajvEOEqldP3DgK7XwR9vb2rF69ms7OTm7fvs2xY8ewtLQkJCSEkJAQPD09Db4Q6u3tpbGxkYaGBpqbm2lubqalpYWWlhZ0Oh0KhQInJydcXV2JiorCw8ODyspKysvLefjw4YDnTyKRoNfr0Wg0dHZ2DlSkmzRpEjExMWMiZGes0avT818vVaFHzv+90AeZxfjcFHke3d3d2NiYb46vTqfj3LlzxMfHj9pCTKPRUFxczO7du4Gn4ZW+vr74+vqyePFiamtrKSwspPjuOZy6u1nsF0GdzSTOl+o5V6JiqrecFcE2THSWGrUA0MvEn7+/P2VlZezatcvg8164cAEXFxemTJny3NdXrlzJoUOH+OGHH9iyZYvB538eTk5O9PT0GCxHtb6+nuTkZKqrq4mKiuL9998f1Z5xIyEiIoJ9+/aZZTSJSqXi0KFDSCQS3nnnHbPbcOr39AkICBgeQfSNApWVldjb25P2SINUDNFuY6dS30ixtbVlwYIFzJs3j/LycvLy8jh69Ci9vb14enri4eGBq6srSqUSBweHFwoqrVaLSqVCpVLR1dVFZ2cnHR0dtLe309LSQnNzMx0dHdjY2ODg4IBCocDNzY2IiAjc3NxeWFp+4sSJTJw48aXvoaenh6KiIrKyskhNTSUiIoKEhARhJ9KA/I8z+TTolPz7WCu8HV4vUW3unr67d++i0WiYOXPmqM3Z3xy9sbHxudUovby88PLyYsGCBdTX11NYWMiT1O/w7REhj1hI5iNvbtVoCHaSsiLYmmnelkjEoyv+urq6UCqVBi+KUVZWRmlp6YAgfh4WFhasXbuW/fv3c+zYMd58802D2vAi7OzsaGxsxMfHZ9hj1NfXc/XqVR49ekR0dPSo9dgzJG5ubkilUqqqqvD39ze1OQP0N6sPDAw0ef7ei5BIJHR2dpraDAGBcYkg+kaBiooKnJ2dSU/XEOUmH5V+U+aGWCwmKChoIPSptbWV0tJSampqePjwIe3t7ahUKkQiEVKpdMDbptVq6evrQ6vVIpVKkcvlWFpaYm1tjbW1NXZ2dkyaNAl3d3dcXFyMEsokk8mIiooiKiqKxsZGUlNT+eqrr/Dz8yM+Pp4JEyYYfM7XiWN3y8nVKHFvL0JXpYfAuaY2aVTRaDRm6+lraWnh5s2bbNmyZVQ9FiEhIbS3t3P+/Hn0ej3vvffeC3N83NzcUCqVZGVlsWbhQlpaWsgtvECN1IsW68n8y502lDkdbI+2I8nbctQ8f3/4wx8MHt6nVqs5ffo0CxYseKXXSy6Xs3HjRvbt2zdQ4dPY2NvbD1v0NTQ0cPnyZR49ekRMTAxvvPHGmA6r9/f3Jzc312xEX0FBAWfPnmX27NkDhX/MEaFBu4CA8RBEn5Hp7e3l8ePHyF0n8KSrhTWh5rm4G20cHByIi4v7VfK4Wq1Go9HQ29sLMCD0ZDKZWYTJKJVK3njjDdRqNbdu3eLkyZPY2toSFxdHZGSkWe6cmjMFVU84WCHB26aPv5sbyaED+9HpdMyfP9/Upo0K/Zsa5lq04OzZs4SHh+Ph4TGq80qlUhITE7G3t+fMmTOv3MwpKytDJpMRHh4OPC1o8uOPP5JfsJ81izZz6ZGYf77dxg1PNbunKHC2Mu73tLOzE4lEQnx8vEHHPXPmDL6+vkRFRQ3qeGtrazZv3syhQ4f45ptvWLt2rVHz4RwcHGhqahrSOY2NjVy5coWamhqio6NZvXq1WXu+B8usWbP47rvv+Oqrr5g2bRqTJk0y2TPs+vXrZGRksHbtWqMUFTIkVlZWtLW1mdoMAYFxiSD6jEx9fT3W1tZk1j8VMXEer1fo2lCxtLQ02wXws1haWjJ37lxmz55Nbm4ud+/e5fr160yePJlp06YJeX+DoLunj3+82YxEasffzXXD1UbCtm3bBnJhFi5caGoTjY5KpUIqlZrFhsYvuXv3Lm1tbWzatMkk82u1WlJSUli8ePErRV9ubu7P+oOKxWIWL16Mo6MjNy7sY+ey5ZT6+nAwv4PfX2hkW5QdCwOsEBvJ63f37l1CQkIMeh/Izc3l0aNHLw3rfB62trbs3LmTH3/8kS+//JIVK1YQEhJiMLuexdXVlaqqqkEd29jYyLVr16isrCQ6OpqVK1eOC7HXj729Pb/73e+4f/8+169f5/r168TFxRETE4NUOjrVu3U6HSdOnODx48ds377dZP33hoKVlRW1tbWmNkNAYFwiiD4j8+jRI2xtbUl/pCHIUYqTkXeYBUYXsVhMdHQ00dHRVFZWcuDAAby8vAbduPl15g+Xy+mQOPC3SQ4D1RYdHR3Ztm0b+/c/9fgZq7+ZuaBSqZDJzC/Ht6WlhRs3brBhw4ZRq/74SzIyMhCJRERHR7/0OI1GQ1FREb/5zW9+9Vp/7u2pU6dISEjgXxZN49OMdj6/386Nqm5+G2ePl53h319RURErV6402Hjt7e1cvHhx2CGPFhYWLFmyBH9/f86dO0dZWRkLFy40+Gfr4uJCZmbmS495thpndHQ077///pjL2RssYrF4IKKlqKiItLQ00tLSmDJlCvHx8UYNX1Wr1ezfvx+pVMquXbvGTKisEN4pIGA8zG97eZzx8OFDLGydKGnuHVcN2QV+TW9vL7a2tmYfPmMOXH/YRXaXgtmuPcR5/tyz6+DgwLZt2ygrK+P8+fMmsnB0MNd+VP1hnSMpyDESNBoNaWlpLFiwYFDH+/n5cebMmee+FhQUxNatW8nKyuLu1XP8/Qx7fhenoKqtj/94qZGjhZ306fQGs72srAyxWGzQXN8TJ04QHh4+4s2k0NBQ3n77bRoaGvj888+prKw0kIVPcXNzo7W19bmv1dXVceDAAfbs2YNCoeC3v/0tixYtGreC75f0/+3XrFlDVVUVH330ERcvXkSj0Rh8rubmZr766iucnZ3ZsmXLmBF88DQXtbu7e6B1koCAgOEQRJ8R0Wq1TwuVaGzQw2vRquF15tatW0yZMsUsQ/XMiZr2Pj6514ZDbyMfzHy+qFAoFGzbto2HDx9y7ty5UbZw9FCpVGYXCnz37l3a29sHLbiMQVpaGkqlksDAwFceK5fL2bx5M0+ePHlh1T83Nzd27txJc3Mz+/btI8lDzJ+WKInztGR/Xif/6XITpc29I7a7sbGRH3/8cdA5d4OhsrKS1tZW5s2bZ5DxFAoF27dvJy4ujiNHjnDmzBnUarVBxra2tkYikfxM+FVWVvL999+zf/9+3N3d+eCDD14rsfdL/Pz82LZtGzt37qShoYE9e/bQ2NhosPErKyvZs2cPkZGRrF69eszlmYtEIqFtg4CAkRBWp0akoaEBmUxG5pM+lNZi/OyFaNrxSmNjI/X19cTExJjaFLNG06fnn261ItL1sS2g56Vl9PuFX1VVFWfPnh1FK0cPlUplVnlM/WGdq1evNllYZ3t7OxkZGUPK6bSwsMDOzu6lzcFtbW3ZunUrtra2fP3114jUHfxNogP/e5IDHRodf3uliT3Z7aj7dMOyu7GxkT179jBx4kSmT58+rDGex/379wkLCzP45kBCQgK7d+9GpVLx0UcfcfXqVXp6ekY8bn/bhuLiYr7++mtOnDjBhAkT+PDDD5k7d65ZXe+mRKlUsm3bNiZOnMh3331HUVHRiMbr6enh2rVrHD58mAULFjBr1iwDWTr69DdoFxAQMCyC6DMidXV1yKxtya7XEO9p3FLhAqYlLS2N8PDwMRVGYwq+yGynpr2XwCc3mBn3am+InZ0d27Zto6amhlOnTo2ChaNLV1eXWYV3nj59moiICLy8vExmw/Xr1wkKCsLNzW1I51lbW9PR0fHSY6RSKW+++SaTJk3i66+/prq6mgQvS/64RMmCACtOF6v460tNZNcPPeSuv5DT3LlzDeZduX//PhUVFS9swj5SFAoFGzZsYOPGjdTX1/Ov//qvXLlyZUSeP0tLSw4dOsSlS5eIiIjggw8+YMaMGWbn0TYX5syZw4oVKzhz5gzJycno9UMLNe7p6eHq1av827/9G3V1dWzevNmgnmZTIJPJBNEnIGAEBiX6RCKRg0gkOiISiYpEIlGhSCRK/MXr9iKR6LRIJMoWiUT5IpFo16vOFYlEf/jpdzkikei4SCRyMOg7MwMqKytpEDnRo0XI5xvHqNVqsrOzf9V+QuDnXK1Qce1hN5Olj5nqpxi0J6nfQ/P48WNOnDhhXCNHGZVKZTZhbnfv3qWzs9Ok7TLq6+spKioaVmiptbX1oBeKc+fOZf78+Rw6dIiCggJspGJ+E2vPf5vjhEQE/y2lhX+720pHz+C8fmq1mtTUVOrr66mvrx+y7S/i1q1brFixAicnJ4ON+Ty8vb3ZvHkzmzZtorGxkT//+c+cP39+WE2yFQoFnp6e/Pa3vyUhIWHUKlWOZUJCQnj77bcpKCjg0KFDLxTdPT09A9+R5ORkvv32W/74xz9SX1/Phg0b2LJli0k3bAyFRCIRRJ+AgBEYbPzOn4ALer1+nUgkkgG/jM/4HVCg1+tXikQiF+CBSCTap9fre15y7o/A3+r1+j6RSPT/An8L/O8jfkdmRGNjI8UaH6wkWsJczK9Cn4Bh6OnpQaFQcODAAaKiooiLizObhby5UNnWyxeZ7UxyEmOZdY2EbVuHdL6tre1AO4djx47x5ptvGsnS0UWlUplFGfX+wilr1qwxWVgnwNWrV4mJiRnW98fa2npIIiU6Oho7OzuOHj2Ki4sLLi4uhLvI+P8WKTlc0MnJB11kPu7hnZhXN3W/c+cOsbGxuLm5sXfvXlxdXZkxY8aIirlUVlai1WpHtRKwt7f3gNfv9u3bfPLJJ3h7ezN//nxcXV0HNYafnx8qlWrM5ZKZGicnJ959911OnjzJ119/TWJiIq2trdTX19PS0kJXVxc9PT3Y2dlha2uLk5MTUVFRBAUFYWdnZ2rzDYpQwVNAwDi88ukuEokUwCxgJ8BPQu6Xgf96wE709KloCzQDfS87V6/XX3rm/NvAuhG8D7NDq9XS2tpGdqcXMe5ypC/JXRIY2ygUCv7qr/6KyspK0tLS+Pjjj/H39ycsLIyJEye+9jvd3b06/ulWK1YSEa5l5wgJDxty6B48XdT3t3M4evQoa9euNYK1o4u5ePpu376Ns7Mzfn5+JrOhqqqKurq6YX+uVlZWQ/YOBAQEkJiYyOHDh3nvvfeQSCTILERsjbRjuo8lH99r459vt5Hioea9KQqcrZ8vZHJycli6dClBQUHMnDmT9PR0Tp8+jZWVFUlJSQNN44dCRkYGYWFhJikM5ebmxurVq2loaOAPf/gDpaWlODg44OXlRWBgIMHBwS/MzXN1deXWrVujbPH4QCaTERYWxr59+/jiiy9YsmQJgYGBKJVKnJ2dsbW1fS0KhVlZWQmiT0DACAxmSzcAaAC+EYlE0UAG8Fd6vf7Zp+ufgVPAI8AO2KjX63UikWgw5wK8DRx63uQikeg94D0AX1/fQb8xU9Pe3k4TtrSqdUJo52uCn58ffn5+dHZ2kp2dza1btzh//jxBQUFMmjSJ4ODg1273W6/X80lGO3UdWuI70/B1dWDJkiXDHs/KyootW7Zw4MABjhw5wptvvjmmF0Eqlcrku/Td3d3cu3ePLVu2mNSO69evk5CQMOy+hTY2NsOqgjhjxoyBnNFnPcj+DlL+1zxnzpaqOJDXwV9dfNrUfdEvmroXFRUhlUoHPHJyuZwZM2aQmJhITk4ON27cIDk5maSkJKKjowd1vfb19VFeXs6OHTuG/H4MyYMHD4iKimLz5s3U1tby8OFDcnJyuHjx4kAYZ1BQEAEBAQO5qUqlkra2NrRa7Wt3vxsuarWaW7dukZubi1wu54033iA9PZ2QkBBCQ0NNbd6oY2lpSXNzs6nNEBAYdwxG9EmAKcC/0+v1d0Qi0Z+A/wP4+2eOWQxkAfOAQOBHkUh0YzDnikSivwP6gH3Pm1yv138OfA4QFxdnuGZKRqatrY3KPgViEcR4CKLvdcLW1pbp06czffp0WltbycjI4Nq1a5w9e5aQkBAiIiIM2sPLnLlU3s3NajWhPcUE2ul4442Rh2VaWVmxefNmDh48yJEjR1i3bt2YFX7mIPpu3bqFp6cnHh4eJrOhvLycpqYmNm/ePOwxrKysht3zbNWqVXz55ZdkZmb+rAKvhVjEqhAbEjzlfJbRzhf320mt6ub9WHu8FU8fn3fv3mXy5Mm/GtPCwoKYmBiio6MpLCwkLS2N1NRUpk+f/srCLAUFBTg7O+Pi4jKs92MIdDod9+/fZ9myZVhYWODr6zuw8drb20tlZSUPHz7kzp07nDp1CmdnZ7y9vQkJCUEmk9Ha2oqzs7PJ7B8LPH78mLS0NEpLS/Hz82PFihUDfV51Oh3p6emvregTPH0CAoZnMKKvBqjR6/V3fvr3EZ4Kt2fZBfyD/mnZqVKRSFQBhAJVLztXJBLtAFYA8/VDLVll5qjVakpV1kxSSrGTjc0FqcDIcXBwYP78+cyfP5+Ghgbu378/EPYVHx9PZGTkuN0NL2/p5eusdty0jYSJanjzzU0GG/uXHr+xKPx0Oh1qtdqk4Z2dnZ1kZmaybds2k9kAkJqaytSpU0eUT2hra0t3d/ewzrW2tuaNN97g0KFDeHt7/0psudtK+C+zHLlW2c23WR38xx8bWT/JlhnKbp48ecLGjRtfOLZYLCY8PJzw8HCKioq4evUqubm5rF69GgcHh+eek52dPayQUEOSlZWFjY3Nc3MK+z2b/a9pNBoqKiqoqKjg8uXLVFdXU1tbK4i+Z+jr66O2tpbKykqqq6t58uQJer2eiIgIdu/ejaOj48+Oj4uLIzU1lbq6OpNuyJgCKysrOjo60Ov1QtVzAQED8sonrF6vfywSiapFItFEvV7/AJgPFPzisKqffn9DJBK5AROBcr1e3/iic0Ui0RKeFm6Zrdfrx10XzuqWbp70SFjqaT7l2AVMi4uLC4sXL2bhwoXk5eVx584dUlJSmDJlCrGxseOq3UNXj45/utWCVKsmRpPDxq0bDC7K+ptyHzhwgB9++IENGww/hzFRqVRIpVKTFk65efMmvr6+w8qxNBQPHz6kqalpxOGldnZ2wxZ98DR94Jf5fc8iEomY52/NFHc5X2V2cCC/k3P6TuaFTR10O4LQ0FACAwNJTk7myy+/JCkpiaSkpJ8d09nZSV1dHevWmTbN/datW8yZM2dQx8rlckJDQwe8UocPH/5Zg/bXEZVKRVlZGZWVlTx+/JjGxkYUCgVeXl6EhISwYMECXFxcXnjPkkgkhIeHc/v2bdasWTPK1psWCwsLLCwsUKvV4+q5KCBgaga72vh3wL6fqm+WA7tEItH7AHq9/lPg/wH2iESiXEAE/O96vb7xRef+9Ps/A3KehoIC3Nbr9e8b4D2ZBbcrn1aRixNCOwV+gVgsJioqiqioKCorK7lx4wa3b98mIiKCadOmvXD3f6yg1+v5c3obT7r6mKbKYNe2DUYTNv3C79ChQxw8eJBNmzaNGeHX1dVl0t5l7e3t5OTk8M4775jMBvhLeORIr5GRePr66c/vO3369AsX2g6WFvzHRAeCMyo4UCzmRFcwvVntbI6wxVLy6mtPKpWycOFCJk2axNmzZyksLGT16tUDVVwzMjLw9/c36WI3MzMTqVQ6bG+ju7s7TU1NBrbKfNHpdNTW1lJVVUVtbS319fV0d3fj7u6Op6cnM2bMwNvbe8he/ZkzZ/LJJ5/Q2NhoFlV+R5P+Bu2C6BMQMByDesrq9fos4JdNyD595vVHwKIhnIterx+9OtQm4E6NChe5Fk870+3iC5g//cVfGhsbuXHjBl988QVBQUHMnj3b6L25jMXZEhV3H2mYqCrgw03Lje7JksvlbNy4ccwJv46ODpM2Zi8oKMDb29uk15lKpeLhw4esWLFixGP1Lw7VavWI/q6rVq3iq6++IjU1lRkzZjz3GLVaTfmNk/zNvEVk9DlzpkTF3VoNv4lVMNl9cELe29ubd999l5SUFPbs2UNCQgKzZs2isLBw0B42Y5GWljYiG1xdXSkpKTGcQWZIRUUFubm51NfX09TUhJ2dHR4eHvj5+ZGUlISHh8eIQ/dtbW2JiIjgxo0br523TyaT0dnZ+dqJXQEBYyIoEiNR1d5HiN3gGvsKCCiVStasWUNnZyc3btzgm2++ITExkWnTpo0JAdNPcVMPe7LbUWrq+PsN00dN1PQLvx9++IH9+/ezadMmk4ZNDoaurq4Xlr0fDR4+fEhgYKDJ5oenlS/d3NwM9newsrKis7NzRNedtbU1GzZs4OjRo1RVVfHmm2/+aryTJ0/i7+9PbFQYscAMHys+yWjj/7nRwhw/S3ZGK7CTv/p7a2Fhwdy5cwkNDeXs2bNkZWWhVqsJDg4etv0jJTs7e0RePnjq6WtpaTGgVeZBdXU1WVlZlJWVIZFICAkJYebMmfj6+hrtuzx79mw++ugj6uvrTRqGPdpIpVJUqnGX+SMgYFLGzmpyjOFibUGrRkhAFhgatra2LF26lK1bt5KTk8OXX35JUVGRqc0aFB0aHf8juR55Xxf/dWngqIflyOVyNmzYAMDBgwfp6+sbtblLS0v54osvOH/+PBUVFeh0r97wMWXokk6no6amhpCQEJPM309hYSGTJk0y2HjW1ta0tbWNeBw3NzfeeecdbGxs+PTTT6msrBx4LTMzk4aGBpYuXTrwuzAXGf/fQiVrJ9lwo0rNv7/YSGpVN4OtT+bh4cGuXbsICQmhu7ub69evD+oaMjRHjhzh4sWLTJ8+fUTj2Nvbo9Vq6ezsNJBlpuPx48ecP3+ef/3Xf+X48ePIZDLWrVvHhx9+yKJFiwgNDTXq5o21tTWRkZGkpqYabQ5zpN/TJyAgYDjMeyt8DOPjION+zS972AsIDA53d3fef/997t+/z4ULF7h79y5RUVEEBQWZRTPvX6LT6/lvlx7SpZXzn5Oc8HC2N4kdv/T4bdmyxegev4qKCk6ePMn06dNpbm7mzJkzA1U5bWxsBn5sbW0Hfuzs7GhubjaZ6KutrUUul5s0f7SxsZFHjx4Nuxn787CxsRlyg/YXIZfLWb16NdnZ2Rw5coTo6GhiY2O5cuUK69at+1U+psxCxJYIO5K8LfnkXjv/cqeNlKqnTd2VL2jq/iwWFhYsWbKE2NhYzp49y+eff84bb7yBu7u7Qd7Pq8jOzubx48fMnTvXIEJcoVDQ0NBglverV9Hc3Mz9+/d58OABvb29BAcHs2rVKnx9fU0SeTFnzhz+/Oc/v1bePktLS4Ns4AgICPwFQfQZiWA3BckPu7mTkYWflzuurq5jKkxPwDyYMmUKUVFRpKenk52dzaVLl3BychromRUSEmIW19Un10opV9uxMRgm+5k2F1EqlbJhwwYOHz7Mvn372Lp1q9GEX1VVFceOHWPJkiU/C4drb2+nra2N9vZ22tvb6ezs5MmTJ1RVVdHV1UV3dzePHz9GIpFQWFiIpaUllpaWWFlZYWlpOSAS7ezssLGxQaFQYGdnZ7Bw2YqKCry8vAwy1nC5d+8ewcHBBg0B7i/1bkiio6Px8fHh5MmTHD9+nBkzZuDv7//C4/0dpPzP+U6cK1FxIK+T319sZFukHYsCf97U/UW4uLiwfft2bt++zb59+4iOjmbevHlG/Z739PRw5coVVq1a9dwWDcPBwcGBxsbGMdOTtL99SVFREW1tbQQHB7N48WICAgJMfo+1tLQkOjqa69evD0QzjHesrKwE0ScgYGAE0WckYoO9+OJOPR4TJ9NRV0JxcTH29vZ4eHigVCpN/hARGDtIJBISExNJTEykr6+P0tJSSktLuXr1KhcuXCAsLIzo6GiT7QCfv1fC1UYbYpQ61k82j35SUqmU9evXc/jwYfbu3cu2bdsMLvxqamo4cuQICxcu/FX+k0KhQKFQvHIMrVZLR0cHnZ2dqFQqurq6Bn7q6+uprKwc+H1/ZUq5XI6VlRVWVlZMmzaNiRMnDtn2yspKoqKihnyeodBoNOTn5xu8P6BcLketVht0TAAnJycmTZpEcXExtbW17N27l7lz575QOFuIRKwMsSHB66em7pnt3Kjq5rdxf2nq/jLEYjFJSUmEhIRw7tw5Pv30U1avXm00oX7jxg08PDwMJvgAHB0dzb6Cp0qlIi8vj8LCQurr6/H39x/4u0ulUlOb9zNmzpzJn//859emb5+VlRWPHz82tRkCAuMKQfQZiQCXpyEtYnt3Ns2OpbOzk7KyMvLy8igqKsLR0REPDw+cnZ2F5qMCg0YikfysH1ZtbS337t3j+++/x9HRkfDwcKKiokatQEhmYRnflligtIH/ONPdrK7lfuF39OhRvv/+e9566y2DCb/a2lp++OEH5s6dOyLxZGFhgYODw6DDLDUazYBITEtL49GjR0MWfRqNhrq6OoOGVQ6V7OxsnJ2dDb5RodVqR1wx8UXjpqam8uGHH6JUKrl9+zaHDh3CxcWFOXPm4OPj89zz3Gwk/P1MR5Ir1ezJauc//tjIukm2vBFqg1T86u+KUqlk27Zt3Lt3j4MHDxIeHs6CBQsMuoHR2dnJ/fv3DS7AXV1dKSwsNOiYhqC9vZ3c3FyKi4tpaGjA29ubqKgoQkNDzbo9gKWlJZMnTyYlJYWNGzea2hyjI3j6BAQMjyD6jISfszViEZQ1PM0vsbW1JTo6mujoaNrb2ykpKSE/P5+CggKcnJzw8vLC0dHRxFYLjDW8vLzw8vKir6+PwsJCsrKySElJwd/fn4SEhJeGoY2U8oqH/Ft6O3prd/52lhIrqfl5r5/1+H3//fds3boVmUw2ojHr6uo4fPgwM2fOJCYmxkCWDg65XI5cLkepVHL37t1h5UtVV1ejUChMWjk0KytrxMVCnodYLB508ZShUFZWhq2t7YCnbfbs2UybNo27d+9y+PBhlEolc+fOfa74E4lEzPW3IsZdxtdZHRzM7yStWs1v4xSEOL/6WhSLxSQkJBAcHMzZs2f57LPPWLx4scG8chcuXCA0NNTg3iNXV1fS0tIMOuZwqa+vJz8/n7KyMlpbW/Hz8yMmJoaJEyeatdD7JTNmzODPf/4ztbW1Jg/PNjZSqRStVktPT8+I79kCAgJPEUSfkZBLLPBxsqa84dfVpxQKBbGxscTGxtLc3ExpaSk5OTnk5+cPCEB7e9MUwhAYm0gkEiIjI4mMjKSzs5P09HSOHj3K73//e6N4PmpqavjXy8W0OUTxQawCfwfzCoV6FgsLiwGPX3+o53AXEfX19Rw6dIjExETi4+MNbOnQUKlU2NnZDfm88vJyvL29jWDR4KioqECtVhukWIhOp6O8vByNRkNoaChisRitVmsAK3/OgwcPfpWbJpfLmTlzJgkJCQPfN09PT1atWvXcPEUHSwv+wzQHZvqq+eJ+O//n1WaWB1uzKcIWq0E0dXd0dGTbtm3cv3+fc+fOYWtry/z58/Hz8xv2+0pLS+PJkyfs3Llz2GO8CBcXF9rb29FqtYhEItrb29HpdMPuC9na2kpVVdXPcl9lMhkikQiJRDKwiaFWq6mvr6empoba2loeP35MX18fgYGBTJ8+ncDAwF8V4RkrWFpaMmXKFG7cuMGmTZtMbY7RkcvldHV1CaJPQMBACKLPiAQobQY8fS/CycmJhIQEEhISaGhooLi4mNzcXHp7e3FycsLb23tMVj8TMB22trbMnTuXgoICSkpKBkJBDUVdXR2fn7xOtes85vhZMs/f/HfKLSwsWLt27c9CPYe6kKivr+fAgQNMnTqVqVOnGsnSwTNc0VdVVcXMmTONYNHgSE9PJyIiYkR5zbW1tRQWFlJQUIBIJEIul3PhwgUsLCxeGGo5XHQ6HWVlZS8MqZPL5cyYMYPY2FguXLjAp59+yrJly17YDiPe05JwFxn7cjs5U6LiTq2a38TaEzPIpu5TpkwhMjJyQGi6urqycOHCIYfKPn78mLS0NN566y2jeH1lMhmtra380z/9E1qtFktLS3Q6HUlJSSQlJQ16nL6+Pn788Ufy8vJwc3NDq9Wi0WjQaDQDbVm0Wi19fX1IJBJ0Oh329va4ubnh7e1NUlISnp6e4yaPfsaMGfzbv/0bNTU1Jt28GQ0kEgldXV1CFJSAgIEQRJ8RCXCx5VZ5EzqdHvEg8jdcXFxwcXEhKSmJ+vp6Hjx4QH5+PjqdDqVSiZeXl0lDsgTGFjNmzODMmTP09PQYrGhHfX093x0+SYXXSnxspeyeojCrPL6X0S/8jh8/znfffce2bdsGXTmyvr6egwcPkpCQQGJiopEtHRzDEX1arZYnT56YrKJie3s7lZWVrFix4pXH9vb2UldXh1qtRqfTUVVVRUFBASqVChsbG3x8fFi9evWAp6u+vp7r169TWlrKRx99RFhYGPHx8SPeNKupqUEsFr8y/NHKyoo1a9aQn5/PuXPnKCoqYsmSJc/dXLCWitk9RcEMX0s+udfGf7/Rwmw/S3YNsqm7VColKSmJ2NhY0tLS2Lt3L76+vsyfP39QnrS+vj6OHTvGjBkzjFYAqrm5GXt7e3bu3ImDgwNyuXzge6TT6ZgxY8Yrx9DpdHz55ZfY2dnx7rvvvnTx3y8Gx/szUiaTMWXKFFJSUtiyZYupzTEq/Z4+AQEBwyCIPiMS6GKLulfHo7ZuvB0H/yASiUS4u7vj7u7OzJkzqaur48GDB2RlZWFhYTEgAA1Z6lxg/BEdHY2DgwNHjx7lyZMnIy773tzczMEfDlPrtxydVsLfJDpgOYiwNHPCwsKCNWvWcOLECb777ju2b9/+yu9RQ0MDBw8eJDY2dkgeCmPSn+syVEHT3d2NVCo1WbjUjz/+SGBg4HMX5lqtltraWrKzs2lqaqK+vh5bW1vkcjkikQhXV1fmz5+Pq6vrc4WKm5sbGzZsQKfTDYTMf/zxx3h6ehITE8OkSZOGdf0XFhYOSSSHh4fj6+s7kH+3YsWKF54/SSnjnxYqOVrYyfGiLjIf9/D2ZDtm+FgOajNFLpczd+5c4uPjSU1N5auvviI4OJh58+a9tHrsiRMncHJyYtq0aYN+X0MlJyeH0NDQn31Wbm5ubNmyhYMHD9Le3s6yZcteOsb9+/eRSqVs3br1lfNZWFiMe8HXz/Tp0/nzn/9MVVUVvr6+pjbHaEgkEqFBu4CAARFEnxEJcLEBoLyha0ii71nEYvFAsY45c+ZQU1NDYWEh9+7dQy6X4+LigqenpxDzLvBc/Pz8eOeddzhw4ADNzc2sXr16WPksra2t7N+/n64Jc6nttuL3UxWDKj1vjvQLv5MnT/Ltt9+yY8eOFwq/xsbGAcE3GM/EaNHV1YVcLh+yiOk/zxTcvXuXR48esXv3buCpyKuqqqK6uprq6moePXqEXC6nqqqK7du3ExQUNKxFvFgsJiQkhJCQEFQqFTk5OaSlpXHz5s1BifxfUlJSwsqVK4d0jp2dHZs2beL+/fscP34cf39/lixZ8tz3I7MQsTnCjiSfp03d/3injRtVanZPUeAyiKbu8DSke8mSJSQlJXH9+nU+++wzwsPDmTNnzs/mfPz4MWfPnqW3t5cdO3YM6T0NlbKysueGQbu4uLBjxw6OHDnCl19+ycqVK58r4nU6Hbdv32bBggVGtXMsIpPJiI2N5caNG4MSxGMVKysr2tvbTW2GgMC4YWxt048x/iL6DLNTJRaL8fX1ZfHixfzud79j0aJFWFpacufOHTIyMqiurh7IcRAQ6Mfe3p63334bnU7Hnj17aG5uHtL5HR0d7N+/H7n/FDK6nVkUYMVMX/PP43sZYrGY1atX4+bmxp49e57b262xsZH9+/cTHR1tVoIPnn4mwxFvGo3GJBtEtbW1pKSksGzZMkpLSzl+/Dh//OMfOXfuHI2NjYSFhfGb3/yGXbt24ebmZrC2I9bW1kybNo133nkHDw8PvvrqqyEtImtra+nt7R12sZQpU6bw3nvvIRaL+fTTT0lPT3/hsX72Uv7HPCd2RduR96SH319s5FxpF7ohVCNVKBSsXLmSXbt2odFo+Pjjj0lNTUWn0wFw+vRpGhoaWLdunVGrVnZ2dtLU1ERwcPAL7dyxYwfBwcF8//337N27l8rKyp8dk5WVhaWlpcFzkscLiYmJ1NfX8/DhQ1ObYjQsLS2Ftg0CAgZkbG7VjxFcbOXYWUpeWcxlOEgkEgICAggICKCnp4fKykoKCgpIS0vD1tYWNzc33NzcDN6QWmBsIpPJ2LRpE9euXeO7775j9erVgwpZU6lU7N+/H2efYM71BTLBwYJdk1/ddHwsIBaLWbVqFWfOnGHPnj1s3759QGg0Nzezf/9+Jk+ezKxZs0xs6a/p7e2lo6ODP/3pT0il0oGQTZlMNvD//e0dLC0tBxq619bWjmoOZkdHB+Xl5Zw+fRq9Xs/hw4dxcXFhwoQJ7NixA6VS+bPj6+vrjeKJFIvFrFy5kh9//JE9e/bw3nvvDcrjN9TQzudha2vLG2+8QXl5OV988QWXL19m/vz5TJ48+VcC3EIkYsVPTd0/zWjnq8wOUqvUfDDIpu79KJVK1qxZQ01NDRcvXiQnJ4fly5djaWmJUqn81d/d0OTl5eHj4/PSz9LCwoLZs2eTkJDAvXv3OHHiBDY2NkydOpWIiAhu3brF3LlzjWrnWEYmkxEXF8eNGzeM2prHlFhbW1NbW2tqMwQExg2CIjAiIpGIABdbyhuNG5Muk8kIDg4mODgYtVrNw4cPycvL4+bNmygUCjw8PHB1dR031csEhs/cuXNxdXXl2LFjA+XmX0R3dzcHDhxA6epOuvUUdO19/MdEB2QWY6Nwy2AQi8WsWLGCs2fPsmfPHnbu3IlGo2Hfvn1ERUWZpeAD8Pf358MPP0StVqNWqweqGWo0Gnp6elCr1fT09NDR0UFTUxM9PT309PTQ3Nxs9HApjUbD5cuXqaioQKVSoVQqCQoKIj4+Hj8/v5duRKlUKqPmKi9cuJDOzk7OnDnDunXrXnl8cXExixcvNsjcOp1uIC8xJyeH69evExQUREJCwq96rrn+1NT9epWab35q6r52ki1rBtnUvR9vb2927drF/fv3OXr0KI2NjWzfvt0g7+dlPHjwgMjIyEEda2VlxcyZM0lKShoIxT19+jQ6nW5E7SheB5KSksjIyKCiosJkxZmMiaWlpRDeKSBgQATRZ2QCXWy4VdY0avP1h8OEhoaiUqkoKysjPz+f4uJi7O3t8fT0RKlUjpmKiwKGJzw8HGdnZw4ePEhnZyfz5s371TEajYZDhw5hb29P+4S5lJSo+JtEBzxsx98to98LdObMGT766CPUajUzZsxgzpw5pjbtpdjZ2Q2reue//Mu/0NzcPOx+aS9Do9Fw4MABpFIpq1evxsvLa0ibTSqViu7uboPb9SxLlizhiy++ICcn56VVbevr6+nu7jbIYlqn03Ht2jVmzJhBVFQUUVFRNDY2kpWVxeHDh7G0tGTixIlMmTJloEerSCRijp8VMW5Pm7of+qmp+weDbOrej1gsJi4ujuDgYL755hsqKyuNGjLZ3ydv7dq1QzrPwsKCmJgYYmJiyMvLIysri48++ggvLy9iYmIG+jAK/AWJREJ8fDw3btwYl6JPLpfT3d2NVqs1Sr9ZAYHXjfG3gjMzAl1sOXa/li5NHzby0f1zW1tb/6xhd2lpKXl5eRQWFuLo6IinpyfOzs6japOAeeDu7s6uXbv47rvv6OnpYcmSJQOv9fb2cvjwYWQyGV5Tl/FPt9tZFmRNovf4rha7YsUKxGIxaWlpY7Z586uwsLDA39+fwsJCpk+fbtCxq6qquHDhAg4ODqxbt25YC3R7e3uj7+xbWVmxcuVKjh49irW1NUFBQc89rqCggAkTJhhEaOTl5dHb20tsbOzA75RKJQsWLGDu3LmUlJSQn5/P559/jp2dHa6urvj6+hIUFISDgwN//VNT989/auq+LNiazYNs6t6Pvb0927dv58svv2T27NlG86gWFhbi7u4+olYZERERRERE0NnZSU5ODqmpqVy4cIHQ0FDi4+NxcXExoMVjm2nTpnHv3j1KS0tfeC2PVUQiETKZjK6urpdWoxUQEBgcgugzMgHKp8VcKhq7iPCyN5kdtra2TJ48mcmTJ9PW1kZJSQm5ubkUFBTg7OyMp6cnDg4OJrNPYPSxt7dnx44d7N27l9OnT7N8+XL0ej1Hjx5Fp9MxZ8V6/vZaK8FOUrZHD70J+Fhk2bJlxMbGDqmX2FgjKCiI7Oxsg4m+7u5uLl++zIMHD5g+ffqI+xiORiPmCRMmsGLFCk6cOMGyZcsICwv71THFxcUG8/bm5OQQHx//XAFpYWExEJ2h0Wiorq6mtraWkpISkpOTcXR0JDExkbiwMMJdZOzN7eTcT03d3x9CU3cAJycnnJ2dqaysZOLEiQZ5b7/kwYMHBhMftra2A83ca2pqyM7O5rvvvkOhUBAdHU1UVNRr37pIIpEwdepUbt68Oe5EHyCIPgEBAyKIPiMT6Pp0t7OsodOkou9Z7O3tiYuLIy4ujqampgEB2N3djbOzM15eXkMOGxMYmygUCnbu3Ml3333HiRMn6OvrQ6VSsWnrW/yXlDbEIvgP0+yHlEc01unvJbZ//36AcSf8goKCuHTpEj09PSOu5JmVlUVycjLe3t68//77I26E3tzcPGqLu9DQUGQyGcePHycjI4NFixYNtA5obGyko6PjhdUnh0JHRwePHj0aVA6hXC4nKChoYPHe29tLdnY2KSkpXLt2jfj4eHZNmcJMX0s+/qmp+yxfS3ZNVqAYRFN3eHr/N1ZFRJ1OR3V1NYsWLTL42N7e3nh7e7No0SIKCwvJyckhOTmZgIAAYmJiCAwMNPicY4X4+HiuX7+OSqUad70KpVKp0KBdQMBACKLPyPg5WyMWYZQKnobA2dkZZ2dnpk2bxpMnTygqKiI/Px+dTkdISIhR8n4EzAtra2t27tzJ3/3d36FWq/nHf/xHvs9TUdHax99Od8DV5vW7Tbi4uIxb4Wdra4uLiwslJSWEh4cPa4z6+nouXrxIe3s7K1euNNiCu7W1dVR39AMCAnjnnXfIyspi7969TJw4kQULFlBYWIivr6/BQjs9PT2H5ZGSSqXExcUxZcoUiouLuX37NqmpqUyePJn/MTORsw/7OF7YRdZjDW/HKAbV1F2n0xktN660tBSFQmHU54ZUKh3Ii2xpaSE7O5vz58+j1+uZNGkScXFxr13UikQiwcnJiZqaGkJCQkxtjkGRSqVCg3YBAQPx+q3mRhm5xAJvR2uD9eozJq6urri6ujJz5kzKysq4ePEi7u7u47YctMBfSElJwd/fH4VCwb8cu0Gm5WTemGhDnOfrGzrl4uLC1q1b2b9/PzqdzmwreQ6HwMBAHjx4MGTRp9FoSElJITs7mylTpjBr1iyDtoVpbW0d9TxjBwcH5syZw+TJk7ly5Qoff/wxWq2WZcuWGWT8oqKin+XyDQexWDwQAlpVVUVaWhqfffIRU6ZM4b/PmspXuWr+eKeNlEo178W+vKm7VqtFKpWOyJ4XUVBQMKoeN0dHR+bMmcOcOXMoLy8nNzeXL774AqVSiZ+fH76+vvj6+pqkN+Vo4+rqyqNHj8ad6BN69QkIGA5B9I0CgS42lJupp+95iEQigoKCcHV15cCBA8hkMjw9PU1tloCRSE1NpbCwkF27dtGqk/O//diIY18Ta4LMIxzZlCiVygGPn16vZ/bs2aY2ySAEBweTkZExpHOqq6s5c+YMNjY2vPvuu0bxprS2tposTM/BwYG1a9dy//59Dhw4YJCct/b2dhoaGp6bMzhc+oVMTU0Nqamp3P/uE+ZPjmFaeByHi9T8/mIjWyNtWRxojcVzvH4ymcxoFVIrKyuHXLXTUPT3rdVoNBQUFFBTU8O1a9dobm7Gzs4OW1tbLC0tsbS0xMrKCjs7O5ycnFAqlTg4OIz5yqDe3t6Ulpaa2gyDY2VlJbRtEBAwEILoGwUCXGy5Vd6ETqdHPIZyoxQKBWvWrGHfvn3Y29tjY2NjapMEDExOTg53795l+/btyK3t+JcrTVjJJMyTPGTvdzd+1rD8deVZ4ScSicaFx8/DwwOxWExdXR0eHh4vPVan05GSkkJ6evpAM21jodFoTH69NTQ0MH36dIN4MNva2pDL5Qb1hvbj7e3Npk2bqK2t5caNG9RkfcG6qARyZGEDTd1/G2ePzy+autvZ2dHS0mJwe+rq6gbsMiVyuXyg9QM8vabq6uro7OxEpVKhVqvp6uqitraWgoIC2tvb6e7uxtraGhsbm4Hqqd7e3kyYMMEon50x8PPzIzU11dRmGBwrKyseP35sajMEBMYFY+NuNsYJcLFB3aujrl2Nl4OVqc0ZEv3NhK9fv860adPG/G6owF+orq7m0qVLrF+/HqVSyUfpbVS39/GfZzoS5bqUc+fO8c033/DWW2+99pXTxqPHLyAggPz8/JeKvtbWVk6dOoVarWbXrl0olUqj2qTRaLCyMt09sre3l7y8PLZu3WqQ8Xx8fJDJZBQVFRmtN56Xl9eA+EtNTcW6+i7zJ83mTrsvf3PpmabuFk83HJVKJQUFBQa3Iycnh4CAAIOPO1LkcvkrUxQ0Gg2tra20tbXR0tLCkydP+PLLL7GxscHT0xNPT08mTJjAhAkTzLZaqIuLC319fbS0tIxKBdzRwtraWvD0CQgYCGEFPwoEujytaDcW8vqeR1RUFBMmTCA/P9/UpggYiJaWFo4ePcrChQvx8/PjaoWKqw+7WRdmw2R3OWKxmBUrVhAcHMyePXtobW01tckmR6lUsmbNGk6fPs358+dNbc6ICQ4OpqKi4oWvFxcXs2fPHlxcXHj33XeNLvjA9KIvLy8PBwcH3N3dDTZmYGAgxcXFBhvvRXh5ebFx40a2bN6Mc2c5oeU/MEHaxqGCTv7mchMPmnqAp17exsZGg89fUVFhtDYQxkYul+Pm5kZISAhTp05l5cqVODs788EHHzBjxgwkEgm3b9/mT3/6E59++iknTpwgMzPT7MSIs7Mz1dXVpjbDoPQ3aNfpdKY2RUBgzCN4+kaBAJenYZFlTzqZGTw2m8ouXLiQ77//nkePHgn5fWMcjUbDDz/8QEREBNHR0VS29fJFZjuRrjLWh/285P6iRYuQSqXs2bOHbdu2jcrC31zp7u7mwoULTJ06ldLSUq5cucL8+fNNbdawmTBhAqdOnfpVmXedTsfVq1fJyspi5cqVo7qQN7Xoy8zMZMqUKQYds6qqiqSkJIOO+TL6xV9tbS3Xrl1D3iWnWjqdv7vazNIgazaFuaDT6WhoaDBYk/PW1lY6OjrGTduEzs5O9Ho9SqUSpVI54KXVaDTU1tZSVVVFQUEBly9fHhCMvr6+BAUFmbRxvLu7O48ePSIqKspkNhgakUg00LZBaCUlIDAyBNE3CjhYyRCJoLGzx9SmDBu5XM6qVavYt28fCoVixP24BEyDTqfj6NGjODo6smDBArp7dfzTrVaspWJ+P9X+uYUf5s6di1Qq5bvvvmPr1q0DvcxeJzQaDQcPHsTZ2Zk333yTlpYW9u3bh16vZ8GCBaY2b1jI5XK8vb3Jz88nPj4eeLrYPX78ON3d3ezevRt7+9Er5qPRaBCJRCartFhXV0dLSwuRkZEGG7O9vZ3m5maTVFT08vJi27Zt5OXlceHKMVp9Z3G+1JW7j9RET5hCZmamwfrp5efn4+/vj4XFi6uGjiVaWlqeKzDkcvlAwRh4ej999OgRVVVVVFdXc+fOHbRaLW5ubgM5gT4+PqOWFuHr60t6evqozDWaCKJPQMAwCKJvFCiu70Cvh4nuY/uG5erqyoIFC0hOThby+8YoFy9epKuri127dqHX6/kko53HHVr+6xwnHCxfvGCbMWMGMpmMvXv3smnTJry8vEbRatPS29vL4cOHsbS05I033gCelorfunXrgPBbuHChaY0cJoGBgZSUlBAfH09VVRUnT57Ez8+PLVu2jPoCXqPRmLS0/r179wgLCzNo4Y7S0lI8PDxMWgwkIiICX19fjh07hrOFI+W2iVztm8iTh6kYqoV6cXHxQOGU8UBLS8ugNjbFYvFA0/h+GhsbqaqqoqamhrNnz9LZ2YmLi8tAXqC/v7/RrnM/Pz/Onj1r1F6MpkAmkwm9+gQEDIAg+kaB3NqnPWaivMd+CfzIyEgqKyspKCggIiLC1OYIDIHbt29TXFzMO++8g0Qi4WKZipvVarZG2BLu8upFSEJCAhKJhIMHD7Ju3Tr8/PxGwWrTotVqOXr0KDqdjk2bNv1sIeXo6Mi2bdsG+vgtXrzYhJYOj5CQEFJSUkhLS+PmzZssWLDAZIv37u5uo/WPG8zcRUVF7N6926DjlpeXm0VxE4VCwdatWzl16hSSiuNIAt6gQDeN9KIq4kN9RzS2Wq3myZMnYzaf73m0tbUNu3hVf0hof5hwZ2cnlZWVVFVVcf36dY4dO4aTkxOenp4DXkNDFYextbVFLpfT0NAwriIyJBIJXV1jp+2VgIC5Mn62gsyYnJo2FJYSfJ3GR+n7hQsX0tPTI5RRHkMUFxeTmprKpk2bsLW1pbyll6+z2olxl/FG6OBbcUyZMoXFixfzww8/jMueUM+i0+k4fvw4KpWKLVu2PNdb4+DgwLZt2ygrKxuTxV2sra1pbm7m+vXrbN++3aTemu7ubpN5+rKzs3F3dzd478HKykqzEUNSqZS1a9cSNjEI1+ITSER6Ps7tQ9U7sgIZhYWFeHh4mDQX09C0trYa7FqwtbUlPDycpUuXsnv3bv7qr/6KWbNmIZPJuHPnzs+Kw2RnZ4/Yo+Xs7Extba1BbDcXLC0tza5ojoDAWETw9I0CubWtRHk7IHpOvtRYRC6Xs3LlSg4cOIBarcbb23vM9DJ6HWlsbOTMmTOsWrUKNzc3unqe5vE5yMX8VYID4iFelxEREUgkEk6cOMGKFSuMVorelOh0Ok6ePElLSws7dux46fWtUCjYtm0be/fu5dy5cyxbtmwULR0+9fX1HD9+nNDQUNavX2/y/ng9PT0mu49kZ2cbvA2HTqdDrVabXfGj+fPn4+DgQNv1HynxXsofb7fyf8xwHPJ9oJ+ioiKCgoIMbKVpaWtrM1okg5WVFaGhob8qDlNZWUlubi4XL17EysoKDw+PgbzAoXjttFrtuHsey2QyVCqVqc0QEBjzjK87gxmi7tVSVNfBe7NMH+JjSNzd3dm4cSMZGRncvHkTR0dHfH19Db5TLjAyNBoNR48eJTY2lpCQEPR6PR/da6NRpeX/meuEnXx4zv7Q0FAkEgknT56kp6dnXFWL0+l0nDlzhidPnrBjx45BeZ8UCgVvvfUW+/bt4+zZsyxfvnxQc5WVlZGdnc3Dhw9JSEhgxowZIzX/leh0OtLS0rh9+zbTpk0blTkHQ09Pj0k8feXlrA5bPgAAqydJREFU5Wg0GoNvXojFYkQiEX19fWa3CI+NjcXe3p4/nEgmQzSPHwo62RQ+9JxznU5HbW3tmM1pfRHt7e04OzuPyly/LA6j1Wp59OgR1dXVVFVVcfv2bfr6+nBxccHR0RFnZ2eUSiVubm7Pfd62t7fj5OQ0KraPFt3d3a9VHrmAgLEwryfROKTocQd9Ov24yOf7Jf1Na7u6uigsLOT+/fv09PQM7FCa20LndeT06dPY2dkNeDHOlqi4U6thR7QdE51HtsAOCgpi/fr1HDlyhN7eXmJjYw1hsknR6XScPXuWR48esXPnziHl2tjZ2bF161b279/PmTNnWLFixXOPq6urIyMjg9LSUmQyGZMmTSIsLIyLFy8CGFWEVVVV8eOPP9Lb28tbb71lVnk/pvL03bt3z6AVO5/FwsLCLEUfPP3+/p/rZfzD1TIOFwQywUHKVK+h5ZZVVFRga2trdt7MkaDT6UZV9P0SCwsLfHx88PHxGfhdS0sL1dXVNDU1UV9fz4MHD2hpaaG3txc7Ozvs7e1RKBQ4OztTV1dnstxYY6FWq4UNZQEBA2B+T6JxRm5NKwCR3g4mtcOY2NjYEBcXR2xsLNXV1WRnZwvePzMgLS2Nx48f8+677wJQ3NTDdzkdxHvKWRlsmFA+X19fNmzYwOHDh+nr62Pq1KkGGddUnD17ltra2iELvn6eFX6nTp1i1apVwNNwsfT0dB48eEBvby+hoaGsW7fuZ1X/nJ2d2b9/P2B44dfS0sKVK1d4+PAhSUlJZll91xSevvb2diorK18o0A2BOTeV9vPz5e+WyPjPV5v4l1s6/nGhC772gxcM+fn5ZlGoxpB0dnYikUgMVlzFEDg6OuLo6Pir33d3d9PY2EhzczPNzc1UVlby5MkTvvvuO/R6Pfb29jg6OuLg4DAwhlKpRKFQmN33/2VoNJpRbR8jIDBeEUSfkcmpacPZRoanvfk8QIyFSCTC19cXX1/fn3n/ent7cXd3F7x/o0h5eTm3bt1i+/btWFpa0qHR8f/dasXZ2oIP4+0Nml/q7e3Nli1bOHDgAL29vWYTLjhUzpw5Q3V19bAFXz+2trZs27aNffv2cezYMeRyOQUFBYSEhLB48WICAgKeu+BycXEZ+DvqdDpmzZo1krcDgEql4ubNm2RlZREWFsaHH35oVovZZzGFp+/OnTv4+/sbJZ9RpVKh1+tNniv5Krw93Pl9XDf/856K/+vHWv5tlS+2ssEJgsrKSlavXm1kC0eXpqamMdOH1srK6mdewYqKCtrb23n//fdRqVQ0NDTw5MkTGhsbKS0tpaOjg46ODvr6+lAoFDg6OmJvb4+Tk9NA2Kg5btJqNJphV1MVEBD4C8IK3Mjk1LQR5W3YRfZYQPD+mY7u7m5Onz7NokWLcHFxQafX869322jV6Pgfc50HvaAbCm5ubmzdupUDBw7Q19fHnDlzDD6HMTl79ixVVVXs3LnTIIt0a2trtm7dyrFjxxCJRLz77rvP3an/JS4uLgNFYbRaLXPnzh3W/BqNhlu3bpGRkYG3tze7du0y+xC8np4e5HL5qM2n0+m4ceMGMpmM8+fPM2nSJHx9fQ3mAbG0tEQmk1FfX29WYbS/pKioiMtnzrAkJI5z6mD+2+Ua/tdSHyxe8cyqqKigt7f3Z97q8UBra+uYEX2/pKWlZUAcWVtb4+fn99yCNGq1mvr6epqammhoaKCyspLs7Gza29vR6XQ4Ojri5OQ08NPfhmI0v5/99Pb2Apj95omAwFhAEH1GRNXTR8mTDhZHuJvaFJPxS+9fQUEB9+/fp6+vT/D+GYlLly7h5eU1kKd08kEX9x9r2B2jIMjJeLke/YJl//79dHd3s3TpUqPNZUjOnz9PZWWlwQRfP9bW1mzbtm3I5zk5OQ14CnU6HfPnzx/0uWq1muTkZAoKCnB1dWXr1q24u4+N+09vb++o5iKVl5fj5ubGG2+8QXZ2NkeOHBkorW8IxGIxkyZN4sKFC+zYscMgYxoSnU7HxYsXefDgAatWrSIkJASru1UcqZTxWdojPpj+4sIZxcXFnD59mvnz54+pMMHB0NraOma9Su3t7Wi12lceZ2lp+UJBqFKpqKurG/AQVldX09bWRmdnJ1ZWVjg4OKBQKFAoFDg5OQ2Ejtrb2xvlWujq6hp3hWkEBEyFsNo2IgWP2tHpIcpLiEWHp96/+Ph44uLiqK6uJisrS/D+GZjS0lLKysp4//33Achv6GF/XifTfSxZHGj8PlpOTk5s376dAwcOcOzYMd544w2zXhReuHCB8vJyduzYYVY7yc8KP71ez4IFC156vFqtJjU1laysLDo6OvDy8mLDhg0m63s3HHp6ekZ1sZ2bm0toaCheXl64uLjw4MEDg1fwXLBgAR9//DF1dXV4eHgYdOyR0N7ezg8//IBEIuHtt98e+LtvTvClorWcK4+sCSpqYlHor4uZpKenk5KSwsqVKwkJCRlt041OW1ubyYq4jJTY2FgKCwu5ePEiixcvHtYY1tbWBAYGEhgY+LPf63Q6mpubaWpqorGxkZaWFgoLC+nq6qKjo2Pg+2tvb4+Dg8OAl9DZ2RknJycsLCyGZU9nZ+eY/TwEBMwNQfQZkZyaNgAix2HlzpHwrPevs7OTwsJCMjIy0Gq1gvdvBGg0Gs6fP8+CBQuwtramTa3lX2634m5jwW9jFaMWYqxQKNi+fTuHDh1i//79bNq0ySw/z0uXLlFaWsrOnTvNMpzL0dGRrVu3Dgi/55XFb21tJSUlhQcPHuDn58emTZtwcXHh5MmTfPnll2zYsMHswzr76enpGTVPn0ajobS0lN27dwNPC5L09vbS2NiIp6enwcLYpFIpHh4e1NTUmI3oKy4u5syZM0RERDB//vxfLcb/twUT+OuTFXyRI8PXsZNQt798N65du0ZOTg6bNm0atyX029vbCQ4ONrUZw6K/kNR3332HTCYbdnj48xCLxQNhnhMnTvzV62q1mpaWFhoaGmhsbOTRo0cUFhbS3t5Od3c3CoUCBweHgeIyz4aQvuz71tnZOe6KBQkImArzW4mNI3Jr23BTyHFTmGfhBHPA1tZW8P4ZiMuXL+Ps7ExUVBRavZ4/3mmjq0fHf57pjJV0dL1tVlZWbN68mePHj7Nnzx7Wr19vVtXXrl27xoMHD9ixY4dZCr5+HB0dB0JmdTrdwO59bW0tKSkp1NTUEBYW9qucvQ0bNpCamsqePXtYtmwZYWFhpnoLg6a3t3fUiszk5ubi6uo6cH+Jjo5GIpGQkZHB9evXmTlzpsEq0bq4uFBXV2eQsUaCTqfjypUr5ObmsmzZshd6NaViEf99qR//7nQN/z25gT+vskQhF3Pq1CkePXrE9u3bB5WfOlZpa2sbMxslz0OhULB161a+//57LCwsDFIQajBYWlri4eHx3M2Nnp6egfzBpqYmnjx5QmlpKe3t7XR1dQ2EjT5bZbS/sIxGoxHWAQICBkIQfUYku6aVqHHcqsGQvMz7Z2tri0Qi+dWPq6urWYcOjiYVFRUUFhYOhHUeLegi50kPH8Qp8HcwTc8muVzO+vXruXz5Mp9//jkJCQnMnDnT5J9Zamoq2dnZ7Nq1a0zk7jg4OAwUd9m3bx89PT20tLQQExPDypUrXyhaZ8yYgbu7O2fOnBkTDbRHs5BLbm4ukydPHvi3WCwmMjKSyMhIqqurOXjwII6OjgYJX3R2dqaqqmrE44yEzs5ODh8+jE6nY9euXa8UbQ6WFvzdbFf+PrmZvztbxlT1PXR9vQbPezU3tFotHR0dYz6c0NHRkS1btrBv3z6kUimJiYkmtUcmk71QEPaHjT558oSmpiaampqorKwcyCOUSqXMnDnTBFYLCIw/BNFnJDrUvZQ3dLFm8vgMgTEmv/T+NTc309PTg0ajGfhvfn4+lpaWwg4gTz0kFy5cYN68edja2pJdr+GHgk7m+Fkyz9/4eXwvw8LCgsWLFxMVFcXFixfJz89n6dKlTJgwwST23L59m/T0dN566y2z8jy+CoVCwbZt27h06RLBwcHExMQMKhQyKCiIHTt2cOzYMb799ls2btxoti0bNBrNqNjW2NhIY2PjCxuy+/j4sHr1ak6ePImTkxPh4eHEx8cPOyfJ1tYWjUYzEpNHRFlZGadOnSI0NJRFixYN+n2EulrxTrQ1X+RYcF/rxx+2Th13Tb9/SVtbG5aWlmYZjj5UlEolmzZt4sCBA0gkEuLj401t0nN5Nmz0l+h0Oq5evWo2odECAmOdsX9nM1PyatsBIZ9vJDzr/XuW5uZmiouLBcH3E9euXcPGxoYpU6bQ3K3lj3fa8FZI2D1l9PL4XoWHhwc7d+4kIyODkydP4uXlxdKlS0c1tPLu3bukpaXx1ltvjcnwLYVCwbp164Z8nqOjI9u3b+f8+fN89tlnbNiwwSwXURKJhKampudWFDQkWVlZBAYGvnRhHxISwl//9V9TWFjI7du3yc3NZe3atcOqImhjY4NarR6JycPm2rVr3L9/nyVLlhAeHj7k85dMdKS44RHX64I4klHD5mmm2awZLZqbm8063HuouLm5sX79eg4dOoRMJiM6OtrUJg0JnU6HVCrFxsbG1KYICIwLhNg4I5Fb2wpApFC50+CUlpaO65ySoVBTU0N2djarVq1Cq9Pzz7db6enT8zeJDlhKzO/rHRsby29+8xusra359NNPSU1NRafTGX3ejIwMUlNT2bp1Ky4uLkafz9yQSqWsWrWKxMRE9u3bx/nz500mRF7E7NmzSU5ONqpdOp2OgoICYmNjX3msRCIhMjKS3bt3ExQUxJ49eygtLR3ynDKZjL6+vuGYO2xUKhXfffcdpaWl7NixY1iCr5/fJXkQaNvH0SoZd0oeG9BK82Mst2t4EV5eXgNh9vn5+aY2Z0h0dXXh4OBgNpuXY4XReKYKjE0ET5+RyK5pw8vBCmfb0W9mOt7Jy8vDx8fH1GaYHK1Wy9mzZ5kxYwYODg7sze2gsLGX30+1x1thvl9tKysrli9fTnR0NBcvXiQvL4+lS5cazcNTWlpKcnIyW7ZsMesm2aNBQkICQUFBXL58mU8++YSZM2cSFxdnarOAp6Go/v7+XLhwgTfeeMMoc5SXlyMSiYZ8rc2dOxc3NzdOnjzJ9OnTmTZt2qDPVavVo9o6o7KykuPHjxMYGMiSJUtGHJJpIRbxn+d58ldna/ljhoZ/dGzDRzk+NzNbW1uxs7MztRkGx8fHhzfffJOjR48ikUieW33THOn6/9l77/Cm7iz//3VVLPfeG8YYVzDYGONG7wm9QwgBAqRPZmd3Z3d2v9uf7fvbKelhZkIYSKP30Lsbzbhg49477t2W9PuD4IWEBGNLupJ9X8/Dg1G5n7eMdHXP55zzPu3tJt9fKQY7d+6ktbUVpVKJSqXCzMwMMzMzVCrVE3/Mzc1RqVQolcr+P2ZmZv0/W1paGm07gMTgMN4rQxMno7yZCT7D84tRTBoaGmhpaZGGtQJXrlxBoVAQGxvLraouDuW0M8/fgqm+4vbxDRRvb2+2bNnCzZs3OXDgAH5+fixYsECnRhE1NTUcPXqUF1980ShLGsXA0dGRNWvWUFhYyLlz50hNTWX69OlDygbpinnz5vHpp59SUlKil02AjIwMQkJCBvXc0NBQnJyc+PrrryktLSUuLg5vb+9nPs+Qu+6XL1/m5s2bzJkzR6elfLYqGf8wy51fnq3jn85V8N5yCyxUpjMDcqA0NTUN21EUo0ePZtmyZRw+fBiFQvGDOXzGSFtbm7TB+5z09fXR2trK9OnT6evre+JPb28vfX19tLe309zc3H+7RqNBo9GgVqtRq9VoNBp6e3uRy+W8/fbbYr8kCR0iBX16oKmjh9KGDtZH+z77wRLPRV5enlTaCVRVVXHr1i02b95Mbbua36U0M9pewZaJplWaJJPJiI6OJjQ0lPPnz/Phhx/qzC7/kWNhdHS0zoduDwf8/f3Ztm0bmZmZXL58mWvXrjFz5kxRB25bW1szbdo0Tpw4weuvv65Tp9dHs/l27Ngx6GO4ubmxY8cOrly5wjfffIOVlRXjx48nPDz8R3vBbGxs6OzsHPSaA6Grq4v9+/fT3t7Opk2b9FLC7Gev5N0YB/43WcY/HMnkv9ZE6nwNsWltbR3WG4oBAQEsWrSIw4cPs2rVKr33zw6Vrq4u6fv+Oenq6kKhUCCTyfozfIOhtbWVgoICHauTEBvja/oZBmRUPBzKHi6ZuOiczMzMYbsTO1DUajXHjx8nJiYGO0cn/je5CY0W/jzWHjO5afY+WFtbs3TpUlatWkV6ejo7d+6koqJi0Mfr7e1l//79+Pr6kpCQoEOlwwuZTEZ4eDivvfYakyZN4vTp0/zhD38Q9cs+KioKS0tLLl26pNPjZmVl4ezsPGTXVnNzc+bNm8fPf/5zpk+fTlFRER9++CHffPMNOTk5P8jsWVtb9++w64OysjI+/vhjbGxs2Lx5s157VuN9LIi1a6FA8OS/DybqbR1Do1aryc3NpaysbNiXEwYHBxMZGcnly5fFlvJMuru7Tcpl2Rjo6emhq6uL5ubmIR3nUT+lxPBCyvTpgfTyhx+2cZKJi0558OABra2tI37n7+rVqwiCQEJCAp+ltZDX0MtfxNrjYW36H2c/Pz+2bt1KSkoKX331FQEBAcyfP/+5+wq+/fZbABYtWqQPmcMOuVxOVFQUERER3Lp1i+PHj2NjY8Ps2bNFyQYsXLiQP/3pT0ycOFFnmZfMzEydljzKZDKCg4MJDg6mo6ODGzducOHCBU6fPk1YWBgeHh60trbS0tJCbW0tKSkpxMfH62x9eDhzMiUlhZkzZxIZqf/M2/nz55Hfy2Rs0HJSWv3Yd/EWq2c+2xTHmMnKyuLy5csIgoBGo+HOnTtGP9NyqHR0dBi9oZVWq6Wjo2PYB+G6xtraGktLS3p6eoZ0nI6ODuRyOT09PQbtSZbQL6Z/lWiEpJc3MdrZCjuL4T3TyNDk5+cP69KbgVBVVcXNmzfZvHkzyeVdHM/r4IUAS2K9h0+ztVwuJy4ujnHjxnHu3Dk+/PBDZsyYMeCL2qysLPLz83nttddEHwRvasjlcqKjo/uDv0OHDmFvb8/s2bMN2lvj5uZGZGQkR48eZfPmzUM+XkNDAzU1Naxbt27o4p6CpaUl06dPZ/r06ZSVlXHjxg3KysqwsbHBysoKOzs7Ll26xI0bNwgICGDSpElD6jHt6uri4MGDtLS0sHHjRoMYFB0+fJiamho2v7IJpZUdf3G6lgM1jvhm3GfKeNMwBnmcsrIyzp8/T2trK7NmzSIsLIzGxkY+++wzfH19TcbsZDCUl5czZ84csWX8JG1tbVhbW2NhYRo96sZCRkaGTgyJLC0tyc/P53e/+x1WVlY4Ozvj4uKCs7Mz9vb22NvbY2VlJTmrmhhS0KcHMsqbifIb2cGJPsjIyMDPz09sGaLxeFlnn7k9H1x9wFhHJZsmDD+3OXg4l27FihUUFhZy5swZ0tLSeOGFF3B3d//R5zQ1NXH69GmWLFmCpaUlarWanp4eent7+/9WKBRGv8stNkqlkpiYGCIiIrhx4wb79u3DxcWFOXPmGMwQZ+rUqezcuZM7d+4QERExpGPdvXsXf39/g+xY+/j4/CBA7urqwtXVFV9fXzIzM/n6669RKpWMHTuWyMjI55obWVFRwYEDB/Dx8WHlypWoVPp3iH5U+rht27b+i/C/ne7ML8/W8eHddjwcq/D1Mg2jpMbGRi5evEhhYSExMTHExMT0z2x0cHBg0aJFHD16FBcXl2G5ydjW1kZzc7PR9/M1NjYOyChpoDQ3N3Pt2jUiIyNN2tQrMTGRhoaG/uDLzs4OW1tbLC0tEQSB+vp6wsLChuy66e7ujru7O1qtls7OTtra2qipqaG4uJiuri66urrQarX95zUvLy/c3NykmYpGjhT06Zi61m4qm7ukfj4d8+DBA9ra2kZ0aefVq1cfGp/ExvO3Fx4gE+AXMXYoZcN7p83f35/t27eTlJTE3r17CQ4OZvbs2U/9Utu1axd1dXUcOnSI3t5etFotcrkcpVKJQqFALpfT1dWFnZ0dYWFhP2nAIQEqlYqEhAQmT55McnIyX3zxBZ6ensyaNUvv2SWlUsm8efM4evQoISEhQ7qIycrK4sUXX9ShuufDwcGBuro64uPj8fHxYf78+RQWFpKVlcWuXbuwtbUlKCiIiIiIn5wTl5iYSGJiIjNmzDDoqI309HTGjx//RNbFx1bBz2Mc+K9EgX8/W8i/L7Mw6h6g7u5urly5QlpaGqGhobz99ttPfU8FBgYSFRXFN998w7Zt2/oDwuFCcXExLi4uRv+6mpubh7zZ84icnBzOnDmDhYUF+fn5rFu3zmTH99y8eRNnZ2eam5vp7u7u/6PVarG3t6ehoYFx48bpbD1BELC0tMTS0hJXV9cn7uvt7aW5uZny8nKys7Npa2vDysoKX19ffH19cXNzw9HRUaq4MSKM+1NvgmR+Z+IiDWXXLXl5eSO6tv/xss5dd1sobOrjV/H2uFqNjI+wXC4nISGBcePGcebMGT766CNmz55NeHh4/2NSU1NRKBT8+Z//Oebm5pibmz/1wkaj0ZCTk8OdO3e4du0afn5+hIeHM3bsWORyuSFflsmgUqmYPn060dHRnDx5kn/9138lISGBWbNmPVeW6nkJCAhgzJgxnDx5khUrVgzqGEVFRWi1WkaPHq1jdQPH0dGRkpKS/n/LZDICAgIICAhArVZz//597t27xyeffNJfNqVQKFAoFP1zs5qamigpKeHll182aKaiubmZoqKip5YDTvEyZ02oFd/c8+c/DybzDxtmGN1cL7VazY0bN0hOTsbNzY1XX331mRm8adOmUV1dzZEjR1i5cqWBlBqG4uJikzBD6+joGHJg1t3dzYULF8jNzSU8PBxbW1sqKys5efIkmzZtMrnzfV9fHz09PQQGBv6grPLRKAZXV1eDZaiVSiXOzs5PfAe0trby4MEDUlJSaGtrQ6PR4OXlha+vLx4eHri6uhrdOWIkMTKuGA3I3fImBAHCpKBPp9y7dw9f35E5AkOtVnPs2DFiYmLI7rDidEEzy4KsiPIceSdOe3t71qxZQ25uLufOnePOnTu88MILKBQKrl69ytq1a58ZhMhkMkJDQwkNDaWjo4ObN29y6dIlvv32W0JDQ4mIiJDKP3+E3t5eysvLWb9+Pa2trXz22WeMGTOGWbNm6S3LM3v2bD799FOKiooGFbilp6eL3p/l5OREa2vrU++Ty+X978fu7m6Kioro7OzsL0d+VJpsaWnJvHnzDF6advHiRUJCQn70/3d1qDVFTX3crJzAB/vP8mcbXjSanf2cnBwuXbqEXC5n+fLlAy5plMlkLF26lM8++4ykpCRiY2P1rNRwlJeXM3/+fLFl/CRqtZrOzs4hbShVVVVx/PhxlEolsbGx/QGep6cnFRUVZGVlPbFpaAq0tLSgUqme2kenUCiws7MT3e3UxsbmiX7Cnp4eGhoayMvL4/bt23R2dmJtbY23t3d/EOjs7GyQMnUJKejTORnlzQS4WGOtkn61uqS3t3fEnhQuXryIQqHALzyGvzr3gBBnJevHjeySxMDAQEaPHs3169fZtWsXWq2W8ePHP3cPiKWlJdOmTWPatGlUVFRw48YNdu3ahbOzM+PHj2f8+PEj9n33fbq7u/nmm28ICgrqvwiOjY3l2rVr7Ny5k7FjxzJr1qyfLE8cDNbW1syYMYOTJ0/y2muvPVdZWnd3N3l5eWzbtk2nmp4XZ2dnWltb0Wg0PxkQqVQqo5op2dzcTF5eHtu3b//Rx8gEgZ9F2/HX5/u4oZ3AV8fOsGHpAgOq/CEVFRWcP3+epqYmpk+fPijXVgsLC5YvX87evXvx9PQ0+h64gfDITdbYX0tLSwvOzs6DKkHVaDTcuHGDxMREAgMDn9oDHhAQQHJyMuPGjTOaDYqB8CjoMyXMzMz6+wPhoStre3s7jY2N3L17l87OTjo6OrCzs8PLywtPT09cXV1xcnKSXEP1gBSZ6BCtVkt6RTNTx+qv3GmkolKp6O3tFVuGwamqquLOnTts2rqN/0hqwkwOfxZjj2KY9/ENBKVSyYwZM9BoNFy7dm3INuteXl54eXnR19dHVlYWaWlpXLx4kYCAABISEkZ89u/IkSOYm5uzYMH/XdDb2NiwcOFCYmNjuXr1Kp988glBQUHMmDFDp8FfZGQkWVlZXLp06blcB7Ozs3FwcBC910ylUmFhYdFvwGAqXLx4kdDQ0Gf+/iyVMv463oG/OqfmdPsoPK5eZ+ZU3Y6nGAgdHR2cPXuWvLw8Jk2axIYNG4bUu+bh4cGcOXM4dOgQO3bswNLSUodqDU9hYSFubm5GH+g0NDQM2i347Nmz3L9/n+jo6B8tI3RwcKCvr4/i4mL8/f2HItWgNDc3m1zQ930EQcDa2hpra+v+/2OtVktrayuNjY3cuXOHjo4OOjo6cHR07P9eflRGauy9qMaOcX/yTYyalm7qWrsJl0o7dY6FhcWIC/o0Gg0nT55kypQp7C+UUdbSx7tT7HGyMK0+BH3R29vLiRMnSEtL49VXX9XZl4FCoWDChAm88sor7NixA41Gw4kTJ3RybFPlwoUL1NfXs2bNmqfeb29vz+LFi9m2bRuCIPDJJ59w4sQJ2tradKZhwYIFpKWlUV9fP+DnZGZmMn78eJ1pGAqOjo7U1NSILWPAPMryDXS2oKfNQ2OXdqUDXxXIyczM0rPCJ8nJyeHTTz+lr6+PN998k5kzZ+rknDBx4kTGjBnDkSNHdKBSXEpLS3XqiKkv2tra8PT0HNRz29vbGTNmzDP7xry9vUlJSRnUGmLR0NBg8hsPT0MQBGxtbRk1ahTjx49nypQpTJ8+nVGjRtHW1saNGzfYv38/7733HocPH+bevXs6/W4ZSUhBnw5JL28CYLy3vag6hiMjMdOXmpr6sKfHexIXijtZFWrFRHfT3uXTFXV1dezatYsHDx6wY8cOvRkT2NnZsXTpUhoaGigrK9PLGsZOeno6aWlpbNiw4ZnlNg4ODixevJhXX32Vvr4+PvroI06dOkVHR8eQdbi4uDBp0iSOHj06oMc3NTVRWVlpNH07jxw8TYWBZvkeZ5KHig3jram38uMPiSUG+cx0dnZy6NAhTp06xYIFC1i5cqXOL4znzZtHXV0dOTk5Oj2uoSkrKyMgIEBsGc/kkSHJ86LRaKirqxvQ/7+npye1tbVUV1cPRqIo1NfXj5iRCDKZDHt7+36jtdjYWGJjYxEEgcTERHbu3Mnu3btJSUmhtrYWrVYrtmSTQAr6dEh6eTNymUCoh257WiTA3Nx8RAV9jY2NXL9+nUmzFvP7tFbGu5qxOnRk9/E94vbt2+zevZvAwEA2bdqk95ELCoWC8PBwk9sV1gVlZWWcOXOGlStXPtfFv6OjI0uXLmXLli10dnbywQcfcPr0abq6uoakZ9q0aXR1dXH79u1nPjY9PZ1Ro0YZjVOco6MjDQ0NYssYEM+b5Xuc5UFWxHqbU+YQwafHrtHU1KR7gd+Rk5PDzp07UavVvPHGG3rrh1SpVMyZM4dvv/2Wnp4evayhb1paWmhraxt02aSh6O3tRa1WD6okOzExEa1WO6DSckEQ8PT05MaNG4NQKQ4NDQ0jesSQUqnE29ubiIgIpk6dipubG7m5uXz99dd88MEHnD9/nvLycikA/AmkoE+HpFc0E+hmg4WZVH6naywsLEz2y3YwnDhxgrGh49ldaIalUsbPp9ghf4pj10iiu7ubQ4cOce3aNVatWsX06dMNtnZcXBxFRUU0NjYabE2xaWpq4uDBg8yePXvQxg/Ozs6sWLGCTZs20dzczPvvv8+5c+cGHfzJ5XIWLlzIxYsXn5k9vHfvns7mfOkCKysrnWQ8DcFgsnyPEASBtyfb4munpMB1Gp9+eUjnr7u7u5sjR45w8uRJ5syZw6pVq/Qe3IeGhuLh4cHZs2f1uo6+MJV+vsbGRjw8PJ5bZ0dHB6mpqc9l2uPr60t+fv4T41SMlb6+PlpbW4dleedgEAQBZ2dnQkNDiYuLIzw8nPr6eg4fPszOnTu5desW3d3dYss0Ooz7029CaLVaMsqbpH4+PWFhYUFfX5/YMgzCzZs3aWpuJs8+iupWNb+IscfefGRvJJSXl7Nz5056enrYsWOHwd3nLC0tGTt27IjJ9nV3d7Nv3z6Cg4N1Eji5ubmxZs0aXnrpJerr63n//fe5ePHioDZyRo8eTUBAAKdOnfrRx5SVldHV1UVgYOBQZOuUtrY2k7hgG0qW7xHmChl/FW+PUqngvst0Pt/7lc7O3xUVFezcuZOuri5ef/11QkNDdXLcgTB//nxycnKoqKgw2Jq6oqSkxCTGHjU0NAyq77C5uRlzc/PncnxUKBSEhoZy/Phx2tvbn3tNQ9La2vqj4xokHm6qBQQEEBsby5gxY0hLS+OTTz4hKSlJCv4eQwr6dER5YyeNHb2M95aCPn2gUqlGRNDX0tLC5cuXcYpeQmJ5D+vHWRPmMnJtix85c3755ZdER0ezdu1a0cr1EhISyMzMpLOzU5T1DcmxY8ewsLDQ+TwvDw8P1q1bx7p166iqquL999/nypUrz/3Znjt3LmVlZRQUFDz1/rt37xpVwAcPL9p0Pc5CHwwly/c4blYK/jzGnmatJdl2k/n6m2+GrC01NZUvv/ySyZMns3btWoMH0fb29sTHx3Ps2DE0Go1B1x4qptTPN5hZlJaWloO6RnB2dsbBwYFTp04ZdVngcHDuNBT29vZMnDiRiIgIsrOz2blzJ5mZmSb3mdUHUtCnI9LLmwEIl4I+vWBmZjYiPrAnT57EOTCCw6VKItzNWBY8Mpq2n0ZLSwtffPEF6enpbN68mejoaFH1ODs74+7uzq1bt0TVoW8uXbpEbW0tq1at0tsa3t7ebNiwgVWrVlFSUsJ7771HYmLigC/aLC0t+2f3ff85vb293L9/n8mTJ+tD+qBpaWkRfXDys9BFlu9xwt1UvDLBhgrBhUyNF8ePHx/Ucbq7u9m/fz8pKSm89NJLTJkyRSf6BkN0dDRKpZLExETRNDwvjY2NdHZ2Gn0/HzwM+gYzHmcoLSCBgYHU1tZy5MgRWltbB3UMfdPS0iLNrXtOLC0tmTBhAqGhoVy+fJkvvvjiudyfhyNS0Kcj0iuaMJPLCHK3EVvKsESlUqFWq8WWoVfS0tKorG8iRT4Oe5WMd6PtkY3QUo78/Hz++Mc/Ym9vz44dO4xmRt7UqVO5devWsH0vZmVlcfv2bTZs2GCQjKqvry8vv/wyS5cuJS8vjw8++IDU1NQBbfBMnDgRBwcHLl68+MTt2dnZ2NraGs175hFtbW1GH/TpKsv3OIvGWjJ9lDm5qiBuVnZz+fLl53p+RUUFf/jDH9BoNGzfvn1QWSBdIpPJWLBgAUlJSTQ3N4uqZaAUFRU9dUi5sdHR0YFSqcTG5vmvo8zMzJDL5YPK9gmCwKRJk8jIyOCLL77g0qVLpKenU1xcTENDg1FUGQ3XcQ2GwM7OjpiYGKysrPjTn/5EWlqa2JJEQ5pyqCMyypsJ9rBBpRjZvVf6wszMzChOvPqio6ODi5cu0Ry6ivoWDf8y0xEb1cjck7l79y7nzp1j4cKFBu3XGQijRo3C0tKSjIwMJk6cKLYcnaJWqzl69Chr1qwx+DBzf39//P39yc/P5/Lly6SkpBAbG0tkZORPGjosWLCAXbt2ER4ejpubG/DQtdNYZvM9jkajQalUii3jR3mU5du+fbtOjysIAq9NsqO8pY88eTxmGSewt7cfkOFGamoqV65cYerUqaJm976Pl5cX48eP58SJE2zYsEFsOc+kpKTE4H3Qg2Gw/XyPsLW1pa2tbVDnL4VCwbx583jw4AE1NTUUFxfT09NDd3c33d3dWFlZ4ejo2P/H3t4eW1tbbG1tDVJ2WV9fP6KdO3WBr68vzs7OXL16lerqaubOnYtcPrKu2aWgTwdoNFoyKppZMmFww0Qlno2Zmdmwza4AnD17lg7vaDKbFbwywYYgp5FZxtHY2MiZM2dYt26d0ZYiTZkyhZSUFCZOnEhxcTEajQZ/f3+xZQ0ZuVyOjY2NqCVEAQEBBAQEkJOTw9WrV0lOTmbq1Kk/GiA4OzsTFxfH119/zbJly9BoNFRVVem1NHWwmJmZGXU/qD6yfI9QyQV+GefAX557QOmoFzh17husra0ZM2bMUx/f3d3NsWPHqKqq4qWXXhI9u/c0Zs6cySeffMK9e/eMbnPq+5SWlhITEyO2jGfS1NQ0pA2b0NBQcnNzB/0eVigUuLm59W8gPUKr1dLZ2Ul7ezstLS1UVVU9ERAqFAocHR1xcnL6QUBoaWmpE/OVBw8eGF31giliaWlJdHQ0d+/e5dixYyxatAiFYuSEQiPnleqR4gfttHb1Sf18emQ4B32FhYXcKW3knscUJnuqWDx25JZwnD9/nrCwMKMN+ADGjRvH2bNn+c1vfgPQPyPMlEpv1Go1ly9fJjIy8okLpFGjRpGbmyv67z84OJjg4GCysrK4du0a169fZ+rUqU+9IIyLi0Mmk/Hxxx8jk8kMYt8/GKytrWlpaRFbxlPRV5bvcZwt5fxlrD3/eLkBx7AVHDq8j40vbfhB2WFVVRWHDh3CycmJ7du3G+X/JTxsOZg3bx7ffvstAQEBRttv1dDQQG9vr1EGzt+no6NjUEPZHxEWFkZycjK9vb06zaoLgoClpSWWlpZPDby6u7tpb2+nvb2d/Px8uru76erq6neNtLe37w8KHRwc+rOD+/btQ6VSPZFBtLW1xc7ODhsbm/5gRK1WS+MadIhCoSAiIoK0tDSOHz/O4sWLR0zGTwr6dEBGxcO6/vFe9uIKGcYMVyOX3t5ejp2+QIn3ApxUct6ebDdiLZmLioooLS3lzTffFFvKTyKTydi4cSP19fWEhIRw4MABrly5woIFC8SWNmBOnjzJ3bt3SU9PZ8mSJf2ZylGjRhmVUU1YWBghISFkZmZy/fp1rl27xrRp0wgLC3vicTExMRQXFxMYGGi0ZbdWVlZGaxKhzyzf44S6mLE1wpadt1uIHfciX375JVu3bu3vdbx58yaXLl0iPj6e2NhYvWrRBcHBwaSlpXHhwgWj/fwXFhaaRMCn1Wrp6OjA2dl50MewtbUlIiKCjIwMIiMjdajup1GpVP3B2/fp6+vrDwjLysrIy8vrzxCqVCoCAgJob2//0ZJSJycnlEolgiAY/YxFU0ImkzFx4kRu377N2bNnmT9//oi49pKCPh2QXt6MSiEj0E2qt9YXKpWK3t5esWXonKtXr5FjH02HRsHfxthjbTZyT+qXLl0iLi7OaHf2H+fxEqC5c+fy+9//nujo6Kd+6RsbycnJFBUV8Ytf/IKioiIOHTpEbGwscXFx2NjYcPfuXfr6+oym5EUmkxEeHs64cePIyMjgypUrXLt2jalTpz5RVtfT02PUlubW1tbU1dWJLeMHGCLL9zjz/S0oauzlXBHMC4pn7969bNy4kXPnzlFeXs769evx8vIyiBZdMGfOHHbt2kVsbKxRGvWUlpaaxHy+trY2bGxshnz+T0hIoKioiOrqaqMwr1EoFNjZ2f3ke+NpxjWPl5S2t7cb3Qia4YBMJiMiIoLU1FTs7OxMYqNpqIzcK0wdklHeTJinLQq59OvUF3K5HJlMNqxKPOvq6jiW30mtwpUtE2wJcDRekwd9U1xcTFNTk+hjGQaDvb09YWFhXLhwQWwpzyQ/P5/r16/3zzgLCwtj06ZN3L59mwMHDnDq1Cns7e0pLi4WW+oPkMlkTJgwgddee40pU6Zw8eJFPv30U+7fvw88zJoba4kdPOwlMcYhwRcuXDBIlu8RgiCwLcKWICclV7pGYebixz//8z/T1dXFjh07TCrgg4d9pePGjePMmTNiS3kqZWVljB07VmwZz6SpqWlIJi6PUCgUzJgxwyjPYc/Do5JSFxcX/Pz88PSUPCP0gVwuJzIykuTkZPLy8sSWo3ekKGWIqDVaMiubCfe2F1vKsMfc3HxYZfv+dDqZMocI4n3MmT/GQmw5opKamsqECRNMtnxl5syZFBcXU1paKraUH6W+vp6jR4+ycOHCJ4wKXFxc2LZtGz09PXh6ehITE2PUX36PynJef/11oqKiOHv2LDt37qS6utqos8QymcyoHIhbWlo4ceIEBQUFTJ061aBrK+UCfxlrj4VS4JZlFL4BwVhYWBj1/99PMW3aNEpLSykrKxNbyhPU19fT29trFBmvZ9HU1KSzMtRRo0b1l4tKSDwLlUpFeHg4p06dorGxUWw5esU0r7CMiIK6Njp61Iz3Mr6yjuHGcAr6rqamcUMxDndrBW9Msh0RteQ/RkNDA8XFxcTFxYktZdCYm5sTExNjtNm+7u5uDhw4QGRk5FOdBs3NzVm/fj3Lli3Dz8+P8vJyEVQ+H492aN944w0iIiLo7u7m0KFDXL9+na6uLrHlGS319fUcPHiQTz75BI1Gw+bNm7G1tTW4DgcLOb+Ms6ehS8ODoKVU1dRy7tw5g+vQBZaWlsTGxnL27FmxpTxBUVGRSfTzAXR2dg7JxOVxZDIZwcHBVFZW6uR4EsMfe3t7vLy8OH78+LCqKPs+UtA3RNLLH5q4SM6d+sfa2pq2tjaxZQyZltZWPruvRaMw5y9i7bFQjuyPYWpqKoGBgSa7y/+IuLg4WltbycrKElvKE2g0Gg4dOoSDgwMzZsx45uNHjx5NY2OjyQROcrmcqKgo/vEf/5F58+ZRUlLCe++9x+HDh6murhZbXj92dnainr8qKir48ssv2bVrF1ZWVrz22mssXrx4SMYZQyXQyYwdkbZk1quRR6wgMzOzv1zX1JgyZQodHR1G9fkvKSnBz89PbBnPRBcmLt8nMDCQBw8e6Ox4EsMfPz8/Ojo6uHnzpthS9MbIvtrUASrFw19hS5fxlO0MVyZPnkxRURFarVZsKUPiN2fv02zuwY5JdvjZj9w+PniYgcrMzCQhIUFsKUNGJpMxa9YsLl68aFS9WxcuXKCpqYkVK1YM6PEqlQoXFxcKCgr0rEy3PNrd37BhA6+++iqWlpbs3buXP/zhD9y5c0f00kpPT0+ampro6ekx6LoFBQV8/vnnfP3113h4ePDmm28yf/58UbJ7T2P2aEsWBlhyplSN+5TFnDhxwiTL8uRyOTNnzuTChQtG4zSt0WhEf98PhLa2Nuzs7HRqxOTl5UVvb69RnYsljJ+wsDCSkpJM8hw0EKSgb4hMG+uCXCZwIadGbCnDHn9/f1xcXIyub+J5OHW7iLt9XiR4K5nlN7L7+DQaDYmJibi5uYmabdAlYWFh2NvbG02Z2qOxDOvXr38uN04fHx+TC/oex9HRkXnz5vGzn/2MSZMmcefOHX77299y6tQp6uvrRdMlk8kMFvRlZWWxc+dOTpw4QVBQEG+99RYzZswwyllfmyfYEOqs5EilFU5jJ3Lw4EGxJQ2KsLAwbGxsSExMFFsK8LD6IDU1VdT3/EBobGzUuVGJXC7H09Nz2PdoSegWCwsLHBwcSE1NFVuKXhhQ0CcIgr0gCPsFQcgRBCFbEITY791vJwjCMUEQ7gqCkCUIwpZnPVcQBEdBEM4KgpD33d8Oun1phsHOUknUKAfOZ9eKLWVEMGPGDEpKSkxi9/L7VDa283mugItKzRuTHUZsH19DQwPnzp3j/fffJzs7e0Alh6bEihUryM3NJTc3V1Qd5eXlnD17lpUrVz63lby/v79J9PU9C6VSycSJE9m6dSvr1q2jr6+Pzz//nI8//pgzZ85QU2O4zbr8/HwcHBywttbfaB+NRsPt27f54IMPuHLlCpMmTeKtt94iJibGqMdZKGQCfxFrj61KxnUhjIb2HpKSksSWNShmz55NcnKyUZRHe3t7ExUVxaFDh4wm+/g0mpub9dJ76O3tTVNTk86PKzG8GTt2LHfu3KGlpUVsKTpnoFu/vwW+1Wq1qwRBMAO+v1X4FnBPq9UuFgTBBbgvCMJerVbb8xPP/WvgvFar/Q9BEP76u3//1ZBfkQjMDnHl307mUNHUiZf9yM7e6Bt3d3fGjh1LYWGhSc2tUWu0/Mu5CrRyK/7fTDfMFSMryd7d3c29e/fIyMigpqaGMWPGsHTpUkaNGiW2NJ1jaWnJwoULOXXqFJ6ennq9yP8xWltbOXjwINOnTx/U73jUqFG0tbXR1tYmin594OXlhZeXF2q1mqKiIrKzs9m7dy8qlQp/f38mTpyoV9OL+/fvExAQoJdj9/X1kZKSwq1bt7CysmLGjBmEhISYlBuunbmcX8Y78P8uPKDcbyFd17/A39//CadZU8DHxwd/f39Onz7N0qVLxZbDtGnTKCws5PLly8ycOVNsOU9FlyYuj+Pq6kpGRobOjysxvFGpVLi6upKYmMiCBQvElqNTnvmNIAiCLTAN+AOAVqvt0Wq1Td97mBawER6mLqyBBqDvGc9dCnz+3c+fA8uG8kLEZHbIwy+lC9lSiachSEhIoLq62qRq9d+7VEwttuyIsMHb1jiGXhuCsrIyjh07xnvvvcedO3cIDQ3l3XffZcWKFcMy4HtEcHAwo0aN4siRIwbfYe/t7WX//v2MGTOGyZMnD+oYj0qjjHl0w2CRy+UEBASwePFifv7zn7Nw4UI0Gg2ff/45//iP/8jJkyepqKjQ6ZpqtZrCwkImTJig0+N2dXVx/vx5fve731FUVMQLL7zAq6++SlhYmEkFfI8Y46DkjSg7CloEukNe5NChQyZZ1TF37lyKioqMwpRGLpezaNEibt26ZdDM9kDRh4nLI1xcXOjs7NT5cSWGPwEBAWRnZw87B9iBfCv4A3XAZ4Ig3BEE4feCIFh97zHvAyFAJZABvKvVajXPeK6bVqutAvju76du8wiCsEMQhJuCINysq6t77hdoCPydrfBzsuR8jlTiaQjs7OyIjIwUvXxuoFwtaOTqAwumOPUyO8A4zBP0SVtbG4mJiXzyySfs378fhULB1q1b2bp1K1FRUUY9QFuXLFq0iLa2NoOPcTh58iRyuZyFCxcO6Ti+vr4UFhbqSJVxIpPJ8Pf358UXX2TatGl4e3sjCAL79+/nd7/7HcePH9dJD3FBQQFWVlY4OjrqQPXDz9ipU6d47733qK+vZ/Xq1WzcuFFvmURDMm2UBUsCLUnrsKfeNtBoh57/FDY2NrzwwgscO3bMKMoL3dzcmDRpktGNlAD9mLg8wtb24TgkU9ogljAOFAoFgYGBnDx50iQ3nn6MgQR9CiAS+Eir1UYA7TwsxXyc+UAa4AlMBN7/Lss3kOf+JFqt9lOtVhul1WqjXFxcnuepBkMQBGYFu5FY8ICOnuHz5jBmpkyZQmtrq9GPcKjrUPPhrTYchXZ+Pt1bbDl6pbGxkRMnTvDhhx9SVFTE9OnTeffdd1m4cKHOLnZNCYVCwbp168jIyDCYjXtiYiKlpaWsWbNmyJkef39/nWe8jJmOjg48PDxYuHAh77zzDsuWLUOhUHD48GF++9vfcvToUUpKSgZ17Pv37zN27Ngha2xqauLw4cN89NFHdHV1sWnTJtauXYuPj8+Qj21MbBxvwwQ3M9LNQjielGVUYxAGSmBgIFFRUXz11VdGcdEYFxdHbW2t0Rmh6cPE5XE8PDwkMxeJQeHu7g5gNMZMumAgVwXlQLlWq0357t/7eRjIPc4W4KD2IflAERD8jOfWCILgAfDd3yadJpsd4kpPn4br+dJcGENgbm5OfHy8UZTP/Bi9Gi3/fLYctRb+YY43ZvLhadzS0NDAkSNH+P3vf09vby87duzgpZdeIjg42CRLzHSJnZ0dy5Yt49SpU3rPTOfm5pKUlMS6det0MvPQ09OTnp4eo8hUGILOzk5sbGyAhxlAX19fFixYwDvvvMPKlSsxNzfn2LFj/OY3v+HIkSMUFRUN6LgajYa8vDzGjx8/aG01NTXs27ePnTt3olQq2bZtG8uXLze5freBIpcJvGBfg6Knjebxazhx/qpRGKM8L9OmTcPe3p4jR46ILQWVSkV0dDTnz58XW8oTNDc36zXoc3d3p7W1VW/HlxjehIaGGm1p9GB45hWZVqutBsoEQQj67qbZwL3vPaz0u9sRBMENCAIKn/Hco8Ar3/38CiD+WXEITPZzxEal4LzU12cwwsPDAYzWjnpnSi2VPSo2BQl42xuvc95g6e7u5syZM/zhD39ALpfz2muvsWzZMuzt7cWWZlSMHj2aJUuWcOzYMW7duqWXNerq6jh+/DiLFi1CVxURMpkMb29vkymjHirt7e0/alrj7e3NvHnzePvtt1m1ahVWVlacPHmSX//61xw+fPgnx1sUFxejUqkGFaCVlZWxZ88e9uzZg4ODA2+88QYvvvgiDg4maXY9YC5cuMDV89/yxng5WoU5hZ6zOXDY9C4RZDIZS5cupaamxiiyBVOmTKG5udmoxrF0dHTo7Jz1NBwcHExyw0DCOHhk9HXq1CnUarXYcobMQB0l3gH2fue+WQhsEQThdQCtVvsx8C/ALkEQMgAB+CutVlv/Y8/97vb/AL4RBOFVHgaNq3XxgsTCTCFjWqALF3Jq0Wi0yGTDM6tjTCgUCmbMmMHp06eNbs5bUlkn58u1jFfVs2jiOLHl6Jx79+5x7tw5HB0d2b59uxToPYPAwEA2bNjAV199RUtLi05d9Do7O9m3bx9RUVEEBQU9+wnPgZ+fH4WFhURHR+v0uMZIZ2cnVlbfb1f/Id7e3nh7ezNnzhwqKirIzs7m9OnT/Q6Ezs7OeHl54enpibOzMzk5Ofj7+z+Xlvz8fK5cuUJLSwuTJk1i5cqVWFgMf2forq4uDh48SFtbG6+88gqOjo4oHLr4nyQtSd0+BN+6zaRJ3y80Mm4sLCxYuXIle/bswcvLS1QDK6VSSWxsLBcvXmTMmDGi6XhEX18fXV1des1Y29raSj19EkPC29ubmpoaUlNTiY2NffYTjJgBBX1arTYNiPrezR8/dn8lMO85notWq33Ad9nB4cKsYFdOZFSRVdnCeO/nm4slMTjGjh3LjRs3KC8vx9vbOHrmqtv6+F1KA7Z9LfxqeYjYcnRKfX09Z86cob6+njlz5hAaGiq2JJPBw8ODLVu2sHfvXlpbW3nxxReRy+VDOqZGo+HQoUO4uroybdo0HSn9P0aPHm2y89Kel46ODmxtn89o6dEYiDlz5lBTU0NVVRW1tbWkp6dz/vx5ent7efDgAcHBwVy/fh1PT088PDx+tPw2KyuLa9eu0dPTw+TJk4mIiDDq+Xq6pK6ujq+//hoPDw9WrlzZ/7pjvc1ZGWLFgWx/vrqZypgx/ia3yeTm5sbcuXM5ePAg27dvF3UMyqRJk0hJSaGgoED0wO/Bgwd4eXmhUOjP0VoK+iR0QVhYGMnJyfj6+uLl5SW2nEEzcrzjDcCMIBcEAc7n1EhBn4EQBIEZM2awb98+PD09Re8h61Fr+Y8rdaj7+virBGdUiqFd1BsL3d3dXL16lbS0NCZMmMCaNWv0+kU9XLG3t+fVV19lz549fP31109c3A6Gc+fO0dbWxtatW3Wo8v9wc3NDEARqamqGbf/YIzo6Ovp7+gaDm5vbD35Hra2tFBcX09zcTE1NDZmZmTQ0NPQ7ebq4uODh4UF9fT3Z2dkoFAqio6MJDw8f8oaAKZGVlcW3335LbGwscXFxP7h/bZg1xU193GEynx66wF+8skz0c/3zEh4eTlFREefPnxd1fp9cLkehUBjF7+/Bgwd6n7drbW1NT08PWq2Wh1PFJCSeH3Nzc4KDgzl69CibN2822coL6apNhzhZq4j0deB8di0/n2M6g8NNHS8vL0aPHk1hYaHoluWf3WmmrF1gmUsjwT7DYw7dvXv3OH/+PPb29mzdunVEOnHqEnNzczZv3sz+/fvZvXs3a9eufe4ME0BaWhpZWVm8+uqreg3Avb29yc/PH9ZBX3d3N319fTrPwNjY2PzAwEWtVlNbW0t1dTXV1dWkp6eTnp7OokWL9JKtNWY0Gg1nz54lOzub5cuX/2gZrFwQeDfajr8638cdzSSOX7jOkjlTDax26EybNo3f//73tLS0DOozryvc3d3Jyspi9OjRommAh5si+naeVSgUWFtb09nZiaWlpV7XkhjeuLq69lc7LVmyxCQ3EcTf6hlmzAp2JaOimZoWqXHYkEydOpWqqip6e3tF03C1tJMzRV34dxfw8uyJounQFfX19XzxxRecO3eO2bNn8/LLL0sBn454NM7By8uLzz///LmdwcrKyjh37hyrVq3S+8Wjn5/fsBzS/jgtLS0GuyCUy+V4eHgQERHBwoUL2bhxIwsXLqSqqsog6xsLHR0d7N69m4qKCjZv3vzMvkcrMxl/He+ATKlif7UjRaWmN07EwcGB4OBgrly5IqqO6dOnk52dTUtLi2gaHvXzubo+dUSzTnF1dR0xLsQS+iU4OJjS0lLu3fu+n6VpIAV9OmZ2yMMT2EVpULtBcXR0JDw8XDSnwfKWPj680YRNVw1/uSBYFA26pLu7m88//xwnJyfefPNNqXdPT7zwwgtMmjSJvXv3kp+fP6DntLS0cPDgQWbPnm2Q+WzBwcH09PTw4YcfDlsnz+bmZlHLdaKioigpKRH1ItyQVFVVsXPnTpycnHjllVcG3KPnbavg5zH2tKuc+O9L5SbZqxUfH092draoM2YdHR0JDg7m4sWLommor6/H29vbIG0C/v7+RuvyLWFayGQyQkNDuXLlChqNRmw5z40U9OmYIDcbvOwtOC8FfQYnJiaGxsZGOjo6DLpud5+W/0lqRNvXwzrfDlydnQy6vj7IycnB3t6e+fPnS717eiYuLo4FCxZw6NAhbt++/ZOP7e3tZd++fQQGBhIREWEQfdbW1mzbto3Y2Fi+/fZb/vSnP1FXV2eQtQ1FS0uLqOYaNjY2BAUFGYWtv765e/cue/bsITY2lsWLFz937+JkT3PWhVlTYzGK/z2Rph+ResTR0ZHAwEAuXbokqo6pU6eSm5srWgbswYMHBisvHTNmDE1NTcPCcl9CfOzt7dFqtQOe1WpMSEGfjhEEgdkhrlzLq6erVzrBGBJLS0tiY2PJyckx6Lq/v9NCWUsfEzrvsGC6adv5PiI7O5vgYNPPWJoKoaGhbNiwgUuXLv3k7vuxY8dQKpUsXLjQgOoe7m5GRETw2muv4e3tza5duzh69OiwmX8ldtAHEB0dTWZmJn19faLq0BdtbW18+eWXXLlyhVWrVg1pDMjKECsmucm52efD0ZT7OlRpGB5l+wy9Qfk49vb2hIWFiZbta21tNZgLoo2NDT4+PlRXVxtkPYnhj6urK4WFhWLLeG6koE8PzAp2pbNXTVLhA7GljDgiIiLo6+sz2O7lhaIOLhR34t2UwfZFCQZZ0xCYmZmRlJTEyZMnaW5uFlvOiMDLy4utW7dy7949Dh8+/INd6WvXrlFVVcWaNWtEUvhwUO3MmTN57bXXUKvVvP/++1y7ds0ky1wep7W1VVRjDXg40sPNzY0bN26IqkMf3Lx5k48//hgbGxt27Ngx5AyPTBD4s1gn3Cxgb5E5WcWmdTHv7OzMmDFjRO/tS0hIID8/3+Dn+N7eXrq7uw1qDhUeHi4FfRI6QxAEtFqt2DKeGyno0wMx/k5Ymsm5kC2VeBoapVLJ9OnTycrKori4mMbGRr3tnJc097LzTgtOffUsG6syugHxQ2HFihW8/PLLqNVqPvnkE77++uthV9JnjDwa6dDQ0MCXX35JZ2cn8LDcNjk5mbVr1/7ojDdDYmtry/Lly1mzZg25ubl89NFHJt3v19bWhp2d+GN2Jk+e/MwSX1OirKyMP/7xj9y8eZNVq1axaNEinc0dtFDK+LuZrsgVCv4zsYmmtk6dHNdQxMfHk5mZKWq23NbWltDQUINn+x48eICPj49Bx5L4+fnR3t4+bKoTTI2uri7u379v8huEj+jr6zPJGapS0KcHzJVyEgKcuZBTa5I7AaZOcHAws2fPxtbWloqKCq5du0ZiYiJ37twhNzeXmpqaIZ/4O3s1/E9SEwptH+PbbjF92k/bh7e1tVFcXExBQYHJmDW4ubmxePFi3nzzTRwdHYddSZ+xYm5uzqZNmzAzM2PPnj0UFBRw4sQJlixZYnQbC76+vmzdupX4+HhOnTrFzp07qaysFFvWc9PW1mYUA7+Dg4ORy+Um6wz3iJKSEnbv3s2BAwcIDAzk1Vdfxc/PT+freFgr+MsEZzqUdvz98VyT+r51c3PD29ub69evi6ojPj6evLw8g2b76uvrn+nWqktKS0vZt2+fwWdfVlVVcezYMW7cuEFVVdWwCXieh46ODk6fPs3777/PqVOnOHPmjNiSdEJfX59RbMA+L5JDg56YHeLKmXs15FS3EuIhbtnQSEMmkxEWFkZYWBjwcC5WU1MTDx48oLa2lqqqKgoKClCr1VhYWGBhYYGdnR12dnZYW1s/c/aKVqvlo1stVLWqCam+wJo1C34w6Lajo4O6ujoaGxtpbW1FpVIxevRolEolOTk5WFtbm4wjprW1NXPnzmXy5MlcuHCB999/n5iYGOLi4oxiwO9wRKFQsGbNGo4cOcL777/PypUr9T7EeChMnDiRwMBA/vM//5M//vGPREREMHv2bJP5UmxtbcXBwUFsGQBERkaSnJxsMueHxykrK+PixYs0NDQQFRXF2rVr9b4bHuGuYl2oJV/ec+P/O5nBX7wYrtf1dMn06dPZu3cv48aNE20Opr29fX+2b9myZQZZ01D9fBUVFVy9epWamhr8/f0N3qeemppKRUUF7e3tJCcn097ejr29Pc7Oznh4eODp6YmXlxdmZmYG1WUIWlpauHLlCtnZ2fj7+/PSSy+hUqn44x//SFRUlNFtYD4vpprpk4I+PTEz6OHohgs5tVLQJzJyuRwnJyecnJyeuHBua2vjwYMH1NfXU1VVRX5+Pk1NTVhaWmJhYYG1tXV/MPi4g+WZwk6ul3UR3JPLzDBv3Nzc6OzspL6+ngcPHtDa2opSqWT06NHExsbi5eX1ROlYfHw8X331Ffn5+aIPk38e7O3tWbFiBWVlZZw/f57k5GReeeUVXFxcxJY2LNFoNLS1tTF58mQSEoy/X/Tu3buMHj2apUuXcv78eT788EMSEhKGZNhhCLq7u+nq6hK9p+8RERERJCYmUlFRYTCji6EiRrD3OCtDbcl/0E1StQtHbxSwZPIYg609FDw8PIiLi2P//v1s375dtIv/+Ph4du7cSXNzs97LnHt7e+nr69PrfL7q6mquXbtGRUUFo0aNIj4+3uCDtPv6+sjPz++fxwrQ2dlJRUUFNTU11NbWkpmZSVNTE1ZWVjg6OuLq6oq3tzfe3t6iG0sNlqamJi5dukReXh6BgYFs2bLliQBv0qRJHD9+nM2bN4snUgd0dXUZRUvA8yIFfXrC1daccG87zmfX8NZM07mwH0lYW1tjbW3NqFGj+m/r6emhoaGBBw8eUFNTQ0VFBVlZWcjlciwtLWnAmj8W2OCrbMW+JBXHsDlcv34dmUzGqFGjmDJlCl5eXj+ZNTA3N2fVqlXs3buXsrIyg8xa0yU+Pj6Eh4dz4sQJg3+RjiTOnDlDR0cHW7ZsEVvKM+nt7SU1NZVly5bh4ODAqlWrKCoq4ty5c9y8eZP58+czZoxxXog/uugylqy1UqlkwoQJXLlyhfXr14st5ycpKSnh8uXLogV7jxAEgT+Lc+HPT1Wyp1BJgMcDQr1NY3ROXFwcpaWlHD9+nBUrVoiiwd7enpCQEINk++rr6/Hx8dHL562uro5r165RUlKCr6+vqNUoR48excPD44mNGwsLCwICAp7Y7O3t7aWmpobq6mqqq6tJTk6mtrYWhUKBs7MzTk5OeHp64uPjY7TZMY1GQ05ODrdv36aqqorQ0FC2b9/+1JL5hIQEcnJyuHPnjsHGDumDjo4Oo/3/+CmkoE+PzA524zfnc3nQ1o2TtemlgUciZmZmuLu74+7u3l8eqtFoaG5upqSqlh37C7FWqhlbn8iUmTOZPHkyXl5eODo6PlcAZG1tzerVq/nyyy8xMzMTrbRnMNy9e5cLFy6wdetWkzzpmQK3b98mJyeHrVu3msScxFu3bmFjY/PEBsro0aN59dVXuXPnDseOHcPV1ZUXXnjhmb1zKSkpdHd3Ex0dbZDy0MbGRqPbVZ88eTIffvghTU1NRtFr+H1KSkq4dOkSjY2NogZ7j6NSCPz9LDd+caqaf7/6gN8ussTRxkJUTQNlyZIl/P73vxf1QnjatGl88sknen/P1dfXEx6u2xLcBw8ekJiYSH5+Pt7e3sTHx4sW7Gk0Gr766is6OzsH5LSsVCr7s3uPH6OhoYHKykpqa2vJzs7m8uXL9PT04ODggIuLC+7u7nh5eeHh4SHad0RdXR03b94kJycHS0tLxo8fz7Jly37yfKpUKpk/fz4HDx5k7NixRnfuHQhdXV0oFApsbGzElvLcGP/VhAkzO8SVX5/L5eL9OlZN8n72EySMEplMhr29Pb86XkhDl5avX4tn0qhFQz6uo6MjK1eu5Ouvv8bMzMxoeop+ioyMDM6ePcvatWtNpvTM1CgrK+PChQusW7fOaEoOf4re3l5SUlJYsmTJD+6TyWRMmjSJcePGcfnyZX7/+98TFhbGzJkznxrQ1dfXc+XKFby8vEhJSWHs2LHExsbqdVOksbHR6Mp0rK2tCQkJ4dq1ayxaNPRzja4oKiri4sWLtLS0EB0dzaRJk0QP9h7H1UrBX01z4Z8uN/D3J/L4zeowFAY27xgMlpaWLFu2jH379omW0XncyXP58uV6WaOuro6WlhadDWWvqanhxo0b5OXl4enpSXx8vMHNWr7PyZMn6enpYePGjYP+bMhkMpydnX/wPmhtbaWqqoqqqirKy8u5detW/7gZJyen/j5Bb29vvW2YNTc3c/v2be7fv097ezuBgYGsWrXquSqWHvVXHjx4kE2bNulFpz6pra3VizGVIZCCPj0S5mmLm62KCzk1UtBn4nx2vZhvs6r5fy+GMGmU7oIzd3d3li5dyqFDh4iMjDTqXa979+5x5swZVq9ebXIlqaZCS0sLBw8eZPbs2U/s/BozN2/exMbG5icv5FQqFfPmzSMqKopz5879aL/ft99+S0REBHPmzKGhoYEbN26wZ88eHB0diY6OJiQkROc7+MaaTYuNjeWzzz5jzpw5ohviFBQUcPnyZVpbW5kyZQqTJk1CqVSKqunHGO+qYtN4Kz7PEPj3o3f5u+WRYksaEL6+vkyePJn9+/ezdetWg/f3dXd3IwgCeXl5ejl+dXU1BQUFrF69ekgbnFqtlpKSElJTU6mqqsLT05O4uDijqIgoKSkhNzeXHTt26GUzxMbGBhsbmye8Cbq7u6mqquovEb1//z4NDQ2Ym5vj5OSEi4sLnp6e+Pr6Dmpzq6Ojg7y8PAoKCqioqKCrqwt/f39mzJjB2LFjBx1kz507l88++4zExETi4uIGdQyxqKmpYfbs2WLLGBTif0qGMYIgMCvYlWN3q+jp02CmMI6eEYnn43ZpI/92Mpu5oW68mqCbHcrH8fPzY8GCBXz77bcGK2l7XnJycjhx4gSrVq16ooRPQnf09vayb98+AgMDTabX4VEv39OyfE/D0dGRNWvW9Pf73b59m7lz5zJmzBhyc3N58OABq1ev7n/s/PnzmTFjBhkZGVy/fp2zZ88SHh5OTEwMlpaWOnkNzc3NRumM6uzsjI+PD0lJScycOVMUDfn5+Vy+fJn29vb+zJ6xBnuPszjIhvwHPVyv9GTPxXQ2zjQNR8+EhASqq6vZs2cPGzduNEjg9yhTn5qairu7u156iKuqqigqKmLt2rWDztp3dXWRl5fHzZs3aW9vx8fHRxSDlp/i8uXLREdHG3TzVqVS4efn90TmSa1WU1tb298neOfOHU6fPg3Qb2r3KCPo6ur6xEbao99zYWEhFRUVtLW14eHhwahRo4iKisLLy0sn2VSVSsXSpUvZu3cv/v7+uLu7D/mYhqC5uRmNRqOzbLWhkYI+PTM72I0vU8u4UdxAfIDU/2RqNLb38M4Xd/CwN+d/Vk3Q2xdMSEgIpaWlXLt2jTlz5uhljcGSm5vbbzJgqic6U+D48eMolUoWLlwotpQBM5As39N4Wr9fQ0MD06dP/8EOuUqlIioqiqioKIqKirhx4wYffPAB/v7+xMXF4eHhMaTX0NzcjJOTcZp+xMTEcPjwYaZPn27QHqXHg72YmBgiIiJMIth7hCAIvB3jRPmZao7UOjAqPZ+p4cZvqCaTyVi5ciUHDhxg9+7dbNy4UW+bgBqNhjt37nD9+nVsbW31WsFx8+ZNnJ2dKSkpobe3FycnJywsfrrfUq1W8+DBg35n7dLSUmxsbIzW0KS6upq6ujrWrl0rthTkcjkeHh4/ODc2NjZSWVlJTU0N+fn5XL9+nc7OThwdHbG1taWpqYnm5mbc3Nzw9fVlwYIF+Pj46O2z7+HhQUJCAvv372fbtm1GueH9fXJzc4mPjzeKzPJgME3VJkR8gDMqhYxz2TVS0GdiaDRafvFNGnWt3ex/IxY7S/2c+LRaLampqdy7d8+gA2sHQn5+PkePHmXJkiVG6744HEhMTKS8vJzt27eLLWXAPG+W7/s83u939epVFAoFEydO/MnnjB49mtGjR9PU1MTNmzfZvXs3AIsXLx70XLumpiajHTvi5+eHjY0NaWlpREbqv0zx/v37XL16lY6ODmJiYpg0aZLoPVKDxUwu8LfTXfnF6Ro+ztTg7VjNaG/jzybI5XJWrlzJ4cOH2b17N5s2bdL5xXBOTg6XLl1CEAQWLFig90z3Cy+8QH19PTk5Ody6dYuOjg7MzMywt7fHxsYGc3NzZDIZfX19tLe309zcTHNzMyqVCisrK5ydnY2mhPPHSE1NJTQ01Kh6XL+Pg4MDDg4O/SZ18LB8s7KykqamJmxtbRk1apRBX0NMTAzl5eUcOnTI6N2Ky8rKMDc3Z9y4cWJLGTTG+wkaJliYyYkb48T57Fr+flGoUZUiSPw0n1wp5OL9Ov5laRjh3vZ6WaO3t5fTp09TXFxsdKWdRUVFHDlyhEWLFhll+dtwIT8/n8TERF555RWj+v9/Fo8cO4ea/VWpVM+d3ba3t+9/zu3bt7l69Srnzp1jwoQJTJkyZcC/x9bWVgRB0FmpqD6YMmUKV69e1WvQd+/ePa5evUpvb29/Zs9Ug73HcbKU86upzvzdRYF/vVjFb1bYYG1lJbasZyKXy1m+fDlHjhzh888/1+m5QaPRcPr0aTw8PAbkLqkL5HI5bm5uT5R2dnV10dnZ2f+3VqtFLpdjZWWFi4uLUY1RGQj5+flGH7Q8DUtLS9HnBS9evJjPP/+cc+fOGV2l0yPq6uooLS3lpZdeMulzo+l8okyY2SFulDZ0UFDXLrYUiQGSUviA/zlzn0XhHmyM0U8PW0tLC19++SW1tbXPdaGqKxoaGkhKSqK6uvoH9xUXF3Pw4EEWLlxIcHCwQXWNJBoaGjh69CiLFi0y2mzT03jUByRWr9kjDZmZmaxfv57XXnuNF154gZqaGt577z0OHjxIVVXVM49RX19v9A6poaGhqNVq8vPzdX7snJwcPv30Uy5evEh0dDRvvPEGUVFRJn1R832Cnc3YPsmORnMP/ungHfr6+sSWNCBkMhlLly7Fw8ODXbt20dHRobPjrlu3jsrKSm7duqWTYw4Gc3NzHBwc8PDwwNfXl1GjRuHt7Y2bmxs2NjYmFfBVVFT0l1RKPD8qlYrVq1eTmZnJ7du3xZbzBFqtlsLCQvLy8li1apXRtgIMFNP5VJkws4JdAbiQUyOyEomBUN/WzTtf3mGUoyX/sTJcL9nZqqoq/vSnP2FhYcGECRMM/gXX2dlJZmYmERER1NbWkpqaSktLCwClpaUcOHCAefPmDbpkTuLZdHd3s3//fiZMmGBygfWdO3ewtrYWtcfz7t272Nra9vchBQQEsGbNGrZt24a1tTVffvklf/jDH0hPT0ej0Tz1GI2NjUY/a+lRGez169d1dsysrCw+/vhjLl68yOTJk3n99deHTXbvaczzt2TOaHMKzQN477Dufo/6RiaTsWjRIry8vHQa+Lm5ubF69WouX77M3bt3dXLMkUxxcbE0wmiIODg4sGrVKi5evMj9+/fFlgNAe3s7SUlJALz88st4enqKrGjoSOWdBsDT3oIQD1vOZdeyY5rUF2XMqDVafv5VGs2dvXy+NRprle4/Ijk5OZw6dYqgoCBRhrL39fVx584dpk+fTmRkJLGxsWRlZfUPf01NTWX+/PmMHz/e4NpGEsePH8fS0tLkrJ81Gg03btwQXfetW7dISEj4we0ODg7MmzePmTNnkpGRQWpqKhcuXGDcuHHExMQ84azX3t6OlQmU+0VGRpKYmEhNTc2Qzhnp6elcv34dQRCIjY1l/PjxJpVRGQrbIu0obeolsWEMfhdTWT4z+tlPMgJkMhmLFy/mxIkTfPbZZ7zyyis6cYf08vJi9erVfPPNNwiCoPOB6SOJmpoak3GfNGa8vb1ZunQphw8fRqFQiOojUF1dTV5eHjNmzHhmr7kpMTLO9kbA7GBXbpU00tTRI7YUiZ/gvQt5XMuv51+WjiPEQ/dlXykpKZw6dYqIiAhRAj54eOEXFBTU3yMkk8kYP34827Ztw93dnVGjRmFpafmj2RGJoZOYmEhVVRWrVq0SW8pzk5mZiUwmEzU7mZ+fT09PzxOGBN9HqVQSGRnJtm3bWLFiBc3NzXz44Yd8/fXXlJWVGVDt0FGpVEyYMIGrV68O6vnp6el8+OGHJCUlER8fz44dO0SpMBATpUzglwmO2KoU7Kt25HZWrtiSnosXX3yRMWPGsGvXrv6qjKHi4+PDypUruXDhAt988w0FBQW0tLRI5/7npKmpySgdRU2RgIAAXnjhBQ4fPkxJSYkoGnJycigvL2fdunXDKuADKdNnMGaHuPL+xXwu59axdKJUBmCMXMur57fn81gZ6c3qKN0Pxs7PzycpKYkpU6aI5vB1//59bGxsmDVr1g/uMzc3Z8OGDbzwwgtcuXKF69evM2bMmGFR0mBMFBUVkZSUpFc7dn2SkpIi+jDd1NTU55pl6Ovri6+vL62trdy8eZMDBw5gZWWFIAg4OjrqUanuiI6O5pNPPqGjo2NAxjMajYa7d++SmJiIQqFg2rRphIaGjqhA7/s4mMv51VQn/vaClvdv1/BvzrW4u7mKLWvALFiwgDNnzvS7euqiH9XPz49t27aRnJzMuXPnaGlpobe3FwsLC+zs7HB1dWX06NGMGTPGJM9XhqCjo8Ogs/mGO4/6mPft28fatWv1NkrkaWRmZmJmZsamTZueOVbEFJGCPgMxwdseJyszLuTUSkGfEVJU3867X91hrKs1/7IsTC99fG1tbTg6OooW8FVWVtLa2srLL7/8k9bX9vb2LFmyhPLycs6fP09paSnBwcHY29sbTuwwpaWlhaNHjzJ37lzRMr1DITc3l66uLlFLf2tqaqisrGTFihXP/VwbGxtmzpzJtGnTyMzM5MiRI6SlpeHs7ExUVJRRu3ja2tri7+9PYmLiTzrcaTQabt++TVJSEubm5syYMYOQkJARHew9ToCjktei7PjghsC/n8rhP9fZmlQwM2/ePIB+V09dBH7W1tbMmTOn/32lVqtpbm6murqaiooKUlNTOX78OO7u7owfP56JEydK76fvaGhooLOz0yTP58bM+PHj0Wg0fP3116xfv94gPZP5+fn9I1PMzMz0vp4YSEGfgZDJBGYGu3L2Xg19ag0KuXTCNBaOpFXwNwczUCpkfPhSJJZm+vlYKJVK0cpmmpqaKCwsZMOGDQO+sPX29ubll18mOzubS5cuYWFhQVBQkEldIBkTarWagwcPMnbsWJPtn0lJSSEqKkrUC77U1FSCg4OH9D6Uy+VMmDABtVrNtWvXqKioIDk5maCgIKKjo43WhW/y5MkcOHCAWbNm/eD/4PFgz8LCgrlz55qcQZChmOVnSVFjHyfzA/nv/Vf42w1zTCqImTdvHoIg8Pnnn7Np0ybs7Ox0eny5XI6joyOOjo79Zl6dnZ1kZ2dz584drl27xsyZM6W+b+DSpUuMGzdObwPMRzITJkxAq9Xy5Zdf8tJLL+n1vNzQ0EB9fT2bNm0atgEfSD19BmV2sCvNnb3cKmkUW4oE0NWr5lcH03n3qzSCPWw58bOpBLjqz8lPrKCvq6uL9PR0XnzxxeceCyCTyQgLC2P79u0EBQVx48YNcnNzpZ6PQXDmzBnUajULFiwQW8qgKC8vp7a2lsmTJ4umoa2tjZycnKcauAwGS0tLrKys+sc+mJmZsXfvXnbv3k1WVpZRvc97e3uprKykurqaM2fO9N+u0Wi4efMmH3zwAWlpacydO5dt27ZJAd8zeGWCDWEuStLNwth9/JLYcp6buXPnEhISwueff05TU5Pe17OwsCAyMpJXX32VOXPmcPnyZb744gu6urr0vrax0tTUREFBAfHx8WJLGbZMnDiRmTNn8uWXXz51vJQu0Gg03Lt3jwULFhi9m/NQkTJ9BmRqoAtKucCFnFqm+Jv2rA9TJ7+2lbf23uF+TStvzBjDL+YGotRz9lWpVKJWq/W6xvdRq9XcuXOHuLi4IQ1gNTMzIz4+nnHjxnH58mUSExMJCwvDwcFBh2qHL6mpqeTk5LB9+3aTyig8TnJyMhMmTPjJ0mB9c+vWLby9vXVWaqxQKPoDOzs7OxYuXMjcuXO5ffs2V65c4cKFC0yYMEHU0s/Ozk5u3rzJrVu3sLOzY9WqVVy5cgV/f39aWlqkzN4gUcgE/jzWgb88U8eZdj98E28wK068DY3BMGfOHORyeX+pp6FK8ENDQxkzZgwnT55k586drF+/fkQamVy6dInQ0FCjn/Vp6kyaNAm1Ws0XX3zBSy+9pPNS2uLiYnx8fER1CzUUUtBnQKxVCmL8nTifU8uvXggRW86IZf+tcv7ucCaWZnI+3xrN9EDDDMUWI9OXkZGBn58f0dG6sSe3s7NjyZIl5OXlcebMGaytrQkODhY1EDB2CgsLuXr1Khs2bDDZZv/6+nqKiop45513RNOgVqtJS0tj2bJlOjumTCZDq9U+cZtCoSA6Opro6GgKCgpISUkRpfSztbWVlJQU7t69i5ubGytXruw3NLCxseGTTz4hMDBQCvaGgJ1Kxq+mOvKr8xp2F6jwcSti7BjxZk8OhpkzZyIIArt27WLTpk0GMyZSqVQsX76cK1eusHfvXrZv327UPbG6prm5mby8PF577TWxpYwIHl3D7N27l5dffvm5q5Z+DI1GQ3l5ORs3btTJ8Ywd6UrNwMwKduWfjt2j5EE7o5yMfz7UcKK9u4+/P5LFgdvlxPg78tt1EbjZGq4/zdCZvvz8fFQqFfPnz9e5Mc3YsWPx8fHh8uXLJCUlERwcrLOT8HCivr6ew4cPs2DBAqPtExsISUlJhISEiNrPmZ6ejqWlJaNGjdLZMWUy2U9uxIwZM4YxY8bQ3NxMYmIie/fu7Td9CQkJ0csw88bGRhITE7l37x5+fn5s3LjxBzvbwcHB/TMHpYBvaIy2V/L2ZHt+nSLwmyu5/KuTg8mZVs2YMQOZTMbnn3/Oyy+/bNCs27Rp02htbeWLL75g8+bNI2YD8OLFi4SEhEhZPgMSHR2NRqNhz549Onuf19TU4OHhMWKuX0yzzsiEmR388Mv7fHatyEpGFjnVLSx5/xoH75Tzs9lj2bstxqABHxg201dTU0NDQwPLli3TW4O5ubk58+fPZ/ny5ZSUlJCenk5vb69e1jJFOjs7+eabb5g0adJPzpMzdlpaWsjJyWHatGmi6khNTWXKlCk6PaZcLh/QZ/JR6efPf/5zwsLCSExM5IMPPuDKlSu0tbXpREtNTQ0HDx7k97//PX19fWzfvp3Vq1f/aCnTzJkzyczMpL29XSfrj2QSfC1YFmRFtXUg/30w0ST71KZNm8bkyZP505/+RH19vUHXXrBgAebm5hw+fNig64rFoyyfrnqLJQZOTEwMkydPZs+ePTQ0NAz5eDU1NYwbN04HykwDKegzML5Olox1teZCjhT0GQKtVsuXqaUsff86LV197H11Cr+YG4hcpvuRDM/CUJm+1tZW7t+/z/Llyw1STjhq1Ci2bNnC6NGjSUpK0luztSmhVqs5cOAAbm5uTJ8+XWw5Q+L8+fMEBQWJuqOdn59Pd3e3zr+cn5Xp+z4KhYLJkyezY8cOli5dSmVlJR9++CFHjhyhvLx8UBpKS0v56quv2L17N5aWlrzxxhssXbr0mdkmZ2dnAgMDuXTp0qDWlXiSDeOtmeBmRq7NRD45cFZsOYMiISFBlMDvkc39gwcPOH78uMHWFYtLly5JY4xEJCEhgaioKJ2YGLW0tODr66sbYSaAFPSJwKwQV1KKHtDaJWVF9ElrVy8/+yqNXx3MIHq0Iyd/NpW4APGazQ2R6evq6iItLY2FCxfi7u6u17Uex8zMjBkzZrB27Vqqq6u5ffs23d3dBlvf2Dh16hRdXV0sXbpUbClDorS0lIKCgv7ZYGKRkpJCZGSkzk1wVCrVoLPTo0aNYt26dbz++uuYm5vz9ddfs2vXLu7evTugzZ3c3Fx2797N/v37cXV15Z133mHBggXPtVGTkJBAdna2zrKNIxm5IPCLGHtcLBWkKMZz8NR5sSUNiscDv7q6OoOta2FhwYYNG6isrOTQoUMGW9fQtLS0kJubK2X5RCYhIYFJkyYNKfDr6OhApVKNqBJdKegTgdnBbvSqtVzNM2wJxkgis6KZxe9d40R6JX85P4jPt0TjYiPOUPRHKJVK+vr69Hb8trY2UlNTiYuLE63Px9PTk1deeYXQ0FBSUlIoKysTRYeYJCYm9s9ENPX+lrNnzxIXFydqL19VVRVVVVU6MyN6HHNz8yGXJNva2jJ//nzeffddwsPDSU1N5b333uPChQu0tLQ88ViNRkN6ejo7d+7k9OnTjB07lp/97GfMmjVrUL9jJycnxo4dK2X7dIS1mYy/TnAApTlHG9y5deeu2JIGRUJCAlOmTGHPnj0GDfxsbGzYuHEjTU1N7NmzxyTLZJ/Foyyf5FwtPtOmTSM8PJzdu3fT3Nz83M9vaGjA29tbD8qMFynoE4FIX3vsLZVSX58e0Gq17E4qZsWHiXT1avhqRyxvzQxAJkI55/eRy+UIgqCXbF9TUxO3b99m3rx5erk4fh4UCgXx8fG89NJLtLS0cOPGDTo7O0XVZChycnJITExk/fr1Ju9kl5aWRldXFzExMaLqSEpKYvz48XoZmGthYaGzPlSFQkFkZCTbt2/vL3X7+OOP+eabb0hJSeHixYt89NFHXL9+naioKN566y1iY2OHvDEwbdo0srOzfxBgSgwOXzslP4u2p9XMiZ1prZSXV4gtaVDExcURGxtr8IyfpaUlGzZswNzcnJ07d1JVVWWwtfVNS0sL9+/fl7J8RsTMmTMJDQ1l9+7dz30ObGpqkoI+Cf2jkMuYEejCpfu1qDXaZz9BYkA0d/byxp7b/P2RLOIDnDj57lSiRxvGvnqgmJmZ6TzbV1NTQ2ZmJsuXLzcqwxBXV1c2btzIpEmTuHnzJkVFRT+wxx9OVFVVceLECZYtW2byTmDd3d1cuXKFuXPnijpXsLGxkYKCAqZOnaqX48vlchQKhc4zEj4+PqxevZo333wTDw8PSkpKaGxsZM6cObzxxhtERETo7Pfq4OBAcHAwFy9e1MnxJCDG25xVIVbUWQfwu1NpdHR0iC1pUMTExBAXF8ef/vQnampq+m9vamqivLxcb+djlUrFqlWrmDJlCnv37iUxMVEv6zyNnp4erl+/PqjMz7O4cuUKgYGBUpbPyJgzZw5BQUHs3r37uUrdOzo6dD7zz9gx7dojE2ZWiBuH0yq5W95EpK90AhkqaWVNvP3Fbaqbu/ibF4LZluBvFNm976NSqejr69NZ1qK0tJTKykrWrl1r0B6+gSKTyZg8eTJjxozhzJkzpKamEhYWZrLz6n6MtrY2Dhw4QFxcHAEBAWLLGTJJSUnY29sTGBgoqo7k5GTGjh2r16ypvb09NTU1Oh0F8Qhra2u9BayPM3XqVHbu3ElzczN2dnZ6X28ksDbMmuKmPm4RyccHz/OLjYvFljQoHmXq9+zZw5w5c+js7ESpVGJtbU1iYiLBwcE4OTnpZe3o6Gh8fHw4cuQI+fn5rFixQu/n/tTUVK5evcr169dxd3dn7Nix+Pv74+LiMqSNlra2NrKzs3n11Vd1qFZCV8ybNw+NRsPnn3/OK6+88sz3mUajob293eQ3aJ8XKegTieljXVApZPxyfzqfvDyJMS7D6yLYUGi1Wv5wrYj/OJWDm60537wea9RBtC77+nJzc2lra2PDhg1Gv/Po6OjI2rVrSU9P59KlS7i7u+Pv7y9qFklXqNVqDh48iI+PD7GxsWLLGTKtra3cunVL9GG1HR0dZGVlsXnzZr2u4+zsTGVlpV6CPkNhb29PaGgoFy5cYPny5WLLGRbIBIF3p9jxV+f6SNWEc+zCdRbPihdb1nPT2dmJnZ0dnp6eNDc3s3z5cnx9fZHJZBQUFHDmzBkqKioIDQ3VSw+yh4cHW7Zs4dy5c3z66acsWLCA0NBQna8DDy/kb9++zerVq/Hy8iIzM5OioiJSU1Pp6OjA3NwcmUyGXC5HJpMhk8kwMzPD3NwcS0tL7OzscHZ2xtfX9webJ5cuXSIwMBBHR+OqHpL4PxYsWMDJkyfZvXs3mzdv/snNwpaWFpycnPTSNmDMCKZUbhUVFaW9efOm2DJ0RmJ+PW9/eYeePg3/szqcBeNMd3izGDS29/AX++5yPqeWeaFu/PeqCdhZ6mcmna7Yu3cvrq6uQ/ri0Gq1ZGZmIpfLWbFiBVZWVjpUqH9aWlo4e/YslZWVhIWFmXxm4tixY9TV1bF58+ZhEcQ+ct4TO3i4fPkylZWVrF+/Xq/rXLp0iZaWFpYsWaLXdfRNS0sLn3zyCa+++qp0YapDKlr7+OXZeuSdDfxdrBVj/U1jc6Curo7S0lK6urqYMGECEyZMeOqIge7ubq5du0ZGRgYhISF6Heyem5vLqVOn8PLyYtGiRTo3iMrIyCA5OZnt27f/4L7u7m7a29tRq9X09fWhVqvp7e2lu7ubzs5OWltbaW5uprGxkZqaGqysrAgKCiI2NhaNRsNHH33Eli1bDDr4XmJwHD9+nNLSUrZu3fqj77GioiLs7OyYO3eugdXpH0EQbmm12qin3icFfeJS2dTJG3tvc7esidem+/OX84JQyE3/wlHf3Cxu4J0v7/CgrYe/eSGYV+L8EATjK+f8Pvv27cPa2hpXV9dBPV+tVpOWloajoyNLliwx6V2q7Oxszp07h6OjI4GBgSYZMCUnJ5OSksL27dtN3rgFIDMzk7Nnz/LGG2+I6tjZ29vLBx98wMqVK/Hx8dHrWunp6aSlpbFp0ya9rmMITp8+TXNzM2vWrBFbyrDiVlUX/3atEZeOEn69ZgIWFhZiS3oqfX19lJaWUlVVhZ2dHVFRUQQGBqJUPnsztLS0lGPHjuHi4sKYMWP0prGzs5Nvv/2WoqIipk6dyuTJk3V27D/+8Y9MnDiRyMjIIR1HrVZTUFBAeno6RUVF2Nra4urqKvpGmMTAOXLkCDU1NWzevPmp10mZmZlMmDCBiRMnGl6cnvmpoE8q7xQZT3sLvnkthn8+do9PLheSXtbMexsicLYWd7yAsaLRaPnocgH/ezYXL3sLDrwRx3hv08kUOTg4kJeXR319PVqttv8P8NSfv/+nu7ubwMBA5s2bh1wuF/OlDJmQkBB8fX25ePEiiYmJhIaGmlSGIj8/n+vXr7Nx48ZhEfA1NTVx5swZli5dKmrAB3Dnzh3s7Oz0HvDBQ5v54WItHx8fz8cff0xdXd2I61XRJ5M8zNkwzoYvMv34j8M3+Kf108SW9AStra0UFxfT0NBAUFAQq1atwtPT87mO4evryyuvvMKBAwe4d++e3kowLSwsWL58OUVFRZw9e5a0tDQWLFgw5M96c3MzDx480ImZmVwuJzAwkMDAQGpqakhOTjZIb66E7li8eDH79+/niy++YOPGjT8oXe7t7R123gIDQcr0GRH7bpbx/w5n4mBpxocbI426N00M6tu6+bOv07iaV8+L4R78+4rx2Jobdznn9+nt7SUjIwOtVotMJkMQhP7egh/78/hjFAoFrq6uJpHVfB4KCgo4ffo0VlZWBAcHG/18u/r6enbv3q3X/hRDotFo+OKLL3BycmLhwoViy+Gzzz5j0qRJhIeH632tqqoq9u/fzzvvvKP3tQzBuXPnqKur03tZ7EhDq9XyX9cbSK3sYYldGa/MF3c0jkajobq6ut+Fc9KkSToxyerq6uLAgQOo1Wq9u0Gr1Wpu3LhBYmIi7u7uzJ49e9BuiufOnaOlpYUVK1boWKWEqaJWq/niiy+wtLRk5cqVT9yXkpLCihUrjNIAb6hImT4TYXWUD6Getry+5xZrP0ni7xeHsXGK77C7wB8MSQUPePerOzR19vKvy8exIdo0fy9KpXLIpSfDkTFjxrB161YuX75MUlISwcHBRpup6O7u5sCBA0RGRg6LgA8eunW2tbWxbt06saVQX1/PgwcPDPa7VSqVepmdKRZxcXF89NFH1NTUjDg7cn0iCAI/i3Hgr87UcrLRjZB7hUSH+htcR3d3NyUlJf3/v3PnzmX06NE6q/wwNzdn5cqV7Nu3j9zcXL06+MrlcmJiYpgwYQJJSUns2bMHZ2dnpkyZQnBw8ICP09HRwd27d6WyZoknkMvlrFq1it27d3Px4kVmzpzZf19vb6/oFS1iYHpNNMOcME87jr89lYQAZ/7ucCZ/vu8unT1qsWWJhlqj5Tfncnnp98lYmys48lY8L00ZZZIBn8RPY25uzvz581mxYgUlJSXcvXtXZ4OzdYVGo+HQoUM4ODgwY8YMseXohKqqKpKSkli+fLlRZFgzMjIYPXq0UWgxRSwtLYmMjOT8+fNiSxl2WChk/M00Z5RKJe+l9VBd32iwtRsaGkhLSyM1NbV/Bur69esJCAjQean/o8Cvra2NsrIynR77aVhYWDBr1izefvttgoKCuHTpEr/97W85fvw4JSUlP/nclpYW9uzZQ3BwsEHKwSVMCwsLC9asWcOdO3e4f/9+/+0ymWxYbfYNFOlb1Qixs1Tyh1cm87sLefz2fB7ZVa18vDGSUU6m5dI4VGpbuvj512kkFjxgRYQX/7JsHFYq6S073PH19WXLli0kJiaSlJSEr68vXl5eAzIj0Dfnzp2jubl52Mxq6u3t5ejRo8TExBhNVuj+/fvMmTPHYOup1cNvUy0mJoYPP/yQqqoqPDwkV2hd4m6t4C8TnPiXKwL/eKqQX68K1Yuxi1qtprGxkQcPHtDQ0IC5uTlRUVEEBwcbJENhaWnJqlWr2Lt3L+bm5gapvFCpVMTExBATE0NpaSnZ2dkcP36czs5O3NzccHV1xcnJCQsLC3p6eigrKyMvL4+wsLBh6cIooRscHBxYtGgRR48excXFBUdHR2Qy2bA89z8L6QraSJHJBH4+J5AJPvb8/Ks0Fr93jd+sm8isYOO4MNM3V/Pq+LOv02jr7uO/VoWzepK3lN0bQZiZmTFjxgyCgoK4efMmSUlJWFlZ4ebmhru7uyhZoLS0NLKysnj11VeHTRbq3LlzWFhYkJCQILYU4GE2o62tDX9/w5bNmbop0vexsLAgKiqK8+fPiz5vcTgywU3FpnBbPk8X+Of9qfzL+nidnBO0Wi2FhYXU19fT1dWFm5sbo0aNIiEhAR8fH4N/Bzo4OLBixQq+/vprzMzMDDpex9fXF19fX+bPn099fT2lpaXU1NSQn59PT08PSqUSFxcXNm7caDQbVhLGS2BgIFFRUezbt4/t27cjk8l0NjPZlBgeVy7DmJlBrhx/J4HX99xi666b/Gz2WN6dPRa5bHgGQH1qDb8+l8uHlwoY62rNl9tjGOtmI7YsCZHw8PBg8eLFdHd3U1RURGZmJtevX8fOzg53d3dcXV0NMuqhrKyMc+fOsXbtWmxtbfW+niHIz88nOzubbdu2iS2ln5ycHLy9vQ06vmO4fvHHxMRw69YtysrKpLI3PbA40JKCxh6ulQby62/O8efr5g3pfdvc3ExWVhaenp4sXboUNzc3o9hc8vT0ZPHixRw/fpyoqChRxlU4OztL8/Ekhsy0adMoLi7mwoULODo6cvXqVVxcXHB3dyc4OHhEJBaknj4TwMfRkgNvxLFqkje/O5/Hll03aGzvEVuWzqlq7mT9zmQ+uFjAmkk+HHkrQQr4JICHZT/BwcGsWrWK119/nejoaFpaWrh69Srp6en9IzD0QUtLCwcPHmT27NnD5uK5u7ubkydPMn/+fKMKYgsLC/VqHPE0ZDKZ3t47YqJSqYiOjubixYtiSxmWCILAm5PtGW2v4KYijD/uPzGo42g0GnJycsjKymL27NmsWLECLy8vowj4HhEQEMC0adO4ffv2sN0kkRj+yGQyFi1aRFpaGnK5nP3793P//n3OnTvH7t27KSgoEFui3pGCPhPBXCnnv1eF82/Lx5Nc8IDF718js6JZbFk640JODS/89ir3Klv4zdqJ/OeqcCzMhlfJlYRusLS0ZPz48axfv55t27YxYcIEqquruXbtGllZWTQ1Nelsrd7eXvbt20dQUBARERE6O67YXLx4EWdnZ71bsj8PnZ2dVFZWGtwRValUDtsL2cmTJ9PQ0PBMMwyJwaGSC/xVgiOWKjOuaELYd/j4cz2/oaGBxMREbG1t2bp1K6GhoUabbYiMjCQkJIS7d+8Oy00SiZGBs7Mz0dHRfPvtt/j4+DB58mTi4uJwdnbm4MGDNDQ0iC1Rr0hBnwkhCAIbpvjyzeuxaDRaVnyUyDc39e+spU961Rr+7WQ2W3fdxN3OgmPvJLAswktsWRImgq2tLZGRkbzyyits2rSJwMBACgsLuX79Ojk5ObS1tQ3p+MeOHUOlUrFgwQIdKRafiooKsrKyWLRokdhSnqCgoAAnJyeD22irVKphG/Q9yvZdunRJbCnDFhdLOX8R50CXwppz7d6cOvXtM5/T19dHZmYmubm5vPDCCyxevBgrK+M3aps1axb29vZkZ2eLLUVCYtAkJCSwePFiFi9e3H+bvb09ZmZm2NvbiyfMAEhBnwky0ceeY+8kMNnPgV/uT+dXB9Pp6jU9F6Kyhg5Wf5zEp1cKeTlmFIfejMPfZWiDZSVGLo6OjsTExLB9+3bWrl2Lj48P9+7dIykpifv379Pe3v5cx7ty5QpVVVWsWrVKT4oNj0aj4dtvv2XKlClGVdYJkJeXx5gxYwy+rpmZ2bAN+uBhtq+pqYmioiKxpQxbwlzM2DLRhnozd85Wq7hy5cqPPrauro6kpCTc3d159dVXGTt2rAGVDg25XM6SJUtQq9Xk5eWJLUdCYlDIZDJCQ0Oxsfm/9qG6ujr8/f0N2k8uBsP71Q1jnKxV7N46hTdnjOHL1DLWfJJEeWOH2LIGzLeZ1bz4u6sU1LbxwYZI/mXZOMyVUjmnhG5wc3Nj2rRpvP7666xYsQJPT0/S09NJTk4mLy+Pjo6f/qzk5OSQmprK2rVrh9UA1+TkZPr6+ozGrfMRGo2GwsJCUcpNFQrFsJ7XpFQqmTJlitTbp2cWjLFklp8FJdYhnMmuIyUl5Yn7e3t7SU9Pp6SkhOXLlzN//nyTPLeYm5uzevVqOjo6yM3NFVuOhIROePDggcFdo8VACvpMGLlM4JcLgvnk5UkU1bWz+L1rXM2rE1vWT9Ldp+Yfj2bx+p5b+DlbceJnU3kxXJojJaEfBEHA09OTGTNm8Oabb7J06VJcXV25e/cuKSkp5Ofn09XV9cRzampqOHHiBMuWLRtWjnFNTU0kJSWxZMkSsaX8gNLSUszMzAwyC+z7tLe3o1KpDL6uIZk0aRJtbW0jwqhALARBYEekLWMdlRQ6x3MmNZO7d+8CUFVVRVJSEqNHj2bLli2MGjVKZLVDw8rKirVr19LV1UVOTo7YciQkhkxLSwteXsO/tUgK+oYB88PcOfJ2PC42Kl75YyofXMxHozG+Ruvi+nZWfpTIrsRitsaPZt/rsfg6WYotS2KEIJPJ8Pb2ZtasWbz55pssWrQIR0dHbt26RWpqKoWFhTQ2NrJv3z5iY2MJCAgQW7JO+fbbbwkJCTHKYd1FRUWiOaO2traaZMbleVAqlcTExEi9fXpGKRf4ZZw9FmYyyka9wImzFzl+/Dg1NTWsXbuWGTNmYGZmJrZMnWBlZcW6desAuHv37rDOlksMb9ra2jA3Nx/2/XwgBX3DBn8Xaw6/Fc+icE/++/R9dvzpFs2dvWLL6ud4eiWL3rtGWUMnOzdF8feLQ1EppHJOCXGQyWT4+voyd+5c3nrrLRYuXIiNjQ0nTpxAqVTi6elJT8/wGYuSlZVFdXU1c+bMEVvKUykrKxOttKa9vX3YB33wMNsnleTpH0cLOb+Ms6epByr9FhAQGMQrr7yCp6en2NJ0joWFBWvWrMHV1ZWUlJRnls1LSBgjjY2Nw2Yc07OQgr5hhKWZgt+um8g/LA7l0v1alr5/jZzqFlE1dfWq+ZtDGbz9xR0C3aw58bME5oa6iapJQuJx5HI5fn5+LFiwgP/6r//iV7/6Febm5iQnJ3Pr1i3Ky8tN2uijs7OTc+fOsWDBAqPMMvT29lJVVSVaZrWtrQ1Ly+FfcSCXy5kyZQqXL18WW8qwRqPR0FN5nxdcW6nS2lNqN8GoZu7pGqVSyYsvvkhcXBy3bt2irMy0HcUlRh5NTU14e3uLLcMgSEHfMEMQBLbEj+bLHTG096hZ9sF1jqRViKIlv7aNZR9c54uUUl6b7s/Xr8Xi7TD8L64kTBelUsno0aN58cUXeeutt5g+fTpqtZrr16+TlpZGTU2NyZUx3bhxg56eHrq7u40ye1leXo6NjY1ogVd7e/uICPrgYbavu7ub+/fviy1lWNLV1UVKSgpOTk78f2+t5OWYUXxypVC072BDIQgCkZGRvPTSS7S2tpKSkkJ9fb3YsiQkBkRHRwdubiMjGTF8t59GOJP9HDnxTgJvf3GHd79K405pE3/zQghmCsPE+Qdvl/P/DmdirpTz2ZbJzAxyNci6EhK6wszMjKCgIIKCgujo6CA/P5+MjAzu37+Pg4MDXl5eODo6ii3zmUyePBmVSsXt27c5evQoCxcuJCoqSmxZ/RQXF4ta+tbW1mYSM9J0gVwuJy4ujsuXLxMUFCS2nGGDWq2mqKiIyspK4uLiiI6ORhAE/m5RKPerW/nl/nTGuFgzzstObKl6xcXFhZdeeon79++TmJhIbm4u9vb2uLq6Ym9vP6wznhKmiVqtprOzUxQTMTGQPoHDGFdbc/Zun8J/nMrhD9eKyKho5sOXInGz1V//SkdPH39/JIv9t8qJHu3I79ZF4G43/PtlJIY3lpaWhIeHEx4eTlNTE7m5uaSnp3Pv3j2cnJzw8fHB2to4Z0xaWFjg5+dHVlYWNjY2RuccWFpaSkREhGjrd3Z2jphdXoAJEyaQlJRETk4OwcHBYssxabRaLaWlpZSWluLv78+mTZtwcnLqv99MIeODlyJZ8v41XvvTLY6+HY+T9fB2ipXJZISEhBAcHExNTQ3FxcUUFhaSlZWFUqnEwsICMzMzVCpVfxCoVqvp6emht7cXtfrhzGGtVotcLkcmk2FhYYGNjQ2Ojo4olUoxX57EMKOyshJnZ+cRsyExMl7lCEYpl/F3i0KJ8LXnl/vTefF31/if1eGEedpha6HQqZlKbk0rb+29TX5dGz+bFcDPZo9FIZcqiCWGF/b29kRHRxMdHU1dXR05OTlkZmaiVqtxcXHBy8sLCwsLsWUCDy+mrl69ys2bN5k8eTJTp041quGzvb291NTUiOqU2t3dPWLKO+HJbJ8U9A2eyspKioqKcHd3Z926dbi7uz/1cS42Kj59OYpVHyfy5t7b7Nk2BeUI+F4UBAF3d3fc3d2JiYmhr6+PxsZGmpqaaGtro7W1tb/cXKFQYGVlhbW1NUqlEkEQ0Gq1qNVquru7aWxspLq6mvv376NSqXB0dMTb29tozrMSpktWVhaLFi0SW4bBkIK+EcKicE+C3Gx47U+32PzZjf7bzRQybM2V2JorsLF4+LetuRJbCwU2j25/9G+VElsLJTbmCmy/e6yVmQJBgG9ulvEPR7OwVin509YpJIwdPvPNJCR+DBcXF1xcXIiPj6eyspKcnBzu3LmDQqHA2dkZLy8v0WbAVVRUcPLkSQRBYPPmzUY5c7C8vBwrKytRg67u7u4Rd/E4YcIEkpOTycrKIiwsTGw5JkVdXR35+fnY2dmxbNkyfH19n/mc8d52/PuK8fzim7v864ls/nHJyPudKxSK/vPlYFGr1VRVVXH//n1u376NmZkZ3t7euLm5GdVmloTpYG1tzejRo8WWYTCkoG8EMdbNhqPvJHAhp5amjh5au/po6eylpauPlq7e/n9XNHX2/9zd99OmFYIA1mYKWrv7iA9w4tdrJ+JqI5VzSowsHs0A9Pb2ZubMmZSVlZGTk8ONGzdQqVT9GUBDlCb19vZy+fJl7ty5Q3x8PHFxcXpfc7AUFxeLPhB3JAZ9MpmMuLg4rly5IgV9A6SxsZG8vDzMzMxYsGABY8aMQRCEAT9/RaQ3WZUt/OFaEaGetqyJGhkW8bpELpf3n2enT59OYWEht2/fJi8vDxsbGxwcHLCzs8PMzAy5XI5Go0Gj0dDX10dvb29/CemjnzUaDWq1+glzLplMhlwuRy6XY2lpibW1NdbW1iNirMtIRKlU0t7ejq2trdhSDIIU9I0wrFUKlkwYuGlCd5+a1q6+/iCw9bsA8fGfW7v6GO1sxcaYUchlA/8SlJAYjjwaAeHn58ecOXMoLS3l3r17JCUlYWVlhaurKx4eHnrpISgtLeXkyZOYm5uzfft2ox82W15eTnh4uKgaenp6RMvGisn48eNJSkoiIyOD8ePHiy3HaGltbSUvL4++vj6mTZtGSEjIoLNKv1oYTE51C//vUCZjXa2J8HXQsdqRg0KhIDAwkMDAQNra2igrK6OkpISqqio6Ojro6+vr7wlUqVRYWFhgbm6Ora0tFhYWWFpaYmZmhpmZGQqFAplM1h8E9vT00NHRQVNTE/X19dy/fx+1Wo2VlRW2tra4uLgY/blV4tn09fXR1dX1RB/ucEcK+iR+EpVCjspajvMwbz6XkNAHCoUCf39//P396enpobi4mKysLBITE7G2tsbNzQ03N7chB4Dd3d1cuHCBrKwsZsyYYVTunD/Go1Kt5cuXi6rDzs6Ourq6H+3JGq48yvZdvXpVCvqewqNB9p2dncTFxREeHj7kz6lCLuP99ZEsfv8ar++5xbG3E3DVo7HaSMHa2pqQkBBCQkL0tkZrayu1tbWUlZWRn59Pa2srDg4OeHt74+AgBe+mSH19Pd7e3kY5v1ZfSEGfhISEhAEwMzPr35nu6urqDwCvX7+Ora0tHh4euLq6PncWIT8/n2+//RZHR0d27NhhMmUqlZWVWFhYiO566uLiQmVl5YgMfMaNG0dSUhLp6emiZ1yNhe7ubvLy8mhqaiI6OprIyEidZoIdrMz49OUoVn6UyOt7bvHljhidGqpJ6AcbGxtsbGwYM2YMM2bMoKmpiby8PO7evUtOTg5eXl54e3tLvYUmRG1trajO0WIgBX0SEhISBsbc3Jzg4GCCg4Pp6OigoKCArKys/rlWHh4eODs7/2TPUGdnJ2fOnKGgoIA5c+aY3EV7SUkJHh4eYsvAw8ODjIwMsWWIgkwmIyEhgUuXLpnc+0fX9Pb2UlBQQG1tLZGRkUyePFlvBkOhnrb89+pw3v7iDjt23+Jflo7D12nkOMgOB+zt7Zk8eTJRUVGUlZWRmprK9evXGT16NN7e3mLLkxgAzc3N+Pn5iS3DoEhBn4SEhISIWFpaMn78eMaPH09bWxt5eXlkZGSQnZ39o0Pg7927x9mzZ/Hy8uL11183yZED5eXl+Pv7iy0DDw8PLl++LLYM0QgLC+P69evcuXNnxO16w5OD1cPCwli8eLFBsuWLwj1paO/hP07lMOfXl9k+dTRvzgjASiVdlpkSgiDg6+uLr68vFRUVXLp0ieTkZGJiYsSWJvETNDU1YWVlNeJ6M6Wzi4SEhISRYG1tTUREBBEREf1D4DMyMrh37x6Ojo64urpy7do1iouLWbBggUnPWauoqGDevHliy8DZ2Zne3l4aGhp+EFyPFBISEjh//jwTJkwYMeVpGo2G0tJSysrKGDNmDK+88orB//83xfoxL9Sd//w2hw8uFrD/Vjm/WhjC0omez+UMKmEceHl5ERwczM2bN8WWIvEMCgsLGTdunNgyDM7IOLtLSEhImBiPhsC/+uqrvPTSS/j7+5OUlERlZSUzZ8406RKimpoaZDKZUQRZcrmcMWPGcOfOHbGliEZoaCgWFhbcvn1bbCkGoaKigsTERLRaLevXr2fx4sWivRfd7cz59dqJHHgjFlcbc37+dRqrPk4ivbxJFD0SQ+POnTu4ubmJLUPiJ+jp6aGtrY3IyEixpRgcKdMnISEhYeQ4Ozvj7OxMXFwcNTU15OTkkJGRgUwmw8nJCW9vb5OaI1VSUmJUF0YhISFcunSJ2bNniy1FNBISEjhz5gyRkZHDOtuXlpaGSqVixYoV+PgYz6y8SaMcOfJWPPtvlfNfp3NY+sF1Vk/y5i/nB+NiI7lnmwovvvgi33zzDVZWVkaxqSXxQ0pLSwkJCRmRo3qG75ldQkJCYpghCALu7u7MmDGDN998kyVLluDo6MitW7dITU2lqKiI7u5usWU+k/LyckaNGiW2jH7Gjh1LV1cXJSUlYksRjeDgYGxsbIZ1aVppaSlyuZx169YZVcD3CJlMYM1kHy78xQy2JYzm4O0KZv3PJXZeKaSnT/PsA0iIjoeHB8uXLycrK4vm5max5Ug8hbq6uhHp1gxS0CchISFhkshkMry9vZk7dy5vvfUWCxcuxMrKihs3bnDz5k1KSkro7e0VW+ZTqaioMAoTl0fI5XImTZrE1atXxZYiKlOnTiUpKYm+vj6xpeictrY2SkpKWLx48ZDn7ekbW3Mlf/tiKKf/bBqT/Bz415PZLPjtFS7erxVbmsQA8PX1ZdGiRdy9e5e2tjax5Ug8RlNTEyqVCk9PT7GliIIU9ElISEiYOHK5HD8/PxYuXMibb77J7NmzUSgUJCUlcevWLcrLy43mQr6pqYmuri6jGNfwOFFRUVRXV1NXVye2FNEICAjA3t6e1NRUsaXolObmZu7evcvMmTNxcnISW86AGeNiza4t0Xy2eTJoYctnN9jyWSqFdVIgYewEBAQwf/58bt++TWdnJ1qtdkSfW4yF0tJSIiIiRqxRknFvd0lISEhIPBdKpZKAgAACAgLo7u5+Ygi8jY0Nnp6egxoCryuKi4txdXUVZe2fwsLCgvHjx3P16lVWrFghthzRmDp1KkePHiU6OtroM2LPoqmpiYKCAnp7e5k2bZrJziKcGexKfIAzuxKL+N35fOb/5gpb4kfzzqwAbMyVYsuT+BFCQ0Pp7u7m8uXLWFlZ0draio+Pj1FVOYwk+vr6aGxsNGnX66Fi2md0CQkJCYkfRaVSERQURFBQEJ2dnRQUFJCZmUlubi52dnZ4eno+cwi8rikrK8PLy8tg6z0PU6ZM4Y9//CMZGRkjtufD398fJycnkpOTSUhIEFvOoGhoaKCgoACtVkt8fDyhoaEmH8CaKWTsmDaGZRFe/Pe39/n0SiEHb1fwywVBrIr0RiYbmZkLYyciIoKenh7KysqwsrKis7NTbEkjlqqqKvz8/LC2thZbimhI5Z0SEhISIwALCwvGjRvHunXr2L59O5GRkdTX13P16lUyMjJoaGgwiI6KigpGjx5tkLWeF3t7e1asWMHp06fJzc0VW45oTJ06ldTUVKMpCR4o9fX1/YZGCQkJ7Nixg/DwcJMP+B7H1cac/149gSNvxePraMEv96ez7MPr3CppFFuaxI8wZcoUxo8fT3l5OUFBQWLLGbFUVVUxceJEsWWIyoCCPkEQ7AVB2C8IQo4gCNmCIMR+7347QRCOCYJwVxCELEEQtjx2X7EgCBmCIKQJgnDzsdsnCoKQ/Oh2QRCidfeyJCQkJCR+DGtrayZOnMjGjRvZsmULYWFhlJeXc+3aNe7du6c317nu7m6ampqMyrnz+/j5+bFkyRKOHDkyYt08/fz8cHNz4/r162JLGRB1dXWkpKRQVlbGjBkz2LZtG+PGjUMul4stTW9M8LHnwBtx/HrtBGpaulj5USJ/9nUaNS1dYkuTeAqBgYFERERw+/ZtNBrJidXQtLW1oVarjfq7xxAIWq322Q8ShM+Bq1qt9veCIJgBllqttumx+/8GsNNqtX8lCIILcB9w12q1PYIgFANRWq22/nvHPAP8WqvVnhIE4QXgl1qtdsZP6YiKitIOZztpCQkJCTGpr68nNzeXjIwMenp6cHJywsvLS2flMPn5+Zw7d47XX39dJ8fTJxkZGZw+fZqXXnrJ6ExnDEFZWRnffPMN77zzDmZmZmLLeSrV1dUUFRVhaWlJQkICAQEBw3rG4I/R3t3HBxfz+f3VIhRygbdmBvBqwmjMlcM36DVFNBoNJ0+epLKyksjIyBFrJiIG9+7dY/To0SZbsv48CIJwS6vVRj3tvmeeHQVBsAWmAX8A0Gq1PY8HfN+hBWyEh+9ga6ABeFZdiBaw/e5nO6DyWVokJCQkJPTHowHwO3bsYPXq1Xh6epKRkUFKSgr5+fl0dQ0ti1BeXo67u7uO1OqX8ePHk5CQwMGDB0fkzryPjw+enp5cu3ZNbCk/oKqqiqSkJOrr61m4cCFbtmwhMDBwRAZ8AFYqBb9cEMzZX0wjIcCZ/z59n3m/vsLprGoGsrEvYRhkMhkLFy7ExcWF9PR0seWMGLRaLfX19YSGhootRXSemekTBGEi8ClwD5gA3ALe1Wq17Y89xgY4CgQDNsBarVZ74rv7ioBGHgZ5n2i12k+/uz0EOA0IPAw+47Ra7Q9qaQRB2AHsAPD19Z00UsttJCQkJMRAo9FQWVlJdnY22dnZKJVKXFxc8PT0RKVSPdexvvzyS8aOHUtU1FM3IY2SXbt2ERAQMCJ2iL9PeXk5X331FW+//Tbm5uaiatFqtVRUVFBaWoqDgwPx8fH4+flJ2ZKncC2vnn86lkVebRtTxzrz94tCGetmI7Ysie/o7e3l0KFDdHZ2Mm7cOLHlDHuqq6tpbm5mw4YNYksxCEPK9PHQ4TMS+Eir1UYA7cBff+8x84E0wBOYCLz/XYYQIF6r1UYCC4G3BEGY9t3tbwB/ptVqfYA/47tM4vfRarWfarXaKK1WG+Xi4jIAuRISEhISuuJpQ+AtLS1JTU3l5s2blJWVDdjwo7Ky0uTsyufOnUtycvKIHLLs7e2Nj4+PqNk+jUZDaWkp169fp6Ojg6VLl7Jx40ZGjx4tBXw/QsJYZ06+O5V/WBzK3bImFvz2Kv94NIvmjl6xpUnwcKzO0qVLUSgU5OTkiC1n2FNRUTHiDVweMZCgrxwo12q1Kd/9ez8Pg8DH2QIc1D4kHyjiYdYPrVZb+d3ftcAh4JFhyyvAwe9+3vfY7RISEhISRsijIfAvvPACb731FrNmzUIQBBITE7l9+zZVVVU/WgpZX1+PIAg4OjoaWPXQ8PLyIigoiG+//VZsKaIwbdo00tLShlza+7xoNBqKi4tJTExErVazatUq1q9fj6+vr0F1mCpKuYwt8aO5+BczWDfZh91Jxcz4n4vsTSlBrZFKPsVGpVKxYsUKamtr6e7uFlvOsKWnp4eOjg4CAgLElmIUPDPo02q11UCZIAiPfGZn87DU83FKv7sdQRDcgCCgUBAEq+9KPxEEwQqYB2R+95xKYPp3P88C8obwOiQkJCQkDIhSqWTs2LEsXbqUN954g/j4eNrb27l69Sp3796lrq7uiX6isrIyTLVaIywsjNraWrFliIKHhwe+vr5cvXrVIOup1WoKCgq4fv06MpmMNWvWsGrVKqOd7WjsOFmr+Nfl4zn2TgJj3Wz420OZLHrvGimFD8SWNuIxNzdHrVajVCrFljJsaWhowMfHx2jNqAzNQIfXvAPs/c65sxDYIgjC6wBarfZj4F+AXYIgZPCwR++vtFptvSAI/sCh70owFMAXWq320XbpduC3giAogC6+69uTkJCQkDAtzM3NCQ0NJTQ0lLa2NvLz88nIyCA7OxsHBwe8vb2pqKgwWRfM2tpanJ2dxZYhGtOnT2f37t1MnTpVb719fX19FBUVUVVVRUBAABs2bMDV1VUva41Ewjzt+HpHDCcyqvi3E9ms/TSZF8M9+JsXQvCytxBb3oiks7MTuVyOVqvl/v37tLW1oVAoUCqV2NjY4ObmJgUrQ6SxsZGxY8eKLcNoGFDQp9Vq04DvNwV+/Nj9lTzM4n3/eYU8NH952jGvAZMGKlRCQkJCwvh5NANw4sSJNDU1kZubS3p6OoWFhQQFBdHa2oqNjWmZSlRXV5uM66g+cHNzw9/fn0uXLrFgwQKdHru3t5eioiKqq6sJDAxk7ty5ODk56XQNiYcIgsCicE9mB7vx8eUCPr5cwPnsGl6fPobXpo3Bwkwa8WBIOjo6aG9vJzk5mTFjxhAXF0dXVxdtbW1UVVWRnJyMg4MDAQEBWFhIgflgaGtrG9Hn7u8z0EyfhISEhITEc2Fvb090dDTR0dHMmTOH8vJyMjIyUKvVODo66nQGoD6pqalh/PjxYssQlYSEBHbv3s20adOwtLQc8vF6enooLCyktraW0NBQFixYgIODgw6USjwLCzM5fzY3kDWTffi3k9n85lwe+26W86sXgnlxvIdkkGMgtFotTk5OzJw5k+Dg4B/c39nZSVpaGsnJyQQGBkrBy3PS09NDW1ubybYV6IMBDWc3FqTh7BISEhKmjVarpaqqitzcXLKysgD6A0ArKyuR1f2Q3t5e/vd//5d3331Xb6WNvb29VFdX4+Pjo5fj64qDBw9iYWHBwoULB32M7u5uCgoKqK+vJzw8nKioKOzs7HSoUuJ5SS58wD8du0d2VQtTRjvyD4vDCPW0ffYTJQxCaWkpBw8eZOrUqWJLMSlaWlpITU1ly5YtI6pU/KdGNkiZPgkJCQkJgyEIAp6ennh6ejJt2jSqqqq4f/9+/7BiHx8fvL29RVb5f9TU1GBlZaW3gK+hoYGsrCwEQaCtrY2QkBC9rKMLpk6dyq5du5g+ffpzZ/u6urrIz8+nsbGRiRMnsmzZMpMr8x2uxPg7cfydBL5MLeX/O3OfRe9dZX20L38+LwhHK6mnTGxUKpVk9jIIenp6EASBpqamERX0/RRS0CchISEhIQoymQwvLy+8vLyYMWMGZ86coby83KiCvqqqKr2MmdBqteTn51NXV8eSJUvw9PTkyy+/pKioiNGjR+t8PV3g4uJCQEAAly9fHnC2r7Ozk/z8fJqamoiMjGTVqlVGmdEd6chlAhtjRrE43JNfn8vlT8klHLtbyZ/NDWRjzCiU8oFM+JLQB+3t7ZKhy3Og0WjIzc2lpaWF9evXG30FhSGRgj4JCQkJCdGRyWQ8ePAAT09PsaU8gT5MXLq6urh79y4uLi5s2bKlv69x5cqV7N27F5VKZXS/h0ckJCQMKNvX3t5OQUEBLS0tREVFERERoZNeQAn9Ymep5B+XhLFhii//fOwe/3TsHl+klPIPi8NIGDtyHWzF5JHLZ2dnJ93d3XR1dfX//f+3d7fBcZ3XYcf/Z7EAaAESSImQSQmJqBeSkdRgSEoirbHEkaixKSmSLNuS4zhxLLeZtInq6dQTu07jdtxpPySeqTNpldiTxGZbV3KcslEaTVg76aS0nU4iDiWLEAmRJilQMkkxFF9gkgDxuk8/4AJaQiCJXRDYveD/N3MHu/dtd+/BwYOz97nPHR4eplQq0dTUxJIlS1i4cGGt325N9ff3s2PHDq6//no+/OEP+zdnEos+SVLN9fb2cvToUVasWFHrt3KO48ePX9Ihv48cOcLevXt5//vfzx133EGh8M4ZlKuuuoonn3yS5557jqamprq8TcT42b6tW7fy8MMPv2v5mTNn2Lt3L/39/axdu5ZVq1bNWtdYzZ4V772Sb/6TtfxV9z/wH/6ym1/6+ot88Lb38sWfu42fvsZ/pOfaiRMn6OrqorW1ldbWVlpaWliyZAmtra0Ui0VOnDjBnj17OHXqFG1tbVx77bW0t7dTLF4+/+YfPnyY/fv3s379elavXu2ARFNwIBdJUs1t27aN7u7uuhsl8ytf+QpPPfXUjLt4jo6O0t3dzfDwMI8++ugFzx4ePHiQzZs3s2rVqrq87u3YsWNs2rSJp59+euKb9N7eXl5//XUGBwdZt24dnZ2dNDc31/id6lIYGB7l63/bw+//332MjCZ+5d4befr+W2hpvnwKilpLKU2riPnJT37CG2+8wY9+9CPefPNNGhsbaW5upqHhndtxjP/fXyqVpv08ImhoaKBYLFIoFGhoaKBQKLBgwQIWLlxIW1tbzQrMkZERuru7GR0d5bHHHrvsr99zIBdJUl3r6uqqy2vZBgYGZnxbidOnT9PV1cUtt9zCAw88cNFiqKOjg4ceeogtW7awdu3aujtTtnjx4omzfXfddRc9PT0UCgXuvvtubr31VgedmGcWNDbw9P238NE1HfzOd3bzB1v38z9fPsgXHvoZHl91vWdU5sB0j3FbWxudnZ10dnYyOjrKiRMn6OvrY2hoCBjrRl8oFIgIImLi8cV+jo6OMjQ0NDENDw8zNDTEyZMnOXToEDt37qSxsZGFCxfS0dHBVVfNzeivp06doquri5UrV7JhwwavfbwIiz5JUk319fVx6tQp9u7dS2trK4sWLeLqq6+ueQM+OjoKMKP3ceDAAQ4ePMgHPvABbrvttmlvt3LlSvr6+vjBD37AunXr6qqQSimxfPlytmzZQkdHBxs2bGD58uXndFXV/LOkbQG/+/Or+KX33cC/e2EX//LbO/jm373Blx67nc6OhbV+e5qkoaGB9vb2OblP3ejoKMePH2ffvn3s2LGD0dFR2tvb6ejomJUvrVJK9PT08NZbb7Fx48Yp73Ood7N7pySp5gYGBjh69ChvvfUWb775JocOHaJQKNDS0kJjYyNNTU0sWLCA5ubmc6bZPMvQ39/PM888w+c///mKtx0eHqarq4v3vOc9PPLII1XfeHzr1q3s2rWLtWvX1ryoKpVKE7FZvHgxnZ2d3H777Z7puQyVSonNLx3ky9/dzfG+IZ5Y08EXf+422q6ony8nVBspJQ4dOkR3dzevvfYazc3NLFmyhKVLl16SLqDHjx9nz549tLe38+CDD172g9dMdqHunRZ9kqS6UyqVOHnyJMeOHePs2bOcPn2aM2fOTEx9fX0MDAxQKBRoamqiWCzS2NhIQ0MDTU1NNDc3v6tILL+uZTpOnjzJpk2b+OxnP1vRdsePH2fXrl2sWbOGe+65p+LXLZdSYsuWLRw+fLhmgxOMjIxw4MABDh8+zLJly1i3bl3dji6quXV6YJj//Df7+KMfvM6v33czn9voGRe9Y3h4mAMHDvDqq6/S09NDQ0PDxN/j8esNyx83NTVN/D2fSm9vLy+++CL33HMP69evn+NPkw9e0ydJypVCocA111zDNddcc951SqUSAwMDnD179pypr69vojgcv6blzJkzFAqFieKw/Gdrayvt7e3v6oY0NDRU0TfTKSX27NlDb28vH/nIR7jhhhuq/vzjIoKNGzfy/PPP093dze233z7jfU7X4OAgPT09HD16lJUrV/LJT36yLkcUVe1cuaCRf/3wrTz7928wMFyq9dtRnWlsbGT58uUsX76clNJEV/5Tp05x+vRp+vr66O/vp7+/n2PHjk08LpVKFIvFiS+5CoUCKSUaGxt5/PHH7c5ZJYs+SVIuFQoFrrjiimnfi2lwcPBdBWJ/fz9Hjhxh+/btFItF2traWLp0KQsXLmRwcJBiscjp06eJCFpaWs57pq2/v5+uri6WLFnCU089dUlvQF4sFnnsscf41re+xf79+7n55psv2b6n0t/fz/79++nt7aWzs5NHHnmEtra2WX1N5dvQaImmotd06vwiYuKWExfrKTAyMsLg4CClUomUEiklSqUSra2tdXV9c95Y9EmSLgvjXYmmugakVCpx5MgRDhw4wM6dOye+VW5sbOSVV16hsbGRgYGBiW6jCxYsoLW1lSuvvJIzZ87Q09PDvffey5o1a2alC2ZzczNPPPEEzz77LLt376alpWXin6HxyzTKn081b/x5+fzJ84aGhjh79ix33HEHq1atuqTFq+anUikxPJpoarDo06VRLBYvq3sMzhWPqCTpslcoFLjuuuu47rrrWLduHV1dXbzwwgvAWDH0sY99jEWLFtHb20tvb+/E9YYHDx4kIvjEJz4x6/eHam1t5cknn2Tbtm0A7xp2ffxzjM+fzlDsk6discjNN9/sPfY0bUOjY906PdMn1TeLPkmSyjQ0NLB69WqWL1/O97//fXbv3s3w8DDFYpHFixfX9Lq2q6++mgcffLBmry9NNl70NVv0SXXNok+SpCm0trby8MMPc99999XdDdKlejE04pk+KQ8s+iRJuoDpDhQjXY4mij6v6ZPqmhkqSZKkqnimT8oHM1SSJElVcSAXKR/MUEmSJFXF7p1SPpihkiRJqsqg3TulXDBDJUmSVBWv6ZPywQyVJElSVbxPn5QPZqgkSZKq8s41fQ01fieSLsSiT5IkSVWxe6eUD2aoJEmSqjI0OgpY9En1zgyVJElSVTzTJ+WDGSpJkqSqeJ8+KR/MUEmSJFXF+/RJ+WCGSpIkqSreskHKBzNUkiRJVbF7p5QPZqgkSZKqMjRSolgICoWo9VuRdAEWfZIkSarK0EjJ6/mkHDBLJUmSVJWhUYs+KQ/MUkmSJFVlaKTk9XxSDpilkiRJqordO6V8MEslSZJUlUG7d0q5YJZKkiSpKnbvlPLBLJUkSVJVhkZK3phdygGzVJIkSVXxmj4pH8xSSZIkVcVbNkj5YJZKkiSpKmPdOxtq/TYkXYRFnyRJkqriQC5SPpilkiRJqordO6V8MEslSZJUFQdykfLBLJUkSVJVBi36pFwwSyVJklSVoZFRr+mTcsAslSRJUlWGRr05u5QHZqkkSZKq4jV9Uj6YpZIkSarYyGiJUsLunVIOmKWSJEmq2NBoCcAzfVIOmKWSJEmq2NCIRZ+UF2apJEmSKmbRJ+WHWSpJkqSKDY4XfV7TJ9U9s1SSJEkV85o+KT/MUkmSJFVsvHun9+mT6p9ZKkmSpIp5TZ+UH2apJEmSKjbRvbOhocbvRNLFWPRJkiSpYp7pk/LDLJUkSVLFLPqk/DBLJUmSVDFv2SDlh1kqSZKkinnLBik/zFJJkiRVzFs2SPlRrPUbkCRJUv480rmU9SsWc/UVTbV+K5IuwqJPkiRJFVvQ2MCCRm/XIOWB5+MlSZIkaR6z6JMkSZKkecyiT5IkSZLmMYs+SZIkSZrHLPokSZIkaR6z6JMkSZKkecyiT5IkSZLmMYs+SZIkSZrHLPokSZIkaR6bVtEXEQsjYnNE7I6I1yLi7knL2yLihYjYERG7IuLTZcsORMSrEfFKRGyftN1nImJPts2XL81HkiRJkiSNK05zvd8DvpNSeiIimoArJi1/GuhOKT0aEe3Anoh4NqU0lC2/P6V0rHyDiLgf+BDQmVIajIhrZ/A5JEmSJElTuGjRFxFXAeuBpwCyQm5o0moJuDIiAmgFTgAjF9n1rwG/nVIazPZ7tKJ3LkmSJEm6qOl077wJeBvYFBE/jIg/joiWSes8A9wKHAZeBf5FSqmULUvAX0XESxHxq2XbrADujYgXI+J7EXHXVC8eEb8aEdsjYvvbb79dyWeTJEmSpMvedIq+IrAG+GpKaTXQB3xh0jobgVeA64BVwDPZGUKA96eU1gAPAU9HxPqy/S4C3gd8DvjT7EzhOVJKf5hSujOldGd7e3sln02SJEmSLnvTKfoOAgdTSi9mzzczVgSW+zTwZ2nMPqAH+BmAlNLh7OdR4Hlgbdl+x7fZBpSAxTP5MJIkSZKkc1206EspHQF+HBErs1kPAN2TVnszm09EvBdYCbweES0RcWU2vwX4ILAz2+bPgQ3ZshVAE3AMSZIkSdIlM93ROz8DPJuN3Pk68OmI+GcAKaWvAf8e+C8R8SoQwL9KKR2LiJuA57Nem0XguZTSd7J9fgP4RkTsZGxgmE+llNKl+mCSJEmSJIg81Vl33nln2r59+8VXlCRJkqTLSES8lFK6c6pl07o5uyRJkiQpnyz6JEmSJGkes+iTJEmSpHnMok+SJEmS5jGLPkmSJEmax3I1emdEvA28cQl3uRjvDZgHxqn+GaN8ME75YJzqnzHKB+OUD8bp0rkhpdQ+1YJcFX2XWkRsP9+wpqofxqn+GaN8ME75YJzqnzHKB+OUD8Zpbti9U5IkSZLmMYs+SZIkSZrHLvei7w9r/QY0Lcap/hmjfDBO+WCc6p8xygfjlA/GaQ5c1tf0SZIkSdJ8d7mf6ZMkSZKkec2iT5IkSZLmsdwWfRGxMCI2R8TuiHgtIu4uW/YbEZEiYnH2/AMR8VJEvJr93FC27taI2BMRr2TTtdn85oj4dkTsi4gXI2JZ2Tafioi92fSpOfzYuVJhjJZFxNmyOHytbN07stjti4j/FBGRzTdGl0CFcfrFshi9EhGliFiVLTOXZtFUcYqIL0XEobJj/nDZ+r+ZHfM9EbGxbL75NEsqiZHtUu1UGCfbphqpME62TTUwVYyy+Z/JjvmuiPhy2fq2S7WUUsrlBPxX4Feyx03AwuzxTwHfZewm7ouzeauB67LH/wg4VLafrcCdU+z/14GvZY8/Dnw7e3w18Hr2c1H2eFGtj0c9ThXGaBmw8zz72QbcDQTwv4GHjFFt4jRpu58FXi97bi7NcZyALwG/McW6twE7gGbgRmA/0JAtM5/qI0a2S/mI0zJsm+o+TpO2s22qbYzuB/4P0JzNvzb7abtU4ymXZ/oi4ipgPfB1gJTSUEqpN1v8u8DngYkRalJKP0wpHc6e7gIWRETzRV7mQ4z9MgNsBh7IvnnYCPx1SulESukk8NfAgzP/VPNLpTG6wH6WAlellP4ujWX6fwMezxYboxmaYZx+AfjWNF7GOM3QReI0lQ8Bf5JSGkwp9QD7gLXm0+ypNEa2S7VRRS6dbz/m0iyaYZxsm+bABWL0a8Bvp5QGs/lHs01sl2osl0UfcBPwNrApIn4YEX8cES0R8Rhj35buuMC2HwV+OP7LmNmUnfL/N+OnlIHrgR8DpJRGgJ8A15TPzxzM5ulc1cToxmzd70XEvdm86xk7xuPKj7cxmrmZ5NLP8+6G1VyaHVPGKVv2zyOiKyK+ERGLsnnnO7bm0+ypNEblbJfmTjVxsm2aezPJJ9umuXG+GK0A7s26Y34vIu7K1rddqrG8Fn1FYA3w1ZTSaqCPsVP+vwX82/NtFBG3A78D/NOy2b+YUvpZ4N5s+uT46lPsIl1gvs5VaYzeAn46W/ezwHPZt0gXOt7GaOaqzaV1QH9KaWfZbHNp9kwVpy8AXwVuBlYxlkP/MVu/mmNunGam0hgBtks1UGmcbJtqo9p8sm2aO+eLUZGxLpfvAz4H/GlWaNsu1Vhei76DwMGU0ovZ882M/eLdCOyIiANAB/ByRCwBiIgO4Hngl1NK+8d3lFI6lP08DTwHrC17jZ/Kti0CbcCJ8vmZDuAwmqyiGGWn+48DpJReYqyv94psPx1l+y0/3sZo5irOpczHmfRNqrk0q6aMU0rpH1JKoymlEvBHTHHMM+PH1nyaPZXGyHapNiqKk21TzVScTxnbprlzvv8fDgJ/lsZsA0rAYmyXai6XRV9K6Qjw44hYmc16AHg5pXRtSmlZSmkZY78Qa1JKRyJiIfCXwG+mlP7f+H4iohjvjErYCDwCjH879BfA+GhATwB/k/U1/i7wwYhYlHUr+GA2T2WqiFF7RDQARMRNwHLGLsR+CzgdEe/Lvin6ZeB/Zfs0RjNUaZwAIqIAPAn8yfh+zKXZdZ44dWfXQoz7MOce84/H2MhnNzKWT9vMp9lTaYxsl2qjijjZNtVAFX/zbJvm2PliBPw5sAEgIlYwNsDLMWyXai/VwWgy1UyMndrfDnQx9gu2aNLyA7wzMuQXGTvt/ErZdC3QAryU7WMX8Hu8M5LQAuB/MHah6TbgprJ9/+Ns/j7g07U+FvU6VRijj2Yx2AG8DDxatt6djP2R3g88A4Qxqk2csuf3AX8/aR1zqQZxAr4JvJrN+wtgadn6v5XlzB6ykdCy+eZTHcQI26W8xMm2KQdxyta/D9umeohRE/Dfs9x4GdhQtr7tUg2n8YMqSZIkSZqHctm9U5IkSZI0PRZ9kiRJkjSPWfRJkiRJ0jxm0SdJkiRJ85hFnyRJkiTNYxZ9kiRJkjSPWfRJkiRJ0jz2/wHvvSc5jbAeZwAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<Figure size 1440x720 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": null,
+   "id": "45a9a4f1",
+   "metadata": {},
+   "outputs": [],
    "source": [
     "import matplotlib.pyplot as plt\n",
     "fig, ax = plt.subplots(figsize=(20, 10))\n",
@@ -675,92 +417,59 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": null,
+   "id": "9c288b21",
    "metadata": {},
    "outputs": [],
    "source": [
     "# access the single geometry object\n",
-    "seine = s_seine_utm.geometry.item()"
+    "seine = s_seine_utm.geometry.item()\n",
+    "seine"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": null,
+   "id": "c766fb27",
    "metadata": {
-    "clear_cell": true
+    "tags": [
+     "nbtutor-solution"
+    ]
    },
    "outputs": [],
    "source": [
-    "seine_buffer = seine.buffer(150)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {
-    "clear_cell": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/svg+xml": [
-       "<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" width=\"300\" height=\"300\" viewBox=\"642223.5431623402 6855624.611642234 15109.236583597492 13113.067078922875\" preserveAspectRatio=\"xMinYMin meet\"><g transform=\"matrix(1,0,0,-1,0,13724362.29036339)\"><path fill-rule=\"evenodd\" fill=\"#66cc99\" stroke=\"#555555\" stroke-width=\"100.72824389064995\" opacity=\"0.6\" d=\"M 656470.8560152841,6857518.546216722 L 655343.1155487589,6858174.160939991 L 655331.5853117538,6858181.58943878 L 655320.7818307271,6858190.040104871 L 655310.7954366856,6858199.442279817 L 655301.7096287367,6858209.717349334 L 652983.4779837319,6861086.3799764775 L 651409.8435902497,6862097.512723841 L 649948.2015080227,6862743.85579848 L 648605.6522962638,6862660.894637508 L 648113.1687603396,6862290.965726823 L 645753.7594769959,6859176.409615635 L 645744.6979546467,6859165.551930659 L 645734.6541702208,6859155.595882011 L 645723.7174020305,6859146.629968122 L 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656443.844368971,6856294.554519023 L 656440.6593160207,6856308.926113836 L 656438.8982625965,6856323.540695272 L 656438.5781686053,6856338.257516926 L 656470.8560152841,6857518.546216722 z\" /></g></svg>"
-      ],
-      "text/plain": [
-       "<shapely.geometry.polygon.Polygon at 0x7f952b4741f0>"
-      ]
-     },
-     "execution_count": 25,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
+    "# Take a buffer\n",
+    "seine_buffer = seine.buffer(150)\n",
     "seine_buffer"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": null,
+   "id": "9fdc30eb",
    "metadata": {
-    "clear_cell": true
+    "tags": [
+     "nbtutor-solution"
+    ]
    },
    "outputs": [],
    "source": [
+    "# Use the intersection\n",
     "districts_seine = districts[districts.intersects(seine_buffer)]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": null,
+   "id": "d9d18b39",
    "metadata": {
-    "clear_cell": true
+    "tags": [
+     "nbtutor-solution"
+    ]
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<AxesSubplot:>"
-      ]
-     },
-     "execution_count": 27,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1440x720 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
+    "# Make a plot\n",
     "fig, ax = plt.subplots(figsize=(20, 10))\n",
     "districts.plot(ax=ax, color='grey', alpha=0.4, edgecolor='k')\n",
     "districts_seine.plot(ax=ax, color='blue', alpha=0.4, edgecolor='k')\n",
@@ -769,6 +478,15 @@
   },
   {
    "cell_type": "markdown",
+   "id": "3d71f1aa",
+   "metadata": {},
+   "source": [
+    "------"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4ed17cfb",
    "metadata": {},
    "source": [
     "<div class=\"alert alert-success\">\n",
@@ -779,7 +497,7 @@
     "\n",
     "In this exercise, we will read the data, explore it visually, and calculate the total area of the different classes of land use in the area of Paris.\n",
     "\n",
-    "* Read in the `'paris_land_use.shp'` file and assign the result to a variable `land_use`.\n",
+    "* Read in the `'data/paris_land_use.zip'` shape file and assign the result to a variable `land_use`.\n",
     "* Make a plot of `land_use`, using the `'class'` column to color the polygons. Add a legend with `legend=True`, and make the figure size a bit larger.\n",
     "* Add a new column `'area'` to the dataframe with the area of each polygon.\n",
     "* Calculate the total area in km² for each `'class'` using the `groupby()` method, and print the result.\n",
@@ -798,123 +516,30 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": null,
+   "id": "488d84c2",
    "metadata": {
-    "clear_cell": true
+    "tags": [
+     "nbtutor-solution"
+    ]
    },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>class</th>\n",
-       "      <th>geometry</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Water bodies</td>\n",
-       "      <td>POLYGON ((3751386.281 2890064.323, 3751395.345...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Roads and associated land</td>\n",
-       "      <td>POLYGON ((3751390.345 2886000.000, 3751390.345...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Roads and associated land</td>\n",
-       "      <td>POLYGON ((3751390.345 2886898.192, 3751390.370...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Roads and associated land</td>\n",
-       "      <td>POLYGON ((3751390.345 2887500.000, 3751390.345...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Roads and associated land</td>\n",
-       "      <td>POLYGON ((3751390.345 2888647.357, 3751390.370...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                       class  \\\n",
-       "0               Water bodies   \n",
-       "1  Roads and associated land   \n",
-       "2  Roads and associated land   \n",
-       "3  Roads and associated land   \n",
-       "4  Roads and associated land   \n",
-       "\n",
-       "                                            geometry  \n",
-       "0  POLYGON ((3751386.281 2890064.323, 3751395.345...  \n",
-       "1  POLYGON ((3751390.345 2886000.000, 3751390.345...  \n",
-       "2  POLYGON ((3751390.345 2886898.192, 3751390.370...  \n",
-       "3  POLYGON ((3751390.345 2887500.000, 3751390.345...  \n",
-       "4  POLYGON ((3751390.345 2888647.357, 3751390.370...  "
-      ]
-     },
-     "execution_count": 28,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Import the land use dataset\n",
-    "land_use = geopandas.read_file(\"zip://./data/paris_land_use.zip\")\n",
+    "land_use = geopandas.read_file(\"data/paris_land_use.zip\")\n",
     "land_use.head()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": null,
+   "id": "b44ba7aa",
    "metadata": {
-    "clear_cell": true
+    "tags": [
+     "nbtutor-solution"
+    ]
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<AxesSubplot:>"
-      ]
-     },
-     "execution_count": 29,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1080x720 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Make a plot of the land use with 'class' as the color\n",
     "land_use.plot(column='class', legend=True, figsize=(15, 10))"
@@ -922,9 +547,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": null,
+   "id": "8aea730c",
    "metadata": {
-    "clear_cell": true
+    "tags": [
+     "nbtutor-solution"
+    ]
    },
    "outputs": [],
    "source": [
@@ -934,31 +562,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": null,
+   "id": "afdb9885",
    "metadata": {
-    "clear_cell": true
+    "tags": [
+     "nbtutor-solution"
+    ]
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "class\n",
-       "Continuous Urban Fabric             45.943090\n",
-       "Discontinuous Dense Urban Fabric     3.657343\n",
-       "Green urban areas                    9.858438\n",
-       "Industrial, commercial, public      13.295042\n",
-       "Railways and associated land         1.935793\n",
-       "Roads and associated land            7.401574\n",
-       "Sports and leisure facilities        3.578509\n",
-       "Water bodies                         3.189706\n",
-       "Name: area, dtype: float64"
-      ]
-     },
-     "execution_count": 31,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Calculate the total area for each land use class\n",
     "total_area = land_use.groupby('class')['area'].sum() / 1000**2\n",
@@ -967,6 +578,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "73e3a926",
    "metadata": {},
    "source": [
     "<div class=\"alert alert-success\">\n",
@@ -975,7 +587,7 @@
     "\n",
     "For this exercise, we are going to use 2 individual polygons: the district of Muette extracted from the `districts` dataset, and the green urban area of Boulogne, a large public park in the west of Paris, extracted from the `land_use` dataset. The two polygons have already been assigned to the `muette` and `park_boulogne` variables.\n",
     "\n",
-    "We first visualize the two polygons. You will see that they overlap, but the park is not fully located in the district of Muette. Let's determine the overlapping part.\n",
+    "We first visualize the two polygons. You will see that they overlap, but the park is not fully located in the district of Muette. Let's determine the overlapping part:\n",
     "\n",
     "* Plot the two polygons in a single map to examine visually the degree of overlap\n",
     "* Calculate the intersection of the `park_boulogne` and `muette` polygons.\n",
@@ -994,17 +606,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": null,
+   "id": "247f0b9a",
    "metadata": {},
    "outputs": [],
    "source": [
-    "land_use = geopandas.read_file(\"zip://./data/paris_land_use.zip\")\n",
+    "land_use = geopandas.read_file(\"data/paris_land_use.zip\")\n",
     "districts = geopandas.read_file(\"data/paris_districts.geojson\").to_crs(land_use.crs)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": null,
+   "id": "ad05ef0e",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1016,32 +630,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<AxesSubplot:>"
-      ]
-     },
-     "execution_count": 34,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": null,
+   "id": "c5ea4872",
+   "metadata": {},
+   "outputs": [],
    "source": [
     "# Plot the two polygons\n",
     "geopandas.GeoSeries([park_boulogne, muette]).plot(alpha=0.5, color=['green', 'blue'])"
@@ -1049,9 +641,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": null,
+   "id": "ea28fa16",
    "metadata": {
-    "clear_cell": true
+    "tags": [
+     "nbtutor-solution"
+    ]
    },
    "outputs": [],
    "source": [
@@ -1061,34 +656,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": null,
+   "id": "0e52f8ed",
    "metadata": {
-    "clear_cell": true
+    "tags": [
+     "nbtutor-solution"
+    ]
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<AxesSubplot:>"
-      ]
-     },
-     "execution_count": 36,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Plot the intersection\n",
     "geopandas.GeoSeries([intersection]).plot()"
@@ -1096,19 +671,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": null,
+   "id": "a87d87af",
    "metadata": {
-    "clear_cell": true
+    "tags": [
+     "nbtutor-solution"
+    ]
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.4352082235641065\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Print proportion of district area that occupied park\n",
     "print(intersection.area / muette.area)"
@@ -1116,6 +686,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "eead4835",
    "metadata": {},
    "source": [
     "<div class=\"alert alert-success\">\n",
@@ -1144,20 +715,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": null,
+   "id": "c666328b",
    "metadata": {},
    "outputs": [],
    "source": [
-    "land_use = geopandas.read_file(\"zip://./data/paris_land_use.zip\")\n",
+    "land_use = geopandas.read_file(\"data/paris_land_use.zip\")\n",
     "districts = geopandas.read_file(\"data/paris_districts.geojson\").to_crs(land_use.crs)\n",
     "muette = districts[districts.district_name == 'Muette'].geometry.item()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": null,
+   "id": "8019d083",
    "metadata": {
-    "clear_cell": true
+    "tags": [
+     "nbtutor-solution"
+    ]
    },
    "outputs": [],
    "source": [
@@ -1167,39 +742,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/joris/miniconda3/envs/DS-geospatial-test/lib/python3.8/site-packages/geopandas/array.py:689: RuntimeWarning: All-NaN slice encountered\n",
-      "  np.nanmin(b[:, 0]),  # minx\n",
-      "/home/joris/miniconda3/envs/DS-geospatial-test/lib/python3.8/site-packages/geopandas/array.py:690: RuntimeWarning: All-NaN slice encountered\n",
-      "  np.nanmin(b[:, 1]),  # miny\n",
-      "/home/joris/miniconda3/envs/DS-geospatial-test/lib/python3.8/site-packages/geopandas/array.py:691: RuntimeWarning: All-NaN slice encountered\n",
-      "  np.nanmax(b[:, 2]),  # maxx\n",
-      "/home/joris/miniconda3/envs/DS-geospatial-test/lib/python3.8/site-packages/geopandas/array.py:692: RuntimeWarning: All-NaN slice encountered\n",
-      "  np.nanmax(b[:, 3]),  # maxy\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "0    POLYGON EMPTY\n",
-       "1    POLYGON EMPTY\n",
-       "2    POLYGON EMPTY\n",
-       "3    POLYGON EMPTY\n",
-       "4    POLYGON EMPTY\n",
-       "dtype: geometry"
-      ]
-     },
-     "execution_count": 40,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "id": "6d39781d",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
    "source": [
     "# Print the first five rows of the intersection\n",
     "land_use_muette.head()"
@@ -1207,8 +757,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
-   "metadata": {},
+   "execution_count": null,
+   "id": "748cd904",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
    "outputs": [],
    "source": [
     "# Remove the empty geometries\n",
@@ -1217,25 +772,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "135    POLYGON ((3752020.694 2891519.323, 3752025.310...\n",
-       "229    POLYGON ((3752443.986 2891171.823, 3752446.430...\n",
-       "239    POLYGON ((3752110.034 2891774.197, 3752112.444...\n",
-       "278    POLYGON ((3752000.000 2891530.298, 3752000.000...\n",
-       "279    POLYGON ((3751954.462 2891571.778, 3752000.000...\n",
-       "dtype: geometry"
-      ]
-     },
-     "execution_count": 42,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "id": "d38d537a",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
    "source": [
     "# Print the first five rows of the intersection\n",
     "land_use_muette.head()"
@@ -1243,34 +787,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": null,
+   "id": "23cb581d",
    "metadata": {
-    "clear_cell": true
+    "tags": [
+     "nbtutor-solution"
+    ]
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<AxesSubplot:>"
-      ]
-     },
-     "execution_count": 43,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Plot the intersection\n",
     "land_use_muette.plot(edgecolor='black')"
@@ -1278,6 +802,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "6f0d770f",
    "metadata": {},
    "source": [
     "You can see in the plot that we now only have a subset of the full land use dataset. The original `land_use_muette` (before removing the empty geometries) still has the same number of rows as the original `land_use`, though. But many of the rows, as you could see by printing the first rows, consist now of empty polygons when it did not intersect with the Muette district.\n",
@@ -1287,85 +812,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>class</th>\n",
-       "      <th>geometry</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>135</th>\n",
-       "      <td>Green urban areas</td>\n",
-       "      <td>POLYGON ((3752020.694 2891519.323, 3752025.310...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>229</th>\n",
-       "      <td>Sports and leisure facilities</td>\n",
-       "      <td>POLYGON ((3752443.986 2891171.823, 3752446.430...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>239</th>\n",
-       "      <td>Water bodies</td>\n",
-       "      <td>POLYGON ((3752110.034 2891774.197, 3752112.444...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>278</th>\n",
-       "      <td>Roads and associated land</td>\n",
-       "      <td>POLYGON ((3752000.000 2891530.298, 3752000.000...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>279</th>\n",
-       "      <td>Roads and associated land</td>\n",
-       "      <td>POLYGON ((3751954.462 2891571.778, 3752000.000...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                             class  \\\n",
-       "135              Green urban areas   \n",
-       "229  Sports and leisure facilities   \n",
-       "239                   Water bodies   \n",
-       "278      Roads and associated land   \n",
-       "279      Roads and associated land   \n",
-       "\n",
-       "                                              geometry  \n",
-       "135  POLYGON ((3752020.694 2891519.323, 3752025.310...  \n",
-       "229  POLYGON ((3752443.986 2891171.823, 3752446.430...  \n",
-       "239  POLYGON ((3752110.034 2891774.197, 3752112.444...  \n",
-       "278  POLYGON ((3752000.000 2891530.298, 3752000.000...  \n",
-       "279  POLYGON ((3751954.462 2891571.778, 3752000.000...  "
-      ]
-     },
-     "execution_count": 44,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "id": "242946d4",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
    "source": [
     "land_use_muette = land_use.copy()\n",
     "land_use_muette['geometry'] = land_use.geometry.intersection(muette)\n",
@@ -1375,130 +829,35 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<AxesSubplot:>"
-      ]
-     },
-     "execution_count": 46,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": null,
+   "id": "927f846d",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
    "source": [
     "land_use_muette.plot(column=\"class\") #edgecolor=\"black\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>geometry</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>class</th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>Continuous Urban Fabric</th>\n",
-       "      <td>MULTIPOLYGON (((3755334.091 2889457.833, 37553...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Discontinuous Dense Urban Fabric</th>\n",
-       "      <td>MULTIPOLYGON (((3755585.963 2889793.822, 37556...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Green urban areas</th>\n",
-       "      <td>MULTIPOLYGON (((3755772.518 2889995.038, 37558...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Industrial, commercial, public</th>\n",
-       "      <td>MULTIPOLYGON (((3755275.182 2889527.443, 37552...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Railways and associated land</th>\n",
-       "      <td>POLYGON ((3755654.921 2889540.054, 3755560.618...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Roads and associated land</th>\n",
-       "      <td>MULTIPOLYGON (((3754820.067 2889843.877, 37548...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Sports and leisure facilities</th>\n",
-       "      <td>MULTIPOLYGON (((3753932.354 2891267.190, 37539...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Water bodies</th>\n",
-       "      <td>MULTIPOLYGON (((3755507.459 2889412.262, 37555...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                                                           geometry\n",
-       "class                                                                              \n",
-       "Continuous Urban Fabric           MULTIPOLYGON (((3755334.091 2889457.833, 37553...\n",
-       "Discontinuous Dense Urban Fabric  MULTIPOLYGON (((3755585.963 2889793.822, 37556...\n",
-       "Green urban areas                 MULTIPOLYGON (((3755772.518 2889995.038, 37558...\n",
-       "Industrial, commercial, public    MULTIPOLYGON (((3755275.182 2889527.443, 37552...\n",
-       "Railways and associated land      POLYGON ((3755654.921 2889540.054, 3755560.618...\n",
-       "Roads and associated land         MULTIPOLYGON (((3754820.067 2889843.877, 37548...\n",
-       "Sports and leisure facilities     MULTIPOLYGON (((3753932.354 2891267.190, 37539...\n",
-       "Water bodies                      MULTIPOLYGON (((3755507.459 2889412.262, 37555..."
-      ]
-     },
-     "execution_count": 47,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "id": "5946bf4f",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
    "source": [
     "land_use_muette.dissolve(by='class')"
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "2f0c8161",
    "metadata": {},
    "source": [
     "<div class=\"alert alert-success\">\n",
@@ -1523,29 +882,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "class\n",
-       "Continuous Urban Fabric             1.275297e+06\n",
-       "Discontinuous Dense Urban Fabric    8.828861e+04\n",
-       "Green urban areas                   2.624229e+06\n",
-       "Industrial, commercial, public      3.629901e+05\n",
-       "Railways and associated land        5.424307e+03\n",
-       "Roads and associated land           2.262708e+05\n",
-       "Sports and leisure facilities       6.039885e+05\n",
-       "Water bodies                        2.921943e+05\n",
-       "Name: area, dtype: float64"
-      ]
-     },
-     "execution_count": 48,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "id": "aa2af958",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
    "source": [
     "land_use_muette['area'] = land_use_muette.geometry.area\n",
     "# Total land use per class\n",
@@ -1554,29 +898,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "class\n",
-       "Continuous Urban Fabric             23.277436\n",
-       "Discontinuous Dense Urban Fabric     1.611493\n",
-       "Green urban areas                   47.898906\n",
-       "Industrial, commercial, public       6.625501\n",
-       "Railways and associated land         0.099008\n",
-       "Roads and associated land            4.130023\n",
-       "Sports and leisure facilities       11.024339\n",
-       "Water bodies                         5.333295\n",
-       "Name: area, dtype: float64"
-      ]
-     },
-     "execution_count": 49,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "id": "577cd57c",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
    "source": [
     "# Relative percentage of land use classes\n",
     "land_use_muette.groupby(\"class\")[\"area\"].sum() / land_use_muette.geometry.area.sum() * 100"
@@ -1584,6 +913,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "a57ae832",
    "metadata": {},
    "source": [
     "The above was only for a single district. If we want to do this more easily for all districts, we can do this with the overlay operation."
@@ -1591,6 +921,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "d74fd1ac",
    "metadata": {},
    "source": [
     "## The overlay operation\n",
@@ -1602,6 +933,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "bbff8735",
    "metadata": {},
    "source": [
     "### How does it differ compared to the intersection method?"
@@ -1609,6 +941,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "534a6667",
    "metadata": {},
    "source": [
     "With the `intersection()` method introduced in the previous section, we could for example determine the intersection of a set of countries with another polygon, a circle in the example below:\n",
@@ -1618,6 +951,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "11d6849e",
    "metadata": {},
    "source": [
     "However, this method (`countries.intersection(circle)`) also has some limitations.\n",
@@ -1630,6 +964,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "0aaaa8aa",
    "metadata": {},
    "source": [
     "Consider the following simplified example. On the left we see again the 3 countries. On the right we have the plot of a GeoDataFrame with some simplified geologic regions for the same area:\n",
@@ -1653,6 +988,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "19573384",
    "metadata": {},
    "source": [
     "Another code example:"
@@ -1660,7 +996,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": null,
+   "id": "8bcd4a28",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1669,83 +1006,30 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<AxesSubplot:>"
-      ]
-     },
-     "execution_count": 51,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": null,
+   "id": "01aee38c",
+   "metadata": {},
+   "outputs": [],
    "source": [
     "africa.plot()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": null,
+   "id": "891b75a1",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "<ipython-input-52-c1250b33ac0f>:1: UserWarning: Geometry is in a geographic CRS. Results from 'buffer' are likely incorrect. Use 'GeoSeries.to_crs()' to re-project geometries to a projected CRS before this operation.\n",
-      "\n",
-      "  cities['geometry'] = cities.buffer(2)\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "cities['geometry'] = cities.buffer(2)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<AxesSubplot:>"
-      ]
-     },
-     "execution_count": 53,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": null,
+   "id": "5866c061",
+   "metadata": {},
+   "outputs": [],
    "source": [
     "intersection = geopandas.overlay(africa, cities, how='intersection')\n",
     "intersection.plot()"
@@ -1753,121 +1037,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>iso_a3</th>\n",
-       "      <th>name_1</th>\n",
-       "      <th>continent</th>\n",
-       "      <th>pop_est</th>\n",
-       "      <th>gdp_md_est</th>\n",
-       "      <th>name_2</th>\n",
-       "      <th>geometry</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>AGO</td>\n",
-       "      <td>Angola</td>\n",
-       "      <td>Africa</td>\n",
-       "      <td>29310273.0</td>\n",
-       "      <td>189000.0</td>\n",
-       "      <td>Brazzaville</td>\n",
-       "      <td>POLYGON ((14.10056 -5.86749, 16.32653 -5.87747...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>COD</td>\n",
-       "      <td>Dem. Rep. Congo</td>\n",
-       "      <td>Africa</td>\n",
-       "      <td>83301151.0</td>\n",
-       "      <td>66010.0</td>\n",
-       "      <td>Brazzaville</td>\n",
-       "      <td>POLYGON ((16.34948 -5.94680, 16.32653 -5.87747...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>COG</td>\n",
-       "      <td>Congo</td>\n",
-       "      <td>Africa</td>\n",
-       "      <td>4954674.0</td>\n",
-       "      <td>30270.0</td>\n",
-       "      <td>Brazzaville</td>\n",
-       "      <td>POLYGON ((16.09773 -2.43295, 15.97280 -2.71239...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>AGO</td>\n",
-       "      <td>Angola</td>\n",
-       "      <td>Africa</td>\n",
-       "      <td>29310273.0</td>\n",
-       "      <td>189000.0</td>\n",
-       "      <td>Luanda</td>\n",
-       "      <td>POLYGON ((13.69097 -10.78080, 13.68638 -10.731...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>AGO</td>\n",
-       "      <td>Angola</td>\n",
-       "      <td>Africa</td>\n",
-       "      <td>29310273.0</td>\n",
-       "      <td>189000.0</td>\n",
-       "      <td>Kinshasa</td>\n",
-       "      <td>POLYGON ((14.03697 -5.86721, 16.32653 -5.87747...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "  iso_a3           name_1 continent     pop_est  gdp_md_est       name_2  \\\n",
-       "0    AGO           Angola    Africa  29310273.0    189000.0  Brazzaville   \n",
-       "1    COD  Dem. Rep. Congo    Africa  83301151.0     66010.0  Brazzaville   \n",
-       "2    COG            Congo    Africa   4954674.0     30270.0  Brazzaville   \n",
-       "3    AGO           Angola    Africa  29310273.0    189000.0       Luanda   \n",
-       "4    AGO           Angola    Africa  29310273.0    189000.0     Kinshasa   \n",
-       "\n",
-       "                                            geometry  \n",
-       "0  POLYGON ((14.10056 -5.86749, 16.32653 -5.87747...  \n",
-       "1  POLYGON ((16.34948 -5.94680, 16.32653 -5.87747...  \n",
-       "2  POLYGON ((16.09773 -2.43295, 15.97280 -2.71239...  \n",
-       "3  POLYGON ((13.69097 -10.78080, 13.68638 -10.731...  \n",
-       "4  POLYGON ((14.03697 -5.86721, 16.32653 -5.87747...  "
-      ]
-     },
-     "execution_count": 54,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "id": "9afc38eb",
+   "metadata": {},
+   "outputs": [],
    "source": [
     "intersection.head()"
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "d6b2fbef",
    "metadata": {},
    "source": [
     "With the overlay method, we pass the full GeoDataFrame with all regions to intersect the countries with. The result contains all non-empty intersections of all combinations of countries and city regions.\n",
@@ -1877,38 +1057,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<AxesSubplot:>"
-      ]
-     },
-     "execution_count": 55,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": null,
+   "id": "f8608488",
+   "metadata": {},
+   "outputs": [],
    "source": [
     "geopandas.overlay(africa, cities, how='intersection').plot()  # how=\"difference\"/\"union\"/\"symmetric_difference\""
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "84928cc9",
    "metadata": {},
    "source": [
     "<div class=\"alert alert-info\" style=\"font-size:120%\">\n",
@@ -1922,6 +1081,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "53ad68d6",
    "metadata": {},
    "source": [
     "## Let's practice!"
@@ -1929,13 +1089,14 @@
   },
   {
    "cell_type": "markdown",
+   "id": "6af711a3",
    "metadata": {},
    "source": [
     "<div class=\"alert alert-success\">\n",
     "\n",
     "**EXERCISE: Overlaying spatial datasets I**\n",
     "\n",
-    "We will now combine both datasets in an overlay operation. Create a new `GeoDataFrame` consisting of the intersection of the land use polygons wich each of the districts, but make sure to bring the attribute data from both source layers.\n",
+    "We will now combine both datasets in an overlay operation. Create a new `GeoDataFrame` consisting of the intersection of the land use polygons which each of the districts, but make sure to bring the attribute data from both source layers.\n",
     "\n",
     "* Create a new GeoDataFrame from the intersections of `land_use` and `districts`. Assign the result to a variable `combined`.\n",
     "* Print the first rows the resulting GeoDataFrame (`combined`).\n",
@@ -1945,6 +1106,7 @@
     "* The intersection of two GeoDataFrames can be calculated with the `geopandas.overlay()` function.\n",
     "* The `overlay()` functions takes first the two GeoDataFrames to combine, and a third `how` keyword indicating how to combine the two layers.\n",
     "* For making an overlay based on the intersection, you can pass `how='intersection'`.\n",
+    "* The default `overlay()` call will generate a warning because some of the intersections result in a point or linestring, and thus not in a new polygon. For this exercise, we are only interested in the resulting polygons, and therefore we set `keep_geom_type=True` to suppress the warning and say to `overlay()` we only need the resulting geometries of the same type as the original geometries (thus, only polygons).\n",
     "\n",
     "</details>\n",
     "\n",
@@ -1953,127 +1115,40 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
+   "execution_count": null,
+   "id": "f15c6be8",
    "metadata": {},
    "outputs": [],
    "source": [
-    "land_use = geopandas.read_file(\"zip://./data/paris_land_use.zip\")\n",
+    "land_use = geopandas.read_file(\"data/paris_land_use.zip\")\n",
     "districts = geopandas.read_file(\"data/paris_districts.geojson\").to_crs(land_use.crs)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
+   "execution_count": null,
+   "id": "43b9d6ae",
    "metadata": {
-    "clear_cell": true
+    "tags": [
+     "nbtutor-solution"
+    ]
    },
    "outputs": [],
    "source": [
     "# Overlay both datasets based on the intersection\n",
-    "combined = geopandas.overlay(land_use, districts, how='intersection')"
+    "combined = geopandas.overlay(land_use, districts, how='intersection', keep_geom_type=True)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 58,
+   "execution_count": null,
+   "id": "4e698a9e",
    "metadata": {
-    "clear_cell": true
+    "tags": [
+     "nbtutor-solution"
+    ]
    },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>class</th>\n",
-       "      <th>id</th>\n",
-       "      <th>district_name</th>\n",
-       "      <th>population</th>\n",
-       "      <th>geometry</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Water bodies</td>\n",
-       "      <td>61</td>\n",
-       "      <td>Auteuil</td>\n",
-       "      <td>67967</td>\n",
-       "      <td>POLYGON ((3751395.345 2890118.001, 3751395.345...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Continuous Urban Fabric</td>\n",
-       "      <td>61</td>\n",
-       "      <td>Auteuil</td>\n",
-       "      <td>67967</td>\n",
-       "      <td>MULTIPOLYGON (((3753253.104 2888254.529, 37532...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Roads and associated land</td>\n",
-       "      <td>61</td>\n",
-       "      <td>Auteuil</td>\n",
-       "      <td>67967</td>\n",
-       "      <td>POLYGON ((3751519.830 2890061.509, 3751522.057...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Green urban areas</td>\n",
-       "      <td>61</td>\n",
-       "      <td>Auteuil</td>\n",
-       "      <td>67967</td>\n",
-       "      <td>MULTIPOLYGON (((3754314.717 2890283.121, 37543...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Roads and associated land</td>\n",
-       "      <td>61</td>\n",
-       "      <td>Auteuil</td>\n",
-       "      <td>67967</td>\n",
-       "      <td>POLYGON ((3751619.113 2890500.000, 3751626.627...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                       class  id district_name  population  \\\n",
-       "0               Water bodies  61       Auteuil       67967   \n",
-       "1    Continuous Urban Fabric  61       Auteuil       67967   \n",
-       "2  Roads and associated land  61       Auteuil       67967   \n",
-       "3          Green urban areas  61       Auteuil       67967   \n",
-       "4  Roads and associated land  61       Auteuil       67967   \n",
-       "\n",
-       "                                            geometry  \n",
-       "0  POLYGON ((3751395.345 2890118.001, 3751395.345...  \n",
-       "1  MULTIPOLYGON (((3753253.104 2888254.529, 37532...  \n",
-       "2  POLYGON ((3751519.830 2890061.509, 3751522.057...  \n",
-       "3  MULTIPOLYGON (((3754314.717 2890283.121, 37543...  \n",
-       "4  POLYGON ((3751619.113 2890500.000, 3751626.627...  "
-      ]
-     },
-     "execution_count": 58,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Print the first five rows of the result\n",
     "combined.head()"
@@ -2081,6 +1156,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "98b05bae",
    "metadata": {},
    "source": [
     "<div class=\"alert alert-success\">\n",
@@ -2108,9 +1184,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 59,
+   "execution_count": null,
+   "id": "2b82c0f8",
    "metadata": {
-    "clear_cell": true
+    "tags": [
+     "nbtutor-solution"
+    ]
    },
    "outputs": [],
    "source": [
@@ -2120,9 +1199,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 60,
+   "execution_count": null,
+   "id": "c4d1bc02",
    "metadata": {
-    "clear_cell": true
+    "tags": [
+     "nbtutor-solution"
+    ]
    },
    "outputs": [],
    "source": [
@@ -2132,34 +1214,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 61,
+   "execution_count": null,
+   "id": "1c9c407f",
    "metadata": {
-    "clear_cell": true
+    "tags": [
+     "nbtutor-solution"
+    ]
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<AxesSubplot:>"
-      ]
-     },
-     "execution_count": 61,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Visualize the land use of the Muette district\n",
     "land_use_muette.plot(column='class')"
@@ -2167,28 +1229,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 62,
+   "execution_count": null,
+   "id": "16d7def6",
    "metadata": {
-    "clear_cell": true
+    "tags": [
+     "nbtutor-solution"
+    ]
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "class\n",
-      "Continuous Urban Fabric             1.275297\n",
-      "Discontinuous Dense Urban Fabric    0.088289\n",
-      "Green urban areas                   2.624229\n",
-      "Industrial, commercial, public      0.362990\n",
-      "Railways and associated land        0.005424\n",
-      "Roads and associated land           0.226271\n",
-      "Sports and leisure facilities       0.603989\n",
-      "Water bodies                        0.292194\n",
-      "Name: area, dtype: float64\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Calculate the total area for each land use class\n",
     "print(land_use_muette.groupby('class')['area'].sum() / 1000**2)"
@@ -2196,6 +1244,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "9e3eb4b9",
    "metadata": {},
    "source": [
     "<div class=\"alert alert-success\">\n",
@@ -2208,32 +1257,21 @@
     "* Select the subset of \"Green urban areas\" from `combined` and call this `urban_green`.\n",
     "* Now calculate the total area per district for this `urban_green` subset, and call this `urban_green_area`.\n",
     "* Determine the fraction of urban green area in each district.\n",
+    "* Check the districts with the largest and smallest fraction of urban green (you can sort the result, or use `nlargest()` / `nsmallest()`)\n",
     "\n",
     "</div>"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 64,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "district_name\n",
-       "Archives           3.678060e+05\n",
-       "Arsenal            4.871228e+05\n",
-       "Arts-et-Metiers    3.181515e+05\n",
-       "Auteuil            6.386649e+06\n",
-       "Batignolles        1.350675e+06\n",
-       "Name: area, dtype: float64"
-      ]
-     },
-     "execution_count": 64,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "id": "d72d47ab",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
    "source": [
     "districts_area = combined.groupby(\"district_name\")[\"area\"].sum()\n",
     "districts_area.head()"
@@ -2241,8 +1279,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 65,
-   "metadata": {},
+   "execution_count": null,
+   "id": "9e2d3dcc",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
    "outputs": [],
    "source": [
     "urban_green = combined[combined[\"class\"] == \"Green urban areas\"]"
@@ -2250,26 +1293,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 66,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "district_name\n",
-       "Archives           4.409114e+03\n",
-       "Arsenal            2.801951e+04\n",
-       "Arts-et-Metiers    7.154903e+03\n",
-       "Auteuil            1.577429e+06\n",
-       "Batignolles        8.471799e+04\n",
-       "Name: area, dtype: float64"
-      ]
-     },
-     "execution_count": 66,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "id": "d2655198",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
    "source": [
     "urban_green_area = urban_green.groupby(\"district_name\")[\"area\"].sum()\n",
     "urban_green_area.head()"
@@ -2277,67 +1308,52 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 67,
-   "metadata": {},
+   "execution_count": null,
+   "id": "b42b2dfb",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
    "outputs": [],
    "source": [
-    "urban_green_fraction = urban_green_area / districts_area * 100"
+    "urban_green_fraction = urban_green_area / districts_area * 100\n",
+    "# some district are missing from urban_green_area (no urban green in that district)\n",
+    "# this gives NaNs in the result, which we fill with 0\n",
+    "urban_green_fraction = urban_green_fraction.fillna(0)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 68,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "district_name\n",
-       "Muette                    47.898906\n",
-       "Porte-Dauphine            42.266546\n",
-       "Odeon                     33.044112\n",
-       "St-Germain-l'Auxerrois    31.534557\n",
-       "Auteuil                   24.698847\n",
-       "Name: area, dtype: float64"
-      ]
-     },
-     "execution_count": 68,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "id": "386b4184",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
    "source": [
     "urban_green_fraction.nlargest()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 70,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "district_name\n",
-       "Chaussée-d'Antin         0.005752\n",
-       "Saint-Vincent-de-Paul    0.332773\n",
-       "Notre-Dame-des-Champs    0.593873\n",
-       "Salpêtrière              0.605310\n",
-       "Saint-Georges            0.654253\n",
-       "Name: area, dtype: float64"
-      ]
-     },
-     "execution_count": 70,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "id": "56729761",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
    "source": [
-    "urban_green_fraction.nsmallest()"
+    "urban_green_fraction.nsmallest(20)"
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "6a752bcc",
    "metadata": {},
    "source": [
     "An alternative to calculate the area per land use class in each district:"
@@ -2345,129 +1361,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 71,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>district_name</th>\n",
-       "      <th>class</th>\n",
-       "      <th>area</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Archives</td>\n",
-       "      <td>Continuous Urban Fabric</td>\n",
-       "      <td>323124.623717</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Archives</td>\n",
-       "      <td>Green urban areas</td>\n",
-       "      <td>4409.113826</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Archives</td>\n",
-       "      <td>Industrial, commercial, public</td>\n",
-       "      <td>33752.922651</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Archives</td>\n",
-       "      <td>Roads and associated land</td>\n",
-       "      <td>6519.347840</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Arsenal</td>\n",
-       "      <td>Continuous Urban Fabric</td>\n",
-       "      <td>274791.191887</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>352</th>\n",
-       "      <td>Villette</td>\n",
-       "      <td>Roads and associated land</td>\n",
-       "      <td>33164.775004</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>353</th>\n",
-       "      <td>Villette</td>\n",
-       "      <td>Water bodies</td>\n",
-       "      <td>3384.185687</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>354</th>\n",
-       "      <td>Vivienne</td>\n",
-       "      <td>Continuous Urban Fabric</td>\n",
-       "      <td>190071.213662</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>355</th>\n",
-       "      <td>Vivienne</td>\n",
-       "      <td>Industrial, commercial, public</td>\n",
-       "      <td>33180.126871</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>356</th>\n",
-       "      <td>Vivienne</td>\n",
-       "      <td>Roads and associated land</td>\n",
-       "      <td>20339.790825</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>357 rows × 3 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "    district_name                           class           area\n",
-       "0        Archives         Continuous Urban Fabric  323124.623717\n",
-       "1        Archives               Green urban areas    4409.113826\n",
-       "2        Archives  Industrial, commercial, public   33752.922651\n",
-       "3        Archives       Roads and associated land    6519.347840\n",
-       "4         Arsenal         Continuous Urban Fabric  274791.191887\n",
-       "..            ...                             ...            ...\n",
-       "352      Villette       Roads and associated land   33164.775004\n",
-       "353      Villette                    Water bodies    3384.185687\n",
-       "354      Vivienne         Continuous Urban Fabric  190071.213662\n",
-       "355      Vivienne  Industrial, commercial, public   33180.126871\n",
-       "356      Vivienne       Roads and associated land   20339.790825\n",
-       "\n",
-       "[357 rows x 3 columns]"
-      ]
-     },
-     "execution_count": 71,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "id": "9b67c046",
+   "metadata": {},
+   "outputs": [],
    "source": [
     "combined.groupby([\"district_name\", \"class\"])[\"area\"].sum().reset_index()"
    ]
@@ -2475,14 +1372,18 @@
   {
    "cell_type": "code",
    "execution_count": null,
+   "id": "b5db3143",
    "metadata": {},
    "outputs": [],
    "source": []
   }
  ],
  "metadata": {
+  "jupytext": {
+   "default_lexer": "ipython3"
+  },
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -2496,9 +1397,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.5"
+   "version": "3.12.11"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 4
+ "nbformat_minor": 5
 }
diff --git a/_solved/10-introduction-raster.ipynb b/_solved/10-introduction-raster.ipynb
index 64770da..e36cc79 100644
--- a/_solved/10-introduction-raster.ipynb
+++ b/_solved/10-introduction-raster.ipynb
@@ -4,20 +4,24 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Introduction Raster data"
+    "<p><font size=\"6\"><b>Introduction to geospatial raster data in Python</b></font></p>\n",
+    "\n",
+    "\n",
+    "> *DS Python for GIS and Geoscience*  \n",
+    "> *October, 2025*\n",
+    ">\n",
+    "> *© 2025, Joris Van den Bossche and Stijn Van Hoey. Licensed under [CC BY 4.0 Creative Commons](http://creativecommons.org/licenses/by/4.0/)*\n",
+    "\n",
+    "---"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
-    "%matplotlib inline\n",
-    "\n",
-    "import numpy as np\n",
-    "import rasterio\n",
-    "from rasterio.plot import show"
+    "import numpy as np"
    ]
   },
   {
@@ -34,12 +38,34 @@
    "metadata": {},
    "source": [
     "![](../img/raster-concept.png)\n",
+    "\n",
     "<small>_Source: Colin Williams, NEON._</small>"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
+   "source": [
+    "Raster data contains at least 3 dimensions:\n",
+    "\n",
+    "- longitude of the pixel \n",
+    "- latitude of the pixel \n",
+    "- data value(s) of the pixel\n",
+    "\n",
+    "The data value can be a single data point for each pixel, also called a single band (or layer) raster data set. A well-known single-band raster data set is a Digital Elevation Model (DEM): each pixel data value in a DEM represents the surface elevation of the area represented by the pixel. \n",
+    "\n",
+    "Each pixel can also contain multiple data values, i.e. multiple bands (or layers). Remote sensing measurement are typically raster data sets that consist out of multiple bands. For example, bands 2, 3, and 4 of the [Landsat 8](https://landsat.gsfc.nasa.gov/landsat-8/landsat-8-bands) measurements capture respectively visible blue, green, and red (each with a resolution of 30m).\n",
+    "\n",
+    "Whereas we could actually store raster data as a tabular data set using an x, y and value column, the representation as a N-d array/grid is more convenient for raster data handling. \n",
+    "\n",
+    "Similar to the GeoPandas library for feature based data, meet [xarray](http://xarray.pydata.org/en/stable/) for raster based data."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "tags": []
+   },
    "source": [
     "## Importing raster data"
    ]
@@ -48,515 +74,241 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Similar to geospatial feature based data, rastyer data is often available from specific GIS file formats or data stores, like Geotiff files, Esri grid, NetCDF files, PostGIS (PostgreSQL) database, ...\n",
+    "Just like geospatial feature based (vector) data, raster data is available from specific GIS file formats or data stores, like Geotiff files, Esri grid, NetCDF files, PostGIS (PostgreSQL) database, ...\n",
     "\n",
-    "We can use the [Rasterio library](https://rasterio.readthedocs.io/en/latest/) to read many of those GIS file formats, using the `rasterio.open` function.\n",
+    "We can use the xarray library to read many of those GIS file formats (relying on the [Rasterio library](https://rasterio.readthedocs.io/en/latest/) under the hood, which is an interface to raster data). An important data format is GeoTiff. \n",
     "\n",
-    "Let's start by reading and plotting a Geotiff file (the file is available in the `./data/herstappe/raster` directory):"
+    "Let's start with reading the Sentinel RGB bands for Herstappe:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
-    "file_herstappe = \"./data/herstappe/raster/2020-09-17_Sentinel_2_L1C_True_color.tiff\""
+    "import xarray as xr"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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2WWJVqn2OXIOCADhb/6rKw7Tw9f2Fy5Jatl+J3rHv4tUHVbWAf+UHFHbi2IsnF3hYFkTgLgYE4f1p5lwKd6MlbkMMRG/8GAIpZaYlkUolOscYIyuWmKS1oN7RhUBpsPbjmvj6+ECh0ncdy5JJtRD+bziNzy4A9F2kDxFN9v+1wTYOw8W/fTzz8mCjgFStvLfyqXDJGbDSUmtlWjN99LwcOuaceT8lfvh6RxccpcLffX80PFYrtZXou+h5PXQIljFVIAisKsw5cxcCXcsaNpJPsMyxqgWKnz1eKLkQBBLmfKmWgagagZSzkkvlZt9xcJ6hd6RcOZ0T/U0wzLg5Xmls6ZQKS648TIkP54W7oePVrsM7y1CEgAMGF/jdNzd8fZyuZC7ijMiscoWCVC04DtFbpiXCbYzXbN6JIKq2STDiOdVKjI79GCnNYexi4GFaeXMzWMmOOdPTbFnxr7848OOHibWaA1xzQVsa5hqBDbTNbLBBiJH35xOdc1QtrAopehZvUEBNDeJRq+I659gHTyfwou+p1aotmqMZGo/hQ+BF5+m9Q9vzcy3Dk1buewdOhalUfHAcp5nzmlCEpEppQV2aI3JeWNbM9/OCirDWwmlJvLkdGb1BirdD5LJmtBrO7ESIzqHVKrkPl0wXzRGsudp9Qei8p/eRd+sDpyUxRE9aLHtM7Xx8cKRcyFnpQ8OqG8yz5OUKeqRcQYVUKo/nhZshwmLEfRA7z6kU3q+Jy5xxIuxix5KsQnuxv2VJmRf7kXVZKThKqoBj7B0fzws5W7XzYhyIseMnHz5QHk7cxIDzti4KioijKGix5I4+GJHeYBrBIJztOqo+8S5OrOocowUZPAzB8/Z2pBeHOBrU2Jy9coXgaOsYcQ2+rXx3WvjDbx+Yc8EFh3NwSpmf3E/89pcBL4pq5S4EjmsGVaIIr1wkesd5LTwuK3MuvN33V77h/pJQgcuS+PIw0IfAuhaW1XilORdEhcc5cSoFcUIfA1qF113E9wNTKpzzwpIS6o1nGUPkMiecwFcvbxD9RyQASMO878aBWgqKGuHWGPjzsrLUSlbLTHbB88PDwFIq3xwnDn3HlFaWUkjVEFpZhQ/TTHSel/ueIVoE/vrhzFwyEXfd2E5hbBF6U0qUtviqWAYwlYJ4R4drzoNPMhXl/Xkmr9mwT614YG2BYVMZbRhkzsrxslL7wMtWVpe1sC6OwxA5TqmpLIy82kirVCsfUubDtPD+HLnbddyNHd45gy+wY4kYft9kM9TqrmWrtqpAs2WKqSp9DPS9Of2NEVhVOedCcMLOe37jxd6qlxD48fHCVAq5KA+p8LIq0btWWQveC68PA513/GOvD/zx/YX7abXKIRtIq60MtmzTKsDRe6ZlJlEtGAdnnEGyNaGNhBUHN7FjCJ6uRR5VuJTCy2FgzgXNGb8F08ZtBDH+qDacGKBvHuJcCrU4lgwhRE7ZiN8NGgrYc8iqBCd477jkAqqkAvfLakRirnxzf+H1vueuj9wOkd477ueEIETniWKkqKJINYhEna2NqkoMEaqQaubQd6zVsucrfl/NEXV95Kh65XUCinPKcVkp80IfPaUaQb4fO1SVi3MMLvIbr2/58f0DZVWSU07rypILlzWx7yJrzdzFyIvDgcfLSlUIVehcYNgP3B8v9tlFEOfJYI5cYZoWnAriFOdg0XpVSVXbBg0OgzH6piZrC7clS2132Zpt116q8WFD9PyFH760JI4mDqBSP8n0WzFxXc9b2Vrb/fv+OPN3vn8gt+psU9eF4IiDo7onwUd0jrdDz8d1ZWhO97JmfnqceL3reTX0nGomesfjtOKdcIgBHxwPufBV35O04MTxYV0558yr0DHVypoKN0PHbYjcDD3nVEx9VhtB3LzG3kduYk90nqoW9BvF8afaZxUAwDbnvg9MSyC3TGlVyzXP02SZdjXJWucdr8aOosI3x4mqRpAe10TSJ0loLoVeld/c7Xk3r4x9x1oMH66trFQ1ovL9tHBJhS/3llmreV+CSitFlUupZKfsnZXN2srIpE9SttRe75xJ6qQq9ZOMPngh5UpOcCyZ7uDxwUr3fMqcV0WDQUGosBsj65JZtRrw3tbzOWfK2aqD7x8Xxuj40YuR2pZ80QZrFEx6xxN0IQgpZ949zKjA3QiMPUWV+5QMx1flVReJTbL25X64BpGXQ0eeFlNJiclx51rBm8M9jIFBLFBGEX7tMHBJiVxsI25VgFQl+sDQd6wlE9UI2oJyWguHGJFcKFWvmVzfSPB9F8jVYKU5t6AvQm2y0tiCg3cGB/kGrym06s6qh6TKpRRzoF6IXkl5IZXEWiseIVeDvfYxMKWMF2/4ca1QlLIUauea1Nfgvu9OMyi83XX0faD3jo9TNulyu48VRUKAahmhEyXGiBdPLYWE8risrFXpvaM2qGeaE330SFHWVAixyT4F+hiojWdBbJ2H4Hlz94IfvbrjD/7Pv0sQKN7TiYkLvn4440QMotjgzFK5GSIfp9T2p+IkMNUFl5Vf++I198cj0uAUUTFI9TBy16qsy7qQa6EPgYmEL+bYQgvaWzbfjkgphSJQixoGHoNBfe3G5lrRWgnBU1qAka3SxWRU0jg7QT5Z85+ufEuALilfVTg4g4qdg5c3Hb92u6NzBvtahajsvKMfLEGd1sq7aeXtYWQXPKlWjrUiaiHjy/1gkF/07HCcL8Zt/mxeOJdMrorktQkQPIIStHJJC2M/8s2HI+eUyJvcICmlq+CEqJ4udORamef0Sz3qZxUAVJV5TRSt3BxGPj6eoWXOm2ZZteng7TdaKmTOdq2VfWx6YzaHzfVf0Qu/cTvy0+PMEGwTiyXHWG5pip6HaQFV3hwG8DSpGHQto1CUWZXLujI6YXCOh7Xw/fFi+vR2niqwFDWHp7TM3DWYxEr8Ddv8cF6QagoAVcPYvfhrSRoQfB9QzU2jven/HVGEOWVcdaQk/G+Xhf0YEVGWXJmL4r1luWHD+tu15Goby/B3uF8TqRa+6Ds651oyZpJBsCpJscrnpgs8rImaC4cxIgHwQnVP79lgMsU4mpdDz3eX2SSUKvghEKsw9oEpJ6ZistLeOWqunFOmd56hM75kDJ7RG2SViqmIaoOmSrUAd1kTuRRicByGjsHbPfIIHpPrmvpHSFq51JZttXW4ltzWXcV5wXnHumaG4Hi56+lj4Ov7M+rgcVkhq2H1NyPvl5Wr92zrYMqZVAPeOXbR40T4cEksuZhLUmFOq0mPu8BvvX3FuOv53//wZ6w58zCvJCqhSZlzVZZUyLXSOc95WgGh5E3pX9m/GPGaCb0nZatWKvDu/pFa4V/6C/84788nHpeMuAiijUsw7uHQG/b91asDx3llWQ0eeZwStzsPtfLj779H9RXHeeY8zcQuIhUO+z3355lpPtP7yKHvidGqU+bKssFztlGgVWiplCaTrVfMXoE1r6Zsornwdnuda+q+BqNZhSDkUiiY7NcJbAyyuYpWAWjluGROKYN76jZwIvTR8fZuR6Sps5RrwEAskVhFWJaF33x5Q8qFuRQeSraqWiFEz9AHfjbNfOEd0cO8Gmyz7yPHS8FjirvO2R/vA7HvKQLffzwxryvTnPCds14i71iWwhIL0VvA0JqZ1000//fbZxcApiXRdZ5d8LyrpvHnmjkbTnteMpehKR8QSjXZZM6F7N31IVwZ/xYI5lwYo+Orm4EPc7JytBrpImq9B2mtlFx4t87Ma+HN3cgQvalnWvapGIm3abSnUvlwmnlaRvZPKeZYNv2vYZjaYATDXotaBhScwTIi5VrSpinjnbDbW9leqxLBMmc1iMqIRMvaVooFqe34VVmKMqUMq7I0h648uahSldj760ZxwJuuuxJLW3PUViUpFUNwlLlUnHO82Hnu+ngNpPVK3AmLKkNrknIi3A0d7+cVBF7vBl7uR7wWRCt/dDao5aLKyxg45sWOUwuv+wE3mFxuyuXaFGYkeOWyrMYNqAWe6B2v+h6PYe0mBGiwWHMsuTkfUcs0U1WWYuJ4bfyPo23SPnKInjF6q3icmF4cqyB77xi951Xf8YGVqq3Pw8Fxyfy4Ttx2gTf7AfGO3RBYzuWaXChKrhBi4DItFuBU+TAtdhwxVVNVYUmZlO3cl6VcM+kK1FTp+mCYdecZd5ElFz5MiSUrPsD7xxO//uaOH335BX/lb/8JU1op3iPOUWtp2LndqyVl1nYsU4oFztOMUlBRPpzPTEsmp8IuGlz34fhAiAHrOymsayJXT3BCrw71ahJNo8ZQnmTJnwbPa4MhUGvD8UWaOG+TNQuIXhNEGny75EKM3qor3ViQxie0/XtJmQ8Xk/puXJL3wsuxxxXBRWlAr7ZqwvZyUlNwfXm7Y+8df+f+yIeS2NLS7T1/cjZF0cc1sUpmdNbMWlMmiLCq8vLNa4au5ydf/4yX+z2lFL75+MB5SdwcOm6iY1qzURYOXIBjuuBX4fXuYOouLfwy+6wCgAC+FGQVsksMwXNJCao0dYY95oclsZ6M+DnOqxGLTda3SQpRw+xqUxwklA/zwq6Vmq/7QCmVc6lX8rGsJqHbcEJtJXcUR8FgCRACthiiM+dxmhOIcNGMV8cIxLbYNkJoUx44BFctmGW0dZva5vfSmk6wEt83aWKHsM7W5OakyRS90IvDqZ3PqqDBUb2jFnMYS1MQOSdsrVxVn0jlVE1t0kXbnLdd5FVz5DSH6a6O37aQE1iL8t20Ih5i503tpEqR7Tna/a8Ck1rm2rdq4NVu4N28sBZljJGeepXlvewCS63MpfJebUMFhDkXalP7bF3FW0CcUyLlcoXyaPfu7W7gRR+hBfbgpPExW5NPw4cbOpZrNeVLgz8Ua4yrFfbRc4jeGqqaI3g99lRRvj5VVq3kovRO6KMpWX56ulz7Q1ThuGaOa8YFz01v5OGbw8C8Fu5n05WLg9O80jthSQWHSUE1mfrIOWFZC8taAFsDpTZOxDWdfjUCW0slOAvj+y6w73suqTClzH1OfPvxIx+PgTUbN3AYd5wvZwqVoP7aAX5cKjvv2Q+BD3PGOaWWVoFWyKu9dthFdp3nkitDF1mSJS9jDIwhsubEccl00ZliCZ6q79bFT3uu8NRF6xrZDk3FI9dshK0Krp9AVtACvLQqHKWU5vRVCQK5Bd1Jlb7zLHO7n87xxU3PD1/sDTptAXzrBheBXAu05rR9cPTeGeyp1k+jW4KlsOtNOh28Q7zwkArrujKVypKLLUytoJmXux1LrZzm2eBp7xDnCFHZ+8CSytVJupaUvT+febEboa2fP80+qwCwPcRSC6maM7eX9VqGZVVcMALwkjKPa8J5h2/eynnhRgJLMQlmaRladNZU82Ywkubl2PFyjPzsOPNxNs1zLVsLO/TB89XrPUMLGL33Tb1Trs48OkGcQ+fcylpzrCdV+itcJG1EhOUHQezvIkLXVCSFlo2q8vqmt3KWhpN6oRePFsP7l1rBYRmJCK4t6hsVcoZMZRFYqnJcMo2SZW4wlJPWZEYbp+Csqc2L4IPDi/U5P4k6HYiSqvL1aWKtRmIhysFH1lxYq3JMmUMXUG11kLTSvjXgbZ3YVeD1bsf3D2emeebW94g3Kd9dDPzJaTbJahFedIG5ZCrCUgpejTeZc7ammXaYTSFCc84xeA5dtAYqTEFWK+RrddJGHmCvrcXUVavqlXhL2SSmCozBsRZlCJZZnpbMVzcjUImHHX/zwwnxjiwwKtzGwLmPnFIm59bU2JKXnz5ceHsz8qqPdF7od5FUClOpvNnvKKXweFkIIvQiJO8o1Te5rAUo4zYctShprYTOpI6lWpfpboj0Hk5L5s2+b1Ww9XTsO8+hCzxezlzWgneRu3HkOE2UYkHVe8cVTCqZh1y45MLNEDlOK1mVfddTRVhSIrXxBGspdLGDpFRvSrfq4bisOKf00bOWijh35bEcYg6S1tfSAo8IVwmnawnddbCG2vpvj9dEGqq2Wtt7B+9M2lsrGa4qr9KSsh8/nnnRdfz+Fy94N62WDOXK7dBdYTap4IKn1noNVNbdbdwjqvxsMjVVnTe+xaqRsY/00eGzrcusgo/eVHWpWI/NGPj6m3f84OUds6vcP55ZcjLyeIiIt8puWS3Ia4O0ilbyWrkZeqcyG+UAACAASURBVLQob24Pv9Slfl4BoGW3G4Fz6DzUwJqtL9j0s9aBeWpKm8430m3L2mph5zw552skFrEFULUytU7J2kDBX7/d8WZX+PrhQiqVROFu1/HViz3OtzkcCofBc1oV8dap7MWUAqcl03WeXmGdV4pALtaoNARvgIlq4xi4wlGqFmgqT6TbdZaPcg0CuVhXsccxRs+tBC6lmES1KodhJK8rSsarIqnBFs6aUXIpXGHQtqkcjZjGqow1VWpRcqww0LiJlsVr5ftpNay/EeIZ6/o9pYJW+4xTrvig9F5aJtSqDoVLLbhW/l6y8u7xzFIyUwUnyg8OYwtmlqlvxOXorUqrCseUOMTAaVmfeIX2zKWpN5wIY/R0XeT7ZeF1DESkcSFKCJ8GZXMgSy22+dUqpjnVq/oExLrMo+HoHy8r95Nh9cdl5dCZdHEUS0CyKqsIvXO8Hnpu+47vLzPnuV6TmKLw3Wmm84Hf+dGvczwfGddMKMLjZcVhlcguerw4zinZ2JKqaDExwTZrKZfKMPgrjAKG3w+NNL3bdU+9MC0gC0IfHG9c5HsVlmwOuRchiyMES7Kig+gC2mDGZc28P03s+o50SdYJXoUggaIFL441KSld8M6TcrEKL/bcny/seo+IqZacc6TSlHC1chM/mfHVoMItaG3VJLRA36CeijX01VTRYuvQoW1v27q1pECIKL3z1yThmBNO7LlG7/jhzYgi10A8Jdt7fXBQK27bl80/HTaCXStrq6J30SNZmbEu9opxf53fCGeDnYY+cEoZLcplTfQSuBk6vv3wkVwKt0NvCRdK19Z1DJ4c6lWdqKrs+sDL2xuO54m0/iMCAUEbYSD2sJ1AMeTTsjgMVnDOMadM7L1tZr2uECiKtjEGTWyCF5v1k4tySpmbPhAbjg5ba/kOnG2IfR+uIOgyr+Q5o6UQ2obvnNCJZYqDCI9V6YKzuUENJkpbKetN3lYx+eYWqBxtFgvlGqRogaroE25ZzAubDNU5chUGcVSnzBVC1xO7nsfjPUEdXhWnisvV+h1Kw6Ixed7gHEtTmyAmqXNiwSiVSqrVeITWHGaqCMPyQfHtfp9rYgzWiOPcNr7DBpABTR2z5ZHCuSpLngmYasPgIuVxzfyg3QDvHC/7yDfnQqFyyoUxeI45MxVl9MoQPOc1XzFbVW2YsHLTB/ouckrFumJrpWsNakGUNQtdaHBPO6b1bsCcTNVRar2OJAEYg/UMlFx4P62kUnk1DjzOmTVZ8Cu1EoJ1gooo0uCq0OA79YGqppBSbNhXUs8/8Tu/x1//W3+b9P179iHycZ0JDkpRknOIq0RxXGoxLbs3MjCnypoy/RgYhmDVSq5IFO7GgTFa1/W+C1eS1R5E08Drk+Qxl0Kphd4Jr2/3pFTIy4xWod9Zl29aFqoa1FXmxFIrl2nh1c0OSQVXHbu+IwDn5JjWmegce+dY1T7/vBYGH3m52+HXhXvnuN2PpujRyi/MvmvO33D7rOY8zTHURuLbdWmt1NZz4wEphdi4rtISGN86ixHjVd7PK3d9dx3eV9tBVU1sIYJJbpOCt74C3+CnfQz0XrhfM/dr5pyz8TdJr7002zDApRYcjtEHsiglVy5rkzIDgmMcjbVz1cbZ9NFzyekKbW09DxoEp8LeeYMDRZCqvH15YE7/qEBAtHXaSDojtyx/LSZDscYXB+MQmdbMldLcMjs1uCI6jxbDv4MItyFy13eGv26dhmrt+b6lyC92HQ9zuioOcirMlxVV5f688NI5QhfoBVyTke6cQ4NnqcqXtyNFlJwrH04ra1GCWBPT04RIy8Y92+Q/25Ab/SX9dv32T6M+bDGXSnTCXK1BaHTCw+mRfdczhNBgo4JvC3+tigRrNtrgralxJc7pVQe/Tb2cio3WuA2evYtXyerroeMxZc4ls7QO0S54VBRpzLgXYUmVfddUNjxN2EwqPNYFrZWosBs7Lmm6Ns4ppuEWgZddx8c5sZbCVAqHEAG7T6eU6b0Rupdc6GMgfjJ9NHjXIL+t6uLK50Rncq5LMnVPVWvIul8Ta670YZuRI9dRBJvMGCxbvMeZXhxTQE1r4bvTStcFxmhzbWpVZmdBpCp457lp3EHQyilZH8CH+wf+m//xf+AHr1/hneeSM30XcLVy03nmlPn2tFJRdoMpoC5z4jStaIUQHV30iNtqFeNj7vYjsWaWJVOiJ3j/cw61NnKbaoP+LmR6Fyi1cJkXxs5zNw485oKkzF4SF+dgN7CTAJrpFmGtsCZrXjr0Hed1ZV0Sg+/oY2RN1ux3mVfw1tW/5MTDZFXej/Yjr9++4TIvHJdESRNXwUHrv5mLopRrr4aINbhtDV5bJW3VJmzuQrE+gY2vcZ/ASGtTGN1Eb5Chbk67JTzOIOi8ZjrvLfAoFC90XQAnTLm22V4FrZUlWQAu3qSt6oSlWBd+KRb4I9ast7TkxYmwC5Ga4bvHkzWISSFLYKM5ttlYl5SvieU2u8uJ4wdfvCRG5Y++ffdL/elnFQCSKt+lRC/b1ETLoGs2IlGwh7MspntVxZx1y8araCNfKy/6Hmlt1WMM3PYRrVZh0DaCYeKWvc6l8nFa+Xhc+Opuz2EI3K8L4sTgkVr4cJr44Ys9IXhrNmmwxY339M4WeVLDDXd9YF5tzs8m0xIwGMI5hhhsVIM4Tmv+RBYneHnqfka4jnEQ2mwUUTKFoI4oNiMHtZk30uAXUUWdhRWnwiAeLy3gVGWvjlprG5es1/P7sCQ+ziu/e+fonDmPvskXTzmzNddoNVjKsn9Txcxr5uNFuBtj26yVpTimspC0EjD4yKHcDR0P8woKH+fEF7sBccJdF3i76/nJaWItlXMuLVBUTkXpnCN6jy9WDtvIBk8qNvQrbVmTwKllg4e2Rno8iHIqylIKx5SvVUoptRHwjte7vg0S3JqLLNOP0RGis4FhapNX51J43VtnupMnyLBWZd66wcWatF52FsxyqrzZ9Tw8HLmNwpsx8OGckFoYQqDWyjcPF1OKBcetcxznxGlKOGekTehMsmUTbpuTa5WKp167gRshs6EoVvRW5f2cmNbCEJ/WQS6JooWz70iYo63es98NuFyp6kglATaao/eeIpBK5jwtuAougjjPfvTMquYkm8PGG0F701llkV0PLjNPR5wY5FK3NUqr3tvZbxCXbhfRLs2ECaYMMkipZcyNWHYt0dtknO/nhV2bjqtsZLO0Kt0EFXPbj9voZVUbA3OIxrl1Xvhq7LjUyilV3q0Ti1a6EJgbT0i7jtr271IrdSmkbGNbDuPIy9CBwyDRlBgPERoEdh0XUYyL2qrdSW30g9TKT99/zw9fv24790+3zyoAVFUeS27O2TTsL4eBh5ybLNEI4KBCwOSfhv9ufXJcNeFjF/hiiNeuSedouOOT8kaBc7Ks9uO08u5xYvCRF2PH/XnmNK8Gm3SROWd8cFzWTGhCIWmYICL0KHfieSyVVOFu1zHEAqXyoo8mgQveZtM4R98WoWDdhNoeeN8FvltW5pQQFSqbJroFEC8M3nPbdexj5LSsfHuccV54TMU6D0WQ4BG1oWi0zaFiEFqVpyxK1EgtK4ocqyiXVPiT08SPDiOjC9gGcQ3uUPoGawVnBF7N1ogmIjzOK945+k5YyC141ha8GizmhKEPzCnbaIslcTd09M6ysjd95NvLwloKDykzNpLYsj0bd1CaDHDOVon1XSSLTZxUtVlGBXjMmSiRMRgU1IsgEjimwlKadryanPW2symru/hJa6WYqsaLcLvrKSmzJCOCQ3Ac+og2tZD3DaOtFmDOS2aMjujgZozMqfKDYeBBVqalcHcYOM4rvlZeDBHvOpZscsjq2pC93rZwauPBRSBGj4/OhAMVSlZCNM4lYM780EdrblRb+yq6CWcAm0675sLgn7qouxjbkL5M33Uoyod5pSuFN7cHfvZw5nGZ6YO3CbVWclJqYdd5pqU2fiwzxKEFZUhZG3qjECprLjx2PR9++jPOc6IPMAbL0t0G7bFxYnoNXtLgLJODN/dfbTZT74SiRl6bqsgma9rK808wq8Lt0EH7yVY+ibTGMQfeWyCprcRYS+HlvmsEuVUKInATAne949XQcZ9sUmheFqq0pKpV/hmFAmk1NVsQbzJQKh9PEx7li8PAuWZTO8rWxGnr6FP/3vo5eTt2vO0D/Xqmv96hv98+uwDgxZGrlfrVW5m+RVKwWd/OmcytbpLNalHae2ewgHsiXmrVJhWRhrFjGHnTZ8+58Lhkvv048cXtyJtdx/f3J7JWVE19krTaRESUj8vCbVMGGf7chlEBh+AZXOA+F95dFta18LtvbnHbl4ngntQNW7auyhA2tbHixLGglJLpJHDTB267iFPhlBM3XTS8XpVTLaQIl84Wpu881XuT6eVK9J7Tul5ln02QYA13GBYVxOb/1HYVodgm+Lgm7lLHGC1r/WocQOHbebZR3BinUTEljWuEXXDGG5zWBfXCdagTLZtum1DEguS3p5kpF45LamoQaeOVhbW9ffTenDUGC9xEx4hn3fR53nHO+Qr3IFxLZRW41MrQoConwugdb1rXsS9q2OvY8wQsGKQgLftHLXj1TnishaTWMTxKoO+tMkql4JzHoTzMC5e1Np4JfrAfjQz3hRexfSeEs7EUf/zhkbu+47vjbB2xXWDKhS/udjhgXjIPl5V5KXhv1aMPrS/GKCXrXO4COx+byswxXCEprnyJE1AnPE6JXG1dp2TD3kQ9ffDUvPLyZs/LccT1PQ8f3vOQCl9/PNJ5z4vYcy4rS87s+47pWo2bms05h/c21yqXyj4GG8csVnUfc+YoGe5t5v0YAh0BJ2HDf5++mGlbr9cdZs9jq96tCmiZsjZ+YOMD0auj3vZZ1sqrviN6U8xtElPFtaGRsOujyag1G6yjm7y4TfWsyooSWnuxw6C04/HCz+5PBlE54xydb2NVnHBwjqNUfBfogoOSCT5CsYp1UePXdsFzEyMfc6a00e6tiGNjRVTgw7Ly1W4gAr99t/ulPvWzCgCCqSuis0FR0uCF37rZo1hr/Vzspiy1mE8XG7Hg1UpILZUpKVOXyU1z7J1clSIA2aS8rK28+nBaeLnveXszMC/JdMrtoatCEXgxdLbRs0EHm1Lh1sdN8WgZtZic8dt14svDYEqg9lVg16Wpm3Tx6drdVqorvO477nyg89Y9mUulamXvPVqMwPy2JL7PNpqWaLBPUTjVRAiOMQYO4jitqw2Bgyvp/eTmbA7P6Bx9F5hyRduYWcW4lE1uisAXY88xp6siq6iSc2FOhUPXcRg7gw20kBMk3eSPdiwHBBWiCjvviH3k/WUhF+W788xN25yK8uXQ8eOzSQWXotzEwDmbSmPfeWLwpDVb1dKe19ZPoMCqal+2ok9VYZStK1nZe+FVDPSjB2/DAU3x2vDk9r7NEW0QnDqbjS8oMTpTGRE4tS+ocWIz5UVssOBv3Y4EZ4O373qToxZn0yLP00oXPOecOa+ZGB2os87PdtxUKufZpM4hOFznrg2JXoTee7IzjuM2eKSaQuTaOKVbA5RdwSUVvrvMNs/fmxpnqYU+CPves2q0MRdcuJzO5JTovOeYkqnLgkeTR51nWtZrp6xv11ul4sWRVNiNA8fT0TqYpT41LFWDjWpVOhFSFuIQnyR7G9j5KW7VgsLGi9lkEnvgVZTHXNk72uC2pyzdEC9zmu/nhS/GkVzyJ0Pn7E2qytB5xja182Fa+HCcWVPmMES64NvgQiPxq7jW51H5uGb+8MOjHVOVIJ5DMAXbslZeDh1vxo6bYGO1p5JxOMQrLw47PjyeeLUfebicjTuJkTd9x9k7vpsmWr/4Uy2kFjD+xv2Rf+rlDS9c90t96mcVAJwTm+/RGk20Wov9611HzkY+FjWFztrbvP3TkrifVqJ3/OhuT2xKidAGjKVSCd4anUp9IsHmnHl3WrifVjrn+Op2IOXCZVqvsrqWTPHlbmDwnn1nJfJ383LNTG+7zkrSRqQ6sXESXuwbpzY8c9MqN43hdfF+2uiyEdq+tvkjW5Nay4y2DVK1daxumX2qbN83aXhnYXGFvbdZ/o/z2rpeG+Ha9phic8bfLRWfE8F7XvSR85pYtfLtNFu2PPY2ZkGVN0PPd8tqJWqppKKMsaPvHIi1+QdM0ZNy5tCmpxrUZBWegy0KmqMDltZVuznd2y5wSIFzymTgNnjWasT2JdsgwKS2GTtvX8gCRghun52bWmLRyvcp83bor/5FEJPQauHH707s+sCLg3UOb+fnpTUgtsDsnY1U6ELHQ1n5cFlasiBXqbLztLn/gkR73rV99ZZvUNY5FZsbVW3A3TQlYnRUZ8HxnAtLslHk58mcv3PGX/VtWGHKhZu+427o+ZhW9rFj3xuJvDk0yx4/SacbbxXEupgF62WpYsTld5cLb/e3DM6Wk8+JXR84LYUQTFoa8fQxglNaszOofSmOijXmRed4OM9UHTinjPPbSHezNVvD5X6IVwXZOWXu+nj19ZvHF7h+idGn++XaXY/QOxM9ZITY9u72jLcsa8rZpq9Srw1i28+N+IddF2wUS1u/fTByfkWv3eUblJqrIRE/Oc98/Xj5ZA/bG5ZkzZFDNIHAVOwLqs7ziheDlWupzOvKTd+R12xd7zmzHM/8xt2BH+5H5pL50Pbb030xu5TK33o483svnmS0v2ifVQAASMuWJbQZ3FhTxnqVUFoZFr3jLngOfeBFb9nrvK4U57gb+yum3QVtsjD7coWHOfFxWq9qFgG+uh04XRamVFkx6aXDOo1fjR23Y2fdtCK8OYw8LGtTGyjnnDlEf3Xo26C3Lw8jS7F5LRvk41o2BrCRUtIwzU+C+zVjQW2zrNk6B2u1byybUmEIwlqFx7VQUr2eX1GTEe56x5Iqt2MkiWuD0rSNmbYpnxWhOFhEbOa+V0LoeDv0/HiaWUrlj44XbrvQNpwRzdrmB0Xnud0Hc07tda1qmb8IL4ONunYilFybM92UGtbo18XAOie8CGu2cchgn/e6N/lk9I6u83SlsFYbobYUvcJCNRVuetdANru3VolAROnE8fc+Tpzjyu+9vbt+JaNg32Z2nFcuKRG9japQ2fogtuDdpL3VYMEpZeY2rbY2RxSjfSVnSoW1FA5D4HEt/I13J3JV3u573ux6ZIjkS2bvDDbZZLSmIhE+LCv7EAldx8fLhPtkLsMXNwPeeY5rYoyefd+xqiJLobrK6m1uvKg1HF6/xrSpYSrCqoJ3HS+7NsdIQb2zRjsnUDMF+36BORfmNmhuHAZWyWip3B4Gvrk/Emq1xIpWTbfg8/F84dCP/F/cvUmsrVmW3/Vbu/m6c2733ot4ERlZ2VWTEp0QwggGIMBiAEJ44oGRkGgGBkvUFMEABowYYslClmWZEUIgZGBAMwQxQAWSwciiZLuqso+I1997T/M1u2Ow9vedm1kRypLMgOBkhiJuc84952v2Xuu//o0TuLsaeH88UYz2nSnrYNvXTa0AsSRCUsLB2k1vX6wQSC2OBLtt4rYqVowYnBVOKZOMYbC6qKdSNsbN/RK461p8JZWsflBS/bFudy3Wmeo0W2gbRxMij8eMKQuf7lstTrMelwIcY+HHHw5Mqc4gC5BhztoNOiPcdNpRfHF/pqTMvmv5jWFHc/Mxf/D6R+SoRpfiHDfDnofpzOAse++QUvj+fo+VMx+mhSQrv65sne0hBP6vd/dfu55+ozaAEBLnxxlE8E5dGicD743FGzbPE9CbW0MaoGscKszTG/Y8L0iGfuggFd7GhZKLDnrPM6CtXGstn+0HFYCERBA2/n5B+I3aURgxNXxZWEJi33hCjDX0ugasl4uFMqK44NvTxL62lOqsCRtJUnS4uA6m1GvtgnXGVDjNi8rCxdA6Q9crNXPX6Ubjl8BxTFiEK++46RqOS2SJmavWa1trDftne37x/kiqjJpQ2DYM6mbniuBR1sWVd9wlx31Q+4kvzjPf2Q8Iyoa4bVrOS6IdtOU3oj5MayCJRd9frqerIkHb8TnHxKn+s2KmpRR+9njmN5/tNxO6G+8oooPm69YpgyQVppR4e5xVc1DKli625hqErIJAZ9S061nT8IPnex7nUM+vLvxTThzHyGoH/sXDyL6mcF2yoxOfH6caBVl43nd4a3Emqyra8kvDVUTdbDuvsX4xKM799hwo3lPOE+KreMtqR+GqqrcTw3EMREm0naNtHdMUQaDzDhHdhLzVOUAW4fE44aX6yK8HGsWg16H5yoGfQ+A8JbzveHbd4s4nvjydGYNameRcOKZAk1V9vqRA4yy3+55YDOfzCdf1hHHiNE9IUf5/qvOXzqnSd2gd1iaGtmHJkbtdz/2oqWS6OessA1F2TG8tfVNZV/UOKevcDh3+XrrglbygVfxYHVynOquzyWJaz3WrtJFQtT9TjDjbadFX7aoVJjK0lVJb0K4xZ632R4Rj0kzqHx8mXnaZm0ojDwV+fH9gror0Ugs5I6rvcUb49KqncxrmPrSWKQqnJfLY9+yWM1KEu8Yyh8jbceLG77HO8NG+r2xFLWS+v99x13h+cjyz5HSBcOtauOT14vvjj2/UBgBUAzdBrLBkDcD+2f2Rl/ue687UrmBd/Curh4utwRqkgRRCSjwugbfnibkqPHP14aHoIvB2WnClesOsA6QCN536tazKRJ01F66MoR86PsxaMQ7WaatdlIYq6I3fOB0snUPUQWo9Y9v/alW3PSrWLmjARwS8dwyd2Xz+4xN6nDOirphZP1fKheMcuB1aWiu8Ok7Vpz0xtJ6+dTxGpaHdDi3ihHNJIGbzFnGiAiJK4cpZHmIkC2qvnZXl834MLEtUT5WS1XclV6FO7ajWc6hdgx6Dt9O8Ma+mmLaN1lvDzlumOZDRcPPnQ18ZVobbznAzqH5j7z1TzLwbJ+6tDh/Xod+SC03RVt8U8LYa5KXCISU+ve4xZ8shJlpj+dnhzJxUkt+2pjKzPI9z5FnvERFOIfJmnJlS2vDkx3nhputoDCx5W5OYQyKlQt9bGiPqk1MUj2+tpUdJCdZb3j0e6fY9g3e8esxcWcfidPFonSWi6m9njbqRVnuHJdUFqhRMRllWZIZhR4gRZ3XWJTV+0BpTDfpqhrUR9r1nyYUlLryfpy0AXaDGHk44Eb7Vdqo8RViWQLGWnfdc7zpePT5gqs23s8Icoa+q+dZbjNdr/Lio/bE1mk98nJcKPdaNyWiG8kdDq3bQtYVbO8y1g8lFN/VDCMy5UqQrNKobXalW4VI7wRXu1O7q3bwwOIctsES9bowxas9QdFhrRNlKeaWPGsO3rga+OJx4f575+WFkDJG/z12zbz2///6R0xy2809dg6zRbmTXOF40TuHmgs4RYuE8BV49vOLjsmPvHMc58hgiU86EwyNN5+qy8GTuUQrX3vG9fc9PT6MGydRfKQXFHL/m8Y3aAMTowr+qMK0V4pIoNb1Hw9oLRlSGra57l3DvHPTE5yK01tCJslT0xLJ1DyuMVERNuu6c3XDG3hpeDj077ze8UahCrKyS85Izzztf7Rhy9Sth82kHPWmNt/z0/kznPLe7BkkZiqmtp+hmoaR6vFNZeikZ5zSMexWvxJw5VSMvZ4XeWT7MiTfnWVkKqIJ5rC5XYuB+WtQbps4Mng0d37keKDFpGhbC7x+OnGvLb4qhMXrbWKM2DDsbOebIMUXeTYGhaXk4TnVxERhBBuWtlycD45ihlMzjHPhiSUzVy/9XGQ3eqKq5txaawhKiYsE503oLkjXhSoR3R+3iUtJNp3eGU0ysAqeQEsdZLQxqJ06p8M8UE4/hsum8PY0cQtig8dYadtbiq/Dn88NCEnUx3YsjiYrkrOj1lXLhsGgEpLFaqeVcXVXR7IO1o6PoRnaz6/npw0HpqKIDyeJ1qHucIt47tTNvPb0TznU+8Pxmx7QExiqCc0a2zldEWVeN97y7v2c0lt5Znu2aCqWVTekcQKGdUnCSeZxmhTJW9LEqbbVChjHpnRJzorOOJUbaxvOjN+/YtToUFbQzOU9qDGjR+3etUPVlVRhZUBpxrou3FcPHXcPeWbWhrg6vKevAel30P8wLU1Kztrl6IeWiepBVvDd4i7XCdevpvWGey5Y5seTMw7TwW7dXusYIlRUEc8q0ooFIpeixF9nco3FG+O3nN/zv42tyVqrzcQ58Pk78/HG8zCaKPscYw77x3LaWW+exRT2ETFHH4JRKPWdKV7a5EDJMWxehQ/8/vD/wcuj5eOhqJ6Bo2E3j+Z4If/fxuEHFFmis/9o19Ru1AVgRrvqGnNSvxxbhYcl4p0rgJAaMYuKpFIqoQVYsTzi9qO9JyBlTIjHVAAlWn6GLBmCt6sdS6Cqu+Nl+YGiUpUFRM6m0dgZQ6SzVN7yxxDW5p75mqm14zIW5ZIIpvFtmPnmxV0VjXil5slHcYrUsyNVXJIWgvHtr+PzDiVNdGMVoClLvrdInQ+K7d3venWamkFToknJlKtRuoSpSxxgxFm6dhSI0Rvh06Pk7j486I6nQlR4X3WQ+aqqXDMI5Fs7TaaOAFtQ91VjDQlLaX1bhVqrDaGqrDmyWEuui3Fi1pWhqO64SrOrzImigjjEsQX2PUp1ZmFLYe8uzvuV8OG/zhLWCNUXtKARlOAkaGfqLxxNZlFV2nIMO7IHWqJq7s4bBa+xowSkMlWsuAEoDtRh2ruW272gKvD6PFdYoNI3dZhOlZAanwd9BMpISZoq01vH2NOKdKrFLSnhrCFaLAu9MdUhVUVsETtPM2uGXuqBfChOh61vm8UxjLNXvj9OcWEzNMqhwVn7iBZRLYXCW71zteAiB16eJDXqvf2vJaqFpawSns0bzL7xFjHC363HGMPQ947Jo7rKVOj/hQligMMXMkiM77yFnOmt40Xc6i8qFKSQOUSnZ87aoaaa3+kLpubRyIVUU1LBx8IahcQze0ljD4AxfhsDjEumM5UeHA9fe40TZQQFVnQAAIABJREFUdBRlECV0IXb1Xg4xMYIWHkV1MUWE277l5X7gy8NJO+oC45w2TctWpiObQvzWO3wtRI0U3o262TZFTSpbbzGmqLV4god6vZrqYxRz4YvTCMDLXVdtzLXcb6zh5a7jsESmkGhEaN3f4wYgIrfAXwX+gfpp/o1Syv/65Of/NPDfAj+q3/rrpZT/UER+CPwXT17qB8B/UEr5j+vzfhf4t1Hj0/+ulPLv/Lr34kS91593XjnGhYrPqchIxzeKW1tj0NpgJX5o6xvqIluihmCvVC/9MCsWvX025goBfTr07JqGdeCUnnj3SFW9SsWdcwHJhabxjHPcWDo5Z5aU+Pk4cb+oWCij4q9VcFSgOjdWimdKnGMmpYSrAqCSEq0zPIRAyJlzztiiN/S5mpYB3PVqvTvHWLmKBcRgq0Okc4Yg8CEF7qdI6js+dQ0CfNQ1/PzklCEkl+F03ae4co4ilvMUkJCqu+aThaIUlilwkswYFEt/aminAzqhN1rlr6pMKypaMobN3npdaFvvwKqf+goBrIynkBM7UYrkrXO4q4GfHMeKG+sfjVlT2NauzdZu5eO+4+/cHy7RnFRIz7dce6XctkazA+J5ZkkGZ+EUEkvF3QfrscayhKisEnQAqvRNpc2WosPJKRSu2gYnht3Q8frhSGNNXfzXwbLCFWpBoJvjlDP7oWWQQhgL99PyRMR0uY4FNeSzou+5ay1z1s1yiolshBTQjIcKB+36hhjTL8Fzg7M861oe5mUjJbTG8tHgWLx23JL1/XmjFgSnEGgMgLJmnHdqkV4N8VYYNpTCaYlUDRtLzrzcdZyXwJt5ZsmJnPX7obYist2fwk2rWo+5GgK2VjaL6VRKdTa1OGOUGZUyjzlrHGwWpqIb+IuacqfdpxZeBkUQ8lrOiBZKgh6zUqovmdVZ4MO08HLo2FnLMHTEknk3LVVnpGuEEUODkkc0V0HzODLQGBXc7buGOSXO80JxlqvrHa+mWZGFWjSt1f3rcSSVzKf7AVdh6HHRLv+q8TSitPDn3d97B/AXgf+xlPJnRaQBvkpZ8L+UUv7Fp98opfxt4B/W4yoW+AXwX9ev/xngzwD/UCllFpGPf92bKKWwxKixgFa4aT271vEwRfaNJkGNMfEQg1bTVcl5Ufbqv7VSrFStyhCIq6wbALksPOsgpRSGxtdFOW1qZESZAoptryEUFZvkQgfLWTUFY4j8fJw5ZVXwhWo8FmLGSsHYqiARSDmqW6dYjKwe9ZcFIiHsuoaSE6lA5wxXrefDuOjNaIxWAd7Qto4gqpQu6wUJlNV4rX7UL5eJaYn8YOixRfitqx1/9/EIgLcWnjDKzginZVFHxKfjCrjcULlsXYbUxXYV9A3G0FqrtND6HiiyrWOr5xEI33txq8HXUiirDkNEVZRF22iX1RzQiLJc9s5uzzdPupdYdKBa0K7yW7ue1gi3TcP7ZammbXp6Tynyom9wFbZxVvho3/GYMm+miTFr8FArGjq/2oYc5rDh21I1Hiv8cx4DvrHcXt8R5ollCTRWDQzXQHTdPzPTkogpsyRhaN3mQ2UofDS0OCvcj8tWiFQejP6O1aKiauE0h7pmYdtSq++g12HfGM4hEFJh17iNMSZFF5PWGt5NGo5y4x03bYN0WmycpshSDfI7p4Ix0O74w8ODCjKdnlvtujQz+bEu/utdl4t26413vDmP1QdL70OLbPeV1hCFXeu4FuFxjjxMAWt0wfdO39ccE4dZPbfWv7IWhleNwyXho66rhVmpszS9gDVudOGqbyjVPmJJGRMN3ikUm2LU12o9z3cdLxpNEEeE7+8H7hrP56eZc4raDRnL/RQYo2463gnv52nTc+Si983jvNBaz1wX85dXN7w7nInErbuGwpLh1WniYVz47GrAiPA2LpXqfnF47d3XhwL/2g1ARK6Bfwr41wBKKQvw9fZyX//408AfllJ+Ur/+C8B/VEqZ6+u+/nUvsJ5AsvD+PGF2HbfO8azzeAOUTGeFYjzTtkhfIABr1N2ylFzFMmrnG50jzkHxdi7NwFp9gS7mY4h0tWJasWqzVa7r8UJv4LJCvNop5KyU0F+Mc8WmL4ucszUDOGRyWnDOYqpOYQ3F1hdWwzJjddiNwE3rmaJwk9WX3lvFTpt9hxPtEN7mQG54MlRexVt1oZCVM60SeueqgChnGjF85FtNgwKMWGLSuLwxJlJQU6tVWa3MEtauF4AWITUrHmuYx6wmedSOoi7Ov1zh1X/XSvTxPNG1dlMLr1WQ8vrzJs5S3xd9bQd8NPS8Gyd9tfq+CrpZNxi+PbTYutn/8O6KL8eZHz0ctdoVYUyZN9PMp3234b8Ieh1UyxGvlx7HaeG67bhtW8Ki2opjjBss440Kw4LN/ODFMwqZvu94exyJcWIJiTlnbBCaxjKOsdoLXK6vcwi8P5z5aNfyfN/xvG/prOVhXAhFB/5rkNAS1UqjlMy19zRWYRArhu/f7Hh9mvhyzOy7hoTGa1KgyaYOcfWYmQKdtXw29MRc2DlLytA43eCa1nG8PzHGRI+SI8Ylcp4jzjqe3+w4TWdlPqFZFI9L2KJbjYia9nnLmCLXTcNV0/A4L9WiYr1SLlDqOSbentS3B1GRZ8yJU0jMow7DkVI7yXoPl8tA/qO+56bTHIWYUm2MBYMhSWFc4pYznCSTs3YX5yWwt0oHpajQ8RwTL7sWYqo51nqybhrPzll+dl67GYW75qTH+jdf3LBvOt49PKj2IWesFa469Zp6f5o4xcxt32JKXc/s5e7QNUbfwx+9P6gZpHfsrjwW4eFRUwgb9/U6gK//yeXxA+AN8J+KyP8hIn9VRHZf8Xv/hIj8TRH5H0Tk7/+Kn/854D9/8vXvAP+kiPyeiPzPIvKnft0buWCQemKnmDc8d3PrE9Gh0lr5c/GaMeUicpIi2CJQvWNWmwGzXvSmLpArpgp8cR75MAfWrF1j7VYp6mbBxrhZRWUpaZDMFCO/OE+cUtoghlJpby1CCHHrTuJq8GRq5Vpq8y0VYoIKOqHGZNZw2zgalNe+85pSJgKDVd94U6EjqZivQegQnhnPp7bl27bjM9vxbdvwzDY17N4wZjWaasXgEOZUuJ8CD6dJ3VTrMfpVppnZYJbCPAX2ree280pfPYdfGfhehm/a4q4VW17LPfLaSbEOcPXfS4zcTzOnEPHW4p3DOVvFfaYmfzVbrGfJBSuWJep5kV/ZDD/qW64bv0FV1gjHlPiwaM2TitJUH+eoXvPh4mPUGMPtvgcryik3avVbyiVcXkTYd+olFVPkMI0cpzPHEDlXBXnTWELIGCe0gyPlQtdbfOMwqK1H32gmr0W4ahwfD61yw1cIyAjWWGKFUULtQEvRLuT9cSJZy2e3Oz7a94SQKEkX2HOom/s6C6tUXLgYkaWUCaEuatWraomJD+eJD2eldD6/vuE7L1+o0lkucxh1ytTja0ToG00UW7n/xxAY2obO2UpR1XtytXAxteObYuL1aeJ+XBhDYIya4RtJ2gXWzk/Q4+9FO811VkK92laL+fU7BmHfeLwI46wbgYYCqVYmp3XYrF5TU0iUakkTkvodlQqBWrkUCFJgMJaQMg/zwo/fPTLWjXA1TPxor8PdK+8YGkcphfMc6Vv/pKjUFaAzWvCltQP2lsYK0ykwnjVMfjd48tc3AH8iCMgB/wjwu6WU3xORvwj8u8C//+R3/gbw3VLKUUT+BeC/AX57O6QKG/1LwL/3K697B/zjwJ8C/ksR+UHZQOLtuX8e+PMATduxrhgiGnQ+50xvBKkBD1Z0cDMmdJAmeiKmnHGiA0X35ELWv1GqQEahirVyLXVTWQ/6OWZejTO7xj1pq3RXWZkNuX5LB7+Z86Ky8h+fJk5VpFR/hcE7vrMb2DnL8bzQVQMv7So07Wv1aVmr15TKRnHNRm2XW2MwJiu2b6RCCCpsEwqDCGOtplsMN9bRieBQkYuV1Q63KoUL/PQ48macmIKqau8ao5GBJRHn+KRGX22pn4DPWzekN2DnDZ1YAokPx5muUZvi1QZia7lWMKBc4OyMqnnPMbLft4Qc1QU0KMSyxEQshWiLeiLVykg3U7US+I2rHa9HzQQ+L5HTtNBYA97wfgk8q0I2KtQweMuH+fL+M4XHmNg5nfkclsg5JvaNY3GGOSZ653hxsycjvL5/VAJBvgSPT0m7qVx0zvPhfCIUVQefqvFa06oqdB1srmy3pnG01mOTsLce17YaMFKPnSnKempdy5wbzpUUkJK6yYoo/AN6XS4x82A02+B2t2NeAiXqppsNRCk8LAtODHuvsGcqukiZCoWdU8ImHeq+O6o+wsdEqVi7fv7Iw2nidtfwrWef8Lc//5JzSoQauyX12lu9rtbCzNSu7uW+4/1x1AW23qed0QV6VbunrAKzq04tV5baBSkMqOpjZejlSmeWX7rOVnuGjc5d8X0y9I3jcVKVrW9M5dMX5pjYtQ1WDD+7P7H3jmYdxJRLoy0ivBon7uegg3VrmVKqIkH4MM88THOllDfVhtrXY6Fxs+Oc2e099/OEd6hfF0qKaFy1FK+W7o0x7LzTmVg1wuxax4Vz9ccff5IN4OfAz0spv1e//q/QDWB7lFIen/z3fy8i/4mIvCilrEbU/zzwN0opr37ldf96XfD/NxHJwAu023j62n8F+CsA++sb7fhRvC6mzM8PZ3747BpJupvnAkPdFZfVW0QuUIOIUgtDqYh/1kHgi6HjHDNjDIRaCcmKq2qjAGgwyGGJdJXmVlgzei/vOcTEaVRqnuscjyVDa5BZ1KyuwEddy2e7XsNE1m4hK4WVorTJmC/v2dUhYMkaUL5eZSWrWChCxfbX1VQna49TRHLhW31T2TzaObh6Mzylsm67DMqkeTcZ9e3JhSkWvnM78PP3R0SKKilrl7JuA4rNKJRU5AIfgCGFxClHUir4Zm08K2ulbuqlKIa/3pyahVyIZPZVK/H2PNU4zxUUKNqdWCFkteItddM+x7j997dvdjTG8Idv7znPVHaYDpPfzYGPe6tD7ALf3Q8sNftgBR/OKfHlOHNnPCZDDpnohLZpeLZ3mJQZzzPnZVFGWm3VT0vYNrJj1LBvkzOnJXG9N5zHmXFaanyhbvoZrZTjnLDW8OndFSKGaRwZdh3W2QoBynadmnpeWytY8ZwX7ZVWoXDIpYobDdbYOpsw/OTdCZsviVE2QZLKoxeYUsKX2pdJNdGrG3QqcBg1D2OuTLrWaOhQFkGccLsbcCbzi9dvtWPO2sEAtN5Uczr9ep0jqf1DxIujcZaU8hbZ2Jg1pKdwPAatsp3hMYetsPCidhawCkLZroNSLhbnUumlqWiKmjF6zgxCNkVdhY2ytVzRjULfR+QMNM7xk9cHnIFvXQ/c9s0Gh6735zlUXQaqK1oDYigq5hycY6hC0I3SKXqsd50u5r3zhJAIErd7TRXWq+ZBdS1WhKE6kMacGXPGAY/jxXDxVx+/dgMopXwpIj8TkR/Woe6fBv7vp78jIp8Ar0opRUT+MRRaevfkV/5lfhn+Ae0S/lngfxKR3wEa4OuTC1gzdqs3dxUVpVS4nwO3XsU6JelNdGsNb6Zccz61PV8ZHqWsYSBsbfTjecY6x3Xf8v40bVWoBU3AWgeSAscQuS2t2hvoQdIFKxfmJTDHpOZhAiWpkGQdMu0bw/PG86Jtf0mGrxxr6FrLamK6QkrWikZY5nVxvCzapTI7KGwDwnXhRNQPf5m1Kkslq/q0KNXyplUrZ+GymJZaL/Ri+P7dFX/49sC1b/h0r7jkhkDK5bysThVmu5lXXEer4FTJ5tOUKKEg/S+NCFCFZalsj0sCk/6/EAtYa3Gix9hu7BgYnOfjoeeq8fU5wpgS98vCm3FSR9JU+B1r+LhvNMC91RBtQcWEp5gYfOLaexDdZL+3H4CR+xD0WhNdwMUKe6tU24Rwt99hjXBN4kePZzXdQ7brwRSluIHa/sais4C+c7TOchQd1oqFprEqQKpewyLQOUeaAsUYbrqGF/sOK3AMaRscahHAFtU5RaUa+8Zs3ZnUmzLVxa9kuN4P+HnmNI742tHOIWJzLR9MxdJz2ZTubu3Mit4XTpQcoDx3ZcyJCEHg7XSmSCbNgQY2okLKBe80t/jJ+q950xvmD1OKYLT4SbHUfGxlzZymuMWNlgxhyVhv8EUIqDvwWvuu3v1iLhtlKgrlXK5D2e6EUuNWvXU821kep4UlZLzTjaXUjuLVYaRUJs+r44j3hru22Tq3XOBF1yKopfs5JdYQF6lFpRXqAm7Un4q6+aKD6KHzRDJ31wPHd4GIpuApQl2210H095fKxEMgpEQUuQTGf8XjT8oC+l3gP6tQzh8B/7qI/FsApZS/DPxZ4C+ISARG4M+tUI6IDMA/B/ybv/Kafw34ayLyt9Ch8r/6q/DPrz6sNfzOR1c6ADrMm8JvSYls1SxrjYKjQNe1HMdzhUMuzJRcWzUvClSfpsCHU8C7xKftbpsddKIURYMQS2YRIRvhlNTb+3nb1GCJrFhhVFdOt3rjF53uYwRXFKf/tGsVizRKodNHdf4shZyFtlG73JQU+yez+ejob1fcOl0uYmUalU0IE1PhsCTNEgA6LzU/QF8jVcuJLfClmqWtlNglqUDnt25Vn9B7U7HTCptBrd612q9N/IZPloJ2UgJjSZQQlTYrdXBoNAULIxxSYKqqWz1XWtWsL7hWohT1/blrGp71nc41qJth3TH/6PHA22muTB79vEUKr04jz1vPzlve199XbFc/yxenif5aqXiZjClw6y3vF6VZrrGdx6T8cURFV2/vDzgnPEj1wE8qnlqini8vBotabUtRynIMNWFMzIVyKbCEjPFqmxGrf1NBQ4TysmCbXhfQlLlpLA+zUiTjk05WdQyRbBUSbFtHmHVRSEUpoD3C7dDy9sMDN/sO6vMaa5U6nTO2qHBJrCHkXBcepwVUbWkChSXmaoVtaRrLHHWQnbJqHKYYuR06PhzPip0nZQI1lamynuM1iGnFuaV2G733OGMp50mhzwptAFgvNQpT8J0aOq7QWckQyH/ses1VRJZSJtqah23MFukoUjVv9T7w1nLVNhxnZUA1zrBExflf3Z/qvFAXXysroUE32IyiEX7o+Fk6k2Jkdc51Wegay1XnGJyuL9bofEPhLi2e5hKZl0znNWQ+h4T1qgkCKiPpAmkdY6gdkH6mOeVLDOtXPP5EG0Ap5f8E/tFf+fZffvLzvwT8pa957hl4/hXfX4B/5U/y958+vDV83Dqetep7LwIPodCiWOybc8Ab4X4OhKLWwKnCEivmmIo6cRrgYYo8ngMZVYnGJXFjLAuKoS9FlQXU7sNXqtj9NDPUljYEneCD4o6IshTezQvH6kGzazzf2fdbnf1UvKPHQ7HmkDKdeJIUAutI1Gr2cbkAPKwbQtGg9VPUNDT1SREel4i1nsd5wgDXfbNV+U73lCevV6GbWk7EDIeQuel8VZbCsijffQ7qmmgNm2iNsmKnZUtawmhVO8Woojurnj1D4/l43/PZVcd5UYbMq9HwxXHSrqyCqAJ4YwglM1jDlXcsMfHbtzeK36PHa8mZwxK0cxF4WAKzcgu3oysGHpeFwxLYNR7vLMcl1nASnX9MMfGTxxPfv9lh0GpxcI7nbcP9opF7q1PsKWXuWoXUPowzpzHSNx4vlq71hFGZO+KEVCEUU73oe2OYLHjn8CmSgnr5NF6D0tUYTyvbxllEDN47MGgWdbkoqp3RYBInT68fNfJbYkLQGVHfq4PusiiD7BAXlkOisZ73h2MF/aoYkLr4ALboPO04qSBtirEuOBryc56XjSCx6lecM/hiaHPhQ9ZuuDSF46SBSSklei+QdaNvrFDMxdJhPV8GpV0epsC+dTStZ5kDoFqarnUaa1kKrjOahVBb5qeJYble3rJeWtRA+W2zKVhTRZpZMwrWTcRaPf5dozCQNYJ1AiTNjLZq5yJi+N7dvnbUCjOt/JpE4X5ZGNVjRjeMrEK2XeewVnjMmU6U/h2LaniswLQWfCUz5cyubepQONA5ZeU19X5f15B1rumMFrhiINr8tevpN0oJXNatEb0wnaCq25j4MEVeHU4cl7BdACs9c9+aCh3oReJruk8GxiURs1q97htL5w0pCacparVsjOLZZlXnVuFJhvcyszf62vUdUgTezpH7RY3FRHRuMHi/LfpGtIVUuuhaoayW0WpbEGMip6SRl0W7gQod6knOuhA4q8EhS+NYAkh1JDyGxLVTFsEamt04C9U9PBc1yto5BVTWjeV+ucRkWt0vtDJBkKJMB91ASmVYqDHXKpIx1RJ3TOorpHQ8PXe71vE7L29xRfnnu8ZyXhLXjeONU+hDjFJQn7eezlliSrTW0jrD5x9Gbj5qts5vzIlTznWeocdl5z2nEGvrnzXmU4yKbwSsdXyy63kYF11MzUX2N6fMh2nhRafurldeGNzAHxxH3o6zLgoVF15iBG9ZpFQLkkSUwm7XsyTdvOawKDmh6PE7BM1KsAj7rmNaFqyzOKl0SCuEVDnpXhkwVsCWRNc4OmdYYqo2DppD3FlLyAkrwvspcJjVEXWeIhRlnPSNwk1NKxzHoEyVSvtsrdmcUVcqr7VV1OWExznU462RmN4Y1Ccr1jmPZm2EmOgbv9lpdM7QLep2++Z4ruIpFYHFpLGIrbeqCaksnLVOjVk7vrDy95dIazXjIdVzaw00rb2IOteFT5TGuep+1qnYWt1rlCpVoU1dPKUO5231EKpiNXOZwe26Vhd2geR09iHe4IKhr35Va1LZyoM2CG+mhV+cxidiPX3vw+C4aiynXDd9YMnKvrK1o9ZGS4uR98cDTVHPpDBN2/22Uq+trP+tny/krD5IQj2OX/34Rm0AlMJpjtC15BLprAZohJJ5f544LaEO09gGZHNINF7oaivrMfha1b5oGz7Z9bw7zRzmRVWlITL0LaUmZYkx2yKRSyHEUlOA1Hvk2lmEwhgzH0JgKoUp5K2yX1WPS4r4it0tVaMg1GFqVqtdr+ozArowUf1W0vb7+rlK7ZONVcaPoPBYSRVSMIbdzvPT1w+VPqlRldd9o5CRgCvKmOiKwRlLpnCYI6kItzttNxunQSiGwlXnef9BU6k0QlfpEuvfExQmGVNUU66Yn8Abysg5LoGfHA58tx/48jjyw2dXCMLOOb2BrOG68QzO0lY2k2ku6WpjjBxrpOQKFXROg9hXKGvnrS6+UTH+xhk+u9nxctepVTWZq67hbmh5dRhV8EetQlHef2YdXuv5e9k2vDtMtFatPc/TjG08Y1a8fDXhmxIs54kM3DYdy1IQsZSiw9zrviEbwycv9qSYeb0sHKZFIUnRyjYuhba1NNYyL5FOVNHrrDLdlkRVvVqFMuri741w1XpenQMJfQ1jKhQ4BhanG8G2GNbbZEpJVdjoYlNKwRU1XhuXRK6gYc6Fm7ZRoVIIFKP+QTGrn03lruCtFkSGmiVAqX75+jebes+aVKBEkhfAVP48mzVETCsZWAuomFQ5LSlDHVorJm/U6qXu4tuyL2xaGRGd5dUvsSLctR7nHLB2garkfz8F7SgL/Oaza2UO1s0gFp1nnXPhISw4J9xetXzUNOyaRguOsv5tuA+BV+O0fQ6KphLuW69QUlH33Poj1jrdQi2aLu6oamdThZhF85dt7Xh0IC/bufXGbiy4MWRllX3N45u1AaBD37enkU/2LY0IE4U5FErI7I3jJGmTlq8ASkowo97k6/fvuobeO7VayEkrrYqdxRjpmobIxQxLGTrCVZWfaxiLIVaa2c/GFcKowENRXP6u8bzoGhVW1RNu6+6ccub1YeL14cy3bnZ8cjfQNGulaggLxBAJSav4OrrYWn5JGTHajQy9r6OPsl3kUFvzUnicFp7tWp7tWsYlkGou6VJU5HIKmVSET650yLgtsN5g0K6k8eqB44y5CGfkyUB6ncnky42nx0NvvJg02OPqxvN5PvNuXJTrnAs/uBrqjGPF8+tza/SmVn2aYdBUu4QV+/eiro1jyoiBz64GHudI53TR9aYGsojy8qFw1XjeyFTFaY5dozbPr8eZn5xHfqPvNA8BeD/OQFGVcV5ZWVrxhaI01HGJHNHYzc8ax2ANc9Nyfx65Gnpi1AJg8Jr3+jBHEIUQSoUHUqqOn41j71ucRJ7tHDFknu86nS/Vm3tMESnaMTROB6wPY+T5rqdLgXleKOhMigK7xtE5S39lialwf56JKePr51ir77XqzUXZVaZoaP2u1SH7eaV61g03CapPpGBLtUJflOaYaiFjs94j1tZrsqjPUUiCJGEyhbbTxS0kJTBkURx9raqXlDgtC7u+4zzPhJRYg1sMF2O7rHvptpgKINX7f/2ZqbCNBjBl2sZv98nOe16NMyLCcYk86+1WaQswpsjrcVSYqArCpqLbpK9ePTkXxph4Pc2KAhhR9XoBj6HzZiOQrBtY1f8rfbVqkJxRkgJFfY1ufcMhBGxVnCOrCY3eh53Vc2kRHpfAMSj9YJu1fMXjG7cBhFKYcuLDaabzDmN1KKYnVtgZyyx5868RCuclYI3hpvMMjdOKs/EgwpvHM+fq3e+qrXIuhb7xHBdlzqwX5ct9x12nVgviNZShlMKRuDFXStHhqjHwsm950TW4ar52OE+E6if/sw9HjZs8z0SBU0rgVHCTsy6UP3o4K65bcXUjgq3Kx1J3phiVBfR9d6WLcL1Sh86z7z2Hs2LMMWVeH0c+2ndYMSRJeDEsOXMfErddw/OhwRp9DV1w9dI/z5HOW8ZwsbiKm31qrvQ5rSmt0fStsG6e68VXn5ii3hAf7TpenyZ+2F9XK2kDVcCzcbULlYGjL3TVeaaYaJ1i4QVVcbqqxnZG/WW8sXxy7bHUZLfDouwWUQvf1uom+/yqVXhpnSlk5V4/zIFBAkOnivJD7dCuvOOQQl1gCqYIrXU0VjfcUIesR1cYTyNNjWFMKTMIWzCqAAAgAElEQVSI4LzHGcNxDjxMJw0AN0btpAHfWkR9Pui9xVLIUdWh3upimcslEyKkOntKqrNYCvSucDgvOCN01lNINNbQN56YlPLorWHfOqZF4YdqYoqRyqwrZYMhBqPHamgcx5h0Q6kLKazCKmU6nUpiKNplLLmey7pJqdqamhoGpXKrHYo/lVQIRbah8nahcRH+nWOioJGwpWRSpprYsRUNayd4UeeXzQRPinYqnmqTjsJeMenQW0Rpsp/sOn50OPEH9wf+wcbRO6fvQurQPV+yPUSEY068XmZeNq1mkhjhiyrukzr7cs5giiIRfS0GO22lL9YddVOQWnhcgGWYlsiXS2LwDTdty+Oi9unjlNj3Hm91A7DG8G6cOUdV6Ofy5Ob7isc3agPI9cbrnWUKiTeHkY+vd3TOMXn1zXFGaI1wLHqidE5aB7qx0A2e3qliMuSszo1OQxdSXWivG4/vWw518h+Sdg8xZc5LYucdXtZhknDXeb48Tduc4Qc3Aw5dkNYbZDWom5fIMWe+PE6K5VeMvWt14IQI0xI1IQu1FTZPKnJXJ/6aYrZSPjPGWqhCsyLKXvj2xzecp4U3D2euWh2+riHWTjS04rwIz4aGu6GhrwrYtCZplcw0J0JUHHKuzJawpo3X5rZktuGhMSttlS3hCz1MWGtYpsTfevWB71zvuOsb/fxrG886xLr4z085M9d5xTEEuiJc4zfWkamLgLOibJuilScpcVwiH04LpyXqa4rhg1/YtZ6E2nGHUhcrHa7womu5dgrBhJx4nBZ6a7gZek1iahrOixIN2r7HlcxYB6K9c7zYDTy/2vPsoxd8/vkXnE6Zx9NMah1X08QHEaxzpMpH37VehWJRNwNaQ2c88xIZQ8AV2Heuesysw8qLRYmIMlom4OV1x+fHEzFpN6e5GYYpF2xQdoiIPr9bobKQyKlsgTx5w8zZKJ/eWOaozJ51q9fOTzeKkJUooV20Yclxu/di3aScUWdLYwSTL/BGqJtgTvq1iFxENULtBPQ+ccZgc6YYoW88h2WpFNcCmJUcXPUw9RLN61BZQ43U/E83DB2yy8b/V+RYeN62vJ0WHpbALw4nvrMfMM7yMC+knLhxjkNSm3SqJuY+RpZS+KzreTvO3C8LGKVmd9bynb4liWUJeu2sc0PDasG9qpZls+/PpVLPS6FpDfM5EU1NO+w6TtPCwzjTdxZfLPfzwnFOIEpCuMBo/z/ZANad3YhwDAlnDA69GJ9fd7w7TIwhMFjDnXV8iFFvLKs+4ocl8NPDie/e7rmqrbRUDFgQWif8xu1A6wxTMbTO8fY0Y0W9UAStEM3WMq/vR1tsyfDtoVURSl20a6emC3tKOmgMiWHwnMcAAs+vem6HDivCNC41w1f/5vuijqcl62kMQZ0nV01ALDrImkLYKllTWwFjoO88n8ienTW0VjDo4Ph+1qFX6zUdrHVuG7K7uiEulYqoDA2NEyzr/Vk7krVYy+uxQP2V5iRPNgm9vZwRclKrgbeniZdXPe/Hhdu+Zam5Dq9PM+O80HvHlFVAB8LjaSGGRNv4DZaDld2hzKVUF3+LQIScqtkZK2VVvd93rScUxUZTzjj0JrVeh5umvvU3JxU5eVNTyRBELF3nKClxPp65Gvo6zG0oot4+bx6P/PTde85j5NPbaz6+87x6/4i72nEYR8TOeGuZYuTxpAl0y1Jx7Vwwu4YilpBnohQ+7nrK08W3Ci6EynWvG8jgEqclbnRIRO2Sl5QIObH3js6q+tsgiNWlM5A2MeIKFzhW3YuQTOGcEqs5nwg1+6C+BytQF1dnZbNloWiB4o1VGK9cKuci65BTq+pGDL7o2CvXN1G4aAKcEQYjKhQsOlvxxpKqs2ciM6+XW30YQIzBWyVGiEAxhmytstRkNVfUFWC1u0g580nfcVwi76eF67ZBlkCsCXWDtSRUFwLrjAvOOakZW2e5zo1mRFiDLXDTNryfI8+7RlMCieoyXHSjbORCPZ1LwhTDatgoRjfWuQQ8mXFJ7G9eUJa3FFTjdFgi5/UasoaQdFbpjGxw8Fc9vlEbAGj1ojbQhZuu0SjFOnTqW1eD4TN7a9gXy1IHAooSwrhEvrw/kvc9jbUsMeGcpTGFm8bROuXEhKgVZKbwsm8ZnNUEJgOU6ihKxUsRPh5a2qJVP6V6gXBp676cZj7MYePpA3S9I8XMi6uOzhriHGrwhQ6k1gUdqMEwVcdQWWZlXYjNhQqGVAinAqFSCl1jnzCoFNu0xvC8d+wbj3MWrbr1plxCZFnC9j51E1DuNrUD2cLoke3YUrQbaI3lXGGxFYu969sazF54P03cLwv74JlC4vnQbirN1hnOsxrvRYBJb5KyJLXXtnarkDYfn8ImgFnhGalQh7dCWHTRiMAxBR79ohxs59RKOyamkvCpVpkGPpwXjeREhVXHstQwe4sXy7QErvpOB/ZWbyQplofzpMHn1rHfNWQx3HpDc7PjfYg0jWfJkUYMD/Ok1L2UauiJksezh1NQq9+usXS16KAuyLn6jawVc8y1e8iFb/UdH5aFU1Am2eqomYqy17wYnC3bddk4i3OGGJLaeleShNQFKebC47joxmoVgtRLsTJ3pF4Botd8zDorWb1+WmvJSXO0ByvV7TZvyneMMIma5j1vPINYHov6YpWybjZCUzeO1Z9oiYm+cZwmZTSt18RTxrsTw7ev91gRfnY46cJbPZEeneXatXoVVZHYBVxTw7tPdj1SMuM4Y43QOqfMJYRSySHHGCDXzhfR9aPAbefJk84uPu07zjlzXgJXrqU1BieeU1Zq9VLKRoGWev8mUYhV6nkXoGsdS1LbkHx4S6w5zTGryWW9/TcWJLXTXxXGX/X4Rm4A0xL55GpQvDwpZniaVbF5NThSyBrgEFV5V5IOEBtRDHAJiVcPJ57vB7IB6yElwyFEng8tY4Z3i0ISz6t6dPV0SaW6TqJVTqzYTO8skqkGb/o4xcz7eaaIVPy0bDdWzjpI++R2x947qJ+DWmWpV72wWlgA9Wf6KNs51p87uzbAF7rZyhZaffenmDgXR0iGZ72KopSSmmqesm6u8xK2as0YwTWeQ1R2hFo5X2iDl/emfzgDZg3nqDerQYdYOessxYtlSYX3KZAs/P7xyBVuG8SvPk2xZFy2T8K7lbVS5oXWGhrnmELQSssIL3Y6uF0ZU1J09rGqrXvvuG49gzfcNBpWH6nDeWfZWctxCnxxmDBiNEs4aqastTAVHXQ+7/Ysy8wUFqzv6V1LqY5fTeORlLmyBr/bMY8zRgx9PS/fvr3hx+8+8OGowqa1mjfmEvpyXmblmxfhZbWqXrURF62kbqZGhJBX/F6DXJxpcSZwPwccKl5aIcSp0jlNgcauyWSC944+Q65897XHKLlwWoJuQAHE6FJ1NfjKQtGqdeXXnydlgMWSmefIvm8wopqCNWfXtI5lCpyNWp+k+mmOOdE79ahaihZCTqBl7ShUkVzQJDKXtODK+dIxuKogvG4bPt33DEbhxc+NMIVUw9LhYV64aTxXFfyXop+1MYY5JRLw8dDqjK3em9bU3A4gJ0M2DuOEd/OMZFQFrIeTxgr7xvLmEIix8IePBzprN+GiE8Ot1TS6DyVeZgtF4d2MbppUAoQYBXNyKYg11TxOr5e0UlrrcZIKMa0UcL+tSH/88Y3bAMiZu6FVdVut9peYmKuTYoejH1rejDPjolIqfZpWSVJx1JjhJ/cHPrm7Yl4i94eRm8bzs8PEGDPTkvlkrxWAFR0QxlhoGw2boF77pazycj1h6wE9pshPj1MdRuv3pC6SpShFbt94VSTWIVkR9ddJtYNYlcumPnk9yTkpDipoe+hrOyv1b6WcN/dBJV5UvNi2uJT4+LrRXNqau2pqF3UaF6Yl6gJgtOIPRvhiGgnbBrZ6nVB3ocvitKQCTpiWRVki2y5VOEzK+OmcJweF5aZZGUzHcebRWm47z5TU+M1LdTRFsdDGWe6GDhHhzWlUnUKIPIRAyYXbtmGpqWfqJaMq1e/eXamLY4HbvmEzrzNqIeyK8O3rgZTh8w8nvvhwItSEKzHC8TjjnKEdHCXpebg/HWis4xwC6Si8GFokJz4/jzST5fE8M3Ut18bycDpjzFDjBYW3h3GDQ1ZKr60UxNYbdr4lSyFkFWpdeU9IuTJXYMU41s0jUzjHwr5RPDulguTCc98Awn2dY+l2oZj7h5wZXKEvDl/VlMbAbvAsc9oiPEuhOtDLdqpLNXJ7PC0bnOOdptNdt63OUyyUAG3nsIgG3JeLKE+soXhDmhMR3fRbp8IobyxNgcdF1dhe6nWMQkMqslqN8oTOeUyJVbhl6LzTjdgYTfHLSnZojHB82uWmTBAhOIs3mh/wsEwq+CpFdSi2oWm86haKogypFjFG0EwJyTRoEtkcEr1Ta+2QtCP4ZNfz5nHU2ZRVq4e23j/OGHY13/uY1PpbszFWO5Gsw+BaDHbecohR78s6W1lCxti0WYA3zlQ2VN086nX2dY9v1Aag8IcO76QucrlklqCh3I01tE75/s/6lsMcuD/PgBpEbf1hgUOITKlQ7o/c7TuNkfTCj98fuelbUnZVgq1wxxzVfVS8LlrOKr1tuyFRuGMUpca9WqIu/r/0AfREdNbwya7n431Hyjooy4ji+hXg1oWvLqJ1IdzELE8+SEGYQyamqEPkUqqjov5cRGXxpQidhf2uofMOW9tpa5VVNE2BEDVlTKzlQwg0WV0s10xlU0PFyzrXqJ977TTGkDjNUYM7nrSdGTgHraR3XruBZ53n548TY9SVpd+1hIp5NmgHcdv1tFJ4CIm5OL7/8R3Xvedv/uQNMUayKAVUU9jgOAYab/CN3QQ0qWSu29Xt8wnDRQy5MlSO08y748Tbx6nSC/V9t8Zq2x0y85QYWk9rHVEixlp8EcYw8+6Y2fdePYqKMAwN+6Hn2dAjYWFKBWcs9/PIGCd6b/FWKOUJS6NuctRjYERovOPtOHHjHKHA4BW/d7Lan8M5FIoYpbHWBXLlZ914S2s63s4LSdNdtlrwUJPMbkSZX25dULuqARgDMWbuZzWzQ9auUxdR3RD09eaQKUvCY5lnjRYVA42sQUkVRjQG48yT60Phw13nue58tfaAxjpV5YbI5j5VCxzHOqgWJGvAUV8dS1urbqvrmx1DYqiZ0bbi/LFSbilwmmeufF9jSbWYXIfg8uSe884iKVeb95q9XWcLg1jwDQc0hGqOykqLJTEtEbIKBXMlkMwUfIVm1k31ynlVlaeZKFlp5jmRZSWSmM3PKOVcn6sbtPcWrMGsEOiKCFRmngHC/wteQP/feIiwb30VculBmmIklszgHVetV4ebUggxMTjHPbNi9whjzlxZy/0ciKXgvS4Uxymwbxtu+4au8dyfAyVHbvqeu+sGKZmbXQM58m48qcx/xd8rbENRQVqsmwtm9QUBRGER74S7tuVZ17DzSmXMSavTtRVXZzV9TY+aT61soSyXzIH1eBjhomFYTfhLqbimuo/OQXjWO+6GRh0hna06AjXCOY2BlBOdd0w5cZhnlpyZ1sWeyq92BjGGEtPGm1YxG3SNQxrL8ZC2TuViiqW4awgJydAMLa+mhTknxBl64xWGEsFaB1UUN5XMXIQFsBQezxPf+/iK797tef1w5vXhzOAdJSf23tauRuENpaPqgXK1dZ/TJbksFU1hiwnujzOvHs61babCZ8quqaevDtIKqSQEw7xEzrPe9PthIMXMnbcci6ETx77A4/tHbq4GQiw8jBPPds8Z48hxGvn4asfrw1ntrFPGmcJ5ilivymbvLHOMnGPkMQTtSCb49q5VCxLRdv9hihpWXt+oreLAUgqSjabBWcNjjNzPda4hmqF9DglvIp21gEY9royUpnVYm/Fh2bqBdRNQ/rzmFhtTO08UunNi+HCcuN6pmjom9WLy1lKMzssohdY5XlypwM07qT46smV476xlrAWREUOiVK+oUj28hNth4BwDRsAXVDlvajCRiFp4e4cRuGs9b8eldl2ZUhdVKWxOrKu1SpFLgt1KYjCi6XNj0Nlg71XA1VpLK4alwrwPFS4TVp8uXQcaoyLDqUa5SslVv6B/x4vUW183N281zS5XDPWwJJZQNygjlOphJVZpp7mel5IzjTM4rNJrY76sF1/x+GZtAKXm+aJtXaxOj6vi01a/9SkkHk4LWdhCJWzdNQ9LAit4q1VmrriyM8Kzm/b/4e5NYmXrsvyu3+7OOdHc7r33NVlVmeUqYyxLSFiAkJggJAskEPKIAcwQA88sJhYCIRgwZQCeWkgMERKiGyCmiAEyEiUZGwwCV1amM/NrXnPvjRsRp9nNYrDWOffJZFaVxITPMXjtvXEjTuyz91r/9W+UfSHZYgcrXT+wTIXzdeL9y8RX9zuWOoO1gdhiUdjG0aQy+KAGaP5V/fhm6PjRYWeYpdCKeuoHMRWvVftb1W+tYBcc44rx6TnAbAZv6yagn6+yOlahiyBc54aTwLu952boNnx+tkXxB3/0nt//4oYues6tcZ0X804x0Qyv8YxeVDmca9V0P/kMd/Se29se7wKnZWEseghEp8OpZapq1Rsd41K4LJkU1M57SD1digxNOerdEMil8GlUQ7eD2QscYuDDaeRv//LCdJoopanaOGgqV23gLfnI2aG1ejctNuh1btUV+K2dv16WbWBcg9Cq3fBBOfdDF3FOWdl9lygIQ4gspRDEkapn6BKexh9+emZpwk++uOPZtCW1Zt7c3NDvIk/nkU4qXx6PvIwLX94d+fZ0ZuiiroGmwrwQFBIp5uu+oINCEeGXTlPVjiEgzeF94JAcWkIoY2otPGzmzuADkhxLFV5yxplcPnrP85L51Bbe9Ik0rJCb4Lxw2HX8hSFxzYXTrDOFZQ08WTcno3uG4PhwupqNhZohRqdrspiwpK2sFl2umu8d/GZ1jsiGsfsY6K3q9l4rWaVUR7zXvqBf8XARwjogR4sV9c6Cp1y4T+odNkTHmJWQHZxe34/Xce0xMH4H8JoroGumWRBQVotxzE2UVwuU5HStxeDxonOTtm5aqKCyetU5jKFyKo1DiCSvxoh2puKMEl0ar4NhdN/Ln0Wh0iCGQJ+CWV94aqvUKuz3kbfHnp9/97IN6n/T44d1ACjgQDat/oLazx6Spj81dMO5TuoHJEFDV3JrW2Htg8PZRrFi8bsY6UPgZ++vzDWzCx2ORvKR6/XCcXfgeXK8ud1zGSdC0OGVpnfJZ8Nd2UIn1DMF9inwk9s9x5RwToVbq8/Huovrxr76/es7XQe7fQhMrWwLs1lp4pBtcGgo0dYtLLWRp8ZlafzkYWCfwha/V0SdJlPUAfYkjadROyJBtjhHbSD1NFnbSt+EuRScCzhTdYqA7xzfPF/5vbc3/M79np9+OCMiHGIkixBjopRFZzDR7Bsq9NFx6HqCVDonzD7yaZ6povBEXqoFejeeW6V3nu50YloWruPCrldrhlKFj/PENYWN490PkRA9z6NZenx27aqo7UKH17zeplm2o9kq6JxFP9cqjS4GimiYeKmNUive1EkxRr4/X3kYOu6GgXPOXMaZ4+7Au4cj58dPvH8ZQeC4HxjHEYcwRM0weDvs7No3ZiqxqTXHaIrbIQQW07R477jkwlQcJ1foXdRsWa+HlV93I1tATty2q+1c4Muhx3ulQ3co9kzTDeKaK10o7Kxg8sYGSnjuvOfYJd7sKmMpPE2Za856KDVd97Q106KZX5KmgM05k+wQ8M5mcCtR371WvN45mnW0tcFcLTvBw7HvuZRig08b0rZGbzOMimzU0cbqiKtr+Fx1PnCXEr+1H/jpy9UGqzp72QWvHkMCL0ve4NeVSbMWZOel8DJlllrNikQhvGtWRmIT+Go/cBwS3zxfmH0liwlUzbspOPXtH3PlmitPUjhGzxf9oPta1bQ0ESFGT/DmxLpo4aV3KPQxEiMUaXy961QQ1hSazggpeYXO1kLoH5YZAGCVPMytcS6Vl1Lx0XMflGUyF632shf6LrBznrQYfu/hdt/zPC/r+uPdXimYuegNdpoqh1i5GZKaqNXCh5cX3j9ftHWkceiiccmN0mnV8Ho4ezQHN+TG25sdh65TjH6tmBwbJr3aH7NhnbAmZQUH+xh4KZU1S0xkxXJNOGJzAS0KhCVXrmPlZZqZ5syP7wZA293WxIZ8QnCJ+2PPL09Xdn3YONDrNXYOC+G2E2HtVDw8jgs3XVJaaOc454xDQ7Rd067m7WHHIUVeigbdp3TLeH7h4WZgFh1O7qUx1cL35yutNfoU6XZJOzubXJ4uk3YIfWQRdUL87vGCBOEh9XhjwAzBbQHo1yZ8mBZud4mlNJIVCNForGPWmL8OT4fTzuYVV9PqFhOKLUJrmZvjQKHivA7h56Vy6Dp8gHHJHFPgi8OeB9f4+acTu5j5eHphkcjRwzUvPD+OHGKg9YFiyrl9r1TY53lhkcbv3N1xnSY1QxOlwOamMMoacjJlPahCDAzJePe2djyO5oQgq0pWF0drqsD9skscY+BxyUhphuerg+inaeF+6DgmDYVPTu2J8XqYHJPjkDxvhmQML2XW/N9Pl01DEKKni8GESLpxJZ8Qr8EtK3Mu+hXXflW/ejsQvFOR4n43EEULhalWVcs39XjCOc6t8eAj2HOuzp9tbU2xrhxBc5Gjit/MOWAplbMTohFEglPYsVXVv6w229clc81163qj6UJya0xZA15C8FAryyR8fRy42UXenyeWqp/bGvsYnGepylAE9dk6p8zehY0JlKJ+/XXJNBqlKATXBc+x79ilxFSE67jQBHYpcHSWiOaF2TIVuhTwvd8641/3+OEdAOgHPNfGyWiJn+ZCFxZ2MfA4LtQopODwTWi+EXoNWs5Vc1ZD1gWdguerfbdt/Ocl827fs++i8pgdOBF2QR1FP100F7aJqohX2lZnG3ER9Ssfc2HMha6PfP88IlW430Wi1dVOUCtZkY2x0zyqavwMr3Nod+J9ptR1OKUrvDm2TuKL2z1RPKXA+Vo4jwtLWbag7OQTS9bq1aHvaVwyd0PHH3534nBIvLkb6JzQ8Ft1D2zMJUUNGiEEUu/AFum4KAWzAX/08cw+RH77fq9VTimUUqF11DISQmIW4cPlymXKfGqN/dAzFxXM4FVglNEKfJmU6jbnuhkgfcqNSqVVx8tlYfAKl6wpU1RUA5LUs+l+39G84/Gifj7Rq83wvDRKbKwAgI8Ol3VoDNpe3+x6cqpcc+G8LMznTOoD/RDZp8QhJc5LVqglRk6t4UXTy6RkplbpG9Tacez3nPOZ764j48sL7/qeSRy3h4H5OhGc53cfHnAIP/7iHafrxMeXE6eSDTrRA7rJuhlE3u2SQpXBmFyithmrAZ9r+j3O3FoFCOK4DZHYe37VJjpxBB9ZqnY7n8aZQ1Q7h7kUkg9KoNCbAY8OJhWvh4jnH31zy/OSuR0SpyXz/Wnkw8uIQ2MVG4pV63Ab06yskI1Wvskk87VpAfLj/R6appMB3HTRXF5f1a2LqCOsC04N59CnDYbXFwARUmnU0BiC565LfJgXLRy852kuWxdS6mqcputlmQtfHnbMdQ1UWnUJeu/ORQ/pmLSLGJ3QSiVFz9uhJ4jju8uk2qVcudbGecnsotqPFFGHXuc9jzmTm6r/c9X5lLPhdXVt81G6zAvPLxNDCPQx6oB6u1cdEe0CnMDupqM52cwKf93jB3YAvPJwz0vZALsswq8ukwqnpOEMv8UWgrNJ/CEFWoP7rldP8agbvUbOtS2b9LIUQvD0hs2NuWhlbxLRy5w3taWirw6a+veMpTLmqra9wTP0ie9eJkQ6vtj3GyNLrBvYMHtpzE3YuZUZopVo7y0kQmTrWtx2oCvLxzeYJsUdayncDB4fdloFxKj2v6WxgvYaHqOdypcPO94/jwxDxA9RvYDWGxQ9hJoNeh0aWK4/WQh9pC51SwSjQegD56yKW++8euQbdBFa4yVrJRiToy5wXWY11RLZAkuWYoHj0gh4fFCRVDGTMWm60Z2XyhQcfQzsusA++a2KVA60aBJTrgoDOliqsiLy3Aj7pKEi+oYIQSEK5zxd1FCejTdjjBNvYpDD0LH3ng+1sE9JB6V12ZLXXopQvWMY9sQqJMlIFfrQgdfUstIaTx9G7vc7oo98OJ3ZdztSmFmq4s21qXLbbS9Dw3J8gI/jQtx3xBBZs6u3zeCzrgDbuBCxISzsvOdNn/g4L2oNET1TFfoQmGvlNC/KrPFqDX4Igc45XXsGo62pd4+TOmMuTbvj37o/8P3LyGXJSn/t3ZYrIaxY/trN6kys0CwuE3LNLCHY/admjjoP84xrqIp1OJfauAmR1+519QN6pVHPrXGtjd5sGfYhbELKnBuzaUXktWHCAW/3O3JrzKWwNLXh9ihGv5Sq69hrMLvzkO37/Vzw3nFIkWMXOWf9WlrjWpqpsVfGErpfNOFatDtXlpSu5YalhTmDJasaUfpemUOlVJJZUesBoHfseq/nqnOy3/T4QR0A24L2nksudCg3eBFLTRKdznvdj1kMm4veMc0F7IPqQ+KcC95VXjK83XfGodYKeayN3vyCGrrwagLcwtKUtaP2yonFvmcqlY/XeZsJtCogjdb0Bvs4anvdBcfqX77CPOuNm6tO8HWTa9YeK0Yd0O9LPpgkXoemh64HPB9ezoylcDtEYohbdeXNfhfYNn/d7GAXI39+d88QA9++XPHBcQjKUNABsEIhgmMNahz6wDSrTUTBAtkRQjG7jCZ4H3l3e8fj6cwwdPQhUmtmbA0vgveqqmbAjOcqrWiVpdWKo9pB4bxjmet22KsDqUJT1ZxYx9aY58Y1q8ArRUexjSWXxrJUo7w6HR5P1WYnr4cqsM0K8tLUT99BTFpZl9w2iM8ZHTB7NfY69B2xNl6qZq82p5tPrY23h6Q5yvb6g6gT5oJZKXvHZVb3yRg9Sxl5HiOP1ytTU0qAS94AACAASURBVNbSa1as3gCdzVAuZaFI5c91N5ioFpwza2f9wB3reNipXHWlLjvHfVTq6vt5AtQDa806WGchk3WLL76wD55DCOxCsAmR4/vrtNmDFOPIR+/48cOB05L55unKZczsD0k3Mthe03ryOtQobSmVWhutNT65iR8d9sbI0gN9Fz1jWT836xickNAOaFxddjHChM1Notd86MdZ/2+XIs4OMO9RP5Xt+qKnpziep3lT4+tcQI2F5qrr/hiTBuRYhb2uyQkhzFoY7ZynRsfVvJ7mWnmcVbujqWgKJV1z0fAclGKu96lsxV81ptKq0Vi9ybxpgPQ1mlUH6toaxXGdhan+f8gE/v/TQwtjz3eXiUmU+idNLZOjue6tFSCGhbZmAqVZOBy07apSOHSR2123YcOh6kUuNhBU7xfz83eeqRpr4/FEM/72Uhpz1dbuaSqsSlxpivsdzWqgM68UTeZThdZUdSgpVjUV0ZP9VJXv20X9aKrA/bBTv5Co2cDXrPOE2hrn6cqSIy/jrLjwrtsykGtTi4TovHUwwURtFe9fF9qf++Ke37o78ne/fQRQhXDQwdU6e2i2GJ2Daa6kPmxWw60JN10HzdF5FUidLhe1nF6E2Wng+qHvuYrS9i5TVtM5gVoaIWqes9giX2ciUlU0pmwnLWtE9MbdaKZ26BY7pmJxW8u8KkhLqUyjHbr22dYqhO3meh0gVpsEBgd50aqs8579LtL5QELMfnkmBc8yZ9483HF61llIqWr01nnPdy8z41IIXcfz6czQJQ67hMNxmhac/SxrKHkeJ9yyzoXY4vxefXq0qJmzXsdrFf7u45kvdh3vdr2SE7YKW7/eiQ330SyH1TveOeE2evZxz7U1vro78NOPJ14Bg1WprM93bY1Lbbi20Hs1kxOvsJKIJuOJg9nWyRA9b44941yYrpnuqOI+78y4TaDzOoifc94sxtcO6XGauUlpK+gOMXINjbGquR92SD3lTIp+IzKss3BvRV21tVOEjaWD6CE9pPhqzObcth+koEQOgc2uuYqSIHJtSG3ULtIlbxGVq72GusJeEXaiz3mbAjd9T26VX75c1Y+qS0TRyMbSlLa+QrzBeaO8snWU1Yb1znt2h8D3j1eGLvD29gBFOaHFXuvcVIV9GrMWOL8ZAfphHQBBuyh2IfHcZi5eh2S7FCyXkw3rXDcKHCxz5S5FipjKNnhu03qRHafJJu8hGEvBrpjoBb2WwvOcyePEw+HAaVy0rbxMTOba6eR1IKsUOL8NE6sInQucl0rr1MkyOCg0HSQ6GBz0vddAipI5WGdQzW++CXQhcegDj9PM3ZAoS6OJ42Hfa9HSYC5K4atNGSTncebmZo+vBiNYe9lFt71unN7oX9/s+VBWfxpRTN4Uw60Kn04T4pTeFyRQpOih0HQT9uItWMOz65TPP4TINDemVmldpnjhaZ6t23Ib42FlH4XgSFGFQCuTw3nd0FflaWsq8lLrDZ1pONOGzEWDsMl6AcXp0BZ4pc1qgcc8a2fQLILRR0fsvT5n0+7Jea223g69WXUDorTDXBsv80IXA8uykIJaU8xFE+B2wTO3wofzlZu96GB3EjWNi5Fd55kWM2ILBjUGv5mLg4WZ2+cmsrp0BmaaiZW00/vWINBu6OyLV+Ggo1E2mM65tesUVlPXiONN1/HTxxfeX0eiV/hzrUKD93gn23S1NmEqWbUnUdfV6jkUAC8WI5obOx+4u+n4+YcXzpeF47GnOWNW1cpUC9KauWLqPE13Z2XlDDEpndSq84c+IbNssE1Du5RN32W37toBKWyjKyfZ+7HzHkHYd4m3nTL0lHXkN9qpUryFS9Zc61wb46LX0gPXs3Zu+13C4TfoiKboQ3COrpk1ind48Xx1GJitcHRObcyDA2cGjCJCdept1Oxav5qwm3rfaeGWa+On3z9y3CdC9Oqcuh5EztF3gWmp/29B6mePH9QBAHBMWlnBkW9PF1wQXDLBVFPp9AoVOacbRC1C7Mwzw3s654k4znOmMwvkYC0VTm0Qiqh9gXKfldLVGgRmzvP82YeiP0zs4pelEY02KOZV0sXIw+HITWibL7qjcVkWuq6nc97MuoTvX650vbocFsPFdTimUnNBq4VAzy515FJ46CJv9jtm6RApfDw/sY86mJ2L3mCtqpPhMKSNTSJNKwd9X3C/7/j+40jrdAIRbEU7tCqZzKW0NqjjjI+BfddBUIgkJOFaM2PNygTxnpu+I5fJrLQLIan9QjdEerOgwCns0veBOVeSj5TSGHPWIXkBWZkMInRdRJzgg1kjm+jOobOCzRjODgxnoPMaJr7ioyDkSSsvvKNW/RopjZ1lD+jnp9GD51zovd+swLvooehzd60hXdLEtADOB/quJwbHzW5ApGq+QC5kaSRRorc4cFHFULE6g/+UNimYl5FzJGPQRDzJR5zLavhnm56I8M15BBHuDMpZr4sXz2fTjK3SVKW0gAgfl4VvX65ayTe1He4NoqjNrhFue47tiWxTSl6RFDVwterVmC9La3x9t+fnn16YpsL9cUBqxbVmgfPmBmqq9Nb0dS2Wtd2HyBb+4oTBeybjxK+dzpybUSf19ZSmtizC6xqxkYNi48btb2hnrVCTijlb0U13UwN7g5iWRSFM66CoML5o570/dOuSMntpDUYS5zhGDQHC6+v78d2eb15GWhMOg87oZmMFrTOPdXdZ41hrE1MzwzjWjZwxl0Y9L9zd9rgAK8UVIEZHap66/EOiBF4FX11yvJTGu8NOvTuC8uStOFtXOK0p9ziZB1BtjcFHboIOd2+C56UU9kmHaKlTD/ilNh4vM0/TzFKb0h0r3Pd7cimkFBiXTEBFLNpZgFiik7IltJK43e94OBw5UDascA04edN3DDFqe1kqz9PasplJl1PMupg//5Rnq1T0tH/Y7YgWS1hrwcnCJS90wdgDTd0fix1wyUztQPUQOFUbTkvh73zzyNOk/i79Lm7e8Qa6mHdO4HSZ2XUdD4eBp2lGnFpFFNdwVbjZJa4FchZaDx/KzCSZfp/oKpD1xmhVOOdMmV9tDPs+kXrH+aQdgg+emFR9rJbeomlTDbpdZGfDwFwUN44hME2ZOddt81/dX1ahUTB4b150zYTorO1Wm4BlrsQuQHJ68Njm+mla8AgtBCR4M6sT+qQsjFPOYGyc6KMNSSuRwNf3N3zz/IJzwr7XtXZtihFH7zgMkVKE5hrv9gPnavOkttapxo7xQiBSRcPZq5h9gbwK/H7xMnIdKm/3HXsfdaDvHc4u8+ug06i+zfExF372crHPwW3dgnaeBkVhRn12H+pBpOSFItA5R+dBAib8MpU6q5kZvDkMXOZCzqo817hF1ELDKasst7YJN4s0PowjP0m3Jqpr5uf/CgWu12ct+Fam0roNBqeMQd+c6SV0nwhoaNFYC7mYzbtXaxCc4+vjDhH4eNUYVB/MK2kqhJU+C7TSGKdM7MOmZl4diptzSPCczaq9Q9/Xu77jJkV+eR4pVbjmypK1GHNim74xrT7/t4eh57JkPuW6rUvvUYW+vNpfL61ucOHWlfyGxw/rALCNfWnC7RC56YWpwHmE5p2GpRiGu2Kwpck20HLecYg6wFrtcI/W9q2449M0c82FS1ZL2uC1CusI1DKz7wdKC1Bh8Hqq51oYRRXG81QJVel9ONingafLldA5hrhacuki7G1TC8HzMhe+eblwtz+QsUANNIpRpeGejGLLMUZyBcqiVbT5katLqd7YpeoN5nB8HGd+dBhYVcNrpgLroMk5Tb1Kis9uRmX2pK0Jpxdtd7uoA7RP04RDw8C99wzec9P3iOt4GCIEOOWZaSz4BnfR88WNxniC41enkV+eLhu7ISSnKUdN9Ca2ij845ZWnoKK/9bV3JrTrkqePQbs3IO46SlPKp0krFe5wahbWJf2+YkWAd/ocLnio6isUgtOgFPS6LlUPj+QDg8n3V6jRO4cX4ZQLD13iZujI0tPHnqfLM0Ea+/2OXJXhdF2uG788Guwwz5V9ihz3Pc45boMn4fg0L1xysSq/QQvE6Nh1kadF50eliFXNr2Hn78eZa6n81nHP7eBNnIjCf3onAbp1ZuDDPCtV04bg6zxixdRXXv4sws55HlJEasMVtX7GO8QOQu9WKwb9WUksE7sp5bHrA7mWbbDt0cq8NEFaJThlyKy26UurTCWzixGx+/Q2RsYmnJZlq5Qbdp+61SUVG6DaYWazMO0YdH1H55gtZjOjHUMMWmV/vE4bOWM1gGtACXohQ1NETCpMhrXHLmzznfV7SxG+f77wO3cHmtOZR+8D+xj5er/j4zxzXXSGWZruUUOn0NDD0Kl2AO0qctMibD8kns8zDh1o3/SRFD2TNIJbHVP1/YYIu5R+4576gzoAwOT7RgdsDboA97vIVATnBsYycViVr+KICK1XYVH0nn7lUDrDkJ0aOL2fFy5LJmPAqJOtEhIH4gWc0sD2nQ4jci0sOdP5QPQ6gByGyP2w5+HmyLefnjmNF5DG++b4yXG3VV4Im6Jx2A9cFq0gY3JMsyg2KrogS2lkqcrwEM38nOfKry4zf+btQUUsrEyJwDmv3HHFqktbqzR968ErpJMtlWroPPdDx+M4KzPBDpEGG3slV+jjQAlXpeIFNhaER5lGKXWqvj1PzLkyDJGXp5EYHH/hzdGonjrL+fpmIET4e9+/EJNHnLBcCzF4Hg49N12iT0EPYszh1D7DuTaSV99+2/+2mYumf+sswTu/zQZyrrTaGHMzirDebCFFmkEWLrwqq1dL6YZwCJ4QAve7HpHG47wQon4uIupzk4JOlN6fRkJwXBiptfC+CF92Hcchcfk0qdunVHItG6d+SJF9l5Tu2rSy3XeBzvf8dC5I0NcaXeDQBW57z00XWET4o6fLVuI1K3YAXpbCz05nfj/ccJPSa9WPwhJO9N1dcuGci0GYr+jY57OStbt2IrztO94MiVIaL9OsJIylUKsOn0MfNJ8Cp/CsrdcjAekdj9O0DW+bzVEcDuRVca6dh9I2e6/Uz0OKNPMAcg7uumQHgHZ5evg1YlUbD71PtNuOzrHY76+CP10qgzkICJ4x1w3aS5+RQIr5EzXrpBdxW9SpcxCqMI4LsQR2Ow2Hd0DwgU/XibFWfvZ85pACX+8HuqBzsi547rvELgS+vVypourp3PTkGkIgpcBczQTOa0HURxU21tqITk33itFHSxUzxNQhdj9EDn33G/fTH9QB0EQpmitev1QdhA3Bs0uOMDW6eCS3hdKWLUVLECaBBxsIrUZmODWI+8PTyTA/t1VBYJznBuKEjBBa5TJdaBLw3vMyLxuNK+Dw0bPvOo59z4fTmVwLc27cHXpyaXycFt7tOoRV0KPc6adp0eFq9DxO43ZDJKuSboaO87RQRXhz0A1/LpXzMjPWyj6sCWGqdwheq/eKMkCoKvB5t++JotXMUgvXWeX1TaDkxpAiD4cdc618eJ6orXE0VlHJlfvB4dpA9iOtNrIT9j6A6GsvtfDp+YWn64Lz8HzVnNubQ9w8y71ZATjgi2HgV92oJnqLVoj3+46vDntVMXrH3WGg5Movzldc08/7ftdvmH/yTvNzvWOpletSOAxR8f6mA2YAOq2SvNfIzdNcwHj9DV6xW/8KF60bwJtDz23qQBoigcdlYSkKFR6HnrlmStY5y5wru07FZ+uA9vl6Zdd13B97plzod0deLmduu8iQ9MRqtia8Y2NeueD48jjwOKmJ2RD0APDoId47+OIw8HHUfFhxOiAPXqvguVa+OV2QmwN91EQ4wdma1nlZMchCpLLaiK4eSrWJESu0+k3iuInmae/hdterSC9joS/g5opLnmY/KzrMM0ct1tduDFELj7VDD5hVsgvcmrtnMMqyoJGgXYh2GKlu4a5LPM6ZVRzmnMW3BsPcRShVIz+DVftr95irQk0N3VMk6EavzDxhcmoouE+BIQSid8xFeKmZuyExG2NNWWMVXxQOup4ndgcNqpImOjR2OnN4XoRDVC+hLgr7oIK75Bv3fUeuE6MxyJoIf/jpxNfHHb3lgAd0uJyris1KqbzkSvSFwy6qJ1DTQ+B21+OAh8Puj91Tf1AHwDoaiRifPXqWZkEY3vOwDyy1cckdL9fCtCzW3murvUgjoxGAzim/fypiSIHboCE+wz5xbP48M0LfPKUV6gJD11Gb0gSrCIeUiD6pH35VlsT9YY9bZppzfBoXpb15VaOuKU2qH2j0KWgMoi36leu8lEqXOvqYyKUQomMXHcPtnn2XXtOT9OVqy15VXBJcU7pqEb7YD4xL4eNl5joXTkvmOmX2u868hDxPl2kT0vQxcbpkhhjofeDjy4Wh62mlse8H3t7eaFqXNK5TZpomnscFnFbssVPXyl1IGxbp3GuWcpXG7z4c+HTJ+J2jT4HbPikNzqAeL2pJ3aTRBW2d3x4GAspU8t7ZUFuQGHjoOt4OvbpHOmd+PrLl215zoRU11G5tJbZoMaAHruLEx6EnGy97CHGDTsSjivO84JrjfteTq9o0S1MhlaB+TJ2ZzpUKj5eZh31HCJFxGhlCYDDNinnKAqvpmR4co4pYeLPvEbwOE/2K5mh5/vW+Zwieby8Tiw0Sm+kLRITHaeFxzrzb9fzkdr9RZ3EOHwJf7gdu+8TzvPDhOjEZlLZSI0XYiAhlaXy4zOy7SPRK89ylSAyeKRcWC7evS8WnQItacb9MmssROw9BY1yXrJ5Ynfem1fH03nGT1O0316aeX7DZRnjMB8zpYXfbRR7nZR35bVj3eh9UEZ7NF+x2SIjh9otZKq/d3hD0dZYmdMFpVkVpDEHfo4gaK05F9SjOebzTiMxDiFy9o7iKK8J80YSwbojEqI6/67xAgPfTwtIaDzt976ozEL4+9tTa+OXLuHWocxP+/vOV+yFxu+vU5VPUedfbIdbEyAldICaHBMdN3/H2Zg+osr20f0iGwIbMbEO7wetQcFVHBuc49pHghWnX8YwOx8Zlou/0sPiUM7ci3KeeGALnad7sbUPQjXf1zdkSm5zihiUL+y4y5UqXEl4cx66nOsXCx3lhOCRK0VzWlHpcrfzosOOT8XmzmBK1qlqviVIXmwh7wmu8onObIZUPKiDKpRKjozlVMd92PYsJZ9bELIDzXPh0WTbzs3dDR2jCh8vE3/vuxFSrWh9Uhb9SqaSotgIkPfBahRAjg1PoQUT4J//Cn+Xv/F8/593xhtvbGyQXvBdO15nLPDPmojBAUJWvVKEG9TNa5zDweuM5B4eUGO6S8vaNCxpMKVmaWubeDJEU94rR4vBSac1cI21lNBFVVjs2W+jg1QNKB6V68ERAvOL+9TOIwwl0BuXsksYwXpfC07RQmgbweK94/21KPF8XHm73/Nb9DZ+enknOMdN4Ee1QO1A7Xicc9x1lyRq1KZVj8gwxWKWv73n9dYUW1iGqd459H1kW2EUTe4l9h3Wrd33ikiufats46WsXu2I6T9OCB74+7vQ6pEg0FfWDT3y16/jRvuecK0/zwi4EPk0LT2aQJlW46dJmf4AIMShOvYvqsaNdCFxntS7ZeU/1cG3gkq5PxbrVMVRK2zQru+DpXCA5hXw0DUupmxrjWKkhsNrTtqb3aPLrOrG5hsDLqLGJtamPUvCvojtvRdWaHR2cDqqrgGuO4oQ+avHxshTG0raCJfnAlBuP40y07kDQ13+3T4QGH05XlutCQ9jtw7bxO9tDllr5OGr3GO88zQecd/zqeeTbl1FnggZdVpNhfpoyc2v86G5HaY3LpLYRzgNV95Brrhy7pIfykHR24x05Nx1s/4bHD+oAWAdUgtnKOs8x6QUOKLxQjat8SI5WA6elcLM/kJcFZ6D/05w5TY27IZq99Osmv35gqxZAD2MhSaBPfuP5L6Xw5c2R/W7H+6cnLjnjHDxfL9wPPaU0ai3k5hhDR3TGGgqJhOM8Xrb35KzyaSL03jM33URaadwOPSk4loqZQwkpakrUWAuIerIIcJ4zeP23n9wfNzrkMUVepoX/9RefGHaRIUXOF5X63+4Hhj5xui4bx9w1uDtqEHn2gZwzXYz8Iz/6ku9/9QGfAt98fM9cGu+GgU+XkfOsyVkhOZqHNunmduijVl+yCri0Mm22ien1ZFOyeg1nRYzv3HWeWsqGD3vr1Jx3loAmhpvLVt3qIaobiO5VTqX/sNE6V43Bpanq2yPc9IrFi5j9sx1cc6kMNhx0Tk25bobEVBeeLxeit86xvlbm4lFBIDBlM8/zjvOiFejBOpwNk7b3ldtrZkHyXsPXBTwNH6JVB7Zm7fU0USjo3a7j07jweJ23w3GdYRUR3o8Tc238mfuj/pzmGJxae3jvuO0S+xh56BOtCV/seuYq/PT5zPma1YqhFSjq/zSWxuU08qPjoF1BUM5P2nVmhyx8+zTSH5NGXCqErzoBB12XqLVq3q9BWKWpRUZ1ouwg9DMPOC7LwiG90i2Dg7sYecoaWrNU3ezWZDhN0bI1tnaUbhXCyaYLWZpGJ3p4pWSKzhtzrfZ1+jq6qIv1NkZuU9Jqfp9wzpNL5TkFSqksY6GIJgKuTZega6Khe9D08cSPDjs+XCdO86LkB697T6uizsXOIUHRuQ/nZVsjpSiUlpJTAaWp3u8OPY5VTKqz0LWb+3WPH9QBAHxWSQJoFbKe/pqprerSLgacK9RcKAJ919Gk0lol+IHWNGLiEOHkdNPFDMWUI+03VergArfDwJILpSrGO3QdfdfxdLow5UrNDafYFE/TrPBDzXQ+cpm0tb6/OeJDYFlm1vALEWWnsEE4ThlHNFxM7Aaliv7i8ZkY1G0x1wrB8zjOJGsF11mHa8KPbg/sjK0jaIWwi4HvzqNRTtWE7TLO5Na4nC9cr8UYOWpCNZeFt8cj3vDL2xj4b/+H/xnxcBMS7w4H3l9GvjuPzFI4dB0pOF6kIEW2iifZ69DNU2jWjur7d5sr6oq/6uf2OoCrtVJK227AYJttMeEPn60FbFtUHUd7dckU/T8VyKioSdAN9qFP/PiwIwicRf3Uuxg5F1OXR8/TvNCk8dCpc+cl6wb45XFn1heGVYs5dyJk0WSnQ9+xSGMpBedUTT3nxjUUbvuEE8fU2gbrOLd2gNjGr5bjQxcs+PwV6gNB2muVr/j2Oktwr1oVee0qr2ZDEUWYs3kNOfWb6YPqBVrTK+nQIJ6H1HHYOYJ4dp3ak59z2Q7VX55HbrvEu32vMw2BQ9fx86cLJE+Iq5CPTYS1duzRqdlcy5VcK81btrXoZKQ2hWTWt9KEjSbaGhyC52WBS1YPKeewtDVL7bNhbh+MAYTbmDLeK8wymr1LEe2kq22aKuTExGSQnGcXPccYOaYECAcXN8rm0CV+5/7IaZx5f5mgNvs8xQoRva4rxHPNlV8+XXi372gt8WxZ3KDF0OprBBBDxKMdrRZKr4Xq+tzLUilVYawq6px7HdVl9jc9fnAHgOK6erOsb6uIYXpg7AYTeATHzmLi5nky5ohWdU/TwhDh02XCO8cQAzN1qz73u8g0Feax4fpg2cCO1CWO+wOlZC7XK+Iduy7QJ6cmcuiQSsyPacyFm8OeJo5vHp/oU8f9YceuH3g8X5iLYomnktl3ES+qhA0hsJTCy3mivztw6BVPX0zbUKti29nEXCvc4HFclqwpT/IqjT+NWj3MuXK7HziPmdKUbRSTpx8CtTRcijpsnwvfzCfe3B7wKE0yOM/DzZ5pVgvnl3Ha8pFvh8RvP9zwB998BzhV0dpif54zd0nDL9yKudtvSsoy5bQNMbUoNaFSrdth4O37myEnaztfWEMC7WARdVQUms5znFZexVqC1bNm8GjVJXrdemAyuGClKUbvuErj07SQq3DbR+6i+trEzzZZQWGFa9VIzGAHTrVqdqpwKoXObIcvRUixso8JQQeRK9SAbRjOmUjP4Kcmr2IuPcQMDlmPf8G8qfTfnXU/qxpiPQiauYMCSiV1OjjMQbdnJzrA9c4jTvUkS9Fr6Z2wC0pTvNbKjGL511x4HB1Mumk9HAZygP2g1tIrJKWHnJjzrQ2Zq9B5XQNza9bRq5YmOW8sl0oXwiaGCs5xypUP48w5Vy3yvCNaEddQ24u3Q8+7/Z4UHD87XSitmZDPkUWN4Bw6rL62ZlkZQTOAnePLoeNoT9r5VWjmDIUwS3Z5VZ3su8SQAh/Gme1f24ZcbYWMXntBPLzMhS92Hc7Bp0mH2jidjXgUmiwmcGxWcAT/yhjzZoOqdaEzdbHjV59emF2j/DFeEH+qA8A5dw/8J8A/Zmv93xCR/+mz///ngP8G+Kn9038pIv+Bc+7PA//5Z0/1+8C/LyL/8Wff+9eA/xD4QkQ+/HGvYx1IxeC2DVZEiM5vnUBDK6zneSFiVWIz7Lc1zc508Ntv3+A8TBn2Q8+YM3O+KiZssJD3jr/4O19yWTK/eD7zsNtzdzjyfL4gUphqxkcNOUkusPM6mGyiHiPShLlVrtPEl/f39N09fd/x8elZB0u1EFykT4mlKff/dlB3x5Ac81kr/ZdpxHtLQ7PFUzF1o+H/0YZprQnvzxO91zCZy1LIVfj+PJGb2macLgvn66zycdGMgGITN1fV9TLPDUnw/nymlsqn85nOeZ7OF0IX2HUdd4cdU80srfKSF07jTNcC15oJJqwRET5OC0sVhTuc8CZFxY2THmQrA+uz1m6zIai1bq07onxxxOIuVwgdmwXYn4sIH5aZXF9tFOai6VLRcOBdCrTgGWvlm2nmx/sBV818D014yqJJYTaX1+qvixtE04RtIwU2293VrNWh61VFdbLRI5OHY594GhfC3pnxoEYGrn5SHpvFiEUg2vpv6MZUt37xFea6Zp1ZrB3B1k7YwDN5z1eHndIj0QpzhZOc19deBaM9GkwnMHRBMxSKCpBSEzqnAfbPPnMqqpkhqwvm3BoyLfRDMIdVu3+tu1ijI50xw5ameQz7qHGeoISEtIkpjha0igAAIABJREFUe94cBpyDxymrtbcIP38ZrTiAo6mA1xle7z2/fdzxsBs2VfTb/cCH66QkDBPQrap4h7AfIi9VLebvu8SDQWI2ndEDkdfPXCt7PWnlsxxuBH77ds/TnBFRtpxCnq+FT7ACx3ldZ++vM0MMHJLODdauPnkVeY2LUqR9eNVqVDshuqDxj31w3HSJKvDxPHL12oX6z2+sf+Dxp+0A/jrw34vIv+Kc64D9r/ma/1FE/uXP/0FE/k/gLwI45wLwS+C/Wv/fOfdj4J8Hfv6nehVOF2gU9bNBNCFITz2/XbjrUliWwiVbRY9WGgHFHlvOjJczN/e3/PbbB8Wwr1djr1TqUjlnVXl+83Lltus4dj1TqeTTk1LtArr524leRdWyHm2VU0y4KvR91M2mVIa7no/PT3x4edbBVqm0oMPYoetZlsKP395wmQufriNzy/QeY0OsMAZby7dWxhhGmnNDSiOFwPcXlai/WDh9zZWb6NmnyMdlwXu/GablpW2UWQcqCLM2uuain2VwzOaHMpg5523fI9e6qSq/PV3p+8gyNs0e7gK3u54lF3ITRkv6Oi2Fhz5y33XsQtAhXBODePSzdagwyG/VIxum/ypUaqyyDb9Wrs6SvOrr7+u6WIf9KXjuBqWSzrlxLsXygh29C/iofvACuAZ7rwEnKoprm7Ga+jShEJf93OAgoRm2GWGWShOPw5OrEKLwW7cHcJBr5DRXHrzSWHs0vlI3CC1urrlRvXCw+MRq12G1dhBef3kxa2f3uuNapwT7lPjx3YHBO6qdC2sHXUXo0DyLYtTPut439hwNmJqqn4PAzgu9g52LDF1gqpVrK4h5sVxLeRUg2edlHn8gziyhHR5hKsW6i2hmcdBhfPfgud13OO94njL/x/snpfCKHuY7s2RWvxvhzaAJaccU2aeoh40VI8eu42madcjqIIhmcWTRA99HRw6N37s5ktbDce3KZH0ra4f2qj9w9rXNrp00oQuBr24SLzlzWjKXrHOsug1kXwWqtWmXmpfCbRc4dJGPY2bfBZ6mzPOUt00/WpQq6891CtvuB12jP/t4onoogc2p+Ddv/3+KA8A5dwv8s8C/ji7KBVj+pO/7NY+/BPw9EfnZZ//2HwH/Fto9/MkPw1mbaMu2OvetJmAVjVt7/6xh22oBod8a9MUb7OCpeeH9x0e+fPeOoe/YhchlUnXdV/e3XKeMx3O6TEj13OwGxnlmqpWlVt24bE6QjTtcmp70yRsEEByXsiDNMY5Xvv/ZGaN9v1YB5pXSqlouv0yZ0zTx6WVkbg3fR5V7K75FcsHeqzDP6iS6i+ptFDy0Tm+asRZOtSFVK/wuOg4xErsOxkxMn7Wha1sZbEC7bZZ2KHinQym/4tyNUoWpKDUueE+KnvN1YQiJ49AxLdk8z02g0tq2CTsHn5bCS9GYx6/3g4qVeDV9K7Vsfu7r4HddzZv5Hq+F7kafMT8QL0YXBrAbB/Qmehg6+uj4NGWSoJa+QVXOycHTdWZZ2ubXfuwi4uDjUnjKFW/K6+g8xz5q+ht2OOEMu9V2fKYxBD2gQ9UB3vvLyO8+3JCC51fnhZclc5PM9MzU6x5hFp1rXcvCVBxf7Hv64Dcb7Ib60gBcTL2+HZZuQ9rw3vH22LMaQ0cUIsnyKnbM5j20fo9CReuhp66kvXPcxMhL0dyLYj+/j559DPQGHV1qZVwqh0OHjjzdpiVYTQtFPSUsc0F/6MOwU/ZP01QswVGAnz1dySJMlk3snSMZvo/TLukmBd4e9tx26XVTtoNxxd53MbBPSqXWN+nwzjo0u49j8tYZmTjQus1X7Y51TCuMAxvDrMqrE6ygHU30kX3wPAbP85gtClRnCmOpyojyzgoV4dNUGKuquJ8WnbVsyKB7zTooVbup6DXTIOwSOTfiPtAHJZCsXfTGLPs1jz9NB/D7wHvgP3XO/ePA/wL8myJy+Qe+7p9xzv0t4FfAXxOR/+0f+P9/FfjP1r845/4y8EsR+Vt/XGalc+6vAH8FYL/b8fEy06fAF4eO5APFbADH3Fiq8P3zlckUffGz5w0oNFAdBGPHhFa5Xi8kcTzc3vI8f7SL5rWarI3DMHDY9VyWiSZVB0XOvPvdGmeHtWu6SHYhcTE+fEqesczsQ8dXDzfM85VpyYAQgm7It13gdJ1ozfPt85W7rufQNyQvzLWx895wPb9ZKCfvGan0TjuIsSoFs4huBofdgZAnFp/xg2OaK99fZ7guxD7g1qyBwqaWXXLbNlRvtEwXIKZAWdTyIUXP7W7HoUu8LBOXWUVKfRfoO891WixMJ2h+8KTKysnUlIABosJUhOIaV6kEcQw+EAm2uTmb3bqt1BF5rcgaCn+tBm9rFbby+b869sy1rcJgyxHQTpGmc4V3ezVra8A5Vz4umS+GnrE2ircbPagRm5qLacBQE7NJ8MIojYxuasWERuK9eud4eOh7jjGQG/heb8aP54lcG7su8m7X+OV5Rpq6ivbB0znt+paqtMGlNZaiFeNXe3Ul3SxPnK65l6lgDZEN3fW6eAfvjjtyE97XhUMI7GPY7Ba2h9jcyq0DSOs2bfYyRM+lqYvpfdLKdu0Spqqh9ck7huApTniui3ZC/nVQ6S3xzuQWZAupX1/nIUULfFKyQDV9zRADUiqdD3x53HFeZjwQfSA6eLvr+eKwJ5kGA7Fc4CbWLaogMnnrgEsxlbBu3ogekh2eGtR0sY/KQWrweq/Y9dLl6Ahe2WS5FPuzWWfYhFYHyZrSdfCeySvhIxh800SZO9lwvGoFrneBby8TT3PW9xAU01cGEZu983YQiPA4Zm6PSeFxYAjeWFX8sS3An+YAiMA/AfxVEfmbzrm/DvzbwL/32df8AfC7InJ2zv1LwH8N/Ln1Pw02+svAv2N/3wP/LvAv/Ek/XET+BvA3AN48PMjdLpkXvjDRFFpYMt8+XpUKZdhZZ8wasPYTMzcTE/9gjoMvZ2YcrTne3txwf9yTW+HLuxvO00yKgfPLCz4GLrVaFqouXodCImtij+5rWsW9vRv4cLpwnRf6GAkxcJ1mxqWSQlDIwdrpea6G62rLltFF+G6/48M40YVV4LYwxLgJpe52PXfHA3dDz/efnvg4ZV0gXqlq07JA76iLHhotGEe/CvNYdPM0TKVWhdO2AW0DqY1urwOwPkXu9z2HoWNcKpdimaV2k9Xc6LqId2rTEH0guLAt9rIeletpaYu9i1FD4tHPszRR4VVTy4JVEKZ4v3y2mM0FlNcqZz3ua4PTov4svW3eUlRRrUIebd2fl8LYGuI0S7oJfLrObIcNEJrf+Oa3XdIDYKvEXnFzbA4Te8++7+wAlZVYxi4FHq+TZTkI352uvLvZ0QVN5vr2MuFy4dgljbMslZQ62jxtqudLLrwfdaMc1k0cs04w+mhbfbXR73nY9/hg9FIcL6XyXIoOV+36DfbnLWPBfZYZIDpvGlLg0irTNBHd2g101o00PagqTLVuMJWGuL862ipVW1+XKmO1SIrO8aY3u20dfKiNib2+FAO3MXAulecFatOq+mHo6WPUAbKFPa3Op0jTYTNaQIjTNb6Uylwqhxi2obvYr2oIqJ7/fXA2O3HbMH6zB/HrmtTuuYqou+kKVxpE7TZyibBz8KO452lauOaiQtAmZiGicE1DC4gCfJyWDS4SxTjV/sEOjmbXNHaBlnVAfx8Tk5dN2X7wgbHqoPs3Pfxv/J/Xxy+AX4jI37S//xfogbA9ROQkImf7838HJOfcu8++5F8E/kBEvrO//1ng94C/5Zz7I+B3gD9wzn39x70QBxbIHmlowMO5VJ7GhWouggXTCLAOa15btWybtGiXSRHl+U554eGgqt5vPj3Sx4QTYd/vKM1TnSMXrTjWgaR4NtXkdSrabTRtx1ZWwBAc90NiFxJLbsxZoxlDcAwxset6uhBJwXE7mAKyFKa88HbXc8066luKMi3wnjkr/LPvIj86HOi95zKrMKWLGpAzzpmPl4vS9JpQix4AxRZNpZL2inV7k5a6dQOIjpt9ZzkBJofvEjGp2vNlnDRAhWZVkMZEeu+4Tlnl8NPCJesAbF4K16VQS90qqK2Vldc5ZcNStKx6dvJqzbDCUVrgv9p1rHTRFQdf/7y0xneXmeclcy6Vj+NMQfF7hSJka+fHXJmzsBRlDpUmJjxSNe91yZxnnWGk4LbB6XrarDbFzqmr582Q8CYo/Gx6wfOSeRwXplJpAlMufHO6AsK7feLdTnMinqfMNRdyheuspnYOy5jwKmZ8nLKpvM0Dpyi8VqtttHZ9bneJfZeYa92ue/LO7Mcb11yZS+FaC0vTqELnnFXFyo7SCbgebl8c9hrf6ISTVJ7zjNTGPga6GJikMhqkiZg9tLht41wzsSPO7I9VjHXfqf2HJt6pYZ9DD7Vms7KlCksuHJxw7BJfHgatcg2Ll6bQZZV1kG48f++ZlsJlzrzMC4hWx6suQDOU9SiPVv94uw6fwzzFNAalqcq91GYEDP29SFPGl1XptapwUy3mPd4HdjHy9c2eh92gm3dQ+OzYBytgzCMpQOz8a5SjdTMrlLm+rhQcedQBfDJfoa/7YYPGwLELYbOU+XWPP7EDEJFvnXN/3zn3522o+5eA//3zr7GN+zsREefcP40eLB8/+5J/jc/gHxH528CXn33/HwH/1J/EAlqdBkuDqwiXRfnZxdkgFp3UB8PjBHUEDE6r/rk1gmhLVq0STMHzbr/n+9MFcTrUaq2SUiL5CM6T3A2n8cyczUJgvbEd7GOHhEYt/w93b9IrWbblef12exozu5030b54XbZClEolJAYgJAZISEwZMGUEDPg2NagvwOdgwKxUlECQlbx8yXvxXjTu4e63NbPT7Y7B2udcz8wKyCGRdxAR8oi4167ZPmuvtf4d7FvH559d8f37O3Ym8vKq5W6OPC2ZUjStdbw47Hg6H0la8Xie2TeZY1zYNw5jRemY0YQKMF04z5SFk26SsBd0FEM8rxIpRMYY8ErYJOOSxVO/KTVlC4oxdScI8xSxvqowo1TgUsdMrRVda3HO0NX3rLXCD59r0Z5yBjXji6UznssLT0ZWFXgJiQ8xYzQcWrcFYmcKbeuYl0hS1Z9Fw65mDKuiKFpJDmwuz/71H60pClJIj0usAdpsOEVrNNdeTK9ygVd9U9cO8LLraMW7mNpMbcU5pEK1Wql2A7Xjq9PG+tO/utzxslldR3X1oleEuqpQCp7qyJ6RdCaryvbyjVYEKlCoMyEr2iCTXtdZrlrP45xIMfA0izL7PE2iWNWV+VH/XlTh/TBx3Tb03nDZei69425cuDvPlFLY7RpOITAOI7vG1nWOFGSlijwHelUbC921tRaV5bNcf29df8+i4LL37CoxouTCTCamQJgl4EUuJEWoxFRdZKLTWjAQXbGSh3lmzPIsHbzj9W6H00aauMpsW593+dyLgMoaGm25qOBwqpoV6mWBqvhg9fxKFKaQ6mUv39MbjY2aU0gSf6qesYRSRD/knMPU719ykbNQz4Wsgqq9fFF/pyPXWjzCUl1B2aLIiyQFGq3EK0lprhqLKg33UyDVvb/WW7QEMYhSv+0t8xBlolDqORyoPhZTSuSYMUpz2To6KzXmyjq+HgfGnOR91z/e5/9jWUD/I/A/1VXO74D/Vin138sLKf8K+K+B/0EpFYER+G9Kbffquue/AP67f+TP+n/9OsfCOUbOMW5gj9ZKBFLxmb8NoIQdVVOfJFow1P1oRg73HDNjnrDG0HpHYxyP5wHrRKTjnGOOkvJkVQ162ZjXggVcdF5MyRrP+8cT153jZWukQBiL8YkPx4njeCamiCmG4zLTN5YxBnKBhzFwaFvIGecMCdBaVAVXfQcUrM5MoQqtsuZunNHKkHUhKcWUAkFH2kZAyxwLFHEYDZVPXpRMBFpr1kgo+e8lAQsjPkXea8EAkMnBKNnJO/M8ijqjZYWVxAfm0jumJdF6Q+uFPqcV7L3QAXfeUjpHKPB+mPBafEtEeCdS+wa1jbeo52UG6tnqeMlrdrBcYCrDOSZu57BiZXW0kBH95a7F1BWEVWpjX2ilauh33kDiWJ5dQFfFcoUAycXUaEYR8EHBZNBF+N/HRR7ojR1SpONurTBS7oaZ47yIUZ+CqAq/fzpzNQeuWs9nh5avYyIVRYnSlSvY1gSlrrpykffibpyJ2fJq12MsfHHouO48j2PgIdXw9HrVVUdr1nd01c3YKpTqrd2Ec81HGpJSxIBsqR3o07wIwF7fr5QyE5kqnqZBiBdLVd0LwYDNeyfz7MWTCvz6+pLOWzFNW4WRWqOs3SxZAmDItFZe12mJIpA0olFQKGIW23StxAo91zUxFcPIq69QgYOXdMD1Qs21+9YV73FWrK1jqlOrUoD8rHVFppRcHlpXamjKRGRqfFwCQ4y86htedg3TEkStq6HkjMmCH940jh+mBae1+AwZeb/Wqfc8RQ69o1O6ppFV37F12okZ5w3aKXprt3nTKMUr3/BmGSu9dl1E/sOvf9QFUEr534D/6O/98b/66N//S+Bf/sj/OwAv/j++/y/+Ma8j5Mz9smxpSUsVz7TWYlpLHoJ4yyuhrJGhM4ZhjnitaLVGU+PWlKJg0UZQ9svdnl3Xcn86UmKha8SydZoXstJcdjtxu5wGOVRIxzguAUqitZaHYeGyEfOuXAqnCJe7lhvrGJfMNC6UmBlyQKE4tDvUfCbkRIlS8cRUOgm1LWaariHlzDAHvGtE4KUjS8xY7XiaB07LvMUadnvZU6clbzF9Wssl2TqhZR5D5P40C1jbGKwzpCLdlDHC+AkpV7ti4eo7o7F6tWtQWKuqv4rhdApoqxhCwjtTATQBqER9KwfN1C7HKfj80GGUQSlZJSmUGGPl5zXPCuitbc8P52kLx1n/FWuXXdb4ShFf5UortUaTycSy7nqfFaFpvUS0mGZ9DDGoIms+WSPUAsFaMKWwrueQmqGwFtBS1zCX3tI7TefEyOui9WKMpqSYhyKq7CFO3E4LvbVcdY4xKsawbIreEHNdi3ykNaiX2FjDxkuRc793FqM101SIs4QZrYV/U+HChp8k1oBys2UhK7kdqyCs0FTOfigSui7v97N7biorxrOCmLJWPFhR0GcK72chDqZaiUuBz3diZgjPCv/VqtwawUHGFHiYAq/2nTRws+Qu344zXx36DaOQta54ba1HRtXuV0SEMg2iocVyT5AlnaqLl3ohAfRWE2saWT0NrJjTKkg0FclWSvCSsRTO08xUd+41rkIKvnoOoiFXx1YlzKrP+oa308JE3tY8uZ5prRRtpcNe7xq+/nCSy6aIVcSa6ua1pneakDMuF4yRzASjet4sk7gc/MjXT0oJnJE9rlEVhFXPXjJFafrGMs/Ctx1yomSF9x7dCSiaSiYr4Ts7bdFK07qOguI8jyhVOI0z3jvO44izokf8+acv+be/+R2Nc3TOMwRx7xShaZaVTZX7H3G8bzRfvX6BCYnHeeL7H24Z5xmrLVlLYfTO0njHeVJ4ZVBWHDxjlvXOzinmAu8/PGIVXLYeFRNFa6YQeDjPQqusBmDrwziPkXlK23uz7x0vuoaDs1u3l86FozPi1qnlAbJWlLspZ7Kq723dBRfkv+mqe2VjxRzOZsEn9n0jZnBLZNf6Oi2ULbIPPtr1lzWso/L+rZGuD2hKda+pD3DJ1MsBUHCOcRPqsa45UeIxU9ge5jo8iHxfK3bG4ZUoP8XzpVJTK53i3zcgb4unOgU1NUlMrKzrHlaURFB/6s5qLpqWpsaM6vpKJFMXXuwafljdHsszHiWFPDHGxMu2wWvPQ4wbbpMV5JDonP1I/yHFdkuU2zAHxWOIm92xN+rvvPfyu61e/0LSXGLmN3dHLrzl0gtFdr3I1imutVI4j8ay8KzPqI2/4A61wMYi2JXTihddW+cnyQJ4miTEpTOGT/f9huVYpTDW1u66znEKppi48FaKqnUsWtMBF1pJZOsSufKCLsSUN9GX3AJSaG3F7VbPrIwIxcYsmQPPRoKKG9+g0rpSEsKEMQIirzv6mAtjipxS4hwDK9teunOpR1bLii3X4qvMyoBS9fXJ53KaI6eYNkZWqWfSaoUvYgQ4B2nGfvFiz7d3Z2LIaKdq6pgQHTorArJzCHRZNgCtMdy4hvdh+tGa+pO6AIANqde6AnJKQh3mUshWYxfp7K6t4SllhlhoG8c0T0ikbS1MpbDrG0qG0zhgjWJYJnatY1wCylgudj3H88hv//gtcwx0bctF2xFDpGlajuOIcYZVmWW1oet6kjJiakXhb799R8iJvW9pvN2K/DzOTPMdL3YdD/OMLUncIlXh4FtCzhyHiXFexMM+RnzjuNn13J5GAWANOGcqWCQsh5VRpBWVZtjwqpOJJOXMU4jMQNvZauOgNtBwtcJbedsKaK2pgdvyM0y1yzXaMC4STQiFpAVsDDXqkFLwNSVKIXTYpJ4LM3WfmlLGW8tN14qLYqnhL6U8F4NC9afZ3urtSwoIm0XAsvGjxcfnwjv2Ri4uV3EilGLImTFJ+VpDw8u2s1dyCdY/M0pvtsC53i7rRVNWqqGGV52nKF0phzUVqu6oxyXxumv5sO8YlsB5idtDv7GcikRPvto5Wu/I87Kx2lJWTCFtv2uq5v29s9uFh6rMtixdqFVyUTx3/tXCoH4oWUkz8jgIOH0/Luz9zMu+5aqxkglcKoZWp4V1pTKVTKtEjOlYufMQ1mAZJft0q2Vt+sI7XMWDphS5bhv2jaf3ThgwSWIhcx3nRIRWOFSPqSUXHs4jh8aLfUSMtNbSAB/GGWdspWWL3bvVzyymUvGiXNlSucClc5zGkanAzogCV97ATFKSO5HrLxJKBXSpDps5M1Ul8bpqzPn5bICsSSVURrCBgNQc6nOFVjyFyPt5EQ3GeqiLfGavneez3jNnMF4zhMjLvuFPXl/wmzePnEOu+J40J2IPnchkchInAu00L7Vnp/59LU59nf+Imvv/q69UKs+2KibXuDqnDaMuYDUqiAlTbxQTEac7RmUEADLCAw8lQcnMMaDWuMFcaI10TSkn5nmmlMy4yJ6+9R7rvBw2Z/i0uyHGhadxxNqGi66l945hnik58Hha2LUNKUWsMQzDiG9bLi8ueTydmecFU6TANNrQ+Y5TDAzzUhOjeGbB5AIq8/503Lpo1xiWWaioJYkS2TvNdSegoLMar4XxUYqsxe5DYEnPfP/1K9fLUdcHvVGaV52nN4bHEBmiMB1sMRKRmNKmJFUaGmWZs+QDd9Yw54zNotLuvdgsJ54dGFcQNiPBJWEOtat9Vi9uq6Ai64cXfcsUEk+L2OHaqpBztYubV9aFvCpiKsy5MKuMq+C/r/a7SikesjBS1pXSOj9skn35NhhUFbVFnnckzxqQXMSOJCMPvlIfMZfqBbbEhLOKn13toYhP/YfzxP04bf4uBZlMvYNr44WqOs719YndQKxyWq3lXeqs3awJYi48xSTgYC5kLYVTQFg2/vrGNiliBz5Xi4dEqcy6iLNS5DWKZUkcnKMxFl+VqNZKhKpS0GiB1YuCThtOMZKTkDQeavaBAg6N42f03M0zr3e9ALmlstTqey02zzLVpiLd7bpyKbkQanpZVIp5CfRG4ZXlnBNDUeyUxpS6b1GVsYMwmkqdEmTVpNkbseC4rorlOQrgmlnZZYpjCByDKMWzEqLHOlHVO1so09u5kHNktpVzZRgpYSiu2/gxZ74bZD2zuhCvYsdrY/mk9RgUOwsuw0OCYYm0RqjVJYmIj1TotAVU9b8qKF3FaPV355/KCghk5FwDy0GmgeMiIQwxPhdxETUVco6c54VD4ykZnBLQ9mEJjMtCKpnTuACKxmmylvi/p2HkOCxc7jpyFnuF4/nIaTiy7xqGOXDQlrZtKUURUuQ0TrTes+93PC6ZN/cPxJjxzlPQtIc9cZlIs6gPjbccl8hF1/IwTkzzwKHzPAYBE1tvcUjmQaqKXlNBKnkoNDEK391pzedXPZeNBLqvisFUMj/MUTzWs6QwbYW/VHwA0SDINqXuxmvha6zmUnumNEOR3APjzLYucqamK6Fx2jDrwsOwsGus8LK1pvOWRVVmSPXpX8GsUqD1wvwRwVD5SPAlhzzUHXNvDa3V9N5wWiJjqIyikAg5y5RQBQGlAtdLKdyHxN4aHIpdnUKcMmInva5Ctltnqx3bV+MlEWqpXkGmysvWQrCkzLcP50os0PgaR5lLYddYWq0JoZBUkjwDrXi9a3nReU5Lz+0w8WGYyEX8m5RSWA3XnVgfn+eF3jqGGChFKJTOKJwxm/+NKoqI2GycK/Mk5EJcBDztnJgJxrKqmCV6MORnbxtT9Sw5F4aqUs2l8DRFQhowShLKEmxe/sZoOiMaAV0ktewhBJwxkBTfPZ550Te86FusMlwpxaGG/sxzlAD1etGUetnHLGEwKWWy0pRKYJAsDjnLGXFRzWg8cGEMQ4ExF6wuWz4BudaBwpbtIaFEipdtI81TESX0eYncDwveSscdSmHaxItyKEKSVVxaQfIqnlNK1Sl1XZ8KHX0lYZSiUFmUx1NRvB2mGlv7/L0Lhc+ahlaL0d5e29q0KHbKkBCCRi5yAZi6qmudkBNEVVyw1aVgiZGcC6d/KheATK7PwSIFKjAAFIUphaveo1JhiAsx1+5TQ6MUxiScBuqIPtQC03u5QY1eAygKrw4tUyo01rFrPa6Ol6dlgZTRObHMM+f5TGMth7bFWMdut+PrN2+ZlwVKYYmZw87x4XhCTZmLrkMrJ/8uF5RveH88M4QZ7UApx8v9jiXLQdfIwXXObN2QVdB3DZ3W3Csx0Ooby+u+IeXCEhOPU6BvHUPOnD6iJ6oKysr9Kf8sIJrI+jNQkozyb4aZXeO4aC0JuB1noOIvWn63ShOnlMTeOemivBSXtERaB49joents4YC6qp/BbIMQ0i0Srq8Z6KtrAXu55mlJBpjsCgareggGn1PAAAgAElEQVRaR2gspyVyX0PGD53bzogDvDPcz2KQdl9XQDtr2FWL6mWlkq4LsFr11bqfr6/zwjk+HMcKfGpaX3fo1Uvmfl54CmlTpWueQdrbeaY3ljEmXu87Lqz7aPUjjJS933Pded7VjIYlZXShJntpusbzxcWBx3Hk3XEg1WZn5zSPk5yz3hna1m0ZELLCU9uKb+tYEQfTKRfRD4RYX6sUe/ECKpzmuCVjjTWwyCjFw7gI7mTl+x7q+mGOgb21DFGmj8/6Fg10yvA4LSilxZjNWMjpo/WMxEWm+n5QMkOUz/9V30g2QBZ8Tddpz2nPMQTGVCR5jIIthb3W2JQ5BmmYbAWovZY1paFsUyOs9hRC5b0bF75/PAu/T475cwhQLc4UcdP19ZLW64dYGw61uW4KEB5CQmtFbz3GGKYlkCKMy7JZogh+Ix9OpxVGBn1JC7QZrYw833WquF2ipBo6jXEiUjzOgZ2VycBq0WRoJRPhOSaeUvzRmvqTugBQwktfVmQ9i9mXRXF1ccmOwMNpZEqFXdcSQ+SmtTIyCWWE1YvbKsNFI8X0blrkcFnDy85L1CDw9cNANonWeXqj+eE44ZV039YLrzzEgvKOp/NE32Zu7yOp3rwK8TQ5zTPDONM6Q0yxKi41+84y50BAEsQaYxlDgKK46DrCcBZaWs4sIXEeA13vuPINl1pzHxI3reeqFf67iFUyYyx0TtMYuK+xhhihS65FrqhnST5FOsiUpfty1Rd+jJn348Kvu4aXvUjd78ZFwqjXMVmJbUSKsMQgqwotneaF8zyeZ44Gfrm/oK7D62cgBSrkzPv7R0ouvGgaLrTGG/nG2ijOS+K741jD0xOd07gi8KpTigvnaK3hHMWnP6bKJTeaGFUNvpdVUwamJXAXhHKY6+fstcZay85a3pwHtFLsnOzAO2v5ZNcS5rCB23NM9eKT22+M4uRaoBrDqXrG5NCG1WiuYhHCVHoO6tjwFTTG1KhLpXiYAvu+J8bAOEecMZXTXdg3QpN8d5aMh5wLv/zkkpTkrK8+MVZLBxmRbjPVbjcBKstq0SixKOicCCLmlHFasWShSZc6KWmlmJeIKjWZzSicUpxiqN7+ioewUAoc5wA506+fzxRk1aPgxe7ZoXMLB6pCqilJ53/hLSkLiylrWcnGat1cFJuSeQxBcLTTyM5JPrizhjfHkWFc+IufvWYcBslVyGIgt4oA51jFcFnyBFaPqRW4ylnU6JnVUkIu0Skk+mrPzooL1VZitWm+cULLLFmEeiiN944xFw7GEZxMWevm6FXjMYXq08SWG7GkQms0rdakDAdjeLlveBwWDHCuJoznUVhkrw4toDbmz+0wM7JeTP/w66d1AfDMZ14LecyZOcEhBHLjuDlolhDkdm60oPB5ZU0g45HReGcI88KUMr98cUFTgTyQ2L+cC//88yu+eRqJOfOYMkuCXWcJYWFcAoe+ozOdeJVYOaBTCOzbnoaE846naWGYpbO73Hm8NjROXCCTlsCQ0zTSKoPThnMOWGVRJfNqf+DtwxNLEZWn9YJjfHVoGZfErz+/5tsPtzLap8IUExHYe/Huz6Vw0XiGlWWg12IDlQsCRW0UxlXQknLexvH7ceFpWrjqGg6N5RRiXeWAVuLFVOp4rVHsm4bTvFCUZCHEknnVtcQsU5diBSWlOB/nzGkJlCKJZq+7hpfO03jh0b0dxurfL0lNx0lxaCVMPajCUMHIg7McvIR0xFQE7E55E4ytHfn6cO+d41evduJtz8qfKfzi0NXuuQCyrooKvh4mWVnlQmeEcqeq6FCv4fIfAcKuFvwxJZZSeL3rJJSkrgxs1avk6jMzLhJdqdezikwoeVn44rDjtw+PWGO47nvGsJBy2swIl5wJIfNwXiBKNq2qlczqGj9ZCqecqkcOlaUkl18sQitsGoOxikUlhpAEB4t5o6POQyBX7OWmd+Kuiyjid8YwJrkImxqtOYbEEhOXXSNK4SWREHX1q0PP3otQcOXXJxRTEp+emDPDLHGOrRMTt2hytR+RyaHRmscp8MfpyJcXvbynFXZprObTz1/z+atrfv8mEaYJqoyzIPTZD5PQxlWSwCjKLPm59b0TcoVgL6WeW2ETPduJrGhQyWprqD7pO3y9LZSSdTQkfGM3YdbBWq694yEEbhpPpzTjEsnq+fvkkikRTimDs8KU0oov+57OWO6HiQutOKnIY4g8LoHbceaTQ4e3mtuTmFd+fn3xo/X0J3UBlAJjjQcMMWG1oTcNLw8NRhV8mMHqGlRe5eDyHFOXf+J7o+D7xzOvdp5P9j2tkWKzjs9eyc43lsIvrnZ8d5w4hcLFrpeQ5QLOWXJK9K3hOAg/PRZh7lzuEl++esGyLLID1IqX+44pJpKmWjQkHs8Lu30v3WJnSUU6a5vKll/w+rDjbhhIVrofVWBAMy4zTx/uWbJ06qcl8DAsfHLR1eIind2l1txb2Z2ua42NcVD/eU7P3iKqAoFrtzOnxDdPZ3pn2TnLhbc8hbhdvrkCnbvWoit9tHWa2/OM1opdI1GX5yHQNGJ1K5eAKB6O8yKiqnoJvR0m3uvAZxc9DeLYKUQLKZSpFB6HwGATvTN4ZxjJqJRptMErRe8MSSvOw7wxhnLVRKBAG/jZoeem8UxLFOprLXJKrbRBuRb2neWv7s8CJtc1z9OyTpEabzW9NnijmBEMQilFZ4UVlXKhcY69s7TGEMjbBKCVeDMd58hpSZsi2SJg8sFZ/uRmT1KaxnpsMRycpeTIOSdyBbwbY+mdYQlR7KiVwimNtYbOyj7YIsBpoZDmJBTbUihao2rO73EUC3FtxMMmJqreRG8rM2MVO2/5Z59fcZ4iWRVOU8Kh+GGYMAF+dtmjS+E8BaYQeZwWOmcl0KVOO7fniSVm9s6iKp5VikxNRitijZ3MRbCmxhqcNXTOMcaEyoL7TDHzya4R7y9Em5FS5mXneff4xO3jkd4bCZ+v2MaUC++GuRqpyTRTUqSUzL51zCXLpACblmYr9EUmgtVJYKUzFLUq0q2sxVaqcsUbcspM51nOl1Fcaoe3hv2y0GjFw1EsVtxarurERdUTnEOgKHH07Yys3oKzz8pzuwL6mT/cnzb+wdW+42kIP1pTf1IXQMqFORRu2hZ0oQEu2wZdxNPnFBOX1ohd79ru6SrAUCvKrlhS4hwiN9nTWulWYhb6GAhDwBnZx1kjb2TOq4+PAJvzsmC9YQqRJYtwqzOezjcc+oayLPiSsfuOYYAPxxPOWea4QEqMMbKoRHw8CrBqZC2UUyGSeH8807UNIDa599OIMhqVE+/HkUAizQuHfc9jiNsDdZgX+r7ZGAUK+KTx/ECRB6eCwyuDasnp714IPC+/11XGwxT427snfn7Zc9NK7N93p5GchVt9sXNosthbK+gbw8MoK4FL77kdJ4zSLIjgzXjBMh4mAeGlOxeALGeEQpsyXeM4tI7jtGz0zBUsW5KogV3dF3urSK4wKei152mOm8XxNgHXh6LUrnOsBVPyhVerkfUiBPGJWX1rKmBe82xXoolVonC2qormilhRTzUkxSjxummM3jAQW9c4K5X0NEvn7FzlwwPDHPlk39Fay7txplGW3ltiidU/H6Yl01gRD161DeM4Ecoafg690lw5uzU+jRUbiGSz9MJpVTtnppSpvyar9sJ4K3biuTAv8n1Lhhf7BlUyn140zLlQdOC69+ynhhgyXWXsXDSOD+eJh2HmPi94o+iNoXcelRWP48wYAp9d7CjAELNEWlZFXVqBVCQQJaTEofUbDvahYibeyl5/PcZaaUrJXDSWlOtzjzR4X9+dCKpOobGuE7zDoMUGxRrmRXAQtQInK1JUC34uhXlJNF68wVYNxBwLeys7eznPVMUwdcrOHBqPM+JPtNOGneuIOTHOkbJI/GnS0Dpba1GdmuukOFWtg1WKvXfczYHWOWHA5cyX15c0znIOgQ+PZzpvcf7Hy/xP6gKgwKt2L+uAJXKza7D1IXdG4bSInbKxYKV7rWpwcQFlHa/hZ5c7vrzaQVnRc7Hh3TI5tXRRX9+euD+OGOtkF7tEDl1HiJGHYeSyaelds8UZioe8oukbpllsn68u9nRdx/F85s3jE1EXri92HG/vSLGw33tAoaKiFMMxTBJoUrNQg850TcNYJeXHacEYxRIK3SxeQFBonOZhDly2TjjcSiDznTXsomEMaaOtLTlv7wnPW6G1Sd6+VmbD0yJFSquEV6pabiv2ncd9dFkAoAo3XUsMgc4o5kn84cmFy52niRasZpgr/VB4dKwmXp21XHReLq+rjn5wvH08bwrU2otVGqPsqJcIMRS61vHtPIhwR6r9StXeVkE5Ff769gmnFL++2nHd+uf1wRoYXt+XWOCz1rPEKAW+gmur0M0bKdqFIvvq+j+OcxBmCBIjuXaKql4CK4lUAeclYa2ql7K81iFFxqdE7x0RS9ELt6eR69bz4nBgWTInHQhxIiyRvLN88cmn3B+fOI4jU0p03kjWQc4S7Vhq/GVtAtYGwSphmWDlklv/W6cNTnUk4F0eeaoK1zfHkffDgtGSRnVhHX98d+T1ZcvBN9t9a5WkcEVklThWgDjkwlXXim9QyLx5PKON4ZfXPeMCQ5bJeRVF5ULF8eA8R1pXV4A51YIqHk1izSCfec7QestYw2NyEVW4tYbTvKCNmM61SqOs2Ejret1YrSrLR/EMCqxUTXlNIRXc2lDU1el163nZtpDLR3Tk1egw826YuV0if/n6kgaYlwiIfujlruEOcQpNqXDMkX1rIQmgLMpihXOWKWbOIVamVsYbwxcXOz6ME+cl8MnlnhvbE0PBKsv0T2UCUMDDcaBvHCklxpTorDA/1uAR5wydl17+YV7dF4XmN8YsKj/gRSNrEa/EdW+lwjm9Mm2ElvZhmNl3DUZrfjiP5Fy4O40YAyihi117y7BEvHPkmBmnCZ0DoVj+9BdfMg5nvrrY8X/+7lte7C7EAOo4yM7YSYJYCmJYNYa4XVRWCSNkSIELY9g5SzaaIUiebyTLaEiNsFRiGyAGV7rW4/rXIkKx8BFPfo1izAjz4O9UflZmg/xhSJk3p5Evdh1Gw8u+5XEWzYLr3BZMUmqbHUPAoIlKcbFveRhmrNec58gUEsXp+vNhoyUVODSeTy67jVHhlObl3mO1YAQxwXFaanEvlY0jttIvLnrupoVYL4rNCqLSGZV6vgRSKcwU/t3dkc4YPt8LLVM468/K6likKP5i39WHRXyVSilifbwOTEoM4taiqq0WrnZdo2gL0ayGXs9JwiGXKrCS7m4MlfmhLR745vGMMZpxWbBGsmrf3D9w3e44eMEEbsfA/fmI1mLK1nnHdM546+gaxzwvW/FPOW/6krWk6a3QPT9nWlWL8CTCvqvOybQbE91O+PNzSMwxMMaIUZqSFTvvsFoTQhTmDvD5vmfnLO+OY81mzjyNM011EY0ZrM4YJTjRVd8wLqlSHuurrOc5gYjoqh5iV4HrtfsXoF7zsEwYa3BqvUAEYH+1axliYsmJkFeKLzWER86w2LAUUMIk0lrVdaf8fFM3AiFmuTTrWmvXOFZe56pNSSKw4cO08G5aiBTOOfHnNxfsu5ZpWQgx0znHoZWzMikJI3oYFy67hq4V5tjTuPB4PsoHlBVzNe2bYiLnxEXbMBD5q+/fcdmJw0HnHeP84/ldP6kLAECVSKMUV7sGZ1a/mPzR2C5jE0XUcGP13b6fFqaUKLnw1a7DFEiLhJG0ja12yGpzNMwIG+HlvuGPTyMvulamhSQ841zgetdDzgzTjDaWkjKvL3b0rePuceDmescXL674n7/+ls47Xl9dEuMdc4i42hOqouiN5ZQiUwmkXOi8QRnFFKKoFlvHHBc671FK46wlpkIMkWylsInnuuLVrtl2rauRldGal33D7TTLoVXPlX5NOGJVvn40AlScXZhIRrMUeDvM3HSep0nUrPJ+R3pvq/JRvoYgHG+UovUNnCdaL6rVcYqsOaUCRoo83ivDq11Lowyh8q9L7cBe7Vuudw0xwu9vnxiXKKuKWrtmCt+fB1lvsf5OzyrYbctVR0JtVlBYBE+/Pw68G2d6KyHmF97ys4sdxxR5yLLjPqw+ojX+0SLFYCn1DlPizZJrMckoHseReXlg5yyfXe7onHzyD+NM3zjuzgtZC/NmrAVWEemq9TNLoXOWF23DEAunympKKVAwjMmAVYRQeDiNXPcNpyngrOaHxxGvobOGIUSxd37+eLfLX5LFxOGy5HpRZkWuUY2qwHXreJoDSkPnLI3TzM7wOCzMIXPVSfRpWYuekilatB6ZgzO4i55cFMMSuB9njjFy8I5iDQ+nmc92ntaKAnffWKExLnHDpHK9mFNKTDHxopN15EpjBXHI/DAI+NkXaL2jFHGnVVpxaD2Xc+B+mAkxgW+YQsFsSTXVMFBvR2VbQ62sn1LP1hKTCEvV8+RWioTPlDrpZSQT++0wVyNK+HCeOc13/PmLSz7dt8RBrGVaa7jZtWKuWApLSKAV744jOeetlsn7mj+aGAvHOfI0Ry5bV5ueQCyKJcw09u91dh99/aQuAKXg833Drm02OlfOaRvvFc8AilKakiP3p4lcJCWraCnq3miJPUT2+UvMNF5SwIYoNsumSsF7a9lby4enQZR4ruXFrmEMQuFqvWWaI9pqPn1xwec3F3zz7p4/+epT/tlf/Jx//ddfY4zj7jQzh5GezHmZ2e97sApdU5N66whzpHEK7yRpKJbM1W6HKZnewKvLhv/79ojWYlkdQmSo2YhOa1JImMbJCFzE85xqhrV3li8vdvzm9nGr7OtKoqJYcsBrd7u+n8aorVMblkjWhWVYeJiWjfnyMGZOU+LV3uOchKgnBUstIIfGYmxlORRhdaT10qICbTlzaCwH63mcZ9mv6ucZJpeCL6IB+LMXl3x3PHN7Fo+TrvXy+a63V/1bTtX1FHmg15AXscCQS6LUrqGUwnFJHBdRt/4wwF2IHA6eSMYXve2lS4YlSR5wh2anhGYpIDCEOk6Oc4BcOOfIEBLHORAo7LwjzVFEdNpyjEvdEZftAos1N0Ajq6FlSVijuDvPhCVxIoDX3A5ndl3D1WFHGAMpZEwF8i8uL3l3nvhkV/DV5C+rVQm8Rh1WgL1k0YFoiEVVcZbw5BsrlhC2NkhLkIjR3hn8ZUsp0GuZupcK3qoiqnDZWydabcT4rUg4zpxE0T0Aj8eRTOF/+fYDX+w7fnkl9tCNNbTWcDcGcpFpYM169kbjnN5W9NZoHufI7ThJEA1q+/yrxyAlZe5GaQRJmd55jFUwRYIqmEZX8FQYXEtOkNlooNJjPetoxPjueW1sNvxM1lAFMbbrrNlIBqsdxLhE/o+3d8w3B7662nGeFpacN/PAhxBlmj2vliHyu68KcLE0EV0KPDP4HitpojWOT3YypY/zP5UVkFKEOsfLL0zlEJetc51j2hRypvpxDFFQ/ZAy8/S8YkkUihIAWUdF0GtakMWQOC+Jd2exH55LrnzuhbtzJsTAlzeXxARd23B56Lk5XPJ4Hrg67NDW8TAqxjlx0VnCMkvSl3Gcw5GpDHxxfUVIiTlExrhgMSxFmBOqKF5eXHDZdQzzzFVreD9OnEtGh4UYE9rKznnnLH9yJTS485J4HAM3vQddgzUK6AyvWs/33m3MG1OzSdeQlFLUpuwFNoAt5oJKmYNzXLaOFDMnrbZduOjwMvdDwBkJL19D5seUUMvEofUMS+WLGy0OqCtbS8kYfd04KXaVz993bnOz9AjwlSm0Gr7ad4Qg5ninGDaNwdoJksv2+xkjKs3WGE51qqLIdLF5zivEL7puyDIwlkxTVaqxCN1RK3E9/X6cCKWwd5abxrEzlr5eBBHFFCQveU2Jo14MoWQehpleaXJOLFrYVB/bmOva8VLP6BSjdKtK0TtN4xoihVOc8c4ypkA4Ldw0st5wGYrVOK3ZXe54Oj/ysvPicV8r5mqJbpTw5lMRB1hKJi3y85aYJSNg7Yzr38fKhjrsfI1whF0juFPIopEQbE3celMpnFOkw255C19e7jgukbfTzGrDOobM396f+TAu/CdfvsAj+M0XVy2Po1yiU0xyWWmzLuApIIW94lKNMTTG8KJr6BvL3RR4+zSglRAaxiVSnEQmLiGB1TSVbZOL0EudsSxz2v5M6g/1LMiZ3XlHb6zgPM5z5aX5KvUCWsOC9s7y64sdfzwOjOuuuX7fb48jIDYZsg6TbUanFBfO8DBHoenW5kUS9oTIsNq2POMT0ggWBY8hsk8yoTzM84/W1J/UBYCier4rLrykX61dZF2/SoiDrfQuo2idZYqivJuCxCA+hsiuMfIAIKPqXIp0T0pzjokpJL5/OOOteN/kJB9ALAmPrFV+eDiJaRciiJlD4PrigtunM3/zx7f85g9veXl1IMVAKJp9a/mb33/Hru95ebkjKziPM23reNUeuOfM+xBJKEKQhzCkxOM8cjuJGjJrUEaxt47TEHjROS693Wya+8ZyO490IbE3unaRhaykDfp01wo4LueMsLoV1kK4dm1KSfHPRQRmjTbcdA4PJKP5fNeScuHtOG/eQksSpsgQYt111044RLzWXHQtGZjDAukZSC258Nm+49K56o2SWSIMx8S+tdw0jQB5+pnPr4DLznM7h63jr43SdqGsK8FS4JO+4ZOu5fvzxA9Vhg/8HRxCUT3bpbkS1lnKogJXhbkU9tnwtESWesE9BYmV3OnIV4cer8Rw7u24kHPB1vSpbZ9dcZVQQcc5P4evOC36AlNEvbraO0iWdcGIwg5jDI23PIYZZWqGgYYxBYoRvn+nDF++usKFM//XSfGUCxdW42r3r6nTTBEx3nFcaidbCCFXBW5tkHJCIzYVmVDZY5lpSfQehimRnCMquSR0kXOllfooXEYmAQHbxTLCesP9/VLP3zPh5nEO/P7xzK8ud0KvXRKHxtI1lvdHSSFbtc2RwuO0CM6iFV6LJcneG7EjQSjjD5P8flkJTlgKeGNprSXGAEqe8ZILWZc1KkNWjPU9K0XOy1Xb8KJruGo9uqi6fi2UJMpfY0wV4+W6uipcesuvL/d8cxp5WJ478tMS+OvbwIvO85cvLkAWC7RW87NDR+8W/nCcoK5+hIX0nEfy8XJHW8O+c6BgCIn348xN27Bv3I+W1J/UBVCKdER348x5MRyqNUJbrXoBztPC948D+8bxw7DefOI6uYTErvMsiHTca5GyiKe57MZjfWunLEHX4xQk91dLp7SOoVZpdl1LAR7PZ76/y4whMYbIu7sHjmPgsNvR9z2Px4GL3vK33/3A65trDn3Lf/Wf/8f89nd/5Ju3t7y9veM8i6Phi8MVd8dHGmt5GkYaK/oA0T88r0MOneMXh75SXgtaa6zVeKX41fWebx8HAaXW0TXLSLpzlqZiIyULYLx58ABrBq/WwrDRSN7CUjJ/fBq49o7r1rHX4vnfO8PbcZbVmTUoownLmgBcu2skVEQFWVEkJbGYGsUcM3vneNE0PIwLd3MElUXJqMTM65VvZVpLyI2NrEvejTNLTuwbzzkEibysv29Gdtcxwy8ver7YdeQCv7jY8bJr+O505n4OG+3zYyrsikhXp2fJV1AKbeB9jgw5yvqrvmcxF+7jwvgQed22zEnUweLJJEyqlVNeKlIcVMGtbw5y8TbWsPeOOAV89dQPkY9sozNWicfLlCIv9x2nmChBPtshR1Sq73eKvLt9z8FqxpjwUXF1aKXQ17jLOQodGURImHLFgZDJ2tUgkRpHswn5Us5gFKcpoLN4+5SSmYPgbusZVUosQXS9YL2Ri6MxhqFkzjGxqwSKUqiKZJks/+buzBgif3qzr2E1BW8dn1603J3EGG3MmR9OYy3msjK66Vp2XteVixAAYn0tuUgEZaaG7BTJ/MhA71p2TcPt04NcxCnTasOUEk4LnuOd5VXf8KL1m54lZgFuE8IQ2mzJtUYpTU6xTj2Ky8bQOMM3x5HbeSalbV5lXiLTtGC02YSCSilu2ganLd+fhNkVcqmgtqq2FpLylgDvjVw+gNPSNBznhZtd96M19Sd1AUAh6dpPpMTTtKBKobFalJZWCtdpidzOC1ZLB2u0Ylwi+8bxqmsZY2bvbR1p1XZY1xqQgMch8HQWIZHSiqzlcButuOw7Wud5GiKf3+zZty3DNHPoPY+nM13T0LUdp2Ekp0jTNfzw8MBnrz8hhpk///mnfPfmDafzxF/+6jVfv3mL0ZrH+cyh3+OtrUBc4vE8UHRe8anNqMxqMR3bfDWLdHVGaayFl7umRuyJF73W4oRqi2bvLeMwb6j58z5TVRWlPPi50mOp2EkshbeTiFmuvBOVptX86tDxZlw4rhYCPOPMpVTQ0BimKdA4DcowhkjRwrK4aryIf4rkEpRoyEw4NK/bRkI1tBYb5Cw5sW+mhTnLADyEQGsMC6L8XfENlKi6O10DwJUix8zOGv786sC7YeJuCYRcOG4YgpwzpaBr7Yr8UUohaOkSndP0QvKWvOCaJXxKiVgmcqwdvGKboNZg9FXlq9AUY6BIQLtWitZK+I6phVMYVX//CRCA0RvNn/78C37/9gOnu/mjkJf1DBe+uXvgqu/ZW83r3vNwmjhNQbpkq2mdxhgrNtOsl4woUDPCdFFG6JaroLE1mnNI6AqqDjHQWs2wgKosnqJ0vSDraynPBo69s5xjJmvFGIU8oJXiVB13VUEyKnLhD08T93PkP3h5wXXjSSXQOot1hqdx4u1RAuqVqsHyXct174kpYbQRX6yUeN03DDHxw3GQVWc9G513lCzxm0rDEiULJGVZPb1oG6zVXDdOokqRiTCExFOSC7t1Fu0lvCnm57Oytj5FyUiplFwMrTb88mLH5eT45jQwxcTOWV51ovnJRZ6hNe8aJO70Z4eWb85C8zRK0VQRpDWa4xzFfE6p6pIrFykJDq3l9X73oxX1J3UBFGTcXSI+aw8AACAASURBVCgcnGNfD9UYEu+HmVwyfWMFbS9waD3nOWz/X1dH11Seja+MAWpXMKdYU6oUvTVc9Q2nRShuh64hx4R3jovG0ejMzetL2SH3DeM8Mc0z+75nWQKlFLrG8eHujlTg9jjyJ198RswtCvAK/uyrT/hf/91vUUWMqhpreTyeab0l5UTjOqGrlYIrmrmkjb43JOk6vJYDJpQzWbsoJJB+qsIaa4TFU4q4pX7eNYwxcQwRrQtJ2pftYim1I26s2dSWKye7AD9MgXNMXHjDdePQWvb+y7ywBFnprKe3taIAnRax3Y4l02orHX8IJJW5nxfGmpPQKvExN0Fx8OIjU8ozFzznwhQzT/Mi2EMWQCxlYcsYveIL0qm+bBv23omCs1StRl2BvOpbXrYNc858mBbejzNjlBa6axzjGMkTdHsnHWQqpCC/WCqiAdh7y1jtElSR13CMy5ZIlsva9cu7lytQ6LXZqJcHL0EoF63n7mmomboipjJKi5VzLUAxFpSBm33D7Yc7dtWZdS3+CunY14yH+3HgF/ue0zlAyTijqnhJPuu+OkkeK2AvhV+orzKB1dVHkbXIvnFMSUylOme4dOKhtOI9Q8rsnfyMWG04nNbbzytA1MIEWo3nnNU0uepUkOdR1WyGhynwv//wyF+8OPDZviXmzHfnid/cDyjgpjG8aBuuO2EQhSQ4AKpaPxfRlny2b7k7z8wxbcLOnBK71vPD0yPHSWxTrroWUy+Ji8bhFRvwWkrFAJCs4eMSuNm19NbgrUbnQkzPzMTVFnq92ErFqEqB60acXt+cR35+sWNX9QtrrsIaXrNkWSX1zvLLq54/PA4EwNdmNNUzrbXamq6cCwcjr2lJiVxWhd8//PrJXQB1Y82QpVhbFJ01dY2juDsHAc1SoXER5xq6mju7mloptTpaZlKqtsopo7Kj8YphWShG0TUNxhqmKUpoyb6n0VkcRQEVRpRveHXoGMaRx+NItyv86atXPM4L1ltSiPzb337NGAN/+90H+t7y7Yc7UhZb2e8/PJCLBLmIjYJ0IDEVvJV81mkUZ89pjiINL3A7Tlx7UeBaURbVCUYOmNFim6yoIquy+igVOqP4Ytfxm4ej0EQN5CQPeUxiKdsYw5zTtiuX7YqqoHLmKRbOKXEfEhfOcp7jtjdl7XaMeBfNK2UTNn69UppGWx7miSVnlpT51W5HLom348xl47nynqclMsbEZ/seleWw343PD/LK4EmlcF4Ce+/wToDuL/Y9n+87MplTqKN4JQwUBRqN0tAqw8/2PZ/sOt6cRoaU8Fpze5pkDbNI3iyKGoYjhSzUDME1XN1ZwxJqtB/P4331Yd0iKwsi+Td1TbFrLJetZFB/yNVnRoAMyNBZSQKz3tIEASmNsjQhMqC4aRpOMWxAIXXKoChedA0pSfKVqiyelTCxNgW9NYxLYq4YwEaBRHCCVH2PQNHW1V1vLZ/3LUbJReis5rLUiUlJDsEUnvENWR0V8UbKWXb2dX2RVaHzonQ+zVH47bWHUEVxCpFvngZ2TsOi+f3DCW3lQr5fAq0zfGY7IYdksfpYQiSX1VUWemP4/GrH7z88sYSE0QVDpC2O1joO3rH3wjrSPFM+g1D9NzYRStZBsTJQbk8DqWvY1Yl4bcS2GSDL/4tW5JRYs6RLKVx6x8GL3qO2BkLpzlLMYy6UlDm0ThpYDL+62PN+Xniq+RLTnDfgWitRkV8aizeKx3nhFCPd9E9FCQwCtigoSrNoocc1Wm2766veA545Zm7PE31D3aU7rLZYldlbMeVai+VqnZvInGfF3WlCKTEJa4zn8qLn4BVdTdUQOpci58Tp6YnxrGkVBG+J88K/+d3XtM7zX/6n/4L3D49MfxUwTvNnX33B27t3/OHunmwLcVrdKzXeuRq5qAlLAKWYlkDjLVeHDp0gGAm+ULUzu18C+9Y/YwNIUbJGbWCjqiHdUHnZSQ7gpZXfbwXRQQp8Yw2dF5perJL8FXBa/6rqRRMLnELkHCIWJVTUIn9+8I6bvuXDcZAwmLKiarDkSDayQvBW6HaFgtNwdbiAPjOcTtyfZ56S+M1Mj5nrdsd5mbibZhE0WbXtmtburNUSunI7zrxsvChIM5u2QyFrQqPVc75vLTatVnx5sZOksJRpkOIzxsiSctVbKIySVZWrFLyiwFuNRTPE51VSqc3Gtmarb2HnPK2CnBMqJh6PE5TMUimotv5OGoV3ujJ9MhOJq0NPGwu+cYRppo2JXynNw8WOb47nrXAbpVBGqJDGGrEaQdWc5vp+1GK1xFLjJqPkKhTQqmwOmiDfJxl57oyCT/ctjZE9tDFysWlWYaFMtFnB43nhs8te9BJJ2DsXWpNzZM6FmFO1XBZue2sNKWdCLhs+B4r308Lt9/d01ggby4C1hpIKf3wcOM2R//DVFa6y/VaTOcqzmPGqsbROwpxCTJKR3TY8nc/kGPHNagazNjzPWMaq9tZAo5WohwuUkolJQqO0kWegIM0W6jm4J1fBX6lOASsT0a05bZVbmovESBZVsb7W4Z2hBPFvckaS53yMPMyBkgvX3nHp7GZJM6bI+znUTUdNwfuRr5/eBbAScimcpwUSJKspITEvkdeXO0rONE5zqIh4Lqoi6LJXXQvi+uCvqDoKzmNCY7DWEUKi05rrVuP0M5i1XhhaVU5zyNxPC9QCeJpmbk9nfvf9Oz65ucR5w3/2L/45cZn57R/eYDrDMiXCErnuW4oGrQ0pZlzvMTlCiALMxfT/cPcuMbalWX7X73vs5zlxIm7cezOzMrOysrqruga0xEMIwQSQWkiADCMP7BHYA2MGFhMksBAMmCEmWLKQZSExshAS4jFBCA+RwEbCMgKbbtzVXVn5uu+IOK+99/daDNa3T9xqVXYX8qi8q5RZFffcOOfsx/et9V//B7YYGtswdB2+FO7PZ2xjOSyBMrRKha3VeVx1XvXm1GFC9ahHK7VSdKN40rW8XTToxTmDZI1uPMekdDRvWJOy1wXM1BO1MnjWDSJZ2A5KEX3iPZumoS26ocSkgTqlPCo7Q0oUhNZbtjg8ws+OZ26LkA0UI0wlYw10bYcReHc+cYrhkmtQkrJfrNPK9lnfctu3vDtNLFmYvVpbX7qjtfQ1uvBLHZ5XqgepCDGlS0rbB2PHrbTMWSGnuyUylUIUweaCcxo92DhL7z3TrI6ea7dwOepgsKl2E1ag80bj/ipNaX8Ij4qjipk7Y7m9GjinzEOI+AIvpjO/dXtNWiJjEXwq/AEJgzrArolq3jv2MapepWu4tQ2rDcWqXA0VGSgG1caIIwft8FxlPlHZTr4qW+egaV8XD6X3tBrGGIrRomIqivM/2fU0jaMkDe/ZeotHM4FfToEZnT+4ynARA0PryIsa6YHQOoetFuOnpPeEFPMLSWIPMfF7d3u8Ucz+uvVc8ibqA+GB79/ueHueOM8Bb+E0R7xzzBUabkCr/LIavumgt7FatYdcLvMk79SiOUZhCQHjDM+3I9YLTvR+QsA6W6m9Gh6fa9FHUX8gYzRHuBQd5ioNWxf/xjuWuvgrNKbf96ZvaYzFtPqInmLg7bzoMNo8suBEhH34R0QJLPCIrc7alm56y7vDggM2fcspJzoxXA0DISulbfRqC900ntW3o+t6zjmSSsZZZbNkU+qQ0jIvgVgSMQnPx+uL3fHFM7zu6iFnvrw/4RqHy5m+7XBWOfZ//4svCVEpX9+8fsm3L95ivWWaI0YMu03HHAuttTy9alhS1HB66oLRmEvMYqDQG8fVMNDtbnh92rPkzJsl8GHbKoc4JZDCkmslIkoBdVYtaJ2pmQAIS0zctg3HFFlSoTGWD56OvD5Nl0Gq5O+qHFZ4QxfU9abch8DONXzYDbydZt7GyNO+RxphHxaGpiGKaGiMqbgoQtdqG/6wP/DVw5GxcZp1K6KisayQ2DkGHlHi+g8DrTF8vBnonePVaWYftAqfYuJHT6/VWVWn+ZfOSLNdBVsK3rrLsN+uK0otFlydB3WuJ2OYJoWFlOpeSFFwtuFp3xObxOvjxFTT6N7HP5w1WO9wGHZDw7ZztMHBOagRoamVI+Vi87wynfZLpPOOIloZf7U/1XhT+DYvHFPBpMQPr694eTyDQVPy0MXhVRUC3bQ6r1H4SkVSKWuVbI1h23uuOs+b00Kswei6kNT87frvuUZvroHq6+8UDKdcSJTLQlTQwHtJBe9cDVYSJAtPO0+qClcLdUEVslg2rWL4yvuvvkTo4Hjdx0udT6x35NtZ7aq/ePHApm/4yYfXNVdAF/KvjgvOOUJUe5hTiBQcpeTHC2VUEW0svNjPOgtoG6amYQmRV4eJJ0PDB7te7R5KYc6Jxir8dJgDu0HjPFunm0fOBetUx9Jai+tawqLUU+0WHnMIYiUjtM7SNZ5Q4zqtNSw5sWTN2m6dxXSekIVv7w5a7ddzvgKQKvJUi5HvOn6tNgCKkI6JtrEk9AE8R2HwTm/o6vvStx6k8Oxqy8MkpHBCpFAkE3IipSq9LwXvDMmqJSzGcpoCOWWiUfaKrQspdbHCcBFPvZ1mvjnOXLctKSutrRiLOIN1jlfv7nn56o4s8Pd+/yu2Y6uKzVQIMRGTSvafjgPbxjE2jk3OvJ5mxFDtauWygJjKsTdFeDZueJgm7k+Ba+uV20zF2OtDYWuVZquPShEh1GhIZzVG77ZreSeR0WuVZusCeVnA3qtk10V3vdEeY7P0/JRcoy5F+GjoeWct355PXDUtn26vAJhz4s0kzHVQazEcgqa3XfU9kg1Josr/jX72/XkhSfmFDyJohWpE84sR4dvjpFh/ZXctubAPgeumqYpgbQJSVr8dzJrGJBjryDHWdlx+8b3qYhzLCiLV7qe2+dumoTMqNLtvHIupjpR1kre29EmEvnX0jXYIXesYm57zkjhMSbnetUrPBjZDxzfHSeElZ5gjzJJYjspoM9ZwSFpBllz45njkt57d8LP7IyaXmmymePS7kNS47VKxm8oic5dhrTM6p3i67XjxMF1EW6A01dZ7DjEytl6xcrvCoVX5LZlUFbDqvK/fMxah9Y7TOTBu+9qdKsTz0dByFzOnpBV/qpBs6y1jYzHzar63bskXME2zPirEeVHZGujHlm2n+cXGWB6WhS8fpnVIhnEK1wmCFWVAbLqeqQh5CWy7Bucs10PD62Pkbsk8sY4Fx/Xuilf7B7Aaj+lrpb1Cb1MsFCLXfavdq1dRYM4FsRbnHcUI/aYlnAOlutmqh5d6CDlj2PTNRT1srN7L51zAW/Wnkmo3YQ2f3e64m2benmYN/mFFNn6R3fjLjl+vDQBt91PRG8RhGZ3DNUojM6KJP8Y5llRIceF6GHkRhHNKuKI2EHPOLKVgncE4z1LDL3JK6pli9Y423pOKYTaON/sjt2Or1SKFXOCcVE4/1YuItTzEhSiFFLNWk9bSeZ0lYIQ5BDrXMZWF1jcV3820CcamYfA6YDtIJIasVZjXmzWURGMtrRNumoad3/LicOJ+CjTOXrxeAKR+V1OhoCyGkDKpJoQ1NRjnEz8QSmHrG+aSGFsHWdOI0vrA8LjOrwvg+oOKYKhhWNtx22o6WSmFXeNxZsO7ZeHb6cSTtqOxuuDMIdF4R+MMIRU2jccX4URGkiFnYTv0dN6RMxxjqEpVBdZXMzhn1L7j9HCuuKrCQV3nEWt4yDqA3nmnlEH7yMZQlo5uAialitkqjLJiwOseGOoGfxntyoooKWzgDJcErZIF44Wmeh+FoGpOJwWyeiFdjU39+zUzwajgKebCbtvxbg58e5x0eKtvU9Xsolx5KfTO46vBnLOGxjpen5d1X9LzVKGHYuHlHGhdz3XryEUXq1zkonQFLTScaJSq0ld1AXbGkYrwMEW2rX9kxqBdajGrsOxyei7/LqildDGGJaa6MHMpIJ60nquu4fW0sMSENargF1HXWmUPmYtQrbWO7+023E8LD/MaU6pQkrWWtnM01Urk7bxwTLoBxXr9vLUkk+ucQKMx9zLxJmW8UV2Dqfqiznu6ruHlw4mua+gq4SSgLEKdea9zD10LHhZh07YX64yh9erVHzPZ66A2IzSDR2JhmeM676e1Vud6pupwjFUDuax54KZCbCGXauKn13hsPKFvuJ9DhX7WbkAIf8wO8Gu3AThj6Zzl4+3A3Xm+MBl655lLxhrPq/3EVes550LjPTfDyNipB48RGDpHzIk5FYIket8gxdFYYdN6Wm8Yqn97whIFpiS8OgdsDdooWdjPkaHv8DR4qzjet4c986TDGWctfa+B3CXpotrW4JQiBe9a5hhIOXPdjhij7bRypRPOGcauI5Ss/uQGQgqo+bPw4dDz4VXPKRZenBLPe/MoXqpxh84aQjQXz5KV6994h7eWBviN6ytOMWKTIKJt+mwKx5BJ7z3Uxjxy0+06PK3zEFcMt11LI6q8XIetG+do+54vzyf2ISgrKBcwQjGiFFdrKDHybtHQiytvKdbTFG2FMepl0zhXq0R9cN0F330cUiv+bWl6zeEtuXBPZB8jn3T9JSyn1IXRGkhGFaXuEgqsmRIGXbwkUx0kH7uQdYNwVDdNDKcUmWICDLYAVu0Qsigd0WHxUjjPhfMS8d4opbjxdI1Tg7Wk511hFz3HalCo79c6e8HKG2d5tul5c5oZO4+zjmOMDG2jEA5y6V4p2oHcLZGx0QV2ihog0nq17shGaoSm8GRoOSyJJRVyDV85JyUYrMQBwRCk6Hmplap68a8D1Ho60U5kGDR729RNZr1u1joGZxnbli8ejpeUuTfnGQNsW38hFzixfHqz5bbraL1nH1SrkzMYVOC5mMxdEGR/5lnXcds6Ns7y+jQRsnbB6oko7K427Of7KsIyiLW8Os141/B8q2yaFAOxRHrxnGNgu+nZzwsthqdjS+dVMDblQuYxSe/p2OLE0DhDcha1ileras0asPjW4VMmJd1Ahlo0xKRzGIxW/7mOleIaI1nv3cZafPVNejr0NN7z+nheL/mjcOg7jl+rDcAZw49vrhjqTth7y7spsNRhymmJNGODM5YohsF5YswsSyDVoUvvPP04cDgduek75lx4sz8wDhrZdzuOnFME7zmHSKawzIJtPEtKDI0ODX3r6WdHbxyvDwdurzY8vdpyDjNF1FfFUtkDRSl4n9zuOMfEtAS6piXFyClp9f7qPPF8HNi1LZumoXeO19PEnAqbpqc44bTMa/3NKSZ+ns98vhs5p8TDFCjSsXVCaxXvLEX4+Krndmy1MnaqfFwHYwp3G0Zn6VzLMaoArAmWIoGbzlyygeeUeQ8pvZTGpnr03PadNtUV+C1IZd9odfz9QbMXvNcF7Kvzmf0SwOuQbxaF5J5vRg4xUkrkg35Qz6NSVcBGs5xzUZthKZBNDVapFY/36qK5zAFBrT1shdDe5cQT41WUVKv3FYYqUijOImKwRemZoC6ZWNVMrLjqCl9gDM83A7djRxYVk6l6Vp/WkJRJtUJKY9tSaiyjiBAXmJfMtm8UNzaGjbVsOmo9SWVyaPhNazUPWxcSJQHcdC0/eLLhftHutbGmhrX0zKnw9rTo9zDQWcXYX54Cn1715LVjRmcFJRUCsOsbtUS3hmNI7EMmF8AbcoZUN0ON9axVcN1o1nmQzoYq4aKsxAtVVJ+L+uOUUrCoHbua7AkfjB3fHideHScdyjqnM4cKwX00jnxvHMii7Jcf3d7wdpp5d5qhzuikwLZt+GBUZ1kBNt7SXG14c57Zh4iz4PDcPRy47hxJFDK7HnqssSruXBZeHycaZ7gdex6iVupNcoxNx0mE+TAjGIqBbaO+/b6S+c8x03pRCmiFa3K1sTBW2T4xFtrRc+U6SiiYSolGtEGy1pCSXGI7c4GSM12jcJKgVh0Ah5B49TDxtG+ZJXPKudKdv/v4tdoAOu/YtHpBS5Hql5+5jxq0jrXcbkYOxzOnJeC6hnNQ979No2zbhJCXidY2RAoxBgbvOZ0n3NiTS+a4aFW+xERMhbHviGGhiDD0HW+OR3IxWMls/ch205KK8ObhxN1ppljIUVkQihEqJe3t/oD1HmvRqrdztFht5wq8mSacMWrAZdTU6rBEnm5GuqbhtYHDstTFSwdwX58mXSqMtutvl0LrqGId4dvjzLb1VcVsL+Kkte2vyDgO2Hp3YSEYo+HhDZbr1tM5wzGWymx47PPbxnDTdGycrXMU1iEJa3zeitEWIGThEBZSKnix3PoBi/Dl/kgp8PJwwhjBe8dhCey8J2Zt7SULySiL4pH+WaMyUdGZbR1T0cSsFaRYh2yHFIk581Hf4erCoOKndROpu1ott0t9CE81gGOFfIx+IV0wuqZeM8dv3mzZL5EpK6Z9CAljtJptnUJQyepG6lF4zgjMIRPzTNc4nvSab5EEbvpGiQal8LAozx9BYTQpXPc9gMr/TcKguQi+Udqtt8o8SvGRXnmKSStdI2yco7O2Qjx6SlMuxOwYnMc4W9WujpfTwrZpmGsQSlpBb1NPklm9cvSsV2JV3ZjXhOUKZXnPsQiNGLoaeiSVEnpcIg9zrOQzZeO4xmp2h7X85vMnOFEX4JQyV95zfbOrTpgLV13L83EVZ+nQfRUAOmMYG88hxCpwExqv3k5j2zE0Da0zCIXzfGZaouoF6uLdrOfHJq77gYfTxCFGjPMY60glcdN5Wq+25ktKGGOYU+ZqaIlZOymspg06q98RQIzBtZ6lCledVb+o0TuuraHPnoc5MJekLK4sFFfvVykcQ+LFXgvGNBVuupbO6r3zj8wMQIuLlYap9E5j1LejMZa2MiXmnOn7hhCzepR7jxEVcyHCVAwpZ6YYab0qGU310NiHhTlFDIXDtCjOh9K7YilYCikJbeu57joakzG5kCWCa4lJyJKV8eAdHjWnKiUQi6EVoW3VRTFJxjml+62L49tp4aONVp+d9/TG0jUtrx/2/PDZNS9P2qICNXi9MHSKz4YS8c4jxvFs60g5k3NWGwhrL7z/soq7EMQZvHMVatDCPnuHjAPvlkVpqMCztsfbwH5JWoVAjQgURmcuIdgY/VzfTBM+w9OxpzHVQ0ng9TTpINvArt/gnOUQEt41zCVoFGSr+bnnlHmIauRnRa0iLklLFc4avdfhqldc+iHFSvmtEZtIHXJaSs48LDpE/2gcEArercwgLhiznpu1dxb2MV4snvW8C2Th4+vNhWFW6uzhSduwE0cswtwXDiHxZl4YXEvMqnxunDp15iKUrFGjpqhpXs5FRWF1s+68oROLw2hnZCCWTOd0U59L5m4OnEOsC7DCNMZAi8OOHedJ2TGnrGJDtWMWKJmxVwDLVf68s1pU2aQF17Zr+Khu3pvGaeJc4+sMZmWcPO778H5HtuY1mEprBbC8WdRh90nb4kVhNCOFUDI/vT9dxIdrJu451dGmMbydTjzfbilLfrQGyYXPr6/4xlqeDi27xj8KsupGlDGcYt1cslSNhWPsOooo9l7ITBlyDFq01PtIjNqNWDRQpvOWd3MkF40L7at1SsJxtyT6IjyvcGEper2HtsEaOBuYQryQM5yxF1vuYgTXahE2R416dMbQtg0bDA7hxTEhtROcJ7VWmYvw9f2kMzKj68ibOfD59cC+fu7vOn6lDcAYcwP8F8Bv1+v950Xkf3vvz/9F4H8A/rD+6L8Vkf/YGPMT4L9+71f9BvAfich/Zoz5T4F/DQjAT4E/JyL3f/KnUZ9yYy1IYQ6RJ13LcVHx1Nfv9oxDqy2d82Rjud32zKIe69vWczieEBHOIVCkhpZPetHHscUITDWntQg8TBPHZWF31XPre26vNoQQOE0BBDa9siPuzzNPrvqKSSaMsZr2BTjnWek5MScwnv0yXZw0c8XSU44cDwmX4POnO37ybMebaUYs/PT1A9djT2c9SbJi6QCivPZTCHgiznqcM+yGDU70eyQpNWtVsd9TjHTOsevUovdCD0zaOTQYOus4WWGWzJIWWmO5ri6qh0X96nddr+E2daBaoW8ySkUs1nHbaaj1McdHCl+GwzLz7phoG0+M6urZtJamtcxFB/W5Dnqber0LdeBmTPV60qHgOQQdkgHVuv9C+UwixJhIiw4Kvzd2DK1DTMNxCfi1qi+PIp06X0NEWFbuOY/Dzc55nrQN1HskULDV1jjXzWnrPVe+ocVySEWzG+q1Puf8yCxzKEwimg98DglvLF2n6Xattepu2ToelkQGnvRqCR1yYaomYbkUmmQvQ+GpZAyF66uOn789IKhqd1WMxqIb1K5i7Aa1a1ghY2OU8hli5rp1xCzsWs+744K3hqu+qZCPXlTdBORS7atwSg0KLYZTzupgag3ZwH0KiDTsrBrWvZ3jo/J83YILl0V8SZnffXHHw9XE9dBx3Q+YappXivAbtztyyhcvHwO1gANEGBpHyEr68NZiCzxrPaYIp5Q4HGfaVqNh181//T2NMTzfDlgD3x4n7s6RbdcxNhp96mxms7vBWrg73nO3tFy3LaeQ+PzZVguXAmPXVkvxxMWSG1WorzGUxlucNBeLbmOVUzW0DU8H4SGEy/fKWb9X5zXXnPrRi8DXx5lnneZRf9fxq3YAfwX4n0TkTxtjWmD8Ja/5X0TkT73/AxH5PeCf0JvJOOBr4L+rf/w3gb8sIskY858Afxn4936VD2PrRZcCnfds+57BtxxzJIdEaxtAeL0/YNAWt+t6phD5+Nk1b6eZnBJXfYN3apC7hKhRdadZB8W9VpSSi4pdjHCaFkp1ujzNUStpC2NoGLuOJ5seyYEpJU6lqNHZduDt8cxhmrm96hX/MzCFhYtFxzpUq5Q9AYoVOqNOlNe9Y+gGghhcOyLSEAjsl0lNrLKyGwSFJsQU+n7DrMY8+LbhPKmrqTeGhxgu3OAPNh3fG3URLyIsoi6or6eFt9OCtWoxbYy2kyvJb9t4jqdIkoU0doixOlSMWvG0tmOxmYTwZpo5LoGmUafDttpUYwVrhblETGNoMERTUewsfAAAIABJREFUyCURYnnElBGm2sK3VRegvtg6tNy0XTXAchQsB3QO40x9iDGklGmt4Unf0RiDzYWmtSTvKsW0BnYU9VlyVmmS51RYclFqoDEcQsQAz8eOrnY91hrEakeqgJK+q3Zb6v9/loJ4U3Uoazdbh6AY3bwKmPJeEPqcmIPOCDadmqZdDx2340AsiTfnWYNuqkXGqn0YksIfp0mFYNe98NHVyDFoOEjJwtCpm+VSCnchsfHaJa4ZEM5ZQhammBVWKCrma+ri/fow0zrL0Hmy1DCtunNc9DIYhQUxHEvhISWylRpyowSAdxJwvuWr48T9XAVLdcj5+PsePfmLCG9OgbHR6+ato29UPZ9iusBzK7S32oevO/e2Ulj3tYD7w7cPbIeBHDPPrzZsho53xyM51zS+rmHwTuMYp4VTTHz/esPDHDkHTenLBTb9wG99/jlffPUlnVO/pE0uPN+pE2cpCtGuMF3KhbAkfOsodYii31nISyIsCbGG6Gv6XHX73fWdMoFyoVDoOs/oHJ/dbPh2P11YQBiYU+HLNFHmfwgdgDFmB/zzwL+pa5UEtGr//3v8DvBTEfmi/p7/+b0/+1vAn/5VfompD6kI7GcNgcAEisDWe252A3sx9K3nbjozh0jIgef9jt/4/vc5PrzG1gFWKELrdKjWeWXEJKcUSWMh1zbTrNYKGE4xU6RcMESLwTfa0psYCGjraY1aL4ecQYTn1TTqECLHFGl9w67zPIRI/CPcOal4jH3vgeqteviUFmLnIWe6ovqHJefLsK0UMK3hECY663i6veIwLdVe1nM3Tzx9eo2dZw6nia9PM4egXvZN40nAzx9OPNRQc1OEY4l0rQ4lXXWubJ1ju9XZgPrLaEXejy1LhgFDiR3nOahJX9uo8KpGMT5pDfss4B0pZZXmdx6V9ho1qUulwhq1GrdaLT0ZR9rG8vXbPcYrVrptOjZDh3g47pdqxaw7SAlKyf18t+X52FWlr1p+DI2yoRIQi5r43Z8D+znSeke2WgXfjK0yt6qB2sfbju9tO9o61C7G8fq4cA4BUFdGqfeJc2o7jtEheSpysfulpjytFsXF1JlE0UUw58LDeWFaItfbnttNz1CDXcRAbx2xKeroWcVkdydltKxsrJgKnz8ZeRcsbxCuvEIkfWOx1vP2tHBlHN4q5CRASMJ+VuptKStElNk4x9h6ppg5zFHzt+t9aurzSYV1KIZJCseiQjQxa9yqFkGrPfY5F7rGI7OKo9aIR4Op+QFcNk5NL4OXx4W+adj2PcsS1IDwAuNRi4dqMSGrqlc7XV9ZZfd18HxeZm67nmwN+7PmfiPwydXA2Djup4U5JGg9n1yN/Ox0JlnBZqFvG1JWX6/f/ek/4HwOfHp7xdbB0HoaZ0klV7qtCusQ1YMMqMo3ZN0IrIEYCqFaPFAgipBLZjd0XLy4SqF3lqbXwKSlFilPxvYS1nNZ8pUv8J3Hr9IB/AbwGvgvjTH/OPB/AP+OiJz+yOv+OWPM/wl8A/y7IvL3/sif/xngv/qO9/jz/CJUdDmMMX8B+AsA23EElA3w6rzwbo6Ihc7AFbCI4cvTROs853kChI93G7JohuqzZubF6UTOia5piUX90PtqG01txTaVhlnqgmWNGjQVwDiDKYa2terTM47sxpG0BPanMyFndpseN1uWJDQ2o0iBMInqERrn+GTb83zT87vvDrybZlY//nUQmRH+77sHvr8Z1PStQhR2mflwO/D2HDhHw7tFMfLGWY38Q5CknvvJFNLxgRgTm6alcR6L5bg/8XCayU7b7LcSOd0d+Gy34cXxzNtT0HXYVn+fAtOcKUb4ybNrRutYgg4lpTy6Xb7en7FOg3qejB2dEWzbMMWoQ2ufGZ3CLq9E6ZExZFxjaVt3oToKlf2w3gSiLBZvdKPtnKONWel1WRRS6zyvpwPHonx+b5WhkpZMCIkfPbni463eP6bSk+5DwNDSuMJXDxP7OeK9xaK0yFioqWuZhzqcExE2jeXp0DA2yjX3zuKd43rcaXj3HHn5cGKOysx5uu0Zc+Scs3q6G9GOoWiHJysKpGby7PqWsfU6tJ0CS0iUDJ3x9NZpHrIIg1WorHVWs3T1edGZCfDp7orbrtVBeCw863qmJRNypq2mdlPMLFkV10aEKeiQMmchJlU0txVCcM4yZzUxW0pWOrKoPqHw6Ehr0WJgCplTjhRfPXHkUa1aRGgqkeGQI5vOswsNoap/V/VxTrqU2bVKrp1GwfDNKTBMkY+3w4XNVInKdftYVbZKNc51RnKOcbVeo+sdW+9ZDGy6luPhxBIjvg7AXx4m7ufIx7uBm6HjXQgcYgJriKFwqM/u/jwRU+bpRpl4m65Rr6J6jamzmXX+ZlB2Wt8YyhzJQSNGw1JzmM3jd0AMS1TIMNXnBmcvGShLLvTOgQj/5EfXHFPmp3dHTksih3Kx6/hlx6+yAXjgnwL+koj8bWPMXwH+feA/fO81fwf4gYgcjTH/KvDfAz9e/7DCRv86CvP8wmGM+Q+ABPyNX/bmIvLXgb8O8MHtrcwx82aJSM0E9U3DxipO+np/ICAcFmVUtE7NuRyGTSscHh44z+p9c14CY9/wMM0clkhBd+ex8WRqJW2qyq9WFzmVC3VuHBq2fcfNduA8L5RporcA6vuhNs6FQ0p465hTpMmFz2+2jK1n17VkET6+Grmb5kqf1AVg9WyhvCc2AYxRfDYvEztnSa0+MA+TsmoqMoaaV9XPsKg97iEutCnSGE/jGq5GxzktnJaFrmk5hcj/+/aBxrcMttHsX1FV7bo5PR96TCrEypFvrL08+Iqb10fPCzj1IBm7jrZX/ncTEucl4BsNgnHW0m10pjAviexr5ZiVyuesMou8owrdjOYFWEfC4IyjaS0tVeRWE6cS+uDv2o5388SzoecH11cad5kS1jrOOXG/JEIWeuu4X4JW+v1I4zxLEnZDq91do3OTOWYswmdXIzeDioKk4rZFBJM1Let28GzbHccl8r/+/is+e+J5Ngw6OwmJb6eZh6BdK6IwjDWGwcglyYp6fq+GlsY5rtqWJ5uekhMpK8PnSdsylcy7JXDTq+V1EPWh2TrLs15FeamG/+SkhYtJcF4ShszdHGm95T4mpYnWBTcmDVLvG1dnA4aQ1VagSOF7m4Ev92f8yfB87B+TyYpwCInWeeKiuhkL5Kp+FqpRXYX3GqPY9T5GbjYNXrSifXPWbN/31cbePIrerDXcnc/sjWHbeZ72mhoncHl9knUreIz+TFnt41e1e8rCXiJLnJkqjFTq/XRcIldDy6ZtuK6L/xeHE4I6BxSjPmJPthtO00Si0DeeoW2Uu68vRNDPbGpZrjkudWbiLNcbx/150QztCn+ta89KO16WxDGoc0CWdVM25KKdVFtN+ahdT7OKQHPBte6PXdz/pOMr4CsR+dv1//836Abw/iK9f+9//4/GmP/cGPNMRN7UH/8rwN8RkZfv/z1jzL8B/Cngd+QXI5l+6RFLIdqGrm84Lmee9IP+LCfmbCjW8fxmxzdv35BR5kgtPHi3P3OcFqaS6b1lKoX7WX1dnDM4nIZ1iwqU1jiG9UOVyuc2Fe9snScV+PmLN5xCZPS+Mn88U0zq6e4gGMglc9U1fHY1clV9wKek4qBt4+u5oF58oXOeZ5uWHkuDXAZdXD6VYopXztJver4U4c151odF1CTLGu38MlxCMIwUnIc5KBXw6bChs5rmVErCG8foPTErHJOLIC7TNY45ZJ60Ld1aydWHePWdR+B7VxselkCSwts17GIwhJTY+r56vCh2m+tDvFbVTaMZCCKC85beqk2106dAMX0HIpnTfGJeCp/ebNmHSKgL724YWVJgX1SMtbGexVl+eHNF5z0GIWN5Oc387N1BF22rMYydaykS2Xqj1b4X3h1PbDr1l2o6D0XYtp7Pbka6aqpXKq4rogttEcF7T9c0iiH3mvUKYMWwazytHXnnPa/OM3Nl5mBhUwsDWzd6qY6cN1cjP7jZcjwvzKl2e3WxfdJ6jlG7WIe6S0LdnCttVGp3W0rBAVLnOqdF5xldYzmXwlSEJhc6WzUQ1tLWyjkVmIsqovcp87xv+GDb8+o0My+Jj3YDbePYL5G7JWJtYrSG0UAnhpxgbmr1Wxd/WxdBX1W/p5xpjHA7tBrstET2NeRcF3Zou5bWWw5hueR6/OH9gXxVeDZ2VaEMWM3BXkV6a2ew5Mw5ZW46nfGFmCsjx5CL4eluxzf39yxRQ+t/fLVT8WFWq4bWOnYosWBudVF36Pypq/O4KSTul4Bzho93m8pi065Fg3u4GEvmIgoDVcQBqNkO9T9mZVkJd/NM5z1PtwM5q4gvZVGLbguDdyQRXh5nTktCcsG3qmb+ruNP3ABE5IUx5ktjzE/qUPd3gL///muMMR8BL0VEjDH/jJ5+3r73kj/LH4F/jDH/Mjr0/RdE5PwnfQ7Qh20pBW9bBtewdYY5wX1M3E2BkDLzstC0SmsMRU95RjgEkKgquq5pyFJ4OMWLWnHTOGxjCTFjMcwhXVwTcw2V73p/+dnhPGOdesq3XgOqt13LYcnEHJWiZrVS+ng70Fpt/1IRQg34NsC0LDUkXc2yWmfJKdMKDI3lD+6OfL6D/rJR1CGxSFWXwve3I513vD7NiK24d8WZvVW/9VK4uGx+8mTLEiLS9oiBJQUGeqwY9sdZ6bQ5E0S3jiLCYYn8fox8OAx8OHa0qIuk1AlbATpneTZ23C+hUjQ7eueZQuC8LFrFO8MyJ4ZePe6jKOtI5NHB0Ao0roJB5tHLJBWgFJ5d7zjIREmZwTVglOueY+AgUc8BhtO84I0qj0+Lin/OKfHF/ZHeNVwNvW6S1jJJoAXezJFzUpqeMRBDwFjDLZYfPn/CB9uWrm3UKNUUyELKGRVoVqGPKUSTOS2Jj27GiikrYcCIoXOWD/ueZ33Pt9PE69OsCww1yGXl1IsGgtztDzgKH/QdpfrVq1pWWJL6Lp1CxImqakGrQvS/Ogy1ioW31iIl12wAi9HTpzCpaP6xQcVMg9ewmVQK5ywc5gpjlcKLw8Su9eohVeDre7X9Nt7QenNJAAtSkKKEDYO6lOa1IjeVMVW/dzFCNMIxBbY1DMhbw91Zr0HjPYnMm9O8ykDAqKnfF4cjxsDzzVAZSEDVYOg5UKfNOSkN+LCorUrTWvZzQARGF4iLgVpVn7NmCY81j+GmaXntAiUJnXW0reP+NJGMPs8hF745nnkxz2zbhhAKTaMZA9dte+k61s8EyjDbn5cLBVlnKGvLX69LUWq7elglDnOoz7hu6N5oELx3tuoo9DNaa2tM5D8cBATwl4C/UaGcPwD+nDHmLwKIyF9DB7j/tjEmARPwZ9aK3hgzAv8S8G/9kd/5V4EO+JtVzv+3ROQv/nEfwqD+/yHtKTmzeOXDnmMipULfenrUSXIp6qviraVYWES09TKGt3G5KGJX3fo5aWXgjWFwavUgBiQWvFc8sKmimVLVetd9V9s0CFGr3ozi4rZrMFn4/lZvyCVmziECOsS2axVn4JPdyIvDRC4aXJ+qYOfp0KqBWxbGVlu+1c105cGvQ/HBWrZNQ5TCQkKMURgFtYBe3rNnaCTSN/C/f/EVbefZ7VquXIu1DZ237JeZtuv48GrD7337rbIOijBl4atypvOWD8eeXKMO1QsevBOKWEbvNZMUHYA1XkNhjFVldtMod39VDFtrMFZf0zq1+sBqKtLKG7VGFZEihg9vnxKmb6EkivcgliAK46WinPrWqrPn0G04h8yynBhbz6loCpU4o9YAKTJ2SgVeloj3Dit67qjP4Qddywd9iy+ZaUlMQQ3R1orNVRhj/S4gxJT5en/iFCJXjVdigWj151AmjQc+3Y5cNw2nmDjGyCFGrsxax+tCb4GHeeHDcUCzRarCXNSvaXCOfuM5TqsDqCDVlG0fE/ch8Ok4gGhIfbIZK5o1UOrrCygcVZHnodpoqwmeZvCmUkh1LYm5cM6Z61G5/F8eJlLO3HTNBZtWVbJ5dLuMUExGnG4CBi5D2rXC1wE6HCXROcuub7npe16eZk4xElbCw3uHZkkou+fCqBPAKCl11b7kUjR/2GgnY9DFdam+VKaax3Vdhzj9JXdzpB1tLdK0w0pGBV2j9fRNQ4iFYwoqpvOWicwU9br9P/f33HQt/9jNDc76dYxbP4/Sz0VKhXz0i62Wg6VuyvsYOedIlEJOEPKM9x5nYNN4alZPZfYZbsaOl2HCNzrbXGG9X3b8ShuAiPxd4J/+Iz/+a+/9+V9FF/Rf9nfPwNNf8vMf/Srv/f5hreV8OlOK3vj7U75UNkPn9WSYll3v8D7zszdnCpkgEK1S7Nbx+KZt8U75tavjphSlpqVcKqdcLiZXpzmy6VtsHWg1oqEXt9sRbz0P54nPdp0mIaXMN6eZYixvzrNiq1nl30hdgEquLAFDj+fb/ZklqRcOBu7mwGfXW1rv+PI4MbTNxTu+rokVehHupoWXp5ksytbovCcDUwjqMpi1GnMWPtkOjBUe+PzZFaW1bFrPlBIpBz56suMmbOj7FoPhw+0Vb85nrRprP/qz/ZmQC8+HVh0kjb0Mg61VU7tUr9H9lFgrGxHVQzgHqcSLeRvogiJWmSL1a10qJV/9kSaJiIE/fPEtWRJTyXQ2AYUpaddlsqqIbdbv+/xmy43NvHi7J6HOozkX9mmuRmlwDEG9ZkKiEzU2s15dGT/sW26Htsr71bDtNKv4R1BVstoNm7VoU41JTLybZ97OC721fDiO7Nr2ci68q3MDgZuu5UnfspTC2/PCt4czT8eeYwh46jyoXnNXxXfOariPwnxqBDd0hmMNy/lmWjjERBFRhXedXUmFFqJUjUWFeES081qNzjwGV5SHfk5KvAj2MqaiiC44yxz4aOjYeM85JZZQ2PSOtbZKNV+4Q7sCmwyzW/2lKkRLtZSgOtiiZmlzznQ4vvdky7s5kMN7tE4enwGH4ZPtyFDZTaVi6OrpX+UdFPZLrIw5s4rVAbVIASGERPAto7OkZOmqZ883x4lN1/IwBZKBrVXq5jQXdl3PhojFV4fhrPkewqWLPIbIP9jv+c3dFaP3lSYLcxX+rffOivfoOdGN65gSU3kM6umssqZijFgjJAxPmw4jhZgV6Bq809wPq8XoRTz5S45fKyVwLoUlJkIpJJSR41vH1jiiZA0QyWfot7w5quqSopinPkCKtVp5LwSmDmdKUcWJoOrMvnXqwpnXikIHL2uqz+3Q8sHVwFTg1TSxdULXOEZjmXNRb/LG8/U54Evmw3FQnLZWJduuYc3vNUWtp8/h0ev8HDMha1bAq3nm7754x2e7geu2wVm1vU4ChyXy8jRf7B1yBrIKXdqK55esncbQeD5oG/0+GL5/NZJEOBu1sc4Fvr0/8Gy3QXLi/rTgbMdH1z0/f/taVbWi5+eLw5lX08z3r0ae9i2ddcraEcDpsC0L+LHnGCKHUhdoazA+w0LNGRZi1rBtZzRvNcojlRajbpMuaycxp8xhnskibLyn7VoeHo58fvuMF/t7jNEsZikQErx6+4bT2IN3HEJijgEjEGOuEIMSEwvQ9A5qeEfvLD/abRSzrRVnFm2rvznMRNRfxtWWvnHK1V5f9+oUeHvW/IJjSZyPB267jpuuw6M02lWdbSrM1lvLZ7sNY+t5d56gQNdUlWnRWMtd2xGSutcWw0XFnbOmhJ2miYdl4a4mQm2849nQ0VQGitRzmyq102Ixki/YtMJuWic5ZzlGYU5JhZQVt18FeNZoLONhTvzwyYY/uD+xLIU2FnUTrd/LrCwGjIaYROHshGIr06rCReumr0aDOpy/blqO+zO913O7cuVRRI3rruXT3UjrLEkKVnR5VdKc3l9rN3VcQoXFlI67jh1vNj2pZKW2LoFt13Ldeo5JBXlj13BaAl3bctN33N/vlbV0PXI4L9yL4TxHQil0XYNYcwl6QQzZCG/mhXNK/ORmx23XscTMtMQ6+3uEOd/3mjqEyCmn6nxg6Z1u4KkYUtGOfgqJvbMK19U1zRh9T2s0M+KPO36tNoBUhOA0VNpYS9+oZW2xyhG+aT3P2g5vDK+rPawAQzFEA7HS1daWt1Qcdb3xjNFhr65csIRMyOo51HvLftYp/NarglYMvDiemVPiauxqS5nZx4zxLcZYxaDniWOMXA+NbizGXsJQ1KETno0991MgZlWHrRVCnZISs3CKmZu2Jeai1rl16KivX6spausoeDFqN230XPxgt9GHu95sa5B5J7oMvZ1P3J0XUol8+uSatrVcba54OJ0o5ZFOtg5uTzHz+w9HDmHg06uR0fsa55frENrgG7V8TiIYXJ056JBWUHOrgGAai0StOEtSbxpx9pLkNiUdXKeywk4KEd0djzq/KZG2cZhs+Wd/+7f56Rdfcj6fOMfI6eFUoUCrAR1CDZDX8+DqcG6dy7TO8tE4XGYSBi5wz/q6nz+cSUYYa9DHR2PH7VD9fYzhPqhSu1DqkFMXgXfLgjOGXdPypO0ugeqglV8qhXOIOKP0wVwFRFkKd1NgaJqqAtU0slQez+Xb48I5J3VYdRZJCskMTrsVY3Ro750nlPB4D7B2LuZyf8xavjNnDWwJKFZ/GVSaxxjHm07NC3/05Ir/6/U9p1wgGIyvscYiRIGtVzgyx4zLBTqjuoi6mWQenWc3rqGxllAyxepnvGo8h6qgXW2dP9wOqhKvu9bKShMp1YjQXmq/Uq8Nl9lVrbRLwWGxJVNMphUtDvqmJRj4+u5A5z1D52vEZss5ZrINnJdArIVR6yxLTIxjyylXQRaPnc1cMr93v+fHV1e4rA+SmNWYkAsEpAZv5dHbX2DjHUsuHHN1X61dzjlqhx1apSV7q/GkjdPY21VZ/V3Hr9UGoPJwBU+9tcxzJKfCsNVQZl+dHwUdIp7SoxtlK4ajCLFuC6FGLq5B8esDglTlqMhFbr2yaBpv2TSeXd/w5rTw483Ibes5GXg2dIRceHUOnNAWUX+npet7+ga2na+t6XuLsApetaqTR+MmA7w9z/pQ1jv4fknc9JklqTulN+rfss4S6tyvdhaaqFSK4fbJwLOu47lrLm9sKuy1Vl6NNXy8GzmEwBwT+9PEQ1g4TjNGDE/HLXOOTDHojKpWKikLr6eZm65h9A4qyKCW19qCGoSnfaetq0AWx0OM+vmcinpFBOOE1tQhJtq7m+qTVCojpnMKZwyNDuSdVW3A3TKDwOAbXr95x3mamFNUJ81cCFnFOIZq0LcOyiu0MxqF+54PPdtqv8Aa74j8wqYZivDN4UQwcmF2fHua+a3ba54Pnq/2Ez+/PxBSVozdgPcG7wSqp9F9WDjFyK5pue17emtADPuUOIZA9RRlrlx8Y2CKgSm2NCjEkrIGn1trOCyJYw37OS2K19/0WsmuVaZBrZhbLMewbuSFdZFamWgiQimGQwykVFjk0VPH2sdiYz0pt4PmUjcVVzmbwpIyT11bIR09h3PJCkeiViOhXPAOvHmMx7xuFIYJIixGc22Xkkmx8LTvuJsXjDF8sh0YagdU6galOcB63aR2AQaIPG7gZb2/ZB22q0nkzdADAs5zSgXnC/vTxJIKyvOwnE6zZoikiPVgGkNfQMRe3KOWkBj6pir0udCoDQayQk2dtaoAFn1eKXVjQkkih5RY1njPatOCs0hV/V/Ed0aFhYc5cHcWeu/4YDteEAW7LojfcfxabQAI2CJ45zlOC3HJPNl23DQtxxh5KInbVsMUPtmMxHxU4Q1Kw2oyJC8467DFECTU1hQ061ffwzdqm9wao9WqwJwzo/eErFjbs+2AlMQHQwtDqy3bHDmJ05RTeUzWssbxdolcz5HrXumBRZSRsqTCq3nhGFX2dOmW0cH06D19q9imd5afH8/aMlJFWMbQNbYqjnlUQla8uBk9R0mc58S57fnMd5j6QOeimKHeiIXOGJ72PfsQeH0+EWOh6eD5bsckhZcPsapNS8UqlYmw9Z6rplFu/3pjrouELl/sc+bFdKY1DTFHBuc5laC4ZYUAMIZAxhU1aMMaljnStw2++uA31mIqBt5aSyzCfdTR5dPNBlcMX3z9kpAzhYyzhsF7lpyxTqv/dWi7zh/W4ePoHbd19nGZW9T77hGiUGx6yYVYF85iDa+nhcOLN/z45pq7OTFXKwEtTIUlCkvUYqRrlLURJTPlibuw8Nl2S9N4vj0ctduqUKExho1vVE2aC29OE5/utoSQ1RbbwjEKh5TJAg+zKrhv+o6nVSlqahDOxQrcFral4xwiSfKl+61fVa9H1uFhqJCQq+fCGFstUjJG4Ae7ESkZscqE+2Ac+OZ8Bnm0mbb1DWIpOJPVOwrDVXGcYwGv7pgGw6ZrGWtoUBDFve/PM8dz5NnYaoa2Ufr0k6FTOFQEUwwxp0rTXr9Mva8EnOisKtS8A0q9rnXzKEYhybGxzCkyWsthiVyNA1em4ThPxJDwVHaZh9NyruI3NQNsnSWkQlr0Pbq+OgHUc+eB7/cD7Xrf6UesRaBga+X/JixYo6rpbeMZvKIGx7peZKmZC0bhLRGFUHNlGTbTzEdXG148nDhHHXp/1/FrtQFYaxg6FXxEYxlHx23fIaUwOseUM2/mhQ+GHgx8drXh69O5Gi/pA+yKoWs9U46Pg9TLza8PtFvFA6KVskExunNMbL3nFFWwcYsHay87fOcdTcVXjXlcjAUQa3lzChpfWYRX08yL88xcJ/TeGrrGM4VYqwVNBPLIhbGR6p84o1VBZ9SP3nvPNw8n7qal/k3B9BbfWJLThy+j5luDs3xgWySrpUVEF39nFFb7wc2GKfV8fTwTjTC2DpcWegNPNwN308JhWcBUNorTiv0YEptmnQPo93ZWA0JSLnx5PBFKxjT6/bPRlCzJgvXv+7UYkinVN8/RdFVFmgpLEUJ9Wde0ZBpiUn+WrvG8PZ25EbjyDS/mBevUHdY2hr7x78U6Kk/e1lzSaiAhAAAgAElEQVTHdbDYeVctBeSyIAKXIZ2gi2i+CHb08642Ictc+PLdGXFCaw1z0TtKB7XVr6feZ401F+GeNYbtoJucq21ZrUVU4Yl2aIIKlFTvo4vjQ0hMRWdFDZbBWsbWadBMxYzXLIimRqeWIhfNSw466bTGXIoCI4YcMwFhrhDlGrco2oATs/Ckb2piWS14gGeblnfzfIE9Su0AUn1GilRtimiedmtUUOUby67RTuIkGYxwd1o4x4zkwg9vNuw6j/v/qHuvXUuSLE3vWyZcbHFURGREyqruKrIbHHJAARCYOwJ8IT4Bn4hvMC/AiwHRxHCmRU13qVQhThyxhQtTvFjmvqN6KhsEeDO5CxlRoc7Z293cbK1//QLDX+zVRVbhW11DBT3YRS7/f/EcKmSMgVebhsMcCSkTjVIwxzlhrOCcxfuWxhWsydyfZ4zxEDNGJnKJTKFShlPE54KtQ+ciosyuyuwBnTOaVO09akfymW9ora1r66KB0TWm//85hPX+d9bRu8VNSYe5G2O4aRoe5kCoYUNzKrW71Yfo8TTSmELrDJ/1W85x1dT/Z6+f1wFAtR0W4W7TamukXTWgD9PTNLNrHFdeh6Ub55hDUvdDCltx2hVUJaoeDHUmUMqqfBVRIVIvlhSVuXO97ZnHuQ7BwFir/juUTyiB9SD5tO2SsqqB//B01mhBqf71JjGGzBwzvXNr9dIa9cgZiBgV0q7DLCvC1lq+vNpQxGKs8KsXe/7Dj5EpF9qNZ/YVLgNtdUU59+/GCW9h7xyhQJYlWk4fWGMMu8Zw3TZkn7muIhJfmSsOj5GMZMM5zXQ1h/nb44k3mw37xtas1ouS8RhVFU29xp11nKPCC6zDuLrFikAsFCcESTTG1jSrzDAlMMJN32OtJ5OVsy/LbCBTGs+QE9/c7fnq61/yf/77/2e9F2nZzACKbswGVTOHknkXZh5SpDHC132nLo1y6QZEqnLTe85Nw9tpopSsm1EqOEw1ocvsWkfn9PM9jbEeEsvgtXabSTfIxht+9eoKJ8IPT2ceziOhaCTMQpFcxFJ6Pmhg+FMYGXIC0YPDG4PrGhoj9YCxdY2r4E3XuOYHxJjpnOM8zyypastszBc4p8S5qJU4ZT2bdfOPKhbbt64eipcOorHCTdvw4TAS63W3BnyWSiJQGNCbilkibLuWznt8KYyymCUm7o8jrTP86u6KvdNCQgWehRwT3tXQ9LrGrTHrs+dqYSYVZwfVQDRGv86Sf7ztHXPIhJQY40znOh7OIylC26iD6TBPeK/rZJoVmZ8yhHFEjLDd+FUsVor6TMVUYI44rxnAd77h2vm1q/zUFLCgFjSnmNZsio1r2Hi3PvOnmPjD4YQ1hhdd4VXX8HaamUNeaaPOSFVJQ2stnTV0BbaV+PHnXj+rA2ARPzljLta/Cy+twL7zzFL4kDRlq8WwcZbRWqbaiiUjjLMKlRaalvq16WW0Vi9ijmpGZkX4N//V5zycJx5PI3PxXHW+hjerT0wpC4e3sLPCMWpVoPm02rnMKSPecxhGYkl8vu243XTEIhxC5O1p4PHpzOd3e94+HZli1EVnWUNcjMDGGD7fb9h1bYUX9HtIKfzixZZ3c+CIMgSkVl8ZrUgKMEjiP8Uzu2ixIty4hp61I17hpxddQwxpHRIa0czhnJP6GRnLVdszVEXmi92OH6eBIalN8iJWMiL8MAz6RQV2vmMOc/WZWbf99b1KnY/kpTUvmbbx7KTDm0DJsGksh7MKj1qjpnoxFw7DmQ/TTIiJY5gZ//B7Qk5MmUr51e+odN9MQnnUcy6cc0aKiooGBIaJN13Lpvryr+HaJWNK5vOuZWMs78PEVGCSROPUqiJm7a60c1A3V4rjNOuAdnF+LBT2jedfvbkjF3UhTTGx9Y6HKSjMh0IMzjhy0Qn27x6f2bcth6j5uU50rmHkT7FhRFZlbCm6ccd8ian01vDqasf752N9thSKmWd1sy31ubqczzpzijHz5fUGj4akJLTwEVTt3FqDd4YYM22r6tS2dlyxKB06lWozIdqdBTLR6XMyTZkPh4HbTcs311s2TghBr2f+BLefY0KsCkYUutJFZssyx9DNdPl3Q1TTvJQLY1KtjLeG682G4zTTp0KfErLZ8XQaOcVEzFqRO2OY0PwOg3BYgltE1b8Gg7cOJWbK+szmWtjdts06RF/+Z+tcJqNV/JAiMSc6p4N7V2HAD+eRPx7P6iZaEt8PA9cxcuUbiln2NtZDzaA6mOOcVQvzL+zyP6sDoJSiMIKBFJSmZuvvZ1uj2IoaC81kerHsGk9rDP/0fEKMBsI3zir3uZY261ZUMUdBK86CCj++utvz69c3SEm8fTjzw9NZ5fI1w9eWpEPfIuy8oYuqPbCmCmlqKTRnmAvEKXK0kc5FNq3nzbbjtvXc9y0GeHSOJLkeULpFtka4aRpe73qss4gxFZct5BR5P038YZ7WHdxUDEEqrFWEVXxjRZhQmEXiTNN02o2sl0HZQaZ2WwtDI+dcH4pCrM6ZG9fQGsMwjvS+5e35RGMt115VpMrcufC+MUWzepepallcGnWjab1l6z1P44xtLS83HZ2zawZAmBM7ZzhNM0Jh2zU4RYjwuhqwXpOgHg5nfSiFFfpRfrWqj521WO+wOdEbnfPobAUeUuCcIl9teu682ndg5BM9RGHvDBvXcYiJ56hOsclkpKiQsBG9R7FkrIXbvuE0p5q7C19db/mfv36B1IAYS+Gruz3/6f1TZSxLTaMyGnpeceEpRGKte3oEi6lDYz0EoHazpZCiqn5zKUwxrgFH1kj1iklsm4bDOONKIQeNEkwUlmSAInVdJB2oX7W+msmBWLkob2uRse88z1NgnBNdY6tbbMFV8zeoYSvGct03hOoNkXLmu48n5ph5uev4y9udKq3jRdG7QCyLQlmMwpBL3vUyyFvXV70W5xA5zIExZ8XsqcWj1etpaic/Gcfd1Z6H88SVtxzmjHO6PnOImsnLpRtrWqf7SO38QdbPKPX9nufIt89nvth0uGoC5yqTccHvNVeZilqoz9RSfA0prZ5guUCOcB8mPsaZnXe0jaqSI6pK33mHN4at11yTjv9/XkD/xbwKypZpvbaeGR3qZi6eNM5oQtNjiuwah8nKlX6z7Xk7zfRGrQEKNVwk53UIs9Txc6qiCzQ6L6bIKegA1ACnWaleC2PIfMIlNwKfdZa3U2KuvGt91cGP0b+/BsHUv7NtHNvqANl4zx+fnjnMylm/bhy/enFNY9R9Um11dWGnkvh2GHlIyileq/1aDa3xjwXEVvZOdSi0COeS+H4auKPh1rU02p8C2iE50SOkpIQphc8qs+SPx4GQM9JknPO8uNmz325497cHvktH/M2Wfesxnzz0lMIUAsM4KvSTC8UId63juvXcj4Gtt9w1nuver5BETInjFJlDpKu4qxM1BzuPk/qzdD3XrePD85GmaxmnwGEeabzFFnVOFBHmEKttheWLqw0PMbJzHmtb3lX/Ij2UYMyFb4eRKSdetd2amLYkiIkx+FK488K1t/wogbfDqNU4QjRaPneNWgmIgavOErNBMPwPX9zpGq22DNkUvrzZ8d3TSSGzrIZmpWStrI0yyBSDhtuuUaiHKhATsNkwV8psSmnl4cektsLWWJAqECq5Jro5ZuMYxkk7oJosVc9/EK1ccy581jfc9A3eyjq/sLU7Wlb6GDIh5koSWGDVP33G9m1D5yxjuXSrj2e1n/7ydsfLXaf6lVJtTEptRfQNKUW2gMmZpvUYYxgnDRVawmik4vIFJVScQlRGX7ls1rYIX9zteTxPPJ9H8jTiRk/fNly3hg+nEzfXe05zUOZh0hkCqODQ+ao2Tho4Vc+ClS680MyPU+CjtWrQVxRqBZ1JlXoggKXzjtYuCXqF3z4dK/W1Xt98KVzJcJgifc7cNQ7nPe/Po+o+EDCGQ1Gq9U+9flYHAEDjrA6sDASj2PbSfkKld4lSIH+cR165Bo8obbAGoWhEIGuVm0XpYrYIxgo2KiQ0C9xtWt4/DYxzYtt6nseZrXeUfPEKEPRm6ZtQwUZvC1NOLDQEWYy9th1XtzsS6ruSqxhKzc6ETdOw3/Zc9Q3/1x9+5HbT8M31jrZaQhuBcVZ44DlF/m48IVkxemVeLEubdREJgF3ENnVIVh8iAZIkfgwjSeBWPFQBmq34eF4eXIFGhMY6/uubK55TZJoTlkSZjvz+4YF933Ce1Fsl5EIgreWhdzU3oeKgxihM0VvLvtFoTiqM4YxhyoUpZ85z0hg9RLuHUrBWIzWfzjPbvmUvhSKGz+9u+Mf3H5mIWCfMove7z7Ye+coe+ubuBiRTYlScXHTIfZoCh2nWLkGEKSW+GzI/jhO33vOm61TRKmhaVX0YtSJFO9CsQ+BStJgYh6AOst7hK3vpf/3rL+isgBhiTEwlcQqZxhq+vNny+MNUNy91dg1Zh6Yx6z2NpXDXKle+NnvrDxnNMGhrsZDrDuCsJafMYoy27CMxRrrGchrhmCvtdGHQ1CJC83ctn+86nFF/rdoIMVNW24LGWLwTjFVn0ZAKvsKgAK4I15uevnWcK2vn4zDx8TjReMuvXt/USNEznQjXxuFYDl5d2KF258YoDdhZQ9c4nBFO54lSocdYq/ohqpPr4l6bFoUwMCX4/vnIw2ngdtOT5kgcR1zKHEfD3WaHdy1NzhzPcbWzbhpH1zlSvQa5FMQJJV5o5KaK7eoWwPvzUKnGjUJhRWdKDXCcJzrr6aqWJtW52sM4X+7vwjNNrIejiDCEzO8ezrzYaOHUO82LWESMawH2Z14/qwPACGxaz2wKIxlrRVkELJv5QlVTgyQ+OflCqV7ssWam1s271CdhqUyWw8Xli7jk43HkMAQVvWxadq6GVdsL1W3Z/0tlVKxUNLSVN8DeC7edum8ZNBi8ddq+DzHSYukbTWa66Rpe7Hpe7/raYbA+cAi8m2e+nUf9vBVqWiRktizxjMvwssICLCi4rO/XiL6PAnycJr4bT7QYfrnfsrOGkmWlqy1YqlZZhTvvmI3FUjdtK3gMn+9u+PZwpLGWKcVLe1uASl1dqrld49h4p1+zbjifzgVOIfE0zCpgqrOOKengeq5JVdMccN4iKXN1tcM9PDLPsLEqggPdQDfeVUMty/3xRKhPiMhiFwD7Tm0fnoaZkBfQrFQaXuK3xzNveoXslk2p5MSUhIdpWj9HLfAq1q8Q1HGO9N7y5mpPRgjFUJL6SlEKnkJrhc+vtvzwfObhNCq7wwj7RmExUGbJ603HrlU/olSr4XW9WUOIivEXqPbBllJyVcAvgwK9npTKQutbOMa6ipb/tFu49p43uxZnrN7nCqtFCiHrAWDq5tzXIPlDSKRU6JzOUTrfKNTjYMyRxloex4kPBzUgfH29IVTvm1gyYyk8Eyt1uRa960xKzeGaquae5qgD2rLAO/A8JaRUm3SRSuNODCXWQsDSWc9cCvOceGZg0zRMGbrG8f48rphL23o2XYfJicNxrPkU9QDNutvaJdAnVPpFfVgX9FOMYciR97MWbBtjaapC20ktbLMWg/fjyMdxpk6xL8Vbghx1w9FDQTusWAwfzjN/8Wqvz9CaMEZV3P/518/qAMjAkUSuniQWqumZbn4rZZOLqvKUIje+IUdU+MVlYees9hJLZ6lpVWWl2xiBU4z87v5ATAVj4fW+u2B+l7Jr3ZxVTV/YGHiu7+W2s7zoGpxRd8RYoDWO1tag9qQhEAatGGLKPIwzH84j98NEbw3/zWe3NAi/G84aBJ4Cl9jDSjOUUnnN+t5tXQAaOUe1619Ur/prWzfcmAvDmIgxM5P5zdOBL7YbXrVtbePrP0pLe60PWWMVUhJRb5jd9prj+cT1dkNKMCeBogRWksHW4JEiqq5+uWtpnR62pmLBi0p6jJnDFNYDdS65csip/GmFA3bOk2LhFCIffniPIfOi7ziHUP2XIJHpml7bdFTdfUz6vpxR5aSI4sL7rqFzjsMUGGOozpsKM2YKP44jsWReevXbLyKM5YLBa5yksqMyOjwvRTfiKaop4HLAGYHeSBU4Cr33OHeJzbxuPDddS+fU5DBlhb823mGNIVRmDEvvt3ZeEEATx0BjNOtL6t9WzxxTq8TMtnG0zjFVZk0xem9TzNxe9+yai0ndMl06ZyVBmHLxQyqCJmlpaYzN1ce/VyrzWNXLJmd2jeeL2612XynyFGatnrnYQzzmyLXYdR2r8aChrWluU4iEGJmmQIwJ5yxD1vjOHHTmVyicUiRVAz1vrKqERfORbS0K913HnBKPgzK8QsyMY+aFd2zbHhNnRjfjrKbXlVRtnF1NQy5FQ6OKvvmLFYash8UUE6dceJbAXdfSVoTC1gJrTpkfz2M19av7TN3DNSnuArdRK/xcg6jGWG1gjMVKqXqSSzH6z18/qwPAGcOucZzyJaRbxSX6AdXz6SJtb41hzoX7MHPTOuRY072Wg+BPWiOp9qleceKsKsu5UE2lCndebSYoC1mrYsHrkVK/pggbZ9i5hLWGb/Y9zqoIZZgqvCKKbevprdYH1qi0++Mw85sPDwxRK5UhwL9/+8Drfc+HPDPXWn5hfKSFCLZ8HNEw7iKQKi96qaBMXT1FClYMPmv60zgFPr0cY6WdeWfZGovJujnWbaMqOBcKp36PzzcNYx74cRr5eErcbXbs+xY7wSFMBDVoX7ujV7uetlapC5ZckSymrIlUKWQ23tIZh+9abLHcXO14OBwxW3gaBq69Y5gmttsNMc0cZ2VLNN5y13e4mPhxGBinwKZtoSRChOum4z6emXPGu8rBt+jDK7BtPY0zTDFyDolTjJpLbA0/jCNPIfB50658/KXp85U6WWeMWhnmy6yqNYbWGHwdrhtR2ibGEGpRcdM2dFd1OFznF/vGkyu8YYxUSqcQwqUIUVEUPB1HZNvSVVdPxf1Z7TxMUQ97agdtC/RAY8xaOUpWbLu1BrHaJ1OLL+0gNT0t54Qz+jULWoxtaoeUUEpp1zgiyrwiy3rodF2Hc5bnMPEUA1KUIqpDTzWQ21l1a12gqSW72ArklAghEubENEdS7bQOc8Y3nut9wxQz5MTpPNE2ZlXMN77DmsLj4UzjHN5YJAvvns6EHJEivNxtKBSezmc2bUtOgVe7De1mw3kamFJQ88C6r+RYA2s04k2T3tAHz9drP0XtELLAwzTz5W6LtVZDe0rhj4dTnTPUV+3Q/tWLa44h8/agGdsLo2gxF/ziZkORJQJTD7qFBvyTe+pP/sl/gS9BB7Njqbi97mT1z2Rd6LL2XLJisu+GeR3QeKtsESPCOcZ1cyyl0NaHTIW5wusXL/jh/Qd96IoyfwSzHjoLS0c7giUrWOXzmcTGWhqvYg6XS/VYVx+iU1AxR2MMW6dUxr9/+5HDNHOsHuXLDX4a1Tqg2XnEVjiwXgdqTqqIWiDbUmXwXDb/2pMqtosOv6Bi1CFpC48ekLGeBLEUfv98ZO89O2vZL3GJS6XDJ5VIUYy5E513ZAqHMNIEx65v6RuPGAM5s208rdMOYA2UkbLCTCCMUbN5+67hTd9zyIlDmPhvf/krfvv993x8fsaJYdu1jBmmEDlOTyTJF/5+Ltw/n1Qg5RvOc2Quhd45HIabTc/DwxmcUiNFFJ/X9Cu9n11NlrNGLRpS1s6kdYYpFf7hcGJnG57mUQexCFfOEWPNgZVKz7Omxj8WbjYNS+5tKVQHWOHDYeTdYao+PhdzuRWuq9d7UVuvrqBLhyPKviq2cH27491pYhZhiomNtzRS7/silrIGqrI6ZzUNtFI0dQ01zGuMcLdraRunM7ciq8AKga21jCvdUjvRgr7ZF/uO0xjxjaPvveoCBESUcmvFcD+MbL3jHHUNOpG1I792jp1xq1rXot2KMQpBziFCLoQQGSo/3znDaQbXGG53HTd9y2kOjCFiXMP984ExJ7IHG0d61yAGNuKVcJAj1sAwqw38OatRnkX4eDrTO0dyhYfnZwqZlNHnMevmv3TVlHX7UZuK1f11obJWexnRE7U4/fX9NPFcO0StBZRt9Wbb03vPthX2recf3j8zhFgZbXC317mKrU9kEe1ynZE1iezPvX5WB0AqhRgLrQjT8vDUD7y05/q6BKovN+FV1/OyJoillFbV4ylYQiocp0CMmTAHQrgo555Pp5VZs1ZRlDW1Z/0m9ZVL5pAyuQ6n5pQ4zYFd46EonfMYIx4qrVUfulzgfph4dzjX0YVS9UrJq2hkTplyihQp+MbStp5csVL/yXugVmGyvO+inUAuij0uC3qa0po1KrBSLamHzjJTeZxnHnPhtm35fNtV4UT93HWgboBd52m94XFUwdsYAlESflbXwl++vmMeZ86i+H1ICWd89ZfRWcpcIMTI03nSyjTD05RJaDv+w/0HHo7P+m8bbbOHOuzbty1G4HmOzCnqcHWK9N7xxdWW60Z4mM+EkvjyxRXfPT2RrW76W6/xmsrfVmaGqQ8yaDdpmoZziExR/ZgMmsC2WPUuNgRbMfjGEnLmOSeGlKpYUPODbzatHpKVHjvnwtNx4sPzxBwSQ720BlMdOmHRDqzZt/V7TVEPrr01eHTgm+sO+mbXMeXC94eBnBtebpo6ENThsKp+E4sNShFovMOERIy68b3a93SdXQuuJKK26rVIENQ5dc6qUC7FqLWzwNe3V/zNd+/Yy6fB8RdVsNSfhxDZGVUp112PO9+ws279PobFw0m/Sqp/N8RcE+5gynA/BqIYrtqG602rs6I5cZpm5lgoKbNvVC80JqV1ftY4jkkwzvJxOFEM+MZw3XWcp5lt13N/PKoYL2d60ZlEpNSBD5SgXXnjlKkXKm3TLehEqehALTT0+VYPsd4IYVamz7bx+EW3lDVk6Yvdhuu2qd2bsGkcf3m344fDwMMwqbjSmfXaGJTGrglxrHTeP/f6WR0A+rAoJBBr+PcaZiFqmbA2AussQD/8bWP1oTFupfGVUtkARfjtw5HvHk+YAo21aNhT5nQcFN9cvuqy0ZZluLIeA8w5cyyZUsU5gh4y3x9OfLnfKrMop9XNcdc0+Kz87KeUuJ8mrvYt85SYc6IkALMGWiiOrBVuGBPzOZLqpr3pO/rGcSQQ5CLfl3KpNJdqO+dCmLNu/vXlKlc8ZmXrlFzDOnJRxpPA4zRzioGvdlt6c1lUInDVN3SNJ6bMGDPrqNGol0vnPD98fGLf93SN+gY9jZrK5HRKpwdASnw4TAjCFzfXfPf8DHEk14rvD+/fqX1ErajUGVTza6cYeb1tMc7zNJwpLrFxHpOF+6cj2QrXfUeWwj+8e484QydWFebG0DnLnBPnuRpuodXowj1vrMFGfTi9t1XoUzjHeVWjF/QwVpYGfOYbSgOPIahfTw09j0lzXt8+njQPmQu0UhcXpSZaWVOzh0tGEV09pM8x8eM488qpn2mWZY3Wd1Kgtwbr4RBmbosHlPrsKistpstQFQovenWlNWK42rVsOw0cyaWySWo5uaxvkcWaxDLlTJbC1nlsTblb/x2X+ZgSIOq8q3aSXgxf9BsOYWaDpTV23SgLeV27Qv16SZ1jx5BqtKjmcIwpc7dt+fpujyb7BT4ejsy50HvPdb9B+bXKvDqHyKu2hSI03lMGQ8mBF5ueXd9jDTydJvatdgjWOM5zoG0cKRXF/lMmpT9NOVuYcyFrvsTiEotdYNNMax3X3q0qcUqhE2idznYy8NV+y23XrJRaqfvcdefZtY5/uldm3K5t9J7UotiK+ROix0+9flYHgADHWfH/beM5pLBuyGnBW9HWOMWEaS2LF0pFH6BUOX4VYZBUGHLder43hpwKvWvYNZnDMF8sIqSO2aoBU8p18kkhAocUGXNWYdgnF9waYQqJ9+eBl51WYAbl2JcCMSbOKXG76fjvb1+R0YPk24cD3z8cmUJasX1ZSr/aJi8PTyyZj6eBq9ziu2Wz0C4gi+K+hjoYj5k055quVecXcuFrG7PAaQqLLNe9pEIxmZSEb48nbpuGrXP0FTJpnNNB8dJu1g4h1ti63gvZwGEaMUnVvd6oY2KoXv8Ly8g7y12/IWbVKhyD0iidNYxj1GhJhNZ7pfHmujlbw9vTmX3f03cNx/MEIjQG5qI5Bs/nqW48mTJldr3DizDPGo6unYhe8JVFZQQyWArPQTfv3inTzDSG52laHzInpsKAIKIPsrOGz7c9v9p1vD0MjHPkh4cTp8p718KwkhFW5W69kMWwOnaWJXFMQ0G+GyYehgnfNrzumzr8F4yx6saaC9SCofW2XqvMEguTU65lklRWSWHjNHyn7wyf7Xueq/vrYjpWSiHJJxV5hTpEDA4tpoxWGYyzkhs+HEa2rSZ2Gambfn1uG5Hq2wM+Fz7vNkwxqjYnZ2Uv1exRZ7QwyTlVRW/GWGGMiTlmhpjovGfftKsLsDNGB6wpM8VI7z299YRsMGLYWsf7cVal8DzT+4ayOMjGiDEW71U53TrHeU51/iA1F1wdcZcBeEwLHFwPgQpr/vPIcwHufFOjQCuqUA+8N5uORxs4TJGrSv+W2plWUhAiBm+Ev3yxZ6ozrAUpoD7rSx64/ekRwM/rAFjYJ4dxpm9cjdbThR7r5qi+70K22opPFDZWBUli9AFyusuRMphicAhds5RfhVSHs+cYlV4nqmL0KdcFrj7vFA3TPtQoycbUiXs9ZBJKV1wGiXN2NGI5pwRFZfk/HCqr5zwRS1YPD+/44mbLy11PSJkfH0+MMdF4A0V4Ok8abrG+tPI4z4EbrzmqE+ryaSoGnUpRT6MpkVPtDmTZCqoyE7QST1pplMxaXS34paDKxuMU2HjH603HN1fblWnRWMNf3O0ZU+Z5DkgqfHW94bOrDU9T4HGYmVNB5sj+ZscwJeaQeB5nGid0jaX1DccQmcJMqoyUkHL1u9FD1xqLNR5roPOt3rdSVcp1w1PYK2Ow5JBwzrDrOkIsbMXwHEZMKVx5z8M0EVNazV8T1+8AACAASURBVO2WVy7qrZ/InMfIMSjWfJwCN31HKsrCcbXyvnUKW6y+87VrtN6TRTiOKlbKrVI/S7lsrLrI6wYquoZcZablOhSNRXOxQ20XIoVTTprutcB3K4Snz0wrltvOV0jwUm2ulaeBUt07Q8rEkBFr1GoB1kp/yYA29efCQr3W92Wsw2dlpFijB3LXOIZJn6fKG1i9p6T26bZWzeeYmEtZrRx0w88rrz/lrJ1rVPZWEeGUNcy+E8vXr+7w3YYf33+g95Ztq+vDWcNhDhzHwPM4sd9s9bnIwt1+x7vDgSlGrvsN3llS8XhjmXPhNIx6rbNCNqXotfcls++35BQ4oDRuWGypuaj4pUJdS2dXD75r33DV+FUopmltiip0Rvh81/F6o0VHqYpnvbtU6q7uhinXWdESkiMX1b2a0wnuXxgC/KwOAGctrWuIuXAcA9eblg9hrngpK6MBEYoTTmOg6Rv+4m7HZ7ue52Hi42mkQWisDocfBx24fBz16/RdS+88Ygr7OdYwEziOGiWYUFz9cQg8zpHNVj15QsXhF+OthY9ZpNQ0JvgwTLzolFb5/fGoA+ha9aac+aePz7y52uImFT0VU/DG8Oq6x4nqBVpnGOOWf/rwzLvD+dM1RUyZj4eBV9cb5qJBJIsgRlIhzlmrKXKdjWj1t1SvakpVobGkh2VGK+dlTqDRj7rYhhD57jhwCJFf3+656xuNsZsDc07cdQ2/utmxbSxFhJu+5fUu8vuPJ05T4jio2Ve7bTkOYEvCiGeYR6DwatsQSmKeAzmjFaRXp0drPfvGMcyRJIHGCHMMNI3l6ayD39ebPd8fn0lkdl3DGALnacB4w1ATlT6cRkopvNpvOMfEcQ56GFrFstdBbC6cJ2WZgNqNIPBYu4xSoDFWs3RTWTsqVwV4WeD+MDIE9ZlXdqwWAYtflEKUSxV/MY2LlWcfyMxFN11L4abxfJwDjzFxHSKvuk671LL44AgZNarrnVsFYctLlh+Wg0AExPDmaoMzOifqsEykdSYRyUovlbVeAtHurOSMNYUUFQP39Z7PcVCGjGG1QciFdU4VqzVGEWXGdcZw1XhijIqZV8w/xMQcVP8TS+FjSpxCworBGdX3/PLNC+72W37zj/9I3Hbs+459zeo4jkEJCNaRskbcvDscsSJ0TgOCJCV2TcNxDIgt7DcbQDilgTgn5jlyte/AWM7nkW2nUbQJvZfOCqHef7tQgOozutzfl13D676r2QS6redSanJh3cNSpqvZ5Dp4tKugrKCdHFVlvzqOitIElujWyvum/PT+//M6AFIdrKSgrpcpZm6s42PWTTyjrb4KhrS1tEV4OI/cdQ1TSDjncM7y9nDmcJp5GmfudhsOFffsmpbbqy2n44mvb/Z8tm85jYH/+N392trfnye+fTwj1tDtWsWwa0WVUX5zKRXmoYpkjHCeI+8Hrd4PcUZQSmCp84YSIr/78ERjDd/cXbH1XtvbEMnWqpV01Er4i+sNpzlwmsInV0hv/sfDQOMt/c4x50SacrW8KGs1d8EHtZ2tjYu2pLX6TKUunvqQU6uLhfcvouZgT+PMPz4eSWWLIPzDh2c6b/nyqmfbOkx1onTWsHMtv35leRojz8PIi23L8xSgMQxT5jjNzCnU6jOvjIm6DUM2YLVTeB7ONE4pUcVYrLGMKWkQhylIDLzsNhyHkSFGQhUmbXD1gNY2/cNx5MNx5F9/ebc6KhYKx2rVrZuPhsrUHRMBnoapHqEqt/cFOmMoUphrkIupZnI5Z47DRGMMf/3Lr3l4+EgMiU9TxxbVaEY1DrlAyKpgHao/vqDfg6KVojXClDJPIfGyu/jFLP79PxzPbDu3dhl1Brxu/sunSYVqEjfjxOJEsBV7bLFMJeucrc6KppyVbQZ0ADFCyRhrKNWLpABvdh2nceb7xzMvrzquW68bPXXmQdbiRuocYilAcqb1jhCTevbkjBMhesOxZM5VIb4xwtYa7npPnk7c33/keJrIufB4HMjArtN0vrFme5Q0selaHk8TULjuO8ZYeB5HzZ/G0baeYZzxHsYwVbgnIEYDXfoGhhQYzgFjoG/dJwEw2t2R6x5Qq3MRoXOWF22jzKvaQVkrhKIdQ8o14AnVA3hrlY4bqxeU1b+n6yTXQ+TCkBLMugZcEWaB9C8EAvysDgBrDB+ejrRO27OHM7y66vFZiKI7mj40WX1TGvVXeTrNvPUjBTWOOs6R3354Zo6ZzjrujwOP54m+a/l4OHE8jbzce253G+acOAwzzlqMgdttCyWzG5X/e20tx7zcCHTIJaZ6l1CtD/ThGutmmT5NLEIXwrK1CNoOv30+cbft6LyjsZaNs2vlFCu98Ndvbvj9/YHH47hu7KBfJITELjWYdGFM6A9aJSw5rMuQfIEiKgypC7mKTvTP9d/qxq+dTS6XodzDMHGubKfP9j1fX22U8lohgs47xblzwQK3rWXfbCi50Ah8HAca0yBWzbXmNPIUFfv3xmCL4vyC4L3j+TRo5ZMzxRj23Z7z4ZHDOGOsKAZ9Gtk1HbtGNyGq1YcRwXzSySwVtxfD1/t2xbTfnibenkacg/MUFN+tW753atC2XiAp7J1WaVPIa7UHIFb9p45j5O56z9Vmw9/+7g+YnHm96StktBzAemjMBeYU9Z6D2ooUViuCZSeXopDKoW4+st5P3azVXsSvecBKAdX7OCTFzlvrcFZnFwpwZXpjK+VT181iHWJZPPaFOcPjPPF126waEgeIMYRadTojXPUNp3BmmJJmLhhWoZeIdplSBIPCWBapgiboveH5PHOa1aQuOjV0PJ0jthS+vNtiRRMAQywcj898ebvjNG15fHzi249P3G17Xl3tuOoaxinSb1qmkC9aojofOAwT1hoaMTgr5JIICbx3IELnM961iFgaaxV+8Y4pzZwmzRp01qxOoGW5D+5CvnjVNjRiSHnJDNb34KxZtQ7L/MfZ2p1bneukGMlJwFjN0EhKnX4cJ3bO0Ru7rkepcOmm9Tycp5/cU39WB0DKCeuUMbLvXTW8ylxZz2NWIdPKfa8bLCIkEX44nYlJzZfmoIPkXespRR0ou8YhFDaNJnAdTiMNhRASc85c9Z6+URuHq97xP+1fgtHqsAwaXIEsEmzl4MayTOALU9RFFhYfouo0MVdIQbMI6sZb0NCZ08TdRrjuGlWG1qogpKSWBsXwxc2W42laN5DF5gELpxjYdq0uTiPkXG0hSrWNlQu+vISVQJWa101m6V6oe5+Usl7hBbdOlas85UKbC3/96oqucYSQOFWB1CLy0k4pV6UyPM6BMaut8UwhzCO7vsNax4fDgYGITULbNThn2Xcdn794wf3pwD99956+sex6zzSPDHOoB69WXfOYKGXktt/QOlfFbwp9LfdhufYv+5ZNo3qN5b1mCp9tOz6cR8Iy6Kut01Xb8TgM68C4s4a9c2q6tg4FFYc1BbWWSJndfsPpfOTpPCNGOKeBu7Zh5y2hKJXyGAOlZLZisJlKBVbrgVwqLly7uJ2z3M8JJ0sFqLuKvithVyHHVNVPSsPMnGa16HAVPknV5dKJ4XmY2biuUhj13jcipKLPQgHIwvvzmZQLk3P09XA2tavIAgvH7IvrDTHpUHcOiWLVhrmpEIawUJ6V2XOeM8dZN9S7XceHJ83UbhqHicLzccKLoW09H8+z0luzEjWGYeT/Ppw5nSeWhK1v7w98OJwZg7aun9cQKSd6kD0OE9YbNn1LCBHrlDxy22+q3Xnm4TBgxLLfdpSUtfMWYQ5z9R/Ta54rFGydWzMwdI1BI4attaRqTa+PYgFZBrj6bEip8x9jLvkkRrC2IYZIzpkaHc6Pw8THccIYteHeeB22t5WG7o3QNT+9zf+sDoAlXzaVzJvNjle7nlQ0nPz5OGu4eG2LTa0ocgGxWkEllA/vvWHjNf9TRGlUIWW21rBxwlXneTcOnMfAcZyZi5pvOW/5/eORK2egE7adw1vLG2n446EOi4qyc3JWquIcEyVnhpAqz5y1wlbxVV04tRqv5wKNt7y+6pXGKDAkhXHOMRFLxhlDLDqwa8ToYqtf21nBdTqUPYSJ3a7BFsPhPGkYhlUTrVRLysWRVOTiiQNLNbn8mYrMMjpwKfWzatWp0IIp8HLTct22WGu4nxPvzjOHaeRff3a7spmM0evyNM2MlU9/s3GcgvranKaEN5Yvb274cDhQUGaWWmdYvLX84uULvr9/IJXM8ziQ8pkhpJVxUbLOXrwoNv3yasP7pyNFlpD1i0C+ceoM2tilKoVDCJQqausbx02BKUUehgknhuOoQ/uF/fFF2+oGVnMOzNKuIySB0xQw1nCz3fD+4ZkX1zfsui1Ppycep4koirXZZXCvJFRl7pR6cBVwUghF1bk5F24ax/0cGLIetvsl/EPgHHO1VVB67Zy0A1PvHu3ExGjucOfspWK1wnEO7Ly7GC0WaDFMNQ/icZzXFKofxomv+lYx66pQXNTUUhTs/+amZ4iJP3w4IgK7bUNq9I2mVDjPavkxznGN7Swog8h5y6/2Pa1RLcwfZx32jyHSVIosFUJprfD3759prdAbiy3CUArPc6R3+rwOc8AZVSs3xuKN8JgixmRKyRymkY31nKt1fGMdnY/MIXD/dOCqbzlOE33n1C6j6OcQgKRrz7jKihKd/5lS+Hzb14HwxWpdtU1p7YaWw9Bbs0KRtaer3YQyFdXSWlPnEtrFDDHyNAaWIJzOW/atX9f5n3v9rA4AYy1t25Fj1AWb1T1xlsLhGEgVozQiGG/ZtQ22WriWsiQSFVqEBTkXlD8cYuZcFHK4ah2f7TfEGHkctc3aNZ7HOXA4nrAi7JqGv7jZcl1Tka4bz/McGUKsRmzQYbjttQJ7Gp+1AjOyVgtGYeh1iFtF+FhneLHtaSscMmWtZFIua07B4zxznqJ2M97QRBhzpvGGpjUEWLnpY4wYEa43rYq/Yqz+NuUTaECqU+RS/C88b/ikr1TcvCwUUtZDYKmEG6PZrLnoUbZtHado+fsPT/ziZsvWOcaYeJxm9bgHEIUWto3BeU/MhsMwMgwFZ0S9nCh0bcf5eOQ0bPnh44lUPfKpm6RuHKUytYTXNz3WKFQxzDO7Tc95Gi7+T/qt2TeOXbVsBlYxmUHFPt7BFkuXDSKW1nkO48A4KfOjt5bO2NVvZ2VWIThvOeXCECP7rmPfWv7x22e++fIXhDlztd+Ty8S7d99furjCCiFQO5ZGDOOiAckZV+9LZy1X3vEYAvdzUHvpCrtNKXI/zrQVchhzqoeKAjtzKdisM7MhRKaU6VvlnJ/niCmwazWPdgG/hpC4P081e0Pf4zElvh1nvuxbNtasnaKpHckyRmqddmtj0CjGrH4oxDkTksY2zlFps0tcp7OWq8bROkOOCSuqhH1/nth6y2ebdu3YFvbT17dbvns6E2Nk6z3XfcspRjZiaNqWh3Fk4ww3bcuu+gllCsMceHV1xePxpMwtq4fiaZzovedu1/M8Bp5PZ8QoucJWRzhdS/rgl1yYRw2ccd5hbeZN37FzloV2bahD8IVSRc2qBnpn8NZirVHbmlo5FbS4DLlCulZhyJQzKWnC4bL5i2i4zsehOtv+xOvndQCIsPUNSbS6D/VDjzHpRRHWqiTGzGlK3G29OkVSxVB8gmujp/WcstpL58KuUTvVXdeQcuF60zLGzB+fj8wpE1MhmkKYJ8YnvfAlF65az8teA+l7a7lqPHcbz5QK/+HdQdOHnFHIp1T/b1GmhYZvKHRwu2256ls23jPFREI3/gUzNSIcQuA4zJymWDUKqg7cep0nKEPlYoyn86jCUCKb3tElx/MwMaUF5BdyvCySwsV9UJaTClk3TkrtxowgRv/+ojv42/dPPAwzv7q74jEExhTYdRYp8N1p4rM2cU6RnPQzVT83EpVZg2BtZt87PjyeOc+R7dZws91ze3fF3x2e+btvf6AYFa3ZiidPy+ZbFOa66RpuWk8omfMYNP1s1zLN6rCoIi/tpL653SkGS6nRGaq4jWVxia3dhDNgDSmIps8lVQS/rFW3WIEIgq5LAOMdD48HSoYPj0f+7b/7j8QMj8ff8M3rz7i9/oL7j/dq61w+oWbWfSHVh3kV8RXWLAhbN5Jr5ziHxJzhEDPeKB8/iRYi5xTpjK2DSFPXkuCtcIqBUNS2+TAF7gpc955d6znMgVQy113DlDI/jjMfzwMFaKxTQkZlG51i5BAcbdXBNFYh2FguDrIF4eWu5xwS94eB+RRWt1NTnXX3TaORhsZUwzvthigZ5zQm9bbxxFx40Xp8nYstm541wqvrK8658P7pzDlnPI5932NM4f3xVD2EVGVrase4sY5XbU+uQTehQN80HKax5uvBnDRW0jWqNDbAseYXGyPkeW2JlVLsPDlqXvm19+pMKouYtKzuBYs9+pQzH4aZozO82fardmfpzBaKeUH3jo/DRAiRTgybrqGxlg/DiFkIHBVdsKuc/D9//awOAAE2jWcoilemUggp8TDMlX97UeUu+G5jHE3hwjygqrdrCZjRYUpOmZjVBC3mUjcUPSC+f1ZfbmMN1sqq2BynsCYPfcwZb4S/ur3CK2F43WRCUp8TpJpDiQ5yNcavhmUI9F3DZ/stKWWepmn1QJEieAOxwCko8+c0xbXycEZ4c93zbhir/37duEv11zEL26NwTqq+bRuDdUKKhXMdYMlSjKxc4tpFlIX7XX8hl2HjMvACvbjRwLeHM49T4Be3O4BarcK2deqRVL8eRl0wpSxqbv0akqEBbtqGEDLFOt6dzjzPgdv9nm/v7yvMYrEWTnNaOymRwlXbcNfpRtJg6K2lYAghsChZFwHUxmsQT1wYUkYYFjYHrN3RAtWVOfP+eMZaYdc2dDbRGlc3bVWtmipa841y/89TIKdq5xFVI3oaJ75794F5OiMS1yrQrP8JARXjtZVI4I0ary0sjwUu3DnPVxvDTedrF6LQgRXDdd9w/zwwk1fGkRUV/imjzKzUXdBucZ81orGxhuc5Uqzl/TDyPE6VLKCwkTdGk+vqc3UfAp013DR6jKpWUiGokEt1LVVh1pd3O4XFirCvaljtBJen9wJclOUgrgWQGOHzTcef/tVFHAmSEt/c3eAwPA6Bbd/RdY7DcKIxhru24W6jzKCFAi2l0Dvh7XmgCMwpIFE4jhNIYZyg2W6xIrzab0kx8nwOFK+GfDnk9X2mnHH2Yn+xqd5jC0wkpVRDp4ufljWGj+dJ9xlRGu1V49l7R+MMuVpCGyM4DGPKPE4TQ1QiwhASnTO86FuctUw58XGc1nnYT71+VgcAJZPDwKZpmZM+eAXRQeOClC3QhCh8sLMWk/InUv1Ly0btCo5iqjOiyvSVvlfd/Urhfp5Xhk9Z/52sqj/QCX9MCzxyidMzqDr0FEJlhghtY8m2rM6lVgy7ztM0imeeU1SMneoe6SxDShzmwHmKTCHqJzHC5/sdm8bx5mrD4xw0e1jU3TGJqjapeOyCMeSSwQmtaDUTprjOLBYzL/2ry/JcC5tPcH/orKW3lod5Zhk8SsVix6jeJot/SxGYSwQxGG9JMa+D1izL8Fq/YyJrvKPA9aZnDBHnDEMYeX13TQbePT7ROI/1jpgHzrMK44wIn287GrPU7oWb1vE8RaYgGKtVpG6TsGuqOd8nBcGc8zrAFEXsQNTa4ONpAgOBwjxNeAxHE2m6lhIipahfvzGCOLNa+sZcE+MW6mTryEb9+R3ViG9ZoXUOMCXF/nPR++nEKNttcb2qxYM3hm1jaZzlKc1rl7tsLItDpNTDLFS/oEVJvkY/Auc5EreXSMW2aXgc59V9kroRx5jJptC6OktC4YmHFPFZ2BqFT1aLhFL/bS1GLcJt3+l3TnmFIqXai69vCC0WFuqybzyxivHW6mS9coUYIxIT02Rpm46rYnlxteO/+/Uv+Xd/+3c8lTPXjbKW1I+JlRAxzqkq/KH1nhACr3dbTikyzYFQApKhM55SDGOI7NuWFDMl6RpWKm11GqBw3Tre7GqaXLlY0GhnLSuMmnLhMKvALebCu/PEx2Hmpmv4xfV2vR5GhHMIdX4W10uQiwrp0jjRWPW2etm3PM+B8V+IBPtZHQAiOjQ9h4l3zzq5v+50WFP3daD+XFSYY7isEzFU+8ylGtKNceOtikDQNn/bNsxBTeOUPaPbhQq2yoo3IpUvv6hY0EWov1R16GpzXL9XKTBNGe8NbWOJOXOz7dg2yiN+nGa8yPqe22ry9HieVDUbU128QmMtX93umJOyQK7altNZDwcrstrzllo5raydutnofK7w2c2Wj8+DityqoEUf1FpeLdCEWY0D2HrLtfMKYTmnFL1c+c9G2SfL5iKil30qZQGlEKML3VujEZZ1c9IBrkJ1FoPzjuE8rHOTP364xztHMJlGCr11nGo3IlItIUwNKi9FlcOim/LGWZ6mwsMUkSp0+vxqs3Ywgj6IMdf6v27+2koLJYoOa2X5PZjIxDDzFAN777XzSGXdcMeoR4lSZlUYlnIhkelxtfspa8fhrQ5/Y2WFAMqrl7r+BQRDV/HpkArV8ASKiuW0iKmixAJdY0kxqao9Z+YFMymGTZ0z1YYRZzWTYAxqmzDMAZMyXgxprSQvepCcwVXvGit6CDzOkWKp0ZiVaDEFjLX4Roeb5IKU+lx9+pCXqlxfnFagpn4pqSGmVAfjF2O4hT+PoKr7VHg4Dlxtt4gY3j0ceDqP/I9/9Vd8+/4D79/+SK/+1voM5MIxBN6fZ6zR6E1yYcgR7ztymBUCihGLYL2l33heX73iN28/1v1Gr0lIGhW5xG7e9o0iDoKms9UOWBlQqvAdc+G3j6e1eDNGM0EChfvzxBwTX15t6Z0WRXNKTFk7yYUksFj+TpXQYgV+sdnwsml4O4w/uaf+NDj0yUtEbkTk/xCRvxORvxWRf/PP/vx/EZEnEfmb+t//Xn//rz75vb8RkWcR+d/qn92JyL8Vkd/Un2//v7wXTc0BETWf+uN5YCDpUeZE/dzrp9p3Hlc3Be+Meo4sLKGVBqktaO8tX99u+evPbrVSMzoEepoTjWiNtMr15cL5XyT61gi3XUNbsVEjVF8gfejXa1V/TLGQArTO0zuv1bSzNEb90Bsx9M4xJ10ET0ddCFBoneWmb/j162symvZkjOKri7X1XOEfKxe65xJMsZyQGfUfL7aw27bkoglbxixNw6UDWLBLY4TrxvPXd1dctw5n4NZ7XjUNO+9W5ey2adbDZ9EurH1XqVCcMTVgXg8YB0wp8+5h4Gmc6TtPyarOTlmTnc7TxP35RKJwjBOneaSzltZarruGb65VjCbL6q6H9L61NI4V5gEdXJvLrQEqN75itbZydQtCjJmH00gueU2Ay3XbTlRV+TTzh9OJx6z5Ae+eTjydhlU93Ti1qGi8rYFAzVoRLtWLyvcVbjPmEyf3ot1A5xy9VzpyqpvlUpgsiuG1dyvKmjkNkedRaZ9jWbj+1ayw5Cq60+/1+X7DVd/xcrflVef5bNMCajFu6sK+HJcqHJxDqjROXTMPY+SPx6EWS7pJbf9f7t4l1rYtve/6jeeccz324+xzzr333CrXyy4TEcshipAQKEIKNIgQdIIUWgEhBWhEdJAIDfrpRopQlA4CCSEhpAANhJCQImgkkbAdx7FNlVWux32d136uteZrvGh8Y869b9nXVRKdlJfsW+exz95rzTnHGN/3//4PJ5CISqmqe+u1y5l308yp2h0suhRdi5bl4MtFqvVS1OPwlAX2k088hMzdENHG8eHZho1KdF7jnWMcRv7Cn/0VXn1wxevjIOyz+mz0MfHFaayBUJohlNUY8YvbA+MYyLowB4kjbVvH3hlUSVxu29Wme4oJYx4Vu/vGVctzajEk4tWSIaeM5GXDm+MonlBQ/fuVPDNJ7uvdNPMH1/e8Ocn73jee4yS5z60RM0Wt9Hp/Ns7wUdehlMKi+LDtvnI//Xk7gL8N/O+llL+ilPLA5o/5mv+7lPJvP/2DUsr3gD8nN0oZ4DPg79e//pvA/1lK+VtKqb9Zf/9f/ElvIitQVqOzbFSxJGkZ6yJQIGk8SkEqPN+2mMU3pCzS86q+qzGLuoDOhY019EEqflsHUv2UeHfqhdNPFeEgmHwpWSwfHkGhdeNfhqiLLcQaVVk/x1J9j1OU0JOYuegcbWNxyuC0rsIxGTI+DNO6cTtj+KXLHd4Z6XCUgtp6tkbTGEOfw0opW0yyn1rCLpvegjPfp4hVivNty2Gca2rWMnj6Mlx23ji+++wMjxxUrY3cDYFWaaxpCDlxiJGcElnpNaGs2tA/Xj+EYROqm6PXimGKHIeAsoq7KVCwci9zovGGQ4okJZuK1eJVcz8PtNbxwdmeGOcVfls2HpRUmA9TxLuWjW9R6oQCPjpv64H1uJmEsmyNy/WS/x1D4hgCRa8AjDyTVEy3PglzLtzOM3ttGaZIpGCNxWSwTnIBTHlkmJSc10NTK74cPF+vXS5gncEa0TLoLDbiy8bfOoGxxuphtURuLsXGGMWbZ/m+GlarCGMkU+HlmUBDIWbuDoPASF5zfZrpx4hW0FnD6UtqaHmzrZFtZJgjc8xM9UAxDHzrbCOsPFUY50TnRGFctMzDSsqYnHkTA9/ZPm5UeiFJFDmAF3hwrfrrXCTXeY1vPeNp4DgnGhvxRvRCW5345W98zHe//TX+2fe+z2/9wQ8YVeYuRHbeihV3CgLX5QU2zLw9DsSUaas987bxDDoy9KNkFlS33Je7FqMVr+96Wet1/tcYzdfOWjYrAeXRNE6Eh7LRP0yB46JQRuyt89JJo1bfpFAKnx56HqaZjfO1K1oWVlqfSGsUV62XIXx5VH5/1etnHgBKqTPgLwL/AfIAzcD8J/2br3j9JeAHpZQf19//u8C/Xn/93wL/gJ91ABS4zZlIIhjhwS4VeH1vKKWhMkNiWior4T6/6SdCSpw3jg82zcrh1lrxfNPyUUG14gAAIABJREFUycOpUhMlq1Nreei1AltKZeUApZDK0hVoQkzV/EkqivJEVHWcAjfjVLHkCsmwYKkCB5yGQAvstCYYtbaCN6eBKUolopEAj+88P8dUhkUqQuMDGHLCGSWh7L0cekt4tlyBUjFEWbq2tt5ZgaqRRq2ztM7y5uEk7CZqEV030+9envFi23DWWEIqTCFVywHFcU7MSaqSC+dorZFBd/0ZpOV78Vh/1mrUpEIsWjqmIoIyp8UyekyBTaM5ZdE/KP3YRjuj2Rsr1MMUUEjmb7aqWhnIIogFkmo4TAmtE887T+cNXz/fitskC+aeajVVc57VI5UxkVlCUJZ5xQqPLQ9ofQ5zKZxi4HK35aGfmGLA6EIIBa8MRmumaSa5QGcb3Fok1OEtMrhTyPO3tbZu/jLs81oYPKnI/y4HyGmK4nuTZbZgAKfEqrtkTSZW5pOh846rtsXVIcNi8fDuMHLoxTqjcw1DDCQKG2vXGVGoHY3Thk3jOE5CNUy5EKo4uii4DYH9MHPVekBx3ghFNdTniiyH56U16ARvppmPKnVbcqyFvVAf8bUD11rXob8Ca3DW4tSSR2EeCy0lBIn7u2v+r9+44fX9Pe8HyZm4DTNqVPRzkKF/AV8EAg0h03nHGKPkIwBbbdEWnj2/RBnPNIvDaQauugajDW8OQjE2Sszctt6s3YF8BmEYmXpQ9Cnx6TBIQWGkEF1mIVKkGjGdrNdTA3OGvh8FOq32FkLBlmfzA99wZu3KL1fLzfiK18/TAXwbeAf8N0qpXwd+A/jPSimnn/q6f0Up9dvA58B/Xkr53Z/6+78K/A9Pfv9BKeWLemO/UEq9/FlvpBThwIaYJeqwtuLLg6HU4r0jisY3Q88U4poytLA7fMnchrheHI208GPKHMaZy86TjaXkzC9t28cDtNJpYsVRp5h430+87gNoXf371fJ/AodbxW5jiXMhVJXowrGvzwQgUXy+tmw9MMTEMC/eItJOvjrfVbqceH/kXAiVyijto2WjLQ6R7Re1ovgsLAr5tWZaYQf5ip3RfNw1OC3eSdOqlJV/tPOeV/uWxgoe23kNGyUeSlqJJ88om2gfM3tjiJWxQqnMlrrJKbU4Woq/y+Itn0uhawynSYZbMWfONlse5pN40Yi0GzEzgGfO8aLxlFKYirBWmu6cOWXmMGKKKGahcJwGLJZcEufesHWWhzHgnVD7vNHEKHMBC6AVY7VfCEXsv719PDSTUuugdJldLFV3KvJZh5BorOPcO+7DzDhHlDLyvbxl6wwWhUnVhlsvNgtS2TfW8nLbcgyBY5CNuJ8TW2/ZO0vrarFR30ZrTB0ILl47Mvz1WvNs27D1W6lOnVBB55C4mwONNbRKsPmcJUy+cy1FWaZc8Bgutg0xJZwxhCLspGMI3A6jrEEWbUjdeIroMj4fJOHswtt1FiZq5AUGkev3TFk+D4E3c+C5Fd6/rVj4Yx9bq2KleN+PUOCj8y3eyNdJVCRrwbTASTnOvL3ped2P6EZmJVNMvAnis6RywRnHfrPh/d2teFa1DoYauB4SoT6798eRYZ7ZWFHYWqXQuvBi4zBacX0cebVrOW/Nuh+VXJ97XeMilVT/h7nmCef6nBrpyHQUGrF0eNI5ay2OBbnOl3zdDx9FnIWtsVw2ft1bFMK+uo9fXa//PAeABf488DdKKf9YKfW3Ebjmv3ryNb8JfKOUclRK/WXgfwZ+ZfnLChv9O8B/+XP8vC+9lFJ/HfjrAL5pUWkpB9b/rK/lj1QpGKO4HwPHEFcIw2iNd4aHlLg9RaFYCpiPLqJIjHkZyYnNg0N45VoprBbaIdgVk8wpE7YNd2NgCdVYXgvFUynFrtOEXDj1hRAfK0kpzFU9xRU6FbYKijbSzZTMeev46Hwrg8uKxZfCaqNbKstpDIEXu44hRN4NA0WXdfirn1yfpJarJYtxYwy/vNuxc46QM9+93HMzBt6eegyKZ13Lx2dbbBX5wCM01HhRGqop8Kx4Yik8hFAH7ksVop4sSCW88zrEMsjhEKo99nEQOqI0DZn74cRs8rrJLWwdDVw4sV6eq13wzmjOzs7Y7be8v7vn/fU1Oc1MqTClhDGWxjtiEYgslcwQIgOyeZp6XVH1GaIaDIbE8RRovF49m+ZcGBe6SoVTylKUKGEJ9XFCZ0OoG7t3jq5paCycpkEMANHrnZANUphgY008S/VaxZyZkxxyU0x01oo9A5kxLClgmtM4088z1mpSlIKpMYbLzq0GdQvmH4rhbpz4xos90zCiFVztO+lgpshpFsGZtZZcq8iU82pM5pRmLjKXWrreny5s5lz45DRy3uxxlfWSstBhVZ37LIPPj7zjOkbehcDHtl0JF/K9H72nYsz0ITGGzKYJXLa2FlwLq67O8OqbCDUD3HjpMlOdMGegaNgZi/MNqhR2bcumMUwpcrltmGPmVLMzyPLk3x175kofts6ua+zMG7aXNSypyGyoUP2qyhIYI13U+37k7TiRK93M6NopF3EqyKk+67WIWUgdsQ67l8Cosm6HiqvGs5iZL+K7OWe23vNVr5/nAPgU+LSU8o/r7/8n5ABYX6WUhye//t+UUv+1Uup5KeV9/eN/C/jNUsqbJ//sjVLqo1r9fwS8/eN+eCnl7wF/D2C/Pys2FsaKqS3VMWqBgZa2XKqzsQ405aUqv7fi21keGKs1jbcMo3CxRZG4KB8X/5cFcyzyxNRK2hphDD3LcNE1DFPg+2/uV3OxV+cboYVpwbuNVpxtLGPI9GN6xOdKWYUbizDw3Gi+e7blrsjQZ7GNXih1SslMY0nyUkUqn41WvDrf8/bYLx20uH3WyjAj8wuK4O5bY/nObsPOe2KS6/HRbsuLXWHXeTrg1dmGXetXamoMAShYJZ771hg2vn7/OXDhPIecGYfIfutW7FZgq6WWk2qzqaZac8xi7Vy7JN8YUT3OMx+dbXnT95VlJBVRSYUfHwZ2zvBh14qC+2xHt+kIKeGtpyhHHyP9NFMSDHlmzrKJ7VrP1kmCWa7D/AV20UqRkyyy96eJIWa2rSWWDEUyHsT7RrrK5aYtIjSQBRgpxGkUM7+mofMG4sxxqVZrqMcyyVw2klI7iC9OA60zYheQH1OxvDF4rZlj5DRF5iQ2JadxZKoFT5hkPaRS6DrLOk1NmaIUt8NMHxWtb+najmGc8UbTWnlO+ylyN4x03nEMgcM0rqy11sj6aIxmylmiEalLryx2JhXGqt3eH973fOd8KyI1FHOINFqcXZeZiwaeW8vnc+DzYeKDxq9eWnIg1zUcMzHA+W7DD9/fc9s4Xl1uaIykYJnaJSydu3eGs63n/UMQ54ACRknBEUvhlCJnRiwo5hhxRiyqj3NaOROHoV/v875rOExSWDSuaopkG8LVGcwi4CtJrGtKLhVBUDyEwOtxJCURMn7r7Iyt1bzuR+4WPystkLGuh5mi0nflUhFKXdD1Uzb6kRm2wMsaaL2V7OeveP1MFlAp5TXwiVLqV+sf/SXg955+jVLqQ1WBeKXUv1y/7/WTL/n3+TL8A/C/An+t/vqvAf/Lz3ovskj0WlmzXNMV5JBXrtgyywau1IrRF8BZg7ZSDSizOPdJFOJl1/Bi20F9kBSs7ASlFjmQvJdUN+7FuCkXGRb2IXIcA7//+o4f3/ar7LvI9aHxhm1nabzGOmkPF+ahVpKMVepg2mvNGKo2QQn8s/EeZ4wYgNWH3RgZioWUebbx7LxbW03BsEV0lWuJapXm1y4v+JeuLjlrmpXhkwBtNDvv+LBt2DaO1ihUEWqZs5q2kd1+TpHGGbrG451j13k2bYOu3PCsJCAnK2rwOVU9LEwFb7X4tM+Bh+PENER0UXRGrBbCFLjadXztg+e82l+sQr8130LJXMRquLo44+UHr/gXvvOKH7++4f408vVXH7JtO77x4Us+vrqicxanHF3TVu0IWGuw1shzwNLgFErJxJgYxsAUk8BLxiDCGikOGqNptVmrLrVGhNaFC5y3IkozSCaw0oYpZK77iYdp5pSS2AqnKAyknFdbjaFSa0VQpSq/W6NK4bof+fw4iFBLK+76gWEO4kNTZLhcmyURJkJVosqfnW88H515zjvP8XSq8xnRZBSksxxjFIqxVaA1XdtgrWfOsvc0SvPMOdrK+Ve1W16KsSU8PuXC9TTzeS/2zA9zwPCkeFmgSSXMqwsnlOi30ywq/XWgvUBrEW+NJABqxd0sa80o2YAX1p0cQrKeXmwFvtTA1suBaLT49ZQsNhXbTUMpmdthJGeBM1FCLTV1JrjtOlEEG8ddPwrkWaEcq9VjRa0NuShCjI85DBXWfDOMK9X4ZdfyvGvwWvNR1/CqbVho10YrvFV0zvBy49lYoauvIMO6puCicXTGfIk5JXqWgn8aUfhTr5+XBfQ3gP++Qjl/CPyHSqn/RNZK+bvAXwH+U6VUBAbgr5YKzCulNsC/CfzHP/U9/xbwPyql/iPgJ8C/9zPfRT1Zv311wZTgJzfX6wOyPCELPz8ulVl9oEuu+BusYi0U9QHUkGWh7BonZmHV4nn90YiAaoFfQCpuydVVQqnMkoUrmF9ZVbIiPquwkhI8U3tF66uMXSWyEj64wA+sfHhnFA9TZC6Ri7alcRIEX+rn8KZSFYukZCmlaJzlVz+84rc/f7PS9h5ZOHKhXrQNl20jEFRdYFkrdCk1QSzz9tQTimzQ331+XgermmIVrlalMecnKlw5iI5h5j7NFAdDydgig9UMK+e7INc7xsRpiDJUVIZN0+Kd5eZw4Hy34XzjUbTc3r/hmXEcQiAi1Nd9Y7lwMlQdkubmOPCsnyFn9ruWz99dc3sceHa+J8dM3u6BzLEfGFLi03jiatex90JfTUXgkVhV4HNMHOZI17kKQVTaaGHtEFuj8UXxMEjYyOLZpJXCKsP5fk9RcDoOUsEuz0x9LkORRCuXhQ0Tkjx3plZ9VhuUKmJ6ljMPITLExP0cCKXQWkuYkgS11wc1l6UXruN2TYU2eaTzKvnClokcqVYU8pjOle/mjSHnwsY3xDRx7Ec2rSegiSlScg2DZ5ktsbJblrcjLrNiS/1+CjTVvK3RC/SlSUUY3AspY68NXaN5PQeuU+Ij7yk5VWaQFExta3l3PKKtIoyRZ13DaRL2j7d6PfTqkuKLYWQqwqAyVX8iATYarRxzTKQQMEZXQzuZu6k66+mnwPPdFq0LzpgaLpR4e9+z864Gyiy7s3Q/MYtd9DLr0NpwmGf6KJ5MLzcdH246qM9EKYWtNbzsWm6mWextUOycpTOGzcbQp8zbfpJUvXrRL7zjRdtU+ruqoCIcZtkztP7qOv/nOgBKKf8E+As/9cd/98nf/x3g73zFv+2Bqz/mz6+RbuLnfllt+JVvfYccTly/v6akgkNRahuZ65O/XBhVg6kFalkeHuEuk0EbJd1ANbZqjNAWj8ModK5luKeq2VllHC1xbwvotJgxlQUGYKEqih+LBDRnsYemPMkBkO/QNZpmEcEXVgFQzqUqSCXD991poJkDl11DSInWOjlgcrV8qBtE56xoCTCMpZAXcQBw1XpedR3P6ua/4NkKqdK1Wg4/OSRTzJxI/OThxKtdJ217kRmFUZopRO5GsTrQNQgl1cEdqjDkhCUTi6GtlN1l8wwxcTxM8nmzovGe866lj4lNuxEsPMz88JNPCfPAi/Mt+9ZxHyLeiHPkrDUbC/NwZHSWu7sHfuXrH/LDz95glOLVi2f0D0c2XYt1lpQyL/Ytn7y/4zjPpONA2bZcdu26aXzRzxznmc4Y9puGy63QW6GK8NaFJmyzxmiKk0rS12GeVRKCE8PEt159KFGO/YCxjm3nSSXRGvH8tKrmPFTar1aKVhkihTf9wFkdWIcaR3gMkbmybi46Q6hZB4pFhCjX19YSpjGGXKEupUV4J2THxa6BNWYwFTGGe38aiDly1mwgBcgZYzQPpwEK4oMfpcIMKaEzFF2LnXqHc3pUlaNEk/BunPl480isMEat0GJFNwXzLnBmDF/MM94onlvxW3rfTzTO8P44sPWensjzdsNz6+RAra65jTc1jEdzCJE34ySHY+2CjVbS+QFZRWIxQr+sLJ0ENPUQHqpF7hAi3jU0zlFK5jQm7saZjXNVwFf3BSVEgJylo5tTZu+sZH2femGmxcKHXYtTMIZY2Yey3+RUuHCe+xBwVmzGc4UeN1bz0a7j3WlkqgfL13cbnHpkx932ExjDZuMYpvinJxNYKbDTgVlr3h+OLIW1wK5l9a2vez2ds9wOE6VCJ6Wa6C0DmQVXn2YZojZWBFU5F/EKt1KhliKLLlbO9r5p6s2qW3iFgoR1mlf6Yaitsi3V7pkqaAGiAq0W7F+yjZeqHmpDo0SnYOoHUsDtOPDmeOCya3m+U6hqahVLYcyJxkr7fJoi584zp4lqD0OjNX/m/JyNs8Ie4NHMi/o5yIkhJe6GiVAjMPt55mGa6OfEty73a7j3lBK/88UNX9wNWOtI80znDb/20Tmt1fQliSFczMwU5qzZGGndQ8lMQ5TKxwos1lnD7enIN37pW3znG1/nd37vd+iahh/85FOe71oCi7sh5KI5hYxRka2zNCpzuL/lx9PAx197xa9/9+vkDK/f3XAfZ4ySYqFtDM45rDlS8sxpTDzbO+aSMQkOY+AwziQyJ2zVW2hapRlQK0lA7rt0mFMWi21d/x5UhQY1jXMcQmI6HevWaDhrGrYbibMsdaB6moXpsww6Q8oUIzxxj+IQIocYyDwNrYcYoxw2avFYUjwGUslicCuE+Ti+lzkVa04AFMZqWT6EiCpUq+VEKJrzTctxDFVIqVEarrY7+nHmbhhptCIp6YCX9/fqYsNn972s09phDzFxOwWuqke9+PLLhqsXSnf992fWobTmbo7ckvAZ3h1GzjYNxirGEHCtYW8d3lRPfWpRNiWMLmAKr4dZqKvyE+X6xkRSEjPrjTx/w1hoi3gV9TEyBvEFoxTaxpBITHGCrGiNRXUC6xmzriAWaH5Kic8OPX3VG8RcOMVIiAmK4qKtMGouzCFWurIkrS3Iwbl17Btb55hVuaygs6LEF9uTjq2VsKUI3A0zJRX2W48m03lLHxdjkz/6+sU6ACiEuZcFV6vwJXlroeEtXOylutV16g6sQ9QlYHmpnpWU5CJRL7XVX1kP8uA8zEE42EacAJeHCeTHLvbKy3zCVBWwbBdP2P+K+n4EzloEWnH9Xo/H9epwqIRWF1U9DJAUoD5ErroWnGdIkcZaYs70/Qwxs9eG+6iZiTil+eZ2S6tlmCQbhewUK76KzCrenQammHHO8FBl5KrAdT+IynC/wWvFp3dHbk4zVltSrvJ0BZ8dBr623/CQIjdTWJPM5pRJUZw1xzkSxyRWAlUuHIrYTby83POb//SfcH2852KzEetvrfjiNK4QR67Y5yllPjuNvNo0tNZyGEd++/t/yDc//oiz3R5lNH0W6b0KM77d8uJyzydv33PedbIgY+R6LnTe8b6fmLJU4SkG2lYcJ40WfUkp0tXELDz7FY8tBV1VmWlhHeUiHPmHI13TQo6ViBD5cx99yCfX9/zo7a1U/+XRint9NoownB5C5JTi6l/0dEMlZWKthHJ+LCCWpn9Ff9VTMeCCE6s1ICfXyv+mn9Ba0zYNaZxISHCSotB1nuMo0Y+lWMI0Y5TCW2FjSWqb4j4EihIFfqtlmO9rtT3lzM04s7fyd+RCrvRIyiNrLtc1vdOG1hvehImUC5uNJ+bMFBKNM1x1HbYKM5fXorCdU+FmHHk/zysMXJ5AnqnIf7zRpAxnzrIxhtZqNsnyRRpIJa0060JBlYQkchlMMMSSOITAs9zQWF0PgCzkgyi6GOcbdJ2VqGlmozUvm6aqtzMPQ2CqRVvTihbHaE1MiWGOtN7SOEsp0sXdjTPXw8zHuw1XjSjuD1Pg7VGcS53VdCXSGVEh7/60BMJQSg22kOm8r4Htc6yWqqV6zRRQdSE6rclLfE5B4uhql5rlP6JILOKj4oyBIn4apylhlQhePj7b1kJJKqdF2CEZA3m1dVXIoNRWPD6kJNV+rQqF/FhnA4V1UZ8Zh684LOVJ61w3gYyYgamyzCAKU4y8HwZCLjxrW1nUscCcyUke3EvjeNm1vNpuhDaYZcCZaoLUcl2W5KF3wyj2uxWtDzHXWEJFSYkf3z3w5jBw2Vg+u+vJKaG0Y2ddzfLN/PChx2jNB/uOq8ZzM4eVxYGC234mxixDVG8IWZw9h2km6sw//N4/5XA3YZQ8oE1reNOPEm6i5MIs/ky+csX7lMklipW3Vrz94nNutmdMQeiG/TjxMA6cK3h7e0M/jJRKm3x2tpX0tyyDWYHzNFdnLVZVT54KgXijcVoCeFIRK/FchyCl7uBaw75p6JDhYT9MNI1low2HKTCHxD/6/ifMMTLlVDdttc4WqGykXMTbZa5Mm9rgrnTEzlliSDJ4retjGUIvimujRCwmt7kugOX5z9LBqno9H6ZQi6JMLolUMq3xWK0ZxolTzkLf1BqnCv08UTCMo2ywu06w+nPnOcXI69sBZxRxzjinamCPbLz3IdF1ZvXkEmhVr3CQfMxK6yzw0lrezIGpJIhS56eUaZVmibkkClFhGaRpBXcxrNRhuY2lri9pyUtOHMeMbzXnXVN9laTCfrXb8PrUrx5Ki61yLhGKYb/x2Dkyp8hnhxPfutgLuws53JZi83zbSeqYlu97Yb2s65g5hcgXp5EQEy/2XYWe4W6aCKVgI2xj4mKnaZzh/jjy7jDSecOFd4JOTIHXxxHlNH0/c2YX1qDcd/2nBQJKpVQczlS1oKof7nHAVfeYdWjXGiMxdPW5pw6lxKdmEX5LVXA3Bz57ONE6QynCQ2/044LRsMY2Uv/NEDNv+omFmqmVwgS1DhWN1nStZ9Z5pRmqVAQWYv3Wq2hL5khiFCWCGoXOahWGLFN++flCa73uT5zCzIVtcEnsgRcLikYpttZKuEwpKJIcRDlhK51PFkemj5JcppXmsmlIIXHpPIck4SiiOYBDmiQQPSn6GEkpMBnDznlmMqcY+IObA43WvGxboSoiNhbkwlDdW1NJfLjbcbnZc3t/ZEpRNpY0Y3YGhwKruC8R7TQmPgbbZ8BqEbp8fLahU4qNUZX9UrjpZ8pU2LUdm8bx/njg2c4LJXCzRyl4d38EMp+8u+GDy0t2mz3GjKASL8/OOU0DSpVK6RP+eUFV40ChZyqlKLFWlUrcL1POhJy4mwXXdcawa7dcXO44vDtymgfmPrExVoai1DjQsowQ5VpPyICdhQ8PjypoZRj6J+6vsJIc7EIvpVQLiEfWnFaPxdJSbWutcYjr7BTTAuHTOUNOkVFbzpqWh+MBq9Rqd16QeRlG0VhP01hS1LTWSGBKZdHst43EdVZr6KLEuXLOhc7W2EOkA1hUrVqJLkdbi/cGHSY+9J67EHk79tK91DUo160IE86KsEulwjHJz5CiQVNUfgQISiEnHmGxJIXCam1eZ4Ivuo53/SC2JnWWodD0IXK523B7HMlkvLbcT7MwCBH4+ZtnO16fRq7v7tHWMqGEjlwim65hqsFIURW61rFr5bC9H6fqkZVJWlFIHG4OYr3SODpryKFwmgMhwcOU+MbLZ4wxEsMDZ9tmpcKue8ZXvH6hDoA5ZX50EG8eow0xx/UhfvpaDoNaLEo7nB43bqVZ5wWptoS20i2/9/6+ir40H59v2NXQCGsUpTwqNZdhrTUKVcPJF5VoKIUSoqg5ncGktFZ1Sim0AZ3Fu/wxELsOletQTitFUALJOGPIqlBiqcZS6kvVe1EwxcgXY8RozbOSOFOW62HmOAeu6Ni2DTstiUOyeMrKgNIUroeRL44DTksLrIvQZb3V+KC5mebaocgGEkohUNh2njAnhimybRwmQY6ZKRf+3/cP7IwMM4NVzCWt5nUqS9TiOERefv2CcZrQY2HIARcMc5ppvBMth1FYB8YZwpjXVl4rYXuUmNDe1Gsrh6f3lqmiAj96d803Pn7JB1uF0i3Xp5kxJS53G5yulL8w42bFedMxxVi7AOhDYmNypeey3iddf621omix8I4pM80JY5TYgyBh3iEnTlPm4dM7MAqPJaQMlpVxlMuSp6uwldo8FdYT4QkyKHCTrtBm3YgjsEC9QvqV58ilxOY4yj2tkI9aqqT6/k3JlagglE2tZTA5h0zTOBqHRB/Wn93PEWfk2netJKlFLcXZZdvwtasLfvjmlhgGrDEMIRIqJpuLuIT2MXE7Rzq7kq1ZlP7308yZ91xdXXJx9SHDOPCHP/whpzChqLbYRfIFroeRrXUC0alMCRIco63h/TCskJpoPR5tFUpW67WT61vW2c5TpKxzhquN5EIXjSTtaZmnve97mq4hh8jGakKmzgnVytB7uWkZ1k4Pgir0QTyrnDVoZfiws3SNZVKarIT+K6I5oZ2nqhaeSsGkTOMsfU68Ps5YHdl3Qkm9H0ZCKIxzpOmaijo8+Tx/zOtn6gD+uXrVzfUUY8Uwa1XM42ZfD+kVK1+qkLU70FIFmUqDU7kOWrVYyk4xM9bW7KYOj5wR0Y5k6T6KTBaut9bCOEILswitKPUvJfxF2BtxKfHqZym62jZrHumiy/us79laA1qJfz5LklhZb+rTs88qTUyZmzCRneVumulDZOsdpUjYzWJfoLSW0GklA+7PjkPl51vaGnAiA0nFpfO8bEWZKf72UqlpjcymLNUqWi7wtvF4bxnnyNt+5GGa0Vnx6uyMi7ZjWzQv2oZ/9Zc/YAgzv/sHP6Q/9dyOPff9yDjN5Kmg4wJtLJUqNJ0E2ShgjglP4dxrWi0MK1Ofi3NvuWoV7+4fUBoeHmZ+6wdv+cGbW97dH7g7jqAybWfoNgqnI29vD+y3G75+dUHnPSFlxinQBzE5W6Gy+pwtMyRnDJv69UsQeOMadt2G1lk+OtvRT7P4zswzznusUjzrOp6fbXFWTO9Slsp/zuVJQM3jfZa/r+o9AAAgAElEQVTzQPQopsJ5a1nKI/5fR1r1e2UOc+Q4CaX1boxcDzP3o8y0jvXv7sdARtFYi1YyvHatldmFKlVjIOKpqc57bk6TwHJG4RWonBjnmc/e3TCHCKUyteYkoUsLtIlAUzfTzE9OYx3qy/s9hMj1NDEYzV/8136dfjzw9v0Nf/5f/GWiMjyM82rGp7Wij4GbceBd33M9TLLOYmYKgUvnJKC9QEl5XXslVRr4suFTr23dLBV1XlLkPrRGs/WOpKEYQBVmlTjlGaeEQht1w7e/+RHfe3vP9WlaPX2c1bwfRyJg0XitySEzF0VGs990PL+65Bgl0zeQudp0fLjf8MG25S//2tfxVpE0tI0X6xDlcHbROWf6eeb61NM0G7ZtR4iG45RkpqbUl/aIn379QnUACkVnDSEpOm/JqTCUQqzV2orBlnq6F3DakIxQG0XRSzUwq99RCZbotHicACsd7TRHCZLPhVIzBFKWSkIEOnWYlKQaUQjGmGtLmuv/1x+1tmQKRa4qyVjkEEoVitKFx65mEdXUfyOHzqNl7nJQLMZkokKUVvfdfOTq+YY8Zz7YiwfMMUT2zqwHWEqJd8PIF6cBqzSdcTTaYMpiK/x4TffWUpqGd9NELGn1tS/AprFYKyH1Wy+D1ZbAqYgwaTGDs8bycueIKdFozT/63uvVbG4Mkc5bGmsoGYYYCFEggjEklNePGQXigYHKhYcp8LmCjzYNjVaEnPjxYUJR+Gi/4VeuWh7mxJh6/o0/+zV+69N7+jHx69/+Fm+u3zHmuRYAmTnNvLt9YNO1TPPMzje8aFv6OfDZ/YmX2xaKhLI0VooBpTWppJU4MI6R4mDXaLrG0E8R7y3pNOGd4b4fOGsd33h2xv04EUKmLzJ0ts7QOEOYo9iIK8Oc0urrpGoVu/WeMMaa9VqfD1Vpqbr6zNdS1lbXzAV+0UrhtRQzMUvEaecUWydbQS4wjzObpmFnLA/9keOQeNZ0XG02hCxJVLlkjJWZVKuNsKi0EWpopQFrBbu2gSBeNMcgqXpZi6VFzNCnxBij5OHmItdkSlwfjvx3f/8fMEyREAM3D/ecO0+cAzfHAes0rZeZWVSFmBNjki5i64W55YGvdw1DEvfPkAt9DZN5Whgv7rFLF1IQj61TTLyf59VqxFSl8VrDKWHlXXqZFf0f//j3cN7SzoYPTMuUEz869Yw5MU2S5+y0QKVeGQ7DRNtkvDFsm4ZhnpnmRM4SD3QYI//w+29RxbBzLV09fIY5EELCucc0vnGOKEY+en6FwXCajqz2gv8/zeD+uXlpJWt/KoV+Dpw1nhiNWDprgzISfl14BNi3bUOZJkISpkCqFhCPWB+UJKZtcWES1CHrnDJjKrQ6ocuTh4TypZZRqpu8QlFrt169fHMuVVUsuOxiY5FVxfwR8zbFUonUvNwMocZfrl3pk3v5+BCrOpzWkCSUPiBU1qazfD6c0AXOjGcq0CgZrBUlpnM5F5xTEj2Y6mZTP0WFoAFojeZl23Af6jCudilL21sKTHGq19Py4cZzdxzF2C4G3twf2XWG1jec+oHWeVIU759SqLL5Quc9266VPNYCNmumKLnNWjR7tI1jrlGgN1Wt+/G2lai8OUApjOnIn31+htWKEAO/8+l7jPJgMt5ZvPUQe1CCt2plCClydzoSU8Ibhd3t8N7z0srwp91seXt3w2kSOwmjFYcpEJNQQTedo2TFxWaDcYY5Rc53Gz6/P0iRESPDFITRsVADq33EXDJjjrIhFTlgQOiYUxKrElWLmmOSyNCyNgBlFfyoJ7vbprE0zaMrZaGmmdWKKaaCUeIwao3mOAXePfQ0dmZTP9+m8ThnGbKojneNhwQJcU/deUfIiVgghITy1Vq6KKZpZtM4hpiwaCyKFAVSWsIrrqeZSyeOoakgbLaYuD4e0NU9tZ9mDjnRp4CzRp63LHO6pbhCwTHNnEYt0ZTO47XkamydJRS4nWbuZ3lecuXcWq048xLOU1ThNkaOIRGyzBBKqSy9/Nh5yLmrwEixgpYQd9eIC+5cRPMw1cG3Q9eDRNhmXimmrFYGWEgCO56GCe8NscggPRShIpciRW5MCepz4nRDqzVjCGgl4jwXJj576JnSTNg4Lrct5nFH+iOvX6gDIBU4znKhxCWw0CiD0SKiSikzllRP6YxXBm1g2/oVEioKTNGMMZCLIqUlQUdRQhSvcoQhUSj0MbMzVKGFvI+lyhe8tvrOZ9aNHR7byJJr/q9wCNfFGSkVd61DKOTfmyoggsrGqL9TunYf5XFwvMIjLMEvj2ZpgiCK984hSC7xpBOv7A6lxVDszUOPKorzRkI/csqiiq7zARSoous8pYrbjKUzlj88HQmp0BhVLbLFUK3PkTkn9rZjmhJKyUyBmp06hcKGhLOOcYoobQgh460M1hQwjYHzc0cIVuygfUspM2OOxCKBOGrOInCp2PQxZn5wEJFSqdX4HDM/uO/prGGOBb3VuBo5+IPPP+Vq12KDJle8vvViNfLB8xd87/NPGXLidHOHQvPB5QUmJ4Z5lHxfFCFp3j8cRdymChvvaL1BKUNR8HA8sWssD6cTzlluTgO7XcNcMjkUhjESdIEKaS2bSlaQyUzVztzUgmDvHd9+ecWbuwN3axe7vBabD6l8ljrB1A5g7RqfPG8YjS6F1hqOc0QZzeWu5fY0MudEnmb2nefMO3Zty+F0j82Kq+0WneG+7/nuNz7g93/8WuCYOdE4T85JhFFaEeaJznp8Cby82nIYBh76iVCELlmU4naaGafI2aZl3zTc9SNOG8YYsQj7Kms4joGkwTjpYkoSWqm2SpxR6wlnUAwpk8vMube0xlKKwqrC89Zx5h1vh0kouhXy2XgniWZkjkPiFGN1kn0kaFRd9VIfyjxMCyytQxS77JQZiPzoPkj+NbBRlkZpOudwxpJT4hjkYHS+ZQgRnQreeb623xJDqEVjw6W3pLbh5uEBjF2h3KQK05TIuqCN4XzbcOonPnn3nkMRWu0nb0duNiMfPdt95Z76C3UArC/FOhwxThNzqjYDC84uwEsfI9PDCfFRUdXTxXDuRFTRWctQpNKP6Bq6IcMwg1Rgm7ZjTBN9koGvN1osW4tU7K2HrnGYkMS1MeXH3rLaPjdFQSjVB6dUXxxFyLlijdLGrnOKJx2KUUo+kxK+kl85yV/uBlIuHGtA9DLsK6WsD7ExhocSGe7v+aBrOQ4zfQh0RhSUl96RQySKp3WdqTz60i+DbxG9Zc6N40RcNQUZGQJarVDerPRIb6udQNsQ48hsFDEUqeysrvYHkSHKp3LGMKbIj24fUAq2TcOYZhpj0FZYPj4LTVUZ4eNmRVWkyuanK0hvtOLFpmXvPT++PfLJ+3tZzBompZnmma03zCpzCKBLJmuPay2mVZSiyVMihcSxP7FpHbtNhzWOw2lkjoFN48lFRHigsKZhHgKnPKGN5lsvLri42PKT/+cHMvMx4qJ5f5gk38DpVe9BkfemlhMeVrFWKjJLUiWzay0fnG85zYFpToSY+HLpUZH2WhTUBUMdpjxpUYWD/7YfOfee4ynjrGLbemLK7NpWqtIpE8KRvfNMMXJzf+Tl5Z6dUby7vhOItcDF2Y5WKd4eDlDnMUkp7voTV7stNmUshouu4SFK1YqCBsnA+OarV3Rtw2/+/veJuWCK5tw7seNoLdZpgcSSHIhey1rr58QcM60XfyytFV11zrweA5mJy6ah0RqrFJ1SfG3b8Fojhm/lcU8xSrGzhvspPFqz1EsYqlmiWsST9S9iyTgK3noikTFmqHAUFsYUaawHpFNOxbLzmg8ur/ji9lrWtLY0WvGrH17x+5+8YQxBBsNF8XA4cT/ObKxkFjdaQoWCymSjmGYRqxprOAyJlBNDEBr4oZ+Z4v1XbqW/YAdApa7JL2VIZ2q1sUz462AOWFkHGrGgTbkw5sBZjW0LOTHOkcMQ0M6IkVY9AFxdqEPfs7Hi3b3bNGtCz8JCaZzmm5d7cR8NErsnW5kmUxjngDFiUhVyJlTJvDJ1cdYqQYRNj2EysnA1Ri04njA/lkyDdbRQ13PKWTJ0jSaqKvRCOgCjtEBQSlMMvBsnUhBa1JASz9sWUx7taFXtIFjeXykr/KVUwSrNpXe0yXA9T0jrLp87Jnmf/VQNr9qGfprpvMVZx7HvUdqwdY4SM0ZlXmw8PzkONMbyfL/lzf0Jb+XAiymgtWFIkX6cIRY23vLNyzNSyrw/DRyGSfx0WQ4uuTb7xnHReDSKi42XZDXkYHBGNB8b47g/jagMxnqO48APP38NyD1zjWU2mTHO5JA43o/s2w3PL7ac+hFjFKdxIkSxbT5vW1ptGcKIVfDJ3YE3p5FEZuf9ei2nOaK0wixOvcvhT1k36xXeowhMBdz2A7oUto14IY2zmJEdp8gUA48LRIqHJflOvk19cOsr1nZyITmcdQbtHTQbxv5IP47CVCuZ86aFKeGspeucQEdNy6Gf2LQt98eRU9+ze3aB6w0hx9VksbUNF+db3ry9RTtLnjJb5+mjQHjPdhtx402Bt28f6HzDGBJjHNHec9l2DNMgtGqjyEqyGkJeApYgJCghs7EaryobL5daCMH7acQpzaX3nDmHV4UPW8/JSLaHVay5IOfOcmcthxgQ87/lntTcAaMplUhSiuQ2bIylsZbTNAkcKOZG9CVLgltJ5LlIlgkCm90eDmhlyRSO08RRZ37rR5+iNDhnhMEzT+JTpvTqu/UwiUUElWqbyRymyNY37NuG2z7SeBGFlgjjEL9yR/3FOgDKYyVqtBJGQow0TYMqasX3671acRK5cLJTqsqPzgisU3JZgzi00VJxpRoqocFp+X5zTNhqM51yVRAbgWRawWDYaFfbRfG8EXFVSyiZt4cBhaHRAgOVWBgR+XYxT9pylr1Ai8mWUuthk8jV/O7xsz3tAlprJbS8PEJAC+NChs/V/kEBTolgJWuBtxAzuVyED/1oEFE3VWrLXgRrdmrxUvG8n2cxdsvSTalSgzys8LMaZ7iZevQMl42ncQ3aWu6PE6c5sbWar2072ZBbzW2voIrexhSZYmJIkZIyjTVcdp5GFZIuvDzbSQxhSXKPc6FoEQe92m5W9etV07Bd7IIrbKSUDK7HmPHGM4SEc46I6B4MgvE6NNqIPTQo3t0fGH3g+dmem8M9/RxkfmQU3mYO80RB2DzvDj33x5mzjecwzOSc6TqH80bgvEVnUu/V8hSUNfR8udnVqkRVDYSS+2q8xmnLZesYguf1YViFZUazDvwfi/7HBD1VvW6SyVXTkPjOi3Ne30/87vUASBCQt4Zn25YyTryrh/lZ13I4HCBF5hkuNhvGGDgOE7qKsKZY0Eozl8D3v3iLKaBSWLOQW2ex1rJvd/TTAz9+d4NFicANjYkzc4pcdi0hR7Y5CaVSSWcbnwhpKqmPIRXmHKXrr/MiFCu0ekyRPkXOqjJ37wxjXjomeTaMKnz7fMM/u30QG5WlBy5PWEP1nL3oJFWt7wMqG867jjHOnIaRxhj2xTJlmRH1Q6TkLPnkuiYWaoM3MleYc+H6OOKdWDtPUZ53pTSNFT2AtYaH04jShhQSRWUab0ErGuvQRYqbECIxiNDB+8Wv9o++frEOgPqSh7n6+MTEYCKtqpvXU4wO8RFfTJaUVis2udzIJWC6wKrwXTdVJTxusWUVTq7Ydz1WWUtXsuCuYqcglryqokG2KG5PA2PMbBpH48Tfo9GGVomQqFG64uy19itUlkf9wKp2AcuPXSDdurKt1jRG1YGVPFBL8L3sIfKFC+umGIXO0GrNaZpFfo5CG0uq4fPCY3/yFupCSDlTamW5MZbnHu5jZMqZpkr/m8ZxsdswjoGN7bifRqwWN9F3hwON98wh4b1njonOG/atZe8Nv3Sx5Yf3Pee+pUHx+nRHjpnWWS47z2XbyL1PhdenA+f7HdenAypKZCRKhDh+Eboh8KCtTpSlXriYM9YoOmuxzjFOM1cX5xzDgXlOVQ8tStpNUdzM4oyqjWY+Hrl+OLLvOrxpsEra82EMgmMPAxqJaXQVWmxaQ05G8Gina+xkvSfrNV6LzaUBW698zgIzWq1XHUtB2OJawfON5aLd8+40MWWpUJ2uM6VSbaIXOOjJ6a5rolaYMz/64gaXC68uzggZbEpMpRCL4uLijNdvr/n85oZSzsXDBrg7nrjcJKapMIfAWdNUvjqcppFcCp3Sa3bEHCOmWJxxeAy3h55xFm78ViluQ+SYAmjFzdADisttyxREcLk1hqgLY8yP3ln180iUonTbY1JYXWSDrbjalEV3EabC3lk21nLTz+wuzDorUUrM/AxLElm9zkrC7TvvGGLgrPVoJQd0YxTHMIs4MUaGCks19d7PIbPtPIrCXhuxk/CWXdNx7E+QhRm36RpSEnXxGMWbyRmZvZ2mwL4oiaB0ns2l583hyDBOtM4QEALBedvybj5hrBGB4Z9A9v+FOgAWDHqpRikil59jkkjCZWNeHor1l08Mz+r3WiiZaREV1TD5Ur27lUKSj5Aq5qJtsOZx4akvv7X169AKlQtLKEwB+ilKKlGRQdZpDBgjVFZvDdvG4VHEartAPbAorIEQS+iL+umbWaTKcY+/lUW9YsCLupKVNbJU9FYpHBpnFf0cxeOoPFoOFOT6LgeewEp6raCp8ECjDfuaZRxKxmnNTOH64cTWWsY4cdl23A89b8KA1ZpfevmCUz/x+u6aSUu/4618uGedJeWO+ynRGFHgGqMpprBvLKZu3u/HmbspkNTARdPQbgx3/YjW8LX9BkrhbpgkPasK+lKdES1V8BASu8ZxM0z0aebd8RpKqG6ecg+Kkm7BaYW1cqh0raOkTD9NbNuGq7Mz4ebHxMPpKPMPJ9yvrZcwGa1FUBiTKKqdUazN+ZNubu2/FJXCJxvMlfOYRZBWh/Ua+b6pSBHQWc1ZFcENMYNW9Cl+SQS2UpMr/DjHTC6iXD/OUj3rnPHGcsiFIUw47zi/2PNnuobf+P4PuTkOfPz8nH6KNCVjrGMcesIog3xvNDbDxnqmeeas83x2d2TTabxzHPsR5wyN62ido6iOaY6cSqHPSWZitcy+H088VPXvAnu01mCsYcqq2iaz3t+cxQFAKxGPzhmcFuW4qgskU7gPoRql1TWnZP6m6jV/0Xle99O6Z7TWsms9nTNsnGBLpRaiyiq8q46iRbH3nuM0cQoZ5zVGy2bcKktRmjHOOAOnYaAfE0mJs2qZIvumJQc5oJ6f7ziMIw/jSAyJ+zzhk6a1HqIc/zkX5pCwtTvfec+L8w2HYab1jrRY4fwxr1+oA6Du/BXKVJVFJl1ACTPGyI1bsFOleMxt1Y8LbFnUpYhvT64Sf61Yla4lF6a52vMWaOpmnbIIyagLcPUeQmiYpS7klapfCn0Ia+IXFYiJsXBIs+DBIbL1HtNqzqnJUPlxAFUQyOkR5a6fYf1tQVdLgVQWp9Mnn/PJ5Vs0ZGKh8vh1nZGAaVujEVURz6CM/pLq9VELW1bxDkWUkVeu4XqeOI6BUIVTfa6umCULBzxmrPa8vrnDVXVqVorbYcJq+PbFDq01XzuzvEiJt8MkeCii9/j0cOKq8ThjuR1mUUzXKMxNVW2mnGkU/Oj6SJ/+v/bONNaS7brrvz1V1Rnu0MPrN9p+8YeYDCQksQIRCKQghiQSIkSRcBgiLAQSRgKJITbhQyK+EAcJCSFBEFIUFAkCjoJAAkwSySiCyMR2bOJ5eJ7e4Nfdt4d7zzl1qnbtvfiwdp17ut0vfrfd03vvLOn0ra4zrbNr1V7zfw3EIbNfVwX0K/PE/kSH0cSB506WVEGrKyoCXaff15daeos2+pyva9o+Y03aXMsqeA4mDcfLNS9dOeJNjz/GfDbh6GSJcRXrITFrdCJaN2QG8mYSXdtG6spjK4vYsRdWSb1Q2XSej5apr5zmtXK5kFmQrA2A3kHtRyz+vJln4Y0FH1jntCl/jinpYPohcbLSfoc+DVqOOBjmTc3RqqUJQfMrVuXk+vVjjlctGMu6j1y+eUxVmgmP1y1ihKbyLLqOWRVYpcTFpiZaUwbUG24u15ybN4iR0ngZyTmB0eKKRbumCZZaLIs8AGW0YkHedcVf6rMQjME6t2kwU29cBX4wp3FSHb6iYeBg1QPOxZvOcEsPgBH1Fq0xXJpOsMaRrNnkHypr8Aay06bLcc4G1hAay3oZ6WJi3jRMmopVnxSjKgRSyuw1DctBc0b9qsXWNXvzGovlxXalpdmxY6+u2feKTHTp/DnWL19l5eIGSl5Sh6sde5PAqtVQphO42DQ004arx0tmVcAFy8HB7BW31NeUApDS5WdsacIpm1dKmVhCHyV7ypjRH0N4BrWkfQmNGDRRVFlL2yeqyt3Sbp9i5ol5Q+0saQT+Kt19Tal5H1GFKFOHjDn1OKQohYQOoD797K0vES3bXKwHluuBK8crLh1MmXu1cg0weIMNZlMaGjatocU6lRFMSzYJ71wsxDEH4E3ZqM1GX4DALFusHcNOOv84xoitKnWZc9qKSuv3jkryNKNgEEmMowAtVjHTvcJdrAetZkgWmqqi61uMZNYxcdKumFWeJjiOe1hmxYjZqzQXs+4zV2Kk3gu4DCeLjpiEq+seIW5+z9Nl8xcgp0RrM8YoDK6xurGE6YwXjo5wxrA30U7nkziQCxDdtAlYa7i2Om2iGhWsiMaQx2lMTiD1GQmBy+2iNPJUXL12Hazh3P6MkzJ0JlvF2CEltSyNwm/4yuHC6JGo9amXePRet2S3WBSj1zVmjJJoKfQA2Gy41kZEtDgiFNA6i+CzELtE8GoRL9tE7TKLddRZAaLFBo0PRDG8vFgQY6ZzBb8qC2274krsWcdESpmDZkoXI6HS695LJkbh6cN9ZtM5Lg9cOznh8qonOMfVZcd80hCGAcmGJlTsT6fE1YoWhQixWIL39FE7YjPqqY1W+7iljwOuhqIcg1VoiCzjfWE21XimxGXjaK0nVQLelCZQyVTebxlyur5ZNJfUTEIpRz3lQHAbb8RQMMWyzpDGagmvSGY+nTGrMkfLJU6EuXO4ELCpY1ap0eExDIP2hVQoFMRBHeiHyIlkXLbcXKyojVPoiCIjDuFktWTqap7Yn/LyyYrHmgaM4eh4RRsHTpYte/OK4fh1kgT21jJxFSCE4ErHrQqJE62TX/W9CoKAnfhbNn+gYI+UGyzpwBNfJggN6I3T94kUR3hZ3diHrDdPU8IBQLlx7cZ6k7JxjNZ5RhhSYlWm/Zze5Lf9sGLBCPC1m8vNd3qnrfmzJlBVChdQkCbUOlQNsPneTMk/cNohaGVswT/Fg8/Fagm2wOgWfrLoWLu2jzTB4bfqEUUyKYFYq0lkERjHDpY1XqTEza5XiI4kNM5zMJ+xzpFFt2LP6ujJm+2Ki6EhVI7KOU2+GsOazOdOVrxtf44M8KVVS5uzlsFaReIcBsVCaXvtvp0Fz/lG5zO0OXE0RFY5sRcyT13Yh5RZxsS663XuMcLXVj0XJxrfb6ynwRKcJw7CXj0h0tHLViVVWYMLdc2eD2AMy0FLhRftmmUf+cHv/Da+dPk6Lxzd5NpiicPw2P4+OQsxrlmJDjRKZQr4pHYMSUgxYSqVKWNu7VAdw4AiwsG0Ut1gtBQxZxltAByUKWJawUY2CoGAGiox6/CdIal3UfsaZ4RpY9QTXbbgSiOknFbCjfOSn9w/wObElWWraKjOMp8EhtWg5dDGaHVLhnMHB+QhsY6Rm+s1bT9g6wrrPWZQ7B8hYL1h2a3xVnSOcTGopt7TkWn7qAnM2p1OsxtDNMYSrCv8qmehRk6BeLanBhPCJveRGTGUNFxp0Uop4yiKRlgVBZqtVgxuF2dovsZuQqtiT+E3ktFBNB7LzAe62HN0knn6wiEHuaHrI2K1Ck9xnzT3ZoNhiuHyuuNgUuG8FprUBRVUJGJNJtjAE/Mpy15Yxp6TNjJtKr712Wf46stH7A2Z2ipUx6qPrGPEV67ki145CfCaUgB15fkDzzyhLm5SaGgppVE6MQeWvWbcU87FzZRNvsBZg0twc93Tx0wftc3eYogxY5wmilMcR7jprehKl9/AKYyESN6UPm6Xhp4O9QaKMLWx9Plu3IDRbzy93be9D1M8iJiEIUfWUXHFq+CZNIGDasJJt1asj/IOjfPn0xh/qQTZhIo2/6risNno68vrxs1O0BmibT8wq7zG+7MqWgv0JG6kRJ8TU+No9JbgatdztVtrJ6/Tlt15U9F1CZcyPnM61Dw4vHE8frDHc1evQRbFPKosy5T41GKBsYZ1SdhZox2fB/OGQxfwAl9OS7w1PD2blMHqwkISazSxu04DXoRuyJyrA64xGALXuoGjZce1NmKsJcaETCa8dHydias4N3FMjGcgc2PQUsY+w+eu3MRbw1OHc9oMi3bF+VkN6Ozgl67f5MrxCW3sSVm9nVXXsV8H6smMPmsXsHGWGNVI0SlqRmuUNzADBaZDStLf6GjGeVPRR43l66xdbV5M5QKP0MlDmZUBIEbwxm3Ax/IgJZxT+mXQruLeZayFiQ+sup6uH6jrwNiGuI49VgwxQZciwXlmswk32lY7lIvMBSdcuXEdRyDGtYZSUZiGmQ9MpzOFUUmJmKFtW60o86qIgjM6pc84qmToU4E88FrFYouXGaPCY4dQQBTLxkyp+tFIgPLUb2EABe+oTOkiVogtKm+xYsE6Xl61XO97JsEyLYaiopfqetrijW2MAjnNq8UsuodkIXiDFc8yZr7w0hHn5hP2Z1OW7YprJ8cMfaLCcm4ypZlOOTep2A9w9aWXy5jZzDOHc66crAiVofGWfhhYxkgXhYlz9N5jjeGzz1/GiJbCn4jQDxFnhUntN8B7yy6+4p76mlIAw5C4cXxDESVDxSRUuCps6udzhr1ps9nItjfkPB4jzJq8SQCvY+RkqZUKIiCDovRJUSiKd5OwuOJWutIMVaYpMVrXtjD51OYAABTbSURBVCTVZLOpQiYO2mk8xnRPN37G7KJaGeZWBSGcWn+C3tjrqDHbyjvq2ukAiuLGqlLSjwzGnsJGbyXExy5ii8WLbPIgY8gDUxJgAhNvuLpcc25Sl1GJ2gkdszZcJYHrlMHqGBal7nvESzo3rWkM7Hk4FqGzjlUcsAVU72Ay4y2Pn+PjX/0ak0Zn6boOfGUUGyerhdWI3VzHPe+54FUpHTaHfG3Rcr6psAZWklE4M0pcXJFBG4FgivIH9uvAIBZT6vfPH86JfeKwUdAz74qVjOWir2hz4lgGOlHMp5OozX515XQjDA5fWT7xwstYb3HBYrPQDT2rrsPInG956gL78xkvXrvO0fJEZTlpiV5wrvRwsEFutGPIrmjzvSqAEZoqlG5fKdVcGuPPWUpfSwmZlOofNQkoGDSWdRqYhQobPMvVSuHVh4jzWhKMCLNmgunW1L5iVnsqX3N9uaRPA/szhTD2Bm4sFsRh0PJR0YY/sbBa9/TDCmNhWtdQQhcIiFEcoFhA77x3eOdxRtTSF+2PMaXpMiaDy4ap8Yo1hHrEpnasuo6UhPmkRhs/S9MhlJJuzclZDI01GlKVfArmaAxiFP+rqnRq283Y00mGQWFPRo9q9O7FqBeRy17jS3f+ekikPhE7RfxMSYsWGquop9dunDBMkzaaSmLSVJhkWK3XBJu5kgJ/5S/8CL/x2x/mY5/6NHt1zUvHS7IFyYlsNOeRk2FSBy5MPIuoMB5pGHB5YDCZSGJaBSQm8qCQ68E4dN7anek1pQBAG41SFtqu5SZtsT50Jwve08aIs1aTmc7iFR6yVNdQYsLa5IWxzJrA+b0ppsTMrbGlQaxn4tR9yhi6LByve1oMddHIoBuqt4qemEkF0hdwWq2xGhQJdPPikTbG/2iiy5ZeKDkEzS6xjW0koi5zPyie/5N7E6LLtEMZSE+BbCiWyjDG/reqk1LO1Cgo3BiWGhXlVlsk0zrw/PGSJ/d0FnBvFK53WjDmeyOsJWs7fmWZ2kBOcL7SpNY6J27kSIvGv+dNzbLrmXuH6Va8/yNX6UWI60gIDokDcwKmUsvWSAlrpBGLCTrJTMRSO89T86l2qqbMy7HTyeKo1dbnTMqR887jt/DrvbMEDyetWrXBek0ousysKqMSUbVugeA8bavzeI33tF1kse5wwbKObpNQdMHgvK57KLMgcoSnHr/IucPzPPfClzk3qxCmLLtIH9fjMuultXoNxt4PUzb/qkzE0nxW3sSoR+ylLFulw4yIrGaj7CFzYVqTBC4vWuIw0PUdE6+YNTdTJCdKvgAmlfLS9T3DEGn8oBVBdUWwluUqcn5/SjKG/ZlW7mhsWvntYiQJVMaxHrIOSxoEmwTnA85YVdQW6romZUtiIIlCtsQhkZJof4DTwUp1cBxWHkkZ71TxHjYT1kPHfhNUPoxefusszjntCs8ZkzUev8HFl7K+gC3rafqeRoQKu+nIl3JPbN+zeZzaJiW3FxNSkE4R2QxVGgYFmExRwfbmTUUSYb8OLLuOGCPeKwR0VQeOV2t++Vf/C7MQqHxgMUS8cVRWZzlTBQ7nNau+50a74vnjjlmoMEVpj0UJ56c1s+BZrCOXTwYMwjpl1v3rJAcQnOGZ/QbvLV+5ueLqcs2mY9XAsOg1jthUmiweTrtaRUCSZvK9d7fU+isIlA5uxxjmkwmL9ZrWaet4FZxa3VVF8IpYWUnF164fMYzDwpcdfUoKKz1kam9pglaqpDK0w4yIXIzG3WlSYDsEtFEIantsnjNb/4gIlTXMrOHLx1qpcWFe0ztXYvujIlAM86Fs/hlFH60wmwopaw3kcTTOZmForDbrnHSRalbT5bxBPTWGgrjoWJmBVhJNFZjZsMFF2ts74PHHL/HBT34KGRKrqEiinVQsjKMKFR1rjZMXhNBVPxDEYWrFellIxGHYD57KWnrJChqX2Wy0V2Onlmhis4GOwa6pKzmf4mm5nNlzcANhGgKJgRASTSiTs2TsExgxeWAdExbLvKnxQ2IavA5CF6EKAUtm1ngWKW66X70FW1uOjo+4fP06wcHMW+aV17LR+R5fvHKZOCTqJpSEplqmrnhbY4ITYwq076lcJEOZi7AFs1yqx+zoaYqox1qu2bmmoh0Sw1oH/wjgvOZVOlElvW7XZXM0mEwJP1rskElZE8AnbU9CmE1qUoraCBV7mrricD7j6o2bSNbRlxhDlyJTX5Ec2EFo12uqpsJYw/F6pfhStSOVxihXkuoxqbdceVfm5mbWw0AT1K/pB+HmquOwsqeNcUZRcYNxxKHAZxg2oaJNW5wZUXVLKKzU/bvifbdDZhpcsf4V+8mgXiBA35fJZCXc28XMsh80T5WEXMpzZ7NGlaAkjtuW2jsm1YQbqxYXPEfLNTENdCkhEykhoMRsPtFmtmBpgsNYnfz2losXiTheunqFYCyXJhVXemG/1nGWHsMTe1OCdTx/fUE0CpT5SvSaUgAGowBK1nC0WmvdbEmUjRYIBqwVfPmPMWOcUMBbvDXM65p11HpnLZss8XbRePfVaydgi7dhoOsTfRo2QqR5Joez2kZf+UBTBwTDoutIUTBW6CTjgmdmtNQzJ9mEoowbhc+c1vbLqfUx1hzwdYpBDwS4VBpLVv3AwbwGCw0Gk0oji1Vo5WxSCRWp5ejK8JFckrgY9XRsLpAOZfNxGC5Na27Ggetp2HwvRpuPNok7q3X2E+txAuuUmITA297yNPsXH8Mb+K2PfoL9ZoI1hss3b9LlxNQ7BgLeWro44GtLTKLJ+dWAq7ULsjKWqXWYYu1oXH4AQeckO4X+HUtSrYXaeM0tmNNZEZJLHFcMHktTVVxfLMFmtUaNJZQ8Sirlxgat/Ucczljms5qZCJITR4uW/dmExoGYzMWmQRCu9qrUPAZyQmTAE5CMzmTOCWvhLRcvcmO1pC3VVs5a9oLHZohW8xizRoedJEoeq2xguchibZ2OjCxw5/p7xz6C0reCKvfgDZUPeGM4ajsGGWu7bu2TGaFJtBRZdOHygBOYN5WGTeqKayeKs7WOa8TAjeWSdbem8k7naFhtnDpea8nnsu0IxtFl4bFpzc2Vwkp3MTKYyMX5Ad44rqyPMdZwsuo0lk2i7XpCUMNt6AbW/aBVfcZS24rDSXXaIIchZwVY3y6jHftZxsqi0QhMWXAWHmsqXly29NbQ5czcOC1BzaX0suQSLVABfdms68pjvaW2QUtDy3wMJ4a6CliEm8s1KSeaUFGFQF0nukHxrNKg+9Sy1x6Cw+lMcx0pYkTnDi/6jnWf8Nbx7JvfxMnJCSbBC8s1kNnbK1PAEHLKXJgGKjfnq9dWTKavl5nAMJY/E/Po9KJXMsGF2ZTV0Cny52YT20TpAZjXFc55Ui/sNQ0n7ZI+6yzRUDbizmTaTquDJHhtbXcaT7QUVFgR4qCVEl1MHK/Wm76DEDzBWiobFOe7i5yzdpPhH5Jwc9XqvOCUyanELe0p33B7YrhQOTevPLPK8/JijbOGWeU2SSqLYgaZNFp5mvxyVgHxgmgpbCoWUM4ylkrodxbNI2XzrCvHMo8gcSWwsB3OysLEesgaYjuc1zz75jezf+ECzlouzSfMay2zvL5Y0FSVDsQW4bCaMKk8x7FnFXsNXRWrbVgPVJXTB7q5RUll7GNZDIFKAGdp0eScdhwbZqPlY0p+pIjKSRepfMDkpBVFsaOLmdlkiulaTa66Ah+Czvfdm8y4vlxxdNJxMGtUmTpLFAhIScyqm39Q11zvOy4v21KLr9aw5EztTzuUjYEnDw846XpeXhyTs/DtF86zHDKTZsYnX3wRay1JBkinsyAE9QgsBcCO8pzIRkZHmTElpyC5wJ4YxVICWA2JfauYMS8t1hv7wlDeYzUkmkdZTEIwni4nFssTJlVNN+iw+qpAFqSUxkYFAEWhTZl5HcBAbzKPn5/xB599gt977kVqICadGLZYnrBXV+zXWpY5qzzeagwfr9Vi09qDwF4TCMYqamrWooQ6OMAQh4EhpwLzoswbW0ACy5qMHmEqWP+I0Bh4Zj7hxXXPWrR6bFLyClbAFG/KAuItdhBEEp1kWi3+LA/NIVkR2lVLqDzOKpQHJnN9uWJ/UrNMHYnMqusBYVZXBG/IDEyrilUHh5OK66uWIWblZRh48YUXmFWGPlt8NlTWUaCHMJz2Px3Ugdnje1xe9a+4n77mFMCYuByz86Z4u9YbxAqNr3T4CyMsxBhG0aNlHwneMKlKk0sWUk5kTuGUfekxWA8DvSTdVO0YN9zGa5Eyo2Act6eY6jlpoisatUASQo9goiamJnXgyYuPMQmJPBhWcaDtBq6dLOhir7NN3am3sQkUlU3PYDisNSRyte2ZNV7DFVm7HYetEkZB0QitWKZYGjQ5KqW6Rz8YZIxvliHb43eO1qCzikpqZYRFPw1PNVaTfN5a3vqmp3nq6WcYcsIZ4egrX2RxcsylacVzV0/oUmI+sUy8Du1YxaiVLtaxQG3VLmbqypZktVZuVVj1apxhNahAm3KFnQBZ+zOi1Y5Nh6FO4wwH7Rz+0qKlKQNn6tqREQ72p9y4sqL2gWXbMS0hGCN24zXNm4pJU/PE45f42tE1Ui/s7085evEF4tDhbcChQ+6t1eThhcmEYD1HqxXLQa3nbEvlTlbYbR2xOTAMkYuzObmgOJ6sBmJcQBSuXlty7kBnGUOZQlbmBwiGaLR801vwYnUjAChQ46aEk0ZZHWfp7jeealDMGdAE+wurNSd93PTXOKMec0at5CFncvFm5xMNhYZgMVZDF77MZB5EgQmNUdk2VojDQF1N8D6w6lo++cUXsClTWcPUV0gxRGrvmRwE2j5STxzdoA1qlTc47zeb+bjpDVkrh24UKIlpSZRLHoM2ZhPzx56WbJvymyRnstFmR2MNtbFMjWE1ZNZGK3Ao62iLBzHmBpw34A1tuZHyoBWJ3lsNR5bGw1hmamd0uqAV0Q7qKuCMoas0dDiCFAr6O57YmyrI4KCAcT44DmoPZNqUub5oyXlgMqm04a8oAWfUEBtH3T4+b15xPzVyS/D50SZjzBXgy6/ipReBq/eZnbPSjqdXT48iX48iT/Bo8rXj6dXTg+LrLSLy2O0nX1MK4NWSMeZDIvL2h83HNu14evX0KPL1KPIEjyZfO55ePT1svn4fnLgd7WhHO9rR65l2CmBHO9rRjt6g9HpVAP/mYTNwB9rx9OrpUeTrUeQJHk2+djy9enqofL0ucwA72tGOdrSjb0yvVw9gRzva0Y529I1ICurgo/AAvgT8HvBR4EPl3HcDv13O/1dg/7b3vBlYAH9/69z3ldd/HvgXnHo6NfAr5fwHgWe33vOTwOfK4ye/Gb6A7yrPfaI839xrvs7CEzow7JfK+U8B77kfawUcAu8DPl2+5weA88Cvl9f+OnBu6/XvKZ//GeDP3Mfr96r5Av4U8OHy/R8GfvBRWKsHKOtnvYYPQtbPcv0epqz/eFmHDLz9tnV9ILJ+5j33fmzkd82MbmoXbzv3O8CfKMfvBP7Jbc//KvCfuPWm+L/lghjgvwM/VM7/LeBfl+O/CPxKOT4PPFf+nivH5+6GL7S57v8B313+fwFw95qvM/L0E8B/KMfT8t5n7wNPvwT89XJcoTfJe4F3l3PvBn6uHH878DFU0L8F+ML9WKe74Ot7gKfK8XcCL9wPuToLTw9Y1s+yVg9K1s/C08OU9W8D3gZ8gC0FwAOU9TPvufdyA/9mH9x5UzvmVCu+Cfjk1nN/Hvh54GcoNwXwJPDprde8A/iFcvx+4Ae2hPdqWfjNa8pzvwC84274An4Y+OU7/LZ7ytcZeXoH6hF49Cb9bBGge8YTsA98cfz+rec/Azy5tQafKcfv4Vbr7P3ojXCv1+lMfN32GgMcoTfuQ1urByXrd3EN77us3wVPD03Wt173AW5VAA9E1u/m8ajlAAT4n8aYDxtj/kY593Hgz5XjH0c3NowxM+CngJ+97TOeBp7f+v/z5dz43FcBRGQAbqJCsjl/h/eciS/gWwExxrzfGPMRY8w/vE98nYWn9wFL4CXgK8A/E5Fr95intwJXgF80xvyuMebflmv0uIi8VD7nJeDS7Z9/2+fc63U6K1/b9GPA74pI9zDX6gHK+lnX6kHI+ll5epiy/kr0oGT9zPSoKYA/KiLfC/wQ8C5jzB9HQxnvMsZ8GNgDRmSjnwX+uYgsbvsMw9eTfIPnfr/3nJUvD/wx4C+Vvz9qjPmT94Gvs/D0/UACnkJd0L9njHnrPebJA98L/CsR+R70Jnz3HV470t385rt5z1n50i8y5juAnwP+5jf47rvh66w8PShZPytfD0LWz8rTG1nWz0yPlAIQkRfL38vArwHfLyKfFpE/LSLfB/x7NH4G8IeB9xpjvgT8XeAfGWP+NqoRn9n62GeAF8vx85x6EB44AK5tn7/De87K1/PA/xKRqyKyAv4bKiz3lK8z8vQTwP8QkVhe/7+Bt99jnp4HnheRD5bz7yu/+2VjzJPlc54ELt/++Xf4nHt5/c7KF8aYZ9A1/asisn1dH9ZaPShZv5treL9l/aw8PUxZfyV6ULJ+drrb2NG9fgAzYG/r+P8Afxa4VM5Z4N8B77zDe3+GWxNjvwP8EU4TKz9czr+LWxMr/7Ecn0djeufK44vA+bvhq7z/I2gCygO/AfzIveTrLnj6KeAXy/fOgE8C33Uf1uq3gLdtXZOfL4/tZN17y/F3cGti7DlOE2P3jKe74Ouw8PVjd5Czh7JWD0rW72Kt7rus3wVPD03Wt9bwA9yaA3hgsn7mffdhbPZ3ZETjah8rj08AP13O/x00kfNZ4J9yh8QLX39TvB2Nh38B+Jfje4AGraL4PJp9f+vWe95Zzn8e+GvfDF/AXy6v/ThbN/G94uusPAHz8vmfQG+If3Cf1uoPAR9CK0P+cxHQC8BvoiVrv8mtm81Pl+/9DKX64V7zdFa+gH+MuvQf3Xpcethr9SBk/S6v4X2V9bu4fg9T1n8UtdA74GXg/Q9a1s/62HUC72hHO9rRG5QeqRzAjna0ox3t6MHRTgHsaEc72tEblHYKYEc72tGO3qC0UwA72tGOdvQGpZ0C2NGOdrSjNyjtFMCOdrSjHb1BaacAdrSjHe3oDUo7BbCjHe1oR29Q+v8jb6eO09102gAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "with rasterio.open(file_herstappe) as src:\n",
-    "    herstappe = src.read()\n",
-    "    show(herstappe, transform=src.transform)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
+   "execution_count": null,
    "metadata": {},
+   "outputs": [],
    "source": [
-    "A raster is just an __image__ in local pixel coordinates until we specify what part of the earth the image covers. This is done through the usage of raster file metadata. It depends on the file type how these are stored on disk. When reading in a file, the metadata need to be interpreted as well in order to know the __spatial information__."
+    "file_herstappe = \"./data/herstappe/raster/2020-09-17_Sentinel_2_L1C_True_color.tiff\""
    ]
   },
   {
-   "cell_type": "markdown",
+   "cell_type": "code",
+   "execution_count": null,
    "metadata": {},
+   "outputs": [],
    "source": [
-    "## Raster data attributes"
+    "herstappe = xr.open_dataarray(file_herstappe, engine=\"rasterio\")\n",
+    "herstappe"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "To quickly scan the spatial metadata of a Raster data file, let's use the [`gdalinfo` command](https://gdal.org/programs/gdalinfo.html#gdalinfo). \n",
-    "\n",
-    "> The `gdalinfo` command lists information about a raster dataset.\n",
-    "\n",
-    "It is not a Python command, but a program that need to be run from the terminal (aka command line). Using a small Jupyter notebook - `!` trick, we can use it within the notebook as well: "
+    "Xarray brings its own plotting methods, but relies on Matplotlib for the actual plotting:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Driver: GTiff/GeoTIFF\n",
-      "Files: ./data/herstappe/raster/2020-09-17_Sentinel_2_L1C_True_color.tiff\n",
-      "Size is 447, 227\n",
-      "ERROR 1: PROJ: proj_create_from_database: datum not found\n",
-      "ERROR 1: PROJ: proj_create_from_database: ellipsoid not found\n",
-      "proj_create_from_database: datum not found\n",
-      "proj_create_from_database: ellipsoid not found\n",
-      "Coordinate System is:\n",
-      "PROJCRS[\"WGS 84 / Pseudo-Mercator\",\n",
-      "    BASEGEOGCRS[\"WGS 84\",\n",
-      "        DATUM[\"World Geodetic System 1984\",\n",
-      "            ELLIPSOID[\"WGS 84\",6378137,298.257223563,\n",
-      "                LENGTHUNIT[\"metre\",1]]],\n",
-      "        PRIMEM[\"Greenwich\",0,\n",
-      "            ANGLEUNIT[\"degree\",0.0174532925199433]],\n",
-      "        ID[\"EPSG\",4326]],\n",
-      "    CONVERSION[\"Popular Visualisation Pseudo-Mercator\",\n",
-      "        METHOD[\"Popular Visualisation Pseudo Mercator\",\n",
-      "            ID[\"EPSG\",1024]],\n",
-      "        PARAMETER[\"Latitude of natural origin\",0,\n",
-      "            ANGLEUNIT[\"degree\",0.0174532925199433],\n",
-      "            ID[\"EPSG\",8801]],\n",
-      "        PARAMETER[\"Longitude of natural origin\",0,\n",
-      "            ANGLEUNIT[\"degree\",0.0174532925199433],\n",
-      "            ID[\"EPSG\",8802]],\n",
-      "        PARAMETER[\"False easting\",0,\n",
-      "            LENGTHUNIT[\"metre\",1],\n",
-      "            ID[\"EPSG\",8806]],\n",
-      "        PARAMETER[\"False northing\",0,\n",
-      "            LENGTHUNIT[\"metre\",1],\n",
-      "            ID[\"EPSG\",8807]]],\n",
-      "    CS[Cartesian,2],\n",
-      "        AXIS[\"easting (X)\",east,\n",
-      "            ORDER[1],\n",
-      "            LENGTHUNIT[\"metre\",1]],\n",
-      "        AXIS[\"northing (Y)\",north,\n",
-      "            ORDER[2],\n",
-      "            LENGTHUNIT[\"metre\",1]],\n",
-      "    USAGE[\n",
-      "        SCOPE[\"unknown\"],\n",
-      "        AREA[\"World - 85°S to 85°N\"],\n",
-      "        BBOX[-85.06,-180,85.06,180]],\n",
-      "    ID[\"EPSG\",3857]]\n",
-      "Data axis to CRS axis mapping: 1,2\n",
-      "Origin = (593476.196863179793581,6577549.603367449715734)\n",
-      "Pixel Size = (40.078139830734088,-40.112603499857698)\n",
-      "Metadata:\n",
-      "  AREA_OR_POINT=Area\n",
-      "  TIFFTAG_RESOLUTIONUNIT=1 (unitless)\n",
-      "  TIFFTAG_XRESOLUTION=1\n",
-      "  TIFFTAG_YRESOLUTION=1\n",
-      "Image Structure Metadata:\n",
-      "  COMPRESSION=DEFLATE\n",
-      "  INTERLEAVE=PIXEL\n",
-      "Corner Coordinates:\n",
-      "Upper Left  (  593476.197, 6577549.603) (  5d19'52.63\"E, 50d45' 7.35\"N)\n",
-      "Lower Left  (  593476.197, 6568444.042) (  5d19'52.63\"E, 50d42' 0.95\"N)\n",
-      "Upper Right (  611391.125, 6577549.603) (  5d29'31.99\"E, 50d45' 7.35\"N)\n",
-      "Lower Right (  611391.125, 6568444.042) (  5d29'31.99\"E, 50d42' 0.95\"N)\n",
-      "Center      (  602433.661, 6572996.823) (  5d24'42.31\"E, 50d43'34.18\"N)\n",
-      "Band 1 Block=447x8 Type=Float32, ColorInterp=Red\n",
-      "    Computed Min/Max=0.113,0.858\n",
-      "Band 2 Block=447x8 Type=Float32, ColorInterp=Green\n",
-      "    Computed Min/Max=0.167,0.795\n",
-      "Band 3 Block=447x8 Type=Float32, ColorInterp=Blue\n",
-      "    Computed Min/Max=0.222,0.777\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "!gdalinfo -mm ./data/herstappe/raster/2020-09-17_Sentinel_2_L1C_True_color.tiff"
+    "ax = herstappe.plot.imshow(figsize=(12, 5))\n",
+    "#ax.axes.set_aspect('equal')  # adjust the ratio of y-unit to x-unit, using 'esual' uses the same scaling for x and y, "
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "<div class=\"alert alert-info\" style=\"font-size:120%\">\n",
-    "\n",
-    "**REMEMBER**: <br>\n",
+    "A raster is an __image__ in local pixel coordinates until we specify what part of the earth the image covers. This is done through the usage of raster file metadata. It depends on the file type how these are stored on disk. When reading in a file, the metadata need to be interpreted as well in order to know the __spatial information__. The translation from the data and metadata to a Python Nd-array with geographical context is done by the rasterio/xarray packages.\n",
     "\n",
-    "The [`gdal` library](https://gdal.org/) is a very powerful set of functions. It is the\n",
-    "open source Swiss Army knife for raster and vector geospatial data handling. GDAL provides Python bindings to run it from Python code, but these are not very 'Pythonic'. When familiar with the command line the [command line raster functions](https://gdal.org/programs/index.html#raster-programs) (CLI) are certainly worthwhile to check out!\n",
-    "    \n",
-    "You can run a CLI command inside a Jupyter Notebook by prefixing it with the `!` character.\n",
+    "As an quick introduction (we will learn the specific syntax later).\n",
     "\n",
-    "</div>"
+    "> Plot of the Red band only, using a Red color scale_"
    ]
   },
   {
-   "cell_type": "markdown",
+   "cell_type": "code",
+   "execution_count": null,
    "metadata": {},
+   "outputs": [],
    "source": [
-    "Important information we get from the `gdalinfo` command are \n",
-    "\n",
-    "* Coordinate reference system (CRS), see also [02-coordinate-reference-systems notebook](./02-coordinate-reference-systems.ipynb).\n",
-    "* Number of bands in the data set: A data set can contain one (single) or more (multi) layers (bands).\n",
-    "* Spatial resolution: This resolution represents the area on the ground that each pixel covers. The units for your data are determined by the CRS above (e.g. meters).\n",
-    "* Spatial extent (bounding box) of the data: The geographic area that the raster data covers.\n",
-    "\n",
-    "<img src=\"../img/raster-spatial-extent-coordinates.png\" alt=\"spatial-extnet\" style=\"width:500px;\"/>\n",
-    "\n",
-    "<small>_Image Source: National Ecological Observatory Network (NEON)._</small>"
+    "ax = herstappe.sel(band=1).plot.imshow(cmap=\"Reds\", figsize=(12, 5))  #, robust=True)\n",
+    "ax.axes.set_aspect('equal')"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Let's see how Rasterio interprets this information: "
+    "> _plot the intersection of the data at x coordinate closest to 600000 for each band:_"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
-    "src = rasterio.open(file_herstappe)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'driver': 'GTiff',\n",
-       " 'dtype': 'float32',\n",
-       " 'nodata': None,\n",
-       " 'width': 447,\n",
-       " 'height': 227,\n",
-       " 'count': 3,\n",
-       " 'crs': CRS.from_epsg(3857),\n",
-       " 'transform': Affine(40.07813983073409, 0.0, 593476.1968631798,\n",
-       "        0.0, -40.1126034998577, 6577549.60336745)}"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "src.meta"
+    "herstappe.sel(x=600_000, method='nearest').plot.line(col='band')"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The `meta` attribute contains some of the essential spatial information, whereas the `profile` attribute contains the spatial metadata combined with the metadata about the GeoTiff storage:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'driver': 'GTiff', 'dtype': 'float32', 'nodata': None, 'width': 447, 'height': 227, 'count': 3, 'crs': CRS.from_epsg(3857), 'transform': Affine(40.07813983073409, 0.0, 593476.1968631798,\n",
-       "       0.0, -40.1126034998577, 6577549.60336745), 'tiled': False, 'compress': 'deflate', 'interleave': 'pixel'}"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "src.profile"
+    "## What's a xarray.DataArray?"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Some of these are also stored in a separate attribute:"
+    "Let's have a look at the data again:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 392,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(CRS.from_epsg(3857), 3)"
-      ]
-     },
-     "execution_count": 392,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "src.crs, src.count"
+    "herstappe"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 393,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 393,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
    "source": [
-    "src.crs == src.meta[\"crs\"]"
+    "The output of xarray is a bit different to what we've previous seen. Let's go through the different elements:\n",
+    "\n",
+    "- It is a `xarray.DataArray`, one of the main data types provided by xarray\n",
+    "- It has 3 __dimensions__:\n",
+    "    - `band`: 3 bands (in this case RGB)\n",
+    "    - `y`: the y coordinates of the data set\n",
+    "    - `x`: the x coordinates of the data set\n",
+    "- Each of these dimensions are defined by a __coordinate__ (1D) array\n",
+    "- It contains 304407 (227\\*447\\*3) data values (stored as float32)\n",
+    "- Other metadata, such as some of the metadata provided by the `tiff` file are stored in the __`Attributes`__. The attributes is a dictionary.\n",
+    "\n",
+    "Looking to the data itself (click on the icons on the right), we can see these are Numpy arrays..."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The `meta` attribute misses information on the spatial resolution and spatial extent, available in separate attributes:"
+    "## Xarray adds (spatial) context to NumPy arrays"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 394,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(40.07813983073409, 40.1126034998577)"
-      ]
-     },
-     "execution_count": 394,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
    "source": [
-    "src.res"
+    "Both the data values as the values of the coordinates are..."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 395,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "BoundingBox(left=593476.1968631798, bottom=6568444.042372982, right=611391.1253675179, top=6577549.60336745)"
-      ]
-     },
-     "execution_count": 395,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "src.bounds"
+    "type(herstappe.values), type(herstappe.x.values), type(herstappe.y.values), type(herstappe.band.values)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Geotiff files can also contain a data mask to define which pixels contain valid data and which are no data. Raster data often has such a `NoDataValue`. To extract the mask, use the `dataset_mask` method, which returns 255 for valid data and 0 for nodata:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 396,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[255, 255, 255, ..., 255, 255, 255],\n",
-       "       [255, 255, 255, ..., 255, 255, 255],\n",
-       "       [255, 255, 255, ..., 255, 255, 255],\n",
-       "       ...,\n",
-       "       [255, 255, 255, ..., 255, 255, 255],\n",
-       "       [255, 255, 255, ..., 255, 255, 255],\n",
-       "       [255, 255, 255, ..., 255, 255, 255]], dtype=uint8)"
-      ]
-     },
-     "execution_count": 396,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "src.dataset_mask()"
+    "... all Numpy arrays! \n",
+    "\n",
+    "Numpy is on of the most fundamental parts of the scientific python 'ecosystem'. A lot of other packages - you already used Pandas and GeoPandas in this course, and now also xarray - are built on top of NumPy and the ndarray (n-dimensional array) object it provides."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We should not forget to close the file afterwards!"
+    "When you read raster data using `xarray.open_dataarray` you are actually creating a set of Numpy arrays combined in a `xarray.DataArray` object. A Numpy array is an efficient data type to calculate with arrays in general. Let's extract the data of herstappe as an Numpy `ndarray`:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 397,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
-    "src.close()"
+    "herstappe_array = herstappe.values"
    ]
   },
   {
-   "cell_type": "markdown",
+   "cell_type": "code",
+   "execution_count": null,
    "metadata": {},
+   "outputs": [],
    "source": [
-    "## Using context manager to open files\n",
-    "\n",
-    "A better approach instead of opening and closing the data file separately is to use a Python [context manager](https://docs.python.org/3/reference/compound_stmts.html#with):"
+    "herstappe_array"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 398,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<open DatasetReader name='./data/herstappe/raster/2020-09-17_Sentinel_2_L1C_True_color.tiff' mode='r'>\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "with rasterio.open(file_herstappe) as src:\n",
-    "    print(src)"
+    "type(herstappe_array), herstappe_array.dtype"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 399,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<closed DatasetReader name='./data/herstappe/raster/2020-09-17_Sentinel_2_L1C_True_color.tiff' mode='r'>\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "print(src)"
+    "type(herstappe.x.values), type(herstappe.y.values)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "A __context manager__ allows you to open the data and work with it. Within the context manager, Python makes a temporary connection to the file that you are trying to open. \n",
-    "\n",
-    "The `with` statement creates a connection to the file that you want to open. The __default connection type is read only__. This means that you can NOT modify that file by default. Not being able to modify the original data is a good thing because it prevents you from making unintended changes to your original data.\n",
-    "\n",
-    "Opening and closing files using rasterio and context managers is efficient as it establishes a connection to the raster file rather than directly reading it into memory. Once you are done opening and reading in the data, the context manager closes that connection to the file."
+    "NumPy supports different dtypes (float, int,...), but __all elements of an array do have the same dtype__. Note that NumPy auto-detects the data-type from the input. The data type of this specific array `herstappe_array` is float32. More information on the data types NumPy supports is available in the [documentation](https://numpy.org/devdocs/user/basics.types.html#array-types-and-conversions-between-types)."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "By extracting the information we require inside the context manager, we are able to work with it outside the context:"
+    "Just as any other object in Python, the `ndarray` has a number of attributes. We already checked the `dtype` attribute. The `shape` and `ndim` of the array are other relevant attributes:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 400,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
-    "with rasterio.open(file_herstappe) as src:\n",
-    "    herstappe_meta = src.meta\n",
-    "    herstappe_bounds =  src.bounds"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 401,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(BoundingBox(left=593476.1968631798, bottom=6568444.042372982, right=611391.1253675179, top=6577549.60336745),\n",
-       " {'driver': 'GTiff',\n",
-       "  'dtype': 'float32',\n",
-       "  'nodata': None,\n",
-       "  'width': 447,\n",
-       "  'height': 227,\n",
-       "  'count': 3,\n",
-       "  'crs': CRS.from_epsg(3857),\n",
-       "  'transform': Affine(40.07813983073409, 0.0, 593476.1968631798,\n",
-       "         0.0, -40.1126034998577, 6577549.60336745)})"
-      ]
-     },
-     "execution_count": 401,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "herstappe_bounds, herstappe_meta"
+    "herstappe_array.shape, herstappe_array.ndim"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "__Note:__ The usage of context managers is a default Python feature and good advice when interacting with files."
-   ]
-  },
-  {
-   "cell_type": "raw",
-   "metadata": {},
-   "source": [
-    "with open(\"../README.md\") as md_file:\n",
-    "    test = md_file.readline()"
+    "Hence, we have three bands with dimensions (227, 447) and data type float32. But with this single Numpy array, we do not have any spatial information on which area of the earth this data represents. We also need the `herstappe.x` and `herstappe.y` arrays to link the indidivual elements in the array with the corresponding earth coordinates and band. \n",
+    "\n",
+    "By using the `xarray.DataArray` to wrap and combine these NumPy arrays, we can keep the data and the spatial context together."
    ]
   },
   {
@@ -565,105 +317,78 @@
    "source": [
     "<div class=\"alert alert-info\" style=\"font-size:120%\">\n",
     "\n",
-    "**REMEMBER**: <br>\n",
+    "**REMEMBER**:\n",
     "\n",
-    "When interacting with file on disk, use the `with` statement to open it within a so-called __context manager__. This makes sure the connection to your file is properly closed! \n",
+    "The [`numpy` package](https://numpy.org/) is the backbone of the scientific Python ecosystem. The `ndarray` provides an efficient data type to store and manipulate raster data, but it does __NOT contain any spatial information__. \n",
+    "    \n",
+    "If we use `xarray.DataArray` to wrap this NumPy array, we can keep the data and the spatial context together.\n",
     "\n",
     "</div>"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "source": [
-    "## What about the data itself?"
+    "## Raster data attributes - spatial context"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We started this notebook with reading and plotting the Herstappe data of 2018-09-18:"
+    "To quickly scan the spatial metadata of a Raster data file, let's use the [`gdalinfo` command](https://gdal.org/programs/gdalinfo.html#gdalinfo). \n",
+    "\n",
+    "> The `gdalinfo` command lists information about a raster dataset.\n",
+    "\n",
+    "It is not a Python command, but a program that need to be run from the terminal (aka command line). Using a small Jupyter notebook - `!` trick, we can use it within the notebook as well:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 402,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
     }
-   ],
+   },
+   "outputs": [],
    "source": [
-    "with rasterio.open(file_herstappe) as src:\n",
-    "    herstappe = src.read()\n",
-    "    show(herstappe, transform=src.transform)"
+    "!gdalinfo -mm ./data/herstappe/raster/2020-09-17_Sentinel_2_L1C_True_color.tiff"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Within the context manager (the `with` statement) we use the `read()` method to load the data itself from the file and assign this to the `herstappe` variable. As with the metadata, the data is stored in computer memory and we can work with the data (without affecting the original data on file):"
+    "Important information we get from the `gdalinfo` command are \n",
+    "\n",
+    "* Coordinate reference system (CRS), see also [02-coordinate-reference-systems notebook](./02-coordinate-reference-systems.ipynb).\n",
+    "* Number of bands in the data set: A data set can contain one (single) or more (multi) layers (bands).\n",
+    "* Spatial resolution: This resolution represents the area on the ground that each pixel covers. The units for your data are determined by the CRS above (e.g. meters).\n",
+    "* Spatial extent (bounding box) of the data: The geographic area that the raster data covers.\n",
+    "\n",
+    "<img src=\"../img/raster-spatial-extent-coordinates.png\" alt=\"spatial-extnet\" style=\"width:500px;\"/>\n",
+    "\n",
+    "<small>_Image Source: National Ecological Observatory Network (NEON)._</small>"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The `show` method is a convenience plotting function provided by the Rasterio Python package. By adding the `transform=src.transform` we get the information of the spatial extent as the x and y axis labels of the plot. We will later learn about other functionalities of Rasterio. "
+    "Let's see how `xarray.open_dataarray` interprets this information:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 403,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[[0.3375 , 0.2195 , 0.17675, ..., 0.167  , 0.17625, 0.2485 ],\n",
-       "        [0.362  , 0.29525, 0.222  , ..., 0.197  , 0.21275, 0.27125],\n",
-       "        [0.30775, 0.26425, 0.25875, ..., 0.272  , 0.28975, 0.27425],\n",
-       "        ...,\n",
-       "        [0.39175, 0.38825, 0.4335 , ..., 0.3815 , 0.3135 , 0.2055 ],\n",
-       "        [0.38625, 0.40475, 0.42125, ..., 0.39725, 0.3875 , 0.3235 ],\n",
-       "        [0.3905 , 0.40375, 0.41775, ..., 0.386  , 0.388  , 0.38725]],\n",
-       "\n",
-       "       [[0.311  , 0.2525 , 0.239  , ..., 0.22525, 0.2315 , 0.24775],\n",
-       "        [0.3485 , 0.312  , 0.25725, ..., 0.23625, 0.25475, 0.29   ],\n",
-       "        [0.31375, 0.27775, 0.27775, ..., 0.28725, 0.30475, 0.26975],\n",
-       "        ...,\n",
-       "        [0.342  , 0.34175, 0.36575, ..., 0.33725, 0.30925, 0.25075],\n",
-       "        [0.34225, 0.3465 , 0.35675, ..., 0.3415 , 0.338  , 0.31475],\n",
-       "        [0.3405 , 0.35075, 0.35925, ..., 0.33625, 0.341  , 0.349  ]],\n",
-       "\n",
-       "       [[0.3235 , 0.277  , 0.26   , ..., 0.25075, 0.266  , 0.27925],\n",
-       "        [0.35775, 0.31775, 0.285  , ..., 0.276  , 0.28525, 0.31625],\n",
-       "        [0.3225 , 0.28425, 0.28375, ..., 0.3115 , 0.3365 , 0.2965 ],\n",
-       "        ...,\n",
-       "        [0.338  , 0.3395 , 0.35725, ..., 0.335  , 0.311  , 0.2735 ],\n",
-       "        [0.3485 , 0.346  , 0.3505 , ..., 0.33625, 0.334  , 0.31725],\n",
-       "        [0.338  , 0.34375, 0.3455 , ..., 0.3375 , 0.3375 , 0.33775]]],\n",
-       "      dtype=float32)"
-      ]
-     },
-     "execution_count": 403,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
+    "herstappe = xr.open_dataarray(file_herstappe, engine=\"rasterio\")\n",
     "herstappe"
    ]
   },
@@ -671,357 +396,127 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The variable `herstappe` does not contains any spatial context, but is a 2D array with numeric (float) values. Let's check the data type of this variable:"
+    "- Coordinate reference system (CRS) stored as attribute to the `xr.DataArray` `rio` accessor:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 404,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "numpy.ndarray"
-      ]
-     },
-     "execution_count": 404,
-     "metadata": {},
-     "output_type": "execute_result"
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
     }
-   ],
-   "source": [
-    "type(herstappe)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
+   },
+   "outputs": [],
    "source": [
-    "When you read raster data using Rasterio you are actually creating a __Numpy array__. A Numpy array does not store spatial information, but is an efficient data type to calculate with arrays in general:"
+    "herstappe.rio.crs"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 405,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(3, 227, 447)"
-      ]
-     },
-     "execution_count": 405,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "herstappe.shape"
+    "type(herstappe.rio.crs) # herstappe.rio.crs.to_string(), # herstappe.rio.crs.to_wkt()"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 406,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[[0.03375 , 0.02195 , 0.017675, ..., 0.0167  , 0.017625,\n",
-       "         0.02485 ],\n",
-       "        [0.0362  , 0.029525, 0.0222  , ..., 0.0197  , 0.021275,\n",
-       "         0.027125],\n",
-       "        [0.030775, 0.026425, 0.025875, ..., 0.0272  , 0.028975,\n",
-       "         0.027425],\n",
-       "        ...,\n",
-       "        [0.039175, 0.038825, 0.04335 , ..., 0.03815 , 0.03135 ,\n",
-       "         0.02055 ],\n",
-       "        [0.038625, 0.040475, 0.042125, ..., 0.039725, 0.03875 ,\n",
-       "         0.03235 ],\n",
-       "        [0.03905 , 0.040375, 0.041775, ..., 0.0386  , 0.0388  ,\n",
-       "         0.038725]],\n",
-       "\n",
-       "       [[0.0311  , 0.02525 , 0.0239  , ..., 0.022525, 0.02315 ,\n",
-       "         0.024775],\n",
-       "        [0.03485 , 0.0312  , 0.025725, ..., 0.023625, 0.025475,\n",
-       "         0.029   ],\n",
-       "        [0.031375, 0.027775, 0.027775, ..., 0.028725, 0.030475,\n",
-       "         0.026975],\n",
-       "        ...,\n",
-       "        [0.0342  , 0.034175, 0.036575, ..., 0.033725, 0.030925,\n",
-       "         0.025075],\n",
-       "        [0.034225, 0.03465 , 0.035675, ..., 0.03415 , 0.0338  ,\n",
-       "         0.031475],\n",
-       "        [0.03405 , 0.035075, 0.035925, ..., 0.033625, 0.0341  ,\n",
-       "         0.0349  ]],\n",
-       "\n",
-       "       [[0.03235 , 0.0277  , 0.026   , ..., 0.025075, 0.0266  ,\n",
-       "         0.027925],\n",
-       "        [0.035775, 0.031775, 0.0285  , ..., 0.0276  , 0.028525,\n",
-       "         0.031625],\n",
-       "        [0.03225 , 0.028425, 0.028375, ..., 0.03115 , 0.03365 ,\n",
-       "         0.02965 ],\n",
-       "        ...,\n",
-       "        [0.0338  , 0.03395 , 0.035725, ..., 0.0335  , 0.0311  ,\n",
-       "         0.02735 ],\n",
-       "        [0.03485 , 0.0346  , 0.03505 , ..., 0.033625, 0.0334  ,\n",
-       "         0.031725],\n",
-       "        [0.0338  , 0.034375, 0.03455 , ..., 0.03375 , 0.03375 ,\n",
-       "         0.033775]]], dtype=float32)"
-      ]
-     },
-     "execution_count": 406,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
    "source": [
-    "herstappe / 10"
+    "- Number of bands in the data set. Each band in the original data set is represented in the `band` dimension:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 407,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(3, 227, 447)"
-      ]
-     },
-     "execution_count": 407,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "herstappe.shape"
+    "herstappe.band"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 408,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(0.11325, 0.8575)"
-      ]
-     },
-     "execution_count": 408,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
    "source": [
-    "herstappe.min(), herstappe.max()"
+    "- Spatial resolution: This resolution represents the area on the ground that each pixel covers. It is available with the `rio`  accessor, but also translated to the coordinates dimensions x and y as the distance between two elements:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 409,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
-    "import matplotlib.pyplot as plt"
+    "herstappe.rio.resolution()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 410,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.hist(herstappe.flatten(), bins=25);"
-   ]
-  },
-  {
-   "cell_type": "markdown",
+   "execution_count": null,
    "metadata": {},
+   "outputs": [],
    "source": [
-    "[Numpy](https://numpy.org/) is a fundamental package for scientific computing in the scientific Python ecosystem. Many other packages rely on Numpy as well (Pandas, GeoPandas,...). The bridge in between raster data sets and Numpy enable us to apply the full power of Numpy to spatial data. In the following notebook we will focus on the Numpy package. \n",
-    "\n",
-    "But let's finish this section with some exercises on reading raster data."
+    "herstappe.x[1] - herstappe.x[0], herstappe.y[1] - herstappe.y[0]"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "<div class=\"alert alert-success\">\n",
+    "- Spatial extent (bounding box) of the data. [Xarray translates](https://corteva.github.io/rioxarray/stable/getting_started/getting_started.html) the the coordinates info and spatial extent:\n",
     "\n",
-    "**EXERCISE**:\n",
-    "\n",
-    "The Geotiff file `./data/gent/raster/2020-09-17_Sentinel_2_L1C_True_color.tiff` is True color data set downloaded from [sentinel-hub](https://www.sentinel-hub.com/). Check the metadata of the file using the `gdalinfo` command and find out:\n",
-    "    \n",
-    "* How many pixels contains the data set?\n",
-    "* How many bands does the data set consist of?\n",
-    "* What is the CRS of the data set?\n",
-    "* What is the resolution of the data set?\n",
+    "    >  The x and y coordinates are generated automatically from the file’s geoinformation, shifted to the center of each pixel.\n",
     "    \n",
-    "How is the data set different from the Herstappe data set?\n",
-    "    \n",
-    "<details><summary>Hint</summary>\n",
-    "\n",
-    "Any command that works at the command-line can be used in IPython/Jupyter by prefixing it with the `!` character, e.g. `!ls`\n",
-    "\n",
-    "</details>\n",
-    "\n",
-    "</div>"
+    "Hence, the minimal and maximum values of the coordinates are still the same as the boundaries defined by the metadata:"
    ]
   },
   {
-   "cell_type": "markdown",
+   "cell_type": "code",
+   "execution_count": null,
    "metadata": {},
+   "outputs": [],
    "source": [
-    "<div class=\"alert alert-success\">\n",
-    "\n",
-    "**EXERCISE**:\n",
-    "\n",
-    "Make a __plot of the FIRST channel__ of the data stored in the Geotiff file `./data/gent/raster/2020-09-17_Sentinel_2_L1C_True_color.tiff`.\n",
-    "    \n",
-    "<details><summary>Hints</summary>\n",
-    "\n",
-    "- Make sure to use a context manager to access the data files!\n",
-    "- You only need the data of the first band. Check the documentation of the `read` method to see how.\n",
-    "- There are multiple ways of plotting raster data. For this exercise, use the `show` method provide by Rasterio. Make sure the spatial extent information is used as x and y axis labels.\n",
-    "\n",
-    "</details>    \n",
-    "\n",
-    "</div>"
+    "herstappe.rio.bounds()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 413,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "gent_file = \"./data/gent/raster/2020-09-17_Sentinel_2_L1C_True_color.tiff\"\n",
-    "with rasterio.open(gent_file) as gent_gif:\n",
-    "    gent = gent_gif.read([1]) # only read the first band/layer\n",
-    "    show(gent, transform=gent_gif.transform)"
+    "herstappe.x.min(), herstappe.x.max(), herstappe.y.min(), herstappe.y.max()"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "<div class=\"alert alert-success\">\n",
+    "<div class=\"alert alert-info\" style=\"font-size:120%\">\n",
     "\n",
-    "**EXERCISE**:\n",
+    "**TIP**: <br>\n",
     "\n",
-    "Using Rasterio, read the spatial extent and resolution from two Geotiff example files and assign it to variables:\n",
-    "    \n",
-    "- Geotiff file `./data/gent/raster/2020-09-17_Sentinel_2_L1C_True_color.tiff`: assign the spatial extent to a variable `gent_extent` and the resolution to `gent_res`\n",
-    "- `./data/herstappe/raster/2020-09-17_Sentinel_2_L1C_True_color.tiff`: assignthe spatial extent to a variable `herstappe_extent` and the resolution to `herstappe_res` \n",
-    "    \n",
-    "Do both datasets have the same resolution? \n",
-    "    \n",
-    "Without doing the calculation, discuss with your neighbours (or break out colleagues) __how__ you would define which data set covers the largest area?\n",
+    "The [`gdal` library](https://gdal.org/) is a very powerful set of functions. It is the\n",
+    "open source Swiss Army knife for raster and vector geospatial data handling. GDAL provides Python bindings to run it from Python code, but these are not very 'Pythonic'. When familiar with the command line the [command line raster functions](https://gdal.org/programs/index.html#raster-programs) (CLI) are certainly worthwhile to check out!\n",
     "    \n",
-    "<details><summary>Hint</summary>\n",
-    "\n",
-    "Make sure to use a context manager to access the data files!\n",
-    "\n",
-    "</details>    \n",
+    "You can run a CLI command inside a Jupyter Notebook by prefixing it with the `!` character.\n",
     "\n",
     "</div>"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 355,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "((57.32777121388223, 57.44833434997156),\n",
-       " BoundingBox(left=392465.9217302375, bottom=6621281.1381751085, right=428295.7787389139, top=6639492.260164049))"
-      ]
-     },
-     "execution_count": 355,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "gent_file = \"./data/gent/raster/2020-09-17_Sentinel_2_L1C_True_color.tiff\"\n",
-    "with rasterio.open(gent_file) as src:\n",
-    "    gent_extent = src.bounds\n",
-    "    gent_res = src.res\n",
-    "gent_res, gent_extent"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 415,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "((40.07813983073409, 40.1126034998577),\n",
-       " BoundingBox(left=593476.1968631798, bottom=6568444.042372982, right=611391.1253675179, top=6577549.60336745))"
-      ]
-     },
-     "execution_count": 415,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "herstappe_file = \"./data/herstappe/raster/2020-09-17_Sentinel_2_L1C_True_color.tiff\"\n",
-    "with rasterio.open(herstappe_file) as src:\n",
-    "    herstappe_extent = src.bounds\n",
-    "    herstappe_res = src.res\n",
-    "herstappe_res, herstappe_extent"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
+  "celltoolbar": "Nbtutor - export exercises",
+  "jupytext": {
+   "default_lexer": "ipython3"
+  },
   "kernelspec": {
-   "display_name": "Python [conda env:DS-geospatial]",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
-   "name": "conda-env-DS-geospatial-py"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
@@ -1033,7 +528,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.6"
+   "version": "3.12.11"
   }
  },
  "nbformat": 4,
diff --git a/_solved/11-numpy.ipynb b/_solved/11-numpy.ipynb
deleted file mode 100644
index cadd38b..0000000
--- a/_solved/11-numpy.ipynb
+++ /dev/null
@@ -1,2758 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Numpy"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import matplotlib.pyplot as plt\n",
-    "from matplotlib.colors import ListedColormap\n",
-    "from matplotlib.lines import Line2D\n",
-    "\n",
-    "import rasterio\n",
-    "from rasterio.plot import plotting_extent, show"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Introduction"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "On of the most fundamental parts of the scientific python 'ecosystem' is [numpy](https://numpy.org/). A lot of other packages - you already used Pandas and GeoPandas in this course - are built on top of Numpy and the `ndarray`  (n-dimensional array) data type it provides. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let's start again from reading in a GeoTiff data set from file, thiss time a Sentinal Band 4 of the City of Ghent:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "with rasterio.open(\"./data/gent/raster/2020-09-17_Sentinel_2_L1C_B04.tiff\") as src:\n",
-    "    b4_data = src.read()\n",
-    "    b4_data_meta = src.meta\n",
-    "    show(src)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "As we learnt in the previous lesson, Rasterio returns a Numpy `ndarray`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "numpy.ndarray"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "type(b4_data)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[[ 5125,  5793, 11475, ...,  6776,  6816,  5662],\n",
-       "        [ 6698,  4974,  5603, ...,  4797,  4306,  4293],\n",
-       "        [ 6626,  5243,  5046, ...,  4509,  4804,  4620],\n",
-       "        ...,\n",
-       "        [ 4764,  4863,  4463, ...,  9234,  6606,  4476],\n",
-       "        [ 4607,  4555,  4404, ...,  4810,  5833,  3926],\n",
-       "        [ 4627,  4902,  4745, ...,  3781,  4430,  5439]]], dtype=uint16)"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "b4_data"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Numpy supports different `dtype`s (`float`, `int`,...), but all elements of an array do have the same dtype. Note that NumPy auto-detects the data-type from the input."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "dtype('uint16')"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "b4_data.dtype"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The data type of this specific array `b4_data` is 16bit unsigned integer. More information on the data types Numpy supports is available in the [documentation](https://numpy.org/devdocs/user/basics.types.html#array-types-and-conversions-between-types). Detailed info on data types is out of scope of this course, but remember that using 16bit unsigned integer, it can contain `2**16` different (all positive) integer values:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "65536"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "2**16"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let's check this by calculating the minimum and maximum value in the array:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(0, 65535)"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "b4_data.min(), b4_data.max()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Converting to another data type is supported by `astype` method. When floats are preferred during calculation:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[[ 5125.,  5793., 11475., ...,  6776.,  6816.,  5662.],\n",
-       "        [ 6698.,  4974.,  5603., ...,  4797.,  4306.,  4293.],\n",
-       "        [ 6626.,  5243.,  5046., ...,  4509.,  4804.,  4620.],\n",
-       "        ...,\n",
-       "        [ 4764.,  4863.,  4463., ...,  9234.,  6606.,  4476.],\n",
-       "        [ 4607.,  4555.,  4404., ...,  4810.,  5833.,  3926.],\n",
-       "        [ 4627.,  4902.,  4745., ...,  3781.,  4430.,  5439.]]])"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "b4_data.astype(float)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "65535"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "b4_data.max()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Just as any other object in Python, the `ndarray` has a number of attributes. We already checkes the `dtype` attribute. The `shape` and `ndim` of the array are other relevant attribute:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "((1, 317, 625), 3)"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "b4_data.shape, b4_data.ndim"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Hence, we have a single band with dimensions (317, 625) and data type `uint16`. Compare this to the metadata stored in the geotiff file:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#!gdalinfo ./data/gent/raster/2020-09-17_Sentinel_2_L1C_B04.tiff"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The metadata on the dimensions and the datatype correspond, but the spatial information is lost when we only store the Numpy array."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Numpy works very well together with the other fundamental scientific Python package [Matplotlib](https://matplotlib.org/). An useful plot function to know when working with raster data is `imshow`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig, ax = plt.subplots()\n",
-    "ax.imshow(b4_data.squeeze());"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "__Note:__ Numpy function `squeeze` used to get rid of the single-value dimension of the numpy array."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "As the Numpy array does not contain any spatial information, the x and y axis labels are defined by the indices of the array. Remark that the Rasterio plot returned this plot with the coordinate information in the axis labels. \n",
-    "\n",
-    "With a small trick, the same result can be achieved with Matplotlib:\n",
-    "\n",
-    "1. When reading in a data set using Rasterio, use the `plotting_extent` function from rasterio to get the spatial extent:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from rasterio.plot import plotting_extent\n",
-    "\n",
-    "with rasterio.open(\"./data/gent/raster/2020-09-17_Sentinel_2_L1C_B04.tiff\") as src:\n",
-    "    b4_data = src.read()\n",
-    "    b4_data_meta = src.meta\n",
-    "    b4_data_extent = plotting_extent(src)  # NEW"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(392465.9217302375, 428295.7787389139, 6621281.1381751085, 6639492.260164049)"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "b4_data_extent"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "2. Add the `extent` argument to the `imshow` plot"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.image.AxesImage at 0x7f090481c450>"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig, ax = plt.subplots()\n",
-    "ax.imshow(b4_data.squeeze(), extent=b4_data_extent)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<div class=\"alert alert-info\" style=\"font-size:120%\">\n",
-    "\n",
-    "**REMEMBER**: <br>\n",
-    "\n",
-    "The [`numpy` package](https://numpy.org/) is the backbone of the scientific Python ecosystem. The `ndarray` provides an efficient data type to store and manipulate raster data, but it does NOT contain any spatial information.\n",
-    "    \n",
-    "Use the spatial `extent` trick to add coordinate information to imshow plot axis. Convert to the preferred datatype using `astype` method.\n",
-    "\n",
-    "</div>"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Reshape, slice and index"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(1, 317, 625)"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "b4_data.shape"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We already used `squeeze` to remove the single-value dimension. We could also select the data we needed, similar to slicing in lists or Pandas DataFrames:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[ 5125,  5793, 11475, ...,  6776,  6816,  5662],\n",
-       "       [ 6698,  4974,  5603, ...,  4797,  4306,  4293],\n",
-       "       [ 6626,  5243,  5046, ...,  4509,  4804,  4620],\n",
-       "       ...,\n",
-       "       [ 4764,  4863,  4463, ...,  9234,  6606,  4476],\n",
-       "       [ 4607,  4555,  4404, ...,  4810,  5833,  3926],\n",
-       "       [ 4627,  4902,  4745, ...,  3781,  4430,  5439]], dtype=uint16)"
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "b4_data[0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(317, 625)"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "b4 = b4_data[0]\n",
-    "b4.shape"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If you do not like the order of dimensions of the data, you can switch these using `transpose`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(625, 317)"
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "b4.transpose(1, 0).shape"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Getting rid of the dimensions and flattening all values into a single 1-D array can be done using `flatten` method:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(198125,)"
-      ]
-     },
-     "execution_count": 22,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "b4.flatten().shape"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Flattening an arrya is useful to create a histogram with Matplotlib:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.hist(b4.flatten(), bins=100);"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# slice, subsample, reverse\n",
-    "# slice + assign\n",
-    "# fancy indexing\n",
-    "# fancy indexing + assign"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "b4 = b4_data[0]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Select a specific row/column:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(625,)"
-      ]
-     },
-     "execution_count": 26,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "b4[10].shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(317, 2)"
-      ]
-     },
-     "execution_count": 27,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "b4[:, -2:].shape"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Select every nth element in a given dimension:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(10, 625)"
-      ]
-     },
-     "execution_count": 28,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "b4[100:200:10, :].shape"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Reversing an array:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(317, 625)"
-      ]
-     },
-     "execution_count": 29,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "b4[:, ::-1].shape  # Note you can also np.flip an array"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([ 5125,  5793, 11475, 10866], dtype=uint16)"
-      ]
-     },
-     "execution_count": 30,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "b4[0, :4]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([10866, 11475,  5793,  5125], dtype=uint16)"
-      ]
-     },
-     "execution_count": 31,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "b4_rev = b4[:, ::-1]\n",
-    "b4_rev[0, -4:]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You can also combine assignment and slicing:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[  10,   10,   10, ..., 6776, 6816, 5662],\n",
-       "       [6698, 4974, 5603, ..., 4797, 4306, 4293],\n",
-       "       [6626, 5243, 5046, ..., 4509, 4804, 4620],\n",
-       "       ...,\n",
-       "       [4764, 4863, 4463, ..., 9234, 6606, 4476],\n",
-       "       [4607, 4555, 4404, ..., 4810, 5833, 3926],\n",
-       "       [4627, 4902, 4745, ..., 3781, 4430, 5439]], dtype=uint16)"
-      ]
-     },
-     "execution_count": 32,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "b4[0, :3] = 10\n",
-    "b4"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Use a __condition__ to select data, also called fancy indexing or boolean indexing:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[ True,  True,  True, ..., False, False, False],\n",
-       "       [False, False, False, ..., False, False, False],\n",
-       "       [False, False, False, ..., False, False, False],\n",
-       "       ...,\n",
-       "       [False, False, False, ..., False, False, False],\n",
-       "       [False, False, False, ..., False, False, False],\n",
-       "       [False, False, False, ..., False, False, False]])"
-      ]
-     },
-     "execution_count": 33,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "b4 < 1000"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Onle keep the data which are True for the given condition"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([ 10,  10,  10,   0,   0,   0,   0,  46,   0, 524], dtype=uint16)"
-      ]
-     },
-     "execution_count": 34,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "b4[b4 < 1000]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Or combine assignment and fancy indexing, e.g. a reclassification of the raster data:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "b4[b4 < 5000] = 0  # assign the value 0 to all elements with a value lower than 5000"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[   0,    0,    0, ..., 6776, 6816, 5662],\n",
-       "       [6698,    0, 5603, ...,    0,    0,    0],\n",
-       "       [6626, 5243, 5046, ...,    0,    0,    0],\n",
-       "       ...,\n",
-       "       [   0,    0,    0, ..., 9234, 6606,    0],\n",
-       "       [   0,    0,    0, ...,    0, 5833,    0],\n",
-       "       [   0,    0,    0, ...,    0,    0, 5439]], dtype=uint16)"
-      ]
-     },
-     "execution_count": 36,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "b4"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "A powerfull shortcut to handle this kind or reclassifications is the `np.where` function:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[  10,   10,   10, ..., 6776, 6816, 5662],\n",
-       "       [6698,   10, 5603, ...,   10,   10,   10],\n",
-       "       [6626, 5243, 5046, ...,   10,   10,   10],\n",
-       "       ...,\n",
-       "       [  10,   10,   10, ..., 9234, 6606,   10],\n",
-       "       [  10,   10,   10, ...,   10, 5833,   10],\n",
-       "       [  10,   10,   10, ...,   10,   10, 5439]], dtype=uint16)"
-      ]
-     },
-     "execution_count": 37,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.where(b4 < 5000, 10, b4)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<div class=\"alert alert-success\">\n",
-    "\n",
-    "**EXERCISE**:\n",
-    "\n",
-    "* Read in the file `./data/gent/raster/2020-09-17_Sentinel_2_L1C_True_color.tiff` with rasterio and assign the data to a new variable `tc_data`.  \n",
-    "* Select only the *second* layer of `tc_data` and assign the output to a new variable `tc_g`.\n",
-    "* Assign to each of the elements in the `tc_g` array with a value above 15000 the new value 65535.\n",
-    "    \n",
-    "<details><summary>Hints</summary>\n",
-    "\n",
-    "* You can combine the assignment of new values together with fancy indexing of a numpy array.\n",
-    "* Python (and also Numpy) uses 0 as the first-element index\n",
-    "\n",
-    "</details>\n",
-    "    \n",
-    "</div>"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "with rasterio.open(\"./data/gent/raster/2020-09-17_Sentinel_2_L1C_True_color.tiff\") as src:\n",
-    "    tc_data = src.read()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tc_g = tc_data[1]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[65535, 65535, 65535, ..., 65535, 65535, 65535],\n",
-       "       [65535, 65535, 65535, ..., 65535, 14221, 13779],\n",
-       "       [65535, 65535, 65535, ..., 14631, 65535, 65535],\n",
-       "       ...,\n",
-       "       [65535, 65535, 65535, ..., 65535, 65535, 65535],\n",
-       "       [65535, 65535, 65535, ..., 14516, 65535, 65535],\n",
-       "       [65535, 65535, 65535, ..., 13844, 65535, 65535]], dtype=uint16)"
-      ]
-     },
-     "execution_count": 40,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tc_g[tc_g > 15000] = 65535\n",
-    "tc_g"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<div class=\"alert alert-success\">\n",
-    "\n",
-    "**EXERCISE**:\n",
-    "\n",
-    "Subsample the ndarray `tc_data` by taking only the one out of each 5 data points for all layers at the same time (Be aware that this is a naive resampling implementation for educational purposes only). \n",
-    "    \n",
-    "<details><summary>Hints</summary>\n",
-    "\n",
-    "* The result should still be a 3-D array with 3 elements in the first dimension.\n",
-    "\n",
-    "</details>   \n",
-    "    \n",
-    "</div>"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "with rasterio.open(\"./data/gent/raster/2020-09-17_Sentinel_2_L1C_True_color.tiff\") as src:\n",
-    "    tc_data = src.read()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(3, 64, 125)"
-      ]
-     },
-     "execution_count": 42,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tc_data[:, ::5, ::5].shape"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<div class=\"alert alert-success\">\n",
-    "\n",
-    "**EXERCISE**:\n",
-    "\n",
-    "Elements with the value `65535` do represent 'Not a number' (Nan) values. However, Numpy does not support Nan values for integer data, so we'll convert to float first as data type. After reading in the data set `./data/gent/raster/2020-09-17_Sentinel_2_L1C_B04_(Raw).tiff` (assign data to variable `b4_data`):\n",
-    "    \n",
-    "* Count the number of elements that are equal to `65535`\n",
-    "* Convert the data type to `float`, assign the result to  a new variable `b4_data_f`\n",
-    "* Assign Nan (`np.nan`) value to each of the elements of `b4_data_f` equal to `65535`\n",
-    "* Count the number of Nan values in the `b4_data_f` data\n",
-    "* Make a histogram of both the `b4_data` and `b4_data_f` data. Can you spot the difference?\n",
-    "    \n",
-    "    \n",
-    "<details><summary>Hints</summary>\n",
-    "\n",
-    "* `np.nan` represents _Not a Number (Nan)_ in Numpy. You can assign an element to it, e.g. `dummy[2] = np.nan`\n",
-    "* `np.sum` will by default sum all of the elements of the input array and can also count boolean values (True = 1 and False = 0), resulting from a conditional expression. \n",
-    "* To test if a value is a nan, Numpy provides `np.isnan(...)` which results in an element-wise check returning boolean values.\n",
-    "* Check the help of the `plt.hist` command to find out more about the `bins` and the `log` arguments.\n",
-    "\n",
-    "</details>    \n",
-    "    \n",
-    "   \n",
-    "</div>"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "with rasterio.open(\"./data/gent/raster/2020-09-17_Sentinel_2_L1C_B04.tiff\") as src:\n",
-    "    b4_data = src.read()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 44,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "30"
-      ]
-     },
-     "execution_count": 44,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.sum(b4_data == 65535)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 45,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "b4_data_f = b4_data.astype(float)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 46,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "b4_data_f[b4_data == 65535] = np.nan"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 47,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "30"
-      ]
-     },
-     "execution_count": 47,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.sum(np.isnan(b4_data_f))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 48,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig, (ax0, ax1) = plt.subplots(1, 2, sharey=True)\n",
-    "ax0.hist(b4_data.flatten(), bins=30, log=True);\n",
-    "ax1.hist(b4_data_f.flatten(), bins=30, log=True);"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Reductions, element-wise calculations and broadcasting"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Up until now, we worked with the 16bit integer values. For specific applications we might want to rescale this data. A (fake) example is the linear transformation to the range 0-1 after log conversion of the data. To do so, we need to calculate _for each element_ in the original $b$ array the following:"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "$$x_i= \\log(b_i)$$\n",
-    "$$z_i=\\frac{x_i-\\min(x)}{\\max(x)-\\min(x)}$$"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "__1. reductions__\n",
-    "\n",
-    "As part of it, we need the minimum `min(x)` and the maximum `max(x)` of the array. These __reductions__ (aggregations) are provided by Numpy and can be applied along one or more of the data dimensions, called the __axis__:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 49,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[1, 2, 3],\n",
-       "       [4, 5, 6],\n",
-       "       [7, 8, 9]])"
-      ]
-     },
-     "execution_count": 49,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "dummy = np.arange(1, 10).reshape(3, 3)\n",
-    "dummy"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 50,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(1, array([1, 2, 3]), array([1, 4, 7]))"
-      ]
-     },
-     "execution_count": 50,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.min(dummy), np.min(dummy, axis=0), np.min(dummy, axis=1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 51,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "((2, 3, 4),\n",
-       " array([[[ 1,  2,  3,  4],\n",
-       "         [ 5,  6,  7,  8],\n",
-       "         [ 9, 10, 11, 12]],\n",
-       " \n",
-       "        [[13, 14, 15, 16],\n",
-       "         [17, 18, 19, 20],\n",
-       "         [21, 22, 23, 24]]]))"
-      ]
-     },
-     "execution_count": 51,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "dummy = np.arange(1, 25).reshape(2, 3, 4)\n",
-    "dummy.shape, dummy"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 52,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(1,\n",
-       " array([[ 1,  2,  3,  4],\n",
-       "        [ 5,  6,  7,  8],\n",
-       "        [ 9, 10, 11, 12]]),\n",
-       " array([1, 2, 3, 4]),\n",
-       " array([1, 5, 9]))"
-      ]
-     },
-     "execution_count": 52,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.min(dummy), np.min(dummy, axis=0), np.min(dummy, axis=(0, 1)), np.min(dummy, axis=(0, 2))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "In some applications, the usage of the `keepdims=True` is useful to keep the number of dimensions after reduction:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 53,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[[1],\n",
-       "        [5],\n",
-       "        [9]]])"
-      ]
-     },
-     "execution_count": 53,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.min(dummy, axis=(0, 2), keepdims=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "When working with Nan values, the result will be Nan as well:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 54,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "nan"
-      ]
-     },
-     "execution_count": 54,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.min(np.array([1., 2., np.nan]))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Use the `nanmin`, `nan...` version of the function instead, if available:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 55,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "1.0"
-      ]
-     },
-     "execution_count": 55,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.nanmin(np.array([1., 2., np.nan]))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "__2. Element-wise__"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The __for each element__ is crucial for Numpy. The typical answer in programming would be a `for`-loop, but Numpy is optimized to do these calculations __element-wise__ (i.e. for all elements together):"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 56,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([1, 2, 3, 4, 5, 6, 7, 8, 9])"
-      ]
-     },
-     "execution_count": 56,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "dummy = np.arange(1, 10)\n",
-    "dummy"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 57,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([10, 20, 30, 40, 50, 60, 70, 80, 90])"
-      ]
-     },
-     "execution_count": 57,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "dummy*10"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Instead of:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 58,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[20, 40, 60, 80, 100, 120, 140, 160, 180]"
-      ]
-     },
-     "execution_count": 58,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "[el*20 for el in dummy]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Numpy provides most of the familiar arithmetic operators to apply on an element-by-element basis:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 59,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(array([2.71828183e+00, 7.38905610e+00, 2.00855369e+01, 5.45981500e+01,\n",
-       "        1.48413159e+02, 4.03428793e+02, 1.09663316e+03, 2.98095799e+03,\n",
-       "        8.10308393e+03]),\n",
-       " array([ 0.84147098,  0.90929743,  0.14112001, -0.7568025 , -0.95892427,\n",
-       "        -0.2794155 ,  0.6569866 ,  0.98935825,  0.41211849]),\n",
-       " array([ 1,  4,  9, 16, 25, 36, 49, 64, 81]),\n",
-       " array([0.        , 0.69314718, 1.09861229, 1.38629436, 1.60943791,\n",
-       "        1.79175947, 1.94591015, 2.07944154, 2.19722458]))"
-      ]
-     },
-     "execution_count": 59,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.exp(dummy), np.sin(dummy), dummy**2, np.log(dummy)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "For some function, you can either use the `np.min(my_array)` or the `my_array.min()` approach:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 60,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 60,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "dummy.min() == np.min(dummy)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "__3. Broadcasting__"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "When we combine arrays with different shapes during arithmetic operations, Numpy applies a set of __broadcoasting__ rules and the smaller array is _broadcast_ across the larger array so that they have compatible shapes. An important consequence for out application is:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 61,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(array([5., 6., 7.]), array([5., 6., 7.]), array([5., 6., 7.]))"
-      ]
-     },
-     "execution_count": 61,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.array([1, 2, 3]) + 4. , np.array([1, 2, 3]) + np.array([4.]), np.array([1, 2, 3]) + np.array([4., 4., 4.])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The smallest array is broadcasted to make both compatible. It starts with the trailing (i.e. rightmost) dimensions. Exploring all the rules are out of scope in this lesson and are well explained in the [boradcasting Numpy documentation](https://numpy.org/devdocs/user/basics.broadcasting.html#general-broadcasting-rules). "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "__Back to our function__"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "By combining these three elements, we know enough to translate our conversion into Numpy code on the example data set:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 62,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "with rasterio.open(\"./data/gent/raster/2020-09-17_Sentinel_2_L1C_B04.tiff\") as src:\n",
-    "    b4_data = src.read()\n",
-    "b4_data = b4_data.squeeze().astype(float)    # squeeze and convert to float\n",
-    "b4_data[b4_data == 0.0] = 0.00001  # to overcome zero-division error"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Take the log of al the values __element-wise__:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 63,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "b4_data_log = np.log(b4_data)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Get the min and max __reductions__:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 64,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "b4_min, b4_max = b4_data_log.min(), b4_data_log.max()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "__Broadcast__ our single value `b4_min` and `b4_max` to all elements of `b4_data_log`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 65,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "b4_rescaled = ((b4_data_log - b4_min)/(b4_max - b4_min))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 66,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.hist(b4_rescaled.flatten(), bins=100);"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "__Remark 1:__ One-dimensional linear interpolation towards a new value range can be calculated using the `np.interp` function as well. For the range 0 -> 1: \n",
-    "\n",
-    "```\n",
-    "np.interp(b4_data, (b4_data.min(), b4_data.max()), (0, 1))\n",
-    "```"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "__Remark 2: Why not iterate over the values of a list?__"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let's use the  rescaling example to compare the calculation with Numpy versus a list comprehension (for-loop in Python):"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 67,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "b4_min, b4_max = b4_data.min(), b4_data.max()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "With Numpy:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 68,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CPU times: user 465 µs, sys: 118 µs, total: 583 µs\n",
-      "Wall time: 383 µs\n"
-     ]
-    }
-   ],
-   "source": [
-    "%%time\n",
-    "rescaled_values_1 = ((b4_data - b4_min)/(b4_max - b4_min))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Using a list with a for loop:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 69,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "b4_as_list = b4_data.flatten().tolist()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 70,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CPU times: user 52.8 ms, sys: 4.04 ms, total: 56.8 ms\n",
-      "Wall time: 56.1 ms\n"
-     ]
-    }
-   ],
-   "source": [
-    "%%time\n",
-    "rescaled_values_2 = [((data_point - b4_min)/(b4_max - b4_min)) for data_point in b4_as_list]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 71,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 71,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.allclose(rescaled_values_1.flatten(), rescaled_values_2)  # np.allclose also works element wise"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<div class=\"alert alert-info\" style=\"font-size:120%\">\n",
-    "\n",
-    "**REMEMBER**: <br>\n",
-    "\n",
-    "The combination of element-wise calculations, efficient reductions and broadcasting provides Numpy a lot of power. In general, it is a good advice to __avoid for loops__ when working with Numpy arrays.\n",
-    "\n",
-    "</div>"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Let's practice!"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<div class=\"alert alert-success\">\n",
-    "\n",
-    "**EXERCISE**:\n",
-    "\n",
-    "The data set `./data/herstappe/raster/2020-09-17_Sentinel_2_L1C_True_color.tiff` (assign to variable `herstappe_data`) contains 3 bands. The `imshow` function of Matplotlib can plot 3-D (RGB) data sets, but when running `plt.imshow(herstappe_data)`, we got the following error:\n",
-    "    \n",
-    "    ```\n",
-    "    ...\n",
-    "    TypeError: Invalid shape (3, 227, 447) for image data\n",
-    "    ```\n",
-    "\n",
-    "- Check in the help op `plt.imshow` why the `herstappe_data` can not be plot as such\n",
-    "- Adjust the data to fix the behavior of `plt.imshow(herstappe_data)`\n",
-    "    \n",
-    "Next, plot a greyscale version of the data as well. Instead of using a custom function just rely on the sum of the 3 bands as a proxy.\n",
-    "    \n",
-    "<details><summary>Hints</summary>\n",
-    "\n",
-    "* In a Jupyter Notebook, us the SHIFT-TAB combination when the cursor is on the `imshow` function or type in a new cell `?plt.imshow` to see the documentation of a function.\n",
-    "* Add the extent to see the coordinates in the axis labels.\n",
-    "* A greyscale image requires a greyscale `cmap`, checkt he available names in [the documentation online](https://matplotlib.org/tutorials/colors/colormaps.html)\n",
-    "\n",
-    "</details>    \n",
-    "    \n",
-    "</div>"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 72,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "with rasterio.open(\"./data/herstappe/raster/2020-09-17_Sentinel_2_L1C_True_color.tiff\") as src:\n",
-    "    herstappe_data = src.read()\n",
-    "    herstappe_extent = plotting_extent(src)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 73,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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Em3Y2xFkYRbXOnWPKBX+2l+b9173TjOU58GR/OhHmlDk52AYheDNlzShdnuImRc2bT6UBCNtrRrZYSEAc1dkzoHFcltVetin8fGMvEFhmAwu5An/vHaLZxu/OZrasjgg19G2AqVS9tYvmUJ3DyOc5W0rhlOyMUsffO+Gmj9wRAeVXLyO4GgJsfqXqeo1tDGRNZMM1qASK5Bp+t7WMYqH/53GxUDGKc44+BLouEqMwzYlSjMm1vagcpxkVx1KNXxe8McfBM+VUx+QazEAQHo4zOGOXe++53XR8vumJ3q1z0yICXswZvgz7OjWWLTpHrsw/KOLc+txSwbarjJWLjjLni5BWczoai3ih9T/aczXU31ZQz2Goy72uGNM+V8DWLrSmlSDVgW82ynRaKoXTnHlaEi8pWVj+4qi2cTmMKR5E2NYoRAvDpQpOz4wWF2ysjd1jjFjvHIN3674sFR22EH+pYLOdETDgOc+Z3dCxSGAusIiQvScvyYiAyiwG79gPPad5YYjmLIYaftX8zwhAMtqx5iqYpmMuEASUQlRvzFB0OOeZS2VwUKOyK1pqm6otXnCOIcJhOi+e6FmVPY4Lm70/L7QaKo7OkfVsCAHQslKZkypd8Ahu3Vmuet7jYkan8w60UJxjrvcMcnYItCF8LjwYbXD/vOGlXbx9D/NyAsZMnbTQKNmWV4AIL9OMBmNgjrMh8955NhXYtYMwZwNJVCAaO28GMRdcdAbMSqkegqx5D/Wf3Awd2y4Y5S7GDEURvtj2fLUfcA5EHFNW3h4n3h1G3h9njjWMUG0fXTCVVnINx9T7NEBrDJwddlfDBC1PRLF16b03bqIeuqJmfHIxwJeKrmuci7GVNm4DHb7qu3bg3YWXJLCCxurgWdg1K6eS2cRC7x2DNw82F+XhmJhzYQgXuTQKD6cZ5wWpIb01lo6w920/GgOzFBiTkjRDXePgHJ13xN7xNC/cLR1vdj23m57uZeIP3z5xv+soORvgo4YZsVCMlwDLAinX+ctMp8ymC0ioilUEp8pUCnMqLDkbi1spcgWKtjyUM/hXtZDtpcccqpJugBxM+XYidOLQyvKIdyxFOS0T09Lxi+cjpzmvDvQQPV9ut7zME8/TDBREtQIyC6uaMa26oYL6IA7nyxpGGYJnTrYnXbBcrqRmoC304aoxt73V8n8AnseEDDaHTqSG841Npu6t5jQtqtx0EQUOqeaRNXVSLYp3noDj+TTx7cuCE+hdoPe2J7MqP7nZ8PXTiaWc53QF3A3ItiOpldHJSvBn586rgekuWJgrVHbFAGJZDdghJZ6XhawGarfR03tPFGPafrTf8PXziWwKe9XfqDGcfbD0Au8cWjIlF252O5Z0ICfwYo5ZLrCQmLIwTRkXJqL3BCfc3wyM88xpnsmqBCd4LOQyLpnojWlqDrVihj9pYUrZ0hu85RY5TI/76Nh3gVd9x5e7gVBVt5dzDpJzxnAaI2gL3lhIEdgGz7RkzA2oBr46CA0gqSqawQ8OXWoOllyEoNBVT1/4D5zjCRfglpaXY3brbLma7k4UacBY6+eBYmfYq6s5WzWHzXmO48LDNHPMiaTn/FxVY+htH5ht2Ytj6wLhQglO9Qy3HCIuxuoaGqx6t3Pe2FtnIchFz+F7X50/rY/jnSPU8+9wjMvCm5uOlI3gcDGwlMK8jDhnLH1R6HygCwFRiDGiao6Mr3v+t8mnBZCcWAwxFzIt4bCiTZGqrDJSBE/mT94+IBLRkrnfdnxxuzXAUUzJlXrgTTnCpos1NJbhjME41nyb80B0pdWjcywNyGAb6I/fP/Lt08hPv7zHO3jO9v3oheA6Ho5HorOY65pvowVVYRE7yK5qNdFiHnX7OYAYlyIqeLmwMOIssRHqjrJrRxE68XwoheJs840p8/g0osHVpEdjvJZxYRcVcYHF2fNtcOBh0YJb7LDveovJT0vBbxyiMJ8Kc/VkpM4T4vAod5ueTQwrpatq4MVV1kKLIE7pvfDDmy2fbXvenWa+q7lKhzmBCGPKGI/FGiotLeB/oTJWZQZoYwPrmi1LIgOphjS8KKhDnSPQGDNb0y54YjDlZVJPq9RQRN0DJcE8Z7TAPGX6TTCF6QykO6ghOvNQgxdqRhlOHJ2zEN6UC2POLKnw9cvEl68GkihOoXemLO6Cx4uBmaVYrtOUSk3GFnrvmIvlGnjXQig1P05h8J4f3+/4+cOL5RhtHOOSWCwewOkwM6YFL2nNt2nsXSnK08vIZtfjO/OUJSsvp4WixYyQGsMg1dutcTUs74YVsDawbKCv7stSalj6nBicUZKDvgv00ePEEZ0p2p8/nThMeQXvN13kr9/fghb2XeRlni3MJVJDtcJSsoGgugLeCUsyAytq/FUXhNe7Hg+8PZx4s9/yMidextn0hji8q6yANJ/eQEes93oaF6ZUeL3pDHwJlU3SaqaMdV0q0L/pI8E7Y/dK5XeasyNmSF6mk7FLwTFjCcxOoPemp949jYZYWkyoKPOciNGvcxRx9D4wp0TNCsNpBah4plLYRb8azWpZV5Ps1Ni/MWe88+w7x+A8t100PZIyU858tu15GCfmVCwdAhDnVmdjqU6bF8uTWpZEzIVOhGNWivf0faBTeNUJLxGWAjkVAoXvHt4TQ7BcPO/IqpySEn3AiTKnmqCLMX+nnOp6V8eh5RSpsFDY9oF9F3mz7bkfIiJ2tpuD4isAEgHxDi01b67qHnOGHD++H4z9pbLaxdhYtTiv6VsFF2p+ajCPy3EuSjGmzK/J1aZPmwqqKEVBpe3Bdq7krKOqBcuInc01NCzmwKk5qoEzo1OKcpoXfnE4kbQB28a0GqDS9cqwxbEXywsrpfA4LjxOM10X+PJ+S5lT1fVN17oahrM9sQ0W9i/FwmVTTsbuN6cfYc4ZBItOYARF9A7Bcb8fSFl5GU8sJbP1PS9T4s3tls/iHdPpSFkWogu8mxYG7ziOI6VA8IFhGBg2W36bfFoASVv1j0dLZi52ALzzFn+uClkqy+JEGFSJeBgz7+Zn7vqOm5ueDLxfMsE7bruISmE/GF0bHWRn1RFZCy1E+f0g16KsFUIiwpSVX3448O3TyJv9YKGnllRUBO8Cf/D1dys78+yEJQg/utmuB6QgLFV9eoxVoQHzxnaIuTyiBjS0IW7ONO1qzxvyF+FWHF4cJ5SDVjBT86faoxWF5ykx5szt0OEwFB7FcSIxieIrXdZFT/COYx1v2MR1DHIxV514fv70wg+2G358s10pZOP9WEMXgqBOzeMpyqs+8mbbocApLfz+N0+c5sySc1VMlofUiUOc8KKFQ7aEXi1CERiyMRy+soKzQlZjhvxFxYiiaCrV01KGznOz6Tgt2bwyOescL7D1gR/tNuucPZwmTnOyvCbvTCnXWfBiytu5TNFMlIhHUCk4H2zVneCB45j49fPIuGQ2fSAL7INnEFsDJ8a8fH2camKzPctcCtOSWNRCDRVCk1LGBwNrSSyfxedCFxz/yo9e87OHZ7wPbHzEp8R8GplSm5tKbbc1dRaqmCJM80wabd+93m1AqCyfeRa+Ml5JFSms3jHVW4vO0TkD1qpKwkKKNNDkHLsuctNFM9ReaiK4GZXghEhN5kYZguP10PO662nhqU6E3gVysbB8Vz1Py5+qeXxV+392uzUmrYpWd/luG9ntB+aS8UuqTGM1YLQE3bMBs9wp0wce0w8fTjP9dsMmSl2z6q1TWYuakJHVQtFvNo6nZeEwLzVNQJnnhaXM3G069kPkT5+OFiJxdq9c4I8fDrwfZ0LwxqL7ZhiFcZ4pGfoustsOfPVmT3z2fHg5mrETyyPzTlimjOzkbGfXdTMjd8qZsbLFwSmDC+xiXFmSORc+TDMFpVPhtuvwwZPFcjo36nh7PPKT+ztexcAhFd4fZnpX2PrIc3EIhc1mw81+x3e//pr0+p5lKaSSGWKgD5Fv3n1nrIC08KPtjc4JfXSk3Nj9wpKVXXQ8zwtjKhxz5r7veNP3xt4A90PHF7sNQ61+js7buazXbzagoLCUGsZveWNV0QLb4PiXPr/l5x8OfP18Mh0rLReW9c/G1DqxcJ2x6FpPbtXkWh2MKk4cTrSC7JZqYryQuJpiUPfinAtfPz4zp8zNNtB5q4p0rtoSCoLppua4LqXwi+PBKgeFdZ/6C7BeFG6c59Z5TktmKoXg4JvTyPOSLDTsQErVgWLg26pDC3ldJ7NHgj37nLMViPhzCDwV5XGceDpMljzuPSJWdORdISLM5YQLQk9kyYnH54nDOHO7G7kdBnwIvDsc+dXLiT565iWz7SMxOJYyMz4e+W3ySQEkBO53A0+nCZ2UoplkhGH9dWVxinnXRYX7vuNH3pGz8qSw2ZlnkLPy4TgypswmBIYuchMDb+52lGSe4uM4k1JhLNkYiQuyBjXl7+u/n6bCdy8zbw8jt5uez28HYq0gAcF7xx9/+4G+giMwb3PJyv/z/pld57kfeobg1qqksRjdv3G+GoKPy5PPJa9r7VAtIa8zorKyGKbQHQvwOM68fT6hLTvZZo2VH0WtKisWxGcihYBjg6eETM6yekEAacz43hI52zqthK4qz+NM1sLPXw786nDEI3y53/Cm7xmiIzjz5TPmZa1lqI2G9o5d8PyNL+845cKHKfFhXHgcZ57GZGFKWEvpz0odJkr9uc3RbhNXKlzEaNaXKdWKOANr99uBECzhtjEwqkrnvBntaKENqeWoDuVmiGTg5TQb45Esv4x6n5KVEIS7rrOKjLaerLoJRHi16wkx8POHAzllVJWAhQ5KDWUdZ1P4BV0TL1MulUVSnLfqv1al5VCywHenCS/GHixJebMd+JMPB1Qd5IykgniHKwlRV9lVyxHACYtTjk5YlrSGJgV4dzhy23WoKNOyWKsIqRWl9cC0qrYoQqh5JQqklbGzKk5R2MfAJgZi8HgvlihfoBNT6kVrCBAFB797f0snrrJRVGbYGNX9EMlj4bSkClTzyiyKtKNU+NXLkWMX+HKzIbqWqyiELvBymkBg03uGzoPC42lhybm2etB1P5mzZhOTKzCPzpLIlUjfFAbnikQPzGuCthn110NHUHDOcxgXnPd0Q8c4HRmL8u//zZ/yB18/8It3z4xTYhc8754mXHCWhF31ixZhPC2oaq2kLWgtiLgdtuR5JtfwaqkMgnjHz96/8MXtll0f19BV3aIcUqql/cZOe7HQt4WnM3NJnHImLRUwLYmb4NiFQPawlEQaHW8PlQ3D4V1H5wrvTgu70DEMkaUs/PK7bykOpg/PdF1PWkY+jDMuREKsLI847vc7NkO/7vs0nyhFePdyNKfAOV6WxJQysZ7px2nhOGduoudu27PrI95bbouxlK1qWM5qEnNkcZXVLy0/RldHWcTReeV+03FKyrvnsYIXkHy+JgqSjOn9bD8wRE/TDB+19rh0eNG1aKCaIdO0BdTZmdVildkPx5FfPBxQlG+ejLl+s+/5wStzaERlLSzKKow58d1pYlHLqSzt+tWpdFW57hBCMV2kBdQrf/J0ZIiON5u+tp9QvMKYMvgLwFcBXueFXQx03ioup1p+3zljo8acGJdMyZn7EFhisgpLsTGXopymRA4WHlzmTHCOV/sdw71ns9nyJ19/R7mBV9sNMUTm9Ew/eERhTAuneaEU08u/TT4pgKRq8e3Oe2bn14oIEEpNNLvA9AC8vtuzU+U0zkjK1WAZG5VLYapK7jAvPKgwdJFNrImKzll+TRayyrp5m6Lwakr/Zcm8P048jgvRO97s+7ViJTgL/T0crIx3V2F5cjCjzBVo3MRAEGMDUjUfLRZ9qiGBKFZi3KhyO2H2rGfMbTOw4qj2MYAgPBxGPpwmY9xsViudufK39TLCaU5MS8E5yzvaBE+IlqxenIGGkpW8ZHwX17tzsQK+hj6hJthVN+TXhxNP08Ku5i7sYuDNtre1rMnTpuTNKAbvGKJYJVbneb2JHOaOP3p/pCx5LUuX+uCKroC2oOc1kwsaWwx8bTuPquX0pKL00ar1shbLPcCAzSZ6OuetcqzG1qXe5DAmvn08IU5IqVBqdU9TbIIQGrDics1anN6euTF2m+h5nBOnQ+LuJrD4wlxz21Ix5TXXULNWFsYeXVdlLk2LYjlVYyp8e9SyJFIAACAASURBVBwZc+FuMzC9nBhEKDlbcrKzMnpxzoCpEyR6irP2DQsGxFpVZ9stc8pob/O6iZHFOca0oBhAaO0UWg6hCvRilT7NtQktUTPY+Y7eWEG9MEwrv9P2vAib6PEuVGPGGup+mS28VaiVqKpITXjXcq7AaTeYS+HDvCAqvOoj2+hRhMdpJqmFC1uYCBVuN4H3z3mdg5W5hpoTZuXJQWriaYHDlPAbX8N7LeFXOc0GbNs9Wp7Zq02POI8jULCeWZ5A9J5/9CdveZpmKMrGO77YbPjuw2hMhxpIbFFnS7Y/V12VrIzLwm2/pfpvlyQFm+g5Fj2X7Bes0grhftvxkhYWLUQvbHyoDITNxFwrGItCzmXN42uA4DAaILvznhg6lpJJKTPPE3fdwE6s/cWoxaonS2YIjjkpm+iJfsuSs7H3AkOIRO9JOTOOMzlGYhBy9kR3Tr1IpYZhMZYl1gRrwYpCpucTX+0HvNE4lVH7GKSsO1Gqhrlg2Bqo0cqWGtNs+9pVlq+VHrectBgc3gv7aGG9XTy3Hmj/X3V3Pc9C27dnOwTGiNp4bO89jTPfPk8sxXpitH3/MiceDjN3m2gOyQrTzRHrvWMq2SoLW+iwWQeFnTiGGnIsBZIo708TfXBsQ6iVa3YiHg4TT3NCHAxddXpQNjWZG3G1utSeyTkY55klWUsLLRaedMEKR1LONYxZWdeS6MUzLok+mEM1LguqhW4ZAeF5Glk0oUXp+sAQOyjm3Kgztspd7P3vy6cFkOr/YvBWjZCSJTTXA95AQnteVz2653nmUIqFrlbkXdkmpfaGsLyPflnYdmFthufEEnRPS66HpiW/CrE2cXs4zZyWTBccQ3RseyvlHpdCqsmrz+NCwaqk1MEiFqIrqtz3HdtoPlpSXasAFMubKWrGIVPIxdHXg4NcHpKLRG5t83A+1M45npbE87Qwpbx6A6x3kkqhaqV1LUcHzfX7kKJyW9srxJooLjWXqixKEsUHt7YgUFVibRSz8lN16MclMeXCcXH03nGMthWjc7Uxm12joJQETgKlhn165+k6x8Z7xhvl4TgSnKuJ59BUilQPr83EOaqta567q95vqzSaa8WDAKLBGjkq3EajqF1LzFS4mF6mlHkeZ3ww/tqtPbBYc8iGGEjJetME1wzqWq+CVkZoyYWSlde7jiF4TrPlTOW2TGqhm6UUtFEheuHtVu3pXHta2+gqUpO5C3FeoCTLI6tJ2JU2gWB7zarFaq5T3ZelNDBm8+Ww/I+UM733DD4yZUcqGV8BX0s29chaDRSchUGjg05sPgbnrKjBWVm1tq2srQHiWVGbJ2mgai4Fj/WFKqVwWjKHeWHMlmO4aFnDCH4NWcuqI5wzZTuXwuM8r2xeFxzvjhOgDEMgICsI77yNde1dtO55YwSAFRy2czZnZVysd5Kv+1yL6R9VqTUWtYWJCLu+szw5TVZ5pULvhJwLv3j7jHhb2yCyVhnhzqxGc47i2nxJ1r5lpQBSHcwGqKnnHzP2VqlWSItVwA0xUI7K6EotXKj7uF5iKYVjyhxTIWdjVfxa6eWYU+EwLmz7yN12yyxAcBSEsSzMKQOu9royxzU4x7YLzGk2IE8N11CM8VFhux14OZx4GRd8mOm9JbSHUHuKwVqN6pyrOZIGQotqTZVIdH675qatZfdV/gxW0o9/13SCVMa6VMYuVCaoVaJ6Z8n/m+jXoozoPfsuVserwYWzKrv8U9pZrjeW9meloudUeDzOvH0ZeRpnG58DVxPxx5x5OMx4Ee623ZpELVge7j4EHmdzbtq+cCi92HpvK1Nbip2rw5J4mROf7/oKjup3BB5OC2Mpa3pCdAYGGzgtRcklo3puFTHOy1qNmrNySpkFy9NrklUtqT94em/pA67amTHNlFwoJSFBmEu2kLEIm75jXvLa/qfvIpsuMp8Sv00+KYAEdvC7EBiiNRHTCnpa1vtlLo7gOMyJl5cTU0p0nbeeHB8pXFmTclsZ4WFZ1vt1ztE7z3Ol2V1N8Nt3kdg7Y49OC130vNrGtbQ6iOdlnngcZ+ZUrLuuKkeUroYYEAvbvB5iLW020Y/+bqnZSZVZlUmUPsSVhfg4tl17v+hFcmW9yqzK+8PEkpLNgVhuTpGmFi9uXDXeZQ+mUqyrc4yOobMeLxEhBqFs4eHDxOwKnQ9Qqd4GiJwz474auPX6MGXznuelcJwWtjFyv7EGY8Ebtb2UQhc8qZyhhD2W8NXNsLIEZzbGgEKmJcA3YMQKjBqLaFNhRrz2sDO2ynluK5vRKhoFLGxWr5PUaOT2nL72a+k7Txd8zUWxSo+kVqX2OC683nbEwFrRcl5pWLJymhNLSvzu53d4gZ99OHFc7JCHYAp+WvK6/q1IsiU2N3KweaGlhoKD92w7C5kcppHowTsPYn14stbcOycsrbt3yhfKUlZwZJ6ceeK7zjOnQhcd2+AIoozBspDWBqDUYgFnncPBFPfGe2JljEIFd02htnVp7R88XLBjZkZKtr0dMDYmpczDOFmoyGE5eqzDX7uIN3DnxVlew2INMGeUD9NCUbjfdnwYZ3IpvHZiDKY7V9bs+8DLbNWoigGTVDs/97XXVfP2S3U8jnPBiRKDUmJtdFv0ohJS8BjwCC6wTBPzPHGYZmM0hs6qTVupK6azvj2NBuy9WMPKCmRzgU101jBS636rFaOHcbRc33r4CzWfLbU1tr0zV4N113uWVNacEo/txT4Y6DzkzDFnxpSZ6nz2LuDFg8qamHuzCcR+y3Q82rrXPTnOC4hnzrr29PLOmrOmknkZxxXUiAh3NxsOh5EQIrMeeZpG8giDC+yDo/haTIPlhbm6p3JNgM+tYtXZXlwLBhoKv3Co2p5vbG+rjkVMv4jYHi1tb4olEg/RM4SAizUnNlj7gJsuEuvZd37tanTWX3UI53uuyhk9c+IXY7QikA/HmV8/nng6zWsvKBertym2V56mBe8dN9vIZc8tULbeETBw3MQJ7DE7KM4xpcKcC2NKvCyLgb5a2rsyXyinWgQlWLVzyWa7nVjKyTRlK8PPZR3fVHvUocJcCu9PMy7b+PvmbBcrFLgNnkUc97uB45Q4zrP1FkyFce3/Y0rDeh91fPvwRPAGum+Hnle7DQ964rfJJwWQBCGEYEnaCqcQmNJCFEdylhNkn2uOsJCbF1wKqdhyZTXKcRc9Wgqz1CS8ouyGSBCLiY4pM2vB5TOCbazM+6P1CVEf2G97hmB9bXCm5L87jLw/TaYUKjDI2Jma6+bbBM9nu4GG4VuYxNZV1hyPVHsANQp1LJnN2r24jersdaweDazX/P1v3nPfRbbimFzhoIWNEyZVUvssZz633ashr+a5vJyMCdNQq4nEGLUpZUop5N6jzsI1+xD55vm4Vgo1cXiy5LXaTbHGkmNKvD/NfP0sfLXfcLfpLC7vTAFdVudBxVliSik6WZWXVqVi81NWoNhUjDprSdAo9Obkq6E4olSWDs55RHVOKkrikAofloUowj5arszdJrIU5c3Q8WaI/Opl4pBqwFSt1P/DOLPrAxv8WpGh9eKKAamMcrftjWFQ4a++2vP+NPP2OHGYlgqKq6dZjZllBzdDa3lcqeZelKLcDNagc0mJl3FiKpnXQ6T3QhYD3gs1xNvK81cl2TxU21wRoQ+BPjjz1h1IZwxWqhVe9/3AabFEXeuOrWsPsM45TqXQdfFcyYasoV6tValtqVtTzK0PHEvmlDN5UYJ43h8WXt3e0vWBaR45JgtHq1s5y2rcHEqxRnnu4hUnAlOpr6qo91yK1uZ4M2PtxP/N44mbTeRu6NjXTse7GPACh9nYUFX7buesz5ev4KhVM4nYmFElp8JUk72t3YgxLn3o2G17TseZ8WTs1avdwKYPZpDmhVQKvfoVLLY2Ep/vB0YtzDEzzZl5stfUTDkzILbvMSN5SolfffvAD242taOxhYYPFRzebPt13KDE4IgS+Nd/5zX/93cPjHNG61sM7vcDBWOPjK2zHjq7GCjFmuJ6hJuh44f3O06l8M37B/b7LeogiHLbDUxhIAzCy3EhTULKVmjxcprJRcAX+tjji5LyQl4yr4een33zDhXLT0kpEzqYNNdScKkYQC00Xc/7nMs5LaEosTZubLq6wSIn52KLphhFHK7q5LMTKpwTrJ2ttVde7Xu+uNtSaiFFC58KCrVyueX3fESAq1IbiFxAIIXWs4hLJtBQ7h+/feGb5+MFg2wsjSZzUqWGb1WUx3mpUQlzFM8NX+FHux2/PBxJJSNYkU30njFlvCi/ej5xTIkheH6w3xLF9tiujwgWsnx/WtZnCc5xEwOb4HlYFv65V3eMp/l8OsVs3/txJgGfDz3jki3XDcWNmdg5JNjnbzcbvrq94R/8/Nf0fiJ2HQ5H9BHvAuOcUG96J6ow+MgmdGiBvje9RVaWObEMmRh/Owz6pACSE6wSpFivk1f7gffPBS/UXJ626cAVRyLz7du31LZjSM1ZyrW1/A+2A2XTU2rvj//rm/fkArdDR/S12Vox1gdqSKaOY1Zlnmdcmdj2PaHrwTmmlIje8+3L8dwbo4KfII7LYP+YMr9+PvF6a9U6zc4J1svDAzOFtbGdGIV/zNagbeeDVTa1Ayct/8pVhqb2r3DeGl1iByJhnrgT2Doryx5r7xrE0Qo515RnPXv0WuDlsDDHQu4C++itGql22x0fZ7ptpNtGxpSgsVkVcIi0PCBnicyYxys2WYT6ua+fTnx7GInBsYmBH91suB36NWnYizXDnBpylNal1YySVhesAcZVyUjNa6ke9ThlHg+TKfZKB2/uA5ve1Q7Iav1CXMstsZywKWe+6Dt2wQj/HKx3SilKF+w1Ca+HiEz26ore155M3mjuKXm8c5YUKlKZFse2j/SdJRKKnn//etshAn/6kGofEllzIIpTiA5NBZyxNLko86Tse8eb2y0dC6dpZK6VmUa3K1uvbOo7rUrKvIxzbYOxEhRVmZsB+OF+wzZ2IEqp3eozMGVFKxPbe8sj6cPAy1Lwy2I7WO07I7Dpw9p/5gKerx23jd1p4DTgAjzMM3NRggh7H9h1PXOyVXp+eeRlslC3U8V7a4UQHXh1SCnMegaPiuWfFYHDvFBpHpY50/fW52lKFbgYXuP5tDAvmXnT8WrTAdahfNdFTkvm3XG0RHQfzJmrSedzztXBsXAmYuF5X2fXmDFj+UrKTKcFjyOXjPPmfDyOM1PJ3HSdvTYltdC3gaPHxxF30+NjICWtTRWruvGOU0p00eO8pwDTnPjqZtMcbDv7DnZDx8s089nNlpIykpWSYdd3xATfPs2Mc6ZV2S6p8I+/+cCUM10MZKxz/JIzyTmCD0zzzC70BCk8TxNDtyX2C08vB9Mb4nhMhUTiy+ELJGRrcEeydIZ+x70rjGXmNFqhwbbvrZXFkvjRZ/d89/hCShZ220UD3qNmxtw6KdfQo1N0gRCkVcoD8MV+u+65BklcUx4VQDSH1YnWUF1jDrX26rH2KS0n0IuFgBXrnTasPcUrkBL7XRFdewuZ2pLVKbzMbbObyVqhujp3asn1b1+O5hhU38AFhywK4VwpGTvHzT5WYCw467Vi+qQYeO5QfrLf8DgvUKwScU6JLngeR4va3PUdrwZzYFMufCiJp8kqaHO2BG7n4KvNwBA8TyXz67SwwUPtiI1SC1aUD3PifbHoRjdKzbO0Zr0lFzrv+Su3t+iSmeaZDx/e8y//lS9YivL1wzPTYnlFU04UzUgWIyscSPBshp48znx+u+cwjuy3A048b59O+HC2yd+XTwogGcKtGwqb3D4Gcs4Mzt4zYUxRwYlRga37b8P2pZw7Mte0BEBXz2FaEnNnBiM6a09v5fX2LZVK84vFrjsn/PBmIHjH05x4PM78zuevrJplbWFu4Qu3UqSNirQ47reHmcNc+GxrVHPBNodKreyhlUgD1G63mKGepVhyq6vv1SnVA1fWvjdfP1nlWNb63rqqFhoYa403reS6mCdfT2WLmwv2cklRAyjLUjhh77Tpvb0YeFzMKytj5jgr4qn0rq4UbvSem23PeJrrq0XOXlPzpp04Qqydk7FXdvzy+cTXH05kLXxxs+Gzfd802PpsFmSrWqzQENOqeISLCrkioIU5JV4m6+EjYrkvJRW0M08xZ+WbaWIfQ31vmK35V8NQm+jVvBzO5cBac7O2XWAshVNNLrSuvrpWqCzZ3g+mvtSwbFn70XSVIWw7d/CO+6HjebvwMM5WZVYVpylJQaIlV+dSjOHpOmJ0PBxe8MBd7GzvFPMaD3OiQ9iEgMsFTaW29m/esJ2P6Jx1SO47S2CvoYlUjHGa6mtFWqfhqRSOx8Q8Z242PdnbngnO09VzFuTjqsPWJ6VQE2TduUt4KoVjKuYtV0Zm0oJLCzDb62ecdUleCpUdhJwz9j5Uo/TvY8dxWqxXUQyIs9CNgVEhp0xZCtmVdY30orwd7FnfHSeKKj/cDbX1COyjY7jZ8HBKLElrj6rLDr1KEWXTD0ix1hFFdc1980XZ7nd0wcOSrDRdFZ+V53lhyoXo/Zr4TAV705yZ54zrHCUIk2bG2dpk+CCE4NFcKM7X9xoGumh5G98dTuz7rmGAZleJPvD6/g0/en3Dz775ln/8y284zQm3tWfcTEfKR9Vgqfb5MtYm50J01uk8q3VL3naR7dDxPC788v0z95vAsImIjyCOnBLHxXEYT7y+veHL1685jhPvH5+Ivefh8MLdds/gIsdp5sPLib4P/Is//Qlvv3nLzWZg0/dMKfH+5aW+azDQuQBklmQ5coeU2EbPSmhXL6AProLFcxrAnFLtYG8ApJSz/thvek61g/dKu7dXYtQu0OLDyuBmBF9ZDVZGquoPpaYl6BpWa2qsWot6zqXdZrUfjemfcrbCmbX2xiqC5WKvdFG420Y+32+IOFzxeGfpE60Sz1I0rMDlPgZ700MpdD7y9rDw3XHkzWZg352fLRWzGYvWaImHWBxfbnsGH3hMiT4GfqcbCEX58HS0XmTB8zDPvB+nteXMAjznVOdBuB06Xmq48OV4rF22hT70HJfEu8dnHp5PxnbWwiFxgiX7KnjHrAuPeuKz/Z53jy/cbbYohSUtKI40/zPSSVvVPBZqabM62G46no4jPp/fwG2btL5lWFc7CRhCdmuGsnx0/ZwVF6w9vTXz85DbKzDOdWLVGTYPpFrfzgl3XWCInu+eDvZix9qjCD2DEV2VLSvLA1YKPwRh14W1D0TRGmarN3YYC9UalirWhTfn2osC6zosApOaZ/wyzpYEWu/XrtVYnUVtLj/K3VJF5WyA20PLR/+y5NKXKdEiuOeqj9YGQAlW17x+y2G5T330a6ix5POcrMEjkdq91tiVWQuh2L+fjkvt+q1s+loJU7Tm0WjtL2Nv4facDXF7bhUbQzN97e3gVGarqDLmzKgWNtgHz66+ugbOPX3KBdJer782RYTOC0N9nUhWY1tisA7a4oTibL8hhtdaztIaFnLNQBsA64PnBzcb3p9m1J3Dqqqg2Ur0pRR2XU+M1lskl0TGignuOl2TpVsJ+kvKRGddqn1wDARA2caac8A536gU5bSUlSnK2oyGrDlB81I7aVdHJaZEF10FXdSUKRv3xYlaQWys+RhZC1N1REqNbapc5IKpknK2Zo0XjFAMyjjXF3LW8zLEwKYLhOCY5mQ6xAlTTpyWbN9PyjYGuqFjwfabdWDWy3xcO7N6fo1Ca79h6+24GyKP40JeWjNbW1SpntWcElLZos5bJdPrmx0/+fKeP/z2iceXE50oS8o8TnNl+qzDfQspW6NcK3W2qsaC763q7jTO5JpMpwXS0t7VpkxF2Q4D+2FgnCZu6sthO+9r41IDo0P0/PK796SUGLqe3/urPySlha8fnlhyQsWfMYGc9+xS3/XWB8+aAqLw5f2OGDzHyUq3VRdwkafDxGYQYoi2H1V59/TENM/8lS8+R0R4PB6Y88JUMnPOTIuFmG/3e4oof/SrbxkPL4jYew6jCF/c3lAkM/iO5zQRciGI5zTnjyqWGigEa4uh1RHWqhdyae08z+Fe+07hMM7nn7d1bmqkgU2xlz97KbUPUFnvpzS94SwMSWVMaCwV5zm+HK9c2JHqoMzZChNWx6alB6jpOnPaCje7nrtdT+/CGnbUi/loz9B6FzlxqFNKtgrYl3nhJ/c31oO0smRJlaeSK3BhzVGMXeBu0/EwJZ5y4j44hlrif1iW+tJjs5ubGJnmGcWcw+hqZZnYHG66gDXBt5fL4q3/3tPLSFqS6ZxSEG86THN9B6taQYOoQ4JjSgt935FUGbw1VX6ZpzUn8jfJJwaQlHFOKzgQL+xjz+NhrG8Fbm+Sb+9JM2lGpIVINt5CXZcVHI3V8WLGxGNNrC7fLL8q53b/+n97sadVJuy8vbB010XKpGtFy6pI9GNjjVpn2FKUD4eFlJVNbyWLWsft2vg4KyNfy7ntLeX2HFGE4qzj7mFJPI0z05yqUbTvrS8C5PxKg3KehBrSsBs735ikyiaJrOxbS7jOtYS6dZltygU1BZ7GbA2+gquVHHaf4BzRW4PPhJxZrRpCE865KeeXF9rhn1LmlMwj19qUcW6vt6gKA4FNcLURW41z1zUuUNkJat+o5qHZZxe16gmpn997T3QWCjgDzDMLd1ag+hHm9mKJhZvgeZoLfX19w9D5+ioSqe+gOoP6urz2zrtVQbb5t+aJwbnV+CrGWt12Eek8JRT2XUCc5dYc5rLm3KX62c47TrVW/1Qy+xLwwbF1lpTusKaKnvPztp5dLdyg1HylnD8C13MycHTuDWOvs4gVGPmqfNfE+Xp938Bz3UEJqX15akM7sVBaa0ZoDSxL7eHT3Ay7XgG0WB7cpgtsu0Af/Jqv5r2Q6pvCU0skLcq2fvaUM5qsZ440N/1iz4tYDsvTlLjrrGKqdf7tgrDpfQ2DtTYkZ/ifa1dgxZjRVknzxasdP3v3zLgkZgrjknieFxArBhCsZ5RbjXRtuZBbJZqyLJl80bVa6zw0zqL96UToRLnd9byMC6XU5qkVFG6CZ5pnfvn2A/e7DV/db/nxZ3csIkyprLl4xrhYQ8VS9W9jnIueQ/S7PjIticOUSNnSI8ZlYcqKzPbOLEviXXj5f7l7kyZJkiRL72MR0cXM3D0iMnKrpdEzNGhgiHDA/z8CNyy3AfXMYKinu6i7urJyicXdzUxVZWEcmEXNombqByTskBkZ6W6mpirCwvz48XvLSquFD8+TjatfFi5rJsRAGc2bELU1UVX59PlCa5kxJMbYCDEyDROSoh202ZTDhyS7ZMmXZbHdy4C1Kqvofn/V19atzXU7UTZHyXa45+4ld/FEBEIMjkJ1eZQbMtRXbufuRbn/mFs121fRXjB5TCxNWUvlx9dlv7Yvr8XeIIh5pc0p7pfb9fb2sCXcaCF3z7dhytsnNxu/boVzzlxro6Bc9SYm22NhFeW1Vj6XbC3dagn/wZMR+1yjuhxTZGmJ85qtcEQZkhnJbjWbrMA4ME7TPl35et1MPFKUYQxo9jZn74Ni5Pd9r2jlda0ETFbG7l1lLdlG///K61eVIIG1wHpmPkjgONgDN2frG78/iCtS301MlWoGi7O3Caz6BTAeTnRfnlyqeUClrgVhKfP9tFg/3FCbxOrwJKr85unA5dOFtYS9yu4VQW+V7YuvKmWzBsPnsrGUymNtPB0n65P78w6+SXdhVU8YemJIwxIGsbbBy7KxbuY3BLor/Gof4/J7xl+gK3BLhOKOUbBX6FqNvxLkpjtk3mO28Pth2bO7fMlGfjwMpDHu3kKqJvDWMPXq5p8TA7t7+K6p57o/WRpVrfUSAgSJTsa2QNGwQ3krbnjZkvFt+r1Tdt+qICDO30hJaOrfKXbdFOUQjWg+9Fu2EyTZ14L/x44G3sfR4lFjSJG2Fd5OyewkPHAqdvg5DcAJpPZmK0qiTwP6uvPq8TQOvOS8J+ljTHxzOpLGRKmVoxjR+jkrL3jLbUisqpxiZESsZYV9z6yNUSKHGMErzq2Y8OR9O7jX0q2vvVpZc6W1unMwerKJCHOw9yxViW4A21kYRkpVP7DtpnW00CbNghcGlshl17EpHe30pKFVpfV95QsviICPoZ8mT458bRaUcYxcHHkRwZ3qDflIIhyj4UKtWRIW/Pve34elVH68LARmpmgJ5RDtc+YxkmuXbNAbohB6Yi17q3saElvOfHy9GgcmCM/rxnk1g9B79Eq17QhnbY3axUixdsrqLaEUw36vJIAWiIMw+hRQrZVDCjxNicch8XnNvKymedZj5zBGPp9X1i0zpcDf/f57vtsq//VPv1CKtdRqMH86a7V0jTnZc4a+XnKprKWy+YaYh8TnZSOFZP597om1VBtoQOCXl2fW0shFySVzmkwhPQWzxbmeXxlcN6i16AlIc+mUlUOc0FII1VHTYP5vHW3UXp3e/blU3a07bjnPLbndEXUPJrezwb5wX8tKD9J+H+5zlv3m2L8VK6Rybea84C29Ww+QvQCwrNRaTH09V7WJsh9ervuv9Enb/nECrkEUkWrItekzBbcfub9I3aeci69fVUtqf/PugTnCJcNFledaKOj+le4N3rfa+ONloTblNCS22vgpr7xJFhOCF8E0JTTbPxduPoCnx0fGNPDTLz8TmvLmMDMOEyVvLMvKT69Xlq3wcBg4HAdiDqxb3R0SrFiyc0lpXMtiSFFtHE5PZNdAtPv4/5MWm2C9+iBWXU8NtnXjOI2s2XgeHaJsDjOLIzZVLeD/8HJhlQMjdzolGDxs47HBtUlssxgJ0ha92pDafgi0Zh5R251rcWm2UD69XhlSJ3sWalMn7Km37NSmQLZKN7kUMfJkLY2SG799/7CT9zr3amuNKUSq+9s0T5a68eYQE3/6fCWXCj6Z1BRmAqOwozJFTecmKGhHMnaUBoJCqCaUWAP7IXRLcOyZRBEkRDOldNJfD3LBE86WG+o8uBBMWAAAIABJREFUkRjFPMdKI28bU7CR5n1Rq1d80YjOzdnkSymkGBhDr3YhaCM1YaOjAbqrAidvow5yKxm7YWNHmspm1XAauvZSINbAIQrvxsTTmAxl0w6hy108vCWs6tNIe+Uiwrk0Pq8bV2+HzFNy1KhD0fvbOYFZvIJTTxgrk1p7jp7Q+TP4/dOR//zx2doZMXIYzJJkRpmDgjYmEQ4hENW0wLZm1yGepGQs8I0EllqZqgWuKUXWalNOvRpUNeTCKk5PNEqxRLRWJJg1ROO2NlIQ/t37J7QZT61giWnRZkR8uaEZiifApe3WGbcjwW7q4khV7WtUIehNEiCrMsbIaQgcR+EQbLKxoXuSrGo2OcblqftzFYFNTfg10j0TEzEIH5bMGgra9scLjrgsufFPny8IZlPx+zfHXSoiBGuRSr0BAQ1cEFL9e9u3vC6Zv//HnznOI0/HkU/LlSkGrl2fyu99kEBDeb1ulOyq50EI0fZabTBEfIzd5CoQKEthq/D4bmaeIlOEU0ymIhyEb08j35wmzrny55eV52UlsPAwH3hzOvLb90988+aB//CHP/DL52c+nq/EEJjHRArGPWmY40AjMjoOGEVJIfLpsjCmgcM4ctkqL9eNNw+PrFtmWTfjuM0D5VJ5nKx9Pc0PjDgPDeHNNENUsiihghZFe8KtjtQ3s2o5pMCfPj6bQXaKtNY4ryutdnpFT3Z0T3zxM6LPUe7JD3dkaX/8/f80/47A3n1QxHR4/GfUE5o+SLMXUD2GNJN6Ka14cmb7vdRmpsjsoib7jrCYY3IFRd02p1eAau+rTvwOEjjNkd++OWIcXEvgxxTJbrWF84yMr2cE9I7MBm/zbxW+OgTmKLzUzEvJVHoy4hIR3Ox3BEv6vjod2EpmTIExRLZa+bws/M3DkVqNwvA5F15KYYqJ1zXz73/zG/6X/+nvCCnxv/0f/ycfPl35KgSWNfO6LLyum012V+V4TCylMQ7GsbtmS7I7bcPisvi9hJgil5I5TiMP05GgkZ9eX/lrr19VgoRCaErnJay1gSjvppFfXm0ssS/lILpXBj3BqCL8tGTKEDmOiV/OC6v7K4lnIhLUR9Xs9gbPusz7SW5Vv9oCqg1+ua7Od7HlIaHyv37/hv/3l7Nl3zHxOa+mFeHbrNZGztZaQ9iz+SjCw2HgN1+dGF0HqKiSMe6VKlxaZcACr3Vqwu5P+em8kXy8O6sRuRFh80Muib3nVtULkk5KlL3a6IKDUbzt02wUN/sGsOPQ0atqB+ZpGCh+QT3wt9KQFBiGSIhinIsGL+u2+1VVxPWIxN2XTcekFINfYzDS7yCBECOSHDZtCk0p0dp4pbT9PUe3a6FZW6ITF7vGyRcVj5hoX2/9zVPizWHwwGoHdCepi6Me94lDJ3Va8mJ//7wW/rysxCA8HKxd+uGy8WkpHOMdf+PuHx2ZcXVHqsLHlvkuTHfwvkAIvDtG3l1GXrbMEAZOw8QUhdGxqB4aphD49jTy45JZigmkfV6zc2DU14zs3K2tVC5bJqVE3deFX6JCrpVLyXavnT/WjWarf4falMMw8P44s7cRmg8oiEIIHKOgTVnUuF7igdxQQZcqUJuOU9zvsCmVbiLMHQrb2KoF46s0xiA8pMiGEKe483ZU7Pt+93TiOATej4mflo2PW7b1kSvX1jgGGziYRBhaJIXAP79UE9rzz27ckD6/NVxr5cOaeTuP1GaaV6dhtuStND5eNp5XEzHsMhQ0+NOnZ757PDFI4/Vy4bJtTGKciRiEl634/bG1l5uyLZXhmNBqSvai5omW10rxM9bGuc0qJ8RAK5VAoGQT9zs9TDs+2ZogIfAwRR7Gkdwa5y3z55eFT5crH15e+PHjRz48r5xOD+QGrVYOw8ibx0f+8POfEVWmmBhCcJ9BdiL60iLbWnk7Bf79N4/8h58+c74u4AVobo0PF4uVr9eN05j49PFnm+RNkSEFkEZEueRGkMjjKfH5fGUkEAZ4Mw/Mw8AYI3UpfHsY+XDZYDbUrDiS1BpIxBBIT1LXbGT+0JMm3z8WJAK38Rp29JzQ2Wfc0JCewsRA1Bs1I0QbaulIYl+7vSAUL3QaQtHK5BuvOsrbXAqgAodofKAulFqa8loqjw8D52v2bsSt+Igp8Ju3B74+TeSKFxjwumSOg3U4Sq27unwINmVrFItOzIC3U+IQTXYkBIuv4puhW/f0W9CpH98/Hvm0rMYFFktA5zExjYn/8vmCStsLKhH49LIwh8DPnz7zv//f/xdDCPz24YknAteqXOvGpsWSWhWG2Yq5jpgShVMceL0W0mgxBo/dNEWi6aZ9vJ65bCsPh4l5Gojn/262AfzaEiSvFnubqiMxH5fsGe8tx+/ISq9QczUi1/vHmRSM98NdH7crGzcM5o7u6fMoyYK42tYpHrjFD5dRZFcUPQ2Fp3GwEUeE3z8dSL6g//FDY2sVETtGTUa9/UVdAm9PE+8eZwZPpgwtM95Ia41L7g7r8oVcWBBhHAeGrXFRa0nYAWNfqgXlrMrBjy3j9rB/5/3UwRb4FO5EzzCNmsEruig4R6QLdAq5Vr5+M1O1Kwv7IdeakWeDkAiEBqOMXGvjZcvUqNZWE5CmSOpKtw5ZK0zAWxVqtt29BZNB0GCH+8elePvBERo1rSn1L7gnMKGjfzdyLZb/EtVaE8RO6rb3Em7TWagllD2ZNERF+OF85TXb+OsQgq8RZYrWAizNWrtJ4HkrPIzRdaHEv2tjh+f9fnbH7ebPCsRNfQ01+ebxRHxZWHPmfK1MjMiUuNVNpkZ+InLdrsZj8SJzFLONubSbVUZtxr8QP9hw6L77jXms8fZuv6bb/cQRxB7sn8bR2i5643GgZgJaQmNtN0sUYDeijns174VBNU6Zrec7npufNOet0UewwfwGQzDV5iOG4rSmLLlyyZXfPp5AC2MQvp1HphD443mh+RRcRWiVXUbikCKnIXLxQ+T2cg6YWmK4lcqPL1dKVd4fBte4sio+DpH3p8m8n1COaeDNwby//vTpldfrirTb5N7DGFlrQySxxLZzBKs2Q4nmhDrCkFyc9HLNFu8G441JtHWfsyVQT08zQUz2YA6BT5eNbx6nvXXfQ4BgbaynaWCOZkZ6LZV/+fCZUhpjGTkMA+oo3J8+fCRq4JIzB3en722i4OstaKE2+PFc+Om88vXbI1Iqny5GzLWJP+GbhxMaA7UWpFqcrCitNHKqZBXmeaRpIOfK4zyxbJm6qU9BFRYplNyICR4OA5tTAsaUdl2fPZERG38fkxGpS09qOnLU46uoJ/BWLRgXUW5tV3rJ1G+kny2wH+bisdLDxx63CZZczIMpile19a5Ak7APsvTk4+wEZIB/PV/MNmgY+Op0Yngb+MOn806WrkWZkjCnxJLtfUrDNMQitGZ0jL6PzX7LTdMdLT4m0y+iWZL5x2UhRJtqPW+FzQnmHVVXPz8fpglEOaa0D+yYHIiZ/L49TVxX0xnr8irTEHl3HDlfFjP9niaWUvksjfWy8rws9FZmFDsrmxe4fX+U2qA2ql9Pf84E4wrnayUl8/YcmgEH/8PX7/lP/Pdfv64EyYOg7mWtvcYUeJySVwI3HpJ3V+ltraJQg8GXrmXtyIHsyRTOf+jjl4ME01W4ryu8AgjCrta8Or/p3WSEr0qHcy3I/tt3R163wqfrxiXb78YY9gmqx3nkzWHgMCXGZFiUjekrj25zsdbKpRQ6L6S3uczXx4iPIQrznGhr2QmcHabdqm3QILYhEXHeVoeM1QNb2JGD7imHWluztyS9H3d7CM2xO5WdQAvsJNfaMPd67xNNBKbjhGKmvFtrLlhn5LnUf88/ox/OWmAUM4ht4TbtRfMIcveQrELTHd0J9PaD3ZHkX6U5r6U2s6Joo1og9MSwE4r7t+1r4eNWeVk3ltbbYr4+xXzjVIRLtgRj9Pbqay4MSXxCzH5+Rzm8bWdaW6bwvrTKFOJeodl7Ks/nhTVvptys8PPV7slhTMjdVIbIPefCXkOMvJlGtsvFv6cl1IvAFE3a4nXZ7pKRTpL3h3C3/TqS2pO6eRqIKfBxW/lqHKit7dcuam24pbQ9yezH0W2PW8FRVF2V2kVSESeFexvVY0H1NY0VkN6uNa0vwcyInxebeowhkGthCoqKtVTHYIrwRcyLr6L770YJTAHezRMPnjAuuXJ1s17t66xvh9r4dF2JoryfJhRcEbqQolXOrVkL47pVrmsmAudcmEIgtrAnsWupluTr7T6X0nlHti8H19tas7eKo3l79eKkKXTxnsPoBh3NW1P+c8Y19OfjxZCdLaYELWLE81yV597aFEOcWqssW7ZhlqY8zBOCIX74ek7B9M5CcKX0Wnk+rwxBmcaI5kpUeJcisTWiAhKpgvvJWUIQg6mQJzISLHqvPhF4nEZSMv7dumVSigzJvkfxdlVVm1S7zaixt7kPQ/jLVcg+nCI3WsX+M3pXOGonW8PuEOAPTbDvkDFyeK6OfHsxkKKlYJsqUm5Mo5tDmjJK933sAcjimXHoLJk5+mTsmBK/fzrte7b4AM01N8R5t3s8xSRxfDvD/Uf4NczJzh1R3VVT+hkRvchKKixrsVgs7D6KfbAmRktmbo03a4c3lOM8QC6cV4OdpjFSVHldNt4fDzzOMxqFz9cLZbFrjTEYeDElCtYu9+1va1dsXyy1ujBmLwDN/mYcI+8eHokYR6+Lmv61168rQZIbdChiB5A2ZRC1xdZM3K94T8VIyV1bR5BolXIuBoGbp1jY0aYvpq8wdHV0YbX7s/fucgyyFNMzWtx9fdfawSsogadpYIqBKQT+5cUqesQUdRV4d5p4nAdCvB3G2VWBU5D9fQcJhoD5RVl7yA7f1admUrS2VlEozkWqjd2PavCAI47dB7WDtNEtQTrs6dYP/sX7Eu/IUSdkf3lPbolKvUMtuj1A8ITDbCpk9z7qR/pWGnMcmaYRWqOW7HYXXqUpu71HUyVUq3irE4X7c+kTaAKOTNi1Fb0Fok65bp4kDljr8/59dK8jb6/czMjzJTfOOd9813zNITdUrrabLkwFWjCCNCLMQ/Tqsmejt6RBPSitauPPya+/iY2rntdtr95UTUH4Zcv2nntAsn+P0Vzse8Xb26PHlGxaT8SEVrHrVGxM/macyZ5h9ZbwF9NdCDEZCXhwi5Vzrcw1MN3fSTF5ha0qc5Iv1k0/tqonRrXfu7u/30pzCQH29+sSAAGbjJxiYI6BFeXzspl10FZYc2UehJdlQybjzvS1kbBkoD+37sw04vYoMTBKYApGYl5K7WJo+1rvGIKJOmYex5lv371nmieWdeXjx585DBGIlGo/p60yxMCaGyQYHeEsavSBitrYue+/6GsxDr7Cmx2CrTWGwSw5+sHU15I63D6NIyUXL17gYYye3Nl1h7v0f185Ysh6cmf7j0smAVstNpGZEseQqSEwqHM3m12PHawwjyNLKYa6AFUr11LY1BKq2tzOZogUFw/siut96jCJUBvk3ChaibFaIl0rk09aJh/Tr63QamVOhqaqFydzTEY30Lp/w36vgicq9+sR2Ne+bUnZ0Whu2+FG9pZbk60LE5vmnqPEWmm1EzQ9mvmUtMBuZ4Sfb/Y7JlNxm8KFzke8rJslvqFP2Np+PA02aKG+f9baWN0fsHobzVpSkOhcq74H+2RspzvcRExBeC1l10CzxBV7Dl9M59m2yLURqr1H5/900MEUL0zyJNSw6xZVdb0ztYnSh3HgUymsuUCDp8Pc34UQTY5nvItDlnQG2mCTuN33cT9RW2MaAqd5ZFkzpWVPou+FgL58/boSJP/SFrhtS1eU0GwSSoIdnr2kFJWdbBajEVJbhVzgMELS6E/LHkpxHQ9tVo0LAUmGNhR8AXnwCVjAyh4gS7XFaIre3HrVsAMu05B4mEY+rQUVJ9n5VMrTYehf0X4e5bJkjjGwprDrnszBfJUuW7HAFa36KcXagNlJymOM1IRbgNgBYwWg9bPtBDeuUtg/1tuMat+zcgsilpC4azS3yrYf6G0vq5TgiEu9+7mGERKbE1SbQKluUCrmb2cHQyVNiXk+ALCtK0u5NYl32Btri4VswXgpdT+oeurTkZguSti/Y3+fPo3Vk8EoVmn06caeMPcH2fdi1cbHdeNab0lD7IdbUwiNS7bqphPb8WuYY+SymUfSYRKv4CwAdhCscQtWGRMk3Yn9GlA1IcGOkPZ/Pm+Vr0+6ryFLHITHcWDp7tgoW618WjPfziNLMRWr7P5UU+2k+cBtRN0LB7+GTmTtryEGpiHapFw1Y1tQPuXM92MfofV2McrSxNlSna/WJQHsBnSOX+trrilrUbO30FuCFsWBQy8UxhiYU2COkZdQ+fFlvU0tOoLxeTXE4zDIvtep5vXX10KvJ0X1ZsrsiMpYw76vjXx+h845CnPJlU0j3/3m93z7/ht++vSRP/75T1Zxp4mPL4u1q5Lxb7JLLkTLgm9aXiK7ppFNbgbCaAVTbXC9Zmthp8A8R9IYuF6do9ELoGb6TENKJDUNqxDgOES3gLkVfsETpvt9ZuHOEtulbJyiWZ6kNHNMkacxsqaBWIQlV2qttheiJerH4wHW1QcuoARoulFKI/VhDbcomYdGTANaK0gg+GRsCpFSjKdp3l2mJt9UDa2oDdQmEhHhsq5M0fe6Cg/jwNtp5IfXK1tfh8mmK3M1jqa2Hge+fJY3dOIW8Kwo+RKZ7ZSA5jHWTMf7fYV1zcy9Hdd5dtkkGUK4TXJaaO7q7bIP6UiPPxgq9rxlxhA4ubcZGJ0BwQVm7WeTCEEiSzXUs+uglWYJefN1vn9pdC/K+zqvDjJ8WDcb9qi3ItOHF+lhqPj/L6pIrSRMjLI7SvTDRtWK4dJjjCrbZu3kIUZO88CUhNeXhUAgJng8zJSaueaN2ikVqvuwS4+jITUi5ruakD3+5mC8Vgmm2hRFmIa48yn/e69fWYJk492W2esO99bWODgRemkZVRgs9zUytyoaAxIDpXQLEXuY9sBdC2Wv4C1Yq9r4fzfRbL2nSYeZZXc4bWqTFOdceJh6+6QfMvaetdlI4e+eDnxaN16zHeprtkXSVYRFbBKiLJmPrXFdM8fTxDgamqW1cRwix5igWWWiEjiNgX+9rDvk79CNEQQdR6/o3g7rprJ9c/a2VPH/r1rpFc0teeoNzP1rgVhbcq3V3Znt95NP3fRxaauUbPOOEtgw1Cs5KpaA4zRwzitDHjnNR+QQOK9nM/DECcHeErPAVklTIGw2uaHcEjrERMdG3+lrddqvAtJ9jmzzdESBBkuz1tbReUKBmxaKBCHFgX/37on/+PHZyO57AuFv3Wx0PnYy4x4EDfFILnS2V4baqCL01l1PSocQkAbXYC2wrWZCVR6nkSGFL3R2VGFp1dbt3g4QYoT388hPl2V/ZEXhWi1ADCkY38Wf+0vO/PZw4DQ0Xreyf0Z3vW8emPokHpiP1ZsxsWBtQbQ5+dbraQk7d0mAOTSuW+A02pqr3lJTfy72tn3qTcnFFKP3+8sN+TMRPHt+k4/2b60xBFN477pM8xD55mHi5/PKdWuc18oYg0/VZL7/2loTtdlofBO7/rO3+LrAoCgMTRjTaGrk2tv67FCqeMKWhohK5Xo+8+m68XY6MtEorXCtmdxuew65oWhRhIeU+LCa6e6cDBUo3hK5LKYMrU2J0QyMk7fM5ymy+vCHoqQh8M3jAzlvvJ8HP6yMt9EtZ/ar9th3KxxuU34ShCkkip/6WzZktzb46nigVCh5g7xRa2VpakMZhxlZM9oy1ArOBU0pctk2i0cxsDa4LoW/+fpALtH8tGpjSIHjYSZuK+npgefrxnlZydWq/zdjJIbEVZVcC2gmBJt6pAq/ff8ObY0fXl75boj8S4XDNPHt2wezyHi9QuhWITcE5PY4b0nKzitC2PC2DULy9bG5J43APgBhiK0lYTbl9qVcTL/7d+HaLZ+UGJLrIsntBxH+dL6wlsa748jDmCj9/LpDiFX7egrkUq1IyqY5lZtNZV5a9I6FfcfuAfowDoYeRUwgslSec9nH/kMv5KpSrpU62nVN3sLMzulVlNdWeJDIRNwnNzfX79q2duOsBhhn13irFRlHNE2U9TPHNBLF5B4qzQaBmroQdGNvSN5pA+ZmU9pzjAwIW0+gU6SsymkYeXg38/g48U9/+om/9vpVJUj3C6pnu+oH0+BTH32SrGpFNOzoTWlKDMpxHGmjcl2yv4vu0wuyw4SOgKg9zCEmcinGW/IELWATJE+DeTEdU+IwJGvdoYiPZ/egvmfvQfhUMykG3vgobwjGW+kiaHWrXF6NkBaCcMkFWSMpRKYxUhXzCatlRzusWmy8GxPX5j33YEJ5BTtItq3wspgAXm04D6tS5d6qwzaJJft2FPXBPquM5C9UWBXcEiPNgmBjtXslRLNq8O65NcVJy0aKLmojpoME3kwDl9J4ubyw5Y05Jk5pMOVVMX5SiEJS2T/nvBY0iCc9QDP/K6p9/trcBgQnmCt+0LNzvQKONgb1azOy6SG56egepOzQGEV4P438+br6xrRWGmISEYfBEAmJJty5bYUwmPgozRKPNTemIZKRHU0RXH8quB+RRJa6GQyshmxecubxYWZ9vjr64Ak8zpfoj8WD8SkGHlLitRi60NGgD1vmzTDyU11QLNFYVXktmSAwRjygWnCZPOEbowd7D3jBCnW2Up3PZsjF3sb17aqeCUwxIbHxslabGt3rUWv1fFgy12qK3KjpojzMA8+Laz9J34PQmiFYXx1G33u2vh6GxC9tZYyONNdGKZXvHic+X8xp/nnZTBdtTExTZCC4ejKmQk1DPenqiYMRfiOnEKlNmUPiuWYuNs5pT1DhD3/6M59fXvjNN+/57v17fv/1V/z5wwuKMo4jpxDI28ZhGpmCFXOXrfBpLSaGK8rpNLHSeJitXfR6VZ7P685bjMmSsNSJ2eiOFApG+p6myMNhZl0uoI3z1ZKr+Y15UfX7eZ8ZRGk7X1H2tWTm3s/rxhwHRhfbzVVNwylVntLMGEZqqUizKV399JnTupjIZAikeeD78YlrNhV0Le6hpYFWKy+XbNylFNyvL/CyXslrJr9emaNRFYY4cNkKf14z358S61pYS6GF7qCgqDY+PD8DRlf4IQZ+O4787nffWpsvV8anxKoBXZ93mKbTOGI0OkBW+559EEA8mbB7xY7WTfGGT1tY9OfgK7sj2/33g6Ob/f52jo+lNcG4lV/ETvv/n9aN7w8HHobkP/vl+dj3Wd97Joxq02pdt2qMkdYqJRs/TEUogp1bh2BDCRVec+F5K6QoXHK2IZSm1Gz0kG28cZKGFNjU9llx6kOUwEsunKUSJTAQPEZ6Yb8n4caRGqIwpZl//uEDfx4+8/VX7/hPf/gTKcLhmMwtAEPhRIJb2RhiWtUK7jVXiw2eKOEFWgqRoPA3v/0OJSNiZ+/LsvLXXr+qBKkCf86Zt0Pi4BBuCLgvlWtNOOFaXLhnkMCirvNQG4tkYnRtI+mVsQVV86Sx90xiR3xphYdpojraBLj658DDmHg7T3YgqB1Og4NKtdZ97Pu+hbNWO0KKKuc189PnKwKc0sBXhwEV4bVm5++Iw/twXjaWXDhOifenA6fDQF6y3QPP/AkwI4wS2RTWZpC6KmhrDCmSYiWXGzkbiVyXvE/KgFWPxmfwUsFgLZYt3zgOtx/fya1RDkhoZBX3w/JGlwBdt8l/MQaTYmjKHjRqaKZjUZUBIZeNXLJpJfUTNhgk2lAkRsN4Yx9t9+cAHHBV2r0atj8PatCyCULYtXT8TgGcJvBSCj9vhccx8W8fD/dYuv1OU74/THzc8i44JmKJuKGFurdI1lYNaCyNebKqbcmF2hrfpIMJFTqBXdQq/9KUQmCtC1lvgVbFpAuK80BErd/fW4ofLhvvjqOpCHs7IAJ/9/aBv//0Ypwjb2F92ja+mk6eVHRFbOFjLjwlG3GPoSGtB1uziJmH4NWiweQxCE1sDn7wIqPX2j+sme+n8Q6ltO8wSYDBXNc7V9D4QnmH3fu6LrX5+9ozSCEw+3TZYbDkoSt144lijLInbnbQBa7Z9t7DlLislc+1sbbG08m4MEMfj1fb+7alwg7Bd2QlBqGIoW1P48C7NDKHyi/bCk15ExMPDzPntfDHH35iu1z4+nFGD4nPi2vk+GTsMSVabfz4euXigxVdufirGBmjmJ3PYge6IZjOG/JDPCU7KJIYgbWWahNxrh6+bcXGw7NJYUydyO9Jld7pewX/nhZ6LOFbauPjdUNUmPwBN60m0tmUdbnaVNExshC4qh24LcLjFFmZSCmRQmArhUzjNI003cgNQlUmKuNoVIJly8RxhADnbePlfGW5Fr6aJw7DADGy1gKSmVPixVt0e3xxNFaSta2jmrjqt28fOJzPtHTk669O5OXKhw8feXl5pY+XDY6kFcULLN2HDDqSFP0z+v2xGNMTIu9udN6Cv2KMJAo7NwzIzTZ0n0qzX5NbYip76AUv8j+uK6JmYRS6G4TcT9PZujdNMivkRwmcl2XnDpXaPTV9ghtFgiU5bw4DwWVkQhSmMPBmHNhUWYqdJ3Vr5GzUDY3m5VgEXkq+46VaG7hJ82KqTytWBgUtzY2pPTYX5WGe+XacWWlMh5FSK//wxx8orTCfJkIwUWibKox761zRnYyfS93vr4BZBmH8UsV4jP/4wx/522+/YZoOfHi9cBiH/zbZ8NevK0FSV+8UiMWyYRHMXToIx2EgTAPLljmEuGe2/WytqLWuqsOK2CKQEChb+UKhurmDe0dUfvdw2K9DxKYzAqa+W6veROe4bZyd7OsLfmvwkjNbU56vG8/Xjabw/dPRXZGVy2Zu2fMwUFp1Ybk+GWafMUVhWzOtJyG9Slf7ThJg8hM7V3GcyKvAaSDFwLJVam3WEkNtMqYHSRHTPgoWgKeUjOjoldIQAx/Oq48i20d3BKYHWvxAu0fcklJVAAAgAElEQVQQQkeeHC1BZN9Qod+7YAegNjvYbe80LlV5iPY3NlUSdi6TqVPeIRV4NeWH9R7AVJkIJBdZq+gtefJ+f707JFZt1Fx4zZVjEpcGkN1/L3Y4m1vVZqPUVtMN0XgxKQg6WFLUifwiZlfww/OVbx5nQsMPLTXTxTax5lc3gryhmohxyAThcR64CryuzTkLdmg/1IEU+rozZGgM8H4a+UU3t1GxRP7H67YHkd46XWtDBtNRjxIYvAUxxL4n5DYNEwJjimyl7iKdEVxDy9qen4olXKOE28Eh5kXWGjZ9VG9+ermrLeJEfARoHIdEbY3jGHmchjv9Kj+k6M/A/mMaEhpMQFNdLqDb4sxDNM/D1vap0X2vimttKfto/1oaWzF20mEwY2YJfb/Y6Py7YeR5ydZeEzOQHseBx3ngcrnydBgQqXy8rijKYRzQZsf6dStW7QbZBzWaF2WbT0Li19bXdhqMI1m9HZa1E9idXB/s2kqrDGPg83VlTIHjPDkHzJMJf0+LkX0d2D5ba+U1W4vlIcXdC7KT9Q8pMKrxsq65oDGCBHJtrK3yy9aopTIm88KTOFC2hojFnqyV7DSH2gIP80g6Tjb95W7yIha3WhDWmolUUhCejjNP44FfLme2Wq2968lEl5xo2pij6Zq9vCzIdGT79JnXdSXnlZfXM1sp0JTjlDy27IHe7lEvFG8nwJ6sdzbdPdrWf9r+236u1grJ/ra385o6RQScA3UjTasIWiAli4x9UOHDsvE4JDcxv6HEAYsfNmjaEWk/v9QSZq192lHuijlDfqchcByTkclbN0gXi0famAT+zeOBa1M+h40XyVyKcSFTipy37ZbswF5QqbfK77mrJZtlmIohddMw8Pbx4IKzgUNKXLfM52Vl2yoP83injm3fsw8z9ABflT3+9BameiFYnOAhGCfp9XrlcjkjtbAu1y+Vzv/i9atKkDpx8+JQT63GdbjWChWGlGgol1Y971ek2aEnsfuRWfBS7UXDLfPWvrrpC6vDdJWH6Whj4XpLmsQh1Ht0oWEBWFXduNQOgdxMxG2tjZe18LJkrlshSuDtcSIGuCyFy5rJtXn1bgshN2sHRRPy4ZoLyVEpq3VvSUbPxqLApFZFXIwWYnHDp/eCmKK1NBMU7JVHCJ2gZ0lgCtZSGEKgtmEnyc8xmMaI3ysR5WkauLbKUs3g8Iv76vdWeiTxVWmfZQTargqdxAifl62wuSN1E+VcClP0lpgnYtENEPuERq8e+vO1dcOeyHQNq05GhV6l+VSTOvLj1WiujR+vG1/PcPJru/2m7J/f3z/6fcODX0co7eC2EffYr7PBdbNnfhonNtSmzQRaKxRPXPDqtIfkzlMJ0cwbx2KHDcC12DTlmAKBuAcKQXkzJs65cC03/aOXXBijtU3/Uh4jBBvxhuAolhuSeiMtiq3RFoSS6z4l2ddXJ10vrTFUISTZlc1tCQgDwQOrEUlz55dxsxQBC1TTmFBV5hS8xdPXlgXyvn1F7dCfx4hGQUujtULG0LZNQXySc0rmy1RrT0A8xIrsAbaq6SjV1vb1KihfH0ynpahNSr2LEa0KYrYHQ4xmylwr58uGHAYTkBwTycX+elXffCGKJ0ipqyjrTYbCQ4yt+2R+fpYE3BDS5kWLtWfMsmcQZSs26TslM6a97Y/7sYVbEtARqtys5dwHKHoLq9/yhhBThFrZEIPhg0lJNIz8f82VVRceZOLxOLNcL1yysuTFZSBMpiT6vkHMNqS1BqJufmsuAkGDJy3KGAZSlD1ONr9XYDHXvlaDZK2b52tlOib0euXlemXLhVKsfSStEkOiz6LtSZIXRD3EGyn47kTV25q5/3OnIdivWBzpGm9KlwTpKJ3/24P0joDI/pE78lSb8v44udxK65f45Uu8gvPkSfq0cD+X/DNR9am0xizRBxUMiUveoVEvxpLY4M+EMEngOCQuxYzKczMUWRsud3KHzu/fz2IYDUruhb/iUt0mB1EbJQWWdeOybuRaeZxH5imy+BSuVdrsCWl/7+qTr395L/q9VqzQejMNjFGYtBLLykz9ooX5l69fVYKkasaI3VQxBMucu+5CaSZst+TCQsBU1M2PpR9SUYw3sJV6N82ldyqnui/GjoLk6rL+sHM4ajMRrta+JFkWtUmz6mVCwSrXczEJ9CVXPp9XttJ8LNnY9jkXJx+6U3oz6fVxiNTs4/wpkFF+uSx8d5gtQPXA6ckNfi1g7UWzdbDFmFvrsigMIZBG4TFamyaGnngExmSHn8jNmkRC1wKxxfhmsqUjvgFEIMbEz1umuklvkFuKFKWPwns15P3mMUbGYDL01jYZQOA5Zl5z4VoLBx/Lfl0LUSxJ6QvCBNwCrRS6ArjVKrfx1D4N1Rqs0tjEyf6exHSbAEGR1sdSG30L/rSshgzFYGKS3I6Vx5QQr7TNB8imNvZ2j6pX9XZo1ez6WL7eYhCuW2NKsEohe6tLNNP3e69Me9XfD8msJsJ5GMwSJTfzGnvNmWmIpBj3yN5UOUbT/bErt3tTVRnEyMR96kZ8HY8h4KQ7xMXrSm0+HRh3A+K1Nk9yOmTeE8IO9SvX1og14BPqexwbgu3pIQRefcS3HyzxLmmfo/FtovSWsj3/ePekOum+k7gPQ6RKYKubwfFRkGLPKlQfMw7WklIVI0PfvQ8Ca65szQxBu1REbY0pBn7zMPNfP51RbC0fUiKgfNpMM+kwJxT48flMceL04zwwJSOsLtnQ260q0UeTbS9bUSJBaMViTevcqGDo6ZCMo9LoCbAXAU3tuXjMSxo5RHheCk+HkTnFfe02xdNTz3b6iu8ohrILdYLxBnNQphBdegTWqsY9yRuHebJ4K4HHeUJTIq0XLjQui/Ha3kwjUjLXzTSLCJbIzqMhgi03ti17SWULcoqBl5Jtkm2Kbv9kCPhSNtOzSgktas/S90eXIsm1kFugBLiUTKBZrC1KlMjNv/VWvBnKe8ONpF+Pb8Yu6CqeaQo9kdHbQe5rCb1Lgv1+q94S0o4e9X1tCbq15e5T1iBmH/Q0DbscyS2P6zHxbnP5LxvCW0hBbi4Qqo4udr9F21e1Oa8w9AlTnwwTQ2nGCNOceJwS16b8vCl//vzMSKRIo7W7SdPbHdifZcQGpboRvKiitXG5rjaGf4j89HxGtHIaEsdhoobmE6zN4msvZkNHTw0N712c2/zl7dXBhOOU+P44MwaL92mIXPNfT4N+VQlSwzJNmulpNG2mkKoN2m1D2OJrFvjVVLOb2MEHsG2G0hii7VVYbTt0PE0DY7ADIGpjHAZDrDzV3M0iW/RpKvYDrDUjjVliHFjXzKW4EYPAP//8Smvw5jjyzcPM20Pix4/dJuWGTilwLZlNA+MQOUwDDeXzsjqc2pff7dUlCG7b3dCLR4GHIPxcMkttXNfC63XjaRr53XcntlxsE99tiB4Lwv1+807W/TIEQYMnmK0xu9aTUcyVSQKHYeLNNBrh2auSn5eF9/NEvDuwDf0rrE0p0qijeXi9RquKD8NgmkMYoW/LDSmFcRi4qCEu4sENEW8D2MZUr+hqf1ieOA5iROwhWsurekI9VBPGbGIk9Z/XjbfTyMk9XhQlaOBvHw78uGz8vKxctBpXxtdIP9REAtVmWTzh9MRBbdG8ORy4LFfqYFFK7iJc/1PnJ7BXq0ZSjzEwTZGjRj4slry8bpXjUEwBN3TVdW8p+5rpQwOKMkWhagCMF2Oef6YvZoihPeeqwuj1VkyRJnDZCghGZL7roBV1dXqvHrKaXY5i+7cBEnT/roNYC3DTSsmVQZUpBaYUmQ/TXnH3hNwSMCW023fqfBBobFWYUuB5KZRSqRjxeIiJ59eNb04zMYghao5GV23cvKTs+f1yWegCntELsdbgdw9HAqZvJgKHGDnFaGs/Xzm9OzHFwOuy8vGyIgKP08A/fTi7T6Ctka8fZ355XTlMkeM4IGp+jOtWuVD4+LzQx/Z7/PIu1j511lX+BUPF0mDoUgqBYxh3hHWMZsDtsyB05eOeDIjvffXi8Jorq0ugqNr6bbWx1sYxJZJEcjPy/mXLfP0wM44TKUQeh4H3f/N7/uE//me+eZh5LY1zrvyXHz8zjZG300hLA+e6Gcq0bYDw1fHAerXWTX/VZsjfMAw+UGFWLCqwrI2t2r6LQyLGZA4FsY/uN66tcb5eERFen5c9MRljYhpsb4wu2hv3gOf35m7L+Zb8ImnpaI2FXj9/XEyu/0oQtbZshTH0e+4K8JbZ0nlE9BBOh2/s33a2Nd5MA/vVaX9/8c/zBye3/dBUOc0D61aRZPycNVuLs/i6wdfA/VlWS/UWWE+clGm0pDg3Q9GuW+H/+Zcf6UKW85hMm6s0ZDDEONHRe+N3nSRwKY3jPKDNNJkGR/Ds3A0cotk8oWJI0mQxLIpwTNEKEYVfarbP86KqH31CT9D64IsX8Qr//HrhISXejNamHILwP7478ddev6oECeDn14uRjcUOytrUA5eNj44S+c0x7mJUualB97WZvkbwNoHza6waMPPGc66kaAfPsjVYbYPEa+FvT7NNqfS2B77I2y3TxhGJrTQOY+SXZWXzcWoFfnq+0hTeHie+fZp5mgdKNei+KSYb4A84AFWEc6t8dzyZr9qW6SapPa1A2DlO0g9Qvf0d3hqponw9jvxwXtg2I5D/3XdPu8ms8Zf6IpO7ib7+ZW0/dgQDbkjublcgwjEEwjTxJiUOKfmzulXBzZOQt8OANntmvZW5tsZ/Kdedw6WKbTRHeFRtlB1MQHM8RMaWGKaB53VzeYK+ySsqjqB4IOn3156T8WrGGPlqTCSE8TDwy2UhYejMVs09fQ8iHoREFTXyC6rwbhptguJytWQBU8c1jhJszRzNc1NGhMM0Mw5xF+Fb2sKYIldHjzry1lGovbJ0CL4fanOIHDAuzXBMfLx+Agnkpny4bLQG3z4c9zXRVDkOgceaeF4L3TQ1ezI4BROMFIRzrjYtFMw8OIoYr0QtGVpzNa6Ur4VG88obiouOJjq6a9cbOiIm1ursAqWorfeHJBwxjZp5iEYyjtbmEbXWU9U+6Qehr9PWdWO4O9w8PCpkhYz7271e+Ztv3uxTjQ+DtbteauH5kjnNyVukPiqstyGOx3Hg63nkOMR9/X93mnbDVONANL55dyQOiet1I5fKYYg8zBMvtXBeC03U/B1j4AjMx2FXMFeUZIqR/PmD6X+FFPb28TBGhkF2U1wrgnzPiiBDIES731orQyi8LMrb40QQM7TdkyHY15shd8bVCGr3/IfXK6sLiR5iYPXviMK1FmJoHOeRv/nqgXJZzFLnuhIk0I7Kz//0B86lMDZ7HlMQcmgs18xwGJAhERnJObNulTlGPp0vXyQXfUp3dFmAeUzMycbflxJ489UDLx8/Q8vkomgTvno68OPry8718TDpfDJHxKoig5JEqVV4+3jwyTQvqPB2p0d3Cyt+1HrMFE9O8Z8xA23dSfRfyKFIZdXIpTTmAJM4NUKNSi1339mYGTdZmSCWIJ5L5u1h3qVWgvTw5mK/ewHv+y0oD5P543375sjiXouXJbPkSt4KEuDbx4NN3freH5JxyBC7B51g3fmC0zDwXz+98Pc/fnSStIDYENQ4BI6zWe2U0ng9Z0prPB0G3h9HjhKZvoostXEcRrZWXNLBHDFKaLx/+8R1XThfF7bW+GY88Wm7MA+DeTKq8nSYCCXyvG0si0129jMIwf03tR9dXljZHvv7T8/8u6cT3x0nUvDv+ldev6oEqXuk1aYQrEVVsIkpESNxPwyB78bRqnyHxpXphs6o8st55bxZYtILhpfVyJXHMTFPN92RWl2S3PpZtMZOihxjIgarcLruCEHcRNBoYdEJix+vKx9fN97MA795MzMP1uY7X7e7QuGWBTdVNoQ3w8AAPLi7/LlUfny+8E/nC4r6tJIlKb87HeitgVtWYMEperVYc+MQAw/HiaQG0wsRbVbWazMxR6pVCVoBmvfd+4Ft/amuUN3AR18a45A4DNEUVDFyomtx+sa1y7Jn4YFchBXlZy17FdstXProa21GJKdX02L6OCVGvpZx96ozMNGPR207OrUfmv75fUtUbVxcj+oQYZwT0oRZE19NEx+2dU9VllxZU2VONi7q4AhR4SFF3h8mzq4bFP15Zi/Xg8C6FtJoFT1UmwxpjUNIaKqM2ZL3EtSDr8tJYPy5pMIogUHFOGh3AfTgxBOLEWbweynVMJu++LGWYBs9aavGNzjXxtsxcRxsiODqatWbV9RJLDm85soQE11ftRfW3Zz0Vo8awXhRYYrm/5REuNDI28bbaBNy0Itk2RfF45CIQ6RX5a0pUYUqjaKmei3gysO3AyiE+yLfYfdmHKoWEhOROQQeY2YIsORGEmGIRthOQfhlXbksmXlIVJQPr+tdMdI4JuE02B6IITrRtRgqGLxtmwZ+XjLbZhIcVUwO4efXC9vWCIO18npr+bJtRg/YbCqsc2p+/HCx7evfKiVrq2VRVj/Iwr6XhCkE3owDH1tBm/A4zbybZo4YGjWPNrTSBQhFHA6W/tR8f/v++uVyNUTNi4rSGlOMbNr2vWm/2/jHn35hnGfCEJg8SSjrlXO1NsrnNfMwDghwaYVhSOTcmDV70ixISEwPM2W5fMH3Cb6+v55n9Chci6FNg8Dna2HLC99/8zUfPn1irVdCgJfFxSb6Zve11VCW1ToPk7dX+/cuVYnphsJbHL7xZzqnJgCtZyZymzgL/nnSpUbkLgQjjDFRSnXXBUtPJ3Hngl1k6y5ayY0IvrbGc848b5lvjgfTzdtRE9kTXjvz8PVpbdqHw4w2Q4PGwThvF6loK4wp8DkXsiqzdw2M52pF3h6r1dD461aYhsiHdeMlF0LsCnG2foppY6AOFAA8nAaXw4gEIuOQeD8kzmvmkjdUbNig1kpo0WgIdWPbCiXDmBLvDic+vl745bowhMCxVKiNwzTy+9OBr6aRP12ufN42i5d6Y772B6F366Eo/MPzhUup/M9vH++i1n/7+lUlSIJ5PZlImrPWo2mltGaeNKchGTHSx/2G2IlxHQ4R3p8mnmblmrPpJtQG2FTc++PEcUq30cu+aP2AN4jdEqtOdrxRXsVFEZUlm9De85J5XTO5GMHz+6eZOUVKrSxbIW8FpA+Zy76pVIQ5CF+fZuNKiCscx8jnYTXOi0JtlRQCB1fUts11OzxUb6OjfXwXujdN7dCEJyvsJD4/nTzBgv0fHlx7Dnbrq9uH1eqUuGbed4iaIu4detDJu7jWh/qHByfBqGPaqpagdrNHM/jtz9EShBIbZENm5nEkt8rzsu0WCvuli+5QeU/zBNhq45NmAsL5YgHskAaOMfJuHPm85f2x/LgsbK3yzWHi7TgYRO0ctDEEnlLitaw0J+/3e1qrI02nIylBSrInxKhVZQpYtl2J1QxNR3FNLY/SQYyUjtxNBLZm1hjCbvjaJ176iHLzm995B1MMHFPkrNZK6+JyghHlr9WUwZeqpKBIhKICIXAphVHC7o/VW1LNd+j9cWsJsqOOwZDZSzPz0W/naf+5PZSJMI2ztc9RPp1XPr6ujCny/bsjcGun7VYn2g1u71pviEtYQIog1Uebx8gmcF02F1Ps5DATuFxd0folZOj8nmLPbhAbalirJQpFG600fMiIhiVkv6zWKhJfG7UZN+yymDjfIDbcYDYigbfjxIfFCKmtKWzqsca5RLDvkVaVw2it/3Wr+yThnAK/fTyytcborvH9GdXWSEPYixPhfkrrbsqUHhyM8PwwDoYYlurj2Bbbkid8MVjxt2ijtMr3xwdSZJdKaAyE14XjPPJGoyEWCm/mAx/PV4ZxpIiZ7U7SWEtBmyGoPZb0KYsoxtdatVHUpg7HOCA0Xq8bHz8/c9k2ihoRt5ZqApR6y1OaKpsjFdOUOA6WpHdu0MfrxnePB0LPE+9QtlsQ6avstsbvX+JIfOdB9laZvaUyBoXW9gSqD8Z88R77u3rxE+C62X357jA7j9QnmlUdbgo7etTfw4ZrrHgunuTf78vDmHjdMosnetG/8xDs+XpEuqFRAiFF/uHzmc/LxjmX29V64hElOreoS4/YJ84x7BZIn5ZsJOkU2XLZz1WC8P7tiX/96ROXWphj4PAw8boVPnx+sasJgSzwWgvb2vg2RZIkHofES4pcirB1Icsvns+tAOt/Lqr8vGzI51f+9vHIX3v9uhIkX+39IPeCBxQXWUz0yY/S7gFOe4Dqh8qUjIg8BGFLNvUTlX0qq2kjNOvhz4ONAOfWMQh8wsuqRkPnnMDalK0W28zeSjpvhctaCQG+Pk5mOruZWONWGlndsZr74G9B4e1h5DBasmabzLxt3hxnfnw53+VTdhPW1pjilxusrwnB0IjTOOwjkdU5OnsS2H/+LxLqnnj1H/I4cPsFuS3G1lydVzw4e2usE7OrmsForZVhiDdyfTOkI+F+aU1pRd0J2lpiu5t6L6Cwg+u6VYYQmEfjOJ0b5kF19z1u1AKDwhH1QHBrM5gWo5IkEkJiimKbr5g717VWdLVn8zSkvSrtdzuJHYy9DVTdeXuaErlVDlMkxO5yrjuMVvQ2GTNIIAEnCYxi7lvdIqRXjDuBdK/w7CGkGHexyv2Z+j7pEhaCrfuHFHfO1RBsIi4EYeorR9xo1RHIrTUKtlYNkg4+oXdDkvpa6Yew7UXj5M1BWIryes28tEas8NXJkV4P0jYsYMVDnxx7uW4MQ+RxSTzM477WhJ7U9WS6V9446mMX1eotUd3cALZWO2hviuy+qPyeNVW03KxOYrT7/rJVqm4cUvRESXh/GKHZ4ahJ2C6KqHHxNst37Hp7zVEVddRZvY0bRQgpseay+80Fn2jr1zVEmxwah0iIgZItViUn6VsbUAjFktGudwO2XktvTXrM2JGSfY3cjuaKGBIigTHa3r3mYm1TvxawQ7S0zq3rU4f2gFozCYi1NGbn95UmHIfEkAY0WKw4zANpiORLYVkz0m66Qz28NLVJOhckIRdQj8+QyCVbIUxkq+XL9e/fr5sESzTbqRjCF3voUspePKJ/EdT6BrqLJ3fR9RZj6Ejq7fP7flB149kQfBjCaA9jL+B7wXP37l3HzMbp4XEc90lTufu52zUFJKihk4NxVxEj9Yt3VOL/x927hNq6bftdv9Z7/x5jjPlYa7/O6z7iMeYKihFR0YKiBguKaCWFCIKPQjRgsCKiBS0IgkUDQUKQWBJRJGpBsSgoSHxEDRoTc5N777n3nLNfa6+15pxjjO/Re28WWuvfN9bOPbkHrHgch73PnnOtOeY3vq/31lv7t///39yWQ1V4My0sVbmWwp1GRvfUqsJmXVM9Oe6i8LRmvni5bmuBdg9u4tDmGeYZeQiBcYgMzoQ/LyuXqhyHDggcBGJKcDzR9wcu5UuWXBm6jhDsfdZaGLseaib7zM2AGXPisfuh78iqvFvWrS2Mr/ntnNyPD1AzVf3iOm9o9u/2+oVKkFCYp7xt/BDEpfS2frPYXKMcm+zbdqqK7AtQXUUk1tfuUuDQBY7R1Di5FHQum8y9BXqxGSfu1u1Q5H5ZLKVwWQvndeV5XlmLyVMV660OKfDp3cC0ZKY5e9vOsnu7Vut2d640GmLg9XG0QGk73gO/mifIMxv8DHYon9dMH4w0+tclOv45HsbOTAKvC3Mt9D4N2Vu3fJhWylYF7SoM+3p32QB8E9l/uuJO2Cbfzu6Rk4tNoC7FprSfolXl1uoxN/Q7EZ5XI2DXYptMxCqpGOImu26H8jElGyfhflF9NP+ZpSXI3oppZMPakud2avm9ValsxCtv5UUJfNT3vJSyZQFLrbydV75zKAxe4avKFtgGV6VFP/D7lDiNHatmouwtVPXkqDYVpSM4XQiMBAaCt+nkBqG0+7uTRmU7QEpV+i7xkhdTetnbc1kLhz61p4liKMApBS7F1tzgsvkuGBdFxBEMPzRztZED7eBe3cFcxNxpP0yQWpUqW/GCmE8QYknv83VBV+X1qXdFmv9d2eXBzbOIYAfxm+eJLppBpIZmDYAllnVPGDc+n9hjnNeCqu3Xxdcdgrv8siVIBr/vWfWymvIq9kYijUF4WQsvS6aLgWktPPSJLpiU3yY2mHIwLyuHXpAqTFgS2XWekBalDSJaSuWbeeYUOwbngKyltWRl+0wCjCnxeOwNyVGjG/QxMnaJIUUupdo+WDOpM/PJJWdiNCUt2rypWsHxreN1O0yEa6nMRSk1MPadJUk6GUpWzHl5qUaOdn9FtOatfRSKqR0zynXKXDv3SFNLlI7DyJyvhFXpTwdqsMkHy1rpsIQdaeOdbM3beBJLJnIpLGvmbjgwDj1DEmIKXNfVRl/s0cj2hxdZQx+pZjrk6Jun166K3mKgfuvebAH0wySprZj2Tbn5rW0KwQflqhjaORUrjGcMee4FqqvajCztqE2weHPJxToIMVKpaAkbKuxvCyKEYJrOcUiMfUuaK+JWJARLgLo+cZ5mXrxQeDstHFLgdBwMHXREqGhzjzfT2t95vnBdVkdnPfFr98vPsYbcga3hPgaGPtHFwLJaQXCdV5ZaGbueU0o8jHcMj99j0ReuWhiwPb3WbFSV2HHqR3Q+g5r449XQc9elLdm5Tx1dMATrrdrZVmV/JuLPpRVtsPNef+t5n/X57dcvVIK05MLz+8krcIN8u07oRLgfe3QprKGQu84OIUdHbqX4Qdxoz2HK9gfjkDj4wrSFagv2Mi2gRsw9nQ62ebVNHbc9M+fKly8TX19nRw+MPCq10sXAfd/xekykAJfZHJSLmD+TUY6NdLoCr4aOjw+9SawFAsFHNrCRKd9cZ6qqo0XuWYQwl8xaW3twlzy34GfyTVssy1p5d1347G7cesYhhO2QUR/Fgf9ciw/S/Hg8W6qw9etb9RjEFD/zYsNy356nzfzPZmZFXt8dzE9pYA86Ah9L4K8+vbCi29DQMQSOfeSj48jb621a6F8AACAASURBVMzLnJmzDd286xJLLKwFU9ClQHolfP7uAtjg3Ko7l0lhU9LZr9wTjs4vJLTPHwJ3ydRJV2/3VWCuhZ9cJn74eHKZuQW2LsBnhwPv5pUYEg93HRKVpa507itSsgvfGycLRaqCG49q8IHA6kjU5mLWHoaddFJt3cylmh9JKaTks5VcAXRdMz9+uvA3fXxHJ2F79kEtYfxk6Hjy8RzJYfYQxW0UhNOQuCyZafV5ZKG1D4zLEItSoiOqjlK0QzSXzBBNqq6OaHx86Hkcev7C5283Q8JNC7mttUqIiZf3Vy5X46SFKKZG++bMr7w+MXayc6vElp0R4m0W4k9ephtOCHx8GLnvey7zClWoobU59nYIvk+2pShCiJFjH+mSuCJPyLmS18pdn7iuhV//5oWPH0+cukB+P9GdBo5BSO4UXXwCehoCZbKB2D0Gu13XYi7/mKgjByUkQxFztkRJnHcZnPibtVILHLtE6gJ919GlxLpm3r+8kBT6rjfhSi0bZCTsBoUqlpjHoES1z7qVO1p5/zIhIYJEiCPjoeOjw8jzsvKjpxemZfEE2Z+dwrVWHoYOqtkZRAl88/LMZ3cHLrlwOgwkSUZS14X314n7NDBXWPPK83UCCczFRqh0sqerc1Xu+kggcM3ZSON9BDIxwOu7R67rjEhk6E588f5sM77ABo97YoDiNh1GQs83BcvBhxWHsC/G2lAk9+/ZsEv1EUrVE/S2ditb/JOqNJ889ThpXlLm3p690FkUvndItj+9kC+leuwT3q8La6mc+khMoNl+gdyYMwUR1FWkXTCuWtrMRqFq2WJzU+l9Pq+ciyHjb6aFPkUexgHWzKnfUa8YDBz4cil8+f7FRHLaEkG7xk0S0ZA3//fDmPjoMG52AhVD70IKEAIvcybFe4buNakGVCZKCdynxGO0cSTvSuY6CfenO6YSUVGOyc4Czwxb4GCUwA+OR375dOR//fob1i3Gb9t7Awa3a9d9OPXv9vqFSpAAtFRqsKA59CZnzUV5e11JwQ6Fr18mDp3w6d1ojsw+GLSdhI3A21o/+IHVkBrww8Apsi6VoWCeNvOa+XqaOfswzw1h8aS+ARHmVGpwex8DP3l/8aBuT6lih2AMexLXuzfStvDkRiVmd4Dv9onvffqan7y/sNZCU2499ANdF+0eqSnu2iJqwUaxSuru1PHF+yufnkY8TdgqfkE2R1ZusnD1qkEER+4c1fAlJwI1WCC4ziufvz8jCHeHnoejVdcNXSq1BTA2omGrau+7jveXlVLq5kbdxchlzhxS4q7rtuRtqSY5nsSP3CCcjiOntfD8PG3BqWIS5VqF+4fODsNW6SgMYgfIWioPXcdDsnHHqyo/PI78pZfL9vmLKp9fZn74eIdw4xqNtce+f//AdDVeya2nCOrTrn23Nj+SPtiIhSBhe9Z9MHWZxWZrWyYf5Jqr8uPLhXOprNUQkhQCnSqnZAZuzWLgWgsv14XHw/AB3yRo4JQCL6p8NCa+e39gTJGneeV7j/cEAk8l86O3z1zWCXNQj9SS/VmbomzJhbvBuF8qwlpslMmYovNyDI1oLbpPxpE/+P2PeFoyl1JIGozkrfBmWfnmfOH1eERFSF1g1kzJkPpA6sOGVpmni7V2gkTmUng/L7ybzUOnoYUInBcjLo8p8undkTfTZUfuxA+w0tSDVjwUVQ5Ha8UnVYLP80td9DUZQTKPY8fYm8P3XDPrFPjq6YlfOoyQAoeY6Ar86HzhMCRWMqfBvIi0KNc5swQI0QbOjhKppfJyXizZrZX7oTNRR9GND1RqZZoKYbFD7jJZ8nccO8bBhntS8wc7v+2Z6kmRLUnZkOh20BWArByGkVfHxKFT3l2V33h6MsTZrTRazEOUL6cz79dIInBIkYd+oE+WpB2SxaTYwy99+siX7y+E85m7w0ApC9d5plahkFEVPjseQYXrtHKdJx6P/dYyGdx7KzqahAhfPL+jFYpRhO8+nvji6Wyt87iLAWzkhRWWa7VYHhBe9R2fPYzbOjQAqyE6bLPvWjFllhtscb8Zk4o4RxAT9ZRqQ3OflpVSje+Xq019aHFtWgsvUXgY3BUeAYlIgae88tOXK58MI5+MIy/XhZIrIVnS0naDKdps3RyGfvNqa8kW4DYyrfDp+dXXd3z5fLHEocJPnq3A/2zs+cHxyOtTxxAjivJ+Wfn1r97enANsZ4V6LEgpmPkj7lSfIr9yd7RC18+2LgY+uztwLpk8K4sUpvLMj9+eyTXz+z575JPDAa0LE0oWIMHb6cr65g1rNG7k2KwBuEl2ZG89VlX+1sd7fvN84ZyzeaC18QfauGHBz4AbSPB3ef1iJUhqEuA2KbAWNaQmAGIeN8XY26hGhpRcqmsKmOroSMuOG8FYMOnyZtSGPXjj2VgyhVqLaFGDPOeqNih08xJpFagrRbxdsVbl3Wymh4dgm1jUKjfYLDAA+M5x4K5P20IUCRsBdfMuwdsXufDZ3YHn2VQw96N5qFgSoJtXUoNANThx1A/kuy7ym7VwXTN3fXO38cRHlFjlhs2JX88Opza0uR1YdvjaYN+YEn0P3w0PBF+AzSTSFqe6L49+kBzZZHYj9dlCN9h2KZUyLcQQeDwMrjqyZ/nTd4amjSmaEvEqHPvOUMXTwJv3V2o11cqrw8Anh54fLZNBwaGpVXzEiBiPRINzfsSIqLkWPzx1+19AeJoWTr0ls+dceTdlrtcZxNq3jaez87BaYNHtgGkOwOJohnh7IaOspfC0Fr6ZFzNj3Fx6HYb3x9OCwpIr98fkJZFbJWAV4v3Y29rzZ1D8OX//bvAD2DzDarGg8DyvjMF4E100czazpGCzSzBpg//uYqM4mjJMMYfdLgQyPmUbgSicYsfbJVOxhKpz5PKLlwsKfD1ft5bH/dgRRRi6nkDg/WyWHffuiFs08rQufDOtLlywYJxviotlI8ELXV5Z1+Iu1E2AYHDF++eZCoxj4jjGbUq7EjivdXOQ793YUkvl1enIN/PMWiuPd0d+/Ys3fNYHHu8PSBS+uUx8cZ1ISfisS7xJlkxnJ2o/HnouWqi5Uj1TUcX4ZLVyOh6IPmut+a+1g9uKOFsTh6Ejr4WHu6MhPLXSBwuVnZvBbgWX7M7KYJy86pEwa+XB99j7aWZdC6UKX5/PDClyXsrmUdRa17YWG0+tEosiJZAVLqVwlABVeb5MvLvOSFU+vT/xeHfivFzJKH0KKIGa1VA3VVQqfZ9A3AfJZ801K/pmBIybDq5q3j6HaGOnrv4+IrZfc61Ik6WrKU8fx467IfmUhe2YoS3w6ipecXuV1pqv1QqEl2yJ/uxtM5vBuPOHbCyV83+iiQfUFb9DChwSHLvg55mje/aR+PI6M4RovK0o5pYvJnIAR2YxJkOg0HVmT9LbuOttmHcTkihWjHcxINrzg8cTP306bwjKWpSv5pXPhkwpHV0Svrou/MbTxW7KDfTewBsRV1NWI3h3MTI4KXsQE8/k6FYb2viekcOg5GtlWldqhT5EHg9HvqzPaEh8eZ05z4tNfpDAtK4gwZ3gjcfVDIObe3mLrXhr74f3R76aFr6ZF645m/2Ex8t2VisgN6ONvv36hUqQRECSbD3i4MFE3Aek9ZoFqxCGFNFVKeod4QCTw43Nhbto9aDjrPsqtInKdvBYEBhjgGYLIMadUd1dfG+uEmhwq7fqXDYfw+6M2mTSEZM2P4wDh2jVqTgmuLW4pCFeToSsZhomVO77ZIdvqR7g3KDPD/4P2jiwtRAl2ODJH7278MuPwfwyks+4WQs7bkTLgOw7wa/LH0KU4LN9LEGKMSISyWoztYqTUMGuozjP4rxmDl20wOe8q6kUzmvhzXXeJsi3pGypdnp3y2pJrhgaM+fsbUF7Xl0MdLFw13XULvG9w+AqMeWQkg2+7SO/8XJhbTA8bAe+kX9946lxn6oKr/uOt8vKVM1Dqory0+vEr8QjIUbmXJimhSbP16rGZdOI9M0zaf9H1OKNVbWtfWJOwLUo74vaqBV1Ob7qJuGV9kx0h48DYi7ZFR6GnmnNzGsmYFyXORfGTnZOWzAk7NAlRISXuRh5vu7Ga0up9DFw6OzztSJsm7OEXdvzbNL5ZgxZq0K0hHetpt7sQ2AphTeXme/enXwKt6FmL2vhaV6MO6L7jKh2iKcQSFptOLEGpqzMZTHPMSpDiMbbCjZN/LlkJ1jr1mpQNbL7F9eVSPChlj4GRsTI02LjTKKb0aZoMSJXGFIwC4ZSCcnW2y+9umNFuMwruRQOIRGi8c5++nIx5/tazdcoK1/NC6GPhl6o8Q2HlDiEaIc7kF3x+NHDkXm1wb3zmmkjfrpgFIFmNeI0P09+lb7rzSV6WckiLNl4hndDYvAxQ7adLVbmhgJhccrxaK5rIQWotdj4mmJt7a3i1oa++55RM2es2PXNNbhIw9RwfbC2WcmZrrPZWj/6+hsIllAc+o7n62QI8NBzWRaW1avCIGzjRJ28bnFBNiR7c/8GLstKiImQ86bOjCHQRnl0EvjoEBliNIGCWhIhTskQrIUVPElTtfb8UiqTJ0ON9G58oro5ORe3xuhdxNM6cCkGxi6Qo257akxWkp9X5a4z8r7qLmp5O018NgwcUtxa2y0ZMFRayNULET/811x4UTv7+hQtgfVrKF4gq1or/Vdf3fHVy9WsWDwQNk+g9/PCNClvpsVUwR8eb1u13rouXYockjDGyCEGTiluz6Uh7FkNkU8I02Lz4foYKCKklPj66Uwg2OBZ3YVLW6DzRPndtDCthceh4+PDsClpt5daadiHwKej8Rx/+yX7W+xnkag4cf7DE/z29QuWIFlrzRASM3o0FY7dxtkHgbbgr9rI1BYM2uKo7ERJQ5J08/xQd9SGHSWpCjmYkqoBe83q3WPLB2RmO3DV21P2n1XMf8E8ZVpCblOZXx8GTn3317XSbNCfJSvbtfomN/Gc0nemUFhWV3w58mHvsF+/TX5uBYAhWV2KPF1W3s8Lh9Gkr8Yfkl1aHuz9HZ3cpic39CM5uVfBLeQNXrLK1Vx751xYnSC71spaTDmRnRw5EFlVeZlX3s/rRoRNcFM1W1vhvFigFlFXIvndct8pkZsqLgqvDz1rtrEEXQiIwqvYcewiT+se6JDd94P2jLRVycJjl2zUgstsUeV5zUxV0FpsbES15FT9ZtVsB5FG0IhbFrA9h1WVazUkBQ8IxYOsOlG2rV2/rA9QuyDujyRtZIsQqjL0ydBO2XkcjdwuyNY+rk7sxRPM1ZWN8Sa5PqbIWk3paUurbQ7ZAuBazME7Bq+Qb5CtRoZWVeZcyWVF5LrNTbqshfOycl5WV6fY726k+uSfMYrtnUUsCUKF1A/M52f63gbhml+WrdXWvqxeKI0pMaSOV2Ph/bS6YaLubXbFxrMkiyWlKquYeWYQK8Kmta0x434dh4E3s/GkVOHtZUICzMCU89aitvesvMyZY/LhHmLE7SFF+qAsNXApZZNXtyKmrmUz3dza89wiFHava1GO48C6rpRsPEck+Hw8ISclBiXd/ownai05soSjFTKtTWmL9ZQSnVRTPeW8JUftyFFPjGOww3D2dZQkbKhXwIUv0RC0Oa+EAGPfMQ6G9kcRToeRUi0xbAaAoRVhze9KG+F83xztOs5roYvWbai5JXEWb4+d+cmdUrLZmh6r56IejxoqinNwLGkBUz1dcmHxMyJKGyLeYrPd2zb+qa39IDC6+WkIMERLjkb3iPjiaTHTWDWX8KLwZp7IxQY0J+9GtLZ5K1YbdcBCuiEFVc2UFhrpXP05ylbcN4DhYex5GHveXmcfOWKF3VSUKJU3k7Wsc/Gimg9f7esYjMN1SMIxRo4xMATZ7E2ieOLmqtnmlZRdUNE8xNZVGbqA5nXvaKiLfjy2FIUpF4slal6ID31344DOHqfUXN5PKXLXJxSxYeGYd10UE8XE8P+TUSNBbLL87VC/IMJd6phWG0ZZvdICuBa7gRIMJcrFZYgiRoStu19MdRihMd3tKzYkZq11g9vb8Mi2IdrubFJKYPPRaKdaVWVS5ST7IddFY+M/DP0HKgppm5+dYLc/eydfi+wmZSGQUqJk6+G3A7qFsOqVeVV77+yHVTuF3s0zn4UjXW+WBkNg8xyKMex8BbWefnZEI4Y2VBOq+7hUdW8lhzkbwmCLeq++TPFiCEUVaxm+uS68LBlV5ZOTBcmX2WSbwYP9ZSnEYAhS8zS3e7K70VZvywWxAbfRH2SbDN8JvO57plJYc7aKdEs/bsKAJ74hCCNG2C5q5o5Jrf15KZV1zZTVCKONDIsISjVS9lyIB7sXDcnIagnJdd39bPZ+uD2/1src6h6xZ2mJkbgTt4kURm+DodVNFO2nbgs+9aCqYOTpauiHeoVa/KA42KlAEDi5FcPTUsh13YikG5Lk17y6atQm3Ps4lQpDF3wgpvoaF6aXCzElogrvptnNXL0lKCCqJAn0EujFuFcpmAxZMUQHCdwNI9fL1R25rXDK/ju0eRwR6ELk2HUcu8RjOPC0rARHihtZVgL0XXPgd2SlWDugE7b2iT0DQ7mupTLNKykYwfr9ZaLvAxetPt+rHRCW3bXioiFXXfQqXwtDMPS3+O++zFa1617vADsHqbUPW/U7l8Ld4ch8vdj9C7KtZRFxZKq6fxXWymPvohsHuSkdrdBoCqYuCq9jR1XhOZuyqM1na9vG1oUlYyrKqo60Vh/eXaFKQ/SUy7oypmgJoUCXIg+HkYhyGAbWUlhKZl7tsAxOFt7XMo4U2CZpTztX3WLMXd/TqakDA5akfDwOto50n9+V1eLEVOxzraqsagFz7KK312wNr60I9SoqBEvgTRxXt5Zjm+84JOOuHXtLdE593OLmEM3bbsVsDIYQSGLP/8cvZ44pMcSWsBtVpD17K3rs4cVgZq23A2lNDZktKfMfcLB0W4MxBL5zbw7bz/OyRdKlKiO2ZqoPcd5u/G2W1Fpc2FifMZrj+tDQOoQQGtJWeL9kAkqZlMfjyHlZ7ZxIQgqWQg99sBEkoYmGrDXZxuxwcynPS0YwdetR4o2/l/2r7b8UAo8+7aDZ27QY2km0Ycs/4/ULlSCJCK9PI8GJ0pd15VXX8dh3/Ghezcclxs3o8d20MA42QLJVu42vMqv38gFU3BHbZjGVNktHsANHTL1S1CwDluwHUDu1bl6tstvhCPy8s6pqCO7LLOZX8fow+mfb38m8JxpSYZtTb4KdNBl9aAS4QoyBvhuYF8v4qwfmGMxV2VpwgMI388LvXCYIwnCMnCdDqQLBTTAdZrYT3zxbHJXJRcl53VyCF6waRK2V0lyzUej7RIiBa/HeeYClWAWaQtg4Rg25OLeZcKp8dj8yr8WGDzsCZRtbobaDQfzeWrJ27KL5iiC8rCt5MY7A948HHlIke1tFVPhsHHi/rFxzITr60KBa45PtSUlTaHzSm4HkUy6kZNDt5Xw1mbk/+9tgYi7q9hkeegtEt7YGDdnZHswW8G1BtHveBdlQ0msp3HmrsA1zvVlmKMFVZFatmSeOHRinzqair7X4GAqhijn1gntC4YiQKqPbGNAnPkP47XfW6mltCzBk1Dv5ZFU6besbEN1EDF2wdlMuhU+PB3793bPxSWzTbPegOtfjlBKDu98NfXToXuiCMi+Zd9cr5Ezfjax1AgJZbFivIT6W5B2HkV4Eiq3hnzxPvO563svC0lRHMbiwoaF7uq3NixZe9ZGcG19O3GcFPn++eEJcqLUw9onoa3RrW23PRuh7i03WIvcESkwR2MXIoUtoLpxnm3HUhAxbmnsDzd2GnhiEvrNQnkQ4DL2PWDJ7jeQWJmGvXRCx5DWktK1XVRspEbwI3AZUS0OPbRbW1ZPm9db0UMTVkZYwDFK51MrLsvBqGLygsffpgnCdV459pHpivqyLtYlqRaXj/nREqXzzVEghEoPNLSt6c8BvMTGQS+HqBVgrUs7zwqvDaP5kpfDxcaRzs9O5ZOPNVeWaKxcfsrqxuvwel4aKeTzvgw30dmDK2sqwqUrFP3+be9enyCFFhuQIpxdItfrMsmDEcxxtMSSvsOTM73/9QArRYrxaFm8GpJXivL3o1wBW9IjDQ7VWE7B0ps4TadQN+3ud0z1+cH/i6TqbB1ex2P9qHIla+MFxoAvw+XW2NcFOhLY14+jjhjJ2HHzfVd/WiHEPnTVFkICKcuoTl2UlBhv308XAu/mFzw4jx3FgCrCEynQt+2drZF1p3DPheVl5Ny3IaLM+WzKtXrC1ojOK0EngYRDO8wrehQqqnH52fvTzJUgi8gr4D4C/3bYS/4Kq/g83f/4PAf8l8Bv+rT+rqv+2iPwa8J/cvNUPgX9LVf89/7k/DvzLQAb+K1X91/5G11Fq5e3TFYL1rL/7eM/roeNymfju6cC1FH8wXlVlG01qc2XUqjr16sXHCbSXwcOB2Qm5sfXoMaO7ln5bhVK2I0Ka1tjexZROHhz3s27P+s+lckiR790d+Wgc7Md8AVetG9doGwOgTa7vUKTLxbXWbdHZlJDqnjQWcFI02XjJlVAVLcqslc+nmfdrIaNoVqQz4uPYJfoUmdeGztlh0AJDDNZOW9ZMkAhh50AU3UnnVh3ZAl3XwsOh4yWXG66QQfDNIPLQdUxr4eyuqhYH3MLBIfGlVK5rttAtlv1LEDSaK3WQBEGZRJk+8DUXFjK/PV/5pBv4QW98JIK5lL8aEnMtTM311eHCIB/CyTayxDZ5iokjkbdPRlpNsFXj6gida9a2ylWAl+crkzsGNwTQl4zD8tAO1DZXKQXhdd9bNeXpz6MfaLr9ywnujihS4dR3zLVyXa3P/tHhyOHQm4me35nQrtIPnehrtSE9j53xdCw4w8dR+O53XvM/f/WONhwTvVHzoJQCvdgRY0mrccyGYND2mCLfOx541ff8lXfPmzrI3wJ7N/O/OhwTD33n7uDeKlcbVv39h5HXh8Rfe3fm47EnxsTLujLlbFw13HsM472IWydcc2aVQkTIsx1WIVng7YMhlGtDhjHE4S51vL3MPB57Pj52hrRV5TufPPIXfuPHDCnRxeitzIpG95KiHbIWpJdSTbWWy9a6KgXQle9/fEf0w6KTynUW5tqSefbn7UWRaKNmW4wSAg/jyLosnlQaz2VIkWvJ7oZtpOTgCUmuyiWvwGpvi7W6Pn048n6et1aWNWM9EfYF/tE4cEyJ82rtcGm1ihh68Nh1fOc08J27kVWVp4s5WKcgSBfRUvjsfqSiPE0L86poF3ns7Xet60QtmXUtaDB1V5DA6uTwSFt3do8vpXBZs6GYWzIJGVNXHVLkusJffPe0tcX05rM5R3/jHjbEpW2wJIHXBzMuXEplypWpVEcnDLnpI4xd4OPjyDfXhUNnZOU+mqGhqCVXuShVxB3YlXmpXNaVg1hnZC4mzrhPvZtf6r49tjVlyVHCkPhSYdXCsdwWzy458qRJxLypBEssFRcFqPL9hztA+Po88fs/eiC4InUIiU9HQ1k+v16t5e9H05ako+YniCXnoSGTHss7CbydVqZqnLtOhLWDHOH7r+6YsvEPL9eZY9fxzfOF7376SCiFNVvhq4LxYjvZYiRbjFZ+er7wbp55PfZ8djwwODQaVHkulXMx8n6lMiZhiAPTnC1WOo/pZ71+XgTpTwD/jar+YRHpgd/Nm/u/U9V/4vYbqvqXgb/THqxE4MfAf+5f/8PAPwX8Hao6i8hnv9dF7DwHG9w658zL0vO90wH1imQulZelEgN8965HsID2shaes7VBAniflq1VZsx446nU6h40N315lK1PG0Pc2zx62xrZm3RtzM/WvDHQCIWN1NeIl6UWa4/tb2L/UnxMh95AzLrxnEQaguBDUV3B1sdIrcb5adXHtWR+e5qZq5o/FALR+tVBIm+vi/XJkyFog6uzpH02tbEmuWSTV4eIhED0dmcKu0TdorY6QRheHQaWYqS9XJQ314U+CJ+czLfZDubKtVqrpQ8dSy6uuLBZWatBMoTUss4GtRvaJtKq3baB7F8BYdHKT6YLX10m/uDjA3NZGTTxg8OBXgJ/5enFnayVPkQkRDTIhgIKQg0wFeGyVubFfI3AAnfjacDeomiJZWv7KeZRskpAN6PFljjbM+pD2P7pgs3PS9shaaf2NjKgebj4PW+ctEBgzoVf+fiRoU+8eXqxZLKUrTo2F3F8bxh62Ph0SaHDpO63iZdiirM28X5TJLdVr0JMQisdupZcV5ipfHoYeXB1YS6Zx67nJa8bH8/yU0vGJAberyspCA9NhKBuj+C/c+wSv/Tqniym0psdAYpiBNmk1s5IKRhqVyt9F61li1CCJVvRn3NLcFEb0yEo61L46pzJsvLDj3/A+Xplnicex5HPv/qGx8PgJGq7LzTpcYNc1NpKuVTOLytdF1jnwtBFa6+JyfI1V0duA6cu8YOHwNOSeT8thgBJ2PlAbjcQbte5QIgJnSbUfX7ac3F3Lbpg6JCWFlfEJwZ7/Ao2f9KMbheGZAaUwWNMwHzaKPYAem/pDjHx9TQZmqPKIUZHBIQUE50qcgxMc3b3cOEyLfS9iQNeHYbt3icJaFDO55dNvRp9byns5ostxqBcs/KyZk9qdzsNPIc/Twtx7Dn1Ha9L5d28tAhtBZ6/L9jMwUYMlpuWp6CE6CMzUmToKlxXnuZMEvMd6pPt2+QydLBC+roaYmwWDXVP4MSB06r0qaN2Fc0mRz+kyPfvDnsR7Ehe8MJKnQiuKEsxJfJx7OzzVzXFsj+Pt+eZ+8NgSXAMVrg6raDx7e6GxMOh55JXDnLb2lfGEPjOYeD10PHFdeKr63Jj1Gt7rKvCeSlclwt9Stz1vfGCkvDbz1c0VEKEkGGeC13X8e48MafElDOX1YrGJAFq5vOv3vH68YG7zw78pd/+gvvTkTnPprzd9qkh4yFEQJly5YuXiW/OMx8dB375/sTzWnibF2at9t5YqzxiZqOJYLxbg4y6KQAAIABJREFUfvbr90yQROQB+AeBf84uThdg+b1+7nd5/SHgr6rqb/nXfwz4d1V19vf98vd6A49ftpzVFtfLvPA7pfK90wg+2uKhj9bXlNY2M8jzKIkUKi9L8dVp72jycraZX8HNEN3Sz5HEnc+Rgikt3l9nmk3/dr/8QhsSgOycAUtsxSBH1W24qrrPzZZo+dtFR4sMxnTYMMiG7rQKpx3jim1s61fv6M6cCz+eFqZs5MLGXRKsxVJRDn2yGVMlg8LsFW0Iu0bA2nk+lFN2GFpCsLZCbe7LfpAFP3RFiCla28dh37s+ceo7FkfUOhE+GQ19isEcd6+5cK2VHKqNv2a78C05skAoWyKytz33CqOpDYNU8+dZrV0XsSGfD7EjhrCZudnnFsT1rxVhKvAyLz4/yNZZYH/uFk92Xlgjuba/EsQUEzNQHAHrYuDUB5asjBocLXRuCbINsVTd30faZ8Y9WDz73qt4W/Mv02IkxmhrOPiMpLYObZAuGGxvdzP4SbUnmA0favdceH0YeT/NNNJt4w1FvwfVr7WNmXg8DNzHwOike0GREPibX93xfl356fm6mf+1A04wVKBfM30wBKBVDwHdkoMxwLXdHyw5Sn4tKLzMK8dqLZEuBAYiSYQxJa4lW8KgLRTo5sDcELtSlCqFX/34IxKVMQT6fuDQd2Q9Mp+fDZGolWnNJj0OwmFMxCjMS7Hkqdj+jlFY5koVS+s1CVMtPL+ZGKPweOjoe0OkXg3CEAPfXGb2l63r5rrcECZ7lusmGhij2RC0FvuQhHeXhe++euDTQ09fC1+8XJnFkPOhS/RdQgK8myYUs0booo2dQPckqeV+glujROG7TqoWhJP7ROUKXbSiYMqV1EWkGPF6zYZQHPpkCTjGYXuaZ5a1EHwgeYrR5yuakWH0O9AKgrkYd8hy0b1IdRDNPZNgKhkV5fVx4LJmcm08obZ3dbu/ghcbvqiqKpdcSNPKkixBuusSc3L+o+9ZQwkNCZ/WvHVDW8LWLDdqU86qF/wiXNbMqR84DYnOi6aIPRsTw4Qt3gbsB1c1VH12BZpgFje2Hp3nJva965IZB0NjmyhJKqyrnRPXnOlE+M7huBlr3oQ2BGuLfjL2HGLi63lhcSpGi8eGwsOqhuStmvjVj+85LJklC9d54TyZkizXzJwz17BusVME+i7wxWUhl8rhMHLoO+MslWyJ/QY6+L2VXVjTQIOiytvLzLTYkGYVIQ4BHYUBt2a4LMyzceRQ4G7kZ71+HgTph8BXwH8oIn8Q+F+Af0VVv+3P/feLyP8O/AT4V1X1//zWn/8R4D+++foPAP+AiPw7wOQ/8z/9jS6kpQNWvXqSUSoXzUbO9gBJ8AnkulfwXbAbWkXoo23m2k4e3SsHxI3/dIfKlV0N1IKEeADK7ZBq1/dhvvRBctSerKpXFzkbl8TftB1UIFsidPvaqlM/JNV3oSl2LLEr1e6J/S2zUv96WTg3mfZ+Iz0ZFJpaY2v1KEhThfmKbFeGV5Qt+UAEcYt7gm5kOtXqSJVy6Gxy9mWxzXPXJ/MGqtVImVGQaP4W0W3xn9fCqspC3aJiu4/tY4hfz3Y1+37dkgDxwJ6wChARRwmddKumdBhiRMVaQbF9bixwP19nMsZzUOtnQmzqwA/vaQu1jRCJX6OqEqoppVr12EUjsr+cZ+6HYVs/ex4o28+3rPvDz3+bFO7/oRhCGoIrEm88tPbnuH/d9kGiSeuNP7UTI/dE/NPjwGXJhpj4m+w5qafqIpiHlz2DY/JRHLiKToR754td17L5KCG7WaNiJpdP68oQgx2OW2JojyAFSNWQNlE7VOJ+ucZvy4VjHzgNHaeu42VKhBg9qBfWuidJKexmgwGzjJiz8PHJJPdDDMTOVH2XxbxVYmiiBbvXVdUVmyaxz81jRZV58RlRAcSJp8+XmWVeKV3iMCY6ddJtjIRg3Me5FGt9+gJoh3uLSaUq13k2qwCfilOBbvPhcll5NNNLUei6hC6ZFCPHvqeKMpe8IeK5PRMMFdkd3f05eFJuHLnIWtxmQ4K126sLCgyYIsZAqYVlMf7ieVkpmAK58QnX4iToUhn7jrvDyDgk3jzNewFE48lZ4tIsIbY4LULy0TBdDO6wbsnLmBJ3Q8/7aaaZuezLe+eLcfNf1ZNoQ+fNLmPNlcnNWIsn1qt/3lwgN6GKv2tAtnggbvaqLdYLzuG0WNAHR5i3s0ChUS62m2/rKYZA8E6BVqWKIV1tSLV4xbSUQl/jlkzjv78UM29dsg04Pjq1QWorwj58jSHQ9UIhuUmtcR2bpUYU2ZTKUyncjz2vjwe+erqQV8irs35FWXJm7COHrnOk0pKw7Pzfy7RSi3J/6M1/rVRIO48K/3yO/drnVfvOmk2dW51THInUAKkzQ9d1tcH0Qay7cCk/20v750mQEvB3AX9cVf+ciPwJ4F8H/s2bv/PngV9V1RcR+ceB/wL4W7YPYm25fxL4N771vq+Bvw/4e4D/VER+qN+ythSRPwr8UYB+GPd0E7ZqG0x2e/Jko/isnZZF42hBVGXK1oIyY7G9xVY9MAk4+e8mi2j/t5mFGVw6eh/+w4Pilr/yrUy8VXzAebWs/VXfcQx7hdBggC0R2g7FHSVR3Q81vf26YsQ/rFKYa+X9mnkzr74wg/Ob9oAiwdohNh/N245+X1XdWHO7OPkAeduehVg3X6Ju3lSWVVmV9TB0FGxURwmVKIElF0o2tU+MshHD27E7hECnbS673qAxe+C55cHY4Ri2ww0sQbyVwh9i3HyiFjW0aPUkuEmSNfh4kgoryqVU3p1nYgob36Buj+am9vTbsiVI7fTCg5GTMoe7RIqWwAumPnp+WXjsui2o3XxMf/P9me3J0m0mvmdn+sHaseqzyRFaFbu/TM69lEJUQ9OOMfq+Ccb7+ZYU/rG30TkXVzPh1Wsjmze/lqqWACxrgcEykHbE2WNTDiny8WFgrZWv82zP2a+xFR/POXPIkceUtjW7ec6oqWyk6X/F1rMCKtZCQ40jcRoH+gQfjwPvczE1LObzs6oFyzbuQfxedykwlGSS82Xl/jAQo8n5n69nIylnM8hEhK6zw68W49fVqsQkhBTIc2GeCsf7jtQHYjJkdplXVHQzxNsSAbU1+zh0XFZhKsXaNOwIhEorAIXrsm7fb3L55tcVRXgYe2IpXLIN+9Q+MXSRu7Hn0HU8O3rTp8isRkLfDDbxwa6yPz9Lknwn6s3XfghrNRpBq64slqh7krn1R/PIisZ/RITDYNMPXt+fOB46cs2Az8T0xHitxuWaSr2ZS7hTFswDqPG09lhwXhZOw8B5WV2NZtdbt8/S9AKyqZrbm1g8Vdal8qSLtascBmoFyB4LZNum+6q3+9d5DK4eLzItfsumybUkkG0ftxY8IhsVIIq14gR4KQul7G37ZjZZpXUmiokQfAfZWebCmJy30VtsFiZ7uN8oIn4vYmCLo67NMXWix6Ki7pq/Zn7nmxd+7fsfG0fW26tFy0ZmP/Ydj0PPIaUtJnQxEGO0Ynqt3I3m63VeqreL95jaCqbYzkZVtNrZVUQJKZqpbq0sVzUrkNWoBjG0uXTRivCf8fp5EqTfAX5HVf+cf/2fYQnS7cU+3fz3fy0i/76IfKKqX/u3/zHgz6vqF9963z/rCdH/KCIV+ARDq27f+08DfxrgdP+gInsWjLS+rJpi7Tia945zUdWziZZKaKk8XRYOB7P0bxJ/C8o21K96Btt4FlmtD5urexj590C5G3rm6jLtm5NSYFP7bJeqrqqPUIp5dTR/mV+9P9J1Yaue/ZO3N2Q7HYXtd7U/bXPAwA715ntxLpWvp4WvpsV4Bi4h2QCXai24QQOvhx5dC+uS6dwQD++js/kLmb1/qAK1bm3H2+trFVVL2IIYUnM/Drw9XwmqHKMlU323GwHu+WhLbiyB6MRmzJXQUsFws1n35KgT4TF0HEJkcGm4Sc2bSrGFLxti26PUIFxr5arqBD626eRRqyVIa+U6LzfTnv3ZhvYMhC1b2p4VoHUb4onuUuLLvPJwTHZoBlOaTLlu/IKGRrUkuEVtYU88zBjPeXFtYzfozO9ewA+jqgzBOEm371+1DaKsXNdih2tV7vuOu9MRtCXsH67fqkCBX3k48tVl5v20mELSHjiKD6+tcM4rqPLJ0G1oR/swW/NTlY9GS7a+vk52r3DvLX/aFfjxZeL+4c6vxa5pLpWv55lSjb+W1wIxuEuwffZDCBy6jrvjAekSP3n3xPdPR774+i0xWNuNDuqq295Kvh5bG+mju4G/9uVbHo4DqRPyWnl7Nq5FyZnLtLq/lMIV7u86G5GwVjSpGWamQJciVWe6PnDoekBYcubT+5H308Inp5FDjNv6b+qxlAJD7JiLjZB5Xs1J3FcDQz/Qp47r89OWoLb2Wjvj51KoEjhXOEQTZayl8kuv73k89LzMK9dFiArrUiiidMlQzyln1iLc9zaep0nYPdQhgNvuGOKFclUYqpOSV1MWvsyZkgunITGt8xbr1lrJ1VzQuxh4fXfP68cjUeDp5cJX757p+uiz40wFe16t/W4Iyc6HScH4iinJtn5sn9jfWVUZVHg19j5QvHj5ZYu8+eIUNfPH6JlKwFTT21YX3MVdnSJxU6wETM2ne0di28eq7mu3R5ON3M9NgRUgVivEtHosu01KZbcS6IKR8c9rpit7cWXWxzbkOIWdr+Zb1QoQ7D72En3PuJpVbe9L2L2TgsfHqsLTvHLO2bsNSi9muvmUF5bSUD3haVn4v798Q6hKpVCDWz4U4fGY+Pg40AVvn/sZdhp9Xh8g0XifwzDQzZZY7U0WQ496hGOfyGreSEWNQxl8/xo3C87XzHxdjaguwmFINkS3++tsJj94/Z4Jkqp+LiK/LSK/5qTrPwT8xdu/IyLfBb5QVRWRv9ceMW9u/so/zYftNTCU6R8B/lsR+QNAD3zN7/HaiabtTFWiRC7rwqodo6Q2iYSge8unYoqn05jM4ySozzpiC7yXpoLD5JRBbdRCVVOFrE6aM7f7wPM8Qa1uUiVbpR18U96W6zaeQTYoGqyHrmSecuZ1F7mtebZjd1MwtSpFN9L4bcVStPLusvB6SJyLDVN9v2YIlr1LCxQqW9/89dDzw7sTyQptzteVLprD9jpnJIKE5Amf6Viboku2Hev5VnV1iEuprCccqGKH+NgnirvQilRvY/nUGAnbexWtboimjEFYqvBcqxMPDQk8SOQUIscQSWLPaycOe1vNA1ZQ3aoxEXh/nfliXnheFndDtwRLGHjsA9e5sOSAUFiXmWCEhA8rQX/Ozu5hexLiflNezbghM6gw9kKSxCl1NqeJwrUU3r2fGIZkkc8RsdaWvW2xbs1etWewCW79MQQPGKWaW3xdlYN23J9GgmTWatPXr7mQnT8w5UKtzU1KSNlaCH1Sr6J977SkV3ajy8+OI3d9z5tpYs2Vx+PIT98+m4sx4i7RkZnKF/PMq5QsAXBUR24O8IAXFGqk4jacuZkWBhHer4UH91SainJZrY2VUO7Hnvu+Y6nWjlrXwl3X89Gre+7Gjlzgm6czT5cLb56e+OThFU/ns903b8fnClct9L2rP11I0VVFpfByudpedHuKl+uCBjtUjseew2AtjCVXlrX4kE/ft9lJxjFuYyCCwrHa3MH7u97ad2FPJLVxF32vjlEYog37vToxdy6FZZlZ5pleAkswYn0fwkZKtbMkIGp2HM+1olkoYsOIuyhM88I0W0tDxFyrc1ZSEgomhV+ulpA8jsPGU1HYXN4bztC4lJM/p0NMIMJlXlhy4aPDQAyBgXYY65asiwReLi9M68w0Z05jx9/2+77LJ/cn/vu/9FegwiXvHkzGw2pli62hLjSFpq1X45nY30ghsJbM62PPGIWXebF2sXN9RAJFbf0efO/F1JBmK3ArsCyZrMrjYFyzUszyIItCESQ4Bu+JjWJk8Fqb/YPzDJsC+qbGaijc9r1WG3u8DeKjRQSaWe+hS1zXiYIpT1MKXrBWLvMCqjyHlUOXGLpE53zQv/rmmVenA1p2i4PtIhxJ3b+2zspvvVx4WZu6tVJVuI/Cc1455w893VbFBqvPlfux43uvDojAy3Xl0+Poz0b3zgiG3pesPB4Sd4eeZ115d56ISZ0i40/er2/oLGsMiNEXemvTdkTzUkuOPobOLH60Q9VG2CS3MfkAl/jW6+dVsf1x4D/yVtlfA/55EfmX/Eb8KeAPA39MRDLGm/wjrVUmIkfgHwX+xW+9558B/oyI/B8Y6fuf/XZ77Xd7tQ/TNkcXEiUXCIHPXybWw8B3DgO55L2Xuyf33MfIm2VlVd2gSMVIpdkryOoLL4RAwgi7U84bIlWKEWi7JDyOJ6bV+ERrqU6Sbpmubhm9dcAqTV7dsIFclc/PE4+uQtgAlQ0V0M0QbK++hRjhcjX5/JIzuSixCzyVyk+nhTU6NyA7DopV1grcpcSn48in40AMJu1ucG6TdQsV0YgPI7JgWAuRQDE9ESGayZZIRCnkLA2q2p6Rzdsycl4SyP4ZKm5t0DQkfvimEFi08Pay8LLaYMcfHAcP/NFvg0trpZn6wUaGb+9389zbPaUqY7BRHM/zuqEEKsLzsrCswi+9OjHXyvPViYghUKptxlJu+FBewbXfah/YPnfQRjX25MVOXCCgbjOQ1WYfIcGqGNmfsThKotoSnz1BRnZFS/u6qDLXQlbs2fh9DNEI+++mmcuSzSmYthf8ABBDDgpGZv3yMvHLDyfaZMLmCdRSwRjNtbqg3A2R++NolXHNfPGuXacZss5ZbTZfEJ6yHSqv+h4RnP9l9/OzceAQA3/hzbvtOdoZYfezoHw1T1AHmz6PtYWDz4abakZD5NB33IWey7zw+nAk5MK7tzNLNfdjKsS+4/1yMaPSUlhX3UQZig227eM+A/F5Wnj7vPBwP7LWAtUMT6/TupHQliUjWum6yJqVNEbma6YsdRvGPB4Sv/LJK4oqb55eSKp853SgqJGkzeJDsJxUQYWw8WT2uICaa3EXIm8vMyvmpTP5dedi6JmKr9UgjMG8sYIrL7Uq5Mq7OfPlb75hSOyovK+7JN6uj96Cx9bUJa8MMW2IdBNKFC+Okie06qHgm2XhPGfzvBFhVmXRuvHKisLq7eyqyjBE+jTy0ePA2EeWUvm/fuvHHPqBl7wgUYgE97mxax06W81NEt7awtsewQukALmsXGalC6YkDGI2BUXE4rM6lzXYrrYh0jBfM8tSN8f+pPAsZowITksU2f7ZiP/q9gHb5t2fY1FYxQx2VarPFbUBv7WaoKWL0QuGsJ0lZgMS3K7AbV48xi+5IM49jIhbIwhRK/OaN0Q4KPzk6zOfv3nhfuh4HHvuh7QNum1Iqm0DaUuS82II0dZyE1NIn13h2tYrGD0jSuB46Dh0iQ4DER4O/VbUie9z9Vj6cEosNVOxvfOdw4n5vFBFXUBpbcYtKor4AHBtwJIlRqFxj4VDTMTebTxKpYR9+saSTWDxs14/V4Kkqv8b8Hd/69t/6ubP/yTwJ3/Gz16Aj3+X7y/AP/Pz/P72Ck5cvnUcbpVmUCPuXXNhqubLk2K86YX7oSHwSei5lry1yopbzEcg6s5dav3N1Q0iG5Tqe5BOhUhl6BISxMzHcmVrfcBWJQNuoqhb0dAIfLuBIRuC0H7slpCO4nSUan30jBHOaoOZYQkWZLsYCV6dUYKjBvDZOPLR0HPXJSdH+gGhrRKzpK3NT7t5YL4g1WeU7UFA80qtxTf+npWEEDfuQBILUs1jZksGPelsiaoWC7QxBnpf8dVZqQUbGlmpLCgLBjF3oUWdGxJ9+4anMNUDeUJ46AKXceDdtFBbsNbIw9Dxaux4XguXOW/31C7REZ7tnff7s6/GnTvQOD9+RllvXIyf8rJkrtn8abTsxpytHazsyElLmPBrWGt1Yui+PgSrllE7aATh1CUehp6gwnVpQ1x3eX571WoH7jF1nLrEMSWrll1arD6eJdfKT6cL11x47Hsua2bsEj+4v6NP1lYbUqDWhgzJhjzlqu5iX4DMR4cB9NYE0Yjcv/b6gd96OnuSsCtUwCahv1kWXsWOwW/oujnlB0QCaOLQdXz66oHzm/dc1VpxSzFSeXBZs61vu39F94n0qHEXptowNbjrEv2QuT8M3A8907wyzashBw1J8c8XIhBsxAywoSxgTtHJ2wgP40B0dPvh2JGd1I2qt1TYWz/qTtwNYfB/bEyDrZvmyi0YhyPcPN+Excy+SwzjYDGmKpJ6vnl+AcTUrVsyvqMYEdnascF92oKbn4aGbPsmMZLsftC11+thJK8XcA5ZzjZOZamVXpw/4odZjJFzXnk4BWqZeXpeKDmzzgtLDPREHw2kTr6GITkZu+17Px+a5UcEHEzbuFJzLoTOfm+KkaKF5rIffY9YQWtr45yzqQ+9kJQQKBnWNaNjJCZ7r6gN4WhxaOdDVk8A9eY5bu3UZrWg+17/AD1qkdm7ENC4s4Z8RYTP7k+8PU8EN4BUfy/bXZY8WeFSmBYrXCumPH5/tWKxd2dvceVyaz5tLVUsCc2+LoMEDhJ48baaNi+b9hkClLmQToZUWjgLO/qo1R3A2/6zpPZu7FizJbvHoSNJ4KzGRfO6BYCjG9/uMZONSlC8oNvut1inIEjg/bJCsvVdSuUy/b9MkP6/8uqCKRRWP8wbjwNaVu6DUHPmzg+XEMI2zbjFjWOyuUdb9up3sXNr8zZfJ7gP0i15DtgWTpTA02VBJTIOHfejSf9bT/4G+CGg22LYwwdbFvx2Wngc+g+Qga0nvfWojZS65sJSCrU0srZX7aV6u8iCd9v0EuGh6zmmwOt+4JTMw2RXStx0YcUOzXGIm6JAtCWGjezbAhEGz7qK4tbrqW10465YctZcs8d4g5iwJwS1qa18AG4s/jmcgLBWI+pVNeUIap+/D3iqefOG/sWu3GtJpzCK8Nndgao22LKTyEPf8/rY0SchZruH6snqB1ycm8Nn/577H0kLSsItIoTuB3GTQ2uFvNbbztoHr/ZE9kPckt2V6ty5m7/pv0OwitV8jnYei6GtZpzW3lQx1dbYJx6HnlOXfHhnYPaDv6jxV2Zvzb1bbGRMM8t7qMp8yAzBPEkOyfSCRvhvAdPaV0XVFWOrJUi0RHZP9F73HS/jwDfruq3r9iwVmGrhxZ14owjHoUN9wO7j/eBole3OT+5GfvNlMrWYmmy80JCwNtjagqiDIVuC2x6sYMVR5wR9z4+31plES+RTNMLn5sPlG7d9hBgDdzFxPk9ojKRoh1HfBV4deuYl2zR4J8laClC3AczS1l0rGLS1tvBCROmSqSzD9hN7nCpVjbTqErfgCfT9YWBdVtZsH74TS7aua7GCSDHDP6EtMHcw3mNTO+RF9mT9gz9zKLR9BsE8lFAl095PIAiTVspaOM4XtBbKmiEX83BaDQHrgkXT1akNfTLCbduWt2VdG+Dbbt3ulG2Iqwlz7Hfn0iw27LUdvGLePW0O2m6vYolxay829aRxf6xVLNt90u1c2Yqpm0K7rcHd+kW3v8NtHNmCGsZF9L8TROhTME5sKT4DTX2szN7Kpv2OXHg/l+19S1Uua+abaYYw8DDciobaj9pD/WjsOa8GBOSq23gW3X6J7Hvb70fyYktENt8xu1d+xoq5sKuLq/oUDRXSCqHy/3D3Zt2SLMd15udTRGTmGarqTrgAKVBid+u5//9r/4F+US+tlkiIFEFc1HSmzIwIH6wfzDwiCxT6/TLXKqBu1anMjAh3c7Nt2/Z+d39kfWlk9kR+MFN3QRR52wJiP7M0HldEAQ4jb2MxOpdKsSS71D2a/uXrV5UgBe+5GyKr71mjcF1v7A9E0Y9rLhxjsjFSG0G/6d5pT9KD9V6BDV1Y+iFjGejWI3WWyct+eBURvp4zQuaH4DlNI3MqXNf6DaIR7b0T2iMvltN3Ih3B8fG6qDibweC9DNBKQTOHPgWyrn26RGHj7kbdGviqEyxrbTTRhGkaIj8NI/cpbIrEzYhJt5tnO7BEE8vQt4YzCIRdUFAPkm8tTHqA0P+0xMo+K3h9NtZVUjSlYVylvaVUrEVXqyZ8tUFLerirlrZ+j17hdeXk/bW3L/uF3aoRC0AT7sbE45hUjBHPuylyn7wpSst273W9OPP/2it49jj5Tey6/TN7hJbQadVP00o53BK/7QD6NqfS/11FA0VpmhR+u5XdngDqO22JY26N3CqDBeqIei/1QOVQUcz7MfFuHHYiuoPJec61cC2V11w456xGqk2D2aupns+18bJmTimom33SFbPQ9V5ke6bNyNStqf/cGG6azfZgPPDjNPCUs1k52P3HNF2a8NYKELmPkeOQWJfCXHUyNUZFN57fVt7dH0hrweWKCwJOpzqxgzz3lpvd7406awd2H/c/ZzVOpjWzwRBi8uTcduQgeqIF7Fb7XhQ96JwFdPE8LTPeuFnTlDgOap8SUiQG1bVRQUG9L910uaLcjJ5IlwZL7tY7ekimoJNQtG+ntzSxbmb/s27UgWVdebw78PaqKtutaSI3BG1rVWPE9oSrIES8EXht34oY2qCv7gvXp4KH4HlbViXRWzKanBLnW60sTX3P+qF2aYUmwufzm6IZqGdZRZBSWUWIZjJbBMbgTJph32v7TugIt6HvVuz2JHetFY+S5x3aaunIW0+MlE8IteghvF2paAHhB21j91ZRjz29gHPb0rYCy2IItt/b9mH93zVa88bHctu50z/WobGz9FuGJUhmO/J4nPh6vrLWSq1YS1yL0WyBW9BOy6fX635Gef375zlzGBPvbq5FY61FnaYeloPzXFzlLWc+rat+s5txQtf3LA4SVmAod9d7cHXTKP3m2vQWdX6ikJtqSr27O/J8mSnrCk65cYcYVGaj33tD4fo971u5y1UUa/f1hHKtzboKO1fwf/X6VSVIAD8/HHBeL/B1LkZAFhX/aqqSqWhEwLkoAAAgAElEQVSG0IqjRWwx9Upbb8bD6cjz2xmkErzXm4iZ0nq/BaPgdeIjeb+1b8BRWuXzy7JZM5znzBA9x5R4Mh1NLzoBN3m/t6zECKBONZkEHf1cmvC6ZtygJMvWuqeQ9o7X3DbdHkHMzK/XR43mHDjP07wq6Rfln0ze8beHA4Pv3CpLeFBTQBVVa3t2BOCE61K5OyqxTb3duit3r0pvkoBeguut2TaWx4iJxmvyXi0gHP2w7JN+snG2XtdCLsJclF8SghrFqhXMrl7SKxRVlPYgvQW6/7k4TZq3rM8Ok3Nu5HXhFAPvHxT639iTVqE36ZNC9l62+frps7fPrElk16DCiVYhCVv70yfPUgvXXPG1KDesb1Dchl722+i9o3h4MYmG/lke5S/ps7zlP7FfP44izRA3RW8eUuTdMHA/DoxRSeHfBPDt0WtF/T+ezrzkBQMTdALT7AucA4LaG3ydV346Tnjn1aerEzidroBrVzl2GjQr8IeXM//5wz3Ki9tFCBDhLjojelqi3eOu3ZhabW83LY6S9xymwC9fXvSAHwJSG//3W+an90e+zq/QGqNXRk/OarEgthe6SUhxfmvh4rVCnXzg85IZD4khRYIPeLoZsJbYUtV2IkfPMOm+zWvVNpYFX0HHqdOQqEXFIx+mxLtpUAPn3NT3Lmoxd6lqjN2cinc2UcSgI0KXNWtbLPRWocd5ncyZr7s/Wt9ba2k4VAvpNCRybfzx0xM///i4HejFWrciwhQi57Z+w6Psyu7BeWvvifGMGlMI3GwN2/3CYRj4+PqkQxRW7VeEKUaqg9i8ajzZug8NXFQS/BhUSBbg48uZ5NRge6l676MlBj05ArbhmCZ9P7Wtpb/9EPveHaJniIk1NOZcGWzf6Ni7xv5VGsGr/MeWlFhLNx10SKTHPd2jyvXsE159b2ne3fGrvTjE63OqVdd09VpEaKtrTzS6vZIHqts5jtjzC/Yzh6RTo8VQHUVt2BCyJjDnqsTn4I3fqi3JMekevqn97IZ1zqh+51OKqiXnBOblBi23BEmE2GAYPTFFjlELARDWVpVLuj2Y/vDAi4OqkhbFTNpfL5mHQ6JWnbJWHowp9je27xWC3p+O0vWeUWmN6nSfJNfRfUXPc9MpzTH9OzGrBWhe9YyO0XP3MPHdIeGaQswASxOuFe58oFYd1XyyxXGIgas0/nyeWcobhyGSfKC2SmuO6prynBCCTSFkUfLcYBVJQRGql3NmrUpDjiHQWmWeV8aa+BC1Lm00cxhvFDEPJGCIyhdwrhP6dMM/LatK14861bVaYtRRLq+nNX3Y3XsNVtfS+JozL6XigQ9M5Na4j5G/Ox32BS83eYwdsho4HL2Q70dm32DR63ddpeiPtYZzQZMSW4aKWpgI3jbfrklG1T6gamV41ShRUlyzCUKNLOLUluSSheiCqlYbKnE3Rnwya0oRnB0WwbltIygVUjRRMvuPflAgO7/ivGaW6nh/HG16r8PfsKwq/Pd0nrnMmTEGrVi8h6holiYI2iLTKQjjT9kNdmhSmKv+u2nQw+Dj+aKH8Q3cDM6mqBy//+6OMepI/px3jsfli5KCO2qWNz5BH9fVhGifRds1nZxlkf/Hu3ccTbUYe2oebS/k1viyrobY7C25F1Mc1naRJuLVtIZaE3zwZBHe8spbztx5z92QeMkFyVqkaEtVD5jOGaE1MvCHpzd+/3hHn7Xq+XkRx0NMvBRFkbwVNV0/BxznWmk47gYzQS1q35FL5e1iBphj4OXtwjREllJYsloLzDUTgicNcTvEIiCl6VSrIR+j6UFFE/YMpdJKZV5WLnMmpagDCpjfod3zSy2bqGJKqi/eqnA4HWnLymKtjylpu+i6avAutTHEoGbOa9HkzW7KNo1FRye0uGoiRlZum0nz8aR8sFJkU0oeh8RcC1+Xlbe1kHzgu8c7vj69ahLdP6cJV6l4Kt55I90qauhao66On94n/vnpjKD2TOtWognNFOSUj+T5p49PWytJ963yQKcU+Wrq6ZN5CzYgFM+zZNZWleDrNdGZl0L2HoKjtaoDGjEyusRaMw3ZWi7d7Nhhmj64bWc4m3iu0lTYcdV21HFIHIZErQVXFa0Wp62ZIMLhmCilkVdFuIYpEqLx9CxDU4VqrD12k431/3Id/ZWtSPc38FBwHUnX92tW3GCipk1AbBDI+25Aq0WLN7HN1uAwJk0aUKHYof/7oEiud55hiDoRZoWLoPHwbx+PjF7/u3dPenxt9pC8dW+WpppavvM/LQwH2XllMXnuB7WJyoaGDs7TUOQvS2NulbWpLMrB67TqYMaQIqq6LSFwnAYFC2plLY3cMlP0erZgCPmG5O33Xew6Go5seUMwLaro1O/zXP6dcJDAIHHXiYkOJx7XIAUNHBlVLPYhMueZT2vh82XmkssW7HX03KvCaAqk6BFXqbZgBTYyoBdMq0GFwi5r5bKo5gJoxXAaPFP0TClwfxx5eskqYuZk44s4p1NfVRrVZAK8Hdze78HudcnU2hixTdH2vr8eklq5heB5Xlae18K5mtiXJQxf3ma+Px14GIZtxL9vBtf/wLGTA7dlZZWJ6Mafl8rpEFAbkYqYBlIYAsoZ0kkLDdLe+FqyIzg2AhxsmwWd52ZBE6PqHc3GV+e18Ol15sP9Hb99f+T1D+owXar67BzHgdo0AezCYM3abpeSdYTcmV4B+xyUM1i7OZ0ae8kK95ZWtmSu98Zb0SmVtWhSklxgCAZP44hxRxB9UG8yMRuGzhHqD/vuOHKthdesnoG9+lIPITgOgQ/HkcfjyPvDiDSdoBm9EqNLU5uQU4rkpu1GfXlcgAG/JW9jCAze8XVdeZcSd1GrttaEX75c+A/f3WvQl/0pL6IijFebTIx2H6rTKc0peM49I8RG/VvbJkg6wplFvfVOd0dSDJysxfySFcHSu+UMrZJtRPnrWjhdZh5HrUYVAHX4Cv/7w5G30vi0rHyyFk3vToiYSjWNMHk+eOH+3SOf18ovT6+s68r7u4nLunIKjofxSDhMnEvl4/OZ43Hi4fTA17dnMLJxa2jibomUHzTlnHPlLiWm6USQwrtJxRU/XhdcVN6HSEcCjM+wNDXlHOOm8+WS4+X8ximYovcYOQyJea0mSaIV8VobQ3D87n5CRCcrc1NUqxciVeCyNAqVdS44J+TkydFzSJHuh5iSI4TAsmo7cSlVJ95sFbl5ZvSeuzGq3IPsI/oNiKb0XS3R9d7z4W7gfz6ft3jVRBOzpVSmQeOob45aGuvaffZkIzAjOjn4tqyKIrWdPuBQjaZzcRTXuJRKfb1Sc2XOjePBb473qrVUyOXC/XEiUzcURZF1Qz3ZUxXlrOrzWkozVFxJ/FWE+8PI5zedhepJQ7NcJnhBvNAM/famIyOGYHj2ibnmtLAMNtzQJ82c1+k7Tfh3PpJKJijCI3rS6+d7PS86sdu53gVx5uVoFiQ9MXAd4W0MIWzIdnTqIzlFx9KaThHimKbIUpRbNaTAlHTya0MCLV71+xcVAmau8D/Pb7yuOhHnvBKdVSBYDJTwnE6J+zFQUA04ZwVYMRzxqZQbX0dNSrPJiiy1I2iOS8m08xsInMYJXwpzKVvh/ZcvgY1fe1v/9+5JtWSsayA2S97+2utXlSBt5GmcIj5WvSenNbHyBsC5xrU1/vn5zLUqqbS2ZpV+79trla/aGZ5jjCy1ql8L5kpv7Q+abvToPdEpWtKa9t/vxsAx7pMjHiHESGtZW2IW+MV5CrIRCntiUpvYJm2WDGkSMISg0yLu9uetasHxp3nlvKp7ebUc55Z9cCmFawk8prBD5W7PqLtacmEPUBqr97ZNbY1cKk0soPSN2fTn+hTFnmQZGoPYBLehON6jQrkWvMfEec3EFPfkzQTsni4XvGubiq/Yc69VUQvYZfj7ss7SiE3bIt5IgFsl4eB5VTLhGODDYUDd2+0n3M4hiNETwm5VgMg+UrrdOwuOxgpU9xK3oVR3x4lfnt+4zvN2vzpBWlWuVatoiJ77Q+J370+MDv7x4wu/fTioYXCCq4kXPo6JS60sPVCiyeuA590YbeTb4fE8psDgw+bBdSmF10XF0Xy0AQRrCRdRTk50e/u32vPDwSklrrVPv7Ffv7XPPI4xeu5TUiuZoDDgu2mkClxLJa9asep+04fhnbaeg3e85Mwxqb1C54xgFep99BSJzLXxtK4bJ4gKY9JptXVe+JMTPuXMAiwtE4NnwJtFjePT9cpBBkKMTONAEYe0QtcCc7BpBwmRXHWceAiexxh4u2a+e/BcL8J8XlhaZYgRvCYOu/QDOmINOJtw68r8rmmLaooRQRi9I9KYix4E0XviNrZteKioR+HJq3/cXCtf58LzVeVGinHTgkGJuTRay5sRrg60QE1eEynb22Inhogw10IoesB1tMUbGlCkqcUImgimIfC0rpoUm1Cld44sqkpemzCFwNIqc6tUj1Yl0knIVjwZEnkcI9d13YizoAjcsXqutudXE1U8jHvbRzXn9Hul6FnWzGGIxs8zDNwmRh1sSV+VxpK1SGpb600j1FwbZV45TZ20rjYiCuWYv2QKOC8bl0pu76ezde10H/ZaRpvobltnXb+s37sqmhz1sGIgtJ1xfjvg9w6WtjjF1rbvsctpwdk9PVOMRES5Q5bENKf2H6+5cFkL0fiCa6m8mxIPo8rMfHuO3CAyosXzL9crl9z23OTmGrwoajoOgSF5DiHw3Aq9rdXvRFdxk6btQjT30rOto/TY93DC6/nKgLZsJ68s1Ksoj6mT43ti324S447YicXmJioW6m6sYERMcuSvvH5VCZKIqhs755CgbvJry2DJTC/gBTWxfTPVWcsPbpDPXdSxVA0uKWiQ0p/VRxQs6apOq+VjjBxD5H5ILKXxdF2Yot+h8KZ8oWlMvGatoJyNTfYHEty+7BSFUd7T1jO1w6uhWg0dPSpVR2NXu4iXVcX/tpYZ3G5FclVBwGB9+mp9oJ5WWNHL1hV3+/t4exvxOlrfD5PeChFLgHqCtC1pi2IbgVMj1Naj7sJpXrzyoLzbNkKMnrtT4k+fz+RclURrmf9SlGR3HKJyUPonOiWvFkOSEj3JcxtEey1VeQvOGQlePzEGtyXMGOJ1HBNrVb84Qavob8KE7NfjnbZ7k11DNfTsmgtZ1L27B0J/88zByOW18bZmvswzf3OYeMuFL9eV746DEZh1jd6ZZ1mtyq9LQduUo/fcx2Bj3ZrIT9GCnD3jJpp8z1krYxE2sbqGylSoZcLOV+mp5yEGoneqW9W+HVFO3vPuMPIwJU4xcIg9jIhO0wyJu3HQYQU7Evr194aHhz25789tL13xznGMgYcUeLpqiymFrq/TWHMhNNUdm3PWAwM0JijDmKU2huiZc6FV5ZbcjwNzXuzw9PvB5fTw1cNWEathmvgQk2pztUpuRbmKpao1SFCvNO+xSTlVzQ72/LTFrJVrHIISjqVt7QtFQHtcCPTEpQdt5UiZfQaBKQqfRddXQ1TrxpLPZrFM0FhRg7fRasdtAdOTeZUcgWsuDIZE9PC4T/LZwWbvcc11fz9nqJawkfPXqodwNk5jDc70v24OoA6HiGxq3/1ve1sx2SFabWF0KQXn3S6A2UNWFTA+VttyLZU6EePXWA7JWtp2GO5RS2gVaqtmqq2t19wyPZT0ZZmito82A5YtCdsvr9u73PL6+vcNbp+S29Y8bGt9NM2jLu3iXS8qzTLLOS61ULpatVORyMcwfPOuwa69DwIhapB+rYrMFdF4G7zjkAIfppG7lAxhs2GcLY7sZ9XXddXWu+mX9dDf71EKgTHpdKb3moxE16d7+1Rl90DVf+Nv7sNt0dsTKu9AWqGa9EuX7HDS77Hb3kvfdie/7+/rjJJhBHi7ttoUQfz/yY9+XQmSQ8XLmtMHjveqWuxVf6I0TS6qCJc52w3uxK3b7NheoijJWvWwUOXm3gveK2fnVKTwbogMxk1YSqO2XXjL3o41V06nuE2LeN9Z8gaDuk4CNoGvbbPtx0hDN/vk1QjzWiqXar+aJk9bx2U/D3GwVaQpOAavflop6ATZmsu2kPpIeBN12MbrwTcEr8Rtrx5S1YHziq5QDIHjFj3aL/52nfXEYh8133Vt9spufx4xeI5TYs3VTD31ZxqoM/NUuXeK/nQYW4AoCrlr+0E2TlcVYa3Cy1KJwXMaIqcxbs7qlp8hotN20Qjklyzb2GdPmL3r0YCtvdufY4e3nehkzOfzVUVIZb8Rbvut2ESgEme/XBaW0PjtMBC85/NlJXnPD0cdgxcRBq/EyYiOYU8hcIjaUlMQbF9bzu3ht3++JpgF6TOJN0WCOod7MnULKN0eJ3r1rpMGqyjxOnqd1jrGyA+niXfTYGPX1h4wNeQheh6mxMe3q90zbI1rS9o7tZBZUYJz8qpFFe2H++HlUUkGLMENXv88l8IqhYdRBSYvfRs45etcSgGBVRpDnPTgXtVs9iEdyFUHIyodldNY4qRxCIG5GHKSBh7vAq+XYoesTpHmUonOzGnNaFSa3oOY9PqjDyyrtgJ0HF3bJX3abX9OYrpMsh06TaytZUhU//nBB8YYyS0Tm8dHtdPp7vWlCa0oKRXRpMs7Nd11fkcPxdYxTtsLY9KDv/U7bwlqZzu2JrQiW5vUbc9Sn+chRhrCy6rq3jZnSnPQvHSbPDsM95Z08EGV3Hu2I/pvtrF3W6zOhpCDNxSxF2dViOKouVCbQ4KztasFi7P720SHSjqiL/08sDhvkCjnNfNwGIkxEFvTFvq29WXn0nV+UJf3sPVaZXvXbdp00/u1OLJr3Um/ZLxznKIWQp0PJjaU4rbJKyxxa3xdMnOtBOd5DzxOw1aM3Ra6zhLu2hqzq8y1UFo1/SeHi47JBU5DVL4lNgkpexLcY0mRxsd5teJjD2xbgekdKQVFG72eZJdWOYVAlxruY/+9CHNuF9f8NqV0Wx7tnWdIwtA8ycMF2RAorND7y0R1I63jrKDVQmOKgbfcDChAlfdz+2Y//uXrV5UggY0kVmExL50f7kYeg+M5FzpkmquwrtouG31gFh0nbf0Qt//pD0qA6AJrq3Yg9nkRXcYez4fDqDC/oTqXNRPEIU4flzf4MwXPvGZSSni65Ya93P7ZrXUStLe2nE6J6eax5K9pb/fTqjLufSxRM44utKcboW+i92Pk+3FU1n5SMp73So5bc/98S8JEeJkzf3o6473n/XHgx7sJHzxxUPJfFZUNSDHSqDTTqOlBvPYLs2p5O7j7ZpWdDF5E2zMxhpvEwW0K3/2Q3keHNYDlJrwuK9+dJtW+kcpaKmuzA8cOuiKCt6plqcKXSyaL56fjwMMUbeza4YMGIB+8Kc6KWi6shSEpiXTNbZsc6cKYe2tSn0H0+/NE9ADqydFNrNoe/s7P0s+vtfv6wc93E//9yytP86q+V86ctZ3wm8PIbUNd/iIgbPiUSf9vkRcNXKU2HMHanPvPC3oN0vR6GkKrWp1VJ3w4jEyx8LYWlty4GyPvTiNTDDppKY1S7dByGlirtSSOMdBh+c6bSyFwTJEpqRXGL/PMn3PmrVa+GxLvx2iBSwPfNVderuoTJ4MOOyTnN0mCKXmmOFDyikilGPekFeGSq4ri1UZMgWMMvBMh1ZUf3j3yy5cnXq8LTeAwJQ7Dgct6xolq6xxi4EOEFc9lzcQYmUuhNCGNAZqSs8E8EL2tLe8Yh8QxREVKU+P+EDj6gbdl5sfDkWjt6W4IrO2uurVMsKRp6O0X0TbQOTc+HA6MLrKshXleNm6Fd5BttDt6OKbI3ZjItTDdjbSmhcbFkhjXVKtHTEUcm5iroqiUt7jRI6VvcBcC51q3gi/5wHGIeOd5WbMq8gvq67UvQTUnteTWifToQ4yRUkCsIGnOeFa2nLwhQBVsn/cCDZwTQlNOU/PKT6pOUV0ZAwl08gzdR2ttNyrbPQGwPer2YnHOhUOKnA4Tr5erTTbqOq7GqcPtiKgmYGaOaihmk03AooNlypvsiR1sMgLKX+ojJsZjag3XhDgMesxb29k14WFI/HKZubTGQ1IE2WTi6Oj2rqOk32+phc95YTUUThMaD0aSzlbseqfFNc5B3YeDigivufCW8zcJoSZHWvSFpusumfK6WPzqfnhbq/72lyXDHUmMTjlkVXSa0Ikw+kAaEj8MI0tWtfhhSGqNI+DdHhe3pBCsbe02xNShDhLPy8o51x2BtOPrr71+dQkShjo0Ec65IC+NcjxwHIx0Zgd3MddeEJLTJZh7F9xZfWobpDXh4+uV6D13B7/xOKoo0fhvHo42+u9YS+VtXvn0dt2Io8ntGjM6Di6kELQ1YYd3Hx09r0pTe5gG3h8SpxR4uWae10xpcD9Ejimod5Y0nufMW6nGX+kaRvvUT/cciwH+/v7E5E0+QNRgcS1qAvl4PDINkZzr5qTtHfy/vzxvMu0xen4I4Af1igpYC1CES6v8+fmiEwdg2hL676ak936yCYrStGpxOOpavkmUTkPkP33/ACjPor+816r+OEWuq+qgij1wB5znzOfzlb99dyL4gJjHYbHEITptB74WYcl6mPz2ceKUoo3iipE3lQMRTLahDyh/fcvcTZFr1oDTVXq804OweYvw6FRjcJ5ctIXTE0bnYIqBq11zby128UavUUVbBt5QpgZDSrwXx2m4MpfG07zyw/3EyyJbb15bmnorwxZiZEPuGjtxurfnvFcjRz0KG4iN6Vui7UUPju4CCKaU25qVBWrL8jiN4ByDRbTcTMm9aqvrslT+8w/vdILFWgTJBULX/EmqfaN+Y7uQ4hQ0ybrURltXJu+ZRSu7YUi8SeOpVUJUSQrxwvNFDWq9d1xL4RQzaYy0KsYzVEJtTHCKI9drYXWN4OEqjmVppHphzTrRdJoi39/f8V//5QsfDo53j3e8ezjhpPHx41e+tELJC661jdORVx3AGE4DZDvaLCt2znFMgctV23SjJZPXkllKIwVroRjHRVEHt/0+d6E/ILSgiYVUzmvlaa7cHRpvS8E5z8HI/blW4ytaa3JIGyIgqAhi9I6jJaBt1pM5es/gFMXp2YwetDaqbm0+h8bC1hzvQuLSKtem5qOnIfC6qtmss0R4n+bSWLsRmIHV64H4Ma/8YLyeYIsvN23sRGfj+6LyLVU3JH3cHBFcE8TvhdaYFKG7lkpeHOkuspio7j76TT/Z7SzRTEysoKgIb6sW3seUeH9/4pfnFyP9wvZhbkdNkH3vCCpD4fpbs39WaVBdR07s0LZiKYI6PtSi30c02ci1EQL4Zse+JYd/93jHf3164cuy8rysfEwz/+dvv/tGV65/Hx+0WHwrxYp9XWsdPa8IH9eFLI0Pw6CTtiZtALC2ymst/NN51msKjhhUfLkV/U4lCzG5bX83lGc4Bf19NDRSEBKO2aJQ5xgJjSLaweiTSdKzS4SX1xl/gvNSGIbEISXO84IUO/uiZqHSTP3da2GZnHZ7nLVo/+H5bVtj2wSh7Zm/9vrVJUhLa98IjLnmeDpfeLoGhhT0/lol1X3RBieMwVPxvNU+yaHlv7PDs1U9ZC6zcDh67lLiYOO+HR3CKaflacmI08pmtMTIG9G0WJVxd0g8L6r1o6dY02kkFGVJ/VdQh/n340A8aAYddvyE6hzn0kyFuLf/lPyq1Tq8HxM/Hyc7GBXxqVaPdFTj5XqlCfzx6czzdVFFbqemqT5GDYhOuDThYIdbV9CtrXFZM1+XQhUheZ3salZBXKXodXvMzFMPjOAgjIlSspK8gdxUtdUb4tBf/UD/3Y8P/OMfnw31sJclnp/PCz/dH5mioXui13iMkVUULl1KoxH423cjj+OoIn9ubwcGAR872VMD6HUxk2KvZrVqStzRDxtMa3tbTwXkVDRQeR+yJUQpBK7sU2vbPqdLEtABJ4XMV+HrdeEhJX66O/Kntyufrwu/ezxYtW1UdwEnbQMQe9ljRbb+V3O4TdJDOWz3Y+Lzeea3j0drqxgYZcltro0xxK39O6JTRB2frE2IZmBcS6OUwue3hfNSTQpCP/y//PKFv3m8U5dwQ2x/8+64kauDldJVZJvuGUNkLquSZAt8lMzj3cCDT3y6rryteUPJTsnz/eHAU1i39fPhMDKliWVZkMFvLvaXKqzzSvEwTNr6zFmYDwOuZJ6uV3443eGr57qu/PPnr3x3F/nBVIL/6U+fST7w/njP09srr+sVqRp8UwzEGLjMmfWSiXjiMeJGj1RheVmpdwd++zixlMK8Zua5MMbA93cHPSys6tZ1L98cmj1ot6Yk7hS8tlSD4zQG/uN3d/xf/+MX5WFYy/WYdCru5bpwHCJ3kyKOi6HOnVMUnWOKHj8lNWrGmkF9Pdmh3belsAv6Ra8JsiAcnOfxOJAG3euLNDxibvfu5gASRPxmadSnwrrXpRQ91DpH6mLTnh7IRVvdDWFIevCqUK7u+eSUNhBRCsV1zUQfOIaoyVvtk1MdNO9IwxZU9JKdfYd9CN9EdjO5NQavyKuK3PZpys5x2jk0zc4j8b3n0D/SThtvyZHdZ485Qzi1UBGT0tB7Z/u+KW+uGZrX7+vJO357PPCn68JrzjzlzB++vvH7d/fGIdpb/OI9ay5EQ3LE3s+2tyrmA0+1sMyNn8ZRixsF5XhZC388z2Y30zlt2rZN0UETxmNEWuNdt9yyqdP7GLVzgxgoYfYilptowecJqKl5bth5aTHO6foTDx/PV+7iqILLwfO7+3te68rrdeUQghVC8Hg/QFPnCIcmc03gtWpBl5LfEvbuO/otW+zb168qQRJ0oXqnBozitE1xnleWKvxwd2AaEmOMqnPSGjgxTRiDN71nZg/snbTau8m1tW2se/BxO1R6vz4Fz3enA7iJL2+KOvUpCRGbZBoTD6eRt3Vlsb5ed9NWBMHbpq6UKpxSsCDk934qynF5SJGP3kY7he2g//39wQjfVnGhsLNzN1UWxrNpQimFS1PBunxbgHoAACAASURBVNkg7VrBOa0GQnAMKXDoSaYFuHktOrop6ij+danb9B/ohis2vVOrsJayHYrVCc7VDUHrSszDkGjL8k3lpZCrZxwiv//NI2suvF5WJZEGz2lIfDiOxOi5bS4m76gOznOlimeIgbvRc0xRA6ttkNY6BKP/NouijLm0jZ+wmit3baIJhFVx+lz3QCsW6PX/9yjsvOqxbNXjzUGzE7B6BafBpubKP3x95eADv3048JvTxCUXSm0cUuSSKxuobe/XBPOXchaElXu3SqNUFeHMYgTttZBb3UjYGynS6ZqPTa+hi9Jtd9ausTShUSHDdc5c1szrYsMP/Zrwm4hf5wkJOupcRatD2Ub+tcXRSuNhjIxhVNuMJsplqZWLHXi0yikFji7wfhoQUb7FEFTY1QmsZWE8HRlco9RGrpACvNQzyUXuhkT0E2OMPB4nfv/3f88//sN/oxYhr+o4/nJeOHz3wJ/PC6eothFrg6flypA0BjRpuOAZoxGamxKS+/30DVpwpEPk3TTy8etFxe6cJuW1NlJ0uA2vk22PqfKGtWUMUepth9b0OY4pkKbAn97OeHprG+Pa6VBAiJ5Laci8bmarPVFBeqtLk93gnU6+CVuLW/ZQt/GInCgK1Ccde8sIUTL4uZmvknPmMtCnAw0pM8SouxOYCoa27b0nt7r9ndg2qdLRQCV7ayLfeUvmlyadC+m2gqE2XZOHFLhkfV5d/0xDXsf8ncE3bEWPFj6KgCZn6Hktlpx5cs4bgmsbGufUA07sWYrcUt0NSbP72ydfekz2NxwpCUbkN76os4R5M6hle4Tb+36YRl5L5VpVyfVPlys/nEYiKg9QHcxmqhyd4z5Fneg2pHvj8Vghl0WJ6mVZ+O04MRL4eJ35tKyqQG/POBj/8hgC71PkLkbeGqzrapcoZv2hsTR6FbbtiiHBCHOOHVmsHX0V6YOPdt90TU5jZJkLIBavVapl8IHHaWSeM6UILtgZgyZlryao2m23YjChXUNvhZ7Y/vXXrytBskXSibIOR5HKJdetQnWGcDycBi5zZskmkCW6KY+mYTHXHRHo7ZH+35dcGXwmeM99iAZKWtvIuY30PIRgSIBW61MKHFNgSoEpBu4HNaK85moHryI/InqIlaZBoj+4W0a/oK2Yru3RPYAG5/kwJu5S2JAm/Xn2HcRNAoMSJUsTzjZ9k1Jgzer6rIRjz3GMHIeo5p9OE4paDO5vupnGoAcewNas6UiSc+pUb0iJeoGxGfR23Qm9j/v0AXCDiuifHqZIitZLXlWfQ/kwQQOX6BoYguctF5wPtgmFIarB6BijHTp7QoLHVKixsWixz9fv0luX/b71jSp2qN/ebOlBfQvQACYNcYvw2ENx20/sa9ihAoHnpbBQ+O44cEiBFBznVWH+4IXSum+SrrvXq469DyaOWptW8dWD90FlAYomDKVodS8Nm4ndv6uT3U5HBTb3b6yjxlYhCzR7v/NSzQdsj9idfD6XwiEF8EY0hq0l26vtYByXaNNzKrOh63uInqdr2Ujhmijo6PAUg042imdIDuc1xIsIy7oyTaMVHWp9IFaEpBC4P0w2it54fXnh7bqyrFWdvqeR72PkdV4oSybdHcjSWFqmSEU6T8jrxZYqOGtBtEWHG1wxZM8GG2IIqslmCXmzez2muPE2btdEJ5Le5tB9Wsw5NVcdvCc44ctFeVPRGcLrbH2iPJq1NWoWBhvSiBv6oLo8OhnlcUHv72Ut22TVHjH2PdAnyxw7h64r2hdR78vbDoWzte1d59vtSUFPEJzF4hAcpbgt0fbOqweb6PrqQx79lonognS4G3RILKZ2wVYhNmEMQe1VnE3l3aBbfW8j32IHwTkGx1Z4dtJ6DB5XdI32z+1K5900un82QG8abPfUnu0GEvbY0WGS4JR36d1WODn6cM9eMG8Fl8Dg4BgCh+C5lsa1Vv71fOXn0xHX1B9tzpW1FhXnvKEILDbJJdZeanbvmsC5Fp5y5oRoPDFPTMHWtmlJDcFzb+bWl1y5H6ImQwheGs0Jq92THk+w5163lbDb6oi4TahV75llR6gI9GtpJEskm2jydTy9J6+vvF5Ximj7ztmifZoz57zHKu8dYwoq1ty62rwePP6bVfDt69eVIG2ZdSelmuhXEx4PiWhUfw+cxoSIiaRVNnuAyUZwqyj03oNQtHZcE5hz4YldMC1af7u/+jRL9FrJCjrq+uE4cDcmsO95SJGXJZNFPZY8Con3aYzgHEP38tkO2v3Q7maLQwxq7SHqev7zadq1G242Y/+GfW1pS0vvz1Ibb7WC1wSptGoOI8pNeDwOHMe0TRmVXMlrpkdhZ5uif8ZW9ckOU0qVDdIUEcS7m7FyjRANWLtyqYBsCggaRba2lHdMQ2IIQZXTbXFv9Einbc1LUcRgjAkVrnMMIRqR121+WNu4rDTWtVpy5Hbo2wiUwQ6UXln16+0Brt/wDcI2REoDv367vpb+zQO6+X0f/5em96QKPM+ZdwddF1/nlbuU1Luv9RxGhVA/zxkvijCp2ruiR2EKDEbonXNVEnETa43sBcC+oWRLvL/5ewv+XjRxd/Y8nSFKPYhtEz52n96WlSFM2zh7EdmqNWfryDudzsSbOrYhncErz+UtN+PTWLUpuqCzDQRU0SQw+UAzc9b5ulCHQQ9JE7NMMSFdx8nW3dfzhac//IGPT2e8CzweJt6dJh7uA//Pv/yZBBTvWUtjLcWE63R6r3nVPCpmCeG9p+RGiF6RW9SiKA2eJavaMU1b0s01kkM5QLVsK2FbIX197g/G9rAeirVpIoI3DSKniWNPYrF/u5aeXEBD9+EheFuXxi+iI0GOFB2+KJqhj1G2RKQf5srTuy3bTCTVqY7W1i6zgqm3yLZ15vYD3nceHuYg4PfEUAz9dtwmijaa3U/XbafdfJv+bwwVEoG5NY5eifSdZ3O7d79NkvZnkJzySQUzw2b3Ywzeb3Gur2exfdK+eTNnidhNEuycFZ7euJn6vfX56iR2R06293D7c+06bV3ospva3qXIXBNLWRAcz9eV99OkiFAx5F8zaLzzGydoKfuo+/aQbj79Kg0nlZg8J3RC8ZJVVBm0uIxO5Ua8rcnHpFSUVYzw7kyMsbGp6Os6uDm3LNlptvaihJu94b7h2XaR1K7vlEvjGCbEv2pCaoly9J7ihOclb2dkfx8RjOphxRdsuoJ/7fWrSpCgI5Wa9dZauC6F6B0P06DEaDACWeV+TFyWjEMFwlZpTE44eK/BtjbtsTvtzWtg1NW+5MrXtlBq5e/fP1hQ3KuhQ4wK29khcxwDp1HRJh2VVOToUkzTBzgOkQ/joDyj7VdP2HaC7bYprD/+METuQmBwnmPwGzzq3F8gG/1BO8e1Vl5K5WXNhpbJdqAF50hToiyFVuH9/cTDOBiC5JBWWRft2QZzKW+NvQq5rRj7dJctulbF+D1uW/y9ubJtCrqHlAWSZgmWXW8PSik6XIg4UULuMUVVIHdKHjyXSggjgy98OIyMIRCDVvBYsPd979tne1D0IGggVLmCdtNa1RFRB5s3lRIW9wu/SdN0kmK7G1a9xqhEbfaDxuRFNElDpz2GEJhi5CoFF9RHb6naVnhdVn57d2QKjiVrEoSoFlKIASmNuVuQOEXGQoXrkvXaq5Bzhdo4TSNdebyvE0fbTjIn+nwrQmU3Ft4ZF5p0Jx+MZL1rSO1j1MKaF04p6WHSOnfF07rqfFD+QPBwzlkRqhs0+G2tlKJ6NMDG+3vKhWvLiAjJR6aUGLzjUldc8wwpUnImhcD7+xPFw9t5Bue1RcwV7z3ny8JwHLjWwuNhoACXXBg9OjQxDmQcxXmcD8xV7WaiOOYq5LVRjbuTS1Fkxik3pq6N6ISHceTL28y6rGSpNKft65QiCYd0tfeegGi/g4YdWq4TgnVP5YYZRztcFR6HEVBngLCN4ev0Yr1JWLIJ4x6itsybU7NZ7/oEqz7Zu8OAEz301mz6OrdbpiOie4VA84oWXksn0O8n0IYmyQ36cYO84HoMt0lY+9Vcb4PoZOlaWr9Nyl8xlN7ohxuXrdvleLo2Gby5xjVnfhgSIZstUTBLJHoyJ/tFWgI5GmKzfSe7nuu6MqSEd041tfqel55AbdkNvTjuthd9Muz9OOJT5Ovlqs/dpkClCctaWELk4BX5VHqkFSK9aHZ9aEQDWXU60FNFrWnEed4Njq+v500oNnhv8hLGJfNOix5bA+qNaAM/ni2ZOY3JApYwJY/3iYKupbmobEW0Il9iYFlX3HDAe88BFV0918q5ZWpzZlmzW+f4ttucgBbR3Yi737Nb+RuRxsPdSF6aehCKUHKjffpniNh0uX6nIQZKLjfSEf2p6ABEL2eD60bHt4niv3396hKkXButVNZcybkwpcDDeNgg+1vQdM6Z05S4rIUiFW9pegiegwiL99p3bY23RWG3MQX1jRFITWhr4b//+TN/+9176lJYi2ofjSnhx0AIju+HyClZsLKK1YvqaqwmeOgRvp8G7TVv6JCZtfaptL94oCA0p3o0Lhis2xRe3ysXsUrD6XSVV42mT2vm2byDepDSVpltfDzfPx75+jYzeM8UHb5VrpdsfCbMoNeIeeFmdHXfuTfVnp220sl3dk031ZV6hAEu0GrZMgu3Xbj29aMz7kvVUfshBear8ry8TdWd18rbUjiN8MNpsgkKjzNumpLV/XbttTWua2ZdVdNmKTf6GeJ4OE66yUR46tC2gPOeFKDkLebr39kPdN7BljA1VRSeXdbbYfdDLOj9eH9kCtpqqfYDHz9deLwfmVvlQxx5fxj4FxMvbHYY9GDtgdMQeS27ia2IFgXXc7WhAXC5EQqMY+I4JJXmd4F9ilPbXdiYd5U+ZWOo0VYN7zMxwTuOyfPVOFLbneiJt/d8vSzMa+E0RN4fRx3xTcLXZeVqDu61Vd6lhMdZe1nHtb+cV5KPJGuFnnPhWpWDJcYLu/rMeV3x4imlcs2Zn98/4g8TT5crz+c3BudtZHjgOIwQ9B4OY+L9mDh89z3Jhi+mIXI6DvyHdab6wD9dzoQYOU0DoXqkVA7DyNProoMD1g5Mfn8mvVUr0VFL95jKyhOzDPn9cULEDJC91dFWWDRMaX2vnTUpd/B8yZxGK1wE3kfPh3jkX1fl5621kc0uBukcD7bD/7wW7lO0dlBvW/o+FKZVtRNS0sT19VzABZLT1qe3BMKgKmpp/Pn5ogmKc7AWgoc0DuYf2HeyrTG7om6pIs4seUTfSws3VcrPdi1DjCTTltKxf+E+BCUae5O5CJ5ahLUVi4ewOmEFk3QRXkrlw5gYgWczxQVhF4c1L0T0IFTHd2dPQBOytVXuY9JpV6cI02xTVxaxdF86VDYBtkLPO8fjNPAfPzwwec9/+/LMOedtkOe2gHPLyugdh+CJVoA42Urh7XlWoKEyDUspJOf43ft7Wim0Uik3iTYOYlBtIiWiOwbfmFKkVfi8zhvKopImjVrEYrRyU0UUgX5IkY+XBQT+t/sj90PkXBu/vL0q/0609exEkZmHGDi2wLVmKs4Gqxpzg4Ge9FqM1ZtG8Zti0nauNLS7A9q2i0ZyzLlxTGkbFuro9vMlWxHu9uQatvvYhyJ60a4yCf+OEqRmLL++OL87HnBuV4p2KLw3r5llbXgvat9wTJQGrCvLUnitjddSN3FBNUcMqhkkurGT04VamuOPn595f3/EBW31LEkh6xYcfzzPfDcmfjqNjCnyZa28FdnaaseYuB90MqB79DiroqpYv94CnK5rXSBZ2GwZQA9Y73RsN2z6JUqCO5fCL5eZ76aBPy/KU9j2VgMfDc62dk1tjec183c/PvAwJkIFZEfJRECspyzGB+mu0h36xT6/r3QRE5nsry0xsATNnlkKkJtupJsQQLOWRjM9KE1ghJorY/JcckYkIXhqiwxBRRWTUx5VkwZV5R1CCHQ5hyUX9XUr1b5uI6ABwEfPlAaKCF/XzGvOnHMxKxm952ve5RS2S9Y60BIMt8WwUhvDlIjOq+6T3YjbnvtqKufZxsZ/uDvyfF25mwJfauZ5qdQB/svljVaEdyGRejnkFMk729rU5EbINI4xUUzIrYsmqtBopkhj9HUTEh2ienDNVc0il1KZUuDHuwkwWLxtS2irsl+ueZN3wP5O9Y0C9ymRYncFjwRn8LmJXfqqVey7YVKBxyVzLsLzdeE8Zx6OA3OuLKKCq9W4byE4FqkUtBV6nyL340QJjvMvn1jyTFjgGAPHh/eKljmgaRs8xqhF0NuF9yL8a8l8//6OZV5Zl4V10ERoGFXt+oe7E3fjyB9fXvk4v/Hl9UV9ADsX0NoI4xDJayVFr8bFDl6uF4YxMo36mSl4PhwmfhjD1sKlFw7YRJelurr6+50VgvO8rY0p6eRlbw004ENKnJ3nXCoXS5QOwWQBtt2pCfy5Vk4u6P20tpZ5mLK0XTbCB8fDaeJ8zRvJ2wLPVoz7YMRauuWGymbk67rtjxQ9p4Ou2G4Muq0X6XxLRT6uWR0B9IBrXK+FdArbvQ4iJNHNtbSm2mXRgzikFs4OTTEtJ7glUs+t8bVkuq5dNGRUOV/G70QP7CI6zVmaDpxEr0h9wnEtmZHYt58iKavKfbQt2epk9MbBJ97fjXw4DBxjQGqltML3x4Eva8Z8v+280hi71sJLiYTgeQyKiKk2UdiCjgue0Qdcw/SoVDB2ckIm0KyFd7s3pTWQgGASGNp/RZzw3TDyyzxvZ2dynp+Ok968xnZPPY77GFhK4vPlylupPM2Zr9eFGFQoNLjdUaKjzkOA6AbOtZDtTxtwqVXjSb9vwQRPUUsU1YfSFedueFLOaddHYNMlc+IYBk+1GB1Cb81tzVCdpjayuPKdLFY3wRWlZfy1168uQQqGnIzBgq53lh2LVbtKAl6LaoMkVA04BseQPAwH/unpzRj2u95H9IEmjVY8I26bWlJYUzPZj29XToeRu8NAobEU4fVtpjXhMUWWCl9zVr7PXHgcB05DREQ5CCok5hQKEl0wye/ZsqW8Rpjb+SGasHSy297OCt5EFHPhl+tCbsKnZTUSNFuA66j+hvbY3+Za+XpeOXgd8w7siYDyrPZKp++YHnx8T7627+kMuWFr53Vo3ZmOzxADhyHhfCB47cV7TOlcRKF6xILonv13PMOHietaOEThYQxEF0hepxMQPQydTRU60cP0sq7KzWjNskM2RdwQtP//XDJvWT3L2s2zcK6jW/21Z509we06KM4gbapwntfNG6r/i87hOS+ZuyFp4OmcpQi0hkjSceKiCdw1Z65zYQmZQ1SisvOOL8tKMf5N7Ppbra//LrjmcQmOY+J+SHy5Liy+qh0AsK6Zl6yKvL0dGbsOiXM4S2B7QKlN0dufH05UaVyyTmAO3vF40Mmy6Pp+2TkpQ1DbgveHYRttdwJvc+bj68LrkjffvZdrxkfPy+usys0OQtI2gcPRcsNHx1ozL5dKbnAcEm/LylpgGgdOU+LkFI344+XMwzTha2FeMs/nK5TIFcfnl1dtv7WKjzravVxXEhqEXxbVd/IhUepCD7m9SHHGd0xJxWGHEBhipCYxqxJNhk4p8jgNrEVbl+Jv9qWTvXrtxQSG8Dnlkq1mqtpfrZnnWBNOIWyGuHVZreXfOXr7iq0ivJr69yEERgKlKVE+OKHYIlVeXODuCMtct4TMb2+oaubeO6hu459Zzri9Sm28XZTa0A+34HXk2gdFbU7jyDIXax92VAmGKZqWltu5jaLTajhr4RmU6oZAnW2AoScqln0GB4fB1KnxW7sum1ZYdE7V0A3JTdby6t+3So89bMKkKQRSdFzzymg2RNUpF1JtNoIKc7pA8nq4JpTaUZtwinFTjcZKzl485WZCliGQvQ4Ceec2/tlSK2vWZLp2nm3UGKixLJARE1pkS760XV43TqR32krMqBxHdO6bPX5eMx/CSHFWaImZgkvjMAR+509clsJSdEKzSZ/wNZ5hPzcdlmQ7Ti5aIl10XxivMtge0TXvCP2MsvftrftDUI2lZrIPDhVzLU0V/JrZhTnAB29tQ7eJUHayu6dt1kbYmq2iiu5/7fWrSpD6hltLITjPcYj2570XrOhDH83uCY72ZLEeteP+MPLp7aqExg2i6xm7bK2Njn4E75iLMNfM2vSmjlPk9brwNmdOKXDJOkXwsmSCc8yzMAyBcQyEoFWEUXO4rpUmqiUxDnHLbHvfui8yJzezXq5PG8jWKz/XxlsuvCzFWnmOtau9wkYb6OhNLy2OQ+JxTGbIGzQA9aRQi4e92tj/Gf2O9ujrDDlxdLjYbb3fzl3YW3Bm61KKqYj3nvP2oPaDA/YS2w6mYG9ySoEpKGdsjIEgbSdKu65HpQfPWtQbr9rUQvCBGJRIOMYITqvSt1L0vvWgaNWrSIdp2fkQdh9EevKkpsetaTu0I0R9xHe/d5rmLaVqMmFJ2nEY+Pp24cdp5DBEVoE567jr5ayq5QVH8Y7clEPQoaMiQkTtSAiDEndFPyt4x5gSP94dSd7z6brYRMf+rHJTU2D1KdIrW0tjbRr8ui1GQ0zPRKvso49MwXS4vDrCS19wN6vEBy0AlsqGPOJgzY0/v848XRaWteo0TfAsrXJMCnlvNjZVKEVbWxFPdJq25VYRcTweJ85LprZKLYW6OrJXon0tlafLVav01pS8uVZ+/uk7fK1Iq0izNu5xwlcliF/WTG6ZUjJj9EyDKqt3ew5psk3zJDNZDtaO9iYgE5zDJ6e8ODCifZdBuCHxGjSz7SzX1zx8XQqnKZFMAn2LDbVPkiqaKBHmqrZE37Rd+3q1gms1tKWKDVw02XhUfZ075xhcgFG/WDOTan28wmstW/LQ42OPLZ00LU0RGXAEZ4r7ht74oh90cIk5qxH27ZoZY/g3UhQ9MDjntzXknN1rW3e27HFOhWvH6M32whvq5xic3iNvZ0XvgHaSbqc8OLu327mwL208juQCBP27yXh5ffAm+khyQBNqrdSbh9G5jRZB+m/0+homU1FpIWpXU/NQRZhEJ9VA+YWD94rY2/dWIWOhNKeyDWKryLGdcf0aPY5+ch58QKoOQAQ7A0UUvVQzeB22ENFrCts+1y9ealMEujWdeLPicf+l+3YKQiNYstiFWqwYR8nTwTuLX/JNvHVO18WSVcbEIYRoU6KovEy3j6mmwSS2y1znSmHJXl9L/dk7tvX8v3r9qhIk0MfSRPukqaMF9OrZVLR7ALGbk8ydXfU1hA/HkZd5Vadza+dgm7tPgGmCAp2EeclqbbG2lSqN92Gi5sZaGg9D4pIr13nlMmfeH0eqBM5L0UNiCuQCCU22rhlFq9AsOpulCCi4ZPNXfJsyGFcE3TTNwbkKX7OSJTekwwLu9nvntj8Mph3xYRr4zf1B1aDrPkYuWHJkB9R+x/XNRLpFwp4qbFWiIQdbctcjvwU5b+2eeS20VrcN2+9zd/XeEo+b5Eo1qzwRdQHvJPchKtycpW2ed31EtrRiyuqqGB6syqtepxQTynO51spieja9cgneUb3Xg6gnl3/xaqJTXk10bHSulVWaGmZacvhvV662EeZSiV41tQBaFd6fRqboeV4rb7OSe1sF5yHFaJURICr1ADrSHbzjECKjD0QaF4GMcg/uxoEfH06cpsin66Ij8BbbxLFZSqiUhLZ456wSAWo46b9Jmr2H0qomSalXXc5I8x3Z3K/fO28IRqNaorOUxrxWPr3OehjUvkZ0+qxVYUqR7OqmR1VWwSXhGAeS13ZB9WJ7OzKExrVlcs3Mi1B85P6gCehaLBh7xzAG2ir8/N07Xp9eyMuykUMlBI7O86U2zstiiSMM6LRXKWyVaLb4Y4NlSvb0VmDJjsKmoInNec1EO72XqohncMpT3KbXbtY7Ttfm69L4+X5ijH5D5cQr0qoxQtsHB+85pUgNwtu6T4gCmnwKNiXlWGojt8aJCPYdotsPbuz5jaM+35Ir6yqbSvPLWrbWZ/+uW/F184c9fphYtaIyZS9cl1BZVhWGdL3F4ZXWsB2d/X80J9bWmmZ09kxvkxr9N8l7TmNijArVbTxDB1NMzLkgtW6tuC6vUjH5AlPC7urZt+0dbbsLk7WynVcRTzWHtU6E6wWVIj+rM/6VxcM+Tdtjac/svHMspXDNnvsUtsDYI78OvOzVZr+/mxE41s70zSbsxNCfajSRXacveNVvcs5xCmrBlDEVc69uEUOMdPpAs2tbc7Eug/FgTfx4Lo1VhIHdbqUjgP3LJe85OUeSxjUX+gh/Q1Gi0pr5AfbntSftnQgvaJHdjcAQMf0pQ3WDTgbu+l37EnKyTw5vUg39rMg7qPCXr19VguScjhQOITCZBlGvcnD9RpvOjmh1dBoj6WY83dvieTyOzMXUgLHbL56CMOHpe1ZE+Djnm7xDK++3y8pPj3dc1qKmi1tmH/nx4Z5Vwv/H3Zs1S5IcV5qf2uLuEXfLzMpawLXZzZmR6X6d/y8yP6EfWigUjpBNAiiAVbncJSJ8sW0eVM0jig38gOqAFIDKvDci3N1MTfXoOUf5+flCq4JUM4F0wjgEdfyuheDr3ooZrNfsenLONcDQN4ghPV+Kqkfq4GCx7b1XKhaxXE80NHjXWnkYB96PI09jJDq3K1Y6d0uPuEaXXLlblZyww7RXI7le8TaDoRuYyqnDyHslZhXObtMg8ougurfqjOAY7LtUMzGpzfEwqJFa8I4YtK2RM7j9/mrwTDlzWbQt4p0eVMF7KvDjeabVyjnnPYnrtKkqljgEh8+KDDXEWmHXTbpX/K3uBmgNnSFkfvf7he083WbjZhDmZEOORXieE795OPBcKo+o+vC0bKSc+ebdO14vJ4ZJh9mWlKjWjgoiYAN+74eBVOGjD3yNkZct0UTbGA/3E//Pf/6Gy9b49PLKauhlBU6lMjoNdt44J0sqOh7EWfsaRVSjg1rN9M2eVa+aq6EHTvRgcVa51do42V5MtfH1AulmxwAAIABJREFUsvLvLzOXVb2KnBnO7G7KUTifVx4fJk4XrcBbuw6nDKM+h2b3MbfG1/NCK1VVXV7IvuGpuGHCe8+3x4MlzIXUCptLuFKoNXOMnuwdn08ry7pwF0fOeVE0UYQY7BDYyj6/r7ZrHJKqT9ibvFhjQMV7dYX2zTGXzOc58RQj304jX9fEEJQecLB77rzGiNLXiginJeFEGJ0esDsS3BTZs68ITRPFb2NExPG7NrPlPm++ewAZ9d4KgLVU1rLyQzhSayPTzMHfiiSnK13VX44QYFWvlCtCbOGmG69eEV9dC1rT6P51rj9ja1MKDNExr32+mXKjYnBIvSIQfcd5p3Mp4xT2odSCblb1pNOkKnrRtlpUD6idJ3XNQ4jOsZZynTdHn3l4VYn1Irx753lR1KjPr7w/DJwuq7qSc0Xa3X5PdO/nZsWv94rwOGEMjiU79cUzw+KuqupKQl3jsqvK+oiiHqudM5T1pgprlpSpxYhy5PS9HaXpPMWeQI5Bkc+AcHA62+ycMqesP/OWEkcUce+J+7wkUi2MLmiIE0UhS1W1rDihOlXXSbNOvSWWDfakbLDn+0ba37t3bhbLWju6Dvo+W2mcN5vW0M9Fy7z7GRQGjwveoL8+UswK79ZtZtS01dv8Thq0wnXG3p94/aoSpAYchmAqFaBdN+yaM7kUkycrevTD3YRYAO+TlgXYtkJs16rJixC8J4hwypkiwlEEVxXm7jyaGLwRytS87Y/PJ94fRw7B8+440BD++dOFf/38SkqFx8Mdj9PI0zESBmFbVj6+/4boZ0YPrWX+8PbGFMLOI+n8GTASuDRclR3dWUth9I5Pl5VgpEwfhZJ0knq7SUJ6BXUIge+OdzzEQHTezLYqtVRz6O5NLD3qG5rMNDv4sEzb03gKkeeUVabflDRfpe18of/lJVe/Ey9aYWnAd1fjQgsAtRhRPkJJlXkTahPuo/DxOHI3qbWCc9dNJNERq15oqTps8jxvCGgA85GlFL5uG3MqeBriVfVS+yaB/Xqx95QMdU1acRg1p+O+feOtKfPx3R1LLbR5Y1nUCLQnfmLXr/cBaNreKOZDMnhVhXkRltb4aU7knBkGYThMbHXjwzSS1pXmtEXQnGPwgUsVBhoinlNunLaN1xCZt5VjCNSceD0lfvep8ZffHPjmzpPWkRMbL5eVT6eF4yFy5wOlJCYJTDHoPCRMYl0q1emBHUSl0pekBouDl31e05rrTrxVdR5Q0HEsxZCLVFi2zFqus/lK1raV3rJmgVRYl0QpGgydE8KgB0KphdzvK4LPQpPKy7rSWmN0gccYeX888vP5zF+PgU/bxorbof7/6/0H/uWff8vgA3d3B8ZjJOTC4/2R//HlK3/17i8IsXHeZj6/zSx15e9/+IY/vJw4LxvNlJWp6LiLJVdSWnWNR+VJfDgcGEQVst2/5eu28ZwyY3SKiIlwHz1//zAqBwj2fVsqvCyZwzjsh2WD3SRR5z7qL9XeyzU+y19OE4nGW868bklnMYrbxRc92awNvi4r3xwmbUtUHX0xOm+mrtekLERHiJ60FYZ1U+UQik5JswLMDro+Bb41cKWpCSI9SbLix+vGP4TA59PCnBJDdHxzf6A5XUs6j7ExjDYGx4kaAvsrAiMIH+4mi3OqiPReW1U6i0t2Z2Wx2PkQggo5DBkqVc1EixXa7ma7TiGwlYz3gafjgUpjzonLujIOgr9pXXV0otCLWpir2hfcRbe3I98PA0uurMWI2E5d2vdWojOUpxM3nVpAdOGKbpLuHXSDtlgWaDpJRb5cN3dVNL2rEreiz2KMQYulotymCLwlbeN9XTfd85aEeFtrC9XEKyBV+UxndIZd9BosKzDZOdJBjP7Klqyk2vDGxdXr9dblUd5db++fkrZ0SzZLFkENNb1QU9tj0J4QNuVg5VZNSWgGoE75b7XYtRQFNOB63v6p168qQYL+kJRwODgxaaUa491Cb9HbDCCzyu+/e0mJ19PGNAaV8jvZGfjewf0QqaVx3nRRitNKKkS/b55ia3ktBUnCX3+446++vUO851IDP78l7kbhOAVyS1wyfPf+I/N64qevr1y2xG/PKyLCX3x44LRdSLkqfAk3DqoaaLIF2W7smGrm3Tjw1pGIphvttso8BMe7ceCbrqTAYN2dQ2CBCyXAYQTdaq2SZgvM8vUdmDoGx3PWINgnj1czTRMHKSkhut3A9iJX0rv3apgmru7wsLOqsum8C95OmVwdURqPo/D+blQDsKoHJtLt61WW+//+0498ez/ydBg4DpH7UW31f3dZdGYTdjBYUtQ1Q72q7IHHoSjSuqW9KpI9vN6swabjVd4/3fH9hwNvSyVX9dTZNzG6tgRVS+QKyyVxGIPaPHhNDE5L4st54d39qDPDbEJ8QPDNc3yc2NKGZJVAn2vhv353z29PFy4p6bw/u4xDayw1EzFCNzqE97//e4GXi84Py4UtV+atEAcjeDqvE7RLI96gXzSrXtFqtTZ4P404k2pvuZANyfDOlKQdxkZbahXh/DbvqNfDNPB8XhlGT07l+lmiAUysNXE8REppLGumbIXDfeQ4TKxVnYEHF7RdlPS90loYQuR+mjgMnt/IwL+9nHlZDE2bRr59PPLjMiPTxJwSvm7cIRymgf/27Xt+YuXt/JU464H8NHiG4ZHff73wdDcRhsDbsjEvG4MRgHtF30RbiT44csk8HCKpNC7ZWlIC3qmytVesp9b47axTxweEuxCUIL5VvI/cxZ4IWXFniZK2tA11s6pPatur5iiO6NTte3BqEhtFi8RsvxNEUYwfz2da1SHLj0MgRkXD+tgO+2AAHu4n3k2qfrykwjllLjkzp+6mbcTqzl0UoZVG7iIRfcw0gd99erMWCYxTYPQ2zNRpqh2D3k8r/MncGF22a+ukk4NdJ7839esqVFxXQXZFCQ0XIne1sZhZ7RUFVQ7N4K4FtXOOd8cHWmlEI1GLGRFqYtKsdWrIG93GQONfMXTpLWceYyTXyrvR87I53ixJ1uG2ev44gZorLxdN+Jt1Q0LUs8x17k7rz1KVsF0ZiLDzO7H18ZY2dbd2juagYzDN0ECHni/9PQS4pMr9EPGigoWtFpxoiz+IWnJE5xgHRfBDFXLp6LNu51MqfBhUESeGbulZoApI71UYRWMnxV9yoaAxJ1cbCh4d26x7qBSFrrz1/bPx4zo61yy++6BJPkUT8irQvPD94wHvhR8/n62rwb6v/tzr15Ug9SqFRrJDOYryMKJ3O/couj6L3X4J3aCpVs7zRkMP8iCC7CMpNKi3orPbCIpeFN/45XCt67vmprO3fnpdlGcQHLU0vrs7cDpfyGlVp10caV345n7kX366EEIgDgMpFb68LdwfIltb9H1vWk+LBdIeDBuaDSeb7ty5A10xBjrB+JvjwOMQOYbA4DvkWq8Eu5usvqcxwpWc3SXeO1S+/73OOZLlajrYl1ajQd1ZKEbevHJxuipBVTsmN676edWy/OCE//mHFz6+e8+9axyj4xCtsqjCSiE0p4habdRSOIwD5yXzX7595G4M5Fb5ed10aK0oCbte46NWt/u3vCF3Ivu/N7QnjvFFFALegS5db4fIKW38+0vj3eHAt/cTtWY+zZt+Nxpb0Z6+VkONMjjCoAdibebqKg0CnNa0ExSDCBHHYRy5zAsfgm7wHAOP0wN/PF+U7E5jTZmUK4cxImhb4LStXESIIhx8I72+8dPrmdHWzxAc98ew76lS1GPEiTC1jmSwP3jlIeh1v6Wk90BudoNVbFtRBK374VAqAW0H9VEzuytza7pfetvW1sOV36IGi+MQlO/RhKUozL8WWCTvCpgQhJy0ijznyjZvHOLIWi8McdB5X1RiFJ5fFr49HKjBI7WQ5o0wBf7p0wtpdTw+Hih5JW0q7T7Egb98bIzR8fnSWBDcMPAwDkgQljWDcShSAQnCMA3KC2yNAcF5z2pt6z7mQNB78rpmYnCkXPh+0nW45MLgAvdR2+dXPuAvtu61xdBu1HG2Xx9CIIrji0vULe12Jr6PKEFYyg0XpCk9Ibe2uz33A1PQNlR0SgY+RtGEaoqsufK2JU4ps1gyeFWCGiprK0WRJKv4sx52tajQwI+6bsoeH9Snq5PAcyn4aLYard9B84VzN3HH/jvIDS8S9u90zpua+2IzLC3ZfDzccVkWTSq9tutLbWxbUum+D8ROaEJj/16QWpxs0qcGsF93Bp6Tii0e4kCUxrtB79vLlmy2WU8eHRJ0jloTOIxR36v2MVf6/6UZQtRuVGS7Ullj2Fqq+vDl67gN35zZNGiR2Yedd6PFyenQ4+3QeDcOvFxm3gzpys1I1LUSnUdEC78YHGOpBN/bYYqSL7my1plSG99OA6OhhjTtVQjCYsakxb5ztJmkxVptrZkfXrPC3SkQgKjn1MFHhiniW+NuUOXgWgqfUtrPn87f6pyv442JsMB+7//c69eVINkl9/9fG2RbKLO1p4IdtIfo9xRAD7xmXjgVxLG1aqaQmH1/24PLrqwQawUZvaZzH6ITpqi92OAcDcfzWYeCRu8YHTjJDGYySKvMy8x4vOPuMHJaEmP0jMGRto15KzZDpl8ZO/wL7EhLf5TaJ1eFzJzLnjg6J/zmbuJhjKqe6cHBAt61OXHzMria1pOF64bvP7yP6UAYrcXYg1GHUEU0iLa9vLMF6GSvLqVdXbZ3DyRRHkKpmmDlKqxJp7qPN7PGehVeDbauRSuK0VRsl6yKkdxUErurIUyVeFt57smRXIl8nUrhuJLO15wZwrgTx3sbqDblKaWUeJ43jkPkMHg+Pky8Lgk1/heOwRPtGuZcuT/ekctGLsoVGIMnITwMAyWpCg7RNTXFSLTKKzfl9GTg9TKTWtKgKXZvTOa6OlXsIULt/kwNXk4nQoxcLjNbUoVdCAZ/29pYcmXOhS1pZa2JoSYuPmq1WKtwWdXRxB6jIQbaBhBxJCo1q0OwBwa8KSL13gaBw+jV2beB7HGz7YilHsgKajonxhDXfdmqiRWctu+wpGoKUf2ScmLZKpMTjiEQgue0baRSeD2vbCnzw9/9hrfXF9bLWdvyubClxMEpainOUY2kn0pRsj86TJfDxGlJtCaI9pUUKXAwiKoka1EyOrR9PQ041moqMz1ZqXaQJBOWfCYRcLQmHEZn/mzCld/T22S/2MD2P+3apkcVU5NzvI8RB5y2RLeacMgvZOzYOppTsdZf2N2WvZUOzukQ1ybX9l4Eog1ovh8Ca1Fkacnq89XVTzt6ZAld3srOmalNkeTgVI7vApyXjEgzXpwmbd58pmxX2w5jL2n6sbC3m3ryKNeY1wytqIiNDtFn8TBEtmbniSWI2MHc3Zdn0UXYkSp9Dj2SXtGLuj+fawFagbdcuI9A00GvD9HzuiW4sWDpR8DBOgiD6L5PGD9nP/p0l/QZaorcXS0jtqJCkK3czCKzZLDfv9qEVPLOES2WMRwHNVMdveM0y07qbvsMzi7AsIHkxhFVtSD74ORUK6txC8+p2Llpg91rY046UHtP/q0jUQxVLKa4zbXan3WFpAaGwxiR3DgviUNQjtjBFKPihBLb9XlbJuQMfJCbUTA9xv25168qQTIAwKBmU601rcROpepASAV/mKIae/VsPpl6puu1mtfsl17x2Prrh6BzKiPNrROYAdEq+hACj0NEmpI1xxBYc+G8KvETWTiOKjV1tijWLXEKGyE4zsvCOASm6Mg1M8+Zx2mgP7Y+vuHa375p8rRmCwkGr60YqQ2pqv757jgZJ0CDTz/RLSbrW+xvJnumvgcx22wiRrTbw8G1zz46Z20rrpkFPVj1t9Vr2c3DRD/YiduTkmYJT86NlCopKVz6Nl/47i4ASs5sYHN4dMZX32TV5J2Ph4Gv80bI6gbcHXKbLZhede/y4f7f9r3k5tRRjof+zpILUymGVLh9DaqLebHWWWNOmcnQroCwoWZy78aB6IWtNlIrHMaJkj1v80wQx90QyTZcdwuNtSkXIDiH94ElrVAqX3PDOU+h8JYSYXC7PLUX06UWzqsGudEUZrVpe2VOG8M48rokzuumZnRhNJKvhvlWzXPHxoL0gcxNIETPHTpCZF6yqonsZFViqrYLB+dIJZvvlPmHCXhzUOgqnBh0jlprV8VXn7jd/aRyqVA0qEXnzX26q5V0wabSaFkD5xiDEV2zyv0Hx/tpNEge3tbE82lRcv84gbySEQoO1xprKUzesSyrKfscWyvkVjmnSkR4GAP33pGyzYZLmVwzWUw1GCM1a2u+VBVgOBGi7Z1S2l4E9cQkt0pO+mdvayY6z10MTPE6bqGvX8HEDrSdg9P3s9y8Z48fDnjwnmaJ2iXn3W+o/34vBguNpVTKpiaRo1N1ltGIGLxX+zbpWJC+ooPoAneDp9ambTczf9yqHlKpmG+WfWZJZf+eYgdw93Py3rHmZff3Al030eYO9uttHTmxdSUWw27t7vue7fe7owneBxt6rajawxj5NC/afjSe1O3sTRFhsYHnU3PKMxRNmjtS1CkI/fyQ//DPap0PmlJDjsGr8IJrYlOrttVcP3NKQfC7yrefQ5rQWDJ3c9DX3srPltS3trfUdBSHjZqxttNS1OZC7BzoKu7JaSIyBs89utbPSUcJNJN56zQIx1JUcessAcm1Mtu0iZ7wXbLF0KB0itwas1lSeEuMxJkVRzVrAWtFd46UCIzeq/mqOA5DhK3w0+lCPARq1fMieMdHH/SamhaRyRzWlXurqNkOgQv4jm79idevKkES0EBhVVDvFTcaW7GhrB7wwjc3yIh602hVl73+/BQ81aTWk/csm3oJLaVyNwWOU1RTvstqh7kgTe3N342DckRM2eNaxVHxogfL46TjHfQ7KakzeuHlcuH5deHreYFzM1KbJiJP0wDY4od9MzmnWZ700qXBlRUjxCbEou2JdyGo+7W1n/og0C5VbXQ/Gnsfy6hrhxwtSPUEdBeocA0woAf6ayk3fhb6YK711PUz+i832+TeddhTq4R1UxnxlitLSszbRs2N8q7iBs3uFU3TYZy5FOuz6/fdcuE/f/+Of/r3Z1wVpslGEuzwfN/8ljhZwnQ9bPui6vdWVMUWHKU1vi6bEjujmY4FoQU9cHU2mI4juKyJnAw5qfDtcWLwzu4/LC0wSeGHH37gX376CZcS38TIMAycUkEGz+l0YmiFkcBaGz+fL6znPpLFEuAP97yaGWgRSyiCJoPzsuG9Zwg6RqOZ2/o0RP71j1+ZU8IFIcbr89Vn3LgfzKXFYeoZ2Jo69p62zGlLfLhXo85jGK5onNNRMEijGL8pFQ1ytWn7o6IQfUZRhS2rXBpuWrm2TpwTpinw9raRUmaIng8PB861gNTeEdoRxJIq7+4mfFBOwmbT5ecmfD9NnNPC03HkMA38z5++8vHuwD/84z/SvKM0R/SB98HzXBNf0kZaC3diM528ZxxGDoeGc4GlJKiF4xjItfE8J7U3cMIhRr65u+PL6wUXYa1NoTWUSzMbedSZqMEH/Yw55b2l0AuQaMq1XBuBDhhY+dJ+ub/Egn4vrMR+plMRcmvcieNhmnjOibO5p1Ohll4XXFOJVCqf5025iy6YYhGOMVBy0cNMrvHYKN+0poXp0xh5N0bU1NGDJZi/e5t5XlWZFwZHLezPfEeHnE5AKE1bsz3x6K8dAbGYFcRRuoACkG67oI6l+4EIHb0SSqkcDsFGYgiDIZMPY+RlTbuk38lNcSiaKKbWGIBRHGdz3Ab2RMkZx4c9ee1UBE3qda6YKuhGr8NjT5Yo9KHQqVQ+b9msSQTJhcH5qx1B03We7fO26s2JXHmZp6Qk+mIxM4rsSI3vSYwlH7k1tlqZugLY6Vn28nYhBM/Hw4D4iUvK/PhaERyXlHebCo3LhWLnSDBCfirXxN43SKGxUgnAuPcfTEzk7B+EtyUpz9ZfC2A9PPTNHqfR2r8m548e+rB3xJI0FD20hePsH28dolYb3hsSOCia0hVzf+r1q0qQgKvpEyDm8fG782rtGs2gX9fC57DxfhyoVE5r5pQyyTXCYIMfW4NcqWpcgp+Eb8JEein4wSuhzRZVKUqEjeIYxfEQPE+HwKdz4uEQeV0SIsKH48hx8NwNkWTJUT+YBXgIjrsPd2wls6SrX4luBjXK67Jx3WRGArQ2VR+e2mHi59dFK/fJk9fGb7+cgcbTYWRwZhnAtajqvk70w7O2PTkqrSJO5d7NxgP0DKfXUqZTUjO3VvdSTuN3D9pXDoLtG0MZGt88Hvnu6aiIF/pd5qVyWbMmR8vKOHjGKKSmDs/ReVKrlJTp1g2I26ut05z47t2Rf/h95e11YxgcD08jhylykH5Q7BFPv7I17rvtgMqD1HiwmWGkEyEGTxw84rt9QCNvhVzUc6RaAP7Dy8wfmyIo3z5O3IXI25ZMOaL92fOyEO8e+PrlM3VLxBhoceBSMl/mC6V63uaFUiovQ+X94z3LW8KPbm9LhuCgFlPLWIWUVIbeUCVmKYpo6egLgVb5+TTTpBGCwv0pVeaWFWlVPwhTg2p1KDRKUafhp+AhqmfOVirfPxwIBz04L2vmZd6QTQO+oO3SXKvZZwhnKdoWreamHLTtXLIG6t6u0cPeEaPjw3Gi5sYcBfHCS9mYX1dqgRAdwyEQosPnyuEwcIjByM8FkcboHTEO/FQS0QVIhS0nnqYJ3xrV6YifkFZGCn4c+Pz7Z/7r//1/8umnnzivK1+XxHnZqF++8Nf395y3zDRNPBwmgm/84+8+MUQhiuNvv/vI3TTwcjrz3/7mN/zu+YXRZda0MWd1Ky/tOmeqO893A7/aGlKFwzjwfgo8Dir59g6a8T3sFqlfD+hMso4EWaahxY9Ni28obtR5jK3yFCKDOF5EeFkTUHmIGiM7F0S5cZWf543fHBz3Q2QKDmnFHpLjdja7VY9qCXLT1uoIcioaN//u6Z5LLnyeN95/eGAIjs/Lxue3mfOS+LQVBu+pVHzwuy9XoSHRCMRyTTxwiqA4CzRXZKXSsicGRTY7J8pJI0rgvzw+KjHaYpMOIde9+zAE3mx+pRYYKKKE3uO1gpPKaEVehitG1fRWD84EJDdYksA+3SFY4jN6x7sx8nYuexv5EHV80lps1BGY8a0m0SlXMwLW4v80w2e3MHnP3zzdGcrqqC2blx2kVizpvqJpTtQxW0SfJNFr0gDM0jg6LTynGhi9FsR/+/TAv72cDf3XgmfJldNWIYr5b3mCE+4Hz5yrDcfV35+ittW/bMla8ragmyZC2Qoe78XI25Umdn46h2vwtqxq15Ebn19mDkFd9r33DDEQfKDYqCVsrSiVAzY69wkOd5EsvajgZq7k//r61SVIXRmkEKNump446Eu5Gr8/LzxviYcYWedEagUftQpZSuFeAkXUA4SmycQ5Jb55OHAIdgBsmYD6yUxmevfxOPDNXaS0yv3o+fF1xUljsqx63Sqprixb5X6KV1+Y2njLidHJjrw4ufJ4vlw2DrEb6+m1DGIVmiVEPfHwzrGumUtKCOofcn+IPBwiv/9yYXkovD8MHKM3ZVwPXD2nsWzaMureeqqtkooGlUmg3LTDHLLLeo/R85w2drdjmrVr2GOmvuy52PtQC2XZmFOjEag43i4rIpWnQ+C7x1G9U3LFGzmlUHVatQWq7m7dDwmA14smRjGr8eXz88r4nac5TxUlvygXymwQkP/QKmjQnB5UDapVNkPfgM4In6ODArloFdUrv9aUNxRj4HXVDfppWZRHY/yKB3HM88oaPWsVWiq8tplVKmcKl/PSZ72w5szntxf85Aje70hglyYDVCyh61C22IFbK82k2i2rU3wSlZvnotXXlgpzSTbyQU0fh6jkcVrnZlmL0shClcZadZYbl8ZclOPQv9ulewWh6sKcFCWSwe+jGvrddk09oxSB1QTWOWGKjnfTgTUVTYSc171Sm5oJmt9ZTkqgP8TAENT24y0vrFZduqoBdj6vjA9H+z5q8VBpPM8bh7sDx2nAO89lLnycjoS06cy7Ijg87x7vWLeVZIODX+YLXy8XRu/4m+/ecU6VNW389Hwm+o0hev7lp2f+/i8/8vvPz8xp02Gy7WpW2Z8jYsiD6enHQdvDu/RcDDOwAqvZXhKxYUO1bzhDRw3SE8FIzJ132NFhLXrUeTswlwzZM5eNwQV79uyJLGZe+7Ylfr4oIf4YPZNTzlxfK9aH16qdKym6JywYUnBKmbctUzB+Uq48joF30xPP88a/fX41l324m2xQnGcnletIqWb+buxGh91aoKtblS5RaVVtFqSLbrwjbRkGHTxbdoFIU686B+8Po3URrHFVO0JnBWVrLAUuhrgkS1Z6bHXmGt6RtX4u9Y7H53Xj3RigOQbn+M1h4sfzRvTCkvPOI5o3RadSbbSazEKhH29i6Iv+6zF4fnN/ZIiBy5KYTaXckaspRoqZWqaiLeNqhfKlFIYYbojijdXi3yRo3EWtdabg+Mv7A5/XxGnbkKrS+Rb0iyylcqhO+bneqWBiy9zHwJzL7rXVnOzqa0F9sjoJOwQdMt1RvGCKZ4xv2FtuvZFZcbyfohpd5kzx+jT6gHW5SZJsTPfeEtVzt7dc/7dBkGR/uM5BaI3TWghNs/m2/5zeyHMqzEmzdmfy5VSrzd1pdki2HWWYvFdZY8tgcOrD8cDLslFdY4ieS6n87nUh18roPcfB7XJJsD53ddxNwxV7EWxwqc4/O44DbEnJ4QiVymXbiH5Q2SnXzd99S3LVPv7bmtRbpbTd4yNvlS1VHo8j0xR5mXWSvBwij2OEm6DVv5C0Pr7jmvz0ijQ34zr00/c2mKNVUu/xwk2cNAj3OoPs2sJyAmoVFmlUTutKo3I3OvOXcmYZXynAOHiV1OaqcHzTVoadA/SMrNTGumb+/uMTfzjN/PR2YV0KL68b7mlSl+Mb1LFDWs2QO8cOhO02ChEYo2M7BKiw1cLkvVkU6KZOVRU0tVxkAAAgAElEQVRuKWs5FMy/JMTAS0ps2VxlxdHwbMDjceJlmXFSqaVxyolV4OP9kROJrWa2VMjmONxN7LpUJdembQAaKSuCllPR++a0tTCEoOhB0cncfbijtgBkb711m4PuyeI3YYye4+B1ykQPwj2pRq9vLZXRO7ZFvVVC1OS3Va2a01bJm16fC8LxEHU1tN6g1RURgiJIU7yqSAfv+XKelaxrpV+1QWHShDAquVww1NV7jj5wdJ6vTsnRQRz3x0itjdF7tnlhy+oOjhOWlEhAXlckjnjxuG3lh/uRly9vzOuGFyU4Z4P5L00Pr2L7cFk2WoNxiniEtSR1+JWJ+zvPv/7h3/k8q7/PZk7Gzok5p1tabmhNs2sJXg0M1znzusL3x5E7m1ze5NpWc9xw+G4R4sYuYuhV/tXNWm43P4M4vpsmflpWpASKxcTJ60DhJVe+uzvoyBw7rEpTsu0sNtfNOSbnuA/a0u57srcDa4VStdXdBOZcWKvyNZvt3a1oEjcFx99+88DP88KSCksqpFaJOGL0+zBxt+//q9Ix17bbjehtVYFCKjpr0wXtAKQ1adzZlCu2k3QRjWvmuL+jdRbT6/5Jen2ZxmvKfDMNduaYlYD9Yml9uKoxyETPo56gqvmsnk9zLtwNAVpjPA4EUeHE4D1LyupTd/Ocxc6r/r1BR4JMwXNaN0DXVKpdzS02TsbpPLbWOT6Nw3HgoWm22WkXguzc000E4zXQmnkmGUobnTcfOfMxoykpPKh5c7ZzYwyeYUeylVKRWuO0JPV1ks53FbzJ4Pa1bGegxhZHrUUT4NLISa0NZBKwfa+Jlramdy6srUtn+6aItvycqJ9baaZS7vNH/sTrV5Ug9U3Yb14T4Zwzvu1/qWM4bGWmUm24orPeuexzeDLs1XdDD7g1FXyD7Nmzz87fSbXiynXw4iF4jjEwp3KDpMAYlJR2dHowiH2vhrqX1lIZo3qJlJytzmuUooZ7h+iN7NasItPruSR1Q11L2bNuEYyorH3beUv6Xk1VXVP2HAcI9FbSvvvpEbO3LK/kV7Ol9wYx37TMqqmVvJhapvXKSTdAdJ7ajFwp6jUSndchj97jxHFeNy5r4pI2vMDDqGRFQXvqTRSu78oCfUb2/VuzhKPZJpI9MYlD5IcHJa7//LZwWjcui+d4iMS9YrhWfH0bNQu3yoO5niLeCzF61iWzboUwOhKFrWq7NbVKrY6WlYfU24hBhK15Ptw/cFnUz8R7pyTi7ibrHFvVZxmCJ9sgU2n6uUrEFVpUAnv/VqU2amo4ry3NYjMHnfGQ8ELJlWJGcPu6lCtayX4HrgdfD4o1a9BITosFZ4T3uif4+nMpawutlIZz+qwcsN0kRwiqMuvhqgdgbOn15LHpLKVSKlmqtiW9s7/TA6KWSivWXrXf74aqqRaS08pzEEXcxhBwa2Urmeiuypie04tTZechOu4Gz5fzRquw2t5xfd0XVYjm1rg18/Ne2EqmrZoIiNPWSy4bqQ68zQuXtLEZVw5uVD8WoHt7Q7DBpGiLPZXKVuHTsnE/XK0Y7Jb9EqHtB4wVX/3DOporIrv8+1bF6kVbqx/iwLMkRXS1OmJwDm9eSEtKvZ7Yi8li63BrmsRkGqOocnhXznb+hyj/5XlJOqvLkohsyU4fE+VEOMTA9+7AOWc+vy37ANLSGtPote1yjWC/SBBvk5quiKMpJy6j+yQXRWXeto1jVPNJs5iywkkJzYNzJNeuw2P75rtJRitqQnlwjmUf02SoRs9FbxJSEdkVxWstLFVn8+VmPNQme6uwI3hy8wYi/PLVQ3nTgv9tSzs/qaNVikSq4kuaKE3hBj1S1Mhd5+px3Z9N9DknQd2wc6VWbfMdnGMTSA02O0e65cDFzCj3IsgSHIG9lbxVnf9YKvThw87iU7X4q8/levOv6NkVbfKiw8+3VPBN9uKv7w8tgK/xxiEkS5wdDm++b7lqHPtzr19VgqSPQwNyQR/IbGaBQSzbb8rw73cpGnNfFw57kC9ou6ijHQHhsmQeh0D3khDRwaPRO3LW7r8XYfCOp4OO61ht1tNeH1vwA60K9+pd1B6gSqDlRLC+6bxteztDFXF+h9qdaDBaS+W0ZeZUfnE/Og/H24LfUt6J0P33ep++kytveQ/7oe5kvy+1aStmNY7DbUCqaOKm5mpe7Qz6wqRn7zYGQ/rcHP33aMqkOSdOl5VE4z6GfdG7pn5BlUaVciNhvQkQ7UadZve7c4UaShAdbRDszyedsbP4AoOS552dMn3/taZqjttqnpuEzHttR5WiqFo2IUCz9dNq5WDDKp1oG7W7tA6HwGVdtUqhEYK25vR+OIp4cFVJzEXHauRSd0NLGjpbaDfh02el1a7+QCv6PZ03Z9lm/jI3AWVXqDhLivY7dz0w+wfkUkm5kpxWZx5FahvmgNu0pZhtaCZoVUjRdllPjnoB0+9lD717XO/ohq35nG/mJ4pK+Eu2I6c2C2Cm7uuJZAPXGlvJmmyh3jzeeVrV6vXrVnAuIOJwru5uxsEC+uB0T2618jxn7o5BuXi28ARVuooIiyURToQQtAW0pYI4GDVjZckrbtH5fqn+cv3qfbzNdK6PQNFTbG2p8uZ5S5xzxTtFqoMVEe32ud3ERSwJvCYO+tO3iWYvIgVtOTwG9Q1zKOrgxMQi4rised//O2JlXMiuDN6arttVhMFI6Lv7sj2T3NAxF073U7ODu7uDC0ZaFzXKHIJj3jLzVijZRC5en638crnqwWeFVC/UOqWhVk2KNK61nVd0yYU5Zyavjlxi960fwlNwpKoJUt3v4k2yYncwVyUda1zUTnTtiWovvm6SD2e/vNl7l6aFe7B7hX1HgX2dpz9xcPc4rm7Wmgidt8S98V6dtz1bOpqoiW+q1yGxIXj2MVTtWuj2uKMxspEtwXCtqfIQmJxH+tmBWiR02sqSC0kUKR1szqRu3bojS1vp7CwTFEgXBvXzaI8Q1lqs9h5XFaxzeg6UVNm2zHEIOOdsJE0xLpY+hc4HK7WRpTKvOmKq5UbLOoe0/O/EQWoV8I45Z34+L6RWlSuCbtBaVPLeXGMIaj+vZLGePFnQ6NJ5C/ypNLa1MB4GskGgDvWKeJxGni8LD4eoJmKihNhcyt5m6VX4nAr3Y9x7qKmoLL2b4g0xcF4v3E0jj4eR3336Sm0d2eochOtolEsqPK95h01vXzmrB8kUtKe9VvXO6HbuYvck0whNXY270mcrCqtvqfB0HKA1mx2kDsgnIyyrD0ZvL8FatRq6G0aOXAPIEIV53kiivhutNqLAVgqXWljSpsTm2nYZ6PFuMkRII3AQVYTVpodFcbq5BYWsg1euRGv1ym8AECGYgdwYPH/1dM9fv3/kH/74hZ9fZ8pd5H4K2grwdpC0rpBp14Sj3gqYNcDNS2a6023iDN2qRjIUhIfjwJarHbwenONtnnlZZoqYFb5o0heqkGpBCIwxEIfAKW18M0Z+fNt2efzOEUM9Y8Kgrtgd+NtSNTK5ll8GjGmLKfQyQAwx0Wq0f1+xqk1qD/WaaN3Kmteqc9pcVst/50w10gxWb2JVn5ErU9snwzvzQ+m3tRu8KerT1yW/mBRepbfAtVCpRW0LOgImrTEMnrtDJFijtqt0twqXZdvNYkWEy7zy7Q8fGdK6G6QGERYTThx8oNI4LYUtbbiqXETxkbe3E7Upyns/DUxe40DJqyYT/dY68xoyv7UlFc5bJpVCEnZulp43wuC8cbbskG2KyOpB0FjzdrWaaIA4/vlt5uDh28PEw6AzJaU2qrtRmfZ7BPu9Atnbch1hLIbqXHMLgVb5EAMPMXLKCXHCf/rwxHnL/NP5mS5i2I95K/7c/kYmHqExNzhvmw2/1QNydErMHYLfk4ze7ku57q3u/v6rFI5D4HGKiINl05FD25IpMSBe8HI9WIFdEq/+dx6PsG2JrRYzn1UH6CqdmN34tKx8N44M3u+JVZfLj96TAlZcXt3Eu9Cm2dp9Tvla0DWN/Z3f2RE3hyLtuTZ137Z7Vu1Z+J3wrj+7WSLbZ01u5YpAdgVuF/T0gd3ReSW3t6bE9ap2N8b7Jrei+6k1tqSIqA6F7cah5vht60Ls4Tb095OGV47O6WxE5xhEGJ3jbnA8jdop+f3bhZc5kbqi0fb81hQF7IPiS72JH1ZYqoJPn2lXv7bG/jsdARWU++VHBT0+PV8UvXZCxdHEUWrefahW0fdIVVt7qRSWNXNes6Vgumk6svmnXr+qBMk78GGgWIXg0EF528Eroas0QoOhaWI0xcBbSkRbELVVrdasT68EUQBhS5VjdDZoz8jbdFfuyvshchcjBZWbnzetolfrdXfy2CFqXTFEz5rVIMs5pzbsMfB62UjAy6rqg4+PD/z8/Mpf/PADX1/fOK+JXApvW1JbfjsUe0Um5jmxFfPwiJ5D8OrH0+BS1fRyMJj5lCqHoCT0WoWi4laGqMHt4CtzaVb5wdELx7tAacJrSjp0s8IkfapzYSlpnzGVqy68/+PjE616LpdMrZXgHR7lMXVn6cOgctXn84wXx5d55aFGVTg4dE4XjocYFFEIjSEouXAIUQ98OnKm9yRGj1hy1FtC+owyf/fhgdfTyrpmvBPux6DcHI1e12nqe/BEo4HBuLXC/WHk6f6B1/PrzXrRg/z0unAfPOvSuBtHjocDdT7z4d075nRh2ZI+l5wp1ROqU9g6aPDNAvUAP+Yzz2+LffTVF0V5V8pxs5Louha6usn2di2mEjNHeW+JTclVA3zV6t15teWvNOX3iPbwu+1+1SyGNekQ5tSuhoetgXhHWpPJ8/vnaxuxf5dmyJEgvL0lTTCbIno1V4ZRPXPCKOY+Dy1ptRt2p1vz0XKOp3FgDCAoT627sQens5tesvK2vFfBQnSOt7cTW2qUptYQudSdQ1TGRqie5jZec1M1aKk8Pj5wWhM5F8ZYyDlzOERcE+5Gx9LU/VwqzPOGeMe5ZIsgei0LWqCpqV2PLoYgZb0vXWgg2KgL1/lC2vboULeTxlZ1wPJjinx7GHXSO60v0x2Q2oUH7erUrUm2Cg58ww7ma+u1WeU+CHw7joxDoDnHf//xZ4Idwl031oCa1acseBttQUdUnL2vSuHXmjlttuejRwytwdDALJrQ9FS+X8dWK3XNBOf5OHriUQuq//H7rzy3hcfHSQ+BmwRAOZ/sHlhLLntSsZSKtyLFi1MjX1EkebHeaHSO0spuhtiAu+5Cvm4k477QrkhSAy6lcDD3aG3Ps/9df1VLAmK1Ai/oLMluQ+KBhJpxqipRdpTybhh4NJPPjjTpPER3TZbsAWgyURECX9ZN6RmiqHHJjZd52++3F6GmwuuXCw8f79Ud/UaNvFMuxDiwDZbWKFI52mdp210veAC2JvzFw4EtFy5JkbfFisYYDSSgo2iOLEXPhHpFtveOmuhg6Z4YWcCjo84iumcG77kELT7ftsRvv75wHAPD4PdWnrrHs3NQtX3sYStXU2M7T/7c61eVIAHEtrJmGMXz8e6BVF5YU2bZFFmYvOM4hZ2xPwU1l1KxiNsr9FqVuAXQamVdM0+HiU31QQYtCzjHwQlucJyXjSEGJcUtq8riBZMhNpPRQ0qZzaqXzgkC+HpeeEmJ05bItXI3DHx/PxFD5N/++JP2F6ga5Nx1U/b8tkrPpitB1I/nVCtLslaWUylqQz1sKBVJheCPBNSnYjKTwyUXTmvib5/u9uqzk8Ib8OW08Dmrad7kHQ6FOqv9TCcj6tgM3XzH6Hk2BCs44fv7kX/8ckIE3h0m3k2DDat9YnSB50tiDB6kkpumbpNX1Ov5MivkP452MGatHL22OBzKIxmDY00ZZ9LQ2tit7oM4/vbbR56LziPSza2tVWn8Yn6TOCUE/+sfX6hNycDBO3LKfP7yoq3aoBHCVa1svn86MriBeNAD6rys3PmBn16+8jhp4vd4PPA0HXh9mzltlZfzGzGMSHAkqbgK4oXxqHycrsCoVkZ5QyE7koboQexESFnh4WpKqFYLGfqppVVa8Daksagah7a71IrmIjs6aMepWlxUaCXvoyFS76t4m592zYds3TZEejJnKIadgOs572ZvIThFxaLbq8ZWK6Xo6J8sfT6bzUAUzFFc9r3QCbENJfj+xXHka8rMWb25jjZ09+PjkTVltqKE8iUnvj08cecyXgLVq0rrUz7jRXh+/kqI4EKgNZhrZag9GdGDRGplS414GBiC2zkMWsELEdlbb125kSu8XpIeRr7zPBRNcwi+BdQGVHlwjmvburRKK5XVOeU04dWEk55k6ZoQaUQcxZWdH7gjhKqgwJu0uXsf7aoLS5pelo3ffn7hUgotq6+bd6pWOnhtK+ZSKVWMJ6Pv01o2j58ueIHmHKHKvh56Mt+ccs50yTYjeOt/JlE8ZSsFqdi6hw93I1/PC8ucGKZAMQHJcdRE5rysikrbXo7WdnNOTKSh9yN4RyvwtiXmXHg/jXwYR5x45ZjtpkuaWD1Ex9e17GguhkD1eLemQuiFQb+VtnfKTu/WomUQr6IDU8aJ0xjqMdXWTfKVWuExRg59f3U+rKFU3BoHYxxbU+HehcDmlOcz254YxO1xrqMrUuD0+czD+4P6ptli7Rzbri7tzapsn1NKQ4LNjnOO6D2kRMHxw/2Bz5eN11XHER2HiEM4xrCT1p045i3t5vC1mvt4s5NHM3cDKNvuK9UTRU2IdQ7dLXBwWTLLmpmGwPEYzRhTzBNKz75z1jPk6X5g2QrrVsyO5M+/fnUJ0hA8Q1CC/Vobv3l45Hle+Xw+c4yeIBq0vAiDCOdS6HaGzTLK2iHT1r0z2j7wUKEgsQNGhxeKtTBSqaSWGYJXcm0peK+zonpmTW2EGGhON/opJU5bVq5FqRTXzEivseTEl8uZi83V0haP7Bt9/zdDNzqkvq6VoRv7taaeOHpmKQo0DBzHA4fgGaUQ3NXbx5u1QZJGahoEe6bePzfXxufLQgmq5gqGYnQH194maR2+TJVLTqy5knJRsjuCMHAIA5d15RAck1Mu0m/ePZBa5HjvcG3lbT6zpqyDMsWxGB/CtQa1UGveIfXBG7wsGm0bEMN1bEBrigzdhcAlFR6myPPzypIzwzH2b74fQL0VIcAQPXHUwZm1Nc5LJvoIRc31IpEYA914tZTCeV4JUSHxjPBq6GUMQVtmpVItuYPCOHp1sy6OMKoaJK4Qe3UpomqWqjPkxB6+91d0KOXKEAZVVdbEUrNNMW+0rAhiC2grpjbzxGlMY7TRK7oP8lau/lU96SjqQ5O2siOGwI7YdVl3R5xa0yqwq3QELGG6tteaOZ4LYt5beu8GL0jWFp93jsMhct6Mp2WcAkWJYNuyGvU5RzTMQuuXxoYFTPR+1dqIKVOC08TAeabQcBJY64bHK/HXO6bauJtG7ofAOW2MMZBz1apXxAjFjpLbDuc3Z0VWU3K9doL1mjdhTzb7wdq5KAVFHrWaVi6jlKbVsPSE0xKMXq2jFzrnzB9PF5Y08O1hsJEft60fW8tVq+r9UEeTp/7qCWZD5dydM7YaEve6bjiUgqDiLk2pbrki4lD+2827agP/llqsQg/wO1lWRH83irAp/LMTmMXuT0cOStO2VBAdMnpaE5dZuUzHg7dRT5W1aFvPixGR7f5F8eb2bGtxV3Dp9+0S/FurlZ54dKRidE7THLmiR9enpIlvwTiZTteeF12P0p+nXctcC0fv1IUf9uGwfV9s3b8HoznskEuzxEb/rNmBD21XYisFAY7e08c31Qar7S3HNcb1y2hNEcHz84J7nBjGK42gGgJ69ZhSxP4NbeEfgrPRMNqFGGKwiQcRd++YBs+X88qa1G4GaZSqY0lyqbB7ELVey1mR6sCGBmNrWgzFUg6bTlbIuZJy1vPa0NcmmhNc1sw4Kle4WihqTbmSg9ciNBk1pkZz7L/NNv/D61eVIAnshNjowZXGXFTqeBd1rIMXdqdSVZhds/+e/4jTJMqKL72ZChZp8sLVv+ZoLNXWGqMTttZYc2E0MzNNutxeLfZBilupnFdFi+aspM2uqKm1MYZIFB3d4Lzsfi+dO3Sza+lkVfsEMw7EKjNd+LVBEXh/PHJ/OGgyYnwRre6vm9ajm/9+CFYtXKHh7jqeakFqr1Mt0KMBLeeuvdOKKLfGp9NqBoHW2/YqJ3+YDowODs7hG0hT0jJS2MpKaxlE5ZbBO0V6LKHdZ8BZ4uODuxKiEcyDfz8UGlfF4cuS+Ol1xgXh+bLRHOSDTqgGrqRE9tuK98I0eJ1AbiMjXBAej6O2ySxBlaCk/SqNpRRG5xmjYwhOHbZF9rEpuTZeciKh3kPDMbDNWe9qVt+iOVeDrJuRPQtl6xtXyLkgElRd5D2FxLqpyHjf4KIVlq5pp2o4UTStGA+uO9cGVIJfnQatYk68NEVVh8FTUtlFD9hz720dzdllv3FXngYWkdiN7pSbJPuQWgVazFBy1Ra0OB1n4qOD7brbRTRROOdCrmY46ZSDE808bitW4BiaKYbMpFZYNvBRidep6B7aambw1mKsune/fbzjsq7XJM9dB5k2UR6FIs56q0PQIbp1N2FUJOQ4RlYaX4wc/Mshs319ogYLtoZ1LlrRIaK9gKYjFdd9oAVV5bnpXJJ3o/IhrxMTbjKqXuPtfZ89+9oTLiyyiMDcGi9b5mVLWrHb1uq/0ZoNK7Xv1dGSDpLZI8ftacS1BSR2yOt1G0Lq1OyjtevzEvueYp/dw1+z9/hwN/LpbWXbCoMvVOfIxdr5RmbS5WdJmq3nbPex7p5F+gHqaVS45MQxDjuSQ+chSttRsdtnaCtz/849Se3FVqfS31qAd7HNUuruhdXfT2z9CNeWVkXbjScr9G/fro//aQ0exmgdksbzvLKYX1NHrcbBs6SG16qW61O3V8WMbyu+mnqTK3l+F8Q0VNnWdDB0V7W61q1M9J5MVrwOhqR+XradeF6azmnrXmyy30srVkXsZnY0275HUzPZp3HAe+GyZm3VN0yNeoP/WNWUs5ri9ljTBFPw6XfNtRmyuD+IP/v6VSVI9IdhwTqI4FNmCo7JH0x5UPCi81pOLVtv8wpr9zbG/pYW6n1PNICOMESnsG9HckbLpjcq0jxDUMVbCGrmV2qjogTot5R4WxNrybvySRCkCgFVA0QfWIswjY2X2RRMrZPvtLXSByBq/GtWdfW0qROH9X7cjSNPd3c45xlaJqC8i9u5Tf1wG5zwNEa6p1RryldZc+F1SSq9Fx2cWe0zsQCQs/blde/qoft82VRB0UxZIrBa8Ln3I4OWYUrUyxnvCss6m3S7mbRbyKXu7QOd56Qcr9JUUXRrRCc9OHVFh92TtRR+/3Lm82mh++lNY9Dp1VfIbH8fLFjXVvfxD8nUYMGrBLkCly2r+Vyznr8oubgHUa8+BQzBm0mkcgy2UqhUnVkEDNEx4BjEkVshBzU3nbfMak7eebtC9JWGc5UcGmHQ611zNi+RHqDBB9lt+3uCpJwXoKppZPBXDkkT5Sltxtvo/KdpCORNx4bs1UUv83DXdsLNTewTwftE9lrrjh650Hcae3JbxRIUr21T8Z0oe010DbxgSTozqsu3m6l3KmJtJ0MizCiu0XgrhaU07qMOkG0hAKrACSJqnkdlcJ7j/YF522h0+TNGdtb1nXt7wLguwTuGICyrWneIV2+h+zEwNvWEWXIxtVd/H3uWHRXqa0a8OYAbqZer6qpHqX7P+qH+eda1cbR2/2jPuifU+6OyxKWfrr3wkv5ILSF5S5nnbVP0zjmqwoU7ZaAnxpXr7C8wxLqvz9uI2rTdGi3LqZbo1P1atNDF/qzaM3VcCc57A0/0YL2fBt4WHcGxGwPauI9me6A7JRs4tZN8uwAh2t4oTa9jyZlTchzjoHG06vNmJ6cLY3AsNyqn9h/+9+aydbXc8Mv6HzsUrVtLJTblEylC13Aof7VbQPR3XqsasWoS3PcrN7NB+zr0tKzPa2tmwYIKJqbBG7WgaDLDTey0+0NrrFtGjBi+nyt284Ub9MwW2SUVtSvxKpePtqCCc3jbX+PR72KhVHSA+FbMAuT23oiYia8lx2bI3P9elbTC/Rg1tpTK6lWtVppFix2hvLawO6+vgwKl9QHNHTHUuOhFk/8/9/pVJUj9JiBXSfrjFLiPugneNpi3RCkbRZQw/ZrzLj/XoKOHUjBEI1u1a4IpOsw6mBFaXyA6AFZVYqNV+6H3YkVN3lItpJx5y5UvF1XQ9AfoRX2ORhzRebzNb3u6OxJ84bwmSlFbgkMMHEJQwmjLJIAukU2VLRWCDwYte4JXyPmvP35gzY2fvn7lm8nx0NtwtP26mqExInDvlc8QDI5cc+XLeeUP55mn6WBSIeMbgVV7lZK1DbNL/J3jlNRATABqY9sqX93G+4Mw0QE2PdSKQgwajGvZIeBkfjeTqV7eVuVqfXsY1QbA6Qbt0GmfB+SdtqRc64lV49O64kdnELPJ4PdSTDpybeMEdG2cZ7XfjzGQcqMU5Ui9pMS6KwmFms26vzXuh2AHtrBWxyGOjF5YW+FSErUVQgvgHMdx4O115uEY+XgceBoGqIoIvm2Z/+/nV9ZVMTQsMW2t6ly4WrlsiWI8mGYWC3TUoTacrUfQFpWuWbMJaJ6Uiw5rNYfqXBtPh4EUFJpPteqIgeipU6QuaT+o90oSAd8DzP/P3bvD2vZl6V2/Medcj733Oee+/o+q6ip3tzFGshBGBCAkhJCQkUDIkQNCRODMIrEQCEFASgBOCCwkQkBCvAJEigiQA1puJDe2we5ndf2f997z2nutNR+DYIy59rllV7sIy1v6P+655+yz11pzjjnGN77vGw6RBwuuKQTjGHkgVHRP2MBaodMQ2Cqk3CCFFyNRlG2zpKKTd6sjqtpMgTPPkWOIVpGreVGJJ1O95Rf8ub7fMikO1Fy4OYxMt0cIAa2Bc1n4+PBECpb8hhR5fTrxtH7kNJ8QtdEkGa941dxdG1sAACAASURBVAqS6PtYVdnWwrY1Xt+NxFk8LgVG4IenmW8fLlxopmhrV4QArklLABuPEBrS5IryKhSnYon2lswOUrPVxlfPC7fjwJt5JE4jQ2BHWmHvcnrR4EeqJx7a/WcqbAHuc+ZS2p5o78WUJ20WN7ztA1fHb70mIhGjNCTx51crQ0iUzVomJprsx29PQoxL1MTev6klKclgJzrKsFZDK26OiXFIgJJz2Qdhq5gyWfqaFOcvtR2L3ovO4uTcjjQvtVJqIXYiebBDGkd6Pj/M/PTpshdf+1HqSI/lJrqrG/u972AeuAG8uImkP6DgyZCZRupe5AZPOLdmsZAIx154KAx7EWCzyx6WbVc8B2Q3glT1Ad8xsKTGjJh4QK8JZe+WXJ42463dTJZciNCi7COh9vNXQJvw3dOZOUXeHSeL5eqO584pNAqE8qPTgQ858+3ThSXXvTh4mY4EsdaZoY5mz9HtYYZgCm0zUbVzavCksKmSL9Z9MR82+3nz4zKkPefiIIIlWor5Hs3RJg0gRiKn/GMk848h7HL0rP3wts12N8EhJS6bcC42pLO0R/CAWlojRWFrZiqXnV8xDsHs5sUmbo9DZIomZ1S/8X3BdwliiEIVI26vrXEpleeceVo3QgyEaGoXz/2tjRF8hlZrBqu3wlYzg0zczkdOsSC1IK1R/LMd0kBrBn2HIEQJlKIsufJ6nvj89a0N1X144v/5/a/47O2JJa/kYaS16Iibz1aTK7SpDjvnqpymAREz3Xty9dyb05Fzzmya6cMwc3EfoOQBzINCUEMmtlyZx5HTwaBfCcJQi88yM05ME5sA3gq8HUc+LKvxF8RaQTfjwJLbvlFya0iIHKLxMIaYXnAaYIzJVViWsAwCd5PJ6EutNtne/ZO610bj6ovSpeslw8eHjdoqd/PMcUxsJfP4vFKohCnxKg6gpmC5ULghkXPhi9sT03Rg1ci5LIxjJIWRKYx8fLzw/vsnW7xN+TNf3PFmtlaf+W8otQXuhsRvfnbHN88LHy6rtS5zAcTaYcmIn21R6tIYJw8qve+DHSzHyeamWTsEalJqsbU9HUdT/zh3Zj5Em/9XG0EiFChrhYSpA5cM7UUF6bD2ftDS20LKlAKlKpfLdeAnnbOjpkYMUVgVNECck9P9ZIfUizYvfmwwps34Eg6jkWhvpgMlmxlqDLJPM+/twZfBPIlVtj/6/IbzkvnZ9w80hD/9+Q95f37mdrQRBR+3zCjwZ758i4TA82XjebXqXdK14RDUDtgkQhVlrcrdOFlRIH7UteZGp8KXr448b5mHzTiITXqi6ImBCFKFMBR+cJz5kDMf1sxWe9uuGSLJladi+9buVQM+rBvPufC4Zn58d0J86KvuOIC1XaOrNrW9jGN2Xd9tG8tWPD7JPq29dzz6KB1rmTqCZBkNChRpe4L+Zp54PQ4kYMlWUBQnXDewuDilPaHaq36uxYp6kO0JoYgZaV62yjENZHyuXbBr27T6/DM7gO2wdjdp9Sn2XkxVJ/c68OraAuUpZ95FG020J3C+1g9D5JASz2XbkSjtD8ITxdZsLmPcV6Al0r3tl8TUfVaIWndj8LMlVzMp7v5nCeFIZKXy7u7EUhqP2wY0TmNyiby9T/W1ECSAk50NqXOUTJ33qso5CJPf6OJJ8oSYqazC5Zw5XzKShNevjoRodhoW+x19EmHNdRdDHIbAaUisrVnbXiwGRRHCEFhK5hgjP7o78LBmntaCBHi8ZJpziuYhMgQzvHw9jfzoZjYEv9lzqmotbtQ4RkMwpHaIdn5rBs3K6ZB4NdvcQFXl2REm73EgIZBUfUi2JXTjIAxz4DhP/PYvyDd+5RKkPs149REJKQiXosTYGESIKKdROAzwcFk5jicueSFo4ThEbqeBj5fNeRFxf98yVZoYlD6E+MIdGdRKpr03bu09+O584cN5Za11d+tNQ/BDpQcj3TP21qDFZlJkIkNIDHHieBC2tXCuNv+sqpK3QtVKCuPOm1FfpIeDcDNM/OjtW6oq988X1ty4OQ48Lc+MAu+XTFb48jBZgie9P98zabhfisGXmLPsWhqPa2Y6DWSpqFS2XD3Y9FpMuZ0Hzqspg4JYe6TzTAaBddl4qg2i8AfPK3/2y1umebhKU6u6/L9yHCOXAlstXjkpTYzy6Zw9vl82fu04kSTsFgKdxCtq8HXpSi+xVtwPT0d+//6RzUdjDKFPpbNA/bLdISKkwTb1q8MbWl7IZWGIkfKiOm3AmAJ3Y+IQEveXhVc3N+QQ0VZJUTnNgd/96gOq8PmbG9ggr2Xn6dwl4zhtHmwFcwPPqja24XZmivB73zwZQhmDJXCL+Q1JUOZ54N1x4jAYoXyIgRAj318uRAKX2k0qbC2/PQ58vGRKrvYzyUJ5FWxCezesS5FREylCLrqXeiGIT+q2uVgxujuxz3oLQfj4sL7oXBpC0MmW8zSg0QOzgoSr1F3VuA1WnVrbx845R1MdxR0PMz+4m9hK4OOy8ZQL8zAQVG3/ObF/CJE5JQRrOf/e1x/McE8MvfqD737Kkjfz3KqWrLxJA989PACQt5UxwO18YE6JD+fnPd3osmprT48cx7TvbUWu0mQn8NxNA6LwdCmGuonu6q9IYB4GXh+tgHozJN5NZiPylAu/++HJjmq5zvvrLt+G6ti9zaq83zYu7zP/zA8/t2LBbS98ddNwhZYnola2KRrh64cLGTvstcOqL5IpDzmf/MHz2v1Zqza+GGc+m0cjnmOV/OOyUdyo1xSXFS0mEMhAnMzb6NrGc5+xwI4IBISkwhfvDnx33tiyqTFFnAcjNn+xuGu5IEZTwMYCwVWKTy+MAhxCMlWhCGszIY8l3bx4pobgvZ4HludiRWtHwcToBOah10AdCQku2/ckMIgRuRMBiWYLYz/ulABxXVDo/2P3Z5bA9+eFeUiM0cbBPK/W3j9OhviOjqZmVWI01Pg5m+O1KY7t98wpslXl4gGvF/zPEUuS/PeKQsumakWvSry+bKYU+erjo42dKY3z/RkR5W40C4pRjYXmqTnTENBcQQNv55HTEPnmeWEaArGF3a5mcRPKn64X3s4TnZ3XLZijCjFekULF/AZTCxSxc6a0xiUXtCU730a7j9XJb1MIqAhLLRyTnfvjkLg9jPte+oe9fqUSJMVImd0GfozBXVmVrUBM7iviUHg6CR+WxmGYbbNo4XHLDFHQZq6m4LCmwnM1Y8RZbFMqzuVQl9tilfilNb46P7N0vwboZ8J1rou+bIH0LNahXVVEG1o3cs6ozDw+Z+YpspXMUgw5EFWeS7asPLrZVlHeHA/czkcezxfWWlmytWXePz9zN0+8vj1x3jbW2njImTfTsMe3jhMLOOFXuRQbQioBpikRAnw8nxGXVUZkn7Om2Hyq0zhQqs1N2jbzoPnsdOv+JMJQlctyYZPGUynEaqoha9UYrBwbloSERlGfJl0LR4dAO7/hed0oh8GIou45VVXNLBFTb1n7zP5ZS+PpvO6+Ia8PE9OQ2Frj+ZK5P6+EEJiGyGGyyfDff7yw5cZwLKQpkZipZCDvffOCuuQ5UZsdchZsGjkXLpfC948bWzbTva/eP1igHCzRmSYjoUdPnDui0PxwEJRDDNwNIzEGGx8SlLqaiiREMyi9mwdeTZOhMo7SxCB8fntkUOH9svK4ZTaHqrdmiJyhP+Lzq/CgbhVy8p7MYTJ1Zu/nq3QipuzTtkXdLTkFG/UShbvT6FVeY/PiRRz+FmcSa1cuOWYQnVSuL55db0kr1h64nS0RFBRaYwqBKUbOpbJVI7tvpXIcR5o21lYpRZFOyq7VjFwRBpmQCCm4sso3RWuFP/5w5tfevaZuI+dtQ9UI+q/u3nD/9IEpBA4p7Tw4CVfKbkdYpaNfTgZtjpx9eTvz/WWzShhDgYcQmKIwRnFisLjZKLYG5pHHNe+uwH3T1nYtuLznaeiuwrePZ94eZw9Gjn75Z6Oxf81IqzbhvMq1VQrXBLUnLT2e9iG6vcXoR+reenozDYy9YPFW12E0q4tLb6/4uivuQSTZjTejoLuEG0dFXxxaAh/OG5sPdO0Ja3JbidL6wHJLYvuhVjBO0BiEQ4xuDGn3eupJgH/Wx5y5GUaPu9fzpokyx8DJ+aab9ntuSacEe7+m1tYbQ/yE4+rfztbqbo1SYV8Lg+/dzcUWwR9V9GL0nLPZVohxf0QhbCBBKOJiDywGTLGjnPa7G8JzNnWxIStp5/Z1QdKGPQNxomsQeLq/cPPq4OTr69e30twJu7ftrfJ+Lo3XzXhGXfU5RFMSTgNIcYsRgWNKPC0LRZRIoA+MVUd7fvfDA++Os60l3DDZe8v9s+dmNJMYA6vz5pbaterCaUxUsdFFneu2VouFN4M5jt8dJ47TsLu+/6LXr1aCpMYZ6tVcl40W37TFH7I9HGFIwu1oZmtVhSYDUYXn85kYbHhdJwVaEm83qjuM9hvXHSEkiM8NU55dmn/t23/6b3WI9uVnV3UZpZpaSbD21PmysFXQxVpKHW3AodKyFWQw5czNaWTbGjILW9lYavX5MnB3OLCsmYfL6tYD9ktPydAWg5jddr1ZNZOb7n4j52zqvCbG9N/pct1/yaNm9VZWDIEU3dyvh0pPYsIg0CJLiVbpy7UdsxvVwW7Xb0Ws8TAKxvcoqPuuKF+fVz6bJ5871Q8KYcmV+7NPTXfiRhwCDTiNI1ldsk1xU8tmA0oxJ/ILjbEJo0SGIbJtG5oS6geD2kOzwKZwyYVarA2bhpGUErUVli3zdN5YusEnFkQJ7KNgpuHFduttQtjJsurJ6xSFz+9mPlw2ymaVcYxG/j6OiTfzxBTNVbabY5q7e9glz4o6qiKUavf2i9vjnsT0RDlE87npc4wUk8EeB2USd/PFD63m5NogbrLp+0KMrHzOhVLsuoonfvsBr/26rR0TQyeRX/dH/69di3AYE8cx+cF7bYsO0XgOa602oqM00iwQIlq7GMYS/kEt0bTf1Wyorroa1r5CKZXL1nj/dOF0GAkxsBVTvUAhSeQ4RMYUfZi0JQbdvKGLOARr3Su2Nqva2JAQA68PI/fLhoqRWacYmYYrEiR9E6gwSOCL40QUU/AZn8v/3oGGzmXpP1Oa8s3zhcdcSCFwOybuprRHpK4+6yqnntx9cZw5l+ocSp8g4MXU9eDweVuezPrSoQsApML9ZWWIhz3R9q4eIoHZJgqx1bavSQS7kGLrSkNDBovJHblpvlalCSVcB34HiT6Ko+4ZSCeg43Gqt8MCxic9pLTzsvYZZH4Lo3tM9aG3tSeVsLeLb8dI1caWi9+RnoRez4imdqh3LhF4x6Mpi48MGvd13+0eLNaiTvns7WKxM6w2a5PZmCFl8DE3XRTUOT+1GefWEkbX2HkyEYC7aaKh7gdm9i7TEFmKFddiVafF1lxZnjfGQ7KYIkLAzI77ve73W7H79bAVmsKcolFTWmVMgQETMZh3F7yZB17NI3/w8ZGlI677WlJrR+vCzZA4DpEhGf/Iwk4nWhufcUwuMHGQIqtyrpUbEkECSXRfM0msPffm5si6ZQ7jwBAj2v2XfsHrVypBgk605vqPWELUiba52eTkrlg4jcJQK7kJRQO1JnIRMhZJUxTEO20R4xdkbSSERNjh7dCToyB7T70nDNfPAi/vte49HA8oXsWp+iw4DErsE9fXjE/9NiJiaaboyjQfyBcYh0TNla30Pq0FiRgjp8MB3QrkTB94e94Kz1tiHjzZ8g3boeGtNdYCy2b6lCFZkKrB522J7Nm9x01LRp3gF0PiOM6sZbOkTJQxqBEtU+Dt0fg86s8OT5DQ7kF0tXov1ZReT7kyueP5Vm021sNW+cHRrmkrhbUYoXjNjQ+L8SjWbO2i02GgauM4TGzrypILoTjJXa8zvbbSWFZDHA7RRkacl5VhMig3BKhrYxpG5mFgduOzVivTGMmbBbeSC5c1c97qzqcxubhckQqxKd09ofBvg/5nsYqvJw+f30w2Fw0bTtsVK6cpcTcMdpB5gN5NA9U+T5/CHoMdtv16X002UiZ3t2FlV7SJH+oqMMdAVUMytmq8k/13iZGjA+IB/Zrk5Vr69BML/FWJybeE9iPZi5B+kOLomcAwDntCbE7O9o9V9Ff+nCUYwQi2nuzNKdooimjBUDBlmSl0ZN8PudnaC37/WjNuA8D9ZWMaZlKI1GDeNNoyQwh7ctRzBuFKiN6/Ij15aq7OMmSnoBzGRGtKFWGSyDxc51Wx3wmPQwKvRktuZMk8a95n1fVkR53N3vdjQ3jYMvebDehtx9n9al6iGddnEIKQEH50c+Q5F86lcM72T61WwPXHZmHsijKpwm6v10CL8qiFw1jcpFH3Ax65joCJbmDYHPHZc0JH+2KykqwqVDFD08tqBqJhDp/sl52Q7nyiLjTpdzEFi99DsBbTEE2d15XGtXcPxHyYXooO+jWKo2uAt+mvAf4lyPUPlsjX+11b45IrS24MMTK+eOT2bo7McU1Ie7dtCD5CQz+dVRiDdUCuBGm73vDiZ4tfa2mNYfegYf/sUYQ5Gd62iZmRos39YmC9bHZWDZEYlWEMbKX6+1+T4P6eT44SNo91g0SKmv1Fn0YBwm1MSAj8TITFE/L+JqIGgNwv2XwDW+Kk9v1dpVj9vBgHmyeX1kAtzVuVlgCupSEpukLO1vucIodh4PYwchiSJ/r2e+tumPoPvn7FEiRFXYJqQdgqgTFa73qp1roptVFiYHSC3pSuHJvzkjnOMw/PZ1puyBh3SaCKBf77XNi0cZsSA2Yu2Gc9dYL4vpG8x9kVXaq4aZY5xop0JMkPIV9cNrBQgMAwJDYndw9DYoyRoMrDerG5Rqdkfe2qPD5f+MHrtzw9PXjl0WHayFoLv3E7kiTwqMJ9KVy2zONayGrqse7d0k3vgssfz1s2lGMc2ftxPb8T9iTJCI7ivXSbBTQPg8/8qTZpHiW4eurLu8OeHGk/lXvwsU/iBEB4KoXWlPPWnPMCQYXJK3MR4bxl3p9Xvntc+Ob+wjhFpilyWYsd+AKymXvyYRrR0tAgNuleIWcL0sdppKgZjuUsMERqs+9T57YFCYQGN+PMzXzkx5+/YR4j58vK07by4fsPfP+4UmoxjgUW5FtVG/GBgqvJCHCI3TRvL5voNgn0A8irz2NK/KlXN6hcXYvVER8jobqbsicp6pm3DQSuJLFJ9WOMPgRUaKXYelXZnwEarjY5XqXaqoQ5WBUmjqgiBsV/yKsn+rIbRyrW8sji6wtBayVo9CTI/iWoQfDhZXJgAf7daWbNhY+X1RJP902xtt6VjT6EwCFFHrcMaonn3Wxt9CWvHF1NKSKsW/VkUohByUWZdV/Q/mwiaWjMw8D7h4sd6EHYmnkOHVL85LN2r7LQDe96NPDDocvjlWsYXlrj1WkkSjIzWVFS0B1ptvv/wh2mWZKUQuCPW+Op+QHe4ZuXbsr+/Jqnn6Upz1vhYcncugCjn2VBels2kgTuYuLdPJqaKRe+er5wv26cS8XGc9vBm90r6wXw0jMlRI0v9riY6hTgNHQyra35KQVmH4572TJLrgzJkI9cDdmLmxIG2RH9zeP14ThwFEUl7Aq6rVYTn0jdlZvSkXkRjsmKkeMQXUJvKEzxQ3Toakh3Qp/GYGayrmbThrWG8efY3HZCO1r4Uq3m+1DdkqWqOzg3a/cveSdU62Cz4URtjmZHxF8qEMHQskEsZhTflxDIrVnLWUx8oZ6M3oyJJ6dVXIruaF23P/n66Yxg+3iIJkJSrEAqDUqAMATmButWCRXWx401CGmOzNNg1IH+6LmiP8YDNRSpdyXeHgJLtaHASYKNpsIoCn/n2488rXUv1Hs7nxCo3gU458o5V9I58+vvTpYUBzE+klz3m/RM1c+WBny8bLweJ9TR8TEasHB3mM0ncYxsuVixhBVJv+j1K5UgKS4n9KAsoc+X6RCu7MhOxODM5gs7ikmR35wC4bLS2sBpPrDlyuP5QtVCPJhxoqIs1WcKhcqX04EgxiM558KTs/iDO0xHCdfsGWy2lC98NbjIMtnoLqWq5h4sgdMh8tXHJw5jYhwitVTWUjnNEz88TCxxpJTCmm0AowTheb3YYRx7+yaS4sjDeeHt3cA3jkodU+Q0JKuIW/NN4z3fqkT/elen9UIjcG0j9HCoGCLQYbIxdqVG5bv7e+IweMugWZ8+BN4cR1MV5npNkHaLePPMCEF4XgvfPq0sa90DeSlwOw68GgemGLg/L/w0wP/7s488LYYKjIdk5pFN2NbmSayhHa+PJx4fC8dpIEcbfBsbbE3MYXwcGYYB6FwG4Xw+85f+wr/E3/r7f8jv/tHXaFN+/O4tP/7xj/idv/f7yDeFIQpP68o5b8yqVKloFOOeVXO/jqPd/95j7eagB0c3Oip3DTbsvjsvF3s3foyeYPckIzdlGpN7d9maWktlGAZenxJptNE3EavCRZVWzJhJCXulnfyQ7y2JzgXoZOQOgLK3Gdll9+L5iqJEDVQxkcRFQJK7RIfkZFRvLTdDC0K0BMmUaSPTEN1Rwp2yY6Tqdep3VWEUbHYY1vqTNPCYMs+XyhdvT/za52+4LAvffch7khKDKYdKvy4JzE7oDN6iCCny6pSYV6i5MM2JS63UVjkOkdeT1d+X1n2s/ND0cUCdO9avc398rjYysYdJ2A8x8v6xcBiVeXTc75qr2cuzJXGV0hQjU4ouRzcDvFr92655tg9gtj+JCI85c3kofHYY+fLm4IRddvVXRJkHcw7q0zJGiZzSiapHnnLl/bIxx8AUAvdb5vtl4yGXa5LUQJrwxe2By7JB033NPuTKd+vGq3HkzWGwog9LJm7GkbvZ25S1seZqrR5VDhq5i4FzbVQJJu4YoDYhRFtHwWHKXAuHYeSybharxFCXoyuwBPdpasoll91OwFrnluZG/1oKgaeSeR27vq75c7SivDVDxA4xcK5mTYJ7Nyl2yDY1NdiW688hfibEMDdvU7F1LmDEEDNz9WZX6B6T9elX74qUasVJk6t/kYjxiL6/ZL59zhxSXzu4GGPkm6eLcw7FHU6c4N6UKQn3y8abaeIQI6Iwhsjvf3+/I2i2qILzcAN0bFHcCPRFfBM19+7va2MpjT/15sSlNSYxrmNujb///oEPl0x3KI/BB9CmSM7ZWojoHo+aKj/9+MxP3p5oDZZSeVxMEdcDaIh2/7XZZ2qDsG6N49FQ2BAC8zjYfEmxIrPgdJOq/3j5IKHNqj6sKjAfjUiMwgHFgI1rRh+Tohp23o2ZwwlvDxPvn89oCLy6O1Gbcn56Jh66oyi7/PRvv3/gy9ORKVkluDiJVdVbBfsN9hk2ETTrXpl3ON4g/cZBDbJPMaIKp8PIsha2tjKHwKvTkS/evuFpXVjef2Rxr4wOI3x8fuJHdye+ebowOu9ky4YA/c6i/PjtDWU5s2yrVX+1MIwjN9PEsq6UUjzpVkKMPG6F1zeTH1p2LYcQODdru3UTr1YbxykyxQTaGGJCJPJtfmYOymXdOExp90O6X1bi8Wrrb28WQZSPa6ZoZShmSHiIiXevJg7TyM8enzkMkdtxYAjCZSs8LZmffvdEHAO3ryeGGFi3ytPTis4DEpS7mwOvjjOI8LwUNs3MMpmCQQ39en2cmMfE8/mCumZdm1K3CzFF/tyv/4DHxwvb48JEYZ1mfut3/m/W2jhfzryaRu7Gkccs/NHzEyrdy8ecvhncDLO554kfknfHkdvDjPoB4xHIJLFe+V6XkewmipW2q7yszWVVtCVgJl8FEyxMc0LzwtAM2u8O0yKWBHbHX7DEJvRhm60P+HwBGe7fiR/iJr1v6klCb5GKVY/arBodWrQ5gK3x6jDyVBvL3s7Axk+IkUZvDrOhrdquhU00A82ValYHTRlEcdtFqzYxJdLtOLKdGt8/P/N733zDFJwj9AJV6+T9HWXDAqT5rBjf6tvHZz47Hal0H7LIORc+XDZOQ2QUSxLMVgRHhfuetgOw9UMiWMtm50uJecKoWoERpDG4h1htfdSF3Vu77c4f8XXZRPniOPH5cUKb8rgVvnteXZyCrwsjI9fuu+GvospX54Vzrvz49sQ0mrggt2z+T9IH5ToiIN0VGW5S4njqsyvheIx8cZhRhD94euZ+3bispkxrNC7SGKoQatuLSQ3K/bbxtBU+O0y8mkeGaMwvZxsYqXgS5iHy4byQhsDPPp55Ko04BU4n+8yIFVOOmRAUkiRybRymwfaDqg9ltSR4iFYANt9TVc14N4kfpv4cRQ2dPw6Rp3VljKaE7G1Ay3WMrK0M1sIJNpGgeczv58tW+iBUW89JYEjQh7+m3nb35xZcGQpXbleIV6PU0uyZBk+Ia2tctmqfxdGrJNaSLO1a0ua2EbJ5CA1RWEsx5aQETjFyMwx82DZ+8+aW2TdM87iUQjCFuCpShXypfN0ekBht1mNfZV5Ega3lHl0qysOW+bvf3fPZPPPuOPHt/YU/fjw7v9DWZv+wrTXqWnfep3U27MwIvp+/eli4myfOa+WSKyG4qXCw1rY5ZytaDfnOWyGPgZujCQWau5I3bAxVqXkXWF2tKf/B169UggSdgNqDvCERlnHaISHR2xaqZm5WFHO6sUWXgjANXRqq5LwhrTGNE5+9ec2Hx3sngNnCfVoLTYVLVZoIh8Hmca3O39mt6H2RW8tDqEH2iq4bs9UKU4NXd3fkvLGuK0ggIry7u+Gyrrw+nbg5HHi4rNw/PLIUl1MHk03HGChikGkK0fr1rdjkZIkmay8bWQJMB6YhoSRuTjMfHx7to/oG7T1sa1leuVQge3KXPNlpKDUIpVa+fH1HCsLDZeX7pzNLLoxJ9plotRpaEYPw/XnlkCLFpbj7TDcnRdZaeTWP3Izjbv9+TLdOkrdbe0pG7Pub55XDGJmSDU18et6QYL3x23kiivBwWTivmculECIsW3bJsq2aIUZ+8sPXnA4ja648LyvPOXNMI+SNwy5zAwAAIABJREFU/+Z//t/4sK08LhdCVe6mhX/hN37E3/zj70hi7YIPy8bSNkKK1KyUXAlJkCF4O7HtSBhYC20aIlqKtZg8GKLQqHvLpqNEpko3rGIQI5AaIfM6o8wkxJ074Y6wxYbCdvt9cYTJEiRBd2jagLygtjOCXBFPbxjQ51R1tMmuq3MA+xBSPED5z4lzP8TqTMVUblEtWXo9jhyCqeG2YMaQr+YBRY374m2FeYyca2GtNlBZx8hNSnsr1toypmJsFX786obJidzyYh1rL4D7mm5Kxpyum1jrfYyBKsqlZF4Nidl9niKRS458XAufzeM+27F4RV+9XbHfNemAoZO3fQGIsqtAL7kwjdENPMXjmXqr/cVr35bd8sCKsCDmfv/142VPsPdcu0N/+zNiP8Uec+HSGoMnjqImu39QfDzTCwdx/5mratfvpeBoh/JunkkaWMkMsxA0WouqVldrGadljIE4JLZceX9Zed4yxzHx6jAyxmtLSRzJeX2Yed4qj7kiQ2BwhCtE4wTafmFf84JCNC+0Qt0TpFgtKcqO9Kgfwl1UYC1Xi0Nb6dceqcVJymqxKobgXYtuCaLMAb6YR75dNxqyqyS7utAc5K1oTcHd3fvjfdFGixgHrGLDmLtjdF84DWFp1URJakhqCFDFhCyLi3CMyO4olrgtglf4zTk+KQi3Q7L959ychJCmiSnGXUzUZ+L9qXd3/OGHBx9R5OqxrRKO1r15Ob+uP4tAV3N6oiRGw/j6ebHJCyh308DHJTvA8QmuQJcrCD1+vYyHsrtodyNQcCJ8w4Zi97eKrnxDfGabMkYbD4SYGXHvaAj4bMvKL3r9yiVIPQCCBQ7LAK9BnH6jQ4cuxSvgq3IqSmDRwhgEmtBaM/WSKof5gLbiyifhNN5yMzaWvHEzjnZTS2VQYfOF+aJo6x/MCdzGOVLnD4wS7D06yTWYI6gEW3ivTzeMQ2TdMs9LJkjEhlOpGYF5hRmTj1joi9dVEEkq0zDy1XnhaStIjLw+BbayQBDWvF29N3r2rMat6gekSOcL2abtELWqD1QVcyFvzTxwYhDujokpmVlawxeoGgerJ0V7a2+/RVdpcxTxlp0dcjFelRoKtIAR5oPdy20zNcVpGpjniW8/PBniILYYKs2SY6/8+v3v3iPvn84c08g8jEzjwFINrv3RZ6/56v6BxafYj+NIa/DNw4UYAjkXzsXaAc15HW9OMzHYsM9z8/ZD7WifX6eYPL/7aOH3ua/JvnJ5kZDYaIErIqDoPltL8L55P/29Esbv826x32FwsUSm/4gA7Lwn+zntC7ejLI4kWSB0dMQ/fAq29pt2jopQpblE3N2vPfkaRRgPI++mkdshkhA2MRRpzWU3A+wKlYYZt4bVSPTPtbAtjXQwNGZZjYS5NWtXvDlO5muiHgE8O+r3tQs3sl6He/bfY62KxtIqh5B4uKzWIvT2h9ZGVmtX3Y2DqTbt5rH0e9Zv2Ys66RqLPv2CaveT6lSAl6pcf75eaHWE7ucuy1bCi0RwXz09i+nJ0YvP1sRc1jf3iurPlH744BwtEabk7viitHq9hH5Bih0qESPBa2sEUWsbRV8btZkXV2u7eWJ20rAWKE/KF7eTzbLsn0VMhfnh/mzt88HmCeKfre+HfjDTkz3MLd7QoOCHv93XokaFQOxe2ignri9fd9GRs9ase9CnI0jfBx4DPbyAKrm0q8WAQS+unO4x3VqXHS0SrCA9DSZYKWqtnT44uJ9bYCjlUhtbu45wsWTORDD0eO3rwhR9jSjBXch9pFE0RdkcI2MMHGJ0Fbb9njGE/bNZgWOfYRoiP7g92sisNfOwZqSpk9Q71uVnq//55TicPcarDWx/VDPvfTMODCHw/bLR+aiK7M/EnoWRwLsIIKgwj4kBm5KRq3szCagjtXuruWGWLJM96+w8LH88JioSs5k4X7JNJsCGIv+i169kgnQNP9cI0ttYPYONwh6E+kIS6cP+7MGOMYBaQK/VBmLeHk8U9SnXEpnnkVY3PuYMR4NXH5aNVpqRz0Svig5/KWZWqBhZtzSlFUUGk0N3+9IYAtIaN6cDy1KZhkRtlWXNlFIZx4FULJHKte2wZA+A2ZOkfoAZ+XjisaqNDijdJ6hYUAuW5JRSWYs5Hlf166hW+YXghmfNqxwJtGgV6pASl62aMsjv55SCWQOgrO3F4sX644q6lNSfF14l4IZqfh25NoPlvVJr/iwtCYTntZiRWzUJ4Zgip8O4j05ZSkGTqb3SYK3LdSk2Jy90grgFysfzwiqVt7fWChhjQptyOs58f15IMXEYB47jwLIWfvZwIWth3TJbMT+TmAKtKa8PI6dp4MO68vyU94AjHkx6QmKjSKpNonfSdU+SXq6cftr1e9jXvMnqezWMrVsPaB1Zad3aQa4oYUeQPtlDXA+Xjjj05xO0t1r6Ae2In1eHffhsF7y0ptTQPe7s+5p2MYwyh0BIgTH0YONIHtaS21Wp0j1glMkPuoZ5OK3amFYj3m617bPRYhBeuSu57u98DbxgSVBuzSXq7O7v4uhPacraKmEQHtd83Zf+VkGFc2lMqTHLlVdU3Nem38MXT/CKAHHNj4obnBrH8RrCeu0a5MXB86LiUj5dH7leR4Jcn6f9lk/AbD7N3UI09Kv7BKnfazr5mG5zYsq3XhRd30f232ukagVVm9cXXxyu/uyKGlqXmyVT/TaZj13mJidS6Lw3V1wm43+Nc2IYrwOXhZ+73r5vBEK7EpWhOwdYp0A17G7KPdkUvapmBUNbhhCcq+oKV29H98/VY+xWjcd5LnWPAyB7UjX4fSh9KK1cB6cfhhFVeD2PnHNlc++6nq6IL5aGx8O9/WPJavHfL1gCYM/vej/GaFYUg4S9mJmjjcuanCj/6arAOEb64vfTzSXh1WEyiwFVHlYTK1z5d9f1KH39yafrDixhSZhL9lYbcwy8nga21viwZH8G3jK9VgB7Iipi4hw8piy57m23ft+vmJVeiwlPhmrVvQ2tfo1RhPNl42nZuJRiw6+7E/M/5PUrlyAB++JVtX5y6SZo/QBRe/DWWv25/LxZxjjG3rv1Bx9sY10uz0Q1+D0OkS2febxspqjIha1UHhdLEG4PM5tYBVppL6pIZZ5tYOlSbVzD6B/wXAo340igz8pJfPn6Fd8+nFnzSs1GVoxTpNTCNBo687QUlmwqseApRsM8VXr185htmOPpMHOcZs7LwsP5wmGeKE25Pc6kELisK5fVFA9brcxpJFd1SFq4PQSfFSQMw8DgzstbLbw/Zz5y5t2rE1OK5GpJwZrNTyJ6gOxtmOCozbVdYM+gYs+uamMplSHW3YVX6AdzT7Qa3z0uRIdIx9FknkTh6+8eCaaRRcU1QyKkMbIuhRgBCTt6WKsR82x0ABzGgRTNQv+rjw/kS+b17cl4Da1StDGOiQ/3z078NQ4QQchbZQjCq3k0o7eqEKydEqJVRuKw+WXLfC9wNwxMhBc8mR54etC2V08OjccQ0HhNUGprPliybwjDUnO9BpC9MvR/mbGmVV09mEn/PS8PYbkGLei2AerrzQwYQ3RViiraHCFtxg9xKsiefMQg3OeNEQuWc/RhnQ3mIexKFjD0wdBu3ZGOHrC/u6y8O868mgem5M7fcg3W2mMC17ZX5yBlR4yCWJvI0IhrmyAiINZS6Z5iUYIpMgVaiDwX4x0eohHKjyHwUBpXSOJFAOrBuieqmAfQqynuB5ugnwxXveZZsp+W10rd/ltVubjr8B752evwa6Kg+4N0RMk4IkOQfcRHP9w6Ib8ndWs12w9Ed/Tnakhi+yyK+1ypz7KszZNUM8acYiAxkJvy2ApbqzYnUa3tM6bAw2JGppbwwXFMvLsbGOaBaQ57gtHX6J7p9bvtiz/6cy0dAW92j+7GxKiBS2su4/Zr8OvqyYvZuchuZWFtMacgqLedpLG2xv1qo2OW1tyyRfbp9X1ciNl72MdNYlL61+PID+5O1gqslaILoVjRlFtlTP1O2z5dnH7Q73kQp2yIDRRHcadse2gxBN7Ng495ubaYe5HW12Lz5EE8Jsi+1npib8908OLBLGcsYdYGffK32IbbZf7SC4JrwLHzrSrTYKa/T7mSm/JuHvjJzcxjLoYT9FPBE6Gs9kyjmEXJMCS+f7zs17Mntx5btXWOq3MLndze90FyU9YYZEcsv3k4UyPoINf7+Atev1SCJCKvgf8S+Kf9Xv47qvp/vPj7fwX4n4Df9S/996r6n4jIPwX8ty/e6k8D/7Gq/ucvfvavAv8p8LmqfvcnfQ5V2IrL+qMbvbVOarsGkijmh2IqHyMVdJNAFeH788KbcejJsx/GwmD5uQXFarJ1YuAnX3xmCFOt1LZwGoXXtydiiDwvG1s72yBHJ9KZSVqAoEgMvLsd+fNfvOPrpwu/8+17SlNOw8Tr21vuTid++t1HG0BK87EljRgMIQgejMYxINGqEzuubBMPKRIRcivMQ+Srhwd+IMLd6cRwOnIYB37yg8+5bI3HpweTvNI412LtjDhyHGe6YZY4XJsIHI+TZ+GNMATOz4XP397xvCzcXy4M0VCAxYOceGRovUQWfzbVfDn6wk3B5/xg3ILzVsjF2jGnIZmxmqNfW6m8f1551kbBNsU4DJQKP/vmo5Ft50grlgC0ZuMzqrtfV8+yJNhnWc+FmAJhDGwU1jXTiiES98uZoMK6ZQiGXsyHxBQH3p1OLMUgWQmw1sJ4k/j64RnpiZtGnteMJK5QtlirKefizVIhZCGrujM3vBsHTjFQCTZV26vQ7tvUYf594wvXRMh/TynZWwQvAiS9mjIVm0Q7Rvsg3KtLsx0c+z578R5LrTzkwupE7iBCqHZy1U5KVTvmB7enEDGn4iENPGIQ9zerJaS/dpgIAVo17seTS4e707cpzqyqj9J2NdAYI2+mZK0X+5R0sz60F0b29U5cz6V4jLHr6nyUnjg0d7w+BePIhaCMfoNNldSQoHwxH/j66UJrBZ3g1knWN4Mpc1ZvI+9+VH4/e6LYY9deyfqtrp7QWAID3VcN9stygz0rNs6l8vXz8iLx3WOo/be9+LP0RFX44c2RQbwq74mRQHeDDg7fCT6ewQ+rrenejmwUOyRxPp0nnlTjUe0jOpqCVgLBVLQa+CZvnKsl2DFgnjsIBKHFQGmNulWmLTPOPkLo5/2CPBl4iXaJWHLTeXGIoMH4aWdVJrl2EppaSwkVBtj3jYgyT4kf3N2gfgObwlIa3z5eSDKwVeWr54Xn3PYHE4NwSOETbyx1HpI3m/jsMPHF8cCYEtKgiI0quR1HqsKHZSP5WjWHblszpi4N9N3ZC895DiwX5SEXhhA4pchNitwMiVNKe1zYU+1OL3mx/un3cl+TvVVs3xT7zhJoTbgZB354e+DrhwuDGMcL/w19zxkShtFJ/C8joElYte2u2Lkp35w33rbE23Hk+3WjqqnNB7GRYZ1XeTMlogS+ejwD1tXYQTDfJqJdgeZWJJ7Qlc1oIEOAV3Pi9WGi4aKF7x/RKbgysO1r4Be9flkE6a8B/6uq/iURGYHjP+R7/ndV/TdffkFV/w7wzwKISAR+CvwP/e9F5CfAXwD+4Jf6FOIOzwEG39hdRNah0+5HZHbnNooEbINkd+Qch8jDkk22rtcFXT3gdnhPAC2V9+8/cHN3y2dv3/B2y9w/PvFxzaiuPFzOVN/QzdGJ7TmjqzJO1rpZtfCHj8/84DDz7jTx3YeFIImxZb5/+EjZVqpY8JBorqV90fX5Yw28x288nVqEeRqJKrTa0AjvBqHqgdgaLVd+8qMv+bO/+Wt88/EDv/23/z4fnp6p7Sqf1gYtqPdhLWiMIVGr8ud+8x3ffHjizdtXbLXy7eMjaYqs68I8GIK1lKuPRacZm4TWArt5togHXrtHptqCS6mcL6amCyJkafxRPfNnP7vj7337SBZDY1pT1q2w5UYS5e08os0qEgEkmu9QiGa1ULbKejZyZhoCORuZNybz/whJCMlOqtYdahWTKA+GND3VbNYMYjyYKJUfvXkLNfNwufD9ZSFpsHlsrHz1dDaI+zCwPBWbmZZsptEQA8d54N3tgZ99fKI240hllBXlaat8WDLHGPjR6cDN4O0KT/qb4skSOygwJUMiSlOSNKcT6W6+aBXeFU1R+gEojljYwUfpzU4+mTNoLFZHV4JxQ4qjfqUqKs0TiN4Ctc/Qib1RzPX73WHiw5Z5rGWfnH6p1STFAVIVpmgO1VOKfH9eLDBjrdIo7ElfcvK0ViNIi5fCVtwEVwHaISPeYtjdlP2+JYwXsvj53I1TFWWKiVJtXUZP1N8dTYFzziYkeFwz95eNeDDuWsQKIAVviVzv+eTZ0FZM8qxENwTsrQFrrzU/TOzZOvrUAs0Pa0P/bXzO81p3tRB+EPesQUR21EXV9tnNPPAbb+/QbMNyu79Q/8dU4YFKI6ip7/oJZ51svx5lR+BKgzVnppS4lcDTunGf6y4CiGLIyRiUGzVe4WfjxNYal1pt0LM2tFVilV3A0oLyuGbzQfIkvhcDxqoxZZoVnyb1H+ltYOMd5WqDVDdplDUTD+YJZwbAUFrlFIcdOQsiDEPk9XHaPfBiCHz1dOH3Pjxyv/oZ4YW4TYoXhtEO76Uawhz0qv4T4NUw8PYwcXSlYFN7jnOKFseDsLbGd5eFmyHS9OrZIGojOAA2tTUxBi84QmA7L/zk5sjJxwOFXh2gXihIP1v3JDqIJeDq36t0D6fg8deeeU8TinMo1Vuyx2Hgs1s4Hicect73PeBqPrmuF2+9qts6JO8o7K164P1i/N8/czszxsi5Kj99utBa44vjhEjgacs8bQv7RWAD0a14MW7rIUVDoJvuN78BwxQZHen/cNl4f15Z1kI8JXJQ1lz3a92D6i94/SMTJBG5A/5l4N8GUNUNYw7//339q8DfU9Xff/G1/wz49zD06Zd6xXCtCMGCAs1mieGbx7LfZpPDscN6rdYvfzyvPK8b0A2nLFHw//OAJHuWLSEQa+H5/iPLuvDq5sSrVzfI08KlFj4+PFGj7sTnXBpDFFpuFC3MKZEIfPNw5v39ytu7I/HNxJozD09ne6i1MU+TlYC9GkJJUdAu3fSWYlWcB6Kct4WbaWJMkbBZglZC44fHmVUbf/cPf8r/9bu/zzQGzutVGqzYJljWwuEoPK3PoI15nBjSxLfv30OD16eZ7x8feV4zSy485mLzoySSX/BAECO6R8Le91V8xljrPeNetXjPWYW7wTxmUrRdurTGTx8vZOmHh7WTWoW7OXI7DO6KbByjRz+8xymY5FMMxRpHS862bCaDXf0X++nrXlqt6v6c91EoTd1E07yrosCWK/fnhVYKpSqnaMZ6mzTmagnlqubifXuYebgs5s8UhJTiziE7xrQfMkX1Cv0HuDTljy8L8xY5+Oyn45CoxXgRL1sOgpGupxCorbopJbZ2+uZwiN7OT1/LTsDZeVF7Wmt/Z+8ddvQhBEsQL0tmbT7XqPGJH1DnwfUp7+Jmp3eTcYOmELiooQRLq9xvmePRRgEoMIeIig2fvCx1V4a1phxC5DA7sXRQnkvlJiUGn8htyIHP8HqpBFNLoKoH62BXxSjBXMCFnZzZZ81tYvy7w2CJzNYqay4MYWZMwo2aseClND5ulc9jgqAmcgg2GNUmxfsG88q9k8FDgA/Lys04cIjR5egd3bB1Vx0V6Wie572egFVWnz3nT3HfU31hqMMrMQTeHmY+O81oqabS2pEgk6Zrb7m6LCD5Idm5Y631OWtXjlL1xSfBKvBlKyxb424abORRR9HE2q6PLROxBCVF4TYljtHwv5+tC+KIhF2nJUDHODiafV3rHQHaV6t6i0ksr3jZjq2d84QZNI7BpO4pCjcuhpmHZMh7kH0gdIqR81b46uMz3z0vPCyZ3BEdsbFV0YnXxlttDEHc0FG4m0a+PB1cLXdNDLowLcbAbjMhhoiehmG3O+h8TGDn1JkyWXY6Rm4NArwaBz7lFvrO9bZ+T3K7XYTt99ZzKDpfl9CTZdnjRx90S9DdvkCbchwSpynxGzdHltJ4KIXHzYxn1T2e+pgc8yUy1Ks0s6ron9KGy1e2JqznZiNFUuTHpwPfXFY+uznws8cLS2m0Jh1opNvqhNC7EvY+nQpiXR9T293eDBS7qTv/NRxs7mjeBTS9cfwpy+/nX78MgvSngW+B/0pE/jzwfwL/rqo+/9z3/Ysi8tvAHwN/VVX/1s/9/b8F/Nf7IxX5i8BPVfW3/6RhcZ+81G6UHZxdYnytnkJwtYO6FNeTi4Zw2Sr3zwuXzQ65Icg1a/cFmfx39Gz6OosNaI1lWUgpEUIiDYnQCocUeWomxUfhOCRenWaezxvN1VjNpYVjUMZhIq5nl9dGSmtkga1kBleQ9OXUlL2/6h+BsjvaKoc0GNrkh9laGmspPFQl18J5K2ylkUJCWtsDshpAwBwD0nS3Q6g1swaT6D+thVILl5w5r+Z8W7WSJVDVEpRu734lxsvOKelBLa+ZaTTn1uibPXYEwEvn4D3/pMLHddvJ6LU29zexxCeqkIaB3KztaD1n68tLsE3ZqjrBHobRXay9wipuP9C8bdO5DRK85dRekD2bvZ/ExhRG42dJoFFZSrb5c2pk/POaQYUpmKLm7jizbEZCjFznhllAV/fT0R29sta+cKmmtrpU4SlXDjnzxeGwJyN4wO3yY7xiu46Q2EMlL2On0NEeecHJ6yvbA21PMF6cu60ZT8YOAoPBq1wdvBXZA3/A7t1xSByGyBAMUbpfNySYN8kQhBC9jRhsjdgQzsLTZfOD72qdEX1tpBC4L3mfIt6LpN2LU81Hx1QwyqRhR2jEP1tPKfADVT1pb2rXl4IYyqK6JzbnXPj2eeGzm5nBEWmRwsNaeMqZ05D2kUGGvgSadJcfdi+bSrB5faVaUjIqh2QOvwHzsPFcjr19rl4IitkDPGeblcY1bL0Mi5/8/+08chhtzlQFJx33FpvHFa6/r3/W/T1U+XlehqVRsiNXfUbhYRqgNW5SoqgNZd1qpYgrYLVSMFuDFMMeo187ugKGppxrRbfKzc1opoj+zDwc70laR0l+/h6Uvqd8DasKbw/zJ2KDgCCDEmIyQUmDVJTHx4v7GVUe18JzLsYpdG5REj+Y5VPy/xQDd4MhRadhYE4Rdc25vNhH4EWjFzDRW9CnceB53WztdSCIvoftjOpO2704TkPYE/B+scZX7CpX3a+/F9WG4r/gJX7yuloP9PvdtKKVq0Ia55aq8a1CCsRgw4nv10xtzRNf20cxuEO6J0eei9HVoUmsYFKF+5bJrfHFYeLL2wMPa+acXQDxyYfV/ezfATPFk1grVNDrCKxBhDUbrSAOwcbYqBUaL9fVn4QewS+XICXgnwP+iqr+DRH5a8C/D/xHL77nt4BfV9UnEfk3gP8R+Cf3225tub8I/Af+5yPwHwL/2j/ql4vIXwb+MsDxcEDV5ovFYFN7BydhmbmqKy1cYdMVNVutPK/GXO+tipfEdWdm7D4ffb00rsoa+4KybZlL3JiGgVALd6cjl6cza6t7hnyaZ9alsGHtMctWYZ5G50IVAkpKgeY6360UUkyW8fvu6p9f9eXhZ69BzN20ZEuYREAi5K3ysGVwH49XNxPHQbgv2frc1/vKOAxoEMYUEDU/iC0XhiHy4Xkx6LVdrd0Fgzc3r5I630peVC+2mft9hdXnqiVx4q1v4oSw7ZtZ/eAKiAZyXfcJ4qp2CK6lsTQbBbC4KmIcDX1o1T+LV+La1JCmcK1A96972+5lPzu4J4uWa4DtjzyGyMmtGYiGQDxvmTHZOBJJcQ9guTWD8YdEUGxeUms2ow47jEx+3PZEQPX6yxQhN2XTxlkr5yJ8dpz3Z77LsYNB2n2tXuOl7Guhv/oTF//9V+KdPahO5Ozf07/PglEzrsOQiNFMWTW8eM8XWVgMoGIuxmMwBZrNVBMOQ/DqOzCGyKLG2ZmTOd22piZA8GdO6Co/2QnnW7EZiZcu6ZbrZHsjsZuKyEjYthjKvpfF9/OVzhwFRhFEbGzJMUYW4/17kWJ+Xt+dF768PZAGQ/GqGj/uORczgXSRhBUI7gfjVbvFIPtvbjYs+dx8OCjmEN+fG7bV9nXSExkw9c6SzTizx4IXl/XJQ56SDfgVEZ5L8ZmUFpf25yvXhMMOio76+svva7eIEN/79vvEeSuW8ExD5Hwp3A4DfdTJ5WWy0gv26rYQybg1N9HaVFawFR5bMbI33l70D6svAorg8ePlPfBr2KoN+r5eE9xN427IWdXHmSSLM6VWcLTkUirn0nbemA1EDrvr9suzoheGx8G4P3fzzHFIu/JvH/rsH0I/+UHLegV7/5sx8bisvBQW9GggXDlDHeUNYn4+ubUdodrvgCdgn/6+vufV+KH0PSs7LaKLYTrVYUfregz37+/XpFgcP/qcv3MutApjCm7q6MWuiCcjeAvM1k1pjSHE/WzLvqC32ohj4v1ltWJS2WOZ7teHJ3DX66zKHmu7TmqpjQmjXAzJkqMe5sYgrB1w94rpT8qRfpkE6Y+AP1LVv+F//u+wBGl/qerDi///X0TkvxCRz16Qrv914LdU9Wv/8z8B/CbQ0aMfA78lIv+8qn71c+/914G/DvD2zRsttfHxsoEHp+Ts9KLGyehmYKUqm1rv+OPzyuOyURSSr77uEdMPGKuucO5CX7CencqVKyC1UItN5Q7rxs2r17w/bwyxgthk8XWzMQEtZ2vvpMjNYeTu5shlWdiaWZ5LddgUQ8CqywwMWPk5FZg/yLGPaBhn7p9Xqw4cPTvNCS1KrRuJyDQkbo8Tc6jcP7v8VK6HMTERh8TtGNGp8bxuPC+ZOSbeP194fTgyh0QYLeiTi80Xqo0pdoM1n1kmgdpnfaFezUdayGYYJ8EN/sw0LMbg6rVoQ2qxBPgu/n/cvUmOZFuSpved7l7tzMyb10VkRDCTWagCQYCjIjfrq/srAAAgAElEQVRAcAcckEOOSA64mxrUBrgMLoCzAgskUMXKRFZEZrzW3dwa1ducRjgQOVfVX2RW5vRRAY/wZ66mzb3nyBH55Zf/T7xOE37wijZYFfN6zrQoyFxUcTV59rvEeVoJyZOXauR8xQu8g3Wu10zYdlRMmF+cuwYGMzNVd3DMq0rlFR7GHW92A8/zhaUUlpJVGK42hl0gFCElp222Vc1aL8vKEPwmSlikcdxpO3ERJWd3BMlZtdcPHW19iCFbnln6VJUKFUaCVpOtr12rN0VNmvshZntn++obiRVnvlI3EfCmWttIxfa5hqCTlntURiI691lI8TYGFfRCIrWA6FEZnON390dVkjZdslyEn+aVtVZ+fdjjnAZ8cYqkYFw1LDkKyhpl19RSphOb9ZC0ajE4mrP2M3B2V+PM2q+QQ4ceLJHDqU/XIQX2ffLSKuQxaiL0acq8TAu5aStIk8XAUiqXyfG0rDyMoylYY4iQxZ+mOlylabt/rYrBNIHzqmv1lIJOy1nrU9Erfa1GF8lTInz3dexEdP3+dh+sWPHO8XY/4J2iMmurYIn0PobPJv9EzLfsujU0UbL0rH+PjjwE0bWm6JpZdGyTi918VjiEQHKOc7G2rIt03ksRRXdVFT2YqbUSrUH3RRRHM9loMSRa44lqSfR72ZM2sfdfjOfm7CTvyUNyWgCCot6tqd/j3gXVMvMwjIkhNhUPrpU0JA4pcimr2tL4PlHmNiTum8Oeu/14Vah3WIyWP0kyNKH/vKWjXolxM3SFm8lNdO917pJziqaOzlO8Tv0eEnT19b4bmzkYd0Sp729Fym1fi7VxvSYQuRY9Q5zT4qe3/V1P5my5NbnhpFnkESFZQdCjzufilaIixz0COXAiZjKtr6FyDI7HNZPnlXOuW6u4E68RtoLr9vveFoa1EzaBy1KZQ+P+lNTlgGtBMHhPpVKlF+DuZgP86eMfTZBE5Dvn3B+cc//CSNf/LfD/3D7HOfcN8L2IiHPuv0HX0Iebp/yP3LTXROTfAl/d/P7fAP/yH5tis1/mzWFARMz5WwXQiighUsMQPJ8z3z29qotzu1Y01TsOIegUlQWb3tbqY5Pu5optCxYoQMmZpVaeUyavhfrjB8aU+Ob9W077HZdl4Wl64T/78gs+TWecCGMacCny43c/8s/+7Cue15mpFMDsUpwG7aU0BtDEANXxuT9E1mpy93aYx6ZS8P/s11/w4fnCx9cL01pYWuYUB5oXDuPIGBJLroTR8TIXjmNS7YfWGJznkDzFKX+n5Uottim89ulfl4WvHu5Y1pXpPPGruwMvpfA0z+zGRPBOiZfTomO8wbEP0XrmzSoez1yFv/jVF8zLwk+fnok+8GleN12RJsJ+HHh3OvEfv/0eH83brlRaufGOOwUuc+Y4Dgwxcp5W5ks2fo6zA1LRj1yaaiC5vpE0SIghjLU2bcElRR9LbpQqvL3bMaTAYUjsh8jHl4nvJ+w7yg3XySHF4QdhnwYyJjbq4Zs3d5wvCxEjscO2CTtF3vontv7ZAmxvb3g8vz7tN+6FGFTdpDK0pnCh9PQInLVRgjMGhuj3daiWzxV9lw3uCO26ysWef8td2iDtrVjQaN29xQSt3LwDF4KZ04IxwsHBH8+zJlHOWq6tT6Z5/t9PV2V373tLoCEF9QlzzgKYcLJ2Vt+Z0qtdlJS98WWw9lSv2EEPwRQ47Qa1g2m6IjxXIb4q8P448jLPPM6FtWiimoLn26cLv3rYq9yF0yr0i3HgDy9nUigm0qeaTP31FGVVJ/RWVr1mXNG9lzWz1MJv746bE3tHYDCUIgS1zMmtqaiHbIyxLUsQq54r8P6wY298oCptcx14zJXHUmgCB+O3xb7omhivpq+E7s3m+lT3hpp0o+VeyXvvGGPguRbabLwb0eC/94F34w4fHN/PMyJCAmgVh2OWSnaVwV/5jIiwSsWZynXrxHdRZJBOhnYbnZ21NT4ti+2Bvo7ZeF6CoridZ+pRztA4hJ5nEoPnLimHZqqNFZ1iW14LzTVG57kfVe1flahl81PrQ0FdhLZrS3Wor+9Pb/EpSO8QaBu/irCUonSHntzhNikI7zpFvSObSozfhR3eX88pQfDN4QL0zd7V8B1sNjidF9VVtjvSBaL2KwYddgRHp7E1cYp2v3PRYYHg4OvjyMtaOa95Ex9uTY3jxRDquAlK6uvOrTAGNZCnKUJ6zkpTqP0MtrgtTpFKV/Xath6zbI3glHuUBo1/ZVGYdS2No9sjzlGMgN+1sE4+WgGh4qX5P5Eh/VOn2P434H+3VtlfA/+Tc+5/0TUt/xr474H/1TlXgAn4H8RSWGun/XfA//xPfK//5CMEnXwJdgUFITeD6MxL7Dxlfng+E0NgcFCs8nKiPJfg3HbQ2XewdpGWYn0cuIkSE3vmX9DntbXw9Dox7AZa9kjKvF7O1JIJPvDNw1sE4c8O73h8mbgsC2+S5+6U+KsfPzLESPMKu/ubzd79ZWrVKPO6ZqLBgz2eifVZX6eJX7+9w4fAbhjZk3icJ8ZWCXHgqS0EvzA6z8vZ/Osc7NNIyZV1XfhU4Tg2XmtgN3jGISBF2x3BRYakCULOhb33imisSnrL1UaEG8SoXKr3+4FcNLB1P6P3hz15Lfzuq/e8vJ5ZL7OK5kVPxXEYkwaXJvzw4ZGny0oaI37vcM3aZM4RomOaMvsxIR7O68o0L3ivPnz92miCZ95rRhxUno7lFFU3lDQhr2qwmoaIE3j3fsfgPTsjWJei4pgPh3v+KJVSdTqxraJVv3NM50aJ6ps2Jk8Bfv/DI6fdbvMVcia0OYlQXNuk9H2/p00DYfe9wgK9N55cJyEL2oZZBN4MDrJOH/axqN6atVqRz1CJHnT64WbJji6+HiB6m1S0zSXwsmQe55VLaZzGyGGMINq2zlUn574+7mwAoFfB12q11kbW7qRO5DXZqreqUvfb2ze5+iDOuW2+at7BF4cdTaoSiO1bCkY07nu0v459/SF6YoyqU2PEJS2KrKluB78A3qkA4PNUmGrRz2K8q2XN/IcfM9/c77nfDexTJIbGwzrwNK1b27NMjS92A95pS3gcB4JrtFY3FEAPO7cd8n/3MuEc3I+J45AYrP1QsdH51kh2SFWu6tZb1oImKocUeXfc8bisCFfFbO9g8IqKNwdTbVzKshH1o3fch6htcLv4wSkqHekkc71IXipNPJ7upabcrxQ8rzkzWmvPC6QmrHPjfjfym92BZnpxr6VwLobUisObRIKaY2vruK+ViiZ/zim/JnT2MkZfRBNNW7qAUg/eDSO/uj+Z2KVsWk9O6oZK5aKE96RVhaIoojpQL4smr7rWA9/sdyrwiybkTSCZca44kzBpnVupm+xqJtyLb8cQ9XOU2phz5XlZzfeskLz60uE0XuGFINeEz0BPtemxfedsXwdDuwWnDhJNjEMJ1VDxGG0CTBRpcsZvcghL1dRcJQeEZuPv3dPUW7dmzoXDMBBCJddKKY3S4Kv7kddp5afLxJwrBY1pYmtkSFYACKxNGKoKC7fa2CU1HJ8zOv17cy+bNKhCq479GGiiSH3/ft1CKkZ3Rf2TXrMxOt7tEosTXoRt2hCLH0PQBHeikv7h/OifliCJyL8B/uXPfvyvb/79XwH/6h/43Qvw/h95/T//p3wOB+yGtI0lAmQj0xICz7kwr4V1LXTZcudMc0auWXjvYYp04pps1VjzOqLnnSZT2EHbs1+c24I0IvzuiyNTblyWwuv8qtXIac88LUhtpBTZucjrmlmb7RoXNAjQNmh620j2QVxpvDsOPJ9X3hwHvPemO9OoufFmf2Beq1ZEXsi58Ju7PXPWQ301M1OCZzpPHHaK7JTWGFLgbjyxNhWCdG0lBEUfdsHzqS1MeeUYEuIcp31i9KPqdqCVYifKgyP6yFIXPr7MpBR0ssxr1RWdY78L/Pu//SNrLqpl5HT89zB6nl4Xci0aUHxg3AVcVCXzat8VEbwJQ1YRXNaUcrxLIELJGMHauC8Gt/meOKGVz3GMPJ8zQsUFRxqCfl7n2B8HzkvmUtULzBv0G+PA83xWzz5LYmPyehgAUbwGiqxJV2lCTI65ZFLwWmlWeJwzcQgbCuL66fX5TqCT5LTLoMlE14Sxgp4hRry5UF+Bn94+uSYc133jzFzVWtC9GOA6obS9ff9ITdG2IqZ+XNXao///tBZiCNwPgxLjads4r2yfVSHxtTZVPJY+Dt/zsit6ocMl+uZV2nYd8IqCpKCtZWdoSUVbRBvyZHvSb6FB73lwij44x9YKU/SoJ5/bV+fDtPCaM82E+uCaQFWBj5eFJqLTYXjWXLgbdnxasvq7iefTnHkYE605Xi+zfhd35ed1Pl9PqsCGL+TaBgg9FjVhWnV0vetBOTuUk7valexT4Ff3Ry4561W4OWXE1pK3w9ZvsUuFch0wi6KnezwPKSFOWCpbjINmGk8CHe0x1CRFx2/f3PFX339iH/zGoctOX595YQ7dnNvzkAZei3rJ6St3pICt9auDJzetFoxbJorSeDS2T6XQuvq/6CG8i4EvjjtrEZkkgPHEEEfO682e0YRnNRHQqTlqqTxEBzHx7bLyF2/u8K1urScM3QFTrO4IMWYWbMmRSXjjHXgfFOEv1c4RJTDXprIlnyY1SfZWsDQP/oYn09GxZEhsilF13TqKi9v4eDSsyG7belIKikBtdGsV2hXt8rZXm2wYpsWovj4FXKNmmA1saFpVqKhkaZx2icOYeJ5XPlxmXhf15/Q2bh8NQXudV7rgpfdKQ6hN+bg+wLIUW3jX9/e+Gz27TXcqV9lQzdF7lrkwV3M4cPA+Dozec58CB2n8VFYl5suVoJ2cw4fAa+kWvH/6+EUpaTtnarvGgMc7qlOvpM040CY3Nuh8O5C6Kmn3YjJ2vfQxUSODilAwhVZhg5R74RbQFtjdbuB0OPDj65km2tKRpuJkIIQYbLQ8EgJcRDjtj7RSOJeVKvrJenKEVUm9FRJc4DTumaZKLsoBCN7zsBt499WJnx5fkLySgiPtAiFBcY65NGvbedtUgTenE3d7z+PrK0NUYuQ5Z1rV/vw5r4ziNyXrEDx4oYjfRk67zsx+GKEsm9N1H3uVqoefq3oIVw9zqRxjxHnHT88XQBiDOit7HPtx5OPzpFpDwZLSwW/3RbR8URK2dZXKWjXoBD3kcq60rDpI/VDpZGzsvqegkHqMnt0+Ml3AJ62qkumLpOBZgyawKiQs6jJdV3DJxrkDLjlarASbQJotuLZmz9+qIGFIKvOPgIx2fZqQhrARw0uppggrW3XsHWbfwjUrcttKYfRB24/SGSP9sP95wqUBtjbhUpUk3JMJsWjakylB+/P35hXVC4bRe+5HdcTugm7RBeLgSUET6mtG1g+0z/+rNtl0mFSTRU8oPW6cHWSyjTw7ub5IcJ43+5FfHUemYtOTlsjdWkwWa8fQeQX27tWq4f7o6NNGcJUbRA2NDUr8VN6U5QR4dCpmTdpqEBx3Y6KuFaQY9N+YgCFqUlRKITc1I2Yr6npLBluf+ueSFZk9xMAuBotHwrvDyClF5qKJ0mQ2P+DZj5HmlOj6YVq0rRbdprWE64mo23ImbxNNfRLQceVfeucYgnJYskHon+fPmiTdNPoAjSF//v6OUtuG6DjRazn7pgMFRdhJIMXA6DyLqlkZonNNmr1oS6xZcq1xW/lhzorWJsJUK2dDgfpnGkPgfhgZrH2jLSo2FLJ04p591w1tFUGc55IzO2fTvcBX46CaY73Nb9chOkWoEXBeNtmEa0LmNtmQJlCMdNyMb9rXfrRr7ZyK7ere6qvVzJBvFq/0exTDFt/7+u0G2QbKbuedUn3k5jXEpmkNxZUeE3RvBIeZKd/ceNevcmO90RDq66fZ/olOeX111FbzVCqj98ytsogmbMGrV5+PbpPGqaJDNcE59vvEMpXPkuPrOrkmTX07ieNq6N70dXCOd7uB5FV24M7rBHUV4W/nadt/V1e6P42b/fHLSpDoN1WDfhFYmooGequeG4DXQ74z5TVYODtr3GcLoq+f3jKp9oMmkFESoSZIsrX17C1IIfDxdVYtDRcYfCAFNZsVg0q916x9SJ6SPeIh10zpI7v95kgPn/1/tM59e9rTwaAxRd4cd9wf9nz38ZmdV3gxukDC85Qb3jdyU5PG4HS0Og2JfUo8oRur1MqcVRl3Vx1L0eeX4Ak+aN/bgzTt61a5HjQaRANFNDmKXiH1xXs7BD2tOXIVdWIXSCGylmpjvoo+hSjkVrUS8h3Kte8umixG71QSvunEiQ9mImrPc8r31rF+z+Zk7z2beWvXInJeDTNjcux2ikYF140pr8l3u0F1vNegUFtlH7V9oEMBkSpNNXU87GKkiTDnQjFOTgzqUTdYu8+HyLJWmn2ebt8yAyEFnhfthDuvZODTEOlL4Vb6IfqgXloW2G7gGrYQfs0PEIFzqTyVTCntOvlmZZRdbgT1jLqNi5rcaIWeDMbKprl0GJLqb/0sKXLb36+j/51D5rek9bOYvT22ZI8e9PXnxyHwMESSFSt9Hkuco1OKq2jC2PPMITgel8zcyXt9r8NnxqXbwezgNEReZ/VZa1zjSRV1XG8o6f68Fp3Ii4FUtJ28lEataia6Nn1uFeVbNadEcssFt4NfuEomrLVeeTeiHofBO467gZYac21c1sKT8+RqHBAb01xq4XFeGFPgQNiSr35NNzzPrmdH1ayjsq395P0VOXWN1nNfsZViRGO5ObV78vf+bse3H1X5JfmrtU9zsEpDTNPG9alYUeHO6jQOO/twHuVv4a4GqH1E3Nl9yq0xlaJtE1vr+xR4M448jKNRLa6+kFtwFbED1v0MKYFLrXhpZmOjz3g7JNba2AfVd6v2GbtWF1ir57OV67YN1ROQKv1+s+kUeVReYiqN6JT7pAiJvkQnfvubm9mR1BA1RqveVLPizPba7bezBa7Isd/WHVZ8itx2U/R7hD5wI80+u2wFzf2YFJmyPd8TjeoUtIgoUrqPHiRu6y1XuUpUYME2sLXIpGfGqCZeSn7bB51mJbYfNwqA/bChRHDnVPLFOfBW1PU7kpxXVFAcP/hF0bStYLG9+Q88fmEJEixVM/ZVhHPOWnkJDNH0HEQXShwCZc50JnxHlDRhug2YsmXOyjvQimuD8AGcbCOcRfSGLK3h00QMKjg2xsguRIiRl8sZQjL/o6C+btFzWWZe1sluKEaYc5sNx617e0WY15WvHk4miS7shshuP/Ltxye8C9ztE8MNy3/vAzuBsmTWdbXNEXFlZedPBCLTqpNoQ/BkL0w5E7zTUeIijMkrVF4boxe8SSg0q7KWvJJiYD/EKz8IVTRFGrUqmlJLY/WwlInjcFSV3RDwMarMf6v88PqCT47RBePc3BgMYiKUaAtL2zHeRMl0GobWSEOkrNn+2yrGeB2QTSko8iQCVZPcwyFSimybr4+K9krFW+IUver2zKXwdtyzFPW624XAVIVLLnhR7Z/aGou1dmN0jEnHmLE15sVxGFT0EFSQcEyBaQikGFVoz8i++yHysEtbRdotQTw6YOBq7Sj0Zxvd2T3qSXafKHlcVhZpliD1AKMnY+fbgR5ML1mHB3BdYFHXYxd7a1lh+K9iYDA9E2+V4Xb40/ehs7auwve+Gk/AvPH6AQN9ckfbztdqXVuMImqamRuKDlri1Stb35seonWuAHdD4tNaFHmz81Eno+z4aGLXV9fw4D1vhsiny0K2wYH+e4J5BwLPa6Y8nXkYByXuJk/dDwjwaWqmsRUodVFCeoe8fCRsvJSbi257q2vEvK6FXCsPu8RpGHAof+oUVG7hbj/w6bKYl5nQvK7XyvXA2q7nDaG1H4AdLcKSoh471JrBb6iHqkpbnJTPEZQuE9ATUufUiPlpWvDeMyZttV0LWm25OcC3ugmkarveuD/tihiJdFRU72egSyloItINiFO4Tvq93+/46nhgjJGlFGpRJLsHehEIIWzXxhqGtg0cz/PCV3tTR7fCwQMf55XfnvY4FQayiWIlvjWM/O2u4+24ph2FqgTgZtcr2nDNreBlP5ePSVvvncPUJ9626a9+b71Z8jidjKaKaeNVpDXdrRYrRPoknVlW0Uf9b9BEr6TsjkSL6CQb3rPWylzqpn5fW+PL456FvBWigiL3udWboQ1N2EbveL9P/DCvqAmw7rupVFIK9jsWI7zus1KFWjKnfVJSdVXEfTUrqGbIonBNrlprSBViCoSkyNc+eqL12reJO1tPb0Lisa5GqFcU65j+4TToF5UgCcIKPK86Juy2FkyzscJ+sHpkEPzqKDYpAGxcBCc6Ji5WWUhTmG5dK+8OA9kurDqW91aS9T8FvFN15HmuvD3u2aXEYTcCjg+fnqnVc3+KPL5OuHEkOp2qe/dwx7iMzPOkEDTCa17ofFmdZOkbyJP8wO9/emQIjrd3J85r4fc/PhNi4r98P1i23IOYIzfHX355x1Qyj5eVp/PMZVpoRfj4MrF3jSXPhBh5fzxyLsLH8zN7mxAKNIYAaUhcXi7s3+zUqkOEMQb2UQXf1jLjg+r/rEUJhs55IpHki7WsIhIHXpcL87pykcxTnqmvggqXNbwll35wm5u2t4qn1oaziiB4RxojYsrXWx+6wDxlfDTI2yv8vB8TxQa8G7L58jUToMtrBec3ToprsKImmr0nH3q1j1bET8vEEAdqE757ft3MWqP3PJ0XSm2sIpwOieggeRPqs4S8t8uaKIHQOaFKVRVxafz2zR2PZ5322Set4hrXVqHabQRC14Hp1WC7JhP9dOwdpuaUPzTnunFpetBwlkBV6Ye2uyEs6z0q0ttTbvsd5/WaLq3iK5s2k4gw+tD54hrQBFXD1VzWUBux99HPXbdn66MhiBFIcUYWdg7nVCqCYM+XPmmDVbtcETXMZd6CoyLFGqCj8Vh0bF/NRA+DEqRxapp6ycV4bIAIxRLFivKpXqeVxykzhAu/uT9wTAkhcS5Vk2QfWetkNkcalYqoxg/ObTHFW1LUEe0+DZVF+PGycL/bqS6QMhOJXluOh/sdXx72fDtPnGvFi6egCG1tvTq+tmfEkpVbZWcR5T31AbbBaztYtdLcDcn4WsgFpylWlMZcVdNGi0inxrMi7Ky4WFu7kp+tUGhNKQwBXfPiIKIaaUvXPirW2vSCa2rU7HxHEDRIikHNvWMlDXZRUXLvYHWKeK7VUFmnhY7KwgTVIattOzwX5zgOgV0INGn2R7/fh/PE18cdo/ckZ96OtpdrdbaytMVViw4e9FaQ6wiY68RrtuKvl+j3KSAEJkNmve+MIE1lihVs2vmAqVbuUzJrFxsikv582boNPS/v4pQ9MQpGZu77UakKUe07auOyViqVD4vqEfVJuBj0PsQQbO3UjVPYkcbcTCvN/gzes3Meku6bpWg23kQHkaIRELfpNFEF98tcuEuRN2Nit0s8X1aeLovxEdk6SLXUzVmimbCni47TmDRxtWpgywuC56s0EJzjY11Zpen6uoafP3n8ohKkpQk/TotVErIFg4YwZT04k9kyxOCJx5HzJeOtTDVJFZ5rpS2VU4okr624NVcOu6T+RKJVrvr3mCWBBGrTEU/vCk70Br873XHYj1ymmefzGVHBEGqBX3/5Bblm5nXFe8+PzxNSZt7d37OWlef5QuHqmg0aU2ptLEvh+3nl4TiQvOfT80Rugg/CNwdN+gJQijBX4VIcbw6Jrx+O/PUPz1zmTC6VMSREhEMKfJhntU4hMGd4dzpQywwBWgs6VVIVTQrJ8XS+cEzRPO0qLy8TX3zxBSHtyEXJ8GtRA8a3d29ZpwthCORSeFpXas4c047n8so0rzr51lBz2Ca8eb83Fe9GWZslurapAZ8NvTBuxWmM3B9HxhiYcuVvnyfKosTTIUXG0eOjVUQ23TSYGWSzCrrQcDaJE+j6PcohG4awQfaCKRADpyFRW2MtlRQjh/sTf3x8obRKPOguTDEyOCUjxnCFiB0gXjaIvTThLniirTsxi4qhCl+dRoLXKjfXokiLfZ7Be3ZeBT3FqT5U1y65EomMqGWf/7wW/vB8wfkePi0hxF2nTJxVqr0QsO8AKmvRkQfhiioE59UqQ9jQxWqHJNiRYQdls7HtYDL1/fXovKTb4CQYeZwNinJdmkGqJW6y/Xs/hJTTZgkV4EQnspamH7Dz6FpfE87xxW5gCEY09irQOngNrs9LZr1BIPQAFDWDtfbt0hprdfy7x1eic5xS5JvDwHkYWEpGpFmVaih0g2VtDMmbro4hMPbVO0G3c1lCF8A1ntiGzjmHF8+rK8QUiGgCHIO207dcsyN+lvCIwUlds0zfTlOvXVBqQG9jbUKqThdff0nNObUVtPM6iKEefZCl2iSmosCKFtl1t+tQgWBxuwv69nY5tpbudp6/2B9UDFWEc6383WVi8IHgtMWbS70iZSL8V1+952E3AiqHEqwgCa77cjpT1Fd0uRVNwpfieF4LT5eJL+8PjIeRJRdaLmCo/TeHHX/z+MrvHo4cY8CJDim0ntiLbO4BfbV0OYStkEEFZ50TojfunfSkfStvlIsjshH5xQmDWfI0lJvlnecuBYbguZSVtRZF3dHr10fZQdepJ2xIYC8se7EzeE8VuJTCxdCixSxj+j3aRIidavzlXBCLnTpCf7N9LeFUYrrGzre7SIqRn+bMD/NCSIEpF4bgr0KuNwm9jyqh4ZxjqUIsja/ud3w4L9d2b/+LXb+0C9tATnCed2PSdnirDPVqTowof/jrsONQE49l5amsG6/p73v8ohIkYKsKeyW0WOYdjbTrnRHypDFEVfHNpQcKDfKlVJuECIw+shsUZYh4so3IiigHqYiYS7mCr8E7oh/wziPe8+PzJ97UIzkrR2ApC2/v7jjPCyHAUoomX/sD//U//wv+j//z3/DHDx82y5JD8sx5QZyj1qobp7P4e2YugnjdJmtu/PgamJvnzeI9+50AACAASURBVPHAF2/uODnP83kmjJ7/+9snvv3po3EadCPu08DSio3KelLU6a3jfuT3P2buxpGAbrrTEPmYC47AUjOOxtg0wMTDgZ9eXnm+zAxBFYj3pt8hObO0yuhgypnneSFI4LFeFBptbTvUvDIBcTjmKZNXI9i5Dv86DrvIGJUEvA+e05j0/tlzStWWRwmeYYzEqAd91Yb/Jv7oUVJws2TEWzXbWw2bTYfX66zo1/UAO6RIrqJq40577XOp3B9Hni+qXJ6bCl6OY8ShpPC1V/JOD/3Od4EbhAXNEzqBkKIcFu8dKQYKTq1g0P5+wm1jz9s52F+vj3zTUSD9x4LqvnzWMroZ++/XG5tu47OQfeXrbXvPXnwMgb1V49kmtUxGa/td73SyR7iiVL1yxp73JwTJ/oVufjB648Z5SzrtVO1TLGKJVbeu6K1Fj3AcI/to3luWuDpDkPpba4tEP/tpVMuIi7dW4+2F7glev052bVYRplLZl8oxHXi8fGK1YgA6KqyV+FIqYwzmHalthJ4I9mvvhW0Cr/vJ6XWxlqiDp6WwSjUrHjF9npv1JR3Fu2o9iXQtGLvDlkhcalNjUBF2IfBuSIrubrfmSqLuybIzQVtvF26Ugb9Fye3eED/EJrp6IoRN8TkMQYZCUwNy+3xNtJWeYiChE2DvRuUcTVknwcq1d8hDTKYcfiVBJx8olI3nh6GHJimkyJ3z5FaQ1vjmtON1ySx1R3Oe7FUbLgFvY+DdEJFW+e51YSqN94cdyWyssqHaPRHr8M22sl0X0rXEx1/vTV9PoEn7LKoWX0WJ763B0tp1CTrH+92Ir1Bd3ZBBZ9QEvb7hM2J3L6RBxZMrqh01tcalZFofk+PWIkqf79H7p+h1b99ejbOtDrJiElz3gsPOMK/SAd9dZp5y2QYpvLty17ZEx+tFETMeD07blOc587Af+Msv7/jj45nzUrY97JNmhNJs8s8rKrxPYdvP2WQkCFbINY2vhxDIEnkq65Yb/H2PX1yC5Hu2KbL1MLX/awvEUKTWmgbN4PBG5u0B7yEELtJYciOgflpFdNM2EQ1ujq1adKg+UUqB/TgQvef1sjBEFT9b84qgROxG5LLoiP/dQTkEIrCuK3/87keWWsDBUipDjNwf9+SSWXMxbZJrCbAfIrlW0s+Ilz4OSBjY7Q8466u74Pn9d4+cS+Z5mhm8ih0Ow0CrjXPJdHg/14oj8/TyyiEOllB41tr4NM9kMXE1L4QQCDZ5Nq2Vl/OFXKsGkaRWA94FVmbe3R/46emFl3klFyWn5lxwTk0hu6Cj9wFpwjQpcVja9Z7qQ8mIb3aJuxRN7v86dbE25SCMu4jUtlUA10BybQv0qa0rwfQ6i/P3nc26hvw27ei9Y28j5p2A3cfud0Ng5YpW9TbftCg0HWwaZmsXAV4ti9SjqCdn29I0Hy/xdqg0DikpF6pp8IDeupJewG7fuUesTgR1XJV4r0mKrTHr0V+5Hp1DZMTLPhWGtdEsaO2C4y6p4apO3XgSKtbatnTH2YDEVUG6f8++HwSxRIfrvXP981x/HpxjF02N2ZDB3q50wlXM0F6jf9ciwpshkoLq9Gyq7/FK4MW+r90l5f4MiYdx4LwWllI+qy6lJ/B2yHZhWZzyt57WQgpCiip+eE1Kr9/ViQnaGqrRG+q9HQJa7apOi+13x5b8gCIxl1woWGEBG+eiXZ9m60D1jzq9QJ/V15EmZH0EvTVNppeiQn7advKbqbej7y27j9sBacm5v3JxqjR25hm42r3fRAqdHqDFa4u3IXZ/ejtHFcYFJdC+GwbOTidW555U2CTYF3t1hE/hisPXpq2grQC6SYT74T4X1bo6RPUO9MHxMq+GfjiGZLHFPCwdOhY+ifDjeeIQA/uovpC6R4Qo17jTrjsNj3UJmmzIWOM6ZCHAMSXympmqxcvmt8Rx9F4n59AD26EUhK3gkquAZkW4FdT0mNK5FftLU8J/pwj8fMqtWQVwOy0WnA7mdFRUFfu7gvZ1823nl14Qgvf8tKy8lsrSGmWLAx215pokNXiXEve7SPDejJEVDX9dMndj4rdvT/z0OvN4WZlX25tiCVufcgy6d9rWdrcGtSjPTlB0K3o1S4c9c7udh/388YtLkCxx3UidoNMYWwXtzFTTG8coeLzr0L8Gh2PQ7HwtjbUJ0QWGFMk56yHcF4dcGf7OwW5MpBhNrKvYezuWkgkhkKJHJOiEmABOEwgRWNbCtz99pJhZ3i4NHPc79sOOl2li9JEhJW1dGXFap2MK/SDzTidnxnHQRKFWatUJvrU2PjydIWpvPfpA6OOuffO0atVBY6VQSuZhv+dpmekifd76uatUDulGsVwapRSWNeODp9KYi1YjMTYSwl0ZuKyZpVS9aBGSeVh1yfet6vewXLRK34p56QTnwH2KnGJkH8M2Iiti1U9pzK3hw5UXoIfjdXzT2b3WNXI9oDuFQNAgkJzqj0zlRvvGntxHdqP3FJMv6Pojqv4dt+mW1lQ8M3lPLqbNwk3AsMcQ/efAxG2eth1g2lbsP9qnhORKKVfGzjbFdhN0bt9q0yqBjQh9WyhJT6TcdU+5m6RSOWFsSFOftnLOcYyR1BM+OqqhSWcnDBcLUKUf+o5NCbqvg+vn+VmCy/V+eqcTXZ23s6VbN8F1q8ZvXkQQmwTsCZcYOuE2DZx+3ZyzKbLa2MXAaUw85JEl+03tvctafPaJDS3AvuclV/JYGCxBQlR3rLTrN5SOCoGp+St6vSVS9nl2KW5Iw8/XRzZouG7ohX2Tfr+5JsO96+Zu7vNWKzaNG5e1bIr2s2jVfUyNRqLEwD56duF6D7y7vk6/H02uU4qNq0mpgXp60GLry2jSrd+1LVtV2kL0itY2s9nZeYcLkSVFVT8WjRuDDzzsRqL3ypFxymXMtSiXpyNl9CTVEEi7+UPwjFYMpZT4NKkh8hC9Gs96z1IqURoDkZ331OB5zoVzhiLd+0/fw4tuum7I627uQxNM8f26d/ufhqKkyTumavYyrZFQU9vRNJJqA1qlda4Ibmvdd9GM2vcunXungqulqfXWrSag817jfd8zIp/FiP7wThHjTjBXgcz+vv0C3/6iBpepNp6WbMrufX/+DDMWXU97H3g/Jt4Mkdk+a7CEb87VfO+0i9CabILFqkXVV6O2/oP3lHIljnNTrGgrWSvcwTnexMRUPf/Q4xeXIOm0k7fpk+tN7RB2v/jqQA41OFzwuNY2jys8HPDUoCiH84H3hyPfPT1TW+UwjLTWWHLWQzYIaYwcdzvWNfMyTTgPLyWTYqS1xgjsgiM5gRRYq04FDDbtNS9FXbtRVOWrd+/41ft3lNLg0yfuDjvujweezxMvCIlrNaVj8J7B6UTcfog8vrzyUxOO8Y5hiFxW/SzeN1xyDONIafDyfOFuP3Iad3woZ0ptlFItWRr55u0DP1wmkMbDkLhPkb95ulADHNLAZc7UajpFXlERb4d8rY2S2xYh/vr7D1zLCR3XHAczpOxkVIF1rYToNul7uyWk4DmMA28PkWOMW4vhSjBWJeBzs4qka9y4a9BzmIt40yDdf3FbGXY4Rq/BZx9U32ep17Hi7QCz1yzdL8+SCnHgPCQfia0ZulSR2iBF7a/fvBdYdeUdu6gWGg3ZKvCOLm1IjsOIxJ4pF45JbV2at0D7syjW13wng4pokC0Gwes18EjrMyCWLDq4DvZ3Zp/d13qt1OGqfO1EVEJim2/T19h1PzPvVCjQuCkI2zOviRgbtN8HJfprNd3k2/30qEBe39/tJrr2ArbnKf3vDp3A6zyvbQXITbraswS7PPO64lBjy8MQ+fK0J8iej9PMp2llaeqHpq1aq7TdNRF3dlI0tLjZJ53EIReqVagax6+JRDH7hBBsgqh/MFEZhd7uuFoh6f24lMaYArn1w87QrO2otjWK+nz1JKuPjl/zUuG8KI/wWjio2ecQGq+5sDahENglTy027csV/QwubMhhFwDsN3QRYbArHlCyvXdOHQmMy+Zo21pV2xqzXxJLk61T4B18udspUmlilW/HkV0K+OA3jlGVbgdk92ZL+G/WMk5bkmCyIypauIuR53lh0dl9YtC2VxRBxPzzYmQXAh/nlad55m4ciMYib9IT8c8P1ta0lRgQXNEkQw/3tiUdMSihefWBS1MZh3djZOf1Duam04ChA8yuC4B6hKpJMyp9M99MoBHgbjdsfpfe6X3QtSXbnqcnj7axtuTHipR97JPGHek3pMwWqU7JXdP5Ko7v5pmpNuUC3tRvAj344EQYnefLMfFmVEeDg3ck0Wt/tvvWi+5jirzdD/zwPCEIrajIJBYXo8nUbOgYFsea4GmM3SDbktNaK/EWdv3Z4xeXIBUL0Ju8v/Xoe+BdRZCmh/1LLlrBbpoLVgmL9vn30ZMl87pUPHuCi0y5EEdPTIldTHgazgs/nCeDN1VS4DAe+fbxieC8SuCjzP7oHZRGyYVpeuV80c8Yg+PxaSKEgFTh9fXCD3gOu5GHw4HHl2fWsvJwuufd3R05r8zryvdPTxzGPcfdyGE3sE+J4IQ8Jt7ejaQI0zzz8jqx3yUGB6/LgpeqXZQAj5eZb9694Vdffs28rjy/vvL6eiH5RHl95X43sCwrU4EWPL9695bXnHk5X1iyajZJbgwh4JOn5Lptpo50peBwO0deddGKNErTazNdivKrrJyUKtr2xLNLgYf9wJsxcUrB3M9t8Yr55ImOb15a47lkG8PWm9HApjLks/YKhiYpB0MrrSoqUPmQIl/vRyX0O22P+EWRup7MOedJznFZKt1AdEtiRN8rl8whJmpZqV6Tm9WqnfOyID6YgrB+rrf7AR+MU8Z1LNu5a/UPnbyr3zlLZZqWTc5Bz562tQD1g90c/HaANWnsUuA/f3fHWiqva+FpzqbdpL361u+ffcDSGmtpJsRq1SJXoqZq2XimoMlnRIX7OtXU2QUafOCt95yLtgKi87r/6Oax+hvS+qSVJqA9ZwGtUAPYtFmkiGmH9d9xoFT762sKV8HIlDxzacotNLPk/u9OZGvTBedIIZhXlQ4MDN7zsPOM3vHmMDDnxnnN/PB64dHMawWdYBTjTnjn2YWBcUwMVQ/0S/C8OJ1g0+ofvYHiNtItDkqB4G/EK2HTlNGH6TuJEuf/7vUCTjkaoMnQVCoSsAETswsxRCE6QPyWeFf7JRElPPf42cfPtY2shU238flhWm3NqkbOZS2M3vO7+yPRCtTTbuDlslBrYzcEXnKhOv2dwXsGQ1aagPPCIXiW1oUUBarGqiUX7seBfVT1/xCsHdngV4c9p5SYSuXr044hRUCTom6t4b1OJonHCuO2jYgPMfJhXhlQTz1oppkGpzEyBv08S2k8zpnfvjvxh+eJSy3snBpGg+PLceCBxg9LxnnPIQTlX6HDC63vS4cdPlCon9ldVCsacUKuKhIqDqap8pvjqMmnxUFpDW/UD7GSozYbVDLJge8vC0stm/RC8DaV61TxvBnyXboaOmrd4Z1VfBZgNNfuCLjGqdMQaEslhJ6c9QEJAa/rtSfZFeGpVD4uS9/QmuhaMtb93sQJBx94FxNf7EYr4DTxiw6OovIbP+Zma92RW2HKdWsxul6fVy16DyHY4FNk7ZZBTlu14pTj5cXhWi8Ihav05Z8+fnEJUs9uoes4AHg7kPUG59K45IIrehhhhN7oHTTlGGnGqW2nlco+DTqxdX8iiuBbYfRqkjkOnud53RCotTYeL5+Y52J+VCpU5lIgoxo4z9PKHx8vCAqXvznttf9s0OCSFx5fMo8vCjn/7qv3fP/pwqeXC+XQuDvsyTTenR5Yy8K0LkzLihPhizf3pLDj66++4PsPn/j2pyfO84I0YZcGxCmlN+0H0v2J83ffU8k8fTzrkdcqwxAYR8cPU+H9w5EhjpzXhQ+XV1pxfPPuxGVZqMqKARwhBUYxn7Ks/IfShOeXleNRFZi994yD9ZidCm/W0jZYE1EF6y/v99yPUR3N0aqk2AbpyMC5NV6bVkKrEet1YqMnQAodNBMhVCRKN29Bq0bv/Gbd4HBWiVlSZAdJ8I4v9yM/XhaSVUhFKi4IY/R4F5iLjsr3Ka6yKmdjGw7wAUF4fs1MIXMco9qY+A5Je1L0zNKvZ2+fmN5Mk22CT9DKd67aAu2HpQaYLgwHGy62lX76oqvIlmB0WYSHMen0X9HqspP4cWqIWpuJSArUCs5GzHq7LYuNegfP81zYJ21/SnCsVXiwkd2eWOmknfX2Dd7plKNmrF3nO/n3ijBdr4vgglfj4OSZVw+ea4LV26liv+96y0er7T98eN5a751kGoKKquo0m97bfQpX/Z8Gxcic0tRuw3uVAhjDwP0YWWrjvBaepoXvz7OFJEe3v9hI7QKH6IluIIXAtCxccmHnIhVLQu0OzkWnenTaCtSK6BrAO9RYaZyzkeKb2/g1RRT1q7OOVu8tKVe0R/dCRj3dut9WrkJtTn3xtivvTGvHby2JtSiBMwbP85QZkmfKlSlXcmn8/kkr+T87HdiNgZfFcVkrO+82e5Pqxaby1GeuiBCbY/RhMwytKJJ/9AOXtfC8nLkbE78+HY0AHahUBgLvgreD1DHPhRC9JfKyXf/o1HKi0nWB9OdTE0pWtF01lbAkQjlGwdZCCkrY/7tPZ/YpqYGttY1Hp9/BC3y9G3kplUup1BgYg8P5CK4qX65Bn+PrNJDe7vT2+brgJ8ApRk53iaUUK8xUPiE3YcmVH19n3h9HXteiFAvRazfVZqK7/qpE7zWmrKWoSK/d5bYhzXJFNL0WQJpcaRGhyKU+Z3AwObWBCdZjFcdGI3BGQs/ieKmVH6aZiN94R1j8xYnxBoXfjqMK6SI8rRmC422MiLSt3R8RjjHaEFPb5Bq8c+TcCKPGKSc6xj8mTxA410prbaOYNIH7lBAcuRaaJXdTrayuO/v96eMXlSB1GLe7Btvaxjut+EWFi7Booi7XJso4DIH7lFiWzMdLwduYofeRvfOc55XxuCe0wj6YSKAFvFq1j9uhRYe2g+IhqTZLX2RO2zu5NY67RBsaIUQ9YNfKMA58GXec7nbUWplXHdPcpcjHlzN5XfBpYFkWLuuMk8Jx2LFPO23ZOc/d8cA///Pf8H/9u7/i3/6Hv+WyrAoTBs/rmhli4O4w8jRdeHq5METP/rDDSeCw25NzBhfw3rOswul45PG8UGplKitTXRnHyDkv/ObLt3x8PZNrIUavVWxTCQIbXFJS3OjZJVWTXrNWpPsUeXt34K13/Pv2wpq1SozOcdolfnu3Izgj0zU14C1NtYie50wYApdambJWRFKEbIu9J1ufCcpZ1Gl2z5q0LdEScXYAKpw75cIPrfHVYeQuRLxzvNsnzrmQRUyvRp+/NuGQzJ0d1fXR9pkqKOdSOMREaJVzXglHT10E8CxrZUG5Dm9OcSP2dv7ThszYqtJ6Vist9dgLvLu7Z3k5U2q5ShAoRr0F1c7s0cREeM3qURVRjlVHT3bBsfORlgJZFLb+uKzblKf3mFJvdxN3mx6Ud0pKn4oiTU915dl1JXXHOQXurf2giYIqkDfHlojdFJOfZUMdmet72tnP+yj5Dy8TrXZbFJ3wGdJVrbm3Pjsq8zhnLq2i+ZkztMkUmX3ZkDfnsIRCuWSXUnjYDZyGpPIQhqppO18PjF0IpJ3nYUw87Aee15VpUURzjJEiah8T7d6upTIvmTGNrLXxX/zZlzxPMx/OE69zpnuLdR+/wxB5GJJOQUrnTmqyUppWwEPQJGVryrUtOGrryl9RPXGyoUpFdJ8BmyFuvwd6QXpS1BAP+53u6ctaSUGFGZfSmLIijZ2X1Ro8zjNv/N4sIhQV8QjiPXcxcIyK9Dxn9Sg8eeVY1aZrb3COv/zqDqlK7h5RP8bvzhe+OR3YRwcEPWBF201iXCxsQravn1obBBVnVfRGEdFJHOdl5ktDj7vgpLdrmM3+pOv+BO84psQPl5k5V/YpEFNgrY0B9ZoLCG9i4Bg8L2vh25z5cmikTROsJ8x6PlXXhS+7ongv7tx2K6o0mwjVc+eyFD5cFp7OMzi4vFQtAptei8430SEC2e6nglOaWMUtCdtGVJQMngYqXROqJxNdakB/1kS4XKrtUSElNTJ3zjH7ooM4TSfAn0vm49ydIuz7GSKsBHBLpppN7tq/N9GkbB2EEUMALam6c9Yl8J7HJfPhMmuh5LQ48tFte6Ch58NGJbDrnGJArL1YqiFpTRPPs6x/X7oB/MISJDAvtpsAq9NNAKZT0ty2aUOIHAROu6BqmU34cSn4lCjVIL4UuEtqHTE4aEE5D1ecSoPucUxqFWETMIN3xqNwJlQWGGPgda1EB28PAx+mFRyMyXO/35OS4+llZpBK80ILDhFPtAVxGtTZvpuPKZS9so8j+2HAx4BD+Js//JFpXXmdLtu0xsXQLE0WVGF0Xlby6nh32rGsBef8pp6as5LFq3esrVJa1t6/XdhWK84Le6/TSuIdl7WraN/qt9iYelXRsb33nPYD+93Ay5w5R8+73YDfuU2deojeApRN0oiaoX6cVh72ibvTnk/LwlzqjcP99Yzd4rlVON75axvsGh/Md0o32jZR5DHuRuWcK2OKHGIges/7w8hPl3mbThLYRERbx48tGavNRsUtmenvAw6fYG7N/K4cK41P08rj68zDm911ug22tkq7hlL778YYR/7jx0fGpkTVAc02NnVYsSTLLo5zju/OE+dSCcHRzNwXKoPZzjhnkzbiSEnf++xVusB52I9Rg6m1oSJGRHWqSn4pqkKrPX611Qjes66Nc6kkE+X0YqPH169k11S2m2iX6/afr89BOUS7ENTeRK6IS24gBWu9Wu/KxtnmWtVn0NDhW3TKAb55GynWxbRKw1Nt6MH4jR2Z7Oeva5+1QHX82XG/S4oEGSqZAlvx1hPsBsQUuTvtmdaZT68TuyFytxuYlnyVArDvXZomrh8uKjTp0bhwtx84xMgfP7xcSdLbkuxNF8waSUxAsCfcHY0y7pshTtMlmzaOuxlm6JOmjuXGIFc90lTNfNuT9hsxmJ7OonxF3+EFj/lh6b3JRr49BLXLec2ZtVVLlIQlF6INTTjYJoq/P0+8a8I+JTbLEfu+Idj9sgvSp5t900nRrkdWgWnNHENgHxWhawKDaMF9zmWLF4qfuk3A95SCtrgsCU3RJodt+qHriB1TIBB5WjMPg66h1fhMhyFu7f7t8Lapv9v13+00vKlZT3PhcVpNOV3b4622K7HfQZ9a60jyJgMi+vq5qXBqjDp91vmQV3JzlyT4vOgUuZlgQ6eNuydn5yMNMWo8qo0gSpIegwMXOee6TfRt04S2p96PSc8VW3eC8tRybeBNZ83WsIeNDO/tusUUIIhSBYIhx7Xy4WXmGKN2i2yVxKCke8DI4rpGptZ4KY3y2ezn549fVoLkNAA4g9qbYL1phcQtymjlGwJv7u5x8yvRdCKWUmnOsxsGprkwJMfdEDhFj2AqtDYh0BeYRlb15yGK1aHCXFQDIwRPcY39EDkNkVKFQ/Q87CIvS1EjPV85jBpY35xGQslbfzQ6j3Oe4IVkVZUGfYXBUwzMWcUYBwe1Fs6vmVq1LdKtK1TyAGJ0vM4L85KVMGmE0FwL0Xu8VyNMj2O/H1jLSpFKdfr7ySwHWmuc1xVB+/Z4z5SzIgZj3A7I1oScGzhVUh5D4JAix+BZRX3yjjFwGiNDCFtl0oyXUa1yrqKkSIe2PsokW8unY8a9pfb5mrgGGHf7Y4Ozm11PkWtCpQcRPK+FMelh5Z3jfhxUKXktKmppL9AwO4em8HC0A6QaCbq2pgmltZcIKqo2hoRDUYRpXqmt8ebtbjsAes0oclNp9oPNDuxvn585psQpJg7BM4j2528AmS1pK034MK2m3aWJqLeESHmkn1+r6D2nlAi+MgS3ta+K7SVV8vbbdS/2s82/iZ7/6FqdW8NV3SHKx0APLWc/s2rOG0H+ecl08MM75UV5HCGYSe04cBoi1bwWsfdrTVtG2LXHKlE9hNUGqJOee7LVv7Nwncjrj97u6hIhwd1U2Tjj7Fy/rTGozBXd4o3XIYbSRRJx5hcGuyHZNF5gWnTNJbuuzhCeIXala8clFyjVCPF6uPnoOaVRERd3/WbYuu58tq6w3Fkmmhz0qSb9nT7K3UrDqwz+hkB5HPsh6PSd7TWlWHq1Tmn9/l/3YTd+nZdsUgF+m2wcbEpM74v+TrL4M7fK2mxviuN1yapZZehNsH0yr5WzW5XHhdpWDDa80mUF+hLYRuebtrKbEdm7Tc1huCKPwTsigeoaoVyLqG71ARqXBqeTZKUJa6nsY2BMkfNltuRB78HOa9vw75aV50WlTpz3KsjbyhaPnKEf4RoBLFG29n81snWpzFW0ndk5nD0xlesgQ2sCwWKb3AyToBPe1aYVvflPcrO++7TsbSXTZXNaU+7OMfgt6Wqi77cptjtNklbJ+KY2TC0KvmoBeh1j0HV0ML7foevn9Xe2bZpLo3q7N84xOt2F/brtguduiORWybmx956ZpoK7tfE8r3z3fOGUApieVOwwOG5DDIvodX2aVsbx/yctNjBNEG2uszYVwVLlVB1hlKb97sF7vn7/jvyp8NPLhac5U0R4ezwQgyNI436MmoH2TWoHhPRRJXvEGHHzzN0QGU1j4dMKLjcOQ2Btnrsx8jAmgij5OzhnCsyZdS18aq+k4PkXv/6Cj4+qLL20xjENhJhY1kUnWpy3qQhHKY2Htyd++viJeV0ZUmA/DByOe8pz4TAO1KJk7If9yJwz4xD5/Q8fWVa1/NiloMaWrm0Kz+DY7xLv3hz59sMjjbpVN6P3DCGxtMJUF4IP3MXEIQ2srVBq+czlvIqOXHrviaNOAZVSya3xzWHguyKUVoyj4bbKuhjnJrdmlbvjz+531NbI06L1ru+TKOCaoeXu88OuV79Xd/brNEZDD7+uCjuEPtGjLsGUXgAAIABJREFUCeXLUhhj5uvTXqfSQuCr447SZtaaAWv7+auqMYDzMMTAPBe8Uy+iagrDRTQQqz9foFahFF0Dp+NgSarr5eIGOytPqQcpRSUfLzO5NR6nmdeQOcTIffj/uHuzJrmSK8/v59vdIjIyE1tVs7g12WSPUdOzyGQavUgmfXE9yWzM9NAz02PT6o3NrVkoFIBEZix3800Px/1GglJ9gJqksVAFJCIj7r3ufs7//BfDnbVCOqSop8p44bR4Ll5y2pYA1kec0fSNYVYRldRWkBigQ0jWQ+HizCHxNK9MZRwKQu7UBaW1riiOtr1UduJr6SCvHbOMg2y5BqYclLaMfxpnedE0/FM8yphGCcowOCuO4U7TGsu+cTireT8vm/SbLKMuU8qX8CwSRKkSQ7C9I7V52FSoXTJTa85Y3tALpUXKrGtHXTtYpTb/qVrAgnSyS8rMa4nbMM+4keWbLj6QsuLOdcyXmZu2wyqLL2aj1Y+FkBgaJ41BSow+knOiLyG47y8zPmdeA53SHAt9IHPt9qG4uBd4SVqsq4O1WC4UFC4rLIq2tfjiGK11MZHVmlf7jvfnCSiIWK6FMUUgka7FWS1GNMRVRou2mKpqRUEtBY1akiTWmyxIfaRc+3KQ+WK10DpDby3GGlqlsVpG99M64Un0zvJiEP8jAU7qM/icepELMVtJkREkdDYrCZW2SQ5OU0JfB2eZgifVayumPmIHUzISl5iYgxe0qG04+ciN0ttYGYQQ3znLt48XQsy8vt3zwy9e8Luv32+2DtdrJ89fVW6mnJlj4mmJqJRYk2IYOhofNvK1PP9iPJnhGvhazviEcKOuaLsUTLGAB3Ut62fXa2tzygNstDzoSov8fq+Lgq0UGSlmkio7SVnjEn4uFAZlLXNMWyNB2W8GbXjTtcw+EkL+LJi3NnAxZiHtUywfrN0KYAUMRmNaB2Q+5RWbFQ01ozMSYuJfPl3orKE3hkPJSaTclyVEjBYBx2n2nMYV1w5819f3rkDyxUClziyrxZMqB9gSRXLdrJHxdGKMhtubPXe7yOpFqWQy7Pci5Y9FsSMbjUQ5yMOhti4mKPjJmwOn88RxXonAl7d7dILWiPOroCCZ+11LCpLq/qNDx0/uB85r5HePFw77gb/74wd2Q0dUmpgSyhisUzw8zmSj6FpH7xpa07ArNLd932zjvJTh4enE0A/cG0VYJozWdLsd3z6daK3FKU3UiqG33O1bztPKj28PrBkuSyQoiFpxnCfu9zvi+czTcWIwhtuh45IFdkw502uRXsbo+fJw4A8fj4zTBWwhtoZE0ziMle77J7uOzmjGkAgR/u3Pf8B//vXvKN5iVDfTWDqkpBR9I91XHVEpJWGjWSkuq99oZRXyrtydbaep9sLyJADiPdMUFKsurk35sK04xegj35xGfny3g4IUWZMRCx2zFc5GyzNR+VIpxK1zVlrRGUebFZ+mWWbgSkYIOYrqEgN3fcuaE13Sn6EZJa6MhCjtUoaQNA/ziFWyccw+Mq+Bk9YMt3tUkIMo1xFbhm+nCWqRjyhj1hgZ1yg8qMFJN4U4XD8Fj9OaNgp5tzeaYd9JEVpcGMcgqhHZ8PTm/5W3ja0Ez+rn0LygCL+837NvHX1BOSuKlIqq5/6LF4K+5dJjFgRO7Arktk4x8vXsQUt3mZMEYbaF8LvEKCHIBdExRslGGBM5XEmaSsmhl1Lm0EgshU+JpcjMHZovbrri7yQ/PyowKhe4HqqasooEfBD/oKzZeETVKiJkQd+8Dzxdzny52/HFoeM/vftIyJHONuz7HnJi8hLDs3rJ5hus8FwuPlxDglNmWQPTPNN3jnnyZb6dt1GL1cUUk+LuXdCTyp2EZyo15PspRnopC1/TOU2rFEMjaPbsI0uI+BjEIuSZkEAKT41fAuMSUEYKvkCms4pWW3yIGCsZb7GM18hwXJfNP6wxil3rSN7LqM6L9UDfOA5dA0CtgzWKy+K5rCt3fccX+50UQwXNlggdcWOWUAxFSJEQPK51hVytCDEISmE0bW1o2xYfEmsIRTWVyVmKEaXFMFblzMfTyrenmV++PGx7QN17Ys686hq+VSN/8Wev+NWP3jD0DY9T4OHD+8JLEkqFTlcSfgTOPnJaA5eY2GuNXz10La1pmE0UZSyQkozQa8NorCgSN1z6OaJWvNtyfPYuS6Hz/J3XUVctVnzIvBl69saQY9z860CK7RwTNoN1GpQV64Dy51aJqu++tRx9JKXIrXXcOytjtCXQNnbjOVbENZVhcP0AISSeiByskYmA2K3TKsOf9QM71/D2MtJlhdWwaFhM2rzYjiFwPHrai+a+b3h9OxBj4nGc+HCeScDLQ8frdved9cb3qkCSmx63RQqyaTpnWEJkWSKdtrw6DNzfNoyPjxxaQ1a6dCqZXAhw11O3bqBVHVO8U5TMOo01fFxWPlwmGg03g5A4FTC0QuRcy/fL+CjRN01h3MthMDjNT+73vD1JGOm6JnZdx66Xjuz945HDYeA8L4QQmdOC1Z5d33E6S2jgGoQjlNGM44x9OvPq7pYfvLojxcgfvn2gsZbH08xh13FPS0iR47TSux3vLp6mQNc2JS7TwnmBL754g5pmhr4R1+auIV7OYr6mDWkJmF6zaztW7/nipsdYxbjO4h9iZTrcWk0MibO2+JAJY0BrxR/++C2LTwQnOUSCHiXenxa6XcN959BJcthUPWjI3FqDT4mxFq05F2UP1CiJuhFUrxdK8eXLGKBucoJMZ0Iu0ulKaiQzes/bc2bfOF7sJAvtRdeiMjwsUkC4QloOPm38C5+kA7o77EgpFG6a5nY48PXjRcaWiMqta5yMd0mMY8I74as5Ix1Y9dBRyAEbQuI4iUIqbbudfIdPif/n8YTVYo/wauholeLbpwuPs39mdim/6izqsTkmPpxlJNBaQ+fErTxmGInkQizutRUlThnl7K2hs4Zzynwcl0KCrOsxQ8obQVoh19YqxY0z/PSwY/RBXNVLIVS75S39HNkktarOzMJ/ut07Pq2BfzlO+JxJPjMHSVQfyZD91Y8q1fwnhUmKJhYkxbKNwOph8LrvZVQSI6EcqiFnvux7DIreWZGMR2l86jOktRQWVovqK8TE0xwK90hGbKL+S8I3ifAQIp3W/MWLAy93He+nSOca5jnQ0HA/DORlpTGK07JI4GuU+xVTLmaFDdkmrBF+Ru8syxpplNhfGKVLoQydkRRzi/BqKhqWFKiQCXMQBEIL+h6jODxvI0arSDbxu4fzNkY2WnHTNjijeFdQpc8KrZxYl4jrZBWmCIeu5d/95AXjGvjn90eJ4PHimdYo4W49nAPLKuTh267h5a7l20/rFRlMuTRHiaFp0FwtO1oljcHjtOK0Lvux2goCrTTGiLnlwzhDTtx1TrhiPmG0ZJqlJKHLawiSIJ8yrdGAJWVPzHUIJE/PGhOXNYLK/Plh2MbXdWQnXEpBBv/VqwMP48j/+Te/xhnEokVflbwpZoIWHmMAvhlF5ShJSYaHydNmTUiB4zRyHGeM0uxay6Kk8Le6WjkUNChtLy//VHWN6mf+YVe/M1VGbXXET6lBfMjcDC031tIAQUt3qgpwoNQ1ISCGyLjKc2GVvIA2isa29N6iWTh7GJwU78fzQkKxhsTQSkJEKkR9VTYQpfLGEVt95GNREHfGFN862SsP1vD70gT1SjNYzZQzD1ny1YxVkBRrhLfnma+fxm2CoLViNzQ44/j9u6fvrDm+XwUSmYuPG/fDKpklXk4Tb/YHvnjR0BqNJZNGz5ITQzZF3vic3Mo2llCqcgqU2JvnLJkyRSa6pshlWpli5Fevb9k1shEMzhbIOaLTNeOrupgORQGyRIVT8LqRjfR3j6t0olE2pL4xkPY8XE4curY4YCu61tJbx989PDBYTecaEnCehcH/8jCw6xpRmFjLD798w3GcmM8XVvJGuHbacpwu3O32WGc5TzNL8JLJ4wMfP7zneJ5pG8OcPescwMjBOi2el32DzZnj6YJWBu0MP9r1fATGKFJUEdxkUS8V7sKsE3POhIsnajmg1yQBgsd55TR7dikyaDEhq15Sqpgh6izz6rXJHJcVijJBmh+1qRy0koLLalEl1nT0WvjmYji30S8LUlVJhgBLivz28cS+Ef5DozU3TUPMcI6BJeSS5waCcEk8zW5oiMFvEmFRAUVe7XreX2QxVin5p1m4SK21hCWRYyY3ml1jQcFaybpJOAhjENVadSd53qimlAlEHi6TWFO0jnPwVAO68rjLL+V96eK9s5T7MHrFrrF0rqBAiKrpGH2RYCsG49BG44PcsypQqGHCUg7I+JNnCM2LvuUvDnsx8wyZ5woZXZsPVZEIuZ46V1+oYoBXgk/9FiUjnWGqaGElWSSIZRzSW8OuFSM4ZxTH2W+FUQacEqR3Kvy/6kPTacNgTRFnyP91IRiDKb5Q5fPJFsFpWXm4BIZWUCulBLGsWShaKX5wGOiMEc5ETLQm0RqL7gTlXaYZVOJ0WfCqKmXlgKrOya2K4tBPZllFcq8AV+wxKkm/cY7eFpKAFi8qbZxwXZQo63JBY4TCJK8TycyLRI2sKROK75c2sn/lmCAkvIY7Y3mIvuRRSiG7+Ih2Mh5OIfPV3cCP7gcsiUHBX315x9Abzj4xeSmoc8yYcaK3mh/d3fCyb9Ekbl/fcZpWHucFVVzCZx+Z/UTfODpb4leKc2yrNe/HmdPiORQbC6NrELSgTp0xpPJeK0FXxCFxe97ImfMozUXbyEHuTMvkA2MIqJRlvFYc1TujcaaG/qrrmkxSVFc01KpMb8Uc1tZnsNgshJgYl8gUEqfVMxdHftlkPBkReqRRlZBsw03fYI0i+rA906UmISRV8LL6B7XxKPtmlnirDOW9l6IkX0fQKauioMvcWYcutgZX9KbCsbL2MxR7kiDvQ8vaU0k4RPvG0NmBXPIapxIP4nShVgRJFrAFhdZa/l93F6NkDVZT1iWKz6BDbfv6L253gOZp9TytK0tIvO4HMvA4LoQsBa1WCmW1+HslMZc0WhMjtL37zprje1UgpQTjGuiMY7COzsoiDizcOktnRXFTiaGdMRxXz1434q6sID6z4pW1pjbyYnlSri2yvBRLcTx9mj1OC/wsNjTyvVuek1Io0pb5YrSmrQsSOK2BtaS0U5Q4McHQGj6Nmmn1dC4Tk2ZcPXeDzGGVNnI4ac3dfuCm66Qj6RrSuhKXCaxIL5ubPU9jCfXL4KyhsZrTNJG8JfpAzJHVSCE3LyshRJpGU80ZjdbkAOgkSc9ZFtPQNqxLYA0G6ywtMIYVpY0og2LiOM+FCyKbSYyR3X4A5AGffGBeRTLqkxiANaVTvZ7u8q+d0twWefDTEq5/kqXwgUIk1GbjGtUZf52iyW2tCJMccrV7qg9BzjCGyK8/nfli1wgXxihe9i27ZHl7makaqhpsuO8bSKmYRtbyS54n1wBjOWgRKfmLvuXjOBUSspiFrkskhYy2Wnh1yHjhtITrCKO8ZsWFKnQek+S0SY2QuR1alPKMdST52VcpKLh2ujnDxUeWeF0L1sgBEXNmiuLwHEOUCI149W+SdaFqY71d21rNLSHyaV6IKcmhluo9KCWuEpPHrK7F0/V1i1kctSMvR4Gq5NMr8pQUm6t4Hc2Zwn0xSgpAKMqVLGjJGmvIJ4UjY9g3jZhSVp6TUtshI6il2t4/XAn0Zd+mfrPYjahSQHkGZ7YDNyvNx9OESopOSZxO4zSPlwtJSTp6LAhSitA1JVpCi3t+Tom4BuaUsNZsPBmVhUd260RIksptECPdolhUYLaittyzMt5pssK1lr7RRaFWmodyHeowzWhRsi1JXNLJgkBFXyJ6SlGoy/wuRBGQ3B9aPo6r7EcUM0bn+It8QGVRvVol9hlWieuzs5qPl5lxlTUv1gkeH1UJk67ZaxpX0LzHeWHfOO76blv7Ywjlcz+j6l9vbSlk2Eb/OcPiAwoJN26tQWvwKjH6tVgkCOdGFZPZWqig2LiF1VlcXL4pRUiGrLY9aQ6Jt08TXpdCXV2z/XSWZ9Zas31etDSPqiArtRKqXGl5C8+6qPp7OouyW2ViSIQyhlaF91ivB4Aq9IHeWbrahNa1VxqZisZWenV5FOibhq4RrlII4sSvlaIxAEYMZXNmaQyjj1vw9uwTRmVaK6aYpGpCWfcDNrFHVeTNJFpjygavxCevuKHvWiHvP44LKsHLruembyRKLESmGDmeZ3wQ2sH9oeVxWviur+9VgZRzxmLY2YbeuvJ7iUPTMmiFzs+2cCXEv+DFYHBT85gC6VEeblVxiesh99nPRKC/l0NLYyXHRSnhGJiyKBR6I2bGKhOG0slqcswi8y5+Q8Y6GfUFkfJ3rmPftozrzOwlZyiExM45WtuIE3MSVZDVkl7flsOxtWJQOIWA0pqboWWOAZ8FzTFanHwv8wzWlQgDxRQWUpAOwZjrwaAyqCj2AylLB1Wly8aZMu4L3LQdSkvKerFwhAyj99fXIuNDLp27HLIhCuerKWZ0c4jisKsK4ZJrMWCUYjCabNymeCpj6K36CWWUIDEQbNe97hV/umVUgmLd3OofxgwfpoX71tIYcQ1vjMKaMnIpI7RqltgYRQp56+AqskUWNVPnJLNPDm25T39cghzEtiqdoHEGnTLWyT1YQmJ+FpK6BaL+yVOpUOxbCbJNObPrnMhfjeY4rcRtLTx/qiuaJi/pY0Jsq+TZckZtV99ZzckHkQiX4mg7DK5v49m/XLvxyUe+Oc+cXODQWm5dUSZRnbO3v7K9xtaTFGQpISOEzmjORYigqRt6+XlbhVJ9pSoKJa/RGonBMOX1dIal2FuoIsQwWgqpjZha/6n47N5uSHO5HdWleztw8zXqKJEZvaBUrTXsk0MZQZOVEsVjiBljhLsxdB0pRHwSZCNFWXNziAzGorShsQ1WW/SysOaIgUJwl/vmymizRqHk6+WU9/bsSagHevUya4youZosxoUV5StORoAgeoJUJOYgjs6exBy9MHAR5G/0kW8vC5dVBAKzFs5OiEn4STHROBl5KQwpRFIUZCxnQQxs65i9xGcsxcB0DZGsVAm4lXfVO41FhBFriEwq0tpQPqjYWoiLu+y1qiCpf7qc8rNfUxKj4epE3RkjHMzSfFl9LbxTytv9l2skzbbKlOJAFz7PNQy1jpIyghNd1rBZFSTKGLfQChorhZFEaZRGIEvxH7haT6TPFtTzRXVFmciy98VYDHQrk7v+1fJXrFa86NtnashnjWZF3ORuUU1kzyGSTKTvLK02eJ0Is9+ev4yoUltrGVoxviTkkopRnPCVcJ8ao8t6KvejjFXlzKgxTRSRT2IMkSlJXqJCzsfOGSYTCFnAit457vc9VmseloWwRhbEuDRnrhYu/z9f36sCCeDgWm5cS8pwXGbmxdMfdlylmfVey3z3tnU4WyTZStPYEtwXU0GO1Gfhlbn48tT/jhlabfjz+wNDKwTDWLxPqnOpKZ1cJm9W8rrMmwUqj7w/jkyLxxQZccypEB8j42I59A0+BebVk1LCYnBK01lFVhGtpcKeFjG1apxhXD3DXmbhl9OIykJyO+x37Iee6APTsvA4zQVpU7RDj46B4zTjvbi93uwbCvIrcGpQKGPx84gqyrdcJK6VC3CZV9rG0rcNlyWQi9w6RYFJjYBkQhSexe8kFvWLLsqHy+o5LuIi3FmZR6NkMeXyvUYp9spu1UzNVpIOWhZKqllXcN0NuG5c9WtDlVRd4LWQLYdA6QpTpkia5f75KF4tRmecMzIWjRGnlJBoiw1BHcOQ4bZtmdYVUwoykxMplkiIRQ4EZzQ3WtEgnKZktHBQEqCqDJfPnml5v+Lcvu8cTRm/ZGDfOfZNgw+Rcc0b2rWxeNXz16pvW/5LOFuQlyhKMqt5WESQoJDOVWfhq2wuz882aF2RlCxd3kNY+TgtNFrzs7sdh6LSMqVg3Cwc6nupxUnZmEOS0dfr1uILMdwagdarfDuVCjghI06ni5s1VySF8t5tebhSCNvPqshxHZnEJPYIWtWg3nrNKlpUO9vMuCZsKQzqB5GDTxGyjDLOi6gcO+dwWdO4hjnMgvKtC703vGgafNswz55OWRobOasRTSJFj0qJmBR933F3cAzjxNefPuGM5hKCjNOcmOBpo4k+bg2KjBETTkEN3q73qxaV9Xer34zKUsjkgi4kyqFPotWWfdPgdNx4n0+XqjiVC3BaA2f/LIT6fWawjr21QrB+nBhD4K9+cA8U1VstaovQhay46RqUVjzMKzFEYhTEUGwGIiFlQdbK8+mUJsXEp8tMUhKf8/MXO3LOTKsnRbknOqstg1CwJbU1CFW1GFIm+0jj1NXSIydQggrtGkfazBzzVpQDhfclymh0zZqse03eRBWdM7w69Jzen8RRvlAitBEBgsKQjWZJQQoHqUKl8LeG6HNRvV6b8axLYfFMgS0NYRVupG20rKu4ohY6pZncOcubviP5QK5+aKUryNuPk4oqZTHQfT8v5HnFGMWrncS/rL7E2KhrW2aUSPTJiqe4IiW4PKejj8WaxGyIWg38NUa4gLWRl+8PzFH8q7Kqakv5fF/sevo7y8dpZlo8789yj76827NvBU1qnSCNj8eZ8KyA/dOv71WBpLLiPC6EOdI2jtbJxjLGxI0r5l0VaCw3MmawWjr43jm0lpvyaVlFVloq1VzaKaVVcYiVrvOYIp01aKM4rl4OD2NK5SueCvUQMShsyUOy6K0LnX3k3Xnh5U0v3iMGnpbAHAXmezzPknFTYOt5jcToeXGzw1k4TVJYNc4ydC3nceSbhyNf3e/58OkT4+JRyvKXv/gJf/tP/8L/8e9/zrxG3n06MT1G9q6n1Y6ma4oTrt98UlxnNqPHmK9xBOdlQim9dfSUwyNGQYWSiSgvsPuhs3w6iYmkj3HjdoQEpoVLqFyQ8jMLWTyskTFB6NxGwKYUq8/dUGX1KlTp7lJVfyglG12+FqQbAVFuqnCOyvNTC6grSfHa4SiET/bH08if7YaSBC/d+G3bCEeoXItp9exKbpKqsHkpulKW2XkMHlscjKPONNpwuGnJle9gFNko3j9O3PSOXd9w8Z61ID+KckjV9q60/VrDrmn54rbbChX5WCJ9Nxp+8vqG98eJh5PYBGijpXDdoDWedaRVdpzpG8eXt3uGzvL7xzOJ4k5P6eS0li4ONvVn5orslMu7vX+UEFD//vGMU4obZ3nZN/z40GNN8Vt61vFWWwe9layZ3mh+tu9LEyM/L2bhS6jyPsZYHJzMFRlM6JIof0V5AExjWdeC5ClNZyxOW3lWtgOY8vzGDSGuyFtCZN9rSOx72S/qwT5F+dwxgTGGndboBG9PIy9vdyxhYlrFZbjyy5bg+c3xiYPruenA6cxtY/npoeHDueObeYV1IufAujpudzuM1gytY/YJkvAM73c9x3HeeHWU96uzmBVu+KwuYc5lkVRSdC3spUFRmzrYlkclIvdLpYgSNjpkMR9c1ohoiDND43BOkgNCFH+aOcYt2ieXhReiYt8KsmiNqLpOl2lbp1Zr7ruWQ9fyOM98c5p57hKdEjyOs4xHjdrOba0Uxlp2jdh6LF4KvkPXoDR8uqxl3RcVbu0hyt5fn72QIZTmzZgSQFsaB0hFrJA3REPlEltkHD5H3k+LmOqWPLkSolR4SzLafdUp/jlntDLbzpWyrJmdMSinWYLfYoV0cTp3RqM8n5GroXLm1FXRVz5bVGKoSJLPHKKgeyGnbYRcwdhdY6TwK3fClH0lqaoUjFRyd0iJd9PKU6FM/PW7R35yt+OXL254ceh4ugSWdam7gaCV1nKnDafZo6OoaRUS8j6GxDouOK059C27XsatMcN5Xvj2MpfIp7yZ3U41x6/sj0olfvPpkS93Az+6GXinJ95fZj5+M/FP33zkVz/8kpQU+8HRaMMF8dn6rq/vVYGktaJxDd570uJps6G3io/nI6+aA8bYsjELkiEXEmw2WKVLarbINofGclmkApYxmhQDH8eZJUgl6rTm3ll2zqJjps2KdQ2sOXAzNCLHrnBs6T5kgRoMmViIZForWqeZfeD1zcAUAjoEsk9c5iCkQNvjdOKw7wkJpnFhXmaGVjYAqy0xZS7LyNB29Dnx6nbHh6cL2rb89Ksf8B/+9c95/PTEx48n3ry65fXtjofHE4d9R05wnEdOl4l58UIobGXMpZPMbVNO+BS5rKsoI1IiB3BO03WOTJbgyBiLf4rwrPIa+dHrFyzLyqdpElTNaB7OM+vJc3fQGxwcU2aaAzf7hrtDJ34s1qAKJF1zsOq0WxCdBEkIm7XrQV03s6qMAkrHIstb3GZTrZbL4Sm+K3IgyolQPVsysGQ4eeGa7RsxgfvxYc9vTiPHacEH2VyaGxkZKYqRnrqOfrTWnMZJTOSsIWeJ+xhsy9tPR/rB0DXiwZV7x+N5ZT5O5FajrC5IVi5oHWwp7Alc0rzZtZsLbS5FnCKjsiFreT9fHHq+OPTitWMsf/3P7z5D1GpPWceqcvBHPk4jfzgJUbMWKXKmyTXLRm2nSq7FM6VQTFefMpVlU1VKkYMoxh5Wz+PqeTctvBlaXvdtSVVX1WFqK2CXBOeYOKZYDOOgRbwiypG+8c06LQd6QA62rbEuDUrdnVXOmCw2ACYrTtPKaVxZVs+LrpWRZeegKPhyLsRuUpHwiyhkiZHDvuG8LpisuKyBpTQBRht6Y0WBlqQ4WObA6gNRiy1Gpw1909K2LaflIodWNsSQ6J3BdC3fPE60Rg7I07Kw7xteGsXp6UjfCJqsjYhITt7z5/09pMzqPUuQ9bKNo+F6jXMJflVZsrcKMV0XbldFaFUp9BWQlCZlIZXnolpcV0/rLP/jj1/wD+9PPBwnCUG2hpvecehFufl4WQleFL3VOuXNTb95tCnqiCMz9A0+RqbFs922DLeNI7SRx4Ke19iYSOJhFGfyzllaa0lBjW4TAAAgAElEQVRa8S+nCy8ay31ni1pSnt2QMq/2LecliA1EoUkkJa9ndHWel308xszsPe8fF2YfuWkdh8aSs6LRxfOsNNNaK6yT8fbHaWFJkZ1Wm9lqiqlYRoA2BalOin/71Qv+4eOZtYxkY04EArFvMalOKGobIf+isoxV/TM1t1L1Hl7XJllo2yqLCWztRXzIKBVwTtAcrUDpjDEG1/ewCmq1fZWiWSVRBmutGWPkw7zyfllgcyDP/O7xzLvzxA9vBv7NFy9h3/L+8VK4Xar4HWleDC0P4yINNc/2mqzZdw5jFOfVM10iPsi4dY5h27k2BDnlawNdNrI5ZX77dCE/XfjqpuOXLw+cQ+TdeeK/vH1LhyGlROtcsYgIfNfX96pASjmzLhO9kTnwrmvorCPTi4IEcWjeiJ+l+Y4kMZREHoaUkOpRxY14uobEh2kphOos/B2gNUKgndawERuVUpznVXgmCHk6FaZ/k5UoRnSNOpCK/6v7Hf/wcKRbLJ2121zbWkMMsHrPlBNNEJuAV7c3nE4nHqeZxjYsyWOU5mU38OJ2oGmE4zJOnqQCMS4QEt9+OvPNw5GfLit3Nzt++OYlHz498YePDwxNy3DbcBwnPl7OOGVYY+BmZ5liYklA2cjSGNn1TmbCSt7ruAQx+LKa17segMu6Mq6JoXMcxxVrHD5G5tWzb1sefcltKz45WiusVcxL3A7yfWNL533VYelCMFUIqfl/eH3Pf3n3UUZQG+oni96gtg5N7nt5DRTx+rtldKo3pEc4K9LJt84IZ8EYLjExjjO71fByaPmnbz6BqunpIvN+Gj03nZXn7hmBxhaIPiiFT5EmZNospnX7oac9SUFQ13bIouq5vesYYyhp9IU0mhKujB5zFkXcF31H14iCKeQsuXBFRZl0Ie2iilpGGgCjMl+9vOH9cdyULKq2dEixYawmaMXJB+mm1RXz2wB7JRyGKg2Wm1A2vYpI1TtYRoRKZpOyN5fR6MUH/nBKfH2epThFuvNDY/nLlwcMmvdh5Rg9gYRH0WC367IRmTLFpVlGuoI+QVAFoi9+VPWRSOVN53glG68p8WFceJoWnNH82WHHrmlorMEZ4S1983jhdmix1nBZIt8eV7LLxdU6bt5VCenO5xDZKUFXc0W+YmZvLbd3O47zypojMS48zgtGGVY/YpueGBJHn3g59EQCXWqwIRND5uPTyKtdC8bxzYdPDH2DVYbH08pf/+NbfvpioLGSZh7KZ1Vl/A/F6K8UfloXkny5l6kUnhWlgGqLKmrCEAJOC7pR90+rFZdpJUZR7WotYaGVC9U7S+gz4xxYQyKFTGOUjFlUyQirz0YSxI6ccdaKWWAS40UFvNh1vNz32zq8LJ4P54mkxI6jDYF922CdY/ae388LP77puGmsKKMUmIL2Hoof0uQjs69oXlWqqq0Ir9fgtm+47aWA0vq5h1mFWmQtPl0mPk0ruSB0a0w0ITJYWzhY13WQMoULpBma0lAnUbeFmLjfDTyNC2sImxv05hKuZH2nnKhhOhUYoBS4ICi8LlylahhZkbZQFLVyNsqnTSmxTAs0bita5XVU6UllX3haPA/LymMpeup+kjOFeB35zeOZyxr41Zt7ht6hDcxLKLwtcUJ/s+9FzJMSoTRF6+pJSqY1S4gb93Ypo8G6balyJsVcgQm2pihntZ3V7y4z78elFMOan9/d8LiK5UrOnkEbUt/wXV/fqwJJAQerueka+tbSGCvu0Llu2vXwLJv11j3V2AN5Qqpr8bx6zqtIFEMUS390gejLASYPtSQJC+pQHya5QVoi09FFnn7yXojFWQ58eSPQG8Nt05Bi5mGaOK+SF2Uw3O4aOqtFLVSKqr4xPFHmyeUBHbqGH72+p2stn44XHi4rP/vha37w5o7dzY7fvHsrNu79gM+K47SIIVqO9Dmxrgtd29H1LfscyDHTuYE5JZyz6KyZwgpRiIESVVG7e1E4NM6w73o5AJO4/Ypct+Ovjxca7TCFaLeuEncyz4G2FWPMEISvEELcoGkp/NNGdK/BvxXdUUrRKXi963l3nkRFQ7nPm4z/2XhHXX/PqGsWkJBsrx2XHAgUWa+4CK8x4qOMYWeV+Dh75hC2jRGKO3eGNIlP0k1nt1iP6rycVYmJSBs2Iu6wTmTzGSFCL0Fk3NISb6XItvHEeKVgdlZzcI6EZlxXbPmZPONAUN5D5R85NMTEi9bR3O54nFcuqxfSa1kkfeOEc1U6TkrheVWY1We+JnzLb225Us9QiivTpb7rq0cVSqGNeMCsRZ1WzheMiiIAeP/Ij1/ccAzS5QsvJhfDTLaGBgQdrMWJoYaHSoEYKWjSVkvJZlpHCYsPEsWTISCxN2vKvDuNGL0U/gjcdg2P48xcyLQ+w5wzy+JJMUEU4nF69okzUgjW8b3K1Z4ApiVIEatkpDuOHoXCGeHvOCMp829PZxkxaEvbG+HhrFLuh9UL7ygnXNPSDy1Gad4eR7687QrhvDTWWV0d6Z/dGdlSZI1F8nWzLL8orcqIVT6PUVcJuVVXE92EFA6+PLsxZLwWArtFbEycUdREGKsEEanclhCv48aK8lQJesiJmMVywhZ+IIhIQrWWy2q5eC/7vjacY2JdJ0KUtff3Dyec0bwZWv5s35GRA08V3tOuUVidOC3hmTqsFo2qjLausRfq+SLLwolLWcxmz6tnKt5auiI+qdiOqJoioDZkJyM8vG/Ok9yLKFlnVhuGxhFSxKUk+XTa8QxGKqtKvL5U1p9zhGpRzHXPu3Y4ZX8oy1HOrFIK1qayIohckXhy3pw1jFYMznD04vqujdn4q/Xhqdma788zf5s/8YuXN0VMBOsaCh9YPpLJmmQUFni6LEwxQlFzxpS29fuZmrU8qKmstYqwPy/W6tDMl4ZO0NFEiJFXfcunZcHnRK8t4+T5rq/vV4GkJPRv1zpaJ6hD7XhqgN7zdZ7Kw7ohDuUPVLmYsXBaKqJQ2fJQfC1KfESBK7YKFSVjn5DUli9Wv38JiV4LD6H+NR8Tk4/0RRF1nFfmKC7SInG05Cyz1MYYOme2EEmr9eZrsu9abnadkFYVzD7w6v6Gn/3wC6Jp+MOH39A4zc3QkTOcppklrOyVhOW+O1+4sOCc4+XtDeu0Yq1jXBeB3VHk4lujTel0uBaVVhsOQ8+u6VjDilGKm86w6x1LCASVUElsDFLOTOtKRiIOdBT0QQMvu5ZQnahz9TFSBbm4Lswrn0W2hFdDy6dpufrS1A5HXwm+FffI+aqYuo7iroe5Qha7KdEfdQ/xJWC1sZrOWglKpd5/KQBS4SOFmFm1cLNSKZ5bq/G+MrzYEsONUvjo6VpHmKRrolyTwVnJEtwKEnlQlWIrdEzpGq1SrFkxrxGXxY+pkher2CBT0RQ5pHKCTmtc10jch8p8CJGUEp2zKK2k0CwbI/XaPltLz9hOn8mDs6IkuNfrXv9GXbTXf7n+61a1bO81ZjZLgfu7gbUQLnVBYnSWTa+Wi7movC4pysgji9Nxo69yd1l/z/g15RLnLAZ0MVVfJ/njmOCyBmIOhPIZpjWSfCR4IXFlbYhaohU0bAf/Z4fQn1yCVJ49UQ2uRU2rtvsqxHThU0TkMJ1CQHnhuYkRpcZYWEJGp8TeGMYUiTHRd4Z+2PHx48zkI4MTR+uaXXY92PmTeyprx+Rroatg851JpaLMqSDzhQtji+qqrrcqslIIEp+RgG7jSkC1s+W2yz7WGBmpx5SEMK2kuJU9NRc1ZUVYRT3qTN2/5QM0RnPbNxgNJx9xxT19XMPWJLwbFxTi2J+Rdfa6bzclmdGK3hmUVsw+kOLVCkFGL3LntDLP9pbrNa0o0RwENQzFJ0wXVLTVmt5qWmvE/ymLn1J9ndlHjssq+2Vp6aySKJ7F+w3lVeXzVH1/RUNkzcv46YqqX+/ttgBVfv5f1B4oFYPQkOU57IxhsK6cmdfGrj4bFaNvjebQWuboOIVUCN554yTKj5MQ9G9OE6+HhpdDJ9chZ8biT1Z/gK6fP8n1qRzAupVk5L2aTf5/PfPlEtXr8nzZPUe/5VNEMsc1cNPJaH8KibMPXOaV7/r6XhVIKIVyZssWcqXLVcVU7nq+5G0jzFk6fpMqB4mt4mysxhnFEqRAkrmu/KUYIuuzihQo6gf58iWLwGqDNuLIOock894CT6ai+jmtgYdplfk+mahqEGsm6sAaAsd5IsbI68OOnbOkkHDGkXQWcnbbse8HUlI8nkd2fct+EjXHaU4oqwlJnFYbo/DBswaPz5lgNKrfEY5nLpcLdzc7Xt/es1pX0JUEKm+xD6YYhi0+Ye111LXvWu53+2J3k3Eqs+8cL247/uM/fU3SijUlVJaN25cEaW0UIYmE9LZ1/PS2l8NJa54mz+wjrTW0Skik1y4nf3b7b51j5yxrTBLeiBw+plQFVWZdzSJTKcBqsnMq3WpdOLaQxeUZkXtmEDh731hu24bTtF4Tt+sCrIV46bYmn1i8bJq6NyxL+OyAjFkOc6NXeuckaDHIyNbq4oRc3vPnnWDtlcTwbmeN7JGpoFhLImdDq2vQKdsoqdFaMorIJcokY3PmzhlUajhNnjVndl3LcVkkGkM9+2xcO816cmqZpWGNKc+3FKKmrJtQY3q4EuGrN9C2K4sGurzR/+/61sDKVaFWtzifru7pIJ//aV55KCP1RivuGsedcxgNDkOrRUqdirRb13vhEz5JCVVFHdcjRW1jqYwYQu6UeQbpJ1KWhsga/dlGXs8wnbeXKnWZfJ4lxkLqL5wVqzkMjoxmKXEOaw6opOjblpQilxwxITAYR28Mp9VzbxxNzMxK/Ib8Gri/MxxuBsZ1pDHC57O6olhXZW4dhdQMPKsKIlPudZA/RMxFpYhMSdD1mKC1tvjUXNdEjWIBGX+JAlSLB5BSm1KpRnv01hKyWGfUNZWVIIux+CdpJc/Omq6u5qrcMcpnuesbhsZixoWg1IZG5dpYlMLv/bjwuHi+uul51TdljQmh1xrNF0PLuyNoJSkNcgRIcSTSfraR87YmlCBH4xo2ZWdKcoFr4PGhcRw6yRNEOY7jIgVRuV7HRdR+8+pR1qDKeD0kGa9no3GY6xiy7Ik6Cy80AzlmfE7bfa0eTGU51Se6oIRsza6zwk3SWn5eaywv2o4bZ6F42NVNrAIHz8fod42j05pfH0dGL83+5whXQbKBby9C1L5tG5QWUn6KaWuIpQLK3DjNHDRrSoTtZ7K9j2c/ouw9dYwvzX2q++az9ahRm/lkBk4hclpXBteQibw9jszxvxOStlIwKvh0Grmxhh8cdgXergstX38tCE5SguBYnUBXjwXpKgdnS9aQLKzRR5RRTEsglnHSkiL7piEgAaw5i6oFpYQQGyMnL2iIK2jP0Sc6rfhwnnhaVtacMcpwmmd6a7fNCsCHwNMykpJsWMdxZpxXwPDmsGMMmdd3e26Gnl0/0NiW+cMH9v2en/zkjtEn/ts/v8OT+c1v/sCLmx6nJRdp193gWst/+83v+frbT4Ci6y0hBB4eT+y7lnGe2Q8tRlvmZUUHhW41H88nPInFR5JPOGX56Vd3sqkaS5glGmGKkX/8+JE5JbIt19XaLcspZ0gxsWuMdBK9I6S0HYZzSjxOHqcUP73fkZMim6u6BJU3UiYp8WbfEXLm0yQoUoXmK9pSCwSthWRpt9ToelQh18EZgci5qpKchp/d7woilIghMDjNbec4+Vj8kPIVzi0b0uwlMqGxmvMathHCNqpC0I/z4ml1otOatmlJ1DTyyLJGKWQ2JFShdYWHFXtnuW8b2SDquAPwPpFSoGk0rTOAYsCgk6hXqmFgTsLTSikxGMPPb3esjeP9qfKArgc95deqBJXyUpdgZ4ByiGUJHv3ZYcfX48T7admQtFrg5lTGnc8KJVWvi0HWaX7W5GrFeQ1FbSr8QaUU38aVO8QpGSQ+4NFHKA4QS8p8M698XAM3zvCicdzalqbcC58TJyJKaU7TTGvEP6y68kttl67E83I+KK0IStZ9RuFzZoqLICw5YY14qcSiZANoUdshKIVkKZISaHP1osmSmUTTwO1+xzR7ptXjYyLmwGE/MI7nbWSbLNgGjskzEXgx9Nzf3bPbH1B+YT8ofnOJjMUluFUaZcTF/nrAF084NE7pkjEoh9KaxeAvxgApytgxpC0QOKREp4QYngvq4IwRW44kgbW6zFt8SEwr7DogVU4U6AQ+idBheY7YFC2FNiJ8WZOMqyq3ZgpC5u2NEY+eLMVhqxVf3e74u49HPk0LVSjzHGFGSYP2+6eRX73ai8t4+bOUMqtPvNg5nO349rRwXsK2T7TFdbqOxerX2QeeppWapVgLzr6gYy/6ht4JSTynzGANf38emXyg1G5bwW8aS/IRW8Qq07oydB3ZR4xVn+9tJFQp2Osb2vrIzeepNhcFfVeQSsK01Zq+cfzocCPBryhWpMmwQFoltaBxhhAl2ufaQiThqhWEsbOWX9zt+e1x5HH1PANq2SgOSETN29OIVorbvuF//9mf8fbjUXhwQI4RlTN3rcR4fTsunMuIXWxcpNgyXG0ZniNm5c1t0wet5X4YxB/MOYM2mnH2ZKP4MK380Dr2zrBrDLO54m1/+vW9KpBqdR21YgIefWCwYJIQNVNBjmrVKOZyFf5LVCl/yAmdoStuqagVVRbDnAT5QAlv4MPkyVbiH2zpDLSqG4EE2SpdN0+KDBLOQfF2XMoMWdM7zdC0nOYVqyyBgM/CP0k5c9uJbDdEya3qreHt8YSPEVJgmVeO9oK1mv/pV7/gH//wDf/1b/5+S1FurOHf/+tfMp+f+N27R5R13N0M7PB8+/GJxjp+8OYlu9ax3/U0bcM///ZfCBF8unDY7bjZdexay9vHE94n+r6TzY1IzJE/vvvIly/vWPA8rSNTibdQWVCB6kYfcsIow92u4f3TzJc3HV/uW9YQ+TR5Xu8a6hP9k/uBXed5GFceppU3+34rEhSizMjlgU8ZbozlpqQyR0StiC42+tuGSpmxl0InVSSmqs7ke2IuwbMxFUdlw3kVDlmjJGE+Al/2DV8Ncrh/WjxfjwsxJkG7kA1Ma1mcPgTOIW5cig2dyJmMZs5iLbFvWzrruKyLBGRmeY7JEJWQ2YNPGAM/u91x14gp5Jozl6U8y1mx5MTqA2FK3PaO130PKhUZvCLrXHyAqFNCkoJTyvzx4RGVCl9pMw26vl9B3woHLwtfxBrplGNMHBrLV/ueQyP+S62+8DAvMj7etlWuSEqunKNyOhRgaWsLEVfoJQUcmmrcEcngNA8+csgQfOboPVGyVsnPDv5E5mmNPMyeX95qds6WAk/ymn77eELlxJJrUKmQjUNOjKvsuqm+prSnhCz5jEaV3SRHyV5LiK2HtKoyRom5OHTrDS0KKguSBeQyWral5c85E9fM6GdMcQH2IXI4dHxzOZEydK0hEKX4tkYKFK15+3QhmZZXd3d8cRj49Mnj2oaHkIgm86I1BB8ZyogrprSpbdeUuPhI4zTrEpgWz+qFj9lYUcyGcLWcWEIUawrkmfBRzPlQEq6rS8EZ6uGcxbOtbRoMWZR8ObBTkGzH6AOtlXFbTln2sfK8rUVaIc1AtVwR28o1RRZkozk4y6rg0zKL1YN0v5IxV3ytrkCIPPz/8Y+f+KtXt9y2tnCIRHxirSbGzIuhpXWWx3FhLp9PECKNNYaQM9+MIyFI46Z0VcBpGiMNilFXPy153DVOF5PSqvgql0kUsApaYco6Y3kxWJSGWS/UIN6csnRZGbSDNYjyqpKpt+ZDsSkRN/QwZl7f7HjRtRxaR2/FiHL2UvSqUsyksmdqJWHc1pjCzRLujjz/uaAx8nc6rflX9ze8HRe+GWdRmhWELcMVmS5o0mn2vDuP8npVfYZYMijEk2xwPX84T3xzWeQpyEL50EqVc6B+tjLOVNc9n9qIoLCNoW8trTMCYDjN6EVo9OunM3eN46t9z2X870TFRr4WQauPfEoLT0azV7Brr8iMLQdXJY/lnHl/njkugZd9i7Wat5d527SloZV57GVcN/TIWo11UjQ5bSnZotINIoTD+GyskBAyW90gmkazJrXlHPmUSSGw61qSziQvKMi6RialiTHRNo6hbXHacj4dWb3nw/lE1pqhbckL/O3v3vHN+wcuqwcMXddyf7unbXt+/ds/sh/27HvHOM/87dcPDF3PD9+85DLP/K//4d+y6xr+4de/43/7n/8N//ff/COLXxmXldM8E0Jg3/X03cD7x0dC9milaZRjjkJMTZUToST/LCspZCgFSkyZbDJf3ez4qu9QWsZcShkZzyjhVlVO0IuuwSrNHx8vvBw6ZJ+R41UAC1lIkdIVGI210uEkLfdDl1EZBbj5TIJeLfIruqEzS0jb2WyUZmgsY4ycQ6BVip0zHBpHoytRVcj+t40o1x4Wz9PisaZwMrQEaJJku6kIIfVxqOOuLONYlpV5CWK5oBJtY7auPOUrKfFF19GVjfdx9vz205mX+z1zjFKQP+trn6aVH/S7Z3B4RieJ34maLZpiiYmP81qKscSha1mjkKRFOi+IT87F+6YWC8goTeXMT+92vOlbMehDnvcf3Q682bUcV8+naeFh8VfIv8Lvz2GqfL1Ccrblz4wDKwkzlnunreIxJ7ySaIFtylPdkbeLLn/z18cTN85x2zhaYzj6SCh5TpS1L6R8VWKmBHXM22uqwn3KmwFlfd85CVq6xiS2D1ZjlGEcVxoltiJShMsYJGcJSK2fNdRIlVJsJBJ5DnTW8oO7HU9e7A1QihiSkE4V6BBKQatEyRoWvv34LWlqWZeFyQfWGElG0Q2O213Ph9MCuQZF5xJpkoqLc0F5U3E1TpkQ/BVILM++NRpXiLaK4qlViNpBZbTK1xBwmY/hc+LpaeGm70QZaEQevxZLhJDUsyywInrJQpave7lBUMvBmTK6k2KlsWKUSdOQUqYvBoNLSDJaLIICa2TdhaJqeloif/P+kT+/HXi9a0vxmEU4YQ05SnPa3HScreZxXAW1NOKd9zivrDHJM2rEgrExmt5ZbtqGzqmtmNhG5DkzlhDi6zOUt9F9zNKgpZTJIeFLiO0aI1YZhqaldQ6tFN98emBwLT5LtJJR0BojuW1R9tnqNC6Eb8uboWXv7BahJAWnorXgoy6cznLfqLyeolZSQmFxzhDWyotia0Rl7J74ctcyOMO7ceZhWWX/qJyUZ0hXzIn1shZz2a0M3pCflGX/e9N3HFzDaQ28vUwiBEhXoUUuZ4EtXWgup3BtqJQVtXRV4IkSGwwabeW7lhh5uCy8uun5rq/vVYEkjWciG+FVhJzQIfPHxXMbrNj3KzEgu+9bcTNFHtAa//GH44V97wTqpKBNBRWqEuh964TLY40kaCsx+6pOyxVHTLl68W7l64ZMXJbA8bKy+Fg2XNmUUBkzB9DQOSeJ4Wvg5c2OkBTTvDIvEdc3/PT1K759OhYlU94iP44XUXL94sdf8XC8cB5nlnlBxcCSETRqXRjnmaZpub/rWZaVv/zRF+To+fbhwuN5QmXF//Lvfsb/9Z/+nmWVpO+cMo/jhVf39+y6lssihOKEcAjO84yzjhjrZ6/dyzMIunj49M4iAs5r2noqXZXWlBm4xgKD0xz6hmoGWBnWlctiykaklWJwliFGphDq8UvNwAJK4O0Vnq/PjnBnyp8HKWy1UrSmckxEnRYRO3yfMl8MbdnI5HWMhp3WOOMYrObD7Iu8WYj185Ke7YLXr0pgfrkfOI8S85K1+GM1WRFQJZhV3l9OUqS/aBpcMSVRyIJ3reZyNAQlifYa8eG6bxpyEp+Ssn9thWPOQIIxZk4hSqdXPpZsxAplzEZoLUNEthcqz3/dTJ1SNAVdUmRi2Qtbo7lvHL3RvOgafn28iLLnu1f18ytE2xby6fW35dkvj0SgpNMbRadsMQpVzCFu7tw5SVETEqQkUH2jxaW8qYT260tvcQf5M8y+7jfy305bkhJuXh2fiYii5sZda4Mq2qh7QR075vJxt+KjHAy14NEZXh5u+OGbF/zX337NHMTUcONq6VpqX/O4Lt6TjmdiCOKi7iMvd45XuwanNY+XhdO04ooqT5X/OSOCghrRUEUhOaft+qSct4bUaOFbadSG5uaU8aQt3uE4rqVoKs0CUnjO3gMWZwwZxbgE+sagci5y62tsSr3xz2+FLuNMcdTQW2GLscQkcTjWSJNjdHy251ICZEVx5ksA7tlHfnucOK5B/LiGDq1UyVqL28/sG8n6PK+B/5e792iSJMny/H7KjDgJkpnFuru6Z3Z2djADiEBkRYADPjQ+AS44AKddyAp2MRjerDqrKlkQZ2amDIf31DyypQf3GhfprqisDHdzM1V97E8eLguHJa0g66KbKnjLvg/s+8AQnEoTWDWtlbWVFNf6Z/d73h7Ooue2fssrgSR4D1imGBmqoXNN9dkr6L4w9kGfSV2v06lC6m4MbINTwptZuyvBiX3MtCSqk2cl5uKW6iTJyZpAS+fnKrdyVeWuKor64kgwbZ8IMWIfPHY7sg2eh1nu1wt8ONbAJogKuUjjSNnYSMsN+N6+V+8sNXi+2vS8nxb93AYEVyalE+2txkJcciHnokQC+YxYKq5eTxqLJPyjtdx2HS78G6H51yoHWdEgkquqFlO5ZMmgSxEwJBg2QWfsSFcpOMPhlEBpoylr/a3J8qRuw50TtWxvLYfUvJuuDxHk75daV3f460U2KwQjfkIpr5UpBsahI06JzkoCZmrFdp7bzcCSKqZKlbkfe242PZclcplm8SUbPJvNwHc/PLBiCUwb71RinNnttlhjeJpmYoXX9/fcbDp++/ZH7vZbHJnL+cJljvzZVxucy+ScWeLVBiXmyLxE7SQIzsA70WuaY1S8gHrkvOiUtNvQNk/Vbps1Is7Zqs/WSWigahBD1/uxY8mZzjR9X1blWasATypsvGPrPQ9mWasl+WDDy/Dzsi2n6GQAACAASURBVO3cjDStVqrB2iteQcGg7dDKSLJUIoxLYucdnb0GbWsMOzWSPKoIYsqqN1PbZ18vpera3fadBlCNeQ0jZKxS1ZXLUlGLA3UNt0bcr0uh76QQ6J0nVdHzsEbu323XKX5GO2XGYIzopRQVxjvGxHNMq25URQqDYoX5NHjHJWb579fcSAO77K99EDf61YpDv2zzNPLWsLeenXccYlqxOXMuHFNSbAl8tiD0Q4ZOxP7WO2euHcFcK+r4sAb0zlpt37fuiJAgqmKbUi0sxVAoLDGvo/e2bkuV4JTLlYFzjdP1uk6tE82W9ZnJvQXRp7LGEJOaztYWXPQ9tQBrU8W2RyTwX2nKo3fsNz273YZt3/FwOStEgBd5qtbJ8saUKsr4D2dYCtx0XoJkhXnJHKdFCBFVA6VR0GqVQvKsbL5SpYO3dt3bul3LILlHvferMbQcgpIwbILnQLwWKS++V6yZUAylWJYsCYexRhmVLcnWAurF/WmrwmrQDlbOhaVU5iJjtGmZpNtq1EvQNIHOos+3qjEra5KaqzgZzLkw5wrG8moIynY1uoYlyXk3LZyXxMO0UAra1WudZ7G3GbxjDF6KrKqWGJhrV05B7282Pc9zIuVZErwX37JUIT8E60k2y6SiiB2VMYVs5f1cE9PVtdQ5K/6atbDtBFfjTStv1A5ENaUEcC8AbGsMY7A6FTHYIkKrLUGRY+xa9FNQy5XrftUUeE1qvRWsZGfVd7GK+XZFSCa7zrENYf2Aaq5FauNt1Bdrr73nXRe4lMJxSSszHWMIQQDsnVd/VJ3SrMLNvIjT+l7SoZLY5dUZYBj0mv7E66eVIHGtyACw4lZ/14uMPE6ok4clEtOFr24GgpVFXF4cVqVU9n3gVON6QBU1YeyD1fmtZOliPVDXJKqB3gSXIZorrZdy7fUJg0Zk0otqokiV88XtjvfpWRI2Y0k5Mw6dqCqbzP1uw9AP7HvDZY6MXWCJC9TC2Du+frPnH37zI/tx4Pt3n8AYxi4w9IHHw4kv73d8+vjIYVoYhpE397dsbOHdMLDkwtB59r1nFwJ/8++/5X/93/535jkKpgbw3tGHwOPhSC5iQ9t3Im45VwHDLjnivBVTRVTgTRORVrmVKoqmHeaK1dHjVs9VnDMKTpdNug+Ot5dZacCs5qtBE6OWvPTWsA+WXfA8LVFG89d9um6I9hKgsll/TqWKfYwVgbcpvazorq9I4f20kLrAbecJ9mVSZ+idjOIe5sgpZfUZM+vFmBcX4Yzhdhx4OJ7lOpwhIx0rnGdTHMlLMMkVvJf5/SknrDGcUuIQRf9mOkduNh1xsSxFTX1DoF/ZLm01yrVIcJPxxHFZRDtG71MtVSnoIhx303diJ1OLfo1WEIgcA7XyxaZn9H69Y1VxTFV/B+RZWAx/vt+uz+RhjvxwmTinrH3F6z2XANkYjy0x4YWEg4rCaSIqB6Kgnq0Vj6yURT9lpmCU4u5ad8/IfirZ4O31PGkHchs8tMNZEqV28KsPlp64BhFBtNZwiZmd4sMe4kQ1MJcsbu+1jSvM+iyko6cjBeS7Widr5WbT4ww8Hk7s+msgealq3A79qwG3PPNzihyOif/+9Z4lFQ6XKEKVVHovfoqtqDKaoEvHxXKco9DUy3VEXl4gbmVJt85uC7R6z4wQTbZdoA9OdL+KASfX7Y0T41UrxrI5y1jyHJN4b9mWjOj9yGU9X9eK30rAa/jPjF3X4yXmNSFtQXDbB5bL3CbzSiHXBOlFQXWOhTlNzLnwV3c7dr0HY7FG5F0K8F8/PAvQuiIyEk6SimAdG+/onV1Htu3ag3Oi46NdmTZmdgjzbsmZOEmneRW41YXYOcem98QCD8dnLsuySr8EBYC3LiXApgu82Q5iyFwzwcqdaEQD6RrateBZcuEUE0OQAl2Z92uxuOQCWJ2OyL5r66CRWsT6sq66dXKfm3+hWJ58selxzvKbpyMAd0Pgy83AYMzqnWlp16dLXddr1jFbE3btvOXrXc93z5VLlH0oOn0Cg3GmsUmrKt7rIr++s7LULT2CTSs6xp1yZv+vt7d/WgkSyE1ratepVlwpdBU6Y8BUxl7MRCuG96eZ5yWy6RxDFyhVqryuD/SdpRrPrBvMGnFeHrsgVONSSFkqoVwKWQ1Urb0GW4MEsfMiT81bWXCUSimRN0NPHnqWWni6LByPE1vXce5lFOIM9JuBN/sdJk1sHfhQsC6xJEcE3uwGlrhwvEz883fv+P79M//xb/6Sd58+cbvZcLrMnKYL03zh79594D/+dz/nn777gTllvrk3dMuZzd2WX375msFbvnv7Eeccf/XnX3M8n/jx/RFq4b7vqMClZJy1PE0Xvry7xdjKeV54OJ15s9+zMXCcpQIyVUZfU8y44K4RBNHWeF4W3miy08YQpko1n0pW7SNe+GwJzT543XprhmFWjEKrgrbW8le3O/6fxyPnnMmUNdihbeBVL6T1o2plXuQZWmc+kwuoWhaaF9+BKqDod/PC+2XhNjjues9N8IqxQDqXupGbsmujIrc0LTjL3Wbk4XT+rHPWmicpZ77qHJfqOSbDOWXBKVX47vkMRn2gnGO0ll3nmPQAtgjZ4NXQCybBCmOnUbGNUMHEHT5ffQMbJZb12xuWlPlUJu7Gjj47LmoSW6tRBmfhtg/c9B1eRy7y3wVv8JkrdoVi1egzyWj51RC47zsel8jb04VjTNLCr3Ldd5ue9w+X9dn3G8+w9froDMQsQqN62dL9kS/TRoQGJVgUOSyDdWJQuhZIaBXMGkRAaeimdT1bbaxrsloKheAEw7CkzODcWkz1QeQPFjXCxaIdo0YaEYze1jkdPUExUu3WuZB7y+uN43bTMZ3O/PD9B775+jUeYZjJ0qxrZ6d537XKGwQP6Q2cTlH1vWTPpNR0hOy6rL2T6pla2XXii9XYmSVd4QixNBslA1a6rlMqaiUk+zHmTO8ctsKm95Q5rVCHauGX2xFnNbnRaLgfHM+XyFxa10fHjC86kbXKmg9ezl1nnAa0wlxFCuJySWTk7zUJhlYEe+tYVrPU6znjdPLQtLtyERr6JSb+l5+/1u62rPeHJRG8Y4kS0M8lY4rhm83Aq6HjdhRWaS7SpbGakOfKKo+gpxfkQraF12NgionzkhTScU1az/PENM8E5/jybs8PMcn+QcbF2+B4sxuxpWJeJPNxWbBe1PUlEawvuifK6spNKVyu6GGaqUYwjq0Asc4wOEtKkIpdE+LGpC1JxpXGaa+tioSGmDtfO1Yg587rvuetPXM/9rwaOkbr1uTImapxVBoNLbkyRpLk5vXoLeyGjr0xDNbyh9PMU0yMnYwjmxdnru37yVTHaj4ghY/606XCffAMnaQ9pxg5LAuvUs+/9vpJJUitg2SMiJsB4mvmHb6+qDm0DXczduyHjuMcOZ2FPltr4fvDRAGGviN0PZ3z3I0d1gnb5xwFnxGMYbGGJYqJZDuMbVUBR6q07UtgDIE+GJa0MOXEYsAOPXGJ1FS4HXt64/jHtz/y7372JRtvsSUTTCEQKfp+OS7UXHD9yC7A3XbDeZ6wRYQhpxz5xz/8jv/5219yiDO5OEK3Y+zv2f1Fz//5f/+9gKlN5d3xyOXXlVe3I8UU/uUHMRssRVSrd4Pnw+nM2Dse5knHDFLJ3Y4j1MrzaSKXQucDc87EJbEdOi7TEe8dw9BxmhZK156Q/GOKiT88nbh9fUeOwtaTjkBLQl7kP6j2SDF8eTsK5spcg1ctQi9d46/2Y2st/GK/4e8fnqAaORiMVoq54VBkvGCyfJ63gmE6qffRGtJXKnerjOoKaq5qUfC4JA4p07nEfee5pEJcroliBbCt8mnjQM+mC5zmiZrAuJcrWoKYN5bJOeKSlU4P0xwl0Opm39rAl13Hz0fPf/r4zJzFT/BuGNn1YcVWfH8+8+1+z76Tb5EUd2GN4cPpzEG9j1oQXLuxa/et8PE4cTf07Dsx0J1SwhnDL2/3fL0b9H0TU22yF2I22UZS7f1skefdxgIG6ZyJcFzPcYn8+vFIMUL/zUlGN1YD5XSOzGcZ24j7eMFrW12qTwHwlhW3JglOG8lgoe89OWYZn+tBaXTtGVBPstbLeoHT0b+Us0ggbIwkkMlU9l3HZhAvvW/vt1zmzIeDJnbUdQwjKv9oomYZes+8FFwQRlQoBlsj2y6Qek8+RkZrubnb83iY+NnNnh9OpzURRZPrliE2kkMjIPzifkdekmJ15HfGTsglUS2CRBRUOk8VEa+86QVjeV6SglmLdudFbDKXsnbKrXa9i36uVwJBLYVtCEyLuAFs+o5vbzaIiW0bSUlChDFswrCe1xmEcJArl1msNYp2fnIumrhqd7AI4SVXjQVI8iZdOkn6jDHsR4+3HYuKj05R2MVVwdKt298ICIc58+408WbT0eM4TIX/8v6JYoRskxW+cYyJXz+fMAbuhm5V7cYos9k00ckmVaodOAemGHyVordYw3cfD6K2H5PiGL2wAY1jioVNCIxdkE6Vl06VyUXXt1m7L9UYlix4Hu/kPrRRarM1quhzyjJSHazl6Szekjd9px11VgxkUXIG5ko6KaAj7orxFmMteWmdSh2TNiXyApjK33xxJzijWtsvyyjX2BcdXCHOoIlpziIcPPaBoXM8XiKdFRzgL7eOU0p8WCKHmLjZBAyVuNTV161ZLSUVm/UIg/WbsWfKhY/zxCkmliL4uXn+N8Rikzll1fUth7PvHHGpeF3wgGKU5Odt79n2nlKl6jktiU+niU2uWOsoZeJ4gnnOfHV/y5JmghHtiqEBICqrnxhIxuoVh5BLppqOYjyPlwNLypRiqYiekoybK9tu5H6/Y6TQKQOhZQkrJNaIBcHxcCCmzHQ+Y1JmCBZswBZwufCff/1bnLMMXccvvnrDX/3FLzheLkz/KQo43Rq+uLvjf/jzv6AL8N/++R949+mI7aR7Ukrl8WiYciJOZZ25B+/YdUH8l0qEdZNAzYUpR151W97c7EgpUWNm3wWe5gXXXaN/1QPsOWVuaxuLyH8zFbACrA0vlKyLVo/WtoOOFTsgz9RAujZODXDnLRYL5kr9bGM8o78jei1K6fYCEl2Pr5ZTtyqTz8XWartgyckU55KlOsVgX1aJ7VAvAJmf3e7Fpfwy47AYX1fW4/rRGsyfpomh6wjVUVNm7AKnuGCLeGYNvXSOUk785Tdf8jgn5stFDu1pYVoyz1VE6/5wOjJMnk3o2HYjlMLbw5GLmgxjwGqwrFyr6XZXrTXMMfKruy2X3PE0R0rKvB66telkMDjsytoUmzoBLK9jAO1iaQy7Jk9Kdb7pOv79q1uW1onyhZLKenitFbi5gp6LAoPl/a0yzMz1uqokPd4ZPKLVtJS6jj/WZ7pi6bXjQ+uloStIGFY344AvFVvV9qRU0hJ5yJndpuMYIw/HiafLIn5ZsAKZm0o1SKJ7mJP4kxm5xnEIfL3f8fHhiZAt2+1ATJmH00Q39rx6vLDbb3gqwq6cFM+xcn+0mx6c4X4zSHJUm0I82rm1IjXiJImpGIzXEUo1ojkUJej2wZOWqAltWYuOdp+8tUzK0LL6+TFljHfrmIMq4+svtoNKCojhb8P/tTn8y71nqAQkccnB8d2nE84a7jcd+23PnOS6l5IoiLivK4WnnHDOYIpZiSSm7XukE1GrSC7gYEoizGhqA4Sz7nRj4e8eT/inM6OXkax0mAtDb7HKVEVxRf/0cOTTZeEv7/eihK9rR2RaNEl4CQdpXRhkBDUoiBhX8CpokUrBmcJ+6PjyZsPD6UBcFoztCVhs1TOQegVPm5Yky3kbjFx7W8stMRW7FIuz8o1TKQxWWYJ6nSIQLM/QWxmlaU9OEmLbNKaqTkmkwziXgqsv3TBlXG2NJeh5rnB8+aZWZlqltPuiRajqMXXOMPRe7ElqXQkGzaJkDI43BjrvhMlthSkec8FVw945fj70HHMiGFHXr1R+fz6z1AY3aIWhIb9Ekv/R66eVIOmGe9l6MMBZtRfykvGoCKB+51UFGZljC1hbXKNrLXjr8SFwuJzZjQPTnCQ5MFJddc4qA6hqtNaEyWhlZCRxyiVzmQrHcxTNFGfpQyAYsB7GQar80UJnr+aaIAeN0BGNXnMl2IpxRl2gDSklcobOB6Y483iecV6Ast3Q89fW0Xcdc46CfzBQS2aZjpwPE4fjTOgs2YhJZFwSJGF2WQPeCx7BAjFlutDLyMBKJ6BUqcSsgedZcDTeOfrQsbVwenhcZ9HXgtdwmBduh+7z4KQ/1CqO2K5eZXjaqEPui8AZoK6HctEKuOpnmSKt3pe4GwOfYSiCyv2LpYCAqps31vrXWgKnp8q1EdIOpJd/pma3WrH1VnwQUlHsgqn8/OZGcG5Lo0xXBV47bR+3j6rrd51TpKpKs8ViFJtSq7SDH61h3HTc7Tc8zk9ibWBkaR5TxHiDs6K2HYvYUpg6c04L55RXZl9FtJ9sNmJ58aKiBuis4avNgANugqe3lktUERbqGgDa/VqZJ/UK2m+dyHXfrptXMWJGxrBQcbpgnDV8ue2ZU1iV6SftAIgOjjCRUOFDb2Us4J1dcUkY6Y54V+mNZ4pRALHXI+OzdWjav1xzLPlni+DVYMnXX69Qq9Duz5cFGdumdVzXknlvLKbI3nbW8M39lpQq76ZJRlc1K8u2cn+zZbvreDxNmJj42nueDhNf9p63tvJq7HGzYUpJgr8mX6P3InRaC5+WmbFa3nRCWy8VKFDINOHEl8ymWqGo1Ud7st6Kgvw5ZShmBZWL99q1k5RKk8iQ7kIu4skVU8JZYYVughO2kDVX9u+LHWU/ex6ybpZSyKZwv+tXFpmzlt4JTrAYR++MwCmqCF1mCx+nqCrueh7rkk4qRBmsAW9ZcvMobFwoTaY0oY+afC+6j7NmHilVEW3Vsy0rK+1hjrw7z3yxQZOkK7ZFcohrVWAMqKQz3sBOx2UfzmfudnueLxc6a9h2HUPfc5ikc3pKmcEnSai8oxWsvDg3WrIMDddzLe+qJi7rqKwaJR0IkFpG60W8GavgQm+GDlPrGqNk/KWJvt7glwSEYO06UmxHSVElzJU0QpPIUbq9vk8Dj7fmQK2VoQ90viWNVzkEdCxnjSEbuFNLncdpYbCWm8EzOs/oLIdl4ZhlhG+063pZ4RTXgvelQvifev20EiTgGrLkZa3oCJkixp7FGGwxVKwIc5XKZggrUNth8E423ypipdm9c5YUM0G7DlVHdZrorq+qEXUVprQCkEsJllSlVeodjUGEVrmdM/QKAKTFpXplbsi3k//zxuC8YKamVLjERMHgq12ZCZbCNCfePx34+HTgze2evg/kJfIXv/gZv/jiNb5W/v7X33O6zPQ7T82VFLOIEBrDvuuECWVl09gCOWfsoD0CmRFQkXa395ZLFD+p4pSCXwx32w1TTlyWRe6nZjnnGMlDj57XEmCMHFHGaMXSAJR6c1WdQZNGrZasHMcv9Y6K/u+u73hcFgH+tmBtrwe8095xzOKVV7Wd9acKh5eF2frYX1a85rOlIDibTth/Jgne5subrRzuGDoj1gpTFgVdwXPYVeCtVqhWu1O5rBWbMVaA11W+dyqVj7PYnoyHI0ta1iQ9F8HjBQzBhzWZyCVzSIlzWrQyZF13VYVVr1WhUpudZRccN71gJQqQimDtRCz12nlZGU5r4qV9UF3f9XrLxFZAP9xo3IiKM7BaxztjFPjqiMWxBPU29IljTKQiEgxF91+tVboHtV7lOhR/0ztPyVXHQXVdS9eN1x749R/CTmsnzHUdBGtWV3Fjrkl4ioVTjdcD9kWC7fRfgndshkB1UuWe9XqcXs7TsvDV/Ui1hrQk+lTYduLN9+gNDzGyD+oFZhpj1rxIxoRBKbpYltEZ7ZrqPjBXnJLRjp5gcPR7q4ZUU1z3znFW7EspZU08WyIOrYj5XGeulMK0ZFKuDE4TfwOfI8wb4L7tNTljM7J+YxUtpb73a9e3/eVUZTzbWRFxNRV8EKHeJyKL3vtWaLZ1cE0VhG04JS1O6vU6nJG/W/S7Jd0rDRskyWS9Xreu11gqH6eZrF2zWiujt2y9/6OCyqzLAx3pemO43fRUHck33BjI+RuTAMGzKZK41bKye18utZeJUamCr7K09a76VhrHWhJircWWsr5JzmL7knU/3QzdasEkSRXrRMDqmWGMSAe0hCsZpTjoWWCsWa1TcinrOLAlr20tNexQ6yRtNDlaCVLlKvvitHtWqpzvg8qMlCSxrLOyT7yB5xiZclmT/9ZYWetBPb9yqZziv5URG3wmAAhStaZFcCbiGFyZUyJb0eWIufB18Gq3ULULZei8wxYBC6ckXmDLEgneM3grB25tNMQK1rFamtQKmdU1W0YVskC8s2yHgUwhxcxljuSSqDXx8/24tnZb4JDVAiLd1YBxsglrFeXRp0WkCYKzxBRx1q/eRADPpzO//f4db2737DcDU0z8zZ//ij/75iv+4Xe/43cfnuj7AFHctqu61YfOMXpHLIZiRKDPe0uMWRI773Ep0+wgvDErI6RQiSVS6kKJmV998YZuSdRUWYpU1NUIhXwqhVCExfKixwOgTCTFD3Bl0ch4QKsd07Q5zAoOLnJqkSl8vR245ERWDR/ZCxZsZfAe68QeYUl5xUN8nhyZFbMh1Rjrz/o4PkvKr1WIdOSKa2w7uYff3t/w9vGZjFRpu07ufVMmdsaIGGYVjEdpqcbL4GwLWxe4rcK6+hQjD0vk/FTgeOJ+M8icvRTmnCXBLRJcllowtZJS5rRcq+sWoFrsKoa1bW8Rls6rvuN2CKRSeZwWDnMilkLwIn7YBwWDqhBccxJXm7YXFHhRp/ZcNZnWr2YMCaAILX61wahXhd7OSmt8FwyvBrEf+DRHmncwlVXEMnPtjPhqVPiz4xBFHqMawaPpcY990eW85kICsA22UdxkvXUOhs6TUiZRqNlq9VklwCumpZkFV42o1sjPfee52Q784Xjh8TyvemICtBYM1VwTz8cJo6rGp5wJneO/XC6cTeVSG0jfc8mCpTKmMpWs1Gbp6Myl8HEWEcaNKoS3av+qbSRYn1ylu9O6Qc6KDEBTDBcByWuRIUmmOri3NWxEhDRpsnpZMjEVghEsSefsZ7R9JVhpnqrgXP39RTXIKiIwKcwrSYyWnMnGMFqjFHbZf0LhFzXnWAqR62hdCsympi0CikPnxYg4i9J8a7+0BElGo3KTit6ra0Bt76xfQsfuh5g4RqHRl1p43QW+2Y44q354jVlqZLRXjchdTKUSnOeLmy3/8P17jDFMqRLTQnCFzTAqQN2RqSylstd9ZXQztSQiKpYml4qxcsI5LRDnmJlKVradxRjputRqyFaSsdbh8UbG0TEXSfh0WtLEa3NCiul6lUjBaKH1osg1ej8lS9QkuFSMlXFZI5KIvMQVq1Ur3AwdqcjYN+e6sgmpBWdEyyzRmJ+ilfRm7DEY5px5nKLuDWHDVXN9b4k6LzpSVXTVHud/I2a1a8aslYIFylkqyn7wjNYwL5nHKZNr5K7rGIaOZCrnnAjV0BvD/WZHzEdSEUCXtYgHUJY5rHQndC5rCw7Ldrfn6XQgJdGQWXLh1aYTTRsDWQ+uV/sd55i4nGe8h0Ii18wlyWElAV+DcQMF2xaU69qtkC5R4TefDtzsBwJQUmYxht3YSdVfBHh5yZHf/PCO3WbkeJ7BwH/+u/+X/+tv/5Z3H564v9tAsRymiZorQwhsN4GlJD6cF0yu7Le9aKg4y4caOU4n9b3SyrNA11thHmn23YJI6DwfPz6x32/52evXPJ5PfDw+S7Jj4f08E2rPxltl0DiWWKgUUXkulWwKnXU0JoRFuoMNpGyxOKtgbi0BBX+T8M6xDZ5UJSEDCfybEPjVlzd8/3ziw/GiiZMGys9wN3UNJC/XWqtqpOvVEqgm+GfX9zqqKvXGef761T3LYaamynfzic453vQDX2+2TDHxYbrgEOq4tZa5FOaUVg2YdVxY4GIit+OGYnRckTMZmGIhTAvbLsg6LZVgLEPnSFH0WmpRQ9u1VXD9Vute0oPC1sp933HXdfTekUvm+4MAGVsmv6TM2+cTu6FjtKjOkAI5WytBehFrVyplYQHaomMOZdEYJ2MXJXx+dlWVVqGb9eeK4Yux5zlmaMaSRtZG1fVZSlHDasuud9x1HTfBcVkiD5eF49JERVU1+GUbSQ92r4rJuVS8cdz0Pf1Q6X2grwUzWc5TEvsfRObBVAnkWSUOgrNQVNAyFw5z5Ejh42kW4GkIa1s/Fenc/uHpLAyiznHB8H1MvJ3PkmwEx6dp4cvtwLd3W37z8Cyj/85zWrKOdUF5opxKgTlyXys3wZNroVewuK111Q1ziss4z1GtGDQhsJZvbgdqNXw8z+LFqJ2IaoomEjrq0U5SwjB6YZzNqXlWsqrrt/d9OZoBuBSVka1NCFQFRfWeLln0u3Iq4DyjXT95XZclVd6MAT8bHpfEXESuonW5jBHrnlKKnn0OFzynJdKmxrFUumApGYLzJFvUykg7bOYa6NtYBtt0rK5yFDXDb75/5vf+xH4T+NWrHV/vRwzSfXVqp/G0ZB7nRPAy8rHGUWtZi7BUiuBP50XPQ6OjJotY+MietkYsY96fRAtqN3TsS2VyTuND4d3TxMN04c2m56ubgW2v+B/1ypmUuOSNsgaN4cNx4s1uFNwrOvI3MHZWjN2LJGFGcavOFDxSSBjvyBWmw4R70UlsMS23Wg1h7zbFdNFl8io5op1lZYNaLLEkHpcFY40Qfqx0EzOFxakkypL5OF3wSkDQGuR63rVO8rrvW7L94s/+6PWTSpColeU5YhH9I+stsyYXp2XhbATIFpxhmYCtzKgbqr4aGIIn58jPbnf87uOZaA1D76jLBe/EksFZYQVMKVGyZTMEHj49cM5Zq3FpM16WxLbzomyLZOPnw0LnKn7wLPNM7y27PqyaGNW2baqBJAAAIABJREFULkHVUQ/YotVolfn3h/PC7x5OvNqPfHW7xVrD02WmlMrt2DPHhAuGpUAwAtz95z/8wG+++1EVSuG77z/JSMdbXo0Du3FLeD5gkIRkWqKqiRR+9uqG286p6Ca82W64L5UfL3JIt5HivCiLhWuSajDYahn6wPNl5tPhzNAHvrq95XCeOEwXDsDZOIIJAnRURmCjK7vW9dEKyyli1liLtU5b3ZWmdpuVWl+qtM0LlW82A7leWJIYx/bOsekdD4cT8xwV42Q0ueNaUuifU1np46ybysjBqAyLl92XFriNbkJTwaZCmsR+5OfbkTdDzw+XC78+PLP1ga+3G36536+/H0vlkiJPRZRlY8kyXmyHcqx8X07suy25ijnkdux4fzjThyABp8im984Sgud8XjjHRQ5xbbOvB4V5cRioEW4plRvfcdsFNkEC3G+fT6I+6+yKqcBIwDrOMy6EVcBSqnhNMIt4NoFUq63QLoi4n7NWPbsgzos+gmtXdpVaeNG6a55SmSL2HPqdWlfDGu0M63oMzrHzHaZkOsB6x7lznDQAWROEcp2v61rGfKJ7VbOh86J3Y21hUMyYobLpHdsglhaHKXKZM9bI9QsAWSrcQqGWyld3Wxbg149HNsHLYW+FUZaK4sdK5fS8sB0DpZNneU7iFxWCfraBD6cLc078T794w4+XhX/++ASITQ4aZIoGk7lWPi2JUuHn+5HzHAmuyV1IMOq1uzN2YZW68F4SArEQKrzZ9TxdIk8XeVbeKljeNmCt4IY6Z5kxUAt9cGz7wOAaK+ra9ZDTRs6/uajyuSbFDdzf+glVOxPFODa7ke9+fOb21Q6rs8NaWDFcJVfuOtElOqTMKQmzNOm6TEUYcMaKTIOphdfbnilmnqeo3S1LMVmYUNrJkvPourbb4dA6nnI/WqEg58h2NwCF0DmcM2vyug3w++czb5/F4ipYw7nKvfAOyI52NyyVwRbOzuKx3Iwj+96TEWxYiyE+WHZWOqnnmDmcIwciX+5Hxq5jMQ7fJX6x/5KHwwP/+OnI683AL283OME1KF5J9l1GusKDtzxdFmLyauptNRmGTWe5LEXZmUXkapzDOU91ah9lYfdq4Pw4t9N17RwuSYpijFFTcHngwTvudj2XRc5Ap7i1JAMG6dLpxLV195oMBZpgNdZ6yonfP5+EIKXJtx7lVKTj3PCYL8ewf+r1k0qQapEbWWslqfO0MRLwewybIIrHBM/tUHl3mvhq79bgOHrPl/ue3z1c2I6VP//F15ynhR8/fmCOkaVUNgEe4qKbpnLMkekkJp0Z2HRicDulwiWJ3pE1jiXDJRYWImSYHyfu9lupWGTwS9Wqrb6YZ9Pa2FYOJ5Cs3KubsrNAksUQa8XlTE4LUy0qNCeZ37bvSaVwviQEzGbZjB232xFK4sPzE9t+ZEmJmAW/0ruO/a7jeZ7prPj1eK0kgzW8GUc+Tap5hKFqRRdTxvkmaS/P4pgXhnGHWSK9hYFK6Tp673m+XDgbYRQ4Z/BF1MlaB6PRxxqLDFg7bY1FU4vhFJN08DSwSTA1BCOOzTddYCkSkQcjmhu99Wy8peLEHfpFl2hNenTjtFGeLhf5K62LVLS+a9e5dvxkzNI7z82gtiT6TIM1vOkHgnF8mmd++/zMX9zcMTqHRXypur4nlsq7y3n9UGugs259jmm+YG1g040M3nKzueWSjwId1oMnlgJz5rwsyixrna4Gy9ZbphiiWqTL5p3lnDLfPZ8lea6iPm2Qqlva6EJU6DrDkYKvmVG7VlYyA7lPiqGosAZCrGZIRsVXVdXX6EijVh2tmhb42hOo69jKGLgk1Q2q16q0DWXbvMwgI5GNd0pvlq5SysJic1LSYoIjBLcmDCVncmoWGyodYCvem1XYUcSQlejhDd225wMzMbZgUVUZuPIffvaaf/rwxB+OF3KVcWGwMnbqjREdGT23llQIwTAtV9mJRQ2vWwdNEjABqP/TpyOh8zIiQoJ+W8erhaExFAyHXHh7uPCLmw1LSoqpFFHGJveVqarELw/NOvlUbx2pCpFFjlQRJR28I9ar8rhF2G8fl8hxzmw7z7ZzK/TIrFW6PiEDUxYvPZ3AyHPQa6nIyKt1kmPNuCRjledpYdd5VURu60DeXbqohhvvOMbEmsHVK5vMVOiDoRb5wlqL4KxVgcQ2vpLfccZQizzzWEQCY034qmK0dENVwDkIG883m55X48Dgm0q44XfPJ56WQh8EX1ZrXfdiHwKTYtlqlZHiXCs1VlwnOM7neWGOE4Mx/NmrvYywlwxWLEY+nCc22x37wVFS4tP5Qszwxasd83khUwnOrljcUpVxSlW5BN1PRhTZm+VSMYa9bcWQPMvgxYUiqW9hKaKDN+eko1orZ+IukKYs0Afdo+K+UFU6xq7im50aKjdDbKOJaO8NPx4vYonyAi8ZKaAMXzlHoPX3rSpkn3JUqMZ1xC6P97rmC5W5gV7/xOsnlSCtSYVGNYvMeXsjOiPBXq1FrJHNLAepWQ//T4s4Rn86XsA+4tWodMrS5jynhHMyvxdqsigelyLAsyULRXBpoDYXqEV0NuasLWgDPoiIVUpJFrsJvNpt+f7xEyVXbvqwquUaU0lRNujTkjjGwhCCMNKqKOz0IRCQue1iyqp2G5dEzZXiLKXAduOxxtC5ABiO50kEKfueWgSXgpojhhCopTB0gXMSbeN9CHKAUhmMk5a2HgAxFy5TYuz96nhe7TUAz8tFxihWVLZfDwJWjDFxyRk3x3X+a5DquVW/VEOqmcF7OfiqJJKZsgZPodA26qkkKY2dZo3hy7HHW8O788RNH7gUmGumGkSOvolDRgUUmvpy27TUiPYvKx2gsiZq18CsP1fYdz0779lapwlLXX+vs4b7LjA4y+Oy8P35xC54tl4MVC2GOSXFMAiGzVnRjKrWcJoWvt7f0BsrGiZpodbCxljmuRCciKAOvccbw+Acp5yYkoyCVpCjQVWp9XlVVjowqG+ZdmxaF0eAuzI6CUGwTbEUnmIkec/GQt80V0C7oNd29svqtHLFEmhDjhUTBesBapz5nFmI3OOP07Iajl6fD+t9lkclyVjTX7LGMpfIkkXNXno7ot1jNZlrINZMS4IKnfWYYohz4nGK3O2Ghg5UVqXcn2EIhFCIsSgr1vDV7Ya36j+XNOHptRPnjVkZjHpnVuxUCFI8dN6xM0HHJrJPoRKcFGZRk0xvxbzzGtRbQmXWQBdr5SllxsvCF5tATEW+Z4aak45J5SwxDsWoCA6nc1CKjD0A5lQYjFk74ZKMCplgToXDOSl20GCbOGvTFjM67tXryuuIs36GBWz3pIlWNrzpkgr3u4Hzkhi8nPOGF2ur6aohRJifb0ZcsLw7zZySsoqt2ppUURB/mhbOsfmu8QKj1ZhXkhDdb0a+3m94mmYeL7NYZ+h5kArUmrWLbARP5+DTErmUyldjz8bLiLRaxxgs5xLJvJScuJLj27/XKpCGVMTcvKak6s+FbAz/8vFEF6wmd4beWnZepAZKLjxfFqZZ5Bou08KcokgqGphK4bAktsGLHIZV6xJNd9s05BwzeZHie+NZk2FrxJkiOEvKIplDLixaLJfKleVoDf3oyYshxiR4J32uDavoncMrY22JWT3/rhIez0sEL5/Zsu2KdpAQ7FPTtbom4vBq6Oms5biktcCnVopp4pLX4uOzc/+PXj+pBOn6MlplC6h0UtE/t2aYsgHH4NV3S0YrU8rE08TWWs4x0V0mNl1HcI5t3zOEymGa8E66I2j70Fr19il6iOom3/Y9OUugtsbirajUBicjnl0XJKireV4xlikWoqrRtoAydI7TLMnVcUlcouBqqg04SYsYvdBIj8tESoWUZE6bY9FgoZlzaMmdJabKtGQ6L95YVMn2axVjRGMNi+JMDJVezWBBsnUKmnSKHkgtYs44eM9SMs2go7XPU0oYU5mL45xgZ4T+etv7VcX5FGWT79y1W9QqZemYXzEKAsSWRMZgrsFb/3tz624t/NFb7uhItdI7Q0mOnIokz4BrY5YqGLK1nV+vI4NWub9YauvnmXax+k9TJXhvvWfnPaGi45jrmxlEcDR4gai/vZx5WhamnBmcJLOnlNZ70DAa6sqGQzoDFPHLm4pouYwqtNY7x+gcphRGHb/aIj5XBgGRN4wHfN6RWC0w2p//0fcGqRa7zmO9hSRFwVLBZvl5zoa9D03aRD+kJWXXZLi1twtVGW0v7uWLRKnlVAIRq5o4izZSXcVDr+vms+fRkoOW9hrDUQsXwazpB9iqiu5N/VwxMFXGGw6RHkhJhGC9X6RDYIXSLJ5mgqdzVkyKXZLkIRs4KlC6YVSq/ixVulkr9msxZ6TjogFvDJ5d9DycF2U3WYJz0ukoohU1Bk+NSRlAfJYktR9qhaVWnpfE7SDWKKZKUphKYUlSrV8fmwSOUkUmo5S6Cqtak1li1tF427PyO3OUkXdLOl+OnKomVVGTUH2k1+T5j16tE6C/DQjdvguWUl2TpFN2sbmurcoKHN52YkERR6gTnJECxDtZH006Iim4uent2JZ0lhZfHF/dbNjrOHtSm44WiFuRKOc/6lVWOSVZc8EaUvGkXOi7jt6BKYULdWVwAivTstllWGvZjgMfjhMxKWFGk5MCfDpPDH2gIrHmpjfs+47nJVGynOlZ7V2mORNLou8CsWYuOfMwiXlx56T4kX0gkg+UStSsNZfKnAqXlBm90/GmWbFdOCloUqrUUlaiRs5NqsVivOgUllLIOa+EBqzg9bweHFGLX7t2vuX+XnLBiWGcFMwvjoC2ZyX5vRJ6TDWMmnRZazktkZMKxTby0Hp2tFz9X3n95BIkEbwSNtPoHF+OA5/qzHFZVhpuK+CDFd0bGwS0JwDeSBh6KoaMbAhDZReCuJnHSKey/NkIK8iazOWyUDOihGyEWthtO85JuiKbzlON6BZ1wdIB+03HecmYJTMMvcrye8Eh1MrTLDoNxVsuxYjGSJbOiWySjrpIENv0vUjGV5GpL7EJ6ZkVzBw6pw/dErNUhp33Ig6mI61qrlVeyeLqviyR/RBwNmiGLiDVRGEMgQKcaiRZw/2mV32VNaSvM/lW9Z9TFksA1au56wMXLOelcEqFaRbAtzOgTT5hXOSCMzKDbq3wYuQYbCrZoB1Bc/2fPHfBXg3O8c1mwzEuDE66bw3w60T5jNBVmJtlDS+Shz9+teDzedbUOlgWQzBulYWgijFlA29b6meyAVvnuA0dh7hwTokpi6QEiOjZogSAtIquVTZBsGynXMTDrMJt3+NMUayMw1KZpomuD1xyJCYJ0MFZgpNAlyqrFH/lTwTU9jIySmrUXuek8zblrG7ngk1aSmbKEix747RrUNtbIP0aruavVm50qWI27cuVsdZaVq3lrzNNCjIOmDSgN6mHl1PSBpIFMe5sHQ8QVtdxeaGDpJGtVsSDrkpVXpuGjhY6DrAI06lQeTjOVNveX4xJW7AF8YUKTvbcw2kWsGlp4xLZp8JIkrFNMS/0aIT+I8+lSJc0u8Ld2HFZVFcoOLWwqEwxC5W866WKzlKmvPQju2YvSv8GHqfIq7Ej6NkDEpScc5qoKb6oQrWoDUbVbrQVCZRc6LFMVdZCKpLwXrKoTS9F3dNVRboio42o3faqe6lKO+JFcr4uvT+KV9cEKNfCdgjYKmvZa0LSfqvqfN4YK52qXHk99loEzJxjXKVb3p9mUWzXTsjgnarFC6DbIJ2V15uBr3ZbDseLYBo7z3F2xJLXUfBnRV5VsUcdSZ1ixlTpIFtNCv0ghtWnRdihlKqd3vZmMtYdNwPBGc6xrqxeo9e7rEmiw1uPcx6sEwacFexcQLqRUZm+22GACPMSOabE1ntsJ4m5MZLIJh2dRmPovALca+E4J8HkvUh8i/5d40QEUiYNdS02QXBbMck55LzFZ3s9b6qcedZKV3VJmc75dc3V2jBk10S/mf+2vyNMx0xn2slhV0ubUiqdtdggN2uJcgbEcsUxtov5/8mPfloJUq1wGwJvNgMbFc0yxvD1fuTjJABTkX0XTY9lzpyXeG2flsLQ9yzG0HtLjpmz9EkJJZFCIKZEAHrr2ISe7d0N3396j6uGIQSs98RSeMwz//D2B7794gsuy8Lttmc/DByPC8VmQtdxToKbmXIkRsPGWqpx9J160tRK5wIxJt7c70S8zxS6xWKMJ86Jtw8H7ncjr25GbjYD/SDZuBitls/E3Pw6QpFjcTME7jYjEcNlupDmgncBXKVESY7OZRHgOpXHeWbOmdfjuLbRt8GzcY6b4HlnJrad48NzxCgVWAQZG+Lkyj+qpXKIiXM+8df3N5gKl5g4xwVTLZYBmxfebDt6Z1ly5vvnGRsM8ZL41astm97g6gtdjZYQmau2C7BSqyUpMVgj3nKnXHDRrCq/NkK1Ihh223kwQim+xLxiDF5uYI3bqtPUKhZoY42A5X474Iwh1yuGRaqUQi1NPbjdHXgdOl55Ga22DpizhrlUfnM8cE5pbXl7YznnwrjpKTGJgKfzfLXf8vvjCchMy8yuD/zqzR0/HCZ6a5iyAnb1c42xUsFRV0B5I7eJO71+sXZgGE1dKlxShppVM6xiqiVSXiigG55rJmDlMHlx+AjgtFXZggW0GpyzGp81oU9JjJoSu8EYhzeVQtYO23Ucsz5uWBN/by1fbEe+2vTMKVGN4ffPR+Yi3dpm7lqNjtXQce2KeZL37DvRNW5dlis2qjCXzBwLB5PYDYH7bU/DFNoKA6IALGQHw8eLvLO3ooHUOcOSszDdYHWhjyXTGTnPpiTg5bve88v7LU9TBLVNmFImpsxtF1hS5G7wBC8YtrfPp2vCodmtM7BxjsFaPl4it30QlqKtRNVBc9aqwrbso6Rjb4uMorxTRlCqfLHtmFLlFDNzZk20u2A5q43EUoQZFXXfnkuiVLNikfQCdf2JdIDsrWvnte2ZxpJ02i3NJWOssKQuuXCnnptVL8QYszKCa4XzHNl6T+wylyiuAD8cLqtYpNWHfkkyNnua49pZuO0C/+5uR1yijD6t5ef7Hfuh4x/fP8jov5ED1smFrClvoLee14MwKWtjRhsBx4exZwyeh8vMeUmkKpiYYL0CmSPv3n3k57uOf3mIZF2DKRduN4MkKyZwux3pgueyLPz+4wHvLekiMIVk4Wle6DvB2qVZbIzG0GMNPJfK4XRhFzynJB3AzklcHLvAx+OZrfcYA1MuXJJ0HHejV1kHSMg5E4xR3GLDMhkRT85gSMQqCfbmZqCzlvkcV3XsVKoa+6qUhJUz8ZJFnqAatZWJDe8kMhWQCcExemH2tfNmqYmgYrlzqbw7XKi58vUwMJXMp7isXoxyvtfV+upPvX5SCdIYHL+62+rNgLblUqnchMCHmDkuiUhl0weiqxAc23Gkd4ElJ9I0U0tl1/fc9B7nPadc+e7dExvj6FxP33tMFWn6/OkTI57dzUhxlmmOLEsSdWbreffwSUURBVRX0sLzZIi9XAPIQeUpvH944BwTN2MQfaScuRl6zvPM4bKQKpymmVIrN5uRX72643G5sJTM0/nCZY48nSYO55nagymGNEU5zL2l5MK3r+/54fmZTT/inWNeFk5LIdcJYzwlqt5L70mx0EUZQx6z+G3FIJvlZ/stpknPW0MwntfjwB+ORzZ94PV+x804sOTCf/v+e6Ftr50Ws/bhK/Dbw4m7ocdbRx+cVq6Gcwl8mAo5LcwpcSmVecoMwfH26cKXu4G7sSNR8cXoZm9gamgMp0azNVrGFaBmMbQtqoXijOGm7wQAWw1zFX8gZww3qh5skgDxy1oaakXN1cy06W/4CvfbLQOCB2mjyVRUTr82oKmONVoHrFVXoPRwqYymmOmxlCJ4la7z/Cr0hJ3n794/cVwSYxe43fT87vmJS0xkKr33nFPhHz+epIuZRFnZGUdJltO8cLfbckxnnJVdU9GOTgXUcqL9OUhF+ep2w2OcpdvQ8GZ6+OfavJWks/dxnnDesCmW3rLq7wiD7fr9VyG5NjI1hsbbbklwrpKcOKRVnotozZg1wsoaa0vNOOkEbbuA0+7IpgtYY/gPr295f5peiMapgGazg9Asq41WjYXRB3oLpSQuc1SsFmKWSks8DHHJfCwXtn0geBmvtz5qRRLy/RAYitqc1MZgy8KoanlkqWy9Z256RMiI4jgl/vqrexbOPE2ROWYMhTfbQQqrzkpSXrRzkauwgnQUvvWOjfcclsjDJCPCv/104JvtwJsxYKxh0wsRxCt+MedKFwznWZmgFQJSePkgyuyNSei9rPVDznTO8jQl6RZW1TRSoHuuojvVhrnSXK1UnCZGuia0sBCtpJeSG1fPOatdNIyM3Hw38DzNdMauY5/WoUpFdImsgVd9YHCG//rj0zqStJpkxyzjIevtOg799mbHn93uZeQJ4BxLztQEGyz/4xev2GwG/o9ff0ezEmnq8LbCtuv4xc2WQTueBtnTK+YSw+gcfjPy5BbenyfByXgp5iieSym8+3Rhf3PDmBKmVEYf8MHQbz3eeVJaOBzPHKYF5+A0LdyPgV0v0wyiYVoWyOB72PTiHXeaIn0vLgYfLpFUDVjLguBA986yHzrActcHXqk20btTJJ8juyFI19Q6Ru95+/REZ816fhQqtthVbLIYFYapMs7e7DrOUyIt4oDhjBCovBWj7mKNsHSt5XA8g5eJh4zoi+4wg0mV4kSipz0DgEknBT8czhznTMqFY0p8sxn4ph/5mBYW9ROkJdj/yusnlSA1cbK1zq/SFWqMjLkU5irVS5yXVi7inWEfDLHAP11mNp0l1cTzBS6KUdiPPTcbz8fniRqV5WEq22D58ZzxxvJ0uoj9RhW8TvGec4l0xrHrA9uh43EqpCy6FI/nmfMUucwJ6xzfvn4Nz8/kSTLo292ON7c3/Pb9ByYiKUMX5JDe9h0PcVGla4+3XtvXiblmQvHMs1TKNoj2zBA8xyUyhI4pLlzmSYBwnWPbjeScCJ0jOIdxMGVRFV+KHEydF2Gz50lYfK/7AeuuCz9Yy/mS8BvHZZ4wteKd48/v7plr5MfDeQXftQqQWmXkdr4IODfLvJtqeT4eMduRvgt0Cij0Tg6ZU8k8L5HeWza91yRkzVzWzeaMaGs0xtUqYCZtEW6cp3rR4zkskVSv19VGZ8YYvlDvntPSzDBrgz+BqevIqSLSAvfdgM2J4MQtvlVP3jSNKNDWEaCjICvaMFWZUTL2LTwuCv5EqvgueL7YbPl4vrDEzAIC2F4ipw8PWANDcGwHMeOMZebrmy02Zd5mGTOIpY7Bu8AlTUo91wdpUBZOFlsa/TOLVIO7ceRpWV6AaeUlHZhrglo0oI3B8+44sXGWL4aenfFrN8NYrfjafW8Jorbj29tXgAbuNIJXu5TMU4oc54Y/0o2ugdAAxjpKzNz2HTddoDPKvnOOYAzfbEe+KD1LFgmNqRTez7MAloskSrbIl9t5IS2ccxZBPC+dC28MrqqGiwrnzVmSk0tMBGfZdp7bscNWGWVWHdX0zrBxjl1wTErwqLTu3tXmQxIAw93Y82rT83CZ+d3htHbDxUtNsJN32259ji3p+/Zux9uHI6bKWNYhpttLyTJ28OLF9WlaOC6Rnfe6tw2XJIKdouFkyFqhL8mo5IaskTbqc9byw2nm0xJJVL7pBt6fZvH64gVrqArRxWiHsqIj/haR/j/q3iXGtizN7/p9a639OOfE696beTOrOruru+mHMUaAhSwxAaQWEiDjkQdmBPbAmIHFBAksBANmiAmWLGRZSIwshIR4TADRCIFsRHtgGw/spt1Uv6oqX/cRNyLOOXvv9foYfGvvE1lU04V6VKeV1Zlx40acs/faa33f//s/tvmzIUi+HYLPOUzriMU5tlBo3zzEvjgdGXyHqLHyPQIN+XZaQSsfpsjbKfJ2juTKtnatzrZRzfW+5/3ZJOmHzrMfAt0Q8ArnJbImJjwv2k6nmZ+6u+bLpxO5VPZd4OOrPa/3Izlm85yqNFPNVoBLy4tUNv7jMSbbW70jpoJoZeh6QlCGvmc+nYy/1Hc4bwXU8Xw0akZrrjpnBOzr0a7XupfUUs0GxlWOKXEYe652Pd453j+emXKkGxq1Qo2In6SS4synN3vWQJlUbRz7YrRw8vOSKLFyGDp2TuglkEtuLvvrc38ZCwIb+o/YXrzreybWCC1jWheBLBbwLWJK5H/kxQ2lKm/OC+cUL3l07VbkXHHbsLt9rVQ+f5o5zYkG0pKq8oPzxIu+x7nmy6WVzV7k93n9RBVI9rrwENbtex17BGck49XjZOw8qwQ2N8gtaeHmcMX9ceLjXccYPA+p0HvPPM9c73rLcVNjvD9m4wA8xGgzY2eHz867BrMaSdhiGSaWUoilMDgjv6VcN+ni+6cn9n3H9RBMWSNCTDOilSWqqZE6x+gtS4qayblSayI5IQRP3wXSU6VUe7AuqifBNQ8TdUpQC5xwYoGUMhhR2Ik04rng3WK+Tw3uWc3ZVISnlHnRW7GylkjeOQ5jj8/VLANK4cPpyCc3V3y6G8nZELwt1qJtcKXazx6aN8o8FaYc2e0C3nuTnDrh5d4z54jXNopoBVsQt42vql4ItbRNQtpmhzQr+XUsohcyaIeydw4ZBo4ETjE2qJatoLn1Pb5PG7G3tJ8hWDHg22gk54rrTLJtML2tzHW/V4Gv5pmaLfD4qu+MjNi+Yb0+uSrv5pm58T0QYQgdh643lVIzhvMuEBwUCjUrfeftkFAYvalXfnCeoVamaqTF1EZCWnWzNtgapfYv3hlnJxbz2hm8Z+w65pyJUtqmZk/ZWngq2gqnC+esaiWmRFpAilLG3oit7TkNIs9+sf287ahZD8PW+KyaHsXe95LLdkA9/zHrtlaL8vpq5Ko3n6GyWhMom4dNaLyEzjmGKnQy2vUpRkB9SgkQBteRSzLH7Oa7FeQijZaGbKRmwOloDsPLj8caAAAgAElEQVS1bAffvvemBms7lcMOSScw4rjp7fqmrVCSdv3tdwnGjVSxENA5V+Z0UYhdnsULsd0hHHzgaeypqTB6k63PjZ9UxdymXUNZKjY2ue4swLuD7c9yu7e994awZKMC2HNoRfe1C8T2/pdSuQoWTXNKhauu48XYCrj2sxpowwqgVi5RKKtdxlpwtzN0QxUBgrNraIReO2BPy8IxJ66Bgw8Evag114pTBN7PkfdzsnEmz95HK7Ar8LQkitZN0XWKkYd54ma3o+s75tmildZYkFWi/9E4IgoPszm2vxx6nCrBs/m0raa6dt3t7iWtzMlc7m38ncwfDsUHz83VwBIz3qllsDl7pxVYVIm5NFXaemFbgya2xaR64dQJyuuDWQ4cY+GY1ZoKVnsctTsitlKdc8wlcz8viPf4xp0rjbC/xIzWym7o2I89IvBi1xOzM6VaVeOXbc1qE/40gvB6FzIVF8x0tGA8pDkVrseh8U2l2cLYBOBuDMQSzcYFaYR4G/u51RpAhKTK2+PCcUnbums9LqUqH2Lip652VKqN8Uph7y8h6z/8+gkskOxTr9LylaSrtLTfCoMzVKQ2q/LTHNHO5pveNzlt1+NDYEA5uMA+BOYU2Y078pxwYlD6Y0xc70Y+THkj4HnnSVUNpqtKUduIctvifZtHr0TpNhXgaZoR2RG8IQUiQs6FzgdDcDpP38zFtBnE7TpPLpUlR2KxDcKUarYJey8tdkQoTU3SecvsKc4KxVqyhU0WbUaSSsq5bfRsXVppqIaILcqHHPFZLjDo0PHJ7RX+PLcRZOGcEsclEpzxlZZSKWswqlxQF9qmYnwdZS7JktFLprlj0HnFi+d6v2OejRi5So/bj2gmYY2o2+D8vtn5o00iawtkBfVZ06c9RrS9EiPxRSkt8bsy1cLojNg/+JUAaJvcku1eOLEuu/e+GZK659QdLlu6MNVKShZroJiqYt8bCjgV48pZTIjlndViXJkqRnJ9WiJLKc2KobnXqvk99b75+LQTJ1fLE6ptO3YSNu7USrwEnvFA7H32wewE/DNEq2i1qJjW+q+IznMH2vVZM+i88XWyjXZkWMNNZZv1F7AuX7lUObURu57h26tR5NqHlmqoD3zz92+oUyte78be/M/aGrZiqrZfpdtBjdomfyUexZOrMoVK5x3nxZSjsURWVtL6zgqN3C5sBXrd3oN9t5lo2h7Re2/XzhlCu45/nRNzk5dArJVYlVmLOTmLcNM4QueWgWeBw5ZSvhZoglyk8eucDiPT3gwB+sC0JGI1zy/B1m0f3HYZUG2WDYXB+y1nTDE+SGhqVstYW3lqRshd/Y86J1x1FlWkqtz2gakFnpplBdvaudxhVqpgu416+XdlK8bhmepPwavgvCOV0hpde16Lmht3kLq5/ttoVltTrEzlsobWsvp5uV7UrE/Wz5ur8jAt7d/hqgsstZi1gBNctcK7tj3x5W5s17DSCZuU/fL7Wj6aPlNmbs+U7cna1MIq9uzsvbAfe4v86LzFhcREoeCcx6/F/w+95Nlz4cSQ7l3nTUWcK1PMLMWCyYfOuLSonTWIWeMgHueDhV/Xyi54ah82dP5xityMPWPfGXE/txgTCSyY476dI9LMYtdnWp5fli1oW9peSzF+om/NSBAr1tYtYww2zqM9L6u4ptSNvWZqN8QsCopuY/nL7zUDaFPlOfbe04uhv7/f6yewQGobJmbkKI3RlxsTHoyPUJdEToXgPY/nmdQXrvcjh6HnnCLBd8yAqnWWV7sBdUJ1joyR6bwX8jTZA1GNIFnag1i9N/i1yZ2tsO0QKXgsGDW2FHKRVaKofDhPPM2w3/VcjT1VnXkc7QIxLqRUjPehpjA4DB1Lzbw/Lc1OfuD2MODFX3J0BMARgpGdg+8N4qyAVLwaETPXYotOK1OKTDkbQW07p7QhckYCfxNnXBWkmKXCJ73n4+s9vYevY0RLpgued9PM43nh5fWOTgrF2UH7PGxwlYCa2kRItZCX0tyQfetGld4H+n5ASzYSnyq+mk1CaofqGqMRqxGsx+AZ/CXEsrbevaUgmZS7qZYKNlLx0jB7p2gVJrWDaGx2+y7Ym1aM31EbVBlw7EOP00LwwpLXw9Qew9U3anWZnkpFk23mZjBaOKbIUi8hqt5BSg0KpmwRC1oLzjtKy5kTZ1l5IjZyKaqkWtp1sUptdSPunSO0btooyfbaVH9YYR1bgR5rZs7WSSvavIouB4p9/ItCpdB2fIUS26hKhNu+48VojumpCs4LpyXi0I00bguubkiQrodaazRXuXGtteWtrSgez3Y7+9dxtTloB7/dfzvo3HqItN+zhlUDBHH0wVCzXoSvZkONirAd7qhdufR8DmaPJuqNH+U2ZMmKpNiUmEEcXWf/9J0ncLEC6VxgjzJna1A6D70IL/cDFfh6mkCcSeObcMAQquYrxPoeWyFQ4TEnBoFxN3A/L42cLm0EI9setKInpVYelsTHe3cZmbZR9aoEXDt5UznZ30vVGr+Uzc+q954lF666gCdyXBIURZ0daislYm1aVNnW3zNb1m3kaGihoVlB2oiuIS9PuRgqCq3ZNRXyWTKKZ+c8o9q5MJXCufG9LhXaCi+tTsqXl3fOJPVOeFoS55iJKfPxYQRxBN9b47tOc9QO513vGcNoB/UajIxsz4n1Zs9MNNRoAbvO1INPy5lOHH0jFnsFlwo3hx2nmFEnFDWfvZQTQ28Gpt+EU23BrrWIYAXX1dBxN/TkWvgwL5xzJmbF58LtfkcpkGrEEKSKeEfne26vrnhz/5acM8cUOaXAzntwjqUUxj7QB08pZjeDmtp1dB2CWLB6teLHeEPrU/9s+qM2hrZGAlQdQ5D2/Niz4rAgXTviHVeNWzjlYqHDNK81We+qPc/7BipswsBnt18Q3kyRl2PHVfDsvKOTPySCJCJ3wH8G/LG2wv6cqv4fz/78nwf+O+C325f+a1X9D0Xkl4H/8tmP+nngP1DV/0RE/mPgXwEi8F3gz6rqhx/j3dDwV5y47StLri2fJTCGjgHPYzJfns735FJ4fzzz6mrPh9OZD09PvLq5auQv4e7mhm53xbsPXxBwXO8O7HcD90viw/GEeGHvzCDRi+PlYaSUbAQ5TD45LdHm+sXIvF3nmznfOlcXSilmYJcTslT8iuJQ+PrhuEV7rB4dN/ueItbZ7bqB290V0/nEUhOLtLTyYr5G33n5kq8+PHB/PjW4P3C961ly4f1x4u5qR1UzR+t94JSXbctYr+PKy9G2UamzTbOoWrjvNFGdcDN69jszY3ycFkqCGnZ4Ve66jq6HN+fJksGxUSBinh++ITi+ty7BUA/HOOxQrXz+9gOHcSChnOfEHnh1NXCKhX0frMtbIl9MC2hlWgqf3JiC6aq3TdXZzkpuh+zqdeNxvEsLX59najUFzq731GYeuVQz9ENhKaXJfTvu50ScKh/yxMlPfPvugOCb8s01dEsJKM55K3AwI7Su76jOcX9euF8WXFgVYO06V4vAkWqdcpXKcY744HlIZ3zvGIPlruVSqT2knIhFnxFbuRSEWGq8QxiDcc6OzR3et3GmOmVaMqVW9mNPUevihi6Y5DkBwTUDRXO/eb7ZOJpfTlZKrpvJYR8uXJBdcBx2A49nk8m7Fh9Q27xMqm4G5YrdM2lco6Q24nRrF9o+5xaOK0YefnXYEbgcSm4t4Jwd+ACrcac2nsM2qlVDaB7myBwrCeP8rEaBnWMbsz7fflRtHWkAqn2/VLb3EMTg/7wUpmio9dUQ2A1raWA/qA+el97z+npPEOEYI3NMBDGVlq5kZ9X2PBpyd46Jw86MYK2TtlH++yVx20ZAhy7gRbifFoIXzlOyIO3BxvjnxXIYe++4HbqN0zc0CwNUtqiM4IVx6Fga4XVJpSFBwpyzPW/e4byQsvIYI6f7zE+92HPVh63AoSEbljZ/KVzWw1PbviNihaxXEHFI8LyfZ47VPNt8Q5VdQ7fOuXIkc+UDr0KHqPLr759IVTfO1+UIZYWr2jo2TlkudUMb1nPlzXFBVfj2zcDYG10jNcf1l4cdcVksL01t/2ruri0U/SLEaKuQH1pGDE64242oFpZSrDDAIq6ePhzpu5735wkc3B5GejmwGz1vn45tjNxsRNqaNt8tKwvGELhrHoFfHmd++sWB5f6JKSe88zxMM0PX48Sct1WhGwKfvfqIX/qlX+Z//7W/ycNy5j5GIsp3rq7xS+LbL67ZDT21PstSY63RbG9xCDUXkqitCycXHtr6MFW9uKiXynyO1lRRDZFToWvPbRCbsQ4hNGJ44SllPswr7/RydUcn/PzLa777/sjTkv5fhfC6h7ydIm8w9KycMr/f68dFkP4y8D+q6p8WkR7Y/4jv+Ruq+ieff0FVfwP4JwFExAM/AP6b9se/CvwlVc0i8h8Bfwn4d/7Ad7LtVsLG2xR7mEdvksrbrme83XP++g1+6MlLxOPY9yOvb+8I0oETPpxmM5IMQv584eMX1yzJPHqGMTCOASjcDDtOaUaEZgznWZbZRhSqtJVtu2mTr17veqqDuFjCvWtmV8POU6LJFs85czplCoCubr+GtoTeIyIsRTkMI5/e7ulCIzJ3SseO0/loadY+0HvPKU7cXe2Z7jPnJXJsrtnX40i/h6wmKZ2XQoyZ3a5jaXYD66VVbOH61VtCQZ3gg+duP7RMIjtcQq1cC9xej8g4UgiMITAvkSUvXA17vJzph8B5MQPM3Ajh6/3LFphhpmF9YJmO3B1ueJweuNrv2O2viSXx3XcPLBronSk+MuYtk9VxtQu8XyJFKx+VgZdjb91/rSSFLLopxu7PM1+e520ZlTaCuh0HimZiKpzV+DedcyAtYkECzmWGfeB23yPA/SkxjB2nJXGaEynZASMu8XL3kkc9sqSF83Rm6DynalJtcqYPNiYrDU6+3Q28f5qIKdF3nutDT2kIVK6ljXZXlZ2Al+bl88ywTnTzAnNiKM8xJ/rm4WJKJUPxgoNEYr/v6EKgFmU3jnz7xS1///tf89mrl3w9P5gxZUMtnLtUM9I26FoK4uAXbg945xids64PG00dj4XX1wOnbMZ8wMYXUaxjPubcvFk6Bm/P8kPM3M+JKWauh8BndzvenhJPMZoKEjMwfDl0dFKfGe3ZJu1p3b5eCLFt1VkxgB1SpVbOMTHnTBg969xSq27iBWkH1/oToCl11DpWVRs3GsRlB7EX4xFVtfHjQ1qoWtn33WZuisDBdQzO472SFjNU7LynD/Aws/kptc0PFXg/JcRbnEJMmeOUmJIZ9fXJ8+27A06Fc0zghQ9PE9dj3zy3WrPVBz4bAqUov/O0cN057vrAkkvj/JkyrGtGZe+PkdGLjWpS2UZttSqHsacTC/5Obr3oleNk/mqOCw8JVUPqm7rNrfs4oGKE2158Q5PMLfnhdGYW88FSDFWWdq21Wnbj6vKcFR5TobQi9XLb5ILsrE6BTVm5mmd2wVR9ufHY9n3gfpr59Kbn/nQm+I4xdPTBMZ8nlpQaKiJbAQsNEWkLz7XnpuKQ2grd9tkE4Xbs6H3HMSfenxdOc+I4Rc5L4TuvX5JKoa/OYol6z+1+x8O54mQhRmuSRIS7XcfH+57Be948nbk/TXz1eN64Or0ceDUOpGzj+7ELBFcRdZwqXO1Hfvbb3+IXf/GP8D/8L79KzbDf77jbm1WB5Mph11s6RSv7zMPIgnhtD7dCeugDpzkyzQkNiusuhfzqMbVGDcWY7YwsRrcIwUbfNIPM4BzqKjvfUbw1VIrQ5cLOOZIaf/O6WSdIsfHxz7285u1x4v15MasSnk37WFWSzROw/0MgSCJyA/yzwL9u61sjhvr8/339CvBdVf3d9nP+p2d/9mvAn/5xfshzPsrqSCvAFw9n5mIXNKkyHU/svc1rX19fsRTlXZvlHvYdHxYboYkzXscwdvTi+eN/9B+jd8p0euD9+3tcrsiAKcYcDEHBNedYMRiZ9kBUt3pdmEqsZqvgrLtyOAfVCBltxi4UqXRBWBaA5vaLUqvDdzDszLn36XikExu53Reh5IWAI7mCeGxWXOGYEqqVj29G6yKdkFHePi2oh9B1DJ1j7wNX+4EfPB03ALTNKTZfiPUzruqD2txfhWdNmABakDjhxj1VC87D4DvmOCFhfYjKM8MzLB5FrVuTIGQq9+dHpCo+THz66hVzyrx/PJmyY39DR2WJkVQKh6s9v/Az3+Y3fud7xDlREe5j5pgKb6bIZ1cjY9/bQSYG+351PPNuvrgci2AHYay8LxNDb4aHrp1z0g64QQI+KOHa1HRbRIe3cd8+BK5ueuOwqBBTRZwdhgLMKXGeFNXK3X4wFEhtyhS85dIdc+TbY2BR5cPKGUIvaeYBXGeZRVlasRIcOZmj+YoErte4QivohZKVq92OITj6zrHkzNcPJ+Jc6EdvfiO6UoqVT+9uudo7vpx144AZ4tJGggIpFnK25+2T3cjrw97WipoFgCV9w+8+TLzeD3TeSJcVoXhHiRnX4hqmWLg/Jd5WS7zvu46ljRqt+XGcY2HOa2iqY/CO66Hj033gMARL+F47UgSc5+0p8nCe2zp2zSnZqLJ1e9ZMNTqN2HPc0C3aObC6SYv4rQMWNb6DKsxa6cXQkyzGs1tSJax9a4NOqsDDOXGaMmMf2I0dh93Add/ROSGI3e+VHA7CzRA4xtgyr2Q75ItWPpyMOLz5NbXC7Bwz3d6Kx+CFJHDYD4xt5BJLITUn7qpw2JkVgLa9dWxS87F3lGqckjUz8YvHqcnjZXuu+mBUA+ccn+4G3rjIY8zgPR+myKurER9avAOY6hY26f+KHgL0OCv+Fe5zYqql7T98QxnbGAhmdttUyw6TiBepvBwG7udgaPwzro5bUUVZ1WVucxc34rNdd9fQ2JQqQ9fxO/dnvn29p/c2MHJy+cztI7T/eQaHtv9cCyjamiuNJuAaKlKrEd21Xr5XnONmdNw/Hbnueq6GDnXCU4p87+1CqRbcjAh3Y+Dl3tbRUgpfPZ15auqtMTjudgMfHwb+4eOJYzLVoeusoNuPOx5PZ8a+Y/COx4d7/ue/8b+SU6ULI6/2Ow5BcLValFGw4PDa9qfVFFQaglhqs7RA6YMwXo8I8DhFChac7sOqVLSRZoqWg0jbw2qjEixSiMXk/4exN6sWJ2TNLNmQ7+CgDz1jt74v4/UdU+YhZvadNw5Xo8E4LmILaFP+DWH80a8fB0H6eeAN8J+LyD8B/G3g31LV0w993z8jIn8P+Bz4t1X17//Qn/8Z4L/4fX7Hn+Obo7jf97Ueaut44mHOPKbEMWbUSyNPKnfeE4sS+sDbJYF4hq7j8XSmC9ZdXQ1dS+22U/ucCgcmroeBr85l8yRaUuLV9YGHydALUHbeW34VRsBbHw5R5aoPnEshN7Kya1In55zN7tcuVcAFZ+O04CxA1Os2Ltn3PS+vru3gXSIxRd7PkaVW81gShYLlRlW46T1vj08cxtAQBBDn+DBNdngW6INytxt4NXYgji9PE5vk2q6wbWQrqtQKp6UUfvPhiZdDz4t28EsrnlQVLRXmhZu+R11gypnHc6YPgYdpsU1eG4TtHVpshmwBpmry4gbDLjnzMJ+JqbDEhHfKnp6sq9+LR3Phd7//JU9PM+KlRWoYfyXnzPxw5Bdf3RKc48MceXeeuZ/iRlwN7jIXR+zaxFg3w8ePDiOf3Rx4miI1G96wjl4E26HX0cFpzubuGxxzLHgX0Gou2b2Aek91FvtCtbFRxfhzV/3A/fGMOuG+FHJVFlVyNPjaBYf0fovRcUhztF1NKNfSSC5I4IbPGEtl8J4lRfa+J2RIyUzudkNgmQsi2YJ2nfDl4wPVeX7v8Uxq0SxejJS7tNMlLhZA2TnHi6Hns6t9y8daK0tsTOI8H5bIq8ZJQpVclC9PMyVVfDADxVyUDlPfnLMiIWyjg9JQN1VlyYYEBrGg3+ve+AadlwtvoaHJzjleX428PPSkYtEyc8q8P07EZ6RkdcLQD7wM8JAzGYW1CdhOZ7Zzz/oXxbWD3NE6Y7l0qC+vd3YX2kg0xsKScxuB2NecCnsfCGLy7uIgIHTSokjawds3IcDKbXHORsi1EVW9E/ah49U4bAaUoTUzPY6P9yPzYuajcy4WZdJ7nAiDh6doqK4PjiG4FiSMrVW3osiV2BqaolYwrmPoqsoQ7PeWFvGQq3LKud0Hi8tRuTCO2mPenn/bxsIzHtScIrFlKLKiPujGv2qPrIlmBII+GzUCjynyyX7kvKSWlwY/7DLqG9KVklXJvXctJgjWbEHX3MMdcMpKrJHOZfz+2c1ey5+1wNX1/cm6nW4RK9Lu1/qMrvvhnFuem1WpFujthOsQOKbM3XBgN/SUaWaKiSUls6HAeIRjCMwp8/Vx5pQywQm3Q8d133E92pTgnI3cjNi6nufCFE2QMC3RLD9EWeZIKYVPbwKDVIJ4G201XpquaF87M0wgtMamPBu3tQmEiDU0Wgo12ed0XSBOycQ8raDatq+27l1jqqVaqdVG+KuwpagBFFnNlmHLHVCT8ztxjN74b6/3A58czHX+6/PChzlu712rUvPKY/7Rrx+nQArAHwf+oqr+LRH5y8C/C/z7z77n7wDfUdWjiPzLwH8L/OL6h20s96ewMdo3XiLy72GmnH/9R/1yEfnzwJ8HuNrvt+o8VZPonnJhrvZgeDFSaO+EToXewykljk3G2/lAypm+XIiufYO7UzVF1JXPLOeF8+lEztk29ZxtdNJuYqqVINI6lEvDsJ5TF5RLt6+1L1z+dfsf+z5vK4mh8y112fOdTz9lmhfmeWZoD+NjyuyDY9d1XPc9H86z+Z0Uc4u9HlrH3z5PzhBzJjjH2HnuRhtB3Q4dVYSrvjPJ+7rwn72vy7KxTfshmnHezntGb4f18++RUum8FZBVTEb8mA0JKNsDJN/84e5yDcCmFHPJ5LlxMNRQhjnGFrRZGTpPEM8yJ6sRm6O4QiNiW/r71+eZXqxAepgjS2qmeGLchNA4Mev9Kw2e3XU26w5YEGR2Ld1e2wah21YACFNKLEXpqqfmSgnKt28OzPPCEiuDE6oTSrWxadZqyiCFGM0lWqvyGC+RGFVNzq9Oti7YNp62hvRy2DxfY9ouqW/GnZ24zQpiHPaEklk04Z2pIR+m2ByBlVwLU4oQPE85N4SwKUSkcRxaDmCtynXv+WS/Y9+ifNoq2K7pOZvUfC51U4Adl8ybxwmA/dAZCV8sEyqUStd59sPAXBM1Zyq1BTS3Q7AaAdg7M/jsvXtm1Hkp8h1qaeq+o6r5uCzZUKApm33G05KIWRmCcPAdffIUdJPIL7U2SP8ypluRNBq66tfTut2MXRcY+8DqtVW8+ftU1BAptTHs2DhCtRZKqaSsDM6hIdg4ssXN7LqAapP5N0FKwIq2qsree170PXd9j28y9FqrKXhbHFMfrDiNteLVioEOYUqFD3NCkcs1FEMhU9FNeViqNif1Rpx2a1BtKxIdm6o3ADvviNWiKx7mxN3YmY8TF5uO1fwRbQwFEYJ4psVMY5FmAOjXhq0VFXDhJz9bb+uXqsKxFG6C427Xg1oBcoq5PTL23K/PnDZEYc3NW3+WPe+VwTuGzoxkSymWWCBcrAyev/RZw7iuxva+nxdINv1o6jZnTUIXnOWMNXFn8J6+86Ql8bAsRDWe2XmxAqYq0JKlihpq8rQkKnAz9tyNvVl4aOHLaSatcTpysYp4Ok9c70ZSZ/yrlG0t3ux6rntDabum/F73mlV0s+7XvhEgNy7S+jvaDXEijH3AZzsvS6pkCqWNaZ+fiZsY4Nk91momuqlNh8o6xRDw3sxbm/8tFTYKx5q0MXpvTQY2rj2mbLmiWamN7+v+kAXS94Hvq+rfav/9X2EF0mVdqD4++/f/XkT+UxH5SFXfti//S8DfUdWvnv89EfnXgD8J/IpeIIxvvFT1rwF/DeD1q5cK9jCeUuEpGn9n7AOnmAnBQmIHcTjxjDXz1Xlh0UqulVomQueNK9P8Y2KprAF5fYAxwFfvHvnwdNo2fVCO82KVfatqzzmTY90q3hUGFifEahd+60RZN/dnN17rRuxFK+I8XefYDx37rmMMgZ/+9kf8X9/9Pc7nMy+8M1VONW+dThy7PvAwz4Z6KNzPkZ+63fPmGA2ZyUY+uw4dIXhu9h2vxpHRmbdF8I5Xu4EpZi7Rrd+4Qc8Wu20i1olUyzlrH0ba3EyAmg2uD8BHu4HHxxO990Qsb0jVUAHdOib7PQ6gSWiXUkit414VQk/LbLlB6oBA5wJd6E2qnaON/9YOqUnmPn8649VUbbmaSWOHZ1GTUa+fz+4HVDH/l6uuwys8nmMztZNLlyvGAdo2CmwDLFSC2kGfXeXV3Y4v3iRyTS1DKdAPBs/nXMy9V4WnNBN6My1LzVun846+91zte04xk7P9lupo5m2smuFtUxZpXVeLDum9t6DhtjgP40AYBoi08ZffQlA7zCKitPu8tC505emsqOLoA09zRNS4N3dDz+vDjjUSojYYXJxxeN5MS+OEZA4ayLXyYTaFkPNCXwND3+OdSc1dLBzGnrv9wMOiIBVfW4I82jg9lSCOQwjcDYEutMBiVlGd2QsYR6WwOq8P3kieo98z5cxxTjzNmXnJ3A6eXddx1zsqcM6Zx5j4EJPFnNRLYbT2PLFWLLLRSOnryPZm6Fk9mkQE10JmtR1wHuFuP3J4xpMrtVKKWrRHsPU+tdHLPji0GlfNODaKw4rCWiu3Xcersd/QqY0Iq6ujvKnoFKUspSF2FmT6fk6ck+VyISafN980Q/poKtTc+D7eWRSFKps9xGqamRQLSnbm44Z0vDkvfHWa6b3jQNhiQHJDjhxcRhxqze20RCuO2rNZnRpL0ZnScd2h1ryuFfGR9hxmtX8+pMjdrqMXZ8UR1gAtxVC09Ex9tY4qTWJ+2f5KVYbB04fA47JsKCCivNgNqxftVsWDxLAAACAASURBVBABG43iG8URNOn6swZCzZtPxELUxX1zjC2YUWpFeft0JDhPHzqO08zYe1OPiSl752RoWwimhrsdbSxXVfniNPPVeb7s5e088s5ctj+6uSIEAw5SisRaud0P5hkWLOA4V0VrZuzCxiNcz7wVzV4/01ZgtoZFxMLYO+9YpHCcI7nm9XixJ2X7zLKhg+umrGpoZqFuNimrQ/oQfBu1NbVxNaf3Xhz96iu1osCqHDpP2EaBzQbIS0vC+NGvP7BAUtUvReR7IvLLjXT9K8A/eP49IvIp8JWqqoj8CWztv3v2Lf8qPzReE5F/ESNl/3Oqev6D3ke7XjgnPJwSTznjgpFCFzXl0fW4M++Fonz79Uv+7m9/ySzKrusptTKlRBGzK1+JpwVth5/j9d5xf/+IqI28ymI3yHvHcSm8vjsQS+Zhms2/om2EFctu23UB7x1zNXlprc2nyNuweSMEUimlUrJui2oYhb13aC6cSmV2if/tb/89YsyM3lMHy+8KQ+ZYBHLkt96+ozbpd8Vkmr/zODE4IZdCSoVBPP/4z71unYkd9kWtS9ZS+ezmwBdPZ3tY121Gv9EUbxJcQfho7NkFjzQJvGCQLXWV8jYIpO0Yv3B3zYeU+Oo48TibxXvBrqmtatspxVucQW1ZH7XB5quUeXaVvgpeKjFHqlb6MNC7nv2h5+vjkVIyvQ/swsBDOlKrJ5XC1WhqmhITw67n/fFIaT45zluhlopJqL91deDaCb00Pyqs05NWeNl4zcYQpeWP7bqAYorJ2lCuv/kPfhf1dmgPLvCdmz0Px5mhGxi14lOmK8o59Dhn9gNjH1qX6fC9mElftU5HnI0pUlWcB69uk097B533BIWhs/uyRp+kUlrxkvj8zdcMXd/4NJCy8Or6gNZk8SCujXmTZa3NYmvWeDueb93sOU2RoQt8drXj06s9fXO99Q4eTgtGknZkKj94OuER7ufE4u1Ammthv++IqTLlxIvba0qpvHs8UhTifOLVXcfowTcxxVILX9+fkMb5eH0Y+aOvbrjd+80DrNaWhdc2S+O6KFrLhY/lA0NwdKGn847vvz8bOuyGttiNf3cVAlch8Goc+YcPTxxT2tRz61OhVHIVM4sVODSzxC7YgWJuvw1pAg77jpiVb10dLD2+KqmNDktR+uBIje/0ejcAwm8+PuKwUaK0/W9RUw91CndDvzV6lmivW1duz7HJ8Wno1dh7ooOSqkmxVdl1niE4Jq2cFhsLv/Qd55TovLTC00YbN6Frh+FllJJVqdUQLcEEDbsh8Nm442FORJSvTzO3Q8dVH+g710KBbaT4MCeO0fLGvMAOmwA4aSrHBLUXvOgzMYJso00w25KVn2UKeGtiP8TE6ByHLvBL+2tUhd+6f2BOdVNW+YYcIWukjBUda3xNzJWHeG7qsIaMJpPN3w391vDKeg9gGydvAcIK3XpOrzhL+3vTUoi5MLTgVu+tUHpcImU2jzeLsrFQ3f3hwNkLMZ2IuQXJOuHnX93wso1ZcysEj7Xy5XkxA0yMCuLa3jwXsxJIKTP0nkTluCRcEL46T9z09tlyMcpD0WqpAbI+a42HV1fCOW2s3oLQVVkz9yo2itsN1nSd5rhdMxEax002Zd76nK+ooVPlKRfw1iCV2hpJpwzeNwsXtqLbrrOFEa+Nw5wrH5ZkfKe1s8WKRv+HKZDa6y8Cf72Nyn4L+LMi8hcAVPWvYgTrf1NEMjABf2ZFhERkD/wLwL/xQz/zrwAD8Kuti/81Vf0L/19vIpbKF8eZ/eGK0Xme5omHeOIujOxvB5aYiNE8V754946okcF3fPTqFUtaWN69M5+TdrT3DfVYiZe/+/ZErvD2ODPlgveOQ4sI6Afh+w8PhjR5zzgE5lLwnee6H9qGaeQ5J9CJUtpMPxt8ARgCUWJtMKp1YyGYLwnOOupcCsfzCcWMuQZv3g9OHDlX9tcDqka+rDSFFqZwcWqV9NXQ8er2io/3Q/vVpqqZc21p2+D9OuFqO7/QfJGskwnesQ+B66HjZgj04ljmSJCVNLnCYWyuxfafTanRfDL2Aj97c+AHInz+dLIRUlV8EDS3WXBVJK3dyMU7ZZ1LV5WLm3DfoeI4zhNj79m7A3f9DrE5JEtKBOcJYsGLY/MSWlLm4xdX5NgzdAOZjLpK30ab90+RUWy8aY7czRDOOeNJcTHeU4Bq8Par/cghF04pWdfvA+HamYqjSivQE3EphM6Km6hCdnDobY1MNW+jshVtqFhDMO4G2xhKNkuHNu61MbJvvBs2VadWC0B1IgzBTE3NwK5yfzxy6Dr+0W+94KvjQqmZqYVsdgKHrufmsGeaz83h2Trs4IRQlAHhug+83I/sh96uj8B5tjHuhxT5/N2ZLx/PjF2gaqUbAnPbnHsZEApZFn7udsdxmTmnaohWB09PC9//+sk63FKIKNeHgf3Yc5ojg/ccOs/VztSksBa3jQdUC2sAakzWqWpb/0ELXRfwzbH6euxIZWB41hkbj8T4Ml6UP3J7zUNMvJ0XnmJqHEQ7SJsanLvRYk7E2Tpd+Tna0IaVe/XJ7S3fuhmZl8RpjoYcaWU/BEM2aDL+pXKz84zOk1XNl0sMAX0lAl23NSKyFkapbk7u61MtgDQ6QHWm+Bq8p4hwXhJzKtzu+40XA8ZlfKuJTrR5ptlz0LVDdtf8ny4jR1iAaTGF7LlWphhxHv7Y6xf83Tf3nGrm/JQ5eM9H1wO7PtCJ42GKHHNmaY2jiNkzFLHIG++EDkGS8CAWrtvOnMs9QDfHMzCRjEMJzvyhplpZUuJcCh8NIy/GAdnB29PClC5q2Jjr5u3T+UDfGZrXe8fp8bil3GhVJs38+vsHPtvveH3YMTSln2uGt+v3rpYTrq0LK6IuKKSI8n6ayRjHL2+IuYkzwpZ2byNP54TPXt7xxe99zjgEppg4xcTYGbe17zwplcbNEm6843b0lAQHZ4VpVGWSytVhoKTMpI7b3Q4/eJacDZUpxhWaYiaWymNMvJ3O/In9jlXlDLbPVGfveQ2hXhF1qxHtOUi1klJiSbYH2za38slkQ5xMIOG2fX9twBHhlCLnbOq7q3Hgbj+yzAvnchn5i8Deu81w9xA83sEpFu7PC2+nyaY7yRDvrheLlFkf5B/x+rEKJFX9P4F/+oe+/Fef/flfwQqeH/V3z8CrH/H1X/hxfvcPv6JWejVJ6S70aK1cd3bH+t4zpcopZd7MC6clgkaeTuYlQWjyWzUjx0SxGWWwMMIgwhcPM1GNXO2r0jnP4XrH9x6Om2x3LpWl2Mlw2wW6ThDXQS48nRfGwXNe8uUGO+tsczR+zooqSTvMbvcD755mztPMHBdDeGLhMHb0wUho10PPYeiZinKeMg85Mg6dmQhqRbyRN/+pTz9iSpldsBmya5toLNaxVmXrsrRWpiVuM/h18YsYTG4y4kJ2MAyBvRd+8zhx1Xk+3g0NSbLPRkMZtodn/b9WrasWPt6NjH3H16czT9OCiCOYL3/jAxhUteuM6Fnl8jMEyM7iNq6849X+wDh43nx44uNPP6J7+EDOldwKgURPiok+OE5nO4y64DmdFzrvOMUZbblvpZqXz+MSmXPipjODtbuh35RZ9Rtzam2jLVjHb2OwTvwUM8eS6Z3DD+YzMoQOlwWvwuMy47MyOMGFDvHw/unM1XVvie5qxa6NUioxGZmd5iPkO0OWOnF04UJgFZVNbaSyYm8WxunEuraPb68Zu0JOhffHhZsu8G4qzWPJ1G4xLrBkPpQETinrqK0qj9PC4D1zMsuG0RsnLXghl0xU5cvjmcc5ctX1XI8DLw47qIrrO6aUeDxNjH1PdsqbRTnlSCwZsKgcGQNnLVAuHfk0RX729gb/8pbbzvHRrqMPgc6b1My5NYDWkNmKRTGsvKQ1s0qc4Ks2U8zKYeyaBNsI3ttEfUVMG4p5EwJXVx2LVu6XyOen00a0FrlMO81TR7drv27yVa2j/623b0n5ipdDT+cgOmXwloPVOXfhZIhwisq3DjveTUvLIGtIrvNbOVDXAY5eZOOdk835u7QqvnNCLoZStiEPgjRe5eUzrM1WrsrBt+eSxg/ywqHzW8OSSyVWyDhOs430n7KtAanC13Vhf+PJqViH7oWI8v4YKenMzc3InIulwTtTIA7uYiw41UKoa8wLuKjQu83oM6NNDdc4PSua2rBuxRli3tCERStTjtyOPY9L4pOrHceYuJ8WlmQB2allqrngSLXw4Wmxn7/5ENhndy3F4Kt5ZiqF1/sdL/cDKwbvLIDw0ii2v1ca1Kft3sVSyTXTISYyUiuUvDh2IfC0WFHhcHSioBMPT8rYCdOSbWQbjNLx/jzz0WGkTUa3acvPXF/z6+8eCKWp9sRx5T0LymOccS6TJLf145mKNWq/8e4e7z2HrqNzjkmVL6cTH+922/5egc5544DaFWfNiLMRqfFBUzYrjZjKZeWq0QE2jZ+yeUmtKrn1cZyzRSaFzjhIc0ycvGMplZhyQ4ouVg8rYbsLnqyVUy7cz5EpVjoxdFs6TxccYxeaz9uPfv3EOWl7hGWJLK6Qq6mcig8suRp3pcFtD6eFWuyijt6x7zo7ILN19dXbmA2hdXJWDCHQt5m8Q1hErYq5YHdAg3oRTqk0ewG7Ob7z7H3H22pxHKY/NDRlhYZ3XfiGF8rgPIchMMWMq7IZugXnOAyBmM0lPOeZ45IJ7abGmKE9UF7gOzcHeoHuWS7VOZVG2m7yTNdm9g2OjzUzeEvJXlPG912gVuW0RJvDB4OsvQuMwXM/J14MfZvHrxiz3w60NlmwTqJ9RlWlF7jtAsPNgYdh4M3p3GYf2IlUreEupZprdCNT5qpbVENVSDVTauTgBuou8Ou//XuknBq+bYjcy8OeD/XMi9sb3j0+Mk1ncPA4GeqkzpAHCi33rBlFZuW+piYAyPz8zRWqdYsKUL0cgLL6uMhKXDQO3CnnbaMcQkffdZzjmV1vuWfeOaoI2oJEd2PX4Hh7UA0uNj6Ic4KKmc9ptfiIoTcCuXOr/4p+Q868dmSCHc6Pp8S3Xt3x4voGLU/MS2TEM8WFF1cHHs4zWuoGzffebU7ZUpU+eHvv2aT9t7sDpcI5mseSiGtE0WLBp8Fz0w1QHfdzIqWITNbZOd8xlWSmoxgZtWKjwO1xaZWFYKnnn+x7dg4ohWHwWxxBVlPj1WodoWU+mU2DoWZ2qq2S9JXzUlosSGwKpQYwtef6gr+4hshaL2S5UW7s6YJwf448LrGhPpVUK875tg4uiCzteViyeeZ8OC/snCGzxqGwdV6zmkFj+3lLrbzoOl6MAzHl9hntja5SezaSsb0/Bd4sC1ddaPl39j6C82i5dP7OCWPnzVLDGAnb+b9yZqIqO+9wrUjqg41NvHPkbCPwosrc4nTqhszb54+58Plx5mfu9vTOxj6nlHmKCe8d754WhtHu5cplgjUYuB2jqlushBSan5whc1qlRd1Ia2LaxL4d3NL272c1L6dSKBXGzjXJt8VtPM1529ezKiWa6GUtbn/owWLFyVNt38/KHbKbre28kG1DtL9e2/eB7cex2X1seXxtrVS1fL+Ukq1GU10gITCIo3OB3Ck4u39VlPdL4tB3Nrp0F3TxOgTj3DjzKXJtivFyNzLNkV0YyAWWnIlq+5bWyqzmqzaXiiumEv7e04mv54XBO14NAx/vRsto824LjV+1ihW7J7lU5pjJLeXC8D07Ey78T9nEOsads5uZqwVHZ60t97QFludMnZTOB5uMqLYge7tH2kbAsZHZnQi7PvC4JFKuNrF51rzk58SzH3r9RBVI9vlNeplTJpdMLYWjs5lqaYs1lmpdtnd0wTdnX9vsLHOtsC5z79xWgcZGgZUm4Ra1rKSF+o1wyMu/2E1M9aLycc6CILWtEK1tw1Twocl0G29i5QycSjIUoxVpfQhNwm0Giqg5/hqhsRKzjUw2FZYqUuDFaPJ72k1fss23V6Kz1vbZ2mFaq3FbPj6MPCzJVD3FZryp2D+KdZR2sAuHPvDuvNicHraNjRWBakXE5pfUHoRKO/gbJKpV6cURvLe4DOqlmFQzEAyymu2Vb6yDLhjp1tXMITgeHo8oStc3SWrv8F5NKeQd17sBhwUTdn3HYbfjk/3IFw8PPE0TYFYLtMIu1brxBx5T4rbv2vVrw9nn6NE3VqehKbvGy8ml5QOJRYJ478y1VlxLLoeUssWorMfLdv3sR64kw6xrppmNO9z6K9vp/tz4bHs3CrVc5NG31zdMcyJNZ5woj3Pk9eEapKEOVLMj0LwVHbI+O22msuvMX2rKC1OMm7V/rJVzbV6pqixacCiPp0jV9VkMiDhSTqSY6Tpvz4nBFxf6WtvU9t4bd6ULtvlpZYmFszekZHQrT0xXUG+7KA42mbhnLSps3F2ofH2aWFKm98Lg/KZmXQ+zVtc3Iq48++9gJHqMKK6YeuspJoqq8RAvtQkKlCZO8A7mnJuMX7ZRoKyoRLtptTUwVU000HsboSzrs9yKhrWYUQxZP6XMfUyGXnq/vQcvq4P4s31ULOLG0LdLyvyK1iasOPbSlGuyxss0ZWkrQlM2FWa6XDp7ZyqcUmbX9xQxx/qsjveLWQqUaMhWcBchiOplP1n5kKt/jVPIycTta7Na28O4fh7b+lZ/tdWCYcXMmkJWKztnDYDzwt04cDeu+99iJrTV3OPXgvT5J5PtE9p7PTQPIXPTZmtKDLlqSKJcODL2R6YYPbdxE9g5sP1sZduDvbOSglaAZQXnzNrCOHMFqZCd8hgTN323KeVyNY+3vfcN3VGS2r59pS3oXDznlCxmCOPy9sETa6FK821qH/xcMufanmVnwctj8wNbnzu9PIjWGEQz390creVyP9bruH5tba7BzlVTwOam6LSCJzgrxmJRQmgquqpI8G0trT/PPn+p1hxdDR0PsxnCiruMBFej4N/v9ZNVIIlQ1AjIuWRKTqDC+xrJGJs9t65m9fUYgicnWDKIU+52PULkFDOCdS5r0TJl43hYbADbgbmUwmHot3yt9kftTdn/Wy+0lrVzxZQJRbf5qMGrRgIsbcGar4ONGETMubYPnjUPzZurIkULXe8Yhp6nc6SKt0q4bVqokUOds+4ttqDctWCxc0i3SATENpFd53kxdHZwFSXmxcIydZXk23ucmxfQvjPexykVdl3l0DWvFFb1zHpgXdQNonZAzaXwMBusHauFhPZiqdS55T5VXTupZ4oOYfscfXDcDB3XnUdLbeR86wR3+57DvqfvhHlJDL0Qy8LtfuDQed6dJl5cX3G9P/D65oopRuYYjYiclVVW59ptTVX54jTRO0f/TOq6dZFqKNLWmjfexFVvhPoUbE3knLbtww6PikjAO8ecjLvjLvo01u5v/V22ydo/a6F+CfZssmJdN8xVlm+FdS6V3eCZltnI5t5RvWOplVMpzE1SXTSTsHHIXDKs7u8iZuoZC4NzHA4Hhs63AikxaaLuek5ZKd5OyVQKsRTGLrCkpWXICaka2oAqx2hJ7GtTs0I52hxQdt5xN3TcjV0bpRgiN0UrljvnGPrQpO+ta93We/txjWS8IglFjbQdUd62GJxd57kOgau+Y2ih0Rvyg3WloVU8K7nYqePV2PPROBK18uVp4hgjUzLUYWjrpVQjuGfVzXdr9dpZx1pmOKn4dshqKwjEQWqfYz/0eF9abIa2MYplEa7BoKeSeTdHVOum+rsUDpf1pNrk0O1Ab6yZi01De26LCOdqRcy+jZdrW08VC9KN2X5XEfNpWT3T1jNS1ZANEF4N3RZDUYDQeZPMt4u9EX0x4rtv79vie6SpPoFc0eBQv76nhuW3gkhlVbsJq8BjvaUitgYshgc68VwNHZ9cHfju/ZFjTKSSt31nXVeXPuRShKlYkO2LYXWXZhPcbFX+eh24uHpLu9YxF84xb+i6qG4Fvf3eiy0Cqkit1Fw4uWrO97VSmyDJFxgGz1PMdM6oEaUq55RZ6ppVqBtaUoHTnOh9IKDEYqKXFcnrfcBJi8BakbF2ZgjGiTqmxPtl4eNRQHwjWF+u9dr8zzFve/pGB7jspDz/S5sBJcpSC3M1E9zUijSLu7I7smihZmVo60CcI7RoHLCiqKitc4Cx8+yHQGpWKuv7zc/O6x/1+okqkLxzLPNESmVbuBXlIWZKNm7P2oUf9j2+GZdp1xOcYx8ct73j+gC/+WUyNCZlalKSOLRxOpwY+/6yOJQxBM4uX/KdqFuXv5W9bRMqpeKC0AWhersBtg05pjkTfWXXB4YW+oc2V96YSMlCJ1/f7JHQMzjP9+4f+ezljo/3naEOd9bZ/d/3J04pI2Jkvi+ezrwY+zbrbeQ51xQf1ZRX2mD1sTeIH60MYofalLIhTq5tKE2WO+XKhzkTi3nzOBG+nha0FQTBXR6OrUCieX60gkAVnpbIm5aBhggxV1KJdMFxaMTT+3jeZOLaLOKdM0M0RHm93/OiC3TQzOfgp18cOKs5xHbBZOsyCDEUSs54P7DvDozdyDD0FAqfv7tncB2fXF3zsMzEZW48hovMtmjlzRRZcuWnr0b2IbSN20YN5f/h7s2aJUuS+76fR8RZMvNuVdXV3bMCICCKFCmZjE96kEn6tvoIMulNbzKTyUQzGgSAJEhgema6e7qr6i65nCUiXA/ucfIWMPgAzRybru6qupknz4nw8OW/OFW/AVZFoA+W3Fat7FPksmbOed26eGPXMc0FxBylx6EnpcglX3sCdg1+O4WtAkPscLTY68lRSw4sEpsyfIzsu3St6cS6df/Pv/9rUjBdpEsu9H1AaiGoGTFPpdD3iVSElYoEM+7txEcSmELuz97dsbtNHJ+P/HBaGfY7zmfDzy05b4FunjNdFwyUnisNlVGxdbhibCGCEFIAscpuTIGv9yMHV8htruBr1c0CZylQ1kLsbURnAbluTKcYArVY6z37E4ohkIGnywooRZSX1VhU/bLwZze3DCluXdl2UIl3A9rAxvaTgFTGKPzp/Q1TLny4zPz25cgYAl/dHTguC0su9OGKh0qtm6s+ChHDT202Lh5HjLMRrRvnnaOx7zhNC7kaluPDbFIEU6lQDWR8E9N2QAUx6nxpb6pN06hu+5NWF8BW1DTnpEZ/Xr2jWYph41Y17Z61FDQKixozNzT1cf+slhw0TbZDinx1GPn98UIMwvPkLLouOOlDN6p78U4HqnSYxEMzJV4VZtQLThvbBN8z1TtgpV4hDVux8eoZWsxVSrGiobgLvSlr2/XXVwlPe4/G4ksEfnazZ0xxe9eKkrN1uIsnos3+pp1VYIKd59V3gweONiL1Y8cIFl1iXS2JyqKUeeU29ry56fn+05GgprHVAfsQeV4Xvj2euR0Hllz4eJp4/3DHy7LyfuhRNccFQuLTaebN7YE7Ft53PaeifCw2xj+eZ2p/Hclagdtuoj3Yx3nhlDNTLvzZ7a15LTrkwKe/BsjWpgz/6sflinkVDa8KEkt4L6VwqcW0mypeECijd3qLwrLqNspNbuNFCNyljlJXUg2b/6Zu60jcBuZKtGlizv/U6yeVIBUHYSo4fqZSRci5Mu7N46iTQEJYaqGIcsxqbKcQiTc7zt2O7364GFCLa9VRAh4g2FR8ieKKuZXFRSNx4F9z2ayOjwG2aqD6XHY3dlQ1hH0SqxDrrmP1YDHEyKRK1sJ0sWr2i8PI/a5DQmBG+c3zCxIqSHUVaDMiFOzg6FwQUELiKRd+94cnfnY7ckjGOovB8CuOhWa/6+mTj9mqKTXXqtz0HYc+c1ocyyP4SE82kOlUCodoY7wlV373dOL7lwtvdz2/vB1Azauu7aVaLQivqvynT0fOuTh4zypPVVPpzUtFlkwMwu1u4GVaaFgLjymglgh8kSI38Yr1KAjvxoG3GP5goTJhOi+XaeFmuON4XjnJypvbPUmUx5cjOcN5ycQQebu7p2rg6WImvxbcGoBQeF4Lf/V45KZLfLHr+Wo3MkRYLCpsui7tmUQJ5FqcRm0+eeb3ZeKDPc50EqvGxlGRE1vAv+aayrpaEEAsUQzJuj9jFzc6sqgxyVQMsGn5Z6ETVzbWytM8E2PYug77PlELvH33lr/+u2/48uGBIXX87oeP5FjoQsSsQEzYbdVIXCKxfOQ8n9jvBpJEcqocp8zT+WI4AIS1qAFfx0Sn4mMC6zJktQo/7SOSAhF/vtjv/+s3t20rbesQtikcMQinxXSKFt3xtsIYfa1qSzEM39Wl6My+Vo3biObffftMluJ32ICkuWT+5uWJt33P/dBzSB2dREr1gqF1Yvz9JBrt3Fr4Nsa4uT/w9WHH378ceZlmU7MOgTGaRU1RG7U+LQt9l3gYe85zIcXEWq2jXXxENA49pVjSU6uNqIbelJU/Tpk5F57nldmT0SiBXoSHsbfEEiuKaqloDBtOUrHuT3JT4bZuW5OgFQANR9UKreRsyWkuzMUNoIHJ96WB4i0eF1/DMVnsCVV5mTLDLvDn7w68TJnnshKLsOCs396AiBVLsNqIPgZLzue1MiRbR32FmJVjqH5f3dgYx4GKuJ+ad1bV/wyX7MDGt7uU6CTy4ccnvth1/P5k66htwGZNItfGGF0UfnbY88XN6F2ZFikM+ybinXyxH27abHg3EDW4xOU1m0ttrIrHtHaOPOwHck1Xw+3eNN/ml8rNYU/OC+d15mOp5GVFKvz87T3TsnBZM/vdwP1uj4pwOV8Iouxj4LaP7MYb5iXzotbFnhxEnbHiuU+Bc81uaWJrI3jG1KQWclW+OZ14Wlf+mzf3lsB6J3hZK+dp+az71LBHqgaqD9vvX8e5HyZXUW8x0A/XIZg9VlY79yFQ1RoBuVryvubCi0CSaNIpPpozEpJZkC1SSeLWRP5M/osBaedqQND11QYNnXB7M2zdhAq20f0wCMAv7w7cdIk+CHldmLV4zm//DCrcVOEsiiRbtLUlO+KVp9rntfGKAT/xjtJWotg8M1e6zZSWWAAAIABJREFU0Rzd5+IHHKYetu8iQeE4Z14wEUEpyq/f3HAz2Bz1Ukw75HmaUBG6qpTx2hGY1oXdaPTckMwyRCQiIZHiwsfzTDr4WEgru9HA1UXVwab+akqstTLGwK4zXZmyiVtc28xZTUtiHOJWAdszqXyYZn55Y/YKtermefXhMnPOplmR1QN0636oAwyv3GTbFFn54rAzdersaqueSPzJuxtn9Xm10SozPxkFp0QTqQhPVfj773+0oBjg4/mFv/j6PW9vb/nx+UJIyi+/+op/+es/5X/9P/83am3A51Z1YCtKDRB6XKxi+jgt/Oyw42eHnR02vgTUsVqhFTSNPhqEIfXMufLjNBN20ZS6QyQOkePpwpKvBxgCAWuHL7Uiva0Zim36LMLLeSGJIDFYpR8CRQueHjHnzOyBfc7FcB++nnOpzMfCV/d3/L///j/y0CXq+UwZet6/3fPd0wsE5fZwwy++eI9W5bfffs/dTceSMx+OE/NctmouxsSimaCGq2r0ZHCJBK6HDD5KNcHP6/giBHjoXJ1Y/GD2dVG1otW8DIPoJlZ4PM9kKjkrd07GiP4cbnbput68G1QRfpgLsUuc1xXlOmYyhW7lwzzzaV4cUwhDTHwxjuy7ziwz2uLwE7MJ3ClG5T+vxawRWqzwPWTKyZbEvUyZPi7c9D2SAsucQY2QEGMkidnlCE08EFMTXjMf5okSIETrlpa1mNFqDHy5G7jt2rhHt86XdVGCYejUu6R+7aUaF86+m4+/toPLi4VaeSqFMQpFAks1OYsF+06xGSdvcJWr+nsSYdaKFrUuYoG/eHfLy7ryzfPZO6KCZqHv4FhN7sDrT2qtzFU8lmHx0rtZKVdqH7bJQYM5mF/ilVrfulkiEKuttTElehG0ZFaxmDqE6CPE11i+usX3nYuj3u96s7vy8b+3PiwJD6Zbhtjz2xxOcCKBd9WLtsJaYeu0t5hjP1FQVyyPJsdxyZSgfPGQCPPEbRDuuoHcBYbbG1Io/P7xadtXEaGUzM/v7/m7SybXzDkXNK7c39/z3fffEfeDK+mbREKuxTrg2RTfo1aXDGm16nWqIli8PJeVf/fxE//y4YG7rmMthZN7Xtr+CNuatNTm+jzad9a24IKxIOsrHOh91xNEOeXCXA24v/qozZJOGyeutbKvifu9MFfvUqlNMILAvk+ES/Dn7gQAL9T/qddPKkGqquQI42iXPbmPSkrB5uPVWAEZtvbcFzcDh2jz7oCJkBW/uYi3PxU6hRsRLlvqZC9r3QMdaDUqcQvdLe9s6P1aFc02Py/qRqO1MZzYFFsN9V8oxQ70h3Hgx9OFWnvudpb4JKdIiir7zjL6gCVgUYTfP5+ZNWwiW9bKVfa7HefLhZdlpY+BdwcTDxMx3EZrLasnFBYezTPs7dizVuV3jy+fBRXUDp3LsvIwdDS8kWw3CS65cEhWZb4smcfZRlMp2i6wtvmrlnJsuhf+/v6BSymUySj54uy5osqQIl8Og3s2/YNuAdf3aQFGUO67npd+NcmFCmjg+XTmbhjo+spD30Nd+c/f/ob1okjf3qThfNiwPWABuFalLEquF6Zc+cXtYau6q7TN3+bsbfO5sWe0Q9w9jLeWewyRsTOtpgbwLGosF7pGffZFuUJOxUZIYNgET+BiMuBvq9AEE0BNwUDo6jj4FKyzKFIZRtOEGpxZWaqNB+uq/E//47/heDzyzXff03WBKWfrzGrltK5M2bpgeV55uLnjw6dnxzK4vclWN1i1mDxgZU9tm/ZJHwN3XccXu/566NCeowXPJu4aKu6RZhix3z8eiUH4g4vzCcYS/boMvN+nTeaiMS4fp5WlrK4z5XYhAilBM39Xri7va13JtbJLibuu5xCTj579uWJJG9gI8GVZ0FpJLtiXXYcINW2q4q3+aS28zAvDlvCbBYYVXa0zaCKMqnCuhZdsI4sowml1mcTQaNWmLNxwgw27Fnxv7fueZVkQjDmJXhlaDYek1+W/rd2WJsxVyVmZ10yprRvoYy2fC7Vhksi1Y2OgJ+XLw8gX+8GZcPAwdvz2xRTJS1HOJXOoZvLdVKXVOz8tGFsMFCdUKElhbUCttmbEO43a+FTXNSgq3A0jrUN20krGfL6iWmL6MPQ2AttUs6XlP+y6xP2ut8JELYlr4zj180lLw2IqTUo3oFcdpGCbf1v/UrcLb1fcusjFmaVdNCxt6a0zpUUZYoAYKc7mWdaFv396Zl5X78TvuB33/ObDR37WtNOKra3zVJjWR/o+sVRLvKvrpr0ZeltnxQg5EgJZSzsuX/cCrt3dCseS+c3LkXddzz5EGtvXvvcrLJi8+rZevLRu5fO6bL8nODNTTfrguGazr/Gubft48/z1pLNWLkvePvt2MIubBlBvQrybBZiyrdd/6vWTSpBUlRDMLwmB4M7TVZV5tpFGCoGuM1Xr1ZOVtn/a3o/B2oONnYJv6ISJi63hmsgrtv9mp9rW2vpO7Zr8v68Z1abmW6q+6m7Ye23Cb8HGESkE7vcd67HwPK2MfcdhSNylyFMwpeaHoaN30bilVM658JQh80r7xhdZjImu6+ii0KdgflSvKp0WeNq1N9SLBTrd6K2vvx84XXxe+Oowfn5zfBF/nFfbtMA5F05uTJi4jg22vNQ/81WxxDWMWSUuCKkP9KPhxx5S5CYlT7b8x+TVBb5+BP5uuxh4tx9MO8NxPM/niX2IHOeJEJLhP16E3TgQ3KdtzdnBn/78Rbb7ayM4M+NMk/D1YUeKcbtfrWoXcRmJltx40LxJiUWMBVTEsCxdjEx5ZXH2DNiflbC9hd0rwdlHlgw0Kq0l5+1QsGd1TdZM2DRnNdZQDJvxLSh3Q08fI1XgnFdnHnWsWsnryuPLC8fzmRiEy2KK0tE7D62juuZKXOLWqWhaJOLX0rpJAaHHxjEqZl0yRFuj+5hMMb6B9P2frbhQ/66quBq9PYOPp5muN+pw22u7lJnXgsqe275j8BHc47zy3fPFmGQ+iqz4QSdsEhjb2sdBvTlbclMrS+rc3ym5dYT9xYIy1cJpNTPMXJVmQbG6xIeJUdr3WmvmuKye1LB1LGrVzW+qqRbPpXIumdlFKnMxIkp7DTHwMHam1gw2AtzWoyUxYwwswnZN7ft93rbQDZvR9IXan5RqjKRaDByetR3l1470du+kEQUw8gM26t35/m1JK1U3kL4o6AK3OzvEWmLUSAcVG/N3TbzSPy9VG6239dbWf++znvZ3OzFT39E7UROFrIoGsbGgqgPohS4YJvKyZu92wqFP3A3Xn7db19BFzlCzSvL659iitTGfFz5+JlxDnny2xl+FCouFakrSKQRu3cYmiFBjJKuxVGME8srTaaLrHBOlLQGLnC8TMZn1SC66aTtpEjpViEJ2Vm3Fiqyqpg0nycRFs19/Gwl+nlPYeLwWJUshR+9YimzheWulqXhDweAVzXi2VusQvdZsFGw8X1RRab6Zjk3j+n6fnR9qHfMmQ9G5XEwfA3eDGXJ7fWD7jmsi/cdeP6kECSARuMyr+WYNdvnLYu3HGISuN5uIfd/xVGcua3a/J9kOldu+45PLncP1ZgWgK8bKaLgFEaUPwmnOr8/iV5vguklku+Wv/568+suV7IE8RJMXSN5NebMf+XSeqIpTKBP3c9z+PYh5M51z4fvLSuhGPiP7ClBt8Q19zxhNIkCA5BVv1itWpl1f+/2lVh7nhcdptu/iiq9b4FZ4nlejfcoV5AZWxT7NKwc36byUsgmHrVVZS/2ckYYZam5t1SYYJg3j4a7hUehH80a7T8lAlK+SzZYfvdYAam/QxidvdwNHx/8spaJ15TIUPh4nVCu3fc9Dv+PN7R7RyFQWTrX6WJQtKWnBdktWVOlDdDaJbPepPYpX3vaWwHhlNMaEiI1h1lp99GGds4xuAVSToC7lv72Ca96ESK6GodkqumozeInhlR6NrYcuBJIYq7N3l2v1m7eLlsxdilVfSy58eX/LFDN/9bd/y8t5YllNcLN4tZckkKWxJCsB5eV42mxH2mFRPYA3r6RWnDQm09uh467vjbKMHzjed/8sSdLrrlL/X6nKy2JCekUi1zocci1Mq8kpfH0Q7gd7Tt88TfxwPG2YsbY9qypLMYB4vLLIbZzj77tq5WlZzLus67jrldvORrkBYdHK87qylEwieFJkV9wwKQ3oKmLd7tO6cF9djJSrjYLWK9NmrabHNTv7LVcD+Wb/7y5GDn3ii91A9M0t4RqN8H1lGFaxFqJ757Tq/bpOt/IeRTaBRFVL3NacEQJLS87lmvgSrgXQ9lJL3EfvLKva3QrikiYSWHCjaTEyQpAE+ko+xJ96BtYls3Mfyjae6cSUvZv2hrR15vu2JfS7mLjtOsT1t1RsBC6YrMdxMYkTqXA79tx1kW+LWcEMXeT9fuCmT4bbknavXsWEJpaolc97Jtci2hhgbX/gjEE2hnGLKw3m2lwNMkrCkrSihvOaFXAFea3GEu1SZDf0W/KxrAv3uxvqfEGJrjhdiV3wMfOFnQiSAlJgrsrF2cVaKtOa6YmkLlIp/zi5Rn39G0j8PiZGV7ffgnT7Ra8/A0JEaSzupZgDRUaJDRCO/do5u7PvotmE1Hbmsq0BmyC0de5yBrVwPq+EYG4Dd2PPu0PHYejMHNqtwl5zI/7Y6yeVICkQazFAVwx0RI7zzHReEIT92HM7dtz2HWUtltR0yl3fzByFIJVf3Rw4r9nBXvbOrQrpcd2SoJttw37oOU0NeshnQaA1CrZEGXxBWVt7Xo1KLV4NqZoy6JjMCqOq8u3jhV+/uSGFnc35y0oU4ReHnY+izGV5Lub4HrqB1tBuhwmtKvDK4XGxmWyUwLtD8gVxXbeqbFX0j5eFH6fJwL8pmQAlbGyudo9W9Q5YMJXaZti6Hzpqrvz+fHGAth3ASy30BNbqTDQ/zOtWNem2eVokFjFGyrhPlCQ8ryvHdeFbhJ+Pe76MyRbttQSwTeKHQ3tvn+URKowSGaNp9Sxa+OFyZM2FUM2M8u3dAT3PSB84fjwb7VOuHapaq+nUaBtJAlX4ize322ZL7WD9LH279slacrCqciqFj7NZHdz2I5d1NrmC1DFjrDfTL7oag7aIoAKXstBVU7DdDj1R5nNmP/buzdd4bt65iVdfohiEMZrR46rKUs3EVILw9nbP/bBjlwo/Pr5Q3HhzzssGfOxDIkgkSzWvtxgYolVsCTuMs1rgD68xSFz3SS/C3dBtrK62zq7AzWvRuT3jtma867D6QW/OOX4weeI+18rfPT3zeJn45e2Bm77jvAJBze/P37edw6XaKH3BQe9BGHvbA5bQ22evFC4183GZ+Iu7e7N6CcJpqfxwPBOwa2t4spaIhRC4SdEkFHxNrbnww+nCz28PzGulFkuccckSVTP6nWuzEzIfsNO8bmbEYxe56U2ewCQPrgrSgoFTJQRKLdyMAxIip2lBuVKgX7+2+OBbKIaAVEy0UE0wsD3LlnhVVdOaM7KhdS5rJWcYkvCL29HEaEu1boeVQzyMA/liBdXWmVDvrOqV/BKDexCKx1pP3Eqt7IaevsJFK2uo4LpWqAF/A8Kh73noe2rOVIWzli0pXatyWVc+Pc+UUvliP/Jm7JEgPK2JZc18dRi56TvXymIrepOahls1sTvb+7KlTnai+JTC9rLdp6148s29dWRUP9sLDe/TWH0qVjSeFxOQvDcQJ49LZuz3vL2/ZZf2zOvEsi6c3Ij5rMJymem6SOqFUlc+HY1UcRJc087OjTGan16uNqQvJZNy4XAYmNbVr+uaHBpeS/m63zP6iFjauaTXTd/WU9vXNRgu75QLp7waa0+EgtCHwC5FetcLDBI458ziRfc2EtZAcAuvbaSLdQLX7GbwFVapnNfM83zhTx4e+MPLkcfLbFOk8Aoq8kdeP6kESQQevnhg+eGJkgvjEDkWRTPc3Q+82w2M0cCIfQi87Tuec+bjPAO9HUDFsp5//nDPd+czT8tiow0qSY2+WRsWIQhdiHRdIsSAbuOnJjyHz5R9yXhvNCZhXUxTJqkQW9UgV9T8abVW5xAMaPu7pwv3u8RIsIXFK/0V5yb2Ee7Gjm8X66T4pbxKCOw3bCEGzmvlh+PM6Bs7eNv5Ugo/Xhb+9vHFExI2enjThVIRA++9WjylwpQtKDU2YRUTrazRK0q1yjeGgERzk++r8ouHA6c58/1xMg87/GDXa12hCrtOSIfE5NFRhA1n9ZRnbvrInSRSxcw+PZybx549g1Y1qioShV/c7/n53Z5LLvzNj09m1Jg6FiqXsvL46ZH9EHk5F+73e8ahcp5njvNkiM7WxZDPFX8va2ZM5kQ/1TYaapgUO6hsuiSmuKzwzcuJ53UlO7Mx12IMyT5RtVCCWhdoqYSk4EJs4jeopT1LMiNb0YBm09tK+4REIa+FfUr0LuB4qYX7lPjd05n73cAuRnPVLoIm5TmvBIVDPxC7jm+eHnlTla/2N3x/PHNaVlKKfHw5crMf0GTEhZSiGa8KzLkSvN0lYj5xtTZavtIg2S11fDP2WyDd1vBnJSdbdywgr6wljI3yvK68TDOh8yRerkAU9VmcKrycMt/pwmE0vEsbb9g4xO7lZknh97h1vspk45YQraPa8ApthPHXHz7x67t7Vq2c87olAaF1YKgb9qyX6PR6e+4BS4DO80K43UMtlFq2sc1xLTzmxe6d2EGrpXKaV0pR7g4dD33HvnVUtHUy7biIIRBjMJZXNdB3rZUxRuJ+5PF0oYvC8qrICn64N7FPxXCUUqxQlADH4hQJ76o6HIgam4KVjV+z+01+ud/ThVcwB2cgicCXNz1zySyTudHf700Jurbn7snzpRhUYlGFXBgluI+mMC+ZIMGseyosCtJ7LIkdN31PBC7rykUMb5Rq4HGyA7Kq8uk4U6vyZ29ueTN2LvVgjNrHaeauTwbKBovFVGqGTL2yXVvSGCJaq5soexfo1eghBrgfDMf2NK1bvGoxH7yj5KD21Nu9KiosRXi/i7w99Hz/NPNhzRQNSOp5mmd+gVC48CSFxZ0WQgoMfWQR5elyJgrWSNjtqCXzPK2IhE2uxfTwVpplSIruTVkM5HxcMgFPnB1c/obEwUkMr/etBl+XXrgreIe/UKvwuCwsWjfrlqI2sN2njq51b1VRqdy4kv/NOFCAj8vClBse2E3C7a2t8aHNL9Ru/7ys/MfvJn71/oYgyvvDzvZfNj+7f+r100qQgOk0cTP2NrdEeRh6UhAzZW1zfrWW8CElnrPJ23dilUUK7t5blHej/cyHy0zA8DJVTMXZMv/IOA58fD672ae03B+w+GoeWC1t8jYfNredLmbVIRWCGqC8egUiWBu4D4EUImAMmF3nDCCH+1vOdWXQjQIjysxVeVe2f4L4gKVRbKfGXBHrxvzhMvFpWphyMbNcrB1bFWrORAnc7/c8ns6vzC/tpVV5Oq88PlunaOgiXWfCg+LlQRD3jcIAorsY+OXbA6nreHsIfH134PdPJ37/cka4mtKGFJCbcG1/4wedV41VlUnhPx+PvO8G3ncD+z4xTYaLycrW+g54xz0G9x+yA+6mT/yLL+/59nhmzvC+H7jtAh3WmXnbJ04lu/5MRy6Zy7q6y3ag4RyiQC+Bv3s68vVhz6Ezc1u7BRWTNCo273a7FDuwhbPrrbQas2C4sTmXzVNsXatphRReCSjCZ8PzChQhSyV1kf0wsBarxLohcZ5WPh0nANa18rQ3hmKWyCoDNze3aFaeTh+JpgHKtC5MzytShCqBMp/ZdZG5FE7nM796/yU/PL8Qekt3qlqX1aA1xlb0lQLBgmingVULjSbfVuqlVnZuKbP9iLQHdx0Psd1zTziimUzfCcz7ge+XxejcTUDG3yMSCVUpa+F4mlhW85y6G3tO08pyhYp4Z83Zcr69RXxEgxKqWKL6Kg71IfA//4tfcj/as/vm8cRffvtxw0S2pDqJAeUJUAuMsdusEyIBxMaetasUNRHZXJUn73ZIsBFph9nYdNK8umRzK/eTeRsxiFheH73QyTVbkREaUSVwt9/xeL54o7UlmbaHgsJc3HopVzNAFeWlFBprr+GNDC5wvTdJbE1kgbuhM2sQXt9rez7q8g/7PjHlwrQWLpfMvhtoI2mwjksf7PkkgUOKJAJLKZtidNFKrfZ+BwnMuXA/DtykHnJhojIF73yr8sPxwvNl2bBhQwz866/vGDrrGDWdvVwr9ymRYqRgthXVQeXGCDZZGUvw7Rua9Ubk6pjXwO916xr1QXjoEvum6eOF3nk1CYWqwstl5vbQg4+ZboaRu/2ex9OJLw5411RIAaIoMfR8KCvzlFlKtnU7CN+eTyCBtdhIvk/mav3peLZCJnqio9ZJMnsOi01RbOycc+FYKvuhY9clSrURL0U4hMiX43AtfSyL9CKj0myY4NqpcW1wx6bqto76aMbvrj28dc+jwLenCz9eZh76jnfjwM1+x8d15ZQNG5ir4/6a4K54wwK3Jwk25BxE+FAKX/SR2y5RNFIOO/4tf/z1k0qQogjiFNroSYmmwK7bbdUGrtocxLxyBOvenNZMCgvv97stkCZxDSFv3UUcEAZOFRWm1XA3hkeQbexkrULdsA+UiqgZ5q7FM9gYCVFJXs2WXDc9jcOup4+B6bIQRDgM3YbTCX6oNjZDq2xRtkRp0ylpkWfbomDNGT+cVfnm+UKMwvNiknlDTPQxuWVAZS2ukKvWclQ/uBpQNAXTTJnyytOyMAymd2EB2D67nXM207VZ8sOu58v9SEGYcyVGGz18fbfjaVpM+TUGq5L6wBr06kj/Cp2s/j0toRDOa+GlLGgy24fVRQjtCbXk9VVlJ60JC4cuUSq8GRKHZEKI0HAnBiZNagq/PR1PUdjFnufpAsFJAH4IFVW+PZ14N+647ZNjvSw5Rwy4GLRuycHLun6mudGwWGNIZHUbiqbC7hfVNvn1RnhVtKrT+6Fo4VKUFCJlVVJnX1pdGFR6Qatwc9ix63pSgFoXPp5PV/ClX2NVIxHEMfJpznRj4qv7A7u3d/zJn/9X/O//1/9tTuMhOMbDEgt8b7RBrp9vVoXy+Xqeql3vaTLmTBKjUH/lYnbia/xaiLTUNNDsOXoR3g4Dx1I4lauXliBG5MiVDmPTpRRInRA00Eeh9pGdP+9cldNimLktoDpYuyUBKpbctABfgRgrt2NnY54UebMbuB0HA09XG9nj36Oo4cNSMJXo1/coiG6jyCFFlrXyUrKri3vM8YQtYky1WuqGaTKzaAfu+zpp7KvGglWE4OOYWq2LY8y6BspuIx5bk4uPxIMfYNnHwir+3to6cHhSpVChDwbYz0XpQ+DWVdAt0ZMNrN8erwI3fceSK6dpRb1TWFwqpGGcYr0Ca6darSuhFnNGCTYq8W5lHwNdl9iHBKWweoKS/MRdVXmZVmPYBuFuSHx9u7c4sjaR02sSqLWyzMaqRXAl+JZUqyvbX5P/EKy7vHU/8JjW0gJpJba6gbBw2fThLMmrVXl/v2NeCkUqU61Unezc6Ht+PJ296yF0MRGT/cxpXcnFJhdNbDeIiQCHYHjGZa3M62IjdYTuELfraTYtbeQXXiU7UWBaVzriFmvHEHnfDY4tlX+07zdsequB1Lq1ReFxnUFMikDVmLz3w+DF7KtzTYSXtfCH88RSKs++3vYh8abvWdQwrkVbdGlngHfE2l4OQugs6ftyP3iHu3KV+fzjr59UggRYO7gFBjCws7fxG0W6BVfFAkpBTSxLC49l5UFsUQQs6UpuBtqihCXn9hnTslI9k23tdZHrjLxuN9d+PyWnqFZhK0f9c756OKAi/PhysWDV6OPVqPzNyFS2FdLeYzsiAaUXYaLledegY9m2HaDNFFEca/LpNNvC6hKHvmNMcQNnX0rlvGSmbFYQ52Xh3c2Ox2kyUoY7gcdgQoQhyVZ1bFgXp3PuYmDXJfYpsesSQ5c4lyueQATGLvGz2x3xNFGjUBOUyAZKbL/amMIyJtfxhACXWvixVI5auI3JtYfEWTyyVR7aduvWjzAc1L2PJj5b/J4kRLGWSBGYg6mgFK3sup7Ge2pA9URgHAdOpZDnyk0X3ZH8mtA1oTkQPrqtyevP60Miqon3mSfWq+fdAkxg+w4o3q1rkcfu0aoVVSMoCOIK7WoCe52ZlY7JQPRGBTYfuBgjfYwk1xaa8opq5VQrT+vK6HupT4FpuoBWZk8WklNO2hpo16f+e2abEezAEMNfLVpZUKiVVa57I6PssomAtsNiezTSnqJslaGoMsbAF31Pt4qbWto9aSO5gnJwP77W1THAtL1PwNbTkKzvuBYh++jYno9n61y1tsBox1/fHsA/I/g4QtTo15M6bm3rIUChEtQVoxvbsBp+48Nl4pA6plqZiuHC2r5uHo+t8xaCULK+LtZpyen1nvk1V90+vyW/xbtkIsKbmz2fTudrmPL1VtW6LaYVU1nUrum1InKbGrXPCq6vtWbbi7d9skSbq/SFiuNE2jWrSTx0jlMzdXrdwPGtH5D851ZtLM9WoNhldz7aFBFSinQxuKiuUoJudUVR5flsyUGfjPn3bjfwMHabJMurr4ToVSeq1GuHrgEtmkr2qxBM1M/6qNtI91pYWxLSGMl4gqdULzCsuOu7QCCyUk2RulRiNmPxsybG6CaranIXuaysPqLtvGueMTJQVCt4F8dLboxiV7LfaO9eHOBirq/XVEueqhua77rEfd+xCy4todfET/3vI5+fT607MJXM2uxHsOIgtU6qtjVra+95WXlZC1O2fXmplbJm5lAhwF2MaDKYzVqazpcnuA336on54L6sIuLsSgyb+V9KglTVtGH6FDYGQgjBsQ4eSF9t3CDmNZblCnL8lGceur0p7wbDBxy6xJQrpZYrsNQDeqmFQPRNdp01q1ply6vquXUhmi1DbV0ZFVIU/tlX98QYGbq4yQb0YpnurrPxXwOmafsy/6CiDgiDieCoAAAgAElEQVSHKLxkX4j+vYN/3+iI3qrX79KFyGU5sRstaO1DMAPWYCaoU6mcuszzvPB4npjXlbf7HS/TYvekddVSQFvX6FUV2cZqY7T3vd8N7IeetSgZ++6tIhE/OA+7yK32nMSUr8vG/rCAekX0tPrMx3GqTCgT8KzWmi8KY4zccnUxvx4Nsm3StnHfjib0tpnPtr/1KhjkUrisZpa4lJU3ww2CshRzvbZWeeB22PHhcuK8rGRNvO2Sj0yvn9c+/dGT7faKEth1PTlnlsVa+K/yOQ8yrxKF9mt1ccOiBowNducEZeyjM9YiY4pmj6PQ9UIUZcqZtVTGFOlDok8dqQuEYGOLZV3JxRhiBeW0zMw+p5/KNwAs1bqiYbvPn78arqJiB2cfjbRQilXDbYS67WuMPfPjvCBDzz4ZhT405NKrRGVDXqrthTd9Ry+Bx7xwqXVjWCGYOKmbWBaqWxTUzWKjJUJDslFJDsJFxBXKfcr96vyLQRhT4M048Kdvb62oQpirMX5KqVuho9v+sHsUxBMWbZi2VhzBH45nvrw5cFxMyA+ueKC+YQQ9BAS5ki1aLBL/x7WIYvv8Wh2bgbpO3BVv9LDfcZyWzXDWlp/FsVBNh2fxe6r+M9t980di+9kwUs1Qe9cFbodkSsXBDUW5Sj4k76qBF6nBkpwoQs7Kbogojflq360T05Wy9fBqnwJ7ZwR3KRFjRFAuGISg+Y/l1Q7cT6eZXRe5H3ve7Qbuxw5R9/tq91DbQd/Wt42+JRrgXdwdYTM3fpVQbYUcbN6A1+dknZ5cjWCw1rqtr/VaSdm6UNj1HVILJSu9Kjsglsxh2LGTwHFZuWT1xMDGdNGV/G2MaYdH8M7UrC4zwPV6crmykq3TEkgKpW4GNZY4t5hT1Tu4phDfAPobC+9V7I1cO3/tT3JVpmJWOaXWzQ2ik7gV2q1lOpfKt8eLw0Q8Ka7KRGFxr7Zf7w4khd4qGjZ98nZW+3lQVNklw0mtRbeEergqtPzR108rQarKy7RwtzfafgWKS7Z3ndFDW6tNESRUO9Rr9VYzGwDZqOU2ctnFyHFZ+M3RRlB3ndmBnNaVISYEqxrkVRLWQGFXtZnrTmn2I+urbkASw9/0Y88//9kDAeX5PPObH0/cd4n/9OmZu75zl2auoD25BiRROwxvukhaM6tPa4IYmLR6VbJNZRBWrzBEhJoLE3AKgc4P0BSEt6ONwp7nld9J4KNMVCo1W1XVxUDqAhv/VFsFLk6gUoYQ+MXtjpuht+TSN1KKCRGT5KcW8Cr5L08na7n7KbVRcrlWyy1cbDTYoNuhFfxnFq/i52xjg6ELjBKNBu1gWAt8alirqg6YvrZk8Y3UDsw2UnhaV28Xw8t8Zt8NDLGnl8RlmklD4vunZ1K0tfjDNFuncDQ6bgu4rXNltPi29qwL1w2JKrAc63ZotqS4FgiJz4MwxpKMi3K8LGgSUh+JMfB+P3Lbd9tBFBDuRuU/fPuR//aXX/A8z1SMQr7Wwr7vOBxGxhh4Oi8G6qxQiiAJ3t3ccl5m5nVlWTPnaSFKouusyl2q77R2YPnBXz3BQCzRHbuOl7yycE3mWnfPAqiNuh515VhWvhpG3g09gzvSK218J2gDMosnYqWyD8JhGDjXymPJPK4rEcNnLbVwM44MfeDD6WLSCmpdoCSmxzSXQqEy9oGhixxn88qqqkhkw7e8GQf+/N0N/+rrB74/TyBiBIUKIoHdYAG4dXxeR4U+JopUP5B0K6i0ClkLj9PCXIwRlYLQI+YdhW5rHQHVSvKkuKpj/fz/LVA0OFb1RH/xToPIVcIgiXA5Tzwcdnw6nhwHY3/WFyiluG+bC3u2Tpa2wuyavNdSmat5TvbRsFLJPytsFfx1jYBDBBwOse8Sb/cjj5eFy1SMzu5Vf0sedl2kV9n0pYIDilWFaa3cH0Z2XURRjlRSsGt4WS0pej6v2xP57372BV0076VS6pboAgYmVospdn+VSnDBXzO57bvEPK9WpEtLZoXqz/Y6jWi36DrEWavpOTW9ubUW8ivfOXAoCabnc3ZplRSEpSp9P3DXd+SoHJfCmhdigN3Ys5xWQvRk3Ls9uRZQMQ0quI7sBVIfqeKkAU8ooiiSIufZhsF4vmJFj6fgFR7PJqHz5WGEqrZK5fpNo90YT7cbo8wIQgQlZ0uOkgSGEDkMPdS63a92NFw8gWtag4rVRxVlXSt/dXkilsCbseO2T1bgvDafFf/7RRkGu8bbPvFcVta6ZQr8U6+fVIIUonDYd4AtMnEmg2KGjsbAsps3iUISBjEhtaLKohbUP5SFr9KwVbqIcDcO3M4rj6t1DhBTHG62Gbl+Xi3X2jJ0AR+PlFrdFAsWb8QmPxxVYOw75mXhUqLrnSh/9nbH//fbR16WzNu7wQ5vxzy1Vk1Tvm5BToLwkAJPxduguPfcNj9vZZ6/xGaxl8WAoVMoPDLz/mak25IJ4aaP/PN3t4T391Dsfv54OnPO2WUQ2Kj6iiUaowRu+44/eXNDjJGTMwv6FDdBwaVWeoWlKI/ryu+WeZvdV2cBmlCnbJiB4BW89+cwI0u28Ui7H6t3MroQeM4rUym8HUa+2u+Y52WbtVk3xyQTRJoeT7wmR2LmuZoLqOFw+hA4lYxguJl1udDHyE3X88v3b+B05juB2/7AnFd+mF74vU48jKONunyzt2eWWwBwvapaCqfTmT88n3AjJy9fm4SCkDrDezXLCRHr0j0cEs9Tx9nX6s0usQ/R5AswpeZ5LUxL5u3dSA8MGjivph0UJDAeAj8+P6EV7vY33I07Pj4X3t11HOfMx9MRQTj0O26GgW9+/Mhh6Ldw0paY+EmdyzXpjLYteLMfuVAY+sh+7AB4nheO6+LPw97JHqsxrr6ZJkSEd70BaNXXhJWd7bPFY0JgU5GPgUPs+UU/8JJXvpsWLnnlN8eFFAO3qQc1YU6hsWYsPsylkH373o2J2yGy5MIpV9aqPHQ9/+rre3795sCSC2/GgSEZo1KAd4eeNzcD/8df/ZaxMxZZwpIwEetwI4Glmm5CCHaIdCGwqGlQ4bi+PgQM9irWhfE91zIYxcejISA+lpimBRElxGRCuKVwccHT4MRowZhthp+pTPNCjInbYeTEymVdSSLkYjYRT8WlUPzAXkshuqBgCAZONm0yYa627u6GxJtddx3HeYyOnkgoOJEmbGs6BuFm7Ix6TWWulU6MPGH0bRPrlCZ+qIZT2qXIXDJfP9yT1OUqRBk8ccla+fAy8XJZQaCPgV+9vcXUqKvTXu22mrJHs56wmx0RNEQbaeN7sHjCMXamO+b721pFnvyhbkXCdr3q7eHnZeGSi40ufbQWvOBUnD1ZlKEXcsmMfWBHJFblkgtPjyf+h//+l/z22ydqWLm/GfjiZs9//MMH7sYdw9hRSiHXwiCB+WTr6tPLvHXDxJYRu6FjLdlNXe05rsW/Z7XiYJvIOCZVke08WufMlE/8izc3LEvFHY/MkzFcZUVyrZxLsXMgRroaKEFdfiQxpI6A4ARzUoTjsvI0L9f71wpzyyRtwqCKZOtG/nBeSNPKXR/50zd7Pq2FInCeV0Thi93ATW9rsQtGrFq1MtfAGP8LSZBKUS7TSoyBsUvs+2gqvt4VmqLhehTrMPQY60Ac15OwjfCimXuSVWjSqi342WFPzAsvcyaFwC6ZkZ9i3m6tKtsMDVtbUa1FHKKY7oIplxGADgN/ZwqneWKIwvN5JQabuV7Wym8+nfj1lzeoukKtXluD7dfXp5I98I56WTgW9RZ4OzKc/gYtk/ED2lhnzQ1+TIkg5rLeRyFEtdGiHxoaAz+/3bPvOj6cL3y8XOwQ0UKshjH4+nbH+5s9qsbSmavSuSicALlY23cfAj/MF36cF15K2ZSLi3/XJv6YRWlaQs35vbW7Bdt87Xa0+1/8vuSG59LCt9OJp3Xmi92Ow6uuFFQCphHUQNxbFVwttIkIHfCm73gzdFTg757PvKxWMTbdmx9PJ35x/8CfD4n7mwPff3zm+8cXKJm/+f4jf/bmhkOfXHjQaOFb+uoBVdVZjd5C3qSiFVoJ9YvbgU4C01p5XjOXmnnT2yjx7b7nQXVTKp+qGdQuqpznzLxm697VwPO6UlFSCuxDR/LRdM6VXUzosrDrOv7ZF/dMOfP8dObd/R0gTMvC7z5+ogRlItMVa4tbFW9Go5fL4nfYvlsMwvvdwM/vdvz98bypO6cY+OJmx/pk3R3dcDKyjQVCCHw3zzzllfuUeDeYfEd5XaS0EY3IP/o9RLnrjH30zfHCqRTWXHlcjdW3+GijkRDGPtElExFsmYgI9M7SnGb4N798x9c3Ax2g0SrchB1milJUSBL51cMNp8tKDSsNpFsUTks2OxiEPkS3zDGLnkvOwMrtbrADwyvo9v1aA8b0EAN9NMDs2LngnhdrlvBl5rWN89wINlhcIkCthVxMjzhho5Qume5MLZVpnkFh8hFXexm+0ZKrpvumqHmslcptSvzqdueMNr0Kgvo9Fv8OlpjaAYrf7vOc+d3zGVFhiKbpVGo1RrG4G5oam291lnISG6m9H3tyzUy0uG9rqary6HijGIWbsedP3t4xdBHWbEm9r9fqLbdrF8e6U2CsvlXbuLV5NVaG2DN0Ped5IeeCaOtqs2EgwQ78lohX2HCfJvSprZylacIF72zGIvzZV+/ou8Dffv/Eh2niZhdJtfI3f/sNKUV2g43JI4XLbB2y+8PAqdQt8QyYmbu+2h8i0PcJREm9eWvWYt3N0rbAlhBZzKq1MPTmsyavxI9zhu8vK++G3m1W7KxpHU5FmZ3UMaRkyvDAPnXsum6DXzThW0X4Dx+feVnzpta+fZq2Vcerrrr9i5EBlJcl8+Fl5svbkSxCdUbi/dBxkwJrUWJQdilxrtmcA0LPP/X6SSVI7WbExohQ0woqCjnolebti/Pa3ry2u8Gy9B905jYmRgkkr0z6FIklImIuxjWrt+G8C+GHMthhXYozvRQkOg6pmFhe9s9csPHTm7Hj5bLyosplKez6jiFVjtPKOHZGNffugUXDVlU78+SVlEBbG7d9Yp5NIfp1dXkVJxRaDvCzmwNDl/xQsGpKpXXQ7Neg2mR/iCEyxEjfJfZ9ok+Rv/vxE6rw9jDw1WHH7dgj7raNB+JSHMQMRFUWlL+8HLeqCSBi3nmi7enY92taOY1JsX0LgSbodQ3Z6gHXkoyiumHAKsqRTJnP/GK8YS+C1Kt6MkAzkrUaji14t73fqr8A/Or2wOOyOhAatNoBoPnCUuC3z88cl8xh7LjMM2fXR0qvpAG2/YwLEUZhSNHYYI0xSKMMb4/TwIshkHqzjsk1ORDfRefE8B9Tre5OLbzMy0Ydbl/ocVrpox1uIRgTZC6F59OM3EZ2KaABnpfMzb7ny7cPPF4mzuvKqgXtIFSv8sRYI6kYo+9S11aME4D9MPDusOd+7Ljk5ToK8qqyVHMqvyyZy1rcILMlN3aPclXO2P57zoZZ+NluZB+bKnPb37otite4PcRVi9uW3Q65JhngYNmqlNksJfpgz6Rh5UQNoP+//Nc/576P9I51qdg4ZdXCWuHiB1AKgV++ueEvjx/M+06dgu2yHp2Yl9RcCjOWxGSvsBU4zospJg+dFQObhIhsNZL6v1TERuzFWL2NAVurjR0VE9gLQXw/avPLpgkyqlhBVLJ9/5QCOluxeFazklEwiRLHfLZSrLZRqjOBvjoMjM70akSEVpSqtsafbo9nKZlOIoiYdEMXmBczF8+5ErrgeB5FXvnsUQ37dxgH7vcmxLtSN5zPWpUPlzMXNzW/3w/senNvr6L8cLlQSuFN6hjFaadcKfivoovFcI/HW+ca2TSAui4QpGPN1rld3ORYaJ2j7ThHgad5sa6TXpXC2718PejJVTmtlY/nC8d54WVeSJ0wdj3TeYJ1IYWRaV2ZFYJ2HIaeh8MtXdfTKaw5M002x2jHQ0o+SgtC76P9Vmi2a5QkkJtXosXI4veg2EHke7R1kpSP54mqypuxvxI3XgXb3u1PqipzMSmZMXUeH/F1ZDH129OZlzUbVOTVs8AhWtoCJIrm1qkLLQCQgR8vK1OtvBl7gkLnllva4otcMZsKn50L//D1k0qQRITD2LmflH3hLMoi6t0Fac9uw/C0598OnQaiW0pF4/asaUu5uGS8AQLlFSn3SgVXTCl44+xXj9bRN5S2JMX/GYR9l5jXwpIr52llXg1EeFozD2OHVMPIsI1m7LVdu76+VtvMYwzEUNHMq5/xFMr/OwJdUN6MIxLEWCx4+xihS40ibKBBVUueJJhvV9cJXbIxx6fjhTEFvrrZWQeqUYulzc2tU9Y23KUW/pAXPubVabnGammdoeqLVHzRt21at38HcbxG85zbNkf783Y4tlrHN5H65z+VhWOFmitSlfe7cXOvvr6PeAB2yrNcrwWEMQpvho5VrRrK3nUTzcb0mTO1QJciMYwspfA4r6ScTXsFsVFde0YeYyzgsz3Y9tmNpv1aYC95cqHBC4CW+1zfEhHheTJblXYItveeS0FVtmRWq3KeLbE5Lyu3Q09CmZaFmy7ycLPjeZ7JWlirac50EuzQlNb9qyZUKRZQW4JrTMLKqWSmBjr2oqZ1APsYCL11si6rcFnXzwKi4OrWWKcpSeCHaeYmJQ4xMsTgAnO+itRxhp4kKfC0rO76/fo+ufqznw6qBpg1DJx1K1Kw0dcYAg+7He8OA3iHpnjhsjQChrAVBCHA250pmcd6jR3Vx1C5Xg2b27iiVN3YivsuseviBjBVT+raHWlrssWUohUpXix6YlLrK4kCX8japCVe7Sv77sZWUlVCNPZXTB1TXWh6kG2dqbaz8Urlplqcve2Td0sbVkq37oF4B6h1ahq71p6adXT7GLgfe75bLgQMQ9In3dhHJvxq92xIiS4lhj5RUWIXzSIEYSmFl2nl+bIwl8q+TxyGjtEtOp7XhdNq8o5k82tLCDc+Nmwx1hwJWjLY4pDtrxiCjWhFtklCkCslvqU6gpiEStWGwtlG5F30J6m2j0pQfw5sYOddn3icZh6PF0qtDF1iXjPTWhg6pUMZkyVmx3nltjcT8cu02F6NkUmwRIiMurF7A8kPKTC5oXv7XLDCrWIMxvactzO0XiEO7TpFzIbnmDN9iazBEubbaD6XVXBVeN28CEs1uReBbVynwPOy8mma7Rz6fNP6ddhf1Grxi38Ux+3eL7XyPNmY8G7fcT92JPFQK61kvTYaPgfPfP76iSVIpubZNl1GmajkqIwSuZ6h9i9O8vns1RyCr925a+auqsw1u+2AbY/4KmFpYMkq1loGDxgtKXKdh5agRa6sLDDD2+NkwLvjZAKEIQpfjLaYoGGK+Oya/Etdk3e1oBrlSiltj/o1G8uEKI31Nnbm3Ix3RaJXCEMyGrZhhYoLsgkhOZtFhJAit0PPzdhzN3S8O4xcVgPxNUxSW2g2QjI8wIeS+f0yo1wpwu3AKFzp4aElJ8Jn3bLtjYVNH2lbzgKhja30mhw3yHzbBo/LzLxW1qWQMNuBQZwNpO0J2q/bnNuvtK2TopXeOzm9BBapdNIwWZZAzuo+Tjc7tCo/HE8GtnVzy8e8+gPUa4JUFaReDx5fR03k727saLYE7XYg8iopvP5fsNHu07Sg0YxrI5+HgbVcPcGqKtNSiK7/kmtFQmJIQllX9g939H0irAHRghQTP73I1aPOGhhqUhoutQDme3acJmoJ16nXFljtgis2lk6xKQXb2KE6wLU1Utu370PgaV25lMLcdbyh4yY6iVzAPdO3vVHUlHpbJ2dbSn8kqWx7zD7froukjCnw/vbAUnQzzUQNuLtUIx40Krv4fhtS4ou7HcujFUMbnsMPlT7aoZa35FU3ksWbsTeAslfPbTz3OlkXvBPm3VqPGiCygeNDiD6hvZplt3VyjRRtv15/L4XAOHQ85wXJsv0+6oKZ/rdb5yB51/3drt/Gb02/SFWdpCLbAdxGwaKm4VW8e9OFwO3Q8Z1c7Ocq9NUwpy2udKlDipJSIvWJGuCUsyX2at6E56UY2aBU+i7y5jCYBICakfDRMVmI8lRMEXqUwCENbJ1sPGFoxa8nPyFYcpSCJZJBhJxtdFurdc3F13dj9k3ZzL+T72dLzmUDd6vCJeftGTSwt0Zh33U8rQulqGsvZesUYkCBqVQOKVIVTpeJnUQu60SqcNgNnuxG+t707mo28cgkYmbpXtBUYbOM2rSqxAvpP9ZakX/wnw5vyWoA7Eux/WFix+avdiOmF7YWK3Ry04bzdaRYJ/WHy2zF3esPkuvZ0jrBuPTM9lLd4kxrlGRVnuaVd7cjt0P3Kg5dsWHW7GiVwB9//aQSpKpm2KoB1qCs0RKZpMHmnnJt+4FeZ93w2U2PXuEcSyag3MbOA4BR3pseRhCjqCNhwwfVVw+11VOx+/+5e5MmV7Isv+93Bx8ABOLNmZU1dHdVczCaUdzITGZa6mtry5VkEqkWRRNpIqvYVVk5vSkiEADc/Q5Hi3OuO16xm/skqnJ6Lx7gcL/33DP8h0AftatVq9dAldtBrgHi8ZKAiefrQhY109t1kb96eaS1eLeBzM01O4eTDU0ofkvoqv1aS5paByU4pXQPHu6i537XqW9XcAwW5hxqtuqcVsxz1sMpeqV+duZQX0RxFlPKPJfEh4eJfa/O4R63+lZlC4DewSkXfkoTn9Ji34r1XmnV1PIQtypwbz+h/mLrZGGN4Bp05PZnrYrp8V9o4HTOcRX1R7vMmZRU/TYJ/KenE399PPCiU+2TWpsujiZp3qNsDdHKu7V0C6pr03vHENROoNihcSl6cHsfbc6uX8n7jrkI12XW0SXb+0XnGJ1fn8aWZOqj7Lzj9WHYEvx1D+u1em6xIBqo3j9NiJEN27aPljo4ryMnrc5M4LFqtNn3kS4GjruRw/2e95eF/u5A+fE9kiuhOpUFiIFJ6m0OifOO47jjNF01uYi65wTH4DzfzVcOsSOIt66N4S+8PrPgVCR1iIHH68wlq1dZQyY7dISohqOBVIUP88I5Z/5ZjCY+KF9cEzgWVF/GRHQBFaRsApfCJvHQvJ30/7r/r6mSa+E3aa+xABV+larWOtE7AmF9l2AYC+cD/+pXb3g4T+SyWOdAQf9DiHxzPPBwmXmY1Wah0Zxf73qlqwPFa3eierd2FlqS1JrVHnVgL95RvN9GNlKo0sDDW3KyZYZbXHI396tU1ZU5jB3jEplMyK/FImfVh1TtdtSiBqK/vd+rIr/c7FV7XoJ64lWxDorFk4iO2cW35ruqS3chmFgjuAqhaLHVec/buwOXJTFJZZFErJqN+JRVAXtWZlgWpbp/c7/jeBiZSuFaElMtpn79JWdJcCQrgIprTvFs3xmILtDHuCbziD0fUZbYkrKNGoVqhhvXZMrYOeGr8M2LPe/nieqgN3uYInXVmXKohET0gSqep0vi5Ysj12mmzq1D6bi/G3nbd3x/SixBwKAl35/PvBlGdn0A0XjnCRx9zxwrORRyquA8cRe4prQqkYvNwN3q4eg026c9b2exa0tEgLXQws6kIapUyNOceU6F1+MAorhIER2zRhzHftD7Z6tajacXPl7nDXPbKqTteDC7HdlYhy0Bb+xqWrKnHqSaYGshI6it11zaua7j5zlvLNx/6PWzSpBAmKQgZrkdsYqF1aB6fVU77FLd1GJvlU+rFKR6ZlcJPnMfexPk8vp77ROrrL3XVg3K9jzsVDORtFIoVRdyY2cF3wDMhW8fT3gXKFUr9713OFfXB7iBd63r5K1aczeJQQGCHYJFeNlpRfLJWARdEP76buDQdwTbhEtVryiCJ6Jgxz4Yt0UUS9C+kwd1fPeq0VJz5ZwKH6ekFg2l8r99+yNO4NcvDvz21ZEqws55/venB2u5inZUNNtYg2U73ItV+a5qMtLwA8FpheMNe9ECbnUWUM0ZuM2PvVe14FRVNbl17GbRau75Uqi5bgeMg+c58f+mB74+7Ph63LGL4Wa2blWyNLzS1hFcK0tbh5qY2iikCrtxz93hyHyZ+POnn/jt118jYiwKFygiXNJpPZhydSwucBcitmgQp6ayxz7y9m4E8ylqR7j6gTVMHKtK8pIL76/z+j33QccPqVbmxmaZiz5Xo1RKtmo2OO5ih8yFzzLx0AW+ff+J33//kdg77obAEAa89/z4fMaNoS15BGGRzMP1yj//5Vf8xz/9QC0OHyK9d7iiB+JS1BxUvOKyxq4j12KJpKydi10XuSxZmXalrLo9qqGkmV+0AiCL8O8fH/n1Ycer2Ftq7RSILMJcNkHIJhOBc6a3IyB+TT40fuiaSTf7u1aYzLhY2VpinRboRT8vi6fv9HCPQcVXS6qcFh119t5z33W83e8Yoqa7b/YD92NHKsrWWosCp2lyF7wy0Czxc9Y1FCviROBctNvbOa9Yv6Q4rWi+jU3GYl2jbIki1sF1qM0NbJpWUgovdjuep6SaOTcKrzqi1U5krcY8NUyY3yT9weKxd4FZivkU6r5GHM7LmpC2HqFHMZXvl/SFqKyrjucls58nCmqtIbi16/E5L4SUeDMOfH2/527X89cvjxQRPk4zzymRpRpQXNYOs5FbSVL5qSS+9p2OFVsB6tYGH100dXWvyX/KldM0K9arqOmwMtqEsatk75mqX+n8FPjj41UJO96xpMKlZsWxipijg1fF+aJaXS/vRy7TmWrj7eA8VE9fAxcclzRTFzHMj7AbIqeU+fh84dVxx4vjgbvdno+nM3dDR0mJy7QQQ2A/7snXZ0rVvYlhwWo710rFN66P97hqnUubtfqbg9ZHv3Yu359nOmNhpip8f7mwix27Tg3Ig6hIsPcoi9BrDL2Wyn85ndmU+Flj3Hby66SBdV2znpe3RzE2+kyu8PpuD8FzMQcLVo06xbEFH5hq+u8nQYre83Y/cpbCLMZm8JgekazaO2tVaa6HN+kOtep4aQwKHCsCz9EiasMAACAASURBVLkwlZm/fTHiTxd8CGuiUpuYYL0FH7I+PJGt/YpzhGpMAMtua60sBjDT1qVqrgwxcBjUkVvxIuaoTJtnc9Mdai13/Q7KGtMFNATPQYzBR+WfvDgwhLAqcguOc0oqAIZTnSURlpTpox52iyh1wQvWEdIDJRXh85z44XThx9OzbiY2tdUfzxOXXPjl3Z4uKkW/idJplYvlRA5p1ZklFhj2olFo9f54pff7lhS2ObhYa9mt1TOiODBxVfEoVRezFwWvnub0Fy1isWvX9/3pMhF84N1+ZIyRnDQw63z7pu0qbguauPV6GuOq4ni368EJeTlxyomC4/c/fWAXIsf9nlfHPc/nC6UMPOcFgiP7yuIzQqcKwqL37ND3vNj1ep8sMQ6tW2fro1VMVZxRdGHwnod5wXeO6ioDnmM3MOxHTk9XfvP6JYfjwHmamZdE3wUeLiOP04W7QcXyckl00fMv//ZXXM4Xfng8MUsll0wnnsPQ8fKwxztPWBLnZeFjyvTO8e2HR97e31NrRqRSREX/XoeBE4kkYhZB2zMMwbqJsolIhujZuY6Y1T8qmUzEuSq2LaBMuGhdqt+fL9yHmV+NO3Zewf+LOJ7NebwltAFnnVXWjgZswb4PnlLUEBpLwvZdZAAGF/BetmLC2jjNbFothfSQCQK7IfDVbgeDKmt7vO476+p4p10UrOPazELb+hIRQvDknNd10GJ4qorl8MXx/nRh7APv7nfg1X8O6sagEo0da0CxBM+v1bWjWuKzhhyB+yHy5y1k6sr34K0zISIMwdFHx0xlR1yJBmtxbxew95GlJu32UE0V2+HaAbzubbgfe75/vOgIUxRb2TpVxWJEGzk2p/jBeQqaRPSxx4fA87zwkBZOJa8ms2BWKKbqXy2IdzjeRe1oKK3d0koruHsDngevT6eUikil5oIXuNh4WlBzWfGOnz7NuM6B8wzdwP7g+fHhwjdfv+Lj04nzkigiHHYdnY3LgtNr6uPA3e5Aqg/Mk418ox7sTqCPkT99eOayLHooAVLguNvx5uA594EpJ9LDE3fjwOv7kW8/fuTVuON+N7Lb77nb7Xiani2O+ZUA4yzxLFUQOye91/yJsk0nBDQ+A9iIvUEpc9XRavQw+MBcEp9nFVc99FHvfdXP9QgP88KHy7x1iuQm8bHiuYrwr96+QHB8nhIP14XLomK9pTUwLFS79heOr48jfefXZlQqBR9gqYVmPRW8Fsb/2OtnlSB559g17y0tAlfQpLQyacWlbFgcRDeFHtQAt8h3DZgJ4eOsKsM3Go10IWwy9s6tQE3YKJrWS2A/jnz17hX/+e+/03GQHcrBe8ah5zLN2vnAQXVI0QsQcQY4vclknQnJOVjrHksY1oBmVY53lei0At51cRUJbAsuOgUVDt7bbFlFCZ+XsmbiAfOfM8HJPz5dcM7xw9OZh+vEbOaZ2PULitk4TQt/nwuHXU82mYVbjExj4sj6K3bhVo0ES6BaQGpjo0aN1e/pTIzPPtvexqNYm/asEL2mORe1enC6Ob6wjLCDsVb4eJm4Lol9jHy13xHs2W5tdrcmqjdw7hWz0hAS7bHlWvh8vioGhopEmGoiXRWr8vblPfvrhaXoJsW6J0MIPC+J49CtiuqhnWl6tStI3bmW5OmhsmS1iTldE8MY+SoGXtwdeDaT5vk6EUPkN7/+Ff/hT7/n6fmqLDznGIfIPvQEbz5TVThPC/OizLUlq8ZS9Spo53CcLpO2r6sw2z54TpmQBDloIjLP+md/+eoFr1/e8e///GeIleIrpTjyBhRbFZT1lirV2HunRshRx2QxF87LYsUOpg6tlgpjUOmKP10nXcPo6OthmbZDHx2VH/rIdc6r36L6DCpRwTm1Igre3eAPIYS2DHQteGfSDM6KDTNHPV8WLrmyHyPXKbO/wWW1hEXXzLaOWjHkXVPfZwWtdtGsT0RIJioYvOMQA95FShHuujsergvffzrzYt9zHLRIK+tWaxi/dfmvy9o7D94baQItwqxCJ2dl+/kbUD+QcgEt/DkMnbJYHWQzKl6PGdsv7R4ffNCEx7Anrb5sgG0rqQjRMXY6ynWKqGV/161CrK1gknXv68f1LvC0JMYoDCHwuMzme7iBp3Xz22d52HvP6INqLTlH9duzQWQFZNukVxMjO7iXRTuilymZdYs+s74LfHiemWrlrh8ZY8cuBvZ95Lgf+e7TE0PneXG4UyXtMrM47cZNUhhxBBKTnAkhkqoaenfO6z7xsbXVqKvunZ5RPjqkG6hl4uXdjpQKH57PZCq7qB6BsYtcpyun69nGZg2D46yzraPTauBnabpDqmGwChUXBZKtAqDVYuEt4DrVFrk0Rk2pMEZ1kUiLShw8LYlP08zTStCQFQfa1gjA67FnjAFnMivHoePhuvDj6WrFfFvwzq5b+JtvXjL00Yr0zZdBnKxFcvDQR7+JMv8Dr59VgtRGXBENhIsF+rymn9DSxbZhv4gL64HzX/+eiPBpTii9dzt0g3NqBuk0iKYqVAqIdn68fWatlZQzT88XtuNUP2UYer56/Yo//Pm7tQ/QWF8NkOxtkbYPbrNutya42ztWEVwVno0VoKOIqq1a6xw5+2kvamOSDQQeMG2PquaGDTS4M4ND0IPyz0/PII7H67RaCNzeq4atylJ5TpWEEHfWPrU7UJ1KHtT18rekrXXhHBv7CdFra8/mC4aa2wJwe26+jeMsrs+lUnI1rZTGCnNrktYAwu0dilSuxcYqTtfUnXlDOXTWvd51K7ZuOrqokJ/q9sTg6Qi82Q98e7rquMbUiJ049qHD5cLQqYjeXdSDeVkKIQZeRcX5DMF/gZNoI5L1izsLXM6+cyqcJ2Wj9TEy9D0FVVBfquB95V/+03/Cw/XE0/XKNc9miaD3ZN8PXG1MUOxgmHLBNDQ1+BpVPIiO81JOerjEwF0XuZig2yUt9D7ig2fvAyLCkhc2Zo+tC7uvXqB3eohlA25pN9VwTKLdFg+kosmSkldkTXw7SxYvORNwDD4QnbDkrJpVdu+Cc4zoKGyuqvxcbf26oh2e6JxVxjZKi553xx3Q8H63PlZK9y9V9XYeLgtTrpyvkZSrVacbRUTPmcb8cStQtKXZt6+W7KuZtK796BT/1gRIvVd1brFnNobAlCtTzqZm7c12p31GO3ysIAp+3cOt8Fqvtap20G3SU6sWFYP37MfIfojEaAmZgwUjfrC9n9Wr6nhvI3ukgWS37t66nhFeHQaWKlyvSUdKpva/9nRd270N8K3/pXYbil+LrnUNNJVq91esmDmEwMFHhtbNkrUOWgHUa15lJ77mDJogShXmnFWrye5QY2gO44iPhleM2nXtY1AHhHFg6AJZ4Dpncnam66fPI0vF10KHowsdzsG+743Bh1kBzerH5jZ3B3HCdUk4HKlU6lRVCV2E65IV8xVV9yzlRKqZGAyjZ+db68aIPbjgFMuJa50ZZ/f8Bn9r11XtHFztRm4S5CoqczN4HWm3Dhxe/ROnxgb9IiXSf/VO/dPejAPBB2NWawc5OKdWK3MBZzAK2ysOuN/12xTD3rthprbcoMFt+EdfP6sESQSuqeCDqS5LXccTax/N/t4Q/+vmt4fpbv993WwNp+B4MfQbZkFsXCfexnZq+OeLvkkuddVwcB6WlHj/8YFaN3Iu6EE+dGYB4fTxNGbEVum57eJuXqtx7W0OSGUpcCrFDk+3JhBzKWqP0nItB4N3uKr0x0Ydr6iHj/MKXq0+rEC6uRQ+nSdlybRby1/cu9bWtL9dl8x9F2xDWScpuBU82ipAMLkE3IpDap24atdlBcv6hcVKxrYBmhClczpSK3UzymxVlS78uvoBraeD3RQHK2NjkcpPlysOx9e7kfsuqieek/VZ+vWw2Q6Tdnx0IbDrIzE4ovd8/6yydUsugKoPO3HIpIB+Ec/9OHK/6/j7nx4JXv0AVwFSdMTYvuft55aWnFY9FK6pMOeK74IKf7rIeVIF5NZlevf2Ff/6//w3zDnZAa2BcSmFe+94XirXecE7x3HXU0vDegRi6HBOLSuqQMqq+hw7x6Hv6IeBKI5SHdcyA5kX444Xw8j1OvP945USNlPoPmgCnwXtKHADJLZ72r6zc6oxFr1aJUxZAd4FseRccEWTESUYqEdjrs1WmFWQ0eMY0H14raqQPddqHRoT7XOGbakanTsTtdRD3BLmulHYSxKmVPh8XjhddaQ7T2V1rW8YKB3Xm1wFW8riXSsOLDHErT9Ti+rX4HQ0NHjVnEKa4aweaocuctd3lCq8nxc+XRd2IfBy7OiH7gu/QdqeWwsV28O1daZb8SjEGEiWpDVzYIfjMERe7AdC1CelYo7KKHaiyt/rpl2LINWA2tTk/TpmbYdTiw1fH/f88elMkkqUWwVuadNuS1j01/zNjky1IknvyeADk2TczbcHVdm+D53u79buWHdYMwe+jbUoHIDbzgpMS9qo6miykorQDzu8Lzgna8dEBXkT97tBqf+5IFKUJJIKXYi2J6qxn4WcM/sQ6GJHrtUspJQdGlxQE1pn0ineq8BnrXSx4/E6UaUy9h1LrmQp3Hc75mxsOzbGozNPGClCzVt81o6VW2NOIz7dLOBNskEwHO3NvbSYHVGnhUMMeBFSylooeB3LBtuzTWupJaRdUDLM3RC5H/o1VmvR5jgOHW/2I59kYspOMY32+cG34qBaQu1WdnaqKpCq+0+T+ltg/l++flYJEg5OS+Ju0AVeTUVaGwxfahYJSr/Nji9GPg2LsL6h24Lyrw874jEYfVnNa3O2KsGpyd1SCqlEsjhKLXw8qydNHDr2wwDe83g60fJTgOfzlf/wX/6oYz1L5or9RdWDXFpV2S7UAIO3SbUuPMX5PJSilUlr4YtWD989PfPr+6MKQYqyLKQKfXScJ5XJd+g9GUJgltZP20Y2P0zTpu0SWpUJzY8pldXv4SYvFU7XZa0eYvAcDoNeRxVmp7IAXpQKLbIBY6vIqiLfOnKO7btXr75a7Tk22HJjpSRr4bZDpt0rR0t0twDZOjJt0wh1TQBzqXz3fGXej7ze9RxioCwmmua30V5LuhU34hm7wBDN52fo6KM38c72vYRUM/eh46fPz/Sd57gL4Hruu0BZhI82Ejr0EeeV1bVOBkRMuwdmEaJXRfnTnEhF6GKkCNRUmGqmiHYAeu95mmb+9f/xb3k4PyOoCB8C46DdiFIUCN51kS54Drs9Q1jYdx0fzjNLViDrtGjQT3MGHJlKdsIxOu5f3vP+6ZlKUK8zD4dDx5wSP52vjH2Ht+ToOPRU0eTR+W79ftUemjdxzdXwVrRzsQuRPqjZ7pTsMHSqS+Ut4Duv4eycZwu6uqhbx2Xw6r91FyI7p0SCq1QuuZgwneIWqwjRtdmajSGsUmhSDAXtHD1dEvOiHUu/xiBZuw7uphBqQOH2Hs1gu02Aays8LHmt3nEXgjJGBRCFFiiYdkvUvVXHb8eBHy8TT0tiFyOvd4HZkknF9OmCKqCWOljyYbCEdYTuFEybiopI1trA6Y5Xh5EQ257X++ClJYMaMxqLssUr1Y/T5KOwjRxD+1xaBxe+uT/ynz+fEIRg4rvVEmWNC83RQD8rGN21TW2rCOcl8TJEPpS63mssAX4Te0bv166z/o7Ye23dpi1Jh2KWIs1odillPWCj95yWwsOSmaRSpwVfHf/8mzcc+m69B9ep8P3jM0UK3gWqvc/92HHcDzznTKqVZKKpD0viX9ztOSPMWYgh8mrf8YcPn4jRrLCCZ993HIeBD6cn+tCpfUgtzCmRSqGmxDhEnmcFI0dVIOU5J2IXtumLbHciWnGQratURcxDUbtiS21ei634tSTa4rJi8XVhv9n1HDv1xWsdqmJj81fDQC6qJp+bh6jti1fjwNvdoL5+ZjeEJfDOKSbsV8eRfRf44WnivGD3DsbeMH/VtXCtT9OpUKvyGHRd3vkvR+B/+fpZJUi1boE0eMeIJ+VMF9zaedAk1MY7zq0I/LWtW3WUUkSBZGsFh2PndYQQgqMLqM9RcDgDlGnA7hCr/pI4UvrMMmdyruSUGAZjj6VK3d587bqIjXxWUSOnmW3FEW8SufUwpwmNGSOoVh6lIs6vLe0W2IJXxscPz2fe7EdlE5imyvO0UKtTAJ0xiboYSRXOaV7tED7MC4+XK+O+43pNGsQNENuiXm/Mr9bWVKKPAVAbMluE59NkiZAmYdGbRcwQ2cXAo1uYpbR1bFozdU0StQYwLZeqC7mzaq2I0OG5mqVGQyy1QyN6h0fxVKpR4lZNlEYbhzY3r0jBggA8TjPPy8LYBf7meLfK1bNqJ+lmKwi/uN+bRYsm1U9LVlFGu45GMw/R88PDM6/uDlzmmQ8PZ+ZFR2739zvOj5lDP1Kk8umy8HLsuB/U56t1B5uMUsqFD88zSSq70PFiPzLsO/7zTx9wXsXelqJYrFIrH84nrZ4WXdvBa7CfXWbvHFIXYlA67MfTid4Hvt518PKOx/PEZc6EISpzKXqOoWPJmefLzHxJXCm8OYy8HHYIcJpn/s3f/5m3hz3iHaNoRSeifm27ztP5gLjKZKq5uR3OKMup1MYYlbXgcQ4OxtpJVfBBqfcFZcikJbPUTO10vzU9FrENGFGGYy2aVPfRc+x7GHTM+qfLlcXqyVwq5+tM86gCmFLm4Tzz4fG6Ppfb9nzrLLVkaNv7sskLWNJnu2QrBtDrzQLnXHi/LHwVPB79ftW5DVOHw7m6WT7YPwfv6XqYi3AuCw+z467vVZ6kAlIUd+UUW1NEv2fD6YS19Id3ux3XObMIOEsMvrnfEU3GobKNY1rvoIriggIOZwBst5IcFBA9hMhcKwmzEsExRo1FVSrXvFi34lb0xNEMituHtsO4s73YJFgarsw5x6/2Bx6XmakUOjyvQodzQYvom5ERtk4CWyerldk562i5SGXJVRNXga5TzmwVZYuqJ6TCGf7Zuzf05lpQa6HkwvuHE0tNiHjGDsY+MoaOS0qUAiE7xOL1eVk0YY49c6p0ncoMKO4tkJNqK92PI/t+IITIVy+O/Phw4fKYGDtPN0RyrYTY0/eBh/PEYezJCNdl0cmLJe95qarDZ0Wwsz2zytmstarjaol1E2RtP++CLqjbYa1U4atxh3dfMqWDFR2+CIGiWD/vKFnv/1f7Ha/Gnp3ZVnmcYY2tmLez03unjNCh4+8/n/npPOGd6kD5yg3UBNvTYhMUPQdFTNH8Zg//5etnlSB5r4DIj+fKoY8ch8h913Mhs5St4dKCVKlVA7Fss1FlmQkuQhXD69hDEC94bwHZAH3egon3AVc3sFepOtJ5MfY8XBdt/4mnLvBmf0fXwem6mACkvRxMWQGcXfBWaQljjCzZQHftMBQN+g5PBp5KMWqvKFUSbcliyRpoAth5z8O04L3nvo80UbulViJhbWlaQaqVqPPEoeNNDLzze4bwllkqz3PmNC18Pl/5fJms4+MoxYIVDVPBahviLACL6DgU+zOCMXDmxHXJHHeDAru5AcWvnSNZE4IGTo7eQPIGRqhFuCzJDFK1/HYW6J1VK960jbRS1+cccJtonXXJrAFnwo3adVS6tvDH5wu/vtsRKqaZpJT8iqr6NiKAd8qI/HSZb6e9CNrCnXMidHC6XOiix0d1cS9zpUwTrw53nKcJj3DsIochkmAN2tWqcgEuS6HvAm/jTtW7Q8AnBbKfUyIUr1T06PASmKasshfOqiqnAn8xBKa0aOJpWjrO7Ce+P114dX/k0DtiGMjO8f78SPUOjPKcgcUsTz5eZ16Lp1JZivpCfbxc6bGktBOoFZczNXR4X8k3+LG2kDSZNTVpX2mEwuDBV6UET1Zx9nhe7XtOcyLEoKr6Jduz18Dddp9gmB5jmEbD4MxZuwx99PzueCB7xyVl7c5lJSF44PmycJ4Wo9MbJqZuQoy6/0xHq7ZOthgXQRNS7w1vcXvIW9ASYBF4ylqkPE0LLkR+eTcSLTFo+y94FchszDpNHrSMavgo5zwhhG2fSuv02Hqv4Cg0y50vRuZVeHmIXJaBH0rlMA78L3/zC/7u/UeKa0elYbWkYYlUFbtYh2wQBYHrm8oan3CO0QdqNnmOTlmctWhX5LoslFSoVE4pkT9Xvn61W7s7DbdVHWtnqtknBTPG7ZxTraac+WrcKX4nZdunjcVYCU5Hl1UctRS9t4bKF1HLFu88U0qrCnTwwSQRhGtSH72LiRbuushv376m986YsOZfFyF0jlduRyqFpQoF4dXdwHHpcHXmiZ5ZBO+VKHC+Lnw/Za6XC7v9nYoin87c7Q58Pj0RfOC6FGpduNuBc5G393f8+HAiVZMO8IF9N/B5mtn13WoiG4eBJRcqgc1QRm+mbm/HnKqRa1oX5stYqWvGnm0bHTtb1VYQ33cdJRd78Nuab8U0OO6HgeI83z+d10Ts2EdLjrwl4zS8jF6La+tPO4dD9Pz29R3f3O9ZamUc47qWLaTrOY5JzwjkVK1wcXxR5fzF62eVIIlUdv3IkpONurQjUdOW5eptV4fsndeWf3RN1Vpstl9Nv0Pft41dgvNU15IO/fXWfmqbG6ts240fu6AAu6oVOOJYzPojZ2XFKaZpa+XOWXWPUpvbOgVG8hfK2LWKGuoVpfv2Idi8VzPw0JhvdpkePaiDc5yXREB4PXbG+NlUhwWhVK1+n5ekDtMpcxg67nc9h94RiOxj5Fcv9zzPB75/vPDDw5nzPJPY2AqtMyfFGQXaI/7mWLodZ9JwNHC6zuzHXmUF7MFV++faYLPN6nEkqo1MNHGpuR0ON2/fgr9AM6NdxdDAFI31eoqNDW4vrxZjZhgTL4vwNM38p0XVbr/aD9xFZUYEFHS5jjgRQvR8c9zzh4dnxbW47WmKXfevXt3RdZFzyjzPM32IJKmMxXHcjQpGBD6er9RSGaKn64K5Y6vOzdh5rrkw7kdSKpznha5XLMrFWvWlWlKB2gY4D32nBpXOeZyP7LqelFXvJucKIZihceZxXsinZ4auR5wCsIud5KecVIPM6UijFB2HnKdFDxAf6L12eRYKr7uB/aDjtKkUyqTCgBh+sLTqH1acSnuWrdvVju85V/NHVLr76ao9n13sELLRfjesCk47KzuvrvfB+VUTqcmDdEELhsPY8/LlgSkXnqaZOWU+n2ZyLuSk3bgtjXBfjG/aRlgTI2ncHuuFGK+9Ha4ObfU7HD6gAO9SeVwyT1MiV+GEfqYyXGmNWRC939hB05KkIkKPJ3SqpdUH9YxzUtdkQpOj2+9RbzbBtpWqmdDmUlnIvD9fbQTe4o1bsUG24dZfB/Wg7GUbeDtr7xu8S4U/u866C0qZx6mGWggG6K3CrFRfHKpdAzrWq61xZtcT7NpWY1zRzl8tRVnEMXDN2Yx6ZXWbd6CJO3aI3sR4gGXJ6/g1GhD7uVSWOaOCkdrVjtHTxY4fPz/zqzcHOhea1qIqg3tlWzbvvTRXcilMS+U3r16S08WINo6Dj7x7ueNhXnDecVkuimN1jlALY98Dhd66ZUtWgtCSC7vdqMmmCF484grLlGBwVFHQexe1SPalcpmzDgZuui25iiWgLTnaQPfBt7PwJkluC9MK5QZveTeOljRtSXX7URFMYUB4GQOHV3ckF/jx8Uxv+Ke2LD2siXlbCw3ktMoNWKwfnGL1Sm2TiJYVqHDnvo+r6bWO1P8b7SN+dgkSijUAShEuS2boI73Jl7ckSTBxObaHs/5e9JALO+dJ2NTIwWD4hQasbPTc1dMI61C1sZxoBtvZQduqQO3MVLyP60is0Ioom+XWijesg4XT9X/tfarApRauFsyCdbmc2zAFq/WJfV+PVrDRguZSKlOudEEZDIuxtZrUwZQLny4Ti1SuKXNNmSln/c6i3mJCIDjHq31PHzxTGikVHi+TeX6pYbD3ijVacmXO2eb0Jt63fkvbcGg1f5kXuk6PjHyj9fRFcuSaVo5V5lkouSJrDibWEm6fwvocV7cAG+8o+LYF9Ja8GdBStvvfNFYaa3JeWUmOpVeJ/5dDb7ijDQ3iHey6wG9e3PHj6cpcC015PeB4ez+qyzSavASnwM4SFMTpKsQx0HWRkJWFMZesbtjBME+1st9HhqCsvVSKVqVzoWS7KWK4gKKAz+BVObYdtu35jmPP/LQwdr2OpJzDB880myKXD4hVnbuh4/OslXoSwbuglVjWDuAuRvZhNGNMtY94TPM6ehxNJ+ypGN6nghqK3wYoY5WYIljbgwV9duelKF6hjXcRLilz1/dA6wpuwVz3L+y85+CDSUV8GVO2gx58F0lVOE0LD+eZUoTeL/T2+beX2vbzX0Sojfl1Ez+8dZYEWfe8xWyyE3KBRRsoxPZbTlWoFyvw3MqI2zi4a2He3syBF8/YOXYxaFEoit0LNxf+xR23WLCKj9rvplJJqTBYcvE4LxpnDbPntq+wCtm2gql9AcVybZoOLa4VHCEGJS4Y/V+ctyROqf5JKinrt22fta4FuZHBsHW7RgBLlhbrABXAe1vPN88woGuv7c+WHpRakaJsq1TrTbKu8glZhItoXPOl8ubFkYIj4zkeDvz5h/dcpsxhdDhTRl+98TzkpWr3pgpT1vt8yhm8oy6KyRx3A0M/cFpMoZ9A7JQw4YPn4D2n6YILER8jWSrXJZmPqLPuIOAFkS05TUX9P4eoTFNXlcU8lWLTCH2pQ0BbV5Y82fnZxjSutX1vEqB2dgXg2HfsQ9jA27bs22q4WXUEhwpYhoi/G9VUe+1obrjR9v5toa1bWTZMmvf+C0kLXTv64cG6R1YW0IgO/y0Q0s8qQfLewJiiXZS5KNhzFyJzTmRLFNqm0SbiVtkJgGGKgugopaB6Ki93A0MXqZGVIlnRrlII5l7d3qTdTydr8FZmWksUKn2MGtScMWTQhVZEpxwNfFrX99PvlqtQcIh3PNdCrsJgXYU2D9ZF1tqUVvGJdk2CdwQLTlkqp5R5EwcAqhQd54SjzgAAIABJREFUSYnSh6+5cFqWFYA85cKUM8Go7mOMPE8YrsXzct/j3ICgQaxVH31wxC6ocNqceJp0RHCdk35vi6h/eTidp4UXflBGhocsZaWUrgwfO+ypIFmoSdbDsRWQtwG4/ZK3cqa5cesmEiit2bQla4AxVAwjBlt3Slg36+O8sNRKHnt2vTCXwhhvEKsCzlXeHQYep4WcVFah90ox/6vXdxyGyDUVhtBzHDoerzPPwJQWXGoGwUE7owSWJXCZJ1IRTZLwiOgI7TwtKLZLK6Zc6or32LhgSlcfY+Bqrc3gAp0LdJ1qit3tooF1tbTItdKHQOziSu/fd5E+Rpaa18qxJTQ4Bcvvhp4pWRfHRjcex3VJTH1k7Dp2sWOu2digzWLH1gfbQdsOFmdJThbhPOfNJBZtvZei17pUVS1uivpaoepBqjRjxR3BliQ5bplmUL3n83nmp8cLn8668Lvoed13GkNE1rXwRbbU/ttt2mXOuy05cppENObQ+nlooTBX7VhGrzpv+y6wJD3cT7noXrQL32yObpM0tx1iVRiHaCSN7bNut167v9C6XX+ZNTbWj3qk7btAKmLjKDHh3YaRNN0lt2GRGnM0I+sBs16LM7yl91QjMqyHllMG0r6POkZ1Zb2PTd9uxaC2hMv9198NNIlPCF1V4L0I3Dm3Gqeu8iw3x7USc1QMs1j3rH1+FpXNuErlWqsJlcJ+t8PHDh873r58yeU6cZ0npeb3Snzw3tF3gcXGbrUqc7Ipc19SMtaoJTQhcC06IcgijN7Tx950kCr7ruM8XW3neCpaiHUxMC9Zb6l1wnMG58XY3tX2WWAIEe+Fse/5PF2pqY1ndTLqb/Y4tp5bQuzsga0QODacYHSOfQi8GvpVlLVtla2RcLPfMVxxFTpXeLsbVAqgmi2Tb8LE2wNu1yDr4t66uT6aJ6Nr8Yk1uerDturbWpH13/7h188qQYrBc9jtmKZZq6IY+Xy+8jev70lSOdWiFYPbuiimvL9moSKCeMfn68I4REIMDF3H717fMXTKLvh0npjnhZTVabl36qxepTIlxUA0oOXnqwqSuaBt1rHrkVIYh44hRH1o1hFx3nOaFsAqKRGyUwVcEcXsPE6JBOwOAx5P51WZtFGWDZYNRpVtCaEmSWbV4ZVuu5TKc0nsDbMUUIXhx3nmkrI6Oxtw2RvBLpXK96czv3x5XM1Di83MK8I+qo1FFz2vukHn2ugBfddH0q7nTR75eJ75/3749JdEPH3Zvkul8vH5ysvDjv3YkVLFOU1YHazmlt5BTULN29jCoUmg++L9NWh7t23l9rtto6Slqn7LqhNqI9O6/dm22bZEbEu2plTIdeZxWvjF3Y6/fXFHF7YEvNTKT88TFzPGfLsb+PVxx76LRiaAvbnYe+e4HyLfPpyZsl5XrsJlmvGu4lzHi+MevDBPM71zvHl1x8fHi+qwoMnpvg8cx5FzVXuLUhobxbOLgUUKuz6aLo+njx3HvtOksMKcFZAOlXlJeISuj1znK0sqyvboe355fMF/+fzRNKXUeHXfRZ7nhZQLj89PuKg4lKXUNVM5p8LvP5345XHPX7858nlynCVxzsUmUzam9Dp2Btbkv4olr1UBslteIqp6bt2Z01VNStVl3doIaMLRNV0cdHTUxq425VtHMoIwJ2V6djGqvk+tqrpsmlaNiRO8eoS1sWNL5LD9F6zzbLuVWkzpH+34ZhQPlTEyiQNpI5YYeUwZ5+GHaWHwgd6rHpDpq7LSrte17ajOU0qh84MmIGvHZO316M+3cZjj5mizlwNM1+v1YaQ23JN1CnZ4rmykBWdxJRvoul1bcHqtWWTt5Hrn6WKAVJBa1BPTabFbRagFAsLd0PO8ZKZsLNeqXYZmj+ItxlfQMRqbkW4RM+yFdQSnmEMIMbCLHSk3TNE2IRARSsqqBG0ejatfV62cauVStKPXMGi9d3x8euTtq1e8uX/F2xd7+n/6O/6v//vf8fCcOOx67nYDfQi8vtvx3ednDn1HNr/P0c6ePvakfDWvN891WXiaEvuo+0txahVHpiyZfuw5dD1LLkxScB52w8DdbiTlzKXOKi5aPOfrRFoq9ztPGEZqFR4vE7337IfIofOcbs6MVCu7rl3jVih67xSjKQ13ah162R6KA171HV+NgzlEYMxOC/qiE4xqe74VQ61/N6dMN4QvcHqlOrzZg6zJkcVlZaKFrXnhNEFrHoxN128dqK2VU1u7tuD/e8Eg5VJ5c7/nOQaenifStDCOHXPK3A89dV44l7yyohp1VrEHOtZofdkgDlc8IXqQyndPZ/76xR1zLpyuiaWKsQcCn54nvjruuObEx/OV03UhhMiv395zeXhmXjJD3/Fid8e/+KtfEUfH7//4Ey/2Pfu449Vu4DhoVfR3375nmotVKQLFc1kyP56uvH+eWYow7jqGoxBRploQdHGJGLVd/W80wd5ajQWd6ToLTjjIFX66TLwaBj5eJz7PM4v5+1QRNaWtpq2CtbFz4fc/fsY7x9hF3t7teLffadu7VOZcwQ63xjI4dh2XJSnDw3teDJFd3ykWyt9Q5NvFGiPOO8/zdeZ8ncE5Xr7qmaxVJ0UxY+A0QWILtjjdtCvQXLS92kQWG0lQ7NBo+KOh96R8W4Hrv6xq26LRVWBtySsYVjdcFfW9ovN8e7qwi5GvDwP7LjKlwt9998hPlwnv4a7vuB87DkM0fI0mQJ1Htby85xgHfhsj746Zj3Ph6TJRSuI3r+/5eJl4PD8xxJ7hMFBy5sPnM/1u4JJmrlnJCULYMHQ1W8ARkEoq0PvIedL2+v1hJHjHp/MZd/W8ejkqVi6lddRw149MZOaUdJTpHFPO7HLgq/sjHx+ewQkJtQLJpbCkwhgCffVIUIafILgkiNcQ9d3pwg8nxVP8z7/7Bf/PDw9kU7aNztEFBcXG6NdEtRg49mqH12qmaW0REdFx2G2Qc5VsPnBfdyOjvX+M2pnRzq3gvB5IGvQdKRUu80IuRVW6+47/4bff8O2Pn1Ts0AbGypRueBq3GmUHZy7ohE10zw4G7Y6pivhS1eVcREdqQVrcdnQO9iEQvSeXzILjnAuHqMKcDQ9Sal19JUFZXP/p44kYG7ZyS55UP2zbgG0MJlpVrfjKNi4+p8LMAqLJT8SKMxESqlzdmKStq900isTiUBVlDCUUHO7QP0/O+KqCiy424LVeW3vmd13g1y8OfAwT3z+c+cMPT/zm3Z0WFy4wNJKJfV7T1GrvEW+S7ix6EPZOGVh98Ox6PTOK/ZlaBKka27rgiD6QqzDVysU1b0mVlJhTQVLlq/3IN8cRqJTpzKdPwrIsRLsnparsSa7C2/sDMUR++erIT0/nG8d6kJpw6cKhj9BHpiXz6XzGu8huDDANEPS5PM8TvR9YTOdIrAgI6Dl2vUxcc6ILnp5gkhiJGB0Ppyv3lqCcjcV2KYH3Z4jR8WLsGfrI47Tg0JHvauPhQIpBNcSv46w1zjotIF/2HS+6jt65VZhYcKYtpXOUJNX2XOuYbV0c7+B5zgxdUCiJdRizaR+uOClL7BvkBIO5iIPiqrJO3YZHa3tE0HG2c5BSRbKqe29qZP/162eVINUKv//uPfOSVRm373i4TDyfJ37z9shdjDgPn4tWX1JNCsG1ToFm/QVH3HVKS6xCFcdk9Gy8gmA7AmPfsx8if3g48cN3Fx7PSofvY+TYwcOkAnFdFzjsB85p5n/9t/+Od8c7YoBv7u94nmbeny/sxyND8NRUTbxOF0TsFFH/lDJERdojJtoVLZCvsU0fZZaq36NUSqtYvSey0VajUzbZIpk5V75LF57mWZdsy+qtghaRNaghTSuKdYRxnhNdDLzajUS0olwMq9QFv5qg5rKp7+6Hjv/xt1/z7779wKN57ayr2wS8NMCbUS+KlxmkR8gsokmNgrFN4aqN6dboYv8QFTZsN2pVKHZi4n76uQ49FEPcrEy29m9r87EW3K3z2AQqwa305SUr8PUPD098vPa8O4yMIfDhqlTTbw47fnHccdcphsv7oArGXrFdDmcMPL0Pxz4wBsfX4x4RFUP75XFkyoXH62J4BTjlmXBKNuoSUhIec2VJ2pWwdBKPPv/GkAxemZOXeUaoysYDFZhD1/QYe7wPfPv4GYdjKaLJinV2luuFd8cjJY4sFK3Wc6VWhwt6GEZUxHUALqUa5kv/0QC+RYTny8y/fPdCbT5Q2Y1Lqfx4nlmWovplviXBwsVscb6wtPAQxXGtuT3EdVE4B6P3jM6v45CcZZUKid4b6Fbv2d1xx1IK1yUx50KMkdd3e373u7/lu/efeJgyg9NxndiK9ZagtDWZrZwWSzqcc8xVOJfC6AO7LnBZFv05W3jilLqtlHEs0VVfRbzHi3CuymDdS1yB0c777T5YhTxL4a+Od3Qoo1E7b0ZmoI37boajove4moZaXitxWJJTRfnW/gaTFpFtizghYeQBcTpGtBFcRkhJmWKjC4xO93frbnpUfLJ13apo0tnGdbuo+mLOO1wXeH+aeHk34nrFbc0mBtrMvB2sWLL2/dpkxoN5wMEllXXsJcWK5QBTqdyNPXPOTLUyO1gClKpqz5dr4tgF/up+xy5oohosUU4pk/yCvxN+/+cPlKKxsVbhdJk4XWfevThwPw784tWRGD1Pl0lFbhGmXOmDForP04KLnjeHgWPoOdwJ51R5mGaeppk+ZpyMShCJWoB+Pp14edgxdjALZBTTs/OBlNVaZ9h17PqBIDAITKiyuQvVyCgLTAmk0plQLl67nI2qWB1KYnJriFwT3M553vQ9+xhWyQVxpvW17kkd73vnVn2lbWQsuCrsOn3nEDwuBHJS0+aStSDwwa9FeYvJZWVzyzoabjjThqzymK6WxfM3+55rrjwtib5hN/+B188qQRr7iEe1IqqHrvoV/PZ4Xbjf9QTvGdCN1sC+iGKNErCUzM4HigfxeuDlUvg8Z+6GnpSqVmEmKTCXwk+fL7bxW8dE3/MPPz2w5EKMnugCXgK5ZLQFkdmPgbHbMafIJVUeT1e6LjLnSnDCbgi82g9QOk6zmfB5Rdp/3fdcnVoodI41GGDtY4dRiqXRihUY6w13kYsqBU9VQYHqJwethdNYXqXKKj63foZbB0tUhEtK5FPlMi+8uVOqajDhP6WHg6ttFKWjs2kpJKn8829e8eeHM+8fL6osLaw/17ZOq4NzER6fr7y831Hmqpij2irhBui8SWREvlQttpc4Vgq25jbWUbHEsOnrtNridpYOfrUesKbBWuHUm0LctwMwVz6XmfOs5r9vjzte9B1f340MhgMRtPuUizB2nY0NlGaq6s1Vx1reukC2vnKpPFxmHqYEVTVkvjkeeV4KI5HLNCv+wMHVVSKqwzKnQu/U4805pbAHp9im65JIudCFQB+tze0codsRYkdaVLtqNtq1iFZdxfRfvpsf+cXLe57nhcu8oMMNNXAdhmjimRafinXj5AYH6WAMgV0XVz0dhx5gL/uIc57vHy5cStHnUBWI3QgBctMJQVTy4rJ6OW19k0rlPsZVFFbsft7kUIBiPgTBh8DT+cq0KDj37jDwN7/4ih9//Ik/fXxkyoVf7Hfsx2Btf1k/p7UyatXALs4xSVWzWYSd9yaUV9cuaAvUOibiBttk4z9blBVNshbZki5g5S8LW/dq12k1rJV53e6GFcgrRV4qcylMuVjnLqo4oLEKr7kQeh3RB/wqOuvQ9V+suOvEq+cgjRSxER0Axtjz6XrlsSy87TuOZgLeCpMYLAaj+6xwg41yyhA+7geerjOxi9RcuXqHCxqXAn79+S05srE8bjUpdxazvVNLm2vKCj7vtBP/dM1UcXz7+UwNTjugzpEMA3o6q9lu10fG4C2p0yImF0to88LDwyf+p9/d8/vTC7777nvyMpOBSSrLp0J9eWQ/DgwxMMTIPGdw8PWbI5drXvFXtQqn64wfHUtJTEm4zollKUiFOVbinJBF7U6G4FlSITuh71R1uhYh5czdrrcJkp5nQ/CM48DlPHG3G8l1YW6WSG2PqrDdlghbDC22RtdujgOp2pF9Nw4MwbeGqf4cdqisGBNnGkitY2+DMmsCqMJ6w9ZaBzpooZdMpqEWFVUW780r0AoBr0LOpzmzi4FDF+gNhtK2vI4LDZqBYUO7yFOa+cdeP6sEqRq9N8SttdxbhTvnoi1Sp1XjuW4066awjazPHvtPWiKagYd5YWkYFQskeW3zq/y6t4oxlcLdoK1a0MUVg+rLDH3kep2Y5kWF/VImJZhzVixIKHivqsJdUPrp27uB+11vTsiOuz7SS+VaNkQ/OBsdrUc5OFkTGzsK9NAVTVRK1YAvsi16S/sRTJEYPXjWgE0LvLpM28w8V22zdkG7EZ33hr8Ru7d6DcnwInpd8O5+z9NF6dIAq+XCltNoYENIqJ9UFwMxa3K30onXLyBrouXdTfenVeV24KyCfJYA1ZtDpf2tHTC37aT1DG4H13rI2m+4ja6MqLhfEa3Uvjnu+ea4Y+ziagpcqko7tPdoDQRn30Vux6X2nkvRQJ5lS+aq0zVZauEwDnjnYZ65ZNXnkRCQVFc/Pk3MPEGEN/dHYoycponH84VpyjgsceqDjVwrSxtlSfMuYu1u5KWQPVyWZAlyx1QTIfq1a9K+Yxt5ree+/Xrwjvuho1N7dDOV1mcQvVoGjDHgxbpWOStg2a6nVa6g49n7ceSa8/bsafvbcWfjJqkaA9QS6CaRbgvBKXbleU4mkSD0fcfb1y/403ff8jwlCvB5XkhVtNsWPIPT5LJYFuwcXE00sFqHJAJh7SzpYdIOHue2pKDqZluvf/CeqxkaN+mONcd09qXaHBntIu/7TuNB3dZYYyO1YijbKGmxPbqPkSb2iq3NNhJRfzsFt65DiJsDxwEdnlnqisWqti8jnuclcUnqup5KoPrN56wVJ67tA3cj82EfMMbAuzuVsoiuOc3XlbUqCF0rWO3zxWJB2/PidN9nS5CvuXCZVWhx3ysu8ON54ZIL55TxncdHtUyal0xKBVedGSfDeSkc+7jiWbFksdTK+Xrlw3Pg8amQkiqQi9NR1SUnfng4c9wtzLlwmbNp5Dne+ACSEBPJjd4xlcrjNGtSh0CALkbsGxJCpEi1Dv7IZUlqWCtVk/ykRfE4doxdp16Lhq/KRXAuUE1XT26eK7DutSa62YDUcvtja6Whxec+BGUFso3NmsCqtDO37V9p0wr9qxWkwbcYuO3yJkTZd9F83OzPVxOOdHoGnHPhlAvPaeESAucUGIM3A3bdH73pZAna2QpO/Q1jDPxjr59VgpRLJktZW9tiVcZ+6EhGZ+/scEglM1v5vwXUFpxuT2aH8x4fhXPKSiGeNWDmajYHvVKao9fEqTUFX+4GHq6ztulF2Wov9juGzvH4VDidJ9o4xom2nXG6AcYuMMbIXCpLKhx3vQJ32zigVAapPOXCkr9McBxb76WBE1uiXuxAbjLxudQVaCnrnWiplJl13nZfYE02ViCd2xgrKVdVvrUMvi3liupniFVdKhjpOE+Jl/ejdrdakHfWObg5FHCO0Gl1eCmJQz8QoqcrasCbjGFiOSHtMr0ButsmF8Eo2Q03dJMw1Vvm23Z4t/wKcSvjbrsuvggeAkYP1t/UDqZtOu94Nfa8GHsykARy1ef3nDJDdObxtl3/lwe7rJ2Oa85MpRCiOtuX6lUFd5pUjbpGxq5fO4jXlHQsuVSO+44YNOnpbJR02O243++5v9sRY+CP108spdAFRyeQ8sIicE0bgBXrcjSsR7GR2/N14m4YGbsIDq5Feynt0KrC+qza/TUCDb33vNkPX3T+WoFZBZ5zou88g4tcknY5wDHEQCntvmsydrumxJ5Ze1y7GBhD0I5Hw9hZEhBUgGjt3njvFfCayjqC88GzH3s+PDyCC+z7jkWET0tmLKJ6WEM0tmhjocFUhEUKERic1wBrB+lW6FhBLcb8cnodpk2Ld459DFzmYoXQzRJcT6kvWZuCY9dtAnnrQYJuzua9OJeyqus7q+qFzQbCuU1wMZXC4iBXT9diLu2Q00+PXpm5czV/OLumUoXP06RdO6c+kMm6HQ3cvW5FS+RajGp/74Ln1a5XmxvZDncs8ejMLLf9uXZrmjyICnRWA14LU9IEyaNdsEOq7LrI01I4pcTQeYMq6PWnpVJSYddrQj/nyue6aBcmujURXTvgufJ3f/rMdZo1cbPn6YClCB9PFy5LUHC+JesBR04qiaLdNSGG/5+6d4m5LMvyu35r733Oua/v+yIiIzPyUdXd1d3uNsbClgdITJAlj0AIJgjBCMTAM8QECTOAASMP8YSBhYQYISQEmAFiABISSJaFaMlyu92N6GdlVlZmZDy+x32cc/bei8Fa+5wbRZW7JEZ5S1kR8T3uPWefvdfzv/5/YZDInG1AoOsiMUWiFMbZhnwkBii2jilEumDcR1WVyzQzTiYJxByJQZcEcPRuwjB0jGVaA5+roGTRw/S9qMFIiVtlG1qiZ/CFQ4oWwLZEASwR8bOqWBtXtAVaa2ttDfrX4GjpdGjDxxljvuJ4Mf9sqxwZaP5UCvfTzJQL51x5nGa6IGsi7/8lsYCp9+AJNWWIX/T6XgVI5gwq2vgtBA5x4NfubqhiwLBc1ccplW/m4oBKlqwiutGPAedsARCiWHa18AR5ZttFYZ8GYmCZdBKBEoSH80gulbttTy1WJfrhizsuJdv45jSDwjybUSooI2oCrSKMOfPmdOEm2odOpXrJO1gvt0Y+V+Xr08g5N84IWbAKJhPgTtqjBlVry7UgaaxWtWlTJWaqWaL8KB9Og7UJg7Zh2xhsFwMvDhtu+o4+xOX3svqESTXdt0suNM2fqqDBAkCpuqilL4EOprMEJo/RbeNSxXqcRwMV32wYJPJ4nngap7XK44cphWDkcn5fTb9rDSgtw9erFmAL0uyla6QkHga6Y1FxDIwqzldn+xBv6WldIrAgVj18dbMzJewYKXXifKm8O114M44MQbh92ftzss8Nnv2jFpSPs2Wyp5IdRyQcNom5CKepcpomNkPP28cjh+2OfT+wHwaOlwtf378nxMAQIrvt1qo0JbPb7Xj3eOLVi+d8tn/GzWbD+6cjl3miUnmaRphGqpojqbWa2nsL26o64aAahX8UaslsuoH9bs9PXt+j0WCTirWdiltK8Q2nYmR5d8PAx4fNgodZbCE25VXBtLcEYoRNStQKt5uOt6eLtwPMofYx8pP7hw9LMmp747NhsNaVWOs2O/u7KsQkSxKA69Cdx2x6UFjAJCGQy8y7+yP73Y6Xt3fGTXY+8XR65G1WYifO92W2yAhPA4PTiISWxARZnveSmqjvXzXMTsPIqECqym2XeD2aM7xUC5in7JU31qTP9qMlQqmN7C5OyQ55rpWzT2Y1kHdCiFFsIjc2JnUL3rMqUa1d2MSrBdOEa+1os6kWIOwkcsqVgrXxp1x4e56Y1So8oDyUTBB4GbqFBqDRCyxtuZY4LDdo03A/uNsSgvCnb584nme71xjY3G2vAOmtp+COuFo7Zc5mf7IPe3TBwakY8P8yF4ZNz2Hfc9clOt+z51zIUyUlZ4xvFZggPIwTuzSwZFHudFMIfPXuPZshcRODVVD8QZ1nb1m7zZYQCAG6EJknY57fdAFc3WFLYLePfHk8sh86Yki8rxemaeJSM1yOBI02NiAz277j/nTisN/SJxNRL6VyPl94Op74+Nme0fF1Eo0bbMar6tfBKi77MRudTnBx6URAxTBJzXc4lJRXm40/sxYwrgFvG5JSp+cRt8k2ILAGU1a8CNcPH3DMYa1kqrOcGw2DJRyVXCxBbUFnS2qz2kSqzH6ofN+LwhADh01nosbRNRt/wet7FSCJCJuUmINFNruUeL4bKM5H03v/+JKVP3lzMXzQkAiRZYxYJNgEcDFwVwrm46ZaqCoLULHhfTpVZjHAql2DLbJmY+YMAuNcGFIgiVKnkZfbgbcxcLvtGafCOBdqMOMv7mirCI+58PbxSM6Fj7eDgXqHjhIBcYMnVm7PwdouY602jllWxlMjAhSO42hcKgXe5pmjH8bW922bB/VSuFhp3fzzyiwhrAzGNvkVudkMpt4eo5HIqYEjq08bHKdsI8vB2IJnN0JGizAa2ZlU5pr9c4yvQoCuj6TOjHtWkGDfybWSy8RZYL/tmXPkPFvwGV0Cpk2vzB7tNnLH1rYxjaFW//LD2oypT6fZJFzTkbNT3EZY1zK5lYDb+W2BXBsGuNsmfnDYUnKl3yS0Gkne3W5D6jue3ig/uX/k88PIs01nsilqmKiI7cXHaV6IEONVmStJoIuw2QRud3sDmt684Lt3D0xTtnHdzcAX8oKnpyOhKu/v76nA3W6LPlwo8wlq4cevv+XHr7/jNI1Eia5956zY1RidbZ1WzAZA6AK3zuH08bM9l9Em2Hqt3Bx6VAcu+QI4SatnoC0gVTUH8OJgOMHQPsOfxlyVn5zPbEI0+gu3tJsuEEgcx5nn+w3fHc/kai3qfex40HEJhAF3OjYqX3LxpMCTB9pz9t0QnLCwi/z49f2SRf8zP/ycX//iY97cv+Pt04Xf+PXPoSgx9nSpI1CYnX2+iy375oPqykrnaJw7s09t9iLMHoBHtf1zmguHzvBSDXS8i0YtMnsl+FwqT7lw5+LPllPZZ5xz4aunCx/tB3oJjHPhaZ455bJUAdIVl0xLGihKF9LS8hBnkB9zYd9blXiaM0+5wADESOcV2ZZMoMpYlG2IjAhvzmeexokUAn1o00xwrkrVjKrw2dY00VggtHZVnQqza8y1oKlVHHIVPrvd8SfjwzI88N3bI88+2lB9/3YS0Fz5+v3ZK/TRqErcMdskqfhnW9A818qcL/zG8xvjqVqmIZVtF3lzyQRPlocYuBkSnxw26xlpNgHDtHSdVTofq+F9+hDYE2ErSAw8nEf6JDblm3re5sK355FtNMqV5JO4N6n7tPffAAAgAElEQVQjRuEv3N3y4+OR1+ORIQSe3+44nWxa+NCb/Xt/NnmWHz6/46lkhtiT+ghk3p0uhC4xjdU4xDDS2lMpvhbrWoP/e3IBaLxiUyz5DV2EYHZbqzFp3HWJZ0PPNM/mPa4KDBU1aSKvjHkh1fnJwsJhFYPQRaGPTUJrhUdEMd4sUSGXgoRACsmqyc5XWMTakG2iLmelVJ9883N4bY+mamt2f5mWAO4Xvb5XAVKKkRI7+j6gtTDnmSjWwupjXapEYJMBYymMJ6totEw1CtD37PvIy93GCegmX8Z1RmYhEBNZQwezfpadgasmV+/tBsf8GLDsi2dGCviunC0CrlCDGhHcbsPjPPPweCFXmzv8+nLhm8vEx9uBT3YDd5sOidYuuxs6gghnMaDqwzgj0SZMkhjAdRMin91t+fTFHX/wzRvisVpwV5Ui6rIkXiYV+z3VxkyrS9neqhoeMKmy3XTcbHpuuo7QmEfFJ9xqdfZWE2+Mai7iXKwCogpPRxt1Va30XWQrPWUqPOVCn4Rhk4hRbCJmSWbWJqi6s70fRz6+3ZLnymU00O6sdRFwXUqu4GPXgeJZ21ohwg8oSymtVRdDtGAqLdp97X/2sFtBZTlKbnCzc7WMufA0zUy1EEYLLMfiek3zzN02EuKBf/Tte17uBj49bHixGUgt475MXKqVyaVFsosWkomVVk9IQwiU+Z7tXhnHwvvzSDla1eYH2y0pJYpukC6x2W95eveON7lwvMwcz6Y+v3bjvR3R7lbd8ao687SdqT4Knxx2RFEe5xmNZsQex8pu2HE8H6/I5Van0SpIkcChSzzb9KCVQliqllb8aeBdvQpqrJI0dEJKnYFB9wdUjcBxuwn89Pi07BSrBAduXQ6mIE4TYUFCbhmrp5IxmB6dIkxzpXPD3Sc4nY78wY+/QoaeVy9f8vBwQgUO3Y4vPnmFxMIf/uHvm51YqjY2iWOxkHrbTFqhyrl1mtO2ylGo9hxyLYvuoHom/fl2w5fns4Hwq/Junnk2RHQBn8rS4tn1kW8ez2xDMhoJNcxFDYaFKtoSJecXQwkEQjXc5lQq1ask9+PEMHS2BYPJlRzHGXol9qaRtba1hNQF7qeJ149nxmJV+xAscQziJXw10Pd3OvOuzPylw56FjsJ2uFUGoyxtFXUnbZUFswM3+4EwBXuuKbiQrAc7uVBdhHhGKcXwa4qL0UqAbBqXGaVPkduh49mmI3SRclnLxF0IvNwMNigxzexi5KON/Wx1IGuTHmn2IsXAX/v8Bb/7+oEyG+v7KJVd6nix3fDmcmE39KRaoUI3dIbrqzNSIwcffNnGaIE35ryf9wNRIqdSeHX3nP/79DU32x1jtj213e+pWvjmeOTxMrJJie3Qsel3vHpxy1dvjrw/Hq1S4xVIKWqYpXJVugOk1OVzG81GrrZ/BrFEfAYOXeSjbc+zrmPM8xKArmffklyLleQDxvwWFB1zpQ+BPtqzafhAUQh1rS6qY+0UI7ada3VFDaM4kJgodWSaZ8DwwSlE00b1DNiSkQ/H+VeiSX7h63sVIAHcbTaM00SpsOl6v7dqTMPJLO5c1CdW2iL7ywOCpynzNBducmHoom8aoY38tiDhChZpBru2t3HyxgohtZaXgSQ/vdmyCcLeg5oXhy37oSOrlQi/fThydL0sGwXFpjIqqFTu88z5XJHTGRwsm2Lgo03PJgaO2So1ncLBGZJ3XeLQG0/K7375hodxNIVz7/fPtZrj9irJGnx4pwFZ7lKw9Ru6wN1+y5BMk22IkZwLj7UsY5pXy2otBpGlAkKF43lyvINlD7O31mIKHLaGXdCIC1zyQSVgPaQsqPHHaaILge22Y1MTxzFzcS2xBqZuFZ7i2Idr5XTVFQR9HTOJ6FItK2qYlyXb8etYJ+/WWHLpyYtwnAs/vj/x/jLzK3d7Xh22jKXwlCcuxbTc+ii8utvSh8i7KXM/ZX6wG7ifp4VNWWgRHwtgvuEEiv8XrFfMoELcwEmVx4eREIT7OPM8wmazY7vfs9/1/PTb18SQ+L0/+5KKyZMYn1NlGKJXcAy422zGspYoQ4y8GDoGU/2lOgBU1QYTbvcDp/OFKPasq+Ata8scq8KnNxs+PmzXFpH4fmvtXFYpAKvStsX2lpAIGqAGqMXaUdM08/HtjrfHi0lEiJG6ftL35GraeGXO7cMQWQHsVRWJgSxwfzybAVfjJPtHf/QVfbKJv6kqv/OP/wkvDntubm/Y7zZshp6ffPPHBpxuwZEul7sSlbqjr8qS3coHz7SFqM7V1jJ4D7r2MZG82qMYjuV+rvQRJv+I4s5+6CLzcSRJoZe0kvj5uQgedQSMrTqK8DjPNhrts4S5VE5+z+NkldpdNPyLqvL+MqHacegTwTXtplJ4X5TXx5NhJX1/RqztVAwh7/YVGvfKWM05tucdRUhJVu46D44aELppzt0MHZsucpkr0+OZh8eJzmV0csMEJQ/wFYYUF0ySBOjFyFPbtG8MxjVX5tnbyi05sEnjjzY9l1J44S2Z9sCqr/0CMkcIYmSMnz6rfPXmnmm2gZsJmwzMRbg9DHTBqiFfPTza3qjKpBlNcQEVV6+6ICaoe5MSnUS0Zm63A5dpIpHoU2e0AZdMjEKfelIXmYoyHi/c7J+b6K5jGTcpWqVN4Khl8QGt9VWLXpkA8wjF262qQinFWt8xsvNkcsFtNp/pCU7wdVlwdqxn4VIqD1NGBPY18azvF5gEizyKT7ixVmebrcCD9CrC68cjl2lm8K/dxI5dZxOx9+PIeXY700odcp3oylqt/zmv71WAJAj7fiCJMM0T1iG3Rc+1IsUMzSlnstYFCHp9++oGpdbKnK11kdIHH/JhRMWqCH5deSjYVE8DqmWpTrwY1laP4hlwJFVhqvA4ZS5e+WgZWjt0hsOo5NnEQ5uQXnJtmpvegpVP+84yXe979ykskyb3o4lsxtD63NVbOE6rryYk2NpqIlbSXNoQChIDd7uBw9AvrKXnkq2vjVmFUlZMlGLcPg3kPuViY/6lgeR9HVWpQTn0HTlWLl7y9cu6WvKrjEbWP7PW5WdDEPo+UEqgUNY2h7Yx/sp1iLu8uciq43b9mGE5OLpUBPzzrwaGWuvu2vm0PXhpOmGYTlrnJf6i1i4NwVosnYixPQNPc2b0bGiRRyE42am3BWGpHi1+11JXK1VHc3i5KBmoMdo0xxMQlO12y9P7Ry55IriCZilK3ydrERdlLm0CSZdKRkVdciaYwfHPHWJwvI6QQnI3vy7qNRWCVgtummSFBX7qa6pe0YC5WoBQGxOoLo/Fgoq2zwQutXCcCpVMiJG77cboC2r1yRRrM0mS9Vn5+yRvK6RoavcTlcfLZA6/msTEw+lCG4lXgTf3D0g1TMM0ztS7E0/HxwVXdA2+Bm8beqCTvVLUebu8nZm6GH93KHq1x/ztOrEAJWGUDTfdasqtLdZW3aaNdoONwpe6nmdjPDZc4xL0u71pFAgGYrbzOlf7oSkXNp0FEibbZGv3OJtTG7qOrPBunHk/Zy5zXoK9Up0QNUZjIG8fjCz3eZ8Lz5I4+Ls944W9YPlvqdi7XEgLcAVj+p6LQRgQG3zpneOoHZYkTkPCes9dI5SVdS1RFlb1NqMQsEm6l9uBQ2eVOft5WZQHvERC8LJZAZ7vNkxz4fE8Mc6zrU2B292GqoVJzZZZQKnceJK76zu6hsfxfSvuQ/pgK1KKS/2oUsRsi1QDo49zw56WRTnh/eOZbR/RrrcWYLBJ7POUCckS1JZUNs6sxW66wRMJ3oHwRKBgJL4tgWx7nnVNWzdH3aa0ExjcZ33zeOLkU82js3Z/0W2X92wv9X3hoZwF3w7WE+BYK4+jJeLNPkNlCoUUlG20YY0QbdBndF6o6xP7s0o716/vVYDUah0mvmqaZwsQDKu2jKVwnEzmIfzMUl89ejNUMdKFSLpyDM3WtQWMAqIr2h4Rn36xKlUjIpypjNkmuDQapsLar7oA5t6cRu7HCYmylPsM68Nygkv1Mn2tS/m8LtkbvNgMvNz0qzd3j9mqHK2U3+5UwcUq1+ysBQpt4ln8pysOjB0Sd9sNEZ9kKcUESn3zFndirSGpWF/3nE2H6zIVzpOrRPt7q9/rtjPc2HfnC4TVGOHXKazj+MvFtb8GC/RKKeskQjI5liRWfjUhyLqM/+tCvc1SDv9wR7WPaet1HZw1C3h1DR5Utr66R7aLMcmqvD5eGHPh05sdXQqLE2yoC8HK+1GEsbQFUJrsQfFgQNu+Zj3ES0ym63PdhsB+6Hg8z6QQmSVwnmbyZUJr5fnNgW/fP9CqMTZtFOn6SJmLCea2qlqzath4975LHPrk00d2DZsYmUIlV8vCpykb23it655sZ0bsOW1SJMWwjBW3aqVigMpzWdsbnjuuKy7rz1KUacocx4kqlT6ZnpTWSlcDWydQlOUXWN6tgfoRSC7YWeZqzkKE+YqGYo2WPekaR6bZsEdSj8xT9qOnyy0HWobLYpeKGqrVAM5WcWq4inxVJVgU068DQ4HbzjCJ2864XRYup6vd2vTk9kPH/Twytf2/eDkaWxWtmpuLBZPVuThyNYxKu/+xFPoUFmeiqgwp8v4yMyYj7jvXwpvLxCXnJaBtycVcKxuJy96/zn9Ulfuc6UPgIGGRZQEj7W3PL6iD9hVU60KFAIZZebYfmEvl/jwSMIHenZOwNsJc461bA7SffenV/1+/1jMrvBj6D35jfT7r+1W/eZ0zm6Hnk9sDXTjx7kmpFJDAs8OW0zQylRm8MyBVuRt6bjedT1dePS9hOUdBIAXl/ThaeykYh9NcjUtpLoXTNJt01FTpYmI7DFwumb4L3MQeqTasMpXCZcw2jBAdb1l1aUe3tWqq9xYcrQeqDzbV+OF6tV9Vx/r6/hR1TJk91OCj5O8vswfOxiU3V+XQ21ToEDzhl3UgaY27xAhk1fbR0zwzFdOPbGd+UhZKmn2X2HSJGANTrcQAT00hwN9Vf87zb6/vVYCktXA6Py0l4V6is7GGpYRs45xlCYRWG9miYltFrWa4tyHQO1BV21Fqh53rDcNqBPCeqQRiUDTYFNdUKnO10ewYrWVgQZRlad+Oo4PWQLzFFkIAqUu5NvuYQOOxUaloMceSFyCyO29lqZKpZ0CdRJS8UgP4fWw2RiCXPZCsdRWfDB4Y9i6Uut8aiPiSM1MtTjlvpd+LT+0MPj4JwrkWXp8uRITjOC/TQC14S/45h77jk8OWV7c7vnm8IB3NziNe2WoO58q3Lc/MkrU18J1rgQ52sWOfOh6Pk7GLNyOaAnNzvM22Wk6yZO22JdQz1Wsz7llc1cUBCSwkoaU9o8Wjib+t7bOHy8zLfaW/wtoEXasHYzWnvOk6NOP/trepsoJTm75V+6xWZShOz9CmuW56A4b3MXIerbLSS+ThdOaLj5/z0bM7Hs5H5mz8R0PXkcvInGXJ3JsLUREQ5cW2527obFzeH0OuNml5SNEMU555PEHfWytCdF1lf/z0yQyqldjXQECCOIVD4VzqEswuBtEvyGI2GzV+umSO42wUBV3gXGbOp5kkgduu5zZ1xnLuGoPQhKbVObzEsS7mjEygtBAxQdZGurhUkNr2C5XUG96mlAbmVZrraOtzrSxvAa7tjVxdA06s6qdEp7q4sjIt+hVZJmbveiP+66Lto6lUB5vaM2qHRRUGD2hQIw11fm6K4rxkK1C2asOIXPEs+T4QhdOc2XZxYbpe7lEhpMT9OPPufFnP19X5qVWZs00bhdAc5do+VIwW4H2eidLRhUgjIlzOvurSTjGqAKustoPYKriHvmffJa9o+zSrtuECC+Dbs9Amc+HPpNlHcZvS3lPE7HetdXn+a17l96hhwSUaHrNQcyZKIMdoEjIibLqe5zcDirIdtnzx8iX3x3te398DmWep41nfLeti58+eT4NDFOyLtVa+ezqxGRKC6QAKFZXC7JPT+23PZcykENhsE/tuw0/f3fPisEFj5ZRnTmNm1koYlWFjbUMtazsrYNp6U7b1Mpyefb8LgU9vNhx6S/DQVZy4+UdUna7mQ2oZs1fCyaVWWrKuwHHK/JM3DwSBF5uej7c2FFR9PxnFzhrIKFbBerhMPmBwlREpTBglRBHlooUuRHYxcTcMjKUw6Uqd8/MC5Pb6XgVIIsJWDBQsKdF3Aw/TiT4lYjBQYa7KOVdrh4kgTqhmwQb2bxEkKvtkZFLatLmq/V5zwAqIl0Ga47OvKxnjUah+sKpai+2cK4feDp8ZibqQ6B3nvDoANxalXIsrLnHycmDwSXJgDZJEV9KUtjlRqtaFr0TcOzV81DQqu6GnTy2LtFJ8F4U5qwGoN4muT8vkyawW2YtaCXUuVkU6DD3BjfWp2LSMFHgYpyUTbiuFBH54d8Ptrl+wBps+cbvveZgmFNN0i9i6z9XxK74U4iOaC+WAT34tCvBiz+hhvHDYDGxD4qdvH62yF+tCi1Dd8ouswdFSmXAHvBhyN57quKSW4R+6jl20mvQ3l8vSAl3gMhVjBpYm/msmoiMQvFZ+zSVSUR7yzJAimzgwTwYylGDGEK+YLS0ZsfVvIwQ9QlDhXAqPc+HZ4YY3j0/sNnHRIgtBOM8zX3x0x+1py5vHR86XiSEEUn/DcNjw9umB8zRSSytkAwSjdBBZKrVWdbRzsO8DXRUexsL98cyPvnjJ29MJ8amhJUASIz1thKJVG+XECgJdgqarvdyCK1tXw0yUUnkaJwqGnbIKjLE8j7nytow8ppldTLza7gjTzFwtoO98QsiqcYqGlfYiBJsMK94um33ftVDv7jAYE3+ZmYtQ8mABiATa5FezC23bR7HzU6s9g6yNisITJNzoq4I4MNbXPrlzmbJyGCzMSWL7YmnreGaO7wuzUNY2jO7MJrX9H6NgsyC28TPOhq1rFaTqGiSrGsC6VS5Xlm+4vdlznibO82xVGsF5x6z63OwXFTQqWmA/JGavUtn12hpkNVmoLPCxCEUDEpSSdT2LYlXxoq69Fa7so1rVMqvjLd0+top/rQWNbc+2KoZfL2sgLm7DYwp+Hy6vM2Wjt6CtnT9jaQzOHjwtu9c+czpf+PLxzH6359nNlu/uH+lj4Ie/9pzf+JXP+eMff8390xmVmV4aUMQqZq0qbZjISIOpz6XwOGU+Puy4n0ZCCBxSxzwXjqeJf+6zV3z3cOZBZ/oEpWTePDzyjiObLnB/ujgHmjLPF3JVPn22Z5ozY161KlVtsGAcKykll7RZIn0+OQzsuujC6WbTSm3Bb/W9vRLpioRl4Kkldj99ONN48NowiPkqU4P47mxacSlGhhgdX2bUDgmY5sK3x5G5Fi4+PWdHQZ06wPZ0cbtQqjKGyuM482zT8fl+6zYzc3Ji3F/0+p4FSCxl4mmeeD9OnM8XCIF+CNz1nU2UOSDZkO7QtrAoy+HsvWyonk21zA1YpSeuMooW2bRcKtTKYdNxfxqNGwK16akAz3ZbHsaJXPKqyxRtHjxcaRaBG0UFIkv2LWJlTRUh50KjYldnF25GsvrnmsCA0HAsNsHjJvMqFT9dZstGu8DQJ2IojFPldj9wM9hYb0WZRPnmdKZ3ACPSMn9liMF1z0y/6ZgzNSuPl9kd4NqLbmytH99uaUR1YBigQ9/z/jK50VevzAqDQPH1WcB9Fu3a83Rn0AKN4CjJSGSqJvvy2Ue3vH7/RCSSpS6EbcWlL0RaJcaNpnuGWg3oX9uzl9WZ3fUdh2iGoVTlRep4n+eltH4V9UAQDpvONbZkjbL9LYsYf1QbE55KoUohRKtE1qL0DhhdgAhOVYAoWmx9kwPZAgY+JdoOmLKxwSes7/5H33zDbb8jS+WsE2MwHMMhbqguUns96rqWtG1tG2A2VDVgrVfcRA1f9MlNTxeg08TTdKFSFxAsAp/f7umv+EYUn6KTwFwNzB0aI3y7Bj+1lth40J8TEm3/WFWoBW7Y17FBgIc68fBuYoiRjzYb9jFaS3wuLvIal/04F7uWuRQvygRn9G3bwrTeBg30faKPtqeL+JqpVR+iCDF4E1XsnBZvEUm1gH9zxXBqEzhqRK1VGGI0rCFWbTIiQdaA0rfB7WCi0G1IwqosTSHA5Iu0VbhKXRzQZellt5agMKTk7OWKOMC+8TMBbPvOAsZSTHU+BJ4uF1OiR5AQefDS95q/L4fHcWbO7xYFiTYtF7AWGbpi995kxyZOlSBtktYmZR8uM6rCYduBRAimt2XxllNiNDqKEMBZ/BvIuBUKQjBpEIEFQIz/XN9Fb4uaQy3ZyBsNMH19LvzMtZ/1QKCLgdhZB+PP3h5NSFat3d+ljpwz/+fv/zG7w57bmxv+ym/+JpeS+YM//GMGqrXX2hougaoNTTyME+8uxmE0pMighkvNc2GaZgqFSTNTEMqUqdmGEfouch5nSg0coqAls+0Dv/XFM3bdln/4Z18vgeWa0GAi1R+cwzVwvhusu+DpjNlex0bkbL9vOKpAUfNdwc/+w5T56uFE0Uab0hLJlY3eCgDw9eOJ9+eRj3dbXu43HqQ66WtVlGpEoYQFD2a2wgKtxv3euN0ouOJGpivWcXjR9Vxq5aunE7/o9b0KkICljy+IA9gS3RA51onLxQxCRtEky4bW3LI8LwIrNgZYKyEKITX03fpHm7Jprw9kSzzQernpeRpnbrcdH+0Gnm97hi5xytYuSTGAYbYZa6UjGG8QLEGBe3maiWkTLNWGPax96JWHfR/5dL9d2m9LBiQtQLJpFlmuul1/4Jq9thbL0jZ9T9obm+w2dcaK6/IGx5LpnZOmqovfxkgnrqyOlTcfzxPX7mxpbaoZuX/2Bx+huKEP4qPUwic3O35y/2Ts4jhtgtYlwFvbHGZwbS3g6uYWsGlZTof16Xd95NWzPd89XUwcswWeXt2xX5M1+/PMsAViQa4yZ4Hffn7L1KQD1A7ajkgfjMvqvABRV8Nx6HtrqWCtjIXI0gHfbY+hLFiDYtGgkQb66HVibQW0iZ6pVh4fJ7Tv2XaRTZd42fW8vRRuDlvePz2RVRejX+bCY7kwUphcPetSZ87TyND1Xr1b24ZDDHx2s6MLoZ0YWz9aQG5r2SNL1XMumTlnr6atsO1VuLWdo2Zs7V6OOTOrLpUB95u4jqjvYtsDT5fWvlUP2q6AoyxbYDnjc628Pp95iJFdSuyi6ew9zDPbi33/PGaoLjSr61BFFCMZrKpsYkKzgb/7EFwc0/eCJxBGl1EJra2H0DQAK9BxheJo7SMRCHFh+W2tQHz/X3Jml6JPOnmVdXFOYJAAXbxqVjvXl9GmFMWTgaYjCXZ/gaVuYsFmdV1Az8RLVX70Ys/tMNjkZK0m/Bvg0AW+fTxz9upJHwJjUSuZ6fosFluj5tAWuae2B1QhGovxnCtfnS58gbBriYH/fxTh0CXn2fAD5KSFUdZ9FCRwzpVjLrwcerToIuOymA0PlBa9QLdYITp4nHoFr1jt/ZXJsXVv/EEF2rRcqWa/NtuBl8/Eq0FQdWY7JJ5KYc6FP/rTr/mrv/0j/uKPfsBpHPnd/+eP+Pbxwsv9ZqENiMGGOFThy8czZ4/qNl1CCYhEjufM7dAz7APvJvj64cjlYt2FKs5FNhuFyGHoCENi0xuIPSJM88jGuwW67E1ldMKurosOvbgKxKUBUWS1n1oJITC3RB68PWxfb0/9OGXenaZ1+lmULrhwrUKZjR/PahXm705z5idPJx7Hic9vd8SQTGs1RfZdx5ePJwuIqUuFz5Lq4EnIWtsLwC5GPt1uFgbtAAwifL7d8ote37sAaWGELnaQEVNTLqLMi7FYDTEIsswPYorRtfJs3zOkNuFwVX5lBUzi2b+EpgzdDpI53Y3LOOCByWHobWO2MSk/PJdSeXMZ6UVYZfGEBUNgP3z1mdeGVJbvh2CZZsOfWJB0NXHQMjNpv9GMqS7GwYIvZZoqqpmuC0gXlpF606yJbGMro/oGEwtsxmpZ0zlnxrks4/Tt6oMYWPQwWF+9T9FGmD3giT4efBjSctDUx/xLu88rA6n+/dAe6gcvD278/VXhUpVCdr2mFvysRrKtGS0l8q/Z+6tl0J6+Bgk87w2HM0cTe5xy9SzH8DOKYdmm6qRzDq6/1s4CfDrLydO0Zf7Lg1oDS1iGDXKtqO+vJMGA6LVwniqnMSMKU7UAqet6qMqw7X1Cy3AruRpi71xmalxNf1HlKY9krVYVScnaUMlY3Hdd5JqAfw1y7OIV8ak8r4Qh7IaeMloFd13167rQ+jcRI0HMunLytL18XZJvOcT9ceZpnrzV2BzXeh7t51twbr/UpluzUxhMMfIiJR4uM09TNkZwD1Yah1ZK/vzFWy2+N7vUEWNyw+JRLnwwvt/uzypRuoQhC9C+3bvffwg+0Whbg1JZqhXBP7+0imzb7motTFoCt+YGTKVyvMzMDjGIIstggPp9tgDA1qcyVRbsj62DffZhsApocie5qaZfpvozXDJZPYG7TievghJ8bdWC6uRTTLkYHOA0FS4uRvz6MvLFbiA5z1NLALsYeJgyQxesMuAX2fiyWku8VNNx/KlWBhEO8UreaFF8h2Uh/K+o7SltC+s3sgSryaqGbUuHFhyHwGU2DFvrQgzF6AuCVxettVro+45n/Y4ffvqSTz66Y7/bMNfCcRoZS2E7Z9PfDIEGNJxLwUAIuvioWpUQhXNuhLtwnmZG8nIWUm+twmkqdClyGTN3uw19CKTF5igvb3a8fTJ6hiWQVWtXtunktkZ9DLzc9n4m2h5ub1VprO2L/1TztRKEc648jZlzE5xt58IDmtpK9p5ctL2oWPXxXmfk6cwmRZ5veoYYTalgiYXEpkQDy2TgImXlryjC88F+99ouB8yn/qLX9ypAUnCOFRPgbHnJPGe0uwp0aFkn1p9vAzgSzAtXuNsNbHxiDK7PzJWpEzBwclhkC5bn6JQxfTSnNbtMCRgo2ZiWwyKQ+O48Lk5z4U7xD27YgJbFrZWTdhOFrc4AACAASURBVCZXhyFXlaDrq217IQa5PuPLz675azMmLq1SIueQqcVEQjedGafep0taVomaIZ2cbXYZ45eWe1iGse86Xuw23G57l/jAJiWkBZn2PttgHCWzUwc0HIXdbXMr65WvV+/3pevfV74NyFQuxdqOXdckI6y1chWLrriK9jyEtWqEjYHvY+KLmy2dCKk39fkYCtMMl2z3vImBnsjsCunnYrpTVStW6F0zmbZOra26tD91bYuiK9Ysq2GmmksynaXCmCsa4FiKbWcVNlJd8qWw6RLNzmatljyogYMDOJeQcq6ZXAr7fsPQdWyD6VuFVlG4Cr5Xg9iqnT6YUCtVhZto2nCneTbyU6yocLvtaYBnfCWMlyYwlex7d5kH/MB4gZhCvMLDaWZU43rhei80586Vs/TfXsr2qmQ1CoqdV+bmudogRXRNQbFhieSVZwRSsoqvYIKW6slVA/62DxLfg2YuVsDqFfLFn7slGSl6Lz4IXYCSdWnHLVOmauD29Z6s7YbYdNN1mLJcjsJlzjS1AMPLWJ/ueuKyNduqeXt6CXQpslMDOxcHuI65eJUMCNaqOI02Vl+qD5NUpU+B8SrZaB/VLE9LGCzIt8GHKVei2Nj1mC34eDfN7FLgo6G3aoq/QxAbvAkCXWo6a+ZAjYPHKBpETSLl3Wh6aYe0+cDuQcM1+e8uj9FCTDv61+1Ctz1BCBrWyWL//NglQjXNxLkYKWbJ5YrPy9qonVZC3/Nbv/oFv/bFx8QAP/3uO756855znihBeCqFjUY2/t5F4VSLV+YCWg2HZXa4olJ5cMHbWgyjud0kcvbuiAiqZkcvl2KcT1cJRBR4tu3JpfB4mWzq2H1h9AGGBh2IErjtE58chmW/ydWaLQFwC2plnXgVhMcpc/QJZ4TFtmQPjEyKZN00H/o0w+99dx7pgiU0t33P5MMzVnELS4LcDFYLHhHHvcbAbd994M/aw/z/5N1Xr18qQBKRZ8B/Afxlf+t/V1X//tX3/zrw94A/9i/9d6r6n4rIbwP/zdVb/Trwn6jqfyYiL/x7vwb8CfBvqOq7f9p1aBAetfK86+gjxFwYJ12tE1dZS4v223P2h0dUQjC9JMOvrFw9KpZ1zu4ggv9+d3Ww7AFbZJyzsu8T7y/TAnKLEug7F1BVKGMm6+xAVx+3Rx0jxMI0vDzU9sX1TK8gYJwTwg1zq8DY4uBq77rGdizfAlHHP9kbWpZrJdjx4UIIpnf1fNdTo0kjdJ6FFlVGtTH+PkTejyPnKV8ptpuD36bE57cHNn2wRo7CNiaMBXct85eqzHPl2W7gcnSQXFiDnkZj0jKi4FVDM4Z8EESKmkE4twCoTaRUuN1vEBWexpHTODv40zLF6zVtFbBG+paAu77ji8OWV4ctFx9J7aNNY80xkM+T6xLZL3US6EXYROV+nqnFsseAtUa0WLDeStPrJm1t32a41+uqWklYYFZq5TzOPDyMxCGy35mMjXjWkCtIrLw/nnmx6TnVwsOcybgGkjvbVskr1SY9M8rjfObFZs9h6Mm1cP90ZogbUopLYGvb0vZ9a2tdcuXtZUaJfPRiIJenpVLRBFf/8qfPWERMBa8U2X1ntdaI0HhOms1qD0dpVduxVGK3sok3fq7G0rviBnUJ5pZz346SKu9zoe8jXY5O3lfZdh19p1QTIrTKTggkiYy50vc9WpXLNKJVuB3SmhgISyvNztR1BbpNBXkwrErqEkOXlspyVjtntbC0YWcHHj9zItyWSWdVDkNvWlzaqD28babBK4eBFDrAKhviTqWtc7MHXYj0zr2z7RIbn/BD4d155M3jmf4yMc5OGXC3ZZwL704zj2cTOU0xsB+MLmGc5w8CxqV9heHshhSNiwlrzRetTGVaEtlWUfiTx7MRZEqk+WmAXZ+YSiXGdeKstWJ6jEerA551iSrC+zwbWS6NdiFgTWzxoYd2rbKwnbe9svBR0VqOdWFcL21qLwixT9wFEArvz+JV30ST+gOzn7sEysRf+s1f5f3DO37y9Vd89fotv//1ayoBDcqpVqa6irmeauFtnheOupZkSBeZJmPLzrP7nWg2+rDZMk2FjUSGbY/eCF9+/Z3xUakPbbjfCMGGLz652XlCf2EqBo4OEhY7FDy4+GRvwdEVUgpVwwy1zgP+vvYedgjnUrmfZlMJiIAPC1RVT35aW201fNKcE17g8M8qwOvThXfniej4zk6sIDFno6koLfG96rAMMfCs6+gac7i6Xbmya7/o9ctWkP4O8D+r6r8uIj2w+zk/87+r6r9y/QVV/QPgr9r1SgS+Av57//bfAv5XVf3bIvK3/N//4T/tImpVSMLr2SNnqczJCnZVV94gkdXpLQKzwR5eqUY1PxebGmjcJfbA4P008/Y8Uqoh6jdd4FcPOwfArkbXxCUrL7cbHi4zl5y5zDP7PnEZlS4YlsEqPqbrkxU2KKM3rYt6xulFx+uWi01tCbUUe4BtwlUC0Sdnir/3UqOoyu/99L61YM2hXZmrJbtrfy4OyDOBUpGpsusjdJGHy8S2j6QQEFWOl4lHheNUluDRAlFBtfIXXz2nqOmwVT8s55yNQ8evI6vLAmjhxX7gu/nstMBAsCnEpsN1nU0s4rPLdSuigdSeXQsuPMuSIBxz4TZFbjcDKUQeThebyvMFWMPLdRz5B4cdH+823PSJIQqbKIRtz2Uy/Eut5hiebwceRssey7L3rIL3sh9IffQgR5f7WDA5rE5bxMQwOw98FXVmXQtkLzkv4rfbrqPulOMpM4llrCF2VCKP55HTfOHFbmBEuThBZRCsgodjJVrbIFhr8BDsPiOF8XzmlOGwvSHnCzWFpbUpTrjXKhkVQWLHdjPw9v7EVDJZM7dDJEgkBuFXnh/oU+ft27Wyk6Lh2Bp7diNNXIoc2qpr9pSKVHLN9GrnqfVelilC2kZse6X9TZYJ0FZxGMsMRfn82TNOY+bd8czDeIGqdF1gns1JhSSkQUgSOT49UQMMfaInGag8GAbGHK/9TpvEFGmtA7ulEAK7ENkN3eLobOKncp6VWWDfRU6TtU36GGnMO6FVo7Va62yATddxGud2ZLydofQCN9uOSG+kgS6DI6qIKF1MbFLHvu94vukJwfTgioOcarE2303fcR9GSoUpZ5NsUmUQI4MlmT2Q0DhyTPz0QvWJJHuQWuFuP5Bi4DhN3F9Gt+ONDUz9Ka0mKQT46fnCp2w4dGnBAB26CL05xQJkbNq14hNfah+bgFddRLXy08vIq83AztcwxrhWjuU6udTlfF4byTUpbqz/JodR58L2sKGrRvK67RKDa7Bdt8pb8Gq0AYH/5f/4+1xq5jiOPFxGHqZ5aRPNZeZ9SYwTHMfRWvalWIu9mki0oqRqrbTzONOnRJcSU8nEEAxvKYUuJQZV8ljYbQd+45OXSAzM45ni/FMpJSsElMrd0NGlyGYYeHe6LGtQFW76yMvdYIzkH1TX8GfYWNrNZ9l/be2E3314WIJZxR6QVOe40nZWl4yfLiSqg9MbVhMRb2sqXeoQhadxZJO6BUTfsLKLe3PerNsQedF1POt7ylwcoiOL34Q2XPHzX39ugCQit8C/CPw79sE6AdOf93s/5/U3gD9U1T/1f/9rwF/3v/9XwP/GnxcgqY3O52KTPrVY+6BNeOHRtrbyQ1hbRFXV+uU+6fOUJ+5zRr1kHj3DWuQWpJXiBA2K1LU0DWItkhDonc/ocZx5c7yw7w5UzYTQGfldUV6kyMe3h/arC1C34RKqViPLUpusaePGP3k8k8/mCKUzUKDYQ/FgwQ0RLCfy+fMenSvnsZJdZQERO2CtZaJrVYqrP/sg7HrzKAeFtOk4l8qcC6Uo58kuwEgB7XODCM+2HT96fsepFJ8ssqi+C4GLZ1xdsDFrEVNAV408223p3j1YXzz4lFnL/PXq2miRPl6Fs9pZBs4LLfZ6U7btK/vY8aJPdBK4Bx7PQtTqavFh+QxpAYwKr3Yb7rYdXXRSs2CVkMOmQ4HLmB2YXvhov+GSM6/Po3EAie0fVfg0DExSKO1e/PraLbXAQ7ER7qKmmzerYQ6iCERh8OgqV2tD3Lpje3M/ctGZ7a6ni5GHhxOvnr9kGo88lomZRpxmwY0FRpVGY9CJ8MVmwy7GZSpkjkrX26TKVBOh3/B0PhPqzLNt83tr9TGXmadLhmrs06fLyPNtx+1ggP+H87Toj3VBnE3cqgpzhiQRqo27dzHa+tZMw94Z1NPOdNeZePHyzMIqyaDt2bfBv1bZ8+pEs5rVsUP7lLg/W4DxYjdwEzd8eZoQLZxzJnWJmBLzDE+XM/vdhiHZdE4vkVTxEXnblOIcLTYgUi1oqXYG9n3idtODGst91Wb4rcXw/jzx8X5DxassfskNX1mdPb4LkdBb62IjiSdmFpJcVVS9AlGtDfxi33Po92y7QMnZxEBTWrA6grW5LqLkuTojv2mAIZBzpguRm6HnNggpRJ5mZTdEHs6jyZAMibttx3k0gtJGnVDU2vdjKXx3PBmxrzrlCKAEFikN7IyZ7JLV1d9NBWGkojwbeqqWpcoE5rQCFsz1MZkGlysntLTns67n9y9nfnIe+WjoedEZy3gIFqxItaBTEWRZ5wapdxsvxpf1p989sR8S+6FjP3Ts9lumcSTDwtotCjEZdQFVF5ZywKeWoWfix28eeXeZjXi39wShGhTh/enMAwIFut74tqjQS8+26+m6xLdv3yAYsHzf29mdZjgceqRGxsvIE7P5MWCbOn78+p6b/cD5cqEL8Hw/kFSdjNLsz7MYOXSJFIVv3p8IAj+83fJ80zEkx4QFT0q0+SCxoMmDouCJVGuZz9U+o/kzw3qZ8ZAUSGo6f0aXIIaFRD1gX+1jECUm6LDgKXvRI7jfbpWja6ydAEngrk/c9t3iUFpKpZ6o5Fr58nLkF71+mQrSrwOvgf9SRP4K8H8B/76q/uy7/gsi8g+BnwD/gar+45/5/r8J/NdX/36lql9jN/W1iHzy8z5cRP4m8DcB+s3GwcJWemtUQO15rZkji4G1f7DgKaoqmyC8v4ycZ2sTCTgDNeBHREQoQdBQ+aOnyxW4D+cFwqpDGqz8jzPjLhUNYZwMvJZa+8jLBsZKH5ZSZEXRzgxLUdsgYy68Pl7IUYjJS8BVF4yCIG4M18kWMBD1NgqHbaRW01Wz6YZWtdRlTaB9zdaugeeaQOUG24BnrZxdZ6lNECrKJkXuNj2f3u04FSM+7MLKW1td+kHASTQtmFQg1UqQStJAqE2vR0FbC2bNVYJfV+O6qH79jViwVW8sx7K/7SXyW7c7+hCtLRSE78aZh9NIVHe7shpdVfjB7Z6hdxCimpBoiDZG2kqHXRe5kZ7TODNOmYDycmN4q7EaLmDSSgmObFBdVKtp4+00Y+I4EDEZ4dz2nt9JcuPdWK5V4VwzYzYm3Q0JrXDRTJBA11XeT5m5+jr4M26swmD7aIjCi65jI7IEEpOv6y4I5IkXH31iAHwtzDlwqULS0Tavr39W26fESB/jOrXiAWhFOc0F8UqG7Q9hSJGGHULVyt7VnlEngaqZtlPnXHl/nNh2VslczgjG52P7t3FkXZ9/VrwgrYJnQdS5ZHaxQ6twqZVahG3fcRoryafGclV2XWLoBi610IVk58LSgsXGCOoVGt/XHrBkp1poyZqqia1mP8Nm6I3t9zJnonQ+YWb4kBhtZLxl54Lhfaa5kFkxda2tb7+r7Dv4aGsDKNbJrA66NQdnlCCBaa58dzF5ls/vDgzBAhTFMEJzNbvVOX6wAi/2W3787t6rmQbkDyESk3EPWdu9Lue/ODFjXjJLJ6nV9WyLGWVvVYYlO7qfbSIyiLU0RXUJRhZAuMLTmOl0rdwE3wcShB9tNjyUwvtp4phnvuiHxTZfiyPjk5ZtkIJmqwFUCH3g3tcqBtimJqyx2p61K2zUACG0Vrpfs9v14Ge6YS5bBd/2p1cvnd16J4mCsPEKlSB0nclNSQwuH2NTa6fzRIqFlOB2v6NLHadxJOTKKZ+oTxPbwVqqDQcWg3Utqp8pUeXVrkepSFFuh0Qfm7U3Li28/WZSI+oB0cp5dJ3Ufn0800Rvf8Y50zL16LQxzQeP8zrv2PysYYgS81x96ASv6qlLOVmAaRG4LlQ+z7qebUyL/0CuZxKt/XcuhcMHTOkfvn6ZACkBfw3491T1H4jI38HaYf/x1c/8DvCrqvokIv8y8D8Af2FdD+mBfxX4j36Jz/vgpap/F/i7APvbWy2uu9Mc4mIXrx8ALM4JkRXS4+dSkrGzGkW5P4y6Rp+hgb6aEyjlA1Cvvzk28hic6dX+XCYfggmASrUReV1+0TLLEMJSjoy+sRqFQJv62sfIZhd4mjIanHX76hKa01tMqDsLERsV1wghNt25JoBIK0N5paX97jpJllgDuk5NSX5KTaDSuKV2nWXGtxvbXEV1kXFoW7BWtQzJ10S1VQQMZ3EcZ374/MDulHh7vnDM+QOczs88UrtSaZNLH/hDGhw2YfpVP9huOKTOjamyS4kfHLac+o6HaXaeqsomWpCnCl/c7tkkC9CW9/f2kFtdP8gmndEMRyl2aJPzRj26OC26Os8GJF/aqMu/3aFWXaeJ/G4ijQW6Lhl4KZWnc/YgC/+eQIS3T0eyrDnwskDqoaO0DNzkSSzIrsy+nklMAX7XR7b9wGbo+fj5LcfLhe/ePzBeMmWe6ZJwnitPk1EGUCuqwtA73qyq8XphGaeG5kjU8WoWSH1QKXRHBZaVV8UZmQtPp5lhs1reNqqexJjdc3uPds/tsF9NifrRcCC/cskTUg3JXqq11EAJweSHNr21D3Wu5GxVl0Bw6obrAqcBzlcmX10coulGmbGeFkJMM+pVzfn3bieKrrphrRrVtAyX5ylCyYVLLYxzNqkKEXI1or+5WqLW+QRjcQkZPJisc0MBCiVELtlSrd6lWk4X47/Z94lPn+1tGEaEKRdeH0ckdDbEoeKgWHMyjbCxXNEFiMBWApPUq8rRtd262puybNJ2mxSUYy68GSfuhs4gEv6M7ZZs75ZqbZMAH1Y4xfY4mFG71MrbnPl4M6yVSFjOsCVaa9OvJY7naeY8FWZnZz9lZZizSW3o+qxA10CBtc3U7qdNXSssDPJpYXvH6GAWtQfQYMH2brOxe65WSRlSYr/pbR9rQVH2vU0clmqSPaMDovOcGZLh5c5zZi6VaSrMQ8dHNxujXpHVL7ag5dnQk7BEqh1NxRMMD6DV16vZMA1r0DqVwttx5MGxmB9SBVzvZ/+aH/3mn1azZe8fg1wlwevvFq2MRZd1vU76K8IhJYawxgoNzAL2mYq1am+7jl/0+mUCpC+BL1X1H/i//1ssQFpeqvpw9ff/SUT+cxF5qarf+Zf/JeB3VPWbq1/7RkQ+8+rRZ8C3f96FiAJTQZqq8nLOdFlMrr68VBV0jYLb968XdcU/XM+L2cawKNUJrNbj0yqMFJ/0qNUmj4qjw8WBtdGDrvauCqx9WpZgz0DIsmTaGmHfJdo0QhWjAnkcjZARB4PGIPTJHLaVI+2TGng0Rdj0ht2Yc/1g4uDKJi2BWfVN70E4Uo0cki5x6jrONSMi3G56boaOvjPJiWaU9Hony/pkWsm5VcAKcBpnPrndm2hoUU5T9uisxQ+ezfnaLYbW37ftAVffInlQ+bLv+WQ7ID7dogqbEHi1HSjbgdeXGRWY58Jt3/HZYYcE4dVhRyN3awGIegbU+uu+YYgxMghULVzUcENJhH0XvSIk1GyVu8a7Is7bs0apa5XJeujNTtl7RcHaJmqVh1KVnE1INAWrZi2HPwXuj2fubrdUWJxVC37b5hNsr47ZqlxJAAmLNEUMgZvdBkJguxnYDh193/F4unA8XZhmm857vBROHtCaUOZsLcZSuQRh6Jpmol3DIj6jdvZEtE1eG8ZO1nVQgctcmEo1XhY1uG/j0xGxSkgKwQkb61X7mwWw3c7XdXJjvlWYaoFSTOgzWMvFGJRN3LKPYdm7xlFUifgkUMtSfY+K7w1zsCtgu1EMVNSreopenbEQxGhH3CY0fpZcK7kUk0xqGbuv4VwLYzEywDbgYXanGg9P6pYzXf0/qUrxoCMXZa7i9BFWhei6nmmauBTbdwh8crNdMCIP54nTNKMyOVGmk2yq6d+hdZEciQ3WoCbQaxXjugbDV8HSh6/1PDT7mlV5mAsPU+Z2+NCJmSuwvaHNVlxFyc0WbV0OiQLv58xN39OxiJaY05QrWIYs70BVeJpmxqnSdZGiwv15ppTCs8GqOh84Il1b561I4o+aRusQgrXHohiWsTaCUvdQbatmlISSUqDkSillSR5TCMxqFCuqODDaqvdP48RpHJ1Isy5akFEi4+zULFV5cbOhVdHtrJhdy8Xoa3onkGwJcdvrLTBqwb74Wtl7mNzW/TTzzeWyPihgiJFtiqQA53pVdBCW6XBLKkBbJ72dKywxXJ+ztkfqIPHrmUR7qlFswjgtpcBWElgDeKPZiP//xvxV9aci8mMR+W0HXf8N4Peuf0ZEPgW+UVUVkX8e2ydvrn7k3+LD9hrA/wj828Df9j//3p93LQL01QQW24FfSfRYInqWwENQMcWhxcuqRcBnXXV2aFFm+4JHpznbF1tlpj3sGMQYaxGO87R8q/imsakAfxjSyu9LqkSbzFmCtubw/S4bqG/Td1CVT2+TM4hW/vTto1d33KAK3Gx6Pr3ZOm8OK7N3W7MYSAnGSbhMhidaAkHHcLX7XMRERZZRwKhwI8L+sOWbPKEhsusTMbjmkTu51ppszyQGEwjsvFrWZCWaIndV5Txnuhi4HXq+uX8yEF/LHPxxrP5dyFeWtUmBJM8wblPHx0PPq03v7R6WIDKIuGaQ8HmKntVVbrrIR7uBbW+ihqXE9eCrMo3/L3dv0jPbluZ3/Va3946Itznve+45t6vMarCpwuUqSzYWZoCEQdQcyQgzREYCBiAx4TPwATzwF6CVkGzBiEIMkMC4CldDVuPMyqxs6raneduI2M1qHgbPWjsi01miJCYkkcp772neiNh7r/Wsp/k3cz3w7JrkWWvIlY0RrKUEDT5aTRsunOdRCuOU8U4TWEXxSr0WfW4FqWPZ2oUgr1VUU9ZtY5mYVYOpLMLQeeYp4XtfO3RFWT/G8tF2y/tl5nmpDten6KaHQlFG2JdxRoDX246XnV8PRu8d28sLUhg4zjOHcSTlQh804cR2TCnr2KQoCLogHMYj+2VeR7VDJ1xuOlIslRLevsMJV7PmirYeVLWLO+bM/X5mqpiR60vVF1MfMlm7DiLCECw2KyMsr8WIrpbGZD11d1olrN2yFDMlFqjji4vNFm8FX4S0zDwenxmGUHV57IqrU7C6qYk/6/4V6jhTdJTyMC0riFdHT6Yqd1d/vtq1VasZPWxTKcwxM+fCGLOOfJ0lpsS4RJZYrXmMZZnTuhesUWX1ttfaXbDr89eu2yFlHsdEJtEPneKeQiDlTHCOi65jSQqA9fZ0YC8pY11EvMVYx2GcWFJSvSSvOkXXvTujW+t66q1lSid2ltSxzArkFq3mm6Jy68S04yyK8P2nI796e7WSTzA6uhxj1E5oLTLXyQ0nQgAYNjW+P+asgpRDRzDmtDdqctTuWwv3BliSEIzj5mLLcZp5/3zg3hrGoePDF1t6b9fk2FAvy57JMNSl55yymy+3gcMhk7LGJS9WQf+aRq7eiynrPjDjSO9UUHFOC3NMjNGuemopayf+GBc+vrrEoMlEzeZ5jhNW4HLbcVyUaZcrnKSZf5t6/QKVMZtJ7jSZ0PVV04sip0JaBIxQxK1E8kNM3M3LmphT4/Or7cDH2w3WCHfTwlfHSX0+a7xodjKUk62UMVQF/lL/Xjsv64iytDGfOTv/9SHsnF/H3u2ZtjGgrTE4OFPHjP98ut5ef1EW238C/Jd1VPanwL9vjPmPAETkHwB/B/iPjTEJGIG/K/WUNMZsgX8L+A9/4j3/C+C/M8b8PeBHwL/z//Ql2sESTHM61u4KVk5Mr7NE5sd4SjXzbQEdEYzV9PVU1cuphS7UVqhbA1lTBXZVOt2gopB6sy0XfeB6GLgYAocla2CVUjc+lSJ9ArS1jlXrgJzYhjV9OU/eWqCvm8JaIYsmGYf5yBcPR53nbi1lZcVJFYrTJGfoquFlLJWtI+smKMacQG5oMM3rjtff98bQB8/zvBCtwXiHcwrMUxxFoQ8KcC4ianjpFQvV4JNrJ0YU3DrFTO8tm6D4lGRMTQDr6VbvRhLV72ltL0MdXxT4aBj4hd2u4p90o1vrOCyL0mFLVo83Z+mMZdMFPJY5JzprlCFjYEkKNm2YLhEYNj3LvBBLIlul+256T8mq4SQkrFOA65xUI6ZkrZ5NFT90xhIpFdjbometBo0lSSF4g7eepYpNtvZxQnjez5SszyVJ4cp0YAK5ZJ6nkWXJbIJn13t+5Vc/4bMfPfDu6cDjMrOf5zU4nED++u9g9bkbhN4ZdhcX3L76kA9vXnD7+oZvf/ae3/nD7/D+/QOvrq/4lW9+gx989RX3z0deXV8SvGdcIncP9zwcJxyOzbbHO1XOfbnttaPgPE6kdkbUbkcxWFXLpEU3qdTfVNYDJtX1uPMeJDEnfcIWqTYPVTy1ADXgNihLfYqnrgS6rlrHeAievrNsnbJ6pkoASQaMdVz0W97NakPgMWx8IbtC5y0dBufdOipvMcZAhWlnogiSM/slaiJdsgKJRbVl+uDoLMxZmHMiVaPtOSUGY9lteo45czgcSTmv1+DkpCnW9kdRpGvFDOnas+T6+yrxgVG6/FCVwo92Sy6Fu6c9Oan9TEFHd4mm4q5jrDllEkdu/ZVaDXmNv8EHXr+8IL/fM+aEQ3GHKjhruHaenXMcUmZMeZUiWZOK1oqvRZrlBHhX1qeOJ7/7+Mwv31wQjOLGDrmotIZTrbE2und1v4k0jKG+vNXu8rtlASl8vBkYjDLgKhejdnBbwaLv+XcX3QAAIABJREFU+TjODE5tVqLVjpu18JgSP/jBW/7mz7/kxdAhdX1bp53ORHMDsCtRJgt8dLElCbwZJ02OjWXTG+3Q1VVvjcbacYZOFl5cX7HdbphSZnx3x1fPB66GHmMg5sKcMp33zDmz7QPWuCpZkzHWs+0GnpYDKWli3TnDYVnY9oGA1aTdqOm1iMrgmJrQC5qVijXEaa4MRA0iLUI3P8LnlHlYIscUlbggiuv6dLPj1WaowrWJm+DZXGx4N0UeqgSAjvuMMgURFIuqmmG3PvDmOHPuPKz7rBU/7YucGkwfbgZ6exp9t26qrSfuc4oYZ3jh+9Mi+Smvv1CCJCK/B/zLP/Hb/+Dsz/8+8Pf/nJ89Ai9/yu+/R7tRf/GXNUjX1QNE6cIxZzWz5Pw6tcQ0Td3x/PeLSsKrGeWpqmzoWEXIawB1FjZBJeyLit5Q0EVpqvqkd4qxuR46Xm03gI5C9vOCrTRrPZBa9+GkGaGXpFTV0wUYxpR5GOfVDdtg1hl/rkC0VAytx94qniitoq1CZbUqsAYy6g9lg9HRHbVLJnDcZwVK0iqDgrGOZpXYOksIbJwldYFjjIwpMgTPZd/ROUtvXQVlq8aUM1qBuXq93kGzOUj1QOyd5eXFlpvdlvsp8sVxv5ZwNWdVzShndNNWxoOBiuOw/OWbqyZnhTVK/U/lJK2AdXVkpR2PKIX348g+aodnyYGPLwesc8SoXQvvLNY5ci6E4NeRrDQQpwNTtGJv5p/GWra95S5n3u4PzKFoBV0qaNnqs2s9KgUb6uFVz7cagE1laxYenic666AmF94Hig1cXhqejiMWuL3c8MHVC5wtfPuP7ynW0YULNtlyNx8JvdPk6Gx8ao3RhN47tU7ZbbEh8OXdI1fbARssf/DP/oTDYeSv/9Vf5t/8V/8Gv/lbv8v+OPGXf/5Tus5zGCfywzOvb19qoiWF52linCO90zFRzqWyvLQavOgCxQjjlBAxJ088EQ1jBt4+jzyMEYxhO6iuTOuGrViuetBGKTgsvbP0TlXbD1FNoU4K7yfGlMFQxFS8FdiiQPJOhPtx4cXlFYPTA2JJmTgXrBRKZd85q0f4PiXSMtX1ZleWpkMYrKMxFntreTF0VYFdi7LeORVClMIc4RAzh0VByUvJisXD8LHdMM2RUD+3eRk2WYTW3WmjRGvVhoJSKPXAr0fu2n3pbKWplcKNn3HAkvXgMKEmeHJyGMDoPghDT2877vd7Prq6ZOtVCT8J/NlX99xeb5liIKeFUjFRc5E1YRisxXh4iqeupnNm1TRai7V2BnK6PmthXzKfH2deD72qwI+RLhfE1wNc23cUA15v/5nMCXRG+KTr+MQHvowL3z8cuQ6ej/pOpV/O4nQDLh9iIUXDJ6+veIoTU0445zGu8PQc2XaeaUmM1tB5R++qBUpNDlpHsQjghMF6ppSZi3YIvQi2ml9XihgOFe5cYmLXd9zsdlgHT4cDx3nh0ln2R5Ch4J0nGEiiY7dmT4NoIm6sCoaazmBHw3YYyFLYjxMP+yd++cMbHpcZinal7aAFWyuq19yjBtvQ6Si2FD3XvDGVAGMJxvL2eOQxRkDNn4s1XHvPB9uBbXBqCJ0hVp+7D/pQu/RN8LaZB2nS1XnL7aZjAC77LY9z4mlJTGsslnqGmvV+A1x0nsvOsS4gWiKnFzSlhPHqVWfcCdv2014/U0raGMMUI731fHy144MXF6Rs+e7Xn3M3Lme6KLV6LOXUQ2o3wVTKtLVn2CCzikRalNpZE1ENXNTKwpr1Rtu6ucdYKhjZ1Jk0q4iaQRd9e3TFVEBaTXJg7TSvoxdTG2GN5XReHbVF0UCNUiueBliNaLKViuoU+brZc62jW1WUajAyVRJ/s3Usi54+Cti1Z5/XFmLzv2K9nlgKeYmkLLy+2K7WDaW0+bLDmFKd3E1t42oSFqwjBMtcMk/jzPWm46994zWf/f4zvj9LdWsVuII/z0rnYCzf2G3XTdUKjFgTy1L00NA5ulZlSy70zvI4zaoMnuD708T9kvnV19d4X2HUeupga/ej0ACzirkppbWFaxKKHipTynx9PFIQnNMKapSsFgLFMFiVqxNz9txN63TUpBmtpuao0g+lJDpn2fgAYrm5HADDnGIVsfMUybgS+fVf+yv80z/6Dl+9u8NK4V+42vH2MFO8Kor7CjDf1O7Fxqldzn5RJlLoPO+PE7cPI9/88BWlvOXLN2/5zf/tH/PdH37J+8cDS/6cb7y+VR0WgUMp9F1PXEalYDs9of7Zm0cugufl5YZt8EpOwGDFsBsCKau2TxNmdbWrUApYb/He0XeeXP9MzYpNxSqVs8OsdkyNUnu33vGwn7nadDjrFJxaZRK8NWuiebHdsuk6ihSyD9xPb8gpr9o6T8us+8Yr/dk7XXwxF+36OU/vNN1NWRhj1FGNTXhjm2i/jgrr2Lyr3Y05qvr+UjRxeq6igDqVMFz2vRqNZlnHiboXhNMA5yzeiV6Xdc0bTNesXedOZrXTaCJ9eUnEdQxxxvqlkIuOxFNS1fGdM0xx0vtlnSaFElniQrCGOUbtttsT4LYzbrXMgVM3sBVtreRXosKpwi+59f1Y4QYgvJ0WjCiesHew7YImV7UT2dLg3LrgrTNlNEV2ImRj+MAHpiKMIjyLcBsCS1zWjo+xOt7vROg6x5gSd/uRWGosE9V5uN702BpTchEWayqcQUeIbVrQri2L8IP9kftFMZCpSg0YUayRM7aeYcJmCJgU2B+PdN6tCW40hu3WY62tOmeGq76DmtQN3nOc1dz3quv5YHDcL5G+84xpIaaME6HvPJ897LnqgnYVvYpzKi5OizjWeyq1OIfQeUpuuEAwRkVXHmNcSQUthn20GXi9HejqODvXokOKrFZQ152n85bHmHmaZ6jjv8E7Nt7jqySOxXDZeTbB8bxk3o7zen9r3VenAZ5PN1vWNvkaU+spbixPc6Qznq6e16aJpf2U189UgmSBD1++VoaJV1rqcZlZYq4K1bRUEf1Ps9L4m1JxTXVWyu5ZD0l/plUw9cYrYt+sSf452E4Pbh33hopRienky6NqpT+WyFZNjFO6e/KLOSVMrcWbi2BsBaEWOQPbaphZk7X6fymV+WSqsBYn9dTWSGuJj2m/EsFZHb91nO5RC8Wrnkyr9LJulnbvcilMEnk/TgSn2CRTfz8VFb2Ddh8rUBW9Z8eYKrjZqthap2Bhj2HhNB5pbERdA4arPnDhFXPz8XZQHEO9vkZZbcrmDTZrUKaYq9/FtO+EAm7nXHh3nLns1OenBW0QStXvaeDEIoodOhwjgz2ZjAra6SilkcH1njUbGmcMU1H1V9sOLM7/r0lvToVpTMSc6xhRk1PrHLvQMwTHYVFX+pQKKWWEQiyZL77+mvvHZ1KK7IJj8J7XO8O+FGZnqjGqJgBjLlw7j2DIKWFlokghxg2Hw8im7/j01S33z3u+/2df4Z3jl775EZ9/+Y55nBm2lj4EbOfIyeC6nouhI+fM8zjx/jgxdBqQci7sOqUZW7TiRUA8TJPaHWDhuoLcN71n6DybcCYct65HVtzB6rUFKyPUG2H2XsdK9Zl3VnErnbUEtPsaU6R3hpvdgH/xki++elOp7r7Rkbjc9GQp632zdU25evi29RZFEz0DuKKaOMYo5uNumnk5DDgDc40PqY5Sk+hYLdU1VUTw6OhoWWJlCZ6uUftsZ2wfYd3/SSqTFC0OlQJekxA57fnznz3FPfmxX6cacw5z5GGaVbSQwhB6eoQxJ1LSuBusZZ4jYtJapBprVNutwvOV5q6sXqpwY0vaGtvQICsO7fTFWldLC8t9zNoF927tItIU3msiWeTEqF0vs8ZNgxCMMtwOpXC/RIJVrbPG3F2y6tJZhNvLnrv9UTW8QgV0F8vtZc91F7RbVK+PDEv1ybTWrNWvWo7A/Ry181gDlhafdXgh6my/EkGMQVxWuQixONHzQDtkDV+pRaE1rRCGpTLbRERxa8UiWMVCUkAKKWsRGclYTPXda739diaZs3tvVpxSynp/2p5SlwR4O00Kmq/3omThpu8ZKuwhl6Iaf7Vp0aj/SXTi0hnDddfxFBPGaoK09Q5TKtYYLUiDsewCZOmU3S2yqvD3zvGy7xmc1XvM6fnHLDyMM9uhp6/q7zEVDjlyOfy/o/n/f+ZljOF2d8Gw6ck58rx/5PP7e3U7rguxtZvbw+YMj6DrTls0oWJG0io0aNb3aMHjBAA/Y6IZ6mFZaYkWPFqF25qQxZxwweOcIcsZgO8smP8ks2atCdeugh5ixehhf+70rd/2/PBv1OOKOVpn+rUqa7HPnBKUldqK6u0422bm7fudJ0fte9buyVmCR13sj/OCNRBLWPEFg/OnMaic3qUJF84x4a2KyM2pMKWJyy4wkWn6QbJ+in6Vi87z4dBz03UYq6J/zcNMOGPu1GDXFNJb56p3hud5OXsWilmYU+bz5yMfbgeuBui8oekWnYKFIaXE87zwPCcejjO32w2lejGJQBcsN33H45JQAKMy9lKl6i9YNrZqf5TKWJRTkpSLCnNOUyLZUmnipgKEXaVvF6Yl8nOffMrD4xOHw56L3YbDUfjBZ58zjqM6Xg+q3ByCxUWqLL8usDHq2s+DMrMMBfJCFlXUvnt4oPeej15e0wXHOE68vHnB1dWOw8Mer9kynbcEH1hMwrmezqnI4JxyRcHA8xTJHRjn6EVHkY0ZmIswpcJ+SWRT6NwWcapgvw2ewTuWVfDoxEVtdYL+u+33mkRbTbAaHdkYTboxjfFTQa4lgji2fccxLgQLImXtvHTeMQyB46KjKFe9xKgU7FiEOStTrREQTCu+aqIw58L9tPCi2ZXEtCZGTdxuzHllZrZ93A6ns3poLfTav1ZatJhVMbyzJ0uR8wDTYpqpMbAONM+66O0vtmJIWblTVDPZVDXMVGdIWVWlZHrvKUXB56XqHjUShg8qNVCyaOeqtDVfx+ZrwdqKIH6smmzdf0GTo2BVc262hW01wT5FJmgYx1V7x5zeqd0DPQeUAp5SYkyZ93MkDD0d2n07LIn9HLWL0fe8ez6w7QLFGpZqXn7b92zNySex3r2KDwUrVRS1nTkY3k1zdQkw1R7n9KRSkVXTTq8BjM34YNaRXVPld6aJpLaY3P5MJSNsrRaXnHiKHmccUgq991iBMQoz6hmX6v5QaES7SS0ytnWjxeUhxipHovhFcdAbx3OMPC5xtZ5qwWxwddRWpT9yFdEVNCFs3p5LNTzfOsUBhmqY3Vc88LkNEQZ6Z7kd1IZnH3UddtZyEQLXXYehxXyzPpPjnDjOia4fGHpNe3LRRNgv8Sd3zPr62UqQMKTpiWI2GOcR47nbH6qR5FmnR1hPyCYu1vZSazDtQiCmimlpSdR6qv5E90JQsLQ5VSmxiEr5W8Ug+OZybJVGZnIhOKcUW8nr6EyzaVkXHkYpmLLSY1uPa42INIpvqT8jsFawLYOWUunTbY2eBT6R2kmqyV8bk7WqEyq+on0GJ8zRyZjIrAtPu1Z6v8WKKlsbeF4WnuYJEWHbBX7+xTUYpWwPvlEutWocqzx+79VrbMoalD7cDfzwcDxVtHXDthHbz202fLDpGXxb5KqOrnPtNcxjjUWqBYj+W+/bAny5P5DyqYpbUuGQJp5nqyrtIrzYGDqn3QtXBdlEhMdp5nvvHnn7NJMovDsWSpyZlkhwlr/xjVtuhi3flYPSqmsQSCJMVSpCrBAqLbe3FotiRrIUci7kqF2obHQU6SzsfMdgHcd5ZE4WYzv+tb/1t/j+D37It7/7bV6/vOGd8XxxeEvfW3bBs+k8YoSvD/N6IJWi97IpnM+lrtP23Evh8eEtcdzzi9/8lL7z9LfX3FwOdL7n7cMz37i9REpCqjhoyobLbdDvXoQkFucCwVrGacECod+Qja/+a6qCb63hcVzYz4se3AL7bME4tcEwOjp2tQs5mVx9s7Rg8bTDsOryIPWAsKsSuchJ8sDVsQQYeuvovWc7bHCbHd/61h9z3SvWLOWEs46t8wTvEAlQ15+tGCJBMKnwHBfmnOvYvHYqpZCLpRgdb0+SVxD2UoRjxaGkmlBkBCWc1LhjDTmnqp+m79FEcbVjpuOvttY12akAbqu4RlqMoK7/ljyiyZRo9cD6JmuHrnafUDmJIpqk97Z6kZXMoVjFvThP13n2x6gHt622RFa/0+ubS94/HZjnJsirRaSTWh3Uz16TNVSD6RjTSQyyxUlR896YCvuY6a3lwrfiUNdEgybYitfyrQiSlkLpyxoFj3tjuIuwXxLvrOWTrmOKhecp8TRFymDINrHdBKYpIUVFI62BwTi8FT0r1gJUN9mcBGvyqnXkrDCJ8DhHqt3fmty0TDjnQs76/EJl2VqxDL3FYUlJaMKkzlm2wTFlxTKpYGup1H6pZ1EbNxakFOYlczH0dC5QgmKUnDiVHPiJpHql39dqPUlhHxNfPh8pAhfBs1jLZDI3m4E341gVttt5Zug6XzFhRokPuUnMyHr+zEUTUl/VxHMpXIXAVefXhN87sOJwKKOxTUy8gZs+cIgJZywv+p6XQ6/yBEVtyErtuh2XxPMUeRk6hk0gZ43VvfMsVniY//+SIBmhJxHKSCqGEmeGoEJYmRN1WLsFhjYnXTtKZy1XZdCwdpxamynnM0WVGjR8RfCJsHoNNd2SJSZM363Mp5JbyDJ0ziDZsCTdSN7CV/uJd+O8zo+dcfza6xcnETTTBMeqFa1UCX7RKiyLzlPFnvpHOkIzGKPg8yQnY9zqP4lF1ZJrrncCw7W0wmjnKdd8qJxdv6pnaxB2dYG6M22pjFQRPbuOl8YY+c67O14MPa82G01uiyYLY05YLJdDhyAcUqwBxhIy7KwnSaKYsqplFzFcd57rLtBbZcY1mQdZCr5W7UV0Fp2KdqcyWvnHkribZj5/GumdAmpjyapPYw23m55jjLwfj8yl8IkYvnnlq9aGYrre7kc+ezxwP0a882y8I5WF46SDBN8b3hwmPr4Y+MXNBuMtb+aZzw6jtodd1SPJmZQVk1GKMARXR5KwzDpeG5dMyYXlwtDVA3EWwyFm7JL4e//ev8vbd5/x9u3n7A/P/M63H/mbv/ov8bvf/j4fXPXsOk8R4fsPB/qgasYxF3ZdUKxIBUT/6Gnkw13hpgv0TbiuGPbjkd//9ne5uLrio9ev+LlXL3lzd8/1Rc8ff3+PsY4XFzsu+57nh0es7Ykx84ufvub5eOTL773j41e3vL1/Yo6Rl8Fw5dW5/pgjzhhSgrtxZopJcRZYpvFA5wKdd1VTR82IUym8CD37FJlyWqnRQjWAltOCzTlDNvgKQHfW1C6PHo5jEQqZZcm41POy37IJns45kmnjUBSIbHv+9l/7BR6XyPe+eM+7x70SJqRqn9VKtZXbrdtRbK3ka7fkkHIdK0dV7zasbKOzoKTvUYRljth6SrSiT7ujythdKfHtsmttVwXQVjbP2oGq3ZUGD6gToXWMbIwWG4cl8/XzCAidM0yxMEVNv0oWPrjsuZsLO294MfRsdzv+rDwjJOacNPaJylM8PS8q4XFzxf3+yLhENv1AyZl9HHHWsNR7adCY9G//9V/gf/j9H/EwLTVuaxKso3olM4ySuQN2rq+3XW9O1tqWrlajJVcNMlM77vUmtU7E1jkunEOM5feenvh0u2Gs61H1wDZ88fBQhUM9rnM4bzClGeYaVnYILc7qvc6i1klTyiw58v1pXB9GS0QpkB1QKf+lVMHYJet+MKr/pZmfdpiuQsdllSl4N84qlFi1ipwH7/xa3BuBHGdigc53zGlRvJOxDF3P87gwloVY1M/RmzpBqaCpYrT4PqbMl4cjylo0JIp2s73n62nCGEcxmTglOue43gQ+2m5W1fsYm1+lrg1rtUuX6+e1M7sPnnnKzDkTqp9jRvFZpQihthiy6Dr9/PlILsKvvbph5yv+V07FgWB4nGYOKRE2HmMdqUQlJFTc8NZburD5c3OOn6kECUTdxo9ptTlIUgi4Copcc3KtUtsv1vrB1KBhyLnKnFfswBpRWuCx9gxnpElPu/mmHtgO7RgFW5OF+vBjFDAZK8rgkKyIXgGuNx3eWW2z14y6/azUCKv4gRY4VByuVVjBaoWUc8E51oTFGChJk5Dg6gat2XqAKkJ50vpQXYlChypHl1opRAzVAfa8ebSOCaUGU2PM2hGLWbDBNPb66aBAuBsnnpeFj3cXyviqisTbEBhTpreot1Wq3ZNc+CAEljmTiiD1O193gV+/vmLjbMVA6RfrrK3jqxPGKdWDsDFIsgjPc+J+XLgdeg4x0veeZRaWFElJkw+1EXCM88x7AxehApqdI4vwxdORt88TRhwUS8yRJRatwrwhWuHbD894Z3m16TFJKKmCYkvFlUlZ7VpyVnZOLGgXRR8aBaXaDruuekZpIoUz7PrA0Ad+/w/+Cd/70VfslxGxGnx+51t/xDdfXrLrnVJu50gGxqSJs7GWY9JDzBq42gwcxpmvjwvHmLkdAlddp8kylnf7kTEXlpi4fzpwe3WNc4Wr3VbXIkKajtxYIVn4+NXtautyc3nJ24cDr64uVLslCdNhUYByMry+2vB2/6yJjqtEAlEtlL4krBiC8YSazFqxTDGy8ZZgO6Io8DrlM3FU2vjNISaDOenvWGMwTng4LoQQkFgwzrN/HvnWt75FFzb0G4dbFq28gbs4Qxy5G28ZqwDf81L91ESLGalx6VSAtaqLFnEwCA+zHviTEYo9jS5sFYpssSU4y8ZbOqE6ubeujlS+h6GQq35L+wy9B8VURs6pmK+dgVNnNRdZQcyKTSqrN5wxho1XRlcRWLKw3QxcXXrmJXJ/OPKDp5FPX75AlsT748ybObIdet48HXFGJVissWoMmxdyFt4dHxXtYgzPxwOI0HvLhXcccuaQ86oh99//n3+KxXLhtNuYEHZ9jxVlG5a6149JuEuOV51fE8F6U1VyzNoV79m63sao6n3r9CGQa3H8jb7jD+6ecAY2u47OWp6mkWAdxSh27Kbz3HQDGLViEslrI6y1YGw1WzXSJg2Fp6wdNGurzlz9M6kHVCl1lG9Q8pBoQdT3gSlnumLYWs9u59VWRpSE8mrbswmOt8eZWSAmOVP4pooLW4ZgiDnRdWo6a6Tw/nkEDNsQeDNOJBE+vdwhdaQiRvQMyplUu81SraCGvme3vSCEQNnf87LfcLy/xwyBFyFw0ykBwzvtHhWEp0knBALc72fEwO1lTxd0jw/eMy06pl0Wnc6E4NhVOQPrdNqTisIDHqaFJGp31aYqbZVbY/i9zx4Yc1ZrFWeZlhk2PVfFrB22dq45OWuK/MTrZytBakFDZAX9WWMVP3OWdJTW/tDs5gxXc8ICxeqf1jRqmqdT68u0v39COLe24+m7NMR95yy9r35UzpDSgrWW+4NqbvimzyOGyy6wC+EU+KRSPUs5tZWNqSJeGW8VtNokntoPDl7pt7ZejxagqkEyUZWYUSGwiTNtjxZ0AVNO5poOuHSe29DVfWpOYOb62QKE2s04WSewsj7W6qh91VK7ciK8H0d2XccuBHbea2JrLC4LJmkSWSrOAbSL5J1jMzg+2W2wAr3zFdtRdTBKWduupc6xGzDaWX3GwRoexsgYE1vvWVJenZ9TTciUUuqIRTjW1vzzvPCHb96zcR2fXg588XjkcVwwRfWM6B12MVxeXXB32DPnhEMFQv/kfg/G8KLveNH3GG/57HhAUJdxZxpyTO/zGCPTnNaupLOOHg0IS5LVRuCYZ2JKHExk/PyH7A8z85QwCJvOEzrPZnC8nxcOS2auYxizdhHPDnEMxzmyDYFpiUqZL7AIjOPMi23g9bZTzNJ85DEuzIuyYFI1YcZmkoN3hyMvO8dX7+5YcmScFw7jzJIicwq8fXrm1fUl15tNFbybKDKzjwuIkHMhJj04Pry9IudltRdIWchFBZy64FgWQaUSLL23zDZjaIKadWPWSsVUBfoWCsBwPQQ2m0vCMqvVgzE8L7AdPEtKbLwnFh0BbgRGHL/7vc9JOa/ejcW0oqnFhnYm1QJqPYANDXg/pnISBq0xSdX2ZT3YRarvVAgs43LCVLZnaFom0CxrTjpmoEKR7oy2LNKKRLvGrFYHKnmj6RHpOyw582Y/1mpfr2OaF2SeiUlxVlebLUvKbJxlFwLvpmm9hlglHTqn8gljqdZBJpBiYoq6znrvCM5zTInOGJwPHItKQpQCtsmB5EJOGderz1tTY29A5vtp4cO+o8Ep2r3QuN301tbHUn9Wx6wtITQVI3ob1ErpMSXmatdBlfxQ3CUrUcZSZREa2Lw+U1vXhcZiWQ/gZj3UjqXzjhOlaBOqAbo5WQvtR90Hm412q7sVyNnivbot2J3lq/2xXqom0gZWaZNUtBhOacER6L3n9nJDysKcIhbLYUl8/rTn06vdOpFwqLVOFu38+qBQihc7da5+Oh4oeOa00FtDsBrbnTGVoapr63GJPMwLhylRirDdBj7Ybpgkrx2+h3lmyXWUjLATVztFiU9uLxXOYYTnw8zdcWJBO0CfXmxXXCVGvRu/++aRfuPpbGCJakBtDWwG7VKXAsXVeNgy2z/n9bOVIBltfRdUVHBrvI6SnK2gtYbVkVPoMKefbZYGoMCtvo4h5girfUDdfHAaL7WF12hHa92obRKWoqqnY04MxuPreCBJVOVfa85AyidsgFqwVRbE2by8vfchqjFpOjvojOihmkUVQZteSa5z6J21HIDc/MTWpE6r1fYZ58tCK2bt1njT2EE6ejrvCLVf2rPvaWqZ2gCjZ8Xzev9KESaJ2m7NmdQFdqFTbygxtOmoaUHLaPu7d8LGey5CV5lcsgb4Zv6YRQ0/GxuwJXOggOR2b5rn3uC9JpZR8IXaedTE2htWNlHWNgpznBAp3B0mEA0Wc1yQlOkMmCUSjOppFKPfccqFz5+P6vO26fCuI0nh88OogV/0u3k3GDm9AAAgAElEQVSn1c/9cSK3dVY0Kd8MAS+OfVoItraPDWy3gUOcOPiZbAXb12GpU1Dsc0ksCLnJSLTYcXaoN6ubInoa3Wx7emuVlSLKNqOCYgHmmHheZnySauzq2QyBXAr3hwNvn4+8urlkSZqsDc4ineNpmZnnEQMcphFB2HQ9m35DTDMiZl3b3ju2/YCKCFZMQtXSMrAewu1g0MNPgaKNxGAqtXqVhOCspqgHhzWW4zLh5oTQJBccxMjFruP2omc/Ru6eJ45z4Wgyx/2y4ll0DGHJJq/7umFg1rhhqq5T24OigG7ODri2Vs8LFpWCgOfjQk5lhQC0QN66UWDWe3CeRDXF+JYktPe17fm3mFU/vNQ9vSpv1/dr0CStL5u/pCY2jsI0LYShowuB3ZJ4WCIiQqhxuFZHK/t2Tgup6Ni/c56uC4TgiHNiztrxvu4C9+NcSRv6PJ2zWkxVz0ypLOI1ZokwllJ9BVti+uNEm1b0atzSG9ESwlOU0rh36SwGx1iUfThXkd/W9aHGPikNM6UB2VWxUGM1qTM1R9e4qOcDdVxmDCqFUEVbtYY8Bf12720tLCUJrgihad6dnQ+t2Omd5eWm526cyfWa1z1iKu6s7qk5JiyG28sNn717UIKL0XHaMRkep4Wboa85tcbA3jk+udywj4kpC4dxAqPdnBB65hxX8dL2BX3VJEtZdcnmUurzgxfbXhnLcmL5pqISKdZrPBtL0T0DfP10ZHDKSozVAWOJmc45tlXJvIj+3sO04DvPhy8umUvmYT+ykBl6xTU1nNq6PM4aIj/t9TOVIOUiPC2KX2iaJK0rcp6A6EvWkLLSLduf1PGbaxU7Z5LrnLVrDat2iJzRFdr7VFwmqegIx5mJjXc4r6alSy509hR4NOCwpm8t6aBqPbS2bNsvU9ZRW9vRpl6FKQZnyoopaBVhypoaNr2jlo2viUtuWq0tKTola80x/hRBT52p03uYqqHU2BmamOTa1VspqpxuZHvPDJQU1TKjZEqGvCQ21tNXUK2pQHgMSNHqwtX3d5jq7i0rYNoY7SJhzdkdbRdYGRKpVGuMgrOKk9oZSEtm5xQMORY9wFw7IFYhNPU8up+WVbW5oElUzhFxjjzP9M4TnCdSmKNW/nfjwmWYyFkxRje+400Z1XTYsK6HIuqRZ0w7GPTr98Hw8dUNx6vM8Tgyzup1NvSBOS4KNvY62quKWxQDx5KVWVlpciKKhXBVs6ktNA3uhillbnY9fe0CBgN9cMSU6uGo44X9ktm4ROeUEgzCkhNJMp/cbnn9YuDL+4ld36tn4HHifp6h6MF9nBdiFnIx9F3HZrNj2I+M6JrXqnbH47jHlMYOreOI2sXNpRmfmrofzLputACotiymrHWOoWKFcq30reFxf8TXnaAJV2Ewhd1Fp0mPsxhniUCmVLkNxWjoEWrWtW9ohUhNMmi4PEtaizVdj+cFREun2paTAqGK6R3neOqIGO3unnTRWpJQRQBZw1tVkD51MWjb2ShzVIs8OdsmNaLUlpN3yhx8nnQNSztsa5wKFWNYirJzjdcE5iEvSNYOg7P2FEetwWRVqG8GrdZYrFf4QhcCORX64LnadDyO8wpMF4sKX1rLuCxkcyK5tPsH8BgTfXWoX5OkNQlq0hz6Exrv9JpS0TG2r8r/eZ7wFnY4grHsBQ5lpgkCa10jtfN2irnrgGHNZg22VP2rOj7Uc6IFcHOKsaYVhadzYX1EFbahQujaoWpyAD/+bPW9r7rAGDPHGLXYXteZHlKrfljRArDzgSKoYrox+Aqwn1M+JUf15azhZdV8ilNkWhaN9VaJMCcP0sKY9blfBq/4tZKZatfeWp3YbGpjIjjHEtUIuD3UNnVYpFCMpbeGu/1IZwyh83hnuWgG6UltViZb8FXvbErC6xeXXG8HHsYJg8F7y+W2RwWQa6khCgE56YT99NfPVII058KfPh3ZOcemKqh6Z1UHSadUp8V4HoHqDTnrKwEtQVGbEMkFkzkFkfbXTK0Sf6ILRGVxuernsp+jarkYXVDeGIL33Gw6Nr7hdc4/XT8/iVQ3Y33zBjTWJESBxE1kcf0pOQVdzEmfx4hRbaFOTpuuHgrOVM+uqlCoDCsN6I0JYs82UiktmLQ6VL/1obIDbA0I1hoknYTZWpfrx/Wd2v3XRXmMkcej+nbdhI4XXc+F96rqm3QR308zLlhMZ1UB3Ni6WM2KLfPWkqt/Rs6aAJ1Gg4W3h5HnWTFGwSljbi6FCyw+qOjYkDOPMTLmxDEWBu8oVY3SitBbx5wLXfBaBXvD4AI5ZdKiByBOvdCsWO6XjPN6UP3p+2c+twduh55v3O7YFcfRyNq2P0aVB3CuagLlOi4UPSS/+dEHbF5e860/+C7L8qCHU4oMznPIcT0smpebJkfKUmrsDzW61OS/jVNqDMJbw3FJjH0G79g4Q+91bOCtXam5SUCsZXCBi+2AdZav7x6JJfPhBxf8K7/4kt1Fz+PhPcNmg3WGKQs3m4GMY7886UEgkGLmMI589MHHPDwd6ezMUtlwve9w1jKmRQHMUhNg9LmSzboSDRqIrejBrQKFSgNPWWcMGb3uJalAnbMqlzAeI1e7gaELik8bJ54Q/GHh/nnG+QBY+uCx4tjHcc1ERJQZVABXsYvnVO9m1qryHplcOBPGY+1It2KJto9rgVHWLErWztS611v8MMJENVSmdQg0qVjSyZKkbUEVQZHKTmsxUfGSrbAw6L3Z9YH8KOpvaE7JSIHaxYW+G3BWXaVdZwnZVdPuE67GGHDG4YzQ+bJ2sOacsMUSM1wMPcFatp0aIru7R4wYtsHjvGOSzNMy6xhd5Z1Pz18qTmyKXHtfD1+zFrXno09B4+uSi46ArNX7FAa6iyu2Q+DNFz/CW42/FoUSpCi4oGtIcaPCcYnVicCuo1+p2nAW8CFgjWBK4XHJvF1iXa8aUxRPWFZavm0ZVsWYrR2ilgi2LuT63My6DmuNsxaiL7cD+VCYq4lt+/1otYhU/JLDWs8YE5cXlzwcn+lrp64Pbo1Fp6RBi9EscN11KyXfoIa603QELHPWxGpMhVkKW2eZcmasY1MjGm82PhBF8EWL7WNMjDHivKrhzyVXwpFiZr1x5By5WzIXFm7CwGXvuegDX94f+OHDAWccm65j0wU2Q8ftxZbjvPA8LhyXtI6dT1y90xl7Lr3z014/UwlSu5B9zjzVVuGLzUAqegCKaW7Vp0O9UfhbOSacgIzOQs4aQPWlf/Hcw0fBnYaudp1aAkNNPlztKswpq6FjnYeKGLZ9YRMcV53HSgHj6LxdK5yWwBQLPss6ogHBWVVxlWxqJd8OhzO6ftIWcAug1hlKViyAdl+A5gYuUscDta1fhM6o51ILzi2f1OTq5DLeZvmFehiJgHPapTrrHjlzAkTWfawChzXYn1f1g3GIszyXzBRHLkvgA9vxw6cDj/MJf3FMmcvthmgKt94yZYGqAJ5qUqTXW6nqosnR14eRN+NIScJFF+icY8mZq66H6gCNQZ3breUxOXKZK+j9VNkjyuY5zBGpcg49gHHcLRPLEtkMgWJ1FLUZAof9Aka4uVQRy7fjxNMXE8dc6C475v1Cbz27odNE1FouvWMms+2VTfXm7sj//q3v8Ou/8kvkEmvVZBklMpaJi9LzfjpivVKPxQritSPYMAzGQtc7Tbpixb606zKG6+3AxVC4ROikEMTWhESfefBKybZeD4qHaa+HXIy8P+y5fXHFzc0H/MPf/i5/+9d/iYvdjrv9kTePjzwc93zj8oICfOPmgpyrMaVRrZb/6zvfw1nHbrPFpcR+PPL24YEuOKY5k2zBdOA7C5IxVkkY1rSeBpSSMdWHTXEl+ue9dzzPC6Aegbko7st5p4roVZpjmhOx6D2XbNh0A/ePz8iSECtqpSOeHJWhiLASKxq1/qzBsJY/Bh0F5LrJ1+4Bp87HeoDVX4dqWZQbAhxT3dvPIx/rodd+Lq/vWljmAg8HtsHTuWaqq5wLarJlbBO71APIVRFUV4uC4xLZT5Gbnbq9p1KYlsx+UrHPy8sBZ2eWksA5XAjsj5Ft75hjIeUE9RkM3mOs0PduvWgfwHU9+8MeyYmff3XDzcWOw5zJ2RBtYZkXmDXBjeY0Vhej/TmHdv5Vgyrzfom8sh0727B9Zk2KchGWnNnHyN0887rr+ebHL/lLn3zC1dUt1gTe3r/nD/8kEQbHm6cnpGjMeLkdVNixqHL6MSfiknmeZ676gVANhtsjEKNrEmuwXvt7UkrFyJyICFKzHRGBcpKqOHX2BClqzVJy051r98HUc+nH14WgBe0Huw3vx5kpJaw1zGhnWYkfYEpmiZk/uXvmahi46C8oaWbJmuxNcyIYw1Wv7PDSivea0L3abjjEyFKyCqJiEGvYDIExJsUoLoXPFuHyoodFuN3uuPKW687hOsfvvb/nmx98xDQfKNnUpM3QG02cnIGxFOZSyEVxWC8vN4gRjvPCfpyJudCFHkeht46YE8dlouD5o8/fIFmYS8IUS0rw+ds9H7/c0DmvyT9631PRYuDPe/1MJUinBLAFDsW0dC4wp6jqyTQG06mC0q1/egtbD+sGOG36RxZ7xvBoDaTKkpETWE//uAYbUdpnsxTJoosGo6Jwj1Okd5YPqqR620lrgwWjHln1j5oBqxQ5mXiaRsqv4MlKCS3WrtVn7bozFgUEZtEOEQid1fDdBBQxgFN9mL6WIakG9KZAjTnfhNLaQlinSsUpF3KqwGp0E7UErGFAVsC7MasPWHsI1hjtjhVhEYjes7ncwfOhCm4WXl5s+Ohqx1CToOOSFFRZFYub/lXz4LI1+XtcEp/tj2Asl716JAWrgmlrcsT6KDAYXoYOj+HtrMHCGE0GMYYxJpyztfUsRBGy1ffovAPRRFtE9YfYKQPwOCdy0pQ3WdUnYSl8cvuC281AcIY3+xH2RzpruNwO/Is/94LLbeAf/fYP6DrDb33r21z0nkWyBqalIB5mUVArooVB6OyZMnytTGs21HWW4oU4tlY4xJy5ex75cOu5HHoadMQ7XVu+iikagQ5D31le9J7vvT9wXBYudj3WOH7wo3fMc+Ef/vZ3+Esfv66Yk8Q8J+YN2F6ZpmLqMxPBpMzjIry63XFzucEbWOKGOQtf3j8gFOIiHOt+GIJHot6/VlEbwBVbk/2yJn4i0HtPcIGnZWY5aletCxapyd+HN7d4o9YHnbO8vhj44d0zX90/6LX7KnAncHV7xf0XR65Cr6rhWopy93wERCU8RPdHQYhV9bq0vd3w0W0bnSXfbQyURbjoAzEWYs7rNjlVarWLUKgigGdvtB7Q+kOuPviYhWQUzxVF2b/ACk/orGMshYua2EiNLXMSXl/vdJxZFDPkB8XtGQ8XYctu17OMs/rEieAdzLGonlPVryoG3j3PGFvYboJ21owhGEsaD/Rex5RfvH/kq/ePiDGEEHAUYk4n4cxSFAvpfvy2FFFGbxZ4WBb2MXE79Hy06TVUiVnf45gzDzGSEb54HvnP/9Pf4POv3vOH3/mM7/7gKx6fR/6zv/sb/I//x7cYY2Z/POpY3gmZogKhTu2lEE1+7+eJRn7qnGXbeS66QMpAUoXnC2cx2w13owqwBixTKacumwXBIjlhjKyJr2JTZV0f61MWVpxd2wR1eayJTGcM10HFep9KXAteX8+EbMB46MSzNZ4hwP1SWKzng9tbfuNXv8F/8z/9NrYIH19t2XZ+/XyL/uPtOBGlsO2CgvKxFFs4LkUlC7yj6ztighe7LQsFNwxM1vL5+ztyEcbxQDaJwTtu+56bTcfnz3t2zvFv/NVP+dH9nn/6g3d88XTUz3FKcBmMCrhmG5iWCQSO84z3luBMTdAN2Qivbj4gTlE1xbzjzeORD64sG6+Cu00Y+TTt+OdfP1sJEk1BVIO4M7DpbaVcJ1wNRrmd56taYgVtih4cdVSs1hzV3X1Ouf3V+knU0QVVM6f8eOfRmOpt5Vc/rnU5t4oHZYbMKWOGTsdgWav9FtAb7kS1VeSUrYui9LPOwU6HemvTipwqEVjHTpIFZ93aiTI1MaqaZivo1RlDscKCakK0752pQO11LPzjFMo2qmlVtF5H7R7ZasGyWiM0Vt76j/amGCN01rGYrKOBkviz53uurwe2u47DuPDB5YaXuw0UHXdNIvRGTS5NvWZvDTHpiObdNPMwL0xV1frC66zbWUfvXFVvLev3knpfMIrZuvBqk/KYImPKLFnwVjFEsUiVd9DrHrxFdgpUTqbQ+8BF3zPNC1fDlnmcWYy2yHMRvPVqVptUz8Nah/OOj14oCFVSoneO73/5xNM868FgYDv0pJIA2HUdl5121VztXuaizI3OOQZrOKakXoLuFFhNXTO4Oi6uCYUReD9GHJr89M5WfSFNnp9i5nFOIMLtpuN60/MLLzoKHU9LZpxHLoaB/+Bf/yv8L3/yOce5cBhnrjdbfun1h9w9PiIp6T0XoeVs1kMi8vi0xxphCCoyuZ8mdt1AHwKhamPNxfDucY818IsvLolJRwgF8AG21lfxyCqSiGESZQYFdNy6xExJha5TnMU47bndbckCsWSWnLVy7gLWGeakbLW+67B54Vc+fMkhKjtPLIiFmYIVg/WqbeWdBuhjZRU6Y7AUErIyb09RTF+N2DB0AVcUc9jAzG1vrz9RiyURcwro5jzGsWIYU2VKqv2S4tsGd9KEMgbF5EGlY9vVogRgE7zq1FRNOO8st7sdTykzjUfuj3t6o8rHJcNl6OiC4XlRVlShYR0EH9Qo2aIHf6w+jZsQyFmYs+Jf+uDZWmWQ3e62PC8LxxgJRskDT0vUkXut/I1Ro+uidane53IShM1FmWpLKRyWyPNRPeNcZ/mv/9H/ysPTyONhZFoWUsz8V//zP4GSedF1dAJjXHgaJ03cvODx64jRGqvGwpLrOSDkRTtb/Toh0PW+M4Zho+rOX4zxrLivNXXVI2sFvSY8LXqex9H2XzV61bZaKfo93s2RRU4FUO0U0HBNhbamauFrhPtpZOsdl7sBaxz390/8t7/5WzwfZoaN5zHOYIWt84BOZn6wPzLWJCTGiM0ax6UWlJ2xdM4hxTClyLwkXlxuiSmxFOFi6ClHFU9OUrDFMMbMMo98cN3zZ28P/OPvfM3juDBH4Ru3L9kfI4HCZtiofUpKlCzEKFwMjpyrvUrFO05RtZS6cWQ39GxdhxG4urzhON2d3dd2aJ725k++fqYSJGvgMvhaEdUMBEPvHSKdSuLnOgtHVraBZtrtgKugQ6stz+D18FxSXs1oqT9zkn3Xz2r+WWtSkoSwaTom64+dXqLjrhbQ2/hHyk+AtmsAbTRSqT8sRXSMVrNc01gz7XRf/+bZL43iNqQeFgosbsmdWamyrRPVTDALmhxlEXy9Fk0GWtJn6kY7JUytld3wVedsm/XVNn3rbtSq2FlLU61BNLgdiVwGRzGGKzuw7cOa0BpUXMz1HlMl/pU0UlNTA3PRVrquDcMmOETUZkJyYwK26kv/jt47BTcYDINzHFJSDEUFNjv5cZZeo+lvOq8aVTXT0mTaqSRBF3BF9WDUa0jbxs5ajotqx2y6wDKPdJ1jqQl4rJT3TQiklED056Umnxbt8gy2w5XEHPXzi6nPKmtbHm/Aq+J5po50jVb4Fu2QqOIzvJ8XllK46jxXoSX8wrspsl8iIjCVwjEXXm/6FU9jbcGawv1hZF4ywXmsM3Tec7Xd4Jzly6+/QNxpzQn6PaQI4zLTT55xWhiXiHc69gnWMmwGpf9PM711TCkp4zQJ2ShH9zBN2OGEd8lN00zqWq7YH1OUTr8shctdz0VnuNj17McZZ+Cjl9f86G6P88K263g+qvK1z4nBDsqgqv9LuTDmQqxmuXpLNR5FDMXo7zujSuxBhJwyOLUUMXUk3yRHigib4FnGWMeb1LV42tNKlVeQs/eeJUZWNJZhTah2XcB5u46yT0WSdrfUz+sUdw4xMdQOrAt19GaEoev4/H6vFkGo7IC/DJAzvTOMScC61cT3MmwpZWEI+pkxRwyG7dCRyXij+D/vdLyqa8GSqmBsKpllzgpEFh2tdD5gnSNVTIpNkbwUTUjrKM0aZTMXkUqeKDzFhBPDJqgq+pwTy/9N3Zs0SZIkWXofy6KLmbl7eERkVmbW0jXobgwwGBAIGxFAOIDwk/EDcBrCDXNANzUOjV6yqiYrlwgP38xMF9lwYBE1i+quO9KIMtPTw8NcTVWE5THz4/eS6gz1xiLW8Pd/+F7jYoIQVMjxx4dnOqs8LKFs+7DIZXyfLWa3idrWysuEVBTURT2PejF1XV7ixdveUfCqq5Qyr8uqBq/byVG2r1rcbCa1rVXbnBNC0SGUkPW/p6QWNtsUnBGcNHmZ9ivq9df1EkpiKI5pWfFWldKfjhNd7y/xpvG6MjwsC+cQ60CTgnVfhCkmxl6nt1XvzeBFmFBXgGldsF1PZx0ijkPfEWu11CBgLCKWT6+BhPDxddkm31xYEXRCMuVIKUkjdo7MMWJzwTj57DzSJE8IISLjjqEfaBU55azay5l5OZn+1dfPCiAJlZhbhatSUaPHYbB4ZwlRCc3apVIX6CYUp8Gm4vByKat1zjJ4x2mpzPzqSN9+HxRSarfzgjtLAWrJO+TrW3yBB+1hhaSij5RchckaCa1NirQrvJ7RaNl+/d71+NmfZKQtoFKrSrmUrY0ogups1KpNyyayXHRxxOhUR+KyRVur7LPAgE5EbSPk7UqlaXx8Bo0umfC299ufSyUqt09eJ39q4ClS8L0jSOEl1OeS9NklFNtYlIgck2brIeftPpcCfQ2cg7WY1ALc5YbJ1d1upOWCZn29NSCOAhyDjoOb9hmhaosUdXg3bYovE0pA6rVYazjUytMcEhKDmqcKnNaVbpr1usPK2HVMU9AqpmiLiKwmtL1zhLb8ShXlNGq/cegc1gTmqAaPuYBF25FZmf/aVq0CdQh6gGZIayLW9sE5ZmIOWwZ+4yynlHlt47kFwqI2Ge/HDkczyYSQAn//wyNLTNwMBmNRsGh0Wk2qgY00wm+pRFjRqYo1KkdoWgK9tyAJcZ5sekDBVtc5mGZiBtf1ap2QAvOaEKOeWaaSaKeYkFwIaBD3zly80TKMXYe32mYSo/897AacsyrAWPdMzpkQ1aF8ilGBSlbhv6VUdygVsNG9XHTIw4pUArFyui61AK3EpFKJzHXNSVH+0bFytD5rqcClywZbzKBWBa4zEREYOov3Zotx1H21VRC27Ep/TyqZkLTSFE2ushjgneE4r9pqLrINTpSScaL8Imcdxlit4IqamFojVQpAL63zlpclb3wklSHQQ/Gc1O3AWo01KWlVM4twXFYFZdW+aa6VfoqCPFMuwwamxgSDTqK+hsgoKjxIoYJbrTT7qqz/fJrYjQ6KIekMAKWoMa+zhjUnQlE+aeNstRO1cJlMNJX6UFBAfk6RkmAomWQtvTGbaW5BJ7usUQuaKSkgPK6xqomXrWq/BVcK+87R1Ym2Ro8456LTYSmzZq2Kx8aNrY9+MweuZ0w7C9oeRHQTZ1RwuQDOeQU3XVOlru9TNDl6WsPmMSjC5k+4pEhXmsWPfmbbYqa5iJPmopwxaw3n88ISsragO+ic5+E4IyZzihq0Oie1M6R6f2sVuRWBkjORxBxVPLdVT1PNwmzFCa4kJEVOa2CJkfOyYoGxazw9+fws/ZPXzwog5QLHqESwtY4WLiEwOn363rTxfwDR9oTooa4WlFBKpiQooofDoe8ZOsfjecKKyrvHrNMn+qoZRMvZygXCWCsbSbJVFz57SR3jzoUlFQbJmg3VDaeb7AI+2juLVBDQQBQaVEu6AmJtwW/gSP+Vk4rotXaGVj4MBSV6b6CJLXxervuSlF5ymaa/QZ1ui4l4BRZ0mK1VaOSzoL29J2wgixrgtERX2IQ4rnawN0IsiaeQSKsGjz4Kf3n/Rh2rc8EbFcZr7uovc9gyRRGdhHldVvrOXA6Vej2t7Cz1yW1VrwrQbp328JWkvvBadT7WXDZieqAQgg4H2NryXXLARJhLYcgd9wevY7Vr4qgCJYSsPKrH45l5DYwHx3s/8DG+cM5ZfZiKTlVGY3gz9JpllgyxMMVI33XEFHlzuMObgMwTxaji+mCcNnaktmwTWFeqAJ8qj5sEc4nbOjToQfJpCbyEyH9xd+BhDhsfz9R/dIw4sfOWc0hqMEvhp/PC7dBptSpH1hRZY+L5dGIcB7XjKRdQmlLBO4dDWz3OWW694/H1FW8NXgqv84qzlq7v6Z3BUFiz5c2bPS+nIw8vJ0oRTlOEwRBTIJTMHDPLosaZd2PH2LnNmb5g1DtsWnC5qBu9wNPrEWsMp3XhyKw8oHpPHk4T3hlt3YXEnCLZckH/UvPWWo1MdSfnXJjSZfmbIhy8Z13XypcTvLE1+zWbVEYDVFe5BK3lknIirumi51b3a+P82To+b69gVrn6qu3k1m7ZeU+prfxcMmtSHqZBNbVCUqufJQZ+fAoc9gOUwqF31Zy7IGJ5mF7w0tFZw+gdxntMBimRYxHuxkGfX0zb6WxFwbwxFu8tXdFBl5AK0/nMGiO73jN2HbLoUIMM+jtj1OcsRuUgYqVbxJyZUUufOUYV2aVVmwxJlPKQUublpNUxayyHoccgrAmOy6LPUKDvlatC0eeXU1HyPeoL1yynilR+T86IWE4xMyf1FbuRptqswFXXYWYAhqHHWcM5JKaYiCVtmlM6JZf5YhjYWV3/ehYJrykxpaS0jwpYMlotbbpaFRdvT18aGW47F1SHbU6RnXcY41hC9Qktejae1qgA1Dse1lU7IfVc8mLU0gWt6K1rpneGzqs10pIinTV4b+j7kQhMy0KKEW+dtsmKIKmwxkjvdbOsOZKK5X63Y+w9U90vqQ5SZRpoK1CTbG88gprzqoehAaMVJ7OemacT//zplWz1bJjmwN2h52boGJ3ddJH+tdfPCoF++REAACAASURBVCCBbqKYdGEYoBThp+PMu31fW2BCU7A9TjOxwEyqI7+a7VOnrd4fRpIkjiFzXxU5m9hYqUDFYTmGADmRSiNv6kKe+8LO6mi9SK2EbD3wsmUXa86cYsLbQucEkarkWrPShGqLbOCnAhlvG+Sv378GLy0+U/+phLM1J3zW6pmp71WK9scxVHNazWapImixwT+pG9g2onZDQmWrWkltyVXO9iaKFwpKVt8+Q81+pVAFmdpHUEsCa3QyrBGkCjStFlsPAKlBFApnE/n2+YVf7Hbcjj3eGXJUjsd3j0cKhc44hsFp4CiiZePS3luvKVbfonbwXAPEDYwWDXgi8NUw8PiiAmxr0iy7t5acM4NXX7XeOopFeUs5UlJhHCzP00pKei2D9xpofU+SzL7v2LuO87xynJ74+u6W53VlXiMlZVzniGHl9XWi88KahTnmrbw9TZEyzHQiDM6pd1teGYYdVjrOaWFOAaQQo0449tZSQiauVT29yGZ7UeqiChn+5tNrbUWW7XBuooJ/9/DKNzcDOWs1yxnLwXlG7/Rz2p6X48TfHf+JX97f8O625/xkiEH3h0G5Ct4V9n3HaV54f3vPuzdv+A9/97eIgdd1YT2rAaazjpvDLX/5/i3f/fTADw8PlAKd7XiZZu4PO7zr+enTM0UyzltiKoiDtWhpvtRKZWcdvrMsz5GYAoM3dMAfPz3xZt8RTeL1uOC95fZ2IOVakcx6z6YKCJ2zpJZcUCeQ6rRVwWyE20sJVY+q47piBAbrGJ3jfjfy17/6kr/9p++qcnjZEqdtv5fLvikinyVorbAB1R+x/r5y2W6bbs4mMEm1SBJNQrxRXRo1gdXPNybHN29v+e7jC0uI29j8sgT2Y09nLZ1zDF3Hvuv5EM4MMZGx3AwDe++Yl8jxlBk7z3/7b3/DP/zhAx9/fKDvlef36XXmzWFHBtYlYkW5gqYzSPFIKex8x6Hb4UPG7Abu9z0/PL3wFM84A9Y4znmprSQFSms1vb4xjt3oKamwc47eeT6dzrzZD0yEysEyeKMVkHHsSYv6HxZBDYMFLFkFPIGUq39iKWSnYoitkkXRZLc3VhXEEY5BpyQP3jE6f3kmWxWw8MXgWTrPw7RyNsIaFfiIKZRYAbhpRuj1yS+ZJSTlttVst61FXSiXffsZlq4FxI0xayAbIVAoKZCTgsIlZAav58nTvPJxmgkFStU/88aoWW8RgmTe9yPGekzJLDHwGBYihZvBI2IYh55lVfHTThxDNyAi3B12UIpSCQyYBJmO25LZH0aic7wcF2JZOHQjOUUkZ2LMnEIkWPBZeDxOVTXc4JxluOmwIjw8nfj+pSrDe8PNviPHwutx4fW00veWu5uBr9/s+XOvnxVAaowYQapXDIhpi0E9yi5EXD7L7vT/L8TiBDyc589UZL0VQrUEUH8Yy2AqpwPLzis7fi0qIriEQBBH13sGqD1bQ055G//3VqtU94cdy+mVGECMBtEm+mYxGOtoopAiV/pMl3C5HewV420rf6v2UJAMY4EUMhHBWQUpzqo2ixH97EtVfDWiWjIRNtdwBZ6araTKF2mgTFDQEqu6ikjt+RrlMpxiy1murnorcdUsu8DLrJMeGLYytjXUqRVbiYQXjpOzjiOJ6fzKfpq57zvuBs/vPr2wJL2Wg9P3mVLi/W4kLXpARD0hq9BkS/wrGKr3ugmUbeT30kQcE3vjWUvEi/KB5pCxBk5rUHBSCf5OBNc7znMzoxWs9QydJYSIN8Jws+M8n0hh5ZgCpe9IK0zLjBWDd5ZsdP18/zzxyzeHml1mqG2bQgFb+Pb5leYTKMZwvzvwHGYsmcF6xn5Hcqq1tBenRp9hJcZcuUhqOpzkyj5jq/RQW8it7aPr8n7w/ObmwFIyT9PKy3nl9x+fFUuLqtqXun6ejhMfnic62yqVQsHwMMMoE5hbzcbHnru3I3RgBiXDmrmQArXQuPLtT5+43ffcHvYaIM8Lw7hnnRdOy8IwWgyurq9EYyW06goZjqeFH5bIMO6YwsKbceQ//+qe91/c8H/87bc8PU4UqxN8Iloh7ovh6bxUfodm6SnpZNW13lqr1JhtzW+NfY1YQiX6KzBxRugkE2PkZteB3PI8LyqKWLkkbZ9vnm/l8t4tEEjdX3arwMr15tNrEE0kr2rUUDLGG85L5GlZFURYy8uc+Icfjnx1P3AYdbBEdeaEX71/x3E6syaVjkjzytPxyPuhZyqZuKx8f57pu46v7g+MruBD4Xd/+ImXZUF6SwAGb/k3v/oKG1dezjNTSiRTKDnhKHTOMK+Rl2VWfR3gS2d4fToSlsiu6+ic4afzxM57VVcWnR72ojy2c078Yv+W//V/+O/59PzEf/iP/5F9b4gh4rEaJ60SwH84zfROuOkdrxTmlDaF99cEbztb95hWredceJ0ynct4b2rrECiGLJl9tYdq3KRPKZBZMcBX+x1dFfRMFErM9MA3u55Pq/C4FAiq6dfATJGy8YYEeDf2PC2RmArN3Py67q9LoFa0Sk2MNqBcV4WiQCCreXYuuGyw4sh1+i0W7b6ENZNTwe20XTfFiHHQO68yNGSMlGoFBVHgrrf8m69+zY8vD3z38QOjc7wZR5ZQCPPM29sd/+WvvuTx5cS3Pz7wPC86MelhlY60FqbzxNN0AmuhzBv1xRnhy/3AOSTtEuwGnLVIjc3xOHF7MzIOntc5s0ZN+OOSeH5d8M6oNVnMPDxOKnHwZ14/K4DUKjsaEypPph7eOWWyNTSySKql4/bKLeJcIppWS+oCE1TlNVOqJ1Amp0znvQItURJwKkU372kFJ7iSt2kPI1K1iFRbQerI/LwGiDAaPYiezyt9Z9n1vto51P54rSK1Mft3tzdEY8lJs5YpRF6mha0kUj9KO8Bau2lvPQklrJakFaKK8SrRt2wZT82BaXcmG1WwzUU1PS5BXhGGQSfWUrlojWhFrgXoC5iifpY28F9qqSJm7bfbyuFpf1UNOKWWq0EPZalVByU/Z1uYciKvC0vlbSDa4ppTojOG3jpchqYEvekybWB5uzigur9v91G2c6b5ur3rPM9RjV6LZJxTAiRoK2nTpSqZ3mrL4LxOzGukc45d13GOmkEdUuJ+PxCN5bguxHnh0xKwBUbf463dhEb/p6/e8E/nhUAm1bL3YddjjfAQzlpJ8R5rDWtYmeKMlMw47phT5DRp9pRLYWXFGsObcWA4jCxL4NO66Og4dZJy68XLRViyPlFT1/evbvYIwiBq4vtsBMm1bJ8ya9KJOytSvaQs74Yda1lZU9Q2Sojc7gZO0xlnhKfnF17nE0Pv6Y0hk5Dekh2UVDhPM1/cHrQF9jiz63v6vmMKgZiSAmjxeGdwRjlkTzHQ1Skn50eGwXHjzoScWePEXd9hnOGUM++L0BkVAn17GMBCTEktXgSmJaqLeyNvmKrJcRWHoIGVSwloE5Zt7WVpFSfl1XxaI93jC8u6Ylzhzd7zZt+REpzmlWkJLKlp1+vvaHwSYFPvNlwI/KVc/2K25PDq+NxiYKZKTxT9HEYMb/eeH+LCac2ELBzGkfHW4b3h8eWEEeG4nHFiGL1aBsUkZDEc+gGxBuMcCTWCtcPIT6eJKSoJvSDMS4Ry5H53gzGRQtASpUDKsvGRyDph+5e//AU/fnig7zveDj1riJznGYfwbhz4OM0UKbr31RGWuEZ+++Vb/vCHf+SPH5+JQTgMI9MSOYVAKZneOrp+4AvrkGoIngSKFWyBUs+RY9S2jdR4l2pitUYd+R+coa8DBgaDMUbbPfVhGaq4qMCP06TJrwijddx5R5urfeMsg+k5dYWnOWAq8DWUK8srnc687xwPJdSJ3Rr/zRV4bvGtqC/aBUQpsDetYll/bOgce9dzmldOscaMJdI7x5v9jmlZAVjqhawpcypB2/NBY9Onecb5gcPhhrye+Xh8RHLibtyDQMi1CiqGuKx8eH7iZQ6csw5FfHW44VwCcZoINrBkbaGlnAiiViJtWMaKMHSe3eCxUpjXhRirf2UqZKNnVOc7jFiO54l1VT21NmTS2uLPzyt/7vWzAkjApR9wSam0rxgTXalTRNZgqpYNLfCXS/uLz/5dqxRFJ1Q25F5UuLFNDmjZvE59ZeVRGCcVTJUtcZOivXBTvWN8PexciZX0DGuIeGer3o9mJiGljVzdDDYdhfdDR+P/hJR5t+tqG1E/QG6HWNH+8cfTSQmoyh+tf5ZRGRTVzdn2j9Fx2O221i+kVhN0q8m2uYRqvyEtK9U72CYddILsMomzva73a6n9cnRiLJBJNURIRVb56kBuE3JSW2NOVCYhlMJrCFuGKQixFDxw8E6za2ORNgfM5WCR7X+u11HLvGVbHSLt4BBuLAzGcE6RU9RWhDft6iq4zLAkNRieQyJnJSWuKeKdY14nYooMnWOKqhck6Hr4d7/8ho9PR+21W+EcF953I/e958O8MCflJ/3y5sCHONHPOjGGKHm2s6auTe3FLynWCbtCWTPRaTuhM4Z95xiswXvLT69nwprVYqONBV8/tw0TqKbUaG17nIzO8XbseJ01O/a2eiOKVFsXHeH2Rng9JY5rIAM33iDWMp1nBu85LwshpEpoVe2ezupkXhAFnq/LTHb6XNKcWX3izWHk3c2el+MZV/Ur1phYUsCJMCcVijt0hdEJs7WkrOtlzYmnaWb9kPj4eubD85GuM9sYvLJAKr+ktkmNbarZZVvj5XpNXX9ju426b1K5wBxNZjRuzbEehNRWCvoWdnCMznBaLWvUiR99j7K9R3u/rR0v1xWsy7O7+kL/ZlH/KxGUaavahvV5CbvB8/5uTz8emJeZEBbCtLCmgDGWLKp5FLMhYslzoOs6Sk4Y4xBjCCHTj3uWeSaHzNj39L1hmgMvpxOd24EtdJ2ji441r3pgtdgCOOfYjyM4VXxPaLzLqWCK4W534LgulSag5uN9p+4Fy4cTx2nepsjudiNrTnTOK9G+xmjvPdkYSo48ns81GdDWMwI2a4U8Vx2vwmVkvqBJcEj6LLy5ANSm1aQdAalV98ySlaSexCAkBqd7UkG+MFRiuu+1Et6ZaqdTOyaxSrLcdZ65iiK3eHo5Ei+JTuOemkoWl3qmiVTNPhF65+jEQMmMvSNkj2BwnZKiY4p0zpBNYUnamrf1jGr8oNe8gBjWeWZeV24Hz7Ssyv8tenUhR05zBJc4neAlrmqAa9TPTjrh/BLwRSCpKXjvnFbDik6cp1oZW2OGGDcSuIho9T1lIjCvkX2nYp6hFBav0gwO0Ypj1RqkfBbx/sXrZwWQGj+kARi4WOCEmnUYEZzVKaDrgfMNAFz99/KHysUJcuVQz1U5sv6YodQpqbbILoKUbQy/ncJ61mq2aY3QGa3TxKwj7YJmfjEnMgbJ5SI/L5dDerTmUi1yliKOUkeWigi5BnFK2ZzPB6dS8tQ2masLWSU3pI6EX3FtzOX+bPfoKrtoOahuYkOp1PRLVqL/mWMkXb2JfpwLiL2eJGveP6E6h18/p+Yt1sZjm/ZQeyYtGKxkcj1QXSWmW1Eick41W+DSMmsXKle/awNPW7P+84uR2pPrjdCLBq+QM3PODLYqM9PIlwpSva1TOVLXVUrcDj1nMxNLZkqBOaoy8cE7grF8dXfD63FmSsrLmkPkaY0cvGNyCckK/G57x6sYTtZgrvSgcgXbBZhCYE2JnBrvR4nAo3f0VnCUavDsmUPPY5g3OYxyWQCKoOWyz94OPU4ue6qzhru+U12l0g7Y6seFIEanXAQ1Up6rO/reqW/SNr2YM0sJ2xSV7gG9Hm1uGNYcyVkrd3OMrDEzGs+477jZDYS8qtBizErQtcJaMraAlYw3hdeSyKLeU0tS/aPjtLJ8fGFeIzdjR0rK/RABnGa8xjQgIpfs83IbPnv9aWxpt6tlvqW+jzOG3ikRF1ESrtkATsHWg9MabfuvSaUO5jVeQfLLy5kmsvEvr03jWfmT71W6ghHVy6kJT86Z3gm/enfg4ynxw2Pg5RhYlwWx2gYXLJ21jN7RWaHEQimJUG3krTG4AmIMOUVMHbu21oE3HOUMIqw5UdB2tdQKUpNvMUZFaROFD69HllKIy6ptXFT4tfeex/O5tnWlVmsHhr7DyJmH5yP7sScXpSuIERYpuGQI5fIcu84RahHL1dgTi2ortQnINrDwGb+r3vFcqhmx7kZiqVIOXIWUKyCtIEtb/8cYGYylqzQDQehEJwDdBo5kW0ciIKUwOsub3utUXljZlEivn3mNm599twpoCmpC642hdxZyYVkDznp2riOLKumvIRBiqpOLKjpKFQcW9Hkta2JBydcx62TarnfkkFgq/9FKnWJLiWwTOagswq53DJ3DO8PrsjCvEXHKPJVS6KyniIqHDp3alMwhKPATqYmVDmcZoz5/1gnLmuicxRkVmFRwpbpLOenwSjvwnPszm5mfGUCCpu3RXs2VXP9JUj3JKBhrNob/Z8ionbIZGseFSrxTANEO9Mv0UyuBNhKkxjShkcG1bKeLt41Tx6h/ywDJmg1ErTFupL4mZpmTijP+yUlOhYSUSymobjTNGUot3RvRzG/wwm/fHCginJdICFmd661O+LUQGrMi6EypEflyIDUye2sXyOVSNKTKZRqpVZdS0Szf1J52u6dbOGjVmraHC4yduaro6fdMI2nXcvY1kGnU+XwF3DCCr6TPLll8Qe1TLiOImGaN0qqISLt7tEz7cyJs/bnSgkzVXDLabhuM4Y33fKiyEK2tWbj4+zR3eVtVaHNW4DZ2nnOM/PHlBUrBi+F+19MZx3GZ6bxV7Z9U6GzH67Kys5Z3fcfbrsM5Q2TlzlkerVZB9r7DG8PzrMaMpQLVmLS9KoDzaqdyN2jlqGQVJMwi3N/csEQdZa9QfVufBTYlaER4NwxcpClqkBXDu6FX89Kr+1pgIzI3YCxGg1jEKW+o6xn6npWApEWBcW3j5iruqlIK+rteQqRUJ9A1Br49fuTNfsdXb+85TjPrmrQCnDNBBKkj5GIBycSspXQjppJtVU3/9bxirOF26PjpZWJetKozDKor1A9WK8bVxDbXA0akJRCXpKhVX+HqUNsW13Yrt2pNyXkzUXVyxa/MqrXUO+HQeSgqqPdjObNu06z6pkYuQxd6+JXPgK6aopjtepBqZtyGFOraVqkPHT//YieczwuSVnLWVvbOe/beERMcho6bwWtccYbfHc8kMZATg4V93/Pp6YWcNd6dzidM13G72/FufyAgnM8rJUdybR8Xofpplio2mHg4PrM+KaF/rZYxRTTWE1d656vCunDoBnZ+5PUciSSezhNrTKQYCSHy/s0t03ndJEliSiyrTuelpNNsMaqWnlSh1oxse/+zqjoFUydVFPDoUECImj52QgU4bIreTZeuAbMs8BoCRyJ7Z9k7hxcFe7ZO+G5Aq+4vZ4zeJCO8HztEFCAV2gBNi2mNt9b0jLjszqJnxl3fY62uoWVRkc4lFt7vd0wpsqbLGo+lsCuGnbGsW7KfCUEJ4wgsYaH3lsF1LDFhcqKIQURjtBXDfuhYSqS4QiyJORZSidz2HR+ejogxzKUqwhuDKL0UI0LnPKRIRCjG0vcdklasqFguUug7y9ipTYqxFhGDNQ5vLOuidmApXbQFjZUtTv9rL/Nn/+T/z6+rU1tqltse2BITc0pIE4O6qggo4KmHv9FD/ZoUTc3AjamB2dp6CDaoo4u6WJ0SawDCXBUfcqaWM6uqtAhFqnKysVCkcqPKlV5RC3flEkxrL/kaM7VqztZ2uvrRnHX81aBZ4HFatHXwcuL1tBIiFHEkDE08scdyJ44hGm6SY8hGrUZM2UqxW54khUzavKI0CDfAUjYctFWc6hdlawGy/Yy3FifaFmhFt60KV7OlLZg3TNiAEdSJBdlI7q0FaUW1XR6nGRFTeTWCiEWMI4vZROXaYdYMeq0p26nXrqGglSAVAq2tWFEF4W+GgTkmuk03S1uGRqfx6XrP1/e3/OLNDWPnCSnydjzgjWFdMw7D/b6nd45E4W++/U6rUaawxJWb24Oa68bEEqMK/VnD4Axve8c3t3uKlHpoWL7Y7fjy9h2pVkFzNahVPZjC25uenXeINGkKYV0T3z48cnvYM/SeQj246x6wdbpQP7OSrxtA1a8r0bTUFkA9itu+K1k1oZp2jojyy94c1Odrv9vz5ft3vLk9YKllexRMKhgXemPYWctYtG1zPgWmORKLErp/eHjhb//5D3x6nulcz+3uBpNVFyUuiZt+pCQ4Lyvvbm4wFTxYVJoipozxlsOuo+TCMFj6sQ5eWKeTel6rdRgF0Eo1r6uxJS9c1mm5KjNcQpXuI1P3bciJU1wb9aZ+5is5EjFbCyClhJC5Gyz/9Ze33A0KiltSY4wwOuVLZcpFT6zRCopsSWSjYrbKN1RleqPV7CkE1pD4m3/8keefHnnXdfz6i7dYZ3h3c8etsdx0qoJ9XCIYj9uPRDG8zDN//PTIdw+fOC4zT6eZYjxrKRznlYfnV3789MDOFE6niWleSRl633G/2zPagfvDAdd8HgFrVeG9t5bBa9U5l8QSVkoxkCyD7RltR1gTH59fVWpijcwhUWLEpkRZI98/PPH4/KhigRROceWn0yufXs98cbvjy5sDIjopXTLcGsdgDLKp1TYoXJPfP0kWOtEqrVArK6kwJwVOG5+oVUeBWBKhQCTzEiPPa1B9pBrXfniZ6nCGrrkWW8XWgaR6XSWXLeYaqVSFOgltjDA4y03XVWkG9Z98u99RJJFyANp0rqFI5PE4UUpkXgLnJTDXM/V5WVWjzhk9GxGkCG/v9xxGz9ubkfdDxxsx9LZjLSu//eYX3Oy8DgtRyFXccec9t8OOg++xWJac8M5wWlfOS+Q4LcxxYRx1cGXJicfzzJiFb4aRd7uBwVj+l7/+LfdvbhnHQbUQQ+Z4XLgbPDElprgiZN4PA/vdQEoF6w2uVpqsNyrb8WdeP6sK0tYzbC0XUZXolC7u47lo+O87u/1cLQ3QDvJtKqQCkBbEcq2kbH5k1K3QMgEgJwUioUr0N0Cl11d1KVpJHrbpLFMzhn3v6J2OI7bgRjbth7eopRvv84pShXZKRk9qIaAgQf80i1QF0bUqTOtmOS2x+phppjn0nl3fYY2SUY3A6K2K5JUrQnvbkbV6lLMaHm5Mh/rHMV82cKs8bJWZXDa03kCS4/K8thsll3Kytmi48JXae+Z6R6Rs9wKUMzsY6EVUV8UYHk5n7sdxy/RLKUryrgraF/Cn/ykt42+lcqnkZdpn0pZt0+GwRvj1uOOP50m/V1tst7uOT7MGkp+eXhk7z35wzPPKcS2MbuD97cC8LvzwcuJHmTiHyFtn+enjc/UIgu9++Ilf3I6klLnfD9yOTpXB63355uBZwsCcDVkM65o4z48qHBfqGL/Xvv5f390zivLNYgVzAeEfXyYyiSUGdkOHGOH5rCTSvqYEQWDfOX51GKmaDJeqUr03LcNWbkYj/OuhsGZtr933HRbh06zKxlMJvDWF//TxO0Ja6Y2q765tcKIe8hGdstw5qxVcVwcIilYq394PxKrf8v3jE84YfnF/y9vbOz69PvPT4yud89yNPSEugAKLVArTmljWxNBZbjrHMVZLl8HXw6/goyGExFAV3pN8rgFWdDltgLptGNmofnIF8qEY6BF2GHpRIdt2cFIPN6mHprM6GRRyxhgVP8UY/urtDgESllNMnFc1GM0VrCoRtrXspD02jZ1y2UtBVP4j1pBlraEkFbmMuZCdY54mKIV//8VbVmN5DB3ncEJE1dznFPn6/Vv+0nm+f/jEcVpIOfP9pxdcZ8ijI6ZCN8DOGvZ9zynCaVqY08r9OOBNx+uaeF3P3A8Do+tYw0rOmZ11fP3uhr//8EwhcX9zq3Yx60pIieM8k3JkN/R0Ysg58eFxxnupCYbgxTGMhg/rCt5oYlxfkczrfOL//v2Rve9U5NVZcskcU2BXBVmf18ja4k+uVZWsccDUDHlOCVs5mLvOa4UlZUrSFqrbwrxWg6o02tZaO+XEaY1aWfa9+j+iz7NUaRqM8q82oWNruOv8RqzeTrYai0Xgdtyx6y27pBVqCxCVpxdL46EWnECJ0HWWu7FDEGLIzOeFzsMxRVJSqZpc1Icw54JZVnrv8MbhCpScOMczX7z9go9PryzzwhoSIScigdFZnuPEzvQMXm1YXs9nziGw7/vKYxVChA/Px2o4n1lY+WMMdE2aIhT+nx8+Usg4Y4mSmcICCfxpqe4EwmIsszV0xWAc22a1otIA8znw514/K4B0qahcQEOuJ1guLRdrQlK1mtMqF23ptAMStsXUyNN6qF+VL+qJbtvEFpcWn9luctlQfN6IHPUZ5GrcmQXZywbuhs7X6pKW1pOUTTzxTxkybJUi2TgQSuQ0FZTItlmU2Fx4XgNLNUndKNhFqzlrSoQpcV5W1bPoPWPnL/c2qaCbMbKVaVt5WYRNIVVbMfp5HMJC2cjaFDaSeLNUaZ+oHRopZ9XgqeUckfLZT/DZ88lX7cTL82uVBkrBNnm8xhXIFyDT1IuVbmFq+6xOBG0XTB1rv5ShEakl76slVzlT+qwz74eep7hyjmp+eloDN9bzHFdyhpfzwqksfLEfeT7P3N/ekOaZeZ4JMbMbem6M5Xbs+R//m3/HPEf+6T99z3B+Iq1qUfFpXchGJyN3TkFSKvDruz2fpsDTHOg7+OZwy3/87icE9QgTK8SYeDif+cU41HVQmFPmYY7EohN4H05nxhpof/3uHuMs3z48s5bIV+PIu7FnqLyhx2kiZ7gbOgZvt4CfastKtnaaZpdGVB8qNYE9K/zuwwOlK/xw/IS3dQ/Ulq96112qLUaUX+ONxSFXfDmhJPCdI1lVKR+cwxlDTIXn04lfvvuC+/EAokMQ33/8xEtY+M3bG5aQ2PWqpN90bkLKdd3XGJJb61SnKrfKpmnrs4LqtuyvkoNr9I3VdQAAIABJREFUAZoL5i70xvDOdexrGyBtmURV/GryAKnQG8ta3dxdPYQdqj6dc6aUzGig67UyGI3hdV22WGBr4mQsG4Btl1+HfVV4tZpAuzp0MIfE0xJUqbpoMvJxChRZiSmzhKg8KrFklEz8P//7v+Z//z//L84hkDJ0Rfjy9sAXX97y+/zI8zFs3pdP4UzXW9YceZpn5hDZ+Y53NyNxXRn7HaYU9foS4RQTlEQAXqczY9fhrOPh5cjQmc3A13vLuN9xB5yWmeNpJklmKqrYHkpS8nUldLe9v9Y9fQqLRlq5AKEoQMocrCVaWIuqV8eUNuLzFhtEH7sXTXKcMzgjrCkTcuU4VS0jI8rnTE0zq5RKfNa99HGdt8nSFjMbSCpkzbZFSd3vh47zad6qvalohXPwnn3fY20tIkgVUq6V/0IF0EBxAqZw8J48B/74oMr1g/fcvB94XSaWY1QZnKJdks7rhG4skR6ra8NYxDpCmPnu44+82x1wTohJIOl+C9UB4WWZCFndApx3vO06KIXf/vIdIUaO80wU+PHxleMUcdVGJ5bMFCLd/sBud8O0zpR5hgyD86Tal+utxkprhZ1z7DrPfuc5nhfmlBi7jrf7HR+en/lH/vXXzwsg1UpAe12qLFQMoA895qI8jDnienv5mascTztqFSa1Qoa0MfsttumKN+1rPWCdbRtEQYWhtRquDvii7ZkcC6uYTZ275EaQNVtAFWoWUrjacDVwbl/LBQxVQHGBElc3REQ96fLnn7kBiXpGVh8uNdKMKRGjUxVjY1kKDFf3mbpR+QzsNH5U2YLuBiTaZbee+NUbCbVCJDp+2qZWmkDn5XrrIYi+R3v/6ylW0EDfMuB2XYbC6NWqc6rTUXYDvc35/cJ/agFoK2G3Fg8XDpNOpdRyAW26UQ+hg3FYK5xJTCkS1lXd0+vUYwY9OITNi8o4q3IJOdPbjjkU/uEPf0QQ1riSY26zfawh8amovMNf3O5oFbDOGt7tYNfpiT1nzaSyv7reXHheAqUUbr3brEVUtFDveUqJwTtuvKtTNMI3b26U42R0+i3EzHcvZ0LJnNfE47So55SzDNYwV7mDQ+/ovVZ71Dst8cM0kdCJFGcsRQq9eEKI2kqyVcC17c22AkRJ2Xe+u/DS2h6pP9NG1b3zOKM6VY8vJzpvebPbkSh0zlGQOkAg+H7P4+lRk5UK8kLKND/E3PacgRgT6xpJVkv9zQR4W4Ny9aW06vNlO7Y1qR+nclmcUOoEohSqBEf9mcKWeHirIoa9UwfyrroEGNFxdJ2S1Ov1zmJLZrSeSCYXFRW1dR3ErO/ZWsohZc5zIJTCzqmIaQxpM8yeQmTXd6xrYg4BaxLeWo7zUisnda8JTKeZ//ef/0Bco1o8lEyIkU/nE+FDJoZQK/CF4zoTQkKstn9jJdXXTcF5XvmFcXSilfJUlFS/t45pXZlKRBDGXg8+jXM65TenoBObWQc3fKc+YzGpYnpflcObkGOhoUZdVE0cVsqF25mvKj1WRDmOpEsyKO0JKzpqaV6oRL62lnRtsUkE6NRg0we7VCU3eY2iyaCumct0aeNLNr06J8JN5/mmwOMSGHq/VeadU6Dm5UJlaDykLZRtZ6IauWcE1xnCvFAwykoUUXPnqC3PXBLOaoVOydSGXafVRPXzzKrRhNr1GAydtwz9gCXyY5j1TLXaapZY6KxhN3Z0KXE8zwxDz+3NDc/zgpRGabkQzKFQ4kJMHd4UTO+0EnVSeYVYk7XBWm76npvDjvN5IURdx4naDZHC+7sb/tzr5wWQ2kENnyN3rg5nFJjMOVBSgSyfLYT2VfNguX6PCqmVvHUFBtoKFi7kSgFI9Wf1ywquLhE8R3Wc3nWKrluGfa3WnYoucg20lfQt2t4p7XeXlr1WTtN14/v68os2gkJMV5lHuzAuf6fu4kxRs9Os0z/eWjqnuk73vdMpGvn8/UEDc3ufUlFL+0T/ErRdfmfFQluG1gjtGmculbAG9FqWW8oFK8rldNTfJQVfGh/p8hk7K7Xao4asUqfmSk3p5eoCS8u04cJT+5ND7nIDr26m/gq8jmuRi3q3SVGH99aCyaVwXgND1xFrxtQZdOpqSeydmnL+/ocHnK0HYCqqE2WNtqRC4tXqlKBv7RgDO2MZnIKeD6cJ5y2YsnHSEoU1ZZ5mbVk461iSfk8tJgqj1RbTztnt+d71HUtOWnbPanHy8TjTDZZTiEwIQ4Ydhud5YomqyuztSO8MOet7n2PieVVweGfVtb1zjrt+4Gk6c2HSVdDdwO4VWB2dJZd6WFE+y6xDTBV0sfHdYsrEFHl4eVEeU9dhxLAfenK9H/MalRfhq1N5zNuBuJlT1+eXqhDqJjpZH/6fgvU/3Wrt/7dvilQx1rJ11a/XWKn3INdJrkYmbsTeteqhWdHPkAobQOqK3SpUJclWkbNItWRK5KygS0Q4LoG4JmJREn8parHkDBhrlcCeMqcQmJbIMFhy1vsdS6kcv0yKkfM58fj4xBpUoNPVAZfn00TIatTc2ppLDCrkm+EwOqTrlBMp2i6ajWWJkTb1Umr1p/eeIVVj5prUiWjC2nkPObMugWCijqX7YZNSaFpsvTGbkWnasji2pKfd/zbMYut9T0W/7+p6dCKkltBuz1gfcslQTLki0l/0xDLtudaET7nWOJFaGW8/rz9zufbLGtIxmMuiUfKy495YVhEGby8G3gZyThixG6DbmnDX50F9v20wx8llLVbAvOsH7m8MISZCuhoyiJkO1RtKkilJTaW9vUxVlqSSCl0n2Gxgrb+3nqPWaGVtTYm9FY7zSjaWzjvCmuiMZbFmo1Y0HpctiRDnzf/Qm2ogPkdizozVA7C3DkHqJG1S7mHJiInMcaUzfx4G/awAUqGQYkN+7dS96JW0V8tcd1bd1kurg1M++8/22r5XH4Bpys6ybTIkN/ykGyejKqi9a1C8Zh/6fk1o8u1h4KvbUSXsi/5AzlDqxF1GBc64qmhA2VoVuisqYGvgZtuZFy6V1ACcS9nK8u1DtskWrv5q+9igwTTlxBxUl+cTEN8cOHhPbwRbW1QF3RhBVAOq+RBRQZ/jMvknDXjUVKWBm2ZhkQufg9B6Ay8J+qWiw9Wm/hOsSymFQ7L1OTUelGyCn4MxrDkTk6pxC5Zi0gbYPkeRF3JNA0itapS3UbcaTGuprBFydb/rZNBh3BGLkhFjysyxTgH1jrkUnHeUJMzryppBStKpIjGEtJJSYOw8g1WuWsi5Zp9wiok33UWNR0QPyecl8hwTMtTSciwsRKYqKZGB5zUikiqp+gK23w4dN52vQU1bQmENnIkMXYezhph1kqXDVKsQ1agZ+p7f/3SEAs4UlpyIST0NC3BKSacbjUo7dNWW5Hbf87osqFBFe7b63LZ1WwN0adNWQtUp08OkCIQl43qvSs2oK/lup4a2L0cVojxPM13f8fZur5Mz5xNafTJEwBpLKfXAbS32+rytM1hn8L2pwqGX1mzdzpfY06pgpbYCW9b2WXJS10t9j0Te1lpLGnKt/KxZW34pqzK0+mTpATrH6gNYAdRWYc2ZFLU6jFCnhWBeVfYhOwUI5ylw06n+TVIBk62FuxRLpPA0T5xnbY05Mcx5VSHcOi2YUuR8PhOrQW2I2sqxxrDvBh6Pr3jfQVSd/pYIZTJ5NYw3jrvDLZ3rKDnSCwzmpGKKpamDWx5OM2Ovrd4pZxWpzfosDpV8u5wm1hgUuADzqqak3uoRt4SVJSTWoodjS4rk6rHoAKtsPC0r1SqyrsVQH2VnNGlBGkm7XAJTafpHbNWOxrzIRteudtmUS2cAY5vvZgMw2t4V57aks8VNhXr5Ihwpyj3NRuh7R4zp0l2oyXcqyle9TkJLm0YWubKPKlrI1DJWNabVKmFMmbe3N7w8v1Cqmn/JBY+2+cS6WjDQlvi+U3/Bzlptr+esgqhZp3dLnfQdnOHgHVKED8cjw2GPcx2n08xTVAuv0XecclBeXU3FRfS9c1IfO1fUoHh3GJiCrtn7vlfidsocH08qC0Ph5TyBFUbxLGvgdZ34c6+fFUCiQJojsYIW64S+ESq5ZKCUi3hUDAVs0dL4tlozpZjP3lf3Yhs9LxtAagtHasYotXG/xMjbmx0gFNEqxZaUlEKY8kYG14TJIKK5w5pUVTXXBWl6U7OKK86PtJBXL1Euh7YUQxtWp/4+avBOORPyFQmdstkVXNUn9VrrvZANeVxI698/nz4L/kaabUrH2HeaaYqi/5RVWXkmI4ENedQ9vWnA1F+7VfTK1SbXj5FJqNlme5atvdXerNSHdQ0nO7N10mltrcZFSjnjRUUkX+fA7dDp309sgUQDhf79kMuWPW5GwhWY5upsr1VDDdQquFZ4jYGHZSUnIe8zry8Lt14l8PfVMf5pOvEaV6y15JBUWXs38v3DCzd3PUbUZmHXeQZjGfuOkLQyY7yhWMM/vE78skv88m5EijCnxKdl5ffnWZ0DBKakDuodjvMcqs5N4bbv1cw1KjE/1CrSu0En3NpSMibztEY+xJW8zNx3PV/d7Xm/ZN7Ywrn3PE4rx/NCioVRLKsUus7yaY6EVHg7eqy1TGvgtu8UPAdYWPnV23f8049PDL4nmZXSyrNGuXhNLuGS7aptw5f9oFNnRqd4YslMa+H2ZmR9LkzzwhoCY+/43/6rv+LbD89MS2AKSuh9eD7zOk38d3/1W7798ROvpxkCvLnxvObIT8ew8RwE5WdQhP3Og6n2PbFAFnKn19omUFvFp62lqhZxxQFUTtbOWx1p141MzqVOZtU9kbcfJxcYbDuEBW91a8XadupEeUOCVF0pavsssibl30UvLCFgZEBspBQ9JHa94xy0wnA86zBBNzg+poSEhSJK2u56R4oJQiHZzCKFm6Fn7x3WCOcQeJ0jt6NnSqtWg4zFOBiHHm+U1xeaNlAuxADee97s36i0RVY7iHOYeZzOnM6rEn6tDrKI9bwsZ+7HHWVZVVF+HLkZe8Zh5Ol45E1vSZ3wuKyQ9TMXEcai8cTheZonrQjljO+sjri3Y6Aov5MaL3UyWtXFR2sJVZFew1BmZ1TkNjT0U+OXtZZmRnxdYjJSz4EWU02plbZCxrLGiG26R0XBmUibVCtbTItyif+rGKYCU9T13eoGrYvR4qupqxnYhDPbWinV1qYl0PpJVCV9WRIxZl7jxMtx5rdfe37z9Xt+98MDSwh4ZyDrKIuxsQL8CvJTZs2ZFDKDs5iipOspK4/pfvQqayEN8MBoDE/TAjZS0OrnaCwxZ7UHwZJiIqRIzIl5juwGx7RE/u3Xv+DNOPL7j4/c7UemNfLWajt1ymql9Wk+IzEjxtD3lrGzODG4sefPvX5WAKn3lm++fKNaGVnH2W0VE0upICQVzzOG0usEiAuG07SyzIFUVQylCHLXfV5qpC0S3VCDNeyd3QBDQf3LYkqsOdPZan1RFAg5a3BWJ7NOxxWKXpuO+TeRAP3/VHTziVT11YqIC1dle8mULBsZnMx2AOatOtOuvGyZzMez8l/aOHaN8ZcPel1BqsCjFKk2ChWgSWETe9kOK5WX/3SakdPMrvcq8OUd1hpe86IZrRFthxS2VsHWday/r/EgZokgKgbYDiNpwFGuNECyUEyVT6C2WNpHKVQPtwa4NtSlgaW2GrTCU3g4z7wbB4xJ9Va0IKb6UKaCt4yC1omELYJrY+xlw6MI4MTytC68rhFyoRPDw9OJXgyndcJZx+g7EIvxnhIDj69HbvuBN7sd3z8+Y7yw6yxvevW+CpWn9uP5zDKv3PY9SWDOASnCPy8T+9nRWcOHZeXDuhIrUMsVzK8pYQrc9h0v08q7w0hI6qlE0XZHEfiLw47em20NZQrfLStTyVANj59j4HR84S++uOH1FHjrDfthxylE5hDYDT0DMKVIlMJTjJxPid/c7dn1A6/Hlbe3nkzhdZ055JGv7w/VVBhiicw517afVptapaaBaylwv+vrvdf1taSsOiZLZJTM2sibxvCbL295nSc+pJXH48zrvLLbDYzdwN/8wx/4i6/e8/WbW0IMvLy+8nhaud+NVdW+CtpJxlgwGXKEFLX1SZvmdFKjRUtA2KoktN1Un4dBs/A3h50KmRa1AuqdUcCk45G6To0oCAJtVW7rV7YpTN/Ae12/yo20nEKoFYxCCJl5ilhvsAZ629f2TSbkxK4TpiVU37OFOKuAo8YdJT3nWgUNkrE7S5oj/Zs9w7jDAfb0ynfTRKaw7zyxKP/v/X7Ht/PC+TTTu43VTkiJNQZyToy7rxUchsgoheelUNlIRNGq6s4r0P74MPHNLvLYO14iTOcFaw2n+Mq6Ro71GrJAiirMOq2ZwZo6vRm43Xc8HRetQraQKJrgmTa6X5+f1Hg7WKMV3KuETe83DGIYjYKCSNFKnFzW52Ul6MtuoEnjSDKFFKFz6iUQUt6SdS+Ct2nTyhMRihFeU+YcIqkkjInb2eTbsFBhi92NlmGokjRyIUFoi0+5WqqrXAhZTd3jWT3x/KDtupC16va7nx4oFN7ejsyz43ieOefAoXMk40j1/Z1YXGdZTspZvN0NDCUzTQsfppXffHmLSfV9a3WcUvjqZk9ELVNSUrXsNWduh5Ff+p7nIpwX7XTMZNJ05rwm/rOvv+TrX36FdY6blEnPR1JKPOfMzhr6nHk4zxyc45UVcUrRTwnWkipf7F9//awAkneOX3/xHrISKtt0UkgaXKWi7FzqOHfOHLqBQ5dYs04ZNRE/qH+//pPJSNYD2oqezsuSOC+ReU36eyqno93O51y3gNWHqj3fC1HT2FZF0WVpqwmmc02orlR0z+U6SiuyUkeda3lVLoCgwKZ9IVfgp5T/r713j70tye67Pquq9uM8fo97b7+nB89MMFEAOY49hIx4REqEo/AHIoJIcYJiESGQCBJIBLAJfyRCQsQggVAkEoSEIiEFCyMgkZMMESj8EYEdO46J7Xg8M3F7Znr6eR+/x3nsvatq8ceq2ufcOz2TdCe2+3f7rNZP9/Q++5yza++qVevxXd8Fu6kQUVaZX9cokR7eqIv4OLxTf0ie/gpV86xELOKzm4xnxMmAc8Kyb9EgnDU90Rufyrak7FSOcFdqC0OrpzOnP4uBWisGtR7RQzi4RspKZApVY0at9f/UiJDDFWC9L5utlSU7nCiP93suu9aMOSrBY/W87JIimT2Z25QYc7Su53hasS1Risf51esbBjV8h2aIPvFi17GNifO+xUlgnJTr643xAWFAR68y9/pTLC0yuMzFckEIjm9cXXGzGbm/aEvZq6dpAmMyIr9f3mwMEIsyYbgWl6vnavfNeWG96njlYslKAmNMvLfbczVMlgYJngeLjto8pM6DgYyIVRxVPJhXeHR9y21MtKNVvL3UO1LXzrxcb20GNkMkZcPHvHm1Y0RIOQKBTGIzDrzx/jt00nJvGfDOCOTaYEbDk5Rmok8FNjHy828+ZLnoSHHi1Ys1bdswJOXxduJmN/DpB2ekUQtDvLGQ/+LX3+FX33vM1WbPGK3Z6m5nbNAvnt3j4c01vfcsm5az1Rppeh5db0u5e4DgCteOrcGYzHhugqUeJBXL/CgSWuemYFVverTWvHd04uk6Yy1OUWdg+tEqK+kymVO31eC3lLz19FKlVLXJvFQrYHo7xZKKM90YfMG+pMRY9F4uLEOrZsl744bWN6wL7UjwjhCEh1dbxinRt4G+a9inSJsFnGe3H5CcCc6xjxAksE0TeVCaEFj2gdC3hCYYsV/OxkiNRczO+paclDffe491tyBnYbPf0Tkt99gxTIlBR4JAiPD6Ky9yNY4kgT5YeXJoW97bPCZNFm1pvbO+pj4wjInGO7II4h1964ki9KFnYLDimWwOdS0aqSD2Ci+KMbGZKtlq4Z0q+vKQjj04eNlRetBl6+OnliobcwH/l6fsS9ufRgx804ZSQIDh4IJzNAH2A4QmkFXZjBah3iaLNF20YYYj1ChR1hIVkqrPSrV1keOKZiiOK3ZdE5khKRptH3XF2DFstEdcw+OrHb/y9iNWfctZ33N5vmaVElfbW7ZPHlvRRbbIT4vnheUKaRsWfc+q8bzQOB54+JWv/AqX6yVRle0wEcTxqcsz2iB85d0nrBbtXFizbhzXuy3v7hL7weABjSttqpqGOI5sdjt+4atfK0z7imiiaQJ74HawxsNdG1BVzhYNMRlGNRQM5n7/nJT5p5TZ77bFCKpEYt5KdUPDTNhYoi4pG+HXsq8pG8P/GHeSGUtaokApa9l4bbe0PLHStx7vi6dbsD1TSuyGkal0cSfzlJERvEPVyLcOoVbBVJUrv1n2HndIJdTwZE0vFejTvCCr7TKnn8p1VuNB1cCYetCaJnbCM3dTDv/OCOjjKJMtOzgo+mKTUDk8BAPNShbybsQ5oQu+8IJYTjhr4d45truqpyPz/ju3Fam3S44uo9p1xWych+XUOHLsu/VovHW7khnYV0cQBPDC1X5g1Ta01bvl4IHVlGTMmTEnphIyjoU3JmBkipqEoXhBYNg1r5Z2WHYdrfc0wNLDKDChtD6QomHkRDJOHEOO+NCz6Jd8+uX79K3jS2+/zyJ4LC6qSMoWkg+OXUrsybga3qzYBpHCbVKUulj102XT0KjQOaHxC5ZN4NF+4OVFR1OiRFoUZSybqHBIt3oRWqwDdshKo2VTEOZSfEEKFYAiEmgbq3gKwXF5tqDiJs77npgtTuDK/axpXC+OtYMJYVQteBHb0Ick5MmitxoTMcGya9iO1hC3EWcYqs6qcb7y1vtspomRbNHHbHMhxcTNsGUYB1ZNAxm6nLm3XtGGhse3twxpIqrh1GqPw5o28eJKWtl4teSowmj22pFyN5nXVVZYdQ2KVYA1wTrKU/SMojP5KcIMoLeP14IOw2Mppjf87PTYDcxq15XqhjzPA89qvWQ3DuzHiVQqcG/VDEbnKYS19qxjhEXXIeNYcGMBccK9xYK4hGGKVjU2jYZDaSwVNBDNMcmJ3TiSo6VKYlYkMevsi0XH9WZPTpmb7a5cc2KSzKrv2ccdZ41RNmjd5BVSaIhxNFypKqqJOFnlpgWgTRl0weM5cNTN1Wql/5w4c07I9vy6xjFowqs3o8KV9GIwssEhRjQa713jDhAAM2j97IwlteIaj6OvdBxZ6cXROqOcqVQsTqTQVkhJuSqIB7F4Y3aZxTLQtD2PNxseD4NFWdUcoTFnOim9A48M9CN7aFbxqewTWoDj1nDckSUzYfMlxkwe7X424tiPVnZU09mOzLoL7KfI1WZHigldLPDBkRA0WUFH0waCeOKQ2A4DF41jd3tDbBs+/elX+T1f+D5+6tVP8ZP/709axFiENjg208j1TlFn5LIKBR/lDNdYHOa+DazbhlUQ3ttHLpZG5oqmssNauj47ZVNIN13tcZit6Xfd27wIDcLNU3G+p+XOGUg32y1zxAQlhAYt5cvZVfCpnZFyqRfQOn0oeV531MX6ODJTfbEjG4HaK0xn5TPFyGaw7uyz2XGUEpg0kVI9XIGmpU9XawvHFFgml+ar9TerEVLcxkPUpPyMKU17yKac6zVrIe+qPMlHUSfmwXyw1AVWd8T5bh17wUen1/smh2tKk21645iKF2qeqHOOzhVgZrmXdbOpZc+Vgr8+t8M1HSI6gsxcNE7rCA3DVeFah6dcDa8jownbXJxAK8J1SuQRVhpMmR3ZsVHtT9UYl11hZZ80MxYvz2Mbkq+4teM55k1BVs4SEWXZOB6lCRUI3lpXxKw03oPznDUNvRPGcWQ/KcMYaRYt+2zIR4mK01K+7Iw/ZS4NxjbR1nl2egDD1r50PoP3zIq7LUbRedfMBmlUZVDjjPHO7nV1FlRL9Ziz9bMoPC/z3Cwb8aKtbOUUxeoNZ7VeME0GwOh8oAkyr8Xj4KUAC3E0XgjZ+MfEKWNp5eO8Y8w2x2LMNJ0vbOxm/HXe0nPOw8PNzhph+vI7KgSFKQljsoq+KWXarudzr3+K9x4/NK82R9I2MY1xdsNTzAeCxXKvVfMR9vEg1dyep5NUo19YtX5+VsZebVFiKdHZ6uDlEvGcA6yAsWFnKtD2qQonmNNGffA0zhpmTzEzxMjSObwzJvzgoHWeqMr1MBi2qjAfOhWmlPDO03ctWUvKU4WgRk3ShQYBttl6DXZtoGsD+8I+3RVCz5vNjpQSeJl7T1Kcj7Zgi5JCTIaLCl5KtZjp566k7mO0eNc0jqwXPVmNxDFjLU5EPCF4vA/VTMSJPXfU8Gq5GLF1bQaxjgFehD40dN7mrFc3G4mGowp4mUxnFVjFsmvxIkxpAqzHW60QqziyrErXBMOR1YiKF3yuUfJD31CjUpIZimFnCGMW2lYgJWJMM+hehJlzrMpTlc1lztR9pDr9tRp4PoYZ0VMq4P9UlF5SouQS3ayM/Ia3q7hTUXM0NvsBHwJ925LHCc3J+MqcN0duHLjICY/Nxc12SxxGLtcrWh/YTXucN6PwdpzYDYmut/SXkcSaUYNzuKhGE1LmThbTN6pivH3iaAQkW6o9qbWC6YLZBlNKbGPNMAh1B6nz8tvJnTKQFOt1AzWqIgzDxDiUEFl1v4pijymz7nv20zQTe3nncN7+QrWOxcKszdxTzax4V5pIVqnlxaoWC5piCVqW2Wilssp7t9fG9aFWDqtqXn2KSrNwJLV8c0oJlwWw0uyUSvUJahuwmDcybyIlrSiaiyfqZgeycp0MOX/r8372wKzp6109OnZsnMkHfbCEso4jNvNbRgCZcmaIpuj61nNv2ZJcZtTIUHqN+VrzXxZ2NZIoAa2kB4AuJUoRpVAilPufxSoiyj5Wyl8PQ6skosdeVTXuuhC42o/sp8RL64Wx3BblMaFMZdNaOWudG0XZqTVArUSkSczgrVUeqaS4XlytrN8PsMmZmBPn3nNbWuHc71aos1RQ4z1r13PhBHZb/s7jx7yzMyb0m3Gaaf2zgkalS47FumWpgjkmAAAgAElEQVSj8SkDyZeI3W2MSC5eIkZkOmoiFCJCh1h14pkvEVTrHXWbEjc5MmGbSGXXrcbTpOZ9eYEuNDOzfDXdM9C3DephO07s9gON81zv9lyslqDOWmYkpeuFPtSWCzLPuTqzWszYabOpylETIonQNEwZpikyjBO+oKI1ZRpnaaehllc7sAImM+a8s9RVRIljhlIe/uKLD/jC938/f/4v/QSrznO57ol5sugzZQO3kktzbLDIkXUXdwW4X6k5mJV7nccitXmrp21M59idzUc1kxZ5cIWivFbpzfN4ntZSfN+nV+N8oiqXhQF5ysrNMLG9tSbAN5tbhphYdw3rtuFmmHi43UN2lhLyHieOKUZeOGtJKoRghkQsqZOrmy2rvoccaZzQhY71oqV3nm8+uSVgTNnBe65ut6Zbi7Hta+oqAyo0TTDiRjAd6SyicrsbETHcTyop9DFFWq900iPBgLXqzEDqmoYuOLqwAIEpD0ZrkKH1IM5DorBf2+9KtpvahsBq0SGqNNLYPU0JweF9wDlHE4ROWsY8Wmpp0eMFdkNl7T84e8aLVyK+pYQfzeSUcGV/yEVZlZoQapseqDhQe5SNKowT0zjRA6vQcKORoMqUecqhrFH4ZxT7PF+PW9+AgfxjzpCUvE8H+hXMCRnGSFvxZ9maoDtgHBLiYN21OPFEFYb9xAsXFwXmogxjZnJK17T47BEnXKxWbIeRt775Fj/2F/4y67bhpbVhGMec0ZzM6NTMhW/nSk4F1Al9Z/3bxMGYJjbFCX/xbM3VZiC4Zh5vcMY5CMpZ17JojOJgPxhpbxdCidIJqeBO50zQB8idMpCCMy+sUriPkzV0zNly/k5q6bjNHAdcJ8MgZEp+2RloTUTxoURZcsGhGOrarHOh4IqshDUEZ6+lbOTOwGhN8NZKBEdwnr5t+exrDxgGC2VnLQR0CZJYo9Antzdsx8mYe/PAsjMivv2UZs/Co0zAwnvWpfLKPEtl0QRTzA7rCyemdDdTMoKubzFswCohDgulerU8FWWp9orMXsiRDTNHaqoNemwjuTmicPiiROZ6l7h0wv11zy57bjQhjRTizEM6sPKBCEJ0ee7uXdNqk2rhAFEbd8GB9JbsYmaokbph273Kc2jIznJlblz0LbuYuN6PjFeJz95b24ZYPju7uyiRjKiw8p4Ozz4lRjXjNjvr36XZQxYum44GULHN06mF2b82bW0jSGZA9k2L88J719esmgavsGoECY7QWs8+cma/TzStGctOoVcYdhHpyrMszynmxEaijc/rvClENRbmTUysfGknQ0lpOhinzDYnnsSJQZW2Kf34NNM4S5FW9TFm5b73M7hfYKajyKpM08h2P7AdI1OC4BteurxgvejZ6DS3uvEu0lasxMHxtYJ/Ocy37X7i0e3eGliOkc55wjhByqgmNvvMWd/xcL9lP6RSvWmecduFUjFkMyOozY0g4FsPzvH6Sy/RN54f/ytfZMrK+zfvsQotrVNeulizXqz52a++Ya0ncp7bSqhCw1HLirpTFf2SSyWkHh4R523DRGXMN6ncabX1jqXV6ifK3Bc/LzapRlq2+V55z2xvtPR9vamC0jhh3ZXWN2Ud3Q6RzWBM2MveqBaSWuWblOXz7vWNlVDnPGMgK0Ht9XYLyZpgi1PeH0a8c1zd7mmCcDYFlk1g8cIlKjAMA7tdom0DIQgpJ4I0rJYdm/2e293IFCM5CU3T0HUtu3FvDVAxw5asnC0XPBpHJCWGIXI7TvgOlr7jvG94vN2yjxnnHEGUdWi4WK9BlKvNxH6KOOdB9qz6pfUdy5AnJQTPsndcrtdsdnvAUqABx7LtuB4n9uOIpolp3Bn5KELQaGm6EgEKCKFpQZSb3ThHqQzTmgv3kjnNNo8OKdQ5piGmZ7V+DqV3sM/KfjfRNJ628WymBHi6YOf4qpdnPVic+Qwq2apDC1VI1AyTUkrwZkxnKoZ51wSSKk+2IwL0bWCqrUKiAdJb37AIDS+e9eSsPN5sSLFUm4lnv4/c7wLSBt65sv3OGt5meh/YxckoI3Kh5Gka1t3CCqGiEXkuuo4X1mu8C4yriTcfP2GfLG3rVHjr4TWuEe77gGZlTIm9ZrbjyKv3V7QlWtgGx1nbklT52qNbvChd6Ije5n2aqqv5rXLHDCTHK2cdDqEJDnHCmzc73nh8i/jSJ4vDQ8/AtB3JqrSNZ9E3WAjUvMOciuVe/ssJivFpoU89YALiZHT3Y9VvRRnXjTAXFeS8cLFY886TJ3jnS9NPa1nhvXB+1tB1wTqFO0/wRmaFgHcrHl1f82RzYwpLYJsTt7c7xmRgM9TuwzQlDCMV6JuAk0L+Fg14OI+sgpSOFw5Pey7VSHravlFQS/8dSpnlaOxF5Ig8r/5AsZxE4XIR+PTliq8+vrH2BVhY+lPnC4YConTFQIqlybBgG1mC0ijWjFYrhz5QLrikhFCI9exXD0ay2nOZx1ivrHDq5JR4ULphv7/ZW7f2IAwpUhtlJtXSvFXmoj6LbgQmHA+nkSBCg2PReHoXiJPxbgVn1Wjf9dprfPr119neXPETP/U3WXctY57YbQdSyrRNQFzA9w2bbFGy3sFeR3YjtF1hE0ZonCN6YTcNnOWW3B7I2FKGW43WyFeF89aYsb1aW4qksI2JIIZlCqXZadTMwzgRKcSRyeZ1OKKZmAOzoqxLO48ySWY2RQF6hMWy57pNvLMb2e8jZ4uW2/2WpAkfjMyt8eZ8ZI7mDhDUQudz9aIzBl1RYdX0XJz35N1AcsZqvYgjwz7xwnnLeWi4GkZ2KXJv2dE1jqtpZJ+TVW1hz0nq2Frlm4/e4s33BU+DNMbWHETw3jOmxOPrJ3zv514HDXzprTfZ7gd0smjdYt0xpDTTb1QPPAhktWc24w/V0gFjinPhxhxAxYyc2nJCC35IBLIKcYqz4VXXHticrGblIXBQnkWZ421wvOBapk55shusz90cuLXnbBidciXFaRpLuyEpa7tiZ8Y02boW8JkSFYEcI0kSrVhPtevhhn2ceOHinJQzV9st3dTgRdgNA64V1v2Se2drlBu2e2u4rDny+GrHar1iGCNTinRtoHUN1xq5XKxJu4HkJpoAkmF5ueTR9bVx3xQDMangAzy6uaHvl+AauqalCZ6LxYLr7YY2rFCfUR1ZLwKPbm7ZDpkYh2IoCJqVF++fc30zkjUVwlB4sAhG7Fh0lzijA3CFbsSpcN63pFINadxEhgmcqSs4YKs0G/bG1blUdKeq/Z71p/TGb1aeW3YG/nZJabwUjiX7cCrKUMp0yQnUWRVyihlnaH8jaizPM+bEdkjsxkQQR997mmApzt0YURFWi5a+68ia2e0j18NI1wmPbresmpZPvXrJbj/x/pMbJDi8b7m+2bMrxrc9/8hjtyWEwDhlxjEhIkQvvHy24Gq/58HZgmFKeCese/j640eMUbnfd3z3yy/j2453rm559+H7iMBmGHlx2XAWGt4eJi5Cy1kbDpxpWWk9fPpyzapt+dWH12U9OUQ9fd/y7eROGUgAiNB4wx7sY+atqy21D1uJ1M/SNp4oqcQOAckFU3JkQBSvdQYIB5m/wzlh1Rqx3bu3W9patg/mjbae7RARKX2UFHJUnjzZ0IemeNYUyz2h6nnn/e3827YgzGv0IogPhXPJmEib4Gh8N+fjsxoZ2zbuGXZWT2dl4YmoRgo4k1gWfotqEFVjyRT0UeSoGjXHrvzsrRbDqRoYwqxMZ9FKFkkNSx3eQnlQcC5jslCt90LXeobSKT5UYwqhKUogOQuDWzorWfNMgaxp7psFENTSa7Ua0S5HwJdyXeuzUKqMDptDfe5B4H7fsgiBt/cDq6VFTMq2MxvZlZEY1RJdEhrxnIeWPGUWIdB7j1eIJZ+XYubV+xe8eHlG3zWsz17hn/pHP8Mvfv1NclT6pmO56hCE96+f8O7NRNs0tM7RNY79BF3bsGisosd58MHb5pcs8Rv3EQmCawxAXRv7LpynF7secsUTwFTSsDXy13nHdU6EIHj1pUqq9tSzcdTUlBNhipmAm+cI5V7mslE6sSKIOGV0gpcuVngXeHhzw5gjizbQLlvUORIWMdSjb8rVZCoetStWR8pKlowmZbFYWFl1zrQamNTSeKtlz6rrWOWGy75hmyZe6Hp6byDP62niNkYa5MA3433ZaCI+W4SjzqOmVDxutxtUhNfv3efhzS03uz0u2P5iTo0c1jJmOKQpzYYQGHh8uWy5GhJKfiqSW9oyl/tY14EQvEVNx6jVoym/U/nS5lVZHIOabjdiQC3fbRgsx4urBe9t9tapXkqEony+Lgwp1yXOoo9zWwqsgW3GouzGxWTepG3kmfWimYmgurZnfXbGk+trvLfIkAGZE64xR/bh1Q37YTBgr7diBOv9FehRur5jiJ7dlIhOubrecdEvGJNyMySux5Fl59ltdqRsLUdSjgaAdopOkbN2zcX6jM1ux+ObW/YxEoLnej8hckUIpmPjbscuRvZpQ98FUMMc9V3g7cdXloIpqi2I8EQyD/oG76thZHACWzZGkJhTnrMHHD8trSq3aOViYJnyUqjcR2rVnKH03Fw44X7f887treFtmlDoCzK9+EJiWbxcrY2LdXZ6sxp1gC8wiKww7CNd6wvExLNsHU3TMCVL36sDaUv1XjHa+76DlBAVduPEo+tdqcrzPL7Zcb7q+dTLD3jjvUeGb1KxiFQ2rsAmQJbAfjDc0LrvabylTW+HEbAGxL4A1t94fMXNbiLmzM1mYNH3vHq5pj9/hcfXV9bwOWbe2Y6MavQ2L91fQcF+VV0yRKu6PG893/3SBV97eMP1fk/TNPTL54RJGw7hTIpi3sU0GxnHkrMS94lF0zBqmhX9kT6wvxr8mL1A2/Bb7+mDN28yJtIE/VkHYiSRWUyZN94VfiJ7EDFlbqeRPJqB1XiLEtWccQVsV2WnWh4kiqYDf1HKmSkJzkU2gylAFeNh2adIJ4F+0RoXiQjU3m4K19sdY7Ry6xmMlxMxKjkefBhXDB4pZG5P2TkcBYSORZ+50XCwiY7eqy1Yzpows/wqlEo3V1I9B94RsNJ6STAVfUS5fw3eQHvVEE4Zn63Uc76+2UCq3rHMm858WcpM3gmmo4I4lo1jFOPcMHVU70/lVj8AomusSosxsmqCha9LGwgRKw9+8fKcV155mfXFBSF4BMdv+dxn+fI332HRtbS+QTUzTANjTITGOm3HooQvuiVNVrIXyEOpWDTDpRUp0UTj/0ooPiu+8fTOF04oiyTUTbca08VaRDGm5qylsk+MZyVhka+cLXpWn2XjLAJqWC+dSemq6q+RuqhYgUIWxAubjRFjilqIfogR1zTGRZLjPMcUJeLIR+vTOcOY+D5w2XWlsMHKt7vGodEzpMz1fiA0DcE7XMkh1nL51jk671iEwEVKbGNkHyOqxdsX6ETIFZiqlsr3pVqpAmKTZF44X3GxXrIdRp7stvOaEWAZAi/1PagynQdQx6PthqyJi2XHTRxopFam1fVfsHcc8E2CFQao6lyMcKjKLEbYkR8yn2Lo2aIf3aE4pJzvvGPdmrMyFKZtV7y66kA9tZ6P9GJ5VTAyNXWo5bkV2otsGLQxZtvgpsEM3iSM02SVe42jNeZI0pRJOSHu4OTU/b2m0m1wji4IA46b3Y6YMr4RXlmu+d7PvcaQ4ee++nUrtCgO5JQzPgnDNHC7uWY/GaZsiokxT9bVQBRxnqZwSq2bhsfbml6zCKuRLxp5a3A15WXXNiZl2Zao0dEmkgvjedZjShkzXoRjY6g8NzjQB3CI7tWUbtVtjSjnXnDLBd/cDZau1NLHT0q3AyrQvTqB9ZdLEYCz39fCw2WpdLv3EZicRZqzCBoPqXWHGexxymy2O1pv2CLnhCEllr01Xx9T4nY74Lzp+HFKuDagWNHJfkpGLinOWM9TNoC9g8aZgbufJov2OSuEuBkmkhqnV54yj293hOYa3zQsG0E0oJLLHm0mfVvWfp26UhzkWsHWe8dr5wsuFi1DztxOlX3rW+VOGkizoaxmSMxgZTsBMM9ORzhbd+zihJJnhVTXfzWKqnsgR/8XnFm+iON6TLRNSxOaQh1QOUyMvdTSL3VTNQxQjHoo4xWxCaXM1TBSNjtbEMVbO1JqUjwB41bK8yKsDTW1caScykZgVUm+NrN0wqprS4M/tSocB9Oo7KeJKaW5iq5wZ5Z0Y71/MgeUntab+vSB+c7NJ8wHa5VUGzzv70bDW4lxT3TB12zA/HWK4WVSSVeC4bo8lkoNpSwzokTLu9E05ktXTEyNeOVsWtYdGWzHZt3Toyglt8GYmdNT78lcyfXsd+Scre1GqdrKBaC97ltSUj716iucXV4Sus7At2qg0c47q1wTZT+O3O731ivMC5r0kNZzjss+sFNzzMcUC7mjpYhyRWaqotHI1pxYma3mTINVtY2aTDEoT4F/oeD2UDNmXJ2bZk6V6flUo9+l8089+BpIOjaqh2hYIKvOcwSd0OCQJLNBF52yWLToPh0qt46fTCFN9d6xahvOFks0K0+2O8ZsLNC9NFbeK9Z/SrF16KRWTR1Fg7ynF8eqURYx8s52yxjtnCD2B1bRkxFUHaFEB0JxPFLONMH6yIXCZ/Zwt7FtT5RFCLy2XvFkGAmLnkY6hmliNw2lQfDImQ9MZb25MsaqD2oU1CHEEt3N81o64L2qEyeHx1BeFGwLsw18sK2K8bosBQXBGV1CU7hgALZTYhtTAYA/tapnPZlhxmAdvUulT2lDYIoTYxzxWVl2nUWNXUJKhbE5ekITAooZpfbdVpCSiis1pcQws4PDetEapYMPNK1xLb10uebJdqDzAs6SjpNLRIQsjnEaGQuVxHnfsBvsHvatpxa5BLGkgfe25tT6QOHEIpc1a9A4mXVWQhlSYp8ci7lfZcWHFcqYYoDY/bP7abrqYAgd7uDBEVGsWrsu73qjBWgd3OsarmMqzVjtWnZTsgpOqXtHVeNufpJSrtCVtHXt15awyPKEEss1qnKAjJSoUx3BNE5IW8dlmMLGOxKlg8OktOq5WPTcbPZ4LwxTPpCvlorJRTC8pTlchmlbBM9+ElaNVRbvJkvldd7TeGc90zRzu9lYNW0wHi7fOHy2uRxKha07usHVCBVK5bHAxaJl1SmbKc77zQfJnTKQ6gOrD7FqgYPBcZiQTbCUw6rvcKNtMHY2RZlQMEZ2545hWnVSZSzCEMVxXsBoccqQrDIJZ9iU2WATnRsQOpEStckMpc+TYCHvukBc8b6MO8ZC5zMiytV+cAYOr2SRhjGAicR+N7ItFW2ohc2dCOOYuFgtWXSNpV6mzGLRoWrgwf00kjWyG2JJVyjTmIoxUDhCCvBE5ptGoRV4+j7N92teyPZsGu94ZdUhInz1yYaYldabcRS8FH6bsnT1wI6dC39UJeebUiI7oXeBpTfAfMoJTeBaB7mq1KMrUjUsR9nwmYkmD9cotWKICui2p5JLRA9hVtZeCs/u0eBVlYUPxGwNOr33LNqGVx7cRxFeeuUVUvEoyRGnysO33mQdPNs4MsTIbpjYDJHXHlwwxsg+T2UOZDa7xIvrJS4mVr6HYWAzDWRKs9qk1uFRLVzuMuiQUKd0mOLunS8EdYpqKtECOTw4hVCeqculMlNKmwPvLF0XbIMYp8SqbG5u9u6Zo7jWBFXZjpGUYdn3kB0Xi8CVKjLV+28Vcp0P7DOGg5AS45IK2rcLdM6xbIUHywVvbwwcu0uJcYRFseDO+477GfY1aihWAKCUKjkp319wgPfbhn3MPM6DOSMCQ05zNGwCmuzI6q21R0mdBIRhP6DOwKOLiwsebTelwtBS4ouu4a3twGrMtEtPK56bKXO9G/GdRaGO6yLq3Ktp3DofteiKWEDSQSoQ+1ApV8c2z2mt2LvDRlc3xRpFaJyj6YRF4xlzaQGBfefbmz1vbfbmqMxWb/mtqgecpR5z8cjrmo0xo62zNSUWqegaz3nbsk2ZRe/rMi88cgbmHWOeuZpqg3C8paj2MTMUHOYwCfdWHQ+nRAiGI92ME7/6/kP2m4F1sEa6CCyDJzi4nRLbbL3CVl3D5aLjph8Zp8xqFebqQwDNiaTKum9K3zJAM03RwcEZPUMqKbPGW7TkyVgKf0rblco/lXJGC7WBlHS1FhyC1Pldl+GRUymCOd/Z3DRzjN2sv0DworyyXvDmZotw6MP4wqKhuvjCkbNfUqLz5CrPx5W1MpEZyhykGl3FQEMK5YEIKSXagpVNhYcKtf6SCZ3bMKmzCsCzvmMYJxrv2Y1l7gRzNvbjxEvrNVM2rJtVgToenC1QlItFSyOCpkgcEquFJ/QNL/adpQgLMW/rhFui6TBVggjLNhwq/MreLFJ73Zk3Is4yF60D14ZDVO8D5E4ZSNW4gYOCCa5igpjLZFErwW+CcDPsSoQllHC5MZGaDhCLGNTw7tEOOEyxtD7wLJoGnyKPtiNguUyX0zyBa7qh/Dht8DbJs1WO5aTzaqgVdEIxAopqNMBswdKYJrGKu1wxDrUCj9KawNJPZseURVjwJqH3bMc9N/vt7Il2W7PK2+aMy/MFq4Xio22g+5QZp8gQE7sh8u6TJ0xjNFoELyV8z2HGUU0nrUOeX9eNqfXOmq1iSkI00zWBrvfcTpHWecuJOynUBUrwtrSrh6pSK30svTZMiT5Yg00NAo0jj3lWPrOoIC4fVbCVxeHqFTLj1hCbA+ogZMdUTOUKTE24sgmVstPyKJc+GJ6nbCCrruGVF1/ge773+3hyfc12u2F1tma4ueLhm99gc3NLUviuyxV/9/EN15uJYYh0jePJ9obzruNe3xCc9aYa0sCT/cQ07Gja1gxxJ/icSQK3Y2QpvjRUtihbGxxxSlz2LZqF/ZSNATwEnux3Myat4hRKYGsmO3QqrEQYnRE1eufKHJPC2VNtq8MuP+bMl65uOOt7yNkA611L23revbkhnK25v+65jQOb3UCTM5+6uOQbDx9x2TZzVMQBTl3hP7E+d8ZZ43k07PlNr77M5b1Lvvnuezx8ck2K8OLlJZMoX37ry7Sd0IVA8oHQBaYpzmR9UrzgLLaJvbxacta2XA8D1+PADiVkSpWdTXMHli5RM4D7JtC0RvQYp4k3Hj7mvO955d49rje35Bh5+2bHfpeI047Nds+wH4hj4nq7Z9F7+vvLcg8tmmzzsFIEGFi7ri6rpoTkzGBp1ADuoVS61XVfU8pirrjN5bKApJzjnRCTzngXLyD5mDIDXl723Ota3t0PvLWx5p1SH3OZ9L4Q7lEAwUYUW953Ri2wGydijHTLBRPJwNfoAU9WjGT7Hj+3t3Bq+s9548RJxei0TS3x9tWG1+4/YEyR3TSQYuKrX3+PdeFlqltD1e1nbeCsPsyiGhZdh+qeddPMsIP6+5KULvh5PKqlchSKkQQ+O2I2HOuyDQw58XC34yy1c/qSYuyXVTLvCZVPqjrn84ahB7WqHFL5aCKLx5UK2plPC8+SbB3rPbhgOto5qzJ1h3CBgejLD2g+OEfqwAchNI4tiaw1k6GkKc+Em01wuFyyNN6ij9sxElpn1ZgKEmDKmaZxrENgmDJX2w3b/UjTOC77HhJG1+AmvAhjVoZoEbaqW2JKnLcNywfnbIdoEfrG89LlEu8dkxrmVsSZg4jhEDfbkd044MUqD8dsfQq19LmrRj6Ac0alkclztahzwnl3TLjxtMhTpdkfcxGR94Bf/Qf4iheA9/8hXc7HXT5JY4XTeJ9n+SSNFU7jfZ7lkzRWuDvj/S5VffHZg3fKQPoHFRH5aVX9/G/0dfx6yCdprHAa7/Msn6Sxwmm8z7N8ksYKd3+83z62dJKTnOQkJznJSU7yCZWTgXSSk5zkJCc5yUlO8ox80gyk/+43+gJ+HeWTNFY4jfd5lk/SWOE03udZPkljhTs+3k8UBukkJznJSU5ykpOc5O9HPmkRpJOc5CQnOclJTnKSv6fcCQNJRN4Qkb8tIn9LRH66HPutIvL/lON/UUTOn/nMPyIityLyx46OfX85/ysi8t9IoacWkU5Efqwc/0kR+czRZ35IRL5c/n7o4zheEfme8t4vlPf753W8ItKIyJ8rx/+OiPzI0fd87McrIpci8uMi8kvl+r8gIvdF5K+Wa/irInLv6PwfKdf9JRH5PXdprB92vCLyL4jIz5Rx/YyI/K7nebxHn7mTuuojzOW7rqc+zFy+03rqO4z395fnl0Xk88+cf6d11QeKqn7s/4A3gBeeOfY3gN9ZXv8R4D995v3/BfifgT92dOyngC9gHF5/Gfi95fi/DfyZ8voPAD9WXt8H/m759155fe/jNF6M7PP/A35r+f8HgH+Ox/sHgf+pvF6Wz37mrowX+HPAv1Fet8Al8KPAD5djPwz8qfL6Hwd+DuiAzwJfvYPP9sOM97cBr5XX/yTw5tH3PHfjPfrMndRVH/LZPg966sOM907rqe8w3t8C/GbgrwGfPzr3zuuqD7wHvxE/+hEe1Bt86wZ6zQFD9WngF4/e+5eB/wL4ExSlA7wK/NLROT8I/Nny+ovAF8rrgBFbyfE55b0/C/zgx2m8wL8I/I8f8B3P63h/EPiL5bofAL9cFtLHfrzAOfArdVxHx78EvHr03L5UXv8I8CNH530RUzQf+7F+lPE+c44ADzGF+9yOlzuqqz7CXL7TeuojjPfO6qnvNN6j9/8aTxtId1pXfbu/O5Fiw8ji/w+xsPu/WY79PPAvlde/H9tEEZEV8B8Bf/KZ7/gU8I2j//9GOVbf+zqAqkbgCpvU8/EP+Myvpfx9jxf4xwAVkS+KyN8Ukf+wHH9ex/vjwAZ4C/ga8F+q6iPuxng/B7wH/A8i8rMi8t+X+fqyqr5Vru8t4KVnr/uZ67sLY4UPP95j+VeAn1XVged0vHdcV33YZ3vX9dSHHe9d1lPw7cf77eSu66oPlLtiIP0zqvp9wO8F/qiI/PNY2uWPisjPAEkwJH0AAANBSURBVGfAWM79k8B/paq3z3zHB3Wk07/He9/pM7+W8mHGG4B/FvhD5d/fJyK/m+d3vL8dazn1GhbK/fdF5HPcjfEG4PuA/1ZVfxumQH/4O5z/Ua774zJW+PDjBUBE/gngTwH/Vj30Aac9D+O9y7rqw471ruupDzveu6yn4JOnqz5Q7oSBpKrfLP++C/yvwG9X1V9S1R9Q1e8H/jyW8wT4p4EfFZE3gH8P+I9F5N/BrNDXj772deCb5fU3OESgAnABPDo+/gGf+TWTDznebwD/t6q+r6pb4C9hE/t5He8fBP6Kqk7l/L8OfJ67Md5vAN9Q1Z8s///j2LN6R0ReLdf3KvDus9f9zPXdhbHWa/kw40VEXsfmwB9W1eM5/jyO9y7rqo8yl++ynvqw473LeqpeyweN9zudf5d11QfKx95AEpGViJzV18APAD8vIjVM7YD/BPgzAKr6z6nqZ1T1M8B/DfxnqvqnS/jzRkR+R0HR/2Hgfy8/8xeAHyqv/1Xg/1JLfn4R+AERuVeqE36gHPvYjLdcz/eIyLJMst+J4XWe1/F+DfhdYrICfgeW4/7Yj1dV3wa+LiK/uRz63cAvPnN9P/TMdf+BUu3xWeC7gZ+6C2OFDz9eEbkEfgLDMvz1o+95Lsd7l3XVR5jLd1pPfYTx3lk9Bd9xvN9O7rSu+rbyawlw+ofxh+VCf678/QLwx8vxfxcDvv0y8J/zAWAyjoCP5f8/j2Fbvgr86foZoMeqSL6CIe4/d/SZP1KOfwX41z+O4wX+tXLuzwM/+jyPF1iXa/8FbMH+B3dsvN8L/DRW0fO/YVUaD4D/E/hy+ff+0fl/vIznS5Tqj7sy1g87XswQ3gB/6+jvped1vM987k9w93TVh53Ld1ZPfYS5fKf11HcY7+/DIjwD8A7wxaPz77Su+qC/E5P2SU5ykpOc5CQnOckz8rFPsZ3kJCc5yUlOcpKT/HrLyUA6yUlOcpKTnOQkJ3lGTgbSSU5ykpOc5CQnOckzcjKQTnKSk5zkJCc5yUmekZOBdJKTnOQkJznJSU7yjJwMpJOc5CQnOclJTnKSZ+RkIJ3kJCc5yUlOcpKTPCMnA+kkJznJSU5ykpOc5Bn5/wEQza0gNlByxAAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<Figure size 864x360 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig, ax = plt.subplots(figsize=(12, 5))\n",
-    "plt.imshow(herstappe_data.transpose(1, 2, 0), extent=herstappe_extent);"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 74,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 864x360 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "greyscale_data = herstappe_data.sum(axis=0)\n",
-    "fig, ax = plt.subplots(figsize=(12, 5))\n",
-    "plt.imshow(greyscale_data, extent=herstappe_extent, cmap=\"Greys\");"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<div class=\"alert alert-success\">\n",
-    "\n",
-    "**EXERCISE**:\n",
-    "\n",
-    "The data set `./data/herstappe/raster/2020-09-17_Sentinel_2_L1C_True_color.tiff` (assign to variable `herstappe_data`) has values ranging in between 0.11325, 0.8575. To improve the quality of the visualisation, stretch __each of the layers individually__ to the values to the range 0. to 1. with a linear transformation: \n",
-    "    \n",
-    "$$z_i=\\frac{x_i-\\min(x)}{\\max(x)-\\min(x)}$$\n",
-    "\n",
-    "Make a plot of the end result and compare with the plots of the previous exercise. \n",
-    "   \n",
-    "<details><summary>Hints</summary>\n",
-    "\n",
-    "* Keep into account that the data set is 3-dimensional. Have a look at the optional arguments for the reduction/aggregation functions in terms of `axis` and `keepdims`. \n",
-    "* You need the minimal/maximal value over 2 axis to end up with a min/max for each of the layers.\n",
-    "* Broadcasting starts comparison of the alignment on the last dimension.\n",
-    "\n",
-    "</details>    \n",
-    "    \n",
-    "</div>"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 75,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "with rasterio.open(\"./data/herstappe/raster/2020-09-17_Sentinel_2_L1C_True_color.tiff\") as src:\n",
-    "    herstappe_data = src.read()\n",
-    "    herstappe_extent = plotting_extent(src)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 76,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "h_min, h_max = herstappe_data.min(axis=(1, 2), keepdims=True), herstappe_data.max(axis=(1, 2), keepdims=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 77,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "herstappe_rescaled = ((herstappe_data - h_min)/(h_max - h_min))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 78,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 864x360 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig, ax = plt.subplots(figsize=(12, 5))\n",
-    "plt.imshow(herstappe_rescaled.transpose(1, 2, 0), extent=herstappe_extent);"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<div class=\"alert alert-success\">\n",
-    "\n",
-    "**EXERCISE**:\n",
-    "    \n",
-    "You want to reclassify the values of the 4th band data to a fixed set of classes:\n",
-    "    \n",
-    "* x < 0.05 need to be 10\n",
-    "* 0.05 < x < 0.1 need to be 20\n",
-    "* x > 0.1 need to be 30\n",
-    "       \n",
-    "Use the data set `./data/gent/raster/2020-09-17_Sentinel_2_L1C_B04_(Raw).tiff` (assign data to variable `b4_data`):\n",
-    "    \n",
-    "* Read the data set and exclude the single-value dimension to end up with a 2D array. \n",
-    "* Convert to float data type. and normalize the values to the range [0., 1.].\n",
-    "* Create a new variable `b4_data_classified` with the same shape as `b4_data` but datatype int.\n",
-    "* Assign the new values (10, 20, 30) to the elements for which each of the conditions apply. \n",
-    "* Make a image plot of the reclassified variable `b4_data_classified`.\n",
-    "    \n",
-    "</div>"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 79,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "with rasterio.open(\"./data/gent/raster/2020-09-17_Sentinel_2_L1C_B04.tiff\") as src:\n",
-    "    b4_data = src.read()\n",
-    "    b4_data_extent = plotting_extent(src)\n",
-    "b4_data = b4_data.squeeze().astype(float)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 80,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "b4_data = (b4_data - b4_data.min())/(b4_data.max() - b4_data.min())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 81,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "b4_data_classified = np.empty_like(b4_data).astype(int)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 82,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "b4_data_classified[b4_data < 0.05] = 10\n",
-    "b4_data_classified[(0.05 <= b4_data) & (b4_data < 0.1)] = 20\n",
-    "b4_data_classified[0.1 <= b4_data] = 30"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 83,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.colorbar.Colorbar at 0x7f090414e250>"
-      ]
-     },
-     "execution_count": 83,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 864x360 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig, ax = plt.subplots(figsize=(12, 5))\n",
-    "img = ax.imshow(b4_data_classified, extent=b4_data_extent)\n",
-    "fig.colorbar(img, values=[10, 20, 30], ticks=[10, 20, 30])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<div class=\"alert alert-success\">\n",
-    "\n",
-    "**EXERCISE**:\n",
-    "\n",
-    "The data sets `./data/gent/raster/2020-09-17_Sentinel_2_L1C_B04.tiff` and `./data/gent/raster/2020-09-17_Sentinel_2_L1C_B08.tiff` contain respectively the 4th and the 8th band of a sentinel satellite image. To derive the [Normalized Difference Vegetation Index) (NDVI)](https://nl.wikipedia.org/wiki/Normalized_Difference_Vegetation_Index), the two bands need to be combined as follows:\n",
-    "    \n",
-    "$$\\frac{band_8 - band_4}{band_8 + band_4} $$\n",
-    "    \n",
-    "Process the images and create a plot of the NDVI:\n",
-    "    \n",
-    "- Read both data sets using Rasterio and store them in resp. `b4_data` and `b8_data`. \n",
-    "- Combine both data sets using the `np.vstack` function and assign it to the variable `b48_bands`\n",
-    "- Transform the data range of each of the layers to the range .0 - 1.\n",
-    "- For the values equal to zero in the `b48_bands` data set, assign a new (very small) value 1e-6\n",
-    "- Calculate the NDVI\n",
-    "- Plot the NDVI and select an appropriate colormap.\n",
-    "    \n",
-    "<details><summary>Hints</summary>\n",
-    "\n",
-    "* For more specific adjustments to the colormap, have a check on the [Matplotlib documentation on colormap normalization](https://matplotlib.org/3.3.2/tutorials/colors/colormapnorms.html)\n",
-    "\n",
-    "</details>   \n",
-    "           \n",
-    "</div>"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 84,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "with rasterio.open(\"./data/gent/raster/2020-09-17_Sentinel_2_L1C_B04.tiff\") as src:\n",
-    "    b4_data = src.read()\n",
-    "    b4_extent = plotting_extent(src)\n",
-    "with rasterio.open(\"./data/gent/raster/2020-09-17_Sentinel_2_L1C_B08.tiff\") as src:\n",
-    "    b8_data = src.read() "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 85,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "b48_bands = np.vstack((b4_data, b8_data))  # 0 is b4 and 1 is b8"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 86,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "b48_min = b48_bands.min(axis=(1, 2), keepdims=True)\n",
-    "b48_max = b48_bands.max(axis=(1, 2), keepdims=True)\n",
-    "b48_bands = ((b48_bands - b48_min)/(b48_max - b48_min))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 87,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "b48_bands[b48_bands == 0] = 1e-6"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 98,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "ndvi = (b48_bands[1] - b48_bands[0])/(b48_bands[0] + b48_bands[1])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 99,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import matplotlib.colors as mcolors"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Using a Matplotlib norm to adjust colormap influence on image https://matplotlib.org/api/_as_gen/matplotlib.colors.TwoSlopeNorm.html"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "- A Sequential colormap `YlGn` with a normalization on the color limits: "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 135,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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ZpDsUpisU5GI2g09WKBgGlusgpCrPLc+yo72DoOJnc7KNkWgUXfbR2RlneTFPtd4cftAwLCRZ4uzKKuvVKm3BADf09LKzPcnejn7CagDbtSmbdWI+nR3tSZ5dWubSepp7x8do2A7+kI4sSkSDKsGQD8dx+YmHP8+f3Xor733t7WxrS/HNm4o2PUJPsEpY1biYL5Cp1zEch8FohKCq0hUMUjAalE2T+XKFXL1Ow7aYurqCEIKfufE6HM/luaUVzmZz3N0/wGi0m9HIAI7XQFd87E6lOLS2xmAkyosry+zv9liolJiMpwgoPt46Mc75uWUW1mrEwyrBaIC3TIxTty2eW1mh0DAomSZCCPxyMzt1+GP8+NgWpgtFFitldraBLCQ+eNs9vLn8TyxXq8R0H9d3dbOno50d7R2czqyzXm+QrtWZX1gnEPFj1E0kSRDUFVRNIdERxXbyhCJ+gmE/u4b68CkK3zh+nnNn1jl/aAWCCvE2P1MLqyzOZak1HDpTQVzbYbVSZW9XBz5ZIaSqXD8xyEgszkyxgCpLjETjlEyDoxdmCCdC7N/VhSZJlLq7WanVuLC0yrELs/z4TbswDYtQQKFat3EcD02TMU2HxaUSPl0jHA/hZsucOjpHOKiiqTI+1SGRCFApVCnXbOpX0jw8e5XhaJL1+im2xMcJaRrn8yt0BIKYjk3FNHnjyAQlq8bN3UPc0z8JwOZEjjY9giwknLDL4bU1HNfFcF0kIWhUDVyn2dIb133osoIkBL3DHVQrdeqaRd1qtpYXa81W20Q0RFjT2JRsI77Hz8lLVwG4c3CQrck2vrEwS6Zg8sT8AslAgK5ggLJV40PHT5DLlHj4p3+at47txPEcZCEjCZkXVppB0oVcjuVqhbCmcXx9nX++dIn7x8dR/H4y9eZNTFQLci6/zP6uYY6l58k06jy/MovHDLvbe5AlmS9fnsYf8jM62cPrRoZ4YnGaDx88wr6hAYKqStWq41d83NbbR3vAz70DW/hvZw/x1199FgBZFgQjGtW6jWVYvP3OG/j5yV0IAScWllibz9AW81Gq2nT26wzEoqxXq0Q0H2/dNMqt3fvwPJOUf42/PPXPfO7oKX71wD6GIt8KjpL6ZnZfN8qRY5eJxfyUyw0s170WZAI0HAs4T1AdBUCRQkhCcCmf5mcnN/PM0ixT+SJvGh3FJ8sMRqIcXl3lF7fcxnTpCsuVCjd1dWG6Nn5q4Hl4nknVLpOt11mvVmnYNjmjRMo/AEJGl7Zce32/so0H9tqs1Qsk9WaeWq0dZK6cZV/HvddufL5d0Syg+78PFfZ3IAmdl25Q+e4aziV80jCOl+avTn2Vm7u7ubFzN/s6JvHJo+SNY3z6wkW2tKWZiMU40NNFXyjMYrVAWDtJzTY4nVkmomnc2t/HOzbfydH1U2QbZWQhsVDJMh7r4oEXX+CO/j6WKhUuF4r0BIPct2mC7ckkI9EEdcchqmnIQmA4Nr+9fx+5hsFbRl+P4xX50uxzhH0+7ujrIV1vcClf4JGrc5xdXEGSJKqlGqmOGBFN5S9vu5POQALHa/auyJKEIgSyLOF5HivVGvOl4rVr4FNk/u7cWeq2zSfufS0vrCzx9dmrpGs1buntQRLNVtOyZbI5EWNsay+Ll1cJx4MENQ3LcXj71q3c3fem73qt//DgRxiIhJEliSuL61xZbP735gM9XaiOhON51Gyb8+l1No/10hkKkzfqnMtmmC9XkCXBfcND3NT5OlyvhhWxrwWZmcYR3vvi09zS2817b7iR+XKRmm3TpndyJrtKXzjc7MkwTTbFY2yKt+N4LmWrzhMLC4Q1jTeNjGG7Lp+dusTWtiRHVldJ12uoksRsusy7tl7PZKLjW9dNljm8ssbpxWW293bjVxVqpsVKrkA8GXnZefGHSvC9FJd/yxFgTAgxBCwBPwG87V/sMw/cSfMf3XQAE/wby0O+IgJNWZLYnEyyVKlQ2+hmViWJdKZIMBpA2ugeDCjNt1u3LTRZRpeDzFUWWavXOLq+wm09wxzo7mIgHMPzXN6zdy9vPXIFy/EoZsqoPhVFlVlfzHP3+BhvGB6mL5RElzVcz8XxHE5lF+kIBPHw6AwECaoau3t7KJom+XqDSrFGtmgQ9CtUKwbVioFfV/h06iJ/fMOt6LJG3ihv/K5wMZ+naJpcn0rRcGxOZ7JoskxEU0nqIcbjzYItKvDc4fNIkkSsPUxPZxJVkriymiagqhQMAyHA81xsr07DMSlbNc5kszy7sEDZNOkJBpnK53j/My9w/7ZJ/sPW3dzZO4Fyj8S7s1+id6yLybZ2yqbFwfIqDx47jWXa3LtrC72hEIbjMJXPkW3U2ZHs5dd37uTx+TkenD7D6wbHqdoN7h8fx3QcFCHoCPgZiyUIKX7GYwmmC0WWKxUkScIf0lmYz9PXF8OoW5QLVdbTVaIhFathsWP7GMmAn55gkGPtERR/DluVIGdgRX3MTKfx+2QkSWAZFtePDPCmsVHmyyVmSiU6A34O9PTw2NwcHh5RTcN1Pc5mcrzzlr0UDZMT6TT5hsFsIc/dQ0NEIwFW5jJM5fK8dnITZ9sTPPZ0czKe63oEdAWfJpFeLSIEdPYmSOdXCegK3SMdyHMZLMPCdlzKVYux4Tglw+Dw2iIvLK9yY1eBimXTcGwimobtuiT9flRJoWLVWa2VSPlDCAQ3dPQR0aKk62nCqh9ZSCxWqpiuw77ubspt7dzV38dQJMZSpcT1qXYOra1RylcAKFfqxCNBcuUqpWylOczErzF9aZGlvhLbuzoRkmC+XOKnJiY5m13n1//mIXRN4tLiKndMjpNrGCxXyrxv/34USSKs+rFcm5A6hu0uczp7hS/NzJIK+LFcF8fzmC2WefLKDJpP5ejqGhXT4Ld27yapR1Alhcl4N7bnMhpNcjJ9Ect1eW5xkfqkzVgswTu3bWW+XGFzIkF3METVtrAMC8O2eWh2Fn1MYWuih4FQOwuVEk8sXuJCLkcgrGPZVbpSQVJ9bZiGRb1c5/PHz3Dv4BAJPchv3LiHj6jHiQcDxHU/29vbWKpUifl0/uiGm2nXv9XqF1ZL3Dc8wuZEnJFonJpt4nhlZBFGFmF+ZedOfunMLJZh8alffAt39N7fzCdeDYCUfy8N5wJ4Dg4lSlaWd23bzp8dPcxSqcSH7ngV9w/vpGw1P6+YL8DXZl7gXVtfy8n0Op+fmsZwbD54+13kjCPMl4qs1mrsbE/heC6GbfPwlRl2tKWIqGkiWu+/qjcj2i6CagnHyyOLEB3+3bgcpWAcJ6CmrgUA39Th3/evzlGzzxD4Lq1fL4ckAteuz8uRrqdJ+RWyxjr3j4zSG0xRtdYIqO14nsVUYZU3jY5wuVjA8Tza/AH6QykenjuHKsm06UESus5v7rqRgDJCxZqmJxjlsYVZFLFK3jAYCLexq6ODwXCEz09fIezzsVStIotm8KbLKn5ZxvM8BiJRwqrOeEzmueWrWO4Cx9Mz1CyboKpxtVQmofu4obOTqKbRF46wVq0iSxKLpRJfn51DFoKbu1Us18b1PKJaEE2WsUwbnyKzVC5z+uoSQhLsHOgl7vfze7v3E1Z3Mlt+mreMbufDTx0EoD0QYCQaYcWo8ar+AfyKyn+9/XaeGF3g4cuXSeh+BqJR3r7pp7/rdf7S7INYrkvcp/H6kREekWSytSrpWg1zo7ftTDbL5nicyfYUrufheh6zxQKZeoM7+nrpCUXo9CeaQaa7jOO5WO4ajpdjvpJlV6qd23tHKBgVIprG4bU19nXkOZFOIwQEFBVdUXhV/yiu57FcLRBUVWRJYq1WI12vsjXRy4+NjvLCyjKW41A0THRFoU3XObR6FUVqBsTbkp1sSSYpGib0dtMZCjKVM9jd1clxIegOh3ns5WfjHy7x/Y00Pc+zhRC/RnMJSBn4O8/zzgkhfmnj+Y8B7wP+Xghxhmao+x7P8zLf7byviECzZJqs1WpMJhNM5QscX1qiYpp4nkcyEESWJCYTce4dmuC69nZ+78mnSNfrnM/Pcj6XIVNvjn25p79Z6Itmg1g4xLZkD4oiEdRlqnWL7Yko86tZbr5ugpFohK5gDElIOF6zFWm5kuHQ6ir7OjvRNu4eA6pCyTDJVEskAwFi7RH8cwUapkPDcKg1bOKex2whjyya4xtt1yGkR6jaDWaKJa7vaMdwHOZKZQzHJeC6pPwBLubTKJJgLBbH9SCzZzOXFleRFInheJyHpqfJr5c4UjXYPzlK0TCo+Q1qdoNH5q5guS4J3cf9Y6PULJuvzMywnCtSL9f55GMvcHh5mQ/cehv7u4Z52537mM7nCagqZzJZHn7xFFfmigR0meWxCmu1GiFVIaH70WWF45l5diR72d/dzacvXORZ7SphTcNxXTbF49Qdm0evzjEej9EZiJHwBQmpKmPxOHOpAmuLWYZG2lhZzPO6W3fyladPUm04FMoWqYSPfL1OsdHAcT3W5jJ0t/tJtIfJpcvUGw6SBG0dEVIb4zzfu+8AQVVnqXKWW3t6MByHg6urfOKuNzFfWWGuVGQgEuWGzkHed+RZnrt4hZGeFDPLaSqlGiuFEnsH+rj3jaNEVJVLhSLnLy9Sa9h4XnMMok+V0FSJYCRALlshv14kHtHwh3Qm21MYdZPlqxlWsw2EEMiyzInZBdK1Gtd1dnA8naFiWtzY1UHVsugJRimYNVZrRR6eneUNw8M0HIv1WpWz2SxvGdtC0awzWyqwoy3JSq3KU/OLTCYSXNce5B8uXGR7exs9oSBjsQSut4ZpWBSyFQbGmy1WQgjC8SCO7WA2LOIdUc6dWqBRbdComXzpygyKdJWHnz1BJdcgnNT5z3ffwQdPnGAoFqcnFKEvNIrn1fCwqZhlzuWeYrFS5lPnzvPXd96N4zn8zJe/jGM7jHd1sKu3h2fPTtEZCpFvNKhaFudzy7iex2i0jSulLFsS3bx6YIDL+TzPLS6SNwwqlsn5XIF7BgZwPZePnT7Nu3ft4l379/KJQ8foaU/w219/nLft3MYbhsfZHE/x5ZkpukMhdk4Mkqk1v8zffd11dAdDPHh5is8+dQRNlolpIW7uHuJoX5otyQRRTeNkJsOxlWV+Z+8ewmoIw5khZ6zT6d+LX9nGeBTGo80WNdttIIswy7UX6Q7cyIGuvTz97g6EEHT492E4V/DJI7heDUk0x77p8mbO5x+nJ5jkV77xNSzX5ff27OEzFy8xXykwHLkVXYZ0/XFOpJf53L1v4Rce/ySHTk4R74hSrzT4+NnT7GxP8qXpK2iywplsjpCq4FdU7hsdYSzahSK1Y7u5/6kb/JtsN82Xrx7hmcVlIj6NHW1JrhRLXJ9KsTu1RljtRZU6/tVx33S1vEamPsu+jklcr/5du1z/PaR/MfbypXwzoP/m44CiU7MLfGnmEl2BAFeKeb46O8e7tm2jJ5hhR9sQmjTI5sRpYloQD/DJE+xqz+CTVDoDY3T4G5zJneds9iyqJBHWmvXcvs4OGo7DHx96lvfuO8Bj89OEfT6mc1l+eccORqJJvjgzxXisiq7IVCybuC9EwteG6VaoWBZH1q8QUjWqlsV1qTYy9QZrtTqDkShDkQi7UjrexnjpgysrfPrF46xVKxRNE11WqNnNMdOqJHH/7m0kdB/7OrvIjY2Q8gd5aGaGgUgYTWpek4VykQ+dOkFXV4K3b91CdzDEYCTR/D7UI4Ag5XcIqz6OrKzwnr172BJ/1Xe83sczj/DJc+e5uaeLbW0JyqZFKuDn9SPDPDG/gCQEC+UKhuNwJp3mpya2sCmR5PeeeZpf3bWTbL1BrmHwtbk53rvn1VwtLzAYDuJhkPInOZs7yXq9yqZYO13BIPPlHKcyafrCIa5PdRBSe7hvOMdnL03xe7v3U7dNVqoFnlpapE3XSQUCTCaaeTugqHxl7jw1y+bGri4WKxUs1+X82jr3Dg9yLpejTde5UizRsO3m902pxNm5JTS/xus3jTOZSLBUrnD06vz/r7z8Q/H9b9HE87yvAl/9F9s+9m2Pl4FXv5xzviJmnTe7lsJ4nsdtvd3cOTzM4aOXyawUmGxr43XDw7xmcJyq1eC69t1UCjXqtkO2UUcWAteDomlSMmu0+aK06c2xjLbn8LtvuoOurgySnMsAACAASURBVAiVms3ZC/MkEmHeMDLMh148zC89/jU+euYI53KLeJ7HQ7MzDEXCWI5DwTTQJJnoRnAlSxKu5/HqsVG2bO0iElTxaRJ9PRFGtvTwvv0HUCWZglEmqgXxsHE9l8lknDZ/s/LNNQwCisJYLEYqEKA3FKZoGBxeW2MqX+DOgX4kRWKypwvTdjBsm/27JtB8KlfyOb52dY6/PnOUxWqRbMMg2zDoD0cYi8Xpj4TpCIa4dG6ZLWN9hBMhjp+Y5qOnT/HpiyfwyTID0ShHV5b55yeOcHm2gCwLDhwYZywep2Y379aTup+6bTFXKlOx6jy/vMzp9TXG4wl2t/dyOp3hjx55kj/56pO8cPoyluPieC6qrHDPwDD94RCO6/Lbr76Zns4kd9+8nZ5wmGrDQdckomGVhuly+Ohljhy7zBPnp5hdqjB/pdl6rciC7t4owxOdaD6V7SN9xJMR3vH1h/jGwhT7u3u4kMsiEHQFA7z76YcIKD5u7BzmGwvzHFy9ymPHztOomUzNrZJbK5LNVDkwNMjmeJyJeIyVWo3nZq/SqBpoioQkCSRB8+bBdDENC0UW1Bo2w5O9JDpjRHWdTd2dOK5HR0KnvzNIpVjDdV2u7+pEVxQ2xWOka1U6AkEu5nP8+pOPEdF0JmLdTMSj+BWVh2dnyDTqPDQ9zaX8Go7nosvNCU1RTePHxkf58vQ0V4pF3r5lM//94AnGYgleXFnmxs4OEh0x2rtiFNIlitU66aUc5XyVN+3YQjFb5vixRXRNRlJkqhWDg1MzXMyk2bJtED3m44O/8Hr+8MlnePXgIG+bmKAvOMgXZ56lYpX4P57+CsvVAh87dZpPnTvP3NU1fvnxR/jyzDT3T24iv1bkSjrT7Fa86wCW4zAQjWK6LoORBDPFIs+vztOwbS4XVhEIUoEAv7dnD3f2DfDVq3O8ZWyMhUqJQ2trHJuZ59DqCrf09PIXr3kVB3p7yC7nOZVO06ZHOZ1ZZaVW46mrV1kql6hZFu2BIFdLJboDzWCyVrM4srbSnDBk1zm5tkrDabYqzxaK/F8HDrCvYwBdnsQnD9MVuAGxcUPYcC5RtU5RNAvUHYOr5WcoGFWeWf4y5/On6QzcQId/H+v1wyhSCs+zUKQ2JOG7Vnc9PDuDLodxPdjX3c2HT57kci7L4dW1a/sMhNp57cA9CCCm64RiQYyaSS5d4alL03zo4FHmM/lrN3In19aI6jqvH5xElXoQaCxUz2A4/7p362Jhmk+cOs3jR87x1OUZ7hvaye/vfhd39b6BuLaTC/lTL1nnGs4UAJtiNxFUVXzyKIfWZ76nFsmXy/OMa49dr4pf0Ti8Psdjs1eZLZX4wOEjuJ5HdyBBQOlEEV0UzAu06yOs1HJoUhfpxgmqlklf6ACms8r7j32V9x8+TNWySOg6AFuTzeBlZ1vbxoRPmWcWl+gLBfn1XbuI6Tp/deoYB7q7+YdLl5jKF9mWTBJW/YCLLGR2pzpYqpT5wpUrrNbq7O/q47WDQwxEwnxjo7t3plhgKp9nuVLmhaUlfAEN03F4fnEZ03H41LGTBFWV+XKzdbsjGMD1XM5mc2xJbOHtm7eS8vs3hh7AjZ17cFyPSqM58Whbspc2PUHK34kkfAigaFaZKmTpiUS+a5DpeS4fPXWKzmCAT5w+Q8znI6ip9IUjrNRq9EUj9EXCPH51jkdnZ/nT/TdjOBZD4QG+8Lrf4s6em0kFAryqv4/37XsnmjRKyh/bmAgHc+VlEr4AM8USn7xwhpJp8udHDnNzdw8xrVlOTmdPsCm6h9cNDfK5y6d58PJZ/ubsWe7q6+f4epqja+sbY8ttVEniNf2bubGrG4CG7XAhl2OuWOBrs3NsijeHuPzkxATdoRB+WeO+kSG2D/bQl4hhOC6m6/Cu7dt48Mfe/H3Lsz8wQrz8nx8B+YEHHviRvPD308c+/sEHbn3LXjYl4jTs5hjG80aJVGecTckkbx7dgiY1ZxxCHSUoYzgOHi5fnr7Cc+emUfwqDi6bEu14NMcU5owKw9Eo/R0JHj99mb6hdtrDIc5kMkxdXOS6sQEimsZfPn+IR5dnGYhGmUwmcfGwHJeCaXB0dZ2abbFcLqMrCnXbxlEEelBlcbFIrW5RzVdp64qxva0DRVKwXIfDa7OczWaI+rSN2XMSQ9EImxMJdrb1kfCFyBtV8kaDzkCAv33+KE+cnmJ5sciBrcMk/TrPHb1IIB5g5vIy/oif+WKRoXicwUiY/nAYVRK4eEwXChzoHiTqUziZXycaCrCrr4cCFtGgvzn7Wwgs1+XslUXml0pEQxq//dbbedPYGOBRNE0imkbDtSmbJnf3j3OpsM6nzp/HcV0cYGd7J584fZz8WhHP85BlmRmzyqnMOjd2daFIMl3BELs623lwaoqIT0eWBJ/6ykESUR8BXaGrN85nfu7H+OdjZynVbIyqQSKi0dkbYWk+x+jWXgrrzclvifYoP7d1krdPbmOtUWciHuf/PniIv3jwGfZvGySqaYzFY2xPjpJp5LhSzPH1q/NcnV9DkiVqpTrTs0WCAZXD5+c4sG2YL09P89TpKfLrJWRFwnFcJEmgqzKaKuH3yaiaQiQeolEzsE2LWDxETNdJ12o0TINKxWB4soeB3nbao2Giup97Bvr58ImTDMXiTCRilEyLkObjz559nvvGR1mulVElwc72FB2BEJuTCdL1Gu/56uN87rkTVPTmOOWYz0fVdvjlbXuIaTr9qSib4yn2d43wt+dONmeVy4JKsUZ6pYDPp/AH997OQ9PT3Dw+Qk0YLK+UmTm5SmdvmPX1Mh1dMX537x76B9qYSMT5nb0HiPgUco06YU3QH44hEAxGg0wmesgYZS5ms1w31M/VbI6HnznNN05cIZEMIkmCimdTsW16I1GGo1G+MjPD+VyWhuNwcHmFVw0M8NTSEnXbxMVjUzzF3184y3WpNiZiHVzIpzmXzeHza8wWi+zr7GIw0kZC1zhZK3BlaZ3/85+/zi/cspu5Upl0vY7tuji2g19TkSWZR+dneGxqmr/9ifs40DWC7Tn8wqNfIabrrNXq7Ons4N07X8Ozy5e4oeNWhFD/Vb2jSG1ocichtZeg0kPMN0C7f5iB8ASdgVFcz+CR+S/xnmee5K0TQ9daBm03jeNVAI8PHH2ET5w6iOW55OoNFjN5HNfjL267h89OPc6mhMqj8xcYjvqo2wYPzVzmj27dz6buFBdKefJrRWRVplaus16pMJvOImkKbxwfZTSaRBI1LhWm+MPnn2O6tMaeDu1aOjzPpe4s8xObtrG5v51fuW4P4BJQehBCYHlLpPx9SC8x83yldoGg4keWYiR1GUWKMxjehBAqrmcghILjlRAo1wLzl8N20y/5ugCOV+JK6TBXSheYKc3TF+rkf0yf4xtHzrFtsJeP3P5zrNSWGYnGCShxVKmd8/lTpPwxor4URXOepWqepB6kYi3yhZkLpAJ+bu/r42q5zNZkkk9fuMgTM7McWVpmtdHg+TOX2drfzkQ8TtWyeHZ5hZCm8BNjkyhSc/WE3nCI27rHSDcKhFQ/OaPE4bVlXlheRQjBzd1dPLYwx/PLK9zW28emeIKqZfL00jJXikUiPh/L1SozM6v4gzpvmBgjoCpcLBYYS8RZqdW4mMlyOp1GSBIfeehpPvrCk5xsFPjM88d4176bkaUYsggjSSs8u7DI3u4uRqLtGzPaLVzP4mplnbPZdXa2dfHW0bd+x8/gWPpr3P+PH2JLdycVy+L9B27jE2fOsDPVTlBVGYlGObae5umZq7xjx1buGxnF9lza9DBRbQsuVR5beObaCiMrtTnmK1O06SEs1+SF1Rkimo+x6O3sSe3kHy4+QcNxubm3h11tfXQEokQ0P3XHJOZTUCWPpWqRsmnxuuEhFiolbunp4Y8f/gbdqQRjsRhrtSqfv3KJxUqZqNZccaZs2rxueJgDPT0IAYfW1jm4ssLrhyZoOCaDkSQzpQK263JTVyeu53Eum+NEeo3HP/70ygMPPPDx73iRfoT++P1/+gADiW+N1fz3/lxO/9Df0ysi0Pz4xz/0wM437MRwHBK6zmSigx8b30wioBHWNPyKhItDTAtyND3H5niSQ6urGI5LzK+zUq/SFgxSsixkySYVCFI0q8xXSgxH2jm0tsSJmXkURSJbqHD05Bym5dLfl6Ru2yxl8/gDPnamUgRVlajmI9do4FcUIj4NISTGEwlsz6M3HOLE3BKrC9nmRBe/Qiisc+umYcaiCZ5anuEDRw7ywvIyS5UKCEHdtlkol3E8l9FYgrAWwPFcQqpO2arzwsoqp2YWUFSFUtnAl9DZkUrhiwV48cgldu4aobGxBM2+nm6iPh8PTl1mT0cHQbW5xM9qtczWZAeqX+X42hrjiQRbO1P0h8MIIRiORjAdF3wqtrD4xVffyP6eLnKNBrIk0CSZ4+kMN3R2saOtObbLp0iczKRJV6tM53K8cWycuwYHeDK9gupT8Pk1DNvGETBfLZPUNV5YWaY94OdtE9t5dG6Wz3/9BOGgQudAO57n8ZmffDNJPcjkcCfPX57BdQEBwgNJArNuYjQsSqUGmbUiP79/NwWjQcrvp2qZ2MDc2jpHV1bIC5df234jObPAfz1+hIfOXmR6ZplAWGf7eD9TM6tMbO5g86ZeaqUqVZ/gpt5ezi6vYdRNDNO5tjxQd28Mo25i2S6G4aBpMqGIH88D23awZbi9v5+jMwtE40HK+Sqnzy4xO5/hQ2+8pzmGWFWp2BYlw+Tmnh72dfRw80AfK7UySV3nf0xfYWsyyYurS1RMi7969iDlQo2GYZPsiBLUNLINg55QkM2J5pfLpngHludwIb/MDV1dPLWw0Fy6SFMQePzGqw40v8yyed4wOsJwKsmb921l355hnjg+zS37N/Gm8XH8qsLlQpHPT03RE/YzEI4hBKzXynzk1AlcLO7o2Q0CZkvrfOqRwzzxwjQLZ9epAbGwhmXY6H6V0c52nnjxLB+9/3WENIUrxRLdoSDpeoPecJiRaJS8YVB3bCqmxWQixafPn+EnJyZZqhY5vp6haBjcNdBPd6g5a75mG5zNZlit1dB0ja1j3bx2cJi5cpEL2SwhTSOXK3H+/AKjA53Yrsvv7tuDX1Gp2A0s1+ZCPs+VTJZ3bN9KyTR5aPYs797xzpcMMv898uYpdEXwM5t3okrStQBPEkEkfBxNP8nx9XUGY3HetWM7v3ndvRwYSJC3Le7u30fVzpIzyuzv2sxCZZWLhTQPTV9G1zTuH95MzjW5tLKG5lNwHZdapYFRNahU6vzs9TvpD7fTcAyOrC8hSwqTiRh+VRDVwoCC5c6hSpDwXc9EbAsx3wCH1o4yGB7B9jLIIgLYLxnw+WUXhILj5bHdIvPVC0S1ZmutEMrG+/QxVXwSjwxB5V8uxffdfacgE0AWIZL6EJ2BTuI+C1l4/L/nTrGaK/Ind91OwymyJdFBRAvgeFVcr0JfaC8uVdbqsxiORWcgxtVyhj8/eoTVWpW7+vrxKQoLlQqO57K3sxNPlhhMxFmplCmUa9w6OsR8pULUp3F7by/94SgAj8zNcHh1jelCgaeW5lipVekMqtRsg0+eO8fbNk2wK5Xivc8+y1Q6yz0jQ6zUKnQGmz0XkhB0BAJoskShYVKwDXqTMcbiMcKaxsVsnqCmoQiJpUqZ+YV1Ti8ss3min2KlRjQWxJVgtpHmhk4fmtzJSGSU9nDzvfSEohs9rB4nMvMcWl3lZyZ+mqjW/5LX13CmuO7Df8CzmRX2DTVncD99YZo3T05Sd0wm4gmeWlykze/nYj7PDb3d3De8iZgvwHy5QEL3E9UGcL0aU8VZBKI5PMWns1Qt89enj/Op86f58YlNjEa3YblLuF6F3R0xnlpcYDQWZTyW4kuz55gt5cgaDUynzmK1xIOXptjX1UnFMnn06jyv7h+mrS1M1bLZ29nFbKnAG4cn2NfZT91pNBs0hOBkJsudfUMcX1+lZFo8P3OVy5UipzMZbu0ZZEd7CklyCCgqd/dfR1dIYXMiyQff//n/dQPNP/vTBxhMvvwWzan1VqD5vfiPH3jfA/f81E30hEJ0BkKEVT+arLAt2UNYk3nfwReJ6xq60lyn7HQmQ1cwwHWpFEFVIW0YzTX6Gg0GIhHqjsXnpi5zuVDA9kz++9lzeI7L7k1DaAGNaqlCKKhxYXqFmrDR/BoRXWc0HqNsNdef0xWFdL1GvmGgShJ120GWJCQEa0adYrZMsdJcgzOWCPKHt97G350/zan1NFXLYm5hHUuBR587y7xdw5MkorqOKkn0heIIQJFkLM/k0Ooq4VgQya+S6orSMC1yhoHjedx33RZs12M0mUSWJCYScYKqQt12aPPreDQHcGcbDQYiYbYlU5zOZkgFAoQ1lZJpUTRNJuJxPA+2tCXZPdBLV9DP8yurZOrNWeeZhkFYUwmoMqoEIdWP53lkGlUuZrN0hEJsa0/SFYyxv6+LY9k0fl2jXG2QDAfRFZW8YVA2LUKqgioLvjY7w9x8lnBQQ9NVbt06Tm84SK5R5z89/DiyIuMPaMiyRFt3HKthogd9KKpM92A7Pp/C5VqZ3nAIXVa4mC/w+qFhFoTJvqF+3rvvFuqOwf9z4jBbknH29HZzrpAD4Ddv3Ms8DXS/Rq5QYetYH68eHCSu+/CF/Bh+ifaOGJ1dcYIxP7ZpIwmB36/iOg66X8PzPPLZCrG2EJOdHfSGQlyulsgs50EIanWbZNRHx//H3nuGSXKW996/quqq6pzj9OS0s2E2J0mrFcogEQQiyREcsDk+mON0XmOfY2P8wnE6zpjjhA0GgwCJIBASiFVYxdXm3dmZ2cmhezrnUNXVXf1+6EW2XoIBX8fYXP5/rblqup6ufuqu+/6HmI+nEgleNzaGU7aQqNf5P+fP41QsRB12HllbxyXLCAJYRJGSrvOhp0+hWBU6RgeHS8XmsJJpNBh0uxl0u6i1mthkiXPZBJfzWepGj+91Yn2dSkMjsZTmhn3bWK9WSTWadLtd0lqT06kUcZeLZL1BIOrmLVPb2BEI4JQVLCLsCgWZK5ZYq5YJ22x88uoCo14Pl3J5PKrBbHGLzy8uMbOwRbsL3Y6JbJdpaG2sqkQ47iPscpHKl1npNNjm979UZDpkmVePjOCQFT6/tESzY7JVb5BuVFkplzmbzfDQ4iKpep2FRJpLpTzPzK1QFbt84tIMNlXFb7Nx80A/Wsck26xzc/8Aj62tMXd5DUEUObJvgvu2beOOoWGuFHKc2Nzkqc1NCnqT+7ZNcT6fZVvAjyKJRO12Jr0j33WhaXYbLFaeJmSNErHtx27pA0zKravYLL2RniBIxB0TbPPr3DowzJ7gHYg40TtbQIftvikidpH5UpozmXW0TpszmQy3DA7ilC2MevzkmzXObaWp5KtUKxpCt0tD6yAJ8O7jN+JTdpPTVknWK7yif4ARtx9ZlHBYbBRbSzjkEazSAIIg0O32xByDzhhp7RIX8wtcys8x5g4jid5vuEZJ9GJ2yyhiP4oUxa+OvIwS8HXYpCoL5Qxxx8R3tYYA86WvEbSOfsvjvfFrlVanzWKlwHKhgKlK7A1GkASRnF7GbrGiiDEeXvsSm7UtonY3NouKzaIQtrvZHfLS6XZJ1Ots1ev0Ox0cDPdhmG0ORqLEnQ5eMzqG6rRyQ18/IbuNvNZEkST++tJlXju6nbO5LWayOaYCQUJ2G5ezObb7fXx+eQWbRabP6WCxVKJhmtw6OsxcscRKucyE18tyucLuUBCbRcIhyygWC5eSKTSzw/NL6yzWq7x5ahuLpTK1Vov1QonURoHNrSqdTotUukYhW8bhtKIJJoLYxqUUccoiMYeVR9aW8aoWmh2NnFblic1N/tven/qWa9rpVnnXEx9HdijsjcboduH2wUHunBwl3azhkGV+/YmThFxOQjYrrxoe5daBHVgEAaskE3d6yWtVkvVFLheW0Dsd+pyua5M3F4okcjga4yurq7xmbJzz2QUu5DYwujUsoshrR7cz5YvzqYVzfGVtjXGvj7DNxlajTtzpZKvR4GAkQsMwSDTqOGSRLywtEXE48FtV3IpKzdApt5r84/w8m/U608Eg4x4PVaPnNLMvFMJptxJ3OvnpXfuYK6bY5t2PR9VZLBeY9PrwKlEC1m2877f+9N9vofk7738vw99DR/P7UGj+QIiBzI6JT1XZqtUZcLqxSgqiIFBu1fnM4lXWc0Ve9GTZrNUp6jrDLhc+a+/mzTSa1FotXKpKxOFgu9/P+VyWpWKBPeEIRV1nxOenpRlUdA2nojA6Fb82LjEp5Cr4vE4OxKLEHA6KusaXVlYZcDoQBIHCNfsfvd0m7nLhVmRGvT7knSLF5xexSAJDA2EeWJxjo1rh69nzk8MxLsyssmvvMB+48TifX16iY5po7TYVo4Fb7vF0+h1BbBYL2wMBVnMF3nPsOiyiyJQ3xMV8imOxUT61eAlVlPCqKtVWi0OR6Etr9/jmJgCqKHEmk8JmsRB1OjBMk7ym94yAHQ7Kuk7EbkcSBCI2O5fzeV5IJAg7ep0Hp6IQsTt4MZ0h22hy+6Adh8XKzkCAp+123KrKo2vr/NfdEeIOH2/buZOLuTwpl5uY08mw28mhSIxH11a5lC/wVGKLn9+/H4csM7eS5PZd2/ixqe0A/MinP8ufveFunkok+IfHT3Hj/m3UDYOQ10WuUsNq7fm76aEO94yPMuH183QyQdhmw6ta2R0KcffICE5LkK9svMhGpcyRaJhDkX6CNhu/8pkv8+5PPMTOHcMMejzIERHrNccCAYFxT6+Tsd3nw6TLl5dXWC3V6RhtRItEfCRMIV2mVGpikQQMvc0dQwPcEBvlQCTCG/7iY1hlCbtVQtPbfPzCJW4eG6Woa4RsdoZcLRJrGR5zODgcjfFD27ZxYmOdO4dGsFkUXkxnGOsPY5cVrpKhpbWo1Jo0axrq4CDDLg9ns2n6nC60dpvNWo2vra4SdTrJZEvktkooVpmrmWyPCrKySTFTxu13sndiiLjTQa6pEXe5eWhpBbeqMux28arhEc5m0mxUK5xLNYg77Lx+bBSPqvI10+TE5iY/sWM3AHMbWyxczbDtYD8tzWD+ahZ/wIHdpjKbTDE20Yfe7hU3a5Uqjyws8nMH99Pn8FFpNai2Wpxe7YmSHge8ITcVXadW10iv9wSOm2t53vPGW/m7Z88wEA8StFm5mM0SsdtZr5S5Y3CAgt5EaxmEBwLcPjXJO3btwSoptDoGcaeL9WqNjtnlkeVlDkejeFQr1VaLPocTQfgncYrWmefJ5AxlXefN4/cBvCTw+Yb9qKvTMtvU20XUax7Ushij0lrAIpzDreyjY5aQRC9T3n1YxGDv3hJEYvadvH70AKJgQ6aJLCzxNxcuIksS+UyZ+3/ojTyyusJmrcBGrY6h97hu/qATvdmi3TEwzS49e3YJh2yjZrQZcQ1Q1AsErANYhBgh6xB6ZxlFHALAMDewiL2iMmLbQ8xuQ+8soUhD33LflcU4LXMDRRz4ln/T7poMuQKYXZ0urZdEPN8JJjzHvu1xSXBSM5rUjCavGx2l1enw2tFRGm2dglbkY3PzOBWF3zrsI69pfHJ2lncfOMCt/Uf43MpJCpqGR1GwCAJxl4u400Wu2UDrGOzwx3HJe2m0LyEKIncMdijqTQAORmIMOOKEbQ5Wq1lsksRtw0MMunrXtivgx6taubk/zlyxiCyJPeW6281qpYJLUagbLZL1GlM+H8vlCgGrypjbw+VcgXql91sWJZGKprFerXHH0ACzhSL5ZoO0mkVVJAr5BjfdMEW7a3Lx/DJry1mWV1L8rmlybPckv339TfzEjr0sVbI8mejt87+0775vupYtc4P/98WHWC4VGff5uK4vxt5QiGe3tqgbLRRR4j1f/CrekJuJUJBJr5cjkZ4oz+y2sUpx2t0Mc4U1XIrKqDuCKuWI2v2cy/VENVVDY+taQd/QWrzvueeQJQmPVaXf6WKumEMRLaxU18jrOq8dG6Xf6SJkc/D3M1f44C13cWu/hipJpJsN3IrCkMvDsXicuNPBVqPO3mCEj8/PcjQa5ef3HmC2kCFgtdGlS90w2BUIUTNavJBMMubz89DKHANOFxbRR599B/eOTbBavYJH0Si3vq1jz78DfP84l98tfiAKTVWx0O90cTqTYaVS4vlUkrjTybDLw4NnL/XI850Ol3M5aq0WK6Uyd48OUzMMdgUDFDQdRZLQOm2ulooMudzYLBaStRqvGR3hTDqDZJG4eGmFock+bhseZncwyEyhwEPtq9w8NIQsijyzlWKjUmZ3KMQNff2c2FxjOujnY1dmcSoKdr2XUPDD26cA+Ad3j3M5WyjwbCLBRrbI1lqOlmEyvqOPI3smONoXY9q/n69trONRFSyiyJVCmpDNxqg7gkUQuWdslB//+IP4wh76nT2vMZtF5Zb4JCZdXhEf4GI+S6taw+Ps2T2pksSn5hfwWK0MOB1s1RuMuD1cyOW4kE7T53Ix5HbT53QgiyINo03QaiNgs1G5ZqxeqzQ4e2qFeJ+bG6YnKGoaEz4viXqd3zn9PLcO9DPlDxCyO0jX673C+5p106DbTb1t0O9ykKg10NodQlYPdouFmXwer2pFBH5i1y4uxmLcN7EDBAERgTcd2I1DlrmYyVCr6fisNoJ2B6ulIjeNjfBiMsk2v49+p4PL+QIfnbnC3kiEm+JxPjxzmTGPmyFnmIy2SVHX2BeJ4FZUZvJpzmaydIw2ptllq1DipsHeQ9Qty/zxiWf4mZuO8tDiIj++q+dJ+JfnL7B8JUG4z4tpdhG7XVJrWQqVFopFpCNAMVPmxEaC2/qnmPTE+Mu3v4GtWp2Pzszw7MlFzPkUrWaLZK3GuM9L0GrF6baTbzS4mMtgduHe8R1UjQZmt8vhaJhnNzdIlipEvG4WlpNUi3WcXgcuRWa1WsbsglO2cjabPzUhEQAAIABJREFU43PPnadYaDDbhUjMjaJaiA33Ruvzi5uk0zVki4iernCeNRpGi9uGhijqOg2jxUalTEnTOBKNktM0Ts0u098fYr1a44bYMKIgctugwMfm5livFvGoKuGQl0ZNw+a0MhoLIUo9y6pKpUHA6yTi6PFM9U6b33/gBFrLJHz8GD/5lS+STBUQBChmKjT1DvsOjHLl4iqi2EuDKlZbdLvgdcq8eWIXOU3jybU19I6JJIiUNJ1j8TjPbKVIVKscGRjgVcNDTPkieBQ7G7UsLsXO7kA/Dy4u8drREXYGfFzMZTHp8uDVBYY9XnYGA//MutjkqUQSvd3hjWM6oqCS19J41cY32PtYRB/bvTfwbOprHIn0veRJOew6TtvMAlwrBHs8z263gyD0KtJSa5Gg9RAF/QwuOcp0sI9d4UhPjBIOs1As9LrqkoU7Bgd4+Moc7XYHXTNAEHjDLQdxyjJBaxBBEBGA0+kMN8VT6B2DgLVX0HbMEhfz8yjSAlPeMVRp/KXPLwo9s8xvVkT//9HtGhT1M/jUA9/0uMMyQEGfYTP/OPuDr/wXz/fPIQoqWmfmJZP5b/zfJobZJmzz0ukWaLbbnE6nqbZaTPn93DwQ5/GNBCcSZ3jLxCHuHBzHrTgQcTCbL/Ll2XlkpdettqoyB2J9pGo13KrKT+/axen0P1DUdY7GYpR1nb84f57bhofZGwyid9Yo6zoeVeWese0sl7M8sLjEdNDPdbE4mUaduNPFbKHQs91rt9kZ8DPodPGHZ86Qbza5PhajS8+e7og3yqNrazy+torL5+CXjx3lT06dZnMhRa4vTrJW57lEklSxTFNr47Jb6B8N8/d3vo7HNq/yW2tpfvjQNMvFIslyhWStyv989kmcisLNA3HqhsEv7/vpb7nWb/3S3/Li6QXGd/XzvuuOIQkin19Z4NaBAQqaxmcWFjFabRyKws/snsYqWfiTC6d488QEoiAStgURu3Z2+S2IggdZjDDovERer2B0TNaqFewWC7Ikcn1fH6dSKV47NooiSQStNlyKyheWV+iYXdyKwpvHp9isl2kaBsOuSe4a3cIwO6xXq/z1pUtcF48z6HIStLoo6r2pod7pcEnI8LrRMR5eXcGtKPzVxYu8fmKCu4amcMojdLp51msb/NqRo5R0jZrR4njfNBfzX+GhlWXevec6AMyuyaV85ru6X78v+I9RZ/5gFJqabnC1WGS7z0eq0YuQqrVaOGUbVocVRZKotXRmEil8bgf1VpVcM4rVIjHk8vLKYRmrZCFodbLVKHMpl0MUBM4vrjNx423sDGwRdThJhEPcPjRA2GYnr2tsVmu8bXqa7X4/n1teodhssl4o8dZt2/jCyhI7/X4mvH6e2kxSNwy6XTizmeCOwQH2BGMcikS4mMtzQ18fD1y9Sj5ZxOFUcUkirWaLFy8tXuNiPsq4x8Oox4tdlonYfC/FDH51c45kvUG13OTVh3eTbzbJaU1CtgbTgT4sgkTY5iVkq/E3mxfZrHkZ9/qIO3pmtEan01OgN5s8uLhEql6j0tAoVupUdZ1hrw+nbGF3MEC/y01ea3IqnWatXGFjJUe52mLCacWuyHgVlTOpNPObKerVJheSW+SSBfpHIvzGddfxnsef5Icf+CwHRwYpaL3oSbeqslnpWT+dWO+9+W4USuyIRXhwcYlfP3wDXtWK3+rF7Ha4+7MfI5socHF7lkSxjNOp8vCLl3C4bBwaG2LE7ebJpRXGPB6e2UpxdivJb15/PQ+trFJvG5h0uS7Wx2wxiV224JBl7pvcQVFvUG8bPL62SrcLSxtVjLbJZ23z3DI8wkKxhN40+POvPs1f33cPn1lc5HwqxczZ9Z5yfrOI7RrvVNMMIkE7uUITn9dKvd7is4+f5q2TE8SdXm7q20m32yKnaWQTBfSGTq3c4Gd2T6NKEquVMtv6owTsdrR2h4v5PG8cu57ntk4x6fNzKDzEiHeZFYqsbKRx+52U81Vk1YKIQMNos93v43X/8I/MzWcQAIfNQrluEB+0EOp3Ioki1VKdyECQqYkBNtN5ut0uyY0CzbpOolgmlyzy86+8kY+du8iVQpWZeB+HIhG+3BfAb7WxWqmyWM6yO9DP+59/gSeenmXtWJkPHDuGRRTJZmrUKk1+9e1v4I+bzd7/FDX0TodaS+ejJ0+TSJYpLFWQozb+yye+QLmsUaq2gB6vs902uXJxlUxBY8fOKOfOJ2nXDNxBK029gyrJvGPnXq4WChT1Xqrao/ML/Nj+3Qy6nKyWywy7XfQ73cwUUgy7PYRtXmaLW8wVC5jdLr/1zDOEHA72R6MsFgq4VZV0vfZSDChA3agzm83xzr17EAUVvbNExL7rGo+Ra7ZF/2TNIwp2QjY7Zrf+sr3KIoY4l3uUR9fW+JX9P4QkOGmZS2idOnmtQtjmvXY+k063QrlV5527d/NkYpNbBwZxWKwErB62GjksoojeaLGVqhHyWQnG/eQbDd528MBLHEefeoA/OObhVGaWIZcPWexxJX/j1Mc4es26Z5v3e4ixuwZVGkVg9Zsea5tZLGKIVqfNhy5c4A+PR3HJe7/jc5vd5rcsMoGXCumlSgqLKNIwDD703IuMxkLEHHaOx0cp6TqLpRJjniQTnptJ1J/lxOYXeXhmDgSBnzmwl0mvnz8/fx6PonDr9ik+dP4CH74yw4TXey3RJ0ijrfH7x29iwBlis97rqI95QvjUPhRxAItwhil/nkORGBeyaQI2GxZB4jUjE5xMrjPp9bI3GOFcNsWrx0YJ2exsVKvMFopMer1EbG6StTrNus7+oX5eER/nocAKzZrGk7MLfLmmsXtqiDdO72DPzcdxKSrTgWEEelHA3pCbX9l3PV26fHj2HOuVKjsCPia9Pl7R9xruHvr2gqyF5SRDk1E+fvc9fGT2Ijv8Ps6k0vzQ5DTrlXVGPW7ecct1PLS4yLR/AEGQ+KmdKtWWTsi6j263gyjYUaVJzG6Tk1sPsVQq4VYVkrU6TlkmWa9x9/AOFstp/uvevfQ5fKQbZYJWF17Vw6nkV0hWq/zE9E7OZJJ86upVOqbJJ+bnef7yEuXbWhyOxIg6HNwc38v7Tz/ChXyS7X4fUbuDTKOBJAo8ldjkQDhMv9NLUdN4ZGWVW/pH8Sge7l98mHvH9tE2DWzSEDl9ntXqGjt8+6i3DTLNEn32nttAslb/tmv2fcf/HcP2/yv4gSg0A047zU6HtWqVxVKZSZ8HrdNBkSy8cnKcvKaxw+9nvVwmX6zSNnoiDo+i8JHZGV47OornmrotYnczMhrGME3apskvn3yMWwb7uX1wiEyzjl+14ZAVXFoT20CvUMk2m8iiyHKpyO3jY2SaDVbLFTyKwmKphEtV6Xe5OJPaYuqaJ2bYNoEgLPLc/DIXklvUSnWcXjuNSpPR8T663S67h+PEXS7uHdsDdF/Kje7SRbiWV/5MMsWzq2vYHSqHo2GsFgsuWWHSG0IRLbS7vUznXf4+fv7AfoyOid1i4YnNdYY9buwWC1uNBpWWTt0wsFks7IpGkESRfLNBolLhaF+Mj85c4ebBQaIOO2W9xeOnZhAE2Le3j2DATaNlXEtc0jBNk7bRodPu8Labj/D05iZ/ceEC999zL8f/6ENctCoM+310MLmw0hvpeK1Wrq6lyGz1bJN2xaLsCQV56xce4I7xMQZdfi7mkuSSRRpVjZDdwYKWxuGy4Qm6uGtsjPtPX2Amm+EXjh7iqUSSS5k0+VyFbLPBz+7ag11WGff4cco2lsubeMwep0cSe93sf7gyy8psAptdRVEk1jdqJLOLLF7eRG+ZFCotJke93Pl7H0UUwWIRabVMHDaJWqNNrdEm2O1ikUQaDYPh0SC1Uh2zC+Gwh9lCgSFXgPVagmbboNnu9NapY2IBrJIFt2JFFCpEnU5KusZnrl7lXfv3kdOS3DP6ZhrtS7zv1BPM5XOUc1V+85U3E7DZqBsGDywsIAgC/3jlCk+fXECRe24Bsizy5jsPcSAc4hOzszgVlVpL56/uuZs+h48XUuvMxXt+lZlkEafHzpt27cDc2WXE7eYN0zvINTW26g1eTM+wsrjFzuv2cD6d4oGvnWYr20SSBMJ+K2fOLvAHNit2WWZ0W4Qf3rebPYE+VMsFwg4HmtVGSddYTmS4dDkDdQNkEVWWaNR10nmNTquDardQqraQRIFCpUXYb2V+Lk27buAJ2RAEeP7/eSdns+vsDsS5Y2iIj125Qq3a5HXT2zmxtk7dMDgYi/HQ4iKfnpvDLst88NY7+I3nn+QPb3wLB0JNMo0TbNod1FotzqZS/OzePdw+MIlLHqLdzb60xwiCwGY6j1tRAFiprjLl7XX82mbxm/ITt3lv5Yurn0GWLr4scWVf8E52+RNIghMAVZqk3DpFsl7hVDrBA1c/x4FojB+d2kW3C2PuUXb4buPOB3+N991wAwGrh7d96Yt0Oib/5aajNG5os1KucFO8D69VZcQVptLa4nzuNMf7DiCLfVwfjbxsbP2TO6a5WsojCgIvZq5yY+zl0ZKdbu2lz/cv4VJhll3+Nqo0/rKC2yKG6HR7dm13DA8hi99drNDXO6utzto3HeG3OmtIonRt35IZcLuwTozw/qO3crmwyQupde4Z3UlOKzPsmiSvnSZm30vckUFWZaIeF3/85HMoVoX79uwiaLPytY1NVItE1G5nu9/HuUyWX3jyq/z29cfwW13oHQNJEAlae/SZp5JneHDhfn5h/wH2hSJ8emEBwzR5+46duGQ7htlhyu/nxMYGy5UiRV1npz/AUrmM3unwyqFhXIrKeq3IcrHAL914lDsGttE2O2htg0ZNI9jnY2IgysJGiotza4Tifh5+w4/QNluUW0U8isovHjmEUx7kVOZ5jI5JwGrlxr5+dvhu+86+w3f/E2XP7F4gUaujWiTaZofVapWFYgmz2+V/XHeUqtHErTiJ2HxUWynMbh1J9PJ44vN8ZX2DHX5vz17JMMhpGn0OB1N+P6uVMl/dmMenWjmfzVJuLbFSKnPH8CCjHi9TgSBOReGZ5Ba3DQzQMU3sssKtgwNMXTNWjzt8dOlyMT/Pa0ZGCNkczBayPLSyymtGhrGIImuVKh+bneVPbhrkFw4eYH+oH71j8Njm57lv4g10MbBKblrmBv/95AneODnO/Vc/w28cfic14wJPp5bwqzZePTLOu76rO/b7gP8go/MfCDHQn37oj957/b0HObG+TrpexyYraO0Of37uNCMeD0GbjX6ng+2BIL96/XHsToWI3caxvhECNgVZlAjZ3OS0Ki+mk+S0KpmmhiCI/O4Nt7HNFwK6nM5s4ZBlOl2TnNak3jbQ2m3KrRbn0mluGx7iKysrbA8EyGs6JzfWuZLPs14qMRnwo1pkvFYr414PPqvJ75x6js3VDKGoD8Nos7FWIBB2o+kG22MRHn9hhrVKhXjAgSj2xER5vUJOq7BSyVNvN7lzcIJPzlzmlultHI72/BgHXV5UqZcq0WjrlFt1Uo0KX15dwyZbuK3/IBWjiCjAoyurnF9PUqjWKdTrFJoak6EgMYediMPBhN8LCMwVCjy7tMaXzlyhZjFRHCrJRJlapUm9Usfi7nHvqtUGifUCscEAh4YHccgyB6MRvnTuCkdH4nTdVi7MrmHz2BAFkWq9SaVQo9lpY1Et1Mo9HtRWocTTV5aYHhvghdUNPreywJNr63TaJrcf2sm5ZJJqqYHDbWNbOMjjs4v8wV23M+Bxc//cPCNeL4eiUe6aGudVg4fROnX++8kTbA/4GHFHkcQOe4N3ErA2OZlcZtTt52I+h+q1IVgtoOuUqgZmSadSM3jHW27EGbHxsTe9nrv2T3K5UUAWoakZ5DJN9KqB3mwjyiLBiItgzMu7jx1hXquSS1Vo1HWemV9G9llZqZT5i3PncKoqNYvJvokhDFVgrVrj/vlZPvX0OS5e3eD8XIJKscq8UUfvtrGIaUJWJ68cupOGuUWmo7M/EmbQ5SHdqPPoyip/87nnmbucQrVaOHTdGNOTffzQ9XsJ2W1YLRZOXl1huj/Gbx69ngFnEFEQiTs8dGnzt488izfoQrUplNoGUacTlyKzwx+g3+nk/rlZTp9ZIJlpEO330Gy3yaXLVHIa03ui/O8338Xde7bzyMoKY34/qVqNFxfXuXFsgESjQUXTaXdNJEFge1+Us/MbmI0OdLt0FImG3iHst9JFoNloI8kibodMPOYiOuCnVdexWCXCfhuBoJPBiI+D4TiyZOFI5Dg2pcSj5+a4uJpg/9gg+WYTURC5OLuKKAgMBv2kmw2G3E4OhPeRqC/wtY119oRD9LlcjPt83DYwSsg6hUX0YxFDL+0x5dY6l6tF7h4Zx6UECNumMLtNHt14iIdWz6JIBYLXbH7+OSa9O1CkPDap+zJRjST0HnSdbpWTW49TMTQStSr7wzES9Tq7gn7+16nnef34FJ9aPMu7H/0oma0CD88v8snFGSbDId59cD/Xx4aYDoRptDUORmI8tr7OB8+f4anEOhWjxc3x6xEF+zcUwj51mHHPdqJ2HVkCrzL4UiY2gCgo39Heq3eWyDTzSEIHlzLwDcIpUVBxyv34rFV86nfezXz5Odwv+2xfhyAo6J0cK5UiE54QNlngcCTGgPP6XoKWUGOjVqTU0uh3BLBagjy68TUeWFxkIhDgSCxGytAwuiZPXVwgabZ4x/Q02aaGT1U5m8nx6pFRjsf7USSJrXqFj8/PcDgSZ72Wp91t8+DSEj+5cye/8+IppoOBaw4IBl5VQZYETqcTLJfLjHrcWCULYZud3z99mgORMAICQ24PkiDikGV+ZGovwy4fjbZOulnGqSgMRILYZLn3opMpUC83mRyO8ccnT7K7P0jA6mTAOcmUby+S6OahlWf4uenbORqL0Oe47nta7+ujozyZvMzVfIHFaol0o4HVYiFkt3H7wDgnt1YYd/vJaRVUycKnl57jicSLKJKIT1W4KT7M8b67yWhLvelRocCkz09BazLocrPTHydkVzgQjnJ9Xx8HQ4NYRLihL85fXjhHv9vNdDCIbJG4ZWCA9z/xDMuVEqM+Hzv8AdyKjVyzxmMbG+wLRfjI7BXsFgvVVgu902bS58OrqhyJHmPY5UIWu4RshxlyhTC6SbR2lpqRQKSF327hg+fO8659+/jkwpOsVvMMON2EbU60jsEHf/ez/37FQL/7/vcyGvjuxUCz6f8UA30vcCm9zO4Hr15lfzSGT1V5YWuLWqvFmVSKoqYRc7qYDgWpGg0sgsBiqUzckaPf4eFkcp2o3YNXsWN0TBZLZQ5Ho7xuZAJZtNGlg0ADl6JQ0jXqhkSlpffe+iQJq0Vi3Oej3e1yy1CP+znp86K32xS1JnZZodxqYZgmEVUhYLORrBept1r4Ix62NnKUCnWc9l6mtyCJrJVLlCotHJ4On5ibQxJFbhkc5G9fPEuraaA1dRRV5oWf/TXu3DbBPWNj+NTeG/SZTJKlcgWzC1dyOfLNBlFnz0h+Ouhnq7HOnmAf/zh/iTtHRvhwvkQpW0WURDptE2Owp0TtmCZ6x+wpkrMlapUGobifD9x4I/1OP28SPsPyXBJPsNcp6Zq9qE5BECjnq0R32vFbVe4cHOfUVJpf/OKjfOIt9zKbzZJM5unrC+D1OSkXaqwsZnE5LPhDLlLJMpV0DVkSmU+kcHscHOmLc+tgPxvVKnPFEseHh/ls6jxOh5Vmu81tOyfZrNXYF4pQ1FusXuvw9DldnEicxaeq/P7xW9E7BgW9zHq1wqBznsc25pnyB3h0fQWXorCykSYc9eELuVlPNejYLFBv84lHXuA9b76Nd33tMf7bwQOMR0JUfG4gQbHcottugywhW0RcPif9Pg+PrKzS0gxsqoTTY0e1yZxJpbDLCvtjfZzdSqI3W9hCFpZmEvzEfdM8l4TqoM7chY2X7u+VuQQfmk8QufdOgjY7cUeWd+y8h6J+PxNeP61OhxG3l6jTya7dfdQrTRrVJu8/doyC1kshenYrhU2SODQ2xHsP39SzZUKg2m7y0Mo8zya2MNomNqeVHz+why8sLLJeqTLsdrFZq3IgHOOusTH+bHaTptbhzLlF9uwZI9bvI5Go8/5X30a20STVaPKu/fsI2exsVatsdLt84IUXuD4ep9ZqcWkjSb3c5ILWIhqwsZHTQBTolHQ6NgslUaCh9YrPsE/lp+46xnWxKAWtyTuvfB5JEHrepZLIb3/hazy6e5z1com/vvOVKKKE2+/k/PkED+nnmNwxQMBuY2AkwrGBQWbzOR44exG9aVDQdAQEgjYrPzf9OixikLaZo9HeoGos41X3v7T+nW6Fk1ur/MzuaYZcNwK9tJT3Pfccrxod4f7TFyhPtzDMDkciQbrd9stSeAadxzj28V/is/e+5WUxljltk9VKHgC/amXCE+aBpRkOR8Ns1upsJHKU9DrbfD6Oj49y1pHEIoo9uzGvh+VKhU/Mz/MrBw+RaWqkGzUkUSTicPCrh44y6Pz2YhroxVC6v7Oa8ptClcaIOTK8mN7gNcMvpw/8c0Rte1/qkuqdxZdxQv8lfCsfTrOrI4u9R5hTtnEkPMrfzZ5mT0Cn1ckQsvZehhyCfK0AbnP7wFFskoU9wX7+auYMU4EgC4UCmkfnSF8fH3jhBY7195OoN7h1oJ9yS0MSRCRRwC5bOBTpRXwWdZ2FUpH7JidZq5ZZyRd4fDPBj0xt52xmC5ei0jFNGu02rx/bQZcuC6UMMUdPiOpWetMUu0UlaHVjmB3WqlnqbYP5QoFEvY7eMWkYBpvVColkHqvDSjwepKhpvShKSSJqO4QgSLTNLG2zzj1jU8jiAEJ367v+Lr8Oi+jnwYtXmIyFEQVwXit0R9xuLuaTiMBz6RXGPX6i9gBvGXditTj4u9nnmPB66Xa7tLtZjkaH8CnjzNizzBXyyJLIRrXKUrkEgCJKVI0WR6MiXbpo7d41HQyHSdZq3DU0js2i8vt33kpBa7I/1IdbcfCRuXNUWy1+7eAPk23O8J6DR/no3GXuGdtG3H4d9fYlpnzRXgdf7CKL8rXrCmIhSKrxFJu1Ms+nUrx9+z663dM8uLjIj0zt4P6FOcJ2O1267Al85/fo9w3/2dH8t8MfffB/v7dxOEq6ViPXaLBSLmN2uwx5vNRaBm5VJWCz41EVtE6bC9k8Q24XXtVKn8OHR7WwVC6gSCKKRUSRRAyzw0qlRLlVwaOqaJ0Wi6UigiCgd9o4ZJmjsT78Viu5ZpPVapX1SuXaeVWcsgW3qtLsmPhtVjYrVS5sJPA47dw3cZh0s8AjS8uk1nLkCk1EUSAY8TDUF6JUrpFcz2KzWggPBJkMBMjVG1zOZbk6m0DomngCLo7vGOdQxEe/086oO9IztlWcXMqn+eipc1zN5ZiZWWNxKcvAgJ9LV9epWeBgJEwXkw888yy3jwxxKpXC0A1azRaegAuf28GE10OrY+JRFepGG03okdb/5JV3MOgK4LBYeSK5SrFcQ7JI6C2DYrpMy+gQ6/dzePso/S4Xrx4eJ9WoktOaXN7YomkR+MWDB/jczDz1hobHZWe4L0SlXsciS3iCLkyjTaXaoqF3kDptXrFzklf09wjtc4USo243OwJ+DKvMkWvqyD6HHUkUabQN3IrC5VyeAbcTl6zw91dm2B8OE7N7STXKZJo1REHgycRiz1hf16i0DJ5PJOgqIonlNJfPbNFttMEi4ghZKZZaPPLYZbKVGnN6mabZwW+zYaoSbpuEJAuEA1YCYRehgIfd4TCVVgtJlrj34DRbHQ3FqtAwDPK1Oi+eXyCVLJFNVxkfDrOVLzFXK5Op1VAVmUKmjGmCqvQKi2Dcx/uP34FHcVEx8rjkcY7FpvnwlSfoczr4q0s9daysWHjndQd4Znmdu3dto2a0+LoG2anIjHk9THhDCAgsVVL86fmz/NmDT7G0lsbrs+HyOXjr9u1cLZUpak22B/xcFxvk8c1V5golqmKHUMDO4moRq12klK3SFQWeWVnlYycucm5xncnRGNDlqY1NWrrBG7ZP8dXVVQbdHoqGjtntojVb6K02qBKtRhskETomRqONxWbBbLQpNzuoERVJkhhwunjghRnMbpd6s43WaKFYBHaOxBn3+1mvVknU6pw8M08+06RQaeFyyegSvGp0lNePj/OV1VVWl9MUiw0uLG/QdSm8fedO+hyTCIJMlw5VI816LU/YFnmpC5jVzjPljTDqPg7Ax69+nPeeeIpqtckTF65y5/6d/NrBGxnzHEcUVBrtqyhS9GV71PZIm5/96iO8bWcvhUXvLJNpZlksl3hsfZOlcpm9oQiL5QL7QhEmvD4mowEGnB4mPVFuiA0w6XezOxRCM7vsCQYQBIg5HTy2vkG20eRLy0sUNY23btvG/tCd/2b7r0seYNgtYnZ7HNmvj7z/OQRBpqBfwG6JY3YrCILyPfuTfh1mt4ZFkOgKDVqmwRPJZRK1OkY3xYnNFfpdDj48M8OU38dSOUHIZqGk59HNNplmlQ9fusQbJsaZLxapaDodoWd/1+9ycSgcpmK0eH4rjVdVcCkqNkvPc/hSPs1CqcTTiSQVo0Ve01iplDkai+FSZKpGiyc2E3z40kWWShVsMtgsIi9m0uS1BvPFIsficU4mkhyM9GG3ODiXW+Xh1VVyzSaqRUKRJCSxR+/yWm3kOjrtdge7VWV/LMZELMz5bJajUSuqFEYSndTbK/jVnbS7mW/Iqf9uMFP8Ks+kt/BYrWzz+UjW68zlcz2jeEHC7HZ5IZ3mVUOTQBdJEHvTsfVF/KqKS5WxSm1006Bllog5XAy6vNQMjScSSZ7a2OTtO6c5sblBQdM5FIlxMZem0TbYajQZcLvY5gugSjJWScGn2rmcz+BSZAacewjYGsSdDgwzT9gWRZUEjvXtxyl7aHezWKURZPHrEwQBUbDSNnM02wsoUpT50mUkQaDSavHltSXum5rCrcjU2i0fZVovAAAgAElEQVQCViuGaaJKEqcza3zmz77y77ej+XvfY0fzyn92NL8nZBoNnr+6jKzK1HSDTrvX8Vg10ji9PVJ8sdmk2Oz5nxmmyR2DY0iCRJcuqmThQi7H49om5jX+vyyKdLtdvlRZ5Ugsj9UisTcU5mwmg80icSg8iFvxIJDkulgfutlhq97AZ1XxKCq5ZpOI3caVfIGTK2usXd2i3ekiWSQWdqwx5o4Qdjl5LtNAtoi02z1eY6pYplnXKVVaKIrY61IF/MxmszT0FoZh4vY7UawKG+Uyj6xdZWcgiGG2abZ1AqqL7X4/8bCfZ56bZ2QizNrTq2zmS8iKhaVUlprRwi7LqKrMl1ZWqZUa1MoN3H4ndreNE89dJre7wfX9cYp6C5ci02y3uXX7BDv9cRptnQeWLjGzsElLa5HN1ZFEgY7ZZXQ8xP7RQeIuF9OBACYmn1teIlWrMzXSxydPnEISBf7otXfyZ+fOkW/2PBwPTY/jusZ/a8eiPPbcJXIlHYtF4hf3HSJRL/ORK1dottvkNY1grY7R6XBjPE5J0/iby5e5d2KcasvAME1GvV6a7TZ/ffky7z16I1qnxVIlwy7/XlarVziT2eJ4fIBqq0WmWef/nL9AvdzA6lCZn8lDqQWqhCfuIBZxotpkLjy7SXmjxlVLEo9LYVOWGJ+MY+0PMjYcI5kr0tIM9kQizOZyOBUVWRTZ5vNy1edjNpXBZlVo00G2yig2hVSqylKxyI6pQfL1OtlEgfBAAIdDwWhrlGoGDqsFWZFxynvodKu45A3MbhPDTHD74ABVo8W418t6pcqjpxe4MrvGbYd3YZgmnW6XutEmZrdTNVoooshCOUWiViXdaHIpnUKSBGyqRCDmw2O3UdJ1EtUKitTz9nsyscoD81dZWkgCMLmtn63NIvNzGZx2C1ZFpFzWuOu2XZw+s8jFbJZOt8uReD/7QgEUUeLO0VF8qsKbJib4H0+fpJSrosoikiQgexWMmgGSxNCgk2qjTaFu4PEoJDdzfD5f4bNmF49TJpXXaJV0KqKANCiyUSnjs9pYLBR6QriiTnzASazfx8M/+mPUDY0++yAb9VXKuk69aSBbRN54yyHef93dWMQIWmcRm7SDSusqPnWAdrfDs6kn2B3oxyq5iNiOvLTXtM0sn56/SsfoUM5VcXjsvHJoEKe8BwC9s4hV6sMwtzC7Vb68dp59oRiHwzfxU3tKPL31EJPeCIl6kd978RRvndrGqdV1xsJBnkwss8PvJ2Lz8oWVWe4d66nZFSmMiI39IZGf/OrnedXICCZwLDZIy2wTttn5nWee4/U7t3P7wCAO+V/RovweYZW2Y3Zb33bk7lXG0DozNNsaLtnzHeWafzu0uwWqRpm60eKPz54lVa/x/mPH8CkO9gX7qRlN/sehG1ElmTPZVf7kwgvsDvrpd7qY8Ib50Z07kUSB6VCIqNPJ+VSKW4eH+dyVOaSd27k+FmOtUkWWRK4UclglCYso9vZ5VWXc5+PJ9XV+/ehRDkcilFs6z21tEbLZuHt4iIVCngl/gIVS77e00+/n0wuLvOfwYdyyjVcNSzTaGmcyl1kslVkultA7bSq6jixJDLo9jHg97Ar4GHW7uJDLcyWbYSabZdzn56krizwcDvPWiSk6Zh1VcvNE4ivc1PeKf9W6/tWly6yvZaiENEQBVkolJEGgkK8Qn5zg+a0Uc+ks6ztyDLlCyKKCgIrdYqGk68wXCxQ1jUyzQbrR5O3br8MihJjtpjkaDeNTFVRJ4VXDwyRrNR5eW+RSNs/tQwP89PQuYg4vTosVu2WYTjePU/Aw6cuSqFXZ7ttAEkSGXUEcFhVBUFGEwDXXBpGakcJuMTmVmWdvsESjreNX+/nS6jMMuFyU9RWanV56nd+qErFHmMnnuXVgkL+dmeFtO7aTrNVwKyqbteq/ah3/TfAfpKP5A5F1bpNlXrt3F3aHFYtiweHpbWB2lw27TWU0GGDcH+B146M8ub7BSqnIs1vrOCwqoiCSazb49PnLPDZzlaquM5/LoUoiXqvKkMdDqtFE75gEVAdvGNvJ60encco2nkvNMeCMMOIa49aBITL1BgfDURyyTLvb5WqxzEalTDZRQFUkKjWD9YU0d/3pR3gxs8af3/JKBEHAabMgigIt3SCXKJBKVdGNDvVmG/laXrrW7lnuxOIeqqUGr942wYFojFv6R/nVp57it188id/qotiqMewKEnU6icU9rC9n6e93EvG6OTo1SjFd5qNXZmmbJu1Oh2cuXKVSqOENunH5nAx7ffjCbtKlCsl6HRHINDQm/QF+dtdeSnqNcqvOFxaXiPUH0A0Tn1tl+sAw0weGECWRe8bHKOratY1HI1mtcbw/jixKBPp8rJUr7A70E7I7KGbK/PyB/UwF/OyPhIg5HQx53BzYN45VEfn7t7+JRkfnH+fnua4vSp/TScBqJWzvRTpWWjo3xHbwzj172KzVORTpPRweX1vlgfkFtvm8vJDeoNLSeT7Ve/iPuHbhUVSGnXtQJJHffuoZmnWNdKLAU08u9QQqLpnrXrON3fsH6Xa7pJJldh+NgyqRTdSQLCJjE31EHE5yuTLJfImBsJ/+eJDrolE+cue9HIxG+F83HudQeIBdwSD9Pg9Wi4V8qsj/fPUt3H5gJzv2DNA2Omym8yzPJlGsMlpdZ3A8xvVHt3Hg8CiRuBeHVaHbNeiYeQyzxROJRzG7zWvWIv8fe+8dJdlZnXv/TqhTp3JOHavz9PTkLI3CSAIJSQSBEcE2YINNcLrms/3ZmHsNvsYsgz8bG9sXrsFkYwQiCGVpGI00Gs1oNHm6Z7qnc6yurpzTCfePGgnLYBvwvf5s1n3WOmt19Tq1zqm33vPWfvd+9vM4KLc0etwu6g2dSqXJSqnI2VSKw4vLxBwOhn1++twenlxc5tMXL/L5SxMkKlUG/AGuPzCCppuk13KsrKb5k+MnqDda+FUbt3Vt4s7eEfq8PvL5GpVyg8XFJJ09fkqlaxqOAQe337SVZ5+boqUZnL44i1+1ck9/HyfXkwRtNs4lN7izdwSfqiIKAqIoMHcpS36xTCvbaGc0LSKLiSrNpsHmbWHuuGWMwb4OuqNBAkE3oZiXZl0HWQRZxONRcStWnIqCU7FSrdS5+5VbeeDXfp6jv/DLeCwREtUSihRnwH2Iv7ntlfzi3dczMNbJh/bfjCjYkAQXhWaFpjHPB44fIVVfIGLbwolEgmKz8rISr2m2EAU3X7vrZ+mKBIj1hxnqjfLA3Hybb2nk+dyVZxEFB6naPB954Um+fPkK04UMa5XzTOcLbNSqTOWT/Lfjz/L40Qk+8K3HWZpJcu/IEPf07yBTr3F4ZZq3DO1DFmVKrRqVVgJRcFHTG9zY1UWx1SJis3M8sUyuUWNXuJN37NjG00tLTOYy2KR2sFfXp2jqi//suqkb+f+t67AoKFRaF6i0LtDUF75/HbNIXb9MW4XWis+6+we4rD8J6loFAQHDhIau86aREWySgl/1IQkiHY5BHly4zHuPPMSVbNuI4b8//jQfOHaMiWwCqyQyVyiyVq5QbjY51NNLp8PBJ+94JcM+L5O5HEFV5VI6e+27q3NsNYFumuQa7ebJhfl1pvN5DEyOrSZo6DoBm8pD8wvsjkZpGQZxtxPdNNEMg0Fvm5NZ11scXl7idDLBVC5/7T5KeFSVPq+PXo+XTf624ockCKiyxERqg6aus5LI8NT4VTb3dnAisU7TmEcWQ8yXFuh0un6owD7AN2a/Rl2f+lfHdb1c4lu/8FYOv/l9ZGt1bu/ro8fjwR9w0+108cG91/GJV9yKX3WSrhfQjRa//ewD1DWdyVyeq7kC88UCFrGd6fzm7Gm+PvMYqizjV22ka23HnmKzwb5ID/1uD4oscSKRpNPhI6T2IIsyl3Mv0NBLyGKIvaH9HE+sIwsxep1bCKrbsUo9yEKYE8lnmSm8QLE5xQNzl9HNOtdHD+GQhyk2qyyXZ7g+FmdbYAezhQL5RoO9kQ72hDvZGogwlc3R7Qzy9tFRjq+t0eN2IwoC37w6/W+eo//HIf4Ex/8P+Kkonf/Z3/zZh1d7FGqVOqVchUK6SLgrgNNmJZsp4nSovGNsM3aLhW3BIJIk8Y3JKZ5ZW2Iyl+ZTp06zNLvBRqpMtMPLSDDApY0UV9JpFgp5VFnm1NoaX714ia9dGSetVYk6FGJ2N7qpIwoaDb3FJr+XLmcAQWh3ei8WSyiyTEnQaDZaVKstPC4Fm0Ph+sEeDFMn3h/GFXYiqgJaS8eiWBBNA8UiIcsiu4Z7kCWJx49fwumx0xnxs6W3g1KzhVe1sj/ajSAa/O0jz+IKOWjoLTxWhS9dHqdWbbCaKBGJONk30MuZlVWajRbb412ka1VapsmpC0s0mjqqVUKxWqiZOp1+L30BP4okUWg2qWkav7VrHxZRZr1a5IG5WRqGzlIyg9OlorV0AgEXBzq7WG9Ued/2PdzW1c/HTp9kvlRiZzjIpUyGiytr3Dk6wkQ6RVGv86vbdvEr+66n1xUAWuSbDZ5PJGjqBjd0dfLqXaMcjPUjCSIjPg+PLSxSbDSIOB08u7KCKAjUNAONKsPeEEulArlGjTvj/ciSyDvGxuhyuTgY3cJ/O/E9OpwOXIrBc+uX2/dfXuT3jz5zTZ9RoVqoAiaaVaKj08nu0Tir61laTQ2LJKLYLPj8KumNKnXdIL1RwBdxUShUeM32zZRbLTTdwCLL7AhGuJBO8D/On+dyLsN4agO7RWFuNUmz3uLbJyaYW0miazqFVJGllQJDozFyyQKq3crSbJJcvsTqcpbV1RJ6q8lor0i+mSNoc9Hn7sAidRO2hZjKz/PdmRmCdjt4LPT1hIl7vczmckynMhyZX+D4eoIOl5PJbA6vqvLWTSMslko4LQqZWpVaq8nwQCeGRUC2yCgWmajTyV3xXVS0IscTq8R7QiysbpDP1wlFPfh9VgLh9gbmjr5eHjk7hW6YvP6WXbxt0xY+OzFOqlphtlBkqZAn4rTy8MICc7kckiyxmq1ARQPDbB8VDU/Ixj2378BQJRRRYjQUZMjnQxBEMo0aWr2ObJXYNBrG6bWjCSYLqQyyLPEHNx5kU8BPXdfpc0cYz86wM7jzpW5rj+Ki2FznuaUVXjUQJ6BuxTCruJU+7p99jMOLizwyP8OZ9BR/sO8N+KxtaR3DbFLTJ5BFH6KgIggqEUeVwzPzFGt1/t8D+9GMMj5rD9uDfhLVabqcB7khFuatI3cz4BnjeysncCsKF9MZ/uj+J0is5XA5LJQrTZ79nfdQajWpaFXcipX9kT6+cOUU51KrbA1GsMkKkujCLkfoduv0uz08urjA5UyW/dEYPa5BLmYW+JXtO7FKEs+tr1DWloi7tiIJXgRBotQ6j26mkIUgmrmBJDgRBfWlNdQ0jR/acPPjoK5fxiL6UeUhdLOAQQPDLFHXV/9R5U78ZwOhfw0veqi/iJXqJHZZZaGU5VB3F1dzOcYCYeyyil0e4u+vPsyza2vc1tPN4aUlfKrK5GqSXb3dvHPzPhKVPD8zsI0XNlZZzBd4w/AQmwNBzm5s8OTSEm5FQZFE3IrCgKctY/X82hqVlkau3uBSaoP0Wo68BX5pyx4GfU6GvD7i7gAhm8IbBm5BkUuc2UhzXTTKqWQSEzgY6+O7c1Psi0Q4kUiyUipxKbHOplCIO+Nx9seijAUCSKLA6WSKb12e5OiVOWIBDz8zMsx0pYhuGGzkCuzp6eSWzjswaRGyDeFR7D9ULaBlrPLN2fN84sxxvjVzjIA9T7970w+cdzl3GJfVwi2dr8OkQdwrMOLz41UVnlta5cGzE3xn5iq39sfpcQU4l1rjZHKZuNtFTdOIOuzopklN0yg0W7xvy8+x2R8jbDO5msuwK9zF3kiUPzt3ihGfD6dFoWVoLBRL/O7ug1hEGUX0UWqlSdcrdDm6+dvL3yFi19gRCuK0mEhiAEmwY5oNdDNNrpHFpagYpskXL09wbG2JGzrcLJSn+cz4Jfo9bsI2N4VmmkcW5hn2eTGBh+bnEASD27p7+MDxo7x77FascptqdC61wa9s38Off/Qb/3FL5x/74w8zEPzx3zix/n9L5z8Jqq0W7oCT1GoWp8eOaZi4rFaiTidjoTBWWUISRKySxFyhQKnZ4lBPLzGHnUSlSjZZQJYEDMPk7PlZcpvqeKxWNgVDpKoVDEw6XG6cikKx0eD+UxewiCKv6x/AJluRRYWovYOYXcakRdylkq3X6PO4+JmhQZ7yeviWfBWrqhAIe+j1eNgd7sBhUelzh/jVI4+iazorq0UkSSDkt5FJVent8bInEuJqrkBTazflLCwlSToLvHH7FgDsso1X9Q7wqdDznEtu0ON0cnR1kV6Ph41EFocqY1UVlosFPA4bxUwZUYBBr5exQIBD3V184KuPYlFklmc3+MW7DmKaJiulEsVmg6mlBKIo4tx/M01DY6NWodhs0e/xsujJUyhUGB3pZiQYJFmt8Lk77sJvdVPT6qwUi5wcn8Uf8RB0OjFNkwGvmxs79zPkDeJS7Ne8wmUORAfo82RxWiysV2u4FQvpWp0XNhYZ9Pj55PlzPPTUeQC27+rjnuEhQjYbFlHk6MoaiXKFO3sHaRotvIqTd23ei0WMYaLxmYlH+fSt9/Ltuedp6BrnNtJopsmhrg62xWIcfmECm8NKOl/H67QgCgKKRSRZKRMMe2g1NTbyNUJdfrq6QjRqTbZs78Mmy3hUlSVjg2+cOEer0cLQDc6em+Gmzg7OJDd48NFx0AzCIRs2VcLhUtse760y+XINZ6XF3v0DaONLvG5sEw9KV7GrCsVc2/Wl2TRwOy0EO3zsDfdSbtVI1wtEbRKS4KJlVttSXaqKgcktvb0cXVrCKsmslUrIFok3DI/y8OwsR5ba0i0dDsc1Jyc7f/fcaXRNJ5koUsxWsDmteAIuwj434+tJ/urSkxzq7OT9O3fzpSsTRDr9ZNbzHOyPk6pWuSPew4FoF5O5NAObIuRTJWayGfLNKifnFliZS6FYRLxBN//j3Hl2RmN0eTxcya4R8Cjkxbb8kiSAokiMbI5e05w1iTqd+FUV3TCwSBKyReLAvmHmltZx+Zzomk6f14czHHnph+7w0gq/sWMnqrSZ3aHN1PUrTGXHeXJpkX84fYEPvfJm/uqO2/ng8WPcM7jGMytrrJSK/PaePdx8bU34pc1vf9n6IgoKdnnbS68lwclNHTcwHJ3kLZuG6XR6idn2cLXwDC6LjR7nDWTqp0nW8gy4dZ5PThBUbfzd+ATPX2hnSaxK2xXKYZMZz2ywNRAh06i0VTPkNK/rH6GmN1gqZZgrFoDLjPmDDHnGKLXWeMemrVglhS9Nnqdl6OwKhzmRWMVntbI9GOLo6ir7wikUMY5hNlFEJxaxA0GQsAgv54/+70C6/sK17ul2QIYg0jI2UMQoDb2F0+LGRCNVXydqc77khvTjQBSs17L4bQ6oRZDRDB1JEEhUyrxv6xt5MZwttSbRTYNtwSDfnJpmZzRKp9POcDzGTV0diDh5LrFOtlFn2OdhfzRC1O5ANw26nU5+ectWbLLMerXCarlMplYn12jgVduVlMnpZbSGhtvvJFurMlvcIFmtoEgSTouVb0zP8JsPPc4bdm7lps4ODBP2RiLMFws09Ba398axCDI7QhU+P77Oq4YHaegGLdNg2BtBMzROJZOcXVllZ1cnmVqVDqeLs8kU5VKNuakk8YEgPquVlrmGiBNRsGIRf/C7/eLkV9BNg/dt3c22YIAvjE/wwceP8KnIBbaGQ3x436+8dO7Xp6/y9WNn+Phjz/Az1+1gTzjMpUyGTz90jJ+/4wArgRJ39vVimCYuyw4CaoJul4t+V5SPnn4Gl6Iwnc0xk8sy6PNTaF5iupAkUSmTbzRo6RqyKPGavj6GPCEqWp2qpnF9LIpVUtANjauFCwD0ugKIgoP3jv0cG/WzeBQnInbKrWnyjTLzpSxxl4/ffPJ7LEwl+PXX3cyHDhzki1fGeWB+ghGfn1f2dnMpk+KZtVV+cXQnMYcdAQHNMHjn5h08l1gg26jxxuEhPj95lFGfn8VSkUqrxZGV/+jOQPyn0dH8qcho/sknP/5hBjxYbQqthkZPT5jXDg7S72kHh28aGkEA3FYrx9cSvLovzh29QwRsCgYGJxaXEYC+4SjdXSFkUSRXqxGw2Sk1G8ii1C41X+siNAwDq2rFoch0OFyYpoEi2REE+ZrWpYxLEaloDQKqnYjDzoDPy3V93XzowG3c3TeGWwmjinaskoVNASdXygXS6RylcotKrV2SdLms7OnrZq1aoW4FiyIjSRIH+ns5EIkw6PXS5z6IR+lhX5/MLd3dhO0OAqqNm7q62Nnbwf7ROFu7ohzs6OBAR4w7RocY8/vpcXuI2F3EHA6eziYQBAFVtWBzqNR0jUGfl2KjCYpE1OtGo0WqVuK+yausl8s0DJ1Ol5sLEwuYioiiWLguFmNLIIoqKWiGxh998zEKhTqFbBlPyMmrhgd55+Z9dDm8iIJA09CwCBKioCAIZrvkJ7Q7jlcrVcotjVu745xcX+Fb5ybwBl10x0PsjMbYH42yWCrx9MoqAVUFAWYKOXpcLqp6A6/VhyS40c08c8V1Ig6Fitbkaj6HLIo8MTNL1TSZzWXJZUtUS21BcUkU6B2KsH9sgDNnp9H0duOV22ujmC1z2+gws6kMHSEvw/4A337qzLWSchObzYLb7yQQ9VIHpjJp+voDdHZ7Keer1BttH+pEsozHacHvseL1O/j5PTs4NbPIyckFVpbzlHIlPD4Hgihg6gYj23o41BfnlV034VQi1PUslVaNSmuFqpbjyaUF3jwywmKxxPmNDfw2G9VWi9nFdbweB5oJl6aWWEvlkOwKbxsdJd9o8IVLl0AUEAQBr99BbzzCcGcUQ4SV1TTlQoU1vU6f14MkCuwMh7hUyFHVNP7rTdezLxrj8xMTBO1Wbu0axmoTODGzyOpyhoZT4ezFWRotA9OEUKePrdEo24IBru/o4M6RQUoOgWarRjZfpzvq4LWHdpLIFQh5nFhECZ/NRrfTSZfLSdRup6LpOBQF2a5QKFbY1BHl+s4YN3Z2cv/Vq6xWqmxUK/S6XfS7R9oe3hh8ffoFvnD8NBarhd85cCNdjk6+NXOR8xspkpUyHlVlczDAO0ffxq7Q9h9pzREFO/cM3EBQrXE8scCwr5+QbRPrtRnS9TkWSjly9ToWqcV0IcepZJJnL8/Samp0DkToiAVoNJrYnTZma2VmCnk+f/EiuWaLHeEQva5BAtYhrFKFoE1ld6gfvzVAur6KbppMZNe5mElyQ0cn/zB1FTCxyRKfOn8Bn83Ga+KDZBpZAtY4oiCjm1lS9TmsksCZ1LN0OoYwzAYNYxoQKGtXUaW2g5Fh1n6kRp2GPkvTWMWkRbaexq92oYhdGGaDY4ljdDt9gMmXp06xJ7wVRerGrfS8xM3UjfzLsqr/EgyziSBIL7uvqraKbhrYZJntgR7KWgqHHEcz1mgZNVyKREPXuK6jg0fm5tgbjfDWTZvpcLioaEWStQpHl1bYG42wPRilaWjYJAWPVcWt2Jgt5DAx6fN4mC0UcCkWFEnGrVqRHQodUT/xSIDXDw9hYvLA7BxWSWZ3uJtzqXX8bic/u2kTiiRRbbW54zaLzNlUAgHodvmJ2B2IEgRtKmN+Pzd3DKLKVlRZ4VI6QapeZyTgZ9jno8ftZG80imxVmF5d5+P33MErugexSiqK1PFDxy3bOMNKJY8stBUWTq6v85c3vQ9dyfLGoSH2Rzv42JkHyTRmOZO6wH3nL9Gst7A7VaYSKXZ2xyg2W5yfXSIS9aOIEhfTad41tgdViuGz1nErNmzyKGMBgU6nnW6Xk4VSmbFggEcX58g16qhym99a0Zo8MDdDXdeYLeT4gyNHObq0zFDQz3o1z5j/RjyKiYlG2LaN+fI5Viuz/JenDrNQyqLIdWQRNFPnj0+e5GBnBzd0d3GmlOHuwX5idhdVrcHhxWXun5pkpVwlWany5uFhYnY/m/x+Cs0qiiSxXi0Stjs4mVjnZ4f3Mpldb1e3pHZiaq1S4fDfPv0fN6P58T/+MIOhNk/zxznG/29G8yeCYZhEeoIk5jfwBF28Ih6n19XuKncpCg1dQxIFUrX2BOt0elkspZkt5JnKFYh1tG0Suz1uCvUGk5k0hm6yXi7js9kY8vmYK+S5OLOEIAhYbQoGJpPZPJ0OJz0uHza5jCz4qGgZrJKFjVqBE4l1WrrOkM/Pa+O7EGiTl0002rLrbUeOUV8Xv7lLY0soxJnEOmvpHK2GRm9nCFWW+O2d+whe34NpVlmurLJSLmKa7SaoTP00AXUPO4Mv7zKt61O4LDbG/NpLQu+qrGCY5jUhd52AdQABmcfvDZBpFGnorZf4Xel6mTlfnqv5PIVmk4dmZ9kolamV2/yaFVFkS28nggCdAS9dbhev6InjUdyASV1vYbMrNFt1uvpCtHSdXeEQy+UNepyd2GU7xVaGlqEjiRoCFiyihS6nj+l8Frss0+d2MZ1P89mLlwgE3ESdLkb8fiqttn5pVdO4uauTjWqNM+tJfn3nTnpdHVzMzLNemWJfBFYrCXaGIiQq+WsKAVWu5grs6OjgkecuMLY5jkWR8QRdhP1uMoUyPUEfftVGR3+E1EqG/p4ob940zMePHEcWBA5sGSBVrTKRStERD7GxnEFRLfjCHkJeF6vrGZ5fXcEwDPw2O3ank1KozPx821qxu8tLvVJHlCVaTY1jKyvIiowoiUR7glSLNerVBg63DcVqYdDn5xXd3SRrlwnbhonadiMKCt+Z+zpbgiE+9/Qpsvsb3NzVyYWNJA1NZ2J+FX/Ey2goxJELk5QKNbbu6GN5aYP7AtMkSiVK5Roxv5dSs8HuWActXafaam9yGrUmxWKdUGe7oWgqm2U8k2V7OELQ7iBVqyEJAl0uNx87dnwMXC0AACAASURBVIIb3tTPrV1xPhe9gLPWJFWtUGvo6LrJX//ia6nr+rVSZHvT1tBN+r0+dt92HQ/PzrIpEMQwTUa7Yrxvezt7KIsiYZsLq2QhUy/x0Nwc6WqVLeEwt/b2cqizi7grxP2zlxgJBPi1bXuRBJFLmVUWy8/S6zyIgIXNfj+GbmLoBu958hH+6tbb6XC5kQSBV/fHuSF648vkiH4ceK27eFWvA91II0lOel1DPJ88R4/Ty+XsBp+duMTTs/NkEnk8QRdmvsL0+CqRmAeXz8Hdm9tC6UNeDzGng6ViifunZ/gvO/yIwgbzxTRDngiKOICAhbniFfZH9rHZb7BYyhJU3dwV7+Xbs3N8eP/N5BpNvjk1xWa/nwFPCN3MIAshBEHlK5OXuJg6wu5IhG2BK5RbZRRR5uj6M/R7vHgUaBlJRKzwQzrHX4RuljDMKgISdb2JV/LT6/p+l7NJg83+KHW9yfn0HFsCASxi5PvrtVmjaSygiPEfeZx/WJORX/VS1WqE1A5OJi8y6o/S0GeoaAUuZ9uNayGbnSu5LPeODHEg2oNuGiyVcrQMgw6HnZu6u/CrNkqtOhZRImr389Wr5/CrVmqahmaYOGQLflWl1GrS53axUavT0HQubiRRZQsHop0kq2X2x2JsD4ZYr+awiCJ/eOBmbLJCtlEiWVknWa2yKxzhxtggkmBnPDuNRZS4saOLoOrGrTgwTQMAQZC5u2+AlXKF+UKBt46M0OV0882ZaR69eJnfft0tbAlEOJVc4pZOK1bpB8cs3zjL7z57mLeNjrLZH6OqNdjs86EZKXaFQiyVS8TdQX5n9wGausZUboN9fb08lriApuqUchVOJ1OsFAvE+sLkam2pvt/buw+PshMAu7yVTP00kjCJLEr4rE48ioM3Det4VZWvTV2l0+ng7EYazTAI2mzopkG23uBANMqWrhguq5WNap1ktcatnZdZLKeI2X20jAR/df48xUYDmyxzKrHGznAQzTQQEXBZrcwX87yyewu3DS6zXqmy5igy4gsw4gvwD1NTFBsNaprGqfV1Ss0ms4UC2XqdmMPOsM/PZDbL5oCfRHWDfdEYc4U8xWaDdK3Ow/8ZOJr/STKaPxWBptbSKGbL6LpBvdokW68z6g9jk6ycTC6wVCrjURRkUcAiiXzs9PNUWy2sUlsPE+DWnu5rOl8VMrUqgkPArVhZLORxW62sFtulRatNYcdQLzbZwnKpyDemZ3hFTxcHIlYclhJWyYIoyFzNZbHJErOFAnsjPQgotIPLFiBgmFXS9TTL5TyZWo2w3c57t+zBs6sfWfRjmA3KrcsslFJkGxXWa+M4ZAsxu59Kq8l4Js13Z2aZLxZ5/4493x8LI8Wfn/8207kcS4UCAB0uNxOLq2zqjlHTWqQqFQb8AcKOF/jA7oN8feYSK+UyMYedEZ8PqySzJ7SHbYHv/zjoZomWscTh5SvkGnUWimVeSKwRjYe5PR4n7nbjt7rRjCYCsFTKIMkiikXE6WqLhVsE8Zrcjta2RZRt5BslFMlyTQFAwy5ZGfH5mS8UWCyWuJBK4VLaMjNbQkECVitxT4RaS2O1XOa5lVV6vR7KzSZPLC3iU9sZkr8bn+DJpWXeNbaVhq5zIZ0i7nJzINrFsM/P169O0zUQZTWVZWSgA1kUSVYq9IUDlJtNLqdTmKaJ6rAyM7/G51tNlhcyXOlO47GqlJtNDNMkHg6QTxXZtaWfkN3BqYUlTBNu6Y1zJZPm0tQS2zf1cv3mQVTHCpVClWqpxka2jsthwR9wsJov8OoD27k+FuWTZ8+iNTWqpRqGYdI3GGM+n2Mim+FSJsOId4OdoW7qeotul4sPn3iOWrnOg2cuYbfIbAuHMUyTmtZCN0yeuTzDwkIOm1Wi0mxitSk89vwlAjEfgiCwsLJBs9Fi1eG8FsC3SGeKbKQqeJzt7FG/x8tyqcjhC1f46zfcjV+18ZtPHeFzd9xN3zYfhy9O8pcXTjGRSrE2t4HNaeUNQ4P80fU3UGk1scsKEZuXy7k1lkrtOfm3Fy7y5TvvwW/t491ja5xNLxGxO3BZ7ATV+IuzDhD5/859l/lCAdM06XS5+dj1b0Y3c/ztxEneM9bP7T0DFJs1mnqLkK2fPWGFdL1AtnEOr3WEzf4IOzfFOT0xy0Jrg18/8gSDfj9/uP8WbP/Ep/yfos1dFK/9/X1P8n8MVRp56dyZwiRhm51zqTY/d+L8IsNbu4jFQySX06gOK9u29vHW0U1k6w2eXFigpmk0dIMBj5tburpo6TqfmTjLoa4udMPgUnaV3SELihjl0cVFtgW6CKg+gmqQy7l5OpwuziXWeMvD91NtNLmlv4+AasMwTTQzjUwITJ1Rv483Do7Q49yGQZk/Of0E13dEOZ/KcKhzFIBSawX/P+Nb/iIkwfUS79UvxV/6/4tjZZglvjlzmV8eu519YRmr1P2y95u0qGs1LIr2L17nX4Mk+JCFBoKg4lQUQuo+1irPcTm3QdTu4MjKMq/vH2Y8k2HEF+CF5ArbghGGvGGsooXvrVzlrUPbWa1mrulZ+rmSW+LEWoLf2LkDl0XlvulJbLKELLatXTsdTtK1OsVmg3qjxbsPbqXfvYc+V50u5xUUyYIiylglEbtsxQT8Vhe7wxbciueluQLQ4UizXM6yyRW7pgcqvIyD6rY4eNtom0fpszo5ub7EseUlZEUmW2/wifOnCagqPS43m33ZlzZLptmiZazy6fHTXJhfocftYcgbRhJEOp0uFKmXveEwonAUURAoNCt87IVTdLvc9LhdWFQLjXoTURQ4vbyC1Wqh2+9ldzRKTdPZFridpr6Acu27XyilOJFIsFQqc+9Q22Y3Xa+xUauxJeCn3GpxINq2W+52OXhF9yCfu3weWRTZGgpxeGGBg2NR7o5v52Jmnk+eO8e2UIitgQD/dd9B3vPkI5w5O4PLY+PPGg3evnUL/R4Prx8c4PGFJe7suY29kTAffeY5rrvzDoY8USayK/zGjt18d+4qLsXCdbFO/FY3MYeLT5w9Q6/bhSrJfHF8nINd3Tw4O889gwPc3r2HQmuDj50+wbfueTNBvvpvmqP/ZyH8p+k6/+kINK/xF2VZYiNZ4u8ePs7NnZ18c3qGIV+beG6RREI2Gz6rymq5zEqmgFOx4rep/MzICDVdx6FY8KkKca+PTK2KbhqIgoAgwI5olPNn53F6ZLrd7pfEZVVZptJq8e25y7yiO06Ho4+GnuFsKs2zy0sEbDYOdXWzbGRZr5SxShJD3iBdjv10OUbocrR3nlP5BCeTC4RsG4RsDhq6Rp+rl/c89EUODvSxK9zObgZUN1G7l5VyidcNDmKRvt9G9t9PfapNUK9WKWbK6LqOy+dkuNeHV1WZzWWptlqsL6TYHAqTqVX59twE35ic5Luv+wW+PHWSX73vQfr6Y1Rbx7ihu4d3b9lOl+Ng+8dFGuPV8XaDRKZ+mpPhBaqaRrrW5rQKgCzK1LQ6va4AT777nQiCyNnUCs+urfP7Tz7FTcMDvHerSocjiiy4CKg2dLOKSdvfumG0pSfOp9PEHHZ8qg2nYmXA4+bGzk781jaPSkAgWW2LKv/Bo0/h8NjRDROraGFbIMrHbvDz4ZPHyDVqWCWJ+65MEnE4US0y4ysJvvi619LrdmGTJJ5YXCRTrZLbKNDl+r4HfL3S3oQEYj6S6zny5RbjV5fp6Q2TWEph6CbTpWU8PjsziRRH5y/jdFqxKDKffeRZVGv78bLJMk+euESt0VYOKFc17rxtC+Vmk7DDycTiKjVN4+nVNRaurLJ39xDzooDVbsUwTW7q6cGjKGwLhom7IqRqeUxMxvw99HpmeV4QqFebfG9hgajTyedf+Qukaovc9YUvUa80CPtVLKqFYqaMYRgc2jXKSqnIwkKSaqlGsMPH6fFZLIqMbJFpNTW6e3wkVvOkVrMcXlrmuliUB9/x8wRUN+878jBNTWM8k2RPuJuReIwvPvYcB/dvQpZF7rluB3f1bkUWQshiiIcX76eutxjxRnl2bZVsvcGQP4BH8WOYFXTTYF94BFFwX9uImbS36hKgc3Fjg2MnJ/H67Iz/5mfazzMx7uxNsl5N0O3cjypd4Imlae6O25jMrfH44hK/tfN6TFMjoHrRTQOtofF7dx3iUGec7mti5qbZYr70HHHXgR9wz9GM3Mu6owVB+meDzaaxzHzxKpl6jQ8dP45Vkkgsp1EVkY2VDC6fk92b+9FMgwGvj0+cfIF7t2ymw+XmXGKNuNvJbKHInkiEDruXS6lzaIbJHb09FBoNzqYW6PeU+a2d1yGLMgISk/l5vjx5hUy1RqPWJJ8qkkqW+LW9uxn07KfcmuarV1/ApVzg9X2v4Pbu7UhCCFn00dQLiIKAX1X54J5XXeNyNpgpJNkX/tHX3n/MmSxrF7FJnYg4ePPQVkTBTaG5iFWaI6B+P2Nsmo2XCeL/JNDNIgJWGkYLlRZRW/u59VqdbPKZ3Dd9hVNraxxZXOTu/n5KzQZ39u5BEeNUtUvopsFGrY5uGlhECQGBC+k5Hl1cbAetNjeSILJRrfH82hrdbg+dTicNXaPc0hj0erl3aIj94VcgIGMI0OPcw5HVJ3h2bY13bd52zYtdRjfqeK0diLxczilmP4BHuchKJU2XI3it8iSiGXUqWh1REOiw+zGBxxan+NPDx7hpyzDzosg3z1zCH3Tzy9u3ocrtILU9tjpV7TLvfPIBut0ehrujHFmY52dHNmGYJr/z9FEKxUfweRx0ezx85LpD6IZBzOliWzBAt8vF1LYhHnvmIjZVRmtp+Jxt84Wvr27w24eub18H/Zr1qspkLst4OsNbNg0jCnA5m6RlGHQ6nRSbTe6fusrbxzZjkUQen1+kpuk8s7TEGwYGidptvG/7dq7m8wgo/Pb3nqKcr7AvFmPA6yOk7kMSH8XttbNvbIDRgJ+PP3SUe2/czaMXrxCN+Hho4TDpep3NHRFWykU6HF6+MzfP+3fsY2swyJi/A0kQ+eBzbbvJLpeLY6ttl7+H7/l/kAQ/D8zfT4fDiUXsRJXyrJVLvPq+r/yb5ui/C/5zxJk/HRzNj/z5n3w4fHMfJpAvNnA5LPhCXsIOOxZRxKlYCFhVJEHgfDpN0zBJVSuUmk22hsOEbCrFaxkq3QTdNEiWKywWCtzY3cPv7LqOW7r6uXVrnDQ6fptKzGG/5tIBN3d2c2vXPkzqbNSSLJdzOC0yb900yq9sexNR+ygx+yB97hg2uca59BoPLpzkufWzfHf+Oe7ovY5O5yCDngGi9hBOi8pHTh1Fo8I9m0ZQJJGxQIBOhwuHrPKVqYu8qncYi2Ty7FqCQ517aRkJPvTMw5TqDZq1Ji6fg1K+QjlXYbA7gl9VkSUJQYCpmXXu3jXK7T09FJpNjoxP84Ytw9zccTfv3r+bbDPNQqHAVDLFp558lq/OHuVq5Sp/evoBpotTDPmaRO0HGPZuZsy/hb3hHXQ6hoAaq5UEuWaFcquBIIDLYuPzl8e5kk6xKRLmSjrFfKnEvkgYRZIRkNscTUAzGlzMrFDVWsQcdv7nufPEXC7cVgVVlvFZrUTtHpwWGwhwPrXOp0+f5W17tjOVz1HTNLaGgnQ6RhCFKo8uTtPncRNQ7Ww06mTrNZYKBZKLaab1KqulMr+x4wAHO2KkGw06gj4USeLe4SF+aetO7rs0zsHNgzgVhddv2UTJaVIuVJmdTiIBoiiwlqridVuxu2z4gi5SiQKtpoZikWg0dRTVwpnxJXTdJFdqIYkCf/z2V4Eg8vgzlzh7eQWPS8HqsHL87BTvvON6TkwvUC5U2THQg9uq4lPVtsVaq4kqg2Ga9LmuY750hfd/6bvUGxrNps7HX/tKrmRy3DNwCItYISU0ODmxgCTCp37uHh6fnOG60QGeOnOFlq5jmiaJZJmNZAnVImIaJpLc3rhYbQqSAFMzOeo2nUPxHtarZb47P839R04zHI8iihI2i8DrB4d5ppDkDw8eJBoL8OahEapaDYMKFlHCIlWpai06HYNczs5z/8UJ8lqTt28+SNPI8ZWr54k4ZCShee2ZsmBiICBiYnAyOUumVsXpsfOu7d+niDgtOg6LQqI6iyRI7AwN47Bspss5zEZ9liFPiFR9BYfFxmv6hvm1G17LzlCb0wxc8+VW8VnjtIxVTLSXaTuKgo33HvlTPvL0Nwh5imzyjSEIIpqRRRRsaEaahj7LifXnWa9u8Bdnz3H/5Svc1tfHbD5HKODG7rOzupilWqyxbaCLbrebuXyeYr3Bm0c38emnT+J023n94BCCAH9z/jx39Q/iUy0IwGQux75ojLDdhSLJPLE0zQeOHeH2vm7ApNRq8ItjW3nz2Ba+cPIM4U4frxkeZKY4w6jvIN0uHZss0jIK2C1t2k7LWOF06ipdLicnEkm8qk6HYwhBkK89x23oZvlf1MX83JUvEbY3MMw0iuTBInoQBSeS6ECVgoCBKkGqnsGrdCIIEpn6aVpGEZv8wzmFPwpM02iLcJsZEpU1vIobw2xilzvQzSw+ZTuCmKRuGLxn6zZu776VTmeAllFgrXqVx5em0cwWiiiSaZQ5vrZKTW+wI9TNZy+dZyGf43x6g0cWZrgrHuemri5MTAI2lZlCkcVigdcNDHBT7NWY1Mg2xmnoGT558TEqrRY7w2E0Q8ejqOimRlmrY5MUFKnzBz6LRYyQrM1il62IgkDLaLBRz+Ow2LCIMk1D4ztzV/jodw7jcNvpDfn5/Ct+l/fsuRu7kuLvLl4i4rBjCjkCqoNsc5JfPvwQ+zo6SNdqTCY3SC6kmNGqnE+lODc+z7bBbn5rzx7uig+QrBV5fn2dgGrlTUP3ELb5eHV8N39x9DECUQ/xaJBt4TBv2bSJX993gGKrTp8rxnjuElF7nCeWHmNzIMi9g1vpd29jo75KudVkTzhGvtGgw+FgbyzGf/3e02yPRXhlbw+Huvq5PR7HJltRZYkvT17huZUVHpgbZzQcZi1fYLZcZKVS5YYOG1cLGV6YmGN1PYNmlzkw1MfxxSXef8N+xoJB9kY6EEWTEb8Pt2Llgblp+txupvIpmrrOeCbJVD6FU1HYEgjy6t593N6zk6hdRhLatrGbfGN4FJHJ/Flu+LNPkUrnsblUVr5z+T8wR/OjH2b4J+BoXkz8u3+mn4pA868/85cfDt06QLPewmm38IVfuJf5YhGLJCIKAg1dJ99osFgqkW820QyDbrebbeEwUbuNhq6jyjIto+2E47OqBO02ru/s5J2bd2OYJmWtTq8zyIDXgSi0RbA7XU52hkLYZAuKqBFQ9+BVInQ5R4nY66RqZaYLM1zOjXM+fYG/uvAUn7l4jlt6esg1GpxcW+OuvjgjvjF0s0BDXyNdT+BR4uikqWk6z64lGPP7Cdhs1HUdiyhyPLHGsM+NgMB0Ps9NHXv46Om/5/JKAski4XS2Mwx9sSCaReB/vvK1WCQDUYDVcoUP3n4jAZuKXbZgYvL0zAJffOEMW3pMYnaVfdEtvG30Lt617S5es93PF547jcNjp2UYnByf5e/PXuDvp5/iY498la8tHGOqPEXYkSdki+JTegjb+gnb+gmoA9jkDm7ssPG6gWFGAx4O9fSgSALpepmgzYoo6IiCSVWrUNHqnEsl2BII8Y3pabrdHvKNOg8eO4/isaNaJKqtOgGbHVmUWK0USDcbvH5wEB2Bg50xYg4nueY6lzLrnN9I4bIqdDicHFlaQpEkltczjAx0ErI7CNrt3D99hXuHtnBTRy+PL87wmoE+Pvjw97jv4jjRqI/Tl2aZX0oy2BOjpRsk0nkO7B5idnGDpUQFl8NCIOhkbibFynKeltaWtrIqEtW6Rr3epkpki038bgWXXea7JycpGnWiXX7y6RKNeotiqUqr0WIunye9nsfpsaPardgsFgI2G37VStzlJmb3EbaF0M0sN//1p3F7bFgVCVW18P4bbuAdoz9L01hElUdJ1We4Us7hDXk4mVwnvZbj6kICQ9exOaxUijVkkZesT0VJRJIl/BEvMZ+H28eG8XU4aZgGt/T0cCqZZKFYoNhq0BsKcGx6jqValb3RCMuVCl5rez7dGNtOrpnFqzgRBB2P4kU3G0hik6peJ+p1Y5FkbuvuQhQkupw2Fkt5gjYnimSjYeSRBYWqtkGxlePk+iqrxSLffOO9uJTua/qPJhYxgiyGSdfneO2XvkI8aiPuatvHNY0035qdJO52EbHtwiLFkAQXdf0KJjqS4HypscQ09WvSRS/POL33yJ8yn8/xvj07eePAW146VxId5BtnmS/PkW9WSNWqHF1d44WFJWx2K8+emcTusvHRm27CrVoRPVZ2DvVya08XI14fUaeDG7o7+fLlK1jtVvZ0dNDndtPhdOJRVRKVIq+J70KVdfLNOudSKRZKBe6busIXnnqeT7z6dgbdA0zmlvnu7Bxxt4vlcoEnxq9id9l4146d7AndiSDIlFsrKJLExcw6nQ43qtzDYnmGToePpt7iqZVlREFgZ3AzLTOBJLhf+vz/mt+5Rdpg0HMIWZTJNadZrcxTaq2RbSzgUdzIou9aOTfD0dXn+euLj7M54KXbefDftN6/KMNUbs2yWMritcr8/nNPcVd8DyeTLyAIaYY8feyLRHhieQZRLPDE0jiLpSyJaplvXZ0mVW8giQI3d/Yz4PFyIrHGTD7NG4eH+dUdB+h12xjPZCi1NE4nkxyItfWRV8sVbujowK0o6OYGLksIi2jyrbnzfO3SBCGXi4vpNFuDAS7nkmwL3IZFLKGIP+gD/yJCtn5mS+Oc3lgm7g4gixJWyUJdbzGRTfBHjz2F02PnL159B7+0+e0IgoBm5Pj1I/dzU08vdV3n2Noaf376CPdNThBxujBMyNXr/MaeXUw0SoyFQnisVt533W4apkG3y0WmVmW2kKeu62zUaiyVZ5kpLuNU6nzj0gTriSKFUoU7xoawSCIWSSBbr1HWNtBNg2NrF9gf7ebo6iIn11d4PnmZoKoiiQJB1cGAO8Qza4tM5/M4bDb2RSPE3V6ithgui4fz6XmS1QpLpTITV5cpV+p86NCNPJ9OsrGew+my8eTSHGMBP8/PLJIvNilXqrxhx2betW0by+Uyu8JhepxRAqqdmXyGB+cXmExn2KjVOJtMslyqsD0cZLFYbjs2La9gkWr80aknuJBJ8qqPfIrfu3srshhGEKwEbTEEb4WUqJFY2CD7vbn/uIHmn74YaPLjHRf+/QPNH6l0LgiCF/gssIV2Xeudpmme+CfnHAL+ArAAadM0bxYEQQWeAazXrnW/aZofunb+fcCLhBUvkDdNc4cgCHHgCvCisuxJ0zTf+y/dn0dReO2WUb63sECny814Os2ecIiFUol8o13+zFwr77Z0g4au0zIMvKqVhm7gURR008B6rQxtkUSCNpWFYonTGwsYmOwM9iCJXoY8oMoy17/EJRMotVa4lFklWZvmI488xZ7RfjpdLmyyRLWlkW80eOTYORxuOw6PjUy9xrnkBpphcnRljTt7MhRbGwgIhGwBDGp0OFy0DJ24203E7kRAoNwsczGzzusHBqhrGg6LQqej7XyUKJfZ3tdFolwmZHegGwafuPl2nBYfJg0G3AHcyv/i7r3D7DjLu//PlDOn97J7tvdd7UpaSatiNcuyjSsYbAgdU/IGCC1AQhLICyHwJqGEhBaSl1ACxOAABhsXDC5yU+9td7W97+m9nzMzvz/OSlgGnIRffr+LcF/XXrrO0TOjmUezz9zPfX+LQlFVyVYrtNjsWA0GUuUylXKVhcU0133yX2kOmLFaFXZs6uOj11zDU0uLODw2/u3mv0DVM7z551/ikSfP09LmR2/2kAinuW/+OA8ePku5WK8KN3cGMJoVhvwB7uzuYmugHYfSh9tYx3Vt8dVbjbpeRBDMCIjYDU5W8qOIgsBsJkWjxcLRlVVGxxcIxYo8/ORZ4jsLbGlspKyqbG1o5kI8SZPNTrJc5vUDA/x8fo53PPQId49s4qa2Dr5648s5EZnhgZlpWtda4sXWRu7q7eFiPMmTc7MsT4U4uX6QXqefgMXCo3MLdLY2MHZpkemJZWwuK7qm89C5UYr5MiurWSy2MEaDiNUs0T8YJBFK4XWZSKbLeDxmapUayUwZh9VAsaxS0zQ2rm+sC/JHc/hcRrw+BxVVRZYEnE4zarWGKIqsLtdtTu1uGzVdI1rIY5QkGixmzLKBYq3Mc6tn+NsnnuX6rYMMeD1EC0Xm02lUXaWmxTCIbVS0JbY3NON12YklsyxPhzBZjdRqKmabiVKhQjpTxuO1cPO29fjNZv7pwWdpcJi5tbubbKVKKF/AKMtc09KKRZZJlysYJRmjWWEqFqNcrHBuaoE/zmX5ycvfgqqXKKlVNAq0WJuQBCc6VXS9iFEyYJZ9rPeUua5pD/O5CxxanWCdp5Fz8RAW2UC4mKKq1fjm2Fn+19BmHpwd58mFRS5OL6GpGnbFQkkdwyTV8YQ1LYYkOPnHc2fYPdjDSi7PdyeeZWewiU2+3Yz4HWvjkizlzhO0NF859uq161cwKYC/2L6TRkvvVUSWePkUFbWKDowm4ozFkzwyeolkKIWm6bQMu5H6mnAYjfzb+DjnwiFevW4dW/x+fja/gMtkZC6TYU9TsN6yFQSenp5le0MAh6KwlMuxrSFARSvwzPISO4NBHIqJb4xe5NzKKsEOP9FigZqewSwbeN/mTZhkmfFEnJ7+FlZXE4jPU2VuMO8gUznNS1qvuWJL2OvcD0DADNuuvxWoa1TqevnKcVUtfNV9/6oYcG3lUuoJOu09+Ezb8K0RyGtajGRlBochjSw2cCg0y46G9rpknOuGFz3nfyVMUl1hI1pK4zKZ0PQsAjCVimEQZRLlLPddmmDA7WG910dFVfnp/DzFWo0PbN7OvRMXqGoqmUqRG9vaqKgaB1eWObC0yGQyiddswW1UGHC7cJlM6LpOk81KVde42dAMGwAAIABJREFUmIhjV4ws5c9jEGVmMxleOtDP3es2MJOO0eNsoNcJFXUOWWj8D12QWq0+nIoFXa/DfzRdZ7WQpM3u5M9vuhazLLOz4XYApjIH+OujRxnyBzCIItlKhUuxGJqu0+v1kiqVOHpuklfu3kJN09jb2sZCJs3upiYixQL+NULO0XAYiyyj6To/vTRJh8/Dp/Zcx1sefYhiocwt122kxWHHbzazyd/IkdASj80v4jaZuK6lmUMrq4gChAtFPjzyDlQ9i6qnORY+zZnYKrsaO1nI5nh5VxdtNh+qrjGaXOVrFx6+Imz/2r5+zscTbBxop1CtUtM0RoJN7GppwakonIvFmE5nuPGaDTx+5Dz5XJl7RkdJdJXodbkwSwqS4OaZlQsYJJFL8RiZTIHNQ40M+rw8PjvL3z13BE3VaPZ7iBfyrPd5ERF46tIUA/1eXv7APXxyzx5G43Hun5pibiFMJJTB/KsYVr9t8TuG0fwC8Kiu668SBEGBq8Ema4noV4BbdF1fEAThMsqnDFyv63pOqG/nnhME4ae6rh/Rdf01zzv+c0D6eaec1nV903/2JnTg1b39bPB613aaOoValRablYVsDnmtqpkolZAEAVXTkGSJcL5QxwEaFWSxnhSK0hro22bjZR3r+fniGCOB5issuyoGZCHBQ7OXKNQu0mAxk65UrryYXX4HM8kEuUoZURDo93pxGY20DzSTimaoVWr8dHaeWCGPJIrECwV0atgM9fZPWS0hCxG6nQE0XSNbLTCVStDr8hIrFdnka+J0dIWAxUJVU3lt3wgl9SKZShkRgUyhRLZYot3tRhQEEuUoNoOJVKXAz+bnKdXqVpkW2bBG6sngaXARCmXR81Vqqol8vsLp8TlePb3Ihu5WjGaF1cIRgpZr+ODICA8+dpazp2a5Znsfi9MRrFaFcrGOsWxs9WKzmsjlSyxm0nz2+HGGGxbZ6BvDbTRiNRioaBotNjtGScYqmwmYtwMw6PYx4MqSrdbdTJ5bWiKfK5Mv1hBFOH1ymllPiNzIEIVajY9u28dUepWSWuOJxXmKqsqG1iYMosiZaAizbKTRamPY52M8meRUOFzHqOYLXIhGiK/WnVG+eWGUf9h3E6/u6+OtDzxIYjVFKlPGYBDxNXuolqrIikzAZaWYK5GKZhAEgbZWF4vTEXrWt/CKvj6+ffgU4dUUiixSrmhkqKIYRHbu7OFjO3fys/kFHp+eYWp0GatBocVhJtte93g3Weu6rV6LmVylyr6WJjb6AlgkIxbZVGf3Gp0IGNnk07j3Na/CqVgxSgas8hoZZa1Sp+kFFLGFJit8ZMcOPn7oEM5BC3ta2/jR8bNYnRZWZiKEZjKElnJ85HY/Xzp4jHi6jMNVxrPm3mNXDPjNZiaSCRRJpsVuI1up0ORxUqzVCLTbKNZqVFWVqlbEKDVilnyUtXEEJEBHQEZHxm8eRBIcOJQkINLtuI5Oe5maHmGLH747cQGbIckdnQM0Wa2IiLTa7Cyl02QSOXZvW4dBNF6VKMqij0zlNO/ZuJmxZJSdjZ1XEVlW8oe4lIoy5AnSYl2HLPp/7RpSVqcxSt1X4TJbbXs4sPwAOxt7MQhNJCoTRIsZFrIZnlpaZjQWZTWcwOaykpYlirkyB54do6nJyZZtG9jX3MT2xgDbG5r42fwsb163nkcXpkkWS/xwYpIWuwNBEChUq9w3MUWiVCRfqXBgZhb7tQpPLy4CsL+1lWarhet7uhkJ+Njib1qTaBFxKmYemJkkVCjysp5uvhZJUtPVq+7NKneiUXzRNXQ5f5xGS8+VeXMZLxN+hF/pXV6ffzf9z0sca1oMWfQhiz78prpOpqrn2BpoxWfahs0/+qLX8F+NcDGCQ7GSrRboczuZz11ga6CLS6klctUiXqOdD2zdikGUiJeKuI1GVrI55ufCHF6dx2c28czyLH1uD36TjePJZVpsNv7s+4/g9Nl53dZhupwOHIqRUq2GJAh0ORwcWFqm3WEnXipitMp8/9IFppJJ3r5hAz5TK02WnVewvJefp5qWIFWZxmEIoEjtV92HqmcxSR4cymYyldNESikazG7cRisnIst89ew5Prj1F4TPbKVCslSk0+nkuaVFFFHCsGat7DVb2N7YSLpcZiWXI10pk61UmIzHqWk66/0+epwORAQeOD/G4kyUVLZCoVhDudFAopRHEgR2bR/gncMbGXQ3IQoCx8NzpCsV1vu8HFpeRm9p4pW9PSRKdfWJyfQBOuwDiFhZ72mmpFZIV/Js9HlQdR270re24YzgNZv4yN8/yG2/t4lX9/ax0eeh2+mgpNaYy6Z5dW8v35u4xLlIhEy5TCxf4OO7dqHpOj967BTFUgW7olCoVUlViniqqzy1tEKuUsZpNPKuazextaGZH0yO0WR3EIulyWeKpOM5TBaFey+M8qWbbuSHk1P8608PsTQf4fuBSY4tLpFJZLG5rHS0NTA+tvDf+rz+fxL/M/LM/zjRFATBAVwLvAVA1/UKrDE3fhGvB36k6/rC2pjI2p86kFsbY1j70V9wfgF4NXD9b3oTAO22jbRaM+hoTKaneWppkUSpTLxYQBZEirUaoiDgt1hIlUu4MKEoEqquk1hro4gIWBUDt7T14DXVf7FHAhly1RKnYo8ynU5RVTWWcjk0HbpdDlRNYzqVoarVZSm+duttfPbk8frOVJIJWi202ezYFQMPVSeJrSQ4c2kOq9PCxpYm3GYzyXIUgyij6ho2g5myWqGkVtB0jbJaYzGX5dmVVaqaxlQqzaMzM3zuuv0s57KYpEGOhh+mz+3hqYV51JqKWlOZKIb58rnjtNqsV+YoVCiwnMmwu6UFr9nMfCaNquvs7Wzn1UPreO8Xf0K+WMNhM2C2Gnnl5vU8MjXFwqVVbo38K/e9qUSfM0hXl5eJyRiTMys43RZkg0QmW8YfsNPe4EXTdQxr2nF7Wlq5e90Q+VqZhUyGR+bmeXJ0AsWsYDIp5HNFBpsP0OF0ErCYWOf2cG3THdzcOsLNrXdRun2U74wf5+GZGQ48PYYgFDi2uMShmTle2b2VPlc7j86fZTlXYD6d5uToDBeWVrl9sJ8up5uZdJLPHTzKUHMjpy/Mous64+OLFPIVLBYDr7xuGw8dO8c/OI7w4a17+ci+3bznWw/gchixOi2U8mXUmordZaXP4+Uv37qL5VyWZ5ZWODmzwJ3XbOJbjx7iMUXB6rTQAJjtJkZcDga8Ptoddva3tBG0eNjobeNPR64nX81iVxoQsaLuTwE1BMGMiBVRsP4SKUXXVZwIaOSIFkfpsO980bbm5eqJIrawq7GFv90rXamKZRI5IqEMsiSASYJCjc89cZD5+QSd7S5cfgfT6Qy3trfxdydOsLUxyHs3bcIiy7x9aAcCZh6eP84XT5zELMt4zRYi+RwCMJ25wFIuy7XBzejU0PU6I7ikpnFKXQAUaxVEQxgZD6JgRNAVrLKRW9s7OLiyjNfUwmt7/YiCiVZ7mnQsyx37Rmix235lNdKhbMahQJfjF3OVr13gcGiGa5uGWecWmc1EcfvbXnT9MErdAFeRfyraEh0OFxW1zHLpDOdjEb5y5gzxSJpoKI3FqiDJIpVSlUis/oJ2O4x85hU3o4gi6zyNHFiaQRQEkuUKgiAwGk/y+oF+vnDqFEeSiyiKjKZpTIYitPjcKKJEn9dLrytAulxmOV/g6aUlVF3njQMD5KtVpLXWqtWg4FJs9LlcDHo82BSF1kYfAXOdABkpHiNSTBMq5Lk2ePW+XdVzVznIHAkv8fLOegJ0MLTAXV23/Ze9yF8owl7RltD1CqVaBU2vYJIGgV8kpP9vQtdVTkVWeElrP9FihvOxBNc0NhEp1nVeW23181/XdC3fm3yE3cFWVvIZ+jxubnvp9ZgNMp8/cQKjLPOZfXUzik6Hi0vJOG+4fgcjDX6ihSItNjtHQyH8FjMNZgtes5k+txO/2cJoIs53xsb42I7dvL4fmiybqWhzXEo9cSUBv/w8SYITl9JDWV1AEpxXuSNJgh1BvEwcDPDE4lluaK2zzncHO/hM7jDn43Fe2lEfv5LPsre1lZOhEOFIEskg0x7wIAki+1ub6XN5yVWrnAyHuW9iklihQHw1RbVcQxYFdF0nXY6yOh8jkijR3+Ph4miMl/f20u1ooFyr1aWavK0kylkuJsJ02J08s7JCs9XKuzdvotfpJ10pcD4ep6xqfObEMf5xfw/R4hgNlg1EMsdpswVptLgxS0OUtSlApcPu40Obt3PT59s4uLqKLIp0OlwcDa3y7bPnGW4KssHrp9lqJWA2873T57l9y0YWcxk2N/jJXbeeE+em+Zvpx1i3oZ3/vXMnBbVMrlJGEAT8Fiu3tA9z78QxxhIJphPxuka0xUg8ksHps7O9tQWLrHBbZwe5G+tOeidWV7GaFMxBD9Fwiqlohlz+hWnOb2H8DzER/89UNLuAKPBNQRCGgZPAH+m6nn/emD7AIAjCU4Ad+IKu698GEOp9qZNAD/CPuq4ffcH59wJhXdefL1rVKQjCaSAD/G9d1599sQvUdR1RMK3JAhUZcNmRRZE/feZpejweRAQ0XceiGLDIMl0uNxVVxWMyYpFlXtE9QJNlJ1CvbIQKKzw0929XksdYscSA24Uiimzw+rm+pQsNnX86d4r5dJoet5tUpcx7Nm6m0eLm5vY2el0e2u1+DGK9/L6nqZuNPh9v+5cfYjHJxKI5QvMx1m1o53hkkRtaBhAFBREjsgxlNURZq8t/WGSZQrWKbw2rNzu5wvu0J/jknj2oepaiWmM6lcIsy5TW2l8Wu5lDS4tsDTZhNRhotllotFhot9vZ5PNxPBzm8bk52p0uQrksr+ntY+rv388bf3w/LT43F8YWuBCNsbutjXy6zmL/x3NnWM1msTot9PZ4KRerRGN5PE4jhjUSSapUQgeS8QxqTcPZ3U3A7EYUJEySgb987Km6z208R0rTSCYK/OX+a2mx2XnP44+RDKexOJ5kuLWZv7xmL0HLNfzB0CBvGhjj3p4TLGRzPDY1jaZqfG/iKK/s2UBV16hqGmMLK6ysZllYTHP7YD81TeOHE5MUskXsRiNOn52jRxegooIgIBklvls8gt1p5ujKMv904Rj7W1r4/jtfR6+rEUWUyVWLzGfjTKVT1DSdyVSSrYFG9rd047zWgSJ28Ykd77rqedT08i8li/XnVEXV04hCjvHkGE8sLnAyHKZQqfKDl34QdI1CbRxN1zi4OsPTyytciEZwmUy8tr+fnY0dfPTw05xf+B5Op5X97R3IokCT1UpFVfnR5CShSBLFrDAYbODPtm6n23EdI/56e/QTO9bxno1buG96nIlEEpui8NzsHLGVJFu3d9Fkd7CrKcjTS0t86KeP8+WX3UqLzY1VNmIzDCAKZnRd5Usn/y9P/3Qck9OIwyKTK9Z4uXYvF07NIYoC/RsP88fbtnFz2050qmvSLZc9ryvYDL/AAVa1MP8+eY439m+hf91WTsUO8/DcLB12O36LmZcMr+Mzu9/3Yr/+qHqGqraISRpCECRMUhuDnjwlNU3APIxDmfyVjikvjFz1LDbDMMnySdS1boJJMpKpFpjPpPj7o8dJRtJk0wUEASRZpJgrkyvUcNsVJEnk+Af/iGOReXpdPrLVAnd1jzCfXeL3BzfyyPwUH966k9PRZbLFEg0OG0N+P5lKhY1eL59++Cn+9x3XszvYyrn4CrlckXSpxO0d7VgMBlyKjWItyWgizFZ/G27FjtXQxGOLz7DR68WqGIjl8kiCSEVb5KG5cUbjSYb9XoQ1YfTLz+a9kw/whr43oOo5BCT8ZjOKWJch6nG6EbGujS/+2ormfxS6XkYSHLTb9171vSQ4qWm/kOP5TUIQJG5pX48s+JnNXOKWjjbcRhtuox2zrGCSFCpqlYq2xP6WTu6bGmfA46bTYedLx04yNbpMJlelvc2JUzFzcHWRXcEWNvj8fOHQMb779HF2b+7nupZ2bm7vxKlYuW/6Im/o20ynPcCFxDIek4n5VIqgZSMXE4cxS6M8ODvG3QOv/JXXKwtuvnTuPv5wgwmJq204VT2FKJgxiM28Y+gtTGUOUNGq2GQz377zDkL5HI8v3c9yLkerzUa+UiVTLpPPFLE5LcSLRRyKEbfRRLZSxmlUcChGUIzYFCOpaIat7a14TSaWc3mWMmnMFoWgD1LJAi1BC/PZLNFSmrds2MCg11sX+tc08tUqmUoFp6IwmUrjMRm5d/wgAO8aHmbQvRVZ9JCunEYWZSTBTsDk4rnVS3z0iad47k0fR8RItLSC22iDNcWMLkeAhVyMJxcX+fQPnuD6vUM0WK3MplO8rLOXDx98mmQoxZDXy5OLS+SqVfwWK05PfYNU1TTcRhPFWpnZVIotjUEy5TLfGj/MwaUVarqGWqv/WBxm2lrqHY1IPs9EKk6Hw4kkCFhkmXylwmf2Xce/jY8zdm6BTL7K8Egby7/xE/r/Qwj8TrXOZWAL8F5d148KgvAF4M+Bj75gzAhwA2AGDguCcETX9Qld11Vg01p7/ceCIKzXdf3C8459HfC9531eBdp0XY8LgjAC3C8IwpCu65nnX5QgCG8H3g7ga3IznztO0OLDILYjClZChRxmWUZEwGk00u92oUgSB1dWeOPAANc23QHUd/Yno89wPPx98tUaE6kUPzpylnymgMVu5iUjg7iNJv7pzBlu7+lhnceHUTIgCSJvX193EclWS1Q1FUEAgxhgd7CwJn7uQaMEaIiChiKKdPY1IogCitFAp8vNG9cNUFZVZMGBRgkdFQEJHZAEiWylQKJUJmAx0+l0sD3QTOLmPbyss4fRRBRJsDPgaqBQPcOdfX202m20251AnfFdVMskSyUmU0l6XE4UUSJbrfB7PYN849ljyIJIvlrh6xcv8qcjO/ibG/fz0aeeYdemPnrcLi7GYuxb10uv24nPZOK8ouA1W/D0mJEFgWS5TLlW4+zSCorJgA7c2NHBGUuEuUic1XwBUZCoaVVEQaCQKzEy2MX44iqVcpWewSb+/MGf0xD08KEd2zkejvCdnx/myZUkz52bYNvQU3x4+3b6nNfzxn4/RXWJPx25lkQpTaiY4Wh4jo1eP3d0bOHN69bx9PIijRZLHfNXyHP34CDy+rpd59sGh7DcpWCSFXzGJmTR/x9WbWwGaLTAjl8BV6uoc0xnDvGT2WlmU2nOLK+QjmUp5cuUyvVNwp+98gaOrYaYjsRIRtKk0yVEASRJQBQEZFmkUlXpP/cefM0eMvEsoiRisZkoFyuEQ/U2/Uw4xoev1bmwtEoilGJ2MsLFs/NIooDZotDe3cj8dAiLzcTX7ryZr1+8yMu/cQ+C8F36upv5we1/jkFspNnayHs2bGIme5T7p6eQJJEtG7o4fmqSscoCp/1zrO9p5em3vBvQMIr9a2zsyxVUgYfvfAMNh/8P7W1OxibiOO0KkxcW8fltlAsVpi4s8c4LSzQED/DufTu4rb0PSZggVUniN7ddkXmpk3oEJlNp3vf0I7yip5t8tUool+ftQ5tosu7ilrYsB5YfYH/zy6+ae12vcj5xAIdiosN+LZI0dOXvZLGuEZqvFhEY58G5UV7eqWMzvLjrz7n4In995Ae8fXgjAYsVRRSZz4Y4tBLi8Ow88VCK7Rt7OHFhmkCrlwunFjAZJTxuE5999W00WmzkqkWKtSo1XV1zEqs3dPK1MtFikZpeJx7+4chmXtV9C7LooVg7z2hyiXfftJsfXJogU6ny5Pw8FqsJn8WCx2TmU8eOsZTNUCqU+dKtN1FQyyxmE5jlBBu9XiwGmUSphMlo4N7JM+xtauVN/S9hOT9Ji3UQSXCj6QVK6hSZSgGfuZ48JsqjzKSjXNNQ12t8fOl+bmx5BZpeRNel3zjJhHqVWNe1NQcgxxU9UkEwIOH8jc97OUTBjijYGAk0czERxm10ICAzmYrQYLHRbA1w/8wZbmrr56Wd3ez97FcplFTe/3v72NvZzl9tfy1fH3uIslqlpum85t9/iN/npM3v4RPX70MSBMKFLEGrk2y1wO+veykg8tjiAcaTSQIWM/9nz17e9tiX+dg1OzFKBu4eeM2LXnODxXKVlublMIjBqz73OPbzfy/+K7e29+A21jH6x8IrjCdTfO3UGXr8PgrVKpJcx/m+ZmCAuWyW0Xgcl9FYf2dYLRSqNebTKRSjgUg+x7HZeT5/y0so1Gp8WYdYLsf8xCqNbT5kQSRcyLK/pYOVQoY/efanfGzHXlRd5wunT/HJXXt4w4/vRxJFPrBlCx8/fIiSWmOlMIpRMjCfjbPVv5+aFuPBuVG+8PQR/u3Vd6JTRaOIJIh88ewRXtPXT6t1iLnsSTxGC2VV4+H3383B1VUenpqi1WbFY0rzqT3X8fdWK/9w8iT729qoahp39XQzGo3gt1pJlUp89sQJ3rFxA+1OF212G+s72zm8GuLMpXmS8RwujxWHx0YhU2QxW8LusWIxGPinM2e5trWVj22/lulMhDu6ujkZCTHs97K0tZdut5s/2rSVlt9qHU1+d1rnwBKw9LxK5A+pJ5ovHBNbq3LmBUF4BhgGJi4P0HU9tVbxvAW4ACDU1Wnvop6kXh5Xpo7tRNf1k4IgTFOvmJ54/j+o6/pXga8CDG7q0Au1ColSGocyTaiYwGM085EdO4gWiwy6AzRZ6/pfb+zPcizyDN+d+C6yWK90TiTTTCQTV3ZviXi9FWgB2u123jywmTa7jUdmZtgdDOJUakyno5yIRNgdDNJsdSMKIjaDGV0vo6FjkGRi5RVcihUQ0HWNBquV9+/azhZ/I5quIwkiTyzO4TQq1PQ0xVoFgyhhlJyECmlOhEMky2X6XC6abXaCVgenoyvsCjZR01VORaJs9h/iT555glu7Onl4epYb2tv42rnzzCxG+Mqdt9FgsdNosbOQTVNZk7TJVqocWJrhNdds5om5OZZmwuSLZfLDw6zm81jMRkK5LNe3tVCo1rijq4ufzMywmi/w0/PjTE9GWLexheGmIHPpFK8Z6Gc0GsEky5hlAw0WC88dGUeWBHJtLahalQdmL3AiHCWZKGCUJdoavYTSWbq8XmzBIEZJ4nvjlzhy4hKqpmMwSjz9znfwyPw4t//jt/A03M9wZwsv6+rklra9NFmHaLKuSbBgRhAkBlx99Du1OgGFKrp+OXFX1uxBjQjUbQk1PU+yPMp8LkaiVCJZLnE8FCFeLHLy0izBJi+3dHXhM5v44rNHSV9OAO31F68kiRTzZQrZIsWySqFUQ9fBapYxGkQCQRf9HUEenp5mfiZEKlWkVNHIF2vIkoDXaaRUrR+jajqqphOKLWO3Goiny+g61Moqgixis8jk0wWssoE2v4dKsUq5kiaeKuNzGwl2+Eklc2zd2M1bhob4ytmzvKS9letbmzm8GubQ/AKDf/eHiKLAn9y2j7etu5sex37eP7yBXcEjTKdT7GttIVup8pPxCe7o7iJdSdFg3lG3/nveaiYIIou5KGajhN1t5Y/fsIF9LU30ujwoooxBlNF0DUEQuJgI87njJ/ja8dNUSlXKxQqyQaJcrLB1fTc1XeOm9nZuaGvhdCTGjS2bORq+SLpc4j1P/pwO9zE+t/vdVyWZqp5D14vIop+N3pt+7aJllU38ePoCb1n3Jlpti4jCr+8xqXoOTU+zxd/L7d1RVF1nJp3ic08cRJRECtkiezb3E3La6Pd6sG1ReOfGjXzMfJBQLMWHrt3JbCaLy2jiWHiJVrudU5FVtgSCVLUaPc7tnI0fZG9TE5OpGEZJYjlf4FTsCEPuZjLVIgeWljgVDpMulVjv9WKURMYTKS7FY3x/Enrcbv5g4wbuGRvHb7azmk+xnM+h6jrtDgdLuSwbfX4mk2l2NAaxG+rPaaPFS1VbRZZ9aHoNUTCgobMt0M49E/fwyu5NVLXwlf/jG5rrhBNRMKPqOeDFmef/UQiCiCS4qGkx0MUrVcxfR8D6r0SmEsJtdDGRjDLsbUIU7Oh6kel0iqDVTrqSJVYscfej97M1GCTQ6KC52cfLOjv51LFjeD70Lh790N0cXl2hzW7n5296Kw/Pj9HjdOEzWxlPxmiy2XEpVvK1Mt+bfJQOh5MGq5VT0Sg2g4GvX7jAP+y7CbeyEUEwXEWiKqszyKIfXa9eue8djU1U1PlfwmlexnQ+3yBg0OPlO+MX+IOhLTRbvdzW7kDgEouZNMcv1nVvfU1uXtHXh8tk5E3NLTy9vEiiXEIQBFRNJ1MuU9U0tnW3c2RiFoC/O3GCTpebGzvaObYa4k/u3o7NoNDn8nMktMB9U9Pc0NbC3+y6nr858RwA/R4vRknh2Td9knPxg/z7xAS/19/HgCuI3TC8Jvt1iKdXfk60WGAk0IhiMnDLF7/JmT/7IAu5GDVdY19LM/dPT6IxiSwIvKpnHXf19NBmC3JwdRUd+PbFi2xpDHJHVye9LhdHZua5v3AJo1Jn7b+ir49ooYh1rUOZr1YZaQjQ7rDjMZq5sbWNBxxjJOI50slC/Z2XLiIIkIxmSDjTGM0GBoeHWczH+NHUFENeN3uCbcxmE3xi9y4uxmP8ZHaC3/r4Xalo6roeEgRhURCEfl3XL1GvWr4Q1f0A8OW1xFEBdgD/IAiCH6iuJZlm4Ebg08877kZgXNf1pctfrB2T0HVdFQShC+gFXtTd3igZ6HOuv7Ir7FlTkdD0CvHyGcYSIQ4s30OhWsMkS8SKJRayORbSacpqDVXTcBiNmGQDgz4/kY40G9ub+fTuG1BEGVEQWefx8OPJSQ4sLdHuSPOJh57EHXDS7XQQtLqQBFB1laqWwG4wk6kUSFfqD7lJqrPal3JZvjc6Tr6nSlFVKdRqnAyF+MCWEYq1CookU1arRErz3DM+TrJUYjjgRxIFvnzmDHOJBBsaG3l1Xy+nomFMssRsJk6+WiVcKLKrpZnlfJ52pwubYuTub/6A1t4gL+vp4fX9G5j+owbpAAAgAElEQVRKR7kQixEqFPGZTZwJh5EEAW+jiwang08fP87mgJ+X9vTw1PwCZlnmHRs2kakUeffGER5bnMLjsbNsjKPWVCqaSipXYDVfwKooZAolYvk0sw0BsoUqoiiwkEnXMTRr1oa965sZ8nqZSqUxywbMskyyVCSaryMxCqUaPYPNJCNpbvzG12lvDSDJItGVJM/Esxw6N8kHCw8RaPWSjKQxKDLuBif/cvOtfPHMaRYzaaqqWrePq6rUqvXKomyQsZoUAlYbI40N+EwmvnzkOKV8mb1DvWi6zsVohK3BJm4cXofXbMIg1gWbu5sDlAJ1DJzDaCRbqeAxmwnnc8QLBfLFMrOXQtgsBlRVY2B9O+fPzDLU2YxRlplZSOOyGRCFeiJqt8hIooDNbkEQBZKJAptHupgcW2Q1WkTX6xXPtnY71ZqO22tF1+FYOEKxVqOYL1GuqHR3edi9vpcLoTCTExG293QwmUrxkvZWkuVK/bnI53lpfy9Pm+ut/L//2bOEC0X+cP02XMYtXNPwEq5p0DgRPcCjc/Nc09bKV06d5pa2ATS9iKpnkAT3VRvnTx49Qmu7h3Qsi3NIodvpxiQpmGUFVdMQBRFVV/EYzbx5/RBzmSyaDlPJJHOJBL83uI69zc08vrjIrqZmvjU2ytHlZWLFh+hyOvjOze+5CisJ8BdHvszPzo3j8Tn43m134TX9emIPwKdOHuRdG+q4xGub7qCi1Yk1VW0ZAcuV85fUMVbyEcLFHDPpFJIocP/kNBaDAZO1Pmfbezuo6Rp/tWs3oXy9QjmaiLG3tZWh4WFmMxlUTWMylWRfcxsTyTib/UEKtQrZShmHYY7j4RD3jo6xrakZURA4ODfPuUiUNscsTqOC32ymz+NhNVu3oNzTHGQ0FsUoyZxYXWV+Lsy2hgBfvO51aHqeRnMrMEqz1c35+CobfX5cioVulxODKHE6ukyhNscdnXtBkND0CiAiCw1U1RifOPIcrx/oxyQNsbuxD0Ew8MzKT57X6cki8Mvwj988dIT/ZkBZsVbGqRTocfn4+ugZ3jqoY5YU9ja1ESnmOBaa5vsXx3jdhiF0XecV2zby9qFNNFpGOHX6u6iazjcvXuRDW7dxIhzieGSeXcEWWqx96HqF+6YmyFYqVDwq7z/wJLIosqmhkWuCDUwkkoiCwNs3bsCp9FPTE8h4riSZ+epZvjZ6ArtiYJ3Hy4h/GEVswSgZOBsfZVugnZqWBNQ1XLYZXa+io6+5yNUhUx8ZeTtPLP+EPcF1FNX6GrmYTOFrclPKlykXK/zw4hjtXjdL2RxH1sTlzbLMVDJBKJcjny5w1/btjMaibGkMcnh2nretX8+gJ4DbqFDVNI6Hw9gMCuFCka0NfjodLp5dnWadx42qa7y0Y4DjkQVuau3Aa7Iy7PdyU+u6K85aS/mDvP4n93NrXw8ek5EjoRVS0QyKQeJIeI5el4eVfJYHZ2bpdDqwyPUK/M8WptjT1ML5+Bxb/H4Ka45vG3xeBtyNhAp5dF1nZSbCe1+6j1Ktxgavl7yzyqVkig67nXy1iqbrLGRzHA9HeHxymqnxEEZFwmJTsLms1Koq6XQJWRIQJQFBEFjN50mWS4TyObY3BhhPRYkUCqTLZZLl8hUVmt/q+J+RZ/6nWefvBe5ZY5zPAG8VBOGdALqu/7Ou62OCIDwKnAM04Gu6rl8QBGEj8K01nKYIfF/X9Yeed97XcnXbHOrEo08IglCj7kH3Tl3XEy92cfOZFH994seYJBmHYsAgigStdXed5XyBWLHEZCJOqlRiZ3MLHpMRn9nEQjpNPJVloKkRm6IgiyK9LgcvWdfHnuYgRklBFEQ0XaPd7qXL7ea+cxdp83swW40oisz5WAK/2bK2uIe5pb0Xk6QgCSJlVaWkVpAEkZqu0mixUtM1LAYZq2JgOpXBajAw5GkGBGpajflsjJ8vLLCczTDS2IjPZKJYq9FgtZIt1xmE89kM2UqVPU1N2AwKjTYb4/EEx85NMT4W47pbBujxePC3eikW6jvas7EVRGBnsAlN13EoJuYzWaKFAgWzmX6vl0dHJxAFiBYKWAwGxhJJAmYLp6JRGi1mfCYz79w0DJuGWckX+PHEBLGVJIccyzQ7HPR5fZwNhxARGBpuoZQv0+ZwXpFpOh+N896RLYQLRcpqjdl4goDDTqfLxdRKhNhKEofdiMtqJkmaeCTL8mIKi1miXNEoVzVsFp14usymYSeTo6uIIiRiOT7rO85cKoXPYsFmtaJqOjZFYWewgYDZQrZaIVIoIokCu4LNmCQD37FfJJvMMxGPcXt3N2W1xr6WJlbzBcyyxA8uTRCKpzFbjRgkCQ0dSRSxK3UBea/ZQrmmMjW6SKDRwcJCEkkQWFqKYrYoPHV4FJfHugbAh57+BjS1jhfymC28qr+PZ5aWObOwTDyZRZQlappOZ7ONRLpMqaJRrqi0uqyYLUby1Sp9Hi+GfonlWJL2gAe32cStfT3EVhJYDQaeWljgYzt38vDsLD+dnsZpMvH0wgI9Hi/NNis/yRf5l0ee455nT7BjfQ97m5t4y7o3sdW/nxbraaKlLK/p68UsBxEF8y+1TnVd46s33MH5+AKfP3WKn16aZFcwSLfTiCRILBYSPL6wwGI2x6V4jJWFKMsrGTZsbuPG7i7evWkYv9nKYjbNpUSCbYEA2wIBPr79VRjExqtIKpfxhE+tPMC/PnKYa7b38YGRLdiVxl+Lg70cf33N21H1+JXPIiaeWnkASRD59ugof3XNXiRR4tH5SfLVKsv5PM/OzrO5uYndLU38fHaeN48Mczoc5S+272IyHWUsEUcU4FI8gaZDm93GfLaeZD46M8PHd+/mx9P1+TgSWma918dMOkW6UsZuUOhye+h0OljK5Xj9xvWs5gsAdeyZ2cw1jY202W18+oEnmRqI84bBQU5Fogx63Ry22eos2/IKbmM7kuAgaF3BbfRxfcsQmp5D0wsMuBNkK2XWextYzKX41vgTvHngJnS9gEYeSfBzNLxEl8vJ3mC9enlZ33Hz8whT0eJFvKZOROHFJY7+syEIxqs0OutJlk6iPHVFdeI3C52ZTJy5dBqLZETVNWRRIlzIczYaZVd7G88uLaHrOluDQVxGG3PZIxjNCn63ztnZJSqbt2BXDBwJhTkdjfL6fhlZkBAFAZfRhKrr/PG2rXzx5Cnu7OlG13XWeb38wdAWHEorImbK2gyiaEQS6thLRWrkXRtuQ9PzyGIAfU0JwCwZGfHvA6CkLiAKAhZ5Y31O9Aiy8Ass8WI2y4hf5NrgJo5FznM+Huex2TkyiRytLf46dj6Rw+6yYpZlctUaAaut/t6p1UiVSuRSeQxGAw/MzPLWDevpcDjZHPDhM1sQBYGdja0cDi3iXvMNb7ZZGQk0czKyzFNLKwx4XFxKptgdzHJ9cz+y0ECTRSbQ5kXVKpTUS6QrdVmyT+zbgySIeE11oqkkS/zBDTt5cnGJS8kkPrOJVLnE/pYhymqNaLHIbDrNSj575Z53BRuZz2ZIliucji3hUIzk00XcAQcPT01xU1cXqXKZH09N0+Vy8tjiItlymZ89ex6LVUEQBNLpImajhM1uwqDIFLNFWroa2GyxsJzKoCgysVCSfx8f583rhxj2+3lycZktAR9Wg4Gz0TjdLgdN1l+Q5X4743fMglLX9TPA1hd8/c8vGPNZ4LMv+O4csPlFzvuWX/HdfcB9/5nrunIMkKlUOLA0Sz5TZLirlWa7ndVclqVMpi5NIYo02mxIosB1La1ECnlEQaChu5Mmq41SrcZCLkesWG87XIgn6HclabK0IQgqLsXMS1pbeW58mky5THdzgOvb2uh0OqmoKqv5HMlyBYtkxLjmd5utRLDIMlaDiaqmUpQrmGWZzx44SC5dxOow4/LaEQUDiXIKgfouq91uZz6dYSyewGMykivVWMllcZpMNFitrPf6GfLqpMtl5jIxjo3NYLIYGdnQhWKU6XC5MckG3rJ5mKVcjqMrKzy1sECny02X00GuWmOD10OPq16RjZfqyeiW9hYmEnFEQSAUSyELdcH7Yk1lNp3BbTQyn8mwzuvBYzJSVVVMFoUtDY10uxyMJpLsbGnhlb09rORyPHn0AplymVKtQofdyQe2jPD506fq8+dyEbLlKFQrpMsl+psbkWSJarlKo83GLNDc6aNcqLdbs6kCuqZRq6pYTRIriTSbtnVyU2cnfrOZbKXCyYl57tzTx+5gE/laFa/JXLfZMzqIlzIELAUuJRIUazUcBjM3dnayc+dOuhw+0pU8uWqNU5EY2UoFp1Ghx+2hx+3hmfEpBEEgIwoslKI4fXaaHQ4mFkPks0WqNZ0nf/9/8YWzR7nn8aOU8mXSmTIj27oZPTdHg9eELEt89pYb+caFi8zE43S53dzU1ovfbK5rv8VzDAy00tOnMXZ+DkkUSGUrmIwS6XiWoeZG7urp4StnzzA+uYjdbePsxTlS3SXu6u/jvTfvJVYs8dLuLk6Ew0ylUuxobsGuGDi5GqLNYWcunSG2nEAxyiwspplfOMlzgTFWCwXu7O5hwHUDTdY6W/hyNetyQvCLlqeIRd7AjoYNvG19hffe+yBPLy/TbndxODTL3z53mKXpMJoGTreFu3Zu5tDSIp/ffz2fOX6cGbeTb1y4yEouy+f27afLsRVdr12pME6lj5KpVAiYbTRYvBjFfgZcDZz68PtwKW6MUt/aOqG96JogCgo/XzzMTa23oOppZjKjfP/SJB1OB5sDAU5Gl3lwZpZsuczEfIhkNEPHumaePD1Gx77tvGFwgPVeP8u5Qt2zWqcOw/F4eEVvN6lymbFEkp+dGWVLXwd39ffx3fE6KeW745e4uaON09EIdsWAJAiIAuxsCuI2KkymUpwIhbEYDIiCgENRCBcKGCWRpxeXeecte7jn2BkOu0MYpXoH5oa2VnYFO0hVcjiUFCLGuhbgyjkqqkrAXDdUOBON0u6wU1bric1oIgHodZazVp+XdLnCWwc3IQgSqp5FEuxUtRB2w6Yrc5uvlTBUFvGa/nsSzefTY6cyB1A1jXPxKEdXw3xqV9dvxEJ3KlZ0vcRPZmZZSKd5YmmCAY+Xz586xanJBXKpPBZHfaNUzJWYjyYQAadR4Suvu4PXfPleQktJKpqK22jCZpBptllxKzaeC02zmM2xvaGBflcT69wKQ54AVU3lXCzMuzdup6zW7VIFQbqSLF6OX9IgXcsHLquZAL+EGTaIzWh6kVJtilgpzalojFd0wbcvPcOpcISTMwuUSxV0TWd+PozRrNDY7ie6nKDV7uBMOEyiWECtafQH6vPp8zlRdR2bYqCqaazmc3hNZqqaiiiIZKp5Oh1Omm0uDizNckNLLx7jMN7WcWqahiJJeEwmbIY6PEmnxGz2ArFSgVixQMBs4Xg4TKvdxhZ/ExcTEUb87YQLed53427OxeIMej1MplL0u910OV102ju5d/IQZrkuyVRVNSZTKTKVKo0WMyZZoqKqfO3cee7s7cVsNSLKIlOji3S63Mym0hw6M8FksweHpS7F5nCZqRQruANOrA4z1XIVf5OHmqqiqTr5XJFCvgRAtVKlUqoSXorTuN3K9oYgx48dY8jr42fz84w0+Hl8YYk7un7LE83LAuz/A+J3wuvcoSg8evQ8mlZXTnrqyAVu2LWBLld9d1muqYTyOa5rbWXI66XH2cKF+GmixSKPTk7xji2bkUWBFpuVZquNc7EYD05Ps8XvJ2hpRhSs1LQom/3NfPrWG6lpGrU1RnrQaiVSKBAtltjb1MSDc6NEiiU61uzddgYbMIgyoiCQKpdosjs4n5mnVqlRLVdptjtQtQrhQoZircrnT55kbnyZf37zXZRqNXR0NnhtnA5H+MTOPZTUKgZRQken3ebj2eXj3LF1I7FCgX6Pm2SxSKlWx6yM/z/cvWecHGed7/utqq7Ouacn5zwaSaMcbclyjhjbOAAGTE6GXQ7LkhZ27+41ZrnssnAMLDnYYIwNOEfZcpBsy6MwkmY0Oc90T3dP59xVXXVftCzAGLNwzzl7P/zf1ad7qnuq63nq9/yfX4jFGYmEyRZK5DN5ZmeC7C/rmK1GavfuwGcxIyIgiyKCIHD/0VMoJZWqeg/5TGVQJosluj0uHEYjtVYbBVXlgekZnpyNViZyh4UjK0HixSLn1NdiNcg4ZQsXtTRxeGyGxVSSeDFHo92LQ7YyEgnjNpmxyga6vRXf00dOjfLhXVtJF4sshaKYJANltYxSVDFZK6tUq91MMprG6rAw0F7P0WOTmM0y+aZm9jY0Y5KMmCSJNpeLVocfTdfPRFwKfOTAIwTSKURBYHtDAzVWK1mlxM7aWsbiMWqtDr48OEit3Y5Fknji2Ajrupuxy0be3NnBjd3dfPLpZ/j0ru384xPP8oWdO3lqcYnHpk5RKJbZtLUFj2kzN3TF+cEjh3DbbLR2uQiF40TiRer8FlS1zMd/8yieahfFgsLJcIivHH0JVdfQdR1d1zm3qYnjoRC6Dv2bWlicCDK7nCGbj2OxLfCQ3cHQzCJaWWd+Kkwqq3De5j6OhsL8w7Zd3Dk2zIlIlOu7OllIZ+j3eVjJ5risvQ2HUWYsGkVVypQUjfPP68NpNHFyaoGfHzzKzw8e5YtXRri+4yaMYiOKFgJgJX+aOuu61x1359b34K56lm/f/wKPdYwRDyfRNJ2bLtyOLIocXFzgn7d/hM3HP8oPT49wXnMj17bv4obOmjNq5oqVU1Gb5vG5p2l3uVnjuZCceopXwjPo6DTZm7l7YphPbHjf2c8taYtIggcJOxnlBMWycvYB/rsct4GqemLFEeyyFUEQOK+pgWqrFZfRzIcffgxJEivWXLEMa9a34rfauLl/DaejcdqcThyylYKqEs4nmEjE0XSd4WgUh9HI9w8f5dqBft57zlZmUilaHE5aHE5eCAR455o+/uPYMdb7q9le28E3T5zgRCDI3rZWelsqXcPzmxu5ofN8BMyU9Rh3nDqAxWDgfevWcufoKDanhZfGZ/jBdVfR6+4kkFtC0zV8JicmsR2NIj5zKztqTIwnglgNMgZRZEtNDZ946An+/cqL6XRVcXFzM/fPPsebWrciCnYEvUixXMZvrnQRJcFBWU8RzE1x/8yjXNzcQq/7Atod56KRRdFWqKQw/b5Y5c8tAeOZfHIzC6kkNlmmxeGkwWYnpy7iNP75QNNp3EixPEVBUSmVywxFoqz1+Qlls2zobGYpleT44Cx3vPdNXNDYxTPLUySLJV4KBLml90bedcU8//PeF7h3cop/3LaPbTUbeHnlOP/z5GEKZZUej5tamx3xTGa5XbYwk4pUPIBFN0axjIj5D77Xq3zLP1Wvp+gXBQsWwzqa7PCBtQIj8ad4YnaWpdU4yWiafF7B4bJgkCVUpYymabj9TgKZCqXjvquu5/YjhzCIIqqm47GYsRlkNlf7CWSz/Hpiks9s24bXbEESRNoctRwOzZBTi5xT33R2HFkM67iuYx2F8igiVgrlGGVNoSzE6XLto9URYjEzxguBRXKqyi/GKvkqqqYxEltmk7+ZiUSc969di9tkZTwewW0yc059HSej41zY1M5qIU2/T+TwSpDT0RitLifxYhElr/Hg6DhmsxGbbOCDu7bQ7/Vx8w9+STibpd3jwWSRCc6GqVrXRqpURNd1cgUVD9BY5yOezfGxTRs5Eorw8LFhtLJGPJrF5anMBf4GL6GFVT714BPcum8Xb+3twSRVMu8fmZljY7WfX01O/dn35F9DCYJwKRXvdInK7vSXX/P6p4C3nzk0AH2A/412nv8qgKZBFDl/8xqm4zFMBgOxTA4RgZPhMIFkivV1dexsqMdqMLCYTvEfR++lzV3poKyvq6PT7eZdP/0VP7vleqL5PDlVRdN17hmf4OvHjtHrq6Lf5+Hyll4a7A4SxQJOo4nFdEUI7zVXtlecxorar85qxWMyo6Hz4PQsh4Ir6DoUVZXZRJz61mqu6uxkjddLn7eaQC6Gw2gkXshTKqnY3Va+c+IE/7hzF7Io4jU7+I+9l/CTseOYJBGTKLGzrh6fYwu76pcYi8Xo9bh5fHYOp8nEuY2VBJDFVJJiuUyjx4WtuqqyRTC/wOmxBT78rQdxOWSu2Leetf4qhsLhik2UJBCYCYEgkCmVcJtMTMSTNNptrOYL+MwmXhydpr7eh2SQyGcKBAoKS8urPPjcUaqbfBSyRZxeO55qF26TmZyqIp3h7e1pbuac+noempllU7WfU6tRWuuquKy1m3vHx5mZipDPFvn1e9+GQRT57AvPE4glsTjM2N1WXFYLo+ML1Db5MFmMbK2pJqMUsclm3tzRx5U/u4ubNq/n5t716GUdkyTzqS1byCgl/vWVQS5paSGYzVDMl7lvcpLJpRB3lF5mXUcTA1VeJhMpZJPMxOIK5/R24DKZaLC7afS4GIqs8r1rryKvKvz0sZfo6qnB7rSy/7rbAVjjuZCZf+znbY99l4WVKJlknhqfmUKxjMNlZnkxQS6VR5Ilrt+wlvFYjBdPTqLrOj2djdx7/BS5dIHNmzrxWCyUWipG8cvBNEazkflUikvX9fHkyDi5bJHenmrOb25k/8IiwWySdElBQ+fh2TnWVXnJqyo/GzrFLZsGOBKKYJWNqGUNX7UDv9XKxa0tHBmeRtfBajfz5ade4N9eeJkX3/WvZzsy1eZG8uosTy+Nc1FTLxapD0GQKZXneWjuJMlohlyhjMtj55oNa3lkfIIX5xf49qUX897+dXzgma9w6P0fPeuh+GqJgoW59PO8Elrmi79+Ene1ixfe+Sk0PU+8mOa8+qvPiFGgy+3m2OrjbKq6FOCsDU/lPMLvdYleBZmaXsRv7mAlP8Gh4CwGUSReLHLX6VFCkQSrwTi9a1s4v7WVXEcH4VyOkUiY9f4qwtks94yP4zWbaXM5GYmtklFUGmxWDq+EuaKtla9ccgHfPzVMl9fL0aVlmux2vGYz3W43dxwfYlNNDRPxOO+49ze8acNawour/HQiyI+1F6mqcXLzDb3k1RWcxo2UyxHSpVIlBrdc5ujEPOPjYQY2NVFjcVHS0hhFGVEQK4snQUbTwvxw9CmuaO1iq/9ScuopArkYx8Jh3rJ5PY/OzfOxgSr2NawFDEwmp+n3nE9WHebW9e/hy0e/yyc2XIBJ6kDRlvjKkcM0Ox1MJmJ0OCuCFgknmp6lYp38/61EwUhZLyIKRs6p38JkcpRvnzjJkblFfvLmq3D+hZojVcvS5XGTKZU4HlqhylLpcJ1eDlLjdfHd919DMJdjJBbg+o6bWM6+yHQyhY7CrQObuKC5kdPRGIHsKkcjp/j3gy9T53XT4nbTYLMRL+QRhTDFsopFMjKfTtFsdyAIMrLQ9LqA8o+BzFdTrV5dDL0WZL4aGvBqNdp284UXv8z0QojVUArZUBH4xKJZaupcaJrGaiDO5VvXsbW2GrtBxiH72VLj5+ejY1zX3c26qip+OTHJQzOznNfUwMaaWqaTSaKFPA/PHOHGnh721u/k344/wJaaatocv/0+irbCRGKR9b6LkcV6fj3zK647E8VqEKr46tFBsorC48+epKzp3HXp3/D4wpPkVYVH5kYZiyWosVh5dG6Iv924ibyq4DaZ6XJ1MJ2awWW08PED+zk5NMOXbrockyQSzhfIFQqU8iX2dbRT0jQ2+mv41okh/uX6S3hqfpFf7h/EYjbgq3PT4HQyPzrN6ESM5jorVT4nm2vr+JuB7eTUIk/NL55tnrS11jIxsUR9azVquYzZZiKfLvDE7CxfO+9Cnl6siKXW+auot9uIFor8/73+kp1z/Q1eO0Nz/CZwERWR96AgCA/qun5Wl/O7u9eCIFwFfOJP0Rv/KoBmTlX5xKbNvO3eX7Ots5WPbtjAZCJBTlXZXFvLSjbHBr+f/zxxEpOhkqIgiQJXNrcynkjw/wwOcu05G0kUCvR4/NTb7YzH4jQ47DhNJtpcDkRBIFnKnu1yyKKE3WgkVSrhNBoxiAJ5VUUWRRLFIgeXAyykknT7fJikygMkUSyQyReZGw8ycnweq1niol3r6fF62FFXhyxJ3Hnlm3hodpLHZ2fRdB1F03h+eYY+bxVv6exjMLTITDLFK6Egbc4l5lIp7LJcUcsX8rx7bT9mg4EDi8ssRRNsb2smV1J4cXiKSFuOi1tbGR1fxGaRiCdLPPDUEFPrG2ivqaqYqFuNFKxFMskcJ0/OsBBcpbbGw5GgSiQQY0NPK/982flsr23m3skRVF3nN+PjlFUNi72yug8uJ1gJJElnFer8Vp6uqUYAWhweLmxqos7mYGddDa+EwlzZ2spMKkWprHBFRwfDJ+Zw+508s7jAZa3tfHBggDtPj/KhgfWEslnKukbj9u0cDoW4b2SUe8YncJpMXN7WgiSIxMNJRmMxVE1DEAWyagGLQcZrsvOONX0EMmlmU2lSJYW1VX7C2Sy7GhvZUVt7xmTbx2Oj45SVMtd0dOAymiiVFe7Ydxn/8soLNNo9DEUq7mqR5RiB+Sicsc1TtVVOx08SLxTwV7mYngxjsxgwmySUgkJ7Tw1qSSUVTXPXi0dxeu1IkkhHax1/u3kTnzvwHFX1HpZW47gbzbx9YB3te5wcD6/S7nLwlWcOkaj34/bYMVlNhJeirOTytLvcLGTSNDvs3DNyGlk2nMkbNlPjdvKDweOMjQQpKhpmo4jXLxDMZPjknQ+jqJXOfFlRyRcqwqn2f3kv937o7Wytvgyj1IJRauHSZgdfOfYkw5H7+fz2HfyPZw9QbbNx/bmbGQqtMDmxzJLXx9cvvpB2RzWPL4xxY+eNfPf8Pb83VieSz/D94WEOTs/y0kvzOG2VKUgrawhUOKFHwks8mP0x7+rdRE4tYpNlApkM/Z6Js1vnr9ZrtyyhAjJPRJ/l5GqEKouZyUSSjKJgl2VkUeKW7ZvY4Pfz7NISVWYzx1IRXhydxmiRMUkSa5bmuWMAACAASURBVKqqEAU4HokwFI7Q5HQyHA7z4Q0DvHvNGg6vBNnX1IxZNtDrdaNobXznyUPcecsNJIoFttXVcjQU5uDgKD9691vYUt3Bg0PDuKsrfDGDLPHNoRN8Y28FOEcLq1zc3Ewkn+O2lwdZXozxH++/iqvaKjY4sijz1MI0fV4vna4aTqaepNfTxw2d67AZfGTVU6hamVSpwLbaWjZWtZ4BM0YOBU/z+PwCrU4HPx39JlUWMx9cq3FlWxuGM4uJk9E59g+OkEoV8PpsBC7K8r6+NyOJbkTBxhuTFKBQHn9dy57XlqItkVRzvBicRUfnsrYW/u3cqylpmT/5t7/9bX/bBdR1DQ0dl8lItc3Gtrpanltc5N1r+1nJ5Wi2V/yUnUaZVKlEqTyPQZTwmU2MJV6iyuxE03RqrFa++NJBJhZX+MeL9nJqNUaD3YpNrvxdolTkWDjCJS0tXNjYhcu49g35wW/w7QGIl47/XoLVq/W7IBMq17XF5WTMasQoVxZQkijgcluwu234HDZSxSKJYoHnlwL4LGbK+iAXNnXxUjBEk93ON4dOYJVl1lb5qLXayKkqVRYzgUyWazs7Ob/hYkTBymc2f+BM9/q3hvrh/AxrPFtQtQgG0X8WZOp6Rdh5SWszO2tbWUwl+eKuncynj/P43ALhbIa1/mo6XE4Oh8K8t7+fDmc7h4In+d6pIwjCYfY1NbGvqZmvnXc+z3R08O0jx+j0VziSWUWhv6UBgMPBimC1weHgpWCIvFpJWxMNEt31NSymkjTU+wgux9m3cy3dXg82WcYgSpQ0lYVUEl3XySZzqEoZo7kyxnPZAqIoUiypdPt8LGYSWAwS13Z2Ei3kWUynmEkm/oLf+P9sCX8B0nwjoAlsA6Z0XZ85c/5fAFfzhwLwV+u19pSvW38VQDNTLHFiNcS+vi7cJhOj8TjpUgmPyYQkCqxk4WgoxGoui6JpfHXvecSKeVRNx2U00ub2sLnaT1ZVkAQRo2jgvKYGOlxuHpqdJV1SOBFepdvtpd9bsXERBAGbwUy0mEbXYSaZpM6qMh5PYJIknj8xTiCYJtgWo6+tnkg2SzKVA13HapGpafGzq6WZqzva6XT5UcoqaSWPy2jj7T0D3NyzgXy5xFBkmV+OT9DmjrCvqQGTZOBoKMTuhgaC2VmGwhGMBgmDIGIUJY5FVtlaXc0Vba1c0tLMaDzOk7OzWGwmTJLEXCrNF950AWPxBE9PTJFJZDGaZU6OL2CxmRAEgVJBwVPtIpvMUVbLzM4EMZpkutvr+fb5V1Ih4IcQBIGbOvt5a/daHpod4/uDx8kkcsTTCqqqYTZJeKqdDAYCvLS0xJb6emYTCT63bTtVFiudLhfxYsUB4CtHX2EqFsVsMpCIpJhMJDEvzWM1GCiVVZ5ZXCRdUuhwObHJRi5raWMiFsdlNiEJIs8vB1jn81Iua5Q1nUK5SKEMJlFG1TScFisb/XVMJ6M8FV9EliR0k4kv7NzJgK+Rw6E55tIpfnnG4P2KzRXvTU3XMYoGcmqB23ZeQLyYZiqZ5La3XsqX7t/PwEAbUPHUvGfqII/NzvHBDQOYJYl4Kks0GKdQUJANEpIkkkrnKRTLuI0Gsqk8Tp+D3U2NxIsF3tTXw8Pjk9y0YR0D/ioenJnFZzaxnMmwlE5zzaa1LKczpEtFPrVzG986PsSR4Arb6mrp8/gQiXJZVxetTjuyKNLmcqMBs243mlomny2ilFRWAgmWF+OIokBtvYuZmSiyQUQUKx0Tk9HAh379ME7fszx+7fsxSZ2YpHb+ftPFLGbn+cnp01zc1sYPn36ZcUcQySDirXFxx3mfPDsmb+ra9HtjVNXiLGVPcSS0gsdsQikquOwyjQ1O3rlnKx9cu/UMN1TkkuZ+Hp8fQUDAZWwkVphma00jr05XxfIUU6lZet07EDCwkBlEFERKmsLxSAgRyCgqv5qY4OTQDJeeM0C8kCeaz6MoKhPxBOt8Pvo8HjR0PjowwO76WgyiyEZ/LXeNjWIQBepsVo7pOj6ziUA0waNz81zS0oyq6xxcXmJvYwMvBVcoqWWqm6r43vApLm9tRRJFpiKrbNnYidds4WR0jg+fs43LWrp5Zmma2x99luOT88xtDtPpnCZRyrGtejvB3DhtPi8zhgCPTs/w2Mwsb1/Ty9bqBi5u7qDWupGvnfgZV7d3EMrN4zE5iBQC1Fk3kywN4zCazlhMNSMJDjS9xDn1VgaqGnlodpSh0Aq/uOI6XMaNzOlPnBVd5VSF/r4WDg9OoBQUvnfoCAW1zHWdvdRZBxAFB29UZqmHUnmOQjmO0/hHKfnIYgM59RRrvJUgC7/Fi0GsIaXEcMj/tbn+d7uAgiAiixXKTEFV2VZby1wqTTiXJ6OoHAlHWON10+JwEi8WOLQyhF2uUHF+NTlFjc3Cxc1t3D54mEaHE3+PjaHIKqPRKAeXCmfT3fr9ft7Xv56PHXiKX1x+PWU9jij8YQDA79I2Xv86VbxeXw9kvrY0vcSxyCQbqqp4zjqPQZbQNR2DLOFv8OK32WhxuZEliWg+h89i5vLWVhbTKT734gFsshGjJHFNZwf5ssoVLWv4zvArFMsaBTVJOJ/n4HIAQRC4tPlaACTBiarFyCgLlPU5aix9pJUZVgspOp3nIAgyqrZKWU8wm54nq6g4jXbqHQ4+/PMHuXHPZhrsdrbUVJMoFkkrCtd2dOA0Gnnv/rsZnQ3Q01rHQjTOY4pKp9tNWdfZPz+PqqgcOT3DO87dwkquIpK7ubeXl4IB7hkb5zPbthPIpjkaivBookhTo5n5eByPzcqRwSmaWqtocVXEZq8Grei6TjSZQSvrxEJJZJOMQZYIhROkYxnWr2vD6bMzE4/zrCyfyX1fYTVfQNE0Lm9t5Xv/tdvyv63+N2iBGoDF3zleouIi9DqfLVip2FXe+qdO+lcBND0WM4likb/ZsJXpZISxeByTJBIrFFnr87LG62MsFuPKzk4mEwncJivd7kaeXjrNqWiMN7W3oeoVUnI4nyZRLFBns9PlquW8xhKT8QRj0VXG4lFcJhODoSCbq2sRqHggZlWFgSo/HpOVrx09isNowu6y0mI0YLTILMYTFPMlLHYzZVWjobOWj27cwM7aZoxnzN+nMhGyioLLaMNwJmLOIVrYVtNCrFggnMuf5YUeOTnN1tpaDgUXiRXyFBQVQRC4prsTRdPYv7DI23p72FrdSr8vh89s4nh4lS01fhzGyh7VdZ0dTMVjlH1uTk8uYTTJJCIpcpkColi5ezd2tTAaWCG8ksLp1LHKRiaTATqctcylkmyurkYSRNJKjv3zC7T4vSxKIr0ChFZSGAwibfV+djU0oGga47EYSrnM144dw240Iosii5ks6WKR3zxzjLpGDw6vnXP7OjkdCRPN5ZhZjWKQJRZjCS7p7mJzTQ3ZM1ZJV3e0M5NKcmgpQIvLxVImS+/aFvxWK4dXlskoKrVWKwcDQc5rbGCdr5ZEqchAdSVers5mxWowcGB5klSpRE5R8VgsXLqxn521tahnyPNZpUSzwwdAWskTKxR4fmGBT73pfG7uqQCqz730AB6ziX/ZdQ522YKm63RUT5KMpnGYZPKZAslomky6iGwQkGSJzGqaVDTNQwaRC9rauaCpEUkUORlZZS6VJq8o/GpikqefP81nb7oQl9FIt8fNSibDi8EQkUgSrUrnjicOYrp8L1e0dnEkfIyHZ2aJ5/PYjUZ6fFWomsY7t23k8dlZttTV8dCxEULBBDV1bvat7aavPc2Tzw6j6WA1SxRLKoVQkmw6z9477+DDO7bwrt6bMUntBDLDmA0SV7d3IIviGcV59euOy7HE03Q5N1PWk4QLSzw6N11ZoMkyLq+Dupo863tb+B8brsEg+hlLPE2nsw6z1M9VrQ1IohtNL53tpAAUyxOs5ELcNTbOrettFMsKoXyGZ5eW2FJdTSSX54HJSbbU1+M2mfH6nbxwYhxRFCnlSwQjOZSSykCVj5PRKE12O4FMlg6Xi/smpxARuLSlhbvGxs4samRanQ42tTRil2WORyI4ZJnxdIZddTV0ulzYZJlrOzv4xcQE3zt5kmg8zfLcKvI6Ax/d/xQb6+rYWVeLKAh0uT30dTXS5vbwq8kpbh3w0mL3MxI/hixKbKqpZry1CpNBYnhikTsKeTbUBnlrdw9+S4KjKyv0e73sa9hAtBjCKBmIF08xl674f26q2sZc+ijVFjc5tSLEq7O1cXmrRo3VyrPLU6zzpaixVAzTFW2FmWSSf9m9m/TWLbz/ngdRigovBwIMrqzwqS0Z+j2bQXhjYYRRakUWG84eq1ocUTD/HjAUsdBo242qxQnmRljKhmi2O9D0P9Fj+SOlahEETNTb7HR7PbQ6qtjbmKXeZmcll8VrthAr5CmVyyiaRrJYJKOUSBZLLKfThHM56m02Pr91J6pe5uWVZTKKwiWtLcwm03R7XJglAxc39/Lw3Agf3biBrw0d4tOb/jD5B3hDkPnnligY2VV7JaoWIZLPc8JfzeD0PJFAHK2sU+9wIgoCm6qryJYUmh0ONvj66XStEi+WSBSLvBgMUtZ13tLZzSvhWX70/CClQoUG8abdG1nJpNld146m5xAFK4q2QrwYxijJfOvUEXbULrKnfoAmux1NzyIJbjTypJQYB5YWcRplHpw9QaJQ4G8vO5cLm1qxyxYmEmFOra4ysrrKVDzBtroa6u0OZm0mFmMJtLLGaibLgzOz5BWFxdU4hWwRs9VIMJtDEgRkSeLllSCHAgF8Fisek4VkqcCFzU08v6OdWDhJqaiwWs5gt8pcs2EtiqZxbGUFv9WGKBgoagr5TAG330FkKcZKOEu110xtUxWZZI6b1/RhNch8/dgxiuUyFzc3c2p1FY/ZxGQiidv8v9Le6399CVSoQ39ulaFKEITf9SX/7hlf8ldP+9r6YwP0KuDQn9o2h78SoOkzW7mytZdkKUtWVSiVyxglicFAEKvBwI1dTVgNlS7k3oZG3EYbqqZQb3PQ7MgwlUjgt1rIqSrPLwdYyWY4p6GBDlcVmqZjNkjU2u2cWo3R5nRhliRGohGqrTbscsU7EFEkWsixt7mZYKayxbuymkAtqUSWYrT3NbK2uhqDILL/5BiTiRTJ0hRmycDm6hpUTePU6ipdLh9OoxVRkAjlYkwmo2i6zvFQmJ++eBTJIFHbUkWr00GPx0cgm2U1X+A9/f1klBJWg0yd1cZ0MoHbZOax+Yo/WW+PlyZ7BSxNpyLIosT2ujrm02lWalwoZ7ihpaLC9666gtF4lN9MTlHMldB1HU0tM5+I86+Dg7ylu4uBqhr8ZhdGyUq+XKTD7cFrNp2NqGw0yQQWY0zOr5Aplfjs9u0cXFoiWSiwHE1w04Z19HjciAiomsaRtgVWA3E0TeecC2qptlq4b/AkSknFVeUgny6wxuumy1VLRimgaJUM+HihxC8ePspDf/92lLLGnoYG7hwd4/BKmHevWcO3TpwgWSwwKMuUdY0GW4VL1+lyEchmeXRunlanHavBwEZ/DePxKBq/XSkaxIpNldVgQtVUvnniBB/fsJGT4TA3dK0/6yO3rqryWzhkK0bRgNlg5gPr1zHR3MhTc/O8q7+fFwIBHjk6TCFbRDJIGGQD8XieeCjJclWa215+GbNBxm40cmx5mRaPh8n5FdwOI68EAlzT1UmDzc5SKMrsYohkNEM6liESL+A2m6i2dHF+YwifpZOHZ2Y5EQ6zmsvxt5s2IQoCwVyOlUwWVVERgJ7WOnbV15IqejlsHyccK5LRdMxGkXyxolrWgnG++sTzBLM5PrP5A+ypfxPn1lU6Nz0b/nAsLmYO8q1TQ8wnEzQ6nFzfVWJD1XpqLO0o2hj3jYzy7Usv4bKWNhbTSdJKCVGwo2ghGm1VhPOT1Nv8Z7Ogi+VxjFILPzz9G3bW1SMIICCwpbqK+6ZGEQSB6USS7bU1LGbSzCRTlMplJEGk2mbD6bNTyJVIrqYxWoxceVEn4WyW7x8bosHjoqCovKWrE0XX8JjNOI1GMkqJjf4qDgVWmIhFWUqnaHa66PN6qLZayasK9wyfZnddLb0eD3ePj9Pl8VBrs+O3WvnR0RlKisbCeABfrZv9wVEO2WZwXGTkkdk5ZpcjrOZypONZ4sUitw5sIJrPI0sSvV4P/9eFe1nOZPjA+vWcikZZ660o/r/w8n3YjUbanC5Wcsv4LW6WshHaHD2klWWMosRKfpzvjwxjkkR6PW4uaupGQMZtqkPV5ytzo2igytwJwHOBl3lmYZELm9pptvv50L4d/ODQEfKqikmS2L+4yIDvkv/SPCwI8plIS+OZ7jQIghGj2Hp2q1nTixhED/W2AaKFUU5FhymWy9S8JoDojQQ1hfJpRKxk1SgOuYU2p4dfT00zHA0wHk+wr6GbOluO+6ZGabRXPGhnU2lEoM/rZaDKy77GBlqdbhyyFYfsRdNzvKWjEe1MGICOAggYBC+iYOXyFp2cWqTD5SGnLmGS0iRKEXymjj+qmC+Wp5AE9++9XtYzpErjWA0uTFLnn7ymBtHPLX0X8u6lHxMJxDGaZSwmmWang06XE4dsJJTN8fTSEoIgsK/hat7R4+KSX32Xgdo6Guw24sU8rQ4Pe9Z38/ihEzg8NkYiYTLZAs8uT7K9poTP3IeqpZlKRjgWiQDwq8kpymeEikfCB/js5hv57vBTmCSJLdU1GCWRhXSKo4PTZIpF3r/mRgbDz7CQThHO50kVi4SjYR599gQdfXWUyxqapuFzO8gUi5wKh1DLZex2C5qm43JaefC5o7T1NVJrt5MuVuzxjq+scPfEKG/rXsOTCxVL7WQ0za079/LvvznA+684h3Pq69B0nV6vmxqLlUg+Rl5VuGJjP6FclimbGctyDJvLWgkoyRT50jMHaa71EQrHebFcZp3Py3q/n4Kq0uxwspBOvdFP899ff7m70aqu6691EXq1loCm3zluBAJ/5L2vZ0/5uvVXATRFQUQWJVZyGdqcbtZ6a7nj5DGu7uqkzelE0zUa7V7unxllNV/gff0VzmWprDKyGmVweh5BFLh4TQ8Hpmcoq2V6vF4W0zGixQLhfAGTZGA+maDGaieUy1Jvd5BXFEqiikkyYBBFWh3VtDh8DIYXMIoSJ1ZXeWh8gny2SKGo8MjBIfIFlXRO4Ymqab60Zw+SIDAWj1Jns/P+/q3Ioo1gLsBQZAWn0cg6Xx3rfQLdbg83vHyaTF7lo9fsIZzL4zRmuLy1jayi8NzSIjvr6plPJzGIIhc2VSaxD/RvIV7McDCwwFwqyYC/BqvBQI97Ow/PzuA0Gump9rOcSlFrdxDN5+hydTCRiLH/uRF27Oyip7WOiYUVosEEqlLm15NTPDA1Q4fbjSQKfGBtxYdOEkXioSS5dGUV6bBVzHL7/dV8c2iIk0MzCAJY7GaC2Rx22UCd1YZFNvCx3dv44i8fRxQFvnPiJFMji0TiRfrW1KAqFfX5//3Ec9wmPM+t5+3g8pZuFtIZ7nn+KJoGeUWl1+vDZbSxs66WHw8P89EnlrBbTNzcv4Y13grvLlUq8eDMLC0OB0ZJosPlpKxrSILIcjaN1SDz1OIi13Z0IIkiq/kcG/31mCQzXz56gOFwiDtODCEKAibptwoGWRRptNtQNBWn0cWR8ASNdhcbq5q5rKUbm8HE1upmZFEkkMlw4OURFLVMuayze20XF7Y08ZXnX0IpKEgGEVUp09fVyWw0xsauFobnl5mo8jMWT/D2rRuospj53N2P4XSYaXaYsUgGdMqc33gR/3a8Epv25XP3sJRJ8aupSW5dv50Gm42pWIwLNvZxbUcHbU5fJSrVYEK9Vueu4WGK+RLRQJyiUmDXth5i+TzHB2eZTsT5zsiP+WD/La/bubn96Hfo8VS2wmqtFh58eYjFGjeLqSQf21hmQ1UzNlnmGxdfhCgI/MOhF5hZDnPvTTewkBkkWcozlYgTyuX5yLpdZ88bKSQpaUcxGySySiXU4FBgmYempkgms1y8pgdZFPnCLx9j2+ZuimoZQRAoqCq3DmzkQaeDf7//GdYOtJHI5ChrGuc0NVFtsXA6GkPTdRrsDlKlis1WsVwmX1aZTCZ59sQY2VSe22+6HIdsZCWbZTmT4UgozN/v2k67y818KsmexgYabHaORSJsr61lZW+Wex45gmLRiATimK1GOhqa+PQDT1AsKChFhVw6j9vvpM3lpMXeT4MtzFI2zOGVAF9//mU+snsbj8zOMeD3cSoaZT6dpqCo/N3mLcymElSZLeTLJeqsXgrlCLtqNrBaDOA1eQlnsxwdn+XIh2/HIHqYSz9Pq2MPV7U28fmX7+Cmrredvb6zySTb6uqQRQPPLk9yU/d6Xlha4gPr12M2GHhh+c9Le5YEO4XyOFk1j0N2ICAjCiZC+cPUWLajaiFEoQZJcOA1taFqKqfjYYrlCZ5aPEmN1YbPbMUmm5EEkSrz1j/4DFlsRtEWSZayWAxRLAYTt+3cx98dfIqN1dWMxgN8ZfAVJueCtDfX4jCZuLarg43+epyyFaNkQNN10kqe07Egvxh/DqfJxO76SmJbJH+c2VSKLpeLe8fHueOCi5hOxnl+uRJffGB5kmihwI2dA0iCF11XUPUYkfw0fkvL2c6uSeqkrCXQ9ByqFuGxhcNECwVu6b2JQnns7P+javE/CCh4bUWyWQwGkXVrWtjR0EC6VOKq1itR9RinY08ythrlC1v3ASAKNpxmM0OBAC9lCsiiyD1Dw3xmzy6OzC6woaURu9FI3F7g0uZdHIkM4TW1U9RUphIJ7LKBNqeD4ysr/Gh4mKVoArvNzFVLXyUWS+P1OogWigxU+TBLBqr8dkILUd67/+t4LOZK9x4BgyASDSYwSAJaWSObzOH2O/FbbZzX3EyL04FS1nhoepodDY2YJJE3d3fT7nJikiRihQJWg8xUPM5YNMZDs5PU2azsamwik8px50vHEEWBnz1zmB+WDqFple1y2SBic1pweGx01vi5vL2Vd/T18aFfPIBBlrhk+zoWk0nmlyMomsal6/oQBIGDgRWcRhMlrcxAVR2/nJj8s+79/4763+BuNAh0CYLQBixTAZNve+2bBEFwAXuBm/8rJ/2rAJqvpu90ufxMJiNE86tc1tLMGm8dTmMdI7ERXEYzOUXl3sNDXNnWRr3NQ7+3kXeuEXhxdJq9a7uZisfwOmwksnnuPjzEeFeMtVVVHAkGSRQK1Nod/GpqklcCAf7Hls00OVzEi3mihTx+i4WXVmYJZDOICNzQNUCqVOSC9nYmPDH2Pz+Mx2Pl8j39DAdXmBpe4saRu/n5B2/kkuZ+VK1iZRTILTObjHN5607Qy2hkeS4wjiSIXHLueh597gRPT0yxva2ZnXX1uI1W7LLGaHycPQ2N5BW10p0xOUkUU+TVEqPxMEuZis+n3Wiky+XjUPAA7+vfjNVgR9HyqFoZHVA0FUVLs5TJ0t5Tw/b6en5zbBib04LfX/Fka3O5uamnmxqLG5MkM5lcYWQ1yngghMNrZ3t/Bxv8fmqsFpxGEz8cHubo0BSarmM2ybS21bKnvo5wPs9LKyG21VTT4nDyyavPZ3g1ytt6e/h8SUEZDWCxm5FlA4VCiWwyRzhWILdbJacWue/F46RSBWqrzCRLJawGE5quUW+3kS+UWA3EsXfUsqO2AZvBTErJsZBO0eV24zIaaXY4SBSLPL24hONMJ+s9a/r54Np1fG94mItammh2OKkye9F0lQeOnGLwQ7ex8wdfpKe5jleH+Z3jd7G9tp6XggEGQ0vsqjOwtboHKtMtVoN2ln87EYuRKhbZt6Of8XCEFw/O4DKbKahlmvwePjgwwJPzC1hlA1aDgenRAFf1dmM6I2CzGWSSxRI7amv5wvUXc3PPdspaAVnyEcpP8x/Hj/DksdPEoxkeOjHCO7ZvIpovcMndP+G+t9zA4eAK9x84ypWtrfznqeP0et188+UjfHL3dj6/cycPTM9wIJahp8/NTy/5ADoKz20d5DOP7Ofuh49w/55J7r3yhrMegCVtiY8/ezdf33sd1z/yYx646gsUymP8enSMXS0tvGdNPx3OXVSEEEM8Pj/Hw6fHScez1DX6mExE6XR7+dfBQURB4Pbde1G0EF8/8SA7amuxGAwEs1kenp4hUShQY7Nz+PgE+YJKWdP5+kiQz771Qj559fk8MDlJl9fH+S3NPDI9zXueeJSdjY3Ut/qJp7Ksa6jjyvY2FjMZ+r1e6m02hlZXue3wYf5p526+tHsf90+Pcvuv9+Py2dE0HYvdzDcOH+GrF+zDajBQa7PzprY+fjMzQo/HR5vLjUmSMEoSy5ksTy8usq2uhqdcJrx+B7fs3sIvhk7xw4uu4brC3ZgkA5/Ysplt1X3MpueptXhQtBVyapFwLsuTcwusa23knIZG8uUyh4Mhzm9uZDVf4M3t7Xxk/1P4rVb+bssWupybKWoLxIsZvCaJY+FlejwFbu7r5fRykC8d+QWf23ITDbbfikxu23ErhfLIWb5gtdXCPz12gDVeL2PxOH3eKmYCEaZbkryr9yK2V1/4Z8/HZqkHs1TJstfIU9bT1Fi2czq+n27Xb9vgZT2J39KGv1Ax7L6y9S2UtCUWMpOIgnAWZKpaBI0iRrHipiEJdiSpj2qLiqaXGI2vsJzJcNuu83gltMBKLsMXduzEsEvEYpDRdRiPr/KJZ5/GZJCYWgqRimUIrebQdTDJInt297K1xo9BFNlRW8fbuzdyMDjFpto6IvksmVIJi0Hi8fk51ni9LKYzbP3GN7A6LDTW+djV0MCbOzqpEVwUyxPMpheRRQmPyUFBLRLMJRmoqiWnlvjP4Z/xobW/zUR/PZBZ1hJnu/qS4OEfdu4ksalAIJslWSyxkskykz5Mm30jjXY7b+7qQBRepUOsMjG+hMtrR1XKHFhY4O7rruEbQ8e5dqAfl8nINe1rMEkystjAtmoniraAWarwUAgd8wAAIABJREFUVyUEljJZmlwu4vk89V4XJoOBweOTeGpcZPJFDszPsZrPMxZdxe62cl53Jy/OzzMxvMzM9i7q7Q5uP/dcrpteRlHLdNVW867z+0iUipTOpOLFCgXanS7aPR7ssoHHZ2bQdZ0qq5Xx6QDVDV4igRj9XU18ccdO5lJJ7hod5cCh06iqhqrpXLZvHXdd8nleCj3C+37+G3at72Kd389sMoXDaMQuG7i4uQdd13n6g+8jkEvgN9sZiYX5p+jzxDI51CqdqegqTS4XvR4/v5wa42goQo/H/Wff+/+n6y8RA71R6bquCoJwK/AEFXujH+q6PvK7AT1n3noN8OSZ2PE/WX8VQFPVy+TLpbP8r1aHh1prBx999k5qbXZ219ciiyKXtbZxNBTiJ6Oj/M3AZrJKgVK5zDVb1nPr+q18b+Qo4VweQ5WILIr0eT2YJBFJFGlxuTGIIsOrEb523gW4TXYSxQx3jo7R6XYxn07x4nKQ514ewWI18b2a4/Q31OI2mWl3e/jYNZU0iGihwPr6OpSCwvRkmNlUkrXeImVdY7WQRAdqbDY0PQ1InIou8M3jQ9yytp939/dzbGqBPZ3t2GUDTqMFSZSQkPjI+gF+MDLC+/vX4THZiRUSJEo53nnv/Vy5YQ2BTIYLm5swSRJ1Vi+N9g7QVTRypEs5UkoegYqCv9bqYp3Ph9VeAUAbO5pwmEyEs1mu7erkipYt5NVKtKRFMrLW08untxp4cGYGUagkpkiiSE5VsRpk/mHHDr5vd/DooSH617TysU2Vh41YAI/JiCxVrrdNlglls2QUBZNRxuWxAqAoKrqmk84qJDMKD01NsZzNkYznkA0CDpeVpTM+csvZBEpZY3tzExN2K5e2tWEQJIrlCqXCZ7ZQY7WRKpUYjcVodjq4trODQ4EgHpORYDbDGm8NH9+wGUVTsRhM5NQc+xcn+MKl57GQOUU8nOLlcIpPW55grW8QmyxTZXbhM8coaeUz0XgSgmAEvYyiVYCx22jDJBmos8v0eL3sbWxAVcpUWczsrmtiZ10DJyIhJFFkJBJhci7Im8/fxCMTk6RiGW7ZvYV+n4/pZIKDwSC76ur4+cRhTkfjfHbLbtzGCo+ukC1gs5tweOy8Egzyma3beG50krJWZlNNNeG1Gb536hR9vioemZnFYbdw25PP4/TZCc6EufrcTTz44hDn/Og2/v68XcylMyRWU9itBkZOzPNm7Rfsv64CNI1iI5/cvIUPPfNL/m7LFsp6gsHwLIuTK1T19mAUDUwkD/Lw7Aw/evYwC4spenqr+fSle3hb1018//RdTCYSJAsFsqUSU8koxyIBXEYjPxo5TSSXpaxpDJ+YYzVRxCiL9K2tRykqxEJJrGYLsUKBYDaL22xmOh7jzZ0d9Hq9DEUitLucVJnNLKQzbKnxcywc4ZVggF6Ph5+NjnJ+SwtT8Tg33nMvX7n8In7wyjG2bu7irb29nIrG+NHjh3B4bJQ0jRqrDV3XSZVydLs9JItFqiwVX76vDA7it9rwmEwkiyU2be4gpyi4jEYsNjM/nzjKr696N9OpWXxmB6uFMGVdYzA8zyZ/E4PhBbZWN/Plc6o4sRpAEipq6Y9v3Mjzy0u4TEZ+cnqUL517Lg/PzrHetxFFD3AqusgXDx3iwavfw9OLS+yqayNWyOFwWLmqvZ2fjj/Ee/re+ZoZ87cPJ0kQyWcKtDhc/HTkNKmSwkd2b+PqtjUVWyPhT2nO/3hJohsJ99lUnEabD53y2ddf3Tq2GEYJ5lZodXRjFBvpdDb+3nkM4uvHjQZzFVqRqunk1TLfHT7G3228iJPRCY6FQ7yyEuLY/BK1PhfxbIWqIBlEqqpcXLtxHVlF4YFXThIJp8mUSuysa8FvbubQyhESxTmWMxnqbFb0MxxSr9mEy2jEIcs8PjtLKlVgdTWHKAp8f3iOx7tmuLFvgXf0bKbN0cIr4VFOxyKUNZ2fjY4yMrZAf28zoiCwteY5+jz1WAztnIgewmOykC4VsRhknpif5er2bhptO1H0ADOpCbbXbOCZpUFihSJ5tYyGzu2vvIKmH8ZjMWOSDHz60H18dfctmKRWrjt3E89OzuDy2BEFAU3X+ecd56HrOieiSzyxMIHXbObS5g40Pc1SNkyTrYFCWWU2mea9/WuZTiSJFfIYRYnru7uYXAqxrrme04EQiqZxKhyiVC6jqRrtTgcbt2ziMZ+Pd/ev4eTqKn974Bm0ssZVezdxMhjkqcUlrAYDLywuoGoa712/DovBQLPDzqGlAE6TieGTs1x22TmcOj3P3NgyNqeFd65ZQ6/7AjqdcU6urqLt6mMhGmd1Ocbw1CL/5PkWj54cxeGxMxaK0OqqAO5ksXjm2V/xx5RFiU/sf5qVuQj/L3vvHSXJXd57fyp0V+ccZqYn59nZHKXVaqUVAglJFhgQEgKuAdtgHHD2DTb3lf0ac32NwYZrbLgOgLFBRCUECOXV7mq1OUzOoadzzl1dVe8fPcjgcK7Ni8P18XNOnzOnT0+H6urf76nv8w0mS1sYZGg6f/mtM7g9Vn7lwYN8ZXGWm7u6cJsVHCYrP/cDn/3/MvXPEQxkGMaTwJN/676/HdDzGeAz/9jn/HfRaBqGQa3VYLmYocvuxG6ykqqv8o7xcebzeV6MbtHtcNDtsCMJItVmW0zyzbV2Y7SYy9HQVD6w5xi60aKuqdhkCyDS0pvc3b+PzcoWH37lFd4+Po4imWjqKrIoMeL1sFos8uL0Is26itNtY894Hz0uN67tVJBqq4XTbCZfb3BDR5iNcoXT9UXsVpk/u3KVWyL92GULBm1OoKbpXM2sohkGL2xGsZtMlJoqdlODXzlxFIfJTLrWRiHncil2+MKcT8R4/3a281R2i8eWV3j46VcwySLfmp5HNkkcCIcIWm1M5zZZLk4hApvlCgu5PPPZDNl0EUM3GO3r4I7+frSWRllVeU1vD4fDEX7phWfZKJUBDYtsRTPaSJ1XcdDvDHNXv875ZBxV1ymrLVRdxyarPL7cznM+fmiCe4cG+friMpvFItOz64yMRhjxeCipKlOZLJcvL/KxZoNKpY7b76RWrmOxWyhmy9SbGpIo8MEbb2SnL8Kz0/M06k1EUSRdaxvMf/Cll3jDyAg9TgcOs4l0rU68WqS5LQa4mEyxO+Dnuc0oU6kkfquVD95wI9VWi9VChUKzybObUd42OkrI6kISReqtBl+dX6ChaXwo+iJqq61sT1UrLMkyfS4niigz6vUxnUlzPZvAZlJQtRYBSwSXqb1RNvQWbxgaRMfg4dk5DnR0cP+enUynM1xyxdAMnU9ducodg4OczLe/i+cvzvDOWw/z/NoaL21u4jSbCFgsrBRS1FSVbyytYDeZuJja4KsLi0wnktjdNiRZopQrU1UsOE0WBiMhBEFgMV9oC7NWY1RUlUarhcOssGPnKOVmg82FOI+evEjPSCflYpXf+c5J1GaLerPtIqC2DCxye9l4KfY47/vio7z+0C4+eORGvrOxQk07yZDby1037yVRqfLzzz/N4tIW1VIdTTe45ZYxfvf4cUyiRLpxgaVCka1SiXuGhzna2cl/efFFVhZjeENtBanVbiGfKmKSRfxuhY5uL39w52vxWaxcSiW4ls5SVpsMedzsCwY4n0hyMZlE1XVCVisbpTIHw2F2+H1cS2eotloEbXY2yyX2hsOYRJHbensoNhr88mPfZqg3zG8dvYloucTtvT08NRSmXqnza089w1v37GRXIMC4N0hT0/AoFmKVEsVmg/fs3MnldBqLLBFx2PmlAwewm0w0NA1RELiSyhBxXGHME+Avpq/Q1DR+atcBMrUaimRi0O1BR6faqlNSm7y0tcF9w/tZLcUIWi10OxwMu91IgsCPDAxgGHVeSSzy5YVFrCYTijTM3QP9eM172em7zufuvBdJFJnwdm8bpcs09Q1Ewf6qp6luVNkT6MDf6aXWUllMpFB1jd868tM/1PX5u1xLp2kPqhFFN5qIghndqAEGvY4OvrV2nf5/QNzeblSF76Ns6EaTkNXDWilFYttNJF2rM5tf5H1ffRyHx44gQKPaYCEXxWq34Au5KeUrZLMlPrd8lq/9+IO8e8dOzie3uK17GJcpBIgcCQ3yexdfwiSJ9DrbAR63dvdiLeaoqS0eXljgtf393BiJUGqqvHNinN86c4ZEucwXpqb585cvIptl9vVE+Nm9ezkZ3aShaXiCLj507GY+eOol/npujpsjRY521Njnfy06VbYqV3hidRGTJFLXVDQji1nsYcglYxI7ORFpIghXOJ9IMun3czYWo9xskqvX2lGNqspvnP08H77xZzna2cGjL1+h1WxxZHIMWZBQdY3VUopPXr6MSZS2BZRfYyFX4EJsiyORbqbTKe4fHyNbr3Jrd4R37dhBtl5Dx+DN+3Zyz8Agj3qWsMkyAauF5UKJu/r7eG5zE7Mk8eM7J3GazRSaTd69aycvB4P0OBw4zGa+c22WNx/Yxbt27qSkNlkvlVnKF/FYFGotlWGvj/PNRUY9HrwhF2qjhSgK/MLDT/Cxtxo4zWaS1RrjPj8mUeLH9u7m4dlZvvj8OfqGO3EpCvPLWzy3tsag14vdZGYpX0CR1rBKbY2GrhuYrWacXjuaqnFiYoTpoIcbIxH6XQEOBI/wmdknWSkWeXDs/2zZ9a9ZbQ70v/a7+MfVv49GE4NCs4osiqi6xuXUBplGnQPBTqxyWwTy8Mwsezs6eN/uXeQadQwMJn0+nGaFboeD72wssicQYsTdiU3+rk9b69XX6LaHefv4GE+srtIydCIOJ06TQlPTONbVyev7+vjw2bPoepvv9zO797FRzrNVLjGVyRGrVBl0Obl3YDfLxRgX4nFezs7RqDWZziY51jlEv7ODzUqSVK1KslolWa0x6HbiVsxcTKY43BGi0GhiEkXMkkSiVubDZ8/ylrExdMPg22tLzOXyPHN1llSiiGFAS9PYXEkR6nAznclxOhprZ6JPpXAHrBTyDZwuMyGfhd6xLkRBoNRs8juPPE2t3mI0GOD1fRMokoX3793D9UyWWqtMQ1exSmZS9QIexclGOcmL0U2Wi0UiDgeubbuIWKXK1VSSsN3B6wf6scumtheaJNKsN9mMpnneF6XH6eDU8iqiKHLu7DJ+t0K13qK7z48kt5OLRFFAFMFnsVJu1Wg2VFy+9u60ls/z2PI8O4Ihuux2Xtza4tz6Ji211Y6T9LhJ12o8OTVL5+H9JCtljnb38I1LUzhuVjgcDvGX09Os5HM0NY3nZhc4MT6CLAjcM9BPvFhi7tom+w4OojZbGIaBz2IlVa0y7HGTa5bxKy4ijgbxapmZbJyGpnG8q7156YbBU+vt9Iw3Du6k2Ggj8BM+H2eiUf7HC6dQGy2OTgxRaapoLY3hgS5UXeMTj7xAQ9W58cYRsvUG9ZZGr9Pxqr/h9eVNPlqtsL6aIJOuYLPIeIMuirkyt+8cR0dHEkVejK6xO+BnLpOmUW+Szpdo1lXCfV38zJ49PL68xFeLTVx2mcRmhh0j3Yz6/Czlc6z7c2TjbV+5+8fHaOkp/uz6FPVKg2evzTGTTvHgjglWi0U6bHZ+84ZbiFbaYsQzg1uUmiobpTLHI514zXZWS1mupJP4LAprhTx/8twZ/txiIraZI1No4vLaOTY+RLRYJE8Rt9+J2mxxZGSAnb4eJMGKIrbPsZVCkW6HA0GAToedgNVCQ9NZLZWI2O3EKmW+ubLGHf29BCwWuhx2ouUquwN+VoolsvU6xXqd7kiAB8bHKDQbqIbOqMPDa4YHeWp2gXqlQaJaY1zTqKh1dgciVNQ6yWqF5UKRGzo78ZXLeBUzn73eVii/EN3ir55/Bb2lcWOkbQyerFZZyufYFQy2RTlWG4VmhW+sLHM8EqGiqngVCza7zNXMKn96/Tp39PVyQ0eEpqYStvUzn5+hZZRwmhVO9EQYcLXH4LdFXodulGjqLTbKOSqtJp02BwMuN5LgpqwWaek5OmxtxLDaWkAzdN4wOc6Tq6v88d138msvvPB/zJH/QUsQRGT8iEKb2yxgRqeKIo5TaV36vtfVjSoNbQmrvAvdKJFpzH9fJnpLj2GVIohCGpfZTLRcYU/AzwvRKA6PnTtGhig1VdbcebYKRSzmtn+SbJax2y00GyqPLC29Kpj62OWzvLa3hx6HG5/ixGNRuK27B4dJ4VomwZcW5ghY2z7BB0JBRjxeWrqOW1HwmO28Z9dOHl1cpsflJFdvcDkRZ7NU5H3f+hYBh4P37t5Nv8vNVqXE/7rtTr4wf51aq8Wp2Cqd9jRddheiICIKoBvQ4wi9iuR+N5XJLPYy7N5go1QmYLFwIZ6gqqooksTcZhxBFEgWS9QPTxGy2tk93scbh4c4HI4QsU/wW+e+hCKJxEoldobCtAyD2WyeJ09dxu62kfJWkQWRh2fn+NVDhxh0ezCJEqdjcZ5aWd6etPHqul5oNHl2dYWNUpF3T05SbjbxWaxMZdNES2UUSeK9O3fxyNISuVqNtxzYzXIhT7RU5paeCGW1RbTURo1v6enh5OYme/YNtKdC5TrHJke4sLaBJIv88ZUrTAZDmCSJZLXKfCzBu3fuwCSK3H54Jy9NLSBHAgwPdJGpVrgwv8bOwW5eqpR5fHGRkN3BRrGA2lC5YecQm8Uix3t7ub2nl9f19iCL7bhlgwYXE0nuGx1h0vvaH/pv4IddP+zR+T9X/btoNAUE1O28Wpus8J2NGQZdTjpsPvwWF2ZJxGEyEbbZ6bK76HF4aOotdAxG3B2MeTp542Nf5HwgxQ7/Fq/rHcAstlFLs2jCYdLRDZ1rmQyXYjE2CgWqqorDbGZfuIOVYol3ju3mj29/Lc9srLM7ENhGRNvMtO8stm1dqpEuvrp0mWfXN7HKJnpGOtB1g61KGYG2D1tAcXMxGWd3IES2XsMqm9gt/83XNOHzkaq1c5HdZoWw3c5MNkfQ2m6On7k6y9ZmnnpTw2k3UW9oBAN2TGa5HdHocfPW+16P+X6Jlq6zUCjQYbMSq1S5nk5zfm4Vk1mmUGpiNom8eG6GxtETWCQ7u3wdnE8kOZtcRRJEjoT70Q2Dv5w9z9fm51lbjiObZAYHOhj1+QlYLegG1NUWl+MxJv2+V2MiF3N5jh+ZJFWtEK+UKTTqWG0Kkkki4GlvNv0jYSYjHZhFicvNFpvRAjaLzJcW5snV27FjEY+LTqeTeLm9oLymv59YtYrDZGZXZwdb5RJHO7vosruZySUpF6qslkr85K5dvLi1RX93iGqriYGBJIrMXF1Hbent72Utgy/o4NTmBo26Sr6scvXSKsGwE4fHwclr8wz1d7JZLpOoFpn09dLt8NJpd1FoVvEr7SZ4OrfApVSCjz97GoDTk7G25dCOCZLVKvl6nVq5jkkxcWGtbWHWG/RhkU1MrUbp7vOTTRQY8vqoqi0uJhIYhsFz6+skCkXGB7rwWawszW6imCWGJ3u4IdKNYRhslkrEq2XuGRykoqrYTSbeOj5Oz6GD/NcnnsYTdHG8p5umpjG47UNnGKBYzUiiSMswCNrs/OjIMDt8fr68sIBXsSAIlnZqiijyxgO76HbaeWxxiXixxIjbww5vBLvJjqo3eDG6SVVtMeRxv5qY9UI0SqHR5LEzl4nGSnhdZmp1jY5OF5FehQcO7Ga5UOS2vl7uGhzkw48/S7OuMp9Jcy2zTk1rkaxWeWRxiV6Xm4au4zC1fydfm1/g1w4d4nw8wZ19fTy6tMzLVxbYHQxwKZHkYGcHVlniZDSGbhic29ykXmnwo2NjXEylkUWReLVKyGrnHeMT3NodYSGXR8fAbjKhSCailSwCAslalWNdEZxmM0NuFy6zQiqe42Q0Rqpa5Y037SNRqXAxHuPuoSGe34hilWUuJZJsll7izcNDBK0u0vU6sihS01rEyhWe29hkKZXm9pEh7uidwCaPIgpW/vfUZ2kZBj6Lgx3eYcLWBF32oywVn2fAeYSqtkq0kqXf5SdoOfx9NkM+ZRLNaCtpG9o8oiByOrbJmNdDpl4naHGiGcbfaTJVPY5J/Lu+kT9IiYLt1b8NmuhGFc3IcO/AJKKgoBklRGyAgOm7nEzRg18ZpaVnkcW2Al8QzOSaK0xl06SqNfYFgzjMZj47NU2vz8ORbXuy61YLkiiSrFRYX4yhazolu4Vcpswv7r0NARNb1TXevWOSp9ZXOByaxCz2cKwrgUkU+criHPuDQQ6HwzyxssqvHjhMyNLNy4kr/M9z53hNXx9vGhqjz+lm0OPmHWO7SNQKTPp9PLa4RI/XQ75e58+vXSeeK3BidIibO09we0+RrywsbIuxOnhmYxNlm0J0Ohpl3OvlRGTy1WP1qanP8MLGJke7upBEka1KhZahsysUxmEy8Uyxwu7uLjK1KslajhF3mF89eAhFkmgZGtnGMk9cvE45X+GmgxNk6zWcisLeYIBDb3od8/k8ox4P5xJJsrUaz29GeV1vTxsxl2V2BIIUG22KV60FYZuVQnObN2+t0NJ1krUqk74wFknCa7XgNpv5y9kZLsfjDHp9eC1mxiQfiijylbl57h0eotRsslQo8pqebh5ZWHg1Dlk2yYz7vKwXC5gVE28aGUEQBKyyxMVkmp3dnVRUlYXpDQaOevEEXdhMJqqqyj0jIzw+N0/YbkeRJGY1Db/NytXFdVw+B2M+HxVVJVWr0Wl34VYabFVKlNUaieo0L16Z46f3fH8O/b/J+sFV5//i9e+i0dQNg1ilgiJJdDtEbu3uoqlp/P6lU2TrdbqdDgzD4GhnNz7FySvJNWotlVqrbZLuVzopFirMCwLHIm2bhJVihkS1Qthmb6OH1QpfmZpB13R0KyRTeToGeuh3tTPNr2a22Bfo5nikm06b91VU9XIqzY19vaSqVZZyWc4ut3PO9wz2MBEK8dwrU3zbs8qEz8+As21tNO7147c4CVrcSIJIQ1cZ83hwmhXOJeKEbVbsJjNBq6ttTG+xIggCDpNMX0+IVKKI329jz45+nj05TaDLS7VYwyLLvHtiF2GbjzbKpnK8y0AURC6kVnl5awuXz8HKbAxVM5AkA2+Hhz+bvsQdfX1cTaW4q3+AboeXj18+x75AN3ZZ4Y1DE/gsCr8dy5KMF6lXVimPNvmR4WHStTr5Rp2be/uQt9FSi7SdqtRoR3xlqlX8Nhsus0K4x4/FbEIzDBoNlbsH+nGZlfamcXkTn8tMt+O7XMQGEaeL/aEgMYeDu/sHeCG6gc9iaZPLr83w+3e/jh3eLkRB5GDQwlM/+W4UycxMLsbZaJQhr48PPPsMQVuba/djd97IxXgMl6Jw9vw8umYwe2UdSRTYtbODrY0cWktHNknY3TYylQqabuBRrDS1JrlG21B9T2CApzemeeipzyObJCx2hWK2QkPVOSMtc3xyhKaukWvU8VqtRA0o5yu0VA1RFEhuZKg3NRSTiMWukCs12SgU2OH3YpFDDLhdnI0lWImmWNUy+Hq78YbcBANu1teSfCtb4uBQHwNuN5dTqW2uscC1TJZ3TUxwIZlANknMX9vgD6NZlDuP4zKb+eX7bmXU6yFWqfLRR57lotXM0EgXbxwaZNg1RLx8mbSzhoidqWurSCaJTL3OSqHQHkV3dpJvNjCJnchGiA+88PtYZRPv372bQqPOfz91ilQiTyZZxO2184aj+3js5csU8jUefP0RdgXajcRKocS7J3fwxPIKJ3q6+ch9d/HZqSkamsa5RIJYtcqp9XUuvbLKLcfHGfb5qDZVVF3nfXt284cXL2E3mXgxGmU5lwPgixeuUq82uKGrk2fWN9hMZPB4HXz8tbfz2y+/TFlVuZZMMurxsDsQZKtS4oijD8kp4jZbqKhNFEmmy9bHaukyFVXllsggm+Ucn52ZYS6dZiIY4O037udr16bJp4qIkojb7+SnDu8nXasTcTopNZsc8Hk50dOD02TlE1fOM5/JkO3p4VjHED/x9GPtZCtB4L8fehe60eBLi1/nnv5J3jl+iGw9j09x8+LWVY51tnmO5xJbfOziJ5kM+Lm7f7htuQaIgoJBC80oIXzPcp+sJfnMzHWG3C5WiyV+YsdB7n/yK9zeP/B31tdoZZ5+5w+n0fzeplUUrAgoIBiIgoNs48KrZubfTYl6VRQjiAh87+i8RrFZZSqT5abOTuwmEzVV5Vo0RqvZ4lwgwN5AALMksZLMoDZU6vU2Zcoo1wlHvDy2ep7X907Q6ziCqm8w4S1g0EAQTPgUG+ulPFeTKR4cnSTXrPCL+w7w6etXeG7p6xzp7eE3jx5lOpuh3KpzLh7HZ1H49PVL2wh0J9fSGfxWC+e2YiwsbzG3mKFeaXAx9rv0uT384S1vZjq3wG+8dJK7h4awm0x8eW4OqyzziYuX2KqUcZsVBt0eCo0my7EUJ3p6sMgSx7v6uKNvgC8tzDGTztCoNvmRoYFtq6hOMvUEa8UCp2PttJ/5bAaz1Ux30MXRrk6mszlEoR3vmmvUORIOk2s0OLO0SqPaILjbxhfnF7hvZJhUrcqprTj9bhd7g0F+6WvfRNM03nH8EHa3jWtXlvHedIydvi5qWpOmpnEtmeRaMskD4+OYRBGbLPPwzCx3DQ3R73Lxh5eXKDebbUsvf4BqS8Uiy8xNrePYv5+3Hd5LXdPodrr4wP79xCplouUqAauFlq5zoqeH3z19FlEUOLe4SkvVaNZVPnDjIT4/PcPs1U0S62ne97qbcCkWXpxexGJXKOUqzOdydDmcvLy2TnRkhB6Hl1GPFY/ZQczIYLUrVFsqG+WXfijn/D9fCf+BaP5Llo7B56anua23F8MwuJRMM+n3cnOkk+Odr2WzcpFMvUK2XuVcYouq2qJlGCSqVZ5YepY94RDvveEAu/xBdvn70Y22AjxgtdHSdXKNOjPZHOmtHDsnelneSFKvNfBbrTjNZvaHgiwV8vQ53dsj7Ty/8vhTvOemg+wNBriQSLX+sA0jAAAgAElEQVRj9DSNeqXOxGCEHQE/f/LkSar1Fg1NYyaboc/pI9co0+8MYhJlSmoNi2SipjUZ94Uxi+1owY1SEYss4TQ56XW5cZjN+C0KAauFT91+Fw/3TfHRR55lM1fgA2+6hYvxBJNDg9zd34cimbYTLNpIsCgqQIsDwX7cyixXY2tYLTLdg0FazRYP7t+N36Lw8089TTFT4p3HDvKzu/uptFTKag2L3PYRtZvMnHrv+3lqfZZvrKywWSyyPxRCkSROrq9t8zZVLLKEqusUGw3qLRVFkunzuyk1Gmi6TsjpoNxs8v69e+lxOmnqOivbCPLBGwaJOF184plT1Mp1mqqG32rh1u4BdENHEiSi5Sq5epP7Rkb5Ts86ryQS7PJFsMgyFtmKRbYCBlZZ5pbePr587gqiJDAZDDHua6s/v5MrcWQyAgdhenGTsd091CoN1peSAOSyFWwOC9VyHbWhMu9yUmjW8SsufIqDmWwauynKz3z2ETw+OwvzSRw2E7WGRrOp8bado5xZWePM0ir5VBHztqG7puk0VB1NazcZggDLy0VM2zGN2VqVstrieirN169NU680sDosXL8SZepqFFkWEUSBAxMDFBsNphIJXinW6O0McMdgP5989gz3HtqNR3HgVfI0Gyr5YpNytcWHn3gOURKRJBGbq33hUqq2sDvbiFC2XsOgxnw2w02RTnSq1BstFMNgJZ+jz+3hYrzNz31qReXZ9U0+dvwB/uTEz7JSeoU/uHSRA6EghWKVnzh2iJqm4Tab+fXPf5PhiU5++c7juM1tw+aRbbXnl+YXeNvYKE9vbHB7Tw/3jY1iN5motlrEqlUUWeYvfvrNROwOGppGolYlYnfw1Po6k8Eg6WoVp9nM7nAIi0lmfj1OKV/l4998kYN7hgn4XRzt7uaVeJy7Bwd5am2NY93dXEtnGHS7UTWdrUqO65k0TrOJpqaRrNW3N6YQFsnMn05dYCqdQRZFFFliwOXCJstMRjo4GW+bU1eKVXKNJncPDHJ6K4pLMROwWPEqDt7x5KPcPTzMh47dzIfOnuVCMslv3HADz21scO8to9RaC7ySXOWr8wucjSf5nzc9QLq+ic/i5njXbr61foG7+nxcTKbZ4ffRZbcRsgZ5ZPkCD4zcgCjYvi+xpmWkAfBbXPza/tfxV/Mv8YknXmDQ7eIjt9zKkGucXOMC3u9Jr1nIZ/5B/uQ/vb5/Y/zu+5KFAA5T/dX7NaPcVpe/qrx2oW9nrhuGRqoeQ0BgJd9GDyMOJz6Xg7fv3UWxqbJaKFBVW6wU8mwsJREEsDsUmnUVTdMpZct8Z22d+4YeQDPKmMQejne1m/ZU/RX6ncfpcZR5ZHmZP752CZMocs/gAKc3N1AbKm8aHmLAFWI6m2GrXMYsSds8Wg+5RoOvLi7RYbeh6jqjfj+ZSoW3jvcx6PHy4toqs+kUH3z5CW7r6ebRe3+Bj17+Gqe3tnjD8DBPra4yFggQsFq5lEyzyx/mvZMH+bX970UzijS0NYrNCn22m3l+7Sneu2c3/+3IEYZduwGJzcp1vrQwy0Iux01dXXxuaop6tYFsknjL2BgjXg/JWg1REPj5x7/FsfFhJv0+rqTSlHJl/B1eUtUquXqNh+cX2BP0o0gSbxoapam3uGHnEPtCITRd5z8fOcLhe/toGRqFZoWPX7nIGwcH+fTtd+FRXPzCC9/Ab7Py2Rde4e7Du9kbDPK5qWl+402vxW+xsF4qkWs0uJLKELY7sO8d4muLi1xe3uCG0UEkUaTX6WI6m0UW24jme3ft4uH5eXaEQ/T7vJw6N0PvWBf/+cgRDoZ6eGDkQQ6nfo5irsJGuUy52aCYKbFvzxBnz89zQVjjjQd24XPaKakqPouHpWIUkyjhU5wcHRviro9+jlyx+cM66f/Z6v+SPhPpoYce+td+D/+/64/+5A8euvvtx+hy2EnV6nQ5bOwJhPnW2iqakeRcIkZdUxly+/mr2VkauvGqOlqWJOLVKp12O6Ig0O8KIAgSLaNtam2SJEyiiCwKXK8WmJtap15t0NEXxGWxsFGuELRZuJrK4LWYkQSBd3/pEX7htpsYdLvRDZ2SquJWFO4Y6OPEyCA3RSJk63UcPifTi1sgGox1hkjXKnxnY5URjxvTdlZry2jbDsmChCiIzObaQoedvjBOc5in1qcwSRJ+q4X7R/ZjFiX2BDrYkhusp7JkW03cikKsXCJst7Mn0I0oiNubj0R7uC8COoMeGyanDdml8DMH9/Nzh2/gWOcgk74u/vDZ5xAEkV29nUz6fewJBIhViwy4Iszlo3TaHbjNdkY8YfLNEm7FygOjt6AZZXrdzm2UuESX3U6u0WCrXEY3DCYCQQ53hFjM52lqGvFiiWZL4/RmlFi9js+qoOoaRzo6eP+eg0iiznytRLVSR7GYWCnk+ebmCn999RozxSzrxQKvLK9TQOP86gbLxTwPTuxCEmW2sxQAnYvJTb48N8uJ0WE+c/fbaRpllgtFDMPg7MwKaUHFa7GiKxJLU5t09wYo58u4PDbcHju6bqBvR8L1hgPsCQaI14r89tnTfPobp/F1uLH57GTLVTLpCpIoYDaJNFWDzm4Ptw0OoEkido8NTQBJlhAlgVazhcdrw+Wxsrha4D/dd4QDO7p55ac/RryxykapTLZWw6yY8LjtpBLtUICeXi+9QyHia2mylQpv3buTC2tRNlYzHBjr5dcPvZnXToR5w8ARYtUE/8/pU0xd30LVDATA67Wyd6KfaCxDs66ST5X4L/fdjsNtZzIY4FhXNz4lwJOrM7xldIywrZc/eulJ8mWVSMSLJIpMBgPoRpvD+faxXTy9cRWENK8kYrxtdAdfnJvl+vQay7Uyi4UcV+IJDu8Y4PdO3MqoJ0BRbTDs8RCrlIlVqlyIxxn1ebl/5BAz2S0++MTT/NiBvWyUyqRqdd48OszjSyu8tBVlh9+PJAic2ooRtFp5ZnUVp2LmTDTK4c5OdANWt5Gfar1FuVFnuDNEt9NBql5nvVzmysom/UEfZql9MdTlsOO1WBlwecnUq1xOZXjj0DAvxzf539eu0tAbzGSy+K1WDoSCHItE+Mjzp7A77PS6XKSFFrJiIuB3cWpmmZ5g2wrptu4R7CYTl1JbHO/u4caOLgzDYF8oiCwIbJRLKJLEjR0HiNWivByPoSNwPNLFuGeMsG0AWehEp8ZKMUpTLxAtVzgYDnM2nqSsFjkc7sZt7kU3quhGBVGwUlavIAoSsuhDN8oYtJjPx7H7XTy5tMRyscR/e/xr/MShQ4iCjiS0qRRPbZzlUOjvcef/nmrpKcD0D5qsf7c0I49htL4vMei7ZRgNBNoXOd/lcX5vCYJERb2CKJjQqbBayvD8+gZz2SynY1s4FIkbOrvYEwhxOZ3i2el5VtYSGNvorro9LQD4vQfu4V079hKtzOExO2ho60iCBVGwYJcj6EaDhrbUjpzNF3jP5CR/en0Km8mEySxT03W+vbbMa3p6CNscnIptEbRaiTicrJeKTGUyjHu9HAiFyNTreG027hsZYTaXo9/rYWErSYfHzTNra/z13MtUWxq/c9MtlNU68WoNv9VCh83GoXAHo54TKFJ4+7goCJj4zOxJjoT3Ea0uscPnY5d/H5qRItOI8ZGLr/D07CI39vXQYbchihI7wiEmgkHKaosnlpZAELm5q4vegI9XYluEtsfMKaNJYi1FtlzhUH8vTrOZboedfcEgYZuLpzdWeTkaJexwMOn3k6hWiVYL3Pzbf8qfnTqPaoXXDPTz6etXWSu1U4pemFkg3Onj1t5uwGBPMIjLbGa5UOCZtXVyjTrHIl3tfU6SOHl1Aa2lUxJa7AmHiFXKfHl6huO93ZyNJ/nU+Qus5vMMeL2cvDpPtdKkWWvy1PQCX5i6xo8fOMp79h3lZ47exZOrlzBLEiVabG6msTutBMMe9oSCKLKJs7E4J3p6eefjj/Lhrz/NVxamWIomUWSBpqrROLMRe+ihhz79d07GfwP1/37kww9Z9rQTCv8pt/rp9X/xz/TvotH8+Cc/+tDmuJ26AUVVJeKwoxs6AauFHd4w+UaNlVIJv6Wdi5us1bGaZOZzedyKmQ67Ha9ipqFrjHnaY51qq0auUaHWaqHqGluVCi+vbxKJBHjPTQfZ39lBxOlg1NO2UmibG2/hsyjcs2OU/aEOJn0D9DndzOXaSQtOsxmz1BbyHO7oYsDt5AsvX0XQNLxBN1ZTm0fptyhYZBnd0BG3r/h1dAzDYCqbZLNc4XjXIILQ4rPTl3FZFC7E4tzW24tJlBARaBh1NmtVllfipAolaloLi8XChM9NQ1MxSyZEQQYMDEPDwMCj2Dm5tc6k38/np6eZ8Hvpc3YCOjVF4zXjg/S5nFxNJzAMnf3BPhYLUTTDYMzTg7Zt5RO0KdzU2cfl9AILhRwhq43NSoWFXJZup5NYpe1fN+Dx0NA0Hl1YQBIEVmJpBEnEYVXQMHhwYoJco8H9I3tfJcunamW8NhsdQS8zy1E6Iu0Gw2u3M7cRY2ekk4jXg8uskGhU2VyM864jBzFLVr4XTZkvJPj21XlEi8wbhiYwaCCKbWFZRtAY8flZymXxWa1MDETwW23MLMcIdHoQZYlKoYoki3jDbk709bJWKvHR509x+co6BpARVbw2G3Nzm2SLTQIehf6xLrRandG+Ts6sb6ChU2k2AQGrQ8FqV9BaGp29QdwuO5KuUlabnDy7yK8/8kUaDtAMg4rapFSt47Ja2dxIIUsCdxze1RZLOc1srqTZN9LDfCbL+noOwwb3jHZRaNYIWHyU1CJfnZnFLEE2V0PTDTrCTv7r8aOc3NykVmkgSSLHxga4raeHe/onCFoDCEg8sznPO8b2IglWfv+5bxIMOtAFONgTwSJJPDA2xrlEgkG3i8OhXVRaxTb3sNXEpZhJSC2S8RwXLkSxSAb/487bqLZUNitFzsYS2E0yVllmq9IORcjW61hNGouFPLPFAvs7w3gVBZ/FjNVk4lw8zs5gEJsscTmdYSaTRpJkJFFkNpHkloEBlvN5FFnmxFA/JyaHOb8WRTZJ2OwWouUSI14vZ6NRbh0e5LnlFUpNlVyzwYjHy2wui2podDudSCKcTyT4xtIybx0bxaMo1DWNVLVGqdXitp4eKiI8M7vASrnI7QMDKGYzl64u8Zab9rFRKuOxKO31CZ0vzs+3XQSsVhq6RrpeY7VY5Hw8yVtHxzkdn2HSF8FqgreN3sKop4+GFmOpOIfbDKJgwSY3WCnmMUnteMuZXJb1UplBtxNZzDFXmCVib6dXmaWOVzmOBiqSYCdo1bmzbwSVFp989EXsDoV37t+HVepAFNo8c6ucImT9Gy/Ov68MdKTv4V/+ww/UaRnJvzdNp9JaQBKU72tCdaOGIPxNEPp6ZYZsI4HX7OAzM9c40tXBbb292M1mUrU6Vlmix+lltZjjynqMWrnORqKK32PB5mw/r9bSkb127hnYQa1Vx2/ZxUppitPx6+jEmMtP0WX3k2skmcmluaOvn68tLTLodrEz4OetY+OYJYFkrcZWpYIkgkWWuJ7OokgiV9IZZLHtrepSzGyUyrymtxsQSNVqPPziBarFKjUFQjY7sihy5vwc34qvU9F13j4xTkltcmorzv5QiJC1F0H4m+GjKFi5lrnGgKvFWinH/lAXLlMnZqkXk1hAEJrUBPjSs+e4VsiyIxyirrU4ubFOudkktc0L99vtXE2l6HQ4mUql2BkMEK9UcXntSBYTt/R2M+px0+1wci6RoM/l5Ei4j6c3lgnabNw7ME6P08lPfflRGnUVl8MEBpidNnTD4C0jo3TabTy/us6N/X04zGbi1SorxRIb5TLzuRyv6+/jQDhMtFLl3oFBTkT6MTnNuNwOut1unKb2BUfA4eByMoVLUajqLU7093MxHuM3b7+VL565xu49/ewb6OEz976JQjOOR5lAFKzc0GnmTGKTsWCQizOrmBWZlqbjdtrZE/TT7XDwn597hsXpKJ0RLyaz3LbBMsm43RZS3178N9to/tZHPvyQZW/n9vTrH3/7j0bzB6xf/8hvPyQMuYkXS3T4PZgkCQODbocTzdCZy+fodzqRRRG3RSFWqZKsVampLQJWKxZJYrFQZCqdIV7L8fzmCi/Ht4hXKlzPZAlYraRqNUqaxu5QmB8dGmFvoJsd3jAhm42r6SQrxSLzmQzrpTLv3XkQnxIEDCTBRKae51o6w/FIhD6Xhz5nABGBT12/ytTiFlaLzGB3GEkUCdusNDWNjXKRXqcHi2xGFiVksY1oltQaAy4XvY5eUvUkf3HxItfmNlAFg32RID6LDVEQ6bA7MAQdm8tOplmn1+9FkWWKap2nN9ZoaHVGPF1Ae0xrYCAJIolans9evsYrL6/Q0xdgT7DdyN3Q0csufyeKDMVGgwOhHhYKCbKNOl12Jw6TgiCImEU7VzMbPBdd40+vXmXC78dnsXA43MXzmxskqlWy9RoVtcnJqwuURI1qtUEmX0LXDRAEZHM7uePNo2PsCXRgFmUQBAyg0qpzS1c/+0JBvjw1Q6lQpaXpyIpMMpbjF28+wgsbG7xwdR5EgWqxhitoxyyppOt5zsRXmMnFCNvsPLe+TtDpoNtlZpdvhD6nE6dZYr1SZiqVJOxw8K7JSW7t6WbE68ET9tDj9ZBs1IhtZPEGnBwfHqTQaHJqc5PoShKEtpimUqigyuD02tlYz5Mvqdx1ZIJso87Ng/2sFAs0mu0xntpQSW/laNZVrHaFB3bv5IHxcT79zAV27+hFcZioFGv4/HZ6fF7W4xmsdgvZfBmH24YkiQiKTLpW5UBnF+FOL8lKlVgyRzxZxmoS8AUdpKo1dvsHaBk1nouus7meolrXMMkiHZ1ujvX38uj5KXK5Gm6PjXce2M0OXwerpTSCoJGqpfnE86f5uRt+HM0o8XR+jv6OAA/umiRZq6EbBvf07+DmriN4FB8I4Db7CFnNfG72Gjd1dTEZ8DPZ08G1zU1EUSAhagy4XWiGwYjXg91k5qm1DQ6GgyzmC3zj6jQrtQr3DA7y+NQcV3IZDnd1slEqE69UcVusVFotREFAM9omzhW1idOssJ7K4nM6UGSZRqvF/nCQpUIRm9vOZjyD1+ckW63yytwq45EwTU0nViiSThepoDGTzTHm82GTZR46fRpDkFjOFyg06uwOBck3GtQ1DbvJxEq+QKfDRtBmpYJBraWykMthNZkoqU3GO8L0uhxIgkC/y83VdAJJEKioKgGrhXi1wrDby1wux539/XTa3AiCzoVkFEWSMKgym1ul0+ZGFiVscgjNyOJVRrmSmSVdq1NrqYx42n6/6XqN84ktPjc1zf2jt/6dNbOd4LOIw+RBEkXGvT7GBgO859A+IvYAOlWgLd5p6Qmssv3vRSG/W5qRQ9U3/kHPy/ZjSkiiG0FQvk9wpBuNNtJJGUXq/1v/k0cU7OhGA50qulEkXS9zKrbOUysrjPi81Fot1ktlYuUyh8JhBAE+9OJLVEs1uoc6sJkFKuUGtWqTlqqDAIVWgxv6wiwWslRbbWueK6kUy4V2sppX0biaidHjdNLt8OC1tK3qxrx+ZnIZSk2VM9Eo94+NcSGZZNjt4Xo2Q7RcIVoqc9dAHx5FQUDgejbLlWSaS8kUOpBp1rlz/yRBu50Bt5uZdBqL08KDu3cy7HWTqdc4EArTabezXMyTqa9RUtcJ2/6m2Z/02WlodS6nYpgk6HYEkAQXshjCJKU4l0jgC7qYCIeIVypcSyaxyDIDXi+NlkbQbufl6CZ3DgxwQ0cHy6UiXosFaXuvubWvl0m/jxvCA3gVOzv8QULWIEW1zCNL87x0bZFIyMnuQIRltYzZqxDp8vOWvTs5GA5xZiuG36qgSBINSeBSPE6u0dgGGiocCAX4TxO7GPUGiThc9DmdpOtVMo0qHotCut7g5kgnT6+tUW9pCAi8dHUek93MRiyD22nHADrsNmZyaf7orjt5YPQANqkXtzLy6nGySB0kakuUmirT0ThOr4NWs0WZFrf19nFr9xArlQJTK1vUqw207XjealXl6P5Rrn7+3P8FjSb/pFvt1H80mj9QfegPfvehiQcOcO1ylHg6w0Ihx9V8lvvHdhCvllF1jVpLo9BsMJ3JYZPbqF+11aLSaqHIMgvZHPl6jcfOXGMhnWEplWahkOfC3Co7esLkm028FgsmUWSXP4RJlGgZOiCgGiqnolFS+RJ2ixm/zcSgqxNJcPHE6hle3NriYizGT+46iFmSMYkyfz59iT974jQmk0hT1bH6rOwLhei02/nC7BxmWSZVq3AhuUW6XkIWDdL1dpZ7t9OFV+kg20hyLZdlZ3+EaK5AXtO4s28EkyhjkUw4zSK6oVPRdXrcLm7s7GA+V+BYVxcjHi9uxQbbBHuD9gh1yO1nvVbkyuImcxtbPL65xJsnJrDICuVWFQwYdPtZK2UwDINsvU60UiJotWI32WhoVfwWG7958kUUk8yx7ggT3hB2k4VopcRaoYDfamPU5+P87ApWlxVN0xnsDKLJArVKHZvNws5QmH2hIC6zlUQtj01WMDDwKnbsJgstXePZ1CbZVAFJlkjF89xxZBctw+D81rZvY0sDQWBLq7NcLDGTy/GdlVWem12mIkK6UcMsSeSbKjd1dmEWrXjMCp+6eoGtjTQOlw0N8CoKfquVMa+HSb8PSZJ45tISdquJlFqnqrVYX0uit3QsFplsvkFLN8ikysiiQbnSRG3ovO/2g5QFnfObW2TieURZAsOgWqpRLtYxNJ3bD0xytKuTBz75RRpNnVKhSLPapG84RKvZIuhuN0eGAT6XHbWdu8bxwX4W01m2SkUCdjuJSplUIo+qang8Ng70d9PQNA6ER7BIcDkd4+WpNSRJRDGJ+EJO9kc6eeTsNA1V51fvvYVbIoP86sln+V9PvcRjq4t8Y2WFxGaGX7jlTUiCA8WUJFWrc1NXFwdDYW7vGWa+ECdotSAKJjDax/9nn/86p+aWmS0XeevoOH6Lhbcc2IUv6OH02gYrpSIDbjeZeoOyqvLo3Bw+u510rcaPTo6TbTRwmmXeMD7Km0ZHqbe0V3/TbsVM0GrhhY0N4pUKhzs76HW5eH59Da/TTrxcxmk2E3E6MAwQRZFxn5f5apF+j5eNRJZyscb+wV4G3S6CTiclWiS2sjywfycz2Ryfu3CF1FaW9WyevqCPc5cWeO3OEb4wO4uq6SSrVa4tb9Ib8oMBC/k8yxtJ7t+7k4amc3Sgl2i5TKxc4XgkQrWlErbZ8VgUjnREuJZJsT/UiVk0sS/UxZCrj2+sXePWyF5Uo8LFVIqA1cKIp4PlYpJco0qXfag9EsfG2cQM+UaT1/UO8OTaCsVmkyG35/9j7z3DJDuoO+/fjZVz6Oocp3tC9+Q8kkY5giSCQObFBoPBZvHuYmzD2nhtDKzBsPYSvWBAmGCBQaAAAoWRNJrRzGhmNDl0zqG6K+dw64b9UIMw9ptYe50en+fpD3W7nu5bfU+fe+45/8B0vsBH9t2IU257tVbqZhJRcP30FcY1se6IvZ31gU50q8Kl9DKSoBO0rUMQJByyg0x9GpfS/v9YgyXB/WqTeTH9zM81RT/73XEk0f9/I50kYlJAFGxI19xtTEtDECQszGuEJg0wqeopWp1+Pnv+FXr9AUZCIRaKRSq6zvpAgJ3RdtYqRR6/OMrODX2UtDqZVBHLNNm/Zz2fvf9OTpXSrC2kOby2TFfAz8dfPMaR1WVCThdRp4ONwdA1zGGdl+NrlPUaVV3nu+MT1E2D4ytxfDYbYaezOcjI5pqTeEXBtOD04hIVy0IRRW7s6MUuCeyMtRBx2hkOhwh7Pbxt/TCbQkG2RVsYz+UYCIUYCvrxqTbyWp14ucx0voAsCuyNdfCD6UkG/A3cSgfjueeQRIuZQpJ1/gA1XedYfJyR0BCCIONVg/zRs4+wpaONTaEgOU0jVamwt72dxUKBbS0xwg4HlxfjbIo1cfTzhSK9Pi99fh+tLhd39QzQ6Q6hmTqaqaOKCrIIsiDyxMwEl68s8fSFCW7f2s+h+Tnu6OtDlWTc1zDUiijhVVUWSiWWikU2hCMsFgqIosTpuQU+uPd6QvYATtmLJBiU9RqlhkaiUiZdrVFq6KxVKhwfn2EpncPptLOuLcqmcJi2UICiVqekNdjdGuPw5Cx2t52D7Xe+OoX/aQiCwHCwE4s0k40yuWyJWrVOQzfweV1sCoY40NbJodQiuXSJznUxXF4n9XKNXLXK6pPj/+IbzV80/jkazb9vWvyvMByqwspsgppmMrtcIp8qMj+2zDuf/nGzWKyu8Xvf/jHxcoWQ3Ua310Ory8lIJES6UuVbx88wnUqznM5hNHRUh8LyQobxS8vksxVCdjsxp4OJTBaPqmKXm+N8wzIRBYGgzYFDVtjY1sJr+vv5zJmzpGprvOUnX+SdX3yUM0vLnDs1h0u2IyJyeHmSr794itZ2Px29EeyqyOx0nJPxVb51dZR0tYoiijhkiQfWbWIkHCVbbwLlBQFsooJhFcjUqpQ0jYZhYHeqvG3jRoqNKrplkNPKXEwlSNVqbI6Eef/WvQwFguxsiTAcaqHVGcS0DJLVNE8vXOZDJ57jvie+zS/9+PtMZjIEvTYyBY3psVU++NLzrzKqnbIdj9JkBkzlc+Q1jbVyhYupOFW9ik1y4pLtmKbJn914Eze1r8OnulgsZYg67fzp9TfyrpFher0eugbbCLlcvG3HFu7q7WFfewfDPR30+P3c1NFOzNnEqubrdepmA5uk4FEcmJaJbhrsae8glakyP59hOV7iu8+c5vaubh5+zev476+/ky3ruhke6kKRJEYTCc4sLRNze+iKhag2GgiC0Jx+WxbjuRUEVBTRz5dvuwetppGrVpnJ5nhkcopcrYZLUREFkZpukC9qZNMlCpkiK2uZpnWbLGKZFm6nTFurl5DPRjZTwbRAUkQ+8eghru9op8N7x9gAACAASURBVCPgQ6tqpJYzmIZJpVjDF3Dxubfex1cePc6Dn/8OPq+NjjYPrb1RPvbAnVRLNfSGQUmrM9Tawhs3baBYrVEr10iv5vjCY0eIzyZYmlrFZ7dxS08Pbp+DSMiJ2++k3e3i++PjCAiAzEqpiK6bqLKAblqIokhBa6DpTTzbeDbHHQ99jR88dY7VRJnRy3FOvTyL3dHM/ZpxlXPJNEGHg9/58bO858dP8Z9ffJrPnzvPWiWBZTWQxCAHv/lnPPvSJZbm0+RrNU7El8nWa4Ttbt6/5R08fO/9FDWNvKaxUi4TdTbF5j2qys1dHXR5PExl0tglmaFAlE+fPculdJouT5MtfXhhkblCE++7WijiuSbSn07kub6zk6FQmA2hIKlqjW9cuERN1zmytIyhm1QaDTpaQjz0y68n4nQQdti5mkpxc08PX3jjazizluBHJy6QWs7g9Dio1zSuJhNEO0N89KnD3NrTw81dHQxHwhwcXsfWcJi6aeBRVR7YtYVtkQh1XafS0MnXajRMg5DDwUKxgFNRKGkaYbuPvbF26oaOW3EQtPVgWGW2RqLMFmdxyQoLxRKjmTRO2cloJk2HO4AkeChoeT5w/OtcSKZodzs5k1jhwcH11A2T0UyGbL1GyL4Ty2r8rWr5MwylIrYjCCoRuw9F7OCLV57j948d4ZmFRdpcUcxr7nKyGCZs/3/HaP7tGA4epNS48PeOy2IbDXPt75wP11jxWSqNJLrZVAgQBZXVysscWXn+2msnplXBJik8NnsZVRKvNYVNq9uow8FkLs+Hjh+hbhjsHurlzPgsmXSBu/aO0L+xk9GZZb4/NcUf7t9PsMVHLlnge1dH2d7dQVnTiJdK2CSRF5YWeWZhnkF/iNF0mqKmMRQIsjEcwbCspiTW6ipV3UASRLq8zXq4UGhaaX5g/x4OdrQxEg4TL+ewsKhdM+2oNnR8qsrF9Cp1Q6eo1XnXyDBOWUY3TdYqFdYqVUoNHVGA5+cX+LVnfsy9fX1UjTpv+NEf86O5Wf7wxItU9Ablhka2XqPT4+HDp76KYRUxrSr1qsbmcAifqmJZcGtPDz0eD3f19tDudhKwq7z/+r2MZbJ0ejzYZZnpfIGt4Rbu6F5HiyPK4eUpZEFiPJtgupAABGpGgz+57jpqdQNFEfnNH/6Edo+XAZ+Pbq+HumHQ7nJxfm2Nk6trvLyywuhqgnipSF8gQL/Pywev33etjuuAhU1qYoE1w0CVJAqNBsvXHh7+4r57UGwyw5EwI5Ewk9ksXlVpSh0pMkG7g/b2MC/MzzNTePHv5VzdmCZVG8ewLEaiUZxeB4GoD0/AxTdfOMUdX/oat/7Pr7A2n2RguINP3XgT920YolRpkEsV/3/n/D9L/IKTzJ9+/XPEv4mJ5h98/KMf1gaC+D1Kc/0KeP1ORFHg24fPcvLyAnce3MzmSKi5Pldt6JbJlXQWn81GUqsiSgJuh517t23i9RsGeXF8GoBKTefNe0e4q3uYXS0RZgs5RkIxZFFCEWXmikn+enycVrcbw4Jjy0tkciUeOn6KxYUm2zGbKmFZFqNWjs+//DJPXRhDFEXcPifrYhEaikAhXSKRK1DWNOo1jboEvzGyi+VylvliHs0wkUWR8Wu40qjDhW41+O74KFVd59beXr4zOkbEZaPT7Ue3DP7y0kXG0mkmMhk6vE5GQh1Y6Dy3OE+n283DE5fo8npIVCt87chpUvEsvpCHhfkEuVyTlejzqHzolutxKgqiIBCwuUlUMwTtLvL1KvOlEl9+/gRlVcShQMiuEq9miXpcVPUG6VqJLk8Yh6ywzh9CFRXKjTprlQobwiFu7e5mwO9vNhgCHF5YIFku0xvw0+3xYpgmFV3DKSsoooyJhSgIZOol5osFTo7PozWaupf5UoNP3vcaFFGizenjNX2DfPniGRKrWVS7itfpQBZFkpUKbR4vXpsNt6IScthpcTpodwcQkLHLEu6Qiior2BWFdLXCTL7AeC7Hx544xPdfuIqiiKiySDZXA13HG3TjcNvJZcqUqzrpTBWfz06jYTC4IYapaTicNmZqJY6+NEFNM6hpBsVchWSuTlubl68+c5pY1EVrR4D4SoFIzItWbfDS9DxOrwOX18lItIWL8VWurCVw2FUyyQIOtx1ZErA5VPwRL363k0JdI1kuU85XcHjsPLBxAz+8Ms6vbt2PRZUfTF3lysQqgiBgVyWWFnMcn5ilVNGRJIFYu5/jp+eQRIF6o4kPbgnaeeY330HA1oNh5fn9H/+QN23ewLG5RcZHV0mlshTqdQZiQb5y5TTv/MqXyKRKqIrIHz94Bz955Qo7+zr5xpWrhJ02NgZHGM9d4T9tvZnVSopuj5fnl5b53Z07+dzZM3hsdryqgiIpbAyFEASL5xYWeP+2PXx/aoKo00HU5cJvU2lxuZAkiR9eGefImTHWkiV0j0ShXuf4xCyqXeHD113H9mgLuXqNmmnSF/Bz4uo0l0o5Hnv+LE9fmGTHYBfrg36+eP4CqUqFd+/fxTPnJ2jvjXD7xiHCLhcXx+b5+P2381eXLnN9Rwexa5amK+Uy26NRtkTC7GyJcWR5iZcWF2n1eNBMg42hEH0+P+O5HJ1uD20uLwFbNxYVnl+aJVkt4lEbuGQ7IXuEIyuTbA13MuBvitFHHS7e9cij7O5po83l5tnFC1xOpfnw3uv57uQ4yVqdgF3Fb1OYzBc4MTfPrwzvAIFXp4h/d/1daMzjkN08Mfscd3QP8Lb1v8SN7f0Igu3ncJT/XySfvx2CIKNKTax7w4wjCR4a5iqy6OPIynN0ewZe/XkNcxlZDCCLERTRQdWYxybFOL76I4b8B+jz/kxLsm7MIAkif3bmJKlKhVu6u9gV3clCaYmJXJ6j07OIssiJeJyFbI4dvV3c0N+DZTWxzalCiem1FCeSq8yMr9I9GKNR1wm4XWSrVa7rbF7LM4mmbeRKucjdvb0UNY2FYpHbu7pJVquIokCmViNbq/HmofWIwHVt3WyOhNkaibIl3MvDE5dpdTYxq8fjq0iCwOV0llu6uqgZDaZyeT575gwr1SoDfh9+u8r5ZBqbJNLv8zISDhN1OHjT4EaOx5e5q6efDtcw+9pcfPLkCWaWErxu03oMy2qKxtvsXNfaxa1f/zQFMcdQe4zlcpk2d/McKg2dGzo6EAC3otLt8V5zFEvQ4nSyIRgkXq7w2NQkAYfMhsB1zBbHeXRmgpl8gSNLS0RdCn3edsDgtbuHCLb4+dObb0UWTT5x/CQvT8yx0qiyWqlwe08PL6+s0OsPsFIssDnWwl3d3bymZxMhh5PpQpKgzYl0bdSVredZKZeYzBdQBIHjy8u4FJW1apWQx83Tl8c4PjbL5fElEkaNvZ0ddHu9vLKW4I2D6yg0mnCzfbGNCIKCaVV5JXmIXz/0OJ977ijPjs8wuhAns5ZHEJqOYw6XnUqxhlbTmPrQN3jbxltpd61jf2w7R0sXyaQLpJ+b+Rc70fzon338w45tbb8wRrPy0vw/+Wf6NyFvJAjgsElkChqWBW+8bScvXhgnvpBG102cdonlYoGlUpmg3cZ0Pk/dNOn0uDmzusZNfb3k6xrVRoN+n5ddLZ386D+8jReXFvnNL/0QVZQwTB2nYuPN6zaTrOV5YWmBZLVKpaGTqVaZSKcQBIF4PINpmk0Gb6GAoZsYusEP3vhmvjl2keVCoemxalrs7O7kUmKNYrbZnJSrOnaHDZfPiUNWOLw8Tczp4uRqAp9NJWy3v+rpHXN6aHOGeMvGjUiiQL6ucWtPNxuCYeKVLO984kfYnTb2tHcwnc2wXC5T1Cq4FJWATSVTr3BLZxcCoJsmnV0RpidXuHphnv4Nbdy0dT1Rp4P3jOzCJfs4Fr/M/tZ1JKpZGpZORPWxv7WPTaEyuVqdqWyGgqbx5+dOsScW5a7uQQ4tTqGbJslqjojDT7FRQRCg3RUkYGtOYFyKgoBArl5DEkVskkSn18cPp6ZZLlVYH2g2oWq9QkmvsVgsMuAPMJnL8ML8PDZVpKYZ6NdY4KbVbIpEQUASJGrlOonVAt6gh1a3G7dq41c2bqTL4+Xp+Tnu6e3jxaVFDMsiXUsRsocBkUG/n8+/cBxZkdnY3cZYKonXbm8SgHwqNkVE1y103eSddx7gm4dPUa9qhKJe9ve0spIvUMo1gfX5VBGnXSbU6mduPI7TLuOwScTTNWqlBlR0zmoreN0KuqYzOZHAYZMIh30UKzVSK1kODg/yH7ds469Gr6DVG9jsCisLSYItft63Zyev6d6LKnXz9bFv8fjkFO/bsZ2VUpE3bxkmXaszV8jTqOsUtDilRpWqrqNIzWa5pplU600P6pDPhttj4+iJMQb7gyTWilRqOl6Xwp88eDc3/Y8vM/9HB7nr0a/w8IOvp80VovO1Xr43OcnJxUUSSxne+9DjNAwLRWpKNNlUmeFQmOu3DLEt0sReXkpn2Bk9ys7IrSyWT3JLxxAPjZ4h5nQwlk3zzs0jjGfzSILI/f39PD4zw8H2Nt62aSOPz46RrtV4+8YRVisFBARMy+S5uTni8ykEAfr6w0yNL3Hr7mHesmE9N3X0Y1hN165fGhzmtq4i55JrJNeK2J02/vrX38xjMzPc0NbGI5OT3NTdTbpWbzoJdQapFKs8OzqBaZiUSnU+efwk+7q7kAWRYytxBgM+erw+ktUKN7UPcGRlGr/NRsDuwC7JZKtVRjMZFFGkx+ulbhiokkGyNotmNLivdyMmJn61m8uZ87gVlXStzoXUIt8aG6NYr/OBXXYEQSDqdCEKPoJ2B68b6OdroxdwKgqLhTz9vhF6PQdJ1f6KHyymf65ZfHL+EWq6zhv6H3z1mCrKCEjsiXXy2y8eot1zit2xKPf17nn1PaZVp6qP4VJ+cRHrn7ra/FQ7c32gBQDdzGJSZq0yhyjM41Ec5LQykiBiE+fRTOPnVux1YxpVbOM9L3yVu3p7iDicXEylOJt8hUMLS4ymkhQyJV43vIHvnrnIYGcMp6LgkCRqksjv795N5KCf1UqWsWyaLysKGyIR4sUiPrsN2zVTjNVKhZjLSaJS5e6WHl5cWuZCYo1un59NwRBhe/Oh9K7ublyKil1SsLD4o5ePUtV1BgIBbuzIUazX0S2TZLXCcrHI6/r7eXBwJ6fWJvnW1VEkUeS+wUHmCkUmclkOtLZzJpFkMBBAFgUCNgdbQzdjWiUevvM6UrXTPD77DE/PL/D1u17LpfQa3xwd44F1TUmmbK3GiXicYrbM4jVXnrphYFkWFb3pjrZWKXND2zrOp+Z5YnaW3962j3PTT9PmdhO2N3Prhs4OPKrK4ZUfcmhhiRPTc2zv7iDmcvM34xP0eUNUjTqqKHJrVycOSaXc0NEbOg1N59aeXvp8HrL1Onvb2un0uFAkkd/cvAtFlMlpZYqNCsulIr3eMKpkx7Tqzetf1+jyuAnZ7Azs2sUPpqZwKzIO2cVtw0PYJZlMrcb2aJh+nx/NNEhUq3S4Pfz6yAjlhoZmzvDYzAW+MzaOU1G4oauLKY+X+XyOpelVNN1kbi5LMVtGlptayL968x7+bkRdLuY89r93/F9a/GuRN/o30WjKskS1bqDrFg6bRL5WY+9wPwN+P1995jhOr4PRCwtMXl4kk9euTaMENu/o5VMHDxJ1+LCw+NjJY5gWnE0scymd5vRKnM4WJxW9wWwxQdDuxqO4uZxO8Ib+TdgkhbxW4eTqAlP5PKvlCtd1dPLAukHskkrN0EhUy8ScTcH4d2/ayf39A3zl8mUmMxmePHqOUqVBTTPxuRVsqkStWuf+HSMYlslMvqkxFrCpdHiaRIKNwQB7Y+1M5dMoosw9vYOUGzUCNjeiIJKpF/jy5csIgoBTUfDbbMzHU8yHwswU0rS6PAwFQgD8eG62ObVUVQRBQLUp6LkqlWKVmMvJSCjUJOJgsr91kEw9j24a2KXm+rTUqPJKYhlBEBgKhdgX66DUaPDd8Qm8qo1bOvqIV/Jopk7N0BAFAc3QWasWrlk42hnPZNgda2WpVORSKkPdMJoTxKkVenx+Pjc2weJMktaOAFOTSXI5jc42Fw/etpuGaSKJAo2GSb1u0N/ZJH/JokROKzORSzSFfBsmxWwJomFanE7KjaYTULxc4eHxMWq6QZvbxUQ+yRbZhkO28+O5eURRRG/onLo4hd2pEup2EYoFmJ7LUasLaA2TgEfl6elp5pcK1OoGMc2kpyNKZ8DPeLGKpEj4wh5uGhqgz+vhq41zLM8m8YXcKIrExHgGBDAzdXKZOlYn2FURt1NhZSmFN+Qh2OLj0NmrJCtlYi43bo+DUqGCrEg4bArv+8YP+dLmC7gUtSmaLon8eG6e514Y5YxnCkkS2L97PelEngOf/gK5Qh27TUKSBAzTwu2QaTRM3E4ZRRZYWW36yvt8Om6ngiQJ/OrdB/jO2BilatOWtcPjZSybxiHb6PWGOdhRRhJFfrSWp95ort8FWSQQcLBhqIvHZ2Zo9zRvQBGng0ytxrH4IplgU+tyMr/Kbwzfzl9efobpfIF9rTFOrSY4Fl/lD3/yPLVKHdtNexgJhYg4HBxos3E1k+DQwiIHO5oYxGy+TKTVTyFdZG25aZd5cnIWVZb43vgkH9i1kw+++CKtHg/bWlrwqCo+n518usSvff379A61c7C9jd2trWyPRBgJdfDk3Civ2z7Mo2cvUy3XsDts2BSRcr6CIokslUqMhIOcS6YZDkWwSzKzxSRT+QIDPi8fOXCAi6kkt3Z1EnO6uZJOMp5rnttQIERFr7FUKtAw0hS0Oq/tiXImscbdPesI2W3saRlmb2w7ryTOM1/I4wm4iNh9NMxlvKrKcKiVu7rvZK1ykaxWptdzkOXycb514TJ3H/j5dfeAL0DU4f+5Y0vlFAVtme3h3TwwtIRmGJiWRaq2SrurD2hOQ/NaGZfCPzhanXvRzSyS4EMWAnS6O179nkNe4+jKCR6KX8SrKuxvmUeVujGtOrpZIlGPc2NnB9O5AueSKdrdbpLVKi5Fodvn5/zpWVLVGus7W/ml9UNM5vMslsq8fcMGIg4/x+IzXE5nOLu6iltVMUwLzTAREbhnYICFQpHdsSjXt7WzUm4+0P/y+mG6PG7ilQomMJXPMxwK8YXzF7i5q5O6aTCbL/K+7dspaRptLh8tjk7m8gVu7VjP4eUJYi43E7ksK6USL8Xj2CSZ+wb6GPAHOJ9McqCtnUytik9VeWZ+gf2tMTxKhXZXAgk3upnkcmaFHdE2ziZT/OfDh/jtnTv5r7uv40omTrvTzXwhR8M00RsGF+Nxwm43bR4PFV1nd0sLhmVxdGWFzaEYVzMZtoRDjGXjfPyuWylpGl+6cIF3bB5hQyCES1GZK+QYCQeZzWWZz+dQJIktkTDvf/EQt/X0EHM6WSyVCNkd3N61jh3RFj5z7hyDAR82SeLQwiJvXb+ePl+IrZEoY9lV/DY7U7ksZ5MptkZCPHT1HHd29zBbyPGdsXE8Nht39nRTbGho17SZ85rGvtYYLlnhIydO4LM1CUYnVlYpaRozqTR3dndzIbnMm9YNY5c20e6e4b/u3ctMIU/DMJnK5tgQjlDMlSmX09gUEW/QTVtbiHy5OYH9uxG0O7A7//FtWP+x498F2/8Jo9HQERomLoeE16VwbmyON+zbyg8uXmV4uJdUqUQxWyZX1JAkgTfesZ10pcrFizP8pHseUQC3ovD6dQO0ulzNZqdW466+Xva2t7FQLLFQLCFfu6iPTU7S5vLQ6mqudndE27itc4gzyXm2RbpomAblRpUn52Z46OgpHn7LG4nY/VhY1A0dpywTdNjpWBejXtXQqg3y6SJOr4N0osgT5y/T1RohWSzRHw6xNRrBME1u7hwkVcvz6PQkiUoVn81GxO7GqzqRBJGqoeFVXLxn82Zu7ergkYkpDs3N8sbtI8TLFVbKJV5aieNWZERBYHeshWMr8aZ4fSJLNl2iXDUoZssUtAbDoSiSIAIiDVPja1cvcEN7O8dW4uyIRrit83Xsir5Eh9tDwObAozrYFAxR0w2m8nnWByK4FJWJXJpBP4hC0yq0putopkG6WmUynwfgK2fPk1nNUSjUcDmaEIhyo4Gu6SyvlFmeaN6cUUQWl5r+uF0DA/x1sUIyu8JAl5ffe90tiIJI3Wjw7MI0nz/6MtY1KEVitcBJfYaFjhCH5ix+a+cO3rlpE0dXlnHIEmuVCkGbnYVSiqjDhyQKHBwexKOqjKdTvHB0lLV4gUjUQzRgR1VELMuippmcPjGLbliYdQPTtJBEAbei0tUaYW4pgd1l4/uHT7NuQydaVcPrd/D6XVsYz6RZXjtPOVeHigEeBZdDZtuOPgqV5kpHUWWyiTypZImz+gwjG7op5sp0xUKs5osUihXsNol8oUJXp4+fjI5Tr2hEXe5X8ZaqInH+SvOholjWMEyLbEGjWjdw2iWcLpVa3UBVRPKlBrIkIIkCiVSZkM9G90ALRU1jfn4Nw2j+PfsCfpLVGnZJxS6p3NQ+iIjA9yunsavSq83mns3rmEqmSBSK/M6+3dR0nUevjLK5rZX//txLKKrMR26/kXaXB1XsYH0wyGw+zytrCWq6zguXJ1hayDI00s5PpqeZyuXYFArR6nIiCQLv2LSJ6XyWJ2fmKOcruP1OXD4ntoaBojbL23gqRSZf4ltj46TjOV6/fj13dHXz/NIibp+TzliYsclFVhZTLA6UuJpK8/LKCvf2F9kUCiEKTfWAteUsTredrqE2KoUq9/b2Um40UCSJYyurNEyDTneQi+kV7ujqZrVSptjQEAWI/C3zh4BNpWrozBVyJKoV+nx+MrUSe2KdyGKUAb+PFscI9/Z6ydYTZOolTqyu8stDm/kvi0+hWwYCTfcsh2TDMFPYJBW3bNAw4zw6PcYDwxt5z/DdGGYOkzqK2EK/dzvJ2vjP1U4B+OboGD27w9zdPczXRk9zIZFkczhKu+tn7/OpLv6xwqKChRMBG5q5iCT4aJhx8lqOPS29HGjdjCjYaJgZLGMaRWw6e/1gepw2t4vRdIpkpcKAz8ex+CqZWpWZbIa9+9ahiCKjCys8pDdxoAuraXZGw/T5dFqczc/Q7/MSc7pocbpYq5SZyecpNRrM53Pc2dPNifgKXz13gZDHxR/u28eJ+CrHrk7Re8dB5vIFIg4HYZeTfa1t1AydLreHFoefmp4iWSsRcVTIaxpXs0sE7XYeWLeOxVKBG9oGKOkN7JLMplCE+WKeFqeTU6txktUqj1y6yp3rBxkMBBnwDbFamSPqCGFZOsulElGHi/du3kGuXuZjJ1/GME0+cf1BOlyDpKpnObu2RrDFxy19zQeEFpeDqNPJfLHIUqnpj26TVIqaxv88cpIP3X4DRa3BG/pHmC8WKWsNPnv+HG9YN0C8VMZvs9Ht8/Py5Gxz6+VxMxQKs1IqczWTod3tpqjVidh9BGweFEki6nDS4nRzR7dOyOFgrVLAq9r53uQU17W3cn1rD4ZlkqvXydTq/MGxl8hXqgiiwNjsCvf195Gq1qgZOr0+L4fnFzi7usp/2r6dPW3tPDs6wfTCGuV8BfUaVKim6/R4vQRsnc3r6w2xVM5hlyRCNjv3D/Rhl2QUSeRHiTyG2SC5VqCcrxBuD/LcuVG47udztG4YlAvVf7Sc/z8V/0r6zH8bjaYoCrRFnRiGiSxL2Bwqhyan2dzVxoXZJRqaTrg9SKfLxrlTcwyHw7gVmZJWx8Lipo4unNd8kludQZLVHG5FxpAlSo0GIbuNXS1tHFqY4wuHT6CoMr/17CG6QgHu6btm++V0Ei9Xmn7IskqHO8B0Lk9yrch7Dz3L5265lR5PlIDqotvr4UoqybbWNuq6jm6ZLObzmJZFIOpj4tISvqCHbCLPiaU0l/1L3LV5IxPZ09hlie+cPM/GvnZCNhcRR4hPnz9KRdeJOuzc2NFJtydCTdeZSqa4b8MQ3V4P9/Q2reWuZrJsCTeFdvP1OntiMR6ZmCSfKtLQTWRZoFJpSq7EnD+VKhFZLKW4nEzS6/WiSCIffek4tX06B9sGcCsOFFFGNw22hDsJO5ycXI1zOb1GyOFgNJN9lU3Z6/UzW8hzaH4RRRJJlSssFYrkkgXqVQ1dN6lrOnaHikOWsSyQVAlDMUAScAfsiCLXtFIttnS2YzQM3H4XD529QL6ucbCjg88ePkE+XcQbdKMqIpmChiiU6OuNYVoW3x4bJ+Zy8we7ridTL/Lc4hwmFolKhUytRklrsFoqIgoCVV3nuv1DlBoaVy/MU60bmJaEALgcMqGwm1q5RqWmE2nxEnN76PS4casqlYbGPf39fJvLrK5mqdc0ZFlirdwkWmzY2IIoioyNrmJTJA7u38CvDQ/zzMIiY+kUt3Z389Gzz1KtG6jFOlMLayg2mXy9jiSL6A2QVRm3x8FUJkNHOECyXKbSaCAIUNdMTEvHKGlEW7wUVvLU6gY1relAtH3PAJnVHLJco1ozKNcMZFHAbpOwLMjk60hKnh8vn0cQf1bVAjYbx5ZXmnaNoQ4m8qt87MgxqjWdhm4iChCOuIk4nVy/bTN/cfQUR5fjtLpdXNfbwwujk2j1BoZu8EfPHeG6gV5+d4cXSRDYE2slYHPy/NIcx65OoaoiAbeT39uzh/c/9SzdXi/rAyFOrsbZGe3i2+PjTfxwtEnwaGg6dqdKa2uQlZU0AL+xZye5ep3Eug5iTgd/cfEiPpuNt+7eRqWhc+nSDP6IlwNt7ZxZSzAYDPLs/CLXtXXjVoq8f9dOxgcGuJhIMrqwwi2bhkhUK6SrNSRR4P7+PuaLBYJ2Bx1uD27ZzpemL72a46fWEmwJh3EqMkFZZmMwwlgmxXKpzIZgiMupDFvCLehmkny9jogNEReqVONyOklZa5DXytyxZwTTssjUE8iiiGbqOOQQQVs7IXtz5HgxleK/7b8RSQgiCiqmuQaALAaI2Ad/rna6FQe3d3fiQCfJagAAIABJREFUt7UhCX5e25vj3t5BAraftwISBbEpTyT8wyyCGmYcARuGlUUgiip2YlkGitiKXxVQxO5XV+aK2E5Fv0ihfolSo8pIKMSRlRWKmkYxV0a3LEJ2G5cSaySXMwT62/nJxVFaon6m5lexLAu708ZkrqnlemfXVubURWbyOZZKJVpdHq5rvZNtkatczSzT4nIyEmplc6iNqmEgCwK6aaJIIm1tIT555AS/e/1eRsJRNMOgrDdwygqT+TzL5VEcssRYJsensqfZ2drK//VX32Pj+i4+d/Pt5Oo1rmSX6XJ7uJLOcD65xvfGJ3n3lpFruEwf79u/mxvb+/CprRxZOYNPteGS7UiCSLxc4eYONxW9xjrfCKJwknNX5/kd8zDv3pKm3eXh14aHuRiLsVAs4VIULiRSHF1axqkoyILIcrHIc0uTHF1cor0tRFFrEC9XWK1m+OX1WzmyPE2vz8eHnz+CzaFyR38/TkWhoyWETZaYyGaRBRHdMpnP5ZjKZFgulZCFSWySRLZaZW/LPgTBxovLj7JSKnFb5+sA6PPNUNN1TicWOZtM0eVxs1IsIgsiPqeDZLaAL+zhpZVVerxuXlxcYj6ZYWo0jtet0nr99bx94yamc1mmlxOE2gLMTyXo72uloNXp9flJVGdpdYbw2zy8sDzLeDaPKMCg349ilxgK+PmhBS6HQqZQR5YEnC47hUzp53L0avYQL1wc4/rNg1zmyX9Qvv+fjn8lfea/DTLQf/3Ef/uwfWsrNlXC5rShqBKZeI6BzhjpWhVBgFqlTi5ZJBL1IDtUxtIZqrqOIIjc0tVFj6cDr2KjrNeYzWeoGTqZep2FYolOjxuHrHA2mWQ2m2V7bxepSoWd7W28sX8DxUaNp+YWGA4FKTYaJKoVBv0RjsUXuTixRK1QYdKosL89RtjuYzqfZFcsxv39g7y0soSJxa7WVvKaRl8gyIXxJfS6RnythM9nxxNwM7maoCccxK0oDLVF2RqJMJHLsCOylbqZ4nwiSZfXQ8BuJ+b04VUdPHz1EvcPruO6th78qpvnF2eJuZz0+wIcWVkh7LDjUVWWyxUWc3m6B2J43CqPvuNB9rR0YZNUlsprzBfXWB/oZE9rBFkUuL93JxWrzEMXL3Ipk2RrNIQkNj3Z3YqXQqPIbCHP5069wleffZn5cpHBljBPzszxyPgYFxJJZpcSlDGYGV8mVSghKxKyImOzSc11fsjNr+3YyrGlJWyYOD0KsZgbrWHgcyvcs2UDiWqVw3Nz7OntZnY1ySunF7g0v8SR1WXSa3lEUaRcqr06qfO4FPq7WkgUSiSzBT558834VBfPL03xtYuX2NPWhl2WKWoal1NpMtUqqVKFSl2jgUWpWKWvL0ZHRxBd1wm3+mk0dJZXCiSzNXTdorXdjyZYnJlb4uLEAsVChZMTc3iDbgRRoF7VsDlsjM/GmZ5dJZMu4/U5sKsidruM3WvHEkT8dhsj4TCXUmmmF9eQJZFKTUeVBXwhD/Vag5jPQzpbIp8tUynVODDUT6JSxq02MWdXJuPIsoBhWIiiQDJdIV9qYJgQ8qlEg3a8ARft0SCWZKHVNEQEbKpIQ7dw2mVkSSQU9WCYJrlcjVJV5w/uehPPLp7i9OISj525zO0bevntw4dZmlollatTb5goiogsWLiDLrZGoygOG25VwbIsWl0uLqyuIYoic3MZ3nhgKx0eN36bzDp/lHZXB1OFpn5kRyRIQmpw6dwckdYA09ksugCZeh23qnIu2ZSaefzMJVS7gt1p47aR9ezu6eBqMknA56bd66VmmmwKBcloGovFMrtjLSSrVeqGwY0d7ayJBivJLC9nEpiWxUAwwL19fTy1MIfnGrt1X2sHDkXkXDLJ/o6m1I9Dlik2Guxu6cC0DFpdfhKVIgG7C1E0eWpmhuFolCdOnMfuczEU8HNzx3qqep2hQIz1gQCKJLIu4MOjOLDLEnbZomqsUtDy/Mnp48RcTt66fiuvrC3xrk27+K0Xn2F7S5gWhw+frQ1ZCGFh0DCXedOPP0O6UuGtG7ahSk1IgXnN5/xc+jkMK49P7Xq1dr796a9yT18fMWcMWQzjVbtQpRJL5QTzpXFeXD7FcGgEw0qRq6/glKO/EDHo74ZlNdCtBIrY8qr7jyCI16SNAk1ZrGvRMNc4sXoZ3dLRDIN2t490rYxLtbGto5X9ra2kazWuplL84c038MUnjyHLIpGQD0sWaI8EERSJ39iyhZVyGUVqoJsmAbudfbE+aoZGtr7IX1w6y4mVOD8cmyDmc+JSREJ2Ozuj7bwUX+JAaytvHtrI5rYoVV3n8NIyQ0E/c4UCnzp1itPTC1xKJTkxs8CV+RX+w76dvKZniLuGB3hyZoaq2aDV7eLbYxM4FInFYonziSR39vZwLpnirUNbyWgl+n0BDi/NY1olDi8ts1IpY6Ez5N9Gp8ckXS/xhQsXuK2rgxs7Opg1yrx5aJBPnznDkzMz2FWV3xi+F5tc5BuXL+O125hOZfA5HEymU9gVhcViid2trdzU2YlHVXh2bo5L6TT7WtvYEGglXSvwxPGLbOhtJ+J0kq5W8dvtRJwupjJpXjwxSqpQpLc1wkf2H8BvU3l8apJLs8t0R4Lc23cbplXBIsuB1ttfFZr32jJ8+swZ3rdtH/tbO/noiZdYTmRIJHIYAmi1BsMdrRy5PMGRS1PMzCVYXm7qD5drOs6Ymz2xNp6em8HrdoAk4Au7yRUqXNfTSbfHj9/mRhIMJNHL+55/jJl0hoZl0uf3E7LbaXO5+cGZy/gjXoq5Ki1tfgzTolKscTR/gU88912+MfocboeNdW1R3jW8mc9+4gf/YslAH/vzj3/Ytb39FyYDlY7M/TsZ6H8nJElAFKBa09F1k2K+wn037uCF82MUck37v3ypgW5YqHKV0fHTvO7OLXzkwAG2hHuYyq+QqqUI2bxoRoOy3qBmGCyXKuiWyefPniOzmiPQ4uMv772Hk6tx2txudrVE8akubmgb4PrWJtlgIrdK/RrOqWGYiKJAS1eYmNtDyOblxeVJ1ipVbh4c4P1HnmV8Ic4btm8mXauzNRplMpulbzDKzEQCRRIo5quYhok/4mW1XGJ3LMpELk+yWuV0PM6Dg0s45KYTyo5oC4O+NgRBRBIsnnnT27AA3dSp6HWy9TrPjM3it9vZEm2h0tAZCbYzmc0RjPp4x8gIj4xP4Le5SdbyPLs4ScMwubtniE+cOcxIKMg6f5CPn3mGWzo7ub9vkKcWpvn+1ATbIhEUSSRRmUMSBD79zFGunI6zflsLHr+Lv3j5FfKp5oTR53OxupLj4uVVduzowNCb7OuBwXamJpYZ2thFrz/Ap14+ycJ0kkiLBxdgmRY9QTdL82leWolzMbFGLlngul07+NHRc0BzgpdN5NF0E9GwUGQB3WhKDmkNk5dPj6PaZO7ev4WgzcPV7BK//JnvoxsWIaeLXx3ehGE1b0YTiSSNegNJlsis5TB0E8swMU0Tra4zd3GZaNDB0MamaG4xW8bmUOnx+WlxubmoLzYdkNojvG3TJr43McnVeoPLV9bwOpv/el29IVqiAWYKVRRVxm+3UzcNBpw+jq/EObew1PRIb5h0dfrYMzzAS6+MUixrnC8t4nUpNHSTDSPdvHR1is/dfxe//fQhpHCTVADQ0E3KNYtaqYGgiNjUJnYy1h3GoShcuDBNvtRAVUREETwulb6hIKVc5VVZpUKuybZVrtFERQEK6SIOt533PPUMs2Mr1yzkhCYDXhFZTlaxjc3zzpERurwe9sVa+eCRF/mDvXv57N2388fHj9PbHuF9W3fR4tjFC8s/xHCZTOan6XAHcEgqHW4PT8/M8N77buAn45N88MBefu+xZ8j2VHnX5s185syZpu1mpkKnLLFzoJtDl8eRVZk/uPE6/vLiRV4/MMCGYIQ2Zxd14wTfGR1HEUVMy8KpKLx8DbMXbPGzvJDE7XPy7XiKt//K2/nQ0aMkltJ85oHXEK8UCdkdRDxu2twuFosltkUiJCpVvnLlIu/dvAPDNAjYmyvDkM3On994E3VDJ1WpcGpqjls6O6joNUJ2L9l6EVEQ+dQrp3nf9u0slXPMl7K0ONzUjQafPneW3bEot3T0sVrJEnI4+JWnHuWTBw9iWhZ/M3mFqdxxPrRrP2vVPK3OAK8fHMAhy6jXyDfwM0JOzOEl4ujmO5MP8+C6twCwt72NNleAdG2RmLOdujGBLLSwzreBRPUUm4M7mS2+yMPjo7x7eDt/Wx7pfydkMYhgqYjXXITqxgQgYqEhC63IQgCgOcnUKvhsNp6YmWVnNEK8UsKtKJxfW+P2nh62hPtodfkYDoXY07INt/tJqqUap05NEo548HTb+e49bySvlbm/r43x3ApRh4cWxwYMK81XrxylrDc4Nj6DrMooNpkPP3GI7739QX4yP8+NHTonVuJcSKbYFg3T4/HS6/VzZ9d6vnj5ND8an+Srd99NuyvMzs98ms7eFm7fsZnX9hzEsPLMF9Msza5x2m5HNy100+RAayepao13DQ+zzrefhrmChc7lVJoDw7tZ778FgJHQOO994TEefvkcf3avwK5IDx84+gJz8RS3rXyVj990kP9xw508OnOJN61fz/w1aaWb/+ZPef7NH+Artwf4yMlj9IaCpKsVRlqa+ZCtVWlzu4g6nQiCQKfXR9Bux6+6EAWZHdFW+jd2MrOaot8foNxokK/V2BSJUNY0TNMim6lw6swEp3t6eEP/CN8dn8CpKLxh3QAn1p7Eq6q8klhjaziFKDQfyDYGbuVH9936ah6kU1/jzAuz4FIY2dzCtvU9jK4m+I837ePt6x/gfPpFbv3UX2GUGhg2icePn+PdwyM4FZVEuUS5WsfpsGGo5jU5qDqtThn5Wq5PX1lGVUSU9TJnE0lOrMSZz+dQ7Sq1Sp2WmIeDw4NcSSbQqk3+wH+57Xqemp3nby5fZSAS5lIq8Q/K9X+K+PfV+T9h/HSjpxsWdU2n3jD42uMn2Larm7cc2MHXnj1OvlTBaZe4bv8Qrxnoo8fjpc3lo25oLJWKjGbT3Nw+QMM0mMkXeOTKKLVyjVs2DbEpEuWJK8tUijVuPvsQdc1ky45O0rUau1u6sUsKoighAyOhLsBiqZxiKpMmHPHQFvRR1RusVDJ8+eIlHtwwxGwhSUVrEI34ObWyQrpc5v71QwyHwwTsDs6dWyYSsLG4WsHj0lDtCldX1rg4t8zH77iZfl+IA21tLJfT9HpDvGVIZMjfgWU1sXGSqAImAiKKCIposLe1lTNra5y5NMPY7AreoJtNoaYkx+V0hi+dPYdhmDzw2CN87vZbub1rHbppMl34X9y9d3RkeX3t+zmpzqkcJVUpp26pu9U5Tp7pGWZgAgNDMAaMCfbYF/Azfti+DlxsLs9+5pp4scEGG5OzPYQJTJ7pCZ2T1Elq5VSlUuVcJ74/SgzBwLXXum9d7N9aWtLS+dWpo6NTv7PP/n733uv89tgeMo0yJ9aSvGPrDjyyxqOLU3R6vTy7tMw3TpwnHg+TLVTAcbg0mQW3TC5Xo15bIlvQEUXo7G9naW6NUsVAc0noDYOBng5mjRTTUyvEOsMsLa9zaWIeURQY3trJ9KVVJFHA43Xh9mnccO0oR+cWsEyLof4Ef/KtH1Cq6IgbJd/kep1K3cTvkQn4FFbSdbyahKqIqG4XwaiPU0srLG7NsP/9n8WxHBDhOw+fx6sojLXFOLW6gm3ZNGpN6uWWh6nmVXEch0qxzuJKCVEQyBQaLKdX8LtlEp0BFEXGr6ocvzpHKVtBVlvgMVmtslgsICkyYb9CMtOApkWszUuk102jv50uf4Bzk/P4t6k8X2/w+NEJylUd1SWRLTSZnsszPFQj3t9G9fIq+kYfZMCrcOTIFKpL4r5PfoXd+1o9Wi6XiG07WJaDrlsgCmweCBKI+CjlKixfbTGQsZBKW8xLodDqSRJFgUa1id7QqTdNpGoTURTIF5p43a0lw3ag2TAo5quYlkO2qCOLAu0RlWLFoFIzCfkUlpZL/JcvPsDrD+/nSj5L3Ofj21enqRoGv7dvH+fW13FJCqaTQhZFvjl9ifVanQPxdrZEYsTdYT53xz3MFNeJaBp/9dxRfCEvMY+XiKaRnF+nWmniUkQ8fjfD4RDxXV6empkjU6/zzl27+MLFi2Trrb/trqEhruvpZq5QJFmpsF6ucNvQIOeWVnjvdQf5wOrTpJdzxPti1K0mtmUT6QhxNJni+s4EO2KdNK3TDARCuCV5ow/T4PbeHlK1AulajYFgiHyjTn8gTEGvU9J1njx+geff806WK3lWqjkuZrN8e3KKt2zbyhtHR/DJGj9YmOeh6WkeefWfYTkFPnbDDparF1ipFtkW6eb+hz/PN1/9WmRBomrWMR2HsKah2yZfm5ziai5Hrl7jQzfd9HMTev7+wqMcjP/I5Nkjy3z+8nleWF7mq69Q8SqbyDUv8cnzp/j1LVuRhHl6vFt5w2aHNu3A/571mp+0WFKlYRrWJNnmFDF1BEkM0bQMjqUW2RQO45Yl3IrM0WSKkXCIt2zbSrvbw8fPPcd7dt1Er+966uYED/3WW/jNx35Ap99Pulrl3uFhTMeix7cP26mzPZJAFmPUzQn+4PnH+MChmxjPrvCObduYLbbENOvb6vgVjT/aezOThSXCmsa1nXE+N3GB3R1x3jCymYZlUNYNdnYmuJhdRxAg3tfGu/fs5hW9NyOLbUhOgAdmHmdwuJO4z89QKEDNNMk3a9w3NMLZ9SQRbQKv7OazF0/wru2vRBbbsJ0mU8XneWxxgcFQkH/87b8FYLV2lJFojI/edBtzpSz/PD3DYrnMfUO7OZWeZrVS5fXDoyQ8HnZ87I/YMtLL3xy+gwdmLvOPL5wirLmZXEmBIHAlEiWmubEch+s64xzu3oztOJT0Eh87c5pivkIpX+W5xQUc26EnHMKjyBzq7kGWJJ785wlQRaYKBVxiEEWSmJ1LIewWWCgVeXJxiYquMxA4zst77/uZ18B33vSr/Gbbw5w+MUsg6uPTh+/GI29/aXu3N0K83Uu+pKPIAopLRpVctHk8GHbLIWN/ZyffOHKaLzTPktveYEesxN1927CpsnvvEPPzLbeAewYHkASB6UKBD0+u4A95ECWRUrPJ+w4d4lwmw2eeOsrl2RUM3STR29JEfGNy8mce+y/TEP+DAM3/FKXzv/z4h/5c2d6BLIvopk0h20A3HPbv6OWJU5colpok2r3cddNOXjeyiR2xNoaDHVTMOjWzwWyxQK7RpM/vp2I0Gc9kOXLyMrIs0dsRJVkpky+2yrujYz0Uc2Vu2z3KjV0JNEXAtC08ioooSOT1MpqkIosSqXqFI2evcnV2nU0DHfQHA+xqb8OtyPz1yVNMnJ9jz6Y+XrN5Ew+eushcrYy8Ydybr5TI5hsosogggGWYFHNVVlZKfPaps3z/8gWO5tJ86egZ3nPtHbS53eSaZdyy+pISzbJNGlaTfLPMs6tzXMrlWSqVEDytEuarx7YQ1VoebJZj4Xe7mZheRpQE3B6NqOaix9eOX3GRqhVpWK1MdFkUaVg6VVOnpOvc2tPLpUqRubkUM9MZVpNlJEkgFHQhILCarCG7RFSXxOJCjnyxCYDPI1MqNpA0kT3DvQTCXtp9PlSvSiDqI58p09YRIptuqdQlQSAQbWVri7KIZdkIsogn5EFwbEJ+F9WaQVO3MOoWuu1Qrhh43DKqS0IUwLZsbMvGpcn81u6D/I8fHAFRQJJEZFnk7MVlZlIpzk2sYTebqKqCQysf2TRMEAQuTWUxijrGhgOuKLRYw3K5SWd3mKVikWyqiOZxMba5l4mZZU7MLSFJEitz6y2GsW4SiWrEO0PkazV2d3XRME2KepOR9jYy9RrFeh2JlrgokfCzlq5Rr1RQFJlioU61brVYRlnEo8moLpHXv+wAm8IR+oIBZjLr+P0q2C3Rkt+nMDSa4PKF1dZ5Mmw0l4Qit3IzBaEFIE3LRlEkoolwSxgkiciKRK7QJOBV+L2b7+Oh+Rdpbwuyc7iXK3NJGk0LWW6xnbbtUKmbmJaDZYMkwvRKCnfYz5ZohNt6exnPZJBEge3RKE8uzXG1uMSFbCttqtjUmSuVaJomu9p6qRg1BgIdXMym8Hs8nDg3jRrS+P7EFTYPJFD8Ks1KixEebIsxFg2zo6Od0XCEY6k1ZgsFCqUq1/X3sV5vsLMtyqcffo719QKKS6YtFODN27fyzPIqOaMBjoPH76Y76EVSXIx1tDEUDLTYHb3OO3ce4EtXLrC3o4OKbtDp87bEBqLEZCFHl8/PpVyOpmVyZGWFb09NYYvg9booNBv0B0KsVMu8fGCAf56e5tdGd1MxGygi3D00RNzTiyQEQLCQhSoNq8mR1Tn6wmG2RdsIuDwoosxoOMKdfTfiU4Ikq6s8v7BIMVvm7fv2EHQlEAQZx9mImHUMAq5+DnTspsv7o4i+iLvAUrnESDTKtfFbmS6+SIe7i8NdB6iZGS7kUqxWV9geOfALIyj/PUMQRBzHQBAkZDGKYa+wXF1oiRldKpnGJJOFNTRZ5sOnTnHX4CDbIgk0udVjbtsO2UaDW3t76PQcoGZO8NTyJE8vL3Hn4AAPnL3I/ft3s6utA7+ika4v4Hf5EQUfhr3Ic8krjEbC/NWpY9w9MEjZ0NkWSXAlv46IQG8gQNWo8+jCPMdXVziRTFEs1+gJh3Cw6fMHeVnvZkynwVShyFqtypu3buX6xL6X2OOPnPs8X3r8GMGoj5f19XFz1wAHOjqZK+dJ1Sq8cuB1PLNylMcWZ7iYzfKqods3zo1MTBtkT1sn1yfGcLCYLb+IIsqMhIOMZ9e4sXMbDnW6fH5CLhVZhDPpNNuiUT504jiv37OdEwvLtAXc3NE3zJlShv3xOIcH+4kF/LhliZDqoj8QJFOvM1/OUmhW+erUZUrNJhXHIpsskFsvo2oKPq+Gg4AqywyHwkyurzE4HOW20SF2xtr5+uQ4mk/llt5eRAQ6vB7uHhzihsTL+PGc+h8fFXOF//nYEfbtGeJzr7ibqLbvJ7YrosG3Fy9g1xsEvArBiI83jI0xW8ozVyiyli1ScSxyuRKdnVFev3kzjgN9fh8OOrpTJBIJMhQKkqxWMW2bF1aT9Ha2cdfoJrojYS5l1snrBpcyGVSfSkckRMM02dXdRa5ep9BsMPvN87+0pfMPfuSv/ty/799fOi89+0taOhcEIQT8AzAGOMDbHcc5+lNzbgY+DihAxnGcmwRB0IAjgLrxXt92HOfPNub/OfCbwPrGLv7EcZyHN7b9MfAOwAL+L8dxHv1Fx+f3aOzeN8jy4jrkqnTEPayl69gOpNJVag2TSNTDPYMD3JAYQhQEVms53vvM07xy0zDFpo4myyyU861kA58HlyqztFzkam+WpcU0iktmbHMPcZ+ftY4cF9fTWLZN07LZ35HA3vBulIVWJrkoiJiOQ75ssGkw0iqf1Wt8+dJlMvkyggDFqsEjz53n3MIyYyO9LKxlCbs1Eh4PXYcP8vDkVdZXcpTLTRyndQO/8cYRpi4tohsW81MpBkc7ee9z3+EDh26iZjYJq34cx0IUZJq2yWQhxXKlzMOz84TdbhRJYkdHnOez0/hdCslaFZckkazWODI7jySL2JbNfLHEJU8GoJVEIgqEFQ1RgIVSkdVqldcMbyXo8tG0dG7vz3Lk+cmW/2NIxhf0YOgmlXKTri4vum4TCWsgCORyrVzsetOipztIX0eUVKXCaiqH6naxvpxFlCVSmTr2paWXAJBuWCzPrNHUbRJdIWzbRlZkOgI+lB6JMydmqdRNXLKIP+iiWjexDQfbgXyxSVtYQ5Fb3o6JSIj/5+TzLwFFSRJa/puiQCwRQlEkUpkajlNFlgS8PrUlVjJMwgEX+bqJk2vS8Eh4QiqNpsXwYITbBwY4trLKW+85TFTT0GSZtx1r2QIVsslWlrsmEY+5CQbUjfcU8SgKt/f18EXTYDqXe2lRcBwHb8DNwW1DJFNnWLhSIBj20r+pndzpZQDcmoxptgQ+qUqZ82spVmc3BCAuGUkUCPlaivarF1daynhJJBJoiS5s20Y3LCyrBWoBjIbB4tUUggCaphBNhAlla/iDLW85v6tluXUqmaKptxTrlZpJvWm1Ii0DrUSSbLGJJLrQDQtFEkl4PWQarTCAL7x4mq/96ms4sbbGF148TTlf5dduO0RYU5ktFPn8xARXi0X+cM911MwGNdPkxOoK1Q2LJdOwGJ+Ywx/xtYSArlZ/7cVsntFIiHyzSX/AB3RzUkxyc1cXX7h0Cbcks3/PJtaqVW7t7+eGzk78LpXeQAmjM0H7Jg8+RaHT5+egIHIstcZ8qcKlTIatsRgVXefm7i7aNB9R1cszKws0LYs72zezUCqyUC5yQ2cPC+UCQ8EAbxzZwp4PfoqLwzneODJCpyfC7jaLiOrj17ZA3dIB2NXWQ1mvkaqfx3Zs1uut+MzLuQz39G9DEERcYgIQEYUUYXUvK9UXadM6uZTL49FcvO6GAwz4b/qxdVnEtPPo9vJPMEY/HH2+zbxlNIRbjlPUJ4hqAU6mL3JN/GZ6fNcT96wBDqaTRCb8i5bhf9dwMBFogZDV6iwLpQK9gSCCoLBQznKgYzd3f+dT9ASCBFwuvjN7mUdm51gvlHnPtQdIeLwMBXo5mX6EU+k1JtYzTCRTpLq76GgP0bRsHFq+uo8vzRHTktzVvwNwGAyGKeoN9nZ08OTSInf0DbBaLdDl9aFIEqZto0kKQ8Eg5XoTvWlQL9c5urjI9Z0JXJLCmfV5El4fA8EQ28LDNK0yAhKmnaFiLDKxnqHWMLEch4TPi24b9Pmu4fbuvZSMC5h2hmS1Rq7RpKLrmPb6T7DQithBST+LLLZu0YVmlYfn5wi6XExkZ9kcilDUm1zMrTIcbKNumnxl8jJ9wRCqJOH1ajxw9SoDwSB/evAgTyxIz/usAAAgAElEQVQuMlUosD0aYbFcYaFUxq+0IiK7vB7e//zzdAeDTK+meePenfTt3cOnzp7DAXZ1dDCZzb3UNvOqw3sYiYQZCoZoWk1u6ulhMl+gZhrE3B7iXh+iAPnmxZ8AkOuNE5xaW2K+XOZTT75Is67zpq2jdHmv/VfXhyJ2sbMzgWmY2JaN6nbx7ZkrfOPUecLRACNdcYrNBrIiMxAKszUS51MTZxiLLlEy6lwtlLiSzeB3JRiLRujxBxAFWKnUOJVa4+LSKlu6E0zlsqTSeTwBN/sSnXgVBXUjLc6nuP63Xe//fwyB/3yl808AP3Ac57WCILgAz49v3ACinwJe7jjOoiAI7RubmsBhx3EqQuvR5nlBEB5xHOfYxvaPOY7z4Z/a11bgDcA2oBN4QhCEzY7jWD/v4GwHru3u4VnbZmm5iNctMzbWzpWrS+i6hUeTMRqtfOHLhSTvfuBhyvkKV+dKvOG9o7xx8y7cUhcTuTMEBYhoGr95+3UcW1khVSyztFxEVURmvGnOFebRGzr5TIVldwrpkMh9Qz8yMvbKKo7T8mYbi4bZubOTP7jxGk6urfOhx5/DNKxWfJZX5ZW37uLz3zqOx6tyw96dTM6tElZVNoVDfOnSZXJrBaLxELaZRVJkVpJlLk/M094TpTMaZmssxnqtxsmZBd5R/j4P3P0+5isvUmg2uFrIEdPciKLAUDBE2O1mKBTkZb09FPUm67Uqqihh2g4n19a4NpGgYVo8Mn4Z07DY19HO9YleFElmuZInonp4PrnIEwtLFBsNDnR28mdHj/DhG1pP4rf29DJ9zyFWSiUOdHby0PQ0tXKdUqmBAHjcErGuCItX17Cclpl3V1cYRVV47ugV6k2LQkEnHFYplHQcwwZRoFIx0DwyvXEvTd1k4UIOBIHegRiCJJJN5Smsl/AG3Whqy97K7VOplhtIUsvrslY3oWGxnm9gWC66/W7ed80hbv5/PweOg7hhXK6bNttGY1w/OoRh2ZyYnmdqcg3TdChVDbq7ggRjfho1nYA3wMJ8GQybesPCrUn82vX7+NXN27g20WI1wprG7z39NP6ARr5UgnwTYm4629yEYz4c28HQTURJxKe0FPaTc6uYhoVLVVpZ7UAg6sctK2zeEud0dYldI/00TIPAzR7u37kDlyTxsVOnOfniLI88dQHDtNFUiWbTwnJawh9xI+FHlkS2bEswdzWN4zi4fRrJZAlZEjAsh67OIIZuksvVUF0SkihQKDYoV9YY3dmDtWGZ5DgOJ5JJTpyZolhtxXn+kNEcHo7hOA5TVzPYZQNvfxCP381SscgdfS3WYzaf4/133Mwnz53j+NU5VpfzFCoGX3riGJ2D7Vzb18epy7OcklepbK/jkVWuSSQIulwkDh7gf54+QzpZQJFF1lbyDG3r4v5du/jKpUvc0tdHRHPzjclJDiYSiAIEVJUH5+Y5f2WBG7q7uHd4iKKu89jcAqokUTctrkvEUUSR/R0deGSZj54+Q7JSZldHnB6/l5VKhe9dvMz9+/ewt72TdL1MVPMyVShQ0Q12t8WxcUhVawRcKrlGnbCmcX59jbe98hpkQaRq6sxX0vR4Y3x58hzv2HoTk4Vpun1Rqkadc5kkqVqNt47eQLYxzSfOnuXM4jKaLLM71olLahBRd1DWK5xZf5Ad0R6OJM9xKrnaEk/5/rUNkSyGWwzpzxiK2IXi6mKl+iKGbdLt3YrprG78jy1yzXmqRoPBwE3YTv0XspqGvYbtlFGl4Z8750fjR+nHnd7NXMg917JQcix2RHux7DyaLDM+t8TjoSCZeoN0voTP13r/Ax27KespJFHgUDzBHb0DHEut8Nnz59FkGZ8iczyV4mAcMvUG/YEAutVkvrxOWPXQ7Y3wru2jnM5c5OtTVzBsmzv6+tCklvVbmzvC5rDOSHuMxWKRX929nVcPjeCRVR5ZmOTR+QWWi0U6AwHu39Hkmo4xivoy+WaFp5YXmFnPcHD/JhRJJFmpokoSsnCCHt/1BJQxBEFhb3sHblni1t5uMo2Zl1w+Cs0zfHL8BPcND7EpOIpHKnM5t0LNMHFJEovlIplGg5FwGI+ssFTJc2d/H4Ig8GIyyWK5QlBVaVoWD8/Ns7e9jW6fl1cNbubTE+dQRJHruxIMBWN8ZmKCVKXC0lSSNS3L9bs38yubtlHUq9w5NMTlbA7fRjtRWTeomyYxj4eKYbBcLrMjanBDVxea3OqFDKmth+eburv55vQFFPESc6USlu3w5MVJ1pMFSlUTVRFxqxIu8Wf3/AqCws62GOPJJM26Ttjr4aHpaSzTptE0WDDzZFbz1CsNyrqOYZtc1xlnoZznE2fO8IrBAQYDfibzBVYqVR5fWGKukGc4EqVuGri0Vhm+1GxSLdfZNdjDpUyGhmmwWCzQGwwxm8n+G67j/4ND+E/koykIQgC4EXgrgOM4OqD/1LQ3Av/iOM7ixpz0xncH+KF3gLLx5fwv3vJe4OuO4zSBOUEQpoEDwNGf9wJZFGj3aNzS18facpa5+TxN3aIt5sXrlkmnaoR8Cn9z9hzfeewc5dUqyCKoEl5Fwa/somaO43e5EBFRJYVMfYa4z48oCKxH3axlapwfTyKKAmG/wlq2QSjgYiK9Rt1ssFrLcTSZ5FIux5/uu56J3ApLlSr37tjKwXgPN3YN8szUDPV0kd3bB6mbJmNtMQKB1lP9sWSS5EqRv3v4OQDeddeNzKWzKKrMoT0jSKIIO0GVJGpGyyPu77/7PB63xO++8mYemplBt+d4aG6GF1ZW2NnewVi0jYCrZT301q1byTbqfH92jt1tMSzb5iOPtYBvIOyl++Zr+S879vCuHXuZLq5zsGM/DgY1Y510vcpKpcxKpcp6rUqxWudbJ8/jCbhZrWUJKB5Op9coN5vsTyT4ytEzZNZKiCK4VZmmbiFt9P0VKzqbR9rxhbyUshXWVvKsZRvYTQtqJrpXxnGcVvOJaYMksHtPN5GgjzOnZn4Y9o7L7cLnVvH4NPLpIoIg8Bt3XcdqpcL4wgqRjhDaSo50pkY44KJYNqjWTQRBwNBNPnHmLOVqq6wuiUJLtBXQ6O1rp6Yb+FwurhsZJBj2MX56jvV8k8npLN6VItt397I8nWLvoR7GJ5I4Drz3Dbdw39BmPLLKplA7VaPB1WKWF5+7yshIOzv3BHlx+iIdbRrx7gjrq3n8IQ87hnsRBYEzqTW6fT4Ge+NcnlyicyBKqdZg26YehsMRjq8ss6UrjuKSWSoVqZXr/MO9d5PwtFgm10GJP682OHliCcveSEaSBBzLQaBV2i9VjRarqyps2taKLkvNr+MAkiRi2zarq0VUl4TPI1OtmzSaYFg2es0k7vNRajZf+ty5N9JUPJqMqoisZRuEg61YzFxRp5ZsGSHPLpWIBlt2TH+y9CS7hnvZm+jk97/2IDNzJaIxjbaYlyN/+CZe8/mvs3Q1xXOWzfbNvbx12zYkQeT9x57F53Lx/v03oYgJYpqbv1IUFpIZMskCpmHxD+PjLF9NUq438SoKQVVjMBAgoqlczGQpNBq86uBOFFHkTDpD07K4PLnIYnKdN+7fhVtReHR2FgDLtplcSWE0Td5++Fa+OTXF4ycu4PaqfO7cOJOFIq8cHOC3Hv8B62sFXndgJ9OFHGXdYKZYwqPIKIKIX3Hx3elZ3jG2jb+fmCDu8dHljeKW+qhbp5GEMB8+dYo7Bvp4YSXJ60c2sTXShij42Boe44/3+ZndnGVf+zCOY6KIcURBxSu78SkuZFGhYZo0ak0mp9N8zeXirr5ZVGnwpf9Tq0z9i93W2919jGfH6ffHuCFxGNupUTWn8StuOtyt5JRfBDIdx8J2KjgY6FbLaP3nz7VxnDps2BhJQojpQpFbu708u3qWmzr38JmLT5IrV7l12wgXMxl+ZWQzrx4aZFOonZjWR7I2Q6ZR4ctXrtDn93NkaZmDnQny6SKNms6RUJjf2D5Gm+bjvbtv4YmlCTQpTLq+wP84dZK6YfL1O+/nUPvLKTa/S0lvsjvWzxsf/hYfuvEm1us5XKLMazdvYjLfsruLu3v44uSzTBeKNE0TVZY51NmJ48B8eZ64J4JPaSVBffmV9/LZi+Oc2QB+dctClWS6vE2uFJ5ja/g2dsfuYGe0hu3U+PyVh/n10X4u58cxHZu7Bvr56uQk9w2ZbA13Y9g2+zvaiWgaum1j2DYLpTKbQiGalkWmUefaRC9Vw+CrV66QqdVQJIn1Wo2LuRynkyk0WWZvRxtRVaPd4+XB+SmeOHKBeGcQt09jz5YB1ms1PnLmOAPBAPlmk8FQkJVKlU2hIGfX1jYcGSKkqg1mC0WStRqdXi/n1zOslsv4XC4SPh/pWo2rhQJPnb/SAofVJuWqTu9AjMZchuZGbPDfnT/PwXjvT/T/Oo7FN6a/wd8+cxR/2IfqduFRFCRBZFU3qZXrdMYj0Bnm1oFBvvj4UcZHNnG4a4TlaoaDnZ2cTWcYDAa4qbuTa+L9fKx6nPVMkYCqslosMdrRAvUJn48pUeTYhRmGBxIkkzmaDR3LtIkFfL/wM/PLMP6D4Mx/E6M5SKu8/U+CIOwETgO/6zhO9cfmbAYUQRCeAfzAJxzH+SKA0PLCOA0MA3/rOM7xH3vduwVBeAtwCniv4zh5oAs49mNzljd+93OHV1G4o3eQr0xeIpoIsZYqc/vNY6iSzHefOEsgohJuC/DE8xdoC6uUqwYdUY2BzR1si8Q4kX6YTm+Q+VKRbp+fbKPGjZ0JNoUj3Pbxf8RxIBJw4fc4BEJuRoa72RqLYToONcPgn2cu4pFlVitVnh6fJFku45JkZFEkrGkICPzLzCVGOjt4YSVHslxmKBJlrVrjLfccYjyZ5MUTV8iVdNJLFXxtblRJYn05y22HtqNIEkcuTLF/dABFkjh9fpL1dJW2di+iKPL+Lz6GR5N4q+cbfPGO+7mlZ5yQy7tBrYtMZJPIYqsMG9U0rkl0ciCe4M5TnyfaHuS6kUF2xtqJqBEcR2dvmxdZaOdq6Rm+dXWKhy5N8vLRzRT0JmNt7Vy/M8EzyysYts2LqysMBoP85cNPc832YRRRpJAp09RbPXtN3UAUBVxCq9c0GlS5OrWOW83hViXWNwQmFQccy6HasPB5FCJBF7btsLxYZvJSEtt2KJR0kARGxmLodZ2aKNAfiWDbNp2BABFNI+hqKZUXSiXWl7MEvDKqSyZTaNIT92BaDqVSgwcePUtP3EtTb7GGqkvCrclMXlxkJeRhoKeD/nCIvmCQ9a4QqqtM10AbRx+eYta9Sqzdj+p2sX1bnAuX1rixq5OI6sfGQRJE/utzz/Ds81eopuucKa9w591jfOIvfoX/+rlHmJ9Zp63dR73SYKGQx7YcfnvvbnKNBmG3G0GATL5MVyzMaqnEC8evcMOhLTQ2TKhr5Tp/dvhGfIrKfzv6DGG3xrMzc5w7u4LVMEFqqcctyyERcyMIAk3dJuRz0ZEIkl7M4PZrlPNVXJoLVTEwTZug30WtYaLIAtW6Sa1hobkkIgEXhYrBk0cuceiaTej2Ev98/Bx7RgcY3dbHCy9Okcq2mGvbdsgVdcqFJnhkRkYj5PIN/F6F1HKecNTHqcuz+EJeytVW/GY21QKkr/jkPxFLhPH4NCqFKsuKxJ89fYSXj25mPJlkMBrlG1fP8cbNfs6k03z85pchixIfPP48L16ewfK0qgX9kTCPz81T0pvc1tvDiVSa8elFBnvjnE+v8eiFK7znxmv4h/Fxbtw9SkjTWCxX2Ndhc31PD8vlCsvlEuVCFa/fzUfPnKZmGOweG2BmJc2V8WXy6SJbIyFu7uvj/jvvo2LUWSjnubN/M17ZjSjIfOPqaTRZZjQaoWlZ/MbYGB84+iKarPCRG1/Gr4/uJN24Qq7RaiW5tbebVLXKD+YX+fOD7ShCF25Zxau4EPEgSRHAwXLKuMQEO6LbOb3+CJ0+Hz6fm2isVfrd/fG/4Pl3v5OIupfWGvy/jvRRxC52x1pxlU1rjmdXrnJr915E4UcMqeNYP9fayKaOJAQBCdP5SbXuvwa6FvzYfubKR3nX9nsBuLV7mGNrj3N6Lc2urk7eODJCv78Ny7FJ1wssV/JE1Tglvc63rk7z0PPnkFUZvW5w9uw0puXgD2oYtsVwoINT6QU+e3Gcl/X2UDYyuESRXe3t3NrTiyImcByLHdE4mqzSMJu8bfs23vHww3z5nnt5dmWR1WqVPr+fLZEoU8UpXt43hNgvslIpMJFdZzQcocsb5LHFWfoDJVarVUbDYV5MLvOrm0f4jW3bMW2bpmWSrFY4n32G3bE7XvrbRcGDKHh46+g9pBtXEAWBqVyOc+tZsvUau2OHSdZOU9YN/vr4UZZm1/nWO9/I3vYE73vhOXZ1dHBzdzfpWh1NcrFYLtPh9ZGp1XBJ0ksMe6ffz1qtRpvbzTMrq5xOrjI/l0KSRKans8TCKo8/d4Fw2M2yP8ur94whCSJl3aDd46ZkGEiiyI5YjO+OX2JHXxdXkmtU9CaRvl52tsW4oSvBSDhC1TDwKS4y1RpdXTFsx2mlnEV8SIqE1y3TKOoYwKkT0yxfl6dtI+2xqJ/ljq9+AaNhoHpUtna0M7GSJFkuk17OUizU6e53IwsiIbebRy5P8v13vgWfoiGLMb43d7wFvGtVFEkkqLo4lV7i5OoqbbEgumWjuhRSlQqz+TwvHxykunWQbL2GbltoXhW3T6NZa1LXfrlL5/Afx0fz3wI0ZWAP8DuO4xwXBOETwB8B/+2n5uwFbgXcwFFBEI45jjO1UfLetVFef0AQhDHHcS4AnwY+SIvh/CDwEeDt/Oxz969YUEEQ7gfuB+jpbUcWJTRZ5t7Nm7m5v590tUbYrREOqEiKhNuvYZoObYMR3nn3TRzu7qGoNwi6vDQsk+/PXd3o4ahQaDYZCgb5wfwcsiRSKLfsD8JBlWu2DzMSieCRZUzHQQQsx+bI8gpTuWwr77xcZjTWxnA4RN20+NrURTL1RquH0yVTLzeYE/PM5LJkkwXcXhVZFjHTdZAEKskqXz47ztaxfs4sLBMNtZ7qJpZWOTTYT6XUUkHXK018QQ9et4QgCBw5eoWjO4/T7QtSNRv4FQ+2Y9Pu9jBdzHNNvI9LuRyn0ikMy+ar97+BkKqhSQpr9QoD/iBVcxVZlDDsJU6uJRlPp7l2sB9NlojLHm7q6iLTqDMWjfBP58c5OjWLN+hh/7YhfC6V06k1NjQyaC4Ry3bQ3C7ifbGXTOH9Hplw1IfmaQEbgG07OnGpCpVCDVES0Lwaiktmy06RzForFaiYLVOoGNRqBoJYRfWopCpl8mtFtnfEyTUalHQdr6zQtEwc28FxoFzVUSSRxeUKibgHWWqpse++aRdffvAEQ0NRSrkK1XorFadWqqNIItO5HLIgIskSXp/Kn950HX9cqDExvobPp1Ir1XG5XQz2Bsk3G5xMLzAYDPPxs2d45IkLVBsWaBKCInL82DTTV5Zb6ndX6yb75lsP8YMrUwx2xPjoMy8y1N1O07IItQWwbYers6v093dw7YERDMsiWSyTTxf51OvvYTgYI1kr8of7DnAln+H4ygo9HR6qARei0ALOs8uVl86vzyOjeVz4Ql5mZjKYa63nxFjIIeBXyeTqRBMhtFKdcrFGKOTG0zDIlXRWMw1s20Gvm4RUjbK+RnK1xGV/ioGOKH19EXJnkzhSy/7Io4mUa61e1tW1Km5VQtzo7zo0NsRgMMRsscDuni6OjE9x5WqGXFGnvc2LoZuUchUCER/1coNX7tzKPQOD3D+2m7c/+n22x2KkGyu8ZniM33v2MT5z66/wl9ce5iuxKA9OT/PanWNk6g2WSkVeOTxE2dBpWhZ37xljvVbDcmx6h4MokkiH14vHpVDRdb7/7FnqhsG9Q4M8MDPD88eu8MevvY2X9fYxVyzwzakpdre3875Dh5gtFoi5PTRMk4imcTazzHemZ3jfgeswLJOcVWKlUuB4MkV/MIAkijyxuMTBRAfZep1sOsXfhk7yOzsO8JrvfZPt8Tgffuw5+vo6KDUahNxuPnbueQ53d7NSrXCgoxvdLqAJXmQxhuWUEQQV087z8PwCD45fIhT2cdf+7WyNhHjNpmHe9PC/UKl/jX+8604G/FsAG1EIIuBioXIUv+Ihpu1/aT09vvYQO6K9SGKYiewSoijwdxefosvrodcfJO7x0+ndhcTPZnkcp0FBn0MSWibyuC6jSVs21uqfBLoODo5jYDhJ5sqX6fP1YTkFJguzqJLEWKSLXe1rPDQzw2OLC/z6lhDZRplLuQwRTeNyYRJNkrlnoJ/dbS0FdV8gRKZe5TMTF+jy+3nTyAjZZokfLCxye18vu2I9fGf2Agc6EhyKxxkKtHMy/Qjbo/1kGhViGjyfXOBVA9dwV99d/PeTn+epmTkODw0wVSjwmXPn+JWtW3l53wCSIHE8lcQGHplf4O6Bfp5dXmYpFKbX7yNdr5Gs1fCXCkwVivzm1r0EXLtRpaepmTqF5hle9d2v87YdO/j10TdjO00EQaJuNvmX6Wl6/T68isL9Y4eYyD1DvtngYjaH0WwFQqRrVb47M0vdNDFth2PJJCeSSTyKTL7Z5Pkr07g0F+cvJ7nxulE+ctOtfHriHMeSKbZFo+zvaOOLj77Ier7lONHQbZKZBl1tbiLxEB63yvfOXyK5lENTW2tV51AHbkXhB1db+x5fXEWSRH57506uiW9Gk7aQb55mvtzq6//u7Cwnzl/F7dPwhbxoHhVDN6mV6oRiAdq6ZEzDolnXeWBmBtN+hP/7iSfxaC7aIwGKtTo+TWW5XKKQKW20Etns2TtEvlIj7vPRE/DzuYl53vLt72AaFsGYH6/Lxe6OOB7Fxe/vPkCbe5DPX36U9VQeWZFQVIUtnXEMyyKdK5Gu1fmTAwc5u57ib06ewePVWJhcRfOqyK5fflOe/zSlc1qM4vKPMZHfpgU0f3pOZoPlrAqCcATYCUz9cILjOIUNxvPlwAXHcdZ+uE0QhM8CD/7Yvnp+bN/dwOpPH5TjOJ8BPgOwZ+9mR9zAp0HVhd/lYiAQ4GI2hzfoJpMuoxWqBHwKjVoT3bJIVStcyueJaR7inhCWvcItvX2sVsr4XQqSILApHOKuG3bz7SdO0dAtLMtGlWS8ikKxqZPwepBFAZ+ikK5WKeUqCIKAKIrs7WjnlYOjGLaJV9b45vQE2XoNl6owN72O3yuzZXs/ocEEhmXhcrtwuV14fBqqR8VoGrR5WtGSV2dXCcb8vOfgPj7w0NPIkkAkrNHWHWV+MtkqB2/05T21vMzv7+5jtZZFFAQ8shu/4mYg0E5Zr5FvNun0efnihYt87JbDrNWq6La1oeScJ6QGSdezZBpJxqIx6qbFl8YnuG1oiITXw0K5xNZIjBsSQzy2sMD5ZJ5SvkqjvVVSXZnL4FJEIhEPfYNxUskc3qCHjx4+zNZwL7/15Hc5OTGNINBahDwKyUydhek0+/YP43drJJM5jKZB1O+lbhgEY35MwyK5UkAWBTZv7yG1lKGwXiLd0DF0k6lshk2RKDGPm5Kuk63VME0LRW6BnpHRdi5cTLUuekkg4FOYyeeQZZH1tRLQKvPrhoUn4Gbi8gLVShNNlYkkQuzpG+Sbk1d5163X8KHik0xO5QiFVXq8Gn3DHWiSRNnQ+d7sNE+fvdwSrFRNcIm4NYnR7V1sibczmc6QWc3h9qrULYvMSq6VZtM0uWrZdHZG2dYV5+jENJqn5QggCAKTq2uUsmW2jfbS4w8iCCK5RoPvzs6RrlZpNlsMwA37tzK5lubk8QUAShUDl0vCNFsCoMxqjlrDwrQcNFUinW/SKQncecsO7h0e5KuXJ5mYmCXR34ahm6yeWMBxWucMt8zOjnYM20SWBQrrJTzdCdKpVoyoV5Mo1QyaBR3Jp2AYNrppU2tYVOsmu/b1kq/XeSKfp8sfYCwe44FMBdtu9XxKssR6qohu2JQqeXwemXPxNVLVGm/btpVKo8lHTp8m4FK5e3CA149s4sH5E9w7cBsH42volsXzy8uE3W6u6+oi4fPS5W0l2XhkmXMbiSRzxTIxzc3bx8Y4n8lwMB7n6bOXOT2zwFu2tsDR9l393De0hZrZoKA32dPRQcytkW80OJ1eRxFFJrM5DiTi9Ph9vGXrFop6laVKmS3hdtrcPl49PMRCuUxYdfFCMoVp2+yJJ9Db21ElkU+On2BbRwfnV5PIisz+RCdv2NxKoOpw+9FklZi7zJ++cATdMnndyGbu6b8eAc+GEbaD4ziM9XXxxAsT9IVCiEKYsKrxB/v3cymX478fO8pK6VG+fc/rCLjgm9PP8NzyKr+3Zw8x7Ufr6VQhT5vbR68vTn8gxnIlR8pVo6wbXMlnW4JA4ReVEiUkQcQte/DixtnorjLsFRTxJwtSouBCIEiydpyA4sGhSaaRp2LoXMmXcXXIvKJ/gHfveD2GvchKdZ1+fzdfuXKFU8lVMpkixUwZy7Jp74kSDfkpVGocHh7ig9feiGGZrNZKFBoNruuME9E0Tq7NE1Y1pjbaG74zcwRZFFmqlBgIhDBsi7pp8YGTD/KeXQf4/pmL6E0DhuCu/gGCLhfLlQpve+Qh/vrmW/AoMiuVGsvlMifX1uj0+VElkf5AgKl8nrVqjaDLRVRVSTcKCMI5Zop5bunagYPF7+zdQ8OyuJB7nG9evcqbR0dRRImZQgFFEhkJBzEdi3StxuOLS6zXarz10B6StRrfm5njdZs34VUU4h43l3J5yrqOIorMF4tEogH++NBB3p19kAuXF3mP/QR9oRDpapUnXpjgtuu28/aXX8fXnzuFbTtEgxam7RCIeCnnKtx2cIjPnnsBsWVG0ervrutE3B4M20bXTXx+N+KGZdCetgaq2Kq2zJUKXMzmEQU2jNFbD7QutwvTsBgd6uK2vj4qhsFSpcK5VIrvnRrnsctTREM+8qUqvkiI4Q12fa6QZ8m1tigAACAASURBVMdoH2HNzenZRfbE44yvpXnw2XMbAkSDfV3txH1+Xr95E88ur3A5m6Vm6Dy2OMPrhr1ENJWe7jYWl9JIioxhWcxmshTWSxxhllt7u9nf0cloxxIhVSOXLtLdFWNqcvkXXO//58d/KjGQ4zgpQRCWBEEYcRxnkhZreemnpn0X+Buhtfq5gIPAxwRBaAOMDZDpBm4DPgQgCELCcZzkxutfDVzY+Pl7wFcFQfgoLTHQJuDELzpGQRCRRZmVSoXxdJ2DiTg102SukOfQliFmE3my6dYNzLEdTq4mSVVrjEbCuCSJmtkgomlUDR1FktjTluDZlUVOptbYH+/gyYiHrt42Xjsywkq1imHbBFUXlmOjGw4uSaLd60XpFglqGiFVwyVJHEstIAkiK9UKp1Jr6JZNvDsKQL3SYH4uhS/kxePT8Hs0br92mDaPmxPJFiCKebyokow83MX+RILPnB9nZKiTZKFEfyxCqdnkctPC65aRJIGewTYM26Zs1PHKrbuIKqmAQNkoM1PKslIuc00izi19/byYXOWmrh5KeoP1ep0LuRTdviolvclqpUK7x8PtvYPENI2IpvEXx47xsoEBVEniqeUlZlMZmjUdw7TxNU3cPo1ou59yvkpHb4xcrkxnZ5SYx8v4+jo7Igf421vu5Pqrn8YX8lIr1xna0k36hWkqNZNirc6W9nZUSWY1m0cRRXJNg5jPy3IxS6lqIAoC+fUShXwNWRJoNC0CPhfZTIlypU48HCTfqFNYL6EbLcP8fK5B/Now01fXkSURRZGQZZF8roxtt9TvAKGwB4/UYjAHhzu5enkJQRQoZspkA15UWWatWufNt1/DN54+wfxqhfauCG/aupW1Wh2PLPPkwgJTMzloWiCAqEr0dbeEGEFVY2dngmOVOvft2c4zCwvs272JTp+fI1emyadLJAWBSqSJaVjk00UuWDYut4vV+QyvPryXP9p3DWE1SFEv0eXzc2OXQNXQsXF4ai7Ntx45TdOwW62sG2yErluYooBVamLbDdrCKoos0mi27JEaus1bt21lb3sfHlnhS7LEH+zbT8Cl8urs15i4lMYXUunrixD3uFmrF2kaNkurZcZDS3QkQqQyDaqZBlQNUCTae1ugJF82sCwH03bY1dXJ7+/dz1+ePMal5BpuuZXrfu7MLPWmRTZTwXFAN228bhmXppAqllkrVXj3whKRoI9srUa6WuXT54r89q6dbApFMJ01hgMdBFwqXzszzi37djEQCHA0meJ3d/YhCODb+DxcyRd4zaZhHpqbI1NvEHNrPLO8wv2Hr/n/uHvPKMvu+kz32eHsk3OqnGPn7uqgDsoRBZAIIsoy2BcMDth4ZmwPngEHDLYHbIZgjK/JQhYYkIQEyqHVUueu6lCpqyvnk3PYcT6ctmycFnctzzX2f636smvXPrVP1drrPb//+z4vD50e49tT07htCrf39OBXfBimwYF4Gy8sz7NULPHwxBRr6RxfvudOEpUqb+4dAkHgQmqdv7xwkdu7Onj48jj3dPcQc7mYyGTw2GTe0NnJ88srLOZz9AUbDV9dPge/sGUHf3FxlFeWFpnL5ShpKnm1RocnjF/pxy0nua8vQaZeo6xpbFTmuZDe4EC8k4hjHyulIsPhED13HaHV7eaW9j7G041AUczVENzX7z+ALEoI2Ik4nfzugUO0uq75iWdop9dHu2eQZ5ePcn3rAEF7jXt7mlgoJvj82BiDwRAh+xygY5d+ssYSGoEjn9KPiAdB+PugT0nbIGj/551PIUcASQhiYRBUNOouDbsksVDIMRiMUdausFRK4ZRtFLU8zW4XQ5Eo/tZWYk4nJ9bWGWmKsy8eZ71c4m+mplksZgBwyzYulJLc2dnHJ0+foN3n5Zb2Dp5cmKc/4GdvPMruaJzR5CZ/cPw4f3DkMNc0NTMSi6OINu4d2c5EKsnuaITnlpfYHY3y8uoadllmJpelqGpMplOkKhUmltZ45P63ci65TrPbzZPzC4wnE0ScDt7aN0iTK0RZr/Ly6hob5TLDoRB7Ys1MZJIsFPK0elzYRRvPLs+zPRphZyRCzOViLLnJQqHYuO9Qo8VmZyRMl9dLzOXi9s4OnlpYxCHLvHN4iD5/gHavl35/FNMy+fRb3sCnTpxEEgXWigUypQqFssb3njnHNz74VsaHOzl55jI2m4j9qmIxTYuxzU227urk0ugCnoCLYrYxRPF6XTR5PCykMmSvVr1+KXGKY6tr/O8b4MRGo6xjXzzG92ZmiIX97OpsI1+vIYkinqY4nX4fk9ksq4UC82tJKqUaTe0Roi4XuVoNy7JIlUrE3W4GQ0He2NtNulZjNJHiF68ZIa+qJIslmtqClItVckWVZKXCcibHvniMt/UP8KHFZ6jWNSYyWV5cnWG9XKE7EGQjkaWcrzBnSxPwuLjx2h5+fGGSjz1/lD+65Qb6AgF+PDuL0+OgWK+zZaiDn32S5n+M9dPOhn8VeOhq4nwOeK8gCL8EYFnWlyzLmhQE4SngAmAC/69lWZcEQdgBfP2qT1MEvmNZ1t9NLv9EEIRdNLbFF4APXL3euCAI36EhZnXgl/+1xDnAdCrBjV/6MtNXsoR8CvM7MkiyyG09Pczn8wyGI3iaW3isPIpa13n/zh10eBu9yi2uMLppcCGVRhYF6oaBAMwXCrw6cYWjFy7zziMj2ESRdwzs5NG5S5Q0Hb+iIIsCkiBQN0y2RMIsFAp8YNt2PDYn2XqZT5w8weTcKpIkYZomwz2t6KaJ3Wkjny1Ty1Zo7YgSd3to8niIupwE7Qpv7u9lvlBkXzyObpo8Ob9A3OVkd1MzB5vjzOQavehPXp5BEgVM0+Jj999G2OkgWa1ilxrG6Sv5DY4V5vnqpXGcskyhXudN/f0sFAp0eD18Z3qaN3T2oYgyiUqFRKXCF0fH+MCuHfjtdl5eXaWqL/HB7Xv45tQF7h0Y4Ob2Dr49PcVfPPpKw3MZcVPJVNDqGlu7Wkm5HNjb4yysJLC7lMaEVxB4ZGqK9wy+gWPrs4iiiCSJrC1nOTLUx0xgGdOySKxkKGRKRJuCjHQ2htrzq0mysoihm5gW/OrbruX58WnWEo2mGlkWUHULwzAb11xI4nTZ0VUdh11mPVkB3eTE8Rmcdgl/0IVNaQSOVhbTVyd+Ih5n41i4OUiL19vAVcnS658Y88UKPfEIz83O8embb2K9VKRy9AJ7m1s4udEwyXf5fWTyDbHU+EELn8dGW3ccRZI4Pr/I4Z4udvW086s7ruGW9g6mso1kY03Xeak0hWVZLEytEQg3hFomWaBaM7jhyDC/NXKAgOLGsnS8NieyINHsCmITZV5YXiWdr9PW4qVSrlMo6430vmXhddswTAtBbzAV/y59/neCzmmXObWZYGu4mZ2RZhJdVZpcfizLRK1rxEIOYnEv/Z1NtHo8qIaJdPWNmZzYZORAF7GQnbJTopAVoayjaiYj+3tIJfKMT25y/517WC+V+NrkpQbKai7BSHsbt3Z1UajUWLy8fjV8ZOCwSwRDjW30D+7dw5lEgrMrq41+6XKNA90dHG5u4rb23Xz2/Av83vHj3NjRzmAwyG9ed5CyqrFYLHAlm2Uyu85UNsNLSytkalXu6u1ls1y++kHRwqsoPDZ6gc5oCLWuYVoWHV4PJ9bWmMn+mJreSNTf0dXJbZ19PDM3x3+77iBr5RJNbhcT2QS9/iBBu50d0QjpWp0+vx+bKPPU4gKff/IooigQ62j4imVFZjVX4EN7dyOLImFHHJsoEvd4eMfQAHGXj5KmslJOs1I+jtum0OrxMhJr4bG5ywiCwGOzc5Q1jbf17uHj11zLtZ/5Ig/cdpCoy0nEsYWhkInbluFzY2P8xU13kajmcMkNWPlNrYeQhfA/8VpuVMqsli9zc/sBFLGNFvckyWqawUAXXf55VkpFtofK/6xH83UPpmWis45ouREFH7q1TqKaI2j/yfNVc5m6kUYRPdSMTWyiwmIpyY8W5tkVjXIg3kWymuf4+iKJao139u9grZLh5vYOHhyKcHxznp2RFibSGSRBIFuvcaCpnQ6vH9Myyal1ruSy+BWFZK3EnniMqUyWqWyavbEYQ6EwHtnJZ8+fYTyZYGcsjs/m5FxyjaizsYu0Wizy7uFhunx+/uz0GV5cXGR5bhNJFnnWu8Q7hgaYyebYHo3yobv341d20+JeZjR5gf6An3cNDjKeTtHijvL92THmCwUubG5wcnWFdw4Pc093mC6fn0cuzxC0K3zp0nnytRof2rmTxWKe0USCvx49z/tHduFTFLaEokiCyEQmyamNBKc2NlgpFGj3++nxBxpA8nwOhyQxEBCwLLAJIkfaOxAEgZfn5ynlygS8CrmiykefeI4/v+8NpKtVJs8vEY57cbjtlHIVJscXuW7fMK8UNUwzh9spo9d1qjUVu89PRzjITGWDSrFKvCPCyfMzPN/Xw1y+wBt7enlqcYF0pUpfKAQ0iA8TqSRBh5OKpjO6vk6rz0dTLMjl7Ar1uoZqN8kVytgUGzf39NDicZOq1riSL/C1l0/x2oc+hFPqxKRE1Onksy++Ri5bxee24VEUUhtZvj05SbvXy57mFnZGQnx3eobZbBbLsmj3+wmEvNy0ZYCpdIqhcITbOzteb4byKAoDgQBP6CY2u8xdfX1MpjP/muz4918CCP9BiO0/ldC0LGsM2PuPDn/pH53zp8Cf/qNjF4Dd/8I1H/hXXu8TwCd+mt8NoFysM/HKCsSc9G9ppj8awWe3s1wo0ubxIggChmWyfbiTXx8Z4VIqhV2ScMky7R4Bj62bleKPCdjtFDWVJpeb7eEQsWv3cyGZ4rqrMG0BgVva+3hifhpZbHAOa4aBZphEHA5GNxN8/vx5/vjwLWxU8pw8c5nNTA2HXcLjlLH1SThlmYos4fE5kCQJmySxWS7hs9uRBYG82thy8isKBbXOX45doKSpFOp11kpFqrpOj9/Hl184TltnjPe/8QgjsSjdfj9rpRKjm0m+df5hnnv7B4k4SvyXl15ibmIVj8/ByJYetkcifGNigm2RCPcPDjKTT9Lm8RF3u3lsdo5ksURV09nV3IrHpvDa+hqaqVPTDUY3l3n44jgz46skElV8AYV6RcWywDBM5tMZmv1eLkwsctc1O1jM52jz+dkoFanpOp+78Cin1te5b+8OvvHscRyKyA9ePMN6qsZQfxjZJjMy0EmqXGFPPMqp9U0ym3lkm0S9pqJpJm/q7eHVhUWCfoWgV2FhrUyuoFIsa1gW2BWRvqAbu9NGIVOmXNZp6/CyslYmHnMSiPqwKTJqXSM1mcBSDUy5UVEajVgYuoHL1hBmNkWmVq5hGCap1QwutwNN1fjdY69QrtZ56y37WC+VmF7bZLi1ifObCS5PrKNn6+CUQRL4yNtu5OTaKh0+PzvjccaTSb55x30ooh+fkqKi6wTtds4sLOMPeykXKthkiUqhyma6StCn0NkT4aMHrsElO3hmeZK/vniJjx08yLZQL2uVdZqcIW7rbOfHwTGy2Spup0y+qGLVDbBL2GwiClCrG1f9cQKJbMN36b4qsL/x0kl+ODHFf7/uEKphcOfXvk4xW2Zpo4zHZaNWrjfENwKZehXNMBshoaLG5PkVDh0aYObKKimlTGK5RFt7gEpNZVtPG60tYTTTwG2zUVBV1jcz2GwSZ5dXuLf3OhLdncR9HsbOz1Esq+iGhWmaON12vnR2lMxmjsWlPD3dQWSbTKpcwbAsDKvCXd3dfOW540xOL3HTyBZavR52R6O8tLJK1OXmuzNX2BuPcrC1hbjL2dheLBZIVGq8sDCPTZJ46/YttHk92EQJj6JgAu8cGuQTL73K+mKKe67fzZu6bkez1jnY1sZTC4v80o4d7IvHGQ7GmM4mGA41UTV0ron3k60X0E2dRy9fxuV1kE03tg8dbjvtwQBv7e9nf1M7EUcbJzfHGA4Hube3l4FAGy+uTBJyOHh8bp6JVJJf3rULpyzz0PREI8zhaOZyKkXU5eLGtrNU9Bo3H9rOm3v7EAWBs8lX+MHsHDGnA49N4Zdf+BF1Q+dQ6ywPDO16PSD0D5dqLNLs9vDw5Ul+c3c32fpZkrU8Xd5+FLGdbeHz3N6xHYc0TEW/SN34R6l2TATAoIRlVRqMTMGJiJse39BVaHxjylkzJjmfmkMWRXp9ccp6jcfnxxhPpbm3r5frWvaSrq3Q5unh6aV5NNPEpwyTqZ/Cp7i4nN/ga+PjfOwaP11+H20eD7sjLRS1KulaFcMycdtsuG02Djd38fLqHBOZDCOxKAVV40BTiEytyope4OWZWRyuBgbIp7gJOZwE7A6Orc+yIxrBpyhsVMrc2tNDyOHge5ZFXzDER0ZG6PXt5bY2JxbW653tNqGFfbEYyeoP+f7sLL89cpinly4Rcjj49Auv4va78DrthBx27JKNmWwWlyxT0XX6Az5a3C0U1Dp/MXaeiqaxr6ON8XSWIy3NuGQ7AcXNZTHNWqnIqbOXibSGuL6jk7yqkqjWeOj0GA/u381yMcPlXJaA3c5QKMDT84tIssRX738T09kMv/3wj1hczPIbjz7Fkz//c2w79VmyqSIRWaKlPYIsilxaaZQw1Co6Xq8Nn8eGpurYRBFJUehojmIaFja7zAM37udvL8/QGwiyUS5xV1cPFU1nvdyglDhlG80eD2Gng4Kq4nc4+Durm8OlkE8VaQ8F+PhN15Gt1QjYHayUSmxWKggCZDNlziYXuaFlF3Ujy/lUiuRGno1UFUkS6FFV3D4XA+EwbpuNmNPBq2sbNHs8rBWLbGTzmFj86p7djCXTvG1wAEkQeHpxCc0wqZQar2mXJERJ4Jq2dh6bnObYY5M/rQT5d1v/UTya/ymagR5+6Msfv+ejd7Bvewe39/UQdzUwn61eD4+cGmNqbZPJ1Q1UweJX9hzk1x79IW6vmy6fjxZ3BIsqb+wZpKyXuLGtHdU0ONTUw7H1ZQ63NlPWNH7n8Wf5rUee4m8vjGJ57YRdLjp9Xrq8foZDMaq6ynKpzGQqyQsr81xKpcnUqiRSZcoVHYddYmk1hS/ioVKsoThsJDcLbO1piNiQ00WmXqdumFR1HafcgMbuiscY3Uwwu5ognSkQ8HvYGgmRwuCWri68ig3dMnHLNhAEIk4nXqeT61r6CTt24XNmuFhogNBbAo0tXFmS2BuPUzd0Hpm6TMCh8OzSMu/bupVdTTGqhs5KKcfuaDsFrcJvH32JV05P8sorcyyNJygVNXwRBy6HjMtla3RqCwL/49bryaoq64UiBatxTz67nalEkms7O7nuKm/t4ZNjdHbHsbntqFWVlhY/8ZYQHo+TN/b1YpNlvvzccRbXU1RrGpLYqCCrlOu8/cA2Hj17Ca/XQe9AKw9ev5ue3ihrm2kqNR3TsohF3Qz3tJIvV8kXa2zf3UGlWEWWRZpaQzhtNuqaztBAE80dAeank/gDdkIhF62x0FWTvUEyXaBe1ajWG57GaJMfn8vJ52++lfft2MuldIIT84uoVZX2aJATF2ZYWi9DRQe7xG//4q30+n30B4O4bDaWi0UWkhlu7e1gMrvMn507x0K+wJMXpynlyyTWciiKjDfgIpepUKjolKo6DgnesXcHLpud1XKeRKXGaCLJRx9/nOH2GAOBJjYrOV6YXyCZKvPzdx3i0vwqtaoOhkVNt9CMxkTYrkjYbRKqbqGWNWq6iaZbVKoa9XKVR89M0NEe4/N33MmapDK3sokoCIzs7aXV6+OBod28urbEd49NUK4a1Msaff1h0ukC/rCXWqmKJ2Bn5mKKkT1dnDt/hctXEmSzBSqmTl8sQkckxGaljKzIrNeqGBakq1WqdZUb9w2Tq5SRr/7/b65kUOwyTkXEG3SjOBXaAn7CTifbQnGaXNtpa7F48fIcK6kswYCXu7v7ONDUwvWtHdzTvYuyXmQik0U1G5PY5xeXuba1mSaPF9OCkNOJU5YoaTrXNMf51qVxCpqOy2lnYmYDwSfjd5a5kk9wZ2cf55IJOr0eFosFLmfT7I418fLqAp89e47tMT/t7hCfOPMaF6YWsSk21JpKMObji7fdxv3929kS6san9GBRY6Gwyq5oMz7FxWhyiSPNfTw2P82eWIzvnrtISyhA2OHgLb0HGAw5sdC4u7eXklZnoZDlNx57iqMnr/Dcwiy9LRG2hKLopsbvf+cZ5lY2ObK1l/915DYON98E5Di+cZLR5HkqxjLNrgbvUhID+JUyI7EWZNHALfcjCkVWy6uE7e0MBAYxrCwgYFHBIQ3+xPO34ZgC0ypjYSIKDiTBDYJ8lclpIgkNr+xiaZyKrtHli+C1RXl5dZJEtcpgMMC1Ld28uj6JX1F4cXWC4VCINo8Hm1Qk5oyjmjVS1SLzhSJ3dw1xNrnGQrGEUxZYLBXo9vk5l0hwdGW1UeVrd5CslvnUd5/nxm19tHjcXEqlWC2XWSqWWC6XMEyTsqZhk00ON3fz3PIcu6JxHp6aYiKTwa8oXM7mqOoG26IRPnbgFip6leXSAjFnO6Lg+AfvgwgIXClMcX1rGy+szHN/31tp9Zi8f++tbIk5ePfQNmySwEIhw3gmw3sGt2GXBXZF48zkMvzOY88i2iVMw6LF72Mmk6FuWaimyonNFeq6zg/PjfMH997KzpYmTqyvUdV0XDYbt/R1M5PL0+v3o0gS8/kCV3IF3j44wHdPnOd9e/ciizDY1cwzozNUSnV+OD/Ns7/yIH919CymqrG+lmVbbxvryRw2wSRf1AgH7cSa/MSjAVw2pYFLMw0y+cYEcqGQJ5Ur0hMJk6xWUQ2NOzp76Q/4mM7m2BmNcE1zEweamkjVakyl06imQcjp4revO8RvX3cj9/UO0+1rYaGQpGroJKpVJEFAFiWW8zm+8txZvvDao3zu6IssbCTRNANBFHA7ZG7ZNUhRVymrGhvVKvub4nzhqVfIqDXiAR92xYYiSXT7A0xns2xUqjx55QoX51Y4d3GepbUS7zq8nV5fnNt7OklWyzz89Dnqhgmjaz+zzUB/+JlPfTxwoP3/czNQ+rnZ/9/v6T+F0PzaX3/549/5w1+hYlTJqSpuWaakNQTK82emyWYaVX7vOTTC/ngbA81BCmodSYBX1xfZFo4gCU6aXE6+fXkcE4ux5DpHWlq4kE7zxVdP4fa7SG4UsCz4o3tu4k1d19DsduNVnKiGRllX+V+Pv8DFiU0mr2zgiTlpiwQoFYqomommm9x1085G8twhNyYydY3R8RVcXgVBFom53GRrNTr9Xto9Xl5d2+DHc3McaG0lqdVJrGb4hUN7rvqEcrR5vfT4/dQMg4VCAZ+ikKpVObO+zqePPsfP7erCaYPlWpU7+nrYHYsyFApxX8827JJASVdp93o4l0gxEo/S5vFybG2N2zt7uZRO0uMPMJvPsCfexPOXZshl6yAItPf6kcRGw44oQKmi41AkJmoF/HYHol3m2vYOCmqdqNPFnuYmFEniibk5Tq6t4XI72NvSQqZeQzdN3D4XhmlSran86OQlHj82TWurH8uySKYrtHSEUKsaDpvA/3NwL98+cx4Li454mBvb2/AoCsGYH1VQ8bltqFWVYMTHcFsTu4baWMvkKOWrtHdHiPu9xNxu1tM5WoJ+8tUaqXSJ5ogLp9eJYJfwO+xEXW6SlTKyTSIc8+Hx2vnh297N2we2oUg2jq7O8vLKMqIs0t8cY3xlnXKugq6b2H0KkbCD1pYQPrudp+bnEUWJ927Zit/j4g9fPkYJE4dsY3ozSWotQ7Wi4fE6eMvhPVxeS5DNVqhrJpIo8I7b9zcQI2qVsNPJTe2dXNfWwXv27CLscFLRqwTsDl5KrPH9B99Gj9/PqewGqyt5bE4Z8er2it0mYZoWik0k4LVhiQKqZmFXRNrbAoiiyPvfcJhf33UASZTYHYvwWmmTTLLI8nKaoZ5m7uzaxkurs7w4Novfa0OzYH0ijSeo8K7DI2RsBrlUkXxFZ/e2NiqGTntPDJvdxo7OFl6dnGVsYp6fv34/k5tJDnW0MxKPsj0a5deuOcBgKMhktUAk6MOyi9QrdSzTwulueI1N00ST4Jd27uK55Vm2hwf5nWOPUSxWcHocLCUzLNYrvLlvB49cOc+e6CB2WeOFlUVMy+LRyzNsjUXZrFR5bXWVfc1NzOcLPLuwQKvXx7MLC8iiyNkLszTFg7z/ur2cml/m+PIqneEQg8EQmVqFndEY356aJuJyMRgI0eULcEtXB7l6HUEweWllmfV0jlhziG+94828e3gnLe4YqVqW/3n8Bf74tSf4wrHn2TA17u4e4Hx6jb2xTjarWW5o7ecPTh5jfHyZO3YOckv7IJ8Ze5nrW3qQBYm6oaGaOlvCESbKeUS7wNiFDV6emuOFjUUKlsmN2/t5w85B/tue96NITQiCjCQEiLkMtoaOsFqe+4kqyoK2zLG1eYaCuzCpIAp1wo5WZDGIIMhXe7jLmFT+xR71BvpYRxTsSKIXQZBZLU/z6PxZgo48idos7Z4WxjOrlLQaBmVkUWSxUOC9w4dYLm/S6vZjYHB8fZ239N7FYnGeC6kNwg4JmyjT5gmxLRKgpFc5k9hEFhsIGwHI1KoUNZ0mt4u4y8WPFueJOB3cv387BVXl2Oo6XkVhs1Ll+tZWnroyx+zkGuGYjxs7OugPtPHkwjQ1Q2M4HMIpyywUithlGdOy+OCOERySg5JWpqyppGpL+JQKNjF+9f5NwKTT66LZtZehoEy6tsDF9CqCUGUouIXl0iq/9sxzrFWrvDwzR9bSiLkczOZzfGt8AiShwYVVNW4a7GWtVKLZ7cG0LNyyTNUwCAd9/O8fvkS8OUKz20O+XsdlsxG027m+rY2/vHCRR85c4NziKgm1isfhYK6Q56GzY7RFg9zS3sXpeoqF+TRaXeO+ka2cKiSpVVSGt3ZQ0TQsSUSWRDK5KtW6QWtrgHKlxsJKgpxaJ+x1IygSUxeXObil0dK8tAAAIABJREFUhysL6xQkk6DThSKJjKUSHGzu4K6uQXZHW6mb9QbkvVSiw+/nI3v20u33EnQ4aXFHEAURMHno8jiXUikM0yLsdLBUKJCqVNi3vZuJmY3GMMGwaGoNosgCmmYwMtDBzGYS1TK4qbMDhySxrauFEzOLVEyD+Zk1EukCr84tspbNk7c0Pnb4EB85dC1beuPcvmeALp+PglplwH8t28IKn3v5GF/44Jt47AvP/UwLzeA1ba9TXn7ar38Pofmzn9//KVZNr7FaTnGouQ2f4uJKPklF38Cr2HjXbQfYLJdxyjJ3d3WjiC72xtpJ16oMhyLc4AwwnVvj+Po6t3Z0UlQbPq2KrlPUVC6lUqQ3clf9hlCtG9z9p9/ixd9RSNeqdPv8/O2VGc5tbFKrG2iaicvRQC1d29ZG1OXmwvoGW+IxegJ+TqyuUamp1KsNKKzLIZFPFemOh3HJMjZRJF9XScoVmt0uXqvUmE6nKZWqvO/2Qzw8OcXNnZ3UdYO4y8mBpg5+4ZknSKTzuLyNrR+HTeYd+3bxe6de4JMHf4UHhqvUdJ0+f4SA3ctGJcPXJsd5YGgYzWOwWalS0XT+ZnqaDq+Hql4n5nLyjalLfH/0ImpdI97RCDGVKjpuj51YWwhdM3C67ExfWEKySWQTedq7u7l/oB+nzYZqGMzmcuTrDRxTrloFQJQkxjYa/cGyTcLlUPDZ7ZgWiJKIqhmIkojdoeByyGwspUjnVO67fRdO2Y7L6ySxliNTrTKZyaGZJkuFPKIokkyViUbcV9/HGn67g71dHdy9dYjJdBqXzYYiSmzvaGEpn2d+eh1ZEiiWVQSxzMHBHprcjXTlu0Z2sj0cZke4FYfU6DyvGxpX8ps8tbBIoV4n4nLz6rkpHrjpABtdFZ47Pc7KWhHDsJjNZimpKrlylZu2b6PZ7ePNvT66fV4upNKMbW6SXM00esIlge7eZn5z90Hu6e7mFx95lNpiluuvG+LXd41wcnMFSRCo6jqGaRFyOHFICtPZNK+ub2ATRSzgfCrBN8YnmJneRLZLGIbVsI6YFh6XiEMRKZQ0JEkg6FOo1Krkihq5sQ1kp8yz8/Pc3z9I2O7DJdv57M03c8/EN8kVVUzLQhBc7IxGkKSrlAePjXS6xo0HtzGXz2FZFh39TXQNClQ0jf9+07XMFgrYRJG9sRgXl9cplnVeWlpCq2v47crVCb6M2+Yg4vDx0B1v5qmlSRaKJV52OEjnS5imSb2iIis23tjfj1t28M7+O7EslYfe8GYenx9ntVSm1ePmj558kQ/qOvf0drNeaSB33tbfz3cuz3Bnby+LhSIXNzd4744dfHd6mg6fn5W5TWyiyM54E6Mb6xSKdSzLwi6JfPDgPs5sJujweMirVRyyxPlUgt87eASHpCAJInOFJD9eWMCt2ChpKodbWhgOhTjU3Ixdajxmn12+REFVibhcbGmOo7RKbI+G8Ss9dPtKVPU6YbsPWQzxX/fuJeZ2847+/dilAe7oXOXDLz3Nh/fs5mIqxXAoxMdee5X379jB51QNt3Odet3g3OgqkxMb9PaG+ey9bwAaAqhuTiEJYVzyDgAGAk1o5gY2sQkAp6zgt9uxqJOpL74OaQdYr5wgZI/wzPIouyLNBOxjuOS+f5JCFwQFEQlR+PviuBb3AD83uBVR8LBeOcO3pk+SrFa5pb2DJmeIdreHwUAzouBGFkQWi1m8ip2Qw4Fl6WwJtTASVZgrbrBSzuOSZYJ2N398+hSnpue5becwnV4fgiDwzckpHLKMT7GRrte4s6u74cN3B4k4UnR4fUxlMnT7vYQcTgzdoHuwmY/s3UtRUzHMGu/fuosPvfA0D2wZZms4zJGWFoqayrXNN7JYHOWpxRne3r8PgU3e/+xTfGDnDt7SuwPLMtCsdUyrgG5q6OZ5XPI2Hrr8VZpdLrL1GhOZJKc3E5RyZa6IAnaHjY1yCZvYxGqpTKWu8pFD+3l6cYm+QIAz6xuN5yIWddNEMgxS1RovTc5gWfDU6Dhuv4sdLc2sFIscW17iwyMjHGhuoqZrvGfLMF+7NMHXXj5FJl3G73fylRPnCNrt2CWJrduaUesav3X0ZX7jmn382YnTtPp8jM4uYVkw+toytoCCQ5HIp4vUahpOl53t7c30BoJ88+gpfF4733/mHLGom7qqNawAms6NbW1sVgrYPA1Pr2FaFNQ6++NxXllb478efYnr29s50tKKgO0qpUBgXzzKa2sb2CWJiXSamXSaaqnG5Pwamm5S10x0Q0Naz73u51YkiWqphmkqvLy8yq/s2sUj05eJNAVJrGbwBNxkkgW2DXQzFI7wkT37iTn6mS2c59e+/jiHDwwyHAqRqFTo9y/hkreza1837xq4jvf+G+qUf+vVSJ3/22+dC4JwB402SIlG3uZT/8w5N/CPKsf/tWv+pxCaDtlGp7eN86kr/OXFC3R4PQwGg2wPN/HmHgcX0ivEXW46PDEEFHJqCd20uJBKsFpeoNvn5QPbbufPxp5shGssGAgE0AyTmqYT74gwN72BZlioqoGiSNz4ya/gccmMfvTDvHtwC3d3d/OrxRKwTktnmJDDSdBhpzvgZygUxKsoVHSdHfEYlVCIsc0NlhfShEIu6lWVlWye4XCEqq4TctgZCARp9+p85eVTxHxetrU1c0NbG51eL5OZLIok8sLyCkvFEjVdR601arV2dMR4Q2cn7d4QVb3OaOpptgRbKWpVBOBccoFOb5AP79zPSjlNjy9O3JUgVa1xR1cno8mGz9QuSTS5XYiyhEOWKGbL+MMeVL1AMOZDU3WaQwG6AwHufUs/T83PMzPZaKXp8Pk5vr5KQVWZS6VxOhRsoogsSZiW1eiIv/q3U2san3vzLaRrVUqqyomNTX6sN4RmvdI4yzAsOtp9OGwyX7wwimVa2BWJuq6TV1XCjkbtmdPrwCaL6KrO5bk19gx18/bBAVTTaGwvyTKnNzao6wbZWpX0epZgyI3Xa6CrOrpmUNE0NLPhud0SCtHpDeCU7Q0YuWVSN1T8dge741HS1UoDWeVQGE+lSFcawtnnsSEKMH1hmQWbSCDiJVurU9JqeG1OTm0kEASBoXAEtsDs0ia6ZjA9vsRfdpxmdzTKV995H79//Dh/ePgINkluYEVMg7phIAkCfru90YV+FXFT0TR6AkG+eG6UzaU0bodMqaKjl7UGZqlukN2sEB4IoJsWxbLOgcO9VKoL5EoqhiDRFHaQSxZYLxeJOQPousGfnxvFJjfCBbIoIgtRSqqKyyFRrup4nDKeZjdnJueolev4wh6uGejhzX29HFtb529nZugJBHni3CWOvO2N3Lt9C1/dyLElEiWVKfDM/CJ39XazUCiyUirT6fXwxNw8QYeTNq+n0cDiVCjnKygOG12xcGOrU5RRzVVkMYBL7uXdA7sAqBuX+RP5KMuFPH91/gLlLRr74s3EnG72xqNIgkBJ09jf2tZg304skWoK4HAplKp1Prh9Fw/b7VTrGrlaja3hCHXD4NW1NVZKJbZFovgVhZlcgZAjxWAgwlo5T8DuYE8synQux9MLS3z+ht+kbswynZvl+eVFHhi8hhtaHaiGzr3dNkws5gqbPLe8jG5topsGU7lNdobbAInh4FY+ujcMyCyVjnEpneb3Dx1huZSny++jrGms5Qv87gsvs7+rg76+MMnNIk67RLVusGegi+FQQ0QKgohdHPqJNLjPtoO8ep6A/arQlNq4sXUXlmUQdzaOLRSP0uxqZTafxBNxcEfHtVzMjDFbSHMw7kWSflJoijjRSQF/n/75u2mfbmYJO4L4FBvXNDUjiyIFrUKLayeaeZ5nl0/Q6fVT1FReWVtDFARWy5docQ9Q0ddQjYY96OOvvYbXbufc5DyaqjO6sc5UOsX17e3opolTkog4HAwEglR0nR/NL9Dr9zX4q5UKnT4vI7E2AoqHI73d6JbJWrnM6Y1NWt0eQg4X9/X3cy6Z4o7OTtbLZQzLpG4scHR1CYCilmc8k2BbLMZMrnCVCbpBRUteRcp1YlLBsPK8vX+YglqlZuh8c3KKZKXM9t52/A4HQYeD5UKB783MUNcNPrx/L9vCUdq9Ph6bm2Mpn6NWrhMc6GUumyNdrdAXCtMSD5HeyGFTZEq5MkuePMVqDUmWcNpk7u7ux7QsVkpl+kMhOkb86KbJ7liEmm6wJRTh5q4uHkmdJ9oUpFKts16ucEt/L3XDpF7VsCkybT0+MgUV07TIF+pcd2iImNvDze1tfObkaaqlOqlcHaddIhD14XU6yNfr7IlFyNfrdHoDrJWz5Ot1todb6fFZTOc22BmJsFossVoqY2FhWNWryCuBA/F25gtFXltZJVksUcgUCcb9rM8lCPvtuH1OlpZzFEoaNptIpWZwIbFJJB7gwyN7+P7MLHZJ4r6+XjyKwu+/dAxREmluDvG+7dto9/hwyw6eXz3KbC7PWqLKd344xvxakmceeC9gYFoVvn7HGxGv2j1+Ztf/hTDQ1eD2F4BbaaAmTwuC8LhlWRP/4Jx/qXL8X1z/KYQmVz9FK1IjWX26XCavqtR0nf1N7czl8zhkmc1qlmaXC4/s5PaOPk4nltkTjbI70s7XJp/j+xcmkG0S92/fimoYPDI1xdLlNZ740INc+8d/RWeLh2hbmEqhynB3CxcvL/Ho3BSvrKyQrVbRVI1okx9REvHZ7QgIdHg9FFWVoqoiiSIOSaZumDy4bSv/Y3yZaGuIbU1xdMskUangURRqhsFI9CC6tc7v3XNz44HhbzwsDjS1UDN0srUamVqVxyenMXSDW3YM4bcr3NHZSYc3jCRIGKaBapRxyt28uvEc35u5QkXTuKmjnbf17WA2n8Nja0wS7+7u49X1ZVLVGrd0tHF8fZNzm5vkEnnUuk4yW0eWBaJBB1q9kcTdGo3ynqEhWl1hunw+virJfO/kGAVVbQRqLAunQ0EzDHTTxGWzUb76ve5AkHiHm8l0itVSkQPxbkzL4gdXZlEcNsIuN2t6lpF9vQQcDlYLBRLlMjPpNOGYv4HdyJQ5pi+wq7WFVp8P3TSxyY2JqJGrMJ/N0ObxEbJ7eejyBd7evwWbKPK96Wk0VW8EAWoqumYgKzK37d/Gf9mzj0+ePsE7BwbxKW7skg3TMhlLLXEumeQ9gzsJOfx4uhzMZPO8eOkyisPG1PwaK0tZJElAsYnYZJFsUWVgexuVQpWziSSDoRAnN9b5/vFRoq0h7hkY4I19vVwORzi5tkrY6SRVrfHRp1/gnXt30ukPkKxWkMWGQFBEqREecjhxyw40Q2ezUuVwSxNz+QJXco3u5YSQRpYEnHYJNWW+XuWJKJAtqJiqSb1ucOHMAslsDZtNxOu2UazoOLJlPnPmLFX9BMVKjVMnFgh4FPxeW6MGlUb7jtMuUVNNdMOitzfE0lIOj1NGqajYJYnTm5s8cWGCYq5MuqeJaDzAZ86epaJquHxOJlJJKsUaCXuRkqYzFAzw+Ow8w8EgVzIZ9rW04pQk4m4Pi9kskk2iORSgquscbmnBIdmo6nV8V6HKNWMa01KRBCeSLKEbBqViFc00ObG+xoND7+CZpYfYH4/zyade5ubdw2yJhKnvGaBQr2OGLLr8Af7i4hh9AT8doQAlVeU3X3yRB7ZtxS7JnN1M0OnzcqCpnYNNAgvFDJ8dO0uT24VpWSwVisxmMzR5vJzcfJJtoTYkUeB8MsXPDSqMJqfYHx8kXUvzt1cmcckymmEynVtiMNBO3VjBbYtjE5swrQp+xUHVWGE8vUleVbmSz3C4qZcL6SU+efYUyzMbrCarzE6tE4v7uOuGnTzx4hg11cQuSYTsDSaoYRXRzKWrYrMxYbJQWSqlCFzVhLIYef1paloVRMHF6cQad3b4Odh0I5ZVxbQqNLsC7ArvxrSKaOYqFT1B3dAI2XsAC1mI/4SgBTDMHIJgYzy9QN0wKGsqnd4wBa2CaVVZKCaoGwZRpw+7JHMhlca0LBaLWVrcFg7JT8he4svj55mcXsbhUlhdK3D/7SO8Y2iAZ5eW6fB6ePfgDp5YmOLOzgGC9hiPzZ8k7nJyR2c/HpuT4WAb88UNjq8vclvHEKfXVtE1g9H1dTr8AR6dm+fB4WHe3r+Pjxz9IT9aWKTX76PH56Os17i1oxdJECmoFTTDRBZEHhzegWou8drGJZrdHrL1GpOZi9zU1kWX9zpCdh8iAk8uzHCwOc5XL16iUlfpDAapaBoltU7c7WF/UxM3tfbjkkNcMa9gFyXK+QqrSxmeqdRxuOwUc2Uy+RJdsTAurxNDN3nw8F6emZ8n7HZT1lTy9Tq/++pR+kMhhkIByprGTe3tWJaFbppcTKf5/NgYlzNpXF4n68spfu2Ww3z/8mXeNjTII5NTbBtoZ3Ju9XU+80BviB1DneyJxzm3uckP5+ZJb2SZXyiAKBDtbLSkFas1/HY7A4EwK6UCumWQr9dJViuYVqMxzW2zsVEpc6S1halsjmS1QqZeIOIIARJhR5CiquK22ag57bhbFGJuD6vmJv6whx19Haytn0dVTYJehVJFI5kr8tw734tT3s7t7RmmcmfxKAqfOnGSoeY4w+EQqWqNbl+AzUqZF1dmuJjO0Ox2sXN7nLGxDf787tsJ2PdgWnXmi8d5enGe92/9pxivn7X1f2GiuR+4YlnW3NXr/w2NWvB/iLT8ZyvH/7X1n0RoCuTVdV5YWcalNKYF6VqNsNPJeHqDgN1OxOHm8+dHefdQFb/iIOr00+rxslwsMFvYZCQeJ3DtNayVyszm8uyORri3v59nbDY6PW30DTfTEvJzpK2Nsxub2CSJcqHKUrFEh8/PmdEZiuXGlqRDkThjWsRcLjq8HrJ1FackkavXqeoGx+YX2CiVaO6Kct/wIKIgYFoWeVXlTT29PL24iGkVEXGzJ9qMaqwgigLH19d5fuE4X7r1dg43t/KD2VlGL83T1dvEgaYYYacTmyhRUCv81aWL7G+KMZZMMxLdz02th9keauJ/HH+FP3vmGDGniy6fn/VykRvauvHZXGxULmOYFo/PzrNSyHNHTw+9gSB741H+5xPPo9Z1juwe4F1DQ/zVxYuMJ5NstLfR4YmzLRzjtu4SFU3l6NQVtna2sjUSZirVwD/5XU72NTdE8lqxhG6arJdK1HWD716e4bX1dR4c3s6NHW10+HzM5rK4m2K0eL2sFAtXJwBOnFf9UmVJol5twNpXCwVcio0Ov5/NqIdsqsTw9k76QmHqhk5Zb2yjLZayfP3kOQzdxOG2I8ri6w1EkixxpLWZFvdOytorRJ0BdNPge1cusVwqM7a5gSgI3N+3BREBj+zEb2+0Xbj9TuamNylWdRRZwONy4gt5iLTIqDWNfLrIqGOVtWKRklrH43MhCAKn1tZ46+AADlnitu5u5q72Z/cGfPzJD55HNyyePd3Ay9arKopDId4R5g+vvRbRJfKJ08fYF48zXyiSqtawSSLdgQCXvU50VW9A4xURNBEME0wLM11viE6bSHKlBDYRrW6AQ0bXTfJFlbPnrlCtGSSzNZoiTrr6Y4iiiMtmQxRcXBNvJ97sp65lMQyLYrbMoUP9nD09ywduvIbTG5tMppLomoFNkQk6G5aO45OzqHUNtaqxIWcZ7mvlnUND5FWVr49PsLSc4Jd27uBQezt+pZHkjbndlFQV022xvJHij+64mZHoDiyriiIZLBSniDr82CUb6VqeH8yd5heP7OP5xSXmUkV6/H7ObCaoGpO8pXcLPsXDp954G58+cwZVN3jf1q381o+f447tw4wnkzR5PGwNh3n8ypXXe52fnl/ALsm0+33EnC7Cdh+CIDGaWmN/U4xvjE+QSOYwdJORgU4uLq/xNw4HJfUS17W18ukjb0G38gyHmji+MYVNFPnFLXtx23ayXjnBVybG2LpnP1uCUQyrUR4gCi7G0kdpcvnpC4TwKgrtngAPz4zymadfoZCrYlkWggCaYXHx/AaiJPC7999Kp8/LD2fnKWtX8Nq2UTfmmcyusicy9PoT07RKlK9WmsLfI4oEQUKgsfV9uLmTl9ZmqOiTRBxOBgJRfIoLzVxmtbzKmcQadklCFkXctnUOxrcCArLw96IVQBBcCAh89twoX7r5HvJqmapR50sXL/CBbRaX0kk002y0Nnk62BvLka5ViThdiIIHAZmyPoeIgMvjoF5TcSgiMbcbv+LgutaW/8Pee0bXcZ7n2tfM7N4rsDd6LwTAAhaxiJJIiWqWJVmSey9xTaw4cYk/+zuO48RxThzHTbYjl8ixHduyZKtYheokxV5AAgSJ3oG9sXtvs2fm/Ng0U85Jcr6z4m9leZ3nH/bCGizsNe87zzzvfd8Xc+kMqqbyuo4BkuUcDkOOu9q3MpNZRFarXE6u0Whz0WipTcSXclE+sHkTX331JGaTgdVs7Xsfj9eyLb+w6wbuP3QQUYCVXI5Nvia8JhdPLoxyTX0TOwLN7Ay0AGCUOulzRzFLBh6bPcOhpUX+fmSUn9+lYyGbwKTT8fa+nbzvhUdIZwskIxksRgPjFxZp7QnQ5/Xxus4NWPR+ptMTfPCZg6wvRglFi7zwqXcSLuT56pmzVGUFRVaw6A28ac8woiAwGo0Ri6XpaKzjw4ObORGOcGt7G+OJBC8uLtNot/PozAx2vYEBr5vzkSjPHR1l8+ZOtrYFmPOlOdDSzgOvHOdn2gRtLhetDgcb/XX85NUzeOvsfOveO9A0jdPr6/zBli187uhRvAE3XI5js+lp7Wvk9X19LGaynA6t4TYa2ODxkCqX+N7YRb5yfQ0V++uFSyxlc/hMJoJWK6IA8+kMzTYHPpMHDZmKUktd6fV6WM7oWclmmIvF0Rl0PPK2N3I2EuLEuUniqVJtfwP+eM81mKTava0TPXQ5Gwjlp9gabKBUrVKsKuxrbuRiPIpVr+elpRUMOok39fTyt+XDfPUjd7DF1wpApDjC2x97nLY6L7e2jv2ndSi/rfotNJqNwPI/+3mFWi76P69/Ezn+b9XvRKOZLGc5tb5ErFji6OQc0bUkN+0ZIi/LjCcSiNR46Coa9RbHFe2VjyZrhXa7H7u+HVEwY9efYNgv8MTcDCdCYdxmM9++8VZUrYwoieQqFaqahttsJlMuY7Gbua4hyHwmw517h/nFi6cplhQqFRVXpcqgz8MNjV1IgkislOZnUxO0OWxc19HGSiZLq9/DCwsL1FltlJXaojF0d/OOvkFUSnx77AhDPh/XNrQiCRJfPHqcgMPOVCrOdDLFazvamUunuKGpiYVsjlC+puvc4g+yr7mRn1ya4NiZCQ60NLM3+FqeX36eBpuNP71jP6v5HP0eH3a9EYOoI1nO8uTUFKNnl2gI2unpbOTs+jq3tLUSsFixe2wkwilMOj1nIxFkRaEgVxiJRuly+vCZnHQ4nPT5/Ly8FufS0hq5SpnbOjupKArjsTjr+RqFSFYVVK125Bsv1I6mfvLUKQ5uneXnd92LJCyykE5ju4Jq7HK7kQSRYyvLlBXlasPZ4HOTkytIokCuUuHpl8awmCS6Bhr5/S1b2ORvIFXOo2oqkiByORFHrlRZWU7R0ODE4jCTS+XxBlzkM0W+duI01zd08OCN9/KZ48/QbLfhMhqZTsRZXYtjshr5yzPHeUtvDx87+AKZRA6z1cjSbJRcsYokgN2qx+62YnVaamam1QSlksxgMIDNYGAmmWA5lUdVVaYvrnD42ASVqsqn3rCfPq+Hx2bn+MeDJ8nkZGwWHXKqSLWq4fVauO/aYdYLef7s+HEsej1/e8N+spUyGhrj8ThOo4EvnzrNwlyMqnKF2lqsRdBKQStKqgxGibqABUXVKFdUcskSOqsev9tEJFGkUKoiKyoWkw6Xw8B6vIRcDWM2Suh7ai7l05EVQqsp/F4LvgY3oiiyween+xYvb+ndRE4+y/dOX8ZoMRCL5gmtjVNXb2N+MYXJKGHUi4QnItyxeQMuk5HHZ+dQNY1rB7r56BPPcPeWQfSiyOn1dd43OMh3x8Z454YNPLWwyL7GGzkaepmVXI5bW3t5/3PP8kfbt3FLy078ZhGzJPHlJ1/mU3ft54KjNk3SiyLT6RBDnuvIyhPc1novuaqMXhB5aPwSNpeVV+bmKeZKvGl/L6+srNDucnNydoEH9t/M/q89yC17NvHW3j5EQeCLZw7zme37kRWVBy+M4jAYKeRKtLbVM74axmg2sJ7PMbOyzovHLvJZ9Vm+9a672dfYw/UNBxAFC4XqGEdCT7K9roMPD21HEKQr+1InmqaiaCl8JhtVVeHQ6jIPT0zwx9u38bmfHcTlsfLp+27ia08fIl9SsFt04DczMxPnEzNP8+67dvLG3h4eGDtFOP8Kl2NRcrkiAfdJ3tTfy6NT07S73Hz+mhpz+1zsWX546TJ1V+KfcnKVt/T2Um92sbO+DYOk46mFS5yPRrHp9ewIBBAQaLDacJtMtNhqGY/jyZkrk0kLLdZ2DFIbABV1hncefIQf3fJOzsfH8ZmsPL0wy/NnLzESCvHcvR9FEjxEihdIVWL0uv2cXF/h0OoKc+lnubVlPz6TA1lVsTjMNDf6yDVXrkY4yaqC11Tm3QefpM/nZyyyztf272fAfQCzFOL9zz/DE3d9lGR5kXy1RLJc4pWVFeZSaXoa6vnDrcNs9Ozgu5ee4PBqiEGvh32NHgZ9fkajEV7X3cWLK9O8tm2QXYFmcnKJJqsPvWgnWgpRVmYIWnYSLpzgnq4u9jQE8ZhMXIiFuP2zD7H07U/xzdHDzK3HWF+KsWd7P+F8DkXVqMpVphJxvnTmGK9Oz/H6LUM0e1z01fspVxUGPE10u6o871/mdGYRp9uGXhQxShKPjo5TypfR6SXGLi/yqKRjWzDAVCrF3oYGJEHgcjLJPn8jK7ksToORY5dnAchUyuhFkbGFFb4kniSbzHPrlgF8ZhMmSULRNOSyzBfvOkCqVGI8kWAulebVlRXCK3E8ASdf/MBwd9+1AAAgAElEQVStrOXyLGfSHJyfZzUcpyorfHdxneaWOg60tbGlvo5vjp5G1eDHzx0H4M0HrmFHIIBeEtE0cBktUFupGCSRPreLF5eWsegNFGSZeChJsVDmibkZXlhYQG/SU1+v4yM37aHRZuW7o2PMpNJ8cvj9ADy/fAkBgde0t2HR6bAZDEQKBb5/YZTv3HwL4/EkOlGg0epFb9Dx+q7+qxN9DfjBnXfw0KVLXEpEf4tdy39GCf+njaZPEIQz/+znB6+QFmsX/Z/rXyPA/03k+L/1B38nGk1JEBiNxVnNZMhlCmzY2MYfD29FA06Ew2yrqyNZLvEHm7bw3OIM93Vt5PnlE2yra0ZAQNFiaDhot2+ioq7y3g1OTDofAgb0Yj0X4gfJxLNct72N9/YPo2gqb376l2iaxtiVB3ybw47Hb2d6OoYgCOTSBdYLRRRNoaoq6MWa0Wc6lWGTz8NELE6pKhONpWl1unCbTRxobuZ8NMK3Tp6l3uVAEgW6XM6aCQMVn83GUjTBXx4/wX/bvZtPHT5MLJllT0MDG9xuPCYTi9kMY/F1TDodJ85NUihVSZfLLOde5Y3dW7jv1z/j+OoK+1pbKStVXAYL3xo9wzd/dZhKVWXX7i7cZjMFWebS3CrNDie3tvYgl2Su2dhNplzm+GqW1/d28+jUNA+PjjMej7M7GKTJbsdpNNDSXEdoPUE0n+dUKEQknydoq00mV0LxGrXGYqTe6aCYLZFPF2qoM0lAVqtYdHrqrFY0Tas9XHQ6dgWDtDlsPDO/wFwoSjyUwuqsHR8lrwTDa5pGrlDFYaphG59dnOaVpRWMOgm3ycyLY5NMz8SxmXUsLCbZvM1BR0eQL+29jr86fZr3Dg4QKiR5emGSh184TTpXYfO2Vt4/vJm/WQgh6Wq60I+/+DKFbBFJqk1EC6UqAmAySjW6kMNMKV8iGcnga3TT4vcwGY+xORDEa7agaVDIlbCYdJTKCqmczKe+8Wv8ASsNTU4URUOShCs4UwGdDqKxPI+fGgWgUqrwhuu34zbYsenNANSZnRSqNdNVfL2Aw2uimqnUopaCllqTWVaw1JmJrOQwuIw019eOfHWSgFxVcFj1FMsKTlstR9TjMKBpIFdrR+ShXBYARdPIFxW8PpEtTY3c1dnBjrpW/uToi3zs0HOkyyVWIgU2DjkJr+eQZRW5JGM2SiiqRqGk4LDpmU+lieQLvKmvh3ORKA89f5ztW7rZ29BAuJBnT0MDkUIBVYM/euo5HnnTG7kQP8ruwC5USkiCm4P3/gFLuWnuefJB1iNJWoJ+4qkSf/SDp7hl3wCiIHBtQwOdjgCnIy/R5vDxwuzPuL6hg5dWZpgKRXjb8Ea+/eIxvnDXAfSiiKyo3N3ZwemFJa77yt/ROdBEvFjApjfhNzn5w81OcnKOh8bHkeUqoVzN5DY6Ms/GLe002O2Mr4bJJHLodCI+v4OP/+IZvvpGkR11MofXZrm99Tr2BodqTmXdBKHCGHPpOHuDtyEIeqZS57Dra9E5t7R08Lbebdz+q3/gG++8i6+cPM2BllZi+67hq4++wuJyDp1RojVYWzO/fPEsL5y6iFyp4m/0UCnJmG0m1lMZ/ubQcf76tpvYUd9LpBih2dbDWi5Hm8POCwu1ye2jd3wGQdBTUi6RqeSYSa9zW2s/c5kIVr2eVlsLZSXLV84f5093fBiAtcJx1nI59jf1cDQ0y6eP/JBb2tuZSaU5Fw6RyxVJy+u4jCYarHWUlCl2Dnayp6GBr48+wx9uegOJco61fI7Dq2s8PzGFpsGRd3yKVGWWkdgKQauFp+57K6lKnpdXFnhzz/XMZ6b45cwsVr2e82fnsew00Of1IQkisrpKRa3y/VvuoFBdw6Y3M5laYyGTYWR9neVEivt3bseq03Nw+SV+fukS9/XVJmN3P/F9Bvx1bA0EOB+NcXdHZ20SW61g15sxSB4k7DRZa3miifJZzsfW2OCpYykb4lPPv8QP7rwD7UdP8uuFR+hwOvj0dbv54qGjnLk4i8NT07cuTEdor/fhNBp56e3vI1xIMJtMsZbLUq5WiZZSWHQm/mbvaznec5n7H32aTDrPOwc2cDYcJl4s0mh3MLEWZnx+Fa/FgkWvYyQaJVoski2XGfR4+PGly3zlhn18/MZr+eHFi4RW4oTq6nnr9s08NTtLNl+lrCicDYdZiiWRdBLvO7CbU+EI58IhPrp1mA8MbidbKVC9XqXO7KZQLfGJIy8wOxdCp5cQhFoMXTyc4vy5OWRFwWuxMHJxnnS6iCAIOJ0mbmxqoqqquAxGzkYiNFht1Jt1CEiAygaPj1PrEUpyFU3T0Ol12J0Sj01MIukkLDYT/S0NPD03x7VNzXzrxls4uDiLoqaIlCYwSBI3Nu6joi7zxPxFHrwwSqvTRWQ5zg/Gx+l0OYgUSmQqBU588GOMxGb4xcxlXEYj93UOsZSNc2NzE0fWfgMu/K9ZggD/SqXyv1sxTdP+dS76b+p/BwH+HyLH/3X9TjSahWoVn8mEXpLYNtDJewcHKCu1SY7baORkeJ3b2lpxG+w1h5pSplCt8uPJMTodDnYEmlG1KH9+8hh+i4W39/fTYrOgE0xU1SgCAu/aux1ZVclVi3iNDh654/W88alHGI/FaXc5GV2P4PDYsJpTyIrGQH8r6XKF74ydYy6VYmRqgduGB2mx21A0jbu7O2m02vj1wgJb6/zEiiVcplpQ7d7OduZTSd43NES8VGQmFcOq1/PGvh6OroXZFazn8bl5muwOcuUyF2Ix/GYzu4IBlnN5zJLEdCpNJlfBatYxkUwy5K1HErx8bd+N3PPDn/JkpsBNzc1MJOM8cuI8VVXjwA0biBeLLEYTLEyv4/ZYKFWr5OQid28e4Fw4jM1gxG+xMJFMUVYUWnxu1rJZfppK8aHNm5EEkWsaGnhkJYpcrnJ2bA65LJNv8RFwO/H7nYiCQMBmZyWTRpBENEVFp6uZhYrVMgZRpKIotDrsTKdSBK0WyopCKF+gXFVIRTO1mJtKFZPZSFWu6Qbr/VaqlSrxdI5osUBZUdnf0kyyXOZiLE6pWGbTlqbahmjU0e310ulyM5VMMB2OEO5oo9tVxzPzJ9k83MGxY1Po9TrOrEcpZItkk3mKuSKiKKKpGgaTnrGRZeq9ZvzNXjr9PlocDtZyOZ47MordYWJHeyv7mhqJFotcSiRZy2bYMthOJJ+nWCizOLOOy6YnXKiyb28/n9u1m2+PjvLK+QlCoQyiKCGJAlVFY3EpidWsI9Dg4u19/WhoyKqCTpCoqlWcBiuf2bWL0bOLZPNybSdSNSjV8jTxm6kqGjqHAVlWyeZldJKAXidiMumxOixkkzmiyRJGvUilqtHa4gIgGc/jNdeOVCuKwtDmRpx2CzsCdWzyNXB4bZbb2ltpsTv41ewsT6sXWVtO4HOZsLmszM7GGL6mnWsamzg0PUsyksGok7gci9LhciIrKnqTnoszy3wPgTqrlSdm59gRDPLajnb2tzTxhZNHmUkk+MaNEv3unQhIlKpRJhJx9rW28mS5zNjYHA6rHrvLwvRimJ9bp/nA0BB60UqH04+sKjgMRnSixL2d15MsV5hPZ3jPvp10Od2cDNceLhPJJPcMbeCwa5kWh5N7ujoxSQZEwcqXRw7x1OkxzNbaJLOYK9UMciWZRoeD1UyGVr+HW/t6uByPce7CLO56JwcXlxjyBhny1iMKVqBm1DFJGwiYo7y4fJBr6hcxSl202PyUFRmXsRdJsLNePMkzr3s/sVKY3a3r5OUKn9n2Hh4fuUihtI5eErA6zITWMrR3+fnxfa+j3T6ITvSjacr/RPRRtAxeU81sd0fbfahahY9uKiNiufq7RrGPZPllOhw1vv0m7w2sFc5wNHSRBpudT2+9FUXLIQk2PEYPRWWRvz1/jPs37bqyXvM02KxIDQ3c2trKQiaO02ikqlaYS6UJXoFpHFle5l19U5yPRigpVWaSCdrrfYSyWcYSFxj0DPDq6inGohHe3reZenMD1zUqjCcu8c3z51nOpLEbjHh9Vu7t6ebG5k68xm40yjgMFgrVEqfXl2my2TkXiRCwWDjQ1oq/v4+bm4e5lJzhfDTGuzduZCmbo9ftZMBfx+mVFaKrCZw+OzsD9Xz34hjvGxwkUynhNzUgSS7ChRNogMtgJVUu8b7nniaVyLK5o5mJRJxX12onikZJ5ImZWXa0t/LUygiJcIq6gIO6gJuleJJIPs/7B7fgMtoY9Hl5/OUR3nX7LlZzGR6bPc9b+no5EVrHYNKjKirfuTCKXpS4raMDWVXxms3sbQwiCgI/vnQZURC4vaOdx6ZnyV9xoF+MR9nX1MGZSASdKGI3GJBVlR6Pl3n7Kr8+MsLH79rHo8UiPR4vkiBwIbLO9S0tDPubsOs38/UL38FjMmGURF5aWmZsagl/o4dsMofNZWVnUzMPLRyjWFZYDyXYt7ONSHseZ6ZAuVhhuKcNQRAoVKvIisJSNsd6IU+rPYNZ8iBgxG92cG5tDc+V/cYbdLE2uw7ABp+f82WZmfUoY+dXeF6+zOaP++jzeCgoC/xi5hJNNiuSYMOi28hNzWUOLiyxPVBPulTixNIyQWsvW/w+AlcwrBbdAkNeLyrw8uo006kUiqaxkEr9NtqV/9T6LRydnwa6BUFoB1aBN1HTZP7z+l8ix/+9i/5ONJq/oSukSiVaXU6OhcLMpVNky2UEQaDH4yEvyyzmam95qqax0evnuYVFzJLEaCzEvsZBtgXqcRsNpMolVG2FLocHVSsQsLi4JlBBEgSsOhOiYEHV0vzF3r18+MlnMEgidqORZKmI22WiVJL55PZtpCplPvvsSxRyJarlKsuZNLKiMJ9K8cVrr8Ug6fjkcABFU1nNp7gQjdLlcuE3m7m2IciPLtXIBO8bGsSk09HpdNFqrx0htTkKzKUz+K1WXhmb4nO33sDu+k04DZNoaDx8+TKxVIViuUZkWcwmqbekabfvwht8lgPt7RxcXOLrvzxEIl2hzmNkbHyBybkUyCr1AQtKVWWz38c3LoxQZ7bQ6fbQ6rDXQoxTKe7s6uKhkQs0up101NWTKJf4xfERJmdqWEW/20gyc+VouxDCutVEeCWOwaRHL0kk4hlS0SyKomK36Pjsrp04DVZeSi3zytQsRpMep8WM32zGYzKRKJUx6iSUqoooiuzb0s9f7rkR6cpr3Xuff5yVZBpBEIgUS2yt89Ph8PLY3BTT0RjuOic6vUTQbmd/SwuhfIFjqyu4TQbq3A4GvX6qapX7h4d5/bd+iqJqxMJJXk1kkfQSBpOBQq6EIAiYLLUmAyAULXDd9n68Zgu7gwGsej1Hz02wFs7x4rlLbPC6ObqyhlEnMeCvQyeKBGw2Ti0uY7MaSCRL1PtNXJpeYXGgptM8PjmHTqpNaRWNKw2hhMWk433XbsdttCEJImbJgKppmHQGDKKOPre/NkEsVNE79EhuI4qiIkdLUKhSAagoCC4jmUIVUQCbRaJSrlKKpBEFoRaJZBBwWCVWV9NYTDqqiopBqjUgNr0Bt8NKIp2jrKh8f/w8b+rp46dTl3l5ZRWzTseBG/vZWB9gMhbjnYMbeNs3HqEqKzgNBt6zbQsPHjtNu9PJ2alFflW6zI09XTUCVFFmKhShrb8Hh8FIwGKm2W7n5ZUVLHoDzQ4nM6kk9ZbLuAwtKJrKzc3b6HbNYtHpOOPx0u/10O+puY7Nko5Wux8BHU59HYlymEyljKIpgEi7w8F7N9zEA6PPsphNc1NLW410pak4DBY+tvnNTKdPEi7kyMoFoqVZljNpcukCTp+dTDJPuaJQLspIeolnDl1gy9ZOZlbWqbPaWE6kqFYVIisJpjwOVE2l2daDiBVVK6JpZZbyF4gW82yrr6dQTaNoo6QreXwmH5JgR1bX8Zn6qaoxGq27+cLOdlbz02iUqcpVSnmZ+mYb4xMx6r0m6v0u2ux9V/Mu/1fYSElwXI06glrY+lphnBbbtVSURQxSK2V1gm+cP89b+/tot3uJFEeoM7eiaRoBSzt6scYw1zSZY6HLLGVz5OQqzyxdps1e08BHinl63S7a7G7W8mleXVvhhkaJoxenufeazZh1Eg6DkXy1hEWn43Iiyfx6rGboqlS5/7kXePR1HvY3N7G/uQmzZMAodaAT1phOJfjwpo0sZNLsCrbybd851vJ5LiXCJErzqMCvpmfwmMwM1/upqirdbhd+s4VBSU+LrY+xxHkem53DotMxlUpRkKscXEhza3sr49EIn3ntfi7Gk3zn/AV6vD7cRjP1ZhcaCsXqGBcTYbb4mljMRZBVlUw6j6ZpbKmr40eXLpMsFWvayqrCDd0dbPJ5OeKxodNLiDoJDUiEU5QKFb7eeBq73sCDTx9hYHMrr2lrw2uqnVh88LGn0Bl0+HxO5qfX0Jo11nJZmq/QkTqdDjKVCs12O3+y4xoenp4mUiyxsh4n3lmix+0mWiwwk47iMhjpcHsw6yQKchW9JNHa20AqnuX6xmZuaGzmQy88j0mn48ObN3Nryz1X75N9TU0cD4eZTuXY6PfT7nRxS2sLP5ucYsBX0782tXrIpgpIV/YLv8VCo91Bp8tF0GrhuaVlbmttQS9J9LidrOXz5KslTFKVGhRAY4O/jhaHg5Vs7RQlGUkTXU3w9HQYq8PM9Zt6UVWVyUthDi4usbcxyBdOHUYUBO7p7EWljKhBVi7wseFh8lWZ0+F1Ll1e5FmjHqmrqyaJE4wMuLdScYaoKDKpSo5wocB4vDZ8+K9e/9mNpqZpVUEQfh84SC3e6AdXsOD/IXL837vu70SjqWoa59YjhBIpNtXV0e9x4TQaWMrm0DSNTqcDoyQRLuRrU0m5yFw6Ra/Hw2QyVQsfx8C9nQOsF1O8sLRIt9vFROJlsnKFm5q76HHVM5Va59DqLOlKhdvbeuhzNfK1229mNBZjNp2h1elk2eXm+Mgkf3nqFO8dGmRLZwvn55bZtLGJD27cSKJU4r/NzvN3F8f45PBOlnIJ2uw17NdSNsfW+no2+5o4tDqLUSfVdKGqynw6TahQIFYsYdZJhPMFpuIxZi4tU9fsRRJE9GIz9ZYQL6/M4TFb6OlwMbeU5oWFRdrsdmYzSwy4e/nkjh1858IFZmbXyBaqSJJAMltzkpOuoPOaKJQUdvQ0YtPXbpEDLc2YdDrG4zGG6wJsrw8yk0pwS083M8kk2XKZX05NMzefQFE0dDqB1aUsBrsBu0VHfcCBRa/nmoEOjpybJGo2sLpUY8kWywpep5Gt/laycpFQvoYeq5RlrE4HM8kaw7fBZsWi0/FibhyruZYwoGoaoFKsVvBZrKiahqppZCsVnAYTk8koR5aXqcpV3A4rdVYbkXyOqqYxFo3gMpo4srJKMl9gOZdhb7AOvajjvpu28czR84TX0titBuqavRSyRSplGYfbiqSXSMdkFEVj87YWjpybpJCv8MOqSmOLB7PNRGE5QylfmxpdWlwjUO/Ga7Yw4HXz84lJnFYzy0WZbLGKz2lgcSnFJx87SHt74CrJyGzUEU+XEQXIF6sUywr9Hg8mqWaW0QBVUzBLxlpzpLfwvtft5snDI3iDLnobA7x66hJz+SpUVUweE6VIEa1QpQRYTBKFUpV8EaxmHXa7AVkpUZZrmXU2c63J1OtEFE29uuaMko47B/uZz2R4eWqW+7q7ORMKYzMYuLS4yj3DQwgI9Hi9fOXkaXZd20WxVOHE2iqtThf7N/RgkiQ2d7UwEaqZh3Zv6GIqEWd7QyM5ucJSJs3ziwpyc43RvsXvIyPLtWmIqnApeYF4sYjPHGfAs4tWexCv6QK7gy0ELUOs5UcpKTITyRCbffVMpi7S6xpkb0PtxeTE+hn63D7WCwsMer0UqjIPXhzj89fcjaZVWCssMxo/ysV4lG8cOcmBwV52BwO4TWbedctuiorCi4UyhXyZfKZAoaSgqhoz06s4PDaSxSJtPg939vfygxeOk69UKCkVVK2AIIoImEEwk5cr9LqC6EUdk6k1VE1l2HcLReUimqagaAn0Yj+SdIXsJfhoswcpVMdQqiofePMeBn1e/uAbT6CTanplRcugJ4isrl+NGPpN1YLFlRqfHJDVVQTMNZe3JYlebALAIHZxTbAeEXAb60hVoshKnIClHUmocawfnvkpt7b04TKZuLO9E6vOWFvDhVzNdJPNMej1Ei5kmUoleWvPTorVHPWNXg4vLyEIAs1OB98bH0UUqJHOKlVS0Sxvu2E7ObmKVWeky+nlUjLCV84f5RPDbjxGO9vqGvCZHIiCyC+mL6EXRXwmE58/ehSb0cjre3txGU3UW63c1NyBXW++koMb5XJijTpLhJeWV4nkc9zQ0ozbZGAymcJtMmGWdHxw8ybubHsd1a44LywfJVku024fJF9dISPHeWz2Mq/r3MBCNkpWrjBcV19b95kirywtYdXrubapkUK1Sr3FQkVROLMe5eZN/bw6v0BsNUE2mSOTqRlbfvr8Sa7fOUCwzc/3br6dJusOVC3DjkCEHYE6vj86hlmnZ2igjY11fkYmFvjVQoT2ziAf3rKZw6shfjE5xds29JMsFtle76ejwc9r2nv54eULqJqGXsygotFgu7JXomGUJIqFMsNdLRwLrfKm7s3IlSr3b95KnbkBVavtYaJgZCWX5f0D2/ibkWPsa2qm392AUaqjw+nDqjPx0VeepZgvo8gKpWKZpyaneG1fD9mKTLxUot5qJlks4jQaSZfLKKpGqJjDIOrQqCAgYRB1DNfX8d9++ix1AQf1Qc/VTExBqGBzWQjarPzFjTfw0eRTnA+HWEylMOpq8XnrhQyNFjMaCq22nYiCkYPLv+Tk6DRGs4HF2TBjTieyuohR6kEn+tCJPgxijPsPfZd6q5X9zY2cmFv4z2xTfjv1W8jR1DTtaeDpf/XZf4gc//fqd6LRlASBD2/ayIn6MBt9PjRN41J8hUZbTQdzZHWN1j4H4XyBp2ZmMEgS1zY0MOQLMJWMopckKmoap6GfWOkkoUKBfc3N+E1OXg3NYxB16ESJZLnEk3PzRAt52h0OrHo9W/ybGPIm0AkO1gpLHA0tIwoC4VyWi/EETTY7kzYT56aX6Np7MwVrmZt6u/n5y6f44NBGOh11nIsuszvQwRt7JDocAfSin5IyyZt7a6i3pWwWu0HPSCTC5XCEDp+XybV1JEnk927by2Oj4/z5y0cwHdCRLpcw63Q1ckWspn28cGGWWHcnWbmCXf8qG70BbmpN8fRzF5FllcZ6C5WKSr5YpXe4HovNxHooTZ/Xx2q+gNdkossZoKzInFm/zNG1MPdvHqasKAQsZs6GQ4RzWc6emiOfqRBssFIsK5R1IqJYy2C0e2zYDAY6XW6mW31USjI66TcCEwVPfU2LqhMkCrJMMVfCZDWRrVTocntqYdp+P6fW17luTx+ZcpnlTJqR2BKRQoGqqjHo81Df3MRSLsed7V2Mxdf5+7FxIpksdpuZqqqiE2sO/59dHKeQLbKls4WT47NYnRa+NzpGo9VOl7OJz+7Yw/7mRj7y0ONEEkXMtjxWp5lkLMvHbr+O1VyB1cYsK5k0NoOR6fFaHEgyWyF8dAm9Q8/QQIC37dzCM3PzbOtqpdfjZoPXw6lwhDf397GUzTFydo5qVSVbqGI160jE81SKS2RytWm0poEsq1fdxU6ThCBQy59TVTQ09KKEAOhFCVHQ1SalLgtul40mhx2n1449WSabqHHbsemhrECqjBCwUJFVXHYDep1ApSSzZbiJtYWaEF6vk1DLGv6gC7fRWAv/vmJc63E5mUimWFuI8srKMrsaG1A1jQGfl/ORCE12B9c1NpCrVHj/0BAnwyGKisJUIsl8KslGn5ebt7Yyl07xy+lZgjYbAauVaLFIncXCeiSJKAjsDjbxxNwMq/k8y5ksZ2YWGR2M0+d24TIaOR4OcykR4w1dd2OULlJvbqOsLPIPExd5V/9GTJIeVcvT6QggYGI5F6fHFSSUz3F4dZV3b9iEomm8pvUAzfaTqFqWpxdHGY8nWcqk+cKu6zncvsod7e0MuBvocXl5z1NPYTYZmJ6KYLfoGdjYzqmT03QPNLAwFaauuTbd2eDzka5UeOZD72I0tn7lhTDIXOYQHY5axnG3s+OqCaikLDHs66uFpJucpCrncV/hk2uagqyuXG0E1woxPnzjbu7t3EBWLvCt++/m/r97Eu3iCi9sG+PmFitoCsu5Vwla+q4aHmrxQ/8k7hKw8Ffnfs4nttxGTp7HZRymqkb51fyLKKrGkLcZo9RBvbmDirqCgBFRqE3arPoaAneL9+Z/EWvU64K5zCHWCwUarQ7ORkPMpjMYpR4EFhmsq+N8OMxSJo0kiBh1OrpdTo6trdHf0sDZS3M8NnKROzcPciG+zFQyiUGSOL66yrGGs/S7A6wXczw8Pcktra0Uqwqf2nozR0NjtSPahgZ2BxtxGgx8c2QEoySSrcjsaQjSbLfz2vZeLsTXSJVKbKyrxQAmSxVOzi+hKCovX5zir+84gCDo0QsBBr31NNuu5ZdzP8cs6bDq9Sxks0wkw/zZ8eP80bZt5CoV4qEUyXgOu8vKP9x2J+969kkG/HW8obuXR2emMel0PH7qArl0Ab1BR0tbPZfHFrFb9NS3ePny3ptwGweufr+i4OPGpm7KqkymIjOTSvPm3h7++swZbC4L920aZGudn2ixSLfLyQavm//+0lGGu1rY4g/w+MwcJqmmYX/k7Ch/sv9aTq9H0YsiZ0Nh6qxWNvi8fH7fdZxeX+ep2fmrCGer3nz1XhMFAyVlnE6ni4+89DSyqvDW3n5s+k0AzKbPcC4Sod/rZdSwRDpZYCVSYGU1Q6vXzU0tLcxlsqxk81gMesw6PclyCbtBj8tUM6QCaCiYJAP3dQ7y9j/dg15s5keTD/P9kQts6WtjwOflbX1DRIsZ2u11ZFJFirk1cvVO7hkeYjWbQ9W4kmfxqu4AACAASURBVBE7fPV+3NcwjCdwlGqlyq6BLiZiUX5w+Ri/t8GHTqy9NKlazdh0fXMjXpMZnf6/eHsk/F/W+f+v9e2/+9qf3vDGa+hyuvjx5QkeOn6OmZV1xpZDGG0mxpfXcDls7A4G2dvUxJHVNWLFIqKo4TAYaLN7sOptqFoOkyRxORllg8dH0NKJTV9hKh0hXc5Tb7HhM5t4S18/Q55+JlOr3PPQ91hQclzXGMBr7GTQ08kmv5FYucSL8/NMhqMkwikS8RxdbR4arHY6nHZ297axns8TLWXRCSJzmTir+SzhQpqDS2Ocj8Z4em6WJrudcKHIwxMTXBid4x27tzKXShFwOri2tYXRaIR8tUpXvR+n0ciJUI2qUFZV5lb+afTf2x5EvhJSf0vL3eSrS2zoCXJycgFREIiuF7DY9ISjRSLRPG1tbl6/sZ9woYBFp6Oslul21pNXCvzszAUm8pkrbHUTvz4zTi5TpFSU2bithdBamkJJQW+UMOgENAR0gkZRVYjLZbK5InVuB3dvG8TksbC8EqOrO8idnT0YJD3noyHmEsmaHkmAAZ+PUL5AtFjkxYUF/u7Aa3jnhmEeOHyEzS0NxEtl2p1Oul1ulnNZppIpHp2e5OmJGSKRJP3NDWzw+cnLMrOROHq9junxVeRShV19HUyurmNzWdBJEnd2dlOsltCLEkadwEgxSTqRxWTWE1pJYrUauH2oF5u+tkE2OZzEigUaGjxokoZOU8hWVKrFKn/4ur0cXl7mDX29hPIFfv7Kaf7+5RFWMkkmChlGZpZJJArkiwqSKGA16xje1olgkDAaa/FNlStaynKxit9roqHJw3UdrbTYPWhazQwoCgLJSg5REHlw/DSnJhbY1tfOYjjGbCTGxESE7m4/4VgeVVaRDBKiQULTiSBS02PKKqmsjM9jRhRFPvva/Xz2pn2MFJOUyhV62oJ0ulxsr+/jO2MvEy8W6HZ76Pd42LuhA0kQmEimWEynWcvnaHe5eEtvL/FSEZ0kcT4aJSNXeGVxkbKioKgaz4xOMJ5LoSAwHo1wR2c7j05N8ZHNmyhUZf541048ZhMj0Qhv7hnCrIeXFpe4MLpEb0eQDR4Pj0xPczkWY1dDAz3uDnpcXShahmQ5wf6mLuyGJmQ1x+dPvchPLo+yI2hGJ4qcCi+TrlQY8Hrxm6wcXFpgV7Abj1FkrRBD0zT63B4+OPQutv3t/8vmjmbe0fsaXlkd4WMHn2f03ApOlwmbRY+7zsGWxgauH+okr1TRWfWszUXIl8uMza3SHPAxnUrygcF34dC7EAUTHlPNRPKbOCNFjfPg+GPUmc202Tdy52PfwWEW2eq/8WoDJwgikugCFEDl+eWTfP4XB2lv8TLkbSBWyvLs6BTFcpWIQeYd/XcjiS6MUpYtX/9zbuq34jN18OuFR8hX/4l1LgpmrmvYxjNLL7DBM4womFG1Ahs8A/jMZXym7Vf3EUVNohMDVx9wPa4NGCQ7GlVEwYCmyawXT5OpLGHWGzkXDdPr9pCtVHh3/z2IgqH2PwhrHFpYosXloslu4zPbr8dr1nNsLUSd1UpKk3nb8CY+PHQNBklkJp3gp5cvM3N5mfamOlocNux6I4/NzlBWFe5ob+fB8dM8MjVJqSwzuRDisclJfn36Il3N9ZxbC9HscnJ7aycZucyJ8Ao7Ay38w/gogz4fbQ4H8+kMXruNTcEAoUqBnQ0NNNoU9GI9D0+/zJdOP05RUfnpxXEOr63ykU2beHJ+gc9ccw0Pjo1hN+r5xN493H/DdXz9+UO8fdsWNtV5mc9k+ZtDR4lUirytv5+LuRTFkkylJFMfcGNxWwitpJg6FeKoGubunno2/tWf8MlHf8Knb7mZSGmZL546Tk6uImsqzTYbzy0s8Olrd9Fks5GTK5h1ep6dX2Axk2FDYwBV1Rjwenlmfo7hei9H1lbZ2drM3sYmRmNR2h0OOlxOPrXtRv76zKtYDQZ2B4OcDIV46vI0X7jhOn69MMPOQAdVLcRUegSP0cdaPsbLS8s4jCZeWVnm9rYaprSorPCFFw/hd9qZXYugKSrpXIVSWcHuMzEcDJAolfGYjEiCSMBiBgRm0xksOgm3yYhBkhAFjaqmYtO3Y5AaEASJn00dYnx+BbPNxPuGhljIJFnN52iyOdnd38yRlVVGL4ZJKAXq3A7MOh2NNitOQwuapvDA2EN84Bc/IxlJM9jTwt8f+DRv6e+jpKQIWKSrE/+SssgDh1+lO1iHXhI5urDMymPj/2URlH/x1S/9qf+6tv/PCMrwU5P/F0H5f1JVVWPsSsjv1vo6Pr5tGwGzB0kQUTUVURDJVYuUFZlOh40pf5K7OjYwn40SLxZ5NbRIsTpHh9PJ9rpO7uroxGN0MBIbYS2Xo6RUsRsMV9Fo64U80eIYkUKBN+weZrjOxz9OnefRyYf56+uvp9fVxme29fL/bKsyEr3AeCJOm91BRVX50AvP4jab6fV42RMMoBNFvnzmDH6LlY1+H0vZHGfDYcanV/AGXaho+MwmhgNB8rkif/vYK2iaxuDmNiYTIulSidt6ukiWagYngOlUGrNOz7ZtXczOhTCaDRxfXeXu7m68plpCc1lR6HA6aGt1MzOXAIErDmcBv8uI0WRgLJ6gzW6nqmk8Oj3DjrpWhv0B7h3eyFImw98/fwyDuXaEW5UVfH4b6XiN7W4163Db9TVc5HqWcqHCQ+95LQGzmzORRQrVKrFikaG+PiL5PBdG5nm/9BQf27aVeKmE3qS/4kbXSDSUmUrEcRiMZLIFREHg/kNPs7O/E7vBgKrBbDrNsVCIh4+P0NPRQI/Xy8TsKgPdzXS6XJh0Es0OJ3pJYjWTweEyUylWGI9G2TvUw4HWZm5o7OZ7l86yOxjk48+9iN9lx6o34PZYWVhM4bDpufe6rVj1egpylZlUmjMLS4iiyJs3DXLJZuOYPEODoqGoGg+fGSVQ7+ZCNE7AasFkNWIvyuQzRXwNbnR66YpswECxrGA26bi+pQW9KPKP50Zxu80srdQaEb1JIpOTEdZSLOWyhAvJK87aak3nVSriMpb4xfmLhEMZku1FrE4Lp47O1sT3uRIDfT7GxyIopSrIKph0yIkyslUPqobXZ0JTNRRF4aM/eJJ6v5W+nmY+uG8n33v19BX6SoQtdT5Ozy0yFosjqyqlapWHxy/z5qEBThSLFCoyB0cnuByN8ftbasQevSQyk0yxtlLT3332luvRNvRxPhpnOpFgONjAF54/TKUk85FHfs2z7303WbnAyfA6vzc4SL5a5FR4nWJV5qP3XI/LZEQnisiKQqkioxNFvnjmH/nU8K3MZuZpt9eTqxZ5YPSXlKoKb+/rx24wEC3m2ejt4fR6iN3BRiqKwkw6xr1d3dQ43ToarXU0WVv45thL/PGLn8Dld7CcSVNWlpnPZNjd0Yakk7CZjcyFU7jrnLw4OsHyUhK/z8qGvhakTpHPXbuHX83O8d/33EVFzaBoOQTBiEYVWVlDFCyIggO96EDTVD4weC+S4ODg8i/Z2dTM9vqGf6Gv/PirX+PL196PIOjJyxeYSqbpGmjmrw4e4vdXnqC93ctfveM23tl/I4Yrk6hQ4QRBy07GP/a9q9e5o+0+AFStjIB49Qj9N58D6EQ/FXWZqqr8i71WVlOolDBJvVc/kwQ7mqaSroyQqeRptG7l1l99gSfvehcLmQzL2TTb6pr/BbKyx+VlT1srsUKeH71yEVXT+PDQdjbX+Wl12BmLrDMSidDqmOJYKEy6VKbT7SHmSzORSPKhoTegamm+cYMHneji1Pp53rthIze1NDOZSPCV51/F5rLS2xJkIZbgjr5erm0IkpGLzKSSTCXTvLGrgetbWpEEAZ0oMp9OsT0QIFYs8YHNm9gZaONMZB6XMUShWuVzu3YTLRZ4fnSCYr7Ec63LfOvRw+xvauSe7k7G4wkeGD3Pjvo67t0zzFo+zWI2Q7JYyzz90W1386Uzx2h1uvD1WSnIFXY3NnJnRxdvzT9GPF3GYjaSruRZjRYpR0t8++Iv6fN4uLmthT63l7l0ilipyAM3HSBSyOM3W/nQC89xb28vd3S289LSCn6zmT0dQSpKlfcMDnIhFuG17e08Oj3DN86f5/DoJLdtHeS9A4P8fPo0HW43vzewHUVTEYVzNHpdXNuwjZdXHqekrPKXZ48gIvDc+BNkU3keeNOd/NmxYwCs5pepaguYJQOVsozbZKSp2V8Loy8sI1dVIksxjgbWODu1yNfvvo0up58T4SUkUcCh1zOdyjDk82MQdQiCDouuDp3oBmAkdpDNfi/TrUGylTJmnZ6g1caPLl1mT6CFnYEO/uy2fdw38XPOnl6hXKzwnX1/iCjUnksZeZSJRBKby4I36KLL7UFWV0mW1/jJ5Yn/wd57B7mWl3fen5N0lEMrtKTO6Xa8+c7cMHMnzwAzzADDgI1hbWxjw9rYGBtsg82a3RdYbNYE2y/B8GKSYcieYWZgcrw55+6+nbsltXI+iuec/UN3r+2y663XW2sbU+9T1VWSWiW1ulU/Pf083+/ni02S6XNV0Q2dHyzOMTncw4FIhE+dOvVTP9EU/vfxRv/m9TMx0fxvn/74hw8bZWoOgTdPTOBSVK4UNjt8u3YDA5OTyXWOJGKIos6I10elVed7Cwt8/fwFSu02LcOkx+HgkaVZ6nqbpWIWRZQY9wXINWo4FQtJrYpumITsDkzgpsgwDb3O1UKBab+ft0xMcXwzQb5RxKBEupZmizfCqMePy2JBlToromy9TshuY9ofwG1RuVosEq+UWS4WcVutKJLMQLefAz09JKo1LmezHJldopSvMjLZQ6vZwumxk9WqOCwWtFYbt6oCJnZZ4Y2jo4x5Pbxxyzj3z4xjdVi5lE6zWCgQr2jcNzzIfGG9IwR32vBHXbQaGvFUDZsq43WrjA5HuX9kiEcXF/neC6dQvTbiWhm3ReZcJsPZRAKLzYIsS9jdNg5OjXLP5Cjvv+UAGZvOyloam1XG6bET6vEhCAInChleNzLO0xvLVFotis0mWrvNfC5HNlVieCDEnu5uFgpFIh43ewZ62D/Uj0NRWCoUWEtlKWUrfO3UaeoyhJ0ubIpMudXCb1U7kz0Mwk4nXtXKcqHAXWMjvHF0jG67DVGAPpeLlVKJcqVGtdJAtMqIskjdMPnE8cO4VCuPLFzFaVOvweHBtIgY9QYffOgutgUCNHUdRRJ5fHGRUraM3ja4aaSfzaqG2+Pg5ulRHtwzzWarTjpfYnYlzotnFsgXagyOhmi3dBYX0qiKQDZfRxCg0TKIRNwINguCIJJp1ujt9nf4naqEeo0pGg67efH8Vb5y+DTfPHmWrxw+w+dfOMnXXj7L554/yepKHt00icVzyIJJ/3CAWLyIYJp4vHaSWa0j3za4Hk2JXYG2QV03MTHRKg0iEQ83b9/CzX09lJst2haR2/r6GPftZ7U8x98+d5qi3uBMPMGldIZqUePMeoLegJdGW6dU0WgYOlvDIUQE1ssVktUKxUoNq0NFtliY8ft5fn2dgN1BslJh10APa/kCXSEP00EfS6UidkXhaGKTP3nsaQyrxM7uMJVWi7ZpUGg0EAQRTW/zzOIyNaPNwd4gI+5beWr9ZXqdXtwWiZt7ehhwBfGpHqKOYVL1dfZ172WjukGvs4tCs0a93SLbyPDcxiLddhWvOkHLTPCObTu5b3yULx46wfb+IPcOTLI/0suvbtvLXQO9KF4rbRNWY2kM3cDuVNGvRXbeOzrCfYNjeC3TqFK0M/HDRBSsCILKc7Hn8Ko1bFKIlhFDFByIgoXF0hUOx+I8PHuFW/tUGvomG9VZgjYrI55O2s/V0gU+dfgox44uI2PisEoc2D7GO7fuxq2MUtcv0zZSeCwzdMyh/7RaZgLdTCOL/n/yvaaxTqGRoNd50/XbZgvPciGXAJp4La7rznnD1GgZG8S0Ta4Ws/Q5FR6eO89t/WGaRuta/G6DqH34+nTWpw5Sba9wJpVGdVmxKhbu6h8m4rTSMgxujEY4EImyr3uEC9lNbuvrZcjjZkmrcntfLzNdQQzKiIKNz5x7ioau0zZ1zqTTHN9McsNQH2+bmuTk5iYToRBRh50+l4sz6RROpWOga1Phm5cvk6nXGfZ6eGB4hKDNxlu23Ea3XaLU0vjO1avk6zWOJhJ85fgZNFHgDTPjDEe7ORLbwOGyoNpU3BaFLx4+yUauwGNHL3B6fh2bv5NWFHVaOVfMYVU6TcFsNke2plFttTCAfpeTbLtFoMfFL83McHQzwTOnFti6K8LJ5Q1u2zJEtdXm6fV1uu1WCo0mA+7O5E4WJS7lswy73ewIhdgbCfORJ1/g9TMT7AruYrm0waVcjr8+foqK0ebE6XlsDivxaoXlagVJFBnxuPnm/GV+8bPfolqtsrGR4T/fvJuPHHqRLQE3M34/b9nyGoZDAvfOjDPk9nE+m+G3d+0iXa9yOB4n6nRwsVJgqVDgjeNbiDjdGG6ZRDxHvtTkyuIm2ayG5pK4s2+A7y7MU2w2Kbda7IuE2drVC4KAiY5paqxVrvCJ049xIplk2ONmZ3eIJ85eYamh0TYN3KrKbD7DnlAPbbPJk3OzWCwSJ3/vt7DLHTpPqnYcj2WQu/p7OJmLMREIcCoR5yuXjyKIJhvljqHJLsv81bkzTPt9PLuyit9u5/DyGgG3k7lvnf6pnWh+7NN/+uHgLUMIgvAv+ko8Nvtv/pp+JhrND3/iox++97fu5PLFFY7m04TdDubyBYpNjdOpTWp6g2l/N0ulPBcyWRpGm4VigePxOB6bFa/VhlXu5En3u5wEbB19TK/TTaFZp8fpotxssMUbQJYEDifiWGWZSksj7HBikyVUSaLYrNPtcJCqaeTqdZaKRdL1EqVWDbsiM+Tq48buG7khbKPX6bhurlgqFqk0m0wHQ+imSVrT6HW5aBpGR8OiWlkuFzu6RlXB73fjUVUsssziUoKllU3WCgUWMjnCPg+vGRglVavwyNISnz1xiq//6CSxjQKC0SJX0dja5+KW6DY+fuJFzi1tkC9UaNZbiKLAzI5e7t89w8290WuxnXXmVxNUqjXmNlOcL+c7sF9ZIr6WoVauEVsvsBJPcnotzuV6iZ88dxGHTaZ3NMxrJsexqRaSxRKNto4uG/yXrz/JSqXAZqvGhXSaSrmGy2tnNBQEAc6nUtgUhYah0zI662GnReXk+UViySqFYoNaScPf7eZqvoBNUSg2WmwLBJjPF4iVS+TqNXp8Xm7v6yVgs6PKMkvFIivlMl7VSt5skd4s0d3bxWiXH5diQTdNLiVTCKJArd0mV6oyHenGplhIZAq8blsHKQQdPWS8qlGo15EtMluCAVaKRXSzw5u8pbeXeFVjs1ACs+Ol1HWDQNiDIAgEw248fie5dLmTSV7XMQyDolahJOrs6A7z8onLlLU20YiLQNBFJqPRbrU7UPW2QbHSolBuUi02aWYbtItNqLYRHAparU130IHVoZJLl+np8aDaLKRSZQzdRLLJmFq7o9dURJxuFY9TwQTauonXY0V1qLxhdBTDNOl3uxj1+ojYR1mvzPO945fw+uy4vA6K2TIP3rCdpXyeVKlCKpmn1dS5bXKMs6k0M0E/PU4HPquNW0cH+eD+W5j2+zi6maDUbOFWLRydX2Y4FKAhmtzS10/AZiNbr9Ftt/GV42f4tYM3XvuAdbGpadTabUa8buZzed4yMU622eSNY2MgGGTqS+wNbUOVHFhlk5bRpkv1U29rfHP+MONeP1ZZJmhzUtebDLqiaO0qCa3CrlCYi7kU35p/hdcNTdFt38f3F1/C6XbQ43RgV0T8apCV8gZPry9TbDR5ZW4Ri6rQqrewqAqKRebnts1wT98WvGoP2cYcxdYaLqUPwyxjmGUk0UOvU8VtmaFlbGDSQhHDmKbBkLuXB0fu4MPf/yrfvniRxzaW+MHlK3zk4DuvTx8T2gJbQn4Mr8zb9u/kzukxPrjn5/FYxq6l+4io0hAmTdL1Myhi69raHdpGBlGw09TXOJ9bIWwPIwoqDX3hul6t0lpitZKhS+X6bV+89DRvGJ6h13HgepOpm2VKzTkUUcUigiAY1No1jiTi7OoOYZdlsvUaFkli0D35j87tCd80T68d4+raJmPhIHbZZK1SIuJwErI7eHptje2BbsZ9XUx3zWCTm2SbNd42vh1R0NFNnUeXz/Dj5WUuZ7NM+Lu4nM2TrFZ4YGSYTL3GUrHEX9z6OvpddnqdXWztCnMyHcNnVRGAHaEQYz4vlVaLPzt2nBYCDSPPYilH2OHg/zl/DkWWERFIlsrcNjSAR7XQZbPislhxOewEbFbyjSaXE0lUu4VkokhbN3nPHXs5k95EEGA+X+Dl9Q3+09QUz6yu0ONy0zJ0au02sUqVEZ8Xl2plzOvlscUlapUyB3dNcOfEKLIo8OJGjO1BP15VJd9ocnxzk9t7R1iv5Oh1OQnabJSaTY4nk1RFA7/NSrdD5CuXL/HVx4+gN1r80V238Nz8ErF4iUqhyshAiH6Xiy+8coK5tQR6S+eO/TOkiiXychNNb3N7Xz83dt+LKNgYcg9jkQr8xbnTuCwWnl9b43I2z4FohI1KmdPJJMVqDbfdzlwuS7XZxNB1mo0Wbd1EkkRQIaU3MU2TW3qiNAwdt8VCj9N7nR7S1Nu8klih2GhyW28PQbudWrtNot0gV68RcTqJOu0cTWzitMByqUhGMrB5rOzrC6ObSVbKs9z6518AXx2nxeRUKsWVdJo3jG/hiz88StUJH9q3n4VinkvZDHO5HKlanaVYitOrccZ6uxn2+jj05Vd+6hvNf2n9ezSaP92z4f+PZVUVBjxeSlP9iILA2XSG1wwOUG21uLOvH6diw6c66bJaOZNMo0gi2VqdfT2d1Vml1eJAJEzL0Blye3l2vZMytNUfRhElCo06mXoNh6KQq9cZ8XpwyAqPLa8QcdiRRIGow8GEryO2T1QrlFtNzmezeMsq3Q4bsUqZi1KaiMNBn9NHl+rAbw1j0uI1g03efeYSaU3DKsuIgoAg+AjbbTR0nZDNytt3budQLM75xXUG/D6Cdgdtw2BVSZHK1ViOVehyW5joi2CVLAy4vGwN1Di8sY5hdLQZjVoTl9fBhUyGg5ER2oZJpahR05rIkoAkCrxzz052BrtJaVUaus5El4+TA0H2Dw3w7aeP0x3potRo4FU7mee5UpN60+gYeeptnnr+EtGwi5t3bmEulSHisHMulcI0oVap84VnD1OqtDqpOi4rLruVXKWOVq5zTDd4+eI81WINQYCusBfZImO3qfhtNgShkyNukUXabYNSo0GhVCXidPLI2XOkdk5y6soynoCL3SNRZvwdiL1NVrmU3cShKDTaOvl6HbuisGVrL90OB6VGgxl/B930maUNco0W+ZyGqojMuezcEI1yxDR5fiPGsMfNqNfDYrFEsV5HURVsVgsrpTJzG5vY3bZOLGa1wlIhz2RvhGxNI5Ut4g24CLld2Pw+FuIpBEHAYZUIeFWGxnsolbUOFL7RQhQERiZ6SR9ZAKBRb+KwSTjtCtVax0hVbzQwTTpr8LreYWX22DupQILARqzIhNfOxHSEQrrElfkM7bYJ14Dwuix0ppqSgN0qYVUlZrYPUqzWeOv2rZimyR8+/yL3bhlje8BPudlxn+4K9hGJurHYLNS0BopFZsDt4sDoIEv5PGlJ5ODIEKvFAnsiEV5cjzEd8BOwWREQ+NKlsyiiyGOzc2yLRkhVq8gWGZskcc/gIDVdp9RssFgsEatovH3vLuyy3OFtGgZhh52X1tbYF+nm/pEhtgZCPLGygtZuU2+3+dy583zxzl2Igocu1cBjqZLQNmnoLQ729BKrFvne4ix39fXT7wzSMmoMukKsV0pIgsSLG3HuHx6i1m7gU2HA5UYVJTwWlUxNo8/RxTNrL/G5Zw8jCAL1aoPIUIhX3zTE6bUNDgwP8uvTnUlgur7Gx44fptxscrD3Krf09DLmPkhdv4JVmry2TpdRhCi6WUIS3Dw8/yPu6R8jMhgktZ4lsZxifLqffxja0aU6uKcvxAODe1Cljtay0DiNV+26do/OfUXBjsfiR7pmBJotPMu45/bOuSlNMO0z0NqLKKKCKHgA+NHKd5EFkV2hXtL1JL2OzuN3223YpC7+YUmCC1EQkYUAbiXAl1fPEKtU2B7qJlGtXDPgiewODvyzZ7fdonD7zBbeNDZG0ObAIimsl3M8trzML03OsFhK0uPwUWrGqOlNBAQ+f/EkvU4HN3ZH+P78Vfw2O2+dGOfI5iavHRrkuQ2ZRxaXuDHSzR/v3ceXLx/mwZFJLudiDLoDvLp/lKVSFp/VSqXZZKFY4EwqQ61S50Q8hnJtyve9q1e5pb8f3TBxKhbum5lEN03m8kWgw5ONOh38p4ltfOXKOSYGIiwk0gSCTkK9fr4zd5VzS+vcvXUCrdUivpTkzywnaBkGzx2+SKTfz3Ckox3XDYNCvc4ji4vcEImwNxrhWDzB9GgXy6USdw/045Q7xqtxrxdRAFWU8apWXkkkSFQ65/WUv4s/v/VOcvUqP1i4wpmNGH2DfiRJ5H3feoxaQyef0kA3OXJhgTe/cQtapY5W7aAA51Npurq9fPulU3z7l3+Op9fX2BHokAsEFHocu1kvPsuAx9sJJmk2+cHVRWLlEj0uNxfPrXDSprInGmUtmyfS6ye5Weqg2oBXT4/zwsoKb52e5nIuR8Bm5VIux6SvE29aampU2nU+c+QEPreD2/t6scsyVknmrZOTfPXSJXTTZD5f5PWjwzyyuNRJo7LbecPYGH9z6TILuSyqLHPzvgmsksTnz5/HIkrcNjDAYqFEo2nw7MPnODo+zptGp/FahjDMR3EqCi9dnMfQDfRrEck/1fUfyAz0M9FoioLAUj7Hz02Mk9RqZGp1ht1dqJKCQ7EiCSICAmG7g4au41VVio0mutFhA/ZYnOZKLwAAIABJREFULLgtFkJ2B6qocGozyXwixQtLy3zs9lsZ9QSZzWd5bmODncEgPtVKrl5nPpfFrsj83NgEmXqVE8kECU3jdcMjLBTyiAg4LApdVivVZotYVWOtUuHvlpZZKeR57cgw3XYHU13dHP7199DUNV6ML3ApmyepaUx1ebFI0rW1u9wRfjcbvHz0Cq86uA2/rcO6VGQRWTao1tvU2y3qepNT6QQHI4NsD4QYf+QiuiIiig4Uq0KiqgHgVlXqtSaGYVKotbnj4CS7Qt1YJYVSs8lquYwkCOSSRX6wcpJGszOBVUSRht6mdzRMsNbk7NkYdqtMdMBPPlmkpjV4+dQsU5MD/N8vHOHi5TQDETuqzUI+X0NRRBpNHdMwCTmcbNke4LGXTpPdLKAbJs2W0WE5CkVkVSZZa5Hy2JAtMi67gW6YeDxWqvUmkixxYXOT5dUC8cRxHNZOlF9zUEcWRYqNBvHKOhGHAwOT1WKBlmHgsXZWdPOrm9w8Ncozq6vk63Vy6RL5UhNVkWi1DBbnY6TSBVqNFi9dnOeYXcWiKpRyZRSLTKvZRnfZaPs7wPF2S0eSRJaLZRRRxK2qrBYLLMwl8blVBFHA5rDyoTsO8p5vPIo/5EK1Woi63B3A8bYJqq0mVlnGJiu47AoWVaGuNXHaFawOlWarg9ERxWuHTNOApo5l1IPTLlMoNfG6LQS7bOSSRRSLjGpVcDsUylobUxZotw2C/S7ShQaY5nU6wMLsBh9706s5kUwTL5f51F13cCSR4M+OH2fcH+CWKHgsw6g2C81aE6vDimpXEQXYHQryyuwiql3l0MoqelvHKitUmg1eNdjPD64uErTbeWhslO/MX2V1fpOxUBCfzcqB0cHrGuMvP3+UP7z3NvaFu3luPdZhfbqcfP/MRd64cwZRENjf28uXzp3njePjfG/+FUa8Pg7HE5xaXec7b3yIVH2Bo5trPDj8c2jt8/itbhRR5XdfehyrIrM/EmbEPYQoWPnA4e/xW9t3sVYq8525q7x/zx6G3VuIVZfI1E+wPRDFKicZ9nSIFoZZ5hunz3PPnhmWC3kypQqFdInHFjZxum2876G9PL5yht2hKD9c7PAFD0Z7+M2nnmI+X+C/7g0iiV4Ms4lp1qm1U3z2wuMciISxyjJeVeVSLsEHDh7gD374E2RZ4j27dtE2MwimBZMmUcc2BFSEf3CEPxe7yoPDHaft/3KYdy53X9dHjntuv76+bpspnlyf5dboCA556rqurWUYfObMaT55m4uZrpt5If4It0Vfxy9s6UQeG2YD3cwhCyHaZhpZEJkrnuRKLoPWavOx/XdjEUcxaVJsznO1sIlVGqBt5JEE9z/Snf7K1DR/fOgVZrp6KTSrlJpV/q+jR/nS3feRqpX4weIiD42OkdSqbFarnEsl+fmJcXqdLlI1jU/eeifFZo3Pnz9PtqZxMZ3m/pGOdvuBoUmStQJ2WUYQBM5m0rgsKiPuIbrtW3lq7Sk2KhW+OzdHKpHj/t1beerSHGeVJBGHHROTfpeTN4/u4wNHHmOqq4vvzM4iCQJb/AG0VpOdoRAfPPQCADdGIiiixE03RgH47ItHUSwyC7ksi0sJ9LbB7KVV/FEfO3eNMBkIsjPo55MvH8VnszKbSdNu6TgtKg+OjlBsNhn1drSK8WqFlzbiTPm76Hc5qbTa/MbzT/Dg2AjFRpPVYoH37t6NVZb5woWzrBaL3NHfxw39fSwX8qysJTFNkx27hrBaVgG4feckT6ysorfadAVdSLLE1kiY1wwN8OJGnBF3N9+ozhKvzuNV47zv5afYKJUoljWCdgdZTSOdLJBMFNmytRdVlgh0e3BaVNKahkVViK9nCEfcLC7lMIGfXJ6nWtT4bOYo7i4n94yOkKhUeSG2ymOLi4SdLu7s66VZb9Id6Qw8rJJM09CxyzK7wmGOxePsjXY2bookcd/wIPvCAzy/sciwx832YIAnl5c52NvLI1evcu/ICJeyWeyyzCvpdXZuC3Puwia6YWCXrchiAFEQcKsWTNMkES8Ri10iGvX8H+hO/nVLEP//RvPfrERBZF80ittiub4CP5WOE7TZ6XN66LZdExan06iShF2WGfF0wOdDbjcui4Uxb4iz6Rh1vc2bxsf4q2oFm6Lwk9VVfOomEz4f7msNqU+1MeGNMOzxXscy+K12/mr5LHMbmyQqVXTT5OZomF6ni16nj2StxKNLS5SaTZptnYDdQbHRpNRscTKZupZRHmJ7IMKOQITFUpZ0rYZXtWKVZAzTwKOqKFInKebKZoqgy0ksXiTc7aS6WkSRO41NtlHuMEQj8NUrl+kb9rB+OUc8rdHV3aJ9za0siwKNZqdRbbUMNotlFFGiobcJ2GwMuD0ErA6+5u0wGVfmN1meixHqDyCKIpVClVq1wY6dvbxr726+PTtLOpbDalWo1VpUW02qxRp6rk5cEXHb24iiQCRgpSvoJuzzsJBMUy1ptHUTX8CJ3jZQbRa8fhd/ccedBKxuzmfjzOZyfOrR5xEE0HWj819nW6dWqVPIlKnVOyzOUNSL02sn6nRgk2Ti1SqH4nEcioV7BwdIFEqcObnG3XdM0Wi2iES62CiVkEWBjeUkgbAX1VLhl+8+wNF4jIXFONVSjTv2z/BLU1Oslct8+qUjuLtclHJlqsUahm7yXPYyLl/nw9zmUHnHzAxv/tsrZPNlcpsFStUWAKlcnC3jIT746NO02gahSBe5TAlVkqhrzY5hRasy1eWl3+XkvXt28zuP/IRGrYnDbcMTcHUYm6qMbpi4HArxdA3nhA+HTaJa0xmMOmi0DG7bM0XQbudSOs0Wv58fc5GNWJFytYXRMrDbJCYDPrJZjemdA7xn9y62+cOsVwq8etDGSrHErzz8d3iDbrKbBZJrGWq3XMAmb2Xv2BAvnJ3F7rbhstm4WigRsln50F23UG21sEgSq6UyO0NBvn91ke/OXaXSbHLkzBw/OnIOm0Pl4P5JDp+aZWZmiF+cnsSlWPj9J54B4PhmivVSkcOH53E5ZD7+lvu4b+skKa1GtlYjrVWpFjUOx+MMeLw8cfYy7zp4I788PcW3r14hpdV4+9QULWMTqzSBaWqIgpuPHajhUKJo7QSPrpzgpsgA796+k4hjiBPJIzT0NnZZxSL24VczfOL0Yf74hldzINyDRfz7ZLbEaoZTqsLu3h72RqM8evEK9WqDF9/1TpyKm0vZMzyxvMovT09RbbeY9O3nntFFHr88x9uLBT5+8FaGXH3oZhu3ZSd/uHsn57JP0uvwo7VjTPrCXMom+Njr7+F/HDmOKknXn98wm8Sqx+lz3nzN0NNp3HYFI//oXGwZCUyzSbWdwSY7sUrj15vMlrFJtr7OqVSaBwbvBtrMFl5mwnsnr+6f5FV9EziuoWu2+zvGIqs0jm4UMEwN3SxwJnOGHf4pVKkfn1pjxi8QvJbmIgoqbaNEpVVj0he9vrb/h6WbFSZ9e8mUfoIqbcGnLuKx2Hnkgfdjmk0a+hU+sPsgT65dYdzn54vnL1BpNjiZTGOVZYbdPlyKnR8uzSOJItVWi+2hbvZ0h7FKFnzqOG3jMv2uKn9++hgfuvEWAMqtHGczF1gplzkUixFbTTE0HOGV9TUUq8LD9/46b3r88/zR3n38wUsvMuMP8vrhYX7/yWcpZiuoNoU3TYxzIZPjVDLF7u4QN3R38+5Hf4zNaeO/7NtPxB6k1+nkTDrNN185xdbxfg729nIkFqfUbOC32Rl2uwhYbQS63DwwPMSN3SHOZXK8aqCPIXcXY54Cjy8v8dXDp1hbzRGJuGHHBNsDAWySRLFR5/NnzzHa5afUaPCtuXleNzLMzlCA4/EYf/7Uy3iDbpq1Jp976H6sUgd7177xBgzDRBZF1itldrzhbr4/P889g4PopslCoXgt995DoVHnlcQabxjeSsjh4Pmjl3j9rbv5wg8OIcsC3QE7/SNBZs+ts25L4HDbWI2nWROgmCmzfdswTzxzkZmZCLpukN7ohHm4u5xEPW66rFYausFHfvA0C8tFtm7t5sTSKg/u2spLa2t8Z26eP9q7DxGB2XyeGX8X87k8DkVBEUTevGUMt8WCTVIZ83bxwnqM7aEAbxrfwsVsni6bjXyjwdunpvjUqVNcOr+C1aowNujhcjbHlfwGw+4mwrUYa4/fRXyjM7GuV+v/G93Iv2X9xzED/Uw0mrlilR9emeukLVz7xQsMUWg00NotZFHi7xbnSGoa410+XtqIIYkC034/DkVBlWS0VoNbolv501NPU2g2uHdkhB8tLNDr7CCNtHaLJ1fWONgT5S/PnOG9u3cz7o1wKRfjmbV1bumN8itbp/kSJqosMeJxs1au4LaoqJLChDfKwLYARzZX+OTJk0RdLr59KcHSlTi/+cCtaO02J1Ob6IZJvtHgLVum2OG3czK9hseiAhItQ+cdMzOsZfOsXd1k1aTDYCzW6e5SCfcHePeOHTy6tMTJxCaqKPI7O27kNQMDHHzfF9CKTeJrWQK7OxnKi/k8pmlSqraIBO0kllO857lnWV9N0dMf5L/ddBNPri3xq/t3U2/r/PfLcdY2qxiGSVs3CEV9lMt1/viBO3hqZY18sYppQjKjsXvvMG5VJdTnJ52vI0sC4YgbURJRLDKi3Hk9elunWW9RqrbQjTJaXccid+L0REHALquokkTLMDpszpaBXZWo1VpUClW0cp1m22C410l0uJtWs8Vtg4M0dZ0TydS1ibKNi+kU6e4QG4tJXnv3VpZiKd576wHOpjOMeN0YJox2dXEsFuNHb3sbf376GCtrndizd99zM5VWi0KjgSKJ/Ne7b6Pb4eDHKyv87YsnKBWqOFxWXj21hXy9Qeva6jca9bMwH0OxKvg9KmPTPcSXOo8piAJur52I08XKXAxxTKDVaOG2KGxWKqyVK4RsNj74/Iv4gm52bxmg2+6g1+Vg+BYP73/0SX71ngN8/vGXsfc4iIbsCKJAsVphabEIkshqsYAoCLhUlS89eohfuf8Az1vnEUWRe6bHkUSBO/v66HN6OJ1O4LGo+K1uuqwuPnriZaqtFqd+8yPEtTn6nTfz2kc+xOcvHuO9O7YSdTqQFQmLLFMoa5T9TfaHu3lqbZ1pfxfPLizy27t2dfS8kshascCrRkYIOOxczWYxTZO3Tk7we7t3856nnuGPHnkaQzcQRIFCvsYPnj1FqdJCUUS0us7pVIYeh52nri5w6dwGD71qFw9t2cLXLl3i8PFZhqd6kUSB5zc22Brwc1t0B2ByInWSPqeXsH0MERO77Od9r3yLfK3GG7eM8rHjh/n4TXfxfOw4marGV1/9ekpNja/OfoMRj4dX1tdo7CpRamnU2guMeW6n2DzDV97xJv7qzFn2hkP86dMvo9pV/sdb7sOnbiXXOI+ByYf27ueHi/NUWm186kt8/bljONx2zl1c5uCxOZb/ZBxJ8JGqHUcQIGhzs1nLc6B7BxZpAPzHuPWvvkA+X6P7Xsf1804ULARtwWuXVUyzhYnBwDXjTl2fwyL2IwpO4to5uu2D113o0OFxrlfmAPjovncDkK2fZMQ9Q8vY5DtXz/HQ6DbKrbM45MnrHE+AI8mXObq5yW9vu4cxTzcWaRCAkHUCw7zMQHcUEDFNA8OsAuC27Pxnz23DrCAJHtLrWZ5Y/RF39G6h2NSI2N3EtMN4VSeVVg2PauWJlWWW15Psmxjm608d4dd/912okoUXYgvohsFH99+B1m5wPhun0moy6r6Jc9kX+IOXXuS/HzzI7+68kWfX5zm6meS1Q4NMd4Wpt9t85tZXsevopxny+lgu5FlLFHjvS98kU6qwXilx3+gov/ztHzI53MOeoX6+e/UkgaCDvz52mj2Dfdw90Mf7v/E4ozO97Bju4ycvnedd6pP87g172Nfd+d084rZx6NgscysJmo0WrWabg7vG8VpV5goFtoa6eWkjTrZeJ+ywk9I0NqurbGo1RrxuensCTAxF2REKkas3eN9Tz5LeyLFn5yjJXJEnZzcoV1vUplsU63WWMlnWF5J4uxy8aWaKu/r6eXRpicdm56gWNYrZSucctcrcuW8GVZb5tW2dgIVDiU0ArmTSPLX8BcJOJ1NdAezyNu7uXyZyv53PPXuYoE/F4bZRzGvsj4bJxPJYHSpauU4rX2XHzhHWV7KcOr1ANGhHtsiMhfycyFXwhb0MhLp45/ZtrJbKfOvZYyysl3F7LBhGZ2uWrtXodbtZyOX4u8VFJrq8zPj9NI2OTOtbZ87zqzfswqnIbIoiWrvNj1dWcVos1xOqTm8m2NkdxirJvOdHP8HhsfPWe/axWixyeSnGi2dnuW94EFnMcCASYcwTZX8kzPtTT/5rcND/j5fAvwqv/V+lfiYaTbfLxiMP/jy1dgNNb7BYzDOby1Nutnh2bYP3/eAnrG8UURWR4fFuRsJBfIqNbrsNrd1mqVgk56yxqZXwWlUObazzzOkr3LNrCrdFocfhZDafQ2s1uZzLk9Y0/uTQIbZ3h9kVCjDq9fByLEGsXGajWKTUaFBptqg0m0z4fFRaNSqtGkGrh33hAX7vBriUzTG7mqDW0Pmbpw8T7PPzkdtvIepw8Y3ZWb6zMIvHYmHA5WLUE+ZqMYEgCEz4QvzGDbv5a+UslYIG8QIOp4ooCXztDQ/Q74wwsWOE77mOcS6d5fMXTrNeLmEN2qhvakiSgF3u/NmD18C8tYaOYlW4eWaUtmGQdZbI58p8/coskihyV38v7//OE9itMqIoEIj6ECWRalGj3TZ4z5d/hNuhEB0OIQgQ6XYScjjIaBqCKNDf56Ve7WRCewIuJEXCaBtorY5W0zTBapEolJq0DRO334rHbmO1XCRs8+FQFHTTZGrHAOtXE1RrLZotA0/AjazIaPECTq8dq6pgGAb1ts6Q242mtDmTSqFKMq8eGuJ8JkvfaJi55TjbRvs5mUrxoRsOciy5SlLT8Kkq94+O8t2Fy9gVhbW1Aqoikq03yDfqHIsnWN/M8IadM9yqquwNh1ndOc6Lxy/T1e1FEUUkUWDGH2SxWCCTK3V0mX4PFjVNOpbD4bZR15pI12D2a8UCgZ4uTi6usn37CJVWi3q9yZVsDlckTHojy2+/5haGPR6+eP4CDkXhqJZEEAS+/OxRPH7ntWSfzsTUpkrUDUARSG7mWb4aR5REREFgzOth5MAebu3p4wsXLnBkcYXvHzmLKAr8xh37mfEHKTWrJLQix+MxvnTPaxAElRPJDRLVx6k0GhSbzc57pq2jlWrkFQlJllBEkU+dOkViPUN16yhXlmJ8oq3TNHR2hSPs7+un1+lgZzBIrreXeLVKr9PFSqlIvdognuhEkZaSNZAFon0uLBaR8akI5XyVE/EYf3N6hUbLoFxtc/TiIoYJ8eUUFkUiaLfzl88fIZsqE+nrQrEe4lN338Wg2w+mSa0dxy6LiIKd9+26ga/NXuTp1XXahsGhzUU+cfwEn7ztdpK1PCGrl2Kzyd7uG3j6jTcxXzzNiHuIul5CN8t4LDsZdlf4/mv/hHzjFNwND43cjSwGSWhHeXj+IrdEozw8P8tKsYjfbkM3TbbPDHFxbo12S2d6+wDJ2iIhWz9au85yKc+JZJJfm97NHxz+IR8/8AC6afDsb7yDr89epNfx96tw02whol6/LgjKNZNRlSObh9jfve868LvPefM/OS8FQaLPOUG1Fbt+m9+65/rlt43fh4AIgkyydoaIfR9tI48s+hhw+ZBEEYs00GmGr5UkeulxHPhHz2ORBgnb/vlPQsPUEAU7YNIV8fLX589zIDKEx2JHNwoEbf187sKTvHV8GyMeH7/18I8I9XYxm83wxw/dg2Ga/NmpI6yXivznHdtJaDm+cuUSV9IZfn5ynJmuNFO+ER59XT+m2SbbKHFjdy+TXX5eim1wLJmk2Ghil2VCUS9PH79IvdZ5bz936jKqzYLbohKytVCtHeC5VZZxOy24fE56Aj5ePDfH60aGkSSBg/0DPHHuMkMTUbR6k0qzye8feoY3jY3xll3beMq3zMG+fiIOO36rlZOpFF86dx5R6ARIFEsamCYWq4X+Li87QiEuZtKIQkfi5FWt7AwGMTB54uRFvEEXfW4PV9eT7Nw+QlrTWLkSo2ePG7tN5a6btvLisUt8+pHneXggwC/s2IpW6iSbOdw2Ag4V1a6ykM8hXUvd0tptKs3Oz26TZW7u7eUXtkxzIbfJeuUV9nWPcXP4IH/x+It0dXu4aWKE48urKJLIzTdMspjJsnY1gWqRWVlLEgg6GR6OsL6R5u27tpOtN7j1/l4O9vTy6NIiRxKbfGD3O5m/o8AnH34RmyrRNxCiWNZQJZnxsBe7YmHE68anWtHabeLVCl02G1a7yrFEgpt7evjJwiI+q43z8QQBtxPDNLlrcBDdMFgrlVjKZDvM0pDKqwcG+PLFS1hsFm6d2cJt0bHrk/uN6iG2+aPsGB9g0OtltVjk/P9L3/HvXv+BNJo/E67zb331rz9svzXKo0uLJLUqUYeDXpeLh6/M8vzRizQbbWyqhG6Y5DIVNuIZdo71dzAw1SqGabIv3MPjKyvIosB8Po/H4+BVQwNczOaZ8fsRBLArFgI2G16rDbfVyp19vQgCFBoNMvU6i/k8TotKIpVnNZ2jJUELgb9bmOeZtVVKbQ1ZNMnWa0QdTuqSwPzqJrfsnaRYb2CIIlZZZC5fwCKJxCtV2oaBKOoUrsUtKqLARqXM/SMjTEVDKD4buk1CURWCTgcz/jDFZpFsvUrQbiVTq+O0WGjYTNauZpna08eenijbAyP82QuPUCzUqDV03nzXbqb9XQx7PCxUS0xHw4z4vDy/uMxkMMi6XqdcqGKzW3B3OVFUGZ/XiSCYJJJVtkxFuGlokE2tiq/bg0WSWbwaJ7GRx+3pHAwIMD4U5a7RYY5eXmZxIYWhG1itcuc+soAApHIN7DYJxWFli68Tu3chm+Wmnijn02kKeQ2rKnHv7hkStSrZVBmz3SYY8rK3r4+g3YYiimRqHbfrZrXC87OL7OyNslYpcWBkiN/ZtYsHhqaQBYk+l48umwKYXMkXKDYaPPzMMVwOC0OTUUIOJ2vFIvcMDlI021RbLZ5eXSWhdaLUvH43tw4OYJNljsVi7OuJ8pPlVWro7OnvJV2tUshXkWWRQq6K2+foRNVV6sRWc6DreAIuZFniwkaCVr2FYlW4o7+Pl1bWeGB6HLfFwqlkElWWObOZQKs1yWcr7NsxRiZXwOWxU6k0UC0iik2mlqmzmamSzdYZ3xqlWtS4cWIQAeh22NFNA6fdxkahgKzIPDg9waVshm/MXubHy8tkMkUeXVzgHdsfYMK3hY8ef4ypYIg7+3qJOBwcTsxzeWMTu8tGQ2vymskxTsUSlHMVFleTaNUGG7EszWaLk5eXWc3mGQwHCdo7aU8f/e4zFGzwzaNnyKVKbGZqNDINUCUQBRqGic0iUSlplIoNMskiqVyjkyYlQE1rMre8iSoLaHWdLYMRdg/2sn20l/snxhjxd7FeqfC3Vy5joNPv8hCrrtNlDSCLLXxWiaDdSk03ePjKFT6wdy9hh4uNSolnN1YIWFWmu8Zp6Ov0OA7Q0Ne5mI/Rpeq0jCRB241AR36y1b8HSey8T8+kT3Pf4CSrlc5a9YGRYe7pH+RCJs3pZJJuvxfFrfKFe15FvlGj17ELrzpAt73F169c4FOHXiFeLHGhGMeuCEQcbgI2CyGbG4sUZlM7ysX8RZpGBb/17x2nullAEYMMuMavO8L/uaq1L1wzdliwiE5Ewcpc4VncioQkejFNA1FQaJsZnll/maVSjqmuGUBCECRMM0OhodHjGPsnj60bBUTBev1601hHEn2IgvpP7vu/3POiYONvLjxFRatz9+gA6VqZYmsTn8WL1i7iVVUu59JMDoTZEQ6zNxLhbeOv5y/PPcPhjXVmL63xzptu5E9PnuBsPMGgz8evTO/EJnmvZb0LmDRp6k1KLQ2PxcEriQ0upNOM+3ycz2apoKObJlabhTt3T6EpJr1BHw+ObaFl6GBR6HY4eO3QEGtmg+WFOA2jjTfo5ra+Po5lUxT1JvVWm0qhyut2TOO0KKwUSzgtFnYEg2i6wVwui99mw2lRWCtXuX9kCB0Rr9XG9kiYoaAfj82GacKhlVUKWo3za3GmomF+cXKSgM3OS7EYos3CfRNjpGo11lI5gj43/W4Pti47bcOgUm/wC9OTnM2k0cqd9e9No4PsGohyYnmDqbFexsPd1Iw2Z04sUa/UmNNK5JoNUlqV85dWcHod5Op1tocCPPgX32RNqhPX8nitNVpOlT39UVRJ5lWjQzy1sMTN/X2k6zU8ARf90QBaq41qs+Cx2xjsDmAiYJUl+l0uFotFTidT3NrbwxbvFAFbnpdiK+zZPtyJ63S7cFosNA2D7YEAO4PdxKsVDNMkVqnSMkxytRojXV1YJYnJri4GPW5eOzbK1UIRSRQ4tr5BKpEnW+oYehaupnnPXfuptJoYwNu3b+Wh0SmcShhJcNM2MjiVAE4lwmJpiUuZNPFKmZXvnv/pdZ1/5k8/HL595F+MN/r3gND/TDSan/3CZz6c3Oah0W6zqzvEVn83W7xhnli9SlsW2Ds9jKPLwfzVFLIkUq62mB6L0uNysjUQoN/lYr1S4kouz0I+T9jp5P6RET794hGeOnSFVxLrrNQ1/Ndc4C6Lwq9v3UnbbOO2qMiigF2RObS6TqXeYHUhRSGvUS1UyAttirU6Nb3NZrVKodliU+uYcXaHQrTdFmbjSa5eTnB5NcZcu8pLR65wZmEdi9eK1taRRYFqq8Xjy8ucz2QpN1ts8fm4rWeM1w5NMuyzIyoya5UKA24bP1yax6damOjy47eqHIhEGfH7OJOOMTwQ5l3bd7BaXubrL52kUmujGyYjQ0FCDjsLxRJLmxl+cfsMIZuNq8USd/T3E3I4aNokdIuIYRjIskSt3iQVyxFjFZEyAAAgAElEQVQKOHj9rq0MuF1cymWJLSZZWknTbOroukm72cI0TAqFGplMAc0qkkkWUCQBu0PF6bFhc9qoFDW6Ak70VhtJBIfXzp19A+QaNc6k0vhtNk6tx8hlNawWicHeELF8gWqxhmlCvd7kNVNj9Lmc1PU2pzaTnE9ssrKapNloobpsFOp1fnVmhl5nF1fyCaIOL6IgYJNUuu0ufnB1lsvpFFtH+lhaS4JpYnfaWM3kGOjyMeT1crCnB1mSmctmubKZpKq3WMznSWkaPpudQbcbqyxz5NIiYb+XcrPJlp5ODvL4YIRCrfY/2XvvKMkOs8z7d2Pdyrk65zjTkzRBMxrNSBpl2ZZsOWODwQnYDz5gvcsCZ1lsfOwPjAFjAzZgsxgbbINtjGRZsmQrjqQZTU7dM93TOVd15Xzz90eNtYiwhD92gcP7V58+t6pv36p671PP+z7PQ3s0zI7eTlbTWZpNk2K2yvpagfnFAqJt8babdzMYDoPXw854nPValTObm+i2xbZkkuvrGSRRYH5xE1VTaO9NUCu2mmqlZhKJexkejpPON6iX6tx2eJwnz06x5Zq8b2IfXQE/SZ8HS5GRPDLrtQbfPHOZC1eWEFUR3JZLwQPjYaKeQe7oDnBPz12cy06SqWfY39bOk8uLuLaLx+eh6tgsraSZniuQW6thigKiIKDrJoWyQaHQIE+T1WaDb7x0gVJFxxAsilstFajrgnlDbIZHoi3uxbIdLNslWzKoNix8moR443l7eyKYhoVlu9x/2y4+fOgwt3R00xv0s1qt4JEkPvXEC0xOr7Em6ixXy3zl6hQ/NHYLomDT7hsgqJrMlPL89J497E70UzObXC1k6QsG6Q6GaPN2UDAyN/LCZTRJJKgkqZhl/EoX0FJdtwCYiYtNT7CbmeI0l7JZLNflWE8PcU+I7bE2uoIaWV3nvv5+/IqMV5GJeWI4boOV2gJj0QgVwUVWZF48c43TWxlWjTpHOrs4t7XGYKiD59cvcHJzkwf6JhAE76sCnr9phg7cOB8DFxNBULCcAi42JXMDrxTDdnO4tMbbv3bmaSbiPiKePlwamM4aF3NX8SsqRzt3IYuxVwU8mtxBh68P3ZnmufWXGAyNs9U8hSJYSGLoVQAJsFS5QERNvQZ8vrZEBEHgD059G1mR+M8HHsR0qvzZ9BSmW2FnvJ2g6qNiNegJBLkp2ckzq8sc7dzFL3z3a1SLNTSfhzdOjDMRj7FQqfLAQB/7U/eBa1M2Z/DK/chCEt1Jk9DCqKKMR3ZZKFdIer0UdQO/qvLTB/Zyvpjjgzt38raxcV4/OMSXrl6lpOtossQbBgbYFR9lMOqhqkmUXYt3bt/GWDTGaFuc7mCIvV3tlASH/e3tvG14L4c7OvjKtSkqpsm1fI6L15boSMbo9PuQRZHzW1leuDbLrQO9HO3s5Fxmi41qhUKzgWk7dEfDoEg8MNCPJIothtXnI6p5aNg2XX4/y3qN23p6GI5EEESJq+tpNpeybEkOqqagGybpzTIVL2TrTbKlCuFQgHStSsLrQ3ct3nL4Jo729nA1nyfk8bC+mee9h/ZyZ28v6XqNa40ia+k8/qCPa8UiB9qS7E6keGl9nUyjQW84wlefP4PpOLxz9w4OdrSTMXWSwSAPDA7QGWg5neQaTZ6YneXM2gaLK2nesms7opjlK9NXqUkumizTuOEccX09w2KpxLl0mo5QAK8s0x8KMxaNMpXLk2806QmF6AsFSXi9LJYr3NbZi6YIZOpNhmIxCpjMXdtE80gYukVWsTjY2YFflrFch4vZNE+tTNLuL+OVLTR5EMetUbMyPDAwQNDj4fHPPfOvG2jeOfzPBpor3/oPoPkvqk/+7ic/8jv//YfJ6wa7kwkSmh+PpJBulPlPe2/ivr5BBiIhTm+soojw1nv2o8kyO+IxZkslEl4vf3TpMjfdUMAeX1zi7MYmO3s6SXaEmZpcw7AMVho19nd20B0M8K25We7pGaBkNHABVZRYrdc5e26eTL7FPjYNm7aOMMPJBCGPB48ss16tMLmZwaOqPDw0yqNz1wn6vMwvbVGrW2Q2itQbFo2mTU9vjDPnZzH8Mvva2rBcF1WUCKoKVcNgtVZkoZTl6zOzvHN0lB8eO4SDSUiVGYnE6fEn6fRHiHj89AVjvOvAbuI+lcPtr+crM89xNZ2hWGrS3REkHA0Q8Wq8uLDI/eOjDIXDXMzm8KsqNdPk65NXEUQBjyKjqQpeRaHW0BkZ6GCgM0V3MMCZzTSzc+soHhmvTyHVFQXHYS1TR8TF71VwHJeNjQKyLBKI+HBdF29AY2EmTbI9jG07GLpJJBZA8arce2Pf8lwmQ3fAz7n1TYr5FqN582g/87k8xXwNURBQFImGKjAei9GwbN4wOEBPOMSiUUf2KLx5fJTzm5v8xfFz/NC+XXT6o1wrbKBIIhWzQVD18cjcNaqGQVcohB2Q+czr7mOjUefS7ArLjSpNx+FbV66yo6ONzkCAwXiMpM/PZq2Kbtt0BAJM5fItNtXvoSMQpD0Q4PzqGjG/j6jXS1soyHKpiAPMLWRo6DZNo/V6m00D24GVepmDfV2MRCJ85vx5ekIhBiMRbu3qRBFFrhYL9HUmCSeCfPMd7yDgUbADHtL5EoZhc/lXfo6Dg12cz6/T0RPj6een2bycZS5d5KSxyWPzc+iuy3AkzEQizkatTqZZB8chmy4TDPswDYsn1ha5rVel3XcIQVD41PnHeevIdvoDu3ly/RKVpo4gQL5aY30lT3mlBjWT9v4QsYQfCRfXdWkYDnqtycpalmrVwLQcjIZBLBlEb7SMqx1ZQFBEoiGVru4oS6tlamWjZd1UMnB9Ms26haiIrS8jkoAiC8wvZ7ioF9mTSqCKEp+/dJlHTl6kVjXIFnXK2QoXZlb51MP38p5v/zmyanEpN4couDzYv53zW2t86Lmn+NPT53j9+AiSILAvOYrlFviz6Yvk9SoXs+uMR1MokkxQGadpX0USIthuAVHwcyH3fVLeOKKgEVB0BMGiM+CnP5jg0YWr1KwGm/UauxIJLm7lWKxU+P7SMg8N3oWAikuOqMdPxCPx4OAQul/l8uwKok/h/RO3MhiKIwgq26IHua1zP7KYRED+e0dnjlunaEyiiApVcxFJBEVMYjnrXC0s8tLGBcaj7YDLy+kzRDwqe5N9LaZTUHDcMi5NxqNHUcQkZeM8Hul/CY0EQeJk+iQjkSQlY4ma1cqsloQYtluioE9xrXiJDl8UnzL4D/Zthyqu2+CPLzxLeyJCwcjRsA2eWljk6eUldqRigM2O2B5cqqR8ffz+heO8bfQWzhSnUf0etne1czqdZm9bivdP3MyO2BF0Z5bPXHqCb81OUzJXQEjTHRjBI/ayVJ3mM+fPsyuZ4HX9Q3x7fo7j56cZ7m7j3NoG35uZY3dXG93+CN+8Ps3dfT1si8UYDKWYK69xJZdjbypFWNPo8PtZLJfYFo0xFo3RFQhSMAwGwkF8isBsKce352Y5Nb1IMhqiWGvw8WN38GdXr3F8ZYWoptEdjzIcDiMI8OzyMg3TpGFZ4LrsaGtjOBrjzUPjPLG0QERTqVsmv/XSK3z35StcWN9gV18XmqxQtyxyjQa9sQgHRvp47uw16g2d9EaZUEBF0mSmJpdYXa/QbDboaY/zvh07ELwqRzo7eSWdJlOr0R0KM7eWYc9AN9+YnmGmWCQW8DM7u85QbxtVQ+erJ88z06jwkUNHSDdqvHNsjN6OOGdW1nnHjm2MRGNMxOOcz2wxlcvy7dNXqJgGiytpXr9rG2XLJBoJULVsinqDUxsbLbs+1+GO3l42qjWK5Rq+QMvS7tJWlvds30FQ0ViplHh6eZm3jY/yp2cukAgFONbdh26b/MX1ab59fZa1fBHddXjD8BCX1zcoFOqM7uzGsmzePDbGeDTOQrnEMysrBFUPz6+u8qahh5ktP4skNJBEl7HwKDe33cpHf/XT/3qB5mc+8ZGOfxHQvPIfPpr/kgqoKnHNy0Q8StSjYTo2W40SD/QPsFAqMhBKsSsuc9++CUKqSkTzYNg2Hf4AuxPtLFeKTCSTpHxe7u4ZZTgS5pHZeR4aHKRsGJR1Hduy6YqECaoKk7kCuxMxnl6dp2KYjEQirNVqtAcCBP0qhtkSrsiyyOL0Bl5NpSMQRBQEIpqGLIr0h4LMlXPYjoumyGzf1cXkxVVuCMLp7Q5RrNQJRHxcW1jnq47DwyPDXMzmUSSRbLPJWq2GiMBYLMpkPsdcqYRh2xzr7qfTP8h6bb51fRQvXtlD1BOkJ9Aa8YlArdygPdWKzPvk7XcgCSKzhQI/s/sg10tp8s0mx2fnsQwL1wXbshFEgfxmEb1hoGoqtw72U9SbOK6LYVt4/R7m5rItxkm3MHUTUQDHcfGHvfiCXlbntxBlCc3nIRQLotd1uvtiaH6N7HqBWsMiv1VmaSnPA1eWGNrWTTZTYn9bClO3aBo2HkOkbpo8ODHOtfYUz748iam3Ite2Gg0OtXfQG0hwKZvlmw++nc16gZgniCjAr689xy2f+ANuOzzOb952J38ydZk3Dg4SVm226nVcx0UUBL5w7wN8+sI51itl9m8fxCvL3NLZzttGR3hmdZWVUolDXV0slcv0RyJcS29xS2c7pu3g4L4q7FJEkVQoyOXpZXwBjVA0wKWzyxRKOn6vTNCvEIl4uTKVJuiT2XfzAIV8he8vr/Lw8CAPj4wQVBUWyhWWKlXyzSYBr4ewpqHZMhWzwYMD2/i94yeZ2NbHu7aNUTbrbI918FN33MKP/+5fw3od2n3s3tnOpbNLdHWG+fZanka1yVvu2M/2WJTuYIAvfP8ETd2mWdcRRIGwz0tv4H+NSecLBdL1Mm1euWXdZFg06zqu65LONSGsQt1i5UoOtdOPKotIUgsMbabrsNUEVSQ5FKbetKkUavh8CoZp05nwsjCZJ5fXyW3UW6lFNx6LIGDVTJBEmk0LXbcJBxWkGwzn6dOz/Mi1FURRoFZtgV9VEUlEPFRqJkePjnK4fS/XJ7/Gp/NVvEEvH7nzNvzyDm7rdPjkiVOcfP/HKBqzVM0GohBEEfu5kv0uj83O8v/s2cORz3yWVE+Cv3jjW2j3HuD5jcc41DZKtj7HjtgeyuYSTyxd492j76bDX+R3L57nkbl5PJLE6wfG2JOQ+NnnnuQ/7dnNvuRuPvzKX9+wOKq/en0Pd4yg2wYfPfgwH775LjTptSbnf7Mq5kW8cgeK2P7q71qjbx8xzz7q1mUinr3kmmd4bu15rhWK3NzWxjtH3nXjWJs7OvswnTUUsQvLKSAIMrZbQZPVV9nSv0/McyA1RrqRIeWN07DqKGLPDXFPnLiWIKiuwI1Ain+oJCFIyTyPabRsrWKaxoFUN7vvaKnGf//SWSzX5b/cFKLbfwsl4yJeRUYSgrxpeIiDbX2sVPMA7IjtQhLiXMw9S5s3yBsHh+kJpLiUW2JbdDfz5StsNer85pkzpPMlFosF3jGyk9cP9vNjE9t5cX2DifY2Liyt8o2ZWfyKwo9NbOfmVD9Fo8aLG/P89ew87x4f40wmw65EgmNdu/jo6e9yqL2T+VKRb88vcHdfDxOxFHXL4JeeeoaVhSwDY+2k/H5+8sH7+L2LF3jkmfMcPDhCTyhIw7JZrFR4dHaOrWKF7Z1t6JbNUqGA5TiYjnOjf0sMhuItqz5RwLQcysUGR7s7SXl9fHnqKvs72hGBzz//CpFkkMXrGap1i/RWk81sg/aEl7039+G68OKJaZ5/8RqDY21c2cpw5lTLr3fgnij+oJfffex5VE3h4K4RdiUTPGtOsVAscOn8ItliC8B+VPWQa9QRgZ/a+SMc6eompUXQ5CSF5kVmNjMYTYN6pUk4HuSDxw4RVlUm4jFCqsr5TJZHZ+cYiESYLxQoNBv8wZ1vYLPeoD8aYalYYuraMh964Da+Mj3NbV2d/OoLL6LXdf7HpTkQBK7mcnxzboahUIiAoqI3DGLhAB8+fAs10+Qn7znMbLHEbx55A/d+/fP4ZYWNWoWvT09TMwzOXp5nZLSL937v11mrlPn5Awe4p+fh/+379l9L/VsSA/27YDQ/8buf/Ij/9n7u7R1gs14l06jR5gtiuw5xzcdfXJ/iC5cv0LQt4l4vr6xvMFvI88Ed+zEdm7VqGb8is9Vo0hcK0BeMsrctScSjcSmbRZFkZFlio1JBlVVUSeIrFy4T9vvJ1BtM5fNs1mqcXlnlw/cf4+TyMn5PK8facWBzvchmJkcg7sewHeqWyc5Eglc20zciJE3uHRrk6sYG2WKTSFAl0RbmluEBbFVE8EiENI25Yol8o07c62OpXGYiHuNYTw8dfj9nMluc2twg22gS0RQQaqxUS4yE21AkmbNbS5zYXOLe3nsRBQ9fm3me05PLaKrIh44d5g8vXuL3XjjBI2/7ERTRj+k2eGTuOtvaU8yupnFsB8d2CUf8BCJ+GrUmgbAXXXTZrFZZq1QIqB6CQS/dvXE+cHAviUiIHCa1Up229hCm3gKQyY4IRsMgu1Xh6I5hUpEgmVKFzeUsC6sVbtrXQ7PaxHXh9bftQZUkwiEfX3/+LPl8HVURcRzQYl7G43H+x4HbWZEaJFJhhiJhbm7rYqaY54OPPspTpybZUAzu7xvl42de5KmFBW4e6iffrHHm/DLHS+t84ra7EASoWzqPzl5vWSbZBl89dxFHFlhez2LJ4FdV8rqB7TqMRiIUDZNHT19iV28Xt3d3UbVt9raluLWjn9+/cJb+SJjf+OYzrOg1mraF6lFQNZUXX5jFMB0cF1RZZGJPT2vMVWu22OxKDdtyWMrkMDWFHYkYPlnhi1euMBaPczGTQZUkJpIJ7u9v7V1eK2ToTcbpDAS4uJXj1o4uLmU3+Nz58yydWIOQynt+6BB3jAxyPZ2mUKizsFIml2nQOxynYlq8sr7GqdMrNA0bXbdIJAMkwgH2tvtf3Qc8lbnISCRMh1/lS1Pn2MoUUdQWs1arGRhLVdAk8EjYuo1R0NENB8tqpQaIERVXlXAEkG54wBUrBgGfTCQRpGpaGKZN6+KI4JPBI9HZHyIc8eAILeNnnyZj2y6lsolzo9lWawbxhJ96VSeWDOL1awiuw/pWA59X4KOPPIbtuMTiAdKrBW6fGGRbbJAvTT/Lb9/+ZmyKeCQvc+VNvjV3nn3JFBWrwNVcjm+8dIHNTI2fu/cwI5EoXlliKHQzohAmqPYh4sMnd7ErvguAkBpnX8pL0qcwWywR9yr8zDNPcaCzgy6/n77gNu7pOXJjJC0iiTW8cgKPNIwmdSAJoRs7hq8t17Wx3Twl4yqPL11jIKS1Pq/OKoazhiyGMN1NTGcTr7ydpj2JV+5gPDqKJBbZqNXYHu25EYMpottz5PUNfHIM3VnimdVTbIvdgUdSKBvTyKKA45YxndXXnI8sxgmrvchiEq/ciYACWIhiS4S0XL1AUAm8mir0D9XjSy/w0uIKlVqDh7aNkvIG+KlnnuKRuRk0WeZqdouRWIiuQAC/Ms7DQ0dwqDAQjKFIEtPFDT524gSnMrP81tnvcCmbJe5TW/Y3osSfXL1C0mfy+OICg+Ewzy4vc2xokJQ/wI54jMl8lvt6xxkMB5guFpjd3OKz9z/AUDhFUFF5fn2e//zkU9w12M/rBgZZrJQZjUa4li/gVSw++8oZHhodYbVaIeb1ElJVSkaTYlPnHTu2syYaeFWV+XyeF9fXuTKzQiIVQvUoLJVK9IXC3NvXx8GODu4fHkKVJJ5fXOJwXy/r1QpXMhkeGBxgrlQg06hxfH2VX731Nn72jqPsGm5DFARimsZQNMyXJyd5ZXaJw9uGQBJ539F9fPzBO/mJu/YzMpRC90oc6u5mPJlA9wvcsWuE85fm+bXX3UVnT4K9Y71s1RvMzK5RqZmIuPR0Joh5vRyZGGSpXObcxTW8HhmfJjGzuMn6eoGC6rBYneOh/nvwSG18b+U5/mRyilQoiMfroVJvEIoFqJkWd/T0oNs2F7ZyfHfmOjG/j4phsF4s0ag0qIoWNyXjZBoNLi6vE0kECXm93NndxdempxFEkUq9SalQQ1Flbhnt5/vz82w1dU7OL2HqFrV6k9sH++kLRlmqlLizp4dOf5z373w7H3rhL3jryDbGYhGO9fVSkl0kUeSndu8m4WsJtXqD3YiCiuGs8rGPfuFfLaP5a5/+jY903PXPZzSX/+r/PKP57wJofvGPP/uRj37obRSNBteLRa7kchT1Bic3N/AqElXTxKeqjEajpOsNwpqHdLXKLV3tGI7FVD6PgIAmSfSHIngkhUvZTYKqh8FwmBdW13hoeJCdySTv376Pgl7BEkUsx8EFXrgyw7kri9iGxZmtDKZh4dEUJFr7ZaIIAZ+C5FWYn1nj1LlVyn6bDx8+zIWtLeYKBa5sZpid3SLglbnz6AS9iRhjsSh+VUWRZOa3cmTyZVLhEFe2Mkwkk7yuf4CaZSIAr2xsMrOV5dKVBZYcnf3t7ZzY2OBkepUr+TTdgQCdgQBt3k5EwctUYZL5fI5QPEAiHGSlXGZPbxeDER9eWUIWJabyWV6eX6JebRJJhjF1C0ES6Y6E2TfYy2h7kgtzK6zOZ3BFKDQaHBsa4O6+XiKqh4cGxvBpMhnZJLOaY/+eYXq6Egwk47x4dgHLdrkyu85WrkAg4qenN4lgG/jDPpaXC8RjPrb1ddCwLCynBdAFoFjWqTZsju4c5Gf23IztutzZM8CV/BaPXL/OnlSSjx5/icsX1nAcuHfPKJ0BLzPFIk3LIl2rMdCWoG40qJUa3DzUBQL8yksvkUkXCceDGLqJrLbWBI4M9tMXDnM5k8F1XZbKZU6sr2NYNgPtSRRJenX8c1tXF13+Dv506iw+1cPc5haiKGA0DSRJxHVdirkqgiBgWi6OA9ViDbupEwx7CfoURrb1ICqtfcQHxoaJah6+cX2Wg52dLFeqXM9l6QwGed1AP5O5PJ86eYonL08zldni/MIq82sZMoLF1Xye2eVN5IDEsXsm+KvHLzC3uUHnQIrMRpl608YXUDh/cZWG2+Su8REW02lsx8WjiHg8MjXd4Hhmg4eHkyhiG3f1JDi+PoftNuiPhDk+u4SiylimzepGFSGk4o95WvuWgtBiOG0HUZNJxDRiYQ8Bv0IyquH1SOiGjSwJ+H0q5VIT13VRNAktoNKW8hH0K0TDKvmSgWW7+DSJjpQfnyajmzbN6yUsy0EJtHYDs7k6hulgGVZLbJWp47oupm6iGw7Do0lqlSZdAyl+bv9BvBLsTU6w2VjGxeGRhUt0B4Icau9FkzS2x9pYrRfYMJvUSjWUiJenl5d4cHDsNbuLf3OE3WIVNfxKFx0+BY+kk/D6qFk2ca/GG/p3vMZuSBAkZDGJJIRe09dst4rpLOPSwHV1QCCnX8Ryyry8uYDp2Dyzukibv0mbdx9bjTmgxJnMNN+cm2RfyosqDiMJPiw3gya7NC2DofCeV/+GLMYIKN2vKtfHoz9QqTuIQgMXHUVsRxISrzFa/9slCMJrdjHPb12gPzSAJAT/t737j658n3jYz0RnB2OxCJosUzINdiQSjEajXMlm0V0Yi2qElG5Md53/fuKrfPyl71G0q0iCwLPX5picXuab73on2xMxcs0Gi5UyPYEgY9EIx9dWuZrP45Vl1mo1FktFVstl4j6NsmFyNrNOrlknpmkc6u/Gc2OVJl2vsjfVzXPrK3zj9CXuGO5nR6yNuOZnNBqnbhk8MDyI44JuW1RNE0kQWKlUGY3GmC7kadgO4/EYBV1nq1Bh73Av2zvaUWWJd4yPUdQNXlpb556+Pr44NcVyuYwrwHK5xOSVRX78yAEiHoXxaKz1cfKo5PUGJzdXOZ3JcD69RV8oyKfOnGU8nuArD72dTKNEyu/nu/PziKKI7Tqk6w3G43G6A614ZlEQ2ZNKUvbAn794lrZkFAGBnck4i0Ydu6njOC7pfJlg1M/TU9d57tnreLwy8bDKz77pDjq7EuSbdR7aMU5U0+gKOFTMDX7p+POsbuZQPQrpYhlVU9jX3U1HwE/K52W+VObDX3oKVXCoGjoBv5f2UJDVzTyST0UQRJ6Znye3XiAUC/KBHTt4YW2d4WiEV1ZWqdywk9sx2sNsocCtPb24QMU20ZsGpWyFN+/dQUFvcGJjk1s7O/HKMqoU5YmlMxzu7CBTr3M2s8XLi8uMJ5M8NDjOdxbn+OK5i3RFDPpDMe762m//XwFl/9T6tU//xkc67x75ZwPNpW9e/g+g+S+pz/3hZz7yo++/j6DiIdesEfG01OFhVcGwbdr8Ph4aGGcsGuNgRye7kwm+duEyTUmgPxikzedjo17n9q4eugNDeCSBqKbilRVUSeadozfhk2EkkiCsRtkW7eZoZwcBVSDTqDO5tMFWtk6laiCYJoViE0URWykxIoT8Ct6Ah4X5LGuzJXBc8vkq1UBL5JOtVtHrOul0hf6BGA9OjHM1m2MsFuXx2Vly1Tp98SiSKpOt19nd1hqV3dzWQbZZx3JbIxbDdVla3SKSCOKRZZJeL6/rHyKuaTwyv8D5rQz39OzBctLkmlnuHh/iJ/btpWw0ubOnl5hXo26arNaKfH9lEb+ikNEbuJLAhw7fTF8yRtW2+OmbbqInEGCjVufExVlURULze/B4VQ73dLMnmaLLH0aTVXbFO8joVb53ZpZzl9eItPm4dGUBw3II3Bgb+8M+UskIo/EEVbklDClsVbjzlu30hUKsVytsVqvk0yVqVZ160ybok9EVh5pgkW6U6Q4EmcxlOX76Ko9NzrC2nEcQBZq6zVq9hKFKFHWdsVgMj6IwHImw5RjousnTc4s8u7FKTyTMXSODBD0aDcdGVWRypSpRv490rc7Zs7OU6w22cuWWH57gUjZ1aqbJSCyO6Trc3tWH5RqYrsWFdJql5Qy2aSFKrXSqQrplBhzwKuhmi/FOxTSKVZP2jjCGbtHbEefuoW0VojEAACAASURBVEF6E3EMxwYX8rqBT1boCvjpC4eJaBolXadp2yxVK+Qz5VeTguqVJlPz6+TqNarFOu+69yBPv3wFWRKoNizSGyU0j0Q0pJJdrIBPwbVs1spFbt07RltXmKXlHKGQhm3Z5LJlxJBEUM2hSSIff+Ul7u7r49b2Ib50+TybawU20zWSUY2gXyFf0FsgUxKQVRHHdPAGFJqGTa1pEfS1Rt6SJNLUbYJ+BV9Qw+tTCUe8+DQZTRHQvCr+oIZjOaTXqi1mlBYTWm9Y5HPNG6lIDoZuY6stZ4nmWo1Kw6LhuJg5nWR7KybWtFzqlSbRuJ+fvvUAB1M7ERDJ6au0e0exnAp7k7to8w0yU5xBlVwWymlKus5ALMo7bt7Jj4zv4E1D25BFAVlMYTgrN7LKbSQhSFE/h1fuRLfnsNw0shClK+DDweK+3r0car8DWUz9g73MdisYziyC0LIec9waqtR3Y4/TQJOC2G6F5WoBRRSJeDyMR9s5u3WOmMfHUjWHJiuczWyxUN5iV8KPIraxWrvMn01P8kOjxwDx1bH4D8p1W/ZYP1Csi4IHWUy2dkEFBUGQcFzjNWDzB5npf18F1QpBZewf7d1/PXecX7r5IK/vHyflDVGzmq2eqapcKxTpDoWIelSGI1Fi2gC/fPJLPPbKRSzTwlAEHr90jVK2guO4PLmxzOuGhwkoCqPRGP3BCdq8E1wrTqGIEul6g75QiJem5igVapxe2+DwQC9fOH4Kxe8lrmnc2T3A12dn6A0GcQCvLOORRULhAH2hEL2BGNlmhcu5DBu1GmPRBKvVMiHVw+5EGz5FYa1a5fHFBS5lttBtm6LepDccZrVcJub3sy+VpD8companNrcIOb1cn/fGC9trNAVDLDVqLO6mceyHC5tZvjg/gMkvGHmSzkcXHyKwvViib9+5SLRaICY18t6tcZSqUjBrGO7Lh1+P4PRCKLQcjHZrNbQZBm/InO9UKBmGoQ8HiY3M+h1g/ZkhFyjcWO3Mo3m81AvN2g2LR7YM07RsZhfyqKpEsPbOnj96BC7k0neu28Pi+Uy14sldMegPxgh02wwlErw0PAgfbEot/Z282Pb9nCks4+AKrNYKfPMxVlyxSb9Q0lGEnEKzQa+oIYgQMLrYzabQ1YkdnR2cK1QRLzhInKsv5fBjiSrjRp39PdxvVCgrBuslEtUqw1kRebQtiEMx2EsGmWhVKbN5yWoerhensZwHM5lMnx5cpJnTkxSylfp7kpwrLsfyzW4kMtycWuLipUjpzeZ/MrZf71A8zP/QqD5jf/zQPPfxY6mIIAiyggCrNdqKKJIm8/PUqVJvqkzEAoiixJ+2YPuWCiizE8dPoDpOMQ0H6ooc61QoNvf20oQwUfUY/LSxhxD4RgNq0FvoAdR8OK6Fll9narZoDcY4lC7w7mBdjLpCk3DJlvU0VSRjXQVSRJRJIGmYXNk2wAzkQyvpOdBEhkaiqNILYP5/3bLQVJePz+uP0qlUOPR69fRZJntsTim3rLMyHgUOgJBOgIB3jI8xOcvX+HPrk0B4FcVSrrB+ZlFFI9CZ7DFjBi2jSarRDWHTK3GuZlF7FvyfHPuFR6bnyfi0bAdl72pNoZDfTdYCxnTWSNdP0vFMBiOxegNtXxCj3S2szOxAxF4ZG6e9UqFbTv70G2baqVBXzyK6TgMhXqpmCUMxyKsdpJrNknFvLz1/u1UDZ16okFTt2jvjuLYLv3dKdr8flbLJa5eWEG54Xca9Wo8cmWKSMhPbqNIpaJTqZm0xTXyZQNFVTi9vsET1Rq3dHTxp0+9zFahSTSkonkkskUdURAwjRbbUGo2aVom/eEwddPCr6h0dSVYW8uS2yjSDEdo2jbvHBul2GzySjrNl2dP8XJtjnK+ylqmzspiGWQRf1ChM+UjGA0gJUWWyyXeNDxESPGRaRYZCIf4+lQF16XlYykK1CoNciWdQr5JV6cfvWoSjHgo10y8nlZ0ptfvwSPJDIZDzBZLnFxbp6g3+cCuXYgC7G/roGIYHF9fYyqXZ2orw1AsjtVns7m4hTegISsS8fYIkWgAX9DL1a0s77j7IN949jTzqxX6O/3cemAb+WaDSMzP0nIR23WZX8izb9sA/+9NN3F3fz/PLC2xVawA8IXnXuFzxksoqkxXT4K1aoWJqJ9YIsS1q2nqTRu/18G0XTBs/FEPtVLLlxBJbLGMlgN1i7wokIppaH4vbT4PvqCGY7f2+SzTxmga6KbTCgYwLEzLpq3LT71pEwmqFMoGhuWA7bRG63Zrsdm6sf+JC+g2puNCSEFRROwbxxQqBl1Cy0u2Yefxyz384ovP8utHIOaJoYgt0cvO2HYadp4TG+sc6eyiL5hEFkRmSpu0+9rwSKM3Unla5uSymCCvnyWodLbsglQ/MU+sBdZI0v23HIcct/Gq12XTnsIjjgM2jlvlxOYsh9sVFLGbnJ6h0zeEIChIKCC0xtJn01u8eXiYrUadmGc3n7v4JL988BCOC7919jSzq2n8YR/v23aEhnWZyXyGqmmSba4Q8zSQpGFc12GzcYqkNtQ6T+Hvjuptp/hqqs/fBqc/iLl0XYcfJA5By/xdk1577D9UK+USX5u5xvu27yGo+KhbBvmmTsrrZSwa5qGBXTiuw2R+lVPpr3ApvUmt0qR3pIPVXJHsZpFC2eC7P/+j/Oj//Es+f+Uyv3LwVtZrJarmClVrhu2xODPFIlv1Okul1hctQRCw7Qqfc17h6vQWpmHxpGmzfLTKzkSMfLNJxOPhe8sL+BSZtwwPEfZoTObX2azXkEWR0UiMpBamvTNBUS9zfGORyVyeoKoyEA5zSddJ+nx4ZInNao2QTyOgqpRNk5Le+myYts1qpcx7n3wEw2mJCQWhFd7g9XtYWcrzrm9/C0mW8EgS9w8OsliucDG9iaxI1EyTlWqNqmmwv7OL3mDgxn1NwyOJrFZrSILIeDyGIorc2T2IR5I4uZnmtq4unpi+TjQVZlssRkBRCKkqRtOkWqzjDWjE/B6qpoVPUfB7ZXbe1MtYKsGpzQxHuzqIeYJcLxQ5fnmG1C1e8nqNY91d7Ih3EPfs5IvXvo4oCKxUcyyWSxxo6+ZtwxN4PyjxE597hMxqjh27JvjAjgky9TpPLC3xwsoyhUwJX0BjeyLOS6urXMm0gPLbR0fo9keYiMXINpv4FIWmZdIVDDFb1ynnq+zak+D922/HRedoV5NL2SzZRoPvL69Q1nX6wmFiXh+1jgilbIUTl2eRb72Lo52DrNXqfPa7x1ldy6J6/2nv4f+r9W9kSfOfBDQFQYgAXwB20Grj73Nd98TfOuYO4HcABci6rnu7IAga8ALgufG3vuG67odvHP9J4EHAAOaA97quWxQEoR+4CkzfeOqTruv+5D92ji9uLBD1aPQGW7uZBb3JYrmK67rULQvdNgnICSSxhuvavK5vnJc3F3h8cR6fLKPbduv/QCXbXOM3z5/i6tYWv3DwZr69MMv7tu/DI2kgiOC6NC0LVZIQBJhIprBvdpiZWmE1U0dTRYJ+hWLZQPXJ3H54G33hMGFNoysRxSNLjMZixDSNa7l8K/UkUGdxMY9pOuRPLfDzbz9Gpz/ID+3eQVTz0OHz85HjL3HfyBC/fuoU2UIFw7aQRJHzUwtYN8ba+3cN8bHDt7NRL/KJU6c4vrrGSCxGe8DPgfEBNuvr/OG581w4s4QgCDzmv8B9R3dyrGeL/W3t9AX6kIQI9/UOs1Yr0BcKMlsssVyp8o2Z6wxGo9iOy+m5JWRVxutrmZz/1dVr2I7DPb19iHgIKXFKRo6nVk4QUlUmdvazv72Nb83M4DouibYQHW0xvLJMyh9gcivDhdOLKJJAsi1EIOil1NTpiEVQJLEVN6nbhAIK5apJe8qPIMDRnm7u6unlt8+eZTPboGk4+L0OQb+K5rEYGEyQXitwbm2NibY2aqaJT5aZK5YIejy4rssaLaHTxZklTMfGclx+YsdexqIp7urp4WMnT3J8IYdTNcF0wHGpZW2u1yx6dBu9aXDX0X4EQeA7S1cxbYe1Wo0Hx0YZOXiAqXyBE2trzEyvYhg21Fviqp9+z+2cnl+ilKtSrTTpao8zkUwyGonw51PXUCWR377jTr6/sshbhw6T1TdwXIc/mr7MerVCXzhMXyRKTyjIQ0MD/NfZ75DeKGFZLrH2CHGfj1pD59rMCrpt0TfWSbm6yND2Hm7p6mQym2OquEitYb3arx59+gILhTzv3bWLt4+N8YVLl+gMBskFWklN79w2zvG1dSIejUxjnSPdPXwvcwkqJluAa9jQsKnJJsgiPk2iXDOxSjaoIoJfwbRdbrppmKvTy/hDXrwBDcu0KWUrSLKIqqlo/ta3b8uw2Co08SgituNiWg6yLFBrOEg+BRuzxWr+AGA27dZrpIqtnz0SlRtAvrM9wNxSCcu0qVsWAWWI9dolfv3IMZLaKGBjOXkkIYwq9aGInRxo22QidghcB9stMhGLUTI2kKyzxDz7EIUuTCdHzZpCROAvZ5/h3aPvBqBknEcVW/ZCF3NPElQ01mplDqSG8Igj2G4F17VYrGwQVssktTFe3DjFnkQ3tttExiLuaXtNn7PdCic3NxiOhFmrVXhhbZ3uwEv88d1vRBJCvLhxnuFojLppMj29yi++/BhdAT+Xt7JcXV7nuevz/M5993AgNYwgiKxWCzy/9j3eOnQvsphgsfICPf5dr4JLSYxgOdnXZKf/3ftDC2Q6bh0Bjd88f5Jf3HfrP9aycV2HQrnGuc1N3jpcJ+bppmYukW02OdLZTYcvjix0slC9wFA4RVTTeHp5hcWgxo72NmbyOSLxAP3jIYbDSd5772E+MHETluMQVj2vJsKd3kwzHI1y4vIs5WId3XRaeeU7Ojl/ZplUVEOSRFLdcb537To/8vBDrb5QrSCJAsdX19EkiZObmxxqb+fWjj4SWjvr9RUWqxnStdqrAqEXlpfpC0fwKwp94QjqjVSzuLfl7XtmaYW5Qp5SrcHK7Cb+oEZHbxLbcfCrKmGPhmE7SLLE8lKeXKbB1tPX8fhkBnrC/JVuMJZK8sMT2/maeI2DnV3ULYttiSR10+Tx+UW2JxL0BIMUdYe6ZeGVJRKaxguraxxq78ByXB7o6+NLU1MIgkBXJMTnnz7B/h1DtN8AuvGOCF2hEA3T4qmrM/yXo4e4trBORzjIe7Zvpz8YxXYdFitZ9rYlmcnnKOkGmtS6l3pEhbNbz/G5F09xdNsw9/aO8NzaGofaW++Ve/uGufPYGGsrWU5tpPnR8QNsi/iJe738T8tm6sISXr/Gk/PzlOtNiltlRgY7+avZOd42MswXJycZjEbZnkgSVBUUUcSvKPziw4do86aYLc8xEb2HmLbMn5w8S097gndvH+dMJsv3rl3nddvHGInF+M7pyyyvFPnC5Fl+cscBOvw+QrFAS6D2A2Xuv+L6N4Iz/8mM5qeB77qu+1ZBEFTgNfOSG0D0s8D9rusuC4Lwg9mQDtzpum5VaBmsvSgIwhOu654Evgf8kuu6liAInwB+CfiFG4+bc113D//EKhs6qiixPdbKB35xfQMHl4RXw3VdTqxvcKSzm6inioAHUZARBIPZYomKYbJZb2DaNi4N1mpZfuw7j1Er1bFtm2dW1nhqapo3DAzR6ZNwcUl4e0loNpOFlhUOQMofINcRYXahhCIJmLZFJKDQ2R1FkxVOrK1ypKeHiKdlXhz1eGhaFqEbYOfR2TlMs8XqKJLA3lSSot4g5fPilWWu5PP81l138srmBrd0dfG0uYgmK1zPbGE2zVdp8YCiEvMkWK8VuLmzk+8vLLBVr1GuNnjvvj186dok1ydbiSDlqonfK7NeqfDsygp/PjXFo2/8b0hCiHZfBzFtBcu5xGyxxC8fOELdavInU5f50tMnEEWBaCpMvtrku+I8Y6kkR7s66fa3bowCEmE1QYc/T91c521jo3QFAhhDQzyrLPOubeNs1hsslyvYrktfOMJySKV/vItcukhI9TCdzqB6FIpbZQRRIBZSyRZ1OtoCZLZqvOHQbh7oH+B3zp2jbOg0dBufJiOJAtH2CP6wQa3cIBTxojcMol6NlN/HSrVKrlFHEkUc10UURWRFxjQsTpye5oJvkUy9zq2d7Rzp7GMimeKkdB0lrGKaDqIo4DQs0G3iqSAvfPAn+PArzyEg8NWXz2LbDpZpE44F+Ni9xzjS2cn2WJTp7m4qhsl3zk9iGhY/PD7G/rYUj83N8cDgANfyRQKKwgN9o4xGoyS9Pnr8uzjcYfAzz3+ds3NL2FbLfijRFiIwpnKgvY3rxRJ/emUKQQDDdBAFgbWFLURJZHNxi7npAlcns/jCKu970y3sSyXZnUwxEonw+PEL+L0yew8OcPHMIj/3ljs41N7Gp8+d4/6BAe7o7ePp+Xl8HpXMVpHfXn+JPSN9XMnlGAkn+cCOPXyq6zmKc2VcgKaNktJevU7lTKOlHHdckERUVWTbWJL1XIFA2Ec5X6VWblCt6Fh26zGhkIbX32I5VY9CwrTZKjTxazL5soFPa7G/tu22wKQggCKBYUPDaoHMqgVeCQSBZFRjbatBvVnFSje4bGX4cvcUjgt39wxyrZCmHmzS4x/gmbWz2K7DPT0HUcUe9iXvQhRUyuZ5ymadbv+tJLUebLd6Y3QcRBV7kIQQprtBm2+Z9zz5//HxW2/Dr3ix3SJ1c5XeQAJJEBkM3c657Hc5nT7FB7a/GRedgWAfsphCEkIc63pjq3Has4j4EYUG67WXefPX/5KXf/TXWK1ewCOJHO7o5rOXLtCwLGKeIC4uF3KTHGofpDcU5qPFIqpHZmorw97UdvZNbOfnphf51Xvu4OmVFfYmK0hCkL2JW9iblJCEILZbpT9429/pr7KYwHK2kITIa3wyf1Cua+LQQEDhE+e+wH+96RBe+e8Hpro9w4XsdfanbuOxxSeIhvx8+f63ArBcvc5iuYzrunxxapKP3PxuHHSGQgc5nXmWP7p8Ba8sI0kS/z977xld13le6z6r7t4LNnolwAIS7BQlUVSxZBWrOq6R4xrbUVxSlMQ5yTmp96Q4uS7XznVLHMdOXGTLkq1OSyJFFfZOgui97d7baufHghXHiXNz8+Mej4z7joEBYGFtYGED+1vzm+875xzPZnj2gd9FM5eYL6+wXMnx+IUr3NbVxVCwjSdmxhgKVXjLhi189skjZDZ3Ickimbzt9OBySvzu9fv5pYtLNJoGkixRzJZxeZ3Ml4o4JYnDS8v4VYUHN21kZ6wHzdT5yzOv8Pu7u7Co8+TMFFGXPZf6xNgEl9MZkosZSrky0WiAm7q7CTud5BsNNNN+XSoOmdGLs4yM9JPxOMjnarg8JerVBlP5GuOOeSwLGk2DWNSDxyVTKGuYpoXDpVAp1lh1l7mhvZObO3r4/tQ4reuCmu9evMJIRxt1ww6VuJjJsiEQIOR0sDEUIeF288ryEi0eN6vVCnf09uBWVHbGY/z2nr08NTvDP/zoGIpTobM7jkOScSsKY/lFNMOkt7OFr952P4ZlIAn2vHnMGWBruJO3DoxwMbvITLGAIMAz81dJ1+oc2DRAudnk4ZeeJ12t8Nz0NF+89XZUSeED27ZyKLBAu8/HfHmNbm83hUaDHx45S6bQJBCosWKYiJKI1tC4Or7AtTe3c3x1laqm8dKVSX775ut4YmqGd2wcYlM4RMLVgSr1MF+8yKOTX+BDwzvZ1d/N5dU1XlpaIe528av7d3Mlm2cql2V6NketbvC9s5f40PAetkVi9LbFuDKxSDmf/49CkP8tJcDrkds/7/X/CDQFQfADNwDvAbAsq4nNQv5kvRN41LKs+fVzkuvvLaC8fo6y/matf+25n3j8MeAX/rO/hEdWmC+V0E07Dqs/aOeMy4JAut5ANy0cooKAg6ZZRhYkJgtriIJAwKHiWd+5vPGRv6ct4Oeark7u6Onmm2NjPH7iPAAvLMzxro2tCJiYVhEBlVZ3gGfn5hjPZLh0aYb51YrNdtV0gn6VYNCF4lQoNho4ZZknzl1hdjrN1l3dXN/ZhU9V6A34WKlUWVhKoygiXZ1BensS/OnRVzn6zr+iy3eCx6evIgsCV7IZPjT8Rkyrws7YRQrNBi5ZZlSVqRZrXDvYx129PYCJLIoMR8J0ej28tLjMa2vT7IjFOL5ygXrTJOBVsSz7Hp3OFHErCu/cvImV6kU+e+40+Uadgx3t3NO7hb3x27mSf4EzyTWub2sles9NPHLVjqfsC4U4emaMFVeGHfE4Vb2GV/YAFoKg0OoOcrDDTpGo6wZ7Ewnu7dtAwh3jxNoEhmliAj986QzBmJ9irkyt0mAtX8QwTArpEpVijaVUDce6XY0g2HOvpmVRajbt5CPDYNNghFy2im5YON0qt28e5HuvneNNe7fxsZHdhB1hvj52jMMLCyiiRKpqZzHPzGRwOSQiUS+JLh96U+e1sWlkUeR8Osv3Dp+kodkgtqA1EQQB2aPgdcvk0iXe/uR3uaa9g4+NXEOP38v3JyZp9/s5PjPPayur3N3Xx0Q+x95EC8dW1tja34lhmXzj6hguWeYP9l9LQHVxY7uBS3bgkR04JRkBga+MPkZdN9BMk562GGvFMrpmcNeOLWyPRZgvlUlWykzOrNDSHaM2tsLimm0NND9ZgGIT3DJvffsevvvEOZ5++Ry53UOcWksyXyzwwE27KTbtFt9v7tlNulbnq5cv8+7hLaRrdTZFQrR4tnBiZY3psSVu2TfMmdkFPIpC07jMrV3d7Njby4trV1AVESvswLJAkkSMpgGKzWCIARVRFBjsj6A6FAzNQJREzHVQLksCsiShKBJtPXFibjdnzk8xtWBHU6KZiA4JdT2qlLph2x/Jos1g/nheUxFAt1vmGBYOl4Rp2iKiclUHt8yH3notuxMt7G9t40cL03gVmdPJJB/ZFuOGtgGStTxYBo9MfYs39z3ASvUYre5r8Ks2oygJPpYq58g1KmwJ7QFMLJpIeLk20cflTAa3bAMQCwOnHOXT557mzp5ehoIrbAn1MRho5RtjP+DdGx/8F2uZZi4h4MYhDdA0ZpHFOK3ufRx7z7WUtfN8e2KUsWyOp2dmmU1m+KObbsDC4sWlcXbFOmgaOgP+m9iVmOaFVy/TqGt8Llfm/u3D/OA9D6KIEv3BEJLgW2/1Jyg0lok699hzoJaGbqUxrByqOIAoqBhWGZMG2fpZ4uuJSD8u3cxiUkEzsjjlfiJOB0HH0M8UASliJzuiKnV9mkfGJ3ArCvlGCQuYyGe5msszEg1zMZMl35wk4tyNZZn0+mNkalVOnJggGvPx67t3UWhO4JFDdHlbOJOa4Yv33Glv9sbPsTEcZrpQ4Fw6TaWmU6jU+NO73sAnjx1n5uoKkXiABz//CPWmya03b+ItQ4P87fkL5FNFZgoFWj0e/KpCQFXpC0Q5tDBK2OliMBik0KzQNHVu6+5hvlRkUyjO7rtaefDRxwm3BPG7bYLjibEJDvR245RkAg6VTK1ObyiM1q1z4cI01bqOqtjdGtWhoMh1phfLuBwSPo9Me2+cxalVXG4VQzNo1jX2bOzl+OVpHvrRIdJreUq5CgObOijXGqhOhRs62ji8sIQZt7iazrBcKjMci/LXx04iySIJr5fuQJDHXz2HL2h75V6IrTJ1eQm3RyVXaNAd8ZJOF0h4fYiWLfR6eWkZlyKzUs0Qcfgp6VV8qhsBAdMykUWFndENWFiYVpOG0aRh2ASIU1IZy6/wlUuXmMhk+POTx9kcjfD142dobQkzmkwykc0y0jLNC7OzzC+W2b6jlTdu38yjR04B4PI6MQyTgUAAr6ry8V07eXl5lYvpLHGPh1yjQX8ggCr1YFkaf3niBF2BIH7Vx9uGBjkeCBB1OVksl/nCiTPcvnGQjZEol9r9JNMV8qkizy2M8XvfeZob920h3BLAsmDyPwtK/j8p4d/00f15rP8Io9kHpICvCoIwApwGPm5ZVuUnzhkEFEEQDgM+4DOWZf0DgGBPj58GBoDPW5Z1/N/4Ge8Dvv0Tn/cKgnAWKAK/b1nW0Z9+gCAIHwQ+CNDZFefeviFOJRcREdgdTwCrrFZrXE6leGjHdto8g1hoZOsrxF1B1qoVyppmg03VzrKN+7x8aGQbHlnlai5D0OFk+1APF6cWUCWJml62Z0GBfDPPwy89T8Tlpq5rDA/3Uq6OU5IENMMi0eLF4VRpVBuokkh/KMZwLM4/LeepFKpI3QJeRWGlUkUSRLb0tjPc18FKucSVS7M0NYPja0/y4uIS+UaDnfEo6VqdiraGS/bS6fPja9T58LZtiILAZD7H586eRTMMevwBok4f3d4YS9UM/+fRYwD4VScN3cA0LVL5Bi6HhEOxfdnmVzNofX1k6xVenprhtnXj6t995Xnu6p0i12iwL9FGj7ePpnmGm7p7cMoSF1IpDE2n0tT5v194Dc00eefgVvxKHGt9wVksl+n2+XHLMmsVO1Yz4Y6xr2Uj+1o28n9dOGIrsk2Lq2NJFMWeaewbaKOYKbGYrKLrFppm0tUZoKc3gSpK1HWdT505jWd9TifeEbFBiyLR5vdzaGwSh0tlczjIQjmLKAgUmk28isqZ6XlWFrK0tIf4wD3XcjmVZGJ0gVymTLTFj9bUOTm/QDFTplLTaeoWTlVAlkWbQJNFokEnLq+TgNOJKMD3pi7w7OwcDknmtalZRi+tsDqX4vDGOXTN4E1DG5BFgWNnxuy8d9OipTPC7pYYS+UKhWaT61pbyTXqZOoNAqpKWdOJOp18YOswPb4IE/kkh+YXSFWrpOseOnxeTq+JBGN+KoUq7T1RFlfm7blFw6JtJEpXf4yarrN3Xxdv3rYFgAvJFOVmk8upJKZlMZfPk6nWKDTqZGo1vnbpMr+xaxc/nJnlnj7b2uiYY4KIy4UoiaiyxHcvj3JXbx9v2biRl49MIAjgcSmUqxp6TbcZTI9MUzPxTMmhRAAAIABJREFUumV6e8NIkoTW1KmWaq8/BwC6YSGKkOiJkfB6GV9YZXw8Z4NJ3QKHiGlY1Ku6DV5/7K+piCAK9vbesMBcf1/UUHt99LT7WFgpv+45F251sy0W5caOTrL1Gu/YcIDl6hw3tDup6w3OpOb55tgYf7xfxSlJHE8eYn/LXRhWCQEZsLAsgy7v9bR7SlzIvMqlTIr7+oZxy30ICHx4+AAvLF6k3etjuTLHlnCch3e8GwGZR6a+Q6HR5B2D2/8VyAQ4l77A9uh2NHMVWYxjWCU0cxLDMsnWSzy84z1cyh7mV556hqHWFg60DVLV69zWeQCLJjU9DcCHh29EM00EQaBpGDwwMEC374DNwtYn0cwlUrU8LkklqA4AkGmc4/MXTvEb2/fjlnu4lD2Mbpnopsm2SDdR5/C/ul5ZDGNYKmltEVlcRpWknwkydTOFKPgpainu+sevc9uWIT6+fS9BtZePHfl7difi7GmJkW80KDSajBfWuMZhYlgZTMsk4HDidSuYpslatYpHDuKQ+pgqHqaiaRQaDWZLJU6srFLUNJ6dnmYgFOYdt+3lps52tkUSPPuW9zKaW6Bu6PzC5/8JQTCYmlrmqNfLL27ZwlKlwnfHxvjD665jkwClZpPFco6vXbrCG/t6CDudPDs/ycZQiJ2x7Xx99DEen5rmd3bv46PX7uHJ6RmWi0X8TltIt1qu4FVVXLKEW1HI1Grs7enilYZGyLIo5auoDgVJkXD7XThceURJRFYkatUG+3cMsVC0VeSzyQxPHj5PMOjiLUNDvOpbwbQsOvw+LqdSZEoV/u7iJYrFKrJob/BOXp5i4Pog1/V0s1wqcWl6kSvaIpZlkV0r0Gga3Lt7KwvjK7R0RnA4i7S2hJmdXcO1ntTT1R3nzNwij7/tHXz2/Ene0NnJ1VyOB4e2Y1g2U7taSZJwhWia+ut/77ph81GGZeJVVA52tHNrdxeKIPLlCxdobQmzO2HPRKuSxHdOX6A9Hmbz5iiiKDIYCtA12Mbs1SX2bxkg5rajc//h8hV+bddObu3s4LeeOIQn4KaqaRxfXaPVE0AAvnDrbfzu0aM8Nn2W/YlOdNPkmkQvf332Va7r62E6n8erqrzlwC7OrK5w4tgkv/NPTyKKAmcm56mWanz41uv4V8Dj/6//VP1HgKYM7AQ+alnWcUEQPgN8AvjvP3XOLuAWwAW8JgjCMcuyxi3LMoDt6+317wuCMGxZ1qUfP1AQhN8DdOAf1w+tAF2WZWUEQdgFPCYIwhbLsoo/eVGWZX0J+BLAhm1d1lguaQsv1pW3g8EwV7JT3Nrbw4ZABPsG0STfrLJUKTBdKKKIInXdoKxpmBakKhXOp9LUdINXlxbpDgTZFIlwbXsrfYEAhxbGibvdSILIofkFNkXs9lDD0PE5HIxHPDiUGoWKRr3SsIUiksh8Okerz4dXVom3BQmFfTglmSuZLJeSa9zY08Pe1gROWebTzx2lWtdp6wzz0R8+w/07hqnpOj5FRRUlruSWcckKqxXbIufo8jJPXLHHWT0eJ7OFPO9/6imiXi/XdrQzHAnT3RLBIclMF3NkalV27O0h7vFw5twUkbYQiUgAv+qgqGn2jaWpM53L0+Pz8dHt2xEEgUuZFKPZNLl6jelCgQ6fh7jLzbWtrQyFIzx68jyxRIgOr4cvXjrNg0Nb8KlulitFFksV6rrBUCjEWq1qD5wbdVTJZn3CTgehRJDRS8uUqzqyLNDVpVKq1bln/3aec49SylXIFRsoqoxhmgTcHp4/dYX23jjXdnSyWimTrdVIdEdRZZmTZyaItodoS4T58pnz3Dm4gXv6VAaDAQKqykw+x/RkikK6hCgI3LdhA7W+PsazOX5w+Cz5UpN6pUG4JUCiLUi1bjMjkiSg6xZel0wo7ufObZtoGLbq/2/PX2BHq53cs2FzmLErq0RbQ4iCwPL0Gn87l+Lg3s1IikSxWEeVRTpDQc6nM/QH/JjAQrlEwzAxTBNFEtkcCpHweHliZoZbOi2WK2UkUaDQqNPh9bJYLjOfzdOsNylkSiysVe32sVNi78Eu9vR30zB0moaJ5jd4aWGBYr1Oo9YklyxiGSYOtwNFlVEkiXSuRKPaIBjzczad5pWxKSayGYJOJ8NDXaxWykSDPo7PzLM2n6bYbHCgrZ3uVi+VmsbKWg1ZFenvC9A1ZD8XaytZWlrDtPn8nB2fRV9nMw3NQJJERFlCVkyi7WF8LifVpkZnIorzVieppSz5fI2GZpLLNaCi2WxmTbfZTGu9RW5ht+edEtQMvBsC9PaEqJXrOFQJl0NCEgW2jHTT6/czlssgCSJz5WmiTj+nUwu8tLTMx0f2EnW5EYA3du7BxN5PS4KPujGKLLRgCToCNqAaidxAr28Up9SKYeZwyVsxrSrbY1kUUWJLaC8IMrqVwrLsDsRKpcrF7CJ7471YVvP1+UfTqrInfgcNY4rnFs5wd89bqOoT/N3oaaqazid2fRCAfn8LXdEQ/33fflRxA0ntBF6lSbq+iICAKl7EJW/lN7f3kqyfRBVlis0qucZpAupmWlx7EASJoWD7T6ynGg1D4w/3PgRgJx+JAhISGwIJZKEFG83/6xKQCChuZCGOS/7ZFkgnksfp8AZ5bHqc9EqOG25rw6/EyTSmuK27i7jbzWyxyEuL9mhPjy+CSZWiNk9Jq7ExEqY+0s8dvb10+HysVJdQxDXKWoODbTs4tHiKb126THatwEJHAdOyGAqHuLWrm4Vykb+5eI6GodPp9fLF515B1002b2u32XfT4odTU5w/O20L5Iq2O4QkigyHO3loR5MOr4+LmRQATdPEsIpEXU4qusYzc1NcyWaxLIveUJiGoZOTRFLVCleWVliNhfGqDroDfjq8XrJdNaZSabSmTk9rlHKzSa5YIdIaQtd0PF4X7906zOlkiodGRvjUmdOUchVaWgPcu3srs6XSevSxg1zNFsW4QgFmVtPs7OlEkSTmV1JYlsUPL46yupChWjNoaAabhtuQFZm52QwAj5+6SLglwEf27OLrl68wdmUeXTcoN5t0BvxsioT56uRxUrUiD+/YT7KWZ19L7+sgs9is8uVLF/m17Xs4l17mQjrNnT29RJ32hkMRZZbKJQQE0rU6T05NMRSNYlngXBc5ZeoN3H4Xp89McvfBHRyfmOGRq2Nomk57fwv/be9+UrUSre4AVV3n+5NT7E3E+YU923hxbo6Iy0WHz8OfHH+FvYkEXX4fD27ZRNMwCDm8HGzrQpW6+cQulU+eeQmAcrNJwu2i0mzS3hmk2dDwBT1YpsX2Lb08NHw3H/8XMOfnrIT/Qq1zYBFY/Akm8rvYQPOnz0mvs5wVQRBeAkaA8R+fsC70OQzcDlwCEATh3cCbgFvW2+xYtmFcY/3j04IgTGEzpqd+1gWuVaucSaVo83jwKAqpWpWYy81qucJ7Nm2hxR3FwrbvSNVqFJsNfKoCTShrOrlGE7cs41FVji0v41ZstdlqucS9/X3sjHViWAaXMhkckkSH18eBtlbqhs4jYxOMtMQxTBNv0I1pmmi6SaWmo6SKBMJeXD4nmWqN8VqGcNTP5licfKNBqlqhWqxxZG6ODZEI5WaTWrluz7fJEu3REMeXl9nX1obt2Am5Rp1ktWonoqwv/Ds62zm/vEJT1wk5nZQbDXYmWhgMBjifSuNVHURcLp6fX2RqfAm3z4VTlmlqBuV8hYGBXrZGI2yNRvm9o0dRHAq6ZafbTORzDEfidHr9SKLAkcVFdsXjnE2leHT8PJ/Yu5ctkRCFbRu5s6cbpyzzl0de5fnZWW7v68O3nsT02Pg4kihyR38/V7JZIi4XQ8EuwORtAyOcT2U4d3YRn0em3jQp56vcvmMLrR43/V0tzEgpG8gs5XF6HCS8Pr72S2+2rTzcPip6g/likS9fuIhbVcjH/fh9btq8Phq6zpNj4+TXc4uXy2Uauo4ii0iKRMztYqlSoarpmJZFJO7D6ajSaBrcOjxETdeZnk5jWeBxyVRqOpph0ZmIsq+lBcOyeGFxEcu08KkqiiiSa9TZs6+PNp8fzTRo9MZxOVXOTM4jKzKFkobHJXF1boVxcZV3/cJ9GJaJS1J5fmGWM6kU7XUfVU1nd0uMA+2tTBXyDARD9PoDHJmb4+mZOe7u76U96OfwK6OsjuagrIFTwhcPICn2Tb8vGGS5XGE6nSGfKtKsNREkmwUtpEsYhoFDUihUajRrTSqFKonWME9PTaG6VNaSOcoBNyOJVm7ubOehrz7OSqqG36vwpQsX+czBe/nwnQf49PdfJBRy4HFK7NoxwP0bBmhxe5DW54eLzQa7WuJ86+Jlsqt55HWjd8uyMA2wTDuuUpFE/A4HEbeLnnCITK3KzNQKPrdMvtSkmG38s+hnXZyFaUHcZQNQ3WTHnm4qhSoFzUDTTCzLors7RMzt4S+OH+dAVzcXU0nevnGI27r8PDc3z5V0iuzGEoPBThbKqwQdVSQhsr5+metgy0AUHGjmCouVcXp9Bwk6dmJaDZrWFJalkWlcIlkr4FFULqQPs6elB5+yndOpp4m63IxEW2lx2absCKCZKyhiK6LgxrJMHFI/t3c5qRtjnEzO4lcUfmX4jQDMl19mNJdkVyLB8wuzvGXAx5+eeJXP3diNJIhEHH2vX7NurRF1bllvh0+QbZRwybOoYj8C0rqlkUjDHKOsVYg5W7AsW7ykin10e5uoUgQBgUJzFq8SxiHZkZK6mf0JM3aJhUqahMvetP+s+uKFixiWyUMjI4yM9NPtD1DVM7hlB1ujcfr9N1JsPsrlpVX+8f77WK0WSLgceOQQWaHIUChI1OnkXRvvo9gcx6t0IOLgm+OP0Odv4bbOA1R1nefnFriwuIyhGby6tES3z8efPHeEG7ZsYLVc4pFDJ5EkgT37Bmj1evnhC+cotJXw+N2oikQqWWKmWKLF7WK+VKbbt8DWSCs+xU2XN06+Webw4gyaMcbdvQOM5dL80QsvcePQAN2BIE5ZYjSdplauk0sW6OyOk6nV2JVIIIkic8USsijSrDXJpctcNZco5asEoz7m5rIokoDfq3I0FGa5XOL5hQUmF9fIZyu0dUVYLVfYHI2wP5FgLJ9nLJvl8sQCTrcDQRS4oaONhmFyfGaO2qqtsNcNi0jETa3aoDseZiGds1PJAi58IS+lXJmHv/EEpaqO22mHjexoiRNffw4s0+TXXnyBb931AI9OTfKLQw5Ori0yHIlRbDYZz2RYqxX4w+ePEAn5GIlFOZtaw6MouGWFb4+NsyfRQrJWY0PYJj5kQWAil+N9w8NEnC7u7u3lnRPf4umXz+H0OIh0tSKJIp+96Q2EHT4Srk5G81cpNTVMLCqaxjWJBMORMKlajVOrSSazWQRBYKVS5YGBAURBwCUnUMRWLMtAFXsZy36f81fnCMb8zORz7GhtRWxt4+WJaZR1TcDfvuHefzMw4eep/kvNaFqWtSoIwoIgCEOWZY1hs5ZXfuq0x4HPCYIgAyqwD/iUIAgxQFsHmS7gDcBfAAiCcDu2+Oeg9RMZbOuPyVqWZQiC0AdsAKb/3V9CEOj127unpmlwJZnlPZt2EnG5+J8njnNNW4JfHNyBInrp8vlZLNlsZrpWx/8TirUtsThHrk7i8jlp8XrZ29bGcDhBoVnmbGqVLp+XkMNJuyfEY1N2PNpsNodXVXHIMoqq0KjaM3ySJFIpN3A4FRxuB0Gnk5MXJ9k/MshisYBhWuiWfQOsVRqMaknifh+BiA9FrTHQGmNLNIpmmAQdDhqGwVq1RsihUtV1nJJM0zAwLQufw/Y8c7hV3CGVj+7ayYG2Pvyql83hJJ88dZIun5f5Uhm3z4WsSASdTiJxP5ZlsTkSYl+ilS5vDIcsEwl4bZDgcnN4cYn9iR78qodHJi/S4nYzUyyiWxabozEenZqirulcWl1jJp/n13ft5L17d3BXzwa+N3mVJ6emCDtd9s49HsMty0ScDmaLBVpcBYIOH5KocP9AH6MHUyRXsqwsFbAsi1fn5gi4XSwspkiuFhEEgbaOIE63A9Oy2BruxLRMmqaOV3HR4YkScDg4srTEYCjMTKGAiUXC62OssMqxRbsVXi3XqdeaOFSRaFuIUysr3NjViWFaPDU9jaLKuLxOFN3gYjLJ3NwafX1R5mYzmJYtRqzWdVKlMrlGg/lSmUK9wdz4CuWOdmZzefKNOhsjUVLVCrl6HVEU0Awb2AuiiGaYqIrK4lyGz7/vXp6dm6XF7cK0YL5UptXrwy3LHBqb5NauThJuL1Gnm9dWlukNBGj1+djdEudKJsvZi9M2yKxoIAoIbR5M0+Lq5RVSi1n27xpCt0w6w0EUVWZtIU0oHmBzawtnmvN4g24OdHXzzeePE++M4Am4KZZrKA6ZznCQkrfJ6MVZrunoZLZo2x1tHAjRrGucGJthducCb+rpZfbmIt98+gS1hsH47Ap/vZTkzTu38c6hzRSb9gav0+flE9ft5/uTUywUC5RKVYrZMopqL0WVZhO3ohByuqjpOsulEuV6A7fPhS/ooTmTpuTQsSQBdBN8it0uF4BC0wacfpWjz43Zx1URTAu3X8XpdnBpepH+rhZeXpjn5p5ejq8meUPnBvYk4rxjaIhubx/H1s4TdblxiBtYrZ0m6rSw1hXpoqAgE6OiraCK/yyMEQUHpqUjCApe2cVQ0MeR5VG2R9tZqWZRvGNsCffw6NRZpgt5dsTqbA71A7wODO31T1xf0xJAipjLzbfHxnl15R/5nd17afe08vTcJO8cGqZhNDGxGEvaQp2YM45pFRkrvEiffxBZiCIKLjKNUwSUOBFHHAsdiyagYmEiCgqmZRBUBzCsLKZVQRKCiIIDtzz4ugVTUPW8rkQH/kXijyioJFwhKnqdU6tJ3vdTqZmXsof4g1dfxbBMlteyVDZv5n1bh/EqLoKOndSNUTo9PQDsindw6B3vId+soJkVTBo4pD76fO1UtSO8ujzKi0vPc7DtGorNBSLO3bR5PFS0Oo9MPsu7hm7BKx/jylqS1bk0fW0x/u7SJX714D56fH7+4sQJbjuwjSPHL5MtlJmZXkGSRBYW8oT9NSRZZGWtRqHRpNdvb/ReW1nh2bl5Ag6VbdEIr62s8u1jZxnZ0M01ba30Bfw4PQ7y9TrT+RySIFCu2f+zpmHSMAz8Dger1SrXJFq4tbOLtWqFF1+7RKNpkE2XkSSB4b4OhnpaqWgauXKVY6NTSJLE348tUq81MUzr9TZxwu1ipVLBIYoc7Gin0+fn1cUFGoZBqWmD/fZIkHjIj2lZnDszTSDqY1Okk+5AELeiUi3V6OtKsJS0wdmDb7yGHxy/QCZdRhYFFsplRqJRCo0moiRy5MVxnt06zl09vdT0Bg3DZKFc4qnZWT590xuIOgP8/k0HSNdq1HWduNuNX3Vwcm2Nd23aiCpJfHvcnst/6fIEpVyZDZs6mSsWKDebKJJIoieG3+3kIzt2cC6d5rsnL9DmiSEJIUyrxDfHxrm/v49wscD3JybZ09pK0OFgsVxmrVJBEATOzyzy1jfezNHlJd67aR+i4Ft/7XpI1y9imCZvv24nuXqDFy+Ns7yYRlZkPnD9HqbyBfbujeOSt/57kOPnpv7LAM31+ijwj+uK82ngvYIgfBjAsqwvWJY1KgjCM8AF7OX9K5ZlXRIEYRvwNeHHOWvwHcuynlj/np/Dtj06tD7Q+mMboxuAPxYEQQcM4MOWZWX/vYtrGgaCINDh9WFYFvf0xfAqG/jFjUmckszp5BrfmjjPjestVkWSkA3b4uRyKkWx0eCGri4WigWiET/Pv3iVB+7YzoG2No6vzbNareJTVCqaRmV9x74lEuJ8Okt6OcuZhoYkiehNnduu3ca5hSUWp1PIkoAkS8R8XhaLRdw+e9HuDAS4r6+Pb1y9SsLrxbPOoG6JhFmOx/n+qQs0dB1JEAi4nHgVmadnZhkKhxEFbPZVkZFFgQ8O72S5kuPXt++1TYlreSRBxCHZuqtXVxbxOxy28jAc5nsvnqTeNJmbTiNJNtP0lZNnaTnoZqFU5HM334YkSBSaFZ6eneGu3p7X527CTieXM1nes2mYtWqJV1dWOL22xnOHL3Hguo38t3172RLuAuC11QUy9QYP79nNULCFE2vznE2lEQW4mM7y2tIiD489ybd++e2IgohbVnjrxo382ZVDRCJu6tUmYxcXEQTB3nl7VbK5uj1cLsu2DZFlA21JEFFEGVmUCTgcvLK4yJ9cdz0WFo9NTbNQLLKzp5OarpNUVUYvzlGu6kiiQGohw9WLS0z0rdHbHsMly3QmoqiSyEqpxFoqz5YNnRimiT/ipVqsMdLbwTMvXSC9nMW5T2K+WGRsLcn2Hf1cSibZ19bOxnCQH80tcOHiDLHOCE6nSqOhIYgizXoTw7BoNA0iETeqJPGh4Wv56OEfEPd42BIOs1KtMl8scevQAB//zhO8/5b9DEfCFJpNXly0xzq+dPIMZ88sUp8vgQlbbu/l++99J9f9+ZcoVjWaVY1iWaN7qIRfdbC9pYWIy01/KERPIMB1ba0kPF4eP3Geq540kUSQRCiAJApML9gpSM6Qwm/t2cOTbe28bXCQFxfn+fh9B/nEl5/B55EpVzX+54kTfO2232VTeJ5iRUOVRZYW8qiqyGPyZa5ra6VhGLy8bKtFR9MpFFHkrRs3ci6ZonckwA+ujuFzOXHJMt2BIKIgYJgmXf4Az48ukMzW8LpkMoUGlm7aiUMNw2Y2vYoNOn/8ed2w/TWrOrS4CMVcuBwSE2NrmKbFpStr3HLzRn5n5x188uzTvLw8w329BzGsHPOVaXbHe5DFEBYWMeeg7ZWJiVvuYKZ0hC5vh20L47kWyzKxaCAKLtyyHT9pt88b3Njm4HMXX+A3t3+A06mnWa1WeHP/PubL83R623j45c/wV9d/HDBoGNOvs4VgpwUlq9M4ZRlZFPnlrcO0e1pZqqxgmBZ+xY3q7EQVO9jZfhTL0pFEH6IQYzBw4F94Xkacu5krHaXFXeFCZo698Tt5ZOpbvKX/7QC45a1YloEkeP+FqvzHINOyTEzqCFYN3Uqjip3/ag0OOnbiVxvsiE+8fsy0GqxWT/MrzzxLwOPi0zfdzLfHx2xmO95G3Gl7eTqlTTSMaTL1U8Sce6kbl0nWSuyM7sG0CmjmHCAzGOxmf2uWZLVKTV/Br3ZwaOH73NLZz6nkApcyWT5z/ikGAgEO9vZwqN7kzPkpfuPuG7m+rYP5UpFaQ2OxWODuG3ayORzikz94gVrDbienFjMkMzU2DYU5PD5FTddp6DrXtiY4nbRb5tOFAlsjEcI3XkOpqXFqdY0fzc5xc08vumVx/KoN6NqCfhYzeQzdoFFr0ttiz5VP5At2bHEmS7Q1yIWLKzgUEcuCQ0cv0dRt6yWHarfz4wk/taoNMg3DwtANoh4326JxUrUq86UiuXqDjeEg7V43Xzp2mvmSPV5zz0A/ZU3nSibLadOiUqiScSrcPzjA1y+vIggCD+/Zzd2f/gbRoJOjM3NoDY3BrR3cPTjIfLFErlFnOBLhTdeMcLFvjSOLS9zZvZFfffFpMtUqEbcbWRDRTQNVTDASLRBzxRBQWKjMcTmTos/vZ3e8m+Nrs5ycmadeqZNNlSnXdFqqdWaKJa5k81iWxUhbK1czaS5nswyFgrz0/g/ilDbzuQt/x+eefZk/vv9WmqZp+2MHQ1xOZ/CqKpZlIYt2d2RhNs0fvPgSLrcDWRC4vTvDYqXA0aUl6rrB7b29PDh0L49OP8mR0QlisTBOWaaiaXhVhXt6R/49uPFzVf+VxEBYlnUO2P1Th7/wU+d8EvjkTx27AOz4Gd9z4Gcc/x7wvf/Idf24nLLMI2PjjMRbOLG8zEd2bEcWzvG10VHu7OmhaRh4FZlOTydjuXNUdX09Zi5BulpjdjnFB+7YQbpe5NXlZZsBW0vy95evoFsmWyIR3LJJfyCILNq2DgmPh8Vyhb6BNtaSObIrtjp233V7OTU1R7mmEwk4Xmdq6rqGN+gmU6ty/0A/z87PU9U0tsfj6KbFVD5HXbcBnaxItHi8hBwOyppGi2wzXd86eZ5w1M/1HZ10+30EHSqH5iep6joH21Wy9SpzpSJv6BzEIbkBkyempu3kmmAApyShqDKVWp160yAedhHrCPPw9ddwS8dmvjF2ikcmJvjIyHZeXFzgurY2is0mnzrzPF+65W7u7b2eiOMUnzt/lgcG+vmVrddxzSufoSXh46auTvr8cSpajdFslmStRllr8r2JKSKuJT44vINcvcHRpWX2t7WSqUUYrc/zW0eOcGd/P9e1tTEQDHDrtdt44sg5fD4nvqCbfLby+iiCaVnUKnWy2RKL5RTv1h7j5VfH6OwO8ztvuJ7bOocIqG4eGNyAS5bJNeq8fXCQp2ZneG1pmVavl8FIFGGbwMnjk7Z4JeBGcSroms7Y1DLNhkYw5ice9uOQZXYM9iEIcDmZZEMkwj17+vjexCT9W9oJOV0EVQcVTeOeTUPkGk0ahk5vwMeXzpyjJxzGNC1Uh2IDJ90WKulNm7Wq1g2c5QYf+NKjeN0/INEdYy3gpabr7I7HGQoGmcjn6e1v5UIyRaWpMZXPc6CjnZVKlQtnF6kvV8Cn8mcfexO/vGUnz86PU28aBL0K2aIdf3r0yASf+uW7uJLJslYps5IvoogSnzxxksWFFGurReqVBrlCA8uy2NnXRbkliCSKFBsN/urUKRJeLy8uzvN/PPIjHv/Iu/i7viCryQr5ssZK2WY5H9r6XubfUea7L56kUtPxuBTSyzne99XvEmkLIYoCPq+LWr1JsaHxg8lJHJKt/q+V6wiiwMVzMyz1xvB5XYRdLlRJZmhzF/FyleR8GkFo2GIfy7IFQW7ZntEsmYQHAmRXq5BpQFWndac9B1au6sSjbsprZWIhJ4WSxtJCmrc/9VWuji8QTgS5vXsrX7t6AsO0uLe2ykI7AAAgAElEQVTPwVxpnp2xDTilIUyrirSegNPp2YpurhBQ7WXNpIJpVVmpnqXdcy1V/QKS4MQhDaKI7fzayNv4zPm/5UPD19A0Z7ntkS/w+9ddS69vO2/eMMDXx77Bg4PvfB1kGlaRXGOMilZHtwxaXCF+e/c+Ojy7MKwsvb69bIuW8CojmFYT3Uzz59feSaYxTotrH1PFw/zFqRO8c+MQc8USO2JxCs0Ge+NDOKRBRiIKl3OHXgeZdeMyzy+OckvHVqaK82wJ3Uqmfgq/2gEIKGILhpVFERNo5tLrIFM3UxSac0Scu3+ijW7Q6ftnIZCFRs1ocuht7yNTz+ORnWyPRXlj50FEIcDJ5CF2xfYhC1FOJi9zXeJONNP2im33hKgby5xJzfHqyiobgn42haPc23sLyfokLyxO0BtIcmvn/VzJ/YiTa0n+bP+bOJkcJVmr8j/2vpk/2KtyNX+a0VyG9z/5FKVcGa2p4/Y6eXpqlfe//x2spmuoisjSTJLr923iucOXyGarBKI+3IqCQ5KYL5cJOFQKjSaWZVHWbAumx0fH0DWd3FqBFo8Ht6Kwtb8TSRRxyTJlXxOvx8nyQpqr3hQ7W1tZKpW5UKuimxbVch2PS0YSBEzLoqmbKJLA4HAH8vrYy+pcCqdDplbXURSRTfE4IYeTk2srPPTFH/DHv3Qbz0xPMzW6yD0Hd3L9hj56Az4CqsrOeCvPzM1w5PwYnb1RZFUm6vWSrNVJreZwehx87tw5bj6wiYVUDr/bSc/IBj6+cwcBh5NOTzunU+M8MjFJ2OmkVK0zoevUDY07ens4vLDExdlFSvkqv7iaJLnwFVxeJ7Vy3Z75dij8zVvvptBo8MAPv8OWWBxZkWyfzriPj+3fyb5Egkcnp3BIErppsifRwk2d7bR6vAwFE7jkjQB89OF/IvvDv0AWJXTT4LUV20t2MBTg80eOE19fr+4fGmJ4/37avD5eXl7ifCrNQrnCbD6PbpksF4sYusmDQ/dQaWr0tcfZlUgQd7mYKhQJOx04pc3/b+DH/7YS+OfxuZ/3+i8RQfnFL332D3c/sIvlcpktsShzxRLfmxhnb6KFV1ZWWK3WODw/z2QxyWefO8pLE7NcKOdZrdZI16oMtMQ4m1rj0Nwcl1JpugIBYj4v2XqNyWQaSxS4o6eX0nqWbaHZJOJ0sz0WJ1mv43Q5KDYbNGoaC1aDfKZMqdzA73fSrGtYqsiv7dlNUdN5U38v6VqdTp+XfFOj2++jouk8cewC83qN7mCA63u7iLldr8+2tbg9vGvjCHdvHuDlpSVWKxUupFLE3R4m8vbQ+lq1QtjpJOHxknAH7eQLs8mv/8NjVEsV/NEAVV3jxNgcparNwAaCLuqVBg+MbKbdG+BMapn+QIBD8/Msl8vUDYO6rqNZsCvewlRxHt00Wa3WWK6UeWZ+ktG5ZfxhL/cNDRJzefjUuROcSyZZLpXoCQTRTZNvPHucb124AB6V/lCQN/X241ZEdg11s7MlzuH5BXoCfsZzefKNBlJAZVtvh20lBKSzNco1HVEQaO+OUK/YzGYqXUAWQGtovDK7QDjs4um5WbZFIzw1O8ex5RU00+COnl7WalWmcnksoMPvp709ghpykFsroDoVmnUNraEhySKVQpX5hQyqQ2YgHuXC2hoAs8sp7t44aP9tJJmwy8VipULU5SLhcdPmcTMYDHJsZY18o45HUVley1KvNrCAxZkUhmagOGQwLVoTXro2tBJvDdLWGiHodvM/9u9nayTCx7/1Q+bNBg9u2kyq3uD84jJ7O9rwqiovzM0xns2giBb33rmT/3H/QQaDQe774tf48tNnaKbqJDq8GCZoukWzrHFmfgEl6KDF68UUYDaT5a1bNjHbqLK8mMMfsE3TPT4n5y/Pcflqkncd2I7X4bBtnlZXeOzwOdp7Y0R8Hg4M9TFVylEq1inlypyoXWZTtEnc7eLFmVlM3aBY1mhqJoVyE8nQWVjMMzuTweuWmZxIceHUIldGV7g8nSSfqyJqGopDJtISJOh0kqnVWEnnUB0KDU3H4VJZWipiNdbnM9djLhFFOwlIFjGLmt0+F0ENO2k0DdxOCdOwzd6/89A7+OH5UWTRwh3y4A16eGjXDjaHN5PwwJOzM2imxuZwjJDajyg4WaqewCkLYFnIYghJCNEwxwGRtdoVLmQm6PSGccqtgI4q9a4DpjyF5jzXt23nD44/zeZwiI/tvJaB4AaKzQU6vBF2xW6kac693ooWkPEoHTjlKnHXHgRBx7Lq1I0Up1JjLFZmGQ634ZRaEAUFUXBjWmUWK8sEVIOYaxdv6tmPJGYQBYtco85KpcILS1M45SQlrYRHdhB0dGNaDSQhwFBwBLAIO3yIgh+DFA6pDcPK0DQXUcQWNGsVWYi/HkH5Nxe/vT5K1EPTnEcW7Tn4bl8ASQxSN8ZI1maYLmQJO1380fGj9AY9dHoDZBqrrNVmOLa6yu74NkRBtdujogfDyvOpcy/wN+dO0xfy0urx0TA0VEmixxeipGWIORO8sjqFKkpkGjO8trrCr4/cx4nkOXyqSp8/TN0ooEgNPvSjp3hlYZHcWgFf0IMv5CG1mOUrv/QAA4EW/ual13A6JEBgai5JpWaHQmwaaOddmzZxei3JQqnEq7PzzJeKzBYKHJ+c49jYDB2tEUIeN5JXZWxqmflMjpZwwAaaioJPdXDu8gyCYLf5SqZOtlZja7yF6XyO+Zk0lglBv80+//7bbuX2HUPc0dfHbT093NDZwalihlyqiGFY3HLdMO/evInvjo/R5fdT8lgYQNDpZH4lzYbOBK9OzfKWLRs5sZYk5nKSrNUYzWZQVIVmrcktG/p424ZNRMJejk3O8Yahfj62Yxf3bRzkps4utsdjPDIxyYVMmufmx3lyapqE10vIoXI1naFWrhMJuBmJxfjO2FU2tbXQ2xrjtoE+lKCbV07NUKnplCoaHrfMi3PzDCVitHp9drRmMovD7cDtdfKBbVsZCiZYqOR5ZnySbKNOfzDIqbUUhWaD/8Xee0fJdZ9nms9NlXPqqs4ZjUZugAAJkmAAwSyKkqysHVPJdBzNjryWZB+PKc14JK/kpGTtyJYsrSKVKDGJJJhBgASRG6HROVZ35Rxu3bR/FAxL2vUc27s+q9GZX58+p7tSV7z9/r7ve593sZxHszaIOFTedd8uIg4fhmVyqbhOU9dRTZNnl5bwelwkvF7Kqorf7uBMJotuGTw8NUXU7eZCJs38SopcqUqrodETC5HwqFwuFtkSDvO1Y6dweJwIgsCHd12PIv5jSMLHP/4Xv7QRlJ/67KcfGrhzDEHgX/R9+Tun/2fW+b9mfebzn3nohl/bS0Vrka03sMsycbebndEI35q8QKmlsise5/TGOqYI+0YH2JtIMJ1vI2/ciq2dFW4YLKaz+NxOgg4Hbx4epmQY+O0ObJKAhUXLNLCw6PMG6XL34bO3xeKmeBRH0M3ps3MgQDTmQ5LbA9//+e5bOdA5xB19Iwz7I+yIJNgcSnB4ZZ5CU0W3TC4tbxCL+jnQ3UW3x0uXx8PpTBaHLPHo3BzHU0kCDhu/vWMf9w9tQrU0Vqs1UrUaXpsNr02hrmt0uj0IWMiiSFVrctEosb5RoKMjyEtzC5TyVRpNA8uCUNBFtCvEjo4YDb3JmWyWLo+bqqYzU8hT13Uqms7mYACbJLAl1MulwgZem8zfnzzDC69O0WhoWJrOxFAP35q6yGsLS6RzZVxuB6uVMrOZLOmNMhvZOp//tTs5nkpxPpfl1fV1gg4HQ34/w8F2q3R/opOo08FgMMgNnZ0MhUN849kzVHJNLEnE65LRGyqNRotg1IdlWoiSSLWq0lJ17t21mR9dvkyH28Njl6c5enyax09cZvNwnHsHhliuVQk6HIQcdgwLwk4X+VaTVlPHwkKURSRJolhs4nEpaC2D2fU06ys55uayVEoNZqwaF7JZbLLE3QP9XCrkub2vl03BMBGnkzOZLNujYWRJoWUYXJhNspGuUS01ME2LumpgmSaxDi9ur7OdXFVpsL6SRXAqfGTv/0K6scqTs3OsLmVYMBv0+X2Md0QpqC2mcllUw8Ct2Ng91MvZtXUiHg+f+umLXLqUa4PKTbD7bIT8dtKpOmSbVHWT7qEQRVUlmynh9DgQRZma1qJeqVG44uxeXSvjdEjEwk5Uh8jXHznOwmqKRrlOqaqxslqibyDKcMDHmVyW2yY2sX2km8mVdXRJ5L6BzehOgcVqGVlqhw/Um23guk2R8HsUalUVj1PGlAQcboV42MHgcAxv2EOjqjLcHaPYbFKrNWnWVfK5Mja7QrOmsj5TuNImN69GT7qCdjTTwixfwft6FFBNGuUWkU43yhUklaqZzFJjsD/Ge6/bzVAgwJZolHeO7Mekgs/WicfWYH9igLCjG91MIwlBXLIT0ypjobWFZ+04Va2GJDb4izPHeXRulreNbkMRO3hi6Sk2BbZQak2TrG/gs7mxiQFsUoWIs41ncckisijikrehWxlsUnvcxLJMTKuCaVWxSX1XTmuhm2XOZFc4l80xEevAb3OzWLmIXSqTrF8mbB8n4ui9AlSXMa0SLlmk37uTHo/EbCnNRDTGllA/Fa3GkO9mAE7nDiMIJVyyH1kMols5ss0ZLhbW6fUMIgshlmuX8dm8fO3SYXZG+q5mmy9VLzMR7UUROxAEO2CimavYpfbcqYDMcnWRndF+VmtZur0e/DY7jy3OMxYM0eUOE3YqKGIN08rgkAeo65PkmhnOZtMUmk3+/Ecv4I15SdUbTMQ6uFTIMpXPsSMyRt3IsSkYJdusc2fvGHapn5hT48XkAg5ZwiZJrNcL3NjVzU09PSSNFpWWiqbqqI0Wv3v9dQTsQX6am0NrthCwaKoGXrdCZ3+EezaNEnY4qOgaO6IR/C43XpudvkAAh9POpu44H9i2lR6fFwORsqCTTeaJxvzYJJnJ5DoLaxk0VcPQDHTd4MCmYQrNJtl6HQRo1Bt0D0YZ6OlgU3+Cp85NMd+okm40aZom3W4Pt/T2sqk/wcRoHwd7e+j2eDmRSjMc9HNNPEG/z0fc7cIRcPPWkWHet3MnPZ4QmtkiYHfQ5/UxHo/y/u07uGN0iH6fD4cssyOSQLfD3f0DzBTzlFSVl5JrvLCyyqVclpZh4LbZcMgyQ34fkiDw/IlL7NsyxJHVVYaDQZ6bWSDfaDC7uMHjr17kLXu38tTJGTTdwgJ03cBqaeC1894tW9io11mslOkOBZhItA2u18XvpMNVYbpcZEskyusb69ze34sJbI+2Z/oRLP7gpRd5PZ0k4mybiMJOJ5tDYX40M8O+ri5cinIl873NDZVFEdUwSdWqlFstGrVmu72uSDQMnZeSa8wWCkiSjOSQubC6wURXgpu77v45bfFLLTQ/9+mHBu7c/C/OOr/87VP/U2j+a9aff/7PH7r+1/agiBIRl4vTqQ28NjuzxRIrxRJDkTBxlxsDi4jXQ8jpZKFUotJqkSpV2N7RgSyJzBcL1BstIl4PIDAeCnEpX2A6n2MqV6CkaQz6fciiSMLlw62E6HB6cSkWLUPn+q5OujsjnFldZ8twN5VWG6LrcDmpaDU2BWJA+0NoWAZfOH2cTL2GQ1YIhDy4bDa2RiJ0uj08v7rKuXQGA5jJ5EgWSpzJZSlqDUJOhUM942wJ+ZkpFahpGqppUFI16rrG08uLbA4F8dlc7OuMcbya48EdO/jGcydw2GVEAQzTomcwRr1cZ1tXnG9cvES1pbFYLuO3O3ArNuYLBaY30rxty2a+cuE8fT4HS5UKr65vkCyWSW2UyRZbFMst5qs5CppKMVvG6bYT8rpRdR1ZlujqCvHgLddwa/cw6/UylgW39vTQNAy+fuECryyvYooibkVi0B/ELok0DQO7LPH1n5wAWcAyQVJEChWNgNdGudj2j1kWKEq7gbBgNJBEkYVSiekLq9Qa7VGEc8kknpAHjyJzY1cnsihQ03Rapkl/OITsUqipLbSWTqPaxOW2t2dfLAtZkZEkEZddQtNNVlbyZFJFDKfI7YP9XJ/oQhZFjiTXCNjtOGUJWRCZKhQ4fPw8C3NFvH47PreCYVrYFJFY3Hc1PagjFmA0HmVxLcOHbtnP1nAnydoGr+XT1Osqmggf2LGdtVqNn5y9iCmLdHrb78EfPnOKqbMbPHtunlSm1haZAKaF7FUY3dbD8nIeq6IhhhwszuYYHYlx3UAfY+EIz5y/THI5gyAK5EsqpmnRFXMhiWAaJucvbSCKAook4vU5kIT2XKnhsNgUi9IbCJBt1HHKChfn15hcXGPnQJzbe4d5dmMF2amQz5SJxryIloUkCtQaOrWGjtoyqDUMHHaJcMSNN+jhwtlVVqcLNPQ6xhUGZCFTptnUcbntbBno5tyR+fYMZstsl4lUA0232ngj35W5zWKrLUYdMi1ZwCaLVBs6TrvEga3D/OHefeyO9lDVGqxVqzgVjR7PbiTBh10u0jRaBGzD2KQulqtHCTm2UGwtUmwVUcQqb/rRw7xzvM2Kvamrj18bGUcROzCtFgM+F7IYRRAK+G0e/svrL/LK+hRjwSBFVWU0kMApb0URO1CNWf724rOMBsEhJdpu2fopvLYEAiKatc6xjeP8/dR5XIrEeq1Ol8fNsY0V/uSlIxT1JmcyGW7s3Iko2Gl7MSGvTuKSuxAEGc3Ms1YrMOSP4JLtdDj3AW0w/G8/+wj3D23CLkpXjD4mLjlEwu3AtBqYNIg6dpBqnMdrk4i7uq4KzWG/C4sWFhqKGMW0aihSG5c0X34Ru6TyhXOn2NPRQYcrzLB/D0GHm+vj+wjZu4E6r6cXEQSTbve1GFYezSxzMb9OQ9cJu1xcTmf4zB130OFyEHG6CDkc/HB2lrv79zDo20VFW+WltRVmihksYYOiWqPYUun3BVgoF1iulLm5azvd7k6u7QwRcNk5k0pjs8ucK+bYHvXxgwuXKGQrROJ+PF4Hf/W2ewh53RSaKnG3i+VKlXJLo9frJuFxE7DZGAj4MCw4kkxyc08PL6+uUVKbGLpBf0eEU9OLlDJlWs0WjaZOSzcRTIu6HRIeL6phEHI68QU87OnupMPtRhRF3rR1DOtK4WOpVCbVaDBTLBF2OOh2u/Hb7Whm+7ioiBIlVWU8HKauaVQ1HZ9NYTKXZSwYZSzQQbdnlI16kmStyrlslrjLRfVKV2691k7y2qjX+OrkeVKNJg5Z5lw6zfZYB1siEXTTYjKd4nwmg8fuoOkQGAqGCLtc3NjVRVbXmFneQBAgla5xeX2dUMCBxynR0kwaqkE44KClgM/lgCus6rVKhX6/H6csMxHdgSjkcSnQ5/PS5/ext6OLht6ioeucy2Y5k00TdDp44vXzlCW4XCwyFgzS541gCgan0yk2qlVqmoaIwNs3bSJot1PWWmQaDaqtFqnlHKZuUK80MXQTr9/NtlgHvz4+zrvHdnDrYA87ox0E7f0/py1+mYXmn33u0w8N/iuE5tT/D0Lzn2sG+uVeAswWi0RdLro8Lrq9Pu7u7+MvTp5kczzG7o4YUaeTd45uZr6c59GFhbbz27KIej0cWV1haTrJ8Fg3kaAX3TSxSxJfnpwkWS5jGhb9oSBOWaap6yxXquyKdGNhIQhOEq5OHJKNHy9MsTceh5v28c3T5zB1k2DIy4sLi3z/yClKd7W4p38Er82FTfTgs9vJ1GrolknC6+Xu/n62huKczKy2HYaKwluGh3jryDDLlQp1XedsJsuXJyfZE0+xORQm4nSgWxZr1SoBmx3NNOnzeQnaPYiCSFPX2RlPIIsCDrtMU9Vx2GUGN3eyNpdiZLyH6xNdHF5apqlrbA7H+c6rp2jWVCzTIt4f5YXVNV49P4eqG8Tcbvr9PoSBXnw+F7ppsjS1Rq3UQG20jVLVUp2B0SGGAj5StQZbIyFu6hziVGYFy7LYFgmhmyaVVost0RiPHT3DKy2N+UIBE4tMscKbt43z/s0TfPT37uQrj75Cua5jmuC8kl1da+gEDAubLKJqJtEOLxurOVxeB7n1IqZloesmhgU2u8J3Xj+DaVgsjg8zFgrS6XGzUim3B/4Ng+HuDiRR5LWT0yg2GU3VaDQ07E4b0a4QsxeT1JttsaLIImMdUU6k0lzO5wk7XSyXS9R1nbv7B3jwyZ9i6AZvu+kavph/idHxBPmNIh29YSqFGrl0hd6RDpo1FYes0O/3c6k3wp8//TLfuXSJj+zbyycPHKCp6/zRyy/zsedf5At33M5atUZNa3FscpbllSLlYgs0E0vQcQfs1DSTGw6O8Kc3HyDkcPLd6WmeL0xChwtFEQlH2m3wbq+HV1ZXsTkU6g2NWrMN8d883kEsGuDMqQXUloHN1q4iq602W9Xrd5ItqizNpnk8MMdbN21itVLhhYszhOMBXHYbTywuMegP8sHt2/jI938KgN1pw660K6ZOezs+slzToWVQaOrousncYpHmWg3qOnPHUxAtQ8sg1u0hGHBQSJc5p7Zd9f/wmUegLTArWruFrptQu8LXNCyoaKiSQEUUqDUNVM1kpVzCJspkmiWeW13l6MwCbxkeRTfXUcReLMtENTQsDCzL5McLM/zW1mHmyxlG/B1U9SZ/d8/dhB39yEIUQWhjggyrhCwGkQliWFXs4iiCoHDPwDwX8zn2xvaRU+e5kF+ly1NFESVMy2q3Zquv8PF9UWxiNz6bq409AqYLl2gYOh/asYdLhRQzxRIvrK6yORTkxoE+DvX2MFcqIgr2nzschuzbEQU7FwuHccoKB7s3o4h9SILn6mUUsYuH73k7i5UNqlqDAW/3VZ6nZWrIQgeqOQXAHx99iQ/v3v1zeef/MFN6PP0ECdciHc5uJNqu9FfWVzjYY8Mly9R1FZsoU1DPknBdi2nVMawKa7V1aprGsD/Bau0YmWaVicidXJ8Y4NWN73A6lWJksJOQPUTCuQfVnEURe/j93TKi4OG11ON86/I0Z5JJ1uZS+MNePnDDNaTqdeIuN08tLfOf9h7ALl2B0TeneGZpiZXZDSwLluczrJRLlHMVEn0RDowMsqcjSk1rkW00mSsUeMPgAOdSabZEo2wOhQk7nDwyN8vBnjah4vHJS3yyWmVPPIHPbuf5tTyTi6u4vE5qpTounxOpoWHqBk6vg5WZdW462Ad+P+l6nbeNjvBaKsVkOoPH1m6fa4aJTZRI1aoslYo06ioviwJel4Ncrszm3k4qqsq1XZ14FIU/O36c0VCYXq+HTo+Xj373SXZ9IMZ6rUqvd4OCqnIxX2Sj2g7o2xxqj2gU1CYhh4Nuj5fxGyJ8c2oKj6KwO9GJLAg8PjvLDT29zF1aZdOWXsZCAbaEQ3zy0efaqV4IvHVkmHOpDfoDQQJRHzviCUYCbc7lwyfOsW2gm3+/qz3LXGmprFQr9HrbM+jZRpPf3d7e9DjlBLf1JCioy/hsbp5avsBiucL5bBb1Cs/z0EA/Z/79n9PQ53n749/jJ4JIYLODbeEwbx/Zxka9QEPXyDQaPLO8xJZwCLsksV6pXA3dCNplqhUNt8/J+nIGVdUYCviJOHLsT3TT7f4fw2n+s+vfwnV+hQj014BE29j9qV84/2bapKGFKyf90LKsT/z3bvNXQmjGnG4G/H5WyhVeqdbINeq8lkqRL1VxKwpzxTK3dvfR4QojixIf25OgoFb5yoULvLK4RNDj4ppdI2xUqzhkmbDLiV2SeP5IO1avayDKXLoNpw7a7aTrDXRTxzALSGIAAYWQPcG7R5187tyrvJ5cp1Ft4vQ4KJXrdEWC/LcP3Mlbvvpt/tJ8iQdv28+DW6/lSwfvQbcMTmfWOJlO88jsHH9w6Wm2DnazJxHn0nKS975+CUEQ+OFvvoeY08dcqczJ9SST6xu0Gi0+cfvNXNsxgCyIlFo1Hl+cQbcsHp6d5LpEguVKmfeOj/OOb30fUZZQDJNCWUWdWue5//h+4s4QF/Kr/NaOHXx/Zpa/eewlJFFA000UWeSG/j5eX0+S6IlwbnaZYMxPo9pkU1eciXiCV1dXGN3Sy8VzS7j9bYzRm6/bxYd27OWxxSmu6YgxHoqxWMmQbzZwyBLZRpNKq8WuaJTr4oP8yb6bmClt8JkTJ1gtlMhtFNl9MMobf/Rt7hoZ4YvvfyPbIjG+c/kyP3z9LOV8jXy5XUmNhRzYFRG1oeELutE1g0JZRdNNgj4bkQ4faqNFS9Vwe50cX1hipVTi3uFBru/s5CtnzrEwlby6A//Dd93G9Z0Jfuu7P8GotaHtDWebbZqIuUll64iiQMTl5uTGBifPzLJn1wi5ep3vnZrkK8+9iiS3xVSh2eSP3nEbDlnmpZUVvv/4GYZ6fUQ6fCTn03T0hknmCgwEAtw/PsY3jp6kXG0wEem96ia8d2SELzz6Ih8/doy3j43yvz/3ChemslgNHTQTf6cbw7DQTYv/7X0H+Q+7riFk92NaOi5lHjnswDQt+hIe+kY7UQ2D6XyBlWwBvaVfIQ+ALIsUMxUqhTag3KaI7QpbtokggGFYKE0dv0dB1y1OnZgjnSni9DiIdwTp9vpIViscm13gwxPXtGccYz7q5QYfuulaNNPkv/7wMNmiSv0KA9PmVmjVNFTNpKWZ7axyaIvE9TpIAmmqpKcKKF1u9nSH2tGSLbPtKNevzGlG7ZBV24LTon261DZc0TJp6SY+t4zbKXPklcvk9l3Lv/v69/FHvLx/7wQ9nmFKrSRepUGqUSFodwImJnV+d9s7yamT7AiPUtNLBO1ddDi7Obz6CNvCCWRB4ujGAhPRLhIuL6JgRzfT7VQfc4O9sU3s63BS0ZZZruTZEdmGZamkGut0uyf4u9v2kVdP8sDTf89XD72Nr146xR29OUb8N1JQm9zatY+KtsGlfJ7f3DrBG7/9LV57/2e5szeFadXYFUlcecqqV4WkKNhRjVnGg7ddeQpWeXn9WXKNBm8afCOi4MS0VCpajbHAQXQzg0UbQhrHYrsAACAASURBVJ+sH6PTdR0tcxWBtoD9P259/1WReT7/DL/x2BM8+6734ZS3MRG5lq9eepQPbrmhfT/MIkG7A7/iYsDvJWBzcyy1wK1d+wHQrTw2sZt+r4sB7z6axgzPrMzz7za9AQBJ8PEfdt7Nf3zpu/zVgfchi0EABOx8YfJhBnxe1mpPEbQ7aOgaKzMbVOs6hUqOT24cRpEFLuzK8Bc3HcR3xbBlWBWeXFziyecnkUWBdLYJkoB8YY2dE+1s+F8f30xN0/jh7ByvzC/yhvExLMvC73DQ7XUTdbq4lM9iWBZ/cvQoU+eXuOuGnbgVhelCnqmldQRBILVWoNrQKZRa+L1NJFEg5LfjDXowdJNPfetZfuPN11NqNvnm1BT1lsamSIRL2QzbIhE006SmabiVrjbXVRAIOBzMLG1w3+6tXM7lmJxcYGp6hVt3jxN0OAk67GSbTVK1GoNjXXx5crI9X5pOcbCnj1S9xqDPS6nV4kI+j99uJ99UeWZpCY9i457BfiJOJ2vVGhGnA5cs8/pr8wwEgnz8rXewL97Jty9fJl2vMz7WS1VrcXI9yaG+Hj538DbmSgUsa5CK1mIsGGYyl+Ebb3sT+WaDAW+Ub1w+w6ZgkIDdzp5YD9fFeziSXMIhtY1jplXmePoye6L9fOPySU6l0hTV9ojBZ289yA9mZzi+vsFvbfVilyLEPV5ObbQr35+49iaS9TwD3jimZdLrVfne9AxNvU2V+b3dExxeWiGbKuHyOllP15AVmXuv2cYHtmxjupjjv75yjIfdl/nO3VFkgv+f65N/q/VvwdG8Qgj6AnCINh/9dUEQfmJZ1i8iLV+2LOvef+7t/kq0zj//pb966BsP/Rn39h/grv5eqkaRu/oGuK6nizOZDCVVxcBiLBhCt0y+eO4Ui+US6XqdiNtNrz9AraWhmQa1VotkqcxSscjKcgGbIrZdudUG7pCbbKNJt8/Lrd1D6JZBppHGrSgIKAgCjAX9/PWzLyGKAnpLp1ZukNrI8/bd20nEAty8aYi/fOJFMorKNR0JfIqLHk+QreEIVb3JUrOGZUGu2SRfq3P+9AaFQpMvHznNi7klPnvoLjaF/YxFIwR8HgJ2G8uVAi8kl3h2dRm7JGECT87PU2ppbAoGmYjeyaef/i6iKNA51IHDJjGxbYAHNt/Oam2Fz5w4wS09PYwGAzwzN0+loiIKAooscnp6hd88cA1L5Qpbezp5z/hmjqc2WM8UmE9naTU1nC47G+tF1IaGYFl86q6DBOweZkoZii0VyzJJ1ms0dIOQw85CucINXZ1sCnbgloPIgouo08fueIC6ZeILeng1meR9O7ZxNLnOfKnEUqXC6VSKT9x6Ex++5SZea6TweWzYHDKyIvOH997CR/bfyNdfP0VL1enuCYJpsp6q4lBEEgMxbhwexGG3sVYs0bRMdsWiqMCh7aOcnF2m2TI4cn6eZ6am+U/3H+Klqbk27Fg3uG7PCA1DJxhyUSk1WNhIkymUMTST6bkUWqtFs95Cb+mIskgw5ifbqOOy2fn64WO8fHypnS0PdPVH+OM7b2IsFuV0coO5ZIZo0Ifd3XawRz02Em4vdlHhmo5uNpQWL7x6HlvAzcduuI6P3nUj07Yq77tzL3OpFLW6hscp88592+n3+WmZGj+YPc8nvv0M4hVYsyILbBqIsyUao6nrLCQzDHbHMGWBQq7GvuuGecfeHRQlk9WVPC3dIl9q4XHJOGwSlYaOrlvccctWVtdzyJLIoT1bQADDspjbyLI1EcfndvLw5Uu8fXQrkk2gpyPCiY0NIk4XHzpwLTds7efo7AKVooqgiJimhaSIGFWtHTUpCW2xKAhtN7kBqCZmqsHqdA57wkW824PlUdriVLP+MRVIvHJduc3ORAD8NjoTblxOmVJVo9rQOVvPcOZEkmq1iRi0cd9gP5IgUtcb9HgGsUsWoiBiWiXu/8nnePfYDpzyZuxSmLO511DNJLujd+KU/RxPn0O3TGQRliuzdHtGkQQ/hpXlkfmXibkcBGxbsEth4q4OJMFLqjFLplmlaaQJ2HqQBBWdKs+uznJ7Xx/dngh2MUa3pwuQUM0iG/USnzlxnG++8S2YVobPnD6MLDZIuJ28njlGj3vzVQanbhao6RtMFSeJONyIuOh2BwjYwSkbtMwkNqkTmygCFrIYYqX2Kj6lm3O5s3S5O2gZ6yhiB08tP0G/L4BpVZDEAOv1WR6dmubWoW4ijkFEwcnFwnm2h7dgWmUkMcBk7hybgyM8tXyJA1076XTbsUs9CILMBw5/kfuHbkAU7FjoZNUVrk8coGWuoIgdFNVTTBXnee/4/chigKZxkQef+wrfnz3Lod5e/ubsWS7m8lwsFDg5t0wuW8PrVtA0E92wiCX8bO6MU1Ab7IiEWKuf5/df/iEn1pPUSzXUlomiiHSEHTjsEjdtGWVHNIKJxZ5oH08tz5Fp1tkWi7JYqdA02k76uqbhUWz4bArnMzlyhQplS+PF16bYO9bPkddnyOYaZDINGg0dt0um3jTwe22IAmTTFe65fgdSUOYje/dyPpdDEkUGg0F2RsN0uNy8klzHqchEnU4Wy2UQ2tGM22MxVAnOrCS5cH4Zw7CIdgU5M7nANaP9/ODoaZKNGg9O7OJ7Fy4Rdru5va8tHl2yjYDdjtdm48n5BQYDfjpcLmpaixeWV+hwe7BLEidTKTaFQjgkie+cOU8yWaLaqrOiN/na8VOcuLTAn9x2gPuHR/mt7Xewr9NHyzR4dmWFd4zspGY08dnsxFxuWoZOp9vHZC7D4dUFZFGkaejc2TuGz9aDadVwyAKqUeexxSO4FJMeT4jvzp7jgbGb2BJx8cDmQ+iU+eKZM/zFgfv4/KmjPDJ/hLeObCfqtng1maSh60TcCovlMl1uD07ZjkcZwS4XqBs6Az4fG/UGdV3n4kKSHeN9fOvdb+KJpQX2dXcRdjh4cmmZgtokuZ6nadPYH5/4OW3xy906/8xDQ3eNI/wLvy5868Q/+Zg+/vGPXwtstyzrcw899JDx8Y9/PAiMPfTQQ0d+5jL9wP6HHnroW//c+/orUdGstFR+MPcYPV4fO8K9PDC2n2wzi9fvJFevk04VkQWRQ719vJJco2UYpOs6IaeTkqqiiCLbohEu5/PM5HKszGywmKwhSQIN1cBhk3DaLabOLtNsmRyRpvDbbJxOpXnx1Yt89C238b7N+ym3SgTtQT54y7X8n6+dotXUEAQY3NSFU1ZwSO35yNv2bsW0YK7Udo4DLJZLeBSFH933Lp5YOs+lfIE/uXY/95W+wcXLOYymzvFXlxk4/2lGRyLcvG0TD27bxplMipOZDB0uJ/s6YkwViqTqdVZW2+0Yt03hxoSJbJPxBd2ojRZqs0Wh0eAvzzyKS5b56N69PLeyzEyxhD/ipVKqU6nr2BQRWRL40iuvY3PaUA2dBw+fwOdzIAgCdqdCJlUhs14kFvdRzleJdof409dew8RiwB9AEgXqmk7QYSfmdHJT1yg7IgVmijkC4fbu0UJjvb7Bbx9+hnKpRn6jSPdIgk89dwSH28GmWIQXFhexyTLfm5nlvkGDa3t7ONjbzaNzCxTVJiW1xU8Wptm/dZjDxybxhTzE4kHMqTXqTR1N1VmtlDl+sh1WtTK7QZfXy4cnrsEmKrxhYJC/PHWKF09NUS43efBLj+B2SkiSiF032RQO89G9ezmTyXAsuc6hvh4em1/k9PQikixSLDTo7gtjWRalbAVdMzB0g4VigZHNPej6Er6QG1mR8Tjs3Naz6QoqReM7J8/x6tIyI7Eopmnx/ekZJmLtaplTtvHA+DhfefQYr80scFNPFxVN4/aBAf76Jy/QP9bJrm2DHD1+maPr6zw+P09Dbzv0bzswztxqmo21Av2jcWaWNsg1GoiCgKxIrKTzuLxOWppJpd7ELklsicaYCybRWzrlqoZhWG0xKAqomsHTL5ynty/EXbvG2R2L8pXJ81zf3c2PcyXStSqX5tco52v8pvgEP3zDQzy6+D1eXm7nvx/o6mZ7JIY/4GRttYquGohXxCatK7GS/7BDF4WfcZXT5mZWNdTpEmuW1a5YCrSNPx6F7i43LqfC9EwBss12VdSrgGqQLqgEvQpup0xDNXj12BL7bxnixqEB/HYbRzfm2BZO8PjiZd4zGsMpDyHiRLNW+Lvb770yg9gi0zzD8VSK23r60M0supVjuVLh7SO7uZBf5FuXp9kRGUTAhiDYONA5QMAeRDM3kIQgupVEsJx0u68n4SpjWSqalcQm9uCznaTH0waEA7TMFQTawjFk3814KM837tyLXRqiaVxkVzTC1nAXuWYKEbhQeI6toUMAyGKQoH03QTt87Njn+eCWbVS0JuPB7ShigmOpx9kXG77qItfNAnFnDyY1JqJ95NRpwo5xJMHDjmgnIKJbZUzjIhGHlw/s203c1W7BmlabMdwyF662qbeEowiCjU3BAJbVxKvsxLySyfHVQx+9+vgsSyPu3E1ePUvANoRptXDKfnaEezGsEiJuTqTn2JvowCHJ5JoNHtq/n2S1yt+cOUMmWcBua+eCe1wW5ZqGJ+BmLBSkoKrMlmf47OnTPPXyOXTdpNYw+PA7bqakthgPBXl6cZFDPd2UWy3WqzUuFpL89vbtfGB1jdFAAFEUcEoSc6Uy37x4kXy2zFhfgpOnZnnzrXvYEQnzN83Xef7cFO2RbgvR1u4ENFSDvoQbt89JvdLA5ZD58MQ1vOfRH/PlyUkUSaJQazAcCOBVbLxeSZNrNOhwu7BLImGnA9UwONTXy0ur7djh/liY5GKGQNhDq6Fhs8vcPzTIifUk45Eo5ZZKJV9lyWFDN00u5XN0uJoslts4JgCfTeHRhUUe3Lqd6UKJ5xYX+Pqjr7F5PM418TgN3WBhOoXaMllYLJDNVGm2DCRRYKlSbscf1xcxLYvFUpnNwSBfOn+cbVd8EE7Zzv7EIJZl8eahXdT1OouVDHZJYrGS5vNnnubC2gaRgJcHtm65muT3By+9yEa6wK5ojJqm8YPZR3l2cRHNNDmyfolv3nM/XsXJYnWelUoFh6wwt7TOe1+ZYvE/f4z1ep6GodLpcrK3o4+YK8PpTIblcoW1SplQ1IsiSbhkOzcM9PPC0jJfefk4Lq+TkN/Du67dxe9uuxnVmLtqaPsfYf0rK5oRQRB+Nmnxv12J9AboAlZ+5rxV2gE8v7iuEwThLJAEft+yrAv/vT/4KyE0FVHEtCxWKmU0Y4Ht4W48ipP1ep5H7nsfx1LnqbY0REScskTC7aKktrBJ0lUAuiQIJDxeIi43pmGxkprHYZPo7vSiqRo7dgwxEgq1B44Fga+9coJmvYUstV9oARtBex8/XX6J92/ezXS+wDOvTgLw+YOHiDkCvHnIT11X6fP6mC+VWKtV+frFS4RdTu4bGKDH40VA4c7ezeyKFuhyD/Gj976Lb1+e4mw6zeEXLlAutpi8kCYQ9aFv2cJIIEi20cRrszEaDDFdLJKslJGuDIwvlytoVpI924doaDr5Rp1ipsLtAwMYlsWA39dOk9A0nLLMXZtGmI5GOHN5kUa1iShLBEJeivkKss/DjdeNUW21yBWrTF1Yp7vLRz5Xo7MzzM3bNjGdy7JcKhJwOsnU6+zuiHFjVzdnMil2x3oQBZEOZ5C4K0qumSVg91Jq1fj82dOUSzUs08IwTFyKQl2WkGSRXL1BIVXC5XMyK+RQRoYQBYHJbI7RUJDrEwkuFwpcyhdwKgq7dg4xv5LGmwhx875xHnv+LLn1AqZpUq5qaIbJwECYn5w8zwe37MRn82Bh8YnrbuLHHTEahsHpVJpjZ6ZZT1UxHTJf+vHLpG+rszfewT2D/ZzL5rijv5dr4h08v7zM5MUlBEmkKxygnK8ymojx3NELaKpOvCPIuw9dy2whz2qhhN/h4PnVGW7tHqHL4+bW8VHStRphp5Oyr/0e9CquqznC53MZag2d+3Zu5fp4Lx975UW+/egJfO42XHu1UMIfdDOVyvCR/fvodHtZrZaZL5XZHovxfHiJVktn76YBXjw1xZ37tjGxZZwhf4AjySSXz60wezHJI047MbebZqNFo2nQl3BRbxpsZBugGkguBUkU2DHci6obNHWDQ/39bNQb+P1uBEHg/mu286PXzrK8nuVi4TB3991JyzB4dnmVdKNGt8fLLbs2s7Jabs8hO2RsNpG1ho4cdqDnVQDcHU7qTQNRFDAcUlt4aleg7AAxBygS99yzlemLy7h8TnyhdisudCDAhXNJKvkmqCZel4x4ZbbzxptHyKeKANwz0I8APLeyyk2d+3jPpk4EbOhmEpCRhRBxVz+amcK0qngVJ/f2D9PjuYGmcQGbOMyh3jwCNnZFDjJ6XYJ0I4dDslHR6gz5bsayNPLqWbw2DZs4jGauYViVq/GT53JPsz3cQ5fHS0lVGfF34FV2AlBQT1JQqwx4exjyxfn+3Kvc01ciYJ/gYLdBQa0Qc44RsK/ikEaoaGeuXjfVeI2ALcqfXvt+msYMPWIHktDe2F3Xcc/PHT9lMYhpOTCtJm97/HtUVZV7R0b4X3e+nZhjGEkIo1spZCFGl1vi18cGUcQ4LWMRRexhb7yHl9fPc7BrEEEQ6XAGOJc7g89mvyo+/8FEZFkmgiD+HPQ9bN+NZi4zVz7Nj+bm+NCO66jrKmFHD1+ePE+n14tdEtkZiTBbLKCbFqsrGVTNJBBsRyjWSnVsdpmQx4XXZuNiLs/7HnmUYrZCUzXaaLRE24hyoLuLSkvjtv4+nl9do8/nZa5U5kQqzYd2TRDzeXlmZYWDPd1opsnDL52gc6DNgDz2+lR706XrRJxtasRgbxyX14koiRTSJcrFBg3VoFTTcHkdSJKIw+2goNb427vv4tcf+Qn3bhljrpCn0tJ4amkZVdeptlQy9bZBLep0sisWYbZY4siFGWLxIEtzG+iG1aZt2AQiXSG+ePYsyYU0ezu7eGppCa2lMxgMMlss8L3L02yJRnny9EXu372NNwwPErDZubXbhWYaJNwuZs6vMTgYwrIsHp+bI58tU67rCALoZnuT6XLI6LrJxXyBoN3B6Uya6WKRjWqN/7L/Jk6mT5JweYg6fbhlO6lGkaeWFgg67CyWKyyWStzQ1cVLq2v8+PBpnHYJ+1YbxzfSTOWypAtlZEXGNC1+58dP8Nn77gLgdyd28dXJC/zm137Y/h8rCHSPxFEbLc6fXWOgP0jQb+foxjz7OvpRRAVJCHN04zSaabItHCZZrdHT2UnY6WJHNMLvPPc0cY+HHr+f4Z0hIk4H+aZK2G7nVPYy6XoNOMmhns0UW9X/d8Lk33gJ//qs86xlWb/IRb96s/8Pp1m/8PspoM+yrKogCHcDj9BOcPwn16+E0PTbvdzQ2cejCzNcn+jDrXRgWToffuEH3D8ywrs3tcvhebXMwe4RDMvEsEwu5Dc4k8kQdToxLJP1Wh3dMol43IxvirBlpIeJjg6OrSX5y5sPoogylmUiCm2u4BtHR/nu6UmOrSV540AS3TL5T088x8HfGOO9W8Y5/PoFAhEvDsmGJNqQMPEq7bhLt60tEpxKu9JZ13U63XaWq2vMlwtc1zEAWPR5BvjYnnEMK8erEzt5ammFrz95jGs6u+hyhxAQGA0kmC+nCDu8BO123jg8zDG3h+2RMPviCWZKl3hw+3YuFwr84PJlbHaZ2/v6UA2D8eAAeTWPahjMFfIMBUPsjndgWCaqrlNoNlme2wDLIhjy4rHZ2BmP81J9EbdTRlYkojEvDllhLBTghs44P56dZzFfYDgYotvjIeYMcFOXG6/iBAQEQWa6uMIjc3O8b8t2vnLhHMlqheHOGBvVKmM9CZZLRWIhH4rY3gx090QxLYuJRALLgreODPPXp06zWimjmSZ39fVzW88wFhbH1peQt2wh3aizVq3TOxChWVfJJQsAeJwyYwOd/P6ePW0uW2GFgN1ByO7jYE8/Uaef23qyHO6I8YdffZJKrW1y+v7TJzm/vZtDw0P8+PR5DowP0+PxIIsi+3aMMBDws1As4Q97Wcjl6eoN88C+CfLNJul6ox0EsLjOsmXxUjLJ6+k046Egd/f38eh8e676mkSCNwwOIAoiAgKzpQwBu4OPvPtWHtw6gc/m5pPX38RER4w//c5hXvzpZa69aYBrxgfxO+wcXl7lg1u3kqzVyDaaLJfLfGTvXjw2GwulElWtxRuHBtga6kQURI6ur9NsGRiGxcnj7aRXRRIIh12Uy01sisjtt29G1XWibjfPv3yJO/p7+czzR3EpCmuVCscvzFEu1lFkkf1vuJlwIshrr8zx9q8/zA8eELm3fy8PX57maxcu0uvz43fYiQbtyFdmWUVZIhQ1kEWBoiigSAI9XT5KpQb5cos3vnmCxdU04ZifV16eodE0iAbtbJno4+beXl49NkO2qKLO5vB7FG7p6WJrd4JHnzuDIov4Ak5aDY1aQ2MiniA+NMR8scTxVIpiU2W1UuGp5Ve4vXcff3vxSd4/fhsibgTBhm5msawGmpVGNw06XH1XjztPLj/azhd3FOn1ZqlpTbrd/dikPmLtUB1M2hVkm9iDasxiYSAJ/wg1HwuM0tAnGfZ1cCa7gk26EvBgXGatlsej2NHMFUTBxZsGt6ObOn9z/u+5b2CULvd+TEul2KoRd4JdDGBZBoIgEbCFUcQuWuZi+zX9GTbgLy7TaicbzZSO8tlbbuOxxTlu7upGEnyo5jyyGEURElcvLwk+WsYi06UZtob68cgOvj11majzGbaG9mMB2WaDGxP/90yOduWzXTFaqx1FAJ5fW+Adw/cgixJbwyHmyhtsCmyjpp3FrSjEXU7uGxxGROSZlQXGgkHUpobDJmJoBhvLWRwOhZnFEv5wge+Zl0mlC1RLDZqNFj63giSJiKLA3nicbKNJl8eNZpok3C7yzSZjwQA1TSNVr/J7Ezt5JbnOarVKttEEILWSpZCvI4kCkiRQ0zRmikUkRWJ+eYNsukIo7KF/IE6Pz8/hY5ModgVv0M2eXZtZKhX5oyNHeMPwEJZp8dLyMuurOQAqjSbDkQjVVotTG+uUSzUcbjuGbnJgsJ9gzM/13T3Umy081SZqo0VvIsrUTLvw5PY6mYhFGPIHeG00yaA/gGGZ5Co1HPEED1y/h4VSmbliGYD9iTgOSSHmcrJ7zxCra1mCIS+ZVBG12cLtkKjULTxOmZtv2MxbRkewiRK7Y514FCfvefJHZLIlfEEPTy/PcqCrk08efw3dNJmIxym3NJKVdp67bppMrawzEgyim2b7vSiL3DE4xOvr62SKFRKhANlqFZujnTN+JpPhTUMjfOr140xOLlCotNA0E7dTZmkqSbmmtQ2h5QbdI3Emoj1UtSbJ2jp/d/5J3jwyzFZ/kKPrqzwwvo1csz172uv1sVKtsT8Rb5t6q1W2R8JohskPZmbp9/uwLHhicZFcs8lbh7f/k5+ZX5b1b2AGWgV+Nvqrm3bV8uqyLKv8Mz8/IQjCFwVBiFiWlf2nbvRXQmiu1wq8tLbE/kTiSvRiewd938gwZU2jrjcxLBNZkHArbsC8ghuKcENilCPr06xVq1RbLURBIOJ2sWnPNrrcbaTGQCBAzBllpZrEtEy8Nhfv3NJOD/jjW2/kjx47zFvT38fQDZr1Fvf9+Fs0my3uvW4Hv7NjJyF7u1IJIpKg47XZSVZrlNQWEacDr83WNlxYJqZlknB5yDRLGFaBQe82LEwkIcQN8f2MBeZ4YPPmK8719ssnCAKfPnGCZKXCjo44Z1MbjIUj3NE3RK/nBmZKzzPij/Lw9AzTF5aJ9YQ5mkyyL55grrzMmP8aHNIk68UycY+Xi9kcWyNRnpqfo5gpIysS+VyNbLqEu8uGXZT4/ev28jn7aW7rH+CRyYuohk7E4cAlKxzo6WZHR4yFYomw08lS5f9i7z2jJL2rc9/fG6reyrGrOuee7pmeHKUZSTPSaCShgARCFgiMQeZggy824As2xjYWNj4+GIyxCcYYY4IAgUCggLJGaWY0o8mxc6yurq6unKveeD/USKB7zvVa+Czf4+Xl/am7qlfXqurqXc/7/Pf+PWm+MzZGRVNZHw5jE0XsksSBnm7anKPs6UjR6nY2+Z02Oy67jecOX6Al6uXJe9/LQinDoUSC7dEI0/kCTy0sct8V+7llIEdBVbmUyaFIMh6bE93UuaKtB93UqegN5gp57vm1t7JSKfPg1DSHx2dYXc4zl0wzGuwBLLw2JyWthmGZdLijmJaO1+bgq08doj3iIhDxotY1suky5XwFWRQIt/iYSKd55uQlJFnk3qt3opkmAYeDRCxLfLWK32vn641j3H3FFgYuO8efuOEaVqu116+kX11ZxbQsXo0v8YX9+2l3BXDLChbNo7iRYCsltcZELodLVrAsk4Di4faBIT6lPYXdY2NxJoXT7eBPd+9mrlhguVLGMC0uplLcPNDPK4kVYqUi//f2HUznCxxNJOlwe2l3hfjqYy+RXakiuW2IInhcNmRZJJutIssitbrxusicX05hmBZfOnqCxFKOp20TfP32W/lIPkepUEVx2SmoanNO2IL4cpFff/Cn/ODut/HNG+7g7p8/yOOTMVKrJcpVna4OL6FWP4n5FKWKRjTkoKfNTaGkUirVm5uiXjt1XSOZyOP2u7jlwAYOvTKBaVosjMf50sUlMgWVW24aZX0kygtjU/z46ZMMrW1j0/Z+2tweXj07Rb3RHAWJl4qMpVNMXopxPOLlijX9KJLERD5PQX2J7546S5fHzc0911LS5nHLXby6ep7NLd0sltM0jBXWh8oIOPjx5DSfu+Z6vj95nk5PgG+NXeRPdvQBYFkaFiZYJm65mWWuSEM0jNk39C5JbGEqP8X64A2EHGmeXhznll4/db1CVddY4+8lVV8m4uzhj488ylw+x4Vzc5zdneKPd5m0u0aJOtYgCk4EQeH7Uw+wWCpzU28vm8OtU7jfLAAAIABJREFUiIILEce/2j9f21of9l+LIEj81voIqtGgblykoJaJOLreEGkpYCPbWGFd8EoAdMsk6nbzj+fO85VrbyCkbGdDqPH6Is8vl11svj7xyhGmCim+fXGMd64dYap4koOxxdcjZjvccR6cPs/maAs7oq08vTjHm/tHeOfwFr41dhJBEHjngSvZEgnz548/j6bq9La7iS9mUBsa5XwVVTPQDQunIlJv6PSOdGATReaLJUzgUjrDSChIyOGg1eViQ7iV2UKGK1vXNudAMzn2drbzVNBNbD6DqpvouoksiyTKJbZEIyQXMyhOO16vg2ymjCfgQpFkbtyziZ+/dIZatcFZu42uoJ/JxRX+fvYQgRYfmXQRf4uXvd2/+EyfSqUJuJ0M9/eQrJSZX25GtdpEkZdiixQzJRp1jY7+KNNzy9QrDcr5CqVchVyjwWq1yl9efTUvxeP47ApqTaXQaOCz2zidWCa7UqCtK0y3101V11kXaqHUaNDaFkQzTUYHOtnf0027y819z77IQ+94B2GHH8symSok+OyJY2RqVcr1Bh2tIU6dnOEfSjUee8e7CDpmyNVrjGey1HWNNo8X07I4tbhEpVQjUamyviXM07LIHddup8/nwbBa+UksyVy5jifgxtAMegfaeHZ+gW8fP83F8wkEAVwOia07B4nPrWKZJuV0DUyYmSsi22XunzhLULHzye8/wVK8wh1/NkC/dx/3j3+NQmOKY4kV7lwzyKC/hYENYY6sLHJzzzoKaoWJ3Cq6abK/pwvLghank4jLxQuLS8wUiv/T+/c/Wv07LJ0fB9YIgtAPxIF3AO9842MKbUDSsixLEIRdNIebMv/aL/1PITSzxQof/PrPuPXAJr563S2AhG6l8NvtlDWdR+bG2BBuYW2gHQEbhlXFa/OiiHVmi8vsiPRwoGsbvzZ0jqcXxzm0nCDsUGh3u/DZFW4f6AcMuj1RVmtNV0wzTJ5fjPFnu3fz8Ruu4eezc8yupLEsi6WZFdaP9vHhLdubIvPykL6AiIlGqzPI+0bDPDZ/kaVyBZso4rXZMS0Ll6xglwwU0Ubd0DifPYXXbqfHPYCJgc/uIqAEAR3d1BGA70+e4ZXj45QqOvomgz29PZxfTZJvVOnxQFVXKWkNTi7E6BxsYyTawlsHRylrNVocflbr4yyVSkR9XtLVCiVV5eCxC6xm6/jcNsJRL509ITIrBcJRP9d2deKx27lvzx5qus5sTwdbIhHskkS2UafX6+XBqSnevW4dZ1Mp3jm8hS5vjJdjMUIOJw5ZQjNNzqymmC8WeT4W4+7hYS5ls82he7ud/XvWE3W7WShlyNbrHJyfJ6wo9Hq9OGWJslZlZ2sHnz3xKgHFge2y6JYEEYdkw6WEMKw60/kcFzMpNre08aHNW7CJIoXBJr7k3U8+SFXT+Mmb340i2REFmVQtg9/u5jeeeJRKuUG1YbBuQy9fvPZ6OtxtfOTFR3jvus1c2dbGJ599nka1gd1p59DSElPzCS5cSmHUdLAschaoU2n+pXAEl8+Jy+tkfWcbdw8Pc//YOO2e5pbwXz5yELfXyYA3il2SMSyTc+kYLU4Xbc4gB/7uG3gDLm7tH0ASRFqdIWq6hsMuYRgWpYrGoRenODk4yJt6+7jnuw/iC3l46K6387mTR8nV62xoCVNSVfZfPg58LrbIVP4cy+kawVYXhmHS3uohkSxjWc3Z1ESqRkfEyeJEgqPpGo2KDgLMTyUZ3tgFwNn0Kv2BIFfc0MWZlQTbo1EOxWLYZYFLf/YRIs71/Npjf8M/HLiZ77zpLfzVicP88JlX8XttVCsN1IUUiUwdTbcQAEkWcTokTNMi7FdYzdaZn1shlWuQPDyLxyXjcsjIksBcvDlH3d/h5jc3bkCRmjNrfkXh0dk5PvfgQbL5Bmgmbp8d3bT48c/PoDcMFJdMqaKyOJfh8++5lU//+GkCER+3bB7ltt47EQQJtywgiy3sat3KUnmGiVyG3W09KOIwz8cf5Z8P/B6WpfKWgQZhpZW9nR1IYoi6MQaYZOsFos612KW+13uVIg2gmzkkwYcgSAjYGPZvIN84RVFtsFguY1Lnh9PneHkpzr6uFLf2DyMLET60eQs/nZmipml8cd+tOKRRYuVDaKbBgG8fNrGddw2/i5OpJxjxtyMJ3je4p5ZlkmmcxC7KKJLvdWfxUOJRjiQSfGjTbhzSMDaxB5ugY1JHNXKYVgXVnHo9Jx1EIo61SIKX1dqrhJVhfn1tljZnEM2KY6ONTvee/2Wvfi1ZqMN1BXPFx7l3/Sixcok9bQP4lRW2RKKcSCa5pj3A+9bdzmdOPADALb1DfObYYb649y7e1DvA+z66l1hlka+cPUu11HQdX4uqjQAur4NcvIBhNjFnNptIt8/PYCDA7vYOfjg5yUgoSNTpxKfYmS0U8CsKWyM91PQytw8MczadoN3tIRz2MT+TxqlIBNq8ePwuom43J5NJhtZ3U601sCs2nKki2WSBfKrI8O4tqJpJIOBEbWi8eGSMWqMZDWtoOYbXdlPVNE6urNAwDF58eZzhkVZ+fPf/xaePP4ZNkhAFgWcmpjl2dIGtW9oRJZFqRaXL68Mpy8zrzbzyodFuHJLM8eQq8UqFu4c24JQVvv02H39+9BVemVtArau4fE48tmaU5nWd/UScvfzTTQIX0imuaOvBLsr47N3YxFaue+9aDiWmKKtLeOx2JnM5js8sUCnVUGsqe67dSdteD9d2d9IwNP766ht4yyMPUC5W6WgJEnU58dubF4ne/h463G5yjQY+j50+nxdJFPHa7TTqKo2qSn93K4M93YQcDt45MsrVn/8apmUR9inUGwazl5ZYTtewyyKaZoJmQFFjLlbkZ5OT7O3pYSnZBOH/xj88SPK/38nda9Ywls2QKJf4xE+e4iM3X8PTcws4bTJ2UaTN5eax+XlGggHeNrie/37iMHcODnLP8Ah/cfQVNMP839Il/97177F1blmWLgjCh4CnaOKNvmlZ1kVBED5w+f6vAXcBHxQEQQdqwDus14aA/z/qP4XQbGgmQYdMrlajotdxyDn+/uwhTq6ssL+3h7OpDHXdoMsdQBREJEHEsnTsksKArwOw+P7k93lmYZF3rh3hr/a8AwE7p9KvkKiUaRgGzy1dYluki3ilQLpWJepysjEa4aGZaRYKRQKKg3WdbcwoNkIeF9+66Q5KahVBEJuJH1QvOwcCDskHWLxlYDugk6kXiFfyPBebx22z0efz0e9rwcLCKctM53M0jEmckp1WZ4CcmkMWRSRBwrRMHhhrIpCCfoXFqQTj55aw20VOjYywKQxd7jA/mDzHraMjRJ1O7hnegiSIHF1Z4EB3K7lGEz0UdjkJO128MDmDzWEj7Df52Fuux7IsvnfmPH905wHCTgefP3GCvd3dfHjzbXzg4HfY3dGOz25jqdwUqYWGSrpS5QMPPExPXyv3n7/AgcFBPHY72XqNVpcbhyRztpBkPJNmbbiFndFeUrUaN/T1oEgSU/kCNlHk4NIS51Mp3rNhPU/MLTAUDKCZJn/z3L+wa6Sf549e5NN33US6XsZjc6KZOrIgXT4OtLFSrZKt1/n53DxO2cZIMMCbereQrJWxiyI/np7hHy8cwiXLvHN4C2GHjzV//lmKZY2Q3849b9rJ2eUEb/rKNwm1BXj5PX+AaqTZFunhgTvfxi3f+Dab1vayMRLBtODs6ZXmIooIFFU+du8tfP4HzzPkc2IaJpeWk3w2n6eYKzO0eT1lVWXHpkE+deVuHLKCbuqUtTp2ScJjc3D/xBnmFkr4Cg1u+cq3uHPfDu4ZGebNX/ouqm4S8jfdKNOyKDRUzqVT/PTeexjPZrj1u99Fa2jcvXsr60Ih0rUq3V4fD01Ps7M1yrdPnKEt7MDtdaA47CTiedxOubnQVNYQBIFyTWfrrn6WX55GtIuYVZ3fvu1qfn9rU0hYNB/32q5uprMZMvVmMtfBP7yXNlfT8br/5jvJNkr4HT5uG+jj2EiMarHGwnwGC4gGFUpVnVxRJVdUaQkqZIsqdptIOKCQy1axLAu9bpCvGxRsWjOGUoDeAT/Pf/i3mMqn2dnaR0Gt8LOZCSws3vfmPTz88ikESSS2XEISBfq6fE3KvyAgigIrqxU+8b0nOHD1BqaTKb7/7FHuGOhne+Tm15E+kuDHwmJPex+tzq3No2lH0yUUBCc9nqvRzAQHut7CQ7M/5M6Bt6OaMRLVRVocJRpGnlPpMbrcfkKKl38eO0mXx81KpcrvbLwXUWgnoLSzp20b2yJjaGaN3xjZznvX7uXg0mmS1SJO6QI9nnV8dMs27hg4SaaeZ6H0GGsCUTqUN2Yz9/uiuG2b33CbaTVTkzw2Nw5pFM1MNHunMct4LsdHttz8S3OTCiYNZLx0e9qo6udxyRvZFV1/+TlLiDQFbNS5CwC7KKOZOnaxm7oxgSyE38DdfK0sS0MQbLy6+iRHV1bo9XkpqhqS6OBoIsnBxSU+s2cfDaOIKJT54MbtpGpFos52imqDZG2JXu82Vqrn+fDB5/j4rp18fPsV3H7/91gZXyUcsDM7m6FSM/B7beTTdTZuaWPDcDdBp4MvnjxFPJ0jHc/y9ffc2QRY53KMhkMUGg2StRxPL8zz/vU3s7VFwMRiS1s72aEShUyJz9x+A3/x9IuEnS6OzS40WcKqjlbXuHHzOo4uxZgfX+anL53knjft4vDELKuxDJIo4HZI+MMeCpky6XyJoM/NfCLNxHgSXTfJrOR5ZukEq5UKe7s6aeg6FzMxhnp9rOvpIOp2c3xpicPHx9h7xXqu3ruTW/r6+MHEJN88c7a5gGiYfPHpl7lrz1Zu7etnLplmR18Pq5UyGyJR/mj725konGClVqTNFWHAG8Rnu4DP7kbAQTMFAQScrA+18bYf/xBBENjW04V2+Xlu3TzIjmgLD4xP8JmfP48v5OGHd76Nbp+fZaH5mkWdTnp8Xl6MxRgIBCg0VDTDpHOojWOJBCvlMmGnk/6BduLxNNNzy8TTOfw+F7+zcSflmo5NFvF4FaIdTs6dTxDy2anUmql2qCZIAmsGgtzYP8CDx88S9CuoapOX+0zsp5zLZPDYZK7u6ubxYpWvvXiMzs4W9nR18uDkFPl6nVa3h0JD5Xeff5J14RYUSeYTL7/E5tZW1gYDv6IS+c9RlmU9Djz+/7rta7/09ZeBL/8qv/M/Bd7oc1/53H2jb9/KjYODXNXey6vJGXp9Pip6cwg87HQSr1RJVIoEHBINU2Ust0Kby4tlmSxVUvT6/OxobWXQH2E8P0OiukRd17muayf9vhB1o8pkLk2ry82p1VV0y+TQ0hI13aDd66WsNsjV6siyxMd37qTH041dtNAtA0kQAYG6UUMRA4DGL2jTIm7ZjSjoLJaLmJZFslplY7itSfIHgg4H8XKJn83OsiEcRpFs1PQGhxPzPLU4x8RqGkkScftdaHWNesOgXNW5etMAV7Ru5W9OP8KWSAuvriT50ZHTtEXdtDgdbAh1YhOjOGQNE41ktU7Y6WChWCAaCRCK+DmTTPLqzCKVYo1NvZ24ZBm7ZEO3LGxSmQ0tYX46Pc2lTIZev58hv5+DsSUS5RKKU0GSJQzDpDcY4JXJOcqWzmCwCUwXRYl0tcpCOssTsRleicd58+AgfsXBdV39/MWLL1KzDL5+/W24bCLZRp2GYfCz4+fQVJ2Lk03o+Hi1gCzLXMyu8pXTJ9kQCdLmauZOP7U4zdHlOB67nZqmM1cocGwlwf2nz7GxLcpbB0dI1SokqlX6fB400+BrLx1DEAXsNpHYaoYrRwZwBFyINom+gEjE6SHdKPD4/AzhFj8hhxNBgPPLK8Tmss1eLQqgSLxr7ybG00kiHSFcToVsqoCm6tgddg6dHAePjU/s2sWgrw2wKKgVqkYdURD48dQkn/zWk1gCdEZdaLrJqYkY979yFk0zX6cCyFIzcefMVIz2rhYuZDLMFYsEfG7S5QoVy8Sj2NkajfLDyUkcssRsochvbtlEzSVxx6ZRSoLBcF8b3hY3qysFstk6kizic9v4tV2biGXTVGs6mmZybjHO7++/hqreXNwZDob56cwkJ2JxOvw+fA4Hu9raCTv6ATCtAivVDB6bwpB/OzcNhjiYjJFeLSBLzfd4oaxh0eS3mmZzjss0LVTdpN4waahmk48pCkiyiKWbtLa7iLQF2NjVylXtgxS1Ks/GZlENg9/ddANuu8rHrr2Wj159E4dri4yfWka1LDp7Qui6gc3WdEdFLBaWUigOG3fu3sqB7kE8ti5UM0bDWECRevDZ3FT0LG5bGFFQyDbmiDh6eY0cn6xfxCk5GAmsQTVn+ebYIW7q2YYi9fOZEz9gWzTKw3Mz/NnhFzifSHLvpk2sC7XgloXLqTzNErBhUUK3DGTRy5B/GJ/dwGvbgCBISIIXp1wm7NhBt2cYt9z5hmNt01JxSK2vO4evlWou8O3xx9ka2Uhdn8EhN+cnZTHItshmJMF/OQ2ogSjYEQSZZO1VTCuFx7aZdP04ktAAdHQrh2nlm7GtgpOJ/HPMFvNsaXlt670Fwyq8wU2FZmJQyDGAZiaYLCxyc+8Im8LDrFRXibqcPB+b5+6RYTaG9nIue44OdwceWx8uuU5BzeNXbHx37BJ7O/2M55Nc2dHB9kgPhxNzuD0uTk3FSKbrKHaRkF9BlkQ04P03X4kiydzQ04Miyxwbn+OGXRu4Y3CEtYEBUvUM8XKZI4kVwk6Fuwavp6TFUU2dsOJjTdDLNQM9XLdukIahs9hoJpCtibawmM1TK9dZXc4TjvqpGzqmaSLJEkfPzOJQZEKtfgItXrKpEk6nHW/QRaVYw+NzsRJLo2kmaze006g0yNos1gSDPDo9zUIqwzuv2MoKKr2BAGsCfhS7nTW97SwU8mRqNb76zBHOTy3ysQNXUwfeum6E+/ZfT6ZW4YbuHQS9FjP5AhsjEQ5099DqCiAJGr3eYRLVC1z/L1/g64eO8oVnX+S0usLmiAOPrY4kuPnL48/SEwqymM2RadQQRIHB3namF1Z4YXKOlWSOYKufgNfFVw4e5v477+KLTz7Put529nV1ka3X8SoKdw0Ns7ezj1v6dmKTa4xlMmQLZVLpIrpl0RYNcmDtGr59yx/yhecf4pbRIb736pnm3zHiZXYmjdshky9r1OZL2EMK6ze3Emh1cfR3P0S/38t3jp7kw7fv41O37OXea7bhttl5eHqGkysrpGpVtnV1Mja/zOF7v0iXp8qRRJyTp6eRvHYuJJKIkki+UWe2WOLm/j7GM1menpll4Sfn/8Pijf76S5+/b+1tG37lZKAz3331vyIo/y319X/60n3vfP8NuG02hgMhRgI9/HDqPNlGnT6vF5sk4pZlpvIFHpme5sRqkrphEHHZWKkWqOsGAcVFQHEznk/y6SNH8NjtbIu24ZZFakYNr91Bl8dPh6uTkaCPdL2M02bHq9ipaBoCAqIo0u7xkms02BmNYAENU6Vh6NhECUXy88sLXAISwuXvVbPBUjmPahrsaG2lw9WKS/aiSAIVvU62XkMWBF5NJjiVSvDIzDQlTSdbbzA2E6dcqAICxWKdlUwdWRa479Z9HFw6htduJ9to8MjZi+TSZTI2k6DTjkOGeCWOU7axLdLLlkiYmt7gTQMDbI1GmS4UOHdmDofTTqQ9iGZZXNHWzsHFRf5o5x4GfG1cyiZw2+ysVmt0eb1M5PIEHAoz6SwA9VoDBIFkrUqlXKdarmF3OxgKBDi6vMzsXIK7dmxitVJFEkUQRXq8XsJONym9zm0D/cwWm05ZTdep6wbn5uPIsoQAxFcqVApVDl+Y43wsjiPgJFYusyXiw2NzYqHywJHTFHUVURYJOBxMZ7MIgsBAOMj5TIqqrrOxJUzY4aKiNxCCTgyvhGUY1CoNHEEXC7FVNvd00u5x8/P5Gb598QIlTSNeKnFmNsZwW4TFUpHYUh6rqjexPFWdYhg+cd1VvGf9Bg7GFnF6HPRGQsSW0vQPtaOaJmdSKfZ1dVI3VOqGhm6aOCQb+7uGqPot0oUcka4Qo0Nd+CMevEEXlq4jYmEYFrIkNLPrw24Mh0SH14OAwBVtrXS3hFgoFtnQ0sJSuUyXx01dNxgNhXg1maTT62W5XOHM3BKCTWRtJEJOrZHJVdEaJiZQd1nYHDZ8bhvVuka5qrNpXYThQJTVWp4Odz/PL41jt8uIokS2VmOuVGQ4oOG2RbGJrYQdnSxXZngmdoJeb5B3jGzklUqS1WQeh12ioTYxYqOjbViaRrjFQ6WqUizraLqJpV0WmXYJn8eGDpSrOqamcTqXYqqWZ0ukhXS9ykq1hlNuUNJUNoauxrDyPL80QyKbp1zTcSkipmbwm9fv5g/27aHilDkzEePGneu5qa8Xv13Baw9RVBd4IT7FcGANCAJuOXg56tGGUy6RrcdwyR4saswU53DLBi8lzrA2uI/hgIUgGPy3Z79JTdf526df5nwsgdfvRm1oPDo1TZvfQ7qebkbpWU3RLoleZCGEIJiYVhlR8CIKzf7y2szjLy/2qMY8AKLgwLIMNHMRmxj5n3rkLwSlB1GQAPlyRGUcEc/lDyEbhpV7HfxeNxJ4bb1o5go++wZkMchk4SglLcfF3CLnM5Ok67PEKyXaXG4iTs/r4tKyNEShuRFlWCUEbKzWZxGFHHk1xXNLi/T63PiVFpYqK0QcLm7sHWJNYBhZDHJ05SQbw3uZKR4hVS9iWCbdngD9AS8uWWZtYDtBh0FZr9HlCeCQ4fmJaRxK88i5WtdJ51UiQYVXxxYoGHV6W0IEFIWpeokPbd1ChzuIQw4SdcoslHKsCwVZF2pBkZojSTVDvbw0usrB2BILpRI/mZhgW3s7+7u7OBKP0xXwM9rZRqQtQMvlUaub163B4XHSsFm8Y+dmNAFW0nlcLjt/fceNJNQG8WSWtmgQxe/EMnQW5rM47BJre9uZzua4Z3QtNwwOcGPPIHcNr+Oqji6uah9hT3sblqBxLJHg97dvxxvy0hINcNeaYdo9Lna39xB1rGU07MO0aoyGhrmt/yr6fBYjgd3IYpicOk/DKHIqFeeF6XkGB9pBFognMgx0tLC15Vok0c9nj/+MjZEIXcEglxaXMQ2TeDxDbKmAoel4vA5q5TqlYo1Ksca7r9jKT6cmKWgqz83Oc2hmgaHWFt7UswVFcmIXu5gpTlLRDc5PLlIrN2jUNVRdZy6fx+cp8sTJMV7IJMinSyg2kU3r+rD7bPgDThanMqzb2cYPPnA3H7xiFxfrBX4wcYGnFuYIhLx4FQfDwQDJaoVktcL5VIqGYZDJlygbGt6Am7cPD3Lz975BIlugWKjh8jvZ0N6GTZJIViocPzVJMBrgY9t38ZY1I3zxr378H1to3rrxV+Zonv7usf+KoPy3lCgIDPp92EWJolrBLSuUNY3VSoWzFkRdTnTToqKpuGx20pUqy6USvT4vAz4/35+Y4Pe37sDC4gfjExiWxUQuT92Yot0d57quAcJKhFQ9ycNLr+JXFCzLYn93Fw5JxmWzcWp1hQuZHFVNI+iwo5kGTtmPKIjN41zxtat7i8uWF5enLADQLQPNNLGJzW3jvJrDZ3NhmCbxcpG6oRNxOWlzu3kxvkymVsWwTE7PxKiWatgUG7lMuYmEEaAloLA1fD1fP/dFbujt5m8OH6NarhOO+riup4c7+neSrK1gF2W+cfE0Lrm5ib67rRuf3c0DU2cJOZy094T5o2uv4lw6iyjA3548yWhLhJlCCoec4yunz7C/t4914SZDMlursbOtlbWtEc7OLSHKUjNdp1TH4VZIzKd4+dgljpwYxxfyYFnNDcxef4Bev4/JbI7dbW1UtDrLpTI7N/fwqVdeZDaf4+qubl6JL+HyOHC4HUS8HroGG6iqTiFdpFqqk0nmUaKtTBUyuG0OrmjtY3RNF0ePT6Kst9Ph8SIIApVClQcujVG7vMXZ3RVhtCWCX7GzNhhgSySMtFVkrlCkpGqsj0SRBYGHp2c5PT7P8nKBlpCTQrFBvqzRGg1gl2W2bGrn1Jll3E6Z9oiLl1+YpsPn4z3r7VRU9fXEH1ESyBUq1Ct1VrUMD3VNcNfQOiIOP0Wtil20IYkyf7B9N6/MLbCjt5urOtqYLTRxIa9kSkS7wizPJqlddrCHN4ZI50vkAkGOTMww3dXK/p4efm/rVkRRoGEYjGdzBBWFM+k0QUVhfTjMF0+exO6041MUDNPiutFh2ltDJFN5dM1AbWhcPdTPu9au5dOvHOHgC2O4bTaOry7w2Nw8n9kdxm23sVAo4FUUFgp5lssl7rz4Q15+zwCi0IYoKEiCSLJa458vnuVTuz7IzQNnOXt6jlJFo9YwiAQdOD0O7A4bgiDQaBgYqvELILu96XJW6zq6aoJlkcw26B2AE7ElFvr72NPeQ5+vgM+u0O0NoVtpJMFDUW1gWhZaw6RaUenoDfNyLMa51RQr5RKffPsN3N7fdPmaIxjLBJX17O0QsKhjWSqi4MNCvZzCI+K3uylrMRbKKdYHh7BQL/MCX+IHk2MUGirHzkySL9Sx2yTau0NkVwuoDY2BgXbWhcJUNI2zmRNEnV66PVdf7gVpbEIbqrVAzVhAEkTsYgjDKqOZC8hCKxYNJCFI3cjhkpvCsMnELKO80cwEmtnmRTWB396LLEbQzRyioGATO9/wcwV1EdWcJWj34LWFkQQ/X7/4KL+zsRk5OegbxbCKhBUfqqnzs9kx7l13AFkIv97LAGQxdPlxV7DQkUQvqmFwKbvMmVSKZKVKw9B5Pn6cXq8fQRBxSnZ0M4tu5rij/0aejz/MSDDKfDHPmkAYp6ywtaWfklbGsLKohkZVb3AmlWSmUMTpcVAsF6nWm/Gpil1E1y0cikQ0ErjMbKzxB7t20ecLoZo6DSONZur0+XxsaelluZqmZjTI1EuohsHawDAXs+fRLYt9nR20ulzs7+qmYej9+3plAAAgAElEQVRsbW3jxt4eUrUqK5Uqh5eXaXW5ibicvH/jRj64sMQr8WU+vG0rJ1uj5Boq17QP8b1L4wiiwOJSipG+dmqhOoEWH1pD4/D5KSRZ5FmHg4jLxflMhnuG13E+k6THW6TH00QerY9EiVfK/PaGbZfDBupc076Gx+YvYBdj3NZ3HXltHqeUxRJrnE4to4cN2lxhlivN+OU1gRCzs2kS8RyBgBNd1fnCwSOcSKZ4/4YN7O3uYVs0ynOxJfKpErJdJpmqUKnpr2P9Crkq+ZJGOGDnq+fP8L49O/j8z57D7XPx0HvfgYDAj6aP4rbZuHNgCN00Ob4cR3HaKRTq6GWVaqWB3Sbx0e88hk0WmBlbxi6LtPa0MBwK0R8I8Njx80Tb3Zz76Oc4nz3Orz/yMBva27i+t5cHx8dJJrLE42nOJVe4srOLo/ElyvUGXqcDr9dFRVUpZsrUjByRFj9zswkCITeyTeLQuUmcboWe9ghvv34Xo6Eg/3zpHHcN/avEnv/j9b+BN/r/vf5TOJpf+8e/u+9t77kWuyQRVJyIgsA/nTuNS7ZR13VMIFurES+ViK1m0AQLuywTK5Y5uBjjxNgcS0adRLVE0KHQHwhQ1jSWSiWCDgdlrU5FL1E3dJ6cX+D4SpJcQ6Vh6iQqFeaLRQKKwjWdnbx9zfX4lAYFrYpdhOeWJvnIs09x//hJ+oJ2+ryd/MLVFAADC5O6XmMyn6FhGKRrNfJqlZJWRZEkxnNpIk5X88NNUTi8HKeq6dgkiWylgmmaCECjoZMtNMXMW2/cxh2De/n70z/n8VOXKGZK2Ow2Nq/p4d71G5BEk0fnJsjUq+xobUUU4Gw6zZaWdjw2Hy1OCZdNYv9gH7IgEnY4iDqdPHzyAqpN4OrOTv7wxRdJLKTYMdjDb63fjoHGaDjETKFI0OFgMpNFV3XSK3lCUT8Abp8TLNA1A62hEVsu8Zv7ttPt87JYKpOv17hreC0/n5+l3ePm4dkpvHY7hmWxVCpRUhsIokg6niUc8XP3yAhrIy3kMMhki0S7wlQ0lZMrSTZEQogCPDY3g26abO/tJlurMje3gs0us7KQZileoFCoUymWWW1UmUimyOgqp5KrPHl+gvZQgNv6+7i1b5iiVuX+545xy5WbKFkqs3M5soUGg71+KpU6qqojyxLpTIVaWadQ1TE1k3Q2z7heIrmYpl5Via9kUJwKhmZQLdaQbTLhsJ/RcICww0umUcKwDF5NLvCZo0dYmE8SDHtx2mxcTGe4FE/g9rmolWtYlsVf3HUTk+Us9UqDvWuH+PMrr0VxSQwFA2wMt9Du8eKSbaiGgUUTCP/CiTGGu9vo8LhJ1RqE3S4csky2VmVjSwupWo1UrsSX7riZmgB2WSKkKHR7vXzg2p2cSaXp8XlZKpdZFwow4Pfxk0vjJEplKuUaH9i5jSNzMd639VpEwY1mJnhw5jjXdHSRadTRrTi39m3gx7MXES0TuyzgC7i5dCFB/5pW5mfTFIpas5v+EsRdlkUMw8IyL/8bGSbRVg/lQoVnJ2d4MrHA29euo8fTRLjMFhf450tHcdhsqA7Q6w1a2wNcs26QQr2BV7Hz0R07uKF7+DKZQuSJhUk+/uJTyHKG55cW2duxB1FwNWcTBTuiYMcmtpKsTfPA1AUWSiV2RPtI1VKsCbSRVysEFTt5VeXQ+VkqdQOnQ8IbcCHbZd66fRNf2HsTR1cWeSm+zKsrK7S6HPT71gIwnj9K3UgSdmxBEuwka0v47d2UtTFk0UWsMk1Jy+GQajikHmQx/HovtEtt/8seaVol7KLj8pH0mtfdxuZ9NQShSeuoGyu0ODY2WZdSD4IgsTXShSg0gyUkwYtplXFI7fjsQ9ikJO2udZe33n/hW9SNSzwTe4mgAxyyF0nwMVW4xBWtu1DkCnev2cJKNc/TC4vc1jeK374GCx3DahCrpAgp7Xxz7DCmpfNcbIlt0Qid7s186uhPublnO7IQpKynOL66TMMwuLqjk4Slcmk6gdcloxsWDruEQ5GItPl597ZNDPn9hJwOnphfZDKX5ZHZKe4Y2EvNyNPrjRCvpEnVKrhkG05ZQbcMWhwDWGRQJJFz6QwbwiGiLjdHEwl+NjlJpqGSazQ4sZJkOBjiVHIFv+JAM3UeOXKOdf0dvGtkMyPBEF87c5K3DI3wUnyR5UwzSc1ySuzq7ebC5CKBqI9GtYFa15mcTXB6IoY/6qPX50GRZMqaRofbz/HVZYIOhfWhMDYRgoqHsXySlWqBTz/7Iiczq6w2VhkOBgjYW5GEEH97+jlyap0d0SE63cN0urvx2SUeWx7DJksUC032Z72qcn56iaPlFIdOTFCwC3R7PeCxU6jU8HoUPnjrHq7ZMMhAWwRX2EUm0+TSpvU6H921k6cW59FUnbJNYDybpcfr5ba+fUiCh4dmDnNFRztPvDpGtM2H1+egWGwSJnTDYu/uteSLFZxuhfRKnnMzS3jCHtpaAuAQGK8s8PfPH2FpPsOV6/pxyBIXVlepFGuIkojb4yRXr7O0kmVtRxsTM3FEm0Q5X8Xtd/Gds6cZv7CEz+9kx9oBvIrCZ2/azzs3baQ/4MNvtzGZL5CrN7CJAg99+Zn/sI7m5778+ftGb9v0Kx+dn/zO0f86Ov+31Ge/9Ln7fveDtyKLIg5ZZiyXAkSS1Qq5eg3NNBAEgY/t2MFzsRiNusbCdIKSqhLyu1nX1cbL56cYz2bJaSrnU6s4bTY6vB6qms6ejg4My0IzTVZrVWZzeYaCQZyyxHKlQqZeJ1mt8amfPs3fPf8kCzS4pW+IFscwLU6Ts9lVTp6a4dhqgtEON+1uN4IgImBhYSEgUdHLTOay1A0TEUhWa0zlCxxZibMx3IJDktgRHSagOFiuFJAlidVKmXg8jTfgpl5VcThksvk6TkXiG2+/jahzkINLx7k4voQowMRUjut3ruGG7iEahkqHx03dMDiSSGBYFtd2dnExu4pDbg6ER51u1gba+cbFc5RUjRt7+7lxZJCw04ldlriqs4vxeonf2rSJqNOPIkk8MT9PWdN4fGyCeqXBSjyP22VnuK+diqYR8LgQFbl5NSaJWKbBrpFeSqrGudUkiUyel1aWmcpmuX1wkLFMll6flzf19vLbG3cyGPRSNAzGZ5exZIFTiRWGWsL0+P0MdbUxlUyhGk3R+dCZS5wpZxAFgbnZBL9x5VaOLi8zMZ5EsQmksjXyRZW6aoIADrtEoMVHKltENU1isymOXpjnJycu8OTKHJ/cs4+N/W3cvWYDD1y6wMJCDkEQSOfqdHb4UBsagz2tiJJJci6PlVdBsxBcMuVchUyuhmiZuDwO/vrNN/K9l05RKGt43DYyap3ZSpnJfJqFUpHvj43xlUdfZnYuhSxCOOJnqVSmoqkMRsK8b9NGvvn0CZLpKocmZ3E6bYxNpIh0+vEqIuvCYc6m07wQW2IkGCBRKSNLIjui3SBa9He2EnAoTOTyvLm/jx9dGkOzTPZ2d1PSNE7FE9ywbpjVWpVur4eGYXBNZzd7O3bxJ0eeoMvno9vj5QdjY7yyssxbB0cJexxsao3yG5s3MlcsoEkif/H0z7h9fZiPvPgwH992FSdScWq6zkvxZZyyyZ9cfYBQxI3mtTExuUzQp/C2K7ZwbGyeumoi20RMvele+gMKsiTQqOogi0g2kc2b26iV66ysVigU6ywt5ih64E8f/Tn/4/GDPL0wy2hnG/0+H3lNZf/GNbxj0ygBRWFPZzv3jGxgjX8DeTWFS1Y4vhrj4OISF8YW2NzXyVyxyGCgSotjgLpxERAQBTem1cApmwQcFmuDYQJKFJcsoEiDfGvsRe4auo6KnuFCLc/yUp62Nh+iJPB7V1/BBza8lT89+gg/OHkOXRLYEo2wPdqKSy7zlke+xIMXxtjWGaXPuxZRcCIJOWTRzUxxitPpBc5n0lzfdQc2sfV1wagaC2+Y9Xyt/uHCt9gZ3YIk+JDEAFFn8HV4+mslCDYaxiyS4McpRREQkUT/6/e/JjKhKUq/cv4RtkXDnM+eZMAX5RuXnmF7tOnoWjQQBYW8Os//OP4Kdw6N4pKHMawSfd6NSIKHNmc/khCkxeFgqrDE9mg/ktBCrjGL376BgraEz+5ld1uEzeF9HOgeIeRoLirt7xrlufhRPn/qGTo8Tnx2hR6vj5Kq8tmHDnLH/q3MxJIUKxrdXX52b13DgZFB3r9+H12eCIO+Ud7Uu519ndu4rX83Z7NHqOkqp9NxFFmi1ellrpRlwBthtpimwxOiwx1mwO/jO5fO8bubd6ObBoJo8fvbruLg0jw7W6Os1uropsnFeAKXw8Edg0Nct36QX1szygNTFzi6kmBfdxcfffopVkvl1+fpRUkklslxw+Z1jM0vk0uXcXmaC3q7t6zhbcND9Ph8dHmCHEnEOZSIMej38ejMDCVdx2uzYaJzcnWVfKPB08cvUsyUKStwXU83EUc/FjX+/tSLjKXTvGf0OiQhiEUDkyJfP3aU47/zp3zsuvfysf1v46PX3slHr7uNiKeCI+jh6PgsGVMl5HQxNbOMIMBCscCThy8yl0qzd+0Q44vLaJqJqRvsXzdId0uQn7x0gVcvLfAnN1/DQqnE1sh6NDNOrLzKl54/wjXb1jK7sMJirECtYVC7DNUvFor8+v4reOHkJKYFfSMdpLJFVjNFCukSl2bi5HI12joCLJdL3LN+HXu6OumIhggHfZydWKRQrrK+v5Pj56cxdJNKvkK92sDldWBZUCvX8AZcfGrf1dwxMEy6VuGnMzNsaAk3L6C9Xnx2O5pl8thXD/6HFZqf//Ln71v/5l9daJ749n8JzX9Tffkfvnjf8iY/0/kCJ5JJHp6a4vzCMi1+Lx67wk39vdza30/E6SFeq5JW60iSyO1bN3DLQB839vayqNeZmIjR3hZ6PcKv39/ccG4YOsuVCvlGg7phIEkSK5UyIYeTsUwWEEhWKlyaXKZQ1oivZPjhufOIvirrQhHuXnOA6za2cCaX5UenzvNwbApZ1ujyuBEwkUSRdD3P3508yamlZURZZjyToaiq7OvqYLpQpNXlosvdSbKWJOxU6PN7WanVWK1VcXoc1Cp1ioUalbrOYH+IP973dixL5wvHnkSxy+RyVVpbnPR3RRkO+Xh0fpYBnx+/orCpJUKsVGI635wRddpkFkoFCmqDTx5+gW8ceDdbIwHe9fjDXNnRQafHg2GapGo1FJuddrcLr02hYWjEysVm0oLHTSydw+Gw0drTQnI1h12xUa7UaVRVKqUatUoDw7TAp3B0Yg7FZWd9WysrlTKLs0lejsU4sGaALZEIawIRDMtipVai1eXk3VdupSZAolzmzPwSOVMj6HCSatRIxtJ0dUUY7W7HJkrk6jU2DXbz0a1X8dTCNJIioKk6bpedel1HN0ycikQo7KZebZBK5Ekni/QMRNBqDZyKzN6Nw9zYM8j7H36YY5kEn7jqShoBidXVLJIo4HHb+Ju33cy9o1t5x5aN/NOrp1BFgY5+H3abSGK5QqNusHFHL99465tZLBX5l5+dBLtEMlXlmu2DRFxufvTySdZ0tjJfLDA1m6K93Ucg6qdhGqxpCfOJXTu5e80mer0hWjo9xKp5UukKGAbRiJv2aIB3r9uAZhqcTaU5Nr/IxXyOz3z/IBeqGQ4M9pKsVlkslbixp4f9Xf3c8dVvMzubweWzs9KocWJ6kbu2bKCkqpQ1jXWhIL8+spNH58b50KM/4q+v309VV3lyfuH/Ye89gyO9rzPf3xs75wg0choMZgaDSZwhhxzmHCRRpGRJlpVlSV7L3rXl7DV9y3mllRW4smTZyokUSVFMEuMMycl5gBnkQUYDndA5vmE/9JBe+dq+u763fFUunyp8QFVXA/2+Xf8+fc7zPD8evukurovFiNqG+Py5l9kZCTGWzjCXy7NRrfDhPSN88Ns/RHVauLSRImizErU7eN/mnXz42ado89m5v/cetgThdGWD5FqW7o4wdSusr+VQZJGB/gBdvQHQGgRDTjBNdMPkhhs3NbXJAni9dhw2mVpNY2pujcGhNh5++13s6ozx3dGLNEy4tq2V84kkPzhxjqV6mQG/n0ytjFvViNg289VLrzDoCzCaShHP5fnw7h1sCfj5+4sXeWL2NdpdVgJWEd1MIwouFDGKImZpse9CFv3IYpi/G/8eH9/2IKnqNLO5NE+duMj1Vw+ys6ud+zb30+PxYpWrFBsVpgo5PnvjTdzStoOIfRey4GcoZHDvQD/tTh9WWaWuz6FIPhQxTNjWS7fbz57wfgB0s/imEeifajIB9oRHfub3f9xkvlFN/ad2ZbIpUDfmEAU7VX0c3cwgX9F9CoJC1VhCFnW+OTHODbEuhgJhFFFCNxOYZuXKhDVDslZgX2QIUXAgCjYaRpyGschaeRqvpRuTBnsiO1DEKA1jCUU0kEQ3HtWJIkSwSDEMyryw9DJdLj+yEOJ06hAjwXZKjQq/98RPOZpa5ztHz/Kt184iCgJLayk6N7XS3dXkl3/mlpuJOuxE7F7OJKdwqxp2uRtBUDBp8IdHf8w93f10uv34VAfpWpE2pw9VknluYZYOl4JDtiGJAsfWl9kTieJVHbQ7/Vildsazs/zlky9RkPQmpvaafdzc3kWLPcCrqwvM5DJMZ3Pc2Bbj+HqC8YVVAgE3lUpz89QS8lEsV3E7bKxnCwSiHhweGz6PE4eicnBxkfds3s7uv/gClxIJTk8tsGTUmJ1ZpW4RcVssVPUGF9MbPHryAqVilUSmhs0q0R7y0emSOLY+QUU3yNVq7Is5OJk4R9BmUmiUeWJ2io+MPPgz7wVBkBnwDnFbxz5+ec/d3Nhh48jqMicvLpHL1zAbDRCaG7Sz00uoiki9YaA1dPI2kXt7enhxdpZCsc4rs7OILiuj6Wnu/9zfsWeok3duH+LRs2Ps3tzDNdu6yZQLCKbBrqt6sHtsvH5qgt27+imWyiTjWVbjeUqFKvWGgcOhUq/rOF1W9vZ18dXXTzKW30AUJQzTZC2fZ+9AN6+dGgfgwet2sauvg9lMhlKujDfk5vvvfoBruttZyBd4cWmBdLWKQ2kiTmNOJ98YG+PRF88wU9hg9amJn9tG87998TP/ykbz6H80mv+a+tz/+OxDt77rapLlMpph4LZaWFpPU8UgmctTFeBSZoMj8VWOzy3QGfBxVWcHBvDM7CxHVlcxgbVUDqfXTnwpxdpahoZNps/nxaUqiILIQr7A1MYG89kNpubjvH3bZtpdLiRRIFurEk9mEMWmC9jjc/DK2UkemxrH4a5T0zQ+tfsAH9v9AHf1BElXS3xp9Cz5RplWh521coGtoSBzhQLnZ5ew2C38131X8/C5c1fyFk1WSwkcqkxd16loGoeWlsjmS5gm1KsN5pcLAAyPdPDezXdxKnmIV1dXWF9KUavrqIrEx6/bw1Z/jB/NTvHy4iKjqTQWWcShyPR7fayXy6yWSkiCQJ/Xh9dqxSJVcak2bu5svxLGbfL5s2d5z+Bm3BaFjVqNb1wa45aOHoaDLXzr0gVmU2nq1Tpuv5Nyvrm2qFcbrM6nKBWqeANO9IaGw2Xl4qUVUqkS6fUcy+spChsl2nqjjLTFkMTm1PjZuVkePnWSGzo62BoIY2IStlmJuJxMbmywr72dhXyOTo+XmeV1etvC3NTRzia/j6phspDL0eNzEnbYOTq3hMNtI58uYJrgdqq4HSqyImF1WMmkirgcKtFYAKfPzi9ds4vbOzv50eUpFitFXj44wVcfPcrYZJyh7a188333c6mS5+aODnwWOybwWn4FBQ2H08J6qkKjooEosri0waJa4S++/gqIAlpVQ7HJnLu4guARWV3KcGpqkWKhRGd/BL2hozd0ZEViV6wFv9VC2OYCBDb5AhzNxFHsEk6PnW297dzS1YF8hUt8ZHWV5ZUUAZ8Lq0thdT7F9r4YHS4Xg34/g74WvGoQT1ihtyfC9tYo5YbG4kqS9+8ZYaFQoKJpvGtgG6VGmb3Rflp8Fro9fiQBiprGcjHL7nAHG/U1rou18Z2JCfL1OpphYpMV9kajtLUEWSkUGV9dY2drCze196MIEnf2dvObL7+My17k6shWrmr18Nzlyxw5PUsk5qOt3Uc5VyazUUHUdbKFOoZmoOnNjM3OtjAVXaNRaxBpC3D9YB+beqL0d0do6AYn19d4ZnSS6fE1ytTJGhonz06zf2QTH9s+TMhm50IqhSpDzOHiUmaZ8Y0Mb+nt5dRGija3i1aHk16vh0vpDE5V5sujZxgKuLDJNcaz54mX87Q7BhAEkbI2yt7IbRhmgY+9/Ahn1xNs7mzhKze9m5vat5CuJqnqOgY6DUPnD/fejyoaNIwqiuTENGvUjQwxRwCP2sXr8aMsFDM8MXuevZEO6sYCkuDEMMtXmrdmk9nEOv6/12qZNKlCgtA0HgmCjCJGkMUQb33qj+jwlGh3DhCy1lBFhZFQkD87dZiyVqXDZcMmxVDEKIIgoZtZbIrOU/NjhO1lvJZOJMGFLIZxK218d/q7fOzp7/OebZ2YNFgszWKXLViljitEoklq+irZepyt/l0UtVW+Nv48V0Xbedczj/O+LVtYNupsjYaZT22g1eoUyk1OfCVXxgB++8b97Ah18Is/epyPjDyIQZIT60sM+t6IaVJ4YfE4mwM+Hp+dZHsogk1S+ezZk9zS3seeyBYskoEgmBimwc5wCKusYpomFjlAWYsDGrv6OnjP0BBDAT9TGxkmsxny9eYZul4u88EtwwSsdr507gyT43E6OoIk17IUNkpkc0UqpRoenxNDERkIBeny+dBNk8WNLAYmXz50lFq1wdahTrb3tNMwdEbHVxnqbaGs6YymUhy9OENiPY+um7jsCqIkMJpJMVnc4AvPvsrJSwu89KEPMptL8e3xCY6trfBHP/oJpmky3A7tzoF/9n3hUtq5u/sa0vZVLl5eIZ1vknqyhQb5XI2brh9iaSWN22Pl/pEt3Nmxk4RcZHo5jmmY2P123rlpgEeOj/He/du5s2M3NofBB4aGubNzgK0dIZKKTl8gQK5WIxzxIQgCG9kikiRSr+lYVBFJEimVG7R1BuhpC/PezZt59uIUqbUNUtUyddFkdirOei6PxaZid9nQJDgfX+fGwT4evvceBgNevjo2ytOzs0ScTu7o7KTF4eAvX36NsNdDrl7nwlKcUq7M7HwW48Tyz3Gj+emHtt43/Ka66H/35+TX/+0bzX8XZiCbLPHugSEmskm+c2mCXdEIF11rSLKI1WKn2+Ph1cVFLLJMxO9hdi3J/liMPo8HUYAfnx7jt265lhva2zm6GmeRBJ19LYTsdhqGgW6YWGUJl6oQdTo5em6KbLbCty5d4jd272Y4GCJdrRGM+ckm86TTZVaWNvC4VD52/V4skkiH240oSFS1Jhnkhlg/d3TcxHJpDL/FRdAaJFFJ8DtXuXgqeJmJTJq/OHmC4XCEdLVGRdP50NAwa5U8giBQ1TVWs3mq5RpSXWNufuPN6+G32QCdjVqN5GoGWZHwuEQinSHS1Sob9QIn5xZ5y7bNtDocrBZL2GUZr2qhputYZZnlYgmXqnJTWxdlrcbJxAIuRSVRLhO02Xj7QD8/WZjHpSr8dG4eSRSZySWZy+cwTFBUmZ6OKHf2dPPjmRkWlxLYnFZcHhvFfIVGXcPf4iUTz2K1SM3JJlCqaIiigM9q5dbOdv5udIyd0SgT6RRdfh+b/WECFjfFRpy90S62h+rsCIU4HI9jkWQ63C5au8O0Ol0YJkiCQKJUYm19g9975RDv2raF/3b3rayWivzO7LMIAricKsVSnWK5wJZWP2+9aSdui4UtAR9zuQIWSWI8k+HYyioRh4NytukQptjg5HMz3Dq2BoAiSmwJhSjU61zX002xLcbx6Tn0xRzUDFCAssGhQ5NXdIfN1ywIIIoCh1+fY1OPF0WVcfkcdAX9XEgXMQyDWqVOr8eNS1EpNip4LU6sksqn9uzhv7zwElZrs+k4s56kYRg0dIPxyyt4Q27GxhepVeq4vTYahoGBiV+1UtcbZGqLfPXEGX7nwDWcTiT51O7dXOjqpFiv80BfPxWtwWOzl8jV6gSsFva3xig0KpxJJAjbrLy4sETUYWc+n+fBvm0AHBydAsAbcrPJ56XX46ZtaJAnZy7T4/GgijL5RgWHYsWqKiwWiiwWF3ApNqItfix2C5VSjZsH+3CoKkePzuDyOXjvLfu4tbOD74xPUKjX+fWdO/nShfOEBu3EHE2koGGaHFtd5ZXXxrn+ukFS8Q1sVolapUFF0yhXNZ47dB6nqnJjW4xEpcofHz7Ce7fmsCsyry2vMJZMoZsmJ9YSNAyDE/F1wg4HB5eW+fDWrfzqyy/xvq1beFvPMKlqjoaxhCp1IV0h7FT0ZSIOJ9uCfjpcbpZL83S59nFL231oZgJRcDLkW0QRIpzPjuJWVf7k5N/z8eHttDlaUcQuBCSqmsY3L13igYF+ZDHE3118hqjdTrpa5Rc33YgkeJEEFyYaAv8QcfRPVVW/hEXsRzPTKOI/6DgNs4Io2NCNN+KK/vnnGQ4019cOZTuvxZ/ixPo6+1uihGx2anoDTV7HpIGIi8v5JVrtXt7S7abVcQ26WbiC9RQRBJHN/gC/deBqDNOkrqfpc9+IYZap6wvIYoivXDzGpVSa/37gdtYqzelUwzD4/LlTVKt1fv/11+n1+Sk3NPKZIg2tiUysVHW6BoK8b2QYj8XKl0ZP8NE9Oylrzef4x7HSH9m2lTZngBvbdBp6s1E9EGtFFsLN6yt5mvdXjJGonOR0YoZ4qcyeSJgt/jB7Iz3Y5W1XjoPztDuDPDU3zkatil1uvrcdspWAdTdu9SUkQeDo0WlMs4nCdXhsZJJFdkaj2GWZPZEI8/k8D88tko5vkC/UWE9X2bo5xJ7WFkaTSRbW01hViROnp7h9/3bsikK1VEMQmrFg0fYAW9pbmUmliAZ5d38AACAASURBVBeLZPM1/H47f3n6CHVDp93t5nJ2g+GhTl569RI/mJrmmn9a2vsz9adXv50nDp0GmtnVkiQgXHGetXcHqVXqPDY5ybZAAN0w2b93kFNnp3nm6VGmx5f4wofvpdzQ+PH8CUqNBgGrG1GQcSgKFU3jm08fIxxy4PY7USwKjWoDQRRQFJFQ1IM74CTkcGCYsBBPcdPdb+Wx93m4/+vfw+a0kkg2taLVUo1A1Ess7Kfd4+EXBjfR5nRxPrnKTxYWm3GAssgzMzM8MzPD+VPzqLLID1In8Ee9FHNlRFGgJWhj8f/5svz/Vk0r8X+Ygf7N6jMPf+ah8C29iAJMZ3MM+L0slopU6w22RaLc3d3FkxcnkRSJvzxwPY+PjnMplaLF42bI72e5UcFttdLudCBJEmUZYi43rU4n33jhKFW7xAMDAwgCBG1WTIeFfKPK5OQKZ6tZtoVC5Os1TEmiPxYhXSpi6DoP3nQVHxzagcei0O2KoBkakiihijKSKKOZeWRRwsBEEAwskkLDbHBifZ14scD+WIyI3Y6JiVWS6PN6SVWaU1sBgR0tEba1t1KWwB1xYFaqFMoN7rtqiE1+k+PrcWYrBVYWN/AFnWzvivHuwc0cXF5iLJXid/fuo9CocT6V5vbOTp6am+eOri5quobbojKe2WAmt8GRtTjXtsY4ttbUcmaqVV5cXGKTz8dauUJF11EliYvpDI+eOE+50aA74Oe+vl72RVsQBIHFehldM0it5cgVGxiahjfgwht2Nx2NktjMhZQFCiWN+/YMEbJZKWo6IbsNu6rS5fGwNRBCFiTOJlfxWlUCFhffnx7nb556ja29MbrcbhqGyeZAk04xlc0xmkxSzJfJpQpEIn7u7d7EkfgKr5yfpVzVcdpk/CEXTpcFQRT4yMgw2XqNmWyeqMPOvmgLZ5NJkpUy8VyBxcUsqBJoJghgWmUa2RrjU2u8enqWk5PL2EIW3rFpgFv7e9F8MgvJDLV0FSwSik3GFMAQBagb6Lk6wpWgdE1rap2ymRI1U+PeXduI18romk5NElkulXhiZoqfLMxS0aucSSRJ16rMz8RJbOQJBTyslUq8MjPH+KV1NvVHQW5KBQb6YwTtDpYLRdbKJcY3Uvz5saMMt7YgSyIdbicbteYa6dR6EoskEC+VCNtt7IlEePjsWZwWlQOtfSwVMxyLr1PVNLL1Og3D4Nb229nk17C7rLSHAtQNneMrK7w8Pcd0Ic/OSBTDNHh0egJJ1DmdiDO1sUFN1zkWjxNz2fn48FV0+J3c3tfDznCY7eEwasBOb0szEN/EoGGauFULs7kcRy7PE/W40UyTkVCoqccWRcqqwUPX7uf7x0fJFRrUaw1MwWQ90cRzLq0lmWqUmFiKk1hKM1MtcTGT5tLEIoV6jeFYKz6rlZ3hMDvCoabut1pjfGODWzo6CNlsVPQKna4QitSCKFiQxQCrpSM8OnOR+3qahKJOl5dsvUqrYxMGRWQxSMNYRhW7OJ9+hfl8lqsiW3huYZy7ugawSCGggSQ6iTl1fjh9iQ6Pm+FgNxF7g8ViDlkQiNhl3GqT7PNGZua/NNnUzAwCyv8t+ugNE5CJhix6WCi8htfS5LnrRpal4km8lk5u7LDjt+wCmmz0R6Zfp6rp7AyHscoyiihQ1xscXrtEn6cHv8XLd6eOMxJqRREVNDNOVV9FFh2IgoWGsUaPO4goiKiSgiyGEQSFhrmCbhYZ34jz+vIy9/cNslbOYZgGmVqFbx05zV/deQu/OnI1DlXgeDzO7MI6Dc3gyx96G+GYn1u7u9gdiXA+lSJZqbArEqbDFUYzNLxWFZ/a+eZ1CtnCXMpM4LZYsMtWbLKFPk8YkzqiYGepeJZMLU9RS+JQLHzuzCmCNjurpRLXtXZhk6NvxkFpZhLd1JnOpTiXTNPucrJaKnF7xzCy6OeHM4dYXkkjSwI2i0RrVxB/0E1yLcv7rh5hdySKS7UQc7oY3chwfnyFal1vDjokWK2XmZ5cIdjqo1Gps7RWomrWmF9JUirV0XQTn99BKVtmbGqV9vYg+VoVvdZAb+hs72njjs5OCvU6Pz58DqwypWyZVLnE1T0KEXvvv/g5Kwlurt1s45FToxTKGrYrUVILy2mqxSqGppNcy5JRYVckxOtLS1w92Mv6RoZwu5+/uuFuzqVWyVSrtDudtDrcJKoZjsTjzOVy5LMF9oz00xXw43fYyFSbMit/yM3F8QS/dtvVTKTTrGfyaHUdxZHmxtgtfGPsINlkHkM3sTss/PFbb+XtWzazOxJhPl9gvVJhMpvlxYUF7IpCrlFDEkUyiRz5jSKFQp3egTC1Sp3fuu0AVrcDwaXQ2xHh4ndP/9xOND/98KcfGr5v+//x6vz414/8x0TzX1NNo0yEi+kU9/f3cmwtwa/v3MmTl+focLu4lMnQGvCytpHj4PISggD1ap39rTG2+LdwKZNhuVAkXiyhmybbw2EMTOyKTCpb5cLEAkfa27m5vYv1SpG39ffy8e3D3PP5bzIztsz/UFW2hMLc09NFq9PFxfV1PEEXV0XDCAJ0uVoxTA0Tk2Ql2xSbuwOEbSFsskTDqFNoVEhV86QrFT65fTdWSWG9kuWZ+bkrIekSlzJJFElEFSVGQjE0wyBXL7ErEsE0TU5tXmexUOT399zEVHaJicwGiaU0bqeK1tAZX0sA8L7BW4k5HASsbqazMzx/6iLjySQ2WaGqdaMZBolyhWKjwY5QCJeqkKlWSFdrjLSH+NQLL7Ozo42Q3cZiocipyXksdhXTMJmaTtEasrPutpOr1zGBjVqdlbkEkY4AtbqOIICmmySX0wwMdVCualhUifa+CEsz6+ze10Ox3uCnC4vc3tnBSCiKTbagijJ1Q2M0s0Kf10dZq7NSTvOdgydYns6R3lcGIOywU6g3WKOMbppohoHNacVit3BmLc6vH3ye2eUENotEwGOhf3M7e1tjWGWJXL3pIu12uzi8vEy6XKbT5cKuyDy4aYCw3c4fGAaplQyTF9NQ19FrOpS1K9ndAnpJ44UXJhi/sEi0zUc45OUP33MrQauVb168yMkjl9GWS+CQm7E9hQZy2Ea91CCXqpCzSKiqhMNW4Nz6Gi67lZ5ggJqu8+hrp5ldyBLxW0nuLBGyOxAFgUymQrZY556rhnnkpeP4wh527Onkho4OvvHqSfwRD28f6Ofo6hprxSLpUon5yThf+9ADrJXLfOnUGW7s7cFjUTm9tsZqPs9V0TBr5Tx2Rea/nz7DJ3ftpNfj428vnuRArI1vXxpnenyJy+0B+sMhNDPJ84vzzGRzDAX83NgWI1WtsFwocmhpids7O3ltdYUOl5OQzc7B5VXeMzREt8fD2USCncEO3OoO7u0eRDNWORy/hFWWuTbWwtH4Og6LwsuLSyRLJS5PrBBq89Pi96KKElOZDOlKlT6vB0UUic8lyNfrtLT5KE4lcdhkEvEskiRQrxvkCnXOn5qjoZvU6zrVWpwXfv0DfJKXmBxbxDrYx2qxyCZviJlcivcN3sD59CV++bGn+OA738Zrqys8O7/A5w5cfyWc3M359E8paxrXx9owMdkR7EIVe+hwNhvBhrFCg5Ur2ZgWhv17GQ5ICMh8cmQHPsuuK0Sr5mRUFr20OJ3cEOtExEnY5uP+nhEEVExqb55/DWMFRYxd0VU6UcQI2doZvJad/8sZuemfPT9r+iyS4MZAodXRjDHSjCQgE7XHMMwyxUaFgCWDSY2F4gRDfj8Hl1cwTZNkpUyL3YvP4uL29kFEnJjoPD0zQ5fLxVURE59lCFji949+nT/eexcxxzCS4Aagqk8CYJo6Vqm51r6rM839vVvwqCMMB5qxSS71KKe2JTmxts4WfwvXtgzwyNQUNrvK0U98mB9dnuSP9tyDQZmKVsHVYeWDP3mGX946wlPz57m1bYCZXJJ8/Xl2Bu+48jfrpKsVetwhlktptvh2IoshdLOIZqyxWsozEuxkrZzhfDpOsd7cLDxz+TLpap5cvUiqcpaarrHF38r59DKPTU0Tstv57sQEc+spnggdxyafZqNapVLTsVkkrBYZWZGoa82z77MnT7G+2MRFl0s18qUG9StQhkZVJ5Wt0bXZwp6d/dzd082nE68zOBCklCv/zJTW7rTSqDcns6IgMBQK47Za6fX5efzoWdZKRd7S28vt+4Z58uBpQlEPjbrGV0ZH+cL1N/OPg/7/ce0M3sEfvmON//qdn1Kp6ZimiXFlG5XIVAj5rGxUK/R5vHxsxwin1pP8l7feyKNjlziVuMxqscSrS4vc1t3N1mCBg8tLBG1WdkVbyBTL3NTRTqHeoNhoEB528MThs1wYW0Or6fzNC4fp7m1hbTGFx+fgsckpDi19gdWlDG1dQR66+Xq2BiKMptZ4fXWVXo+buewGy+kshmHgcNoYDASJOpws5XOUCxUEUSTgsTDS2caYuoZbVfnNnVfx6TMnWCsW/sVr8fNQ/19IZv4t6t/FRPMLX/rsQ//p4/fhUCSssoJXVXnq8hx/vv9m3BaRJ2dn6fP76A0GeHpyGotN5ZqeLhqGTqKyzusrqwwG/GzUmod3v8+Dx6KSrFQ5fGEera6RtRj8wuBW0tUSnS4vMYefbX0Rvvv6BQR0fuPafXhUC8lKmdt7uhkMBbmmpYOyVkMWRap6HUkQuZBe5fuTk1gVkXani0+99jwhu8JqKc8zc/P8cHKSh185zPcuXaAr5OfBvq0M+oLkG2XOJlNE7Da2+qO4FBtWWcWrOpnKJklXK1gliV2RCG2OXuLlVX40M82+/m7ixSLXbxvgk7t2UmzUeejY8+yKhAnYHBQbFS7kMoydWyCRyHJ4fZV3bB3CZ7HgVhUcikq3x0PI5mShkGNnOErQZWfI7yfmdPJXL75KPlOklK/gC3vYt62Lrd0xljdyPH9sjNPlDQ6dHsdiVfB6ncRXstitEu09IW4a2YxTUTk3uUxDM0mu53E5VN6+extzuSwNw2AoEGBveD9WyUQRBU4mFji1nqDV4WDQG+Pbk6McOj/Lvps28dkbbubqaA/bg2HOJtd4cWGBnx6+wP7NfSRKJRRFosvnI10p0+L3UGnUyWarqHaF9VqFhVyOC3MrKHYLT01Pk0kXEC0Sd3Z3s1Ao0O/10e328fjkJNVyjZ5NYfLVOkgCelmDN9YYokBnj5v2vggLswk0TaM7GuL5uTniSynefuNOvvPxB3k9v0I8U4ZiAz1bRwlZMco6FpfCLTcN4vQ7mbq4yEBXC1VNY3c0Sls0yDuv2co9OwZJV6vkazW6fT76uiKsJlJMLKxyeTJLJOZiUyzC3T3dLDQqfGzXCKlKlSMrK9zW083o6jrpZIFILMDWoJ9Xl5eZSaTwOx18ZNtWpnNNjWe/z8ftnb3siYZxyAp/d/Eiv7XrJpaKSe7t7eOV5CrpeBaH187b+rpJV/OcWU9wa2cHf/DyIdZrVf6vfTdzXVsLXxkb5ZqWKPOFAl8fG2N6eY2j80s8dnqMwxcvM6Hlmc6NE7SVidr3cDEzhoHJvV13cFfXDSyXJvnE8B4EyeDxw+MUNspkN3JYvDb2t7VxaGEeBJEnXz3L6U/9ZzLVIi8vLLCpv4Xkeg67w8JyvITDLtPdGySdKbORqjTD9QWBvz98hkwyz749AzQMg26Ph5Vyns8eO4GgVAnbbEyU8vzS5h1s1IrM5vLc3XUATJ2aPk3MsYc2Rz9uVWcis0qnu5tEdQrYQBRAFkOoYozz6SP4LeKbk1AQCVibDd4bEUGvrDxJl6uH6dw8t7bvRxQcCKgslc5xcOUU/Z4AE7mThKxdV9zeBiC8GeZulVv+t89PWfQ3n1+Q32z+mk5zE+XKpNGntlMzZjHMMoqo4LYoPNB3F1G7D4ei0+q4GkGws1w6j8/SxSde+SJDwRDZep1Bnx+P2kOuPs+55BptLoml0gIt9jfoRMErr73ZUFb1i/gsu3h24VWG/Nve/D9dSju3tPfwe6/8mLlynrBdbuYdq7A54OOBvrfyhQtPUjMqzOUz9HnCLJbyPHz2NAYCB2KdzfMmtcYW/zYMs44kujCIE7C6aXVsQxLc1I0ZPn/hGZLVNHvCndSMOi7FTlWvocgy7xvcSdChsCt0HT61j5hzgM+ee46vjp5lIrPBerFIslDiwrk5ME1+emaCZ89NMjuTorPDR7jVCwKkVrNkkgU0zQDDJJ0qYrcrBKNeevsi9PdGcPvtJJIFHFaZnZs6uLWrkzs7N3E4HWdlKckn774ei9fBkbML6LpJrVxDa+iM7Ooh6nQxvp5gWzTCWrHAx67ezdv6+klXK8znC2TqFeYvp5hfyFOoFblu0Eb0yj35l2prQOFPn32ZYlnD6VDwOBVEUUAUBHLFOj1dIXZFoyQqFV5eWCBVqXBzTzdnEkn6vB7OrK3xuRvuwm/pIldP8t3xcSbSaU6fmOPHJyY4eH4WzSPR7fUythDHpgrUdJO7DmzDZ7WRLpdJJgqUCmU+f+/tXD/Uw4d2jJCslPm1nz5PV8BHn8fL50+f4dCrU9itEo2aRlvYx3qpRNBux6YoFOo1PAEXWkOjqsCBri40wyBst1LXNZ48foHkT2d+biean/nipx/a/paR/+OJ5tGvHf4PM9C/pr7yt198aO/bRzi0ssKPpqcRRZEuj5st/jAOpel2PbG6wvu3bOHFuXn8Djv9fh9ei4XVUomVYpGI3c5beno5m0wyHAwylc2xXMgzOb9OrM3LRrrA0ew6hgD9Xi91o0G2XuVseh1v2M2BtjaGg+0cWVuiomlXVsxljq/FiZdz9Hj85OolXl5e5I6uLpLlCrrZ4P1Du1ElkSPxFVaKJXTTwONxEPG5SZQrVPQqLy0tcDwe53d272fI18/RtSlm8ym8Fgs22UK704dJ81tsvFQkahfZqJV5bPQSOa1OLlPk2r5ONvn9hO1O0rUK35uYIOayMbmRJeJysWHWqZdrbGRKPHH2IstCg22hILph4FAUZrIbXN3SSrJSxqEoLBYLvLi4zGo2xzuu2cnv3Hwts4UCMZcbE/DYrWQqFWJBH+3RILGQH4eqsnNTO4ZLvmL0ucJFcklo5QrBkAunx0be1Ik4nPitNr514izj5UX2t7bw1Uun+M6lS5xdWKEj4OWJy1N8+8Vj3HNghIqm8YEtVyOLPqySjVQtxfPjMzjcNibnVpuGkZCXmNvF4kaWqfFlfu2O64jGAvQGA+xtaSHqdBKvV9gRiXB2bhmXz0HY6aTV5SRbq5Gt1fjI135Id2cEVImhWAur6+nmhFYVMawyFq/Kt3/nnaQUg6DTQUurn6XFBF1tYcZml6mVahwfXeCvnz9GOOTkmx+5nz9+z22MuUq0dfgoFMvYrRK/eet+Pjq8nadnZ7lr8wDvHhxEMw0uptP4rFZssowkSrx0ZpztnTFanQ6yqsHcXJotI62sr+Y4cmaRpF3DbbHS5XHz5WOnuXhhmZRU58LZJWp1g9dHL9PZHSJeLDIYDiOLIgYGb+3t4xPDBxjy+5EFiYVCht9+9RDvHdrMcjHFcLCNy/k0S6UyNw318SdX38h0bo1MrcqptXWOxeP82fXXs1YpI4k6v/LscyxvZHnixBhzuSzlUpUP7NvFUEuEbZ2tLFVLTE0vM9AR5Y6OQWTRR8BWI1Up0utpcruHA8M4lBg9Hp3bd/Zw20g/r84tsraeRXZaCdjtTKwnOPThj+KQO+l0beK+TWFGMylCES8nzi68mcNZLtQwgfaYi3rDwDTB51KRJQHFbeGq1lbe0T/AgdZ+hlv8/NXrR4l4XPR4vQRsKgv5PL82cu0VN7aAIJgIgg1RUJAEH61OgbpRwKO2Y5XaAB3DzLFaHiNi81BsFChrq0zlxjCFFIdWjvPKyglcapqgtYcfTL/K1S397G85gCQ0yT2iYMNn6eJM8jztLivtzu0IyAiChZo+iyq2UTfmyNUvY5dbgTfY4v8wpdKMDKJgo6yN8trqUTpcrYAOgEEZUbBgmvqVNfDGmzQiQRBZLI4Tse3FJrfiVv2cSrxGi6MV7xXWuiiouNQA65Uz3NO9iY1agX6vl253CIsUxS63cm3rZj5//iW8FpUul52V8hg2yfwZ17wshjFNnSH/8M+c800TlJX3bd3LHR03UDVW6fW6+dDWXWwN3Mxa5TSSYOBUFHo8fvxWDwdX5hiJRDjQ2kq/dztutZetgWFS1ZOcSZ1HN9dQRBkTk4XCLDpJGkadA61b6PN4KWgV1ss5TidX6HB5uJzLEXFYkAWBh449zYGYg9HMOR6bmuTRe97JOwYe4P1bbsVpS9Pf1cJytYQoClisCj6vjVSyQClXoVyqUa3pOOwKhVIDu12hozdCa9SPIQlohkGhWKGQKeFQBXTDRFfhtr5uetxBvn7xHI1ag56WMIVGHcUtY7dICIZBtCOIosoUajVkWeLWzk78NhtuVeWLZ8/y2KlRjpyZpZivUNcM6pqByyZx/ZZe+jyb/8XPWsMsI2Dha6dfIhpxImJitSps5Ot4XSrBkItKvc6L05f5heEhvn/4LAsLCabSad65fYgb23s4k0zwrfHz3NoZ5nIuw+GVFRRJYuZyqmlEkwQkDG7b3M9oIoFqkdk+3MHllQSJQpHfvflaHn7rL3L3cBdOxcqnDr3C906fR7Fbub23m5lcnvFMhid/eoFKtkZiMU9io0JXbxCLLBFzu1jI5ag2NLwuO6uLKfYP9ZEol0mUK5S0Brd3dXNVbzvf/uxzP7eN5qe/+JmHRv4VjeaRv/+3bzT/XazOi406R+NrTKQzbA4GcSpN3dHjl8eQBJEetwuL1M5fnDjB3ZsGmM/leOLsGA6Pnfdu3UKH2833Tp3n0NISv7F7NwGrjZ2hVj5//jQP3L6T83PLfPH+uzmdSPC9s6N89vGDSJKAYZjNiIoWPw5F4eXlKUaCIR6bmWVPNMz2UISS1sCtquTrZb43OUG6WqWsrSAicGNbFzW9jm7q6KaJIokE7Q5ssoxhmkykU/zw+dPs2dvL+7dt5e8vneWalhauaenh1dUZXlqa4+b2bkzTRBIFutxeInYH84UULy8tIysyNkVh60A7umkytZEhaLNjl2VkUeRvL4zisViRRRHVotDaE6awUaJWrRNxOEiWK0iiwPjGBg3doKQ1CFht3BDbxGZ/gUz1POGQl/F0iqmNDJv8TTrQfHaD0clFfGEP1YZG4AqZSZVEXhudwh1wMZfdIGR3UKzX8Dvt2Iba8VqtVDSNXx4eZrM/xLnkGj8+fJaw3cZMLsGhpSXcFgvRNheKKPLYwZP0b27nFzYNkK3VEJAxzRqaWUA3TURJRFZkVEudeq2BLIjUNR2rqtC3uY3ziRTjqSRbwxESlQqPvHaa1q4wZ9bXkVUZj9XK2MwS17XF+OiW3bznucep1nSmJpbo7GthfHWNSMyLy+dkbSFJqVjD4bQQtNlJ5YsYusHZM8uEfRYEgabIXZXxBF2cO7/KxfF1fmnlEW7YP8SfXXcd35mYpNBTIhTyoBnN+zXY3Uqx0eC/Hj5MtlBmIBomU61R1jQidjv5bIUnjp0j1hHi1LHLFEoNDgz18ULlEpl8nWd+cpE/eN/NiIJAOp4lnahw4tg8NosEGIT9dvq9HqYyG7x/yxDPzs8zmsrw2edf5+R/+s8cX1/gmmg3B5dX+PQNNzCWTjOe2eCx6Rk+fd1t7LupH8PUObQyxefPnOHRe97B8/OLV77IWTm/vs7V0SgbiTyGpuP0Ovjwvl1s9vtRJYmDy8tcSCZZmEkgCnBydZXfKr3AJ7YnsMkKg/4IP7r8CPFymQOxGJs8u/GqQ1zb0tQLbvtghGSlRKJS4veffpHfuOVaDNPEoNRcN1t387kDw/zpqW9QaxhodYOA39IMUPfYME2TYlkj6FVRLTKFYp10PMtCMM+J9TiPTE/z0txlIl43umESL5UxTJPVUomTiVn2hCUEJPKNDA45jU3aQqFxAascwikPYFBGN3OYZo3x7ASbvG0YpoFH3Yxu5glZdzOZe4UvnTuH22JhNpvHb53lt3d+gE+++gXaXU5+Zdte7PJQM5LHbPCOvltQxAgrpSOErDFEwY0kWBCQsEh9P0MGekOD+Ua9QexRxSjXx7qRBCeakUEW7Ug0HzuaeYmgzYVdsmCRmvz0ujHN1y9dZHckwWZ/kLF0kv0tXUzlxojaVnAoNiQhgGGWueeb3+L4R/+E0dRhtgXhr88d4zd2iHxr8gyf+8lr3LF3G3d1vAWDChFbHUVsRTfzTc67WUOnwJ+deoo/2P0gouB+U0pgYlDT57DL29CMDDZJZavfj0XqwTQbVPUaAZsNj2qj3TnCWnmMP7/6VzDRaBjzCIg0jCXKWoaVUoZWh4uA1Y0siFikVqzSMhbJQbqWRhJc/MnJ5+j2uNjfEmNXqJV0rcT2UJAfTE3xladf45FPvBurbOHQyjLtbs+bpiCAd/S9ixeXf4TPqjK5keXk4nKzMfQ7iXjc5GpVSsUKpgmSUsDtd2JRFbwWK+vJLPVag1KuTKXaRLB6PVYUVWEsnSFZGeUvDhzg13/yAolymel0mt5wkD+//wHqRoOT66t88eQZKsUKhWyZ1z0efvDsaXTDxGaR6Oj04/NaKRSb7vH2qJ1q3eBL585zQ+sIFqnnn/ycXS0d4d5vfYepqSTdXT76ulu4q7eHrx4/zXqqGbPX29WCz2bj9OQcNV3nhY9+kIeOv067y025oXFweY5f3TGCW7Vw/w9/wE39vXR7fWxUK4S8FtbSFapFnbyrwtOzs2wk8rj9Th7ctIlfuHcXuXqBQqPManmZhUKO333uRWRVZqSnHVGAHo+H5WKRTV4P7nfdwNeeOUIiUyMWtvHXN97M773+KhZRIlMosSUWJepwUhqs0+l20eKIoEoSy4Ui737kcZxexz95HX6e6j9W5/+G9cW/+euHrn3wKno9Hq5pbcWpKGRrNXTTJGi1Erbbqes6qqxwfm75RgAAIABJREFUZi1OvFikUdMwTbiQSDK9nqRcqLKvtwufRaXcaPDcwhw3trVhk2UiPg/nUimcqkrY7SJRLpLPVXHYZCyqxK6+TnZFInx1bIx4ucLrs3N8YucuLqQSCIKAKklMZDL84MIYUbeLneEQoiiSqZWxKyI/vjyLVZKwyjKqKOFQmwi+fK2GLhgMtITp8XjY7A/w0tISnzt9nOPxOPf19mJi8tdnT+NQZNqdHjqcMQxqPDo5SaFRo1ipkatUub6jHa/FilWWaHE4KGo6JpCtVOn2eenx+wk4HBTQuKa/h6rWIF9v0Op04LNYOLyywoVkClMQMcwaBgY7wxF+cPESJa3Bnb29RB02npyeZmpyGQCb00rA6aBQq7NeKjJ9OY7VrlLKVag1GlhtFvx2G4IgcHl+jUsTqwz3tXFndzftjhBtTg8VG9zd3c3B5WVmNzbYqFR4/9YtPDs3T6ZU4vdvuJZrWjbT5+1GM3I0jArFRoW60SBn6OiYVHWNO0eG2BONkKvX8VptZKtVio06vT4/VU1r4hErJU6fXKBSrWCxW4ivptEbOm2RIHYVziWSBKJufAEXI9EW5lJN8tE33nof79yxFV/Uy2wuy662Fna0RJFUhSPn5nE5FASXQjqZYyNxJSlAFIgE7ORLDdYTGS7W8/T4fNy/dROf2L6bF5cWWSmVuLe3m+fn5wnY7WyORAjYrIiCgF2WeWVxifv3bOXHr40zMZmgWtIwRYEL08usnE6gqSIhv5VEtUhvJMhbdmxmLL1GMl3B41QwgQ/ceQ0HF5f4xaFBVktF3IrKVZEIVrcdj8VkMrtBzOkgW68ScTjYE27nqkgL3R4nl/NpAtYmiWu+kEGWFG6M7WVvixu3KvHqyjIOi4U/eeQFSqUGHf0Rtne18e7Bzby0tMQfPfkCDavEwdcv4XFbGNnew9z8GrNL61RtMiY6e8J76PNEOJG4zDcvXuTLF15hKj/L7rCMJIq4VDcdzl0MeId4YDhCl9uHiclsfomAVWkSfVB5afkUZ6YWsdlkNM2kXNEwDQNVlVEksKgipbJGtW4QibpJlyvEvB6SlQp+m42Hrt7/JkfdqyqcXFunpDVodSq8tjqNx6KiSgpWKYhFiqCZq1zOj+FWFRZLs8iizo/npqnpFbpcXdSNNVSxjeXScb48eoFPjGznrb2biDntDPr9tDgGWKtM0+f1MuS/Ed3MoRkJktUpVLGBIkZwq+1Iopdc/RLFRgmX2oFh1t+cYP6vxJ9/XBPZo0RsTRJRrn4J25UJKEDQGiBfX8epWDHIoIhRXl09wtt6t1BqVNniG6TP68Mu+bFKJu969nGubYvitQSRxRBeb4nh4FVcH9vM6/FRnp6Z5rauTh569TVUq0LI5eT2zgFARJXaaJhxKtoyVrkd3cwhC0EQ1ul0dSEKDnQzhWYkeWr+FQ7HF9kRbCNZnSZs67/iuq9i0sAu60Tt/XgsHZhmFbfaiSioaGaKxy8fZsDn5sXlc+TrZWIOL1F7C1a5g8XiLD9ZPEuvJ0C2nmM6m8JvNTmdjHN97H+y995hkpzl2e+vUuccpyfntLM5aldZu8oJJAQW0YDhGGOSAX+AbWzjALZskXMStiRkJEAJ5bQ5x5kNM7OTQ093T+dcXVXnjx6tBQ4X37nOdT77XH7+m+m3qmtq3nrfp+7nue+7mQHvVjymLsK2CDl1EZfJxPFUHJfDRrKcZTiZZCGX586u1ksIMECnq5+wLU+smOee/j5u6u3m2vY2NoXD7GhuZldXB1d2tDKnV2j2eslWKiwXCqjVGpVSFV3TkSQRtabj9TtQKyoXluKM5bMsFEsMn5vGsMvkskVK6LwwM8mVzc0kyiVeHhlnenKZ2fk8x8/Po2kGJlnE41RwB5wYukEuVyZXrGtGqzWDmdllNvT7fw3VTFWO8S/jL/PI2G6+ceIo8fllfH4bLW0hpqdjnInHaQn5MEQdraazurURm6IwNhvldD7NrZ3dvDA9xXgqyZMHz/D4wRFempliS0cLR+NLdHm9OEwmTJJMY6Mfm99Mc6uHy4Y6sSsKt6wd4PNXX0mfN8g/XTjJdC7Ft0+d4u+feJmfHRrmvdduJVNTMUl12T+rIhGx29jR2MKGUAjZZ8UcNPOtN9+KTVEI2q3EiiUuRGMgiyzkcvz89rfR7nJwIh7jwMIiD714mOhSnkIqT+bVqf+yiOY/fOMf/nz9nev/txHNfT/Y+z+I5v+TEAWBPq+HmRWdy7JWQ9V1prJZZnJ5FFGkqmkMx2N4LBbiqSwAfrcDv9VGrJCn0V93GjBJEbo8XgJWG+lKuS4FY7Gg6QZzuTxOkwm1WsNpVyiUajidZgI2K5IgML4QY2xuie29nTiUusjv7rkoN7W18eLMDGpFRRAELJJMu9NMslLmcHQRVddpsNuwyQqj6TS5apVctcrmSIR7B/r50p4DTGcyvHtoFeuDAT68ZjOSIDKcXODxiQk6PW5CNhtjmWUUUaLFvorbuhf5xrHjTI1GaW4PsH9xkTaXi3f0raLZvpotoTS/nDyAquv0e32YJZnTiTglVaXL7eInh09gtpnxWsws5POIgkBRVXn2/CiPnxjmQ1ds4baOfnZ1d3E0usipWBwdg0K+RLDJhyjWEd/JFV1MwzDQVA2TRUExycgmmXy1gl1R0HSd2FKOiqrjtVoIWd0ci0/hs1j50JpNvP+Fpxgbn0eSJBSTTLFWY9+Rc3z9nXeyI9KJiBnDqFDSqgDsj07z7NQ060MhVE3DZ7Xw7oFBRlNJuj111Pf7I8OcjEbZNzyG1WlFr2mEgh7WrKu/IVZLKqIoIkgCQZuVfzx6jO/uvBm7YuHM8hyHolGsdjMTF5YAkESRG1rbCFgsNDqcNNvdbAk3Mn9rljPnpllcWMZit9DT4OXiuTnau0Ok49m6P3ymysJcgj21+rzdGIxwT08PrY4Ah2PTNDldXNPSRMBi5aELo5gliYlUmmtaW9gYDmMyieSjVahoYJcpaga4TVDViacreN0FXpmZ4Z2DA3xg52V8+vvPoMgiDpvCeCrJ2wf7KdZqJEplUpUKj46OkimU2D01xYc3byRTLfHyzCwDPh/DyQU6XX46XEF+cPYEM7ksb+4cot3lZlOohaq+iMfkpcWhYZJk2p0Bmh0O5vJ5tjWE2Rhq5sjSLA/sPoKsyJRqNWwWmf6BVu7p7+Xv5mKMT6b46r/spqfTx+A7AqzxX8+A18vBhQVOH5tm5sICC7kcA34/t3V2IAujdLpWEbBsvrQm+C1ZZvMn0Y1Zmu3tzOdyZFMVMEmIQp2DJUkqtZqO3SojigJL+Qpep0K1otIWCaAb8J7BAf5lbIyZfJpujwefxcKFVAq3xYxNlnng7FnGU0lW+YPsWbjIDa0WFKFpxRc9ymxhBpOoYJFMvKV7kFKtQqoa5ZmpUd7R18D/2vsqP9h5B1Z5NWXtAg+NnuWP1t9MsXaGp8YnuKuvB92oIAgmZvOTRGx+ZvIxetxVRMGEYWi4lC4yXFxZC/9Vnkgz0pecg3SjRE2PIQluJNHDoHcnAOPZV2i2/7q2jSR6Lvmu60YJgO2RASxSH1vDDYiCm0J1GY+pn2RlkvetWU2LI4QiRlbGttbPIzi5trmFRruN4eQSH9i4jifGL/IH69aSV+dwm+oIoIgFp7KWbPUENUPHIi2xPtCCqs9zIXcMkyhjlhQCFis7Iu0sVy4iAKnqBOlKgQabl4JaZrGYYak4xvpgEy/NXuS2jsEVbc8wd3Vdh27kGE9n2BgKUdIqPDV1lDs6duIz151gylqVklbFZTKhGTrv6B+gwepD01MgioCAJIjIoojf6+RnJ4e5ureLXS0tfHFunrOpMdYHfh0NHEnGcJpMCIJAzai3aGiGgY6BYQgErTY03aDBYWd4eh5N05EVqe4aJEtoFRVdN8ilCsiKRLGoMju1TL6nhMNjZ+TUNP6Qk0K2xLIo8HlxP0uZLIvz6focV0Q03UCSBEoVjcVEGU2LU1V18qUatWwVb6ODQklFFAU+96uX2PXBXfUE3lDZ9cCPmJpIYLfKOFwWOvqamJlYQjMM3rp9PRuCQWqGzsOSRFFV+ej6DXzlxHEMvb4XPDx6jhs62vjID5/EbpEplDSS8Tw/HhnhsuZmvvPUXhrbA7xl3RB/uHYDPosT3TAwS2aS5TQ/OHuab5w6SblW4+VjZ+tzUjfq+p/r23l6dIx8uojVaUGSRNaGgqh6hfOpBDm1ilmS+NimjVS0Gsdiiwz5g/R7/ai6zi8PneK+N9+ISfLRaHPzw90PUqvV20gUSaCpI/hfWt4IQPw/fQG/Zfz/ItHUdJ3HxsY5u7hEk9eNw2RemawSVVGjqmsM+Hy8ksgwX0nUS82ShFmScJnNKJJIrlJBEkS8ZgshiwenUsVrKZGtVti7EMVnsRBWFI4sLmIYBqVyDU3TKeQrPHPmPCPxOPfduJNOlx+TpDCZTbBULFGsqhxYjPLxDRtY7CtwMZMhUS7jUGQ2hyOELB4MDPJqiWOxBfq8HjpdXv7q0EGanQ6Wy2XuHBrg3HISl8mEXa6fe0PwCoLWNAGrBaskcXQphiQIrA008vjki2wMRnj5LX/Foxse408ffQ5zZxuz2RyKJCOJHiQ8PHL+AT63bSvlWo14qcQ/vrofs9XEJzdtoqxpPHTwBJPpDH6blfcPDXF0aYmfHD2Jw2Pje4eP0+508Y7+QQ7Mz3E+EUcURcxWE36bHcMwKKhVCka9vSCTKl76f9mcVsqFCqlYlve/aQ1dbg/7D18g0uTh8PQsv5d6monJKLquc/maXvbvH8Xns9LV1UhRVSnWanzi9mu4ItJFulqgqmfwmBxohs6BxWm+eeIkjQ4nd3T2sSPSyOMTE7Q6mmhz1NnAUOMP1sjcduifKBerVEpVAk0+BgNBvM3NAIwmU3R7PYSsVn5y6gwDDSH8lhaendnPhVQan8VMj9/Pt/74eprsfnJqCbOoErbZsEjyiui1yMaGBhwmM0W1imNFomlsZBYEgd+7fgd/8uPnAIjHciQTeZYX04zEY3xw7Rqa7D5ixSIOk8J0NodTMfHeVYNUNZ2vnDjOTC7PVDZHOluts95LNVBEKFXAIePwWhBFqFRrnB2e4gHDwG+18Y47NmOVZc7GY0zFlpmM5Oj3elBEEYskMzG+AMAX7tyF12zBJpv428uvxqlYERFIVfMcj89xR2cXrY4gS6UUL87M8IFVW/jx+WOEbVbu6LiZgDXNaPo8BVXlw2s28cTkeRypGN87fYZtQ108+fJJJqdS6IZBplzm/t0HGT67RCRgZSlZZmExy2f27OZLVwpc03Q1PxoZQdcNljNVnnzpFK/aZH7RPIxskrn/hgrd7jClWgVREInYttHuvPLSnPvUpk30+epo01cff4356RzZqobgsyBJdSawSRZw2BQcHjurgyEOLswTslm4uqkJl8mMy2TGoZhIV8o8eOQkl/V2ohsGx06O8/FymT/bvp29i2Nc29SNjImw1cNjF0d4Z9/lCIj4zS4WtRkuZuK8uWsIWQzy9WtuwiqvpqKNo+olGu12ND1NtJjkp7e8HwkHomBmOncYj8nBwehFPvLo03zxzhQ3t92OZsRRxKZL0kNvjNcTPwBRsCKLDaj6PBL1fkjNyGKXreiGemlcvafzX1FQATOz+b2XEk9JCCIKJuLlDOOZ59kQvJzR1PP8+aGXuablAtc0buRobJ5WR925qM3ZQLuzhU/ufYqFXI7v77oVq2ylVCtxIvESa/1bkcUAulGkWKvgt7QjCXWkFDS6XXYMSmh6jcaGVTwy/it6PT7SlTJNDic97lXsXTzM+VSK3x24jK+f/CWdbg9/+8yrXPG+Vvan9tDp8hIt5mmyu+hyu/nphVHe2tfLWDpLzYhhlU2cii9jlmSCVivrA1ejGUlEIcpzM+dY5Q9icJGZXJYfDg+zOhhEEkWqpSofXbcZp2LlU9Fn+dbpU3zrmh2X5I72R59iJJlEBBYKBQACFgvCSkUip1b56YVRzpyZZLEpjclqwmoxMTO+iMvvYDmawea0sGPzAC6zmXy1iiKJGIZBpabR4nahajoHZ2aZG48ST1WwOiwIokC5qtUtXGUBalDNVkHV0ZwKcwv1awkFrXRtaqSUr7PhbRaZ5ViGD7z0TVxmMyeji4iiQMBnxWI3I4oit3V383ChzGe3bcUsyVikug6mz2JlNpkmXirwyolz5LJlCvkKPyud4p/e+iZsZolKVaOialx92SDpSplb2ju448NdWGWZVKVM2BZCFnwciR/mS4cPk61UOHV0CrtFpliue6FbzRIVVae7J0hsKU02mSfSEWJNpIHLmyKcjC8TtFmZyGTIVlU8ZjPRQpHxdIarmpsurVfHo4toNY3V/giS4EU15vmd7Rv59tO7sVhN6HqVBu+/2rD+V43/LqXz3yrRFATBA3wfGKKuMv1ewzAO/MaYq4EvU5elThiGcZUgCBZgN2Be+a5HDcP4/Mp4H/AI0A5MAfcYhpFa+ewzwPuod6l/xDCM5/6z66vqOtF8HpvVzJpQiAPz8wwFQ3WCg9/HzpZWRpYThIMeEukcToeVT23ZQr83yBMTo4ylM7hMZjrdLgZ9jSxXsoylkwz5w3zsJ0+SL9W47bq17Gpv5Zl8nnKhUncAkkWsFhlBEjl+Ypzvygpv7e/lmqY+Vvma6HQFubW9l2PxOR4ZHeP9Q0Nc21yXGfmDV57mysZ2bHK9Z6qkLRIvlfBjwalY+Z3+PlwmE2eTSQZ9PtYHgwSsNkQBnpueZv/iI/R5vYSsFiqaTsBiIeKw4zV18+zUK/zVc6+xHPseiizw/d+9my2hdv7k4Cvcd/wQawMXuaKxhR6fj9fm5tAMg0a7nf3v+xSpapR9i1N8eM123CYTPzl1hnBbK4ZhELZZuay7gwd/dYh7btjEYrGARZYJ2uycHp9B1wxMFoU2j5cBv5/Hh89id1ixtQSwu23cPNjH8aUo4xcXMAyDuYUcH3vgSV7+xHv51UfezR/v3k0snub8wixjk2n0vMrYRApNM2jr9LMqGOLA7AwXM1k+tm4zoiDiMzvJqsW6fiICj1y4QLlc5YbVbQQszfjNJf5ofYSFQhSzpOC3eDEMnT3zs4iSiMNtQ9d1WnweAlYLTpOJrQ0NlGoaF5aTrO3r5S1Dgzx45CTC1SY+98SLLEWzNDZ7sDosWDebmM3Hidh8JMpZTicS/PTQizg8dnwOG0VVpc8fqNtUmkyMJlNMjmdQazrqap2OFhepVIlSpa7nmUkVOJa4yKfG5vnOvXcStFrJVKs8ffEiD548Q9jjosXt5utX34QgCNz+84eQJAFrh5PCUmnlYQRf2IZhQK2m864btjOWXGYmkeKt/X28OjtHk9PBiZk5vnzjLiazGcbTGcZSadYGA2xe3U3E4SBVqbJ3IcpCLsu7Vg1SrtXY2tDCp/e8wue3XUZF0/jOyBEePHAcu9vGXd39XNnUBMBn9v+IP91yJZPZNO/qX8uhpWn+8pEXGFjbyppwmCdeO073YCOJhRSTs1kO7pugs82NKAok0hVqNQOPQ2FuNs5oOsmQz8tPrv8D3ml8nX2HLpDKVplZLBBdLhMJWLj1/n/CYZPxhd185023YJNPMJKcp8MVIGLbRr/nOvo3XAfAHZ1dvDAzTapS5UfP7sNkUZBkkaAgEG4LsrYxws8OnKCns5Gfj46RSGbZ2tXOTe1tfP3ECZ64/TNIosibO25k1X0fp1iucfb0NP/k8fK1q95PTV+krKUQEHj/4Ls4m3qR0VSS1YEQql5vB5AEJ1V9Dp95IzU9BYg4lXW8vbcJWQzS6ojzwuxuWl0uetz9NNmHkEUv1zZv4tgfrianJlH1uUv9dLpRwkDj9PI+ziWXubf33n+zToqC+df67yTBRcS2DeMNiaYgKKj6IpqRQsSOSWqj2b59ZZ2dwyQ2oxsVGqxectUoj4w9iWboPH/qHK+OXqR2jc6GYIRi7TTH4tMM+RrRjRK3dLTznu/8DOfNH8KghlWGVb52Fotn+cXEKB9YtYOSVkERG1gqHSJoWc9fH/0J7+wfZDKb4vLIFmTRy9MTk9y9azNmqXfl766wVCywe3aWD666h6tbmpjP5+jtauJUIspENsvGYAtLxTxfOXmce/t6aXY66XD56PWEuOfpn3JdWxtbGsKEbDZGU0mGlx9joVDgXGKZ5VKR5VSO2oo7l2JWWBcK0eBwMFaY502PPMziTJ0QeHD4Ih/Qv4EoQJPTyZGFBe7q68VpMjGyXJffuizSQEFVORSN8dj+41SKVebjJVLZCgG/DV/Yzc3b6+Q3+2oFkySh6jouk4JuGKi6jk2WUUSR+594BU/ARSFbJJGuC7YbuoGh1/v9s1UVPa+CLNZ3bVmko8nBdduH2BqJ8I/PvMb8QoZyRasTcEQBWRI5PHKRUr7M0nKZjdvaqVZUrA4LuVSBVKXKYGOYo0sxdrW2UlBVXCYz+86O87uXb2b3/AKFXJlsQa2vP1qavziwn8f+4O08OTHJO/r76XB2k1PjZNUCVsmM1xzidOIQH3rsy3iCLgqZIuVilXSygM9lZvXqDp55aYRyRcMki7Q0u7lx9QA/+tU+rrhsAIfJREWroRtwNhHn99eu5WImQ7pSYSabJZrPs725id3zC7w0O8cTB0+SiBfIFFS2fOnr7PnkB0iUihRUlYHVbcTjGdw+B3f0dPOL/yzx+J/4reO3RTS/AjxrGMbdgiCYgF8zzF1JRL8J3GgYxowgCKGVjyrAtYZh5IX6a/JeQRCeMQzjIPC/gJcMw/iiIAj/a+XnPxYEYRB4G7AKaAReFASh1zAM7T+6ON0w6PB4ieZzlGsaHouFE9FFrm5r47mLF8lUqnVrSrOZrKXMm/t66fUEGEkucTqeQBZFWj1uWp1OFgp1du2ePaPYrTJ2q0wyW+WFPcOMRWOU8mUEQeAt121mayTMZx95hsXpBKFGD2PRGF8t5Pmb3ftJJ7J8eNfl3NTWyeWRLrY3dJCq5JEFJ6qe4bNbtpKuFnGbMsiig3KtittsYqlY4tHxs+ybn+evdlyBQzExncusuCeoWFbcIxRR5MzyMpOZLK+cHeWqgR5u7ehHMxJsCIc5F42RSxUINvt4bX6epyenmEyn+N6uW3AqTr51Zh+XN0Z4bnqGo1MzNPq9fHn3Abw+J2G7g1vaNvGegRtY5ffzmVdf49DsLLG5JF09jZSrGg88cYjmt+/kWCzOsbMTmC0mPEEH5WKVNaEgiijS39iAXVE4Nj3LOzas4T0DGzmXWuDp8CTf+uUeqjWdfFHlqr//Pg9/6G3849XXYBgGXzlxgqX4EXbespaFZIatHa1MpdK8NHKBtkiALrcLt8kDaGi6ilOxkVdLvPf5J5majKJrOueTaVQtiWboqLrGfceO0OR0cG1LC6t9zXz74FEWFnM0Rpx1mzOt7jlfUFV63RE+u6mVaDHBo+MXeHZigsRiiu6/+jjpnIpJFkkl6hprO7/2fa7bNnQJGX9s9zHKZZVysYLeoKPVNILNVnQMyjWN1cEAvoiNzoFGhhNx/uLOXSiCyP/1g18gigKVan2aZ7NlJjIZtjVEiJWKmCWZrR2t7Ig00O3xcuujD/Kp7Vt5+PY3E3hLhHh5gbc9/nNmxqIoskixXMNuVWhoD3JlUyPXNDdx35EjfG3/ETa0NbMlHMahKASsduKlEl85eZiPbt/CYqFIplIm4nAwns6gahq6YfDj4RGubWvj+yOnOHNhhoPt7dgUmW8+9VodYfA7WChk2BDYzMXsadpdTmq6xg2tl5Eoz/K3u/dz3Y5VJMsl/uWFI0QTZWaXirQ3OhgaDBMKe2hze7g4dYhKVUcr1Zi4mCEUqfHRB57g8A0x/vqyd/Pm3h5e3nMWwzAwmSRqNZ1EqoLbacLhtuHxO7nrWw9y4+VrSZVL3N3Xy9Zwjalskq3hfsxSL32e6+hbITnf09PDP58/z6N7jxNo9NLsdfO7qwaRRJFWl5Oji4vsWDPIvrk5fl5TscoyM/mjPHz2PKt8AQ5+9A95fvYCdkVh70KUvzjyEH+2+W3YFT8X0ocwSadJlIrc2XkPNb3O4n5w9EFMksQdHZczmnmZXve1yNTR1tnCWX46ep5Pr38rN7XddUlXU9PTTOZe42ImRbPDSb/nul9bA+tlcjvNDi/ib4AcmpGlXvZ1/rvrZ0k7T14tEbJuAepIqEIdDVX1pUuSSfUkswSI2OQW/uLAL/jnG9+Ey7SeyUyOc8sJInYHbY7LqOqTNNqdPDV1jnt772RLWODem7auyDQpCCgcix9ifXANH1zViEls4YcjL3JrR55EucTOZi+/t2odsijR51XIqQu4FJkHb/gMVf0ik7nXWCzkWBdoxSrLnLkww9caH6bL7abd5eHBm+5GFsLUjBhVrUKhprJnZIy39fWyq7WFr548xvVtrdze3c1TFy/y4JGTxBZS+IIuPnnd5aiaTshuZ204RLVFQxIEAlYL93SvJVMtcGY5is9i5eevHSXSFkCSRERJZCyeoJQvMb5CJOn1eNkQbAPO8vDZ83zt+Emuam1hV2sLVzY18tXjx0nuGyXgt2G2KNSqNRxKvXRff/FVmM8XkQSBZoedYq1GqlIlr9bwhd34/C4aGrwY+hzL6TILM8tIkoDHZaJa0xGdJpw2mbbeCOVCmU9cddmlXn2z1UQyk8LnNtEQdtDaHsYqK0zMRJEkEb/HxPz4EtdtG+LM/CJ/duu1fP3IcQIOO7dt7CRZKeMymXlxdoqn3/Mufj5+jm2RMBd3rGbfyVEEUWD9YAffuOZmnEofjXY3yXKBidw4L8/OsLO1jS+fOMDlTRF+dGaYakVFkSQqpXqSaXeYUSsqL756Fq9TwXAoCAL4Gjxohs61O4ZIlUvMpTJsammirNV496pBBEGo601LEuOpJFe0tLJcrtDqdPDwmRHisQI13SDss9Cf2edTAAAgAElEQVTeF2E6l2Y6m2OV30vEbqPUUV+D1wWD/+ZZ+a8UAvW2wf8OIRi/6cn1mwMEwQWcAjqN/2CwIAgfAhoNw/iT/+Q8NmAv8PuGYRwSBOECcLVhGIuCIESAVw3D6FtBMzEM429XjnsO+PPfRFDfGI0DEeOGr74Fl9nMQi5LtlIhuZzl9nVDnFxaoqhWuauvjiRe29zG106dwCxJnF9OYJYkmlwu2pxOnCaFfq+Pvzl0iOFTk7T2RFiYWCKbV6moGpIk4rLLdAw08aeXXcaD5y+woynCj0+eoabWMAwDxaSQSWRJxPPUajqBoJ0v3LGLFoeLJocHj8lBupLDIpuxy050o8LFbJQfnh2h0W7HIkkMJ5Y5OTPPrv4e/nTzLUzmLjK8HMcmKxyNxVkfDNDocHBkKUqyXMGpKNzW0UOzYy1HY3uQRZH7jx/n6OmL1Ko19nz8QyyXc/zk3DlanfWyjtdi5q6udaQrOb43chKv2cSN7R2IiJQ1laDVxanEAsdjMVKVKj0eF6OpDK+NXWRqLEY6r6KqOjfsGmB6agm338nqpggVTWNLpAGv2cRkNoduGIRtNu7sHMAuWzi9PMNfHzrE2Og88US9hNPdF+bxt92L2+RCN1R0w6CiqcwVlvngE78CQDEr7GhvY0tDCEUUubqpB0U01ZnmGLwwe5oPP/A4qWyV3r4Q9992A412Jzu+9B0cNoWdl63m9NwCJovC2oYIz+w7Sb6oUixrdHT4uHXDEE6Twjv71+A11zf9bDXDYjHFaLq+ud/9jYewW2U8QVe9sVoUyKULOD12ysUKnqCLTCKHtLKIpxNZ+rqb+cSmjZxO1OWoFElEpG6n9/XDxxAEgfuuu4a7vvkQiXQFj0PB57Pha/Cwvb2N1QEfbU4X64OtpCt5UpUiS8W6JFefz8dSoUBOrdLt8ZKvVtne0MloJsqD588TLeS5uaODAZ+fVLnE7oUFHjtwkhs2rmIhn+P2rk7WBEKcTsQ4vBSjwWbjyfOj3NjbzXQ2i4iA32ZFEkSmMxnuv+p6DMNg4999GYfLSntH/UUC4P6rdvI3Rw7w1r5etjdcRapygc/se5n3Dw2RrlZ4bGycTLnMQjpLg9vJbCzJ+bNRXHaF2WgBu1XG6zIxN54h1OZEECBfrF2qHHR2BXjPtg28vW8tN/7zA8QXUiwkSmiagSyL2C0Sn3vbTkRBQBFFPvuTZ7nwl5/g/hOHuaWjnR53kPPpJYJWG33udSuyRP8ax+LP4DSZGV6OsyXcTKyU48/27ePL11zLpi98m0jYgdVuJhLxIYkiP77hDu556lGeedNHqYuxVFacd8J8Yu+X+eL2uxjPXKDL1ckz00e5reMmRGxoRpIX5/ZwWUMHbtN6zqZepMvVymRuloJapdHu5bHxs2iGwYfXvJWl4mnCttUImBnN7OH5mWk0Xef3V1+BiAVJ9GEYNQyqCCiXWOX/UdT0OAb6peQRQNUXL5XZDUP7d0W7q9oUBhqZ6jIBy1p+MfEL3tx5D+9/6e8J2e10upy8vW8DNnk184X9TGaXmcvnWBcMU1CrrPYN8OTUwZU1uAeb7EQQbMRLMwQsIZKVBCPJKOsCTWSqBQIWNw6lB1WfRRaCiIINzcjws/FX2NrQSMDiZioXY3g5wfNTM2iGzvhCjK1d7awPBvBbLXzhxd189MptrA+Guf/4MfxWG9e0NNHr8XM+mWAym+X5qSkaHU6y1Qp7Dp7D7jDjDjhJx3PoNQ3ZLKOY5Nf3MDxBF4ZhUC2rRHweZFFgfbiBtQE/NUPnB2eGuTixyIaBDiIOB6v8XpyKCR2DiN1BTdd5dnqahVyeNcEA17e18cTEBJtCIT784OOE24L80bbNyKKI22Tmtfl5Xpupdwp+dttWFvIF0pUKAauFTz/0NG6vHavTittlY2E2wXIiT3tPmGpZZWk+hcdnx+aw8NErtrF3foFrWpupahoBq5Wzy0kePnqK61b1UdHqCe5UOk2n18MrE5P1knqhQm9XE6MX5xEEgWpFxWxRLonEf2TnDgZ9Pj7z6mtc19nJFU2NTGYz3N4xgEmUyaslkpUC7c4QPzh7lB/tPUJ8KcdX330bmWqVXFUlU6ny2KGTJBN5XB4r1ZJKuVLDF3Dg8Ngv6TRruoHdYaalI8yVra08fnIEs82MrEjc1NXFWCrFOwcGyFQrfPPESRRJ4h2DA2QqVXwWMx1uD4uFPH/y9EuIoojTZ+cD69axJhAkXSnzy4sTnEvEWU7luLK3i1S5xPd3fvWYYRib/tOH6v9QNA5EjN974H3/28f95da//v/8b/ptEM1OIA78SBCEtcAx4KOGYRTeMKYXUARBeBVwAl8xDOMnAEJ91ToGdAPfMAzj0MoxYcMwFgFWks3XUdAm4OAbzj238rv/MAqqSjSfYz6XZSAQ5IoWNw+mTrJ3doYOj5egLYBm6JxZsQVTxDrbLFMu47VYaXHUk69UpcoPhkcA2Lihh82RCF87t4Ao1j1kAXLFGtPnF/iT0msMNdcX5zcN9JGpVhmJx8lUKmzfOMSZ2NKlCfvDM8OcH55mcHU7O9vb6fN6qOk6PR4fpxIxfj46xtpQGLuiIApQ0TT0msZSscDBpdOXemAKar3EtSXciigILBUKpMpVAlYLrY7tpKsneW1+nssiEbKVCqVitS63Y2nh5blXWSoUOBOrk1ccJhP75hfxW630+zxsbWjEJltQtRrL5RIFVSVfrfL06BjdgQCdLie9XjeVznYMA4yxJXLFGufOTNPUFaam1nh27ymuuWwIt8lErFTGrig4FJkbWruxSiaOxCb5/L59nD42TbWqYxgGba31PphnpkYZ9Ptpcfgo1sp858xpHn7+ELoBmqbj9doYdTpIlksM+Hys9meJ2BoxqBItJvjl+AShRg93XtPF+1atotfTxh2PP8BSssLg+jY2hkN1+ZhUiuVSkabuBtwWC9FEmu09HUhivW/KJptXZpVGTa8xm8txMp7gH/YcJF9UcdgUrA4LvcFA/R773cRTWQY66/aOWjDE2lCAck3jpyfP0O71cGZ5mWy1TgSzShKJcpkz8QROm4X+QJD9i4tYTBI2i0ykycPNG1axMRTkRCyBy1Qnd4ymF2lx+HGaNC6kk5gkiYpW40gsxq0dHVgkmRejszwzPc0frd/CH23YSqqSRxEl5vJZNMNgXSDA/pYAqXKZT27ahE2u66OejC/jMpl44twF1jU18uLkJD1+P0GbDVXXGU8nOHJsjOWt22iyB3jfTZfz1Omz3N3bS6fbzbdOnWL7P3yTpWSF01uj/PCW+oYK8JUTJ/CYLeSrVQ4eGcXptiIrEidPzlPJqxRKNagZFFIVCvMFsEgIAvzOjVs5OjtHbCZBraZh6AZnl5MsFlNcO9jL8+o5CqUasWgRs9uEy65wMhbHaTLxsQ0b+FRN53hsjpvb29gaWoMiNuM1T/LE1FEylUMcj8W4qb2TNscQkuBe6RX00ePSSFZOsNa/mh9e7+Mz+15h19WD+K02QnY75VoNRRK5/dGHyCXzfGb/I9zb38eGwI2o+jwA0+kMXzj8BH+y+QZMYgtXNqXIVEdJVXLM57PsiHSyVErhUHL0e64gXR2myzWEqsf45pmDPD85yX1XXc1S6Qx7F6fp9mQZ9NatA9/dv56KpmKR+ihr50gU5wlauzCMMooURtWXEBD/TSINUNWmAYOSlsJt+tdE8/Uks1Q7g4GBTa7rV75eKq9q00iin3ue/kcAHrzJyeWNHQiCyP1XXs/JxCyNDidmqQOApVKWWKmISZJotgewSHU29u0dFnLqPDbZzYGlEVRNp8nhZDh5mtX+CEO+BqZzCVb5WqlqVUDnuyMH+Mia9wIgoHBv772o+jwPXniZBrudtYEQW8KNBCwu5gtJulwNKGIDF7OnWdfZQqvTxa+mJvm7y3eiiDKxUprX5mZIlMv88OWDeIIu/FYb5yYX8AbqeqWSLGEYBt6wm5aQD4fJjKbreK11YlW6XCZXqeCzWnCYzAwn4hxZXOTwkVEMoGeomSuam5jMZvn24eO8a8MaimqNaKGI22yi3+vhru5uji0tsXdhnr2zs5xcWsITdBFxO2l2ODFLMm6TlZHEKYqlCm67lWihQFmrEbTVr8PptiFK9X3JZ7UyuGGQZw6eRjHJJOaTuDxWXD4HoiTyhSde4neu3ESuWkURRbLVKkN+P6va6lurWZJRdZ1kuUQnHtZEIiiSyHQ6w8RMFFmRyKUKmCwKnqCLVCxLtVwnX56Mx/nG9dfT5gySqxbpdPk4EJ2iVKuRrao8MT7O5c3NdbQwV8ZqlnCYTPR5/XxveJiXT5wjkyqi6QaFXJlQoxe/38XacAPJcomnRk8iCAKaptMYcLI6FCZWLCGbZIrZEjW1xuPl89w62Ee2WuW1uQXmZxOYLAr6gHFpvxMQmMxk+b0rtvDKzAzdPj8HFqNohs5cvsDp2BJei4Wa206Px81i4d9XbPivEwLCfxOv898m0ZSBDcAfriCRX6Fe5v7T3xizEbgOsAIHBEE4aBjG6ErJe91Kef0XgiAMGYYx/J9837935/4NkioIwgeADwDYQ05S6TzX9PdwU1srLU43BVXleDTKtkgDthXv5pPzC7xvaIjZbIZ2jxdJFOualYBZFBEMA1XTsCsKQZsdAwO320IuW8ZuFUmkKyiySGtfhNEzc8xNxJGvErmlsx1ZFDm9FGMgEKTD7aTf66GkaURsNmyKzMLCMqMX5hg+NYkn4MRkNfGWdUOoms6aUIiqprFULLI1HOb5yjS5dBGP2ULYZmfPwjyHFhcRBIF7ensJWbeg6vN0e/KMJJO4TWYEQeLBC6d44twFnh4dYzmaxmoz8embryavRjmbTFHValQ0DZuisFwqsVwqEbbbKdVq+C1WNoVa2D0/yUwuz41t7Vjk+vQYnp7HYTIhiQJmSaIj7Kc3EmL3obNIkkghU0SURMymuv6n32qhxenkfDLJ+WSaIX+al+cmeGx0jKva2uj2+Tgzs8D4hSUsVjOxuSTfPngUv7e+iMxkMxw5Pka2UE+snXaFWKKAXpvCbDNzPhjn+rY24uUl4qU8u+fn8Fut9LVG+NKOmxFQOBwbYfzsLNu2tXJLdzcNdhuKKHJFU4Rmh4tPvPIys/MJrlzVw/WtrSwVi/gtFjRDX5mEMhVd5YmJCc7M1skxVrNEuVJje1sbqqZhU0wcGJ2gXKxiaVRQNQ3N0FksFEkUS6xvbWY8meRCIsEVLa38cnSUbK5IYj5FKluhvTPAvYN1KRGn105V1bl1wxC9Xjflmka2WiWvqpxLptnRGOFAdIp0pYoogM9iwSYrbAgGCFntuE127umRabYHmMzFUEQRu2zm1flpCqrKVc3NdLla2drQyvH4HK6VDfKVuXmubWnixZk53rluNZIg4jSb2dYQYiqbYyKTRZEkfGE3H3/1Zd67eoh3DwxxXUszIZudr5w4wcjYHLlinRw3Nx7lByMjrPb7KKgq7141yO9+91GuuKyfXLGGIJQQhBSVWAlPiwObRaZQqmFWRGRZRBBgw8YubIrMru4upkNBdMNgOpNmZGmJslbjY+s284s9xzAMkG0y77ljG0cnZ7iqpYlOtxtJEHn0Y2/jJ2fP8qPr78Yk1b27zVI3L0z/nM9tvgwhLNBib2f3wl4en5hkQyjIvb070Y08VtmEJLgIWbfw3Wt7EQU7c4VDxEo5cmqVTz71wkoFA/ZPTLGrrYWp3G4mMkl6PJP4bVbWBf2YpbrLyutEnVOJx2l3ebBIYU7Ez9PpNFPRLnAkNs1VjRYWi8v0e72sDQRwmsy8MjdByGrjmakp1vg28vOLL/HpDSEUUWIk9QLdrk4QBMYyZxjwXAvUmd41Y/nSOqkZeQREBJRL90Ezcm/4PHvJDcgqr77EMq+fq95fIIshBGS+ds0urLKZE4kJtoSupKKN4zKtZ1NIQkBAN3KUahep6TrlmsY93dcTK40iWxYRDTsGNZxKEweXjrBnfpFYscjnNm/Hb3GSV0ucStTZzNHiORbyBd4/uBar9AaxeWMZw6ihCI3c0tHHoegMZ5MJtoabmckneHF2FlWb5BPrb0YWJbZFGhhLp+hwORlJLlCq1bArCifiCSq1Gi6/k3y6SNSVY0NPK6en5jFZTQw0hMj5PDQ46tazqqZTqtUoqlWWCgXShfo9UiSRiMNZt7iVVUoVjUpVZ/LcPF8cXaRc1fAHHPUyvM3KyzNz7DlyFlGWuHbTIPvPjtd7Kg2wuawolvq+M5ZOYZVlzFKOZqeLiWiCaDTFN0snyCSyrOpp4fq2NnZuGORiKsn09BLLrrrRhFpRSS5l8IbcaJqOYpYxyTLhVj/FWo1yTaNo1JjLFxhPpclXK5glF2sCfso1jVihwHwuh7Ti0rQuHObAoQvIkojNppBIFGloC/IPb7oRWawz8NcHWnlm5hwvz83S43aTKJf44q9epZAro2n1rTuZK3DvmiFks8z2oW4UQWQym+bJ146j1nQMwKTUzRTymSKr2ppwm024zSYaGj3k0wUqVQ2n08ZEKkUsm6uXyC0K+XSBfKbISzYzv7owxuevvJxyrcbRyRlyVZWzyRR5VWVCzrJYLNLhcnFnT/3ZdJlMvDa3wEQqxbpwA4okcmVTI4ejMTaE/qd0/v9W/DaJ5hww9wYk8lHqieZvjkmsoJwFQRB2A2uB0dcHGIaRXkE8bwSGgSVBECJvKJ3H3nCuljecuxlY+M2LMgzju8B3Afy9IeN3NqzBaVJodrixy1beO7iOZ0YfZTZfYGdrC7phMBKP4TPbuLu3h70LUTrcbpqddipavcHaMAwanU5WB3zohkGiVKZSqlLTDETRoLnJRSZdIjqdIBhysrq3FZfZTLlWd+XZGGmg1enghakZrmtr5XwyxalYnGghz7b+TqZXbBU/sWkTdkXhVDxOi8OOLIpMZLIU1XqpMFet4A25+MDq1fjMLpodGX60sMRyNM36UIidzSBg4qnJiyyXK9zYejuHlp7mn4+fplwoE474cPudxGaXubt7DS/OXmA6k2G5VEIUBBqdTsaWl/FZbYTsDiIOO9FCgVgpw3Ut3cRLWYaX4xxYXOKh297ERHaZbLXC906fqb+J+v2ErFbab/DwyCuHKeZK6FpdumMisUyzw4lTsXJgcZEHnzvEK2dHuby/GxGBJrudG9ra+IXFSnw+iaZpmMwy1bJKtlhm9/gEyWgaURSwmCRKFQ2zImJWRERZoqklQLlW41Q8Tk6tcia+TNNKO4CIwHeG99LmdPJ3ew/gCbh40+pBBr1ezJKEbjHQDYNzyWWuamtj3pd7fS6hGTrRYhERgUQ5QUVTUUSZjeEQPouVZ0+MUKpohBtcjC4vU66puC0WcukitWqNM5NzKGYZWZGZTKcpFSsoZplr2zvYHA4xl8/z1oF+VF3n/thuWlq9+AMuFgtFLm9sxOqwEFYkBEHAqZgwSxLXtjSRV1VEAcySxNpAAyZRrstIqWWy1SpBqw3dMPj66SO0Oh0ML5/hvYOraLIHmC8keG5qir0Hz/PLNa38xY4dbAj00ect0eroxiwqTGazHF2KI4siBxeiAKwK+LHIMrtn5zh3YQaH20ZXc4gPrlnDoK8Bn6WJRls/Z5LH2BgKMtmWIpsukMnV2x663C7MkohdUej3BNF1gwOHR3FYZdI5laVkCgo10tEiVX9dOgxAFAVMisjO9nYAyrUa7W43NkXmzOQciWiag6sWua3DQaglQCY7T9Br5sjFadxeB6OpDN89dBxBgH+5625Gxp7n/tBubmibxiYraIbOuwYGaHVsp9lRIlO9QL8vzKCvgTsefoiLmSzvGhhAQMCpHCZk3XLJsabFcTktDjiReI77bt3F988Mc3x4gpmLMS6sS2OWJFRDx2t20u3xcEVjJ7pRRTOWUcQIhqEStNpwKjYEFC6PtHPfiR+zpSHMFZEe4qUUrY4+WuwdVPQUB6OT7Ii0ELJ62RHpRhCsvH/VEE9NnaPN6aLJ4cKgSsDcSMhST+IMA0TBhoLl0jr5OgP6jWF9g7D460nm6/G6HNIbj31m5mluabubRvt2NCNP2JonWTlHwLyRRPkIJ+Jz9HoCNNpD/N3xfZglkU+tvwnNSKMZOhZpEMNQWSpOoRsG/d4wj1wY5ZqWJoLWIfLqGCHLBhpt55nOx3AqVgZ9KpqxzFCgzkg/tbyHXLXCtoYNGNSwyxY8ZjPbGtpwKC485iJPjr7Ez26/m2R5jgarlxvbzCwVs3z5+HE+tmED9x09SqVWo1CukoplCDb5GGhqYGM4xG0dPZQ3Vnl49AKbQiFOxBMkSiUClrpurSAIaLrOcrGeZKbjWaoVlRaXG5e5XnVw2RUEB2i6Qbla9yefj5X4gXSE3vYIy6W6QPt9b7mZQ9ElcqkCiklGUiQa/R6OHR+nptaBAEUSMYsSkihyWVc7X37wNcxmCcOAc6MJDoYuEGkP8rHtW7jY1EyXx0XYamP/2k5WB0M8fWKEakWlpyV8yQREEUWixRJFVeXkUpTZsUXWrenkyMICH1u7jX3RCUZjcVp8HjxmC7PZDMOxJSwmiXXrunjfmiHMkky+WmVXy5uIlQ6TrORIVXJUNY3/m733Dpbtqu98Pzt0zvn0yTncdG6+V7pXOUsIhBAyYKLBGBswPMx4bD8H7DG2GaJtEJgaEwwSIIEloZzDzTmfnEP36T6dc/dO748+dYvBU/PG79UrM9RbXae6T6faXbv2Wr/1+6bjsRg/n57mYEcHLq+D+FoJt6NZYsTmkjh2mfjZh97N8XiMhycmmUwkSefrRMMO1jNVbBu/r1yqM74Sp9frZS7XzCnXNtaXnS1RVooFUrEskY4A6XgWX8RDvdrgpq4urqRSHIuvMRoKcm9fD90uDyGbjecXFhlPJukNBPBZLETsXhyyCZvJxBvj05itZuxmE7siEZKVCpOZNBbpV9w8SODf8LF/Vcf/baFpGMaaIAjLgiAMGYYxSbNrOfZLb3sS+LrQDOo1A/uArwqCEAKUjSLTBtwKfGHjMz8HPgD83cb9k7/w/COCIHyFphhoADj5PztGURDY5PejGQY+i5OXlqd4dWkFh9VMolzGLsuEbXa2hiPUdYUnZ+bo9XlpdzlYKJRYLRYJ2e0IgsANba3c3L6d2cI8Xzt39ioXJZWrU61p+H1WJFnk4pUkVyZS9HW42P6O26mpGsWGwr6WVi6sp+n1eDgWi3N8fJZw1E9d09gcCuO3Whn2BVF0jTlznrl8kS0BP7vCYRq6RrZW4w/27GZfuBuTKJGo5qipKgd6u3mpOE68XOGPj32dz+zYS6pW49poBEEw8dCFixvdxSrLtQSGAe+/7RokQSRTr5Gr1Ta6cCZmMhl0Tefevl5eXVpmOZ/HbjbxzaOn+Kvbb0REoKQoPHfsAk8fOoeu63iDbv7unlu5vnUzh+JjPHTuPJtDIT79lhv5+2ffQFV1bE4LJrPMU3Pz3NHVxcNHzpAvNhjxu+jzuHnj8hRfvDzP4KZOLp2fRxAgs17C5bFu8FtlNFVrGhYrTePsWrmGKImYLCbMFhOlap2Ay8ErS8t8ZtdO7usdwWP2Mp5d4J8uXuTieorpbA61odLfGSFss101qtUMA3GjYb4tGMBjNmOVZVbLZZaKJXaGghgYOGUbC4UMn33hFQQBQj43oijypQ/dw5lEkuVCHlXXm1ZL5Tr1hobdpaBrOg1BwRO1URMbeG02VMNgKpdj2O8jXipze2cP294fIlkpM5bJMuTz0tA1JFmikCkxnUmTLJfxWq0cX1lmb1s7iq7z+soKI34fc/kC10SjfPKp57l2sJex9SSf3bOH7cEgfquVsM1OplZltTyL22zBMAy27+zlT/fvZzQwSF0r8tPpaR4YgPl8jhvbO9geuJmp/Jt84Imn6I+GWMgX+P6bp7hj12YuNea4Y8swb+npZkdoFAGBPz76U1ocdqqqxvdeOEJrb4R8ScHlkNE0g++du0AqluUDN+/jzdgi99y0nSNnJxAlEY/PTj5bYaXNgZGsYm2VyMTK1KsaeZuE4GjSLaDpEacZColyhUjYy9pqlm++eoy3//YIX7rlJn5z/ieUKgqCJDISDJGr1ZgaX6Na13hAfRS728ZzUzO8ND/P9pYW/uaa2ylYSzT0RRp6AYfswSGLpGtr7OjpoMPp4KfT0/zw0Cme/cgHAEjXTqOjE7I2hTI7gncAELY5+IHbw+5wkDdX4xyPxbiju5sXlyf4w50fBUDR167C0kXlMqcTa2wKKGwLuJnNr/Op0VsxUMjVs7Q59iIgoLDGNy+d5PdHD5CtF8nUC3zqtZf5lzvuI1Yu8tVXj5JZL3Dt3mH++da3YZa7aGiLpOuzXEzFuK3j7RjUELBT12YQBAtm8Rf37bBUOkyH49qr2eLNeV7/7/7/xXFP1wNXH0uCk173DRtm8BIffOHnFCo1fnPbZlrscf5k9w1ouookBig0Jq/aIr2y+gw3th5gIneeLtd1fGBTmpDNgWHUyNQLCFzmdHKRI/E1fmfLThRdZSJ3ns2+VlQjQ8Tu5uGJk1gkiX2Re7DL27impRsMHUn0YhJLjJ1b5lPuF/notq2EbHa+fekyf7b3IIdOjJEsl1icW0NTNAKtPuxOK/9674NYZSsz+VWijh5Wy7P4rVYupFIMeN2UFaWZqFWrY5eb0HLQYafV5WLWaiaZaFqRNc+1jtNjZy1eoFhVsVslnDYZTVOolutcmVpG13RcPgd/e+gomqphsZn54gN38dOpGY6dm2wqtRdSjPlcHGzvYDydRtE1AjY75Bs4ulyYZZG15RLLRoV0foVPTa1xYP8Q9/b2ELS6uamzE5fZxA1bBkmUS+yMRIiVy3S6nPz40hVKuTKSJOEONPmPS+sZzBYT47k4T8zMEp9P0hXw0eZyslTI82fXXEPfnQGm82luaN3P8cQpTmQywKN87fRp6tUGNzrq8rwAACAASURBVA/18/iRc9yyezPbQmEGfR6UkQE+vHcnBUUhXirjsVi4rrUDh8nKdy9cpD8YZPpKjEpNI5evIQkCdquMKIlUKgrlQpXlYoG5dJpitszW0V6mZ1bp93o4txanVm0wPxGnVtdIZZrF/6mOOP982728+9l/ZX9bO9e3tfPdsTEidhvZWpU/vGYfi4UiW4NBLqfT2KTmxv6du7ZR13Te2tvDS0tLfP7nr/Kpu66nx+3+5UvhV278OkHnAJ8EHt5QnM8BHxIE4WMAhmF8yzCMcUEQngcuAjrw3wzDuCwIwjbg+xs8TRF41DCMpze+8++ARwVB+DCwBLxz4/uuCILwKM1iVgU+/j9TnAOYRJGKquCzWDmfWmEsk0XH4Nu33cWXzp7CLpuJBnzYTCZcJjtv6+/lYGsPh2PzXEwmePvAAC6ziTPJdaqaSqqW4KWlRU5cmUWSRGRJx2mTkWWRVKYZy0hZRdMNlBY7DpOJTK3O3d1dlBUFbQOC3xQMsNyWZ2dLlL/5l1fYs7eTJx58F3VNYSqXJmyzMeL3IwkC2XqdeKmM12IhXipzTJ8nWa2yJRDgxcUlzi4s88EDu7HLMqcSCd79zOPsa2vjzs7r+PMTD3H6yiyNqoKiashmme+8737aHB6+cPYQWwN++nx+xlPrlGp1kisZOnojpOt1gg47S/kcsVSdYq7M//HoMzg9djpagpTLDco1DZMksD69zicefpJPvyXPh0b2cW1LL39+/DUUXec9N+7lsWPnaFQbVMs1TsXjnIrHiccKWC0SXR4vX3z8VVaTVUQRavV5bFaZekOlrSeIYUC9UqeYLSGZZERRxGoW8ARdKA2VUJufOwb6UDYgudlshveODOMy2fjBxEX6PR6ub+vDZ7NR11SS5TKDHS30eLyUFIV0rUpV1bDJTRiupCgkKio9bjdv/5tH6O/1cMv+LUS7u9ANA0kQ+Mdz5yjnK1w+vML2m7oIt/h4fn6B1VSWwWiTTlxVFZxuKw69aetUSJdo7Ytw38AAAO8d2s1SaY0XFxc4vBrnyvo6BUVhyOflcjrDWrnCVCbL1FqShekETruJyYWmv1ulWGPn9j6eOXmRXZt6aXc5+ZcrY9zT20uyUmFHTweLuRzv3byJIV+IC6k4370yxt5oC90uF2+sxoiXSpy9MMtH7zzIVn8Hf3/hVT6yeRdLhQJ/duQwQbuDVqeDzb4YHc4Qr733w8QqaY6vrRIvlTi1ssLgpk4W83nCdie6UeStT/6A9bUskiwhb4gkxi6ubBTzUK1rXLmwgsMmY5YkXlpY4sS5KQzDoFhqEEtW2DISolbXyJtEMqla0/uzeSHj95g5vZakz+dltVTCbTYTtFmZHl/GYTNhGAayKBK2OXH7neSLGfqjIc4srzAUCeNzmbBbpebiPrGOx2XGZpHIJvN0P3eC26/fynIhz/pqc7O1vFqkrmhYTBKPmkQ0Tee+23cQtoVQ9XVMosR8Mc1C4TlGg1swjDoWqZ9Nvlv522uapuf39eRAkJAEF6eSz7FcOsxsIc1/fu4VHn3wnbQ5hqnrCoM+HzuCo5jENg5Gh3gz9nOCNjubfLdS064gC628sXqa+/sHMIsDRO1m5gpv8Mhd7+VQ/BKpaoW9Qz2ct65wQ0f71c6kWeoiYutiNHiSqfyrDLhvQDOKV6H7mjaOVWpSNBraIs8tzvCuAQce845fmEU13vLkX/Czt7zvqnWQpjeV7PBvoyxTtUsEraN8bHQUt9nMgDdMTatzMjHLF06e5PF7P4LHvIlk9SRus4eb2+5gttCk3j85/yg3tw8iCSKi4KDLeQ1H1l5AMww+vm0PZ5JLFBp13tpzE/PFK8wX5rmxdRuf3rGbmtZA1VOslsc5trbMbR2DG7xSC129AbaGQvz1seNc39HJWqnIZ954iW3be7m1q4st+/ZyJBbnR4fPcOP2YWRRYrawSpvDzxNzhxgNhmmx2/CYm0LGneEgumHQ63FzNJ7gRy8dp39zB+lEjlCLD0kWia00uYCGAdVSDZNJxG8yU6o0DdBtFglN0TA0HbPNjK7rFLNl/v4dd1NTVd5YjXFuaoH/9r77+fzx45w/M8/iQoJWl4t2twurJJMol5H8VgZHWqiV61Rqze61rkNd0Yll8uTrdcI2gT0bHblbO9uZyuZ49MJlVubW6eyPkF7LEWz1NfmbASc2l5WZsRhDW9v5yunTFOpN276/2H8tLpONNoeDHaEOzqdW+N6VKzy/sMgrFycY7I5ydGWF394+yr9OzVBqKAwPdvD4q2eacK4oMLy1k/07d3CDL0DY6qWi1nhsZpxejxuTSabX6yEQsJPK1fH57NSrDRqKhsNqwmzSqBSrTCTWcVjNNBSd48eniLS4eXVpibmJVbyBZpzvnv5u/vXlM9QaOmvpPJ8/fZjJS0scbO/gny5fQtMN/u7RV/D5bPx9rUYmVWB3fxfnF1ewOppeyFuCQa5ra+NyOsUTYxPoqsbPJidpdf6PXRp+VcavG3SOYRjngV9WKX3rl97zReCLv/TcReAXZ7NffC1Nszv6P3rt88Dn/1eODaBYb/BXL7yB2WbiLZuH8VutPDgwSLxS4K+vuYlENYtm6LTa3Zxdby6K69UCNU1jX2srPx4f556+PpbzBXaFQzx06SzPnr1CNl1G1XTauwIsL6QxyQKqKpBc3+AxmUQ6BlrYEWplZ0hgrZLnL44eYU+0leVSiR+fv0QmkSedLSKKArcM96MaGudTcR4ZbxpZ/9WBA7hMFn44MYmq68znssxMrSLJIu+4die5eh27ycT2rnakjc7crZ0dpMNNuKeuJXh9bp5CrorLbaV/pJ1KvUHAamM2n8Ykioz4g6yUyqxXyiSNMnft34rbbKbf4yZeKuO32Tl+aYVwixub00piKc3aShZNN3A7ZGRJRJIEFEWj2+XCLPah6nE+PrqdsNXL5cwKJklkPJWmze3GJIoUGw327u/n8OEpXjh0keIG39Iki6iaTiJdxWoWSa1mkWQRm9NKrdJAklXqDQ2bVaZWrhFq8xNyOXl2fIr+SIiQ3c5gIEBNVUlUijxy/Bz7h3vp8XgwiSK7wy1cSGW4sr6OJDa7mOpGkoRjAz5dKBQRBYGLqTToBmupKsfHZ7m9u5MuV4CjyWUSpVLzHDubha/D1PRq+43tWzi1tsbY7AqlXIW+kXa2hiP0eT3k6nXm83nyjQbxUpmx7BK/8/jTSKYmad1kluncvAmbJPPS3Bwf2raNg63tfPj5ZylXVWoNjXJVIVto4LDKnLswiy/swWqSGQ0GGUulORqPszkQoKqqHOhop8ft4XBsic2BAO8aHuTZ+cWmDcvgAN8fG+e9t+znwf5h/uDQC5ycXmhCca0tmESR71++wpagn9se+zp3DQzQ53Uz4gvwYP+NPNh/I9P5cTxmOxHbABczZ7j/B//Q7DZXGshmmWKxRr6oIMsCPpeZSk29eo77N7fx1cdfo60zwNR0lp172rlhbyffe/wkl6+sI5nEDTNpvYn/SE0cyO0wUWo0mM/liTgc1DQVzTBoKM29pqJozBdSeC1WUok8VovEQiqDqmhcWlgh0OKlNeJnWzhCYu0QsixuGFEXMckirx69gs0iE2zzsb6SwWaRcDlkGopOra7R2e5mczDAycQkb6zGOLqyzD/ddheHY0ssFY/w5mqcY1Nz7Bvo4Z6ebu7svP8qvA6wJ3wXAG2OKs+9t51YOYNqpPjL44cI2m1sDbTT0FJYpFaub30rAKqeoaiU8Vtkbm6/Fd0osFY9Q9ASpdd9Aw1tgVvb72OpdJj7+67j1ZVTnF9PkayexG/puSr8CVg30dAvIAgSEs1FUtFXrxaZ0CxKf2fzB//NHKoaKR6750Es0iCanmuGOojeqwbump7DwEAz0jw8dZROl5sbWwcY8Pp4dWWZfm+IHtcNtDtWefptd9HQ5zGMOm6zi5/NnsZpusA9XbdSVOaxSE1RjkO2Ml5+nb8/e46LM0vUynWu2zXMH+7eS5ezhxOJkwx4w7yydJl2p4uwzcOFVJwup4pJkvnSa0epXaexORDkselpJJPMVCbLzpYo69UqrS43sWIzBW5PJMJj0zNkazXcfiftLhefPfQidU3jvv5ehnx+zmwIJe/pHuJ74+c5t55mMZ/Da7FyT283h/tbUDWNK+NJWlNFrt07jNNsZi6bRVFU/EE3qq6zvppBlioUKyp2a9NqbD1bQ5JEQl4LdruJf758mX6fj/0tEUZu8fE7P36S77zn7Rzv7eXhU+exSDKb/D4WCkUaukZX1M7CdBJBgEKugWAScdhkShWViSsx1g9W6HXrrJSKVFWNsqJQbCh0hwNk4jmSy2msDgtDkTCiKJKO51iN5ens9KFpOrtaotzU3kZuT51OZxtfOPMKR1aW+faps4R9blbiaXIRH+EWH2VF4X2bN+E0mZFFgUdfPMW779zLmMNMLFFGlkWuXFjk02PLhDsCDEYjTVP2WpVro608dOttfOvSRdaSZWwWCU/QRXwxhc1uRtcMqnUNwzBYX82wUKqjagZWi0RqvcSLq5fxeyy8/pGPYpFM3PLId9iys5tyqco37rmTRyYmEQRQDYNjswtcOr+KwyYTavPzp/v3o+o6QZud/1Kt8Mqb48xaV1kc7aLP46GqNr1SBzd10tA0Ls78qucC/e8Dnf9aZJ1//qt/97nQ9T2UCxWKks56pcK3D52kIMK2kJ+6pmKgY5FMfPfKZX5r007Gs0kytToAFtlEQzewyjJRp4O/efRl8rka1YaGJAqo9Qa1epOwnMo1MIpNE1zZaeLjt+9nWyCMgcELi/McHp9jXauTqzdw2iw0RAOH04bXbeZju3dQURskKhVKqoZmGLS7nLy5GmM8lUIQBG7s6CAaCbC1q409kRDJapXNQT/tLid22YRJFBEFgXani62B/RyKneVIbBV9IzprbibJpv5W7u/fxKPTk/ynnQdpdVxD0J5nLJNlrVQiWS6zNRRCNQwObxi2i7LA0kIGf9CJw22jVq7h9Tux2sxYbGYEXaelO8SfXXMzJjGCLPpwyPDk/Fm+evoM3V4v7xoawmmS6HW7GQ2FKKkqy6kU65kaut7kR4a8Fiq15rGaTSINRSebr+P3O/AGXTRqCvVqA1kSEWWRzkiAmqaSXs9jcVioaxqdriasNZvPY5hlXBYLFVWloirsCIe5rrWdiMNGm7MJsVtkmbqmNTtuqsYLc3Ncjid49IXz6AIEvBYMVeP4aozOsId/vniJtZU0lWKVqg4+jwXRZsJrtbJUKHBrVxc3DfQSjvrx2WxsCwZ5aWGBDrebqUyWI5NzzCfSPHz4LPVqg1q5Tq3SQNc0PnFwP39y6BCf2bObeKXCrlAb6UaVY2MLOG0yiqIjSgLRqBul3rTMqgg6w8EADd3g6OwCZouZfp+Pc4k1BEHgSjpDp8u5YaFi50hsDQGDfKPBeqXCodUVvDYbmiwwlkpxXXsbPxqfZDQSodBQKCgNdkciZGt10rUaC8VV2h0WzJKMXbaSqyea14/TSlc0CE4zs7MJShV1I2VLoFRRsVmbC5/FLOINOFlfKyAYOv0jTVeCueUkyXQVBHDaZRxWGZNNRhNAtjZhd0XTuTy9RjKdZrGQZ6VQ4PTkIuVyA1EQsJhlqjaZu7uHeHp1llymjCwJvHvvdj66awd/evAdPDCwhZva9vB89jLegBNvyIXaUHA4rVQrDe6+bpTb+nqYzGUJt3mx282YJAG3y0xbT4R/uPGddLn6cFsq9Pm8fPq5F3nLYD+CIHA5neauoQHqusbLCwsE7Tmscg6LqP13BacgmLBKERymMg2twWg4QMhm5VBsEc1QWCwuoxlruExuZNGPQ25DNwrolJCFMG5zNxcyx3GaStS0MieSpxgN3M4PJn5OSVF4cGALYdteJnLH8FlcNHMyVByyC83IAQKiYGEmfwq3uSlu0o0Kc8UjpGpzBKxNhbhhaGhGnnhlEo+5fSM7XEUUzGhGkbI6iSRIyGIAzcijU6asltkf6WOpNEdD1/BZLPS5u5FFP6qe5PmlN/jquaPc1hVBFr34LDrDvijLpXnC9k78lk78lj5c5i4CVoNPfPcnqA0VSRQ4MNzLz6anWK4keXZunl2REG/pHqbF3o9JVElVC8zmlzDQeWVlmXcOD/HswgLPnL7M1956J2G7jQGvl5GAD4csI4oSPV4P37l0iYmlOBVBR9cNtkbC/NbmrVzbGkUQBN6MxTgZX2O9WqOkVNkRDtNit7M5EMBntdDn8fLYmUvUqw1CQTu/e/sBtodDWCSZiqKSKpQ2oh5FXB47rVE//ogTh8OCrqg0FJ1yVcVmEQGDS5Nxjl9ZxB52Muz3so7K946eYSKVJhr2oRsGy8XSxnkUuTyzii/gQBYFvB4zNquE1SKxdUcn14/289bePr516QJRh53n5hfQjKbSurhxfds9djAMLo0tkU0VqVUb9A1H+cvbbuTtQ4O4zSbOrK/z0PFTfO5nz5CXNWxmE7f19yEgUBV0ujxexmdW+ej+nfgtVmyyzNZgkCm9zKmz02RyNVwOE16PBU3T0XTQVQ3FBA6zGafFzGqpTJfbxUqxxJHL89QVnUKugt9v523X7mB8fpVcsYGAgFkW8AacBFs8yLJIIlnGZpXQNIMnF6e4UkrzqT27cFutqKLIc/NznBlfINIRZCqeJL6YplrTCAds7B7qps/joc3l4lIqxXeeO44sCfj8DiJBL9tCIUQBSqrGp3ft5K6eHvIiHP/u0V/ZrPOvfOPLn9v/jt3/7qzzl//p9f8/6/z/6YiEvbz55hTFfA1fwIEn2OysffnMabo9bm5qb6ck1okVi0zl1jgUixG22QjZbCi6zng6g0kUiZcrV3PMbVapyQHJ1bFZJBqK3lQe+yyoDR2rWeLOri4auookiExncyTjOcrFKhlPgR19Hezu6sBjsXBcWCFTq/L6SowWh52QzUayXObHE5N0uD1EnU7u7O5iSyBMulYmX68z7AtjlZrij6qiIgjNVnmh0WDEF2a9OsbPpmfo9/u5ubuH84kEF6tz7IlGcZns/MbgAPYNeC1fr3FyYYmukJ9Lk0s8oah0+Lz0+fy8f2SYE2trPBOaJbCR8W7oBoIkUivXkCSJQKsPv8uBqmtoYh4BGUkM8sj4BBfOzLMwFeMR2xmG+9t4cHiI0WCUj23xEbbZ+P7RM6yv5anWNQplhbrSVHbLkoCqGdSVZr5vh8+L5PdjG5ZZLuSvktjPzi3R2RHmzp6eDTh2gR6vD0EQCDscrBYLzGYz+Kw2LjtS7BrZRtjq5amFK6QqZcYNA7vJhCgInIjFGLu4iKLqeJ0mBMHE9dcMc+7yPDsHu7HJMruirUyNLVOqqLjtMhabmYjDQYvTRalRZzZfoNRoYDeZeOPyNNm+Gm1uNz85fYHEag5V0zHJIja7GQwDQRQRNR3ZLPO3J0/y/i2b8ZgtzOcLHI7PYzfJeJwmLGYJp0smm60gCALlqkqxrBCLF/lascpd2zYR8DpZK5XYGQkzu5psCrPsNv74hVfZ1t1G1OnivcNDnFhbw2U2sV6pcG9vDzVNxW020eVyUVZUBgMBXpudY3FqjQ/dfQC3xcx0Pk+23iBTq7HJH2Qik+bseooH+vsZ8Y3yyW0dGChM5Va57/wca6kqFosEin41Cs1ulYhEPcTnk2QKDSo1jS+88y4eOneeVDLfjMncEHnVGhqiKGAxiai6AVKTpwVQKClU61ncbiuxtSIWk4SuC5SqNaqqgkP2ce/mYb40FkcUReKlMsfjcVZKTTuqAY+Hu/r6eOzMRfYP9jK6eTNfeuZ1AkEnt3d18tPpaXb3dXF8Yg6b04qWKtI33M617R1X03N2he5iLPNDdFXjZCKB32qloij8y5nz7O/tZk80ylqlwnWtXbwRO8FC4Q2GfD5CNjvD3lsQBAmnqZny4rVAnxtGA+eYyMUZy2R4dGqa3xtV6PcMs1icJmh147PsIl07TcC6m02+TgxD42hyjt2hZn74znAEv9WBXbZQVM7T44pQUVewyxKiYMMkRpjMvcKbqyv89uYP4LU4uZy5zM5gOyvls7Q7OlktL/PqyhPc3H4fgiCxXp0kUSkS3YjhEBBZLh1mOpdCBw5GTaDL6EYZVW9wbcsuTibO8dziIttDQeyyTK6RImiNIIsRPBYLl5ZizBfXGPEO4jDFMIsyFVUlWV0iat+GZqQRhSZ/1WZtUlqcXgcvT88S9brJ1GpMrqzxW/GnedvWEaySRE3TUDSd02txPr5jO4VMkddWVvmNwUFuam+jzxNkf2QvkuBipXyWsK1Mqlaj0FCYHVvG7XfS4/WxXimzPxrlcjrJjlArbQ4zfe4K/R43yUqVfS1Rnp6f42JyneVCnqFAkEGfD1VpUlqu27eJ0VAQgFaHkyfGmvxjk1nGIkmYJAndaCb0WOwWgm1+BClHsZwjsV7D6zVTrTc329laEx0bCgS5NL6IpupMZUr4W7y8dWSI5+fmcFssOF1WVEVDVTWCUR/tkQA1VeGTO3cSsNoQBZFEuUxd8/HWvl5S1VqTUuZyca6h0qgr5LMVekda2dXehkM20edxcymd5vJ6inS1gs9mo1Ko0jMY4da+XoI2K9l6nYauEV9JE1tO0ag2uJTKcH1bK1VNRdF07uzt5aGxFf7oN27hO68eR6k34yIDQSeBqJdsMo+7pYWzSyscy5YRBIHbOzv4iiBgMUnYLBJWu4UTqyvki3XsVhmn3YSiaFQKVcr5CoYBkYANi92ML+xhtDVKj9fDfKFAq9OBouuka05CdgcmUeTlY5fw+h2Eol5SazmOXpwmXanyqZ07+IcjJ9A0/Wrc7LVtbewKt7JSzqOzgtdipcXm42Brlq/9f1ax/L8fwoafxP8O49eio/mlb3z5c713bUI2QWK9hMdjJbaUZr1apiE1Y7uOxuKslsvk6jV+eOoCkkWmpulUNRUD2BdtYV+0halsntl0pulnWVfp6gtRyleRJAGrRUKWmxeHxSrhdsjcsnUAn9WKKIgsFPMslwtIkog36MYsSRvJXyKdHg9v7R1kW8jPlkCQXL0GgsgN7W0M+Lx0uJyE7Xb6PS2E7V4idhfpehGn2cyIN4ouqKxXK6i6ziZ/gKjdz5fPH+fV8xOoZpH7+vvYGgyiOs386xtnaG9zk6iUcZqzOEwWLLLCcq3U5D5tHebGrg7G0xlqmsrFVIoXJ2YoFiqEfW76fH6u7+nEMEnEM3kcHjsWm5maopBRqvR7JeyyA0lw86ePfZ/1XB1V01EaKm1tAaqazo8mrmAzS2wLBtneEeXPb7+Fd+zdxNHkKmuJIqLYjEWrK001YUvERcjjwiJL9Hq93NzVQbfHzY3t7WQNnUy1QllVcZrN/PzNcyzncuQMhfMTC8zMrnH95n4SpRJbQ0E6XBaKSpUXlhaJl5qm8flGndOLK8xOxVhL11CU5kSzc3cfH9yymd/atwtJFFgrVzi0vMT07DoOm4zHa+Pm7cO8b2SEf3z5MJlKhbGFGGuFIi1+D9f1d5OqVjk7v0xyNYtJFrE7LFjtZtx+J7VqA7PFhNVh4ZbtI+yKhLGbZIZ8QR6bnGCxWGIxnyeTL1Eo1om0+SgVqtRrTc/OWl3DYpYoFmpMr67R2hKg1+fjZCxOq9/LTDZDulplS1sUv9XGNx4/xH27N7FULDLi93FPby+aYfDM3AJ3dndhliQmslmmMhmS6TyyLDEVS3B0aQVVEujyuNkdCTGVy/LDsTGKjTqPXLjEaNTKMwsT/GD8IqcTCW7dOsif3H2QnN0glcqRTtco1zX6+4PcNjrCq6dnqVdVVMNg20g7h85PYnNYKJRrNIoKuiAgm0R03cDrMiFLIrph4HGaKddUHDYZp9OMrmokM3XcThMmWSSZqeEMW3nfpp2s1zK8eHGK/sEoI8EADpOJ//rM64wtxJhplPjEjh28ZXiI6XyepyenuHt0E3PpDKfXk2xriSAKAts72zg1NsdHbruWxXye3xndRk1bRdGT/MGhR3hxdo4WnwdBEGhx2JlMZ6koCmvFEg3DYG+0hSPxCX4wNsaR+SV+fmGc2VqZmcI488UJ/NYibnPn1bnKLAZZKE7z8tIye1oiXNc6xB8dfQqLJFDTGkTsdspKjpeXj1JUy7Q6wvS6e5AEhYcuPc32YAut9s0UlCQXUiuYJEjXS5xeH8dnqfOj6Vd4bWWVc4kEDwzcgEOO0mLvQhBkPOamn6XP0k2HM3JVYe6Q/UhiAYskIotBKuoYzyxOIQDTuRztThtH1y4z7NuLLLqZK1zkz44c5mPbRjkSjxMrl6lqdcI2FZvkpce9ne5QHYskE7DKuM1bKCurWCSJsK0bWQwiCS7StdNk6jlmjAo2n53OsJ8Pb9/GSMDP+4d28PZNQzw+M0Wu0eD4/BLxWoXLy3Eks0yry8kdw/080L+JbucButwdQAFZtKIaKQw0FgsZAjYbOjrLRp2BaJhOj4dYqUiv18O3zl+g3eWgoau8tLSMRZbYHgrzNydOsKelBUkSSVQqLKyniXjcbO6IYvHaecdgP0/PLWCXZYI2G4YksVouoTRUDLEpz/DbbJhlGUGEiNvFeqaAomjUFR2bVUbXDTpbnNQEg7OpJDPJFGpDY9NAOw3BQG2ojMUTfGDHKE8du8hv3riXukVgsDPK3q52jl6eYbCtheva2mhoGkvFPD87d5m3jQzR4nAQdTg4lUjy8EvHcfudfPaWg3zyxv3c2NnJ2USShXyedw4Ncia5Tk1V+cyuXSSqVc5PLuHyObirvwe/1UpBUXjiyHka1QaFYh1dN9g71MVysUSiUmVPSwsjfj87Bjr4wNANDLQ7qdol/GE3imCQXMmQTJSYWIhjMUtUClUmYwk+deAA3z1+Gou5yc8WBZieTPKxt1/HQiqNAZTKCiZZRJJFfCE3H7hhL5s7otQMndPj80znspxPE8z4lwAAIABJREFUJtElkYZuECsWaXe5uL6tlbtGh/D53AxHI0zGEwiiwNzsGq8vLnLufBxJEhnY3MZoWyvr1QoH2tqwyybeXFlh2O8jZHNzKL7M89967Ve2o/nVb3zlc9c+sBtB4N/19+J/QEfz16LQ/PI3vvy5u37zIMuFPIVshXK5TjxWRtdVapVaEy4RDJKVCquJDO/YuZXFfJ7DJ8dQ7DJDfj9HY3HeXF7h9NwSB0b6Ua0i4RYvn7lmLxmLwcJiE9p2uyzYbDKNhobNKqE4zLy0uIjNJGIAC9US+UyJL911G4YgUGo0xUH/add+BCBk9eA22+l2e8nUmsrjsN3G7nAbXa7mLlkUzJxIzHA0HqfYaLAt2IvHbCLqcKHoKntCd/CjqZe5kFxnfiHB6WNL/OTkFUYGozz68mliqSrn4zGm6iVeXJzHaq4wlUsxkWkWJbsiEWRRQEdE0XTMksx8LEkxUyZXrXJTfzd+q4VNAT8mu5X59TSlXJmw30NZUVkuF5jJL2M1pdFcFk6NL9FQDUQBKpUqA+0R4qUiZxNJVssVfm/bfs6tr/Dq8gq/v283f33v7ewYiXJ+LUah0OTg3HtgC26rBQGBXL3OWqUplFkulTFLEvlaneVsnhaXi3CLj6nZGC6/g/awn3KtxlI6y1MPvpdEtcCVTJIfT05ydH4Rj70pEKqpKsnlNLpuIADlmoosiXzhrbeSrde5mEpxY3sHyUqFfENhdS2FzWbiT992Cx8a2cHh2DJHJuYA6OgMc31fD8eXlilrKr1eLxOxJPNLeTxOM4IASl3FYjOhqRrBVh/v3rGN7aEgit60zdIMjePxGPl6DYfZTCyWJldskFgrYrNI5IoK3QNhtFqDbKGOpkOx3KBUKLGpu5VBv4/XrkxTqysbBaKX16ZnCYSc7G5rpdXpJFuvEbDa6XT5sJtE8o06s4UCM7kcS/k8k1dixBIlREPn9+84SJvLSaxc5qmZGVRDYGIpztJCApNZ5qWFRQ6PzTIfWydVqXCgu5MnZ2ZpaBpfedvtfOPB+7FGzTx3dIKx+VWqdZ0NCz1yVJidzZBJl6nWNfSKCiYRTdHRBYFqTUMUBEQB+oYiuJxmtIZKraaSLTTQNrKbARqqTjBg571bt7BUzHB0aYmVxTRVm8BiocD4RJxou5/ZiTh/dOvN3P/9hzl2YY6P37yfB/qHGCvlaXE7+eToDt7eu5OHJ86TrTZ/z8mVGGGnA0mEU4kYKgYXZpb5wK5RUvUafquVXL3O1MwqRz/8WQ7FZ2jozeCBAb+fD27bwif3H8AqG7xncAcjvha8liG+fulh9kWaVPU/P/Et4pUy5xNr3D84yLH4AjvCIW5qG+BQbJF/mTjNda0d/P7zz/O5A59EFgNIgpt8Y4kXl+YZz6aRpRxz+QyqobMjuA1BqLMjeDuSWGLEH+DurrfywMANAAiCyPn0K0Tt/Vfny4p6iZXyPH5LN4ahIApWysoKK+V1vBYLNnmQTG2OL546xasnx7h/5yYcJjOtjiFEwQKkcVkk7ui8n/0tdjrdNqySRKvDhyjK5BszPDU/Q8Bqoc3pxCRGMEterLKJhpbGLLXQ0Fc48M2vUjA10RpJFLizu5t3D76HDqeKw9RJpp7gQjqFzWRiX2c7NU3ld3fv4I9338Cu0DDdLgcuUweS6EIULFilKAImLmXOczS+zIuLSwwHfHQ4Xby2vMzbBwe4r6+f9VqVfq+H2Xye4/E4Hx/dz0jAw9nkOo9OTrK/rY2nZmYIO5zc09OD1+ngfCLJzR0dZBsKNU1jTyRMn8e34Wlr4Y35RQzdwGE1E7A3W8P2jdSsuqZRaygkEyVUTSfks+Cym3D5HLRG/BSKFeanErR1hzhzeo5KoUqkw0+92uBPDl6H4TTzw1eOY3KY6Q8GkAWRsqQzu5Lkwzt3oRo6P56c4t3bNtHqcOExW5jJZ6lqGm/fuYn3bN2Cqut878oYPzx9ntMX5ojF0jx1ZYL59TQdET8f2Xw3i8UVzizHWFlIczmbpsXvodhQuDC91Jw3NwqVscUYW3vb+fDmbbhNViySiTaHmy+ff5MDrW1MZLP4bDY2R8IsFQuYTSKVcoNKuUGtruF0WXhwxxY6OgK8cn4Gn8+O2+dAbyjNBkK6jNksg9GMl3W7rfzW9Xvxmc3M5Qv89IUzVCsNRAw+dnAvVVXj5bEpRqIR9kRC9Hm9bPa3cnP7VkaDPm7f3MszUzP0D7ZSqdQxCU0+9lB/lB6vl0SlzA3tHTyzMMdSsUCf14vTJFNTFX7yDy/86haaD33lc9c+sOffDZ2/8B9QPP9aFJr/9R+/9LnVqMT8XJpIxMl91+9kajVBtqDgc5mRTRIhrwu/zY7P5UA1DHa3RBjubiNeKlFWVFRd58Nbt7ApGqKsauxrbeFt/f1EHU6ua29noLeF1y/NUqupSCJ4/Q7CHUFUDDrcbno9bgQBnj11hR2bepjMZHGYTEQdDiJ2O6PBKKIgougakiCi6CoXUwly9QaSILAj1Irb1IcgqAhIJKpptoea3a+I3Y2iK9hkMx3OIPHKNP/5pVdwO6zsG+phLpGkuFbh9fFFBAHMJomu/jCtHjd1TWM0HOLlxWUcZjODfj9vLC/jMFv43a276PY4GA0F+YNrbuC+ncP4vE7eP7yTkM1GolrCZTazr6OVloCPt/T20uv1cCK+htNswmuxELBZsIXtTC2soaoG9brK+EKc7o4wM5Mr5LQGDUnj/r4d3NR+C62ONmYL0zw7v8hX77qdz9/zIDs2eTm5FificKDqRtMcXhA5OjmHzW7FIkucnVqgVq4zvrJGXzTEaibHcHsUr9XKQFuEkbYWro128PjsFJdSKc4vrXLr0ADnF1dIrxdIxbMMDLdTrTVQGypelxmPz8bHr9lPoVHnjZVVujwuvn7qLGvZPHfs3sT+gW6enZ3jp1Pj7GyJcGx+GVEUODjYx65IiFeuzDAxuUpfV4S8rrBrpJ2aodGoq9RqCvlclUi7n9XZJGWrgN9m47n5eQ5PzHE2m+L3tm/n56cuUzM0irkysiSyb38/mq6hKyrZdIlytbkIh4J2DF3HZjNx/UgfS4UiR8/MUshV+OM7rmfQ5+HJk5d5/oMf4HeffIqP79mLVZY4ubZGpl6h3+vlEz96CpvHzruGhpomzq0+Lk6uIokCs4UsLQEv59bW2BwKN+GtwT6OTc5TLtTIZ8tIkojNYaVRV1nT69zQ0cFIMECiWuHhySuUFIVXP/yXvHd/Dz29fubzKapVBYsskMrWCHqt+FxmcmUVygqYJcxWCbWhY7FIKJpBKOigUqwRSzbFFGaTiNthotbQkaXmY0PX6Wn10uPxUDFLXJheweowk0kXObhrkKKq0CjX+Oabx5rn2u8gHPTx6OQ4X7vxdm7p7OZCKs4tX/kmDdlANkl8Yvcuru1o4/tXrtDmctLmdPGT8XGeedf7mC2kWcwXubu7hwcHt/J711zLE/NncFvMJCtVbLLMV558jRdn53hiYZr3b9lK2NaKSWpjtnCKP3ryBVaMRWraEluDQb5x4nTTtLuzk+VSkft6D3IiOc5iscj/uedtWCQ4k43T5S5T1+OYxTwu02ZeWDrFX+57DxdSU7Q5nYRsDlSjjM/sJF6ZbsK2ogVZDFHTriAKXgRBImxrQRTMV+dLkxihpsZwmTuuxk0WlRWWijl63dsRBIlcY5HJbI6KoHFnfx/bA01lvCR4kcQyy6U0bnORT73+PM/OzXH/wBAh6x7yjRm8lkH6vQKbfLuRBA+iYKOqTWCVBjBLLUBT/V40KTx66AyPvPOdyJLBqUSSgjLPDyfH0MkABiOBAEdWV0lVKwz4A7xncBS7qR9ZCGKS3GhGBknwbtiXFVD0Jb51+QwmUeTS+joLhQKCIHBtWxsmUcBpMnN7xwDr1SI/PH6ORDzLqlCl3+PlleUlxudjFAyNz+7ZQ6HRYLFQ5PjqKmZZ4vbubh6fmuKa1hZa7E2hGsAXT57CbjEjyCL1hoLd3Cwwyw0Fv81GXdVo9XvxtDhwO2QiHQFcPkdTaa1rWO0WVpazCKqCquqsLhVR63Vy2QovrMxxeSmGy+8kt16gqyWIy2wm4nBycWYZt99Bj9tDu9vJqUSShUKRPZFW0vUKEZudn8/O8+TMNN9+6igziwncHhu/fcs1PPbuz/OJA/fxu/vu5d6eA0iCk52hUfb3m3hg71b+yw2f5kB0F9dE7fzz2VN0D7Ty2oc/wh/d/HEeOvE0osNETVf51sVzfOvICdKGwmd27OMfzp9lVyREj9vF07Oz7Ohsw+yw8P5rd3BheRWP1046VeLnU1N86fa7+dnUZdLpMoKuM7y5i3dt30qwxUcRlXt2bWFTf5S8oTBfLLCrpYV0rUbdamCoGkpdYbArSr7RYCASYtDnJVOvAwYRhxOnKQxUiZdzPHz4DCePLBJPl2lv9+B2WdBFWCkWiMUzHOzt5AsvH+LukUG2BIOUFYVvX7zIlUfO/MoWml976CufO/COPRv5QP/rt/+ILu2vRaH5pa9/+XMD7xjlhq39/xd57x0m2Vmd+/52ql05d865e3py1ETNSBqNhCQkQIAEGBl8TDIY2cb4Hs610djY2MY2YDC2RQ4myggJjdBoJI00o8mxe6Z7Oueu7so51977/lGDsH3gGvs59x5f7nqe/mNXfb139e5vf99ba73rffncPXdxR2snBzd08sPLI6iKgChJCBaZD2zcyIHWFq5Ho9zb0cnOhmZqrCoruTx3trVwZ8s2Gm0mbIrAen8NTlXFMKpciKJWJqhUsLrMiKJAMV9C13XuGuglWSrx8uIiL4xO8p59O/jQxm2s8bm5EAzR6XKymsvR7LDw7NwUsWIWt6rwxZFhhsNhWpwObm9pwWd2IIkyoBMthpBFaHM041bN1TK+XrUwtCvref9L3+Ds+WkCqzHMXgtb+9oQXAKCVgVQPr8dDAPRJOO1WlnMZChUKrxrcA13tnYTK+XQDRjwevnnqUnS5SIOU9VJZnNNE4ooYZIUul21dLm8XAwGOL9SBYLNdge1VivD4QgNNit7Gzsp6WU6O+pYCIVIZcsk0mUCKzHKZY1QKM2z58cZ1sK8obsNRWzAZ3bQ6tRJl4qcWZ1kZ0MbrU4704kkbU4HA14PR2dnWZoLcWh9P/PJJPNLYVw+B4IgEMxk2Lemh3aXi16vmzanA0UUeXpmkh63C1mScNlsTMSiFDWtyp0yK8xNraKYZOxOC6VCiUAwx199+2WOTk3T01FHUdepczj42K6dXI9Geer8MBXDIJ8tcD0Ro9bvJpcrslrIYVZVFlJJrDaVycUg0ZU4BV3D6rRgdZjRNZ3aJg+bW5spWgRCq3EmUwlS6Rwer4OdTc20OhzMlLKsa6hn/5ouLk8tsKWnjRuTS6gmGbNZpndtC5JgoFV0nC4LqsVEqFJgcimI1abg9Ts4Nb3AuVAQm9PCF0+eRbWY8DutFHWNdf4axuNx/vT4Sd62czPT8QTnVlZIlUpMrobo7q1j37puPr5/D1fCYZodTpyqiZdvTHFpYZnroxESmRLpXIV4sojHpWJ3W8nligSKefq8XmRRpMft4qvHz9FSbzAUCXJ7Sxtt9V7c9U7WtjeRNQqEo1kqFYOyYaCXdRAE7A4TxWwZ1SJTLOk4LDJ2l5VgKEOppGGzyJQq1ayh22FClkUCwSzOegeH2roYioRYTCTYM9BNo8+N32olWS7ypTfdR1wF0WFibiZMuJjltp4u/uSVV7i/r5eCVuZqNs5H99zC727fzZMzE7Q6HFwOhShoBh6ziTOLy/zF08cYiccoS/DU2DiyCebTUdyqmU8ceQmrw8I716whb5P58n1vIGOUuRAMMui3U6hEKGglLE4Lb+zu5oM/fJZnb0zS1uDno9u2sbmmjW21VdegDx9/ijf2dNPl3IYouJhNT3Jf+72Y5SIWeS1Xosd4uGcvOjksisZcOslMMsFYLMrm2kEkocw/XD9PppKh172GUGEaq+zkpeWjdLvW/09rpl1pQhAEhqJH8aoKVsVNp3MDoqBS0eNAihcW5jApMk9PT/GWvnqs8nrKxirZSoI13n4cSjN/P/QifX4/2+rqcZisrOSWObUyzLbauyjpsyCIjMTOciIwxzpflS/+9bFvYZIMXt+xjbds7udiaJEHOu+hxpqjwWbn1qYWXKqJRruHHlcDJwNzdLjdJIpF7m4b5OjCKRQpglftIltZRBINNCOJZoSRBC8VPYHbrNLv8zKdSPJQXx9XIxEGvF4abE785jZUqcih/i5Eh4UjF67x/Ows7926iXvX9LKYyTKRSNDncbGay/NbGzfR43FzbnWVerudu9u66HE10GBzcS64yGg0RiyZQVZkDN3ArJgwMLApJpKFIhVDZyEUpXKzqzkeThEPJW/uLeDzOBBFnXA4SyZXoVSoIJtl0rkKizNxvB4zewZ7CGez2K1mHKpKWddZ09ZAl8vJYiZDv9eLRZY40NzK/zh9kquhEE+PjtPu8wIG80sR7ty3jp+85ZPsqt/8C/fSBms3Lfbe145L+irfGhni6EOP4FA2AvDBXffzQHc3Hz99lAF/DV11fkq6hioZFDUN+WbDqlUxMRqJ0OJ00WizoVkVsnqFUrZAQ0ctXR4H1zMJWlr8jE0EkSR4ZWgSq8fGFw4eYtDnxWVSGInHiEaSqFYz0/E4Fy/MUCxU5QWTJo0WpwtVkniodw37m7bTYJMZjgb4yuhZooUUn7l0meXFOJl8BUSBYqGMxSQim2T0StU9ye+0M51Osbmhjj6PF7uiMpaI/5duBvr0F/7msT0PbvsPl85/8u8ATUEQ7jp8+PCRw4cPP3r48GHrY4899uovGLft8OHDi4cPHx597LHH/q22+r+KXwmg+Xf/+JnH/vB33siupkb8FgeyIOFUzYxqSew+O7d0t/PBjRvZ5G8hWcqx3l9Dk82Dw2Sh21VDvU2h0e7AoIhDsdLubGQhE8FyUw5Hx8BntnJ7axt7WlsYzSZx+xzEY2nq/G6OD43xnh2bOTO7yPBKkIoCVlkmlM8TyReRRYFgLke8WOTrl4ZYLOSI5vNEclnS5TL7m1vQDI2ClgEqfH74IiOxKAYFmu0/tYGTcJo2Mhp/ga9dusr0jQjpYI7x6RDxTIIP37mXil3m4Lp+dFXi0tV5ZmejTM+HyGsl9nR18MaudaiSwo76di6GFmm0Wzk2P8+x8Wl+PDaBrogMeL1YJBOyKL123T889TLDV2ax+Gx4zCqDPj8us4nHh4bp97vYUdeKSTSweh2Mza+gaVU5IZNSbaAqlXWWlmN0djjoddsQBTvZShinyUKfuw6/eQ2aEeENndvYUlvPdDKM32bjrvW9lDSNU7Pz9Lc3cE9vN111fsyqiiJJvGftBlZzaQqahiKJyJLE9UiUaD7P/pYm3BYLwwsB/F4nDwz2M7S4gs1hRtNuNq8YBulIAatHZftAOwYC3W4XS5k0F1ZWmZgIkE7m0EplJFnC47Ax2NzAcizBuaEpirkiNldVA06v6CQTWXLJHK3NNeSLJRKhFL2tDWxraGAiFqPF7+EjO3dwsL0dh0khWijQ7nLRYrczlUwxubBKTZ2bpUCUjv4m8rkSiWgau8eGYpIQJYlioUQskiKTzGGxmZFVhWKuRHApTjlfRNd0VgJJapu8HGxtI5zPcT0ao6vGx9nlZQqVCu9et5Y2p5O97a3c0drGP7xwmrxZosluw62qJIolLgxPk0kXSMaLoBsIctUi0uNUqanzEFyOkc0W6GyoIVEo4TApbOts5ZXlKj/wt//xGZ6/OsmtG7p5e38f19MJ+rsa2Lu2i0QxiyiDx2OmoclFMJSlVNKp85txOC0sLsRRFBFNBwwwDHA7TPgbXDi8drq769hSX8+BpnV87OiPCa2mMBwyiUKB6zPLvPi2/wOv2c3B1mY21nl5JbBINpVnOhYDQWC5lGOD389CNkOiVGaD34/TJDOfTrOSy9Hv9fDCwgL39/YQlTU+dMtWnrs+jt1hZTqewGOxcLClE5NT5Vo4xIsLCzzU10dRL2M3yXzu6Ks01rko6VXQeSMaYyWXJaKV+MienZwJBBj0++lx16OILYDG6zrraLC6UKVaREFhY41KWY9glvpIl4d5eWmODpcNs+Tjs1df4UBLK3VWK1fDES6Eprit+R4EYYWj8wscbN3BtegQ9VY7bY46ZNGLZqRulr2rUdQnAIPR+AxtjgYqRgJJsFHUJilqMTxqBwuZRW5EI6iSxB1trTe5pgIWqQ5Z9FHUJnhr3yC3t3SSr5SwyBpL2Rh2RcVvNjBL/WTK0zRYW9hYsxeARPEyqgypUhG7qfoFvtXhxTCSWGUTy9kk63x7yWshRAQKWpFOtwOf2YzTZGLQV8ufXzjN2/q3IKKiSvWs5EaJF6OokoJF6qTd6cck5Qlk02yuq2Wdt4Xh6CptDgfBXAarXMZrdhIppBnwenlpZYnf3LGZQa8fv9nGg91b2FjjYSWbYjGTxaOamEulCOXzbK2rpdtVi0myIKDzzbHrJAoFmtwu6u12nGYzbW43y+k0mm6gyhLRXI5SoYxWqWYvDQMcbhsOjx2tomOzmdEFKGYLeNxmNAGKJZ2Bvloi8TyZbImB7nr2dLSxmstS0jXCuRytDiePDGxnva+LWDHG9yem+Mq1YVKFAkVNQ5REbCYTM6EoD+7ayN/sfde/cn76ZUIUrPzTxCl+fe0uZNH72uunV1/he2euspLNECsX2dvcxHOz89zT2U62XOZGPE66VMZjtlBvs3E1FObCtSke2bmFqWSCSkXnXDjIoc5Onnr5KrIksGdTHx6/g9/atJFcpYQqyUwkEgz6fVhsFr537AIXri6RC+aQbHKVux7LcNu6HlwmhV0N7ehGjrxWJJBNIQgGLy0sMnJ97mZVyEK2UFUA0HSD5nYf79m+hQ2N9TwxPs7mpkbanQ4kQUCRRO5p7+cvP/Gd/9pA883b/8Ol85/8/Uu/8G+6qXn+HHAI+CTwt4cPHz7x2GOPhX/OuG8A48CVfw9o/kp0nVd0nUSxiCyKdDiEqjdyOsbDa/q5HIown0zwnfEJ3Oo8e5oa2OBrxGmyU9KKLGYjTCTi9Hm8fOjFp9hc38DHtu0hUSyQrZSwSDLpchmXSUUWRRptDj64aRM/npnFYTIxEg6hWkyYJAmf38m62jqOz8/z4twc/f4aJFFAEc2UdZ3j8/Oszkc4W9ZQzAqSJNLocPDs3CxuVWVvYzXLcLC1hSOzc2iGwXhilX5PAybRTqx4iY+fPlXt0G51sBTMYcSLLAZz6Bjc2tLC1ro6JmMxsvkKsihgtyiUimUUscpxkwQH2Uo1o2tXzPgsVv709v08v7BIk91GvlJCN9LIgohbtVPRNVbmwmQLGhfGZvGYLTTZ7FgkmU63h7+7cpV3Dq6hz+NDx6B05za+8tQZRFGgVKl2lyuyyOvv2IBm6PzFpaN8cMN2Wu39gIEo2BCx0eeuSqqW9CX6vT5e174TAZGnZ0/yJwf2kdcqhHN52pwOdjc08M0bN3CZbLQ5XVyPRJhJpVlOpdhQV0u6VEIWRbKlMofW9vPDM1f4caWCr8GN32nn3LlJNM3A67XAzSavfKXMS1duICsSpWKZ1WAGl02hVNFJpktoWgKtouNQVba0tTBht1DSNLSKTm9XE2OTi2RyZaw2mcXlCJVyBavTwmwiztVAgGtDywRcESq6wXIkTmAugtVmwtB0ZFWmlK9mrMfmV/DWuQivximXKmTSBUxqtfM9k8yhmGR89Q5Ui4kHenu5tBok7s0T82UBqh2edhOj4RDXo7XECkUWUlXe0Sf27GUqGUeVZIK5PKeWVzjY1sK9t2wgnMsRyebw26w8f3GEYlknV6jg9KqvqQQUcxXGpuI4fVUZqly2xEQszh1tLZglCUkQ2d/cjMOkkMjnOfPqDN987gxTsShXrk5z645Bej1u4oOd/MGvb+f44gJ/8oNjIIuQKRNPl3DYCgDouoEoCpS1qtKDphtkknnu3N7FH2zZwXQqiip1cde6AR6ffZVoMEE6nsXhsfG9qaO8vfftmMQW1nsb+ML9Bo9fG2YyGiW0GKV7i4tGm4u3D/TzybPnWOfz4rdYabDZGF0JMh4KszIb4u6Odn593VqOzMxhtplxqWbC6QxWWWYiGWY6keRNfX10uVy8sLjITDyOx2zhrfu2cHd7L1cjyzwxPs493d3c1dbGhZUVejweNtTV0+JwoIitpMtDnFqZodvtxa5YWE68RJezlVA+jkOxYJYq6IbBw727WMou8tUbz/Cjc0O8vb+fWouLlWyWtT4vhqHRYndTZ7MylniRW+rurN5HI1V18aEKMieTx+lxHcAs9QGw1lvPWGIcl8mC3zyPSapHEsvIYg2H2tp5enKSt61Zw9XwCo3WHBV9BUnwIAoWZNGLrkUQBYWFTAKXyUaz3UumnMegStC1yS3Iov+1tTpeSrPWuxHdyLCUXaTF1o6BhkGFsq4RzOd4deUYFV3n1qbbKFRmWettQRacIEjoRpqD7a0sZYO02p0YehSXyYYq2VClXnSjhEEZzdC5frNxS5UkNvr9jMXjbKqpxQAmkwHsiolgLsObBgdodTi5Eg5yb/sABhXG4yHW+Wv55ugNzgeW+Z2tW5hLpXluboEN/gZssoOn567zpSdP43ObeMOBrRQrFbo9biL5AtlSCVFVcUgqNTYb09EMmUSWSlmju62e2eUwtw/0cHximonRRXwNbtx+ByZLleKwtJKhVCzjd6t09NZxYWwW+uGPd+5hIhHFMAy63B6OzA2TKVf4ytUhdrZWm87+4c5D6IbBK8tLfP7Yq/zZA3dyf8db/lN7qyQ4eOINb0aVutCNIhdCL3A5HOY710awOS14XDZEQeBsYBWf1UK91U66VEI3YCGZ5LMH7uB6NMhyJkOlVKHRXuWl3hiZ5z3338bGmlrmD2yx0J5pAAAgAElEQVSmxWFnU00NPouFuVQSRRR5ZnaOJ45f4MSj7+P+DhOf/vrLUNZBlXDaFPJFDbNJQQQihQIruRhNtjqWMjFsioIqSqiSzC2b+6i323n2wjU6WmRmFpLkChVOvDSFYcChni4eHhjAqlSbu1yqSrPNz3I2+p+6Z/9vxf9Dgu3bgSnDMGYABEH4LnA//7Mb5IeAfwa2/TIn/ZUAmi6TilWRWcxk0IwFNMMgUSySK1d4dXYOWZGYWQlj6AZus8q22jbAIFJIcXY1wAvzC3xrZJSF8QDxUJKpeIxcuUy3p+p5fm9ntQOvzuLgUniZxXSGQDrF1Pwq4WAKr8/ON0dG2drYyHsGN1DSK6RKBZ6bn2c8GqPJbufJ8XEC00FkWaRYqJbdRVGkvdvFq0tLxDI5kqUSr+/ooqhpmGWJkViMvY2NWOVOLodPcfjMabxmC6Io4qt1oBsGAaCv20eTzY4kCMylkowvVfmSVruMJAkUcyWOz8/hNCmsZHPYFIVHN+4lV8nht5ips9q4tamRxXQal8lGxdDAMIgV0xS1Mg63lVAkSySUZjoe57zZjCwK9Hg91BZt/OPQMB/avInNNc187foIbofC4moOAJNJ4qF7tvCnu2/FLpsJ5hM4ldbXRKahan8H3NwQFfrdt1PQbhAvJtnf1E2smMYsKZQ8FeotHkxSLd8aGyNfKeI3W8mUy1wLBVElmdFIlHg+j0dV6XA5OLkcQFYk5saWMZkVwotRCiUNXTdIJPLUdTh41927uBJcZWm5KsFUTJexuky0tXnJZ4sIAkSjOYikiDbkWF9bw5ETV0hly2QLGg6LjMvxsyYgk7nK05QVidVQnEwyR0ujA7vLyumz42RyFRw2mXS6SGA8UV0xzBIoIm0VjQdv28ZL18Zx1zhw+R0UslUdUpvTwm/s2spsMsXGGj8dLheFSoXHf/IqqtWE1W7G7rSyEI2wOBvkGZsNTdfZUl/PIwMbWEhHMYkiF0MhVElCFkVOLK/Q6nTw3KURUoksubxGtlDBZVduZqar/yNZFNBUCUGA1YUIbziwlZFwiOtzS+xqbEARRVRJJJjL88WhIVbnIzisMq/fv4myrtHS24AowLdHRykVy3x7bIwdDXWc/v33844jT3HqxDTFVImIWaatzYO7xsnY0CKpbLmqr1nWCUWyLCaTfPCl5znY0c7eBthSW4NhwL61vTy2Yx+yKGESf+bvLYt+ttXezaYDW1nNTyAi8K3x67y4OEuN1UKdzc6Tk1O8f+MG/vux44QWo2TzFdq7a/nG9RFWlqLEoxkcTjOCIPAbmzZwKhDg3Wu2sbNuHWU9zVOz11FEkd9Yt45UqUi+UkHTNQ61vJG9DT0cmR+h1uLiI1u34lQs/N7mHZxYnqHZPoLfvIYBb5pr0SADXj8ek42R2Ay5SoUBj4qACbe6mZI2T4djF+HcNd68cxO9rl2U9Dm63C7e2b+bTOUaQ5FVttXWMpWI02K7gU3ZAFgxjCIj8fOs991Jq73ptXujGzlC+RShfI6CVuFccIkHuwbIV24gYqbXtZk+3xWGIhFypTL3tKkoYvtrtneK2ISAicXsKDvqNiGgkMtP0+XcT1kPUtSmUaWu165X1peZSsRosEYQsBAr5Gm1q7waOMeuhl686gbubVN5cuYaV8MRdjeEGI0H8JjN1Ft0SnqFdCnHA51rUSUfJrEZ3cijiM3oRhrdKCEgkSqtoBs6a7xuvnrtOu9fv4EGq5cGW5SVbJqLoVWKmk6rw04wl+P52VkAdjc2okoKP54bptvlIZLPsa2hAUkQuRKqUoWuhsI8Nz9NrDDCt169RLGssRzKs6exHo9anSOXQiEcrW2MhMMkiwWWVqLMzFTBYaMi4zabaaz3IiLQ11BHOp6lrc7HuYsTePzVL5EOm0IinsPrs1LvcfHoHQfYUbcGgzJnV5dJFIt84sQpcuk8NpeVqdEAj991F/d0tNPnOoBmRPnnqSm2r+3+hSDz88NfIVIo8N+33Pmai9TPizrLDsr6Cg89+ziXLk+SSJfZeksnH9q+hYPNvWiGzvtePMIHNmwgXapqYL6hqxOtswNZkJhKJNB0HW+9my9cvoLNZKJnoIV0qcxyJs2Blia21jYhixJzqSifPX8RRZHJpPOYzAqfuXqR2UScnk4XjV11TI0sY1ZlRFEgna0wHotTa7Mxm0pgkVVGY1GcJhNWRUYSBTbV1tPudHJmfgGfw4an1smZ49NIVoXZsQCfH1lm365+9jY332yGU/jajcusZHO/HAD5/174BUG4+C+OHzcM46cZziZg8V+8t0TVUvy1EAShCXgDcBv/fwKaBlUJo5KmMZdKIwkCDpOJdLnMuoZ6jl+6QV9fM+tqatnX2MRoLMBSJk2H08Wexma+NTJKYCaIrEhkEjlWLQk+ddcdPDc/z7Hr49zX1UEwm2U4HK56gM/MMDO1gtlqQpYEVldSWGwq93R14lHtqJIN3SjS6axlKLpEWdNxDg7SvXs3R2bnuLQSoJArUsyVyJQrqJJET40fESjpFfY1bOL/PHacWp+LN3f3U9EjfOPGDSKxFGqNjNduJRlJYbMotDYIPPHIW/GbXSxloySLRbqaapmcCCFWq8OYLAplXeeJ8XFy6TyVssZHNz/Cp4a/yrdfucAXK6fw1LnobazjpYUF/mzPPlTJhIzIUHyFUCCOzSxTKGpcvz7L9PwK7a11VX6Oy0m9tZMnJ6f5+IsnGB9dQVWqbiz5osYtuzs5vHMPDsVCvlLiWjSEIt6g3fEzoPlTj+WKHsIktQEQKyaot2xEN7JcDp9kOZPBZlIY8JSpt5b43K1vJFmKcT4YYDgcQRZFZLHauby/rZVEochTk5NsrK/nrk2DJAoFksUCFy5PIolC9UcSefuhHQyFgly9PIPNImM1y1RsCm6nyuRkhDqfGV03+Pr73sygtxaTpGASq4D2K1+vUlcKdgWnXcFuq4rbO70O8pk8gcU4WihNQ5ObxkYfC/NVDmsuX8FmkQjOpaBQAbMM8SI4TagmmadPXmZmKcNAr49cOk//2jYuXZjm1t0DnA2s8I6BfnrcPr47McaV4Co1zV4cDivZbIG2Gi+bOloYWlxmcinIY3fciiJJfGNsmFcWFuj1+tjf3MRoLEa/10OuUuH7l4ZJJbIEY1Wah9+tEk+VAMikS8hq1RPYbJIolDT27Bigx+2i3enghGmJsyurvK6jDVkU6fd4eGjNAB+7NE88WcJvtfJAVycLqRRj8QQ7Ghr49NETfDtwgaccFm5f18ftHZ384PUPUqiU+OjJ47xy+gah1RTpbBmHVaZQ0skXNZrqbYxNLBKP53GoKu9Zk8ClqtTU2vBbLHjULb9wjZDFGppt1Tn3+5tuIVy4yFQyxDcOfQyAdx37c9Y0NXDmkc8QLpzn4SNPcu7MNMlIHrvHTDJTZjWU5auSwN/cdhsLmRChXJXLpxk6HlXlubl5Lq+ucF93N9+bHKXbvYxFVihpGjXm7ZwrPsFXR05xaXyWdDzLwLp2PndbgaVMivOrIa6EInxow3ZeXl7mw+sfwKDIcOwFNvgOYaChGXHeOTDAy0uLBHKXSZZynF9ZYZ1vhGa7k1ylzOaaRtyqA1H4qWyMlYK+SL3VjWFoiMLPbPUMo0Sfuxu/JchqLkkol0dARpWabpbabeTKZcajEb546K6bmccSmpFENGwIggVRsHF8aZ7bmgWuR0OMJxK8f62dfKWEy1R9lk+vPsO3x8Z53/r1eM1mMuUcXxo5xYM9PciCn32N+zgfOs03Ro9wIxTint4ecuUyJwLX6HR5+PML5/jk7tuYT0dYTKfZ1aDy9RvH+OD6d79WCtaMBEPR4/S5G7HJZmJCmgc6N7G3sZ2zqws0tvkYj0cZikR4c08viWIBzTCwKQrJdI7vXBzi0Jva+avL1c/1Z+fPcUdrKw929/C9iQmevDiMxW7G5bDyTyMj9Ph8vGvfNiLbC/zkyiiZcgWnySBVLDAVT6DKMoFoHFmRWZiNks6W8Tir2crjZ0f42Bvv4G+OnmQ5kMKiSoQas3SuaWbq+hJ1TW6aWr383f13M5GI83DPXazmx3njj7/Go1u28MWhISpljcXpEDu399Ln8zM7vopbtSOLEjoZ/mniKMOhIH+ye88vfCb+6cowEzdWERH4o+2/GGg+Nft9PvKdI6yEc6iqxI6dXdTbHfjNFuxKVah/sOY8Zb1qQjIUiVDUdO5ub0M3dL786gX2r+vD73WSLZW4dnWWQlFj6PIMv/uGA7yuvQNJEJEFCb/FyvDlBcwmCY/Hgq7pPHdphHKpgrvGiSAIuD1WopEsHb11zE0GKWoaLtXEP90Yo9uziipJ1FttWGWF965bR6xQIFcps6O1hS8/fRqAukYbXf0NLEwF2XVLH71eHz+cmKDb6+XqzCvU+F2srMR+4T35rxLif+7XIoZh/Funx5/Gz0uRGv/m+DPAHxiGoQm/ZEb1VwJozkRijMbi7Kyv53o0xnImw4YaP8liiX6fl/KmXo6dusaYZZEttTX8xcnTfO9NDxItZMmUS+xvbePJUJJKWWP35i7e3t/Hai7L4e1v4493KPx49icsZDK8MDeHz2Kl2+tjJDdHbbOXjpY6suWqa8m2unrm0mEOffpTNDZ7+Oz9dzEZT/DSwgJ2k4lGu40HujqxyDLnA8vEyxrnlpdQJYmKblBrtaDpBo+e+AFWh4X3rF9PnaWRaGGVQDpNMpphaS6C3Waiu6+ZaCLNxw/so93Rhm7kabPXsCRE2dvczDt/aw3z6RRfvzSEKIn4LBbePjCA32Lh765epeXj72bL1i4cbivjY0GWg1nmpkIA7DkzhiQKtPXWV8F3roJugCJVJ9VqIEksnEHcIlBrMTMZT3Ds9DC+ejeiKBBPl2mqqW4A7W4PLlM1s3ZscZLpRIpsuUxTzyqiYMcwCkiCC0FQXgOZAHWWdZT0WczSGg62vAGA86FnabR5cZvWUtJnWM7GeXP3rby128pq/gZfHb3G6aVFXjx9nYpmUNfkpuTXONjRwkgsxn0dnTxZW8fTIzdIx7P84T0H2FrbyBNT43jMFhZTSQIzQXK5Mg/u38pULEaqWOSPdu1ik78d3dCqfXuCyF/uuZ2RpRXOnZiFsk65omN1WLA4LHjtVrBbEQQBURJ566Z1jMfi7NrdzNeyZ5AUicVAuurx7TSBTYa0gLfJRjRRIDqdApPIxHQMu1Xm1VcnuGVXN2cvTPCOQ7fQ56nBMAwGvG5OLy8hiiLL8yGsdjODfj9fe/4MfYOtvH/jBkRR4PsTE7xv/Xr2NjYyn05xJRxBFGA8nqDFYUdSJG7ftY5nXrpKoaQRTRQxqFIezDal6lZ0cz2xW2Wefn6I0O4sb+7rY0djA0cmJvnU0gr/bftmHh8aZmR4DoBDdw7Q43YyHA5zZGaGFpeL6UQcgK+/8038+jd/yJOvXCKaLHL4a8d4/1t287cHDuE59EGuxc7z3h89QygQx2EzkUyXiMWrXtLxdImRuWWWc9dRJYn+nhZ85p/xD/+9EAQRv3ktmjEMVDPq9XY7n9z5QebTJ7nts49jtqn82r07ODE8XuWI1jhZnQvxYH8//zw1xd7GRlRJoslm4++HhthYV889He1cDa5yZHqaD2zaSDRf4Ep4CU3XeUv3KuOJBA/2dPPlO/7g5nXLGBg0WhdptbsZjYX4/PAFfnNwI4Jg4mTgFFtr2xiJHyOQyXCgaQ+dzhZ6BgdJlBapt3gIZ7Pc2niQ3d/4GO/dvgWTpGCV17GUPcVY/Efc0fwAqtiDV40hCBKK0MAXrn2VtT4fW2vbuRSex2s2M5tM8IF1bydeusKJ5Wl2N7bzxNQJvnLw/fz2K18mkElzKvB9HurZ+VqWUjeKiIKVh3s2kankua25lztaZM6sTpKrlLmnbSsTyZf46IvHubuvh4+9epJP7N6DV13LoxutgEjFCLGam2VzTSfSoMALTgfv6F/HUDjA48PD/P3tr+OPduwmXyky4GliS816yvoy7183wFz6BEORVe5rv5+yHmSDby+iYEUz0rTZbShiPR61zD3tG/jdE0f48KZN7KxvJ13OsdG/nooe51p0gUe2bKDT6eL0SoDf37yHP7t4gnA2yw/Gx4kUCjTabbxrzzZmkykShQJ2kwm7yUTdTRkjh8dGo81GRddJl8p0ul1YZZmtt2zlSjiCrEiYZZl8uUwmkWN2Jst0MoXL50BWJLx1bhodDhKFAn/68F3c3zHIVCpIv7uRZ2bn6PzjR1FNEmabyjPeOYILEQq5Eu9+3S7OLS9zd3sbv/+Hu7DKXdRZHPzVlS/yzv4N1NusNFi9P+8RAOD+tWv4fq7EC9Mz/NH2n72+kjvLX146y4WFReKhJJIkYXNZaBIF7ty5nqJWwWex8oWrV+lwemh37KPZbudqOEy8WKTVYWc2mebZ2Tm++pNv8ze/di+PPfUCOzf0MlOK4XRbqbebUa0qtVYLRU2jrGfxmR3VJiKzjCwJJBJ5ZEmkUCiTy2uMT1e52267QnOrl5nxVcLxIq+cHWGkwY2kSDQ67GysqTYnRfIFJhMJHCYFqyyzvb6WnzQ4iISztHXWUMgWKZaq1bREscjQxTkSbSn6OxrRdJ2+9gau/NKryv+OEPhlgd5/IJaAln9x3AwE/s2YrcB3b17bD7xOEISKYRg/+kUn/ZUAmoIgcHR4jNlEgnq7ndtbmul0uen3eEiWiryxq48mh4OnL13ne+PjHHno1yhqZUySiCSIPNjTQ65S4c093YzH47Q7PdzmrOrQ6UaRE4EAp6fmONjfQ7ZSxjBgcH07ZlmhUCmzuhylkC1iO6Awl07icqpkElke/fFROppqUGWZTKnEQjqDVS7Q5nTQ61lDJF/gR2PjiHK1nLKQzvDpl89w9/oBHujtpdFuJ12O8utHn2HmxhLxdAmzSWJ2KY3Tm+C+DYNMJRLsbchikup4deUMw9Eo630+zLLMpppa2vbvRbtZmg7n8nzp2jUi8TRdA42UNY1KWUOSRHRdp2ewidW5MNl8mVJZZ3JkmUyugt0qYxg/zY6aMBUqJDMlRscWMCsyw2PzCIJAuVShpcXN7GyVI9rZV0en202ylOVCcImZZLo6TtdZzk6gGwYlvUKnsxeTUJ3bhqEjCCKSYOfup77Fsw98BIOqe8Ym/yZ+NPsKD3auxywNsMk/gKYnqBhRroQDfHTzHZQ3ZvmrplPMxBNYFYVEscBT0zO0u1x8+vJlLs8s8IHd29laV0+uUubLo9eYTybRDYNCroikSBzc049uwB1tVQ/ctd4m+Kk4hCCSrxRIlrJ89u6DfJhjnD+3QCCcx2ZX+dvX3cnWmgPoRobfOflNrgVXaXM68Kgqf/nsyxSKFcxQFS82ALOEooj4OpzkChUkScTT6SRf1FAkAU2rchWff/4GoijwvRfP0+VyssFfw5+fOI1e0VGtKrpmoKgKT16+jrvGwYG2Nr52fYSPbtvOobZWTgcC3N3eyfnVVSL5POv8PiRBZCaRZGR4mcBcGEGAimZQ0QzsVhlFEjDJInmx6j0sywKVikFmPs2xxAgrixGaW2uYG1+htaeelWyOudlVUtkKpZLGqycnmZtexeG20tVUy/HhcQ5uGMDptfPR4y+zfk0bs4EwS6FVmuss/OD5CwTSaRrsJ5mOx/iTQwc40LiFYH6RDx9/gdNnx7GoVScRraIxn46z0d9Cr2+O65Gfz6cyDB2dDCI2ykYAWfAhClZEwYpdMfPXV79EoVLhkzs/CIDLZMPushIKpnnypUs8+sB+ziwHGBmdY++2NZxcWmI5nmRLbQ2fvXSJfa2tfGjTJgpahdMrK9za2sbRiUn2NHTw7Nw4+UqZXo+HD738TT5z6+sxjBLTqZcBcJvs2BQbc+kAnzh3lt/dsoXfHGyi3noL0cJFLgSD7G7YTJNVp8fVj06BldwqsEqH41bK+irv2bCedHmcP9q/F1kU8Zu3MZF8iTqLhzuaH7i5PorIgp+KHkcWPXxg3bteW9u21Uo8Nz/CbCrNdyaf4Cezc0RzOf7g+8/ypUfehCx62NfcyKmVFS4EVnhrzzaihYvYFQ8msZ2yHsQir8MsaWhGikRpigNN93M58tzN+2/gslnocjn5nY27uMksQxK8gMgXR57l3Wt2EStEeXp2lod7+/CpNdzZOogsijw5Pcrb+zZR0MpVniawmJni1MoiD/fcRbtjH88t/JADTRuYTp2jzdGDiBmT2EK8NMzDR/4Zi6LwxD2PMhQ9i2YkSJeKZCs3uBwK8vlXz3P44H7Msszdbd2MJQJMJ+LsaWnl1qZGXlhcosPpYI3Xy2gkyv6WZtZ4fRxfWmQqkSReLGI3q1wJh1ElCb/FTJfLSVHTUSSRLbU1lCoaL14cxeasqkas29zKoxu38/Ft92JQAiRAYzG7TEnTGE+s8tFXXubK+TmsZolsvqomko9lOXruGm1d9YwMzXNfRwfb6mrxmS38cPo6B1vzNFi288jABhyKC8NYwi53/NznQjdyZMtlpqYiHDxQx2ruLJfCS7z3K0/y+v2buK+zg2dPXeWNt26lyWblR5OT3H5LO1ZZ5omRGwzU17KnuZlvjo3ye5vc1FutCAJcmQohCgImSWIkEsbrs/PRbx9hcH07+UqZwZpazA2NhHM56mw2bLLCciZNrlLmlnobU4k4Ta1e3rF9Ex1OB5++eInAbAhJLBNeyqA5TfhrbNTXe1hejGEY0NRVx+UL8zisMkcyBXpvd5Mtl2l1OHCrJhLFEslSCVWS+G8HbuFSMMhPjl8jtpoDUeDlV29gNklIgsD2vg621NXy0sIC18b/a3ud/9Qp8H9xXAB6BEHoAJaBh4C3/csBhmG8NqkEQfga8Mz/HciEXxGgaVIVivkSV0dm6exupFTR+MTObr49McRKNke91cadrdUuzR+MjZEoZvCqDqL5PPPpFD8Yn8Aiy4zGory+YxCXqSquPJ8+yXML0zx7eohSsYJj/Roa7TZaHXYShQJjgVWcDiuNzX5Gh+eRRYmypmNzWpFkEafHzmBNDXZF4ZnJSUpa1XZM03WSJR1VFHn3xg1MJ1P0e9yIArxl63oKFY33Du6nqCV58zM/YObGEpFEkXxRI50tYzZJ9Lc08MjAWhLFHOdDczhNAZwmE4/0b0AURGLFFCVNo6RpRAt5iprGcjZb/QyG8doklWQRRRKoq3GQiqZx1zjwm2QyiRyGYWCEMiSzFcymqi95KpFDNUlYzTLBcJZoLsftm9dwamyacrGMrMg01NmrunZljecnp1jJZGiwV3lzmqGzlM4ymYiy3t+IVsqhGykK2ii6UcEq/0yK5ZN79yIKNgraMqF8nFqLm/vaN6BTQDSqftKS6EbCTUG7AIiMJwK8Z3Ajq/kUDkXlC8NDzCcSzCTibGtsRBAEji8ucXplhWuLAUyqwuamJsqaxo1MgXAoy7W5Jf76njvZ5G9FFISbnDQBQTCh6QVeWJpkOBzl4b4+nn7r2/hk12m+9ORpnF478+kUDdZLGMBbensYXlmhpGmE8nlESUQQBO7btwlNNxgOrnLy5SlMskjuphtOoaRTqVT9mEVFvOmcZCAIAoYB/iYvY7EEl4NhFJOCoRgE58N46t1sbGliKZ2i3+fn1OISqVKRb42N4VJV3t7XxwuLc0iiyJt7uvnu+ASjkTD5bIGmBge5bBFdh1woD5pBrt5KU40FRVUoF8sk0iUqWhVs4lWhojMyFmF+IcE77t3OgdZm/vrEGcan4phNIi6HiY2bO9jS0MhqNsNMPE6lrOEwmfgft+7hU2fPcbC9naOGwdRkmEi8SJ2v2mzztoE+zk/N8bnLV7gYDHFvRwd/vm8ff2WzstbvZzKeIFeu8rt21LrxmVWefPUyx7qffC37/dO4kXiJDxx7nnQsi2JWeOeWDbxrYCsWeR0OZSO/t3HjvxrvMPVy//YNfOv5M2RyFf7suy9S67Pw1UceRBIEnpye4eH+fjRD51O37ud6NMyJ5QCr2QxmWeGu9lauh0PYFTPNDgd7RYEvXRnid3dsYzhaFbhvtDmZTsbocgmcWZ3jQFMPX77jIa7HJqgYGpHCBVRJ4fWdXQSyszTaOpGFWr45/n0e6X8HRW0CzcigiPW8qeshgvlz3Na8DlWqytJ0O/eQKl3/OStl5V8dVUXOBxGEEbxmlTd1bcJlUjm3GmR1Jcazc/Nsr53ijV37yJaDBDKvEMiu0uHYQ1lfQieLItZR1GZQxCZ0MrhN1WznRt8BrkSO8v4fP4vTZeOu1n6s8noK2gglfQqzNIhhlLmzrZ2nZy+zxuvnwe4ebIoJVermXPAI+5u2c1K4wLngLDvruxEFO2U9SKfzVjoc+muUmzua9/ObLz7On+6+FRErwfwYfrOfr964QjKZRfW5CORGmE+n8KhmTi4H+OiW/fx4ZpY1rVVueo/HzdGFKb41Msrh3btJFAu8sLhEvdXCMzNzfGTrFtb6fexsaGQpnaLL5WQhnSFVKhFPZXl2aoo2t4d3rV2DQzEhiyKruSyqVPVab++q567OTj6w7nWAjiR40YwYgewCqVIBURD4xo0bfP3IGZLZMgDFokauICJJIpl8pWrVWqrw21s287HlKA02N36zHbdqp6Qt4lVdCIJEjXk7FT1GulwimB+l0bYLgE9f/RI9bjfHFhY5MzFDKp5lzbpGQqk0t/7tPwDQ1l3H0VNDdLpdbNnYTafTwavLASRB4LunL3Nw0xo2NlWz+cuZLGfmF9jb1MDnLl/mPRvWs5BMMjS7hM/n5Pb2dk6cHqOtq4bV1Tj13a1YFYXbW5tpsjkYioSYTqUoVCrUWa2ky3kee+kEd60boNVup8ft5c6OThKNTUiiwBdSJ+kbqKfe7yacSGMY4HWZmBoN0NJgJxjJsbKc5AunzvNr2zbezLxaCOWq+x9AUdO5MRsgm69AUQOXiWJRw+82Uy5r1B2RUxsAACAASURBVNpsXAmFsSomDm4ZZIRnf0kU8r8nxP/FONMwjIogCB8EjlL9BvQVwzBGBEF43833/+E/c95fCaDZZLeTzxSqUjqaRknX+OsrZ5hOVEXTr0UiHGhup8PpJ1ep8NvHX+DOjg5USeSLpy6SCKeoaDqqJNPt9iAJKzw1M8MTL19kJZwjnS7j8ah8/+Iwd68fIF0qUdY1kpE0DV43ZkVG06s0ht0NHXzsYIV0qcxiJsvr2ts4sbxMh8eLIAikSyUUUcSuyIgIbKyp4cGudcSKabKVIjZFYXdDJ7JYw1dunGD48hyqSUSSBOxWmWJRw2FTeKCnC49qx2920e2Csl5hKrnKicAM/R4fjVYvJb2CW7XhMWd4bn6edKmMAKgWE/abpUbVqlLf4MTuthFciOBr8KCIIopJwaIqeOvcXDo/R76oYRgQTRSRZRHrTX/ia1fmcN6i0ttaz4XLk4iigMVhxmI3Y1IVGl1ObkTCTMdj3N7ezoaaWiySzFQywb7/i733jLLsLs98fzufnE9VncqpK3d1TuqW1AoNEpLICIHBIAfAMA4MBgweY8a+NkNmDMYBk4wsyWAUrIRyt7rVOXd1V87hVJ2c8957PpyensvynbXsuct3uKz5r1Vfaq1T69QO//3u932e39Psx6d1UDWiPD53GUkQeGDT6I2u5lb/PvK1cS5ElyjrOi7VjlcdRhCkeqfKLGCYZQDe3LkbVexgi9/FpfgZToTDZKtVetwuDjQ3UTUMHIpKJJ/nynKYWqWG02snny5wfnWVFo8bl8/Bu/dso9vtZDmbRWCJUX8rqlTXVh0Lj3MxGuXJ8Un0ms7upgYarG7+cNd+bIrMT149cyMtx6Wq6IaJTVP59tkL3D88yO8eOkCkWOT3t92Oic7l+DxftNo4fWaKZKaCP+CgVtXZiOap6iaqLN7IAbdqJoWSzsp8lCO6gaRI3NTdSbpcwm230uf3ky2XiaWy3DI6yt2dHTwzv0DIYedsOMwjU5M02mys5fKkynXDimEYzE5s4PNa0HWT6FIWEnWtqCTU88a7mjysLdUj4QzDRJIE3B6NUkWnXDFobnLQ5nLytWMnOXV6GUUWyOeqtIYcvHdokL88eZa2gJegzUbOVeDFmVnG4zGqlRovLy4y2tDIXFeE1eUE6/ESicwSF7s66WoOMrUQ5mf5Iv986RrJSJqu/hZ+fXiYVoediq5zsKUbVepkd2MjfxbN8wfPvMSF3VF2NTayJdDK0bVZHro2Ti5VQLEoWGwqX3r8ZV5aWOTOzrP85vBdKGLoxj5SNcKAgSKKNLT6sKULzC6kaO5uxKdZ+ObFi/R43NgVhT8/eZLP3XQTh5dX2d8S4lB7G4ok8ez8AjZFZTVfNySc2cijqQpfPXmGVq+b9wz0szXQTodzG4aZp9O5m4qxQLS0zoivB0VsYzlX1+krYt3JLyAgCirv3rSLbPUiiVKWkE1Fkuovb27VhSb1YZpVqsYaC7lZREHArdZ1nbIYQDdzxEqzNFjdPwdvN8w6RuZKLMF6a4zzkShv7+3lucvjjMeimFSQCRAuTPDpnXVPQNmYRBSciNTlMSYlREFDppGqsYSMD8MsMJ1KEF1N8PYtw1hld32/EQcpG5NUjTCy0Mj5yDpfOXycwx/8EFWjRqyUBmDI18JCdoomu4PpVIJ4KY1TqRAppWizZxFQiJRWaLXvJ12Z4wv7DxK0DpMsTxC0NLNRXMEmy7Q1+Pjsnr0cDy/R7fawmM0QsFr4/KmXWMlmuLm1hQuRKMdW13hwZIj/uHMnkUKeTpebTlcREQG7orCWy7G9oeF6xnyF89EYA14Px1ZWaQt42d/aSovdhohAxTBYy+fYHmxhMZtgIh7Hb7Px4ZGbkAQ/hdo4keIMFaPGfDpFo93O5469ztnTM6TzVcplvT4SrdW9B4pdpFzWkUUBi11jxNdIT2cIm6RxJR1jLLHBXx89zWo+T6vjLBVdJ1ku89TYBI81TvPAwAqnNyIcnV8kupogEssT8FjIF6t4AiZ9DUEcVo14PENXg5+ehgBPXJvgzk09HF8Lky2XsSoKTp8DqyxjlWVS5TIOVSXodJAolWi0O9CvOwcr5SptbjeqJNE/2srHdmzna8dOokkS2xsC2GQFv8XJrkaJhyYm6HY56fF4mE4laAt6eW9//3UZQoU+r5vxRBJFFNm7rxdJFFFEEdM0aev0s7acwG5VsDosWDJlUrkqly6v0dro5x19myjVdCRRYCNb5Go0ilPTCDS4cSzEMQIWqiWdfLrCVL7GQJ+PEb+Xs7UaNtPEcR28/4u8hP9HSeX/u2Wa5rPw8xX2/6zANE3zg/+av/lLUWgC+Js8uL0OMuk8l5I5piY32LytnTcP9NHicNBg9VDWq4hAJJrieyunecPWIQY6QpyOZ+noCfG5fft4ZHKSWLHIy+evoVpV3I4qVk2irdNPpVTltYUFyqUqqWiG1XCOpo4Sa+MbqHL9gWCT3dzbuROTCiv5DVRR4fa2Nno9bubSGQAcioJTVchWqrhUC3ZlC4nyMRKlIuOJJPd17uDhqYd5emaGhkYn6WSeVKZSH12rIo0hNwdCXfz3XlvN1JEFkWKtxs8WFnlyZo6qofP7O3cSsnlQRZlEqUSmXMZjsbBRq5EtlpEVCbtVw96iYVXqcYkbyzGcHjuKVjcxvGWwn1u7Ormwsc4LL12jUjNvALRtFhldNzl3foaGVh91NKWJ1WGh0ee+gVRyahoz06v8zDDY2dDA80tLtNjtjCWm8Wo2Opzb+cnkP3HbdTzHf+9USKIHm9DLnkYHAnLd5Xo9ycQgx9Hwq9zUtANVbEXGC4BuJPjOlTF+dWiQVoeHPz99go+M3IduZshWE6wXCnxix05Wclm+e+UKK1WdNq+HNpebT+zYQbZaYdTfyqXYCm0ODyZwPjrP18+dY3J2DZvLiiBAT1OQRpsdAQFFlHhwaJhXJmewKQpbgw34NCdfOneSVpeLeSNFj9vNrS2bbsgYwGTY28Jf3RHg92SJS+MLGIbBylo9m12RBHTTRDBBEgRsVgm3Q2E1UuSTb9/Cl376Cvl0gdu3DrKvxY1umPzW5i2889qjfO/KGH91590IAvx4chqHqtLudJCtVHFrKiF7veAMz0WwWSUEUcTttWEZlukeaOE9g4Pc0daFLMo8MTfBn8y/QLmsUzNMVOo8TbddQXDAhw/dxN+8eJzJyQQ2p4IAVCUDi13jKy8dQ1Ykmp0uXjp/ja6uJu7s7OSZmRnePDxIp8vBVDJNW0uAy2Pr9fNqwivTs/Q2BtkIp0nHc6iajGJRcKgaXzpzlmSpSIfbw5s6BgE4ELqTyf/cRNWoMZeJ86kXXiabzGNzWKiU67o4WZVRNYVossy58zMkigU8msZ7+/7HVEg3k3zm+Asooki1XCMayfGmO0cYDgb58fR0HdodDvPNp45QrRnMDKUY9PuYT2eRBJGQ3U7VMBgO+GmwehAFgdGAn+dnZwE4c36azcEgb2z3AyIL2QnObIR5V+8d6EaMfC2LV1XpcN5MxVjhYuwsd7TcQ8VYxDALvLh8mYMtm2i29yEJHgyzgiioWKT6cZhMv4YAfOLIYb528DYEQUIW6mghSXAQtAz8XJFZP945xuJxhnxe0pUiHx3dSbqS5z/deQsHmruwSEOU9GsMeG4iVRnnv5x9nV8dHGTQuwdBqD+ILdIQ0dJpplMRbmq6l1Mbz7Cn8R46nC4e/s0H2B7ciSz4MM0q6coVPFodGG6aOoVqjZ6WBip6lYvxZV5eWuGPdveiSX42ubeiGyk2Ckd4emGK+zr7aLU389ziee7qGGEhk6DRGsau2LFIgzy//Bh7G7tYzi+QKhd5cHAXumEQsrkZi0dpsXvQzXoE7ImlJebG1+i7x4fbYqHb4+ZSNMZYLMandu7mSjyCdH0f2tfcxLC/iYcnx6gZBkO+ILIoMuJr5PDyar1L19bGTCrJWj7H5kAQl6qykkvy6VcOs6U5xG+MjDCZWqLHVeHxuSvEiiVubW0lUS7xoe89BkAyW0WWBERRwOVQyOSqGBWDQqleLNksUMiWeHVlgd/evg2Ptp2tAZEL0SXu2lq/Bo6urJIsFbErCpvbQtgUFVWU+OnR8zR1BEgmC3XTpCwhCjW6mgJcmlsmGHSjaAq5SoUOt4e7+zcRzuWZWo+gaDJ+m51Gt4vFdJqaabCWyZBLFRjoCHEuEuWurk52NISojZg8Kk+wls0yGYnSFwzwo6vXSEYyvBK5RoPdRp/Xh2EaVA2d9XyOQ+1tnFpfJ14qkyyVWC/k8GlWirUqxVqNPq8Hv2bhcjTK5FKY9lDd1BcNp3A5NXxNHmKriXoqniwgyxIvH7nG1sZGFrNZFtMZTk/OE11PY9FkLHYNqyYhCgLRRJbNe1pweGzEVhNkKlWGfF4EQeB85OfQkb9wq443+t/9Lf5165ei0FxMpdnvdZDNFnC57XR6PNw60Eu0UEASBG5q6uaR6Qs8MzuPVZHJZ4voNYOjM3PcPdjPb//a/Xg0C8/Mz5Esl/jZ8UuIskQhXyZXrI80JUkk1OqnyeFkNhqjWKigqiIr02Ha+5rpcHuwKxZM6i16ARmP6qBYK9Nq99Nk87AjqDOVjjKdTJEolTm3EeH9A3uJlc7wmdeP0OX28Lnd7+TUxjN88fkjWK+bS2RFJpmpUKoYbN3awqPvfAdWWa2PdQUJw6iRquSYSCaZ2IgiKxIH2zuoGQavrMwym8owl0xSrunkqxUqpSoWm0ZsLYmiythcVnTTpJgr4W/yMhpqQpXrMgCAkN1GV98mNvn8/OWPjyIIAoWSTk03CVzvbs1PRyhXDTRFpElVcKkaZb1GRTeIp3OsR/LksmU+mzvMe7ZtJpwvEM7nGfWPcmrjCH975z14tfZ/cW4lwYF0Hb2hm5n/MTIXXOxu6EYRmq+bEjQMs0y0FGU9n2Mhk2Fnwxb+4tY2ZDGITBBN6sGlzvD+nzzO1u42Dra38YE3vJ3TG7OsF/KEbG58RpXHZq/y1u5BNEkhVc7x9XPnePm1cbL5KltGQ/gCLvq8Pp5dWOBDwx5AxaM6+PTNN/HY9AwWSeFqYp1UqcRCKkm5WGEhm2WPXsUqqYCEgIQsyozF5ylUq4Tag+QyBfqsGucvhbFbpHoHURSwWiRURSKTq9DWaOORC1doaHLx+3ce4FI0zqOvn+czdx/EpdpweOycOjPN7K6dbPa10LnTy+OzUxgmLGaz7G1q5KFr41w8P0dbTwM7O9rQDYPf274dv+aipFd5aXkGp2LDo22n1bGMpopAnSlqGCa6btAYdHLfvi1cjESZX87Q1upAVUSKZZ3BoUbuGO6nrOu8vbeHiUSC841uIvE0S74sB9rauRaLEc7neeboBeLpelfaMExKZZ3ZiTCZRI5azUSXTQRRRJIkqoaOXVHY2tTExfV1/uulU3x2Zw9/d+0xIoUS2xsCHGrdxeH3DXFq4yr3/+UjZAs1Gn0WNIuCZlUZGQkRCycJOZ3c0dbzc9eaJg7w/Omvkk6XsFlkFv/083z5/Ks3pB/fP3YGQzfxNbrJZ4q8srTEx3fsoNXh5cWlWT793Eu89uBHeXjqDA8+/yR/fNM+9jR28Mi9rfg0H2U9j0MZYiV/hifmpvjoyJtpsnm5EDvHtsAWTAwWc6/T6bwFRWjmtpbbmUq/xuV4lCPLq7x3oB+r1IIgWCnpkyiin0emD3N/70GqRoIBzx3UjASP3zdEzYj9i3vJxKBmxH6OaykKDh7oGyJg8ZGuZAhYdpGrvsa+UAc+bYTV/HEarZuomQlWcnHe3N1FolS88fl89RIWuYegZTeBxvred2pjA4v8PHsa7wGgpI8jC0Ey1UvYlWYqxjLH1s7R6wnwK/2H+OBgE2996o+5v78fiyzxd1df5B+vXuObhw4x6n8DB0J7canniZVy+Cx+hv1BZKGB4+FjzKRe4lf6D/GVC9/hvf0j/P7RF5AEkfv7NlHWK7ytZ4B8rcSepma+cv40i6kUv719G7lUgTsPbOaJc1e4Z9swt7W28tVz55gLR/lY+kX+dP9+nF6No2srtFuddS1/bz/funSBe7o6OdS6E1Fw8af7JLLVIoooc3hljVPzi/zGrm28vhrmPQP9vGWwn0ablQabg4lEjKnUONsbGkmXy/zWk8+Sjmep6SZWTcLjUEhkKjjtCo0BG3v3NdNgd/DECxfwOFVq1/XaD4+P8+g97wBAFiR00+Teri7ixSJvaG/nfCTCRCLJ5qAfr6bxV5cuEWj28gd79zIzMMA3njqMKAncdtMwU+ENouEUtw/30bXJxeGlJVYyGXY2Bvnhiye458A2JEGgyW6rc6CTaean1lFkEd0wGepo5qWrU8w1B1nMZtkfCuHSNHKVCh/buZ0/f/4I5WIVWZHYMdTNXDLFR596jh++7c08MTtLtlwmVS4xl6pjBS2yTMh2vVOvaTe6pIIA7x8a5GFB5OrCKpVSlYZmDw1BD3tbWvlp9iKiLOFymuTzFVbW8vzds8eQRKGuOXdqVKoGFg0qxTrnM5mt8IM/eoBDbd1cioX51BPPA2BTZNYLRXTD4Bd9/TtoNP9d1i9FMtAX/uJLn++/ZwRJllAliQtX5phcXmctmuSzt93MP0yOsZDJ0uv10OlycWamjomyO61MRWL8bHqWa5kUz527xoVri6RzFfKFKoVSjUJJp1QxSKcKHBjpYVtDEJvVwq6+DlJU+PDBvazn8/z65hF63c0Ua3m+euEodlVHN2tciK3TbHdgk+uMNb/Fjiya/MP4BIc62tkS6Gc6vcBaoYDXYmFv4yBfv3iY05fniUayxKI5VAmaW720dfjIJHJ4fQ5GfI0gQFWvIosSr67O4lJVYuUKw4Egd3V28OLSMo12Gw9fHkNVZDo9XoaDAdq8HhYSSR7cs53jk/PoNZ0P7d5OTZNZWNnA43agSBKdbhdLmSzRYhGfxYJLU7E32ojFUpSvO63zpXpMoN0qY9XqSUDxSIamZi/RXI4en48Ov5dYPks8WYRKlQ/v34nPomGVZUSxwKjvNhxKO4aZZS1/CafSiCD8y3cgUdCuj82r1MwIUEIRGzHREQSZ97/wRa4l4iiixEwqjVUu0u/ZeaMLWtLH+fjh51ma2SCayXJlI8LP1uZ4T/8wA95G/q8zr+PWFA4vr3CgpZUfjF/k0ckJgnY7iUIOCQOLVSGymmC1lMfrsNNo17DLCn5LkE2eEHd1dqBJCpos8EDfVt7Y1cEPTpyjoopsbfDhs3gQEAEB3Szy8so858JhCrlSvYtcquJQBeLp8nUtrIQv6CQeLyCKAoYJI32tjLa3MBoM4FZVzq6t4XE5uL21mzdt6uGF8CKf3nsbVaOGIskM+YJ8+vkXcNisHF9d5Q927+br932GXxu9nY3iHN9/8QT/NHaN7546zeMzk7xwbpzvnT3LtfI0Hxu9g28cfgWvz0Y2VyGTq6LIIrIIt4/0cn9fH2/cvolTa6u0dTWiajLv3rkFn6axns/zyJWr3NrZjiDJDDc1MhGPoykyTx+5yIWJZaKpMvmijqKIdWanCblcjXiqhN0qoSkSyXQJvaqjOlRESaSi66TyRWLlIvtbbEQKBZ6ameZvnz7O48uXyQl5BAG+846P8Id3vY99AzYuZpMsz2+wsprm5n0DrKYyOK0Ko4GBG9fagYc+wcJ8go+85WY+sGcrJzcWeW5ulrm1CAuZNFa7hXKhzPpanVCxZ7CL9/cfwiKJBKwylzIJNLlGyG7n6akZXl1doclhYWtgN4rYjCY18uzik/zmT57g9r4uhv2tfO7kc7ywsMDWRhsN1q0cC5+h2xUEwaSsL+BUbGzx38ybOvcSsFbRpG5q5gZPzJ2lw2llW3ArkuDmi+efZshvMJ2e4cvnDnOofdPPsWoBriReYzW/SrOt8/o9I2FQwK10o4gNPLt4lD6PD6fqxa0OIQgKdsXLd8efZGfDdkK2QVodbXgtVVTJh4CCKoVudEkFQeTR6YfJVCq8tft+woWTOJVWdDN7PSNdRBQc6GaUTlcvPm2YF5ae53Mnfsr33/AWVMlEEuGrLx5lcS7KS0sLTJVnONQ2iG7myVTKPDF3lTe23c5GcYJcrcT9vXewkp/kZDjMvqZWejxO7uvq42w0zPZgN/88P8ZEMkG3202sWCBTqdLjcXMlk6Rq6OQKJeaicVIY/MpgPxeTcZYXI/zz2AQpGe5sa8Mqyzy9MItHU7h/0yhezYZulriWnKJsVNFNE1WSOR5eI1Ys8PGduxkNBuhxBdDNGo/PzPL3ly8zkUrx0vQcP3jtLE9eHsfQDUqFCqJY11+vxUoYFQNJEenf3MK7Bgd4Y2cnf/PSeXKFGoosIMsCyWiGhy5fxOPLUTXKqJLEdCrJ14+f4s7uLiZTKbY3BEmVKzw7V2eEvv76DKfXlvH53ezr72IhlcLvcrC9pZnPHLqZvU0hBrx+PjB4M1eSq6znC7Q3B/FYLKTLZeLFEh1uDz977Souu0JDiw+9XOXSxBpbh9v54PBwXfMvCFyKRrk6vkRBFelo9JMsFvn83bfx4PA2RoM+np6a4WdLC8SKReaXI5gWlcV0Cp/ViiKK+GwWNEnCMOv8XqeqkiyVeGh8ggvj87x11ygZQSebKaJj8pHtW3k9uk5ryM89WwZpaw0yNreGVZMJNnswdZ18voIkCqRyFYplneT1gv69+zbT4fLh1jQevXiFpoCXdqcDj6by2soKk4+c/4VNBvqv3/7a529/995/c9b549968f9knf+vrK986yufv+OBm+jz+elye1jIZWho9uH3u7i7q4tosUiiVEY3TURBJCZUcXjsdPl99DUEMSWRhY16ByCXLWG3yteZgQayJKAqEt3dATaFgoRzeS6Fw6wX8rx7ZIjlXJ7P7NrLkLcXqFLRq3z26ec4srZKxtDpdbvRTYOKUUEVZfK1EtlKmbu7uijqVfK1FM12D/uaWrm99XYuxU9yIhwmp1egViObr7IWLWIRDTbWMxQLVaaScS5kY+xrDuFUrSzkIjTa7Jze2ODmlmY6XC4ylTL3dm5CEeH5uXkEUeBQZwcbhSIT8Rjh9QSfP3gbTy3MsrGS5JlzkyQyWd69fwdWReHc2iq6WTfvyKLIpUiEqmHitlgY6W0hXcqDrqPKIn0jdQi0ZlUxqvWIr0gkxdt3j/Ke/gHe0jXIgU2tXMjHuWvbMH9/+TJX4nGqCNzd0Y8kuhAFFd1MYZNFJlNjeFQZSfT83HmuGQnqaUIWJMGJbuaYyZzHpUr8eOY5ZlNpUuUSDlXDY7GwJRig3VkfKZX1OV5euYxD01gt5hjoamZLawsL6RTfeu41fnTpIr+7fzcHW0a4tbWFw6uz7G9u4fb2diYSCTSHxt1bB1gu5LhlpI9ktYwiifz44hiqVWFLIIAkaBRqeb5x8RSfe+J5bhloo90RxO+3sJzNkaqUabDJeLX6/3UxPseXj50gHk5RyBRZWUpSq1Rxee04NJGWdj/BZm+9AM0Xae4Mcse2AdwWCzsag6iihFvTCHicPHT4NB3NbrwWjd/YWmfoZisFNopZXlyaZ6WY5zN79vCJ7R8iU13ifc99l+9cfIG+gB+Hz8nO7nbyssn89DrVmsHB3UP4bTb2NTXwzSOvE4sXaW1xkcvVTUGJdIWLs8vcu2OA3Y3t+H12hgJ+WnweZFGkVNNZy+VQZJlnr0xwoKsdSRQ40NrM49cmqJWrLK9k0Ys1kOuMznKhBqZZRyqlythdKk6XBVM3sDs0Qk0+Lp6fp1Ato1oUFFki5LDx9p5bOBOZ43du20OT180Pjpzm1fE5/vCnT/Ja4TJv7Orm14d38Du3HMQTsjAaDPDq2DSnFpb561PPcSJ1hc889j2qlRqbRzvo8XhpcdjZ1djMza2tZDBYiCXY09nOYGuI2bV1DMNkbHaVf16/QpEii9k0giDSbLeTKJXJGzoNNjv39w3hVBqomHMkygt84vDLtAV9vL6yyvsHd3JbWyf9PjvdriZEwU63249JjaXcVXTToKiX+dirD3NPVyNWeYSqESZbCTPka8KlbkNAJVa6zHohTafLzSdfO4xDUVnOxdnduO3n7h2/xUZBzxK09mJSw6CEbiSQxbrs5NGp1zjYsg0RDVGw1D9nblDUU3Q42lkvXsAwUxxencFjKV+PpKw7mCvGEqLgYNDbSqy0xoB3GFUsIQkeZLGuT89Vp9GkBhSxGdOsopspmuwKr6ws0OdzUtENetxe3rZ5kJdWF3n/3u0cWVzknp4WBOB8dJ2qYbCjYQCX2smgtweTKppUI1pKs5BJMugN8p2rlxmPxxnyO+hx+whYLRxdW6VQq3ElEiGn6/ynPXv59ovH0SwKDUEP6/kc44kkZV3nxff9Gj9dnGRHqInj4XVipVIdmRcIcC0RwaHKPLc4w/HwOh5NJV0uY2Dyrt4R7uhqQxYk3v2jH/PFp4/w0zPXWF6NkYhmmZ6LcPv2fnb0tlNQTCJrCSpVA5tdo1CoUq7qGICiiASDDi6shHllYZH1cBq7VUZV6rIVi03D4bJyLZPi/v4BctUqVd1ga3MT4XweTZZxKgoNNhtOTePi+joKBvNLGaKZFHt62/n4nt20Oh3UDIOVXI7bWjZhYHJyY5onp6f43e3beUN7N9lqkY1CkVKtRqZcRpd0quUa2XSBbL5K/0gLXpuV7790kjuHe/nZ4iJWWSGcSDE2sUJZqsf9fnLvzQgIOBQL69UiK5kMLk2jbBqsZ3NsDYVQJZlmp5PL0SguTWU5m+VrZ87w2e+9yNnVJZqbfDQFPfisVnxWG+evzuNrdHMxGsNvsxErFFAlmWKtRqaQ4wN37uV9W0Z402g/hzZv4lf3bWPPUAe+JjepQpaewRAPjowSsLgwge+dOgM2hbf2bmLU38I7Nw3xhT99+Be20PyLb3/t83c8sPffnHX+2P+GQvOXYnQuCiJzySR+2a9C2gAAIABJREFUm5X9zc0EfC6mx5d5y83bWc1luZZI4NUs7As1IYsCPR4XiiBiYDKfyaJJEvf1dPPC4iLDXfWiyaVpHLs6jdVhwTRMjvzqxxAFN8XaGvFyhlZ7PyY1LkQvEbI1XcdUCDw0eYlysUpDSOPm5mY2eXz8+elT9Pu8vLWnl1S5RK87iCoptDsCxMsZrJJKpJii0erg9MYGumEy0BHiak2nXNGRpDqb0qLWQeiFTJGKXiOcTxO0uIkXi1hkGdM0abDaMTGRRRGXaufL50/T7HGRr1Z5dHycVpeLD44M85/DMc5urFIuVOq6Sk2iVqnx0MsnUa0q7R0NTIQ3CHqcaJKEx2KhVKuSq1TwW20Mb2rFPWLhkWfPkEvl0awqpUIZzapSqRmUKzo/+NlxfG+10D/UjIBArVrj2bNXWF1NkyvWOGGZocFq4cMjjehmkocmX6XP4yVXrZIoXWJvk4gkeJEEB6ZZRRBURMF247zLgo+JxFm+cPo0pmlSrFURr+fUZsplvnr2HJ23+3ErNr47fg7dMJhLpZBVmUF/AI9FwzBDyKLI1sYmIsUCk6kFTq6H64YQq5tCrcyBlhA3hZr43tgYp08uMOteo3+0jYvjCzi9di5Fokw3rzPo7eRKfI339PcTLRR48JHHOfFb/4Fut4e/jlxgNhHnro5OCrU0qigznohTyBaxOa14/E78+RLxtSSaRa2L220aqiTR4/XxyZv2kK/WyFerN9hpmUoFt6ZxqL2dm3+1mRaHh6VsEo9qJ18t8cWzp3jilfPcfesWfnrfezi8Osm7n/0TNnI5YmtJopEcf3x5BbddoVCq0drhx99QN4ZlyiVylTKKKOPz27FqJSIbOdwOhVyhHm9aLOt84G9/wjc+cB9Ndhs/npwmVynT7HSxkc+Rq1S4q6uLTCiERZYwqiZPz87T7HEhigKTUwmQBNBN8iUdSaujnqpVA+z15A/NquAPeQh5XPT6/Hj3WylUqow0NBDJ57HJCorYwnsH+vnWxYvIgoiv0cP8xBqaKhJZT/LRZ55D1RRu6+7ins4unKrKV992Nz9bWOQnL5/h1eNXqekmmlrFqarc0tLMjuDgDV5kx64Ap9vbqBkGbU4nL41NkM8UedOeUXo8bvo9Hk6sr/Pi2CQr7Vne1beJD2/ezLCvnVQ5y/cn/plosch/3HonXzl4EMOEE+thRMHGeHKCJpub1XwMv6VCsVam0dpHt/MWDHIkyhPc3dVJqVZEFpJESws0WbfcuAdipYtIgsj7+m/jgy/8kPVIkmgyw7m5Je5sf5k+980YZp4TG6/T7vDQ7z4IQMVYQBIcCIJMSR+nopc4trxEYfsiLnXbDaOdIjRxc+gOJMGBT3OjiG3c29lOpDj1c3uwJvWwmD3Kci5Fq8N5/Xe9GGaBQnUai9yNW91GzUjemEDIgo+p1EW+c8dvEy5cZCxR1+luCWzma/e+gV//wU+RVZnzkRW2BEL0e70ookTNXEPXa3VOraDhULZwX6fOP85c5uWVeW4KNdHq2IQkisRLeU6Ew4wE/Hx/7BrFapV4ochEIk6oM4he09nc0Mjbe3tIl8u8Hg5zcmOOaqVKWTdIFot8YGiQ2XSKZxYW0SSRZ+cXuKO9lQeHhvBpTgKWXRwLP8XHXn2OXKXCgdZWYtEsJuB3a9jdVjxBF8VsPTRiPZ+jVKkytLmTTKmEaZgYtQiKLBJPl+np8nHf8AA/fPkk8ytZVFm80fUEECWBhqCHzQ2NXI5FGE/Uc8VrhsGHN4+Sq1Yo6TVixQIzqTS5VIF9W/rQq+MMDLShiCJL2Qx+q5VvnDvH3T09vLo6RbFWQxIEbuvoYDwRJ6zl0KT6y2y7y0miVCLsd5FLF1EAqaJTyBTp39SDMWqimybruRw7m0IM9bZyLDZBeDHGvbdswzRNEOrmFaeq8Ds7d5ApV3hkfBy7ovJAfx8PT0zyvoEBaobBfzl9muNnJ0hkKridCvu3DzAU8LOQzrCYzlCqVfEGXaSiGdSQTMjh4MyFafLpOi3lN+7cx10dHehG/XupDpH5dJqlbA4AT9DJ6swGf+I+wZdvvgPDNCgXK7S6XMiiSLiQxKX+23Lh/z9fgvD/m9H5L0WhWSiVWViJQGsDw/4A/Xt9/GG1imGanI1EkK+Luvs8AbyaAxGBslHjcnyFYq1Gm8POPR1DFGo1Hpuawm+1UtYl2tsaEAWBoM3G1eQc/e4QsigTsgUo1sI4lCE2+9swzDKCIDGXWeOHx8+hajKSIFCoVfGoDnY2NfKBgR3IoodVcQ67YqFm1PVMjVYfr6xMcktzD1eTL3IpEmUxnaKm68iKhM2qoCp1PYwiS5QrOs3NXkaCQVodXmKlNBZZZi2XYy2fJ14qUjMMtgebkUUL5VoNh6qxkcthV1Ua7HYux+Ls7Omgahp4gy4i4RQWTUZWZURJoFKsEA4nCDTWO2+pUol4sYgiingsFjLlMuu5HBXdQNdNwqspOjc1YHNa0WsGHq+NRDxPJl/je0dPE7BYWMvn+fj+PXzt2CmyhTiGYaLKAo9eucpoIMDepu28t28HAgq6WSJVyV/PXK/ryQRBQULBMCscX3+e2XSaQq3GyeuuSIeq0uH2kCyVEBGomQaaLPGT6WuUdR2bLDObyTIZiVEpVSjUarxwZYZSvt4dm1WTTMRjnHRvcHZhiU1NDfgtFoZ8QTyqRq+7kZube/lSIMix5aU6OcBjZ3Emgstp48fT07y1u8aZjQ3etamf/7B1K7IospKP8s0LFyjlS5gmXIpFeHJulnf0buKxqWlUTaGQK/HmvdvrMY67RL51+CR6Qacv1Mje5hB7m0KkyiUuRGNIgki324kkCLhVjUKtyqC3iVy1SEmv8MzCAhZphZOrKzz71BXe/rbt+K1Wfu/Is3S4XUwthDFNk6vXYpCugEshna3S4NMo5ktYrBqHentwayrbg0H+fvIc/b2tjE8uEUmUafHasVlkhjZ3MnlticVwjq+/forX3/8ltgbO8zuvPs/JuQUO9vUyFY/zxo4uHp+d5vunLyCrMg0uJ8ValXOnFtBsdTMZgM1SH5PrpklZgHyySrGs09oapFCtokgSi+kUHs1CvFBgMZ3m5PlJMpUy+0MX2BkcYU9og3ipzOWZJRpbvBTzJfYN9rCWyxLL5Hj45VN8I3EEgNHt7dzc1cFH7r2ZRpuVvzl+loDfxcH2Nk6sr7O7Yf+N/cWn7eCu9h3XXd2r/M3bTH48Nc3berp5fHYO6/Ux34N7dxCy2bi9tY+j4RkUsZ3vXvsHFjMZOlwuHp0+wTt7NrNRTPGh4f1cTZ5HESUcigUTE6ukEi2mMJnEqzqwK1vwqX0sZy9yZG2GXQ0l8rUyWIUbiTherYN/mHyB29tKjE0tE4tmsagSvcNt/GhinD5PmPf03cr2QDsWqYPzsRfYEbwbAQkBK6LgoFCLYJObKWSKmIBu5kiUr+HXtqKb6RuaTlXsvX4vqgQsLawVTtBs24eAhmlWqRg1FrMZ3tlT76jXjCjJyjwruSQ9LgOb3IEkeG6Y/QC2+AeJFC8TtDTT76lR0qvMZybZ7G/hm+97C9lqhaJeI1stMuzr4PmlMVLlErsbe4gUk6TKBUb9/VhkK5vcHqbTKWyyTFmv4VatvLA0xVu6e4iXivzR3r382alTXFtaI9wc4g1d3RxbWUaVJEzTpM8T4LnFxXpHPl/mwvo6u5ubCedznFmP8MrVKdpDAX5vx3YGvE11Dm0mikO5xseffp7bhvr45I76KPPc+jpTC2FkRcLhsaNIEhVFYm4tiiiL+NwOpiaWsbtsNDR6ECURl0cl0OjC6bVTM8y6ySVRxGGT62xKi0JHTxMDgSDjsSjtTgeqKNFos3F7m59vXrhAtlomct2bkCqXuRwO09EcZH9LiDd1ddDudPPMwjzrhQJ7mzq4p6eHQq3GJx55mkq1nvr1tt4eriVSrGbXuLi0SjqWpbs3xEYkxaa2RpYMg3KlXuiPdLeiGyZ+q42qbjAUqMs1nJpGTTfZvauPt/R0UzFqyKKELNRDDuLFEhciUSLrSYJNXtocPkIOOxZJ5QeTV8hVKgRbfYz6PNhUhUK1wmQiwWQkikVVSGcKZBI5Wjc10enx0GCzkc1XqNWS3H/HHlZzORKlEjZZxjDh9bVV/nHsGvFwkl2be2n2e9lYivP0S5d5c0838VIZl8+BJkl88shh2lxu3rHpf56W9Iuy/h2A7f8u65didP6Fv/jS50feuY1Wl4sB33X38XVo7Hq+QLSQJ1OuMOBzY5VVFEnGqWzCqZZ4PbzK5WicN3YMcWR1juVMBgMwTJNyrYZT0wjn6mOVt/TsAaoIaGhSM5LowDDTmOhkqnmemZ/h0to6hm6QyxXpb24kXs6xlisgiFWqRha7rFExajgUC5IgcSm+xMVYjAOh7Xzq6DMUalU2oila/R4WFzYAkBSZWq2ez+3y2GhvCfJAfz8GOuPJGBVd5++vXcNtsWCVJVocDjqdQRayYS5Eo6iSRKZcxqlp1yHdSZyaxlo+z2w4iqpKGLqBw23Daq9jj6wOCw6rhkNV0WSZTLFE0GFnI59ncSVCMpohnS2QSBUpVgzEWhXbdaSRKInUyhX6NrficNk4vrDMXCZNxYR4uYjbruDQRHxBJ6IocHR9jVQtiVsTuBxbocfdgU22IAlenpz/GQPeHgRBYSH7Gn925im+8eJRjozNMJlJ4XXYqBkmAZuNdLmMIAisZjN17iQwFo3S5/Px6MUr9AYD3NbZjt/j5OjcApl4Dm+Dm8hKglQmjyFCXq8yEqpndx9obsapaiiiyOX4Or/36osM+v08ODLCG7u6cDtsnJtdJBnL0tTkJV2psl4okCgVqRo6jXYbfZ4g33rtOB0tDXxk93ZeXFzmxQvjZGXo9/mx2iw8uHsrD2zawiaPl4/84+Pk0gV2jPaQr1SYT6f57S3v4Up8nB9duAySyPaGBlRJYldjOw1WB27VxZHVGb529hxlXefM8grT11bRLDI/fM+b+ceJCaZXNjh+aRpFlZmfT1JJ1A04mGCWdQRVoqM7yP07RkmUSmwJ+jnU9jYuxy9jURTmkyn0coVwrEhXT5Df2LGVZaPE5Ng6HT1+fn30blbzk1yMxmj2uFnJpJmaW+O55QVubm9jc3Mjzxy7gr/BxcGODs7NLCFLIi67gt0q47DVDXfpXBWbReKeu0cJhlzkSxVCLiduzcJMNI4siyTyRdYiCSw2lStjS+waaKHD2cgfvPocy9k0O7vaKQoG4ZU4WWqMX11hZjaO0ybj8VjRazrr4TQnriywmIxTUARGW0J87dY3sKuhh1gpSa+7PvL9vy9BkJBEDyFbL72eMqIg8IOxKyTKFd4/OMgb2/fS52lnNrNAwGLnx9PHuauzE7sik6lUOR0O885Nd2GRiizn1kmWSzgUlQZrH6JQRBJV/JYWIsV1/JZmJNFD1Viiy2VlW3AzVlllvZDg9fBFBrybWMqdYCW/wq0tw/zT7GWOTy5QLtcINnmoVmqgiGwUi5T0FCO+PgRB4m/GjnFLcy+x8hKTqRmCVhmr3I0setjfpdBsG0YSXRhmlJoRYzY7S4O15/qlUqZm1p24plDjRHiCTZ6hG4WjRcqyJbADVWoF4Fz0NeyySlGvELS60M0Mr4VP0u0auHFMJdGDRdKQxSascgGfpQWrZGJTgvS6BjBI8Ho4zFwmQ7/XQ7crQJerHVkIYpEqhGxDiIKFZxZfZXtDK18+c4qAzYZuGmwLjtLjUel07sClVphKRXlpcZF7BvqYSaVpczoIORzc3dlBwGrHJlv43thFnp+bI58uUhNMLi+scjWb4iNbtvCpmw7yjt5BqmaVZxdm+MPDrzCWjLM56MblsuO3WnhsZop9oWbe1jNIVtZZzmfJZwpE1xKUixUaQz4uXVgin87VzZyiSKlUQVYkRFGgpSVAs8vFq1cmyWeKuD02Qp1BHn3vO/nswbfhtemYQK5SZWdTA7lqlaDVSqFaQxJF1vJ5CtUqx9bCvLy4yPZQM90eN8vZHBXDYDadpslmo9XhIF8rsbuxmel0ktVaid7OEH0+H/FSmfcPDpGslHjy8GUEDPK5Im/auZnjYzM4vXZymSJOp4WBthBWWcZntRArldEkCZsis7Oxgc6ORhKlIl6LhQFvgAuxNV5fW2Y8meL5mVkiuTztjT7ShRJv6O7g+1euMJ/L8PjJizjcNg52d3FPTxf7m1vwWDSevjxOrVKjphukY1ly+Qpun51b2tuwKQrjkQiiJBLXK5QMnbs6OxmLx/je2BjPnb3KRw7sxuVzMZ9KUjMMCoUSigDH5hZ57coMDrcVUZW4dHGOe7cNsiUY5NtffOIXd3T+V1///KEH9l6nPP/rf/7pmy/8n9H5/8pqcTr50wMHOLexwZHVNaqGQbpcf5BqkkT1Oqz1M0eP4tEs/NO9n0IUbMiCRJvDzkI6Q6mW577uLl5dXCBbLtPscHLp6gJ2p5VAg5u1bIa3P/W3fPXWg/gsToKWXm76+4+zvb2VT+/cw9MLM/S43fzRoVuIFIo8Mj7OrsYmHp6cxDBNXKqGV3NgkVQmU2FKlgqxUp4up5+QzcmpjbOcujBFS28TuUyBlLeE1WnB2+Amm8xTyFdo7gzgddvZ09zMkC/EVGoDSRA5H41imPDR0S2s5bIErDYk0cKR1RWihTx+q40uj5fZZIKyrhNJZlAkCeO60/zglgHaXU6mE0mmEnFSsSy+Jg9+qw1JFEmXSmwsx+nwelmbj2DU6py3RDyHLNUjw6LJMpAgEPISWUvxzjvrEY7rkTqoW6/pnMzMUy5V2NHfxXQ0hiBAdDVBKpbl75biRG8qMuD18IcnnuLP9t3Dt688jWGa/NGp77Il4Kes66zlssiKhKLKGDWd5WSKwYYGLiyv8rmDB3hyZo5cpULNMMgUSnT5fVy8jql4c3cXZyP1mM1yocxtWwd4+rXztPY2USlXmbi6hkWV2HNvCyICfzc2xtnJBb5w7yFG/U2k4lm+deU1/lIAb8DBW3eNMvYHn2I5H+U3n3uOW1tbWMpkeGZ2lunxZX74a/cTsDTyidv389VXXueEx41L0yjmSgz769oxl6rQ4XShiH4em32R4b42WlwuXrs2Qy6Vp1Ku8c22R3hxYYHV+QjFXIk7O9q4KdSKU7EiCCIL2TW+fvIMVy+tANAYtKMqIoOj7fxofIJIPE1D0MPyQpzljTiVkl6PvdSvxz1dN1eOXVohk8gxdjXKXzkUsl97FxZJxmex8JZtI/ykdAFRzDM/E+XljmXePTDA6nKM797zJgAempjggYE+XKrGQxMTuIYsPP3KRZpvOYAiiYxs6eQ3R0e5Ek9Q1U103fxv7L13lGRnfef9ualy7urq6py7Z6anJ2eNZpQDCkgICWTJ2AStwQhsbNaG18ZgL4aFF2wwIEQwQhJISAKRJFBCAWk0mpx6Ok3nWF1dOdeN+0e1BrO77571nt19bR8/5/Q/N1Tfe7v6ud/nFz5fnPYazsXhUBBEkav3D7K9IYJDlnhitMYrjbg9hJ1OSprGhVgcr9dFIVNkc08bVw70s1QsslSa54tXXcE9j/2UF2YTmHqN+7qlrZnYXAJdN5lZKuLzqGQyKhQ1sGC4qJHNlLli3wAh+3Y+e+IblHWDmzp+4wleNSbQzCK6aeCQnchClFbPNkTsPH3LNmLlEZ64MEKXL4Jm6szn8xxs6qXVm6DdE6Hfv51UdYLh9DLnU4fZENxBf2Az/WslyIaVZ66wSos7jFdppz9wJculN/jYa4/w/s2b2BRqoajF+cKpw3gUhfdv3IEo2HEpdr5y5iTv6DO478XD+IJuLt3Wz1cPvoOZwiSfOvw6J8ZnmclkOBKLc2VrC5/YeQOyGCLi6Odc8mXmi/N84tCjXN3RzvXtfUhiANOq4pTasSjR72+++BxEwYUMCDh4Yf5lrmzZTtUYRxajSIIPRQyQVicI2FoZSp1la3gHgmCnzhHHvkaOuKK5H8PM/Fb9tYVaE/FCAFmIMFMep9MbZSRzjGdmZ7i7f4D7h87wzOwFUpUKY6k0/8+uvZxJLNMXiDOdy/LszByXNnXXcEZuN7ujbWhmEsuy+NKZxyiqNQj63GycH+eKrC4kufnuWzCxiJdK/HBikm6/D1EQSK+ssTybGviLXXvp8B5AN9Nc9uinsUyLra3N5FWV6bEY5aYKX7afRBAEPrvvCkxM/EqEheIsf779RhrdL/K5n/yKVK6KIgmMTY7S0+Yjk6sSCtno7WoiUSigVjV0VSdXqhBw1BpHs9kKiUyVXtPkwy/+is7ASXqCAXoDfh48dJyrO9qIuFxrdJEF3piapTUcJOrxMp/N8vsbNxJyOHhyYoKx+Cor80mCER9b21qoGgabwmFueegRjDVcXXwhSbVPxyZK6JbFFa3N9A82MzW6TKS1DkUUmZxKsX13B/t2reP0+Wl+d/06Xl9exrAsru/o4CunTnN2ch6Xz8nVPd10BYN85NtP8emAna3buom6ax3l+XSRc+dX6O0MUCmpLORzzC0nmFtOUMiVcSkK6WqVVxaWuLGzg75AkI9dsZ+8qvHrhUWOposEgk5SKxlSXVUG6oLUN4eollXCHjcLK0k++MxznD4+R7Gos/+ybn4xOcVSMsNNG9cxlcnyq6lRGsNOUqkSVc3EF9LJVCoYpkW718sfPvvc/w558n90/GtJnf+biGh+61tf/dQf/sHNfPHEUUwsREHALkk0uJyopsV4Ksnw0CzJlSy6YDKnzTObH2VTXRPxSo5npqa4e/1ufjJ1nvOJBEGHk+VCnsxqDrWiks+V0Q2TlnCQ7548zfU9Xdzy5NdYnl4lL+j8aHSU00vLHFpc4hN7r6BqVmj2+tgbbefVpXkOjU1i2WUkwcSlSDUhrFYoaBpfPHGMd2+4m08f+yk5XaXO50EzTTTdwOawsaGhAcVhIxZL0dUZJVeqsKOpkXafl9OJFZaKRcbTaRRJYkuknl5/AxFnHQI2XlueIK9pLOZzIEBeVVleSrK9q40tDREOTc0SDvvZ19zED06fY2I2RiaeQ9d0PAE3O5ubQYCwy8VKpUTJ0nnX7m2MJJJUyyrlio7dJnHNgUHmFldRNZNMukQk6qOlPkRF11hYSOBw2bA5bGQTeWx2G8lSiWjQz2oiy2o8T7GoEQg6GZ5ZIiubXNrSzH/89TPsa26ioOskyxVWSmUcksRrU7O4fTXkkyAKOBw2UpUyg9Eo17Z3cH1HH7+cmWRmZoVwnY/tjVFOxpap93oZy6Qp6QYOWaYnEubo4gIDXS2spnOsLqTI5GsOOOemFujqaGCgrg7LrvDQa8eZ0or87paN3PfW32dzT5CfHB1idHGF+w8fZntXC3+z705G09N87vGXOPfGHFnVZFTP8Mz8GLIk8enLL+NsIsHOhnp+b882rmzpZzyzimaZnIyv8szsED0BP+/buJXPPPcipXytdksQBF49P8XSUhpJFBAlgffv2UmXr4mKUWW5lORPX3mZ82fmyBY0WhrcOD12GtrCtPoDDNbXEfH7eemN82QLKqV0tVYRXtZr9ZEVA2wSwaCd3vWNeAMu9EqFs5/4MF5bK2eTZ8GCoN3OtFqkWqnS1FbHpy69hOvabuY9O9fxkVee4Wunf8FkIslsscj2SASvTcFjs/Gj589xy/4Btte38tDZ0xxZXqbF7+flU1PohoVl1ew4BUCxy9i9Dm7s6sJrs7FULBFw16JTHT4fL41P0tMYwWuzMzO3wrGzc7xzzyA/m5zkQEszYYeHH0+MMzubZilWIlvSOT26iKbXbDVNCyp5DdEmYgmAINDS4iFU56EsmLgcCVo8HiRB4IM//wFNdSUC9gLfHj5C0CGhiBIFrYQiVnBIbQiChCg48dna2NOwGYfcxEj6LC/MLzBQF2JzeCu6lefDrzzOwZY2BoL7qHdEEQUHU/lDHF05yafeeJJbu3cTcfThkJsQBAXNXOHoygiaZXFtWx+KKLNayXBqNc7Hd9yES26nao6jiBJvaX8LH3jx+8RjaVrbItwzOEhPYA91jk4ub3Xz0/kLZBJ5ZldTVGWR6zq2IQkeRMGJLK7itbl4ZXGe7oCfX83Pcj41xK6GHYiCE0nwXayJtiyTeOUoXqUTQRDo8EZRpEZksQ5RqGVB8toUPlsrihgl7PCiiBFMq4giRhAECdWYXTvegW4mLn62JPioGpMU9QS6lcAp2bBJfhqcPbweO4tm6tzVvwkTnednZ7mQSvLIyTP0NdRzsHkQp2zytu6NqKbG9miYVq+f0fQqZxJLuBWZvmAQhyzy8JlzWKZFtVxFreg0NdXxDy8f5vXlJSwRzsTjJFI5Hr/zdj5+4L3c0LGFo/Fh3vHoVzlfnKFoaBQLFYoYLCUyPHTX27i6r4sNoRCiIDBY14BHcVM1CszmkwTsEo1uJ2W3TNVpUcwW2bKzg+XFDG6njKrqFMtV3D4n1ZJKpD5AfDlNU32QjkgdhlMkFHBQzldIrGSYX0lybjHGJy67kvOlDBYCP75wgaDDydt7e0GRODI1xwNvuYWQS2G5WEQzTVKVKqfOTbEcLyIZOsOTy4wmEzxx+AyVkordYSOTKtLcGaGiarjtdlp9Xlo9Xq7p6sTf4GdoepGXj42xaUc7umrQEvTT09zAdD5PplprtH1sdJRbe7tpCge5vKONqNvFX3z3WYrLJUINLnb1d2JZFg5FYbVS4q37NlJSLHKpAh2tERaqJeILSbwBF9G6APubm7i9tx9FEjEtKOsaJU3n9cUFErE0ddEgsiyBTcImy6QrVURZxO9wMDI6z4HBPr531638yXWX8NbeXpp9biSbDVkQiZeK5LJZou1hcukSbqeMIAgUCxWWYgWWbCqvvzaJcXThX3RE85o79yEIwj/r5/F/ePbfu87/V8Yn/+6zn9rxtk1c095B1O2ioGl86+eHePnMJK+cmmKhJLySAAAgAElEQVR5MUO5qoMFmXSZSGMAn91Os8fJfD7H0ViMH4ydYiKdxq3YSBaLzI0vodgVSiUVyzRp6ailK9tCQZ6bnUG3TGSHwmBrE/vaWljfUI/P6eTIygJ39e3GokK6WuT1pWV2d7RxNh5nIFzH3504TovPw4ZQhIVCDkkU2RvdyjfOPkO5oiIpEoIsYugmpmHSFamjrGlULQNLFvA67PzHHXspaBXOJRIUdZ3+UIgOv5dTq6u4FIFEOYPfprA90sy5ZJzh2AqaZdLg8VAydd67ZZCoy8UL41P0NtQznEgyOb6IrEisX9eGKQvcsamGq5jOZCmoKtOzMT54yS4My+Lw+DRatdZd3tYZ5u0b13H5YC8nZ+Zr23oaWMpk+fCO7ezqacOyyUwvraKpOpIsYrMrFCtVMvEspmnhdinYnAo2u0LI72FLQ4TjsRh1a6v1p46fI2WqvHvjIM1BH1XTYmtTI1VMwi43K+kcbaEgT09P0eJ18urCApJdIeJxc2R6juv6ejlyYYahkTlMj8yWhghTmQzNPj9uxcZAYwM3b11PxmkiaioAi4k0B/q7ePfARpYtlXqXi8ViiflCjG2RKL6wl5ilsjSf4o2lRQRXmUubW6lvDtC1PsqNu9bhdTjY3RjFKct86+xZ/nznLubzeT53+A3eN/gOegONOJUSN3fu5qrWnfQF6njvc0+SK5TxBFzcvHMTFYdAOpGjLuLlwLZ1fOWG6+j0RZgrrLD3/72Pzzz8MslcAZejZguqKLV6M13VWdccxW+zMZFJkyyWmFvIg2HWIplirQkH06J/MEz/xlbGzy+iFitUVYMnx0b4wO6b6PULZLQCDS43IaeTaH2IR274SyxWefdzD/L1E69jYIEgkEsVSBdLPPDiMQ4OdHM6vsoD77oZ1TDQTI0Gr4fTsRU6QyGODs/W+KCyeHFVrqk6bc1hyqbJfKHAcydHyJTLBL0ekmt1wjOzKyytptm0oQNX0MF4NkPU4+VcMsmeaBNZS+fs5AKFvIbXW0vL5woaDruEYVhYJlimheyQa13tLgXZJvEnB/dxIZNlZ0OU04lVtrQ08aUXD/GtQ0dIChq3922gwdVO0LYOu9QEwEz+13gVP6LguJg+bnL3cnnLTny2VgraBWySnYBdoNkd5Oczh9CsBI2uLkL2LnoDG7i1ez9LpRO4ZAfPzz9HuzeEIjbQ5g3RF7SjrTFyG5xBNofDOGUHkhAgWZklXS0gCGn+/oVXiLTWcfu6dawL1V1MdSuik3dtuIRISCeNyQ2dHShSEbuUQxEjBO0dmNYq7X4XQYeDt/dsYW/0movz6puNO7oVx7Qy+G0DmFYJQVAu1oi+OTRzkScmjrKxroHRzAkckoYolDidPEOjuxmL6hrWS1nDjUm/hTCTxRA2yUfVSOK19aJby6yUZ7iiZTM/nzmPbqpE3R6uaetif2szfQ119AaCFPU8rZ4wQ6kFHr8wgkup1TMalsloOsPXTp3i0aNnePCFE9SFPbh9TpLLaeoaA7x6apzG9nr2d7RzQ1cnf7bjIP9h260cjp3jK2de4D+//jSnEqtc1tdNfK0kZlNzIx2BAD63k6cmJ7mpu5vlYpFGt5tDsXnsksl8Ic3p1TdrKGU8ishwOk1vW5S5hVVcbhu6quNw2+nqasSwLDLJPDoWlmmxmskztbCCJIoUsiXKZQ3NsKhUddxuO5Gwm1cXFugJhpjNZjm1FONrvzjEd257O/s6mgjY3LhsIm/EYmSqVdYFAzx7ehxJFJAlgXLFYCVepLHZTz5fIZUpE/A78Nd5afb72NPcjGVBqlJBEOCRU2cpFyoUCyrBOg97O9rZFA5zIZ3m1p5uxjMZegMB+oJBtLXFcdBhJ1tVmSlnKagau3b38ld79nJ5awd7ok28Y/0AV7d1I0kWD/3oBAObWwk5XYxMLWGZFssraWJoNHncnFyNk6yUKeo6Dlmmzeej4pQwRQGH04aIwI6GBpLVCgVVZSmZwW5X+PxVl+NSHIgIiKJIVq3wjVePsr29mWS5wg1bN9AeCvLG8CztvQ1cPtjHJT3tzGSSHH19DkESMI/9yxWaX/n633/q2jv3/rNT54/9/yA0/02kzj02hW31zfz5ay9xSXMzQ6urF51rMrkap8xuk2ovG9NibCFGWdcYS6UwLaiqGrlUAbffhVbRyGeKBMI+FIeCZdZcThRJwqXYcCs1Zly7P8Ch6izn5pcoqCpuReGy1hZGUxnOp6Ywqbnk3NTViVtRSFcrpKoVPrB5M3ui3ZxJzjGVy3F3/yAZ9RRlXedAXzdHFxcQRZGr+nsv+ukapsWyP49Nlrm9v5+yUWU6l6FqmCwXivQG/Dw1OY3Pbqfe6aLNU48o2LEslaF4nPhcAqfHwYqYQpIkXl+OMRAK4Q24uby1lV9MT+P0OKhvDiGJAn1NDby+uMgV7W3sbW7i+ekZbt67hZ3RKF8+dQq1rGFZ4PfaGGhvJlWp8uiR0xTLGjZF5MzxGRRJYHwgQ0/Aj8dmw7JqoOs3fbtX5hLkChr1ETeyXEuFC4LA/pYW6p1OPrJjO6lKlQeHhrA5FNr8AfJqlQaXi6DTSX/QT7paAaClLoBumiRLJTaEolR0nUqxwpxucM+ubeRVjR/eeQe3PPQIs+NLHHd7GJlb4rrB9ZR1nZKmcSqe4IbubgZ37+axsTFePTnGV196nUcahilkily2vtbF6lFkRtMJ7ujdwNHlZcTN7fSF6njPhj0Mp2eZyea4pqON/mCIoN1DwObmZ9NDyKLA7d9/nMbGEDtbWtCtOJqZosvXRNlIM5w+j2GajI7Ps3Owh3u3bKHB5eWu/n7uqzvLbT3d7Ix0UtKrjKQXufaLD5JcLIIikkpUyNprgHe/Vyfks2F32oi4nGypr2dnQwNFTWP8QoKSZkK1hhRCAAJ2TMMktpikqSXIzEySStVgeWGZz574Br+3bpCNoSiqqRG0O7jjgR/w4vAfkYxlqIsGiEaCFFQVn93Oqm7Q3dbAxvZmvvb6Mf76qoPcf/YsRU2jJxjALtX+zqphUCjrOEwJUQCbIl58CY5NLvGjp85gAegm3R0+4iEvTV4fcxdWaO4MszidIF2pkFhKs2dzL4dOjhKI+HHv2k/IYaelPUQ6ryLLIrmihigKKLJIqajXhLajhi9z2iVkWcTjd/GO3psYyxyhqGk0uFz47TbevX8nlzQ28pkjR9nzua8jigLvun4v17e30ej20uZpRRIDF1PBbxoHvDn8tq2U9HOsD0Wpc+zgnb07KOnnyGvDeJReJDyYlFBEGUVs4JLGLkyrSEk/i0veRJ1DYzZ/gVOrMZzyKkG746JvddS1h5x6ive/+DRun5O/2b+fA003/9a8KItB0tUT9Afr+LOddQwEr+ZU4lmenRvhxg4PithErJRGEgTCDjepapaQfWzN8tLNSnkGj+zEZ1uHQY6CdobPnniVT+16O4oYRTfTyGKQrHoKu+RiMpvDQmVj6Gp0M41p5ZAEAdMqoBpxNNNAEBZwyU0YVgaH1P9b1ysJHlxyFAEbryyNoQgioaiX/7j1duKVMVbLOVLVIp8+/AZTczH+9sar0E0TiDEQauFCJs26YJiVUoFvnxvi9MgMumZQKas0NHiQZAmXomBZIEoiW7d088A1t+BRNrNceoPRzCJfPvkUF2JxdFVnV08H6pp7jVOWUasaObVKUdOwSbUF0sdffZUr29vx22ykKypPTkxQNQxeGh7nyHKMz1xykFipiM9uZyaTvjjPSZKErunML6xiGiaGXivvetNAgyTMTSfQdBOHfS3iL4uIksBnn3qJ9f1t9AZ8NLrX8flfvoxlWTw2cZZbutZjYRErFkhXqgQddv7+hdcI1XlIrObJF3W8bpn6SC2FLYkCHqeMKIkokkTQ4WQslcKyLDbU1aEaBr+3fTPfPX6a+Eqe+6+/jg/96gWOzM1zXV8Px1ZWaPZ4uK17gBcWLrC6hkJySBLrQyGu2tjPvZfupmwYPDMzTcUwmMnmsMkS17e3M1+ozWHHFhbY39GOzS5jc9SoJwGHgyMrceLFInf09+Kz2UmUS/hsNk7Za/itdKWCicVzs7PMxJO0hoP85XVX0+L2IwoikiBiWLVnuzEUJRnPYpgmFV3jjcVFLsws07exifds24yIQEnXcTjtNDa6aO0Ic/R/n0z5PzL+tTQD/ZsQmlXd4GfT4+xqbOTw4hIb6+tZ3tzK/PgyzQ0uShUDh63WuW1XREqFSs3ruVxhYSWJIIk0N4fJlSqMj63Q1RXmysF+ttSH+eKLh+hrj6KIEslyCbskUVBVhhaWCfrc7Ozu5PTKCpZlMZ8v4rfbKOk6k5kMDllia30Dze4wHpuNRLnEroZOnNIGZGGBvKoSde3hG+e/y3VdXWyLRDgXX+HsqRlOHZ3moT98Ox5FZlO4n/muTmySxFKxQKpSXrMpFOkM1DyE53NZ/nDbVoJ2D5LguuhQNDUXwx/2UipUyGbKOGwSIYeDy1u6sEsiHT4/S8Ui9S43QbudsmEgCwKD4RBhh5NYqcT1XZ10+HxIgshsJoMn4CK1mqc+4qfO6SRdrSJKAv5ALcrR2O5hZGiJT37vOTYMNmFoBqVCTRS6fE60Sq1eStVN0qkSPq8dTdW5cscAG0IhYsXiRQzF5e0dnHWu0O73czwep6Tr/PzIGbqvuZTtkXqGkmn6QnXkVJVSReWbQ6cwDZP6sJ+uYAjDtBhJpriuXeNtOzfz3IUJTo/N0ttVi0p1B/zM5Wsvkv1NzbR4gtzYpXJ6ap71bU1MxhMMdrTw0W17eGpmlAfODfGhbdsQBIEN4Xoef/0k9191LS/Mn2egrp5rO9p4fbnWxLUj4iRezhB0OLhn0ya+K0lMxlaJtLXx7eHnyVSr3DOwjXg5i2YYxEo196SCqmJaJqIg4JLt/PXug9glhWQ1x2eOHublkyNYwLrNEUYPL4EoYHhkEAUyyxoV1UQ3LPw2G5Ioopkmm+ojPFoxQFuLaDokurp8zK+UKJZ14qkMdqVmLalpJt6Qg0cPneTLT75MU0uQcrHK9XsGCTeFKOXLuH0uDN2sXatpUtV1OjuizMWStEXruHPbJj7y+NP4Qh7mpxPMb25jU0MDxWyJSWcKyjqVVBUkgZLfRrTOQc+GJnrqw6gVlbHxNGgmkwsF3L4Ev54a58CBPjw2G26Pk3y+RCxeQJFEEqkyxaLKd0dOc3lLC68vLhJbSFOq1v4H7DaRaMSN36NgmhbpnIpuWmSLGh6XzHduuYk/euWbHJuc5SMH9tDodvHTySk2hcOYFuxsbOSsawpBgO8/+wZfyb5CJORg05Yu3rmuny5/gO31lwIW8BuhCTCWWWJ9sAPDzCAKbkp6haVimk11NcalJHios9d8yiuGiksOYxe6AMirMUJ2LwebXfhtATLVNFDjVs4VjvMnL7/I8RMTNHeG1wTXfzsu++Y32dDTwgc2b4YghOwuNK+BKDgQBScd3g0UtAXskg2XPAjUus5VY5pnZifZ19iEzyYjIJFVi7y1qyaGi9oZBEFEEGQUUaakF7hr3TpsYgcLxUOohk6Hdy8tnhgWBrLoxqJITivhVXwYVhLNXEQRm3/rehWxEcPK0Reow2/z8IWTh/iz7QcJ2f1rAljgk/v28lTjNFGXm3ipxAPnhxkI1/FHm2/m8yd/jCSIvHF8jIa2MKV8Ga2q0d/Tgm6ZRD0e7rnzBraEG3ErDgRgNPMr8qpKXq1iWCZfvPYqvnDsOA1uNydiyxRUlR2NTWxobGA+m0UzTVyKwskTk4TqPPxc02j2+shXa2zdnlCIawbXcX51lZcXp3ErCkMzC5QKFURRRJJFGhpDpJI5sok8iqP2vbQME10zSMUyaw5c1ho/U8LldVAuVHjfpbt4YngEpyKTqFS4kM6SSpbYsr2T56ZnKWk6f7BxNxGXm13RCE+O18gaplkrU9mzt5fru7rw2RR+cmGCoTW8ncfvIpct8uzwPDaHjVCDn65AgEeGR4h6vOzqbCfk9zCZTXNFRyevzc+RrFS4srWFhUKBU6vzJMoVJrJZrmhpQRIEJjIZVkslNoXr0EyT6XyBWKFAs89XIzVgsVzIgwCFTIlmtxvLtHD5nIQcTuZzWUaml7huy3oeHh7hbb09vDA7T6ZaYWRqEUM30VSdfL72bvF47EiyRMjuuCjAaoENEcEyMbGINAWRRJGFdJaRswvUh93sH2hCpIbFW86WaG0Os6m7lZKm/ssWmv+ON/q/OywshpMpNoXruLSlmclsjpv6+viJbjI+tIimmShSDQotyxJqWaWkaRimiSCJ6GotqpWMpVEkEU3VGagLcWwlzr713bw6NE6kMcT1XV1MZrIs5/Ps6Wqn3eulahjc0tPDZDbHhXSaD27ZjF2SGDYNXp9f5vHRcf7zgQNsrmulrKvIog3dSuBWFLZHIkDNn7Y/VMd4Ok0mVySbV3E5ZL549Bh2WebPd+1iX2MNPP78/FleXVqiousAhJ0OFgtF3DYbDknCb/NQo5XJHF4Z5YbtG6nqBq9NTFMuVFE1k90NDQTtQVq9KWbzOfw2G66AjN9uo2oY1DuddPoCpKsVjsRW8Nvt7G1s4okLF4gtJnF6HMiSwLrWRiq6zmI+hyRLdHc3Ued0Ue9yEQn5GB9b4NTJBUQBgj4b1trK3eZQCEZ8lCo1YLHdWYvAuRWFU6urnI7HqWg66+vDNLvd7GxsJKuquGSZn4+OYxom3zl+its3DdAf8NPl9/PwyAj1Xg+PHTqB0+0gtZIl6HTy6MgI9199DWGHH0EQuLSrk2OORf54+zY6vEFS1SILhQtIosBrS4tEXCl6/AG2d7cT9bj56I4dWFj4bQHe2buFg81t1Nl9aJaOahjcc/keIs4AJ4xljsQWKWg6M5k0iVKR75wb4u4N61kfqsMuybx340b82+08MjrG9w+d4PrtG3FKNnr9G7mQ+TU/GBmjUNaZjyWYyGaoc7pwSnaKeoWsWuS2Rx/D4bYTba+nUlIpl6p4O7yUKgZGTgWnBHaBSkUnX5KQRIGCqvLrxSWeHRpFkQUCTW5U3aRQ0rE5bERCJn/3uzfyj2fPEY+lEUWR+iYJtarh9rsINwXZ3BDlYHMTo+kMbaEA9voa7mY8vkqpUMHUDbJqHrvLjq4ZTM3HWcnVOvoDXhfnzy2TSxdwN7egqToTszFQxFqNqCCwY1sTkfoAiiQxNLOAoZtrEVcR1FpTT1dHkCs7OmqR81yJeLLMndfvJFYokMxUQTN58tww9266hE9fYuMPikU21Ef41ZEhHn7PHbR4gpxPxtjf2MOjF07xyR88i2FafOnum3l2doafvnSS995wCSulMk9NTWOXZLY3NPDNc2d5W08PVdXAtCw8bhuqZmCYFrPzcT7w6ihOu8TW7a/zxI134JI3/dbctKXuqotuVjltiPvOHufO/nW/dcybwHSP7EQzF7CERs6mjtDja0ARZZxrArDekSFePkrEuYuFQpbDR8YpVw36PE7+6pVXWdxR4PjKKp/cfYCQfTsPjn6PvQM9HBufIb2un2PxX+K12dZQZW8KPAuX7EURW34zn66J3rv7d/Da8jgB23EqhkbUFSLibF87dwJBcCAg4ZB6mMq9Ro+/GVFwEnWux7TyiIKNiGMHFjqWoFI1Jmhy7cWyDGShHmENCv9fDwEHLe5+TKvEn2+/FknwIYthIs782u8b46NbO0lUEszkskysJjg6NMGfbLmbmzq7KOsaQzv6+bMdO5nKZnh6epq/P3AdpmVil7y8ujzE18+dxq0onIgtc2N3N4oocm1bD9++aj3Pz58lUSgw73KyPdrI6ZWVmslFPE0ylkGSROqagrgcMrIisbKQJC6kkBWJSqlKsUdjR2MjI+dnecXtJmB3MDuVRFFESpXavG3oJoF6H4Zukk8XMQwTURZpjQRZzeRZXUiiGSZOJBxuO4VsiWh7PYcWF1mYiBF3pPj14RF0w8TvdyAKAkvJNPedneLeTTdyfOUc3z5zlmK2hKRIGBWNurCb9w5uxCUrfGdoiAszy9x5YAfj6RTPvTxEtNGHzWEjnijicNl4bmqKuwYGOLOa4Kq2Fu7ZuJGfT0/R5ffyyCsrBBwOfDY7vX6ZRKVM0G4jYLPjkGV+MjHJ8Zl56gIeHh8bq0WEDRPDMqmmU9za28MPRseZyaTpHwjjcNupdzkRRAG7Q+FCLI5a1tjW28bNnZ38xWuv8cZyrZxgeiVBT3uUJ58+g1kxIK9BwEZ3h0RmNUfF0PEoa0JzTR8IQKZa5IoNfXT7fOxsayGxlMbuVNBNE5dSk0KxYoGp6RhXbl3PpS1NPP7PkyL/Pv4/xr8JofnmShdqoeRbu7sIOpx8+ScvEwq5COgGdpcdy6p5xd51yXbavB4mszlO21ZYzuQoZkts39BFprvCG4cv8PHv/wLTtNi8vRNN1Ym43awLBmucP0Nnf1MjZV3nuZk5Cn4/umVx75YtJCtlnpudY7VUYmx5pWaLKcoICLgVLwvFJSr6AsOpBDsiTejmKslSiTlZ5sWZFPH5JKIoEAo6GD+3QEOjH4dciygIgp0fjk9wa083dU4n8/k8qUqFLfX1vKWjE0WUmc3HmM5l0UyD7w2PUOd00erzcufWQZTtIrFSmfc/9jMcLjsb2pvYWB/GJf/ma5AoV3jwzDmu6+0hViyRLJfY0VDP83Oz/HJkjIbmOqJuD/t6O9keqWc4VWtEevuBXjTT5KcTk5R1nXZ/gM7dQSZSSU6dmCKRqeLzOWppIUAtawRDLjb2t3FuZJZyocKZlRiiIJDKFmgJB5lIpbixsxPDNHlpYZFDi4v81aWX8NGfPMPUWIyGPTvx2208Pn6BY6PTxJay5Es6bc01NuPpkRnu2LeNeocfp+zkDzZu46dTI5xcFnhpfoEPb27GZ3NzaVORs4kE8XKF3dEoW8L9fHynj18vzdLhrccpR8mo8+TVMl7FxR1P/5BMKs/8TJKP3X4lf3v8Vc6vxrmtr4+Hz55jaSrO1XsH2dpQq898cGSEvY1RPLLClnALn9wdRTNNLqRSrFaylPRV7jt1miOHJ2sd2G4HsVKZWLFAo1tgqZjnvtOncbjtVEtVVmYTpPO1kpCKauBxyRQFMAwLl1vGsECRBDyKgl2SCDsdOD0OwgE7Dpu09rKTyWTK/N5b9nJz5zZcssJfPv0CkYYAu5ubKag1a73pTJrVUpEvnziBZVm0B4Kky2VcNgVDN1DLKpVSFVESkWQJxaGAZZFPF6iWVa7s6+FTH7uEe574Kc+cG2FyMkF9cE1gKCIYFmOjcc6cXcZhk1B1k/qAHb/XRqGkY5oWseUcG7e3c3lLC/f/4tU1a1idZ147QzxVweGQqVSrzFxY4Y6nf8CTN36YDfWnSFfKvPSh/8AzcxOEHW4ONG1CERs50Jxg/q+/D8CJ1V/y6uIy6ze38651AywWc4wkU/SHgkxns0hCjR17zw37+cdfvoauGXhcCrmCRmo1z2fedT2iADO5Al84eQhFOsxEOsNf7NpDl+/gRftTQVDw27by8R0RKsbqf3cec8qDPDj6PX63/208cH6Yrxy8DqhFGC1LxaLMUzOjvGf9LuqdLu65aT9dPi/DqTS/OHaOL710mGKuxAc3byZkh28eO0k2mScY8XN163qenx/h88++wSt3fYKidga3shndXF0TYC7K+jmc8iCCIKOZBgGxl/2NEjaxlbw2xkR2mb6ADclSsUs9WJZJUT+HU+7Cb3MyV4jR7rEDAnapa+2+JQQkwI7PtvXiNlmop2qMczzxa3ZFrrv4nABEwYaFm08d/SF/u+fei9sloUYC8Cr9iIKLRlcr/cEibx9YT7qnimrO0eKpI1XNcde6dXgUOzd33sTNnRaaucyh5XOcSiTWoN8ZZmdXeODOW/ng08+gljW+7jhOOV8mlSjQPdDM8PIKx8dmiC1lkCURVTPIFvVaBDDgxu13MTmZYPe+HgqFMqlYhmyuimoYXN/ezuPKCRZzOZ45fZbGJh8belqQRBGfzY5umSRLZWxBP6fyU0iyyNJMArvThsftwIgG8IcNXF4nq4spfueqPdze28sd//goC/EykaCJqplYFhQKVVYTWaYmVunoCvM3R39Eslym2edjolgllSohSSKhBgcRl5uFQp7plSRzcxkGrguyKVzHz547w0BvC2/p6uSBc0P83saNXNvWw51P/5iP7tpJu9dP2OHlh8fOcveerTR3RriqvR2XLPP80jJPHDuDw20nk8hxpreVeC5Pe6TWJBUrFNBtNjoDQRZzOeYTab6w8Dp1YR89oRD7WluZzWa5ormHLzUeZWUuQaDeR0dLhKvb22jxBDFMk0y1QqJYwud18YdbtvDRHTu4/euPMj+ShopBLFEmqBp85sgR/u7gVVhYFLQyry7Nc3Jllf3NjWxvqCdTrfLr4QlSqRKCAGc8i3QF/DS4XJgWZFJFvDYbvYHg/6ok+b8y3qy5/Ncw/qeEpiAIAeDbwEZqi4T3WJZ1+L865jLgS4ACJCzLOigIQivwEBClBlH5pmVZX147/jHgzSKdAJCxLGuLIAgdwAgwtrbvDcuy3v8/uj7NNHh7bw/rg1HmCykiTi9FvcKVl2xkY32YqMvJVa09nEsu8/DwCPsaGwnYHWyPNPLHm/diAaZlslBMcsUX/pFrLttIQVVJF0usC9fz+YMHAfjRxCTrggEGQiG+dfYc7YEAO6MNaKbJVDaHTRLZ29DJY2tIo31dHbyjr48W9yBnkm/Q7WugpGl4FDtVw6DLd5Anpx7jE3v3cjgWo6obJLxpfJqJYlfobqljINrAtvpeQGC5OMPXr7iB86lFBkIt7Io4OZu8wCNjY3x23zWU9DLTuTg/m5zij7dt47K2NvoDAdaFGnh+bpJfTE1hl2TedWAnsVKJk0tL/M76dTw9PcP6UJBkpcovJydJx3P8KHeOHT3ta7gQDxGXi784eJXKsSEAACAASURBVAmvLCzxo5eOEQj7qNvhQDNqKbuvnjzF7evWsTnSwHAiUUtx2u3sbmomkcxRHImhVXVKuTKiJNZg9B4HQ2NzLMUKRCNuihWVYrZEtaKyADx4y81EHAEevXCWxXye6zo7uf/MWV685x5+tXCBZKXKdC7H+dU4To+D/kEvlgVhj5uI282v3hjilyNjvLW7iz5/Exm1wHdPnkFXdc7Z4zwzN8oNHRsYCEXIayptpskvZ2Y5n0xyaXMLV7R0MZRa5KHhFzg9t8iv7no3S6UUuWwRxa5wz037afG4ccgSL41eoNnjIRnL0NIb5fjkLGpV46FEgVC9l+V8nqpu8N3z59FNi3u3bmFfdD1nkhOMpVNY1JrBG+rduB02fjY6xsOHjlPKlSmWdaqqgbYWiTTNWkrN51GwXArFso5e8wulmKjU0tIeheFUms3hCJIgoKk6xXKtgStX1JAkkV17u/n4jv1Ylkqb18dtu7bw8zPned2ysEkSdkkmVSiRlSsoksTzz41w01sG0QyD6fkV4rEcPRua6O9ovFjrqogiJU1DrWhIssQPXzvB06fOc+/l+3h+ZobUShZNN/F4bVQ1o1YTnVNxeWq1c5YF+ZKOwyaSXVHBJXPpJesoaSrfHR5BkkSiTQEgw/RsnqYmN+F6D/MLWZZXyjhlmXf84ms8ccPHyGvDuOV25vKnSVdLtHvrMaw8qUoZfJCpnsS04PjyEg9dfxM/maxNOevrQmwIhTi6ssLmSD0/GBtnQ12QcDSAruk0NtVxW38/V7a2U9Y1un1tlPQ8sijxneETOGWZ4/El/nH4qzS6XNy76T0X5ypFbP5v0sX/dLyzdw8nVl/hSwfuASBVPYEiStjFABUjzb7G2rldvo18aLOXC5k4BU3D0M1aFNrn4ttDQzR7pvnhbXdwcnWB15ZiCMBbO+/grZ13YFgF3ErNXUgUnCyVFgiZJwjat/OFU9/iI1veid9WR9kY5eGxE9zWneO+s8f50637kIV6RMFG1RhHwI5LXo9uxWn17P/v3o9upoiXRwk7mrFJ7WvbEshiGLvUR9UYXWsU+o3j13LpDUL2MH+75150M41uLeGQBi7uf7NbXRSc9Pq7+c7weZbzeV5ZOs/WcAuGaXJ9+20AfGfkIf7+udfwh73omsFfXrafhXyBh6+7ldVKhhZ3lGvX9/H82ASzEytYFqymK6wemsLjlLHbJHxeO6upcs1vXBLw++wodploOEBPexTTgpxZolLRUHWTlcUkt933CKWKQUunicdrZ6C3le5AkKVCAUkUieUKSKJA2OVm62AXk4txHFUNj93O7MwK/rAX0zAviq4r21p42/3fYyFeQtNMElkVVTXYPBglHssyP5OksztMpD7Aydgyu5uaKagq1VKVYNDFpdv6ibo9LBTyTGSy1AU8ROqcvOfrTxINu7j00j7qnC5+MTXN/PgyGw8e5OA/3M+33vU25gt5nrxwkqvbW/ncjVdz76M/I5MpU8iViG1cT8Tl4LHfeTtfO3OGc64VCmqVoKcGcF+Mp6iUqhyfTvN0olwr3ZFFXvrc+5jKZmlye/ir514is5rDOHA1lWKV7r5mPrx9GxGnm7OJVb569iQbIw1UdI2RsXnedfku2r0Bfj49QbGi07e1nqV4iUK2iigKHDtxgZ+1tXFyZYWCquKQFUzLwq0oeG02fj4xxepyBqdDQtVMNrc2c2RpGc002NnYxIG963luZJwXxi78j2THv4jxb61G88vAM5ZlvV0QBBv8k1mBi0L0PuA6y7LmBEGIrO3SgT+1LOukIAhe4IQgCM9bljVsWdY7/sn5XwSy/+QjJy3L2vI/exM2SeLlhUVGUymWiyVcioxLlrmtt4dGtxunrOBVnAzWNfLpfWFG06tkqlk2h6MYlolNcqGbFZyywp49fbx+dJRSRef3b9jHcqFA0O7mTGIZpyxxMr6KYVm8e3CAr5w8Raffxx29GxhJx7mQTtPmqTkjZKsV3je4kcG6NmQxyKa6bfzOL+/n0tZW7uzbyDt69nMy8Qy5qspb2jcxEGpmoT3Bua4OPvf8qzQ3h/nedbeuhf0VagkAixOrs/hsNhaKq/gUF98bHSVXrWJaOoZV41vqlsndT/wYxa7wjRuu573PPoWyZmVY1jReW1ggWyqjqTq/nJnFY7OhmSZVw8DvcJCWBN61ZxudPi8LhSJVQ0c3TTJVlaOLCzjcdkqFCk+dH2WwtYlEsYRDlvnmr49gmRZuvwtd1ZEUiXVNUXZ2tyNIItlEHlmR8dV5UNb4pp6AG5fHgaTIZBO5Gu7GYeMtG9bR4g7z0uI4siDwB5s2MZRcZTBSz8uLE/xo/AK7mpo4vLhIxO1mUdVpDQYwLYup5VVa/X5a+xrJpQp84Oln+NZNbyFoc3Ogp4tXp6YpaRoPnBuiyx+gP9DIJY3tPDI2xC3dXfjtDkzLIlkp4FFsNatDj4Pff+4nfOuqm3jvrm14bUrNHchmYzAc4YnRMf728GF6upuIJTJkVnNIikxHfyOtwQCLuRyGbnLvzm0MJdM8NzfPo2NjXNLUxDVtPQynMpi7LRKxNBMjC8RTZRS51iRz4JJ1HD5a6xrNFXWuu3YD79k0yIcf+BkBj0LQq2B32lhZLZKarNaWgqrJsaUlmt1uegIBBEHAMCz8nppTRjhgp9HjZbWS4cHhYWLFAivFIk0NIUQE5mMJjh+aY/fBTqplFVEUedfbdjGfy/LSk0O1Ok9JINoUINzYyFQ6hUtR8NsdJEpFdL+LSrGCJNciVROZLJph8qnbr+XBofMcOTSJpltIbqU2B8Ba3XGtKWhlrlBDLwG/emWYpqiHoNOJIIlcsqGH+88fAt1k985e4rk8dfkK2bzK60fGuPu6vVhWGZfcyNOzL6CZJk9OTjKUTLIuGOREPE6H9ygWFq2eIO/fspkziWU6fD4+9pNn8QTcSFKti/xDe3bwnoENnIivcNeuLexvamZ9sIm5wioPjpzn+PIy927dygNDQ9Q5XfzJ9u18PnmMeqeL/7T7rZhWkbx2GpvoJVZaosU9+FsMSQDLMi5G9OxSDzsjv3EkCdm3M1d4jfvOvsqndl9BmyeCZsZYLk3wmaOHOXTuAg0tddQ3h0itZOhpa+DQ1AyZRI6Qw87RWJxWr+di+j2vneZMYp7dDduQhDB2qYdObxuaOUOqeoL+YIDl0llkQcKtOHjfhtv4T8ce5o7evoviVDdTCNiQxCALxaM0OKO/dT+aGUMUXAjYEQU3dY4GZLF2jGmV0K04guVAQGR3w2+XEZhWlTpHC5qRRDVPo4gKklD3W8/qm8MPM5xMc6C5kZs6L+Hz++5GFNxATXxG1hris+opbu5cz+9+6BoWimOcWV2hauiEHA5G00s0uv08Pz/ES1PTZJN5REFA1Q0+eMelzGWzhJyO/8Lde4ZJdpZ33r8TKueqrtA5h8k905NnNBrlUQAJSQgQNsHYBJNEsgXrgFnWGDBekkz0EiRElFBGQiMNmpxz93T3dE7VXdWV46k64f1Qjcxq98J+972utXmfL119+jmn61SdOnU///sfGEsmKWZLNT5gVUfXDdb2t/L1G27i5blpHnrpGInlGp/c5bKg65BczqOqOh2ddfSHI2xpqPHBs5UKAZsNqyyTVRQyShmvxVLrGnW2cm93F16rlXfNP8nCRIx0vkpHZx3v3j7AfD5PNl9Fqeh4XSbSuSoep4lCpogsCZhlkZnJBJVylc/ccQOfeeEVyiUFk1nm7t0b0Q0YjMdr8ZPZDGuCIbY3NvGzo2e5b+cmXp6a5GJ0EbNZ5o5rNvLI8AjvuGkHo+k0hWqVu7s7sUky7R4frZ0RXIksvY0RNgbrauKc+BJOk4l2r5dEsYQgCMSyOXRd5+K5xZrLxUoHA4tErFjk8dGrxOJpdvV28mz8HMOpRfb1r6p5Q8finI5e4s2rermltRm32cIT4xP09jbxnecO81XlFXZs6+HUp97HTV/9LpIk4PRYKJRU1vU38eVnfoMv7MHmtBJyOEiVyzx+dZw7OtrY3lDPSfsY89E8NotEvlphXSjIL09cIF0osbGxAUtTPV0+L/8TmvafcIh/IJjmv1loCoLgBvYA7wAwDKMCVF4z7X7gccMwZlbmxFZ+RoHoyuOcIAhXgEZg6HeOLwD3Adf/n55EsaoylckgCqBjkFEqPHZxkNRShqMf/gCXE/PY5TQGBrphsCXUimbomEQJWZSo6iUK1TJfPneWNcEgi01p7lq/BlGAG1uaOb44i9ts4eaWFk4uLfGlJ1/mx8UT/PmdezAMg/HMMiW1Sp3NRqKcZWskQqKsEC8VORWbYEc4jIDEzW1tBG02rJIZWWzAZ5nGZ7VQUotkK0UOL8xTVjXWdDbx6R07KaplAtYQipYhpeS5sLzI3sZuHh29wLtWXcf5xCD5SoVitYKi1SI3AeazWXTdILGY5uvnz/PV629EN3SGUwlixRI+m42qphEvFjk7N097wM9sNoNNlml2e0j4CrhNJkI2O2PpDMlymWMLi2QrCovTy1isJpBBKVY4OzZTyzcv11Iv2r0+TgxPYLaYUIoVDp4YJBDxYrFb8IXclAsKxWwJu9uGw2xG03XwOsilCqiqhiRJNDfW0eJyEiul+eHgEBvDEZaKBaKFInUrZsb3r+pjKJniutZW0mWFoN3BhmAdF+LLLHscnBifwuNx4K9zU1FVfjB0hQf6N/PutetIlUpMZ9IUyxX+9sgRPrRpEwCJssJENsOVxBR1NivPTUzgMJmYXVzm/bu38e1TZ/n24GmuplLU2R28cnWcN/ev40R0iYDNRqPbjYhAulQimVFYtT6IWtXo8tWU2XaTiRenZyhUqzS53AyEQ2QqFT52cD9r6ur4wrXXoukGnzx0kOyZKTr7IsyMx7i9o4NEqcSaYIgXT1ziwwMDbKhr4KE/uZMPfv8pADRdoVLRkcM2VFWHXJU6ux2TKPKPp04Rm03gdZupb/ZjdVho8LhZLhZ4ZPgK05nMSm60gHPlPelqCpPqzpJcTOOpc/EnOzazLRLhH0+fRvZb0HWDOq8Fu9vGaGIZgIDNjkWWEIRajKlSquLymQl6XVyOx4hGk3wnX+Cj27YwNjhLsayiKRoIYJhrKUG5QrWGzuoGKBpUNDIljXUbmzlyYhh/0MXXf3IYyioDO1sQBIFULMPYSIpgg4O7b9jMra0tvPfAt/jrbbvY1zLAkYVnkUSRZ8bHca3qI+Kw88PhC/xmZoa7ursxDIMvHjiCw2MnlymxpqeZrQ0NOE0yU9k8A6F6PGYz3z5+hq89/QoOt41bN6/l6WMXsDut/NfsYd6yYS3fPXSSs9NzvG1gQ427XDiLz+JiNB2lP9BGUhnjycnHef+6+5AE5+/cY/+1bawbRaAWMVnLGy9ilcx8ZvtdyEIIgwoXE0fpcIf46MAAq+sC7IjU8+jICKaWZvY0NvC1s2dJxTL85SPP8dn79zGeyZJUzuC3DGCR6thdX1vDG4aOpqdBkDCJbdz79N/wtRtu4KdXr/D+dbsoqyVipUEmUmnMkkRFm0IWw1T0OeKlDJO5C+ypv5kXZp7j1tZ/LY7z1Xk85nVoRhJVjyMIElV9FovUhSjYV6IsDXQjy+XkOP119ah6CknwcCb+Mt8bHOTQxVFSyQKqqvPA3dfx1t7VBG0+VF3ljV3rcPR5EAUbBgYg/i92S4o2ilXy8PWLv+YTmyJECzmenZzknu5u7unq4mh0gWenpnn6zCWWl7IEIx5u2t3FQDhEq9tFyNZL0OZgKpfi8ydOYrKa2DEQoKRWCTucPDZ2FZ/VzOu2rkM34PxilNHBGawWibKisWGgnYjTxcn5OfrqggTsNjRdJ67UEqVEQSDicHJsfo658UVsTiuT6RRv7Ovlzs3reOb8EJKUIxzy0uP18fEXXqJvfRPy0DzRhQKSVaap0Y3ZYmJnVwsj0SWmx2Joms5f/vxXCKJIuCXATR0dPHl+kP9ywzXsbohwNLrIqeFJfCEP68MR7ti6nqqu0+h2M51IsS4U5obmJkJ2Ow88/yI7O9podjmRRZHlconnp2d4YGAAQRCQRRGXyYTfamc2n6PP78VrtWD4DfLVKv/w4iWqVR2r24zPZWI5rSA4TDSH7bWIzHyRYJ2HeLGAp87FUqlIoli7N2dyRd69ZRMmQeRbFy7S6HITcdhZHwpTXFNFqVR51/q1OExWvvOOe/ijb/0Mr9fGUryAruu4A07KRYU1zQ0E7XYiqkrEYWd5JZbyvj2befzYOTKpIi8fHuSgJDK7WKClvkBvqI6JVJIzI5P/pyXJ/7XxBwJo/rsQzQ4gDnxPEIQNwBngw4ZhFH5nTg9gEgThN4AL+IphGD/83YOstMQ3Aidec/xrgCXDMH4Xp24XBOEckAX+yjCMQ7/vCeqGTkmtMpXJELDZcZlNZJN5vvf2e/FbBmh25fGYHZyJz9LtCTCUWmBdoAmz6AI0RKEKlFnM5zh7fpxN/Z2sDfhpdLooVCtcWk4StNtY7Q9xQ7MF7rye4wtRzi4t8p716xnLpMkoFXbW14Defa2dRItZ6u1uXpiZYFu4hIiVuzvXcDExj1l0UlCHuBBfwmk2s3/uKrlKhZ8MXcFuMhGNJvFe6+TwwiQNzjRNTh+ZSgmfxcqlxBzDyRQIEp3uEO9Ys5pkubSSM5wgX6nQ7K6ZoQNcmJ3nVFMjN7d0c1NzCDA4FB179bX76pmzqIbOYj5PPl1AU3V6WiOUNI0Xpmc4PDdLPl9TboqiiMtbO0eztcYlEgWBnkAdY8kEmUKJISWG21+zEsml8jWz7KqGUKogm2toplrVKBcVqhWV/sYGTk/NIkkikiRRrajkKxW+cewUc+vypMtl9k9NUtV1TKJI2GFn//Qse5sbmc/lsZtMLBVql+KXnnuFrp5GCoUyzSE/8UJNJFVQVBKlEg9dOsvHNu7gfRvW85EDB8inC2xqauTXM7Ms5LJsb2jgoTNnafF4+c1Mlm/edDPj6RQPiyJPjI1hsZlJlhX+y9YdfGfwYg0FARbyed6zfj0FtcrTE5Nsb2rGIstkMoValKfJRFXT0CSJkdgyGxpq2eVzOYmSqjIVS7BcLDKSSPKmvh72dXTw5lWrWCoW+ZUk8rr2VWwMhRjPpNkcDrEz0o5NbuD7l5/DJNdQzlxBJ1Jnw+a01iLvUgo22cSZpRjRmThVVSPS6GN1SwPxYgFV17FIMmcXF5FXPCBtsoyIwG2dnWwIBnnXUoLMco5qReXQ3BzPjI1x+uQ4mzY20hjy47JYMEsic9ksQbsDSRTJKQrLySwmq4lw0Es8lWV8Ikp3RwO5VJ5ELMMnJp9lx5ZeUi9dwuaTMAyoagb5ooqqG1gkARwmKKi1YtNc8wYtlWsIOPkqiAKSJHLm7FWmh9PccM86djc3s74uwCd+tR9BEvk74whf2H0Df7ftOv7+9EFuaW/Hbbbw4HP76W4Os7elhb1NzXzl3DnS8Ry7+7p4Q28vy6Uy8WKJlnAQj9nM/T97jFDQy9zUMpJYoyE8cfAMpbJKd08j8wsJHj51HkEQWJxZJra6xM+Hh/FYrVzf2sr71t6CZqT5/tAQ3V4PT04+S6fHi9/i+F9azr9tC+tGhZJ6FbNUV4sqxLRiWSbS563FU5rFHLe1tXNyMcrp2Tk+tG0zOyJthHc7+KRxEKZi/P3j+/m7N97CYjGFz6xhFpteRVAFQUTARkq5hNvczDvXraXdFeGdq/yYxTaQJ/FaNvFP10pcTi4wnDxNm9tDhzuCjk6iVEIUbPT66lD1OLIYRNWXMYsyk7mj1Nv9SIIVEDGokFJq7XnNSCIJPgx0NtbtrRWJK1+Yi8U8g0tLuHwONva1cWl8luML8zxxcZCf3HsPrc7dzKRfpssTxiQ2rrTha1GhaeUsHvMG0pXzK9eznXu7uslWFunyhLCbrlJUq3gtDm5r6+Yzxw+zuJDBJIts7m3jszv24rM085dHf8qehgamchkGE0nu7unh0Nw8f7JuDY9dHafN48ZtMjFfKOA0mbi+qYkWl5P9JjOj4/MIQoVkIstDN97Em3/yGKNigqaq+9XizGk2U1KrLBbyxBeSiJJIKV9mfnaZx0URWRSJhH001Qf4x2v3cv/PHudLt99M0GbniZ5xvv7UK5QUDbvTiiiJnBudolxQanZxYQlv0A2ARZbZ1VBPp8eNomnES2UmUinWd7UQcjjYUR+uBULMR+nw+ri2qYl6h4OzsTi3trVxz/p/pSvM5/OcjC7xytlhspsUBsIh+oMherwRpnNxzsTiJIol+gJ+3r5qPX6Ll0dfOkE8VWb1ukY2tTRxbm6eRp+HLr+f+UKR9Q31iILAybEapSaj1PyKRUHgUklhKJmi3mFnIZNlZjnF4nScn7/3rfQHA5yJxTkbW+b21nvx1U/TtfoIdU4HojTPyNAifreZugYfPquVLeEggiAwm8uzPVJPtlLhaiaDP+zFarcQDHoYH57D6zZjd1gI2OzMSBluH1jLBZ7+faXHf+gQ+MNJBvr3FJoysAn4oGEYJwRB+ArwIPDXr5kzANwA2IBjgiAcNwxjFEAQBCfwGPCAYRjZ1xz/LcCPf+f3KNBiGEZCEIQB4AlBENa8dj9BEN4NvBtA9loZnlxANtXUsttXdVIuKDQ6PCTKp3GZ7MiixHNTUzQ6l7kcX+bL1zZS0XMkylkMDB4ducK1zc1c39rCUCLFw0NDfHzzFlpcPnY3Vgna7CwWs6z1N/Gmbg+9Xi+aYbA13MbZ+AxFtbrSgtaYKaTZGu7CJDTgNM0gIDNXuILDZKXXF0ISAkSLMzz4i19xzUAfHxvYzPeHhqiUqzjMZuxuGyklzycff4E/vXEHH1jXxXhmmVy1gstkZk9jPRgaLlOQrSEvk7lpprLLLJdL5CoVev0+FE0jEy4zMbPIt8+f50oyRUlVeX1HO7Fikdta+8hVi0ScLhpdLlrcHoRGgdlsrVg/F4txbnwGw6gltqhVjf7uJprdbmyyxEQ6jaJqZCsKdpOJOrsDp9lMulymXKliN5nQXDZs1nzNYkIUcDttiIJAUaiSTxfwhTxkFYWQ302xWkWQRCIBD/lKhXUttbZ4wG4nUy4zlkqxpi6AeyV7/eDcAhVdo1Cscn5kilsH1uL02MkrCpqmE7DZ6QkEuBJfppAtcio1QUWpJUt8YEM/71y3li8fOMaNLU1YJZlnJqe4kkzywObN3N62hz0P/wP/Y3CQHfVhdjY0cDWdYU7Icm4ximvTDu7t6mYhl6eoqgwvx/lSNsP2xiYMw2BrJMS+thYuLidYKhaxShI9gTpmMhmafB7Ozc2jVlQ2t7cQzefpb24kUSrS5vVyeGERWRAoVlVaXE4evfMNWCUT2UqFJ8cmmEom2dfaTbx8hampJXQdVFWnUFKZnM+j6TmsFgmv00TI4SBWKNDUGiJfKCObJIrVmudrwGYnVS5hoYZAGoaBJIpc39LMdU0dWCUTd61bzSOHTiEIAtPxJJqm47TLlIsK68MhspUK+UqFNcEQo8kEXouVgM1Oqc6LJIjMLi4jSiJKUSFVLpHNV3DYZBaXS2wIhXlevIxJruWblysaqlrjnqqqjmwWUT1myFTYdls3t/Z2Uy1XOfzMMN4mB26HiXPn56kulTCFbTx62xsYSkV5/0+fxmSWWVzIMDGyxHgywY9uv4tPbt7DJ4/u51cTkxSyJW5obeXezrXMFRJkFYWXP/RnDKVivDg9i02WOTg2QTSf58rMAmpVY2x4Dl03qFR0ZhfTGIUqkstMYDaGoRuk4lnS+Sp7d/fx5p5e6h128lUVp0lGFup5ZfFpLi4t0uFxY5NkDs3PsykUosmhYVChrI29ai8ENUHMb1vVlxKn6fZWaHJEEAQTNnkdhqHR6DBjUMHf6uYfXzrMdY2deMyt+K15NF3ng7dcg8ds5p7OrSvon4Kq5zCJYaDWWnbIrRTUMtHiRd7SfRuakcQkaoiCDbPYjm6UsMsWEqUSuUqVXfXtCECrcyvDyecAaHNt4beVYkmbo6KpBK0eYqU0XrMTm+zFKq1GEhZXzk5DQEQWagXib/1HFW2cpWKJilJFEAQSpSJ97Q3Uu1wMXZ7i3S8+z3N3raLT3YF5RSUvizUHBEWbwCbXfEmXy1ny1QoNDi+d7g1cSp4hZHNxR3sbQ8kku+slfjx6ift6ugE4fmWcP127Fo/Zy6GFGg5yfLGmNA/a7byxuxuX2cSZWJxen5d6R20xcGppiY2hEH995Aj5fImIz4PDbUOSRbKJPB/9zQFKhTJuvxMdA7epZjdmEkUsFiuGYZAIuFiajiObZWwOC1cuTeH2O3H7ndQ5ndglC2/d0k/IZqegVrm+uYnMvh1864nD2N02ZEkily4QjRUoVzSmplKsXldDd9OpPEOJJH0+H0VV5Wsnz+Bz2ukPBzi7GAXguuZGdtSHUTRtpYmgclNrC0vFAjvr64kW8jw7McWFiVksNjOlosKb+3oYSiRZKOSwSBIltcrmUIjN4QgWsUZTG03P8v13vpGZXBaP2cJcPs/epgaCNjufPX6c5aU0167tYS6bIRCoFcZOk8xoMsG1zc1Iosj+i8Pcs2U9Poed5WyeiasZ3v/s87xl43ra3W5enJriVOxX5KsVZq5GcW9oZ29/Hz9aOIkoCuiaTkYpoxkGfV4/OyOteC0+looxTi0c49r2NiRR4PzSEqIk0tTooZQvczWZRBQEfJb/2arsP+MQ/v/SOgfmgDnDMH6LRP6CWqH52jnLKyhnQRCEg8AGYFQQBBO1IvNHhmE8/rs7CbVoiLupFakAGIahAMrK4zOCIIxTQ0xP/+6+hmF8G/g2gFzvMuKLGSJNPlqaQ5wem8Yf9vDy7CRdXi/rAk2ousbI8jJOk5nP774ekygRLSYZTi0jCSI3NjfztfPnec/69UQLRU5OTvP05CR7Ghu4sekmBpPHmC/mqLdnKagKv56ZT38dRAAAIABJREFUxSpLmCQRt9lMUa1yLBolrShsj0QYTc8RtGXp8HjRjCw/Hr3Cjc0t9PnqkQQnlxNxzFYzyVKZHw0P87ZVq9h/eYSqw8qDu3Zw3T99l/mFAo/Yz9DpdlM1dA7OLXBHRxvNLjepyiI+c5iUsoxVMnE0OU+Hx0NRVTk0O4ckCiRLRTwBF2ZZZjAex2qS+crZc2yujzCdX0bRNNwWC6IAHouZ4UQSEYFOr4dj8/M1A3tFxeGwYugGYYeDG5ub2BRsJVsp8JkTR8hWFJaLNZXeb1tCFbOFVLmEJEs4PHYEQcDusmGRZRS1JgjKpQosRbP4ttmQBZG+QB1z5iz94TCxQpHFfJ5v3HALc4UMj10dI+Kwk61U+cb5C0RjKXb2dHBjSzNhh4OLDRGcJhNbbtiNVZaZzuawyzIlTePP1qznDQ//mGQihywJLOZzfPH0KY5eHiOfKXI+nuDFsXFWh0O4zRZua93Gl849xcXz8+TTRTy7zeiGgUWSWBcK0uJy1s5RU7HJMsfnFxg6N8u7Xr+LJ89dpqpUyVcqmESJhXyOt69dw0PHTlMulDGZZYr5MjdvXsstrS0cjS4ymU6hGjrTsSSFapUGl4uAzc7h6SmWZpb5cfsV7u3t5W19u2jc7ubx8VE+8PLz7G5qolwoU6lq5NIKpBSoGuAyUbaILCoaY8kEA5F6JtMpYrMJ/GEPosdNwGYnZLdTUlWquoZFklE0lZ0NDexr7cYkymiGzr3d3Tx88CSSLFHIFlGrNc5kuDHA+aUlttTX4zabmc3mWF0XxCzVkMeh5fiKMbaBUqrQt6aVa1ta0DWdHS0tHJuZIWK3Ua7opKNZrAErVUUHSUA3DAwN1IqON2xDqrcTn08y0pAEYOM1LZQKClNzWaqLpRr3a2UEbXbKBYV8tozXYyWWKJFLFvjcqWN8fe/H+MqedTwx8TO+Wi7xvTPneeTyZcyyzA/3vZ4LiQWeHJvg1vZWbm3Zy7q6/fzVz59nOa1QKKkrEXWQzVd55CP38t5vPUGuqCJJEoF6D3PzGd7xuu3IgkiPZyfdniq6USBbmUcQRI5Hl5i7usjXry5y3fY1BO126mx2BEEiX62JkMraELIQerV4+u24rvFOAF7/1N/w4Nat7IzcgSBInI2d5jPHjvG+/n4euvt2hpJR9jRsoc21mWffECBRzhCwurFIXVT1eTQjw1DqIhsCt2AYGp4VFXjE3kadNYVOAVkMoWg5FG0Ci9SBpqexy+u5p3M9ifJpbLKTbCXDS3PPsinUyOHo0/R4w/gt7SvtfoOhVJQd4V0o+hheyybK2hUMQ+f5mUPc1nrrq2IoRRsFRMxiO7P5w3gtTmyyhNliolyoKamn82Wm/U5aehp4b/8GJnKXaHf9q9G7qseRBD8CIt8ePMxMLs9bemt//9tjB/niNRbq7R68Fhednio2ufY+nlpcpKRqbAwHKakqTU4fT06eqZmtXxxBU3XqGnzc19vL+XiMrxw8Tk9zhIVcDqfZTFlVqbPXCs6pkXmUilYTMpYqZLIK5YqGJ1bzPZ0fW8TWJ2NfoSPFCkWsJhmLJOO0WXCvamb86gLpeBZVMyjlywQjPrbU1/Pg4d/wsc0DpJUyVb3mrRuyWQkFbNhMtZjXjg09PLV8jnxRpaMjQCaRY2kpz7btXfQHg7w0O8fRuVmu66x5J/cH66h32BlPZ5BFkWihSK/PR7Orpur/4EsvMTsa5XV7NnJ9UxOf3LqVputex5fPH2bDnl3Igsg7Vg2gaFVemBnBa7Eymk5zX9c+NCPNkehFspUKm8ONtLkDpJUCfquVdKUm3sxmCoyOxpmfTXHbdRtqIMMKMndhcIr5VIZVkRANDQF+PTZe63Kli/gidvLpIodmZ4k4XcynMnzh1EkGh2eY+ptHeGH2cQ7OL3Dz3jWcPnuVbKrAQl2O2XyBdrcXn6Wmn/BanLy3fwNZpcK3T50lFctSUVRWr2rlhZcHcU5HCdf7ySivZQj+5xt/IIDmv11oGoaxKAjCrCAIvYZhjFBDLYdeM+1J4OsrhaMZ2Ab89xX+5b8AVwzD+Kf/zeFvBIYNw5j77QZBEIJA0jAMTRCEDqAbmPh9z9FmN1MXdtPWECTscHLuzDiRJh//9OJhIk0B1gRDKKpKMpPH3WrCLlsQBYmwzcdMLsPnTpygPxxBEkQyisIjLx4nm68yMbLEgbWNHO2K8qEN2wDIVct4zXb+6/b7GM1cpKyqOExmZnJL9Pl8/PP58/QHgyylk5TUKpuCNf7fW3vX8KWzp3hDV4X1AY1zsWXsbhu5isKx+TkuxJa4b/N6rLLMT0ZGOPPgh1gsplnj28J8YZDxTIL1dQHcZsvK6tMgW43jNttxGTbu6VxHVVc5uDBBolAgm8xjc1qRZIlWjweLJBMvFhAFWMgXiJeKDCWS+K1W3GYTY+kMjpUbV7RQpNHlxm4yM5fN0OT2kDCZaHI62B7eABgcW7yCKAiUqlVKqoogCNhkGYfJxGgyQS6VRxRXWrIuK7quky6VsJlMeHxOPD4nc5NLHDk2wt7dqzBJUs0vU6nwyS3bmStkeHF2gmS5xmmazxfQDQOf1ca7blhLyGbncDSKJAooms6moJ9eb4TTsWna3W66fX7ipSI/GhliW18HR9Ux2trCBGx2Gl1OFtsj7H95iO2RMGcWowyEQ0zlcrg+8jHsVomPvmkv05kML4yP88716ymqaSySxPfPX8IiSdTbHSiaRqJQoLnVz4Hhq5itJnb1daKoKh1eD3d0tgOwpb2FrKJwfUsz09kc71m3ieenr7KzPkJ/MMCppTiSJDI3FyflLhD0urhyfoaGJh+xxRSfG9zP8R2LdHg9vGftAGsDAT798kEyuQqJTKVGsK/UCjUqNc6jN2iiye2hrKnMXY2Syiq09znxWW24Voz5m91uptI1Q/n7+3ppdPh++xkEA1wmO2/aNcBjJ86jaTpmiwmL1UwmlScDJHy1WESAyXQGv83G8HJNcPCeDevp9Pg4F19iNlfgYixOOp4lH6mwrbmZt/Rs452Fx5DcZuxWiUpFw26V8bhMtPVEKBcUFudTpLMVuldFmMmkqQ/5uDo2z/DQMiSV2rlKItV0hbt+/mP++trd3LFnE4cGr9LeHOaxXTs5G1ti//Qsnzn5DewmmY9v/DPu6riPQvUCJ2NT9PpCBKztwALrgnUsFUvc/czXWE7nXkV6u9q9LC3lEQWBTRsbeVP367j/S/cj3Hcbo6NxnHMpTJJAxG7n9vZawaVos4iCA7MkoxsKb+zu4e2r1rPp77/KgeODNPfU8zdbbkYzclxYnuWbFy/iNJn58w0bsMoy7a7VGIaCJAYQsSEIEk+9/jNkKue4mjnAoYVZdtU38uht9/DBA7/ivRvWs6fh9eiGwnD6GBlFYSiZZDSV5g1ds7S7AkTs21nn92MYOoIgUdZGGE1P0+oK8qVzx/jYxh04ZRdmycxHDj7OX2zeSsQe4GzsGFtCtxKwbmYmf5h8tUyiXOZYdJqbW/pwmfoZSb9Eq6sBh9xGkzNLUhlDEEA3SshCBEEQubXlRpaKF2lw7GQotZ8Gu5+yVsFvMWEAD108iWboWMwmSvkyj7zxDVQ0jfFMik3BBqLFLCOpBB0u28p3k46BxkTuED+4MsR71vbz1OQoq3xNpJUcn925l/lCgk73aipaDNXQXk0G+trefZxYmubZySlubG3mwNwEqbLCr6em+KOdA1xYirGqLsB3L14kNpfAZJY5NzSJ2WLizl2bOTg7h6brLJfK+CNebE4rSqlCsMEP44s4VZ256QR3XNdPvlIhUy6jqNoKAqgiiSKqrtPs8TCbyeALuTEMUCsqlXKViqpybVMjPzhwgkP1ERqcDr546NirgkpDN/hv11zDo8MjHB6bZCmpIEsCW3vbOXRhhKZmL5Io8qmDB9nX2UXQ7qCiafT6PPxidIwPbuxncyhMg8PDxromLiYW+Paly7yuox21qvHe264hXVaYL+RZLBa5q8PJ+9ZtQRJEXpobQRIcjKaHmS8UsUoyNzY3A1BS08RKRXKVKoPJJfwWG0ejC6wJ1EI1Xt++isdGR1m9Rie2kKKqa3T4/GxvbKI/WEPaq+UqbR4P1zU347GYaXN7KKsqT4xP8Mvj57HJJjaG6sgqCkdOXeGx974VVY/z5NgE46lkLeTDOo3JLJMrKcxkcyT8pZX4VLFmc4jA1w+dYH4mic0iIQhw8MgwdV4LmqpzdXCWxPJrm6//uYaA8AcjBhKMFQHJ750kCP3U7I3M1Iq+dwJvAjAM45srcz6xsl0HvmsYxpcFQdgNHAIurWwH+JRhGM+t7PN9avZF3/yd/3UP8BlqinUN+FvDMH4vUcLZ4Tc6HthFfVOAdq+Pqq5zamiC7o4GlvN5OgIBymoVRdWwmmT6/AE+unEnoiCQVvIcXJjis88e4Ed/fC/PTE7ynecOo+tQLKt4XWau37GWrZEIb+3ZyM/GLrA9Us9fHTmMbsC+9jba3G7cZjMfeWE/qViWf7jnFv753HnWhcJ8astuTixOMZfPc0d7L2HbRlQjhogVWQxS1q7w9hd+zqO3PoCqLzJbmOdfBgf5474+SmqVZyen6PN7yVaqPDYywr/ccgearmGTLdgk80rGsoFm6EiCxFRuib8/eYLFfJ5KRcVtt7K6Lsh0Jo2iafQF6nCZTTw/Osb4lQXaeyLs6+vBaZLJVarkq1UUTSNfqeCxWtgeCbNcKnM+HmcileIL115LolTidCzGz09eoKEhwMZwBB0DTTdQNI2ryQRlVUU3jJXVaAGL3YLTZaPB6SJfqZAsFZElicXZZZKJAi6HmX95+z0YwE9HRoFaW2ddXYinJsZZKBRqEWHVKhcXoty3bg3tbhdHo0srgqgqmyNh1gUCDCYT9NcFGU6lOBuLE3HYeeeqDTx08QxjqSSb6+t5/NIQXlcNbe30+VnIZXn50BV27ezBZ7XVIkxNJrIVhauzi1gdVj62fSsAv7h6lU9t3YZuGLz9Z09w84ZVvHhpmIaInwaniw6vh4Ozs7S4PfT6fUxlc6wJ+KnqOkvFItc2NvLs5BTPn76Mrun8xW17eWRoiPnJGIauU1FUzFYTakWlXNEolFTe8bodnJmbp8nn4dM7dtLkqAMErvvBd5gcixOPl5AsElquguQys25NiF2ru1gd8JMoKzw3NMyOzjYcsomCWkXTDdxmM5Io4LdaCVgtbI80YpMttZQMUUYSRJZKKT748kskYhlMVhOiKCBKIuWCQmtDkB2NDVzX1MRXzp6jUK3RR4J2O/vaWvnx8AhdvprCWkDg+fExohMxqlUNb52L+7dv5E3dq/j86RMcOHeFDavauDIxT3Q+TUdvhLEri0Tq3YQa/VQqKmazzPpwmEeePUF8MAnm2kIGScRab+ddd23HZ7USttsYTqb43K4bOB2b5onxCaYzad7c11srFkMRutzXMZU7yE9GhxkIBRkItXAqNs1IKs2h2Vm6/AF+8NxRdN3AbJJQVozj44sFvvzAnbxn7W5cH/4Usixit0o4bTKP//lbaXYG0A2diH07RfUiR6LjHIlGeXDgWhStgkWyAxoPXTrKhaUYP7zlU1T0OcxiE5qeRjWSJJUY9fbtHI4+zU9GRvnwxk10ufcgCBKakUfATK46yDtfeJJmj4dPbt6B1+LBMFSkFfP3H1x5ia2RCBsCtwBQ1kZ4ZWGQGxuvp2rMY5XWUNWXkAU/gmDiauYAHe4BhtMn6HQ38I1Lx1jl99PscvH89DQf6/9TDEPjm4MPsz1Sz2/mZnn/uptIV5YwizJu8yoMVCTBRUWb4uX5s+xp6MIs1q/c5waxiKuYLx4jUymyxnfTSuu+Cc3I8vHDT3J2Zg6z1cQHNm1kTSDIB17az6/v/jQA55Zfxmky0+ZqRdFy/GzsEk1OJ9c3DjBbmCRTKXNmaYmIw44siDQ4naz3X197vQSZscxJ3vHk0/zTvpvQdJ1Gpxt9xRM5V1EYTCbIKBV2NzTy0uwsI8kkZbXK1dklNFXjl3/8Fu7/5S/xuOz8t2uu4XuDQ/isVpwmmYOzs7xtzWp+OjzKmbNXuXPvAN1eDw+fOs/M5DKyJLBhUwfd/gBWWWY+m8Uk1eJXVd1gfDlBpVzB6rDgtdlIl0oE7A4CdhunR6Z487Z+TkWj7H9liDqvlbKiUlQ03nb7dn59+jJjk2kEATYNNCObZMxmmbt7evjOoZOU8mXWrG7j4X13M1eIc2Jxgfu778BAZbYwyHQuzcXlBF9+8gB19V66msJYJJl7e7qwSTIj6TR2WaZQreK1WNgQDPLdy4NIgsDJ2Tl6Q3U0ulw0OZ2IAkiCSFpRUA2Dq8kU2YrCp7Zu4zdzs/zo3EUeun0ff/HKK9zV3c3Dp87z8b07WSoU6fR4mMvn+c6x0wTrPPTW1XF3ZyeiIDCXzzGfL3AyGmV1XRDDMKh32FkTCPD89Aw+i5nDc/OMjM3hCbi4bVUP+ycnSUTTFLJFimWNG65ZQ9DuIF9RuLOzkyPRRZ4/N4jb78Rjt/HBjRt541d/gsMmE6734KlzEXI4+Nnt3zpjGMbmf7NI+g8YvRtajW8899rm8r89bmj68//r5/TvsjcyDOM88Non9s3XzPki8MXXbDvM7/EUNQzjHf+bbY9Ra7X/u4ehGyhFhfmZOLl8iUfuuotPlMvc1d3Ji1Mz9Pr9zOfzXFmOr5CCs4ykF2hzBfnqhTM8+usTrNnQyjOTk/T4vOzZtprRhSWyyTy6blCuqngsZr49eJJOj4dz8SW6/AFihQJFVeWXY+McujiKUlToW9NKs8vNAwMD7Gt5PQYGff48vxwfZ2dDA3Z5CI95I1V9noo2haZX8FltaEaWfDWL2+Rgc6iOhUIeiyRR1XVkUWRvUzOXlhOouvaqAXxFV9EMHYdsxSSa0fQqJbVKvFCgVFQAcJhMdK/EVCqaRp/Pi6JrBFwOSs1+UrEMv0ifY3VXEz6rjWxFwWky47Naecfq1ThNFs7GoxCH13d389D5C1yensfqsLC1tx2n2fwqwgpwcmqGrnAQsyRSWeHI2K1mMtkiDU4XTrMZt8WCJAosF4t4g26q5SrVqsZcPsfaQJCQ3U6+WuXzB47wwJ4dnF1a4g3dXZxZirOjIcJkOsVMNkfAamFDnZ+lYokzi0sUVZWj0Rr36KnJSTxmM9siYX4xMspEOs1SoYBJlHhiaJjWoJ9oLsdP7ribu372Y6auxti8tYO+QB1WWSZZKjG0HCeXylMuVpBkib955iV6Oxu4b6Vg+dX0FPduWc9H+2/g9rZWEuUSPxke4aaW9egGTGUyFFWV84tRnj9xCWklV9tnsfDrs0MkEwXWbmyrxdb5/CwvpvCHvbR6vFyZizIzEaekaNR5rdzS2sIHNvTzR798gnc9+xwPbNvMHW1b+ME9d5JSyhyan+fHx88xPLpM2F+L9NQNg6lsjgtLi6xurKeiajhNZiySTFGvMpPN0uP3YRg1lehYJskaf3glWaOWE1xn9fBnG9bz34+cqLXCi5VXbVOquoZZkshVK0SczlevsVihwHNTU2yOhDkXi+EwmV+NSBQkkRu2rOLhp07xwXdfi4HKxwe2oBsGvz56kZKiEUsq5M/N0dHuf/UzbjbLFAtljl6drF1rdVZIK2CSoKxSzlfZf/Iyf3bzLn507iLjw0v4bTYux2Lc1N7GnsZ6dODowiI3NvcwlTtIm2sPDw7sYaFwlKcmhtgQDFGq1pKLZmNJdN0gV1TpbHezsJAhsVyLunvwe8/zve5TfPyt1xGw1nhcJlGk0eHjw795gVva2vjjvrXIop89DQ4+9fxLfOOJQ9x/6za+uOvtqEaMD62/g3RlBs3I89lTT/FA/zaemBjksZFRnrrzL1H1ZZqdXj61ZQeCIKIai4i40I0CAlYK1RJvX7uam5tXIQhmTsdGaXJ6cJkUvJYe+oMhglYXSeUMblMLy+VlBoK1bPZ4cZg6a5LLyfM4TWa63NfS5d7DK9FnSJRKdLpbWOX3I4sine4GbmmBz535Fh/pv4Y72rpodu5mYx1cTr7IWv9NAGh6mjPLR9kS3MfV7FV6vAEkwYEs1tqVVmkNijbGUxOjvLlnPbqh4DL1rtg/JTk+OsEbB9YTttcWf5qhsy4U5lRsP8vlEjZJpssTIVtN8vqfPsreni6cJpmx7FVanEFanGH6Az6mcscRBAhZvcTL52qG+5ZeJjJpDvzRezi2OEq728eTE1c5H4uzpi7AtkiEPp+foWQSWRSRRYGAzca1TZ2Y166h3u7EIpn55u238oXTp0iWy9zc2ky8VMIqyexrb8dnsdLq9XDZLCMKAhficTKJHD1rGzGZZNq9PrKKwngqSbZcplysoFZU1GoNwTSZZfb2dpEu166xHY0NHJydpbMlTK5SZWI2hsdpItjkZ3Y8hqJo/GL/KQpljaZQjYby19fu5jsXLzE6Ps/Xp2Lomg6CgM0k88TEJbq9Pnq8PtKVMeYKCTrdEQYTcS7F49x13QDRXI5rmprYUFeHw2Ti+GKU/mCQkWRNDDuezjKWznBLa0stXjLgZ3MkTFXTmcnlyVUqlKpVUuUSTrOF2zvaqGgaI6kE0WKRSqnKZ48dR9N1kmWFLZ2t9Hr9dLg9tLtDfPnZx3jrln5KqsZyqcTRaJQ1AT/LpTIbQ0HqbFZGUxkkUeDA7Cyr/H58FjP7p6bIFEp0dTaQKhQ5MD2NSZJoawsTT2UZvVLjBoftdqyyTJfXx7WNXTw4sItji5PM5nLc9vlHsFkkXE4zH9i7nZRS4W9/8ML/mzLkP2T8obTOpU9/+tP/0c/h//P43Fe+8Gn/rhY2rGpjb3sbp2NL/ObMMMen5vjTbRtZEwgQtFlxWayMJpNYZBm7SaaolpkvFIhrJd68bg0tLic2WaagaiwpJQRJpJApsrCU5KnTV7gSi3F8Mcofr1/LvtZuGp02HCYTV1JJYpkc6/pa+Nw1exhLp3hlfgFJihEtTtDn7WBnQ4h6ex02uY3F0lmenDjL2fgkl5NLrK0LUNGzFKplctUSA8FWTBJMZFKcXopxJZEkVi5R77DXMsdFEbNkYiS9yEIhg06Vl+bGKGll7LKJqVwOURYp6xoBmx2zJNPmcTObzfH81TGcVht3dXUS9nmYyGZILGUQ7KYacqNq+G023rZqFXVWJwLQ4vIRsJkRAL/VSkewjqphsLelCbMkoRkG6bLCK5dHUYoVBKuEzVSLlPRabfhtNuYTaZKFIn6XA5/VikWWKakqmUwBu9tOMVfi8PgMssvKcDLBuclZ+juaWSqW0HSDkN1Oo7O2ryFIrA74eGVuntX+AK1uN7Ik4bdYCNltHJybo8fnY75Q4KWpKXxWG/2hECenZjl+YoJ9W1bx+s52zi7FePDh5wj4HdjsJlY31aOu8KAEUSBTLtMaDODxOjBbTCzOJ/AF3exoqKdQrTKdyzGVzXIkOoHTLJMqKxycnOa2rk5WB/z8fGSYK7E4qqpRF/KwHE1jMst4PE4ujcwQCLkJ+tyYZZl1dXWMlXJ4LFasskzA5SRXLlEqVtizYxUf3LCTTKXIS/Oz7G5txWsxE7Kb+ODLLzKZzdHidnNjTwcfvWkHOavIYjqL220nXS7T6vXR7nGjI2ASBTKKQkZRsMgSGUWhyeXCADRDJ2izYl5BMyVRRBRELBIcWYqSSRWw2M3YHFZCdR5W1wWxyhL/fOoMKrWFxlwuy9nzY4wvxDhwYYxwxMsvXjjLqs56ZpeSfO3u21hbV8f/OHCeT9yyA0k04TTZ2RIO8si5C0TjRbrbveQLFRYWC5TLFZwOE+sa60mrFSZHF2lpr8PllElECzWvTauEyWsmmVH4/J038KuJCbSygsltxSxLmCWZtKLww7MXsFnMrAp4ORdfoqzNEbBWsMl2Ghw26mxuvFYzb1y/hus62ljT1YAlaCNTLLFv2xokj4harfLo++8jiU6r28VCocD71g2wt3EHX7lwgGcPnWd7XxvrA3VU9BSy6OKZuUGKuSKdzWFubd2FKNjIVCZRtCqykCel5GhwuGhxuXhL3wZsUj0aWb55+QgIGusCG9CNIonyNIPJcVpdGyhqC5xcihIrZUgpWYI2O7P5LF2eDkximEZHN2apypHoCAlliU53A25zTVzkNDWxXL5Ei6sDgzJ2OYCqL9Lh3sx8YRybDM1OH8vlPBZJ4Gh0nv5gkEZHA37rhhryqi+yUIwBcVzmZkTBStjmQxIdaEYcWZTIVjI4TDKSUBN7GFTo8zmwSCbMUiOpykVMooxFdvPy0iif23UzXR4/QZuN56YnGU4so+g6d3asptHp44mJQU7HovSG6rimoYE1/hAJpcThhWl6vQ7KWoywbSNukw9JtHElNcZgMkavr4eCukSmkuOfL1zgTGyJD/dvQ6Xy/7D3nsGW3Ge97tNx5Zx3zjN7wp6cx8qWJcuSLNk44YCDMOYYDPgAB5NEMuADDmAb4YQDDjJWliwrjMIoTM6zc8575Zx6re4+H9Zcn3vvh3PBdQtcFP9V69Oq6qquXtX96/f/vs/D49MzPDE2iWpVua27h2ytykKxSF3X6fW4Ob66zrMLCxyMRfnzUyfZHY0Sr1TYKLfkAIuFIg5FQTcNnpycJrGeZ3JxnYWVJFu39+C2WvjI9u0IAmyUK1SbDeYnV0kmiqyuFcjlamCaFPI1BnsiTGXSLC3E2dXbwXgqxbZQmBs72jmdSYBpojcNctkqdpuMJAm47ArhzgDoOjWLRI/Xy2q5iN4wqJbrRLqCNEyDdK3OMwvzDPq8pGolAlYbNlnFIgs8OTtLv89Lpdlks99PzOHEKssMeQOYGDw4OcWA14NFkmhzOTixvsGPJ6cpNTX2RKKk63UUUaTW1ElVK3gtViySxI5QiJdWW1rebreTZ8+Ok8mVsDmsLBULWGWZ0UyGIZ+PdoeXH06N8is7duJQZUaCQQ7HOqmZLaPSAAAgAElEQVTpLdXv0VgfOa3FyO73eLi+owO7otI0DUbTaVxWK3cO9LEzGqGumyQqZVLJPNVSDVPXObplgC1+Ly8vLTOezXIhtcFjs5P84/Mn+PZzl9ANE7tV4qN3HOVDw7u5nNrg2fPT6KdW1u+///6v/Efkmv+v9cUHPn//Xe99A8K/8fPNzz71735O/ymC5ve+9cD9f//nH+Q9m7bxhrZe/vy146wspvmlWw5we08fr6+vkahWeXZ+npDdjt9q433D27HKEq+trVE1dfZGo+imyYn1DV6emaNarqE3DZKJEnt2D/DEB95LUmhwsKudkVCYfL3Kv0zPsCMU5PtXRhElgd5AgHcOHuQnSxNc39HOSLCNNocPixTDoURQxQ5EwcJyaZLfeeIZnj8/wbErMxwZ7sWmtCafl0tFOl0OrJJCyG5nvlig3+uh1Gjwnk1br1UvZap6nQenWgaiiWyWx6Zn0AFRMHFZVM5txDnS2cWOcAiPReWJ2VkWM1k2RcIs5HN0e9zMFQpMLm5QqzYIt/nwWW1kqhV2R6O8pXs/NllGlSTqeoOQzYEiCeQ1jWJDw2ux4lZVpnI51oolEpUyKCKbO6IMBQI4FIUbO9uZy7fMPRuLKSRZZDAWxsREEgXCdgfxWuUa1qhBpVxnLp0h4HPhddm5s7+PS4kkZ0dnGc9lCLpctDsc5Op1lktlHnnhPBczSax2G4diUV5aXW1x3Ww2OlxOJjIZSprGwfY2onY7x65M8c6b9/KZo7/Ahx9/kFdfmQVBQK9rVCsaiseC22pBlkQqWoNkpcLE5DLVZoMmJuV8hSYmpxNxXl5aZj6fw6laCNntfPfcJR5+6RJms4k/5OFbV0epNpt85U238cOLV5AtMp3tQax2lVdOjROIetk10M3sahyrzcpLS0vkUkU+sGcHLy4ukK/VCPrdDPREmF9LUpGbbPEHeXllhcV8jtVymc+/8Ao3DPXz8ug0Pzk1xlghS9jr5lN7b2drh4upbA4TCNvtyKJIwzDwWyy0uxyslyu4VAuyKJKp1XEoLRdxQavT6fSgSgrSNeyRQ7HQ43ViWhQOd3eyUavQ42n1gCUqFU6cmeYtu7dSaTRxWywMdkWRXRaWl9OYlpbJx25Vsdus/M6ej5KqzfPVp07x6MIoo/UUt3cPYFcc7OkP8/TVcer1JvlSA11vsTqzmTIHt/Qxs5GgWdXIZMookkhZhGZRA58Fh12hlq3z4uocLp+DX7/hEDvCIQ63tTOeyZCr1eny+yhqdVK1Gnf09OO12LiaXmW+mCJRLbHNfxM2uXxN4iCyyRfk3Zv28cu77+TNPQc50m3j7j3DHI4NczaxzOMXRzlzaQ68Ko/PX+DhV88jKzKfuvE63KqV1XKS88k5loolBKfK1265C1kMgNnAofThUqJMF0a5kkqz2RdgrpClzx1BEWMIWNCMdfo9fixSA4eyhT869SNu6ezBa+lmoTjOaDrN9R2dfPnSRZ5ZmOdoRwcNo0zDiLNRnSZs202fu4ewTcIi9mNQoaZPoYgRErUZgtYRrJIDUbCxUR3HpYTpcUVRRY2XV2dRJAlNb/DN0VEkUeRw7BCCoLBROY8sGsTsbdT1Ck7lf3vSDWqoksa9j/8AQRLYE+pAFBwIgowkOLHKMXLaIqoIxUYWl9qDJDg43GYjaN2HIlqp6VmmsikSlSq/vecQsiDxByde5EdnLnPH8AAf3Hw3HU4voqgTsbkZzyQYzyaI2G0oYglFCiEKDh6ZO4FTken3WAjbwvzl2eNcXlplbi3Bzq4wd/QcwWapsVwpE3Y4Gfb78VqtZGtVut0uFgpFmqZB1OFguVjgxfEZRFXh9Noakth6EXt+do4D7W08OTvH8nKKe96wi4xRp1qsIdoUynWNjWqV+XwOu9KSE6RyRZqaTqWuoxtgGCayJNDVGUQWBWp6k32d7cxks2wPhxj2+3lqagZMKBWqlCoNBre0US1UKZYbCIZBuVRnaT2Fy+9kLZ6hWqziC7v54L5dZGp1BEHg9NkpVoUGQ34fmq4z5N2EaVYZ9LtRRIFburqJ2B3k6jXyWh2HoiAI0O/xUNAajGVzPDkzQ8Bu58KVOYZ72hj0eZFFkWS1xha/HwPI1eq4LRaahkGlqdPutGOXZfq6IiyVCiRXMvz2zUf4+k9e5wOHdiKLIoKg8/1zl9jeHqHf48OpqvS49hK0GhxbmcdtkTgQGeBfZsbRdJ2VUol8vc5SsYRFltkRCrFSKtPhdDDo83K0vYPTiTjbutsR3SqbA36CNhvfO3aalUyOE1fmeP3SIvV6E90wMU0Tl11h51Ane8JRJrMpTk8vUnpl8ec2aH7pgc/ff9f7rkMQhH/T95t/++R/Bc2fZX3tq1++/49/8xdxKF5EwcK3rpxmfiHDH91xHSGbi9Mb63z2oRdZWszw+XvfxB29A4SsHiRR5MHJUZ5/cZyxTIKTiyuk9Tq1ch1JkalX6jzz8Q/wsZEP4lQ62B2W+NLFcxxfWabP6yFerfDo9AwRl4uP7dnFvQOb8FmG2RN24LNYWSikKTYqROy9aPoCihihrs8wkV0laTa5NLlKIlnjT+++gdVyidFUmly9zpl4nG+NXmamUOCegX6OxLqJV4oUGzWaZhNVlJgrpPnO6Cin5pboCwW4sbODotZguVTmUiJB2OHgQDSCJAropsmFeIJyocJ9e3bwns1b0E2TH01MMD0ZZ//+Aa7r6kISRep6k3xdY1vIjt/SjiSo2GSVcqOMW7XS7nRSbNTZqFT49qtnyeoaHquVoN1OsaHhs9nYEvDz5NgEv3vwOtL1Ek+9chlZlqhXG4gulZjTRbGuoUgSw8EgC4UcsiLBtZ7OfQPdjK3FWSqXCNjstIV9bIlGyNZqRB12dobCJKsVfnJmmmq5Rlu7nw6Xg2pT5+WlZQ61RRlNZ7EpcmsAp1giaLNQUyX+8MARvj1xisdeuozHpZJN17BYZRCgoWlYXVbm0hlqepNctkQ2XcIXcnPXlk2cnFigXtGolmpsrOUwGk1cHgc+m42b+3pYMat4gy5evTTFjp4Obu/t5bsTE/zGof2cXF/nwrk5FFXiD99yEzcN9DKWznDy7Bya0OQ7d9/NN18/y3B3jPF4EpfVwuz0Gp+741bmqxVeW1jEZlVIVqu88Mo42WweX8TLubF5CrkyoihQKdZ4ZWyOJ5OTvL62zm/t2cN1HR28uLJMslJFlkQKmsbbBwZZKbXQTADlhoYqy/itVjTDQBBM/FY7Jiaa0eShmTE+99opREXk+OQcuVyJsN/NeqnESrHA0a39nFxaZmxqmdmlONOLG9h8dvxBJ/HFFM2GjsVpIVOqsFSdYXsgyAM/OU1qqcD5sVXKYXhDWwfnk6t88/nLFBJVjHgVdBNPyEZTNxnubyNZq9DQdIxmE6fXQSjoYKNQIxS0kU1WoWaQTlS4746DzOYLVBpNXl1dY9jvo9fj4dTaGu/bOsxTs3N8ZOuNOJUwHtXkr86c4L+NHOSOR7/AcjnHa+ur5Oo1npyfwyo36HA60M00EdsOulxbaZoJdofD/HBylI6uIGGng+88fZJqtYHVIvHefTvxqAFkwcCtKiwWC/zdDXdgl0eYKbzCq+tX6HHLKGIYu1zCoUCfZwiHYrBUShC1D1A3pvny5bNU9Qavb6xgk5O8vraGRRb42ugxDsXakESo6a1acr/Ph0tVWSsXmcylGM9keHTuVVK1RZLVHH3uIUTB9lO0kc/SA7RMOoIg4VRCP4XEa0ae5VKOb4+NMRIKUTdM7u7vJ2RrTXNbZZEPPfcD/ur5Z0iYGhV9kdc3zhFzNHlk7gQ7g7v5zvir/I99h/jK6ClWSrNs82/l1Y0nWS5NMejZhSR4cavdaMY8hlnBrihcyVyg0kzx9bErHI7FOBCNMl/I8sTCNPdtG6Eom3xk6wEEQUISfYgUsUptmEKO55aXKWg1DkbeSE67iio6GPDK7AjuRBI86GaG9WqOimkyO7fOO/aNELDKBK1WKkaT27q7cSgqqijzu88d45b+Xr4/Pk6iUmHQ50MQBPxuF0fbY2wNBgk5bOwIBnnq8iSnFlYwVYkjg730etzMFguk4nkqhSoWq0K+ViOZKpAoFHE6bFQbDeqVemv4Rzdo6iaqLBJu85Cttpzpu/s6GU2lONzWhmbonFpbJ5sooFoUtFqDeqlGsN3Ph2/cz1KlRKNaJ5GpYQtYKeUqxHrDqFaF586Mc3VilcHeKFV03rZ1mG63m6Ox7SyX55EEEbsiU2zUydZrpKoVfrKwSKpWY0cwjN/iIubwkK2XKTcbJMoVJEFgNZllT18XG+UKDkVhrVRGEATeNjBIl7s1vb5RqbIzHGS1VObU+gYLuTy3bRqkbBH40LYRzlUyFLQm5xMJDsZiHOnpZDSTodTQSFTLnIqP8unTrzGRTnNifZ1HZsbY5PeRrNZomgZhu53LyRRRh4OzGxtcXY9zXVcnx1fXCdqshFxOYk4nYbsDt6ry5eOn6OmLsjifRJVFqjWdYqV1D9zU76Neb5LUKuzsDPOjqSn+5M038M+fe/rnNmh+8YHP33/3e9/wU+f5v/b7T/8VNH+29Q//+IX7P/SR2xEEEwGJtXqW5VSSQNhHl9vBmXiCe/Zu4e/ufQtdrgAWSaGma3xj7CI/ef0ym3d0YnVYWJlP0qw36OwM8yt7d/Gp628gXinS7mjx1uxyG3PFSTTd4IdXRplci2MoInZV4eM7DuJRffzthcd4ZW2Rb49d5vmlRayKwha/BYcywpev/BOpWgbDNOnxuLicTtDe4eHebZuZzedYKZb4pa1buJBItnp1BNjk85Gqluj3eilpDR6emeXByXE0w+S23h5KGDRNk3StzlQmQ13X8dls7AyFiDjs1HWdBycmyebLlPIV3jg8QIfDQ8jm4M7+AfYNd9BEQBJEdoUCXEqmyNdrHF9ZIejQ6XIFEQUbDtmBJBjopsG3xq6Sr2s8+LZ3MRRw86beHi6n0kSdTsaSCV44P8EnbjzMp0+9zj89cQpVaQ0siaLAtt52jl+dIlkqY7NZMAWBgM1O2Wgy1B6lrz3E3kiYLZEwlxIJ5taSlEydXq+XRLnM2weH2OztbTnnh8KM5VIkiiU2R8KMpdO8fdMgfR4vM7kcpUaDJyenGYmEObW+wVdufht/fe4VtgX8LAo1IjEv2UyRdKKK3aGQK9Qp5gpUClVK5RqpeIFYpx/VqvDqhSkS6RoOm0wiU0MSoanp3Lh9iAsb60zncrivBe6RnnZsssyT09M4VJVtwSA3dnVyvpBmbTHNuY118qLJpZkl+jfFyGdK3L51kM1dEf7p1Hl6IgHaXW7+xw2H6XEHubmrhzsHB3hwapLXz00gCpApaBQyJURMnB47NruK2+cgn6uyMJdhfj7F+VycgNfFb+48yD39ezkVn2etWKTL46Ld6WSxUEQQQJVaSjztGhS/qGl0u11MZZPc+8B3ePi1cayygOBQcNgtOJxWcvU6a2tpSvkKNQVyqQJjkykaTQOv18aHD+3hLQMDSF4747OrbO3twGOzUdIaPDYzjc0lEV8rQanByYU1vn7yFE+dn2T3/m6WJlJgl8Gh4HIqvP2Ne+l0OQk5ndw01MfOgU4uLqwiiALf+PBbcYRdnLy82LoZ1HWe/cSfcy45TrpWwzRNPrHzIAcjt6IqCb4zOs7h9nYOxbaSrM7xg+mrHGmLsT1wE4oc55mFBa7raOd7Y2MspDNcyWYQRY2gzUKmvoJL8aBKXTiUdi7mRrlncIB/evUsWq2BYUK0M8DH9xzGRKParCNeq9b0uoNcTp/n5MY6R9u6OJtc4Eczr/DpU6+yIxTCawG7bMGj2vna2NPEnBacqsQfPPg0n7zhCMlqhV3hELd2HsUql6jpTVLVKr/32HNcWYuTp0lFN2hz2AnZ7XxndIwhn494pcr1HZ0oUhFJkNHN4k+tRIZZpWFu8L2pJ9jqb+O55efodvmwyh3opDgQi3Egch3Xte3DZ/H9FCY/nj3FYzPThH0eIk4HUYedNqeTjz/7DKl6nbcP3srdA36WShlWy2Xqus6ecDvdrs10u7ZR02cwyPE3Fx7hSGwEVeoipy3y+QtneWJ2hvdtGWZfaDudzhHe9+jX+fptv0LAuoXNviY+dSerlStAAUmUsEi9tDkGuamjhycWLtPvqfPQzBg7QwHcyjYQBFbLlwna9rE3vJM7+7axZ8hF0Grjr8+d5PmlBZyqyhZ/gF7XMDW9yNuGN7M90Eu+UQRBpNvtIlGpcmxqhj3tMWbzRTK1Ot88fxFRFrlt+zAgsMnvY7FQpNfrJaXXESURxdrSw5qYGLrB4b4eEo0atWodWWgBt7WGgcsuo9gVQKBarnHdUC8T6TT7Y1G+OzbB6koKQRSw2FRqlTq1uo5R17i6Fie5nidbqFNvGLhsMoZuoNUaIIAv5MZqldEkk2q5xitXZ7lt6xD9nu1cTk8w7NvOVG6Bv79wgePLy1zX2cHNnV38cHKSR2emWCjl+cdL56jqBmfX1/ngtq3EKzU+deMR4pUqbovaqiRLEl0uJy+urJKsVnlmfp7xlQ1en18mqzf4lZERJnM5zqyvMRKO8A8XznH34CBDfi8HYlGeWVzi0ZlpQnYHq9dwS+lajUqjSYfHjUu18NqZcSZTaTrDfm7t6kIRRV5eWWZXOEyqVmM9k2dNq3FjZwdvaOtiIpdBFgQKjQbpWg2fx8liIoPTZWV6PovXpeK0y1RqOp1dPrRqnWSizBOXxrkwusZ91+/hG/8Boexfu774wOfvv/tnqGj+R5zTf4qg+dWvfvH+D37k9p9iO7YGfDw8M8GTr4zyyMWrXJha5tzcMnft2IxHtZOs5fnG6GVeXl5Csam8cWCAG7o6Eb02fvnQHu4/8DaGff3YZQeyoGGTbYiCnWLjItsCIe7u244uNXjxwiQOt537do4QtNr4wfRFBrxu7IrMb+w6xFg2xam1NSyKybbAdn7t6W/hsNt4/+ajiGKd9abGYDTETR09rJUL2FUVSRC4Z2CIrUE/v7bjViJ2Cw/PTjHk89LvCXBDezd7omE6nA6GfAGOtnewJxymYTS5mEiwPxbDdw1ZZAK5usZYKkUxX+Hdh/dwc2cPsiihGQ1ssoUet5+o3UK8UuGpuXlUSQQTas0mmbrGloADl+JAN8sooo1X1qZ5+Mo4M3PrfO3UaZp2FcM06Pd68FhURFEmUS4zFA1xZnGFcr6CdO0G6fI52dYeY3otgWGYVEydcrOBLIoUtDp/cvgw6VodSWwhKPbFoqzWquimwWqxQL5e5+HxCW7t6yBg9VJulrlreBMf3rUHuyLx3SujHGhvo9ft44m5Wc4vrPDhPTt5YmaGj2zfzi899hCyKvPS0jJBu52VtTSqLGAIYFElwmEnTrcdi93CrqFuJmbXEQVYmk+zka5SLTVomiY2i4QiixQrTZxhOxsbWUSLxL62dqYzaXq9Pg7FolxKJllN5aiLAsdXVpiaWOa9tx5kPp3B53bg8zjYGYmyuSPK98fHCdjsHOnpxKaovKm7ix3BDmyyBVWSsUgKN3R2kbOYnBpdJJvTsFglgiEXH7p+P3t6O4n4PBQknVKujMMm83t33MCbuwf4wfQoTy6MsT8S4td3HOKl1UW+eukSVlnBIsk0dB2boqAZOroJBa3BhUSCj37pMYqVJj63isUiM9ARxiLLJLIF8pkiqkWhoz3Ib+3dy5H+Lk7OzxMMOfnMvbexIxgmVaugGQbv3b+DiUyWSzNLNBWBN3R20hby4+t1t3q6nApOu4zbbaOQLuENWiloBh63gs0isZzMUFGh3tR5cXqOqmlgWiU+un8Px1fX+OTufQxtDvPU8TFomvzhL9zFq+tjzGQz3Nbbw2w+TcxZ47W1FZaLBZ45dZWsWmBPOEpOqzKTy5OszvLSyionzk7S2xlhVzhM1TR5z/Am3tDWQ7Ze5qPP/oSameZs8hKGucp9W9/HoNeOaWvy/Xf+GfZQgS/cci82eSuKGMEhx7DLLrpcKq9vzNLvCXE2sU7EbqXH5eN8Ms5yoYDl2sBeTitTbFQJ2qz8wWuvErLb+edf+FPCNhcBK2zx3cTF1CtsD3RQbFY5vrpGXRWYnV7n4HAfZU2jw+UkXqlwpL2NO3s3U9ZrHIwcIKslkIUaoqiS12awSEFMGihiiB3BESTBRa8rhiK1IQoWApYAXlXHpEa2vsivv/wQd/XtoW7MsVpO47W14Pwf276HHy/M0efxcHJjnXaXmzf3HEIUSnQ4NnMgMkzQViddj+NRLZg0sEi9LJfHmC/k2eZ3YZFi6GaSf7h4hj87cpSRwDZkMcTnLv0z37jtU4iC/drwUBulxlU8aoCaXqVp6NjllkdcEpzc2LGPV9fPcGvXIM8vjzOeHWOrfzdeSzc1fRRQqDRbwfLllUWemZvl+q4unpqZ4daeHqL2IRpGmmy9xGfOvc4Ht4yQqVWI2O28tX8Yj8PCd0bHuKW7i1KjyUAwwFQihSYY3NzdhSJKnFxb487+XuL1OsvrKVSr2nrAiwJ6Q+fVc1MoiojDbecN2wbR7SJWRcBqU38Ke28L+znc1sZ8ocDtPX08MjHZCpfl+v8OmYaJ1++guyfCweE+JubXaYs6+Zt7b+NjB/fzyMQE5ULL6OYPeViZi6NVNT5w80Heu+kuTLNOpyvEqfh5tgc6qBp1PDYbblUlaLPzrk3b2Bz08u1LV1iaWWelUMAQBWbyBdqcTmp6E6/FwlKxxEQmQ9Bmo6brdDgdBG1WsppGQW+wv6cLu6zw6Mw0E5PLeP2tgdBsvcZQIMDp9Q26XC7OxxMsJtKktBrbQiHqzSZn43FKDQ2nqtLudDCbz9HRFuRALMZULsdENsd0Ms1Ll6dI5IsYusnOznZkSSRVLZOp1cjXNWayGVRJ4tnXLrOykqeYr6IoIoPDMa7bMciZq0sU8zW27e6mpzvE2loLq3bj7j4e/uJzP8dB8wv3v/X9P0PQ/Jsn/ito/izrH/7xC/f/8i/fhWEaXM0s8sOpCV44PYkiC+gGNBoGlVqTA1u6WSpmmcpmmMxmcagq/aEAXovK03NzvGvzJrpcbtyKgEXyYdLkfHKG5fIyVqnlStdNg+n8Om1OJ0tmnY14lpIEpqAzny/y5p4BHp6Z4S09b+CGjnbeMbSbbf4Rnlp8kodeuYjVZ+dAzI+AwM2dvby17xYqeobvTowRstkQBPBZLXx3YhJRqhCvFEhWq4RtNnxWO7rZqip6LFYkQUQQBJqGQcBqpd/rIadp7ItE6HC62KiUeXJ2jlqzSdMwuHOonx63H0EQsIgKkiChijJBq4s+j5u1aol0tXot+Gl0uNwcirbTNDWKjQqpWpaGaRByu4hF/MSrZT6ya4Sow0HTMLDJMlv8fnpCfqayObZFw6TNOtViFVmRcXkdNAWTgbYwFpuFfLGMDiRyRbLJAqdzSdwWK6lalYfOXOZCIkGxUqNaroEoggCqIvOVl05y1/ZBJrJpRgJtNAydTL3KrmiYLpeb3375JWZXEhSzZR47McYNI4Pc3NXFXz/0MrlcgfhajkDEQ63RmvoUTAO3x0Z7d7iFPKlq1EQDs9GkWqlTqjYRBQHVImGzSPi8VhRZpFJrsrCYItruwW618NLJUdKJPFfmVjBdVnp9XlaLRWZXE/SGAlRkk8nVDQzdZHZ27doAlsxtPd2Umjrn4xvoCKwWi9QMnV6PE4dixTBNZFFCRCDisPBafJVmvUEwYGd6PstKKYfisDAc8HNTTxc3jwyyf1M3b+4ZpK43OBOP85beHl5bW8ciG4wEQ7gsKm0uB7phkrp2zXXDZL1U5PXxOR554So+V4txKYoi5XKDTCaPYZHYFI0wO7dOrdKqgn7r2FmePD9BIOyikC0zo1dQZYkfz82zPxrh4ZkZZFHkVw/s4Xw8zlwux+/u20+x0STZKLd0pLqJqkqoVoVKWSOfq2OxytgsEp0DUWqNBnZV4WhPNzZF5fbeXjpcLh4cHUMX4eGJCVwehUiPl3u2d/KVyxcIOVq2Kpss8cT8LE+NTzE/s44gCLxzz3Z6XD7WywXanU6qepO/fPAF3G4reUlnfyzK4bY2Dkf7+OixH/Oj8XHq1QYdfh8n11YZ8vtYKk0z5D3MrlAbv/3a1xhLpzkYC+G1dLNROckTC69yMj5Gh9PBJ196gafnZ1BlmTt7NhOx7efb4y8gAEfa2yhodWZyOVZLJWRR5INbd1JtavR7uhAFBzY5Sk0fI2KPkKylObGxyoVEgtV0DkUR+a3D+zm5voFhwpG2dkaC7QSsO9js7SKvLeBRXVilAeaKlzBMk0++8kOKzTVC9jKmmcIwU3x/+hW2+WOY6EwXznLfc09xKbPGvf1v4639RxEEhaaZpM3RR8Ru8tLKMna1xbgd8HpQZJk/OfB+RMHCI/PPsS2wl6YRJ2zbRbo2T8DaxVJpDLcaQBbKHIxsYbm8RqW5xomNBe7bvotOZ5CHZ8+S01Z49+C70Y0comDFRKfYGMMut9E085QbVSL2AximholGywsC/Z4uKs0Ey6U8O0NRfGongiAii2F0M4emF1ElhV2hfuL1LN8/dZF6RWNnZ4xBTwd1Pcep+DKr5TJBq0qp2SBdrbFeKdDhdHKko4OGYTCRyXJDZzshj4u7+vv50dQ0xyZnSaXyrDXrxJwuqgokVjNotQYWq4ogCtgdFiJhHz1+PwdiEVbLFYqVGrWqRiDk4ZbeXtwWK3f1DbJUKjDs9/PkxBTlfKU1ja8blK9V4NZX89jdFhxWCxuJNOlcjUi7n6qukdYbzMxuYDR04mtZQlEvT33o/ewMRrDLLirNVep6icfnphgJhhn0+rmhrZdjKwtEHXYaRgNN13ns4hgWm0qlVGNnfxcLyTSKqnAoFsNvtWKVJYavbWd7LCpBm5UOp4uYw4EiK4TtdiRRZJM/gN3jIFersZzKUqvUuVHqakMAACAASURBVLK2QUOEQ23tvLKygqRItLncbPJ7Ob0RJ2Czsz0YZCqTRZFlUs0ae2Ixtvh9XN/exc5QiLKps1QoYHfZaDaadAf9LBUKnI8nSFUrnF9awWGzUtebLC2n2LStnXymhN9rpVbRWFxPkVouIlgl3nFkBIskk61VyOVrvHHXIE98+djPbdD80gOfv/+e9133b946//p/QND8V+GNft6XaZp8+uzLiAI8ePwcs4s5ogEbtbqOogjYrC2f8h8+/CzJdIXOTi8jA13sioQRBYHFYpHd0RjJahXTNMnWa1ikZVyKSkHTuL17D2CyWl7lxwuzfOfMRd62axu/f+Ag88M5np5f5IsnzvDuXSMoosyl+AYn4yfpcweoGw16Xf18e3SMd9y8H49F5aHZKc5tbPA3b7iJ5fJlilqddwwNcnJjgwuJJKPpLHZFwaEojKUzlBpNFosFYg4XmtFkrVRi0BtAEVuXTzcNDFMiXqnygc27UEQJSbDy0uoKNllhbjnBQFcrfBqmQcNoYpetNI0miighCgJu1c5IMMCuUJBHZ+YIOxxUGg0em5vCqSiUGg3ilVaTfMxhp9PlxK7IXN/eCjPpeoGrqSSSIGCYcM9AH2Gbgzf39PAPly4zk0xh6AaJYolCvU7I4aA3GsSpqvisNpyKwkQmzWgyQTpbRFFlZEWmkC7i8NhbWjoN6hWNuw/uYDKX4ktnz+M/aiNVrfCd0TE+uXcv/zI9Q7FaJxB0c2hTHxdX1/j763+LwU9/AKddwWJXAY1ktkBiOY0n4CST18gWNNKpMsGQk0y6TK2iYbEqWHUDM6/htMsYhklDN6lUGsiSgMPWQpns6urgyZcvoioioQ4/Q7EIL5wdwxty0x32s1CpY5FlNkXC/MGBg3z82HPkUgXeONBPUdP44oUL6IZBvljh+q4uTszMc+bKDPsiEYKWVi+xbhoIgsDDs7OkNnJ4XGqrp7Wqc+XSBnPzGS4MRwgE3USdLjrcLgJWL3W9znqpRNTu5qbODuyKwlw+h0tV8FqsuBSVO/v6OL66wpmNDS5NLLKymkeVRZx2mWxRI7dWhoqO0eviTe1t1wD9oNpUDg718mK5Rq1cJ5UoEol50ZpNpnN5Dra1EbTZ2RWJkK9rCAI0DAOXamEym2Y8ncLlc1IuVMlmqwTbfJRyZTIFDUoN8iZ4XQrr8wns1yDdUaeLklZHkUL88fFXySby2LfJ7IjGIBrDZ7Xw3PIc/23XTv7s+Gs4FJXT62UkQeTt27fwlH2W5FqGxUKRXG2SV1ZWuTyzRHt7EJdd5g/vupmw3Y7fasUhKzw6N0pJq5Naz2K1WRgJ+rmwscFfvPwad20dptv1In3uvXxs+w40o8lfnDnBF67zMpGL84VXTuLxOXnnwAi7ozGu72gjXqkiixJNc533Dm/mheUVVkplPrRlN8lqjpdWl7mpYz+mWWNvWEQSnNT1GbL1FIZpELS6SVRLvLVvK7phMhdP8dZ9I4xnM1hkialshsVCgTf3dnNP3zYWSpf4/uQkHx/Zx7p2GkWUWCnlGF1Z560DffxkcRqLJLE/0tZ6cDdX8Ki7iNn9fOGmm7HLVnQjhyR6MUwNUXDytxce4y29vVhkiZdW1uhxO9nsizGRzQACq+XXeXFphQ7nU5Q0jZ2hBD2uTvLaAn3u6zFMDVWy0DDyzOSzxBxOAlYbmtHkB9MXeUvPZoLWnQBIYovDKgoW3MoOGsYyFrETv3UVTV/EpI5FGgJalqCmmaTW1Li9awvzxTiGWUQ38qhSN6LgQpVS5OpFpnIbTGWy7BrsZn8swtZAiPXKBL/x0jHeM7yJ39i5F9M0sSkKJ9fXmc7lqTSahGw2Ko0md/b18j9Pn+b3Dx7kcPQws/k868Ui9UqdU2eniN6wh2pNw2jq5Et1LDYVURTQag1mJlfYf8N+PKqK32blSraEKIkc7ujgaFsbT8zNIwD9HjdrpRJDsQivr2YRBKhpOsPb2pgcXcNulUmvZ5kYXcNmkfC7W6itfzx7gVyygM0qE+0JkVzNIKsyTtlGopZr9atWMjgVKy5VxS5ZmK8kwGKyNxxmNpcjr2lcTaVQVJn15Qwen53jp8fQdYNPHNqHbpp89tw5SvU6HZ4WPs9rsfLxnTtZLOZZLBa5s6+X709OYVdkgjYrPmsEp6pyMjdPPlNCEAR+54bDDHh9yILIPUNDfPG5V9kVCWORZNqcDvxWC9MbCabWExSzJRI+H+VGg4dmpik2NB47eQlRFFlZSGFRRC76N7ArCvlKlV/ft4cTLjenlpbJJvLoRmsGoFhuUq23uLiiKIAi8vvvvQWAzQE/DNNqc4i1/fsEmJ+zJQjCbcAXAIkWE/2v/l+/3w38GS02epOWWvzV/9Mx/1METVEQ+cz3XsBhk2k0DCyKhNUiUSg3fvqwLFWaNNfKWGwSy8s5PAEXN3d3MZXL4bNY2eTzUGk0WS1XKOfyrJXKbPK1JmsfXzhDpdHkSipNulrF4bFjmNDjClNuaExl0ty1bZjbu3tZK2fxWqx8b2KS920ZRhVF2h0rdLg9xBx2PKrK4zOzpEpllktZ/vuxF9kcCfHpIzeyxR9juZTh7y5cYFsoyBZfjOlsjgPRMHZZYaVUoMPpxmuxIgqt8PF/hU0Tk7ymsVBMELF5mCsskavVaXe7qXc1ef/WLfitdgzTRBFl8loZt2KnaeropkFOK+FVLYTtDu7bvo0z8TjpWp1Ks4lTUXh6bo6jnZ2kqjWKmoZLVa/pGC1YJOhwtOFRHTT0JjZFYcATIq+VcaJyQ2cHk+txqqWWhrFp0REFgQ63G6us0DAM1kqlFsg9X6JcqDLU345DUVi5pm00dYN8tswv33KIN3Z1Y5Vl3jeynRPrG1SaTfLlKl+8cBG7qtDl95KuVEhXKzz3zg9xOvFjavUmogB2p5V6RWN6fINyTefO63eTip+lWtep1nWm57JEA1ZS6Qp1zUAQwG6VAJAkAY+r1QJgdViQZIlqqcp4KonW0Nk80oXPamU4EGAqHOeWTYOMpVIt0LkgkKyUGU0n+aXt2/jTxQQBq4Wnxyf59lvvote1hzc+9GleWFhgqC1C5/Agx5ZX2OIL41EdmJicSy7xyCvnSGbr7NzdydzkBpQbAJRNOHd2le6OHMmQm1FJ5J2DQ2z330jTfA5JEElVq8QzaTyqynSuwHJxhVS5wvVdnfS4XRyOtXGxo4O/e/410qkSLq+dSq0JWQ17rwvDhBfOjVEu1bFaZNq7QkQcDt51dC9/9b1jeJwK58+t4nKr1LZr/MKbbiZks7fwL4ZBrzvAHxxUSdWqPD2/gFVWMAyD3v4YnnyJcr6CaZhsH2lj3pYglavTt6WDfKqI02sn5HCgiCIlTSNks2EYBr94dC8Pjo3xi1u38vLKCvVmE0UUObW+QaffS0mrM7uRZDAa5vmFBa7r7ELq7sZrsZCq1ihodcIxP5+94UaW9hTYG+6ioFUIWN188fIp7IpM1OnCMaByU3cXC4Uir52YRJIEHtUNVksl7h0ocCS2B0WI8bbBIv/z/Ktc19HGI+9+NwuFNE1T588P3YJh6oiCheeXr+BQlqg0Gwz7fbhVlXKjSrsjyIeGt5CuLfLaemuL129dIV1LELL1Ml8cJyy4GAn0kqvnOdzWxt5IFKei8sziPHf29XJ6I0G8UqZhGOhmjg5HmIPRHC6li2JjEt3U6XR6GYqFeWJuno/v2EnY5iZVK7LNd4BzqdcZ8BjM5hNcSScZ9gcIW1sDE7qZIVFdQjcNPvL4U9TKNSIxP7+5861sVLKUGk10I81fnjnBVCbNB77xQyLdIf72lpvYHeyjbqQB0IxZMvU8U7kEg14//a7d/N6Jb7OYz/G2oUFWy1n8lhI1ffX/4X8XBBFFbEMQFHQ9T8PIkW9UiNhKCEiAjCK0EbQ2EASZXL2GTpHx3AQF7TJD3jAR2wES1eMcX1tlJp7kHSNbuaNnEKdsY66Y4DPXXU+noweTJl++8hKDXi/Dfj8zuRwNwyCvabhUhfl8ni2hMPsjg4DJnnAEuyzzuZdfR1UkXro0wVfecRf3Pfg4+rWeSXfASWo9h8Nt47WVZWZzOaY3Wm1EvZtidDid6KbBaDLBdC7FibUNmqZBw9Bx+500G03s7tb/vlrXaTQNFFm4tmOn0x6SePryOPlUEa2hU6vrvL2nm1eB2fE1fjR7lfdtOoooOOh3+9DNPPdt2UTTTLJaLtLnjrI9IHI1fRW/1crLp8coV5vcdt0IAGGHg3Pra+wOR7maSrK4EMcwWi/Aa7NxBjZ3MJFJ8/D0DPFyiWOLi3S43LhUlecWFujz+Xn/8GYurK5RWkoTjnqIOZy8sLzE08dGGe9Y5t3X7UU3DG7u7GClVGKlVObsmWUMwyTst2KVFfKaRrvTjm7Y8ARc+NwOnF47sxMbTF5eIhT1UM5XOHxXD/siHfxqOsXllSW0pomymGLT5jDVco1kokwk6mrZgEyD0WQSn83G/miUdLVCulb9d8kvP/MS+Km68/+3QwqCBHwJeCMttfgZQRAeN03z/26DPAY8bpqmKQjCCPBDYPP/6bj/KYKmgMCvv+M6vvLoaxiGicel4o96SWQ2WI5XEEWBptYCRmuagSIZzE+ssz44yKDHgyKJWKRrYUIU0A0D3TD46sunsDmtfOzQPg5EY7ywtMy7N29ieyBKvFpgpZyixx1kb6yNp6dm2BsOczjax58dPcImz14y9WkcsoXp/Dg+i4WGYXA1naGuN+nweWh3einlyqw7bHx99CK/sfMIm71W/uiglXZ7D5fSY6iSxGKhiCpJhGxW7LIFE5P1ch5REIk5Wuqyuq7T6XQwk8vy9MIieU3DNE2SlQozS3H+Ol/EMAxiXjc+m41ao0mnx837Nw9zNZ3k2NIKn9yzF6ukoogit3R14VJsxOwD6GaGvKZRajT48fgkbqeNfLHC+3bv4HxymojdSbsjjEvxYMoaXouT19ZnORLr52pmhWS1isWmsjSboKs/jCAKFCs1mk4XXouFsVQKVRIZ8gfYF2vj+8fPUGlohOx2uj1eTi1Os3dbPx+5fRu7Q92YmCyVkmwNBDix1qrMhG1WfjI3x/kLM4TbfNyzaxvDPh9/dPJ5ZjIZXG4rhUKtpT1rmvzeu25hi8/H7zz6DOGYl+RGnrqmY7NIfPD2w1QaTb737CkkUcBmVzF1g0ZDZyNVpVDSGNjS9lPYcno9RzJbp641aA+HeXZ+jnK+ykw2w8nTU2wa6eT46DT5dJFfPT/HwLYOLvzapxEElV/d/kHOJJ7mg0/+KcVsifO/+lk+cfwBHjx2GqvDwpt7utkRaFXzZnI5ZhYL6NUmr78wA7IIXgs0DARFxMzUsQ21WiMqxSpX0ym2+Rt0utwookyny8VfHDvOH73xemRRYCaTodnQ+eqLJ7E5LHTFQtzW28Oz932IW7/6DXZu6uFEaYK438K77tjLR7Zv4+mFRT734EvcfGM/SytJvjcXp6c/SshnoVrTIVGlCJw9s8x/5xjRgIdPHTjAxx5+Ck/AxUAwiM9q5bHnL2Kztl4G7dbWbcjlsmB3Wgn63TAk8MmRLVSaTb47dYqVpQwbbgvRQy72RKP8YGKK94xs4wdXx9CbOolqldVCAY/VylwuS9Mw+JWdO5AEgS63hw9vbTEkH7hygU+MHKTYqPD0wgzxTJ6I38Nnzp7ls9fdSrKWI12tcDUT56Hzl6lVND577+0sFouc3tjghRNXCYWdJOIlGvUmz526QqpSJluvcW/fO3lT57009B/x2Ow89x/o4oFLl5laizPS3c6be3p4S89NvKXn7VxKP8PDV2f54wNvwKN28oVLT/CeTdv41uXHWS4U+fThm3hlfZamkWG5lOW55Tl+Mj/P124J4FB2IAqnuZRMclfvVmRRJl4Z40A0Rq6+zEgwiCi0NspUsZedoTJPL73E0VgfD1w9S76u8QcHD/LxY8/zhQsX+LvrbyNk24qInZVSEY9qod3h4WwiTsTmRBaDNIw4ihilze7hA8Pwyf/F3XuGSXaWd96/EyqnrtBV1V2d00xPT09OmhlpgjTKQhISghUYEMbYgGEBY4zwGozNGjCweAk2YJAwBgkBQjnHyTn1dM65urpyDuecOvuhxrzsu6+914uxP3DXlzpddZ1zXdXnOs//ue9/2Hwd1z7093xu9248pg5+NPoEfR4Pj00e4/FXz/Nfbt7B8aOTFEthnu2cZYf/VhqtPpLl89gMAQrqCnuDu9Gpsu/Rv6Y36OcLe67lM8eOsBhL8sVDJfY13AqArmtUqlOIggNJ8KJVo1TJ8+riGMv5Ag/0epAFL5qeoqCmObM6R0lVeWFmDp/FykgiztNT0xxsacFpnOau9mv4cL+Pezp7CFjc/GzyMi/NzvK1624gYNnAVy/+mKeGRtnW2swDvVv4q7NHGI/H2dPUxA0trUynU1jsMm/r6scodpJXh0mWimSVCkpFpVhS+ea7bqLeYsfptVPMlZiaSeBN5LGYZWSDxNJ8lGJAYWFqlZauABaDgUeGh1nr9fHgzp2Mp5IMrkSolBU0RSWTyKFpVWRZpFKqTVQy6Qq+OhOSJKBVdRKZCoKQRVE1rDYTDS12chWFTY2NpKIZ7uvqR6dETqlFP1aqCpeiVzjUvIF9jX0AJMoRbmxt5fDiIh+9bR/fe/M0mXKJhdUEb7t+H3d1tDOfqcUVb1zbytmLE0wNLdHaE8RpMjGfyzGfTlHIFCkVKhg6JMqahkGSePzNs7w+PE4snCSTUwgZZWYyab5/7CylcIHRRInN9/qIFoukymWemZzk49u24feYKVc02rr93N7RhlqtGcS/OjuHruu8bc0afjI8TJ3TRDJdxmg2sLWnl8l0lNcWFnAYjfRtCFHIFvE3eCiWKqwup7BaJNSrgDxWKBK0OyipColSiY9t3cpPRkb/80DMb1jCbz+Ccgcwqev6NIAgCD8F7uTXYsd1Xc/92vdtwP81XvJ3A2gKAu9cu4Zng5e5c/dm/BYzj128QrGsIcs1L0DZKP4/INRpRDbKjMTjtDlbWczlCVhri7nbZCZSKDKfSbOuq4m5aIILq1FOh1e4b003PXVerLKJgMXJNy+fZ4PPy7NnBrA5LLhMJmTRwkw6TZ/bjapr2AwbeXLqOziMtZ3YSj5HNJZm0/og3x64iNFkYG42Qr3VxufPvMFdHe1sq9+DIJjY4FV4eGiIu7s6ka8KZJSqik02Y7ObqepVBASS5SIFVcEoStzY0stbO/qYyh7mi2fOIAqwGk5himcxWY20+TxcmJzHbDPxytFBnj49gMNtIxXNcOzKBM0t9fzgxtvR9Co+cwCDGETTEtzc2sYvJicI1DlxmEwYrgLzlUIetVqlydYISCBUUbQSW/0tpMo5JlMpnhkbJ5PIcffBrSiahiyKnBiZYqQS5sWpASLxEi0NVt554y6OLsxTLlZYjaTIFkpIkkjf2pr10uGlZdZ5GjCIMq/Oz/M/nnyDxjYfJy6N854DO7l3zRq+sVDLkD8XXuH+nnVcisW4cHESq82E12cnlchTreocbGqm3VGPJ+BieXqVckWjpd2LxW4hWSojiQKdvY2U8mVEWSQ8F0NRdcpKFdtVZWe5UMHqtLC/pwund4Zv33gjg/EobwyMsbCYolwooyhVJgcXa6bzQScGo8yN7R186M2HADh+eZxcpsQ33v0WBuMJ3vbcV9jWEOQjt+/j288f4XPHj/PIrXcRLqT51usn0LRqLdvcIIIs4rDKZFNl9JIKZol0uohskJBkiacmJ+mqexOv2YQoCDTa3EiyhEU2UFBUysUKmqrR3OrHb7OzO9TAsaVlCqrKK3/wPl5bnGIqGsNsMzO8FObji2Gii3GcdgO5SoXOlgAnTo9z6fwMuq5js8i1xJ6qjlbRCM/FWJqJ8tF4mjXtjbx2ZJgxaZFP3HOANz71Pn46Ns5QNMr5C5PcsKef4aUwEyMrDA5HCHotnPaFKWsqrWsaWV1KEGr20eywczYcxmux8uOLA7T5vUwtrXKgqYnHTlxkJJKhtaOeBr8bWRSZy2QZWI3g2VKHWerlM9tcaFUFm2zmHT0bec/a/Tw2eZTL0TjxUoYmW5CB2AB//suXqQ95mJ6K8f6HHqexzUchU0Q2ypTyZaq6zspyCotJ4vLwLO/p6+OZ2Z9zR9vbmM9mee3iCIuZNPMrccw2ExPxOHcc+CMkwYlajbGYy9Jot7GcT/KRN15EEATe1rWWj27YxVIhTp1pC7e0tJOsTGA3GplKZ7iprY0vnT/G53euxWm0cXdHP6Jg48jyZe7t7sJlrEWn3tTazmgyRlXPoOtlnAYvh5odXIhOcW1jI3UmM3bZzJNv+S8IgEEMka6M847nHqfXV89Phkd5X38ft7R28N3BAR5YpyELEh3OAKJgIWjp4amZ1/j7O29ju/8WLsdfwirLHGhaz51P/RNlpcqzRy+ycWMj79q6kRemp5nLHsVmMOM01JOtrNBqX4ck1BEunsPjsHHk7AgXpxb4ym2HWMxl+ZtTp2EX7G+8Ex2VaCmKz6xSVFdIVfIELEFubb0Ogxj81Townxmi2b6WXneRD776IslMnl+6XHxs016CNhttDjdtjus4u/oCG73raHOsu5r3nuLT23dcbQqcZCSewGQxck9XJ5pexW+xcia3QKfLiUGUSJVL7Ao2YZRkND2KTe6loE5yT2cfZknmay8f5eM/ew6j2YC9zkZF0WhrceFrcJNJ5BgZi+JxGjGaDOzeuYYOt4enTlzkhQ+8l1cWZtjgrfFAAXKpPLquY7Ia8QbrMBsNrK4kaW/3MD+fItQZxLiUwFFnJRZOYbGbKcRyGCoqa0JBLiwu8cPbb+f3+/oQBIEPvPok/3XLZjZ6t7GUH+X2tnt5ZPwR3tq5CVkI0ukUKGt5lOo8z09NsWt9J3OpFImVFCOJBBcjq1wen8PqtFDIFElny1jNMvs72hmKRnnkwgCaorKmpYGjp0bo8XoZj8eJpDNUqzpz01F89XbEdAUAp9HIlaEIWCU+/YEbmU5nOLKwyOXLU7StDfHw4CA7t3ezKRBgf1MITdfJVCpYJIndTSFOCfCT4WHu6u6G7m48ZhN//IOnuXRpiUefPYfDZuAv3n4Ir8XKydFpPrVjB59+/lUUtUrHmiDbWpspKBVG4zH66v0U1Roff6O3hefNs/85AOY3LAEQf/vJQCFg4deOF4Gd/8e1BeFu4IuAH7jt/3bS3wmgCQJr69p540Mf4JmZMWYyGVLRDEGvmXyx5jMXqHdy045+Hty2G4ts5NzqHD8YHORrb57AYDLwlr5eOuucrBQKnFxapt8f4Hx4mVt61yCLAmeXw/gtNuwGC+OpFZ6Ymubuzg6W8jn2b1pLWVPpdPrR9BInV1YYTnyPXcEgjVaYTKZocjgwShJXxuexOCzc3NrKQjbL++69m69fOM9UMoFBEnl5foGNvmX+aeQMj42MsjEQxCLL+CxWhhMxREGsWWXoOpquga7hMVvwYKHFLjKbjRC0VrgcjVBUFSYXI+iAbJQxmgzMxhLU17vw2+zsbW/j9eFxVhdqi6Hb7ySdL/Ll86f4xOZtV9WeRRKlFCfDNQCyK9RIv9fLQ4ODzKQzVDSNbf4QVb2MIBgBMEpmjFQxijJddXU8sHEDdSYT7S4XXzl3jnZXHeGlFMFGFy6XGV3XUTWdx49ewGo3UyhpGDIF0vEsmzd14jSZubW9lWsaWhlJrvAnz73Mpw/u5a4DWzk3t4DVbubhV07wP+9/C1+660ZOhSMs53L85anjvHFykMa2ehx2C/FYBrvTzPsP7CJkcyOLEn93ww384ZPP4vRUMdvMhGdWWZld5W/ffhtr3HW8OjvHXDhGe2+IVDSD3VpkfiVPKprB6bEzO7HKQ2MRAG4e/yGiIJAvqbXo0rKK2SRhkAXEKiSiWTZu7sBqkPGYLWTKZQ5uWcdcOsVgPAFAe10dn/vhyzz9p+/kXdfvYrPfx2MTQxxoamZtZ4h0Mk+xpJHMVlASZYpSjWNEpQqiTjhcwOuzIxskMuUyi7ksh1raWC2mWchl6GkM4DQaabTbsFhN9Hi8/Om27bw8P0O/10e9xcI/Xh7gldlZwuEEf3TtDrrddXz17DkS6RzTS3laglYa7Q7OXJ6gXNEwGkQUVSeWKhNssOG0GymVVSKJEvV1JgYvLzFrjeB2mkhnKzw5OIJVljkyO4vVZOSGPf2cHZ7G6jDT0uohvprhv917iF+MjTE1soi9zsoD122nx13HYi5H0O5gLp2iqlU52NJCplzmkdExHG4bDreNazvbEQSBF2ZmmUwk+NYNN2AQm6hUF5CFIFVhiXQlj1kyMlEYYXN9gOubO3EYLJwIj7HNH+LSx/+cHwwfZizUyF2dHXz28FF0HbgatfovXdh8USWRqfDe7/6cxiY35ttlPtT/AEVN469/8gr1bjPjD36fh0cf5cdjT3N/zyGqeppDzTt5Y+GX1Fuc7G9p5vDCIgZJRhYddDpaiZXOUmds43Mnj6BUNW5pbyNks5NRFETBxKnIJJOpNLsbGjm1EqHf62EoMcOD26/hdGSBu9rvQhRMVPUyT848yQ1NPbQ5vQzFV9gZWMfLCyfZXB+i3tzOqchx1rgD3NXdjUEUubG1meub1mEQGvnCri0s5C7wt+fO8M39W6hosxTUMn93+hw/uuMOCuoA46kk6UqFopqjrKoMfvajeMwunp8bZJMvyB3t3fz+y8/z6j1fZCl/gpBtNwAXYy/RYHXxxB1/ytebf4okCHTX+ai3WNF6dRayWSrVBSTBTdCyhipFxlJzhGwuBOT/DWT+49A/8d7eG3ly5g0Ohrrw2+yMDs7xvGGCP1y/9VcgE+BEeJl+b8fVrHcvn9t+B/FymNORWb569izXt7XxZ9u3s1rI89TMME8PjRB0u0iXK3zx7Gne3duLy2jjxMo01zU4GU0fZTaTYpqLAQAAIABJREFUocWR5MJqlHKhjKpqdHWHWFyKEUuVMRokJoeXSWcrdLW72bqug6HZJX72zCVIlTH4zLiMNm5p7eLI8hQTqRSri3HCkRyapuOtq3X7XX4LgiBQKlQY//yf8J4Xn2IiVSSfK2M0iExMJ/C7TfhCbtrrXJwfnuZEeJH7u29H0SN8++CtSIJIRpnnsYkR7uyo4LWYiZWS5JQwZslApaoyFI+TSubQAbVaZceWHkRBoNXl4tab9/G3rx5DVTUMsoggwLGFeTwWK1tamzgzMcv5K9NYbSZmU0mcJhMlmwVbbxPpdJ6ZiVWcNgP5bBGjJPHWWzdysLUF+1VD+/a6Oup29lHVdWwGAz6rhXf39hEuZHl2coq5dIpb2ttxGY0MjsxzxzUba1SYUon//vybJNIVdLVKWamyYXMTY8kUm/z1/PGmTZglAwuzcSRJQK2olFWVoM3OfDqN3WigoMg02mwsF2K8u3cdX/gPxS3//voNO5o+QRDO/drx93Rd/xdx0P/XCf+PjqWu608ATwiCcB01vuYN/9YFf0eApk5WSfP45DDT6QyiINDf14ZFrt24fpuNBruN3Q0NuIweQKCgKjTaHSw4UkTmY7xonmRXUzNmWaLfX0+704lWrfIHfZs4u7qAy2jk5EqYiVSSY0vLtLmcWGQD6zw+ptMZ1nu9WGQjoiBxT1cXn3j9dX5/3RayyiWOnxtBlES61jRRKavs39iKWq2ynM+zmMvw1u4uttQ38+rCOJvrg8RKScaSKfa3tDKVqu0kb2510+Vyo+kaZa2mPDeKMrIoISD8Kmsc4OX5MRKlMgVFQamoOJ1mbE4L/vo6VlaSyAYJj8XCH/Zv4EMbNrOYS+G32jBLRqbTcb4zMMBnTx5jX9MMNqOBoXgCEQGRmtCnzmzmq9cd5Hh4AR2dpXwaq2zCJGkYRAMCNWslHZ3ZTJpT4RVW8jk2+APc0NLCoyMjdKwJopQV2psDLK0mGLgSplDSWBtw4Q846OtuJmi3E85maXO52BFooqIppMolPrHvGkYSSVZyWf5g22aenJxECbr55eQkg4thjGYDH966hWNLYZZWi5iMSbKWPFa7GafHjkEUmc3G6HOHaLC6ePu2jTgNBiZSad4slBkZjvD9K1e4sb2dnY2NzCxFmR5eoljS0HSdUkllYiaFZTmL2SghSUKND4yMxSzhsMqomo5BFimWNQKBOuIradIFhTOnJ2i0O/BaLbx0/DIGkwFJFjl8ZJS9e3t4/vkhZKPIw4OD3N+7lhdm5hiNx5jLZokWCrh9DjyCwM6Qj1MnJ9B0nUJJQ6o15HHaDZQLZSSnBa/VSlFVKagVXpqbw20y8cD6PnxmGxt9PgqqyoGmJlLlEj11boYTCUI2G9e1tLA9EOBbFy8ynEgynEjyqe3ba521PSkUrUqT3U6uUuG1w8O4nSY0rUK1qpMvqkSTZbwuI2ajhM1mxGY3oVZUKoqGz2fFYbfwz2cv0dbgw2owcnpwkuZWP8ePTfCBu/dwbnmJb71+ArPVRGNHgD1trTx28QrX93ZzemmJkMOJy2Smr7ebrzz9Ops2dLA31IAkilxYXOLI5DS3rVtDTjZwW1cXnc4m1GoUTc9hFO1IgoUGqwkQqepVLsfCRIsFeurq6fM2XKWPBPlQ/wPoepVKdZau+hEG8yVK+RKqVsVqM6IpGvmSis0sUeexsbKcwmexUtYm8ZnNdPfUs641RLk6SZerjpfm53l65g021zcQtHr4s23X8POJIQ42t9DqcOIxefjF1EmuxBJMJBJ8fOsWzk3NoSoa9/X0cDoS4d6uXhZyx0iUSjx8/hJPuyZ5x9q1NDscbPU3MpWOsT/UxWuLz3NdYz8mqYu729/KueirvLG4wEc3XkuyvMShpu1klSix0iw2g5EfjQ4wn8nWbMqMJmShAR0FWaijxb6Nt/UkeXb2BR4ZGWVPUxOSLPLOJ57EaDawv62N7QE/j44P8OV9+zBKBq5/5AdkEjk+e/tBvCYzq0sJHhl/hPt77mcuexRJFLHKMpFimnDhBO/oWcdjEyNkKkU6nEFmMrXN19+ce4bPbHsLq8U5ApYutvtvQdOzDCVOst7TgCiYKGvT3Ne9gapeZGegiYyS5529a4kW8tzb08NyIUGPax3zuWMky3nWetw8PHISWRDY39RCp3MPRjGKz2Llk9u3szvYwXIhwcVolF+OjBLy1hHN5/nO0TO0NflJlcv8/svPcGtnB/WWMTKVCo+PjfHz4RHKJQWLw8zmnjZ2NAT50sVpHFaZfFEhmixjNkrouk5Fqwnq0HWwyiiqzqX4Am6TmQ5XHf9w6RLVq0CuWq2SK6r4dZ3JiSUcdTZ8gTruf/4J9rW0MHJlDlEUyOYVWhvtOD12cqkCL42M07+mhbd1bUanikEIkNMmGE9FKKkqd3Z0Igoi+xvXAyIlbYFTK4vc3bGTP9pQ4S2HLxNfSdHc08CDO3ZwLhLh1PIyi9ksA4MRBAFaG2wAZOI54pUUgf5u/nDPdk4sh5mMxghns6yr9xO0O5hOJsim8qhVHaWo8oe3b+VnYxPkFYUnxye4f10vPnPNYilaKGCSJSpVjX6vh0ZrKyVtkhaHnQablSaHg9MrK/ibPOwM+klXKjXOdaqAfnUtFCwyHpcdkyTxnWNn2HjXbVQkjaYWD6VimfWtIe7s7KBS1dgWqGdHoIUT4VncJjPhfI4tvpb/LADzG5f4m3E0Y7qub/tXPlsEmn/tuAlY/tdOpOv6EUEQOgVB8Om6HvvXvvc7ATQzlSKxUoZkuYzdaMAgitzW0VGL3DObsMoGippKb10DgmBCq6ZIlcs02Kx86cB+JEHguwNXqLdaMIkiPosZr9lCXlH40egAG+t97AwGOba8zDdPnqEzWI9VljkeXmZLfT2tTgdr3F6kqx3Ab168RHh6lelMlES5xunLFlSWI2M0NTrwms3YDAYGVqNcFwoxGI+xJ2hkq78Bh8HKP48N8OENm/iHgctsCfh5fX6ehVyeG5qbkEQBWRBrVgUCaLpWs78RJMpVhal0kkihSKfLxQc3bWSsNYVJEnlpehZVryKIAhaDgYvhZb4BHGwOsbU+hKpXySklCqrKl/buZyS5ynA8wdOTU5gNMlsCAS5EIsxFE+jovK27h32hNvJqiRPLS2QqZZrsDlrtfiShiiDIRIpJqrrO3+w5iKZrfHvgHP9w4ixrmxsYyxQw28w8uHMnNoOB62ceIpNXiC4mCHUFCNrtmCQZn9WG22QkXSny+uICBlGkomk02e0cbG5mi6+b/U2tfHdwgCOT07gcVuLxDAZBJFMuo8ZLjFeqIILNIrOuL8iLMzM1b82NGxCBTqeTC9EYTQ47t27oZXU5ycmTE+i6ztarykNZEmq7YEXHYBCvepJBRa1iREQUBGRZxGw2UCopKEqVdFZB13W02ThWs4yqVskXq5wenUZVNMpKFZfXzOJ8Ek3TOXx4DEQwGkRePTLM+/rXs5jNoOs6siCyp6mZOYeTbo+HRKlEsj/EhbNzlMrar0YoPrepdlyocG1TiN0NTeg6vG/dZi5GF+mpq8djcuAxOVhTF6yJhMppkqUaL2o4kcAiyxxeXGJnYyPRYpEOp5O/OHKURCTNX95+kOtCrRhFAwZJpMFvw+a0outZykpNPGW3yhgMIg6bAVXRSGUr1DmMCAKYrSY63R5+eX6GD+7eTrhQYNhhIV+q8KF7ruXaUANHRic5uKmXkN3GbDrDyYV5uhr9vDQwyqH+tRhEkXu6Ojm8tMTebb0cam3GZ7Fydnae63u6iBeLDEajLKczfGrrDsxSL3nlMivFJE02Gwv5BSqaht/iYji5is1gwCIbqLc0M5keo9Vej1JdRBI8yKKPSjXDPd1deC0Wnj56gXROwWyp0W8UpUhdgxNd12lq9fL18+f5xv567u+5geenp9F1nb89/yZddS52BPw02Ow02nwU1RJatcrZlQiNNht3tN+MVk1yfVMnAgIXV8JMplJ0NQXQdZ3LsRhzmSyfPPIat7S38fCVQdSKyrZgA/d13YIomMmrY/R7LRTUMs0OJ/FylIDFj66r9HmaWCnkyCpZXEYfeTXGPw5d4O7OTvo8ffgtDl6an8QkSXQ4veTVWUySCVF3cT56GIMo8tPRMVbzeX54/BzrO5o4MzfJg3ccJK8o+MwWHhsdJ2C1sJjL8nc3HeKzx4/x18+9wY1b1vHXd1zPne33kSif57Mnj/GF3dfS5mimoGb55uUzfHrbHbx7rYHFXJJkOctMJsu+UIhL0SiLuSnqLS4WcqM023VAo9nuJa+OYJGaSJZXcZt8mKQOTFKUSKFA0GrjL3btYp0nxEfffJE7OhNEi0W0qs6uhgZenJ3n7PwCx5aW+cb+OhzGIJt926hUF8kpEVxGGweamlkt1gQhs8tRSsUy0WyOr5w6jVKpiQsbrG5OhIeZHFxEFAXqG+roaAnyyW1b+YfLA0iiQJ3LDIJAtaqTzCpoapXZ1TjZVIGgz0JrZz0rC3GWcjk2eJv4+sVTLC/HkQxyTVVvFOnoqie8kCCRqRCqqBgtRkr5Equ+Io46K6lEHoNBpLWrgdWVJAuLaTb4XXxi67/gCQFJrMNhcBGylZBEkYCli8X8KDUYoNJo9XJ9s42skqDP3cUt123AZTITtFl5fGKSR4+cQxDA2+BGq+qE/Ba61zVTUlUcJhOr8TSZSpl9Tc2sFIpcGJ3BYjdzbGUCt9/J6mKCeLyWLGQwitzT1c3Xzp+jzVVHolRktViiwVYLE7DIMs1OB9c2NhK02REEIy32eg40V7FIRjS9SqJUpG3LJq7EEzTYrCSKxVrSj1qjFvnqTDQ5nPitFvav68ZqkHl4aJhDG9aSVxRubG1mb2MniVKW0VSUidQqSrVKXlUYjET+0/DLv6d+y1oggLNAtyAI7cAS8A7g/v/9mkIXMHVVDLQFMALxf+ukvxNAM1kq8+TUFFUdrm9uRtOr5BWFdLlCTlFJVypECkUOhHRAYKkQI12ucUT6PU1IgkjIMUuqVKbRbiNbUbAbjBxqaWMhm6bZ4eL8apjD8wtkk3kmqzpatYrPaiNgtbC3oQ2nwYKAAShy5PQQvgY3OjoXV2sgXwAsJgmb08JqocCZlRV8NiuvLyyy3ushr5awyDV7iqdGxoAaf6XT5eL5qSmOLcxzqKX5qiWDSKWqYtChShXpqgJ9LpPmr145jMfr5D3r17MrGGK9J4ggCGypD5CulPnh0BAiAvet6SFTqTCTyRKyZwhanBhFGassM59N0uF0U1RUsoEAW/z1rHF7ub29gw+88CKvzc6yLxSiyW5AFiR6PR4Wc1lemJ3lljZotvkQhSqrhTznIlFub+tDrQrsbWykxWHnSizBFw8dwGYwELLXIQsSX3v37XzjxBkmR1fY6/bgNpkpqCoXF5eYSSVJlMoo1SpVdAJWK9c3t+M3NwE6DdYQscJJNK3K4nyUQrbIdy9fxm40gs0AigYmiTqnkTqPgwa7ndva23+ljtwcbKCrzoUsCOQVBZfbRjgW5/SpSS4apqkoVcwm6Woerows1kBnqaJhNcvkiypVXUcQIJkqUShpuBwGmjxWFpezFMsaDpuRYrnWoViYT5LJKwS9For52kKSKejYLTLrNzSyupjA11DHPw0Nc3/vWmLFEseXlnCYTPhtNmLFImvcdVxZjVDIKshmqWYdVKoR2w2ySC5X5uejY9zT2cfPJ4cYisWJFwvc3J7n7d1bUfQiIiIlrUKmUsYs1x4FhYpCq8PBG/PzfHTzZr4/OMhSJoMkSxhNBiKFImciiyRKZf585y5iGzbw7Owsj7xwGkGo3eO6Xttp54s1GxFZEkhkKpiNItGVNM6NJvbt6cVmNKDmdDLJPB6jXFOLr6yi6zozqSTnF5dYG6gnHk4BcN26LiyShFmW+NalS0iCyHv71vGRXz5HoMGDyWLk5NIi17e10WC3o1arBK21VC9ZtDAYHyNVLuIymREFgVQlh6JpqNUqp1ZW8JqtNNm8mOVOJMFOvHQOmyHKSiHJrmAr671+tgXq+fIrR6mUa96Ifo9CPFYbb97a18mdnR3YDbVOyKZAgB++cpJMXyvLuRytLif93gBmqQ+jWEEUjPz33TUunlJdIK8UCVp34bUs8/UDB/nRyAjXNjXRbLdzbnWVszPzZOLZmlhR1fjiTQfxW21IggtFX0YSRFJKjolUjFipyL5QB+hVVD1CppLnYKgHi9yDgAlBlnjX2j4aLH0o+jLxUo4bmjtwGW1MZSJMrcyzO9iC1zzFBm83737pEZZTGUr5Eh8/uJubW3t4pqWJkUSS969fj65DplImW1Fwm02kyiX+fNcuviic5lNbr8Fv2cHJyHP8bHyCd6zpYSaTQKlqhGwNBGwWfjD8Ktc2hvCYrJyNLPLh/rehViP8zwsXWOf10GLvJ2RLka4s4DatwSYriIKNqp7Hb9nAUv4C4cI40+kUm+sDWGUTPrMDAYGxcITTAxN0dYc40NLCI6NjfHTTZl6oc7EzGORKfJE3Fk/zof5t2A1uLJIRQZaoM/r5yAYnC7kk08kkFy5PsTi5gtVhZktvbXT7l6eOcGF2ga71TawuxhFFgaDdTkFRuX/tGp5+8yJqQUEQoKxUafJbuGXXBpazGQxGA/dc28quYIAzK6s4jUZ+OTXEj146WRN4VjS+8b47eHJikonZ8K8ShIJtfgA2d7Zw+MoYBpMBs0lGEAV2NIZ4JVOgf3Mzn712DwAD8Tk2ee2UtTg/nTjDXR19WGU/kuCmwVpPpLiM22RnPhclV6lglmVCNnhrVxcbfQ2cXV3kzcVl9m7u4djFcSxmIzazRP+mNkRBoJgrsSnYQJ3JzGI2w2Pj47w+O0MsmiMgS6iKysTQEplcTfiXKSjcdqif7165wvnxOQq5EtdtXYvPbMZ4tdHTVediWyBIulxmKpXEJE4SsHpwGa0YRJmVQgpJEGhwOEmXK5RUjUg+h9MmoyhGWltc1NU72RYMsD0QIFupkC6XWc5l6a+v54aWZi6srjKSOENBVbkUieC32VjJ1Z4JyXSe5Vz+PxK2/LtLuPr6bZau66ogCH8MvETN3ughXdeHBEH4o6uffwe4B3i3IAgKUATeruv6vykI+p0AmmZZYrVYIJYvsMXvo9PlpqJptDqEX7WWp9LJmsE5ApejEepMJrrq6pBFAyfC44TsVn4yMMSulmZ2NQQI2R24jDZaGzr4k2OPc22ogdHxBURJRK/qLKUymGUDXS43p1fmOdjUjUEQETGRyilc19KI32JnMBZF10HRdHQdelsaSZVLNNntHF1c5PXLo1jsZj5yTYV7Onfx5vIljEaZfEXh3u5unpiaZFtjI7e0ttLnaUQWayKcsqZQUMtoOoiIpJUic9ksbq8TnRpB+vmZGdZ4vHxkww4abE5CNoEv7vHxvcHLxIpFbm9fS1YpUFQVJFHCoFdpdrio6jpKVcNlMuEyGSlcTe8RdYGbuju5FInwsWdf4sdvu/uqD2mGiVSapy8Ps5TLc19PN2vrgjiNJu7p7kJHxyybeW1hkQ9v2Mzexqaro/YataGq6+xpaIbd8Lnll7g8OU/rVid2g4ymahQUhcurq4gCpMtlHrrpNnxmD/9CHckoca7ML7M8F8dklIilyjSVK3z/xtsYuDLD+FAchCp1biuSINBfX8/Nrf0cWw5zfngau9FEq9OBKIm0Ox2ElzO0NzmQDBKRSI5UVsEF2C0yLrcNPZGv8YcsMslMBYMs0hJyolZUFKXmufn+2/awyefjz372PIqiUSipKAUVrDKFXM2SaDlaxJQuU0iVcXjM/MGdexmLxQjPx1EqKovJNNc2tvGR11/m2uYmcopa86MzGXnoyiBrvT5eU6uoRR2DVQallpusaBo2s8zk0ALFm8qkyxVCDjvL2SxvLiwSzheYz2T4xNatmEQDLXYvPxkbIFNR6HLXUdV1LLLM8fBKLTlI0HCbLRTMJa5paMRpNPHk9CRz2TQvzMyx0e/j9U/+PoPxKH/xi5colVVUTUe62gV22gwYDBKaVrNlGY3H+L11vQzFk2QqFTwBFzet6Wa1UODIwDiCAL2+ejb3ehlOJEkl8qiKxpAsszAToas7xLZgA1aDzHw2g7/BQySc4IG92/nWc0e4YrMhCyLv39B/dfMHgmDBb7XhM9uRRJGqqKNWNWwGA5lKhT/d/H5WS2exyI0o1TnSaglBEDCKHTiNGSRBZCGXpqrDJ2/YS7+3nnu++xNWYkU+8Y4D3NvVxTr3ZkTBzmz2DHUmOx/dsIuqrjOwusp1TSGixSLN9poLyFTmOIeXFnj/undT1qYoqClcxlZ+OPLPPDlZi2H89NY9DCeX+fHIKOt9XjRFZf+WXu5b043HZMEkSTw6PkayucTu4H4K2ionVxZIlst0uupwmxp5du5lbmnZSaQ4z1+dPkZZe5F/OHg/y4U5LLKJc9HjpCpl+j1BvnrxDIeam5nJZFB1ndORBZ6aPMz3rr+PPU1NPDRzBkmSWOfx4jWt56cjT/L+Df0cX17CZzGzOxTihZkZ/m7/jfxw5DJz6QxPvOWPkAU/T838jJ+OjmGWawlUn3/5KD+/917Oro5xf88mXpwfxSTJLBcytDpdLOYukVfLfOvgzdQZW4kUR3liapQPrD/0q/9ppbqCWepC09McD89za2svfoudRlsHyfISV+Jh1GoVj8vOx3bv4DuXLrO7oYHzq6skSgXu6liD1+zjoZETbPH7cJv6KGmTmKUuRKHGRbHISRptLj6wYQMPixKXx+d4556tLOXyfP65N1gJZzAZRXo3tvLDd97D50+eIFMpc2R5iZ+evUS5ouGrt3Fgcy/nZuZJx3M8c+QCoc4AseUEL6YLvMgIskHCZjBweTXCLdduRBQE1nk9hPMFTpwep3CV951JlImEx3F6TNz0jk7G652Ep1dRFA2z1cjl1Qjv2lKzI/rlxCQ+q5W393RjlFpZzp/gO6fPcai5g5K2iN/sp6SWkASRF+dH2VIfpN0b4Gh4kkfHxlnMZih2q/gtVubTaRYjcRKJAtnsHO1tbvw2OyVVwR7wYhBFJiNRNjQ18trMNNGlBC2d9VSKCmarCXultukUBLjzpg185bqDbPnCtyiWa5tjl2eRVqeTsysRUuUSzU4XDqOBi6sxXCYjlaqGJERIV8oUVY21bjdT6QyyKBKy2/j5+ATj08tomk6potXEuTYLJVVlLJngwmoMn8WMSZJ5bXaWrjoXp5fDXLkyjdProL7ehUU2ELTbWePx4DObaXc6+dZ/OHr599VvODr/N0vX9eeB5/9ff/vOr73/MvDl/z/n/J0AmhZZZoPXS9hi4UR4hZFEku46F50uNyZJRuDquFkQ0NGZSmdIlMqcjazyI22EJ16/QCJTQVWrOK1mdjcEyVbKKBaVi7FBrg018NjoODanhVDQSzSVJRFJ0eR2cTm2SqerDrPkBuDxqXOYDCKb/PVYZBOnzo1Rqmis7faSSxewGgzMpJJciSeYDcfIp4toapUTy2FaHEMAfGbnTs6trvL87AwtDjtvLiwxEIvR52nEIBpQqgomycBUepXnZue4s6Od7wwMMB6NUcqXWdNUI8nnKhVemZwinMvxia1bscsWEAQur0Y4ubRIrFTirZ09eEx2lvMpBmJR9odaUfQaEJJFkVdmZ9nb1MyRxXPc1t5GyGbjgQM3MhAL89aHH+Wx99zHBl8tUUeURVKlEg8PDWE3ThC0Wnlv7yaUqsbHDj/DNY0NfHvgIu9b18fDw1fY5q9nU32IeCnLux97AtlQ6xrGVzP84sRFbt62nv09XZQ1FYssEy8U+cp1B7DLFnRdRSWLKIg1v06nFVWLopVqmepDlxYw32XE2+iGqRRUdSSpNuL++xdr5P2xWIwPHLyGlUKRXo+blXyezz/1Gl6vleWVLJIkYrfImE0ShVLtIRmLZskVVFx2A846C30bW1mYiaBWVJweO4FWM6qikasofPChJ8kXVfweM9WqDgUV3SJDSQO1imaDQlHFG7ASDDrQqlUiqQwmo0QuVeBjB/fwyvwUH9q8ic+8+BoH1/Xw+uwMSkVleGAJaZeAO2AlGSvWQCy1jmK5Uq3xwBSdPzv2JslikbevXctKMk3MkOfwqSHsLis7ggEm0mn+fNt1XNPQwJfPnOWaUCNlrTYRAFjJZfnYtq1MpzNs3LaVXnczc9kwz4yPE1tO4va7GFgO85ePvswf33Udl//0QeKlOJ888hovHx4im1cQALOpWvPkO7Ce65qb+eqxU2xsDrHG48brstPisLPFX+s0/GhoGFkQGE2kWOuu44Fb99Bgs/K1p16nZU0jK6tJLggCBUXhvrVrScYzmMxGBqIxvvV7d/LIyBgj88tsvu4GZNEDgFpNstnXwWdOvsR7e9eh6lXsBiMzmQzvXvN2Xpx/gu2BFgpqrTOYU4o0WLtZLpwjZN2FjsZat1aLRvW3c3JlCumqqb3XbKLfc+BX6TQOowWb7AAkzobDxLN5HhkZ4Y83b2YwcYnhRIztgQZ+PDjIu9aMYJZ6EYUIL84fZjiR5LFb38lYahabIQQs87d7D/HE9BXevm0jH9lwPcfDAzw3O8PdnV3sbWzk8ydO8PgdTVgN9bQ6wshijhaHE6PYzO2tDi4nTvHw0DC3trdxS+uNSIKTNkczajWJz99LSZ3BKPn5YH+ZD7/2Cl/Yu5d0ucyZSARREJAEN6/PzbGvfw1+qwWP2YqAgWfufB8/GT/BoeYOVgpZ8k6FNqeDxVyS9/RuwGNy8friGb53eYDTZydo7fJjMMqMeDzs7GjlA6+8wKd27MAkurm1pZdnZoeoM5nxOM28uTTHOo+Xl+YG+GC/g5lMbTL3J0d/zt9d+z4ux0fY7r+FifQbhGxenp6apsXhJF0u02zfRL3ZS70lRaPNzTcPevnKubP8/PZ7kQSR9Z4mZNHCSiHCDY+QcxqNAAAgAElEQVR9D03V6Aj4KGsat7Ss+xXInMse5QOvvMAHN21iV7CFrcEY17e28Pj4OEMDM9hdVkoVjVJF48TRCT6WL1HMlQgLAheHZ7htxwY27vMSsNr42fg4TpeNd23dyEe+8AtyBYU6l5lkNENV1wmEPDx5ZZhCpkg+U8DmtPJsIs9ipIDVLGG3ylRUHTIVyCpkShr/+PwxrDYTOzf3YDUYODs+SypfpMlup8ddu+8LqspzMzOsdaUoaQr7ujupVFUUtcxQ/Dk2+pooamVG4kkeGrhCtlBie0szx8emMFqMfD1+lgOdHdzT082Iz8ePFhNIooDBKLOUzaAoKlubQlxZjZBPFzhbmKWQK1FVa4boVqeF4YFFqlWdilJ7Bnz2mmu4/6knEAWBvr4gvS2N3NvThcNo5M3FJcYTccYXVthc72Uuk8Eiy2zx+zGKIs0OB13OIO99+UmsBiPPjU9wc3cXw9NL1NU7USoqy9Ei5VJtYnl4YYGqDrlKmXSmgCBAvdvJF18+wtaeVr709pqFlkWSsRhkBmJx+r1eTq9EaHU6/mMAy2+xfvsw8z+mfieAZlXXaXI4eGvnes6tznE8vMJIIsWz0zM4TCYW0ml2hUL0uFMELQIes5kHv/8iVaVaU+sKgKbjDFiwGQxMZTJMZTKkK2V2BVt58OhR3GYL+3q7SZdLxDI54rE8F65Ms7OhgTanTpU8s5kVHnz0ecxGia1+P1fitbg7SRL5vb3bqOo6J5aW2BxsYI3bRXDXVjxmE09MTvGhjRsxSTJPT08C8J7ejVxYXeRCNMqGeh+nlsMcamkjZDOQrRQwyybKWm3h+87AAGpVZ197O1ZZ5umhETRFw3o1O/eZNy9yYW4Rb52dB3fuJFepUCkrXInG6PN4uBKPc0NLC29p7+XpmRHmMlnqrRbmMlkS6RxaqMo39n2IVGWY7w2eZzod4y9fO8xyOMs7H3mcb731NmYyWcqFCrlKmXixiN1oZCQaRRJF0uUKl6cXGFleQZIlLq2EKZQrjMRj2AwGPv70S2iqhlJRkQ21GMJSvswTb56jt7+NequNsXyOTcEgJqmWHazpVWSxtrCPJuJsCAQYFeepVnUMkogowutLk7idNrZub0JTNWKRDBaHhc6eECPRKCZZ5ueDw/zepn7qLVb+eXgUXdcRBAFfnQmTxYiug8Eo41RUlsI1HmLQa2ZptUgiU2ElksNmlqnz2EhGMySjGUwWI8/FBnC7LfSs9zA7FiZfVKlvc6LpOklNR89UQICWVgf7dq1jMhLlmZOXSSQKmIwSFUUjYLVSUBUkQeDoAx/jvx7+JZ/fs6f2e2lVlhdjBP026t1mcoUaMBQEAbNJwhdwEl1Js8nv54WpKb5z6hwul407u7v54miN2/3c9DQAT04PstUfQNOrPHr6EgBGi4HXSiXuXbuWJyamOH5lgmQijwD4G+ow20xYbDWqh89pR1of4vTyMmdD4+wMbOIvdl1DolhkcniBdE6hUNJQVR2DKFFSVdr8XurMZp4aH6eq6cRKJVRdJ2i1EkllSBQLALw+C00uF5vqvazrb0MWRBSryu5QCFEQ+MeT52hpqGej389yLsffHD+J2Wjg4LoeBKGmbA5YHCQreS5FV3lLRzt1JisFtUy2Uub65nYOh5/nQNNaRMGJgEi0OEfQ0oQseHAb7ZS0YcxSHx7TOrrrqlyKz5NVKqzGi2halYePnSVgtRApFNkeCLDO3YhR7EKpznOgpYU1bjcnwitkKmVS5RInl1eYzWTZ3NCIUewgp1xGEkRub7uXgPUFJjNzrHNvR6fM6ZUVLqyusjUQQBJEHho5gtds4v19m3EYHPxw+FXmx5dxGTcDsKXeiN86SsjWCUBBm61NcZbDaNUqNzQvIUnOq9GNJaq6RqSYotXRzlgyzt/fcIiyprLF97/Ye88wyc7yXPdeuXIOnXOY6Zme6clBM9KMsgQSAiEBNggbMLKNA94+mI3DNvbeBowxNkbY2CYjokAghFAOowmanDvNdE7VlXOutdb5UbN1Xeefj69jHx8ff3XV/+6q+r7vXe/7PM+9i03+GRRRQjdT/Pitv0/DiPPP40fQJBUTE00aYCw4w0M/+hF7+rp5+0Afqigx4A4hCiKxcqqZAFKt0NLm4dHdO1grFslUq/zS8BAGQ+wN3c1M7nX6XLt4oE/lSnKBC/Eon376FUrFGic+9htcSsyhShJbg0EGvR7+aeKnPLrpYWr6IrlahddWLvCne/fx1fFxJFHk+9Nf4oObN3E6GuWJ88/wTw+8hT/cvQ9JELmQWOal5WUuRaPMzaxhtVvYM9yLYZpMpzMs50/zoREDRfLj0RzcNzBA3TSIlXO02m0ErTbWY2kkSaRSrKIoIrpuYtUkZqciaGozh/f2zk5+PDPLUr6AYZpcWV7j3WOjLOULbNzTytp6gVKpTrDFzabedi5MLRDu8NMd8vHq0UnK1fyb5BpFEVEVCcPQm/pDAElAU2WsdgsXpxf5pZt2UOpt5+L0Irpp8GdvnKDT5abdYefWzk6S1WvEywUGPG5Orq+wO9zO/tYhfjZ/ia2BEP0eF89NlUiupWl0tL+5r7eEwiiiyHPzC0wtR+joC1HMlahWangtFh7ZMcKrK6usLMVxeGzs7OnCb7WwKxxiX0svsiDyv7qPciUW5blnxvnQe/bxR8eOMdbexn/bt5sjK2us5HJkqlXsssIGr4eqbrBsz/LK0goH29v43POv8+2fn8I04S23j/LpA7ewMRBkMhGn0+vh2PIyqkUhHcshiAIbh/w4PHaKtWaxGbI7kEWBbLbIo3t2slwock9fHzZZJmi14VBUvnr1Ku1OB/FymSNLy3x0x3bsN1zw/1GXIPzbdDT/LdZ/Ctb53/z95z+544FtWGRwaRrpSgVFFOl3u5EliSGfj23BIN+cnKTbZeOzp09RSDc5257/HTQtQNUwWVhKUHOJWBWFqWSaJ65NIAgCPqsVwzTRZJmy3sDhteEPurm7t4d9LX2Igsh7n/0pqVgOgHfs2MyPZ2Y5P7mMJAokpTqPjo2xp7WFoM3CJl+Auqlzc1s/vW47XouVtWKeQa+XaKlEploiWakQKZWbTjuHnWfmF3BpAl8Zv8p8PsVkKg2AJsv0etwErBohq5VTy6vEV1MUsyUq5Rrlcp1Qq4f11RQvXJ8j5HPhtVmRBJFsvc7PL03w5edPEm5z4bNY2N3SypDHy6VEgsFggEc2juBV+xCFHLtCrQiCwRNXxqkUq6iqxNOXpzgzOU82W6FYqTDYHmI5liKfLVGTBaLFAolUDkVTCLqdbPAHQID1QoGrySSFapV6tdmREyXxTeKBZlUplipY7RbimRyz0QRul4YqmQQsTZwmwFoxgypJnFtaRTBNVEXE6bLw9OkJ3rd/O08dvcrCUg5FFqFeZ+dgD6OhIIlSCZfVQrGhM5PJEisVmZlbp17XUWSJUqlGoVjDokkYusFapITHpRKJl5EkAb9bw+PUUFWJjp4QWwe6uG/bRnb1d7FroIvO1gCmIJDM5HE6NKwWCb1hYFElfEErugktLS4uXV0mGs1RKtfxe60IQKGs88rUNVao4dY0vj5xlj/Zs5+fzs6wkM2QT5fI56uUSnVEETCbJiJJEmjoJkZdRzdM/sedt1AzYTGXQ5YlMtUaezb0ktKrCKIAgsDZ9XXe0jfAF58/iqLKqFYVl8tGpVzjcG8XmwMBDg/3krcI1NCbpCbDJBbJolkU4vEsxXwZUZP54z3v5mz8PN+enOKDo5v52OFbMHwKnZ1+yo0KrQEvPW43gtiMX3rhtXEiq1muLK4Qlepcise5uacbh2bBqWn80shG7u3pJVWtsCUYYEdLmA9s2cJEKsVXnjtOZ3eIQZ8fRRKZTCSQJYkzp+e4Y+sgwx4va8UcFaPOP1+5ynMzs9zd14dVlrFIKj6LA78WosVmo6LXkIQ6IFA3qlhkNzVjGU3qQhGDmNTAbGCRFHSzyAcef5J6rYHDptCo1rH5XexqCTHkCSCKAjO5cRTRoNNp51wsyoc37aTH5aPVbmd7KEjAauX9Gw5Q0Vf5wfXLTKVjbPFvoM3ejVttMJWZIlOL8/P5OTwWC/d2b2SlmOadA9tQJIMPvfALFgopji8vsWOoh/t6m3o8SXDgVNyIgg1R0DDJEiklubOvlx3hEKoosF6+jt8yAKZOvr5Mrl7CpRq02J0kKwVqus7P5s+zr6WHc7El/ubCCe7uHkEWw3Q6a6wWs7TYAlxIHKFmGGQMneVslvlcng9uuh2b3IcoVPnS5TeoGwZ729roDQUY9nrQTYOaYeCzWsjXatTNCMcjq2wPbqFuxDkXW8ataUSpk84VePz8RQ4P99PhcBO0OrgUj9Jqt9Pr9lNqJDDQcagKT87O8ttbdxIt53j7wADRUokfX7vGru5OrmeydDhsfOC5n5Oq1bmru4uX5ubxB1y4nDYUSWJnS5iwzUaL3calZASvZuDRvOwKb2GpsIJNlhnw+Gi1uanK8LaxETSPnfV0GodN4YFD22lv93HHlmHmMhkamJxcbUZWtdrtXE0lSVYq3NnTzflYjEalij/kolKqMr8YJZ2poGkSf3rLAb525AKiIKDIIsVyg4ZukE5WKNUNyNagYaJ02PG6NOxuG/Vagwtzy9jcNnQBFE1jvVhgIZvhUGcHiXKZscAArbZWlgoRvFqTUe5QNGyKSFVvEC+XSTcaVESTRKVEuVBlS2c7dcOgojcY9nnp8PvY3dHGcq1ENpEnmcmD3cKl9XXet3OMrF7n/LUFAl4XO8NhTq6vkKjkeez5Y9R1nWq1xkN7RnlgoJ92h51So4FTVXFpFgzTxMDEBI4ur2BTVIr1Ggu5HNdmIiiyiAmsRlL86Oo4E0sRNJeF6WsrlCs16tU6lWKFerWB3WkllypSLdXweB34LFauRWJk4nn+4NABRnw+SvUa8UqFS4nEm4kbqihRrDeoGzoVXef0epQ3vn78Pyzr/O//8QuffPhXbr1hTP2Xv7/0lz/5L9b5v2Y14yJ0XltZQRFFtoVCZCoVIqUiVkniQizGT65dQwC+fLnBoN/P8B0BZtIptre0AvC1p0+QTFRoGCZLs+usryTp6QnT7fZwaS2CKko3nNASh7u7KTd0Lkaj2OVm7liqmqdYKJMt1Ont9dHnDnBu8RUK5QaGCbGVJOfjUX514+08MXOE09EINlnmUnKFveENrJeiJMplivkcIasFWRSbUUt+H8/NL3BrVyd+i4XVQoE/3LWfx6evkCiVGPZ5CVgtXE2meUf/KN+enKCQKeH2O5FkkeWFJNW6QTbZJAOZpsmetja6nU4ipRL5Wg2vz0lyPcMfPf4cnd0+7ty6kUMdbfS7XeimwUohR69TRMRGqpbmO1OTzM4kGNrYQqVYwdBNSsUqjYZBKlnk7KUZ4qkyum6STxfwBJtB5eVChS3Dg5imyTuGBjmzHmM8HqMl4GGhFEUQRCRZIh3LolpUHB47A61BBrxetoRD/PDoOf7shy8QbvfywX07eM9gk/aiSRIXY3EatQa60TTlVMs1dMNElSRuv3mE145NEgw5CLb7SFXKHL1yDUmW+M1b9uLWVI6tRhjy+TiliEiSSKNhoBsmFk1q4uN8DjRrvhnWbkKjYSBLAnu2DbE1HOLo8jKX55dZyWUpFCsMhINkqxUGvD5auoM06jr5dAEEAUkWqVfqmIBhGIgiZDN17FaZxbUCNotEsdygUKrz6L2dLOULVBs6xUaVh4eG+NnFcSRJoLXdy9hAF197/DjUDSSPRnvI2vweqlVUWWS5kMOhyIiSQMMwmmJ/RSYVzbKSL5Mp1An6rPyx5SilYo219QKDg0EUj0iL182Q10eL1YVV1nBrGl8Rr1LXdVyaxmuZCSqlKnrDwBd2o6oyomCnUKtxdHKGp18/z8HdI9za1cG7BodIbBjGqWpcSsQp1eu4NA2Hrdk1kEQBVZIYCQSZz2bRJIkBr4etgRCPT01RqtcJWK1MJBK0ONZ55vRlytUGq6sJAjY7pbpCwzAoVWpsHuvkI6N7UKUWfrH4LMV6nWupJJIs8dLSMh/bfphCPYdHHSBXn8MhBzi6dpZbO7aim0U0SaFhNLndomCjok8jCy4k0YeIi2NrKwgCuBwqum5QqRm8cmGSe3q6+cH1SX59827a7F7cSpiGqfPDyUlG/M2kgJ2hdnqcPXxj8ll2hfqxSl2EbdfxW6xAAwGFdDWPRZb58pXL3NrZwc1tPaiim06ni7848ypX4jHGwi2cWFlmYWqNz/76ISbSLzHs3okgqOhmEkVoGpIuJ2dZzOeo6waKJNLtDNBq9TGRfgmbrNLjvBmfBsnKWe74+tf4xJ03szfcwc5w89J/en4BRZIwqROvXGCxkMapqBTqSzQMg2ixSKfTwdagn7u6Bpoh8Th5bukcp9ZW6fV4KdUbtNqb3XmnqhIpltAkieV8gclUGkUUuZ49RpvdjwGcikT5/C13cGp4ic8cOc43Jyb4/M134FYDHGyvcSURY6lwvWnitAXocbQy6A4xl4vzgY13EC8vkq5W2RJuYTIRZ0soTKfDzzs2bOChgRGqepPgoygyxWKZ4UCASqPBWDBIpdGg29ns+jnVGIfaBtkb7mE6s44kSExlosiiwEafnxNrEfaNDbGYzZAul/nd7dv4xsQkrQ4HxVqdtw0OspDLc3R1jZFgiC6Xi3ipTLVcI9Di4X/edRi/1crVZIK/euYIsiIxk8nQ1+Nt/n2qTCReRl8pQpsdajoYJghQz9WpBhvkM0VCHX6cWnO60BXwkq1UmJ5eaWZwbhnFparIYgtgcmfXBuZyUayywkI+TrJS5mI8znKhgCyKdAW8XL22TEdXkJlUEpemcXdvL2fWo0wk4mwNt2CYJqZhkkwUefXiFIIAv/7gA3zl6B/i8jm5vauTxXyOr164xNWLy4gCTM2keMs9m2mYZhOcommkK1VabbamyVJV2Ojzs5DLcrCzg7FgkJ/NzfOL4xebGFxAEoQbOM8anoCLmfFlZLl5Z+QyJYrlpqRJb+jsGO1jajFCMpNnczB0w3i1nUS5hEez0Gp3oJsmK/kCZ6LrZCoVAlYL6XKZTpebY8vLjIbC/241zL92/X+jn/mfpdAEYuUyLy8s0Ovx4tZU0pUqmiSRrlap1JuhrP+bZrOayyGJIvV6gy6ng2GvF99Dt3JuPUKuWmVmYhlFbX40G31eppNN57jbYsGhyLTabcxmcowEAsiiyPHIHCfXo2TieTxOhf922024FBuJ1dSbT6XxZImDbR2oYgfv7N9LuhrnxPoidd1gpbjGdDrJlWSKit7gUHs70o2WuE2W6Xa7mUpnWMxm2BUOkauV2BMOcyEaJV1tUkpEAY6sLJGuVpvjFlUmE8+RyDTdvslYHqtFRrM0GeWb/H7e1ruJ5WKCnaEQPYcO88A/fJuejhDbQwE0SeJAWzvH1laZTqcJWl8mXi6Rq1V57Cev09nhRtUU8ukC2UyZRsPANGE9USbU4kJKV6g0dBKZKsVyikCwiWJ7oL+PYr2Zh1mq17m1p4frqTSNDoP1aJpCtkS9YdLa6uFAbw9jQT+qJGGTFe557zu4nEjw5z98gT9fepGTB9f5pQ3DTKbTCIKArMoIAhiGSanSQBQF/vBbz5L+7A+YO3CEL1+5xGw6zfj1Ze7ftYUXxqf57sQEh7t7aLHbWCsUcbhtxGN5gDd1Rbph0ojn2DAU4O5tm5hKJoimstTKdUJ2O1PJJPFMHr1hkIhlScayrC/Gaegmj7xvhBNzC5imSbDFS4vdwUw0zvy1Zg7d6lqeUqWB1SJRKNRBEqhWdTRNwutUSVWaUoQdLWGeWZjnw5t2snOgm0uazD++9V7sisobl64xeSqCnqywVKw3N4Qq0tnu4MjKGqV6ndFwC+PxGG6LhalonGuzKWqJCkgCNk3i0vgCpUoDl0OhmCtx577tTCZTHFtbw60m2N/azuVEkplInORaGqfXjqw2CUmtPUEUsdlxMM0qlRsZgdWaTqFW5YtHTxEKeritu5s7uro51NHJW3uGeHJ2kntv2cIrb1xt/l4LJX7pwDCPT00xHo9RbjRwbLSyXiyQLpd56dIkuzb0okkSLp+DW8Y2cPTyNU6en2Z4pIv3bhrhVCSKRZaxK1uJlE5SaTR44vwVVKuK32nnXUNDaOIADekqkdI405kEt3fs4GtXv8GtHaPY5C1U9HHKjQoOpQNR0FDEIA0jgYQPUdB4eXGJTKaMIos0dBOXy8L7b95FwzCYTqUoNar4tAFk0UfQ0tyHJyNRbmpr5XxsjRF/jUMdbTw5e5GK3mC1UGJfawuGWUUUZFptezk68z1Ozi3yx7sO4lJ6SVVn+KszZ5hdjTX3SL9Or8dDobPEmH+ES8lJopVJivUK7XY/KhKlxhW+N32N9wwPEbTa6XJsRxKcNIw4spikzdZNqXEFmzyKRxvmiV9+mH7XIbK1C8TKecZTK0SLBRbjKfK719FEhV3B3chikCdmv8/rK2toksSHR0dxyBbqhs5nzh0B4NkrU+zo66LVYSdgtTSza3N5jiwtU6jX6HE5mc/lKDeaRcTpaISHBzrxahZeX17i7p5u/FYr79m+he9fvMKZ2BKj/hpdjo2ErV5ORucJWq2ErSAKTcTq9sA+JMFBvHKVhmHgt2isric53NWFKsrc0t5OoV5mvVTg927aQ7FWx8BkIplmwOOmy+HlI688z+xchFu2DvM727YRKafodmxkwG0Qsu5G4Bh//OrrDHrc3NzRxs9m50hFM6RjWT5Vr7OeznJosA8Dk/VSmaquc3FmCcWiIPV2sbu/j0Ojw0SLBe7pfhCA7YE0n/npy1yfjvJC6yKfuOcQn3nuCJVipXnBaRKyJNAwTNBEsEhIDgWLRUGv68xNrFKp6rgcKvceGCNTqdDT34I7UOTH09d5ZNMIAiIrxfMELG7sN7qFkVKB7cEOPnn0GLf3N6UWVlnmc++4GxGBf7p8mXdvGOZqMkXYbufC8iqnF5Y4NNjHs/HmBCa6nuOLH7ifSGkCm9PKvUODjPgDxEpFJLlJ4yvXDbaPtXGgo5MBt4vpTIZivU6sVMahKJR1HaskUag32NvSyslIlM1+A7emYpomxYpO2KdhGKAbzdxgOVPE7rKiagq6rtPS4WsW3Z1+kpEMO8JhvBYrl9YiPHPmCqtrOUbCoWYOst7g9dU1Xpifw2ux4lAUPJZmygnAqN/HWj5PplL5tyxb/h9Ywn+Nzv8916f+7rOfXOvWWF9McPOmflrtdkxMarpBvFxhMZdFkST6PB6yteoNdJOALEsU6nV+ND7Jwxs3sL+9jdFAkLds3ciuvg52hsPopkmfx4Miy8ymU4wEAjw8sJXTsRVm0xnWSiW+e/EKZy7NUijWME3Yu6GXEV+Qx147QUtr043ssit85OABnEoHJjWuJK/T6XTRMJqdhq9cGeeNuQVu7+/hbDTOty5dAUnCqSo8NLiJLqeNs9EYyWoVhyqxN9xHSS9j0DwclnN54pUKkiBQVwWiaynK5WachCDwpgvY7XcgqAp/f/wUT8xPErDZuLm9l6DVzRv5KIurcc7FYwwGfHQ4nDfGjBKvra4wlUrznSvjaKpIe0eA8UtL7Nk+gGiT0W+IrzVFRNMkqpUG/YNBzFodX9BBNlXENOF4OsYLM3NcTDbDfHWzSZ4o1mro3DDsiKCoCi67lQGvBxPY4PWxwdvPlkAL62qFc5PLXJtf58jsHBeW1ohkcqxHcuiGSaWqo6nNmB2f18qGbpOXl5ewyDIf3b6DhKjz4qUp0rEsI73t/Oilc8xnUsytxqiWqmQLze6iw6GhSCKq0sSYSrKE3+ek2tDxOmysrCY5c3WBK9cjLC2l8bg0vGE3+VSB/pFO7ty6ge+cu4Te0CkXqsRWUyyuxLHaNbxBB/FoHuNGFJBugt+jIiAQ9Gm0t7moVuo4A3bOT8xzYXGN8UiU65UsNkXhc7feTpezBYdiweO38IvJWYxSA81vQU9XcYWtPPt7jxC22Xj8zEUCbgc+q5XnXr9CJV/CZZfp3RjG49fI5KqYhsHm7T38z3sPs6hXGA0Gef/GURQRlgsFanoDTRL59ovn+Ng7buXC4iqSJFIuVNjc24FT04gkM9wzFGS1kON8Kk4pWyIQ8lCu1ylXa1Qw6fXcMGw4fexr2cquFjeukJPe9jD7OzsI2aycjERocTgp1es4NIlRf4CfjU9TLdfw+1wIgsB6Ls+V8QX8bV52DvfwxcN389rqEq0OO5/Y8RCJylWuZ2PMZLP0hfwkK2Wi8QwnkzEeHtqNJGiUGxnsqszxyFke3bITp7KJQuMymthGrp7ALocQBSuS4EQU7EiCE0FQ+PrEi0QiaVK5GnarzEOHdvLw0BCrxQItNjtlvUaPswMBK/n6LG1BB2PBIF+9Os5iLs+z83OMhUIcX4vwe9veQtiuszO4D1Gw8Olz3+AL55/h1bkFhsIhvjV+CZ+twu++9ALReIbr1+IYusHKcoqSWWdjVxt2tUaPy8vnzp9mT2sLYeteKvo4p6JzbPb7GfV3IgsSdqUXAFGw41AMVLEDSXRQqE+gSn582gCR0hv4LBu5nJji5ZUVXjx5hfVIFlfYxs1th5FEDwB2JcV8LsPHdxzAMA1EQeKJmUm+8eIbjC+uUS3X0NxWHKrG90+c58LaOienF8gVSiAKvHPjBiq6znwmy7VkkolEkncP70QWawz5PExn0tzVuYGtgRaSeoUel4vVYo4fzZzn9o578VvKqKKEXfEhCTY0KUCmdo25/BQD7haCVjupaoHR9tZm9JioM51OcTEe5+R6lEc2bOWPjr3Gn+29lWGfC4DrmSTffukUf3DfYXrdbp5bXGRvSzt2WcOtjjKXO8LVZIywy8mAx0NZb/Dt184wOtyFaJGZn18nEHSjSBL7W1uIlso4VZWFXJZcKo/dZaXL5cStqjW/hgYAACAASURBVJxdX+d9G2+78X1Y+ebky1SKVTrb/Tw8NExFkzg/tUS+2ICKjs2rUSs3IFODhoEatOCyKxRKddbiZRw2hTsPjPLquUm8PgeCILA4u048nsEV9LAnbON9zz5F2KEy4m0jUsry+mqE69kkv7ZlC05V4bsXLjOzFOV39u3DrWlcz2bZEgwwkUxTbjT4yI5tvHB9loYIPaEAFcngtu0bONTZyZ+eOEY0lmYyEuN0Ok6Xy8mjW7bx3v1bmJdK/OEt+zkXjfHTqWm+9/x5Ng+20zAMcrUauWqFcqNBoV7nYjxOtdHg5aVFLq9E2DvST75SIJEq09BNqrVmcoXHY8XpbWK2E+s5Ng134Pe5iMUyLC1neOSmrfx8ZoaNwSCyXeW+nSOEbDaOr67x+soKL12cZG0xic1nY9DnI1IoUqjViZeKHOxo51Rknf3tbTzzD6/8hx2d/8M/fuGT7/6VW9+MOfqXvr74mSf/3f+n/xSF5mNf/ttPdt83wj+/837e0bcHVWpqO0zAIkn4bTZ0w+T3tu3iUGc3AZvKfC5PtlKhxeHkT2/az7V0Gpeq0mJ3UG402OwP3vAImUSKZRyqwlgwiMeiMZNNcLC9HUkUeW1pkVgkRSJVRpVFfH4Hf3XHPTw5N8HjL1wiulag3DDYt3+Qu3t68WjdCCjYlSKnY6u8sLiMS1X44ZnLeHxONvr9dDodjLWEee/wKFZF5OWVBRyKyk+np+nyeNAkkUQlz3cmJul0OtnX0sKry0sIgsDTRy4wO5/A47Hh9tkxb3QVDb1ZxG3f1EO6XGF+Zo3oWpbXxmf5xqmzPDEzwWA4iNdl55aebnqcLk7HoiwXCqyXSlhlmfdt3MwDg0M8tGUTXz1+hpnZDNcWYvT0BQiHvTyyfzuPPXAfXz1+ikK5gVlv5seVSzXKFR2rRaZarjExFcNulWj1e6gbOhZZYT2Xx223sjIfw2JVySbyrGdyXM2nkSSZs9Eo3W4Fn+bkts5+3rV3I987e4lwp59A0I0pCuTSRSyq+GaMhq6bLK0VSdobbAoESFQqrBbydDodHOzv5t6tG/j2q6fRVAmH10FkJcODt+3EF3ZRbTSwe+y0dwQ4vGkIwalSrtbxuOy89Po4kfUUlUodWRLo6PTx4bv3UVMEFmabhifFonDy/DUkWWRzXyeSVSYayaDIErMLaYxqjVJFx+9WqdYN2oI28qWmQ/0TD95GdzhARm7Q5/Nzx8YBZLuFZKHI7EqUf77vbZyPr9LnClAz6nQ4HRRdcHZ8GbtTwR200j8YYkdnG49+7UmSiSLRRIqTF5eoVHW8XhtOjx2by4pmVRENA6fLyvJ8nPOJKAPhAIokMeT1sCt0L7tCw5T0CPO5HGdnF7H5HCyuNgseURSwemxkqxU0TeFLLx3jyOwiyUiG1XiZlg43M5NrGPUGO/u7WczlKTXqOFSRfD3FMwtzfHjTHnrdNm5p28CD3/sukXSWo6evc20+yk/PTPLQ3s388PXzyIrE/HyUS9MrrK1mqdYN6sUKK+tJno0t4tAs/OboTpzqIHalnWuZCR47eorptRg3D/TR6ffiUDX+8uTPee/IVq5llvFoNnTT4MWleQY8TZ2YXRnAKtmQRR+6WWC9fA632o9u5vn9Y//As69epqEbPHzPDhSPhS6Xm+9PTvLC5HVSep3FXJ57uneyVj5PVa9jmAb//dUjfOHW2xnwuJjL5bmjq5tsrcqe0AFcahVZDFJsXON/vfE6lUaD5975CJdTUR7dsoWPPvkL1pdTSJLAp999Fz84No7DJlMpVfmTO29hkz+MLEp89fIF2pwONnr7UKUOvjLxEm5NZbN/D3al583z8qn5HzLgbqWir/Hqyhm8FgsOJcgjL/w1mwM+AhaNQc8WelwNvn/xCq1dfnZ3tGEQp83Wz3LxOP80fonf3bqHeCWLJqmcjS3z2NFTZNIlREFAFAWi62k29rSh2TUmJlbwh9x8/LYDJGo1Dnd2MeIL8MLiPFcuzfO+fdvZFerHFKrEygVeWVrBFGoMe1tIVXN0u1z0uHzc232Q3z7yZR7ovxNNqrGQX8au1FClDtZK0/z5yTfwWiU2eAaYz0e4vXOAhtmMZ9sZ6iRg1ZBE8Fs17u8fpHYjweO5xXn+7tUTuLwOZgo53r1hA3tb2vjJ3HX6PXYcchterYs+t599Lftos/u4mpxjplbk2tQy2VQBURJJRDJEM3lCAS+lep0Xrk5RLlQAgUK1ymA4SJfLybNXpnnrBg9OtQlhuWPIzhdfPkWlVqGoipxfj1DKFknnapiSgCEKGDUDig3QTe56eIz5+QSt7R5awg5+86797AiHeOb8JDuGepheXsfhtdNo6LxxaY6xoRZS1RplXedg2wjt9n6OrF3lyQtXOdjbjWGCxarxKzu2MpdLM51Oc6CtjZeWVxjxe5lJZxhPppieW2NxMcbsYow7d4ywNRigbuhsDQapKCLvHN3IiaUV4pUq9/YM4NHs3Nvbz58cP8pMJM7Viyvki3X6+oIU63VKjTo1XcdntdLjcvL4yyeZW40RXU8zN59mYjZCNltFlkXqdQNBEGgP23F6bNicVqqlGm+/aRubAgFaHHZcTjsNC7hsNiZX1+n0e7keifGbO7ZR0Rs8O3Wdq1cXKBZrFCs61XyR/q4Wul1OlvM5NEliSzBIodEEobzyz6//xy40f/VWhKbU/l/8/q9C81+5Hv/6Vz/58l//BUGLh6upad5YX0MWxaZmq6FjkaXmhjB1ep0BvBYL7U4b9/X30+1y4lRVPn/2LF957g12D3exI9SOQ7GgmzpBq41yo45FllktFjm2uopXszDqDyOKJo+/doZquY5uNLUn27f28r6ND/A/TvyU6zPxJoe6YfDnbz9E0GolYOlDEGRqehSnKnFbRy8n1le5fbCP+/r7WS0UmE5niJfLWBWBJ67PsNHnZcQXotVl55XFRURR4lw0xs6WFkI2G3XD4KlLEzQkKJerbwbD5zMlDuzYwHosiapKuJwam7rbmV6Lkk2VKNd0AOoNg1Khyl/ffydhu5VMtcZ8LscTZy/zypkpElKdAZ+PZKXIsDeEbhoMtwU4uTBPNl/HoYnUGzoHe7voc3nQnSpX5le49+AYFVVAsyj83j0HyamQSOSwayKFfBVDMqkYOqlSmUw8x8JcFIu16Sq3OiwomoKhG8ysx2nxulnK57maijDk8eLRHLxn9xaGgj5+f/tNjLUGeHlu/s2u8si2btKJPG0tTgwRzs6vMNbZhiAI/PLQVr42fpkvPHUEARNv0MXqYhKHTSEr6GTyJd61cwvRapmAzU6328XpmUUkRebeoQFmUgkK+WoTPVmoUy3XiDVKPDI2ytnVCG2dAQq5EoFWL5pV48TJa8SjWSxaU3vp91iQbjh4qjUDj7M58urtDmI6FM4srPDSG5OMDXfR5nDy3WPnOHt5kXy2RLDFzXevXubPD97Li8vjDHk6bjxMyVzIR0FvBra/+8BOPvmD5wm1urFYZG7btYkr11cRBIG15TzpfAWpUUdWZVSLwsxMko5ODw/u3ILfaqPP7WQynWZHcBhRsNFia+efrrxCslhkZi6CKIkUC1U6B1soV+ssXVsjHPYyOxMjmSqh6ya5hRzzyykGN4SwOSysFfJMLUV47uQEHz18gG5HL+laEoskoEkKFklmS0eA7Z1tfOjgDq7Ws4imzrdevYDTZeHDt+3j4tIaFk3G0E0auvlmEHy1VGWsrwOPJhG2GYBMpLTM05enAJOplSj3bx5GkUTeOtCHKtUZ9gxglUXa7H5eWJ7hpeV5FMkgaC1jV5qF5d9c/D5Bm4ZfU1gtTvHlC+eIr+d4/CMPU2ro1HSDHeEQ+Xqd/T2dvHNoCE0S2RbcTqw8T7dzLy61zDfOnuWuoT58FhvvHnyIgLWfN9bPsyPUzs3f+Bu87hLDnhCPjOzlJ7NX+cwvXmRnf5NMs1Ap8H/cfhNfuOchfjw7zt+94y6CbV662oM4VYVzsXVu67ibPW0SZb1Bu13ANMt87uxr1Ay4u7vJ+H5x+SeU9RX2t+xDlTrJ1ZfJ1kps8G5GFv1sD4Pf4mQ2t4YqFfFpTn4RWyC2muLwhj4sksQb0fPMZtL0uJx0Oj1IiGRqRT7+0quU8pUb+xfK5RoCkDJqROMZjLrOfXu28DtbPsjuVoluZz8vLV/l6UuT+Fo8dLjdHGgdwCK76Hb6+dtzRzkbjfL84gwf2LQVp2JhPB0lWY1yuKOTz198ibs670ST8likNr42+WO6nW4+tOkhOh0y8UqMHcEDWCUXnU4LryzPUmpU+dbkBL86MoZHteNW7RTrFc7HI7y0uEi2WOZz997BzR0dlOp1ouUi+VqNDd4AVllDEl2I2Ck2rnJk7TJfOHeOtw0O8srF6zjdNg5uGWLPcC+3behjpVBoIoAleGDrJn55+yh3DPbz87k5lnIFJq8vM9rXyrBnEwBerYenIscRRZGRthZ+e9s2XlpeIp0qUa/oKBYJvdSAUgPcKo6ARq1SZ9vmHrZ2tvPi9VmeOn4Zm8PCb+zbwZMnLpFOFgm2eamXqkSEBp/YvZvD7f28tjqBJhd5a88BHhjpZiaTZLVYQBAE9re1o5sGAYuVXywusSMU4MTaOpv8Pp6bvM78fIpq3UCRRRKlIu0BL/FyhcIN/e2FWJxUqcSdfb1s9Ae5kojwoSd/hsNh5UPbxwh3+IhnUnS1BxEEAUWUkAQRh6qSrdWYj6dIpkqIooAoNidxXpfajFSSRQJ+O5gmDo+d2FISi10jXq+Qq9W4EI3y3pGN7O5o4yfXm3jepWiSjx+6ibphsCPcwheeOUq9YdLS5qG1w4vFpmHIAkcnZhkIB0mWy1gVhf2trdzS0cUXPv3j/9CF5nt+9bb/2x3Nv/t/odD8T6HRbGbkK6wUZzkbi5Kv1VFusHSztRqaJBIplmi1O/jDN17hL/bdyoHWXppSWhPDLPPYbTI/6bnOnz79Ml9+1/3sCo3QsBiYpkG/201V16npOnmnC6eq8rnzZ0iWS2RzVWRJwDCgWtc50NGBKNi4PrmMojVHrla7yrZgGI/mQDfzVBpz1IwGs5k0DrXIpVicmzvaSVXKKFITnVhuNHhtZZU7ujpZL5X48pVL7G0J89Ht2/mzY8epFKuU6jW2hsL4LBoHNgxw4toc7z6wk0ihwPnFFTSLwtd/fIqw38L+vcO4NI2AzYooCpim+aYOVBAEHA6NZKWMS9V450Abf3/5IulYFlkSWJhaY7W1jWGvBxMTq6xxU1sPP/+t9/Oub/6AWDSHJIn89/lfsHj/QS5Go1gtMl6rhaDTwa/s2818Lo9VlmntCRJfTXFwyxAjfh/Pzs3x+UOHeX5pkR+NT2LRFGKRFJpNa5J2ollESeTc4jI7ujvxWDReXL5O2GZn0O3nUPsgsiizPdjL197zdh547HF6h8IM+fys2aPIikQq0nTnP3VlktHOVmxKEN006OgJIKsyFotKKNgcCQ76/DQMg59OTKE3dDSpaf555/ZRjiwt8djzx6hV6gSCDgb72piYXGR6Povbneevnj/KQG8r4+MLlMp1Bkcd5NMFduzuI5srcf7cMhZVYmhzO6bZNAIBJNbSvHjmKtVys0jOFOqUKg2uLa9z4vQUy9FSU/R+w+QUavFikTZyd5cXwyxR04t8e3KShYUUiiRi0SRabFYkUSCfLvLWg9vwahrlqo7frVHXm5pezaZhsWs4bRZsTiv5dOHNPfPY6XMsXlun1Gjw/g1bERBYymaZm0uhGyaa2mS6V0o1UpE05aqO12qlUG4gSwLlqt58yEpUOH9iiVCHgwfv2IHRavKztQxvrC/z9r4wvS4P66U8T87MkiyX2RIM8La+QTyag18ZHUUbE/nm1XHSmQJfeuZ1VItCtVxHkUWqdYOwz0Kx3EBTJTocDg603k2yepHT0XFkUXwT0eYNuej3eGhvNDjUvh/TrKBK3W+eIGOBK5yKxhARkEWJ55aeRJMkHhwYQhIklovzTKTiiKLIo287wFy2GeJ+T283p9ZjvGt4iIOt91HTF9geaBCrnMej2REFDU1SGevrpN8VpqRXaZgxkuVFPrzpJj55+gkGO8KMBcI3Irs0nrr/4/zZ6W/x+tIS79u0iU/dfDPDnhZ2PfbXvPror3E9G8OvaTx5ZYIzKyvs7+4mXrnAa6vLBK1WXl6ZYk+4mx6Ph7f191HRpxEFG7Io4tXsfO7CT/jY9ncTsOziptYCdWMVQy+QqzU1ad+cmOTZ4xfxtXp52+hGtu3cyZ2db2G9dI6QrYhPs1PSq3jUdn448xpno3HiK0nu2r+Vl86Mk0qVCIaazO2Ax8n1WI56Q8eraSwXjvHa6gIH2uq02R0YusEf7N/DofZBFgvzLOQyHGgd4Et33IFNVvnK+BUuJiLsCLWzM9hFWa9S0xt8YGQTDTOFXRlAQOPenkGuJCO41asIAnTYb8I0DQx0QOb+vg1YJZVhb4BEJc9CboX1Uond4Vbe0r2NYW+TbjbgDpCoFPj460e4paubj44d4I/feJFP7e+kpi/SMHPY5AFu72gnV6vSanfw6/cdRJFEzkbWiRaL/Px8FE/QxdpcFG/YzWqhQKZSZTHblL1sDQX4TrLMXDb3f7nF3nj/37L3679LwGphxNuFx+dEkWMImoSmiFQ1CTwqolfDNE08PjsNw+Ds2iqxpQSValOX/p3JKW7atZHLM0tvTh1+Y2wrR1dXePzKc+i6wYHeHn5nrKmBvJxIcmxlmV8f28rnz51jWyhIrFxma8DHCwtLPPXKBd55x06Mho6umzhsMj6/g9ZWH1fiMS5emmPTaA/bwy3YFAWP3Uq+XmciFW2S3EIewnYHWwJBHho4zGjAx/PzC0iiSLWhk61WKNVrXJ1e4kOH9/L3vzhK+0CYarmGJEt86vAt/O258xw7MU0mU8ZmkVhbiGO1aU0ZzWKcqJigvTvEmWicuUyavzhwkJDVQUWvUTcMfjRznb88cRKn00JnX5hburp4eWEBWZH43M23ciq6woV4kqVcFossk65WeG5x/t+mXPn/4fpPUmiCgIJFVrFIMmmjRsPQcasqNllGk0QUUWQ6ncYqy7ywPMWIL4FHtePVgry+dpmK3iBRrjC2sQcAkxpezYuAjCCIzGbjPDs3T7JYJGC18Ftbt3LPF79BtW5QqTWpP7pu8pHRd2CYNao1nbZgM/g30OLhU6dPAzAavIxNlrm5vYMhr5/vTk8RLRYoNRrIokC0WGa9WOBwVxexcplHv/YTXC4LO0b7eCyyxpDPz8rMOmNjAzg1jTORCDvDIX5zy1acqsqDAwN8b3qaUq7M6loWs950mg/6fPgsGk9PXcPndqBuUlidiVK4gUczdYNvjI/z22NjhKxeHhzox6bIPHH8PLVynWffuMS5lVU+MFbh9q4+HLKVb01Okk6VmnE6hkE6X+Mvv/cKHmfzAJtLp7mnr7d5kC0vo5smLk3jpj1bed/GjcznMlx1uelyhHjXoIOQ1cLjE5O4fE6yiRyGYaJaVMqFCmsLCfLpIo7dW3CrKqfW17ErCiGr54ZGTGbI08Ij9+zj56cvc+LqDOlMBdOsYNEkqjWdSjVxo8iucy2e4Ldv2s0fff9ZhjZ3vck1PxdZ48HhIWLFAi5NI1kq86Nzl1E0mVuHB+j1eFnKZVlbT2FRZNx+J6MWlS3DXdgUBRGBaLuPerVOr8fLtVqDbK6EqRv0dbqQVZlsMs892zfzxvISM+OrOBwapWIVw4D23gC5yXUaDZNr0zF6erxYM1XqejPfMxXP4w97+MQbj/Hpfb/FufizxEolFjMZFEnEMJtGqKdmZnjb4e1MJuL4LBaev3Ydn0vFaVdwuy0kkiVK+TKjw13EikX6A34WFQndbCI+P7Z/D3/FKY4vN5Mc3KraRIAaJnaLjNUi4Q+5iCwm0A0Tm0Xme0+dwzSbrHMAJWChXmyAaRKLlVnKZWl1ONi3Z5hBj5ejkQmcikK+XiNdqXBrVwcbvH4eu3yBAbebsM2KTVb46M4dfH/qGoWQj0SpxNzEKqoiIlagf1MH4+cX2bltkMMdnYiCil/bzK3tVp5dmsBi0xBEgQ9t28pcNsu7BncABqrUfcMUEwRgMZ/HKklUdZ10tcBYoI0L8VWOrCzx1QuXEMUmqzrkcjKVTLCUy/Hw8CBjgTbqhsEz8wvsDs2gSQM0jBQ2uUDDaE4MbPIof7AzwUoxwWfPnqXH7UaTRH4xM8Pbh4epNnRytQqFepSNXhUROy5Voc3pYoPPx7V0iq9eHefWbRv5zNkT7GkNs5QvUKvUODS4gY9vv4njkTneO7SN5WKCy4kY356+zG9tHaPV7kETB4hXzrG/ZSMn1if5+PYPIggSuplHEpwgtiFiZ7OvC5MKhzvWEQ8I3NrZzi1tAyiiTLp6hfPxNfa2dONQbAjVPFU9gWGafO+5U4iiwNZggNsfvIt4ucKBtnbmsxmuZTLkq1XKxSqTyRRfrV/m6fPjdN/vomYYKKrMJ376Ag/vT/DOwUHaHU5kMcCgy4VBkft7ezkTjbLZX+daOs4nnn+FB8Y2MeRx49UcOBUvshCgzbaPNhusl07iVQPNO0EQkQQPhlHFreroRp2n52f47I9fAeCT77qTEe8A8co6iigx7AlwPr7GhXiCX9u6BVWU/k/u3jtKsqu+9/2cVHUq59Q5TYfpnpw1GgWUIxIiw8Pka2yT7Gsb8LMfGNuYa2PCBUwQcEUUBglJIAmURtJo8vSEnu6e6TSdQ+WcTlWd8/6oscyyn+/zXfd5Pey9Vq/udc6u06dO7frt396/b0A3Gtze1Um1keT7U+exKzJ3dWm4TP3sDIap6zqXU0k+f/0tHGqJcWJjg53hEIVanUdnN5CV5ndhdyjAnd1dLOaylOp1Ah6VbFWj2pjDLDWJOIahUy5UCFstaI0qhUL51XJnsdxcuLnb7agmid+/6SCxcoXvHT1DOV9BUmT0ch1vxM1CrKmwYLGrrC3EUS0mPvrIUxiGQaVY5a5DO7i2JcKpjWV0DHYE/bQ5bJgliW1BP11OJ4/PzvKzs+NEV9PIksCjz4+imkR8bhNVTUfXdQqVKrF4hlCnH49q4elLU7xlx9arIv8CE8kkPzo7ht/rxGexkNOqKGKE/374OPl0EafXjjfooqHrnDk7gyxLRGxW/K0e/uyaa2i1OyjV69R1nT8/eJA/A55+fgJdN/C4Vax2lVyqqeRRyFX4xF3DHF/b4NzkPObdezBLCtFynqNraxyMRHh5aQmXz86Az88ry8uIgoDVovKVsXNc39ZC4ypXwCrLeFULT88v/nunLf/b7f9rC8p/r/afJtEEHZ85RKczRlbTyFY16oZBulqhUqtjN5lQJJH94RCL+TxfO3+B+zZt4h2DFvwWKx994Xl6vT563B7GEgl6XQG8Zj8gYJFM/PDyFBMTC7T0hkiUK1Qadf7s9bfyiR8+jSgK1Go6boeCLHrIaudo6fKTSxW4Zusm2h1OdAwuxuL84PhZ9g32UDcM8prGVCpJKlsgWiphGBZUWeID27dxMNzLajHBMxcvIwgCE3Mr2N02nj85TmtvmDank1JNwySJ/HR6huvaWrHIEj+ammJXMMBhn52FxTQOlwmTIvGr85PY3TYKmSL9wQBFTcNiV+kbasMiKyxvJGjoOj2uABulFC02F3d1dfOatjbe+9Aj1GsNLo+t8LlYlnM7EpgliQefOIamNejp9jLU18r41BKyLLGxnuO99x7Erij0OJ08dH6MW/p6efjIGYLtPt4/sg27YiGouhnyBBlPrfDN8YvsCYXIFstYVRMFWaKaLyOIIiaLQqOhk06VeOrMOJNdIRRRImixsMPfBYaObtRRRJn3DA/ztz84jM0iv1rattVlSpU6qknCmIshCCaqZY10pcrQlk6mxpewOS2sLMep1xs8p6pMTC/T0xMhlW/63YqiyFq+gCgIVOp1VJuZYb+fkM2OKsvUGg3aHXYefPEktWoN1WbGbjJhUU0MhgIsZbPIJpm6Vkc2yXQ57aT9ATZscVxeO3K+TCpZJLGaRpFFGo3mAiEVL2CzyLhcKrlcBafbiiI2d8pPxZ7iwfFxAlYrO8IROm528ezRi+RLdTaiae7s6aFSr/HE+XHu2zHCz4pVAAIBF6o1ic1pZTWTpVyo4AwEuaGjk2fm55sr+kKJ/2P7FhZyBc5FYyiSSCFTxGaR2bOvD6fZjCgIzNtVuoM+Zlai5Ao1GoaBLDZ3NFsCFqJSFbMi8sBtO7m3t5u1YgmrLBO2uvjqhSPUdZ3f2bYVUQC/auHltVWOzy3wUrnGLVsHUWUJRRT5r7t3oYgSL60s881siWK+jL3R9PxuaXMz7PfT7zoAQF2Pka+VObK6zm0jg/Q4HSiiyJDXx3Jhg02uIYBXk0yAQq2GVZEZ9PpotW0nWZkkVa1wbH2duwb6eW5hnn6vj2KthkWW+cPde+i0jwASD/Q68KvTfP78C+wIjnEhnuD1fQNoep310nNs9tzMuXgUVZJIlkskyyUuTSzS1d8CQIfTwXgyeZW5b3Bs7QRPXJ7mobvvJmTx8tDkJcr1GtuCAayyzMFIJ3d3DvOuzVuu4tC9+CzrxCtZuh3dOE1WHr8yRb+7nT8++ks+e9DBbDbG7kCIG1tf++p7Hksew21WabdtRhcMCrU5bHIXd3bu47rWBIlKloahIxgN0lqeRLmMXbFR0zXOxJbZ5o/w5VdOUW8Y9PY0n2Wf20NOi3JkdYV2h4N0tdo0bUjkeWY+TntvkPZWP8VajZlslmyqgMVm5uTaKh/dcYD1Uoq53CQC0OkIs90/SLvdA4JAr8tHa9BLrFTmTZsG+eSJl3nr4AA7A5uQaFYkwtb9/2JmMIwKouCgRpJtAT+/c+8hfj5+mTa7nXwtxcdeeZHX9vViEkVkUeSZmVnu7bmFnKYxlVnj+pb9bJRmmc/mw5TrSQAAIABJREFUEAWBOzsNirVZHr8yx5n1dQ62tuIxtyKJCQY9Hs7EYtzX081qLsfeSBivqnIqGuPs+jq3dndTaTTwt3qZSMRZKiyxydV7dQYrYLaY2BEIs1ZKkVhN0WgYyLLYrJoZ4HGYkCSRx2bmGA4EWFxME/Q2jTmqWgNBEFiYidLdH2ZPVwcvpIssLGYwm0RcdgWHx4bLbEaVZSaSSZbzBQa9HgY8HqbSabb4/HQ5fSTSeZYXkiiKSCDkRKvW6O6JsL6eIpvMU6vUmLu0hqbp7Nrbw8snJwh3BvjVlStsDYUxSxKPTU8z1BZhbG6ZXo8Xu2Li2eWfcfuOYR566hjF1QzC1ZKDxa7S1R3mheUV2vweHCYzOU0jWiris1hoGDrv2jLMK8emSOc1tLqOtaDh9ljQGwaqSWK9WKLf60HaNUxNbxAr57iUSrHZ56VhGITtdqZTBXLVKluCIUq1GuuFAs+cn2ST20WX00Gm6uMXc3NkKxWqZe1/Py3592wC/2H0jf5TYDS/8Y2vfPJ973sAUTDTanOyVopT03UWc3nqusGAx93EUWlNN5aGbrDZ7+e1PQPYZJWAxcMXnj+M2W7GbjaxUSwxkUqwM+DDJJmBOj+ZmSRfqbK1vYVEuUSPy8WT8wvkyiVKRQ1dN/D67PzO/nv48czzoMi0Bb1sDfhZK5Yo1+vEigVquk66WmE5n8dnteI0q1hVM5vcbhRRpNPp4EC4G5MoN0vUPa1cymdxu2wsLcWIdAfp9HjQDZ3L0Tjv37GN+/v62eJrQRQaTKZSLObznDw7c7XEKSKKAvFkCUU0mgLbuQKJtTQIoAsC9wz143HauaG9jVfWlklVS/zDzDS/uDLHGweGeO/+vTQcCmdnl/D47Iy0hElWygz1hhmfXmP/7l5GAkG8XidzSxvcenCE8xsb7G2J8OjMDIoiM5dJY3VZ+e4d9+A1OzBJZhRRxa7YyNfzfPPUWc6trpNcS1OpaPzNvbdxOh2nlCs1HTIEgXpdx+GycPfwAA//6jTtHQFUWafV7qYptF3ndf/wY+KxAm6Hgig0IQ1aXUcrNdDyGpoo8LFbX8O3Ro/x7OlL/ODNr2POKPPmHSMMtIYYnVkins5TLlQ4P75OZ7ef+0aG8NptPHNsjJLQwGe3EY9n8bjsvLF/E59/5gjbu1r52fkJJi/HKJbq9A6GWYgm2FhKsJ7OIZllFLPM+kKcL7zuTtaKJQa9Hi7E42RTBfK5CpIootV0coUakYDl1V00QYB8oRn07A4Vp8vKhdllRjMJej0e7IpCtdGg3+PGF/KwEo3zwVsP8t3RC1yeWiHc7ufY2AwtrT6q1RoWi5lySSOfKRJdTSNgEAq4Wc3nWFiIck1fN7maxq5QiHRV4+FfnmJuKcaBrX28cf9WdASsioJWb5DIFYinc9S0OhaLTDpdwWqRqVR1qloDSRTo6fFxy0Av48kUiiiymMsz4HHxlv5tXNvawpMLV1gvFFkrlXjfyDY6vU6OLa0wtbLBhbkVOiMBfKrKz+bm+NC2A7xnx3UUzRUsXhtX5td5w4GdXNfaQoejH0GQkEQ3b/vlN1BEkYuxGO/fuo0XV1e5r2cbE8k1up2D/yKG7A6aORjZj0Np8ODkc8TKeU5txJiMx/j9Xbt5x+btHGpt5039hxj2q7RY/QiI5Gor1PQ6VkViwONhu7+bXcEIRzcW+PHUNKVGnUEPjCU2+NLJM9w30M/R2QWy6SKfuv1GvnZqlPsH+plMpbEqMre2v4Z3fu9Bnvmtd4HQtM59fnme69paabXZOBWN0m63ErK68Jp3slKc4Utjx2i32+hy+lGlENHyKn1uNyejC9zU3sFPZsd5dmmJkYAVm6zznue+zv29h/CrFnzqFkTBhoDMidhpIItH7cUsNmE+nzp5hKlMgtPRKHd1d6OIElOZdQ617OHr40fZ097KixfncLktKBYzL6+scnhpiWdOTfDc9BVGLy2QTxcolpqwEL1Wx+G2sVwo8tTZCRSTjDvg5Ld3bGcyFcOjmnlqYZ7RWJxb2u9irTRJwBLCJrupG2UsisiIz0usXCRgtfA/xif4+ZVJ7u7xoxslBMH0qhWoblQxaKBTwjDKSIJKq81Om8PCb+/cz49mJtkfbkUSDT752LO8srDE67ds5g92H8JjtnE+sc6IL8JSYRlNryMJ0OV0ErRamc01tSc/uf9GDoT3ICADeT525CX2hMP0uFycjcW4rauLnKbhU81MpzNs9vvwmFXOR6NYzWb8NpUhTy+CoGCg8ZHv/QBfm5NHZ2YYvbhCtdYsfctXjRgiESdWh4WipvH88Uk8TjOqTUWSRcolDZMsUixobB/q4PTkFVZWcwQ8ZhLZKsGgHafHzkDAzya3i+VCgb946HlS5hqv6exgIZfjibk5wnaVLZEgOQtEN9KIooDNYWFHextdIR/9nRHW83nMZpmugTBOs8ofvOYaNqoVfm/HDhqGzl/+w3N0dwRZjCboiPjxWFSOra0hiAI/vzhJLlPGMGBtPU8yWcTQde7auZn7+3q5vq0dp6mJy3SYTERLRUDgi2fPcv7ZOXRJwOE0I4rQqDUolWsMbungvVu2oEoSJlkkZLVRN3RE4Ka2g5TqKa7kcvzBgb2ci8UZ8noIWq10uZz0t4Qo1GpUGg1kUWJ8aQ2trOF02bjyDxd+ozGab33Xzf/LGM3/P3Cn/2kSzf0P9PC5cy9wIBKi3e4iXs4zkUzyQF8fBk3tLANIV6o8OTdHrFTirYO7yNUK3P6D7yDJIi6Htcm+k2VkUUQU63Q5XJQbVb5y/AQ9rQF+d/t2YuUK+VqNn7xwmkymjMtpplyus3t7D2/sv4H3P/ZNDh+fRrfAZCJJrlZFkSRcqhmbRaXb40USRbpcLlxmE06TmZeWl7kQi/H4qYts7QxilgUkUSRkcXFvzyAhmwmry875qUXsTis1vcGulhZe2zOAy2TDrrTxrYnjtDscPHFugpXVHKpZwu22kEhVaG9zATS91fWm5qXFaubPbrkOl8lEvFzhybk59re08MraGoZhcE1rC/O5DCO+ENeEO/jOqVO0dwW5b1MvC9k806kkfT1BgnY7y7kcBa2Gz+/kVy+OoyoiL5ybYmE5QTKR5U9uuQ5RkvjexDgPbBpGFEyACAi879knKBTKlItVTKrCp+68ieta+3jz0Ah51WA2GqfR0LHazdy5ewt///iRZjncKmI3m7mYWCdsU0lXi3zzueOEQnY8fjuZdIl0qoogCeiCgNVpwmmTuXtnF88sL3HTtkG8qhlZkpjP5XjyKjFhdHSVfKlOd5uTdCxL3gSTi2skk0UyqSL7hrrY3BZhyOvhO+PjdIf9PHtmgnK+QkeXl51bu1i4sk42WWDzli5Es4QkS2QTeUIdfv7ie4eZjW3w9NkpGlqdbK6CKAp09od59/V7+KNbr+FEYoNirgyCQCJTBQRaWpzsHe7lrt4eXpyY5XN33cpSvkDQYkHHoKYbDHndtLcG+NC22/na8ReRTTL3jAwxtrjKdQN9tHjdqLLMleUoTq8drVRFEEXetW8HY/E4y4sxjl5cQBB0ruvr4ofnxlhZyZLOaXz4lr28sLTEZr+PWKlEXqtSqtUIel1IqoLf62R6Nka93sRx9Q2EuGXPEJoMHU4Xz03PMpfJcG5ygT+54S5+OD1Kqlqk1W5nOp1mOZfj6YUrHFtd4xOHrmGykGVzZwubPG6+ffEi92/qo25o1PQSt3fs4mxiia2dLfS4HNzcPoAsel5NMj738iPMLkTpbw/x4soyd/d00+scwKcKnI6dodMx8Gr8aOgZREFlLneBT598hQMtESI2O9+fGAeg3NA5srZCi12lw74Lm6xjkQdBkJFFDYcyhE/txWlSGI1f5mR0hRabnaDNwht6t/GtS6M80LuZ7507x0A4yGAowEq1yOZQgGOzixzs6WCpUGCtWOTm9kEO9HuZSsdZK+ZosTl5bmmeX0xMsVIp8eEdO1BECYeiYpL8fOjFh5lNp/j4nnswqFKsJ1kuZPjB5SlK9TqvadvErmArN7V341U9QI0fXR7jQKuM1zzy6jP41qXvsy/UTod9PwJmJNFGXY8xkY5zen2NsN3OA733cDJ6jr2hzZikLg6Eu9jmd3L3rj5+b+9BJlNxDo9PE19NoWl1yqV/IknW6waBoAPVYkar17llUw9Ol52esJ/7N/XxnYvjjCcSbJTK/OTYWcbnV3nTrgiGYVCo5XGa2ig3UnQ7PWz2dvGLhUv84MI48XiWodYwyUqWLb4uKo01BMG4KkUlN39Qm85O6CSrKX4+P0uHw4Yqi7jNZoJWC1WrwsxqlK0dEVrtTvJamc+PnuFN/VuYTEX5+thF2ux2buscIGTZx9H1M9zc3kXAshlBkJBFD2ltgW8fOc3YRpSPHXwdj81d4L7efnpcXhym5pyyxe9nJpvFYjYzEvDz0LkxPv3Yj3jw3NN84/SzeFxm0tQ5cXKmKctEM8nU6jqCAB6XGV03WFxIEvDbcHptyIrE6lIzIWzvDdHS6sVlVnnD9mFeuXwFkyKxdVcX+XQRk1nB73Qw5PXytXPnmD+1SqpYZudQG69p76Cm67y4skJNN9ABxWWmkC8jSRJ94QADHjeiINIfCWK2q0iCyPnzszx1boo/uvkgnz5ylHMr65hkkQtjK5QLFYIRNwupNNe0tzGVTHF5bhWPz44v5KRaqjK8vR30BpdWNjgaW+eltRWKjQabvT6ssomVQhMP/Wd/9QQAf/yh29kx0M6pS0tNtzcB9EaDLR0Rrol0MeQN4FcdfPrkMfq9bnpcDmYyUQY8Huq6jlmWyGgaWa2GWzVhk+XmLma1Sq5axWRWWF9NIUgiq49P/sYmml/7+pc++dZ33/y/7Az0xc/89DeTDCQIght4EBihKQf9bsMwjv+zPjcAXwAUIGEYxvWCILQD3wXCgA58wzCML17t/0ngfUD86iU+YRjGU1fPfRx4D9AAPmQYxq/+Z/eXruT43ad+ScTr5hvjo3xo2wHu79lLp9PFVCpF3TAo1eukKhXmM1kA3rtlC1OZJf7wpZcoFyqoVjMvvzKFz2Um0h1kW0uEJ68s8MuFJSyyzJ++5hBhq42GYXBHVyePzV0hndeQRIG1aJGK1uAzh64jWj5JOpaju9sLQGwpQclto1RtApv/0WmoWqoiiyJWRaFUq5GtVLAqCt6Qm48+8hQfvOUg9/UMkdNKjKc2KGgaZ9bXuGPXCAGrhb2hENv97Yg0B8+Xx37O8aUlWt0uvvLaOzi2vs7Do2OsLSWxmCWsdpVKqXoV8yOg1xuoNjOqJKNIIulKhbv7+vjO2BhtLheruRyKJDHk9fDRl57lY3v28u13vZ7PnDzJ//X0YVLxHPF0U5N0174uPrx3N+diCTaKBX77vmv52dFzSJKI2SLzqftu4frWXg62dPGVsTN8eew4vz2yh7qh8+LqLIZhUNPqOH123rljG9e2dCMJIoIo8PFdh3j74CB/PzaGKsskyyVuOTRCTmsK8j85O8ut3T18/uwoP3vhLFWtgaJIyEpTR1O1K2haA7tFpqI1yJfqfOr4MUbCIW5qbyNss/O+bz+Kx2tD13XaIj6++cH7eP+XH6dUqtE73Mqbh4Z4RJrmL+94DYeXm8zti7EoC5k0sWSWe/b28rNfnSfkU8lnSozFr6DazOzY0ceZ0RkcLgvh9iZuTJEketrs7Nrex9TCOo1Gg3fcvJ/lXFMkvtVmRcfgLTu38rcLL2BWRGrVpm2lYpLZ5PHww8lJRoY6OB+PY1VkTJKEgMAmtxOPWWVfuIWbHv4iw71tdLvc/PDoKLVK0wvdYzZzQ1sLJ8bnWJyL090fQpIlZjNZbuvubhoWjK+wbVMnk6kM6WgWt9OEVdX58eXLhO0O1opFrqTT3N7Tw3lZZmxqiehGjmS2yQB32GQqms7Zs82J5uO3X48qyfQe3EepVse8SySjpdnmD/DQ5CT7IiHGV9bZ3dWBTVFIlkvUDR3dMDg1MUe0q8DH9+3jkZlZbu/q5KKW4IWlY9zV08XdXcOIgoKAhCg0MdE/mXuYfKaEYpKZXYvRE2mWdR+cfJr3br6D3UGFF9cep8XmoNe5h5XiRZ5dnuf5xUVu6epk2BviYmKDD+zYzhNzTT94iyTRZnOTqZ7FprRiGDrVxjwrxTjdDhuK0Mo1D34WWZEwqSZu6OlmJpXEpii02W14zSPcsWUQWRB4en6ekbYIP52e5q4dw9hNJnYFAyznC+RraxRrNfaE2vmr08f4/JlR9ra0olpNtDmcvLSyQqle544umM8+QbXR4NF73kjdyCIJIg7FR4dDo81h5x0D28lpRWLlPH7VTrSc5lw8ysf27UUSROp6mrqxwaX0En1uF622axiNP41PtdHluI5nV6Z5/8gOvlCvs5zLoenL1AydpcIyD08/x7093fhUOzlN48sXTvHU5BQdbQE2VIVMIs/aRgGnXUGWBLwBF4IgsLqcYsfuHq5vbeXU+jqz63E2e7387N7f5tB3/htnajU+e//tdDic3POjH+DzOHjnlhGubzGRr5X45PFjfPLANfzw+FlsTiv37drCWwcGERDI1WJYZTO6Ufwn/CkgCmZkQtSMJTxmO2uFIk8vXGFPKMyHDj9HrlLhLcObee5978Fn3g7USVUnePjO32cqc5K/OX2aAZ+frKZxPrHCT6YPY5ZlfjI1w0d35ehx+ghYegiqbt5xaDffPzbKh178Ed999BSVeo27e3so1Op8f/QCp1pC7AgF2RUM8OPLl7kwuoxuGGQLTc3jXbt7GQ4EOV6eJV9sfmcdNoVGo5mwW2wqd2wd4oWZObLJPPH1DNlCDY/DRKjDh99qZVc4QtCq8tCFi2zd0YXPamXyymrTOa3eYCIe48ilWVaXUtg7HIxsa+FgpJVfzF/hx6cuYHdbubQRYzAU4NaeHua9PrrdLrb5/UymUogCVBp17urpIlGusLASY6SnjbDVzt7Odk4vrQBgs8i0tHtZWoyhWk00dJ2pZII3X7+b4ysrHGxrZzoS4MLlRepanXBXgEqxCqqpubixWrihrYNtgRDL+Rzf+sxb+eA3fs5N7W1c37KTv77mgwCU6hcp16t89eIZ/vTRr1Gr1vitW6/Ba7HQZnfw6Nx5XlxZ4ZpIhLlsDpuicHYjyvZQCBGBcqNBtVqjYTKTq5aZnVxGFMV/Esz/DW7/QSrn/2aM5heBXxqG8XpBEEyA9ddPXk1EvwrcbhjGkiAIwaun6sAfGIZxVhAEBzAqCMKzhmFMXj3/ecMw/vafXWsz8GZgGGgBnhMEod8wjMa/dnO6AUMtYZxmM0NeN2AgCCoOxUSuVmM+m6Ou6/S5XRSsVr72mrsRBBMvrk6gX5VKOPbyFURFxDAMmI/htlm4q7eHzV4f2/3tFGoVkpU85XodSRB4eXqOWk3HYlcwrt5Hu62dPz/9SyrVOmqtQbWksWmonZFAkPVCE9/X43YTstm4nEyQq1bQDYNKvXb1fRiYTDJbBjr4/FMv8w3HaQRR4J37d7I3HOavDl3Hs0uLKKJIwGJFQMCgOdg2+7w8bVK4ubODvcHbGfKMcWx1lZXFJKGwE6vTwht3bWUmlebi6jrZZJ7OgJd4uUyxVqOu6zwyeYlyoUJ/Vzd9Hg+r+Tw/nZoibLfzkcMvcPyVWeoN41X3HcNoCrQXMiW+OzHJzlCYM1MLfOK265kf7sZuMnF9Wyu3dzQnAtEQed/wdr45cZ5fLFzCLImcjScoaho2l5W7Bvt5fe8IJkluruRFCd3Q8Zod3NzRxrcvTrAaT6HazFTLGonVNGariV8ZBh0ud/PzMEvkixqZXJV8uU4lp6E6TVRrzeFTqTb46WPnKH/tMzx0+SRfGB1lcGsHDV2nWtYYPTPLW4cGaQlY2Lmrt2nl1lO6+jnV+cGvTjCwtSk7tNnv52BrK7+cXyASsCCbZPZs6WN6PYpqbTJDG7rR9ElfiGEYMHrqCiZZ5Nz4PKrVRGwjx+PnJwj4XVzf0cHZWIJSbR27yURrl5/l+QStESuaprO+muGrP3+Z2w5twWE2M+z1ErLamv61Fy6wye3kQLiX1z7xI+YurRGzJXixXKOzL8S+4R4KmnaVVSkRafej6zq5VIH2ziCXkgleWSmzPL1Oz+YWnj48hiwLyJKI09nEh166uIhzdz+jl+YxqQo/yVzkgR0jTMytoJolVJOIy2FCNYk4HWZkUUAxKzQMg5dW1yjWNEb8Tc/rmUwMiyw3VRAEge/e91oMDP7rSy/S7fbw9QtjNAyD7u4wqXyR5UKerQE/mt7gW+cuYBgGfz23zIktUT6970bMUjup6igCTTetvVv7uLy8zrWbeqjpOleyOY6srPBbg1E+8MKT/PmBa+l0HAKgw36Qdw6O8I6BHNHyMs8szZEoV3j7wAj39RzifGKM5XwOv9pCw8iTqi5gFlf5ysVTdDjs6IbBoLsVt8/B6nwMQ9c5ZpKpNxp85MdP8ndvuBNRsHBTezs/m5vDqigMeL2kyhXSlQqDngBOxcrj8xO8uDqLIoocX1+k1W5jLq0QsVn5mxtu5Ewsyom1dX5/104mkgn++tkjiJKISTIBMmdi0+wN7cChWLm3uw+AoCVIqlpkrZSj1ebi/p7dJKtJSvUK37n0BENeLxGbg7xWJaudY9jbQbKSJVUdZW+wlS9fOMPbBwewyWZyWpbt/laSlTx+i0rE6uGJ+cusFAr8zpbdZLQqf7L7WsqNKuPJKPFyiflsnsVcjlaHg0JN45nqBDd2drKczzdJG6qJo6trREu/wOq0cOroHA9ZLLxxcID37N3JHV19PHTpItv9RfK1KleiTQLSX99zK5lqhafnFzm8ssSBSASboXJ4dZpelweXKUHk1/CaomBCFoNIRoNBj5vvTkywPxLh2rY2rLLMmY0oDd3gtwbNyKIDj3mIvDbNC8tL+CzNMbpaKBKwWGh3OrDKMhuFPP/93Dn+7MABDq8+z3gyzcVYlE/fdRM+1cKxzbPEikUUUaTdbsfnceBSVcbiCcYXVpicjOK0KfQORcgmmnAdu8nELe1tPGSVqTUM6nWdak2nrumIskA6luXJcxO4/A584SYu1WI109UbJh7PMr8aZ3J6mXfdsA/VpLC2kuBKsUoyU8GkiPhMMlWtxoev38/fPX+UXKqAzW5hvVjggb5+REFgNp1hbGWNDqeLpVyelXyO+UyaLocdiyRxOZliPpOm3+3mxNo6n737Fg6vrDCfy7A3FGSrz8ejMzNUKzWsjqZW73V9Pahy04b4uZk5trZE2BMKUjcMxEGBy8vrVIpVlucTeH02HB4bU+kM2wIB/KqNToebUr1Ge8TOT2Zm+MbYGP9lW5PLoEp9WGULt3fGecI9zubOVnQMrm9tYdjbw2Nzz/HiyQnc1zVl5SqNOuupDLIoUPP58VtUBEEgni9QypWp1Q1EsUE5/x8h0fyPkWr+vyaagiA4geuAdwIYhqEB/xwl+1bgUcMwlq72iV39vQ6sX/07LwjCJaAVmORfb68FHjYMowrMC4IwC+wFjv9rLzDLEnsjIfaHI3Q7goiCgiwE8arLuEwm5tIput0eTJLEtoCPudwG/a5+2h0OWh1OpnIbDGzyMLuYo1CqU6+XudZi5bXdg3jMfmp6EVVS8KkO4uUchVqDXKpAsVxHEAQkScAwQBK8lOt1ugYiJNbStHUEePeWEdrtTi6nk6SrGiv5AtOpJKlCCa1aY6ClaZWV1zSyxTKi2CyZt/WEKJeq9IeD3NLRidNkY6OUZdjnZZPbS6vVh0ETuzgaX+Tr58fwqCqrhRLnks/Safcz5A/gvE7lYGuE+WyeiNWKy2RifyTMWDKJLIhojQYRmw2LolAuVNB1g6cvTVEuVKhpdcqFKr3XjNDQm3qP2byGoohUtCZZRZIEssk8vqCLZKWCKIr8cn6RxXiKDx/Yww2tva+ybwUEVMnEvT09fHt8gm63iwf6+pAEkcVsc6f5meUprol04DM33Toy1Twnost87fwFsukCVqeFoM3GcFeAv585wsBwB91uD4+9MApAPFPFaVWo1hpoV3VCHRaZeLTpSIIiQqWBIrby/s1vI1p8kHt6evj2xATjsSiiKPD+rz+G06awsBTF4bbiUU1U6nX+4rmXcThVYqspCo4SuyNhHpmawjAM2jZFuHxhiY1CnkKmRClXpm+zl/4t7bQ4HMyuxfB7nYjSOsE2H/l0kUKmiCQKDHW0kK1WeHpujmK2RHdLgJDNxnv37eJU5wZX0mm2BEO8cP4Sum5w5OwUoXYf9/X0sMl1Deulc7TabXzqycN8rPA0b7t5H6VyFa/DxvJKHLfDSl03qOs1VjNZphbWuWnLAEGbjaDNhsesMhaLkssWsTpUMvEcgaCNVLJEQzdY2yhctfVskDk8QUvYjmKS2dHXznqxhNVhIR7NUa3pxNMVTLLIDTc0cZA3dnayUSrjVVXsisKZ9Q1KtTrFq+Qb3YAvHTlJa9DLF2+8mc8cOoQkiPxsbpZaQ8dhUshqGquFEtFikV63i+RGmtW1PI2GwffefD9WeQuVxgSFWhlJELm941ZubtvOy2sXeXhqmtGpeZY7wugYiIKZ1/b1MJmK0en4xwjSwEBDEMx4zA6ubWljJpPiz08eZUdomnRF496eHr5y8Tlqus4rKyv87o7t5LUa7Q4Hi/ksm5wZPKpKwe9AtZpQRJFioUy5VEWVZSqNCZLlMkenruD1OljM5TEMgyGvhxbrbgw08tp5HpmeaTKZxxexOS10doZ4cm6OD2y5Fb/FyhMzM1xKJTnU0oWsHCPgd3F8Y5Yup5ufzMwiCgI7A92YJRMW2YVBlYDq4PDqPKejUd7QJ7OcTxG02lnKFwjbrLjNKt+7dIk/jR9hayTC3nCQvFbj8NISy/E0Q14Pb+y7nYw2x3wuyeV0krVCkXgly/lYnPv6enEoFj66fTduUx8uarTXBquvAAAgAElEQVR1HPwXcbramOOxw6M8cWmKJyWRSrGKoRucn1lk2hFj7NwKBjA5vUx4507ipTLlusaBcJiXVpd5YnYWv9vB342O8qf7DjKXTfC6Tb2M+IIUaxqZaoGtvjDjyShDXuXV/9swCkiCHUlwUjdSvK53hNVikWy1ypDXAzThUq/rHSZVzfP4lVHeN3wDZ+NLfOfYKJ+45RBb/SE+cvh5ctUqFkXBriikymU+vn8fTsWCRZZxmUy8aXCATLXKkNfPQ299HT+dnaFQq9Nis9Pn9TGdTDB7aYVId5CWgBWz1UQxW0IrN8mZoiBQbtSp1nQaDZ16w8CqiBhmEZMsUqsbRNezVEpV7C4rWllDlJumJCvlODaXlUqsyuOXptDKGt1dYZL5It5InWK2TClXJtDppKEbKCYZw4BSsUKsXKJxdTwu5vLoDZ1eVzMG+1SVcqPB5XSGRLnCqZkF3rZ3O6V6nR2hIKuFAtsDfrRGg7reND24b1Mf2Y4OxhNJpqIxNnvdnInGqVWbREqrolCq19jm93IgHOJxReH4xCzlaoNyoUKozYfPorJeLBAtFYmWyqwUmqL43/zJCf7Hh19Hh8PJdy6d4brWDUJWF2ZJoiMSYGcoSKfDwe2dW1jIr/DYyQuYLCamU0l8FiudTidhjwun2cxsOoUoeMml8tRrDTLpErLUTN6K5dr/JE35zWj/SKb6TW//lh3NHprl7e8IgrANGAU+bBhG8df69AOKIAgvAg7gi4ZhfPfXLyIIQhewAzj5a4d/TxCEdwBnaO58pmkmoid+rc/K1WP/ajMMg363h4DqwCJHWC/NcjE5wUqhQKFWZ3c4QofDjipLXEikePjSZT66u8gPLl3mhaPjlKsN/D4rmwf8mC0KFruF/3PfflwmG3W9yHR2nbDFiVVWcZutzGRSaJUa1A3K1QZWVUIUwaBMQzfIpwu4fHaKFY1HZ2YZ8QewyBI1XWejUMCqKFxajOPw2nnPyDBtdhf5WoUfTU3jt6hYZRlJFJjL5HCZTXjMDlwmNxbJxMurK1wbabIU/9HnNGCxspbM0B7wsF4o8MLyMtXGAlPJBE6zmVSlisOkIApgvzq5X9sSYaVQ5Ew0xnwmTSKZQ1IkasUqS/NJUjmNRqkGokBye5nhQJDGkM7lsRVKlaYnrCKJSJJIe3+E923bimEYTF/1hf/UjdfRMHR0Q8dAYq2YYjQWpdKoc0dnH7d3dfLElSscXlzkL6+9locuXWIikeD46ionNqL84c79eMwuzsbXOB9PUq5oKKpCvdZgJBCk0qjT3R/i2rZ2lnI54pkqVU3HpkpkChqKLKIX6yAJpHJac9sbmnaSHjOS4ORz5x/kRy+f4WI8RrXRoFKqYlIknDbYSJYxmyTuuHGEaLHM4tQa2bzGX779diZTaaLFAi8uLlEuVLhzeJAHnzrKtp3dtLtcXBidp6LptLTlyKUKDPr8lPIVcqoJq11ldXYDp8/B8OYultbjaI06BU0j4nAwnS6Sq1Z5bPQi4bCHVqeT4UAQSRAIt/rQGg06XG7u7+vlo0/8ip2bLjGXTrM0tcZytITNIjclvmJZNkdC2HtM7ItEmM1kOT2/RC7ZLNFnqhV63B7sikypXqfd5SJks3NxYwOHRSVXqtCoNcgXNOxWmXyxRmvQwtJKAadNoVLSmHBGsVvMrFyJIQoCEb+FaKpC0KsCkEjleCQ7cbXkZ6ZcqJBYS2O/Ziuf3H8ds9kY+ZrGidUVbuvp4rNnmqGh1+3iUEsLS/k8iUqFdwwOM51J8sNLl/n+iXMkE0VKlTpdLXY67UEEQcQib6HdVuMbkz/gpvYqhgG3tN/P7uAof2s/TtBi4aWlZWTBR6/LzX954ilcd5jZF7wWSXSjCCE0fZlfLEyQrmqU6nVOTc3zwydPM7y1hRNrq6xuJLlpeIBOl5uNYgmvaiZstdPlaKNmrPPOkWE+/cIRDN1AEkVkRcbhsnJ4ZRW7YuLurptZ21vkl/PzLGabsj/vGNyJps9hFgcp1esk80Va3E78LR7uGhnCrsgkKhXi5WXWi3m2BEO8srpOqlLF7XVwV28vEZudiWSc+3p72BPchdaIAVDXCxyPzuNTLXQ4nLTbDQq1MhGbiyNri7ypv5/Hr8zx9QtjrK4m+G/33Mrfnj6N02wCIF2p4HJaua2jH93IUaiVWchneW5xmXt7u3l6YQG7ycSxtXW2+lqYyyVxmua5nF4jbHXSZt+BJNhfjdNH1y8iis2YpVVrhDuvlkqBTCKH3SqzZ28f9/b14TGrhG1WNkoFupweul0e9obCVBsNpjMpKg2NnYFO1kpJXllbYXsgwKnoOjuDIXpcbmLlAmbpBEHLZnSj/Op9yKIXm2Lio9tlHpm7QJfTxd9fuIBZkglbrQx4fNzY3k61kcUkNR2+LiRSTGeyFCpVghEb7XYb5UaDmq4zlkgylU5zINzCNn+I5UKWum6QLJfY7t/EgXCeuq4zlkiQqVSwKiYq1WYJe3hLF1fmN5pY6WqNXKZEi92Bx6zSaBjYLDIOq0I8U8WqyvhcJhSTDILAa3Zt5tRs04I1tpzkxJkpJEnEF3EjCAKxpQSCJOLy2NHKGqGAm46Alwvj81jk5pjqbmmKppfyZbqdLnYGNgHgUEwcao3w1bPnaHE6UUSJhqHT7nQRLRZ44+6tHFleYTWW4vrBPoJWC5V6/aqdLiQqGmZRxGM2sy0YYMTvYyFX4GIsiiAK1GsNNooFPnt4FsWscOdQP/dv6mUln8NiV3nvvl3Ey2UWsznOrG+wtB6nXKzib/Hw2HvfRqGmcTq6wWohz5eefpn3/MGfAOByh/jmLT4+8PyT+Pt6UaUhUpU5Mqkird0BzJIEwEQiTqJQbOLaGw1m02nMqglDr2K3m6lVayQy1aak1G94+4+RZv7bEk0Z2Al80DCMk4IgfBH4GPCn/6zPLuAmwAIcFwThhGEY0wCCINiBR4CPGIbxjyq1fw98mibm89PA54B38//87Ix/fkAQhPcD7wfwRty4zGYSlQKnoi9zYiNKrlp9VYrk5OUriKKI2WoiE8/h9Np5cn6BX708Rq7QXLUkkiU+cN8h7unppq7rtNoCpCpZfjp3mXPRGNe0RHhtzxBW2czFZIpytUEgYCGd1xBF4aowtERB09ja14HHYsGnqnQ6HfhUlQGPD7fZxpWWOF85f4HO/haG/AEahkGbrQuDBvf31rmYTOIxmzBJEg3d4G0D2zEMA92oosoWXt83hFkyoRs6AvCtydO8e/MeMvEc3SEfg74mNnQsFqfW0LmxvZ1nF5d462DTFSVWKmGRJTRdJ14qk6tWOTfaxKF19AapaXXi8TLUdWgYIDa1y7Zta+Gtu7bx9au7SU5VQqvpbN3dxSf278enWvj2xARvGhyky+XEJEp0OrwcWV9gMZdnxOfjq0dP8eV77kARZQ6Eu3CZzfzViRN85IUXSG1ksDgsuJxWxmMx/ubsCYY8HpYLRXpdTlybBzmyvEymXOYXY5PcOjzAzo42Hj13kUuTUTRNh3KdEiCKAqVkBSoNnB12cvGrJRChuSjxOExUGpf49uETDA20kyyXSa6nMVtM3HbNVtocDp6cmiYVzfDsxcvE1zOEWj188O4dDHg8RGw2vjc5yXoqg97QWc7nUGSBcq3GpY0YgiAQDto4eWIOsyJyvN6g0WiQTxe4PJ1AFgX8rV7i+QJWh4WFWJLV+QQrNhPDQ50srsXp6wgxObPChjlNMV/mzdfv5nUD/RwIt2BTTJyPb2CyKFgVE1ZFYTVeRivUiPhVdoeCLG0fZD6TZjgQ5Jn5eaKxDFOXY5hNIn5fE4O1f0eY6XSWaqNByGolaFWxm0zohsFyLssbh4f40pMvIcoSpUqDcqVBJGyl3tDRagbr8zG++PZ7+etylVf+YRy1y8FAnw+rQ6WoadSqdeq1Oi6/E8MwyKUKqDYzPosFh2JhxNvKsY15/ub665nPZvlx4jJbgiGemZ/n+rY2bIrMnlA3QYsbQYBDba1Mr0WxqjJ//IabaLXbUKV2DEMHGtSMDdIVjbc/+jjX9nXz5/t9OJVePrW3G0FQ+MBIiiPrR1FEkb+65Ub2BHcjiW4qjUn++OivuJSIM395lZ7BVg60tXHb9s28eZ+Jv3vkMGlXFqvDQrfLwWPTM/zRrr189sxJHpub4/d3DGAS2xiNvkhsJYXDbUX3GazONcuxP3r+JHe9pxNZbGrZ3tvXh1c1c2fndgQEHp45xZs2Cdze2cX2QACvqvLDy1N8fNcDjMZP8d6HH2ciHmctmuLgQC/nV9c4u7RCOpajdcd22m1+vnD2LAGrletbbmE+P06LzUusksGmKDhN5qul8SprxQKX0xne0j9AsV5jKpkiUyghyRKffPEIc5fXKVU0qqVqsyyvmpCEppxWxBrGY04RstnIahotNhs3trXhU214zZu4oWUvlcYl2uwegpZODKOC/mvY2USlTKQ7yFNveDtveeoR1taSyIrE2MV1tm1tIZspMz21QmTbVjZKRbb83+y9aZQcZ3n3/auq3ve9e6Zn30czmtEuWZYtb3iPbTAYG7CxsSEBQhKyEAIJIQSHgBOTkCcEDIYYYzBgvOFFWLIlW4u1S6ORZkbS7FtP9/S+d1dX1fOhhQ/hPW9OnvfLQ3Leu8+c7tOnu6amTs19X/d1Xf//zxsgXi6ymM9gNxg5uLzEG3PzNNrtvDozi9dsYWQlQj5dxONzUCpXuWNNH59cexnlWgWDpEdRk2goyGoNVatglDqQBBsGMcz7unTk5Tz39JXxmsz8y6lTLKYyfGrLRvLVKnsWFvjsldtRNJXnJqf40NpBLHod2aqMoqp8eHCQPrebR0+c4OqmFpYLOQyixGZ/M7P5GP88so9+j5t/P3eORK7AjvY2nj94ituv3sCphSX2vDGGTle/ttuHujFKuneSB4qiYTCI5Ev1Tb0oCticFtLJAus3dPLSW6fQ6yT0Rh06nYTVaSGfLlDKlfEEndRkBYNJz/xkBLPNxINrB6mpKrd2dhAtljBIErKiMje9SmOTG0kQ+LMDLzOTTpEtlrmtr4fHbriRu3/0DDaXFYDfGxripRmNlWKRqqrQFPRydGmRdqcDi64eSqgaOA0GRAGsBj2HIhGuampidiXK6lISi81ETVYQEcgm63ZOr4xf4Fgkws0dHTyVHWUum8NlMpIql7gwtcRqLIfbbaHR4eC+Z1/A53GQLZY5engGg07EKHW9ExdYKHDvmjXsnl9gvX8PJ2Mx3F4b2USOOeBDG4Z4eWqKxEqKrCGHJEnEpDTTU3F8LhMfunYbogCzmQzZSoWffvPXc16/XaPubvTfI9T8rwSai8Cipmm/ykQ+Qz3Q/M3PxC9lOQuCILwFDAMXhLoM9OfAU5qmPfurL2iaFv3Va0EQvgO89GvHav61YzcBy795UpqmPQY8BtA6ENbms1lcJiMX0mny1Sp6SaLBaCRaKOAP1csjqUSWe67cXG9mrim8//otHJmZY2pihac/eTeXh4ZB0IGmECsv8sOJc1xMpZBVhZHVOH7zLBsCjZxPJKjVVGwWHe1dfpbmEyiKhkaNfLXC+mAIp9FAslxmPJliY9CPy2jFrrdh0qVIlIqErDYubwzRZHOAICJodaHIaDyBoqr83tAQLXYHJskOiFTVLDrAojMiqzI6QWIstchPzo7x2L4j1Ko1ZmP10kCpVsNhNFJRanQ6nbh7ujmTSAB1sUmpVuNCKkOT3UqL3Ub/HV6e3HeUh7ZsYD6XZ+Li6yiyWt+eGiWG14V5aMsGfjAyynB/G05vgqWZVT5+25Xc0NpCtlrln06e5OiZSXarKr6why9ffSV/e+QgXouZT6/biiiIvHX/H6JqRaKlFIlyjm6nn8dvuJX7d/0CQRTIpfIkllO4Aw5iVit/OLwRo6Rn39I0g14P5VqNvXNzSHqJRKnEplCAqqpw7OgiXCp3aKkKilUHJQUs9dJQ3U1fg0s72qc+8X4e2lO/FZdiSVKxDO6Ak3Kxwq6D9Wb4jb3tvHxuCUdZxhd08udXX87ByAqapvHMuXGUmoLJYuTkkRlmp+K4HAYi0zGy+Sot7T7OjEYoZ6sIRglRFLj28gEKsozNZUVVVArZIgajHpfHjiiJlCsKNaVMo93OsjFFJJPj8zdfxSvTs8wkkrw8MsZ1g30cXj7JB/p66XK5uWt4kFSlSjyZpb/Xj6qqqIrGW0vLuM0mOt3t/PDQCXKpAs09DQQ8Rq7aNsjN7a1849gJ/urHv6S9N0TY7UQSBGKlIv1uN7+YmqLP60MviVgcZpYXM5iNEl6fjZpcY2YhS6PPhE4vkalUCTjsNK/zUVPqytiexiCzyRSxSJpsQea+oV5kRSERSVOTFWYy6UuEqUbSlXGmMxk+0DPMTy9cYN/IBMVcmb/Q3uKnt76PWCnNDyZOUlIUvv7cXpo7/JhtRlKVKvf0DKITPVSUC2gonEnM8LNjI/gCTn665xifXr+ZTV/5BC3tPp67+24ardu5stELqDw//Qo7f/hVktEM12xaw0w6happVKoKkcUEe0oVVhcSOH12btg5hMNo5PTSMhdTGUqyzGf27+UT69YRMFtYLkzTZm/ihZNn+Z2dGzh8cYZNzU0AJKNpFFnh8wcO8PId/STLFa5pbsEsGdALQV6Z3837uzeSrmTYPT9Hk91Gtlrh94fXk5XnGU0k0DSNG9rb0XV00Ol0ErZZabBayFZl9i9FGPQG+MLWy6mqNQRMtNkD6EQdBbnEk2Pj/OWW7ZSUGrKqIgoCJklHs81PopzlmpZmFnNZyoUKckVGpxP51JaNPPzqPiSdxON33Fr39oyO8MbCEp1OB3+6YRsB8xa2/fsfse/ej2EUu99R+5ukfsJWqCpz6KX/WIh67uIk2USOlWKKY0cmqdbq7hfvv2UjIxfn6B5sIjofZzSe5HwyyfbGBo6urHB9WysnYjEeXLORDYEAY8kkRyIRRlYilHJlrlrTza7jZzFZjEQLReLlNHa9hcXCKqXaMoOe7YCG8B+kBXWanE1vYXOgGbfRx2PXNZGtFrHojBRqZcLWOG8tL3Po4gyaqpEoFtncGKbFbmMsk2UskahXqhSVb42e4da2Ntb4mpBEAz+7eJFd587z19dcyQ3t7UylM7Q5bDS0+QnbbZjaW7k4FqkL7Rxm7luzBqtez8dfeIU+t4sbrx3g3MQ8eoOOhcV6W1EuXWTLpm4W4yk8fgftzUFanE4qtRqjkRWKuRILs3EMepHB4XYeWruWH09McPjYBSYzWfbMzJBM5hAkkcRyCpvLQrjZw9RUnIeefgFfo5titkQuXeAV/RR+s5mGBg9es4XLwo2kKmVeOnoGh8fGzIUoV13ez139/fzbG2/zz3fezGI+x8V0lr1T0wQcdjRN44rmJpLlMrKi0NMZ5qG1g3zv7Fn2HjqHIAjkC1XymSJmi5Fvvn6I7UPdnFyJMBQMISsqq7EckiRic5ox6/SMnY3Q06tgsZtpClh45ffv/w/32EI+ykvT9UqEWadjR2NDvUppNZJezfKvrx0k3B7AajcjV2vk0wUURSXgs1IpVXlq7xEu39BHtJDn7MhvPxnof0zpXNO0FUEQFgRB6NU07Tz1rOVv9li+APwvQRB0gAHYCnxdqF+Fx4FxTdMe/fUvCILQcKmHE+DdwNlLr18EfiQIwqPUxUDdwNH/7BwrisLJ1Tj39vWzIaCxkMuzks/hMBoZmZxH0zRu37SWtcP1bN+wP8h3z57Fptdz20A/V15/LZsCwwjo0bQyo8lxXpieoc1ho6o4kFWVraEg1zWvRdWqDAUCvKWbIJYsE0tGkC8pAjWtzM7mZiqKgk2vYzmvkCyXeHNhiS2BMHa9/R3hzWc2b6bJ6kUSRVQth4ABSRC4ub2VDf4m7HonmWoaRSshCiKpSh6DqMOiMyIJIoqmsnt+gdh8HFXVsLut6PQ6JhJx3CYTlzW2szO8AUGoB7A2g4F/PzfGIz97neY2L9ev7SNTqeO5MpUqD157GU6jga3WAD/+4/fxtTcOMDEe4+N37mB7QwhBgD/eupnnJqfoDgX4g8s20+F0sZjP8YNzY8zOR7E6zCSjGVq8bvJVmU9v2IBNb6aqyCAIJCvzVJQaj587R8hi4T1d3WSrFZbmYthdVgxGPUpNxeaycmtHOxa9EZ0g0ev2cia+SpPNxhe3b2fI20SkmOSWf32C6cU80iURl2DVo2jUecB2PYgCucUCOA2gaeiMEuuGGxlLJhm9MM9Nm9fy8uEzzEUKaBqsG+5gYnKJbLJATVXZsrWbTreHmXSKz/7kVQQB2nrDeO1WOtxuDp6fJpuX+dz9V/LUa4frwhmbgVKhgk4UcPlM6CURm0XPqYk67swfcjM2Msfn3nsd//TSvncyEV39IW7q7yVaKNDkc9Pp9uA0GLm5o42ejRv45Mu7ODg7h1yVuRgKErRY6310U9PoDDpCPhdbGxu5rCHEUj7PdCbHwYVFulpDXNQizJ9fRqeT6PW4WcwX2NrczMLFFUZPLdBwtYOVQp4Bn4/ZXI5/uupafnrxPN85eJzP33gVX3rpDaIrORxeGyMnFrj26j727Zsg6DXxy9k5UuUSmzd1sxhN8IFNwzx55BTlYoW/vftGnjh7ln2nJ7ht6xB/8zvXUlUUrm/ppe57GuWOjvUsFlY4EJlmYyjESj7Pd9+/k2HvGkTBQYstyUcH+riQOcstn/4I9/zoGe7cvoHtDSHcRj81dRVRcKAXQ6zz2Tj+uzcgCBK8BxQtR+TLP/q1uUzmQ7u+zhPX38udnXdzZ+fdVJQL3PL89zFKEjPji/QPteC6ZNj//fvfy+f37+f6thb2zC2QimVI+H3c2t3N82MTxIoFhr1NWHRNPHz8W2zuaeOpV47Q0eUnX61wfnSpLo6yGokuJoiVlvnE2gfYu/QC/3zyJHf2zDGWTNPr9tJiayBWKlNRVPRSXeznMVUIWSx86IrNdDgdJErlS0bWMjvDHhLlEvO5PAv5DNuC3Xxj5AB/tv5yTFIjsrqCUTIgiSLpat2Ldq03QJPVR6qa5+TqHKIgkKpUqMkK8ZU0qgr5Yo0vPPsamWyF1q4AbmNd7LQ10EGzzUnYGkRAIls9xX0bh0mU04St+v/HnBwtLdBkbUYQRDRNQRCkur2KKLBnYY6mFg96k57l2ThBqxVfyE2jzU6g38rzo2N0hHz1ebehAbfRxI6GNqz6Tjb5PWwJWLmvt8D2xx/BaDbw3L7j+Ju8HLjvr3l2ehfjyRjXNF3LkxPPkJdrdDlC7JofZ9gfpNXWg14M18Ea5Sn0osRcLsGJ1UU2+ptosGzkWGwPazxhut0ulgoFbhzo5cP9A/zTqZOIAnhM9U38ci5HXpa5ubODk9EYK8Ui23RGRCzc1d2DqsFiPs9bCwscO3mRf7jnVh6/4Sb+7uhhzswuYbfqyOarXDw8x1MtE1gMeuKRFFlZ5s6ebpYSaaYmVmgI2ZiaTSPLKpNzKxjNBqxOC5lKmZFoidhigo6OBqLKKg67EaWmshJN8QvrDJsbGiitr/HG7CwXzs3T0luHBIiXSthytYJBV++XVWoqpULlHTzukcgKXR4v17U08cOxcSRRxOayUilWeejWy2l12JFVFV+jmxenZzi5tETAbmMgFERWVfYcHGVdMFhnmksSN3W088rsHPsPTaCq9X56nSQQ8ppQVJWhvla6XC66XC5emZyk3e2hf10LM+NLBPwuHhwcYN/hsxjN9cqkJ+ikzd5NTV1FQ0XT6pWr45Nz3HLdWr6w5Sr2Lp1H0uswW43k0gWsDjNGScLvczI7s4LepEeo1rPGZpuRZKLIL14/jaJp2C2//Tyb/x5h5n9ddf4p4KlLivNp4AFBEH4PQNO0b2maNi4Iwi7gDHUbo+9qmnZWEIQdwL3AqCAIpy8d61c2Rl8TBGEd9bL4LPC7l453ThCEn1IPZmvAJ/8zxTmXDiAKcCwawWk0oBNFOtweTkaW6+WD6VWORyJsDQYJWW387eHDbAgG2Ds/T01VKdZqDHmbyVYLLORTfP/cGEVZ5sjSIh9Ys4ZBr5dBTy8aMqqmstbrxee3kcgkMBkkQEWT6jvkWKnETa2t2Ax1vrlOFIkU8lj0RkDFb7ayPtRQFwvUyrgMVmZzEZqtPvpc7fS5pEt/lYJZMpCq5NCJEv9r5CTDfi8+s5ltwXZOxOYYTyQwXep9K2SKGM0GHlw/zPOTkyiaSpdjBxX1AgtajIJc57+LosDImQiFbIlPvOtyJpIprm4OY9HpserrCmFZUdnW04HBZGAsvsrljSEMokROrtLicOAzm2h1ONGJIl978xCXdbUDkMgVaDAbaHE4+e7oKNe3tbE+EKDF5mY5n+b7584RttsoyzW+9/phfnJ0hKWFJIWSQqBQwdfgpqsnzEfWDjLsCyIg8OrcecpKDbvBwAuTU+wTJcq105w+N0sqV0UQQKkoCHoRtVIDnQBGQ72GI6v150QZJIHwoIePXraJsWQKURI5Mr9AMlnEZtGRzFZ4Y/84iqIRCli5EIlyU38vL54+h96oQ9KJxJMlbvD6GPJ5mc7maGvwMeNe5dDCAol0FYtJIhhyYLbWzYTb2jyYbXVxjclioKXBj1mvQx5sYj6Xx+G1s7qURKeXKBernIlFGQoE0UsSrxw/y9vz87x/YA0fe/Yl7tmyjnt6Bvju2dPYDXoS5RIHFxcQBIFNzU3UVJUDCwuMxxM4TSaOz86j1lRy6QKSTuKqbYO8PXKBpXwBVdM4PjvP+vVdLKzEGfD7ObCwgEWvJ1+tcvv3n8LqtNAe9nMiukoqWcBklPj9LRv5k/MrfG7rVt5++yKFUo3XD57l3ddsJFMuY3Va2D07y4auFs4trZCVZZodTqplGYtOR5fTTYvNR6ZaYG+sLpK4sbWb1+ZmeeytI+RSBb501408fvYs72rN8NZShE1BP+/r2kiyXCZdKTwtoiUAACAASURBVPPUPXfiMlhwGKzUpyN4c/kwVzZuRMJOPYBdIVmZw2fq51xqNz3OtUiCG0WL8/c7ruKDu57gyRvfR0WpYJQsPHnT7fx8coyww8FLe0/j89ux2Ex88dAhrAYDz1+cZDmZoaOjgRPjM0z5E6wLN/K90bNMZbLc0h7nA7393P3TnyPXVBIraUZ1Eh6PmXy+Qv9gK5liCaveTFkZ58XpGa5obuZCKsPnN12BWddJTV3mhtZmOhxeAmY/exZG+d7Zc9zZ3cWDA+soyhUKtgpmnR5ZVUlWSsiqUsfmyTIg8a6WVqrqFNFSDJfBitPg5QN9vYwn42xvaCNk3oKqZVnMH8JjMvPVY8f4q23b2BoKcdfEjymWlboQQhDwB+xsaG3CJBko1src88qz3NDRwXXNFfpcnSQrOdodDsRLZfXfHHa9BQ2ZqrKEKBjRC2H0Yh2LeHg5Qk9riPl4iliqzK4TdRX12dgiwZAbr8vGkD/AkydH+OD6IZpsdsaTK2RtBYq1Gs9NTaFoKrlkHntnkM41zUQW4nzr7HM8NHAZj517mxenv8snh4dpsQV4YuIEvS4XZ+IxmqyN/CosdhiaULUi3S6V+MoMZp2R+fwRvnr0GO/tzbAxEOTunl5WS0W+PXqGNV43i7kCK4UiIgJzs1GU7m42BoMMeL18/9w5Brxe+lxWDJLEuzs7uZhO0uX2UBpqZyabZTyVIl+tEl1KYnOY0RtqeH1WDo5eJLqSoyIr/M2PXuORD9/MP934Lh5IPIvRZEBRNPQShBo8LM6vsqE/hCgI3NndxfGWGG9MzeALe4jOxzHb6j3SYbudRquVfLXKhlADpb4a0YU4RpMBd9BJKppBkER614ZJr2ZZno5ishpZ09dCq8vJrW1t7Fta5ifnL7BaLFK8tM5YHWaOLC8xEtNhlCQeGhpie0MTji1XkK0WWC5kWSrkuSLcyBNnzxKZieEO1lGT89kM4WYPNpcF10yMpu4GVFXFIEksJNO8t6eb5XyBHp+PLlcdtPKmxcJNHe0ELTZu27mRFruNV6enWV1JceOz/8qfbN7EmXiCh3+4h9+7cweSJNLl9qBpKmK9X4pitoSkk8gm8pisJuSKTDJRpKnVQyxTolxRMJskrGYdVVlFFOFv7rqR+/96738lPvq/NIT/ORlNAE3TTgObfuPtb/3GZx4BHvmN9w7w/xJ0a5p273/y+x4GHv6vnNuvRkVRGU0kOba8RGo1iy/gpFiuUsiW6OxvZGtjmF63j0dPHmdqNc5nNm+iy+XkyEqMXLXKq/PjHFpeQVYUSrUa85k0qqLxy9lZQlYLGiVWSymSlQItdgcf3bmVv43sxqAXUVSNqqxSrCXYGQ5jMxhosfnRCRKCoKPNDnVz8hphq5f39/QwkUqwxuPHa6qX9SXRhIaGgIaGgqKWEQQBs87ISHyRt+fmeez5gxj1Ig/ddjmZcoUGm418eyPxfJ6V2VW8bjtWg55mh5Ovv/wmPS43g94G0pUyry8s8OLeU6ym6jvWjq5G3tu1hol0lGS5jF6qc641TeOZixeZjKxiNOlZXk5w0LfCFeEGrHo9Q34vy/kCy/kcFUXld9b2877uHl5fmOdiOs1KPo8kCsiKwme+9wo7d/bywTVreGpsjJqqMpVKomgaFpuJudlEnf5g1ZHKyXiCKpsaGlgpFGlzlJEEEb0kIqsiazxetE6Nfzl6AofFhCfoxOmzszIfJ5mpI8kqsgqKisWho1JVUSrKr24OMOtYt76TvCyznMsSDvsYH50lFi/T1GDBbNKRzFSQayqlYpWrmsPsm58jvpLG6bYiiHWEm6wo/Oz8eZLxLHJFprMvSC5VF6dIkoBOXxcQBDxm8ukiOr2Ew2MjHc8Rs+ZIRtPoDTpeTxfqJSOrEbPNxFBTI4lSketbmvGZrSznsmxuaKDd4aCQLXF/3zrOp1e4q6cHt9GG0+BiYvRnBMPuuhJWp2N6MsLAZcMcjyxjshgZCoYo1WQ8JjNHlhbp6WniyNQsIZ8Lv7veN9nVFCRerJv4Z8plHMb6xqWYK9Ee9LL7zATVqorZJPH0xHmG1rfxqedfRZZVWlq8NDf5ObdaF5/saG5BFOpQBFVV0QkCXW43s8kUxyIRzsXjfHbzFi6mk0SLJe7r2wCAz2zCE3TR0RLiH/YcIJPI8+bJCWrVGkcaXLiNJv7t9Gk63B4+MuDAZ25G00qImNGJPrqcHpLlJfSijguZKKdiMTqcToa8CqulIgHzPNHiKKqmYdHr2dHUxPPTpzmyEkMvigx4PVzd3MzHB9/DbR170Esi3zhxkjOnZtDrRPR6CVlWSJjqyLyV2RhtHjfRWJoXckXGE0maHQ4+sm0D+5uWeGnPGYrlGNWaitVUx3kOhIK8OD3GHR0DfHX7u7n5+W/xyrs/iUFsoqauEi3F+PbIGQb9fm5ua6fdWVfR+s1mqkqN47Elhv1BGiw+3tvVSbZaRVZUwjYrg54gIkZsegOS4MauzzGeWma9z8PWwHXIviWycpysPIJN30mb3cex2DzxYgGrzsxGfytf++AtfOmFPZyfTtPZXXenu3dNP1adm1ue/gb7PvQJDGI7k9kDnIyfpyDL9Hv870zuv8paAiTKx4G6f6VR6nhnjp5YiHBFbyd7RsZx+R0UcyWMepFMqsBf33I13z97jqDVhtds5lgkQiqWJWi1MJNJM5PNcjQa46MDGyjKEzgMl7J68dwlIU2JSLHIp/a9yKEzF7l8uAeHwYJe9LN7ZpYHbr6H2dwi2WoGo0lGEPRES1MEzK1YdDYCZiuxUprT8Si3dXXSZLNhkHSMJVfRiSJdLic6QWCd34vHZGZbQwNj0Rjf2HOQX370AdA0Pjk8jEVn4CsndrGcz3NzWxunVhNc1dSIisa/7z/G/VdspiDLON1W0skC7f2N5JIFOpoDWGwJ5qZXkRWNLz+zmwdvupzP3XI1f/C9X2C36pFllXg8g8liYDK6ynvWrmEkHudCMolclelrDJFezeJrdNfdP0bH+UW1htFs4MWLJ1lNldE0aAo7yCbzyDUVRdVwVWvIFRmX38GW7jZMeh0ug5EvvLmfba0t9QpaTUEuy9jdVkSpvrlIZPJ8dNN6bm3r5ztjJ/ij4bvwGB2X1jtQtRLv7hxk4yNfR6fX0edxE7Ra6fJ4CJgtlDva2RkOM5pIcCGZoqoqpCsV9JJIu9OJrKrk5Rrbw2F6XG70oo4/27gVNA27wcDz0kUS6Rz3fvMZMrkqrQ1WEqUSgigwEouiolGsycSXU+QKMg1hJ7JcV7WrioqiaIiiiF4nUiorGPQSok5CFBVWU2VubO3+PwlB/q+M/0k9mr/1o1yrES8WMEo6wnYHuXSBmfNLSHodTp+dLr+PZpsVQYBEqQiAJIi4jSbu6++notT419MjNNhsJMtlREFAFEVEEVKlEo+NjOIwGGmxuevBjygykUyRzst1PJgoICsqhVqZrcEOXl+coNUWuCTYqV06yzrlQdFUEuUSP7twkUeuaEEUbHUKkWAFNNBUNKqIgogISJLIgKeBP71sC386u0quUOXpPUd41/YhIvk8HnN991oLK3S4PeydX+SNY+eYms9y72M/5blPfohoscC/Pb+fRKZuTWQ1SSQzeZ6ZHOPmtk6suiLzuQxuo4lfzMxy5MQF+gdbUTWNVCzL23PzdLucdDqdWHT6OuZLknjs2HEGAgE+sWc3Jp2ORrsdj8nMseVlDJJETdU4cWyKlZUU3oATp9HIYjxFo9eNt9VMqVChWq4iSSI+v4mbhtawKRBgqZDHYTAiCiLrfEFSlRJBs4vmFh/DvgAPHzlCJJKkJiuYzAYsFQW5oCEaRNSKRrFUQ68XUSSh3rtpEMFl4L6BNbQ5nDwzOkZ8Kck1lw1ic07X8YlWE54GN9lEjtaOEAfOXKh7qgLFXLluPSIJHJ+cY2Uphd1uopCvUCnL3HzZEMGAi/HROYq5EtFIhnxRplrTyBWqhJs9lIpVYvNxHF4bO/q6eOXgadxBJ6IkYrOZOT23iCAKFGSZnJxmZ3MzLXY7XS4PG9d2sFJKMRKP8+G+jSwWVkmUc3j8dnb0dZIolnjt7TMUSjVm0qm6WGLvKOX+Cl6XnUpNYWdrKzPpDEWnlUgshcFswGjUs5LKMCUrtIS8TC3G6GkJ0TvQgkmn5/ipSSanM9idBixmiVShyFBDA30+P5qmcdvGQZwGA+0OB4IAOlHk2yNnqNRqiKLIQr7AvguTyJUakZkY2zf18fLsDLsuTmIxGtjRGKbfPcyVjSVkVeWR3ft59PYbqaoqX95/kPhyilQ0w1Ihz19v347baMasMxApzJKsFJnMnKRQld/BN673+zgRi3MissyAP4BRkmi2O/jFzASqBpuDQRrMHp488nOMZgM9oQBLuSzfffEQW7d18r0b7Ly1tMyA18PXr76Gpc053lhY5Gf7T5BMlLj78iGG/D6ev3iRmqayqauV0/NLADiNBtocDj5wzTpmYgkURaVcKFPIlFiZXWV5OsqHP7qGaClFh6OBewcGeGv5OFeHXShajuOxJVbyecanl3hzfp6hQJBUucREKsVf7N9PqVCmrzGEzWBgSyiAqsF8Ls+Oxgb2Lc3x/m4vZp2BmpbErm9hvT+AgAFFS5OoRAmY+nh2ehd+8zzbQ4P0ugtsbGhkJhvnUGEWAG/IRcslNGlTe5CaqqIXQ3zt+msxSj0AdDg20WrPMJmZoNHaAVqNijKNXmyiqswhqzkchmZAZffCc/S5/TTbdrBUOITLbeOrl3+E7r2foLutgaDNRiFTxB1wsikQxjiso6aqREtF8tUqrvW9PHluDL0o0efzcmdXF05DK/evSdFo9XAhVW+BEUURt9dK2GrlhcMjVEsy8UKRfz1zgo2BeT6/bSsmqZcma5mqWmM0ubfu6ViTCZhqiBhZ79vGfP4MW4NNTGUSbA9tJy/PcyY+wQuj49SqNSRJpFSocP2GNXS6nPz59q08PznFj86f5ZrmZkJmBwcic2SrVRRVY60vQI/bQ65aYd/FaQRR4MDiErd0dnDCZuPsYgSDToeqqhw7cRFJFMgVa/hcRsw2I0+9eYxv3XM7t14zzC/fPEO+qDA3naC1w0tsIYE2qHEsEiGWylLMlki4i4iSyNJkFHegzidfWa5nEK1OC7Waeqnf0UKtWkMUBaKJEhcnVli7oYXBQJDTS8ucPj6PJAmYjRI72tvo83pIlOr/+0ap3h+/WiywobOdG1q7eerCCAMeD5Lg+A/rsiiYceiH0Bv16PQSqUqVAwvz3NHTwy1tXSTKBVYKeZbzBdKVMje11w3gnYa6PdhtHZ384Ruv8/dXXlkn9kkGAuYt1NQUbuM8iXSOTDxHaizF9rsG+NjmDexbWKSjwc9N7W2ICBglHXJNpVxV0FSNYrlGk91MYiUFgKKoWB1mrtiyBofRyGgsSmR2le3bu98Rwf22jv9OYqD/EQjKh7/+919869G/Y63fxngqyexqglpN4fbt6+gL+qkqCq/PzXEhnabL7ebqttZ31JgmSY9db2Fj0E+P28VUJkOT3cZljY0cvDhLqVRBkcBvtXAhnaCqKMzlsrwxN0c+lSNXqKGTBMwmHet7GxjwDPPn+39Bu8uKQq2en9TqJS5REJEEkUgxzc6mJlrtzQiCnlQlgdtY731CAI0KoNUfmoYkiHQ6/RQtGgu5NOsHO7DqDfwqa56ulEnGs/zjDdeRk6tczGdYiWQplGocT0W4UMhyajSCABj1dWNtnSQQR2alVLwURHp5Ynycg+enaO9oYPLiEvl8CUEAURI5tbzCdCHHllAIi06PQZQY9Pv40o93s6mvFZfJSKWmIIki0XyeLo+XMxeWkGUVlBqlQpmVlSThJh8WvZ42lxvJaqBYrZLPlTGZDTywaZhut4cmm42A2YVFZ6SmKZxNxDiwvMDGQBinwcqOcJijqSixeJpKsVq3NkpX0XQCOmO9F8xslKgJoJUVEEU8jVYeu/NBTJLKyWSMxWiCkfFFlmMFGhocmG0m2gIemoNeFE1jdiZGMl3GbtVTKMk0tPooZEvMz6cplGrEEiWqNZXWdh8VoW6TMXE+Rq1W9xe99dp1nJlYwucyUsiXsTvNNLQFmLmwwuxyjI3rurims51ouUQykSWykESvF7ljbT8+sxmHwYDPbMGok7i6uYU/eXMfBy/M4HMZ2Roc5NYffA+X38Fav58rwo3MaRU0VBKpHN+85SZCYTfjq3EanA5y1QrjsVU6vR62NzYyW8wjiAKRuVXsbisfXD/ED3cdxeW2cFVnBzpJ4vndp4lEi7jcRkI+C/2DrQyHQmwJBZnJZEjXqqwUC6QqFRKVCqqm8S/7D5MvlPC7HMzNRpGsBsJuF/Fsjta2IK1OJ88cOMnU5CoLC0ksQRtdLomAOcALM+MYrSY0QeC+3g9ydbsJ2aLn0Vtu4Orw1RRrUXxmJ6IgEDD38OTEYb7+8j5eO32B+WSSpWKBgMNOn9tFQVHZGPAzm8tRqslsCISYy2b5q1ff4Ofz53lo83oqaEQLBQrlKi63hYWpFc7X8nz7Z4c4eH4ai9/Gu1o6WB8Icse6Pl45f55IKsNMOU8+U6Q76KemqZQ0hdu6OrmYTuMw6LHqReJylbHpJUr5Cps3dPOhLev4xm13YNMbmMwk6bB3s1iYQ0Cg3RFCEAw8fu5tJmaW6WgJMTu7QkaoMTsb5fD0Aul4jrbmAJlyPcv/6tnz+Fx27utfQ6cjwM8uThC2G9A0lZBlM7IaRQC+evIlBDFTt//xBXAZBZYKWXpcw7gMHk4nJus9hItLHI1EcFksDHaGKUsat/f0cFv7EF85/gL39t6NcGnRFYW6NZjH5EMneNGoIIlOVC2DJLowSa1Igg1Nk/nHk6/T6rDRbOvggdee4Be3/y2fOfgtjpybI5nIEFlJks5Vae7w02C3MBpPIgoC7Q4nvR43j+87ws6+Tk5MznNDTwfXNW9B1QoUlTwhyyDrA3qSosq1vR1c09uBUZJ437pB/EE3l4cbuaurn/X+DTTZ1qFq9bns4MoYr8zOMpvN8NzkJDe3tRIvx7HpHdj1Zv7q7T3c0NqO2xggXY1h1onk0cioMpVShXXdrfzl1stxGw30uoNc29xO0GKiweoiJ5fwms30uFy8p6sfWa1R0xT+ZN8+TEY9ydUsT7/vvaz3hbki3MTL89M4jUbmZqNUZJVIvES1qqKTBFLpMksrBU4lI3zvttt5/NBxFEUj4DWzrq+NyGqKRblEsVKlnK+AANHlJKqqYbGbEEQRnV5CLldRFYXeriaMLhOJWIZwix9RLyFJEtlsmbtv3MTvrhvm6dFzjJ5awGCQsFv05Es1ygaV1UqZ6cllFlZTfO36a2mwWtg1cZHjZ6Y4VoyzrbGBW1qve8dd4NeHRo3PPvNjjBJsam/its5OXEYjFr2O0cQqJ1ZXGY3FiCQzNLqcIAgELWaeOD3KTZ0dBG0WziYS9LjdtNuvRNMUpnKH+fdzY6AT2dnfxf5T0zS2uzkTj1OoySiaRofLxTpfmHPJFZ4/Ok6tplEuyVRkFbVcQa7WCDa6MNtMBHxOtjQ2oKKxks8TDnhYWFrlSGqFcz868duLoHzsG1+878HrL9H+/us/jzz8k/+fdf7/ZXz50a98seXmFrpdPuZzaU5MLRBfzTO7Guejl23gynATkWIJy6XSr9to5Adj4+gkgSabnVgpT01TqSgKY8kkH1kzjCBojOcypNJ5Nre38L7uHvJylUORFVxGIy++dQpBEDAZJDJ5maqs0tzu54bWq/mLnzzOd14/yb7IHK9HFxhNxjm+GmE6E2ckHmExn2etL4DL4ANBuBRoBqjvURQ0lEs3hVQ3nxdNSIKBNqeRxVqZoM1GVVEYWVhmZHSGZDyLP+yhzeXAptdzeHGZZDxHW7OTK4d6qSo1spnspVJBPbdaq9ZIJzK0Nwe5qa0bk2Tg8sYmTFY99/T1s6hW+IPtW3C7HeiMeuLZHIJORAYabTZipSJWvYEprcBafwC70YjdaGAunSGdKxB02DlzYQmrSYfDYUKtKaSzVbKpHG6/A5vBSLvTyVXdbYwnEnzkis1sa2hEVlVcBiulWoWqWuPje3bxj0/v59WjF+jocjPgDQLwhZ++QrVSw2w1YjGI6E06ShUFVa2byOskEbfDQD4ng1liaGMzH1v3Xv7uxPPsPT1BdLVull4s1yjkKzS1+XCaTETyeUqyzNJCioYGO9WKTFNHgCaPi1g8QyZbwWrS4Xcb8futlHJlxiZWmJtNEvCYcLrNNHUEOD+xQEODA6ffQbjZR6UsszAdw+EwI+lEKqjklBoOo4kTp2e5bscgtwz0cjwaY/fcHKdiq5xNJNgcDPJvo2e4q7cHj9POP+/aj+BQ2dQSZjQao6KpnE0kiGZyrC6lkCs1Wpp8WHQ6Xj40ytkLEarVCm2Nfm5sb+MLT/+SxkYPpUqd2rEmGGAmk0ExwP1bNtBgrTfLn56ZJ19ScDsMuH12TBYjE/MRxEt9u1e0ttLj8dJgs1KuKVj0Oq7tasdms6JqGjlFZnU1TQkFo9nAB9es4VQ0SiSW4jsPvJvjK0v85D1/wY/O72cmF+WhNVu4vWOY0cQCQz4rbkMnQ34jIfMaZG2Jt5anEQSFZ6cu8Jdvvcjhc1MUC1XC7T5euvdD3NzdgawqLBWKxEsl7usfYjabosPpotMRwKjTWFYqXBYOcy6erKM2pyN1exi9RD5dxOA0ccOmXq4Y7OT61hbeXlnir/btQxEFfvieB7l1oJVfzs5w9swi79+2loDZzKZQiJlslt2j51nf3Mi/nDrJVc1N3LluDas6lbnVJJPZDD6LgXi5wOOjo7S7VK4O387exaM8O32KDpdAi93GH122gzcWZ7E4zIyNzGF3mlnb3YxoNTA7FSERTfP7V27lid0nKFvAZjQgCgoX0mnaHHYkUcBjMnIsNsqn33wFv8XKU2Nj7Do0yrgSZ2dTC16TmTeXT/GFQ6/yieF1PLxvP5JeIl0oMT8bpacpxB093by3axC9aEKSKrTa26iTrwVKyjn0YhBVyyKJNkSMKFoGnRBEFEzvcOM1FKayU1zV1IFN38L3zr7Ge7p8ZOQ8e85cpFBW0DTIFmRCDQ6i5TKKphEvl1kuFmh12Pnczmv43Auv8r6tdYHZlZ0+fKZG/KYhQGEkfo5MtY7bdBtNtDvrnphXNTWRk6us84V5+PgurmhsRCd6AYFmm5m3V2bxmEws5HJc09xKTVMwSQpmXT9NjhzfHBnh2uYgLuMQTTYH2xt8PD1xFrvNzL0D/Qx7+7EbRByGMAZJI17OUJArHIos8+jx41xIZ9i7OMfOpmZabA3ct+ZKWhwCf/euO3Ebh4lXpnlhpt73qJckliJJiqUaFpOETidiM+sJNTgwSpDLlXl5bpIDv/+7PDN2BovdxLmJRfIFmc/csIOSpjJ2foFKqc5QMVmMdSKPw4woiahKXdkfT2RpCLoRjRLjo4uUC2V8jW5+8eDd7F1YYNfMNLlMkVq5iqqCySiRzsvolBqqoBFdyRJPFDmUWWGpVGLX6+MUKwq1apnPX30VLmPPf1iTVa3I60sv848nX8XX6GCguQGP2USn00mxJlNWFBqtNhx6PWficWYvRhhoCyMrKggCE6kkPR433zx1mu/8/Ajv2baGdkcfk9k3kVWFn56f4NUfn2L/2BytnU5ymRJms4HYQgL0IjlV4baOXkbiEV49eZ6KXL8O5apab9dymrE6LQw3NdJotzOXzdZpfdkiDqsZRYKpyQiJ3VO/1YHmhx+8AeH/8PHIw0//drLOf9uH3qDjJxMTpCoVbHo9f/yuK3hpaoo/2LABq16PhsbdvT2UazUEASw6PZ/fuhW73sJyIU1ZqTGbynBLWz8PrDHx7bOnuSwU5CtXXMEDL77EarHAF98+hM9ixWU0kq5UCHeGOPL2DNWqgsWsw2U3UNNUAEwmPbJSplSo4DDWUYROg4GxRBKTXkexKnNTm3rp7AXSlTKKlrlUWq9nM0VBQhTsaFq1Ti1BYjQeo8/rIVkukxcESvlyfZfb6KTH7+OWtmG+fvotREmkf6CBTe0tpMolFhNp2nobaChWWJiN4/ZYKOUrmG0mXj81zrDPy63tfZgkIweW6qXA93R38fCbB6gUq2zpbafR5yZRKDCZShG2WhlZjdPpclKtyPWATa4hiSIzC1EEQUAnitgtOmqKhiIrSDoRo0Hkrmu3YNJJ7J2e4c41/dzVtZZDSxFem5lhg9+Pz2whVsqRKJco12qcODbFv338Nn42cZ6PfuknhP7ewu98sc4CMLmMmIwSbr8Dv17CvJgkmizjsOjR6QREUcDoMeKy6Tn04a/xwV9+hR6Ph57OMJViBaPFyNBGH1e3tpIolXnp7RGqZZldf/AA244/RjZdolCuISwkMJj0WB1mLMkiuWKNTEHGWaoRDDno7LLyl9ddyQPf+jlXbF3D+eUoiXSZ6moRv7vIbClKMlvlkYdu5rv7jlDKlwm4HXxsaIg/euZlHr3/Vn5w9hxj8QRFWaamqewIh+lwOjm8EmH3iTEUVePm9laCt+zkVCzOeHyVT2/ZzBdffYOOlhCJSBpV06hWVb70s9fqCniPFZtTo6M9xLpgkC+/svdSL2oeTdOIzMVxWEzEU3UT98V8npFYie888zZqVcVo1aFpGr1tDbhNZgYCAcpyPTstiQKZapWFbIbNoRBNNiuzuTwVReHtkQs4vHZURSMZzWCxmfjmwaOY7SZu2jbEjsZO2ttCKFqWOzp7CZl7kAQXVXWezcEQGjJ7l/cxEo9zPrmP2XSaWzo7WecP0uVy4rVa+eb919PpaOTvT7zJrvlJ7ugYYL1folxb4nCpyLfPnqSiqLw4OYnDaMQo1Tnxz50d57NXXMZ4MkWr00VNVXEajTgG+jkdW8VmMBDJ53j87DkabTa+uPMKWuwO/uTAxzjJNgAAIABJREFUs5xaXOK5O9+PeqPGainLZ/e/hawoVBWFYq7Etw8eJZcu8u10jiafmyaHk7GpRQrZIp8aeQFf0MHWvg6sej1lZZyPDnyYJyZ+SEGuuz/sXrhIpVbj2NFJLCYJuVpDVhREQcDps/PtW2+mpqnYzDpMOh1dLifrfQOELA7uePJHPHr7jXxk9w8QEejxeNkSDPCxwWESV+X50tuHuePJH9HdGqr3uq4dIF0pc1lnGyVZxqrX89nLt/HqzBwmnYRVH+SV2UPc1n4HomBA0fIoWhyLbi0AkuCtz16CHjRQySPhQBJdl2Y1Pdc0N6NoKmcSr3FlSwvr/+FRVuIl7FY9LpOIqmmYDBIfGh5kjcfLfC7LjoYWGqztfPfcbjqdbvQGHU/uPYqv0c1MJkOv03CJ0R7DIEl8sHeQLx7ez2c2beFCum7hNuwP4jM5mc5G+ctNOxGQeGLihwDc03MVnU4H/7jnAO/buo63V2bxmy34TU7m8wcYcG/gc1sc6MUwAhI6wYvH2Mwv77Tz6txpRlYTvDj1NFc1h0mWT/LazAw//50PMJX93+S9Z5hdd3n2+9trrb322r1P7zPSaCSNpFG3LMnduOEGGIONCQmE8EI4oYSQegXeFAghIZQUjA8YSAAbjG1c5F4ky5LVRhrNSNPLnpk9u/e62vthKyIk50o+nJPrIpz/tz2zrjWz917lWc9z3/dvhUcvXiSfK/HQjbcgi1a8spuiWsRv6yRot2NS46mFH/M3J0/y2stT2G0iW7d34vI5CDR7cXodSILAUCjM2bUo03NpajWdYjnO544dxt/k4dzoCu1NDjStjlO20ufzcSLgRK1rmIaJpmqXOpkq7oATp9vOaiSN3SYxP7+G2++kqzfIR/fvYaVU4ra/f5hQewCvu2H+q5ZrVIpVEpmGGz1XqNPSY2Pf3kE6PB5+c3gzsVKJkeYmMrU6w6EA13/jW1z8TIMEVdHGuOWn36darvGDu+/m9EKEb91xGxPpJE/NzlPRdHY1N7FcKDCbyzObzRBL5wh3NKQQu1pamM5mqZZrPLewxNipRYy6wYNjY3R5vHzqtVc4f3ae+Yk0yAI33bKJ6YkIqmaQz5QwTZOA10W+VmOxGKeqN6Rr6qVCEyCZrREKu/C6HTz3xllUzcQfdDI1m8ZmFYh7Uty0fyuptex/T8Hy/8P1K1FoOq1WrKLIdCZLplphOhKjVqnzFRN+b/curIKIZhiIFktDt+UKEFTa0Y0sy2aaVqeLoGKnrFVRRJkdTSEsFguyIHHD4AAvz85TKVYoNPm5f+MQI+Fmvn/0FDargKY1+NpbRnrYfCksvVpVqVcbxdWOlhb6vR6ms3manA7G4gnCDgcltd7oXJomP5qeZrGQxy5JzOcLvKN/A/FKgSF/JxZs6EYdUZDZ3dJJdnGGs/EEhmniDbnp7AhjmCabQyEUcYjfHWmm3/scb0ZjxEpFZpZjSLKELEtsbG7CIljIJgsEmr3IipVitkzQriBZROYLcSSLQKpaI1Iscc/WzZyJxcnXawwEAqwPBnl1coaldJYtrS10e9zs6OwgWa6gGQb5eg1vyM0Dw5tJVqoN99/GFuqVOpVSDa/cuDluDYXZEgqyu7mDfzx/kouRKG+dXOb82Xk+d89NnFiLNRzGj7wFuslKsaGrxQJv/71vg12EagMxWavriMUqpmlikxtmBJdDwu1z4Am6+dTGDVgFgVRtlInZZZZSGWrlGsPDfVQ1lW6vj8VcnrVSkeGN3UzNrXL3t39AZ4eX2Foer9NKsawSnYuTzFYJBx00NVuZmExSruosLGbYeUUffz86it0m8vzrDfpJsaKxbaSDawYHiORznD4/x/eOn0G2WVHrGqNnZrnn8EVsVoE/e/JlDE2n1qfR5fWxlMpQbm7mm2fP4bfb2b2pn/FEnH9+5jihgJ31GzpJxbIcj8VZ19PKmdFZBMFCa3eIQqbUELjLEuu7W+j2+mh1OlgpltA0HX+LD7/HyfSFCOFWH1MTSwiSiFWW+OGLx5lbKkJdx9/soL3Nw+/ffDWvL68QtNuZSCZRDb3BAzYMNgf9nIutsVaucHNPLyfjSZocdq7dsZGzq1H8TR4+e8VehkMtVPU6Xxsd5eqONubyMX50ywNIliZ8cgzdzFPWlqkZKo/PzTKRTpGsVjm2skLQ7uCa7m7OJ5McXVlhpLmFr197I0GlCROdz+64ir8dPcLzS5P4FYV/uXCBLo+XTLXGS6cn2L9lPQN+H987cpIJj52gz81oIkWmVkU3TGqaRp/Xg2GanJla4JotG9gYDPKPh47w+3dcR6fLzdHVFe4e6Ofd69dx109+xPT4Kh6XzBfvvYVr2gc4n17lkalpRldWaW7ys7qaIpLIEImnqVVU3nvVLi6kkthEiaJa50wixg+npvj8niHuXbcXE5W5/AqL+Ty6YXLFnvVcnIowPNhFt9fHoCjyyW17WSmnqNR1WruCvPraFOfOLNDZ+zLx5TSbt/ayp7mX8VSK+wa34rftuHx9dFvPcH1PN9FwmHiphGSx0O32cjYZJ1Uu0+1tSBKSlSrvWDfAFS0DCCiXHtLrNBLrQLT4UY0oFmxk67P45B4kIYxhFjDNGqL4c41eTZ+ipKqE7fB7r7/Gd266nW8+faQhadEMFFnANBrn6mw2z3KhhGoYXExn2dsaI1evcz6Z4O/uvBnTNHlmYZEXliKUtRfx2mz8w+hZEtkCHUEfS4k00aECqm6wkEwzkU7Q5qxyMraGQ5JRjRg/nZ7mI9u2UtOT+G0K63paUQ2DRKXKtR3reXNtlk63l5n8OdZ796MZa0hCgLXyOVLVAtlalV3NHfx0ZpYNwQDRUpmhgJ/f2bGDml6jyxXmnYODnE+lSVSLNNs9FNQi8/kkXjlPn6eZC5llPvStxxAEC81BhVDYTblQoanFzw29vXz1idfwB510e710eX2k+wI43Apj51ZZyGW5ZeMGzp9dpVrT6ewJMpPN4ZAk+rpaeOvkNIosojhtuLwOsFiQJYlEoYIkWpBkiZ0bG13/Ax3tnIonqOkalapGYjmN0C1QrNQaWEqnDSFXp6YalPN1VudibNrXzHgizjse/BcmfvcbuORXeX1lhbd1DlK+XePLo9/i2MoK8ysJhns7eOidH0ewODj2gYN8efRbJCtVtjY3sb+1lYqusbe1jcdnZiirKk6XnU6Pl8VclvOSxDWdnXS73ZyOxdmxu49CscL5qQj3Rn6CJEsoDpnr3rmFVCxLRVVx+RykE0WKhQbPfW4milWWeH/iCRSnrXHrsFgYHAiysJjBYgGn10FN1ymUNHTTxKMbaFoD+9nV6eNCMoEo/nJrNOF/jkbzV6LQ9Mo2PrNrF1PZDF975U2iK1kcisRSNMFPZ+e4f8MgNkOkye5CFq0Eba1ka1GKaiOjMler4rLK1HUNRZLZFmrlTCJKpJjjrv5+1vt8VHWNHrcHSRA4FY/R3Brg7IlVSNdItcOFsUUyvY2YH0kUaG2209QRZG9LCx0uL6OJFAHFRtBhx2+3U9Y0GsFMFjyyTLJSbUQeqSqjyVXKmoZDkmhzBrFgQTPq+GQnd/Vv5q21RtZ9wO5gbmmNdT2tLOQLLBWPsFjIMJZMU9M0IuksNocNm82KX1FQDYP2cIBMLEcmniPc3riQvba8yrlkilt6ennohvfzgRe+A8Cgz4tXUTgxt8g1nZ2ciSc4uL6fqXSKw2NTLOVz3NzXx3Pz8/T5/XhsNrY1NREvV6nrOtdcuZEv7r+GM4lVvnLqFPGVNG+trTGWTNHv87KYv8DfPfYquZIGdZ3VRIVPfPsprKKFXXsG6Opys3Qhwxf/5SUcigSCBVerg7YmBzNLeYJeGyPb+/nItq1YLQLHYzF8NhtvrKxglyRcso2hQICXI8tc982HKOXLrC7nEAQQrRJ9LSFePX2hgRMFNg73YHcprCwksSsSLW1eujqbSGTyXByP4nfL5HJV3M6Gm71c1XDaJZqdTrLVKnXVIJNvuB8B5iZjrMwnsFgsuNw2SvlL7GvFSrGs4Xc3dLblUg2rJPDp3btJVioc7OzAZZWwSSKGaTac96qGpptoqs7CQoyWjiBvRpY40NVNpD3D6kISm11Gq2vIikwuVWBiZpkJlrG77dyycZCdm/rJ1arMza9Rqmhoaw0HazRWxCoKZIt1EC109XrYvXMd25qbUSSR1WIBlywzH4kRavYRbgvw+twCc5kMhUqNSD5Hj6ufxdxRdMPgA5s381f7b2C1nMIp2Qkrg8QrF7i2s50ut5fhwEEEix3DrCMJMjaxHwuLLJemeeb0OHO9bVzX3c1t/f2MJVPEyxXOLaywq7+bHx47zc093QSVUEPTjIUHNgzzzh8/0jBEJIuUhzs52NWFy+vg2IVZXs5XKFU0giaMdHXQ6XISsiskyhWcl1jqj4+OM9TXzp6WJpYKRQKtPhRJpKxp7G/vYCGfYzQWI5vIY5oQTZT5g588x2dvq1NUVeKlIjcM9CMKFn6ymqKYLSHbGl3w2/t6uXugn7Di5c21BYqqSrpSIVo+RkjpwTDL1HSdSLHE5MwyX7jrbYy3tfHdV49zWtX5tRuuQDN1jq9F8dlsdDYFsO624nE0jIAWwUIs0ehov3dw6y9g1ExTxSuP8JHNIwDoRpZ3PP23jCcSfHbPbjbvDLFWLvHI5DTr/X7eWlvjQOtBVGOZQX/4MrpRN7NIliCCxU1dnydo237ZZQ4CBuVfuCaLFjfDwQ7c1m384/Uanz3yCv6Qi1S8gE0WEUWB6/Zt5NTUIiG7wnQmg9tm44rWZlqdLv7gyRfQNJ2/uPNGHho7T7fXxwc2beS15WVmc3l+Y3gz51NpxhIJZJsVu2RlKruGpur8bHaegx3tnEumiFcq3NjVzX0bh7ix4+0UtXGu7xzm1eUVru5oZyab4/mli4ynMsznLvLeDYMcXvkhb+8dpNneQVjpRxYW6XVbsVgEPrptKz6bQrZWZUuwF5sweFm/+qFN21guvcH5VIyXIxF+e8tVREsLKKLE80sX2dPSSUubl/hanvauADcND/HW6gpXdHTw5MUp0rkaFgs0OZ1YBYFZf5Jr1/WjqToBxc6WUACv20rfxjbqVZWyqjGTyVLRVIpljWpNx6EaGLpBS3eYcqWGL+RBV3VsdivdXg9lVWM6kyNeKnLy9DQAyWyVQmkVi8VCKNz4vmVJIJeu4vLaCLb4yNcbqNBSqU5Jm0K0WBgOBanodQKKjes7+3hqepov3XoD+1t3I1h+Ho7/0eF9/Onxl3h6Zoanpqeplmusa2ni5MkZ1g938rHtIyzmC0ytxYnJJV6JRKhpjQzq3W3tvDo3T1tnCEkQiCwlqFdVItNRBElkg9tNpVBF1Qxa230N1nupisvXcLeLFoFFVxRBsFAr1wh4ZUwTTN3AMAz83sbEsVyo0ha2094bZltnOxVNo3IJj/rLvP6HpBv9ahSaJU1tBMauRcmligAYpsnvXnMlhmmiGQYOyYpDUvDIbopakopWI1UtczGTYqlQ5Lbe3ka3YXkRqyCwORjEZ7PT497BkD/LammegM3Na6szPDI5SV3XIVsHu0RzwIZsFbFfIs+Ypsne3esRBQsn4nFsosjO5jBPzy/Q5mqcyNlalYqWQxJE/DYbiiTislppctgbo3mXi5KmkqjkkEUJwzRxWe1YBZGPbdvG354+RdBhZ9++HdR0nQupFF84cYx8vUaqXCaTK1EtVenoCCMLIvlajUg8TTFXJptvRBzVF5KEmhvF85ZQEBOTicx5toTDJCtVXokss6UpzBlZYjKTRTMNFEmi0+OFHmhxukiUK6SLZXLVKook0eJy88DGIYpqHYvFQtjexc4mgV7fLJVyjRaniz6Pm/OpNC+cmSCdr6MWVLBL6LpJc9iJ2+fAJkp0r29laSFPV4eHcqkGHpmBgSCPve9enJKdpxYucjGdJVOt4rRaUXWDY6tRMpUK2O1cjCV4fXyaXKrQGCdpDddhXTMIpApMVlWSyTKaYeKySywuxkgmiiiyQGtvE2sLCdbiGYJBD2G/QiJTxeuyEktV8LqtNIed5HJVnnphjL5eP3XVQBQtzM/mEBwSpaqGKFqxShZ2bu5na3MTj41NsDAdY9++9Zw+Ncv2Hf10e33c0tNNTdfZFAxR0VTG02nWBYK4rBInolFuHOgnHcvhDblZ1xRmMtqQKNzY3UWX28WD2kmcskxOLdDV6kWtqbQ1ByirKkuza6SqVfa0tvD49DSb1nVwPDdFOl/HUdcJem1kCw33v2kaDA53cfT4FPGNRa65+ir8ip3Ta1F2DvbQ4nRhl0TKqkZN1/nw1i28tLQMFoEPDQ/z/QsXkQQBqxDGJ9dwW708dOEJ0tUaZVVDMwy2BGrUjBVs4gCSJYxuFjCo0mz38e49I3S5Xfxkapqb+3rRTZM3JmfJJvKMe+JYZYmn5udp3eTHIzdyVCRBpF5RMQwTj0fhwugSutZg18eieSTRQq2uM3Y+zvsP7gJgNpsjaFdIVxshz9lEns4NA6SrNUbCYY4GvBgmGKbBxXSKZ+YXODGzgE2RCTc5kSSRhcUMX/jZy+TyjVGcX7GztSnMe/eOcF1nF9+9cIGnj5zhj994g16fn/3trVzR0snFTIJNoSBPzU+iSDO8b/B+hvxVJtJJHC6FPc3d7G3p5jsvvYnNYWMqneHLZ47zzoF+JEFgfiXB/zqwm4PtnZgmvLq8RKvTyUopSbe7Aws/D1D/V2LPvy5R8PGdG+9gvpDgofPn2dXSzA2dfbSMOPn66Cgf27aNvDrHV0aPc31nB2eTj/L2nnchCx2X96GIGzEoX8othYnMLJ2uAIr4878jiz1YLFFeXX2CHeFuVgp5YtE8gmBBvWSY88g2utvDHJqbYyAQwC5JvBxZIVud5a49WwkoCtvDbfzxwkvsu7KDsOLCYZV4/OIk96y7FVkUef+GYbL18qW4JT/paIaLDpmzkRWqpRrpRIH+93rY39bNVO51ZFGi3eHnEyO7uPeJx7DZrNzY20uz087JtSiffvwQisOGZprc1qPRbO8gqOxEN7JYLDZ2NzWkA3V9gYqeIVU9Rrtz3+X33ebYRVhZ4G9OfgerINDmcnI+laSkqqSrJX5037u4/Z++xzPvewC/3ItmJnl05hTzwQDnpCXCrT6ubm/n2FoMra5hYLJ3fS/HpubZ3drCDfuHcckyh89P8ejrJ5FkqTG9GGxiU3c7dw708Y0zo2TTBYq5Mnu2rGPFqfBb27ZyKp7gtYszXLVhgOu7u1nNNtzqpfkULW1eBnvbWIgmG80JRaZa13E5JNS6xsxyjD9/27Wc6+/n6YUJru0YYEuwm4+++jDPHT7H/J88xA9vEwkpmyjUp/DKTZcfRKxCJx0uJ98fi5ArqMiywLGXZnEEFba3tPJKZIU3Jmbwhtw4rVZS5QrxfAFN1dkxPMxqsUhN1/DINsbSCximiSgYWHWDM2NzWG2NY1yta5TzFWS7lVQ0Sz5VpFatU1cb5J9Mpkxru49quY7X76LJ6aRjxAvA8VNT1FSNbCJPLOCjrNaRbf8RRPDLtSz8T4ls/5UoNCuaxovnLhKL5qjWdTTNwO938PTcAp/csZ3lYoGQYqcglbEKImWtypHoEi8vLSNYYDgcZjyVotPtZl9rG6OJOC9FIpxPJPnW9b0YlHhqfoZ71w8TsjtwyTKLkXgDayhZsMkihmGy8dLoXBQFlmMp6jWVsYlFnu9vYaSlFZsocUtPDx9/5nlGwiEUyY4Fma3hEDVdJ1osNZB+DjtNdieCxYIkiJimSd1Q+cLJ06QqFSL5HOlkno7WILFSiXg6z0BrmMVclny5EaorWUWcHge6YdAXDHFscYlcqkg63eg8CAL4gy4+ff1+fDYbpgnzuSwG4JZl7ujr56HxcU6trXHjugF2tzTT5nRxMr6GyypR1VR8isJT5ybIJvLIdhldM7j/9o3k6zU2BsJsCjRhmnVeiMwwGAjw7sFBHp+ZJV4q8ezhs6zEK+iJSiOCSLCw+6pePG4HIy2thOwK3zx0BMEuMTTYxbduuI2FQpKPPfsct//f/4xa19DqGi6fg1dkiWKuQiZTJltU2b6jQTCVrCKlfJmb92/j2SOjKDaR5qDCcrzCXVeO8J1n3qRa1xkYCKGpegPDpxqNTMz5OG6/i962xrgRwO2woukmhgE+l5VYovHzdetC6Fqjy+m0S5SArhYn/iYPhWwZt8+B327nJ+fGmZqI8r/vv4mvv3CE7oFm1uIZ8tUq928YpKxpzOYyPHx+HJcsc31PN4eXV5iYihDLF9m7uZ83x2bIez1USzVWlzM8v26JuwcGSI0M89bqKpJVZGMoxE29PdR0nbKmYXT38MT5Cfa3t7K2nKJlsJub9m/l+OQ8qXgOwzCp1HRM02TL5hZ2tbWxNBPFJlvx2RTev3GI47EYsiheZhqLFgt/sOUgRa3CU3MLHFs7w5ZgO39zsAu7FCJXX+Z4bJE2p5svPvYSv3XrAb7x08Mc3D/INR3r8MoN84AkhADQsfGl089zei1Kp8fLSjLDI5e6KIpT4cM7t3I4EsHdbCOSL/DI9HnuWbcZ0SJw71OPEV3JIFsFBIsFXTe5eG4Zv0/BYgG7IpEtqJCs8CcPP8fffejtCJaGrtghSYzGYvibvSTKZRRJ4seTk2xtbuGHFy8yObHEluFeFqNJViMZPB6FtXgRt8NKS8hB10ArosXCxLlFHnrsGDarwOBgE+PJFJF8jp/+1v3MZDN85kfP8EZglkff8y42+psYDrZS1eqUtBpr5WMEbK3cM3At63x+Hpk+z9lEgut3b+bdg+tpd3pIVcsEFAffODtKfDVL2G5nPpdlLp9jpVhmUzCIV3by8MU3uXfdVmz/puj798tn286IDb5ycDcWi8gT88/y1lqc09NLDF/7CdYqp/jhy8dZ2J7lm9ffAcDWr32Ye/dt5/d3fBjVXEVAAUuj0BwJvY2KNoZuZC9rNHWzyFp5lteXV3FbZXKZIrFUtdFt1U1QdR63nOLGfVu4pb+PXL3OHX396IbBmUSMXc1tzOTShO1BXvjQr+OxOjgWm+MH4xMMt7Tw5VMn2doU4nQ8wfs2bOZ8KsFXj5/kj+++gR9dnOTkm/O0NzvpXt/KXX17OBGf4EI6TUCx0dbTyblklN/bt5dUtUrIrtDj8dLudPFXpeMYhsFDR08SLZX57a0yYbHn8vv61yWLPchiD175Fz9bwSIjCwM8ecf9WJB5dvE0bqWBI17v6+ZI9AKnP/kJyloNweIiUZ7k22PnL6EQG4Q3gKqusaWnnXiphNtmQ9d0tobD7Gtr4+PPvcCnrt/Ph/7+cWxWAV032byljS3hEN84M8qFs4vohonFYsEwTfa2d/Do5BQbQ2EcHjtem0yv18umlmZeW72Iyy7R3BrAqyi0NvlJFUqEmn1YZQmn14FfsdPr8zXc3z4vq6US35s8x5bgCqlyhdHPfgLBYqPZvoeCOsqvP/8km8Jv8rk9H0CwOIgUz/DKUoSOnhDaTJxYogqaiSRa6HQ3Gi+jXgeaqmMVRLJalb5QkKlonP/re09y/9v2squpCafVyssnJ6hXVWSbRL2mNQyhLgWvpzEiT6zlqNX1y52+Sk1HkgRKxRq5koaSLHDPdbs5dGqcvLuEzS6zp7OT/buGOPTaOWqVOivJzGWd6y/zsvArhKD8n7Dquo7b72RpMY1osaCaoNZUpqIxvnH2LH+69wCSRcRttVPUKry4NMdQIIDa3iASBO0KO8Kd/MXJN/DIMnOZLO+5dOM3UZnILBGvVHlzbaERKCuIONz2BkPbaeXmA1vZGAoSL/98fLRnXS/z2QzZYpmtzS2cT8T5i/0HkEWRzFqWTrcHATtYRGRBJHJJHN3mclJUVSLFPIZh4rRaCdkdpKoVTq9FqdVUqqUG3/eN2XkicwlME8r5Cns396MZBr3BAG1uN27Zylw2x2qxgFZvOLTbHDKFTAlNN/jNq/fgslpZLhZxSBJOq5W6pnFzdx8OSUEUBM5dXOSe29czHGzhXDLKq0vL3L1ugKGAH6sg8sTRUXaOrONARweL+QKy2Hg/PpsTCxYOR89x+pJ279NPHKJeVVleLTTQkIqI3OKgs9mBv8nD1s52dNPgx6+cQFasTP3+w/zWy1/ix8+fIvTMWYyyBgX1MtccRaR7wMDrd2KaJpreMBc0+T0MhcJ8+6k3uOrKIcYTcewuBYvFQrDVh+JM8d1n3ySertLf7SW6ksVpl/AGGzdOf7OXmckY1ZqOO+DkYHcPj8zHAZ1iWSPok8kUVHwuK6IosLqcoVxtXNy8Livlqk4yW8M0cyxGywS9FTLxPL6wh83bu0hWqly1bQOzmTQziwnetnkDdcPgw999DIfHTnNrAN0w+dbRkyhOhXdduZ2XZ+cZW1qlvStMsljkwKZ12LaItLmcDPl7mMlleOzMGIZu0u1x0+PxcCIWI5IvcOjIWWqqwQ8CF9mzqY9ivc7rZyeZXciiyALZVBWP38bIzi4+vmcnqWqN2/Zt48+vuBXDrFCo13hbdzf/cPYsX9x/PY/NjhMpFDGBs8lV/vnQcarXa9zRr3NN+wGy9XmeX5rm68dO8ODbb8Fml3li/CJ/+cBNjaKjXsRlzSJeKlQABIuL23p7uKu/jz9+4w36WkLcOdDPm9EYP3v9NJ4tm0kXyxQyRfKhRrC8VRC59Uffp5Ap4VAaxiXFacMwTXJFFfNSAd3WGSCRqYIsUl4p8cLCAh8cHuYn0zOEHQ7uGxriRWeEZw6PsnFLD91eHx/cvImziQRfTeUYPTuHrht4vQrRWBGLxdJAngoW7tu4kdPxBPOOKIMDdmSblbXVLP6whyank48cep7X3/Mldvx2O3/4xut89vBrfG7flZyOx2hzOWl3enhuaYYtoRwb/QPsbrqFfP1xjq6u8qHhYbYGB5jOLXI2mcAmCvz01ZPWUoPEAAAgAElEQVTccc12bum+ifH0m3S6PURLRXY37eHJhZdpcTjI1Uv/oQD6f1qS0IBFXN0+wF8fOcaXb78RzYiSrBTYsKmbr11zM4q4CYDoap4/+/6L9Hs93DPwnsv70IwEi8VxulwbLhdjkeIRBIuAJEj80a5fRzfTSPILmFUNFIlNG0P82e038LlDr3B0YoYzS8s8fOft1HSN8VTyUvC8wB8++zKJ6GO094Z53/at3N67nrs3bOC+9Vv40fQYLy8usTncxGePvMqulhYO3fMAr65M8xf7D/DdYIjnT08QW0ygGkV+ODnF+4Y20O7yMZq8wJ+9/gYPbN9CplrnZCxO2G5ntVikmC3R1RHmu++6H4fUgyT4MU0VzUwhWJy/cMzW9FlsYv9/+FxVI4Jq1HFbN3JjV5kPvvgkZy8sMJvL0+V2cU379WTrYzy9+BR9Xh93rl9Pi8POZ1afplKs8vrKKsPBAM/mC0SLBUIOB31dLUxmMjwweBB4gd/97tPU6wZDg2EA1iIpvrb8GrF0Q3usyCIff+dVtDjsHFuNcnZigd3XtbK5qZkNfh82UeS67k7ORlZo6ggweX4JdaNB2NFA4q6kYuzZuZ5P79zJQj7Hb3/nSa7aN8Qnv/wEt75zG5/ZtYsDrbdxQ+ddv/De3dZtvGNwnMenZzDReWXlCZ6eX2B9MMindu7g0ekZXjl3kUSsYUCMFIr0XOKi1yt1Kl4VmyRRUlX8XierZpKnTozxSKFKuarhUCTsLhtun5POpgBLaw06ns1hQ7KKOFwK0UiayFqpcZ7WdQJhO5pu4FBEsgWVfp8HURJYi6So1nQGm8Lc3NvNZDTGVev6WcnnsYoiguW/YF//Eqz/jjLTYrHcBPwdINKgPH7h3/3+PuD3Lr0sAh8xTfPsf7bPX4lC02ezoesGbW1ekokCLkki1OZHq2tUVY2iWqFJ8WECbqu3MaaWZZodDva29FDTVT768rOIgsB9O3YwsK0FqyCxLdzOoaXTvBmNEbIrKKLEs/OTANw/soWjh6bw+RrIS/WSexWgUtU4fH4Ki8XC4mKGmYlVxv/4UyiilXPpZTRNRzfMRmSIqVPTddqdLhyShAG4JCuGYRJQFHw2Basg0ewQ2NfRiWoYBBUbry4tkY5msdskKjWNSrHK+eUo3eEA2VqVu9cN8LauIZ5ZHOdb58aQFRmP10kmladzfSt/ffXVnE+lcFplBv02ziVTJCtVHtiwjZJWZTwdZTwRR1N1Pv/cq6zrbuFPrriCXx/eRLGuMpnNouoGH7/pAM/Nz1PVNF6dnOHQmXHevXeEA20DJKs5MrUqqq6zLRTicUVmfj7dcP95rIRDdh78zbvIVGv86OJFcrUq2WqVyT/4Br/50t9jeeDtUDcaHZCK1sBJNtshVwfBgqfJjmCxICtWlldypBMVBgcDpItl1pQigYCDVw5fYPfeAdS6RmtPmPRaFq2m4bBbcTk0puay9LQ5CXcEKGRKyHaZ+ek4fQNhOltCLCzHOW2LkslWUXWTgXVhNnS3MruWYHkuQd/GDl5/dQpdN+lpc3LFrg2MTS1RLlRRNZ3tI20Uc2W8ITfZRIGR/s7GzXrdOt79z4/y+XtuoqLpfPDhn3D93mHeml7gN7YM8+TsHG5/Yxz79z89THun7zJa7qreXp4+00Bj7jywl7l8hI2BIENdbVzT2UGmVuMfRs8iCRZGmlu44+odrBbyHH7zIoNbOtFUnUS8iEMRsYoW+vu8eINuovNxnmteYnMoyLl4DNUoIQkS20JDFNQUt/X18vTCBZ6dm6eqqVzz8IPkMyUqNZ1TixEOvXGWr7/PpM3pIlYu8/ErdvGT6RmcHjthnxtVN/jG6ZN8fu1FPH4nr3/gI0iWJlK1WT7+ynOsDwT5yPAI507NEww5CSh2tjeH2fWum/jkwz8jEHCgOGxUSzVOrK7wjp89it/jpFyoUFcNFJtIqNVPqdDQ08bTFZx2iZH+Tn5t73b+/JEX6Rts4flXx7k4u8I9V4yQq9X54uGjtAZ87N89RI/PR7vLwfOLi/yv4av5k0cPUapoWC91RVTVoKPVhUUQ2LKxm2S1SqpSpqkjiN1mpVyt0yYJBO12bGIjOmnvd36H1YUk971tL3+6+0ZS1SSvRCIMh8Nc0TxMMVQnV6/y0MTrvK07wrXtt9DhcpOolDm0NMqAz8/9g7u495kf8uCvvYODbftRjSWcVpmKpvL8UoSP//gP+ekH3kOuViVVLdHuLHIy/hp7mm/9L6+fryxP0xrwcaD1WnL1KX4wNcX7Nm3EIbVe3ib6Fw/y2sqLXNtxJ6bZIOuYpopBlRa7H6vQcnnbTtd+qvoFKlqZJ+ef4M6+e7DaJNwBhZ5uP4Zh8CdPvUS9prISLdISsvOhZ59lR0c792/YwGvLy/R4vEhWkXdct5Pf3b4Xj9VOpJRkJBzmkZnzfOEnL2GTRW57Rx+RQh5ZFJnLx3DJVt6KrfH6xRlkxcqeTX1IgpUNAT9tTi9fOXOSeKmEYRgcXYmykE4z3NJCvl7nQjzO52++lnaXm8nsGq+tnOSu/nX0uvcwX5hoFD82Oz3ugwDYxH7m8q/R57nqFz5PWexGFrtRjShnUxG+dvVNvLxuhnS1iqobnE+/wXKxgF2yYhdlPrL5Fh547kEcThtfvfVtTGbSPLewRLxUJBnL4u2yIQkWHh49x2qxRK1cI1tUsSuNaZfL56RSqpHLVbnywDomx5bYuWMdIbvCTC5Hn9+HtqmXpUKR00vLWEWBvS3NeG02btiwntcXF1DsMrVKnZHePna3NXjo7U4Hy8UCe1u6GP+jT/FXp98ERaTN7cYmiswXDvOXJ47x9avvuhzoD3Df+vu4b31DD3xFywY2B1t5dHqcaKnIRCJOOlHE61UwDJOjcwscBUI+N2VFpqyq2ESJWC6PKAr0bWxD1wxqlTod3Y2kg1yqQD5dZEHTUZwKd24Y5Pn5efLFCv3tTRTzZVgu4vZZyVc0CiWVqiRQrenIskisVGGor523MlNkClV+/Nxpmu9ysqm9hVSljFdR8CsKZfWXu6MJ/9+bgSwNvcM3gBuAZeCExWJ50jTNiX+z2TxwlWmaGYvFcjPwTWDPf7bfX4lCM14o0WWx0NIRxCpL1Kp1NLXRYTo/t8xXbWc42NHGjqZW7JKN8VSG5WKJdw4McjK+BICqG41cQIcXySIAJifjEfw2hTv6+zgVi/FSZLnhHK3XaHbY2XZlJw63nXPRtcbN5hKlRzfMRjRJVcWhiJgmvOPJR2jzeOj3+bG7FCpa4yC20IgqKakqHtnGYqGAIop4ZRuSIOC0KtT1hrZNNw2eeusc792/43JEjc0hI9ut1Mp1qqUanf1eji1FOB6Ls7ulk1t7Rvibo29hs8vIoojNYbv01LzCruZmSprKeCJDpFDkd0Z2ohoaXzr1Ft/66ZuIFti8pQ2rzUpJVfnZ3DxXtrWSqjbMPqvFEgfb12MTBeLlKndu2US6WqWi6Ty7dIHrO9Yxmkjy+CunyddqPHLPu9jyua/htIs4FDtD2zr50tHjvHFknqBXZv3GVloDPrZ//dOcm0ggeWS0S51PXFaoaMhumboi4XFKtLW4SaXK2HIVursDuOx5rty+gaFggB+fHSeXq2KxwJEjU/z2Ow5yMrrKgeH1LOVzAKSOTtHV2ogWqZZqaKpONNbQ+GqqztFjk8hWAdM0aW33Us5XkGQJj2yjNeCjnK8wfT7C5o3NTFyMY7VZOXzsAqJgIVOoo+smK/FVHIqIJ+imuSvIZDxJ0O7gZDxOqMXPmXiS8UScvsF2nj96DlUzmM3lWe8PsDkUxioKnLBNc8v2TTx7ZoId3Z1c3d7GSqFAqlLmu+MT3NTby/amJj40vJmZbJZCXSWylmTPul7quk6Tw45hmuzevQ6bKLKWL+BySLT1NTN+dpkzn/kkn3z9OYwBGAz4ef/QCG/v62cqF8VllXl0eoonRseJLKTwemyIVpHWrjDFfJlstoIiCxQyJZweO0/PL/CJkR0M+LwcjcbodDlx+RwMh5uIV6r4PU60usZVGwb4q1OvMZvNErDbGQqFsAoCzy/NMrCxjWsH+onk83xndIy5i6u4nTKra0XaWhrjols29fDBTVu59eHvsRzJ0tUdINzsI5crIYkCsmLFY5gMb+uly+Olqutcd2ATL7x2nuaQgwPD69ENk4qm0Rzw4rfbsUsSz09M4vE66fR4KWk5+oc6SMSyDf1XocLAQBC1rtHV09zQFYoim0IhdjQ3EytXeHNpicGuhkym09Pofk1V4nz3N99FSVVZKq4gCxJ/tHsf3e4DVPUJJjMp1vsDWAUBu2TjRPwF9jTfyjpPlmRtkhOxCE/OzfHApo00OZzoZg4QmM6m+f6FC3zuiiu5s6+XdLXCq8vLDAeDbPDN0+9t+i+vnYZZwW9TePCG27DQ6BJPpVL85RUfu7yNbhYQcHJtx50YZo2SdgEBCyvlFPFyiS63H+e/k7Mp4hCCJcKBNhPNSJNLNo45QbRgsQgk4gXKVR1ZFiiU6rR43OiGSb5eY1dzM+t9G7h76zxXtbc3SG0WgUytSpuzUeQMbenCKoqEFDs39XTz1ZOnGEskGQ6HuKW7n5ve08/DF8aYTKURcHJTTy/v/NEjtDcF6Pb58Pd2NwhvkSjXd3XiU2zMZTJ8+/w4dqvExNwKxWyJ0J0KD2bGuLKthUSlgs9mQ7QcoazVGPRdR5/nKgyzzE/mnuTGzkG88sjlz8AqtNLqcFPV65c6piUcksSHn3yGpqCXP923DxOT04lTxEolnvzQ/bgkO2G7kzOJJOdmI+we7KWiabx/4xAPjo3xgY3DtDodfGT0STRMsqkiLp+T1p4wTZpBsVjhz+69CcOEz//4OT5y60GOLq/gkK0cn1/k9k1DvHdwI8fWIuxt7uXqtm2ciz/M9iu2YJckYuUymtGI3jsZjQJweCVKl8fNR7eMMPhJL/FKhRcjEUZjMcYmFvlr78v84c5GoakZGXQzTaoW5/nFGfa2tvHu7z3CzqE+MpUKdV3nuis3s5DNMHkugmQV8QbdWAWRbq+TyVicrqCfZq8Hn6IwnUgiWUU6+hsPMsVCBYfbTrlQQVN1cskC8UqVbS2tfPfZNxkbXcahiDjd1gaS2ARVM1GTZShpVP02ru5o59Pb93NV5J8ollXS+Tr/9MQR2jq83L5zC+t8HhRJIlb6RYPbL+P6bxid7wZmTNOcu7T/HwJ3AJcLTdM0j/6b7Y8BHfwX61ei0MQ0SUUzuLwO6jW1keNoFbEIFtR6lZdPTWAVRVSjMSpfzOUIOR0cXl0iVq5wIhpFFCzM57Lk1QpOqx0JgaBip6DWOba2xvHVKLFcHtlmpV5TORFLoDhtiJJIu89zybDT+HeCIReyzUqg2UcqmiGeKDE7voI6oNHiciEIAookYUGgoqcJ2JSG801rZH75bQqOS+Qi1dAQLmltRtfWKOUr/MuRUw38olPB6bUjSiKxxSSaqvPA0BBXdbTx4Lkxnp6fQhZFkitpdN24nAUpyRJPTE2hGgaT6Qz5WpWb+3qp6SqIMODzYhgmgmjh7NlVfuPOvTQ5HPxs4iLPz8/xlWuuZSQMT8zN8dD5cd7e18ubq1ES5TLL0RS/tX83t3QNMZVb46WLM8iSwGqxwANPP0lTwE5bXxOdfh/LuVyjOG528L/vvYnvjY/z5KFzaEUVTNAME5wS2ESQBZytDhyKRFdvkGKuTLlUo6s3iC/oRpEa3aO5TJqartHdFCC5kqZtqI1qqcp3nj1KsMlNZDHO2lqBUMCOpje+sPhqlrbuIJViFeulJ996rfGQYHcptLYGGAyFeGsxwvzkGqdORnAoIn19QZwuG/l0ke5WV8PEpFhJxBvEKEEAwzCRJIHOsJ8be3o4uholZFd4bGyCdS1NjCfiFCs1Dvb1MH52ka51zaQqVS6mkpw/O49il7lm3yZ0wyTU7OPV0YvkazUCdjuCpWHImkilmcxk+djWrbwcWcZrk+luDeFXFCq6TqSQZzqVYnNTM5/esYuvnz1DIVNifjJKva7jlAa5vX8aCxaCdjuPzY7z6MWL7GnvIFkuMx6L0dkSwmqz4nIqpFN5VFVDV3VUzcDnlrE7bQSCHg6fn8JptXJlWwsuq8SRlRUkUWQ4FCBTq9HS18eoO8HGgJ8WpxNRsBDJF8jXarhkmXi50nDpWyXCDgdlb51T+ToryTwoIrlIEQQLVpvEBzdv4/59O/j06NOUqgk+cnAPPsXGa5EVXjg2hsUCN/T0sN7n42Q80Ti3/Hb617WRrlawiiLr/D5cskxV00iUS7x9eIht4RCPTE5zaHGSDaEw+UK5weW2yyTjBa7YvZ5ru7tZLhZJVqrk63VORVcRhYb7OJLJcvfQBjTT5IbuLn5n+wib/DuIV6d5KTLLNR29/M2ZE7xzIMezC4vc0NWJV1a4qbsfVdfocgeZy7+GYLHQ6drObT17uKLlJI/MnOfQwhk+uV3gxaVFMrU608sxxpJxdja3809jo+xoCqObJsulJH3ukf94rfw3q6ydYy4fI1Or4rH2AiaPz51nZ2sLpqliolHRppAEJxaLDdF0s1aZYDqXYEe4m0K9Rl3XUcSfz+lVI4ZVaEYzMoynG/emroYMj7pqkIgV8HoVtm7v5ezpeTq6gyzNJ1mMpVgfCNLl8vGDqQtUNI12lxO/YudELMKt3XezPeRkIjPNcrHAe4aG8Cs2WpwuKprKx3fuYDyV4ZELF+hwORkJt/GpkQP87egR5gszpGtVrtowQEXTUCSJlXyeqWgcXTeYymbpcLm4baCPH164yFomhyfoopAp8eXnDxNo9rJcyBMvldjV1sbBtj5y9QqR4hFCihebOMhYMo1uXOCWbi6P3AHanf08t/QmJiYvLCzw9oF+ZEWmxeXiucVFBv0+Ol0eysUqhxbmeefARvyyG48sY1WsDPh9LOYLlC41Js6nYvza0PX8kfcQ8bUymXydDqDL66PV5eJ3tu3ixm99m1q5hqzIWCwWHLIVmyhRyJQI2RXandvYGqrx6MwYt/du4J7BQU7E4lQ0jeV8jvXBIGvFxuhZ1XUuppKcWF5mOtPQBltFgUJdJVWpUK+qXEg18kuPRH9GSVN5aWmZ8UScJqeLh86c5crN61grFvEqCrf096GbBh6bjVlxhcWlHMMBF7IoUFbr6JrO8ZOTBFt8eLrb2dLa6DLubG3lB2+NYrE0crMtgkClWMU0TE5HVxluaua6KzehGjqj4wsEQxYqxRo1r410vk7VLoFqgADj6TQ9ngDb+joxdJNwrYGSziSLvDQ1w0w4wIZAkJGm0P+7uuS/ef03WYHagci/eb3Mf96t/A3g2f9qp78ShWbY40Jx2FheTCFbBdxeB9VyDcWpIAgCN+zazBWtzZyMJQgoCulqBcUqcXhllbHIKlpdIxTysikcpssVpq5rLJYSHFpcYiaT5vP7rqSq6Xz37CySLDU4sf29CIKAU5Hp8flxWSXW+/2UtTGsskS1Umt0HO0yUKKuGoR9bnyyjf3D62h3+tDMAqcTjY6qbpooosimYBCn1Bj/hBSFp+fn6fP5yNXqnDszz46dAzxy6wd5dOZ1vj12nsRqGgDx/3D31lGSnee97rOpmKG7qquZYRg1IBiLJZPIGGNMcRzfG3Qcn+TKdhx2chxDLMmJKbYsWZYMYmk00ow0zNMzzYzFjLv23uePmqM4N7nn5JybnHj5q9VrdcHa1NX7e7/3fX+/Rxb5kzffTJPVTaezicvJFJ//4QvkSyqqqiMIApJUxZpuGCYDPJZrrArNNhPfKRZRRJFUpcoPzl+iVtMQLTI+lwmLLDOXzdIe8GKRFQ6vLNPn9fD+oQ08OHqBg0vLXFpcZWd3BzZF4V39W0hUcjxw8SL1Wp2mNj/jFxdZmc8zvKWJj27bykMXLiBLEk+86x3Eynn+5uxZjr86jSgKiDYZSRJQMzXa+jysxstoJZVQm/P1FZzZ0jiHjpYgK/EU/Z1tnDszi9NRIG3PUi2rDfb3+Cq6AaEWD2abickra1SqGi6fg2yuSktnkLnJKJGgj7qqIZtk1Ir6+t+uvyfC4lqChfkov3rDbr62kECSBHLpKpfHYrzltk2sZXLYrWba3W6Oj86gaQb7r+9B1wxOnZjHZpawKSaOLK+wkE5Tqavk00VizgIbm5qJl0pcisVo7Q6i1TXWiwVu7Ojg3v5+6oZByGbj6xcuoGoanqCLtUKeM1dm8QRdjBF7ffVvACfXVvnUrl08cmGUqqbR4nAy6PdT03T+bN9NaIbOtqYgoWts7Ghu4nQ0xgOXH+HhS5e5tqsDSRB59JVTqLU6q0sJAJpb/fhtVtrdblbzOYq2KoIoYHNasZVUPEEXXr+LteUkfd0tmCSJHreX0WSKLo+HN/d0E7BYGU9lcJoUmm121ktlJFFAFASG/D4uJ5M8d+ISI0MdbAyHsMgyNkXm4pUFSqlqo3Wipl+FZxmceG2BTwSeo9vrZeeuVvKZEt+5cIk7BvqIl4qEOoP87jW7sSsKYbuDp+bmcSgm7tiziVixSJvLzQa/l4l0tlGGHxggWSrR7/VwKhrjvv4+vjc2Rr5WY2t7K5cUGUEQEKUMDpOZbLWGbsBcNsv5qQXSiTzlikZnTxC724ZNlhnwetnoj+AxbeJc8iCT6RSKKPKZ1w5zS2c7n3rpZWbH13gy5MIdcPKbO3cQtNoY8oYp1su8tLTEiD/PtsAQFa3GR0buQRiRKdUnqWiz+C1m9vZ347daUUSZF8anGE8muLatjdFkilJ9jMDVKovbZCJgtVCp1zFJEhv8TcTLRV5bXWVzIIBBDVHw8VfPH+FjB/ag6iuYpE5S1TxhWwsCCoX6HF6zg6X8LHuaXYz4TJyKzZCu5vGaGwGmLDQEkbLoJVOtMJ5O80JtgVQ8TzBgJ5UqceOOEe7r6+W9lxb4wdvu5YnZKW5ua+dcPMZMNoXXbKbd6aLJZqPD0crZ2DqCIDKfn+Nns7NcTsSJl0oc6OhkIV/AbTIRcThwmRQ+umUzz80vIgsiHkuG9w5uRDd0Pn/iOL+1fTuabvCV8+e5saOdY2cmeMcbdjHi91HXde7rPcCIz8/zi0t0u528FlyjqKokSkXOj83z/Ec+iNfkwCz14DdbyKkXWCrGmUiP85buLnQDHpu5xDv6JOYL55EEkWarjyupFAGrhWQmT7fLzaZwiHcPDfLa6iqrhSK9bi9v2jDIXT2DjKWjKKJIxG7H4bTyV4+9hMOmcHp+EU3V+Gw0ya3vexPNIRextSIepwm33Uqry8nN7W08On2FaqlKIlMh1NwgyI0EAhRUlQ39bZTqdf7szD8yn81y5Mw47ntM3BDpJK/WOLkWZSQYpKQ2Eh5dHg+5ao1ut4vRRCOYrOoN0WNV04ivpNB0g+m1ON+f/D4hmw1JECnUasxOr5FucvGuLRuvzrkVRhNJLiWSLGQzpAol9u0eYHY1TmeTH1kQWS8W0FSt0WetG2QrFW7tbOflpRXMoohWb1DGEASsdjO6rpNJFkglcxQ8Xu7o7mRPqI1LQ1EuJZM8OzWN2aRw6vgMVbOEIYugGXzlhVcZ355GESU2dLciiyKSKHBmcoFCpkhLVwcjfi9bAv/UOvKLOv43S+cBQRBO/9zzBw3D+O+koH9tg8a/8hqCIBygEWju/5/t8Jci0DRLErduGeb7seNYLAq6rrO6lMPnMiPJIslyiSMra6zm88wJvN4HAmDoBopZIeRwUqipjKaWGfFGOBePMuzz4DabrpaHHLT1h1meWqdnqPX1Ph9FkmiyNjwqh70tPLNwGZNFYWIyTshvwe6ykSuqqBWNc2dnaLrWzm2dHfgtHhbyazTb7MTKRbwmM16zDafJylcvnsZnsXAxkeTi/Arn9SXaQgFKVY2hQJC/v3KQR8fGGsH01QDb6TRzINJHTa9T1VTu7R3g69IRsnkVA5AlofHPW1Cp1XVkUUCUCniCLorZMs6rrGGbInPl0iqi2Pj8wKY2LsdjqLrOe0YawgBV15lKZ6jU69zd28uVVAKzJLG/JcztHVuQBB+/e/jHzCzHKGRLFApVVpYL9PR7KOQqmCWRsNPJ2Ysz3PX9H2DoBucurGFkqoheM1azhNksoVtlMvkamqaDDrmCSldvkA/v2cGfPfEigYiPZw+OUq5oSLJEsNnJpcsxWoJWaqpOMlujyWemo7eJa3u7OTw5QzjswtvkJr6Swu0yc8tgH4dMMhPTy+iajsVmbuAWx6L4PWZWE2nUqsrcQoZ/eOUksVQZXTcIhe3svWaAZLmM225lbmYNe7+JYMSHVtcQRZFWt5PVoJVKTePEhSkMTefu63cwnUqiVlXSmQInVRWTLPMb27ayvamVj734DAM+P10uFwda+ziXWMRtsjA1tsSWTd20udxkqxUS5gzNbhfbQs1sDgQ4uLTMH772Kj3exkT/qWv3MJ3NMZVK02S18Nvbt/Pw5Hn2tUS4LtKBSVRwmzz83fkLHD0xQaHcmFxESSCVrmAxN/q/yoUKmWSeKV0n5HCwGE/xuTdcz5Nz85wsL9Ac8bC2nCabyLOwViCTKqJu09jg9/K+wY08ePk8VlnmD144xN6+LlocDVVtolLhkfEl7uzpoVSvc+LyDHanFbMscVdvD/FymX6Ph0cFoFSHq2b8WCWoG/T3enjt2ATSvkFESWTvln7OTy/yjWde5X237GVvJEKzzU63K8jLK9M4TCZ2NgepaBpTGROdTicCAhdjUbY0h3hmdpYer4+vnT2HSZJYyuUJ2OwcOT7O1tv3MhRqIlYssiHU3KB71eu8cGkcXdORZAmb3UxdqxBfTTPQGuLdA1uRRR8CEseiz/HQpVH2toS5LtLKl46e5N7+Xt402M/3knmWFtPE1nN8IVcm1ORlS3MIRRT5xKadOJUu0rVpHrp8no+MgCRKaIbOJzffhkls+2f3wS/fkaM+46YAACAASURBVKJcV8mrNc5E46i6zoVYnF6vh5lMlnaXk4jdRkGts5DPYBiN6sXecP/rPZaFbIm5bI5YZYVWeydBqw9J8AAiNrkFwyhjliREwYoidnNNs5/zibN8a+xZ3tG/GZvcg3TVXun4+jrT6QynrsySyFSxVzREEV69PMVqPs9H77yWbtf1+M2LdDiDqLqGVVbY6A+zVEhTUGvIYogOl5uKdoVivSHk2Bz08/s/eo4fRTPsHOzibf39zOdylOp1hn0BXl5a4dh6lOMry3R6PPz2tu0spjIcWVnhYxuu55Nb4U9OnGRhrcBPTl7kdFuA39qxg7Pxs3jMFu7p7WWlkEfVNQ60NaqCwuAgh5ZneXf/u6/eA9dxKD1IwhwBi5vPnzzCrR3tfGDwV6jps3Q5uxEwYVDhjV1RfjQ9zf+1dxfpaoXf3LadDscmjq493nAXaO/g/UObyKtlvnX5Cr+1fRv39G5ge3Mz05s34TVb+Oi3HqdQquO0y3xv8lF6u8INjGTEx6bmEB8YHuHx6SmenJiiWFaxmiWi0QLfOXQSl9fOLSMDhJ1OXlteZmx6Bbvbyodv2cfZWIJb2wbodLk4vR5lR1MTP52d5Vc3jPDwxCRnF5ehvRWzJOG1WNAMnfVikZNT86RTRWRJYGk2zh8sPs0X3/1G7ul5B8O+Y+iGwZDfx8VEA1SwK9TEvkiYtWKJ1XyOTeEQO0PN/OjqXNzv87KQTlOrqvT1Rri9uwuL1ACYTCQTnJ5eIBjy4lBMLEeTKCYZXdcZ2tgBgMNk4vDyKruaI+wLd1HW6ui6QcBmo8lnxTCgKAkYBly5EGN6LsPb79yGw2Ti1PQ83ZEmwmEf7x0ZRtV1vndl/PUY4Rd3CP+7pfOEYRg7/j/eWwZ+/sbSCqz+iz0LwibgG8DthmEk/2c7/EW/kv+mIQBvaI0w8LZbeej0OXRdp1qqUVM1vvKuN3I+niRRLrOWylAt1TBZFaalJLMTKwTb/Gh1japWZzKV5BujVVymKWavGggP+f20OTzohsH+tnZeqqgc6OjksTMXSccapd+DhsHulghu01YOLh7E4bIxPNyM2WZGraiopfrrxzq2HuPN3d2IghOrnKJUr9LqcHExEePrFy/it9potlkJ2azMZLK4vQ7SyRw2k8IN+4f44PAIf3ziOAfv/Txn4gd573ceI5Gp0trhJ17J4jbZKdUreMwO/uydd/ArX3oMtdqgHiAIUNNIlevYvWZKlSKlkoov6CCXLRJ3ljh+Yer1jGexrKHW6iQSaX7/tuspqioFVcUiy4yn0hTUOrPZHPf1DXFvzzUYVHhy/iwPXLjImZOzCEBdM3A7FPxBKwtrRT523z4++HePk0tXcbjNFCbTjWwVEOn3YJJFoqkKTptMNFWlVtNAEAi1ORja0k58JcVn/vEZYokK0WSFznY3mqohSiLd7SFyqSKlSp2+kRY2W810uN3YFBOHJ2eILaeoawYT00nUigayyEOF19A0HVEUcLutaHWNW7cMU6uoqLU6ybUMM0t5rr2+h3OnGrYhkiQysKmV0dE5svkqkTYfoiQyOjpHKltF0w2WVxuqyo52D4qqoZhkJmeSPPzCcT77ttuYXYoRX0nR3B7A77NzbbiHlVKS4UCQVKXCpWSKBy48hsNkYmxqmet3DZMologVi6QqZR5+5z38ycmTmCWJP3rmJQLNHnK5Eh/fsoVKvc6QL0CTzc47+0dwyBaK9So3tpmJ2Nuoalmmc1He8dTjjJ1fxB+w82fvvoGfTs8wtxp/3ScynWzYN9UqKuVChUsrKVw+B09Mz3BucZlitowv5IblNLPLeexWGU03MEsSuZrKmfgKZ9fX+eTm3TwemSFotfLg0VN4fU7u7O1ldnqVhWATr0w1fBObbTYW8wWWCnn+4meHKJdVVuPlhgjsqpUYBiguhba+MGFN4/L4Iv6wl4lojGKuhD/kYSaT4fauDh6enGAyeZQ7e3rocjv50skz3DM8yM/OXsZsM7G/p4ugzcZYIk6T3cH3nznRsEgSBS7rBjfu24DDbmIxl0PVNbY1h5jJZOh0u/jKTw9TKNdpb/eQTeaJ9IRweqrkM0UW0mnMUqMUDQLbgt3MpQ/zp/tuQMfg4bvv5tOvvsLo5BJ1VSMScfPOvdt57MxFzp+dJd1fwuuwcfvl72KxmfjJW9/JH2y/g7KW5tbvfxur3cxXb7oZj2kJQRBZK2Wwy0oDpWky0WsKckvbPhTxnzIyhqGiGuvU9QyleoWCWiZVLTWCRhrm2ucSz/GeW/ZwS3s7ql7n0emHuZJKsylwiV3NrVxORVEEkbd07Xp924rYDMCDR0/x0LHTZBN5Ir0hWp0u3trXQ7vTwSNPnSboNeMNOFlbybC0mGZ+PsUF9wwAHxjazNG1WQ60bmAsPQ9As83JDvtuDFR2Nx3AQGfYK/GNS6PsCDWxf2s/E7EEutHIru0Jh7FICqIgsCvczIPHz/CRa7YzmkyRqVZ428gQK8USsfI66WoVUQCHTaFWqRGLZji2ts5berr5zvgYZlFCx+DXNm9GFkRGkwnOxuLsbQlxLPoUZVXlW5evcFtXJwCXkin+dO8tPDB6jE3+47T8nK8mmBj23sQR2zJvaO3FbxkGdOp6ggOtrQx7vZyPR9kdipCrVenyuKnrOnbZy1Nzp/jMjvfxe689iKYbDf9ISeTLR05w+AOfYP/al7llsI+PbNjKswtTzGayaPUGQ75a06hrBitreb72zjdzaHmFw1emKWRLuLx2fmf/NWwMNDGTSTOVXUdEwCzJjKfTdLndxEolhv0+joxOsprPs5JIs7OrnRaHnelUCl3TEQWBd958DT87cQG708pXz53n1dV1VvM57urrxW0ys1Ys0eN28dz8AjZFIV+t8v3bPwTIKGIz17a8wssrC9za3sv7hzbzawefocvj5fm5Ba6ML7JpuIOIy8W55SQzsSxmqwm12rC1q5ZrNAU92BSFbLWCIAiYJQVREPnszw4SjPjIVavY3TZCkogn4KSUr9De0US+XEESRV4bnaJSrFIIelhciPH5+WgDBapIHF75F/HVL9z4DyADnQL6BEHoAlaAdwDv+mf7FIR24HHgPYZhTP5bNvpLEWg6FAt5tUasXKY/1ETsagr+/bu34bdY8ZpNvDA9w4H+Hn51eCMfeeFZTp2YQTfAbCvgD3tYSmXY3BJmPJkgaLNhURqrrO8ePslXf/oK1+4Z4vq2Voq5EhOpFCuLKUyKiCAIaLqO+Squ6qXTV7hr/zb83WZubGvnkclJ5qbjqJqO3WFG13SmMhk6XeM8cOkS06kk8XiWYNBNr89Ph8uJLAious6Ry1N89vYDtDpcWGWZPz5xnF978Xl+cOfdXEge4qHRS0S6mnB57WTiOX738CH+/uY34zM7qel13ti5gbe9cZaHf3IGvWbgcivIciNj6PZYKeYrtPWGCDod5GpVXjo6SqjVx937t/LDQ6dwB5zc1t9LtUcnW6vx6NgYuqZzY3c3b2hveLM9dNObORWbpznczYnoFb45epljr07jcSp09IfYEA7x8pkxuocjqLU6X3n4SEM1LosUryrJBZeJtpANm91MLtfAiCmyhCwK7Ly2h3KhzJbuNhRRYnlqjcFNrQTXM6RTJdSKSqlSx5TINyyedg5w/NQEbruVTL7Ekek1MtkKn33XrXw5+xp2p5WhTQ4mRheJp6u0tHlJx3JXS+UK4RY/Pzl6jomZNOGgFafTwkBP4zOCABazhM9lppyv8N6bruGHJ86jmGUuXFrH41SwW2XKFQ2nTcLlsTK/kKGr00sxV8KsiJQrGje2dfFsxxyZZg9bQmFOra7wyZef5UMbN3BDa4R/HBvjzq5OTq2t8undu/mvssKzhy/S2h1kQ2uYja5mcrUavV4vqq43BHA1le/c9WZ0w2C9VEA3DLYGOtENjVQ1z1PzUzgUhXt6AtQNnecWFjh8aKqBY7PKdLnc9Pp8TC9GKeQqJDNV2iINGxBREqhVVLq7w4iCwJnZRYIBN7GlJPFYI6B22hVcdoWWDj8vHRkjUSrx8W1b0XSds/Elhvx+bIrMHZuGqGo6YbuNP3rzjURLZX5z325ms3lWC0V2Nge5nEzzjffczfv+/lHq8QrIIphFEAUkq8xvv+sAb+3uosXuZrmYpdcd5HMnXuWF1TS7ejtxms3oBuxqbqJQq1Gp1xlLpdjeGsFpUvjkDXvIVKucWFujWtcwSzKHjo1it8qodZ18qZENOX15hjv2bibicKBqOhfjMRaTaWbiCcwmCbNJIrqWe31h5vTYkRSJ6GKCwT/9BB++bR+f3Lyfvzl/hK/cdDMukw1JsPH10Vc4fW6atv4whUwjmN/XEub46gqSJPK3t9xMRavzewcPUcqV+eK5Y6wV8tgUEw/fcxft9s0IghXNyKAZKULWnczkDzOWStDqcFE3CqyXLpCrncRlMjOXzRArV2h12KlpGjpQrKn0eDxsD3ZgkjqpaQtsDdzK1p9rS2t3FAhYXmR/y3Y+e/In/OaW3UTLDePy/z50o8rpWBSf30W1XieXbrTg5GpVrgm18+lXX8ZsljCbZD5y3W5Or0dRtQb1JZHO84OjZ3lhbo47eno40ApTmRQrhTwes5mIvYqARE2fZyy9yKcOv0JsLcWN7a10ut3UdYNoscDBpWWOv/IqD73pTl5eXiJbq7Gjq50hn5/doTDNVg/PLSxiGAZTmQTlusq58zOEmuxct3WQqqbx9JUJTJLEtS0teM1mFgt5crUqFxNJbu/ootfj4btj49zd28NsroGs/dxPXuTDN+9lKZclWckw6POi/+tVRj468n4AvjX2Xd43+G4UUcGpLPBcYoHTa+v85cHXePzd72BncxPPLy4ymkyy0e9j+9/9FoZhEOkKMjuxzsJaEYvdgiS4+NW9O9jV3Mzdjz4CV+k2xVyJSk1DFgWagnZaOps4tLzCRzZsZtDr4Xy8UX1qstmxyWZ0DB6fmeHunh56PG6qms6burqZy2V4eXGRzX0diILAgW2bOLq6yun5Rfb0dKK26rxt1xasskRnVwhZFJlfjLJgttDmbpDwgjYrFlnisYkJKjUVs0nhXUND6EaZ9dIafss6nz9xjHv6egFIVRv3ElXTMEkiG4bakUSRrc3NpMtlLLLC+NQSoijwpu0b2RL0Y5MV/vr0aa5vayNbq/GtsYuU6nXMFhML46uEOgJIsshatEA8WWLjtnY0XWdjKMSF1TU0TUdSJARBoJgrU6rUGzZpNjN/+d1D/14hyn/MEP79yUCGYdQFQfgE8BwNe6N/MAzjsiAIH7v6/teBPwL8wNeuZlTr/4MMKfBLEmgKgsiWQIiJdIZ+n5dkuYRW15nOZjkfbwR521sj/M7WPdiVZp5866/wQfsTHHr1CqV8GX/Yg99pZzQWxXHVoshlMjOdzrBhoJ35tQRXZpa5PLVEPlPiyMkrDaRVTSMRzVEqVHjX8BDQyP6cX19jY1Mz7Q4/giCQLTZ6/pZX86yuF0iupXl1eIXbujoZ8HqItTTwkzZFJlOtYpNlrqTSWB0Wbm8/ABhcSJ7GLMlsC4W5//hhbu5o45pwiGSpTM1WR7EoZKtVXlubZV+4m4Japliv8MXrbuLs6CzFQpVqTaO1w4+hG9hcVjYMuJBEkYn5NYq5EqFWHxa7GZ/Fwl+8/U5quka2WuOZ2TnO6hoD/gB+mxWnSeHZ+QWua2tjrZRixBcmU1vCpijkSg3SSlPYg8mscGZmgUSyRKW8wvZtfZjNElVRgIqGYZVpCtnweiwoZgW1qiIKYDVLlKt1duzpYuLiEq0dfs5OLZJPFwhEfAw1NWFrjTAaiyEJAhfPzeMJutB1nVePj9M5EEY3jIZqNOKjvdfM/Q8/j9tlplSosKEjQirsoaamSEWzdAxGGDu/iEeSkESRfK6C3SpjkkVqlRqG0eiB3bqrk9WZaCNboGo8cXaUSqnG5bE4WrlO+ud467F0hWiqglkRGZ9KEPCY2ba7i3KhgixIbAgG+YdnX8NhMvHVG2/CJMmcja0x5Atwe1cnryyvsC0U4tefepZmn5uhTe1k0gVEBCJ2G58+cpidLREmk0l0Xec9mzaiGwaaobPRF8Gu2NH0GodWpji2vs7PTl7iN27cy0cP/pSww/G68t7lUPAEXTwzv8BcJoM74CQdz+F2NnCWgiCDIqHVNVZWG551i3ON3k1vk5vsbIJyVUORRWqqxtRElFxB5cjL00iiyI5wC0/PzxOy2Xj43EVEUUTTdFbzDY9AiyyTLpRw261sbGpmOtOwxPrMK4dRTDK+LifZvIrbqWCSRWRJYNjnYbGQx2+1IQkCyUqe545ewOqwUFRVujxunpydZSWTZX5ijdI1Q4TsDhayGVqdjb41t8mELIiMhAJcTiTI5FU62j0kEwU2bm0jupRkYTHLN5aPcsP1g+wMt7Cay7E+H6dc1XA6TNSq9caiyKKQvpptMVkU3H4nbq+Dg/PzOE0mPjC0GVEQqWgq09lJDi8tk83XeOzAAdocAe564gd0uXz8+pYtHFtbp88dIq+Wqat1Wpp9fGbHLRjU+PHsef7k5HF+d4dGr+saKtoaFimIIEiErD6+tHCOqlZnyOcjWipT13V2hZpYKRR5YmyCdp+H1Xyefp+fkqqSrFbZHuzAMHRWSvMo4jLfHR/FYzazmG+4L3xu970oYoj3Dg7hN2/jSuopzsZeJmi18dDFS3zzljdzLhYjncojyhIWm4mNTc3c0dlBQS2Tr1a5ft8gXzpwEy6TnYB1Apss0+pwcj4e5/959Flq1ToHJYnbOjrJ1VTsisJqscDHXvoqn91zHTVN5cm5OWr1Bvnlj597hampBO2troZAK51nT08nT87NIYsCjxw9x5+/5RZ2Bvcgiz6q2iTLV78vJkni21fGMNsaQpkhv49YuYy9s1EtHPD6ObS8gNtkYiqdod/j4cWlBZptVob9PrLVKvtbIogC/EgU+fGVcba1RvjO+CWuj0Qo12v/A3/NKDtDIeKV03hMzUxmkvzw5AVi61kGN7Uzlo5zXUsvP5mZ5bmZGe4dHKSQKXHr3k1IoshNA308+NMj2FxWnl44woeH38hfn3/8aptTg4zj9NrxNsn0tDbR7fXR6XKwLdjMU/NTr4tVL8fjXIhFedfQIK+urLEz1Iwsitzc3sGZWJRPHX6F7eEWDh8dZ+O2TmyKwmgiyc5QiGtaWvjp1BTlUpVlp5NyvU6xVmN/WztlVSVVLjESDNLqtOOzWDi2ts517e08PTaJzWxCkUT+9MzzbAkE8FnS2BWFa0Id6IbBd8fGqdbrXI7HmB5dRhAEvH4720Mh3jY4yMGFRcLtQcySRJ/HRavDSamu8r4NI7ypc5iCWuaxmXF+fG4USRa5ZucAfquN2XTDUi+xnqFSrFLWK3itVgRBwGRWaG8Nsq05xOJCDFEsN1p4bGZuvHmQgw/84jppNsRA//5yIMMwngae/n+99vWf+/1DwIf+V7Yp3X///f8uB/efOR544Ev3f+LX7kIU6izm81gVhRafB1XTmc2k6fJ4uLm9jR53EFGwIgpW9rZ4ORhfIhnPYzHLNHsbQZddUUiWy4iCwNkrcyQzDfsIs9WMy2PnupE+lpJpDE3DpIiE2gO0twW5/5qbkcUgf3vkCWSrgmySWS3l+OHRsxTytQanWjPwOBU6B1uwmxTCDgeZag3NMEBo9JpqhkFBVTkyM0cpXybg08jWYqwXizxxaYykWsGqKDxx8QpT+Ryrq0mq5RrhiB+32UxFN2hzWWmxBzgdX6TV7sUftFMwC2joVMoN2opibrCS51bj5DNFFLOCxW5GFEW6fd5GgFytoUgSS4XGNd0baSFVqZK6yoa/obWVHlczp2OLHFpZRDd0yoZBWajh9jvJJvJUKyoOh4m/uO8OvvfSSdS6QaWggkNh2/YI/mYX+lX1t6zIKIpIraYhAL0dITxNTirlGrlkHnfAyfauds7OLZGolQnYbCQLJSKtfrw2G7f1dlOyCtjNJsIOB7LNxK62VgZ8PrzNbpajKaqlKquJDLIik82UMSkiK4tp2rsCCAJsaAkztbiOw9rAfi6slqhrBplslVwyjyCAy2PD6XOQWksjyRKx9SJuX0OM0xpx4/HaMOoaoihQqtQJ+a3IskC1VMMTdFJFo9Vhx+Sxc01LC1sCLcTKeb567gJ9Pg+5msq5aIyqphHLFxBkkWvb2lkq5RkKBFF1gysr6/z+nt2cjka5//r9qLpGrzuAWVJosm6iXI9yKbXMKyurHJmfJ5cqcOjiFOuxNIdOzxBPpPG5TJgVCQQ4P7nIsXOLpBN5XC4LZrPM9EIOq0nE7rBQKVaZnU9TypWxWSRqFRWbw4LHa20YLVc0dlzTw+J8Et0ASRJYmE+xZ0MndkXhmYkpLFYzN3R3cWNPBy/PLfDOkWEOXppEEEWGQ810ul2kK1VS5TLpaoX52QQ7dnXT1umjo81PuVgmnqqgeWRubO8gUS4x7AvzxbOnWE5liMfyWP1WYsUSJ09PMj/XqDrYfTZGAkG63G6OrawwGo+TV+tsCAYo17WGObNHAUHAZjdj6AbNYR/FbJGP33Ut7xgY4GwszuT8GqViFUkUcPuc+MMeMAxSySLpTIVEooBaquJwWREkgVQ8h91pZ184QrSc5WJijb86dYpzZ2dw2BS29bayyb+JdD1Jl8vJWqnAje0djGei/PWZ09x/7T7sJoURn5/xzAp5tcYTF6/w7dfOcDI/SUkr02QTSFbmmMzGuL2jhw0BPyZJJFmpsl4s8tz0DIv5PAOBAG6LhV6vj32RMG/t7cOuyEiiTkVb57YHvsXLyVXu6O5i2O9nPJ3i8Owc37zwKpfz47y993Yk0UaHcwC3OcNkJkVF05FFjVPr68RTObS6xq/fsId7+/qp6TrfvHyF+/fs4wMjN3IlNcf7f/pjfvzqBRKyzkwuz6DPw6HxGfZtGyReKvL07AwtThc+s5lEpcJ9fQNES3nWSwWOrq6SqVaJr2ex2s3YzCJqrU61XENRZKaXo0zGEyhWE/MrcboiQYZ8Fgr1ZY6uT2NTZCZTaZ6cmSaeLxJq8rK6lOC+HRs4urLKKyeusK5X2dkSYjlfQMfAZTIxnc2xnC+gSBK7QyHS1Qqno1GytRqLuRyIAm8f6OfHU1Psj0QwSxKqXsGpBP8Z/rOupzi2fuxq35+BTpWfzU5z8OQEPr+Nm4b7uaW9E68pTMCmM57J8NKVKVx+J+OzKyh2M9lqBdEq8bvX7uGGSC9mycPvH3yCcqFCtVSjuT2ApEik1jPUBINbervYF26U5B+bnGQhl+NDGzZyIZFgPZvn+fMTtDf7ua+/n3i5RLZa5Vw8wQuvjaI7ZBAMREUinc6TrlXJqjXcZgujC6soZgUNg1aXi5qmUdN15lbi5NNFgn432VqN9wxu4h8unSfkcCArMtdEIvxK/1Y2BYIkKkWssswj4+Nc29qC02QlWysync7S6nKh2yQq+TL5XIXbtwzw0KmzmEwKXR4vn9q5i4Vcwzv1qbkFriRT3Nt3O7qRYb2U5WI6SdDjwiorfGTjRtbKZQxZZH4xgdWqIEoifc1NqIYOJok3dHRwcm2NmqFx9+7N2O0WcmqNdwwN8bOvHVy7//77H/yXUcd//njwwS/f/6sfvpN/0p//236+8Lnv/h8/p1+SjKaAItppstnQDTi/vs6WUAiXyUSmWmEylUQWRdqcLvrcDhBMBCwB/vD6/fx+7nnWllKUChW2DHUiCSID/gCXouukUyVkSUTTNExmBZfPgddsxh9u4LkAIgEvXR4PkuCmpi2QL9ZRolkWpmM8Wa6jaQZup4K5KqIb0D0Y5gvXXstkOk3Ibucr585hGAbbwy0YhsFKvkBN10iupalV6/ze958i1OanVlEpZIp0NDXEHopZQRIEFJNMNpknHs2Qs5lodbmJFouEbTVGfM28sjrDgdYOVgol5pai1Kt1ZFki5HVT1TREuVHyr6sNQc2OtgirhSI7mpr4wcQk17VG2BkK8bPpaWayOXrcLhRRpNlmI2RzoIhOJtJpboi0cjYWxTAMtvW2c25mEU1rqARFUcAsSVy/axhFlDh6ZbrBse3vwK6YmEwmmBpbQlJkioUqu3f2E3Y4+Myufbztx49RVzV8YS8Br5PXRqd4w5Yhnjl2gZniCl6/g7JFIVbXWE2msdjMqLU6brOFZntjVR222fCYzZwcncZqdzA9naS1BVK5Gm6nCfnqNViaTyKbZD75lhv4mycOMbK5g0hXhdELyzjtCpqmY3daMFtNjF1cpr3DR7VSg6v4RsOAUr5Mc3sAu7XUyHrVdPIlFbtVxtB1dM3gictj/Jdr9/HxzZsxSzLn4iucTzSyhPO5HI+MjZNLFdA1nbdu38iry0t87+WTvPuGXWiGzv6WME9dHqff3cKHNlapahpDvgCXUuv0uf2AQKqaZzydIlEq4bXbiBoJ1LqB2QSiKGC1NgKreLJEOl+jUKpTr2pgV8jlKqwtFwi3OpClhiH+2koGta5TKDVY8UG/QjaZf10h2triZHkuimE0hGfWqwzEZ86M8u592xvscU2npKpU6hp2iwm7orBjoJMmux2nYmIum2Mxm2U1mWZ2IkrfUIiNTc3kalV+9tJ5oslGtvyVV8f54IYNbA6E+eyJI7x2ZRpP0EUsmmNydJGaqlOqaNx96za8Vgtes4VTa2toho7XYmUtk+PKwgp+mxWHYsJrNrOvtY3vv3gcb5Ob5FqGpbkEd71hG3d0dpKtVtF0HYfHzsxMkkjYiTvQ8MjMphvoQOFqJl7TDZYXkoRbvciKxP5IGIdi5eGpKzz0wlG8TS46+kOszsaoajqXUuf5jU03UqqnCVo9uE19wBnOjM0xfOBODi0v89j0RcIOO7/zvSe5dvcw779lGJ+lAZAwiTKJSoEvnj7Np3ftYoOvlUFPC/vDPWSqBa6kfFhulwAAIABJREFUYkxls5yLxmhzOdnWFORvz5xle7iFAa+b/eEh1ktR7ti9ibPra5yOxrHIMiM+H/FSidHlNQwDVoqjtDm2Iwo2Op3X0ebI88bOaT538jBhp5P3v3GETf4WXIqV8cwq3x+f4I3dnT9nbn6J1fk4Hr+D7aEQb+zqZth7Exs+3oRNtrDrz7+MARwxmSjV67x/aARZlGi1+3ltfRabYsIqy7R0BsjE84iSiOPqoiCbzKOYZDxBF+V6nc6eEOW6xkOXz/DewU0kK2U6nC763B48FguXEgm+PTpKIOzhi6+dIJPIYXNaqat1/uuZs2iGzjUtEZoDNjKVKk5T47tZ0zQ6nE56PW5+PD1Li9PJbZ0dxMtlPrl9G4lymWSl3MBZ2qrMFU7S6diPIEika9NcTqVosdswiRKbgy3c3dvL8k15xtZjbAsGaLUPoIghdjZV2RmKErTZGfZ7+fPHDzI1u4phGHzsDY22j7c8/gM+tn0rPU0BziRmyWcrWGwmrA4rvnDDWunFhUV63R7yao2Xjlxh/94GGclntXJ0LoY/7MFtsSAKAoOeZsbSUeayGZzuBmygq62ZbLVCfKWEYlbIVCpkq9WG9ZvLit9mJVdtJC6Wc1lmpuO0Rhrzik1R+JNTRynWalyMxkgWi/za5k1MZtcZ9vawJ2TmyOoMh16c4EOFCv3hJjY1BUlXyiiSSIvbRdyUopavcS6WQDHJ1HSNt/X3MeLdxVPzj1BQVV46P8a+DX3U9XV+MnuFo2tr5FNFckYBs83E0bVVjs8uYBgGmYJKx1XVesTh4P3DQ9SvCnq/c/gUn77jBnxmC4NDI3x77DK9Hvd/bODy/3sIgPiffRD/pvFLkdF88MGv3X/f+/fx4tI8FlkiXioxthplpLkJt8WMRVbIVSuUtDpbAn5k0YkgSHQ43Xi9JibLOZYXUiSSGXSbhM9q5fL4EmazTKVax+6wIIoCuVSB1VoJw4B8uoDL78RlNdPj9XB95AA/nHmaM6urmCwKqXQZwwBVM0hnq8T/8g/5wtMvI2t1VIeZiMPOxkCQyUwWh8nU4FJrdTKVKjZZYXK+cYOv1Q3UcpVquUZLdzPnzs6RKRYx20yszEZZXs2SL6gN5q2qkdFrPD8xw7PLs7ypp4+gtWE50e12MVctsZbKUFd16qJBWVUREHAHnNw0MsDbhwbwmM10uZ04FRPbm5t5eHycUl2jyW4nZLeRq9UwSSIvLy1zb+9GdFRqepXHpqbp8bh4amaGaDbPto42nj08gVquEWr1MV/IEysWiZdK3LN5A0G/53VvtU6Ph7JZIBHN0DnYwn/Zcw23dfTgszh5ZKpBwJFlibXlBLduH2E6naJuGPhDnqtcd5nlxTSZZJGhnjB729p4z9AgpXqdU2trPHl+jJosUhENwkEvsgLZdAmbReYr730LgbCXmgBtrQFy+RLn5pdZjxZAa2AMK6UaI1s7iK9nsVhk9oz0kquVqVVV5hezdHS40DSDXLpKulTHroAn6KJe18DQKVe0BupsUyt729uZiye5b3iQqlZnKpPCY7bw7Pw88VIJVTeYW4mztpympy9MvFyiy+NlYm6ViWic6/q6aHE4iKs1djQHCdncdDj9KKLMcwuzhO02Hp58lW+OXmIimWYmlsBpsdDbFuLclRWiyTKabpDOVZFFoaHYl0QsZolyqU5Z1anWNLxeC067Qkt3M4IgkM8UCTU5uGnfCJfGV9E1nZqqUS41jOl7hiOsLaUoVBrUK6dNRhQF0HUGOlvY1dpCXRRwmEws5wssxFP8xo6dXEgmmEunmUgm8dtsTK7H2NAapmyotDb5OTvZwL5m00Xy2RpIApIo4Ay5yKkVWhw2Ti+tUquoJFMlqqqOJAr89n0H2N/SQkFVWS00MlSr6Sw7IxGm40lCAQ8DPj9eswlRbDDSR5fXmZ1NkSk0zun/vmk3fquVV1fXePtAPzd0dnChmMThsZNczxBdTmI2SVSqDT9RQRAQRYGWNh+3bhrkD6/bx/G1KF86d5Jhv59DF6a4c+cG9rS1kpQ1doZD7Gzq4NvjJ/j0wRe5a2AAt6kXhyzyhaee5nfe8A4uJqd46NhpfnrmCn909018aMNWBr2DBK1BNKPMeDpGpV7nju5uPvH0c2wM+5nKxonY3fgtHfR6Bml11Hl0cowPbWiw4X90bpTx5XU+vnsHigjfmxjlzd3d/OjcZQJuJ1ZZRhIFLLKMKkBRrREtF6nrUbpcgwCIghmzFObmtp3c1rGXblcHNtnJpdQELy0vMez3ckfHztf9JD956Pts7+tgKZEm4HXzrv43IQpm3CY/H3j+H1mPNsqbPR3NdHvc3Nl5N4nKLJla8aqopEipXqegqWiqRjbdgAWIGLj9TuxuGzNjayyuJNjc18YHR0bYG+7Aa4nwtQuvcWp9nd/Z9kFCti42+bvo9Rk0eV1M5zLcu3UDl5bWSMeyaBL8wb49PDM/x5ZgkHxd5dmZaZocDiIOB3O5HINeH0dXV+hwexj2+ZjL5RhLZUhXqwz5fCzlc9hMRdwmB4ooUanP4DFv51T0HC8vrZBVVVqdNjb5bub6VjvfPH0am8PGDZEBJMGFLPrZ2dTG84sX+dnFMeYXMjQ3u/gvt99AqlrlocMnyCbyZGSDv7vxTr5x4iSiAMVcBUkSqRSruP0O8tUqU9ks25ua2dgX4be3X8NEOsbDo5dJxXOYLSbeu3UjXnPDAsugwUe/sLqOYlVwWyy4TGbcXgfFShWbxcSlmWXKxSoYIFsUNjUFuamjnUuJJOVCsUEvM4lMrESZXo7y8D13c0dXL+8Z3kKf+3pcShFJdKIbZeLlHC+OT4GmoyoCY7E45XyZj+7cym2d3USFOslcjrpZxCTL9Pn83NLexQtLZ3nglROEA148Hgcvn7yM6IaarpOpVFmMJSkXq9x/2wGOr62zlslRypXJZMrYLRLv37sDzdDZ0RwmUSlxJhqjKeBlKp3h0NIiG4MB3tDaSYvdw19+4ZFf4IzmV+7/0IffiPC/+Pjjz33n//g5/VIEmg888Lf3p7d72BwIcHvHIH6rwqd2X0/dqBEvN+w4JlLJRlmuVmRbsA8wEAQzw94Qv7JhhJ+uTDE7myKfLrKeyqDXNbS6Tl0zaI54MQwDX7MHAagUq4iSSCTowyRJdHs87GraxPNLp5hMJAEBydAplFRMSmMiemp5jPVokUpVQ7PDTV2d+CxWej1utjQ14TQpCIJA3TDodrt56fw0ytVMm9kiE+kN0eH1YlhE/AEXNrMJQxLQKjVKlTqVah1D0xqlTZeVfK7EhkgT3a4gRa2BiXOaBVa0Kpl8kbpaBwNG2lr4+JYt7Gtp4ZWVVXo8LkK2hgWNXVEY8vu5nExydmGZe4YGaHU4qNTrTGUy7Iu0sFxI8ujUFMdmF3jm7Bhen5NMMs9QJMSJywu4HQpun503D/Rxbj3K+dMLnJ9dJGvUyJcr2CxmAlYrA34f920focXpIlur0efx8UfHXsEiy+xoaeGlY1ewOy0sZ7KYLSYqVRVdN/A3e6iUquiqisUs0dTkZTqTZrlY4sz6Gk6zhdVYinOXFpmdS1ErlRrKTN2grum8trhIXK1iCHD+v7H3XlGS5NW57y8iMiPSe1smy9v23o/v8RaYwWnQwMEIjrwACZ0riYUMQhJHAxzhQcKJGWYwI4bxPa6nfXe1r6oub9N77yLjPmQv9HCfxLm6S/cs9ku+ZK40K9Y/dn57f7/v/AK5TBm3306jUsPttzM3l6Szs41DOnxwEyWtyfTcOj29fjLpAs26ysjmbqam42iqRkfAhNmikEsVicTKOKwyDoeBWl3l1q0jFOo1hrwefjg1icdsZncgSK5eY9Tl5GIiwd7ODt66NIvVbuLeTaPYFAMdFjNr9TLdHR4uJRJk6nUeGx9nwD6KWRfkhdXT/PDaFNOpFF977STPn7zGxMQaskFD0utYmF2nr9tPpVnFbtEjXDcslKsqaGAy6ChVmjjsChajji07Qig6gW0b+0llCzhsZv7xgTv468Pv5v7+LfzTW0dIZus4bTI37dsARpG1ubaa2RG0IUsajWaLar2tLCpuA902G6v5PKVGgyGni5v6epjLZji6usryapx6vYFilDnyxgy7x0M4bRZUrcXk1DqteoPuAT/NWo1qXSXoMVKUWjSBj23ezc2D3Tz+i+PoJYEHbttGLJnhjk3DTKYznA6H6bLZcBmNmBSZc5EI9VoTVYQeuwOHInMuFidfr3NwuJ8Hd4+zeaST3l4PTQ3UVot7+wYRBIGfzs8h6iQWliJEogVq9RaNRotytUmx0v4z0RNy4PBaEQWRjR43d/WMIIgqTU1j80AXI04HLkObZvHlMxM8vzJPsdmky2HnbYMHkAQbomDjROUqXzjxIhfDUcrFKr39ARS9TLSc58+OvYheVyFWLmDS69gf2IgiCWRbdR4Z2ke/rROLfowfzz9LvZWgy+LhvaO3YNar/ParLzF5ZZ2D24f5yMbHeHP9JOVmk0Md/QwHnLy1vkaqWuWluUVmM2n+dO8enrh4hVv7ezgZjbE3YECW/j1uUhDE6496WlqJL18+ypH5Bca9HuqtIh5jDb3o5ysXnuPvbriZK6Usj99wJ4oUAuCxlz7PC69doXfQRzFXweO3o9fpOBaZIF4pcXv3TejECn6zQrZe57k3L7EaLmA0SNx542Zu2TjEiUtzKAaZj92+nw0DnRyfWUAxG3AoOlyKjtlckhGXixOxi6wUZ5nNLXB79508PXeC+wb6eXJ6GqPFQDyaIxkv8sq1WabnovT1+Lmpq5OQ3cZT5y6RaTUYdDp5Kxyhy2ZFJ4qoWosNbjd2RWaHz0e+Xmc6k+E7V69QVmts9/Yxk1tEEtPc0HEnr6ydod9u46WlZcxKkl6rnxsGggw5HHRZtgMQKZ/km5NHubevj7fiEZrlCt9+70NcTaX5/okJUvECiXQVg03P1VySw+NDLFWK5DJtY5mkkwh6HdRbLeYXIjx/5RoPbh5DluBHs7MoOj2YdNw1PsLhUB+/+W8/ZzKXYdjhwCzrOba2TjKawe9xEC4WULUWdrORQq1GtVwj0O2hXmuwty/E2wcH2eYJcSEV5aUTs+hQaWkahUyRn3/wUeLlIgP2Tow6H5JgRZYCSIIdvWjjyNoEa1rl+sqWQK1cI7qW4fjyKi2zglWRGez00223EbLbecfgEPl6lRPRGKJBh89sptxscOP4IJOpNNFSiY1eDy1Zx97h3vak0O8j1WywIRTk03ffwKM7tuI0KHzl7ARTuQyHOru4rWsYr0nPeqmMUa/n4aGNtNBQRD2f+6sf/pduNP/bh+79D7/u143mr1ifefyzn04MmchpLebyKXYHgry6tsjZWJzT4XXOzS4Tj2a5OLnGG5fn+enKBB/YugcEAVEwIAoGHt00xo/nLtPV5/2lC1nTwGSWaTVVdHodXW4H83MRpmdTFAtV0qkcqWyROzcMMeby8+WLbxKOZjCYFUr5Cul8HVFoI36i4QIdfhPdISdul5W6BpdTSSKlErOZLOVmk3ytTrWpYpH1vHVlEQSo1Vu4vBa+cf89PDq6jcc27+KmUJBYtcrlhXUeOrSdP7ptL3dtH+Hpk5OUKk2ikTxr4QLPXpxG9srM53Lk66W2SqGXwaBnvCtIn9dNsV6nrrXI1KrEKxV2+vwci0QIl0oMOZwM2L28tLKIxagQq1T54rNvcM+WEd6/YRtv+9GTFAWNbquFDoedWLOGotfxD3fcyg8mJ9m/qY+WUSQdzfLI1o0cXVljaSmNy6aQSZXwdTpJ5Ars6AyyzetlX6CPvYE+nAaJf7p0AUXXjvH77isnsDpMGC1GTFYj9UaTPzm0j7ooMrWwRjKWx+W1opd1KGaFD2zayE8vT+K2mrm2HKGzy4Ni0mEQNXwhDxaHmW3DPVRaDerVBofGBjk1MUM6VyMQsLJ3pB+nz4pe0TM6EKAuaPSG/LxxfJLFpQydXQ7ikQy5XIVsscHsZBxNAEkvUa42KRRq5EsNanWVdKZtwvK52qpiulJGkXQ0Wy3sBgNbPD6MOh2KJHFkeZmLkRjbhnrY1tuFXZaJlkp8auchNgRcaAjc1hPigf4hjqytstXj5HfeeJLvHzvHUIePV09eIZutYlQktuzuJb6eptVUqVQazCxFqZQb6CWBdK6O/jrGR21pZAoNFL1EMGCh0VDRVJXbtm8gVirisVn41J49bPP2svXzf8O2fgfPX71GMGhjNVKk2qhQLdUolduqer3WJFNo4LDKtFoaDqvM2YvrLGeSGGwGWprGVp+PY+thYuUyAy4nFUljb28P9w30sWO8m5evzbK1I0hT08jXqyQSRRwuM1aHiXy2TLDTSY/fzd8evAWL3kCuXubFhTl6hwN85sABXlxe4vjcMhu7glgUhYDZxG+ObeKRoT2cS6wQcjuJFgu0gGi5xM5AAJ/RiFmvI1Iq02Exc0uom+1ePweCw5j1FjK1HEfXw+RrVT5/12F+fnkSnSSQyLZ3NgNuI52dVix2U3v/WVHYFQzw34+8iElW+NNvvcjwoJ+ja2uEbDY2ur385PxVouEU8XyRgNvOPb3bkAQLqpZli0/HS2vLmIwKZpuR6csreP0OHhocYiqdIWSz4TIYUCQdnWYfF5ILvGNwM0bdKJLooNlKs9G1k5/Mv0WkkiNRiYLQ4t2jm/iLO97HZp8eo67Ibzz5IxYqRUY9dnb7exEllal0mm3BIFv9Pn6+sMjH9+5C1TS2er3UWzV8xv+n0QVAFMzc0rWFD2y6izfDZzHoJNaKGfKNZd5YW+NfL15iYyDA3b33A/Cd6e/zgyOnaLU04vEiXp+FBzaPcTgU4obObm7s3ImIwpn4FZyKgTfD6/SGvDyyfxOy08haPse1eJLubi+Lc1FW6yUuzK7yp3fcSKRU5tXVFTK1IquFIr+YvMbTRy7yb2enqVpF7MY8H9xwmE+++QvcRiPnzi6g14nI+nbj/L8ee4B7eofRiwKdFiu3DPbx48lpphJJ/se+PfTYbKwWinRbLKwVS5wIR+h32PEaTXRbLAy7nHznyhVeXZumz2HDrNdhlU2oZNjs8TKVyXBzd+g6nH0rZ+NX+eBz3+ap+TdxmWRGnS5ORaM8fuM7+MDerYQsvYhimZuG+nhmYhKf38L6ShqX384jIyO8ODePxWaklCvj6XBiURQikRTxaJ5yucHr03P89Mo0y/E06WKJvf29HF9Z4Xunz/OObRt5+eI0RquJiVicmYUwt2wfp9nSePvwEA6jEYNez0avl+lIgm2hTi5PLVOQNYZdTmayCX58dQq/34rNaSafLlKpNHgjG+XR8c3M56I4FAFFCtJsZRAFIz+cfYrziSRnJmb57Zv3cf/QILt7OhEcBpxOCyu5HHaDgZu6OlEkEYtez1QmjSjAVq+X+/oH2ezxsMnjptJs8sjwCCa9RLHe4KauLrwmI6qm8cOpaS5eWeDtWzdQVpvYZJl0tcrezg4OdnYyaPdTaJT57vQUVr2eXpuVMaeXifgaNlnm8c8+/V+80byP/+iO5l995ju/bjR/lfrsF//u05/65Nv50YkLXFqJUJZFNnncPLewQLPVotFosrScod5otQ8SVSUQlBlxOhGEdnyagMREMczM3Dr1ehOdJNFUW7i8VjStDXavCxrrK2ny8QrIIkZFQifCh/fvotHK87lnjiBfH/OmUyXKVRWbWYdeL1GtNHnjTz/Ie7dupsdupdBoUKg3kCWJWkulWG8zKkuNBopOx4W51TYXTBb5wC17ualzAL2oQxQUJEFjKpPg4nKYLp+LWkvlWiaL3iljMEjUKnUK+TpWs56R/iBBs4ltPh9Bk42G2mS1WEQUBIr1Giv5HM2WRsBs4p7ePsZdIS4k13mof4hOsxuDJHNrdy9Pz04RstnYNtDNDV1dLBeyvLa0zFo+z3wuy7DLzY5ggHy9wT+fnMBhM7OaSLO1u5MDw31s9HhwmIxcWlml3mhn6WaTBTYNd6MCS/kCCA2+enmCcKlIt9VKplbjmbOXaNZVPnzbfnSyHqfRwJWrS1SMOvrsNgpii5ag8Z492zgzs8yh0QHeWlunKWhs8vm5bbiPBwcGeWl2AY22e7xRa9DSCSzORuno9TK7GsXqsjA8HCCdyLGwFmdzfzeRYpH9XV1cXFhjYS6K0aBrN40BG+V8O/5tbMxPs96g1mi1WXettulLEMBlU9DLIjaLHpvDxHIshWbQcXlqmZHuAF1WK9u9nSiSni9emGB7wM8mv49Yqcz59TB/c+hW3jZwDxoF3IqRMZeLcVcQp+LiyOocH/v+U8RybSTIQixJPFEiW2ggiAJ/88CtnIlEMZgUgj0e1tcypLJ10rkaTpuM1dTeMTYqOnQ6EY+jvUsr60TWIkUShSyZbJG3b93ADZ39NFsqLyVX+dejExSKdbxBO8VcGVkSaDaaOFxmsrkqtevpPalcjaDfSjpbpZSuUayrCK0GFrsJp9HEsfkl/nj/Xo4sr/zSDTyXzfHi9CyR5ST7Rvsw6PRMrUaoVhoYDHp0eolGpUY2U+GdezazxRNAFASWCmk+cehG3jO6ibrWYKZUYF9/D8NOOw8MDDLkcKBqLURBRdFp/GxmhpaqsSngw200ETAbeXFxie89f4qyUeNAZye7/SE6zO5fng0uxcet3Z08OLgBVWvwrWNnaTRbNJsaQa8Ji82ITi9hdVpQmy3+9sYbydVqTKXTHDk7STZf41vvvpePbjpMl0XP3587w4GBHlZKRWxOC3pR5N7+EHrRjygY2fv5v+D0Rz6Doi/gt1jp7PQw4nJSbzW5u68fv8nMNk8Iq6wQryRJ16oM2w8iCCItrUa+MUOpGebGjrv42cJxXAaFDrONPzt+lK9fep3f2/ZBVC3FR3e9k3cMd2DQSdTUBuPOAKdiYVSthSK1sS+XEklu6Oyiy2LHojdi0bch5lX1Ko+++DUeP/1vfPnkz3l2/SRb/QIeQzf7grsYdW5AllJIosCejiADHhe/u/kwDS1MvrHCj+euMbnYDocI9bo5MD7IoMNOl9XKuPM2RIyoZHEaRPKNMtt9PlLVKj6jkb3BANv9fvKqSjjXHo0m4wWcHgt/d8t7sCs1ktUqHRYzBzs6eWV5mVgkh8sms39sAL0oMmA3IUoa56JRBkI+7B4L5WKVSqVBVoGgxciVVIIfzcww7nJxc2+IbQE/f3XyJKIoMuJ0MJ/LU2w0sSkypyJRco0637x0CZ/ZzM5AgHeNjOIzWui2DNLSMqwVM+zybaTXrqPXMsB6KYzb0MNfnnqWW/r7OLe0hsVs4lo2y2Iuxx09IRLVHGulGAZJx8srK0zMrmI0KXj8NlSdwKDTQYfDTk5tMjsfJ9DVxp4lw2nqjTYjWNMgk69RrdTpGwyglyTml6LIio5n3rjC2Hgn7x0bY4PbxbLaZlK6TEYUSWI+m+PEtUVWSwVGOwMs5bJkUwUkSWQs6G8neKWS3Njfx1wiRTpRoKm20FSVp65e5aO79xCv5vnSpRd4cvYkuwM6TsfCHF9fY+9oP+8aGSVcKrQTqzxuemw2nj17leVEioVaiUOdXRzsCOE1GsjWqvx0fp5hp4NYpUSP1UWf3UmkXKDZanE8HGa738cLSyt864VjSNcT5BqyxAfGt5CpVcjWqphlPbFyiR/PzeA06Nns8fBQ/z66rDK5egmP0YRLsf7/QNG8n/94o/kvvzYD/SrlMhgoN9r8K4BXJ2eYSaVIZgvo9O2vGPCayOaqlKsqzWyVr5w7z63dg9hlGwACIjZFQZTacVSapiHrJZoN9ZfvE19LkSk0QJGQde3nKSaFoNnGxWSERlPlDw8fAuC54AIL63EsDjMhu4PJ5XXssoF6q0m+XqemqriNBnqsVkw6HclqlZlMFosskyyXUdV209Ld7+eRoVGMOgMgoNFgIR9nNp2hvy9At9XSzgQHIuEU0XCuPRIVBEQRNnncPDs/z81dIXSihE5sf8+fn7pItVTD5bezq6OTeKXC+UScDrOTB/uH8Rt9SIKTM4nTDNi89DmcHA51E7I6yNQq/NvCIgGnHYss0+tw/NKFmatWCfidbPL5eSNXxKYo7AsE+OATz/D42+6is8+H2WwgHkmjGGSOvHkVnU6kd8iPSaej126jobaYyWSpNJtEwzn8ARtXUymWs1kcBgOyUSZfq15PqzBSdFt5c3UVg0lmNZ+j0mxw79AQx1bXmE2n+dqbp6nXGlRLNVqtFha7icW5CJ19XtxWMyuzUXLFBh0BC/lCDU0Dp8FALlngG5NvoZPaEHlBAK+jfY2kczVaLYiuZ/D5LWQKafwuBbNRRzJbo7vLjsNr4+rFNURBoFapc+/+rWz3eQkPlBi02xl2uig1K1j0RtLVCu8cGWYh1/78AbuVdK2AXc7zvenT3Nwdotps8sTsJHpR5IXpGWp1lfBCGlUDRS9SrauYDBKKXuRrFy5ic1u4eHYZl02mWG5ikNvs0mpdRVU16s0WVpMO3fUbUaXSaKtzHiM3bR/jaizGxUSCfcE2GLtebxKOFhAEAY/LRuCAg2S+yMpsjFymRKWmIutEZJ1IS5FYXM3R6TUS2OrH7raiNlXS6QIRRxE0jS9OTLDF5ydeKZOpVphJJDl1cpmRASeJSgWv0Uh3yIdibKNodvf1IPb38tyxCzx9eZJktcoWj5t7esZJVvPM51L87enTXDm/xNtu3cFvb9lNpJxGEkQEIFzKsMndBowbFT0mnY43V1Z4dUmlUqrx8O27cBoMvG1gKyJmNBq0tAotaoiCiiBIhEsJXlxZ5OFbd5Eol+mwWslUKpy+toh8/aa2u7uLPqsfp1LifePjnDo7g8Wk5wfXpvnrvbcRMPnosV3krt5e3j+2mZ/MT7NSKFJolLne/6MYZXSih/v7NvLa+gwPD44ymY4zlU6zXiwx7HSQq1fpMFuJl0scCm5EENoGrHz7DG+WAAAgAElEQVT9Kg55M7XWDBoqf7B1P8ci87Ro8Rtjo3z69aOoWpE/PfESd/f2ss/fR4dpnHhlmppa5/aebnSCyFqxSLRUJlYq8oXzE2zz+/n9Le8CoN5a463ILK8fmyLY6SDU5SVfq/Gxl1+iw3aSQ11d/NaG99JjbZ+H2GGT6yLRyhpPzExyONSOqy1X22ENsfUMix47H9i4ge2eO9vnbXWCpXyS7d6tZKUiboONO3s0Xlhe4kIiyS6/j3v6ennP6Ainh2McX18nX6vx2vppNA2GHHYOBHtoaS22dXYQcjm4sauLu3oHMesMnE+sMuiw89DwMADn43ES5iylYo1Lk8t8qV7nj3fv5ouvHufu3l5u6BxgLhdjf1c3DbXFD6emmV2KMDeXwmrWMzAW5JWTl/n7d93DJ598jmiyzMGDw/Q7XdzQFeFAIMRiPk/OF6fH4kOWeriYPM7R8FOIgsD3j59jtK8Dk17HoY4OcvUax6MLBM0WbLLCX5w4Rr/DyeZtfZj1erptdl69fI35XJ5Pbn+Qf9CeITyS5fK5ZZw2hVKlgU4SsFgNNOtNmtfpHufPLNJ161Y+eecNxCtVvpE9Tr5YQdU0rHo9LQ1SlTIAmUqVVKWMbNCj1+s4fm6aTK5tAHL5Bb74yjHec2AH6VSea4YkOr2EySxTKrafk08XORFd4UIixXIuR6nR4Hdfe5H1TA5JEvGYjBwLr/H1MxOUcmUMJoVcqoCn08Xunm7cBgMug4Fuy0F8xgXStQs4FAOnolFu6e5mPpfiUjLJvX0DXE4mGXA4eWZ+kW6rBU/QSbVUQxAEemw2MvUSr6yskqnVeGt6jkqpHRN9NhLmYHc3fqOVqUwCmyxjkxWquvr/bmvyn1z/CSDN/6T6P0LR/NY3/+nTj//J+ynoVa5F47RaGg1BQ22q5FIFbtg4zK7BEMlWjXq5yu49Q/z5/v302do3HY0q35p8i28+dwwRkPQ60DSMFgONWgOdvn2AV4pVBATMJj06SSSdr2OWRT5yYC8nouv85NgUFr+FrT4PNoOBaL1CeDVJrlLhd2/YS7fVRrXZZK1YQJEkrHo9NkVuZ60K7Tgpk07HZDLJ4koSSRQY6A/w2Hh7d0cUZKrNMr/z6ktEi0WK5SozqTRusxlREJgNx1EbTdSWhsnYNmMUTQJXLi9icVvpsJjI1WvUVRWX285wT5C7RgYZd7moNJt0WS3M5lLs8A4iCEbKzShLhTSJSoFbQyH+eXKSxXyWXf4gP5qe5t3jo/hNJmqqigYcuTTNhq4gjZbK0YvX6OvyY5FlymqTE1fmefrMFcYGO3n85ls5no6Rz5WJRAtk8nU6O22YjQb2BQPM5fL82/ELRHJ5dJKIzW1BkfVcvLBAtlDC6rQQT2SJNWuMebx0WK1MR+McGB7g6noEj9VCqlJlMZJAlCUEnUiz3sTmtqIYZXLJIrOLWW7cOciVqWUEDQIddhp1FX+nk/X1PGemV2jU6sh6iUKpwdCwj0DAyupajniswKZt3fT0eVhbSSNq7Z3E3j4Pd+3ehOxSqFUblPIVRK3Flm396C0Kf7RzJ41Wix6bjReWVzgTi/HE9BSDTivlZosT4QjRcpk7ekOsFUvsDQb50dxZZEkiVi7xNydP8rNXzvPi2VnEZpNytdlORqmruO0yjYZGta6i6CWi4SyTx9eopqpg0eN3tfegiuUmputgcoBCpR0LKIrtA8vmMBHo9XJzKMSezg7OxWLs9Pt5cz3Mk89PUKy0m9SuHifVZhNJJ1FIF9A0KJabSFKb7OZxGZGuj+Z3bxvEYTbitlmoaCpXr63w0M7NiKJIuFhkNZ8jWS6DAKVsiUC3i0c2jDKfzeM1mSmhYjIp/MW+/ewLdhCTVMqtJmuFApOpNKrQoNdm58+PH2N5IUq9rlKkwYe27cMo6WhpLRRJT6lRp9/WgcsscT4WpwU4jEZsioGgw8ptoW4e6h/BpHMgICIggdCgqTWRBJHVYpwXlhfY6fPhNRnYEwxwb+8w23weNnb5yagq79swziNDG6ipDVRNxSrrySgCFeoIeomHBrbQ1CKslZL4TSa8Rhs/X5yj0mwy4rLhVETStVm+fvQkv3PoXsrNKCa9SJ/1RrotMv84cYyTiyukm+1o2Q6zhZDVhVnvvz52LyKLJlRypKpx5vPXCJh6MOga1NQmm93d7A35eWP9Akv5AnZFj1Ev8Pr6JUx6iYDJyaC9baJstBrsCvgZdDq4r3+Aw13DiIJEsTGPLFo4HZulbtHxwIZRdgT8jLk9bPJ6Mcsy8XKFphZmwD7QZrECshTALofY4TOxXEigai2aVj3FSoVMtkpXl4uHBocxSAUi5VmWC2lMej1mPbS0FlfSEd4Kh9nodnNbqIflfJ5Oi5WQ1U65WeedI6M8feEqPzt9maZJ5sGBITwGOzbZTb6RJVIqc3dfH0P2rXzlyqt86cQZ3Na2waeutjg2s9BmUjZVNoz1cPXyEmcLKVKRLKciEfb0BSk2GsiSQKRc4fT0Ii1Vw+k0UirWSMYKFMoN9B4TFoeZi5MR6uUSNQWStTq1VoOtHg891k40VKDJ/7r4Fi/PzvOLn15ELwvct30Du/x+FJ2Oz506jazXk6lVqTYb2BSF9WKJj23dwiavl9VikYvXllnIZnlkYz8Hgj38YPISuXSRptrCYtKjaRp6va5N/9DaEcQtTWOkP8AOv497ekfYN9zNsNfNG2thrqbT+Exmjp6Z4oFtbeNlJJXDbDHS0jTyqQL94x1sHu4imsiSSZWIqhXcTiuLKzE29XTx0QM72TkSoiBpRGMZTq2HsVhNeEwmOiwWDHo9E5cW8PodnL66wC9OT7G6koVWC7QWFoeJHp+bdw4PIwgC0VIJpyHHk7Pn+YcjbzG1HGao08+oy8lvPf1zDo8MUGk2uZRK02u18OjoJoYdTkSDDlUW+ejuHdzZM8DJ6Do+k5EnzlykXKgwPtKNbDGQjGVZymTx2s04FIUOs5U3w6ucT0R58Wuv/5dWND/4oft/BTPQP/96dP6r1De+8dVP/9ZH3ssev4eg18JEMoGs6Hn13Z/gsZ3j2BSRb5+/SK1U43u/+Q7eOTxOh9mJLOkREPmd15/hey+dpNVq36hlnYik19GsNRAlEbPNSL3aQBAEdKKGIou0NI3segm338TvHbqR19eXOHZlkUf2b0JD46tvnGS008+BoT6i9QrvGB7BolcoNeukq1UaLQ3xOmZBEkUOBvsJl3JMxOJMTC1SqzawWxXu2jzGgY5Q+xIRZCLlONlGg61+P6+cukopV6a/249V1jPc4ScjNMkmC+h0IvWmxvpaGotV4eJKmLIsMuRwcPF6OsSA3ca4y4VdUZAEgcupFCNOJ/VWhVQtydevXKDUaCBLIrt9Y2TrGX587RoOg8JUOs3+YAdNrUW0VEESBfr8XhBgYmaZK5MJGtUyD23fwAN9Qzy3vsjScpZEIktHpwsEkemlMIVinV17e1GbKj6HlWvpDGv5An6vnY/s3k5P0As6iemVCM1aA4fHxkf37eJKJkU+WyJSK2GVFbwWC3uCfswmE4V6HUkUqQgtfGYztZbKzFQUGg1KuQqSTkLWCeRKZbKZCv5OJ+M9HYTjGW7ZMsJqNE6x3MTjMmIwGyiXaty+ewOTM6uYDBI2S9upvDQXwyBLxNI1BKCj28GBnm4mVsPUKnU0TaNaaeILOOh1OlnMF6ioKuFSiQMdQZ66OslGv59rmSwPDvRzNZ1mV8DHTn8Qv9nIbCbDk9PT3NDVyc1dgzw1M4nLa0WWNJbWC+SKTZzWttonAEaDro3xKDSwmvUUayrk67SsehRZolBqUk/VEAwSer3URvLIbY6mQZawWA14O1102e1s8Xr48qmzLM6GSeo10tUq0/NR6pUmLUFA16yTjOXIJPKUKyq1egurWY/LZcJg0LFpQy+ZTB6DUcblsmGQdKxksgiCQLVcRzTpGXA6EAWR1XyebpudhaUoj962m0yjhkVRWMrmEAUBp8GIWZY5G4tzX98Ywy4rLywu4jQYuK2nh79/5jXmWxUi2RzlQhVJgFarRUwsc3PXAC1auBU3HqMNEPm9157HKivc2hMCBE4uLnNmYoGJWJTbRvrxGb1oqAgIfO/aSXb6NnIuMcXnzpzm4eERJFGg1+ak2+LGpFP4xdIMBp2OD23YQanZZsxKokTQtAOXEuKWLgctuUWtpXFXzw2Igp1UbZlj4TBjLjei2MKo03E1lSZRTdJsNXhtbZX3b9+GTtRjl+3t3HRBJllb41omTbHZYJvfT6/NhkuxEq2soxMyVNUkRl03omDhUuoyww4/Fv0o5WaYM7F1VotpDgXvpdPSYF+wg+3eEFW1xsefe4nvHT3LT1dm8dsNbPfspdfWQ4e5j5dWz+MxGukwu1GkAaLlWY6Gp7icTHNvfx+NVou5XJ5YucxKvkDyuho94LBzIXmFPpsenfjvkUM60UvIOsJWzyYGXFVsTgurxRx/dHAPO31b0QseprOz/MvkJIIgYJMlCo0aCDDqdGLU6XjfEz/hfCTGsXgUiyKzyx8kXinxyMZx3r5tIz02K2PODs4mluizjnEuMcWPz1ziluF+uixWnpq9zLmL87xv31a8RgMvLy2xM9TFXCTB0EAnpUadUKeXkyfnqDVaLJyMcPtNYxxdj3AuGqXLamV3bzcnJudxdziJRvKIgoBOEphZjJLNFAj1OGk1VMqFCrlalXtGB+my2jDpdOQaZdLVBA/2j2M0iBi7zWCQeOf4KHpR5PFzE/z1wUPc37efU9E5dgeCREsl7uvvR3/dhKShsdKqUq816PXaGHVupEiGi0trGBQdrqATRdHT2+snFk6TK9QxG3XcccMm7ujtYYvXh1028/lzp5EliU0eNwGTifOxOF1BD5Ik8rmDh7l7qJ8tfg/PT81hdVl4aGyUtw0NcWRlBbXe4IaNwwQtVgpCm398oLMTk07Hw8Mj3LVllF29ndgNChvcLpLVGpFikd+9cQ+PjIxS0gssrMfaq0YeCwazwncffhCv2ciXz1/gxMoa9w8PcjmVwCLrKYqwc6AHv8mITZF5c32dh0aHSVerfPfEBI9t38ywYxeKVGVvoJeDnR2ES3m8RjO9NgeFeo0nj17g5j3j/NnefWz3+3hjbY1SvsIfHtiLy2BEQyNZKRMulXnjm0f/izeaD/IfH51/+9eN5q9Sf/2Fv/n0733s3UiiiRFHBz6bwAsXp/nY7nehF118a/I13jg5hcVuZGOnn4DZzFevXOBQxyBTmSW+deociXjxl6pOpdpE1gl4u9yEOr0oip7n3/ObrFJhbi1GudpEFAUK4TIOv5H/fuhGJhJrzCXiTMUSPLRxlJpe4sjJK8h2I5lShXsHBxAFgVPRCKlqjUarhU2W2eYN4jaYULUW5xJRYuUy0UwOraliMBt4387NTGdiIKgoEjz89FNUUZnNZBBkCbvHxse2b6GmqlhkmdPzK2QzFRqqhqpqDI0FKOXaHLRNoQ66LGZUNJKVKjZZxq4o5Os11oslHhvbxl+dPo7PbKTSbCeMLOTyPDqynb+deI1NbjdPHb/Axp4OfnvrNp6anWW1UKSptXAbDESLJY5MTDI9k0SttUgXGxy/tsDL4cX2rmCnA7NZ5tzSOvePD1NRRFKpLN3dXlQ0FlfjlMUW8USW7aEuFvMFOi1m7IpCslkjmy1x/87NWGU9r0xMc8+uTRQbdTLVKjZF4bWVFe7s7eVUOMzJMzOojSb/867DLOULRFNpFJNCsVjDG7BDSyMSK9IdcjE1FcMZbCNSXnjtCn//6D0sNQqk4gU0VcVsMTC3HEEQBNxBZxsMbtS3YfRWBbWhMr65k6GAjzfnF6mV6/SEfOwd7OWhHePc0NXJv548z91jbfX4H145ykDAywPDg3zz6GmefX2KC/k4377nIbZ6RrDoJfwmC11WK4lqhR/PzPDM/DUCVivpQolULMeGrd2kEwUsJl2b33h9LzSVq6PXCQwMeBCEFsVkFbWmUte1sUANSaDRaNMU2hsXGpVaez3E6WwbWebmwszUiqzPx1iLVzC7DfQ6nSQLOZKREogCiGAzy2TydTRA1okUK038ATt6Wcf8XIR6XSWTr6FYdEh6ifBqkoEOH0GvkzdPTHHThgFemV8gaLPy4GA/J9fCjAb81FstEuUKYx43NkXm5Poa0UyeSC7PTDGDU1F44rWztBQJSaejZhDIVissTkcolesYFIm1SJGiWKcqNTjUMQ7XFcoPvPwkn9i1iz6HnVSlytlohJDLiWiTiawkeSMWZiRg5PvXzvM7P3qCJ1+/zFeOHyGtaHzt1vsoNSv4jFYUSUYvSjy7NMWX3jiJzWZhk8fNQi5Dt9XBXC5Br20DgqBDEpx4TSUGHTbi1UUCpj4++NJ3OTW5yL6BLkDgoYEdvLgyyy6/n0+8/Cq3jQ9xY0eIVDXOXC7M5dRlJhJXGXW5mM8XWFqN845NY231URnhydnjNKnTaXaglxyI6OiydGLS9QGwkL9Kqlrhvt6DxKtTfOPqBDpR471P/IiHN4/z8b3v47cP3MFvbBrmw794hjPpGe7p3YJOdLLLN8LZ+BU2uPaiakXOJaY4FYvTaLVoaC1i5fbYtdFqYVFkyo0GdkVhtVjkUiKJ2yjw6tpplovXGHVu+OW5LQgCQdMgB4I7cLkKVJpNYpUYvbYOeqzbuLt3C5HyEl+7dIknrlzFbjIy5nLzVjjM7+3bzTOT0+zr68Go03FDRz/PLMywN9DNly6ex2VQiJRzvLKyxm2hbTw1ewqP08b+jiBBUz+7AlZOl5O8PLdAqt7gHcND9NltnE3EyWTbRrFYLE13nxedoDGyO0SkUmEyFufm3h46LWZ8RhMf2LeDrz5/HKfdgM1hpFyuk8zWKVWbmPQCszMZtu8b4IbBPp6dm+e3Nt+BqpV51y9+wjPzsxzsCnJr1z3M5Wf5+u3/DaOuxod/9nNu6O/loYGbaGk5LiTX+ezxEwRsVlYKee7u3ccrq5M8Nno/vzl+B8NBld9/4lk8Pvjg+DtZEsIsJVLIih6/z8F6OIUgChzaNUpVa7IeS1OTJSrNBq+tLfPsyUtEtRpBi4WFXJ7l6xMGvdR27KeqZf51apqmCBaDwqDTyQ+npllfS5BKV8Cu45O7dvH9kxNUmk0mcxn6HHY2uIOUGtX22P/515hIJXj7yDDb/T52+LqwyyaqapWqUYcv4MDmMKMK8M1XTnA+kSAZz5GIZLlQSPPHew7Qb3dxIRnj6OwCb5y/RsWg4+3jI7y0tMI9ff1cLmR4ZGQj5WaSz5w+ytV0lFu7d1Br5em1+rDJHkrNPJdqOd42PERdVUlWK3x42zYe2bIBr9HBfC7JyWiEkNXKrd29fOGzP/51o/n/Qv0fsaNZaTZpaZXrGbwCd/cc4gu+CVQtT6O1Tq5aY/+eEeZWYjx+/DTC9YYyUiywryPISFeATp+LCxfmKVebuN0m3EEn6ViWD+3cxuHQAGfjy3RazNjcVgShSLnSAEnA7rZSapQ4urrK6noes1HHI194AoMs8fDtO/nUrn2kqkUUSWo7yxsNrLKeYqOJqrW4ko7RYbZyJZXAZzQw6HCQDAUY2+Fhg8vFWrGIx2DAY7CQr5dZXUiwOdTBUjaLpJP4wNbN2GSFcZeb07EYPR1eirkyTp8Nh83M1YvLOFxm+vsCXE0kqKtt6K0iiUymMgiCwF09fegEkTu/9y/sGepjr7+bslojaHSxxd3FB17+Gd86/ACn44s8etNu3lpb44WFBQIWKxs87ZjNUr3BhdV1llZyNMpN9CZdG3Le0pidiWMytHcXRVGgv8fOV0+e5aaRQcw2I9Vmkw67jU6HHUXSca3SViRNosjnfnKEjl4PilFm03gPdVXFbzIyMNRBtdlkcSmK02/n8MZuHAYDjVaLxdkwHp+Vj9y4l8ee/Bl7RvuJJMpU6wWG+p3cv2Ucj9HAX/7oZcrFKkGvCb0ocu3yCrW6yudeeJNUsojdbmB0uJuJ83N4gw5Wl1I4vBq9Q37OT6zR4THw5/ffyh9851kkncRLb15G0zTecXgnAw47VVWlz25nIh7n8JYxZjI5ZrN5vvPOh6ipKi8sL6M2W/hcBkIBNwbJRrmZxqLv5mr+LF84P8EOv5/piyt0hlzUmk2uXFzj0KERwuEUfreRSKKM1azHYTcQGgyiTSwRjZY5f2K13RAaJUjXqFWa1PwmkNrXvk4nthXX+r+jjmqVOpden+fmO0ZxG438wW/cz2ffPI7ZIBOyWTm8ayNr4ROIosDGrd3MTYap11WCXhOJTJWekBOry8zSdJh8qYnF2FZYK8Uqqt1CJl1mMZYiFc6QKzT47I9ewWzUMbbfx+trYe7bsgGnIpOv1ag2GzwbSzA7GUGRRUY2h6iUa7x2aZrFbIZHbtmFU1FYyOY42B2i2Giwt6sbuyLz2vIygd4GkcU4/3OpnVf8kY27kQQT/3z4Xdz43S8x1hXk8uIa79q1hYlYjFQkS7miUsyW+NC//IRMvtbmgZr1jG/tIVYqEqtkcBksKKKepqZSUetU1SaVUpW5TJqa2mCjx4vXGKLYqPD5C9/koxv3oEg99Fp302sViVYm+JPjX8VjMvHt99/Nz+ZnOR2JcCDYj0Wvw6qX2wDxMR0ff+tZ/q/dB1gq5HArBt419B4OfO8PabXaOdPXMhk2uoMISG0kmN2LVb/l35FDWpNi4yIAuVqNfL2BTgjwxMzzPDw0zDt/8BRffvu9DNpubh+kAkg4OProZ7iSPs57X/gKF68s8vi77uOhvrddRxhV2ezpoNCoczISo9Ro0GVtN13nE0kK9TqKpCNVrVJu1LEqCj+Zn2c1l+PJu/+Eb1z9Dt1WK3eG3vbL8ztZPYNNlrm5czeaVmW5OMmV5Bs4DAo/X1jk4eEhvnBuAkEQmM2m8RgNPD03RzZRIBoqkqtViVeznI/FaLRadFstVNUmry+s8dDAAMuF83xmz/189eoR/vzYMf7pVjN/fvwt/mzvPtwGE5PpBJFSqZ0GZDKQqjVoVBtUy3WajRzvPriD7712mpXZKJFkOxr0z248yGwuh02WeeDm7bx04hLlQgVRFHDbZYwGqS1YWGVymSINfwtJFPncuWe5FI8xcXqRnXv6+en8HB/f1sundnwEVSvw0VdeIOBz8v7xTTRacV5dm+FcLI7FoLDZ4+au0H1MZd/kPcNbEQQdydp5nplfxGgx8NnnXqdyu8pn99/OYjbD1ctLZBN5FKNMIVfmhdcvYTW3U+sOdXbw/clJZi6vopMEysUqmVodq6znj3fvZpsnRLyS5SfzM8TKFQC6rDZ8ZjPffvM0S8sZLCY9breJVCTDly9eZKAnSKpcYnU9yf84N8+Hs8+g6EU++rZDhNcytFYyfPjqOmajnvnVAiaDRClb448/dJiPbtrKh15+vv0bSiL5TIlstoLdbqCUK/PPk5d5YXqGDpedgNvOp27cz9cvXiJosfCxLVv47tQUGzxe0tUixyLrTCcTPPvmBHVV5X1jGxAQSNfijDm7+OE9b+daNkrQZKXTPIKATKo2w1cun+GluXkUWU8mlWewy/+f3rv879Wvge3/n9Zf/uPfflp/g489gSACekDlN8b3cs/P/pEeu4F0rY7X1N77mFoOE15J0d3t4XBvL+dicSLFAplSmdH+DjKZPNlclUyySKDbxYmFFU6lo3z36FlSNDCbDaQSeQwmmVxT5dPvuI0Bu5NvnjqDohPwBe0kkyUEUeDtuzcy4nRh1OkxSDIz2RTxcplKUyVTq6FIEplajXCxSF1t4VQMGHU6RtxOOi1tliUa6ESRkNXBSjFLWKqzv7OTSrPJJr+fEZeDuqoSLhUp1Bvk63VUg0ij1qRSrmGxGdDpJewWE02txZDThUYLi15Pv72dfCCK8OZ6mAN9IfSSxKVUnE1uL6fjKzyzMMtsOs13rl5gT7CDH0xOYrwOz01Vyrx7dJS62uTpySky8RypTBVNEPC7jRhkCUVuj5rXYmWa8QqBLgsurw1N01hKpvn4rQeZSqdZWo1jt5nIVqt8Yu8efmvjY9zWvZtZbYHxgA9V08hUq8zFkrw8Ocej2zeTrFZp6AU2+f00Wi0Wsjk8RiN3bhzhyJVZXjgzTbPeYG4lxvi2EIrQwuGxcX52lW6/m8VUmk/cdSO/ODtNuVBGr+gYHu/gQ3t3ULdI3Do2xJVYnI8fPki4VqVcrTI9k6SQKSEKArJe5LmJawgCJGN5Qn0ebA4jRVQ0BE6urjKdyTCTTrOUzrA9GGBfMICqtTgdi2KVZX5//x7et28b9/QPcCI6h6IT+Pbkcf7h9bfY2xtiIhrFYDcg6XU8MDpCy64jnSty09gQ/zd37xkn2Vle+/53qKpdOXZ3dY7TPd2Tg2Y0o9GMpNEoCyQjHaIMmGgj26QDOIDBgLExxghjgzEgQAKBEEhCeTSaJE2OPT2dc6jqUN2V8073Q7V1ZPv8OL7nw73Y75eurnrfXbt+Vft5n/2s9ax1ZWyO5tZQRbw6XWJsLEY6rVYI4rqJ1Wsl3OAik1Ehq4HLgkWRkGURl12mscGDrurUBO2sJEuIgolNkZFMg2Qmz9Nn+vjITbs4NT7D1cgCgkViamaZcJUDTCjkSqi6iSwJtK6pZtfaNmaXE7g8dnZtbMdT5cLhrqg6rMRSRBdz5FMFYskipbKOaYJik9ixtpV0qUh/LIbTpnC6f5xSWcXrdWLoWsWpxFHxFpctEg67jXafD6soUu10cHBiAsM0afF6ERCodbs4dnaQ6oYg+7d2c3BwhO5aPw0uH4ZZZiC7wg9+dZrI2QWEFjv3dHRw+MoIwWoXt2xdR6yYJ5PK43FauH5XN/NLCSKTS/zi6gA3drXQ4AoiCgJ/fvIovzrfx81be3jH2i7S5RJ2Web+px5FEwX+aNP12OV1LBUukdUWcVlqSZbnODEfodnj5R5wvwQAACAASURBVPGRId7c3k6jx43XauHRgX7u6ehiSdBwW63c1tzEusB+unw1IGQI2Fp4bOwoPqeDz+7cQySXojtQg0NuZmNIwmlRMEghCX4EQUQQLMznR1EkC2VDJaAo1DkbSZRn+dnwCE/d9yUaXZ3/IZ6Kgo2gYmdfQxWy18bjw8Pc2OjAbW1EQKakL7GUz3B7SxvJUoEtVdXMZjOUDAMByGsakUwGiyTR4HZR0DSqnS721oUJ2UVCdgdHIme4Gr9Kk1ujoJVQDY3x1BxNrnpKRo5NoXZkUcevWGnz+nh6aITeuXlsioJFErm9uYWkFbbWVDOeTHJkdpa3dnVhYvLy1BQdfj8f3rCNLn8tz04NYZVKNLhd7GtoJF4qcHNTMw5Zpn8lhsdmpcntocsf4NHzvdRU+XA4bFzT0YLisnFyaJLbt68nrhWxmBUHtQW9jF+xc3h2lrymMjq5SKGoU9bM152pggE7uqbjsMu8ZWM3J2fmWCrkafB4qW8M8M6ebnaEwwwno1jEZTzWBg60+FgpFbBKcCw6xf0dN2G35KlyOPBabYykhlnrD6OaGoaZwin7CdhNji9EsTmsvLWnm/e/9AyP33kfhxNzROfiFPJlbr1+IwPj85RVAwGTowPjJJczdG5owh10kUvlieSzCJLEnvp6Ti3MsFIssMbv59H+fgRBoNrpQjMMWsKhis2w34nitNFSX80HNqwnpaos5/NMjVeg8O6NDexY38KvXr6AzSqhaSYl1SBX0OhoC2DqBpmMirvewd3ta7i6sswzrwwiAulsCQEBSYBbdqznM9v3UBA12n0+7l2zhg5vkEcv9vI3N+znH3ov0eRx8+a2dk4vRJEEgY1VVUSEMgu5HPP5PC9Mj3EhtkC8lOaRwaucX1xgOJkgUVrk4cETLOYzXFhcZGYmRiqRJZcqUDA0os8M/hZXNP/x8x/4wL3/rzmaX/zL7/12VjQFQfAB3wPWAybwe6Zpnvp3c24AvgFYgGXTNPcJgtAI/BgIAwbwXdM0H1qd/7fA3UAZGAfea5pmUhCEFmAQGF499GnTND/8m85P13QOTU1ybW0Nu2q6GU/PcDm2xNRwhH+09vLgls3YJInzS0t8+sD1/N3RkzgsVo7PzuGx2dAMgzqfh6DdwfYta7jcP0kiUWBieIEdO9cwPrNIIVtkbiZWaQgSBDx+Jw9s7uSetm5msitIFgmn2051lY/5uQS6bmKaJrIgoho6uqnR5vXxk8HBCqm6VEKsrSWkKIiiiNta2ZBlUUA0RVTDQJEkDAk0w2QosURBrzT6lHUdl9VGyK4wlkyRLqusDfgIKQorhTyFXIm22irWhUIUNZ1oNosoQNDuYGB5mYxaZjKZpN3npzvgp9Hl5ZZmkYtLS9zS1MxCPstLM5OciEQ43zvG5FyGjlYfkVyOqxenqKp2M+tNoDis/HhgkJVCnnQ8SyRaEbF2KhJer1Lh4olCRaS+oIFTxiKLSBaJDY11vGVNB5uCdbw4OcV0WePW1laur6tnMZ/jSORpYoU8f7lrH4OJBc4rChcWF2mrb6DR5WSlWKLa7mBjVxc/HRzkRCLLYiTOePsybodCqK7iSOJwKRRyJeYnl7C7FBzOigvGRCKJw6XwmUefZ93WZor5EoVsEbdd4XOPvcTCcoGDLgvVATt/PvIihmEST5fZu3cNhZJKMpYmGc+xeVsrx4+PYJFFivky3pCbbDpPdUMjt+24hocuXOD3t2zGKkoE7XZOROexyxXZmLtb19C3vEDY6aI3tsgXXjxCVdjP9Y1N7FnbQb3TyeZQCJ9iI6eqZMplfnddD4dmZhlYjuHxOdjQ2kAg5GGwdxpdN1FLBu3Nbqbnc1T7bYQb/EQiOaxhB+XRFGqVAhYRzSbxlt1bOROZ4zM7d3Lb3zyCzSpRW+WmkC1imiaK08aFxSUcbjsWUWRDdRWeW7YAcPDVPuLpMg5FwjBNLDYLDW430YCPsMvFl3bfyLf7zvEvva+RzFTE9SUBlpMlTN1EWW2vVjWDvqVFNtXUoMgWnj17hXfvvYaCpuOwyByRJBaXEoQcDmZzJVrDIbbU1JApq5R0nXS5TIvPT1FTkQSBkm6QVzUaO8JIskidy8neznZ+OjTMWl8NL82M8aNfn0abSIND5rVXR3FarLx9/04csszJyBw9TXXIVpn3btvM4ZkZorNxymWD5rX1BBQHgiDzLwNnOD04zrauVm5oqMNnU/jY4VdYmIohWSS+NXSciwsLfOum2zBMg9/5xeN88444Le4QX73u7YykrjCfq8UmSXzt6EncPieqqmGTLLxv3TrWBw5gmCXy2hUM0+TMQpQ2t0qHP0B3MMBEepl6pwuftYdk6SIWUeJCbJqzi4t8ZIOGIq1DNzPUO9cRzQ/gkK10+3eSVYeZTmd4aO/HfmPMt4j1hB311DjG+NQ111Dn3E3vykuUdYOsWubacAsO2YtNivJqJILbakUWBBRZqig2FIvYZZm8plVQiuVlTsxf4aaGeyjpI9S3tHNy4QKR3AplXWcpn+fWpuvQzUpl2CZ1UuuQqHe2k9eWuKdnLf3LK1xaXMBtbSReLHBfZweZchlREEgWKyoUx2bncFosTKWzpMo57JKVqytxjs1GeO3sAMGwD0EQOPaeD/FHx57jRO8I9+zazLpVh590PEtHQw3vX7+e9cE6Xpuf5JDDyWw6xUf37ORHV/vZXlvHXCbD4MoKNU4nhmny0XtuoC+2jE2WkAWRZKlIjdOFIIDHamUonmRrfT3ba6pQJAndNPnVyCgnzgxjmCaaZvA/79/Pe7u38Mkt9/Kdq8/w08t97G9s5suvnSS9ksHhsWN3KvS3Jvi9ns18f+ACr87OkVPLiJKAU1H46zNn2FnfgGrofPn663m2voFHDp3m8KmrOO0VY4ZUttJvAFAsqaypCpF2OFhIZVANncOzs/xez1YOzY4wm8nwud27gUpX/lQ6w3giwc41rdS6nEyn07x4/Ap3H+3n7gObyBUr3PSO9Y10BAKc6hsDIJ1TcSgyZc0g6LUSW6qoV2CTGL46w9yuJPVuNwGvFZtVJOCyV/jifi/3tLdR1MvUOhy0er1EsxnaPEGWown64gtcjEQ4NzNLLF9gpVBgZdVN7cbmZn5++hIz00vcec0GUsUiWVVFMwzODkxQzJXw33od+xsb2BKq44aGRn4/mSKTyFHTGHpdxea3e/zX6Dr/z0LnDwEvmqZ5n1ARnnS88cXVRPSfgNtM05wRBKF69SUN+IRpmhcFQXADFwRBeNk0zQHgZeBPTNPUBEH4G+BPgE+vrhs3TXPzf/ZDmKZJdGqJLxgn6QgM0R9ZYGZ8iZ5Nzfzpzh2sD6xBwEJGvcCmYB0jm1JopokoQEk3qHN7CLtcRDMZwi4Xja015HMzaLrJYixJPlukpqlCZk/EK8Ln0ZkVPrBrOzbJQl5VUUsqFpvMwkKCfFFn57UVUePZbIJqhwuraKGkF/jAhg0UdI25bJa8qlHtsGOVJJrdHmRR5LVolL5YjDafjw6fF4skMpPJopsGJyLzLCRSDLtchBwVuNdttTCVzlQCLgK3tbaSqi+zMRQk7HAyEF9BEAQeWNvNC9OTnJyeod7jIZpOE1DsiAIICAwnEpR0nd7lGA9fuExsbgVREkmlS1gkgamZFNFsDk03yWcKxFdyxNNlRtxzOO0W0tkydWEXciyHQ5EJrhLRY9E44wMxyGnUdHgqvud2Kw90d7M+UEfZ0OgKBiit0Xh8aIhfDg/z3QO3UzY0ZFHk0Owou2ub2BRsYH1wimcnp7DLEj0BP7VOF89MTFZ+g5JIa3c9FklianwBf7WH/7F3O8/3DmCxyqSTOTLZMtPTCZyKhOKwMT8bZzlZxiZJBIM+psvLHDs2jNdtoTqgkCtoxNMlaqqcKA4bVkuGGqeLBbIYRkVIvyMQZPc79pMoFbk0P4+hG6xrrOPk3Cxdfi+1bjdT6Qz76utpdgfZUd2NICjMZcdJlwtEcjnOL8UYSyQo5Eq8cmiI0l6Vd21Yj9dmpd7l5uDMNN0BP187cpJrO1vp9PuZTiaJzqfps8+xrqWeOY/CYryI4pDJF7WKfalqYJqwdm0QSRbpj+Qgq0KNA9M0ubq0xP6WFhKlIq2NHqYjGQpFHbsiYeZV6porgv4emw1JqPjbDy4vc+j4AKpq4LZXwkcsUSJxbprUcobGpioM0ySvFWn3enA4bdgdNuYXswhCpbtdNys3YaYg0NRWxauvDjPVsMCejZ3cec0GNoVCLBXyfOPYKQzdZGUpRWIpzTtuvpbNVUHOLixVEgtZJl0q4bRYkEWBVLlMtqyyUsjjdzkY6JviZ4sp3AEXmqrzYu0oH/7O05QmM6BIIAjkEyWeP3SV0Jt3IAsii7Eka9eGaFvbyYlIlJubm3jhcB837e3ha/tuwG91843Lr/LZh19i08ZavIqNnkAVJ6JzRMcXqW2trviYl3Wu9E3yjcA5XBaZj+/Zyd9fuMBKocA7e3p4U2s3be4wkujnW3cLfO7Ea8RiaY7MjTObzbHGO84vx88wnkpzR0sz0VyOL53/Ptc31OG12hAEgYuxGHtqFxlNLTKZTnJv215SpVNMZxbo9HZjmirx0hjV9irixRVy6jgPHnmBHxz4t/ftJX0ci9iAKNj+Q2zdUVNLl28/l5ZfYjqdos3rI6tCJBfHay0QUuxsqQpjFWWemRyl0+ejP17pFlcsMrF8nkypxGIyjWoaq0eVOB49z3OTU6wN+Hhv936SpUtYxFomkgN4rRU0xyZVYqjXWs8nt2whmj/FL0YH2FQV4tnJKWqdDiLZHNc31OOyWNhUVUdWUzk4Nc2GYIBMucxcdgaf1ca14RpOnh9iXXM9B5orrkQ7wmF8q/aLPxsawi7LKE4bDW4PgiDw+OhV5rI5plNJkpk8/5ztRVN1fIqNZKnEpbkIqqGTV1XS5RJba6oZS6ZwWiwUNI1YPkdB1XBaLCRLRVZSWX75ynnSORVJEsgVNOw2ieZmP+Pjcb782CGK92t8amsdPYEAD912C6fmI3zs2mvIqSqTqQwhu1KhcFm9vLt7C7e3tOKyKGiGjihUrFSrlDACMvVOnT+75jp+b91Gbv7H7yFqBmXVQJEEdN0kEHQwcnUO61aZreFaZpfiHGhuxmu1cmJ+gi1VYWyShflciqVCnkgux8ByjPn5OGqdTqZcpm98lniqhCQJzMSTeBwKXRubqHd7eP5YL8aq9F59nYdSvowkCqtVTR1JEnAqEqIo8NDFi5w8M4wAWC0itS1VPLB+HQMrCX49Mcl3entxWKz83d4DHJqZZSiRpKmrjp8ODjM2EGXf7m5214aZTKd5bjyJQ5a5qaGB9FYVv83Kvvp65rIZPFYb33jhVdZ3N/GHW7aws2Ydw8lxVENHFkWq3S6gUrxKx7P/2RTk/6fxXwc6/z8mmoIgeIC9wHsATNMsU6lCvnG8A/iVaZozq3OWVv/OA/OrjzOCIAwC9cCAaZoH37D+NHDf//WnEAQMwyQ6vsjEQARVM/AHHNzf1cX6QNfqHJE94XbOLk0hiQIWQUQzTJrcDkJ2BVU3wO3GJolc19BIuz/AubEKx0W2Vbhm89PLaJoBq9aQW6qqEBF48FfPEVvM4FBksnkVj1PGZbVyZGaG58fH+ei2bWwM1QKwu3YNAGW9zMmFSWocTuqcPpyywsHZIZ4cHsarKDgsVlo9brKqRlHTOD4XIV4oIltkDLNS1SzpOo02FyG7gkilgz1RKtLkrniVl3QdRZZZyGUZiMd4R+cGDk5OEkmnqfN4uLO1BUWW+YfeS4zGV0hm89y1tgtBFIgu5ckVVzVEyzpYRL7/wglUzWB6Po9iFbHKAjW1XoZGlrFIApH5DBZZJJUtc+XiDEGvjZVUCQoaa68Jo5U1JFnk3q4utlU18+xUBZLZFAoRUBRenpxiOhLjwlKUgKIgCgI+m8JYcpmgPcfOmhaen5pme3U1Y6kUh2cjXFlc4MqFaWRRoKraxUduvo4frFScnV4ZGcPtdxKLxGloryGfLnB1YBFVNeg9P0VLS5BUtky+VCY6t8zKcpYDN/bQ1zeJYjVJ5zXuvHEjM6kk2WQeSRK5MjZDciWLP+SmVChz6Fw/f3vf7dS73Lxwuo9QnZ/ZdIrB3hkOuj1sqKoir2lcXo6xOdSFICgUtBh+m4tDc6P8emyM0ZEIpUKZbz7wZu7+3I+4dG6aZr+f21pb+P7Vqxw5288X3nILnoCLU0MTPLuUIpYooWoGc9MrzEyukMqpmCaUSjpx08TrtlDWDPquLhAOKhRLOttvaye+WFlbF3ZT7/FweWmJQ1NTfP6em3n/Pz1JSdVxO2V8IQ9v37qRM/PzLCXSiJLI3z75CrOLeXq6QsSWMuSLOopVZN8NnbT4fAwtxjhzeow+RaYvMk+5WCaTLmKRRUI+G8WyvipsX2lIsjlkwiEfZ3OzxFdyZNUyXQE/Xz9/nr1NTbh8TpajCdxeB/9w/51sCTWRLGX5+dAwNknGJleuhZVCHrfNxtHRidftRa/vaifVXsCl2NhQXcNrk1P89bNHeOfd1/CDx07CchHcMq6AwsYtDZy+Ok4xV0TXDQ4WB/BVeZgYiJLeXiJc7SKWyeKQFeyyi4eePIpilchnCryprZWpdJJHBwZw+ZyYJlhsMqGAyK4tXThkme6An4NTM3zxuj18/OgR/uyxF5i5K1vh865Zw/mlRfY2NdHvcDCbzfHUwBDv79mGbhrIooAsijgtFr536BSfuusGsmqZmUyG21taKBnJ1VgU5tJyL9urG5jPp3hi4nHsksxMJsOb2jopaGV+OtJPLJ9DFgP/Jnwu5KM0uZr+Q1hNli7S5dsPVOi+u2qbccg21vp86GYGQbAylIhhFWUuxea5pbmFasXHMxMvc9+aDuayWQ5NT/OedevQTZN9dV3MZF+j2u7ns68cxe5U+OK1N/LtvoP8wYY7+fbVH7K3vp5ILsFXzv89n9+5F79t2+vnU+fYxR9t3MGJhedp9Xp4rK+fpdll7t+7nY1VQc4uzHJLYxctbi9Oi4VmVxUDiShv71qLVZR58cH3MptNEVIcnJwfJa9p9C4usBBZIZPM4wu5KRdVPrxhE3/y2jFyqkq2VGJlPonb72RmOIppwg8WK2iIIAg88dwlGmsc5Is6A921pFey9F1ZRLJJ1FXZaemqZV7VOHd2hnBQweVWuGb7GrbXhmn1uNkZrqfe0YKJioCMLNYhClb21d+MgMjGYBRJCKKbMaazcySKBXw2hWhuAUWy0OpuZCAxyUIui4GJXZIRA5X9qUppQxLcNDivo/cTYXb/y9dxenXUskYyUUBXdWRZZLB3hnyuiOK08fjQEIl0jt/Z0MPOmooGaUBxMJZKciYSYWF2GbWoYmusZjmfo5Ar0VjvQVc1bmpvZSKR5PLwNIPLMyyuFAmHFOaXi/j9Dvbu6OFU7whLsRzZvIrFIqIWdJZEgeMnh4AKwvGpe/fTu7SMiMB0KsnVwRnS6SKff+stOGU3LR4XXzv4KlbFwo+fG8DqttLhD7A2EEIzDe7q6EA3DBRZ5k1trVhFkW9eusy2cA03N3bx7ht3Mp5MsJDPYVKk27+OpyZfpctXuS5ikTgf2L+LZ8bG/q9Tkv/vxn+fimYbEAMeFgRhE3AB+GPTNHNvmNMJWARBOAq4gYdM0/zxGw+yColvAc78b97j94Cfv+H/VkEQLgFp4M9N03z1N51gtduJWlKRJBGXR+HBA3tY66/o/M1kpytB1NSxSAEevvocE8sr3NOzFqskMJfNUuNwkCyWsIgi6XKZoFLRyLu5uZFP/+x57E6F08cnUdxWgl4rkijw6d/Zz7aq21kpnmchmiKeruTeilVi+HN/wlcunOBg7yAL0SRfKam8fcM6bmlqQUABdCyiwd66DkRBovJjMRmMJyhki3hXIfAfXe3HYbHS4PFgk2SiKwl66msxVr2qF/MFVMOg3umkJxjEIko8dPEiLV4fb1nTga7rzGWybKupRhZFZEHiE9u3U9Q0MmqZxXzlLvX87Bzjg1FkSeS0x8XZ01MEPFZ8HiuyJKDYrcTjBTJ5DV03cTlkXA6ZpXiRvv4lqvw2lpMlNN3E77FiGBW4P54uU+WvBOTRySQuh4zPXRG1fnKij9FkijU+L1ZRRBTgQGsLtLZQ1DUMTHJl9XV3krlshnZPGLfVysP9/czGk5TyJVrrqrjp+m7CLjd7G+r405+/wMaNbeTVSvJosVnoXtPAxUtjWBUrdVV2quv8fPWOm7n3Gz8lX9S5fH6aXKGSVE9HYviqKhtOoyjQOzJNT0cDZ4bnuXvfVg5fGkRRLMxHkmTzGrlkiZlbM3ynt5c9WzoJOZxcmI/yzXffzd8eP0WVw8H19RX9vZVSjKAtiE2yAgKPDQwx1D9dgbMyZe7+y0dANVB1k7NXxnjlRD9feNutnPVO8uePv0ixrJNMl1m/qZ7piQkQYFEz8bktaJqBoZlUBZXXu8+rA5XK11KihN0mkUnmCdR4gRQOl4LPZqMnGOBnV/sr3MI7ttE/HcHlcxJ2ufjqLw9XKhBlHVEUXldbsNkr9pKZnEpJlUispJkajjA9n8PnspDJqwz1R7HKIgtLBYIBGw5FrohH22VK+SKGbtLYGWR+KcFb7trE8HiUV17tx9hj8pauLprdbj6wbiunF6a5uBTDbbHy5PhVtlRX0xEIoBsmkijgslroXVxkdHYRQzf4g33XcntzB0HFy4GfP0zvpUmGrZUmr3ROpXdilh27mzl/YQ4jr2GzisTmVoinSrgcMp3rm5gZm2c2MY9umPzq2cuQUZmaS/PUxkF+eP4yZc1g45YGeupq8NsUnp2cpFyuyIotzqdw2mX81V50wyBeLPKDvqvc19nJV8+fY29jIz6bwj8/+yrbt3cwm0nz5d038sjwFaKZDG/r6uKF+x+gPz7HL4ZH2BoOc2FpkV8OD/O1++/AabFwZC7CA2u7aXa1YhEb6fDqTKSX+Lvz57l3TQe/03Ydr0Zf4amREf5h/35skoVax0Z21+Z4cON1r8fNhfxpXp4dI5LN8/EtLViFRgBMU6VkTOCzbX19brdvLQU9jkPuwDQLmJSxiI3srROwSZ1YpFPEi1m+N3CBv7ruBoK2Tl6dP05e09hb10G8lOGpiV7mc3nOzs/TEPLz2O0f4T0v/zM/uuWPkAQ3H1x3Hxdix9kYbOEdXQbvPfhrHr8jhFVqfsM+IrGn9m721MLmqud4fnKKhVyW/liMz+zcwXNTgwAUdY33nX2WDY11fHHXPrxWN0fmBhFFAa/Vzj/39VHUKi5sPR0NBO0ODp3rp2dtE4+NDDITixPwuihkiqgllTXhamJzcQzDYC6SxmnPY5rw+Mfeyh8/8uuKwYZu0NRcjVWx8PEbdvO2NfciCnYA1HctoJspAHSjSDQfJ5JL89zkOMdmj7KYztLg95Itl2lwe7gwMcN33nwnGwI76Pmbj1Iq67g8Ck63HVGWmJuMYZpw85713N+5hkguS4PLxYn5BWYyGV6YnETVDe7qaOOFiUl+evsHOfPBz/PZMz/npcsDyFIRm91a0RVO5JAtMl+8/nrymsrxSJQH1m5cVVUY4a+fP0pLa5hPbN/Ou88/gddtZXxmEdM0cbgU7tm2gaBSqYZ/5+lXSWRUBAEaauwYBgS9Vm7c1k1B08hlS6ykKpQqxSpht0ls3dnKcjTBh268ll8OD7MhGGJrVQ2PDg0xMbtES3uYsMvNVCaDZhQIKAodrXV8ZPMmVm4qcmw2wsHBEdq9Hp4dH2c5n+fBrVt4YnSMu9ta+YuTJzFNkw/0XMPllRleGB0lsZTGa1NwW4a4ob6TFyenuWFPG3saGugfmuHh4+ewO/9jhf+3bQj/XSqaq3O2An9omuYZQRAeAj4DfPbfzdkG7AfswClBEE6bpjkCIAiCC/gl8FHTNNNvPLggCH9GBWL/yepT80CTaZorgiBsA54SBGHd/2bdB4EPAnhqPHSFfeiagcNj53R0nh01NSiyjCBA2cigGwafOfEKsXwOTdX59dAIOxobaPG4aXS7WOP1MpBIUOuswIrjqTQhu8I7b97JF/7hBQCKRY0VwOuUWR8MohnLvOP5X5HNa2zcUMvl3nmyeY0PvfIsr50eIpWt6Bmm4xkeudjLjpoaGpw6ICMKYJglSnqZrFrgl+NDvDwxwdaWRmL5HEPT86xtrqXa6eS+jg5qHC5+qChkVRWLKCIIAqliEa/Nynw+z69GRzl8ZBCrVeKSTaLe7aLV42ZzVRVem40WdxUuSyNr/WWCtloORy7w4/4BlmJJVhaSNLVVAXDo8AAN1Q5auuvZWd9AvdPBoxd6GR5cob3diyfgYj6SZDlZQs1pCLaKoLnVIiJJJsl0mZqggqoZ2KwS8XSZ7OqGbpVFUlmV09Eo71m/jtFkJfCWdJ2SbhBUbHisFdmYkq5zZnGRjaEgdklGFiuw0MWFecZGIhiaTjavoqk6X7j9Rv7ihSP88tB5RFHgyKuDGIZJNq/RWu/iVCRBbYOf4aEl7r99G6OxZW78i4epCtnRygat3VU8cN12Hjt7GcVp4+Y17RydmiIZz6El80wuLFNSDV44cRlBELC7FWpdCpvXNPP0ocucjEa5oamJpweHsdmSfHnPHnoC9cg3iNzU0Mlr0XEmUnG81gg3NTiZyixR5wjgttlYXCngdVlRbBK5xQIN26t4z+27OTE3y6nXxvn+uYtkkzm61jXht9sZmIhg6gbuoEJmNgslA9NlYcO6arLJfMW1p97P0olplpOlVVcgA4/TRvuaOryKwozDxthAFGOziSwIPHjNNs4uLpEulVCcClvDtXz3yRNk8xptDa4KzA2s7Q4jyiIDfVGKZQOjbBCqsjMyvEQ6q0JJxxaoQJGpnEqibOBwygS8NmKJVVcuzUBxWnj3m3ZQ53LxxOnLxHI51LLGxq0t9A5OoRsGjDWmEwAAIABJREFUnYEgn3rmIPs3ruXc3Byvzc3y7D2/j6rHOdBU5Cunz+Cx2XBZbVQ5nExkoly3uZMHuq5DFqswzAwP33UnD6hPc+VyBMUmoVgl5ucSdG1spLnWycxCHp/bSian0tIW4vruDs7PzoEgMDFa+W1avVbKuQJFNceHv/4k996zhdYbQrx//XqWiwW+d7WfQ+f6MTQdX8jN2Ngy1WEPqZUM5xI5SoUy77v9Og5OTzMbT3KguYm3d65jYesW2j01zGSXeWlmhB8cOc1NW3u4uhKnJ5BkS2gd/3IgxD2PP4YoiqzMJzjfuMRKocgdLc2ky0UkMUSifBnTNGlyB3nk1t/l8soAi4VZGlxOdtTV0+gMcXhulC1VJS4uLbG9ejOGWcKkjE2ycHQ2wpd278MqVpLMX4z/jHixyLvX7gLANA2WS+eZSMVY669DxIYgupCpxAubVGkmCtu3Uq2k6fbvwjBTfPT4D+gM+PmDDXcxkx0kmstwfV0LL0yPMbW0wnfuuh2TEu9cuxbVmEGS1mGYGVrcFYpStcPFR7dtRRTc/N3l73F/x1qaXHv+zca0q+ZOdtWAbqaZzFzggwdfYldDA01uF0PxJIszK6wJVzMQX6Dbb/Kxx58jGPbx2X17aPN6ODQ1RTyVZSKRw1/tpak9TMjp4Nn+IdLxLBarhfKqlnK2XEa2VvYTJa9RUg28Tguf+vlz/OVbb6Pe5WJ//Z2oRpS0usREOsZnz3yfU3NzLM2tkEzkX//919e60VUNxanQ1FzNu9ev469eOsblaJxA2Mf9XZ3c0tzEiWiEE9En+cn73sZTE+N8Ycc7ERAwKLBUmMTE5MOHXuTTT77Ip26/gYtLy7xv3SZCtjq2Vtfwnsef4jMnh/F5rHyl6ike6O7hS9e+h921L/CV4yeJTMbYd20Ps+kU66tr+NalS3zzxlu5LtyOJIiU9IpNssOlkCmU+MnQEHZF4oGbr6U76McuySzk80SzOW5pbuF3n/o1hgGtDW4ki8Sf3nkj5xdjTCeT3NLcxB/+6GkS6TLoJrJSuR6T2TKzI/P4qz34FRu3t7fzw4EBPFYbfUuLlIplBvum6dNNGlqrONg3RFNtFZFYnEvLy0yl0lhlib+9dT9HZuf45PbtrPXXYZomr0WPAzA+GkVxWDkWHcUqiizNxbl+axe768KkyiVsUhcL2Z/zsWMv0x0M8vHb93F5KUbZ0Dn7m3Oj34Lx36eiOQfMmab5r5XIJ6gkmv9+zvJqlTMnCMJxYBMwIgiChUqS+RPTNH/1xkWCILwbuAvYb64yb03TLAGl1ccXBEEYp1IxPf/GtaZpfhf4LkB9d525q7ONvqVFivkSw9FFztQucE1NmPlcllghzz9evkz/+BzVYT8ej4P52WV+NhTlnhu3osgyLR4P7R4PFkmkpOs8PTpKfCWNoZtQNsBauXMoZsrcdWA919fexYeOfI35uRVU3WRifBndMKGg0TswhcNuYW46QyanYrOItDfX8pPhYb6yqxPQ0M0SY6l5Hh0a4onD5yiWdVo7w2TLZWyyTGdTGJsks6UqxNaqdiTBg9s6gtdmJVUqY5UkJlat+wA2VdeQva7MxMAcsiTSF4uxs6YGj7UCoTtlNwDz+SR/ff4ULR43exoaifj8XLDOsr+zg28+cQyHUnEU8tkUvFYrGVWlo66aft88NU0hxofmkWWR9evCXO6dr3h7G2bFD940kaQKByebV7GVDVTdQJYrQsaqXuEIJYtFbqhbT7XdyXQ6RU5TMU2TlWIJRZKwyxay5TI1DgeZsgpWMDWTX09exW214q+ucKg2BX1sqKrm0YFB/uauA1Q7HHz42RdYiSbI5lVyBY133ryTb/7yOB0NNQSCHhZzWV59eQTZISPLAvW1DtSyhqobhGv8LCcyvDgwQnd9GNdmGyMDMwwPLmAYJqWyzrYdbbT7AyxkswxGFti+s42f/OIclrdeS4Pfy1wixdfOn+ctnSluqG9jJDnPxlAtm0K1KLKNb105jSgIfPu5H7EUL6EZJvPLBbwuC94ePzarxDceP4bLIbNpUx0Br4tP7rmWnw0N0+z14llr48XXesnmNfytHhrqveQzBYq5EhabBUGoOFhJFhG1qJOSNDTNxOlWaPb68CoKRVUjE/ZQWm24Kes6klARgR7tjzDQO0s2r2GqBqpmkEiXKakGH7yzm5fHxuleX4eh6aSWM8zH8rgcMsWygWCTKP0rPK6btNY76ehp5N3r1/Pl5w4TiWZIqwYtDR48NhvPXhmgrTWMZhi0blrDHW0tPO0cZ19DA72x5Yr8kqbxoa1beKS/n6vxfj559Ch7GpvYEq7FueqaspTL4XDbKesGk5kprOIcDa52nhofoz1cRaY5R3wlh2KtdNzrmo7FItHR4qVcVPF6bGzvaObywjzzk0t4Ai6cARu5aJ5ysQBVSoXbmlZ58odn2HhDE95t21guFnjhtcsUSjr+gINSsYzLIbO8mKa+JUQhW8Tjd5IqlWn2+iqoRDbHfU88jsft4KPbthGyO7ipoY3U/jLff+0cf3/PbQRsbhYLc5xamOWOnrXMZ7OsBH2s9fuYkrL4FIVufwO6scIvxvqostvZVlWH3+piS2gLuplkW5XJz4eG+fnoFd7c1s3huTH++chpvvLEK9x5wyY2hkJ8aP2tfP/mT78eU6/GXybscHKgoRNFqlCOUuXLyIJE2OHBZWlHM5eRCVEJ6/9rFPVhZMGLJNiwiPV87fo7uBgbZzh5hS5fD6oxRF4rcXdrF0+NjqIZBl889zSfvebNSIKfpcJZVEPjWGSKNT4/a/21XF1ZYrEwRI/fX7lmrv4Ql0Xmga53oRkxZLGS7EqChw7PjRy+70YW8qf5kxNHCdrt/PXb7+DO5h5UQ+fluRGKhTLJ5TRjySQDKyvMTC8higLNLTVkiyUcFguRdJo14WpGgXKxTDFfJpctcfL0MLIkEKrx0L7WQ1dtDX+2cycNzmrixRSXlqN87sx3eeHKIBabzNxEDMPkddtXSRJJZ1V0w2RocBl0E3dQoaO1Ft0weNfubTy44SbmcjMci8wQzeV4anSUfK7IiaoIX993gJI+g0UKIgvV6OY4Dx4+yJVLFY76zwYH8dvtvOfFZ/j+LXdQ5/TzxTtu4qELF5EEgf7lGJ84eoRPXpPhtqa9tN/t5wunTnLotavcc9NWHn7qFD3rwvx6YohdtXW0eWqwSjLdgQDJWIZsqsD7tmyi2unkU9v28VfnjzKRTNDq9TGWiPOuXz5FOp7F67YSbqlC1wwSxRKj8RUCip2RZBKXW2EpUYKSjiYLKDaJoGTDE3ARCnmZzeTQTYMd4TBr/X6CdoWfzK1UoHe3BV3VqK7yYbfIhAIe7mvv4vnpcVKlMkVNY0e4hkSpYn1Z0Mvk1IrVc0t7mOjcMhuCYe574nHUksrxC0PkVZXN1dWIghXVMJheSRB2uljI5XFZra83TP32jv86FpT/x0TTNM0FQRBmBUHoMk1zmErVcuDfTXsa+JZQ8RqzAjuBvxcq39T3gUHTNL/+xgWCINxGpflnn2ma+Tc8XwXETdPUBUFoA9YAE7/pHIu6xngiTpXDwWgsTV1dkINTUzR73LwWXWBDMICIgNVmocrp5PLgFKlExU/84swcXsVGlV1BFkUaXR5yq3ZfdS0NPPbs+Qrf1iqCbhKosvMXu3ZT0Pt5/lgvqYyKYq0EEQoanWsDHPrw+zi1MMP//OlzTE6kmF8pwqUxfLvWo5s5REHmWGSEBx95imS2koiaJnhcdiySxGIui7pq66jIzcxk58mpMyRKZTYEA+TVij9w2OWm2mFnIpnilZExmqsC7Ll1N8cmJ7nYN8ETNoX71nSw1l9NSc8xnBxHEgW2VIWI5HI0uV2cjMzhctmZTqUQhMrFL1sqcHVGLXNydpZCtljx0LZbyRV07LYKWbquyk4sWaK8KlXjcVlQrCJL8QoM+a9Nex6HBcUmoelGhVpwdYbvtL3K/Wt6SJWLZDKVxNk0TUaTaQ40edGMSoUzks1R1isJ0aZQNYdn54jPJ9m8sY0v77mez586yexygo88+hSfeNONLEcSxNMlDMPEaZc5F41is4g0e31EU2lShRzd60LE43nsNglZligXVJ7qGyC1nCGTKVLXGCCWzzE2EqGlqx7XYpJAtZfI5BKnT47RZ5dZs64em2LBZbGy//Zu1oWC7AyHGYrHGYgn6PQHWCllWMjnEAWRV6MRfnTyAi0N1YxORGnqqsMRTbze4V0o6eiGTiRWoKHawYFdG9gRrmEokaTLH+R31/Xwi+FROvx+3nrzTr76yGF03aBcrFA23H4nNoeN9EqWbDJPbUghnVMpa2aFAiFXusMBcmqZzrY6xhJxREFgjc+Lahh0+ANcsoispEqYJR2KOtmChs0mkS/qPH7yIrJFYmhkGcMwK45xpomqm9htElZZxGIRKZR0Ah4rt+/ZTE8wQFHX+ND+3RycnOSllwf4w1v38I1njyJbZRakSuK3dWMtIcXO7ro65nN5CppGPJbl6SMX2fOuO9lV38AfPPsi2WSOYYeDjdXVGCYcaGzkxekZeprrcFosfPPSJXKqyoc3ZXl+fJzOQJA9GzvpnY2wNLuCw2NncmieRLpMld9GoVShBfzshfN4nBYcDgsb2htx+52ceHUCfakAqoGr1kl2PgclgysX5tH+h8G3zl2sfG+6gSAIqEWVsmpQFXKSjKVRnDYUp41z0QiNHi+aaTCVztBaE8IuW/ApCg9dusjburq4pqaGNXcfwG2x8tjoFU5FogQdDv5o8xbqne08PfkaQ/EkZV2nyxfGIW+goPVxeHqGKwPT3LdnK5/bUWl6TJZSWCSZz167i7OL8wD88cPP4HFZUawixy8MsdLdzIfWg2GWEQUrZWOWgM1Jj38vomAjp/Zil7uJ5uOs9e3GaxXQzTgZdQGv1YXE/0o0TVNHMyp8P2F1S7GKbWyrkjge7efnI09xd1srm4M9JMqLfHv/rThkhYVcH7qZRBTcnJyf4tuXL/OlPdfT7q1mNrtMULFzZXmeneEWHLLC+3puxiLUEi9d4MT8BLc17cYi1v+bvSDsuJYf3HwNmrlAVl1AEEQS5SRXYivYHTaSiQJf/dVhiiWdNT21lcRzaI7qphAzczGK+RI//sjv87FjBxmanaeQL1Fd5+P2jd00uJ2s8fpodHuZy6b56vlzHD3TTyanks6prFsXxu5S2NpQz9s2b+DCwiIFrSL5k08XsMhxFlYq0l4Ov40/vn8fZ6MRfjE8SlFT+eimKjRjkuvrGpEEsaLFq2nUOd2kyjlOzk9xZ8tGLiz38eAzL7A8n8AiCyhOGyVdZzyyhGyV+dSrR/jydXvxWG10BUPIosgNDXXMZrL8U28vHf4ZPrNtH3+3bz/vymY5cmGQhjo3hVyJ//nD53nLrVt5aN9tiIJIp6+KXdu6OHFukH86cZZivoRx7U30BHwEFRt5TcNrUxBEAa2soWo6C9PLNLeHeXFyElEQuDI2w+BMFNki0VBtx9tdTTaZo6a5Co+jcgP8tq5OXFYrs5k0S/kCQ4kE7+nexNBKnEi0giRpqs7E2DzVDQE6q0K8ODPOUDxBolDgY5v3r7phXeRibIaDM7PMplKYpskPbr2b3uUoiVKOr992gPf/9EkmplN4/At8ZPNmsmov1U4nhsPBqclplmZX2LWtiw6//zelHb8l478PdA7wh8BPVjvOJ4D3CoLwYQDTNL9jmuagIAgvAleoyBh9zzTNq4Ig7AEeAPoEQbi8eqw/NU3zeeBbgA14efXO4V9ljPYCfykIggbowIdN04z/ppPTDRNZFLFIEharjE9RuKutlZ5AFT+82o/XaiXkdLDodzE6t8jKcg6HIhGsdpJN5jkyMs6mUBCHLLJSLNDs9uGxKYzHV9i0sY6zZ2Yrb2SavPXW7awPHODjr32DYknHbqv4oKfTKjtvaOOL+/cRUILc0ezm/M3LfPWRw2iawfR8jolEnCcn+nhL+xZ+PDCAbpgEvTY0zSCZVbFIEnPpFDMTizg9dmqqfai6wXQ6xYn5eTxWCw5ZRjNNvFYLdlVmuVAkWy4hSiLxQoEuv5fBZRehbhfpcok6l5vpbII1XgsL+SyGadLp99Pi9fDZo68SnVgkkVEJeiubrsdZ2UBcViutHg8rwRDnE9N43VYyiWylaaoEkdkkDrvEvbdtosrh5PClQcqFMoIkkl9tIjJME5tFxO+1VRo7FlPYrBLpbJmHfn2Usb0p3tPTjbIqg1IyTMIOO3PZDD/uH2RnXaWBSjUMrJLE31+8yHQqid2tkCgWccl2wk4XhYBGR00VNlHiC/fdyjPj4zz/Sh/VATux5RSbt7VSNnRCLhf9s9N4Ay5CdQFki8TsyDyqpmN32sinC/w/7L13kGRnefb9O6lzTtMTevLMzuYctStpJa0iRoCwCEI2GQxGxhgbDPglOADGYINJNiB4hQFJoECQ0CqtkDbnvBN3ck93T+fcffqc8/5x1vrqdZW/8lv1ff6w63uqpqaqa2bOdM/0c+7nvq/rd4mCwEisnStzcTRVo93lZlO0nWG/l8XuHn6w/xCx4Xbq1SZ/sGML4/k8L45N4lQUnIpC1Omgw+VE1TSOLi3x/Mws712/DqsksrSYQ9M0tq/up9ZqMX1lkZ1bV1BRVSYnFrFUGmxeHeOOgQE0XSfXaHBnbw9fPnUKv91GzOshVa1yMZVEkgT6+gJ84rYbeGx8gsPHxvAHXVRLNdL5BmG/DV/AidpsUa81cQdc2BWFNw4OcH1HOw9dvozLYsWhyJxPZ1gZ8FNWVdSWTqtlmO4PyURTyZKALAv4wh4kScTnMg1F8H9lpGuagc0pUW9oCAJYLRIXEgnOLyUYaQvjsljIl6ts297DwcVF8sUmDrvG1rWDDAf8uC0WpgoFco0GPR43D79yEt0wcNhkNMPUY27o7uSXV8+SKJZYF4lwLL7IZC7H5dlFZIuM02Unmypgc1r5WOYlZsaWqPc3WNHexpqudqZdds6fnEE3TF2rfi2jfildM7srETeyIjGbz3Fzfz+apnPoN1dRrCLdMQ+XyypUq3z1I6/ji8dPkF8uXtOwGmQzFVwOBb/His1hQdd0mnWVG/r6qKoqz5y8iNVmYe3WdcRLRSbTacZzOcbmlmgODSIIAm7FQq7RYE9HJ9l6nfWhEFOFHBczR9jXNcy2SIXHp8ZwySEWKoewS1buX7UKY9VKInYHJ5dP4bfaGPSs42dTzzGeK3D3QD+qboZQlIp1mqqO36rwnnVreXjiADuinfS6VzFbmmTIu/fVPdUitWHQYNAzRFOfxSL2km0soBk65co5Ohwjr8ZK1rRLOOQudBr86yhPQGS6NM8/nz/P1fkUs4UCq0IL+G0W3rlyH4rYyZf3yNik1TS0SU6llvnC9TfQ4w7x5NVL3NEzzPl0iqlCkVWBCC45iEXsAiBg3cxt3REmCldodySYL2dwKhYGPDea1xYkFKETv7UTzSgTsLa4o7eHeLnMpfk4fp+LZCKHYpUpF+t4fA4qhSqNehNdN3ApHsrNJn975y30uQPkm1UytRpL1QoPXrrE2NwSmUTB3C9dFjq7A4gLOS5dStDf6yfh87A92oYimek7/f4AcbmI1WFldn6CwQEfb7l5O9lajYDdzlKpRNjpJNcYJeYK89jUedaFwvS6vYiCyGKlyLAvSsTuo6JW+dDT+6lXGzRUnVZLx+G28z927eR/XrpColzCJiscWppnW1s7N8e66HS5+Z+XL9Pn87KtvR1V17mUnWNz+A5+Z/gKz8pXSSVyJOIF+vuDvG3lCAJQ10x5RcTppFBWGfC7sLtt/GZxAq/FynOz8wz5/TgsimlEbGrYrDKhDj+VWoPUXJp4qkp/X4C71q+i3tIQBIGZfI7zo7PUK3XWd7TT5nTyq+kZFkpFPBYrOgYTmQzfP3WWuckkAY8FxSJTzldxuG2UchUqPi/3DKwmVT3FeCZNQysyWZzgOxcukqlVGZuK06w3+VT9FTp9HlaFwmiGzqNjExTzNeoNjaV5s6z488MvMJVYZn2sk6DXRXoxi47BY8fO8tu8zJyf/yYdTQDDMM4CW/7Nw9/+N1/zJeBL/+axg/w7IgLDMAb/nccfwxy1/4eXJAq0dJ2o08WGTRH2dnUx5I1ildx8cc+NvLw4x0QuhwGUi1UsikhT1WnUVMKdAbSWxpOTU2xvb6fX4+ZIYoHZQp5sokCj2sDtVigVmzhcCn+4YQOTxQPsP3ERj1NhOdegUmuxc3cvqzujPDYxScThYIUvhmEY5kmspYNm0Kg1+enYONujHQA43TZajRaFikp71MQqDPgDdKz3EHY4cCkKyWqVdL1Ot9vFdKHEaM50mU5kc4QdDg7PzvE3N15PslrhF1PTSILAX+zYSaWl8uDFi3zl9Cn+8YZ7yNSTPDl5lYjTyf0jI5xdTnH78CBfOb+ApukkMzXaAjYSmRq+hsb5yTnmCua1StkyDo+dWsXsFCo2kQ1b+3j98DDrQyEUUcJvs/LzkxdoNkw3YaFkfpYkEcWmIPwrT1M3aLYMhIbG6bkFhvw+Hlj3TpaqR7mYWWJNsJ0nr45yeXSO1eEwsiAQstteNQY1my02DvfS5nQSdexgwDdKvFziyKkxjpwa49ifPEDQbuPIiTGyxQa7tnQwkUhR8jSYW1qm1WwxNppkw9YefFYb54sNHDaZhYkEN+9Yw7OHzmOVJRo1lc7BNkRBYP/Zy9xw1y2sDgTpv/f2VyNEHx0dZX5uGVmRWK7W6HWHkQSBUrPJdNHsEI+Oz/OZdI5GrUmupPLavQP0er1k63V0HY6dHCedb1Bvagz1+9F0nc2RMMcTSR6/PMpSpcofb9rEO376c/pjEf5g/Xo+tGED76j/im/ddTsxV4ghX4A/qVYZPTfHv7zvXj75wkvUynVqpToWuwUMg2qxxoVUkk9nM4QdTvw2O9vbo9hlia8cPEaiXMKhWChVzJH5NfI2mm5gtUj0dtj56M7tPHTpMvK1zqV2LeZU0zRUUaCeNmUcDpuZo55fLlKvNOgLBri8ZB7WBiIhXjpymRVrO/E67ezp6iRst/Hi/CK6YXAxlWR7ZxeaquFyWanXzASat6wY5jOHD9Nq6SxNp3ha1cini2QCLlLxPJIosHJtD3aXDa2lsZzK0tJ0itkyzm6zQIl5vEw7ZcrVFi3NLDLVlo4sCQQ8ViRZIhTyUlNVji/FuWtkmMTMMlaHBUmSGOhx8+733cHldIYjx8aRJAGrIuKwyTgcCs1Gi0rN7Oq4PKYJ5OD8HOV8hbft2cpMoYBuGNwQi/GdV47zi8kpnF4Hy7UaC+UKFdVkg97Z28uaYICxfJ6ZQhGnouC1WNkV3cXvjdipa1k+fvAlPrdzNzd3rUAWw2Qbs/x47AIrAwEU8RI7ol10OF30uMK85mc/4m/vuQMDg8899xu++ju38/jkFK8b6Ofh8TFu76kz7Iv+b3uqLEQwqCMKHn42eYibY0V0w6ChNbmUWean+VE8isLbVmziN4sTHEse5A/WbqbN3sWjkz9hsVzBKklcGp2jWVe5oM8zkUixra+HK7lLrAt24lZMgp0keFkd9LPC186hpSlemlug1FR5Xf8w6doEUUcbouD5334/RYjyl0d/wHK1ykgwxJtXDNPtSlJSF9AMDa+lA4vYhSS48Fo2siu6kSFfhO9eOsOzV69SqzQoZEo4nFasdgubBrt5YMNGBr3dWKVhvnfr73A8NcvjU2M8cWXMPGQ3WywsFk1pkUvBahHpGGjDIkmkE3m6Oz1oLY0z56bY0dHB21ev4qnpGaZyWfx2O367nU+9ax9vGBwkaHPy9XOnaek2WrrOV264GZfiYLGyTK5hpivJokSyWqbX7Wf/3Civ69vFy0tneMfWDXzukefoiPlZWsi9Kv1ZGQxw7PwEd+0bYF9sEL81TMTuwyG7ee9aCUkUOJlMkqxWWa7V+Pypf+KjG1+LLDzND9Nn2LptkB6vjy2RHhQpSKE5xyMTlwnZ7Wza1k+vz8eHN24ibPfw+ZOHSVbKrAkFKTUaSJJIuN3H+3dv4+ErV1iYStBq6fg8FvZuGOHA1WmWF7M0ak18IfNvWcyUeTZ9HovdQrlYo6UZfPC11xMvm272c2cXEQUI+600K018ASf+Ni+CIOBUFEpqlcdPnENWJL5y9ggvTF7FZbdyz4oVJuYuleHwwavYLCKnIg4ir93Lawb66PK4eeLQGRKpCn+y/3lCPjf3bVzHU1NTiILZIf7ghg18JLH8f1KG/H+0/msUmsJ/DSjp//1qG4ka7/3Bu9gYCbG7vQ+vxQeAgAUDDd2ocjG7wP84dJC5mSTFvHlDbGkGkTY3N64fIV4qUm42cSgWrs4laDZUdM1gdCpnesJVnQ2b2jnyvgd4bOo0f/TgL82xmyBQzta5/e61AK+6xO/o7eHerz5MKd+AlgF2iQ++eTdHx6fpiAZI50tcuRjnS2+/i88+sh/pGtvshl2riHnMxB6PRUHVdA4tzKNIEluuAd47XE4qqopTUbiYzvLS3CzfvGUfumFwZjnJyeQy2VqNsXiSN6xbxWQ+z9a2NuZKZWyyxJNnLvLA9TuYK5X55YUrLM1l0HSDTKGJpumms1gRTVegTcJmt7BxTR+j03HsLhvZZIE/uuN61odCWCUJWRQ5kUzy4MkzpOYzLC7X8LsVltJ1BMDrVhAEgXyxiSwL3H/3NpKVMsVqHZtVIebxcmk+bqYZVeoYBmxbN0iP14siimxti5CoVHhycpLZ+RRWh5WQ382/3P566lqT33/mF7z88gSxNgffvP/1vLwY557BAf7ohRcoZSv84+vu4DOHD5NczLAUL5oFgkVkMVXDoogM9gdQrAp3rFvJc6MT6C2N/HIRxaaQz9VwORTedvMONkVC9Li9PDI+zq093Xz3wkXaXS42t4XpcrnpdvmxSgodAC3UAAAgAElEQVQB6xrGC4f57JHD6IbB5FwSh8dOIV1CkkVuXLuCXx85h8Vm4cLZBPtes5o/3ryZy7kc51PLBB12crU6VxYTvGHdKjZFIpxdXmZTOMx8uYxVEtEMg68fOcHTb74fURBp6iaI2Cop5Bol3vfcflKLWSrFKhabBUGAUrFOraGxdnMPq8MRpnJZPrljBw88+WtcPidrom08/MxJqnWTLtBq6VDTQBKIdjj56Btv4vELl5mbTLKUrqOpOojgcihYLCKlimpif+rmgcLvsaCqOiMrI0yML7N79zAeq5UTpyfYu2M1PqsN3TA4GV9EliTymRLlfAVd04mnqhhAe8hOuCvAmq524qUShUqNVrPF/HQaVdVZtTFmxkeWauzetopys0mqWKKYKRFs9zN+YZ6BlR30B4P47TaaLY1kpczV+RSZVIFaXcPlMPmJbp8TxargcdhMdJjHi99uY6lUYi6d486Vw5TVFgfGJjlzegFdN3DYJDxOBa/fQTFfo1RVCfttiLJEe2+YWrlOo9ZkpK8Dv82OVZYZz6SJx02+7XWrBrk51sVsqcSxpSXanC5u6e5C1XRu6hrikYnznE9ncCoKgz4vN3bF6HK28cPRE7x1eAM2qQdBcLBcPwNAQ1P5p4tn+emBE5z66B9zLDnDP507x97ubq7r6EA3oMcd4n3P/4p7hod489Bb/919taKewyGvoanPsO/Rb/O+zRu4d/BGBKwYqDw6+SIzxRIH5+fRDIMvXn8D64N7eN+L/0hLN3jryAo+/LOnMHQDp8duBlP4nHS6PbxmoI9HR8ewyjJvGVnBXT1vRDea/PXJB/n5mYscfNcHELBxYPEsDllGx2B39Hpk0Rxnnkj9mvt/8CiiJKKpGvVGC1kyiQjekFk4/MGaNzBbPkePeyO6UeLvzvyKD6zdhtdihg78q2zg366GNkWiGufuh37EusFukpUyhUKF+ek0X77/LgDsssz5dJaD83OMXZxj17YRcrUaiaUss7M5NE3na+9+LUfiS/R4PRxaWCCTL2HoBt94ze2k6zV+fGWMm7pjtDkctHSd/bNzfGXPW5gtj/L0zDQbw2Een5wyTTENlXqlzgPX7+CJ8Uk2t0f5wbNmHKwgiWxZN2CC8ZMpAA7e92Vejv+CdL3Gno4+3IqXRDV5LaEuxcNj4xwdvcrm4R6+c/PHyDVO8YEXn6bYaBB1uZlMp1lezLJ6pJsdnZ3s7eoi5gqwVMnz9XNn0XSDTrebXL3OwUsTzM/laKo6K1eZBxbZItNqtjh7Ls7v3rWRHq+X5y6OMXolic9tIRz1Ui3X2by2n4uT87x7zzamCkUuL6e4cn6OhWSVP//9m9jXHUPVdD7y+K9ZTpXo6Q8zEmtnbDHB725cyy9Gxwg6nUScTg5dmOC2zau5b2TEvDdmlnnTVx6lt8OFxa7wrTe9lm63D6tk4WvnTvL9Xx1ieE0X37vzTmKuKE2tzvnMArOlIkuVKn+47jrs8r5ThmH82ybbb8XasmW1cezEo//H3yeLa/7Tn9N/iwjKb377Hz7zj5/4fYZ9bRgYyKKAIAg09QqiAIVmma+fO8OxU+OojRZNVcduk03zjq7T2xnGKstkajVCDgeJTJ5GpUE8WaFZayFIApu3dPLQva8nYPPxJy89QyZZxGoR8bktDK2OcuuKQRbLZRYTGa5MLXI4vogiGGQydVB1urrd3LFxhLMzC1TrDXLJAgf/9L1sDHfx2MRlatUGFkVipKcDt8WCZhg0dR1V11kqlylV68T8Pg7Oz3N4Mc7Ht+5mqVpkTTDIcMDPsC/C7/3y5xydmWchVyCxnGf7YC8bImE2hMM4FJkul4vbuvv5xcwUN3Z389zMDJVGk2qxSirXoN7QMHRTX2wK2SU8Hjuv2bme3x0e4oahXhbrNaq6xoFTV1g/0EXIbgLnLaJISm1SFXVssplw1NXpJbVsdpDVlmkQ0XQ4PxYnvphFrzfQdJ1DJ6ZIJkosxIvkiw0yuTod3V58NhuaYZCp15krlfHZbaxob2NtexupSpWo28qgN8KVfJqubj/dHWE+/cNnyWk1BttCpOsNaoJGTlXJ1WrIDguyYOB0WUmkKugGvPmuLbQFfdw81E+l1WKkLcTZyXk0VWdipoDbqbDvurU8d+Yyk/UKmUaT58cmyGka71+/jvuG38iw14dAg6lihl9cneRnk0fMfPOebv5s8/Vgg7LWYn5xmRWDXRy7OInL66CYLbNiXQe3Dg1QbrX4wcvH6YuGKDaatLvdJGplXj45yrlqnl2dnbwSj/ODF4+xc6iXGzp76Q56OLK0wAp/kM+fPMI3zpzg1t5uXIqdbo+T0WqRXKaEYpWx2BR2bRymUC2zoS9GodEgW6nSH/AxXimyOhJhV2c7f37rHv7izpv5xcQl0pN50AwQoKIZzGZTWO0WlpaKjKwIIws69WuGIa7FSZYqpn7Y0AxESaBS1xC0Fv/87tdzMZ3h+lgX/bEoHquFV2ZmmcvmyCzlyCYLWG0mbqVRa6JpBvWmRibfwOOUaVoEBv0BxqdNKL7WbBHt8hML+fH4nNRVFckm88YVwxyanGH9YA92RaZUqzM5nkJ2y0TdbhpaC7fFSokWszOmRlU3DGQBqqU6yaUC2XQBh8dOKldkOpmhPxLi1sF+MvU6z18aAwPaO73QVGlpBh6nBZvDQqXSwOuyoFhlJFnC43NSLdd53aa1HJucQZWg1lIZDARxux0sJrPcu8F0mp9MJLius5OlcoVUrY5LUbArArWWyqZIBDD4vZHrCFj7qLSW2RzpZyy/gEtRKaiLjOdTZOsV/unCeZ4+eoH33LqLeCWPU1YwBIF+j4eo00XI5ubzJw/zhd03sTG8Bulap/AvT3ybL5/6BW8avo6mPku1NYPbsoF04ySfPvYCqWqFDo+HrZEYFqmLYnOcl+NzvGFwiJ+cuYAgi7xt5VrG8qP88Mx5vnTTXj558BUqxRqNWhOrzYIoiaiqRqZY5kx6mW6vj5DDwQfXvgOAA4u/5N6h9bxv82v400NPcCR5lR63m36vn5+MjfHlU8/isWXocDZ5bn6K/afGaammTKdW1yhVWxRLDXLZCstik5nqPK/t28VyfYp0PUWPx80nD/+Gh668QLoxTam1wMuLJ3hs6iCfPfhTvrT/EX5w6TlcbtjetooHTx9lqD3CXDbHt++6g0DUx8sLC6/uS71uN3u6urhQzSOKIitDYUJ+N5aAFa/bykP7T+Fvd5Ov19nYFiXTrBML+HliYoJnr0xy75qVGIbB9y9e5DX9A4wE/HzrwiFuiQ3ynQtnCDvsnEkmyRTKZJN5nB4HLVHk87tvJGC30NsV5uTMAk6Pg3JLJb6cRa23sNot/HDsRT64fju6oTGWS3Mxu8i2ti2cSI3itVrpcrtY0RHhE1vu4rqHPsOunnbevmozhxPzjCZTqA2VWFeYqekEssPKnb0DvByf4fn5Bba0hYk6naRrdU4vxckvl6jUVHbvHmZFWwSf28nV2QSZVAmPU2ZxKcPpsXlifRE6Yn5iXSHWxzqxuKycvzzLYrxI0y0RtDs4NzZLPFlGbejctnWYe/rX0uXyY3HLLCsmhzlfqxH0uriay9Fsabx51UpsikxR1Lmtt4cBbwCXYqfWavLK1SlS2RqdPUFuG+xnha+f2fISZbVJwWqwr7+fO3uuw6COJMrkGkVG/GGenJrg5fgMR79/+Lc4gvJbn3nPe9/Evw7R/6Mfn/vsN/7Tn9N/i0Lzoe996zP3v+s2bJJCXTMdzBZJRhIk0vU8P528wsMHTtBomlo8iywiW2R0TcNiVeiKBmh3uYmXSsQzJpx7IVGh2dLRdZBkkX07V/Ke1W/lp5PP86P9J9B0g5YObe1eBmNtzBQKFGt1UvMZDN1AbbaIDbbjdkmogsFPPvBmjiwl8Htd3DTUzxduv5WI3YcoiNw20sfziXkiHQEe2LSRRLWKZhjomDpHj9VKSzBYEwqxvaOdktpisVrkaqHEfLnEuVSa3x28mX8+8SKCJLKyM8p1Az20ORx4rBa+euoUFlnBwOBrZ06RyRRZ0R7mlu4eVrWFuZDNsBg3R0LCNfi9RREJ+W2Uyg0m5hOcr+XZ1t7Or66MU0iX8AbdPPbKeTYNx/BYrJxKpVB1A1mRSGWLzC+WWM6YhhuHTaLWMM0CrYZmFiGyiMMmEerwU85XQBBw2mQcNsnMwLZLWOwWLiVS+J0OFFGi0lRJVissV6sMBAJouo5qNIjY7UzmCxw7O86ajT0sL+U4n0uTLpapFGskKxW+dNNenFYrxydmmZ7N47DJ7N49jF1ROHxylAQNqqrKTCHP9NQyXr+TW65bSVVtYlhEKtUGTV3j0mycdX0x3rVmDSt9HaRq0/x67gIfeeoZwj4P3332EHPpHDsGuik2m1RaVX4+OcmF0VksVoX2sA/FaeHY8Vk6u3wMx6IsFEucXlriPTu28OOXTuD0OehwufidwQFKVoGA3UG6XidVKaPK0BIEHpsYI+hwMJ7L88upCY6PXuXyxTihTi9rgmHqWpO6bhCvmukWikVm72Affr+HDZEw1ZbG6nCYqNOB3+7gZGKJdeEwEYcDl2LnwaMnqAtg2CRkh3koK1dV9IZKb3+Y4e4oiWSWfEmlPWRnz3UjiDIspytoOqAZ2OwyzbLKx952M6O5HCPBAA5FZiqX58UL4yAIrOmK0tUWZHVfJ2/bsJp9w/3kFJiYTmKzSEQCNiw2BQyD169awQvnxpBk07Dmj3iQRJGQw8mewV6cFitzpTJ1QUcUBKbjy5x5Zop6vkmqXKMz5sMmyzgsCkulEulEkWpDo1RpocgChbKKpoPfb6dSrCGKIlaHBY/Djsti4aFnj1DM1xAEg1qpRrXWwmmX0HWDWrWJrkM0FqBSqIIgIMomliih1oiGfAwFgwTtDjZGwgz6fORFjZFAEK/FwvVdnYTsdi5lMvzt7ttZ6Q/jVuzE3F7cFivHkktsjkSQRIWvnnuJiEMiU6tyOZdiwBui1KyRrFbQgYHOCO9fu5l3/stP2dzfxalkinWhEKqu8cj4KJ/f9X5cSverRWaiepSW0STocHA8dZ5qq0C6XsJjKdPQVE6lErxv/TqWKhXWhoIIVHh44jQPX7iEy24jrTbZ29NDv9fDV8+coaypTBYKTM0nUSwyoiRiuRZ64fe7CLpd5EsV/C4nVVXlNX2ryDUukawW+fgrBzi+PMGHN27mnoHfpaTOsVAusjnSxp9ufiv9Hi9n0lMUG03OJRIEwh7KxRputw2/30FHzI9VEbl97QhvHBqk3bEOqywStI7gkKtc19HOvcNrCDusiNdkOSOBAHcODBBrD9Ae8LG7owNJ1HhyegJBFNjT083ujh6emp5E1XSeOnGRc8kUgtXCkxMT3No/wNGxaTItM3ayrrWQrTKZVAEDjU/esJuI3U7U7eJYPE6/389961bT7nAyUSiws6Od1cE26lqL75w8jWITuL6zk16Pl6jLSbxWpbc9TLZSZWouwS9mp7hnxQqOJhJ0RYP0hoMcPz3BptV9vGn9KiqazpGj40xS5t7h1awJrGNdcAeakafPPYhDbiKJBpl6DZ/N4E2rVzNbLjDkHabNpTNVLOJ1OZAEgfhSllSuyGvXDqOI0rUAgEH+4sWXuDA5T63cYHCwA3/EhappdHo8SILAXDJDsdi4ZhgEl9uGpEhMjy1Rqzc4dWmWxXiWcNRLtMuPVZGvBWNUaGtzIxgaf3jDNno95iHrz3/zAulr5slKoUp8IUM2XcTpdbAu2sZTU1O0dJ1Oj4eV/hCiKFJtNclbBKpaA4/fhd9u50J2HlkU6fN4mS9X6Pd5mcjPcWp5gV/NjKOIApsj/cyU0hSazd/yQvOb/2UKzf+oGei3emmGgW7o1DUVEYEruSWGfW2M5hJ84+xZTp+dQtdNrqPaUBlaZSb/vHJhHHfAxUgwyIszM2baTrWJpmoEvVZiAxFS8xkqNZW/ue5GGvoEz0zP4LCZPDXFIlMpVLk8sYDVbsHQDTwBF41ak0gsyC29fcj9/YRsNhyyzPHJGf7i5uu5JbYKVW8iICAJAl3OEJ+6cTd/d/wEC+USAIJg+jetikyf18POjiivLMQBcCoWnr16FadiQcfg5MlJwo5HmJtO43ZayAQ8aLrO1bRZ9FaKVc5bbfxsapG5eVPE/mfjT7NnzwqqqsrFi0u0WjqSJL567ZZmMLtURhQFwj4ro+fmeeepOWwWEZ/PTiDg5uSpBT71yK/53jvfiFNRcCgyLsWCy+dAlgsYLd3sZmqGKbJ3yFhkgXrTFLEnsw3eMzzITFuYuUKBLR0daLrBS2OTVIo1gnYHS6USM7k8frudqmo6rPP1Oj++/VN85vg3eeXceRLlMncNDnK1O8SaSIRMqkClUGP32iEmc1lOH7/K66/+iGDEjWFA0GtFEGBdW5TTS3F8YTep+QxnlmZoqKYzPtYdZmIhiaxI3NHfjzI0yKZwGwPeCA7ZhW40qbUaLFQK/HRsnKWFHN+vncAdcOFw2zkwO8ugP8CA10OXx4tvnY0riRSariOLIps2d7E21sFisYjXZuO+1as5EjcRUpquM5kzheqqZnZsnjl0DotdYcfaIZarFSauxllIZvj9bRt56ORZyvkqTrvMFx99Ef/9VmqaxouTU/S2h/DabKwLhYg47BxZjOO3mazL0WwGHQNJENENA90wmaY2SWEpVaGSqCIHbTRVHRoaqgC5YhNPqUauViNfVokGbfSv6qDT4+HAK1cQBFMzjVWioeqs2xil0+lk2O9jrlSmoen4bDa8ITeVQhWHYmFfd4xqSyVVq1NRVSRBJF9SEUVoi7pZTpXITmZYWF+mf2UXm9s7KDUaXEgkyBTK2GWZRKWKVZJIlEu4LBbKzSbHj8yZ7XmbhFUxk8AAmi2NbKKAKAivmvkKZRUBCPqs5PM1PG4ry4kC0S4/a8MhdMOgvS/CwlQSm91KIpfH67Hib/MiKzLTY0vIksD0RAqv20Kt2sBes+KPeFnR3sal2UUAFNF8raNOB29btRLdMHh8Yo7beruZLpa4d3iYVC2LRZS5nE3wq5kZPrBuPe9evRGnvJLLuZdZLJX5+Csvc30shkOWeXj8PLs7OvFarRQaZszf0cQsH73zBm7tHjINfMA/nDrNYCBAonoSu2zFb93M41cf4UuvHOEju3fQ1DTGczmy9To726Psnxvj22fO8uEtWxjL5UhWaxxNmiDwI/ElNse6+OYLh3nzdZt409BKLJIZBfrRbVv5/MEj2JxWWs0W+WyFTq+djqCf4WCQQzOzKBaFxVKRBzZt4mjyEJIo8tz8AqliiaDDTrZRpcMxw4AngtdSJF2vkKpdZrKwzOOTU2wIh/iDm3ZSbbU4Fo2zo7OT7W1trA/FOLM8x4g/SsS+DYBiY4nR3BmenZvHY1EI2+08MTFBo9XCoVj4hxtvJubazUbT28TXzz/Iu1f3MxQJc+TMmKmtPPwbbuvtNkkD5TrVUp3Hl06aIQrZEqGIlzsHBqi1NPx2G8vVKuWRKAGvi0uZDG8YGCHqdPHg0dP09PTgtVpRJIlOlxNV0zmXNgMCFKvMYrnK1rYoy7UqK/1BtrSXCVqt6IbBhrWr2NrWxocPvMimaDt7uzoZ9gdwWywYhsF3jp9mbmoZv99OulohXskRtK5BM0qka3McXJrlqaszfGzrVmyyhMfiJFMvErY7eHjiADvbO4m6XBQbDc4vxOkbbCexlGWhXEISRFKVKo+Mj9ETDuDuascum+bR2UIeEQGHoqCK5n1EkgSaqo6mCVTTFZr1JtEuP5JimnYVi0w45OXuwUEy9Qb9XjffEs7S6/NzuDjKYrnMmfQ0bXYXhgEOl40rl5PYrRIr13fR4/eTqVX53KPP4vbaEEWRnzVUNF1nR3s72boZaPL267bw04uX+d7BExiGwSdvv5ERX5i9sU6mCkUUhwOv1cI2R4Snp2f4+0PHCPrcnDj2258M9N8J2P5bv6qtJufTS6wPd5CsFhnP5zi9vMxTk5PMTydpqhotzcCqmBFXfT4/t3THqKgqE6llziVTrA5HKDebnMhXWL22l7v6+6m2Wri3KJSaKgFrhI8efJKXj12mWtPwui3IFhmLVSHU5iOXKeIJufnIli187fRp/DYbmqEz7PWzNmhGv9+3dQMzxSK6oSEiYGAgCjICBj1uL36bnedm57mtt5ulShX3tU2xzeGk3eEhYnfwzu/+DH/QZWru8lVqDY1qvcXXH3+ZoNeKP2I67JIVU+u2vJBl747VzObzpJfLryKHDAPOnb5qjrR1w+zAiKDroCgi3VEnyUyNkM9KqMNPvdoglTCZ+c26yuzVBFu3xjh7dpGy2mS6WKRQb3B2doFauU7QayJ1iuUmlVoLp93M4O6OOskUGgiAy2G6zVcEAqwJhfBaLDgUmTaHnVyjyRNHTRj1leWymSwji4iyZMYDFn9DvaWRqdX4+I7tfOKpF5icXObksRnsVglFFjk9M0+zrlKqtsjkG1zb/4h2+dk22MuP9h/l+p0rub67m+88fZBytWViakSdsUtz3LVnI70eN4Vmk21tbUQdbhyyndnSAqIgMl8q8MSUabC6cdcqLozPobU0dF0nkyrgtlh5aSHOyZk5nC47Q5EwdlkmaHewri3K2cQSpaqZBHUxk6WhtdjZ3U2Xy8nf/+IAF1w2CrkqjaaOJAmsG+lkuVqhzenEF/bgtluZyBd4747NPHxl1Cywi1X+7pcH8IU9rOiK4lAUUxeZSHJHXw/1lkqHy0mrZDDoD+C3Wgnb7ahdMUQBXpif57m5OexWiaLdRFStXhEkGgvxO4ODHJibY2t7O2W1xXw0Rb3SYGY0zsTFRVotA7VloMgCHqfChq193DEwgNOicHY5zWg6gyyao2q31cpSNsXPXzrFsdgMkaAXl8XCfStHSNfq3H3HOs6enURttl7ttLc5HOyJdRO0WUlLZiexUqySUCRSlQqDgQD6NZ7rcDDE06kaCOCOOtiyo5+Pb9vGh556Br/PhSSLBIMOcrkqTptMsarSqLSoN3UUxWQfGgZ8ee91HE0kOTG3wA0DfeyvNJi5msZhk6nVVKzFGk6vA6siki02zddAa5g4MIcFtdHipu4u4qUiVVUllylyaXQOm8PK5uEe/DY7qUqZlxfi3L9qJZtCK8k0knzj/CnmikXqLZVcvU6fewtNfYofjY3R7/PS7/PitVgY9PnYPzuHx2IjaHPx2MQkf7PrJh6fusi2tnYkQWJHewd/9OILzE4usfmmKEeT89zVs5HHph7mmZlZRjqifPP0GZZmlrlt+1oeWL8dh2xDFtq4d/B1HEq8wHKtRrHR4OHRcWIeD51uNx6Lwn27t7D/6hSKKPJH62/mr3bdyHcvnUGSRIIeJ5lKBX/QaQYdKDJVVSWbzPPR267narHE1rYYXoubTxx5lgPnx/CG3BQbDYa97TT1AguVDCt82yg0j/Dw+EVu7IrxhsEBDsWXsMsSXS4nb7lxHx2Ora+yPa9rH0QSXBiGSk0bpaGpvPuHjyNKIrIis2Y4Rr/fz96uTnTDYDS3jCQewSoq+KwjvG3FBkSsvH+dqbufzGbZHevmxbkFkrPLZoOh0kSRRRRZZEN/jEK9zmK5Qq/HjUuRmS0UiIX8dHo8Jn1idpb3r19PpVQjYrdzYH6RhqaxNhRAEkSenLxKh8vFA1s2Y5dMGdcTE1O8eWSYfbFujiTi3D3YT6Jiapd3dnZxcimObhjsau/ljzdsp6TWmMxlmZ1MISsyn961iwFPlBOpA2wIrSBoC/GVg4/SrDX5VSiIVZIoqzWKzTqiIJKq1QlaPXxuxz4+deRZesIBGi0Nf8TLjy6PcnZslru3rWNtKMBELoemG68e/q9OJ/jgzTspNJroBmxY2ctLhy8jSwKGAbIgkC02UVs5tm4Z4qUrZqhGIVPiwXyJ7nCAbL1OyOHg+YMX8PjsfP/cebLJAp6Ai2yygC/sJhKw4vY7+esbridsd/CT8TEunJ0h1O5n8nKcUqHKj0o1zvYuc25ilqHedvZ2xXhibIxSroIn4OLYUpKQzc66UBt9Xh89rhCVVh2bZKGsNrmwlEAWxVfvlb+961+7lL/9679FoRkvlPjJ6BjllsqVTI5NkTCdLpGflEw8p9NlRW20qDfMm9ZzRy6wIuDn1JVpZmZzXJIWeMsdW0mUy5RyFTJOG2G7nYjDQapaZXdHF6Lg5MCZK1RrGnabxODKLoplE5S9MJ1kcKiTP9u6lV5PgC9efwMvLcyzWK7Q7nQxVcixPtTBGwdG+PTRVziWvMrO6AgtvY54Dbgac4X46NYttHSdtcFORvMJFsolmprGueVlvrZwhmpTRZFFcpkyH7r7BrZEIuiAJAgIArzvoSfILxdZrlZZEw4RLxXxr4rR7/WRKJfxeW04bC1ypSb1pk4g5GJ+oYDNIqGqpkxAa2qIIrRaGpIkYHeZjLRitvLqG69ab7GUrmFJlokGba9CgROLGeqVBv6oj/RyGZcs4fPZibTJyIpMs6Fid1rxBA2CIQ/DwRCJcoUfPXuU9p4QQ9EI+3q76feaI70HsxVS2TpqTUO0iAS9VgJ+Gx/atZX7nniC21YOE/N4SVWr5sglaGcuUcGiiNitEkG/m0vnZ7jhxmEqlTr1SoPZmQxddgudbhebNg9QbDT42fFzLOcayLJIZ9jOzbvWsDESoayq/Hx8nH39/WiGQVGtEbGb+tnPHDnIXDrHzcODOGSZA7NmkpDTZUVWJBJxE8M0FAgw0hFFFgUiTieJcoWq2qSiqmzt6OTJY+c4Np3ipp1rsEoyPz9sIjVm4hWG+hT6Rjo4fXyGgf4AqyMRMlXTyLYt1sXBiatMiSJb2sK8Z/06npiYZD6bZ3Y8QafXQa/Px5DPw3dOn6NWrnFHXw+ZfJl0rc6V9DK39fWRbzQ4mUqxWCzy6/OXKeWrNOsqDS8bbV8AACAASURBVFXHF7LhsMkUclVEMcuP8+fYMdTH+lCIv/7NQVxeB1pLp1qoIUsiXR1u8vkahmHw9rt2cUt3jFS1ys/GxrHJCtma+X4UBDO/u1xpXstsT1OvNlg10MXBeIKnT17E7XfiDbrJpIoUyyp9XW5kQSBZqXB4YZ6RUBi/y0HQ7aTRarE4n6bb68OpKKwMmeNoAO+wj/Wbu9ne2cm3z5/H4baTzZb4i9tvZNgX4Gtnz2CXFS6kkhzYP4pWUdEUEd0u890Pvo7HxidIVSoMR8KcSyYpZcvYrRIut5XZhSKJTB27NY8sCbRaBooimpGdAuSXS8iKxCOjYyzOLbNyqAvV52RxLksrXaVWafBXr9/H6cQSQbudmqoiCBba7GtxWy6gGToVVcVntQEGnz3+IkcX5nnfhvWM5wrMFkusDATYFo3w0JVLvH3VWj657TqeXxjn1u4Bcs0KLV0zu6PnZnnDzVv4xOb3IAgS9/zqs1wenSPU4adRbfCXt+1l1R0Rvn3xLH9z4iDbohFuja3gSGKaTpeb96z+fdSVCd789D/R63FxOrnMTD7PTd0x/mTrVmRRRDeqhG3r2D/xMwzDIFevsT0WQ+oRiTrsuC0KJxMpqqU6RVXl91auxGcJYtBgTTBAfeUAH9ywni5nCIsUREBglX89AA5ZJlWr8+VTJ8lUq7xj7Rpe27eBBy8fRTd0wOxMz5UP0unccO3/TEERI0QdfRi6QSpbobc/xOh0nGmHhfWhEJqhcySXxGWxcHhpCcM4gyKKSILA3liMHq+HNeEQT01OsjCxhMNjR1ZkM4XObUPXDV48ctEMdNjQj99mdh57vF4eP3iGTF+EfKZEOpFnYWCAD9+6h/tX7GI0P8V3L17iSHwJl8XKjbFOdAPWhSIslIvEyxXmiwUuZ7Lc2BXjTHKZuwf6WRcKcT6d5tDCPM2Gyie27sYuWVD1Fm32Dv7hxltZ2LKF71+6zJbwLYiCBb8tw7uef4Tv73s7L77tXRxOTPHyYpxfjU2ws72djaH1HE2e5sWZGWIuJ7fEhvnCrt/jdb/8Ch1uN1fnEoxfmKfW0HjkwHGS21fT0nXqLRXdMBAFgfauIPWWhiKaRsWVwSBjXX5mr2YAUDUdXTdMVvLVOIWSyuqREKIkMtzexmw+x8xSGtki0zfSwbr2KJ1uF/vFKVaFI7zllr1M5fN8Vz5LKVumrml0OIM8PzGF22tjaTqFy6HgDbmJhH2kKmXm5/OEon58VgeyJOHyOWg2VI7PLzCezbA+0satPd1YJBmH7EUzdHa1d5NaU+doPM4HXr+Hz37h5f/3Cpf/R9b/X2j+py0Dg6lUmlNuN31eNz6rlalCAav9mgC9rqJYZTRNp6UZbN88zGd+9Cy1hkajaYpIvvfEUfbcMES+UKfZSPJDzxX2dse4sSvGkGcPtz3xaWbnzLHzt++/m4fOXXj1Z75j12bu6hvAIdsQBYE2u5uNkTAuRcFvtdHjDvD18yc5EY+zOJtiIpPh27e66HFF0I0WoiAjCyJrg12U1ToXs3EeeOJpHn7rG/nqmTNM53P47XZ0DJ7+0NsJWN3YJAVZlNAM/Rp2RCXQ7mNxOk1L15krltjb08uZZILVQT+pWpVEIIekSASbZremUqyhSAIN3cQwabqB06Wgagb1pk446MDhslGvNChUVPxuCzPxCnarhG5ApWZ2AC+fnWVkfTdfvPs2bunai0Gdob/6U9p6wuSXi1RLdeLLVWLtLl63fT2JSpkrCVOcH69UsCgSuVSRc8UaV9MZXrtyBSv8PtxuG3arzFyiQqulU6m3kIoN7hu+j88//RIbwyEuLS/z07FxwrEgcxNJNmzsoiPoYzAQ4JlTl8yOXDjC0+Nn8Ud9bN0+QKFU5VI6zfmzV9mxdQVWu4V7bt/IdV0drPD7GfKG+cn4JQD23/NuNKPBucwc93znxzjcdmxOK7pucPHcIlOjSwiCwL49ayj1hbE5rPR4fVTLdSIeNy1dZ2u0jasFsxs8nc/x3vXr6HZ7+PtTp9i1ZpBBv48ji3FeOngFAXM8PdjtYdVIN4pkSg+SiaIZcxfr4guHjjIcCSOIAnu7u3l2Zo7dnR28Y/Vq/vzXLxAIu/iLnTuxyTIjvij7Z2YpuxxEnU5EUSBVrTIxucj8Ypr+WARVN7v8x5bGsVpkJElEEgUsisiGTX1859a7sEkW/seRl5gvFDiWsPCxPbt4ZGyMeYtMsN2HrulYbAqBthZtPg+vTE3zw2eOMrCyA5/TTqJYYnZ8CZvdQqjDTxNYWq5hGFCqqJSnsxi6wS23dLMYL+DIlBEEgUyhwfBQCMMw+MbBY7h8Tja2t2MYMBIMMRLw8YUnXzAZhfkcPrudJ85cJLGQA5tE2G/j0tl5pkeXCIRddHaGCLeH+PujJ/jYrh28bWSELpeXvz5+lJG37ua1A/08PjlJn9fLK4txcnXTNX65usTSbJpM/n+1d97xcZx1/n/P7Mz23tRWXbIs917iONWJ00NCCSUQDn5wwNEOuOMox4Uf5WhHO9pBCHAQCISSXpzixIljO25ykWV1WdJKq5W29zq/P2YTDJcczh35EeJ5v17z0syzs492Pq/vzjz7PN+iZk6Yi8bxOg3PlRUslMBmUm+n6XKVbL6Cy24gFssSWVBdUCZDEdxuG3UNDsKhJP9w5fl8+cm9rGhpxCxLxIsFHp3aR380yq2P7qVrURPLfH7qTE4SxWE+u/FGbrHfyVef2MPK9gArfT5kUWQoluC9Kzfw+f1P8emNF7CpvgWjTs90NIRTb+HGO+9kxep2/u+mCxAEHTft+FeOHRsjtJAjHs/xgesuYHvz9Vx55yfRiSLf33YFToMdo24J21uW8Xjwfm64//8SjCX4h3M2ck3bdQzH/4PDU0E8ZjOjsSgL8RQddQNsDTTR4/eSKZVIF4uMRKN4zCb2zwQJB6MYTDKvunAt13Us4rHpCd7ccw4D8V1c0NRKo8VKu62en5w8SKPFTI/Lw2JnE8niYcYScT64aj13j52kxa66wIwlg7xz6auYzhxjZ/BulnoaqDc1I2J+7tkgi3WUqiEURaGhwc6arha+ft7liILI07PD7JqZ4XBotpb7tsrR+TA3bz6HW44fx6E3E0ylGIlGKZcqFEsVti3pYjQWxWDS0+J1MRGOqPacL3G0b4yTJ6fYvnE53U4HG1Z0MRwKE59PYnOY+fdHd3Pf296CTnAyEI3w8L5jbNuwjIPTQf5x7SbuHDvJ1w8d4vquThQUbrn0KrzGNqYzg9y0dAk3797N7ieHkWWRyy5cxvu3rsGokxlKzPDTgZN8fP251Jk2UmeCledtpKyE+faRB/n+I3vo+/uPUa5GMejauaipk27HQd6/cj0feuJhMqX9fOm88/nutstw6C3cvO8J3rU8zm+u+hueCffjM5u5a+8RTNkic5EcU4kE57W00Dc3R6laZXRslvdv21IrJVx5TvutvV2s6mjmgV1HMQo6zGaZbLaEqBNw2GT1R225yr70EP5mr+reZtJjkiSmU0ncJhOpaIarN7bj0BuoM5sxGvVU7CY+cteD7H3XB2jzutna0c4vHt7LXLTA2maP+h3MFZBEgV2PD/HT1lZuWLyYR0ynOHBoGMWvkC2VOBKew6aX2TM7yy/2HiaXznPTxZt4a+9KZjNZGiy/t6OXL9rS+f83BEHgXRvW0GSxIokidwyP8MDuPr7z5uv43J49RENxdJKONcs6OHBkhNvu2K+uHVdqiakNqg/XzEQYS+1hcfz4OG9e0kuXo4XZ3AF6PF5e/fZF7J6eYdfUNImFJKt62vjsFVsB0InqL2pFAQSBdLHEz0+coMvpJFsusXtykmQkTaVcJTwVIVHII9h0lCoFUqUUj0yNscTtoW9eddyPLaTYPRskls9RqFRIF9UyW3UmJ0adHkHQoaZrLVOlSrJS5EPnbiK/qUy+XGEyleaJyVO8Y+UK7hubYMfuo4SjeTKZMvV+E8vXtFEpVyiG0+SLanUTvV5HJlVCb5ZIpkvkChXyuSKFYhWTQcd0OEslVyaHurxe5zbywesu5PDcHPOZDPO5LCfjB1jsXIu/0UU+k1d9WQ2SuoRSVTBKOlrsDpKFAnqdDqusx+mzE5yMYrGZSEXT/PrQMd6+eS0Wu4lcJo/JoKMiiSiKgs9vQ1FK9B+f52b9TtU5PFNkPl54rnxmKJYgFEuwcWkHh4Yn2dU/jKdRTYsyMjBNIlWku96P3W2h1eHErjewqaGe0USSHeOniGQz/Mel23EbbRyPjvLo1BRPB4MsX9rGIo+Hm3qXEMvnaL/Bx9ZvfIeOnibu33mUxkYHbX4PyUIBURQJxRLs2TdEY8BFW8CP22Sk2e7gCzt2kUnkWLq8jfMCTRxdiLBn3yDRSB63x8j69c2IOpFCpUyjzYVRL7Jl82LuGRri59EMaxe1sqWxkdFohJ2Tk7TYHXzrsaf58Ruup6XBy9+vW0som8EqycznEwRsdh47McSe2RBep40jM7O8avMqZlJpVvi8HJ1fIJ7Pk81VkCUd+UKZqoK6fJ7N85VD+7goEODDa9dx0733Mm008vNH9mKyGpmaTuBzGXH57WRTOYwWI6vq6hiQdERmY8wHo5R8NoYHQkSCGXR2PaFwhnS2jNGgw2KUCLS40Eki06ei/PBAH1aThN1pxmI34c2XmA8lsFhUH+ixE0Ga7HYK5TKXd7QTTGdp7KxjtD9IqVCi2uxFEARkSWDZlgCeBif6YJRSvoTJauQHl1zBJd+/lQ3LuriwqZuyUuW2wT6+dcEVmCR1qXRj3UHGkmE++dRTpNM5ZL1ELJwkWYuo10sieZ1IKlOuBbkJ2M0y2XwZp12P2ajm6tTJOnpXtXBVdzcHQyFGg2GaHQ5i8TQWk8Q3H3uaT11+IY9NTlOuKmRKJcySTI/LicVmQhJF3rNiNRWlil1uQSc6eXvvNXiNRr68aw9PHhrkHZecw1t7V/B3j93Pe1at4v1PPMjBwXG+ft3l3D9+ih8f7ycdz7KxvRWb3MO3jt7K0OwcTV31VCozfPst1wGw4Zb38Y7N69jc0IjT4MKo6yFeOIRDv4oel5//3H4jkcIcboOTn5y8nSaLmadu/ApVMnx0962sq2+gzmLCJusxy3pm02kyxSKlYpn5VJpYWF0CbfI4+eymi/j5UB9vXLSacO4ws5kMd44c5TWLunjgVD83LV5DspilVC1TrkbJVYpUUbh77CTXdy7Fpq9jKH4SkyTzuvu+wVKfj7f0LsFr7KVvYTcnok+ypaGZWEHNF3lR01r8LV7+bdtFbKy78rnnxgVNXSSLd7G5oZ5jCxGuam9H1omYJQNr/F4i+TQ2g4EGmw1Z1LG5tYWf79iHpBPI5SuMiSFaO33csv1y3rHjQeLpEtVkkUguy2K3ixa7ncGZOepavGxoa2EykWAiGUUUhvnc/Y8jG2Tu3XWYS7Ys54Jvfk+t8JMr0mC1cFPvcgbjc9w60/fcLGFFUQg0WMnlSsiijpWeZh6eOsn6umZ6XE5+erKPty+RkEUb+8P9NFhsvLV3Ne9bcT2/G9/BU8FZRqIRPr91K8vd51GqzvCv517At44eosXaQ6m6wN7QKG12GwGLl6ORQdb7F/HUzAxOn53IbByTQa3sli+XWVXfwHw2w6Zlneh1OqooeGWjGjhYKpGVJO5+4hDVqgIi5PMlAGamE+QLFYx6HWajhKfBhSBAQ52b5X4/9zxzDFkvEU1nURQFm16PUZIwShKpeAZBFDBajNw+dIAOp4sGi5mFWIF8JM+Rg1MoikJFgWpVoa3Ryj3HT5LP5AmHkmp99fkkTp8du97A/tlZPr5xE9+86wlSmRLfumsXvS4X6+t8zKQzL/XQ5X/Ny79MpsorIur83779bzcn13i5p3+QuFLl6FyIS1b00ut202K3k5dFMtUygwNTxBMFsskiOrOMAiCL1HmMuB1G8vkSpYqC1W7k8s0r+MDKtwMiO6YOcV1nN0cjC1zd0c4jk6dYCCdIFQu8YflyqqgzigpQpcqpVJSbdz1FeDrKxu4Wdk5Pc2xkilXdrcxFE/ia3FzS1oZCmcPzQfqj8zRareyemSWSL3D7wAAdzX6GYzHi+TzL/XVc29XBdV2LKCslnAYXAjoEZARBYjQZJJRNY5P1uAxGnAYDE6kU4/E4DxwaoNHnJK9TCM0mUQQolhXESpmLVvdStYiUs3nyJTV/piiJ6o1BUOuTC6h/3R4rs3MZqCpYLDJuu57ZhTzHJ6d5/G2fpMVR5eYHdvKGlcv58K47mZgKk88VEUV1RjmVKWPUi6zqakEUIFMqU2+xAHBg+BQev7pcHgmnmJlN4aizs7SxnuY6D4l8hnSmiFGvY+Cjn+L82z6PqJRIJvLEkkU1qbgski9WoFikqdmL12YhXy6Tq5RJRFIEp+LoRIV1K7vY0NtGg9VCVqcwOBem0WHn9j2Hed2qJTw5Pc25zS0MRKPEChm+eegQR2dC6GWJ2XCMD23agE4U+MIzz3DL4YP09YUYGQrT4DexYUUXkVyWUrVCncfJ7GyUdLoAlQpL2hvZ3NjAfX0DSLJaDrKkKETKaj3eeDaL1STS2dvEeV0dlAWFoeEge45MYNSLRBbiNDV5MVsMjE6H2dDSxIHgLFPjc4QzGXo7mriouYVWhw230cgip4/Hpid5cGICnShyYWcbi5xO9JJEplxiKpnEYTDwcP8Qb12zgkaLhX2jE2rWAUVB1gkYDBKBJi83LFqEWZaZyaTYOTLB5tYA+wZOYTLrcTpNZLNFLDYjdreV2655FXeODnNifIb29nqOHA0SDKoVdBwuA6BWE2rwGjHodThdJtra6unweylKataCdDxDNlNEqVRpbPZSKpXJZ4uUCiXqW2uzZYUiAYeDw3Mh5hcSamEAvcTiQD0z4Sj1rT7euHYl+4cn6WxrIBJLs6q7lf84eIDWBh9XdbbT6fAgizqMkkLAup5idZxSNUi6nOeLB54hXsirPsEmE163nXKlhFCtUihVqfeY1IpJOgG7VSaZUd1aCsUKFpNEg19NLl0qlJkr5Hj3ypXsnp1lLpGiwe2krs7Nh8/dxOPTM6zwebn94BEOzMyyPtDAdDpDSRJZ6vWxxOPm8PwMHpPCg6eepMcV4NHpE+w6PEgsXqB/KoivXk0FtqWhmc/e+RCSLPHQ4AiDE7OMjM3hcFv40rYLsest/G70CB/euJ6dU1N8+KItZMslfnDkKKVKBYfVgsdkoMu+mKpSpKwk0IkiTkMvVdI49UvJVYJ87+hh7jt4nBP5EX47so83Le7hDd1Xsr5uE0vdPaTLU2xoqEfUSTQ7nRSqVS5f2sO3tl3FG3tew6n0SZZ76jFLLu4/dYxwNotJlnAY9Kzw1mHXWzFLboySgCjIHImMEM7m+M7uZ/A5zdj05Vqpwgx/v3oL5wc6GU2EmUxPcCgc5tWdyzBKeoZi80ymU6zyttDrt7DOv6p2z1R9OQVBImAt8/DkGG12GwoK94+N8+uhk9RbbRSrFSRRZCqZJF0qcu9jR6jW8g1bzTKyJFLOl3D77Cz1enn82CgmowRGkYDLwR1PH0YURZLRNMF4gpKgYDYa+Mhv7+OHb3gVss1E1ijw9L4hsrkyjW1eFkJJJIeBlT4voUyae0ZHaXU4eMvi5Sz1uXnvls3MyxVGohHanBaC6QzLPHXUWYys9jYgiRL/vG8HPS4XD0+eYktDG1UlRbfTT380xGQyyal0hhZ7Hr+pk3wlymWtS+mPnWQyFeXQ/DzXdnZj1OlptHgYTU5zfcfr6PYV8focKA49o2PzGGSRNr+HmVSKdfX1WGSZUym18lyyWOSRiQkefeo46WwZl9OIgEK+UEVRVP3qvUaaO/xUyhUuW9nLXDZLPJnhphXLyMkisklPPJkmsZBibVcz8UIei6xnKJ0kOBPBYjdjMZnIlkpE8nliuTSZYpn153QgVsp43CYaGx20tteRSmZpbPRgdpio1KoTGcx6FFEgmcvzrpUb+PJDT5IrVEhny8zo85wbCBCw2vjJV+97GUedf+/md77zjbzYqPNPf/obWtT5/4RCscz8glrX9MnjQ2zo7WB7awvRfI6JVIqDJ8aolKtYHCam5jJY3UbKtSjobL78XKUQv89CPJ7Dajfz7uUryVdO8Km9j/DhNRt572MP8rlzt9JirUdRnqFarpDLFHh4aoz1dfWEcxmOzC/g0Oux6WV0ksjF65ZwXmM7HoORU4kEb1+2FKtez2u7u2i2ufnFUD+yKPLA6CjnNjeTKpY4HJplic/PB1atwaDT0x8NscLT+FwSeoUyUEVBfK7Ym9/kIFUqUqpUyFXKHJgL8/T0FEcOTJDKlFjSFcBiMVKqRYHXe4zIeon9E5PcuHoFR+v83PnoIWKpEnpJjYrViQKiKJDOlrFbZTLJLI1+E5lcGbNRolSuIoqwfFUbFSXJvpAaof3AqXEOnhgjkVBnIpVKlWy+jCQJyLIOg04kUSxi0OkoVasE02mW9bQwOh0mFIxhNkps3rAIv8WCSafDbTTS2lqH0WxgRVsT19x1C5VyBZvDTKlYRtKpzujeOjvTkzHaepvI5IskUlmmRsOsXddFq8+Nz2zBJEn4zCbuHR6m/9Ak33/Hdfx2eIS5TJqfvul6Vri3scjpImDxcGB+krtGxgiF1Mobkdk4Tp+NmUyK5Z46/BYrT+weYMPGZhwuKw8/fJJS+QQXnrOUsYUIKSVPMVckmiyiKLDrwACbrq7nVWuXc8eew4yMx7GE0pTyJZa2N3HFumUcmQvxxiW9eI0mHj52kny2+FyUv7/Zw8xMBJ0kkk5k2Tsb4preHg6757isvZ1jCxGOLoT53fAIsk7H25cto1St4jGZ2NE/SKSlEZ/ZxG8OHsPqNLPE52elX539m8/mCGVzFIoVVq/tZGMt+t+mlwmmM3xqx07mZ+PIkg5JL7F/dpb167qJ5rIoNb+rT247D7fRRMC6kvetzvCvxQIDw9PYreoDWScK+ANuwtNRyhUFX8BDuVhGNkgk83kssoxOJ7Jn9ziVXBn0Iq061cIL2QLpbPm5YK5KpYrfbsNh0HNddzcfOzCMzWFiw5IOmmw25hrStDqdHAzNIRtlYvkcDo+Vj65fz42/+h03nbORJW4foVwMo06myeLinok7cRtNBNMpmqw2APKFEiaDjEmSsBkM2HvbOXRynEwwAUA6p+bkLZXVqO62NheyXiIVy+Bv9mI0yETCCc5vaeHbfX3k0jl0ko5Wp5NtLQFG4gmmkwn8ZhM9gXrWNdRzZCFKt9POJzZuxKG34DG2M5Hcw2QqgkEnsT98ELMksWxZO3PxJBaLkb7wPG9Z0suOyTE8jS6UqkIymsZo1hPoqKPeasUuW3jXYz/jrkcOs+gtTn511WsYT4YZjscwSTKTkRQ7sgN4TUbOb4wSK8S4c+wkN/YICEgIiFSVHKFsjHAmzcJ8msPTQZ5607+RLh3hgts/i91s5I1LekkWS1zc3MIKbx0TyThbGuvpcXkI5WJ8+cBXCWXSRFIZvnTxhZzTEMCptzKfT9BuW0mVLMniLA69F0mQKFQW+PrBQ7hNRpLRNPeOjZGvlOlyOFnirmMoEeLJmSDnNzVh1Ek8FQyybzYEwHVdnVzdtogHJw9glCQemtxDqVplc30r9ab1CIIOg66OQ3NhplJpmqwWRATe2NvLKp+fJ2emkUWRSC5HaDqCLIvkChW1Opok0NDqZXVLgJ2TU5yamX8uBVhwPMy3h0I4nSa29nZxeHYGq8FAMpvnjoNHWd7Twh3DI3hNJlb46/BsNjE4McuJo9M0NzspPZuZwt/APm+YHePjXNvRiSSIfO6ZPXx0/Qb2hWb4zFNPq/7g2RwXNwdo9QVQKDMWi3H/xCmuaGsllIvytw8/xJWdnSxxu1jkdFCoVMmWS4RyJ/Abm6iSpdHsxGNsw2k4xq39x6kzmzBKEq/pXMaTs/dglfWc29jIxc3NJFavZjabYcfEJIlM7rnncEWpkiyWmIjHCZ4Ko6BmoMjlShj0OiSdGnhqt8q4vDZSsQxrett5YnScbr+PU9UqX91/gG63h3UN9VzY0syP9x7is/ftRBRFPnjJFpKFAr3dAQyShEEnsi+oFlexu62s9tnV1QOb+v1dyGZZ5FGzbnxg9WpOpZIs5PJ89e6dtLT6qdaCDu4eH6BcVlft1m/u5KZlS7FI8h+4Abx8+fMvnQuCcBnwDVTH51sURfnCH72+GPgRsAb4hKIoX/lTfb4iBpqCKCAbZaKhONVKlaGFBX5QLHJidJqmJi/rl3ZSqJSZTaUIxLM4PDbS8QyJVIFsHnKFChUFksk8ZqPE1p5OOuzrORB+go+t28JMNspnt5xLVVH4Wt+TxJIZ9CY98WiGz9/5CD1LWjBJEpPzUarlKivbA7Q6XfzT+k0YdXrObVxHm93F3lAQiyxz99g4z0w9yfjJGQrFKr0rA6SKJWbTaa7t7uYNi1ZgktQSdlsb3MCzA8xnq5vqaldeAUTsehtdDjiyMMOJSJTj8/MMH58iGi9AscrJ8Rny2SIGWUehVKFYqtIZ8KATBB49NYleJ2LQ66hWixRKFdqbbMzH8kiigNEqq2X1SiI2i0S5rC5h2yx6bBY9NyxeTLGaZCGnLpv+595DRCNZ8sUK0WQRj0Nfe38VQRRIl8oYRB1GWcKk0/H0wCgmm5Fty3qIdGYxSjJ2g55koUgasBv0nNMUQAwEuDDQzM8HB5k2JDgemSSXLeLx25D1EoE6D8GpGOsaGtk1PMZCMMrKNR1c2dHBVDrD/pkgsk7VbWpolvPPXczJWJx6q5Ur2ltZ7OwkWjiMz2hDEnXceuw4rQ4nXr+DybE51i7v4M4H+3hT3x24HQZmF3Is7vZw22uvw6G3sH3ux1idFtbU+Ynlcxw/Mk6uoM5u2a0ysXie/zx2nHWNTVidZlob8uTyZcqlMtOpJCdOzSDpqw0D9AAAF6lJREFUJYZiCX46c4K5YAxRFLDbDATa6/CYTMwHo5gsRmxOM+lSkd8dO0GDW00b1GK3YdPrmZyPUilV6NpyPo9MTXFeUyNuo4HBaAyn3kCgwUO704XDoCeaL/Da7m52z84ylUyyYnU7Nyzu4fqOFZSVCrlykWfmJrmmo53vHT3KVDSOyahHFkV8ZgszkTiX9i5i2ToXNz+wk71/+xGqSoaJZIIbl/TSfc4WRhMx3v2jOwFYmIkhSSJQZXxoDlkWsZhkjBYDK+vVuvb7OAW1KHNFgVyhRCJVJJIo0N5kxWg2kIpl2DcYwmM2oRNEWrrrWd8SYOfRQY4aZERJJG02MzAWJBXLkC9W0IkC3zx8mMZ6NzOZLC22IieiC+yZCbG4Vmu92WanUKkyGIthlmWKhRLtHjfFSpl07X4SDCYwyOqsf0OdlWpVYSGSRacDURQp5ks4PDZsJgOlahWXz859Q8PYTEa2LVuM32TkRCTCofACS9xO3rNqFW6jkTqzCVkUCVgtzGVzZMolykqKijLKkYUFDs/NsWf/Sdat6eYtS5fQaLOxqamRJW43sUKeOpONOwafwm4zU2ex8s7LtvHRR3aSTmb57jU3kChm2PXMCT71xkvZVN/ALf0HKdUKQrQ6ndiWGihWypyMRHho8gj3jU8gIhCwDnNRk4BO1JMrh8mVS5wTCDA1OY/bZmFn8C7Oqe9lY0szsXye3w6NcOzYGI8s78BuMHDo5DjBYEKtWV9VqFSq+Pw2cuk8ffPz9IsR8uUyXpMRSRTxGOw49Z3oBAfh/AFchka+eN55HF+YZzQW4yvnXcxUKsrBcBiPyYRZkthc38DB8BwXBlqJ5XIs99dhliV+emKA/eEwiXyBaD7Hm3oXs8pbT6FS5ET8MRQFsuUSq/w+JpIpREEgms9xbmPLc0GaZknCYzIxlsrRvSyAoii0Ol3sfLqfcrHMUDRCaGqBeCyLzSyrgYhWIxa7SV3ilSQMkkQ8naVcquD3OYnkcjzw2HEEAT5z43bes2IVH60+TqVc4c1b1vHk1BSTqQR+k423LV3KpoY64oU8++bmuLC5mZPRCKt9dfT4vew/Oc4D2UEua20lXowxmYoyOjZLNJ1Fr9PRYU/woXVqkOmu4AzvWr6SZusiZLGBSP4AClWMuiW4DCOkSkHMkp6lHhd7Zuc4lYiTLZcJWC18Yd8+Jk+FWd7Two8vfSs6rFjlHXw7nUKv01GsVMiWyozHY4yNh3B4bBRzJfKFAiaznnKxjMkooTfpqVaq5LNFNq3s5l0rlvPPu3cTTCUplyqkMxniFgtLPG5WeL2s6WoB4MTsHGZJwm4wsKGxEZdBz+0DJ59L2K83yphlmb3BIEt9PgYWFjDLMpsb6phMWWiwOOh1NVJWqjy4fJyxsVnymQKKovCBg5MEGm0sXtzMV8+/CLNk4JfD/dwz8vJPb/TnDgYSVJ+8bwOXANPAfkEQ7lYU5cRpp0WB9wOvOtN+XxEDzUqlSmQ2jlKtojfq8Zgt/PaBPiRRoFyqcOXFndj0MqOJJOMOJ2MLEUIzah1vg6xDNEClolAqValvcvHJ9VvJl0dZ6zuHo9F9pItFzmu8hkTxMKu8XurWr+ZL9z6O3WmiXCxz6OAo9U0uUtE0b71kM2v8PvbMhvjZyX4uaWlmNjOK32zmO/sPojfIzE7Mc/G6JfhsVlb61WCM3+3t49pNK3nL4jVIohGoAiIKSm1gqSOUnaPB3Ig6o1lGUSrP+WqWqhXqzRZcTQbuHh4mEi9AoYLFaSA4Fa9VMZLx1nkoFkpYZT0GSUf/8DSlYhm9rMPnMhBNFpkJZ0mnitTXqWlbymWRtk4fhw4H0enUvJo6SaRULDObzXLd3b8kncoRHJ/HoNexcWM3jz0+gMehx243ks1nEASBRLLArffvRlHAZtXjb/ZSqVSYn4pw50yMV21ZjU0vky2XyZdLTCeT9Pp8GHUSkijwsSd38YG1a/jRsTg6ScRqN3LxisW4jAaOhsNYzRJek5HPbruAiqLOMt124iTz2QyZfJHFdT4GQmG2bV5Oq8NOsVLhX895K6XqNKVqhn/Z+wRPHBmkLuBhfWMTAIs8XgJ2B7sPD6LTCSQTarqm1kYL3R2NGEQZSdDxg9dczS8Gh1jIqbNzzd31DPcHcdv1LF7WSqZYJBlJ81hkkGq1yjVb13BvLcJ85Pg0pXKVm67YjMOgp1xVK1cpCuhkHclklkQsjade9TNdmIlyKhLjPZvWMZ3OsG9mhkK5jMdspr3OS75UxijpcRnUoKXDc2HGFiJc29lBtlSizmxG1okYRJHjkQi7JifpcruZnY1SXqwgCCKiUlXz5uWyHFlYIF8qk1hIIfrtlGSZi1sCVBWFwcgCU8kkfQdnkEQfpWqQHxw5ynvXrKbN5sdrtGO16CkVy5QrVWRBp/rG+tWaxZ0uFz1uN9d1dvOr4UE2vfMKdILAY6cmeWzXAIVsAa/PQqWqkMqUKJcqlAol4qkidz/aR3u3H6/bzkP7j1MpVZiajNG7IkAin6ex0cOB6Tj+Ohs6SeTg2CTFQonflNRce9tb2wH45clBPrFxI0tcrfiMVvoWZjHodBRzRSyyjEWWSReLVCsKmXyFji4fhVyR5hY/bqOJodkw2WQOSdZRyBcx2020OpyIgkCbw86v+47T4/eRL5cZjsWJZHN0u1zsng3xyfVbscutBCwTfPHg05xYmMdjMnN9Zy/xQppfDPUzGI2yyu+nfZuTGxYtQhDg9T2LOJVK8t2+Pr598aX88MQxXtXdjSyK3Nbfzzp/F9t7pilUK9jkVdx64odEkwXevexyLv3N11EUhXetXkV/JEapUsFtMnEsPMd0LEGhXKFQKWPQSXxx9148F5v4+clB2hx2FrtcmCWJf9i+lWabDRGBXTNq4Fy3y8VT01MkUkWm5yJ88uKt2A0GHswcYSGex+c2cd3Wtew4MoC7zkH/QoRzGhuoKFV0oohDb8Woc1FW5ihWg7gNLVSVFJVqlWabjQ2NTbgNdvbMnqLX7cYiGTDq9BxZCGPT6wll088NEO86cAyr08ISr5fRWJS7r30f79n5Ex41THNpSwvr/M1YJCPZcoGHJyfZNzqBa2kPbqOJO8eGyJfLNFktjMaTHDoySqC7nmqlSpPTQbZUxGSUGB6ap7E+p5a9dJqIx9RVAYsoIOpE0vEsDx7uZ0VXC2P5IjqdSKlaJRFLc9PVm/jtowd4YmqKG3tW8Y0LL+WLtr0kCkVev7iHO4aG6XS4aLK4aWttZDYbZrHLyUIuT7vDgUEnYZb1rF/czj+sW0+rtYVIYZ4Gix1FUSgVSyxks1zV3savh0d4dVcn5wcaeWz6FP9nyfkAeIzrUJQKVaWIQdeFTohi1AXxGKM8vP84ixc10+mwc35jN/lyhYH6Bk4lErzjkZ/SYLXywVXrueq6y4nkB3jzA3eysSnA008PIQhQzBfJ5ErYLaqbQiZXRvdsqiO9hM1pZiwS4TN791JvsRLKpInPJ1EUBbvewK8HB5lIpsiXykRyWYq5IotcqstZg8XMGl+APTOzjMfjRMMJHF4bVUUhUSggItBoszOfzXBsIYrXpAbp6nVGZKXM6xcv5uN9Y0QTRURR4KptKwgmk1ze0c6rf3Y7HS31eMwm9jz1ch9oCi9FHs0NwIiiKGMAgiDcDlwLPDfQVBQlDIQFQbjy+bv4r7wiBpooCp4GJ6loGoNJT7pYQC+J6HQChWyRu4aHEQWByzo6EJ1ObrvrgJpYWidQKFaxmSW6ujxkEjm+dvV2qigUq2W+3/8bNtbXszcU4rxGsEhtXBRwAFUOrZ/n3StX8PPBQe7aewRJL/GWbZswSxK/GR5hQ309G+rq6XG2IYnjFCqV5xK8extdeM0mGqwWZFHkmakZnq05LwoiAgK/T+FVZT6/wJGFWWbSGdrsEXpcPgD2zU2zyOmi1erjI7sepX94Cp2kY2EuSSZVYsO5bXg9dkaHgnT1BIgm0hw9PI3fbaRYrTAbjjMfTlGtKmouTcAgiyTjRQRZVFO2VBQKpSpzwSgum16dHdKpN1JBELj7+ADhqQgzC+oMKJQoHBhhxZpmkpE0ybjqUF1VFCoVhXBUrWUeSaglPh1WmaZ2H+vamhmORllVV4dRJ7HY46HD6cQmy7TabfhNZvrm5vjczqeYGAlz/pZefGYLoUya/dPTDB0PYrfqWcjl+dXQENtamrnl6DHetGQJp5Ip7js2wDKvl+2tLQQzWd60aAU22UuuPE4kn+QTT+/i4LExivkiC6EY/bX8k2ZZ5uTsHBPTSQqFCoIkYjFJGI0ymVKR/miYTXVttNv9nNuY4tfDw+hEEY/Fwoiguh4MnZjE6rQwMx2jocnJ6zetYTgW472XbWXHxASehiKdLhd37+nj5mu38TfLl/HdbIGRgRAOYD4T4Yqtq+kLzpBJZLlm8yp++8QB5PUi+2dmOdo3RltvE6ciMdLxDMVCifQFOf797ifUoBqHmVa/m1uOHaeqKKyr83MoHGYyk8VtNPK1Cy/kgYlTHNFLjMQTfPfYHg7OzaETREySxHmBJoaiMf7u4s1srm/EZTBjlowMx5PsPjbM1159BT+uPM3n9t+G12TEYzbz9GyIhVyeuWyWaqWKbJDJJfOks2VMViOrGxqQBJFNDXU8NjnN55/Zx2u6uzgWiWCWJDKlompnkojBqEcviyxf3YbPYqHktJNOZAlHC+SzBaaz8yzubmYmEiObLTF4LMgNl60nXSyyaFkTkdkYmWSOtl4n8ZEQgiAQbMziNFjY1tzFj44dJ5TN8KMn7uXqznaeCYXpmwvxqe0X8LVn9uOymDFJEu56B5sanJhl9eG5rFYvHqBYUKNpt69fxtG5OfqmgnidNs4LNLKmrZlzmxq57cQJPr91K9F8nnK1yt1j41glLxUlilky8KHVG7hr/CRvXLQKAYFD4Wlu6l2JQ65DEIxUFbXKk0KBESXIyViMW7dfjUNv53g4jKmxgS6ng6mhWZ6YGeDuvn7+8aItZMvH+P5je9i6pYe/efhWrunupslqIVsqMxqPUamqlZT8FgshJc3A5AzL2gJIosCFnR202FwcnQ1xYHyS+WAUQRBw1zlobfCywl+Hw6DnwuYmGi02JFHgoYdPED0c5N9dh5kLRiiVKuglkfo2H0fnQlSrVRKRFMZAgLX+Oow6PRbZiEPupKQEeWRqgEub1yEJdfxiZCfnNrbQaguw0zTNp5/ZSZvdylVt69k1c5SPPfAoy9sDTCUTfHjdOi5tb+en+w5jshoJTcxzwGTg45s2IYv1XNLawrXtGyhX0xyPTvOZvXt518qVfGT1pbxn+XoW8kneevc9zKRTLPX5uay1jYVcHkEnopckJgaDzEkRQmF1ltxk0FHMl4hGsxj0OkQBkpkSFmuJSiVNMVdEJ0scGT7Fd19zNZvrrmTlv/8tNreVr5z7Ov5l43l88umdXPXr2/jEuefwta3voayotcqvalvKZHqe7xzbz0qvl9sGBvjnTZv59dAB4oUC71i6npFohB9eehVOgx1ZbMRtKPHxPQ9x25tfw7f6+hiPxwhYXVzR3srn9u0jYFMraT01ew/nNlyNoqizgf+4+7usq/NxYaATURD4yYkTLFoUYCYS5z+OHOWy5m28rqsJUTBz5/g9PD0T4vh8mJsevJvIws/Y3NPBQ9d/nnxlgHsOHiOTyBKOqBlNRFEgkcgjSyJ6WSQczaHTiRTzRWYW8sg6gQafmXPWLibrKZDPFBAFgTX1DTzWP0R3cx03LVvKHYNDfGzXLkLBCJ+/5hJssonlPh/jcXXSIZvMMVML8plyJLmqo53heIKnpqcZGZ3hX362g1JZfdY3t3n4zA2Xsb6uAbNk4B077qeQK7LOX8f4eJThYTVPrtOmJ/uSDlz+HPzZg4GagKnTjqeBjf/bToVnBzh/zQiCMA+c+kt/jufBCyz8pT/EXwmaVmeGptOZo2l15mhanRmaTmfOK0GrVkVRfH/pD/F8CILwIKrGLxYjkD/t+PuKony/1udrge2Kovyf2vGbgQ2Korzvef7/zUD6rPHRfBkbwgFFUdb9pT/HXwOaVmeGptOZo2l15mhanRmaTmeOptVLi6Iol70E3U4DzacdB4CZ/22nfx3ZPjU0NDQ0NDQ0NF5K9gPdgiC0C4KgB14P3P2/7fQVMaOpoaGhoaGhoaHxP0dRlLIgCO8FHkJNb3Sroij9giC8q/b69wRBqAcOAHagKgjCB4EliqIkX6hfbaD50vKyTPT6MkXT6szQdDpzNK3OHE2rM0PT6czRtPorRFGU+4H7/6jte6fth1CX1M+YV0QwkIaGhoaGhoaGxssPzUdTQ0NDQ0NDQ0PjJUEbaD4PgiAYBUF4RhCEI4Ig9AuC8Ola+0pBEPYIgnBMEIR7BEGw19rbBEHICYLQV9u+d1pfa2vnjwiC8E1BUDMKC4JgEAThl7X2fYIgtJ32npsEQRiubTf9/736F48gCDpBEA4LgnBv7dgtCMLDtc//sCAIrtPO/VjtmgcFQdh+WvsrXic4c600m/ovOr229l2sCoKw7o/O1WzqDLQ6220KnlerLwuCcFIQhKOCIPxOEATnaeeetXZ1pjppNqVxRiiKom1/tKFmQbXW9mVgH7AJNSLr/Fr724DP1PbbgOMv0NczwOZanw8Al9fa3wN8r7b/euCXtX03MFb766rtu/7SmvwJvT4E/By4t3b8JeCfavv/BHyxtr8EOAIYgHZgFNCdLTq9SK00m/pDnXqBHuBxYN1p52k2deZandU29QJaXQpItf0vot2rXqxOZ71Naduf3rQZzedBUUnXDuXapqDevHfV2h8GXv3f9SMIQgNgVxRlj6J+k/6T39cHvRb4SW3/18DFtV9824GHFUWJKooSq/2flyJf1p8FQRACwJXALac1n35tP+EPr/l2RVEKiqKMAyPAhrNBJ3jRWr1QH694rZ5PJ0VRBhRFGXye0zWbOnOtXqiPs1mrHYqilGuHe/l9kMNZa1cvUqcX6uMVr5PGmaMNNF+A2tJBHxBGNf59wHHgmtopr+UPE5u215YanhAEYWutrQk1AeqzTNfann1tCtSUAkAC8PD8JaCaePnydeAfUYuzP0udoiizALW//lr7C13b2aATvDitQLOp6p86Ec2mXoxWcPbaFPxprd6GOvMGZ7ddvRid4Oy2KY0zQBtovgCKolQURVmF+sttgyAIy1C/YH8nCMJBwAYUa6fPAi2KoqymtuQgqP6bz1eI9Nkw/xd67b97z8sKQRCuAsKKohw807c8T9ufuua/ep3gf6SVZlNn+JbnadNs6vk5K20K/rRWgiB8AigDtz3b9DynveLt6n+g01lrUxpnjjbQ/BMoihJH9XW6TFGUk4qiXKooylrgF6h+O9SWVyK1/YO19kWov8hOX2I4vZzTc6WeBEGQAAcQ5SUqAfUSsQW4RhCECeB24CJBEH4GzNWWTp5dQgnXzn+ha3ul6wQvUivNpv6LTi+EZlNnqNVZbFPw32hVCzq5CnhTbZkXzl67elE6neU2pXGm/KWcQ1/OG+ADnLV9E/Ak6hfMX2sTUX1O3nba+c86incAQcBdO96PGkj0rEP0FbX2v+MPHaJ/Vdt3A+OoztCu2r77L63JGWh2Ab93HP8yfxjg8qXa/lL+0MF+7DTdzgqdXoRWmk2dptNpbY/zhwEumk2duVZnvU39sVaoPoAnAN8fnXPW29UZ6qTZlLb9aVv6S3+Al+MGrAAOA0dR/TI/VWv/ADBU277A7xPevxror92YDgFXn9bXulofo8C3TnuPEbgD1cn8GaDjtPe8rdY+AvzNX1qPM9Ts9JuSB3gUGK79dZ923idqWgxSi0I8m3Q6U600m/ovOl2HOuNRAOaAhzSbenFaaTb1vFqNoPoF9tW272l2deY6aTalbWeyaZWBNDQ0NDQ0NDQ0XhI0H00NDQ0NDQ0NDY2XBG2gqaGhoaGhoaGh8ZKgDTQ1NDQ0NDQ0NDReErSBpoaGhoaGhoaGxkuCNtDU0NDQ0NDQ0NB4SdAGmhoaGhoaGhoaGi8J2kBTQ0NDQ0NDQ0PjJUEbaGpoaGhoaGhoaLwk/D/xLXSQ8Up3cwAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<Figure size 1008x360 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "div_norm = mcolors.Normalize(0.1, 0.8)\n",
-    "fig, ax = plt.subplots(figsize=(14, 5))\n",
-    "ll = ax.imshow(ndvi, cmap=\"YlGn\", extent=b4_extent, norm=div_norm)\n",
-    "fig.colorbar(ll);"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "- A Diverging colormap `RdYlGn` with a normalization on the color limits in two directions of the central point:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 102,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1008x360 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "div_norm = mcolors.TwoSlopeNorm(vmin=-0.1, vcenter=0.4, vmax=0.8)\n",
-    "fig, ax = plt.subplots(figsize=(14, 5))\n",
-    "ll = ax.imshow(ndvi, cmap=\"RdYlGn\", extent=b4_extent, norm=div_norm)\n",
-    "fig.colorbar(ll);\n",
-    "plt.axis('off');"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "---\n",
-    "\n",
-    "## For the curious: Some more building blocks"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Numpy provides lower-level building blocks used by other packages and you will once in a also need to rely on these functions to do some custom implementation. Some other useful building blocks with repect to reclassification could potentially help you:"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "- Remember the `np.where` function?"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 103,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[1, 2, 3],\n",
-       "       [4, 5, 6],\n",
-       "       [7, 8, 9]])"
-      ]
-     },
-     "execution_count": 103,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "dummy = np.arange(1, 10).reshape(3, 3)\n",
-    "dummy"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 104,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[1, 2, 3],\n",
-       "       [4, 0, 0],\n",
-       "       [0, 0, 0]])"
-      ]
-     },
-     "execution_count": 104,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.where(dummy > 4, 0, dummy)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "- Clip the values in yanour array to defined limits can be done using `np.clip`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 105,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[1, 2, 3],\n",
-       "       [4, 5, 6],\n",
-       "       [7, 8, 9]])"
-      ]
-     },
-     "execution_count": 105,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "dummy = np.arange(1, 10).reshape(3, 3)\n",
-    "dummy"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 106,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[2, 2, 3],\n",
-       "       [4, 5, 6],\n",
-       "       [6, 6, 6]])"
-      ]
-     },
-     "execution_count": 106,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.clip(dummy, 2, 6)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "- Numpy provides also a `np.histogram` function, which is really useful to get the bincounts over a custom bin-set:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 107,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(array([  2550, 139818,  55757]), array([ 5, 15, 25, 35]))"
-      ]
-     },
-     "execution_count": 107,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.histogram(b4_data_classified, bins=[5, 15, 25, 35])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 108,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(array([0, 0, 0]), array([0.001, 0.1  , 0.2  , 0.5  ]))"
-      ]
-     },
-     "execution_count": 108,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.histogram(b4_data, bins=[0.001, 0.1, 0.2, 0.5])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "- The `np.digitize` function return the indices of the bins to which each value in input array belongs. As such, it can be used to select and manipulate values containing to a specific bin:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 109,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[0, 1, 2],\n",
-       "       [3, 4, 5],\n",
-       "       [6, 7, 8]])"
-      ]
-     },
-     "execution_count": 109,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "dummy = np.arange(9).reshape(3, 3)\n",
-    "np.random.shuffle(dummy)\n",
-    "dummy"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Define the bin to which each of the values belong to, using the bins x<2, 2<=x<4, x>=4:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 110,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[0, 0, 1],\n",
-       "       [1, 2, 2],\n",
-       "       [2, 2, 2]])"
-      ]
-     },
-     "execution_count": 110,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "id_mask = np.digitize(dummy, bins=[2, 4])\n",
-    "id_mask"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 111,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[ 0,  1, 20],\n",
-       "       [20,  4,  5],\n",
-       "       [ 6,  7,  8]])"
-      ]
-     },
-     "execution_count": 111,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "dummy[id_mask == 1] = 20\n",
-    "dummy"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Besides, it is also a practical method to create discrete classified maps:\n",
-    "\n",
-    "  1. Apply digitize to create classes:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 112,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "ndvi_class_bins = [-np.inf, 0, 0.3, np.inf]  # These limits are for demo purposes only \n",
-    "ndvi_landsat_class = np.digitize(ndvi, ndvi_class_bins)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "  2. Define custom colors and names:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 115,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "nbr_colors = [\"gray\", \"yellowgreen\", \"g\"]\n",
-    "ndvi_names = [\"No Vegetation\", \"Bare Area\", \"Vegetation\"]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "  3. Prepare Matplotlib elements:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 119,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "nbr_cmap = ListedColormap(nbr_colors)\n",
-    "# fake entries required for each class to create the legend\n",
-    "dummy_data = [Line2D([0], [0], color=color, lw=4) for color in nbr_colors]  "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "  4. Make the plot and add a legend:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 120,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.legend.Legend at 0x7f7e641648d0>"
-      ]
-     },
-     "execution_count": 120,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 864x864 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig, ax = plt.subplots(figsize=(12, 12))\n",
-    "im = ax.imshow(ndvi_landsat_class, cmap=nbr_cmap, extent=b4_data_extent)\n",
-    "ax.legend(dummy_data, ndvi_names, loc='upper left', framealpha=1)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "- Find the modal (most common) value in an array is not provided by Numpy itself, but is available in the Scipy package:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 121,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from scipy.stats import mode"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 122,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(ModeResult(mode=array([4220], dtype=uint16), count=array([367])),\n",
-       " ModeResult(mode=array([20]), count=array([139818])))"
-      ]
-     },
-     "execution_count": 122,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "mode(b4_data.flatten()), mode(b4_data_classified.flatten())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Side-note on convolution"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "In case you need custom convolutions for your 2D array, check the `scipy.signal.convolve` function as the Numpy function only works for 1-D arrays."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 127,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from scipy import signal"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 128,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "with rasterio.open(\"./data/gent/raster/2020-09-17_Sentinel_2_L1C_B04.tiff\") as src:\n",
-    "    b4_data = src.read()\n",
-    "    b4_data_extent\n",
-    "b4_data = b4_data.squeeze().astype(float)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "As an example, apply a low pass filter example as window, smoothing the image:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 129,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[ 1,  1,  1,  1,  1],\n",
-       "       [ 1,  4,  4,  4,  1],\n",
-       "       [ 1,  4, 12,  4,  1],\n",
-       "       [ 1,  4,  4,  4,  1],\n",
-       "       [ 1,  1,  1,  1,  1]])"
-      ]
-     },
-     "execution_count": 129,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "window = np.ones((5, 5), dtype=int)\n",
-    "window[1:-1, 1:-1] = 4\n",
-    "window[2, 2] = 12\n",
-    "window"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 130,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "grad = signal.convolve(b4_data, window, mode='same')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 131,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.image.AxesImage at 0x7f7df67c9410>"
-      ]
-     },
-     "execution_count": 131,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1152x432 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig, (ax0, ax1) = plt.subplots(1, 2, figsize=(16, 6))\n",
-    "ax0.imshow(b4_data, extent=b4_data_extent)\n",
-    "ax1.imshow(grad, extent=b4_data_extent)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python [conda env:DS-geospatial]",
-   "language": "python",
-   "name": "conda-env-DS-geospatial-py"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.6"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/_solved/11-xarray-intro.ipynb b/_solved/11-xarray-intro.ipynb
new file mode 100644
index 0000000..eaa5a75
--- /dev/null
+++ b/_solved/11-xarray-intro.ipynb
@@ -0,0 +1,1798 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<p><font size=\"6\"><b>Xarray intro</b></font></p>\n",
+    "\n",
+    "\n",
+    "> *DS Python for GIS and Geoscience*  \n",
+    "> *October, 2025*\n",
+    ">\n",
+    "> *© 2025, Joris Van den Bossche and Stijn Van Hoey. Licensed under [CC BY 4.0 Creative Commons](http://creativecommons.org/licenses/by/4.0/)*\n",
+    "\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import matplotlib.colors as mcolors"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Introduction"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import xarray as xr"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's start with reading the Sentinel RGB bands for Herstappe again:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "herstappe_file = \"./data/herstappe/raster/2020-09-17_Sentinel_2_L1C_True_color.tiff\"\n",
+    "herstappe = xr.open_dataarray(herstappe_file, engine=\"rasterio\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "herstappe"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ax = herstappe.plot.imshow(figsize=(12, 5))\n",
+    "ax.axes.set_aspect('equal')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can make the dimension information more useful by renaming the labels of the 'band' dimension:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "herstappe = herstappe.assign_coords(band=(\"band\", [\"red\", \"green\", \"blue\"]))\n",
+    "herstappe"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Or just rename the coordinate names itself:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "herstappe.rename({\"x\": \"easting\", \"y\": \"northing\"})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "These __dimension coordinate names__ can be used to extract (slice) data making data selections very declarative:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "herstappe.sel(band='red')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The data type of this `xarray.DataArray` `herstappe` is 'float32'. Xarray uses the data types provided by NumPy. More information on the data types NumPy supports is available in the [documentation](https://numpy.org/devdocs/user/basics.types.html#array-types-and-conversions-between-types).\n",
+    "\n",
+    "Converting to another data type is supported by `astype()` method:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "herstappe.astype('float64')   # .nbytes"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Using xarray:\n",
+    "\n",
+    "- Data stored as a NumPy arrays.\n",
+    "- Dimensions do have a name.\n",
+    "- The coordinates of each of the dimensions can represent geographical coordinates, categories, dates, ... instead of just a positional index."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-info\" style=\"font-size:120%\">\n",
+    "\n",
+    "**REMEMBER**: <br>\n",
+    "\n",
+    "The [`xarray` package](xarray.pydata.org/en/stable/) introduces __labels__ in the form of dimensions, coordinates and attributes on top of raw NumPy-like arrays. Xarray is inspired by and borrows heavily from Pandas.    \n",
+    "\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Selecting data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Xarray’s labels make working with multidimensional data much easier:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "herstappe = xr.open_dataarray(herstappe_file, engine=\"rasterio\")\n",
+    "herstappe = herstappe.assign_coords(band=(\"band\", [\"red\", \"green\", \"blue\"]))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We could use the Numpy style of data slicing:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "herstappe[0]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "However, it is often much more powerful to use xarray’s `.sel()` method to use label-based indexing, making the selection more declarative:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "herstappe.sel(band=\"red\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can select a specific set of coordinate values as a __list__ and take the value that is most near to the given value:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "herstappe.sel(x=[600_000, 610_000, 620_000], method=\"nearest\").sel(band=\"red\").plot.line(hue=\"x\");"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Sometimes, a specific range is required. The `.sel()` method also supports __slicing__, so we can select the green band and slice a subset of the data along the x direction:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "herstappe.sel(x=slice(580_000, 620_000), band=\"green\").plot.imshow(cmap=\"Greens\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The __positional indexing__ as you would do with the underlying Numpy array is still possible as well:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "herstappe[0, 100:200:10, 100:200:10]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Use a __condition__ to select data, also called fancy indexing or boolean indexing:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "herstappe > 0.3"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "However, with xarray we cannot use a mask like this to directly filter the array or assign new values. \n",
+    "\n",
+    "One typical use case for raster data is where you want to apply a mask to the data and set those values to some \"NODATA\" value. For plotting, this can for example be `np.nan`, and for this we can use the `where()` method:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "herstappe.where(herstappe > 0.3).sel(band=\"red\").plot.imshow()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Reading NODATA values"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "GeoTiff raster files should encode the presence of NODATA pixels as part of the metadata. Check the metadata using the CLI tool GDAL, we see for each band the value that is used to represent NODATA pixels:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#!gdalinfo ./data/gent/raster/2020-09-17_Sentinel_2_L1C_True_color.tiff"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In this example, the data is stored as _Unsigned Int16_ values (i.e. range from 0 to 65535, 16 bits are used to represent the magnitude of the number and none are reserved for a sign) and the value `65535` is used to represent NODATA.\n",
+    "\n",
+    "Rioxarray will use this metadata by default to replace array values equal to `_FillValue` with NaN (and scale if a scale factor and/or offset are in the metadata as well). This is because the `mask_and_scale` parameter is by default `True`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tc_data = xr.open_dataarray(\"./data/gent/raster/2020-09-17_Sentinel_2_L1C_True_color.tiff\", \n",
+    "                            engine=\"rasterio\", mask_and_scale=True)\n",
+    "tc_data.dtype"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Whereas the data us stored as UInt16, rioxarray returns data as float32 with the NODATA values converted to NaN. Let's count the number of NaN values in the data set:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "np.isnan(tc_data).sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To explicitly ignore the default behavior and read the data as stored without masking the NODATA values, put the `mask_and_scale` on False:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tc_data = xr.open_dataarray(\"./data/gent/raster/2020-09-17_Sentinel_2_L1C_True_color.tiff\", \n",
+    "                            engine=\"rasterio\", mask_and_scale=False)\n",
+    "tc_data.dtype"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Whereas the NODATA value is not applied, the nodata value stored in the geotiff is still available as attribute of the DataArray or accessible by the `.rio` accessor:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tc_data.rio.nodata, tc_data.attrs[\"_FillValue\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The data is read as UInt16 and the number of values equal to 65535 in the data set:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "(tc_data == 65535).sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-info\" style=\"font-size:100%\">\n",
+    "\n",
+    "**Remember**: <br>\n",
+    "\n",
+    "The `mask_and_scale` parameter is [by default `True`](https://docs.xarray.dev/en/stable/generated/xarray.open_dataarray.html) in xarray for the different engines. This might lead to unwanted data type conversions when the 'NODATA' is not properly included in the raw data file. Make sure to check the data types.\n",
+    "\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Let's practice!\n",
+    "\n",
+    "We'll look at some Sentinel GeoTiff data, this time from the region of the City of Ghent:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-success\">\n",
+    "\n",
+    "**EXERCISE**:\n",
+    "\n",
+    "* Read in the file `./data/gent/raster/2020-09-17_Sentinel_2_L1C_True_color.tiff` with xarray and assign the data to a new variable `tc_data`.  \n",
+    "* Use the `mask_and_scale` parameter explicitly in the reader function to mask (and scale) the dataset with the NODATA metadata encoded in the geotiff file.     \n",
+    "* Check which value the `tc_data.rio.nodata` attribute specifies as NODATA. Why is this not 65535?\n",
+    "    \n",
+    "<details><summary>Hints</summary>\n",
+    "\n",
+    "* Whereas `mask_and_scale` is by default `True`, adding it makes the conversion explicit.\n",
+    "* After masking, the NaN value in the float data is np.nan as `tc_data.rio.nodata` attribute also specifies.\n",
+    "\n",
+    "</details>\n",
+    "    \n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "tc_data = xr.open_dataarray(\"./data/gent/raster/2020-09-17_Sentinel_2_L1C_True_color.tiff\", \n",
+    "                            engine=\"rasterio\", mask_and_scale=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "tc_data.rio.nodata # after masking NODATA value are represented as np.nan"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-success\">\n",
+    "\n",
+    "**EXERCISE**:\n",
+    "\n",
+    "* Read in the file `./data/gent/raster/2020-09-17_Sentinel_2_L1C_True_color.tiff` with xarray in the __original raw data__ dtype and assign the data to a new variable `tc_data`.  \n",
+    "* Inspect the display of `tc_data`. What are the different dimensions of the array? \n",
+    "* Select only the *second* layer of `tc_data` and assign the output to a new variable `tc_g`.\n",
+    "* Plot the second (green) layer.\n",
+    "* Create a new array with each of the elements in the `tc_g` array with a value above 15000 set to a new value of 65535.\n",
+    "    \n",
+    "<details><summary>Hints</summary>\n",
+    "\n",
+    "* Use the `mask_and_scale` parameter in the reader function to make sure the data type of tc_data is the same as the raw data.\n",
+    "* To select a specific subset for a certain dimension of a DataArray, use the `.sel()` method. The argument name to use is the name of the dimension.\n",
+    "* You can mask an array and set the masked values to another value with the `.where()` method. Check the help of the method for information on the keyword arguments.    \n",
+    "\n",
+    "</details>\n",
+    "    \n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "tc_data = xr.open_dataarray(\"./data/gent/raster/2020-09-17_Sentinel_2_L1C_True_color.tiff\", \n",
+    "                            engine=\"rasterio\", mask_and_scale=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# Get the green channel\n",
+    "tc_g = tc_data.sel(band=2)\n",
+    "tc_g"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# Plot the green channel\n",
+    "tc_g.plot.imshow(cmap=\"Greens\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# Convert all values above 15000\n",
+    "tc_g.where(tc_g < 15000, 65535)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-success\">\n",
+    "\n",
+    "**EXERCISE**:\n",
+    "\n",
+    "Subsample the array `tc_data` by taking only one out of each 5 data points for all layers at the same time. Use _positional indexing_ to subsample the data.\n",
+    "    \n",
+    "(Be aware that this is a naive resampling implementation for educational purposes only). \n",
+    "    \n",
+    "<details><summary>Hints</summary>\n",
+    "\n",
+    "* Slicing in Numpy is similar to Python lists, but in multiple dimensions, e.g. for 2 dimensions `my_array[start:end:step-size, start:end:step-size]`.\n",
+    "* When selecting all elements in a given dimension, just use a single colon, e.g. `my_array[:]`. When slicing from `start` to `end` of an array, leave these values empty, e.g. `my_array[::step-size]`.\n",
+    "* The result should still be a 3-D array with 3 elements in the first dimension.\n",
+    "\n",
+    "</details>   \n",
+    "    \n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "tc_data = xr.open_dataarray(\"./data/gent/raster/2020-09-17_Sentinel_2_L1C_True_color.tiff\", \n",
+    "                            engine=\"rasterio\", mask_and_scale=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# subsample the data\n",
+    "tc_data[:, ::5, ::5].shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-success\">\n",
+    "\n",
+    "**EXERCISE**:\n",
+    "\n",
+    "For the file `2020-09-17_Sentinel_2_L1C_B04.tiff`, the NODATA is not stored in the metadata of the file. Still, the elements with the value `65535` do represent 'Not a Number' (NaN) values. The conversion need to be done manually and use `mask_and_scale=False`. \n",
+    "\n",
+    "NumPy does not support NaN values for integer data, so we'll convert to float first as data type. After reading in the data set `./data/gent/raster/2020-09-17_Sentinel_2_L1C_B04.tiff` (assign data to variable `b4_data`):\n",
+    "    \n",
+    "* Count the number of elements that are equal to `65535`\n",
+    "* Convert the data type to `float`, assign the result to  a new variable `b4_data_f` (numpy does not support NaN for integers).\n",
+    "* Assign NaN (`np.nan`) value to each of the elements of `b4_data_f` equal to `65535`\n",
+    "* Count the number of NaN values in the `b4_data_f` data\n",
+    "* Make a histogram of both the `b4_data` and `b4_data_f` data. Can you spot the difference?\n",
+    "\n",
+    "<details><summary>Hints</summary>    \n",
+    "\n",
+    "* `np.nan` represents _Not a Number (NaN)_ in Numpy. You can mask an array with np.nan values using the `where()`method\n",
+    "* `np.sum` will by default sum all of the elements of the input array and can also count boolean values (True = 1 and False = 0), resulting from a conditional expression. \n",
+    "* To test if a value is a NaN, numpy provides `np.isnan(...)` which results in an element-wise check returning boolean values.\n",
+    "* Check the help of the `plt.hist` command to find out more about the `bins` and the `log` arguments.\n",
+    "\n",
+    "</details>\n",
+    "    \n",
+    "   \n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "b4_data = xr.open_dataarray(\"./data/gent/raster/2020-09-17_Sentinel_2_L1C_B04.tiff\",\n",
+    "                            engine=\"rasterio\", mask_and_scale=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# Count the number of cells with value 65535\n",
+    "np.sum(b4_data == 65535).values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# Convert to float and make 65535 equal to Nan\n",
+    "b4_data_f = b4_data.astype(float)\n",
+    "b4_data_f = b4_data_f.where(b4_data != 65535)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# Count the number of cells with value 0\n",
+    "np.sum(np.isnan(b4_data_f)).values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# Create the histogram plots\n",
+    "fig, (ax0, ax1) = plt.subplots(1, 2, sharey=True, figsize=(12, 4))\n",
+    "b4_data.plot.hist(bins=30, log=True, ax=ax0)\n",
+    "b4_data_f.plot.hist(bins=30, log=True, ax=ax1);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This is what the `mask_and_scale` option also does: it reads the nodata value from the metadata in the tiff file and uses this to mask the no-data value with a NaN value."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-info\" style=\"font-size:100%\">\n",
+    "\n",
+    "**Remember**: <br>\n",
+    "\n",
+    "Whereas the `mask_and_scale` parameter is a convenient functionality, it only works when the NODATA metadata is stored in the GeoTiff file. Make sure to check the value distribution with `mask_and_scale=False` to check for missing NODATA metadata.\n",
+    "\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Plotting"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We already used `.plot.imshow` and `.plot.line` in the previous section and exercise. Similar to Pandas, `xarray` has a `plot` method, which can be used for different plot types."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "xr_array = xr.open_dataarray(\"./data/gent/raster/2020-09-17_Sentinel_2_L1C_B0408.tiff\", \n",
+    "                             engine=\"rasterio\")\n",
+    "xr_array = xr_array.assign_coords(band=(\"band\", [\"b4\", \"b8\"]))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "It supports both 2 dimensional (e.g. line) as 3 (e.g. imshow, pcolormesh) dimensional plots. When just using `plot`, xarray will do a _best guess_ on how to plot the data. However being explicit `plot.line`, `plot.imshow`, `plot.pcolormesh`, `plot.scatter`,...  gives you more control."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "xr_array.sel(band=\"b4\").plot();  # add .line() -> ValueError: For 2D inputs, please specify either hue, x or y."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "xr_array.sel(x=420000, method=\"nearest\").plot.line(hue=\"band\");"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\"facetting\" splits the data in subplots according to a dimension, e.g. `band`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "xr_array.plot.imshow(col=\"band\", cmap=\"Reds\");"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Facetting also works for line plots:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "xr_array.sel(x=420000, method=\"nearest\").plot.line(col=\"band\");  # row=\"band\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Use the `robust` option when there is a lack of visual difference. This will use the 2nd and 98th percentiles of the data to compute the color limits. The arrows on the color bar indicate that the colors include data points outside the bounds."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "ax = xr_array.sel(band=\"b4\").plot(cmap=\"Reds\", robust=True, figsize=(12, 5))  # use False as well\n",
+    "ax.axes.set_aspect('equal')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In case you want to show the output with a __discrete colormap__, one can define a [set of levels to split the colormap](http://xarray.pydata.org/en/stable/user-guide/plotting.html#discrete-colormaps) on:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ax = xr_array.sel(band=\"b8\").plot(cmap=\"Reds\", levels=[0, 0.04, 0.08, 0.1], figsize=(12, 5))  # plot without these levels\n",
+    "ax.axes.set_aspect('equal')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For more control, defining the `Figure` and `Axes` Matplotlib object first provides more flexibility in terms of further adjustments. One can pass an `axes` object to an xarray `.plot` method in order to link the output:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig, (ax0, ax1, ax2) = plt.subplots(1, 3, figsize=(20, 4))\n",
+    "\n",
+    "# first subplot a histogram of band 4\n",
+    "xr_array.sel(band=\"b4\").plot.hist(bins=30, ax=ax0);\n",
+    "ax0.set_title(\"Histogram of b4\");\n",
+    "\n",
+    "# second subplot a line plot at given x coordinate\n",
+    "xr_array.sel(x=420000, method=\"nearest\").plot.line(hue=\"band\", ax=ax1);\n",
+    "\n",
+    "# third subplot a histogram of band 8\n",
+    "xr_array.sel(band=\"b8\").plot.hist(bins=30, ax=ax2);\n",
+    "ax2.set_title(\"Histogram of b8\");"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Reductions, element-wise calculations and broadcasting"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "herstappe = xr.open_dataarray(herstappe_file, engine=\"rasterio\")\n",
+    "herstappe = herstappe.assign_coords(band=(\"band\", [\"red\", \"green\", \"blue\"]))\n",
+    "herstappe_red = herstappe.sel(band=\"red\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Reductions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The __reductions__ (aggregations) are provided as methods and can be applied along one or more of the data dimensions."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "By default, the array is reduced over all dimensions, returning a single value as a DataArray:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "herstappe_red.mean()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In NumPy, the dimensions are called the __axis__:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "herstappe_red.mean(axis=1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "But we have __dimensions with labels__, so rather than performing reductions on axes (as in NumPy), we can perform them on __dimensions__. This turns out to be convenient and declarative:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "herstappe_red.mean(dim=\"x\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Calculate the mean values for each of the bands separately:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "herstappe.mean(dim=[\"x\", \"y\"])  # read as: 'take the mean over the dimensions x and y combined'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Or some quantiles:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "herstappe.quantile([0.1, 0.5, 0.9], dim=[\"x\", \"y\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Element-wise computations"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The __for each element__ is crucial for NumPy and Xarray. The typical answer in programming would be a `for`-loop, but Numpy is optimized to do these calculations __element-wise__ (i.e. for all elements together):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "dummy = np.arange(1, 10)\n",
+    "dummy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "dummy*10"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Instead of:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "[el*20 for el in dummy]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Numpy provides most of the familiar arithmetic operators to apply on an element-by-element basis:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "np.exp(dummy), np.sin(dummy), dummy**2, np.log(dummy)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Xarray works seamlessly with those arithmetic operators and numpy array functions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "herstappe_red * 10."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "np.log(herstappe_red)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can combine multiple xarray arrays in arithemetic operations:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "herstappe.sel(band=\"red\") - herstappe.sel(band=\"green\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Broadcasting"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "When we combine arrays with different shapes during arithmetic operations, NumPy and Xarray apply a set of __broadcoasting__ rules and the smaller array is _broadcast_ across the larger array so that they have compatible shapes. \n",
+    "\n",
+    "Perfoming an operation on arrays with different coordinates will result in automatic broadcasting:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "herstappe.x.shape, herstappe_red.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "herstappe_red + herstappe.x  # Note, this calculaton does not make much sense, but illustrates broadcasting"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-info\" style=\"font-size:120%\">\n",
+    "\n",
+    "**REMEMBER**: <br>\n",
+    "\n",
+    "The combination of element-wise calculations, efficient reductions and broadcasting provides NumPy and Xarray a lot of power. In general, it is a good advice to __avoid for loops__ when working with arrays.\n",
+    "\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Let's practice!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-success\">\n",
+    "\n",
+    "**EXERCISE**:\n",
+    "\n",
+    "The data set `./data/herstappe/raster/2020-09-17_Sentinel_2_L1C_True_color.tiff` (assign to variable `herstappe_data`) has values ranging in between 0.11325, 0.8575. To improve the quality of the visualization, stretch __each of the layers individually__ to the values to the range 0. to 1. with a linear transformation: \n",
+    "    \n",
+    "$$z_{i,scaled}=\\frac{z_i-\\min(z)}{\\max(z)-\\min(z)}$$\n",
+    "\n",
+    "Make a plot of the end result and compare with a plot of the original data. \n",
+    "\n",
+    "<details><summary>Hints</summary>\n",
+    "\n",
+    "* We ask to do it *\"for each of the bands\"*, but you don't need to do it separately for each band! Using broadcasting and element-wise operations, we can do this in one go.\n",
+    "* First calculate the minimum and maximum value for the bands (i.e. reducing over the other (x, y) dimensions). This should result in an 1D array of length 3.\n",
+    "* With those min/max arrays, you can add and subtract those from the original `herstappe_data` array to obtain the scaled data.\n",
+    "\n",
+    "</details>    \n",
+    "    \n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "herstappe_data = xr.open_dataarray(\"./data/herstappe/raster/2020-09-17_Sentinel_2_L1C_True_color.tiff\", \n",
+    "                                   engine=\"rasterio\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# Calculate the min and max for each channel\n",
+    "h_min = herstappe_data.min(dim=[\"x\", \"y\"])\n",
+    "h_max = herstappe_data.max(dim=[\"x\", \"y\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# Rescale the data\n",
+    "herstappe_rescaled = ((herstappe_data - h_min)/(h_max - h_min))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# Make a plot\n",
+    "herstappe_rescaled.plot.imshow(figsize=(9, 5))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-success\">\n",
+    "\n",
+    "**EXERCISE**:\n",
+    "\n",
+    "The true color data set for Ghent `./data/gent/raster/2020-09-17_Sentinel_2_L1C_True_color.tiff` contains 3 bands. Plotting with the `imshow` function can plot 3-D (RGB) data sets, but when running `gent.plot.imshow()`, we get an error. This is because matplotlib expects data in the range of [0..1] for floats or [0..255] for integers. \n",
+    "    \n",
+    "When working with the original raw data of the file, the data type of this specific array `gent` is 16bit unsigned integer. Remember that using 16bit unsigned integer, it can contain `2**16` different (all positive) integer values to represent the data range (in this case 0 to 1):\n",
+    "\n",
+    "```\n",
+    ">>> 2**16\n",
+    "65536\n",
+    "```\n",
+    "\n",
+    "In this excercise, we will convert the data to floats within the data range 0 -> 1 so we can plot it as RGB values.\n",
+    "\n",
+    "- Read the data file and assign to a variable `gent`. Read the original raw data without masking. \n",
+    "- Try to plot it with the `imshow()` method.\n",
+    "- Convert the array to a float array, switch values equal to 65535 into NaN-values and call the result `gent_f`. \n",
+    "- Now divide the array by 65535 to get our data in a [0-1] range.\n",
+    "- Plot the result with the `imshow()` method.\n",
+    "\n",
+    "<details><summary>Hints</summary>\n",
+    "\n",
+    "* To convert the data type of an array, you can use the `astype()` method.\n",
+    "* The `.where()` method is very useful to convert all values not according to the condition into NaN values\n",
+    "* Masking out part of the data based on a condition can be done with the `where()` method.\n",
+    "\n",
+    "</details>    \n",
+    "    \n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "gent = xr.open_dataarray(\"./data/gent/raster/2020-09-17_Sentinel_2_L1C_True_color.tiff\", \n",
+    "                         engine=\"rasterio\", mask_and_scale=False)\n",
+    "gent"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "gent.plot.imshow()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# Convert to float and convert values equal to 65535 into NaN\n",
+    "gent_f = gent.astype(float)\n",
+    "gent_f = gent_f.where(gent_f != 65535)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# Divide by the maximum of the int16 range to get [0-1] data\n",
+    "gent_f = gent_f / 65535"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# Make a RGB plot\n",
+    "gent_f.plot.imshow(figsize=(9, 5))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-success\">\n",
+    "\n",
+    "**EXERCISE**:\n",
+    "\n",
+    "Next, plot a greyscale version of the data as well. Instead of using a custom function just rely on the sum of the 3 bands as a quick proxy.\n",
+    "    \n",
+    "<details><summary>Hints</summary>\n",
+    "\n",
+    "* A greyscale image requires a greyscale `cmap`, check the available names in [the documentation online](https://matplotlib.org/tutorials/colors/colormaps.html)\n",
+    "* Xarray provides the common aggregation functions as methods to a DataArray, e.g. `my_array.sum(...)`\n",
+    "* To do a reduction over a given dimension, use the `dim`-ension name as parameter of the method.\n",
+    "\n",
+    "</details>    \n",
+    "    \n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# Make a Grey scale plot\n",
+    "gent_f.sum(dim=\"band\").plot.imshow(cmap=\"Greys\", figsize=(9, 5))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-success\">\n",
+    "\n",
+    "**EXERCISE**:\n",
+    "\n",
+    "The data sets `./data/gent/raster/2020-09-17_Sentinel_2_L1C_B04.tiff` and `./data/gent/raster/2020-09-17_Sentinel_2_L1C_B08.tiff` contain respectively the 4th and the 8th band of a sentinel satellite image. To derive the [Normalized Difference Vegetation Index) (NDVI)](https://nl.wikipedia.org/wiki/Normalized_Difference_Vegetation_Index), the two bands need to be combined as follows:\n",
+    "    \n",
+    "$$\\frac{band_8 - band_4}{band_8 + band_4} $$\n",
+    "    \n",
+    "Process the images and create a plot of the NDVI:\n",
+    "    \n",
+    "- Read both data sets and store them in resp. `b4_data` and `b8_data`. \n",
+    "- Transform the data range of each of the layers to the range .0 - 1.\n",
+    "- Calculate the NDVI\n",
+    "- Make a plot of the NDVI plot the data as such using the \"Greens\" colormap. \n",
+    "\n",
+    "__IMPROVE THE COLORMAP__\n",
+    "    \n",
+    "Using these default plot settings, the NDVI visualisation is not very informative. Actually, whereas the range is [-1, 1], values of around 0.1 and below are not considered living plants. Consider two alternative methods to improve the colormap taking into account this information:\n",
+    "    \n",
+    "1. Normalize the data linearly into the range 0.1 to 0.8 and apply this normalization to the `imshow` while using the colormap `YlGn` (so values just above 0.1 are still yellow, whereas only higher values are green). Use the Matplotlib function `mcolors.Normalize` for the data normalization.\n",
+    "2. To improve the contrast between vegetation versus no-vegetation, use a diverging colormap (`RdYlGn`) in combination with a [`TwoSlopeNorm`](https://matplotlib.org/stable/api/_as_gen/matplotlib.colors.TwoSlopeNorm.html) to normalize the data. Let the range vary from -0.1 till 0.8 with the center on 0.4.    \n",
+    "\n",
+    "<details><summary>Hints</summary>\n",
+    "\n",
+    "* For more specific adjustments to the colormap, have a check on the [Matplotlib documentation on colormap normalization](https://matplotlib.org/3.3.2/tutorials/colors/colormapnorms.html). \n",
+    "* Check out the available normalization by exploring the `mcolors.` module of Matplotlib. \n",
+    "* The `imshow` method can accept a `norm` parameter to normalize the data.  \n",
+    "* It is a two step process: 1/ define a normalization; 2/ pass the normalization to the `norm` parameter of the plot function.\n",
+    "\n",
+    "\n",
+    "</details>   \n",
+    "           \n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "b4_data = xr.open_dataarray(\"./data/gent/raster/2020-09-17_Sentinel_2_L1C_B04.tiff\", \n",
+    "                            engine=\"rasterio\", mask_and_scale=False).sel(band=1)\n",
+    "b8_data = xr.open_dataarray(\"./data/gent/raster/2020-09-17_Sentinel_2_L1C_B08.tiff\", \n",
+    "                            engine=\"rasterio\", mask_and_scale=False).sel(band=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# Rescale the data to 0-1\n",
+    "b4_data = (b4_data - b4_data.min())/(b4_data.max() - b4_data.min())\n",
+    "b8_data = (b8_data - b8_data.min())/(b8_data.max() - b8_data.min())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# Calculate the ndvi\n",
+    "ndvi = (b8_data - b4_data)/(b8_data + b4_data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# As a quick reference, plot using the `\"Greens\"` colormap as such:\n",
+    "fig, ax = plt.subplots(figsize=(14, 5))\n",
+    "ll = ndvi.plot.imshow(ax=ax, cmap=\"Greens\")\n",
+    "ax.set_aspect(\"equal\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# A sequential colormap `YlGn` using a Matplotlib norm to adjust colormap influence on image \n",
+    "# https://matplotlib.org/stable/tutorials/colors/colormapnorms.html\n",
+    "import matplotlib.colors as mcolors\n",
+    "\n",
+    "fig, ax = plt.subplots(figsize=(14, 5))\n",
+    "\n",
+    "div_norm = mcolors.Normalize(0.1, 0.8)\n",
+    "ll = ndvi.plot.imshow(ax=ax, cmap=\"YlGn\", norm=div_norm)\n",
+    "ax.set_aspect(\"equal\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# A Diverging colormap `RdYlGn` with a normalization on the color limits in two directions of the central point - \n",
+    "# https://matplotlib.org/stable/api/_as_gen/matplotlib.colors.TwoSlopeNorm.html\n",
+    "fig, ax = plt.subplots(figsize=(14, 5))\n",
+    "\n",
+    "div_norm = mcolors.TwoSlopeNorm(vmin=-0.1, vcenter=0.4, vmax=0.8)\n",
+    "\n",
+    "ll = ax.imshow(ndvi.values, cmap=\"RdYlGn\", norm=div_norm)\n",
+    "fig.colorbar(ll);\n",
+    "ax.set_axis_off();"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-success\">\n",
+    "\n",
+    "**(ADVANCED) EXERCISE**:\n",
+    "    \n",
+    "You want to reclassify the values of the 4th band data to a fixed set of classes:\n",
+    "    \n",
+    "* x < 0.05 need to be 0\n",
+    "* 0.05 < x < 0.1 need to be 1\n",
+    "* x > 0.1 need to be 2\n",
+    "       \n",
+    "Use the data set `./data/gent/raster/2020-09-17_Sentinel_2_L1C_B04.tiff` (assign data to variable `b4_data`):\n",
+    "    \n",
+    "* Read the data set and exclude the single-value dimension to end up with a 2D array. \n",
+    "* Convert to float data type and normalize the values to the range `[0., 1.]`.\n",
+    "\n",
+    "To reclassify the values, we can use the `np.digitize` function. This function return the indices of the bins to which each value in input array belongs. As such, it can be used to select and manipulate values containing to a specific bin.\n",
+    "    \n",
+    "* Apply the `np.digitize` function to `b4_data` using the `xr.apply_ufunc()` function. The first argument is the function to apply, the following arguments are the arguments that would be passed to the function (`np.digitize`). Call the result `b4_data_classified`.\n",
+    "* Make a image plot of the reclassified variable `b4_data_classified`.\n",
+    "    \n",
+    "\n",
+    "<details><summary>Hints</summary>\n",
+    "\n",
+    "* The `apply_ufunc` function requires as input another function (in this case `np.digitize`) and is a way to use [external functionalities within xarray](http://xarray.pydata.org/en/stable/user-guide/computation.html#wrapping-custom-computation).   \n",
+    "* The `np.digitize` function would have as inputs the data `b4_data` and the bins to split the data, `[0.05, 0.1]`.\n",
+    "\n",
+    "</details>   \n",
+    "    \n",
+    "    \n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "clear_cell": false
+   },
+   "outputs": [],
+   "source": [
+    "b4_data = xr.open_dataarray(\"./data/gent/raster/2020-09-17_Sentinel_2_L1C_B04.tiff\", \n",
+    "                            engine=\"rasterio\", mask_and_scale=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# Convert to 2D float array \n",
+    "b4_data = b4_data.sel(band=1).astype(float)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# Rescale the data\n",
+    "b4_data = (b4_data - b4_data.min())/(b4_data.max() - b4_data.min())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# Classify the array into 3 bins\n",
+    "b4_data_classified = xr.apply_ufunc(np.digitize, b4_data, [0.05, 0.1])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# Create an image plot\n",
+    "fig, ax = plt.subplots(figsize=(12, 5))\n",
+    "img = b4_data_classified.plot.imshow(ax=ax, add_colorbar=False, interpolation=\"antialiased\")\n",
+    "fig.colorbar(img, values=[0, 1, 2], ticks=[0, 1, 2])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-info\">\n",
+    "    \n",
+    "__Note:__\n",
+    "    \n",
+    "When only interested in a discrete colormap for plotting instead of the data manipulation, xarray provides the `levels` parameter whe plotting, see http://xarray.pydata.org/en/stable/user-guide/plotting.html#discrete-colormaps.    \n",
+    "    \n",
+    "</div>"
+   ]
+  }
+ ],
+ "metadata": {
+  "celltoolbar": "Nbtutor - export exercises",
+  "jupytext": {
+   "default_lexer": "ipython3"
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.11"
+  },
+  "widgets": {
+   "application/vnd.jupyter.widget-state+json": {
+    "state": {},
+    "version_major": 2,
+    "version_minor": 0
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/_solved/12-rasterio.ipynb b/_solved/12-rasterio.ipynb
deleted file mode 100644
index 56ed96b..0000000
--- a/_solved/12-rasterio.ipynb
+++ /dev/null
@@ -1,1480 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Rasterio"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "In the previous notebook we focused on the Numpy package using [Rasterio](https://rasterio.readthedocs.io/en/latest/) as the interface to read the data from disk. \n",
-    "\n",
-    "Whereas Numpy is a powerful package for calculation, it does not provide any spatial information so lacking support for reprojection, warping,... Apart from reading in a broad set of GIS raster formats, Rasterio also provides some of these GIS raster operations."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 48,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "import geopandas as gpd\n",
-    "\n",
-    "import rasterio\n",
-    "from rasterio.plot import plotting_extent, show, show_hist, reshape_as_image"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let's recap with what we did so far with Rasterio:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data_file = \"./data/herstappe/raster/2020-09-17_Sentinel_2_L1C_True_color.tiff\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "with rasterio.open(data_file) as src:    \n",
-    "    herstappe_data = src.read([1, 2, 3], out_dtype=float, masked=False) # Note the float as output\n",
-    "    herstappe_profile = src.profile\n",
-    "    herstappe_ext = plotting_extent(src)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "((3, 227, 447), dtype('float64'))"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "herstappe_data.shape, herstappe_data.dtype"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Convenient plot functions for data exploration"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Rasterio provides dedicated plot functions `show` and `show_hist` for quick ddata exploration:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1440x720 with 3 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 720x360 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "with rasterio.open(data_file) as src:\n",
-    "    \n",
-    "    # convenience plot functions from rasterio\n",
-    "    fig, (axr, axg, axb) = plt.subplots(1,3, figsize=(20, 10))\n",
-    "    show((src, 1), ax=axr, cmap='Reds', title='Red channel')\n",
-    "    show((src, 2), ax=axg, cmap='Greens', title='Green channel')\n",
-    "    show((src, 3), ax=axb, cmap='Blues', title='Blue channel')\n",
-    "    \n",
-    "    fig, ax = plt.subplots(1, 1, figsize=(10, 5))\n",
-    "    show_hist(src, bins=50, lw=0.0, stacked=False, \n",
-    "          alpha=0.3, histtype='stepfilled', title=\"Histogram\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "__Note:__ Rasterio directly interacts with Matplotlib objects, so adjustments starting from the figure can be done using the Matplotlib syntax. For example, change the title and the legend labels:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['3', '2', '1']"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "_, current_labels = ax.get_legend_handles_labels()\n",
-    "current_labels"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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9bo3xnXbaae3GV0pqrytyh3YsDQQGRsRTkvYEngROBL4ArIqI70v6JrB3RFwkaQRwEzAB2A94ABgWEZsl1QFfBR4H7gGujoh7JZ0PjI6I8yRNB06KiFPbi6u6ujp25N4ttbW11NTUbPf2XVFeclKua+Lyko9ycT6KOR/FnI9iHZmPpUuXMnz48A7ZVyV1h8duNWrtZybpyYiobm39kvXERcSbEfFU+n4NsBQYBJwAXJ+udj1JYUfafnNEbIyIV4BlwIS0GOwTEfMiqThvaLZN475uBSY39tKZmZmZdWVluSYuHeY8DHgCGBARb0JS6AEfS1cbBKwo2Kw+bRuUvm/eXrRNRGwC3geKB9vNzMzMuqCSz06VtAdwG/C1iFjdTkdZawuinfb2tmkewwxgBsCAAQOabiK4PRoaGnZo+64oLzkpmPzUrh09lbzko1ycj2LORzHno1hH5qNv376sWbOmQ/ZVSZs3b+4S55HFhg0btunnX9IiTtLOJAXcf0XEb9LmtyQNjIg306HSt9P2emD/gs2rgDfS9qpW2gu3qZfUE+gLrGoeR0TMAmZBck3cjlxv4Os3WspLTrJeE7ejp5KXfJSL81HM+SjmfBTr6GviusK1ZN3pmrjevXtz2GGHZV6/lLNTBVwHLI2IwlsYzwXOTN+fCdxZ0D49nXE6BBgK1KVDrmskTUz3eUazbRr3dTLwYJRqpoaZmZlZJ1LKnrjDgc8DiyQtTNu+BXwfuEXS2cBrwCkAEfGspFuAJcAm4IKI2Jxu92VgNrArcG/6gqRIvFHSMpIeuOklPB8zMzOzTqOUs1N/HxGKiNERMSZ93RMR70bE5IgYmv65qmCbKyLiExFxUETcW9C+ICJGpctmNva2RcSGiDglIj4ZERMi4uVSnY+ZmZltm7feeovPfe5zHHjggYwbN45JkyZx++23VzosamtrOf744yt2/JqaGnbkdmeN/NgtMzOzbuCu5zNeGJzR1IPav6lmRHDiiSdy5pln8qtf/QqAV199lblz57ZYd9OmTfTs6ZJkW/mxW2ZmZtbhHnzwQXbZZRfOO++8prYDDjiAr3zlKwDMnj2bU045halTp3Lssceydu1azjrrLMaPH89hhx3GnXcml79v3ryZb3zjG4wfP57Ro0fz7//+78BHk0BOPvlkDj74YE4//fRWn6W6bNkyjj76aA499FDGjh3b9BivhoaGVre97LLLGD9+PKNGjWLGjBlN7TU1NVx00UVMmDCBYcOG8cgjjzSdx7Rp05gyZQpDhw7lwgsvbDr2fffdx6RJkxg7diynnHIKDQ0NHZpjF3FmZmbW4Z599tmmR2i1Zd68eVx//fU8+OCDXHHFFRx11FHMnz+fhx56iG984xusXbuWG264gb59+zJ//nzmz5/PtddeyyuvvALA008/zVVXXcWSJUt4+eWXefTRR1sc4/TTT+eCCy7gmWee4bHHHmPgwIHtbjtz5kzmz5/P4sWLWb9+PXfffXfTvjZt2kRdXR1XXXUVl156aVP7woULmTNnDosWLWLOnDmsWLGClStXcvnll/PAAw/w1FNPUV1dzZVXXklHct+lmZmZldwFF1zA73//e3bZZRfmz58PwDHHHMM+++wDJL1Wc+fO5Uc/+hGQ3DPttdde48EHH2TJkiVND6t///33efHFF9lll12YMGECVVXJXcjGjBnD8uXLOeKII5qOuWbNGl5//XVOOukkILmFR6O2tn3ooYf44Q9/yLp161i1ahUjR45kavo8xmnTpgEwbtw4li9f3rSvyZMn07dvXwBGjBjBq6++ynvvvceSJUs4/PDDAfjggw+YNGlSB2bURZyZmZmVwMiRI7ntttuaPv/sZz9j5cqVVFd/9BjQ3Xffvel9RHDbbbdx0EEHFe0nIvjJT37CcccdV9ReW1tLr169mj736NGDTZs2tdi2La1tu2HDBs4//3wWLFjA/vvvzyWXXMKGDRtabNP8WK3tKyI45phjuOmmm9qMYUd5ONXMzMw63FFHHcWGDRu45pprmtrWrVvX5vrHHXccP/nJT5oKr6effhpIermuueYaPvzwQwBeeOEF1q5dmymGPn36UFVVxR133AHAxo0b242hsWDr378/DQ0NTb1/22PixIk8+uijLFu2DEjO/YUXXtju/bXGRZyZmZl1OEnccccdPPzwwwwZMoQJEyZw5pln8oMf/KDV9b/97W/z4YcfMnr0aEaNGsW3v/1tAM4880xGjBjB2LFjGTVqFOeee26LHrf23HjjjVx99dWMHj2aT33qU/zxj39sc9299tqLc845h0MOOYQTTzyR8ePHb9tJF9h3332ZPXs2p512GqNHj2bixIk899xz272/1qi7PeCguro6duTeLH5ETEt5yUnWx25NbX/W/FblJR/l4nwUcz6KOR/FOvqxW8OHD++QfVVSd3rsVms/M0lPRkR1a+u7J87MzMwsh1zEmZmZmeWQizgzMzOzHHIRZ2ZmZpZDLuLMzMzMcshFnJmZmVkOuYgzMzOzkujRowdjxoxpevj8Y489BsDy5csZNWpUyY5bW1vL8ccfX7L9b01NTQ07cjuzrPzYLTMzs+4g680ys8pwU81dd92VhQsXAvDb3/6Wiy++mIcffrhj4+jG3BNnZmZmJbd69Wr23nvvFu2zZ89m5syZTZ+PP/54amtrAbjvvvuYPHkyY8eO5ZRTTqGhoaHF9suWLePoo49u6u176aWXAGhoaODkk0/m4IMP5vTTT296nNdll13G+PHjGTVqFDNmzGhqr6mp4aKLLmLChAkMGzaMRx55pCm+adOmMWXKFIYOHcqFF17YdOz77ruPSZMmtRtfKbmIMzMzs5JYv349Y8aM4eCDD+ZLX/pS06O0sli5ciWXX345c+fO5amnnqK6uporr7yyxXqnn346F1xwAc888wyPPfYYAwcOBJJnr1511VUsWbKEl19+mUcffRSAmTNnMn/+fBYvXsz69eu5++67m/a1adMm6urquOqqq7j00kub2hcuXMicOXNYtGgRc+bMYcWKFU3xPfDAA+3GV0oeTjUzM7OSKBxOnTdvHmeccQaLFy/OtO3jjz/OkiVLOPbYY9lpp5344IMPmDRpUtE6a9as4fXXX+ekk04CoHfv3k3LJkyYQFVVFQBjxoxh+fLlHHHEETz00EP88Ic/ZN26daxatYqRI0cyNR0anjZtGgDjxo1j+fLlTfuaPHkyffv2BWDEiBG8+uqrvPfeeyxZsoTDDz8coNX4Ss1FnJmZmZXcpEmTWLlyJe+8805Re8+ePdmyZUvT5w0bNgAQERxzzDHMmjWrzWentvf89169ejW979GjB5s2bWLDhg2cf/75LFiwgP33359LLrmk6XiF2zSu396+GuO76aabspx+SXg41czMzEruueeeY/PmzfTr16+offDgwSxcuJAtW7awYsUK6urqAJg4cSKPPvpo0zVu69at44UXXijatk+fPlRVVXHHHXcAsHHjRtatW9dmDI0FW//+/WloaODWW2/d7vNpjG/ZsmVtxldq7okzMzOzkmi8Jg6SXrPrr7+eHj16FK1z+OGHM2TIEA455BBGjRrF2LFjAdh3332ZPXs2Z511VlOv2OWXX86wYcOKtr/xxhs599xz+c53vsPOO+/Mr3/96zbj2WuvvTjnnHM45JBDGDx4MOPHj9/uc2uM77TTTmPjxo1txldKaq8rcod2LP0COB54OyJGpW1zgIPSVfYC3ouIMZIGA0uB59Nlj0fEeek244DZwK7APcBXIyIk9QJuAMYB7wKnRsTyrcVVXV0dO3LvltraWmpqarZ7+64oLznJOrs+w6z5duUlH+XifBRzPoo5H8U6Mh9Lly5l+PDhHbKvSlqzZk2bw6ldTWs/M0lPRkR1a+uXcjh1NjClsCEiTo2IMRExBrgN+E3B4pcalzUWcKlrgBnA0PTVuM+zgT9FxCeBHwM/KM1pmJmZmXU+JSviIuJ3wKrWlkkS8DdAu1cDShoI9ImIeZF0Gd4AnJguPgG4Pn1/KzA53a+ZmZlZl1epiQ2fBt6KiBcL2oZIelrSw5I+nbYNAuoL1qlP2xqXrQCIiE3A+0Dx1ZJmZmZmXVSlJjacRnEv3JvAxyPi3fQauDskjQRa61lrvIivvWVFJM0gGZJlwIABTXeC3h4NDQ07tH1XlJecFMxgb9eOnkpe8lEuzkcx56OY81GsI/PRt29fVq9eTd4HqTZv3syaNWsqHUbJRQQbNmzYpp9/2Ys4ST2BaSQTEgCIiI3AxvT9k5JeAoaR9LxVFWxeBbyRvq8H9gfq0332pY3h24iYBcyCZGLDjlw06otwW8pLTrJObNjRU8lLPsrF+SjmfBRzPop1ZD5eeeUVPvjgA/r165frQq47TGyICN5991322msvDjvssMzbVaIn7mjguYhoGiaVtC+wKiI2SzqQZALDyxGxStIaSROBJ4AzgJ+km80FzgTmAScDD0apptqamZnlTFVVFfX19S1urps3GzZsKHoSQ1fVu3fvpidMZFWyIk7STUAN0F9SPfDdiLgOmE7LCQ1/AVwmaROwGTgvIhp71b7MR7cYuTd9AVwH3ChpGUkP3PRSnYuZmVne7LzzzgwZMqTSYeyw2trabeqd6k5KVsRFxGlttH+hlbbbSG450tr6C4BRrbRvAE7ZsSjNzMzM8smP3TIzMzPLIRdxZmZmZjnkIs7MzMwsh1zEmZmZmeWQizgzMzOzHHIRZ2ZmZpZDLuLMzMzMcshFnJmZmVkOuYgzMzMzyyEXcWZmZmY55CLOzMzMLIdcxJmZmZnlkIs4MzMzsxxyEWdmZmaWQy7izMzMzHLIRZyZmZlZDrmIMzMzM8shF3FmZmZmOeQizszMzCyHXMSZmZmZ5ZCLODMzM7McchFnZmZmlkM9Kx2A2Y666/m7Mq45taRxmJmZlVPJeuIk/ULS25IWF7RdIul1SQvT12cLll0saZmk5yUdV9A+TtKidNnVkpS295I0J21/QtLgUp2LmZmZWWdTyuHU2cCUVtp/HBFj0tc9AJJGANOBkek2/yapR7r+NcAMYGj6atzn2cCfIuKTwI+BH5TqRMzMzMw6m5IVcRHxO2BVxtVPAG6OiI0R8QqwDJggaSDQJyLmRUQANwAnFmxzffr+VmByYy+dmZmZWVdXiYkNMyX9IR1u3TttGwSsKFinPm0blL5v3l60TURsAt4H+pUycDMzM7POotwTG64BvgdE+ue/AGcBrfWgRTvtbGVZEUkzSIZkGTBgALW1tdsUdKGGhoYd2r4rqnROtmzcknHF2kyr7eipVDofnY3zUcz5KOZ8FHM+WnJO2lbWIi4i3mp8L+la4O70Yz2wf8GqVcAbaXtVK+2F29RL6gn0pY3h24iYBcwCqK6ujpqamu0+h9raWnZk+66o0jnJPDv1hZpMq61Zk213U9uY7FrpfHQ2zkcx56OY81HM+WjJOWlbWYdT02vcGp0ENM5cnQtMT2ecDiGZwFAXEW8CayRNTK93OwO4s2CbM9P3JwMPptfNmZmZmXV5JeuJk3QTUAP0l1QPfBeokTSGZNhzOXAuQEQ8K+kWYAmwCbggIjanu/oyyUzXXYF70xfAdcCNkpaR9MBNL9W5mJmZmXU2JSviIuK0Vpqva2f9K4ArWmlfAIxqpX0DcMqOxGhmZmaWV37slpmZmVkO+bFbZp3EXVmfHkbbkyrMzKz7cE+cmZmZWQ65iDMzMzPLIRdxZmZmZjnkIs7MzMwsh1zEmZmZmeWQZ6ealdi2zDo1MzPLyj1xZmZmZjnkIs7MzMwsh1zEmZmZmeWQizgzMzOzHHIRZ2ZmZpZDLuLMzMzMcshFnJmZmVkOuYgzMzMzyyEXcWZmZmY55CLOzMzMLIdcxJmZmZnlkIs4MzMzsxxyEWdmZmaWQy7izMzMzHKoZEWcpF9IelvS4oK2/yvpOUl/kHS7pL3S9sGS1ktamL5+XrDNOEmLJC2TdLUkpe29JM1J25+QNLhU52JmZmbW2fQs4b5nAz8Fbihoux+4OCI2SfoBcDFwUbrspYgY08p+rgFmAI8D9wBTgHuBs4E/RcQnJU0HfgCcWooTMSuHuvfuyrzuVKaWMBIzM8uDTD1xkkZt644j4nfAqrb2ZuQAABl8SURBVGZt90XEpvTj40DVVo47EOgTEfMiIkgKwhPTxScA16fvbwUmN/bSmZmZmXV1WYdTfy6pTtL5jUOgHeAskh61RkMkPS3pYUmfTtsGAfUF69SnbY3LVgCkheH7QL8Ois3MzMysU1PSwZVhRWkoSeF1ClAH/GdE3L+VbQYDd0fEqGbt/weoBqZFREjqBewREe9KGgfcAYwEDgL+OSKOTrf7NHBhREyV9CxwXETUp8teAiZExLutxDGDZEiWAQMGjLv55psznXNrGhoa2GOPPbZ7+66o0jl5f+P72Vbc0LdDj9u3jd01z8f7GcNbuznjisB++3TsuZRSpb8fnY3zUcz5KOZ8tNTdc3LkkUc+GRHVrS3LfE1cRLwo6R+BBcDVwGHp8OW3IuI3Wfcj6UzgeGByOkRKRGwENqbvn0wLsmEkPW+FQ65VwBvp+3pgf6BeUk+gL82GbwtinwXMAqiuro6ampqs4bZQW1vLjmzfFVU6J3c9n/FashdqOvS4bZ1y83zclTG8pauzXxP3uWltHLwTqvT3o7NxPoo5H8Wcj5ack7ZlvSZutKQfA0uBo4CpETE8ff/jrAeTNIVkIsNfRcS6gvZ9JfVI3x8IDAVejog3gTWSJqYF4xnAnelmc4Ez0/cnAw9G1m5FMzMzs5zL2hP3U+Bakl639Y2NEfFG2jvXgqSbgBqgv6R64Lsks1F7AfencxAej4jzgL8ALpO0CdgMnBcRjb1qXyaZ6boryTV0jdfRXQfcKGkZSQ/c9IznYmZmZpZ7WYu4zwLrI2IzgKSdgN4RsS4ibmxtg4g4rZXm69pY9zbgtjaWLQBazI6NiA0k1+eZmZmZdTtZZ6c+QNIT1mi3tM3MzMzMKiBrEdc7IhoaP6TvdytNSGZmZma2NVmLuLWSxjZ+SG8Dsr6d9c3MzMyshLJeE/c14NeSGm/vMRA/4srMzMysYjIVcRExX9LBJDffFfBcRHxY0sjMzMzMrE2Zb/YLjAcGp9scJomIuKH9Tcy6rrZu4rtlS/Yb/JqZmW2vTEWcpBuBTwALSe7jBtD4QHozMzMzK7OsPXHVwAg/EcHKKfPjtMzMzLqhrLNTFwN/VspAzMzMzCy7rD1x/YElkupIH1QPEBF/VZKozMzMzKxdWYu4S0oZhJmZmZltm6y3GHlY0gHA0Ih4QNJuQI/ShmbWserey3aN3YS9ppY4EjMzsx2XdXbqOcAMYB+SWaqDgJ8Dk0sXmlnXkLV4NDMz2xZZJzZcABwOrAaIiBeBj5UqKDMzMzNrX9YibmNEfND4QVJPkvvEmZmZmVkFZJ3Y8LCkbwG7SjoGOB/wGJF1CnV1lY7AzMys/LL2xH0TeAdYBJwL3AP8Y6mCMjMzM7P2ZZ2dugW4Nn2ZmZmZWYVlnZ36Cq1cAxcRB3Z4RGZmZma2Vdvy7NRGvYFTSG43YmZmZmYVkOmauIh4t+D1ekRcBRxV4tjMzMzMrA1Zh1PHFnzciaRnbs+SRGRmZmZmW5V1OPVfCt5vApYDf9Ph0ZiZmZlZJlmHU48seB0TEedExPPtbSPpF5LelrS4oG0fSfdLejH9c++CZRdLWibpeUnHFbSPk7QoXXa1JKXtvSTNSdufkDR4W0/ezMzMLK+yDqf+7/aWR8SVrTTPBn4K3FDQ9k3gfyLi+5K+mX6+SNIIYDowEtgPeEDSsIjYDFxD8tzWx0nuTzcFuBc4G/hTRHxS0nTgB8CpWc7HzMzMLO+y3uy3GvgyyYPvBwHnASNIrotr9dq4iPgdsKpZ8wnA9en764ETC9pvjoiNEfEKsAyYIGkg0Cci5kVEkBSEJ7ayr1uByY29dGZmZmZdXdZr4voDYyNiDYCkS4BfR8SXtvF4AyLiTYCIeFPSx9L2QSQ9bY3q07YP0/fN2xu3WZHua5Ok94F+wMptjMnMzMwsd7IWcR8HPij4/AEwuAPjaK0HLdppb2+bljuXZpAMyTJgwABqa2u3I8REQ0PDDm3fFZUqJ1s2bsm03vDdO/i4W2p3cA8NRfsYvnu289gWefoO+nemmPNRzPko5ny05Jy0LWsRdyNQJ+l2kkLpJIqvdcvqLUkD0164gcDbaXs9sH/BelXAG2l7VSvthdvUS+oJ9KXl8C0AETELmAVQXV0dNTU12xF6ora2lh3ZvisqVU7uev6uTOstrevY407Yq2aHtt+ypZaddvpoH0tXZzuPbfG5aTVbXaez8O9MMeejmPNRzPloyTlpW9bZqVcAXwT+BLwHfDEi/mk7jjcXODN9fyZwZ0H79HTG6RBgKFCXDr2ukTQxvd7tjGbbNO7rZODB9Lo5MzMzsy4va08cwG7A6oj4T0n7ShqSTkJolaSbgBqgv6R64LvA94FbJJ0NvEby+C4i4llJtwBLSO5Dd0E6MxWSCRWzgV1JZqXem7ZfB9woaRlJD9z0bTgXMzMzs1zLeouR75LMUD0I+E9gZ+CXwOFtbRMRp7WxaHIb618BXNFK+wJgVCvtG0iLQDMzM7PuJustRk4C/gpYCxARb+DHbpmZmZlVTNYi7oP0erMAkNTB8wHNzMzMbFtkLeJukfTvwF6SzgEeAK4tXVhmZmZm1p6tXhOXzgqdAxwMrCa5Lu47EXF/iWMzMzMzszZstYiLiJB0R0SMA1y4mZmZmXUCWYdTH5c0vqSRmJmZmVlmWe8TdyRwnqTlJDNURdJJN7pUgZmZmZlZ29ot4iR9PCJeAz5TpnjMzMzMLIOt9cTdAYyNiFcl3RYRf12OoMzMzMysfVu7Jk4F7w8sZSBmZmZmlt3Wirho472ZmZmZVdDWhlMPlbSapEdu1/Q9fDSxoU9JozMzMzOzVrVbxEVEj3IFYmZmZmbZZb1PnJmZmZl1Ii7izMzMzHIo681+zayZuvfuarV9+O5bWLq69WVmZmYdxUVcd3VXxiJj6tTSxtEJtVWcmZmZdSYeTjUzMzPLIRdxZmZmZjnkIs7MzMwsh1zEmZmZmeWQJzaY5dBdz2ebfDH1oO43McXMrLsoe0+cpIMkLSx4rZb0NUmXSHq9oP2zBdtcLGmZpOclHVfQPk7SonTZ1ZJU7vMxMzMzq4Sy98RFxPPAGABJPYDXgduBLwI/jogfFa4vaQQwHRgJ7Ac8IGlYRGwGrgFmAI8D9wBTgHvLdCqdU9Zbh5iZmVmuVfqauMnASxHxajvrnADcHBEbI+IVYBkwQdJAoE9EzIuIAG4ATix9yGZmZmaVV+kibjpwU8HnmZL+IOkXkvZO2wYBKwrWqU/bBqXvm7ebmZmZdXlKOrEqcGBpF+ANYGREvCVpALASCOB7wMCIOEvSz4B5EfHLdLvrSIZOXwP+OSKOTts/DVwYES2u5JY0g2TYlQEDBoy7+eabtzvuhoYG9thjj+3evuTef79j99e371ZXKVVO3t+Y7VzWru3wQ++Q3jvBhi2lPcbuu2dbr2+vrf/8Sq3T/86UmfNRzPko5ny01N1zcuSRRz4ZEdWtLavk7NTPAE9FxFsAjX8CSLoWuDv9WA/sX7BdFUnxV5++b97eQkTMAmYBVFdXR01NzXYHXVtby45sX3IdfU1chnMtVU6yzsBcWtfhh94hw3ffwtK1pe3knjAy23o1B9WUNI4sOv3vTJk5H8Wcj2LOR0vOSdsqWcSdRsFQqqSBEfFm+vEkYHH6fi7wK0lXkkxsGArURcRmSWskTQSeAM4AflK26LuLLEXhlhJ3O1kLdRkL16kHlTYOMzOrnIoUcZJ2A44Bzi1o/qGkMSTDqcsbl0XEs5JuAZYAm4AL0pmpAF8GZgO7ksxK7d4zU83MzKzbqEgRFxHrgH7N2j7fzvpXAFe00r4AGNXhAZqZmZl1cpWenWpmZmZm28FFnJmZmVkOuYgzMzMzyyEXcWZmZmY55CLOzMzMLIdcxJmZmZnlkIs4MzMzsxxyEWdmZmaWQy7izMzMzHKoks9Ota4kyzNWAaZOLW0cZmZm3YR74szMzMxyyEWcmZmZWQ65iDMzMzPLIV8TZ+WV9do5gGGlC8PMzCzv3BNnZmZmlkPuibNOa8CDddlW3GNCaQPpDjy72Mwsd9wTZ2ZmZpZDLuLMzMzMcshFnJmZmVkO+Zo4y72Dns527dzzh/naOTMz6zrcE2dmZmaWQ+6JM7PsPIvVzKzTcE+cmZmZWQ5VpIiTtFzSIkkLJS1I2/aRdL+kF9M/9y5Y/2JJyyQ9L+m4gvZx6X6WSbpakipxPmZmZmblVsmeuCMjYkxEVKefvwn8T0QMBf4n/YykEcB0YCQwBfg3ST3Sba4BZgBD09eUMsZvZmZmVjGd6Zq4E4Ca9P31QC1wUdp+c0RsBF6RtAyYIGk50Cci5gFIugE4Ebi3vGGbdWLb8qxaMzPLlUr1xAVwn6QnJc1I2wZExJsA6Z8fS9sHASsKtq1P2wal75u3m5mZmXV5leqJOzwi3pD0MeB+Sc+1s25r17lFO+0td5AUijMABgwYQG1t7TaG+5GGhoYd2r7ktmwp+yEbgNoSHHdd/+GZ1tttz2z7G75beXLTeycYvnv5fw6tqa1UGAW/I53+d6bMnI9izkcx56Ml56RtFSniIuKN9M+3Jd0OTADekjQwIt6UNBB4O129Hti/YPMq4I20vaqV9taONwuYBVBdXR01NTXbHXttbS07sn3JVWD4rHbLFmp26vhO3bqVSzOt98KL2fZXrpv9Dt99C0vXdo6J35/bq0IHLvgd6fS/M2XmfBRzPoo5Hy05J20rexEnaXdgp4hYk74/FrgMmAucCXw//fPOdJO5wK8kXQnsRzKBoS4iNktaI2ki8ARwBvCT8p6N5Ymf7GBmZl1JJXriBgC3p3cD6Qn8KiL+W9J84BZJZwOvAacARMSzkm4BlgCbgAsiYnO6ry8Ds4FdSSY0eFKDmZmZdQtlL+Ii4mXg0Fba3wUmt7HNFcAVrbQvAEZ1dIxmZmZmnV3nuHDHzMzMzLaJizgzMzOzHHIRZ2ZmZpZDnemJDWadgmexmplZHrgnzszMzCyH3BNnZVVXn62Xy8zMzNrnnjgzMzOzHHJPnFkOZb1ujyN93Z6ZWVflIs6sk8hcmJmZmeEizmy7tVV09R47nIOeXtr02bNYzcysFHxNnJmZmVkOuSfOrMQ8TGpmZqXgnjgzMzOzHHJPnJl1vLvu+uj9li3FnwtNnVqeeMzMuiAXcWZdWNabK0+o8uQLM7O88XCqmZmZWQ65iDMzMzPLIRdxZmZmZjnkIs7MzMwsh1zEmZmZmeWQizgzMzOzHPItRsy6sBdezLbehKrSxmFmZh2v7D1xkvaX9JCkpZKelfTVtP0SSa9LWpi+PluwzcWSlkl6XtJxBe3jJC1Kl10tSeU+HzMzM7NKqERP3CbgHyLiKUl7Ak9Kuj9d9uOI+FHhypJGANOBkcB+wAOShkXEZuAaYAbwOHAPMAW4t0znYWZmZlYxZS/iIuJN4M30/RpJS4FB7WxyAnBzRGwEXpG0DJggaTnQJyLmAUi6ATiRrlrEtfXYIjMzM+uWKjqxQdJg4DDgibRppqQ/SPqFpL3TtkHAioLN6tO2Qen75u1mZmZmXV7FJjZI2gO4DfhaRKyWdA3wPSDSP/8FOAto7Tq3aKe9tWPNIBl2ZcCAAdTW1m533A0NDTu0/XbbsqX8x8yoAajNGN+6/sM7/Pi77dnhu9whO+3Wm93Gdvx5llLWn9/2aPf7UYnfpQqr2N8hnZTzUcz5aMk5aVtFijhJO5MUcP8VEb8BiIi3CpZfC9ydfqwH9i/YvAp4I22vaqW9hYiYBcwCqK6ujpqamu2Ovba2lh3Zfrt14uHU2i1bqNkpW6du3cqlHX78rDMwy2W3scNZ91THn2cpTTtyQsn23e73oxK/SxVWsb9DOinno5jz0ZJz0rZKzE4VcB2wNCKuLGgfWLDaScDi9P1cYLqkXpKGAEOBuvTaujWSJqb7PAO4sywnYWZmZlZhleiJOxz4PLBI0sK07VvAaZLGkAyJLgfOBYiIZyXdAiwhmdl6QTozFeDLwGxgV5IJDV1zUoOZmZlZM5WYnfp7Wr+e7Z52trkCuKKV9gXAqI6LzszMzCwf/NgtMzMzsxxyEWdmZmaWQ352qnWIuvq6Dt9nZ5t1amZm1pm4iDOzysl665ypU0sbh5lZDrmIM7PMPakTqkp3PzkzM9s2vibOzMzMLIdcxJmZmZnlkIs4MzMzsxxyEWdmZmaWQy7izMzMzHLIs1PNrPPLeisS8O1IzKzbcBFnZplvrDyhqrRxmJlZdh5ONTMzM8shF3FmZmZmOeThVGtXljv5r+s/nN3KEItVnp/sYGbWebiIM7Ouxc9jNbNuwsOpZmZmZjnknrhK25ZbJ5iZmZml3BNnZmZmlkMu4szMzMxyyMOpZtY9eQKEmeWcizgzy8xPdjAz6zxcxJlZhyu8n9y6/sOpW7m01fV8Pzkzs+2X+yJO0hTgX4EewH9ExPcrHJKZdSUedjWzTirXRZykHsDPgGOAemC+pLkRsaSykXV+We+8b7Y9Coddd9uzvWFYPwHCzGx75bqIAyYAyyLiZQBJNwMnAC7izHIg6zV2WYs9qGDBl7XHbs89SxuHmXUbeS/iBgErCj7XA39eoVg+4hv4tin7P9pmH9mW780LL3ZsL/OwoVmPm229qr+s7n5/R3io2awk8l7EqZW2aLGSNAOYkX5skPT8DhyzP7ByB7bvipyTYs5HMeej0I+dj2acj2LOR0vdPScHtLUg70VcPbB/wecq4I3mK0XELGBWRxxQ0oKIqO6IfXUVzkkx56OY81HM+SjmfBRzPlpyTtqW9yc2zAeGShoiaRdgOjC3wjGZmZmZlVyue+IiYpOkmcBvSW4x8ouIeLbCYZmZmZmVXK6LOICIuAe4p4yH7JBh2S7GOSnmfBRzPoo5H8Wcj2LOR0vOSRsU0WIegJmZmZl1cnm/Js7MzMysW3IR1wZJUyQ9L2mZpG+2svxgSfMkbZT09UrEWE4Z8nG6pD+kr8ckHVqJOMslQz5OSHOxUNICSUdUIs5y2lpOCtYbL2mzpJPLGV+5ZfiO1Eh6P/2OLJT0nUrEWS5Zvh9pThZKelbSw+WOsZwyfD++UfDdWJz+zuxTiVjLIUM++kq6S9Iz6ffji5WIs9OJCL+avUgmSbwEHAjsAjwDjGi2zseA8cAVwNcrHXMnyMengL3T958Bnqh03BXOxx58dLnCaOC5Ssdd6ZwUrPcgyXWsJ1c67gp/R2qAuysdayfKx14kT9v5ePr5Y5WOu5L5aLb+VODBSsdd4e/Ht4AfpO/3BVYBu1Q69kq/3BPXuqbHeUXEB0Dj47yaRMTbETEf+LASAZZZlnw8FhF/Sj8+TnLPvq4qSz4aIv3bBtidVm5C3cVsNSeprwC3AW+XM7gKyJqP7iJLPj4H/CYiXoPk79gyx1hO2/r9OA24qSyRVUaWfASwpySR/Cd5FbCpvGF2Pi7iWtfa47wGVSiWzmBb83E2cG9JI6qsTPmQdJKk54D/B5xVptgqZas5kTQIOAn4eRnjqpSsvzOT0uGheyWNLE9oFZElH8OAvSXVSnpS0hlli678Mv+dKmk3YArJf366qiz5+CkwnOSG/ouAr0bElvKE13nl/hYjJZLpcV7dSOZ8SDqSpIjryteAZcpHRNwO3C7pL4DvAUeXOrAKypKTq4CLImJz8p/pLi1LPp4CDoiIBkmfBe4AMj6pNXey5KMnMA6YDOwKzJP0eES8UOrgKmBb/o2ZCjwaEatKGE+lZcnHccBC4CjgE8D9kh6JiNWlDq4zc09c6zI9zqsbyZQPSaOB/wBOiIh3yxRbJWzT9yMifgd8QlL/UgdWQVlyUg3cLGk5cDLwb5JOLE94ZbfVfETE6ohoSN/fA+zchb8jWb4f9cB/R8TaiFgJ/A7oqhOktuXvkOl07aFUyJaPL5IMt0dELANeAQ4uU3ydlou41vlxXsW2mg9JHwd+A3y+i/7PuVCWfHwyvXYDSWNJLtbtyoXtVnMSEUMiYnBEDAZuBc6PiDvKH2pZZPmO/FnBd2QCyd/HXfU7kuXv1DuBT0vqmQ4h/jmwtMxxlkumf2Mk9QX+kiQ3XVmWfLxG0kuLpAHAQcDLZY2yE/Jwaiuijcd5STovXf5zSX8GLAD6AFskfY1kNk2X69rNkg/gO0A/kt4VgE3RRR9YnDEffw2cIelDYD1wasFEhy4nY066jYz5OBn4sqRNJN+R6V31O5IlHxGxVNJ/A38AtgD/ERGLKxd16WzD78tJwH0RsbZCoZZFxnx8D5gtaRHJ8OtFaY9tt+YnNpiZmZnlkIdTzczMzHLIRZyZmZlZDrmIMzMzM8shF3FmZmZmOeQizszMzCyHXMSZmWUkabOkhZKeTR+X9b8l7ZQuq5EUkqYWrH+3pJqKBWxmXZqLODOz7NZHxJiIGAkcA3wW+G7B8nrg/1QkMjPrdlzEmZlth4h4G5gBzGx88gLwDPC+pGMqF5mZdRcu4szMtlNEvEzy9+jHCpovB/6xMhGZWXfiIs7MbMeo8ENEPAIg6dOVCcfMugsXcWZm20nSgcBm4O1mi67A18aZWYm5iDMz2w6S9gV+Dvy0+YPrI+I+YG/g0ErEZmbdQ89KB2BmliO7SloI7AxsAm4Ermxj3SuAO8sVmJl1P2r2H0gzMzMzywEPp5qZmZnlkIs4MzMzsxxyEWdmZmaWQy7izMzMzHLIRZyZmZlZDrmIMzMzM8shF3FmZmZmOeQizszMzCyH/j/AeVCDzAlqVAAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<Figure size 720x360 with 1 Axes>"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "ax.set_title(\"Histogram of RGB bands of Herstappe\")\n",
-    "ax.legend([\"Blue channel\", \"Green channel\", \"Blue channel\"])\n",
-    "fig"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "__Reminder on opening and Closing File Connections__\n",
-    "\n",
-    "The Rasterio library is efficient as it establishes a connection with the raster file rather than directly reading it into memory. Because it creates a connection, it is important that you close the connection after it is opened AND after you’ve finished working with the data! That is why we use the context manager to work with raster data in rasterio. This will handle opening and closing the raster file for you."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "__Utility functions for reshaping__"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#plt.imshow(herstappe_data)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Python image processing software packages and Matplotlib organize arrays differently than Rasterio. The interpretation of a 3-dimension array read from rasterio is:\n",
-    "\n",
-    "`(bands, rows, columns)`\n",
-    "\n",
-    "while Matplotlib and image processing software like scikit-image, pillow are generally ordered:\n",
-    "\n",
-    "`(rows, columns, bands)`\n",
-    "\n",
-    "We can rely on Numpy to transpose the data : "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.image.AxesImage at 0x7f646036a690>"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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hJljEV9WcwDdPF0ouBIGEGVaqRQ6qRsbkrORSudl3HJxn6B0pV07nRH8TDKNtRlUa8zilwpIrj1Pi/rxwN3S82XV4Z5GFEHDA4AJ/+u6Gr4/TlRhFnJGCVa7wjKo5viF6i5BEuI3xGoU7EUTVDgBG4qZaidGxHyOlGYNdDDxOK+9uBkujMUN5mi2a/cmrAz97nFirGbc1F7SFT66RwUA7qJbKhxj5eD7ROUfVwqqQomfxlp7X1GAXteyrc4598HQCr/qeWi1LohmRofEGPgRedZ7eO7Stn2uRmbQU3DtwKkyl4oPjOM2c14QiJFVKc9jSjIzzwrJmvp8XVIS1Fk5L4t3tyOgN5rsdIpc1o9VwXSdCdA6tloHdXzJdtEO+5mrzgtB5T+8jH9ZHTktiiJ60WNSX2v344Ei5kLPSh4YNN+hlycsViEi5ggqpVJ7OCzdDhMVI8CB2n1MpfFwTlznjRNjFjiVZZvVqf8uSMq/2I+uyUnCUVAHH2Ds+nRdytizl1TgQY8fP7+8pjyduYsB52xcFRcRRFLRY4EYfjJRu0IlgsMr2HFWfeQ4nli2O0RwIHobgeX870otDHA3+a4ZcucJitH2MuAapVr47Lfzlt4/MueCCwzk4pczPHyb+9pcBL4pq5S4EjmsGVaIIb1wkesd5LTwtK3MuvN/3V3z/4ZJQgcuS+PIw0IfAuhaW1XicORdEhac5cSoFcUIfA1qFt13E9wNTKpzzwpIS6o3XGEPkMiecwFevbxD9N8C4S8OY78aBWgqKGnnVmOrzsrLUSlaLKHbB86PDwFIqvzxOHPqOKa0spZCqIaKyCvfTTHSe1/ueIZrn/PrxzFwyEXc9tE5hbJ51UxSUtrGqmOeeSkG8o8M1w8BnEYby8TyT12xYo1Y8sDajv6lxNswvZ+V4Wal94HVLdctaWBfHYYgcp9TUCEYEbQRQqpX7lLmfFj6eI3e7jruxwztnkAJ2LRHDy5u8hFrdNZXUFs1rtggvVaWPgb43g74h8Ksq51wITth5zx+92lvWEQI/O16YSiEX5TEVXlcleteyXcF74e1hoPOOv/X2wF89XHiYVov4s4Gi2lJTixItcxu9Z1pmEtUcbXCG0SfbE9oITXFwEzuG4OmaV1GFSym8HgbmXNCc8ZujbFxCEONrasNlAfp2+s+lUItjyRBC5JSNRN3gmoCtQ1YlOMF7xyUXUCUVeFhWI+Vy5ZcPF97ue+76yO0Q6b3jYU4IQnSeKEYwKopUgy3U2d6oqsQQoQqpZg59x1ot6r3i5dWMTNdHjqpXHiWgOKccl5UyL/TRU6qRzfuxQ1W5OMfgIn/09pafPTxSViU55bSuLLlwWRP7LrLWzF2MvDoceLqsVIVQhc4Fhv3Aw/Fin10EcZ4MZqQVpmnBqSBOcQ4WrVc1UbVj0CAqGKNvqqu2cVsg1E6X7dn27KUa/zREz9/70WsL0GhEO5X6WYTekoDrft7Szdrm7/vjzF98/0huWdWmQgvBEQdHdc/iiegc74eeT+vK0AzqZc384jjxdtfzZug51Uz0jqdpxTvhEAM+OB5z4au+J2nBieN+XTnnzJvQMdXKmgo3Q8dtiNwMPedUTKVVG9narMbeR25iT3SequbQG6XwW8cfhHEHO3j7PjAtgdwinFUtRjxPk0XI1WRbnXe8GTuKCr88TlQ1svG4JpI+yyJzKfSq/PFuz4d5Zew71mJ4bG2pnqqRfh+nhUsqfLm3iFjNshJUWnqoXEolO2XvLJXVltolfZZzpfZ650xWJlWpn0XiwQspV3KCY8l0B48Plk7nU+a8KhoMnkGF3RhZl8yq1YDutlfPOVPOFtV//7QwRsePX43Utp2LNqihYPIznuEEQUg58+FxRgXuRmDsKao8pGS4uSpvukhssq0v98PVQbweOvK0mJpITJI61wrejOlhDAxiTjCK8MPDwCUlcrFDtkXvUpXoA0PfsZZMVCM7C8ppLRxiRHKhVL1GYH0jlPddIFeDeubcHLoItUkrYzP83hlE4xvkpdCyMov6kyqXUsw4eiF6JeWFVBJrrXiEXA2K2sfAlDJevOG1tUJRylKonWtyV4PgvjvNoPB+19H3gd47Pk3Z5LttHiuKhADVIjknSowRL55aCgnlaVlZq9J7R23wyzQn+uiRoqypEGKTPgr0MVAbr4HYPg/B8+7uFT9+c8f/9H/8C4JA8Z5OjKj/+vGMEzHYYIMYS+VmiHyaUjufipPAVBdcVn74xVsejkekQRyiYjDnYeSuZUeXdSHXQh8CEwlfzGiF5pC3KLxdkVIKRaAWNcw5BoPf2sTmWtFaCcFTmvOQLUPF5EbSODJBPtvzn+98C24uKV/VKjiDb52D1zcdP7zd0TmDYi2zU3be0Q8WfE5r5cO08v4wsgueVCvHWhE1d/DlfjAYLnp2OM4X4xK/mRfOJZOrInltZL5HUIJWLmlh7Ed+eX/knBJ5o+6TUroKTojq6UJHrpV5Tr/Tqv5BGHdVZV4TRSs3h5FPT2doEe+myVVtOm/7jRbCmCFda2Ufm56WzRhz/Vf0wh/djvziODMEO6BiQS0WE5ry5XFaQJV3hwE8TS4FXYsEFGVW5bKujE4YnONxLXx/vJj+ut2nCixFzZgpLaJ2DbqwtHvDEu/PC1KNKVc1TNuLv6aJAcH3AdXcNMibvt0RRZhTxlVHSsL/elnYjxERZcmVuSjeW3QaNmy9PUuudmgM74aHNZFq4Yu+o3OuBVEmmwPLbhTLWG66wOOaqLlwGCMSAC9U9/yeDbpSjBN5PfR8d5lNRqiCHwKxCmMfmHJiKiat7J2j5so5ZXrnGTrjJ8bgGb3BSKmY2qY2uKhUc16XNZFLIQbHYegYvM2RR/CYZNVUMkLSyqW2KKntw7Xktu8qzgvOO9Y1MwTH611PHwNfP5xRB0/LClkNG78Z+bisXC1j2wdTzqQa8M6xix4nwv0lseRi5kaFOa0mv+0Cf/L+DeOu53/7y29Yc+ZxXklUQpPz5qosqZBrpXOe87QCQsmbkr2yfzXiNRN6T8qWZVTgw8MTtcJ/+Pf+LT6eTzwtGXERRBt2b1j/oTes+as3B47zyrIaZPE0JW53HmrlZ99/j+objvPMeZqJXUQqHPZ7Hs4z03ym95FD3xOjZZXMlWWDzOygQMusUilNKlqvGLkCa15NAUQzz216nWsquAZtWWQv5FIomPTVCWxsrJmKFrlr5bhkTimDe1bTOxH66Hh/tyPSVEzK1RkgFiSsIizLwh+/viHlwlwKjyVbNqwQomfoA99MM194R/Qwrwal7PvI8VLwmDKtc/bH+0Dse4rA959OzOvKNCd856xWxjuWpbDEQvTmDLRm5nUTjv/m8Qdj3Kcl0XWeXfB8qKZh5xrxGi56XjKXoSkEEEo16WDOhezddYKvzHgz8nMujNHx1c3A/ZwsRaxGYIiatj6tlZILH9aZeS28uxsZojeVSYsaFSPENg3yVCr3p5nnLWL/lGJGY9O3GmaoLbU3rLOoRS7BGVQiUq5pZpoy3gm7vaXStSoRLOJVg42MlLNoa6WYA9quX5WlKFPKsCpLM9bKs/kpVYm9vx4CB7zruitJsxX2bNmNUjFURZlLxTnHq53nro9XJ1mvJJiwqDK0Ah8nwt3Q8XFeQeDtbuD1fsRrQbTy07PBHxdVXsfAMS92nVp42w+4wSRjUy7XgiYjlCuXZTUsXs2pRO940/d4DNs2Ur1BVc1o5GZYRC1CTFVZiom/tfEtjnYA+8ghesboLVNxYnpoLPPrvWP0njd9xz0rVVsdg4PjkvlZnbjtAu/2A+IduyGwnMs1cFCUXCHEwGVazHmpcj8tdh0x9U9VYUmZlO3el6VcI+AK1FTp+mAYcecZd5ElF+6nxJIVH+Dj04mfvLvjx19+wf/8z/+aKa0U7xHnqLU0rNrmakmZtV3LFFWB8zSjFFSU+/OZacnkVNhFg9Duj4+EGLC6isK6JnL1BCf06lCvJlM0KgrlWZr7uWO8FscBtTbcXKSJ2DZpr4DoNfijQapLLsToLSvSjXVo+H07v5eUub+Y3HXjbrwXXo89rgguSgNftWUBdpaTmtLpy9sde+/4i4cj9yWxhZzbe/76bMqbT2tilczorBCzpkwQYVXl9bu3DF3Pz7/+htf7PaUUfvnpkfOSuDl03ETHtGajCBy4AMd0wa/C293BVFBa+F3jD8K4C+BLQVYhu8QQPJeUoEpTMdgSPi6J9WQkynFejaRr0rZNVocaRlYbM59Q7ueFXUv/3vaBUirnUq9EXllNRrbhctrS4CiOgkEFIARsoaMzw3CaE4hw0YxXxwjEtpE2cmVj6B2Cq+aoMtqqIO1ge2kFE1ja7Zs8r0NYZyvQctKkel7oxeHU7mdV0OCo3lGLGYOlKW2cE7YypKrPBG2qpsrooh282y7yphlpmjF0V6Nux8MJrEX5bloRD7HzpgpSpci2jjb/VWBSizj7FsW/2Q18mBfWoowx0lOv0rTXXWCplblUPqodloAw50Jtqpit2nVzdnNKpFyu8Bpt7t7vBl71EZrTDk4a/7EVqDQ8tiFWuVZTiDRIQrGirlphHz2H6K0YqB3yt2NPFeXrU2XVSi5K74Q+muLjF6fLtf5BFY5r5rhmXPDc9EbEvTsMzGvhYTbdtDg4zSu9E5ZUcJgcUpOpdJwTlrWwrAWwPVBq4yBc06FXI4O1VIIzF73vAvu+55IKU8o85MS3nz7x6RhYs2Hxh3HH+XKmUAnqr5XJx6Wy8579ELifM84ptbTMsUJe7bXDLrLrPJdcGbrIkiwwGWNgDJE1J45LpovOlD3wnDW36nLausJzdadrxDU0tYtcIw227LV+BiNBc97SsmeUUppBVyUI5OZQJ1X6zrPMbT6d44ubnh+92huc2ZzzVqUsArkWaIVV++DovTMoUq1eRLfgSWHXm3w4eId44TEV1nVlKpUlF9uYWkEzr3c7llo5zbNBxt4hzhGisveBJZWrkXQt4Pp4PvNqN0LbP79t/EEY922BSi2kaobaXtZrapRVccHItEvKPK0J5x2+WSLnhRsJLMVkiKVFVtFZQci7wQiP12PH6zHyzXHm02ya3lq2smnog+ert3uG5gx675vKpVwNdXSCOIfOuaWaZjRPqvRXCEdaWwLz60Hs/0WErqktCi2KVOXtTW8pJg2X9EIvHi2Gry+1gsMiCRFc27A3KuQMmcoisFTluGQavcncoCEnrUCKVsLvrADBi+CDw4vV3z4LGx2Ikqry9WlirUYIIcrBR9ZcWKtyTJlDF1Bt+Yu0dLsVj20VwlXg7W7H949npnnm1veINznbXQz89Wk22WYRXnWBuWQqwlIKXo2nmHO2go92mU1JQTO8MXgOXbTiH0xpVSvka1bRyuyx19ZiKqRV9UpipWwySwXG4FiLMgSLCE9L5qubEajEw45/dn9CvCMLjAq3MXDuI6eUybkV5LXA5BePF97fjLzpI50X+l0klcJUKu/2O0opPF0Wggi9CMk7SvVNMmrOx7gERy1KWiuhM7lfqVb9uBsivYfTknm371v2ajUL+85z6AJPlzOXteBd5G4cOU4TpZjD9N5xBXhK5jEXLrlwM0SO00pWZd/1VBGWlEitJH4thS52kJTqTRFWPRyXFeeUPnrWUhHnrryRQ8z40eo2mlMR4SpjdC1YuzZzUNv/bXlN8KBqu7W9d/DO5K21kuGqhiot4PrZ05lXXce/+8UrPkyrBTq5cjt0V+hLKrjgqbVenZBVHRvXhyrfTKY6qvPGb1gWMfaRPjp8tn2ZVfDRm/osFashGQNf//IDP3h9x+wqD09nlpyMiB0i4i0jW1Zz4NpgpqKVvFZuhh4tyrvbw+80q38Yxr1FpRsZcug81MCarV7V9KFWGXhqipTONwJri7ZqYec8OeerBxWxxa1amVoFX20g3E9ud7zbFb5+vJBKJVG423V89WqP862vg8Jh8JxWRbxV0HoxRv20ZLrO0yus80oRyMWKbIbgDcRQbZg+V4hI1ZxI5ZnAuvaGUa4GPherdvU4xui5lcClFJNpVuUwjOR1Rcl4VSQ1KMFZIUUuhSvs2A6Mo5G8WHawpkotSo4VBhoX0KJvrXw/rYatN3I5Y9Wop1TQap9xyhUflGPxr58AACAASURBVN5Li2BatqBwqQXXUtJLVj48nVlKZqrgRPnBYWyOyiLsjQQcvWVXVeGYEocYOC3rM47f1lyaysGJMEZP10W+XxbexkBEGveghPC5wzXjsNRiB1st05lTvao0QKz6ORpu/emy8jAZNn5cVg6dyfdGseAiq7KK0DvH26Hntu/4/jJznus1QCkK351mOh/4Oz/+CcfzkXHNhCI8XVYclkHsoseL45yStcqoihYj5rfePblUhsFfoQ0wvHxoBOTdrnuu9WjOVhD64HjnIt+rsGQztr0IWRwhWAAVHUQX0Ab9LWvm42li13ekS7IK5SoECRQteHGsSUnpgneelItlZrHn4Xxh13tETN3jnCOVphirlZv4Wc+oBt9tDmnLAqE58Qa/VKwYraaKFtuHDm1n2/atOXwhovTOXwOAY044sXWN3vGjmxFFrk52Snb2+uCgVtx2Lpt9OmxktVbWlv3uokeyMmPV1RXj2jq/kbcGBQ194JQyWpTLmuglcDN0fHv/iVwKt0NvwRRK1/Z1DJ4c6lXFp6rs+sDr2xuO54m0/hsAy0ArmxdbSCdQDGm06AtL9Z1zzCkTe28HVa+rD0XRVjrfRBl4sd4xuSinlLnpA7Hh1rCVM+/A2Wbf9+EKOi7zSp4zWgqhHebOCZ1YhDeI8FSVLjjrQ9Ogm7Sll94kXhWTMG5OyNF6e1CuDojmhIo+44TFLKxJMZ0jV2EQR3XKXCF0PbHreTo+ENThVXGquFxNz18a9otJ1AbnWJoqAzFZmRNzNKlUUq2G27fCJlMPGHYOim/zfa6JMVgRiXNbywhrVgU0FckW/wnnqix5JmDqBoNwlKc184M2Ad45XveRX54LhcopF8bgOebMVJTRK0PwnNd8xUhVtWGwyk0f6LvIKRWr1qyVrhVXBVHWLHShQTDtmlabAHMy9UOp9doGA2AMpokvufBxWkml8mYceJozazLHVmolBKtQFFGkQUihQWrqA1VNSaRYY6iknn/77/xd/umf/3PS9x/Zh8indSY4KEVJziGuEsVxqcW02t6ItZwqa8r0Y2AYgmUZuSJRuBsHxmjVwPsuXAlLW4im8dZn2V8uhVILvRPe3u5JqZCXGa1Cv7Pq07QsVDX4qcyJpVYu08Kbmx2SCq46dn1HAM7JMa0z0Tn2zrGqff55LQw+8nq3w68LD85xux9N+aKVX+uT1gy74eRZzTCaYaiNELfn0lqprabEA1IKsXFLpQUnvlW8IsZjfJxX7vru2uittouqmnBBBJOdJgVvunnfIKF9DPReeFgzD2vmnLPxJUmvtSJb47ilFhyO0QeyKCVXLmuT8wKCYxyNJXPVWqj00XPJ6Qo3bZp+DYJTYe+8QXQiSFXevz4wp38TYBnaHmyElxFFFncWk2tY0YaDcYhMa+ZKD24RmRqEEJ1Hi+HNQYTbELnrO8M7two4tZJw30LbV7uOxzldmfmcCvNlRVV5OC+8do7QBXoB16SUO+fQ4Fmq8uXtSBEl58r9aWUtShArwHnuFGhRtGfrAGeHbaOSpN+e3/5pVINt1FKJTpirFbeMTng8PbHveoYQGpRT8G1Tr1WRYIUyG+Q0NW7COb3qvLfuh1Oxdg63wbN38SrbfDt0PKXMuWSWVrnYBY+KIo1l9iIsqbLvmhqF506LSYWnuqC1EhV2Y8clTdeiL8U0yiLwuuv4NCfWUphK4RAiYPN0SpneGzl6yYU+BuJnXSiDdw2G27IlrvxJdCZ7uiRTwVS1YqKHNbHmSh+2nityLX/fpLZgUd4DzvTQmFJoWgvfnVa6LjBG65NSqzI7cxBVwTvPTcPqg1ZOyXTu9w+P/OP/8X/gB2/f4J3nkjN9F3C1ctN55pT59rRSUXaDKYUuc+I0rWiFEB1d9IjbcgzjP+72I7FmliVToid4/yvGsjaimGpN4S5kehcotXCZF8bOczcOPOWCpMxeEhfnYDewkwCa6RZhrbAmK7w59B3ndWVdEoPv6GNkTVaodplX8FZtvuTE42TZ2Y/3I2/fv+MyLxyXREkTV/K+1ZfMRVHKtRZBxIqztuKkLQO2LBE2c6GYDn7jR9xn0M7alDg30RuMp5tBbsGMM1g4r5nOe3MqCsULXRfACVOurVdUQWtlSeZcizd5pzphKVYdXoo59YgVmi0tMHEi7EKkZvju6WTFTVLIEthoha3X0iXla9C49YJy4vjBF6+JUfnptx9+p039gzDuSZXvUqKXrXueRb41Gykn2MQvi+k6VTFD3KLoKtqIzMqrvkdaKe8YA7d9RKtlBrRNbhi0RZ1zqXyaVj4dF76623MYAg/rgjgxyKIW7k8TP3q1JwRvhRINSrjxnt7ZBk5qON2uD8yr9Y3ZpEoCBg04xxCDtQcQx2nNn0nDBC/PVbkI19YBQuu1IUqmENQRxXquoNZDRRokIqqoM5fhVBjE46U5k6rs1VFrbS1x9Xp/90vi07zyp3eOzplh6JuE75QzW2GIVoOKLGo39ci8Zj5dhLsxtoNYWYpjKgtJKwGDdBzK3dDxOK+g8GlOfLEbECfcdYH3u56fnybWUjnn0pxA5VSUzjmi9/hiKaq1CfCkYg2i0hbtCJxaFHdoe6THgyinoiylcEz5ml2UUhuZ7Xi761vTua0wxiL0GB0hOmsupdaBcy6Ft71VTDt5hvFqVeatSlmswOh1Z44qp8q7Xc/j45HbKLwbA/fnhNTCEAK1Vn75eDFFVXDcOsdxTpymhHNGkoTOpE3W6bQZsJZheOq1SrURIBuyYclqVT7OiWktDPF5H+SSKFo4+46EGdHqPfvdgMuVqo5UEmDtIHrvKQKpZM7TgqvgIojz7EfPrGoGsBljvJGdN51lBNn14DLzdMSJwSB126O0rLvd/QY76fYQ7dGM5DcFjcE8LdJtJK1rQdwmZfw4L+xal1RlI26lZdcmTpjbedza66pa65FDNI6r88JXY8elVk6p8mGdWLTShcDceDnac9R2fpdaqUshZWsVchhHXocOHAZTpsR4iNBgqWuLgmLcz5alTmrtBqRWfvHxe3709m07ub99/EEY96rKU8nN8JpG+/Uw8Jhzk+YZmRpUCJgE0vDWrX6Lq+Z57AJfDPFazeccDed7VqgocE4WjX6aVj48TQw+8mrseDjPnObVoIwuMueMD47LmglNUCMNg0OEHuVOPE+lkirc7TqGWKBUXvXRZGDBW68T5+jbBhOsyk3bYvZd4LtlZU4JUaGyaX6bc/DC4D23Xcc+Rk7LyrfHGeeFp1SsIk4ECR5Ra6BF2/gqBmtVeY5+RI0gsmTGsYpySYW/Pk38+DAyuoBtftcgCKVvUFNwRobVbEVUIsLTvOKdo++EhdwcY22OqUFVThj6wJyytVNYEndDR+8smnrXR769LKyl8JgyYyNcLUqzEvvSpHBztgyq7yJZrPOgqvXGKcBTzkSJjMHgmV4EkcAxFZbStNHVJJ23nXXb3MXPSv7E1CdehNtdT0mZJRmpGoLj0Ee0qWq8b5hoNedxXjJjdEQHN2NkTpUfDAOPsjIthbvDwHFe8bXyaoh417FkkwRW1xqy9XY0U2sBLQIxenx0RsJXKFkJ0TiOgBnqQx+tME9t76voJjABrEvpmguDf67u7WJsDd0yfdehKPfzSlcK724PfPN45mmZ6YO3TqWWKlJqYdd5pqU2PiozxKE5XEhZG6KiECprLjx1Pfe/+IbznOgDjMGia7fBbWwclF4dkzSIySTRzbRX6/XTO6GoEcGmvrEOi7bz/DP0qXA7dNB+sqU9Iq3oyYH35iRqSw3WUni97xrZbBG+CNyEwF3veDN0PCTrGJmXhSotYGoZe0ahQFpN9RXEmxSSyqfThEf54jBwrtlUgbIVINo++tx2t1pE3o8d7/tAv57przP0m8cfjHH34sjV0u/qLXXePCBYr2bnTOpVN9liNe/qvbNU3T2TGLVqk1RIw7QxTLrpj+dceFoy336a+OJ25N2u4/uHE1krqqbSSFqtMx7Kp2XhtiloDO9tjYuAQ/AMLvCQCx8uC+ta+NN3t7jtix5wzyqALcpWZQibmlZx4lhQSsl0ErjpA7ddxKlwyombLho+rsqpFlKES2ebznee6r1J1XIles9pXa/Sx0bcW7EYhg8FsX4ytT1FKLbBP62Ju9QxRos2vxoHUPh2nq3dMsYhVExx4hr5FZzh9Kd1Qb1wbRJEi4LbARMxB/jtaWbKheOSmmpCWgtdYW1vH703Q4yl6jfRMeJZN42ad5xzvkIwCNf0VQUutTI0+MiJMHrHu1YN64sa1jn2PCf7luZLi9pRc0y9E55qIalVso4S6HvLaFIpOOdxKI/zwmWtjdeBH+xHI5Z94VVsPf2dtUL4q/sn7vqO746zVWp2gSkXvrjb4YB5yTxeVual4L1lfT60ug+jcKyitgvsfGxqLMdwhYm48hNOQJ3wNCVytX2dkjUGE/X0wVPzyuubPa/HEdf3PN5/5DEVvv50pPOeV7HnXFaWnNn3HdM1izbVl3MO761PUi6VfQzWclcsWz7mzFEyPFjP8jEEOgJOwobJPn9pzrZfryfM1mPLui16bxGuNjx+49/QqxHezlnWypu+I3pTlm0yS8W1BoOw66NJiTUb1KKbxLZ1d6zKihJa2avD4K3j8cI3DyeDjZxxfM63Vh5OODjHUSq+C3TBQckEH6FYprmo8Vm74LmJkU85U1r77pZ8sbEQKnC/rHy1G4jA377b/U67+gdh3AVTIURnTYWkpfx/cmPfzrSWylzsgZdazF6LlfV7tbROS2VKytRlctPUeidXRQVANqkqa0t57k8Lr/c9728G5iWZDrctqCoUgVdDZ4c4Wzq/Mfq3Pm6qP4uExSR9364TXx4GU8y0r2C6bjvd5HvPz+629Fnhbd9x5wOdt6q+XCpVK3vv0WJk4Lcl8X229qNEg2KKwqkmQnCMMXAQx2ldrWEYXAnkZxNmfV1G5+i7wJQr2lqJKsZdbJJLBL4Ye445XZVLRZWcC3MqHLqOw9hZKq+FnCDpJgG0azkgqBBV2HlH7CMfLwu5KN+dZ27awVOUL4eOn51NLrcU5SYGztnUDPvOE4MnrdmyjbZem15egVXVvghDn7O5KFu1rLL3wpsY6EcP3hrJmeqz4bftfZuR2WAxddbbXFBidKbGIXBqXx7ixHqCi1gTuj+5HQnOmivf9SbJLM66Bp6nlS54zjlzXjMxOlBnFYntuqlUzrPJfUNwuM5di+m8CL33ZGecwm3wSDUlxbXoR7fiHXuCSyp8d5mtH7s31cpSC30Q9r1n1WitFbhwOZ3JKdF5zzElU2EFjyaPOs+0rNcKTt+et0rFiyOpsBsHjqejVdZKfS62qQbl1Kp0IqQsxCE+S9s2APJzLKkZ/I2Hsm4YtuBVlKdc2Ttak6/n6NpQKDOIH+eFL8aRXPJnDcrsTarK0HnG1r3xcVq4P86sKXMYIl3wrcmdEeJVXKtjqHxaM395/2TXVCWI5xBM6bWslddDx7ux4yZY6+SpZBwO8cqrw477pxNv9iOPl7NxFTHyru84e8d300SrY37OYdScwZ89HPl3Xt/wynW/067+QRh358T6RbQiCa1W1v1215GzEXlFTcmy9tYv/bQkHqaV6B0/vtsTm6IgtGZUqVSCtyKdUp8JpTlnPpwWHqaVzjm+uh1IuXCZ1qu0rAVBfLkbGLxn31na+t28XCPK266zNLGRkk6shYEX+6afDT/ctLhNZ3fdmJ8XaWzksK+tn8VWYNUimm3zV22VlFtEnirbd/gZvlhYXGHvrRf707y2asxGXrbzo1if6A9LxedE8J5XfeS8JlatfDvNFuWOvZX2q/Ju6PluWS1tLJVUlDF29J0DsdLygClfUs4cWhdNg38sM3OweTgzYsDSqj03g3rbBQ4pcE6ZDNwGz1qNJL5kaxqX1A5a5+3LMsDIte2zc1MVLFr5PmXeD/3VdghiMlIt/OzDiV0feHWwitbt/ry04rnmdL2zMv4udDyWlfvL0gIBucp1naf1bRck2nrX9pVHvsFL51SsD1G1ZmjTlIjRUZ05vnMuLMnaTZ8nM+zOGV/Ut8Z2KRdu+o67oedTWtnHjn1vhOxmrCzq+ywMbjxREKuuFaxWo4qRgN9dLrzf3zI4204+J3Z94LQUQjB5ZcTTxwhOaUW4oPaFJSpWVBad4/E8U3XgnDLOb227bazZigX3Q7wqrc4pc9fHqx3frLnA9QtmPj8v16pvhN6ZgCAjxHZ2tzXeIqgpZ+vCSb0WN20/NxIddl2w9h9t//bBiO4VvVY9b/BmroYg/Pw88/XT5bMzbG9YkhX2DdHI9qnYlwed5xUvBvXWUpnXlZu+I6/ZqrFzZjme+aO7Az/aj8wlc9/O2/O82LiUyp8/nvm7r56lpL9p/EEYd4C0bN699VDGCgrWq4zQUqPoHXfBc+gDr3qLOud1pTjH3dhfMeQuaJNGWeP7xznxaVqvqg8BvrodOF0WplRZMfmhwypg34wdt2NnVZ4ivDuMPC5rY+WVc84cor8a660p2JeHkaVY/48NhnEtigLYCB5pGOJnTvkaaaB2ENZsFW212jdFTakwBGGtwtNaKKle76+oSel2vWNJldsxksS1plraWglbt8eKUBwsItYz3SshdLwfen42zSyl8tPjhdsutMNkpK22fjTReW73wQxPe12rWsQuwutg7YydCCXXZig3RYMVqXUxsM4JL8KareUt2Oe97U1CGL2j6zxdKazV2m0tRa9QTU2Fm9414Mvm1jIIiCidOP7lp4lzXPm77++uX3Mn2LdIHeeVS0pEb+0RVDad/+aYm7y1GlQ3pczcupbWZmRitK85TKmwlsJhCDythT/7cCJX5f2+592uR4ZIvmT2zqCMTUpqagvhflnZh0joOj5dJtxnvQC+uBnwznNcE2P07PuOVRVZCtVVVm99v0WtWO761ZBNNVIRVhW863jdtb44CuqdFYk5gZopWH/4ORfm1pRsHAZWyWip3B4GfvlwJNRqQRMtC26O5dP5wqEfCQKvb3bcn86os3yxVCOJY3NYCmQtpGLk/ZYFX/+ywRIt8BH81UH7VpHhxBG8cC6V4hw7bwa7qF6VKQ9r4vXQE5tAY+svJK3f0qt9jw+udRxV+i7QpczTqeJ05YeH3gLPavOiwCkrP/10ZC6N81OgwlItiwtOuBssE/jm4YKWymHo+cluT3f3JX/x3b+kZmuKKCFwtzvwOF/YBc8h/p/UvUmvLVmW5/Vbu7HmnHO79577c4+IjCYzK1MgISaICRMQElNGMGWAVF+BGjOqr0DNmCDBpAQjBEJiXhISjZRQVZnRu/vr772nMbPdMljb7LwoMoKkkpI8b2bIu3fPPfeY2dpr/de/cUit/OxwwMqFT3Mgy8pDq9tEeoyR/+3D4x+sqd+L4h5j5vK8gAjeqVvfbOCjsXjD5qEB+uCqgT4MnUMFY/owXpaAFBh3A+TK+xSoperS9LIAOl711vLDw07FCzEThY2fXhH+qE0CRkwLohVCzBw6T0ypBQC3sOl6tclFFId7f545tDFPHRZhIwqKLurWJY/6cl2xxZQr5yWoFFkMvTMMo9IT94MeIj5ETlPGItx4x93QcQqJkAo3vddR0xoOLw789uOJ3JgnsbIdBrSDzFXBo+yEG+94yI7HqJYH314WfnzYIShr4L7ruYRMv9Mx3Ij6+qxhERZ9f6VdrobObJ/PJWXO7X8rRllr5dfPF/7kxWEzLLvzjiq6tL3tnTItcmXOmfenRTn1tW6pTqsvfSwqZnNGDZ5edB1//PLA8xLb9dWiPpfMaUqsls/fPk0cWvrRNWs3881pbvF6lZfjgLcWZ4qqdS2/s6hE1NV08BqVlqLiyu8vkeo99TIjvgmPrE4CrqlNBzGcpkiSTD84+t4xzwkEBu8Q0QPGW8XdiwjPpxkvzQd8/aBRzHddQK8c7yVGLnPG+4EXtz3ucua784Upqn1GKZVTjnRFVdEhRzpnuT+MpGq4XM64YSROM+dlRqry23PbdwxOFai73mFtZtd3hJJ42I88TpoGpQev7g4QZZGM1jJ2jZ3UnpC67snQRep1el2JANp9T83Jc267MZstpvfc9krBiE3bMqeEs4M2dM2SWKEbQ99opRWd9krRLn1COGXN8P3Fceb1ULhrVOpY4RePR5amlK6tSTOi+hVnhK9vRganwda73jIn4RwSz+PIPlyQKjx0liUm3k8zd/6AdYYvDmNj9WmT8rPDnofO88vThVDyFVZttTCU9eb767++F8UdaGZfglghFA0D/vXjideHkdvBtG5+LeyN/cJVSr+GHCCVmDPPIfL+MrM05WFpvi5UfcDfzwFXm9fIuoypcDeo/8eqmNO9beXGGMbdwKdFO72ddTr+VqViCvpQd06XNJeYdCnZrsb2f60b274ati1o+EICvHfsBrP5tKfPKGLOiLojFv29cqmclsj9rqe3wpvT3Hy2M7veM/aO56RUrPtdjzjhUjOI2bwqnKj4hVq5cZanlCiCWigXZcN8nCIhJPXoqEV9PEoTmbRJaL2G2u3rZ/B+XjaG0pzydoh6a9h7y7xEChr0/HI3NiaS4X4w3O1Un3DwnjkVPkwzj1YXeesCLZRKV3X8NhW8bWZquXLMma9vR8zFckyZ3lh+fbywZJWB971pDCbP85J4MXpEhHNMvJsW5pw3/PZ5CdwNA52BULZ6wxIzOVfG0dIZUd+Vqvh3by0juuC33vLh+cRwGNl5x5vnwo11BKeFoXeWhKqSnTXqStksBUJuxadWTEHZSBR2uz0xJZzV3ZK0SDdrTDNza5m/RjiMnlAqIQU+LvMWBi3QouRmnAg/6AdVRCKEEKnWsvee2/3Am+cnTLNydlZYEoxNzd17i/F6j5+CWtxao3mupyU0OLAdOkYzZ7/Y9Wr520avdTJcJ49S9cA+xshSGk24wZV6iNVmBy1tglshSJ2KPiyBnXPYCiHpfWOMUUuAqotPI8rqKSuF0hh+cLPj2+OZj5eF3xwnppj4190th97zFx+fOS9xu/60GmSNThH7zvGqcwoBVxS3T5XLHHnz9IYv656Dc5yWxHNMzKUQj890g2tl4bM9Q63cesdPDyO/Ok8a8tH+SK0oDvgHvr4XxV2MFvVVHWitkEKmttQUDa6uGFHpr7qvXYOOS9SLWqrQW8MgyubQi8bW9a/QThU1dHpwdsP1Rmt4vRvZe7/he0ITERWVOddSeDn4ZgFQmv8Fm8826AXpvOVXjxcG57nfd0guUE0bB0UPAiWN451KoWstOKfBxKvwIpXCuZk+OSuMzvJpyby7LLrNR5W1U3NEEgOPc1CvkYbRv9gN/Ph2R01ZU4gQ/uJ44tLGcFMNndFHwhqV/u9t4lQSp5z4MEd2Xc/TaW6FQ2AC2Skvu362fE0Fai08L5FvQ2ZuXuz/4ubfG1XbjtZCp2nx55i4K4XeW5CiyUIifDjp9JWzHiijM5xTZhXnxJw5LSqbb9MxtUEyc8o8x+uB8v48cYxxg6J7a9hbi2+ilW+OgSzqZnkQRxYVeFnR+yuXyjForJ6x2mGV0tw1Ue/6dRKj6iF1tx/51dNRKZmiy73qdUF6mhPeO7Ws7j2jEy4Nj395t2cOkakJuJyRbWIVUXZS5z0fHh+ZjGV0lhf7rsFbdVPgRlC4pVacFJ7nReGFFRFsClDtbGHK+qSkkhmsI6RE33l+/u4D+14XjIJOFJdZTeQs+vyunaW+rIr6KkqlLa0wWzF8OXQcnFWr4eb0mYsuf9eC/mkJzFmNvZbmrVOq6h1W4dnOW6wVbnvP6A3LUrfMgFAKT3PgT+9vtMYIjT0DSy70omE1tepnL7I5BOOM8Pde3vFPpreUonTf0xL5Zpr5zfN03QVU/R5jDIfOc99b7p3HVvWkMVWdY3Ou7ZopZdeWSiwwb92/LtD/8vHI693Il7uhdfCKUN11np+K8M+eTxt8a4HO+j9YV78Xxd2KcDN2lKz+L7YKT6HgnSpUsxgwikHnWqmiZkqpfsZZRX00YimYmki5mfuz+tZcOe5rNz7VytBwvB8eduw6ZTNQ1Xgorx09NNpH833uLGlNTGmvmdtonEplqYVoKh/CwlevDqq0KystTTaaV2oy+dJ8KnKMyiu3hm8+nTm3oidG02dGb5VCGDM/eTjw4bwwx6wijVzaRr91+U0pOaWEsXDvLFShM8LXu5F/+vysO4kGJ+nnogfIF13zJkG4pMplPm80yIq6aBprCGSlvhUVHeW22KWNz8BmY7AW3M6qFULXRmSVDzXfEEHDTowhRPXRyW1HYGrl4C0vxp7L8bLh92vnaapaIAjKBBI0hvG3z2eKKPvqtERdfgO9UZXxYA07r1GOFafQUGm+7igV0mLYu577caCr8PYyNaih0nV22wXUWtg5DUGOUpCcMXOit4735wnvVCFcc8ZbQ7R64HtnmlOmCrIScJ4X1qm7tmJ9bTqEYexZpgudsTRvOM5LJpjmRd8gpvKZt0yplZ2z/Phmz1OMvD3PbFB3+1mhqJWibbGGzhrNL/AWMcLDfsQZw24cmULQnForbV/BdflPZU6FUBJ776EUBmt4NQ66+ymVOWaOSWnJy1awNANZfYb0Wlq5EhQqau6384Zd59h5S2cNO2f4LkaeQ2Iwlp8fj9x6jxNlnVGVaZPRIuvasxxTZgJtKqrqPqoI92PP68OO745nnYQrTEveNBtbe41syuV77/CtyTRS+TDpQdpVNTTsvcWYqvbRGZ7a/WqaL04qlW/PEwCv90OzqtY2vbOG1/uBY0jMMdOJ0Lu/A8UddOve9Z6Xg1cObaXhYSqQ0VWI4sTWGPRMXwkSOo7GVkBr0kDgle4EXLfo6z+K+qYYtNjtu451eZM/84KRpsaUhvOWClIqXeeZlrSxWUophJz5zTTzGFToUlDh0iqWqdAc/BrNMWcuqZBzxjXxSs2Z3hmeYiSWwqUUbNWH9dIM78c0qwAAIABJREFUrgAeRrVXXVJqfL0KYrDNKdA5QxT4lCOPcyKPA1+7DgG+GDp+c3bKpJHroredQdw4RxXLZY5IzM1l8bMiUCthjpylMEXFrj83P9NllzAa7c5XtaAVFdwYw2ZhvBbR3juw6oe9juUrMyiWzF6UJnjvHO5mxy9PU8Np9YemoulX67Rl25Tx5TjwTx+P17hDGszme2690k57o97v6bIQssFZOMdMaDj3znqssYSYlH2BLhOVwqjU0Vp10TfHyk3f4cSw3w28fTrRWdMK+7qkVQhBZe968M2lcNj17KQSp8rjHD4T4FzvY0HN26zoex56y1L0IJxTphghR9Sjv0E0+7Ejpfw7kNnOWV4MPU9L2Bb8vbF8sXMEr5OyFH1/3qjs/RwjnQFQdonzTm2wm3naCo3GWjmHRNNfEUrh9X7gEiLvloVQMqXov49thJDt+RTuetUyLM08rrey2QjnWpvDpcUZowyiXHguRSM2izBXPZxftXQxnRq1qTLo5F/WVkW0CRL0M6u1+VxZ3b09zYHXu4G9tex2A6kWPsyh6Wi0RhgxdCgRQ33xNU+hAJ1Rsdhh6Fhy5rIEqrPc3O55My+KCLSGaO3K304TuRa+PuxwDRqegk7nN52nE6VGvxz+DhT3WishJY1as8Jd79n3jqc5ceg0gWdKmacUtQtuCsOr4lT/qh1eoyu1TXpapcQAyLWorEuJWtl1vhXcvKlkEd2oK5a8BgQ0LJArJaoU5cxPMfGbaeFcVFkWm0lVTAUrFWOb+kEgl6SujWIxsnqMXx/+jLAfOmrJ5AqDM9z0nk9T0AfNGD29vaHvHVFUwVvXmw2oq0lX+1W/CzNzSPzxbsRW4U9v9vyz5xMA3lr4jFV1QTiHoM54n68H4PqwlLpNB9IK6SpG2xlDb61SI9t7oMpWo1YPHRB++upeQ4ClUledgYiq+6qOtq6okZwRZYMcnN2+33w2daSqy8mKToM/2I/0RrjvOj6G0Ay+9PKec+LV2OEalOKs8MVh4DkX3s0zU9FQmF40gHu1qjguccOTpWkYVkjmMkV8Z7m/fSAuMyFEOqtmd2s4tJ6NhTlkUi6ELOx6t/kaGSpf7HqcFR6nsDUZjS+if8Zqw9B0XJrb27KDbW1dc9T7cOwMlxiJubLv3MaskqqForeGD7MGV9x5x13fIYM2Euc5EZrB+eBU7AQ61X56elIxodNrq9OSZsw+t8K+PnWl6pTdece7y9R8lfQ5tMj2XGl/UNn3jlsRnpfE0xyxRou5d/q+lpQ5LurhtP6Utem76RwuC18MQ2u6attd6Q2sEY6Bm7GjNsuCkAsmGbxTeDSnpK/Ve17uB151mriMCD877HjoPN+cFy456RRjLI9zZEp6oHgnfFzmTa9Qqj43z0ugt56lFerXN3d8OF5IpG0qhkoo8OY88zQFfnizw4jwPoVG9746fY7uD4eofj+KO622FOHjZcbsB+6d48Xg8QaohcEK1XjmrQBfx3Jr1OWw1tKEHmrZmpwjLVHxba5N/No1gRbqKSaG1ums2LDZOk79HsXsTFN66mvktgA6p8Rvp6VhwdcC5mzLTI2FkgPOWUzj4a8BwfrCam5lrC6OEbjrPXMS7or6inurWGV3GHCinf37Eikdny1oV+FRKwKycoJVtu1cE7+UQieGL3yvKTyAEUvKGkE2pUyOaoC0Kn6VgcE6iQLQI+RuxT8Ny1TUUI02CbTC+7udWftr6yCfLzNDbzcV69q9KG+9bMIi9RHR13bAF7uRD9Osr9beV0UP4g7Dj3Y9th3kf/5ww3fTws+fTtqlijDlwrt54etx2PBWBL0Pms2F11uP0xy47Qfu+54YVDtwSmmDSrxRUVO0hT9+9YJKYRwH3p8mUpoJMbOUgo1C11mmKTVJ+/X+usTIx+OFL/Y9Lw8DL8eewVqepkCsujxfQ15CUvuGWgu33tNZhSasGH52t+fteea7qXAYOjIaWUiFrpi2ENXPzFQYrOWHu5FUKntnyQU6p4dX1ztOj2emlBlRosEUEpcl4azj5d2e83xRhhCaJfAc4haHaUTU4M1bppy47Tpuuo7nJTRbhPVOucKbl5R5f1YfGEQFiqlkzjGzTLpYRmqbANszXK/L7S/GkbtBffBTzm2gFQyGLJUppC2XNUuhFJ0KLiFysEqJpKpI75Iyr4ceUm65v3qx7jrP3ll+fVmnEIWglqyf9Z+8uuPQDXx4elJufylYK9wM6l308TxzToX7scfUVs/s9enQGqPv4a8+HtU40Dv2Nx6L8PSs6W+d+zvAc79ifnrR5lSwvapOc1WpvEHdGlePmFBXzrVsDopAUxoK2agXyZLMZ6PZ1b9CMTj9pm8vEzDwqvHaW+3Zivha5EujZq3LqJILc8789jJzznnrTGvRrXuPEKMWARr2ty5VTVWMFlmj7poYp2F4L3c9H88F3zkNBaYyeAPNVGxnLbaqgKi25a9CIkKPcDCO0ditIbdURutb8LdhKgkRTXWKVcfIyxw5XWZ8W2ZSV9jj+tX2daRaWebE4V5ZOpeQOF8i4/5zwdB6zkj73Zo4pR02Kw1vzfBcHx9dhicuIbK3joe+1+7LXK/D691ALoVjM3sqpWKdJgx1ohCetIU8qMr2w7TwaVkQ9IE+5cynEHjZd+RamXLhFLN6haeCePX/H4zSAqHyMPQ8LoHROqakB2DXiNuHQb2J9ruOKWRO84VLTGpT7SxdZ4mxYJwGeC9TYndw+M6RQsJaGDvNMLUIN52jF+EpRM6xSdKNYI0lFfUgiqVskJo3wsfTTHaWH97v6bzj15+etSsUjZcr1dJb2w5CvddyU27nqovrWLVgzs37KLSsUG8Nzlhe3t5xdxhZQmiLbbaOPTZs3Igwdpbet3RhqZxiZNf3hKyagBVa9SLEwhZpNyddYPoWHrKmHIlcWXHr8G3lmhYWqdueYs0s2E7P9lwdOk/OhWlJDM3i2xRdUK+pX1LVu2iOGdMOgjUYZqURW9HDfyl6SO6M2lGcYuIXH565HVSVmpqV9BeHkdtOFaVz55hi4bIkxt4ztci/9X0PRifTHIvupbyls8J81ixWaw1j7yh/uHH/fhR3/VqLqoY+L6UwGkGa+b4VXYJMGV1KCVAVq3SiyznHZ85qgEht4g5pBVZ/kmKZshXpSyq8mRb2nfts1FHgd2UAlPavdIlauASVMv/iPHMua569vt7OO36837F3ltMlMDSzJ50GNGVp9f1Yu86c60bzLEatdXtjMKYolm6kjfUqyhIqOxGmdhL1GO6sYxDBoQINK6vlaVOwVvjVaeLdNDNHVXs+dEZj2GomLemz3rpei3C7PNsNKAqHDN4wiCWS+XRaGDq1ol2tB7ZRaR3Q6xU+LqjK9JISh0NPLEndIKPCHiFlUq0kW9Vjh2thL6h8/Y9u9rydNEP1EhLnOdBZA97wMUReNBEWbfzfecun5fr+C5XnlNk73bEcQ+KSMofOEZxhSZnROV7dHSgIbx+fdRlfriHMc9YpqFTdq3y6nIlVVavnZtLV9apWXJeEKyus6xy99dgsHKzH9b2GP7TPzlRlB/WuZykdl7Zgz1ldRUUUkgG9L0MqPBn1pr/f71lCpCYtfcVAkspTCDgxHLxCkbmxzkyDpy45Y7MuSD+clP/vU6Y2bFt//8TTeeZ+3/GDF1/xf33zHZeciS3uaC2Cq3eS1vbGhquF14eBj6eJOeZWtNUywnBVYeei4qibQW0+QpteFJpTVawy2Uqj9Mrv3GerJcBGaW54OgXGzvE8q/rTd6bxxStLyuz7DiuGXz+eOXhHty4+6nVAFhHeTDOPS9QltbXMOTeBG3xaFp7mpdGqu2Y17LcGs7eGaSnsD57HZcY71P8JJRh0rtlGN9vuzhj23ukOqpkmDr3jyk3667++F8V9LXJKxzOkXPjN8cKfv7hFsp5mpcLOqvdMWL0q5Dr+iyi9LtaGsBddqr3aDVxSYUqRmNlO5rUjaOJJ3dyHxNCoXpU10/T6PmPKnCelp7nB8VwL9AZZRI3NKnwx9PxwP2rQQ6ktJk1pnFSlDqZyfc+uLdRq0bDm9Q6qRYUuCVrnslZK7aWf54SUyg/GrrFetDNx7Ub/nM65nSAo4+TDbNQHplTmVPnx/Y7ffDwhUlXhl0ujbF6xcRVa1RbmvVriGnLMnEsi54rv1jmhsTvagV2rYubrg6fZsZVE4dC0AO8vc4tIXAf1St/2JrGo3WptB/Ilpe3vf3S3pzOGv3z/yGWhsah0MfthiXw5Wl0IV/jJYUdo3vUrIHDJme+mhQfjMQVKLCQn9F3Hi4PD5MJ0WbiEoMytNj6fQ9wOqVPS4GNTCueQuT0YLtPCNIcWCacHekE73LRkrDV8/XCDiGGeJnb7Aetsg+Vku09Nu669Fax4LqEAZQuzjqU2YZ7BGtt2AYZffjhjyzWpx2bI0njignbilXZNm+FaO3xzheOkeQZLY5z1RgNhigjihPv9DmcKv337HmPV28a2vqj3phmZ6T+vexu1HEh4cXTOknPZYvA6swaoVE6nCFV1H88lbk2DF7VQgFXMyHYf1Hq1sZZGscxV06uM0WtmEIqp6i5rlNXkqh4C+j4SF6Bzjl++PeIM/OB2x/3YbRDl+nxeYtMdoLqZNbyDqkLEnXPsmohxozWKftb7QQv16DwxZqKk7VlT5e/K6VfdhhVh15woUylMpeCA5+lqzvfXfX0virtmkjZv5SaIybnyuETuvQpNatYH5N4a3s2l5SIq73VlQtS6BjWwjbbPlwXrHLdjz8fzvHWPFjR5aF3uCZxi4r7226i3tvalVJYQWVJWoymBmlUEsS5sDp3hZed51fe/I/1WDjEMvWU1s1whH2tFYwHLWviuBbk2BgSVbdm2FkVE/czDot1UrkVVkVXphne92vUK10JZ2zk/iuFnDzf85fsjt77j64PigBviJ9frsrojmO1BXbEW7V5zI1PPc6bGioy/A8mjyr/aWBHX5Bv9/0qqYK3FiX7GdmORwM55vtyN3HSeFSebcuYxBN5NszpT5sqfWcOXY6dh1r0GCgsqhDunzM5nbr0H0QP0p4cdMPEYo95rosVZrHCwSjfNCA+HPdYIt2R+/nxRgzZkux9M1ZxjUGvXVBV7HwdH7ywn0cWnWOg6q+KZ5icrAoNz5DlSjeFu6Hh1GLACp5i3JZwe8Gzxh3NSuq3vzDZVadFaQ2EqtcDtYYdfFs7ThG+T6BITtrTWwDTsutRNge3Wiarqc+FEF+3K41ZmmYgQBd7PF6oU8hLpYFv651LxTnNeP6vtms+7Yeww5wRGG5ucassTVnbJeU5bhGMtEEPBeoOvorCLaANT2/Ow8vPXQzBXzUS+3oeyPQm1RVh663ixtzzPgRAL3umhUdsk8OY4URvj5c1pwnvDQ99tE1ep8GroEdS2+5I1qJ3W+EjVxlKLs1G/I9rBii51d4MnUXi43XH6EElo+pgRPQTW10H0z4fGWEMg5kwSuYZn/56v70Vxt9bwZ1/c6DLluGzKs5Azxaqx0hqvRYVh6DlNlwZRXBkcpY1PXhSXPs+RT+eId5mv+30z5teIvNGoQjPVQhChGOGc1Zv5Zd8103/F5mJSd0a3eptX3YJjBFcN953j66HHt6Vf2pRjzQGyVkoR+k4tUTUFXtQArwmB9E+3hWi+3qDKyKmbiCPlyjFk9YIHBi/N/11fIzebgy2MoxlrrbTQkFVc8qf3yr8fvRaKVYS1PuCmdeltsGZ7hxWdgASmmqkxKXVU2hLOaPoQRjjmyNzUoHqt6rpa2A7ZOavRkreGh67jxTgoVZJ20LXT8K+ej7yfl8Z40d+3SuXNeeJl79l7y8f252MuhGaH/e15ZrxVOlpBMcx7b/kYlGq4RiGesvKjERUMvX884pzwJM3DPKvwJyS9Xl4MFrVTlqq03RRbspOYK+1QIMSC8WrVkJofUEUDXkoI2G7U4pgLd53laVGaYPpsAlWefqJYhen63hEXfeBzVRrkiHC/63n/6Ym7wwDt+zprlT5cCraq6Eas0eB3FHqq0vZWRbUGIZVmd6y7giXpUjgX5fDPKXG/G/h0ulByIWdlzHSN0bFe4zUkZ91VSZsSRu9xxlIvs8KRDW4AsF5avKDgBzX/W+GsWiBS/h/3a2kCqJwLybb8YGO2mDyRptdqz4G3lpu+47Qo3t05Q0jqS//m8UxbTZGpzSOK7fAsKIrgdwO/zhdySqwOqq4IQ2e5GRw7p/XFGmXxKQSljdFSE0soDF4Dt0vMWK+aF6Axd64w0ynFNrno77Tkco22/D1f34viDnrCfdk7XvTqWy4CT7HSo9jnu0vEG+FxicSq9q+5QQUrxperOjIa4GlOPF8iBVUvppC5M5aAYtahKnOeNjX4Rpd6nBd2bcyMUTfdoDgfotv8D0vg1DxN9p3nx4dx648/F56A3tAF7bAH8WSpRNo4jNWs2HoFXViLfdXQ6XPSFCr13RCeQ8Jaz/MyY4Dbsdu6c6fnxWev1+CU1gakAsdYuBt8UzxCCMrnXqK651nDJriirlhl3RJuMNqNzimpYMyqB8yu83x5GPnhzcAl6BL5zWT49jTrNNVASwG8McRa2FnDjdcg9L93f6d4Ofp5hVI4hqgTh8BTiCzKr9s+XTHwHALHENl3Hu8sp5C2IHNjhDllfvl85md3ewza5e2c42Xf8Rg0xmx1DD3nwkOvMNenaeE8JcbO48Uy9J44KcNFnJAbrGGal/hoDLMF7xw+J3JUb5jOa2i0mqhpR9o5i4jBewcGze6tV6WvMxoa4eTz+0dN30LKCLqTGUd1Ug1BmVbHFAjHTGc9H4+nBsQ1IRutsAC26v7qNOuSdE6pFRMNYLksQQsibPoM5wy+GvpS+VR0iq1d5TRrmE3OmdELFD3EOytUc7URWK+XQamHxzly6B1d7wlLBFQrMvROowJrxQ1GvezbqPt5UlNpt7estxYtXHs7SCrWNIFhUY/59YCwVj//oVNoxhrBOgGyZuxatRARMfz04dAmYYV+Vs5wpvIYApP6muhhUFSEtR8c1grPpTCIUqBTVY2KFZjXZq4W5lLY9x21Vi5LZHDKXuva877WkHWP6Iw2r2Ig2X+R7vC7X3+r4i4ivwCOqI1FqrX+WyLyAvivgZ8CvwD+41rrpz/0OnU90tCbzgmqBk2ZT3PizfHMKcTt4q7slUNv2jivN4BvqSoFmEImFbXzPHSWwRtyFs5z0i7XGMWPzaoabaKJAh9l4WD0tds7pAq8XxKPQU2oRBSn33m/FXQjOtYpZXLtLFZbYJXKp5QpOWuMYNUuvkF1egGLPuTOaqhD6BwhgjRnulPM3DqnVKmqfPvOWWjuz6WqqdLeKcixHhqP4Ro9aPUs0I4CQaqqS/VwqI0SqiZOq8DDNNvTKatPjVLS9Nrte8efvb7HVeVX7zvLJWRuO8c7p3CEGKVhvuw9g7OknOmtpXeGbz5N3H3RbRPbVDLnUtr+QD+XvffKGEG7tFp03B+cmnFZ6/hqP/I0BS2U5ipZW3Lh0xx4NSgb6sYLO7fjn58m3k+LPvANhw0pgbcEqc32IpOkst+PhKwH0xKDLvqrfn7HqF73FuEwDMwhYJ3FSaMEWiHmxujxKkCyArZmhs4xOENIuVkHaG7rYC2xZKwIH+fIcVFnzGVOUC0xa+pY7yxdL5ymSMyF1KiPvTWbQ+ZKZ7W2CZKc8LzE9nlrzKA3BvVdSm2volkJMWXGzm8WDoMzDEFdT9+dLk34o4yylDVqrvdWNQ9GrjRT9MCa18UrlRoSvVWP/tyurTXQ9fYqSFyLmiiVcdW1rFuotSvXeEqacphWGKUtuu0WDG5kVe/qzms/9I21BtnprkG8wUXD2PyPrgw7/bkG4d0c+O15+kxopu99t3PcdJZzaQc6EIrSi22bhHVA0kbj4+lIV9WDJ87z9ryt9GMr69/r7xdLUV8doX2Ov//r/4/O/d+rtX6e1PoPgP+p1voPReQftH/+z/7gK9TKeUkw9JSaGKyGG8Ra+HiZOYfYFlNsy6YlZjovDG289Bh860Zf9R1f7Uc+nBeOS1C1Y0zsxp7aEorEmK0AlFqJqbb0FfWyuHUWoTKlwqcYmWtljmXryFdKVshKczOi6VG6M2iLyaJ2ql6VU0S06ND8O/L25/X32hLqrTJjBIWsam5jvjHs955fvX1qFEKN/7sdO4VxBFxVZsFQlbZWqByXRK7C/V5HwM5pSIWhcjN4Pn7SNCCNHFVawfrzBIUuppzUwCmVzyAHZa6cQuSXxyM/GXd8d5r48xc3CMLeOX04rOG28+ycpW+sH9NdU62mlDi1mL51fB+chlKv8NLeWy2sSTH1zhl+eLfn9X5QO2IKN0PHw67nzXFSsRqte0R57YV1EazX73Xf8eE401u1eLzMC7bzTEXx6dWwbc4QLjMFuO8GQqiIWGrVxejt2FGM4atXB3IqvA2B4xwUJhTtSFOo9L2ls5YlJAZRpamzyggLmabGtAovtMLujXDTe95cIhl9DWMaPDdFgtMivxW69pjMOas6GC0ktVZcVZOuKWRKA/JKqdw1KuhzjFSjfjSpqD9K43jgrTY7huYFT21+5/ozu/bMmlyhJrIXwDR+OJsdQcqrvZc2RymroldygbYAVgzcqL1IO6G3ki5sWhAR3Z21f8SK8NB7nHPAOr0phfbjHHUSrPAnL26VYdcKfaq6P7qUylMMOCfc3/R80XXsu06bibr+bHiMkTfTvP0eVE2DO/Re4Z2qLqrtP210YgutIbq6ZKqFShMRVs2rtW1S0eW2bNfWG7uxxaZYlH31B77+VcAy/yHw77a//y+B/5n/t+KOLlDfnye+OvR0IsxUllipsXAwjrPkTc68gho5w4J6S6///mHoGL1TeX/RBHXbsKqUEkPXkbgaJymTRbhpkmcNyjCkRrX69bTCCg0MqIqDP3SeV0OnoqB2MW07VXMpvD3OvD1e+MHdnq8ednTd2mEaYoAUEzFr991WBdsYLrkgRqeI3ejbqqFuNzC0cblWnufAi33Pi33PFCK55TiqDkA4x0Kuwlc3urDbiqc3GHSa6Lx6qjhjrqIP+Wy5u+5AyvWh0s9DH6qUNXTh5s7zTbnwYQrsOkctlT++2bWdwoqft+9tcYbarakHfdck+ivW7kXd+6ZcEAM/vNnxvCQGpwXVmxaWIYbR6gN903neydyEVY59p1a+b6eFX14m/mgc1M8e+DgtQFX1a1nZS9qpxapUzCkkTmiU4Q87x84alq7n8TJxsxtJSQ/3ndd8zKclgehYX9vInnNzfuwcB9/jJPFi70ix8HI/6D6nPbhTTspfz5XO6bLyaUq83I8MObIsgYrugKiw7xyDs4w3lpQrj5eFlAu+/R5r17x2q6UqC8lUDfDe97qwvqx0x3aYZkG1dVRsbXbXQal+uTUptugzYm27J6v65sQsSBZmU+kHLVwxKxmgiOLWazcccuYcAvtx4LIsxJxZQzUMVxO0oufkVigFkObdvv4306AUDccp9J3fnpO997yZFkSEU0i8GO3WIQsw5cTbaVLopomZ5qpHoG/eL6VUppR5Oy86vRtRVXUFj2HwZiNjrIdT06UrhVPfoFpSN6yqd5Z733GMEduU0MhqfKLP4WD1WlqE5xA5RV3lb7uN3/P1ty3uFfgfRLed/0Wt9R8Br2ut3+oDXL8VkS//Ji8Ua2UumU/nhcE7TXTPq8+IsDeWRcrmhyJULiFijeFu8Ow6p51i50GEd88XLs173TXr3FIrY+c5BWWYrDfc68PAw6DyfvFqmF9r5UTaGB616qLSGHg99rwaOlwz6jpeZmLzA//1p5NG+F0WksA5Z3AqmilFi+DPny6Ko5ZrUrttirzaTp2UlC3zM3ejBbbdhbvBcxg9x4tiuikX3p4mvjgMWDFkyXgxhFJ4jJn7oePlrtOU+AZZ65JLRRSDt0zxaoeUNhvN0ihk2gtao6lHcT0Y1xurfWNOerN/sR94e5758/G22QUbIF/VuA0TVaaKvtDN4JlTpneKPVdUXeiaStgZ9SvxxvLVrcfSErWOQVkgojatvdUD9OVNr5DPiuEX5RY/LZGdRHaDKp2PbbK68Y5jjq14VEwVeuvorB6msS0sT64ynSe6Fm2Xc2EngvMeZwynJfI0nzUM2Ri1DAZ8bxH1lmD0FkulJFUtequFsNSrp3/MbdeTVUcQKoyucrwEnBEG66lkOmsYO0/KSvvz1nDoHXNQSKCZWWKkMdBq3aCBndHPatc5TinrYdGKJChsVFFG0LlmdlWng1DatWwHkKqAaWlNUBu/2KGYUM2VWGVb0G43GquDp+6xKhqzWWshF5rhGVtDsE5wV9V43QzTpOqE4WlW2CgUlbIukEWUKvrVfuDnxzP//PHIv9E5Ruf0XUhbYJdrNoOIcCqZt2HhdddrpoQRvp0vnKKyq9wagVgVQRhbozfoCHy1i2gFX1pTcQV7VSj2XcjsfMdd3/Mc1CJ7mjOH0eOtFndrDB+mhUsTzpX62cP3e77+tsX936m1ftMK+P8oIv/n3/QbReTvA38fYBxHCppcP8fMu+PEl7d7BueYvfqwOCP0RjhVvQi6c2zL0VQZdp7RqSdyLEUd/Jwa4udWRG87jx97jm1DHrN2/SkXLiGz9w4v62JGeBg8353nDdf/47sdDi02682/mpktIXEqhe9Os2LnDdMeel3eIMIckiYTodaxq4CuVnBtM+5bYIXSHgvGWmgiqSq65f/Rl3dc5sC7pws3vS4y10BfJxoocAnCi13Hw65j7DTAI68JRrUwL1nT6Q0qZS+VuCYvt4GzFrZFnDErdZMtWUkvpE4jYc78H28+8ePbPQ9jp7//OlqzLoSu/uFzKSxtP3CKkaEKt/iNnWPaA+6sKCulasdIzpxC4tM5cA5JX1MMn3xg33syarkcaytEFcgaE3jrFBaJJfM8B0ZruNuNmoDTdVyCLu37ccTVwtSLy1AGAAAgAElEQVSWi6NzvNrveHlz4MUXr/jmm285nwvP54XcO27mmU8iWOfIjW+9772KnJIWenrDYDxLSEwx4iocBtc8S9bF39UWQ0SZHzPw+nbgm9OZlHUK09wDw1wqNiqLQkS/f1jhq5gpuW5hKWXDqNloj95YlqQMmPUY14lNDwENQm/7ATGEkrZnL7UDyBl1ODRGMOUKOcR2wJWs/yx6U2/3TGnUVKqG8dhSqEYYO88xhEbzrIBZCbKbUrVCE/OwBc6oUZweBrqwlo3frmiu8LLveT8HnkLkt8czPz7sMM7ytARyydw5xzGrFTZN8/GYEqFWfjiMvJ8WHkMAo/TkwVp+PPZksYSo9866pzOsNsuy7bBWiLzURr+ula43LJdMMi1lbhg4z4GnaWEcLL5aHpfAackgutC/Qlv/Cot7rfWb9te3IvKPgX8beCMiX7eu/Wvg7e/53n8E/COA+/v7uuK4p5hxxuDQG+3l7cCH48wUIztreLCOTynpQ2PVB/oYIr86nvnJ/YGbNt5Kw1wFoXfCH93v6J1hrobeOd6fF6yot4agnZ3ZxtiGOYuOvVLgR7teBRStILfpSYt2zrq0i5ndznOZIgi8vBm53w1YEeYptMxT/Zkfqzpf1qKXKEZ1IFw576nqUmiOcetATWvhjYFx8HwlB/bW0FvBoEvYx0UXSL3XVKbeuW1h7dphFxodT5kMGtFW12evTRJrk1XWzwL161myfHYA6KOj0m2Vt78/z7y+Gfk4Be7HntB8+d+eF6YlMHrHXFT8BcLzOZBipu/8BpXByoJQhk9uhd0ikKDkZozFSttU7+5974lVschcCg59AK3XRaFpb/3dWQU63rQ0KAQRyzA4as5cThdudmNbjHZUUa+Yd88nfvXhI5cp8fX9LV8+eN58fMbd7DlOE2IXvLXMKfF81uSvEBqOXCpm31HFEstCksqXw0j9vLA2QYHQuNztcNi5zDmkjRKIqCVuyJlYMgfvGKxpNh2CWC2LkbwJ6dYR3rHqOtSi45Izq5GbCM27vr0HK9AKp7PNcKzdG6VNUrXKVtChblYYBe2GOzH4qmum0t5E5cp5d0bYGVGRW9VdhjeW3BweM4Vlvd3alwHEGLxVkoEIVGMo1iqbS1YjPq0Aa7pRLoWvxoFTSHycA7d9h4RIaslgO2vJqO4B1p0SXEpW467Bcls69fi3Blvhru/4uCReDp2ms5HUbbbqIdi1wo7AUjOmmgY56eFkxbDUiKcwhczh7hU1vKeiGp5jSFzWe8hqsHmutTWYfxiX+Zcu7iKyB0yt9dj+/j8A/nPgvwP+E+Aftr/+t3+T18ulNqvfyt3QaTxdW+CMvWsh2YWDNRyqJTQAvlmdMIXEd48nymGks5aQMs5ZOlO56xy9U+5ITNr5FSqvx56ds5p8Y4DanCVp+CTCl7uevmq3Tq1tJLyOWt/NC5+WuPHQAYbRkVPh1c3AYA1piS2UQJc7a7EGWmhH4+k3plVdi6y50qGQBqs04FGqpravhRsUS7TG8HJ0HDqPcxbtlvWBCzERQtzepxZ45SbTJoctmBvZPluqdvG9sVwaVLVinw9j30KqKx/nmccQOETPHDMvd/2mHuyd4bKoSVsCmPUBqCGrhbK1W2ezMoyobOKNFTKRBj94K8SgBSEBpxx59kE5xs6pXXLKzDXjc+sODXy6BI05REVBpxpasLfFi2UOkZtx0OW31QdEquXpMmsItHUc9h1FDPfe0N3t+RgTXecJJdGJ4WmZlb6WcwukUHJ08XCOauc6dJahNRS0Yluax8Xa6abSuv5S+cE48CkEzlEZV6uzYq7K8vJicLZu92XnLM4ZUsxq3dwIB9KKTSqV5ynooWkVFtRbsTFcpN0Bovd8KrqbWL1jemspWXOHd1aa62nZFNkYYRY1WHvZeXZiea6p+TOtB4nQtUMht/svpMzYOc6zMn/We+JzRrcTw49uD1gRfn08a1Ft7qzPznLrer2LmsDpCnipOdpX+xGphWlaNHzcOWX4INRGtDilCKVNrIjWjwr3g6fMuiv4ehy4lMIlRG5cT28MTjznovTiUOtGA5b2/GZR2FPadRdg6B0hq1VFOb4ntVzbVNQQsT3+G1uQNqGvytff9/W36dxfA/+4jZEO+K9qrf+9iPwT4L8Rkf8U+BXwH/1NXiyXyhwSX93sFJ/OitGdF1US3uwcORY110+qCKtZl3GdKOYWYubN05mXhx3FgPWQs+EYEy93PVOBD0FhgpdN1bh6hOTa3AfR7iQ1vGR0FinaYa8f5TkVPi4LVaThlXV7aErRpdRX93sO3kH7PWjdkXqNC6ttAtD+m37V7frpf3d2HUqvlKuVVbP6ps8pc6mOmA0vRhX0KC0zt/xZPTiXELcuyxjBdZ5jUhaB2vVeqXPX96Y/uABmDU5oD6JBF0Kl6O7CiyXkysccyRb+4nTiBrcttVffn1QLrtjPgoyV3VGXQG8NnXPMMWqHZIRXe12CrswiqbprWFXAo3fc9p6dN9x1GtydaItuZ9lby2mOfHucMWI0ezVpBqe1MFddGr4cDoSwMMeA9SOj66nNGrLrPJILN9bg93uWacGIYWzX5Uf3d/ziwyc+nVSUs3bhxlwDOS5hUT51FV43O+KV+7/54bcj1YiaaSleriEbzvQ4E3lcIg4V3qyw3twojaZCZ9dEKMF7x1jU5M6aq2q5lso5RD1cIojRMnSz842tod3myh+/zMqUSrWwLInD2KmJVWXLJTW9hp5fjNpt5PbbnEpmdOp5FKo2OU6gZ50EVClb0QQol5uRV7l2+q6p3277jq8PIzujkN83RphjbsHR8LQE7jrPTQPbperv2hkNA8/Al7ted1rt2bSm5S4AJRuKcRgnfFgWpKDqVP046axw6CzvjpGUKn/5fGSwdhPdOTHcW00B+1TTFcuvCrkW9ECkkQnEKMBSakWs2guI0fslr7TO9jlJg31WGrTfKtJf//UvXdxrrX8F/Jt/zb//APz7/59fsBQedr2qrlqXHlJmiUk3xjjGXc+7aWEKKgPSb9PuRhpumQr88vHIVw83LCHxeJy46zy/Ps5MqTCHwlcHPbmt6LItJU09z7Vqh9ygF5U068VYP6hTTvzqNLfFrv47aQWwVqWJrc5zqS2cqqhfS26d/6qoNe2b1wtYcmlp8Tqy+TZiSvtZuSi/e5U3r29AbI/LmS9vO83xbDmVpk0/5ykwh6QPt9FOPRrh23kibofT6p1BO2GuhSeoNSdzCMqm2E6gynFWZszgPCUqVDYvyvQ5TQvP1nI/eOasJmFejOoRUOyxc5aH3YCI8O48KQ8/Jp5ipJbKfd8RWtqUepOoevInDzcsKUOF+7FjMzozahPrqvCj2x25wDefznz76UxsyUJihNNpwTlDv3PUrNfh8Xyks45LjOST8GrXIyXzzWWimy3Pl4V56Lk1lqfzBWN2LbJNeH+cNohipbWuTpa9N+x9T5FKLCoyuvGemEtjeMCKO6wHQ6FySZVDp/hxzhUplZe+A4THtjfSo0Ax7k+lsHOVsTp8UwIaA/udJyx5i0WsleYgLtulrs306/kcNojFO00Fu+37ayh4hH5wWETDvutVUCbWUL0hL5mEHui9U1GPN5auwnNQlbCXdh+jcI0KhFZTNWFwHlNTEx0ZBu/0kDVG09OKEgc6I5w+n05zIYoQncUb9X9/CrOKlWpVnYXt6DqvvPyq6EBuDYoRNBNACh2aALXEzOjUPjlm7eS/2o+8e550F2TVXqBvz48zhn3LQz5ltXfWbIPVwqLoYrU1eoO3HFPS57LtMkIsGJs3m+fOmcYaagdDu8/+0Nf3QqGqkIQuwqQVsFILIWpAcWcNvVM++4ux57hEHi8LoGZC28xW4RgTc67UxxMPh0Gj+bzwi48n7saeXFyT/SoEsSR1oRSvBclZpXhtDxsKQUyi9LA3IWlh/51fQD/kwRq+2o98eRjIRZdOBVEcvQHKWtRagWxFbhNifPaLVIQlFlJOupCttTnr6X8XUSl2rcJg4bDvGLzDthHXWmXfzHMkJk13Emv5FCNdUTfDNYPWtIDluu4R2u+9TghTzJyXpKEKn42CBbhE7YD3Xrv4F4PnN88zU9KqMe57YsMYO7Tzvx9Geqk8xcxSHT/78oHb0fO//vIdKSWKKA1S06/gNEU6b/Cd3cQfuRZu+9X18TMmiBhKY3Kc5oUPp5n3z3Oj2On77o3VUTgWllmDxHvrSJIw1uKrMMWFD6fCYfTqeVOF3a7jsBt5sRuRGJhzxRnL4zIxpZnRW7wVav2MzdAOMNpnYETovNPUe+eIFXZe8XLXJkgRuMRKFaNUzlb8Vh7Tnbf0ZuD9EsiavLH1cMeWIHUnypBya7EcGsd9iqRUeFzU+AxZp0UtkFrs9fWWWKgh47Esi8Y1ioFO1hCbBu0Zg2nB8NvEAuwHz+3gm50EdNapWjQmNjej1rw41qWvIEXDZ8bmXNlbdd1c3+wUM7tmS20brp4a7ZQK52Xhxo8t6lEbxXWhLJ89c95ZJBeFfEvLKm5Y/k4s+I4jGhC0JGVvpZqZQ4KiIrfSyBgLFd/gkvXAvHFe1c55IUlRqnXJFFlJGWbzx8mlhc63BtB7C9ZgVlhyneQbg80A8e+CtwwiHHrfREj6AcwpkWph5x03vVfHlFqJKbNzjkcWxcoRplK4sZbHJZJqxXstAqc5cug77seOofM8XiK1JO7GkYfbDqmFu30HJfFhOqu0fMW7G5RCVTFVagcHZvWZAEShCu+Eh77nxdCx90rnK1m7ynU8VhcufU2PGhWtrJrSjIK2M6NBRRtH35ito1YcUV0olyi8GB0Pu06dAZ1tPHk1VjlPkVwyg3fMJXNcFkIpzGshp/GHnUGMoaa88YJViAVD55DOcjrmbcK4GigpzhljRgp0u543c2ApGXGG0XiFhkSw1kETdM21sFQhoD7zz5eZn355w08eDrx9uvD2eGHnHbVkDt62aUQhB6Vk6gfl2ji95GtiVK6afpUyPJ4W3jxd2ihLg7SUhdIu3+bPnWtGMCwhcVn0gT7sduRUePCWUzUM4jhUeP74zN3NjpgqT9PMi/1LpjRxmie+vNnz9nhRy+JccKZymRPWq+LWO8uSEpeUeI5RJ4kZfrTv1fZCdAR/mpMGN7c3apuwrdaKFKMpXNbwnBKPS9sjiGYOX2LGm8RgLaDxeStzo+sd1hZ8DFsXvxZ45YdrzqsxbWJE4TQnhk+nmdu9qnxTVm8fby3V6H6KWumd49WNirO8k+bLIlvm8d5aptbsGDFkavMeqs0TSrjf7bgk9S73FVV0mxYaI6I2zd5hBB56z/sptGmpUFvBlMrmyLnaeVS5JoethAAjmvo1Rd3FjV7FR7219GIIDXp9ahCWsPo+aR3ojArk5haPKbU0fr7+HC/SHn09uLxVr/bScM1jyITYDh8j1OaJJFapl6Vdl1oKnTM4rFJMU7nWi9/z9f0o7rXln6KjVmqOf6sS0Ta/7Dlmns6BIppXqDJuPRCOIYMVvNXusDQc1xnhxV2vLIUaW5RbpusHwpw4XWbeHWde34+EvEAbzWg3gkIpQqmZwVg1yzJXVd6LoePr/dgwwkpJ6olua1OXti5969bbeNZZYVoxNa3xLM0MbH3A9dop+2EVaVQql6Ug1fJqZ7gZug0PX9oF/19+8Y4//uKGzhlOpXBZQvPiaIIPZPNVN1UVrTFnTUyrn+F8xnB722PE8hwCU9IC70QXPWHOasfqhCkkzkEDFlIuDL6n846hKAe7GywxJT5Oav61b5L2vbO8f5743397Zn6eSamoCtZqGlIuYFrijLQDafUCCm1pKrLy5s02Yl/OYVu+ZlspuT3MVjnlQ+cQUdZx33kSlcE6QkrYKvhsGDqPofBXH58IpfLjL+54atqJnCMvbm7oR8fjaaKrmS8PB45T4Mu7A989nxg6p/dAUVGZtQpTpObLHdClW62V34qmWR2spRbBGMveC9oeKLNobSra/lpDHbwQcuUYI9Jk3M4YnkLkYwm86D1+WGGwipjKfuz41wbPJSaeF8XwQy5bJGWpQKM8Wiu8f7406wQ1znOi92Rqwomysj/0dtU8ZGs2O2tq3TBt4yx965aN0Q5UacUOY7Sf71f8uVbsumxGGxH1YoLHmLj36kU1OGGKSkq2op/vh8u0zgY0rgRw9YXXe0ah0kuKaiNNc5X8v6l7jyZJ0iRN7/moEfdglVmk6ewCWED2BhH8c/wDXHHAAbIiCxFgp6cb010kM4M5MfIxHFTNPAHs9Fxr4lBVkpUR4W5upp/qqy/hZrsRjNxr3llskz1F3YoWIgYsVnj8kyu858rBeYIVEz09LzFKC86V25IVqXvpq3hJKnjn6IJTuwVLqYVSGuPo+XDs+MvPp33p/fe+fh3FXUAAkurDV8Ri9BAkBLcixeQ6i79McxKIkWrdG2LrDEaLwIZ9D97TOcefP11ZSmJwEUMlWM/1euE4HHibDd/cj1ymGedkEVSRzuS2KG17IEDQ1J0xOP54P3IMAWNEdLT5RmwVWor25tcu73RbknbOMde833RVWwpD25dwitzsXf5aKmmuXNbKH596xuD2SLPcxHEweFkGz63yOskk02h7RJ4MdXJSbKOerY0lZ4xxGFUbtgY2Gn58u/LvP9zx+8eRP30+01rj4D2pNbwP5LzKzsOrZUCBzhsOscO1QjSNxXqel4XSBDJIa9Fw48pbLXTGEt/fmdeV67QydGIHkEvjyzJzDW7nMHe9x3nL26Q2El9du9JE6h+xkm9aJftzUim/7DXkcy2tEr0jNwlWzqWSS8GqssZ7zy/nK0995KHvOafEZVo4Dgc+Ph05vzzz6TRBg+PYM00ThkbvxYP+Qz/ota8sFHwVO4hJlaC9c6yq2bDWcEmZORveTaYzXrI4rRxEdqs0egOZZvaKNRjHd32HtUIJjgjWS5WH/5oK0WUGbYassmYClgdrOcbAN0NhypnXOXFNSQ6cKvc9dcskqOq/48AYlpQIWuCt0Z3XHpl061StMVSdREuFpaj3vYVj13HJWZeIuvCslU53BoW20ycrmzOq3MPnInj8Qwj8duz50+mqS0rZdQzOimdNg9Oadkh0Y5xszdZ5zZzmxFqK2l8IrHZNwtyrDb4fe4594Me3C4sttxB39QJyRnzXp1S4psJryxy95duul7pWJKWqtYb3FmfVkXOVpkqeUOi8x3vIrfLDEEXMVAUuTjRCsAJnbU3OvwXMHTQuq8FSK+dcOOWC9ZZHJ2yMJUuXlmyji47BWMKqeLmF+1GS3PXe4uMoNMSU5eF5nwsHX7jrgxhulczn04lPbxcZ56gcoleutNIatYvdDlWL5Ia6VPlwN3CIUTDxrdMx7BjwZnHLji3CllDkjKTPn3Jhy3BqbcNOVfSgOLwc5o01Fa5T4TQvzEviDw89ICNorU0XZg1nAo/Hjr++Xxk6t3N8t2tsDBpIrNV+mzAsvEyrpKs7C9FwTgmDBAqbKtPIh8PAIXhOWUK/Q7hnOp94uutZmiz6xlaZS+aX85VaK13wxCHIRKZbwPfLLJ1951mbOOL9/HKhucZT6LDKFOmd2cOgr7XxeV65HwJrrgQ9/L1SOack+ZgRS0SiGDfDK/SQrajIaW3Umrg79mQKxspCe1kLhxixDqY1cQyObw8jT6byl+d3Bp/48n5ibZ6jhWtaeXuZOHhH7RxZVV9jJ3TQt2VlbZXfPzxwnWcxzmpCA01VoI0tgGJOcgg57+iD8sr13rEYqmm4tqk35eaoVZSh38XA0Tte1iQRgchEu7bG87zy2EeOQQKygxELWqwcFMdgOATLN31QJpQwUP6v18vOkXfeEr1TEY0UpWADzUqoxsYw83bDkW+qTKvF3hoR2I1Dj2/SBMyliIq7imcQxnCulSfrQX/m5gBZt5ESnaZpSI6sF+GWKtrXXDibhleyhTMCBdYi+o7NSvm6Jq6p7NOqV91DqpU5SfiGcxZKYZ0bPxx77gbPp/PMWuRz26L0nLGsRZh8IL5N55AYjdsZM8HL37+uiUolZ4HForMcu8gQAnNuXKeV2mAIjqPRJCrbWNQTPwaH7ew+0f5LX7+e4o58eEupvCs173nJRLcyeMfLtFJ8IziDrY1qK66T0NlUJJfSJblZg7N8P8a9qJ/XxMexY4xeeLoGTGsMTpwlny8rIFSq9BV1KWqRzU38pqeUmVImdp5f3iZaaTwOHq/9sGmIXWhrO7OlWkRt9xU+ZpCpwtpELtuiR+7eatgngG/vR3yz5Azna+Y8rax53UODgw2sSbpOg7ynaU089JF//PmdwyHwzUNPNI2K3btyYGf4yCRfcc4ROgN6A06r0BAr8E9fzozO87vHUbqTnMm5QI2UPOFcYGmNz5crlznxXCtj37FkEXtgRRyTkM55nYXutaSyG+o8p0qhUIvhdFnprUAYW7oPBdE4BPEAehwj1RpeLuIP461YyS5rJfvKNpRbbzBJFrAgI+/d0JFC4Zoy53VlOSdC5+h6zxgChxA4r0ngD+95rxXbJDWq5cRcC12FUiLHbuSczvx8nZhOJz52HXMz3B96luuMM5Z/eHrC0PjDtx95v858Ob3znpPCGXL4bvm80Xk+DkHgQ6eMpyZWDZtZm6nyPUZdOxvgmuHeeXxn+Vudic3grGctMqU8TwsHLxYCS84E64SMIA8DFlnyCT4OHst//809b2vivg+8r4lf3ic+nyYMElVXEWxYFsWoJmODUaRjDSrlLlWaiz+MI1RJhQK4i17dPm+qy7WJM6hxRszJkB/rFB/PAK0RcqW4Su8sDzHweVmlKbCW1yXv00Mum8mW3C/rkvnuMLCULexm493Ls7tkOYB9kO5/Mo2aC8FbPvQdrhl+vsyizUmFa6mc18TgxfIiN3FqNdbykhKpiio9FdkHGV0EF1N3X57LsvJ2mumdo/Nelr37s2rwSPduGgx3kWrabmz3L339Sor7jWd6XvMOkKXW+NtlFtFPqxjFS9EP2ejG+hActcJj7MQT2ksRlxivumc5XtaMc5ZOsbApZenIVbp4WdKuAhS000AVP5gpF6ZUxJrVWfou8PNpprXIt2O3s5KadvE7Rt4qS20MZmNQSAfZWTXwb22fNsx+EAsbxlaYZ8H5Ss7c9RbrBjm9vReL11zZQHIJ9pAJ47ungU9vE33vsb0Xb5nt4UMOmKpLU4MhRnWxo+E6T1nLnsREBdc5zkmUoNZY8ThXOMHVyilJB+eDoaxwXRcxYGptD5NYsyxfa6s4LNaJwCerIVWrUsTOa2F2hs47hugYg927P+H4NknASUWgOQNrEfZAWipuDBL4IG8I5wQ2MMYSvQSm7PwSZWZYFTsc+shoLZ9LZgxBlo5l3ROvTrlRrKHvR3xphJZopdG5CFbSonKtvH6eeBwHvPV8fj8zxoHgFtYi+G6poig2+8uQIBPr4Mu04seId54t63d/0L/q5tGiRGu60ITBWr7pAl+WVewIvGUujc45llJ4X1ZhoFixfz44RzRG7j2Ftra0sZdZHBLXKlPtbx8P/HKauKxJKKCd2XMBGht2vk2hsoPKVI0ghFQSq3P6/Inxn+yfLNMWeKGTyaVU7pznNnVu/jI3KvFSK9dS6dQKYHRuFwGmVFlUC9Fugw4G+DAOpFpZcmatYrVsEUx8zUXuY2sJTnzTk36/XTLWGg7Bc4yec5K/S61cc1WV8MbsQepFbVyzTNXCJpJ7uaIpTUahwiKmhbYThk3OhaB2w1Lc5YndnvVUZC/1975+FcV9v1mt5ZIyEeG+rk3Tappssa3UWlbFwrw1zEsG/RA6FzinjDWFU4IPY1SOsHS2U6l06j9TkZuqBMCsrFXYLWKhG1j1e+Zc+HJddgy+lgatUqs8PF8mGXmjM2z+0xv0sj2UqcimWwpY1ZFVMGGHfF+wTmXYsoA8xA6wfD6dmXLmvvd45/euyKrFKrAXdilkMHjP/zA80nvHT6cr1hkOTjb5skwVeKJh2MLv+s4xL2JNkNFwahouq0VDbVjr+Xj/wMv7mb6PdM5TSmKqFdsa1oralx41KSvULN2RdBmGooeAsYZ1KftBLk6UAhcVdeScamVZKtck4qTgDVmLRsqVdS1K+zSyiJ2L7ipuByawY/NpreKHbsAH6YhzqjvsZpQSl6yYQB26iC+VU5GsymqksJRS+XAIkjurr981cURcYU+8vyziQui9Zc0Tb5Pn5XplrsLuuWVrygMQdWdxySu5Ff5DvEPFnmCM2vfKB27YVq1GZJQbfdcYHr3QNz8tMyCeSptX/bZ7mHXKO9nM6CwH5xic042M4ZfrvFtSZOWAe2v4w9OB9zXx4+uVy5QYD0GKFOyvaTtVDWKqteZCKZVaK89m5jeHUZlLclgP3jLl7XPTTt80AjK5TJvbKko+0D2Ft5Kn+7LI/xuCx+jhZC3i4bFfX+RkbIa3edlV4oLDi1HNUuS+P/og4SXaGW/35EzDLdL0DMZSvOGq3kFLKbwsok2RNCqBd64pS7AJQrPeAuu3xq4oo2fTIGxeV1Y1LvIa1R4Cce/0zXBdGnP5N5ChKg2t5efLzNyE/taq2OJ6dV/bOjcUe6xVxTVL43CQUai0zCF67oe4Y7GuyAXMulwTLxH1YzeWuSi74eWdqvzkNVeWIuPW65zZFKKtCs52VHl7VO8NSTsTddFcZMHXtNvJTU7k9yJ81ujlkpcGj/0g/hNeslSvSfD7Uivn+cqaPKdpERx2iHtmbKkiy/fG6uThVJBVsPZ2E/2Hbx/57cOR//yTZKUcglOZuNmx/qo3mjEwL4XQud1OttbGXYxQDdGKuOf9chFb4bWxGAmfPnQd1ybUtcucxKCsQclVEtyLMobUcdIArYjgSVhB0o60Jg/lTrXUAzXrEeSz2cfYTdmYc2Ge9EDVz7aUhtsfnNsyruhWzRlIq3RT0VrGwROtI9DUYnchOMu6JL55euD9TXYPud0m4loAACAASURBVIgpWLSWn08L05pxMfL2fqaPgcMQMBje5xWjv0sHQd6mGbNuexj2iLSb74s0LEuS63gtjf/8cubbIfJx6GTRv3fG8vdN00U54sW/eX8b07j3ltGPXGvl+4cDf/ryzm2I3xS08vOutXIpFVNXOivGY80K1NOaJJI1A4veJ723fHPsmJbMfE3EowjTrFGTrwbRylJ7SWm3kd4mm5d54S6EvVk7eM/VVaYiRnDoAfSaEsHbnRSw7ZWtNmxF753c2NksNDmA++BvJl7G7PUgOCFFNNgteUsTQkEqlVYqJXpisBr7t1k6iDvolcbQ5GfeB8dd15Fq4a+nq/gbxYBvEoOXq1C3N9jVGau0T/ZJsOji21jLcHD88nKlj44P9wfIwovM+lqXKurg90kD3v8+KvPrKO5OJhsGF3irCxcrC6chOM0xZMcWtyKAgXUpPARPbqr+dJb7sF1Aw/usG2rndJuvV6PJxbrmzNuSSNPM0+HA+7TKqHeZmdW90bTbclNoYHZfzJXWiMZxXgs1iqOhM5CpspQz0BvoOithATlx0I6+qF94bRBd4NA5XuaFhz6Q10pthqexk2ajwpKFxlaqMC3O08Ld3YgtOtrryBe92V83Rh7iH+5GPufN76QJBq5K1loaz+8zzQjFzTVHblkKfpUCa5vV0APLEIWv3jvPvFTmWqgxkW3jdVl0SjI7M2Bj6ThnCF5ELBvjwVgp1psislYRKIndg+wQjGofliyhwCS5gM3IAhS4UUelMWNZpKOvGmtnvcF3Vn5mlanHWOmSPvSd2jEDTah3qVROy0r0jnVdCU7sEJYsyVuDsyw18/l85W5ssiSdmxiMec8QLfOqpl1O4T9ndwNp0GBn/dxa29waHQtVhTYyof2ksGTso/7lTfRmqOQdOjNmmxYbm7mnx/BNjPzp5cSn64S3Aklu3aOzFmvavqkstTHnJNoKL/fV5mHjANs0mjFVBut4uIv85fOJ82XleOyoRhlIpTAXCX4Xd0TZX0nlFfZK74NQKrWrfuoCbWk7lFKR6WLXJumju00uAqXInRP0/ehZTqMxxsCHKEw2YefYnXopNOfGJUkOcCqVaZVraYHrWSaucQgY7A7nUAU1cMYQq9pxWINtlu8PPYs2hcaIVbUzYNSsr7VGMeKVU/Va34y2VVVupClLpfKnX144jgHnrThoboeMMXTRMa/l/y+m/P98/SqKO8AxSEcER356v2BcwwQV+1SR627wjTHy8Jfc8FE9GKwlGovHcF4SUW1unY45GJHe5yaSeeH2Cq2pVnAsnJflqwsuv6zphc1rxSt1rqn3RfSep8ORO1d3X2tD5bKuxNgRjVVjp8YvpyuxE7e7rDi0LJpE3tyQU97RMYRIypmn6PlmHFhapLXMl/Mro5cl55Ll4alFHO36Puysi1blxJf3BY9j5JcvEzUK4u/0bjVINzGrW2WpUKYF6x1jjOAEtnChcS2JqSRhTFjLXRdJeVa75IwLIvmPvadT2wOMQCFd51hSIVhPzpUpJVk4Z2jbxr81YvQ007BO7W9VMGYQbH43EdPDwCjIuwUrb3gkNNIsHRPWUIr8nZYrg3rHy+cncW7nlOms3e2eo7eQ5WfHWmkxSFKVA2MdXezwznA39LRWxB8+ZVKrhCZE5mbAeBHy+GIUkhPqYEO9cYwhKNPEYwnWY0wSczgtaK01fjxP0BoPCq9s18U2y1fbg71DFAVvg9b4sq78dLpKB17FWrZT2KBUvUaY/WfsP0gLTrCCboiRp3adyhBZa+WHh5G/PJ+Y58zjsaeVgqlVw7fVFVLV0rXK61o1m7hznj2YwzR6a5mV871NKEuqSh+U15OrWIE0bveIQvyCRSt3vSITscA/IkSsWQrqrlK1Cvusq8CKOvlQYDrJxDwe4nZLqYWwhNY0Yzh6CWjByuv7w8PIj6eJWhuHXnZii7Jnth3DVl22iMtSm6psYZrKTnRYcqWcVx7uO4yDjeYJ4L0hVEtZ/w0oVDexUgyGU658PAziBeGEB65N1Xb3Uqtwa4N6ypRa6a3nzsmi9M5ZTjkzBllIhSge3mupvFwWXueFtVSh/BV47EZSzoTgmNaEQwQYMhFA0yQdYRVIB3A/DjwdjhzIOza3hU9800V672Xky4W3eRuj1NDJCEac1V99Tot2GHJKPw0DXqPeSsmYtnJJK9GZPcU+GBV5WENQAzQQvj9GVHDzmvlPP77wOotfSDf43ftbgRD1YnG8XxaGGHk69LzOC82IPUE2FVMad0PgmiGlRu3gc16YW6IbA7EASW76WhrnlMjLRuyHrguEznB+l87eOosPoooV2+YmKT8V4uAZdLGWsuC03jnmObGkshf2zU1kE8k4hdyWVe4Z5w1bcHqulXUp+OggGDlUtHA+zyuWRnWO5qwamzW6IGyF95RAWSveel04FjyOHx7v+PHthDGNsZN77VoFk/XWcOg9OTeqqXwce85F9zd16y+VRWIbDk9pElRdmkrm202c9s+niWtf+DBGRutlOW4NRi/zbWmodNdq+JIyfz5d9HMwe5cvE6PCQ6ipmz6HcsgIESA3CZyPFppDRUuqnmYzvoJvDj2XJZOSKKIlwg6xbTDCvkq17qLD3Cqfp4k/hnsVhFX1Y7/Bc9v12Zq5jdGzlThnhFlnq1E9gNQJhwTKTCWTslp5W7GjwBh+OA60Bl+uEi1pnXrvzBm3UUiBmivTnPCd21W2m1NtNYbmLGe1447I+/rYRe6C56/niVwa11RYkzRapmlBV0bS13/21Hdc1sRzKvt9aS2iHG83i+O1lh3C26eJv/P16yjuWrTX2rjvPXddY85wnqBaI0EWiplumOeWcC83mOHgZRm0WZ4edRTbcL7XeeGaMpcktqPOSvcUcZS8MHY9uToo0Fs5jVPJTE2Ur8tccEUobhgYQ8/r5YqLht5v9k1yg3VasJyznJbMj6cLD+OBhIYdIPF2Ike2JATL9d6TCpBX6X7VT1rcKuWhzUUeHoPhy7Twm0PPpmbdPPHZljbGSNpQEDx0N7XSH1pr4/0kI2j0sox6nmcMEoxsraW3lruuo5nIU+/BwXtamKeMrfDgLd/eSTQiGP72PvHX98vOAnDBSLpMbfKAaqfujPCmgxPB2vbao4rEYrB03snUBfghkqvQHlXyJxCEEWOpGOT7sh7w1sjPMM5CEZ8a54yEWCDXdS1yMATr6FUyvsF/1hhsa7ynzFMM3PWR1Do63/F6ecO1yjgOpCJMoOt63fnTXqGAZSmMwXMcO4wx3DtLwPC8rFxS1u68QnV4bxii53WVfU3OTbvdW/Dzp2nhmgu/PY7c91aFdQgkJ08SIGUxAZ+XReiKulDe8P8Nw95450trDMbyFDytVEwWe1+soekhZ80m/5ffFTQcfK1C+4udI5W8L4kt0lHn2mi14IwwSTZr7LUW5pwYvKfpc3rvPVNtvK/r3uFW9Dk1m1smuozUg0p3T9Lpy/3tjWHR6MKEdPreSXf85TrvRIfNLKwC2cmFdFVQqlZgVmzbR7fvU7bvzbnxy9uF3z8cqEZ2DJ11jN7zwzjwZVm4rrIzzFVqVB8Frnnqo3DjkWkgVWmwxj7wdl4wyHL4rvMEb5lbxZnNOVPer/MwhPB36+qvoriDSsaVElcrRAePg2fODWN6pjxz2BSZzeBp1E5EMd5auo1HaBSzNWL282lZuayJhAKRpu0dTDPQbAMjVKgxCvifSmZNiWgd3soyr+89j/3I092Rn57feJ8u0CqfquGPx2HvmGjsSrt+7Lms0vn5YJiXJlhkk5st50pqRZgQTTISl6Xwt8vCv/twEAEGG6PAcU4bN1qw4Vy37kreurMCsyRNA+qj5bGPvEyLbPD1gKiwszxSgc73ZHcVOppjZwtYhJETQhRV6HlmSYW+95xeJ7wz/Mdvjkp3lN3JD3c9zsN/+eWED5ZmGus1453l6dBxFwNdcHLIok6X+hkupRKs+K5rbdt3HJKELNi9NXbH4lMq1FKZUlWarDxILniqwgjG3RS/m21wpXFwFuccj0NHa5WXZcV5+VxaE9+U4GSD8+l9wjnDhYlSMp9y47sYOfaBy/Msro+tkEreOeN98IwxCOWzSkc6Rke0HX9aMs3Ja/XGcYiO+85yFx1ra/zT62Vvzao2MgCnNfPn9zP/jbvjLoRbt45ABabJu7ukzDllhRVviNXXu4ltKjat8aGLfNMHcq6c5kUIDWumFFnkus5JvgBGIFO9X484Wmd4med9EVp1b2Ew0G5KaJkYhLrYWaE/HoKnqqeMMfAQgxZ3mc7kYKv4ItYR8pzIlOyNYdV/38Rqcqv0qmxvWKZUdrgtfEWoyOp3U3UCXpvZ4yONAVca07Tis2MYJCjbAM46nq8zUyn8+e3MITh+GHuik71UdJbHGBic46fLldJE1ZuqnEq9c4TgWIoahllpdjovorxSKt6IQVtWCmUuTc0TZSHc9Z5DF/9uTf1VFPfahKa44eNrkaVS7yxDMLi5Ev2RVFdyXff0okZjbvCky5XN9AojZmL/+P6uGJvZuxdQTm+FZhqJhquFy3yhNoe1ltOy7lQmh8F6yxgjx67j8/uZVDJLqjwcOlKufJlXPg6RxiZGEW7w67zKotJbXuZpv9mDdjd3feQ8r5TW+OYgxXzJhfO6MJXC6LZkJuHzOytdd0GYEhQRp3wcO3yTLmQtmesiku7aIKdKHzxPh4GlFD6/zZRaOSr7JqfCY28wtSfZiVoqyTRG66DJa88l8/x24vW6Yiy8XSUX9O7gd89pq/JzA3zb9/wtTmK4tkpn9zhGvj+Moq6zhodDT06Ffz5fMVU+78eh2zH2YI3kjVrDWgrXNXPoveDrVZa1AETpbqyVGMP3JYPy1ivcsFJ7g3C2h/ubQ8d9iNAqrTle1pU1C3x37DuWkshJ9hpLKgxRhFPbsvPtemWIkcdjx5wy3XDkdDlzHz19kNOo6j1hDTtDyTjDd8eel1kMr3onxd0iB3Rn4NtDz5dJ8jSbkWWzs9K9LqXw4/uFdneg85LE1TB6T8t+KiuM0Fphs5PcPHlKbUpSkK41NMOdV09yC/dDJwKzhAZygFkKJliq/i5vUA8WsdHepiia2EZsk7VD7XCN415dHp3SdhsSsxid14NGePkPMfCyJDZhkzEaiekU426NXCRG0WmXvk19qQj8U5Ga0pwUcWGwNWYj5nNjcPTO4a1hyY1TSTz0gUWZXcKuKtgsEM31PDMcJESoVQlON0Yw/re1cfDiTRN9Y3QiFgu28thFUpmZlGlVW+Mfn9/54TjQaW6yQxa1qYhQKufCKRW8zRwGLx4zVQr8/dBhgKfD8K/W1V9Fcd/WDB7la3vLWjWkwFqeRsdaKpcUOV0z87rqyC3j79oqCYlVM0b463NuOr2bHa7hK6wRw+73stDoqiXXTFmhj5FShSpXmiSnexvEz7wIm+DxMGLWhWoMz9Mq1C8rKsktHeeLJtF3wUm0nN7QG5d3zYUYIp0PpJxx3jB4Q38/MsZwS62RlytjdBFhhDNVKJu58e3YM62ZL5eF65J5XxPXOTEOUb1pLK+XeReBdD7wfkn03tFZx5fThT521FwZu54P93eSktQq1zkxzzNv0wpGOm0fxb1wcGHH/oy5Zc+WVvmHpwPPl4QdDF1w3HdBqGAKv9gmtsO1VaKTcfbDocchjB5rjS6IG807nmLkQ9+Ji6Ax6g/T9jzQa8rULKbJtW4EEDno5TAVXPbYdyTlHffO73BGs4gSOq2YangcOlIRK95WRQTUEH+fqAZlucDLZeFpjDjnmeaJ3jl61WSotyiwGWTJoTCJSINvxo6GlcWc3RAWaat/GDt6Z/npMrPqUq4qf761xsu88rIkPg4df7wfd/ooxmCd47ux574LvC0rn68zs8JbGz2wNfalfl4rny8LY/R4K1THIXi8s8wps2rQd1kLNjiql075NEuugo8WnERjrkk8lqK1qkWxdNZwF8T1NZUqHlKwWxVY1FfKyEF2Hz0vy7qt2HZseXsOSmu8qc/UfR9oipOvapu7TWm9k9eZayM6I1kDudI7eY+tiQnfnEVvYYzFGokdPDjP1RqyKZjcWC6SzBR7j9cg9w2fb8CneWWtladB3rvw6Bs/HDtKqfz1NO2T5VIb//fblcc+cD9EcXts4sBq9YCqTRf90eGDoTnDXRf5cDcCorjO9d/AQlXRkn0B1ltZsG2qPWcMx87jbGMeIm/IomlaZ7ooB8FzSty3xmPo8M5xnpfdwtQ5KaqbD8uelGMEp8upMUbPnAoxBGwzHGNHMYI9T8tKfwhkTR4PocOUwm8OA8/KV01NFZJFVGS1CX2vtsaIu0XWGbObF1kn4peUC94bqhF17X3sWFX0sSUVAZyXzPNl3Y2yPvYRVxufLzP/5ed35lJEbl8Ekgq5ELxI2QlymNUCznt6I3BAa43/6T/+t/yn//MvfDzecX9/R0sZaxvv14XLsjClLKO5E/VpK43ixB9n23vA7aEyBg4h0D8E4aUrH9Kpgi9XsUW96z3Bj4KJYrCtUKu6B+qdUVsTxa9ht/51VjyFZOkoh4oHmhWcvXwFO5gGUeGVIUi03XXNvM4ruUo4irWCr9+HwNt15el+5LePdzy/vhGMYaFyajJZRhDLVdM4jpG8JokvbIVjsPTeaYcu73n75zbubwtJawxj51lXGLwKlZp+h06ZD13gkgrPpe6c62363HCW13nFAj8cB7kOweNV3ftkA98Pkd+MHedUeF1WBud4nlde1UyrlcZdDLvkniah5E99ZPDi2SLTA1wXscsYrKVYuFYwQe5PwZbFObLlumsyBmeJxhGMwDCSQiT0RYnGKxTn2GxKa5VnNNjtPtE9QoPTJFF0pYovj7M3wZjVhmnL2nVGlr6lgamGbBqdl8bitGamXPdmJFjHnCov04LXrr4hr/9hDLgKn9+vrNeVSmMY3V7UjdaQtRS+TDL1+QdLtQ5jDX97m/jpNMkOTuHEohLC5zmx1MpvHgZyrVxmsSowFihSQ66pcIxBDtw+yK7EGlKqsiT+O1+/iuK+LXsaah1qLMcgF88hI39RLu4hGGpxvK+Zu/FAWleMguyvS+J9rjz0Xi2EbwV8+zA2rrscoo3QHF2wO499zZnv7o6Mw8Cn11cuKWEMvF0vPPYdOVdKyaRqmFzEG2XXuEDAcJ4u+3sy2rHU1uisZalSIGqu3PcdwRnWghoJNYKXdJ6pZGji8dGA85LAyp/98fG4UwKPwXOaV/73f36mHzx98JwvIi+/H3v6LvB+XXcOtanwcJRQ5mQdKSWi9/x3v/mOX/72GRscP375xJIrH/ue58vEeZHEIhcM1UKdpXAdOi9dU9vER9JRVi1Qcj3ZFZZWwixpyueN0VJy3vFYqxOWsUaTp5ri1G3vSuWAlOIgdciI3Bx2auPGob9UUSNbGnedYN+tqcWvHkpLLvS6aDNGDJzu+sBcVt4uF7zVia/cOupmETEbMCc1WrOG8yqd40Enkx0D1veV6s1zPlgrQdQNLBXrvJ78es/q66lN4JmPQ+R5Wnm5LvvBt+2Mcmt8mmaWUvl3j0f5PdXQG7GTsNZwHwOj9zx1gVob3w4dS2n86e3M+ZpE/l8zZPETmnLl8j7xm2Mv3byGyIchquVt46fXie4YJDZQIHPhwRuIMVBKkXxUhZVyFVuGYpqwaJDP3GG4rCuHcKMcOgMP3vOaJFBkLVLItkQuSS/Se2ybBM0m4mq77mGtEkdn4UZLbLLfS6Xo35PXEb3crPfecx+CdOFjwBhLyoW34Mi5sE6Z3CSJbRuWGnJPVKQGzV/e+c1h4PN15n1ZhUhgpfbU0sTB1hiaE8Ts83nd75GcBd4KwYj4T9XYD4cOwyaElN3jNoX9S1+/iuIOfNUBAkj3sJ3aki8sqsfoHcZkSsrkBl2M1FaoteBsT61i/3/w8G6koKLmU8IBtrtasjeO+75nTZlcBFPtY6SLkdf3C3MqlFQxghfxOi8CCZREtJ7LLOPu490R6xzrurAFE7QmLA52WMUIM4eK8YGhF7rkP7+84Z247qVSwFlepoWg49m2WzC18Zv7A4OyWhpysg/e8fN5UtqlGHZdpoVUK5fzhes1K3NFDIuWvPLheMQqXnjvHf/z//K/0izcucDHw4FPl4mfzxNLyxxiJDjDqWVabnunEvR1SGFsVB0R5f2b3R1zwzvlc7sts0op5Fz3h8tpIc0qWuGrewEteaJTqDe3xCb/T8QdIshpSPF86gJ/OAy4BucmftjRe85ZVc/e8rqs1FZ5iuLgeElS3L47Dmq3oNhwUwdHGqlJos6hi6ytsuaMMaLyXVLl6jL3XcA0w1zrDrUYs01uaFEXW+k+bsHgN/gNGq3eunPBkzfs3ty0GO02DV7V+sC3xpLUu8aIf0nnhA9fq1xJg4SkPIXIYTC4ZhmiWFCfU94PzL+eJ+5j4OPYyQ6hwSFG/vJ6gWBxfhOhsQuItknbGzEmq6mQSqFazQJusokoVWCS7a3Uxk6VrBUOznJa4ZLEk8gYNOVK09J0Mdo5ZcpgdkaJtQJ9TGopkptMwEULoogQUSEUBGMZvOXoPccQgMbB+J222MfA7x+PvE8Lny4zlKqfZ9MmQ67rBrtcU+Gvrxc+jpFaA2+aXQzS6Gw+OQDeeSwyiUoTdGtCt5+9roVcBFoqTRxUr5O4jf69r19NcRccVR6E7SXnphgaKAtAxQnOMGj01rLMyrCQbux1Xuk9PF9mrDH03rFQ9q5xHDzznFmmiumcZqkaQgwcxwM5Jy7XK80ahujoghHDMWTh09S7Z0qZu8NIbYYfX17pQuTxMDB0PS/nC0sW7O49J8bosU0Ums451pw5nWe6hwOHTvDrVbn7pQiWnFSItEEAFsNlTZKu025y7PdJTv0lFe7HnvOUyFVYOT5Yut5RcsUEL4vrJfPj8s439wcsQhV0xvJ0NzIvYtN7muY9T/a+D/zu6Y7/7cefAUm5327ktyXxECSYwGwYt/5LyEuq6NWFoDSTKrIpZS/0Vr+/KpqxjdiZLXhND40mznqNKvsTIx1T1lZ+80DpLdItNbluHTDrCL9R9bw1XFvleV5JpXHfeR68+KT4rwpoQ0b9a5GYQaeHSdEudC7wnjNRrWUvuRF8YfSBhiz1tvEfLQbGqMBMIaHabkIkOaAUotiO9oZ6HcmfG51aNrb/VuSrukQCQqc0soRLTkqvabIMtcbSjOgl1izX0prG4ISqdy2FBcHOrynzMhmYpSA9HXqSg7H3GvSunyli+iYOqLqwLY1o5R5YatVJXLQiwVhlgxSic7uQxxnDeyp8nhbOqUgDZw1eG7SKWC186Ds+jiPBGf78fiHXqiI0Q2piGmaQxe+1Vs06kNB0awzf9ZGj/tBoN5GUUfRAbbfbTVUxxkAfHJ+nhf1P644m7U2KXPtGs3BaMt8OEWPgeZYFMUZ2ERaBC7OK86o2E87emFVW7TCl5zOqejX87fnEYir5X/Ef+FUU9225453Zi2drDW/s3sFXpDN6W1Y82t1VxVprlaxBA7/78A3Gwpxg7DumlFjSVTBYhWqsNfyPv/+Oy5r457czT8PIw+HI2/lCa5m5JKyXAIpgHIOVJV9t4lnRamOphes8893jI118pOsiX17fZElTMs54uhBYq3Db73tx+XPBsJylQz/NE9ZqCpXeGAVV3Sne7nUxVWvj03mmsxL0cVkzqTR+Oc+kKlYN75eV83URyXITj/es2ytTxP0wLZUW4NP5TMmF5/OZaCyv5wsuOoYYeTgMzCWx1sIprbxPC7E6riXhVBTSWuPLvLKWJhCEaXwTvOC0QQ6pjan01Ui2S99LKfs4TRM+NE0jBDfIGsXe9b9za3xeF1K5SfeXLKk+XnHXITiqs0yl8OO88IexxxQ1akOSdVKThCbdcUvXFv0Om9TGXiSB3Vp1M+00yP0qgrC2UwSDhWMXeJ1W3GjUpE5i2DZ/IovuPprGyun9X5GiU/Y57wY9XZPsCLZOfh8DdHkYrOX7wyAUQbbwdJV5WXntpaHUP4XOGvTRiQd+FvFMqI1oJMz7zSbes2hCSOKGuNRKm1e63qnTpj6/OhVscXxGGVRrFT/90UtEIshyP+xCwI5vDj3GwMucxL65Nf5ymvTgh6OqU7edWWctvzsOPA39rtb9MPZ8vs5CaFDx16bWNjTG3nMqYiP+GANPClPpNkQOO26fuXTkcoq2r3KLafC7+5HXJdGasMoEhrw1NU6bF2PlPvt0Xei94xAEp9+m8WBFoDStQhO27qZFKFr9o5NIvc4Z7mKgNPhynrhamR7t1w/Wf+XrV1HcMXLz+Sb+KDRJZpHTyu4X5bpm1jVzSdqJIx2CQ7C+mhLT5czd4z2/+/AkmPH1qiyPQlkL5yTqwx9PV+5j5Bg75lxI769CN3NIYdeTuDRRcVpkfA0+YEqj67wUklzoHzq+vL3y+fQmS6JcqE4Wm33sWNfMHz7ccVkyz9eJpSY6i7IGNmiBfQzbOloUk0yp0nIlOMcvF5FFnzSou6TCnbeMwfNlXbHW7uZaaa07bdSAiJl0tC0pA9CcYVF/jV5NGu+7jnYtu9rvp/crXedZpypZrdFxP3SsKZNqY9KEpfc189R5HmNkcE4WWrUp7CKfrUFELXbv+tgx9JvIprLJEuzWcRpNUCq3f2/3xbY4D87y0AudckmVc86ar2rojMN68fNugKkwWgmfEEFX3U24xPcHgZ309zoDAcn8TDSWVqjNYrCk0nC+8dv7AxhIxfO+FJ6sUDk7JBJQHn5pXK6pUmzjoJF0Ra/DZifQuP3jpPa95lZNdcKBMQT+8HCgt4aiNX+bfEtrRCSPICv9sWzPjf6MCsxVVLmuwWAbnYHBePromEvhWjNN/T+uOd/EM/p5qR8cNKO2vwZLY85ZpwKvxmISkt55cQ+9HyPGGt7mxP/x6VVorE0O6kFtd8U/pfFNL8lUx+AZeJqcOgAAIABJREFUg4SGe200jjHyOi+ysDTgmmQppCaHufWG5Cr//u5I2A6+bZpq21vZJqsbv97o36167VptROf4/i5wSon3NXFJsjcq+3LzJq4sVabLtGbuo+MQPV+mxBgdr3PibU57QfcaT8n2e41AqWMv9+ifv7xTLGTH7lj790v7r6W4K65Zm4xRm4PbZhhVkAirT28SPCy2A/KtDgSmaILJlrTy6csL3338SN9FBue5zKL6+v7xnuucsFjeLzOtWO6GnmlZmEthLUWKkuLySbmxucoJHayO5c5wySutGqbpyi9/PqO05tvprd4btYit7mlOvM8zz6eJpVZs50ViLJgTwTh9r41lEUfJwYtXjrNQozwQU8m8l0or0plHbzh4j48RpoQPX42G26jndNm5F0It+NbIgsduuHIll8achR7mrCV4y/m60rvAsY/Ma1LPahVX1LoXWGPgec2cskTn/TD2IrThZhCWS979uLcl6nan7kZt3BrUnWaiHhS2KWUWQB8KkAfkqY903vA8J0JDbFudqG+DgdfrwrrW3W/7GD3NwJc185oKVhXB3liOnZfULfTgwShWKiPyQqV3cvi6IsuwT5eJf3i6IzjL384rpzVxF9QgS1XVlsbSZI90zStzNnw7dnTO7lbHFfE5Abioqno/CM2OfmGt4cOxYzP/9QhskdpNqJfUy2b7HoFvtgNN3Ck7Y7jznlOW3IKsv7/zltE7OoVzLqUwrYXDISLrQ7Nz5TeDuyY+BuqZL7/0qR+EJVMljahhyMCfX6+k1pg1y9UaQ1A8HSPTzV1wfDiM3MdwK7h66G1Y9+AdYxA6sbxJgzU6Welz7IPViUaFbTol3rQpOuls0ArsTKzSbo6gDZlEvPWMzvLiLG9T0nhFwfCnXIQ5ZI02IY3nOTMVURe/rrLb2NE6c/Oqz0WmIG/Fk94NgZQqfnR0TsgY2/S7M7D+ha9fRXFvwJfLQhcc3x4iwTqy2sFNqbKWxi9vV2ZVmm0jF0hxz61RDDhlkbhauF4vhGZ4ur/nbfmiF8RKF1gqh77nMHRc1pnaiixdjHqvmy0iDB2h5AYYXOCifO8QLFNeGF3k+6c7luXKvCag4ZwU2/voeL/O1Gr56e3KQ+w4dJWWVpZSGaxVHM3uNrnBWiYKnZHOfypCQ8xNHvTDcMClmdUmbG+Yl8Iv1wWuK75zmM0rPrOrONdU92JplZpoHPjgyKvYDARvuR8GDjFwWmcuiwhsuujoouU6rxp04iRvdRbF36wqP/kgZZSdcyObyrUVXDP01uFxWriM7kHN3qK0duukKgJJbWZgW/e08dW/P3YspW6CVfWBlwmPKjj+x1GMvSpwToUva+LbvmMqlWz1IXZi2iVGVBL+UptK821japWEFKysIplmrXixWHjqOo7ekSrYTh60L+eZVCpD9HwcKn89L7Qq7pKds0Qj09pahDq31sqapdP7fhR3yt1mw8g9d5ozOsjoAluuizXw8TiQauNTWTk4x+jdLvG/PVy6JzLbMk+nRN119N5yqeJm+RikI926+7lIgHewht5Zsmm8lVUmGHtb+llNGlM5AUkDu7fXeQhew3hk8V5UP9J7R8uFaB3fHQfO64IFvHV4Ax+Gjm8PI0E1BjTNUa1NpzwR8wWrk2vOql6VwkyTAzAiIelLKnReuDoVbs+KXi+5HQ3OCusq5az/rXYNuu2UpaykIx2sZbZCnnAKqdQmDJek2FrR5tUax0+XmdclyXtwgqEL04bdwncv8q3xMiXuj0Ega6B3VtlH/Kut+6+iuAM8DEG9zBszVcb9NfHTy1XoQIpVRWWggI6EqBFWU+EK6jx3OrNgqNXw4e6Ox+NIqpnvHu44zwvBO86nE9Y7LqVodqTcmAaBKbakFKlZ0n19eOj5/H7huqx03uO84zovTGshOCcwgI64y1IUR5UxKiE32Mdx4PM0E90mzlrpvd9FPg9Dx8PxwEPf8cvzK19mSVw3Vuha87pCZyirHAjVKQe9NJYpS2FUnKMUgbj2ZWeFVipxlGVSFzyPY8ehj0xr4ZI141EfoJIqMXqsEWsAbx3OuP1GztsxuJ2EeiNH7yUwG/k8c20iGqoik9/ETIKvt69uVHWD5NadbEd5qfC+it9Hp4W5ZVH6ighFxum3NTPVSjOSvVsbPF8X9oMEcNXufOr7GKS47x3UDadG9x6+s4xd1MOxbQQshuB4uc7qxd/4+f3Kx7uB6CQR6afLjEmZYwwSEZgLIUTqMu9q3EvKfJqkCPZbgUbl+kqhrJt3MvI9T2OHdUqxxHDKhbecZVGp16/X/9498s1Xnu9N9jt9cFxqYZ5nvNm6+KhTRJVDqMBcyg4dSaD1zdlU6MryukSxKQ2QN4ZvOrVUlkWDWGfo6wvece8d51x4W6FU6Yaf+o7Oe1nGahDP5oBJq7K4RZqDZuQeX3NhyYWDd/sCu+k/xTxOPNs7Z3RXYfbF9m5JYbd7Uqbe0pq4XG4QosLGZidqNAYDv/Ejr/PKNWURMdamthUCoVSkOcjAl3ndIZwmuKNYDuihUPWa+uioSZbdjz4w27Yrrg/WMRVZGv+9r19FcTfAIXrFW+VNn3PhNK27A+KEphaxcacVI0MsvnWxLFgX8lDMtfL9/R1rLvz4/MLvPn6DaY2xG5hSphhDzdIptKop5EaUkw64zpn7MYirXaqMXn5/7ww+BmieNYnhfs6V4A29CeKK5zyOwhAD71MlZZErfxg63nUkW3ORjtNallSIwXGMgfvQYa3lsoioInrHtIrR0rQWoarVRlHe4IaXFgphdNQsGHTV0dPqPHvsApcpCevDWjrvKUXCgGurGBPAVu1exG2SBtdZiv01F1KQXcOyVorRLExzc7rbskC3nV9DY9Gcu0EcykzYbmTgKzxZivTefLYbMrPWys+XhS5YRjyXJXHfdxoCgrI15HCZkjyUq2LzUtQ1wINGy5K4dOwCfbTaPd0woRt1zXDoJYquWbnj7FeI59uaeJnEC6Uzouj88f3K7x9HPo6BXAsviq9aGqkY1iwGaIYbC2KtjXVOfBit2E60Jt5DpWoxMDvT524IjDFwKWkv2sEYWhPHxWsS87JqLeB4jJ7UboeVhZ22563l28PI59OV7BtLq6S0cHCeMYo8/iWtO0xEk2fUNXFndUYi4BoiRLtm0RI4LI9RLCdA1MbGid3FVkxThoJlTZmDaZgY+NgH3AaVoJ/LdkOhJdtIozOvGYyowmnS1W4MHg0elNelvY1Vq+OvoZesxdgag0WXsTpBCePIqI+9wjhFNn3S5QvmH42lv/O8TCs/n65CtbWW6AxfpqosJWnCvLO0tWqim0AwzsshljRdJjhDmjJGxWiPMUC0/JwW9dc3DM7d1Pb/wtevorhvjnO5wrU1Lqvwj7PRpSay0XaKfzXEGc4Z6daXWnFNxqSixSE4y8dx5Jf3C83IgqjWQgiBYD0YSzB3vE9nlqSy9a1HNDD6SHOVkuHYB377m0f+9umZg8t8fOx5XjLva6U1S+8DH+4OvF9OFGt4uywcu8oprxy7gPOiwKtYki5r7kNkrsK5dkW2/DaLeVo0hZIyU05EI6yLaa3iid41TTeC5pxicLDMGR9VHZjlYWg6+llrGHpPCI5Br1nvhf+8aEGeawWzEJtncJGH+0hF4AOiBGanXHEW7vqwhwNXGn0fWNZMMer3YeGgmaymGZo1kpupBVS6oNuN2ZAieVqzhgmz7wV6Z3mKYpBUG3w7dgoFwMdhoBd/WrQJ2mGLVBpq3aESd+3UdErYfvsfHw587Db3Sate4oak8IEx8K5jdEVScbxp+8t31pDQpZutpGrok0xow+B57CNvS6HkxPsiiuHLPIuS0ipDQv/dTOPTdeap7xij46GPPMTA87TyfFlorXE4dJxTYrpOHDqvEIt09sY0eQ7spoKVQtV7j6nyWW7v2+r7bAYexshBSQatNhYquSTSIuEbYglsSErOtE0mMWtl52B1N/G6LExVnqW7GPjucCBYR1EKatNDeKcM0mRBa6GznntdtBbVZKBNGkb3ceohVWjMqWgjIT8zOovPlnMqEilpbth9a6KPCSHg9Oe32uRe0PtC4Bm1EG/m/9VJWyueU0VhId8MdZWENmeNeO8Yy2PnMa3jZU4Uxdmt3aMByEkU5P3oWa5ZJgFjbsEt+ljMpVBzxRnLQx8YvNSYRx/4p+nKVItcd7vxt/7rX7+K4g5wyY1Lzlxy3hcn1hoR9+QbPxnACENI03Ykri0pHlmRG3fJlanOeOfoY6BzgbfLFR9EYBJCYMmSruONhnDszGLB3u+HKAZWXeTT25mnIfCxd/LwO4+Lhc+nmdN0IZeMa47TujB2niknaoPXKXHX91ArITgKYK2w5h/HAWh4K91zyw1TLc/TgjWOahvFGOaSSDbTd7IA3DpzH8Qu1Flhk5Qs2YtbFI/8fUkewkmHE6MVzB0Z+512ycHdxsPgrMBKRXxFHmJgXgt9dPRRKGTWwDEKJe4QPW0IpAafrjPRig+GiMZE3t3xVadubgAD5mZnu9Yta1WngQqXXPiypG3vpCOBjM0fDz1OYQFvzM5SsMZoAHLdF6653dwgNyWtrtOozWncnYjPoOEq2Cb85tMqD+vOomiyh/l/qHtzWNm2NM/rt6Y9RcSZ7vDGHGumodVqAwwQAuEggQQOLbAwWmoHCZfGwmqpLSysNhCNwdAeeEggJHBQC1pCUFVkVZH5Mt/L9+50xojYwxoxvrX3eVnKymxaIGWF8e59ce45J2LH2t/61v/7D50V5sbduHBcvJi6KYiq8JOnM1dL4Kpr+OTQ8UVMpKIoMeKruGk9upcKP+Ui1+JuWojZ8mo3YCx8dui57hsep8BDqkHSdRurDSDrFV11IbaKfAZrN9FX+y2NRCliVuXlQvC0eBlW1+uVUmYmU0W9tAiJwVc1uAzr2bxcMs/eLqnAb11f0jdWDLZWUZ/WKGs3G5AAGDKdldd18lHEfUY4+ApFzGKNrZXYXecK3VJnBnn1qSlwaCSVbd0sc4WKdJ2vOCv2xTElVoNBkN+1noCUko1B60qPTJmIeAo9egk3fzW0vOxbZi/h6KJazpgs87qb1vF29jitxbfGyPVSdXZ3niOHwdErXVOgqo9VFtJDihnXGLRTDNZusKRRildNyzd+qhTTFRz85Y9fW9yVUv8p8K8D70op/3R97gb4r4HvA18Af6OUcl+/9h8CfxNJx/v3Syn/3a/7HSFn7r3fUmp8FX501mI6Sx6DeIMroW2RoTeGcYk0WtFpjaZGWClFwaKNQB+Xuz27vuP+dKTEQt+KLee8eLLSXPY7cT2cR1kwSKc3+QAl0VnLw+i5bMXoKZfCKcLlruPGOiafmSdPiZkxBxSKQ7dDLWdCTpQo1UyMg5PQu2Km7VtSzoxLoHGtiJN0xMeM1Y6nZeTkly0qrt8LLpx83qLPtJYNsHNCTTyGyP1pkcFna7DOkIp0QcYIMyakXC1phYvujMbq1SJAYa2qfh2G0ymgrWKskJEMo2TYI6pQWUCmdidOwaeHHqMMSgm8o1BiopTZjsTrcGxtV96e5y24ZP0Sa3dc1khAEQ7leoS2RpPJxLJiq89KxbRuEFoMlr4N6asCpbKUlK43P2sxlKK5rkOqB/5aHEuRG+yysQxO0zsxfbroGjHRUlKoQxG18BhnbmfPYC1XvWOKiin4TWkaYhYDPL7Fpa8b1FSDl0uRdb93FqM181yIiwTNrEV9U4fCNq9IrGHNZsuOVbLzVZhAYCSVRXWbWK/3s4tqKutMZR0IygzqYEXZnSm8XzxQQ6brNfp0J8Z38Kw8X+2orZG5w5QCD3Pg1b6X5myRnNrbaeG7h2GbCZQCuXo3rUtG1a5VBHByikNDh+WegNhfVFuvutkADFYTawpUXQ2sM55VTGfqVFgpmU9MpXCeF+aKcde4ASnm6jkkhFydO5UwkD4ZWt7Mnpm8pZLluqa1UnSVEnq9a/niw0k2kiL2BGuaVqM1g9OEnHFZguAvrMWogW/8LOr7X/H4x+nc/zPgPwH+828997eB/6GU8neVUn+7/v9/oJT6p4B/G/grwKfAf6+U+t0iph5/4SNDxe/qQFM9e5MUpRlay7IIn3TMiZIVTdOgexkwppLJFXt22qKVpnM9BcV5mVCqcJoWmsZxniacFZ3c9z5+yT/60Y9pnaN3DWMQF0cRQGaBUarE/Ijjfav57usXmJB4XGa+fnvLtCxYbclail7jLG3jOM+KRhmUFSfHmAVy2TnFUuD9h0esgsuuQcVE0Zo5BB7Oi1ALq1nUeqMtU2SZ03Zt9oPjRd9ycHbr0tK5cHRGXBu13BzWiqI05UxW9dpWV62C/Ju+uhi2VozEbJZ5wH5oxTjMR3ZdU7v8ssWgwTO2vtra6lq4jDXSrQFtqW4o9eYsmVr45f46x7iJzGozL1/QPGfY8szX10jnvDOORokiUTxEKj2z0g5+2aF1A4Pq6aVdQ9OLlHmlagtt5SYvFHZWc9F2tDW6UddXIhmk8GLX8nZ1/ZND1aZXmGJiiomXXUujGx6i+PnHUge4IdE7+y19gxTSLcmLTaPLY4ibpW1j1C9ce3lvq1e7IM4+Zn50d+SisVw2QhNdN6n19NVZKYpHY/E86w9qwy4Ydi2esYjEymnFi76r5x7xcn+aJWCjN4aP94O8YlVzea2tXXE9fymYY+KisVIwrcNrTQ9caCUxmD5y1Vjp3lPeBEtS4aWIWlWpilUTkRGR05TFM/7ZdE5x07SotMI8Qj4wRgaydRsg5sKUIqeUOMewofbSVUs9slpgr23OY1amkKqvTz6X0xI5xbQxl0pdk1YrmiKmcUuQRuv7L/Z8dXcmhox2qqY9CWmgtyJ+OodAn+Xk3hnDjWt5H+ZfVVZ/6fr/xZuhlP8JuPtzT/8bwN+vf//7wL/5ref/q1LKUkr5CfBnwD/7634HsHlK6Hrx9NqRa4WyevNxvrZCkxpjkUVRMmOGsBadUtj1LVpbpmVBUxj9zK5zxBjwKdO3LSlG/vRnX7HEgDaGi26gwXDVDhjEpnZVFVlt6PuBpIwYIFH4s6/e8XgeabSjr11KzIXjtPD17R27piEUyCXh/cKSguCzOXMcZ6YaIvL2NPJhHikqc3uaBIs10HaGrjd0nXhc+CWzhu7uGsvLvuV1L/xoSuHJBxag661I/ytMIctz7WlXXnKRY2qVzINQuaxR1XZUOOKt1XS9Q3VaYgpjJkRZkFuRdZqkKowGFXIpEjigFS92vQyOSvWZqYO8daiVN0UyW6def8wm7hC2A88qSCsMl70xdEozaEOrxas8FvEIh1Kx4Io/U20N1s2jGpit1q9rB7ays3OVcSoFr/pGwk6KyOmNkjWqEF/v133H630vtsb6WZyl12JW4G72FFXoGrdRHsXvBObqJlqKnDQyMDi7bWYoKfI+V1m9+pYHSX1f6/WSDlGapPvzwodp4aePIz9+PPJ+8tupWKAs+V45pdRwZlajtoJDVW44BFXWACwaa7BabDNeNI7XTUtrBPK47lr2bcPQCr5ttASiqFWNi+DWh6Zh34rp2sN5kvjImmfQKbGM+DAtPPiIz3K6X09tGlV59RU7L1lEiilz6RwpZ+aYcWiunOPSOposwRiqyv9RAuVOIfIURI176z3v/MIpRokxrIU9bx+EQJcS+FHQWj9Teyvsp7TiqYYEraE5pTY2GnjtGv7gasdeaYbGMobIvrH89uuL2lwh87QijYeu+gifkpi2Zfm9L13D95vhV9bUf1LM/aNSyjcApZRvlFKv6/OfAf/Lt/7dV/W5X/tIpfJIq5JvjQBz2jDpAlajgizswShmIk73TMrIMMUIzzmUBCWzxIBaI9xyoTPS7aScWJaFUjKTF1y8axqsa3DG0jjDx/0NMXqepglrWy76jqFxjMtCyYHHk2fXtaQUscYwjhNN13F5ccnj6cyyeEwRE6NWG/qm5xQD4+JrUg8b/ibgb+b96bh1v641+KXe8EkUso3TXPcyYHNW02hNKnKt5ly4D7Jx1XW7PURiX+ogClqledU3DMbwGCJjFJ66LUZi51LaFI5KQ6ssS5Y81d4alpyxWdTDQyNWuolnJ761tGUkVCIsoXajz+yFDZ4pAgm8GDrmkHjyYnlqq7rL1e5riWlT74EYbi25sKiMq4P0plqsKqV4yM8MDkEkVvuBtcMFlIjNRJAVecYtnjUOuYgFRkZu6meGz3oEkROOs4rvXO2hiM/4h/PM/TRvfiEFOVE2Dq5NI3TNaamvTyTusco8tZar1Fu7yeFjLjzFJIO2XMha2DUy0GTjZ68daC5i+bxUW4GEuCOeYsRZxVxkg/Y+cXCO1liaqpC0VmIplYJWaFZCSNCGU4zkJISHh+pdr4BD6/gOA3fLwuvdIEPRIjOg9VqLla+cRlORrnSFQUouhJoaFZVi8YHBKBplOefEWBQ7pTGl7sBKYCARW8ksZIX1jNbsjdg+XFcl7RJleJlZ/dcVxxA4BlEwZyWkifUkVGu10Ia3dSHryGwwsKwmq0QjsaLfU878fBTIZHWjXYV618byUddgUOwsuAwPCUYf6YzQi0sSxhep0GsLqOqnVFC6bhb1vfP/ASzz/+ahfslz5Zc8h1LqbwF/C6Dre8EIK+QAwks9ejHIj/G5QIsgp5Bz5Lx4Dm1DyeCUDEAffGDynlQyp8kDitZpcu0gnsaJ4+i53PXkLJL+4/nIaTyy71vGJXDQlq7rKEURUuQ0zXRNw37Y8egz39w/EGOmcQ0FTXfYE/1MWkQVZxrL0Ucu+o6HaWZeRg59w2OQwVzXWBziWZ+q0tTUgY8seE2Mwud2WvPp1cBlK+HWq5ItlczbJYpHdpb0m62ol4rHIxx7aoeWKuxhEPXhpW6Y0wJFfOuNMxuE4yo1y6Bx2rDowsPo2bVyMtBa0zcWryqDovqsr4OhUtgohmuX+ixWkgUcKqY7WENnNUNjOPlYaaoS/CAdm7BuVkzbKIUvhfuQ2FuDQ7GrHblTRiyDV3hi21G2urA92kaSeHz1nqlkze0m9ynz1cO5DumF2qarOnfXWjqtCaGQVBI/eq14vet40Tec/MDtOPNhnMlF/ICUUlgN173Y254Xz2AdYwyUIhbEziicMZufiiqKiFg7nCtDI+RC9DKI7J100LGs6lqJcwv52SvFVL3GSmeNlQnyNEdCGjFKkqESbF7sxmh6Ixx4XSQt6iEEnDGQFD9/PPNiaHkxdFhluFKKQw1kWZYoYdJ1Eyl1I49ZgjpSymSlKZUMIFkKspYz4qaZ0TTAhTGMBaZcsLps/vLkWgcKWzaDBMYoXnbtdhIKJXP2kfvR01jNGKKoYjfhnSyKkAQeS+vAuQq/VEUUxBhNFK9CuxZCQykKlUUROxfFm3GuUaDPP7tQ+KRt6bSYsu21rQ2JYqcMCSE75CLF3ayUayeDflG7FmxVz/sYyblw+v+puL9VSn1Su/ZPgHf1+a+A73zr330OfP3LfkAp5e8Bfw/g8uqqrF3H2jnVMzIUhSmFq6FBpcIYPTHXrlFDqxTGJJwGiqjpxlo8hkZ2PqPXcIDCq0PHnAqtdey6RvjBZE7eQ8ronPDLwnk501rLoesw1rHb7fjimzcs3kMp+Jg57BwfjifUnLnoe7Ry8rVcUE3L++OZMSxoB0o5Xu53+CyLWCOL0jmzdTFWwdC39Fpzr8RsaWgtr4eWlAs+Jh7nwNA5xpw5fYuip+qAU/ZG+bsMpERKnpHjXlCFb8aFXeu46CwJuJ0WoM47tLy3nFeoJLF3TrqfRgpH8pHOweNUaAe7GYCtnXJZbwRtGIMcs0sWVFSKq6Yoxf2y4EuiNQaLotWKvnOE1nLykfsauHzoXUWdFQ5onOF+ETOteyVNwc4adtWG2K90yhWUqhVdrXh4fZ0XzvHhOFX4RdM1FbOu3iT3i+cppE0trXkeeN4uC4OxTDEJJGPdhq9qJcyNfbPnum94Vz32fcroQk1U0vRtw2cXBx6niXfHseolCjuneZxlnQ3O0HVu8/AXB0m1wW5bp4k4Wc658uNDrK9VCrl4yxROS9wSiaYaJmOU4mHyMuex8nMPFRJYYmBvLWOUU8Mng0SJ98rwOHuU0mLiZSzkantWeIZL6vWgZMYon/+roRVv9yzzLF1PaU43HENgSkUSnyjYUthrjU2ZY5BmyNZhb6NllmMo22mPDXoUOuvd5Pn68Sw8uKXWngr76lp4KeKq2tQNWK8fYm0m1Oa+KEPlEBJaKwbbYIxh9oEUYfJ+s+GQeYl8OL1WGDmgS0qbzWhl5P6up4FbHyVNzmmME4HdcQnsrHT0VhuaKtCKuXCOiacU/8ICDf/kxf2/Bf5d4O/WP/+bbz3/Xyil/mNkoPo7wD/8tT9NCe/arxPoLMZQFsXVxSU7Ag+niTkVdn1HDJGbzsoxRqgVz6IMZbhopVDezV4WjjW87BuJbwO+eBjJJtG5hsFo3h5nGiVds20Eww5RRBVP55mhy9zeR1LdMRXikXFaFsZpoXOGmGJVAmr2vWXJgYAkN7XGMoUARXHR94TxLJh3zviQOE+BfnBcNS2XWnMfEjddw1Un/O6YRMwyxULvNK2B+xoVh1FbIDjInyv2SpHOT1LepRvPFKaYeT95fqtveTmIGOJu8oKTr0dXJVYFKYKPQeADLR3ihWt4PC8cDfxgf0GFJ+tnIMUn5Mz7+0dKLrxoWy60pjHyg7VRnH3i58epBkkneqdxRUaVTikunKOzhnMUn/VYM3aL0cSoKp4p8E8GZh+4C0K7y/VzbrTGWsvOWr45jyJIcpbWaHpr+WjXEZawDYqXmOqmJjvbFMXRs0A1EVN1jcmiDaspmZGbThg9zyEK6wDRoDGmxgcqxcMc2A8DMQamJeKMqZzlwr4VquC7s3j051z4wUeXpCRrffUdsVo6v0ipWD2V9QIqC9xnlMxSeieEfwkgV/gsVOFSTzhaKRYfUaULkJdFAAAgAElEQVQmYhmFU4pTDNWbXfEQPKXAcQmQM8P6+cxB4BcFL3bPTo1bcEuRIj4n6dgvGkvKwvbJWmDSWO15i2JT2E4h8GLX83Ca2DnJU3bW8M1xYpw8v/+d10zjiK6nfKufPWKWmBlDYsniB796FrHOJLKopMWJc20BZPYxVAvudU60Ag+rFe+NE2piqcJFlKZpHFMuHIwjODkdrWjOq7bBFKrvD5vvv0+Fzmg6rUkZDsbwct/yOHoMcK6GfedJ2FavDh2gNobM7bgw/XJQZHv841Ah/0vgXwJeKqW+Av4jpKj/A6XU3wR+BvxbAKWUP1RK/QPgjxC9x7/365gy62Pl665FOubMkuAQArl13Bw0PgTZVVst0+q8sguQI4vRNM4QFs+cMj94cUGr2LzFdZEb7699esWXTxMxZx5TxifY9ZYQPJMPHIae3vTifWFl8c0hsO8GWhKucTzNnnGRjuxy19BoQ+vEDTBpCXM4zROdMjhtOOeAVRZVMq/2B948POGLqA9tI3OD7x46Jp/4rU+v+erDrRy3U2GOiQjsG/Fez6Vw0TaM6zRePw8oK2cCqqou1WuaiwyU1iPy/eR5mj1XfcuhtZxCrPAKaCXePqUeeTWKfdtyWjxFiZd9LJlXfUfMclpSdfEWpPAel8zJB0qRJKnXfctL19A2wiV7M07Vf10Sco6z4tBJsHRQhbFkOqU5OMuhkQCFmApPFSddxU5rJ73euHvn+OGrnXiTs/JMCt8/9LXrLYBASFHBF+MsMFIu9EZoZ6oK5vQatJ1rEdCy8bTGMKWEL4XXu14CI+ox3lY9Rq6+JZOXOEC9rlXkZJG957PDjj99eMQaw/UwMAVPymkzrvM5E0Lm4ewhSpanqlXK6hrpVwqnnKrnCpXNIxtbLEKta1uDsQqvEmNIMneKeaNkLmMg11nHzSAK0YIotXfGMCXZ5NoaVziFhI+Jy76lsYbZJxKZ0QdeHQb2jYjcVv54QjEn8X2JOTMuEpHXOTH8iiZXywvp+FuteZwDP5uPfH4xyDWtY47Waj7+9DWfvrrmJ98kwjxDlSAWhEL6YRbqtEoS5kNZJG+0Xjtt6oluVY7ybLW7du7r9KVktTVLHw09Td0JlBKIGBJNazdR0cFarhvHQwjctA290kw+ktXzz8klUyKcUgZnhVGkFZ8PA72x3I8zF1pxUpHHEHn0gdtp4aNDT2M1tycxOvz0+uJX1tRfW9xLKf/OX/Clf+Uv+Pd/B/g7v+7n/uL3wFQj10JMWG0YTMvLQ4tRhSYsYHUNbZYbdKVnVbBNfFQUfP145tWu4aP9QGekkKxH2kYJxhpL4ftXO35+nDmFwsVukMDZAs5ZckoMneE4Cv86FmG4XO4Sn796gfdeMDeteLnvmWMiCR2DkhOPZ89uP0iX11tSkY7YprL5z78+7LgbR5KVrkUVGNFMfuHpwz0+S4d98oGH0fPRRV8Lh3Rkl1pzbwWrXKGGVTJfPweW9OxVoepQbe1SlpT48unM4Cw7Z7loLE8hbhvrKvnedRZdKZSd09yeF7RW7FqJDzyPgbY1m31Eroz+4+JFEFQ3mDfjzHsd+ORioEWcG4UAJUUwlcLjGBhtYnCGxhkmMiplWi3y/8EZklacx4X1rebK+UeBNvCdw8BN2zD7KPTPWsCUWqlzUvL3veUP788ymK3Qy5NfT3+axgoLpzGKBcH8lVL01rEkUa+2zrF3ls4YAnnr3LUSr5/jEjn5tCllLTKYPTjLb9/sSUrT2gZbDAdnKTlyzolch8etsQzO4EMUy2GlcEpjraG3gr9aZAhZKKQlCc20FIrWqJqLepzEJlob8USJiaqn0BuMZaxi11j+6qdXnOdIVoXTnHAo3o4zJsB3Lgd0KZznwBwij7Ond1bCNuop5fY842Nm7yyqzo9KkdOO0YpYo/xykdlOaw3OGnrnmGJCZZmzzDHz0a4VLylEe5BS5mXf8O7xidvHI0NjJIi7zhLmXHg3LtV0S04hJUVKyew7x1KEwZVh04psRbxIJ78q3FdqQFGrUtoKVLXSdSu+n1NmPi+yvoziUjsaa9h7T6sVD8eZGmG8WaToitsUhOJYlDi79kbgsODssyLarsPxzE/vT9ss/2rf8zSGX1lXfyMUqikXllC46TrQhRa47Fp0kazDU0xcWoPekmbk4uT61xWD9ClxDpGb3NBZ6TJizqwZ90ZrsRQtyA2nBP+MMYuPu9Ys3mMbI6nvWURHvWnom5bD0FK8pykZu+8ZR/hwPOGcZYkeUmKKEa8S8fEoQ0ojUE1OhUji/fFM37WAWKHezxPKaFROvJ8mAom0eA77gccQt5vlsHiGod0m7wr4qG14S5GboqweKlSvkvSLxZ5nsHmFFx7mwJ/dPfG9y4GbTqLUfn6ayFm4wxc7hyaLp4+CoTU8THJMv2wabqcZozQeEWuZRmYHD7MMtKWrlmFTzgiNNGX61nHoHMfZ1yKttsGTT6JSdRWfbawiucKsYNANT0vcbGy3U2ld8KV2i1MthpLHutpbrJscKFU71iiHShHCqI2fLoNJUd5aVQVfReyG5xpgYZTiqmlojd5mDrZCKysn/7RIx+tc5XsD4xL5aN/TWcu7aaFVlqGxxBKr/znMPtNaEb5ddS3TNBPKGgQNg9JcObs1Na0V64Fks/SwaVXhZuaUievZWUnBMo0Vy+hcWLz83JLhxb5FlczHFy1LLhQduB4a9nNLDJm+MlsuWseH88zDuHCfPY1RDMYwuAaVFY/TwhQCn1zsKMAYs8QEVjVYWoeSSFhFSIlD12xzpw91RtFYwdHXZayVppTMRWtJud73SPP2xd2JoOrpcTWoahwGLdYb1rB4mTuodVCxTmZqMc+lsPhE2xjW1CNVYImFvRWMXNYzVclKPR1nDm2DM+J3s9OGneuJOTEtkeIlUjJp6JyttaiedusJb65cfqsU+8ZxtwQ6J8HlMWc+v76kdZZzCHx4PNM3Ftf86vL9G1HcKfCq28sR3Ududi223sDOKJwWoU42Fqx0nVWBLG6QPPN+v3O54/OrHZR1yixWq5sRlJbu54vbE/fHCWOF/z75yKHvCTHyME5cth2Da7eIOPEAV7RDy7yIte/VxZ6+7zmez3zz+ETUheuLHcfbO1Is7PcNoFBRUYrhGGYJm6jZkUEL536qMubj7DFG4UOhX8RbBgqt0zwsgcvO0a7mRxQZIkbDFNJG3fI5b9dko4Y8N7fbY2UAPHkpQFolGqWqrbJi3ze4b20EAKjCTd8RQ6A3imUWf29y4XLX0EYLVjMuaeNsbwNWhN530TeyMV31DKPjzeN54wnXHqpS+QQT9hFiKPSd46tlFNGJVPJ6c7HBMzkV/vj2CacUv3W147prno/0dWRf4XJigU+6Bh+jFO86qFpFWo2Rglwogg/Xb5yWIAwKJJpv7fBULfArkVIBZ5+wVtUNV17rmCLTU2JoHBFL0Z7b08R11/DicMD7zEkHQpwJPpJ3ls8++pj74xPHaWJOib4x4lWfs8TllRopWDf4dfO3StgYWNnA1n/rtMEpYai9yxNPVXn5zXHi/egxWlKALqzjZ++OvL7sODTttpdaJelHEYH3pjpsDblw1XfiQxMy3zye0cbwg+uBycOY5cS7Cnpyoc7N4LxEOldhuZxqsRTPH7EDkM88Z+gay1SDPXIRtbK1htPi0UbTOE2nNMqKVbCuW4nVqrJhFM8g/EpXlNcUUsGtzUKFM6+7hpddB7l8i5Ir91gqmXfjwq2P/MHrS1pg8REQfczLXcsd4hiZUuGYI/vOQpLhrCheFc5Z5pg5h1gZTZnGGD672PFhmjn7wEeXe27sQAwFqyzzX4bOXQEPx5GhldT0KSV6KwyJNRTCOUPfSA/+sHjmOunPSnA2leQY86IVqKJRkjq00sGcXhkpQs36MC7s+xajNW/PEzkX7k4TxgBKKFPXjWX0kcY5csxM84zOgVAsv/P9z5nGM9+92PF//vgrXuwuxCzoOApG6yS5KQUxN5pC3DYhq4Q5MabAhTHsnCUbzRgk/zSS5bhGjQVUIlUXM6Q1Nrz+t4jlafgWD3yNt8vIhP4XqjorA0CeDCnzzWnis12P0fBy6HhchJPvere5AZbaHscQMGiiUlzsOx7GBdtozktkDonidP39sNF3Chzaho8u+4154JTm5b7BasHkY4Lj7GvhLpW1ItbBLy4G7mZPrJvAZj9QKX1KPRf4VAoLhT+6O9Ibw6d7oSYKJ/tZ8RuLFLzv7/t6E4hPTylF7G3Xg44SM7G1YGqrhYtcoQ1tIZrV/Ok5eTVkSe9BSVc2hcqQ0JYG+PLxjDGayXuskWzPb+4fuO52HBrB4G+nwP35iNZi4NU3jvmcaayjbx3L4rfCnnLe9BNrudJbEXu+zyScPKNSQhW46p2cUmOi3wk/fAmJJQamGDFKU7Ji1zis1oQQheECfLof2DnLu+NUs2wzT9NCa4XZETNYnTFK5jJXQ8vkU6X91VdZ13MCzhVKA8WuDoHXrl2G3poHP2Oswal1c5Bh9atdxxgTPidCXmmu1IAUWcNi/VFACeNGa1UhSPn9pp7kQ8yyIVaoadc6Vm7jqr1IIiDhw+x5N3sihXNO/N7NBfu+Y/aeEDO9cxw6WSuzkqCYh8lz2bf0nTCsnibP4/koH1BWLNXgbY6JnBMXXctI5A+/fsdlL8r7vnFM1frhL3r8RhR3AFUirVJc7VpJOae2ottRWo4yFMkxnapV5/3smVOi5MJ3a9p98hIU0bW2Wt6qzdkuI1P7l/uWnz1NvOg76fKT8GhzgevdADkzzgvaWErKvL7YMXSOu8eRm+sdn7244n/84iv6xvH66pIY71hCxNVeThXFYCynFJlLIOVC3xiUUdViFy47xxI9fdOglMZZS0yFGCLZStESz2zFq127YZur6ZHRmpdDy+28yIJUz1V8TZYR9srWGNevyV+d1Tij8QXejAs3fcPTHDj7WK93ZGhsVeTJYwzCYUYpuqaF80zXiJpymiNrrqMM9kSS3SjDq11Hqwyh8otL7Zxe7Tuudy0xwk9un5iqjet6KlsofH0eBXJifU/r8Pb55l+Pcto8Ky7nkvnJceTdtDBYCXS+aCzfudhxTJGHLJjyYfWTrJF6FrnRfan7kxKvj1wLRUbxOE0s/oGds3xyuaN38sk/TAtD67g7e7IWhspUi6ci0mstZl2+0DvLi65ljIVTZf+kFCgYpmTAKkIoPJwmroeW0xxwVvP2caLR0FvDGCK6XoTtgFU3dkl0EqfDkusmmBW5xt+pAted42kJKA29s7ROszjD4+hZQuaqF4vnshY0Jadf0TJkDs7gLiQofvSB+2nhGCOHxlGs4eG08MmuobOiDN23Vqh8Pm4zoFw33ZQSc0y86AUiXKmcIE6JH0YZJA4FusZRiriUKq04dA2XS+B+XAgxQdMyh4LZUkSquZzelsoGDa3smFLXlo9JRJHq+cRVigSDlHpCy0iG8JtxqaaF8OG8cFru+L0Xl3y874ij2Jl01nCz68SIrxR8SKAV744TOeetlsl1zd866RWOS+RpiVx2rjY0gVgUPiy09s91bX/u8RtR3JWCT/ctu67dKE05p+3IrXgeRiilKTlyf5rJRdKJipaC3RgtUXIIfu5jpm0kfWmMYqVrqvx4sJa9tXx4GkUh5jpe7FqmIDSmrrHMS0RbzccvLvj05oIv393z29/9mL/6+9/jH/7xFxjjuDstLGFiIHP2C/v9AFaha1rNYB1hibRO0ThJeIklc7XbYUpmMPDqsuX/vj2itdgShxAZa96c05oUEqZ1ciwt1TKgGiftneXzix0/un3cqvYKE9SJkCze2pWu19NUr+jGGkYfybrgR8/D7DeGyMOUOc2JV/sG5yRQOinwtTgcWouxlQ1QhP2Q1g2JOrTKmUNrOdiGx2URPFM/nz1yKTRFOO6/++KSnx/P3J7FM6PvGvl8152p/pFTdb9EbtY1gCOVwkrFLLUjKKVw9ImjF9Xl2xHuQuRwaIhkmqI3HLhk8EnyU3s0OyVUQxmoSpA5wLQEyIVzjowhcVwCgcKucaQligBMW47RV0y2bJtTjAIpagSu8T5hjeLuvBB84kSARnM7ntn1LVeHHWEKpJAxdSh+cXnJu/PMR7tCUw3hsloVqmt8XB1Wlyw6Bw2xqCosEh54ayWk2dbmxweJbRycobnsKAUGLadlXwehqohaWXDiRKeNmIQVCS5ZkiiNR+DxOJEp/M9ffeCzfc8PrsQCuLWGzhrupkCuEv01G7cxGuf0Bolbo3lcIrfTLCEhqO3zr350lJS5m6TJI2UG12CsgjkSVMG0ug4ihenkc4LMRoWU/ulZJyImac9QrtnmVQINFcQErbdmG9gLrVTYUf/HmzuWmwPfvdpxnj0+581o7iFEOYWeY71P5L2vymQxeSubXcPKdHusBITOOD7ayel6Wv4ywDJKEerZWt4MlSNbto5ziWlTbpnq7zBGmX6HlFnmZ9gjUShKhrE6KoJeU1okQOPsE+/OYjG7lFz5yp67cybEwOc3l8QEfddyeRi4OVzyeB65OuzQ1vEwKaYlcdFbgl8kYck4zuHIXEY+u74ipMQSIlP0WAy+CMNAFcXLiwsu+55xWbjqDO+nmXPJ6OCJMaGtYLw7Z/ntK6GCnX3icQrcDA1ocQTJRbI1XnUNXzduY6iYmuW4+siUojbFKbANq2IuqJQ5OMdl50gxc9Jqw55FQ5a5HwPOSJDzGrg9pYTyM4euYfSVD23Eg+bbalStFNetk0JW+epD7zZXwwYZImUKnYbv7ntCECO1Uwwbh37t4Mhle3/GiHqwM4ZTPQ1R5FSweYYrxEilolYZmEqmrerJWITyp5W4X349zYRS2DvLTesktKIW+YhiDpIvu6ZzUYt+KJmHcWFQmpwTXgvr6NtW1bp2qtQ1OscoXaZSDE7TupZI4RQXGmeZUiCcPDetQA4uQ6meQLvLHU/nR172jXiU12q42l4bJbzwVMQJlJJJXn6fj1k83teOtv45VdbQYdfUWDzYtTLnCVk0ADLLEn+aVArnFOmxm1/+55c7jj7yZl5Y7TinkPmz+zMfJs8///kLGmRe8tlVx+MkG+Qck2xE2qyANwWkaNc5UGsMrTG86FuG1nI3B948jWgl5IDJR4qTGDofElhNW1kpuQjF0hmLX9L2nNQf6lqQNbtrHIOxMldxjYRllCzxj1RfIGSN/NbFjp8dR6YV/60/96vjBIg1g0BUgkL0SnHhDA+LBNzn2phIspmQAlarkOd5gDR5RcFjiOyTnCweluVX1tXfiOKOonp2Ky4aI77Y3zpqKqQzL7ZSnIyic5Y5iiJsDhIt9xgiu9bI4kaOj0sp0vUozTkm5pD4+uFMY8VLJSe5uLEkGgTqePtwomscGRFzLCFwfXHB7dOZP/nZG3700ze8vDqQYiAUzb6z/MlPfs5uGHh5uSMrOE8LXed41R2458z7EEkoQpAbLKTE4zJxO4tKL2tQRrG3jtMYeNE7Lhu7WfEOreV2mehDYm907f4KWUn78vGuk0GzrCHC6lpXi9zabSklhT0XEUe12nDTOxogGc2nu46UC2+mZfOq8UkYFWOIFVuuHWyINFpz0XdkYAke0vNQsuTCJ/ueS+eq10bGRxiPiX1nuWlbGYrpZ766Ai77htslbJ16bXC2zWKF6UqBj4aWj/qOr88zb6v0G/gF3F9RPbelKRJ2VsqiTlaFpRT22fDkI75uXk9Bovp2OvLdw0CjJCTizeTJuWyGZht+XOcYoQ7wlvwcjOG08OdNEVXlaikg2b8FI+owjDG0jeUxLChTPeg1TClQjPDZe2X4/NUVLpz5v06Kp1y4sBpXu3ZNPYWUalI3+dqBSpqYKENr85MTGrFGyITKssrMPjE0MM6J5BxRyQagi6wrrdS3gj+kg5fBtdgU2MZwf+/r+nsmpjwugZ88nvnh5U4opj5xaC19a3l/XCjp2f0lUnicvcw1tKLRYoOxb4xYYCC06YdZ3l9WMpcrBRpj6awlxgBK7vGSC1mXNepAYL96zUqR9XLVtbzoW666Bl1UhUQLJaUaNG+qkCxXOKlw2Vh+63LPl6eJB//cSZ984I9vAy/6hj94cQECCNBZzXcOPYPz/PQ4Q4VjhK3znCfxbcBFW8O+d6BgDIn308JN17Jv3a8sq78Rxb0U6WTupoWzNxyqHL+rdqwA59nz9ePIvnW8HdcdS9wHfUjs+gaPyJUbLTIM8aQWLDrWyzZnCf2d5iA5qVo6nPVoaJVm13cU4PF85uu7zBQSU4i8u3vgOAUOux3DMPB4HLkYLH/287e8vrnmMHT8a//yP8ef/vhnfPnmlje3d5yXBWcsLw5X3B0faa3laZwk4q7kyu9/higOveP7h6HSPsUBzlpNoxQ/vN7z1eMoA571OJnlmLhzlrbOIkqW4evm6QKsmaVaCxNFI375vmR+9jRy3TiuO8dei2f74AxvpkXgLGtQRhP8mphau2Ik8EEFgQ2Syhgjs40lZvbO8aJteZg8d0sElUVhp8T46VXTySkrIbsxAmG8mxZ8TuzbhnMIxAo3rTehKpLa9YOLgc92PbnA9y92vOxbfn46c7+Ejfr4bTroOt2tbr7ij68U2sD7HBlzFEiqXrOYC/fRMz1EXncdSxLVqnj8CONo5UyXOnUNquDWi4Nsqq2V+MQ4B5rqiR4i37IGzlglniFzirzc95xiogT5bMccUale7xR5d/ueg9VMMdFExdWhkyKehEq6RKHkgojgUq5zF+RE7GrIwxoXuIrQUs5gFKc5oLN4xZSSWYLMudY1qpTYUOi6eTZGNoXWGMaSOcfErpIRSqEqZeVE+Cd3Z6YQ+Z2bfQ0SKTTW8fFFx91JTLSmnHl7mmqhFhjnpu/YNbrCIDJMj/W15CIRnJkagFJ0dWyEwXXs2pbbpwfZZFOm04Y5JQm+R9E4y6uh5UXXPMd9ZhmCJoRJs1lPa41SmpxiPa0oLltD6wxfHidul4WUtnMmi4/Ms8dUF02r5XtuuhanLV+fhAEVcqkDYlWtFBAtANA0RjYWwGlpCI6L52bX/8q6+htR3KGQdO0DUuJp9qhSaK0WBaCVonTykdvFY7V0nkYrJh/Zt45XfccUM/vG1mOm2hbien8n4HEMPJ1FBKO0ImtZuEYrLoeezjU8jZFPb/bsu45xXjgMDY+nM33b0nc9p3Eip0jbt7x9eOCT1x8Rw8Lvfe9jfv7NN5zOM3/ww9d88c0bjNY8LmcOw57G2jrUSjyeR4rO66xnM7Vasxc3f8Ui3ZhRGmvh5a6tsWXiJa61OGLaotk3lmlctgn0M36oqrpPbupcKaLUWUUshTezCDGuGifqQav54aHnm8lzXGXrPM9sS6kDOGOY50DrNCjDFCJFCxvhqm1EuFLEV75EQ2bGoXndtRJ4oMUWVoI4Mt/MniXLoXQMgc4YPKJIXecJKFEb97qGIStFjpmdNfze1YF348ydD4RcOG6YvawzpaDv7DpFo5RC0NLdOacZhMTMaYmEJHTaU0rEMpNj7bwV28lnDYle1acKTTEGioRVayW2wikJNz7k6k7552ZhBRnWNUbzO9/7jJ+8+cDpbvlWAMe6hgtf3j1wNQzsreb10PBwmjnNQbpbq+mcxhjLHNI2TJfCnrfsBGWEcriK8TqjOYeErgPKMQY6qxk9qMp2KTUzdEUgUnk2+xuc5RwzWSumKIN4rRSn6ryqCpIxkAs/fZq5XyJ/5eUF121DKoHOWawzPE0zb44S1q1UDdnuO66HhpgSRhvxWUqJ10PLGBNvj+Nm1YwSJknJEmmoNPgoWQ5i+QsvuhZrNdetk/hH5CQXQuIpyWbcOYtuJFgn5ue1srY1RclRUCkp+p02/OBix+Xs+PI0MsfEzlle9aJpyUXuIaWeyQmd0Xzn0PHlWaiORonVeOMkbvC4SOj6mixmtWySJDh0ltf73a+sqr8Rxb0gR1BP4eAc+7pgppB4Py7kkhlaK1PpImn35yVs39fX42QqzyZJxgB1N19SrOlAisEaroaWkxea16FvyTHROMdF62h15ub1pWC2Q8u0zMzLwn4Y8D5QSqFvHR/u7kgFbo8Tv/3ZJ8TcoYBGwe9+9yP+tz/6U1QRU6PWWh6PZ7rGknKidb1QtkrBFc1S0kZhG5N0C42WxSO0K4FCFBLOPVdRiDXCdilFXDM/7VummDiGiNaFJG3HtmmU2sm21mwqwJVzXIC3c+AcExeN4bp1aC04u188PgjMsq7MzooycfZirRxLptNWOvUQSCpzv3imGlTSKYt2GhMUh0Z8SUp55jrnXJhj5mnxgvVnGS6lLKwSo1c8XzrMl13LvnGiLCxVi1BhiVdDx8uuZcmZD7Pn/bQwRWl9+9YxTZE8Q7930vmlQgryxlIRjvu+sUxVoq+KvIZj9JuneC5rty5XL9ehW6PNRj88NBJScdE13D2NNYNUhEBGabHrrcUlxoIycLNvuf1wx646dK6FXSGd9urRfz+NfH8/cDoHKBln1Ob3b7VmqI6Cxzr8lqJODUtfTxu1E8+wbx1zEpOi3hkunXjyrPOVMWX2Tn5HrNYPkgfAhs9HLYyZ1aTMWU2bqw4DuR9VTSN6mAP/+9tHfv/FgU/2HTFnfn6e+dH9iAJuWsOLruW6F6ZNSIK7o6q9bxHtxCf7jrvzwhLTJkrMKbHrGt4+PXKcxarjqu8wdQO4aB2NYhtillIxdySb9egDN7uOwRoaq9G5ENMzg2+1/l03rVJnQqXAdSuOn9+cJ753sWNX+fmrL/4aLOKzwDuDs/zgauCnjyMBaGqjmeqaljB5+d6cCwcjr8mnRP41zi6/McW9IsSMWQqxRdFbU6EVxd05yAAqFVoXca6lrzmdqwGSUquzYSalap2bMio72kYxek8xir5tMdYwz5HGWm72A63O4iwJqDChmpZXh55xmng8TvS7wu+8esXj4rGNJYXIP/jN848AACAASURBVPrTL5hi4M9+/oFhsHz14Y6UxTr06w8P5JJZFklP6mqIQEyFxkqe5TyJw+O8RJEjF7idZq4bUYZaUcXUk4csHqPFGldRBUJl9eUp9Ebx2a7nRw9HoUoayElu4JjENrQ1hiWnDZsWxEPVAW3mKRbOKXEfEhfOcl7ihlOydilGvHCWlbYIG39cKU2rLQ/LjM8ZnzI/3O3IJfFmWrhsG66ahicfmWLik/2AyrKQ76bnm3RluqRSOPvAvnE0TobGn+0HPt33ZDKnUI/HdfheFGg0SkOnDN/ZD3y06/nmNDGmRKM1t6dZoBEv+ZwomeOYWqRCzWVbg6adNfhQ49J4PnJXP84tBrAgMnNToYNda7nsJLP3Q66+JTI4gAy9lQQm21jaIAM/oyxtiIwobtqWUwzb0I16OqAoXvQtKUnikKpsl5V8sG74gzVMPrFUzH2jASK4/BpeAYquwmmDtXw6dBglm5yzmstSTzpKfOTn8DxPEDhHAlJ8zoKRV0ghq0LfiAL3tEThb9f+QBXFKUS+fBrZOQ1e85OHE9rKZnvvA50zfGJ7IVpksZfwIZLLFmXPYAyfXu34yYcnfEgYXTBEuuLorOPQOPaNsHM0z7THIFT2jXWDEogmVjbH7Wkk9S27epJdm6ytd8/yvWhFTok1e7eUwmXjODSiZ6jbvtCasxTqmAslZQ6dk+YUww8v9rxfPE81H2Be8jYE1krUzZfG0hjF4+I5xUg//2VQqIIMLhQUpfFaKGKtVhtWfDU0QMMSM7fnmaGlYtcOqy1WZfZWDJzWQrjaoyYy50Vxd5pRSgylWtNweTFwaBR9TTwQSpMi58Tp6YnprOkUhMYSF8//+uMv6FzDv/ov/HXePzwy/2HAOM3vfvcz3ty946d392RbiPPqYqhpnKsxdprgAyjF7ANtY7k69OgEwUgogaod1b0P7LvmGYtHCo41ahvcSSScXDqtFSnJ4rq08v7WgTRI8W6toW+EqharDHwd3qz/VXUTiQVOIXIOEYskHKUizx8ax83Q8eE4SlBHWSdU4HMkGznWN1YoZ4WC03B1uIAhM55O3J8XnpL4l8yPmetux9nP3M2LiHGs2vCftavqtARi3E4LL9tGlI2ZTbugEOjOaPWch1oLSacVn1/sJKEpZVqksEwx4muyUSoKowQ+cmtqkILGaiyaMT7DO6U2Ehv0VS9h7xo6BTknVEw8HmcoGV9pmLa+J42icboyYjIziavDQBcLTesI80IXEz9UmoeLHV8ez1tRNkqhjNABjTVib4Gqubb1etRC5GOpEX5RfPELaFU2J0WQn5OM3HdGwcf7jtYI7muMbFqaVRQnJ9Gs4PHs+eRyED1AEpbLhdbkHFlyIeZUbXWFu91ZQ8qZkMs2DwPF+9lz+/U9vTXCWjJgraGkws8eR05L5J95dYWrrLjVkIzyLMS7ai2dk6CdEJNkCnctT+czOUaadjUgWZuZ59nBqkLWQKuVqFoLlJKJSQJ9tJF7oCCNFOo5VCVXsVqpCvaVsedqxlWp/MpcJJpPgtALh87ROEMJ4gfkTE38ipGHJVBy4bpxXDq72aBMKfJ+CRWhkAH/r3r85hT3lXBK4Tx7SJCspoTE4iOvL3eUnGmd5lAnx7moOmkWHHMtdutNvU6fUXCeEhqDtY4QEr3WXHcap58HQ+tmoFXl7IbM/eyhFrfTvHB7OvPjr9/x0c0lrjH8i3/9rxH9wp/+9BtMb/BzIvjI9dBRNGhtJM18aDA5Qogy5IoJnRVOO/q2xebMwziinea4eHLfCB20dtVh1SjVhSfgvdri45wVHnpIheu24XaREA5jFCVJHN4YolCyrGJNDV6Lk6oXamW6rMU/atj3QpO8tpadczRZNosQJewk52fFoY+RTKGxmj0GS+GL08hNLiQFWRWmnNAK2qZFFbgbz5yD33zpcxSWiDbSkb7sGm66hrvzxJIKsxX74u1Us7asSop6qYPoSokg5kKIcUvHej203JSGOQkMdL8EppwJpaBTlshBLfTRzlqmWZwd1y5/e9Qhm6sWB7pAaxVLliJMKTwd/bNapmLURmluDj1jTDz6gM3wZhr53ZtL4hIYcsHGzI+JKMQJdE2ystbwFILoMVrHjXas1gerotLX03pWiPajGJKXk5mpDCEqK8hWxeXs4xZCr7TaaJLrqSwraRimLLj69UWHc4YcJVhlbzUWyVB9O3lmBO83lQlSFPSNIS1pCzBvjEFXG+lzlDVRsvqFBKfHEPnR/RNWCUZ+2Vi2vIB6Q1jgOzcX3I4T4+yxGs5zwBrDXOFaB9Kd59UcTIamTku3vcYPogTuTBlCKCzeo4zi1X5A24IpNcy8gDa60lslSDvVho5cajC95K7mLINRoSJLYXfWsNTCLnCVvN+rrsEpjWrkFj0Hz+28yGBXPbPFSik8+b8ECtUCz1jmLEfFXae5Oy4YYNc1nFOkLYpD3+OT0LoGK9a/zllWH4i27RhTIOaE0cL6SCrXgZ9mXjwhR0IsvBouN0vbzfO57sY+Jb58OGOcwaRE17SS4G40f/TTL/FBaE9fv3/LN29u0VYzzQFVFBe7ljlkGq15cXAsMUhQN7UYOLVF13kynTIc+p724or35yeWlPiweD5qGuHIxggls6TaQRShQRotNqNGVU93CkuI3DSOUwwsMeOU/n+oe3Nf2/Isz+vzG/dwhnvffe/FnJGRlZVV6u5ykFqo+QuQcBAeFgZGW3hYeEjtMjhISCAhhIGwsbEwALUwWupuUU1nURUZw3vxpnvvmfbevxFj/fa5Ua3KSNQ4UaeGzHzx8t5z9tl7/db6ru/AR89H3p6n61Ky5t934q+QgxTL9YY7hMDeOD7uBt5PM+9j5HnfU13lEBYG54i1SqCHajgklc7LaPx4OPLt44nRGckGrVUET1lgqksMPKGy7f8p8Erx2WagN4Y355lDkO55iok/fn4jDpuyGb9ONFo3JkcpWG2ui3O9VovWCJi2f+lMT0YxTQLVCJW7kGLFaMfzvie6xNvTxNRSwH6MSRit0NZgUOwHx7Yz+GDgEsS0TrWOj3K18l0ZQYcl0llDqdLRfns4t8hIeJUXTqmgUuJXNzt+OF1AIelkyIP/polYbr3sRwRSEoFPytLdaqXY9pZdZ3l3XogtJFqKxJqRKv86tzjDNVx6/ZkVxTkXEuVaZAoS/l1TwRrTQm8qNVeed5bUlJcaWrGs5KrZeMHMhdfefG6QJex6Rpe2D1jvyPezWBJ//fqRTe/4049vmi+8FOlvTwvGGEIUS5JziBQMpeSnL0qJUldpeH2YBXv3jsk5lhB5c5x4Njg+2vdiMVAKc044LZDQcQ7sB4lI9EYOhpwL2ohOw2uN6TxhEfqldPlPPvKxLfa90XTOEloEotaKJSeWXPFO4E7VWUKuvLo/SpfervkKCopAUWwtfur1syjulEo6JbzTJOThusTKYI3crM1HpPcWauHFbsvjVEnhTK2FUjMhJ1Jqcu9SsEaRtNh+ojTnKZBTJipheehWJGmFCMVV+PN+mvn+NHPjPSkLtasoTTUKbQxvPjzww5t7coV//ttv2Y5elISpEGIiJpGJPx8Hts4wOsMmZ95OM1XRLEnrtTioxiFXpfJi3PA4TTycAzfaCneXhmm3G1637ko3X45SK6HF7Rkt0WR3nedDjYxWuivdit+1OP2oA10L6noTPcUVyfUpucUH1sonQ88HrXl1ObNzni+2OwDmnHg3Vea29NQojkFSs3Z9T82KVKNIzpW898NlIdXy195IRTpLVSXvlVp5dZoEW28sqCUXDiFw41xTqkrznrL4t6DWFJyK0oYcYxuR61//Xa3QxrICO21qaaP31jk6JSKpB2dYVHMmbFuxdcxOtdJ7Q++ks++8YXQ9lyVxnJJwmVt3nRVsho7vT5NAPkYxR5hrYjkJ80tpxTFJ51dy4fvTiT95cctfPZxQubREKcF/P4QkJl/XTls1tpW5Lj6Nkr3A823H68fpKjgCoWp6aznGyOitYNN6hSibIrlmUlNminO6fM5YKt4azpfAuO3bVCmwyyeD5z5mzkk69dRgUm81o9OoeTVqW4/bK8AlWQ0NdryqPxX0o2fbSd6rUprHZeGbx2ldSqGMQGiViq7CJth0PVOp5CWwbaHdN4Pj7Slyv2SeacOC4Wa/483hEbREDtrWIa9w2BQLhchN72XqtCJoy7lQtcZYQ1GVfuMJl0BprqbiCSWeNEYpNr27qlqVlnv5kgtYLX5HtVkcaMWXd3vup5n351lCWVgRib/OAvx9r59HcUdG8FTkyzdoRmMwTqhUqkrSijKGJRVSXLgZRl6HyiUlTBHrgTlnllLQRqGMZWnBBDkl8eDQcrcqa0lFMSvDu8OJu9FLl0chF7gkkXBP7QtCax7jQqyFFLN0gVrTWcHuUZU5BDrTMZUFb13DUzM+wegcg5Vl1bFGYsjSPVm5EUNJOK3xpnLrHHu75fXxzMMUcEZfvUMAavusqsEzuSpCyqSWzORaaMnndiCUwtY65pIYvYEsKTBpfRh4quFrcVv/oKEKYi7lO+68pEKVUtg7i1EbPiwLr6Yzz3yH01JM5pBw1uCMIqTCxllsqZzJ1KTIubIdejpryBlOMTQFpQDZq3GYUWIZcX68NBxTIJqus1SteMyyzN1bI7Q5/cRaEDaLFHiVUsNIBdpYMdf1fAvt8L6uSeuK8sgobxTX5KKSK8pWXPPSCUFUhqYWyOKtsxtd++83z3slYp2YC/ttx4c58Oo0ySJUfk1TWVfhgtdCbyy2mZEZrXDa8PayrGeOXKcGBxQNP8wBb3puvCEXKUS51KsCE6SJMFXiKYXCKcXVKEMqlccpsvX2iUGCTJdFraKo6+W5/mtBbIOLUiwxtaLLtTl45i27zvF2WlhiQitRltcq7qXCslFXkZXXhk/3Gx6mhcd5jX4UeEdrje8MrtlXvJ8XTkkOl9i+P6s1SeWGy0vc4KFOvEsZq4S3r5p+prOWrnP88Him6xxdI28EhG0n++N1zyC14HGpbLy/2jUM3orXesxkK0vPTMUNlhoLyxzX3Tlea9mjqaYzUVrMxrLkJ6sGe4VcmuGbfMejs4Te8TCHBsesXXwl/IHq/rMp7kZpOqP5bDtwf5mvG//eWOaS0cry5jCx85ZLLjhruR1Gxk48XVSFoTPEnJhTIdREbx21GJyubLzFW8XQ/LcTmlhhSpU3l4BuIQglVw5zZOg7LA6rBTd7dTwwT7LoMFrT9xJOXJIUTN9CLUotWOOZYyDlzI0fUUpGXOECJ4xRjF1HKFn8pRWEFBCD38rHQ8/Hu55zLLw+J1726kl40yLkjFaEqK4eGCuX3VmD1RoH/NHNjnOM6FSpVUbnWRVOIZN+9MAq9cS91usisu0fTFHcdR5XRRG4Li43xuD7nm8uZw4hCHsmF1CVoqrQPLWixMiHRQIJdlZTtMUVGU9R4o3ijGndnTyU5oqnPi18BW/WuF5yS0suPBA5xMjnXX8NMimt6GkFSYnS0VxDVCUTQCGFqWaak+DT9LAWf0NzVURxTpEpJkChC6BFgp+rUPIMGlsLl7lwWSLWKqHVOkvnjJhxJbnuAoXINRYzO/l93ugrNu2M5sWm5915ZuwsRhtOMTJ4J7AK9Tp1UmRyuF8io5PiOUUJd/BW7CKyqi2WsPJs8ByXxJIKuQVjXJIs69clfEURapHr0jpM8VJfl5HtciITxDBIVrFqB8j6vWltGIxm9J6vH0/XdK93lxkFbL29LupN1Xxxu+Wu6/DWcgiiRckZFCJOXFTmPlTq4cKLruPOGzZG8/Y8EbJMr+KfV9nvNhzmhyYgUlSteXOescbxciuskxQDsUT6arnEwHbTc5gXPIrno6ezInaaciHzlGD2fPSYqnBGkYwEgcMa5A5UjfUGmzIpyeEwtIYgJtl7oKRrz22NE9dovnbvOq2xzYfn+dDjrOXt6bJ+5U/CmJ94/SyKu1GK39zuGNoJ1lvNhymwtMXEeYm4UZLVY1UMxhJjZlkCqS0wemPpx4Hj+cRt3zHnwrvDkXGQGLS7ceSSIljLJUQyhWWuaGdZUmJwsoCz3tLPhl4Z3h6P3O02PN9tuYSZUsWnQ9O27EVoaJ/f7bnExLQEOudJMXJO0nW/uUy8HAf23rNxjt4Y3k4TcypsXE8xlfMyr30z55j4Xb7w1X7kkhKPU6DUjq2peC34YimVz3Y9d6OXjtaIIm9dMgm8rBiNpjOeUxTxkguaUgO3nbpmqc4p8yNk8trSqub5ctd3Mug2oLVQG0tFutpfDOKdb60Up28vFw5LACsLs7kKTPZyM3KMkVIiH/WDeOiUpk5Vkn2bi1jJ1gJZtdCL1qlYK26KyxyoiJ2EbrDWh5x4pqwIalrXvUJDpRaK0dSq0EUoiiBuiWjRBKw45gopoBQvNwN3Y0euIoQSVac8iSEJ42iFeUbvKS3qrtZKXGBeMtveCU6rFBut2XS0PpDGeJBgEq8lP1iKhCzUbzvPL59teFhk6nRatSCNnjkV3p8X+RwKOi2Y9g/nwBe7nrxOugg2X1IhAPveie21VpxC4hAyuQBWkTOkdtBJVGLrXtshsu5fZBfTyAtlJTGI0vdSxG+llIJGLLfFkK3y0djx6jTx5jTJgtMYwfgbLPbJOPLpOJCrsET++O6W99PMh/MMbSdWC2y946NRHEYrsLEat9vw7jJzCBGjwWC5fzxy0xlSFRjrZujRSoswcVl4e5pwRnE39jxG6bBdMoyu41wr83GmoigKtk58120jq19ixtsqNMgGoeRmnaC0sGJiLPjRsjMdJRRUowVTZbDRWpFSvUYh5gIlZzonEE9F7CEAjiHx5nHiee+Za+acc6P8/vTrZ1HcO2vYePmySqnN7zzzECV0Gq2524wcTxfOS8B0jksQF7iNEzZpopKXCa8dkUKMgcFazpcJM/bkkjkt0k0vMRFTYew7YlgotTL0He9OJ3JR6JrZ2pHtxpNK5d3jmfvzTNGQo7AFBJMTWtb7wxFtLVoj3Wpn8GgZsQq8myaMUmLWpMQA6bhEnm9GOud4q+C4LK0wyTLru/MkZUDJCP1+KXhDE5pUXp1mtt42da2+CmvWUbwh0Rhga811W6+UBCk7NDfe0hnFKZbGAHiavb1T3LqOjdFtb8G6lGCNJFsx0QKEXDmGhZQKtmru7ICm8s3hRCnww/GMUhVrDcclsLeWmGXcrrmSlLANniiQLX4QEUxpb5iKJBWtwMG6sDqmSMyZT/oO0x56Ee6sB0Q7sVqbXNoDdm7hCCsMo+QDSTHoXPvODL++3XJYIlMWDPkYEkpJF+qNwEJJyyFpEchMVZhDJuaZzhme9ZJPkCrc9k6W9qXwuAiPnYpAW7Vw0/cAIjlXCYX42lsn1FOrhaGT4hPF8ByTdKiqsjGGTusGu8gllZB1w2AsyuimwjT8MC1snWNuIRVpBZlVu0hq9V6Rq94ISO3QXRNpG7xkLadScVXRtUCa2miRpyXyOMdG0hLWinFashe05tcvn2GquMGmlNlZy83tvjkiLuw6z8txFRbJAnsVrxmlGJ3lGGITZ1WcFa+g0XcMzuGNolK4zBemJQofvhVmt14fnbjpBx7PE8cYUcaitCGVxG1n8Vasq5eUUEoxp8xu8MQsExBaUt6Mls8IUJXCeMvSRJdGi//QaA03WtFny+McmEsStlOuFNPu11o4hcTrgzSDaSrcdp5Oy73ztwJzl6ZgpSIKxVEp8YFwSuMbo2DOmb53hJjFY9paVBUhErUyFUXKmSlGfEu9V82T4RAW5hRRFI7TIrgaQnGKpaAppFTx3nLTdTiVUbmQawTjiamSaxZmgDVYxMiolEAsCl8r3oubXqoZY4Tytha+99PCJxvpGjtr6ZWmc563jwd+9eKGH84yNgIthLowdIKHhhKxxlKV4cXWkHIm5yzWA1pfee1lFSZRqUZhjWnjvzTk2RrqOPBhWYSKCbzwPVYHDkuS7gFa7FplNOoaCIyS9/X9NGEzPB97nGqePBXeTpMshRXs+w3GaI4hYY1jLkHi9bzkjV5S5jGK6ZuuYk9wTbhpENNorSwqreDAjyk22muLLaS2haGm5MzjIgvpT8aBSsGalUHDFdOVa7POs5VDjFcbX7nuFXLls5vNlYlVGtb/zDv21RBLZe4Lx5B4Ny8MxhOzKHKdEcfGXColS3yjKmKwlnMRQVM7iDur6KrGoGSiURBLpjNyYM8lcz8HLi1kvCDQiVLgMeix4zIJi+ScRSgnlrsVSmbsBVQyjR9utDRMOkkzte0cn7SDeeOMJH0523YeKzPj6UyHH09Sq9++atROAM27RZxWn3mPrQJtqVoIJfMXD+ercG7NEL2ktiZUivfTmZfbLWXJT3YUufDVzY7vteb54Nk7+yQmaodMRnGO7eDItWkIDGPXUapg3YXMlCHHIA1Ju4+qEosLjYR9dFbzYY7kIhGMfbPrSBjul0RfKi8bhFeKfN+Dd2gFFwVTiFeig1H6ar1cVMV4abDmKPF5Rim8d2xQGCqvT4naJrh5EjuPuVS+e5hkJ6WkjrybA1/dDBza+/6p18+iuMtLfKaV1lALc4g86zynRYQ/3304MA5exixjyUpzt+2Zq3hkb73leDpTa+USAqW2AOdJvtBx9KgKU8u1LBUep4nTsrDf9dzZnrvdhhAC5ylAhU0vLIKHy8yzXd8wwIRSWlKWAGMsK40l5gTKclimq6Nibth1ypHTMWESfPV8z5++2PNumqka/uLtIzdjT6ctqWbBrgGq8LbPIWCJGG0xRrEfNpgqnyPV0rIpBWs9x0hnDPtObFivFLkkHb9D0WnDWVfmmlnSgleam+ameVzEb3zf9RI80paTDWomI3S8og13nQT8nnJ8orFlOC4zH04J7ywxiruj8xrnNXORpXduS1PXvu9CW14p1byDZMF2CUEWTkCzXr/SHlOtxJhIiyzdPh07Bm+oynFaAnbtxsuTwKTtqqi1sqzcap4WhZ2R9HraPRIo6GZdm9vBs7WWnXV4NMdUxHu/fdeXnJ8YWAaBLqrkqV5CwipN10mqmNdaXA694XFJZOBZL7a/IRemZiiVS8ElfV2wTiWjKNzsOn73/khF1KSrkjEWOXz2DdNWiEXACtEqJbTHEDM33hBzZe8tH04LVit2vWswjHypUuDrtUsX0Y+Y2WkU55zFyVJLSP1DCtTq2GsxN3s/xydF9Hq8Fq4FekmZP399z+Nu4mbouOkHVDNYK6XyR3d7cspXbxgFrTkDamVwhpCFQGG1Rhd44S2qVM4pcTzNeC9xm+vBvv4cpxQvtwNawavTxP0lsu06RidxkkZnNvtbtIb70wP3i+fGe84h8dWLrTQlBcbON9voxNV2GVFOr9F+ympMdVcbZqWFezR4x/Oh8hjC9XPlLJ+rs5IDTXvrpcJ3p5kXneT3/tTrZ1TcpSNr0widtWz7nsF6TjmSQ8JrB1TeHo4oZOzsup4pRD57ccP7aSanxK53WCMmqEuIEv91nmXp2ksnWHMRoYaqnKeF0hwPz3OUDljDGBxj1/Fs01NzYEqJcyliirUdeH+6cJxm7na94G0KprBwtXxYF1SNtlaBoiudEkfCm94wdAOhKowfqdURCByWSQyPsrAAKgIXVFXo+w2zGL1gveMyibulVYrHGK7c1482HZ+OUqBLrSxV3DDfTgvvpwWtxUZYKRnxVqLb1llO50iqC2nsqErLgi5Kp+J1x6Izicq7aea0BJwTxzvfrIjRFa0rc4kop3AooirkkgixPGG4VKY2VvvGexfvY1kAbnzXzJIMBc0R2XsY1R5QFCllvFY86zucUuhccF6TrGk0yxamUMS3x2ihCl5SYclF6HFKcQwRBbwcO7o2rWitqFomSQF55LfKlCT+7ZdaqFY1ncU6hbaFIkoOpgKq/CgUek7MQTD5TScGWzdDx904EEvi3WWWEJJmy7By+4ckkMR5EhHTTV/5ZDdyChLcUHJl6MTVcCmF+5DYWJnuVg9/YzQhV6aYZdQvIkRzrTC/Pc54oxk6S64txKidClc9CEqgOhSnUnhMiaxrCyCRZfqHGjDW8+1p4mFuYpu2MHz6eU+e6qVW3p0Do5PvzWpD70TVnWK6QmYr3LZaRK+n8rbROA+tOfvL949sh4EcMy93GzZDx4fTiZxbClrnGKyRiLtp4RwTv7jZ8DhHLkHS0XKBTT/wJ199xdfffkNnxH9nkwsv9+LIWIrApit0lnIhLAnrDaUtLeQzV/KSCEuiakW0LfWrub7u+04YM7lQKHSdZTSGL283vDpMV7YMCuZU+CZNlPlvA8+dht813uthFoN+VKBU2FrL7X7gUBW9t9xPF+YQCTnwst/zR7/4BafHt+i2DAql4o0sqDorzJFkhCaoNOQ2+qlVzo/iHDOllitmp1FYJ2O2ioGAjINaib1uyBlq5WUzGDqGyClFvHXsO8tjiMR/hT9WG0aif/Sw9Fo8YYqH2FnIma4Iv3/J+bq4KgWUVxzDRKcNz7c7jtPSLEQt9/PE8+c36HnmeJ747jxzDOJF7pwlAb97PPPYAp5VqZxKpPOy4DPNwdAbw3YrWLz4lUgn3Y+eJcOAosSOyxzE0M07EQ21eLtnXnHIFawhpSxy8M4iklMlhmapNKihddFaupxn44h3mu/eH1BWsMmt69gMHdXC6bA0u105HUoQWupX+y0vx64pUMVmYnDCGkpALGL49nAJHOaIt4aspXu9Hb0wnJrZ1mfbjk+3Hb4tiIsyvD0tXEIAxJ2vtvvEGLGWRsnCOZV6tXSlpeusNrRFtR1AkQKXc+HxsjAtkZttz92mZ2ihG1VBrw3RFXF2bEKo+7MwP1bWUkyFr56NfAiad1R2VmCL3mm0trw/L+yUwWqBgSoQUuUwC/20lBW2yWyMYfSWKWaOc5S84nafqvZ80qAWimKqhVMREVVVa4SlNDirBfIlFzpnqbMIe9bYqHDPiwAAIABJREFUPIW6Bpevh6KkRsEPp4XeObZ9z7IEMau7Qmu0xqDZGtRVbSoTqm3sq4e2xL0sM3ddT9aKw0Vykqnw+W5gdIaHaWEOCbzl893IX50vJF3RudJ7R8riE/Xnf/EvuVwCX9zt2BoYvMUZTSq5UU5FFEYVvcOAqE9DliKvFcRQCM1WgAKxVnLJ7IeOq7dTKfRG43oJs1laA/Js9NcglWs5l937T75+NsUdZGv+5rLwYY5UDZ2CHbBUxTfnCW8sl3kCKp/tN+QqmZMv3Mzr85mcE53zxCJ+1n2zBqaNR5tGRSytGGklZj4FUEahisJ7Lb4v48h+HElL4HC+EHJmv+kxs2ZJFaczMr1Xpip8e2cMn297Xm56/vzDkQ/TzOqnvi71MpV/dv/ILzaDGIQ12EAvMx9vB95fApeo+LAIJu2Mlhg1KjWJZ3pShXR6JMbExnmcsWg0p8OZx/NMNjL6vq+R8/2RL/cbXp8uvD8HqbG6+cUUmOZMUZU/fXHDqA1LkAVfLU+uh28PF7SREJVnY0enKto7phhlAWwzoxEo5E0VimAMGeM03psr3a/SWALXr1zYHlbJIdoZg49ZKGa5CszVWd5OR05F+OpWC5MjLZkQEn/8bMdn2xGoqEbjeQgBhceZwrePE4c5Yq1GI9TAWGhpV5nHtuiqtbJxmueDY3TCpbZGY43hZtxLkPEc+eHxzByFwfJ82zPmyCVn8eRWVTr9IpNZXZEZMQNn33tGb2UBOgWWkCgZOmXptZH82FoZtMBX3mjJHqVxrqt0/1/sd9x1XpbKsfCi65mWTMgZ3wzQpphZsiiBVa1MQRZ+OVdiEqWtb2O9MZo5i+HVUrJQcqvw7wtPzqQaOeinkDnnSLHNY6U+qShLrbhGCjjmyKaz7IMjNFXqqorNScqUXrvbNiEUFN+fA8MU+Ww7XFk/jazbjoZV/Sl029x2EpcYV5suut6wtZZFwabznI5nlhixbZn8w3HiYY58th+4HTo+hMAxJtCKGArH9uweLhMxZZ5vhLG26Zx437TvmLYLWfddCmFx9U5R5kgOEtsYlpZbq54+A1XCyKkSvZlrAaOvGRZLLvTGQK38G5/ccEqZv7g/cV4SOZSrRcTve/0sirsC5ph5t0Rqy1C0zrHRgku+PRwJVI6LMA+8ESMng2LjK8fHRy6zeKlclsDYOx6nmeMSKcipOjpLpnXAqqnPWleQU7nSx8bBse07brcDl3mhTBO9BhAfCbHqLRxTwmrDnCIuF7663TJ6y77z5Fr5bDdyP82NQigP9+oBQvmRUAJQqiXALxN7o0leHobHSdgnDa1CjI7ae1jEAvUYF3yKOGVxxrEbDZe0cF4WOuc5h8j//f4RZz2DdpKVWkXtuR48L4celQqxccCd1teHWnDq9ljZCkY8Lcauw/fCb3YhcVkC1klIh9GabiMY/rwksm0dXxY6m9HCwLGGJtJS4veuDQmFUQbnNZ4m0GpJPwl5qPe+48M88WLo+eXNTiIEU0JrwyUnHpZEyJVeGx6WIB16P+KMZUmV/eBlKnOyp5hjRlP5cjdyO4igpTactNSKypJSdDdYtn7PaYn8b799w5fPLC+GQXYVIfFqmnkMMm1SBRrRSjGoek0Qol3f3eBxxrDznmebnpITKQsT5pn3TCXzYQnc9mJrHKr4mmyN5kUvgrLUgllykqZEJbgsCUXmfo54q3mISaiSrZjGJKHSvTMNi1eELFL2Ugufbga+OVywZ8XLsX9KhCqVY0h4Y4mL6EI0kJsqt9JMzRrk5pRgxYcYud04bJVO9N1FslB/rIK16kmwpbXi/nLhoBTbTgLEaffp+vdTXcv8U5xiymIRvqqwU64camSJM1ODdkq7n05LZDd4Nt5x0wr718czFVG0FyW+VM+2G87TRKLQO8vgnXDT5S9SkfesWjstGRttR2E0NxvDw2WRzOEGSa21Z6XeLkviFETRnut64CpykQnINwM32rTiVgFjLhhvfrKu/iyKeyyFqB1d7zgtF571g/xZTsxZUbTh5e2e79+/IyMMi9Yw8OFw4TQtTCXTW81UCg+z+IQYozAYCS6uIq5ZrfLXBVppfOU1JN0bSyrwu9fvOIfIaG1jyFimmMST20BQkEtm1zm+3I3smo/zlETYsnVyadeORFHpjOXFxtOjcdTr0ojruxIMb2c0/abnm1p5d5nlQahiqKSVTGMZrgEFqhaMhTkIHe75sKHTkqJTSsIqw2gtMQtEkkulmkznDHPIPPOebu3A2gO6+oZT4dPdhsclkGrh/RpEMChCSmxt3zxDBCvN7QFdu2HnxMO+1oqxml6LFbGRO1wwdAO1Zs7zmXkpfHG75RAioRXV/TCypMChiJBooy2L0fzqdkdnLYpKRvPDNPNXH45SkLVE23XGU2pka5V06bby4XRm04lfkesslMrWW768HemaAVtpOGqtUkRLrVhr6ZwTzLaXbEwAXRV7Z/F65IO1vLnMzI3BgoZNO/R1O8Rrc2a83Y388nbL6bIwpzaltUL6zFtOUaZPg7gMQjt4G3Wytqm0lIIBatujnBfZH3ROcymFqVRcLnS6cfy1xreONxWYiyh1Dynzsnd8tO15c56Zl8Qn+wHvDIclcr9EtE6MWjEq6KoiJ5hd61pbYdetwNmmRj3njFOVu8FL6M4SObTAZyna4DuPt5pjWK65DH/5cCTvCi/G7hqqjpbc4FVgtnb0S85cUua2k51aiLkxVxS5KJ7v93z/8MASJcD7N7u9COey2AN4bdgjS/rZS8E2yL6na/uvKSQeloAxis/2m8b2kmlDQlW4mhDmUgWaaUgB0Lz52/+olY1UuZ9nOmt5vh3IWQRoKVexYdYwWEOqlR9OM+clUXPBelHZ/tTrZ1HcS+tyrPYMxrE1ijnBQ0zcT4GQMvOy4LxQ+0KRy5mpHAPUKOquzjlyLTye41VFt3EG7TQhZjSKOaSre15uAdtdb69/drzMaCOe4N5KWO+28xyXTMxRaFpaOpzPtgNey0iWSiW0sGMFTMvSAqPFWMkbTU4ZX2Fwmv/n/sRXe+ivh0BbuNbaVI/wi+1IZw1vzzNVN5y54bpWi192KVzdFj9/tmUJkep7qoIlBQZ6dFUcTrNQSnMmVDkWSq0cl8hvY+TjYeDjscMjboK1basK0BnNi7HjYQmNptjRG8sUApdlke7bKJY5MfTiUR6rsHNqfXKy0xWcaQCNevLGSAUohRc3e451oqTMYBwo4XLnGDjWKNcAxXlesEoUsedFhCuXlPj64URvHLuhlwNQa6Ya8MC7OXJJQlVTCmIIKK24Q/Orl8/4aOvpvBPDTFUgV1LOiHCwiVRUIarMeUl8cjs2DFeW76oqOqP5uO950fe8mibenmcpHrSQjZUzXiWs4f5wxFD4qO8ozW9cVJyVJYmPzzlETBW1J0g3h/yvLBa1YM9ea2rJzdtdo+TyCXRZJS9WIUKcwUoQSCqFS64c5wYtlcLr48TeW/EkKvDdg1g7K6vwVl2Tl0It1CLkB4W4Vea1k1aNWdQ+d1GVqCqnFNi2oBarFfcX+Q6ctSQy787zKnMAJQZwXx9PKAUvN0Nj6gBNYyDXQBwX5yRU2OMiVh7Oaw5zoFYYTSAuClo3fMmSvTo2P/1b53lrAiVVOm3w3vBwnkhKnueQC9+fLryeZ7beEULBOfGIv/H+Oi2s7wmEiXW4LFcaruws1lG9fS9F6N3iiZQ4zqE943JYWyWh2NbophOQ96i1btF7f0tgmRAyIR0oObNY4XteYiKlQu8tPeIouBTx6bBaUzQstco4pBTv43JVaq5a6UuSE90qxWDEXqAqqLFgreBvrgk+SlOR3fRdG50gROlWM4JD686hcuUXW7nZlpi5hAjIQliv3ZeCz/cjr48TuUiId2pik+eDF7OvXBm9jGGrq+XK814XzIPWbJ0j1sJCoiol0AZi87v8yBLA1Ujv4B9//S2+s+z3np3xaO3orOawzPiu4+Pdhn/x6pVs50tlypVvy4XOaj4ee3KLjxMvb7CmUqpmtFYyHJFlkrMS2KG0KIadE276qmTVWqG0/B1vxF4CLWk0K3dSK1Hq1ar4+O45YXoFJVGshaoJVaC1VIQz7rU4PA7dhkvILMuZ0VvORdJ/qlEiR0+RsRM67LJErDXoKteO9ox91Hk+6j22ZKYlMQUxz1o7LdOghfWzQCWmzHeHM+cQ2TkrS/oqXZtBGCcW+GI7cuMc55g4xcgxRnZq7b+liGvgcV74eByQ3IemfK7i/zMYQ7+xnKbVCbKKPw6KQ0w8hMAX4wBVAruTzugqXvGl/f0CAhE1pHdoVslimCaZpakUUqsTMRcuOXMzClf9m+NEypnbzl2xYFHLqifXwwhFZaqRAq/guvBcO3NZRsOpJjqj2fee277nh/PMOUbCSh740UuyAIQFc2WeVUAJMXPVduRSJK9VyQSikMK5NJ8j1YzGuq6jGvkh93PEj7o1YDIZJSVipFFbeucIsXBKQYRgVjORmaJ8b//XwwO3nefv3d5itF1Xou39CAW71tJgGPlgqz1daQfuIUYuORJrIScIecZai1GwcZaWo9IYcIrbseOHMGGd7BJXqO33vX4WxV1rzeV8oRS5qQ/nfO1Ihs7KB1WefW+wNvNX7y4UMqFC1EIzW9fIG++xRvijq/NiLULPSrk0znS9GiKd58im9+i2HHJVAgnutiNWWx4vE1/uO0mgSZnvzzNFad5dZsEys0iOqa24lNy26Yoey6vDhSWJtwoK7ufAlzdbvDV8c5oYvLt6f7d61+CQyv208MN5JldhNXTWkoEpBHGby9JFGQ2fbwfGNrJ/9WJH8ZqNt0wpkXLgk2d7bsOGvvcoFB9vd7y7XKTbazPiXx0uhFx4OXhxElT6uljVWgzQUvuOHqbE2pHUKnx/YyCVeDX6AikWVQujon2sa4djm9/OVCNVwV++fkWuialkOp2AwpRkWlJZ1K06y+d9ebvlVmdevz+QEAfKnAuHNDdTLTiFIN4lIdFVMcHSVtz5Pu49d4NvknIx9zrPIlypiFpWLGXV2myJhiImPswz7+eFXms+Hkf23l+vhTUNp69w23me9Z6lFN5fFl4dLzwfe04hYGn7l/admyYcM1qCVwR6E9OwoVOcWpDJ99PCMSZKraI8brui2sb9WJuGoMEutcrEtJpiWRSmCM/6koTEEPR1LUSpUkyWOfDJ0LGxlktKLKGw6Q1r35RaHmuHdPM6KWaz+hU12JRmY0BzMkWMteac6TB8+mzLhzmQw4+ojTw9AwbF59uRobGASsOsxZO9yRcoHJbYmGVqFVEDYssBlRASwXpGo0lJ0zUPmO9PE5vO8zgFkoKtFvriNBf2Xc+GiMY2p9ks+QyV6/R3CpF/eTjw6/2O0dpGFYW5idbWe2fFYOSayKF0SompPIWodFrYRTFGtKokFM9dh6qFmAV8GqyR3AYtjeZV+Pf76uofKrxKqf9OKfVGKfXPfvRn/6lS6jul1D9p//fv/Oif/SdKqd8qpf6FUurf/kM/H9op25J/zimjjMJ2hpuhQ2mYSmYOF6Dy7iRqwHV8vVbEtqC5BnTop+6XRrPKudB5Qz96hsEzDo5+cFfaG1Vwwc9vNqAUb6YJZyqdM9z2ntuhozOGruv47hL43eEsroBakod6ZyR70QsM45RqSTjSMdYqvhQhi9f74xz5J68/8PZ8EWxuTX9Jwhr64Tw3S4FCyJl5idSYJWi7wUGliIfLR16EJ1UpfrEb+dx3eCW2uapoXj0cMVZRc+L+cMLojk9u7sQQCllSxVz4+njhn384cB8jWoua0TtDZ8QobbCa0RpejD1b58TKVGuU0WjbIIO2gF3pnFRhFsQqQcQFeZ9LkX/ubGNXzDPHxsDxnWcJia/uXoiJktUMnaMaTUjw5v07vjtPZGt4DIk5BlSFGEV567RmDXRyvQGv0VbTG83fud3yovdPuZZVqIzfH2denwKnpRByFbgIKZ6mLaDfnAPvL4Fc5AH9y9ORb84njknu31hKW5zrq0FYrzVf7jd8vt/KlFckZlH2OxIVKN+pbrshgTmoImbxneNcCz8sC/dLZGn3z4uhwxnVDszmhNjojbrZIKxY8OqJUBB2zFJEPj/XSoIrE8bqZq2RK8c58atnGwYni+gYiywcG6TXGnJA4ZRmiDTxV70qf1dsWdArmbp6Zbj1ntPhQm+lBK04tBR1ORj/zosbdp0l1SL+6a0rzg0O062AnZZwpVXWVkABbjc9Y++IukoADnDjhUGTlGbsO84h0nnPi92OOUmI9cubDbVUHrLiMouGoNMGp8VOWq24OpV388I//XDPQxDXziVmpkX2CQLXPHk91QqqVqFNR7FKUGh6Y6goUlGkkom5MoXEYYmEskJ18vOykl2dM7o1sb//9f+lc//vgf8K+B/+lT//L2ut/9mP/0Ap9XeBfx/4e8BnwP+ilPqTWn86yTWVSjASsKu0pndiS1q0cGBvveWF77BK8bZZgFZgKIqoIDbK1jqGllb419qvlCxOyRUsLCETsnjY9FZzmGVbvbWi7KwKXp8uzCmxG7s25mUOMaOsRyktmO88cYqRm8HRe4nSW4MqxKkRXow9D1MgZrkE68m+3vExV84xc+s9MRexR20LPPn7axdEG+cqtiqxFFZyLX653wiNrLU+a6hzV+XQej+fub8spBL54tkN3mt2mx2P5zOlPFGq5C3J+/nt44ljGPhiNzJa2yLSclvoKqwTW99UKwrTMH5ZeFbECClQUU5ToxT6ksTrpBp9TdCakiyBU1mhIIFt7k8n2ZeUiHcGlTX/4M/+jL/4+hsulzOXGDk/nhs8p+Xhr7QwbbkOpi261j2IN5pPxuG6A1BwhWDWv/e7xwtJVcYWwvDJ2HE3NL8YpXgIuR1QpS0M5QH/sCwYpdg7zzPfXcOlQTq2VAqXEDFKDrrcxC+5Fu6nwOBcUydKgU3l6Vq+Py1cchKnTaOpSWCSwciUoZQswK2xhBKe7gHWiUNd749Z2m7mLGEaAcHGr0s/9RSNd9uJ0d0fP9vxT98+cM4FgkLZFgNbK7HC1gpEmGPG5AKdEt5/gxgzTw6kG+NwWhNKpmh5jztnOTZl52rd+/F2EPUy6grzlQZLiWmdNCVPZDT5jOvvqe0aGzS6ZIrK+CqTWe88QcF390c6axk622ILPZeYyTpwWQIRMfbyRrPExDh6zrmJiXiaSOaS+RcPB36z22EyrcFZTey4wjJiBlaevNkrbKxhyYVTbi6cbTq5RJmMgxdqrtUS+eiMRImuit+fev3B4l5r/V+VUl/9wZ8kr38X+J9qrQvwl0qp3wL/JvC//+TvoMXIWhnV5zmSU2HYSkCtbQ6AFVnIndOTK6GvilOtxFbyQ4uxW0Oz15uf2hSNtV4lvivbxFnNxln2vePdeeE3m5E7bzkreDF0hFx4cwmckbFNfqam63t6B9vOtnHxRwVWhJiElJs1rPy5At5fZnng2t35sCRu+8ySxKXQKvEDWbH7tkNDqdXKoFCK4u7ZwIuu46Vx11+sGhS1js9OKz7bjxxDYI6Jw3niMSycphlVFc/HLXOOTDHIvqdRtFKuvJ1mbjvHaA0rO1dsjWUsVFSe952MkxVyNTzGKO/PiNi01ooyFa/aQhCZp1Xz3SmNOdIZgRgGJ8tto4X7fr/MUGGwjrfvPnCZJuYUr1NOyLl1QK3jXJfODW4ZlXTCL4eebZP8rwn2UP/agRhK5fvjmaDqlQHx6jzzJ3c3vBws3x4mfvdwJKQsmLYCaxXWyHSYauUhLJxjZO88d31PrxVUxSElTiHQvCWZG9dcKZhiYIoeh8AeKcsUp7XiuCROLYjlvAg+ftt7brxtTKzGvtAKj+YU1kO6sBaglbFVa6UUxTEGUios9cmjReunRmK9KHeD5Pi6hnVcVGFJmefGN5hFruFcskCEiL1FKFcMAqueIgdvnEAjoVYWJTmgS8mkWHjed9zPC0opPt8ODM0XqbTDR3JT5XurSg5lBUSeDuey3l91XVyLoeDt0AMVjOWcCsYWDueJJRWEM6E5n2fJgEgRbUE5RV+gVn11I1pCYuhdU45zpRIrFGSBfzqtRZla5XmltEMHIVwcU2JZIxObNQhGU5sa/SocUyKKO86B+0ult4aPtiODs1xCC675A/X9/w/m/h8ppf4D4P8E/uNa6z3wOfB//OjvfNv+7KdfFXSpWGM5TQtxyTzbdtw6zylGHkvizovR/eebkZhPIhpBqEguQ7IVow26KEINbVwEyUaV32GdWON6paTLrDDnzGgtIQu29WI7UEvio8HD4FEIm+BcjaRC1qdEI60M75fIzRy56YUiV6owN5ZUeDMvnKJIdtaRE2TJO1pL7wVLtEbzu9NFRjeagEgpOqebEpYnhV7DZ91oOdXEZU5cfM+XtkO1h1VS2mu7yQqdUjzvew4h8PZyJsaC6+Dlfs9UCz88xqaCLA0blI391lp2zgl3fb3p1gIgpYlDzryeLnjliDkyGMu5BMEJG7MCpQhkTBEzL7RimSO9d9jmY+60RjXM2WtNLJWHKGvA55sNpii+/u4HQs4UMkYrBmtZckYb6drXBeiK96+LvNEa7tqu4bonaPfdegjWKljwkguxFcWiFW+nhePrd/zm9ob7OTE3+bo0lJUlVpYojUbnhN0Qa2bKE/dh4cvtFucsr44nmZLaMlApxcY6UTnmwrvzxBf7LSFksT7WcIqVY8rkCo+zKItv+47nTcGoWkjJ1e5ZF7al4xIiqebr1No+qnwfWRZxAWkCTLsWSulmy5FRFX65H6klU7Uwxj4aB76/XKA+WQnr9gtiKRiVxYsIxa4YLrGAFZdEhWLTecYW6BKq4MwPl5nTJfJi9JI5rIRC/GzoMHqlhSpiTo2qvH6Ydl9VMFV2Q6H51QvmJyhUQQ72qiqj08wpMmrNcYnsxoGdcpzmiRgkCP48L1gL5+XShFtiHOeNJqRCWuR3dH1TqLdrZ4Ff9AN+ve/kLbYGr6Jbx/4uLGglat6tswxWpv1Tqxe5Ns98JZBTbTBmbmw8N818stvw+vHMJcoC+ade/7rF/b8G/lG7Z/4R8J8D/yFP9evHr7/xfFFK/UPgHwL4rmfoRKwQlWYcDXd9Ry2F0RimnHk3L3w09KDgy92G786XZtIjD6cpis5bphyflpLXG1seVrOS4xv2pVANB09sreUcRWxwhwWtrydzZw2u4ZlKPRXaClSteXcOEglYKm+mmdeXmbltsq1WdM4yhdhOeUlisdQrsyG1f2KUnOadEj9xay3fP565n5b236yoXmOdJhl5sDJi1DQYzUfaU7PYKESksBslUNcvbzdMqee704WoKqM3mLTQK3i+GbifFo7L0tYXzVESOIXExhlRlbbPbbRgyikXvjmdCSWjnHz+rCSdqOaKtj/2/1AkVZrHmsF1Td2YCkuphPbXOufJOGISv4/OWd6fL9xW2FnH63lBG3EJ1U7RO/ujqDzB2nXLyluXdJ01TcZer8Wu3YNXiE9rTb6KTeT9rtYUy1z45sOFaipeK+Yid5QsPZv/S7vPnFZX0ZlWiu0gB5hp41TrM0R5iExWFRHXiFZFCt9jSExFsHWHZtCa0RsJATGqddmyNHQtjrKUetV05CBbQ63U9cBXVZFjJlCZG2y4RthVGZwlYL13LSmqNTPAi43nwzxfoYjSOvfUnpFSm/aiSv6wVyIGsk6zdzIBnGsGVbk/L1xipubCr2437DuLQfOrnbiJCqQq91BFDm2lnv796mFTKWgNL0fPMSRiLiQtNMQ5ZLRRWGtwrsPbitGF95eA1g5SQauFUhNLbLTZnHClYtoCtyolDKgV50cWqjo3S4k2SXzkPJ0x7d560njIPSb//hDj9fvvjWWwqzuPLEZHrbn1nvsQiS0IJuTaplJ5iB7OM15XOqv5aNhwSVet99/4+oML1b/pVWv9odaaq8x+/y0CvYB06r/40V/9Avj+9/yM/6bW+vdrrX+/a8s/rxV3Y8fzsf8RZ1QelMclcMnp6iQ4Wiubf+Sh2iixwo1NIWm1LLRWfHVdiAEYA4OTZCIDPNsM1FqbGyFoY67JRitP1bTR6q8dVaq2hPXK7x4v/Pb+xOOS6K1l9PIzQpJwD93eS28sKRWmlNAi8BQcu3VRG2P45e2WwXs6Z/j18x2DNRitGLce3WuKuc7ZguNVmRLul4AwFODqQrH+fK3ZesdN57kbHXedE86z0dw6zcvBcdM7blyHBvqWW/vt6czjktrCrsW3NZzolEStC/JQr58ttYNthcaeCO0iz48qtwohrIvLkjjHjLceYxyFIpx0JVh8yIlqxQ3xy7sd/+DP/u419UZceqVQqVZwNOKrDsK3fxMDv71c+HqarpFxKw66hmpro7lxjjvvZaRvi9Fa6tU7XKvCtrPc9pZnjc9f61PjIOEgkh6UMnil+fXLPb9+vuN26IT22Q7y1RdlfeCl9kt48lSFnUErLr3W3PWevbft8DBCmTMapRu8VcX/nSI2yetngydKpG6T6qXka+crHb0sMWMSodOus+3Ak2WsUuI6edt5YiotDxXQ4qpolXyPU8lNeyF2vRtr2TuPq5WEODrOMfP+NFNy5o/udtz1AinmUtG1UlIWfncr7FRJPhOrZHmulZLCtT7bXmt8c71c82I3g8Uo8c2ZUyBjuL8EcpL3fM6FN5eJSiVTOIdALWKQ93iaucwR03Z5ITcRnpIktCUkSAIN3RjHjXXX5enVkrg2UKwiGcwIjDlaJ3GGra6dU+Z3xzNvZ7Ekf9l7TPMYWqmTtkGU3gqx4VlvGTW89O5vLtDt9a/VuSulPq21vmr/8d8DVibN/wz8j0qp/wJZqP4G+Md/6Oetwh2r9ZO968rNqrDrHUFV3mVJN+oQxsZsjLAxlCJrxRxEZLNSlcTbSy6RMfKllyTGVUYp/q3ffMr9ZeHhPBOqY9839ocS35FaV45qZWsxzqJ7AAAgAElEQVQUpySnueR5ik9KyAXlHMdpJtXMp5ueZ2NPqopjTPxwnnh4vPDp3Y4fHk8sKeGcppgGf7TCNGrNp7uRbd+1kV9+h6qVXz7f8CZETginfS1i6wNbgUllfpsubJPBKMWt9Qxcp9TrSPW896SWEi9dmGS0lpLFH0cb9t3A1JSCz7dbXi8TUxYr3FVoo5Xi1TS1GwK2rifE0HxL1jnjqeCqto8o67hcC513bFWP05FaYPSG40VEM50WA7ZUKsfpwrslEFPmFAPz774mlsxSaIwB+Y1CeS1khCccSuXy/zL3XjuXpFea3vO5MNv8LjMrs6rIIrtJdA80o8HIQMcj6Cp0BTrQRehoLkPHAnQLuoWBDEbTVmyym2Uz87fbhPmcDtaK2FnAdPFAgFCbYCWq8rexI9a31rteUwqmiiBmwMAw8a5r2aiv+ho0XAu2Fj7vWjbW8SFOTBUmk2m82COkIlORdPzi6kn1nGZZdi4OgJXKvgn8y3d3lCpulDlltsHzOEWB3pDD0ltPqbIN/sPTC/u25ZAkb9Qb2SNY82MsFj2QQM7NmCRsZYn+C87y5mrHh5ejPlsCj8yzuJpWfa7q+kbJjielwpfXGwISYJERuMkgKtzWWYK3pFRoW1FNtjoppSqU4FzV2sDIVBUpJC/PyTQVPh4GbjctX11v2XhDjHI9yyc4+ZwyxokgYjl8QH4H2RtIoVw+b0hisJZLZcyiBQnOcr3ZcJxm+lzpc8ZsdjyfRk4pk4p00t5aJiR/wWI4LKEaRlSpFktwHiEnmvWZLanQe89t26wL6eV/TvcgBem+h5xIJdN5WYJ7heY+nkf+eDyLq2TNfDsMXKfEVWiodqltlwPLIg3wcS6i9fgT1ftPFndjzP8C/FvgtTHma+B/Av6tMebf6FP1B+B/kBut/kdjzP8K/BWQgP/xTzFl9PNktLeQo1C1nP734jTeqsoXnCn0xrFrAq21/MPLCWMlHLvxTri92pKsZUYxPgOa4i6ihV/c7fnt2xtMzfzweOa757NItDXz1NUsC9Rq2AVLl4Rb76yKQJJ0pHOBuUKaEkeX6Hxi0wbebTtu28B9L93wk/dkU/TwkfLXWsNN0/B21+O8w+jEUamUnPgwTfzTPK3V2epcbxRqqoZVOOKMYUKgD5NmmqaTsIv1MgiLxioOvjAZSil6w1eSOihufENrLcM40oeWH84nGue4DqJuFIbLhdeMrZJtumwo6+LWJ0WkDY5tCDyPM64VKmXn3erhHufMzltO04yhsu0a6QgLBJ3PXJAEnsfDWR44wwrHCH9Y+KbeOVzwuJLprXSrssuAxxw558QvNj13QSwjsOYTvn9l7y0b33FImZckjqHZFkwVEVyjNMdUC87Bbd9wmrPmlMIvrrf8N798hdHwDkflF3d7/p8Pz9p1Gk0BshIArTjsFBNJe5oeg8PqAlYKPKCS90pOokYttTKltIbPOGvUeySzbRoO44yvlRIlni1TWZzd6zIAZllOX7VBjcfAOHNRhGoDse8CL1NknDNd49Q1tOLVKAw0CMM6rvuGqH4EuRS+eTgxp8LrXcef3+5EAZwuStMF9liUs8YKNLjkAy+Ls/X+0mtxjonDHBlLEYwcbQydXE9bxHRvsp67qz2P54mr4DjMBe/l/iwxSYapwrQYaFovdWSZ2LUr17tdlK5z4uuXM19sOrwahnll/C14ueTQip/Sxotv0dJYDTmvHlOlQklwHyce0swueNpG1LIJUUvvgidYyzZILkXH/0dvmVrrf/+f+M//8098/L8D/t2f+ro/+hyEVdIGGQcLsiAtXDxOvJVknKecZKwp4J3j3bbnh2mmtyJHr2jwQynrQmPpv+esggEkjizlxCnKMtECp1noTguzxlqr56+gCJ91jh+mzFzqZcGpD4q18vFrSId+zLbxbNUJsAmBPz6/cJiFk33deH7z6prGiguhWKfKTZtr5uth5DELZ3bt0rWLWSP1KhiHQghCs3MYzjXz7TRwR8Otb2lWeEQmG69jZM0ZWyufKQPjj8dBuNpNwfvAq5s9++2G93994Jt8JNxs2bcB+8kDTa1MMTKMo8AMpVKt4a71XLeB+zGyDY67JnDdhxUmSDlznBJzFCir1Io3YiR1Hifx++h6rlvPx5cjTdcyTpHDPNIEh6vioGeMYY5JrRIcX1xteEyJnQ841/Je/XDkwIGxVL4eRqaSedN2a1LVktxkrCXUyl0wXAfH9ybywzBKF40hWWl7u0agGWPhqnOkYjFY/osv7uQeVSuAYitf3uz45vkkMFZBA8qLdMRWmFaC+cJt1wj8gsKJBlyxzEobzVknLyPumblknHVgVNxSiyZpeWbrGcZJJhdN9NGzHcxFK/FZ33DTNwRn1n2B06lmudPHWIip6MK9qvncj5+xfdvQecdYL1Pm01kshr+83fF611GyNBmlSJN02fwKbJUr2FJo2oC1lnGSwBc5bOR7Zd1znFPmFBNLaPhSiF01fHG35+k88XIeKdOIHwN923DdWj6eTtxc7znNURh6WTB7ELGcD6qCzRIGpHV+pcwuVOvjFHlwTszcqsCfIDugqsUeHF3wtG5JLqv8/vmo9E+9vuUT2LfAYUr0pXDXeHwIfDiPomvAgLUcqtCNf+r1s1CoAjReMOpqIVphfSwjISjFyQgN8Pt55I1vCIhIyGpIhcSusXanxQgu5qrBOoNLAtPMBu42LR+eB8Y5s20DL+PMNnhquejTDQsuCmDpnKF3lUnxUKi62TbcbDuubndkxMejqJBHjLEMm6Zhv+256hv+93/6nttNw1fXO1q1/bUGxllG9pec+JvxhCmCDwpDYbltWW8QA+Auu4GKcLuTdvLZZL6PI9nArQmg4heHuj4uD6WBxhga5/mLmytecmKaM45MnY784+Mj+77hPIlXRyyVSF7buuDV917xY6sAeO8c+0biDlFowVvLVCpTKZznLNFkGOn6a8U5iSl8Ps9s+5a9qVRj+fzuht99eGAi4bxhViFbX5we58Ky+eruBkyhpiRYrZGF8WmKHKZZuntjmHLmm6Hw/ThxGwLvuk6UlgZJCdIHTTpJZHIsslCtVRqFcYjiJBoEC2+s5b/7F1/QOQPGklJmqplTLDTO8uXNlqfvJi1MglXHIphuKvKeplq5a4ULrkPa+o+CeNC32ggUfbq9c5RcWEy0lhqRUqJrHKcRjkWplwvTRBsEySt1fL7r8Fb8mnSAYaauUvnGOoKX3UROwiwLCk0C+Gq43vT0rees7JaHYeLhONEEx2/e3mhM45nOGK6tx/PJSsbIfmS5f5wVkU7XeLw1nM4TVeHApN34kMTRc3ExzaWuWPWU4duXI4+ngdtNT54TaRzxuXAcLXebHcG3NKVwPKfVsrhpPF3nyXoNSq0Yb6jpQqW2KhRb9hYfzoPSbRuBp6q4QjbAcZ7oXKBTrUjWPdbjOF/e34VrmVkPPmMMQyz84fHMq400Rb0XLN7qz7o2V//M62dR3K2BTRuYbWWk4JyRbTtLoV7oWmKmwycnVqzqpZ00Y1ILc9W7fOkoloPDl4sw4uE4chiiCDY2LTuvwb3uQvdaantV5sFKx0IVd8A+GG47cWpalnmtl5F6SIkWR9/IMuima3i163m763UyYH2YMPB+nvl6HuX3VfhnkT+5ukTeqSx+GdVZUGez/rzWyM9RgYdp4pvxRIvl1/stO2epxayUrQW7lO6ochc8s3U4tCA7Q8Dy+e6Grw9HGueYcrqMnBVQ+ubShe0azyZ4+ZpaTD7F4U8x8zzMIr7R3cKUZfk8a0LQNEd8cJhcuLra4R+fmGfYOBFwgRTHTfBqvuS4P56Ievcbs0jUYd+J1cDzMBPLAmRVpaJlfn88864XGG0pOLVkpmx4nKb199DGTLF1gYWOc6IPjndXewqGWC01L2n3lUCldYbPr7Z893Lm8TQKC8Ia9o1AVSAMjLebjl0r/jZZu9j1fnOWmARTr6AWsY5aiyqzF2BeridV2Vp9C8ekd9Hyf+nyr0Pg3a7FWyfvs0JdCVGZLpT1WAu9qrAPMZNzpfOyt+hCI/CLh7EkGud4Gic+HsSs7u31hqheKqkWxlp5ISl9V5vVdQckRmKNl7CVaU5Mc5LfEagFXqaMqWqFbYxSmTNDTXrIOzoXmGtlnjMvDGyahqlA13g+nMcVB2nbwKbrsCVzOI5yv+skl4pUUreErUThpy8P64JIGmsZSuLDLM3Yxjoa9Xj3RpvWIo3e/TjyMM4sAeRrY5ahJCk4UvBlMkrV8vE882dv9vIMrclOqBL8n3/9LIp7AY5kinpcOFCDLClsK22Ri9rvlBM3oaEk2WB/etOWIpYGy7QnKUF15QZZA6eU+MP9gZQr1sHbfXfB2C7t0lp4LUCtbCy86M9y2zledQ3eikteqtBaT+s0tDqLQb9FTvqUC4/jzMfzyP0w0TvLf/bZLQ2GPwxnCUXOEpbhFqYDalWsHYk16o1u0Bgv1G59UWPKvzstpqlUhjGTUmGm8PfPB77YbnjTtjpa6yflZeSVB6hxAvMYI14ju+01x/OJ6+2GnGHOBqpwP8gWp6EQ1Yjq9/WupfVykFrFXhf17pgKhymuh+Vci3KkUX6wjOg7H8ipcoqJj999wFJ41XecY1Q/H8gUuqaX0RlRHR+z/FzeiqLPGMFh911D5z2HKTKmuMq6nRFWxPfjSKqF10H80qsxjPWCeUtEn7CICrKIrlWK7JTEQG45vKyB3hoV5xn6EPD+EkV43QRuupbOiyFeLgJJbYLHWQmHzyxFRCqgTEwQQZKeEEuN5WX0o8WDxWp3V9g2ntZ7pqTiGyvvbU6F2+ueXXMxNFu2OecihAJbL/461SAJRtLS4or6sPdC5x1VVWtLYdcEvrjdytSUE89xlq4Xsz5TTyVxbdx6H4tJnaXVFK0pJmJKTFMkpYz3jqFIJGKJsmOrVE45kdVsLVgn6lUjTDinDd++65hz5mmYqFXgpXEsvAqebdtj08zoZ7yT1LCa1arXa3psrRLoU+WHX2Cxhe2UitwDp1J5MZG7rqVVZMFp8zTnwvfncWVs1bV1RxO6LhAY2pkXDQkak+TDNtbh1IJgPcz/mdfPorh7a9k1nlO5BBaLMEJ+ePEHusipW2uZS+U+zty0HnPUVKWlyP9oXDFqkRkEly2i/psrakBUuQtibUBdCEuKva7HhX5NY9h4y85nnLN8te/xTgQUw6SQhxEsWU5doU85K3Lih2Hm7z8+MiTpMIYI/+GHR97uez6WmVl78IUZkRcy1PLrGAkmrgay8n6XzsfqnVFNxRlLKJK6M06RTy/HqNSr4B1b67BFCp+WBFUWLrRR+R6fbxrGMvD9NPJwytxtduz7FjfBIU5EMdhep5o3u55Wu8sFu1V0ialIElCOhU1wdNYTuhZXHTdXOx4PR+xWwsuvg2eYJrbbDSnPHGdhFTTBcdd3+JT5fhgYp8imbaFmYoLrpuM+nZlLIXjlmDvkwTSwbcVPaEqJc8ycklBsG2f5bhx5jpHPm3blmy/DmoR5awNitKMrl91Qay2tFU+hxf42aLsftWG4aRu6K1206r5g3wSKQg7WGqU1GmK8NBgi6IHn44jZtnTq7ig4O6uFhK3iQY5Ovq5CD0oV1GagCJbcOotxMt+ijZVMfpJaVUrGW/maQhmUCbsiTJpzznSNJyEMJYpZD5Su6/De8RInnlPEVOH0ywJRzMZ2Tlw7F7hoyXp1BkrOxJiIc2aaE1knpMNcCE3get8wpQIlczpPtI1dldxN6HC28nQ403hPsA5TDO+fz8SSMNXwerehUnk+n9m0LSVH3uw2tJsN52lgylGM5rSulKRhIhKtJQlbyIMX9NpPSTr7YuBxmvlyt8U5J4EqtfLHw0lxfX3pZPUvX11zjIUfDpJJvDBvFiO6L242VLPQZ4v6WS02ZT9RV3/yb/9/ehlkyTlWxcmlSunfmfUmNuscZFYM9P0wr8uO4IRVYY3hnNJa+GqttPoAiWDU8PbVK7778FEeqCoMGYNdD5SFzSKd/JKtKpLtQmbjHE0QIYIvVT2yhR99iiJEaKxl64XO97c/PHCYZo7qMb28ec+jyNWbXcA4hd/0OiyGZ8aI4ZKrKr3mUth1ThQsFVkkgWLCMctYjRx+Sat8qpV/fDmyD4Gdc+yXCLqlQ+GTDqIKptsZ2S8UKoc40kTPrm/pmyAp7qWwbQKtl859DfswdYV+wDAmyTLtu4Z3fc+hZA5x4l/9+jf8/ttveXh5wRvLtmsZC0wxcZyeyabINKLUgvuXk4h7QsN5Tsy10nuPx3Kz6Xl8PIMXeqAxgodL6pC8n50mejkrtgC5yETResuUK393OLFzDc/zKEtNDFfek5LmZhqlqDmrkXqVm03DkhNaK+oEavh4GHl/mNQXRii/cIH8FlbKogJe3SGXycQIS6m6yvXtjvenidkYppTZBEdj9H1fhD7Ogip+S8mUXHGmStoVYq7WWMPdrqVtvOy4qlnFQRjYqn7DaKOT9Z7Dwqt9x2lMhMbT94FUl8dSaKfOWO6HkW3wnJPcg96YdZK+9p6d9auK1KHcdSuw4BwTlEqMiWEWJxbvLacZfGO53XXc9GL6NcaE9Q33LwfGkikBXBrpfYOxsDFBlvcl4SwMs1h9n4sYzDkMD6czvfdkX3l8eaFSyAV5HosU9mUapq7lR6wRVhfQhc4p1ykYOS2rl3+/nyZedLKTc15YSe+2PX0IbFvDvg383YcXhpiU+QV3e9ljOH0iq5Hp1FuzJkD9c6+fRXHPtZJSpTWGaXkw9JdZRmZ5XcKllwv8put5rclNOedVjXeKjpgrxymSUiHOkRgviq6X02lloKzdD3VNS1m/ib5KLRxyoeiiZ86Z0xzZNQGqUBqPKRFAqZ3yQJUK98PE+8NZVwWLw6CYm5UqhbieEtVUQuNo20BRbDJ88jOg3ZNZfu4qHXypgvUtN+s05TWb0cBKN0QPlGWH8TTPPJXKbdvy+bZTYYD+3rqctsCuC7TB8jRG/un5zBgjyWTCbBjmxK/f3jGPM2cjeHnMGW+D+pXI7mKuEFPi+TxJR1ngeSpkZET+7v4jj8cX+dxGRt9BF2f7tsUaeJkTc06yqJwSffB8cbXlujE8zmdizXz56opvnp8pTgr6Nkhk4eoYajTFR2+p1lps03COiSmJv4+4F5rVjnWRvm+NJTSOWAovJTNkEaeUInmrN5tWDkCliM6l8nyc+PgyMcfMoJfWIrYBcjuoL46yY6p+rynJobR3loAsT5dg8He7jqlUvj0MlNLwetPock0WraJGzSzWG9VAEzw2ZlKSovZm39N1bm2msjFina0NgEGEbHMR5WytVux7Dfzy9or/85v37M2nIdoXtarRP4eY2FlRz2pF4y407Jxfv49l8QSSr5L1Y2MqmiwGU4H7MZKM5aptuN60spuZM6dpZk6Vmgv7RvQwYxZq42eN55gN1jsehhPVQmgs113HeZrZdj33x6MYwJVCb2QHkKi6YIEaZZpuvDDaolIX/YIqVJ3qtYmQ51s8qXpriLMwYrZNICy6nCIBOF/sNly3jU5dhk3j+fO7Hd8dBh6HSYSB3q7XxiJUbknmYqW0/nOvn0VxlwdBxvSkQchr0IARmf7awK/Yu/xit42TB8L6lcpWq27Nq+H3j0e+eTphKzTOISE7hdNxEDxx+apLEa3LomIt8cylcKyFqsISgxwg3x5OfLnfCgOn5NXVb9c0hCL84+ecuZ8mrvYt85SZS0aY/3YNGxDcVjrTOGbmcyJrQd70HX3jORKJ5iIZN/XSIS5dcimVOBcp7PryyoVORVgttWiQQqnCDDLwNM2cUuQXuy29vdwwxsBV32gSfGFMhXVtZ8UbpPOB7x6e2fc9XSM+NM9jUstdq++lCEI+HiYMhi9urvnm5QXSKArAmPinD+/FskA7IXGIlLzPKSXeblusDzwPZ6rPbHzAFsP985HiDNd9RzGVv3v/AeMtnXFYpMB33jGXzHlWcybUAlo5042zuCQPXghORSqVc5pX/46KHLTCZoDPQkNt4ClG8X/RAOiUJRfzh6eT5MdygTv05qJqkpCzmtVaC4KgygF8Tpnvx5k3Xnwti1nuUf1JKvTO4gIc4sxtDYDQf72yt1K+LCih8qoXd1JrLFe7lm0nYRClKutC28Dl/jZmscNwTKVQTGXrA07TxdbP47KPEjKB7pd0AgzG8kW/4RBnNjha69YiWCnrvWvQr5fFQXSMWeMaJUdhzIW7bcsv7/ZIolrk4XBkLpU+BK77DcIxFYbSOSbetC1UQxMCdbDUEnm16dn1Pc7C82li30pn76znPEfaxpNzFaw9F3L+cbrUwjCLRfIBFrdQ3AJlFlrnuQ5+VS9TK52B1ssupQC/2G+57ZqVVmq0zl13gV3r+Yd7YZDt2kbeE214nbE/Ik381OtnUdwNcJwFb982gUOOa7HNC76JjKs5ZWwr/seCUSojoqoEXAUEZBE1XLeBb62l5ErvG3ZN4TDMKtlW2TpVcy1lEbtwnBJwyImxFBE1fXIxnZXk8g/ngdeddE4W4ZDXCillzjlzu+n4N7dvKMgh8fXjgW8fj0wxr1i6WVo2HV2XByPVwsNp4Kq0hG4pBNK9FyM4q8iwIaVCnoumGum+wFz4yNYuEJdAFct1r7lSbSFnw9fHE7dNw9Z7eoUxGu9l6bqMgNrZJ40C64OhWDhMIzaL6jRYcc6L6tW+sHGCd9z1G1IRLv4xCpXQO8s4Jonrw9CGIFTWooXXWX44ndn3PX3XcDxPYAyNhblWSi68nCctKoU6FXa9JxjDPEtQtEwQcsFXtpFaIDgqL1EKc++FkWUby8s0rQ+QN3aVlRsjD6l3ls+3Pb/ZdfxwGBjnxHePJ07K65aGThf7q6JUL2S1rM6NdUl6ksCGb4aJx2EitA1v+0YX6QZrnbhylgraDLTB6bUqLJEdJRdtgYyyLyobL8EofWf5bN/zoi6gi0FVrZVsPumkFX4wxuKRRslKB8E4C1Hg42Fk20pSkjVa0PW5bYxRHxgIpfJ5t2FKSbQnpQjLR/McvZWmo5SsStOCdeI3P6fCkDJdCOybdnWD9dbKsjIXppToQ6B3gVjEpmDrPB/GWRSs80wfGuriJKph6iGIorf1nvOcFe836rMvzqjLMjnlBaLVAq9Q44/3e/JM3YVG4xUVDdDD7N2m48lFDlPiSinQRidKJc9gjCVYw5+/2jPpzmiZ8NFnfclPdj8Nuf88ivvC0jiMM33jNa5MbuKkhU98uw3FyXg8Udk4EdMYKw+HlwpGLmCrxWPomqVtqmRddJ5TEoqZEXVdyEVvXvHppkqw8EHj+Rqrm2k9QDJC2VuWcnPxNMZxzhmqSMG/Oyj75TyRaqF1ImT44mbL611PzIXvn06MKdMEC9XwfJ4keGB9ScdwniM3QXInJ8Tt0Srmm2slp0qeMiVrV2+Wx1wVgyAddJYOoRbWrmjBCw2iuDtOkU3wvN10fHW1XRkJjbP82d2eMRde5ojJlV9cb/jsasPzFHkaZuZcMXNif7NjmDJzzLyMM403dI2jDQ3HmJjiTFbmRsxSpIJuhJ11OBtwFrrQyvtWVT2rxUygqILFUWLGe8uu64ipsjWWlzhia+UqBB6niZTzaoS2vEoVb/RM4TwmjlGw3eMUuek7chW2iteO+dYLlLD6huu050KgGMNxFKFNaYX+WOulaMpNrsXRyD3klcFVdMGYquQIR23zE5VTyZKqtEBqK6wmz0xrHLddUJju0iWuHaOFqi6OMRdSLBhnRd4Pa4e+ZOZa/bOy0I/l57LOE4owN5yVw7ZrPMMkz5Pu4AUmMNJEGIRMkKtMInOtq32AFPOy8tZzKTJxJmE5VWM4FQn27ozjl2/uCN2G7z98pA+ObSv3h3dWgi/GyMs4sd9s5bkohrv9jveHA1NKXPcbgnfkGgjWMZfKaRjlWheBUWqVax9qYd9vKTlyQKjMoPg6XNTlRuGnZSLTQ+06NFw1YRU5SUqWoAGdNXy+63i7kYaiqhJX3l2UvirVMBfdzeji0JqLGlyMzAz+T4DuP4vi7p2j9Q2pVI5j5HrT8jHOik+ybv4xhuoNpzHS9A1/drfjs13PyzDxcBppMDROFq1PgywvHkb5On3X0vuAsZX9nDRoAo6jxLNlBMd+GiJPc2KzFY+XqLi34PF6sYu8WcGJsvDjMPGqE2rht8ejLHPNYiJU+IeHF95dbfGTCHaqrQRreXPd443w4VtvGdOWf/j4wvvD+dP7hZQLD4eBN9cb5iohEYuYw+RKmot0QWrOtMidlq6zomNdFZaEtZqmo/imAY3TkxtpiIlvjgOHmPjt7Z67vpFosDkyl8xd1/Cbmx3bxlGN4aZvebtL/OPDidOUOQ4jrbe025bjAK5mrAkM8whU3mwbYs3Mc6QUpPML4vjnXGDfeIY5kU2ksYY5RZrG8XyWJerbzZ5vjy9kCruuYYyR8zRgg2Uoooz8eBqptfJmv+GcMsc5ykHnBDtel5qlcp6EjQFicYGBJ50OaoXGOskezXWdhLyKx4qB+8PIEMUnXBiicsAv/kMCGy7d98VgLCmPPFKYqxRUR+WmCTzMkaeUuY6JN10n02VdfFUMBTHX6r1fxUzLyyz/WIq8MWAs7642eCt7mQ7HRF53AIkiFEuz9kJgZKqqpeBsJSfBnIO+53MahEliWaX3pbLuhZLaMVQjDLLOWq6aQEpJMGrF2GPKzFH0LalWHnLmFDPOWLwV/cqv373ibr/l73/3O9K2Y9937DVr4ThGWeY7Ty4SP/L+cMQZQ+clvMXkzK5pOI4R4yr7zQYwnPJAmjPznLjad2Ad5/PItpN4z4y8l94Zor7/bqHK6DO6vL+vu4a3Gs5etGSXKmptqtawXOg0y1kWfW4VQ1VkAkPV36vzpJGV+xKHqdxn6k/X9p9Hcc+6pMgxMRUx9rpxnociBbog47eIXasrICMAACAASURBVGTcc9XweB656xqmmPHe473jh8OZw2nmeZy52204KM7YNS23V1tOxxO/vNnz2b7lNEb+6pv7ddy+P098/XTGOEu3awUz1k6oIPzdWhV6QQUe1nCeEx8G6boPacYgtLiq+H6NiT98fKZxlq/urtiGICNnTBTnxC44SQf7xfWG0xw5TfGTKyRv7MNhoAmOfueZSyZPRW0W6tqFXfA4GTF14JAxUbvGXPXG0AcY7QoWXrsxYiT1PM787ulIrlsMhr/7+EIXHF9e9Wxbj1VHQu8sO9/y2zeO5zHxMoy82ra8TBEayzAVjtPMnKN2jWVlFmiJhWLBSYf/MpxpvFCHqnU46xhzFnc+WzEp8rrbcBxGhpSIKqrZ4PXwldH543Hk43HkX395d4nKo3JU10opLBL4odUQAzwPkx6PIvEOVaLyqqnMGrJh1XislMJxmGis5V/8+pc8Pj6QYl4dCw0XNWNBOPylQiyirBzU39wg34MqHZ6zhikXnmPmdXfxH1n81787ntl2fp0OdJ+6Fvblt8kVNRSb8cbhjcEpHtjimGqRvZbuZqZShJUFdAApQS1YZ6nqf1GBd7uO0zjz7dOZ11cd122QIo7uGCjSuBjF/ZfmohTa4IkpiwdMKXhjSMFyrIWzKpc31rB1lrs+UKYT9/cPHE8TpVSejgMF2HWSijZqNkPNE5uu5ek0AZXrvmNMlZdxlLxePG0bGMaZEGCMk0IwEWMlbKNvYMiR4Swxk33rPwnnkKmMojVAu2pjDJ13vGobYSjp5OOcIVbp9HPR8B2E7x6cE0pqUm8hJx8n90nRA+LCJDLY9R7w1TAbyH/C0P1nUdydtXx8PtJ6GZkez/DmqicUQzJSreSBKOLD0Yhfx/Np5ocwUhGToeOc+P3HF+ZU6Jzn/jjwdJ7ou5aHw4njaeT1PnC72zCXzGGY8c5hLdxuW6iF3Sj81mvnOJblIiMLI2PVCwOV28uDM2ohzJ8mxSBv8lI2DDKi/vBy4m7b0QVP4xwb79aOJynF7rfvbvjH+wNPx3Et2iBfJMbMLjfYfGEWyD/kdF+sipeF8wIPKOwnN6kKJuTv5XOlqMtEUuplwfU4TJyVFfTZvueXVxuhferY3gUvuHKpYp/cOvbNhloqjYGHcaCxDcaJEdOcR56TYO3BWlwVXN1gCMHzchqkYymFai37bs/58MRhnLHOCOZ7Gtk1HbtGCgxqL2GNwX4ygSydcjCWX+7bFUP+4ST5tN7DeYqCp2o5D17MvNYLZCp7L93VFMvapQEYJ35GxzFxd73narPhr//wT9hSeLvpFcZZDlc5EOYKc07ynoNYWVRW+ftSpU0VmOOghcWs76cUYrG0CLqQr3JA6/s4ZMGqW+fxTnYFAjoVeuuU9ij3zWJX4Vg80g1zgad54pdts2okPGCsJWq36K3hqm84xTPDlMUz37KKlIyR6dBUg0WgJYdRMQ70wfJynjnNYmiWvJj/nc4JVytf3m1xRpLXYqocjy98ebvjNG15enrm64dn7rY9b652XHUN45ToNy1TLBetjOLxh2HCOUtjrAaYZ2KGEDwYQxcKwbcY42icE0gkeKY8c5ok4807uzpC1uV98Bciw5u2oTGWXMqPOnHv7MrlX/Yt3ulU7WSPklOiZAPWSQZCFvrw0zix855es1ulBAiEuWkDj+fpJ+vqz6K455JxXpgV+96rOVLhygWeiohwVm63Fk+MIRvDd6czKWuKe5Sl7K4N1CpOhF3jMVQ2jSQfHU4jDZUYM3MpXPWBvhHrgKve81/tX4OVrq4OEiqAWWS/wjFNddlUV6YkN1BcfG3U3WDWMb/UulICa0UCQU4TdxvDddeIYlFP85izyOir5YubLcfTtBaHxVoAB6cU2Xat3HjWUIpaEVS1BjUXPHcJkgCVN2sBWaYOtK6ZWtcrvODEWbm4U6m0pfIv3lzRNZ4YMycV9ywCJZlwiipo4WmOjEWsa2cqcR7Z9R3OeT4eDgwkXDa0XYP3jn3X8fmrV9yfDvzDNx/oG8euD0zzyDBHPVSlW5rHTK0jt/2G1nsVbgkctbwPy7V/3bdsGtEjLD9rofLZtuPjeSQuSzMdea7ajif1fQehA+69F4OudcFm1H8csTPIhd1+w+l85Pk8Y6zhnAfu2oZdcMQqdMJjitRa2BqLKygdVuTupSoOq9PXzjvuZ/E2N4pjL4AbGHYKA2ZV7ggVsXCaxRbCK6SR1e3QG8vLMLPxndL45L1vjCFXeRYqQDF8OJ/JpTJ5T68Hr9VpoBhYuFhfXG9IWRakc8xUJ1a7jcIKhkuweMyF81w4zlIs73YdH58lg7hpPDYZXo4TwVjaNvBwnoXiWYT0MAwj/9fhzOk8sSQbfX1/4OPhzBhl5PxcA368kUPqaZhwwbLpW2JMOC9EjNt+o5bWhcfDgDWO/baj5iITszHMcVY/K7nmReFZ5/2aYSD3mPj2b50jq/24PIoVzLIMlWfDVN23WE29qkK9dq4hxUQpBY1a5vth4mGcsFasljdBFtetUrGDNXTNT5fvn0VxX/I4cy282+x4s+vJVYKaX46zBC3rqGq1EygVjJPOJyN87xAsmyB5icYIlSjmwtZZNt5w1QXejwPnMXIcZ+YqRk0+OP7x6ciVt9AZtp0nOMc70/DHgy5eqrBYShG63pwytRSGmJVHzdoZi3BIbwrtorXm0wTH26teqHwGhizQyjllUi14ayXtPRYaY+VG0q/tncF3suA8xIndrsFVy+E8kXJRz3qrOaUXZ0pjLh4rsHSBy9+JQKogC46qv6t0izLu2wqvNy3XbYtzlvs58/48c5hG/vVntyvrx1q5Ls/TzKh88ZuN5xTFJ+U0ZYJ1fHlzw8fDgYowmMSuwRGc41evX/Ht/SO5Fl7GgVzODDGvzIRaZNcRjGDBr682fHg+Us0SOH0RZTdeHCIbt3STcIiRqoKsvvHcVJhy4nGY8MZyHGUBvrAkvmhbKU7qU2+XERpDNnCaItZZbrYbPjy+8Or6hl235fn0zNM0kYzgX25ZggsRUxguVQ+lCt5UYhXVaCmVm8ZzP0eGIgfpfglmMHBORaX8QjGds0xO4gUjE5SxktPaeXfpNJ3hOEd2wV9M+Sq0WCb1838a5zX957tx4hd9KxixqusWla+pAq5/ddMzpMw/fTxiDOy2DbmRHzTnynkWm4lxTmsUYkWYNj44frPvaa1oPf44y+J8jIlGaaIorNE6w99+eKF1ht46XDUMtfIyJ3ovz+swR7wVFW1jHcEannLC2kKthcM0snGBs9qDN87ThcQcI/fPB676luM00XdeLBqq/B4GIMu9Z72yh4zs22ytfL7tdbl6sdMW7U5ep5jloAvOrvCgzmI6BQijT2yLJe0rI9PHkBLPY1QKtPjl79uw3uf/3OtnUdytc7RtR0lJbsYiLnqzqRyOkayYoDUGGxy7tsGpTWetMv6lWmkxLEi1QfixMRXOVWCAq9bz2X5DSomnUUafXRN4miOH4wlnDLum4c9utlxrGs11E3iZE0NMatoFHZbbXjqn5/FFOidr1lPeCuy7LkRV+I3zllfbnlYhiqlIB5JLXX3mn+aZ85RkCgmWJsFYCk2wNK0lwsq9HlPCGsP1phXhUkrql1I/GdeNOgYuTfvCY4ZPZj3BqetCo2Qt8EsH21jJsixVjqlt6zklx99+fOZXN1u23jOmzNM0i0c5gJFxf9tYfAikYjkMI8NQ8daINxCVru04H4+chi3fPZzI6nGOFkApClUZTYa3Nz3OCnwwzDO7Tc95Gi5+QvKt2TeendryAqsQyiJCleBhi6MrFmMcrQ8cxoFxEoZE7xyddat/y8pAwuCD41QqQ0rsu4596/jd1y989eWviHPhar+n1In377+9TF+VdaxHJ43GWMZF41AKXt+XzjmugucpRu7nKBbCCoVNOXE/zrQKA4wl64EhYMtcK67IjmqIiSkX+lY41ec5YSvsWsnvXACpIWbuz5NmJ8jPeMyZr8eZL/uWjbPrhGd1kljWNq2XKWuMEm9XxIODNBdilii8OQl1dIlA9M5x1Xhabykp44woND+cJ7bB8dmmXSethSX0y9st3zyfSSmxDYHrvuWUEhtjadqWx3Fk4y03bctO/WkKlWGOvLm64ul4EoaTkwPvNE70IXC363kZIy+nM8YKUcGpe5jcS/Lg11KZRwkD8cHjXOFd37HzjoV6bNGF8kI9QrN9gd5bgnOStJTravxVkcYxFoVZnUCDuRRylmS5pbAbI8EnD4M6nP7E6+dR3I1hGxqyka486i80piy/sGHtJlIqnKbM3TaIYyAq5OETHBk5ZedcxEK4VHaNWGbuuoZcKtebljEV/vhyZM6FlCvJVuI8MT7LRa2lctUGXvcSzt07x1UTuNsEplz5j+8PkvrircAwVf2bjTASJBhBxvnbbctV37IJgSllMlLUF4zSGsMhRo7DzGlKysEX1do2CH4vTI6LiZrsdipDTWx6T5c9L8PElBdQ3VDS5QaoXFzozHIKYdaiSNUpyhqMlY9fePV//eGZx2HmN3dXPMXImCO7zmEqfHOa+KzNnHOiZPmd1PuLjDJQMDhX2Peej09nznNiu7XcbPfc3l3xN4cX/ubr76hWBFdO8dtpKaxVoKebruGmDcRaJArNGG52LdMsTnsiUJIJ6KvbnWCeVI01ECVoqotbqE4B3oKz5Ggk9SuLUvW1dsvGGUhgkPsSwAbP49OBWuDj05H/7d//FanA0/Hv+ertZ9xef8H9w71Y99ZP6In6zGd9UFcBWmX18ndaJK695xwzc4FDKgQrfPNspMk450RnnS71rN5LhuAMpxSJVax5D1PkrsJ1H9i1gcMcybVw3TVMufD9OPNwHqhA47yQG5SVc0qJQ/S0qvNonMCiqV6cRCuG17uec8zcHwbmU1xdL606rO6bBmtFESzmaDLFUAveO1LK3DaBVCqv2kDQPdRS0Jw1vLm+4lwqH57PnEsh4Nn3PdZWPhxP6kkj6k+rk97Ged60PUVDSGKFvmk4TKPmmsGcq9hTNKKAtcBR816tNZR5HWWFVusDJUm+83UI4lBpFiFkXVX1iwX2VAofh5mjt7zb9qs2ZZmoFpp1RWrHwzARY6Izlk3X0DjHx2HELmQIRQXcKnP+T79+FsXdAJsmMFTBB3OtxJx5HGbll17Uogue2lhPU7ls6FHFsLZuBVlMlFxIRQyzUqlaLKT4f/sivsrWWZwzq5JwnOKa+PJQCsEa/vL2iiCE2LWAxCy+GRg1EjKyFJVoNA0yMNB3DZ/tt+RceJ6m1VPDVEOwkCqcojBkTlNaOwZvDe+ue94Po/qna1Gu6tdiF1ZE5ZxFFdo2FucNOVXOugwySxOxcmW1+68Lt1n/xVwWd8vyCOTiJgtfH848TZFf3e4AtMuEbevFc0e/HlbcEE1dVMbyNUyBBiSLMxaq87w/nXmZI7f7PV/f3yv04XAOTnNeJyBjKldtw10nRaLB0jtHxRJjZFFYLuKdTZCQlLQwiaxhWFgPsE41C3xW58KH4xnnDLu2oXOZ1notyKKmtCq4Co1w289TpGS1kEiiXTyNE9+8/8g8nTEmrd2bXf9viIiQrNWlfLBi0rWwIRYIb+cDv9hYbrqg04OM885YrvuG+5eBmbIyc5wR0Zowr+xKXwWZ8vZFYu8aZ3mZE9U5PgwjL+Oki3eBcoK1khimz9V9jHTOctPIESk6P4GFYqnqXimioi/vdgJVVcNeVZoywS1P7wVMWALIl+bGWMPnm44ff+gi7AOTM1/d3eCxPA2Rbd/RdZ7DcKKxlru24W4jDJqFBmxqpfeGH84D1cCcIyYZjuMEpjJO0Gy3OGN4s9+SU+LlHKlBzNtKLOvPmUvBu4vlwka9rBboxtSqBkEXfyZnLQ/nSeqMESrpVRPYB0/jLUVtf601eCxjLjxNE0OSpf4QM523vOpbvHNMJfMwTuv+6adeP4viTi2UOLBpWuYsD1XFyNJuQaYWuMDISL9zDpvLJ/LwyxiFdvNHY9UhT6ThQmFTl7dauZ/nlQlT188zqxoNZBOe8gJZXCLKLKJaPMWoDApD2ziKq6uDpTOWXRdoGsEPzzkJpo26CHrHkDOHOXKeElNM8ptYw+f7HZvG8+5qw9McJavViMtfNqImRPHPZe4vtYA3tEa6kDildUewGD/Jhy633tqQfIKzQ+ccvXM8zjPLEs8o9jkm8cpY/ECqgbkmMBYbHDmVdWlZzLIIlu+YKRKZZ+B60zPGhPeWIY68vbumAO+fnml8wAVPKgPnWURd1hg+33Y0dum5Kzet52VKTNFgnXR/UgJh16iR2yeH/VzKugw0gqKBETn9w2kCC5HKPE0ELEebaLqWGhO1it+6tQbj7WrbmoomdS30wdZTrPire9S0bblDFXefsmDtpcr76Y0VVtjioqSNQbCWbeNovOM5z+t0uhSNxSnQ6EEV1X9mUTivcXrAeU6k7SWmrm0ansZ5dSFEi2xKhWIrrdfdDQIZPOZEKIatFUhjleVX/VxtIh2G276T75zLCg8atZBefyCkEVjou6EJJBWSrZ3HeuUqKSVMykyTo206rqrj1dWO//y3v+bf//Xf8FzPXGu4ufj7sJILxjmr8hzaEIgx8na35ZQT0xyJNWIKdDZQq2WMiX3bklOhZrmHhU6qCngq163n3U5TvOrF9kQmYrNCm7lUDrOIs1KpvD9PPAwzN13Dr6636/WwxnCOUfdVab0EpYoILI8TjROvpNd9y8scGf9EFNPPorgbIwvIc5x4/yIb7utOFh9aswH9s4qoxHK5B4xFbRSXLkaK3iY4ETAgo/e2bZijGIwJy0RKwZJYvuB7GOWDLwoM5AaTfxXV4mplq9+rVpimQgiWtnGkUrjZdmwb4ck+TTPBmPVnbtUQ6Ok8iZozZb0xDY1z/OJ2x5yFLXHVtpzOUvidMasFa9WOZ2W3aCGRXVfls5stDy+DCLRUjCEPobZFC1xgV7E62+C49kFgJe+FplaU32uFpbEUDmPksk+1LkARxspNHJyVWEAtPLIMFfjMYfHBM5yHdU/xx4/3BO+JttCYSu88J50ijFEbAquhzbWKotVIwd14x/NUeZwSRkU6n19t1snDIA9ZKtq3a2GX8dZQk5HFp1n+G0wUUpx5TpF9CDIx5LoW0zHJMSG0URE15VLJFHq8Ti11nRSCk0VqUvYEILxxo/e/AYOlUzw45oqabEAVoZc0KCqoq9A1jpyyqK1LYV5wjGrZ6F5HBz28E0/5MYpUf5gjNheCseS1A7zoHUoBr14ozkiBf5oT1aFxg0pamCLWOUIji0JKxWgo/Y9KT1VF9eLuAZq2JASBlLMumS8mYgs/HIOowXPl8Thwtd1ijOX944Hn88h/+Zd/ydcfPvLhh+/pxcNYnoFSOcbIh/OMsxJnSKkMJRFCR4mzwDIp4TC44Og3gbdXb/j7Hx603sg1iVni95Yow9u+EaTAIKlYOrkKU0iUp2Op/P7ptDZm1kqmQ6Ryf56YU+bLqy29l4ZnzpmpyAS4LNwX69dJySHOwK82G143DT8M40/W1Z9FcQctUhmMkdHjj+cBqBgP0notkIQE9XoDRj3DaxUNjNEbwiCY5j54+uC427T86vZKOiwhmPI4RRpjxZtkgR+0m1ryHRcHxduuWZeg1iA+M6paW15ahsQKwEDbBnof6JxVGXZR21OjrpKF4zzzfJzW1rn1nk3j+PJuR0FSdqwVPPP704liFjGXUVxPitGnpmeoLW+tluoqu23L43EglaI5raymY8CKFVoDV97z25sd5ykyxsxtCGyc45BlKZepbJtmPVhkXK7r06oQLNbaNTzbsgRoFB4eB+76hm3XMo+VvmsZ5xHnLedpIs0TmcoxTRgjS8XkHH3jeLftLuCc4hayhBNnQzOh/i1SgO3lrQGU+60PncWsC+yUCo+niVKFpbD0zzJpCMzxNM0cY+SmaeiyYZxOwhvXrrTxlsY7OdSso3XNpZPTw3/ZGVXMSoNbLpqzkj60HNS51DXgQZqOxVtES3sVqu1pSMw5E7zcC7Jb0KWe+qgsU87n+w1XfceurWxMZfSWf5yjRFAatSAodb3GKQv7yPvFqMrwOCZeauSrbY9TrvY2CCRnsrQQy2XPpfJximycZasaBJZl7IpJsy7xF2KEXQ4Is8CJRvxm5sK273h35aFkSuOYkmUcRv7b//pfUf6q8h9+93s+3/ZsgugwjjHx3WnUtCrLGDOt7nS+ezzIJN4YUhSeftcFtsFRSuZ22/F0GsnAlEQkuQwq+zaorbUOzggUBkJesN6SauWH4ygeQ4gXjhyc8pBkK4Z95/vE213PXd+ybxu+PgxyPxjDXOv6nBYDG+/4vO8xxuAxvOv6n6ypP4viXgwYb7FF4tVSzTLG6Q1uQFJQjIFceb3tcIsPRV3kzqoK0+g6W8GWysY7zjFrmrrcuucp8+F0Fs46KiBBMPBai9gMXICatbNaFpKLFcEa/6e/x9I1j1OSQIpUuOkDXesJxhGsVdGTLOxehmmFVIJzfHW7owlOJhMjt00qhc5ZWuc4l7jSqhYj5E9tP5eCtuC6zznhjeF623EYZ00rWpY4P4awrtvAX9xd0SChCp1PPA2Rzli8a4klc0iJkjPF2DUZSm3EL9cPYaJEdfVrrGGYEschYrzhaYpUvLyXJdM2cnhkI4eUt+J98jwPdD7w9mpPSvMKiS0HEXq4vUyJJnRsmg5jThjg8+tO7QUunX9cCu16veTPMWaOMVLtCorIPYliqHonzKXyOM/srWeYEomKdx5XwAfxdXf1wsSopawWB9bw4xBuvXalgg8O74Srb4tYRS9FvQsCLY3qibTEGC6NxJjE62X5uhZWewLnxBP/syuBa2IqPB0GgXYay/1p5jwmrIHeO04/UunKD9s5KQ/DnJhTYUqygnQM/NnVRthrpjLOmT6I8rVa2T/VXHCl8EOK/GZ7KUJ2IRxUYf4skN3areseouh+pOkaxtPAcc60PtE40cNsbea3v/qSv/jzX/B//+3f8X/8/e8YTeEpJnaNF7vlHAVCKwuUV3h/HEi50KkF77ZtGGxiOEsA+uKa+tmuw1nD909nedZ139Y6yy+uOjlAkMN9MRgT0ZxsCF+myHFRziIWxmWZgDGrD0+sla8PZ16mmU1odJpZHqy83pHeGV51jSy060WR/FOvn0dxr/BYColMdNJBLYZKsGBaFpRBkXJdMZpYKj+cJ2LOXLeBt5t27d6tNbzedPzx5aT0PMk2tFZuaGvA16rsFaBWcpVyWaslpqxGQZpC/4kg6DhFHsZJsVuFSViwSxnRT0OkA3bWEp1Zx7OH08CUpIOwSLjCb15f45SJkKtQ2QCGkgnOSED1WQ60JUhYrkC9LEaRE72gB6ZGyXTB0wXPDy8nYQGhza8Wyr+4veLNtuWq9cRcmWJWmbvhOGfmnHDWcBMCnXaoXr8HeflaehAq3JOouFxJ1cqkU0UMFazYAo85smktpyL8fmMvo21wlr3zQr/LEYNkpBZv1qml1kqqkE3LYcpYm3ndN/SN45fXW+nWWDBugbes0Vxcc6HzZQpLQMWyH1ghq+UG1fuw1MopRW53W17OE1OKOFuJsdIYh7OWaZrJIdL7lrA2ALoIRZZgBrn/tt5rYZfFWWOF6ZKr/LkcDqcpiY9KESzfAcGIHXMtlkJShpCjbwKvuo6goP5iK/DhMHI4i11DH1qGFMlUNt6vO5mok0iwjk0bOGqgeP5/qXuTkOvyPM/r8x/POXd4hneKiDciKoeqrKbsLgq1EaFFBF2IG1eKG3HR0JsGEVw47BtcNbhx0eDCBkUbVHQjCg2NKFUtdlfbaVVa2WZFZsb0Ts947z3Tf3Lx+59z36jKii4okfRmRsT7vM/z3OGc//D7f3/fIRdCFe0WBXchsB9mnrYeUFw2cuoIdVyRZWO8tgad4PU081GlL0vur15Pycv8Bjnx5QpNYg3OWpxa8gTMuYhSQjZ4uL/hf/57t7x6eODdIDkBd2FGjYp+DtJAL+CLwJIhZDrvGGMUf3tgqy3awpNn1yjjmWZxuszA067BaMPrg9BsjRLjr62vsDDLZxAmjqmbQJ8SXwyDFAtGisyl9yAFqIS6U6+nBuYMfT8KnFktFYSGLGPzA99wYe3KsVbLzfiWxy/F4l6KcDxDzBIfVzGz5aYLpUh4zlZpXg89U4hrusvCgvAlcxfi+sE1gv+OKXMYZ647TzaWkjO/sm3PG1+lncSKW04x8a6feNUH0Lr6r6vl/wI3WMVuY4lzIVT14sIhr/cbkHgzX49RPTDExDAvXhVyxHt5uauUMfGSyLkQKp2vIF7TG21xiFS8qBU1Z2EbyJ8104KN1J/YGc3HXYPT4sUzrQpO+aWd97zctzRW8M/Oa9go8eTRSjxeRlkg+5jZG0OszA5KZYDUBUypxdlQIKjFGzyXQtcYTpM0imLOXGy2PM4n8TYpy+FWNrsnzvG88ZRSmIqwO5rukjll5jBiiig5oXCcBiyWXBKX3rB1lscx4J3Q27zRxCg4vAXQirFK/kMRi2dvzxtiUmptOi69gqVaTkU+6xASjXVcesdDmBnniFJGnstbts5gUZhUrZb1Iu2XiryxlhfblmMIHIMssv2c2HrL3llaVwuJ+jZaY2pzbfFukUaq15on24at30pV6YQOOYfE/RwEalCChecswdqdaynKMuWCx3C1bYgp4YwhFGHxHEPgbhgrzLZoH+qiUkR38NUgyVJX3q69J1HJItBELcefKMtXIfB6Djyzwmu3FXs+nz9rNasU7/oRCnx0ucUb+TmJ32MthhZrjRxn3tz2vOpHdCO9iSkmXgfx7VG54Ixjv9nw7v5OPJBaB0MNnw6JUMfuw3FkmGc2VpSfVim0LjzfOIxW3BxHXu5aLluzrkcl13GvawSfkqr9MNf81VzHqZGTlI5CpZWTmZx4tRYlfa79HF/XzfetdAAAIABJREFUw7MAsbA1luvGr2uLQlhKD/F9B9k//vilWNwpoJaY93Vh+sa35WdKwRjFwxg4hrjCCkZrvDM8psTdKQqeJzQYdBGlXMxLe0usBRzCm9ZKYbVQ78BK9ZhlgIZtw/0YWAIPlsdCc1RKses0IRdOfSHEcwUoBbWqu69Cp8JWQdFGTiElc9k6PrrcShOwYt+lsFqllsoGGkPg+a5jCJG3w0DRZcVn9XvXJ6nlaslE2xjDr+127Jwj5MyvX++5HQNvTj0GxZOu5eOLLbYKVOAM1zReFHBqCjwpnlgKjyHU5vVSPaj3JpsSXnVtCBlk4Q/VAvk4CCVPiv3Mw3BiNnldwBZWiwaunNjrztUSdmc0FxcX7PZb3t0/8O7mhpxmplSYUsIYS+MdsQhslUpmCJEBWRhNva6oOoaoZnQhcTwFGq9XD6A5F8aF1lEhjrIUHErYNH2c0NkQ6qLtnaNrGhoLp2kQszj0eidk8RPG1FiTplK9VjFn5iQb2BQTnbViCUBmDEv6kuY0zvTzjLWaFKUYaozhunOrmdlcYZNQDPfjxHee75mGEa3g6b6Tk8cUOc0ilrLWkmv1l3JeTayc0sxFAseX0+ofLVrmXPj8NHLZ7HGVHZKyUEJVkZPd0kT8yDtuYuRtCHxs25W8IM999jKKMdOHxBgymyZw3dpaTC3sM3m+pYYPNTPZeDkdptqtzUDRsDMW5xtUKezalk1jmFLketswx8ypZh+QZeTfH3vmSqG17twDufCG7XUNsimi+yhU/6OyhHnI6eddP/JmnMiVlmV0PeEWUdDnVMd6LVAWgkSsjeMlzGdtyaB42ngWw+pFODbnzNb7P7qSfuPxS7G4q1KwsTBWDGupalELNLMclaWqkvCM9bcrf7XiyVkGg9WaxluGUbjGopRbFHmLn8iC8RUZDbUCtkaYNU8yXHUNwxT48euH1Yjq5eVGqFFa8GWjFRcbyxgy/ZjOeFgpq+hgEaxdGs2vX2y5L4l941msgZeGk1LSQ1gSlFSRimWjFS8v97w59supVlwfa0WXkX4BRXDurbH86m7Dzntikuvx0W7L811h13k64OXFhl3rV3pmDAEoWCWe6dYYNr4+/xy4cp5DzoxDZL91K1YqUNJSg0mV2FQDpjlmse+tpxvfGFHjzTMfXWx53feVjSOVTEmFnx0Gds7wYdeKsvhiR7fpCCnhracoRx8j/TRTEgx5Zs6yQO1az9ZJcpRs1GWFQrRS5CQT6N1pYoiZbWuJJUMRj37xUpHT4HLTFgEVyOSKFOIkjbpN09B5A3HmuFSZNXBh6Qoui0Splf/Xp4HWGZGo53MakTcGrzVzjJymyJzEGuM0jky1mAmTzIdUCl1nZaApBSlTlOJumOmjovUtXdsxjDPeaFor47SfIvfDSOcdxxA4TOPK7morQaExmilniZujTr2yWGhUaKme0v7woedXL7cisEIxh0ijxeFz6XFo4Jm1fDUHvhomPmj86s0km22dwzETA1zuNnz27oG7xvHyeiNh60VOXwunH8TK42LrefcYRNFewCgpJmIpnFLkwojtwRwjzogN8XFOS6uLw9Cv93nfNRwmKRoaVzUzsgzhas9jEZ+VJHYpJZd68lc8hsCrcSQlEeF97+KCrdW86kfuF38kLTCurhuVolJY5VIRSp3Q9VM2+sygWiBfDbTeSlbutzx+KRZ3mQA1onkZTyvXcdFjyo4XlyWkHs1WqARWxkJJwqtd3Ni8FbuA59uOu2GWoGXkuCwuivqMF6slZbysXjZzSoSY1hf60XBPcQbtz97VWgnPXSlFjCLsCHO9T4WVH1uWJm8qjCHRNaYO2MLGe4E88rIYsSb4hJR5smnYeUfPOWxhaQgvzAyrNL95fcmlE3VlQY5+qRQ5lhrNhykTS6Y1ClVy9UvRWO2Z55m5RBrnsUajVGSnpVE0xkSapaFa6oRcbEpls4SNNkxJJuoQIn0vQh+nDJ0Reb/F8HTf8ckHzzDvHvni4e6905Z8MGM0VsPTqwtefPCSjz95wf/wv/6Qxlo+ffkhn3/1iufXV5zGmfvDI6UofCPaiJ0T++eFBZXrcX6hfsaYGMbABGxbjymKPhRKpYkui0kowotX78UuShwaXLaerkIZOQuOOs2RhxSra6bHqoQtmihazNWDZ6j00oWRJfCRYoqFm37kMAdUgcZZ7ntpAKpS7X7ll1BIbwIQ6mGdB5cbzyWKoDzH06n2Q0RzICyawhijXF+rCFHTtQ3D0ltRsqA8cY7HGOlzrNBf7XuxiPhqPvA00/WaT3cdj3PAcC5MzML3VtJAvHKW2znwZpp54gRPX+auwF0Rb40kr2nF/Rx4eHXPP/3JE1Y7inonct00n29bvuwHFLD1ltMU6rwV6m1jDcZZTuPIXd3UppQkIL2mhBUFne84jgPeOO77kcZJzF5rxcZgPbloGVchVq+X+r5izrwexiqaK7zoOp51DSklPuoaNlrzeVUBi3mYkCiuvOU4J/qcWMXkdW1TCq4aR2fElXRxDp1TBqPp3o+G+wWPb1/6/7961B3x+0+u+MGz56LcW+gEUpaiUkHlQpzTipAUWPHRRbq9NMfycuGziEF2jRNjqWrju740Iv4p9eVKrZQlh1QwQTHzEZxsiRI7nzDkl5IScZH2inaj6TqDbxS5UhPXTahWk86IIOhhnKBAY91776Oc3eMAV3MnG2f5cx8+Ra+0OlYd06Lifd42XLeNdO3rBFC6NjUrZPHm1PP61POT+5MoSVX1yLcW51ylMuaaYyoLkDGaY0k8pJnoCkPJhArvp/rPgp66WgGehljd9AybpuVivxF15X7D5aZF0XL3MPDEOJpaGTbGcNV6rr3FaM2QNLfHgaGfIWf2u5av3t5wdxxovaezlovtnsvdBp1hmBNfPEjCVWM0W2fZeCsYa5Z7McfEYY7SwKawxC1S738uhdaI0x+hsFUWXxQ6gU0KVwyX+wvcdstM3egyayh5QTaGQ0ocY+QxBN6OE++miVMS6MTWe+q1oeTC4xi4Hye+PJ64mSaOOXGcpnPXUa1TgYWfL5qC6hukROEpYSCFlgkVZ5yunynXRQHRUeRcaKycHI/9SGOFoptqItGY0iqaUrCOp0XRsGT/aq14NwXe9DMppTV5SKFZkNaFbrvXho+bhpgLNymhqiBKa7U+d9s63h6PKKsIKbFrnJxiopzunJYewsKFejWMTEWsRuaYcEbjlOgiWtcwx8SxH4TNVJPa9o0TmrESnv7OepwuOGPIJdNPgTcPPcMsa4CpLo5i/iZrjHjLGKwRoeQpJfqYUKXwYtPx4aaDuo6kVNhaw4uuFTuKev92ztIZw4uN50XXiFvn4hOo4Mo7nrdNdZo9O3PK2JU5/W2PX4rK3WrDD773q+Rw4ubdDSUVHIpSj3a5ckMrtCtpOosfSj36FoSbSwZtFM4a8f8uUompAsdhFErT0ihT1RirMnOWCK1l4VyMe8pyNGeh64m/h4TVZrEAprzn4y7P0DWaZhFe10UDquo1S6RfjIW3p4FmDlx3DSElWutko8rVZiBnHkOkcxanFR7DWAq5hgUAPG09L7uOJ22zNNiZl02gDgzZ2GRwppg5kfj544mXu06O0kV6AkZpphC5H6Xq1jWkItUmGKow5IQlE4uhrbTVBfIKMXE8SHo7WdF4z2XX0sfEpt0I9hxmPvv8C8I88Pxyy751PISIN+IgOGvNxsI8HBmd5f7+kR98+iGfffkaoxQvnz+hfzyy6Vqss6SUeb5v+fzdPcd5Jh0HyrblumvXCvfrfuY4z3TGsN80XG+F4glVQKZhgRE0YrVanEB9vjbGrJKAkhgmvvfyQ4nH6weMdWw7TyqJ1oj3o1XVp79SX7VStMoQKbzuBy5q8zfUBfUYInNlp1x1hlC96hWLgE6ur63lSWMMucJPSotoTAh/i0UAa3RbKmIi9u40EHPkotlACpAzxmgeTwMUxMc8iqYhpITOULQsRsv2ndNZ7UytJN+OMx9vziQFY9QK91XEUTDmAhfG8PU8443imZUT5rt+onGGd8eBrff0RJ61G55ZJ5tldU9tvKlBKZpDiLwep1rRn08L1lZ1sorEYoSCWNksCWgqHDJUq9QhRLxraJyjlMxpTNyPMxvnqvhMrSfUVCBnsUieU2bvrGQjn/p6Kix82LU4BWOIlaVXTzqpcOU8DyHgrFhJ5woHbqzmo13H29PIVBvSn+42EspeT0l3/QTGsNk4hin+2TNUlVKfAn8T+BApHP5GKeU/Vko9Af4r4LvAT4F/vZRyV3/nPwD+MlLQ/dullP/x218D7HRg1pp3h2PF+JZjbFl9x+s6Tucsd8NEqXBGqWZqS3NjwbGnWRqSjRUxUM5FvJ6tJsZEKTKhYuUk75um3oi8VtmpTowlqV2YPZWjLGvXemrIQFSg1YK1SxbsUo1DRdKU8PBN/UAKuBsHXh8PXHctz3YKVQ2QYimMOdFYUbqepsil88xpohZiNFrzG5eXbJyVLjtn4yfq5yAnhpS4HyZCjRXs55nHaaKfE9+73q9Bx1NK/PDrW76+H7DWkeaZzht+86NLWqvpSxLzsJiZKcxZszFynA4lMw2RKSSKFaiqs4a705Hv/Mr3+NXvfMoPf/+HdE3DT37+Bc92LYHF5Q5y0ZxCxqjI1lkalTk83PGzaeDjT17yW7/+KTnDq7e3PMQZo6QQaBuDcw5rjpQ8cxoTT/aOuWRMgsMYOIwzicwJW/UEmlZpBtTacJf7LiXSlMVGWdfvg6pwnaZxjkNITKdjXfYMF03DdiMRgaU2J0+zMGKWpmFImWKEB+1RHELkEAOZ9wO8IcYoG4laPHsU5yAgmQxuhRXPrfAlNH7xeQeB/+YkMJkqVDvdRCiay03LcZQIQqM1SsPT7Y5+nLkfRhqtSKqeXOv7e3m14cuHXuZplpPaEBN3U+Bp9Rgv1VM+IVRAtazwwIV1KK25nyN3JHyGt4eRi02DsYoxBFxr2FuHN9UTnVpwTQmjC5jCq2EW+qa8olzfmEhKoju9kfE3jIW2iPdNHyNjEJ8pSqFtDInEFCfIitZYVFehNrPOIBYofEqJLw89feXTx1w4xSiwbVFctdKkmnNhDrFSdiXhavFrurSOfVOD56mKWgWdFYW4WG10bK0E4UTgfpgpqbDfejSZzlv6uJhp/OLHnwaWicC/W0r5DeCfBf6qUuqfAP594G+XUn4A/O36NfV7/wbw54F/GfhPlFLmFz5zfSgKYe6Z+sdvmNkvarylYy8L+BnjXsrWReikKyd9yS5dOuGmNkwWNRyc5eiPc+BmmLkbZxa2+vK8AtHkVUBTiuz+fuUvn/8njVD5vVTO4qK4Ptd5m12d7pTQ84qqCz2SvvLzu0eO88wUJdwg18Zb388wRfba4Isc76xSfHe7pdXSmAGZWJSM2HdJtZ6Bt6eB4xyxzjCkKF40pXDTD7w+9swpkXPii/sjt6cZqy0piyS6V4kvDwPPnOfKOJGn13PinDKHKXIKiUMfGMYk3y+CJYQiFgcvrvf8/X/4D3h1f8vd4UHsnbXiq9PImDJTyvQpozScUubL08hYw5wP48j/8eM/5M3NHXOIKKPpM6A0Kga8c7y43mO04rLr2DYtxMjNceYwF971E1MWI7IQA07L4uidoXOWvZXwZKgOo+8NuMUwTCGTe4qJ4zRz/3ikayRVSytJWPrBR8+4rvjz3TjTL0lKdaSn+pxzPY2dUlz9cJaixhkNSRqaC3srLtYH65w5/2GxwtAV/1Xr1/IjQ4i8PQ70IdE2zVrB7hqHUdB1vtpiSIxkmGaMUngrSU5eaS6MrSeDCnlog0H8kcRWoHA7zqs7Kvk9uIszu6zUCnunDR/6hmMQFs1m44k5M1W16AddJ0Es760TYlGsmVPh1Wnk3ThJMVN55AtElQrVH0rsDi6c5VnX8rT1POvayrdfLCFAGrsJpcVX3SjB1Q8hCIZejyji8xLpo+g+nG/QvqH1DaoGdrxoGgk6iYmHIfDuNPHF3YnHakZoag7BMEdSRoRw1cXycZS16LrxPG1ECX6YAj+7PXI/BiYFsUTh1CvY/VnDOkopXwNf1z8flFI/Aj4G/lXgX6g/9p8Bfwf49+rf/5ellAn4TCn1fwP/DPDb3/IiNXRAuti+hlfP8YxvKxZoRryQndbkJbakCNllwU2z/EuUckV8OZwxUMSf4TQlrBKxxscX21rgSMWziBLEIz6v1p0KcFavWGlISar0Ws0JAVCtG8gyYS+Mw1fp8SKMkX1JJnhGjKNEHSsfZoqRd8NAyIUnbSsTNhaYMzklrNZcG8eLruXldiPUuVwoJZNqcs9yXRap+9thrG092QBCzDXqTVFS4mf3j7w+DFw3li/ve3JKKO3YWVezTzOfPfYYrflg3/G08dzOYWU7oOCun4kx0xhN6w0hi8PjMM1EnfntP/iHHO4njJKB17SG1/0owRMVaFz8fnzlQvcpk0sUu2atePP1V9xuL5iCUO76ceJxHLhU8Obuln4YKZU6+ORiK6lbeeJxmivEpnl60WJVtZ2osIQ3GqclHCVVDDdXymepq7PWsG8aOsRFtB8mmsay0YbDFJhD4nd+/DlzjHUjqb+4FNGVtZOLeIXMlZFSD6YrJa9zlhiSNDHr/JA24uIrI4u3M2tdycL2okDOueK2cj0fp1BhvkwuiVQyrfFYrRnGiVPOQmHUGqcK/TxRMIzjTAF2nafkxKXznGLk1d0gAfNzxjlVw1RkUX0Iia5bSAIVztB6hWjkY1ZqY4EX1vJ6DkwlIR3FQkqZVmmW6ECieKpQIUGt4D6GlT4rt7HU+SVH6ZITxzHjW81l11SfHqmMX+42vDr1qyfPYp2bS4Ri2G88do7MKfLl4cT3rvbCgkI2tcXB8nLbSdqTlue9sl7mdcycqvVBiInn+67CwXA/TRLQHmEbE1c7TeMMD8eRt4eRzhuuvBNUYQq8Oo4op+n7mQu7sOvkvus/Kyzz/kMp9V3gnwT+LvBBXfgppXytlHpRf+xj4Hfe+7Uv6t/9iY9USsW9TN1VVX3j52ZRXT9YGmCtMRLtVcc0ubAoARe4ZmGR3M+BLx9PtM5QivCsG32eDBrWKLz6eRhi5nU/sdATtVKYIKeK5WjetZ5Z55Vqp1IRqIYzC2ARHImjgZgKiRhEobNaRQ1L1SWvL9TOm/7EKcxc2QaXxAJ2sT1olGJrrXjelIIiySaTkyTWQx34mT5KYpRWmuumIYXEtfMcUlwZFCXBIU0SDp0UfYykFJiMYec8M5lTDPyj2wON1rxoW2l0IV1/cmGoLp6pJD7c7bje7Ll7ODKlKItGmjE7g0OBVTyUiHYaE88h3xmwWkQaH19s6JRiY4QlUkrhtp8pU2HXdmwax7vjgSc7L7S4zR6l4O3DEch8/vaWD66v2W32GDOCSry4uOQ0DShVKq1N+NUFVU3mTG0kK0qExfEv1ZSckBP3s+Cozhh27Zar6x2Ht0dO88DcJzbG0mgxxNK1oblU76kUJrIUEQvfG87qXGUY+vdcQGFtINqFYkmptgN14ykCBa6N/lola61xtbqeYlo73p0z5BQZteWiaXk8HrBKrZbWBelPYRSN9TSNJUVNa42EWVRDv/22kQjEav9blDgYzrnQ2Rolh4Ky6D1knKdU0NbivUGHiQ+95z5E3oy9nDrqHJTrVjBGo6yIklQqHJO8hhQEmqLyWtAIts0ZqkpSBKz+UbUH97zreNsPYqUhhTkKTR8i17sNd8eRTMZry8M087xaKHTO8t2LHa9OIzf3D2hrmRBfLMlVaJhqaE1Uha517FrZSB/GSWiaOZO0opA43B7E7qNxdNaQQ+E0B0KCxynxnRdPGGMkhkcuts1KB13XjG95/KkXd6XUDvivgX+nlPKo/uQn/kXf+GN7jFLqrwB/BcA1LT89iNeL0YaY4zpA/+iTLFW0qlg3Z/IMSrPi86mw8t3HkPiDdw9VsKT5+HLDrh6FrBEzr0VBuDQ+rVEoI0KFRb0YSqGEKCpDZzAprdWYJJiDzuI9fQ4Hrg3a2uDSShGUNCOcMWRVKLFUEyL1jaq7KJhi5OsxYrTmSUlcKMvNMHOcA0/p2LYNOy1JLzIxSmUKCbX0Zhj5+jjgtKG10nRzVmhgPmhup7meLGRxCKUQKGw7T5gTwxTZNg6TIMfMlAv/17tHdkYag8Eq5pJWozOVJb5uHCIvPr1inCb0WBhywAXDnGYa70SrYBTWgXGGMOZVlamVFsZNTOhKL9VGNkbvLVOljP307Q3f+fgFH2wVSrfcnGbGlLjebXB6JGeYwoybFZdNxxRjrd6hD4mNyaIqVGe2ka5/1lpRtNg0x5SZ5iRp9lUopLUh5MRpyjx+cQ9G4bECjdna3GdJj5J7a6u53bRAPmcYut5zgYASZV1kI7BAq2qhwCpwKbE5jnJPlVrHz4LvaK0wJdemvygetZYm3xwyTeNoHBInV1+7n6OwTYyWqLkCUUvhdd02fPL0is9e3xHDgDWGIUQCUj3nIm6RfUzczZUOKu+WRYH+MM1ceM/Tp9dcPf2QYRz4w88+4xQmFNX6uAiN+GYY2VpXGS6ZEiTUQ1vDu2FYYS6Brc5S/pLVeu3k+i7clPdWJiUb3NON5OgWjSScaelfvet7mq4hh8jGSii49OXk9bzRvNi0DOsJDYIq9EE8kJw1aGX4sLN0jWVSmqyEAiuCL4FUU2UDTqVgUqZxlj4nXh1nrI7sO6FlPgwjIRTGOdJ0TUUL/oSV9r3Hn4oKqZRyyML+n5dS/pv616+VUh/V738EvKl//wXw6Xu//gnw1R99zlLK3yil/MVSyl+03hGKNCYEM6zVLOeFvG6uKza9VA9rVa+lejECuqNybVpqsQ2dYmasx6Xb2ohxRgQnkj16Fkhoda7mlameNkaBVpT6TQnmEJZDXEqzZUDpas2rKz1T3ijrf5R09NFK/M9Z8Ouy3rD39zWrNDFlbsNEdlbc5EJk6yUIfIxppYQqraVnoaRZ/OVxQGkJr2hr+IQ09xTXzvOiFcWg+JNLhaU10uexVDtgucDbxuO9ZZwjb/qRx2lGZ8XLiwuu2o5t0TxvG/7Sr33AEGZ+7x99Rn/quRt7HvqRcZrJU0HHBW5YKkxoOgkZUcAcE57Cpde0WphIpo6LS2952irePjyiNDw+zvzuT97wk9d3vH04cH8cQWXaztBtFE5H3twd2G83fPr0is57QsqMU6APZ4rd6pgJK1PJGcOm/vwSity4hl23oXWWjy529NMsPibzjPMeqxRPuo5nF1ucFYO0lKVin2ufpxac632WtV70Fgt+vZaTnJvxwsZanitzmCPHSWid92PkZph5GANDSBzr9x7GQEbRWItW0gh2raVrWlASQVeyCH+m2l+5PU0ClRmFV6ByYpxnvnx7yxwilMpompME4ixwIwIX3U4zPz+NtUEu7/cQIjfTxGA0//w/91v044E37275p/78rxGV4XGcV+M2rRV9DNyOA2/7npthknkWM1MIXDsnYdUFSsrr3CupUqGXxZxzn27Z93TdUWOudFfvSBpKbbTMKnHKM04J7TLqhu9/9yP+4M0DN6dp9YhxVvNuHImARfpwOWTmIl2u/abj2dNrjlEyUAOZp5uOD/cbPti2/Cu/+SneKpKGtvFiV6Eczi7620w/z9yceppmw7btCNFwnJL0sJT6xhrxix5/GraMAv5T4EellL/+3rf+e+DfAv6j+t//7r2//y+UUn8deAn8APjfvvU1UHTWEJKi85acCkMpxFplrZhn9ZktRbjfyQi9T5SmVeSxPKMS7M5pEZrIZ5HnOs1RuNm5UKoHfMpSAYi4pHqJJKkiFILp5XpMzPWf+lLrMUnEFXLUjkU2mFThIV0/h2xGZ/bAouPTS0OsPqdSSxNWVXWcHD/fzkeePtuQ58wHe/EUOYbI3pl1c0op8XYY+fo0YJWmM45GG0xZrGPP13RvLaVpeDtNxJJWX/ICbBqLtRLYvfUdISZaAqcSpImlgCRNoRc7R0yJRmt+5w9ercZkY4h03tJYQ8kwxECIcmwfQ0J5ffaYF98FVC48ToGvFHy0aWi0IuTEzw4TCrGv/cHTlsc5Maaef+kvfMLvfvFAPyZ+6/vf4/XNW8Y81809M6eZt3ePbLqWaZ7Z+YbnbUs/B758OPFi20KRwIzGykavtCYVwc11UYxjpDjYNZquMfRTxHtLOk14Z3joBy5ax3eeXPAwToSQ6YvQH60zNM4Q5ihNdGWYU1p9glStPrfeE8ZYszHr+FCVmqmrT3gtQW1tCi6QiFYihjJGmD0xFTqn2DqZ4rnAPM5smoadsTz2R45D4knT8XSzIWRJAMolY6z0gFpthG2kjdAjKxVWK9i1DQTxNjkGSTPLWmwUYoY+JRFLKbF/fhgnwpS4ORz5m//t32GYIiEGbh8fuHSeOAdujwPWaVovPaqoCjEnxiTV/9YLw8kDn3YNQxIXyJALfQ36eL+gXVxEl9NDQTybTjHxbp5XewtTRWtrfaaEvXbtpTfzP/3d38d5SzsbPjAtU0789NQz5sQ0Sf6t0wJfemU4DBNtk/HGsG0ahnlmmhM5S3TLYYz89o/foIph51q6urEMcyCEhHPnFLRxjihGPnr2FIPhNB1Zrej+XzAO+0vAvwn8UCn1D+rf/YfIov63lFJ/Gfg58K/JolF+Tyn1t4DfR06Vf7WU8q2cHa1kXk9F0tIvGk+MRmx7tUEZCQIunAHtbdtQpomQhLqYqu3AGVuDksTgKy5eIbVhOafMmAqtTujy3gCgfOMYJ1VJXuGh9QRdBVY5l6p2FRx0ETZlVTF2xOhLsVQQNV80Q6iRgutJ8b37dB6gqjZ6NSQJ6A4InbPpLF8NJ3SBC+OZCjRKmlRFiUFZzgXnlIjCUl1I6qeokC8ArdG8aBseQm1s1dPFchQtBaY41etp+XDjuT+OYoIWA68fjuw6Q+sbTv03/nR1AAAgAElEQVRA6zwpipdMKVSpdqHznm3XSn5lAZs1U5ScW63lsraNY67xirdjYIqJj7etxI/NAUphTEf+wrMLrFaEGPjhF+8wyoPJeGfx1kPsQQm+qZUhpMj96UhMCW8UdrfDe88LK82WdrPlzf0tp0ksDIxWHKZATEKH3HSOkhVXmw3GGeYUudxt+OrhIAVEjAxToBQpDk4xEqokdS6ZMUdZbIpsHiCUxCmJPYaqBcsxSQxjWQv3sopV1Hsr16axNM3ZnbBQU6RqNRRTwShxmrRGc5wCbx97GjuzqZ9v03icsww5UrTEC5IgIS6aO+8IORELhJBQvtoHF8U0zWwaxxATFo1FkaLAPDUJhZtp5tqJc2Qq0FhLjImb4wFdXTT7aeaQE30KOGtkvGXpiy2FEwqOaeY0aon7cx6vNZ21bJ0lFLibZh5mGS+58k6tVlx4CU4pqnAXI8eQCFkw+1Iqm20RQlb2VykKjBQiaAm0do2o3+cinP6pNpEdum4Shc47vFJMueobSiYkgQJPw4T3hlikKR2K0HFLkQI2pgR1nDjd0GrNGAJaaWKKuDDx5WPPlGbCxnG9bTHnFekXPv40bJn/5Tyk/tjjX/wTfuevAX/tH/fcyyMVOM5yEcQtrtAog6nUtJQyY0l1d814ZdCmSseXhVeBKZoxBnHNS0tyiaKEKF7TCJOgUOhjZmeoIgF5H0t1LvhopZJl1kUbzke7kmteKnqdnKjqNaOWSqAs1wNTxS9QWQv1K6XrqaGcm7ArZAErpW1hBghiJ14uhyA5rpNOvLQ7lBbzqdePPaooLptGPlfKLNLx9XmKrv2LKswyls5Y/vB0JKRCY1S1QRbzrT5H5pzY245pSiglGD41a3IKhQ0JZx3jFFHaEELGW2lSKWAaA5eXjhCsWP76llJmxhyJpdBYg5qziDMqFnyMmZ8cRGBTahU9x8xPHno6a5hjQW81rsa4/eSrL3i6a7FBkys+3nqxt/jg2XP+4KsvGHLidHuPQvPB9RUmJ4Z5lDxUFCFp3j0eRZilChvvaL1BKUNR8Hg8sWssj6cTzlluTwO7XcNcMjkUhjESdAG7yO9lwcgKMpmpWlabutnvveP7L57y+v7A/Xr6XB5LEPUivJfHQudbT3vvjTeMRpdCaw3HWWij17uWu9PInBN5mtl3ngvv2LUth9MDNiuebrfoDA99z69/5wN+9LNXApHMicZ5chYFqNKKME901uNL4MXTLYdh4LGfCKUqvpXibpoZp8jFpmXfNNz3I04bxhixCEspaziOgaTBODl9lCQaA22VOGTW3cugGFIml5lLb2mNpRSFVYVnrePCO94Mk1gVVxhm44XuGckch8QpxuooeiY7LNYjZbmOpaC09Nh0iGKJnDIDkZ8+BMkLBjbK0ihN5xzOWHJKHINses63DCGiU8E7zyf7LTFUOiQN196S2obbx0cwdoVXkypMUyLrgjaGy23DqZ/4/O07DkVEWp+/GbndjHz0ZPet6+ovhUJ1fSjWRoNxmpjTmjAT6wAvQB8j0+MJiaQTWpk3hksngoDOWoYiFXpE10AEaSwZpHLatB1jmuiTNE+90cI3LVJptx66xmFCEve+lM9bXLX2bYqCIBxupYWlg1KEnCu2J0fLtS/w3snCKLWmGBWyDGqWpvH5kqRcOFZfm6VxVkpZB6gxhscSGR4e+KBrOQ4zfQh0RpR9196RQyTWw5NCqvKF4bM0kUWwlbk0jhNRjNaUUEKHELFaobxZKYLeioR90zbEODIbRQxFKjKrMVmqliHKp3LGMKbIT+8eUQq2TcOYZhpj0FbYMD4LVVMZ4aRmRVVKysKmKyhutOL5pmXvPT+7O/L5uweZqBompZnmma03zCpzCKBLJmuPay2mVZSiyVMihcSxP7FpHbtNhzWOw2lkjoFN48lFBGSgsKZhHgKnPKGN5nvPr7i62vLz//0n0mMx4qb4cJjEn97pVc9Akfemlt0bVqFRKtK7USWzay0fXG45zYFpFj+jb5YVFdlWC4ddrafRFbyXAUrWhTf9yKX3HE8ZZxXb1hNTZte2Uk1OmRCO7J1nipHbhyMvrvfsjOLtzb3AngWuLna0SvHmcIDa/0hKcd+feLrbYlPGYrjqGh6jVJsoaJAMg+++fEnXNvz9H/2YmAumaC694zBHutZinRaYKslm57XMtX5OzDHTeiN+/FrRVQfFmzGQmbhuGhqtsUrRKcUn24ZXGjEHK+c1xSjFzhoepvANOxBUjdCrFsHvN6VjyTgK3noikTFmqBARFsYUaawH5ISbimXnNR9cP+XruxuZ09rSaMWf+/ApP/r8NWMI0mQtisfDiYdxZmMl47XREvgSVCYbxTSL0NJYw2FIpJwYglChD/3MFB++dTn9JVncK31L/igNL1OrhKUTXptcACg5SWnEZjTlwpgDF9VgJ+TEOEcOQ0A78e7QdXF3dRIOfc/GivfybtOsySgLW6Nxmu9e78WFMkTmvLgeajKFcQ4YI25xIWdClWmrGj9G3d2zqk3JimPLpNQYteBmwpBYPOlXKL/O1ZQlyd4aTVSZJf0pVSaEqY24YuDtOJGqv8mQEs/aFlPOlqOqVv4s76+cBRpKFazSXHtHmww384Qcp2scXZL32U9ijkTb0E8znbc46zj2PUobts5RYsaozPON5+fHgcZYnu23vH444a1sZjEFtBYxVT/OEAsbb/nu9QUpZd6dBg7DJJ6pLJuSXJt947hqvJhRbbwkWiGLvjOiadgYx8NpRGUw1nMcBz776hUg98w1ltlkxjiTQ+L4MLJvNzy72nLqR4xRnMaJEMWa97JtabVlCCNWwef3B16fRhKZnffrtZzmiNIKs7ixLhs7ZV2IV8iNItARcNcP6FLYNoZ9YxnnRD9HjlNkioHzBJHCYEkck6epA7c+Yj0GLoSBi86gvYNmw9gf6cdRGF0lc9m0MCWctXSdEzinaTn0E5u25eE4cup7dk+ucL0h5Lga8rW24epyy+s3d2hnyVNm6zx9FFjtyW4jrqwp8ObNI51vGENijCPae67bjmEahFpsxAxszImQz3LCkKCEzMZqvKqstVxqkQPvphGnNNfec+EcXhU+bD0nI9kMVrHmOlw6y721HGKAIgWO3JPqG280pZIySqmxl8bSWMtpmgSis1LA9SVLclZJ5Fli9hICZd0dDmhlyRSO08RRZ373p1+gNDhnhOkyT+J7pcTHKRd4nMSWgEo3zYg4cOsb9m3DXR9pvBWxVoRxiN+6qv5yLO7lXEGaavA0xUjTNKiiVjy93ocVu5CLIqvgkp+ZEail5LKGJGijpVJK1fBfg9PyfHNM2GolvKjdlBGYpBVchI129QgnHioiDGoJJfPmMKAwNFqgmRILIyIZLua9ozLLPNeigFRq3UgS4lOxHD+XhuryaK2VAOdyhmUWZoI0cqvlgAKcErFF1gI5UST5pQjf92xKUBdM6jG6CLbr1OLN4Xk3zzgjDbE5SeM6piycYxSNM9xOPXqG68bTuAZtLQ/HidOc2FrNJ9tOFttWc9crqIKtMUWmmBhSpKRMYw3XnadRhaQLLy52Eu1WktzjXChahC0vt5tVAfy0adgulrCLaZWSJvAYM954hpBwzhERXr9BMFWHRhuxAAbF24cDow88u9hze3ign4P0a4zC28xhnigI6+XtoefhOHOx8RyGmZwzXedw3gjEtugo6r1aRkFZA6CXm13tMVTl+Cu5r8ZrnLZct44heF4dhlUUZTRr8/xcrJ+dUlVVdyezmL8lfvX5Ja8eJn7vZgAkpMVbw5NtSxkn3taN+qJrORwOkCLzDFebDWMMHIcJXQVEUyxopZlL4Mdfv8EUUCms2bGts1hr2bc7+umRn729xaJEnIXGxJk5Ra67lpAj25yEVqjkRBrfE4pU8htDKsw5ymm99mdQrHDnMUX6FLlwjlYb9s4w5uWkI2PDqML3Lzf8n3ePYt2xnF3Le+yauodedZJm1fcBlQ2XXccYZ07DSGMM+2KZsvRk+iFScsZZMTWLqYA2eCM4/pwLN8cR78S+d4oy3pXSNFb47tYaHk8jShtSSBSVabwFrWisQxcpXEKIxCBEfu+/Vfj/S7K414cM1OoLExODibSqLkzvY2KID/RiyKO0WrHA5SYlasUMq/J0XTCV8JSNVngrnFOxejpXR8tpYsE5NQIR6AKqIjS2KO5OA2PMbBpH48QvotGGVokIplG64tq1ZitUNkT9wNVlbuWkLhBqnbVWaxqjavNHBovWS+Dw+QcXdkoxCp2h1ZrTNNM6K8IHY0lRvBuFp/3eW6iDPOVMqRXhxlieeXiIkSlnGi0+7E3juNptGMfAxnY8TCNWi6vk28OBxnvmkPDeM8dE5w371rL3hl+52vLZQ8+lb2lQvDrdk2OmdZbrznPdNnLvU+HV6cDlfsfN6YCKEsOHEhGJX0RaCGRntTlv/kWaqNYoOmuxzjFOM0+vLjmGA/Ocqk5XFJ6boridxeZBG818PHLzeGTfdXjTYJUcmYcxCG48DGgk+s5VuK9pDTkZwX+drlF+rM6F5b3/vndwWq98zgL9LbYbMsaFDa0VPNtYrto9b08TU5bK0unawykyJpex9f7OrWuSUZgzP/36FpcLL68uCBlsSkylEIvi6uqCV29u+Or2llIuxRMFuD+euN4kpqkwh8BF01Q+NpymkVwKndKr9/8cI6ZYnHF4DHeHnnEW7vdWKe5C5JgCaMXt0AOK623LFEQsuDWGqAtjzGcvpvp5JJ5OTsljUlhdZPGsWNeURVcQpsLeWTbWctvP7K7M2ptQSozfDEsCVL3OSoK+O+8YYuCi9ejqP9EYxTHMIqyLUayRY6ap934OmW3nURT22oA2aG/ZNR3H/gRZGGSbriElUb2OUbx+nJFe12kK7IuSWD/n2Vx7Xh+ODONE6wwBacZfti1v5xPGGhHH/WOI7L8Ui/uC+S5VJEUk2nNMEvO2LLrLDV//+J45Vn2uhZaYFkFMDdYu1XdFKSRxBqk+rtoGa86TSn3zra0/h1aoXNbAjgL0U5Q0mCJNodMYMEbonN4ato3Do4hV6k/djCisZv1LIIf6ozeqSHXizl/KhF0x10X1x8quWCpxqxQOjbOKfo5c1Mi6ReZekOu7bGYC9ei18qUe2Rtt2Nfs11AyTmtmCjePJ7bWMsaJ67bjYeh5HQas1vzKi+ec+olX9zdMWs4p3sqHe9JZUu54mBKNEWWoMZpiCvvGYurC/G6cuZ8CSQ1cNQ3txnDfj2gNn+w3UAr3wySpRVWMlmpPZqlehyB2sbfDRJ9m3h5voITq6ij3QPzoRfVprWwYXesoKdNPE9u24enFhXDPY+LxdJR+gxOO1NZL0Id4z0jFVhIizT8PoLN6kjOmKzQ2WTyeOo9ZxFSrZ7o8byqywXdWc1EFXEPMoBV9it8QMK303AoJzjGTiyiqj7NUvTpnvLEccmEIE847Lq/2/EbX8Pd+/Bm3x4GPn13ST5GmZIx1jENPGKUp7o3GZthYzzTPXHSeL++PbDqNd45jP+KcoXEdrXMU1THNkVMp4lmupDBDwcN44rGqUhcoorUGYw1TloV8mdOivhZluvj4ZOYMTouiWdUJkik8hFBNteqcU9LvUvWaP+88r/ppXTNaa9m1ns4ZNk7wnsUDR1nxH5Jwa8Xee47TxClknNcYLQttqyxFacY44wychoF+TCQlDptliuyblhxk83l2ueMwjjyOIzEkHvKET5rWeoiytedcmEPC1lP1znueX244DDOtd6T0rSTEX47Fva7qFTpUlUkl1XsJM8bITVmwSqXOPu7o8+RZJmwp4gOTq6xcK1YFZsmFaa4WrAWauhCnLCKomhKyYqRS5eq6EJxfS5VCH8KatEQFR2IsHNIs+GuIbL3HtJpLaiJPPjdzCgIDnVHl+hnWLwu6ythTWRwv3/uc712+Rf8klhznn+uMhO3aGjeninjQZPQ31JhnjWZZhScUUew9dQ0388RxDIQq+ulzdUcsWTjOMWO159XtPa6qJrNS3A0TVsP3r3ZorfnkwvI8Jd4Mk+CPiJ7hi8OJp43HGcvdMIuSt8YLbqqaMOVMo+CnN0f6FAkxc9F4fKXKfnjR4a1mDpE/PJzwTlgIHsc0yevNlSuuEZHKk6ZhmDNapfVeeme57FoeTyNfv73h0w+es9t23BxOKOMZY2LbShLVFDORvCaADUOg8RbtNUUvGk15yOlR+MmLrYFWCuuN9JFyvZG5ULKI16yBpsJgueQ1j8AqDdYx5rRSgENKEtIdE4de+PxzikLJi4pd23DTD7TOST9Dyzi5u3vksR9AacY58ObhsZrjFR7HgaIKrbccp4mtd/Qp8axtCFrVsG7Fw2nketdSVKmiwUDOCZQQFY7DSOs0TdEccwRqXF11YK1xJsy54JRCG7OKo+QULQM+ro5uAt+CQLNOy8k111Nwhm9w3FWRU55WihebDq0MSasV7/daYRVkI4JB2RAArXCtZjwFppDYtS1d6+nnJJ5HzpFSZt+2nKL0aOZ+QDcN+12DRvPV0NcQ9Il903BhxenmxZNrxtfv6E1Y7cJLmjCNYd85+kHgRVPgWdvSblrePZ7YeodxmsvL7bcuq78Ui3up6jOlq4CkLkwpSSBEhKUTydL5XiAzhVTAtsIVCmm6eK0Z5oT35v1eEylkPty1NDWYYFEOllJoK6d7calB6VUJtpwUSl3wExLGe37u916kCHXxOEZOY+TtY8+Lyw07K9WpAqJVaKdWeqRbJYu1qiyL8VJZm8e5VnYL5m5VXYTVuhdAgW3WaL1AQZIXG0JAey/H2JzeQ4HldZcN8IzgK0pJLPFqGi2e11YsFsYoXf+kofWeaR5QJTOGxGHo2XpL6wyPM5yyeI7svfQ+xjnzNgSavcNkOBwnQiq8G2cKYf08H9eFvQA5JQadUUqsTpWWRcNttnx5c4NRin0nCtxDiORqWrZpHVorbvuzAGjZPEsRzNZUsrgpkOZMcY43w7GKUDzvbu9AK64vthz6gSElshbPFlKSirAmFVlvMG45SahzQP166nxv7NZqYTktLR2aVIQOHAGdFbdDoBQhGrhqcKYp2FwIU8JZqWRPQ6IxmeMYxOu9SOO+tY5QFK+PR0LITKb6IeXCMPS8DTNjSKSUuWw3TCHgvNz3uWRCKHx8dcF2s8PkyO3hwJt+xhnDu9PErmtxMVKyonWei82G0PcMiC2FRuOsZQ6i1MzICWuptpflegkWinXjc1rsCHJZ5oVaWWuqYqVhqbKTLPBWVQFjyXhr3yvS5PrmIr2btnOVknl+BwWzniIU1aMqS+YuWmispWR2my1bn7k5nTClsDMG4xw6TWy9FBQWRYyie/CI/cBl45hj4FAyJmsejj2NMmJXUMeIoXDoT2xMw4cXG14fep63LSjFzWPPECKH08B+54mP/z9oqFqt6YwHCs6ZqgSVAWCK8MD7eZabXEB39hsLO1C9LOrkSRJGYa34ZERkUsxzIoWKd9cTQMwyMdp6RAfqpNRr1VXqorBU1ZlCTIk+pEpLrE/53vouX5d14L56OK2vaY3Iwbetw3uRqFd3A6nqZHVfXzdT8X7OyjVdFtn32c8712rDaRF2Le8nl4LVimEOtM5I4stSE5VMSlC0loZsKf9Pe+cWa2l23PVfrbW+y76cS19mei5tz9goBKyAgoUipKAIRQgSJ8LAUx4S5QGJF5BACCFHkVB4BAnEGxI3KSJAXgLCyguEm+ABJcTJ2B574tgej+Pp7pm+9zn79n3rW6t4qPXtczzpbs+ITO+T0S5p99nnO/v0rlN7fbVqVf3rX1DokgvQg0VKPOp6o4VISusDR/MZmxxZdCsOXIOI8Gi94mrVUtWe2nsrZIqwIfP10xXffzhHB3hrtWads0FBnTEyDoNxa6x76wqdVYHLrfHrr3Pi3hBZ5cRBlXnpyiGkzDImNl1vc2JR3ln1XJ1YPr11gRZH5QNxUA6aCZGOXs8hjooNrjQNB6ECEZaDwWUX6w3LPvKjP/DHeev2A27ce8T9xRKP8NzhITkrMW5YqQ2bSWUi8qTxRs8bE1LbmhL57s7JMTWnqhxNa/P7YnC8nHXc37dDxn3h/iGLtd1jQUjMNhhlSHYqaEKDF2Xa2qjH0+UafGni0zPE2Dhf9sXDI1xO3FmujRXTO+aTimE1GCRYxFAgGS4dHZGHxCZGHm02rPsB19S4EJDBuGSUCheEZbchOLW5ryVYmoZAR2bdRysGNn4bHcuYNhFH5XzR104EFsDYA3cWDKFsaw2ZkZPHUoiOMnDeUzYRZVU2x+wMWXce6GD1kbPJZerOKB+S2JCQgGMWKrrYc+808/KVY45yS9dH1BlazXiErNblKmGKcHvTcTSp8cFAG01hh1SNOMlUruKF+ZRlryxjz+k6Mm1r/uir1/nOu/c4GDKNM3qIVR/ZxEiofanPPD3pfiGce1MH/tj1F+zYmYz+Vws8yCaVwLK3ynTKuRz9dJuf907wCR5tevqY6aO1djuEGDPireiaYt7mugF86T4bOKMuUM1b+N95eOTZgGOgLJR1LP2n2/B9PMud3crnTw1SIv+YlCFHNtF4oesqMGkrjuoJp93GuCPKb1hePZ/l1AtiYpu+2f5rm4LLYq8vrxsdmaLUXlj3A7M6WH492ybqgJ7Ew5Toc2IqntaWO3e7nrvdxjpMvbWSztuarkv4lAmZswHPlSeI59rRAW/evQ9ZjUOndixT4o3FAnHCphS/nFgn4tG85dhXBIVvpyXBCS/PJmXItLLQxAYrkm7SQFClGzKXmgrfCkLF/W7g3rLj/joizhFjQicTbp08YOJrLk08EwkMZB4OBufrM3z9ziOCE146nrPOsFivuDxrgMwwJG49eMSdk1PWsSdlO6Wsuo7DpqKZzOizdaeKd8RoAYhNrxLD6W5b2ws1hJYCuti4u3lb00fLndvMAGu8S+UDHulxh5y3na0qNsR8JKrKg5YUS+kHwbpde2+D5CehYtX1dP1A01SMLXSb2ONUiAm6FKl8YDab8HC9ts7ZsuYqr9x5+ABPRYwbS29i1ACzUDGdzoy6IyVihvV6bcirYJtM5cWmo4mnTkKfSpt9MLSHK6fDGI0CuaoK4V5xuhR0jJ3gTaf+HKdMFTy1lO5Wo2yiDg6nDpzn3dWaB33PpHJMSxBoLJZmT1dOUdsNX8/qWDGr+ZCsVEFwGljGzDdv3ePSfMLhbMpyveL+6QlDn6hxXJpMaadTLk1qDiu4e+vdMrozc/14zp3TFVUttKEMQ4+RLioT7+lDwInwu2/fRtTg4Keq9EPEO2XShC1J27KLT/WrF8K5D0Pi4clDYxasaiZVja+rLT48ZziYtlsndd7Z5vE5yqzN22LqJkZOl1bRVwUdjK1Ny2Zh/CkJhy9HPV8aecoUG8ao2JUClW4dJmTiYB2wYw71zKkzVuosOpDvdv7KWdSm2E27iZYjrYOnaTxNMLztuMmUEz6VuDNq4HPF5bG71eEIqtu6w5iGQEoxSWEShLvLDZcmTRk/Zx26MVuzUFJ4gBXPAsKi4JpH/p1L04ZW4CDAiSqd86zigCsEbEeTGa9cu8Tr33mHSVsRc8Z3EGoxrpVskVGrbvs5HoTAlWAbznF7zDuLNZfbGiew0oxRX1Hy0MYQ2SpUUjZ24LCpGNQhBZ9++XhO7BPHrRFkBV+iWxxXQ806J050oFPjEDqN1qjW1N6cXOUJteMrN961AQ6Vw2WlG3pWXYfonE+8dIXD+Yyb9x9wb3lqazkZTK3yvvQosGXwc2MarezUB3UForR1VbpQtaCeLKees5a+jZLGKCgZ2+4pnCaOTRqYVTWuCixXK6PQHiI+GCwWVWbtBOk2NKFm1gTq0PBguaRPA4czo6kNAg8XC+IwGIRSrVlNHaw2Pf2wQhxMmwZKOgEFFeOViYUgLQRvQyhELUJX6/+Q0jAYk+CzMJVg3DXYSVYaz6rrSEmZTxqsabE0zEGBNVsNzCG0TizNqfmM+E8EFeOTqmublvUo9nSaYTCqjfEkNJ7KVSz6z8XXhNI1Pg6Ej50xP6ZkAIDWGfvl/YenDNNkTZKamLQ1koTVZkPlMndSxc/81Z/gv/6fL/DFN36Hg6bh1smS7EBzIovVGHISJk3FlUlgEY06Ig0DPg8MkokkpnWFxkQejFa7Eo+NXHmyXAjnDmXSTFbW3ZpHrEvUYF6qCoF1jHjnrDDoHcFoAgsKhZKDtQYlxDFrKy4fTJGSo3biSnNTz6QMl80IXVZONj1rhKbspGDOMpRxaplUaFsBb6iG1WCMkNsXj7IN2sfQWs/5/JKzt0oN57lyVO0Y2w/Gx/7iwYToM+vBBoYohSagRBjDmGs/h+JJOdNgBGJjqmjcBM+16zFtKt4+WfLigc1O7cUoWaeFI7wXZaPZWsBrx9RV5ASXaysQbXLiYY6ssXzzvG1Ydj3z4JFuxX/+rbv0qsRNpKo8GgfmVEhtEaloSTWkkdsHOs1M1NH4wEvzqXVQpsy7sYNQIiygz5mUI5e9TQkaU1/BO6oAp2uLRisXrDjnM7O6jJ/DtmwHVD6wXtv8UgmBdRdZbDp85dhEvy3O+UrwwexeFS7/HOGla1e5dHyZN298m0uzGmXKsov0cTOa2T5aZ5/B2NsgxbHXztPWvtSP8jYnPHL5ZD0Hn2Vk5pTtRg6ZK9OGpHB7sSYOA13fMQnGgfIoRXKi5OdhUpsuXd8zDJE2DIacaWoq51iuIpcPpyQRDmeGcLFcsOnbxUhSqMWzGbINshkUlxQfKrw424QdNE1Dyo7EQFKjCYmDDYoOIXDgbehNU3mO64CmTPC2qR63EzZDx2Fb2foQ+/hdGUQdU4ackWz57y2vuRb7Aq7YU/qeVpUat+0U13JPnL9nx8H0oqWWFhNaGC9R3Q68GQYjI0zRiNnmbU1S5bCpWHYdMUZCMJrfuqk4WW34pV/5PLOqog4ViyHno34AAA72SURBVCESxFM7m31LXXE8b1j1PQ/XK94+6ZhVNVI25LHAf3naMKsCi03k9umAoGxSZtP/Ici5V164ftgSguP3Hq24u9yw7aQUGBa95e3a2gqvw1m3pSposop3CP67sOxGGGRDrBFhPpmw2GxYe2tXritv0XJdUwVjLqy15p0H9xjGwcnLjj4low4eMk1wtJUhOlIZqCAjexNjUHaWhD+fltk6e4sZtj+Tc/+oKrUTZk749okhGq7MG3rvSy59dPLGQT0Ux54xFsoa2SKJnBPI49iSrWFonTWanHaRetbQ5bxlvxShMO95VjKw1kRbV8xcteXZOTg44tq15/n1r76BDolVNEbJTmsW4qmrmo6N5aULU+SqH6jUI41xhyw04hEOq0DtHL1mIxjLbJ3o3dhZBJnYOscxATX1pcZSTkg+Zw48PESZVhWJgapKtFWZWKQjDn7keIFNTDgc87YhDIlpFWwotCp1VeHIzNrAIsVtV2Zw4BrHvZN73H7wgMrDLDjmdTDo5PyAb925TRwSTVuV4qBFlL6cksZiISKFvvVsXSSh8Nqfo9ItKCs3nhBV7aRZPrNLbc16SAwbG8qigA9Wx+jUNuDNelMcnyCZkhJ0uCGTshVTT9c9CWU2aUgpWhNP7GmbmuP5jLsPH6HZxgkiQpci01CTPLhBWW821G2NOOFkszK+osaTSlOPLwXqaLMGqYMvc0Yzm2Ggrew80g/Ko1XHce3OmrrE2FEr8cShUDYI2/TNtqVLRnbVkp4quHZfTs3rITOtfInajUtIsNMbQN+XiVAlBdvFzLIfrC6UlFwgqrNZaxucJk7Wa5rgmdQTHq7W+Cpwb7khpoEuJXSiJS2TmM0n1ohVOdrKI84mbr1y9SoRz627d6jE8fyk5k6vHDaBqfcEhBcOplTO8/aDBVGMVPFpciGcuyBGtuOEe6uN4UJL0WmMHBBwTgnlG5ExL6cQHMEJ86ZhEw3Pa9DBkt9Wyy/fvX8KrpwSBLo+0adhu0CsZuPxzlq361DRNhWKsOg6UlTEKZ1mfBWYicEdc9Jtekj8uLDkDLuuZ1HDWJvn9zl9e6LA86UpYtUPHM0bcNAiSCpNGM7oc7Okkr6xiM+XwRB5nB8rdkJxudAIFMfiEZ6fNjyKAw/SsH1fxBpntkUwZzjyiQt4hU1KTKqK73/lZQ6vPkcQ+N+vfYXDdoIT4fajR3Q5MQ2egYrgHF0cCI0jJpv+nlYDvrHuvFocU+eREqVYHnwAtUHj3hu96wjLdA4aCZbLlzOuf80lb6pCwNHWNQ8WS3DZokhxVKVukQrkVjBsO+rx4pjPGmaqaE7cW6w5nE1oPahkrrYtinK3tw0rIJATqgOBCs3YDNuccA5euXqVh6sl64JK8s5xUAVchuisbjBrqzLLtNSNinPKZS02ztsYvkJpbX/viJMvfRnYxl0FoQ4VQYR7645BRwzUd/eBjHQYBsdVM1we8ArztrZURlNz/9R4mzZxgwo8XC7ZdBvq4G0OgrOmn5ONwR6X645KPF1Wnps2PFoZdXAXI4NErs6PCOK5szlBnHC66ix3TGLd9VSVBWVDN7DpB0O/iaNxNceT+qy5CyFnI9E+DyUd+zVGBM4Y4KWseAfPtTU3l2t6J3Q5MxdvMMxc4IeldueAGuiLI27qgAuOxlUGjyzzDbwKTV3hUB4tN6ScaKuauqpomkQ3GD9SGsxPLXvDyB9PZ1ZbSBFRz6CJRd+x6RPBeV79+Mc4PT1FEtxYboDMwUGZvoSSU+bKtKL2c75zf8Vk+v85Q/VZSYH3EvN4EMU+pQRXZlNWQ2cMkFsHtc2KAzBvarwPpF45aFtO10v6bLMXq+JkO8msO0PRaBWsndpb/s5RmD9ViYMhCrqYOFlttrj6qgpUzlG7yniau8gl57aV8CEpj1Zrm6+aMjmVPKE70xveW2QtUq7N68CsDry72OCdMKv9tuDjMA4aSWN0ZoUk74w8rVKDg6YSueSsI6TA3rPsKlocY1N7lnkkFCuH/fMppqxMXIBsaa/jecOrH/84h1eu4J3j+fmEeWNQwweLBW1d2+BxVY7rCZM6cBJ7VrG3dFKJtobNQF17e2COK2oqo/SKMRRqBbxjjRW6rBNWmI0Ri5R6RFkqp12kDhWSkyFvYkcXM7PJFOnWVqj0hbICm4d6MJnxYLni3mnH0ay1jdI7okLFOBjdjt5HTcODvuP2cl2w5hbFas404axzVgRePD7itOt5d3FCzsqnrlxmOWQm7Yyv3ryJc46kA6QzLn/FInlHITuj/Ex1u0bHNSMlh6+5UG2IcfMArIbEoTMOkluLzTZ2EMrvOEtT5nEtJqWSQJcTi+Upk7qhG2xwd13a5FNKIxAfwNhIU2beVCDQS+ba5Rl/4tUX+PKbN2mAmGxS02J5ykFTc9gYNHFWB4KznDnBUFXTJoDCQVtRiTP2zGwF/qbygBCHgSGnQi1iyosrhHLFJuNJLhWudlRpBa7PJ9zc9GzUUFaTksd3ClJOQQ7Q4HCDoproNLM2AGR5WM3GqbJeranqgHdGH4FkHixXHE4alqkjkVl1PaDMmpoqCJmBaV2z6uB4UvNgtWaI2XQZBm7euMGsFvrsCFmonadQ2SCc9fccNRWzawfcXvVPdqhcIOc+FgHHKraUE6gLgjqlDbUN5mCkIhhTG/Zs2UeqIEzq0qCRlZQTmTPK3FAw9JthoNdkDtONebrz/B9aOObHEWbGiZ2TFY2iWOSQUHoUiVbkmTQVL159jkmVyIOwigPrbuD+6YIu9jYL0p+dErbJm+LQBOG4sTTF3XXPrA2WQsjWhTecg/Epxkrn1DHF0WKFRi0oGPuPQcd8Yhk4PL7nGMV5Z+yUTkda67OUUeusYBac45Mfe5mXXr7OkBNelHu/9y0Wpyc8P6158+4pXUrMJ45JsIEKqxgNEeI8CyzG7GKmqV0p/BrCqcbZacQLq8EWq5RP2CuQrf8gOusk9AhNGjn4raP1rcWatgwDaRpPRjk6nPLwzoomVCzXHdOSFhF129POvK2ZtA0vXHued+7dJ/XK4eGUezdvEIeO4Co8NvDbOSvEXZlMqFzg3mrFcrCoN7uCcMlGrWxjCweGIXJ1NicXNr/T1UCMC4jK3ftLLh3Z7Fco058K/7siRDEIY3AQ1NlNDlDopKWkeMa1Os4ePWwD9WAcJmDF6hurDad93PaPeHFlOLpFt0PO5HIKnU8sPVlVDnGWTghlhu2gRmInYmtbnBKHgaaeEELFqlvz1W/dwKVM7YRpqNESZDQhMDmqWPeRZuLpBmuuqoPgQ9g66tGhDdkQNg8LfcG0FJ01j4kU2ebYcWewZSl/k+ZMFmvUEyc04piKsBoyGzGkCsWOrkT+Yy7eB4EgrMuNlAdD7oXgLEVYmuZimUGcsaluTtU6e+sKL0JXWzpvJLRT7O944WBqhHSDkYuFynPUBCCzTpkHizU5D0wmtTWrFQfvxYKscXzotXn7VJ8q+l1J4d2IiNwBlsDdXevyHrnKxdMJLqZee53ev1xEvS6iTnAx9bpIOr2iqs897gcXwrkDiMhvquqf3rUe5+Ui6gQXU6+9Tu9fLqJeF1EnuJh6XUSdHiffg1dsL3vZy1728odR9s59L3vZy14+gnKRnPs/37UCj5GLqBNcTL32Or1/uYh6XUSd4GLqdRF1+n1yYXLue9nLXvaylz84uUiR+172spe97OUPSHbu3EXkx0TkayLyDRH53I51eUtEviwir4nIb5Zrl0Xk10Tk6+XrpQ9Zh38tIrdF5PVz156og4j8XLHd10TkLz5jvX5BRG4Ue70mIp95lnqJyMdE5H+IyBsi8hUR+Vvl+s7s9RSddm2rVkR+Q0S+WPT6B+X6Lm31JJ12aqvyPl5EfltEfrV8v/N78AOLFgrSXTywRtNvAp/EOn+/CHxqh/q8BVx9z7V/BHyuPP8c8A8/ZB1+BPg08Pr30gH4VLFZA3yi2NI/Q71+Afi7j3ntM9ELeBH4dHl+APxuee+d2espOu3aVgLMy/MK+HXgz+zYVk/Saae2Ku/1d4B/B/xq+X7n9+AHfew6cv8h4Buq+qaq9sAvA5/dsU7vlc8Cv1ie/yLwlz/MN1PV/wXcf586fBb4ZVXtVPVbwDcwmz4rvZ4kz0QvVb2lqr9Vnp8CbwAvs0N7PUWnJ8mzspWq6qJ8W5WHsltbPUmnJ8kzsZWIXAd+AviX73nvnd6DH1R27dxfBr5z7vu3efqN8GGLAv9FRL4gIn+9XLumqrfAblzg+R3o9SQdLoL9/qaIfKmkbcaj6jPXS0ReBf4UFv1dCHu9RyfYsa1KquE14Dbwa6q6c1s9QSfYra3+KfD3gHzu2oVYUx9Edu3c5THXdgnf+WFV/TTw48DfEJEf2aEu70d2bb9/BvwR4AeBW8A/LtefqV4iMgd+BfjbqnrytJc+5tqHotdjdNq5rVQ1qeoPAteBHxKRH3jKy5+JXk/QaWe2EpGfBG6r6hfe76885tqFgCDu2rm/DXzs3PfXgZs70gVVvVm+3gb+I3a8eldEXgQoX2/vQLUn6bBT+6nqu+XmzMC/4Ow4+sz0EpEKc6L/VlX/Q7m8U3s9TqeLYKtRVPUh8D+BH+OCrK3zOu3YVj8M/CUReQtLE/+oiPwSF8ROH0R27dz/L/B9IvIJEamBnwI+vwtFRGQmIgfjc+AvAK8XfX62vOxngf+0A/WepMPngZ8SkUZEPgF8H/Abz0qpcbEX+SuYvZ6ZXiIiwL8C3lDVf3LuRzuz15N0ugC2ek5EjsvzCfDngd9ht7Z6rE67tJWq/pyqXlfVVzF/9N9V9ae5oPfgU2XXFV3gMxii4JvAz+9Qj09iVe8vAl8ZdQGuAP8N+Hr5evlD1uPfY0fRiEUFf+1pOgA/X2z3NeDHn7Fe/wb4MvAlbJG/+Cz1Av4sdgT+EvBaeXxml/Z6ik67ttWfBH67vP/rwN//Xuv7GdjqSTrt1Fbn3uvPcYaW2fk9+EEf+w7VvexlL3v5CMqu0zJ72cte9rKXD0H2zn0ve9nLXj6Csnfue9nLXvbyEZS9c9/LXvayl4+g7J37Xvayl718BGXv3Peyl73s5SMoe+e+l73sZS8fQdk7973sZS97+QjK/wMSUkmNIbDdzAAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.imshow(herstappe_data.transpose(1, 2, 0))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "But Rasterio also provides the utility functions `reshape_as_image` and `reshape_as_raster` to support the conversion:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from rasterio.plot import reshape_as_image, reshape_as_raster"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.image.AxesImage at 0x7f6460060350>"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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hJljEV9WcwDdPF0ouBIGEGVaqRQ6qRsbkrORSudl3HJxn6B0pV07nRH8TDKNtRlUa8zilwpIrj1Pi/rxwN3S82XV4Z5GFEHDA4AJ/+u6Gr4/TlRhFnJGCVa7wjKo5viF6i5BEuI3xGoU7EUTVDgBG4qZaidGxHyOlGYNdDDxOK+9uBkujMUN5mi2a/cmrAz97nFirGbc1F7SFT66RwUA7qJbKhxj5eD7ROUfVwqqQomfxlp7X1GAXteyrc4598HQCr/qeWi1LohmRofEGPgRedZ7eO7Stn2uRmbQU3DtwKkyl4oPjOM2c14QiJFVKc9jSjIzzwrJmvp8XVIS1Fk5L4t3tyOgN5rsdIpc1o9VwXSdCdA6tloHdXzJdtEO+5mrzgtB5T+8jH9ZHTktiiJ60WNSX2v344Ei5kLPSh4YNN+hlycsViEi5ggqpVJ7OCzdDhMVI8CB2n1MpfFwTlznjRNjFjiVZZvVqf8uSMq/2I+uyUnCUVAHH2Ds+nRdytizl1TgQY8fP7+8pjyduYsB52xcFRcRRFLRY4EYfjJRu0IlgsMr2HFWfeQ4nli2O0RwIHobgeX870otDHA3+a4ZcucJitH2MuAapVr47Lfzlt4/MueCCwzk4pczPHyb+9pcBL4pq5S4EjmsGVaIIb1wkesd5LTwtK3MuvN/3V3z/4ZJQgcuS+PIw0IfAuhaW1XicORdEhac5cSoFcUIfA1qFt13E9wNTKpzzwpIS6o3XGEPkMiecwFevbxD9N8C4S8OY78aBWgqKGnnVmOrzsrLUSlaLKHbB86PDwFIqvzxOHPqOKa0spZCqIaKyCvfTTHSe1/ueIZrn/PrxzFwyEXc9tE5hbJ51UxSUtrGqmOeeSkG8o8M1w8BnEYby8TyT12xYo1Y8sDajv6lxNswvZ+V4Wal94HVLdctaWBfHYYgcp9TUCEYEbQRQqpX7lLmfFj6eI3e7jruxwztnkAJ2LRHDy5u8hFrdNZXUFs1rtggvVaWPgb43g74h8Ksq51wITth5zx+92lvWEQI/O16YSiEX5TEVXlcleteyXcF74e1hoPOOv/X2wF89XHiYVov4s4Gi2lJTixItcxu9Z1pmEtUcbXCG0SfbE9oITXFwEzuG4OmaV1GFSym8HgbmXNCc8ZujbFxCEONrasNlAfp2+s+lUItjyRBC5JSNRN3gmoCtQ1YlOMF7xyUXUCUVeFhWI+Vy5ZcPF97ue+76yO0Q6b3jYU4IQnSeKEYwKopUgy3U2d6oqsQQoQqpZg59x1ot6r3i5dWMTNdHjqpXHiWgOKccl5UyL/TRU6qRzfuxQ1W5OMfgIn/09pafPTxSViU55bSuLLlwWRP7LrLWzF2MvDoceLqsVIVQhc4Fhv3Aw/Fin10EcZ4MZqQVpmnBqSBOcQ4WrVc1UbVj0CAqGKNvqqu2cVsg1E6X7dn27KUa/zREz9/70WsL0GhEO5X6WYTekoDrft7Szdrm7/vjzF98/0huWdWmQgvBEQdHdc/iiegc74eeT+vK0AzqZc384jjxdtfzZug51Uz0jqdpxTvhEAM+OB5z4au+J2nBieN+XTnnzJvQMdXKmgo3Q8dtiNwMPedUTKVVG9narMbeR25iT3SequbQG6XwW8cfhHEHO3j7PjAtgdwinFUtRjxPk0XI1WRbnXe8GTuKCr88TlQ1svG4JpI+yyJzKfSq/PFuz4d5Zew71mJ4bG2pnqqRfh+nhUsqfLm3iFjNshJUWnqoXEolO2XvLJXVltolfZZzpfZ650xWJlWpn0XiwQspV3KCY8l0B48Plk7nU+a8KhoMnkGF3RhZl8yq1YDutlfPOVPOFtV//7QwRsePX43Utp2LNqihYPIznuEEQUg58+FxRgXuRmDsKao8pGS4uSpvukhssq0v98PVQbweOvK0mJpITJI61wrejOlhDAxiTjCK8MPDwCUlcrFDtkXvUpXoA0PfsZZMVCM7C8ppLRxiRHKhVL1GYH0jlPddIFeDeubcHLoItUkrYzP83hlE4xvkpdCyMov6kyqXUsw4eiF6JeWFVBJrrXiEXA2K2sfAlDJevOG1tUJRylKonWtyV4PgvjvNoPB+19H3gd47Pk3Z5LttHiuKhADVIjknSowRL55aCgnlaVlZq9J7R23wyzQn+uiRoqypEGKTPgr0MVAbr4HYPg/B8+7uFT9+c8f/9H/8C4JA8Z5OjKj/+vGMEzHYYIMYS+VmiHyaUjufipPAVBdcVn74xVsejkekQRyiYjDnYeSuZUeXdSHXQh8CEwlfzGiF5pC3KLxdkVIKRaAWNcw5BoPf2sTmWtFaCcFTmvOQLUPF5EbSODJBPtvzn+98C24uKV/VKjiDb52D1zcdP7zd0TmDYi2zU3be0Q8WfE5r5cO08v4wsgueVCvHWhE1d/DlfjAYLnp2OM4X4xK/mRfOJZOrInltZL5HUIJWLmlh7Ed+eX/knBJ5o+6TUroKTojq6UJHrpV5Tr/Tqv5BGHdVZV4TRSs3h5FPT2doEe+myVVtOm/7jRbCmCFda2Ufm56WzRhz/Vf0wh/djvziODMEO6BiQS0WE5ry5XFaQJV3hwE8TS4FXYsEFGVW5bKujE4YnONxLXx/vJj+ut2nCixFzZgpLaJ2DbqwtHvDEu/PC1KNKVc1TNuLv6aJAcH3AdXcNMibvt0RRZhTxlVHSsL/elnYjxERZcmVuSjeW3QaNmy9PUuudmgM74aHNZFq4Yu+o3OuBVEmmwPLbhTLWG66wOOaqLlwGCMSAC9U9/yeDbpSjBN5PfR8d5lNRqiCHwKxCmMfmHJiKiat7J2j5so5ZXrnGTrjJ8bgGb3BSKmY2qY2uKhUc16XNZFLIQbHYegYvM2RR/CYZNVUMkLSyqW2KKntw7Xktu8qzgvOO9Y1MwTH611PHwNfP5xRB0/LClkNG78Z+bisXC1j2wdTzqQa8M6xix4nwv0lseRi5kaFOa0mv+0Cf/L+DeOu53/7y29Yc+ZxXklUQpPz5qosqZBrpXOe87QCQsmbkr2yfzXiNRN6T8qWZVTgw8MTtcJ/+Pf+LT6eTzwtGXERRBt2b1j/oTes+as3B47zyrIaZPE0JW53HmrlZ99/j+objvPMeZqJXUQqHPZ7Hs4z03ym95FD3xOjZZXMlWWDzOygQMusUilNKlqvGLkCa15NAUQzz216nWsquAZtWWQv5FIomPTVCWxsrJmKFrlr5bhkTimDe1bTOxH66Hh/tyPSVEzK1RkgFiSsIizLwh+/viHlwlwKjyVbNqwQomfoA99MM194R/Qwrwal7PvI8VLwmDKtc/bH+0Dse4rA959OzOvKNCd856xWxjuWpbDEQvTmDLRm5nUTjv/m8Qdj3Kcl0XWeXfB8qKZh5xrxGi56XjKXoSkEEEo16WDOhezddYKvzHgz8nMujNHx1c3A/ZwsRaxGYIiatj6tlZILH9aZeS28uxsZojeVSYsaFSPENg3yVCr3p5nnLWL/lGJGY9O3GmaoLbU3rLOoRS7BGVQiUq5pZpoy3gm7vaXStSoRLOJVg42MlLNoa6WYA9quX5WlKFPKsCpLM9bKs/kpVYm9vx4CB7zruitJsxX2bNmNUjFURZlLxTnHq53nro9XJ1mvJJiwqDK0Ah8nwt3Q8XFeQeDtbuD1fsRrQbTy07PBHxdVXsfAMS92nVp42w+4wSRjUy7XgiYjlCuXZTUsXs2pRO940/d4DNs2Ur1BVc1o5GZYRC1CTFVZiom/tfEtjnYA+8ghesboLVNxYnpoLPPrvWP0njd9xz0rVVsdg4PjkvlZnbjtAu/2A+IduyGwnMs1cFCUXCHEwGVazHmpcj8tdh0x9U9VYUmZlO3el6VcI+AK1FTp+mAYcecZd5ElF+6nxJIVH+Dj04mfvLvjx19+wf/8z/+aKa0U7xHnqLU0rNrmakmZtV3LFFWB8zSjFFSU+/OZacnkVNhFg9Duj4+EGLC6isK6JnL1BCf06lCvJlM0KgrlWZr7uWO8FscBtTbcXKSJ2DZpr4DoNfijQapLLsToLSvSjXVo+H07v5eUub+Y3HXjbrwXXo89rgguSgNftWUBdpaTmtLpy9sde+/4i4cj9yWxhZzbe/76bMqbT2tilczorBCzpkwQYVXl9bu3DF3Pz7/+htf7PaUUfvnpkfOSuDl03ETHtGajCBy4AMd0wa/C293BVFBa+F3jD8K4C+BLQVYhu8QQPJeUoEpTMdgSPi6J9WQkynFejaRr0rZNVocaRlYbM59Q7ueFXUv/3vaBUirnUq9EXllNRrbhctrS4CiOgkEFIARsoaMzw3CaE4hw0YxXxwjEtpE2cmVj6B2Cq+aoMtqqIO1ge2kFE1ja7Zs8r0NYZyvQctKkel7oxeHU7mdV0OCo3lGLGYOlKW2cE7YypKrPBG2qpsrooh282y7yphlpmjF0V6Nux8MJrEX5bloRD7HzpgpSpci2jjb/VWBSizj7FsW/2Q18mBfWoowx0lOv0rTXXWCplblUPqodloAw50Jtqpit2nVzdnNKpFyu8Bpt7t7vBl71EZrTDk4a/7EVqDQ8tiFWuVZTiDRIQrGirlphHz2H6K0YqB3yt2NPFeXrU2XVSi5K74Q+muLjF6fLtf5BFY5r5rhmXPDc9EbEvTsMzGvhYTbdtDg4zSu9E5ZUcJgcUpOpdJwTlrWwrAWwPVBq4yBc06FXI4O1VIIzF73vAvu+55IKU8o85MS3nz7x6RhYs2Hxh3HH+XKmUAnqr5XJx6Wy8579ELifM84ptbTMsUJe7bXDLrLrPJdcGbrIkiwwGWNgDJE1J45LpovOlD3wnDW36nLausJzdadrxDU0tYtcIw227LV+BiNBc97SsmeUUppBVyUI5OZQJ1X6zrPMbT6d44ubnh+92huc2ZzzVqUsArkWaIVV++DovTMoUq1eRLfgSWHXm3w4eId44TEV1nVlKpUlF9uYWkEzr3c7llo5zbNBxt4hzhGisveBJZWrkXQt4Pp4PvNqN0LbP79t/EEY922BSi2kaobaXtZrapRVccHItEvKPK0J5x2+WSLnhRsJLMVkiKVFVtFZQci7wQiP12PH6zHyzXHm02ya3lq2smnog+ert3uG5gx675vKpVwNdXSCOIfOuaWaZjRPqvRXCEdaWwLz60Hs/0WErqktCi2KVOXtTW8pJg2X9EIvHi2Gry+1gsMiCRFc27A3KuQMmcoisFTluGQavcncoCEnrUCKVsLvrADBi+CDw4vV3z4LGx2Ikqry9WlirUYIIcrBR9ZcWKtyTJlDF1Bt+Yu0dLsVj20VwlXg7W7H949npnnm1veINznbXQz89Wk22WYRXnWBuWQqwlIKXo2nmHO2go92mU1JQTO8MXgOXbTiH0xpVSvka1bRyuyx19ZiKqRV9UpipWwySwXG4FiLMgSLCE9L5qubEajEw45/dn9CvCMLjAq3MXDuI6eUybkV5LXA5BePF97fjLzpI50X+l0klcJUKu/2O0opPF0Wggi9CMk7SvVNMmrOx7gERy1KWiuhM7lfqVb9uBsivYfTknm371v2ajUL+85z6AJPlzOXteBd5G4cOU4TpZjD9N5xBXhK5jEXLrlwM0SO00pWZd/1VBGWlEitJH4thS52kJTqTRFWPRyXFeeUPnrWUhHnrryRQ8z40eo2mlMR4SpjdC1YuzZzUNv/bXlN8KBqu7W9d/DO5K21kuGqhiot4PrZ05lXXce/+8UrPkyrBTq5cjt0V+hLKrjgqbVenZBVHRvXhyrfTKY6qvPGb1gWMfaRPjp8tn2ZVfDRm/osFashGQNf//IDP3h9x+wqD09nlpyMiB0i4i0jW1Zz4NpgpqKVvFZuhh4tyrvbw+80q38Yxr1FpRsZcug81MCarV7V9KFWGXhqipTONwJri7ZqYec8OeerBxWxxa1amVoFX20g3E9ud7zbFb5+vJBKJVG423V89WqP862vg8Jh8JxWRbxV0HoxRv20ZLrO0yus80oRyMWKbIbgDcRQbZg+V4hI1ZxI5ZnAuvaGUa4GPherdvU4xui5lcClFJNpVuUwjOR1Rcl4VSQ1KMFZIUUuhSvs2A6Mo5G8WHawpkotSo4VBhoX0KJvrXw/rYatN3I5Y9Wop1TQap9xyhUflGPxr58AACAASURBVN5Li2BatqBwqQXXUtJLVj48nVlKZqrgRPnBYWyOyiLsjQQcvWVXVeGYEocYOC3rM47f1lyaysGJMEZP10W+XxbexkBEGveghPC5wzXjsNRiB1st05lTvao0QKz6ORpu/emy8jAZNn5cVg6dyfdGseAiq7KK0DvH26Hntu/4/jJznus1QCkK351mOh/4Oz/+CcfzkXHNhCI8XVYclkHsoseL45yStcqoihYj5rfePblUhsFfoQ0wvHxoBOTdrnuu9WjOVhD64HjnIt+rsGQztr0IWRwhWAAVHUQX0Ab9LWvm42li13ekS7IK5SoECRQteHGsSUnpgneelItlZrHn4Xxh13tETN3jnCOVphirlZv4Wc+oBt9tDmnLAqE58Qa/VKwYraaKFtuHDm1n2/atOXwhovTOXwOAY044sXWN3vGjmxFFrk52Snb2+uCgVtx2Lpt9OmxktVbWlv3uokeyMmPV1RXj2jq/kbcGBQ194JQyWpTLmuglcDN0fHv/iVwKt0NvwRRK1/Z1DJ4c6lXFp6rs+sDr2xuO54m0/hsAy0ArmxdbSCdQDGm06AtL9Z1zzCkTe28HVa+rD0XRVjrfRBl4sd4xuSinlLnpA7Hh1rCVM+/A2Wbf9+EKOi7zSp4zWgqhHebOCZ1YhDeI8FSVLjjrQ9Ogm7Sll94kXhWTMG5OyNF6e1CuDojmhIo+44TFLKxJMZ0jV2EQR3XKXCF0PbHreTo+ENThVXGquFxNz18a9otJ1AbnWJoqAzFZmRNzNKlUUq2G27fCJlMPGHYOim/zfa6JMVgRiXNbywhrVgU0FckW/wnnqix5JmDqBoNwlKc184M2Ad45XveRX54LhcopF8bgOebMVJTRK0PwnNd8xUhVtWGwyk0f6LvIKRWr1qyVrhVXBVHWLHShQTDtmlabAHMy9UOp9doGA2AMpokvufBxWkml8mYceJozazLHVmolBKtQFFGkQUihQWrqA1VNSaRYY6iknn/77/xd/umf/3PS9x/Zh8indSY4KEVJziGuEsVxqcW02t6ItZwqa8r0Y2AYgmUZuSJRuBsHxmjVwPsuXAlLW4im8dZn2V8uhVILvRPe3u5JqZCXGa1Cv7Pq07QsVDX4qcyJpVYu08Kbmx2SCq46dn1HAM7JMa0z0Tn2zrGqff55LQw+8nq3w68LD85xux9N+aKVX+uT1gy74eRZzTCaYaiNELfn0lqprabEA1IKsXFLpQUnvlW8IsZjfJxX7vru2uittouqmnBBBJOdJgVvunnfIKF9DPReeFgzD2vmnLPxJUmvtSJb47ilFhyO0QeyKCVXLmuT8wKCYxyNJXPVWqj00XPJ6Qo3bZp+DYJTYe+8QXQiSFXevz4wp38TYBnaHmyElxFFFncWk2tY0YaDcYhMa+ZKD24RmRqEEJ1Hi+HNQYTbELnrO8M7two4tZJw30LbV7uOxzldmfmcCvNlRVV5OC+8do7QBXoB16SUO+fQ4Fmq8uXtSBEl58r9aWUtShArwHnuFGhRtGfrAGeHbaOSpN+e3/5pVINt1FKJTpirFbeMTng8PbHveoYQGpRT8G1Tr1WRYIUyG+Q0NW7COb3qvLfuh1Oxdg63wbN38SrbfDt0PKXMuWSWVrnYBY+KIo1l9iIsqbLvmhqF506LSYWnuqC1EhV2Y8clTdeiL8U0yiLwuuv4NCfWUphK4RAiYPN0SpneGzl6yYU+BuJnXSiDdw2G27IlrvxJdCZ7uiRTwVS1YqKHNbHmSh+2nityLX/fpLZgUd4DzvTQmFJoWgvfnVa6LjBG65NSqzI7cxBVwTvPTcPqg1ZOyXTu9w+P/OP/8X/gB2/f4J3nkjN9F3C1ctN55pT59rRSUXaDKYUuc+I0rWiFEB1d9IjbcgzjP+72I7FmliVToid4/yvGsjaimGpN4S5kehcotXCZF8bOczcOPOWCpMxeEhfnYDewkwCa6RZhrbAmK7w59B3ndWVdEoPv6GNkTVaodplX8FZtvuTE42TZ2Y/3I2/fv+MyLxyXREkTV/K+1ZfMRVHKtRZBxIqztuKkLQO2LBE2c6GYDn7jR9xn0M7alDg30RuMp5tBbsGMM1g4r5nOe3MqCsULXRfACVOurVdUQWtlSeZcizd5pzphKVYdXoo59YgVmi0tMHEi7EKkZvju6WTFTVLIEthoha3X0iXla9C49YJy4vjBF6+JUfnptx9+p039gzDuSZXvUqKXrXueRb41Gykn2MQvi+k6VTFD3KLoKtqIzMqrvkdaKe8YA7d9RKtlBrRNbhi0RZ1zqXyaVj4dF76623MYAg/rgjgxyKIW7k8TP3q1JwRvhRINSrjxnt7ZBk5qON2uD8yr9Y3ZpEoCBg04xxCDtQcQx2nNn0nDBC/PVbkI19YBQuu1IUqmENQRxXquoNZDRRokIqqoM5fhVBjE46U5k6rs1VFrbS1x9Xp/90vi07zyp3eOzplh6JuE75QzW2GIVoOKLGo39ci8Zj5dhLsxtoNYWYpjKgtJKwGDdBzK3dDxOK+g8GlOfLEbECfcdYH3u56fnybWUjnn0pxA5VSUzjmi9/hiKaq1CfCkYg2i0hbtCJxaFHdoe6THgyinoiylcEz5ml2UUhuZ7Xi761vTua0wxiL0GB0hOmsupdaBcy6Ft71VTDt5hvFqVeatSlmswOh1Z44qp8q7Xc/j45HbKLwbA/fnhNTCEAK1Vn75eDFFVXDcOsdxTpymhHNGkoTOpE3W6bQZsJZheOq1SrURIBuyYclqVT7OiWktDPF5H+SSKFo4+46EGdHqPfvdgMuVqo5UEmDtIHrvKQKpZM7TgqvgIojz7EfPrGoGsBljvJGdN51lBNn14DLzdMSJwSB126O0rLvd/QY76fYQ7dGM5DcFjcE8LdJtJK1rQdwmZfw4L+xal1RlI26lZdcmTpjbedza66pa65FDNI6r88JXY8elVk6p8mGdWLTShcDceDnac9R2fpdaqUshZWsVchhHXocOHAZTpsR4iNBgqWuLgmLcz5alTmrtBqRWfvHxe3709m07ub99/EEY96rKU8nN8JpG+/Uw8Jhzk+YZmRpUCJgE0vDWrX6Lq+Z57AJfDPFazeccDed7VqgocE4WjX6aVj48TQw+8mrseDjPnObVoIwuMueMD47LmglNUCMNg0OEHuVOPE+lkirc7TqGWKBUXvXRZGDBW68T5+jbBhOsyk3bYvZd4LtlZU4JUaGyaX6bc/DC4D23Xcc+Rk7LyrfHGeeFp1SsIk4ECR5Ra6BF2/gqBmtVeY5+RI0gsmTGsYpySYW/Pk38+DAyuoBtftcgCKVvUFNwRobVbEVUIsLTvOKdo++EhdwcY22OqUFVThj6wJyytVNYEndDR+8smnrXR769LKyl8JgyYyNcLUqzEvvSpHBztgyq7yJZrPOgqvXGKcBTzkSJjMHgmV4EkcAxFZbStNHVJJ23nXXb3MXPSv7E1CdehNtdT0mZJRmpGoLj0Ee0qWq8b5hoNedxXjJjdEQHN2NkTpUfDAOPsjIthbvDwHFe8bXyaoh417FkkwRW1xqy9XY0U2sBLQIxenx0RsJXKFkJ0TiOgBnqQx+tME9t76voJjABrEvpmguDf67u7WJsDd0yfdehKPfzSlcK724PfPN45mmZ6YO3TqWWKlJqYdd5pqU2PiozxKE5XEhZG6KiECprLjx1Pfe/+IbznOgDjMGia7fBbWwclF4dkzSIySTRzbRX6/XTO6GoEcGmvrEOi7bz/DP0qXA7dNB+sqU9Iq3oyYH35iRqSw3WUni97xrZbBG+CNyEwF3veDN0PCTrGJmXhSotYGoZe0ahQFpN9RXEmxSSyqfThEf54jBwrtlUgbIVINo++tx2t1pE3o8d7/tAv57przP0m8cfjHH34sjV0u/qLXXePCBYr2bnTOpVN9liNe/qvbNU3T2TGLVqk1RIw7QxTLrpj+dceFoy336a+OJ25N2u4/uHE1krqqbSSFqtMx7Kp2XhtiloDO9tjYuAQ/AMLvCQCx8uC+ta+NN3t7jtix5wzyqALcpWZQibmlZx4lhQSsl0ErjpA7ddxKlwyombLho+rsqpFlKES2ebznee6r1J1XIles9pXa/Sx0bcW7EYhg8FsX4ytT1FKLbBP62Ju9QxRos2vxoHUPh2nq3dMsYhVExx4hr5FZzh9Kd1Qb1wbRJEi4LbARMxB/jtaWbKheOSmmpCWgtdYW1vH703Q4yl6jfRMeJZN42ad5xzvkIwCNf0VQUutTI0+MiJMHrHu1YN64sa1jn2PCf7luZLi9pRc0y9E55qIalVso4S6HvLaFIpOOdxKI/zwmWtjdeBH+xHI5Z94VVsPf2dtUL4q/sn7vqO746zVWp2gSkXvrjb4YB5yTxeVual4L1lfT60ug+jcKyitgvsfGxqLMdwhYm48hNOQJ3wNCVytX2dkjUGE/X0wVPzyuubPa/HEdf3PN5/5DEVvv50pPOeV7HnXFaWnNn3HdM1izbVl3MO761PUi6VfQzWclcsWz7mzFEyPFjP8jEEOgJOwobJPn9pzrZfryfM1mPLui16bxGuNjx+49/QqxHezlnWypu+I3pTlm0yS8W1BoOw66NJiTUb1KKbxLZ1d6zKihJa2avD4K3j8cI3DyeDjZxxfM63Vh5OODjHUSq+C3TBQckEH6FYprmo8Vm74LmJkU85U1r77pZ8sbEQKnC/rHy1G4jA377b/U67+gdh3AVTIURnTYWkpfx/cmPfzrSWylzsgZdazF6LlfV7tbROS2VKytRlctPUeidXRQVANqkqa0t57k8Lr/c9728G5iWZDrctqCoUgVdDZ4c4Wzq/Mfq3Pm6qP4uExSR9364TXx4GU8y0r2C6bjvd5HvPz+629Fnhbd9x5wOdt6q+XCpVK3vv0WJk4Lcl8X229qNEg2KKwqkmQnCMMXAQx2ldrWEYXAnkZxNmfV1G5+i7wJQr2lqJKsZdbJJLBL4Ye445XZVLRZWcC3MqHLqOw9hZKq+FnCDpJgG0azkgqBBV2HlH7CMfLwu5KN+dZ27awVOUL4eOn51NLrcU5SYGztnUDPvOE4MnrdmyjbZem15egVXVvghDn7O5KFu1rLL3wpsY6EcP3hrJmeqz4bftfZuR2WAxddbbXFBidKbGIXBqXx7ixHqCi1gTuj+5HQnOmivf9SbJLM66Bp6nlS54zjlzXjMxOlBnFYntuqlUzrPJfUNwuM5di+m8CL33ZGecwm3wSDUlxbXoR7fiHXuCSyp8d5mtH7s31cpSC30Q9r1n1WitFbhwOZ3JKdF5zzElU2EFjyaPOs+0rNcKTt+et0rFiyOpsBsHjqejVdZKfS62qQbl1Kp0IqQsxCE+S9s2APJzLKkZ/I2Hsm4YtuBVlKdc2Ttak6/n6NpQKDOIH+eFL8aRXPJnDcrsTarK0HnG1r3xcVq4P86sKXMYIl3wrcmdEeJVXKtjqHxaM395/2TXVCWI5xBM6bWslddDx7ux4yZY6+SpZBwO8cqrw477pxNv9iOPl7NxFTHyru84e8d300SrY37OYdScwZ89HPl3Xt/wynW/067+QRh358T6RbQiCa1W1v1215GzEXlFTcmy9tYv/bQkHqaV6B0/vtsTm6IgtGZUqVSCtyKdUp8JpTlnPpwWHqaVzjm+uh1IuXCZ1qu0rAVBfLkbGLxn31na+t28XCPK266zNLGRkk6shYEX+6afDT/ctLhNZ3fdmJ8XaWzksK+tn8VWYNUimm3zV22VlFtEnirbd/gZvlhYXGHvrRf707y2asxGXrbzo1if6A9LxedE8J5XfeS8JlatfDvNFuWOvZX2q/Ju6PluWS1tLJVUlDF29J0DsdLygClfUs4cWhdNg38sM3OweTgzYsDSqj03g3rbBQ4pcE6ZDNwGz1qNJL5kaxqX1A5a5+3LMsDIte2zc1MVLFr5PmXeD/3VdghiMlIt/OzDiV0feHWwitbt/ry04rnmdL2zMv4udDyWlfvL0gIBucp1naf1bRck2nrX9pVHvsFL51SsD1G1ZmjTlIjRUZ05vnMuLMnaTZ8nM+zOGV/Ut8Z2KRdu+o67oedTWtnHjn1vhOxmrCzq+ywMbjxREKuuFaxWo4qRgN9dLrzf3zI4204+J3Z94LQUQjB5ZcTTxwhOaUW4oPaFJSpWVBad4/E8U3XgnDLOb227bazZigX3Q7wqrc4pc9fHqx3frLnA9QtmPj8v16pvhN6ZgCAjxHZ2tzXeIqgpZ+vCSb0WN20/NxIddl2w9h9t//bBiO4VvVY9b/BmroYg/Pw88/XT5bMzbG9YkhX2DdHI9qnYlwed5xUvBvXWUpnXlZu+I6/ZqrFzZjme+aO7Az/aj8wlc9/O2/O82LiUyp8/nvm7r56lpL9p/EEYd4C0bN699VDGCgrWq4zQUqPoHXfBc+gDr3qLOud1pTjH3dhfMeQuaJNGWeP7xznxaVqvqg8BvrodOF0WplRZMfmhwypg34wdt2NnVZ4ivDuMPC5rY+WVc84cor8a660p2JeHkaVY/48NhnEtigLYCB5pGOJnTvkaaaB2ENZsFW212jdFTakwBGGtwtNaKKle76+oSel2vWNJldsxksS1plraWglbt8eKUBwsItYz3SshdLwfen42zSyl8tPjhdsutMNkpK22fjTReW73wQxPe12rWsQuwutg7YydCCXXZig3RYMVqXUxsM4JL8KareUt2Oe97U1CGL2j6zxdKazV2m0tRa9QTU2Fm9414Mvm1jIIiCidOP7lp4lzXPm77++uX3Mn2LdIHeeVS0pEb+0RVDad/+aYm7y1GlQ3pczcupbWZmRitK85TKmwlsJhCDythT/7cCJX5f2+592uR4ZIvmT2zqCMTUpqagvhflnZh0joOj5dJtxnvQC+uBnwznNcE2P07PuOVRVZCtVVVm99v0WtWO761ZBNNVIRVhW863jdtb44CuqdFYk5gZopWH/4ORfm1pRsHAZWyWip3B4GfvlwJNRqQRMtC26O5dP5wqEfCQKvb3bcn86os3yxVCOJY3NYCmQtpGLk/ZYFX/+ywRIt8BH81UH7VpHhxBG8cC6V4hw7bwa7qF6VKQ9r4vXQE5tAY+svJK3f0qt9jw+udRxV+i7QpczTqeJ05YeH3gLPavOiwCkrP/10ZC6N81OgwlItiwtOuBssE/jm4YKWymHo+cluT3f3JX/x3b+kZmuKKCFwtzvwOF/YBc8h/p/UvUmvLVmW5/Vbu7HmnHO79577c4+IjCYzK1MgISaICRMQElNGMGWAVF+BGjOqr0DNmCDBpAQjBEJiXhISjZRQVZnRu/vr772nMbPdMljb7LwoMoKkkpI8b2bIu3fPPfeY2dpr/de/cUit/OxwwMqFT3Mgy8pDq9tEeoyR/+3D4x+sqd+L4h5j5vK8gAjeqVvfbOCjsXjD5qEB+uCqgT4MnUMFY/owXpaAFBh3A+TK+xSoperS9LIAOl711vLDw07FCzEThY2fXhH+qE0CRkwLohVCzBw6T0ypBQC3sOl6tclFFId7f545tDFPHRZhIwqKLurWJY/6cl2xxZQr5yWoFFkMvTMMo9IT94MeIj5ETlPGItx4x93QcQqJkAo3vddR0xoOLw789uOJ3JgnsbIdBrSDzFXBo+yEG+94yI7HqJYH314WfnzYIShr4L7ruYRMv9Mx3Ij6+qxhERZ9f6VdrobObJ/PJWXO7X8rRllr5dfPF/7kxWEzLLvzjiq6tL3tnTItcmXOmfenRTn1tW6pTqsvfSwqZnNGDZ5edB1//PLA8xLb9dWiPpfMaUqsls/fPk0cWvrRNWs3881pbvF6lZfjgLcWZ4qqdS2/s6hE1NV08BqVlqLiyu8vkeo99TIjvgmPrE4CrqlNBzGcpkiSTD84+t4xzwkEBu8Q0QPGW8XdiwjPpxkvzQd8/aBRzHddQK8c7yVGLnPG+4EXtz3ucua784Upqn1GKZVTjnRFVdEhRzpnuT+MpGq4XM64YSROM+dlRqry23PbdwxOFai73mFtZtd3hJJ42I88TpoGpQev7g4QZZGM1jJ2jZ3UnpC67snQRep1el2JANp9T83Jc267MZstpvfc9krBiE3bMqeEs4M2dM2SWKEbQ99opRWd9krRLn1COGXN8P3Fceb1ULhrVOpY4RePR5amlK6tSTOi+hVnhK9vRganwda73jIn4RwSz+PIPlyQKjx0liUm3k8zd/6AdYYvDmNj9WmT8rPDnofO88vThVDyFVZttTCU9eb767++F8UdaGZfglghFA0D/vXjideHkdvBtG5+LeyN/cJVSr+GHCCVmDPPIfL+MrM05WFpvi5UfcDfzwFXm9fIuoypcDeo/8eqmNO9beXGGMbdwKdFO72ddTr+VqViCvpQd06XNJeYdCnZrsb2f60b274ati1o+EICvHfsBrP5tKfPKGLOiLojFv29cqmclsj9rqe3wpvT3Hy2M7veM/aO56RUrPtdjzjhUjOI2bwqnKj4hVq5cZanlCiCWigXZcN8nCIhJPXoqEV9PEoTmbRJaL2G2u3rZ/B+XjaG0pzydoh6a9h7y7xEChr0/HI3NiaS4X4w3O1Un3DwnjkVPkwzj1YXeesCLZRKV3X8NhW8bWZquXLMma9vR8zFckyZ3lh+fbywZJWB971pDCbP85J4MXpEhHNMvJsW5pw3/PZ5CdwNA52BULZ6wxIzOVfG0dIZUd+Vqvh3by0juuC33vLh+cRwGNl5x5vnwo11BKeFoXeWhKqSnTXqStksBUJuxadWTEHZSBR2uz0xJZzV3ZK0SDdrTDNza5m/RjiMnlAqIQU+LvMWBi3QouRmnAg/6AdVRCKEEKnWsvee2/3Am+cnTLNydlZYEoxNzd17i/F6j5+CWtxao3mupyU0OLAdOkYzZ7/Y9Wr520avdTJcJ49S9cA+xshSGk24wZV6iNVmBy1tglshSJ2KPiyBnXPYCiHpfWOMUUuAqotPI8rqKSuF0hh+cLPj2+OZj5eF3xwnppj4190th97zFx+fOS9xu/60GmSNThH7zvGqcwoBVxS3T5XLHHnz9IYv656Dc5yWxHNMzKUQj890g2tl4bM9Q63cesdPDyO/Ok8a8tH+SK0oDvgHvr4XxV2MFvVVHWitkEKmttQUDa6uGFHpr7qvXYOOS9SLWqrQW8MgyubQi8bW9a/QThU1dHpwdsP1Rmt4vRvZe7/he0ITERWVOddSeDn4ZgFQmv8Fm8826AXpvOVXjxcG57nfd0guUE0bB0UPAiWN451KoWstOKfBxKvwIpXCuZk+OSuMzvJpyby7LLrNR5W1U3NEEgOPc1CvkYbRv9gN/Ph2R01ZU4gQ/uJ44tLGcFMNndFHwhqV/u9t4lQSp5z4MEd2Xc/TaW6FQ2AC2Skvu362fE0Fai08L5FvQ2ZuXuz/4ubfG1XbjtZCp2nx55i4K4XeW5CiyUIifDjp9JWzHiijM5xTZhXnxJw5LSqbb9MxtUEyc8o8x+uB8v48cYxxg6J7a9hbi2+ilW+OgSzqZnkQRxYVeFnR+yuXyjForJ6x2mGV0tw1Ue/6dRKj6iF1tx/51dNRKZmiy73qdUF6mhPeO7Ws7j2jEy4Nj395t2cOkakJuJyRbWIVUXZS5z0fHh+ZjGV0lhf7rsFbdVPgRlC4pVacFJ7nReGFFRFsClDtbGHK+qSkkhmsI6RE33l+/u4D+14XjIJOFJdZTeQs+vyunaW+rIr6KkqlLa0wWzF8OXQcnFWr4eb0mYsuf9eC/mkJzFmNvZbmrVOq6h1W4dnOW6wVbnvP6A3LUrfMgFAKT3PgT+9vtMYIjT0DSy70omE1tepnL7I5BOOM8Pde3vFPpreUonTf0xL5Zpr5zfN03QVU/R5jDIfOc99b7p3HVvWkMVWdY3Ou7ZopZdeWSiwwb92/LtD/8vHI693Il7uhdfCKUN11np+K8M+eTxt8a4HO+j9YV78Xxd2KcDN2lKz+L7YKT6HgnSpUsxgwikHnWqmiZkqpfsZZRX00YimYmki5mfuz+tZcOe5rNz7VytBwvB8eduw6ZTNQ1Xgorx09NNpH833uLGlNTGmvmdtonEplqYVoKh/CwlevDqq0KystTTaaV2oy+dJ8KnKMyiu3hm8+nTm3oidG02dGb5VCGDM/eTjw4bwwx6wijVzaRr91+U0pOaWEsXDvLFShM8LXu5F/+vysO4kGJ+nnogfIF13zJkG4pMplPm80yIq6aBprCGSlvhUVHeW22KWNz8BmY7AW3M6qFULXRmSVDzXfEEHDTowhRPXRyW1HYGrl4C0vxp7L8bLh92vnaapaIAjKBBI0hvG3z2eKKPvqtERdfgO9UZXxYA07r1GOFafQUGm+7igV0mLYu577caCr8PYyNaih0nV22wXUWtg5DUGOUpCcMXOit4735wnvVCFcc8ZbQ7R64HtnmlOmCrIScJ4X1qm7tmJ9bTqEYexZpgudsTRvOM5LJpjmRd8gpvKZt0yplZ2z/Phmz1OMvD3PbFB3+1mhqJWibbGGzhrNL/AWMcLDfsQZw24cmULQnForbV/BdflPZU6FUBJ776EUBmt4NQ66+ymVOWaOSWnJy1awNANZfYb0Wlq5EhQqau6384Zd59h5S2cNO2f4LkaeQ2Iwlp8fj9x6jxNlnVGVaZPRIuvasxxTZgJtKqrqPqoI92PP68OO745nnYQrTEveNBtbe41syuV77/CtyTRS+TDpQdpVNTTsvcWYqvbRGZ7a/WqaL04qlW/PEwCv90OzqtY2vbOG1/uBY0jMMdOJ0Lu/A8UddOve9Z6Xg1cObaXhYSqQ0VWI4sTWGPRMXwkSOo7GVkBr0kDgle4EXLfo6z+K+qYYtNjtu451eZM/84KRpsaUhvOWClIqXeeZlrSxWUophJz5zTTzGFToUlDh0iqWqdAc/BrNMWcuqZBzxjXxSs2Z3hmeYiSWwqUUbNWH9dIM78c0qwAAIABJREFUrgAeRrVXXVJqfL0KYrDNKdA5QxT4lCOPcyKPA1+7DgG+GDp+c3bKpJHroredQdw4RxXLZY5IzM1l8bMiUCthjpylMEXFrj83P9NllzAa7c5XtaAVFdwYw2ZhvBbR3juw6oe9juUrMyiWzF6UJnjvHO5mxy9PU8Np9YemoulX67Rl25Tx5TjwTx+P17hDGszme2690k57o97v6bIQssFZOMdMaDj3znqssYSYlH2BLhOVwqjU0Vp10TfHyk3f4cSw3w28fTrRWdMK+7qkVQhBZe968M2lcNj17KQSp8rjHD4T4FzvY0HN26zoex56y1L0IJxTphghR9Sjv0E0+7Ejpfw7kNnOWV4MPU9L2Bb8vbF8sXMEr5OyFH1/3qjs/RwjnQFQdonzTm2wm3naCo3GWjmHRNNfEUrh9X7gEiLvloVQMqXov49thJDt+RTuetUyLM08rrey2QjnWpvDpcUZowyiXHguRSM2izBXPZxftXQxnRq1qTLo5F/WVkW0CRL0M6u1+VxZ3b09zYHXu4G9tex2A6kWPsyh6Wi0RhgxdCgRQ33xNU+hAJ1Rsdhh6Fhy5rIEqrPc3O55My+KCLSGaO3K304TuRa+PuxwDRqegk7nN52nE6VGvxz+DhT3WishJY1as8Jd79n3jqc5ceg0gWdKmacUtQtuCsOr4lT/qh1eoyu1TXpapcQAyLWorEuJWtl1vhXcvKlkEd2oK5a8BgQ0LJArJaoU5cxPMfGbaeFcVFkWm0lVTAUrFWOb+kEgl6SujWIxsnqMXx/+jLAfOmrJ5AqDM9z0nk9T0AfNGD29vaHvHVFUwVvXmw2oq0lX+1W/CzNzSPzxbsRW4U9v9vyz5xMA3lr4jFV1QTiHoM54n68H4PqwlLpNB9IK6SpG2xlDb61SI9t7oMpWo1YPHRB++upeQ4ClUledgYiq+6qOtq6okZwRZYMcnN2+33w2daSqy8mKToM/2I/0RrjvOj6G0Ay+9PKec+LV2OEalOKs8MVh4DkX3s0zU9FQmF40gHu1qjguccOTpWkYVkjmMkV8Z7m/fSAuMyFEOqtmd2s4tJ6NhTlkUi6ELOx6t/kaGSpf7HqcFR6nsDUZjS+if8Zqw9B0XJrb27KDbW1dc9T7cOwMlxiJubLv3MaskqqForeGD7MGV9x5x13fIYM2Euc5EZrB+eBU7AQ61X56elIxodNrq9OSZsw+t8K+PnWl6pTdece7y9R8lfQ5tMj2XGl/UNn3jlsRnpfE0xyxRou5d/q+lpQ5LurhtP6Utem76RwuC18MQ2u6attd6Q2sEY6Bm7GjNsuCkAsmGbxTeDSnpK/Ve17uB151mriMCD877HjoPN+cFy456RRjLI9zZEp6oHgnfFzmTa9Qqj43z0ugt56lFerXN3d8OF5IpG0qhkoo8OY88zQFfnizw4jwPoVG9746fY7uD4eofj+KO622FOHjZcbsB+6d48Xg8QaohcEK1XjmrQBfx3Jr1OWw1tKEHmrZmpwjLVHxba5N/No1gRbqKSaG1ums2LDZOk79HsXsTFN66mvktgA6p8Rvp6VhwdcC5mzLTI2FkgPOWUzj4a8BwfrCam5lrC6OEbjrPXMS7or6inurWGV3GHCinf37Eikdny1oV+FRKwKycoJVtu1cE7+UQieGL3yvKTyAEUvKGkE2pUyOaoC0Kn6VgcE6iQLQI+RuxT8Ny1TUUI02CbTC+7udWftr6yCfLzNDbzcV69q9KG+9bMIi9RHR13bAF7uRD9Osr9beV0UP4g7Dj3Y9th3kf/5ww3fTws+fTtqlijDlwrt54etx2PBWBL0Pms2F11uP0xy47Qfu+54YVDtwSmmDSrxRUVO0hT9+9YJKYRwH3p8mUpoJMbOUgo1C11mmKTVJ+/X+usTIx+OFL/Y9Lw8DL8eewVqepkCsujxfQ15CUvuGWgu33tNZhSasGH52t+fteea7qXAYOjIaWUiFrpi2ENXPzFQYrOWHu5FUKntnyQU6p4dX1ztOj2emlBlRosEUEpcl4azj5d2e83xRhhCaJfAc4haHaUTU4M1bppy47Tpuuo7nJTRbhPVOucKbl5R5f1YfGEQFiqlkzjGzTLpYRmqbANszXK/L7S/GkbtBffBTzm2gFQyGLJUppC2XNUuhFJ0KLiFysEqJpKpI75Iyr4ceUm65v3qx7jrP3ll+fVmnEIWglqyf9Z+8uuPQDXx4elJufylYK9wM6l308TxzToX7scfUVs/s9enQGqPv4a8+HtU40Dv2Nx6L8PSs6W+d+zvAc79ifnrR5lSwvapOc1WpvEHdGlePmFBXzrVsDopAUxoK2agXyZLMZ6PZ1b9CMTj9pm8vEzDwqvHaW+3Zivha5EujZq3LqJILc8789jJzznnrTGvRrXuPEKMWARr2ty5VTVWMFlmj7poYp2F4L3c9H88F3zkNBaYyeAPNVGxnLbaqgKi25a9CIkKPcDCO0ditIbdURutb8LdhKgkRTXWKVcfIyxw5XWZ8W2ZSV9jj+tX2daRaWebE4V5ZOpeQOF8i4/5zwdB6zkj73Zo4pR02Kw1vzfBcHx9dhicuIbK3joe+1+7LXK/D691ALoVjM3sqpWKdJgx1ohCetIU8qMr2w7TwaVkQ9IE+5cynEHjZd+RamXLhFLN6haeCePX/H4zSAqHyMPQ8LoHROqakB2DXiNuHQb2J9ruOKWRO84VLTGpT7SxdZ4mxYJwGeC9TYndw+M6RQsJaGDvNMLUIN52jF+EpRM6xSdKNYI0lFfUgiqVskJo3wsfTTHaWH97v6bzj15+etSsUjZcr1dJb2w5CvddyU27nqovrWLVgzs37KLSsUG8Nzlhe3t5xdxhZQmiLbbaOPTZs3Igwdpbet3RhqZxiZNf3hKyagBVa9SLEwhZpNyddYPoWHrKmHIlcWXHr8G3lmhYWqdueYs0s2E7P9lwdOk/OhWlJDM3i2xRdUK+pX1LVu2iOGdMOgjUYZqURW9HDfyl6SO6M2lGcYuIXH565HVSVmpqV9BeHkdtOFaVz55hi4bIkxt4ztci/9X0PRifTHIvupbyls8J81ixWaw1j7yh/uHH/fhR3/VqLqoY+L6UwGkGa+b4VXYJMGV1KCVAVq3SiyznHZ85qgEht4g5pBVZ/kmKZshXpSyq8mRb2nfts1FHgd2UAlPavdIlauASVMv/iPHMua569vt7OO36837F3ltMlMDSzJ50GNGVp9f1Yu86c60bzLEatdXtjMKYolm6kjfUqyhIqOxGmdhL1GO6sYxDBoQINK6vlaVOwVvjVaeLdNDNHVXs+dEZj2GomLemz3rpei3C7PNsNKAqHDN4wiCWS+XRaGDq1ol2tB7ZRaR3Q6xU+LqjK9JISh0NPLEndIKPCHiFlUq0kW9Vjh2thL6h8/Y9u9rydNEP1EhLnOdBZA97wMUReNBEWbfzfecun5fr+C5XnlNk73bEcQ+KSMofOEZxhSZnROV7dHSgIbx+fdRlfriHMc9YpqFTdq3y6nIlVVavnZtLV9apWXJeEKyus6xy99dgsHKzH9b2GP7TPzlRlB/WuZykdl7Zgz1ldRUUUkgG9L0MqPBn1pr/f71lCpCYtfcVAkspTCDgxHLxCkbmxzkyDpy45Y7MuSD+clP/vU6Y2bFt//8TTeeZ+3/GDF1/xf33zHZeciS3uaC2Cq3eS1vbGhquF14eBj6eJOeZWtNUywnBVYeei4qibQW0+QpteFJpTVawy2Uqj9Mrv3GerJcBGaW54OgXGzvE8q/rTd6bxxStLyuz7DiuGXz+eOXhHty4+6nVAFhHeTDOPS9QltbXMOTeBG3xaFp7mpdGqu2Y17LcGs7eGaSnsD57HZcY71P8JJRh0rtlGN9vuzhj23ukOqpkmDr3jyk3667++F8V9LXJKxzOkXPjN8cKfv7hFsp5mpcLOqvdMWL0q5Dr+iyi9LtaGsBddqr3aDVxSYUqRmNlO5rUjaOJJ3dyHxNCoXpU10/T6PmPKnCelp7nB8VwL9AZZRI3NKnwx9PxwP2rQQ6ktJk1pnFSlDqZyfc+uLdRq0bDm9Q6qRYUuCVrnslZK7aWf54SUyg/GrrFetDNx7Ub/nM65nSAo4+TDbNQHplTmVPnx/Y7ffDwhUlXhl0ujbF6xcRVa1RbmvVriGnLMnEsi54rv1jmhsTvagV2rYubrg6fZsZVE4dC0AO8vc4tIXAf1St/2JrGo3WptB/Ilpe3vf3S3pzOGv3z/yGWhsah0MfthiXw5Wl0IV/jJYUdo3vUrIHDJme+mhQfjMQVKLCQn9F3Hi4PD5MJ0WbiEoMytNj6fQ9wOqVPS4GNTCueQuT0YLtPCNIcWCacHekE73LRkrDV8/XCDiGGeJnb7Aetsg+Vku09Nu669Fax4LqEAZQuzjqU2YZ7BGtt2AYZffjhjyzWpx2bI0njignbilXZNm+FaO3xzheOkeQZLY5z1RgNhigjihPv9DmcKv337HmPV28a2vqj3phmZ6T+vexu1HEh4cXTOknPZYvA6swaoVE6nCFV1H88lbk2DF7VQgFXMyHYf1Hq1sZZGscxV06uM0WtmEIqp6i5rlNXkqh4C+j4SF6Bzjl++PeIM/OB2x/3YbRDl+nxeYtMdoLqZNbyDqkLEnXPsmohxozWKftb7QQv16DwxZqKk7VlT5e/K6VfdhhVh15woUylMpeCA5+lqzvfXfX0virtmkjZv5SaIybnyuETuvQpNatYH5N4a3s2l5SIq73VlQtS6BjWwjbbPlwXrHLdjz8fzvHWPFjR5aF3uCZxi4r7226i3tvalVJYQWVJWoymBmlUEsS5sDp3hZed51fe/I/1WDjEMvWU1s1whH2tFYwHLWviuBbk2BgSVbdm2FkVE/czDot1UrkVVkVXphne92vUK10JZ2zk/iuFnDzf85fsjt77j64PigBviJ9frsrojmO1BXbEW7V5zI1PPc6bGioy/A8mjyr/aWBHX5Bv9/0qqYK3FiX7GdmORwM55vtyN3HSeFSebcuYxBN5NszpT5sqfWcOXY6dh1r0GCgsqhDunzM5nbr0H0QP0p4cdMPEYo95rosVZrHCwSjfNCA+HPdYIt2R+/nxRgzZkux9M1ZxjUGvXVBV7HwdH7ywn0cWnWOg6q+KZ5icrAoNz5DlSjeFu6Hh1GLACp5i3JZwe8Gzxh3NSuq3vzDZVadFaQ2EqtcDtYYdfFs7ThG+T6BITtrTWwDTsutRNge3Wiarqc+FEF+3K41ZmmYgQBd7PF6oU8hLpYFv651LxTnNeP6vtms+7Yeww5wRGG5ucassTVnbJeU5bhGMtEEPBeoOvorCLaANT2/Ow8vPXQzBXzUS+3oeyPQm1RVh663ixtzzPgRAL3umhUdsk8OY4URvj5c1pwnvDQ99tE1ep8GroEdS2+5I1qJ3W+EjVxlKLs1G/I9rBii51d4MnUXi43XH6EElo+pgRPQTW10H0z4fGWEMg5kwSuYZn/56v70Vxt9bwZ1/c6DLluGzKs5Azxaqx0hqvRYVh6DlNlwZRXBkcpY1PXhSXPs+RT+eId5mv+30z5teIvNGoQjPVQhChGOGc1Zv5Zd8103/F5mJSd0a3eptX3YJjBFcN953j66HHt6Vf2pRjzQGyVkoR+k4tUTUFXtQArwmB9E+3hWi+3qDKyKmbiCPlyjFk9YIHBi/N/11fIzebgy2MoxlrrbTQkFVc8qf3yr8fvRaKVYS1PuCmdeltsGZ7hxWdgASmmqkxKXVU2hLOaPoQRjjmyNzUoHqt6rpa2A7ZOavRkreGh67jxTgoVZJ20LXT8K+ej7yfl8Z40d+3SuXNeeJl79l7y8f252MuhGaH/e15ZrxVOlpBMcx7b/kYlGq4RiGesvKjERUMvX884pzwJM3DPKvwJyS9Xl4MFrVTlqq03RRbspOYK+1QIMSC8WrVkJofUEUDXkoI2G7U4pgLd53laVGaYPpsAlWefqJYhen63hEXfeBzVRrkiHC/63n/6Ym7wwDt+zprlT5cCraq6Eas0eB3FHqq0vZWRbUGIZVmd6y7giXpUjgX5fDPKXG/G/h0ulByIWdlzHSN0bFe4zUkZ91VSZsSRu9xxlIvs8KRDW4AsF5avKDgBzX/W+GsWiBS/h/3a2kCqJwLybb8YGO2mDyRptdqz4G3lpu+47Qo3t05Q0jqS//m8UxbTZGpzSOK7fAsKIrgdwO/zhdySqwOqq4IQ2e5GRw7p/XFGmXxKQSljdFSE0soDF4Dt0vMWK+aF6Axd64w0ynFNrno77Tkco22/D1f34viDnrCfdk7XvTqWy4CT7HSo9jnu0vEG+FxicSq9q+5QQUrxperOjIa4GlOPF8iBVUvppC5M5aAYtahKnOeNjX4Rpd6nBd2bcyMUTfdoDgfotv8D0vg1DxN9p3nx4dx648/F56A3tAF7bAH8WSpRNo4jNWs2HoFXViLfdXQ6XPSFCr13RCeQ8Jaz/MyY4Dbsdu6c6fnxWev1+CU1gakAsdYuBt8UzxCCMrnXqK651nDJriirlhl3RJuMNqNzimpYMyqB8yu83x5GPnhzcAl6BL5zWT49jTrNNVASwG8McRa2FnDjdcg9L93f6d4Ofp5hVI4hqgTh8BTiCzKr9s+XTHwHALHENl3Hu8sp5C2IHNjhDllfvl85md3ewza5e2c42Xf8Rg0xmx1DD3nwkOvMNenaeE8JcbO48Uy9J44KcNFnJAbrGGal/hoDLMF7xw+J3JUb5jOa2i0mqhpR9o5i4jBewcGze6tV6WvMxoa4eTz+0dN30LKCLqTGUd1Ug1BmVbHFAjHTGc9H4+nBsQ1IRutsAC26v7qNOuSdE6pFRMNYLksQQsibPoM5wy+GvpS+VR0iq1d5TRrmE3OmdELFD3EOytUc7URWK+XQamHxzly6B1d7wlLBFQrMvROowJrxQ1GvezbqPt5UlNpt7estxYtXHs7SCrWNIFhUY/59YCwVj//oVNoxhrBOgGyZuxatRARMfz04dAmYYV+Vs5wpvIYApP6muhhUFSEtR8c1grPpTCIUqBTVY2KFZjXZq4W5lLY9x21Vi5LZHDKXuva877WkHWP6Iw2r2Ig2X+R7vC7X3+r4i4ivwCOqI1FqrX+WyLyAvivgZ8CvwD+41rrpz/0OnU90tCbzgmqBk2ZT3PizfHMKcTt4q7slUNv2jivN4BvqSoFmEImFbXzPHSWwRtyFs5z0i7XGMWPzaoabaKJAh9l4WD0tds7pAq8XxKPQU2oRBSn33m/FXQjOtYpZXLtLFZbYJXKp5QpOWuMYNUuvkF1egGLPuTOaqhD6BwhgjRnulPM3DqnVKmqfPvOWWjuz6WqqdLeKcixHhqP4Ro9aPUs0I4CQaqqS/VwqI0SqiZOq8DDNNvTKatPjVLS9Nrte8efvb7HVeVX7zvLJWRuO8c7p3CEGKVhvuw9g7OknOmtpXeGbz5N3H3RbRPbVDLnUtr+QD+XvffKGEG7tFp03B+cmnFZ6/hqP/I0BS2U5ipZW3Lh0xx4NSgb6sYLO7fjn58m3k+LPvANhw0pgbcEqc32IpOkst+PhKwH0xKDLvqrfn7HqF73FuEwDMwhYJ3FSaMEWiHmxujxKkCyArZmhs4xOENIuVkHaG7rYC2xZKwIH+fIcVFnzGVOUC0xa+pY7yxdL5ymSMyF1KiPvTWbQ+ZKZ7W2CZKc8LzE9nlrzKA3BvVdSm2volkJMWXGzm8WDoMzDEFdT9+dLk34o4yylDVqrvdWNQ9GrjRT9MCa18UrlRoSvVWP/tyurTXQ9fYqSFyLmiiVcdW1rFuotSvXeEqacphWGKUtuu0WDG5kVe/qzms/9I21BtnprkG8wUXD2PyPrgw7/bkG4d0c+O15+kxopu99t3PcdJZzaQc6EIrSi22bhHVA0kbj4+lIV9WDJ87z9ryt9GMr69/r7xdLUV8doX2Ov//r/4/O/d+rtX6e1PoPgP+p1voPReQftH/+z/7gK9TKeUkw9JSaGKyGG8Ra+HiZOYfYFlNsy6YlZjovDG289Bh860Zf9R1f7Uc+nBeOS1C1Y0zsxp7aEorEmK0AlFqJqbb0FfWyuHUWoTKlwqcYmWtljmXryFdKVshKczOi6VG6M2iLyaJ2ql6VU0S06ND8O/L25/X32hLqrTJjBIWsam5jvjHs955fvX1qFEKN/7sdO4VxBFxVZsFQlbZWqByXRK7C/V5HwM5pSIWhcjN4Pn7SNCCNHFVawfrzBIUuppzUwCmVzyAHZa6cQuSXxyM/GXd8d5r48xc3CMLeOX04rOG28+ycpW+sH9NdU62mlDi1mL51fB+chlKv8NLeWy2sSTH1zhl+eLfn9X5QO2IKN0PHw67nzXFSsRqte0R57YV1EazX73Xf8eE401u1eLzMC7bzTEXx6dWwbc4QLjMFuO8GQqiIWGrVxejt2FGM4atXB3IqvA2B4xwUJhTtSFOo9L2ls5YlJAZRpamzyggLmabGtAovtMLujXDTe95cIhl9DWMaPDdFgtMivxW69pjMOas6GC0ktVZcVZOuKWRKA/JKqdw1KuhzjFSjfjSpqD9K43jgrTY7huYFT21+5/ozu/bMmlyhJrIXwDR+OJsdQcqrvZc2RymroldygbYAVgzcqL1IO6G3ki5sWhAR3Z21f8SK8NB7nHPAOr0phfbjHHUSrPAnL26VYdcKfaq6P7qUylMMOCfc3/R80XXsu06bibr+bHiMkTfTvP0eVE2DO/Re4Z2qLqrtP210YgutIbq6ZKqFShMRVs2rtW1S0eW2bNfWG7uxxaZYlH31B77+VcAy/yHw77a//y+B/5n/t+KOLlDfnye+OvR0IsxUllipsXAwjrPkTc68gho5w4J6S6///mHoGL1TeX/RBHXbsKqUEkPXkbgaJymTRbhpkmcNyjCkRrX69bTCCg0MqIqDP3SeV0OnoqB2MW07VXMpvD3OvD1e+MHdnq8ednTd2mEaYoAUEzFr991WBdsYLrkgRqeI3ejbqqFuNzC0cblWnufAi33Pi33PFCK55TiqDkA4x0Kuwlc3urDbiqc3GHSa6Lx6qjhjrqIP+Wy5u+5AyvWh0s9DH6qUNXTh5s7zTbnwYQrsOkctlT++2bWdwoqft+9tcYbarakHfdck+ivW7kXd+6ZcEAM/vNnxvCQGpwXVmxaWIYbR6gN903neydyEVY59p1a+b6eFX14m/mgc1M8e+DgtQFX1a1nZS9qpxapUzCkkTmiU4Q87x84alq7n8TJxsxtJSQ/3ndd8zKclgehYX9vInnNzfuwcB9/jJPFi70ix8HI/6D6nPbhTTspfz5XO6bLyaUq83I8MObIsgYrugKiw7xyDs4w3lpQrj5eFlAu+/R5r17x2q6UqC8lUDfDe97qwvqx0x3aYZkG1dVRsbXbXQal+uTUptugzYm27J6v65sQsSBZmU+kHLVwxKxmgiOLWazcccuYcAvtx4LIsxJxZQzUMVxO0oufkVigFkObdvv4306AUDccp9J3fnpO997yZFkSEU0i8GO3WIQsw5cTbaVLopomZ5qpHoG/eL6VUppR5Oy86vRtRVXUFj2HwZiNjrIdT06UrhVPfoFpSN6yqd5Z733GMEduU0MhqfKLP4WD1WlqE5xA5RV3lb7uN3/P1ty3uFfgfRLed/0Wt9R8Br2ut3+oDXL8VkS//Ji8Ua2UumU/nhcE7TXTPq8+IsDeWRcrmhyJULiFijeFu8Ow6p51i50GEd88XLs173TXr3FIrY+c5BWWYrDfc68PAw6DyfvFqmF9r5UTaGB616qLSGHg99rwaOlwz6jpeZmLzA//1p5NG+F0WksA5Z3AqmilFi+DPny6Ko5ZrUrttirzaTp2UlC3zM3ejBbbdhbvBcxg9x4tiuikX3p4mvjgMWDFkyXgxhFJ4jJn7oePlrtOU+AZZ65JLRRSDt0zxaoeUNhvN0ihk2gtao6lHcT0Y1xurfWNOerN/sR94e5758/G22QUbIF/VuA0TVaaKvtDN4JlTpneKPVdUXeiaStgZ9SvxxvLVrcfSErWOQVkgojatvdUD9OVNr5DPiuEX5RY/LZGdRHaDKp2PbbK68Y5jjq14VEwVeuvorB6msS0sT64ynSe6Fm2Xc2EngvMeZwynJfI0nzUM2Ri1DAZ8bxH1lmD0FkulJFUtequFsNSrp3/MbdeTVUcQKoyucrwEnBEG66lkOmsYO0/KSvvz1nDoHXNQSKCZWWKkMdBq3aCBndHPatc5TinrYdGKJChsVFFG0LlmdlWng1DatWwHkKqAaWlNUBu/2KGYUM2VWGVb0G43GquDp+6xKhqzWWshF5rhGVtDsE5wV9V43QzTpOqE4WlW2CgUlbIukEWUKvrVfuDnxzP//PHIv9E5Ruf0XUhbYJdrNoOIcCqZt2HhdddrpoQRvp0vnKKyq9wagVgVQRhbozfoCHy1i2gFX1pTcQV7VSj2XcjsfMdd3/Mc1CJ7mjOH0eOtFndrDB+mhUsTzpX62cP3e77+tsX936m1ftMK+P8oIv/n3/QbReTvA38fYBxHCppcP8fMu+PEl7d7BueYvfqwOCP0RjhVvQi6c2zL0VQZdp7RqSdyLEUd/Jwa4udWRG87jx97jm1DHrN2/SkXLiGz9w4v62JGeBg8353nDdf/47sdDi02682/mpktIXEqhe9Os2LnDdMeel3eIMIckiYTodaxq4CuVnBtM+5bYIXSHgvGWmgiqSq65f/Rl3dc5sC7pws3vS4y10BfJxoocAnCi13Hw65j7DTAI68JRrUwL1nT6Q0qZS+VuCYvt4GzFrZFnDErdZMtWUkvpE4jYc78H28+8ePbPQ9jp7//OlqzLoSu/uFzKSxtP3CKkaEKt/iNnWPaA+6sKCulasdIzpxC4tM5cA5JX1MMn3xg33syarkcaytEFcgaE3jrFBaJJfM8B0ZruNuNmoDTdVyCLu37ccTVwtSLy1AGAAAgAElEQVSWi6NzvNrveHlz4MUXr/jmm285nwvP54XcO27mmU8iWOfIjW+9772KnJIWenrDYDxLSEwx4iocBtc8S9bF39UWQ0SZHzPw+nbgm9OZlHUK09wDw1wqNiqLQkS/f1jhq5gpuW5hKWXDqNloj95YlqQMmPUY14lNDwENQm/7ATGEkrZnL7UDyBl1ODRGMOUKOcR2wJWs/yx6U2/3TGnUVKqG8dhSqEYYO88xhEbzrIBZCbKbUrVCE/OwBc6oUZweBrqwlo3frmiu8LLveT8HnkLkt8czPz7sMM7ytARyydw5xzGrFTZN8/GYEqFWfjiMvJ8WHkMAo/TkwVp+PPZksYSo9866pzOsNsuy7bBWiLzURr+ula43LJdMMi1lbhg4z4GnaWEcLL5aHpfAackgutC/Qlv/Cot7rfWb9te3IvKPgX8beCMiX7eu/Wvg7e/53n8E/COA+/v7uuK4p5hxxuDQG+3l7cCH48wUIztreLCOTynpQ2PVB/oYIr86nvnJ/YGbNt5Kw1wFoXfCH93v6J1hrobeOd6fF6yot4agnZ3ZxtiGOYuOvVLgR7teBRStILfpSYt2zrq0i5ndznOZIgi8vBm53w1YEeYptMxT/Zkfqzpf1qKXKEZ1IFw576nqUmiOcetATWvhjYFx8HwlB/bW0FvBoEvYx0UXSL3XVKbeuW1h7dphFxodT5kMGtFW12evTRJrk1XWzwL161myfHYA6KOj0m2Vt78/z7y+Gfk4Be7HntB8+d+eF6YlMHrHXFT8BcLzOZBipu/8BpXByoJQhk9uhd0ikKDkZozFSttU7+5974lVschcCg59AK3XRaFpb/3dWQU63rQ0KAQRyzA4as5cThdudmNbjHZUUa+Yd88nfvXhI5cp8fX9LV8+eN58fMbd7DlOE2IXvLXMKfF81uSvEBqOXCpm31HFEstCksqXw0j9vLA2QYHQuNztcNi5zDmkjRKIqCVuyJlYMgfvGKxpNh2CWC2LkbwJ6dYR3rHqOtSi45Izq5GbCM27vr0HK9AKp7PNcKzdG6VNUrXKVtChblYYBe2GOzH4qmum0t5E5cp5d0bYGVGRW9VdhjeW3BweM4Vlvd3alwHEGLxVkoEIVGMo1iqbS1YjPq0Aa7pRLoWvxoFTSHycA7d9h4RIaslgO2vJqO4B1p0SXEpW467Bcls69fi3Blvhru/4uCReDp2ms5HUbbbqIdi1wo7AUjOmmgY56eFkxbDUiKcwhczh7hU1vKeiGp5jSFzWe8hqsHmutTWYfxiX+Zcu7iKyB0yt9dj+/j8A/nPgvwP+E+Aftr/+t3+T18ulNqvfyt3QaTxdW+CMvWsh2YWDNRyqJTQAvlmdMIXEd48nymGks5aQMs5ZOlO56xy9U+5ITNr5FSqvx56ds5p8Y4DanCVp+CTCl7uevmq3Tq1tJLyOWt/NC5+WuPHQAYbRkVPh1c3AYA1piS2UQJc7a7EGWmhH4+k3plVdi6y50qGQBqs04FGqpravhRsUS7TG8HJ0HDqPcxbtlvWBCzERQtzepxZ45SbTJoctmBvZPluqdvG9sVwaVLVinw9j30KqKx/nmccQOETPHDMvd/2mHuyd4bKoSVsCmPUBqCGrhbK1W2ezMoyobOKNFTKRBj94K8SgBSEBpxx59kE5xs6pXXLKzDXjc+sODXy6BI05REVBpxpasLfFi2UOkZtx0OW31QdEquXpMmsItHUc9h1FDPfe0N3t+RgTXecJJdGJ4WmZlb6WcwukUHJ08XCOauc6dJahNRS0Yluax8Xa6abSuv5S+cE48CkEzlEZV6uzYq7K8vJicLZu92XnLM4ZUsxq3dwIB9KKTSqV5ynooWkVFtRbsTFcpN0Bovd8KrqbWL1jemspWXOHd1aa62nZFNkYYRY1WHvZeXZiea6p+TOtB4nQtUMht/svpMzYOc6zMn/We+JzRrcTw49uD1gRfn08a1Ft7qzPznLrer2LmsDpCnipOdpX+xGphWlaNHzcOWX4INRGtDilCKVNrIjWjwr3g6fMuiv4ehy4lMIlRG5cT28MTjznovTiUOtGA5b2/GZR2FPadRdg6B0hq1VFOb4ntVzbVNQQsT3+G1uQNqGvytff9/W36dxfA/+4jZEO+K9qrf+9iPwT4L8Rkf8U+BXwH/1NXiyXyhwSX93sFJ/OitGdF1US3uwcORY110+qCKtZl3GdKOYWYubN05mXhx3FgPWQs+EYEy93PVOBD0FhgpdN1bh6hOTa3AfR7iQ1vGR0FinaYa8f5TkVPi4LVaThlXV7aErRpdRX93sO3kH7PWjdkXqNC6ttAtD+m37V7frpf3d2HUqvlKuVVbP6ps8pc6mOmA0vRhX0KC0zt/xZPTiXELcuyxjBdZ5jUhaB2vVeqXPX96Y/uABmDU5oD6JBF0Kl6O7CiyXkysccyRb+4nTiBrcttVffn1QLrtjPgoyV3VGXQG8NnXPMMWqHZIRXe12CrswiqbprWFXAo3fc9p6dN9x1GtydaItuZ9lby2mOfHucMWI0ezVpBqe1MFddGr4cDoSwMMeA9SOj66nNGrLrPJILN9bg93uWacGIYWzX5Uf3d/ziwyc+nVSUs3bhxlwDOS5hUT51FV43O+KV+7/54bcj1YiaaSleriEbzvQ4E3lcIg4V3qyw3twojaZCZ9dEKMF7x1jU5M6aq2q5lso5RD1cIojRMnSz842tod3myh+/zMqUSrWwLInD2KmJVWXLJTW9hp5fjNpt5PbbnEpmdOp5FKo2OU6gZ50EVClb0QQol5uRV7l2+q6p3277jq8PIzujkN83RphjbsHR8LQE7jrPTQPbperv2hkNA8/Al7ted1rt2bSm5S4AJRuKcRgnfFgWpKDqVP046axw6CzvjpGUKn/5fGSwdhPdOTHcW00B+1TTFcuvCrkW9ECkkQnEKMBSakWs2guI0fslr7TO9jlJg31WGrTfKtJf//UvXdxrrX8F/Jt/zb//APz7/59fsBQedr2qrlqXHlJmiUk3xjjGXc+7aWEKKgPSb9PuRhpumQr88vHIVw83LCHxeJy46zy/Ps5MqTCHwlcHPbmt6LItJU09z7Vqh9ygF5U068VYP6hTTvzqNLfFrv47aQWwVqWJrc5zqS2cqqhfS26d/6qoNe2b1wtYcmlp8Tqy+TZiSvtZuSi/e5U3r29AbI/LmS9vO83xbDmVpk0/5ykwh6QPt9FOPRrh23kibofT6p1BO2GuhSeoNSdzCMqm2E6gynFWZszgPCUqVDYvyvQ5TQvP1nI/eOasJmFejOoRUOyxc5aH3YCI8O48KQ8/Jp5ipJbKfd8RWtqUepOoevInDzcsKUOF+7FjMzozahPrqvCj2x25wDefznz76UxsyUJihNNpwTlDv3PUrNfh8Xyks45LjOST8GrXIyXzzWWimy3Pl4V56Lk1lqfzBWN2LbJNeH+cNohipbWuTpa9N+x9T5FKLCoyuvGemEtjeMCKO6wHQ6FySZVDp/hxzhUplZe+A4THtjfSo0Ax7k+lsHOVsTp8UwIaA/udJyx5i0WsleYgLtulrs306/kcNojFO00Fu+37ayh4hH5wWETDvutVUCbWUL0hL5mEHui9U1GPN5auwnNQlbCXdh+jcI0KhFZTNWFwHlNTEx0ZBu/0kDVG09OKEgc6I5w+n05zIYoQncUb9X9/CrOKlWpVnYXt6DqvvPyq6EBuDYoRNBNACh2aALXEzOjUPjlm7eS/2o+8e550F2TVXqBvz48zhn3LQz5ltXfWbIPVwqLoYrU1eoO3HFPS57LtMkIsGJs3m+fOmcYaagdDu8/+0Nf3QqGqkIQuwqQVsFILIWpAcWcNvVM++4ux57hEHi8LoGZC28xW4RgTc67UxxMPh0Gj+bzwi48n7saeXFyT/SoEsSR1oRSvBclZpXhtDxsKQUyi9LA3IWlh/51fQD/kwRq+2o98eRjIRZdOBVEcvQHKWtRagWxFbhNifPaLVIQlFlJOupCttTnr6X8XUSl2rcJg4bDvGLzDthHXWmXfzHMkJk13Emv5FCNdUTfDNYPWtIDluu4R2u+9TghTzJyXpKEKn42CBbhE7YD3Xrv4F4PnN88zU9KqMe57YsMYO7Tzvx9Geqk8xcxSHT/78oHb0fO//vIdKSWKKA1S06/gNEU6b/Cd3cQfuRZu+9X18TMmiBhKY3Kc5oUPp5n3z3Oj2On77o3VUTgWllmDxHvrSJIw1uKrMMWFD6fCYfTqeVOF3a7jsBt5sRuRGJhzxRnL4zIxpZnRW7wVav2MzdAOMNpnYETovNPUe+eIFXZe8XLXJkgRuMRKFaNUzlb8Vh7Tnbf0ZuD9EsiavLH1cMeWIHUnypBya7EcGsd9iqRUeFzU+AxZp0UtkFrs9fWWWKgh47Esi8Y1ioFO1hCbBu0Zg2nB8NvEAuwHz+3gm50EdNapWjQmNjej1rw41qWvIEXDZ8bmXNlbdd1c3+wUM7tmS20brp4a7ZQK52Xhxo8t6lEbxXWhLJ89c95ZJBeFfEvLKm5Y/k4s+I4jGhC0JGVvpZqZQ4KiIrfSyBgLFd/gkvXAvHFe1c55IUlRqnXJFFlJGWbzx8mlhc63BtB7C9ZgVlhyneQbg80A8e+CtwwiHHrfREj6AcwpkWph5x03vVfHlFqJKbNzjkcWxcoRplK4sZbHJZJqxXstAqc5cug77seOofM8XiK1JO7GkYfbDqmFu30HJfFhOqu0fMW7G5RCVTFVagcHZvWZAEShCu+Eh77nxdCx90rnK1m7ynU8VhcufU2PGhWtrJrSjIK2M6NBRRtH35ito1YcUV0olyi8GB0Pu06dAZ1tPHk1VjlPkVwyg3fMJXNcFkIpzGshp/GHnUGMoaa88YJViAVD55DOcjrmbcK4GigpzhljRgp0u543c2ApGXGG0XiFhkSw1kETdM21sFQhoD7zz5eZn355w08eDrx9uvD2eGHnHbVkDt62aUQhB6Vk6gfl2ji95GtiVK6afpUyPJ4W3jxd2ihLg7SUhdIu3+bPnWtGMCwhcVn0gT7sduRUePCWUzUM4jhUeP74zN3NjpgqT9PMi/1LpjRxmie+vNnz9nhRy+JccKZymRPWq+LWO8uSEpeUeI5RJ4kZfrTv1fZCdAR/mpMGN7c3apuwrdaKFKMpXNbwnBKPS9sjiGYOX2LGm8RgLaDxeStzo+sd1hZ8DFsXvxZ45YdrzqsxbWJE4TQnhk+nmdu9qnxTVm8fby3V6H6KWumd49WNirO8k+bLIlvm8d5aptbsGDFkavMeqs0TSrjf7bgk9S73FVV0mxYaI6I2zd5hBB56z/sptGmpUFvBlMrmyLnaeVS5JoethAAjmvo1Rd3FjV7FR7219GIIDXp9ahCWsPo+aR3ojArk5haPKbU0fr7+HC/SHn09uLxVr/bScM1jyITYDh8j1OaJJFapl6Vdl1oKnTM4rFJMU7nWi9/z9f0o7rXln6KjVmqOf6sS0Ta/7Dlmns6BIppXqDJuPRCOIYMVvNXusDQc1xnhxV2vLIUaW5RbpusHwpw4XWbeHWde34+EvEAbzWg3gkIpQqmZwVg1yzJXVd6LoePr/dgwwkpJ6olua1OXti5969bbeNZZYVoxNa3xLM0MbH3A9dop+2EVaVQql6Ug1fJqZ7gZug0PX9oF/19+8Y4//uKGzhlOpXBZQvPiaIIPZPNVN1UVrTFnTUyrn+F8xnB722PE8hwCU9IC70QXPWHOasfqhCkkzkEDFlIuDL6n846hKAe7GywxJT5Oav61b5L2vbO8f5743397Zn6eSamoCtZqGlIuYFrijLQDafUCCm1pKrLy5s02Yl/OYVu+ZlspuT3MVjnlQ+cQUdZx33kSlcE6QkrYKvhsGDqPofBXH58IpfLjL+54atqJnCMvbm7oR8fjaaKrmS8PB45T4Mu7A989nxg6p/dAUVGZtQpTpObLHdClW62V34qmWR2spRbBGMveC9oeKLNobSra/lpDHbwQcuUYI9Jk3M4YnkLkYwm86D1+WGGwipjKfuz41wbPJSaeF8XwQy5bJGWpQKM8Wiu8f7406wQ1znOi92Rqwomysj/0dtU8ZGs2O2tq3TBt4yx965aN0Q5UacUOY7Sf71f8uVbsumxGGxH1YoLHmLj36kU1OGGKSkq2op/vh8u0zgY0rgRw9YXXe0ah0kuKaiNNc5X8v6l7jyZJ0iRN7/moEfdglVmk6ewCWED2BhH8c/wDXHHAAbIiCxFgp6cb010kM4M5MfIxHFTNPAHs9Fxr4lBVkpUR4W5upp/qqy/hZrsRjNxr3llskz1F3YoWIgYsVnj8kyu858rBeYIVEz09LzFKC86V25IVqXvpq3hJKnjn6IJTuwVLqYVSGuPo+XDs+MvPp33p/fe+fh3FXUAAkurDV8Ri9BAkBLcixeQ6i79McxKIkWrdG2LrDEaLwIZ9D97TOcefP11ZSmJwEUMlWM/1euE4HHibDd/cj1ymGedkEVSRzuS2KG17IEDQ1J0xOP54P3IMAWNEdLT5RmwVWor25tcu73RbknbOMde833RVWwpD25dwitzsXf5aKmmuXNbKH596xuD2SLPcxHEweFkGz63yOskk02h7RJ4MdXJSbKOerY0lZ4xxGFUbtgY2Gn58u/LvP9zx+8eRP30+01rj4D2pNbwP5LzKzsOrZUCBzhsOscO1QjSNxXqel4XSBDJIa9Fw48pbLXTGEt/fmdeV67QydGIHkEvjyzJzDW7nMHe9x3nL26Q2El9du9JE6h+xkm9aJftzUim/7DXkcy2tEr0jNwlWzqWSS8GqssZ7zy/nK0995KHvOafEZVo4Dgc+Ph05vzzz6TRBg+PYM00ThkbvxYP+Qz/ota8sFHwVO4hJlaC9c6yq2bDWcEmZORveTaYzXrI4rRxEdqs0egOZZvaKNRjHd32HtUIJjgjWS5WH/5oK0WUGbYassmYClgdrOcbAN0NhypnXOXFNSQ6cKvc9dcskqOq/48AYlpQIWuCt0Z3XHpl061StMVSdREuFpaj3vYVj13HJWZeIuvCslU53BoW20ycrmzOq3MPnInj8Qwj8duz50+mqS0rZdQzOimdNg9Oadkh0Y5xszdZ5zZzmxFqK2l8IrHZNwtyrDb4fe4594Me3C4sttxB39QJyRnzXp1S4psJryxy95duul7pWJKWqtYb3FmfVkXOVpkqeUOi8x3vIrfLDEEXMVAUuTjRCsAJnbU3OvwXMHTQuq8FSK+dcOOWC9ZZHJ2yMJUuXlmyji47BWMKqeLmF+1GS3PXe4uMoNMSU5eF5nwsHX7jrgxhulczn04lPbxcZ56gcoleutNIatYvdDlWL5Ia6VPlwN3CIUTDxrdMx7BjwZnHLji3CllDkjKTPn3Jhy3BqbcNOVfSgOLwc5o01Fa5T4TQvzEviDw89ICNorU0XZg1nAo/Hjr++Xxk6t3N8t2tsDBpIrNV+mzAsvEyrpKs7C9FwTgmDBAqbKtPIh8PAIXhOWUK/Q7hnOp94uutZmiz6xlaZS+aX85VaK13wxCHIRKZbwPfLLJ1951mbOOL9/HKhucZT6LDKFOmd2cOgr7XxeV65HwJrrgQ9/L1SOack+ZgRS0SiGDfDK/SQrajIaW3Umrg79mQKxspCe1kLhxixDqY1cQyObw8jT6byl+d3Bp/48n5ibZ6jhWtaeXuZOHhH7RxZVV9jJ3TQt2VlbZXfPzxwnWcxzmpCA01VoI0tgGJOcgg57+iD8sr13rEYqmm4tqk35eaoVZSh38XA0Tte1iQRgchEu7bG87zy2EeOQQKygxELWqwcFMdgOATLN31QJpQwUP6v18vOkXfeEr1TEY0UpWADzUqoxsYw83bDkW+qTKvF3hoR2I1Dj2/SBMyliIq7imcQxnCulSfrQX/m5gBZt5ESnaZpSI6sF+GWKtrXXDibhleyhTMCBdYi+o7NSvm6Jq6p7NOqV91DqpU5SfiGcxZKYZ0bPxx77gbPp/PMWuRz26L0nLGsRZh8IL5N55AYjdsZM8HL37+uiUolZ4HForMcu8gQAnNuXKeV2mAIjqPRJCrbWNQTPwaH7ew+0f5LX7+e4o58eEupvCs173nJRLcyeMfLtFJ8IziDrY1qK66T0NlUJJfSJblZg7N8P8a9qJ/XxMexY4xeeLoGTGsMTpwlny8rIFSq9BV1KWqRzU38pqeUmVImdp5f3iZaaTwOHq/9sGmIXWhrO7OlWkRt9xU+ZpCpwtpELtuiR+7eatgngG/vR3yz5Azna+Y8rax53UODgw2sSbpOg7ynaU089JF//PmdwyHwzUNPNI2K3btyYGf4yCRfcc4ROgN6A06r0BAr8E9fzozO87vHUbqTnMm5QI2UPOFcYGmNz5crlznxXCtj37FkEXtgRRyTkM55nYXutaSyG+o8p0qhUIvhdFnprUAYW7oPBdE4BPEAehwj1RpeLuIP461YyS5rJfvKNpRbbzBJFrAgI+/d0JFC4Zoy53VlOSdC5+h6zxgChxA4r0ngD+95rxXbJDWq5cRcC12FUiLHbuSczvx8nZhOJz52HXMz3B96luuMM5Z/eHrC0PjDtx95v858Ob3znpPCGXL4bvm80Xk+DkHgQ6eMpyZWDZtZm6nyPUZdOxvgmuHeeXxn+Vudic3grGctMqU8TwsHLxYCS84E64SMIA8DFlnyCT4OHst//809b2vivg+8r4lf3ic+nyYMElVXEWxYFsWoJmODUaRjDSrlLlWaiz+MI1RJhQK4i17dPm+qy7WJM6hxRszJkB/rFB/PAK0RcqW4Su8sDzHweVmlKbCW1yXv00Mum8mW3C/rkvnuMLCULexm493Ls7tkOYB9kO5/Mo2aC8FbPvQdrhl+vsyizUmFa6mc18TgxfIiN3FqNdbykhKpiio9FdkHGV0EF1N3X57LsvJ2mumdo/Nelr37s2rwSPduGgx3kWrabmz3L339Sor7jWd6XvMOkKXW+NtlFtFPqxjFS9EP2ejG+hActcJj7MQT2ksRlxivumc5XtaMc5ZOsbApZenIVbp4WdKuAhS000AVP5gpF6ZUxJrVWfou8PNpprXIt2O3s5KadvE7Rt4qS20MZmNQSAfZWTXwb22fNsx+EAsbxlaYZ8H5Ss7c9RbrBjm9vReL11zZQHIJ9pAJ47ungU9vE33vsb0Xb5nt4UMOmKpLU4MhRnWxo+E6T1nLnsREBdc5zkmUoNZY8ThXOMHVyilJB+eDoaxwXRcxYGptD5NYsyxfa6s4LNaJwCerIVWrUsTOa2F2hs47hugYg927P+H4NknASUWgOQNrEfZAWipuDBL4IG8I5wQ2MMYSvQSm7PwSZWZYFTsc+shoLZ9LZgxBlo5l3ROvTrlRrKHvR3xphJZopdG5CFbSonKtvH6eeBwHvPV8fj8zxoHgFtYi+G6poig2+8uQIBPr4Mu04seId54t63d/0L/q5tGiRGu60ITBWr7pAl+WVewIvGUujc45llJ4X1ZhoFixfz44RzRG7j2Ftra0sZdZHBLXKlPtbx8P/HKauKxJKKCd2XMBGht2vk2hsoPKVI0ghFQSq3P6/Inxn+yfLNMWeKGTyaVU7pznNnVu/jI3KvFSK9dS6dQKYHRuFwGmVFlUC9Fugw4G+DAOpFpZcmatYrVsEUx8zUXuY2sJTnzTk36/XTLWGg7Bc4yec5K/S61cc1WV8MbsQepFbVyzTNXCJpJ7uaIpTUahwiKmhbYThk3OhaB2w1Lc5YndnvVUZC/1975+FcV9v1mt5ZIyEeG+rk3Tappssa3UWlbFwrw1zEsG/RA6FzinjDWFU4IPY1SOsHS2U6l06j9TkZuqBMCsrFXYLWKhG1j1e+Zc+HJddgy+lgatUqs8PF8mGXmjM2z+0xv0sj2UqcimWwpY1ZFVMGGHfF+wTmXYsoA8xA6wfD6dmXLmvvd45/euyKrFKrAXdilkMHjP/zA80nvHT6cr1hkOTjb5skwVeKJh2MLv+s4xL2JNkNFwahouq0VDbVjr+Xj/wMv7mb6PdM5TSmKqFdsa1oralx41KSvULN2RdBmGooeAsYZ1KftBLk6UAhcVdeScamVZKtck4qTgDVmLRsqVdS1K+zSyiJ2L7ipuByawY/NpreKHbsAH6YhzqjvsZpQSl6yYQB26iC+VU5GsymqksJRS+XAIkjurr981cURcYU+8vyziQui9Zc0Tb5Pn5XplrsLuuWVrygMQdWdxySu5Ff5DvEPFnmCM2vfKB27YVq1GZJQbfdcYHr3QNz8tMyCeSptX/bZ7mHXKO9nM6CwH5xic042M4ZfrvFtSZOWAe2v4w9OB9zXx4+uVy5QYD0GKFOyvaTtVDWKqteZCKZVaK89m5jeHUZlLclgP3jLl7XPTTt80AjK5TJvbKko+0D2Ft5Kn+7LI/xuCx+jhZC3i4bFfX+RkbIa3edlV4oLDi1HNUuS+P/og4SXaGW/35EzDLdL0DMZSvOGq3kFLKbwsok2RNCqBd64pS7AJQrPeAuu3xq4oo2fTIGxeV1Y1LvIa1R4Cce/0zXBdGnP5N5ChKg2t5efLzNyE/taq2OJ6dV/bOjcUe6xVxTVL43CQUai0zCF67oe4Y7GuyAXMulwTLxH1YzeWuSi74eWdqvzkNVeWIuPW65zZFKKtCs52VHl7VO8NSTsTddFcZMHXtNvJTU7k9yJ81ujlkpcGj/0g/hNeslSvSfD7Uivn+cqaPKdpERx2iHtmbKkiy/fG6uThVJBVsPZ2E/2Hbx/57cOR//yTZKUcglOZuNmx/qo3mjEwL4XQud1OttbGXYxQDdGKuOf9chFb4bWxGAmfPnQd1ybUtcucxKCsQclVEtyLMobUcdIArYjgSVhB0o60Jg/lTrXUAzXrEeSz2cfYTdmYc2Ge9EDVz7aUhtsfnNsyruhWzRlIq3RT0VrGwROtI9DUYnchOMu6JL55euD9TXYPud0m4loAACAASURBVIgpWLSWn08L05pxMfL2fqaPgcMQMBje5xWjv0sHQd6mGbNuexj2iLSb74s0LEuS63gtjf/8cubbIfJx6GTRv3fG8vdN00U54sW/eX8b07j3ltGPXGvl+4cDf/ryzm2I3xS08vOutXIpFVNXOivGY80K1NOaJJI1A4veJ723fHPsmJbMfE3EowjTrFGTrwbRylJ7SWm3kd4mm5d54S6EvVk7eM/VVaYiRnDoAfSaEsHbnRSw7ZWtNmxF753c2NksNDmA++BvJl7G7PUgOCFFNNgteUsTQkEqlVYqJXpisBr7t1k6iDvolcbQ5GfeB8dd15Fq4a+nq/gbxYBvEoOXq1C3N9jVGau0T/ZJsOji21jLcHD88nKlj44P9wfIwovM+lqXKurg90kD3v8+KvPrKO5OJhsGF3irCxcrC6chOM0xZMcWtyKAgXUpPARPbqr+dJb7sF1Aw/usG2rndJuvV6PJxbrmzNuSSNPM0+HA+7TKqHeZmdW90bTbclNoYHZfzJXWiMZxXgs1iqOhM5CpspQz0BvoOithATlx0I6+qF94bRBd4NA5XuaFhz6Q10pthqexk2ajwpKFxlaqMC3O08Ld3YgtOtrryBe92V83Rh7iH+5GPufN76QJBq5K1loaz+8zzQjFzTVHblkKfpUCa5vV0APLEIWv3jvPvFTmWqgxkW3jdVl0SjI7M2Bj6ThnCF5ELBvjwVgp1psislYRKIndg+wQjGofliyhwCS5gM3IAhS4UUelMWNZpKOvGmtnvcF3Vn5mlanHWOmSPvSd2jEDTah3qVROy0r0jnVdCU7sEJYsyVuDsyw18/l85W5ssiSdmxiMec8QLfOqpl1O4T9ndwNp0GBn/dxa29waHQtVhTYyof2ksGTso/7lTfRmqOQdOjNmmxYbm7mnx/BNjPzp5cSn64S3Aklu3aOzFmvavqkstTHnJNoKL/fV5mHjANs0mjFVBut4uIv85fOJ82XleOyoRhlIpTAXCX4Xd0TZX0nlFfZK74NQKrWrfuoCbWk7lFKR6WLXJumju00uAqXInRP0/ehZTqMxxsCHKEw2YefYnXopNOfGJUkOcCqVaZVraYHrWSaucQgY7A7nUAU1cMYQq9pxWINtlu8PPYs2hcaIVbUzYNSsr7VGMeKVU/Va34y2VVVupClLpfKnX144jgHnrThoboeMMXTRMa/l/y+m/P98/SqKO8AxSEcER356v2BcwwQV+1SR627wjTHy8Jfc8FE9GKwlGovHcF4SUW1unY45GJHe5yaSeeH2Cq2pVnAsnJflqwsuv6zphc1rxSt1rqn3RfSep8ORO1d3X2tD5bKuxNgRjVVjp8YvpyuxE7e7rDi0LJpE3tyQU97RMYRIypmn6PlmHFhapLXMl/Mro5cl55Ll4alFHO36Puysi1blxJf3BY9j5JcvEzUK4u/0bjVINzGrW2WpUKYF6x1jjOAEtnChcS2JqSRhTFjLXRdJeVa75IwLIvmPvadT2wOMQCFd51hSIVhPzpUpJVk4Z2jbxr81YvQ007BO7W9VMGYQbH43EdPDwCjIuwUrb3gkNNIsHRPWUIr8nZYrg3rHy+cncW7nlOms3e2eo7eQ5WfHWmkxSFKVA2MdXezwznA39LRWxB8+ZVKrhCZE5mbAeBHy+GIUkhPqYEO9cYwhKNPEYwnWY0wSczgtaK01fjxP0BoPCq9s18U2y1fbg71DFAVvg9b4sq78dLpKB17FWrZT2KBUvUaY/WfsP0gLTrCCboiRp3adyhBZa+WHh5G/PJ+Y58zjsaeVgqlVw7fVFVLV0rXK61o1m7hznj2YwzR6a5mV871NKEuqSh+U15OrWIE0bveIQvyCRSt3vSITscA/IkSsWQrqrlK1Cvusq8CKOvlQYDrJxDwe4nZLqYWwhNY0Yzh6CWjByuv7w8PIj6eJWhuHXnZii7Jnth3DVl22iMtSm6psYZrKTnRYcqWcVx7uO4yDjeYJ4L0hVEtZ/w0oVDexUgyGU658PAziBeGEB65N1Xb3Uqtwa4N6ypRa6a3nzsmi9M5ZTjkzBllIhSge3mupvFwWXueFtVSh/BV47EZSzoTgmNaEQwQYMhFA0yQdYRVIB3A/DjwdjhzIOza3hU9800V672Xky4W3eRuj1NDJCEac1V99Tot2GHJKPw0DXqPeSsmYtnJJK9GZPcU+GBV5WENQAzQQvj9GVHDzmvlPP77wOotfSDf43ftbgRD1YnG8XxaGGHk69LzOC82IPUE2FVMad0PgmiGlRu3gc16YW6IbA7EASW76WhrnlMjLRuyHrguEznB+l87eOosPoooV2+YmKT8V4uAZdLGWsuC03jnmObGkshf2zU1kE8k4hdyWVe4Z5w1bcHqulXUp+OggGDlUtHA+zyuWRnWO5qwamzW6IGyF95RAWSveel04FjyOHx7v+PHthDGNsZN77VoFk/XWcOg9OTeqqXwce85F9zd16y+VRWIbDk9pElRdmkrm202c9s+niWtf+DBGRutlOW4NRi/zbWmodNdq+JIyfz5d9HMwe5cvE6PCQ6ipmz6HcsgIESA3CZyPFppDRUuqnmYzvoJvDj2XJZOSKKIlwg6xbTDCvkq17qLD3Cqfp4k/hnsVhFX1Y7/Bc9v12Zq5jdGzlThnhFlnq1E9gNQJhwTKTCWTslp5W7GjwBh+OA60Bl+uEi1pnXrvzBm3UUiBmivTnPCd21W2m1NtNYbmLGe1447I+/rYRe6C56/niVwa11RYkzRapmlBV0bS13/21Hdc1sRzKvt9aS2iHG83i+O1lh3C26eJv/P16yjuWrTX2rjvPXddY85wnqBaI0EWiplumOeWcC83mOHgZRm0WZ4edRTbcL7XeeGaMpcktqPOSvcUcZS8MHY9uToo0Fs5jVPJTE2Ur8tccEUobhgYQ8/r5YqLht5v9k1yg3VasJyznJbMj6cLD+OBhIYdIPF2Ike2JATL9d6TCpBX6X7VT1rcKuWhzUUeHoPhy7Twm0PPpmbdPPHZljbGSNpQEDx0N7XSH1pr4/0kI2j0sox6nmcMEoxsraW3lruuo5nIU+/BwXtamKeMrfDgLd/eSTQiGP72PvHX98vOAnDBSLpMbfKAaqfujPCmgxPB2vbao4rEYrB03snUBfghkqvQHlXyJxCEEWOpGOT7sh7w1sjPMM5CEZ8a54yEWCDXdS1yMATr6FUyvsF/1hhsa7ynzFMM3PWR1Do63/F6ecO1yjgOpCJMoOt63fnTXqGAZSmMwXMcO4wx3DtLwPC8rFxS1u68QnV4bxii53WVfU3OTbvdW/Dzp2nhmgu/PY7c91aFdQgkJ08SIGUxAZ+XReiKulDe8P8Nw95450trDMbyFDytVEwWe1+soekhZ80m/5ffFTQcfK1C+4udI5W8L4kt0lHn2mi14IwwSTZr7LUW5pwYvKfpc3rvPVNtvK/r3uFW9Dk1m1smuozUg0p3T9Lpy/3tjWHR6MKEdPreSXf85TrvRIfNLKwC2cmFdFVQqlZgVmzbR7fvU7bvzbnxy9uF3z8cqEZ2DJ11jN7zwzjwZVm4rrIzzFVqVB8Frnnqo3DjkWkgVWmwxj7wdl4wyHL4rvMEb5lbxZnNOVPer/MwhPB36+qvoriDSsaVElcrRAePg2fODWN6pjxz2BSZzeBp1E5EMd5auo1HaBSzNWL282lZuayJhAKRpu0dTDPQbAMjVKgxCvifSmZNiWgd3soyr+89j/3I092Rn57feJ8u0CqfquGPx2HvmGjsSrt+7Lms0vn5YJiXJlhkk5st50pqRZgQTTISl6Xwt8vCv/twEAEGG6PAcU4bN1qw4Vy37kreurMCsyRNA+qj5bGPvEyLbPD1gKiwszxSgc73ZHcVOppjZwtYhJETQhRV6HlmSYW+95xeJ7wz/Mdvjkp3lN3JD3c9zsN/+eWED5ZmGus1453l6dBxFwNdcHLIok6X+hkupRKs+K5rbdt3HJKELNi9NXbH4lMq1FKZUlWarDxILniqwgjG3RS/m21wpXFwFuccj0NHa5WXZcV5+VxaE9+U4GSD8+l9wjnDhYlSMp9y47sYOfaBy/Msro+tkEreOeN98IwxCOWzSkc6Rke0HX9aMs3Ja/XGcYiO+85yFx1ra/zT62Vvzao2MgCnNfPn9zP/jbvjLoRbt45ABabJu7ukzDllhRVviNXXu4ltKjat8aGLfNMHcq6c5kUIDWumFFnkus5JvgBGIFO9X484Wmd4med9EVp1b2Ew0G5KaJkYhLrYWaE/HoKnqqeMMfAQgxZ3mc7kYKv4ItYR8pzIlOyNYdV/38Rqcqv0qmxvWKZUdrgtfEWoyOp3U3UCXpvZ4yONAVca07Tis2MYJCjbAM46nq8zUyn8+e3MITh+GHuik71UdJbHGBic46fLldJE1ZuqnEq9c4TgWIoahllpdjovorxSKt6IQVtWCmUuTc0TZSHc9Z5DF/9uTf1VFPfahKa44eNrkaVS7yxDMLi5Ev2RVFdyXff0okZjbvCky5XN9AojZmL/+P6uGJvZuxdQTm+FZhqJhquFy3yhNoe1ltOy7lQmh8F6yxgjx67j8/uZVDJLqjwcOlKufJlXPg6RxiZGEW7w67zKotJbXuZpv9mDdjd3feQ8r5TW+OYgxXzJhfO6MJXC6LZkJuHzOytdd0GYEhQRp3wcO3yTLmQtmesiku7aIKdKHzxPh4GlFD6/zZRaOSr7JqfCY28wtSfZiVoqyTRG66DJa88l8/x24vW6Yiy8XSUX9O7gd89pq/JzA3zb9/wtTmK4tkpn9zhGvj+Moq6zhodDT06Ffz5fMVU+78eh2zH2YI3kjVrDWgrXNXPoveDrVZa1AETpbqyVGMP3JYPy1ivcsFJ7g3C2h/ubQ8d9iNAqrTle1pU1C3x37DuWkshJ9hpLKgxRhFPbsvPtemWIkcdjx5wy3XDkdDlzHz19kNOo6j1hDTtDyTjDd8eel1kMr3onxd0iB3Rn4NtDz5dJ8jSbkWWzs9K9LqXw4/uFdneg85LE1TB6T8t+KiuM0Fphs5PcPHlKbUpSkK41NMOdV09yC/dDJwKzhAZygFkKJliq/i5vUA8WsdHepiia2EZsk7VD7XCN415dHp3SdhsSsxid14NGePkPMfCyJDZhkzEaiekU426NXCRG0WmXvk19qQj8U5Ga0pwUcWGwNWYj5nNjcPTO4a1hyY1TSTz0gUWZXcKuKtgsEM31PDMcJESoVQlON0Yw/re1cfDiTRN9Y3QiFgu28thFUpmZlGlVW+Mfn9/54TjQaW6yQxa1qYhQKufCKRW8zRwGLx4zVQr8/dBhgKfD8K/W1V9Fcd/WDB7la3vLWjWkwFqeRsdaKpcUOV0z87rqyC3j79oqCYlVM0b463NuOr2bHa7hK6wRw+73stDoqiXXTFmhj5FShSpXmiSnexvEz7wIm+DxMGLWhWoMz9Mq1C8rKsktHeeLJtF3wUm0nN7QG5d3zYUYIp0PpJxx3jB4Q38/MsZwS62RlytjdBFhhDNVKJu58e3YM62ZL5eF65J5XxPXOTEOUb1pLK+XeReBdD7wfkn03tFZx5fThT521FwZu54P93eSktQq1zkxzzNv0wpGOm0fxb1wcGHH/oy5Zc+WVvmHpwPPl4QdDF1w3HdBqGAKv9gmtsO1VaKTcfbDocchjB5rjS6IG807nmLkQ9+Ji6Ax6g/T9jzQa8rULKbJtW4EEDno5TAVXPbYdyTlHffO73BGs4gSOq2YangcOlIRK95WRQTUEH+fqAZlucDLZeFpjDjnmeaJ3jl61WSotyiwGWTJoTCJSINvxo6GlcWc3RAWaat/GDt6Z/npMrPqUq4qf761xsu88rIkPg4df7wfd/ooxmCd47ux574LvC0rn68zs8JbGz2wNfalfl4rny8LY/R4K1THIXi8s8wps2rQd1kLNjiql075NEuugo8WnERjrkk8lqK1qkWxdNZwF8T1NZUqHlKwWxVY1FfKyEF2Hz0vy7qt2HZseXsOSmu8qc/UfR9oipOvapu7TWm9k9eZayM6I1kDudI7eY+tiQnfnEVvYYzFGokdPDjP1RqyKZjcWC6SzBR7j9cg9w2fb8CneWWtladB3rvw6Bs/HDtKqfz1NO2T5VIb//fblcc+cD9EcXts4sBq9YCqTRf90eGDoTnDXRf5cDcCorjO9d/AQlXRkn0B1ltZsG2qPWcMx87jbGMeIm/IomlaZ7ooB8FzSty3xmPo8M5xnpfdwtQ5KaqbD8uelGMEp8upMUbPnAoxBGwzHGNHMYI9T8tKfwhkTR4PocOUwm8OA8/KV01NFZJFVGS1CX2vtsaIu0XWGbObF1kn4peUC94bqhF17X3sWFX0sSUVAZyXzPNl3Y2yPvYRVxufLzP/5ed35lJEbl8Ekgq5ELxI2QlymNUCznt6I3BAa43/6T/+t/yn//MvfDzecX9/R0sZaxvv14XLsjClLKO5E/VpK43ixB9n23vA7aEyBg4h0D8E4aUrH9Kpgi9XsUW96z3Bj4KJYrCtUKu6B+qdUVsTxa9ht/51VjyFZOkoh4oHmhWcvXwFO5gGUeGVIUi03XXNvM4ruUo4irWCr9+HwNt15el+5LePdzy/vhGMYaFyajJZRhDLVdM4jpG8JokvbIVjsPTeaYcu73n75zbubwtJawxj51lXGLwKlZp+h06ZD13gkgrPpe6c62363HCW13nFAj8cB7kOweNV3ftkA98Pkd+MHedUeF1WBud4nlde1UyrlcZdDLvkniah5E99ZPDi2SLTA1wXscsYrKVYuFYwQe5PwZbFObLlumsyBmeJxhGMwDCSQiT0RYnGKxTn2GxKa5VnNNjtPtE9QoPTJFF0pYovj7M3wZjVhmnL2nVGlr6lgamGbBqdl8bitGamXPdmJFjHnCov04LXrr4hr/9hDLgKn9+vrNeVSmMY3V7UjdaQtRS+TDL1+QdLtQ5jDX97m/jpNMkOTuHEohLC5zmx1MpvHgZyrVxmsSowFihSQ66pcIxBDtw+yK7EGlKqsiT+O1+/iuK+LXsaah1qLMcgF88hI39RLu4hGGpxvK+Zu/FAWleMguyvS+J9rjz0Xi2EbwV8+zA2rrscoo3QHF2wO499zZnv7o6Mw8Cn11cuKWEMvF0vPPYdOVdKyaRqmFzEG2XXuEDAcJ4u+3sy2rHU1uisZalSIGqu3PcdwRnWghoJNYKXdJ6pZGji8dGA85LAyp/98fG4UwKPwXOaV/73f36mHzx98JwvIi+/H3v6LvB+XXcOtanwcJRQ5mQdKSWi9/x3v/mOX/72GRscP375xJIrH/ue58vEeZHEIhcM1UKdpXAdOi9dU9vER9JRVi1Qcj3ZFZZWwixpyueN0VJy3vFYqxOWsUaTp5ri1G3vSuWAlOIgdciI3Bx2auPGob9UUSNbGnedYN+tqcWvHkpLLvS6aDNGDJzu+sBcVt4uF7zVia/cOupmETEbMCc1WrOG8yqd40Enkx0D1veV6s1zPlgrQdQNLBXrvJ78es/q66lN4JmPQ+R5Wnm5LvvBt+2Mcmt8mmaWUvl3j0f5PdXQG7GTsNZwHwOj9zx1gVob3w4dS2n86e3M+ZpE/l8zZPETmnLl8j7xm2Mv3byGyIchquVt46fXie4YJDZQIHPhwRuIMVBKkXxUhZVyFVuGYpqwaJDP3GG4rCuHcKMcOgMP3vOaJFBkLVLItkQuSS/Se2ybBM0m4mq77mGtEkdn4UZLbLLfS6Xo35PXEb3crPfecx+CdOFjwBhLyoW34Mi5sE6Z3CSJbRuWGnJPVKQGzV/e+c1h4PN15n1ZhUhgpfbU0sTB1hiaE8Ts83nd75GcBd4KwYj4T9XYD4cOwyaElN3jNoX9S1+/iuIOfNUBAkj3sJ3aki8sqsfoHcZkSsrkBl2M1FaoteBsT61i/3/w8G6koKLmU8IBtrtasjeO+75nTZlcBFPtY6SLkdf3C3MqlFQxghfxOi8CCZREtJ7LLOPu490R6xzrurAFE7QmLA52WMUIM4eK8YGhF7rkP7+84Z247qVSwFlepoWg49m2WzC18Zv7A4OyWhpysg/e8fN5UtqlGHZdpoVUK5fzhes1K3NFDIuWvPLheMQqXnjvHf/z//K/0izcucDHw4FPl4mfzxNLyxxiJDjDqWVabnunEvR1SGFsVB0R5f2b3R1zwzvlc7sts0op5Fz3h8tpIc0qWuGrewEteaJTqDe3xCb/T8QdIshpSPF86gJ/OAy4BucmftjRe85ZVc/e8rqs1FZ5iuLgeElS3L47Dmq3oNhwUwdHGqlJos6hi6ytsuaMMaLyXVLl6jL3XcA0w1zrDrUYs01uaFEXW+k+bsHgN/gNGq3eunPBkzfs3ty0GO02DV7V+sC3xpLUu8aIf0nnhA9fq1xJg4SkPIXIYTC4ZhmiWFCfU94PzL+eJ+5j4OPYyQ6hwSFG/vJ6gWBxfhOhsQuItknbGzEmq6mQSqFazQJusokoVWCS7a3Uxk6VrBUOznJa4ZLEk8gYNOVK09J0Mdo5ZcpgdkaJtQJ9TGopkptMwEULoogQUSEUBGMZvOXoPccQgMbB+J222MfA7x+PvE8Lny4zlKqfZ9MmQ67rBrtcU+Gvrxc+jpFaA2+aXQzS6Gw+OQDeeSwyiUoTdGtCt5+9roVcBFoqTRxUr5O4jf69r19NcRccVR6E7SXnphgaKAtAxQnOMGj01rLMyrCQbux1Xuk9PF9mrDH03rFQ9q5xHDzznFmmiumcZqkaQgwcxwM5Jy7XK80ahujoghHDMWTh09S7Z0qZu8NIbYYfX17pQuTxMDB0PS/nC0sW7O49J8bosU0Ums451pw5nWe6hwOHTvDrVbn7pQiWnFSItEEAFsNlTZKu025y7PdJTv0lFe7HnvOUyFVYOT5Yut5RcsUEL4vrJfPj8s439wcsQhV0xvJ0NzIvYtN7muY9T/a+D/zu6Y7/7cefAUm5327ktyXxECSYwGwYt/5LyEuq6NWFoDSTKrIpZS/0Vr+/KpqxjdiZLXhND40mznqNKvsTIx1T1lZ+80DpLdItNbluHTDrCL9R9bw1XFvleV5JpXHfeR68+KT4rwpoQ0b9a5GYQaeHSdEudC7wnjNRrWUvuRF8YfSBhiz1tvEfLQbGqMBMIaHabkIkOaAUotiO9oZ6HcmfG51aNrb/VuSrukQCQqc0soRLTkqvabIMtcbSjOgl1izX0prG4ISqdy2FBcHOrynzMhmYpSA9HXqSg7H3GvSunyli+iYOqLqwLY1o5R5YatVJXLQiwVhlgxSic7uQxxnDeyp8nhbOqUgDZw1eG7SKWC186Ds+jiPBGf78fiHXqiI0Q2piGmaQxe+1Vs06kNB0awzf9ZGj/tBoN5GUUfRAbbfbTVUxxkAfHJ+nhf1P644m7U2KXPtGs3BaMt8OEWPgeZYFMUZ2ERaBC7OK86o2E87emFVW7TCl5zOqejX87fnEYir5X/Ef+FUU9225453Zi2drDW/s3sFXpDN6W1Y82t1VxVprlaxBA7/78A3Gwpxg7DumlFjSVTBYhWqsNfyPv/+Oy5r457czT8PIw+HI2/lCa5m5JKyXAIpgHIOVJV9t4lnRamOphes8893jI118pOsiX17fZElTMs54uhBYq3Db73tx+XPBsJylQz/NE9ZqCpXeGAVV3Sne7nUxVWvj03mmsxL0cVkzqTR+Oc+kKlYN75eV83URyXITj/es2ytTxP0wLZUW4NP5TMmF5/OZaCyv5wsuOoYYeTgMzCWx1sIprbxPC7E6riXhVBTSWuPLvLKWJhCEaXwTvOC0QQ6pjan01Ui2S99LKfs4TRM+NE0jBDfIGsXe9b9za3xeF1K5SfeXLKk+XnHXITiqs0yl8OO88IexxxQ1akOSdVKThCbdcUvXFv0Om9TGXiSB3Vp1M+00yP0qgrC2UwSDhWMXeJ1W3GjUpE5i2DZ/IovuPprGyun9X5GiU/Y57wY9XZPsCLZOfh8DdHkYrOX7wyAUQbbwdJV5WXntpaHUP4XOGvTRiQd+FvFMqI1oJMz7zSbes2hCSOKGuNRKm1e63qnTpj6/OhVscXxGGVRrFT/90UtEIshyP+xCwI5vDj3GwMucxL65Nf5ymvTgh6OqU7edWWctvzsOPA39rtb9MPZ8vs5CaFDx16bWNjTG3nMqYiP+GANPClPpNkQOO26fuXTkcoq2r3KLafC7+5HXJdGasMoEhrw1NU6bF2PlPvt0Xei94xAEp9+m8WBFoDStQhO27qZFKFr9o5NIvc4Z7mKgNPhynrhamR7t1w/Wf+XrV1HcMXLz+Sb+KDRJZpHTyu4X5bpm1jVzSdqJIx2CQ7C+mhLT5czd4z2/+/AkmPH1qiyPQlkL5yTqwx9PV+5j5Bg75lxI769CN3NIYdeTuDRRcVpkfA0+YEqj67wUklzoHzq+vL3y+fQmS6JcqE4Wm33sWNfMHz7ccVkyz9eJpSY6i7IGNmiBfQzbOloUk0yp0nIlOMcvF5FFnzSou6TCnbeMwfNlXbHW7uZaaa07bdSAiJl0tC0pA9CcYVF/jV5NGu+7jnYtu9rvp/crXedZpypZrdFxP3SsKZNqY9KEpfc189R5HmNkcE4WWrUp7CKfrUFELXbv+tgx9JvIprLJEuzWcRpNUCq3f2/3xbY4D87y0AudckmVc86ar2rojMN68fNugKkwWgmfEEFX3U24xPcHgZ309zoDAcn8TDSWVqjNYrCk0nC+8dv7AxhIxfO+FJ6sUDk7JBJQHn5pXK6pUmzjoJF0Ra/DZifQuP3jpPa95lZNdcKBMQT+8HCgt4aiNX+bfEtrRCSPICv9sWzPjf6MCsxVVLmuwWAbnYHBePromEvhWjNN/T+uOd/EM/p5qR8cNKO2vwZLY85ZpwKvxmISkt55cQ+9HyPGGt7mxP/x6VVorE0O6kFtd8U/pfFNL8lUx+AZeJqcOgAAIABJREFUg4SGe200jjHyOi+ysDTgmmQppCaHufWG5Cr//u5I2A6+bZpq21vZJqsbv97o36167VptROf4/i5wSon3NXFJsjcq+3LzJq4sVabLtGbuo+MQPV+mxBgdr3PibU57QfcaT8n2e41AqWMv9+ifv7xTLGTH7lj790v7r6W4K65Zm4xRm4PbZhhVkAirT28SPCy2A/KtDgSmaILJlrTy6csL3338SN9FBue5zKL6+v7xnuucsFjeLzOtWO6GnmlZmEthLUWKkuLySbmxucoJHayO5c5wySutGqbpyi9/PqO05tvprd4btYit7mlOvM8zz6eJpVZs50ViLJgTwTh9r41lEUfJwYtXjrNQozwQU8m8l0or0plHbzh4j48RpoQPX42G26jndNm5F0It+NbIgsduuHIll8achR7mrCV4y/m60rvAsY/Ma1LPahVX1LoXWGPgec2cskTn/TD2IrThZhCWS979uLcl6nan7kZt3BrUnWaiHhS2KWUWQB8KkAfkqY903vA8J0JDbFudqG+DgdfrwrrW3W/7GD3NwJc185oKVhXB3liOnZfULfTgwShWKiPyQqV3cvi6IsuwT5eJf3i6IzjL384rpzVxF9QgS1XVlsbSZI90zStzNnw7dnTO7lbHFfE5Abioqno/CM2OfmGt4cOxYzP/9QhskdpNqJfUy2b7HoFvtgNN3Ck7Y7jznlOW3IKsv7/zltE7OoVzLqUwrYXDISLrQ7Nz5TeDuyY+BuqZL7/0qR+EJVMljahhyMCfX6+k1pg1y9UaQ1A8HSPTzV1wfDiM3MdwK7h66G1Y9+AdYxA6sbxJgzU6Welz7IPViUaFbTol3rQpOuls0ArsTKzSbo6gDZlEvPWMzvLiLG9T0nhFwfCnXIQ5ZI02IY3nOTMVURe/rrLb2NE6c/Oqz0WmIG/Fk94NgZQqfnR0TsgY2/S7M7D+ha9fRXFvwJfLQhcc3x4iwTqy2sFNqbKWxi9vV2ZVmm0jF0hxz61RDDhlkbhauF4vhGZ4ur/nbfmiF8RKF1gqh77nMHRc1pnaiixdjHqvmy0iDB2h5AYYXOCifO8QLFNeGF3k+6c7luXKvCag4ZwU2/voeL/O1Gr56e3KQ+w4dJWWVpZSGaxVHM3uNrnBWiYKnZHOfypCQ8xNHvTDcMClmdUmbG+Yl8Iv1wWuK75zmM0rPrOrONdU92JplZpoHPjgyKvYDARvuR8GDjFwWmcuiwhsuujoouU6rxp04iRvdRbF36wqP/kgZZSdcyObyrUVXDP01uFxWriM7kHN3qK0duukKgJJbWZgW/e08dW/P3YspW6CVfWBlwmPKjj+x1GMvSpwToUva+LbvmMqlWz1IXZi2iVGVBL+UptK821japWEFKysIplmrXixWHjqOo7ekSrYTh60L+eZVCpD9HwcKn89L7Qq7pKds0Qj09pahDq31sqapdP7fhR3yt1mw8g9d5ozOsjoAluuizXw8TiQauNTWTk4x+jdLvG/PVy6JzLbMk+nRN119N5yqeJm+RikI926+7lIgHewht5Zsmm8lVUmGHtb+llNGlM5AUkDu7fXeQhew3hk8V5UP9J7R8uFaB3fHQfO64IFvHV4Ax+Gjm8PI0E1BjTNUa1NpzwR8wWrk2vOql6VwkyTAzAiIelLKnReuDoVbs+KXi+5HQ3OCusq5az/rXYNuu2UpaykIx2sZbZCnnAKqdQmDJek2FrR5tUax0+XmdclyXtwgqEL04bdwncv8q3xMiXuj0Ega6B3VtlH/Kut+6+iuAM8DEG9zBszVcb9NfHTy1XoQIpVRWWggI6EqBFWU+EK6jx3OrNgqNXw4e6Ox+NIqpnvHu44zwvBO86nE9Y7LqVodqTcmAaBKbakFKlZ0n19eOj5/H7huqx03uO84zovTGshOCcwgI64y1IUR5UxKiE32Mdx4PM0E90mzlrpvd9FPg9Dx8PxwEPf8cvzK19mSVw3Vuha87pCZyirHAjVKQe9NJYpS2FUnKMUgbj2ZWeFVipxlGVSFzyPY8ehj0xr4ZI141EfoJIqMXqsEWsAbx3OuP1GztsxuJ2EeiNH7yUwG/k8c20iGqoik9/ETIKvt69uVHWD5NadbEd5qfC+it9Hp4W5ZVH6ighFxum3NTPVSjOSvVsbPF8X9oMEcNXufOr7GKS47x3UDadG9x6+s4xd1MOxbQQshuB4uc7qxd/4+f3Kx7uB6CQR6afLjEmZYwwSEZgLIUTqMu9q3EvKfJqkCPZbgUbl+kqhrJt3MvI9T2OHdUqxxHDKhbecZVGp16/X/9498s1Xnu9N9jt9cFxqYZ5nvNm6+KhTRJVDqMBcyg4dSaD1zdlU6MryukSxKQ2QN4ZvOrVUlkWDWGfo6wvece8d51x4W6FU6Yaf+o7Oe1nGahDP5oBJq7K4RZqDZuQeX3NhyYWDd/sCu+k/xTxOPNs7Z3RXYfbF9m5JYbd7Uqbe0pq4XG4QosLGZidqNAYDv/Ejr/PKNWURMdamthUCoVSkOcjAl3ndIZwmuKNYDuihUPWa+uioSZbdjz4w27Yrrg/WMRVZGv+9r19FcTfAIXrFW+VNn3PhNK27A+KEphaxcacVI0MsvnWxLFgX8lDMtfL9/R1rLvz4/MLvPn6DaY2xG5hSphhDzdIptKop5EaUkw64zpn7MYirXaqMXn5/7ww+BmieNYnhfs6V4A29CeKK5zyOwhAD71MlZZErfxg63nUkW3ORjtNallSIwXGMgfvQYa3lsoioInrHtIrR0rQWoarVRlHe4IaXFgphdNQsGHTV0dPqPHvsApcpCevDWjrvKUXCgGurGBPAVu1exG2SBtdZiv01F1KQXcOyVorRLExzc7rbskC3nV9DY9Gcu0EcykzYbmTgKzxZivTefLYbMrPWys+XhS5YRjyXJXHfdxoCgrI15HCZkjyUq2LzUtQ1wINGy5K4dOwCfbTaPd0woRt1zXDoJYquWbnj7FeI59uaeJnEC6Uzouj88f3K7x9HPo6BXAsviq9aGqkY1iwGaIYbC2KtjXVOfBit2E60Jt5DpWoxMDvT524IjDFwKWkv2sEYWhPHxWsS87JqLeB4jJ7UboeVhZ22563l28PI59OV7BtLq6S0cHCeMYo8/iWtO0xEk2fUNXFndUYi4BoiRLtm0RI4LI9RLCdA1MbGid3FVkxThoJlTZmDaZgY+NgH3AaVoJ/LdkOhJdtIozOvGYyowmnS1W4MHg0elNelvY1Vq+OvoZesxdgag0WXsTpBCePIqI+9wjhFNn3S5QvmH42lv/O8TCs/n65CtbWW6AxfpqosJWnCvLO0tWqim0AwzsshljRdJjhDmjJGxWiPMUC0/JwW9dc3DM7d1Pb/wtevorhvjnO5wrU1Lqvwj7PRpSay0XaKfzXEGc4Z6daXWnFNxqSixSE4y8dx5Jf3C83IgqjWQgiBYD0YSzB3vE9nlqSy9a1HNDD6SHOVkuHYB377m0f+9umZg8t8fOx5XjLva6U1S+8DH+4OvF9OFGt4uywcu8oprxy7gPOiwKtYki5r7kNkrsK5dkW2/DaLeVo0hZIyU05EI6yLaa3iid41TTeC5pxicLDMGR9VHZjlYWg6+llrGHpPCI5Br1nvhf+8aEGeawWzEJtncJGH+0hF4AOiBGanXHEW7vqwhwNXGn0fWNZMMer3YeGgmaymGZo1kpupBVS6oNuN2ZAieVqzhgmz7wV6Z3mKYpBUG3w7dgoFwMdhoBd/WrQJ2mGLVBpq3aESd+3UdErYfvsfHw587Db3Sate4oak8IEx8K5jdEVScbxp+8t31pDQpZutpGrok0xow+B57CNvS6HkxPsiiuHLPIuS0ipDQv/dTOPTdeap7xij46GPPMTA87TyfFlorXE4dJxTYrpOHDqvEIt09sY0eQ7spoKVQtV7j6nyWW7v2+r7bAYexshBSQatNhYquSTSIuEbYglsSErOtE0mMWtl52B1N/G6LExVnqW7GPjucCBYR1EKatNDeKcM0mRBa6GznntdtBbVZKBNGkb3ceohVWjMqWgjIT8zOovPlnMqEilpbth9a6KPCSHg9Oe32uRe0PtC4Bm1EG/m/9VJWyueU0VhId8MdZWENmeNeO8Yy2PnMa3jZU4Uxdmt3aMByEkU5P3oWa5ZJgFjbsEt+ljMpVBzxRnLQx8YvNSYRx/4p+nKVItcd7vxt/7rX7+K4g5wyY1Lzlxy3hcn1hoR9+QbPxnACENI03Ykri0pHlmRG3fJlanOeOfoY6BzgbfLFR9EYBJCYMmSruONhnDszGLB3u+HKAZWXeTT25mnIfCxd/LwO4+Lhc+nmdN0IZeMa47TujB2niknaoPXKXHX91ArITgKYK2w5h/HAWh4K91zyw1TLc/TgjWOahvFGOaSSDbTd7IA3DpzH8Qu1Flhk5Qs2YtbFI/8fUkewkmHE6MVzB0Z+512ycHdxsPgrMBKRXxFHmJgXgt9dPRRKGTWwDEKJe4QPW0IpAafrjPRig+GiMZE3t3xVadubgAD5mZnu9Yta1WngQqXXPiypG3vpCOBjM0fDz1OYQFvzM5SsMZoAHLdF6653dwgNyWtrtOozWncnYjPoOEq2Cb85tMqD+vOomiyh/l/qHtzWNm2NM/rt6Y9RcSZ7vDGHGumodVqAwwQAuEggQQOLbAwWmoHCZfGwmqpLSysNhCNwdAeeEggJHBQC1pCUFVkVZH5Mt/L9+50xojYwxoxvrX3eVnKymxaIGWF8e59ce45J2LH2t/61v/7D50V5sbduHBcvJi6KYiq8JOnM1dL4Kpr+OTQ8UVMpKIoMeKruGk9upcKP+Ui1+JuWojZ8mo3YCx8dui57hsep8BDqkHSdRurDSDrFV11IbaKfAZrN9FX+y2NRCliVuXlQvC0eBlW1+uVUmYmU0W9tAiJwVc1uAzr2bxcMs/eLqnAb11f0jdWDLZWUZ/WKGs3G5AAGDKdldd18lHEfUY4+ApFzGKNrZXYXecK3VJnBnn1qSlwaCSVbd0sc4WKdJ2vOCv2xTElVoNBkN+1noCUko1B60qPTJmIeAo9egk3fzW0vOxbZi/h6KJazpgs87qb1vF29jitxbfGyPVSdXZ3niOHwdErXVOgqo9VFtJDihnXGLRTDNZusKRRildNyzd+qhTTFRz85Y9fW9yVUv8p8K8D70op/3R97gb4r4HvA18Af6OUcl+/9h8CfxNJx/v3Syn/3a/7HSFn7r3fUmp8FX501mI6Sx6DeIMroW2RoTeGcYk0WtFpjaZGWClFwaKNQB+Xuz27vuP+dKTEQt+KLee8eLLSXPY7cT2cR1kwSKc3+QAl0VnLw+i5bMXoKZfCKcLlruPGOiafmSdPiZkxBxSKQ7dDLWdCTpQo1UyMg5PQu2Km7VtSzoxLoHGtiJN0xMeM1Y6nZeTkly0qrt8LLpx83qLPtJYNsHNCTTyGyP1pkcFna7DOkIp0QcYIMyakXC1phYvujMbq1SJAYa2qfh2G0ymgrWKskJEMo2TYI6pQWUCmdidOwaeHHqMMSgm8o1BiopTZjsTrcGxtV96e5y24ZP0Sa3dc1khAEQ7leoS2RpPJxLJiq89KxbRuEFoMlr4N6asCpbKUlK43P2sxlKK5rkOqB/5aHEuRG+yysQxO0zsxfbroGjHRUlKoQxG18BhnbmfPYC1XvWOKiin4TWkaYhYDPL7Fpa8b1FSDl0uRdb93FqM181yIiwTNrEV9U4fCNq9IrGHNZsuOVbLzVZhAYCSVRXWbWK/3s4tqKutMZR0IygzqYEXZnSm8XzxQQ6brNfp0J8Z38Kw8X+2orZG5w5QCD3Pg1b6X5myRnNrbaeG7h2GbCZQCuXo3rUtG1a5VBHByikNDh+WegNhfVFuvutkADFYTawpUXQ2sM55VTGfqVFgpmU9MpXCeF+aKcde4ASnm6jkkhFydO5UwkD4ZWt7Mnpm8pZLluqa1UnSVEnq9a/niw0k2kiL2BGuaVqM1g9OEnHFZguAvrMWogW/8LOr7X/H4x+nc/zPgPwH+828997eB/6GU8neVUn+7/v9/oJT6p4B/G/grwKfAf6+U+t0iph5/4SNDxe/qQFM9e5MUpRlay7IIn3TMiZIVTdOgexkwppLJFXt22qKVpnM9BcV5mVCqcJoWmsZxniacFZ3c9z5+yT/60Y9pnaN3DWMQF0cRQGaBUarE/Ijjfav57usXmJB4XGa+fnvLtCxYbclail7jLG3jOM+KRhmUFSfHmAVy2TnFUuD9h0esgsuuQcVE0Zo5BB7Oi1ALq1nUeqMtU2SZ03Zt9oPjRd9ycHbr0tK5cHRGXBu13BzWiqI05UxW9dpWV62C/Ju+uhi2VozEbJZ5wH5oxTjMR3ZdU7v8ssWgwTO2vtra6lq4jDXSrQFtqW4o9eYsmVr45f46x7iJzGozL1/QPGfY8szX10jnvDOORokiUTxEKj2z0g5+2aF1A4Pq6aVdQ9OLlHmlagtt5SYvFHZWc9F2tDW6UddXIhmk8GLX8nZ1/ZND1aZXmGJiiomXXUujGx6i+PnHUge4IdE7+y19gxTSLcmLTaPLY4ibpW1j1C9ce3lvq1e7IM4+Zn50d+SisVw2QhNdN6n19NVZKYpHY/E86w9qwy4Ydi2esYjEymnFi76r5x7xcn+aJWCjN4aP94O8YlVzea2tXXE9fymYY+KisVIwrcNrTQ9caCUxmD5y1Vjp3lPeBEtS4aWIWlWpilUTkRGR05TFM/7ZdE5x07SotMI8Qj4wRgaydRsg5sKUIqeUOMewofbSVUs9slpgr23OY1amkKqvTz6X0xI5xbQxl0pdk1YrmiKmcUuQRuv7L/Z8dXcmhox2qqY9CWmgtyJ+OodAn+Xk3hnDjWt5H+ZfVVZ/6fr/xZuhlP8JuPtzT/8bwN+vf//7wL/5ref/q1LKUkr5CfBnwD/7634HsHlK6Hrx9NqRa4WyevNxvrZCkxpjkUVRMmOGsBadUtj1LVpbpmVBUxj9zK5zxBjwKdO3LSlG/vRnX7HEgDaGi26gwXDVDhjEpnZVFVlt6PuBpIwYIFH4s6/e8XgeabSjr11KzIXjtPD17R27piEUyCXh/cKSguCzOXMcZ6YaIvL2NPJhHikqc3uaBIs10HaGrjd0nXhc+CWzhu7uGsvLvuV1L/xoSuHJBxag661I/ytMIctz7WlXXnKRY2qVzINQuaxR1XZUOOKt1XS9Q3VaYgpjJkRZkFuRdZqkKowGFXIpEjigFS92vQyOSvWZqYO8daiVN0UyW6def8wm7hC2A88qSCsMl70xdEozaEOrxas8FvEIh1Kx4Io/U20N1s2jGpit1q9rB7ays3OVcSoFr/pGwk6KyOmNkjWqEF/v133H630vtsb6WZyl12JW4G72FFXoGrdRHsXvBObqJlqKnDQyMDi7bWYoKfI+V1m9+pYHSX1f6/WSDlGapPvzwodp4aePIz9+PPJ+8tupWKAs+V45pdRwZlajtoJDVW44BFXWACwaa7BabDNeNI7XTUtrBPK47lr2bcPQCr5ttASiqFWNi+DWh6Zh34rp2sN5kvjImmfQKbGM+DAtPPiIz3K6X09tGlV59RU7L1lEiilz6RwpZ+aYcWiunOPSOposwRiqyv9RAuVOIfIURI176z3v/MIpRokxrIU9bx+EQJcS+FHQWj9Teyvsp7TiqYYEraE5pTY2GnjtGv7gasdeaYbGMobIvrH89uuL2lwh87QijYeu+gifkpi2Zfm9L13D95vhV9bUf1LM/aNSyjcApZRvlFKv6/OfAf/Lt/7dV/W5X/tIpfJIq5JvjQBz2jDpAlajgizswShmIk73TMrIMMUIzzmUBCWzxIBaI9xyoTPS7aScWJaFUjKTF1y8axqsa3DG0jjDx/0NMXqepglrWy76jqFxjMtCyYHHk2fXtaQUscYwjhNN13F5ccnj6cyyeEwRE6NWG/qm5xQD4+JrUg8b/ibgb+b96bh1v641+KXe8EkUso3TXPcyYHNW02hNKnKt5ly4D7Jx1XW7PURiX+ogClqledU3DMbwGCJjFJ66LUZi51LaFI5KQ6ssS5Y81d4alpyxWdTDQyNWuolnJ761tGUkVCIsoXajz+yFDZ4pAgm8GDrmkHjyYnlqq7rL1e5riWlT74EYbi25sKiMq4P0plqsKqV4yM8MDkEkVvuBtcMFlIjNRJAVecYtnjUOuYgFRkZu6meGz3oEkROOs4rvXO2hiM/4h/PM/TRvfiEFOVE2Dq5NI3TNaamvTyTusco8tZar1Fu7yeFjLjzFJIO2XMha2DUy0GTjZ68daC5i+bxUW4GEuCOeYsRZxVxkg/Y+cXCO1liaqpC0VmIplYJWaFZCSNCGU4zkJISHh+pdr4BD6/gOA3fLwuvdIEPRIjOg9VqLla+cRlORrnSFQUouhJoaFZVi8YHBKBplOefEWBQ7pTGl7sBKYCARW8ksZIX1jNbsjdg+XFcl7RJleJlZ/dcVxxA4BlEwZyWkifUkVGu10Ia3dSHryGwwsKwmq0QjsaLfU878fBTIZHWjXYV618byUddgUOwsuAwPCUYf6YzQi0sSxhep0GsLqOqnVFC6bhb1vfP/ASzz/+ahfslz5Zc8h1LqbwF/C6Dre8EIK+QAwks9ejHIj/G5QIsgp5Bz5Lx4Dm1DyeCUDEAffGDynlQyp8kDitZpcu0gnsaJ4+i53PXkLJL+4/nIaTyy71vGJXDQlq7rKEURUuQ0zXRNw37Y8egz39w/EGOmcQ0FTXfYE/1MWkQVZxrL0Ucu+o6HaWZeRg59w2OQwVzXWBziWZ+q0tTUgY8seE2Mwud2WvPp1cBlK+HWq5ItlczbJYpHdpb0m62ol4rHIxx7aoeWKuxhEPXhpW6Y0wJFfOuNMxuE4yo1y6Bx2rDowsPo2bVyMtBa0zcWryqDovqsr4OhUtgohmuX+ixWkgUcKqY7WENnNUNjOPlYaaoS/CAdm7BuVkzbKIUvhfuQ2FuDQ7GrHblTRiyDV3hi21G2urA92kaSeHz1nqlkze0m9ynz1cO5DumF2qarOnfXWjqtCaGQVBI/eq14vet40Tec/MDtOPNhnMlF/ICUUlgN173Y254Xz2AdYwyUIhbEziicMZufiiqKiFg7nCtDI+RC9DKI7J100LGs6lqJcwv52SvFVL3GSmeNlQnyNEdCGjFKkqESbF7sxmh6Ixx4XSQt6iEEnDGQFD9/PPNiaHkxdFhluFKKQw1kWZYoYdJ1Eyl1I49ZgjpSymSlKZUMIFkKspYz4qaZ0TTAhTGMBaZcsLps/vLkWgcKWzaDBMYoXnbtdhIKJXP2kfvR01jNGKKoYjfhnSyKkAQeS+vAuQq/VEUUxBhNFK9CuxZCQykKlUUROxfFm3GuUaDPP7tQ+KRt6bSYsu21rQ2JYqcMCSE75CLF3ayUayeDflG7FmxVz/sYyblw+v+puL9VSn1Su/ZPgHf1+a+A73zr330OfP3LfkAp5e8Bfw/g8uqqrF3H2jnVMzIUhSmFq6FBpcIYPTHXrlFDqxTGJJwGiqjpxlo8hkZ2PqPXcIDCq0PHnAqtdey6RvjBZE7eQ8ronPDLwnk501rLoesw1rHb7fjimzcs3kMp+Jg57BwfjifUnLnoe7Ry8rVcUE3L++OZMSxoB0o5Xu53+CyLWCOL0jmzdTFWwdC39Fpzr8RsaWgtr4eWlAs+Jh7nwNA5xpw5fYuip+qAU/ZG+bsMpERKnpHjXlCFb8aFXeu46CwJuJ0WoM47tLy3nFeoJLF3TrqfRgpH8pHOweNUaAe7GYCtnXJZbwRtGIMcs0sWVFSKq6Yoxf2y4EuiNQaLotWKvnOE1nLykfsauHzoXUWdFQ5onOF+ETOteyVNwc4adtWG2K90yhWUqhVdrXh4fZ0XzvHhOFX4RdM1FbOu3iT3i+cppE0trXkeeN4uC4OxTDEJJGPdhq9qJcyNfbPnum94Vz32fcroQk1U0vRtw2cXBx6niXfHseolCjuneZxlnQ3O0HVu8/AXB0m1wW5bp4k4Wc658uNDrK9VCrl4yxROS9wSiaYaJmOU4mHyMuex8nMPFRJYYmBvLWOUU8Mng0SJ98rwOHuU0mLiZSzkantWeIZL6vWgZMYon/+roRVv9yzzLF1PaU43HENgSkUSnyjYUthrjU2ZY5BmyNZhb6NllmMo22mPDXoUOuvd5Pn68Sw8uKXWngr76lp4KeKq2tQNWK8fYm0m1Oa+KEPlEBJaKwbbYIxh9oEUYfJ+s+GQeYl8OL1WGDmgS0qbzWhl5P6up4FbHyVNzmmME4HdcQnsrHT0VhuaKtCKuXCOiacU/8ICDf/kxf2/Bf5d4O/WP/+bbz3/Xyil/mNkoPo7wD/8tT9NCe/arxPoLMZQFsXVxSU7Ag+niTkVdn1HDJGbzsoxRqgVz6IMZbhopVDezV4WjjW87BuJbwO+eBjJJtG5hsFo3h5nGiVds20Eww5RRBVP55mhy9zeR1LdMRXikXFaFsZpoXOGmGJVAmr2vWXJgYAkN7XGMoUARXHR94TxLJh3zviQOE+BfnBcNS2XWnMfEjddw1Un/O6YRMwyxULvNK2B+xoVh1FbIDjInyv2SpHOT1LepRvPFKaYeT95fqtveTmIGOJu8oKTr0dXJVYFKYKPQeADLR3ihWt4PC8cDfxgf0GFJ+tnIMUn5Mz7+0dKLrxoWy60pjHyg7VRnH3i58epBkkneqdxRUaVTikunKOzhnMUn/VYM3aL0cSoKp4p8E8GZh+4C0K7y/VzbrTGWsvOWr45jyJIcpbWaHpr+WjXEZawDYqXmOqmJjvbFMXRs0A1EVN1jcmiDaspmZGbThg9zyEK6wDRoDGmxgcqxcMc2A8DMQamJeKMqZzlwr4VquC7s3j051z4wUeXpCRrffUdsVo6v0ipWD2V9QIqC9xnlMxSeieEfwkgV/gsVOFSTzhaKRYfUaULkJdFAAAgAElEQVQmYhmFU4pTDNWbXfEQPKXAcQmQM8P6+cxB4BcFL3bPTo1bcEuRIj4n6dgvGkvKwvbJWmDSWO15i2JT2E4h8GLX83Ca2DnJU3bW8M1xYpw8v/+d10zjiK6nfKufPWKWmBlDYsniB796FrHOJLKopMWJc20BZPYxVAvudU60Ag+rFe+NE2piqcJFlKZpHFMuHIwjODkdrWjOq7bBFKrvD5vvv0+Fzmg6rUkZDsbwct/yOHoMcK6GfedJ2FavDh2gNobM7bgw/XJQZHv841Ah/0vgXwJeKqW+Av4jpKj/A6XU3wR+BvxbAKWUP1RK/QPgjxC9x7/365gy62Pl665FOubMkuAQArl13Bw0PgTZVVst0+q8sguQI4vRNM4QFs+cMj94cUGr2LzFdZEb7699esWXTxMxZx5TxifY9ZYQPJMPHIae3vTifWFl8c0hsO8GWhKucTzNnnGRjuxy19BoQ+vEDTBpCXM4zROdMjhtOOeAVRZVMq/2B948POGLqA9tI3OD7x46Jp/4rU+v+erDrRy3U2GOiQjsG/Fez6Vw0TaM6zRePw8oK2cCqqou1WuaiwyU1iPy/eR5mj1XfcuhtZxCrPAKaCXePqUeeTWKfdtyWjxFiZd9LJlXfUfMclpSdfEWpPAel8zJB0qRJKnXfctL19A2wiV7M07Vf10Sco6z4tBJsHRQhbFkOqU5OMuhkQCFmApPFSddxU5rJ73euHvn+OGrnXiTs/JMCt8/9LXrLYBASFHBF+MsMFIu9EZoZ6oK5vQatJ1rEdCy8bTGMKWEL4XXu14CI+ox3lY9Rq6+JZOXOEC9rlXkZJG957PDjj99eMQaw/UwMAVPymkzrvM5E0Lm4ewhSpanqlXK6hrpVwqnnKrnCpXNIxtbLEKta1uDsQqvEmNIMneKeaNkLmMg11nHzSAK0YIotXfGMCXZ5NoaVziFhI+Jy76lsYbZJxKZ0QdeHQb2jYjcVv54QjEn8X2JOTMuEpHXOTH8iiZXywvp+FuteZwDP5uPfH4xyDWtY47Waj7+9DWfvrrmJ98kwjxDlSAWhEL6YRbqtEoS5kNZJG+0Xjtt6oluVY7ybLW7du7r9KVktTVLHw09Td0JlBKIGBJNazdR0cFarhvHQwjctA290kw+ktXzz8klUyKcUgZnhVGkFZ8PA72x3I8zF1pxUpHHEHn0gdtp4aNDT2M1tycxOvz0+uJX1tRfW9xLKf/OX/Clf+Uv+Pd/B/g7v+7n/uL3wFQj10JMWG0YTMvLQ4tRhSYsYHUNbZYbdKVnVbBNfFQUfP145tWu4aP9QGekkKxH2kYJxhpL4ftXO35+nDmFwsVukMDZAs5ZckoMneE4Cv86FmG4XO4Sn796gfdeMDeteLnvmWMiCR2DkhOPZ89uP0iX11tSkY7YprL5z78+7LgbR5KVrkUVGNFMfuHpwz0+S4d98oGH0fPRRV8Lh3Rkl1pzbwWrXKGGVTJfPweW9OxVoepQbe1SlpT48unM4Cw7Z7loLE8hbhvrKvnedRZdKZSd09yeF7RW7FqJDzyPgbY1m31Eroz+4+JFEFQ3mDfjzHsd+ORioEWcG4UAJUUwlcLjGBhtYnCGxhkmMiplWi3y/8EZklacx4X1rebK+UeBNvCdw8BN2zD7KPTPWsCUWqlzUvL3veUP788ymK3Qy5NfT3+axgoLpzGKBcH8lVL01rEkUa+2zrF3ls4YAnnr3LUSr5/jEjn5tCllLTKYPTjLb9/sSUrT2gZbDAdnKTlyzolch8etsQzO4EMUy2GlcEpjraG3gr9aZAhZKKQlCc20FIrWqJqLepzEJlob8USJiaqn0BuMZaxi11j+6qdXnOdIVoXTnHAo3o4zJsB3Lgd0KZznwBwij7Ond1bCNuop5fY842Nm7yyqzo9KkdOO0YpYo/xykdlOaw3OGnrnmGJCZZmzzDHz0a4VLylEe5BS5mXf8O7xidvHI0NjJIi7zhLmXHg3LtV0S04hJUVKyew7x1KEwZVh04psRbxIJ78q3FdqQFGrUtoKVLXSdSu+n1NmPi+yvoziUjsaa9h7T6sVD8eZGmG8WaToitsUhOJYlDi79kbgsODssyLarsPxzE/vT9ss/2rf8zSGX1lXfyMUqikXllC46TrQhRa47Fp0kazDU0xcWoPekmbk4uT61xWD9ClxDpGb3NBZ6TJizqwZ90ZrsRQtyA2nBP+MMYuPu9Ys3mMbI6nvWURHvWnom5bD0FK8pykZu+8ZR/hwPOGcZYkeUmKKEa8S8fEoQ0ojUE1OhUji/fFM37WAWKHezxPKaFROvJ8mAom0eA77gccQt5vlsHiGod0m7wr4qG14S5GboqweKlSvkvSLxZ5nsHmFFx7mwJ/dPfG9y4GbTqLUfn6ayFm4wxc7hyaLp4+CoTU8THJMv2wabqcZozQeEWuZRmYHD7MMtKWrlmFTzgiNNGX61nHoHMfZ1yKttsGTT6JSdRWfbawiucKsYNANT0vcbGy3U2ld8KV2i1MthpLHutpbrJscKFU71iiHShHCqI2fLoNJUd5aVQVfReyG5xpgYZTiqmlojd5mDrZCKysn/7RIx+tc5XsD4xL5aN/TWcu7aaFVlqGxxBKr/znMPtNaEb5ddS3TNBPKGgQNg9JcObs1Na0V64Fks/SwaVXhZuaUievZWUnBMo0Vy+hcWLz83JLhxb5FlczHFy1LLhQduB4a9nNLDJm+MlsuWseH88zDuHCfPY1RDMYwuAaVFY/TwhQCn1zsKMAYs8QEVjVYWoeSSFhFSIlD12xzpw91RtFYwdHXZayVppTMRWtJud73SPP2xd2JoOrpcTWoahwGLdYb1rB4mTuodVCxTmZqMc+lsPhE2xjW1CNVYImFvRWMXNYzVclKPR1nDm2DM+J3s9OGneuJOTEtkeIlUjJp6JyttaiedusJb65cfqsU+8ZxtwQ6J8HlMWc+v76kdZZzCHx4PNM3Ftf86vL9G1HcKfCq28sR3Ududi223sDOKJwWoU42Fqx0nVWBLG6QPPN+v3O54/OrHZR1yixWq5sRlJbu54vbE/fHCWOF/z75yKHvCTHyME5cth2Da7eIOPEAV7RDy7yIte/VxZ6+7zmez3zz+ETUheuLHcfbO1Is7PcNoFBRUYrhGGYJm6jZkUEL536qMubj7DFG4UOhX8RbBgqt0zwsgcvO0a7mRxQZIkbDFNJG3fI5b9dko4Y8N7fbY2UAPHkpQFolGqWqrbJi3ze4b20EAKjCTd8RQ6A3imUWf29y4XLX0EYLVjMuaeNsbwNWhN530TeyMV31DKPjzeN54wnXHqpS+QQT9hFiKPSd46tlFNGJVPJ6c7HBMzkV/vj2CacUv3W147prno/0dWRf4XJigU+6Bh+jFO86qFpFWo2Rglwogg/Xb5yWIAwKJJpv7fBULfArkVIBZ5+wVtUNV17rmCLTU2JoHBFL0Z7b08R11/DicMD7zEkHQpwJPpJ3ls8++pj74xPHaWJOib4x4lWfs8TllRopWDf4dfO3StgYWNnA1n/rtMEpYai9yxNPVXn5zXHi/egxWlKALqzjZ++OvL7sODTttpdaJelHEYH3pjpsDblw1XfiQxMy3zye0cbwg+uBycOY5cS7Cnpyoc7N4LxEOldhuZxqsRTPH7EDkM88Z+gay1SDPXIRtbK1htPi0UbTOE2nNMqKVbCuW4nVqrJhFM8g/EpXlNcUUsGtzUKFM6+7hpddB7l8i5Ir91gqmXfjwq2P/MHrS1pg8REQfczLXcsd4hiZUuGYI/vOQpLhrCheFc5Z5pg5h1gZTZnGGD672PFhmjn7wEeXe27sQAwFqyzzX4bOXQEPx5GhldT0KSV6KwyJNRTCOUPfSA/+sHjmOunPSnA2leQY86IVqKJRkjq00sGcXhkpQs36MC7s+xajNW/PEzkX7k4TxgBKKFPXjWX0kcY5csxM84zOgVAsv/P9z5nGM9+92PF//vgrXuwuxCzoOApG6yS5KQUxN5pC3DYhq4Q5MabAhTHsnCUbzRgk/zSS5bhGjQVUIlUXM6Q1Nrz+t4jlafgWD3yNt8vIhP4XqjorA0CeDCnzzWnis12P0fBy6HhchJPvere5AZbaHscQMGiiUlzsOx7GBdtozktkDonidP39sNF3Chzaho8u+4154JTm5b7BasHkY4Lj7GvhLpW1ItbBLy4G7mZPrJvAZj9QKX1KPRf4VAoLhT+6O9Ibw6d7oSYKJ/tZ8RuLFLzv7/t6E4hPTylF7G3Xg44SM7G1YGqrhYtcoQ1tIZrV/Ok5eTVkSe9BSVc2hcqQ0JYG+PLxjDGayXuskWzPb+4fuO52HBrB4G+nwP35iNZi4NU3jvmcaayjbx3L4rfCnnLe9BNrudJbEXu+zyScPKNSQhW46p2cUmOi3wk/fAmJJQamGDFKU7Ji1zis1oQQheECfLof2DnLu+NUs2wzT9NCa4XZETNYnTFK5jJXQ8vkU6X91VdZ13MCzhVKA8WuDoHXrl2G3poHP2Oswal1c5Bh9atdxxgTPidCXmmu1IAUWcNi/VFACeNGa1UhSPn9pp7kQ8yyIVaoadc6Vm7jqr1IIiDhw+x5N3sihXNO/N7NBfu+Y/aeEDO9cxw6WSuzkqCYh8lz2bf0nTCsnibP4/koH1BWLNXgbY6JnBMXXctI5A+/fsdlL8r7vnFM1frhL3r8RhR3AFUirVJc7VpJOae2ottRWo4yFMkxnapV5/3smVOi5MJ3a9p98hIU0bW2Wt6qzdkuI1P7l/uWnz1NvOg76fKT8GhzgevdADkzzgvaWErKvL7YMXSOu8eRm+sdn7244n/84iv6xvH66pIY71hCxNVeThXFYCynFJlLIOVC3xiUUdViFy47xxI9fdOglMZZS0yFGCLZStESz2zFq127YZur6ZHRmpdDy+28yIJUz1V8TZYR9srWGNevyV+d1Tij8QXejAs3fcPTHDj7WK93ZGhsVeTJYwzCYUYpuqaF80zXiJpymiNrrqMM9kSS3SjDq11Hqwyh8otL7Zxe7Tuudy0xwk9un5iqjet6KlsofH0eBXJifU/r8Pb55l+Pcto8Ky7nkvnJceTdtDBYCXS+aCzfudhxTJGHLJjyYfWTrJF6FrnRfan7kxKvj1wLRUbxOE0s/oGds3xyuaN38sk/TAtD67g7e7IWhspUi6ci0mstZl2+0DvLi65ljIVTZf+kFCgYpmTAKkIoPJwmroeW0xxwVvP2caLR0FvDGCK6XoTtgFU3dkl0EqfDkusmmBW5xt+pAted42kJKA29s7ROszjD4+hZQuaqF4vnshY0Jadf0TJkDs7gLiQofvSB+2nhGCOHxlGs4eG08MmuobOiDN23Vqh8Pm4zoFw33ZQSc0y86AUiXKmcIE6JH0YZJA4FusZRiriUKq04dA2XS+B+XAgxQdMyh4LZUkSquZzelsoGDa3smFLXlo9JRJHq+cRVigSDlHpCy0iG8JtxqaaF8OG8cFru+L0Xl3y874ij2Jl01nCz68SIrxR8SKAV744TOeetlsl1zd866RWOS+RpiVx2rjY0gVgUPiy09s91bX/u8RtR3JWCT/ctu67dKE05p+3IrXgeRiilKTlyf5rJRdKJipaC3RgtUXIIfu5jpm0kfWmMYqVrqvx4sJa9tXx4GkUh5jpe7FqmIDSmrrHMS0RbzccvLvj05oIv393z29/9mL/6+9/jH/7xFxjjuDstLGFiIHP2C/v9AFaha1rNYB1hibRO0ThJeIklc7XbYUpmMPDqsuX/vj2itdgShxAZa96c05oUEqZ1ciwt1TKgGiftneXzix0/un3cqvYKE9SJkCze2pWu19NUr+jGGkYfybrgR8/D7DeGyMOUOc2JV/sG5yRQOinwtTgcWouxlQ1QhP2Q1g2JOrTKmUNrOdiGx2URPFM/nz1yKTRFOO6/++KSnx/P3J7FM6PvGvl8152p/pFTdb9EbtY1gCOVwkrFLLUjKKVw9ImjF9Xl2xHuQuRwaIhkmqI3HLhk8EnyU3s0OyVUQxmoSpA5wLQEyIVzjowhcVwCgcKucaQligBMW47RV0y2bJtTjAIpagSu8T5hjeLuvBB84kSARnM7ntn1LVeHHWEKpJAxdSh+cXnJu/PMR7tCUw3hsloVqmt8XB1Wlyw6Bw2xqCosEh54ayWk2dbmxweJbRycobnsKAUGLadlXwehqohaWXDiRKeNmIQVCS5ZkiiNR+DxOJEp/M9ffeCzfc8PrsQCuLWGzhrupkCuEv01G7cxGuf0Bolbo3lcIrfTLCEhqO3zr350lJS5m6TJI2UG12CsgjkSVMG0ug4ihenkc4LMRoWU/ulZJyImac9QrtnmVQINFcQErbdmG9gLrVTYUf/HmzuWmwPfvdpxnj0+581o7iFEOYWeY71P5L2vymQxeSubXcPKdHusBITOOD7ayel6Wv4ywDJKEerZWt4MlSNbto5ziWlTbpnq7zBGmX6HlFnmZ9gjUShKhrE6KoJeU1okQOPsE+/OYjG7lFz5yp67cybEwOc3l8QEfddyeRi4OVzyeB65OuzQ1vEwKaYlcdFbgl8kYck4zuHIXEY+u74ipMQSIlP0WAy+CMNAFcXLiwsu+55xWbjqDO+nmXPJ6OCJMaGtYLw7Z/ntK6GCnX3icQrcDA1ocQTJRbI1XnUNXzduY6iYmuW4+siUojbFKbANq2IuqJQ5OMdl50gxc9Jqw55FQ5a5HwPOSJDzGrg9pYTyM4euYfSVD23Eg+bbalStFNetk0JW+epD7zZXwwYZImUKnYbv7ntCECO1Uwwbh37t4Mhle3/GiHqwM4ZTPQ1R5FSweYYrxEilolYZmEqmrerJWITyp5W4X349zYRS2DvLTesktKIW+YhiDpIvu6ZzUYt+KJmHcWFQmpwTXgvr6NtW1bp2qtQ1OscoXaZSDE7TupZI4RQXGmeZUiCcPDetQA4uQ6meQLvLHU/nR172jXiU12q42l4bJbzwVMQJlJJJXn6fj1k83teOtv45VdbQYdfUWDzYtTLnCVk0ADLLEn+aVArnFOmxm1/+55c7jj7yZl5Y7TinkPmz+zMfJs8///kLGmRe8tlVx+MkG+Qck2xE2qyANwWkaNc5UGsMrTG86FuG1nI3B948jWgl5IDJR4qTGDofElhNW1kpuQjF0hmLX9L2nNQf6lqQNbtrHIOxMldxjYRllCzxj1RfIGSN/NbFjp8dR6YV/60/96vjBIg1g0BUgkL0SnHhDA+LBNzn2phIspmQAlarkOd5gDR5RcFjiOyTnCweluVX1tXfiOKOonp2Ky4aI77Y3zpqKqQzL7ZSnIyic5Y5iiJsDhIt9xgiu9bI4kaOj0sp0vUozTkm5pD4+uFMY8VLJSe5uLEkGgTqePtwomscGRFzLCFwfXHB7dOZP/nZG3700ze8vDqQYiAUzb6z/MlPfs5uGHh5uSMrOE8LXed41R2458z7EEkoQpAbLKTE4zJxO4tKL2tQRrG3jtMYeNE7Lhu7WfEOreV2mehDYm907f4KWUn78vGuk0GzrCHC6lpXi9zabSklhT0XEUe12nDTOxogGc2nu46UC2+mZfOq8UkYFWOIFVuuHWyINFpz0XdkYAke0vNQsuTCJ/ueS+eq10bGRxiPiX1nuWlbGYrpZ766Ai77htslbJ16bXC2zWKF6UqBj4aWj/qOr88zb6v0G/gF3F9RPbelKRJ2VsqiTlaFpRT22fDkI75uXk9Bovp2OvLdw0CjJCTizeTJuWyGZht+XOcYoQ7wlvwcjOG08OdNEVXlaikg2b8FI+owjDG0jeUxLChTPeg1TClQjPDZe2X4/NUVLpz5v06Kp1y4sBpXu3ZNPYWUalI3+dqBSpqYKENr85MTGrFGyITKssrMPjE0MM6J5BxRyQagi6wrrdS3gj+kg5fBtdgU2MZwf+/r+nsmpjwugZ88nvnh5U4opj5xaC19a3l/XCjp2f0lUnicvcw1tKLRYoOxb4xYYCC06YdZ3l9WMpcrBRpj6awlxgBK7vGSC1mXNepAYL96zUqR9XLVtbzoW666Bl1UhUQLJaUaNG+qkCxXOKlw2Vh+63LPl6eJB//cSZ984I9vAy/6hj94cQECCNBZzXcOPYPz/PQ4Q4VjhK3znCfxbcBFW8O+d6BgDIn308JN17Jv3a8sq78Rxb0U6WTupoWzNxyqHL+rdqwA59nz9ePIvnW8HdcdS9wHfUjs+gaPyJUbLTIM8aQWLDrWyzZnCf2d5iA5qVo6nPVoaJVm13cU4PF85uu7zBQSU4i8u3vgOAUOux3DMPB4HLkYLH/287e8vrnmMHT8a//yP8ef/vhnfPnmlje3d5yXBWcsLw5X3B0faa3laZwk4q7kyu9/higOveP7h6HSPsUBzlpNoxQ/vN7z1eMoA571OJnlmLhzlrbOIkqW4evm6QKsmaVaCxNFI375vmR+9jRy3TiuO8dei2f74AxvpkXgLGtQRhP8mphau2Ik8EEFgQ2Syhgjs40lZvbO8aJteZg8d0sElUVhp8T46VXTySkrIbsxAmG8mxZ8TuzbhnMIxAo3rTehKpLa9YOLgc92PbnA9y92vOxbfn46c7+Ejfr4bTroOt2tbr7ij68U2sD7HBlzFEiqXrOYC/fRMz1EXncdSxLVqnj8CONo5UyXOnUNquDWi4Nsqq2V+MQ4B5rqiR4i37IGzlglniFzirzc95xiogT5bMccUale7xR5d/ueg9VMMdFExdWhkyKehEq6RKHkgojgUq5zF+RE7GrIwxoXuIrQUs5gFKc5oLN4xZSSWYLMudY1qpTYUOi6eTZGNoXWGMaSOcfErpIRSqEqZeVE+Cd3Z6YQ+Z2bfQ0SKTTW8fFFx91JTLSmnHl7mmqhFhjnpu/YNbrCIDJMj/W15CIRnJkagFJ0dWyEwXXs2pbbpwfZZFOm04Y5JQm+R9E4y6uh5UXXPMd9ZhmCJoRJs1lPa41SmpxiPa0oLltD6wxfHidul4WUtnMmi4/Ms8dUF02r5XtuuhanLV+fhAEVcqkDYlWtFBAtANA0RjYWwGlpCI6L52bX/8q6+htR3KGQdO0DUuJp9qhSaK0WBaCVonTykdvFY7V0nkYrJh/Zt45XfccUM/vG1mOm2hbien8n4HEMPJ1FBKO0ImtZuEYrLoeezjU8jZFPb/bsu45xXjgMDY+nM33b0nc9p3Eip0jbt7x9eOCT1x8Rw8Lvfe9jfv7NN5zOM3/ww9d88c0bjNY8LmcOw57G2jrUSjyeR4rO66xnM7Vasxc3f8Ui3ZhRGmvh5a6tsWXiJa61OGLaotk3lmlctgn0M36oqrpPbupcKaLUWUUshTezCDGuGifqQav54aHnm8lzXGXrPM9sS6kDOGOY50DrNCjDFCJFCxvhqm1EuFLEV75EQ2bGoXndtRJ4oMUWVoI4Mt/MniXLoXQMgc4YPKJIXecJKFEb97qGIStFjpmdNfze1YF348ydD4RcOG6YvawzpaDv7DpFo5RC0NLdOacZhMTMaYmEJHTaU0rEMpNj7bwV28lnDYle1acKTTEGioRVayW2wikJNz7k6k7552ZhBRnWNUbzO9/7jJ+8+cDpbvlWAMe6hgtf3j1wNQzsreb10PBwmjnNQbpbq+mcxhjLHNI2TJfCnrfsBGWEcriK8TqjOYeErgPKMQY6qxk9qMp2KTUzdEUgUnk2+xuc5RwzWSumKIN4rRSn6ryqCpIxkAs/fZq5XyJ/5eUF121DKoHOWawzPE0zb44S1q1UDdnuO66HhpgSRhvxWUqJ10PLGBNvj+Nm1YwSJknJEmmoNPgoWQ5i+QsvuhZrNdetk/hH5CQXQuIpyWbcOYtuJFgn5ue1srY1RclRUCkp+p02/OBix+Xs+PI0MsfEzlle9aJpyUXuIaWeyQmd0Xzn0PHlWaiORonVeOMkbvC4SOj6mixmtWySJDh0ltf73a+sqr8Rxb0gR1BP4eAc+7pgppB4Py7kkhlaK1PpImn35yVs39fX42QqzyZJxgB1N19SrOlAisEaroaWkxea16FvyTHROMdF62h15ub1pWC2Q8u0zMzLwn4Y8D5QSqFvHR/u7kgFbo8Tv/3ZJ8TcoYBGwe9+9yP+tz/6U1QRU6PWWh6PZ7rGknKidb1QtkrBFc1S0kZhG5N0C42WxSO0K4FCFBLOPVdRiDXCdilFXDM/7VummDiGiNaFJG3HtmmU2sm21mwqwJVzXIC3c+AcExeN4bp1aC04u188PgjMsq7MzooycfZirRxLptNWOvUQSCpzv3imGlTSKYt2GhMUh0Z8SUp55jrnXJhj5mnxgvVnGS6lLKwSo1c8XzrMl13LvnGiLCxVi1BhiVdDx8uuZcmZD7Pn/bQwRWl9+9YxTZE8Q7930vmlQgryxlIRjvu+sUxVoq+KvIZj9JuneC5rty5XL9ehW6PNRj88NBJScdE13D2NNYNUhEBGabHrrcUlxoIycLNvuf1wx646dK6FXSGd9urRfz+NfH8/cDoHKBln1Ob3b7VmqI6Cxzr8lqJODUtfTxu1E8+wbx1zEpOi3hkunXjyrPOVMWX2Tn5HrNYPkgfAhs9HLYyZ1aTMWU2bqw4DuR9VTSN6mAP/+9tHfv/FgU/2HTFnfn6e+dH9iAJuWsOLruW6F6ZNSIK7o6q9bxHtxCf7jrvzwhLTJkrMKbHrGt4+PXKcxarjqu8wdQO4aB2NYhtillIxdySb9egDN7uOwRoaq9G5ENMzg2+1/l03rVJnQqXAdSuOn9+cJ753sWNX+fmrL/4aLOKzwDuDs/zgauCnjyMBaGqjmeqaljB5+d6cCwcjr8mnRP41zi6/McW9IsSMWQqxRdFbU6EVxd05yAAqFVoXca6lrzmdqwGSUquzYSalap2bMio72kYxek8xir5tMdYwz5HGWm72A63O4iwJqDChmpZXh55xmng8TvS7wu+8esXj4rGNJYXIP/jN848AACAASURBVPrTL5hi4M9+/oFhsHz14Y6UxTr06w8P5JJZFklP6mqIQEyFxkqe5TyJw+O8RJEjF7idZq4bUYZaUcXUk4csHqPFGldRBUJl9eUp9Ebx2a7nRw9HoUoayElu4JjENrQ1hiWnDZsWxEPVAW3mKRbOKXEfEhfOcl7ihlOydilGvHCWlbYIG39cKU2rLQ/LjM8ZnzI/3O3IJfFmWrhsG66ahicfmWLik/2AyrKQ76bnm3RluqRSOPvAvnE0TobGn+0HPt33ZDKnUI/HdfheFGg0SkOnDN/ZD3y06/nmNDGmRKM1t6dZoBEv+ZwomeOYWqRCzWVbg6adNfhQ49J4PnJXP84tBrAgMnNToYNda7nsJLP3Q66+JTI4gAy9lQQm21jaIAM/oyxtiIwobtqWUwzb0I16OqAoXvQtKUnikKpsl5V8sG74gzVMPrFUzH2jASK4/BpeAYquwmmDtXw6dBglm5yzmstSTzpKfOTn8DxPEDhHAlJ8zoKRV0ghq0LfiAL3tEThb9f+QBXFKUS+fBrZOQ1e85OHE9rKZnvvA50zfGJ7IVpksZfwIZLLFmXPYAyfXu34yYcnfEgYXTBEuuLorOPQOPaNsHM0z7THIFT2jXWDEogmVjbH7Wkk9S27epJdm6ytd8/yvWhFTok1e7eUwmXjODSiZ6jbvtCasxTqmAslZQ6dk+YUww8v9rxfPE81H2Be8jYE1krUzZfG0hjF4+I5xUg//2VQqIIMLhQUpfFaKGKtVhtWfDU0QMMSM7fnmaGlYtcOqy1WZfZWDJzWQrjaoyYy50Vxd5pRSgylWtNweTFwaBR9TTwQSpMi58Tp6YnprOkUhMYSF8//+uMv6FzDv/ov/HXePzwy/2HAOM3vfvcz3ty946d392RbiPPqYqhpnKsxdprgAyjF7ANtY7k69OgEwUgogaod1b0P7LvmGYtHCo41ahvcSSScXDqtFSnJ4rq08v7WgTRI8W6toW+EqharDHwd3qz/VXUTiQVOIXIOEYskHKUizx8ax83Q8eE4SlBHWSdU4HMkGznWN1YoZ4WC03B1uIAhM55O3J8XnpL4l8yPmetux9nP3M2LiHGs2vCftavqtARi3E4LL9tGlI2ZTbugEOjOaPWch1oLSacVn1/sJKEpZVqksEwx4muyUSoKowQ+cmtqkILGaiyaMT7DO6U2Ehv0VS9h7xo6BTknVEw8HmcoGV9pmLa+J42icboyYjIziavDQBcLTesI80IXEz9UmoeLHV8ez1tRNkqhjNABjTVib4Gqubb1etRC5GOpEX5RfPELaFU2J0WQn5OM3HdGwcf7jtYI7muMbFqaVRQnJ9Gs4PHs+eRyED1AEpbLhdbkHFlyIeZUbXWFu91ZQ8qZkMs2DwPF+9lz+/U9vTXCWjJgraGkws8eR05L5J95dYWrrLjVkIzyLMS7ai2dk6CdEJNkCnctT+czOUaadjUgWZuZ59nBqkLWQKuVqFoLlJKJSQJ9tJF7oCCNFOo5VCVXsVqpCvaVsedqxlWp/MpcJJpPgtALh87ROEMJ4gfkTE38ipGHJVBy4bpxXDq72aBMKfJ+CRWhkAH/r3r85hT3lXBK4Tx7SJCspoTE4iOvL3eUnGmd5lAnx7moOmkWHHMtdutNvU6fUXCeEhqDtY4QEr3WXHcap58HQ+tmoFXl7IbM/eyhFrfTvHB7OvPjr9/x0c0lrjH8i3/9rxH9wp/+9BtMb/BzIvjI9dBRNGhtJM18aDA5Qogy5IoJnRVOO/q2xebMwziinea4eHLfCB20dtVh1SjVhSfgvdri45wVHnpIheu24XaREA5jFCVJHN4YolCyrGJNDV6Lk6oXamW6rMU/atj3QpO8tpadczRZNosQJewk52fFoY+RTKGxmj0GS+GL08hNLiQFWRWmnNAK2qZFFbgbz5yD33zpcxSWiDbSkb7sGm66hrvzxJIKsxX74u1Us7asSop6qYPoSokg5kKIcUvHej203JSGOQkMdL8EppwJpaBTlshBLfTRzlqmWZwd1y5/e9Qhm6sWB7pAaxVLliJMKTwd/bNapmLURmluDj1jTDz6gM3wZhr53ZtL4hIYcsHGzI+JKMQJdE2ystbwFILoMVrHjXas1gerotLX03pWiPajGJKXk5mpDCEqK8hWxeXs4xZCr7TaaJLrqSwraRimLLj69UWHc4YcJVhlbzUWyVB9O3lmBO83lQlSFPSNIS1pCzBvjEFXG+lzlDVRsvqFBKfHEPnR/RNWCUZ+2Vi2vIB6Q1jgOzcX3I4T4+yxGs5zwBrDXOFaB9Kd59UcTIamTku3vcYPogTuTBlCKCzeo4zi1X5A24IpNcy8gDa60lslSDvVho5cajC95K7mLINRoSJLYXfWsNTCLnCVvN+rrsEpjWrkFj0Hz+28yGBXPbPFSik8+b8ECtUCz1jmLEfFXae5Oy4YYNc1nFOkLYpD3+OT0LoGK9a/zllWH4i27RhTIOaE0cL6SCrXgZ9mXjwhR0IsvBouN0vbzfO57sY+Jb58OGOcwaRE17SS4G40f/TTL/FBaE9fv3/LN29u0VYzzQFVFBe7ljlkGq15cXAsMUhQN7UYOLVF13kynTIc+p724or35yeWlPiweD5qGuHIxggls6TaQRShQRotNqNGVU93CkuI3DSOUwwsMeOU/n+oe3Nf2/Isz+vzG/dwhnvffe/FnJGRlZVV6u5ykFqo+QuQcBAeFgZGW3hYeEjtMjhISCAhhIGwsbEwALUwWupuUU1nURUZw3vxpnvvmfbevxFj/fa5Ua3KSNQ4UaeGzHzx8t5z9tl7/db6ru/AR89H3p6n61Ky5t934q+QgxTL9YY7hMDeOD7uBt5PM+9j5HnfU13lEBYG54i1SqCHajgklc7LaPx4OPLt44nRGckGrVUET1lgqksMPKGy7f8p8Erx2WagN4Y355lDkO55iok/fn4jDpuyGb9ONFo3JkcpWG2ui3O9VovWCJi2f+lMT0YxTQLVCJW7kGLFaMfzvie6xNvTxNRSwH6MSRit0NZgUOwHx7Yz+GDgEsS0TrWOj3K18l0ZQYcl0llDqdLRfns4t8hIeJUXTqmgUuJXNzt+OF1AIelkyIP/polYbr3sRwRSEoFPytLdaqXY9pZdZ3l3XogtJFqKxJqRKv86tzjDNVx6/ZkVxTkXEuVaZAoS/l1TwRrTQm8qNVeed5bUlJcaWrGs5KrZeMHMhdfefG6QJex6Rpe2D1jvyPezWBJ//fqRTe/4049vmi+8FOlvTwvGGEIUS5JziBQMpeSnL0qJUldpeH2YBXv3jsk5lhB5c5x4Njg+2vdiMVAKc044LZDQcQ7sB4lI9EYOhpwL2ohOw2uN6TxhEfqldPlPPvKxLfa90XTOEloEotaKJSeWXPFO4E7VWUKuvLo/SpfervkKCopAUWwtfur1syjulEo6JbzTJOThusTKYI3crM1HpPcWauHFbsvjVEnhTK2FUjMhJ1Jqcu9SsEaRtNh+ojTnKZBTJipheehWJGmFCMVV+PN+mvn+NHPjPSkLtasoTTUKbQxvPjzww5t7coV//ttv2Y5elISpEGIiJpGJPx8Hts4wOsMmZ95OM1XRLEnrtTioxiFXpfJi3PA4TTycAzfaCneXhmm3G1637ko3X45SK6HF7Rkt0WR3nedDjYxWuivdit+1OP2oA10L6noTPcUVyfUpucUH1sonQ88HrXl1ObNzni+2OwDmnHg3Vea29NQojkFSs3Z9T82KVKNIzpW898NlIdXy195IRTpLVSXvlVp5dZoEW28sqCUXDiFw41xTqkrznrL4t6DWFJyK0oYcYxuR61//Xa3QxrICO21qaaP31jk6JSKpB2dYVHMmbFuxdcxOtdJ7Q++ks++8YXQ9lyVxnJJwmVt3nRVsho7vT5NAPkYxR5hrYjkJ80tpxTFJ51dy4fvTiT95cctfPZxQubREKcF/P4QkJl/XTls1tpW5Lj6Nkr3A823H68fpKjgCoWp6aznGyOitYNN6hSibIrlmUlNminO6fM5YKt4azpfAuO3bVCmwyyeD5z5mzkk69dRgUm81o9OoeTVqW4/bK8AlWQ0NdryqPxX0o2fbSd6rUprHZeGbx2ldSqGMQGiViq7CJth0PVOp5CWwbaHdN4Pj7Slyv2SeacOC4Wa/483hEbREDtrWIa9w2BQLhchN72XqtCJoy7lQtcZYQ1GVfuMJl0BprqbiCSWeNEYpNr27qlqVlnv5kgtYLX5HtVkcaMWXd3vup5n351lCWVgRib/OAvx9r59HcUdG8FTkyzdoRmMwTqhUqkrSijKGJRVSXLgZRl6HyiUlTBHrgTlnllLQRqGMZWnBBDkl8eDQcrcqa0lFMSvDu8OJu9FLl0chF7gkkXBP7QtCax7jQqyFFLN0gVrTWcHuUZU5BDrTMZUFb13DUzM+wegcg5Vl1bFGYsjSPVm5EUNJOK3xpnLrHHu75fXxzMMUcEZfvUMAavusqsEzuSpCyqSWzORaaMnndiCUwtY65pIYvYEsKTBpfRh4quFrcVv/oKEKYi7lO+68pEKVUtg7i1EbPiwLr6Yzz3yH01JM5pBw1uCMIqTCxllsqZzJ1KTIubIdejpryBlOMTQFpQDZq3GYUWIZcX68NBxTIJqus1SteMyyzN1bI7Q5/cRaEDaLFHiVUsNIBdpYMdf1fAvt8L6uSeuK8sgobxTX5KKSK8pWXPPSCUFUhqYWyOKtsxtd++83z3slYp2YC/ttx4c58Oo0ySJUfk1TWVfhgtdCbyy2mZEZrXDa8PayrGeOXKcGBxQNP8wBb3puvCEXKUS51KsCE6SJMFXiKYXCKcXVKEMqlccpsvX2iUGCTJdFraKo6+W5/mtBbIOLUiwxtaLLtTl45i27zvF2WlhiQitRltcq7qXCslFXkZXXhk/3Gx6mhcd5jX4UeEdrje8MrtlXvJ8XTkkOl9i+P6s1SeWGy0vc4KFOvEsZq4S3r5p+prOWrnP88Him6xxdI28EhG0n++N1zyC14HGpbLy/2jUM3orXesxkK0vPTMUNlhoLyxzX3Tlea9mjqaYzUVrMxrLkJ6sGe4VcmuGbfMejs4Te8TCHBsesXXwl/IHq/rMp7kZpOqP5bDtwf5mvG//eWOaS0cry5jCx85ZLLjhruR1Gxk48XVSFoTPEnJhTIdREbx21GJyubLzFW8XQ/LcTmlhhSpU3l4BuIQglVw5zZOg7LA6rBTd7dTwwT7LoMFrT9xJOXJIUTN9CLUotWOOZYyDlzI0fUUpGXOECJ4xRjF1HKFn8pRWEFBCD38rHQ8/Hu55zLLw+J1726kl40yLkjFaEqK4eGCuX3VmD1RoH/NHNjnOM6FSpVUbnWRVOIZN+9MAq9cS91usisu0fTFHcdR5XRRG4Li43xuD7nm8uZw4hCHsmF1CVoqrQPLWixMiHRQIJdlZTtMUVGU9R4o3ijGndnTyU5oqnPi18BW/WuF5yS0suPBA5xMjnXX8NMimt6GkFSYnS0VxDVCUTQCGFqWaak+DT9LAWf0NzVURxTpEpJkChC6BFgp+rUPIMGlsLl7lwWSLWKqHVOkvnjJhxJbnuAoXINRYzO/l93ugrNu2M5sWm5915ZuwsRhtOMTJ4J7AK9Tp1UmRyuF8io5PiOUUJd/BW7CKyqi2WsPJs8ByXxJIKuQVjXJIs69clfEURapHr0jpM8VJfl5HtciITxDBIVrFqB8j6vWltGIxm9J6vH0/XdK93lxkFbL29LupN1Xxxu+Wu6/DWcgiiRckZFCJOXFTmPlTq4cKLruPOGzZG8/Y8EbJMr+KfV9nvNhzmhyYgUlSteXOescbxciuskxQDsUT6arnEwHbTc5gXPIrno6ezInaaciHzlGD2fPSYqnBGkYwEgcMa5A5UjfUGmzIpyeEwtIYgJtl7oKRrz22NE9dovnbvOq2xzYfn+dDjrOXt6bJ+5U/CmJ94/SyKu1GK39zuGNoJ1lvNhymwtMXEeYm4UZLVY1UMxhJjZlkCqS0wemPpx4Hj+cRt3zHnwrvDkXGQGLS7ceSSIljLJUQyhWWuaGdZUmJwsoCz3tLPhl4Z3h6P3O02PN9tuYSZUsWnQ9O27EVoaJ/f7bnExLQEOudJMXJO0nW/uUy8HAf23rNxjt4Y3k4TcypsXE8xlfMyr30z55j4Xb7w1X7kkhKPU6DUjq2peC34YimVz3Y9d6OXjtaIIm9dMgm8rBiNpjOeUxTxkguaUgO3nbpmqc4p8yNk8trSqub5ctd3Mug2oLVQG0tFutpfDOKdb60Up28vFw5LACsLs7kKTPZyM3KMkVIiH/WDeOiUpk5Vkn2bi1jJ1gJZtdCL1qlYK26KyxyoiJ2EbrDWh5x4pqwIalrXvUJDpRaK0dSq0EUoiiBuiWjRBKw45gopoBQvNwN3Y0euIoQSVac8iSEJ42iFeUbvKS3qrtZKXGBeMtveCU6rFBut2XS0PpDGeJBgEq8lP1iKhCzUbzvPL59teFhk6nRatSCNnjkV3p8X+RwKOi2Y9g/nwBe7nrxOugg2X1IhAPveie21VpxC4hAyuQBWkTOkdtBJVGLrXtshsu5fZBfTyAtlJTGI0vdSxG+llIJGLLfFkK3y0djx6jTx5jTJgtMYwfgbLPbJOPLpOJCrsET++O6W99PMh/MMbSdWC2y946NRHEYrsLEat9vw7jJzCBGjwWC5fzxy0xlSFRjrZujRSoswcVl4e5pwRnE39jxG6bBdMoyu41wr83GmoigKtk58120jq19ixtsqNMgGoeRmnaC0sGJiLPjRsjMdJRRUowVTZbDRWpFSvUYh5gIlZzonEE9F7CEAjiHx5nHiee+Za+acc6P8/vTrZ1HcO2vYePmySqnN7zzzECV0Gq2524wcTxfOS8B0jksQF7iNEzZpopKXCa8dkUKMgcFazpcJM/bkkjkt0k0vMRFTYew7YlgotTL0He9OJ3JR6JrZ2pHtxpNK5d3jmfvzTNGQo7AFBJMTWtb7wxFtLVoj3Wpn8GgZsQq8myaMUmLWpMQA6bhEnm9GOud4q+C4LK0wyTLru/MkZUDJCP1+KXhDE5pUXp1mtt42da2+CmvWUbwh0Rhga811W6+UBCk7NDfe0hnFKZbGAHiavb1T3LqOjdFtb8G6lGCNJFsx0QKEXDmGhZQKtmru7ICm8s3hRCnww/GMUhVrDcclsLeWmGXcrrmSlLANniiQLX4QEUxpb5iKJBWtwMG6sDqmSMyZT/oO0x56Ee6sB0Q7sVqbXNoDdm7hCCsMo+QDSTHoXPvODL++3XJYIlMWDPkYEkpJF+qNwEJJyyFpEchMVZhDJuaZzhme9ZJPkCrc9k6W9qXwuAiPnYpAW7Vw0/cAIjlXCYX42lsn1FOrhaGT4hPF8ByTdKiqsjGGTusGu8gllZB1w2AsyuimwjT8MC1snWNuIRVpBZlVu0hq9V6Rq94ISO3QXRNpG7xkLadScVXRtUCa2miRpyXyOMdG0hLWinFashe05tcvn2GquMGmlNlZy83tvjkiLuw6z8txFRbJAnsVrxmlGJ3lGGITZ1WcFa+g0XcMzuGNolK4zBemJQofvhVmt14fnbjpBx7PE8cYUcaitCGVxG1n8Vasq5eUUEoxp8xu8MQsExBaUt6Mls8IUJXCeMvSRJdGi//QaA03WtFny+McmEsStlOuFNPu11o4hcTrgzSDaSrcdp5Oy73ztwJzl6ZgpSIKxVEp8YFwSuMbo2DOmb53hJjFY9paVBUhErUyFUXKmSlGfEu9V82T4RAW5hRRFI7TIrgaQnGKpaAppFTx3nLTdTiVUbmQawTjiamSaxZmgDVYxMiolEAsCl8r3oubXqoZY4Tytha+99PCJxvpGjtr6ZWmc563jwd+9eKGH84yNgIthLowdIKHhhKxxlKV4cXWkHIm5yzWA1pfee1lFSZRqUZhjWnjvzTk2RrqOPBhWYSKCbzwPVYHDkuS7gFa7FplNOoaCIyS9/X9NGEzPB97nGqePBXeTpMshRXs+w3GaI4hYY1jLkHi9bzkjV5S5jGK6ZuuYk9wTbhpENNorSwqreDAjyk22muLLaS2haGm5MzjIgvpT8aBSsGalUHDFdOVa7POs5VDjFcbX7nuFXLls5vNlYlVGtb/zDv21RBLZe4Lx5B4Ny8MxhOzKHKdEcfGXColS3yjKmKwlnMRQVM7iDur6KrGoGSiURBLpjNyYM8lcz8HLi1kvCDQiVLgMeix4zIJi+ScRSgnlrsVSmbsBVQyjR9utDRMOkkzte0cn7SDeeOMJH0523YeKzPj6UyHH09Sq9++atROAM27RZxWn3mPrQJtqVoIJfMXD+ercG7NEL2ktiZUivfTmZfbLWXJT3YUufDVzY7vteb54Nk7+yQmaodMRnGO7eDItWkIDGPXUapg3YXMlCHHIA1Ju4+qEosLjYR9dFbzYY7kIhGMfbPrSBjul0RfKi8bhFeKfN+Dd2gFFwVTiFeig1H6ar1cVMV4abDmKPF5Rim8d2xQGCqvT4naJrh5EjuPuVS+e5hkJ6WkjrybA1/dDBza+/6p18+iuMtLfKaV1lALc4g86zynRYQ/3304MA5exixjyUpzt+2Zq3hkb73leDpTa+USAqW2AOdJvtBx9KgKU8u1LBUep4nTsrDf9dzZnrvdhhAC5ylAhU0vLIKHy8yzXd8wwIRSWlKWAGMsK40l5gTKclimq6Nibth1ypHTMWESfPV8z5++2PNumqka/uLtIzdjT6ctqWbBrgGq8LbPIWCJGG0xRrEfNpgqnyPV0rIpBWs9x0hnDPtObFivFLkkHb9D0WnDWVfmmlnSgleam+ameVzEb3zf9RI80paTDWomI3S8og13nQT8nnJ8orFlOC4zH04J7ywxiruj8xrnNXORpXduS1PXvu9CW14p1byDZMF2CUEWTkCzXr/SHlOtxJhIiyzdPh07Bm+oynFaAnbtxsuTwKTtqqi1sqzcap4WhZ2R9HraPRIo6GZdm9vBs7WWnXV4NMdUxHu/fdeXnJ8YWAaBLqrkqV5CwipN10mqmNdaXA694XFJZOBZL7a/IRemZiiVS8ElfV2wTiWjKNzsOn73/khF1KSrkjEWOXz2DdNWiEXACtEqJbTHEDM33hBzZe8tH04LVit2vWswjHypUuDrtUsX0Y+Y2WkU55zFyVJLSP1DCtTq2GsxN3s/xydF9Hq8Fq4FekmZP399z+Nu4mbouOkHVDNYK6XyR3d7cspXbxgFrTkDamVwhpCFQGG1Rhd44S2qVM4pcTzNeC9xm+vBvv4cpxQvtwNawavTxP0lsu06RidxkkZnNvtbtIb70wP3i+fGe84h8dWLrTQlBcbON9voxNV2GVFOr9F+ympMdVcbZqWFezR4x/Oh8hjC9XPlLJ+rs5IDTXvrpcJ3p5kXneT3/tTrZ1TcpSNr0widtWz7nsF6TjmSQ8JrB1TeHo4oZOzsup4pRD57ccP7aSanxK53WCMmqEuIEv91nmXp2ksnWHMRoYaqnKeF0hwPz3OUDljDGBxj1/Fs01NzYEqJcyliirUdeH+6cJxm7na94G0KprBwtXxYF1SNtlaBoiudEkfCm94wdAOhKowfqdURCByWSQyPsrAAKgIXVFXo+w2zGL1gveMyibulVYrHGK7c1482HZ+OUqBLrSxV3DDfTgvvpwWtxUZYKRnxVqLb1llO50iqC2nsqErLgi5Kp+J1x6Izicq7aea0BJwTxzvfrIjRFa0rc4kop3AooirkkgixPGG4VKY2VvvGexfvY1kAbnzXzJIMBc0R2XsY1R5QFCllvFY86zucUuhccF6TrGk0yxamUMS3x2ihCl5SYclF6HFKcQwRBbwcO7o2rWitqFomSQF55LfKlCT+7ZdaqFY1ncU6hbaFIkoOpgKq/CgUek7MQTD5TScGWzdDx904EEvi3WWWEJJmy7By+4ckkMR5EhHTTV/5ZDdyChLcUHJl6MTVcCmF+5DYWJnuVg9/YzQhV6aYZdQvIkRzrTC/Pc54oxk6S64txKidClc9CEqgOhSnUnhMiaxrCyCRZfqHGjDW8+1p4mFuYpu2MHz6eU+e6qVW3p0Do5PvzWpD70TVnWK6QmYr3LZaRK+n8rbROA+tOfvL949sh4EcMy93GzZDx4fTiZxbClrnGKyRiLtp4RwTv7jZ8DhHLkHS0XKBTT/wJ199xdfffkNnxH9nkwsv9+LIWIrApit0lnIhLAnrDaUtLeQzV/KSCEuiakW0LfWrub7u+04YM7lQKHSdZTSGL283vDpMV7YMCuZU+CZNlPlvA8+dht813uthFoN+VKBU2FrL7X7gUBW9t9xPF+YQCTnwst/zR7/4BafHt+i2DAql4o0sqDorzJFkhCaoNOQ2+qlVzo/iHDOllitmp1FYJ2O2ioGAjINaib1uyBlq5WUzGDqGyClFvHXsO8tjiMR/hT9WG0aif/Sw9Fo8YYqH2FnIma4Iv3/J+bq4KgWUVxzDRKcNz7c7jtPSLEQt9/PE8+c36HnmeJ747jxzDOJF7pwlAb97PPPYAp5VqZxKpPOy4DPNwdAbw3YrWLz4lUgn3Y+eJcOAosSOyxzE0M07EQ21eLtnXnHIFawhpSxy8M4iklMlhmapNKihddFaupxn44h3mu/eH1BWsMmt69gMHdXC6bA0u105HUoQWupX+y0vx64pUMVmYnDCGkpALGL49nAJHOaIt4aspXu9Hb0wnJrZ1mfbjk+3Hb4tiIsyvD0tXEIAxJ2vtvvEGLGWRsnCOZV6tXSlpeusNrRFtR1AkQKXc+HxsjAtkZttz92mZ2ihG1VBrw3RFXF2bEKo+7MwP1bWUkyFr56NfAiad1R2VmCL3mm0trw/L+yUwWqBgSoQUuUwC/20lBW2yWyMYfSWKWaOc5S84nafqvZ80qAWimKqhVMREVVVa4SlNDirBfIlFzpnqbMIe9bYqHDPiwAAIABJREFUPIW6Bpevh6KkRsEPp4XeObZ9z7IEMau7Qmu0xqDZGtRVbSoTqm3sq4e2xL0sM3ddT9aKw0Vykqnw+W5gdIaHaWEOCbzl893IX50vJF3RudJ7R8riE/Xnf/EvuVwCX9zt2BoYvMUZTSq5UU5FFEYVvcOAqE9DliKvFcRQCM1WgAKxVnLJ7IeOq7dTKfRG43oJs1laA/Js9NcglWs5l937T75+NsUdZGv+5rLwYY5UDZ2CHbBUxTfnCW8sl3kCKp/tN+QqmZMv3Mzr85mcE53zxCJ+1n2zBqaNR5tGRSytGGklZj4FUEahisJ7Lb4v48h+HElL4HC+EHJmv+kxs2ZJFaczMr1Xpip8e2cMn297Xm56/vzDkQ/TzOqnvi71MpV/dv/ILzaDGIQ12EAvMx9vB95fApeo+LAIJu2Mlhg1KjWJZ3pShXR6JMbExnmcsWg0p8OZx/NMNjL6vq+R8/2RL/cbXp8uvD8HqbG6+cUUmOZMUZU/fXHDqA1LkAVfLU+uh28PF7SREJVnY0enKto7phhlAWwzoxEo5E0VimAMGeM03psr3a/SWALXr1zYHlbJIdoZg49ZKGa5CszVWd5OR05F+OpWC5MjLZkQEn/8bMdn2xGoqEbjeQgBhceZwrePE4c5Yq1GI9TAWGhpV5nHtuiqtbJxmueDY3TCpbZGY43hZtxLkPEc+eHxzByFwfJ82zPmyCVn8eRWVTr9IpNZXZEZMQNn33tGb2UBOgWWkCgZOmXptZH82FoZtMBX3mjJHqVxrqt0/1/sd9x1XpbKsfCi65mWTMgZ3wzQpphZsiiBVa1MQRZ+OVdiEqWtb2O9MZo5i+HVUrJQcqvw7wtPzqQaOeinkDnnSLHNY6U+qShLrbhGCjjmyKaz7IMjNFXqqorNScqUXrvbNiEUFN+fA8MU+Ww7XFk/jazbjoZV/Sl029x2EpcYV5suut6wtZZFwabznI5nlhixbZn8w3HiYY58th+4HTo+hMAxJtCKGArH9uweLhMxZZ5vhLG26Zx437TvmLYLWfddCmFx9U5R5kgOEtsYlpZbq54+A1XCyKkSvZlrAaOvGRZLLvTGQK38G5/ccEqZv7g/cV4SOZSrRcTve/0sirsC5ph5t0Rqy1C0zrHRgku+PRwJVI6LMA+8ESMng2LjK8fHRy6zeKlclsDYOx6nmeMSKcipOjpLpnXAqqnPWleQU7nSx8bBse07brcDl3mhTBO9BhAfCbHqLRxTwmrDnCIuF7663TJ6y77z5Fr5bDdyP82NQigP9+oBQvmRUAJQqiXALxN7o0leHobHSdgnDa1CjI7ae1jEAvUYF3yKOGVxxrEbDZe0cF4WOuc5h8j//f4RZz2DdpKVWkXtuR48L4celQqxccCd1teHWnDq9ljZCkY8Lcauw/fCb3YhcVkC1klIh9GabiMY/rwksm0dXxY6m9HCwLGGJtJS4veuDQmFUQbnNZ4m0GpJPwl5qPe+48M88WLo+eXNTiIEU0JrwyUnHpZEyJVeGx6WIB16P+KMZUmV/eBlKnOyp5hjRlP5cjdyO4igpTactNSKypJSdDdYtn7PaYn8b799w5fPLC+GQXYVIfFqmnkMMm1SBRrRSjGoek0Qol3f3eBxxrDznmebnpITKQsT5pn3TCXzYQnc9mJrHKr4mmyN5kUvgrLUgllykqZEJbgsCUXmfo54q3mISaiSrZjGJKHSvTMNi1eELFL2Ugufbga+OVywZ8XLsX9KhCqVY0h4Y4mL6EI0kJsqt9JMzRrk5pRgxYcYud04bJVO9N1FslB/rIK16kmwpbXi/nLhoBTbTgLEaffp+vdTXcv8U5xiymIRvqqwU64camSJM1ODdkq7n05LZDd4Nt5x0wr718czFVG0FyW+VM+2G87TRKLQO8vgnXDT5S9SkfesWjstGRttR2E0NxvDw2WRzOEGSa21Z6XeLkviFETRnut64CpykQnINwM32rTiVgFjLhhvfrKu/iyKeyyFqB1d7zgtF571g/xZTsxZUbTh5e2e79+/IyMMi9Yw8OFw4TQtTCXTW81UCg+z+IQYozAYCS6uIq5ZrfLXBVppfOU1JN0bSyrwu9fvOIfIaG1jyFimmMST20BQkEtm1zm+3I3smo/zlETYsnVyadeORFHpjOXFxtOjcdTr0ojruxIMb2c0/abnm1p5d5nlQahiqKSVTGMZrgEFqhaMhTkIHe75sKHTkqJTSsIqw2gtMQtEkkulmkznDHPIPPOebu3A2gO6+oZT4dPdhsclkGrh/RpEMChCSmxt3zxDBCvN7QFdu2HnxMO+1oqxml6LFbGRO1wwdAO1Zs7zmXkpfHG75RAioRXV/TCypMChiJBooy2L0fzqdkdnLYpKRvPDNPNXH45SkLVE23XGU2pka5V06bby4XRm04lfkesslMrWW768HemaAVtpOGqtUkRLrVhr6ZwTzLaXbEwAXRV7Z/F65IO1vLnMzI3BgoZNO/R1O8Rrc2a83Y388nbL6bIwpzaltUL6zFtOUaZPg7gMQjt4G3Wytqm0lIIBatujnBfZH3ROcymFqVRcLnS6cfy1xreONxWYiyh1Dynzsnd8tO15c56Zl8Qn+wHvDIclcr9EtE6MWjEq6KoiJ5hd61pbYdetwNmmRj3njFOVu8FL6M4SObTAZyna4DuPt5pjWK65DH/5cCTvCi/G7hqqjpbc4FVgtnb0S85cUua2k51aiLkxVxS5KJ7v93z/8MASJcD7N7u9COey2AN4bdgjS/rZS8E2yL6na/uvKSQeloAxis/2m8b2kmlDQlW4mhDmUgWaaUgB0Lz52/+olY1UuZ9nOmt5vh3IWQRoKVexYdYwWEOqlR9OM+clUXPBelHZ/tTrZ1HcS+tyrPYMxrE1ijnBQ0zcT4GQMvOy4LxQ+0KRy5mpHAPUKOquzjlyLTye41VFt3EG7TQhZjSKOaSre15uAdtdb69/drzMaCOe4N5KWO+28xyXTMxRaFpaOpzPtgNey0iWSiW0sGMFTMvSAqPFWMkbTU4ZX2Fwmv/n/sRXe+ivh0BbuNbaVI/wi+1IZw1vzzNVN5y54bpWi192KVzdFj9/tmUJkep7qoIlBQZ6dFUcTrNQSnMmVDkWSq0cl8hvY+TjYeDjscMjboK1basK0BnNi7HjYQmNptjRG8sUApdlke7bKJY5MfTiUR6rsHNqfXKy0xWcaQCNevLGSAUohRc3e451oqTMYBwo4XLnGDjWKNcAxXlesEoUsedFhCuXlPj64URvHLuhlwNQa6Ya8MC7OXJJQlVTCmIIKK24Q/Orl8/4aOvpvBPDTFUgV1LOiHCwiVRUIarMeUl8cjs2DFeW76oqOqP5uO950fe8mibenmcpHrSQjZUzXiWs4f5wxFD4qO8ozW9cVJyVJYmPzzlETBW1J0g3h/yvLBa1YM9ea2rJzdtdo+TyCXRZJS9WIUKcwUoQSCqFS64c5wYtlcLr48TeW/EkKvDdg1g7K6vwVl2Tl0It1CLkB4W4Vea1k1aNWdQ+d1GVqCqnFNi2oBarFfcX+Q6ctSQy787zKnMAJQZwXx9PKAUvN0Nj6gBNYyDXQBwX5yRU2OMiVh7Oaw5zoFYYTSAuClo3fMmSvTo2P/1b53lrAiVVOm3w3vBwnkhKnueQC9+fLryeZ7beEULBOfGIv/H+Oi2s7wmEiXW4LFcaruws1lG9fS9F6N3iiZQ4zqE943JYWyWh2NbophOQ96i1btF7f0tgmRAyIR0oObNY4XteYiKlQu8tPeIouBTx6bBaUzQstco4pBTv43JVaq5a6UuSE90qxWDEXqAqqLFgreBvrgk+SlOR3fRdG50gROlWM4JD686hcuUXW7nZlpi5hAjIQliv3ZeCz/cjr48TuUiId2pik+eDF7OvXBm9jGGrq+XK814XzIPWbJ0j1sJCoiol0AZi87v8yBLA1Ujv4B9//S2+s+z3np3xaO3orOawzPiu4+Pdhn/x6pVs50tlypVvy4XOaj4ee3KLjxMvb7CmUqpmtFYyHJFlkrMS2KG0KIadE276qmTVWqG0/B1vxF4CLWk0K3dSK1Hq1ar4+O45YXoFJVGshaoJVaC1VIQz7rU4PA7dhkvILMuZ0VvORdJ/qlEiR0+RsRM67LJErDXoKteO9ox91Hk+6j22ZKYlMQUxz1o7LdOghfWzQCWmzHeHM+cQ2TkrS/oqXZtBGCcW+GI7cuMc55g4xcgxRnZq7b+liGvgcV74eByQ3IemfK7i/zMYQ7+xnKbVCbKKPw6KQ0w8hMAX4wBVAruTzugqXvGl/f0CAhE1pHdoVslimCaZpakUUqsTMRcuOXMzClf9m+NEypnbzl2xYFHLqifXwwhFZaqRAq/guvBcO3NZRsOpJjqj2fee277nh/PMOUbCSh740UuyAIQFc2WeVUAJMXPVduRSJK9VyQSikMK5NJ8j1YzGuq6jGvkh93PEj7o1YDIZJSVipFFbeucIsXBKQYRgVjORmaJ8b//XwwO3nefv3d5itF1Xou39CAW71tJgGPlgqz1daQfuIUYuORJrIScIecZai1GwcZaWo9IYcIrbseOHMGGd7BJXqO33vX4WxV1rzeV8oRS5qQ/nfO1Ihs7KB1WefW+wNvNX7y4UMqFC1EIzW9fIG++xRvijq/NiLULPSrk0znS9GiKd58im9+i2HHJVAgnutiNWWx4vE1/uO0mgSZnvzzNFad5dZsEys0iOqa24lNy26Yoey6vDhSWJtwoK7ufAlzdbvDV8c5oYvLt6f7d61+CQyv208MN5JldhNXTWkoEpBHGby9JFGQ2fbwfGNrJ/9WJH8ZqNt0wpkXLgk2d7bsOGvvcoFB9vd7y7XKTbazPiXx0uhFx4OXhxElT6uljVWgzQUvuOHqbE2pHUKnx/YyCVeDX6AikWVQujon2sa4djm9/OVCNVwV++fkWuialkOp2AwpRkWlJZ1K06y+d9ebvlVmdevz+QEAfKnAuHNDdTLTiFIN4lIdFVMcHSVtz5Pu49d4NvknIx9zrPIlypiFpWLGXV2myJhiImPswz7+eFXms+Hkf23l+vhTUNp69w23me9Z6lFN5fFl4dLzwfe04hYGn7l/admyYcM1qCVwR6E9OwoVOcWpDJ99PCMSZKraI8brui2sb9WJuGoMEutcrEtJpiWRSmCM/6koTEEPR1LUSpUkyWOfDJ0LGxlktKLKGw6Q1r35RaHmuHdPM6KWaz+hU12JRmY0BzMkWMteac6TB8+mzLhzmQw4+ojTw9AwbF59uRobGASsOsxZO9yRcoHJbYmGVqFVEDYssBlRASwXpGo0lJ0zUPmO9PE5vO8zgFkoKtFvriNBf2Xc+GiMY2p9ks+QyV6/R3CpF/eTjw6/2O0dpGFYW5idbWe2fFYOSayKF0SompPIWodFrYRTFGtKokFM9dh6qFmAV8GqyR3AYtjeZV+Pf76uofKrxKqf9OKfVGKfXPfvRn/6lS6jul1D9p//fv/Oif/SdKqd8qpf6FUurf/kM/H9op25J/zimjjMJ2hpuhQ2mYSmYOF6Dy7iRqwHV8vVbEtqC5BnTop+6XRrPKudB5Qz96hsEzDo5+cFfaG1Vwwc9vNqAUb6YJZyqdM9z2ntuhozOGruv47hL43eEsroBakod6ZyR70QsM45RqSTjSMdYqvhQhi9f74xz5J68/8PZ8EWxuTX9Jwhr64Tw3S4FCyJl5idSYJWi7wUGliIfLR16EJ1UpfrEb+dx3eCW2uapoXj0cMVZRc+L+cMLojk9u7sQQCllSxVz4+njhn384cB8jWoua0TtDZ8QobbCa0RpejD1b58TKVGuU0WjbIIO2gF3pnFRhFsQqQcQFeZ9LkX/ubGNXzDPHxsDxnWcJia/uXoiJktUMnaMaTUjw5v07vjtPZGt4DIk5BlSFGEV567RmDXRyvQGv0VbTG83fud3yovdPuZZVqIzfH2denwKnpRByFbgIKZ6mLaDfnAPvL4Fc5AH9y9ORb84njknu31hKW5zrq0FYrzVf7jd8vt/KlFckZlH2OxIVKN+pbrshgTmoImbxneNcCz8sC/dLZGn3z4uhwxnVDszmhNjojbrZIKxY8OqJUBB2zFJEPj/XSoIrE8bqZq2RK8c58atnGwYni+gYiywcG6TXGnJA4ZRmiDTxV70qf1dsWdArmbp6Zbj1ntPhQm+lBK04tBR1ORj/zosbdp0l1SL+6a0rzg0O062AnZZwpVXWVkABbjc9Y++IukoADnDjhUGTlGbsO84h0nnPi92OOUmI9cubDbVUHrLiMouGoNMGp8VOWq24OpV388I//XDPQxDXziVmpkX2CQLXPHk91QqqVqFNR7FKUGh6Y6goUlGkkom5MoXEYYmEskJ18vOykl2dM7o1sb//9f+lc//vgf8K+B/+lT//L2ut/9mP/0Ap9XeBfx/4e8BnwP+ilPqTWn86yTWVSjASsKu0pndiS1q0cGBvveWF77BK8bZZgFZgKIqoIDbK1jqGllb419qvlCxOyRUsLCETsnjY9FZzmGVbvbWi7KwKXp8uzCmxG7s25mUOMaOsRyktmO88cYqRm8HRe4nSW4MqxKkRXow9D1MgZrkE68m+3vExV84xc+s9MRexR20LPPn7axdEG+cqtiqxFFZyLX653wiNrLU+a6hzV+XQej+fub8spBL54tkN3mt2mx2P5zOlPFGq5C3J+/nt44ljGPhiNzJa2yLSclvoKqwTW99UKwrTMH5ZeFbECClQUU5ToxT6ksTrpBp9TdCakiyBU1mhIIFt7k8n2ZeUiHcGlTX/4M/+jL/4+hsulzOXGDk/nhs8p+Xhr7QwbbkOpi261j2IN5pPxuG6A1BwhWDWv/e7xwtJVcYWwvDJ2HE3NL8YpXgIuR1QpS0M5QH/sCwYpdg7zzPfXcOlQTq2VAqXEDFKDrrcxC+5Fu6nwOBcUydKgU3l6Vq+Py1cchKnTaOpSWCSwciUoZQswK2xhBKe7gHWiUNd749Z2m7mLGEaAcHGr0s/9RSNd9uJ0d0fP9vxT98+cM4FgkLZFgNbK7HC1gpEmGPG5AKdEt5/gxgzTw6kG+NwWhNKpmh5jztnOTZl52rd+/F2EPUy6grzlQZLiWmdNCVPZDT5jOvvqe0aGzS6ZIrK+CqTWe88QcF390c6axk622ILPZeYyTpwWQIRMfbyRrPExDh6zrmJiXiaSOaS+RcPB36z22EyrcFZTey4wjJiBlaevNkrbKxhyYVTbi6cbTq5RJmMgxdqrtUS+eiMRImuit+fev3B4l5r/V+VUl/9wZ8kr38X+J9qrQvwl0qp3wL/JvC//+TvoMXIWhnV5zmSU2HYSkCtbQ6AFVnIndOTK6GvilOtxFbyQ4uxW0Oz15uf2hSNtV4lvivbxFnNxln2vePdeeE3m5E7bzkreDF0hFx4cwmckbFNfqam63t6B9vOtnHxRwVWhJiElJs1rPy5At5fZnng2t35sCRu+8ySxKXQKvEDWbH7tkNDqdXKoFCK4u7ZwIuu46Vx11+sGhS1js9OKz7bjxxDYI6Jw3niMSycphlVFc/HLXOOTDHIvqdRtFKuvJ1mbjvHaA0rO1dsjWUsVFSe952MkxVyNTzGKO/PiNi01ooyFa/aQhCZp1Xz3SmNOdIZgRgGJ8tto4X7fr/MUGGwjrfvPnCZJuYUr1NOyLl1QK3jXJfODW4ZlXTCL4eebZP8rwn2UP/agRhK5fvjmaDqlQHx6jzzJ3c3vBws3x4mfvdwJKQsmLYCaxXWyHSYauUhLJxjZO88d31PrxVUxSElTiHQvCWZG9dcKZhiYIoeh8AeKcsUp7XiuCROLYjlvAg+ftt7brxtTKzGvtAKj+YU1kO6sBaglbFVa6UUxTEGUios9cmjReunRmK9KHeD5Pi6hnVcVGFJmefGN5hFruFcskCEiL1FKFcMAqueIgdvnEAjoVYWJTmgS8mkWHjed9zPC0opPt8ODM0XqbTDR3JT5XurSg5lBUSeDuey3l91XVyLoeDt0AMVjOWcCsYWDueJJRWEM6E5n2fJgEgRbUE5RV+gVn11I1pCYuhdU45zpRIrFGSBfzqtRZla5XmltEMHIVwcU2JZIxObNQhGU5sa/SocUyKKO86B+0ult4aPtiODs1xCC675A/X9/w/m/h8ppf4D4P8E/uNa6z3wOfB//OjvfNv+7KdfFXSpWGM5TQtxyTzbdtw6zylGHkvizovR/eebkZhPIhpBqEguQ7IVow26KEINbVwEyUaV32GdWON6paTLrDDnzGgtIQu29WI7UEvio8HD4FEIm+BcjaRC1qdEI60M75fIzRy56YUiV6owN5ZUeDMvnKJIdtaRE2TJO1pL7wVLtEbzu9NFRjeagEgpOqebEpYnhV7DZ91oOdXEZU5cfM+XtkO1h1VS2mu7yQqdUjzvew4h8PZyJsaC6+Dlfs9UCz88xqaCLA0blI391lp2zgl3fb3p1gIgpYlDzryeLnjliDkyGMu5BMEJG7MCpQhkTBEzL7RimSO9d9jmY+60RjXM2WtNLJWHKGvA55sNpii+/u4HQs4UMkYrBmtZckYb6drXBeiK96+LvNEa7tqu4bonaPfdegjWKljwkguxFcWiFW+nhePrd/zm9ob7OTE3+bo0lJUlVpYojUbnhN0Qa2bKE/dh4cvtFucsr44nmZLaMlApxcY6UTnmwrvzxBf7LSFksT7WcIqVY8rkCo+zKItv+47nTcGoWkjJ1e5ZF7al4xIiqebr1No+qnwfWRZxAWkCTLsWSulmy5FRFX65H6klU7Uwxj4aB76/XKA+WQnr9gtiKRiVxYsIxa4YLrGAFZdEhWLTecYW6BKq4MwPl5nTJfJi9JI5rIRC/GzoMHqlhSpiTo2qvH6Ydl9VMFV2Q6H51QvmJyhUQQ72qiqj08wpMmrNcYnsxoGdcpzmiRgkCP48L1gL5+XShFtiHOeNJqRCWuR3dH1TqLdrZ4Ff9AN+ve/kLbYGr6Jbx/4uLGglat6tswxWpv1Tqxe5Ns98JZBTbTBmbmw8N818stvw+vHMJcoC+ade/7rF/b8G/lG7Z/4R8J8D/yFP9evHr7/xfFFK/UPgHwL4rmfoRKwQlWYcDXd9Ry2F0RimnHk3L3w09KDgy92G786XZtIjD6cpis5bphyflpLXG1seVrOS4xv2pVANB09sreUcRWxwhwWtrydzZw2u4ZlKPRXaClSteXcOEglYKm+mmdeXmbltsq1WdM4yhdhOeUlisdQrsyG1f2KUnOadEj9xay3fP565n5b236yoXmOdJhl5sDJi1DQYzUfaU7PYKESksBslUNcvbzdMqee704WoKqM3mLTQK3i+GbifFo7L0tYXzVESOIXExhlRlbbPbbRgyikXvjmdCSWjnHz+rCSdqOaKtj/2/1AkVZrHmsF1Td2YCkuphPbXOufJOGISv4/OWd6fL9xW2FnH63lBG3EJ1U7RO/ujqDzB2nXLyluXdJ01TcZer8Wu3YNXiE9rTb6KTeT9rtYUy1z45sOFaipeK+Yid5QsPZv/S7vPnFZX0ZlWiu0gB5hp41TrM0R5iExWFRHXiFZFCt9jSExFsHWHZtCa0RsJATGqddmyNHQtjrKUetV05CBbQ63U9cBXVZFjJlCZG2y4RthVGZwlYL13LSmqNTPAi43nwzxfoYjSOvfUnpFSm/aiSv6wVyIGsk6zdzIBnGsGVbk/L1xipubCr2437DuLQfOrnbiJCqQq91BFDm2lnv796mFTKWgNL0fPMSRiLiQtNMQ5ZLRRWGtwrsPbitGF95eA1g5SQauFUhNLbLTZnHClYtoCtyolDKgV50cWqjo3S4k2SXzkPJ0x7d560njIPSb//hDj9fvvjWWwqzuPLEZHrbn1nvsQiS0IJuTaplJ5iB7OM15XOqv5aNhwSVet99/4+oML1b/pVWv9odaaq8x+/y0CvYB06r/40V/9Avj+9/yM/6bW+vdrrX+/a8s/rxV3Y8fzsf8RZ1QelMclcMnp6iQ4Wiubf+Sh2iixwo1NIWm1LLRWfHVdiAEYA4OTZCIDPNsM1FqbGyFoY67JRitP1bTR6q8dVaq2hPXK7x4v/Pb+xOOS6K1l9PIzQpJwD93eS28sKRWmlNAi8BQcu3VRG2P45e2WwXs6Z/j18x2DNRitGLce3WuKuc7ZguNVmRLul4AwFODqQrH+fK3ZesdN57kbHXedE86z0dw6zcvBcdM7blyHBvqWW/vt6czjktrCrsW3NZzolEStC/JQr58ttYNthcaeCO0iz48qtwohrIvLkjjHjLceYxyFIpx0JVh8yIlqxQ3xy7sd/+DP/u419UZceqVQqVZwNOKrDsK3fxMDv71c+HqarpFxKw66hmpro7lxjjvvZaRvi9Fa6tU7XKvCtrPc9pZnjc9f61PjIOEgkh6UMnil+fXLPb9+vuN26IT22Q7y1RdlfeCl9kt48lSFnUErLr3W3PWevbft8DBCmTMapRu8VcX/nSI2yetngydKpG6T6qXka+crHb0sMWMSodOus+3Ak2WsUuI6edt5YiotDxXQ4qpolXyPU8lNeyF2vRtr2TuPq5WEODrOMfP+NFNy5o/udtz1AinmUtG1UlIWfncr7FRJPhOrZHmulZLCtT7bXmt8c71c82I3g8Uo8c2ZUyBjuL8EcpL3fM6FN5eJSiVTOIdALWKQ93iaucwR03Z5ITcRnpIktCUkSAIN3RjHjXXX5enVkrg2UKwiGcwIjDlaJ3GGra6dU+Z3xzNvZ7Ekf9l7TPMYWqmTtkGU3gqx4VlvGTW89O5vLtDt9a/VuSulPq21vmr/8d8DVibN/wz8j0qp/wJZqP4G+Md/6Oetwh2r9ZO968rNqrDrHUFV3mVJN+oQxsZsjLAxlCJrxRxEZLNSlcTbSy6RMfKllyTGVUYp/q3ffMr9ZeHhPBOqY9839ocS35FaV45qZWsxzqJ7AAAgAElEQVQUpySnueR5ik9KyAXlHMdpJtXMp5ueZ2NPqopjTPxwnnh4vPDp3Y4fHk8sKeGcppgGf7TCNGrNp7uRbd+1kV9+h6qVXz7f8CZETginfS1i6wNbgUllfpsubJPBKMWt9Qxcp9TrSPW896SWEi9dmGS0lpLFH0cb9t3A1JSCz7dbXi8TUxYr3FVoo5Xi1TS1GwK2rifE0HxL1jnjqeCqto8o67hcC513bFWP05FaYPSG40VEM50WA7ZUKsfpwrslEFPmFAPz774mlsxSaIwB+Y1CeS1khCccSuXy/zL3XjuXpFea3vO5MNv8LjMrs6rIIrtJdA80o8HIQMcj6Cp0BTrQRehoLkPHAnQLuoWBDEbTVmyym2Uz87fbhPmcDtaK2FnAdPFAgFCbYCWq8rexI9a31rteUwqmiiBmwMAw8a5r2aiv+ho0XAu2Fj7vWjbW8SFOTBUmk2m82COkIlORdPzi6kn1nGZZdi4OgJXKvgn8y3d3lCpulDlltsHzOEWB3pDD0ltPqbIN/sPTC/u25ZAkb9Qb2SNY82MsFj2QQM7NmCRsZYn+C87y5mrHh5ejPlsCj8yzuJpWfa7q+kbJjielwpfXGwISYJERuMkgKtzWWYK3pFRoW1FNtjoppSqU4FzV2sDIVBUpJC/PyTQVPh4GbjctX11v2XhDjHI9yyc4+ZwyxokgYjl8QH4H2RtIoVw+b0hisJZLZcyiBQnOcr3ZcJxm+lzpc8ZsdjyfRk4pk4p00t5aJiR/wWI4LKEaRlSpFktwHiEnmvWZLanQe89t26wL6eV/TvcgBem+h5xIJdN5WYJ7heY+nkf+eDyLq2TNfDsMXKfEVWiodqltlwPLIg3wcS6i9fgT1ftPFndjzP8C/FvgtTHma+B/Av6tMebf6FP1B+B/kBut/kdjzP8K/BWQgP/xTzFl9PNktLeQo1C1nP734jTeqsoXnCn0xrFrAq21/MPLCWMlHLvxTri92pKsZUYxPgOa4i6ihV/c7fnt2xtMzfzweOa757NItDXz1NUsC9Rq2AVLl4Rb76yKQJJ0pHOBuUKaEkeX6Hxi0wbebTtu28B9L93wk/dkU/TwkfLXWsNN0/B21+O8w+jEUamUnPgwTfzTPK3V2epcbxRqqoZVOOKMYUKgD5NmmqaTsIv1MgiLxioOvjAZSil6w1eSOihufENrLcM40oeWH84nGue4DqJuFIbLhdeMrZJtumwo6+LWJ0WkDY5tCDyPM64VKmXn3erhHufMzltO04yhsu0a6QgLBJ3PXJAEnsfDWR44wwrHCH9Y+KbeOVzwuJLprXSrssuAxxw558QvNj13QSwjsOYTvn9l7y0b33FImZckjqHZFkwVEVyjNMdUC87Bbd9wmrPmlMIvrrf8N798hdHwDkflF3d7/p8Pz9p1Gk0BshIArTjsFBNJe5oeg8PqAlYKPKCS90pOokYttTKltIbPOGvUeySzbRoO44yvlRIlni1TWZzd6zIAZllOX7VBjcfAOHNRhGoDse8CL1NknDNd49Q1tOLVKAw0CMM6rvuGqH4EuRS+eTgxp8LrXcef3+5EAZwuStMF9liUs8YKNLjkAy+Ls/X+0mtxjonDHBlLEYwcbQydXE9bxHRvsp67qz2P54mr4DjMBe/l/iwxSYapwrQYaFovdWSZ2LUr17tdlK5z4uuXM19sOrwahnll/C14ueTQip/Sxotv0dJYDTmvHlOlQklwHyce0swueNpG1LIJUUvvgidYyzZILkXH/0dvmVrrf/+f+M//8098/L8D/t2f+ro/+hyEVdIGGQcLsiAtXDxOvJVknKecZKwp4J3j3bbnh2mmtyJHr2jwQynrQmPpv+esggEkjizlxCnKMtECp1noTguzxlqr56+gCJ91jh+mzFzqZcGpD4q18vFrSId+zLbxbNUJsAmBPz6/cJiFk33deH7z6prGiguhWKfKTZtr5uth5DELZ3bt0rWLWSP1KhiHQghCs3MYzjXz7TRwR8Otb2lWeEQmG69jZM0ZWyufKQPjj8dBuNpNwfvAq5s9++2G93994Jt8JNxs2bcB+8kDTa1MMTKMo8AMpVKt4a71XLeB+zGyDY67JnDdhxUmSDlznBJzFCir1Io3YiR1Hifx++h6rlvPx5cjTdcyTpHDPNIEh6vioGeMYY5JrRIcX1xteEyJnQ841/Je/XDkwIGxVL4eRqaSedN2a1LVktxkrCXUyl0wXAfH9ybywzBKF40hWWl7u0agGWPhqnOkYjFY/osv7uQeVSuAYitf3uz45vkkMFZBA8qLdMRWmFaC+cJt1wj8gsKJBlyxzEobzVknLyPumblknHVgVNxSiyZpeWbrGcZJJhdN9NGzHcxFK/FZ33DTNwRn1n2B06lmudPHWIip6MK9qvncj5+xfdvQecdYL1Pm01kshr+83fF611GyNBmlSJN02fwKbJUr2FJo2oC1lnGSwBc5bOR7Zd1znFPmFBNLaPhSiF01fHG35+k88XIeKdOIHwN923DdWj6eTtxc7znNURh6WTB7ELGcD6qCzRIGpHV+pcwuVOvjFHlwTszcqsCfIDugqsUeHF3wtG5JLqv8/vmo9E+9vuUT2LfAYUr0pXDXeHwIfDiPomvAgLUcqtCNf+r1s1CoAjReMOpqIVphfSwjISjFyQgN8Pt55I1vCIhIyGpIhcSusXanxQgu5qrBOoNLAtPMBu42LR+eB8Y5s20DL+PMNnhquejTDQsuCmDpnKF3lUnxUKi62TbcbDuubndkxMejqJBHjLEMm6Zhv+256hv+93/6nttNw1fXO1q1/bUGxllG9pec+JvxhCmCDwpDYbltWW8QA+Auu4GKcLuTdvLZZL6PI9nArQmg4heHuj4uD6WBxhga5/mLmytecmKaM45MnY784+Mj+77hPIlXRyyVSF7buuDV917xY6sAeO8c+0biDlFowVvLVCpTKZznLNFkGOn6a8U5iSl8Ps9s+5a9qVRj+fzuht99eGAi4bxhViFbX5we58Ky+eruBkyhpiRYrZGF8WmKHKZZuntjmHLmm6Hw/ThxGwLvuk6UlgZJCdIHTTpJZHIsslCtVRqFcYjiJBoEC2+s5b/7F1/QOQPGklJmqplTLDTO8uXNlqfvJi1MglXHIphuKvKeplq5a4ULrkPa+o+CeNC32ggUfbq9c5RcWEy0lhqRUqJrHKcRjkWplwvTRBsEySt1fL7r8Fb8mnSAYaauUvnGOoKX3UROwiwLCk0C+Gq43vT0rees7JaHYeLhONEEx2/e3mhM45nOGK6tx/PJSsbIfmS5f5wVkU7XeLw1nM4TVeHApN34kMTRc3ExzaWuWPWU4duXI4+ngdtNT54TaRzxuXAcLXebHcG3NKVwPKfVsrhpPF3nyXoNSq0Yb6jpQqW2KhRb9hYfzoPSbRuBp6q4QjbAcZ7oXKBTrUjWPdbjOF/e34VrmVkPPmMMQyz84fHMq400Rb0XLN7qz7o2V//M62dR3K2BTRuYbWWk4JyRbTtLoV7oWmKmwycnVqzqpZ00Y1ILc9W7fOkoloPDl4sw4uE4chiiCDY2LTuvwb3uQvdaantV5sFKx0IVd8A+GG47cWpalnmtl5F6SIkWR9/IMuima3i163m763UyYH2YMPB+nvl6HuX3VfhnkT+5ukTeqSx+GdVZUGez/rzWyM9RgYdp4pvxRIvl1/stO2epxayUrQW7lO6ochc8s3U4tCA7Q8Dy+e6Grw9HGueYcrqMnBVQ+ubShe0azyZ4+ZpaTD7F4U8x8zzMIr7R3cKUZfk8a0LQNEd8cJhcuLra4R+fmGfYOBFwgRTHTfBqvuS4P56Ievcbs0jUYd+J1cDzMBPLAmRVpaJlfn88864XGG0pOLVkpmx4nKb199DGTLF1gYWOc6IPjndXewqGWC01L2n3lUCldYbPr7Z893Lm8TQKC8Ia9o1AVSAMjLebjl0r/jZZu9j1fnOWmARTr6AWsY5aiyqzF2BeridV2Vp9C8ekd9Hyf+nyr0Pg3a7FWyfvs0JdCVGZLpT1WAu9qrAPMZNzpfOyt+hCI/CLh7EkGud4Gic+HsSs7u31hqheKqkWxlp5ISl9V5vVdQckRmKNl7CVaU5Mc5LfEagFXqaMqWqFbYxSmTNDTXrIOzoXmGtlnjMvDGyahqlA13g+nMcVB2nbwKbrsCVzOI5yv+skl4pUUreErUThpy8P64JIGmsZSuLDLM3Yxjoa9Xj3RpvWIo3e/TjyMM4sAeRrY5ahJCk4UvBlMkrV8vE882dv9vIMrclOqBL8n3/9LIp7AY5kinpcOFCDLClsK22Ri9rvlBM3oaEk2WB/etOWIpYGy7QnKUF15QZZA6eU+MP9gZQr1sHbfXfB2C7t0lp4LUCtbCy86M9y2zledQ3eikteqtBaT+s0tDqLQb9FTvqUC4/jzMfzyP0w0TvLf/bZLQ2GPwxnCUXOEpbhFqYDalWsHYk16o1u0Bgv1G59UWPKvzstpqlUhjGTUmGm8PfPB77YbnjTtjpa6yflZeSVB6hxAvMYI14ju+01x/OJ6+2GnGHOBqpwP8gWp6EQ1Yjq9/WupfVykFrFXhf17pgKhymuh+Vci3KkUX6wjOg7H8ipcoqJj999wFJ41XecY1Q/H8gUuqaX0RlRHR+z/FzeiqLPGMFh911D5z2HKTKmuMq6nRFWxPfjSKqF10H80qsxjPWCeUtEn7CICrKIrlWK7JTEQG45vKyB3hoV5xn6EPD+EkV43QRuupbOiyFeLgJJbYLHWQmHzyxFRCqgTEwQQZKeEEuN5WX0o8WDxWp3V9g2ntZ7pqTiGyvvbU6F2+ueXXMxNFu2OecihAJbL/461SAJRtLS4or6sPdC5x1VVWtLYdcEvrjdytSUE89xlq4Xsz5TTyVxbdx6H4tJnaXVFK0pJmJKTFMkpYz3jqFIJGKJsmOrVE45kdVsLVgn6lUjTDinDd++65hz5mmYqFXgpXEsvAqebdtj08zoZ7yT1LCa1arXa3psrRLoU+WHX2Cxhe2UitwDp1J5MZG7rqVVZMFp8zTnwvfncWVs1bV1RxO6LhAY2pkXDQkak+TDNtbh1IJgPcz/mdfPorh7a9k1nlO5BBaLMEJ+ePEHusipW2uZS+U+zty0HnPUVKWlyP9oXDFqkRkEly2i/psrakBUuQtibUBdCEuKva7HhX5NY9h4y85nnLN8te/xTgQUw6SQhxEsWU5doU85K3Lih2Hm7z8+MiTpMIYI/+GHR97uez6WmVl78IUZkRcy1PLrGAkmrgay8n6XzsfqnVFNxRlLKJK6M06RTy/HqNSr4B1b67BFCp+WBFUWLrRR+R6fbxrGMvD9NPJwytxtduz7FjfBIU5EMdhep5o3u55Wu8sFu1V0ialIElCOhU1wdNYTuhZXHTdXOx4PR+xWwsuvg2eYJrbbDSnPHGdhFTTBcdd3+JT5fhgYp8imbaFmYoLrpuM+nZlLIXjlmDvkwTSwbcVPaEqJc8ycklBsG2f5bhx5jpHPm3blmy/DmoR5awNitKMrl91Qay2tFU+hxf42aLsftWG4aRu6K1206r5g3wSKQg7WGqU1GmK8NBgi6IHn44jZtnTq7ig4O6uFhK3iQY5Ovq5CD0oV1GagCJbcOotxMt+ijZVMfpJaVUrGW/maQhmUCbsiTJpzznSNJyEMJYpZD5Su6/De8RInnlPEVOH0ywJRzMZ2Tlw7F7hoyXp1BkrOxJiIc2aaE1knpMNcCE3get8wpQIlczpPtI1dldxN6HC28nQ403hPsA5TDO+fz8SSMNXwerehUnk+n9m0LSVH3uw2tJsN52lgylGM5rSulKRhIhKtJQlbyIMX9NpPSTr7YuBxmvlyt8U5J4EqtfLHw0lxfX3pZPUvX11zjIUfDpJJvDBvFiO6L242VLPQZ4v6WS02ZT9RV3/yb/9/ehlkyTlWxcmlSunfmfUmNuscZFYM9P0wr8uO4IRVYY3hnNJa+GqttPoAiWDU8PbVK7778FEeqCoMGYNdD5SFzSKd/JKtKpLtQmbjHE0QIYIvVT2yhR99iiJEaKxl64XO97c/PHCYZo7qMb28ec+jyNWbXcA4hd/0OiyGZ8aI4ZKrKr3mUth1ThQsFVkkgWLCMctYjRx+Sat8qpV/fDmyD4Gdc+yXCLqlQ+GTDqIKptsZ2S8UKoc40kTPrm/pmyAp7qWwbQKtl859DfswdYV+wDAmyTLtu4Z3fc+hZA5x4l/9+jf8/ttveXh5wRvLtmsZC0wxcZyeyabINKLUgvuXk4h7QsN5Tsy10nuPx3Kz6Xl8PIMXeqAxgodL6pC8n50mejkrtgC5yETResuUK393OLFzDc/zKEtNDFfek5LmZhqlqDmrkXqVm03DkhNaK+oEavh4GHl/mNQXRii/cIH8FlbKogJe3SGXycQIS6m6yvXtjvenidkYppTZBEdj9H1fhD7Ogip+S8mUXHGmStoVYq7WWMPdrqVtvOy4qlnFQRjYqn7DaKOT9Z7Dwqt9x2lMhMbT94FUl8dSaKfOWO6HkW3wnJPcg96YdZK+9p6d9auK1KHcdSuw4BwTlEqMiWEWJxbvLacZfGO53XXc9GL6NcaE9Q33LwfGkikBXBrpfYOxsDFBlvcl4SwMs1h9n4sYzDkMD6czvfdkX3l8eaFSyAV5HosU9mUapq7lR6wRVhfQhc4p1ykYOS2rl3+/nyZedLKTc15YSe+2PX0IbFvDvg383YcXhpiU+QV3e9ljOH0iq5Hp1FuzJkD9c6+fRXHPtZJSpTWGaXkw9JdZRmZ5XcKllwv8put5rclNOedVjXeKjpgrxymSUiHOkRgviq6X02lloKzdD3VNS1m/ib5KLRxyoeiiZ86Z0xzZNQGqUBqPKRFAqZ3yQJUK98PE+8NZVwWLw6CYm5UqhbieEtVUQuNo20BRbDJ88jOg3ZNZfu4qHXypgvUtN+s05TWb0cBKN0QPlGWH8TTPPJXKbdvy+bZTYYD+3rqctsCuC7TB8jRG/un5zBgjyWTCbBjmxK/f3jGPM2cjeHnMGW+D+pXI7mKuEFPi+TxJR1ngeSpkZET+7v4jj8cX+dxGRt9BF2f7tsUaeJkTc06yqJwSffB8cbXlujE8zmdizXz56opvnp8pTgr6Nkhk4eoYajTFR2+p1lps03COiSmJv4+4F5rVjnWRvm+NJTSOWAovJTNkEaeUInmrN5tWDkCliM6l8nyc+PgyMcfMoJfWIrYBcjuoL46yY6p+rynJobR3loAsT5dg8He7jqlUvj0MlNLwetPock0WraJGzSzWG9VAEzw2ZlKSovZm39N1bm2msjFina0NgEGEbHMR5WytVux7Dfzy9or/85v37M2nIdoXtarRP4eY2FlRz2pF4y407Jxfv49l8QSSr5L1Y2MqmiwGU4H7MZKM5aptuN60spuZM6dpZk6Vmgv7RvQwYxZq42eN55gN1jsehhPVQmgs113HeZrZdj33x6MYwJVCb2QHkKi6YIEaZZpuvDDaolIX/YIqVJ3qtYmQ51s8qXpriLMwYrZNICy6nCIBOF/sNly3jU5dhk3j+fO7Hd8dBh6HSYSB3q7XxiJUbknmYqW0/nOvn0VxlwdBxvSkQchr0IARmf7awK/Yu/xit42TB8L6lcpWq27Nq+H3j0e+eTphKzTOISE7hdNxEDxx+apLEa3LomIt8cylcKyFqsISgxwg3x5OfLnfCgOn5NXVb9c0hCL84+ecuZ8mrvYt85SZS0aY/3YNGxDcVjrTOGbmcyJrQd70HX3jORKJ5iIZN/XSIS5dcimVOBcp7PryyoVORVgttWiQQqnCDDLwNM2cUuQXuy29vdwwxsBV32gSfGFMhXVtZ8UbpPOB7x6e2fc9XSM+NM9jUstdq++lCEI+HiYMhi9urvnm5QXSKArAmPinD+/FskA7IXGIlLzPKSXeblusDzwPZ6rPbHzAFsP985HiDNd9RzGVv3v/AeMtnXFYpMB33jGXzHlWcybUAlo5042zuCQPXghORSqVc5pX/46KHLTCZoDPQkNt4ClG8X/RAOiUJRfzh6eT5MdygTv05qJqkpCzmtVaC4KgygF8Tpnvx5k3Xnwti1nuUf1JKvTO4gIc4sxtDYDQf72yt1K+LCih8qoXd1JrLFe7lm0nYRClKutC28Dl/jZmscNwTKVQTGXrA07TxdbP47KPEjKB7pd0AgzG8kW/4RBnNjha69YiWCnrvWvQr5fFQXSMWeMaJUdhzIW7bcsv7/ZIolrk4XBkLpU+BK77DcIxFYbSOSbetC1UQxMCdbDUEnm16dn1Pc7C82li30pn76znPEfaxpNzFaw9F3L+cbrUwjCLRfIBFrdQ3AJlFlrnuQ5+VS9TK52B1ssupQC/2G+57ZqVVmq0zl13gV3r+Yd7YZDt2kbeE214nbE/Ik381OtnUdwNcJwFb982gUOOa7HNC76JjKs5ZWwr/seCUSojoqoEXAUEZBE1XLeBb62l5ErvG3ZN4TDMKtlW2TpVcy1lEbtwnBJwyImxFBE1fXIxnZXk8g/ngdeddE4W4ZDXCillzjlzu+n4N7dvKMgh8fXjgW8fj0wxr1i6WVo2HV2XByPVwsNp4Kq0hG4pBNK9FyM4q8iwIaVCnoumGum+wFz4yNYuEJdAFct1r7lSbSFnw9fHE7dNw9Z7eoUxGu9l6bqMgNrZJ40C64OhWDhMIzaL6jRYcc6L6tW+sHGCd9z1G1IRLv4xCpXQO8s4Jonrw9CGIFTWooXXWX44ndn3PX3XcDxPYAyNhblWSi68nCctKoU6FXa9JxjDPEtQtEwQcsFXtpFaIDgqL1EKc++FkWUby8s0rQ+QN3aVlRsjD6l3ls+3Pb/ZdfxwGBjnxHePJ07K65aGThf7q6JUL2S1rM6NdUl6ksCGb4aJx2EitA1v+0YX6QZrnbhylgraDLTB6bUqLJEdJRdtgYyyLyobL8EofWf5bN/zoi6gi0FVrZVsPumkFX4wxuKRRslKB8E4C1Hg42Fk20pSkjVa0PW5bYxRHxgIpfJ5t2FKSbQnpQjLR/McvZWmo5SsStOCdeI3P6fCkDJdCOybdnWD9dbKsjIXppToQ6B3gVjEpmDrPB/GWRSs80wfGuriJKph6iGIorf1nvOcFe836rMvzqjLMjnlBaLVAq9Q44/3e/JM3YVG4xUVDdDD7N2m48lFDlPiSinQRidKJc9gjCVYw5+/2jPpzmiZ8NFnfclPdj8Nuf88ivvC0jiMM33jNa5MbuKkhU98uw3FyXg8Udk4EdMYKw+HlwpGLmCrxWPomqVtqmRddJ5TEoqZEXVdyEVvXvHppkqw8EHj+Rqrm2k9QDJC2VuWcnPxNMZxzhmqSMG/Oyj75TyRaqF1ImT44mbL611PzIXvn06MKdMEC9XwfJ4keGB9ScdwniM3QXInJ8Tt0Srmm2slp0qeMiVrV2+Wx1wVgyAddJYOoRbWrmjBCw2iuDtOkU3wvN10fHW1XRkJjbP82d2eMRde5ojJlV9cb/jsasPzFHkaZuZcMXNif7NjmDJzzLyMM403dI2jDQ3HmJjiTFbmRsxSpIJuhJ11OBtwFrrQyvtWVT2rxUygqILFUWLGe8uu64ipsjWWlzhia+UqBB6niZTzaoS2vEoVb/RM4TwmjlGw3eMUuek7chW2iteO+dYLlLD6huu050KgGMNxFKFNaYX+WOulaMpNrsXRyD3klcFVdMGYquQIR23zE5VTyZKqtEBqK6wmz0xrHLddUJju0iWuHaOFqi6OMRdSLBhnRd4Pa4e+ZOZa/bOy0I/l57LOE4owN5yVw7ZrPMMkz5Pu4AUmMNJEGIRMkKtMInOtq32AFPOy8tZzKTJxJmE5VWM4FQn27ozjl2/uCN2G7z98pA+ObSv3h3dWgi/GyMs4sd9s5bkohrv9jveHA1NKXPcbgnfkGgjWMZfKaRjlWheBUWqVax9qYd9vKTlyQKjMoPg6XNTlRuGnZSLTQ+06NFw1YRU5SUqWoAGdNXy+63i7kYaiqhJX3l2UvirVMBfdzeji0JqLGlyMzAz+T4DuP4vi7p2j9Q2pVI5j5HrT8jHOik+ybv4xhuoNpzHS9A1/drfjs13PyzDxcBppMDROFq1PgywvHkb5On3X0vuAsZX9nDRoAo6jxLNlBMd+GiJPc2KzFY+XqLi34PF6sYu8WcGJsvDjMPGqE2rht8ejLHPNYiJU+IeHF95dbfGTCHaqrQRreXPd443w4VtvGdOWf/j4wvvD+dP7hZQLD4eBN9cb5iohEYuYw+RKmot0QWrOtMidlq6zomNdFZaEtZqmo/imAY3TkxtpiIlvjgOHmPjt7Z67vpFosDkyl8xd1/Cbmx3bxlGN4aZvebtL/OPDidOUOQ4jrbe025bjAK5mrAkM8whU3mwbYs3Mc6QUpPML4vjnXGDfeIY5kU2ksYY5RZrG8XyWJerbzZ5vjy9kCruuYYyR8zRgg2Uoooz8eBqptfJmv+GcMsc5ykHnBDtel5qlcp6EjQFicYGBJ50OaoXGOskezXWdhLyKx4qB+8PIEMUnXBiicsAv/kMCGy7d98VgLCmPPFKYqxRUR+WmCTzMkaeUuY6JN10n02VdfFUMBTHX6r1fxUzLyyz/WIq8MWAs7642eCt7mQ7HRF53AIkiFEuz9kJgZKqqpeBsJSfBnIO+53MahEliWaX3pbLuhZLaMVQjDLLOWq6aQEpJMGrF2GPKzFH0LalWHnLmFDPOWLwV/cqv373ibr/l73/3O9K2Y9937DVr4ThGWeY7Ty4SP/L+cMQZQ+clvMXkzK5pOI4R4yr7zQYwnPJAmjPznLjad2Ad5/PItpN4z4y8l94Zor7/bqHK6DO6vL+vu4a3Gs5etGSXKmptqtawXOg0y1kWfW4VQ1VkAkPV36vzpJGV+xKHqdxn6k/X9p9Hcc+6pMgxMRUx9rpxnociBbog47eIXasrICMAACAASURBVGTcc9XweB656xqmmPHe473jh8OZw2nmeZy52204KM7YNS23V1tOxxO/vNnz2b7lNEb+6pv7ddy+P098/XTGOEu3awUz1k6oIPzdWhV6QQUe1nCeEx8G6boPacYgtLiq+H6NiT98fKZxlq/urtiGICNnTBTnxC44SQf7xfWG0xw5TfGTKyRv7MNhoAmOfueZSyZPRW0W6tqFXfA4GTF14JAxUbvGXPXG0AcY7QoWXrsxYiT1PM787ulIrlsMhr/7+EIXHF9e9Wxbj1VHQu8sO9/y2zeO5zHxMoy82ra8TBEayzAVjtPMnKN2jWVlFmiJhWLBSYf/MpxpvFCHqnU46xhzFnc+WzEp8rrbcBxGhpSIKqrZ4PXwldH543Hk43HkX395d4nKo3JU10opLBL4odUQAzwPkx6PIvEOVaLyqqnMGrJh1XislMJxmGis5V/8+pc8Pj6QYl4dCw0XNWNBOPylQiyirBzU39wg34MqHZ6zhikXnmPmdXfxH1n81787ntl2fp0OdJ+6Fvblt8kVNRSb8cbhjcEpHtjimGqRvZbuZqZShJUFdAApQS1YZ6nqf1GBd7uO0zjz7dOZ11cd122QIo7uGCjSuBjF/ZfmohTa4IkpiwdMKXhjSMFyrIWzKpc31rB1lrs+UKYT9/cPHE8TpVSejgMF2HWSijZqNkPNE5uu5ek0AZXrvmNMlZdxlLxePG0bGMaZEGCMk0IwEWMlbKNvYMiR4Swxk33rPwnnkKmMojVAu2pjDJ13vGobYSjp5OOcIVbp9HPR8B2E7x6cE0pqUm8hJx8n90nRA+LCJDLY9R7w1TAbyH/C0P1nUdydtXx8PtJ6GZkez/DmqicUQzJSreSBKOLD0Yhfx/Np5ocwUhGToeOc+P3HF+ZU6Jzn/jjwdJ7ou5aHw4njaeT1PnC72zCXzGGY8c5hLdxuW6iF3Sj81mvnOJblIiMLI2PVCwOV28uDM2ohzJ8mxSBv8lI2DDKi/vBy4m7b0QVP4xwb79aOJynF7rfvbvjH+wNPx3Et2iBfJMbMLjfYfGEWyD/kdF+sipeF8wIPKOwnN6kKJuTv5XOlqMtEUuplwfU4TJyVFfTZvueXVxuhferY3gUvuHKpYp/cOvbNhloqjYGHcaCxDcaJEdOcR56TYO3BWlwVXN1gCMHzchqkYymFai37bs/58MRhnLHOCOZ7Gtk1HbtGCgxqL2GNwX4ygSydcjCWX+7bFUP+4ST5tN7DeYqCp2o5D17MvNYLZCp7L93VFMvapQEYJ35GxzFxd73narPhr//wT9hSeLvpFcZZDlc5EOYKc07ynoNYWVRW+ftSpU0VmOOghcWs76cUYrG0CLqQr3JA6/s4ZMGqW+fxTnYFAjoVeuuU9ij3zWJX4Vg80g1zgad54pdts2okPGCsJWq36K3hqm84xTPDlMUz37KKlIyR6dBUg0WgJYdRMQ70wfJynjnNYmiWvJj/nc4JVytf3m1xRpLXYqocjy98ebvjNG15enrm64dn7rY9b652XHUN45ToNy1TLBetjOLxh2HCOUtjrAaYZ2KGEDwYQxcKwbcY42icE0gkeKY8c5ok4807uzpC1uV98Bciw5u2oTGWXMqPOnHv7MrlX/Yt3ulU7WSPklOiZAPWSQZCFvrw0zix855es1ulBAiEuWkDj+fpJ+vqz6K455JxXpgV+96rOVLhygWeiohwVm63Fk+MIRvDd6czKWuKe5Sl7K4N1CpOhF3jMVQ2jSQfHU4jDZUYM3MpXPWBvhHrgKve81/tX4OVrq4OEiqAWWS/wjFNddlUV6YkN1BcfG3U3WDWMb/UulICa0UCQU4TdxvDddeIYlFP85izyOir5YubLcfTtBaHxVoAB6cU2Xat3HjWUIpaEVS1BjUXPHcJkgCVN2sBWaYOtK6ZWtcrvODEWbm4U6m0pfIv3lzRNZ4YMycV9ywCJZlwiipo4WmOjEWsa2cqcR7Z9R3OeT4eDgwkXDa0XYP3jn3X8fmrV9yfDvzDNx/oG8euD0zzyDBHPVSlW5rHTK0jt/2G1nsVbgkctbwPy7V/3bdsGtEjLD9rofLZtuPjeSQuSzMdea7ajif1fQehA+69F4OudcFm1H8csTPIhd1+w+l85Pk8Y6zhnAfu2oZdcMQqdMJjitRa2BqLKygdVuTupSoOq9PXzjvuZ/E2N4pjL4AbGHYKA2ZV7ggVsXCaxRbCK6SR1e3QG8vLMLPxndL45L1vjCFXeRYqQDF8OJ/JpTJ5T68Hr9VpoBhYuFhfXG9IWRakc8xUJ1a7jcIKhkuweMyF81w4zlIs73YdH58lg7hpPDYZXo4TwVjaNvBwnoXiWYT0MAwj/9fhzOk8sSQbfX1/4OPhzBhl5PxcA368kUPqaZhwwbLpW2JMOC9EjNt+o5bWhcfDgDWO/baj5iITszHMcVY/K7nmReFZ5/2aYSD3mPj2b50jq/24PIoVzLIMlWfDVN23WE29qkK9dq4hxUQpBY1a5vth4mGcsFasljdBFtetUrGDNXTNT5fvn0VxX/I4cy282+x4s+vJVYKaX46zBC3rqGq1EygVjJPOJyN87xAsmyB5icYIlSjmwtZZNt5w1QXejwPnMXIcZ+YqRk0+OP7x6ciVt9AZtp0nOMc70/DHgy5eqrBYShG63pwytRSGmJVHzdoZi3BIbwrtorXm0wTH26teqHwGhizQyjllUi14ayXtPRYaY+VG0q/tncF3suA8xIndrsFVy+E8kXJRz3qrOaUXZ0pjLh4rsHSBy9+JQKogC46qv6t0izLu2wqvNy3XbYtzlvs58/48c5hG/vVntyvrx1q5Ls/TzKh88ZuN5xTFJ+U0ZYJ1fHlzw8fDgYowmMSuwRGc41evX/Ht/SO5Fl7GgVzODDGvzIRaZNcRjGDBr682fHg+Us0SOH0RZTdeHCIbt3STcIiRqoKsvvHcVJhy4nGY8MZyHGUBvrAkvmhbKU7qU2+XERpDNnCaItZZbrYbPjy+8Or6hl235fn0zNM0kYzgX25ZggsRUxguVQ+lCt5UYhXVaCmVm8ZzP0eGIgfpfglmMHBORaX8QjGds0xO4gUjE5SxktPaeXfpNJ3hOEd2wV9M+Sq0WCb1838a5zX957tx4hd9KxixqusWla+pAq5/ddMzpMw/fTxiDOy2DbmRHzTnynkWm4lxTmsUYkWYNj44frPvaa1oPf44y+J8jIlGaaIorNE6w99+eKF1ht46XDUMtfIyJ3ovz+swR7wVFW1jHcEannLC2kKthcM0snGBs9qDN87ThcQcI/fPB676luM00XdeLBqq/B4GIMu9Z72yh4zs22ytfL7tdbl6sdMW7U5ep5jloAvOrvCgzmI6BQijT2yLJe0rI9PHkBLPY1QKtPjl79uw3uf/3OtnUdytc7RtR0lJbsYiLnqzqRyOkayYoDUGGxy7tsGpTWetMv6lWmkxLEi1QfixMRXOVWCAq9bz2X5DSomnUUafXRN4miOH4wlnDLum4c9utlxrGs11E3iZE0NMatoFHZbbXjqn5/FFOidr1lPeCuy7LkRV+I3zllfbnlYhiqlIB5JLXX3mn+aZ85RkCgmWJsFYCk2wNK0lwsq9HlPCGsP1phXhUkrql1I/GdeNOgYuTfvCY4ZPZj3BqetCo2Qt8EsH21jJsixVjqlt6zklx99+fOZXN1u23jOmzNM0i0c5gJFxf9tYfAikYjkMI8NQ8daINxCVru04H4+chi3fPZzI6nGOFkApClUZTYa3Nz3OCnwwzDO7Tc95Gi5+QvKt2TeendryAqsQyiJCleBhi6MrFmMcrQ8cxoFxEoZE7xyddat/y8pAwuCD41QqQ0rsu4596/jd1y989eWviHPhar+n1In377+9TF+VdaxHJ43GWMZF41AKXt+XzjmugucpRu7nKBbCCoVNOXE/zrQKA4wl64EhYMtcK67IjmqIiSkX+lY41ec5YSvsWsnvXACpIWbuz5NmJ8jPeMyZr8eZL/uWjbPrhGd1kljWNq2XKWuMEm9XxIODNBdilii8OQl1dIlA9M5x1Xhabykp44woND+cJ7bB8dmmXSethSX0y9st3zyfSSmxDYHrvuWUEhtjadqWx3Fk4y03bctO/WkKlWGOvLm64ul4EoaTkwPvNE70IXC363kZIy+nM8YKUcGpe5jcS/Lg11KZRwkD8cHjXOFd37HzjoV6bNGF8kI9QrN9gd5bgnOStJTravxVkcYxFoVZnUCDuRRylmS5pbAbI8EnD4M6nP7E6+dR3I1hGxqyka486i80piy/sGHtJlIqnKbM3TaIYyAq5OETHBk5ZedcxEK4VHaNWGbuuoZcKtebljEV/vhyZM6FlCvJVuI8MT7LRa2lctUGXvcSzt07x1UTuNsEplz5j+8PkvrircAwVf2bjTASJBhBxvnbbctV37IJgSllMlLUF4zSGsMhRo7DzGlKysEX1do2CH4vTI6LiZrsdipDTWx6T5c9L8PElBdQ3VDS5QaoXFzozHIKYdaiSNUpyhqMlY9fePV//eGZx2HmN3dXPMXImCO7zmEqfHOa+KzNnHOiZPmd1PuLjDJQMDhX2Peej09nznNiu7XcbPfc3l3xN4cX/ubr76hWBFdO8dtpKaxVoKebruGmDcRaJArNGG52LdMsTnsiUJIJ6KvbnWCeVI01ECVoqotbqE4B3oKz5Ggk9SuLUvW1dsvGGUhgkPsSwAbP49OBWuDj05H/7d//FanA0/Hv+ertZ9xef8H9w71Y99ZP6In6zGd9UFcBWmX18ndaJK695xwzc4FDKgQrfPNspMk450RnnS71rN5LhuAMpxSJVax5D1PkrsJ1H9i1gcMcybVw3TVMufD9OPNwHqhA47yQG5SVc0qJQ/S0qvNonMCiqV6cRCuG17uec8zcHwbmU1xdL606rO6bBmtFESzmaDLFUAveO1LK3DaBVCqv2kDQPdRS0Jw1vLm+4lwqH57PnEsh4Nn3PdZWPhxP6kkj6k+rk97Ged60PUVDSGKFvmk4TKPmmsGcq9hTNKKAtcBR816tNZR5HWWFVusDJUm+83UI4lBpFiFkXVX1iwX2VAofh5mjt7zb9qs2ZZmoFpp1RWrHwzARY6Izlk3X0DjHx2HELmQIRQXcKnP+T79+FsXdAJsmMFTBB3OtxJx5HGbll17Uogue2lhPU7ls6FHFsLZuBVlMlFxIRQyzUqlaLKT4f/sivsrWWZwzq5JwnOKa+PJQCsEa/vL2iiCE2LWAxCy+GRg1EjKyFJVoNA0yMNB3DZ/tt+RceJ6m1VPDVEOwkCqcojBkTlNaOwZvDe+ue94Po/qna1Gu6tdiF1ZE5ZxFFdo2FucNOVXOugwySxOxcmW1+68Lt1n/xVwWd8vyCOTiJgtfH848TZFf3e4AtMuEbevFc0e/HlbcEE1dVMbyNUyBBiSLMxaq87w/nXmZI7f7PV/f3yv04XAOTnNeJyBjKldtw10nRaLB0jtHxRJjZFFYLuKdTZCQlLQwiaxhWFgPsE41C3xW58KH4xnnDLu2oXOZ1notyKKmtCq4Co1w289TpGS1kEiiXTyNE9+8/8g8nTEmrd2bXf9viIiQrNWlfLBi0rWwIRYIb+cDv9hYbrqg04OM885YrvuG+5eBmbIyc5wR0Zowr+xKXwWZ8vZFYu8aZ3mZE9U5PgwjL+Oki3eBcoK1khimz9V9jHTOctPIESk6P4GFYqnqXimioi/vdgJVVcNeVZoywS1P7wVMWALIl+bGWMPnm44ff+gi7AOTM1/d3eCxPA2Rbd/RdZ7DcKKxlru24W4jDJqFBmxqpfeGH84D1cCcIyYZjuMEpjJO0Gy3OGN4s9+SU+LlHKlBzNtKLOvPmUvBu4vlwka9rBboxtSqBkEXfyZnLQ/nSeqMESrpVRPYB0/jLUVtf601eCxjLjxNE0OSpf4QM523vOpbvHNMJfMwTuv+6adeP4viTi2UOLBpWuYsD1XFyNJuQaYWuMDISL9zDpvLJ/LwyxiFdvNHY9UhT6ThQmFTl7dauZ/nlQlT188zqxoNZBOe8gJZXCLKLKJaPMWoDApD2ziKq6uDpTOWXRdoGsEPzzkJpo26CHrHkDOHOXKeElNM8ptYw+f7HZvG8+5qw9McJavViMtfNqImRPHPZe4vtYA3tEa6kDildUewGD/Jhy633tqQfIKzQ+ccvXM8zjPLEs8o9jkm8cpY/ECqgbkmMBYbHDmVdWlZzLIIlu+YKRKZZ+B60zPGhPeWIY68vbumAO+fnml8wAVPKgPnWURd1hg+33Y0dum5Kzet52VKTNFgnXR/UgJh16iR2yeH/VzKugw0gqKBETn9w2kCC5HKPE0ELEebaLqWGhO1it+6tQbj7WrbmoomdS30wdZTrPire9S0bblDFXefsmDtpcr76Y0VVtjioqSNQbCWbeNovOM5z+t0uhSNxSnQ6EEV1X9mUTivcXrAeU6k7SWmrm0ansZ5dSFEi2xKhWIrrdfdDQIZPOZEKIatFUhjleVX/VxtIh2G276T75zLCg8atZBefyCkEVjou6EJJBWSrZ3HeuUqKSVMykyTo206rqrj1dWO//y3v+bf//Xf8FzPXGu4ufj7sJILxjmr8hzaEIgx8na35ZQT0xyJNWIKdDZQq2WMiX3bklOhZrmHhU6qCngq163n3U5TvOrF9kQmYrNCm7lUDrOIs1KpvD9PPAwzN13Dr6636/WwxnCOUfdVab0EpYoILI8TjROvpNd9y8scGf9EFNPPorgbIwvIc5x4/yIb7utOFh9aswH9s4qoxHK5B4xFbRSXLkaK3iY4ETAgo/e2bZijGIwJy0RKwZJYvuB7GOWDLwoM5AaTfxXV4mplq9+rVpimQgiWtnGkUrjZdmwb4ck+TTPBmPVnbtUQ6Ok8iZozZb0xDY1z/OJ2x5yFLXHVtpzOUvidMasFa9WOZ2W3aCGRXVfls5stDy+DCLRUjCEPobZFC1xgV7E62+C49kFgJe+FplaU32uFpbEUDmPksk+1LkARxspNHJyVWEAtPLIMFfjMYfHBM5yHdU/xx4/3BO+JttCYSu88J50ijFEbAquhzbWKotVIwd14x/NUeZwSRkU6n19t1snDIA9ZKtq3a2GX8dZQk5HFp1n+G0wUUpx5TpF9CDIx5LoW0zHJMSG0URE15VLJFHq8Ti11nRSCk0VqUvYEILxxo/e/AYOlUzw45oqabEAVoZc0KCqoq9A1jpyyqK1LYV5wjGrZ6F5HBz28E0/5MYpUf5gjNheCseS1A7zoHUoBr14ozkiBf5oT1aFxg0pamCLWOUIji0JKxWgo/Y9KT1VF9eLuAZq2JASBlLMumS8mYgs/HIOowXPl8Thwtd1ijOX944Hn88h/+Zd/ydcfPvLhh+/pxcNYnoFSOcbIh/OMsxJnSKkMJRFCR4mzwDIp4TC44Og3gbdXb/j7Hx603sg1iVni95Yow9u+EaTAIKlYOrkKU0iUp2Op/P7ptDZm1kqmQ6Ryf56YU+bLqy29l4ZnzpmpyAS4LNwX69dJySHOwK82G143DT8M40/W1Z9FcQctUhmMkdHjj+cBqBgP0notkIQE9XoDRj3DaxUNjNEbwiCY5j54+uC427T86vZKOiwhmPI4RRpjxZtkgR+0m1ryHRcHxduuWZeg1iA+M6paW15ahsQKwEDbBnof6JxVGXZR21OjrpKF4zzzfJzW1rn1nk3j+PJuR0FSdqwVPPP704liFjGXUVxPitGnpmeoLW+tluoqu23L43EglaI5raymY8CKFVoDV97z25sd5ykyxsxtCGyc45BlKZepbJtmPVhkXK7r06oQLNbaNTzbsgRoFB4eB+76hm3XMo+VvmsZ5xHnLedpIs0TmcoxTRgjS8XkHH3jeLftLuCc4hayhBNnQzOh/i1SgO3lrQGU+60PncWsC+yUCo+niVKFpbD0zzJpCMzxNM0cY+SmaeiyYZxOwhvXrrTxlsY7OdSso3XNpZPTw3/ZGVXMSoNbLpqzkj60HNS51DXgQZqOxVtES3sVqu1pSMw5E7zcC7Jb0KWe+qgsU87n+w1XfceurWxMZfSWf5yjRFAatSAodb3GKQv7yPvFqMrwOCZeauSrbY9TrvY2CCRnsrQQy2XPpfJximycZasaBJZl7IpJsy7xF2KEXQ4Is8CJRvxm5sK273h35aFkSuOYkmUcRv7b//pfUf6q8h9+93s+3/ZsgugwjjHx3WnUtCrLGDOt7nS+ezzIJN4YUhSeftcFtsFRSuZ22/F0GsnAlEQkuQwq+zaorbUOzggUBkJesN6SauWH4ygeQ4gXjhyc8pBkK4Z95/vE213PXd+ybxu+PgxyPxjDXOv6nBYDG+/4vO8xxuAxvOv6n6ypP4viXgwYb7FF4tVSzTLG6Q1uQFJQjIFceb3tcIsPRV3kzqoK0+g6W8GWysY7zjFrmrrcuucp8+F0Fs46KiBBMPBai9gMXICatbNaFpKLFcEa/6e/x9I1j1OSQIpUuOkDXesJxhGsVdGTLOxehmmFVIJzfHW7owlOJhMjt00qhc5ZWuc4l7jSqhYj5E9tP5eCtuC6zznhjeF623EYZ00rWpY4P4awrtvAX9xd0SChCp1PPA2Rzli8a4klc0iJkjPF2DUZSm3EL9cPYaJEdfVrrGGYEschYrzhaYpUvLyXJdM2cnhkI4eUt+J98jwPdD7w9mpPSvMKiS0HEXq4vUyJJnRsmg5jThjg8+tO7QUunX9cCu16veTPMWaOMVLtCorIPYliqHonzKXyOM/srWeYEomKdx5XwAfxdXf1wsSopawWB9bw4xBuvXalgg8O74Srb4tYRS9FvQsCLY3qibTEGC6NxJjE62X5uhZWewLnxBP/syuBa2IqPB0GgXYay/1p5jwmrIHeO04/UunKD9s5KQ/DnJhTYUqygnQM/NnVRthrpjLOmT6I8rVa2T/VXHCl8EOK/GZ7KUJ2IRxUYf4skN3areseouh+pOkaxtPAcc60PtE40cNsbea3v/qSv/jzX/B//+3f8X/8/e8YTeEpJnaNF7vlHAVCKwuUV3h/HEi50KkF77ZtGGxiOEsA+uKa+tmuw1nD909nedZ139Y6yy+uOjlAkMN9MRgT0ZxsCF+myHFRziIWxmWZgDGrD0+sla8PZ16mmU1odJpZHqy83pHeGV51jSy060WR/FOvn0dxr/BYColMdNJBLYZKsGBaFpRBkXJdMZpYKj+cJ2LOXLeBt5t27d6tNbzedPzx5aT0PMk2tFZuaGvA16rsFaBWcpVyWaslpqxGQZpC/4kg6DhFHsZJsVuFSViwSxnRT0OkA3bWEp1Zx7OH08CUpIOwSLjCb15f45SJkKtQ2QCGkgnOSED1WQ60JUhYrkC9LEaRE72gB6ZGyXTB0wXPDy8nYQGhza8Wyr+4veLNtuWq9cRcmWJWmbvhOGfmnHDWcBMCnXaoXr8HeflaehAq3JOouFxJ1cqkU0UMFazYAo85smktpyL8fmMvo21wlr3zQr/LEYNkpBZv1qml1kqqkE3LYcpYm3ndN/SN45fXW+nWWDBugbes0Vxcc6HzZQpLQMWyH1ghq+UG1fuw1MopRW53W17OE1OKOFuJsdIYh7OWaZrJIdL7lrA2ALoIRZZgBrn/tt5rYZfFWWOF6ZKr/LkcDqcpiY9KESzfAcGIHXMtlkJShpCjbwKvuo6goP5iK/DhMHI4i11DH1qGFMlUNt6vO5mok0iwjk0bOGqgeP5/qXuTkOvyPM/r8x/POXd4hneKiDciKoeqrKbsLgq1EaFFBF2IG1eKG3HR0JsGEVw47BtcNbhx0eDCBkUbVHQjCg2NKFUtdlfbaVVa2WZFZsb0Ts947z3Tf3Lx+59z36jKii4okfRmRsT7vM/z3OGc//D7f3/fIRdCFe0WBXchsB9mnrYeUFw2cuoIdVyRZWO8tgad4PU081GlL0vur15Pycv8Bjnx5QpNYg3OWpxa8gTMuYhSQjZ4uL/hf/57t7x6eODdIDkBd2FGjYp+DtJAL+CLwJIhZDrvGGMUf3tgqy3awpNn1yjjmWZxuszA067BaMPrg9BsjRLjr62vsDDLZxAmjqmbQJ8SXwyDFAtGisyl9yAFqIS6U6+nBuYMfT8KnFktFYSGLGPzA99wYe3KsVbLzfiWxy/F4l6KcDxDzBIfVzGz5aYLpUh4zlZpXg89U4hrusvCgvAlcxfi+sE1gv+OKXMYZ647TzaWkjO/sm3PG1+lncSKW04x8a6feNUH0Lr6r6vl/wI3WMVuY4lzIVT14sIhr/cbkHgzX49RPTDExDAvXhVyxHt5uauUMfGSyLkQKp2vIF7TG21xiFS8qBU1Z2EbyJ8104KN1J/YGc3HXYPT4sUzrQpO+aWd97zctzRW8M/Oa9go8eTRSjxeRlkg+5jZG0OszA5KZYDUBUypxdlQIKjFGzyXQtcYTpM0imLOXGy2PM4n8TYpy+FWNrsnzvG88ZRSmIqwO5rukjll5jBiiig5oXCcBiyWXBKX3rB1lscx4J3Q27zRxCg4vAXQirFK/kMRi2dvzxtiUmptOi69gqVaTkU+6xASjXVcesdDmBnniFJGnstbts5gUZhUrZb1Iu2XiryxlhfblmMIHIMssv2c2HrL3llaVwuJ+jZaY2pzbfFukUaq15on24at30pV6YQOOYfE/RwEalCChecswdqdaynKMuWCx3C1bYgp4YwhFGHxHEPgbhgrzLZoH+qiUkR38NUgyVJX3q69J1HJItBELcefKMtXIfB6Djyzwmu3FXs+nz9rNasU7/oRCnx0ucUb+TmJ32MthhZrjRxn3tz2vOpHdCO9iSkmXgfx7VG54Ixjv9nw7v5OPJBaB0MNnw6JUMfuw3FkmGc2VpSfVim0LjzfOIxW3BxHXu5aLluzrkcl13GvawSfkqr9MNf81VzHqZGTlI5CpZWTmZx4tRYlfa79HF/XzfetdAAAIABJREFUw7MAsbA1luvGr2uLQlhKD/F9B9k//vilWNwpoJaY93Vh+sa35WdKwRjFwxg4hrjCCkZrvDM8psTdKQqeJzQYdBGlXMxLe0usBRzCm9ZKYbVQ78BK9ZhlgIZtw/0YWAIPlsdCc1RKses0IRdOfSHEcwUoBbWqu69Cp8JWQdFGTiElc9k6PrrcShOwYt+lsFqllsoGGkPg+a5jCJG3w0DRZcVn9XvXJ6nlaslE2xjDr+127Jwj5MyvX++5HQNvTj0GxZOu5eOLLbYKVOAM1zReFHBqCjwpnlgKjyHU5vVSPaj3JpsSXnVtCBlk4Q/VAvk4CCVPiv3Mw3BiNnldwBZWiwaunNjrztUSdmc0FxcX7PZb3t0/8O7mhpxmplSYUsIYS+MdsQhslUpmCJEBWRhNva6oOoaoZnQhcTwFGq9XD6A5F8aF1lEhjrIUHErYNH2c0NkQ6qLtnaNrGhoLp2kQszj0eidk8RPG1FiTplK9VjFn5iQb2BQTnbViCUBmDEv6kuY0zvTzjLWaFKUYaozhunOrmdlcYZNQDPfjxHee75mGEa3g6b6Tk8cUOc0ilrLWkmv1l3JeTayc0sxFAseX0+ofLVrmXPj8NHLZ7HGVHZKyUEJVkZPd0kT8yDtuYuRtCHxs25W8IM999jKKMdOHxBgymyZw3dpaTC3sM3m+pYYPNTPZeDkdptqtzUDRsDMW5xtUKezalk1jmFLketswx8ypZh+QZeTfH3vmSqG17twDufCG7XUNsimi+yhU/6OyhHnI6eddP/JmnMiVlmV0PeEWUdDnVMd6LVAWgkSsjeMlzGdtyaB42ngWw+pFODbnzNb7P7qSfuPxS7G4q1KwsTBWDGupalELNLMclaWqkvCM9bcrf7XiyVkGg9WaxluGUbjGopRbFHmLn8iC8RUZDbUCtkaYNU8yXHUNwxT48euH1Yjq5eVGqFFa8GWjFRcbyxgy/ZjOeFgpq+hgEaxdGs2vX2y5L4l941msgZeGk1LSQ1gSlFSRimWjFS8v97w59supVlwfa0WXkX4BRXDurbH86m7Dzntikuvx0W7L811h13k64OXFhl3rV3pmDAEoWCWe6dYYNr4+/xy4cp5DzoxDZL91K1YqUNJSg0mV2FQDpjlmse+tpxvfGFHjzTMfXWx53feVjSOVTEmFnx0Gds7wYdeKsvhiR7fpCCnhracoRx8j/TRTEgx5Zs6yQO1az9ZJcpRs1GWFQrRS5CQT6N1pYoiZbWuJJUMRj37xUpHT4HLTFgEVyOSKFOIkjbpN09B5A3HmuFSZNXBh6Qoui0Splf/Xp4HWGZGo53MakTcGrzVzjJymyJzEGuM0jky1mAmTzIdUCl1nZaApBSlTlOJumOmjovUtXdsxjDPeaFor47SfIvfDSOcdxxA4TOPK7morQaExmilniZujTr2yWGhUaKme0v7woedXL7cisEIxh0ijxeFz6XFo4Jm1fDUHvhomPmj86s0km22dwzETA1zuNnz27oG7xvHyeiNh60VOXwunH8TK42LrefcYRNFewCgpJmIpnFLkwojtwRwjzogN8XFOS6uLw9Cv93nfNRwmKRoaVzUzsgzhas9jEZ+VJHYpJZd68lc8hsCrcSQlEeF97+KCrdW86kfuF38kLTCurhuVolJY5VIRSp3Q9VM2+sygWiBfDbTeSlbutzx+KRZ3mQA1onkZTyvXcdFjyo4XlyWkHs1WqARWxkJJwqtd3Ni8FbuA59uOu2GWoGXkuCwuivqMF6slZbysXjZzSoSY1hf60XBPcQbtz97VWgnPXSlFjCLsCHO9T4WVH1uWJm8qjCHRNaYO2MLGe4E88rIYsSb4hJR5smnYeUfPOWxhaQgvzAyrNL95fcmlE3VlQY5+qRQ5lhrNhykTS6Y1ClVy9UvRWO2Z55m5RBrnsUajVGSnpVE0xkSapaFa6oRcbEpls4SNNkxJJuoQIn0vQh+nDJ0Reb/F8HTf8ckHzzDvHvni4e6905Z8MGM0VsPTqwtefPCSjz95wf/wv/6Qxlo+ffkhn3/1iufXV5zGmfvDI6UofCPaiJ0T++eFBZXrcX6hfsaYGMbABGxbjymKPhRKpYkui0kowotX78UuShwaXLaerkIZOQuOOs2RhxSra6bHqoQtmihazNWDZ6j00oWRJfCRYoqFm37kMAdUgcZZ7ntpAKpS7X7ll1BIbwIQ6mGdB5cbzyWKoDzH06n2Q0RzICyawhijXF+rCFHTtQ3D0ltRsqA8cY7HGOlzrNBf7XuxiPhqPvA00/WaT3cdj3PAcC5MzML3VtJAvHKW2znwZpp54gRPX+auwF0Rb40kr2nF/Rx4eHXPP/3JE1Y7inonct00n29bvuwHFLD1ltMU6rwV6m1jDcZZTuPIXd3UppQkIL2mhBUFne84jgPeOO77kcZJzF5rxcZgPbloGVchVq+X+r5izrwexiqaK7zoOp51DSklPuoaNlrzeVUBi3mYkCiuvOU4J/qcWMXkdW1TCq4aR2fElXRxDp1TBqPp3o+G+wWPb1/6/7961B3x+0+u+MGz56LcW+gEUpaiUkHlQpzTipAUWPHRRbq9NMfycuGziEF2jRNjqWrju740Iv4p9eVKrZQlh1QwQTHzEZxsiRI7nzDkl5IScZH2inaj6TqDbxS5UhPXTahWk86IIOhhnKBAY91776Oc3eMAV3MnG2f5cx8+Ra+0OlYd06Lifd42XLeNdO3rBFC6NjUrZPHm1PP61POT+5MoSVX1yLcW51ylMuaaYyoLkDGaY0k8pJnoCkPJhArvp/rPgp66WgGehljd9AybpuVivxF15X7D5aZF0XL3MPDEOJpaGTbGcNV6rr3FaM2QNLfHgaGfIWf2u5av3t5wdxxovaezlovtnsvdBp1hmBNfPEjCVWM0W2fZeCsYa5Z7McfEYY7SwKawxC1S738uhdaI0x+hsFUWXxQ6gU0KVwyX+wvcdstM3egyayh5QTaGQ0ocY+QxBN6OE++miVMS6MTWe+q1oeTC4xi4Hye+PJ64mSaOOXGcpnPXUa1TgYWfL5qC6hukROEpYSCFlgkVZ5yunynXRQHRUeRcaKycHI/9SGOFoptqItGY0iqaUrCOp0XRsGT/aq14NwXe9DMppTV5SKFZkNaFbrvXho+bhpgLNymhqiBKa7U+d9s63h6PKKsIKbFrnJxiopzunJYewsKFejWMTEWsRuaYcEbjlOgiWtcwx8SxH4TNVJPa9o0TmrESnv7OepwuOGPIJdNPgTcPPcMsa4CpLo5i/iZrjHjLGKwRoeQpJfqYUKXwYtPx4aaDuo6kVNhaw4uuFTuKev92ztIZw4uN50XXiFvn4hOo4Mo7nrdNdZo9O3PK2JU5/W2PX4rK3WrDD773q+Rw4ubdDSUVHIpSj3a5ckMrtCtpOosfSj36FoSbSwZtFM4a8f8uUompAsdhFErT0ihT1RirMnOWCK1l4VyMe8pyNGeh64m/h4TVZrEAprzn4y7P0DWaZhFe10UDquo1S6RfjIW3p4FmDlx3DSElWutko8rVZiBnHkOkcxanFR7DWAq5hgUAPG09L7uOJ22zNNiZl02gDgzZ2GRwppg5kfj544mXu06O0kV6AkZpphC5H6Xq1jWkItUmGKow5IQlE4uhrbTVBfIKMXE8SHo7WdF4z2XX0sfEpt0I9hxmPvv8C8I88Pxyy751PISIN+IgOGvNxsI8HBmd5f7+kR98+iGfffkaoxQvnz+hfzyy6Vqss6SUeb5v+fzdPcd5Jh0HyrblumvXCvfrfuY4z3TGsN80XG+F4glVQKZhgRE0YrVanEB9vjbGrJKAkhgmvvfyQ4nH6weMdWw7TyqJ1oj3o1XVp79SX7VStMoQKbzuBy5q8zfUBfUYInNlp1x1hlC96hWLgE6ur63lSWMMucJPSotoTAh/i0UAa3RbKmIi9u40EHPkotlACpAzxmgeTwMUxMc8iqYhpITOULQsRsv2ndNZ7UytJN+OMx9vziQFY9QK91XEUTDmAhfG8PU8443imZUT5rt+onGGd8eBrff0RJ61G55ZJ5tldU9tvKlBKZpDiLwep1rRn08L1lZ1sorEYoSCWNksCWgqHDJUq9QhRLxraJyjlMxpTNyPMxvnqvhMrSfUVCBnsUieU2bvrGQjn/p6Kix82LU4BWOIlaVXTzqpcOU8DyHgrFhJ5woHbqzmo13H29PIVBvSn+42EspeT0l3/QTGsNk4hin+2TNUlVKfAn8T+BApHP5GKeU/Vko9Af4r4LvAT4F/vZRyV3/nPwD+MlLQ/dullP/x218D7HRg1pp3h2PF+JZjbFl9x+s6Tucsd8NEqXBGqWZqS3NjwbGnWRqSjRUxUM5FvJ6tJsZEKTKhYuUk75um3oi8VtmpTowlqV2YPZWjLGvXemrIQFSg1YK1SxbsUo1DRdKU8PBN/UAKuBsHXh8PXHctz3YKVQ2QYimMOdFYUbqepsil88xpohZiNFrzG5eXbJyVLjtn4yfq5yAnhpS4HyZCjRXs55nHaaKfE9+73q9Bx1NK/PDrW76+H7DWkeaZzht+86NLWqvpSxLzsJiZKcxZszFynA4lMw2RKSSKFaiqs4a705Hv/Mr3+NXvfMoPf/+HdE3DT37+Bc92LYHF5Q5y0ZxCxqjI1lkalTk83PGzaeDjT17yW7/+KTnDq7e3PMQZo6QQaBuDcw5rjpQ8cxoTT/aOuWRMgsMYOIwzicwJW/UEmlZpBtTacJf7LiXSlMVGWdfvg6pwnaZxjkNITKdjXfYMF03DdiMRgaU2J0+zMGKWpmFImWKEB+1RHELkEAOZ9wO8IcYoG4laPHsU5yAgmQxuhRXPrfAlNH7xeQeB/+YkMJkqVDvdRCiay03LcZQIQqM1SsPT7Y5+nLkfRhqtSKqeXOv7e3m14cuHXuZplpPaEBN3U+Bp9Rgv1VM+IVRAtazwwIV1KK25nyN3JHyGt4eRi02DsYoxBFxr2FuHN9UTnVpwTQmjC5jCq2EW+qa8olzfmEhKoju9kfE3jIW2iPdNHyNjEJ8pSqFtDInEFCfIitZYVFehNrPOIBYofEqJLw89feXTx1w4xSiwbVFctdKkmnNhDrFSdiXhavFrurSOfVOD56mKWgWdFYW4WG10bK0E4UTgfpgpqbDfejSZzlv6uJhp/OLHnwaWicC/W0r5DeCfBf6qUuqfAP594G+XUn4A/O36NfV7/wbw54F/GfhPlFLmFz5zfSgKYe6Z+sdvmNkvarylYy8L+BnjXsrWReikKyd9yS5dOuGmNkwWNRyc5eiPc+BmmLkbZxa2+vK8AtHkVUBTiuz+fuUvn/8njVD5vVTO4qK4Ptd5m12d7pTQ84qqCz2SvvLzu0eO88wUJdwg18Zb388wRfba4Isc76xSfHe7pdXSmAGZWJSM2HdJtZ6Bt6eB4xyxzjCkKF40pXDTD7w+9swpkXPii/sjt6cZqy0piyS6V4kvDwPPnOfKOJGn13PinDKHKXIKiUMfGMYk3y+CJYQiFgcvrvf8/X/4D3h1f8vd4UHsnbXiq9PImDJTyvQpozScUubL08hYw5wP48j/8eM/5M3NHXOIKKPpM6A0Kga8c7y43mO04rLr2DYtxMjNceYwF971E1MWI7IQA07L4uidoXOWvZXwZKgOo+8NuMUwTCGTe4qJ4zRz/3ikayRVSytJWPrBR8+4rvjz3TjTL0lKdaSn+pxzPY2dUlz9cJaixhkNSRqaC3srLtYH65w5/2GxwtAV/1Xr1/IjQ4i8PQ70IdE2zVrB7hqHUdB1vtpiSIxkmGaMUngrSU5eaS6MrSeDCnlog0H8kcRWoHA7zqs7Kvk9uIszu6zUCnunDR/6hmMQFs1m44k5M1W16AddJ0Es760TYlGsmVPh1Wnk3ThJMVN55AtElQrVH0rsDi6c5VnX8rT1POvayrdfLCFAGrsJpcVX3SjB1Q8hCIZejyji8xLpo+g+nG/QvqH1DaoGdrxoGgk6iYmHIfDuNPHF3YnHakZoag7BMEdSRoRw1cXycZS16LrxPG1ECX6YAj+7PXI/BiYFsUTh1CvY/VnDOkopXwNf1z8flFI/Aj4G/lXgX6g/9p8Bfwf49+rf/5ellAn4TCn1fwP/DPDb3/IiNXRAuti+hlfP8YxvKxZoRryQndbkJbakCNllwU2z/EuUckV8OZwxUMSf4TQlrBKxxscX21rgSMWziBLEIz6v1p0KcFavWGlISar0Ws0JAVCtG8gyYS+Mw1fp8SKMkX1JJnhGjKNEHSsfZoqRd8NAyIUnbSsTNhaYMzklrNZcG8eLruXldiPUuVwoJZNqcs9yXRap+9thrG092QBCzDXqTVFS4mf3j7w+DFw3li/ve3JKKO3YWVezTzOfPfYYrflg3/G08dzOYWU7oOCun4kx0xhN6w0hi8PjMM1EnfntP/iHHO4njJKB17SG1/0owRMVaFz8fnzlQvcpk0sUu2atePP1V9xuL5iCUO76ceJxHLhU8Obuln4YKZU6+ORiK6lbeeJxmivEpnl60WJVtZ2osIQ3GqclHCVVDDdXymepq7PWsG8aOsRFtB8mmsay0YbDFJhD4nd+/DlzjHUjqb+4FNGVtZOLeIXMlZFSD6YrJa9zlhiSNDHr/JA24uIrI4u3M2tdycL2okDOueK2cj0fp1BhvkwuiVQyrfFYrRnGiVPOQmHUGqcK/TxRMIzjTAF2nafkxKXznGLk1d0gAfNzxjlVw1RkUX0Iia5bSAIVztB6hWjkY1ZqY4EX1vJ6DkwlIR3FQkqZVmmW6ECieKpQIUGt4D6GlT4rt7HU+SVH6ZITxzHjW81l11SfHqmMX+42vDr1qyfPYp2bS4Ri2G88do7MKfLl4cT3rvbCgkI2tcXB8nLbSdqTlue9sl7mdcycqvVBiInn+67CwXA/TRLQHmEbE1c7TeMMD8eRt4eRzhuuvBNUYQq8Oo4op+n7mQu7sOvkvus/Kyzz/kMp9V3gnwT+LvBBXfgppXytlHpRf+xj4Hfe+7Uv6t/9iY9USsW9TN1VVX3j52ZRXT9YGmCtMRLtVcc0ubAoARe4ZmGR3M+BLx9PtM5QivCsG32eDBrWKLz6eRhi5nU/sdATtVKYIKeK5WjetZ5Z55Vqp1IRqIYzC2ARHImjgZgKiRhEobNaRQ1L1SWvL9TOm/7EKcxc2QaXxAJ2sT1olGJrrXjelIIiySaTkyTWQx34mT5KYpRWmuumIYXEtfMcUlwZFCXBIU0SDp0UfYykFJiMYec8M5lTDPyj2wON1rxoW2l0IV1/cmGoLp6pJD7c7bje7Ll7ODKlKItGmjE7g0OBVTyUiHYaE88h3xmwWkQaH19s6JRiY4QlUkrhtp8pU2HXdmwax7vjgSc7L7S4zR6l4O3DEch8/vaWD66v2W32GDOCSry4uOQ0DShVKq1N+NUFVU3mTG0kK0qExfEv1ZSckBP3s+Cozhh27Zar6x2Ht0dO88DcJzbG0mgxxNK1oblU76kUJrIUEQvfG87qXGUY+vdcQGFtINqFYkmptgN14ykCBa6N/lola61xtbqeYlo73p0z5BQZteWiaXk8HrBKrZbWBelPYRSN9TSNJUVNa42EWVRDv/22kQjEav9blDgYzrnQ2Rolh4Ky6D1knKdU0NbivUGHiQ+95z5E3oy9nDrqHJTrVjBGo6yIklQqHJO8hhQEmqLyWtAIts0ZqkpSBKz+UbUH97zreNsPYqUhhTkKTR8i17sNd8eRTMZry8M087xaKHTO8t2LHa9OIzf3D2hrmRBfLMlVaJhqaE1Uha517FrZSB/GSWiaOZO0opA43B7E7qNxdNaQQ+E0B0KCxynxnRdPGGMkhkcuts1KB13XjG95/KkXd6XUDvivgX+nlPKo/uQn/kXf+GN7jFLqrwB/BcA1LT89iNeL0YaY4zpA/+iTLFW0qlg3Z/IMSrPi86mw8t3HkPiDdw9VsKT5+HLDrh6FrBEzr0VBuDQ+rVEoI0KFRb0YSqGEKCpDZzAprdWYJJiDzuI9fQ4Hrg3a2uDSShGUNCOcMWRVKLFUEyL1jaq7KJhi5OsxYrTmSUlcKMvNMHOcA0/p2LYNOy1JLzIxSmUKCbX0Zhj5+jjgtKG10nRzVmhgPmhup7meLGRxCKUQKGw7T5gTwxTZNg6TIMfMlAv/17tHdkYag8Eq5pJWozOVJb5uHCIvPr1inCb0WBhywAXDnGYa70SrYBTWgXGGMOZVlamVFsZNTOhKL9VGNkbvLVOljP307Q3f+fgFH2wVSrfcnGbGlLjebXB6JGeYwoybFZdNxxRjrd6hD4mNyaIqVGe2ka5/1lpRtNg0x5SZ5iRp9lUopLUh5MRpyjx+cQ9G4bECjdna3GdJj5J7a6u53bRAPmcYut5zgYASZV1kI7BAq2qhwCpwKbE5jnJPlVrHz4LvaK0wJdemvygetZYm3xwyTeNoHBInV1+7n6OwTYyWqLkCUUvhdd02fPL0is9e3xHDgDWGIUQCUj3nIm6RfUzczZUOKu+WRYH+MM1ceM/Tp9dcPf2QYRz4w88+4xQmFNX6uAiN+GYY2VpXGS6ZEiTUQ1vDu2FYYS6Brc5S/pLVeu3k+i7clPdWJiUb3NON5OgWjSScaelfvet7mq4hh8jGSii49OXk9bzRvNi0DOsJDYIq9EE8kJw1aGX4sLN0jWVSmqyEAiuCL4FUU2UDTqVgUqZxlj4nXh1nrI7sO6FlPgwjIRTGOdJ0TUUL/oSV9r3Hn4oKqZRyyML+n5dS/pv616+VUh/V738EvKl//wXw6Xu//gnw1R99zlLK3yil/MVSyl+03hGKNCYEM6zVLOeFvG6uKza9VA9rVa+lejECuqNybVpqsQ2dYmasx6Xb2ohxRgQnkj16Fkhoda7mlameNkaBVpT6TQnmEJZDXEqzZUDpas2rKz1T3ijrf5R09NFK/M9Z8Ouy3rD39zWrNDFlbsNEdlbc5EJk6yUIfIxppYQqraVnoaRZ/OVxQGkJr2hr+IQ09xTXzvOiFcWg+JNLhaU10uexVDtgucDbxuO9ZZwjb/qRx2lGZ8XLiwuu2o5t0TxvG/7Sr33AEGZ+7x99Rn/quRt7HvqRcZrJU0HHBW5YKkxoOgkZUcAcE57Cpde0WphIpo6LS2952irePjyiNDw+zvzuT97wk9d3vH04cH8cQWXaztBtFE5H3twd2G83fPr0is57QsqMU6APZ4rd6pgJK1PJGcOm/vwSity4hl23oXWWjy529NMsPibzjPMeqxRPuo5nF1ucFYO0lKVin2ufpxac632WtV70Fgt+vZaTnJvxwsZanitzmCPHSWid92PkZph5GANDSBzr9x7GQEbRWItW0gh2raVrWlASQVeyCH+m2l+5PU0ClRmFV6ByYpxnvnx7yxwilMpompME4ixwIwIX3U4zPz+NtUEu7/cQIjfTxGA0//w/91v044E37275p/78rxGV4XGcV+M2rRV9DNyOA2/7npthknkWM1MIXDsnYdUFSsrr3CupUqGXxZxzn27Z93TdUWOudFfvSBpKbbTMKnHKM04J7TLqhu9/9yP+4M0DN6dp9YhxVvNuHImARfpwOWTmIl2u/abj2dNrjlEyUAOZp5uOD/cbPti2/Cu/+SneKpKGtvFiV6Eczi7620w/z9yceppmw7btCNFwnJL0sJT6xhrxix5/GraMAv5T4EellL/+3rf+e+DfAv6j+t//7r2//y+UUn8deAn8APjfvvU1UHTWEJKi85acCkMpxFplrZhn9ZktRbjfyQi9T5SmVeSxPKMS7M5pEZrIZ5HnOs1RuNm5UKoHfMpSAYi4pHqJJKkiFILp5XpMzPWf+lLrMUnEFXLUjkU2mFThIV0/h2xGZ/bAouPTS0OsPqdSSxNWVXWcHD/fzkeePtuQ58wHe/EUOYbI3pl1c0op8XYY+fo0YJWmM45GG0xZrGPP13RvLaVpeDtNxJJWX/ICbBqLtRLYvfUdISZaAqcSpImlgCRNoRc7R0yJRmt+5w9ercZkY4h03tJYQ8kwxECIcmwfQ0J5ffaYF98FVC48ToGvFHy0aWi0IuTEzw4TCrGv/cHTlsc5Maaef+kvfMLvfvFAPyZ+6/vf4/XNW8Y81809M6eZt3ePbLqWaZ7Z+YbnbUs/B758OPFi20KRwIzGykavtCYVwc11UYxjpDjYNZquMfRTxHtLOk14Z3joBy5ax3eeXPAwToSQ6YvQH60zNM4Q5ihNdGWYU1p9glStPrfeE8ZYszHr+FCVmqmrT3gtQW1tCi6QiFYihjJGmD0xFTqn2DqZ4rnAPM5smoadsTz2R45D4knT8XSzIWRJAMolY6z0gFpthG2kjdAjKxVWK9i1DQTxNjkGSTPLWmwUYoY+JRFLKbF/fhgnwpS4ORz5m//t32GYIiEGbh8fuHSeOAdujwPWaVovPaqoCjEnxiTV/9YLw8kDn3YNQxIXyJALfQ36eL+gXVxEl9NDQTybTjHxbp5XewtTRWtrfaaEvXbtpTfzP/3d38d5SzsbPjAtU0789NQz5sQ0Sf6t0wJfemU4DBNtk/HGsG0ahnlmmhM5S3TLYYz89o/foIph51q6urEMcyCEhHPnFLRxjihGPnr2FIPhNB1Zrej+XzAO+0vAvwn8UCn1D+rf/YfIov63lFJ/Gfg58K/JolF+Tyn1t4DfR06Vf7WU8q2cHa1kXk9F0tIvGk+MRmx7tUEZCQIunAHtbdtQpomQhLqYqu3AGVuDksTgKy5eIbVhOafMmAqtTujy3gCgfOMYJ1VJXuGh9QRdBVY5l6p2FRx0ETZlVTF2xOhLsVQQNV80Q6iRgutJ8b37dB6gqjZ6NSQJ6A4InbPpLF8NJ3SBC+OZCjRKmlRFiUFZzgXnlIjCUl1I6qeokC8ArdG8aBseQm1s1dPFchQtBaY41etp+XDjuT+OYoIWA68fjuw6Q+sbTv03/nR1AAAgAElEQVRA6zwpipdMKVSpdqHznm3XSn5lAZs1U5ScW63lsraNY67xirdjYIqJj7etxI/NAUphTEf+wrMLrFaEGPjhF+8wyoPJeGfx1kPsQQm+qZUhpMj96UhMCW8UdrfDe88LK82WdrPlzf0tp0ksDIxWHKZATEKH3HSOkhVXmw3GGeYUudxt+OrhIAVEjAxToBQpDk4xEqokdS6ZMUdZbIpsHiCUxCmJPYaqBcsxSQxjWQv3sopV1Hsr16axNM3ZnbBQU6RqNRRTwShxmrRGc5wCbx97GjuzqZ9v03icsww5UrTEC5IgIS6aO+8IORELhJBQvtoHF8U0zWwaxxATFo1FkaLAPDUJhZtp5tqJc2Qq0FhLjImb4wFdXTT7aeaQE30KOGtkvGXpiy2FEwqOaeY0aon7cx6vNZ21bJ0lFLibZh5mGS+58k6tVlx4CU4pqnAXI8eQCFkw+1Iqm20RQlb2VykKjBQiaAm0do2o3+cinP6pNpEdum4Shc47vFJMueobSiYkgQJPw4T3hlikKR2K0HFLkQI2pgR1nDjd0GrNGAJaaWKKuDDx5WPPlGbCxnG9bTHnFekXPv40bJn/5Tyk/tjjX/wTfuevAX/tH/fcyyMVOM5yEcQtrtAog6nUtJQyY0l1d814ZdCmSseXhVeBKZoxBnHNS0tyiaKEKF7TCJOgUOhjZmeoIgF5H0t1LvhopZJl1kUbzke7kmteKnqdnKjqNaOWSqAs1wNTxS9QWQv1K6XrqaGcm7ArZAErpW1hBghiJ14uhyA5rpNOvLQ7lBbzqdePPaooLptGPlfKLNLx9XmKrv2LKswyls5Y/vB0JKRCY1S1QRbzrT5H5pzY245pSiglGD41a3IKhQ0JZx3jFFHaEELGW2lSKWAaA5eXjhCsWP76llJmxhyJpdBYg5qziDMqFnyMmZ8cRGBTahU9x8xPHno6a5hjQW81rsa4/eSrL3i6a7FBkys+3nqxt/jg2XP+4KsvGHLidHuPQvPB9RUmJ4Z5lDxUFCFp3j0eRZilChvvaL1BKUNR8Hg8sWssj6cTzlluTwO7XcNcMjkUhjESdAG7yO9lwcgKMpmpWlabutnvveP7L57y+v7A/Xr6XB5LEPUivJfHQudbT3vvjTeMRpdCaw3HWWij17uWu9PInBN5mtl3ngvv2LUth9MDNiuebrfoDA99z69/5wN+9LNXApHMicZ5chYFqNKKME901uNL4MXTLYdh4LGfCKUqvpXibpoZp8jFpmXfNNz3I04bxhixCEspaziOgaTBODl9lCQaA22VOGTW3cugGFIml5lLb2mNpRSFVYVnrePCO94Mk1gVVxhm44XuGckch8QpxuooeiY7LNYjZbmOpaC09Nh0iGKJnDIDkZ8+BMkLBjbK0ihN5xzOWHJKHINses63DCGiU8E7zyf7LTFUOiQN196S2obbx0cwdoVXkypMUyLrgjaGy23DqZ/4/O07DkVEWp+/GbndjHz0ZPet6+ovhUJ1fSjWRoNxmpjTmjAT6wAvQB8j0+MJiaQTWpk3hksngoDOWoYiFXpE10AEaSwZpHLatB1jmuiTNE+90cI3LVJptx66xmFCEve+lM9bXLX2bYqCIBxupYWlg1KEnCu2J0fLtS/w3snCKLWmGBWyDGqWpvH5kqRcOFZfm6VxVkpZB6gxhscSGR4e+KBrOQ4zfQh0RpR9196RQyTWw5NCqvKF4bM0kUWwlbk0jhNRjNaUUEKHELFaobxZKYLeioR90zbEODIbRQxFKjKrMVmqliHKp3LGMKbIT+8eUQq2TcOYZhpj0FbYMD4LVVMZ4aRmRVVKysKmKyhutOL5pmXvPT+7O/L5uweZqBompZnmma03zCpzCKBLJmuPay2mVZSiyVMihcSxP7FpHbtNhzWOw2lkjoFN48lFBGSgsKZhHgKnPKGN5nvPr7i62vLz//0n0mMx4qb4cJjEn97pVc9Akfemlt0bVqFRKtK7USWzay0fXG45zYFpFj+jb5YVFdlWC4ddrafRFbyXAUrWhTf9yKX3HE8ZZxXb1hNTZte2Uk1OmRCO7J1nipHbhyMvrvfsjOLtzb3AngWuLna0SvHmcIDa/0hKcd+feLrbYlPGYrjqGh6jVJsoaJAMg+++fEnXNvz9H/2YmAumaC694zBHutZinRaYKslm57XMtX5OzDHTeiN+/FrRVQfFmzGQmbhuGhqtsUrRKcUn24ZXGjEHK+c1xSjFzhoepvANOxBUjdCrFsHvN6VjyTgK3noikTFmqBARFsYUaawH5ISbimXnNR9cP+XruxuZ09rSaMWf+/ApP/r8NWMI0mQtisfDiYdxZmMl47XREvgSVCYbxTSL0NJYw2FIpJwYglChD/3MFB++dTn9JVncK31L/igNL1OrhKUTXptcACg5SWnEZjTlwpgDF9VgJ+TEOEcOQ0A78e7QdXF3dRIOfc/GivfybtOsySgLW6Nxmu9e78WFMkTmvLgeajKFcQ4YI25xIWdClWmrGj9G3d2zqk3JimPLpNQYteBmwpBYPOlXKL/O1ZQlyd4aTVSZJf0pVSaEqY24YuDtOJGqv8mQEs/aFlPOlqOqVv4s76+cBRpKFazSXHtHmww384Qcp2scXZL32U9ijkTb0E8znbc46zj2PUobts5RYsaozPON5+fHgcZYnu23vH444a1sZjEFtBYxVT/OEAsbb/nu9QUpZd6dBg7DJJ6pLJuSXJt947hqvJhRbbwkWiGLvjOiadgYx8NpRGUw1nMcBz776hUg98w1ltlkxjiTQ+L4MLJvNzy72nLqR4xRnMaJEMWa97JtabVlCCNWwef3B16fRhKZnffrtZzmiNIKs7ixLhs7ZV2IV8iNItARcNcP6FLYNoZ9YxnnRD9HjlNkioHzBJHCYEkck6epA7c+Yj0GLoSBi86gvYNmw9gf6cdRGF0lc9m0MCWctXSdEzinaTn0E5u25eE4cup7dk+ucL0h5Lga8rW24epyy+s3d2hnyVNm6zx9FFjtyW4jrqwp8ObNI51vGENijCPae67bjmEahFpsxAxszImQz3LCkKCEzMZqvKqstVxqkQPvphGnNNfec+EcXhU+bD0nI9kMVrHmOlw6y721HGKAIgWO3JPqG280pZIySqmxl8bSWMtpmgSis1LA9SVLclZJ5Fli9hICZd0dDmhlyRSO08RRZ373p1+gNDhnhOkyT+J7pcTHKRd4nMSWgEo3zYg4cOsb9m3DXR9pvBWxVoRxiN+6qv5yLO7lXEGaavA0xUjTNKiiVjy93ocVu5CLIqvgkp+ZEail5LKGJGijpVJK1fBfg9PyfHNM2GolvKjdlBGYpBVchI129QgnHioiDGoJJfPmMKAwNFqgmRILIyIZLua9ozLLPNeigFRq3UgS4lOxHD+XhuryaK2VAOdyhmUWZoI0cqvlgAKcErFF1gI5UST5pQjf92xKUBdM6jG6CLbr1OLN4Xk3zzgjDbE5SeM6piycYxSNM9xOPXqG68bTuAZtLQ/HidOc2FrNJ9tOFttWc9crqIKtMUWmmBhSpKRMYw3XnadRhaQLLy52Eu1WktzjXChahC0vt5tVAfy0adgulrCLaZWSJvAYM954hpBwzhERXr9BMFWHRhuxAAbF24cDow88u9hze3ign4P0a4zC28xhnigI6+XtoefhOHOx8RyGmZwzXedw3gjEtugo6r1aRkFZA6CXm13tMVTl+Cu5r8ZrnLZct44heF4dhlUUZTRr8/xcrJ+dUlVVdyezmL8lfvX5Ja8eJn7vZgAkpMVbw5NtSxkn3taN+qJrORwOkCLzDFebDWMMHIcJXQVEUyxopZlL4Mdfv8EUUCms2bGts1hr2bc7+umRn729xaJEnIXGxJk5Ra67lpAj25yEVqjkRBrfE4pU8htDKsw5ymm99mdQrHDnMUX6FLlwjlYb9s4w5uWkI2PDqML3Lzf8n3ePYt2xnF3Le+yauodedZJm1fcBlQ2XXccYZ07DSGMM+2KZsvRk+iFScsZZMTWLqYA2eCM4/pwLN8cR78S+d4oy3pXSNFb47tYaHk8jShtSSBSVabwFrWisQxcpXEKIxCBEfu+/Vfj/S7K414cM1OoLExODibSqLkzvY2KID/RiyKO0WrHA5SYlasUMq/J0XTCV8JSNVngrnFOxejpXR8tpYsE5NQIR6AKqIjS2KO5OA2PMbBpH48QvotGGVokIplG64tq1ZitUNkT9wNVlbuWkLhBqnbVWaxqjavNHBovWS+Dw+QcXdkoxCp2h1ZrTNNM6K8IHY0lRvBuFp/3eW6iDPOVMqRXhxlieeXiIkSlnGi0+7E3juNptGMfAxnY8TCNWi6vk28OBxnvmkPDeM8dE5w371rL3hl+52vLZQ8+lb2lQvDrdk2OmdZbrznPdNnLvU+HV6cDlfsfN6YCKEsOHEhGJX0RaCGRntTlv/kWaqNYoOmuxzjFOM0+vLjmGA/Ocqk5XFJ6boridxeZBG818PHLzeGTfdXjTYJUcmYcxCG48DGgk+s5VuK9pDTkZwX+drlF+rM6F5b3/vndwWq98zgL9LbYbMsaFDa0VPNtYrto9b08TU5bK0unawykyJpex9f7OrWuSUZgzP/36FpcLL68uCBlsSkylEIvi6uqCV29u+Or2llIuxRMFuD+euN4kpqkwh8BF01Q+NpymkVwKndKr9/8cI6ZYnHF4DHeHnnEW7vdWKe5C5JgCaMXt0AOK623LFEQsuDWGqAtjzGcvpvp5JJ5OTsljUlhdZPGsWNeURVcQpsLeWTbWctvP7K7M2ptQSozfDEsCVL3OSoK+O+8YYuCi9ejqP9EYxTHMIqyLUayRY6ap934OmW3nURT22oA2aG/ZNR3H/gRZGGSbriElUb2OUbx+nJFe12kK7IuSWD/n2Vx7Xh+ODONE6wwBacZfti1v5xPGGhHH/WOI7L8Ui/uC+S5VJEUk2nNMEvO2LLrLDV//+J45Vn2uhZaYFkFMDdYu1XdFKSRxBqk+rtoGa86TSn3zra0/h1aoXNbAjgL0U5Q0mCJNodMYMEbonN4ato3Do4hV6k/djCisZv1LIIf6ozeqSHXizl/KhF0x10X1x8quWCpxqxQOjbOKfo5c1Mi6ReZekOu7bGYC9ei18qUe2Rtt2Nfs11AyTmtmCjePJ7bWMsaJ67bjYeh5HQas1vzKi+ec+olX9zdMWs4p3sqHe9JZUu54mBKNEWWoMZpiCvvGYurC/G6cuZ8CSQ1cNQ3txnDfj2gNn+w3UAr3wySpRVWMlmpPZqlehyB2sbfDRJ9m3h5voITq6ij3QPzoRfVprWwYXesoKdNPE9u24enFhXDPY+LxdJR+gxOO1NZL0Id4z0jFVhIizT8PoLN6kjOmKzQ2WTyeOo9ZxFSrZ7o8byqywXdWc1EFXEPMoBV9it8QMK303AoJzjGTiyiqj7NUvTpnvLEccmEIE847Lq/2/EbX8Pd+/Bm3x4GPn13ST5GmZIx1jENPGKUp7o3GZthYzzTPXHSeL++PbDqNd45jP+KcoXEdrXMU1THNkVMp4lmupDBDwcN44rGqUhcoorUGYw1TloV8mdOivhZluvj4ZOYMTouiWdUJkik8hFBNteqcU9LvUvWaP+88r/ppXTNaa9m1ns4ZNk7wnsUDR1nxH5Jwa8Xee47TxClknNcYLQttqyxFacY44wychoF+TCQlDptliuyblhxk83l2ueMwjjyOIzEkHvKET5rWeoiytedcmEPC1lP1znueX244DDOtd6T0rSTEX47Fva7qFTpUlUkl1XsJM8bITVmwSqXOPu7o8+RZJmwp4gOTq6xcK1YFZsmFaa4WrAWauhCnLCKomhKyYqRS5eq6EJxfS5VCH8KatEQFR2IsHNIs+GuIbL3HtJpLaiJPPjdzCgIDnVHl+hnWLwu6ythTWRwv3/uc712+Rf8klhznn+uMhO3aGjeninjQZPQ31JhnjWZZhScUUew9dQ0388RxDIQq+ulzdUcsWTjOMWO159XtPa6qJrNS3A0TVsP3r3ZorfnkwvI8Jd4Mk+CPiJ7hi8OJp43HGcvdMIuSt8YLbqqaMOVMo+CnN0f6FAkxc9F4fKXKfnjR4a1mDpE/PJzwTlgIHsc0yevNlSuuEZHKk6ZhmDNapfVeeme57FoeTyNfv73h0w+es9t23BxOKOMZY2LbShLVFDORvCaADUOg8RbtNUUvGk15yOlR+MmLrYFWCuuN9JFyvZG5ULKI16yBpsJgueQ1j8AqDdYx5rRSgENKEtIdE4de+PxzikLJi4pd23DTD7TOST9Dyzi5u3vksR9AacY58ObhsZrjFR7HgaIKrbccp4mtd/Qp8axtCFrVsG7Fw2nketdSVKmiwUDOCZQQFY7DSOs0TdEccwRqXF11YK1xJsy54JRCG7OKo+QULQM+ro5uAt+CQLNOy8k111Nwhm9w3FWRU55WihebDq0MSasV7/daYRVkI4JB2RAArXCtZjwFppDYtS1d6+nnJJ5HzpFSZt+2nKL0aOZ+QDcN+12DRvPV0NcQ9Il903BhxenmxZNrxtfv6E1Y7cJLmjCNYd85+kHgRVPgWdvSblrePZ7YeodxmsvL7bcuq78Ui3up6jOlq4CkLkwpSSBEhKUTydL5XiAzhVTAtsIVCmm6eK0Z5oT35v1eEylkPty1NDWYYFEOllJoK6d7calB6VUJtpwUSl3wExLGe37u916kCHXxOEZOY+TtY8+Lyw07K9WpAqJVaKdWeqRbJYu1qiyL8VJZm8e5VnYL5m5VXYTVuhdAgW3WaL1AQZIXG0JAey/H2JzeQ4HldZcN8IzgK0pJLPFqGi2e11YsFsYoXf+kofWeaR5QJTOGxGHo2XpL6wyPM5yyeI7svfQ+xjnzNgSavcNkOBwnQiq8G2cKYf08H9eFvQA5JQadUUqsTpWWRcNttnx5c4NRin0nCtxDiORqWrZpHVorbvuzAGjZPEsRzNZUsrgpkOZMcY43w7GKUDzvbu9AK64vthz6gSElshbPFlKSirAmFVlvMG45SahzQP166nxv7NZqYTktLR2aVIQOHAGdFbdDoBQhGrhqcKYp2FwIU8JZqWRPQ6IxmeMYxOu9SOO+tY5QFK+PR0LITKb6IeXCMPS8DTNjSKSUuWw3TCHgvNz3uWRCKHx8dcF2s8PkyO3hwJt+xhnDu9PErmtxMVKyonWei82G0PcMiC2FRuOsZQ6i1MzICWuptpflegkWinXjc1rsCHJZ5oVaWWuqYqVhqbKTLPBWVQFjyXhr3yvS5PrmIr2btnOVknl+BwWzniIU1aMqS+YuWmispWR2my1bn7k5nTClsDMG4xw6TWy9FBQWRYyie/CI/cBl45hj4FAyJmsejj2NMmJXUMeIoXDoT2xMw4cXG14fep63LSjFzWPPECKH08B+54mP/z9oqFqt6YwHCs6ZqgSVAWCK8MD7eZabXEB39hsLO1C9LOrkSRJGYa34ZERkUsxzIoWKd9cTQMwyMdp6RAfqpNRr1VXqorBU1ZlCTIk+pEpLrE/53vouX5d14L56OK2vaY3Iwbetw3uRqFd3A6nqZHVfXzdT8X7OyjVdFtn32c8712rDaRF2Le8nl4LVimEOtM5I4stSE5VMSlC0loZsKf9Pe+cWa2l23PVfrbW+y76cS19mei5tz9goBKyAgoUipKAIRQgSJ8LAUx4S5QGJF5BACCFHkVB4BAnEGxI3KSJAXgLCyguEm+ABJcTJ2B574tgej+Pp7pm+9zn79n3rW6t4qPXtczzpbs+ITO+T0S5p99nnO/v0rlN7fbVqVf3rX1DokgvQg0VKPOp6o4VISusDR/MZmxxZdCsOXIOI8Gi94mrVUtWe2nsrZIqwIfP10xXffzhHB3hrtWads0FBnTEyDoNxa6x76wqdVYHLrfHrr3Pi3hBZ5cRBlXnpyiGkzDImNl1vc2JR3ln1XJ1YPr11gRZH5QNxUA6aCZGOXs8hjooNrjQNB6ECEZaDwWUX6w3LPvKjP/DHeev2A27ce8T9xRKP8NzhITkrMW5YqQ2bSWUi8qTxRs8bE1LbmhL57s7JMTWnqhxNa/P7YnC8nHXc37dDxn3h/iGLtd1jQUjMNhhlSHYqaEKDF2Xa2qjH0+UafGni0zPE2Dhf9sXDI1xO3FmujRXTO+aTimE1GCRYxFAgGS4dHZGHxCZGHm02rPsB19S4EJDBuGSUCheEZbchOLW5ryVYmoZAR2bdRysGNn4bHcuYNhFH5XzR104EFsDYA3cWDKFsaw2ZkZPHUoiOMnDeUzYRZVU2x+wMWXce6GD1kbPJZerOKB+S2JCQgGMWKrrYc+808/KVY45yS9dH1BlazXiErNblKmGKcHvTcTSp8cFAG01hh1SNOMlUruKF+ZRlryxjz+k6Mm1r/uir1/nOu/c4GDKNM3qIVR/ZxEiofanPPD3pfiGce1MH/tj1F+zYmYz+Vws8yCaVwLK3ynTKuRz9dJuf907wCR5tevqY6aO1djuEGDPireiaYt7mugF86T4bOKMuUM1b+N95eOTZgGOgLJR1LP2n2/B9PMud3crnTw1SIv+YlCFHNtF4oesqMGkrjuoJp93GuCPKb1hePZ/l1AtiYpu+2f5rm4LLYq8vrxsdmaLUXlj3A7M6WH492ybqgJ7Ew5Toc2IqntaWO3e7nrvdxjpMvbWSztuarkv4lAmZswHPlSeI59rRAW/evQ9ZjUOndixT4o3FAnHCphS/nFgn4tG85dhXBIVvpyXBCS/PJmXItLLQxAYrkm7SQFClGzKXmgrfCkLF/W7g3rLj/joizhFjQicTbp08YOJrLk08EwkMZB4OBufrM3z9ziOCE146nrPOsFivuDxrgMwwJG49eMSdk1PWsSdlO6Wsuo7DpqKZzOizdaeKd8RoAYhNrxLD6W5b2ws1hJYCuti4u3lb00fLndvMAGu8S+UDHulxh5y3na0qNsR8JKrKg5YUS+kHwbpde2+D5CehYtX1dP1A01SMLXSb2ONUiAm6FKl8YDab8HC9ts7ZsuYqr9x5+ABPRYwbS29i1ACzUDGdzoy6IyVihvV6bcirYJtM5cWmo4mnTkKfSpt9MLSHK6fDGI0CuaoK4V5xuhR0jJ3gTaf+HKdMFTy1lO5Wo2yiDg6nDpzn3dWaB33PpHJMSxBoLJZmT1dOUdsNX8/qWDGr+ZCsVEFwGljGzDdv3ePSfMLhbMpyveL+6QlDn6hxXJpMaadTLk1qDiu4e+vdMrozc/14zp3TFVUttKEMQ4+RLioT7+lDwInwu2/fRtTg4Keq9EPEO2XShC1J27KLT/WrF8K5D0Pi4clDYxasaiZVja+rLT48ZziYtlsndd7Z5vE5yqzN22LqJkZOl1bRVwUdjK1Ny2Zh/CkJhy9HPV8aecoUG8ao2JUClW4dJmTiYB2wYw71zKkzVuosOpDvdv7KWdSm2E27iZYjrYOnaTxNMLztuMmUEz6VuDNq4HPF5bG71eEIqtu6w5iGQEoxSWEShLvLDZcmTRk/Zx26MVuzUFJ4gBXPAsKi4JpH/p1L04ZW4CDAiSqd86zigCsEbEeTGa9cu8Tr33mHSVsRc8Z3EGoxrpVskVGrbvs5HoTAlWAbznF7zDuLNZfbGiew0oxRX1Hy0MYQ2SpUUjZ24LCpGNQhBZ9++XhO7BPHrRFkBV+iWxxXQ806J050oFPjEDqN1qjW1N6cXOUJteMrN961AQ6Vw2WlG3pWXYfonE+8dIXD+Yyb9x9wb3lqazkZTK3yvvQosGXwc2MarezUB3UForR1VbpQtaCeLKees5a+jZLGKCgZ2+4pnCaOTRqYVTWuCixXK6PQHiI+GCwWVWbtBOk2NKFm1gTq0PBguaRPA4czo6kNAg8XC+IwGIRSrVlNHaw2Pf2wQhxMmwZKOgEFFeOViYUgLQRvQyhELUJX6/+Q0jAYk+CzMJVg3DXYSVYaz6rrSEmZTxqsabE0zEGBNVsNzCG0TizNqfmM+E8EFeOTqmublvUo9nSaYTCqjfEkNJ7KVSz6z8XXhNI1Pg6Ej50xP6ZkAIDWGfvl/YenDNNkTZKamLQ1koTVZkPlMndSxc/81Z/gv/6fL/DFN36Hg6bh1smS7EBzIovVGHISJk3FlUlgEY06Ig0DPg8MkokkpnWFxkQejFa7Eo+NXHmyXAjnDmXSTFbW3ZpHrEvUYF6qCoF1jHjnrDDoHcFoAgsKhZKDtQYlxDFrKy4fTJGSo3biSnNTz6QMl80IXVZONj1rhKbspGDOMpRxaplUaFsBb6iG1WCMkNsXj7IN2sfQWs/5/JKzt0oN57lyVO0Y2w/Gx/7iwYToM+vBBoYohSagRBjDmGs/h+JJOdNgBGJjqmjcBM+16zFtKt4+WfLigc1O7cUoWaeFI7wXZaPZWsBrx9RV5ASXaysQbXLiYY6ssXzzvG1Ydj3z4JFuxX/+rbv0qsRNpKo8GgfmVEhtEaloSTWkkdsHOs1M1NH4wEvzqXVQpsy7sYNQIiygz5mUI5e9TQkaU1/BO6oAp2uLRisXrDjnM7O6jJ/DtmwHVD6wXtv8UgmBdRdZbDp85dhEvy3O+UrwwexeFS7/HOGla1e5dHyZN298m0uzGmXKsov0cTOa2T5aZ5/B2NsgxbHXztPWvtSP8jYnPHL5ZD0Hn2Vk5pTtRg6ZK9OGpHB7sSYOA13fMQnGgfIoRXKi5OdhUpsuXd8zDJE2DIacaWoq51iuIpcPpyQRDmeGcLFcsOnbxUhSqMWzGbINshkUlxQfKrw424QdNE1Dyo7EQFKjCYmDDYoOIXDgbehNU3mO64CmTPC2qR63EzZDx2Fb2foQ+/hdGUQdU4ackWz57y2vuRb7Aq7YU/qeVpUat+0U13JPnL9nx8H0oqWWFhNaGC9R3Q68GQYjI0zRiNnmbU1S5bCpWHYdMUZCMJrfuqk4WW34pV/5PLOqog4ViyHno34AAA72SURBVCESxFM7m31LXXE8b1j1PQ/XK94+6ZhVNVI25LHAf3naMKsCi03k9umAoGxSZtP/Ici5V164ftgSguP3Hq24u9yw7aQUGBa95e3a2gqvw1m3pSposop3CP67sOxGGGRDrBFhPpmw2GxYe2tXritv0XJdUwVjLqy15p0H9xjGwcnLjj4low4eMk1wtJUhOlIZqCAjexNjUHaWhD+fltk6e4sZtj+Tc/+oKrUTZk749okhGq7MG3rvSy59dPLGQT0Ux54xFsoa2SKJnBPI49iSrWFonTWanHaRetbQ5bxlvxShMO95VjKw1kRbV8xcteXZOTg44tq15/n1r76BDolVNEbJTmsW4qmrmo6N5aULU+SqH6jUI41xhyw04hEOq0DtHL1mIxjLbJ3o3dhZBJnYOscxATX1pcZSTkg+Zw48PESZVhWJgapKtFWZWKQjDn7keIFNTDgc87YhDIlpFWwotCp1VeHIzNrAIsVtV2Zw4BrHvZN73H7wgMrDLDjmdTDo5PyAb925TRwSTVuV4qBFlL6cksZiISKFvvVsXSSh8Nqfo9ItKCs3nhBV7aRZPrNLbc16SAwbG8qigA9Wx+jUNuDNelMcnyCZkhJ0uCGTshVTT9c9CWU2aUgpWhNP7GmbmuP5jLsPH6HZxgkiQpci01CTPLhBWW821G2NOOFkszK+osaTSlOPLwXqaLMGqYMvc0Yzm2Ggrew80g/Ko1XHce3OmrrE2FEr8cShUDYI2/TNtqVLRnbVkp4quHZfTs3rITOtfInajUtIsNMbQN+XiVAlBdvFzLIfrC6UlFwgqrNZaxucJk7Wa5rgmdQTHq7W+Cpwb7khpoEuJXSiJS2TmM0n1ohVOdrKI84mbr1y9SoRz627d6jE8fyk5k6vHDaBqfcEhBcOplTO8/aDBVGMVPFpciGcuyBGtuOEe6uN4UJL0WmMHBBwTgnlG5ExL6cQHMEJ86ZhEw3Pa9DBkt9Wyy/fvX8KrpwSBLo+0adhu0CsZuPxzlq361DRNhWKsOg6UlTEKZ1mfBWYicEdc9Jtekj8uLDkDLuuZ1HDWJvn9zl9e6LA86UpYtUPHM0bcNAiSCpNGM7oc7Okkr6xiM+XwRB5nB8rdkJxudAIFMfiEZ6fNjyKAw/SsH1fxBpntkUwZzjyiQt4hU1KTKqK73/lZQ6vPkcQ+N+vfYXDdoIT4fajR3Q5MQ2egYrgHF0cCI0jJpv+nlYDvrHuvFocU+eREqVYHnwAtUHj3hu96wjLdA4aCZbLlzOuf80lb6pCwNHWNQ8WS3DZokhxVKVukQrkVjBsO+rx4pjPGmaqaE7cW6w5nE1oPahkrrYtinK3tw0rIJATqgOBCs3YDNuccA5euXqVh6sl64JK8s5xUAVchuisbjBrqzLLtNSNinPKZS02ztsYvkJpbX/viJMvfRnYxl0FoQ4VQYR7645BRwzUd/eBjHQYBsdVM1we8ArztrZURlNz/9R4mzZxgwo8XC7ZdBvq4G0OgrOmn5ONwR6X645KPF1Wnps2PFoZdXAXI4NErs6PCOK5szlBnHC66ix3TGLd9VSVBWVDN7DpB0O/iaNxNceT+qy5CyFnI9E+DyUd+zVGBM4Y4KWseAfPtTU3l2t6J3Q5MxdvMMxc4IeldueAGuiLI27qgAuOxlUGjyzzDbwKTV3hUB4tN6ScaKuauqpomkQ3GD9SGsxPLXvDyB9PZ1ZbSBFRz6CJRd+x6RPBeV79+Mc4PT1FEtxYboDMwUGZvoSSU+bKtKL2c75zf8Vk+v85Q/VZSYH3EvN4EMU+pQRXZlNWQ2cMkFsHtc2KAzBvarwPpF45aFtO10v6bLMXq+JkO8msO0PRaBWsndpb/s5RmD9ViYMhCrqYOFlttrj6qgpUzlG7yniau8gl57aV8CEpj1Zrm6+aMjmVPKE70xveW2QtUq7N68CsDry72OCdMKv9tuDjMA4aSWN0ZoUk74w8rVKDg6YSueSsI6TA3rPsKlocY1N7lnkkFCuH/fMppqxMXIBsaa/jecOrH/84h1eu4J3j+fmEeWNQwweLBW1d2+BxVY7rCZM6cBJ7VrG3dFKJtobNQF17e2COK2oqo/SKMRRqBbxjjRW6rBNWmI0Ri5R6RFkqp12kDhWSkyFvYkcXM7PJFOnWVqj0hbICm4d6MJnxYLni3mnH0ay1jdI7okLFOBjdjt5HTcODvuP2cl2w5hbFas404axzVgRePD7itOt5d3FCzsqnrlxmOWQm7Yyv3ryJc46kA6QzLn/FInlHITuj/Ex1u0bHNSMlh6+5UG2IcfMArIbEoTMOkluLzTZ2EMrvOEtT5nEtJqWSQJcTi+Upk7qhG2xwd13a5FNKIxAfwNhIU2beVCDQS+ba5Rl/4tUX+PKbN2mAmGxS02J5ykFTc9gYNHFWB4KznDnBUFXTJoDCQVtRiTP2zGwF/qbygBCHgSGnQi1iyosrhHLFJuNJLhWudlRpBa7PJ9zc9GzUUFaTksd3ClJOQQ7Q4HCDoproNLM2AGR5WM3GqbJeranqgHdGH4FkHixXHE4alqkjkVl1PaDMmpoqCJmBaV2z6uB4UvNgtWaI2XQZBm7euMGsFvrsCFmonadQ2SCc9fccNRWzawfcXvVPdqhcIOc+FgHHKraUE6gLgjqlDbUN5mCkIhhTG/Zs2UeqIEzq0qCRlZQTmTPK3FAw9JthoNdkDtONebrz/B9aOObHEWbGiZ2TFY2iWOSQUHoUiVbkmTQVL159jkmVyIOwigPrbuD+6YIu9jYL0p+dErbJm+LQBOG4sTTF3XXPrA2WQsjWhTecg/Epxkrn1DHF0WKFRi0oGPuPQcd8Yhk4PL7nGMV5Z+yUTkda67OUUeusYBac45Mfe5mXXr7OkBNelHu/9y0Wpyc8P6158+4pXUrMJ45JsIEKqxgNEeI8CyzG7GKmqV0p/BrCqcbZacQLq8EWq5RP2CuQrf8gOusk9AhNGjn4raP1rcWatgwDaRpPRjk6nPLwzoomVCzXHdOSFhF129POvK2ZtA0vXHued+7dJ/XK4eGUezdvEIeO4Co8NvDbOSvEXZlMqFzg3mrFcrCoN7uCcMlGrWxjCweGIXJ1NicXNr/T1UCMC4jK3ftLLh3Z7Fco058K/7siRDEIY3AQ1NlNDlDopKWkeMa1Os4ePWwD9WAcJmDF6hurDad93PaPeHFlOLpFt0PO5HIKnU8sPVlVDnGWTghlhu2gRmInYmtbnBKHgaaeEELFqlvz1W/dwKVM7YRpqNESZDQhMDmqWPeRZuLpBmuuqoPgQ9g66tGhDdkQNg8LfcG0FJ01j4kU2ebYcWewZSl/k+ZMFmvUEyc04piKsBoyGzGkCsWOrkT+Yy7eB4EgrMuNlAdD7oXgLEVYmuZimUGcsaluTtU6e+sKL0JXWzpvJLRT7O944WBqhHSDkYuFynPUBCCzTpkHizU5D0wmtTWrFQfvxYKscXzotXn7VJ8q+l1J4d2IiNwBlsDdXevyHrnKxdMJLqZee53ev1xEvS6iTnAx9bpIOr2iqs897gcXwrkDiMhvquqf3rUe5+Ui6gQXU6+9Tu9fLqJeF1EnuJh6XUSdHiffg1dsL3vZy1728odR9s59L3vZy14+gnKRnPs/37UCj5GLqBNcTL32Or1/uYh6XUSd4GLqdRF1+n1yYXLue9nLXvaylz84uUiR+172spe97OUPSHbu3EXkx0TkayLyDRH53I51eUtEviwir4nIb5Zrl0Xk10Tk6+XrpQ9Zh38tIrdF5PVz156og4j8XLHd10TkLz5jvX5BRG4Ue70mIp95lnqJyMdE5H+IyBsi8hUR+Vvl+s7s9RSddm2rVkR+Q0S+WPT6B+X6Lm31JJ12aqvyPl5EfltEfrV8v/N78AOLFgrSXTywRtNvAp/EOn+/CHxqh/q8BVx9z7V/BHyuPP8c8A8/ZB1+BPg08Pr30gH4VLFZA3yi2NI/Q71+Afi7j3ntM9ELeBH4dHl+APxuee+d2espOu3aVgLMy/MK+HXgz+zYVk/Saae2Ku/1d4B/B/xq+X7n9+AHfew6cv8h4Buq+qaq9sAvA5/dsU7vlc8Cv1ie/yLwlz/MN1PV/wXcf586fBb4ZVXtVPVbwDcwmz4rvZ4kz0QvVb2lqr9Vnp8CbwAvs0N7PUWnJ8mzspWq6qJ8W5WHsltbPUmnJ8kzsZWIXAd+AviX73nvnd6DH1R27dxfBr5z7vu3efqN8GGLAv9FRL4gIn+9XLumqrfAblzg+R3o9SQdLoL9/qaIfKmkbcaj6jPXS0ReBf4UFv1dCHu9RyfYsa1KquE14Dbwa6q6c1s9QSfYra3+KfD3gHzu2oVYUx9Edu3c5THXdgnf+WFV/TTw48DfEJEf2aEu70d2bb9/BvwR4AeBW8A/LtefqV4iMgd+BfjbqnrytJc+5tqHotdjdNq5rVQ1qeoPAteBHxKRH3jKy5+JXk/QaWe2EpGfBG6r6hfe76885tqFgCDu2rm/DXzs3PfXgZs70gVVvVm+3gb+I3a8eldEXgQoX2/vQLUn6bBT+6nqu+XmzMC/4Ow4+sz0EpEKc6L/VlX/Q7m8U3s9TqeLYKtRVPUh8D+BH+OCrK3zOu3YVj8M/CUReQtLE/+oiPwSF8ROH0R27dz/L/B9IvIJEamBnwI+vwtFRGQmIgfjc+AvAK8XfX62vOxngf+0A/WepMPngZ8SkUZEPgF8H/Abz0qpcbEX+SuYvZ6ZXiIiwL8C3lDVf3LuRzuz15N0ugC2ek5EjsvzCfDngd9ht7Z6rE67tJWq/pyqXlfVVzF/9N9V9ae5oPfgU2XXFV3gMxii4JvAz+9Qj09iVe8vAl8ZdQGuAP8N+Hr5evlD1uPfY0fRiEUFf+1pOgA/X2z3NeDHn7Fe/wb4MvAlbJG/+Cz1Av4sdgT+EvBaeXxml/Z6ik67ttWfBH67vP/rwN//Xuv7GdjqSTrt1Fbn3uvPcYaW2fk9+EEf+w7VvexlL3v5CMqu0zJ72cte9rKXD0H2zn0ve9nLXj6Csnfue9nLXvbyEZS9c9/LXvayl4+g7J37Xvayl718BGXv3Peyl73s5SMoe+e+l73sZS8fQdk7973sZS97+QjK/wMSUkmNIbDdzAAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.imshow(reshape_as_image(herstappe_data))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Extract the data you need"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "In many applications, a specific research area is used. Extracting the data you need from a given raster data set by a vector (polygon) file is a common operation in GIS analysis. We use the clipping example to explain the typical workflow in Rasterio.\n",
-    "\n",
-    "For our Herstappe example, the study area is availabe as vector data `./data/herstappe/vector/herstappe.geojson`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>gml_id</th>\n",
-       "      <th>UIDN</th>\n",
-       "      <th>OIDN</th>\n",
-       "      <th>TERRID</th>\n",
-       "      <th>NISCODE</th>\n",
-       "      <th>NAAM</th>\n",
-       "      <th>DATPUBLBS</th>\n",
-       "      <th>NUMAC</th>\n",
-       "      <th>geometry</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Refgem.202</td>\n",
-       "      <td>642.0</td>\n",
-       "      <td>37.0</td>\n",
-       "      <td>59.0</td>\n",
-       "      <td>73028</td>\n",
-       "      <td>Herstappe</td>\n",
-       "      <td>1831-02-07</td>\n",
-       "      <td></td>\n",
-       "      <td>MULTIPOLYGON (((5.41544 50.72148, 5.41568 50.7...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "       gml_id   UIDN  OIDN  TERRID NISCODE       NAAM   DATPUBLBS NUMAC  \\\n",
-       "0  Refgem.202  642.0  37.0    59.0   73028  Herstappe  1831-02-07         \n",
-       "\n",
-       "                                            geometry  \n",
-       "0  MULTIPOLYGON (((5.41544 50.72148, 5.41568 50.7...  "
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "herstappe_vect = gpd.read_file(\"./data/herstappe/vector/herstappe.geojson\")\n",
-    "herstappe_vect"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x7f645fff3850>"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "herstappe_vect.plot()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# herstappe_vect.crs  # vs herstappe_profile[\"crs\"]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Make sure both data sets are defined in the same CRS and extracting the geometry can be used as input for the masking:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "herstappe_vect = herstappe_vect.to_crs(epsg=3857)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "[Masking a raster](https://rasterio.readthedocs.io/en/latest/quickstart.html#dataset-georeferencing) with a geometry in Rasterio is supported by the `rasterio.mask` module:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import rasterio.mask"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#?rasterio.mask.mask"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The `mask` function by default just masks the image, but it can also be used to `crop` the data by setting the `crop` argument to True:"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "As the required input for the `shape` to mask with, the documentation defines: \n",
-    "    \n",
-    "    The values must be a GeoJSON-like dict or an object that implements\n",
-    "    the Python geo interface protocol (such as a Shapely Polygon).\n",
-    "    \n",
-    "We learnt earlier that GeoPandas relies on Shapely as spatial entities:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(geopandas.geoseries.GeoSeries, shapely.geometry.multipolygon.MultiPolygon)"
-      ]
-     },
-     "execution_count": 23,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "type(herstappe_vect.geometry), type(herstappe_vect.geometry[0])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can do the data extraction (masking) within the context manager:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data_file = \"./data/herstappe/raster/2020-09-17_Sentinel_2_L1C_True_color.tiff\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "with rasterio.open(data_file) as src:\n",
-    "    out_image, out_transform = rasterio.mask.mask(src, herstappe_vect.geometry, crop=True)\n",
-    "    herstappe_profile = src.profile\n",
-    "    herstappe_ext = plotting_extent(src)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "numpy.ndarray"
-      ]
-     },
-     "execution_count": 26,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "type(out_image)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can work with `out_image` as a numpy array, but we are lacking the spatial information by doing so, which makes it hard to combine it with other spatial data sets for further analysis:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.image.AxesImage at 0x7f6460456dd0>"
-      ]
-     },
-     "execution_count": 27,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.imshow(reshape_as_image(out_image)) # extent ?!? extent=herstappe_ext would not be correct."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Remember the `meta` and the `profile` attributes of the Rasterio object? The latter contains the spatial information and the settings for the GeoTiff file:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'driver': 'GTiff', 'dtype': 'float32', 'nodata': None, 'width': 447, 'height': 227, 'count': 3, 'crs': CRS.from_epsg(3857), 'transform': Affine(40.07813983073409, 0.0, 593476.1968631798,\n",
-       "       0.0, -40.1126034998577, 6577549.60336745), 'tiled': False, 'compress': 'deflate', 'interleave': 'pixel'}"
-      ]
-     },
-     "execution_count": 28,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "herstappe_profile"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(3, 77, 59)"
-      ]
-     },
-     "execution_count": 29,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "out_image.shape"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "To contain the spatial information or save the file for later usage keeping the spatial information, make sure to __update the profile metadata__ according to the transformations you did. In this case:\n",
-    "\n",
-    "- width/height of the cropped image\n",
-    "- affine transform function"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "herstappe_profile.update({\"height\": out_image.shape[1],\n",
-    "                          \"width\": out_image.shape[2],\n",
-    "                          \"transform\": out_transform})"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'driver': 'GTiff', 'dtype': 'float32', 'nodata': None, 'width': 59, 'height': 77, 'count': 3, 'crs': CRS.from_epsg(3857), 'transform': Affine(40.07813983073409, 0.0, 602814.4034437408,\n",
-       "       0.0, -40.1126034998577, 6574661.49591546), 'tiled': False, 'compress': 'deflate', 'interleave': 'pixel'}"
-      ]
-     },
-     "execution_count": 31,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "herstappe_profile"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Just as before, we can use the `plotting_extent` function to extract the extent of our data, using the outputs of the masking:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "new_extent = plotting_extent(out_image[0], out_transform)  # see docstring of plotting_extent on [0] requirement"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.image.AxesImage at 0x7f64603b3750>"
-      ]
-     },
-     "execution_count": 33,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.imshow(reshape_as_image(out_image), extent=new_extent)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The updated profile information also allows us to store the output as a Geotiff file:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "with rasterio.open(\"./test.tiff\", \"w\", **herstappe_profile) as dest:\n",
-    "    dest.write(out_image)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let's read the data we just saved to a file:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 864x360 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "with rasterio.open(\"./test.tiff\") as clipped:\n",
-    "    fig, ax = plt.subplots(figsize=(12, 5))\n",
-    "    show(clipped, (1, 2, 3), cmap='Greys')\n",
-    "\n",
-    "    clipped_ext = plotting_extent(clipped)\n",
-    "    clipped_array = clipped.read([1, 2, 3], \n",
-    "                                 out_dtype=float, masked=False)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Quick check if the extent in both situations are the same:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 36,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "new_extent == clipped_ext"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Rasterio workflow"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The workflow applied to clip the data set is typical when working with Rasterio. It consists of the following main steps:\n",
-    "\n",
-    "```\n",
-    "# 1\n",
-    "with rasterio.open(data_file) as src:  \n",
-    "\n",
-    "    # 2\n",
-    "    out_image, out_transform = rasterio.mask.mask(src, \n",
-    "        herstappe_vect.geometry, crop=True)    \n",
-    "    \n",
-    "    # 3\n",
-    "    herstappe_profile = src.profile\n",
-    "    herstappe_profile.update({\"height\": out_image.shape[1],\n",
-    "                              \"width\": out_image.shape[2],\n",
-    "                              \"transform\": out_transform})  \n",
-    "    # 4                              \n",
-    "    with rasterio.open(\"./test.tiff\", \"w\", **herstappe_profile) as dest: \n",
-    "        dest.write(out_image)\n",
-    "```\n",
-    "\n",
-    "- 1. Read in a data set using the context manager\n",
-    "- 2. Read and transform the data set by clipping, resampling,...\n",
-    "- 3. Update the spatial metadata/profile of the data set\n",
-    "- 4. Save the new data set with the updated metadata/profile"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Similar operations are:\n",
-    "\n",
-    "- Reprojecting a data set, see https://rasterio.readthedocs.io/en/latest/topics/reproject.html\n",
-    "- Resampling a data set, see https://rasterio.readthedocs.io/en/latest/topics/resampling.html\n",
-    "- Create a raster mosaic, see https://rasterio.readthedocs.io/en/latest/api/rasterio.merge.html and https://automating-gis-processes.github.io/CSC/notebooks/L5/raster-mosaic.html"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let's apply this workflow to resample our data set with a factor 2, i.e. doubling the pixel size:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data_file = \"./data/herstappe/raster/2020-09-17_Sentinel_2_L1C_True_color.tiff\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import rasterio\n",
-    "from rasterio.enums import Resampling\n",
-    "\n",
-    "scaling_factor = 2 \n",
-    "\n",
-    "with rasterio.open(data_file) as src:  # 1\n",
-    "\n",
-    "    # resample data to target shape  # 2\n",
-    "    new_width = int(src.width / scaling_factor)\n",
-    "    new_height = int(src.height / scaling_factor)\n",
-    "    out_image = src.read(\n",
-    "        out_shape=(src.count, new_height, new_width),\n",
-    "        resampling=Resampling.bilinear\n",
-    "    )\n",
-    "\n",
-    "    # scale image transform and update metadata  # 3\n",
-    "    data_profile = src.profile\n",
-    "    out_transform = src.transform * src.transform.scale(scaling_factor)\n",
-    "    data_profile.update({\"height\": new_height,\n",
-    "                         \"width\": new_width,\n",
-    "                         \"transform\": out_transform})      \n",
-    "\n",
-    "    # save the output to disk # 4\n",
-    "    with rasterio.open(\"./test_resample.tiff\", \"w\", **data_profile) as dst:\n",
-    "        dst.write(out_image)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "__Note:__ Other resampling algorithms are available in the `Resampling` module of rasterio."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#!gdalinfo ./data/herstappe/raster/2020-09-17_Sentinel_2_L1C_True_color.tiff\n",
-    "#!gdalinfo ./test_resample.tiff"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<div class=\"alert alert-info\" style=\"font-size:120%\">\n",
-    "\n",
-    "**REMEMBER**: <br>\n",
-    "\n",
-    "Many of the typical GIS operations provided by Rasterio have a common workflow. In order to preserve the spatial information, make sure to update the attributes such as transform, width and height accordingly before saving the file.\n",
-    "\n",
-    "</div>"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<div class=\"alert alert-success\">\n",
-    "\n",
-    "**EXERCISE**:\n",
-    "\n",
-    "You received remote sensing data from Ghent. You're wondering if the remote sensing image covers the entire administrative area of Ghent and decide to make a plot to make the comparison. The vector file of Ghent is available as well, see `./data/herstappe/vector/herstappe.geojson`.\n",
-    "    \n",
-    "Make a plot of `gent_data` (band 4 `./data/gent/raster/2020-09-17_Sentinel_2_L1C_B04.tiff`) together with `gent_vect` (read from `./data/herstappe/vector/herstappe.geojson`) using Matplotlib.\n",
-    "    \n",
-    "    \n",
-    "<details><summary>Hints</summary>\n",
-    "\n",
-    "* Make sure to align the CRS of both the vector as the raster data set.\n",
-    "* Remember the `extent` trick to use the coordinates as Matplotlib axis?\n",
-    "* The Matplotlib `plot` function provides `color`, `edgecolor`,... to adjust the style of the plot.\n",
-    "    \n",
-    "</details>\n",
-    "    \n",
-    "</div>"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "gent_vect = gpd.read_file(\"./data/gent/vector/gent.geojson\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "with rasterio.open(\"./data/gent/raster/2020-09-17_Sentinel_2_L1C_B04.tiff\") as src:\n",
-    "    gent_data = src.read(out_dtype=float, masked=False)\n",
-    "    gent_extent = plotting_extent(src)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 864x360 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig, ax = plt.subplots(figsize=(12, 5))\n",
-    "ax.imshow(gent_data.squeeze(), extent=gent_extent, cmap=\"Reds\")  # Extent trick to plot numpy\n",
-    "gent_vect.to_crs(epsg=3857).plot(color='None', edgecolor='grey', linewidth=2, ax=ax)\n",
-    "plt.axis(\"off\");"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<div class=\"alert alert-success\">\n",
-    "\n",
-    "**EXERCISE**:\n",
-    "\n",
-    "The files `./data/gent/raster/2020-09-17_Sentinel_2_L1C_B04.tiff` and `./data/gent/raster/2020-09-17_Sentinel_2_L1C_B08.tiff` are respectively band 4 and 8. Combine them in a single Geotiff file:\n",
-    "    \n",
-    "- Read in each of the data sets with rasterio\n",
-    "- Stack the two data sets with Numpy\n",
-    "- Write the resulting `(2, 317, 625)` array to a new geotiff file `./B0408.tiff` with data type `uint32`\n",
-    "    \n",
-    "    \n",
-    "<details><summary>Hints</summary>\n",
-    "\n",
-    "* The workflow is similar to before: you want to read, update the metadata/profile and write the output\n",
-    "* Stacking arrays in Numpy is done by `np.vstack`, converting the dtype by `astype(...)`\n",
-    "* The metadata/profile to update is not the transform, neither the widht/height. Check the `count` and the `dtype` keys of the profile\n",
-    "    \n",
-    "</details>\n",
-    "    \n",
-    "</div>"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 105,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "with rasterio.open(\"./data/gent/raster/2020-09-17_Sentinel_2_L1C_B04.tiff\") as src:\n",
-    "    b4_data = src.read()\n",
-    "    data_profile = src.profile\n",
-    "with rasterio.open(\"./data/gent/raster/2020-09-17_Sentinel_2_L1C_B08.tiff\") as src:\n",
-    "    b8_data = src.read() "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 106,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(2, 317, 625)"
-      ]
-     },
-     "execution_count": 106,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "b48_bands = np.vstack((b4_data, b8_data))  # 0 is b4 and 1 is b8  \n",
-    "b48_bands = b48_bands.astype(\"uint32\")\n",
-    "b48_bands.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 107,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data_profile.update({\"count\": 2, \n",
-    "                     \"dtype\": \"uint32\"}) \n",
-    "\n",
-    "with rasterio.open(\"./B0408.tiff\", \"w\", **data_profile) as dst:\n",
-    "    dst.write(b48_bands)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<div class=\"alert alert-success\">\n",
-    "    \n",
-    "**ADVANCED EXERCISE**:    \n",
-    "\n",
-    "You received multiple remte sensing layers of Ghent that need to be resampled. Instead of copy pasting the same code over and over again, you decide to write your own function to resample a file with Rasterio. The inputs of your function are:\n",
-    "    \n",
-    "- input_file : reference to a file on disk\n",
-    "- output_file :  where t write the output \n",
-    "- resampling_method : method from rasterio.Resampling to use when resampling\n",
-    "- scaling_factor : factor to which the resampling should be applied, default is 2\n",
-    "    \n",
-    "Apply the function on each of the Geotiff files in the folder `./data/gent/raster` (make sure to not overwrite the original files!)\n",
-    "    \n",
-    "<details><summary>Hints</summary>\n",
-    "\n",
-    "* Start from the code from the example\n",
-    "* Try to make the function as reusable as possible\n",
-    "* Make sure to document your own function using [numpy docstring](https://numpydoc.readthedocs.io/en/latest/format.html)    \n",
-    "* The [pathlib](https://docs.python.org/3/library/pathlib.html) module is very powerful for working with file systems. Use the `glob` method to find all files with a given extension.\n",
-    "    \n",
-    "</details>\n",
-    "    \n",
-    "</div>"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 543,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def resample_raster_file(input_file, output_file, resampling_method, scaling_factor=2):\n",
-    "    \"\"\"Resample a raster file with a given scaling factor using rasterio\n",
-    "    \n",
-    "    Parameters\n",
-    "    ----------\n",
-    "    input_file : str | Path\n",
-    "        Input raster file to resample\n",
-    "    output_file : str | Path\n",
-    "        Output raster file\n",
-    "    resampling_method : rasterio.Resampling\n",
-    "        Method available in the Resampling module of rasterio, e.g. Resampling.bilinear\n",
-    "    scaling_factor : float, default 2\n",
-    "        Scaling factor to use for resampling the data\n",
-    "    \n",
-    "    Examples\n",
-    "    --------\n",
-    "    >>> resample_raster_file(\"./input_file.tiff\", \"output_file.tiff\", \n",
-    "    ...                      Resampling.bilinear, scaling_factor=3)\n",
-    "    \"\"\"\n",
-    "    with rasterio.open(input_file) as src:  \n",
-    "        \n",
-    "        # resample data to target shape\n",
-    "        new_width = int(src.width / scaling_factor)\n",
-    "        new_height = int(src.height / scaling_factor)\n",
-    "        out_image = src.read(\n",
-    "            out_shape=(src.count, new_height, new_width),\n",
-    "            resampling=resampling_method  #Resampling.bilinear\n",
-    "        )\n",
-    "\n",
-    "        # scale image transform and update metadata  # 3\n",
-    "        data_profile = src.profile\n",
-    "        out_transform = src.transform * src.transform.scale(scaling_factor)\n",
-    "        data_profile.update({\"height\": new_height,\n",
-    "                             \"width\": new_width,\n",
-    "                             \"transform\": out_transform})      \n",
-    "\n",
-    "        # save the output to disk # 4\n",
-    "        with rasterio.open(output_file, \"w\", **data_profile) as dst:\n",
-    "            dst.write(out_image)    "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 541,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "resample_raster_file(\"./data/gent/raster/2020-09-17_Sentinel_2_L1C_B04.tiff\", \n",
-    "                     \"b4_out.tiff\", Resampling.bilinear, scaling_factor=2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 546,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from pathlib import Path"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 552,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Converting 2020-09-17_Sentinel_2_L1C_B03.tiff...\n",
-      "Converting 2020-09-17_Sentinel_2_L1C_B08.tiff...\n",
-      "Converting 2020-09-17_Sentinel_2_L1C_B04.tiff...\n",
-      "Converting 2020-09-17_Sentinel_2_L1C_True_color.tiff...\n",
-      "Converting 2020-09-17_Sentinel_2_L1C_B02.tiff...\n"
-     ]
-    }
-   ],
-   "source": [
-    "for file in Path(\"./data/gent/raster\").glob(\"*.tiff\"):\n",
-    "    print(f\"Converting {file.name}...\")\n",
-    "    resample_raster_file(file, Path(f\"./{file.name}_resampled.tiff\"), Resampling.bilinear, scaling_factor=2)    "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<div class=\"alert alert-info\" style=\"font-size:120%\">\n",
-    "\n",
-    "**REMEMBER**: <br>\n",
-    "\n",
-    "When you need to do a particular operation a lot on different files, convert the functionality to a function that you can reuse!\n",
-    "\n",
-    "If you are familiar to the command line, using the power of [GDAL](https://gdal.org/index.html) is a valid alternative to programming these conversions with Python/Rasterio. Have a look at [gdalwarp](https://gdal.org/programs/gdalwarp.html) in particular. If interested, the [Using GDAL for preprocessing](https://ocw.un-ihe.org/course/view.php?id=11&section=3) course provides a good introduction to GDAL.\n",
-    "\n",
-    "</div>"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## BONUS: only download what you need\n",
-    "\n",
-    "Rasterio only reads the data from disk that is requested to overcome loading entire data sets into memory. The same applies to downloading data, overcoming entire downloads when only a fraction is required (when the online resource supports this). An example is https://zenodo.org/record/2654620, which is available as [Cloud Optimized Geotiff (COG)](https://www.cogeo.org/). Also cloud provides (AWS, google,...) do support COG files, e.g. [Landstat images](https://docs.opendata.aws/landsat-pds/readme.html).\n",
-    "\n",
-    "These files are typically very large to download, whereas we might only need a small subset of the data. COG files do support to downloading a subset of the data you need using the masking approach.\n",
-    "\n",
-    "Let's use the Averbode nature reserve data as an example, available at the URL: http://s3-eu-west-1.amazonaws.com/lw-remote-sensing/cogeo/20160401_ABH_1_Ortho.tif"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 478,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "averbode_cog_rgb = 'http://s3-eu-west-1.amazonaws.com/lw-remote-sensing/cogeo/20160401_ABH_1_Ortho.tif'"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Check the metadata, without downloading the data itself:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 462,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "4 37645 35405\n",
-      "{'driver': 'GTiff', 'dtype': 'uint8', 'nodata': None, 'width': 37645, 'height': 35405, 'count': 4, 'crs': CRS.from_epsg(31370), 'transform': Affine(0.03999903195245761, 0.0, 190775.64103407107,\n",
-      "       0.0, -0.03999903195245824, 192409.09031570077)}\n"
-     ]
-    }
-   ],
-   "source": [
-    "with rasterio.open(averbode_cog_rgb) as averbode:\n",
-    "    print(averbode.count, averbode.width, averbode.height)\n",
-    "    print(averbode.meta)\n",
-    "    averbode_extent = plotting_extent(averbode)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Downloading the entire data set would be 37645*35405 pixels of 8 bit, so more or less 1.3 GByte"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 457,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "1.332821225"
-      ]
-     },
-     "execution_count": 457,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "37645*35405 / 1e9  # Gb"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Assume that we have a study area which is much smaller than the total extent of the available image:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 515,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "left, right, bottom, top = averbode_extent"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 516,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "averbode_study_area = gpd.read_file(\"./data/averbode/study_area.geojson\")\n",
-    "ax = averbode_study_area.plot();\n",
-    "ax.set_xlim(left, right);\n",
-    "ax.set_ylim(bottom, top);"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "In the case of COG data, the data can sometimes be requested on different resolution levels when stored as such. So, to get a very broad overview of the data, we can request the coarsest resolution by resampling and download the data at the resampled resolution:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 517,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Available resolutions are [2, 4, 8, 16, 32, 64, 128, 254]\n",
-      "Resampling factor= 254\n"
-     ]
-    }
-   ],
-   "source": [
-    "with rasterio.open(averbode_cog_rgb) as src:\n",
-    "    print(f\"Available resolutions are {src.overviews(1)}\")\n",
-    "    oview = src.overviews(1)[-1] # list of overviews, selecting the latest (most coarse)\n",
-    "    print(f\"Resampling factor= {oview}\")\n",
-    "    # Download a resampled version of the data (http://rasterio.readthedocs.io/en/latest/topics/resampling.html), aka thumbnail\n",
-    "    thumbnail = src.read(1, out_shape=(1, int(src.height // oview), int(src.width // oview)))    "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Compare the thumbnail version of the data with our study area:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 518,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig, ax = plt.subplots()\n",
-    "ax.imshow(thumbnail, extent=averbode_extent);\n",
-    "averbode_study_area.plot(ax=ax, color='None', edgecolor='red', linewidth=2);"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Downloading the entire data file would be overkill. Instead, we only want to download the data of the study area:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 510,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "output_file = \"./averbode_orthophoto.tiff\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The resulting data set will still be around 35MB and will take a bit of time, but this is only a fraction of the original data file:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 492,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Only run this cell when sufficient band width ;-)\n",
-    "with rasterio.open(averbode_cog_rgb) as averbode_rgb:\n",
-    "    averbode_rgb_image, averbode_rgb_transform = rasterio.mask.mask(averbode_rgb, averbode_study_area.geometry, crop=True)\n",
-    "    averbode_rgb_profile = averbode_rgb.profile  \n",
-    "\n",
-    "    averbode_rgb_profile.update({\"driver\": \"GTiff\",\n",
-    "                                 \"height\": averbode_rgb_image.shape[1],\n",
-    "                                 \"width\": averbode_rgb_image.shape[2],\n",
-    "                                 \"transform\": averbode_rgb_transform\n",
-    "                                })\n",
-    "    \n",
-    "    with rasterio.open(output_file, \"w\", **averbode_rgb_profile) as dest:\n",
-    "        dest.write(averbode_rgb_image)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Thanks to https://geohackweek.github.io/raster/04-workingwithrasters/ for the inspiration"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python [conda env:DS-geospatial]",
-   "language": "python",
-   "name": "conda-env-DS-geospatial-py"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.6"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/_solved/12-xarray-advanced.ipynb b/_solved/12-xarray-advanced.ipynb
new file mode 100644
index 0000000..17b020c
--- /dev/null
+++ b/_solved/12-xarray-advanced.ipynb
@@ -0,0 +1,1516 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "37c046a3-39ce-4b51-9fb5-07a2d93d3fa2",
+   "metadata": {},
+   "source": [
+    "<p><font size=\"6\"><b>Xarray advanced</b></font></p>\n",
+    "\n",
+    "\n",
+    "> *DS Python for GIS and Geoscience*  \n",
+    "> *October, 2025*\n",
+    ">\n",
+    "> *© 2025, Joris Van den Bossche and Stijn Van Hoey. Licensed under [CC BY 4.0 Creative Commons](http://creativecommons.org/licenses/by/4.0/)*\n",
+    "\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c4b0d759-b8e0-4950-b6b1-706edad1b1e2",
+   "metadata": {},
+   "source": [
+    "Acknowledgments to the data service: https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-land-monthly-means?tab=overview"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7c2ad45b-1592-4128-a126-e984bfba1a4b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import xarray as xr\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "import cmocean"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1015efcc-929c-4bc5-9dfb-7805fd888a45",
+   "metadata": {},
+   "source": [
+    "## `xarray.Dataset` for multiple variables"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e6ce0cd6-3a30-4085-ae16-0fd6083d6959",
+   "metadata": {},
+   "source": [
+    "We already know `xarray.DataArray`, it is a single multi-dimensional array and each dimension can have a name and coordinate values. Next to the `DataArray`, `xarray` has a second main data structure to store arrays, i.e. `xarray.DataSet`. \n",
+    "\n",
+    "Let's read an xarray data set (global rain/temperature coverage stored in the file `2016-2017_global_rain-temperature.nc`), using the function `open_dataset`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "47aa01dd-cf62-4312-9596-1f5a4ba7ff25",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ds = xr.open_dataset(\"./data/2016-2017_global_rain-temperature.nc\", engine=\"netcdf4\")\n",
+    "ds"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d62db744-7f64-42e6-8e45-638b88421c53",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-info\" style=\"font-size:120%\">\n",
+    "\n",
+    "**REMEMBER**: <br>\n",
+    "\n",
+    "xarray provides reading function for different formats with the `open_dataset` and `open_dataarray` functions. For GIS formats such as geotiff and other GDAL readable raster data, the rasterio engine (`engine=\"rasterio\"`) is available after the installation of [rioxarray](https://corteva.github.io/rioxarray/stable/). NetCDF-alike data formats can also be loaded using the `open_dataset()` function using a NetCDF-compatible engine, e.g. `netcdf` or `h5netcdf`. The netcdf-engine will be _guessed_ by default.\n",
+    "\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e586c171-c6d7-4f90-8f5b-df54089c7cfa",
+   "metadata": {},
+   "source": [
+    "Let's take a closer look at this `xarray.Dataset`:\n",
+    "\n",
+    "- A `xarray.Dataset` is the second main data type provided by xarray\n",
+    "- This example has 3 __dimensions__:\n",
+    "    - `y`: the y coordinates of the data set\n",
+    "    - `x`: the x coordinates of the data set\n",
+    "    - `year`: the year coordinate of the data set\n",
+    "- Each of these dimensions are defined by a __coordinate__ (1D) array\n",
+    "- It has 2 __Data variables__: `precipitation` and `temperature` that both share the same coordinates\n",
+    "\n",
+    "Hence, a `Dataset` object stores *multiple* arrays that have shared dimensions (__Note:__ not all dimensions need to be shared).  It is designed as an in-memory representation of the data model from the netCDF file format.\n",
+    "\n",
+    "![](http://xarray.pydata.org/en/stable/_images/dataset-diagram.png)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c0e48ee0-c828-4f80-a6c8-5428155bef01",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ds[\"temperature\"].shape, ds[\"temperature\"].dims"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "72266de5-77e4-4b48-81fe-7388b7dff5b4",
+   "metadata": {},
+   "source": [
+    "The data and coordinate variables are also contained separately in the `data_vars` and `coords` dictionary-like attributes of a `xarray.DataSet` to access them directly:\n",
+    "\n",
+    "- The data variables:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2460b4e6-a5f1-40da-967d-325aa0287d4b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ds.data_vars"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ddc9f07c-0d81-4aab-a753-da40f57aa735",
+   "metadata": {},
+   "source": [
+    "- The data coordinates:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a67e0be0-204b-4164-a6ed-9bc6eb34eaed",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ds.coords"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ccc8e94c-17ca-420a-a3aa-c7c71fa76636",
+   "metadata": {},
+   "source": [
+    "If you rather use an alternative name for a given variable, use the `rename` method:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bbfc46d8-e4b3-4cc4-bd83-769258548716",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ds.rename({\"precipitation\": \"rain\"})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8efa9dcb-128e-4b01-987f-a970c7abe40f",
+   "metadata": {},
+   "source": [
+    "__Note:__ _the renaming is not entirely correct as rain is just a part of all precipitation (see https://en.wikipedia.org/wiki/Precipitation)._"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d8e17106-8575-4f55-bb92-ab6c2d90a6c1",
+   "metadata": {},
+   "source": [
+    "Adding new variables to the data set is very similar to Pandas/GeoPandas:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6ff98dc0-737f-4e55-8c73-2f9b62f89749",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ds[\"precipitation_m\"] = ds[\"precipitation\"]/1000."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1c8aa29e-6d87-4ad8-80e7-a265bb683ca6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ds"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "035e068b-d776-4f13-93c6-c91929d8ce26",
+   "metadata": {},
+   "source": [
+    "### Selecting `DataSet` data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "14cd95ad-d6fc-4685-a899-16084de71edb",
+   "metadata": {},
+   "source": [
+    "Each of the data variables can be accessed as a single `xarray.DataArray` similar to selecting from dictionaries or columns from DataFrames:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "400bcda7-b4d8-4aa9-a4ef-7ab0158c1f28",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "type(ds[\"precipitation\"]), type(ds[\"temperature\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "38951a5e-7515-4665-ad08-82cce47a75ac",
+   "metadata": {},
+   "source": [
+    "One can select multiple variables at the same time as well by passing a list of variable names:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e0e2ee9e-6882-4ddb-a01e-c7a64fae2c17",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ds[[\"temperature\", \"precipitation\"]]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6c3db5d4-6fe9-4c54-9180-c90e75b4ed80",
+   "metadata": {},
+   "source": [
+    "Or the other way around, use the `drop_vars` to drop variables from the data set:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a114abfe-0950-4262-9e13-9ecd78ff6f49",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ds.drop_vars(\"temperature\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "148ca2f1-14fb-4825-9c77-c761b275de66",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-info\" style=\"font-size:120%\">\n",
+    "\n",
+    "**NOTE**: <br>\n",
+    "\n",
+    "Selecting a single variable using `[]` results into a `xarray.DataArray`, selecting multiple variables using a list `[[..., ...]]` results into a `xarray.DataSet`. Using `drop_vars` always returns a `xarray.DataSet`.\n",
+    "\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "449e461c-b584-4e51-905a-eda5fd95e80a",
+   "metadata": {},
+   "source": [
+    "The selection with `sel` works as well with `xarray.DataSet`, selecting the data _for all variables in the DataSet_ and returning a DataSet:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "07b43c50-8b78-4ee2-9b03-85526b98f5f7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ds.sel(year=2016)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7db298ec-66a7-46d8-ac7f-0cc6389e598e",
+   "metadata": {},
+   "source": [
+    "The inverse of the `sel` method is the `drop_sel` which returns a DataSet with the enlisted indices removed:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2e0c699f-da00-41e4-811c-a5efbe72f046",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ds.drop_sel(year=[2016])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c4c20f29-fc0d-4743-9be7-a88ec04c8a96",
+   "metadata": {},
+   "source": [
+    "### DataSet plotting"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8fa04333-e701-43cb-aaa3-217fda033ace",
+   "metadata": {},
+   "source": [
+    "Plotting for data set level is rather limited. A typical use case that is supported to compare two data variables are scatter plots:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c0a5372f-c674-42a2-bbbd-6162a17cd125",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ds.plot.scatter(x=\"temperature\", y=\"precipitation\", s=1, alpha=0.1, edgecolor=\"none\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "79bf5a3c-d95e-4f35-8535-f7245c175cf0",
+   "metadata": {},
+   "source": [
+    "`facetting` is also supported here, by linking the `col` or `row` parameter to a data variable."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c4423911-7fad-48b0-8790-3b4ea5566b70",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ds.plot.scatter(x=\"temperature\", y=\"precipitation\", s=1, alpha=0.1, col=\"year\", edgecolor=\"none\")  # try also hue=\"year\" instead of col; requires hue_style=\"discrete\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a1be8b3c-2eee-42d8-9085-0affa62098f3",
+   "metadata": {},
+   "source": [
+    "### DataSet reductions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "68c5401b-bf33-4d18-86f4-e0d20b5d2927",
+   "metadata": {},
+   "source": [
+    "Datasets support arithmetic operations by automatically looping over all data variables and supports most of the same methods found on `xarray.DataArray`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0573e3b5-1260-44bf-ab12-245fbf2d12d9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ds.mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7012d2f1-094d-4493-8995-4d0f86d75e4d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ds.max(dim=[\"x\", \"y\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "da29365a-b560-49e6-a357-5f0e78955363",
+   "metadata": {},
+   "source": [
+    "__Note__ Using the names of the data variables (which is actually element-wise operations with DataArrays) makes a calculation very self-describing, e.g."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "797f8990-f31f-4745-8d91-e120317de979",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "(ds[\"temperature\"] * ds[\"precipitation\"]).sel(year=2016).plot.imshow()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1382ea01-2c2c-43c6-819a-ba6e54e1640b",
+   "metadata": {},
+   "source": [
+    "### Let's practice"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ef13ef35-d144-4cd7-ad8b-02763d44dd78",
+   "metadata": {},
+   "source": [
+    "For the next set of exercises, we use the [ERA5-Land monthly averaged data](https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-land-monthly-means?tab=overview), provided by the ECMWF (European Centre for Medium-Range Weather Forecasts).\n",
+    "\n",
+    "> ERA5-Land is a reanalysis dataset providing a consistent view of the evolution of land variables over several decades. Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset using the laws of physics. \n",
+    "\n",
+    "For these exercises, a subset of the data set focusing on Belgium has been prepared, containing the following variables:\n",
+    "\n",
+    "- `sf`: Snowfall (_m of water equivalent_)\n",
+    "- `sp`: Surface pressure (_Pa_)\n",
+    "- `t2m`: 2 metre temperature (_K_)\n",
+    "- `tp`: Total precipitation (_m_)\n",
+    "- `u10`: 10 metre U wind component (_m/s_)\n",
+    "\n",
+    "The dimensions are the `longitude`, `latitude` and `time`, which are each represented by a corresponding coordinate."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9c01ab8b-0c75-4992-8f7d-68759c1fb6b2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "era5 = xr.open_dataset(\"./data/era5-land-monthly-means_example.nc\") # engine=\"netcdf4\" is here optional\n",
+    "era5"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "724d816c-4f6b-4a8b-b9ba-34b13bff41b0",
+   "metadata": {},
+   "source": [
+    "To get a feeling on the spatial subset of the data set for these exercises, the following code creates a cartopy map of the average temperature with country borders added:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "86c9a88b-a63c-4903-a207-284f100aeeef",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import cartopy\n",
+    "import cartopy.crs as ccrs\n",
+    "\n",
+    "fig = plt.figure(figsize=(9,6))\n",
+    "ax = plt.axes(projection=ccrs.PlateCarree())\n",
+    "\n",
+    "ax.add_feature(cartopy.feature.BORDERS, linestyle=':')\n",
+    "era5_mean_temp = era5[\"t2m\"].mean(dim=\"time\") - 273.15\n",
+    "era5_mean_temp.plot.imshow(ax=ax, cmap=\"coolwarm\", \n",
+    "                           transform=ccrs.PlateCarree(),\n",
+    "                           cbar_kwargs={'shrink': 0.65});"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d52c2c00-fb94-4fbf-8442-defce3f55178",
+   "metadata": {},
+   "source": [
+    "(See notebook [visualization-03-cartopy](./visualization-03-cartopy.ipynb) for more information on the usage of Cartopy)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "050db69c-c7e0-4210-a6c4-530cba7f6cc5",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-success\">\n",
+    "\n",
+    "**EXERCISE**:\n",
+    "\n",
+    "The [short names used by ECMWF](https://confluence.ecmwf.int/display/CKB/ERA5-Land%3A+data+documentation) are not very convenient to understand. Rename the variables of the  data set according to the following mapping:\n",
+    "    \n",
+    "- `sf`: snowfall_m\n",
+    "- `sp`: pressure_pa\n",
+    "- `t2m`: temperature_k\n",
+    "- `tp`: precipitation_m\n",
+    "- `u10`: wind_ms   \n",
+    "    \n",
+    "Save the result of the mapping as the variable `era5_renamed`.\n",
+    "\n",
+    "<details><summary>Hints</summary>\n",
+    "\n",
+    "* Both `rename` and `rename_vars` can be used to rename the DataSet variables\n",
+    "* The `rename` function requires a `dict-like` input with the current names as the keys and the new names as the values. \n",
+    "\n",
+    "</details>    \n",
+    "    \n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1403ca2d-3bcb-41e3-898f-578ce6ec1971",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mapping = {\n",
+    "    \"sf\": \"snowfall_m\",\n",
+    "    \"sp\": \"pressure_pa\",\n",
+    "    \"t2m\": \"temperature_k\",\n",
+    "    \"tp\": \"precipitation_m\",\n",
+    "    \"u10\": \"wind_ms\"\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "597e057c-1f36-48cb-9b5a-22b4af6a8a59",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "era5_renamed = era5.rename(mapping)\n",
+    "era5_renamed"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0cd469ac-818c-4eb9-bc4c-91ada227339d",
+   "metadata": {},
+   "source": [
+    "__Note:__ _Make sure you have the variable `era5_renamed` correctly loaded for the following exercises. If not, load the solution of the previous exercise._"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "988e64dc-ef51-435e-b24c-d362f20ea912",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-success\">\n",
+    "\n",
+    "**EXERCISE**:\n",
+    "\n",
+    "Start from the `era5_renamed` variable. You are used to work with temperatures defined in degrees celsius instead of Kelvin. Add a new data variable to `era5_renamed`, named `temperature_c`, by converting the `temperature_k` into degrees celsius:\n",
+    "    \n",
+    "$T_{^{\\circ}C} = T_{K} - 273.15$\n",
+    "    \n",
+    "Create a histogram of the `temperature_c` to check the distribution of all the temperature valus in the data set. Use an appropriate number of bins to draw the histogram.\n",
+    "\n",
+    "<details><summary>Hints</summary>\n",
+    "\n",
+    "* Xarray - similar to Numpy - applies the mathematical operation element-wise, so no need for loops.\n",
+    "* Most plot functions work on `DataArray`, so make sure to select the variable to apply the `.plot.hist()`.\n",
+    "* One can define the number of bins using the `bins` parameter in the `hist` method.\n",
+    "\n",
+    "</details>    \n",
+    "    \n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0f5095b1-5e3d-4ba2-afa8-33e22e61d331",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "era5_renamed[\"temperature_c\"] = era5_renamed[\"temperature_k\"] - 273.15"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a2230a30-b3a8-4298-9666-30ecc58e4849",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "era5_renamed[\"temperature_c\"].plot.hist(bins=50);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4d372680-c12e-41bf-8bef-9e8c998dabde",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-success\">\n",
+    "\n",
+    "**EXERCISE**:\n",
+    "\n",
+    "Calculate the total snowfall of the entire region of the dataset in function of time and create a line plot showing total snowfall in the y-axis and time in the x-axis.\n",
+    "\n",
+    "<details><summary>Hints</summary>\n",
+    "\n",
+    "* You need to calculate the total (`sum`) snowfall (`snowfall_m`) in function of time, i.e. aggregate over both the `longitude` and `latitude` dimensions (`dim=[\"latitude\", \"longitude\"]`).\n",
+    "* As the result is a DataArray with a single dimension, the default `plot` will show a line, but you can be more explicit by saying `.plot.line()`.\n",
+    "\n",
+    "</details>    \n",
+    "    \n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "733a1027-f906-40e5-8958-af99391dedd4",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "era5_renamed[\"snowfall_m\"].sum(dim=[\"latitude\", \"longitude\"]).plot.line(figsize=(18, 5))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "633ece8e-0fcf-4822-a14d-d45e6677adb5",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-success\">\n",
+    "\n",
+    "**EXERCISE**:\n",
+    "\n",
+    "The speed to sound is linearly dependent on temperature:\n",
+    "    \n",
+    "$v = 331.5 + (0.6 \\cdot T)$\n",
+    "    \n",
+    "with $v$ the speed of sound and $T$ the temperature in degrees celsius.    \n",
+    "    \n",
+    "Add a new variable to the `era5_renamed` data set, called `speed_of_sound_m_s`, that calculates for each location and each time stamp in the data set the temperature corrected speed of sound.\n",
+    "    \n",
+    "Create a scatter plot to control the (linear) relationship you just calculated by comparing all the `speed_of_sound_m_s` and `temperature_c` data points in the data set.\n",
+    "\n",
+    "<details><summary>Hints</summary>\n",
+    "\n",
+    "* Creating a new variable is similar to GeoPandas/Pandas/dictionaries, `ds[\"MY_NEW_VAR\"] = ...`.\n",
+    "* Remember, calculations are __element-wise__ just as in Numpy/Pandas; so no need for loops. The numbers (331.5, 0.6) are broadcasted to all elements in the data set to do the calculation.\n",
+    "* To compare two variables in a data set visually, `plot.scatter()` it is.\n",
+    "\n",
+    "</details>    \n",
+    "    \n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "75b9ea92-7563-45b8-918d-0ee24092f1eb",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "era5_renamed[\"speed_of_sound_m_s\"] = 331.5 + (0.6*era5_renamed[\"temperature_c\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "dc82bca4-bd8a-4ff7-89ef-2e007cc058ee",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "era5_renamed.plot.scatter(x=\"temperature_c\", y=\"speed_of_sound_m_s\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4bb8d66a-c910-4108-920d-d780838c9717",
+   "metadata": {},
+   "source": [
+    "## Working with time series"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "775b5de9-0cd2-4e73-993a-a4b2ed741ba2",
+   "metadata": {},
+   "source": [
+    "Let's start again from the ERA5 data set we worked with in the previous exercises, and rename the variables for convenience:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2053ddc5-1a08-48b5-b3be-3267441bbaa5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "era5 = xr.open_dataset(\"./data/era5-land-monthly-means_example.nc\")\n",
+    "mapping = {\n",
+    "    \"sf\": \"snowfall_m\",\n",
+    "    \"sp\": \"pressure_pa\",\n",
+    "    \"t2m\": \"temperature_k\",\n",
+    "    \"tp\": \"precipitation_m\",\n",
+    "    \"u10\": \"wind_ms\"\n",
+    "}\n",
+    "era5_renamed = era5.rename(mapping)\n",
+    "era5_renamed"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f09ee5e8-6a10-4831-95a0-6c9577437992",
+   "metadata": {},
+   "source": [
+    "Apart from the different coordinates, the data set also contains a `time` dimension. xarray borrows the indexing machinery from Pandas, also for datetime coordinates:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0bbf03b0-2b2b-4c69-90a3-90c91b783688",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "era5_renamed.time"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c964676f-5baf-4ea6-9e23-c38883cfe4fe",
+   "metadata": {},
+   "source": [
+    "With a DateTime-aware index (see `datetime64[ns]` as dtype), selecting dates can be done using the string representation, e.g."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a5577359-e2c4-4fc8-a157-8a580682d5ea",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "era5_renamed.sel(time=\"2002\").time "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "01dd349e-6fc3-42be-ae2d-96c1f4ed66ec",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "era5_renamed.sel(time=slice(\"2001-05\", \"2002-08\")).time"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0d6edf5a-506d-4217-82c9-1cfafbbe34ff",
+   "metadata": {},
+   "source": [
+    "And you can access the datetime components, e.g. \"year\", \"month\",..., \"dayofyear\", \"week\", \"dayofweek\",... but also \"season\". Do not forget to use the `.dt`-accessor to access these components:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0a6d3d01-763c-4197-97c0-1b6df3aa80eb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "era5_renamed[\"time\"].dt.season"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2775d2ed-eb40-4a7b-8ecc-1535e2e9594f",
+   "metadata": {},
+   "source": [
+    "Xarray contains some more powerful functionalities to work with time series, e.g. `groupby`, `resample` and `rolling`. All have a similar syntax as Pandas/GeoPandas, but applied on an N-dimensional array instead of a DataFrame."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c346962c-7ce8-4f55-a728-043b1f94463a",
+   "metadata": {},
+   "source": [
+    "### split-apply-combine, aka `groupby`"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "514eaf9e-1127-42cf-ab61-12551b73ec86",
+   "metadata": {},
+   "source": [
+    "If we are interested in the _average over time_ for each of the levels, we can use a reducton function to get the averages of each of the variables at the same time:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a397d3e5-991b-44de-a3df-70ad02523df8",
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "era5_renamed.mean(dim=[\"time\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "be63a859-d1d0-4dc6-a4d9-d92a328eb884",
+   "metadata": {},
+   "source": [
+    "But if we wanted the _average for each month of the year_ per level, we would first have to __split__ the data set in a group for each month of the year, __apply__ the average function on each of the months and __combine__ the data again. \n",
+    "\n",
+    "We already learned about the [split-apply-combine](https://pandas.pydata.org/pandas-docs/stable/user_guide/groupby.html) approach when using Pandas. The syntax of Xarray’s groupby is almost identical to Pandas!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7b2f35df-5bd3-46a9-85c1-0800bccd4b0a",
+   "metadata": {},
+   "source": [
+    "First, extract the month of the year (1 -> 12) from each of the time coordinate/dimension:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cdaf9650-9c43-4c10-b1b9-d9fb157409fa",
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "era5_renamed[\"time\"].dt.month  # The coordinates is a Pandas datetime index"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "71fa721e-685e-43ef-83f4-8a6ce44f7192",
+   "metadata": {},
+   "source": [
+    "We can use this array in a `groupby` operation:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8efd054d-939c-488e-9504-0a710e4a1e03",
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "era5_renamed.groupby(era5_renamed[\"time\"].dt.month)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9711cc34-8f5e-40f6-8ba8-88312a29b98b",
+   "metadata": {},
+   "source": [
+    "_split the data in (12) groups where each element is grouped according to the month it belongs to._"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c778cca9-a129-48bd-9064-9ef2bdc9fa87",
+   "metadata": {},
+   "source": [
+    "__Note:__ Xarray also offers a more concise syntax when the variable you're grouping on is already present in the dataset. The following statement is identical to the previous line:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fe0045b6-420e-473d-b5f4-0f0a46c9aafd",
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "era5_renamed.groupby(\"time.month\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b59f7bc2-9fee-4cfd-8843-87e9331796d1",
+   "metadata": {},
+   "source": [
+    "Next, we apply an aggregation function _for each of the months_ over the `time` dimension in order to end up with: _for each month of the year, the average (over time) for each of the levels_:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "353ce952-5452-4c1a-b64f-49109e45865c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "era5_renamed.groupby(\"time.month\").mean()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "49043296-6e04-4f36-ac97-9edc2568c2fa",
+   "metadata": {},
+   "source": [
+    "The resulting dimension month contains the 12 groups on which the data set was split up."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0fbab008-bd82-47c4-b9fd-f1075811b7e3",
+   "metadata": {},
+   "source": [
+    "<img align=\"center\" src=\"../img/pandas/06_groupby1.svg\">"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "797ae1ed-2940-4199-b297-7d6e9acab715",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-info\" style=\"font-size:120%\">\n",
+    "\n",
+    "**REMEMBER**: <br>\n",
+    "\n",
+    "The `groupby` method splits the data set in groups, applies some function _on each of the groups_ and combines again the results of each of the groups. It is not limited to time series data, but can be used in any situation where the data can be split up by a categorical variable. \n",
+    "\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6d51b68b-9a92-450b-b500-59b371a3eef5",
+   "metadata": {},
+   "source": [
+    "### resample/rolling"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fb9cdebc-c4bc-491d-81c0-3d6b2cce3b79",
+   "metadata": {},
+   "source": [
+    "Another (alike) operation - specifically for time series data - is to `resample` the data to another time-aggregation. For example, resample to monthly (`4M`) or yearly (`1Y`) median values:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6e8df3de-72ea-4281-b355-6216117c2e83",
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "era5_renamed.resample(time=\"1YE\").median()  # 4M"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6b846207-a635-42f7-8b31-0c38e3fa8b17",
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "era5_renamed[\"temperature_k\"].sel(latitude=51., longitude=4., method=\"nearest\").plot.line(x=\"time\");\n",
+    "era5_renamed[\"temperature_k\"].sel(latitude=51., longitude=4., method=\"nearest\").resample(time=\"10YE\").median().plot.line(x=\"time\");"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "da0d5e1a-3752-480f-a7e3-6e5fc92fa7f2",
+   "metadata": {},
+   "source": [
+    "A similar, but different functionality is `rolling` to calculate rolling window aggregates:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "44c7ffc1-fa4c-4e38-a57a-b2128cb93040",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "era5_renamed.rolling(time=12, center=True).median()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "eb325086-089c-45e6-84c5-131db680cd19",
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "era5_renamed[\"temperature_k\"].sel(latitude=51., longitude=4., method=\"nearest\").plot.line()\n",
+    "era5_renamed[\"temperature_k\"].sel(latitude=51., longitude=4., method=\"nearest\").rolling(time=12, center=True).min().plot.line()\n",
+    "era5_renamed[\"temperature_k\"].sel(latitude=51., longitude=4., method=\"nearest\").rolling(time=12, center=True).max().plot.line()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "71085f04-5124-4a10-9e5c-cbaa2674d564",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-info\" style=\"font-size:120%\">\n",
+    "\n",
+    "**REMEMBER**: <br>\n",
+    "\n",
+    "The [xarray `groupby`](http://xarray.pydata.org/en/stable/groupby.html) with the same syntax as Pandas implements the __split-apply-combine__ strategy. Also [`resample`](http://xarray.pydata.org/en/stable/time-series.html#resampling-and-grouped-operations) and [`rolling`](http://xarray.pydata.org/en/stable/computation.html?highlight=rolling#rolling-window-operations) are available in xarray.\n",
+    "    \n",
+    "__Note:__ Xarray adds a [`groupby_bins`](http://xarray.pydata.org/en/stable/generated/xarray.Dataset.groupby_bins.html#xarray.Dataset.groupby_bins) convenience function for binned groups (instead of each value).\n",
+    "\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ed0ed970-502e-428a-8306-5af774aad525",
+   "metadata": {},
+   "source": [
+    "### Let's practice"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "19baaf1d-8013-4e0e-8acc-93baefab799e",
+   "metadata": {},
+   "source": [
+    "Run this cell before doing the exercises, so the starting point is again the `era5_renamed` data set:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9b8f3607-8fea-484e-b899-cb8f35302105",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "era5 = xr.open_dataset(\"./data/era5-land-monthly-means_example.nc\")\n",
+    "mapping = {\n",
+    "    \"sf\": \"snowfall_m\",\n",
+    "    \"sp\": \"pressure_pa\",\n",
+    "    \"t2m\": \"temperature_k\",\n",
+    "    \"tp\": \"precipitation_m\",\n",
+    "    \"u10\": \"wind_ms\"\n",
+    "}\n",
+    "era5_renamed = era5.rename(mapping)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ce2fd02d-6d03-46cf-8fa6-8f3235cb29ca",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-success\">\n",
+    "\n",
+    "**EXERCISE**:\n",
+    "\n",
+    "Select the pressure data for the pixel closest to the center of Ghent (lat: -51.05, lon: 3.71) and assign the outcome to a new variable `ghent_pressure`.\n",
+    "\n",
+    "Define a Matplotlib `Figure` and `Axes` (respectively named `fig, ax`) and use it to create a plot that combines the yearly average of the pressure data in Gent with the monthly pressure data as function of time for that same pixel as line plots. Change the name of the y-label to `'Pressure (Pa)'` and the title of the plot to `'Pressure (Pa) in Ghent (at -51.05, 3.71)'` (see notebook [visualization-01-matplotlib.ipynb](./visualization-01-matplotlib.ipynb#An-small-cheat-sheet-reference-for-some-common-elements) for more information)\n",
+    "\n",
+    "<details><summary>Hints</summary>\n",
+    "    \n",
+    "* For the yearly average, actually both `resample(time=\"Y\")` as `groupby(\"time.year\")` can be used. The main difference is the returned dimension after the grouping: `resample` returns a DateTimeIndex, whereas `groupby` returns a dimension called `year` and no longer contains the DateTimeIndex. To combine the data with other time series data, using the DateTimeIndex is preferred (i.e. `resample`). \n",
+    "* `fig, ax = plt.subplots()` is a useful shortcut to prepare a Matplotlib Figure. You can add a label `set_label()` and title `set_title()` to the `axes` object.\n",
+    "\n",
+    "</details>    \n",
+    "    \n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3042fd53-7a4e-4ef0-8fe5-9e9885231b0a",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "ghent_pressure = era5_renamed.sel(latitude=51.05, longitude=3.71, method=\"nearest\")[\"pressure_pa\"]\n",
+    "\n",
+    "fig, ax = plt.subplots(figsize=(18, 6))\n",
+    "ghent_pressure.plot.line(ax=ax)\n",
+    "ghent_pressure.resample(time=\"YE\").mean().plot.line(ax=ax)\n",
+    "\n",
+    "ax.set_ylabel('Pressure (Pa)')\n",
+    "ax.set_title('Pressure (Pa) in Ghent (-51.05, 3.71)')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8867e7b1-949f-4df4-860c-8c2947452502",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-success\">\n",
+    "\n",
+    "**EXERCISE**:\n",
+    "    \n",
+    "Select the precipitation data for the pixel closest to the center of Ghent (lat: -51.05, lon: 3.71) and assign the outcome to a new variable `ghent_precipitation`.\n",
+    "    \n",
+    "For the Ghent pixel, calculate the maximal precipitation _for each month of the year_ (1 -> 12) and convert it to mm precipitation.\n",
+    "    \n",
+    "To make a bar-chart (not supported in xarray) of the maximal precipitation for each month of the year, convert the output to a Pandas DataFrame using the `to_dataframe()` method and create a horizontal bar chart with the month in the y-axis and the maximal precipitation in the x-axis. Feel free to improve the axis labels.\n",
+    "\n",
+    "<details><summary>Hints</summary>\n",
+    "    \n",
+    "* You need to group based on the month of the data point, hence `groupby(\"...\")` with `time` as an existing dimension, the `time.month` shortcut to get the corresponding month will work.\n",
+    "* Plotting in Pandas is very similar to xarray, use `.plot.barh()` to create a horizontal bar chart.\n",
+    "\n",
+    "</details>    \n",
+    "    \n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "781a3ec8-6080-4871-950b-d2b3f9112421",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "ghent_precipitation = era5_renamed.sel(latitude=51.05, longitude=3.71, method=\"nearest\")[\"precipitation_m\"]\n",
+    "ghent_precipitation_month_max = ghent_precipitation.groupby(\"time.month\").max()*1000\n",
+    "\n",
+    "fig, ax = plt.subplots()\n",
+    "ghent_precipitation_month_max.to_dataframe()[\"precipitation_m\"].plot.barh(ax=ax)\n",
+    "ax.set_xlabel(\"Maximal monthly precipitation (mm)\")\n",
+    "ax.set_ylabel(\"Month of the year\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f56b741b-d6bb-42f0-b1bb-106468ef1ef7",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-success\">\n",
+    "\n",
+    "**EXERCISE**:\n",
+    "    \n",
+    "Calculate the pixel-based average temperature _for each season_. Make a plot (`imshow`) with each of the seasons in a separate subplot next to each other. \n",
+    "\n",
+    "<details><summary>Hints</summary>\n",
+    "    \n",
+    "* You need to group based on the season of the data point, hence `groupby(\"...\")` with `time` as an existing dimension, the `time.season` shortcut to get the corresponding season will work.\n",
+    "* The labels of the season groups are sorted on the strings in the description instead of the seasons. The workaround is to `reindex` and provide a correctly sorted (i.e. `['DJF','MAM','JJA', 'SON']` version to sort the `season` with.\n",
+    "* Use facetting to plot each of the seasons next to each other, with `col=\"season\"`.\n",
+    "\n",
+    "\n",
+    "</details>    \n",
+    "    \n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3cfd5a68-7ea5-4078-b244-3913e1f75142",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "seaons_temp = era5_renamed[\"temperature_k\"].groupby(\"time.season\").mean()\n",
+    "# See https://github.com/pydata/xarray/issues/757 for getting well-sorted groups for plotting\n",
+    "seaons_temp = seaons_temp.reindex(season=['DJF','MAM','JJA', 'SON'])\n",
+    "seaons_temp.plot.imshow(col=\"season\", cmap=\"Reds\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f437e339-2074-4aef-8cf2-0e3cd3ac2a3b",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-success\">\n",
+    "\n",
+    "**EXERCISE**:\n",
+    "\n",
+    "Calculate the average temperature of the entire region of the dataset in function of time. From this time series, use only those years for which all 12 months of the year are included in the data set and calculate the yearly average temperature.\n",
+    "    \n",
+    "Create a line plot showing the yearly average temperature in the y-axis and time in the x-axis.\n",
+    "\n",
+    "<details><summary>Hints</summary>\n",
+    "\n",
+    "* You need to calculate the average (`mean`) temperature in function of time, i.e. aggregate over both the `longitude` and `latitude` dimensions.\n",
+    "* Year 2021 is only available till June, so exclude the year (e.g. slice till 2020).\n",
+    "* Resample to yearly values.\n",
+    "* As the result is a DataArray with a single dimension, the default `plot` will show a line, but you can be more explicit by saying `.plot.line()`.\n",
+    "\n",
+    "</details>    \n",
+    "    \n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5bfd0ba8-4580-44ce-970d-b18fc0b75623",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "temp_mean = era5_renamed[\"temperature_k\"].mean(dim=[\"latitude\", \"longitude\"]).sel(time=slice(\"1981\", \"2020\"))\n",
+    "temp_mean.resample(time=\"YE\").mean().plot.line(figsize=(12, 5))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e87b7994-ccc2-473b-8317-5e86c7f84fbf",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-success\">\n",
+    "\n",
+    "**EXERCISE**:\n",
+    "    \n",
+    "Create the yearly total snowfall from 1991 up to 2005 and convert the snowfall into cm.  Make a plot (`imshow`) with each of the individual years in a separate subplot divided into 3 rows and 5 columns.\n",
+    "    \n",
+    "Make sure to update the name of the snowfall variable and/or colorbar label to make sure it defines the unit in cm.\n",
+    " \n",
+    "\n",
+    "<details><summary>Hints</summary>\n",
+    "\n",
+    "* When selecting time series data from a coordinate with datetime-aware data, one can use strings to define a date. In combination with `slice`, the selection of the required years becomes `slice(\"1991\", \"2005\")`.\n",
+    "* From montly to yearly data is a `resample` of the data.\n",
+    "* Use `.rename(NEW_NAME)` to update the name of a `DataArray`\n",
+    "* xarray supports _facetting_ directly, check out the `col` and `col_wrap` parameters in the plot functions of xarray or check http://xarray.pydata.org/en/stable/user-guide/plotting.html#faceting.\n",
+    "* To update the colorbar unit use, the `cbar_kwargs` option.\n",
+    "\n",
+    "</details>    \n",
+    "    \n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fa90d488-c4c2-40d1-a72d-f44178c6d643",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "snowfall_1991_2005 = era5_renamed.sel(time=slice(\"1991\", \"2005\"))[\"snowfall_m\"]\n",
+    "snowfall_yearly = snowfall_1991_2005.resample(time=\"YE\").sum()*100\n",
+    "snowfall_yearly = snowfall_yearly.rename(\"snowfall_cm\")\n",
+    "snowfall_yearly.plot.imshow(col=\"time\", col_wrap=5, cmap=cmocean.cm.ice, \n",
+    "                            cbar_kwargs={\"label\": \"snowfall (cm)\"})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "06a4eaf2-0c04-4586-8280-5ff7808d0178",
+   "metadata": {},
+   "source": [
+    "## xarray lazy data loading\n",
+    "\n",
+    "Values are only read from disk when needed. For example, the following statement only reads the coordinate information and the metadata. The data itself is not yet loaded:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "531a290d-2721-4201-89fc-b7109d0cb087",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_file = \"./data/2016-2017_global_rain-temperature.nc\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "36901805-8511-41ab-95ac-e4863358a2c1",
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "ds = xr.open_dataset(data_file)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "56ea00c4-2fbc-405a-bc04-1678ec50ef20",
+   "metadata": {},
+   "source": [
+    "`load()` will explicitly load the data into memory:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6d0d1e85-838e-4993-b504-a7d88d526f86",
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "xr.open_dataset(data_file).load()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "98492efe-ff37-41e9-ac78-a37007e5fd07",
+   "metadata": {},
+   "source": [
+    "## (Optional) From DataSet to DataArray and back"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ba05f4a7-1916-4c10-b307-2483bd4a3fad",
+   "metadata": {},
+   "source": [
+    "Consider the sentinel images of Gent used in a previous notebook:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "69c45250-c760-4b2f-9769-e8cdd22a0aa8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gent_file = \"./data/gent/raster/2020-09-17_Sentinel_2_L1C_B0408.tiff\"\n",
+    "gent_array = xr.open_dataarray(gent_file, engine=\"rasterio\")\n",
+    "gent_array.plot.imshow(col=\"band\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b447c79c-1e7f-4c25-bf2c-823b87c96ff5",
+   "metadata": {},
+   "source": [
+    "The data is a `xarray.DataArray` with 3 dimensions and corresponding coordinates. Assigning the names `b4` and `b8` to the individual bands, makes the data more self-describing:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bc9f62fb-2df7-490b-8a69-5570cf68eb0a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gent_array = gent_array.assign_coords(band=(\"band\", [\"b4\", \"b8\"]))\n",
+    "gent_array"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c927b116-bd41-4ecf-b94d-0a12e31f7e87",
+   "metadata": {},
+   "source": [
+    "In case one wants to work with the different bands as DataSet variables instead of a dimension of a single DataArray, the conversion `to_dataset` can be done, defining the dimension to convert to data set variables, e.g."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5a6b5f39-4861-4fed-b92a-963b1bd508b3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gent_ds = gent_array.to_dataset(dim=\"band\")\n",
+    "gent_ds"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b2324c6d-d5e0-4143-9b31-abd08795f13a",
+   "metadata": {},
+   "source": [
+    "Consider the calculation of the NDVI for Gent using a xarray DataArray:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "12ece407-f5ba-4978-97dd-3b1e45a3b55c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ndvi_array = (gent_array.sel(band=\"b8\") - gent_array.sel(band=\"b4\"))/(gent_array.sel(band=\"b8\") + gent_array.sel(band=\"b4\"))\n",
+    "ndvi_array.plot.imshow(cmap=\"YlGn\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "628c0255-eb78-4b64-a55b-b3ef56376de4",
+   "metadata": {},
+   "source": [
+    "One might find the usage of data variable in this case more convenient, as it provides the ability to:\n",
+    "\n",
+    "1. work with the variable names directly\n",
+    "2. Add the output tot the data set as a new variable\n",
+    "3. The output can be converted to an Array again using `to_array`\n",
+    "\n",
+    "For example:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "131a85e6-a7d2-40dc-99f8-e98616a1ab6e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gent_ds[\"ndvi\"] = (gent_ds[\"b8\"] - gent_ds[\"b4\"])/(gent_ds[\"b8\"] + gent_ds[\"b4\"])\n",
+    "gent_ds.to_array(dim=\"layer\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "jupytext": {
+   "default_lexer": "ipython3"
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.11"
+  },
+  "widgets": {
+   "application/vnd.jupyter.widget-state+json": {
+    "state": {},
+    "version_major": 2,
+    "version_minor": 0
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/_solved/13-raster-processing.ipynb b/_solved/13-raster-processing.ipynb
new file mode 100644
index 0000000..86401d9
--- /dev/null
+++ b/_solved/13-raster-processing.ipynb
@@ -0,0 +1,2575 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<p><font size=\"6\"><b> Raster operations and raster-vector tools</b></font></p>\n",
+    "\n",
+    "\n",
+    "> *DS Python for GIS and Geoscience*  \n",
+    "> *September, 2025*\n",
+    ">\n",
+    "> *© 2025, Joris Van den Bossche and Stijn Van Hoey. Licensed under [CC BY 4.0 Creative Commons](http://creativecommons.org/licenses/by/4.0/)*\n",
+    "\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "In the previous notebooks, we focused either on vector data or raster data. Often you encounter both types of data and want to combine them. In this notebook, we show *some* examples of typical raster/vector interactions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import geopandas\n",
+    "import rasterio\n",
+    "\n",
+    "import xarray as xr\n",
+    "\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# `rioxarray`: xarray extension based on rasterio"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In the previous notebooks, we already used `rasterio` (https://rasterio.readthedocs.io/en/latest/) to read raster files such as GeoTIFFs (through the `xarray.open_dataarray(...,engine=\"rasterio\")` function). Rasterio provides support for reading and writing geospatial raster data as numpy N-D arrays, mainly through bindings to the GDAL library. \n",
+    "\n",
+    "In addition, rasterio provides a Python API to perform some GIS raster operations (clip, mask, warp, merge, transformation,...) and can be used to only load a subset of a large dataset into memory. However, the main complexity in using `rasterio`, is that the spatial information is decoupled from the data itself (i.e. the numpy array). This means that you need to keep track and organize the extent and metadata throughout the operations (e.g. the \"transform\") and you need to keep track of what each dimension represents (y-first, as arrays are organized along rows first). Notebook [91_package_rasterio](./91_package_rasterio.ipynb) goes into more depth on the rasterio package itself. \n",
+    "\n",
+    "Enter `rioxarray` (https://corteva.github.io/rioxarray/stable/index.html), which extends xarray with geospatial functionalities powered by rasterio."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import rioxarray"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_file = \"./data/herstappe/raster/2020-09-17_Sentinel_2_L1C_True_color.tiff\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_raw = xr.open_dataarray(data_file, engine=\"rasterio\")\n",
+    "data_raw"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Once `rioxarray` is imported, it provides a `.rio` accessor on the xarray.DataArray object, which gives access to some properties of the raster data:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_raw.rio.crs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_raw.rio.bounds()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_raw.rio.resolution()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_raw.rio.nodata"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_raw.rio.transform()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For the remainder of this section, we work with the integer converted version:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = (data_raw * 255).astype(np.uint8)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Reprojecting rasters"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`rioxarray` gives access to a set of raster processing functions from rasterio/GDAL. \n",
+    "\n",
+    "One of those is to reproject (transform and resample) rasters, for example to reproject to different coordinate reference system, up/downsample to a different resolution, etc. In all those case, in the transformation of a source raster to a destination raster, pixel values need to be recalculated. There are different \"resampling\" methods this can be done: taking the nearest pixel value, calculating the average, a (non-)linear interpolation, etc.\n",
+    "\n",
+    "The functionality is available through the `reproject()` method. Let's start with reprojecting the Herstappe tiff to a different CRS:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data.rio.crs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data.rio.reproject(\"EPSG:31370\").plot.imshow(figsize=(10,6))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The default resampling method is \"nearest\", which is often not a suitable method (especially for continuous data). We can change the method using the `rasterio.enums.Resampling` enumeration (see [docs](https://rasterio.readthedocs.io/en/latest/api/rasterio.enums.html#rasterio.enums.Resampling) for a overview of all methods):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from rasterio.enums import Resampling"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data.rio.reproject(\"EPSG:31370\", resampling=Resampling.bilinear).plot.imshow(figsize=(10,6))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The method can also be used to downsample at the same time:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data.rio.reproject(data.rio.crs, resolution=120, resampling=Resampling.cubic).plot.imshow(figsize=(10,6))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Extract the data you need"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In many applications, a specific research area is used. Extracting the data you need from a given raster data set by a vector (polygon) file is a common operation in GIS analysis. We use the clipping example to explain the typical workflow with rioxarray / rasterio.\n",
+    "\n",
+    "For our Herstappe example, the study area is available as vector data `./data/herstappe/vector/herstappe.geojson`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "herstappe_vect = geopandas.read_file(\"./data/herstappe/vector/herstappe.geojson\")\n",
+    "herstappe_vect"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "herstappe_vect.plot()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "herstappe_vect.crs"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Make sure both data sets are defined in the same CRS and extracting the geometry can be used as input for the masking:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "herstappe_vect = herstappe_vect.to_crs(epsg=3857)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "clipped = data.rio.clip(herstappe_vect.geometry)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig, (ax0, ax1) = plt.subplots(ncols=2, figsize=(12,4))\n",
+    "data.plot.imshow(ax=ax0)\n",
+    "herstappe_vect.plot(ax=ax0, facecolor=\"none\", edgecolor=\"red\")\n",
+    "\n",
+    "clipped.plot.imshow(ax=ax1)\n",
+    "herstappe_vect.plot(ax=ax1, facecolor=\"none\", edgecolor=\"red\")\n",
+    "\n",
+    "fig.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The above uses the `rasterio` package (with the `mask` and `geometry_mask` / `rasterize` functionality) under the hood. This simplifies the operation compared to directly using `rasterio`.\n",
+    "\n",
+    "\n",
+    "```python\n",
+    "# cfr. The Rasterio workflow\n",
+    "\n",
+    "from rasterio.mask import mask\n",
+    "\n",
+    "# 1 - Open a data set using the context manager\n",
+    "with rasterio.open(data_file) as src: \n",
+    "\n",
+    "    # 2 - Read and transform the data set by clipping\n",
+    "    out_image, out_transform = mask(src, herstappe_vect.geometry, crop=True)\n",
+    "    \n",
+    "    # 3 - Update the spatial metadata/profile of the data set\n",
+    "    herstappe_profile = src.profile\n",
+    "    herstappe_profile.update({\"height\": out_image.shape[1],\n",
+    "                              \"width\": out_image.shape[2],\n",
+    "                              \"transform\": out_transform})\n",
+    "    # 4 - Save the new data set with the updated metadata/profile                   \n",
+    "    with rasterio.open(\"./herstappe_masked.tiff\", \"w\", **herstappe_profile) as dest: \n",
+    "        dest.write(out_image)\n",
+    "```\n",
+    "\n",
+    "The [91_package_rasterio](./91_package_rasterio.ipynb) notebook explains this workflow in more detail.\n",
+    "\n",
+    "One important difference, though, is that the above `rasterio` workflow will not load the full raster into memory when only loading (clipping) a small part of it. This can also be achieved in `rioxarray` with the `from_disk` keyword."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Convert vector to raster"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### [Intermezzo] - data preparation Zwalm catchment"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The following section provides the data preparation and data downloading steps to prepare the 'burning' of the shape of a river into a Digital Elevation Model (DEM) of the river catchment (all locations around the river from which rain ends up in the river). The 'burning' itself is a subtraction of the DEM pixels at locations of the river with a chosen value, i.e. decrease the elevation at locations where a river flows. To do so, we need:\n",
+    "\n",
+    "- A Digital Elevation Model (DEM) data set (raster data)\n",
+    "- The shape of the catchment (vector data)\n",
+    "- The river and tributaries (vector data)\n",
+    "\n",
+    "To be able to subtract the corresponding river pixels, the river vector data need to be converted into a raster with the same resolution and bounds as the (clipped) DEM."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "__Load DEM raster and river vector data__"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As example, we are using data from the Zwalm river area in Flanders. \n",
+    "\n",
+    "<div class=\"alert alert-warning\">\n",
+    "\n",
+    "**! Manual download needed !** <br>\n",
+    "\n",
+    "The digital elevation model (DEM) can be downloaded via the [governmental website](https://download.vlaanderen.be/product/936/configureer): choose number 30 on the map and then click \"Downloaden\". Finally extract the zip file in `/data` directory. Inside the `data`-directory, the file will be available in the subfolder `DHMVIIDSMRAS5m_k30/GeoTIFF/DHMVIIDSMRAS5m_k30.tif`.\n",
+    "\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dem_zwalm_file = \"data/DHMVIIDSMRAS5m_k30/GeoTIFF/DHMVIIDSMRAS5m_k30.tif\" "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dem_zwalm = xr.open_dataarray(dem_zwalm_file, engine=\"rasterio\").sel(band=1).squeeze()\n",
+    "dem_zwalm"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "img = dem_zwalm.plot.imshow(\n",
+    "    cmap=\"terrain\", figsize=(10, 4), interpolation='antialiased')\n",
+    "img.axes.set_aspect(\"equal\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Next, we download the shapes of the rivers in the area through a WFS (Web Feature Service):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import json\n",
+    "import requests\n",
+    "\n",
+    "wfs_rivers = \"https://geo.api.vlaanderen.be/VHAWaterlopen/wfs\"\n",
+    "params = dict(service='WFS', version='1.1.0', request='GetFeature', \n",
+    "              typeName='VHAWaterlopen:Wlas', outputFormat='json', \n",
+    "              cql_filter=\"(VHAZONENR=460)OR(VHAZONENR=461)\", srs=\"31370\")\n",
+    "\n",
+    "# Fetch data from WFS using requests\n",
+    "r = requests.get(wfs_rivers, params=params)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "__Note__: A WFS is a standardized way to share vector GIS data sets on the internet, typically also used by web application, see ['A bit more about WFS'](#A-bit-more-about-WFS) section for more info."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And convert the output of the wfs call to a GeoDataFrame:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create GeoDataFrame from geojson - this is vector data\n",
+    "segments = geopandas.GeoDataFrame.from_features(json.loads(r.content), crs=\"epsg:31370\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "segments.plot(figsize=(8, 7))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "__Clip raster with vector__\n",
+    "\n",
+    "The catchment extent is much smaller than the DEM file, so clipping the data first will make the computation less heavy and focuses exclusively on the region from which rainfall is collected."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's first download the catchment area of the Zwalm river from the Flemish government (using WFS again):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import json\n",
+    "import requests\n",
+    "\n",
+    "wfs_bekkens = \"https://geo.api.vlaanderen.be/Watersystemen/wfs\"\n",
+    "params = dict(service='WFS', version='1.1.0', request='GetFeature',\n",
+    "              typeName='Watersystemen:WsDeelbek', outputFormat='json',\n",
+    "              cql_filter=\"DEELBEKNM='Zwalm'\", srs=\"31370\")\n",
+    "\n",
+    "# Fetch data from WFS using requests\n",
+    "r = requests.get(wfs_bekkens, params=params)\n",
+    "catchment = geopandas.GeoDataFrame.from_features(json.loads(r.content), crs=\"epsg:31370\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Save to a file for later reuse:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# save to file\n",
+    "catchment = catchment.to_crs('epsg:4326') # geojson is default 4326\n",
+    "catchment.to_file(\"./data/zwalmbekken.geojson\", driver=\"GeoJSON\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "geopandas.read_file(\"./data/zwalmbekken.geojson\").plot()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "1. Clip using rioxarray (rasterio)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As shown above, we can use rioxarray to clip the raster file:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dem_zwalm = xr.open_dataarray(dem_zwalm_file, engine=\"rasterio\").sel(band=1)\n",
+    "dem_zwalm.spatial_ref"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "clipped = dem_zwalm.rio.clip(catchment.to_crs('epsg:31370').geometry)\n",
+    "clipped = clipped.squeeze().drop_vars(['band', 'spatial_ref']) # remove redundant coordinates"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Using rioxarray's `to_raster()` method, we can also save the result to a new GeoTIFF file:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "clipped.rio.to_raster(\"./dem_masked_rio.tiff\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This DEM raster file used -9999 as the NODATA value, which are read as NaN values:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dem_zwalm.rio.nodata, clipped.rio.nodata"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "img = clipped.plot.imshow(\n",
+    "    cmap='terrain', figsize=(10, 6), interpolation='antialiased')\n",
+    "img.axes.set_aspect(\"equal\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "2. (optional) Using GDAL CLI"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If we have the raster and vector files on disk, the [`gdal CLI`](https://gdal.org/programs/index.html) will be very fast to work with (note that GDAL automatically handles the CRS difference of the raster and vector)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "rm ./dem_masked_gdal.tiff"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!gdalwarp -cutline ./data/zwalmbekken.geojson -crop_to_cutline data/DHMVIIDSMRAS5m_k30/GeoTIFF/DHMVIIDSMRAS5m_k30.tif ./dem_masked_gdal.tiff"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "clipped_gdal = xr.open_dataarray(\"./dem_masked_gdal.tiff\", mask_and_scale=True).sel(band=1)\n",
+    "img = clipped_gdal.plot.imshow(\n",
+    "    cmap=\"terrain\", figsize=(10, 6), interpolation='antialiased')\n",
+    "img.axes.set_aspect(\"equal\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-info\" style=\"font-size:120%\">\n",
+    "\n",
+    "**TIP**: <br>\n",
+    "    \n",
+    "In the GIS world, also other libraries do provide a large set of functionalities as command line instructions with a `FILE IN` -> `RUN COMMAND` -> `FILE OUT` approach, with some of them providing a Python interface as well. Some important once are:\n",
+    "    \n",
+    "- The [`gdal` library](https://gdal.org/programs/index.html#raster-programs) is the open source Swiss Army knife for raster and vector geospatial data handling. \n",
+    "- The [SAGA GIS](http://www.saga-gis.org/en/index.html) has a [huge set](http://www.saga-gis.org/saga_tool_doc/8.0.0/a2z.html) of CLI commands, going from flow accumulation to classification algorithms.\n",
+    "- The [WhiteboxTools](https://www.whiteboxgeo.com/geospatial-software/) is another example of a library with a lot of functionalities, e.g. hydrological, agricultural and terrain analysis tools.\n",
+    "    \n",
+    "Other important initiatives like [Grass](https://grasswiki.osgeo.org/wiki/GRASS-Wiki) and [PCRaster](https://pcraster.geo.uu.nl/) are worthwhile to check out. Most of these libraries of tools can also be used with QGIS.\n",
+    "    \n",
+    "__NOTE:__ You can run a CLI command inside a Jupyter Notebook by prefixing it with the `!` character.\n",
+    "\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Convert vector to raster"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To create a raster with the vector converted into pixels, we can use the `rasterio.features.rasterize` function. This expects a list of (shape, value) tuples, and an output image shape and transform. Here, we will create a new raster image with the same shape and extent as the DEM above. And we first take a buffer of the river lines:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "clipped.rio.to_raster(\"zwalm_clip_test.tif\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import rasterio.features"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "segments_buffered = segments.geometry.buffer(100)\n",
+    "river_raster = rasterio.features.rasterize(\n",
+    "    segments_buffered, \n",
+    "    out_shape=clipped.shape, \n",
+    "    transform=clipped.rio.transform())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "type(river_raster)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The output of the rasterio function is a Numpy array. Let's convert to xarray objects"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ds_zwalm = clipped.to_dataset(name=\"dem\").squeeze()\n",
+    "ds_zwalm[\"river\"] = xr.DataArray(data=river_raster, coords=clipped.coords)\n",
+    "ds_zwalm[\"dem_burned\"] = ds_zwalm[\"dem\"] - ds_zwalm[\"river\"]*20"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As the river is now converted in a raster with the same pixels and boundaries as our DEM, we can burn the river into the DEM:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig, (ax0, ax1) = plt.subplots(1, 2, figsize=(12, 6))\n",
+    "\n",
+    "ds_zwalm[\"river\"].plot.imshow(ax=ax0)\n",
+    "ax0.axes.set_aspect('equal')\n",
+    "\n",
+    "ds_zwalm[\"dem_burned\"].plot.imshow(ax=ax1, cmap=\"terrain\", vmin=0)\n",
+    "ax1.axes.set_aspect('equal')\n",
+    "\n",
+    "fig.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Let's practice!\n",
+    "\n",
+    "For these exercises, a set of raster and vector datasets for the region of Ghent is available. Throughout the exercises, the goal is to map preferential locations to live given a set of conditions (certain level above sea-level, quiet and green neighbourhood, ..).\n",
+    "\n",
+    "We start with the Digital Elevation Model (DEM) for Flanders. The data is available at https://overheid.vlaanderen.be/informatie-vlaanderen/producten-diensten/digitaal-hoogtemodel-dhmv, We downloaded the 25m resolution raster image, and provided a subset of this dataset as a zipped Tiff file in the course material."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-success\">\n",
+    "\n",
+    "**EXERCISE**:\n",
+    "\n",
+    "* Read the DEM using `xarray`. The zip file is available at `data/gent/DHMVIIDTMRAS25m.zip`. You can either unzip the file, and use the path to the unzipped file, or prepend `zip://` to the path. \n",
+    "* What is the CRS of this dataset?\n",
+    "* The dataset has a third dimension with a single band. This doesn't work for plotting, so create a new `DataArray` by selecting the single band. Assign the result to a variable `dem`.\n",
+    "* Make a quick plot of the dataset.\n",
+    "\n",
+    "<details><summary>Hints</summary>\n",
+    "\n",
+    "* Rasterio can directly read from a zip-file. If one wants read the file `./data/gent/FILENAME.zip`, add the `zip://.data/gent/FILENAME.zip` to read the zip file directly.\n",
+    "* The `crs` is an attribute, not a function, so no `()` required.\n",
+    "* Selecting in xarray is done with `sel`\n",
+    "* We will improve the plot in the following exercises, but as a quick solution, we already learnt about the `robust=True` plot option of xarray. \n",
+    "* Just pick a color map that you like.\n",
+    "\n",
+    "</details>   \n",
+    "    \n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "dem = xr.open_dataarray(\"zip://./data/gent/DHMVIIDTMRAS25m.zip\", engine=\"rasterio\")\n",
+    "dem"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "dem.rio.crs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "dem = xr.open_dataarray(\"zip://./data/gent/DHMVIIDTMRAS25m.zip\", engine=\"rasterio\").sel(band=1)\n",
+    "dem"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "dem.plot.imshow(robust=True, cmap=\"terrain\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-success\">\n",
+    "\n",
+    "**EXERCISE**:\n",
+    "\n",
+    "The dataset uses a large negative value to denote the \"nodata\" value (in this case meaning \"outside of Flanders\"). The NODATA value stored in the file metadata is - by default - read by xarray as `np.nan` (see notebook 11-xarray-intro.ipynb), but this behaviour can be excluded:\n",
+    "    \n",
+    "* Read the `zip://./data/gent/DHMVIIDTMRAS25m.zip\"` file (band=1) without converting the NODATA value to `np.nan` (keep -9999.) and keeping the original data type.\n",
+    "* Check the value -9999. is used as \"nodata\" value.\n",
+    "* Repeat the plot from the previous exercise, but now set a fixed minimum value of 0 for the colorbar, to ignore the negative \"nodata\" in the color scheme.\n",
+    "* Replace the \"nodata\" value with `np.nan` using the `where()` method. \n",
+    "    \n",
+    "<details><summary>Hints</summary>\n",
+    "\n",
+    "* The `nodata` attribute is provided by the rioxarray package. Rioxarray loads this information from the geotiff file metadata and makes it available as `.rio.nodata`. \n",
+    "* The `.rio.nodata` is a class attribute, not a function, so no `()` required.\n",
+    "* `vmin` and `vmax` define the colormap limits.\n",
+    "* `ẁhere` expects a condition (i.e. boolean values), e.g. `... != dem.rio.nodata`.\n",
+    "</details>   \n",
+    "    \n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "dem = xr.open_dataarray(\"zip://./data/gent/DHMVIIDTMRAS25m.zip\", engine=\"rasterio\", mask_and_scale=False).sel(band=1)\n",
+    "dem.rio.nodata"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "dem.plot.imshow(robust=True, vmin=0, cmap=\"terrain\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "dem_masked = dem.where(dem != dem.rio.nodata)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "dem_masked.plot.imshow(robust=True, cmap=\"terrain\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "__Remember__ Alternatively to masking the nodata value manually, you can do this explicitly when loading the data, using the `mask_and_scale=True` keyword (see also notebook [11-xarray-intro.ipynb](11-xarray-intro.ipynb)):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dem_masked = xr.open_dataarray(\"zip://./data/gent/DHMVIIDTMRAS25m.zip\", engine=\"rasterio\", mask_and_scale=True).sel(band=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dem_masked.plot.imshow(robust=True, cmap=\"terrain\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-success\">\n",
+    "\n",
+    "**EXERCISE**:\n",
+    "\n",
+    "We want to limit our search for locations to the surroundings of the centre of Ghent.\n",
+    "\n",
+    "* Create a `Point` object for the centre of Ghent. Latitude/longitude coordinates for the Korenmarkt are: 51.05393, 3.72174\n",
+    "* We need our point in the same Coordinate Reference System as the DEM raster (i.e. EPSG:31370, or Belgian Lambert 72). Use GeoPandas to reproject the point:\n",
+    "    * Create a GeoSeries with this single point and specify its CRS with the `crs` keyword.\n",
+    "    * Reproject this series with the `to_crs` method. Assign the resulting GeoSeries to a variable `gent_centre_31370`.\n",
+    "* Calculcate a buffer of 10km radius around the point.\n",
+    "* Get the bounding box coordinates of this buffer. Assign this to `gent_bounds`.\n",
+    "    \n",
+    "<details><summary>Hints</summary>\n",
+    "\n",
+    "* Remember the introduction on geospatial data and the shapely objects, e.g. `shapely.geometry.Point`?\n",
+    "* The Latitude/Longitude order is a typical gotcha. The `Point` expects `x` (longitude) as  first argument and `y` (latitude) as second argument.\n",
+    "* Use `geopandas.GeoSeries` to create a new GeoSeries and add the `crs` parameter. The lat/lon of the Kornmarkt are provided as EPSG:4326. \n",
+    "* In EPSG:31370, the unit is meter, so make sure to use meter to define the buffer size.\n",
+    "* `.total_bounds` is a class attribute.\n",
+    "\n",
+    "</details>   \n",
+    "    \n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "from shapely import Point"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "gent_centre = Point(3.72174, 51.05393)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "gent_centre_31370 = geopandas.GeoSeries([gent_centre], crs=\"EPSG:4326\").to_crs(\"EPSG:31370\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "gent_bounds = gent_centre_31370.buffer(10*1000).total_bounds\n",
+    "# or first extracting the Point again:\n",
+    "# gent_bounds = gent_centre_31370[0].buffer(10*1000).bounds\n",
+    "gent_bounds"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-success\">\n",
+    "\n",
+    "**EXERCISE**:\n",
+    "\n",
+    "With this bounding box, we can now clip a subset of the DEM raster for the area of interest. \n",
+    "    \n",
+    "* Clip the `dem` raster layer. To clip with a bounding box instead of a geometry, you can use the `rio.clip_box()` method instead of `rio.clip()`.\n",
+    "* Make a plot. Use the \"terrain\" color map, and set the bounds of the color scale to 0 - 30.\n",
+    "\n",
+    "<details><summary>Hints</summary>\n",
+    "\n",
+    "* The `gent_bounds` is an array of 4 elements, whereas `clip_box` requires these as seperate input parameters... Did you know that in Python you can unpack these 4 values with the `*`: `*gent_bounds` will unpack to 4 individual input.\n",
+    "\n",
+    "</details>   \n",
+    "    \n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "dem_gent = dem.rio.clip_box(*gent_bounds)\n",
+    "dem_gent"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "img = dem_gent.plot.imshow(\n",
+    "    cmap=\"terrain\", figsize=(10, 10), interpolation='antialiased', vmin=0, vmax=30)\n",
+    "img.axes.set_aspect(\"equal\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The CORINE Land Cover (https://land.copernicus.eu/pan-european/corine-land-cover) is a program by the European Environment Agency (EEA) to provide an inventory of land cover in 44 classes of the European Union. The data is provided in both raster as vector format and with a resolution of 100m.\n",
+    "\n",
+    "The data for the whole of Europe can be downloaded from the website (latest version: https://land.copernicus.eu/pan-european/corine-land-cover/clc2018?tab=download). This is however a large dataset, so we downloaded the raster file and cropped it to cover Flanders, and this subset is included in the repo as `data/CLC2018_V2020_20u1_flanders.tif` (the code to do this cropping can be see at [data/preprocess_data.ipynb#CORINE-Land-Cover](data/preprocess_data.ipynb#CORINE-Land-Cover))."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-success\">\n",
+    "\n",
+    "**EXERCISE**:\n",
+    "\n",
+    "* Read the land use data provided as a tif (`data/CLC2018_V2020_20u1_flanders.tif`). Directly select the single band.\n",
+    "* Make a quick plot. The raster is using a negative value as \"nodata\", consider using the `robust=True` option.\n",
+    "* What is the resolution of this raster?\n",
+    "* What is the CRS?\n",
+    "\n",
+    "<details><summary>Hints</summary>\n",
+    "\n",
+    "* `rio.resolution()` is a method, so it requires the `()`.\n",
+    "* `rio.crs` is an attribute, so it does not require the `()`.\n",
+    "\n",
+    "</details>   \n",
+    "    \n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "land_use = xr.open_dataarray(\"data/CLC2018_V2020_20u1_flanders.tif\").sel(band=1)\n",
+    "land_use"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "land_use.plot.imshow(robust=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "land_use.rio.resolution()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "land_use.rio.crs"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-success\">\n",
+    "\n",
+    "**EXERCISE**:\n",
+    "\n",
+    "The land use dataset is a European dataset and uses a Europe-wide projected CRS (https://epsg.io/3035).\n",
+    "\n",
+    "* Reproject the land use raster to the same CRS as the DEM raster (EPSG:31370), and plot the result.\n",
+    "\n",
+    "<details><summary>Hints</summary>\n",
+    "\n",
+    "* For the sake of the exercise, pick any resampling algorithm or just the default option.\n",
+    "\n",
+    "</details>   \n",
+    "    \n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "land_use_reprojected = land_use.rio.reproject(dem_gent.rio.crs)\n",
+    "land_use_reprojected"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "land_use_reprojected.plot.imshow(robust=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-success\">\n",
+    "\n",
+    "**EXERCISE**:\n",
+    "\n",
+    "In addition to reprojecting to a new CRS, we also want to upsample the land use dataset to the same resolution as the DEM raster, and to use the exact same grid layout, so we can compare and combine those rasters pixel-by-pixel.\n",
+    "\n",
+    "Such a reprojection can be done with the `reproject()` method by providing the target geospatial \"transform\" (which has the information for the bounds and resolution) and shape for the result. `rioxarray` provides a short-cut for this operation with the `reproject_match()` method, to reproject one raster to the CRS, extent and resolution of another raster.  \n",
+    "\n",
+    "* Reproject the land use raster to the same CRS and extent as the DEM subset for Ghent (`dem_gent`). Call the result `land_use_gent`.\n",
+    "* Make a plot of the result.\n",
+    "\n",
+    "<details><summary>Hints</summary>\n",
+    "\n",
+    "* Check the help of the `.rio.reproject_match` method (SHIFT + TAB) to know which input ou need. For the sake of the exercise, pick any resampling algorithm or just the default (nearest) option.\n",
+    "* The data calling the method is being transformed, and the input parameter is the target to match.\n",
+    "\n",
+    "</details>   \n",
+    "    \n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "land_use_gent = land_use.rio.reproject_match(dem_gent)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "land_use_gent.plot.imshow()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The land use dataset is a raster with discrete values (i.e. different land use classes). The [CurieuzeNeuzen case study](case-curieuzeneuzen-air-quality.ipynb#Combining-with-Land-Use-data) goes into more depth on those values, but for this exercise it is sufficient to know that values 1 and 2 are the Continuous and Discontinuous urban fabric (residential areas)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-success\">\n",
+    "\n",
+    "**EXERCISE**:\n",
+    "\n",
+    "Let's find the preferential locations to live, assuming we want to be future-proof and live at least 10m above sea level in a residential area.\n",
+    "\n",
+    "* Create a new array denoting the residential areas (where `land_use_gent` is equal to 1 or 2). Call this `land_use_residential`, and make a quick plot.\n",
+    "* Plot those locations that are 10m above sea-level.\n",
+    "* Combine the residential areas and areas > 10m in a single array called `suitable_locations`, and plot the result.\n",
+    "    \n",
+    "<details><summary>Hints</summary>\n",
+    "\n",
+    "* To select for multiple options, one can either combine multiple conditions using `|` (or) or us the `isin([...,...])` option, both will do.\n",
+    "* The output of a condition is a Boolean map that can be plot just like other maps, e.g. `(dem_gent > 10).plot.imshow()`.\n",
+    "* Fo the `suitable_locations`, both boolean conditions need to be True, so combine them with either `&` or just multiply them with `*`.\n",
+    "\n",
+    "</details>   \n",
+    "    \n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "land_use_residential = (land_use_gent == 1) | (land_use_gent == 2)\n",
+    "land_use_residential"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# Alternative solution using `isin`\n",
+    "land_use_residential = land_use_gent.isin([1, 2])\n",
+    "land_use_residential"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "land_use_residential.plot.imshow()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "(dem_gent > 10).plot.imshow()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "suitable_locations = land_use_residential * (dem_gent > 10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "suitable_locations.plot.imshow()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-success\">\n",
+    "\n",
+    "**EXERCISE**:\n",
+    "\n",
+    "In addition to the previous conditions, assume we also don't want to live close to major roads.\n",
+    "\n",
+    "We downloaded the road segments open data from Ghent (https://data.stad.gent/explore/dataset/wegsegmenten-gent/) as a GeoJSON file, and provided this in the course materials: `/data/gent/vector/wegsegmenten-gent.geojson.zip`\n",
+    "    \n",
+    "* Read the GeoJSON road segments file into a variable `roads` and check the first few rows.\n",
+    "* The column \"frc_omschrijving\" contains a description of the type of road for each segment. Get an overview of the available segments and types by doing a \"value counts\" of this column.\n",
+    "\n",
+    "<details><summary>Hints</summary>\n",
+    "\n",
+    "* GeoPandas does NOT need the `zip://...` to read in zip files.\n",
+    "* The first few rows are also the `head` of a data set.\n",
+    "* The `value_counts` provides the number of records for each of the different values in a column.\n",
+    "\n",
+    "</details>   \n",
+    "    \n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "roads = geopandas.read_file(\"./data/gent/vector/wegsegmenten-gent.geojson.zip\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "roads.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "roads[\"frc_omschrijving\"].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-success\">\n",
+    "\n",
+    "**EXERCISE**:\n",
+    "\n",
+    "We are interested in the big roads, as these are the ones we want to avoid: \"Motorway or Freeway\", \"Major Road\" and \"Other Major Road\".\n",
+    "\n",
+    "* Filter the `roads` table based on the provided list of road types: select those rows where the \"frc_omschrijving\" column is equal to one of those values, and call this `roads_subset`.\n",
+    "* Make a quick plot of this subset and use the \"frc_omschrijving\" colum to color the lines.\n",
+    "\n",
+    "<details><summary>Hints</summary>\n",
+    "\n",
+    "* Selecting multiple options at the same time is most convenient with the `isin()` method.\n",
+    "* Use the GeoPandas `.plot` method and specifh the `frc_omschrijving` column to the `column` parameter.\n",
+    "\n",
+    "</details>   \n",
+    "    \n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "road_types = [\n",
+    "    \"Motorway, Freeway or Other Major Road\",\n",
+    "    \"Major Road Less Important than a Motorway\",\n",
+    "    \"Other Major Road\",\n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "roads_subset = roads[roads[\"frc_omschrijving\"].isin(road_types)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "roads_subset.plot(column=\"frc_omschrijving\", figsize=(10, 10), legend=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-success\">\n",
+    "\n",
+    "**EXERCISE**:\n",
+    "\n",
+    "Before we convert the vector data to a raster, we want to buffer the roads. We will use a larger buffer radius for the larger roads.\n",
+    "\n",
+    "* Using the defined `buffer_per_roadtype` dictionary, create a new Series by mapping the values in the \"frc_omschrijving\" column to the matching buffer radius.\n",
+    "* Convert the `roads_subset` GeoDataFrame to CRS `EPSG:31370`, and create buffered lines (polygons) with the calculated buffer radius distances. Call the result `roads_buffer`.\n",
+    "    \n",
+    "<details><summary>Hints</summary>\n",
+    "\n",
+    "* Use the `map()` method to map the values using the provided dictionary `buffer_per_roadtype`.\n",
+    "* The conversion to EPSG:31370 is important to be able to work with the meters to define the buffer size.\n",
+    "* The `buffer` method can take a single value to apply to all values, but also a Series of values, with a buffer size defined for each element.\n",
+    "\n",
+    "</details>   \n",
+    "    \n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "buffer_per_roadtype = {\n",
+    "    \"Motorway, Freeway or Other Major Road\": 750,\n",
+    "    \"Major Road Less Important than a Motorway\": 500,\n",
+    "    \"Other Major Road\": 150,\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "buffers = roads_subset[\"frc_omschrijving\"].map(buffer_per_roadtype)\n",
+    "buffers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "roads_buffer = roads_subset.to_crs(\"EPSG:31370\").buffer(buffers)\n",
+    "roads_buffer.plot()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-success\">\n",
+    "\n",
+    "**EXERCISE**:\n",
+    "\n",
+    "Convert the buffered road segments to a raster:\n",
+    "    \n",
+    "* Use `features.features.rasterize()` to convert the `roads_buffer` GeoDataFrame to a raster:\n",
+    "    * Pass the geometry column as the first argument.\n",
+    "    * Pass the shape and transform of the `dem_gent` to specify the desired spatial extent and resolution of the output raster.\n",
+    "* Invert the values of the raster by doing `1 - arr`, and plot the array with `plt.imshow(..)`.\n",
+    "* Recalculate the `suitable_locations` variable, using 1/ land_use_residential, 2/ dem > 10 and 3/ outside the road buffers.  \n",
+    "    \n",
+    "<details><summary>Hints</summary>\n",
+    "\n",
+    "* Access the geometry column using the `.geometry` attribute.\n",
+    "* `shape` is also an attribute.\n",
+    "* `.rio.transform()` is a method, so it requires the `()`.\n",
+    "* Previously, suitable locations were `land_use_residential * (dem_gent > 10)`. Combine this with the `(1 - roads_buffer_arr)` output.\n",
+    "\n",
+    "</details>   \n",
+    "    \n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import rasterio.features"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "roads_buffer_arr = rasterio.features.rasterize(\n",
+    "    roads_buffer.geometry, \n",
+    "    out_shape=dem_gent.shape, \n",
+    "    transform=dem_gent.rio.transform())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "1 - roads_buffer_arr"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "plt.imshow(1 - roads_buffer_arr)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "suitable_locations = land_use_residential * (dem_gent > 10) * (1 - roads_buffer_arr)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "suitable_locations.plot.imshow()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-success\">\n",
+    "\n",
+    "**EXERCISE**:\n",
+    "\n",
+    "Make a plot with a background map of the selected locations. \n",
+    "    \n",
+    "* Plot the provided `gent` GeoDataFrame (a single row table with the are of the Ghent municipality). Use a low \"alpha\" to give it a light color.\n",
+    "* Add a background map using contextily.\n",
+    "* Plot the `suitable_locations` raster on top of this figure: first mask the array to select only those values larger than zero (so the other values becomes NaN, and are not plotted). Then plot the result, adding it to the existing figure using the `ax` keyword.\n",
+    "\n",
+    "<details><summary>Hints</summary>\n",
+    "\n",
+    "* The `fig, ax = plt.subplots(figsize=(15, 15))` is a convenient shortcut to prepare a Matplotlib Figure and Axes. \n",
+    "* Make sure to define the `crs=\"EPSG:31370\"` for contextily.\n",
+    "* `where(...)` is a powerfull way to exclude data as it - by default - adds NaN values for piels where the condition is not True.\n",
+    "</details>   \n",
+    "    \n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import contextily"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gent = geopandas.read_file(\"data/gent/vector/gent.geojson\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(15, 15))\n",
+    "\n",
+    "gent.to_crs(\"EPSG:31370\").plot(ax=ax, alpha=0.2, color=\"grey\")\n",
+    "contextily.add_basemap(ax, crs=\"EPSG:31370\")\n",
+    "\n",
+    "suitable_locations.where(suitable_locations > 0).plot.imshow(ax=ax, alpha=0.5, add_colorbar=False)\n",
+    "ax.set_aspect(\"equal\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Advanced (optional) exercises:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-success\">\n",
+    "\n",
+    "**EXERCISE**:\n",
+    "\n",
+    "We downloaded the data about urban green areas in Ghent (https://data.stad.gent/explore/dataset/parken-gent).\n",
+    "\n",
+    "* Read in the data at `data/gent/vector/parken-gent.geojson` into a variable `green`.\n",
+    "* Check the content (first rows, quick plot)\n",
+    "* Remove the rows with an empty geometry (`None`)    \n",
+    "* Convert this vector layer to a raster using the spatial extent and resolution of `dem_gent` as the targer raster.\n",
+    "* The `rasterio.features.rasterize` results in a numpy array. Convert this to a DataArray using the `xr.DataArray(..)` constructor, specifying the coordinates of `dem_gent` (`dem_gent.coords`) for the coordinates of the new array.\n",
+    "    \n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "green = geopandas.read_file(\"data/gent/vector/parken-gent.geojson\")\n",
+    "green.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "green.plot()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "green = green.dropna(subset=\"geometry\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "import rasterio.features\n",
+    "\n",
+    "green_arr = rasterio.features.rasterize(\n",
+    "    green.to_crs(\"EPSG:31370\").geometry, \n",
+    "    out_shape=dem_gent.shape, \n",
+    "    transform=dem_gent.rio.transform())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "green_arr = xr.DataArray(green_arr, coords=dem_gent.coords)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "green_arr.plot.imshow(figsize=(8, 8))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-success\">\n",
+    "\n",
+    "**EXERCISE**:\n",
+    "\n",
+    "For the urban green areas, we want to calculate a statistic for a neighbourhood around each pixel (\"focal\" statistics). This can be expressed as a convolution with a defined kernel.    \n",
+    "\n",
+    "The [xarray-spatial](https://xarray-spatial.org/index.html) package includes functionality for such focal statistics and convolutions.\n",
+    "\n",
+    "* Use the `focal.focal_stats()` function from xarray-spatial to calculate the sum of green are in a neighborhood of 500m around each point. Check the help of this function to see which arguments to specify.\n",
+    "* Make a plot of the resulting `green_area` array.\n",
+    "    \n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from xrspatial import focal, convolution"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x, y = convolution.calc_cellsize(green_arr)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "kernel = convolution.circle_kernel(x, y, 500)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "green_area = focal.focal_stats(green_arr, kernel, stats_funcs=[\"sum\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "green_area.sel(stats=\"sum\").plot.imshow()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The `scipy` package also provides optimized convolution algorithms. In case of the \"sum\" statistic, this is equivalent:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from scipy import signal"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "green_area_arr = signal.convolve(green_arr, kernel, mode='same')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "xr.DataArray(green_area_arr, coords=dem_gent.coords).plot.imshow(vmin=0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-success\">\n",
+    "\n",
+    "**EXERCISE**:\n",
+    "\n",
+    "Make a plot with a background map of the selected locations, i.e. land_use_residential, dem > 10, outside road buffers and 'sufficient'as green area. \n",
+    "    \n",
+    "Define sufficient green area as the `green_area` pixels where the sum of the convolution (previous exercise) is larger as 10 (keep those values and convert pixels with values lower than 10 to 0 values). Multiply the different conditions/categories, so the green area score is included in the final result.\n",
+    "    \n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "green_area10 = green_area.sel(stats=\"sum\").where(green_area.sel(stats=\"sum\") > 10, 0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "suitable_locations = land_use_residential * (dem_gent > 10) * (1 - roads_buffer_arr) * green_area10"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import contextily"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gent = geopandas.read_file(\"data/gent/vector/gent.geojson\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(10, 10))\n",
+    "gent.to_crs(\"EPSG:31370\").plot(ax=ax, alpha=0.1)\n",
+    "ax.set(ylim=(190_000, 200_000), xlim=(100_000, 110_000))\n",
+    "contextily.add_basemap(ax, crs=\"EPSG:31370\")\n",
+    "\n",
+    "suitable_locations.where(suitable_locations>0).plot.imshow(ax=ax, alpha=0.5, add_colorbar=False)\n",
+    "ax.set_aspect(\"equal\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Extracting values from rasters based on vector data\n",
+    "\n",
+    "The **rasterstats** package provides methods to calculate summary statistics of geospatial raster datasets based on vector geometries (https://github.com/perrygeo/python-rasterstats)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-warning\">\n",
+    "\n",
+    "**! Manual download needed !** <br>\n",
+    "\n",
+    "The data can be downloaded from the EEA at [download link](https://www.eea.europa.eu/data-and-maps/data/world-digital-elevation-model-etopo5/zipped-dem-geotiff-raster-geographic-tag-image-file-format-raster-data/zipped-dem-geotiff-raster-geographic-tag-image-file-format-raster-data/at_download/file), then extract the zip file and save the file in the `data` subdirectory. After unzipping, the file will be available in the subdirectory `data/dem_geotiff/alwdgg.tif`.\n",
+    "\n",
+    "</div>\n",
+    "\n",
+    "To illustrate this, we are reading a raster file with elevation data of the full world (the file contains a single band for the elevation:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "countries = geopandas.read_file(\"./data/ne_110m_admin_0_countries.zip\")\n",
+    "cities = geopandas.read_file(\"./data/ne_110m_populated_places.zip\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dem_geotiff = \"data/DEM_geotiff/alwdgg.tif\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "img = xr.open_dataarray(dem_geotiff).sel(band=1).plot.imshow(cmap=\"terrain\", figsize=(10, 4), )\n",
+    "img.axes.set_aspect(\"equal\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Given this raster of the elevation, we might want to know the elevation at a certain location or for each country.\n",
+    "For the countries example, we want to extract the pixel values that fall within a country polygon, and calculate a statistic for it, such as the mean or the maximum.\n",
+    "\n",
+    "Such functionality to extract information from a raster for given vector data is provided by the rasterstats package."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import rasterstats"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For extracting the pixel values for polygons, we use the `zonal_stats` function, passing it the GeoSeries, the path to the raster file, and the method to compute the statistics."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "result = rasterstats.zonal_stats(countries.geometry, dem_geotiff,\n",
+    "                                 stats=['min', 'mean', 'max'], \n",
+    "                                 nodata=-999)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The results can be assigned to new columns:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "countries[['min', 'max', 'mean']] = pd.DataFrame(result)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "countries.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And then we can sort by the average elevation of the country:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "countries.sort_values('mean', ascending=False).head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For points, a similar function called `point_query` can be used (specifying the interpolation method):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cities[\"elevation\"] = rasterstats.point_query(cities.geometry, \n",
+    "                                              dem_geotiff, interpolate='bilinear',\n",
+    "                                              nodata=-999)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cities.sort_values(by=\"elevation\", ascending=False).head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "-----------\n",
+    "\n",
+    "# A bit more about WFS\n",
+    "\n",
+    "> The Web Feature Service (WFS) represents a change in the way geographic information is created, modified and exchanged on the Internet. Rather than sharing geographic information at the file level using File Transfer Protocol (FTP), for example, the WFS offers direct fine-grained...\n",
+    "\n",
+    "(https://www.ogc.org/standards/wfs)\n",
+    "\n",
+    "In brief, the WFS is the specification to __access and download vector datasets__.\n",
+    "\n",
+    "To access WFS data, you need the following information:\n",
+    "- URL of the service, e.g. `https://geoservices.informatievlaanderen.be/overdrachtdiensten/VHAWaterlopen/wfs`. Looking for these URLS, check [WFS page of Michel Stuyts](https://wfs.michelstuyts.be/?lang=en)\n",
+    "- The available projections and layers, also check [WFS page of Michel Stuyts](https://wfs.michelstuyts.be/?lang=en) or start looking into the `GetCapabilities`, e.g. [vha waterlopen](https://geoservices.informatievlaanderen.be/overdrachtdiensten/VHAWaterlopen/wfs?REQUEST=GetCapabilities&SERVICE=WFS)\n",
+    "\n",
+    "Instead of downloading the entire data set, filtering the request itself (only downloading what you need) is a good idea, using the `cql_filter` filter. Finding out these is sometimes a bit of hazzle... E.g. quickly [preview the data in QGIS](https://docs.qgis.org/3.10/en/docs/training_manual/online_resources/wfs.html?highlight=wfs).\n",
+    "\n",
+    "You can also use the [`OWSLib` library](https://geopython.github.io/OWSLib/#wfs). But as WFS is a webservice, the `requests` package will be sufficient for simple queries."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As an example - municipalities in Belgium, see https://wfs.michelstuyts.be/service.php?id=140&lang=en, _WFS Voorlopig referentiebestand gemeentegrenzen 2019_\n",
+    "\n",
+    "- URL of the service: https://geoservices.informatievlaanderen.be/overdrachtdiensten/VRBG2019/wfs\n",
+    "- Available projections:  EPSG:4258, EPSG:3812,...\n",
+    "- Available layers: VRBG2019:Refgem:,  VRBG2019:Refarr:,...\n",
+    "- Column `Naam` contains the municipatility, e.g. `Gent`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "wfs_municipality = \"https://geo.api.vlaanderen.be/VRBG2019/wfs\"\n",
+    "params = dict(service='WFS', version='1.1.0', request='GetFeature',\n",
+    "              typeName='VRBG2019:Refgem', outputFormat='json',\n",
+    "              cql_filter=\"NAAM='Gent'\", srs=\"31370\")\n",
+    "\n",
+    "# Fetch data from WFS using requests\n",
+    "r = requests.get(wfs_municipality, params=params)\n",
+    "gent = geopandas.GeoDataFrame.from_features(json.loads(r.content), crs=\"epsg:31370\")\n",
+    "gent.plot()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Cloud: only download what you need\n",
+    "\n",
+    "Rasterio/rioxarray only reads the data from disk that is requested to overcome loading entire data sets into memory. The same applies to downloading data, overcoming entire downloads when only a fraction is required (when the online resource supports this). An example is https://zenodo.org/record/2654620, which is available as [Cloud Optimized Geotiff (COG)](https://www.cogeo.org/). Also cloud providers (AWS, google,...) do support COG files, e.g. [Landstat images](https://docs.opendata.aws/landsat-pds/readme.html).\n",
+    "\n",
+    "These files are typically very large to download, whereas we might only need a small subset of the data. COG files support downloading a subset of the data you need using a masking approach.\n",
+    "\n",
+    "Let's use the Averbode nature reserve data as an example, available at the URL: http://s3-eu-west-1.amazonaws.com/lw-remote-sensing/cogeo/20160401_ABH_1_Ortho.tif"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "averbode_cog_rgb = 'http://s3-eu-west-1.amazonaws.com/lw-remote-sensing/cogeo/20160401_ABH_1_Ortho.tif'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Check the metadata, without downloading the data itself:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "averbode_data = xr.open_dataarray(averbode_cog_rgb, engine=\"rasterio\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "averbode_data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Downloading the entire data set would be 37645*35405\\*4 pixels of 4 byte, so more or less 5.3 GByte"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "37645*35405*4 / 1e9  # Gb"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "averbode_data.size  / 1e9  # Gb"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Assume that we have a study area which is much smaller than the total extent of the available image:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "left, bottom, right, top = averbode_data.rio.bounds()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "averbode_study_area = geopandas.read_file(\"./data/averbode/study_area.geojson\")\n",
+    "ax = averbode_study_area.plot();\n",
+    "ax.set_xlim(left, right);\n",
+    "ax.set_ylim(bottom, top);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In the case of COG data, the data can sometimes be requested on different resolution levels when stored as such. So, to get a very broad overview of the data, we can request the coarsest resolution by resampling and download the data at the resampled resolution:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with rasterio.open(averbode_cog_rgb) as src:\n",
+    "    # check available overviews for band 1\n",
+    "    print(f\"Available resolutions are {src.overviews(1)}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "averbode_64 = rioxarray.open_rasterio(averbode_cog_rgb, overview_level=5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-info\">\n",
+    "\n",
+    "**REMEMBER**:\n",
+    "\n",
+    "`rioxarray.open_rasterio` is used here instead of `xr.open_dataarray(..., engine=\"rasterio\")` as the latter does not support the `overview_level` argument. In general, both should be comparable in behaviour.\n",
+    "    \n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "averbode_64.size / 1e6  # Mb"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "averbode_64.rio.resolution()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Compare the thumbnail version of the data with our study area:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "averbode_64.sel(band=[1, 2, 3]).plot.imshow(ax=ax)\n",
+    "averbode_study_area.plot(ax=ax, color='None', edgecolor='red', linewidth=2);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Downloading the entire data file would be overkill. Instead, we only want to download the data of the study area. This can be done with the `clip()` method using the `from_disk` option. \n",
+    "The resulting data set will still be around 100MB and will take a bit of time to download, but this is only a fraction of the original data file:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%time\n",
+    "# Only run this cell when sufficient band width ;-)\n",
+    "averbode_subset = averbode_data.rio.clip(averbode_study_area.geometry, from_disk=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "averbode_subset.size / 1e6  # Mb"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "averbode_subset.sel(band=[1, 2, 3]).plot.imshow(figsize=(10, 10))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The rasterio way:\n",
+    "\n",
+    "```python\n",
+    "output_file = \"./averbode_orthophoto.tiff\"\n",
+    "\n",
+    "# Only run this cell when sufficient band width ;-)\n",
+    "with rasterio.open(averbode_cog_rgb) as averbode_rgb:\n",
+    "    averbode_rgb_image, averbode_rgb_transform = rasterio.mask.mask(averbode_rgb, averbode_study_area.geometry, crop=True)\n",
+    "    averbode_rgb_profile = averbode_rgb.profile  \n",
+    "\n",
+    "    averbode_rgb_profile.update({\"driver\": \"GTiff\",\n",
+    "                                 \"height\": averbode_rgb_image.shape[1],\n",
+    "                                 \"width\": averbode_rgb_image.shape[2],\n",
+    "                                 \"transform\": averbode_rgb_transform\n",
+    "                                })\n",
+    "    \n",
+    "    with rasterio.open(output_file, \"w\", **averbode_rgb_profile) as dest:\n",
+    "        dest.write(averbode_rgb_image)\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Thanks to https://geohackweek.github.io/raster/04-workingwithrasters/ for the inspiration"
+   ]
+  }
+ ],
+ "metadata": {
+  "jupytext": {
+   "default_lexer": "ipython3"
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.11"
+  },
+  "widgets": {
+   "application/vnd.jupyter.widget-state+json": {
+    "state": {},
+    "version_major": 2,
+    "version_minor": 0
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/_solved/13-xarray.ipynb b/_solved/13-xarray.ipynb
deleted file mode 100644
index ccad43d..0000000
--- a/_solved/13-xarray.ipynb
+++ /dev/null
@@ -1,11705 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Xarray"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "import rasterio\n",
-    "from rasterio.plot import plotting_extent, reshape_as_image"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Introduction"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "By this moment you probably already know how to read data files with rasterio:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data_file = \"./data/gent/raster/2020-09-17_Sentinel_2_L1C_True_color.tiff\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "with rasterio.open(data_file) as src:\n",
-    "    # extract data, metadata and extent into memory\n",
-    "    gent_profile = src.profile\n",
-    "    gent_data = src.read([1, 2, 3], out_dtype=float, masked=False)\n",
-    "    gent_ext = plotting_extent(src)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.image.AxesImage at 0x7f1254dc2c90>"
-      ]
-     },
-     "execution_count": 31,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.imshow(gent_data[0, :, :], extent=gent_ext, cmap=\"Reds\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Rasterio...\n",
-    "\n",
-    "__Benefits__\n",
-    " - Direct link with Numpy data types\n",
-    " - Rasterio supports important GIS transformations (clip, mask, warp, merge, transformation,...)\n",
-    " - Only load a subset of a large data set into memory\n",
-    "\n",
-    "__Drawbacks__:\n",
-    " - Coordinate information is decoupled from the data itself (keep track and organize the extent and meta data) \n",
-    " - Make sure the bookkeeping of what each dimension represents (y-first, as arrays are organized along rows first)\n",
-    " - Functionality overlap with GDAL (and sometimes installation issues)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Meet `xarray`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 81,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import xarray as xr"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 265,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "  margin-bottom: 4px;\n",
-       "  border-bottom: solid 1px var(--xr-border-color);\n",
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-       "\n",
-       ".xr-header > div,\n",
-       ".xr-header > ul {\n",
-       "  display: inline;\n",
-       "  margin-top: 0;\n",
-       "  margin-bottom: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-obj-type,\n",
-       ".xr-array-name {\n",
-       "  margin-left: 2px;\n",
-       "  margin-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-obj-type {\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-sections {\n",
-       "  padding-left: 0 !important;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
-       "}\n",
-       "\n",
-       ".xr-section-item {\n",
-       "  display: contents;\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input + label {\n",
-       "  color: var(--xr-disabled-color);\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input:enabled + label {\n",
-       "  cursor: pointer;\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input:enabled + label:hover {\n",
-       "  color: var(--xr-font-color0);\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary {\n",
-       "  grid-column: 1;\n",
-       "  color: var(--xr-font-color2);\n",
-       "  font-weight: 500;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary > span {\n",
-       "  display: inline-block;\n",
-       "  padding-left: 0.5em;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:disabled + label {\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in + label:before {\n",
-       "  display: inline-block;\n",
-       "  content: '►';\n",
-       "  font-size: 11px;\n",
-       "  width: 15px;\n",
-       "  text-align: center;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:disabled + label:before {\n",
-       "  color: var(--xr-disabled-color);\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:checked + label:before {\n",
-       "  content: '▼';\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:checked + label > span {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary,\n",
-       ".xr-section-inline-details {\n",
-       "  padding-top: 4px;\n",
-       "  padding-bottom: 4px;\n",
-       "}\n",
-       "\n",
-       ".xr-section-inline-details {\n",
-       "  grid-column: 2 / -1;\n",
-       "}\n",
-       "\n",
-       ".xr-section-details {\n",
-       "  display: none;\n",
-       "  grid-column: 1 / -1;\n",
-       "  margin-bottom: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
-       "  display: contents;\n",
-       "}\n",
-       "\n",
-       ".xr-array-wrap {\n",
-       "  grid-column: 1 / -1;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 20px auto;\n",
-       "}\n",
-       "\n",
-       ".xr-array-wrap > label {\n",
-       "  grid-column: 1;\n",
-       "  vertical-align: top;\n",
-       "}\n",
-       "\n",
-       ".xr-preview {\n",
-       "  color: var(--xr-font-color3);\n",
-       "}\n",
-       "\n",
-       ".xr-array-preview,\n",
-       ".xr-array-data {\n",
-       "  padding: 0 5px !important;\n",
-       "  grid-column: 2;\n",
-       "}\n",
-       "\n",
-       ".xr-array-data,\n",
-       ".xr-array-in:checked ~ .xr-array-preview {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-array-in:checked ~ .xr-array-data,\n",
-       ".xr-array-preview {\n",
-       "  display: inline-block;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list {\n",
-       "  display: inline-block !important;\n",
-       "  list-style: none;\n",
-       "  padding: 0 !important;\n",
-       "  margin: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list li {\n",
-       "  display: inline-block;\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list:before {\n",
-       "  content: '(';\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list:after {\n",
-       "  content: ')';\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list li:not(:last-child):after {\n",
-       "  content: ',';\n",
-       "  padding-right: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-has-index {\n",
-       "  font-weight: bold;\n",
-       "}\n",
-       "\n",
-       ".xr-var-list,\n",
-       ".xr-var-item {\n",
-       "  display: contents;\n",
-       "}\n",
-       "\n",
-       ".xr-var-item > div,\n",
-       ".xr-var-item label,\n",
-       ".xr-var-item > .xr-var-name span {\n",
-       "  background-color: var(--xr-background-color-row-even);\n",
-       "  margin-bottom: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-var-item > .xr-var-name:hover span {\n",
-       "  padding-right: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-var-list > li:nth-child(odd) > div,\n",
-       ".xr-var-list > li:nth-child(odd) > label,\n",
-       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
-       "  background-color: var(--xr-background-color-row-odd);\n",
-       "}\n",
-       "\n",
-       ".xr-var-name {\n",
-       "  grid-column: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-var-dims {\n",
-       "  grid-column: 2;\n",
-       "}\n",
-       "\n",
-       ".xr-var-dtype {\n",
-       "  grid-column: 3;\n",
-       "  text-align: right;\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-var-preview {\n",
-       "  grid-column: 4;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name,\n",
-       ".xr-var-dims,\n",
-       ".xr-var-dtype,\n",
-       ".xr-preview,\n",
-       ".xr-attrs dt {\n",
-       "  white-space: nowrap;\n",
-       "  overflow: hidden;\n",
-       "  text-overflow: ellipsis;\n",
-       "  padding-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name:hover,\n",
-       ".xr-var-dims:hover,\n",
-       ".xr-var-dtype:hover,\n",
-       ".xr-attrs dt:hover {\n",
-       "  overflow: visible;\n",
-       "  width: auto;\n",
-       "  z-index: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-var-attrs,\n",
-       ".xr-var-data {\n",
-       "  display: none;\n",
-       "  background-color: var(--xr-background-color) !important;\n",
-       "  padding-bottom: 5px !important;\n",
-       "}\n",
-       "\n",
-       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
-       ".xr-var-data-in:checked ~ .xr-var-data {\n",
-       "  display: block;\n",
-       "}\n",
-       "\n",
-       ".xr-var-data > table {\n",
-       "  float: right;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name span,\n",
-       ".xr-var-data,\n",
-       ".xr-attrs {\n",
-       "  padding-left: 25px !important;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs,\n",
-       ".xr-var-attrs,\n",
-       ".xr-var-data {\n",
-       "  grid-column: 1 / -1;\n",
-       "}\n",
-       "\n",
-       "dl.xr-attrs {\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 125px auto;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt, dd {\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  float: left;\n",
-       "  padding-right: 10px;\n",
-       "  width: auto;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt {\n",
-       "  font-weight: normal;\n",
-       "  grid-column: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt:hover span {\n",
-       "  display: inline-block;\n",
-       "  background: var(--xr-background-color);\n",
-       "  padding-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dd {\n",
-       "  grid-column: 2;\n",
-       "  white-space: pre-wrap;\n",
-       "  word-break: break-all;\n",
-       "}\n",
-       "\n",
-       ".xr-icon-database,\n",
-       ".xr-icon-file-text2 {\n",
-       "  display: inline-block;\n",
-       "  vertical-align: middle;\n",
-       "  width: 1em;\n",
-       "  height: 1.5em !important;\n",
-       "  stroke-width: 0;\n",
-       "  stroke: currentColor;\n",
-       "  fill: currentColor;\n",
-       "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray (band: 3, y: 317, x: 625)&gt;\n",
-       "[594375 values with dtype=uint16]\n",
-       "Coordinates:\n",
-       "  * band     (band) int64 1 2 3\n",
-       "  * y        (y) float64 6.639e+06 6.639e+06 6.639e+06 ... 6.621e+06 6.621e+06\n",
-       "  * x        (x) float64 3.925e+05 3.926e+05 3.926e+05 ... 4.282e+05 4.283e+05\n",
-       "Attributes:\n",
-       "    transform:               (57.32777121388223, 0.0, 392465.9217302375, 0.0,...\n",
-       "    crs:                     +init=epsg:3857\n",
-       "    res:                     (57.32777121388223, 57.44833434997156)\n",
-       "    is_tiled:                0\n",
-       "    nodatavals:              (nan, nan, nan)\n",
-       "    scales:                  (1.0, 1.0, 1.0)\n",
-       "    offsets:                 (0.0, 0.0, 0.0)\n",
-       "    AREA_OR_POINT:           Area\n",
-       "    TIFFTAG_RESOLUTIONUNIT:  1 (unitless)\n",
-       "    TIFFTAG_XRESOLUTION:     1\n",
-       "    TIFFTAG_YRESOLUTION:     1</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>band</span>: 3</li><li><span class='xr-has-index'>y</span>: 317</li><li><span class='xr-has-index'>x</span>: 625</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-070bf2a6-2c13-4ea6-9d8e-d8cd767c7855' class='xr-array-in' type='checkbox' checked><label for='section-070bf2a6-2c13-4ea6-9d8e-d8cd767c7855' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>...</span></div><div class='xr-array-data'><pre>[594375 values with dtype=uint16]</pre></div></div></li><li class='xr-section-item'><input id='section-5e5e165e-1e1f-42df-a688-e28fba94f94f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-5e5e165e-1e1f-42df-a688-e28fba94f94f' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>band</span></div><div class='xr-var-dims'>(band)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1 2 3</div><input id='attrs-70a240f1-91bc-47e3-bf19-971d73e57ff8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-70a240f1-91bc-47e3-bf19-971d73e57ff8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ad358cd8-d5da-4d14-82ad-2f98f8a5229b' class='xr-var-data-in' type='checkbox'><label for='data-ad358cd8-d5da-4d14-82ad-2f98f8a5229b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([1, 2, 3])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>6.639e+06 6.639e+06 ... 6.621e+06</div><input id='attrs-2b984b2c-e88c-4f2b-be9f-3f9b48b01ee1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2b984b2c-e88c-4f2b-be9f-3f9b48b01ee1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-99897377-b6a7-42ab-a58c-0bb5c6debb57' class='xr-var-data-in' type='checkbox'><label for='data-99897377-b6a7-42ab-a58c-0bb5c6debb57' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([6639463.535997, 6639406.087663, 6639348.639328, ..., 6621424.759011,\n",
-       "       6621367.310677, 6621309.862342])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>3.925e+05 3.926e+05 ... 4.283e+05</div><input id='attrs-fdaa54f5-1d51-4415-acb6-9d09d02f8f62' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-fdaa54f5-1d51-4415-acb6-9d09d02f8f62' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1af669de-ca81-4066-a2d4-d7b29fd19a2a' class='xr-var-data-in' type='checkbox'><label for='data-1af669de-ca81-4066-a2d4-d7b29fd19a2a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([392494.585616, 392551.913387, 392609.241158, ..., 428152.459311,\n",
-       "       428209.787082, 428267.114853])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-2a15a100-41b3-47ed-9969-3a37084ae63b' class='xr-section-summary-in' type='checkbox'  ><label for='section-2a15a100-41b3-47ed-9969-3a37084ae63b' class='xr-section-summary' >Attributes: <span>(11)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>transform :</span></dt><dd>(57.32777121388223, 0.0, 392465.9217302375, 0.0, -57.44833434997156, 6639492.260164049)</dd><dt><span>crs :</span></dt><dd>+init=epsg:3857</dd><dt><span>res :</span></dt><dd>(57.32777121388223, 57.44833434997156)</dd><dt><span>is_tiled :</span></dt><dd>0</dd><dt><span>nodatavals :</span></dt><dd>(nan, nan, nan)</dd><dt><span>scales :</span></dt><dd>(1.0, 1.0, 1.0)</dd><dt><span>offsets :</span></dt><dd>(0.0, 0.0, 0.0)</dd><dt><span>AREA_OR_POINT :</span></dt><dd>Area</dd><dt><span>TIFFTAG_RESOLUTIONUNIT :</span></dt><dd>1 (unitless)</dd><dt><span>TIFFTAG_XRESOLUTION :</span></dt><dd>1</dd><dt><span>TIFFTAG_YRESOLUTION :</span></dt><dd>1</dd></dl></div></li></ul></div></div>"
-      ],
-      "text/plain": [
-       "<xarray.DataArray (band: 3, y: 317, x: 625)>\n",
-       "[594375 values with dtype=uint16]\n",
-       "Coordinates:\n",
-       "  * band     (band) int64 1 2 3\n",
-       "  * y        (y) float64 6.639e+06 6.639e+06 6.639e+06 ... 6.621e+06 6.621e+06\n",
-       "  * x        (x) float64 3.925e+05 3.926e+05 3.926e+05 ... 4.282e+05 4.283e+05\n",
-       "Attributes:\n",
-       "    transform:               (57.32777121388223, 0.0, 392465.9217302375, 0.0,...\n",
-       "    crs:                     +init=epsg:3857\n",
-       "    res:                     (57.32777121388223, 57.44833434997156)\n",
-       "    is_tiled:                0\n",
-       "    nodatavals:              (nan, nan, nan)\n",
-       "    scales:                  (1.0, 1.0, 1.0)\n",
-       "    offsets:                 (0.0, 0.0, 0.0)\n",
-       "    AREA_OR_POINT:           Area\n",
-       "    TIFFTAG_RESOLUTIONUNIT:  1 (unitless)\n",
-       "    TIFFTAG_XRESOLUTION:     1\n",
-       "    TIFFTAG_YRESOLUTION:     1"
-      ]
-     },
-     "execution_count": 265,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "gent = xr.open_rasterio(data_file)\n",
-    "gent"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 266,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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Aam3BbDO+/Gx8AkOeM8mhNZTW3HBLa4L9BsTGzaIXCsBkTEXkMtmiB0f0dHfu8Tlk0wEgjgeIdzqxijlJgCy23qAX0GvJtPWmx0MaqEoN2H7EzVUIaSwECkI0zTc2+of8vfH2AQu3aDGUBosGeI3RkAK0+xjaD6C8Jp9tNqaSmo4B14fOUqBY4lxoTS8a4L2iLhWxeHeqVGOUFU+hFs9QUXkBcLwvYWqbz1Qrca76h6LYQ2DtQh7JqIDenap3eP80BcY96FHXRl7KAdbPSuLbB1qLNhlsPGyloGOBYDrLNApeADy6A6yfg6rU6QREExo9P4Byfei1s5Q1nVF2ohmQyEbbus28yuIKcyOHu7zuaMDN7XlMjJo1MDmLLIU2ePdbr0A/egSsrEAFAQ0EANx9W4wqoS+UKsCZS1yTJOaah2VeWym+dzpnInttk4YqmUO1O9APH0AfHtLL3n4Etb4JhCXCaEoR+tNM/OvXX4He38f8aIRsHCGLE5Rf+hghpIMtqLWLuVip9iqyo68B8zlUsw39rZehfuJnuZdcl4qoUAQmI87vPKYC7u1z/hzXeprRlO+WCt7t+dA6s9BrluWYPhyXyrveIaSVJHY/VZuU1+mIxjBNgawD1V6G7h2KIxYQ6nM8IJpA1VrQl29w7V0XOHeV+7+5BNXovJswEM+Iz4/7vHb/mM8VVqh0dx9bDNxxGd0AotTnNlm/cZ7XPNqjQ7K4xnvKPZAkvKYfWIcqLEvkP7ce/eCYe0ZnNk9XqnAO3mO8b8r9/9UwG7rVodVfFri+2pDQ5ESyMBWoJigCgyNObqVOwZkO+fPeoVWQjsuNb7zOLAW29qFbi4JrFWzY6geEG7oH9HAlqaoKIZVO74hQzWhAD9VYWyMY81gSjGJM4shmxueCJYZVLrzJJ7SY3NRxxOcthhR8gNfd3yG23VphVJHMaQBrTet9YJLDUHrUp1B6gRgzj/ORpdxYJnEzj2kUJyMmvOYzSYTOCRUoB8p1CenEM6h0zmfcegC9eoZRhOtbFo/OaGCHopAPdhkii+Km0Qn4juOhNRDjAfR4SIjJcfl/OhclPxejMgEKJYb9T+4C/W165fMZ9DDjMxqlrzWNZypKQDlUzJU6IY7QowEHiEPv3JPN17TQUaHI/MDdt6GP9qFKZW7gWoteq+txncdDemdrm9DDLvTv/Quo6zeAc9doyMYDbsbldeg33kC6fQC/WhOYKII+2IWqVLnmqXh8jkQY4x6fJ54BFbJzdJc5DbW+wc9OCZHp4yOo514EZjPowz3rNW5IIm8eUQkKu0gtLiJ99ASO78Ft+1bWzl6ijK9dtNGn50M98zz0a98BphOoz/243bNBgY5MuUoWjmG8jHpAqUbD5nMfqlobAKCNs+ALayaaCuziU4kO+4yiPZ8R6bDLeWxJNDsdWqfGMHUMYcFAogDXfTqE7h0At14HXvo090+9k5MS1MIaZcR8PkstS+ngiZ2DeovveLDDn3k+11A5mP+f/xzZPEHh5/8d6qtoyuvV2zTQ0wn1BCA6SvJj0wk/nyZcm7bkDtO5OEQtEkmqdSAMaciAnExCaDJ4T7X6wVDuABemIlZ70GVW3fPoQSZzTtTeFielvWgpf8Ou9d6MJ2wm38AiUWQVDMAJThOhhxXs/aMp9MOb3JCzKTf50ioV0sGWbLwi//5wnxNvkn9KmBpaqHFB0XpxyZzPFFZpPExSqLFAj71/SKGdToD1C3w3462eu0bPFoAu1egReT7x/pwKqEUxcU5Ua5nXNNBQQXIA4xFQrpAZ4wdQq5KoTOZAJiGemTMzVyYKcn2uz3gE7O9A11o0AvOY7A3X4zsWS9YApymhlrFAVgPxgDYuindKOikAed6MMFOhxMTWdEQl73k5NU4tb1iG0GwMvX2fc2ggIoDzU64J3uxT0WSJVW4mTxFNyKzqHVNph0JVmw65dvUmVJoC1Ro9Kv9EFDMWTNlgwU/uA/U6Q/CdB9D33wEePADW16EAOC9+GPG/+Cq8t9+C+tTnAd2DWlimQ1OuWPx1MqJ3KLANHBe6f0ijZ5LMKxs02GEZmBwRalk9ywjs+ADZzXfgXLuSG1C4Pj876FLGWgtQjgICF267AdRqVqF0D/KcklFu6ux14NE9ft895Nz0jxlx+IGlAbs+n8HkutIUCBTlTSkaHc8X9lab9ymEjEDS1OZmHJfKrBAKY4YUTiaJI0DFFgaZC8VSYEDUWsS4kzn0w1vQb/wFWUWPb0P/wVehfvJnuN6VGrB57d1UR/PMjgssrNE4GdZdNKVSdV3CK3EE6Az+Zz9J2M7zqKOS5F05PsxjeuzHe3y31fN5Qp8Qi5ALDOOrUuczGBaNcvi53jGwuMo5WlgmRLlQe0+V+sFQ7lnKxQKIy6UJJ2o2hj7Y4cR1jyzWpEVR7GxBD3pQTz1n8TKdcTFMIhKC7aYpsdV5TMUTVizWq3XuOSIo8vfNNgW3tURvuHtgrxEUGWFkmkI37NMDrza4WElslWIhJGNFZ2RvLG3Qm1EKqrnIzx3tCiYcUuFI/kD5gTBiKCiqWIJeWAdqEyqzTLixQRG6u8v3D4oMKbOUOYT+ISONOCIENY+hp0MoVRODQChBP3gbqDZyDyvH5EdMXqtylbCJUvQG05QhuB8AS2fIsFEOlbWJxAwdctgDiiHU2gWG4uMBIQuASvfJfW4iyRHoOIJyPWLvkutQFYlU5hqqvQK9dVuSlqHdlCbE93yLGytF2YgTXk/yMCqs8vkdh47A7TehXvqMGAlR5EGRXttMFMjBNj/veXQYTiavkznURz/Nv1negPI8xN98BT4AHcdQC0sofuZjiF/+Dvzf/12oDz0HbF5AXq8QCEujfwisnKVcGvpqdw+6sch1qLXJ2BgJ1dbI6dEe1ziKcPTKYyysLAlFVrxPR9HIaw3UGnA//jHohw+AVgvKcQgbJDHU+gXSX00Oy1BgWwvQj7lOqihKd2+b9/c8SU7OGdnWW0Dd55p6PuXRdaHqHVLSDQTh+vTKh33K5mxKDDwoEPIbC3HCeOOGHl2pE/rrHgkFtQeUZY9HU+7rQsj8R5ZBf/2PkPzhn8C7uAHcegO4doORp3ciagEs6w4gKyWdA3Xx6CcjGtxYoOMK9wA6S3wG4xQVQ0Ycc6E7FiQfNhfFXywT8kvn/DqaApEYq6CYw7TY35E5IrEAxZAGtS1RQKVGiPA9xgejcZgpHsikEMJQ01yXi23oVcVQKIdlZtFdF0pJlt3AItOJZb8Me8L4kImvNIHjQ25M182VpyqWeE2De66dg7p0A+rsNWKWJsmaJMD5p4CrzwJnL1OIs4z/m+H5ufKG51sL/vYrwJ23oGcTUjLFm9GH2xTYeczEWv849/51lpKfK5xeeLIZpiPLX9Yaev8R/yaORMlpeni9A957cY3RzmRki2BMMUUqIerGZRZmSV7hXRik50ELxU61V6AEPlF+YGljJkFmfjaPqQzvvs1rlGp8j3nMe8p9cbjLKKhcBc5fz5lG+uCJQA/c0DqJrQI3Hv7SGUZbJkEmhlAVwnfxtXNvCrBeolJQngecvUol2+rwuoVQoKFUklqSlDZevSkMMwVCzTaV1MoGVLlOjrTnQ995G/7ls0Clguhbr0PffAvYOA//hWe44VfPEBIQhhAmIyur0yGNajyj8ZakI4plZP/qt6D/5Pe4FxyX8AsA/c6bhBCLRSz8+ItQH/64VYo64+eMRzjokY3T6QDDIZlGh/vA7hbpoZMhI+K8MJARJOZzoN8n7z6aCsU147Mrh9DJxetCGw2BpXXL23Ykr5alnF8zyjXun2jGvef6AmempDVqLd54xSp2oytMDcrKutBHg1wOtSFNFEPo8QTeSpuw1Ztv8Hkd792EgO9uw+K4UGGVcmqMb01g4n6XhsXs96HUThhv20BKhnRhqNY6Y+Q9lihdCt7ofAi6MOzRIL3xKkb/9Nek3mPIyGt6InfR6BCufY/xwfDc/YLFvg2MkfNdA+DxXeFSu5bqVK7Qki6tMVQMI054LEJy5jy9iM6SZYh4AfTmJQoNQGWVprZiztATRfj0bALlelDNJejRQCplZ0wcJS4t8MIqFcbSGbtRXVcqX8V6H2xB94+hNs7TkAD0oHuHFLR5zOs4Dv/fugusnQPmKTSmxOTNXO09prIWfFMPu/Qsay0LAxmBSlPgwW1iqdGUG6BY5ucKIec4Tfme5TqTosMuKWXzmMpveJQnSfXKWQq92YTRhMm+ygn2kPHKzNeNJpWZ40K5njW8AL82bJRKjZs+EcVv6JIlYtRKKSZfBbPVSSJMp0UaObPZD3eBK8/K9U+EyFlGzviZi3mtA/xACl0zYPMqqZfjAb3lx/cIyWUpo6mDHUtxG/SIo3qeJPeGhAWqwlfefQgcHUHdeAE4PkTx3Hmg3oJ+eBfqxkuUoaVNRkZBQeoOXN53zkQqFs/Q+Rg+5jqFFWDU5zyEIfTDe1CtNnS/B7WyDnXmHOW30YKtrhQD57iUs2JolXuaAuevQE0l2T7sc78sHgGH+9DBDtTTH82Noqq1gOc+Cv2134aex9w3o6E1MgsrhBpM3skUBJUbZMLMI2idSYGiRB1mv1SEmJCJlIcV5oMW12ydisnveD5wcECj11mkshfYErHkFsBEMHQGLG9Cnd0kHbLZgj6WvNnZxEZ6OFERnleeAvrRO9zXACGk8YAGDkBevepJ/sXQKpXoLZPAXlyzhAUAmANIhtYhNPkfUyAWUj+oq9dRufE8dVlNYMmlVV7L5BVPOpXfY3wwPHdoS/0zVDtRMABoodY2Gf4Xy3axlUMlsrBMITMTfnwA3H8H+sFdYqpmOA4XvblAmpOhSpqnSBOpTGOiRylFT1tnTLhVavx8UKTi05qfrbdzr4/QjSRulcPFuHsTankdWFyDTmLoaEqlPBly07UWrNU3PN+DbYbahsYXTYj7R9G73oc435wKLp1TiPxACliqZJhkKbnXUhPAMuhMCkACKkxJ2CiDU+uMMEbvmPBK/YTxMFhopWGLv0pVKUrybP4gFY9FIiVkKaORWlsMuQ+0V2ioAXLak1igMaFJjvo5U0JJObvuHTD6cX16QWGVyrLeATrLTPwqx8pINGXRm2Ci2lA3DfUuKAKOYnSyR69UHx1Q4R3v0ZsyRrjRYbVtvcV3e3IfuHcT+htfZ8WuRH/qmWcpL+cvE6MNClDPfhSod1ixWhIF1j2weLXricFoAIfbhC8E7sophVeeAaZToCvMpUqVsNvCMu8HQK2f5byelJNL1ykPpoTf5KdKFd5/ZZ0MNS+A/tafQn/1KzY5aEa9wzYQ/T73m1FMhjVjkutZCtTaEsWIkUkTgfl6NJQzibBnE8mTBZy3ziqvs/uYCs8kGKOJjTzCMt/VVK7GUU6NRb3FKDGJWYviBVBnLzBCGQ2hPvklqBufYMS79xD6wVsWGjOKPUmg3/wm4n/2q0BvnzUhAu2OfvnX2LagWrfOSbkqkJjM13zG2giAMjybcp7rLeqRWoPPfbJg0RUDHAkJ49J15AWCJto9PrC5Ey2Q8HuMD4bnbpKHxmuTZBs8wa8NL1rKbm2BiSy2coGqCNL2Y/KHd7aA0Qj6+BDq8T3ocJdJs2hKxecIVdEPgKIUBo16UlRzggFiDEa1CVSbTG56zPQr8YL1SNF7X78IlXrQsfHghUfv+WRsmIVMYv47PhDsnUwA3T8E+hLuRULxmk5k04uS7CzaxF7vKMekUSxTwL2YRkNnFKrmgijK2GKR89hCPtMR/5/HlolgICtPEnHFErn95Zql+xmmAmChmUS4uL0jC0sNevTqZmPoYpmJUoDPUQipqHvHrIyVfiBajLcqlllAsr8FvXqWczKP6UEBLB5yXSqRQgg1j5l87x/SSyzVaEQhcFEqXniWQJsoEbAhtWHL7G1DXb7OgrqXvwq1eoZJYOMhegVg/zH0zdeRvnkT7pWLUDeeI5XwnVeBSg16+zGUMYjFkPNSrvGZJoO8QAkLkrA/3kNeoW1C9HGPchfLNq1IDiXLEN99gsLGBg2HMeYHu4RKukfA2cB6owpUzHuviXffJlxx/x2oVofOxcIa9L/8dajFJbIzVlehn9yFOv90vj9VcwH4yA9Rbntd5p0M7JjOCaMGXuQAACAASURBVHkadlQ0AcoNwmuGq++4wFTozcaJcRQA0zfIJRylMym1l4g6CFipWhDWiFLA/jYVvB9QP3SPaKDKVXEMI8D16MCMhsB4TCP3zqvQhQIT0G+9Ijkq0jIxm7KwrFSDevaHUbj6PPTuI16jtw996w18+ZuP8Lk7v4ln/gtw7167wbldWrOwofKBtU2ohTX+7XjIyGseWb6+gUWLJf5bXAPeeY1r3FoSKneZeY2wJLTQFp+xtWA5/+8xPhjKXWt6yemcG01Cq3xTGMbEbExv2xQPxanlvRoecJMJQeW6hASabU7GeEBvALAc9CyVii9yvFEsUwlAkj5Zyqo112cxCQBTRn+yB4sqNyw7pViGKtVI55uOuJgv/DBsmb0CElDpieektSat04SpJmIZDzkXrisFThLOmRYJA3pvJsGZh62mgrNIzqwST1gn7EVDr1ZYHodP6IGbQiHl5LkCpTW0ULT0dAQVlmlolAPohMbQ8PEF89Zj+dnRfl7goRbWqMSLZUt7E/aHKleByzegJ9IyQTBIVW7ws8ub3KyTEQ2U69Gb3brHUNULuIH2HkreQnDmwTH07qO82E0fSA+gQoHyUHKoPLPUypt5d5MUBqAuXZceJBlxWs8Dtu5Cf/PrwNISKztNVaKhNI4GUO1FoWXGhAVLtfx75UvSW+6jwgp0tSnFeQVGCnVpASHGCa6fRzRqfQP+zg7QWYa6fIPvqYUj3lpiAtrwos3wfGBhCdjdhn54FxgO6YWHJeLd0xHS2/eBuw/g3nga6sw56O+8DLV+kcZbhjpzGbp3THaYI8VDwz5zOvUmMOhB/9HvQX3xp6Wf0kPmM7wAiKeWjVIVksGobyN2YwT6hzaHphTXejIC1s9xzbtHfJ/RgM7PTNbrQArUJKcAgD1gCgWu0fExdPebUOfP07G4eRPqpY/lid/0D/8ATudbwMXLUC9+kgrVVHL7Ae5++SuYZRo70xjPTKdQ7Y6NGExEaxyv/jF11WwsxVwiw2Xy5VWxTCcGoEM3GdKBPdzlnBpnKk1tewJA6m9GQFimU/Me44Oh3IXLrsIq9LBnmSimmEj6fGA8sKG08doBi89mqW1TUK3RYrcWhBYpWJnhv5ty/SylMJXrNrM/HtKzaHSgD8d5laWejuyGGfUsr3Y6pletM8Ar5gwDZbLmpgtdlvJzs4lVKPOYGG0xZNGPzuz1phPoNIXa36GRqtQEA5YwNE0JV5kCrsmAvVEuf4jJ1AILmbSpCkxieo5ZSogjnjGx6/k52+Vdnnkh5O8N/9dxGV6PBxS8k83G4hmVM0DhNOwCqUlQzSXocY80xs6qRGaFnK+rjGI2VbOeJOBMIdKozyike8DNcuFprmnvEHjwMmGJclWijBr0rb+AvvsOqauAQDsO1NVnoJpLyH7vN9i46/Nfssphb5uecL1FhTUXalpzges0G0O//Af41j/+v3Hj4xsotNvEci9csY3TxpJ4N5WL9Y5AXZ6U5s9tPYEU/WjDYTaJPUNFnUlRWmtJGrtlrMGo1uB86nOEmu6wOpVtA3psbra2SeW1+5BKoN5ho7NyHZjepmL3vLz9AI5Ir/U+8VEkL38T6sozhIZefwX6cNsWNSlhFhUK0N/4OtT1Z2xB1v4On7vZhvqCdPce9ZnzMl5rIsQAA13uPwHOCC02TZA34Br2GRmsnpH2DVNR6oey9y3lNXf+lCP8/sfAuauSVHegNi6Tf1+vA0kCdeUpNggshMCSVNrWGkCSwCkVoT77Y/TmJwMo1CijonMu/Fsfxi92e1CeC/XCx/gMpotnPGPriaU1iwiYKEMpqNYK80S3X4d66bP8m5K0HgnF6LWXmLs52OU9nzykHitXmfP6zjegLlzmPbUmXfM9xgdDuRsWgklQzKaWpgTYai2T9DRQRDSj1T3c5UY6WbHZWpKsfYkT2Fi0FWSJYFhb921oDljqnOlKaHq1mLYFJrnjeVQyZUksjg2zQXBTg5UZBovnAb5EBaqe82j1RJK0JmFmvOmwzDDSD6B8CFNAcG/lAM7MhsPKoQKsSMWp79seHZ5H656lEhVNoX2rQFU1pIIYD4jDm1anrm/bD+dl+LYfjJbCLnrJmWCqc4s5hhUmf2bTnHLK8LknHGPOgU4TqHIdunvAHIZSgOOzYMrQD8MKE42La4SP2lJ56vrs02I8mtGQXmiW5pi6un6D/Ye6wkAybSDiCKpcBi5cAbYecAO1FqDv36HRq9ZovEwSf/8J9IPbwGAAtb6BwHFw5zvbePonvwC88HEa2tYCcySOC2xuWnnTGesaxLfIS+sBybHElkdtZMd4tiYpPhtDK4fJ+J7wyxsC+ZhCOeWQ/37zLwSHngG33+BnX/iUred46jlGYJIA1EeMOFXIbpTel75Eup3nQ/3QZ+mISBGZkTd16YZ0a30ibSRCm/MyztdUImzPs/TUtG+pqUGRij2aWvLEZGChnc0LhDsGx7zHrMtkpqFDm+s0mlx7Exmvn6chNDz9OGKLiOde4rU3Lub7SX36xwXK9ICHd6E++VnK7HRC5+Tq83z3ucBkq6tQlQrUmbPv1hsGYlsSPDxL87oIFVbyepR3VdCa/NRYog9D6V07Zx3X1TOcw1Gf6358jPitryD47A+zZ9NJjv73GB8M5Z4mtsWvaSlrJk9nNgtuFtZ0IVSKmfawTO/uJHUpSWyBEEAvNKhCT4ZWIZjrGkzLhNGeZy1ve4VUPKO4pkN6R45Lj2dlXUrfHUIXJnw3hqFYzpMnWqhMOQWz1rYsAENLTBJWz5oiLMOR9XwW9xRL0Kal6MGOTcpMBvS2PI/GrtEBXBd6PoNyffKjB8d5sy7VWpK8g2c5/ibXEE2ojILCCRaRhh51LQUynQOJaztSSuMuwhx9Xm/jIpWp4d3ffhNoL9IbPHOFTBsTUSmH3lK5wc0QTTiPMpQXUAkFRTYs6x/y/cNSXoCGQY8hu2E8ZRlQawi+X5DOogvcrIsrQDRD8q/+GN4XvgB4AdRHPsmbGY731l3O4Svfhrp0FVhcgd59ggvna/jTm0e4vr1FnH82hbryNCOVlQ3OrXKgJyOutWHcGJwVsHgyYD19E8XUydDQhtZ6gsmlnzxmBLK+IcV8UgH98Db0zhb761ca0Md70P0ujabp3WQiBNPkKplDbV5gtJFl/DnAHjQLa3RAdh9AH+2yRuFkjqVUYWVsMbR9ZEzy3xQn9Y4434a/beDGk1ix5wEjcYAKQnUej7hWD24xIotmjOZLZRshmVYEhYLVC5Wq5JZUbjj1sEvD1lzgv1KVEHAstOBhn9c4f1kouRUpJjxPh8gT560QQm2Q9ZS3IjfvYxLfprWAgdSiKbSJao3juHGZRWntVSr+7oHlyQcio4Nj4vBBx+7H9XNQl28g2H1oK3hP9ID6XuODodwBCsbBdt6EKC++MD1YDPTiB1zEcsPCB8bbHve44KKUdHeP2LMp008SbrwstZMaz8TrdnmPslR9Ge6pUVpJzGvlbQ0cYolhhSFlWLJ9YAJAD3tMvprQKyhClVIW8GQpaXlHOzkUgWFPaFUdeh6NRevZpSm9vzSlt1sIoU1DtPvvWCgh72gnLKBCiXCLzJNa2oDefcDnSlNJOrGoSJkCLjMMlKNpdPR4SEUsjCCdJqTDCR4PSNLS9VkEs3zWtg8AOOcvfYZfj3tsZKUceT5lE3GuC5UqKjYAprBEH20TtprP7JpmGbD9CHo6hSoWoe/eBppNu9btBW7ana08KlSFENpx6X26LrwL120LWkNfPdzmvQ/3yOA4d5FG98FdqFoN1Z/9PJ79374C/eQJ1KVr7BdTqtl2ufGMBrQQsj9O/1gS96WcFaE6q3QGKlR02iSgDUyjM4u5pnNW6wJQSyvQ79yE9n0ax1IFGPahD/YYjSytMsLpHlhSgoF7GovA8Q4pop7Hv3UUZTesECYZ9MhVBxhZxjGgRjTIndV8PVBrQS2tkBI67FuFanrIm57lWZpHkag2bLLfnEsg/XnyYiVTPdw94LOMh9ADdvNUUcQIYTSwJAXTtqHZpnfruPaMgMnQ1sAYRsywy06upQr34MoGjV6pBvXMecrpm9+21boGJTCQ77BPYzaPbcWqUvTAjcJNEks1zpugjcnqkgZrenDMdtqlCqHFRemVdPctrkepljtdOp0zKiyWWWQ2JvVXfzeb6bvGB0S5M6zX24/Z8jQs5eW9JxNJhrmiSqyu1AdbufLD3hNO6vIqkAiXNxKlvcfNqs9egtq8wlt6HrEsnUEP+pa9YLpNmtLyyQA6iahQTJhs+sQDeUipmkvQg2PoO6+xCCqs2oIgQJQRW+UaSCKHnpIk92rZY10DSmiEEsKrRBTadMzras2wtS1GwbWKCw4PPGGjqfRdkATKDTISBscUOAMfALagwvUBV1MJCJ6pakL9E6gmb5Rlvjb5D5MYM4agWLaJRNM2udYi1JAm0HsPmRQ198hbDvjW0+yyN45qLgHVFcqKo/I2C6p/TEU8ug917iL04wfMSzzzPHn+pQrnVqiRyhTIZRIJBJLEvC8RmVBE6YVOCdP0u8B4nCvY5X/w7xMaqdTf3Z9EmsPpwTGdDcBy8A28ls6lPmFse8LHMz5LPLNrYIap/RAsV332i9wT+9t8xoVlUiJnM9vC42ifnRwP96igwjIbZJWrVAzjIRk6xvERY6D396BqDTZYm02g33oV2N+HunoN+JGfkP2hoeodZHs7zJU028I2mQC9vp23Ros9yAsh9PGu0B2rtqtrHFnHajalUfAD9t258zbfaR4zOR0UgKN96H4X2N1lrqO9SE/bcSQCLwM7j7hG9Q7nWHrA5HDuoPvuNtt+AETf5dgUCrYPjGmpoRxgKoexjIQKGpZonNrLlNvvvMz8yy1hvZhmcxNpM5AlNvE6PLKVuKYaejri3xRCOmBS20HHg720GNXOgLXztgX29xkfEOWu2ZSovcjm9H4RCE/wyOcRvzYshkJIBT4dUamfvXKC1jaiYje4t+kJkjFRqbfvUxBMMjVNyaxxpPw/nfMalRowm0JdfIbhlUlkAjywISzRgkcR79NcokAJk0VjyIU3DY3KDVLwZmP7DqaAylS/VevCn8+AeM5WsUHR9l73q/Q4ThQp6XPXLPVMOfxdUDyhbGfIOd9+EapR5GaQznc5F9nxbHhrlKvxSgQSyb2w+dhuDFNVCQhf280ZLMr1c46uHg/tSTtSCKUKJaBcF0U4oNHyAsJpwy6w9wT6rddZEPT5H6NRShLbW97gu/UWsLgOde05YDKgB1uqcD1vfIxK3BgWWXedJmybPB0DvS709pbQIQOo5z7Cr588ZIjePeLPz1/kXJ2/yvuW6V3pYRdwPOYiiiULMWYpZblc5XX6x3lBFw63gYM96GvPMd9RKNkTvszBJVl8ovZB8j4r69I4StMZqkqv/otPScuNEh0MaX+r792D+uSJrRYYKmqJSmk25rxKK2S1vAr91us8aORgF+qpG8DyIRPCoy5UYzFfa/WJz7Gn+PxEvqXRzutQVK0lUdcuFZzpZBgUuTeKJSYQy1XrHQ+6hC7lHAW9s0UFL/x2lcyBz3wRmI6R/MavQ8cplOfA++iHgStPU97u34JuHrLDplHqs7G0CbYJXS3VzHr7AZ8zTQlbCv2XzDZJAu8/sUhBkghOXiTMU2kwGr/2DNfqsjxHMmcO5NIzcp/70I70y5qNLcMuLJ+I+guWKea4UKUq9VY05dyYPXa8B3NK2PcbHwzl7jgM2Sp1W5puqscGR9L0P8xDLD0eAPdvQj+8D4Qh1JnzTCaFFSr84QAoFuy5jY22PS2l1gICUU79LhdqbZMJMa2B1IE6d42TWZWK1WIZKiM8o/uHrIB8fJsepTBCdJqIManY7L0pL56Dm9z0/Y4m/KyhBiaJ0Po8KjrpF5NjrXMqMmWKcpQDOJAeICXyebPENs9KImsQTZ3A2jnORaZtgjooUhEZzz2a5LmKv9xQKbH9cowyVw7vZSiUWcrrZSkAep66fwgVFKHCEqElnQEZnx3RFKqxSKhsHtOLj6fQA503kEq3duCuLVtsPUttJac5aME0sNKaNLNLq5xH4fQDAFL2EIJmpSp6B0D3iIVuYQiUSnz3UolyOBvbRHBVoLpqnfIjyUXluKS7zWMgnQAVMUqOwABJArgC63kx0FhkzxjTFre1BHN+rypV+LnZmIq7t2+Ng/HeUhZxYWmNG308ZlJ4eSOvi1BhBcnv/g4bh1WqSI4G8OczJkwBqfHwrDNULBMWMC12Vzegag3KZ7XOyuHNqzzHwDDMDMyTxLaWwS8QLjF4dEMiEpF7tXmFzkqlbguXDHGhvWQVp+Myl1IuM8FdLgMXrnItbr8J3e9DRdxX7jNPAffuYfzGFsqXDtnFtVKVE46KFvoJirYydjax0M9MlObyGSpvE7FNx0KbDfn8b/wZ9UMgebb+MfNJ0ilSm3kMBnlSn3tKopp4xj458Ux6Rwkyce8WcPk6Iad6hzlDP7BtkeXYUJ2lhHcNRGSGeb/vMz4Yyl1DstvCPXal6hE4keSb2j7pyZyTvbdDTPDhXeDKh6i0HJdZZhMKA4LXnwhthYeeh4d+YJOBfkHOZJXEaEpLnTc7ysS6r2xwkx9sCZ1sB/nhyqbE34TrwnhAOiclynSFHB6J5y4Y4ViSPKZASBgxOo7o9Y/69Oz9gF54UKDX1TtguHu4bTnZ4zE3mKFqTkb04k+etDQbUxma+xis0BRymWc3SVvXpzI3nn0SWQqa5zEE9wK2CWiwNFwFRXLfB3IM3gmDlWOvAj+p9UvcCN091iSEJXhf+lvQ929D33wd+q234fzM37EJ3FaTFFbTAM5AeVrz3WZj4tbRlC0Gdh4Brovsz76B8R+/jvJP/ghQqQCjEVSLsJN+602oa89yjs9dtqwkzxeuesb7zMbcdEpRDqJJzpJQvhhNU09h5Gw+g4Yo6wR5dKW15hqkkrg3cFTvmJi26/FvimXLPtIaannFnh529y22Hp6N4SwvskS/WoN3+Vy+D/TeI+iXfx+q0WT4v36BkZv0O8pzWisbgmXLiV2uD9Ui7KhqUvI+j5iI7R5x7ZqLzMOYs1uFp67HfStzyhEoqkEYrFInhfLxXTof/SMaiqVVQDl48F//EjINnP/xZ6B+7j8Ert0AXvlmjuerRhN49nlUrksy28C4l67nNOA8UQ3XNpMzxA3jwJj3Bmy9QBQBBVD2Vze4N7P03Qn86ZjPW61zPlpLjICePJSWGlLFOhO9tX6B8h1HPBO4uWTzMqYuYTok8QLgmmRs+qaCgvDifWG1Zf+GtPw13N5MW46oCWujCCgHQEkOw5iK1V86A7Ux4veOsq1GTWg7HtLDAag8e0dcGM+XREtCy+sHwOK67RttWuQqBbVylorGPKMxAPUmvTat2Y/FHEIwjwWfU/IMM4vTa03u8UlFaRSQYXOYE1x8CcHzNgI1dr5zI+hKHSpJgPnAJooGXXpNrQWG7CYaWVihomgtWAjFnFpfCGkMtu6z3864zyROvWONVBJbKMV46aZtgEAAADgv0xHynialmi1MmQ5tVW5WtHUCBvvOUkCdENJCCLWwzr7fR7vA8SFUuQJ95zay2RxOHLGQRg7u1iN6jXomzzYaUJEPelRS3QPot19jMnVpFag3MXv1NsKLi2SKzKbQ0UPo42MmTjtSmBIIwyIs0+sbD2iUTXm9OVDBEejKD/iuZXNoizBg/CJQDqi8SzXCSZHL2oYk4dGGchCzno4svHGyQvtwn8oYIOtp5wFl94w0mtt9zOeqd1gY9vmfIO6rHCr/vcdkfTkOsL2Nx//7VxFFKc598iLcj3+cXPRCEdAJlW0hlOZvEam7gPXCJwPbGO7RPejxCGplnXJdsHRIPTiC/qPfBeIY6nN/i96tORovSXiIuSlWKoa2CGk25UEaWYpz//i/kkKnIZ+lVONh0/1jSyCYx9B7R3jzv/9VXP/7n4L6wk/nlePIUmg5lFpLy+icUm0UpHEcwxJ1iefRqA56LJra3aIumk6g33wD6ke/xH5GhRKwv2VPilLKnsZWrUO/8QrUlet8znkMlOXoxdkESIfQzQVGszn7xyMsaaDmjYuAW4TyFCNO14Vav8TzDPJK6X8jlLtseMP7NvhuqcZJnI2B7p49h1Gy3izFL1J4D3etx+B5dgIMpiyHDquAmKfuSz/s6Zi4mFS2YvkMWTv1lvVipyOGnWNp2pMkQKVMGlz/kN5MpX6C6SAJzHLDUv36h8CdN6WsmqwYNBb5PJmGXl63pfVVHizBhlgaquaTnz4kHqmNkRGDgc2rQP8QqrEIHT6gMMYzoVSV5TQbDcR9oXy61quZjOg5RTOo536Iz2p6gRhcvr0iydUTPTiUk1McVa3FzW1625jklNlE8UwOTa7lUYCqlpEfkG0+qzNGcUoRqqk2oaUXtlo/C6/RofExuKPOiM+nCZRDhpWWA7zV+ae4KSox1PnLkjhjj4/w7/9dTH/5/0A4m1LhFQrSmnUC9fFPAdW2nE/Lw0/04TbnzRSB9fZ5wInhdbuuNe4JaX2mYVbei78vsIHhPpuK4v6RrdYE7PFx5t+oz6ThZEQY7bjHZw1Cym+1wfa3VWltYNhX1Tr0/Ts4+sqfod1oQF19kdHelatYX1/H8a99DX/8G2/gpe0uwk99mF7w6gYxfYB7yjRG80QmdAbdO4TqrNEjB4jpL60IjCVdTgXGUxvnoN94VeT/GKYgi6cQibe8eobOwv4TtgppL+YFY3lh1PI68u6U9RaTlNGUxwsCQBji4nMrhGiNYodEoia61HMq1nKdTkOjbbvGSlsT/XtfYQvkK9dpZHa3AN+HPtxD+srrcDfX+Oy9I6ABaX8gesGRyvB6iz+/I73kP/eT3H9GZ5lmiMMu9YMekXwxGTACn47zfaOUsmc2mOgiKEhOMSPd+T3GX0u5K6UeABgCSAEkWusXlVItAL8G4CyABwB+Vmvdfc8LOQ4TM5U6Pd+5JCkNRHP3LeDCUxR0xwWqUswRR2xF2l6itTMFEIUiEGrStSoNUokczy56/1Cacs0Ax2HHRtM1r7XETVOq0bsMK8xmR1P+7GCb56ueOc97pXMxFFWbG0gSQiCGXjjs2v7MxwfID5AwBxNkCRfaHBhtPIywYsvWyzUL2SjHJlRcT7A4j0yLZltCR59YarGM/Di9nLeuWWEblimMR/ssGimU7PVHXeiv/w75zIUSaVsmkWtgBskX6MfviEJpQjXYj11PRsIvlyP7zCHXUk2r40h47ingKHrijms7TAIwRx9CaIOIpoR8UkXcXKID5bj0eoRbDHOm7kgafjVawM3XoN98k32/4xjF56+QQ53MoaTXCjfMIv83LKVqkxxlR7FnDSDw4cAyR4ol65nO4/wzqljmuo0HfC+d2XkpV/LDU/S4D9VcoqwNu3Qmyiew6UaHhvNI2C3lGo1vJi0AWouMCHr7XNsKD0RRYQn7/9PvIvydr6Py6Z/g/d9+C+NXH6Ky0cIn/+4Xeai3YYWMR/zanM2Zkm6njMOkHEZCYZnruLAE5/kXkXcANY34zPkJK+vk3bse4aXtB0AgrQEyoU4a2Ku1ADUcIPvTP8Hoy7+K6r/9GahCgRGo6fQ6kQp1P2Vf+Q+9wPn+5tdR/Ns/Bmxe5PyZNiKpKHTT82h6IkruHXFNO6t5byX1wkvQR/t8h9YC5SZNoQB4C8v83jiNgTiV5RodyN4R3yusAkkEdeNFetuFkJCuoQUrxT45wz50uQHTsgR7D+Wgm9TW9CgHyves0zgP7FGUSWLpyt9n/E147p/WWh+e+P4XAXxNa/2PlFK/KN//l+95BYOBDbq2nHdvm6XNxmLP47wboaq1SfnrH9MQVJr8vemtDUjCZgo9FVpgRQqdHIfCPxnB9I5XQ9mohQIFqLmUM1L0y19l0mMs3N1SRaAdCZ+iKZkuykF+OpTh0JtsfVhhmNU/lr7d7OimDCYnXps571L3D6mYXRfahMAm5NfCTDDNs0xp/61XADxhQyTDBDLHtJk8QDoHDqWplzlxXnjOavMqfx9Kx8juHtvJJnPg1Zehm22+/4XrQiX0oYqk/r2rCZfj2gIkQydN5vmZmSzJrhFbNMreJXtE+4Fwez3rUXmEk1TQoZBHU2tsHUV8+3DLdszrrCJvVeFJ7xSteVjIs89JUrQh9RIGchADaYxKpi2uGs8sK6HatgkxgMZQFGBekDSb5Fxwnc5ZC+AHZKekcyglXP72KrR3aFldpn2DF+S02jzyMfmJ40Pmlo52GcUayCyakiaqlE3cFUKg0cFT/8N/Kr1IqlDFDPiZfxeV529CbwkspBQ9YxOBjIYs/V9Y4lym0kt/MrBtoycjC5luXuDcm2pUISDY+o2uTQi3lyw0YSrRtabCVA6wuALnh34Y1cu7wL17mNzZRemnP09FC0gPGJGN/R0pYCwBYQi9twPVaBG+fOoFKsDpyBqukjTs6h5I1DWhgUwSJko9n9GPSRg32zYCrdQ5T/OI8GeV/aZYmVsiInBG6hVMQVpzASjXSIQohNDTlIZgNmYCvDag/NYWKBfmIPn2CedJ2HQ6jhiVmQTwaGhhz/cY7wcs81MAPiVf/wqAP8S/TrnP54RV2ouWl+77bJjjuEza9A6ZdEkSKgXj0foFbj7PowIuupZBUW3S05iObNFTEtE4hCUqZ+VQkHtdCttsSm57uUEouN6kYjLFU/feYfFSMbQl51sP+M94BbMpvUJDbzLGadingizyc7p/KMnJwJYja/BvDGR0vIf8vElH2gEEBXZEFKhKp3NpcyCtB2pt4M4bhELOXqIRm0jnw0ab4abJTRzu03iZ80/VWDo1HkKtb0LfvwPs7ADNI3KLl9cZKhp20MkiDyDnwSOdM19hWBHDATs/Vuq2+CKJgVSwagDwi7x34UR4bSiArk+DYqiH0RTYewx95hIhChM1maMZB0fA9LBUggAAIABJREFUbEIu/MNbnN+NCzlHO/fODbxkchLjgZ0LwEYcpsDGcYjDF0ImVgE52zax2Ot4YA1HUZyKaErPOqxa+NH1hBJYpmI3jsmJ+gbMpBNmoWAZKKaFhmmctXEReRdOcwRgoZT399Hbj6GSiB7tyllg8QyJAFv3oG/fBNQjqGZTFOAMeneHjJnxgH3to4jwSRRxX+1t81kAypDZp1LDgfkJj7LGk5pUWGFuZNi11ETT8iKJuYZBgadQXQfwsTFKQ5nXTBhanWXLJlEqPwFNLS1B7+/bw+WHRxZTD8vIO1C2Fi2TJUuo/L2ALJr9Heg/+hrUx38Y+WFBhrLsFZjfSefUNybfsrBuO1BWG2yZYZ4tS3kwh3KA9irl2Q/YS+irvwH1/EegFqVHjDmgpn8oEI/LuR/0OEc6IzwJcD5MD33vvdX3X1e5awD/UimlAfzPWusvA1jSWu8AgNZ6Rym1+L3+UCn1CwB+AQA2Og0ugumBvH7etnetSmfHUoV4+6M79LwiofrNY+DCNSZjBj3ihp1Ve+yb6cw2OLLZ8VKNQpfdJFvC0MQAcn+HUnxSqlCwukdU7L0ucOEq1OY1Fi3s0fvRkeCsJkNuEm2uz8RZtU7u7f4eaXdRRA+hVLHC5gfQQqNUjQVovyBnvc7ppU1HloLoFSzf9iQv/exl5CfMB4GlDpqop3vA3vOLK4RijknlVGuX8ipCfbhNKtqwTwO1+4TMm9ksL6LK6ZCua88f9YO8KRlc8dAlUYi6lFGfLBQ52pcikNT2URFhVUaBmW6drm8NiPIIlRis3mzghVX2qUli6P3HUo5e5zMuSCWl6xMDlx7g+s7rxG9NXsDcI51T2QYF5nQAW+VbrrP4ypxhavoBdQ+sgqvUcwaN7h9aI+UHVDzmmvHUluU7Kj9qUBtvPkvl9DEWfcEPJI8SSuJ8DszY/E0fbNF7N/J9/22Z+xbU2saJjecAngO1cg5YOQf19Es8lMJ16fU221Am/3R8QKVzeEBYTmtWjG49ItxiDuyot2i0TfJf+vgr1xOoLyHX3cAl0jCNc32iEaDDOgslSXltaMOSkM+ZZqWabZ3r+cDVD0FdBfdpSdoHmFJ+42wY9psp/AmKeYsRtbDGVM9LH2MSFaCzNBVu/LAP3ejQGzcRnVQNA+DhIiOJTA2DxZF2A2FVImQvl4/knfvwOws8RtGVE59MRXM845wYrzwssZrZ6LpkAv3WX/BwFgMRfZ/x11Xun9Bab4sC/6pS6uZf9Q/FEHwZAF68uKHNQQMIAuLabRHUYZeb/t5NMgTKVVpZY9EHA2K3nSULDdx7C7rVYfh1+3WGndVm3jVRS7dDXQihtx8R2wuK9I5MKwHxLtX1D1PhxTOoix+yAhNWuOnKdThPvcD7ur7NwIdl6N4h9M03oOoN4nWDPuA4UM0WvS1ADiRIgcUlWvrJiIdNl6p5+GeadOmTx9NlCT239jIVU2fJJqJNS93pmM847PKZI7aUVQ0mc7WpIXAc5P1pwipQm9JYRjP2TAnvQu/vQy2unEh4Z/TYls/wa2nUpGoM2/V0aDHzsEq2RjSRQx1EMZdrdlNXhYG0/1gKvgxnXqp682Q58V0VFKA3r3LOTNWvgSRmU8qJ40KPh8xbeD43oMMe9ugeAA/v8POrZ/lchhZrlH2lLnCFrKvBh01/IhPy+0XhNwuc4wdMoMv1lEnMz1jhCsfj7z0PaK2Ipx3yvaOJ9Jd3bDQyG0N//fehzl+iwVzdFJbHjL3jI1Z3apPvKVXojChHosmidQxODlE+6vJzbFewv3NiPeiQ4NwVKNPIz3WBfhfq6Ru2dqR3DKydY5GaI4y32Zg5hoLkaRDkNFjWN0AchRHfweQUoikw7kO7vm3VoBwgLEK59kQnxFNLnZ4MrEJVyp44ZZSwqS6VpCmGXeZi+szZmTYUKITA0y9aKE6q11EIuUamW2s8ZZRx8zXuabOPzd4RSmveFmE6BIJCXteBUgX+f/Kf0zCbrrECZerpSAgbMSMY6UHFs4Qj5h6jCLh/H9g4j9kv/8p76ti/lnLXWm/L//tKqV8H8BKAPaXUinjtKwD2/7UXMv3FjeUz3qgpSTb89Dji8XmGF9xosZBocZUT5Rc4mUoBjx8Au9uETip1CkMhZMIsEh5pZxFq0KNQ14TCNhnz9CITKp3AtXSWQpkOccohdvjwDrLeMatciyHw7CdyLrCq1IGNc3lOQa2eIdbZXuD3nVUK8rjHrL4f0BtMEyoQOcA6Pw3KHPDcXuHGMYLVP6ZRnMfSWMkBHtxmksgo7rp0DTTJWsflxg4K+RFg+bsar9i0Gq63oK58iBWMxbKE1nLwgOkd/t29pQ1jyHhfgAizMEoWVvie0zENycWnuQHCCpudOS6VfRpDv/4NHqLQWrEFPik5+WrxDPTWHeKYgi1DlLnl78cMd5M5OffVNo39h17K10qf7K2TzIH2CqGEaCpdMCuMCmYTy+ByHCb5/YDznsypWLsH9PK8Ar1QIL8OT/EKoGOTtBTIydBKTY/5MMw9S0QRqx/9QM7flMIp0xpbZ7yf4wHpVHBu6bGkFDAakq1j+iaNB5I0Fe9SgxBfmgKepsduWjBnKZO+9+9Ara0TpjQRzv4O+/U7LnQ8YrLPHAoTVmwOA2AdQq9LDvj6WauEm5uWadRYlCIuYQ4ZIkClDu1pqMaCNOCT1smuy8SzkXGpSckPu4hnnK9SxRpck9erNXN4TpuzFUZ9aZsgkab5F02E8y/nAEtr4/y0sZOdYwE6OwZKNd1NTfvfeGZRCcehE2eM3rArucMFGovGIqDTExBeCh3NCFkvrsGt2z7732u8d8/I9xhKqbJSqmq+BvCjAN4A8JsA/p587O8B+L/+ChcTHFpK8vtdm1RoLRAeOHuJQvH4HvTNN7logy7xz8UzUE1ptGUwrI3ztMSdFVrC3r5gnUKNKpZ58HCrYxfKLNbuFhNc8xnxQ50RRnj1Zehhj0k9c2BHHAHzOXQUsX3qvTehR13o3YfQr36dG/7q84Q5TAe6eQz9jT+Afuvblk538pgug9mZQ4ozyYwP5WADOdhD1dqSyPK5yc0ZpL7ANuMx9O23KbBBIe8iqefSLc/QxEzSzCh1w+ZoLTCCuvwhqI7ghmZI50MAtnDIQEAmsab1u/nwkyHvayiQBk9e3eTpN9UmDY5fzNsbKONxGT655wkziZCJNufHCg6vTCWzZidEPRkIvJVafvaom3t8+kTjM5FlVtQazNZ0wZScAot+BFc1FY0GUpKzBNT6JUJn0YS4vHjuvI7mMxk2x2QgLX8nLIRzfWK5Y6kPSFPkB4yEZRrC6QTY2YK+9YZ1NMy8y5GEAOQabAOtTEHdeAB9uAV99zXCiicL/WQ99f4e+/O4LiGJB/epUFyfLZRvv21bD5/oKKqPd8izXzlrI0IpVOORjgW2bjBw4sO7NnHeWuJeyztlShLZeOjRBHrcl2Zr9byFABzHzoEf8Jme3CO9edgj398LgEfvcO5WNji/rscE9IkEOAzN1rDYDB05ngI7D2y+YG+Lz3jhKSrtJ/elbUDdGjRzrq8XcL2rTRb2mcjVtEQxHj4A7G5h8kv/C50Dre25C8bIlipQC8tQn/5RqEYHwT/4h++pVv8/K3cASwD+RCn1KoBvAfgtrfXvAPhHAD6vlLoN4PPy/XsPP6A3BcjmG9jFM+GkaclbDJmANf02BLPUx3tU4DMJq4ICk5GP7zIJaqzwZAhsP4A+fMLN1lpgCGoKiLKUitK09CwIPTIoMIQ83D6R7FP828AWE+i9bVKkBsfQD+9B724DT+7yc0pxsaKIzZG2HrCtrB/QUAVFm4gbnqjU9Qq2z/vBHhNah9vEWR2X0IzhD0tbU3LVxZORyj395l8Q8koE7xz1oY+2oWdjnjVpiq/SlP16Lj5NgQcIZ8xjep/Gm0nn/BujvLW23osXWLZMliBvmmQObcgye1yb6cjpuMD+Y6uYe4fQ3/5D0tPyLoKpdJ8UGKm3z80QVuXACkCVG9YQmSSogXaCoiTjM3vosRyQrvJTpjIxgL28Q6MWyiXAiExVGlQQridHpZXtXGUSeZqunEZhmaS4WRPlENIxlDaziQdy1OI8smcA7z2hPG6cZ295gDDZZMTnvvMmi9LqHRr9NCWG3j1gfsH04dEZ9Bt/Tmjz5d+HvvXn0Lf+nM9fqRLCKRSAyYTynqZQTz0DDAY4+qV/Jn1yplKI1LENuvyiTfaafWuojua8T1OkZfJniyu2lYTxgOXwmLznkiloBDgvxrCWKjSYYcUmwwsh+xHdv80GY8cHrHY92uXpU9GULDWRgzwCyKSy1XXtgecyVxj2aUx7wl6aiAd+8WnbzLD2/1D3XrGyZel52Ld2qtoVT9XJ4ebUt3P3hJ4ZTpJmhhRFmZQly6ZkELRBQIQhwIIBw7Tg9CTDD4YeCYGABOnFsgRINGUbDjKDGMThcDihp9PtcPM9+ZzKaVftvfzw/f9adenpHpqQhesCGt197j1Vu/Ze619/+ALRelYTtHzB+zHqMunYf8BDtXfqK9wg5LpczNgunI6AtS1UfuYbePK3/nvYH3zbS2Bol2B9C+aFz9AdS+G5n/D6U7dlrLV3AbzyQ35+BuBr/6/erCh4GpdS9g1nE29ZBvheZEWcdr7+U8wCFhnQPZOMYSL2WxHFx5zPasHJfhjyJndOhaBU8T3NbEZC1MkRF5LC97IpscfTMR9gT8SsAAbUfMGsM4xgH97ltU4mHIxdfR7mch/24T323VfXvP+hoB9MELiA5GRMm2v8rms7Ht2TlFgOziY+KHTPeC23NZDPSRxZWWeWdOMFYvfrcliEIczla7Df/w7MdEp9nN45cMLrtqUUuP26y4C8xnvEzw6M00FhVhY571KnCwKIREHmS2uVb3CyyolXr1xpE63TP+cz1o0csleORQbzma8SZ60yzpo99c68OJMcNmb1KjfQoAPTWuemcaqcwrKtNARCKC0P6afrc3ZZYZGzzbGYc9hWqXlWMcDrSescNApjl0PYMg/AKPLrN8+B+cAfjMqk1v7sZOxJVto3XlnzWv6FdagJNNswr28xaBc5h+JRzB640PttGHp0jRpl7N91zlmmUmOQNkew3/kDFE8OEH7qdeDSdeL+W23Yfp8QvPMT4NJ1mOs3MPq1b2N1pQ0TxTwIVCQN8M+72vCVUxA4eQmzdVHEr869o9ryQFC1eIwcdCohrId45Dkk9ugh/98Y2PmMiVBa4c92LsNs7kryMnDVntm7zPe99z6szlJUDkNbLbq/lC/x8CMGd23rhDGweYEJ0ngI+69+neJpCZg0ymAU84zZ/HBAXaJKDaozb0pt2ChmUlSuspIe94HTQ8x/9deAwGA8XqB48y2E157jfDApcX92T32yp1LGn/B6Nhiq2rNKqzBhyEBQqvAEnU14AsopbUd9PvSKKCX2zkWZUbNu1Y5YcPChimzjvodXXhVM9zwT/1ARFEpK7CmG0m7JZkTV2EJYgSWPre2fM5tqrJBQsb7lCUSi/2FuiVvKyQFst0MWYHudmGsVvgJotNA5g4ljQhibK3yP0YCQyTznAj54zM84OfTVgsOKx8Djd2CfPOAAWaVodUA2mzhfSfvuW8z66oImOT7goNcYIW+RGad5gdF+ZpmwMufdmIjCpAb2+Yxls/SQnbyASu1GCWwYCglj5lsTy+2daoND5CgCtjwiwHZP+HfCkL1PHTyd7AM74r4zHcEJsL3zR97yTN2OeqcewZOUhXZeg+r4mGrdafGYclVYt4RB2mGP3zsk3NQOuvx+KgUdhhzST8b8bqUKUGTO8NnIgUmN9BnUmAU681g2ZRmNvOiZqCMCYKBprfP3xqJ+Cngs/ICsUdfeVPXSIID96A5w4Qaz/DYlJoyoKIaXzhiEtIJotoDKMZw3bKUG7F3Bxf/kZ5nN9zu8rkrd752Y/BM10bBD6fEvH1omYAvt+Invu6+sSTJXXZJEFtKPagSZgPvZBGzd6L5Rp6UokkG7zIHyOZOIizc5oJ9nTOgOH/M7zTOZ9xT+WQBw/sDTCZMOrczXNxmoz48Y3BstVq2f+oLH9acM1BgPpUoZy/0UKQ5JlFTbxhnOKLhg+yKiK7tY3H2M63/xdZhXXhfkHd/frKzTZ1e9DLQt+AmvZyO4h0J4GfW5SXau8mRcxv4CsPfe5c2eiEhTtcEBaLcjFPKUi7Ha8AfA2QEhVXPBKu9egRod28UCplqnFsl07O3CNFgBXDiq7the54LRm3pyyAWoXo8VYZQaAzvowD54j9m19E3toA9z9AR4/lPAlVtcVMMeXVYWC1YeccIF0lrld10sgNjy+yn5Jor9AFh7fIZQOjubweQ5zMtvMLA4Yg4zErOzC/voITHBN1/iYruYcliZlBgIxF3J5r6Ud6/aimTrEZzblIiL2f4ZUBEd+yCELRYcINbEDclaLmgnvyrPzKEdAjr+aAWj6oPTkes92vvvwhrDNVKuUqNEArUdiQZJWmU/9Mk9afmtMMBXGh4nXchAe0ivVJvPYVZ3eB2q6dM9fkrXBY02PX7FEtBmUx5CWhUAAt8MncImbOFbRNKqcS2LaUcqAAkMdSHXXbjMSuZ0XzSADjkwnoyJO7/+IpnD778L8+pn+NkrbQalcsXNTuwPvk/zkvkcKMscISkzOD2+R6PrkxOY3V0mRM02D4NBH2Z7xyNMxkPxrX2OAbIlPA8l0gXGcwfyOex4xIN+OvKBWJnN2vpz7OyC+3/Yce1DxytQtExSclkuHddqcC5d6mvQP+Oh0zvnZ2xf5j5VPZt8zn2njlj5gv90RdlVPXpnE8aPzhl/Xm/6Qfmgx4ppPCRiKbG8Bzpr0hZOWqFrlGspC6ErDAkGqDaYhCgHZDahn0O5jPirP8YKqlzx90CqCZOU/DPRhO0TXs9GcAf4BZIymYvTEe3cdLMJIcY+vAtz5YajfWMy8tP7KGJmO+zx52mVdOeiAC7dAO4/YN8sDCnWVKq78ts5G7XWeQg48+krXCAHD4nzLpW8Hrsxjp5MM5GK/x4mJKTryUNgMIBZk2GYLTgYVkU5Rbio/+fZMTeNzhfC2JemUQ1YA+/J5duk7J/uA/MZJRZ6pzTTrje8XZoGKSVERRED1UiQCrqJSimDm01c1qrsVjubuExah8h2PGDPWaUN8jmHz4H04pUZ63RjCv/MShUGgsmAPcrbr1NKAPBZvut9534D1IRUMyMhyk4nhKYu253FLNWdSuXmBW76sfRQlf+Q1ni/NBCb1PdANfBGMQ+81iarhrjEVlSjzQMBINktimHqFaAQ8o44LaFckWHigtVmqcJg3Vr3vATN6KIICGs0ozjah1nd9BIc1TqRKsMBg/drP8YM8uwY5votDr7lEHWS19Mxq7aLF9H/p7+JxmeuAY2Gu0fm8m2uod4p5y7f/X1WC2enMBubbOecHMn6K+BEsSpijLG6ySCr2GwlLS2Lcul+mE/l+Zc8zFTbpartn7JdRLigrJ/TQzfQxbXn/cB+OgKiFq9d2zrLEtTnx+S99E6JNimEhZs22N6YZx47H0VexylcsKLYvgycH8GeHRMB195wLSDHYZmMCE4ocgqzVUWnvt7kwZfWuGZXZ3wOStoqcv7Z+RE7AsYAifAHVjfwg//2H+OlX/w6q+rFHChnfo4D8EDQ1/+XPfd/7S9rWc7qS9Aftn/OB1Wtwzz/qrA2T33gGPQIa9q8SBJQ58Tj3UXNzTTasKsbDKKDHtBcJQklkGyrXIVZ32M2Nux5AowJCPNalWHlyRF9Ha/c5gNSxmeRc6KuLFhAjHYnhLRdvcWFXa54aYKzY57ymztcZOW6y5xRrXu8dFz27Dgt2yNpZwiiwpapCWOabaKEhMBl0hpbXAMZLvW7rD4uXxO8fiBBmAxZO6bQlWvJ6MAaIDxRCDywBTfiPGPlc7rPrKneBKrM0q0G3cUCTt9c+9WakcUJ2wR6CGVzoC7MTCtBxRZCOBOT760LwPg9ijOprKsJgMWUsw/Ra7fZjG2UpMRhYu/Uw0AjGknY/XuuxHVDSNGst/mCg/HNC0I+IaPY1Fc4B6m3eG97pwwYsQyke+d8fvMZnEXe2o7cByNDcglulcaSjougWj79Jb8fTMAgXqk4HRx0jzkYn83Il6g12FooyWGdZczMbQEcHiIvLHBFmN2TAWcK8mzNygYDYL1FpM7pkXAEGmwfLhZMmGYT2PfehrnxHN9bkhvlHxibeJmGIIIJpKV1ug9nM1cSNdFy1cNHl4zSAbC3DnCfXL7F59g/57oToTKnY7Oyzmw/LpNtOxk70IIjN54fwGxeIgEom4rZy9yTzRSJkpSBYM799+FbhHe++jkRKbT+76lkcbXGVvDJoXNkmv9P/zPiv/KXGbxV0z6tc++qamhS5t746F0J6FseQlkq49rza2wdp1Vpf3WFfV32exDyHfOFZ0h/zOvZCe7GuIm70bIjimEqNdjZWGzhJgywmzsMYPqAqg0OPYMI2H/En7dW/U0yATfp8QE33Ptvkommvp3zjHhZbQMonjgMhW2YerbaeAjYd5hh3XxBrj1gn7BYEFLYOQE6ZzAvvOqyE5f9VBssBXWYu3mBgUax7Is520mSjaiqoKm1yNqbDL2tnZar84x47+Yag6UuSGWFziasem695D0btY2lr/GAvexsJsPTOYNpIrrxiwVs54g+mo02p/8KCQUYwLMZ0JDDLc/Zw44Ea72MiJBs1RShLzN1YKWuT8DT7TG5z6bRhr14jdm/sgDVjkyYkXbUI0xy2CGjefcKA0b/nL1OCORxfZem3CukpVshRJlIglWzzaH66haD0DnlFExri4lI54RGFqePCXmrNrystMJAm2sOqmc2qbeuxhpkrHb8/yvFPJv4jO1knx6xKuiV54LrDtzMxOZzmFqL/eXQk2jMG19A+/JlYHMH9sM7fBaARzVJgmRW1hks105hH30o915YsqM+0O9Q777W8K5JSuzKZl4ddG3HH2hTqRRtib8jMgumXGHLRLkS9YpI/xo+o1JFBuJyj4+fcN5x4TrbbrpH3vqWsEmnDLDTCb+DJDrYIn7eamtL2zqARznFJaH6dz0bvnPGROjSTTlwE59U2YJxSGR9WWlGsN/9Fj74nbu4EfwzLsckQvQ3/jOX6Jla07OsbcH4pQxz2SsII9hFwblcc81j3oOAYnlJCaawPADznPHi7OATQ+ozEtwlSwkssLB+8Y761Cc5O+JCaK9z8JMsWbE1VhlEgtCVy/bgMTOOJCGV2hakxtebXHClEheBDnKmI5azaY3QLmP4s2EPtnNCavUHP4BVoaMnD6FGwGb7snzmfR4ISRlm9xoRC4u530gKtTQZh0jtTRkSV0h2UQKN0qRnY9Lcs5kMkTLCFUMKJ9meOA1pO8sW7KnqAhIImhENeXPhChELGviXe3e2AFJhU+qmVR16xT9PBp5so4FJyRg6/Guu8RpUArfW9P10yc7NUn8dkfGlOkq+9HZM28jD3NR0obAMxmmVf14w2BlBVFkhN7mepjJvAzmoz0SvfPOSDJEl84+kmlgsiIaSQ43PLHADQzsd8XfCkO2JImd2lU38QFJ5CnpfwhCIQs6QRn0e8HqATMfEXZ8fMetvrRMWrOYqtQbM5WvMmGtNav5cvk501nhIuY3JiFWEkpm6ghprtvlZ9z+Avf8ARvToi1/9e8CTJzCf/yLM61/x1WZa5+8pS3M6YRtvPhfCTU1acRnbagDbHgPyTWznmC3CYY/3vNdl4DVyCA86PEDrLZ/oLOYU/uudApMzoC3CYBNyIrJ/+qtI/uZ/Cmd4o/OflVUmN/c/EFLSCgXVRn0ekr1TqOIo+ueU3ShyBs7xwCuguhBkmZQB3ndVTX0EHmwqNdjmKj8nLrG9uUNph/nC4v/6X9/BtChwo57ixb96AHPhFteoyvfmCyabOkdpb/MAE4RR9bWrcHIMWs3KfnTzpFLFw4vVOOVjXs9GcFf5Ss0I1ALr6IEXEdvc5Y1qSUajgyqnMSFBaG0DZjaBnUyEQdgX/RfB16pBx2LBYFxr0rDh6AHM5iWSDVaFbqzDjlJKZuw73yN8cX2LutfSV7RKnNKyH+B1ybDOEV2qdZIqrAVe+bwTnzKFZbBS0SoldkgGyixVNqBmnrUW2ZYKz5unXtxqIRIBtnDWcq78B4DaClsIWhJbqisyiIUePSAHnj9EpSQMAqAI6LLUPWY7Ss3GRSHRtNYle4t9mSqtLkfPTqsOY+5eruqQ8lqNPpRtOhky09TerSob6oE0HfEejjt0gtq5yqB//IjXUA8EWz0V2YCqk4I1aY0BeNCRlqAIUFUb1O7X94+FmNI7hy2EwKXsyihxhwHF1TIgZ2BDexsIR/Ieua9oHr7PjD8RTL6ixMZDl6Rg7xrf9+FdoFSmefTalohnbQibUw5bDU7ZlACAyQhF9kcI9L6enGDywT7Cg3+O5Pvfgbn9AiWCN2VWk2Wwb38fqNdhXv+cF7BqtB3yxTTX+KwXC96f9V3PEVH9F9V0sdKCUp2dhQjdxVP+nhB2MO4LaWgKe+d7wHiI5Otf4ZxEe9YfvsNYsCG/9/A+8nsPESQRTKMO89Vv+N62tlhnEltkpqIzKichoYf1yirvJeCdwxz3Y871MB6wBTSVgfnDu8BggG/2RnijUcELf+Ya4p//eZi1PYfzd7rsRe7VPtd34MxvbEHE3fUbso4Xwp8Qclsm8FkVBlzMPCT5E17PRnAXiVmzuuMzByN92t1LzmEGDz7gl04S2JMn1GsHoIbQptGGnU9hP7oDk6YsnwDi4I8P2MdTiFssU/jJkPj1chm2Qc0Qo+w0FftZLGA2LngHGFUAjCJWFhrcRDrWKmFDtUeqDQaS2ZgZxukRFQ0bK95weNhloFeWoS24qFVO12WFkqmjDMzLDs5JY4KhR29oXxsQwsgFL/w/7DITaK37HqxorhhlTY26AAAgAElEQVRp1dj9e9Reaa0C2xd5TzQIq5ftuM/qpb3NFo4JgGxMl/bVbR5W732Xg+xPfckbbYiWjDtAl69Vg7oeZnowKt4b8NWJZvOGv281W1aClboixWVuNltwc6U1aqnMJjAXbjKQDztMFkQPHtmU0ruC7beAx+ebgH9v8wKvv8ip6W4L3g9dYzp418N/JNDF2cxDKIV5CIAsx0Yb9u47rC6HA2Br12P5AQaoKOI6Wsw5b6qtUMsnl/nGZMz1mJTd0DhsNZgtblyEuXQJ6c4OTKMJO+jD/tEfco51e7IELYyw+O7biG+/7Ko7x32AJDSaaKxueTkKGcBThTTgMzMB94MOF7MZ92a/C1s98byCcoUVtorktdfJIp9N+HmNNgO7skQrNZjtHYTHxzAvvAg897KYglivAQ/4Q08qcruY+ZmOAh2SlG1J7dfvPwDWQj6nxVJ7RtFn84xVjS1gvvw1/OIrrwObO4wT2orVNQzgKeMNmT0omQrZlGvn1oueYDceeKlhJSOWUj9PDCJm8p/wejaCezZjZlNr8USt1GDiMk91CSqENbEl4xh+Sy8r2iEwBualT4n70IKndxjzYcwm3JxqHr2yxmy0vQ376APexPGALZXZxLk2Ia1yMW5dpKnIdEqUznBA3Pv5ES35hl2W1/OMCpe7l9g/XwjksdFm1m8CaqtIlmqV9KMPEvBQMVsAOfwQNYyf/v46M5izhLNByPtXLCnUbV2ECWOvHRMnDB69U2qohzFQiqmNrgJQh4/lsBHqesC2gqmu8HkFsvDrLX9dk4Ff0LMxv/P5iVe/XGRAUJbvDf5bN4J7kPK9FHWhwy+AiJ3+OT9H9ekVKmuMJ5wlZQ5IbQGYCCrTi/EATtt+cMZsbP8jBpUGOQCm0RbJgIrv/YviJfXuqwKbpMqknY39zET+rsv4TcCAvrrlsd71lmtnmaaI21nL3vCoz/tfqRGN0e+KjducazYMgSs3qQqoh+1synVZqjCgj4fE5su8AI8/IlRYMdvC7jSVKrDSpt58swX7nW8Tvre1ByQhzIVLiM7O/JBZqgM7z+gYlJQYiBxbm+00u8h8tavkrDjhvhDPXtfSUtiv+rcamra4StNaYNT1QVHaifaDd3j965vA7iW2XutNvveUMgdmfQ/2yQdcH6m0kvTZ60xElTdLVSaW+RxGh9M3X2HyUkqFlSrzK2spYaBVQZHz+e5eZcu0yAEbAta4+6BViRuASqvRK2PmQpI8923i8ZBrQ4X69Dlo+/ZHZO3AMxLc7WIOFBb2MWnVjjlmAp7ijz/il09kCJmGvBFxmf26fO7hbuJEY2pNqBktb9SWG9I6MgTAYUUQ0kdVcbbjPkyD2GSbL2hYEYTsue4/EHW4Kd9PmIH2yUduIo44YbaumOPZgmiSg8fAzReBK6tQs2r3wGdjDo8u3GAfXvrFbmAZJx6jKwgapwBoAuBUB5sSKCWDN0HG7N8xNUMgrPjeOcADNSnBNNcYqAYdYG0TZnUD5vJtkQaImI1J750QyZDzCMHg0iBh4FsU9z9gu2F1g8HRmTsXT1+rZrj5grIQaq9ocr8RgxCIyjCpIHCWMkdigS1gIzd7sKozY60nLqkQl7YP6i2up+Mn4rakWTl9LW13nxh5a93BaJprrNZKKVs9xlBW+uzQacuwfM69r69WXTrArDZ5GMQJB5LdYz8TCEJJTHJqrXc7MF//i35ImZRhPvsVvnfvHGi0KKdbaYgHaMy+uwrlqQzE1pZfA+01Jh8Hjx0pz7z8Ko03rGUrc2Mb5ht/nn9/Y9fj1icU8jO716DqmXY6Fpy5MkUjxyZ1Mg9lmT9o8Iwi2kJqm0aRbiUyfs1KnZ+nbN9s5kS/zIuveRnp7rFXkB33eagZw2G62lyKljqyqddVHwsSRqs79YaIS1C5adMg1NlWalwvaury/ls8kLb2gKTGttRSZWsHHYdWw3Tkg7jOurQNHZd8MifEQFNtsqocDgS5Vvi4NBtzndVXyDJeBkT8kNcn81f/Db2MZJtIq1wEww7LTIDBcueynFyyOAZd13+yww7sh2/6QYS8bOeYQ87uKdRtyLmXKCQNIILidN/1+lSjwk5H9A8VJpjrldWbfA/Fiis5Q5XlKjXBvae8zqNHPgttrvj+nn5+GLKHpi2P033a1DmNFs4fXICYZ4RXSUA1oehmr24xqJarzMQ1cNrCHSAmplGzokGcYmFSAqZjr3HRF5XJ7cuOHWkFUWD75zwMdNOKeiOshanWmY3OxhRuUg0Q/R7ahnCD08JXLACf8ekh7IM7sG/+K9gH7wqcdYlbUBEpVsVJq1RsIdfz+CN3nzyBS+5tXKKoV1pncND7sHWRAWtZb346gv293xTc+MhVUnbQ4aYPY6GrSwugvekHqEoTb2/6A63InZSvnU04MOwc+1lBkTNYT0dQtyfz6mfIkRh1vfGLzoEiYVvHCfD4HqvMBx+K3r6Qa5Zs2Mzmtpf0bbbZq7eWVWitwcBeb/khuGo71ZoeQ6+tsKRMpFTvVDJ1kXNQDL8RvoAGIBMwiKp07rBHXXgHTQxYOcnhZrTXrY5SSqRqtBwQwfWcVwSHHoQcbBtDrPvZIZwzGsDkb9iD/cE3qTIahkxoSinhvGLO4rJ5EXlTZjUWhLaaeotkrr0rT4EK7KDj/nFOXAs6h9Fkx3AtKbFQqnVTqXHdaTY/7jvPBXt+xP1drvK7VBtSpc9gJ2NPpvyY1zORuaPGgYztnfrsYjJkH9kWXvshDH3/fZ7xoR4+JDyy3vQZmvZij/aZlVy+TciY2oMBfqECfBiDHrMYSFmUibuKyqvOM0INd67AVuv+Rp/se4lQgTShXGVGUal5SGKjxWxHlAStBsZSSqhnFBM/W2kQr60PP4yJ319kDCixZLEfvQVce9HDChX3rwgR+Y5WWYT1ljeQ0AXpEDMCC5sMYceF64e7/ruKOykK5/gJ73cUwarevARSm82Aj971bMfxEOicUbelVPGbR6sowN+jUgpz7UVnsmxqK/4gAJ7OfmYTJ7RlRwNmSVFEa0PF2M8zfrdl3RCFaBaWB9b5MbB7jTwH1fBvrMIOuzA/+ZehOiMUSOMA3IrxuLn4HDNXHdZmGZ+Tro2ArSjOkgT1MJH2TcbWoO0c+dZGWvftl1GPh6vOG9R4RSGjOpROjEAOLZ9JEPqDwFp/4MQJ7OETObjLtFAsSZaqCo8APyeVlldlzSsjnp94c3elzgehbxNqEFMRPDW2CEIRwYt4MOs+TlJWeaUK5xWjPgPf3fdgr7/gDztruYaqdc6nbAGrKCYlnIUhDdWzKdfObMrka22HWPnJ0KN9VtpeTypOCKTYvcZKeNh1bQ87mzBJiiIAoTvEbRiyItdKA/D8jeV5kR4+xQIYjf3+1fs56DF+lSrAvMtqUxy7EMbiJTB3VaIeenY89HpUGis/5vVsBPdsShZgT7Lj5irhYKMuh4E6wV6IIJH2L4ucbNOdK2wJ3L/DhQDwZH3+U74FEJWAaAbMrR+QFDlgBIcdhILtzbjJAMnQB3D2doMzYG2PDySbeoKOmosYI5C4icefr+/K5g38Nbt++dQPwpQoEYZOtN+qrnsUEyEyzyh7uhBikZBo1EdVTbKdRK3qqpgAtnsifWI5UHRwHIQ8VBVad+tFAGC2UBF52yf3vNyBau4rzwCgYmJagRPC0oqgyDljeHiXswzRdjFrO8BC8doajCIp5ysMjg/eJ5lKjA70Wp05xrgPZGA/XAP7YsH/zueErU3GwpkwHo5nC9jJzCOb6iv+eSRlZuzdYwft5MEsfdPFQoZgTd9eCgw5Bp0j6S/X+Examx65o3oigw4Fz/TeBSEz58mYrcS1HWLpp2Nf4eU5D54gZKshFou3cZ9EuM0dnyFv7zGoTeXgU4GyRIaCZ6I2Wa6yMptOYKfM7s21W6JlJIQjK7OOcZ8/L6ce6BAw6+VgNPcIL9W5d5mooMDSKrPfUoXfT8EH0iqxo56DU+LSdWa4/a4cXjIfshb2/rvAyqqfAcmcw9RaS3DOmu9jzzPOwbQSATxxCIBKntizQz7D/jlMXLi2iS0FMLnxrdMwFPaz8AWabR5IJ/t+Payseento0eslpptChPmc6BIyHzeuMCESgetqt2kB/hsInMfWZuzCVuH5ZTM+6svkDPwCa9nI7jnBXD4iDevUhPqdcZsYTzyMrhRzHJLoV+Ao8obALj2oneArzadl6btnwOVjA9CDSYSEeRSYSIV/s/n0nMV/PM8AzZ2HV7cPv6Agw4V8tKAOB2x71ld8YYLrXUfDKWl4XDeUQLs3xMiUgoEsZ+EA3A+mUUOU17qC5arHoNsAp/9qhKfqhM6zLsFbO7FpJyy4oy9wHt3YAcDsiOfe8n19uzxY5gLIvWwd43v//0/YMC4/jwcezebAffehd27SqGog4felHrQ5/PbucgDbNBj/3a5dFe9k8XCHbomEHGxk31WEYp115eIjxlAxL3kuydlmFQ2SBgD4YzZ+aArbZjIEZ04pI6JvZZWkZG5CspV2EfvM+tOpc22mPtDetQnLn468sM+ncEMexLMMyeFbOfSShl0+bxV+33Q8YHRFr6nvbYjzzQH9q7BHtyHuXybQWExgz186CvN0yPK9M4z4sqrNbaosikQpGy8igS22dhwSqfUP6nBrG7w900AoPBtmMMHcDaKyuQOQpJ8di64qsDUmtwTqtszGzuBOq43qbziMuzJY/78zptMvM6PeG06E+t3uT6OHvGeVGsELbTXmfmrJaIhsYfSAkRtGdUoCkMvqxtFMEmLHQDVolEF1ajEGNDedJwCk5RYWc2nXvFUDnQbUEvH1KsukNs5vLz28YFH1GiPvdvxcwc9FFXLPfhjez0MYYJE5ic18REoc+2cHfI96i3e2945W9LnnU8Mq89EcLdKtd274p3VNQiWy948WzJUtwmkbWJ0eFOuMssA/KAV4I3Rvq1mFpEshCj2/VrVNFGluEYb5tItN9g15Srs/iNvmpEkwPo1r/UcMlho+8io8bWU9ghDlo2P7tLFXjOKJHVZGgA/RDp+wuwqimGSdY+msaKXk8+djoWdDGVBVgjjmojD/PaeE1qyamo9m/DPO2ewnQ7xzOWUkNBKDQgTYGOPqJAgFDXOrjcwT2uc/s9LXNzbF4Hz46cp4mHIz6hUvc5Ie8MPLVVnXVs9SejQQ6g0mAGJk5DRZ6XVzyJnb3+ekc1YbzG7rwgpRpiwJoxgSxW/sXLRERIJBceaHfSIBAmMtFrAzGoycM8dNnPzAkVRGJUNVkSPqvVpm0Uz52GPQTNaahFp8hBGzOb1vqgS4Ok+ZwH1ddg4IVS4ucY/6y9tamlR2DmZmvbOW9Ritxbmx/9tb9Jc5MCFK4TgqkBefcnmcDz01dmo6yDAqDcZTDYveNbpZOx0423/zJNphKnK6xLVzUXGDDWtMeOuNdmCyef8rpPQz6BqYlPXWheOSAZs7vE9Rj32103AalcHs5MhrTOvPgdT2fHQ4rn2u+U+KcR4kXG/2ykrSZHNsBm1ZzicT/zzCQyrGFWjFT6LHfb4PvmCMWTnIkXVRn2PEKpKe6vfZQWbVJ6aVbkBu8is2LND3pOi8GCS7jHbfa11wiknY1ZsG7vAO9/9xLj6TAR3GEPMaFX0VZT631rnwtJ2zdkRs01xUSHefApkDZfJobkqbMK2L6POhaa75CRkx33f3hE/RTx5QIhitQZEKXDwEPbCDSE7DMhcvXDZa5ir0uB0xBt+9z2SndrbzAIGHaB3CrNxkcFRiUXHh2xjbO35jQe4TN2VeM02kJ8QzbHs0rQcGKMSEFB3xpYErTLPYDunMElC9UnAzxhGPeDe+0RMVKswz71ILPvRvmCoE1Y29RVP/gpDZj7tdW5kRRYUObOvIoe5eNO1nazao01F6VAHgIAviQHZZBaAzFSKwh245soLfniIAljIIaDDqHnhYZuRSAYorDKKGXTynNhlwA8XldQ1GXqM/XTC7LW1xe+s/qZq7iyYbSO9b1NpcOCt7krzjJnkeCim3DIEy+eca4SxZ/VqED96xAGhKiVGQonXw6m17jJBk9ZgISgM1WVRCefDxx45FEUwcQxbq8FcvOp9XLX/rq5OiwWffxDyQO92GFg1oZrNGMzzOauNFaJ3cHzA61zdhFMr7J06hJreY/sv/0+YL3+dgVG+nx11YVIx/967RoG6apU2k0ooKiz1YjSwplU+Px1c98/YokpKQGoZ5A8fU/GyRbawvfsWob9Oj4XSASZKYI3MjaKEn5PQ4cqsbPB5SlvPTqhIa5IS+TRh5HRhHOtcBsEojN9bzRYtMEdd3rtqg6qims2HM2odWUtj7lqTwXoyIPT1+ABYzag4uRD4qwnEQpH7xr7/FjCZwLxa8XOSj3k9E8HdVKrUWAlj2L6Ug4oSUanbmbicnAjTdDphMF7dgE26wDvfgX3+dT/QWGRAmPJUvPs+7GwK88JrzDo1Y3Z60DEZY40VOVhE4Cgd+2EJ4DaLWd9j9hE2PLEmmwG3XiYjUsWJ8gUlh0ddOOf1WpNQrgMVO1rxCBLAMxyNYe+/1gL+6Le9mJiRjbqspBiXOHw0BrZ7RiOQq7dEwnjJGu/kMdsTaYWbt8iBx/d4QG2HDjpmKg1mqMIwNUEoHqPSa4wTDiUN5YFNXJbB3VT+PkWYrDGsInSItnWRGySM/fcFpJ0krRVb8p+jYk1qBwgw+PbPROqh5DVmAN53zJ9eXDkdlqw8czubyLpKfWW3sctA0lil6Ue9xfbe2TF1aZQFW2mwLTLPeE0qjayImlLKe7c0VzFhTNYv4FmYQSgys322I1TKFuDzEjSSPRny2a5ue59axU2rzkil5mcjSRn4sZ8gdLd36u6X0zufihqieJ8CAC5eg5NKMAEPD/dcRGdpKlo3F6+zpXLxOqtFlWao1hhcq01WGD/2Z3it3TPPCC9V+NxKFTcAtkUOJ5BX5Axw7TW2YpptOGPpB++TZPXKG7ys2UTc0CIaw48GHgWnGve712TWIgdrWDAzN4Z7V4eXUczAXljYfMrqYtgTsbIBddQ7R7zexRy49x5VKkWXxlQanH0BzqN2GRhiR0Mgy2AGPa6fIHICdzabiewCZRXsbArTXuegdnVTEFFtruvDR7xfeU7kky2AFz7zQ6Kpfz0TwR3iwmKPHvlSUAdteS4KhpLdJDpQGvL3koQDzKREQbCXPsehlw5UHr0PPP+aYJ4lU1RxJMAPkNSBSUkL46GHT2qGWUo9iWDYITNzMeFDu/q8p8VHwFO0Y4XqaWak7Ym0AlTqHBYaw7ZG4Yd7Joo8TRpYysDmwAy8L0/uApMx7NoGN1il5slc67sSiDI4q7KzQ/+9Sik3+RICw5RSZqGP7hN1sH2RCo+DM2Blg4Fe1R6TEgP8m79PUxQ9qPQZDbo84LQFcvwEuNr0PUhAUDYBgNxln65yUWSMMlkLCSidE2aTmYjCqQG3whMFhaTGHTAB+/MLMVDQ76v90EUGE4hZRZ6778Wh4ZTfodrks4gTHpKVmvfRlZeabT8lAyH+oUYGv3Y2oVZSrbEEzQ1dcLP3PoBZ20DxrW8i+Imf4sxiZQ0qPGUffwScHsHuP8bi3Y/wnX/xPj77S38J5uXP8FoFaeRaXHkO+y//BZCmMJ/9os8ic4VEdoQVKWCCKGJACUPAlDn3aLYZ9CIekqbSgD0/9GtKETmadGj1ubrlGanZzB/aioxSlu7KOu/t6gb33cYucebCaLYXrlHISxmxMizHbCw8hQOp1BZkiS4zpgHORoqc37W1zr9XafDf0wlQzf2BvJhz341IfrSdM68dVOQ82BStNBnILCf2zPRx3+0/qo82YQ8PmJwWOedZUcQkAABWt7gmeqcwL37KV6s9QTAp6e3JA0x/4/cRr9URXL2M4LXYieB93OtHBndjzN8H8BcAHFtrX5SftQH8YwCXAdwH8O9aazvyZ38LwC9wt+I/ttb+Hz/qMwCwBFoWmuqf+3K2c8KAmM2YdWzu8qHqIQCQDToZA48+gF3f8dP79gYziEqD8MF6U0x0H/B3N3dJtlnb4j+xaMRkMwZYJZyovolis7vnsEFIhMNkTK2PIgcQ+/aEYloffcSyf9Dz116tEzWiRIbFzA9cdGHGCa8T8I5BihZSLZuTA9iTY5gipymJLfg7L7zGzEBbD3o4LQbMgkop+QPtTd/r1005n3LWIHoqplyFHfWZcQhCwRY5jLUsd6+/wKzoqUOo4EBcFfVy7VfzUDaq1LcQSKvCIbVNpborsTJz5TkXM7KBR32nQmhPj7gJDDXQOfyWYagGLYDBNZ/LQSFtnoLf23ZP+P+jPpw+f6MlGiJzluY1sXuME6e3Ytp0SrLnR544pQqAaisYChIrkH786qY39VCtnSKnnsvREebfewcIDIJBH1jfpDz1fAazd4NaMf0uzKWriK/fwhv/1p/3e6bRFiilkH5ECdN89ceBj97juti9xDacJjRZBpihx7GPhyKa1oA1OSvabOrJQNdue3jeZORQNHY8JAmutcFnrMJ507FPJIQ4Z4xhm6l77NzGMBoyUVNNIkUZKRNa+AB20GH7SW0Zg5BB7vQA47/7D1C6sIrw536B1ZdwQlBvUYKkueZp/LrfFD1j5x4GOZdkKJtS1jetsqpXt6dHHxDl1F6Drbc8AGA+8WtW7iMhzlXvzKTzQYVoDjpM0tIar+fue8Swv/MuzI3rMH/2p2FWt2BX2ujeP8fv/dZH+OmfmiN+5S7v/Se8/iQkpn8A4M/9sZ/95wB+3Vp7A8Cvy//DGPM8gJ8F8IL8zi8bY0L8qJcxHCaYgCdaUibhAeCNjRMGeBWtmoy4KPYfCRzL8gb1u8Sxdk9Z4h0+4I1UzO1iQWu8iuhV75C8QlGrundVUVJVEHIhu/6+aHSbgLriKtWqru2KrxbRJmcJdnbMQ2ZjW967wawkLvlMHRAMuOjW9E6ZnWiFsmyt1Tnjz0d94MJV9s2jiPcoilm+ytATSVkYn+mSeJeBfef7wJN7flg56nHG8PB9nxGbgIHyXGB+2i4yxkszAL6vG4QyAxDtFSXnLAQemVagXpLuAHArMXpqPQAgymk6IrZ8NuEgdNRlWR2XGET0QBj1KVus+ht6SNiCZJJRj5tJLf+qDUJDa022vpaxyWHoPUqN4d9JZX1EJag5jFnb8Z4Dw57/XMDJXaDI5Xci9m+Vhp9lT2PSwxjm2k1kRz3EX3wD8Ruv++Hs4UPgvTedUxnWJSNutLhOe5SHNe1NBvRhj/+ez8jo3LsBPP8a7KMHsN/6XX62UucbLWavuu77XVYWAHHfctjZZSKdGlOnVWa1glTRSsakFRLskjIPNF0bAOcQ2YwMcl0DhYXZuwazfVXctTi4t4uMyYRWr9rqqq44xBQADvd/5zeR3tyBzRYk5EliYxcLBvaEAd0kJT+rEm0po/OVJ/coVqjmMEEoSB1ZGylZv/f/q1+mEQ/A2KPyFFqFuRarwD1lP/zev/9f4L3/4JckGTgkpPHxfX5eWufeqDeBszPMz6ivo2xr3HwF5XKIRhggeukWDwtNyj7m9SMzd2vtbxtjLv+xH/8MgK/Kf/9DAL8F4Jfk5/+jtXYG4J4x5kMAnwXw+5/4IXGJAfgH34Id9GCLgn6il677m9Ro86GkVWbAwwGRILUmN2I+Z0ZiAmZG80wGnYd8j8YKyz61XEtKkunIDU3rpFDPM69/IYxCqH9nKeXCObjvdN5VHsCePIHZvMi/N507khJWVqlkefiYA1Sx2LKLDDh8QPVEQXJgOubGqje5YeOEbLjhQNoEoqC4uetddxYyrU/K/KxB15uCLMn/mnKVmN97d+iifvGqv/4gpHH4PGP1kpQZ0EspMFnCrdslyzj1clQzkBWBp1ZUOybwFmWllM9NN7Q6Zwkk7amXIoUko7Wn+wwmlRrhdMs694Kqcu5IgceGu1dhSZJRin9S5uEexp5IVq7yeWjWnk15H7IZEBsfhOOE7an6Kts0OpQPpTea57DFxMNdAyJjXBCyBavIMCLuOUo8s7rWBDa2UfraF3l/1jYcjNDc/jTsxT6D1MkhD/daA/ad7wGDgTAnqcJoWltAKlaH2ZQB0xbAoAvz2mdZtZ0dU9//M1/idzo75v7LJkSDhSHlOFJBr5RSBplBB07ls5RynU2G/EdtEdUbVls15apLYEwk5L35hMS9QZcoEOelQKNyFDmNKRJWtTZOeGA117hHAkGNnR1QQ7+5BvNnfwLmyvMwD+4sPfvcOzYFgZsRoXPCSq9Sk5lNBDMdwZbI1Mbqlp+bqWMSwIO52sCln/kUzO2XPRJunnni0XQEHAvhS5/5bAa7WODSVhVr11b9IVCV+d97bwKf+Qqv7923gOkUya1LMJ/7KgO7+CQ3v/46vvHXNoDbrzJOiB7Sx73+tD33TWvtAQBYaw+MMYI/wi6Aby79vcfys//Hyxjz1wH8dQC4uCW93N1LXMwqXzoZMbNQGrtYumGeAVdv+v6hmlJnM/7OYqmMr9a5cE6P+SDGQ8HSy++K6h8vSnqBSdnLH6g4UrEgmxEgRHJpuOX6u5qJq5RBlFCL5FoskDDxsSyLEpxkw7Z/LtmC4Pmba1x86v0aad9Z2jVJQgkCK4FWlAmJhki9XGyjRdapBptFBlx/wT+EIPBQvWzG+6x6LAAX6qDLA1EghXY2YaZjJYhHsTcRj0rSbmBryly+zfeJl9outqAPqWxchAWHxGFIREyxAOaFz7yVci+HilELQv0z7d+LSBirnf6STLLAUGXQaWriJJTPeQ/nGWz/jMqKgGDERR1x2OPayoWBOReV0GHHY6l7pzSJGPX4T70FoOmH+kFIOJu17LvWGnD6RtqSmk04xNu5ymCV51j8/b+L8NWXYHYusULJpg6yiduvAr1zmPVNZuHdM2BtC/bwPrkak6EcIEJw0vbg6RHsO2/BfP5LZDTrvWOJjJAAACAASURBVKyLKFph+f1nM+DJXZjrL5ORmU05NCxyYNCF/fX/Dfjxv0By2u4l0XMJiP0vLA+WtM6etD6bQrDpUrmie8y9Vm26zNie7Lus3bVlKuK7EEp7sVzx2HnFqAchVU8nQ0GY5X5PAHx+Rc5n2qJgmWmtM0FRv9mTx6yoZW5izZI7lK5FuU7zEz/NtdjeIrJGCIGmIsbtpVRkpTPYE1ZBplTC3t/5L31bVVvQs4kkrB26xm3vALdehP3et/gd6i2iavpnMC+8zAF/VUxTluQlftjrX/dA1fyQn9kf8jNYa38FwK8AwKdvXbV2QP1tHD0GDp/AjkdUe1vb4gYYD58OxJUGF622Q6YUe0K9JS4mkL619LHFBR5TCXxJiQ8urfnhV5FTa2Y64g0UirKptxjYntxjr206oVmyyoMqgaLgcM4qUiGbwcZCWpnl7Amr2I8RV6HRgNdRXWGpv77rb1oYAuBwzii+Vj9T+5EqiqQORgrJc0JYhgFI/3w8YDBSaKKSTgIjfWEpK5MyA61q5Sj5YzaGXYiejPTojSIQpD/vBteqha06OoJOMeUKe7RqZL6MFlLMtgxG6VYv7lCFz8ht/1wo2nIvShUXsNFo+8C1WDjDbxOIrHEiXrhCMkKRM7uOyzxME9mclRqDSRB5F5/ZxDNMJyMefEOpsioNthu0hRCEXgwvrTEYFWwbYDrhmuyc8DqbrExNaxP2zncQXrnIwH0q5JjRkK3D7T3eRyUKdR/6Pq4aU0xGfL/Dh7zPdVEwDUKYN77gZzeDrpelBhi4tvck6VkQ5SVSC9AB4GwC8/kv8f6++OmnkTaAyGkncFIE+spmbD1VGkBgYHPZU4sMppAWizhiLc9u1E/ZrUHZi8tzKZPWODPRvaVmMeoVUEr5LBVfH8WiEbTqPsvUW6xstQWjB4S26pSzonIHS3Booy2nKIKxBWxUElBBDrO5w99f3+IaUIelueDtF3OqW/a7bN2++Gmu1U9/0VceW5eBIKKX7ngIPPcqkxRFe33M608b3I+MMduStW8DkJQWjwFcWPp7ewD2f+S7GcOFlla46AHRvE7gFADz3BOcJmM3lHTiTTOhcZclyKvH5qDDjdE944Jf3RCEhfTLFTccqSzw1JeT7U1nc4c8Z5uhUuN0fjqWAZ3A0oICQOiDfZ6L/jvJTKosZ6cjOF2QingsVleYMcwztiIks0QgvW1h4eHoEe/L5VtcSNqbzheOpu82xuoGVFbWipIfqejSzhgMRZ2vxAyr3nKHhimn0rKIHEFHceQmTtxQ1PXgLXhwjocOymnntAaz2YyLXwercr2uvJcNaKd9QmG7x4IUqIpcciaIhJJ3WlJ0jEpNKKJGh7STgatkrBhv6GY0lQYz++VDUUXBkAF56KUlJqJh1GjzWmJBz0Qlj93vnDDQnhxIkiCHW6XBeY2aNI8G/LubF6SFI0G92uD/nx3CHj+i/lBjBeamVD1F4VtV84ztusYKUIv9uk0r/G9tUTbbQPcM41/+e6j8hz8L3GwyoPbOxf8g8bj2Oz9g4JmM/dqxVozmh1xvpVSY3IbPYn2Pw/VMTCPGQ2bC88yzo4OFh/+KqiHbLHMgSIgt7x4D+/eZ+V5/mbOg/jmfTRTxPj+6A1tKaTDjCFsZD9Qo4bXpdcveM5UadVoAd/CYchX2+AnXbLnC9xn2WIFrZVmp8zupHpNWB/1zzldmE0ca0/aiUQJbIE5wCvtV/svNV8hpWSx4eCgQIpuKE1XGe3/4GM76soi8LWC14Q+ga7c8Ca7W8knTx7z+tMH9nwP4eQD/nfz715Z+/j8YY/4OgB0ANwB860e+mwVLznnGhZuUYK7c8M4vmmmUSgykzbZkvTSLIFQsYg9Ls9rFXLQbZMA1HMCencBcuELzCWW6njyBM51eEsDCsOc3fZET/rb/iH3z1Q3252tNGRjJ7wgu2MRsU1AXfuyCi52Mvdm16G0ofd4FYIDZpyI9tG+eTQmL63XI5BX5BUVoOEJPueqz9FIKZ6t3fuwPzrTCXqcaI2gJ/P4P+N4qcDYjxt+EMTMswA0jAXhCh1YJgDO34JCoQm0XpWPb0LVZTCn1wzKAA7kEXOjHB8Cl62LZ1vUaGroOgogCauUqg4yySAG2BNSntLnGIaNuQB10xwnULMOOzl0pborcG3tXalKpiFKoitdVG0wYAKGtZwyC25dJdDu47xmWWn6PBlzLjTYPmSiGDc+9iUS1Dvu//DOYr3wN2Mi4FyqCXlGhsfa6r6qiiKCBZpukO4BrVVmd+RxYzFH5WbYP7KgPc/M1HnZvvwnM54RGvvFF4PIN7pvFwq0/2zlj1VxYZpOjHhAocmnBSkANsYWNaZ98RKMKQFqnhszbpMTEQZFYScnDiW0B7F5dgr2WXDXrFEtXt7mHTvdhFVppLUxbUC7aftX9oi2kbAKzusPERwUFV7d4/6uEvdrpWETScgbqUIaXpZT7dLHgs5Z5g6ksVQgqTSFMXluIZEp/6CHbctA4yKhycAr5b00Em4KMq7d4mJco+W0abdhxn/j6IISqS9pv/Q7wpR//kWH1TwKF/Efg8HTNGPMYwH8DBvV/Yoz5BQAPAfwVXrt92xjzTwC8A2AB4G9Yaz95pAvwprRWqY1RFGSwzSYwaZ06H2eHvsTToJXNmE0dP3G2aE4WOIo5FNFTNB4Dr30BZtDhhhx0mA3kczJaM2Fh1prAUd9rsQ86DMRads0zwvuqNT9IKSycCXQ+p4xrIIdLbcWXqyKyZGckCdnOEdDaZCBPSgyeWtKrj+myR2laZzUyoI63VcW67jGwfdW3GgDJJCK2M/pnwIfvwh7uA0UBs77BzLDe8lVEEHJY+/LneK3jPll2pRSYTP37avmtJbK2s4KQyIp774kswoLBLAg5uwgMyTw5kSNmZYOfoa98IU7xZbajpDR1GTfAbDytezejhZCtwhBGDVEEoWLqLdi0TqJVvFR5RXLtRSyQwZhtsOlI6OSyVCuSzZ0fSStv7KV7xwM/P9DgbgvXqsPKKkk3FRnKT0IGe8Hj20XIw8AY56dqGqvAz/0i733niIG2JuJ1xwd8LzXIDkKX4Dj3o/37fP/NXefApLo0bkalhLDzcwzffIjaF19kq+bkkEN7bXeutGG0lRFF/P4V4ZnkzFjt6b7LVp3YmerhKJu81mQg752LPpAkStMxDTH0oFpQBM2aQPx7LQN7YUVK+4z7oaJoJcGU5zmD39mx358VuWdP7nLgPKLyos1zLwtiZWBeqoiUbi6SxSIFEIowHcDPU1/ZeeYlsUVHyKoZuAzOKf5X8m2u6YjkP0VRiVCaQ2HJdaAQeK3qU+W5ryg6R4QvZ1Pek8UM2bffQunGbSL2PuH1J0HL/NWP+aOvfczf/9sA/vaPet+nXoF+8Rl7T6UycH4Ce0OErAIj6owyQFNcdB7C3HyFN10GEi6bnAxJ6iml7IENzniDm21XdppGmw+vkBJSM2iAm1+0S5wo0t5lOhSdkrGGkyPg5suSeYS+fFLf0VFf+uSFGzIiSrjoVF8kps65MUsoE+3v6qJVCYCSKP0p9VstzFSfReFtgkJwqovzDGZjE7ZzDjsccGBc5AzMOqwcD3jI1VrMYtZ25FBlUNAWC2ZjbgoRbNP2illZg1XWY8y2hV0spGIZwAby3yeP3cDMCOLC9s/ZNhDRLIc2mWeewPbHtatrK/7A12rL0NcVYQjj4Jwiw/oUZVyHsNLnL1XEd3foN1VaIz66f8a1oXrcqnYoZi/Y2vPPsn/OMl6Ns/UZzia8j9oGVC/Z6MzL1KY1Ds40kVF9mluCyuic+AB87w7F21SnpNHyAUP1k+ot4F3RHtm5wH7+jZeAfIH6V2NgNOJ+uXDl6fukAAUVdStEkE01z0cDr+o4GYgjERmxVn0MZlMeRupxoENoHaILjd/UWjS5abZp/ThWE2hJ1JKyDPozDps7x3DM7YgVm7uepMwEYzJklaMy0EHI/aZzpEQMOdyAf+GAC7ZzwkG8Mn4XS4dinHC/hTG7BHGJa+D4iTMIIYNZeukTmXupMKHMDUy9xT1Q5NI9GPu1rutiOvLXYK0HltSasN/6bczPhki+922Yn7n5iWH12WCoQmjDccxs6OSQAUA3wjxjBtNe8/99+QYfbLTFG6aDVe2lGwYLI64ldjHzB8RsApSknI8TZt6ipmiULQdwoS7mflNHMbC2RYrwTMpDJ88peGfV6J6Mmc2Yp1s2boMq+04WgaflFw4B4lyiNHCvrLOikB4ibEEyyIM7nKIPlgxDjp/ws9d3gM9+lZj2tMJsTft284wbJZsyeGiGCXgmZXsdmBVk1aksg2DFXUBRws7KhkdIzIkEcYbThaUC37DHHu2NVxjQZxO2TsSmzyrVX4dmtgDCxPXVTZTAFtbPQmzhR/Y6wNX3KGQoFpc8TwDwpby1njnZWGUGPR1xiFddYfuo3uJfVVGsO28SIeLUHRswW1d47cePPA3eGj/Y0zlHEACJDNXC2AeFk31gNIK9eFUCJ7Na01zje54fe/hhFPHzBz0+myUBNSfBq/untcq/l80cS9M+fgDMZli8+xHin/xxwiq39ny2meXChH0Ac+GaOAMdeXZpucJ1pn3m8wOiwNb3PPxXq+IkIas5TmDLFQZyJZ51T/hsDh97JUpbAHHDz1BMwIG1wlOLnEnb+p6YYhQe2hyEjANCWEOciGWn9L4L61Fyi4W08pbafTGRbd7GrkwZ4N6ptGxiIC2x1QqwotAKpdpkHCilsPfekQNIkGxSaWCSe+E31XE3AUx9hd0BZeLPxgR1zKeMcwrjTEqsND/zJdS2dvHef/0reO4n/9InRtVnI7irHOz2RS5Y7ZsrBCxOxB1esp/JmO2RMGRWPM8ofLW5w4Dc2iSFeLEQkwhqflgRdLKnj6k0qY7yszGDUJ6Tddre5v9nU6rGRVIWNtoMgpWa9340AR/OWCj/ejLPMxekVC7YmQEI484ePXDMPKvGAovMiR5R7XHkMw0Vu2qsSgAQ0aLzE0eYMFsXaZi9f9fDI7MpfTb7Xd4jnfSnNUFDVHzvdnkCfybw0Z3L7Lvncw5WA8NqR5mvWgEAXkFQ32tINT9mUWQmYm1Hrk1ITHqvygIJ00BT5B5HrpDIWBifire3At8LAoeOobOVEL80wC8jdpbJV2p2ns/dANksD+Os5YE1GTJQXLvNYAlwran2/jwjDPHskM+4ybVif/AdmGqVxDJV5eye+aSlUhO6/Zj3Tof/aQW2+ohDfEHSIEp4GD98H/Z734a59bx8p9wjZACK150c8PnNSP+31Tp//+iI2kOlCPb0CGZ1HU74SlFWGsAiT0xylnfSRsR4SKZwkXsCj1YMwx7XeH2V76WSzuJlaieCh0/KMK9+gYmZwjUDgalWGmJXx6rNziZcr7UV/nw28aYVo5zBtdLgeu53vAyyCXwVNBnBxuLl0D8TFFXJDX5NrUntoHHfDUpNWudnN9reJSsq8V52TmD2rsl+kkomFRXSZTSLIo2qDe/UZAt2CmYTD/M1hiqmhVTlGud2L8HpyM8mwLXbuPWTz3OdfMLrGQnuuddeUKLSnR/APnpIVMjnvsxNIBoLtt8HshnMhSuwb3+XJaa1FEza2uONLaUwsQS3xVxkXoUCrr6J/XN/EgNQRcKnUCeaUQQBe8KRwOKiyDkWObGlbErhrnLKYJpNgSDwPetlsax8zqypc8Yevlrk6TUW1vXxnOSuogGqdb1gXt/eFZZ5u9d8ia1O9LMJMMkZwNXh5vRQRMUqMhQrURdGxcKUfp1WSQLSVoXqh2gPVzV3sikzOGs9AUR78ZKp6dDZCY6Nh6K3krpsClHEKks0hYxq1ysZaDqCMQ0PeVtmuCrTNww57dG1VOQ0/TBLbZqKQNpUAyQIoP6rHK7HPsvPRUZiOBApjDOyoBcQ/f+FN8QGmH0pY7e9AfOpz3v0iQ7uK2IuovDOYd+30Mqp11hRyYrhgP10E8CkFdjti8CH7xFCF0bs9zvLxxnsD/4IuHsX5qWXMf/dP0B08zKrtiu3YT79BuzxAcLplKqh8jyWNW7sH34L2NzkMxx0PARQWxTVFaez79AigHgZ9N36NklJDoMyM99KzbUObT7nTAvw7VZNanrngsgq5LMTP4OyBdshlRqhrdJys9kMpi5zMzUzjxMmVoHhv7WFlM+FjwB/aMsBp7Mx1RVygIQi5wE+GQKjQ0p3nB2yWj878u266QRorTK5VNSWaLjb/hmc7/GgwzhTb8HEDa4la4F7b8M+vg/z3MsM6lHEKkJaNlakUcwv/M2n1/8PeT0bwT0MvZa0bozWKvDeu0Cawn7wNow4BNnZjBKfV29Rja7IYd/6PolB5ZTZ0bBDDfRSCkrcgItukfkgCQDTCSno5SqHQoMe+6VlmaCXKk7FzkQJnWCKnBrTc52WR+zzRgns2b4MbCXY5zmcIp/K0VorxAgD+/g+MB4zE82msGdjft4MzkrQbF0URE0uuOuqZEE1bzc46BEeqeXroMNSWK3PCjjMPuFsbcowzA/JVlWkgR4kxYCb+tINVhjKQowSP1w6O2CZqbosSrTqyMDZBISvGQM7n8IuZuKNKdjkCkW0TDZz5bMJ5fdylt82n3OoLq0SjAaeCbl8UGofXUTenHNOnsMsQUZ5SEuZrOxCWwCqBxQuBfXllyBCmJGvSGAOmW2fn3DNXbrhh216KDfXOPgeCbtS5X21ZaQQ2kjmSkEoVVjs+tKukgXc55rmGvDGl1lhqrcBADXENq9/Hti7BJRTxN+oUvYim7LaKqccrL/6Oj+ntSaH8dy3DuOYWknlqsgqSJjon8uhJRBhbWd2H/H+a7sGIHQwDD1irVj4Xno+B457MrcpcW1GCVuJm3s8cPIFEFc4B1LZhSDkflvb4/WoSU+16So301h1w3U7ny7p3Cz8ga5y2bpHNYO31s2W7GIJcuvIfjLXK5W9Jky/A/Q6rECUZHmyL6bzZeCUVZiptxw4w8hzsr1Txp7+GZ9F9xR48gDjX/stVCs14KXP8hpsQbeo9V0eSp0Tto+XQQk/5PXsBPe0sjTkKLgIq8S7GkUOADDXnxO4pGiIrIVA+Q4wn8M+fghz4zkgIV1aMzGrWFttR6huiBJ0RNbUfvAu++ndY9K0VzcEJZHCzocsc9VFp74C58+p11ZtUryryLkhooRZxkiw9FWxdFOGXpYBkwns4T7MzgUuwEGX2WGzBZTKzEiiBDbK2FJSrLqSrmZT3o88h3pZqkcqPnybw7SkzOsRiBXKEVtg2pKxgtHXoSPgAwwg2WVPUErCFMxmZEQCgpGWIFVOPSFLhcZ0iAbIQDgGigWcwmQQshweFdxA/S6/09zAmhEPhTDmwtZBqfZG1YRBg7IGOllXzilpOiIbUiF2s7EMx2I85TSkL/0cHYZXaryHCs8FGJA3d0W7XhRMyymr0EzK+0qDm3AycLA5Vw3Oxg7ah9lUTNVlrVy5weC/uk0YbudEIHldb91Y5MDBfa8PowdwTVjdB4/4fbUiOafJgzl8ws/Y2JaZ0th/7yCEeeNLgpU/5loTyQwMukxeyhWvknn0iOYz159nIqIHkRW9fQUuyD21wx6z0Oaaq6xcuycIeYBrRRzGPByUxavEtkbb38M8989vNn6q5WYAWEwdVNckZfbkw9j38JcIUnY2ofhZpc4B70SGyjo7muo8b85qS81+9i5zLhGR8Gg1uSkKoqe0EugNHHHOKvlOCZPnR8BH78FOp6j+Rz8H+83fg7n2nLuXT4E1ioKdCCXhfczr2QjuiwWHL4Dg0xPY+x8CWQaztcXsRUvH1roM2qzDT5vVNdhBnwFybYulcjaFCaqAFSZbxGGtM+IOhWxz8hhm8xL15NOq34A1KdUGHQbB+gpbPr1zqki+8nleb57jKZf1PPcMxv65UJwTZnlq3xcnhMBdvQl7TxiMccKgoGSVw8fA5Rsc+sQlGGu5+OqEV7q+49oWA+90xBaTOjUZ44N+EPphjQ5vkxJbTKWK73FDFt1CM9+56GaHXkd8IszOpMzvB3gceaXBaxp1qd2S1viZWs7r313MmF0vO81rT7zS8Jhl0XOxopoIwdvDBE8janSwO6WKpUMSAVg2jGCGLEteD+ai8BWWvjSwhyElH1Tqt3fGdaC4/jDmPZhOGAyjmJl3S2Yig65vO2imr8iOvkAGkzJ77jrEf+5lD7EtlfjfnTO2ZuoNJiF5zoMrTvi5QcjDRz+rd8rvcOEaM9/JmL18gLj1ao2Jy1T+TO+lDO/M7jXXQjAb267FZ8upXNNMYIYxD5Zrz/mBXykFYjGqEcckCxC2KN6wTs9oPvfWl8Zw7tM/5346fkIDmJBkRavBfTaBPd2naYcxhFFKxs1DPncHH634LGOAkp3iMqvaZX/ZeeaZ1YXMbJIy5xvCaCVpKvZV1PqOHKaRX4Nxmd9b20sTIV2q9tFsAgQRD8Bxn4ek861Nge09mOMDdg/iGLj/ATkHr36WSJnZBKbWFPnqhW8df8zr2QjupbLv68nwy8QxH8DVW3Dyr0qBr4pi2/kBZUwvXoVZVknL5wI/S/xJrn0vdW1XO7Pp2A9elnU4lDbvGGwSlNXnNZYWhSo2im43Rj2SJ8ZULsSoxwWWVjw8Sh9KrQ7zpT8HnAm7cX1bCEAzauH0zkko0gB9dgA7G8MoAmHcBx7fZQWgksNpXajeETe+tmL0tZjD2gKm3mImo71NzVInIwaElQ2aWvfP/XtEJRKYBNVjq02SwGRju0HRiMQyEydee0c2PecrE9fjNSmHlmrpZ1UUqsiB+qrg9wlxpQyEQPZUgnbQIXHIwRpTHq4mgBOYUrlhUbd07FTg6QAfggGiyIFQ+vOLOYfUuUgUVBvMolU0LU74u6eHrIZWN56GbS4FEEQixau/rwJp50e+7VATev2ExuiYjXmAb1+U+cmI16dYcB1oazvr8T3Y996my9bnvsJKQB2xlCioQ2RVlNTeeKPFcn8qQ/z+Q9i0xmBS5MLQnfDfSqNXmJ60QUzcYNsnKQHre8zUV9ad6Y1JK1yjwy4P7TihsX0p5aEjsx9z7UUvHbE8FwPgdF4AopwAn8VrhTjs+OQBgNPpz6ZOyE1Zy3bQETMcCcolQQb1Tt3sx4axZ1nrs2uuuYGveyZa/Racz9j33oL58jeAVGZ0eQ784W9xb+5d5u/ovdy6yMA+oWro7H//dSRvvMqWpVhJ6uwJUcmv4Y95PRvBHfAb5fAxy8UbL3hp0tLSF4kF6z2b+EFTnnvHF1VKGw940qU6eCn4cFVaQINyte6xwYKYQaXme6KK2Dg/Ydl8/wM+cBXFAqCmEiZOYMNIsh4t/zIu2lSkEhTPPOozI0vK3roO4OJpb0gmTwy0Wd0mdnvvBlT0yj1olR7tnLJ3Px76lopq3qjBsxpUCPUaAIN0FnrzgCL3/XNFwwC+DaJMuYUVNcBVYpNlWMbeIvurdtRnlh8nzKQOH3DGoSgUHa4poiWt84wxAZAzONvuibBuY3dAu81jLfv6u9c4D8lzQRrk3re1JJpCSzLGepBwgCj97yCUCi/ym1TlAWYT2O6hqCCOHPQVJ0fcoLWmiK9Jy0plDdSkQuGB+4ccOCtrtd9hhRgnovxpREIjZZCfS8tGyED2zlswb3zV652Pugy0etglJWZ/rVXhH0gLqFKjocvRPszl57i/+mL7qJpLwt60o54Qj1aIelpkRHU12rIXZtyD2hZSVnhr/SmIoZ2QVYrVLdghobAqUQD1WphNKL4VRiJ2FrOiDWNfeQn3wwQhbC6JSL8PbMw9mU71iUwAZw6jvystH2einU1FU6oOlGI/QFU9mWpD2pvSqhr2mGSm8j11+DoawImtKTdgyesZ5RV+vxdeEUDAGJha4M1vwWYZzMUWY8rOZanqRnwOInhX9Ico/Xv/Dhm8SrJrrhEi2TsnHyT6/0NwNwEXz2ToA1IYw9x8QQR/BhyaAE/D7jb3+DvTCRBD1BAHTtea7RfFNBdLxIgpMBSDAtWqnk34oPpdbgz1hFwI5C+tAqM+7Pk5+/pp9WkYXjZlINL+oXp9llMaaJer3MBZxr78u9/1ZiMaXLR3HMXA+q5I+mYed6298UD6zK1NmM9+mffv4YcCe8vgjBhSUZVUXDrgy3cZWjIbHFLHXX071UR7Medmqa49vWEKydBToejHDSHodGDrq0sBZ8EDb9AB3v4ORa+Eyahele5aVE0P4ObTzDitsQUThNygdQl6Mszj71VoYpHNWFZLEHdIGyXj6CBNNLifahUBvr2jfdwgZFaV1rxa4PEj3qeTffEAGDy9lvXeIPOMxOmY92RlVRILqZTUoQvgWtBMWDP/MJLEY8KsenObQWA0EFTGuh/2AR6zH2Z+aCvP237ztzH77nsov/YFYfLOgEiy7mCp8tT1CwBv/xHbRLOZwERzr7o4k+9UqvC/x0M34Lfnp8Dt12Cuv+yer310h4mU9om1X22tT3z0JUmYKrOaqOax82MArdQ/MyGfYToSjfk6++cjHpamtf607Ec2YWVZrsrayvjzUkoCmciBOGXVaoP7pC5Jo3YYRL0TBdF4GHR9+xggS/bhR8DtV3wXAYA9PoZ57gXCGJeTTZFEgCCvwr/283yfQYeD1jphmnqQGNWK/4TXsxHci5yLJy4zE5fMx6ysMwvUG6oDkEBo99qn1QzzwUdkrl24CrO249AcFCC6y5NSM/YrZHeZUsqsLwgZFNe2gHt3qEp5+To3kPaFRZNDEQfeHajwG1NlexWHPerxwYz6PIwqdZZ79SZwRbL/+YyLvnssGXLkKeqTMTB7AlshusCI2Ya2PEgqConWGA+AcoWu9MeP3PwCA7EKS8qAicWggxBBp12/WPhDs9/hQHn3ktPlVggjAAaB8VJQ+/4fAK9+nlmHEnQyarLY8wNq8jz/GstzLZOjhAPXfOE5DeLFqhBIW+QiySDwqqBk5QAAIABJREFUUFW3VEq9QiXlcHB0cuBpRArAjEclG/64yYEerj9sXdpCGMVj3qORZJhqbh3GT/etY9GXPzsEcsnkdfaQ1kknjxPgw3dgv/89mJdfAa4+5+99EHJtFPnT19XvCIQy9n326ZiMWGP8sP10n39PUEY63Def/gLKn/sy/380hD07hoFo8O9dhZMfXhreoSww1VpdgA1ScRoh9gUh1+54yPuyse2rn/Mjl4kD8NBDW/AzpyMmS6Ipw+8RAN1j9ujTGtuAKo2b596vVFEpUUxOSRAyCRr2BLJZ4vB49xqD/GQAW10RQTnrTNvt0QNZe33vrbs84E39QWySEqy2+wAoj8KIFLnVvV5pAGdHsL/zG/iNv/+7+No/vOETKgDmM1+QKifzQ/A4AWZzj6hTOK4a8pRSEqqyGVATombqQSYf9wp+5N/4N/Vy6nGCMpmOGKD6Z1y8pRSm0SY0zhZCTDBsw7TWXV/ePrgPvPcm7KMPYbsnjjKPrT2iT/o92OGAQbjImd2rXny5ykX40mdhrt+G/egODZ4D6bHtXobZ2vUHDSAZnhAlKjVmlkHIbFMXgtpoiV+qWVkndDFOmGlay/nB+28Jg1PwsbUV9uFqTY/PBdxhY8oVmEpN7s0qhalWNri4tf2hWex85nvdyoIV1q0JQoo+abAMQ7KB+5SyVdMELMSERBE1A9K97XRCGNtE8fyFZw2u7cG8+gVmvrMJ7NkBNxLAw2bU53fXtk8huPIwpnyEzij+b+reLFayLLsOW+feGzfixhzxXrz55Xs5VWVmVXVXdTWr2WSTTTYJk6YpyJQMWYQM27IB6kOyYcA2JH3ZgCxAHx5gwIABGhZsAjZFE4YhgaJBk6bcnLqr566ha8rK8eWbp5ine+/xx9r7nMhmVRZNAUQ5gEJWvnwx3XvOPnuvvdbaYYGeNKoZUBx7EVfVRrKyrrS0TypkQVRqLPn1/aPIbySFn7SRmueOVml0EpcegsMevcyVkdXqiHNkVbLGHqmmxwd8vTTlGlC/nqMnwNIKzMufg/3B29JHkkOs0eYaFPsD1/MZjzxlcIGRgntvA8WyUEW73qpaFZw67k0dVwsxJ5BdiD1GS9gcacp1d37Cz5xUgKvPc01URaWr/jvnx8xk1U+lsQRz8yWuPc1Qv/ZVBk+5zmZ5g6/bv4C9PGZWroF9LuMgJ0OY9jpFfEHEClz549qTULbJZAT75K7vORQTrvlSmT9rdZ5mI2VzBsRC7Idoa4BUHxsdNajCyCBkfFHWV0qhlokiajQEPrKZ6DoKMYN1KQHW1ijteHyfUHPvkteuvcI9/uAD+YwTb12ucM9swkSn1XHD2+2gy8lyb7y+UGkuEDk+4vHpyNy1ERfGvlmSZ7Bvfhum3gRWN2BXt2Uzy4a2OUx7nZm6dpzjIlAuw56dsalTrpL+phnunVdhti9gv/8N4M1vMoAtrbLzrSyTcd95K5sv/RwzoQ/fYSBYWWcWo0ZA1srCnMK+9Q3gynVP9SomMEXwedp1VzGOLpzpiM01gIFgbUuwX0P/ELzHgbztVWAS8vC6OGbGoPRAqR7s6MKPmNOp9NpbmAv7RYyV7LDr+LZWS3kNjE2Zu6KiERMwWM2nbtoQ0hnhmsAwY77zMuw734c5PwHuvOqyKjeIAeBrfP9rrIh+9GfYN5iMfGNURT2ToRe/zGd+U6sHjgZmzZoFz3fZeBSxstOegV5PxfkLOQ9eDWgayNWeQCETZPIdamSNLK3Bvv89/l4pATrSXM+tt43WfoWMt8PREwqe1OpW9Q6lxGX75ktfFihNGpyzKRCJKje3ftbq6iax1umYjdFYst1qg4G8EDOwnR/z++icVrXb0KEQjz8EVjaAIIDtdp3TJ5vxVV/ZKKcdYMWUVOHGJ+r3kQPXZefzKe/r63+CrDtA9Itb/qAVUzed5mSSKuzFEUylDjsn7OPuu4rL9LMF0kQvhHzebEIMHmAyOOyysaoWHlrN5rKG4xIhjSyDTYu8/sOeh051rUxGHh5VCElHTObe0sJeHHlGkyY6s6l45PSAWgOmWMRP/c0v+Z6J2HFgPADu/gCv/xe/gS/8+j/ifTo9pBCqXGUsms+AcZ8VeLAQwLeu87/RwH2fZz0+HcFdvcBLCcs74YSb1pJvWOn4LrFddbM/ZYwaSmXYlXWYtS3gaJ8skPMTYryFWHzd+0CtCfPSq7BvfYeTTyqklzn5fnsdtlznglHjoFuf9fhaY9kdLsjA4DcdC2d4xk1QqrgM2VoLk04JDYkU24jYhbCKlL5tkYE/eQhUqrAH+6SlLa0wg1tUAspMURfAXcZQ8ewX9bQo19jg6p4Cd98CVjcJdynDIgjJNqnUWb4vTKi3JuAiCkOgvMzSdDQAHn7Ahb5zk5unVIYplnh9T57AXL0De/QINs9ZUagl8pVrokqtyluEsOU6WTsCr2hmblQRKRvPTme8bvqIS44vr/RK7+EiLJQA/hA2xgfxMPEVDCSALTKKtL+gsEgE4rpJme856PphGRPxtldhUzYH9h+xv7B+hXhpqeKtGMpVPjfLGAhuvcz7o4wL7deoU6H2obQ6KcT0EVIbaIBQgPLoq4QccHrI91G/oskYWF3mAQHA3HkJ6Pd4sDRiII3FhGzJv242B6zxwV+89d1cTz1cAOHqS9+qVEL01/4dwlMne249AYAOS7ciKrIzQpImip4+vHUougr/AKgrJcKCsHdi7iulGUfCzlIWUp6TwZPNmVCZgGsjUVZcIBV13SdK6Ry4FAOxQiyHmqF/U+9SfHikKr9Y6OklZQm6KQP9+jZMnsPuPYZpLcE+fsB41lyB+cq/ji989gs85KotL9Abj3iYlBJ/vd74Oqmm5aqMyuwCD96jSdv/PzB3yZaOD7hJVKq9ueP9ZABfcqepC6B23OdCzjMKNJIaGygyaMGe7lNc8b2vA5eXQLMJc+M2zGd+xJetvUvyh/s9bsqNHQbAw0fMvlbWmfWUyl6tmM3pO11t8HXiIm+QOkJab25lZ1MeJGrZmor7ZJZ5/3WxX7WHvAam0eAQha3rPhMvFHzTT3ng2ZxYvVK6hOlh4pLAQTUpT/kZ8MHbsMsC3azvUim6tE4xSnOJr6EQifqtSBZthz0fnI4PvMeK851nELWHD1h23nmV/y48Yis8eJ2uYwdke9ijh2RzXHmOuKoePNowRU6la++c2KqMzENuvU2yMqi08eYosCGvLyAQiSgUtdzXgC+e3i5zVwhehTPDnqc9VhueBQN4zrvi+utbbKKvSH/m/IgzB5TKKQZ0GI8IPxrjh09kQlNUr3BtwIqdgQtwaeb9XAZ9eT05aGpNmswNLrzKVC0QVPG6fZ2HdJ6xgd3X3tAlnIuh9n3qwoiSCtuO6a9iZOiFMt2s0CuDX/5b3jtHCQx6LxezzSDkayjNWPdzsQwznzFz1qpe7CnssOuqKasDy2st/pv6wyitNxfr3GIZRvB22zujclirjckQ6J9LYrfG9bu84XtmgM/k623n5c57HrL3stCrMNs3ePB1T4Ef+TKMiLHM2hbZbnLYmu3nPRRTW2K/sC4aismYvPbeGe2vh33um4sT/v7SChGF85NnhtVPR3DXsuhEPE/qLdg3vglTrojisUaZtDHMuowRUYBMMDk5cJvVAvRaaXekzJ3A3vsBcHbGhmhfXBHDkFlJmroBIbi8gL33AfDgQ2DrCrv+fcI05qXPewOkPGXDLAiFlyseK0G4MEA69l4xeQ57ccSbFXKaEQU6JV/WiaeOubLjefS1hniGT2gPquwWlYoD/PdBn4dQUhFVbZkG/3HJ82737vuNcrAHlBLYVgdGjc20iefG77GacVa/6vY4GnDj6jBga/l6GgTnMw5g7nVh3v0e8LmfZKZ1KcO6ylXh1/cYqLKUlFDAldVGpzQVYs+zBzz1LZvTglbfU9WGCo0oru6aqrkP3MrCWVSmKu3RHSaA86GRjM6s7gBzUVgWE+9qmFQ8jKB2sAoPZKkXOAUhoY7FUXCtJd4XtbYuVbw1bkw/EWac4que5+77I8/9dVH2D+AzfG146qi4Qd8PAEnl+UsrwtE/9k3huASA1FSzcZUBVytmPQQBNil13JxWpLUW7+uiI+LhI2LhUcHTisMQ6I69MKe18tRndyPsxn0marWW8/N3g0IKMv5SD9xArr8MKnGmcWIpYceS3QuV2BjDqrraYjWqzqHCfrHdU9lr2jvacAmA1YlwOjMC4EE0GYoXTUDPmHLd8fTN6o7f59YCBq4yNO1V2Ou3ud5/6zdhrt8AbsshPBCdQu9CKNUi8FOq8zMen47gLlxvay1LF2NgtnZhH34IHB/BDZloLdGgPswc1Q5hCDuf0zag3uSFGHTZXCzXYR+/D/vmdxjYAVIRv/M6zJWr3vLAWqCUwFy9QevVk0NmXvJvZvcmG17aRZ/OnRLV9iXTGXCQgJ3PmKEXSgyEYegbfwBweUaHPn0UYp8B71z3QUWzwvkUiBMGdsXtdHzgdEqGTxjSO3085GleLHHjKuMiLNA07PgJLRRKCXDtllQ5fTZGz44paHnuBaBGLYGpNLkwTUCur/qmKFNjaQ344E253k1+vtVtBnAdwLx4j3WqjQ5Q1u+9Lk1jzbAFCoEJmKXJprUne4QRag1mMZKt26/9LrBxhcIX5Ssr1gv4IK7YrzbYrCz/dO61A6OetzY2xmP4OswhkeA2GjBAzmYM3qcHXkxmrQ+kCi2oP5C6GQ57TAySsstsTb0N21iWBlvq2U7TMZDBTw/TQzVKfANcexI298NFNJhWavxvcSyh/jm+8OpwtcSVodO20mRi0ViGHUiWOepxjTXbwPkRrNhEm5CNaedomqVcu51NN4jc4fWTERuJauAl1FubZ2wOC+vFRBFMc1kYMMt8XWdcV3RQoj15wgpA75s2nI1xVs2m2qBArnvKA7re5rqei76mf8nAPB54awFVDwNMUPoXbMwrb77WYt9Ilefp3DWGqZFo+GEz+qcJACsQl9JggxBm8wYPsNd+jHtRNBwcoygBPc/gBtlncw+Jfczj0xHc5YaZK9d4ESZjBoibd4hBhyHs6QlMlnq5bkn48KUE5s7LXjiiQ46nY2YRJ4f+PYrCvx6NYN9/BygUYLau+Gw1LgK1OlV0owED1NXn2PSwVqTV5OBb2SgmLjKzUfXabMKpMmruBXjcMZRmWqkC2DI3cExmANau8HcKRY+3qo2sTFJHe5VqVhXKtMWmVwUagQFk4hKnxGeSjYKLqd1x2a1pil2qYpD9D+Asc1UdKdilMyjKMqqDVTZ/9FhcLYWFsLbFhldYkLmnomTNUh60wkJwHumLatPxEDa3PBg1UAUhm25Vlt042OPnO9qnyM3EZF5cXrgB0mZ1x/PbNYtVqqmz9pWDUyGYRThmMqRY5vyADCL1/1BRS7EMe/qE33tr1x8WgWTiir8D/DdtUEcFlvXahK2A+He56szWbPfU+x9dntFmQMcgutm7kiQoB15H2AEMYBcLpXoU86DQtaCCpkj496pHaK/4BnMkPjpLopbW6lLXpLUUXiVVBuksAyY9TkZqr/qsPRdvGWVrDbt8HYW0tJ9xdiRziDMfrKKIuP5sCjPq+wlLWhmWKrDDSx4oJnPj6Iz456ufkImLgr/TRM9EMScjjQfUmuhamM+AaoM4e6tDlt7+Y+D6JveVrBHTlr5aXIRBnTTeIGQQno6A++/zcN+6TnaYXnM1XstSn3hoJaSPcR/5b/06zOY2WUqFEivEt7/JWHj9RW97/Gd8fDqC+3zGQDGfMwPUjCMpc+JMnrFRGobA3gPgyib//3CPqrvlVTY6MPOvqaIlE8C0lwjXTCa+vNVMHpAyjN7Rpt1hkF/dZGYFuNLO8eolkDoIpi82sCZw7BcrkmxlFcAYlqjlKpwtgT6/WHYYnolilqujAYO6QgvFovhRJAvsA9lw5bofSlCV0Wl5Jo22BSMta5mlFRM6WFZbvnl44zb/rpn+XKAOYxmYT/akGRb7928ukw+vMNHhHjfNfOLwYhsWZGyaNDqLEbFSQLIdad7Np47eaNOUmGsU0YxNMHrceIHXpN/19hGVOsztl2A/eAc4PebBurIt7Ccp1wtFV44/FditBYxAHVpFGCPJgbARSuK9r2Zz1QY3+MY2ezXLa7znUkHhaB9YXgWM5fcZ94VRskrVdJbC1BueW32yz4Pi+c9w858eMnh2z2UQRJ3XN01d89E9+pfCxqJRlb089Qd6oQgUFnoLxQSmKWypcZ/r71SqOJ1mlI29QZpmozlg3/k2X2dpBW62gNJqtcmt/j9KDw1Dl+Ha6YjNxyDwa0fhr6QiTqdT9ibaq8DxE9hKjWyR7ulT7CY7uCR8KVUF5iD+n1Q50Wo65iEVGNjOFtywnd4FbHOZVUm5yj17ec5qtdECmsKokiBuD/aAiB45Ru1OdN3EZa6XVLyXtH+kkJOSH9Q2RFlfwx69rFa2ZQSn4UHTPaWS/jOv8rOV61y38xn7Wx0K/1yM0Olli3v7Ix6fyHM3xvxjY8yxMeathZ/958aYJ8aY78l/v7Dwb3/fGHPXGPOeMebnPun1AfDGqblRFDPDcaPIpKHX6gBrO5zAlM25AFc3OGxAu/vKeHBS8gKdJdvLMJ//IswLn4EbU1cu+9eOCjDtZWaNxjAABsZz2FX1qF14gKc4IAZeU0+5a7S9+ODiyPONi2Vm56vbhEcePyCTQtgBppgwiIhtgam1eB0A/ukmrsce6y+W2bg53feZ5dKKMG+stzKNOG3erGwDrVWh5Ekp7oQuxKlNrcVgHBa4gMRUyyxv+KxhPuWGLAnGbwK+z9qW59ZL5myiSCyBJWCcHUiFM/YBorkiHN8uS/M8g5VFbwX3tMeP+d5rO5Rs61CP8VCUvkKZ27vvZ3yq1iAXnFk3qGaTCt0EgafzSdVh1nel6c3GuRuY3T0VQ61tegFF8j7HB1zDOzcZoJ48ZONMv5uoGo26KRYTmKUNHg67N32Gt7HLyrXd8RXAbCqj7+bezVIbp5UmM2jlc6uXv3x2TMY8fCYjVjnnB9ChJI5C2mh72Eibr+q5JOZtKCXeQVEPQg26ap+s1g8yIIaNxXPy5wuxaFjkoNeKZz6j6+VkxEM7t2S4LUBMdtCFPXlCCmIYEvrJ5jxwtRlbKNKmo1JfgMIEY9d9VqnD7N4W64YBD5ad66zML858g7PagPnCzxIStFbM9ObAfMqka3DpmVEBs3DTWCYBZGnFN3UVEdBBMoWYFZGKMgFeK5kvazavsxLTXkYQAms7PLhUOTvqs98n4rRnPf4smfv/BOC/A/BrP/Tz/8Za+18u/sAYcwfAXwfwAoANAL9njHnuE4dka1DUDaml+uE+1XtRxIxtNiYLIJ15rLze9I1CfS1taAQhnQk1u7hyg5j+4wewZycMmJ01LxMHPGtHsiU37UWbLYuy9jD0lC2lQdUabP6OhZ88kZmQQ1kQ3VNgNIQd9GDWSasyYjdKaMASFtEZoaK+Q2uV2c3FyYIlQtkHMBVyKB8/CIEnD4DrL3gHRVW2AvyZCK/swQNf3qsHzfCI7J3t6056b6oNBvzJiCV4mtIp8NbL/PdiIg2kiPLq7jns9TsMhApbJcLPb67w/lwee8GIzFW1NicVEkXBwQc++M4mPrOOuKmRVGC++BWug/6Fl4+LuhZB5jdjoeizS4VTpMpjc3ABXwf8prw8ZvApL3jOK6bf1OZXz6+/3gVQfYUUT2s503U+o8/K6SGwfYPwwdkxD0htnlnLtbMm16xY9lBMKlnnqOeDGgAd4oJFhaYOa2mv+qooFVVvOufz601ex4EYiikkNpvw9ZQfrxbXYvuMUplVhjaOo0hmG/DAtMMukxtRoU7+l99EYamK8K/+m95io1jk8y9PYb/9OsyP/gScUVpSAfIA9v47xOajiIeYO9hyIIi5dsU6xFl8ByHMxlUeyCImtE0q1Y1OXVIVaFJm9bWQFBqtKACuFdWKCJRilqRHks19Zi9zhhV2s/0LJoq59RXKuO/Uy36cpnXxw47EhylNPQtJ3tPNWVU9hc15TRY/60c8PjG4W2v/wBiz+0m/J4+/DOCfWGunAO4bY+4CeA3A1579JhBXQ+uhj3IduCrSeHdCl7iw1eRq1nWZN6Q8wtFjZhm6QNU06+wQuLzg89a3YDZ3yFRI51y4VenI55lvDmrDQxgldBgUWfwiRa3Z8c5wQSiCGYGFJmP/b2Xxxb52i0ygLIOzRxVoxhRi8Z+3xBTVzXIyZMakcuzH97zj38YVmaok2fHpIbORjR3XqKJpkmRcofioS4NTD1UjE2ys0vxkSK9VK1cTcHGqDDuKiN3HJVjtcYSFBWrilFYO10OfQReK7FNMx0BBoK7TfbmfU9rY9i/gJuBEMSED7bVEsQSUjBoFgNm+TrhJ58DDD8iy2L0tohzRJcxn3LA6J7WYuCCgTBCTlN2hYPOM92Byyuc25HuXKvyuC3axtrNJE7WRaCl+4hc8NbVY9myhsyNWJpMh4a5aw29yVWQq4yPVA1Gao3oPZ1Me9uM+X6Mp1atYU3DQ/DmwtE5W0an2UGLv6bPYF7A57J98Feb2ixzDqH4v3XM25wWnRn0Ji949RumOKkJTrrsGdhl+E9YSmFAqqygCUPKCKxMwsAP8t+Mn/P/LM7KIShWu6Qfvwu49RPCL/9ZTh6+9OGK11lmDWdulgn1MywHUWs7gzKjhVzol/Tfc5P47euKFjD/8MEbMuazTYSBQwaWsc62QignM2g6hl4sjJkHnJzDPfdatUZOm3HNqja06mCj2zqrhhFg+4C0v1L7AKfPZbP4ktsy/jP3A3zHGvCGwjRhHYBPA44Xf2ZOf/amHMeZXjDHfMsZ866Q3ELe52Lu2XZ56IY5yyVPS7DjQ4VwECFXPkDEBceAxLWJtKg5xH74DnBzBnh7BHh/AvvMGcPcHzLILMUvCVDaN0v3U1+X0kM5wwx7ctJz5jGVn/4K452TIm1ZM4Iynai1+ltVNL7gJQ39D2h36Vc8mfI3hpV4Y/u6oD5vNYfPMd/EXhgRr19weHcgQEblWYyk3o5iLaND1akMrU4rk8wMAjvZkAwiFKyrClOswqztkn1RbwHTkKws9cADB0Fk+ujJSZmOi1WHJ2+4QZorFLlgHA8clL1KKi2Id2xZ3RcG762JCFhYkW5y7QcQYDbiJpJoyOukoSwkPlauwvTMG79zC9i8J8/TOyNYZdNmw06Zb74xrwASETazY1hrDhm65TmiqVGGWqhawEAxbHQkBYNiH/ea/4LqIigz4l2cybzWG/ZM/YMP08AGrGpvzHgI+iUhkEpImGHGJ2K9QVNX/3Y56HNhRLPPaKTe9Jvft3g84tKN3TthrLJBhnnmzstzCvPSyD+yq1K5Ufc9HIYYn95kpizDOaubZPYU9fQKrDX+tmgBEL78A02qSTZSmZNucS8WmnvVJRbDwHq/N0aFUIgKPVuvA6an/fMMe19ob34B9/Y9hv/t1SSBSUibXr3mxnAoeY3GzNAEx7kIs3umELQGwufunoxWMUiXDyMEqpt4GaktcY3OZGSBT3OzXv8rPpcNzDh/5AeLjASfClSqezhkVvWoboCWIrgE5WMzWTa7JMa24bf/yWfH5z91Q/e8B/AMw5/4HAP4rAP8epJL9oYf9qBew1v4qgF8FgM9f37auLFX5sfKHo9hj8qWEjITp1HfeyzVujHQOPPmQ/ueNjNS0VsfPN52OOTNyZZ0L9eTQCzekIeWoYCvrQL8Le+99mGKRAWo0IAQhlpxOphwXualVMaa0N2Ah8zdw3uLpDKbaodgpm3Oj1poevwSIU49SZo2AG96NqMCG8qBHfG884vO3r/M99x/x+ijrQOlSknXbXBq86sKYp0+V+Q6ucf7sAQOy8NLt2aGo+RbEJ0Ek2eKCA6MG+GbRqQWpHJSBwwfiyzLoSu8gIRtg2CPzQSsHm8NN19GGmrU+402qPBTiEoOK+IBwtqmYi4UFMiuSGpkS8jOYmdv8xGObT8/ZdLRMBi+jbpbCMjIAA6UeqLWmrxZPD1n9RQVpFB57B9AghPnyz/J3soxmVMcHbO4tix9QmvqmtOohco47NJ1NV9nRtKxPyOr4sX9uLpJ3Ze5U64Q3ajL2MIqANKA/TmuJFcOwB/vP/w+YX/plXsPZmGZzoz7cLIXLY9hvvQ5z/Qbs7Ve4R3vieHq6L/BjXyieNdocZBnM7c8COwMeFoBcC2GMqEBP9/ntV3jADbt8rvaH6m2YrRtS1Ux9ovUjX4Z59SfhhrJEkW/42hyILOz5AQVE2vhXdbg03+2o5yml6tfu7rL+b+CzdRMAUQDkAauA031HabWXJxKsI5iv/JzvWZSr/lqp9bCytMQLH+OhZ8vVRV2s9OOKBn+p4M+OvPHgxzz+XMHdWnvkvrMx/wOA35K/7gHYXvjVLQD7n/iCisEpR3U28M1ENQaqNRhY5zNguA/n75LOxe+lsCApB+yDu2yIzGdcwJUK2TJlwQaXV8iYUWl3LjiqzbmYmsswL7zCxpjSLtVYqFByNqPkyJd9SaoDkNXf2xlRWVYjK1tUvgaCzVYb3AijHmDHsHnqG1KjHgNQLkraRpvTeGp1mUh1APOZz/P6PHkI++BDmFLCpiPwlKmWmx2rVUQhhomqhGeyVHj8E+LdUmLb6ZgVidof2JwsIbVUnY6o/qs2WB5Px6RnKvwzm8KWKjwIlPMdhvTw1mCkVhJxiRi8qv/yDDgVX4+GcJyzlFPrNbt1M1VzNkEFD7YZKX0mqZF5dEjzN7ce4pITYdk8c3itPX4M+9a3uU6uPQfceBGmKJRVpVVmxKspGb9k4F3eYBDUOZjp3A2wwMkh+zo6r2AiLLDmElCtE9JRG11jeIDqsAjVNdjcpUh2OvbXV4UsUcbrMBmSfVNrAPfeZaN2NOBnaXYYiM6PeB1nE+6LQkxMvtYAOh3g4BGoeL9vAAAgAElEQVThkP6lv+dqDTDownxRIJT5VBrrZVZCl+dCkRQmifqqP7rL5EgSJOcZdHHGPRiLwlSbt2HEQ2x5Q7679Vz1xjKcZwwABKQcW/WHsTmcojqbw/bOhZiwRKhP5wgrHz0IgHpR9oSQFRZtKBbzUoUJdW1q4gHIa8oUqJyfw2zvytStoqf+Xpx4yDYMYffv8flqH6weUFol5anvF6qALJXqsNkmYeMZjz9XcDfGrFtrD+SvvwRAmTT/DMD/aoz5r8GG6k0A3/jkFwz8KVWImfX0L3lxglC41ZKRKN1IaGa0Dx0DnRaNqqzl4rh+izf14syzXDa2HWTj1HjzmWTVqYcFhtLdby5xsYp7nCkmzLbnC/NM5xM/KgzwZk3KXghlsRw8EtGRBJazQ2B1S2xMdaCDWLpmGTOnLOPnuDiBffO7XDBRREaGNpUB2D/4XSBJaFmg/QLAY80amGZsrLGxJKWz+HGbxjIpWbUW7N4HwigSSl657iEUuVemXAdyMj+c+1+pwqpDbHpNqUJoLCr4RpUOHq8vAaf70lfghnEOnkmFm7TRZrC0OWEI/QwKySmcVWl6a9i+uB1W6h6f7Gz5pqTqB1RBDDBwlCo0pNq+CvvwQ9g/+QOY7WsyfDnifdVmqSZ2mmxcHsONU1zZFBtX+axrZU+LU58btZ3VNSRzNk1d7B8mQ1/Fhp6h5b6DNov1eiozKp3BSDZnK0KNXd12UJ5VFpMmUZOxp9k2V2A+96NkCY16PstVPrs0RzlPNZPexciZy6EkzfzhwFfay2v8boUY9rtfZz9DBt2TgNDyNtn6nYIAKEg/KmaD1lRqdHjVdaaPmH7yNBwruYYuAMYThU/cMHmuL9Nc8a6y+tD3s5ac/QUOulWVr/bvJkOZbRwJXNZgE7XWglnmGE9z51XoxCekKZOIWou9l8kIqLZ4WEEObLX7vThhUrQJHtRZShNDdaZ88B6wfoV6jkWR4Ec8PjG4G2N+HcBPAVg2xuwB+M8A/JQx5mXwaHsA4G8BgLX2bWPM/wbgBwBSAH/7E5kyfKLP7KzljdIBEyqKGAo1a84sFSYAnnuRb1OSha6y9fmMJaAO4Vi55pud6RwYyWmo1K3mEvFxxc2zOecX3r8H86Wfdni5zWN/k+XG2m98FbOvfRfFf/tv8LVbywyilTq/x3TErO76HQ+dXLvFIKADewPjRsjp3xEWZMO2gEEP5rk7vD4rm95zIyrAvv6HwHgM02rR06QQA48/EA76CRtGq5ss4eqi9Bx0uSjlupuldW6M3jlzld4lN2etTgzU3gPWd4kLajap9gQKZZyTB24Vl1cOfzoFggbvzYJDoImLsJUaN0JrFcjloJxPaXaWVIl/qima0u3KdV/NKfQw6nkjqWGP054AIIq8CCoseEm+isBMwPuTZWw2Lm8Soti8Cqy8zaCufjFaSusjm/usCnCvbdq7QCLWCLOJn79bb7M6i4tMWgZdn2is7dAyYjbhGlRGVmPZv4/2OVJh5YyHwlYJ5T1aQAr2C6QSQgmehRMXiREXmjxQBGZCX2DL/gXQaPMzTMcej68IrW8ilNNynZ9BWUSqWK7UvbDudJ+Qz+ZVYCkGuucwuzeEmRL4sX8He1x37Y6DEk25ziqwmOCpmQmabavtRhTBlKTBnaZAmD2tZRAY0I4HrAw1luT++XzDAKYUehaNzYEcQBDA5rnTI5i4CAwmrDjPT4DVTQb4nduSaA64L0NWiHbYZ1+if8HmalKBLSZci41lHo7yvQzgrTSSChPAuaAV5ye8fytXgLtvwh7uw9x5lQfmJ/Dc/yxsmV/+iB//j8/4/X8I4B9+0us+9QjDp93olAerGLjyrZOy50ZX6mxSrW6zGaabLwgdhohRj5ns0RM3Q9I+fsAM+PgAdjCAuf48338uEIqakDWXYF6gET9ZC5IhyIAJO+gyMN64hdgYvk+aep+LvfvE7mcyzCCpeqoiwJuczdmojUsMqtLINbUWKZz33mU2u3OTVYkcXvbiiM/58F1gPofZ2KBwZmMXOD2E/fY3gE4HptmCHY04X1apVzOhvg0HxK0rNVEFS7DonjLo6MixRtsHUzXIEqm+0V5Fq8MFe/dNVj2dTUI8qQRz3XBBACD3z28ss2E86nnxjX4GQ/tfznLteTuAyZBN3skQKECgjiGQp5zYI9RVOx7ABJHjRDszLOVlZxlQCP0hNJ8B6ZSbMqkCr3zJM2xUWCXWyHYiQUbFZ+M+qwc1+zKGh9flsegMCr5Rr2taf1aIKahRibkqg7VvUij5Ck61FLUG8MHbPlBuX3t67UX0L7Kq9AVIS9VDQHjUthDLQHmp8BSr1iHv0si3X/0dmB/7aQ6qsbmnhKoRHuANwqYj3gP1YtLqYdz3wy/aHX5OHZmpfiyWa8OZpsnhabunVH8ur8BRoJsd57Zp+xesntRHSV9Lfy+SKm0yZN9p0RZacXJVqGqTXP9tMiRsV6oQW+93fZ9Aq3UdlnL0kPdbem5WrJLNxjUhhVgPKaXyubM59+Rk4bAWjN6EBdikxqo0ioDWEsy159gfUTvoZzw+HQpVgDffWpahSsNT+9r5jDSzXKiFa1swV29zwwnuaPOMF1/xVBnu4VSP3/0jqs4uLyl7T1OYep0bRXm03/kjSsq1EtCufxA+rZ6Mizw1C0V6N3/uiwx6laavFpoiP1Z4R6xu7fZ1LgSVkx8/4WLPM9/gqtS5+XZvkgWg3PVag9ehmAjbgA1ZOxrBrG6Io+JjYG0NZn2bi2F1kwFc1aB5Bjy+D3uwD7N7Ddi9CXt2yIEfIpyyOnCkVGGjB/CDOESqDRPAPvkQZvM6sfJsxveRspVDukNisqqcrDb5u0LD5FxbgWvSufe9VxvUqpiySdluWqtORYi4SPfJWstnczYHUgsUC2SWaNAC4Nw0AR78OrVJBW95RnOpIPSmYKUK535GRQ7smE8oYhl2uTbbIljR62tzBtDhpetr4Gif16LW5HP6XWD3FhOSPOPm1/J82PVZrfZtFKO1OTCdEaZKKlwPxwcMeIrDFxMGHBnNiMcfAtdfEBYHOfxGYZ50/rQ4TrUBDkoQMV29DfzsXxJMeAJnUhYYsTNOCDHEJcHfz33Vrfdk3Bf/qCVeR+WZ63oSSiCABdrx3PdDpmPYi1NCaNduMdl78B7syrpvfN97l59rdRMoREwyLs+5nlNWhaZQ8kN0xn0G39FAfILEhlhG++lQaoq8liXDr8C2loHg3B2E9vzAHyajAe9lpcmDvdnh313jlL71rhpRGqUOCZrPgPkUprkMOx7BpsKM0oOuVAFqqfQtRO/xjMenI7jraXxxKoMzRDKsXPGGUOKGl8yQ1ZWvVJbMKILJQ9jmCn/ndB+oSYBQ9eXOdZhrtyhxHghWv7mz4AEe0jdZ3RSPnrAZtrkDdDYYLMZ9L2aaTXgQafY6HZOiWSguZGvGl8VhASgujArTclobsQd73NDSDLVZys24sc3PUW8yA4giBqGkDLOyBntyBNMkn9e+/U3YR/dhfvxnfNOsLaPUlK5ZSmDPSKNzqk3p7tuxNIh0Q6lZWXvVN3R0YQahC6w2TYH9+4SMRgO6dJarQCClZyYNyd6Z2MVKg2484IZxpmgyxCFNnReMSao8YAZdWhtMhqx64hKZDmnqvWeK5QUhVuh58tbCHj/ic5bWBXoTrNpaIJ97cVQ6Z59hZZMy+Cf3efiKT78RR0w77BN77Z0BsymhICPj2M7GwOEHdMb8wk9Jj6BGhXKWco3OpiL5Z0PQqPXz2hUvodd1ppL9UkWwWZHKhz2aty0KvBa9aK7ecsHDrFwhRDCUtRiEnIClcJEyndRWW5vM0zEz7A+/483QcgtESyI6mgLpCWxNej06HMPmwJN7JDZ8/ku8j3kGm0pjVOFBVX/KmrKLsItOXWss8zo+uU8cejwiiSKWPseoB/vgPjPo3ef9AdXv+jU2ncFiwrWigTyp+alMZ0eMJ5Um+0ZjUm1Nk8ZoqvxlIpP5xC0uiedUCVaauXY0ALKUh4MeYjpZLqn6HlAQMmHRCmg69qZpBRmjOeoLDVsq38ay14lo1fQxj09HcLdgZnh+SspWZ12oYSsAZmwuKr6kbpCTsS9llc8O8IaPxCt9NoUj/ms3vLMBJGJilM2BPPLUM7FfNe1V2Id3mRHXxN9Dm4n5HPb+e0+rQyXzW3TUQ1LmZ1EsMoh448pVlz3YuMjPUWkAu6L2O3jAny+tc/Ht3ASu3vYzTC0nCdk4IVe+ucTsaTQAhn3CTHpQAtx89aYLchj02UBLEh5c5TrZLOp/YXOYIGbzLQydNaq7xgonaKZXb7MUX1qDc+tTQZpSDxVKS8pUbq5skGHTz3lIp74Ut6kEv/mMTaOAQhK7tEbrAhWPRQVPTVOVsEILag2cC/zhhiEM6Ai4vOGrMYF6nMS8WAa2mjwIxcrXvv6HnGK/fdNxi42OwQP4/D4FcqbSlCy5QAUyAPvb/xTmx7/M+7W8AXv3DeLRup6H4jNTTPhdRz1gmvOaqLZh0PVqUWPIgCmW+HPF6QH+fp09BJwdAivbHFyu9FStWspiH5umXCtLqwsqZzlEZDg45hPg1ud4Xw4ee8uHs2Pg+m3xuBE44uJMWFEFYGMXwY3PwGolMR3TDlqx88Vmv7NJnjtrAiusFKPwy84tN+hacXQ7n3ANf+krDtahAdgqZ6j2zljRFxPAxvSDLyZMxNI5YcfZhGuimMC+9Q3Y3efhBsFos3o2IRyqlUu5ziCbpQ764uxc4ZmU615BDDh+vf5nu6d+0I5qJIQ6a7tTXtOqQLHjKSHoQgxz9QUy3/LMVy0f8/iUBPecQScpi8f0nItWT+fepccgQyPjxibMrqKIFzzLnXQdF2cef9MMMyxw8R4+5oJuND22HIaER6Q8tcMeVaRJmVicYoCFmFWB0isXPTrSOU/+etuJjFy5HZeEw8qGrI1LMBB16+qm88E2pQpZDkrpzOFx2SyDncowhvnUNxSHfXLfxcbXNbkAfw3UfVDl1lkGs7VDLDRNGWBFaAQjXOxEPut07Bt6WQqcHXrLYvV6104/8LTsv5hw058f84AplNjpr7V4YAz7zEJbq4LvLrgILq1D7ZLt5TGvsZa4KqGPIphCXWijAw5GUDZOIWamo424PAMQAiFoy1xMuHnn4lQZFoD1q7xPgQFWd1gpHDyA2d6BufaC5yinM2ZU996kCjSpshmrQ1jihLCJBGXzs/+qa4LaB+9wreu1Gg2AzpZnXS2tMdMu1xlsHr/PA7FY8mptdRZc2RQKZtE3CotFr1wuJcDDd2Wu6kQOs4BQ03wGWyxzLS2LBcewL7TbntdtDAUaUtpfvcn/ylXCQjr398qE6ynpPh3Y8oxMj3e/L/BqS5rm8pmMQLHKmtL7Lw+jUn0NFfMZIRWAYqW4CBRDx2JzB5Qc/G7u7uEjp2ux1RqDc1Jl70ob7SYg5Kv7sVD1DWpt5oNBXCmXpn/BjFxpnvrvymbSyqsQe1jRGLHwqPuqDGDfSCtZnQQ1E3FUo81EY/8um//rVzly8xmPT0dwDyN6evcvmEVciMtimtKudjbzvPBQmk2b19ksU18Sh6UNYA+fMDvurPl/D0O4iUSrm9zEenIXiryYrQ5/t3fOi9pZY2mojygmU2d5RYyHJItSrw9VXarJmDxMWICdjXmA1FtAkQGeXGBuSlNfYomXzilBno7hvE8yYaFodaAq1mHfD87oXfjgAPjxfQA3d6VGIc98BtNqM8iKCZmV6sW6ID73smd16KzErDCkBNUmJ6dNFR2uaLWp3RQbWbWxHfSBDv1O3PCP5Q02E/VA0KDdO6dtQVQACgV6AqmoQxuLs4nrOaAQ834vUgQXsHZrLSEQ6QO4UYKB3CNtsGpfQTzJTbNDSGo8lCQidUNA7LvfgSqi7XQMk2f8s5gAy5uEBbOUzKWowKSltQp8+JYnBsQl/nm6z6AchORmu2b7jAnDbEIL2hu3uRe06Tbqe4hLeeLVZQb2+YxrYCL0Yh28LR5Ibo5vOoObKlVMGHgLRcIUrY7zBbIXR9Jjanq4aG3D7zvthaxu+yB9dgC7tM7r/fxnfLPTGJHhV6EzW41I7K0q0zWhAcRnCHydPKMY7eIEODmA3bzK3y2WSTEEPNymQRVgQNf+V1iQGcSEQoz2HrI594pk6mqOx31iWAGcHdOOQ/xqUBXP9maHVV2eirdM3dO784yJ2IJNg1nb9YmYJAVmeUPskHOYSplBXskkq9uEhOS62fMjv44/5vHpCO7Wel6yuibqYIEoYpadpj5riovOsc1xVfW0P3riIYRSGW4orSoilyX4NZeZZU9GQPdA+L+Jx/+0G16IWcpLOWiPD4iDH+6xlFXhEmZPc9yz0EsgZhM2S0vSHCtXvWDKGFKq1LNaFXCKbV9Ib0HtBdRIajpmUFfPkTCE/ePfJxNo57rfdGVp2AmmblZ3WDmon7sJgGzG106F5hiKtLpcozJSONtGWT3aDE5nHpooJsQZ60vEKqMItnsphmQp70nv3IuOigkDfJYB5ZivPZByc3lDoDJhNpwewvYvYTqbQFH80dVgrhADqfH3WBWz1kKnBnHzrbBBJ2Pu7HTMTL8oysLjx8xEt68xM6o2Pe788D0Gus4WIZiTPdg//gNgexvB8y9TYq7BSpuT5RpMngPPveLVj8Uy8PzL3ksom3u/fhmt5uT+kGAWxbBJxRuuddY8Jq6Wv4Ou7xXpQ2nDK+tiUzxnQBUar4kbHq5QfB/w1W5S8YI3Yyg80z5Oj3NYOXauzObwdOx8c9BoMzPevE54RQeMmECM2wKu6VSC6WhAi4jjx/wujTZQaLvPYy0TGqMeSNUm0FqBfe+7/P1Wh9WBevAAUvVNCYV+709YMeze8uymYgJY4w3WRPCng1qMCTiiUz34FXKdTamEl2lZUO1AGDM4D3vswzhaZuT7dApXaY+vf8HKABCnSoHnkooXSgH8fsoGHHT5Oh+8DdtaemZY/fQE93TO0zvLiK82ZlJiJx5SUapkg1xnHXlGSEBpYBFMWaTxysvW7PNonxujXOUmGfSA3iWVnavrwJVrhC1qDTJNynVCNOMBmyStDsyLnxMceeqym6d8rAHeVFUKatOn1mBjdGnFmY5Z9Qnpn7GsBLxBVxRLUzXkARSGDADOjU8biKnLPM0rr/kMQYK+WTigcPiQ2fDaFY9Xdk89dJVlLMPDxPuaKMQDMPimMxGobLASWsQMxX3QLG84DFl9Z2yewbQ6ZFPowa0bUbI501phoNUBEuUaMX/B7W33lJsripgZqahEg3yW8XqEBR+kLBx2btTx0QqENx3D6PWrtWDWrzFj/v7XYG+8wM9jjZeOtzpemLK7C7N7QxqypI2aWgv24lgMtaZ+LCIgGWCRfRRVN2vmNRoQg085jMbKyDbMY37e3Ao1WN6jfyE9JIEz4oRrRD9bXKJT5yJjJZ94XxzAq0iVUhxFzqMc1sJsXnd+MYgKXCfnp8C1OwzqhZgVzWTI+Z86nzWdy3hIztuleZq4VF6eyQEcu8PNBCFsIfYDYQqxuIA2/BrJqfy0UYFZfGClst7wLDZxYDQC8djzI2dnbfceszd1+JAV0OaOrF9pXKsbbZ5xqlNnk2tuMoRNqq6qRq0FdC9g33mTFVSlKqP2JJO3Qh8WEz3TWPb0W34ZMpl6Zx6uUV/6sMD1ckL1vVXWX73Ne5ZKA1rZMzJn4FmPT0dwD5ip2v4FTKniO8UAg8l8wlJZhy7MZ5RpX5zCFkswO89LiWnJ9a63fADRUq3fhT0+5M2sVmn9q8OIazWgsyp0rxkwHHBxpzNPwRMnRLsktKRrDMKYyQJRs7GheHGoEKhS9arbpMyyrlThjVeL1WqLUuLJmFOFdm/CTZZKqh5LbK+y+ZNUeejY3Dcr+5fSzGSWRhgj9Bau3VPPoR50YQtjr5qU0tOePmBmPxl6BoV2+2cTBv5Fr+2k4rm2JcksVS8AEMuutRw9Tg25XLVVboiyUP6eZdxUYg1sBJ80AGwYskEoQ4Ftsy2CppyBXul5eebhFnnYdEYITxk0JoSbrCUPU9GDuArc/pwf76f+O+KlY4KQGPmPL5GnP7x0ylj7w7M7p2Ng/wGhkTuv8nfHQwlsWqGB2LlWcnGRMI1WcoIxc5qSQGfpjL2Ce+/zft64I+rQMe+bSvzVdjYTbnpYgM27VFSe7Hn6pA6GGPSAIGDGrYlVlvmKdG3LM3KUZaY0QIAB79otSUSkyTwZAUnA9Vius6GZiO1FnjFgKRNLqaLLK3C+OG7yVOhUwmTwxN5+GSB0ks05DCUXib4xjBnttocpN6XXNB179pH+LkC4BJJk7b7g+zYBexX2ne9yelscM/kZDwR2vHTGZKbaIOe90hTtQQCdowzAxTBt7NtT0Wz0L6SBO+YQkcBwcEg647pTu19VOv/LWv7+hTzyjLzVx/fpTPf8y1xEDz9gUBwNiXFv7vgseTyU6fET+pFX6t4DpbHM/9cBD+UqoZxbLzLjrpDWhmN2ts3KOkvBYxkksbIOHfWFWoEe3NJwMXGRB09cAt57gwtyY1s4xmVmJ6fH/mRe3eRn0KbmfMbvutzlAOKwwNdb3gDefwPp7/8LRH9DlKZnh8Q41d4zz5gN6ZT7QZdYu2Kw2hTTaiKKmCnmKT2uB11fxQx7ZKPkDKg2nVFdqN7cyohJwQVVKDKodU89XALAVgnLmFyGGgDCuOF9sLMpWQTTMYNxNic/enVbmpqWQySUUiYTfUy5/rRAZjSgIGU85MGZZ5wpai1l2f0LlusOT5fnRgX3Pm6t5RbO3CyIPC6qgpp3vgP70ms8EIKIB/ECXAJAMr10YSB6CfZsn1nfsC+/nwPbN4HhJSsQBDDVJoOeTtmq1Am9KbtKg2qh6BliCr2pTYc2+ASnJy4/9Y1sURC7hv7hEVkulSowoN8PKnVgfuoVpnrQVRsLw7+rTIDuvwfbvYC5eQeoRp7lkmU8eMpVrnOFIqKI91rdR9O59Ho6XAvnR56tVBJWjqq3AeDKDX9A/dC9dH2h3LpYYITKC2XRaD8hCIFCEeaVL/L5tZbg9gLnLtI/9aHXYTFw6vuXKjCvfcVXTsOeg/5c5a7JTkU0GnkOoEDI6dFd9hKlae6vgRivnZ/wdZdWgGabA4EKMu9B2VylCpOs8ejpz/0Rj09JcM/9n7OZ92RvtPmFTo+8JHdlk0EvKpBNE4Rs1p2fMvhrOadqR8XLkgo3QVIRjDHl8wfq5y2vl1QYNKSKQDGBsVWeuiYASmvSCBtRWBHHvgGkTchE/LJT8atRvHw24cJ9dJcKWXWZVIOvPEf0V/4KmUDHB2wmnhLKAeBFVVHsg8PhHnFi9ckBWBYWLANspQzYyIlVTGPZN2alG2+iyGXMVrxCTFwEMvjGlM1cxqUwC+YzZmTplEE8CJ3bHgC+Vp7BjvrMXlSQk1R8c2gycoHTjgcscTVrVnqmMfyMkyFL8XNCBLaxD3P1BWb8J3t87+4pYQDADRkBIA15A2TWv9fjuzDXJOvVLDCKgPUt2K/+Nn1QGkvi+tjySkEAJkwcN9kZojU7hHraq/6epnNYtUwOQtjTh6wKB13fwC1LdSflN32F5t6dc8CMzpRJ/bXqlLm0wvt+esjAHRcBIwdkYLxrJcCG5t59HghnB0x+kgqD8NkB73O55gd0mIANWwC48QKMeJ5QTTl23jwoxJ762rvwh9SgCzcVKp15HUUYetV2ucqkQ9diHJMGraMF1Q5EKauB4WG8OB/BxRBZd0mF8ODxY+47sWE29TYTgfe+x1m7Wzd9X0tNwT7qoVWJQMfq5GpqLdiTfcHHc9gP3yKmP585ozx79JD3ZmXT05ploIzZuOaZMmrOdn7C16sRjtE+jtF5qqMBcPIEOh0Nqkv5mMenI7jDAo02zIvSUFKpeXOJGW9njYthZZMQSZVGPa5Jlw+ZbReKvsy6PIV9/23eiPVtZhbK144iYJYCG9dYxqotcLVBjrs0TqziqWeHgiW2nm7UffY1Yt9D2QSjgdwYoVkKw8dEMafEz6ZcmNvXGJRLZQY5zIE3vwF7dAizugYr3jem3mQjaP8x0F6GfXSP2VNSJvvDWkJQiyPQAmEFKe9bs83pyJe4wFNZhj14wIC5tAKzfUMyW5+lmmJCfH46hqk1iW0u/Lsdj8RJMickVEwIxdhcymcqV00Qkumg8y51VJzCKDZno9daOFtXAM7e9vyY66K1xP7FWIyr1HdFFK8mimFnU9jLE240wItONEAUSizPHTxSELMoA3P7Ndjt52Df/Dq9/4MAZmUdttGC2b7J4BMsBAYNEgIhYdhzwcKOep6TPx1xDUUF3rNSInNu+zywhe1FdkXC65ZlT1sTqJuhGmNpA1OZF8b64DyfMghpA3z7Gj+n4vOForCOZqwcwxC4GHka44Q6BlNtEhZTdlZcFI+brpAQUo+1ZxkJEc0lBrZWh99lPqPJlzG0Pag3WbUKDRelCnD7FR5iQQh7seeNzQAGuSiWhn7mtCvGGJ80IGVV8OBdoLXMWAFJMkoV4OAeufmddV8JaVDPM89OW9wjANSqG+mc93bYf5rN1pOhJrOJgwiNtZw38I2vAdUqgr/6N3ntNCGYTYCGVPICjWLjisBduRdxFmJen3IDph1419RC7Gc8f8zj0xHc1UM5LjIAajnaWAZC4ewCDLKlCocjPPzATyHSSSWFIjC4JBbauySXu7PmG6lKs1KPc23WiL+KiUsM7Po74yHLpe6ls1m1zq0v9AGwIWPoOlKaNqXxlntc15SYJdnuKbm35TrLSZtziMLxEczaBqdEiSIVZ8eEecRy2FSqhKcCaab1LhnoJmPvgFhMYAMDDC7ZPE1l8ZzsAZUGm9BR5DJ0fPAm7N5DVkx7D3nAtTpAKpa/6Zye7oMubFz0o+d0AEKaMkOWgGwEDzSdLVY34jBohEligtBvNj2gyyD618kAACAASURBVAJHRLFQARuekqmPMGSg2LvPe5qUCRPZnAdBociDVMehFRNXmVipchwDphB7+19ppHGTa/bGAR3miz8P+/kZ7B/+lu/HRBEPdOcPY3yAmI4dlqwWxupaSFiqJthwxqAp3io2mwP336PVbmOZ2RnAw/f4CczVO76qGolNh/qVa7BQdW6DBnSoNuW7n/mZqhGYyU5Fidvq8Dq3OmQ5qS202vaGIYdMbAReQKW4cFzk75UqQDjxFYwyb9JUBEJj4uglVjpWRUGtVa5VgfKM/tugy95Fmnqlq8whtqrElUEkOL+g/bODr8RoTFkxhZjffzLin8M+ewIfZbhlAmA69ImlQJhOCap9Cf1/E/D7qde8wqKZeiVJQrK6ChwdeY8ahXuKCd8jirgW4yJ99rW57Sx+F7x2hAFokhrswjjIjw2rz/zXv7CHIWSiMvRCTGOkW5/hglLKj85MfPgB7Pe/A/PqazCrV7h55xNngO/c0rTs3ChwsWpTRWXPBWHTlGu+uRYV2HEHYMdHhEd0IR8fyIJOfAlfb3NRyRBcbQSbIAR0tJji1yYAKnUOjyjXiRULrc3cetHjxasi46/WGUg0Q1pakffJWdHM5wIrCfXPnMIKz9jUWm4QM2zOQDgZ+oBwfgQc7cNenDnutNm9Drz1Ldhrz5HffHEi5kkjVwbbbA6cHxAPVFdGzXY0AwEYKNLUNzvnM8evNqUKX6ciGHyW8WBV+2V9aFNSLJtNa5VZv2oD1F9fNp0plcXQjQHENDuEUwDKyfPMlfqmEPOz/JAmQR86HBtJDcFP/xJ/Zi1MECB/9B6l5Wp/KwebwkoIQq80nU1o/iQHqhpSYTpmFqqH5M2X+Kc6OFbqfL1qg5YB6sRZTDz9tr/PdfHkCNgBG+5ycJqkytefTbgWshTOY73VEVgvIAS6tgM3/LuU+HI/kVmel6f83Uf3CH0WIiYe5SoFdNUaXUP7F86v3jSW/WhB6YeZuMi9KtbcAARiyWGl6ehov9UG1994xJ9L5o+DB1SvAzzko4jJACAH9YLyVWmbwwGx7icPgZsvsErRrF0/h1wriO+Uo2/mmTBaJHNPyVqifUbf+UBp8932zlm5jEfA5i7M5hW6q84mcj/FuA0lXp9qk2spLJB+KYplWjx4WElhJ9NcEWKCfboH9BGPT0dwz1KeRlkG1JYocy4lsG9+C+alz3MCuFL8JkPY1hLMv/ZvkH4UhOxOd+cs3aTxaQHYt78H8/CuH4JdrsLZAvcu2KhVfxgpeTm4QRbIk4ewp4QCzPZVHjKjIUVW1RpQWjCtyvOFZk8Gi9Q33HSoQs5ZpnaxgTeSIKL8e22u9S7ZXJ7PgGlMV7wgBLZ3ffY+6fE14iJnql65zs1SiJ825VIMPYzEf/2x50EHAcyVXdoFl+vAxRnsG98GXlORxxSogJvt0fscmn16AmxdAa7fEf8MoeRpFi0iESuCJQPwgMjmdLrcucl7pmMNowIDO+AzbhXlLJbJ0gTVe8zrnpFNIFQ+Z+0QxbCTEXnLOnOzseyHklyeAkEAe0G2g8kz2KjI4AIIW0nuZZ7BRAUYnWI/GwNowdlOpJTTm3qbuLKU8643MRkyuIlYxirfXLUFvQGVroUS7OPvwj5+AHzlL8EN6rj3LtdCXPIKxt4FYRBlleiQdoHGbBjy9fKMh0OeeZhDB4z0ZMCGMmymMixCjfNqLWF9DRlg17c8G0iV3UsrTHqKR/w+fTqx2sGFv4aFEtXe85mvIgCu0SD0wTupevuB0YB7k7/ILHnc597b2BaMPmE1pvTAXOjQpgA7FajoYI/JUnMFePHzrEzDAgOoVm3iumlVUSyMPcQlYSf52cOAPOdkjwFck5LhJROeYgJ7733g8BBmbYv7+soNstls7llonTqrWxEpktkT81BUqCxpCK12AkQ1OK8qZc/ZZ4fvT0dwDyNvppVOnVObuXKNRjulMoPDo/eZnbRW/fCH+YwXt9L0gb13xvJmMoGdTmEqVdhBn1OK2svEqQGgISWV2A1rCWZKFdizI9hLqsNMW/xb2itA24oRVNmXSknNZwrpfAHLnDCTTKpssgnUYCpN0pyUpaLK26Tiy+ytXX5GHUaiJdr7b8P2ejCdFf79lR/jwknF1z6dEbJqL3NTtFcc399OhsCTBzwMJgK53P4MDbjGA75ee5nVwL13eZip6VeBnHJ7fMSD7PQYZusqbCEW0ythbmhGsYjJz6bA3ofA6hbhpr17sGtbzGKyOTM61SyoV32euck5zK4NME89u0EwZvVNt6lwtqcjqC2EUVZJEPrmqgRsU18idW5+zg2qjI1FgZce8kEIG9SIyRsDs/uCa5ZC5xCokllcI7lBp25Ckh33fVN94dB1Dc/5FNbmMC++BvPia6T8DYUm+NxnpNcjTdqzI2ahrWUGunbHs2TUOGzYg50cShA/E4l/6CGo2URodZFYyGZ+OE6ewRnPqbVvqcyqUSnGTRG5haEoYccM7LU6cPcd4NpzxNrF49wpuIc9EhZ0ihkA520+6nnGSFJhI1J9c4oJ993NF33z1An/LLNmhXjioowTLEj1MXfQDtIFu+72Ol0day1/wAH8mVuLfI6ptaRhHXCtmIC9sCvP8fNqRl2IYV56Fah9wGsu3k328tT3d+KiF7Zlc1pniFOsUdZflnnjMWO49z58i9+9vertJZ7xCJ75r39RD23umYCBr1xl0Fq7wo2lApm1HZh6m0FAT2ttMs1Z9tjjx8CH78Du78NcuwnzhZ+g1D4uwiqrZjJ2/htGSmtrrR+am80Z9Ne3aLDVaHORTEYsm48PmOUqZj+fCBe54vm4gNDUKhQudE8dPdLO5SAJC1Ki5uwnKFVRm1fvv8XP2lriDV7bgh2xTLXjEX3FH90lDt1aEvgng33yCPaDHxBblYkxVumFN15ittXuAM+95OySTbnOANVsw7SWYO/dZQDprMO+9R1WU0urQJVCM7O67jx7OBme+LhVP3ANfhPxaomLwP5DZoGVGlBpMJsTFz1TrkvmdAkdM4hC7AO04Lx2PoE9fAB7fiQ4ak8aTlWn0jRJlRnPIp1NKwst2SXgmHqbA8F1GLGMULSXJ7CjARuzAtHYLGN1mdFl057tk/YI8HM4YzUp9wtF6VkIbHD4iBCgjsRTnrVO3hL7BptnsOeH/HmcAEePYUqEOnioMWmxxwe+mQwQqptRzekgTp2Xq38HgDe/yfeLS6RInhyJT/lMdBsz3+iLiw7mM50t/l1Nx8YD7gn1mxfFqT14wmTAWi+RV4pnucYgVms5HQN1ApeONEGvfIFQay1m26WKSPapDDXVhtgXGxckOdxmTnqttfz3nef8PdH1EIScxlRM+J1655622Tsn2SHLPIuoUocO54FYY5tqg5WA9ndsDvv7vw37m7/G57z4eYff2/6FWIBUWQFXmqxqZXg7TEAIT6dNmUDETudsEIuQEoMecHkh/bYLkgqe8fh0ZO4W3HDjATvp6ilz+MjdLLSW/NRwkUSj2qRJ/+U5L5xI/O29u6R8qRjovTeAnRswswlwsAf7/ttsTq5uMLgJZcwK0wImoB/MygbMaOgx9vmMDADdPE4ZOPTQR1wESmIpqsMHjPGNFuUyA7BmxrmW1oqbXsTDJghhlealGGutQUponsNcvQFntpbnLPscO8bC7F6HPdp3LBo7GTKz1spIm2OaRY6H9NouxBzYvGkpohBWgdnapa3DjRdhbtyGvfsOvYCiyLkM2snQqYFtLtRHVWcePyFGWyyRkbNFZaeVQ9CUq2w8KvY9GTKTrzZILdTAERWIZVdbDn5yatx8wU9EMztjAASkvdrANSHtguoWAEye+l6JmGqpvwxCaQAPez5THPYoAhKLBFuuCo66wX+vtbiORJhnJ/LZWh2KnaYjfl7N5uOYazjLnFcJv+OIB0lz2TexraW51fkJ2S9HT2jPMRzw+jZEvBY0vBLW2RvUGPyvXOPvr2yJkdfMWWljMmL2XK56VkdSZaM7KjABUC/xuMRD71t/DPPCy1TFTscwP/0LDMLZ3FMI0zlMNqfXTnvVO6ZOhsDckgkXhG5NODw5t8xgU7ES0JCh9x8gM0UnYanATl+jEMss0xKzd2UCSYVsxwMPxQEMvo1l7pNaywuHFAqUhM6qD37vjPqQ/gUwHPIeTidAVdbi4AK4+wPY4QDmJ36ecWVwyftarj89/7XZ4cF6fgQ34KaxLLCj0ERlsDrGI0fy+LjHJwZ3Y8w2gF8DsAYgB/Cr1tr/1hjTBvAbAHbBUXt/zVp7Ic/5+wD+fZAp/R9aa3/nmW8SiO/20Z7/+3TMBTkeAWWxyxwKrUwHd6gl6fEBM/KVdaDfg9ncApZXiIHmGRfudMxgKI09JGWYq7fhRD2LOG5gyJrpnjPLVR7yaAB7fEhvGcCLhxYH5wLek1rL0UAWaiEWpVnJwSBsqkXA1nVu/DBkJllMYFe3CLFkGT9LS7ycHz+A+ZEv+TFsiusqfLG6CdPueOxaZps6BoKyEBYsYJXqSP70GswrX2CA00EQ125xo+08T5qbCXj9H34IXLslTbsM9vf+GcxP/iu+239+Arv3AObWSyy1lWcPsFqwOWGUPPc2E6UKcEGGAS7PYFe36R3y6H0KQKwlzDWfseooJrCXx8IvXzhUtdlaiJklhYRq1HoAkKZsQDsDC/iqQy2iC/x99j5G7CGEYtalswEE6rDihGmW1rh+eucyc4AUVZOI7fPlGa+ho2FGrOBOD7iZu9LAVGdL5ZPLfTbr14DWKiuXhXXnPL9nU8fYQe/cB7jpmElQFPN3jGEAMwaLJnYIQ1aryicfdrlGnnxI8zM3p6AP1JuYfestFG88rwGDfaN6Czoq0BgDOxnxe2mWr9YU1Qav1XTMjL61Cru+ywNZgrkFmKmXKkyedIqSUnk1EOq6DkPXRMegy++uB36eEUKZjhnA45JzBkW5SuRABqfz87V4TWX+KgJhmmmSGRZ843h5mdqURBg3qVzjzhrMi1tcr068Rt94mAA2ZWJkGjXvRxWyqWwqdWb+hRLM2hXYksBct14GPnjzmWH1z5K5pwD+Y2vtd4wxNQDfNsb8LoB/F8D/ba39R8aYvwfg7wH4u8aYOwD+OoAXwCHZv2eMee6Zs1R1cEK747nqswlJ/6eHXHijgQSULoM74BWPK+sM4LUWN7/M3LSn+8wOOusMGodP2EC89SJvuPqiKJVtPuPFTWdi2zsjthgX+TlOjyglby/7RogyRooJ30/lx6OeN/fKyMs1QmG0s6kMpZCmoTbOpDnplJ5RkbNPNZtZXpWOeZufX8QzyDIO4I4ifv8880IQ/R0A6o1tc0uM0TWwJoCpO465Hfa8wlAnxIi4xSrkoBVFUhY2i2EQ/NLPMNCnKfD4+8Tmd657EykZ0mBqLTiZvj4CwWXnU5i1XWbHwx6bV2u7DIiTIbUIKjIb9WHLNY+bqxxdxwHmGemsh3uscBIDO88c1m3FDdC0VsXJL+SKB7zYC4B6FRmAwaiYMIBV6t7WQCo7e+9d+pxrxnj0mO+1tC7NMVHCjgde5JNnwPYN1+gzSY1rSJ0Fiwns9/4Iw3/8G6j8p/8BJ2DVl7jmwtDbWqtrph7ewiBzWexkDKR9slt0gIvCFoBXq2pjv1z3gqRyzQfExjIPjrCA4t/521zDQQj7+78N8+qPcq0tb/Dn2rC2OczVZWb/YsCmmbBprVIDAfBzDkZMaOSAtnnmldNhCOTGV8cCARr1TA8LTkDnPIcAPzpwPuM972z5qrp3zj5MwOrW2KL4SsW8x5oIFeD2MwDup1EPproC/NQvwI36jLxjo7l6h9l+75zfCeBeEh6+00RMOavVxkUOvDncg33+ZSZ9QiQwtZavPFXc+DGPTwzu1toDAAfy/31jzDsANgH8ZXBwNgD8zwD+HwB/V37+T6y1UwD3jTF3AbwG4GvPeBORLceOAaOZhe0ELOsBQhNVKY2rTWZz05wLqISFbKvo7X3TcxHKhMDVm54VoNL3SQ9P+bLPZ8waRgOWy4VYSuM2q4iLc+D8FObOrmu4udNYZd0qawZk7N7U8d8xn7G5eO0Ob+RswtJNXSiNeFnoIlZq4JUb/LOYEEYZdL1vBeBNm+KSMDomQtWjIMkGhiXhyR59sBvLDB4qgz58CGsCmLUrhIrODiUYDH0jULHHD9/1giDxdrdn+9QTXH+BwpT77wHTKcwXvgyzcoW/0z115aazx81TtyGdrF4CvllaJ7f5gn4ypk2PdKqFDYc2iOUueueujHVluQkorjWBHIYFN1RD529SoCX3Pgod397ZXGhTVV7TjgZP8+gVL1XanGouzo/5nt37/L0l0vYoVko5V6BSZVasA1VUQwGwatFZrGkKe3EMe3zAg+nk0B1oKJV5fQY9N3jCOSuqhcC52GGsbAJ2xGQnEKy71uKBc7QPrG/7KuD8hJ+/3SHEuUi9U7Xz2i6z4zxymLX5mV8kPbBc92ywcpWNRRMQkuhfyPWO/fdVyGw29YZ0jbaHJ1Rxrkwz+VOvjxtiHjCZUfsKWxAdxlRG4o2EmVSuc8+Kv4+LO4rZpzTts2OZqauVkM25P6SHZsfiKaUHyTk9ojDsMznV/VhMeC9k/ZDtNvQwkYwXtf0L2G/+IfJ33kNQLsGUSrwH0otBoSTWzk0mTM94/H/C3I0xuwBeAfA6gFUJ/LDWHhhj9BjZBPD1haftyc9++LV+BcCvAMCVTkt46CGzWm2YyoxUu7zmBQpZxlLFWq8SU9tNY+AGOUcFBvXZlMIP7S4f7HGxtjq8MedHXPRpSvxsMiZ2OR5y8wUhdMKQPTkkriZdc/Lm5aSeCAVuXnpq6gyqde/3Ik6WLgCA5aZVdonCK1Hsp7zMZx6OiosAEh46mlGrFLlQ9HYL46FX3q7Ipc8tlbD9LvD2d2Bf+4qn7k1HzuPEpimg05EWMymxZDZxEVbHEeq8z6gIvP2HyN/+AczeQ26u1Q2Y177CLE2mXplEdApRgSKrdM4MXqsgha/yTL5viSW9HnTzCT+vDCTWYG0qTWBtVwYsxL55ls1dUDDXJIucDBlAxI7VDe6Yz3w/RDNr1SYATABSEfbklofEfMZAtrrjIYKLY66vNOW1VkfHoz3Yb78Oc+cl4Ppt2D/+KsxLn2X21Vn3mLr2Q46fMLgmNZhaC/lX/zns3Q9R+fkfxfz//L8Q/yc/4i2S6y3ea80Kg4CfRx0gG22vaqwKFq/WswA/36r0C/Q5Syu8B2fHvCaNpg9UapaltgBxiWtg3Gf2K69hzw49t31wyX1Qa5KFEkWsaNRSV/ewWnnYnNXLbMIYcPjAT/uSZu+i8RsTu4IXm0llxvUlepNKk9RONWjTPpoOp46lyo0imLjFoSDTMWmdi7Yb46EXB85JWrDFRNCHVWbzvTPuGROw6pBqAdWGDL8JvXJZK+vZBPZ3/inSBxRdmmgOc3kBbOyS3947o8FgFPG6KnPpYx5/5uBujKkC+N8B/EfW2p75eNOaj/oH+6d+YO2vAvhVAPj8resWqXiUZymweoXPsLkPfLoACmKiNezDzdy8OHKZGINt7rHHuMjG6+MPeGoWiw5HtZpByEKxxQTYewB7fko6k1LGTMDsaDiEWV2lmErmK1p1/iuWxLhLFK7TMeGbXOAmNQiLS3y+Do2OCi7LUVofg0qF7n2ziW/sTIbM8st1LqBCTMihVHETo1zTaf8xN1BRaI8nR8TNZVAyHr4Lu7LJgBCL+GNZpgeNh/7gUgFHnrkJOKazSThgPGIjNophr9+COeGUIPPyazC7d3wGHBWcoZQabVkVfygOOpsws9IhxvMZM+JBl/CKCkXEY5xl94DMoXRG+psGJmkuE8tdsGKw/y91bxZzS3adh32rqs48//N453t7HihOIimJViiHsh4UK4GROLYQAwGSBwdIgCAPcRAgLwLykCgPeXAQw3mwYSMQEAOeA9myJkZDiyLZbPbA7ntv3/mf/zPPVbXz8K2197kU2WTiwOgcoNHd/3TqVO299hq+wfFe9c9h3peeOBUX+bNJiQNTkwHIMrYX4hiYpaqfo4451qNOl9qWiALkdTaGcw/Y2suIuZbPfpGV5+Up5Ff+Q94X+4ymGWSCVK01rWYSHlovvU4p63IFhb/y+dBGMZcla5dV6yQddTa5RzRLlOYaZxZRxIEfENBcwz6D3uYun8fJM36/2Q5ZafeCB3kUswV4/QXf82cbTofp/XPliuTA+TO6dpUqPORrTa6tb30DeOOL7E07RxRQmvI52BwiWwaGsUJivbRznhFhpVh0j8ha0fPxqokuBxZLttTqBbjWWmjvGHtZZRowV9z6bOKlsrHUAWelDm+DmA/CrMS5oI9jVelixkNZzWfcbEJMf6kKLLpe+sD8lEXtDl2WAVUOd+NaEdJpKwOdhCopVdgWtYNwHAbMP+z1EwV3ESmAgf3vOef+gX75RER2NWvfBWCeT08AHK78+gGAZ5/4Bg7BemwxZ+9NHW9QLCsbU6nflRpdheoteIeYLANmSrM2Af3lPPQfRz2Sj5ptVgBr2/x674KaNlHMh+McMB1Dqrrgz455cys14OgJ5M3PK3miRO0aE7bavcINbFnDxh6zvCwjxMuGK5khOYSQM1UqdN0TQi+Ngq2EKDFxfxMFe/qQn6Ft/WXhIK5YZo8ZgDeFmIzgRiPI1dtAs8IN++Q+M7TNHX6+2pAb1mRbjcJeaypLss3AqsMs0593RkFvKSy1WIZcewlob3CjmpqgYXkBnW1EQEHfa5oClTj0PHtnvt/s2lvMUEwGt9GBF1eKYm1jSch2FzO4+STobi/pnSu1RjB19trmLqAqbFhqz2SZhmH3dATcfS+wJSUiVh0AErMX1EPR2luzMSGMixkz0kYnHCZmd2iiW9ORsj8vGFTHQ/5j+uClCtzpY76nc7y/+zd54ImKgpmOjs0FLENf22SAb66xao1iuJNHXAOb20xEBj1fGWBj53lYcbMN3P8+ZzyWWNl86vyY/x5oK3HQg7v5EqTW5tqdj+GO7nM9711nJZiq81alAdm5SjtD603boLNSog3hCgJEigjrp9YmzNmUEXXGIq0NtdfUVqTOdTzMEgiIqjQny7lYJi7e5MCjiNdXUPXR5QIoZeHZForw9n3WKRC9NlM8tTXlodvKaAV4aLhyqJSMIGUtQkV9CQB8/iso7B2QL7B9GBKsJAlCegAPpVWUzw95/SRoGQHwtwG875z79ZVv/SMA/xGA/17//Q9Xvv73ReTXwYHqbQBvffKbgEHG+rhHj0IvymBIag+Gw6bve/ryyzlOn03DwyQ57Xd7F8xWtB+HQpEL5fF9HhBxwr7kk4/h0pRomGpd5Wo7iuYpBD0OI2CYvKkdIpgDUYkPZDaGlxa1PrJzQByRat/agGmh0BdyTHGmPPftGZcufDbqBmY8HPPztTcCPFCZdGLtDeeA1z4PfOsPWHHsXqM70/0PQyXRaIU+oMJQMbxQDeolUOLgRozRBwSUSZqGGcNixves1CD1jkc+SBTDnT6GbF9hkDNjDYkJS4xilX+YKzZ+GYS00jmDQ7nGLOjoAZEMG3tKxprqfXVANQ60c8PHq7b3cy/T4wGCxoxI6KerdyzSBbPcYpk8ACVYAYD5inqUz3zKANxQAh20qhlcsgKwgejDu6ppMmEA7l+yz15vsG1nFoCGk4ciQMpV4rGLZQ0+ZDB6OOdH78Bt7RKSa/shz5RcpId9vUUocGddfWx1X/U16TAxsPVtJeGp9szVWxp01fimtQGpVOFKJZKUZlMmNZUqMOwxiFvAL5XY4mtmKnF8wRbr9lWuzXGPh5SiXqRNQp5T6KZ0tuFF6HR9uu4jZVQL72u2JJRwMuCBs31I7aTBJTP63IH+rHGYwVgFaT4I5sNrFW+RSChptNUYo+CHvwD4NXMgU5kFI6+JtUQlCqgzIBwGQBAec4rQ6myyx/7BtyEvf47PubMdjHAMvmwcEktmzL3qx7x+ksz9KwB+FcA7IvId/drfAIP6b4jIfwzgEYC/pBfwroj8BoD3QNzBX/9EpAwA745kr4vTYMI7HocT3TliZbMl+8JRzJ85Y2HgliX2vk0Uaj4Fcu0LG3Lk8hxy7SWKE914MagrqpC/nB6xhxnFwHRKlEWShD6syZg+ug/3/XchzRb1KpprzFbjUthYcRw+l2F+s4wbd9UoolRh38rEoBKVu81zBn7TbLlyKwSuNPUkGJeFTeADcKkKufMK3HtvQxokTuDOq4qGqAKW6cYxS9z1Pd+6cMs5+QON9WAxaFlDkgBSAXJtbc3GAKYB037+jNl9ucYS3PqayialdVyBlddCGbx5GobJAD/b0SO4W6/yPTQj9UHWAqSiRLzyo2GPowhYBpz0c1aM+vzcqAsprqjqWVvA5UCq7EDzqgW8U5AkRbjuMTfefEKxuEpjRUlxTNJStcFDbTaGe+F1oh8UKonD28DT+8Cj+8i++z1E+7uQ1z6jDkbaSukSa+4mA4jLgyBZmlKZ07gfUQxzwEKiGaaxig3hsrXLQPToPg8Uk+kFwrDSOAPDboAxqgQHqnWIEgpl7xawdUgl0dlE9VbS0AppNGBkOq+1lKYMWlEEd3EE97v/HPLlrzH6OBds6XQo6SYDNZmuhjZha4Nr0hAyEmmmrDOZbMkhZxwTmmhaRKKHvCVnSYmY+cElq9MVYhMt/VSobDlnZhwX/Bpzs4m2SKZaYcXBujAnj0WMrY4MLovD/NASQLMuHF4wcRlcov8//x20/ps2E5pGR5O9ERBNWWEpTwDlmlanOkQ296of8fpJ0DLfwA/vowPA137E7/wagF/7cX/bv6I4MD5dDqxtwt3/CHLrBT6se3chr7wWsojLU9Lqhz2e2s5R6P/KLXhXorIq1vXOA+rF5XSTKVUJzZpNAlxs9wof/uU5A43ZXV2e01B6fYuZ5Vhdca7dhuxd4cLrqelBlgX9w44dOAAAIABJREFUmlo7IEGG3WDfZ33hLAPEMajn84BXF1FRMR3K2oDY8PhlZcqaWNLT+8DOVdV+pi0dsiWzy6svAus04EDuvFuTtDeASpVZ9pgwN+dyDqpnYwaEQomZ2sWxUtNbkFgNitVNyJncQpYCjTIrp/UdtZArB5tBwKtuItUMNSqG551pX97++4PvwA0HdMvavRZMJ8zEOIpVSrYcBtES8fAAQk/SOeL+VxEvavIgzfUQDC3jsgyvoK2dapNVmB0a2ZJ8gOUC+OBtyJd/MVx3nrHqimIAmmmpcBgGl3z+L32GzyXP4JodYDpB/ObrcA8ewH3wPbJ+D69RB6W55nXkfUtLRb7cw+9THuJQyUj1ZoBUmqPRirY/1nfYGmqrt4B91iyjEuKGZspOghiaoViOnxE6nGUBlmltrmcPgZrqj+dOgQm6HlYz5qIF1AtgcAn58te8N6mvwq39AlAtE+C+MU+FSOBGBT6LLFMFTmpBiUKiSUbMdR6XkPBoFpStDR761iK11kieAXqY+HbjZMjDxBAtEtH8+vKEn6/e5L4xPZxR38NwnXO+svNQzYQDdilXWY3HBR6Qwy7QXEPrr/4S3Dd+G/LzX9fZUszPef9duIcPgN1dyMYO948BPKxK/4TXp4Sh6vzgSyoNuGt3qGVeawDvfRuYz+kEc+02F25zTWUKmuxxmkTuqM9p/CpiZmOHB0BJYWOlKtyTj/i+1TpPv2KFmWC5Atx6hQFk0OOhMJnAXV6QHHXjBWBjLcDCfDsGeqj0FTaWh+AyG4UgZAQOG4BFOvVe5gwMhnc1urJESpzIQ1thNoGpJBLqpv3EYY+aJkoZdxYQojjoq2/sqdsRB18sO+tK29bA4LPfOAyyPFlE++jDXvj8OuMw1IvY4NhgeOlcMzDtmdvAuNIIlYppnqgfpxv0ySd4eI9qlgZBKxQJ0Rx0IQc3OVhNxxzuKozWqXiYm460VC7B+8xKxGBmg3J7mZVcpcEgcfyAA+pCORCgXM5sem2bAef6Hd6/KAYyfX4rpDHMJ8EUw1pGmom6PGMGv7kDHFyHfObL4XrSlASZKAaaKwbIBpubMHBh95Brrd7gWrWZlMFw7ZAt10IQKqni4+UZ90uaqslHORxQtlbV0lJeeg1Y2+bA9Owps/i6isWtbRCCacNHiYB0HMhvVhUM+/q9FGi0VSQwhTjHQzZnpeFGfSY2yxkEZR4K80mwcIyTsF5Sqli6WhuIUnVj0sO6WOK9070mxueYa3WWlNhC7J1q1p1zDeUZhQsb2uI1fsnwgm00Y8Wa3eeqTpBHzY3hCmWyrodHrAScat+cPeWwvtFipZyU+Ddf+Sx+87/9u/j6K68rBHSDlU7nHBgNIUnCQX1S4pyhWicrd9WM5oe8Pj3BvcLBkzMv0IqWRC+9CfT7DPbNdZ+RGEKCAj8lpbbvaPbcCFTm5YKZBcCMIkng/vB3gHKZeu8A38/0mCPNBMdDLsZCAZhMgOUy0H0tCAJcZNU6H0ilqsp8Djh5DPd//mPIa68DN14IdH1DxBSKAaObqHLhZMhBa6EMTIbMpLMlD6f2ugbECYOrwvBIosqZNU0VYhUlOlR03gfTrOLceMj+rSE9JPJ/150+ZglaqRLuNxmoWbFO/ivssTvtO3tPyGzJoXW97Re9NDrUZTl7SnKUtqGcHiIy7IbNY+gIgPIEkxGhruenwNN7kMM7obVSZb/RKfXfSw4r3l9qLf6td94isueNL6nnqwMWk8AFSBcBXhvHvPZ0GQgqucojDC7YRlrfJXt0MmAWtn/r+TWs6Cw3Gaiq4YCf+eGHDL4vvM7nffcdBqnOpheQohfrCufg/Jh/Y3OH73nxTANXGhi8C60seudhyD+bwhuB2M+lC6K6ZsqNGGspH8fh3vUv2eqyPm4UM5gVipCrLzIR6J7xPbyU8Zx97s6MMiEbO6G/bEbOgA7o62GWspjz2RXLrPysnWdCcPp83PiUM5aqrk3T8KnWfQtKqnW2IEd9uFIltCSb6x4u6HkPir6ShooGRqL+rg0126hQiiF3DKJmHJ8uGPDrDNIeZVdS5nGSAE7ht0khCNSlWrWb3+x4GKDKSVFF4XQOFwm+/jf/K+DqHY/gkkKR8ieNFpVav/MHHIav7TI5O3nIxOETXp+O4J5nwL33eMOvv8TyRyLe9FoD0myydDRYm7maV5schhze5vfOn/H32ptwiyEhTmqZh7NnpPgvZpCvfl2rBaI/MFQxsDuvMmAeP+IGjFPI1ZuEsxnpI02DFG1Ze4LWGmop+87lcB+9h8XpANk//Jeo/MIIcvtlBmgjqhiDzWYIhSIX8mwCMwR3M82C2utcTKanM5uEbKiqmfd0xIxiMQcaVV8SS7UeiDlxDFnM4WYRgFyzJF1ISQJkhIW53hzS2eJGMAhitvSHh+HCpdbyJuIO4IE65UAVzXXNunQTpCkHrUIRMWe8gCwDnA5oo5gHZL0Vyu+nD+G2DoPuTCSQ3WtKLlEZ1GKJh8F0wsFmnrHtdPQEuPc9yCtfZCB3jhljtgx2bL5U52Y0rQ8psFfqdHDujrX3bMNUk2mdU5bAYICUyWUPWfZvwllFmBRYPT19yLUMMMADcA8/4Pppb/Hg3Tn0rQcbZrJfry2ixTwM9sfDFfCBU2OXUkBmlKtcRyUlO7U3AlzQKslKPVRj5sWqVYCbDj1bVVr7XvnQV3x5RlP6YTe016xlOJ8+hxiS9T24eMz9tbYVUDhDFcOytaZCZVbRu8ko+KxW6+ylKzRScgdXmPqM30y0n3NYAnhQmd8tQMjt/k0GWpfzmiz7t0TDOSqXWrt0FYGTJAG2qRr+HmhhwA2n1XhSAuIJxQFrDap6Wot2ueDzuvYCk1utgN2qguj1F4A8JxDAMP9PHwIvb3xiWP1UBHc3HsJ955uQgwNKaA779MpMErjv/DHw8CG1UnauArUEXgGue4L8j34PqNchX/m3uQmsHbGcA4MukRWlErC2ySA2GRCbXSwxU602+Y+JM50o3Gr/Kvu3xSIDfWmldJ1OgPY6s9P3vskPoZA8j2DZ2kXxswvg+Bjz3/smSv0+5M//Mhf+fEpCTqYl9PkzZkFAODCspI6SkLEDYfFOJ0CpDNnYgzPWmjmzTIbM2CwoWIYAaMtA2aGVOkRU9KxQZJtDs3U3GSmDdg4UHIeWNrgadjUDioGxZl4SEc446gOb++ztb19VXPUksG3TNFiF5Wnw6aw2Ai5+Zw8yn/J3G8zEXUwXJykUuImjxGOwaaIRPwetkzd+Bmi+y4PXnJ9EiE6aTykjbC2IlX6v9fC9WUOtzew7EgYxZdd6ks1kwAM4joN1W72lYldalXX24LWQtvfCs7ZgqvBXKRThnLbZSpXgSAVws6vRAyQKCcb2fjicPFJIZxONdSYKRvbrbHryF+cHOrizbL97HuB7VR0MGtKss8ls06rX82MibCYjipcd3uQhYJBVQ5vYsBZgG0QPXkQREzN7NjrHoURGg+tkcEGdJcvWARK3DEZpVZfh6p8LKivoqcWMyUapwuzdgrFWokgXbFNlGdtKpWpAgk0GXoIXokbV9gwybTFaomb672bLZxwOl3NfF0pB0tokhVc/h+olwTkPTPBryaSnraIw7Z9PeH0qgjszjjJcvw/JM5Z4IpS87XYhL79CKq8xu06fwt3/kNTcmarqPfiA0r6718kSnIyY3XTPgt6Kbf7eKaGISZGLeTLkYp5Sv8Z991uQ9XW4xQLo9djC+cwXeAgMLvWmt7mxL8/glkvg5Bnk5os8JADgzuuQegOuXEZpcxNy9Qaz7ONHPLySBKh3uNn2b7KfnWpGKVFAPFhfEAjWgAVm4e7u+yGDbK2pznxdne0XvgfsljP1ONWAasJNQJApXi40yyiSZSfCa8pTwCUK57TWRxteH1716tE7pdb8cgk5egL3+hfZJ1yoMYZKp3qt7bOnXPCtDWartg4SzUJ3DxmMGh1uSMvsTR65vanYc8owuCQh1noxV3PvJf1gd689h0xyBp/VgZRpkJiGO1tlc51D6DPY2IKXrDUXp+4ZW2aTERBPCRVsb/G+ZBkrr9EJ3EfvQq7dDgPgvY7/W1IsczZSb4VKbdTnMHU+Ze820kBig0GbaeQrwm++/bZk1RvFgYRm7R5l9Rqfw/9dC77LBSGyD+/xWgsltcPbpGBV94RQx36XGOzzk1D1lJSLYm3SYS88K9OuMeON3gUTk7VtuPGAUMlRj4FOGcFuNuF1awtE1nf9INwOHTfu8zmvaskYyWlV6tmSLiBo9u/f5CFlA8mVobwhnIwVLms7vDaFJvvfWcx8UDc2uURlr7nkJALyiZcrdsMuEDlfzUpznaQ9iSDtfX4+GzAbmq5cVb0cATIHN+mHed7hLfI9PuH16QjuAE/yazd54fU2A8DWLnvtGzsBatU940Do/Bw4OIDs7hIF4Bx7b91Tbj5zin94D/LsMfDFfytADFsbZJV2NuFlCoCAUT0+RvbwMQAgmywgIkiKRcgLr4abHsVwp0/h5nO2bdIU7ttvQV55g/31uEB1xTwPmZG5uY8VzdA/D0zPw1s87Re6aa20tjaAWbVZ0Dm4wYB3ecbN1VCpUlOeLFeZUZZrzFLtFRfUBKDoN5PJA3hKdxyzbJ2O4GxhA5ByxEVYLMONmT3L9hV1VBJu9iyD611Cjh95pIesbbMlcfyQbY71HX6m40c8lJQkREYo1Nxk1wcdUWlUUSijHNzy1YhUm55c5PrnPAwGl6H3KSvtNIABNV0ETfH2Rjh87EArKbSyoRLQ5SozJiDoFokwkKq+t5uOgptUc80zT2XvUOcho5B53/0esHsF3oC9Wue6bnSedwgS8YqGUqoQEpelvjLz0DrjKpw987A8DHrMVrsXcI/+BPLaT3EtWTZdqSuxSFUij54A6uGLdMF2zxrbH24+VRmMIQeonXUevvVmINctF8zIN3ZUU+eS68HmUI02A9rZkc4nZsBiCjcr8H4vZzqULnHeZDaGE1Xd1DaagJU++udwnU1m4efPVMBOWayrxDkzUhEh2qRQZGBfzBisxwN+3qTInzt+6HkbAILZiMFEJ4MATVZ5CzefUKc+z5icWgKQqSm8iZilS69V5UY97t3cwdU7/LzZUtfSEKgWCJDIlhBRomAkyjPR2Uj+Z4j/z70+FcFdiiXIL/xyyCwALtwrd3ijuyd8IEnCjXD9Bci12xz6nDwlvNFo2L0zwvGUICMvf4abLIrh7r/L96s24bZ1km+l92QAT38+OEB0fo7l03M/yJNGkxvm2hYQkRKO7gWrh0KBWb7h4od94OA6A+zWPrMNw/IOuszOBj2g3QHOTz09HfvXFYo10GFukVWJZmjOSn/Mgk/j9j77oe0tD/d03TMPH/TVikkCiwAoBLXA5YKyAka11346zZhHYQic0GxYLLMZ9b0sgkjEa9vcJZa6UAitB4Cwyg5RJl5OYjGn8YBJEJcqHnEiJgCVLkNGmoKBRpUMpbMFM7fAsBc+++DieUxxmj6/4SsFZSSH7F1KFQ3YhAiKQnNd/5xQ1ZnODuZTuGf3+Qyz1JOCnJLUZG3b999Jt1da/HLBbHy5AC6PWAkYZyDP+CxL6s6jksYolgLjOc+89yyAkKmb65JlnXvX4GU8DDYHcF3e/xDYOvR9Y2fenJUa5wif+RkO6SyLb62Fe9Ha0IpHq7tnj+il2r8MkLx0GbxXF/PQxhz0gGY7eH9u7DCLPz8OnsarulDKMHajnhdnc8cP2OKzfnxxQbN2y9gvToly8wlCxjZjpQavFZSlikDj83CjLuNAknC/mdiXIYdqDd8OcUOtHJczj2CSpMjPU2mEtZUued2mNrlcMHNPU65TUxat0LAEjVaonG39RTGvI8/IobBnCPD7fYVqm0vXJ7w+FcHdLZcep4zlnIYVjk4qDuDGMOPjSMvzagdoazthMQeg5ee8zw19cUw9jN0DYO8636jIdoQb93iTGx1CKUddqhge3mCmeOU63OkpCle2+Z5LRcS0132GJ5U63Gtf0BI/geQpN9ugR2nhj78P7F1hwEoXXBRG+FgueG1nJ6r9kgFbe8wSprrAikR+OHOJMYndLKMI1b3vQza2+YAVeSCaZVkmKVreubvvcKPefkP7yUSNSLHsswvS2EfMZqx/K8JsKy5ws5gEgUREMhhqKEn43lfvQM6OiHhZ2w4PONd+qLIO/Us1tNE75zylVCUe2g6iKIYkBbjxUFstGeGJJs0KQJKSNwvxdok26AMCbHRVcz9JIIkpcFIMzM05o5AZlflozdaD++Ad+mF+/Ze5kfuKWT+8Adk8YMsoiimsFet8RLXfpdHh+v3we5A3vkSp5Zn2mNMlg1VzDShNA4QyW3rLNWTzMKC21tJyoQzozMP4vNuSKSaazv90SGetjU26kL33p2xvGoa/VOX9NBhlc53VSGst4NpVOlqaa0o4y4ni0TkBShVa/V2eA9UayU5xISChajPg/Ijv314PgWnvWhD3KpbhBireNbjUQaxq6iR62CTFMFMydzODFO8dKkSyoC2oKGDyjVDoGaO5h0d6OQlDGen3PMu70tBWWMIhpmrTm3sYqnWtqFK/ZhnYo1CRxQWVZS4EkUAL/Lke0pMhnM0g8swTK33L0AbmtkastbhYfGJcjT7xu/+mXi4HPvgOs1uAEqcm6nP3e3zgnU2WtbZRbTJukLLpRDPMAhfvaMjMoFLnJrOeu0IDMbzQ4SozFOxfDVC0CQWpMB7DDYbMdN79LvCA+HgLdFKqBMRGRk1qbO0Dt17m5/jt3/QKdjh/RpywtSCiKJAV1japEXN5xMy1uUYscbUBWd8JGO1ShQfRfAa59SI1w5trvhfvDC1kOPX5lNnFk4+Bk2cUMJqMaDbhF88KJNJo24WiGhc04TXgjQg0GQGDC0L3XB68MF1OPPG126xAbMC5+lrthQ77LKXHmjUt58DTe8zCDSUSx+zLFkth2BvHDKKGiCiW2Mvf2AuQVhtmWeY76gULQ6O5m7pgrpokxgDVz4vZBDh5CqnWIHde4OfunTIIdtap2/79b5Fmb/fGDpQVUo6Ua5DP/iyHifOJZmYDnamkaiOoQTISBgC7FguQ5Rpbjqorg2ozkOFM4GyivXfL5Ic9smKLJYIDNrYYlE3+dzpUZNQp3KjLA7TeYmCxof1iBje4IKt03FeZgi0G/0ab6/bilM9y/yqwtc97myjooX8OfPC2rnnhMzB5AUA5FYrNL9eYzRdL9GswslUck+tg0tbpkteiirFct2X+nTiG1Jr6OZowATffJ8+zMLAslik/cnbEOVlLNaQW84DRL9fIizEyma1hC/6a3DgbmgJhLmjfO3vCTFxE5Yyn1PlZzngP51NtOS2YkCVFJimmympaPi4P0GM75OzfP+L1qcjcJVZCRFJgIF8cccEPB3D9S0ippHoxhUBUMPlQC+jtdYUDKlqgd8EMsrnODZsU4dZ32cu/OOYCfPAhWwgtDZDVOuT2a8h/6x/CzRfI50tmr8MxoqX2yyYjuDznwoxjXjsA9C/gntwPw6xrtyEvvB7U7KpN4HtvsaS9dpsZ++U5kQhrm8CD7zNoxAX/oHH+BHL1ZdV96Xo4Id2o1oiJ//gDBskrd9iKKVX08zb8QNC9+jmyCZ/cow1ha4NsuekIiIt+Yi+J9m51uAMbuqVzZixJgXoYaujLFkrVk1AQCQeYLoe70GFRpRrQBKsCXmru6z7+iPMWwzFbhmpokCSh0fGoG7J+Y1haMLXr1Oxcqk0vKuVp7UKau5QqXhDNa7SP1Akoir3fLE6ehsF+UTOnYjlgwZ8+hHv0iIG/XA3XbfC3QplDP7vPhlqyv6W4dXf8gIlCZysMeqFVW5ktE3f6KEjUAlwPWRYSE2UQ4/xZgDeajnqaAvUGHbQkAiIJ1WBSAAo1Zu+RaLWQBmhkpR7IVwZBjvRwXT3IrL0yGgCVGoX/IiI9cOslJWSt8fqOHsGZdtTeNT/jkXINbuvA8z84YM14KJqtpkpgSFJkRVQokmleKFIkzvgMJjiXK8TWDt9ME7xCkYmQ6fmY5aQlgLUK26Bmqj0dBby8VgSGqXdnTwhhtUNK442JiQXf1ojX0z1j5VetAa7H97a2VGJEsGXA1JcrKqtQIJrJLD+n48Df+RGvT0VwR7XGRf/wbng4iuDwBrsff8iv37gTBIS6Z0GGtNLgYlXavhuN6P1pmy4SLgobbiRVbhjLrNUEw1VqkEoFKJcQx5HinHOg06GGzGLGls98Tiz1+jZ7poZaAfj3L8+YKfUulORUJr758X1i6g9vKIGkG7Ia7c866/+lKTdKw/Tuq4owATPdd78N1+0C7Tailz4XynRDhcU6zEkKxNGakcN8worG7NvSZUDLWACxgGkSCtWmRyNIpcHML4rpa2t8ASCgGnyvPtHNGYVMDAAOb8ELnZmxs/ZppbVBostszHI2iil1CnBgbuWpDXEt2DuwXJ8MlBG8VCkBIofE5AcAtnJUrVFaG1733c0mPEinE2qyGKLEKptSiZnqS29COhv8fJencIsph8BJEjRRJkPKOpRrwMaeb1W43/kXkJ/+GWaXJmtrKAmnptJxTBZi71QJS/Wgux/FvIY4ZtVjGWlfoZYuJyJE21PSJnDAPfowBJM4YYZrWeHlCZClxKJbEM+WQBqHvjWgXxOyupdz1X1PKDFdLvss1Ov0VxpcH6ZTX62rumkHOHsGV67QPKPRYUVs8xJr+7icFdOaSiTMKdUhxZKqd46J/44LSm5bqNbQygFkLFGXA4tFkHPe3FZS3ICVfqUaDEIqVX7drAIN2DAdqgKlBu9KPaxp+/d8SnRac533NUm4x7pnQS1yMiawYi1noN7aV5hqDDeZ0qrx9DETWKvidq/o53HB6vITXp+O4J4pKmRzhx9YxXIA8KQqlrmImm1llF1wwVtpXScWWuodZtWn9/m7nc3nboDXaxHhIOnmqxyQRXa6O97oQhG4wn55tH+FsK/dAyVZJCGzGQ/oqNPZAD74ri6Y3ef7yq014OKEvcgbrzDjmE5IRioUyd4bqp53rclhXaGoTMlKgGxVG6xYak3+3Q/ehjunUJKsEcZHmeNSGCCmbCE5MyMY9RWjvxGQMnYgeVeaOPSnI2vVZCG7AEL5Pxuzb2kBeHBJ0ti4xwGoZSL+AXBA5/KMB+24zx5woUSUwrPHFMuybFvNIZhJP6HevPpTunHPZ8NiEs523Ur/Z69+4bMht5gDUUr+QpIwCBhuWyLS32tNuItT3gfTA1ou+CzM/Wo6YZAxDoW2mNz5M7JpFzO4j97m97f3iTQy39LxCPKln2NV0D1TwlCBn39jJyQgS5UjSIrMesd9JanNwqFZKDGb272iQlPaa7ahd6kSIHzDLtfrcsE1CoUGRknIyFsb3iHL1ag7j1i9CUYL77rldeeXCPf8yk3veepFvExqYzphUDcpho0p71lD2ye9Uxq3rG3rYeNC9lquwUnEDFlnT2IM1IIN4p1XhxVzYotiHrKjXvh8BR2CFsvcxzNCWL0Msx3g2n6U/Zs87BezwDPRl2eSKhMbyxkQsyXmRn32z/vnbLVW6tq/T3n9OqzH8WMS1tLliuAYyIGJhIeKyYBUtAXTP38O2vtJr09HcAcoS9rUPt7GFh/GBfHqsnc96HgsZsQX1+Cp8f6VLbnABz3IrRc53MhWSnlVgqM3o4MzTYxCmUM9+/mrNyFnJwz023sMQLkOW9S6TmpNZpfjnio23uBp37tkq6Va48K5OAYuTuHe/RPInTcgh7e5KJprwLzCn1lfcKOtsvuandBDdi6UxooucIMeW0ppyozJUEY+Q078/fI9wEgYpFaYem424YZY7YfnqXefQbpk793QN/YypElVDxuF9bl0wSHsxREREumCaCUbbK6STeICMOlyMxpKxDni1QcXWn4q0/jqCyu63Mqu7J97Q2kpVcKwCYDJpbrxgMHAnKAmA7jRjFn8Kj4cIJ19MmIWt3MQXIt8n1XL/EaL0MF2h4fR3fchiznbbQkRHdjYpeH1bArZqnEYuVx4KQKkqbZNlsDZmQ7WJzz8Y1UwXMy5wQtFuD/5HerZVGrUUmqvca2XK2y51Rv8mmnQrDJQ33kr9MrHw9AmG/cYeM0dyWYPVU1eukpganao3d/aIIdkOQ/tGQ2aAChVq5WHn8XEBSJP1neVAzALc6JBDyhru8qClnkYmDb7qM/7ryqm7uyJ7kFTvowAaPJjFb9l/qtD9Jw9cKmz0neG75+NeV9Oj7h/U0oJ+163JoLEsRs3YEpWKwDX7anjWiO837hHRnOzwyTAoJP1jrfz8+CHWitcs7WJzMzDdPtNjM0UaQF4U5dPeH06BqpwDKIdFUqyNsn2AXDlFjWP++csU9R9CYUyN1+ivcjZmAMHW6DbB/qn80APrtaDRVi6DM7wNlgdXoSbu73HIRHAv3f8yLcqbMBHuFUDePoxTN9cbr4KXNPW0elTtnpOT+BOODE3308A8FZltSYPooJqVNjswBYT4CGZRsOWL3wVcvU6g8JyyQG0fXaDs416HNLUWqpfUwyUdxuCrjpq5Skz3uMHHOx0tnmtmgHS11YPDolYfVhrp1Ak4WMyZE/eyujZmH13o25HcbAQBFSbI2fQ2toPuH6JlBj1PeDe+55daNLNYhmbGVuM+nCnj4JSYJJAai1i7FMaLJsqoWeSWr96dZg8G1MyoEGqNyp11QOaM5GQCOhdEmsdxcDTh1wPlSrFoHSQLJ2tYAsHsEoolkglf/dbfN96h/1aO5DKVZVdUHnXnSuh8to5YGVj1YKJnZUr/Np8Dhw/CwNGHbZJo8Ogb6zKps4b8kyZuxUNjMsg6rZc8Nqv3GSrrEb2sEck2VAS4DPtX2qGPgImA64ta2eZDk0UMynLdRhZawfp5smImjPDLrPUPKMukVUokbo9ZaoLZCY+lUYgS61Wy6sVpu4ft5gH+Gs19LphAAAgAElEQVSpynWQZWybJkVWPx6Cu4C8+FO+TRZkhgUm/+1+6x9rq8YBBzch21eZzMynbMmazaJVAt0TIrMsJpVrTFrzzA9uYe04PdydifhJBG+jaZ8/ikMi+CNen47MPSkyozW9ZkWfIIoAFyuyQNhLf/A+J9vOKUZdsau9C3i39pc/w0zBpGfjGFimwKO7cA/vQ/avsN+meGavhe2cX4h+Wl9UVmitSby20YVX2w1G/374EdyNl0Mf/+QpkQqf+xIdWzTw+NYPENofSYEZ6/mzwETNM3hPSeuXL+Zw732H5K5XvwA5uB7K5BUqvRuzhSCVOrPpQhmu0uDsYUWn3LNY++d+KEXFukKoGNKVFo6RZ5T6D8APYcmyNXp2HW58yXu+e4U45VLiB1wCkMnZ6LC8ThIGJTVSkEKRhhl7hx7J4mFn0wkPuyThNUfaYlEEkNkVykqZLVFMQtZ0RE6AIoagLFM3GbDn6XKS1SJtDywVGtdoMeutVOHOjiFGnGu04J49pvxzMVy/uzyByQy72ZjVkZm6v/C6KpKO1Oy4wVZZrICC1kaQqM2GgQPRWec629jRQWlHGZdXmT26nP82oEFZ6e3NNoP/csHDXZ2jZOeaUv9HZFTq+vCZpVcxnQPlhImK8S8M0meDdGPz1lsBs53r0DfLOMQ12eVyjc/DZhjTibY7awQoJDog1WzbMOJSKsJNx6w2rWK0vbFcBOKRREC+EuzLNYVbXoRZwtYhcP9dQpDVIpBVMLWQ3Pkz3g+bOWUZXMZ1ifkU8vpnw2Ex7BIvP81Y1Wzth8PP4oPpuBupMc+ApEQ9pFE/+CUYIq3W5FqZzFltVBq8futO9M+pHPlJYfX/XTT+//i1XMB9+xssAa/cAJYREGlfLopZ3lhLolhiFt29IIGn1vTsQxw/A262uGitXIwVGmlkiW4X2Nlf0VUnIgaqLufxvgZDSwqUCq3WueBWSVZZRjjbzVf9YvbWWcasjWKyYR8/AG6+COzeYNvj4gjonvF3U6Xdz8aQ3RtB9yPV9zLMseKX5XrG4fOf/h7w+heUKp1oFSN+mIf+BVy9pRlBgRodqpCHbMkMa9yDtLcgrhqGplUuJDE8eUGCiFFceB71MhvzIDHI2XJBS7TxIByYx49p1WfCUEbmaJbh2YpmlhGr/Zi68phiplMdDTF5gsUMsnvN65FAVAHRStmkyHnAFHy/Sh1Sr2n2LaHMjWIGlTxjELGqyVoDtXawgMz5ueXKDQCA+93fgvzsn4Ns7wUjclWFlM0DJisqJ+HSBYOjWgBirutzOuLvFsuKMWeQd0f3Ce+0rA3g4ZepNHHvVCGfwpZCJGE+MOwpy7IekFrppR+AI3ccsprhda0Jc5JykwHcdAzZPGAbpHcaAqgn28jzVeZiASSOQ8/5JCRaJgVwccz3t777fOJhgd7DNYqppGqDVUAThuUKs5TEOw7iU3IgYl0fw+7zhtkShaBtWkFWwZcqXCe7VzSLv+ChZM/8BwexNpayw1uEJL1c7+3H7wd+gcIx3UrLRGrNoMtvB9yK7LQzToL1/ZVb4Gxf16l7I8Uy3GgWQAQWo37E61MR3N14BHdxBrnziuJMlYqsGuDuvW/zYSwWkJdeVyjTGfuckxF74u11nv6lasB/WgDKMxXyn0Nu3eFw1NAnyrSURgcw6q/h4aOYm3U8ZOb97GNKCmgQdSoH6/uELmfm1b0AtvdI/NA2iRv0gLffYva/d4PtAuc40Ernwamo1oAbj4mznoyonpeR8CLFMiAVuB2Ffh4/Ad7+Y96LzS3IT/+CH0ZJe4Os0WHPoyUMDuZm4+cV8oDQf5xPKXVQWOnBT0ahLeRylbNVlcqS6l84B9c9Drh/FfTC+hb708uF4pFz/0y8/G5SBLIpvEyq9Y1FiC6qN7zYE60Hq0Dceh5HHynqI0vDENB8L3NmjtJSdIshJ7BCbhKhnV9rTQ8flXIdXCi6IWP7Y2OHglm9c8jXfhHebcuMmvXvud//J3CTMROCQhGyc40mJ2ak0dogw/LiGHh0F7j+UuAz3H8X2DnkYM7w1TYUBJisdC8IPtjaD5rujVaQ7jVIaveUydD6tud4SKfDA8OqZJOlXcyVqc3K1V0chfddzLkGqk1mjZY8nR3zHlRUw94krfNMwQTHbFOsbcIkQtA7Z9unUOSebXZYcZTKnGGot6mhqVyuFZaZxGRLVn7OQaKIiDKrdFfJSnnql4fEhdDyA3TdabBf3/WfSZprQX5ZIkhF9W3aW/qXYlbYJl2dJHCddQ/qwL334IwxbwivwWUw2jDkVbXOpCLSKrra5P2e9HiN2ouX9hbc+VPN+pW/0FDj8hW/2R/2+lQEd0D7WrNpyIxLmq2UKpC1TbgH99hjFrYwUCzCXV5Su+Xmq15dz9ujmULeZBDQFzde5AK/WFmQS4pEuacfs3fW2mCgNQst2/jDLhegsf+yDOhfADtXiMqoaFa8scfrPj9h++DgJnWaAWZYxbL3eJWdqwozU8s2gz/96TeIomm2VctdnYgU4yvlGgdvV27BHT1mYD09AXqnzPwVTiqVBsttGywWioHGrgQPaW2wZJ4OvV4+kQA6dKyr7k5SCozUTMvPuBBmA6ZCadnJcsFsuN4CtrJw0AJhAybwxCFEAjdXwlG68LBLbGyFvqkJN+UOKBUC81WzNDdSjezljJutewa4Ccv8ektdlEDFyWJZGaEpyTxP7mL6t/4OKn/h5+g/KxGD3nQC9+AuZGs3kGFcHmzWSlXfEsJyQZekQhF47QuQR3dVATGmabREkKsv8BCvaBBrtBn8rEedLSEv/lSYSbicph8WEJrrPHB2r7Bf3b/gQPelN1imZ0sirsZKBktTBk9tF3kkyXjAjHG54DrY2GP7YuOA+0IH6W4+ZcIyHTNAmzF3a4PPY/8698GgGyqH81P2+WtkSMurX6B+z3TE1lxHA333PHBUDm9yX5qY3/EjuKZCHG3gmC68FITLTxBtrczVDO1lej3zqQ7Hu6TxQ9daXIC7PIGsbbP9CFAvH+Dzu1AiVQq2doHnzedNwM3lvm0lW4e+JWhsV6nUFTixzlmQQV31JSYlohLhHuFWb2n7VZ+dMYnjhGvCkpOk8GNRMz92oCoihyLy2yLyvoi8KyL/uX79vxORpyLyHf3nl1Z+578Wkbsi8n0R+fqPew9AgOkU7pFCGNtrXMi5I3a3UOCHmk25MFrrkNsv+56nWJbXbPsBDD9dxMx5vnJo5C4MyyYj/uzlGdxH79Pe7fQxS8bFjFnFggYDqLeA6y9yeGZa6fs3+e9KHW42VuaZ6mHvHnLzvf2HfEhb+8DrX2Bp1T2hKqLhhwvsx2I+1Ws4hXv3e5Q96Gzy+8ePOKxMifEX1aeW2y+TI9BsMruwrLRYpuyueZMeP2K7I9Y5gzL5XO+M11BTk+flnEEny4KBhaETUh3WWj/eeoQqG+GlUs1xaDHn+1eqwOVJYNgZu9iGYIUShcoqig+v1MOBurHHqmnUY6spW/oWh9/I6ZLVT7rk31ZRNm+iEsfM8so1Qg2nIz5ns03TtVH+ypscpJdryiLN2DJ4/Jja8IMug0tV5y/NdQbJu+/o83/IQD2maJg3wNagKVuHvCfVZoDylmvE00+HcO9++3n243jAa1zf5fpbzBjYLVBU69Q3evOnmXjcfQee/fzkAXvZnU2uR1v/AO/hdMjBc2tD5yFVPzA0uznXPw/+Axs7DLSjAY3klSWKaoNVggX2xZyHQFOTgsUM7vgRDD4r7S14o+pyJZh2L6bhefbPeR/VfBqzMb8mkX4eFbPLUlaHhRLvqQ0vAR5OFniXi9COzFNtrxhcWFtbpQp/tqqqqlnqbRLdbEzo7YIdBTcZBFmA3HGf2Pznhdd1fRS9QY4Xc/tBWWIz6Qa4ti9PFcmXsxU4HTPZG1zwfltffjmj7WX9X9+sIwXwXzrnviUiDQB/KiL/Qr/3Pznn/ofVHxaRlwH8BwBeAbAH4F+KyJ1PMsmWUhHy1T9PV3XPdiNlF40mcHEKufUChcSMXr+2BXn1TQDgpprPid+1wK5lvDQ68PK0xkrUxUGCTQXYq0DmM7ijJxQCu3Ir3PB7H/DnNnYZsFSmlBTrnKd6HDPrmI4ZjKyf21rjpjt6wA1gjLmEi9mdPWHGlBSItz8/5ns/ekij4XUVmHr2AO697wKNe5Drd+AObnLwVCxDDlRONkoCwcWCHADPeNN+ntR007kcnlWnG5/G3YTEycaelrlR0MRZFSqygLFccAGubbMiUMKFe3wXOLjJw1Xp196PcnDmVRvdsPucbjmylEGzkcBdglVElgFu4Yk0Um3CW/apFIVB5qRYDuzAlG72qFNrB2akHReAQs7sNkn8kFDqdaC9wYxVIt7DvSuQz4FBq1jiQRTHQRGyucZh2vkz9p6Nl5EtiZMeXHLG0j8Pejva6pAibdNI4MogP/uLML0RcTm/V1E1wosT8ikqdV7L2TFw7UVdpyMeoMdP2YuPYrjTI8jahq/QPJxzdEa3szwDrr8c9kueK06+DDGwQrpk6+/2q6HV2RK2T3SWJfUW772atROIMA4IJOfgfX91uO+DWlOH0sUyv7++o/dnSs6LkZqWZfosRDEwTQKBaDbmGjYbxRCEeDhNBiG50ZYeTKcoXwm0pWqQD56NuT8VVusFv8xBKlVLP5dTJGzVHc5kMwpFDkr99TzP9TDGrfu9fwp3/x4lxV94mWKD2ZIgjvOPeA9SnQmWa/yc5RrMP8BNRz8qpAL4CYK7c+4IwJH+91BE3gew/wm/8u8A+N+dc3MAH4vIXQBfAPCHP/I34kThV8p2S1P+d6TDotmUEDIgwOgWM97wB/cgW9vMFuwBW1DSMpcKhyrVWdAT25AZznGjvfgZ4OYr3KRqsIsnH3tPTqQLuLffAk5Pgb/4V1V6dBAm36XSCmIl4WJtthUJsfX8sMeMEKwf/Owxe8uOMwb50s8RL18sAb0LVhUAMBxSJ6beoN2WIgTkBzWsV2n5tq7Wd0iE8YOxFaTLki0Vp+0mMXOC5YxZiGRh2GNZkAYAl6YcpGWZOmcRxSA3Xg5tks5myGJmYwZnQ0EsF3C9c5avo66il9gPlr0bPGjU8R3OEZkwGRDeaQgHE5OTKMjuFksM3OZInyTBV1URSrJ9lZ9lfMLP99KbDDgAzcEBBq8sIw9DhGSqzV2yD60PfPIkPPv968wGu+esjmw4CIR7FxfZO352l5XB1j68q49bsvcaRXy2nW1WPJUag+SQ2kJoaCZvJKH96zpzIjRSbr2kWd7ck39QVBlpY8Xe+15IRJYLVsGHd4LGUq0JvPElkm7ee4vtnXIV6Gyz1zyfsKfvtU6KoepSZUPv2mTw2fmE92zQA7b190ZK0CpVdage8/k6B/f0HquWzV1t61R5X7QC88YblrXbfCBJQhKQqL5/VAowTmvNxQVItAgItzQFemdM0ibD0IpxNGeBKZGqpIRLF2FYqkgnABwam3+wGdTbMNXaYZ//KmRrF+6734K79yERV9VmqPrWleimSqlOgRCUF9Gk5xNe/49w7iJyDcBnAPyxfuk/E5Hvisj/JiJWI+wDeLzya0/wQw4DEflPROSbIvLNs26flORhn4GitRb0Xq7eDtKd2ZItg+UCuPc+3PFToFJhv9ls56xXCbDvZv1k02VRRUfMxsyo1aLOXVAd0n3wDnD3PeD0iH93saD9XqMD+cyXIF/+KpDOueHSRSiVs4yLNp0zg9zYo0CSZU6rJdl8AvQv2QM/fUwd7jgmkiPL+M/mDkvTcgXy5ufpNnV4DbjzGlEEJw/hHn8I1z2F+/hdeFd6f4Oj5zIGKZY5tLGhX0X1qNMle/2jHjN7w856Wr8LWZINqWwwpJWRVGnC4eZT3gPbKAAXYk3/piedgENdLZul3qZxgeGsDZOsJfdzsNFKnYgZHTKv4qClWGLLyjJiRb242dir/bnjR8ByxsrB2ksXx2GIPh2xHZE7Pp8nH/HzXLnp9XAwm7C8r3fYu73xMuTGq+RWGA4ZgPdjLVW5bu2ABPgej+57CKy0NzhPKRTZw06KwGwaRKnqjWDQXK7wn+kIXh54OuLQtLkO7N2gU9j+TWa5iwX/XrFMkxPTwimVOVP4+CPg5BlwdsLrtMqi2gyetLtX+N7jPrXUzSQEUBSbth5MGfX0iN9TI2sOPHVvForAns6bCiVWrJVaGNpnGVuX7/8pLRT/5PdVIrgWvm8SE4ZzNylpBhe2dEoVBnbjUhgHRKWEbU27ud7nWpN/4+gJk7xSlQEdYFVqLVRby/a+3TP12i2yUhz1+LmSEjDqEZxga99L/k48C11+7hcgB4dhAPtHvxtQQVUSLZ312aOEezmOw179Ea+fOLiLSB3A/wHgv3DODQD8TQA3AbwJZvb/o/3oD/l192e+4Nz/6pz7nHPuc5uNGtzTJ8zWXR6gaNZHO7jmZTDx4dvAd/8IrncBufEC5JWfoseq6XKvlJluuVBNckWK3H2Pan6DS/aA33/HKz1iOgbOjiGlElyvC/f4Y2aPm9veUQhZSgu5px+zTF7Mw2FSqQOHtwOu3nrT1SZ7pxOKPbnzZ8z0WmsMQIppRaEQcLoP7/GwuvYiscydbcgbX6CsrOJsAbAdM58yw7VhjS0igxwCmt3GkEabi9Rw2HGBjlKjvkebMBhZdrvShikqCzZaKfYSDa5KThEVLXOTgU7+B4EToBvTTYcM+O0t7ekrRNVQM/MpA/2wy6wwXSG+uBxucEnP0UIpHD5WUcQxJSjmU0+mQq7m4MUyg4z10pWa7i41az9+DKm3eCAvZnDjnkq16rPsbLLvfO0FyLZC6KYj3ltrwzQ6nr3p/vD3n2tjSa3lGb/u8pjr+OotLeF1bVwecc3uXCE4YEe1REzDxAAHhvIAQpBWGVspa4uhXAvD5zwLiYjBa3sXfHaVKmRTIbvFkpKtVqpAgwpb4Kw2dX3wmUq9DVnf4T2OVUOls86EqMADCgvdh/Y3Whu8f51NVrGHtzwsEAArnvaWryjki3+OB5cKnKF35hnb7vKIMzKA1xRFYQ6jrTksZmooE4XEJM9IbBt2ed8s4641eP1ZBpw9YTIoohpT3Fti1UixTKisafQUy2Sybh7w0FcnMWLTSbxjUpEwwUwK3Lu9S6S/+w3GonQJuX4L5gtsbVyJE/5jZDgjsX3C6ydCy4hIAQzsf8859w8AwDl3svL9vwXgn+j/PgFwuPLrBwCefeIbFFQtbzphuVks85Qf6dAmSVjmHT2Ce3AfaLVYdmrf1U0mz5e/QHjIS0NYZHDDIZ1SHnwf7vQoZNsff59QzDK1KqRShbs459+/+bJeQwkog+4o3/4mXLeL6M3PAJ/9WU+V9q4uWQbX1CA7vNA2yJTBYjKiGp5pN49YtbjxEGL6OXuHzH6yLPiYzqbsqZqV2dkRs6OXPxs2ujHpVl++RaODnYIuUmUCAtrDtsNhNtbAiVBum542FspinXKTRxEPSj1Y3YgmydIkI9I9vst70lRY3arno7YxrKyX9iYz9vk0PHsgBDKrLIoluO1DdU/Kg3mIRISbWpvMUS5C6m0+h7EaoJSVKTvqMfu0AWz3gtl0/1IXtR52xrSNIs4VShVutvmUCB9TyATgFUnLNci/96uhehJwp0kU7vPOwcrgP2YmFxfU6lE4hxmtcAtM7Go64WfwYlTHXDPNznMOXlKtB87F4W3eo2cPiLxaKJrIgu3uAYe6tlZGvaBvZJVbpRaACUtKSqBY4hzMOVbH435o6aSL0KIZ9HjN6QJISJoCoGu56gk9WC48isiJsNVqSohzB9losmIuVZghr/rWGoLLsOhp5mG/AHiNZkidZ3CjCQNlROMWWVBoTLauwJWqASQxmzAYK4zUmUyCsuhFhcNcFHPeY5o+eexbOuYv4LIlxP6mzQmiIrC5h+SnPw/3ztuI3vwKXF0NQFLV9p9PeWgYIihN+TzyH9jrP/D6scFdyE//2wDed879+srXd7UfDwC/AuB7+t//CMDfF5FfBweqtwG89WPfZ3uXmakt5ijWD1FlFtta8ybXWN/ig0oXwHvfVqU4PYVNrN/pw748o4+lKQj2ukBnnVCkzS24s1PepEKBPpyHu5BrLwEXzxRXW2GPzUhUNlDLHbCx7SFPGJyHTNm3ERTDar0yk2F99gBuQ3Xa25Q6lkf3idQwjZTDgjJJY26c3gXNPxodDvM2tpXFpgNdkRA4DFNrxKI8A4oNvr9JBWhp6mFerhiCjWml5zmDekGCYYT1b+1giGK4VAfVWRYQOE7V7rYPdZYQKgm3mEOm1L2WRgfeQMTlPBhmY50pKDvUBMiShGUpVoZXCm9DpcYMyzxuay0+74QDN2lvAZsHHGxW6kQrWYYnEVEt0wmDpCEYTHemWGbWOBsT3rbSv5bWhlYP/Flpb6oglw7njCyXLnVW0Gav1OYyxpiE9vcf3yW3wTLPJOHfno7CsNYMQ6o1j8iR1gaBBe2tMDMxJcZv/i7Xi1ZWvm2WJOzfW5U30sM3LoTqZjYmdDOhYJ6bn6k9ZaLzGA2sxTKx+5U6A3BSXBE/0/cxNFShyJ8bdsOgOiWU2aWaQIyHbFtt74W2jwgQJ5zBeHnfpX4ux3anacRnS+4/27tWUceqU1Op+3Usmc4DyqxYpL3p4ZTI5pBEZavTMa9bdelpUUjVU2muBy9g1fXx900liCWKWbX1LwkYaTThtonQkZ//i5CvrMg/SMT2i8YTMZllr+I6DyCEH/H6STL3rwD4VQDviMh39Gt/A8BfFpE3wZbLAwD/KQA4594Vkd8A8B6ItPnrn4SUAcBFvHtAeE+txYGpkUd0Uo2e/v/OQYB7lSrAndcgTz5WSdSVzND8GNOUMMuIJ57rXXCIMZuxf7RYhOC4WMB99B5JM2u7cPEFN5VptluGefMO5OCQ+hNW6mUZAPbQUGsp4UiI8+6fBwOA5loI/inRKbJ3Ha69qX3siD8bx0BUgOuehSx22CdV/ukjyNYOcP3F8Hes/WG61db3TQp8CsYwNaKJSSmb8Um16fuWz3mQWiVgwdns3VwWFtulmjksZuQHADrUKnDwpaQpl47DfRn3uCnKNaBEXW5kmRdKsswYAIWY5lNq1M/GPGwXM1WD1H6q3cuNcqCP63zAqVa8mDRD70zvr4QWy2LGPj8Q6Ow6qKYtnw6dBxd8f5MjBoK/ph2OlRr/3tEDYqyLZW9A4mZjtiKOH5KIdOcN9vZHPQbmm68Gkpm1EzJFOxVKhMuZWFy5wvWkByOtG7dY1SxnIUAB/J2jx2w7mM57oUhPgbVNBlNjyuZjuEUKWczYIqu14ZZ6QKt5t7mduZzicW4+9ZK1qLUZrI9OWRE01bPXjKcl4jVW60C16YXd3LCrQ/kWK4ubLwY100KJgb9QhOuewPwFfBYt4AFscwloAlAocS27XNcW26hkm8/CM/OomBm8EbpEJBBBZySWbIlADl+AqEKpfy99UUp6wXmPVUB6TdLehAOQ/+Y/gxQKkH//r2lPPuX6m47DLEF/V8yvdek881UArvFPeP0kaJlv4If30f/ZJ/zOrwH4tR/3t/0rThRGlvIBnj8D+j0OFfuXPPHNn9FIPcryQzRi71KErkWAR3NIuQp39SakQ/s1zBQ6d/yEwd9KO4CYcuur3fuAtGYTA6vUObVPl+yBv/A6PAVf9brdxh7hWkoK4fQeiqtfV+/Dmpamhi4IVH7vF2kwsVEfrtHhsGaiFO96A9i7ytbEkweQ/SmAtgZelR0wNt+qIl5S0GyMBssSMwOmpZ+JPGnfMknYd84zIHIhyNngyiFUA0mi/fo49OoBDYqlkPkuZoQWOjXM2DzgxpkMuKDTKJA0sozlfbEMlPTAVly/35xJEmYA5YLvL7vBJdtDvVOYHrg7e+qx+O7cwX33m5DPfJHZkJpwu96pZsYKlbTBvAV2Vex0oz4rAPMTAJQo0+dwK3f8PLnj0CxTrL/i/00aQYp1rpdimXDM1gYPGQ0U0lyHO3nklRS9l6wIs/pmm8lH94Trt1zlNe9d53sAmiBo1dhe11nGJTAn+ssdndBb1QAGF6cMjhIx4JZSvm9rw4MZMJ/ygLP2jKpmkiSYA0Z6mgwC23k+ZRUG8G9cnjHYr20r4ekZzVhMtiBVElZrLVQ9kxHRY1duUUSuUIBT6VxKXKQr8hixz9alVIGbnCku3fGZmXlHpgqmZt/nUiYD5qJlFUa9HYTG4hjS2iMnZCWwh7+XhrmGJUKFIgM2wIMwLgAPPsLiaRelOwdhzyi50JmUiiluahXtzGJz5TBdVXf9Ya9PB0NVhAHbGIhHT+AGPUiW8kOaVkWtASz04Tc7wGTARb6Y8eYDfCjKvpNyjd9Piopw0BP02SNmyNaeWS7gZjOg14NsKnvOglqhCLecwX3vW5Abd0LgiBPK2io9XIpl1XVesPw3wsF8wsXT6OiAsRCghBpw/UFRiJkB2iS9VFEruiFweA2ycQB3eQS58wawcxiC8FKJGD5zB4BFwLAXS56MYT1pN+wyeylVV5i42iO2ygGAF46yzxIXwkBtPPBSu2YBJ9Umg7TLmZEBFPByORCV9BnOyApsb3mFTQAQI45tHcLr0gA+kzHBM4M7UoaVbQE3pqywM82VqaKq4oTDuCjm9azveKau+/Bt/v2dA2DQZVUGMLNUPLJbzBn4q00/jLOgYDR5SKQHQMlD+VAsUQJiMvKtIJSqZO0OLrku++fP74EoBrIpD90StVmk1gTqLf5spIfycs6MWkXX3HLmWdhuqObd6RIoxkReNBqQw+uQr/w83Df+FVx8AlxS9lpefo0l/sENKoC6HBB99tZKGPW1tVZkxVWqMLBb22C54P0B1OtY1/PLn2UL0dpyacq1NB6stBhS/kyhyLVuf8+kBLonnDc1WlRZ3TlgEmfMTgUQUMdHTa2trbZcMDufgFWCPis4zhKk3gkIPN3D/o5M7IwAACAASURBVOX0MKi3ggjcaORF6XDzdUihRK0gw8cPFeVk8hKlSkC5WDssioGNbZT/8r8Lef1LrMbGA++0xZnSfth/UcLPYL6v85xVRZL4ivRHvT4dwV1hfJRMFbi9KzypZ9PQe6woxVuE/bspSUh+g0zHzKysnJkO1SZsyUWRpqSym3JikrAc3Tkk9hrggHI6gTs9YdC5djsM5mo1ltFrmyHwiFChrlKHW0w5C5hOOJBKCsCMCApv/N3Z5iKsNrkY1FzD3X+PG/nKHcX6xmxpmN7NyoIVFZaS3Rtc2ItZYOU6B0gSKNtpykNlSryuWDmuk3uqEF4EB3cb0GTa5kmU+WttJAvsNqQuVbybETK+l8vS0M+cTwOBBoAXFoti3gs7eOxVrsF7ZS7ncO/9CeT2G2HG4RwrCxGf5bujj4PmepvoIQz77KEbTLLWYtvIBrAAg8zlGQf55wnk9S8H1E2uw6tyLRDkMoVWVpvMJOstHopaucit13yv101GPnMW1Rhxg0vg+AE/b6NDXfJyjRu3UIQ7fsg2U7okPHbzgC246SgI1vWPgUEP7uO7kL19IIqR37+L6Fd+VQ/uElArMtHRgCMHCmGs1YGjJ5DrN7k2X3yN1XFNNeUnI2D3engWOYORMxatR6GtdFgVqifmb5vUA3JkFalmSU2SsE1TVyHA/jl/7v4HZHQbu9oIc71LPse1zbAGTbune8zKqlwjCWjvBqtd1XsSiXgwq9SGqCm5W6jKYroM/W2n/r82PLZWpCVOeRbMzkd9uHe+RamH9V0O10Xg2psM5t0TotuqdQZfw7YvF6xs8ozm4LnjoTIdMb4USx79Ip1tru88A5bT51BrojwSN+z9WfDED7w+HcHdqLoWrI+0bTIaqFFAWzM+bR10lBX25AEfdqXKnzMIZRwrichx0bfX4N3T58wCZNDj3zKqfrrkwssyyO4h38sCVJYBr3w2BLosC6gI0/gGmGHPZ+yFjodAZ5Obw+UkQWkpJeUae9PjAQNE/xKoNRT6twCa6zrJT5gt+9aKEkusl6pQMadiT2KDzoqyN01syDRglgt45Uj1xHTzaegbA8BoHAay1oaxUlYDs29/pAtuXMAvckRkzokOCWlockqz74TtHZel/HzjAUvyVPXfrZJZLrgxTO2v3vbZuikDon/OrOf0qWqurAVs88cf8tC2zN25gLW3AVuhCBxe51orKR1doEFED2/TJLLy2+5RqcIWUbEEbOx6DRLr8UtF2yTqNiXFEgNKucbDfaKImHGfgmKTEa364jhITk+GnBN9/H24wxs6fI24VsZjuPfeBZZLuOUS7o9+ixaQNR7+btgNh3p7jfrz73+XtoCRAK0O7/XZEbD2OlCuUmHT9gMi3ntrIZo64WwSWm0F5RpkoCCaSn34/rklUVICHn4E3HlDs9+uN5h3uQsuZBen8EYhScL5gYjOsJphPQ57cD01EUkK1JUqlpkMTIfAbBIq2mzJfnuhrD6kMaQTKnM3mzAZtHlYXGDbMFK3sFo7wCbPnrL9VW8h+uVfDS0jTfJERH+nFSqSxcw7lkEimFuZtbQkSeAQsb1kcGHrzaepV+C0WZCbjriOLMn910XL/Bt5WfltmOV2B+ieUwXShmp9zUaqdWZS0zGlCYyibFPpbMmsaDQAtqBIiA6D5gffYWYxHsJdXkCuuRAAJSPqPy6E7EOt+7ypcFyA11hRyJWbTvgQFDKFyhoXrEkRmI5KtcmHXa3zYZmuzWLGjH+NC1LaigRazhjg4ji4xdvAx2cXP0BSUucaMulWWHgW9O2gKVU1I3HPB37jGKzi2y2bkihkq0sdOs3G9NxMV/5WJGzD2KbJlnQk2o654WdjLuLFnDR/05/xEq25Dq8UhTG4ZNadOx7weUY5XaXSY20TaKzzsFGZYve5r+qQmeWwO3oA2bkCl6bcUIb13txn+2Rjj8/QqiYgZHT7t3jomlhcrtKrxmzubId7lGdw548Drh7gph8PmZ01OlT5M2Zp94K9480D4Modtlp0piCNDpEVZe3b5pn2uMf0VlVFR1kuMf+dPwD+1f+F4s98Dvjpr+ksIicR71tvQf7SX4O88xbcd/4U8gt/gUHx4gTZt99GfO02+/HHjyCvfTkkEtbmWy7gWePNNR2kHikcs+gN6J1WaU7Nrr1/7WQQ0FkuhwN7z+ifB0hkZ53B3A7TpYPrXkB2Wb1gMmIPXtt8bNOq3eJyARkPeE8BRfRM1QidcxCYVr9VkKMe71/MQTmWC7jRU8hWxMSrf06+RPeYM77NPe7l8QDud/455Jf/CqTa4BpIF3BRTPkQgOtj/yYgC///viouFFXvnlWd0z3pbOYVx5CoFfRuJOJh3B+oIOJK+29VmvhHvD4dwV2iYKI7nxLi19kIsLtqncG6RBKDe+dbXNjXbvHGW5YwHfGGxzF/FniOFYjN3eBLatmYTcwjAfLIlzpSrvrTHy4OU/s4Ierg8GZQnrw89X1Wr2559Q7bIMsiXKmipbJCCvMcXgKhpv3p9V24p3dZ7uUZtSlKlcDOtRJMqfMko2h2YvDJ2ZiZSbEELAVu3icbM1G1RuMPmEVZocjDUyIgBjG3hum2hWNa4obJB56DQqJILRI3H8BFoiYKS/YpAV7/9oH2zbW8XcwYwC0TVkyve/BukARYLgITs94Jipa6yGVjj5naqvyAc3CLGX82KTDAJ0UfIEUHzJIUta2j/IRShVVSugTANqA7f8YB4o1XWWKP+pB6g9egJBT39B4zZCV8ud4pA2r/kvdl5wqTAjVZdnlGSnupqkPjEnDyFO7kKbB/neX4dMS5xYxqlji8FdoFiWaEpQqkBAblXg8SR4hqBUihQG2ZuBAckGo1uL/7v/D3bt3y14o8R1Svcl8ViiROqWmM35Y61HNWneWObTybR1WK/ExWJRbBYGVM8GIJqO3zUFD5B7Y4m3DFMu8hwD21f52AhLMTIBGa258QqYKN7ZAJl6qQm7sksqlwnXt6D9i9FmCpqfk3sBXk0gWQLngoX574frb/fIsZDwVrHwFMwJIS44hVQukC8vO/RL8DS0gLyg42GHK5xsPIEqXl3CevrnfOe+xNTpIwcM1SXr/NxQyqCpDfo0mNRG3unfn0/ydtGYM1zRXSVW9xGNNb0a2o1Rn41GEIJVVn7F9Cdq9R58OGHemCyo8uV4aeytiag026hDSNEu8AcWGajthTqf0DTBcsTb/7FnDrZTV1LnOTpnOiERZzLgCj5WvZ5pbUH3flKg8Bs8qy16ALbB3wmlobPnMWo3PbzMHaI0bxtuzdIIxACLoql+uHS84BczUrMLRJovfPykYAKNY8HJIthgRAOAjdcgax/mWmGO88U8x3jegk08Wut0PwTUpARuKGt04r14gU6V2qZPMM6PfgVHIZzXVPr3ajvqr/6eGjZiZSVm/SxUzNTZbwkrbeCDmm7IK6N5ncsSjzzz17yPK80QFG5mw/Yz++3ghKmvreUiwFLH29RVPszX1gvmCLqKjPb+eKHpqJR4WgUuPzWs6AtV3IfpV94Qcf0MVr/yoRPNantX52tgQy3QfrW8B8Dvftb+LuP30HN792B8Wf/xkSmda3GSg6LaJKRn3IwRW4chlSrXJw7DSxuP4ipKW973KN8ExdU+78GT9v/5wiVrMJn3FfJXonY2bSJq1h6Bmvq6KMW4mAoXrwRjS0dt0zuItjzpfimMY6uZKDas1QLbTXOSMbj3hPTZvFBrzNtSC/YMJtpktksOCMAVQUbeIWM+XBiE9u/DC13mK/3OSwjZPSURKb2nP6oXtKeWA6RrmQMMYFP9z1XrhGtjKYo8387FUss0J9+AGBJY1OmB2KcO4w7MP93+19WaxkWXbV2vdG3Jgj3os35vByqpxq6OruqnJh04a229iYpsFMNv6w4MPIIJAA8QG2LEB8IAEfiA8QkoVBLRkwpvEARpbBDRi1h65ud3XNnVmZVTnnm4eYh3vv4WPtfU5kuyqrjaucg96RnvJlvHjx7rn3nH32sPZa1SbrCfUWD0UDkbzPeDiMezohFC4uQlo1GKuaMQ26rVt8mHHMzVKpQI6ucTF3D4gLLlXY1VYoeIwpcscw3MXBoHV2icSxDleAN9BY4VTeikUZ8Q0U7mtfgssyyPUrwIvfrcxy5PVwWXoPZFAa89wEWcZIY1+Lvq2FQC6UTskh0mpr99s0cKeP+sEIR4VgvM2jLibaXJSEHK15d8WEpEX9rhKKlZi/Vek6H+JHhQDDsqYxk0dT/LBEkXZOxiEFMaUXZKgc99ZXgdNPAoBS6E6YejBc+JDFRZ+LnYzv7aQdj8iBXq5ALnwCnvLXY3tVYX7YBbY24ZaOk4OlMc+DVYRwUW2iAUA0y9wyveByjTntgSISjCbVmrCeeIbanS5n1HHzMg273cud9SDEML8Cz9+tlAtu/QbzsZMRjXe9FSBwEtEZcY7rt1wDhl2mjnI2CUmxDLd2jo7FsE/ed03rSLnq8fEoltkQs3ScDKPPvIBzf1Hx+AYhNZRJscSD6bWvA4uLNI4tpdHua5qk2lTepT4LgLWWr2fYnpt+/vMofu9nmM83yOuwz/todSGJ2H2s2gmoz/Orv8/P2d+E27wZAAA7G8DuNtwMdS9RXSMe9I1W4PAZjwlQsMJspco6wpsvE/7cmKOGwfIa3DtvkgdofonrNCkzShoPNH0y5RqsqWDHoKd59Bp/P53oQdYPqLPOhLh0y6vXWj7Cc8Me3M/+NPBHvpv1HUDTsQMeTIMOPP9RqamcS7peZ/sM0qlScAgbm+7cBJ55Aa5aVyrziOyiRuVhOHwjkLvPeEiMe0ou9KREZMiNy3wIsXJLK+mRxEW4SgPyyT8cvP10Sp6Y9gq9nkh5IPIsNAVEEYtGlSplvJpz7LIzDpbpKBQndu4yjZJnzMUaJGlhmZ7leAS58jpw8bnAKaLFMyu+ucmIB4N5weZdpCkNl6Ys5PhZFq5GfRZ+LC8eFeDb1Scj+E7XXPO6JoFnhh4INKpq8MmKqPh2U3GyYnA8cxhkaUj1RKXg/YJQw3uiDMsrD3q8lwMlqyrQg5Pmgi+4sRtvIzR+jYf0jEzwA+AiP3qSG2fxKNdBTxWTTJxYIiAnrNV7hkYnm44JN8xSxWEPaDjHQ992zi5Iht30ICPeR5fzerQnQeJ5eqzplM/aDKVRGTQXYOIc5v3S22xQl3XlJK8tEt+d6NOBFTXSSnXg6nOhC1KZKl1vj0XlNlMQbjrSRqyMB3izHQiaFGcv5Ro9xEIhGNl6ixHUkROQv/CXaJQtZVBtMuK1Rp6jp/l7nZ0gHJ/nvFcnLqL4Y3+NXrt1Y46HbHo6dpLF8emEhfKFI7zP/tCfhrkDPJDUkZALn6RnG8U01LaG4xqwUAwIt5Hq5G6v82AyWomDXRaEAb8+3GTM90RCh8uYH53jIerrKeIjWWd57ShmTccOLy32GjzTmfNUqvJ3JmOtodUhf/5HuJ4yrYeUq74/wqeqtteBxlAL/krhkOveS6fkybE93GgxQ6FUB5hOQtrLJB4rdS+LONs49V7joTDubjqhEbactN2EG+/A7e9Czj0JLB+Di5SeVI0qigm9eyMUKteANPXiuS5XVEuueWVx9FiefC6EZqO+ht0lFop2NpWJshGMUKkKnLoI2dmEG/RJPtbbUyM0kw+3SrdBw6z4aWRfpUogSLJ0k7YqU0lKcbuJFm4NIQKEJo1Mi67WKGVe/UQhdQfbMFFrKWoLuFXiYz00jP7YmiQsDwgEb34W628b1Q6f8YChY53oHU++ZdefajNaqUpvvbPLzWOkXfOLxAHPYNzZ4p/d2y1nRV7/89K9hT6TQmulHjGEPOPBMxlrE0sPiKYBazzohk2/s6HGfoaOtVxh05myhfpGsOmE/N3LJZ8ekNoc3HST6kVG+WtQU0VaOENujLhusLIWNAY6u8AbXyGP93SiXCyRz+s7IKTfLD2XTX1U55IypBAHL1AL6O5gWxtwWswl91XazoQ3qg0fjbnOLg1Xd4+RgbXyT0Ys9E5GvLfW+Tm/wMglKQeHKClD5pe5/vIMeOdNXl91hgBMc+Fu8ybnWVQKZ1NesrUIaMFR4b2LqyrNp0RnhSKfWaWqbKYHwM0rwOknPczV66kWClSFqjS4F5Tzxo2UubRQBHp7/LtzqjLlcj6r5WPcx5YatXWYTWGcVtJc8B3VkAId0jd/R8VK2sCwc09WQIplAgGs92UyYs2j0SIMu9FifUolIp15/gDt2FiFYtKUv//75XP/Axubd4HTF+59sM05YHsT7vpVdoR5OlrSkXoh2UEH2N1g+DIaAJ0dyOppSJ4qvak2R7hcqUrvbdRAXOYDWzjChdpc0O5J4z4hba07fQF49Sv0HN69BHexcq/WqA7X29PCcGum8Khpn91Nhm0Ar9UWtHmaeQYxXgpdkG4yZk4xnULOfMzzqIttREDTKUozOp2w8FWueVSBJ3My3nAPjSwEA2J8NKUqn4Hl75GHw0DJsdzuXXrc5RpD22ZbvX9tnOrrwt7d5KLNpnBXL7FD8ju/N7RY27VYoa9e8AcEii4YPcO/2/VYXhVQIQMess7oVqfjgM6x4l13T1NETIW5uTZTMOrBekTCsAuvOlUml4l50e5gm1GGyxWVdcBySLMdnpmxe1brbFrKMx6oa+e0kzWH+8ZX+Z6TZxU4UCIdcbUe1nm5xrROpc7PNNFlTZ2gmLDQa06D0jqTCVFJ2OywOXmRe2U85DzMo59OSO/wX/4j8AM/GHLgb3yFQiWrp4gZjwoB7XXrbYrEeJF5TQvFRRpEE7sZduEOxkyVFBOgUIKceUZBB32mwkqV0JVdawav1oi+1Glw3Q5FREoVpmPGQ38IYTTk/lCostS0c7a3H/LXiu66BzNuPDGNORZb+wfwTJTaLCdJiQ1twIzHrWvXaEvMARr24bY2gLcvQc5dAM4+HYxCnirdtlKn9A5Yb1tc5X67fpVQbEsJ9TqKd58GO7V1F26lwMyGRSv3GdF9f/oHNoQXOlTe7e4+F97iMpuHxmMqIm2vB0NlSAvlw6byui4SIxmyLlCRgIKxQqWd3vawjQJgfolplVqLaBfLy2dTLtJnv415teEAuH45nOgKQ3S33qZHaMRN3kOO6I0f7IdNWExCgWQy8pwlhk5w0wk9g61bcN941TfYICkFFkzrZK23gJxQPxQTNnWYFy4RUxCG8bZik+eBGYVQttKA59Kp1MO1A7CmKZlbYniYZcotwgYQt7/NXCCgiJcxu4EV9SSf/HbIZ/6UN8a+w87uoUVTivH1ikomimHY3jQN1wgQOmhIEks/Oefx5Z6SQJtRAPBz55Z58NimlogGobOrBxlZC71Iggg1eXc3uDlHAxqW65dp9EuVcM8ietQe9pfQk3S9Ax6MC6uQU0/yGe3v8kvb+6Vc44G+fiM0iBkTo8IqXb9Dw5RnLBh3dlnQ7+8HFIZFPhYt1pqQ+WXI/ErgAk9KdKyWlui9xjHyn/88xr/434OweiQhhywSPHeJ+PpUo04r6GdZ4Hm59S5w5XVVf0qDUbSmon4nRGXmeAHaLKfkWCvH2HvS6yrvC8kEybU/hrvxLtw3XlXOoKKnRBBTBzOCOUsNaaGTiKhRQOsNeowQoFF/JAE2WyhCqnXP+eNrNoZpf/cbcL/239D7pV8H5rQvJynTwStV6OTYc8hzGvZaPdiqs0/SCbE9O78UbJOlX0wBbOko192td9/bnNq2uO9P/4CGVCqsjJvRXD0B31z0zPPAKy/B7e8Bgz6kXCGOdKaj0g06XpFFjNPBvMiInCbIMj787h69Iwu3DbsONZb9DnD5ddJuHj3p84mizUdYLjNcvvkOsH4rsDsC4Zr3dwmh6h0ArTikiQYdFqcARR10PTKHsMkZ/LzBQve26LGfezoIC6dThtuarxNTTQIUmpiHQ069cfKJ63vGAw+5Iza/Ac/zbYeifW+RhRldsGAJMywm36YG0JpFfErq7FNsF58hc5IoBsqtkFqzwzd3HkbmJmOmJl75DaDWIOWCRRUWfSAPtYNakymdbEoMc6nCvHg6ATLNlcZE6biDbQqFxAW43W3Wdip1GvWGcub3DriBjp5kDts2oZKvuXRMqubtDcjqMXrKyUg9c82Tpux0dS4nesjojEeD0KV59wafwXAAnLkY0kMuJzzQ7nN3j9dTUajd4lFeb7nKSFFmEloH20HIHfDGS4yfJo6BqEzPsHcAnDgLaSlN83gIdDpI/si38bDIWGOBCaQMeqx7TcfEi08JMaSxZuqKhnlE737lJA9WgM/A0EzTMXUNVPBZGvPM/XvIbqY5bB5eWIkU864HwMIKC6tnzkOefd4jUdzeRkiB2vpWLLtX8yomkPllNo1V6qHZ6MgpPi8j5ErT0N9QKND5Mi+9kLD2ZE5WFAGDAYpzVWAw4PXZ/m3Oa4/AJJC8zanX3Tu4B9jhege+VkYbMIDxwdua9ighub9v/lAYdzgXquzDHjzHiURAsw05fQ7u8ps05kYvOjMM3cLP0g2f50QeNBaUdKoL92v/jTnr7/sBNVaTe3C9LpsCN67C9TrkOWm1CecyIWjVVTRss7t7F7K2S352g6xNxvSuuh2mlSpVUuJ294NYMUDDrhh2Q3NQuYmesmgzlbMOyZx0uvDkU+WgX2lQxiKVnWRZq/cW5VSbSkFQVOTGAg1hpRYoBmxYgdWMLeALxUjHgU9l6w7fV6nDbVwPmquKWUevCxx/gq9bV2mWkSiq0lBMcjnk0POMYbwipwKh24Se5ZmnA264XAsennn242GgWI1jInm0u1iqdQ39J4GuIdeipyGQrl8GTp7nZ27cCbnS3U24UtWn96RUgdu5C/fSr+tnV1mncQ448xTc1m1VhFLc/d4GI08R/u6Ny/x7hYQHxNIRuK/9JmRxhWF3uRKKitbkVihSqayYsF5h5HSWg07ZLY0Dctx7+o1Zp6Pf0fb5fUh7Re9VEsQk6mpcsxTy2T9DeCfAe9nbY9pg5aiu732mrvR33ZioNCg5HNIJxVYsZalEWq5QgJjxT8rE8O9vsWFv4zqwdNwjcKxxzisumSNgnbLTCelB4iK7fPe3PBoJ6RTSXmUq5GA/pFiA303UlWc8GHp7QTltOgledrEMxFmAPGZTOJMMVOoIqbfgFlYg3/snUf4skWzSXiFIZPk4n/tkxPWeTeFe+nXIXJuqalt3FDwSh5RjWRE9WQZMhjBNV5lb5jPq7jM1bF327zMeCuPuhgPg8qvAuY/RuzJ8dFV5LzbuAJMJ5NQT9NqN0tY8zGpDDb+2vGdTPkiJSEfbWuQJubEBefoZfuZkHB5gUlEs7CiINSwfCVJ/9j7jR49iLqztDX4tHQ0463INWDnGQklLhSIK2gWqfB0wGNxMyzPSVPO9A9+eDRF6VyPySPvWfvMOzXib4XSOiCEA1k7NRaKETYpAQp4F5AnAuccFeoczcEw3GnjtU3LIpCFvefrJgDCYWw5FqmKJB/KJiwEFYEYqKRMSZ00agntTMnERIhOiX7p7cEmJOclqPQgEmwKTzd/oCrp7/LeY+OYYL3qsrJBeVHh3A257HTh9kUyDJpC9cIROwfXLoYV8vA/M91l3iQv8m+98A5hqr0S9EXKlN9/mM0ynhMdNRtyALofbuUOysNe+BvnYc2HxJ2WqbFkIPoM2YQfviJBZ41Zav6WQwBnOnlKFLJTWlt4/CNEDwKa788+yeNtqw9nBI0JnSqGRsnKCzsb2bQrBWxRsmsZz2mRTLAcjWaqGgyOKCX/duROgtekE6PcgJ4+HiKyYEYM/v8zfM172seLpjfddhClXdaxcOgmSliIsTlvOPi6ovjIjVpdOeHDEWkvT2ocAIT2JIh2/LAM2b1PLeO0M52nPw9aXpbmimPbG5aQ1uPMOi/AGEHBKeZKmXOsGEgG0j8dR+WrtDNep0Tbvb4VitvaK4GCXDmGt6aNtVBt0cLKEe+M+4+HIuec53G99Ce7L/wu4dhnuP/+MwruEi7lUIbb9qefpbRolZ55yIfrOryi0OgP8vZq2++7t8D0nn+DPhn3gYI/Y1Czlxhj2KAjSmmdYlU499wRVe7QlPSlRWuzjL4bcuuFVxwPmvxdW+LetiaXe4iYFuDmMXa8x79kT7bNheH1jWpxbhstSGlg1qHY9ooo8bjxkBFDSDlxD+lhBt1zzUEqGqeMAF5sqZ48tWiO9UoIzt7sR+Dc0jSJxkfC/QQe4fZU5RW1emoX9eY4WQ0MYvHC2ddroDQoz6IRiQhqF5WMqsZjf+34ghNsGp6vWfagqccFHRa53wPtfqfOQnFPIphWYTWln2OfmO/9x5VTp+uI3/67j4fLkc5ALT0M+/TnIx76dRnZnM6SnVtcC53kxIcvpr/wS3O2rkFNPYPqFL2jz2IAHarEUmsmGAxrjzl6AulWqjCrSlMpE/Z4yJc557iNndaRBh55/fT6kONaeYEpj+Qjvp0VYFh0NesSjd/dYt1k6Dlmk507hjQKvQdMXUm8xJWSNbLUm1/OwS8NmKYRSFZ7TCRpJRQI3GVPlyMR1rG+j32WT0wFTZZ4aww7z7p5GSTmN3u4Gi9qDbih6b69z3R5sw331S4B9XqaHnTGe5s5DSOEcay9PPU/v3Vgl0wn7HCzN2VdPOdNriWMa5u4BcOcdzjOh5q+7/DIlAPe3CMmejCBzy7xf9QYL8XnG6Gd2f9j6VvUm3LnG6x72aPQPtkNt0ri23mc8FJ67VGpwzkHeeQduOgXOnw8G5qnnuQhGA568hlk1zyRLaUiMjlaLaq5/4LHE+c/9NPNgJ0/yYcwiWIyDZdRn+NybKfA4JcdXSKXLpkFt3bg2ImH+ffkIkRcnzxGxsrcR+Gac4yk9A6tjPrzgvX059oQWv4hykFg9bz3kJCkprQCNmWh3HZWgRlp07AUxEDN6/U5AeJSqWt1X6OOwywUyt4R7hMVFYPwWmHxLyAAAIABJREFUbjwkksAMQXcveG2mfFNrcIMDNMrNdugfmG2uAkK+dRbWmE0BZPSOi2WgzMPLGVY4m4Y0BQCTNPSiFlUVzTaOF/W6rTAtlXpgqwT4macuBLhjUmae3uVe5UfWzvKw7HcCTfCesoUCkAvP6aHEAqPrarGzOReeU6kC0wyQJuGPmF9A8a//LRoKw4pv3GIkVKlzzbQJFfUNY32mKt1vfYn0vRfr9BYHXfL8N5SUqkoPD7sbPMxihTaa/KPJzM0W1A0GKxGNZTb1VMzOr1V93pNhiE4Veunb+RvBQWGDl+OaKpaIBOrs+H1kKBumUwuaIlQqgt4esKu48DgK6bF0zANq6QhQn+N846KmdSZaE4mAxVXOoT4PeeICr//aJcgzL7LobI2JXhqxCGnGjPh0vTNCusGegKVjPu1nKV6mRpl+lKXjyg6pfShxzPmc+zjtx5VXgZMX+T6rRS1onW6oBlyyQPRWSDzyColCcQ26bHtogToEMptOfY/xUBh3lCuInnsB7urbVBg6dpI5vvYiQ8dqI3A+zLIYZhnzy5rDxJQ84bK8xjDGjMNwiGz3AHGrpQ9BT2uPqx0FD7Oh2pLDXqCgTQKXiqc5MAN4/DQr3wN6/ZiM6T2cfpIPJikT21uf84VVGCZ7NmQD+N5pF9amz07B23yoN68yGphf4vwNkZFN+bkuD9KDcVHJubTjcOFIOEiKCYwpkSyKVZ/vZvpEZc+s4zDLuOlyR4Okm8ht3eJnLB4NkMqD7UDg5ByNnuL9Zek4DFPv+gPC2ip1D7d0e5sepog0D4pNGblj3HBAtsW4wAOwt8cisaF/inFIwQBAUQ3eWA/Yzg4PA6NjNYk3K+jqcKM+jBUQR0/CM5aqbJ7buk2nwb5/4llSPKdT3rf2sjKSton9nowYGRVUdObuLXrnR04wgjNDefkVeBK8qT5DS4k4B+xtQ/7sD1PkPY6ZMrSaQb9Dx8EOzrLyIJncXlJWBsilUGPJMwD6fusBqNaB9ZvsIF1cUVhwOxj5qiphDXtBTvBgO3C7m5buLPzUqGsBpvwy5q/daMB90VokmABg/r9KFBYGvcCFFBGuK0dPB7GZgtIpNxfg7lwBtm5TB2DQURFqVW7STmnbb04PcxkPA5RTIhbNjUbXkGzjIQ8PIy4bgXz21SYLwNaH4qMLjUA9zYdGMMoKi2lOZbXpiJFPUuaayaZAVPN9G27UZw+FsdUaA6lFg+MBawAfgszeRz+iGPLCp4FnXmDK4be/CNy6BSwva/dnMeCnJYLLBzRw2kzA8JM3VeZXQsFGIp6uf+gPI/7yb7Li/eX/DanW+LBUU1Seep7h53DA183rSidAXAlpoO4ePLRx2KOnfux0aANW1jeZW6LhtKaa+ZUZlELMbkfz4gcdRiTGXOlyPtj1a8TXzmsHaL3Jotr6LbjzzwZhgbgYcq9pGrz9WYZHM3rW7Wietw3naGgi3kPX26MHnZQh0VRZH/UAPdjh5yoGWeICvS5r+IliNo1Uqr6r2B9keu+kXGXU4btl84BTz9LAIKjIGocIUjfMs2PqoNkOAhrT8b3QSIDrotYMTVSlCmSeOUov3L2ijoEVzaPEpyykQAk4z21v9Qw75A+2+Ww6O0GI+vSTuh5Fefc1tdhUyul0Annh03A761x78xUaw0g8kgd7G7wuW9vJPD08FVnH4jJTkzeusEZVrWuKQCMlEeD6O/z8tTOQtXNw23fgrl8F3noNcu4i00aatvKGySCTm3eB1WOMHApFpjpUeMI7BoUiD+OIBG4oFHiPN2/D2f4pFMOacI5rS1MTGHZDI1ypwkjXCvHFMlCKNM055v9ntIEF8GsFg67uDYW33r0GzC0wrx/FvjPaa++2jwRU2HQSmtSUUVWabR5cUcxIdWeD72vOBzGSca6oo0h7CGZ6O7Kp7qFYVdemkDNP81CxFFylzr9z/RK59+tzRG2NhzT49RZTNsZV4xyLrht34G7fhrzw7UphXgpptfcZH2jcRaQM4P8CKOn7v+Cc+4ci0gbwnwCcAjVUf8g5t6e/8xMAfhRABuBvOud+9b5/ZDoOudadm/SsrK23vRxybjayqVLbao7VIF7joW8AkeYC3LU3uamfeAaYW4R742vAwQHcYMBQF4A8cZGfaWidzm7wKLMsFG/UaHmUQqUOJB16t6Wyth9rM49E9Obsmu2ENUNrhkgNvacqSBHgTemUh81Ic92NFotzlm8r1+jxTEY0VFXFzKpgr2eXcw5wmtNzaYBd2QYBQsjnUz5l33HpldpnC9CVOje1zSFSmbzaHBAJUySVRshdat3C83UnZUhUCJj+YhJymL19RgqqPAOJAnWCedhRIRxohu4x1S59zQ179NpKFaYrIiGFa1RgKs3WlMsBaApsf5NiLVFMI6RcOGK5X4PouZyRSMrNLEk5eFl5xlqERRTlGiQqwOUOiOr03LTxi12fZV8MR6FIQjErEgKqZOT4t+cWQkrPPMLdjeDdG6vgoMdiW6sNd/casdtbW8g2tiHXbiBqNSDPPEu2xYZ63VffpHd45gIwt8Q9uLtBbvy1lI5Gs00juHiUVAnOqTKRrq+1s8F77+6FHgwR7kOFoZrughSTcLgbYZ8xgurhILZ/fNOQdieLANGURnt+mc1VZaZdpJBQc6DapFG27lj1fOl81RkBDjrU750pvrt3lHGy1wXOXNRO5BFMNNxZEdp6BYyh0RzK8SDs+/EwUDOUKkz7WYSmuXUfdVmjVarNZYtHuS9abXJQtZcYlUnkCfruN74Vz30M4DPOuZ6IFAF8SUR+BcCfA/BF59w/EZEfB/DjAP6eiDwF4IcBPA3gKIBfE5Hz9xXJNv4H89Cbc5CPPwccOwXPoJa7YCy1QObGQ4ZyljcvVYJHnOekhbXGltYi5PR5uK9/RYuUbeDoCXJ2WDtvvwNcfo3h6+JqwBuLFgqLCRe5FWa0+88jW0yDs9+hUVTuckngkRok5pfwYMwgaXFEKlXO1ROHxXygUcwFOJkw9+lyQPi7xqUti0f5vv0tfqZ5zHZPPDrIBU/avINh7x4qBdfva1dhAdjdocEAWJAzUepiSQvKJBeTpERCJW3wIdwxp2HtHdDwTxN/CLnpJBRqAR64CyuMBtQgG+rC9fd5aNTntBhoIWlOMrjduzSqpQqNkiJpXG9M/hMRYH6VDUTVZhCdsPvk8qDTO+p7Th8xpI+9L4rJAJmONaWVa/v9Cmsb8yshn2t1gkJBIZpFuJKu4UEnHBZ55u+D6LpmvYR8/ig1tGincMj2shqKtt8/rrtHjzOKgYvPUqT67jWuFwBotRDt7mGyfoCDr93A8toacO0doN2GnHuKjJHWc2HKS5MxDwDVSsWUOgrOotGYKlcUohmw+Di3RK9Tob+e294UvOxZe8RUFCIyi8L1PkutFe5hNvV1IN4zR3x/nocmL11v7u471EH+xHcwdeNyIB1rmq5Dg9pjtEVx6kwPSO1MPXme2g/jYXAGegcBvaIoNqhEp1cbM8dsRp/CzeoZT0ZcN3nK6GwyIg4+jlnrKxRJc3z0hBr4sUZrRxntdJVy2/LzH4Bz/0C0jOMwEoOifjkAPwDg8/r65wH8Gf3+BwD8rHNu7Jx7F8AVAC/e948YokQiehJHTnBRJWU94csBO5wrr/M3kdU7E7+w17Ip5etef4nhUaVKz6dWg5w+S0phY2c0QZBBh+GUQbHM6Gmhzg1I8mQhJuoNetYuZ5hULAWYlbXzR3FglRPhKT5Qj9/+RpaGjlnDlrfaHs2iD4KGNkmorKO5TNfveAiWr7CXa8oG2bkXzWNcK4DvSnU762wyMWGL0YDXuL/FYvLBNjfmyhrkiWcg86v0qI3sbUy9V2fEYKY3OehwAU4n9JbsgLZnN9EisPUbFBN27JmSVlLmPVWvVSp1GnaRAOfUL9fRrsKdu6EeAsDLIW7cDGstzzVFs6JGQ4t86ZRsi8vHmMcddMLhbR6lolHcoEOE1mQUjFIc3yPScc/f99zfCfsTShW+t1Rh08+g4wvhbmed9zRPGaUNepSfXD0ZEGTdfXiRcFMpiuIgiaesjOj3GOK/+jJkeQUyP4d8kqL95BFgfx8YDuEuXYJ74+vhsBPherDI4cgJ/msNbVbMfvsVpmy0MU5ai8TG720QEgoEb9sOoWICqVR5ePc7watX4RHPpmkRVToh2mTjOv/tHYTUbGeHUY85hAVGguirstPJs/ciUEylq9LQaNQpHz7RN86U0iLV2l055vsyUK7RcWpot2m1EZBWxYTGVjtuAfg+DGfoljzjmu0eeFZUMeeqVAKqDYx+5j/BvfkKm/5OPcnP2VaKD1svp88Tkqr32zsn7zO+pZy7iMQAfgfAWQD/yjn3ZRFZcc7dBQDn3F0RWda3HwPw2zO/fktf++bP/DEAPwYAJ1aUIW9/k0Q/UcQcc61JL4J/RFMFkYfNWW7WC1ZYQ9JkxKaGQgHodZjHTcq8IU9c4AO309Zy6dMJvZzlI2ETSQSAXoMJD7tiiQdAMVHcOIURxHDsikgRUfUm8/4k4gL90hfhhkPI534wFBnDTfHoGpQqNDTTSeBsT3qBNGh3A67WUIx/xgLX7l0WKi20BfjeqRoYDZmN59p0Vd3BNrG+zjH6aMxRE3Yy5MKuqNEtBJ1HE4BGro06FlYOetxI23fgai3g0tc5rxNnaWjaKxpqx2HzqfyeF5lotoMhiWJlBi1CSrMNLvp8xkrvO+jwOifaJTqh7JrMLynTom4EI6Ur16hib52z5p2XKgzrp5OAxjHkkRV4LW2gwtcuzyAGdwUCtNOK9tOxwgzpcfpwvrunXq1S2RYTOhzr1/m77WXgYAdu/RphtXO6ocs1ICqQS77ZpsEA6BWmKXD1TbirbwNzc5BTZyGfeJ7P9lPfhcrRo0znFRO4K5cQPfm06prOhShsNODaVYZRCFk7Pdok0zTGu5d4vaWKGmY9jGtdpjcnI6XrLYQDztakwRCtLjQZA+OOT0eJRndSqfPvZozGnUF1jRLa6LEtRWlO3vwSn52P9ve5lotJYKU0nqR+hx5+UmYxuL/P9blyzKcM3bBHLL0JbSdlfmYUexy6Tx8B2vBWJTJGSeLk+BN+fZDUUFFFpSrKf+5zwNE1WJeqnP8kf2/zJllgW4uky7AmTSDAnd9nfEvGXVMqnxCROQC/ICLP3Oft74XPcb/rBed+CsBPAcAL5087bN7VVms1TlkGLKyExgMReHa2VjuIMEvEkN2acmbTD2vngOa8L4C48RBy6kl6S1ffAs4/wwezfSdAw+aX+D1A7LYIDxqJuMGE6AQ0F4Kh6+zBFVUn0x5uuQaZUqfR9VUma9ADdrRhqd8B2lqkHPeD9+E5O3oBVggwVxup8S9XYNSyuPk23JFTkEIRsnwC7vYVyJHTRJckymBolfYsYweowt3s5BctTLphj9JuOxtMTRlL4WQMYByK2IZcGij5lJFVlao0zJNRaI9fOcZQc/M2m9EGPUA9EylVeB/zlF6UUTVv3WKhqdYKuVktjiPOQ77cYHLFRBkwJ3C5UiOY3JttuEKihdIkRHiR6l7aQaG6AW48JFHcaBCMzSxvitUtTHUIIJFbUgopmRnOISKHTnCeacrNq3w8pvjlCkV65M02UVH9Dg/XSpXpopQpDWkfCZw7cUwnxuWs3dRaLMR/5ctwaQZ3dwPxuaeI+nnrZbgrlyDPfIJrfDoBttbZjDfrYBRLQTqvs8tUy/Ix5VpRQ1ZMmKI4fjpEDvpz3jctAGdpiMJma0mGOBsP4LpjpTXosKZUTIDVE1pgHPDQmQUGzGowGA2FUXdIRNhqtUlPfDqB9JR4rdYMnna16feDURSYZJ4kZbiBwkarTSLXLHoVytxJY17ReGP19ItAbFH4NNgSgHs9z3gIr18DLr0GXPgYD0Vbv3FMjH0Uw116WR3HRkjX7WywS9py+ekEbjLTSPY+4/eElnHO7YvI/wHw/QA2ROSIeu1HACiBBG4BWJv5teMA7tz3g019vZhoc0YLOH5GyYcGZKQrJsTAWrHOwt/xkEbVEC16073Bai0qwVKq3Xd3gOtX4ToH5GC58Gww5kAQARah52QNMsYn7wWhCyGMLCbciLMEW1FMNIBzDCGbC8DyGuR7Pwtce5sbs7/v+VCYX1YEQyFi/lpb6n3ReJYCFKCn1GiFheRy5p21rdwNtMvQ/kaa0mAWiuz0A+iJWh1h2CXU7tQ5wr2qzdCAUa75IitihPsAaJfflCmZ+SWPOXf2d09doAdWKPqUkkjEg0Zx7m4y1oOjwDykhbtAoBuYjoBCQz2pLiMfnTfGQ16T6H0cDyl3Z5Jqq6eUITOCcykkjiEup4eeG/RVEVLNBebpgcDcqZ6mFJWWIZuya9GMuhVcgUCVDIR8vgqSmyG1Zh96hEXOud4K6UZLN1TqhOKqIDuBA6oe1JiHkz5w+XXIJ1b0YQjkh/4SZOsO3LWrfH/vAKjUIMfWmO7LM+DuDcjZiwGlYyNPwzrTZiGpz2uBesQ9CXC9WhRo2PHJyNd/EBFt4pR7nhQYPbhXXmJ377knqd1gnO5JBXLxbCgyFsBq33jAA2PQCdGX5ebjmDDkpaMBcRURDCFxAc7lPg0qtZaypNb5XLdu04AaV9Gow72/dMwjmyA5O39Hg2AXokIoFluEbc8rJbeS1Oa8E+hUtIQIuz7BF6MhZE7YdWud6sUkHFZpqmpfCZ2kvR1mNQCYTrERxt1vfCtomSUAUzXsFQB/DMA/BfBfAfxlAP9E//0l/ZX/CuA/iMg/Bwuq5wC89AF/RD3jBguZi0cDHG7QAQbafp1lQKyV78099eIXgyL4mLk0KRRobLZuhfB/f4eb593LcL0eQ7ytDciJHo3IqK+Me4TVyfEnAs5YuUhoeJPQDGPImJMXQ2rBhrUtDzrE3dfn6CHOLcItrzFdU6lzM+UuGOhhD77df3czeAG1RvAmzEMoFALaAtBilPMHkjTbvEdaiHEq0gwg8H6nUx5M3QP+/oVnA/91Tr1RD6/MVADF8o0KEXTf+Bohoe2VIO2nRFUuT4L6+/GzcG1lvDQemELBq7v7DtGTF7VwpgZdG4zcdKIpLiGs0TzG8ZBGxPK3SGn4rCM3S+/5exgP4aB0ERIBoo1WOe8hi4QKAfXoIgdAD3pMgSiBpCnTWUacZevY8xw5xrHFMjd6nkFUNISC6+A9HHQJpy1V6IUedHnQJKV7QQTFhOkjg28mZa7XMxd9v4Jx/Lh6i/fRin5A0AwoJIyoLPWVZ4CLea/Nu51OuB/ml+jYZFO4/QmjiMYcjU6ujstkBLzy23Cbm5Bjx1nbShJ2+gI8xIwBtNcDyrpmewekK+juMRWbm35CBTIe0jlIVZoxIcukJCXtu9D02OLqTKqwwgNk1OcaHQ3Z8NPZgbt7gxH/2vl7Dl/W0MTzELl+hyRveRYOuJral90NPhe7h1b4tsbKQpFRjx3okTCVaXWbuUXI0TPkuTJSsjvXKKxSUt77J54KzLjNNgvsDYVBW/d5ocBmyQ8Y34rnfgTA5zXvHgH4OefcL4vIbwH4ORH5UQA3APwg17N7Q0R+DsCbILjvb9wXKQNwES8f0fBLtPimHoF21YkXGO7xRu5uKkHQEd746djjW93OOtxXfwNYX4d856cDc+Gtd4Hjp4DLb/ocNC6/wbRLQ8NNhUS57h6J98dD5uKffl4N14QY2GE/8NcYgZNtFhEgzYJ3a/zovEFBSCAqhOq/VvQBRb9MxmTHnE4otLC9AXn6pC84+XRBvUVP0+WhMDXRTaqLy7wnqbcUKXDASnylxsOkkDACqDZD4wlAY1RIAJfxoEpTGn3D0CdlFmhPXWDeu7/vDbvLM2KNkxLpjC11FhUCRHHYJY1BXKTRsvDd5UCkMLJhLzSyAF6QgsXMnHPpk7fb89nnhOAZxNSpMXSjAdMgmab3rHkHsccX+yJgNqW4guKi3XREx8EIrHLHw6ZaDygtj64yJBJ0LhHrRxZWO8f7mk15bd193v82cfMolVicTFPel7QfmnvSifIWtX2KSNonQx3JN7dlwJ13gRPn6UQkJXqrlbqntcXBLjt18yx0S2p/hkvTALvbWw89FFsbehBVuYanGddStQ75+Bpu/P1/iVqtgPanLkBOP6WC0R1G3ZU65FOfYcSpaCBnaCJrqLM9lJRpZLdv02iqY+O2bnnlK5dMWbA0hA0Q0oS1JqRdDFS+i6vA+k3+v95S7eJjcDcvU/4yKQM1RYDZ+s6d8jo16HAtrKpAxgxwIyfbrLSPhPSRNQsak6xSeSOdMiV85zppjA2p5wneHK+11mRKs3fAuRw9w9c2bgcE3Z3rTLfdZ3ygcXfOvQrgk+/x+g6A73mf3/nHAP7xB312+AV4w46KKc234CYTGpPVE8xtme5nlsFZW7DLSd4TF4hB/Y1fRf76G8iGUxSaWuhpar776EkWFRfVYzWDXOap6mlBC0W43/wiMNKmkDiG3LgCV6szVWOdq5YLNt4Ke0gi/mdSawWoF8DrtVCtuUBc92igm1KYm7bQ2boGyzXg7jUuNIDXY6kjS7tEMaOaqeLRSxVuRqUqgBV4gVDsu3mFn19t0ktDhzq21TqRMuMhXKlCA5a7cHCVajy4OjtMjYz6jALyFCgkvkPRTScQoaCHs0NlPODPFEpq+VmpNHwh0uUZUyaTMYuLnX16NFa40zk75V33HYjVhhrAcdigACRNgwerzSrIMxLWqRMh5ea9WONCCSgVeR3pBHjtJaDRQv7V38bmr76MlX/0tynybPdylvvGIKZG6ZBOA4rKvOicAuwSxXBGbzAZ+14GN51ATj7lC7aIA4DAaVORl1v0osslPpfePikxAGDjpo9q5ORF7pnVGl9rtcM6NaitNUOl2himTVlIJzwAKlUW+rt78OpnxRJw8ePAZIy1v/o5oNOBLK/A7a4zDXPrFuTCRab76qyjYP1GSF0pVNn2hxFsUfC67CkfXHcvpEKSEiQvsJEsLmoTYs87CNaEh+4eDWOpAiytBnTb2jmmOhZWtRDb8U1rrj8OKUhbx4UCn1GuRILT1O9p9mNIcEh9lzGft1MpRWktcq9bncY5NqXtrMN4dMj8GpHsUB1HyQpw5SrrYEb1ceV1poruMx6SDlV2ulFRSSvZzXZoXgHguclt803GvFn7WyEHV6rCTadwWY7iH1LmvWNnWDCtt4Lns7gSHl5Dc3F7+jk3NskxY8M5SKMRQrBagyG/yeNVydDn9jdpoCztkc0U2MyrMBRLv0scsuUJRQIc09Rd7L2JNhRV62zvN8/fsySqJ51rJGJe0XgIN9qGF26wvP+gywNg0ANWT3AxTSdwu1skTVtt0LMuJsyRWx6wIIGUykSUc4XF7W34PD8snM0y36TiBr2QrlAeaklKcNMx/06awk3pdYrmHt2oz+e8sMovpZuAGmq3t0mo2InzvG/1udAlmKZw+dh79zK37HOi5lW5vtEtMFXljH9Fw2VpzHt2P9y6yn+7B8CRI1j5sVN0GGbx2cboF8UA8gD7TJTmVXHtJhqNqMAoQrlIXJ7xGRkaw1KFxYS/Y2k451jHmeG2t5SCX0MSBaTHwS4FU46d1GL1beDSa5BP/ymgtBiu34a13RtE1+VEsK2q/NvKGtNKpSq95kFPdQva9Mxf/K7AidM9gCytwu3swN29Azn7lKY5S3y//l2p1APCyBhM663w7DQPL4aXz6kkJdoM5ak3RGj41m/AtZTrZrbBEWCaaTTgHqi1PNwamzdp+OcW+PNylRHVoAc5+zGmHw3TXqr4XhtpzPNZWSZgtjhtqReA0NmkDGnMwZ192h+oTte1G/U1ChHPjYTuHl/XPYfGnDYAxnCNFjvk7zMeDuMOYmil2tQiXEba0GqTm888Nuu2dDnz6nfeVWREgZvN5ZBPfR/iU2eBo6eCgdy4TSM+v8Tw6dhZYNDl30hT4O3XKLABwI0VTmbeUJbBdTr0XttLHvrlBaoxU1w0jG5BDySjyNWwDjll2igdV+N84iK9JoAndjGhQW62+ft7W/d6hfub9NyimJ8xGTHdUGsFj8cWWKlKIzro8bre+ho9r2LivTY3GdO7fuG7uYDfeY0K7B//DhazjAI4VeIt5+jBFZNAqVttarrG+VZ/SHQv9TJyQgw1beREZgqQI3hCKoCH8eZdYoDPfYwboZDAZcoq2O+w1f3lr0BWjrMoZR6xNowIQGIpy8cqqRMAmDqX1FvhcIxjSGNFu3/bPMDiIr3FYkKYWrMNSSchKrJhaRDT4Jxt6e9q+swEW5QQj2pBaUhF+T6PecjSMSJ1evueK0jqLY8okoKigyp1OhHTAtvZTfy6XOUhEReD4k+5wgOkVIE7OAC0mUtqTTZ2NRcoZaniz1403pqp0gn3kBpWGCWFzd20UOPY03/I3BJz2IvLzBmnUzZ7DTrksFlYoTGejPlZ9Xl+jtWyLMXl4P/vcgeJ2ZXqrrwa0lOlSnDAShUPeZViwjmvX+P75pa1zqAdpSZd2Gxzzj2tPVU0ImnM+chMShWtA4wDFLra9Os7IHkiYLDHCLTahFTiEF0YW+agy+7hWiM0dMWFoIva3Qe21uE6b0E+9gL56Y2s7d23GOFdePa+VvXhMO5qi5yRYEENZu/gXk1JIPAa37rKB7C/y+aA8Rhy4Rnmz010wagE2kueXhcuZypk8yaLQrsbcHs78AxrhQJ5umer2wA9l1JJc2iqUTmdQBRPa8gWmU4CQ6JoQcWUXqCt6aun+PMs9RwUhnmVlRMBv1qpMce4v62fR4ikrJ7iPFSWTKLYt2tjZx1YOqbUBGN6qDffhqcwWD3BtMtwoMRipRBtWGNTvwt55w3gxNmgC2qK9sWEG9YQINkMsiOOAyyyqNTL00losoqEEZcWlH3XcZ4HHdQo5gF0dC2kDOICvd3JGC5eobdfrgCLGsqbd5OUgYkW4Q2bPhnB3b7KMH4hciKvAAAFuUlEQVThSIjyAM6p3uI9tQO2mATD5XKKba+sAS//BnC2HBR1rCPaiKKGvaC0ZDl1ha76KCGOuc5KFYbkVsSfXwpomWGX3Z5WgKyqR1ppwL37BlNBdrDkzHe7O++qw1BlQb1QJMJkoiRiTz2vghFMV8mL30mvz7o6a3OBJtsiq7jIXoftTQpDJBV+TYbwYtnpFMhGNLC9AzpP+jnOipbm0T7zIu+RicacfpIoFoWo+ly7efAiANTAx0UgcapSVODzBLh/N25yj6YTruO4yPy+etEeCXXzGtNCWca19tpLbGRMygrD3GOj2IwUpZSrpEJev8HfO3qKefTGPCPvSOC5hwyqG0V0QOtz5E7SYqubdiHt1YC+KpZY+xj0CEYAtAlS0XGNOTqRug7dxg34Rq+jpyBnmx/YxCRutrngAQ0R2QLQB7D9oK/lQx6LePzmBDye83oc5wQ8nvN6HOcE/P/N66Rzbum9fvBQGHcAEJGvOudeeNDX8WGOx3FOwOM5r8dxTsDjOa/HcU7Ahz+vD+SWORyH43AcjsPx6I1D4344DsfhOByP4XiYjPtPPegL+AjG4zgn4PGc1+M4J+DxnNfjOCfgQ57XQ5NzPxyH43AcjsPx4Y2HyXM/HIfjcByOw/EhjUPjfjgOx+E4HI/heODGXUS+X0QuicgVlet7ZIaI/FsR2RSR12dea4vI/xSRt/Xf+Zmf/YTO85KI/PEHc9X3HyKyJiL/W0TeEpE3RORv6euP7LxEpCwiL4nIKzqnf6SvP7Jzmh0iEovIyyLyy/r/R35eInJNRF4Tka+LyFf1tUd6XiIyJyJfEJFv6P76jo90Ts65B/YFkp5eBXAGQALgFQBPPchr+j1e/x8F8ByA12de+2cAfly//3EA/1S/f0rnVwJwWucdP+g5vMecjgB4Tr9vALis1/7Izgvsga7r90UAXwbw7Y/ynL5pfn8HwH8A8MuPwxrUa70GYPGbXnuk5wXKkf4V/T4BMPdRzulBe+4vArjinHvHOTcB8LOgBusjMZxz/xfA7je9/OFpyz6A4Zy765z7mn7fBfAWKJP4yM7LcXy0OsAPaIjIcQB/EsC/mXn5kZ/X+4xHdl4i0gSdwZ8GAOfcxDm3j49wTg/auB8DMKNe/N56q4/YuEdbFsCstuwjNVcROQXSPX8Zj/i8NHXxdVAx7H865x75Oen4FwD+Lig1YuNxmJcD8D9E5HdUbxl4tOd1BsAWgH+nKbR/IyI1fIRzetDG/VvSW31MxiM1VxGpA/gvAP62c65zv7e+x2sP3bycc5lz7hOg7OOLH4YO8IMeIvI5AJvOud/5Vn/lPV576Oal41POuecA/AkAf0NE/uh93vsozKsApnD/tXPukyCX1v1qjL/vOT1o4/5711t9+MeGasri960t+4CGiBRBw/7vnXM/ry8/8vMCAA2F/w9mdICBR3ZOnwLwp0XkGpjS/IyI/Awe/XnBOXdH/90E8AtgSuJRntctALc0YgSAL4DG/iOb04M27l8BcE5ETotIAuCHQQ3WR3mYtizwu7Vlf1hESiJyGt+KtuwDGELu458G8JZz7p/P/OiRnZeILInInH5vOsDfwCM8JwBwzv2Ec+64c+4UuHf+l3PuR/CIz0tEaiLSsO8BfB+A1/EIz8s5tw7gpohc0Je+B5Qi/ejm9BBUkD8LIjKuAvjJB309v8dr/48A7gKYgiftjwJYAPBFAG/rv+2Z9/+kzvMSgD/xoK//feb0nWD49yqAr+vXZx/leQF4FsDLOqfXAfwDff2RndN7zPG7ENAyj/S8wPz0K/r1htmFx2BenwDwVV2Hvwhg/qOc0yH9wOE4HIfjcDyG40GnZQ7H4Tgch+NwfATj0LgfjsNxOA7HYzgOjfvhOByH43A8huPQuB+Ow3E4DsdjOA6N++E4HIfjcDyG49C4H47DcTgOx2M4Do374Tgch+NwPIbj/wEErj94QF+slAAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.imshow(gent.sel(band=1), cmap=\"Reds\");"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Xarray brings its own plotting methods, but relies on Matplotlib as well for the actual plotting:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 267,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 864x360 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "ax = gent.sel(band=1).plot.imshow(cmap=\"Reds\", figsize=(12, 5))  # robust=True\n",
-    "#ax.axes.set_aspect('equal')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "As a preview, plot the intersection of the data at x coordinate closest to 400000 for each band:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 281,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<xarray.plot.facetgrid.FacetGrid at 0x7f12a4823b10>"
-      ]
-     },
-     "execution_count": 281,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 720x216 with 3 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "gent.sel(x=400_000, method='nearest').plot.line(col='band')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "But first, let's have a look at the data again:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 88,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
-       "<defs>\n",
-       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
-       "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
-       "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
-       "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
-       "</symbol>\n",
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-       " *\n",
-       " */\n",
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-       "\n",
-       ".xr-section-summary-in:disabled + label:before {\n",
-       "  color: var(--xr-disabled-color);\n",
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-       ".xr-section-summary-in:checked + label:before {\n",
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-       "\n",
-       ".xr-section-summary-in:checked + label > span {\n",
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-       ".xr-section-summary,\n",
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-       "\n",
-       ".xr-array-wrap > label {\n",
-       "  grid-column: 1;\n",
-       "  vertical-align: top;\n",
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-       "\n",
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-       "  color: var(--xr-font-color3);\n",
-       "}\n",
-       "\n",
-       ".xr-array-preview,\n",
-       ".xr-array-data {\n",
-       "  padding: 0 5px !important;\n",
-       "  grid-column: 2;\n",
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-       "\n",
-       ".xr-array-data,\n",
-       ".xr-array-in:checked ~ .xr-array-preview {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-array-in:checked ~ .xr-array-data,\n",
-       ".xr-array-preview {\n",
-       "  display: inline-block;\n",
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-       "\n",
-       ".xr-dim-list {\n",
-       "  display: inline-block !important;\n",
-       "  list-style: none;\n",
-       "  padding: 0 !important;\n",
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-       "\n",
-       ".xr-dim-list li {\n",
-       "  display: inline-block;\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
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-       "\n",
-       ".xr-dim-list:before {\n",
-       "  content: '(';\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list:after {\n",
-       "  content: ')';\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list li:not(:last-child):after {\n",
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-       "  padding-right: 5px;\n",
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-       "\n",
-       ".xr-has-index {\n",
-       "  font-weight: bold;\n",
-       "}\n",
-       "\n",
-       ".xr-var-list,\n",
-       ".xr-var-item {\n",
-       "  display: contents;\n",
-       "}\n",
-       "\n",
-       ".xr-var-item > div,\n",
-       ".xr-var-item label,\n",
-       ".xr-var-item > .xr-var-name span {\n",
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-       "\n",
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-       "  padding-right: 5px;\n",
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-       "\n",
-       ".xr-var-list > li:nth-child(odd) > div,\n",
-       ".xr-var-list > li:nth-child(odd) > label,\n",
-       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
-       "  background-color: var(--xr-background-color-row-odd);\n",
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-       "\n",
-       ".xr-var-name {\n",
-       "  grid-column: 1;\n",
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-       "\n",
-       ".xr-var-dims {\n",
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-       "\n",
-       ".xr-var-dtype {\n",
-       "  grid-column: 3;\n",
-       "  text-align: right;\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-var-preview {\n",
-       "  grid-column: 4;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name,\n",
-       ".xr-var-dims,\n",
-       ".xr-var-dtype,\n",
-       ".xr-preview,\n",
-       ".xr-attrs dt {\n",
-       "  white-space: nowrap;\n",
-       "  overflow: hidden;\n",
-       "  text-overflow: ellipsis;\n",
-       "  padding-right: 10px;\n",
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-       "\n",
-       ".xr-var-name:hover,\n",
-       ".xr-var-dims:hover,\n",
-       ".xr-var-dtype:hover,\n",
-       ".xr-attrs dt:hover {\n",
-       "  overflow: visible;\n",
-       "  width: auto;\n",
-       "  z-index: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-var-attrs,\n",
-       ".xr-var-data {\n",
-       "  display: none;\n",
-       "  background-color: var(--xr-background-color) !important;\n",
-       "  padding-bottom: 5px !important;\n",
-       "}\n",
-       "\n",
-       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
-       ".xr-var-data-in:checked ~ .xr-var-data {\n",
-       "  display: block;\n",
-       "}\n",
-       "\n",
-       ".xr-var-data > table {\n",
-       "  float: right;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name span,\n",
-       ".xr-var-data,\n",
-       ".xr-attrs {\n",
-       "  padding-left: 25px !important;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs,\n",
-       ".xr-var-attrs,\n",
-       ".xr-var-data {\n",
-       "  grid-column: 1 / -1;\n",
-       "}\n",
-       "\n",
-       "dl.xr-attrs {\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 125px auto;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt, dd {\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  float: left;\n",
-       "  padding-right: 10px;\n",
-       "  width: auto;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt {\n",
-       "  font-weight: normal;\n",
-       "  grid-column: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt:hover span {\n",
-       "  display: inline-block;\n",
-       "  background: var(--xr-background-color);\n",
-       "  padding-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dd {\n",
-       "  grid-column: 2;\n",
-       "  white-space: pre-wrap;\n",
-       "  word-break: break-all;\n",
-       "}\n",
-       "\n",
-       ".xr-icon-database,\n",
-       ".xr-icon-file-text2 {\n",
-       "  display: inline-block;\n",
-       "  vertical-align: middle;\n",
-       "  width: 1em;\n",
-       "  height: 1.5em !important;\n",
-       "  stroke-width: 0;\n",
-       "  stroke: currentColor;\n",
-       "  fill: currentColor;\n",
-       "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray (band: 3, y: 317, x: 625)&gt;\n",
-       "[594375 values with dtype=uint16]\n",
-       "Coordinates:\n",
-       "  * band     (band) int64 1 2 3\n",
-       "  * y        (y) float64 6.639e+06 6.639e+06 6.639e+06 ... 6.621e+06 6.621e+06\n",
-       "  * x        (x) float64 3.925e+05 3.926e+05 3.926e+05 ... 4.282e+05 4.283e+05\n",
-       "Attributes:\n",
-       "    transform:               (57.32777121388223, 0.0, 392465.9217302375, 0.0,...\n",
-       "    crs:                     +init=epsg:3857\n",
-       "    res:                     (57.32777121388223, 57.44833434997156)\n",
-       "    is_tiled:                0\n",
-       "    nodatavals:              (nan, nan, nan)\n",
-       "    scales:                  (1.0, 1.0, 1.0)\n",
-       "    offsets:                 (0.0, 0.0, 0.0)\n",
-       "    AREA_OR_POINT:           Area\n",
-       "    TIFFTAG_RESOLUTIONUNIT:  1 (unitless)\n",
-       "    TIFFTAG_XRESOLUTION:     1\n",
-       "    TIFFTAG_YRESOLUTION:     1</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>band</span>: 3</li><li><span class='xr-has-index'>y</span>: 317</li><li><span class='xr-has-index'>x</span>: 625</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-12986e5c-cf38-4ae5-95c4-e1b62a80a8c0' class='xr-array-in' type='checkbox' checked><label for='section-12986e5c-cf38-4ae5-95c4-e1b62a80a8c0' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>...</span></div><div class='xr-array-data'><pre>[594375 values with dtype=uint16]</pre></div></div></li><li class='xr-section-item'><input id='section-50ce0bd9-632e-40a6-ba6c-3492f925d39f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-50ce0bd9-632e-40a6-ba6c-3492f925d39f' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>band</span></div><div class='xr-var-dims'>(band)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1 2 3</div><input id='attrs-61e32bbf-9f11-415f-ad2d-1b7e9c6843c8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-61e32bbf-9f11-415f-ad2d-1b7e9c6843c8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-17d16f99-6865-4285-b248-3c80b61edff7' class='xr-var-data-in' type='checkbox'><label for='data-17d16f99-6865-4285-b248-3c80b61edff7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([1, 2, 3])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>6.639e+06 6.639e+06 ... 6.621e+06</div><input id='attrs-ffdffd22-9cce-4903-868e-8ee07009623c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ffdffd22-9cce-4903-868e-8ee07009623c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d0eff3f9-4c79-4190-8d7a-522858740fe2' class='xr-var-data-in' type='checkbox'><label for='data-d0eff3f9-4c79-4190-8d7a-522858740fe2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([6639463.535997, 6639406.087663, 6639348.639328, ..., 6621424.759011,\n",
-       "       6621367.310677, 6621309.862342])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>3.925e+05 3.926e+05 ... 4.283e+05</div><input id='attrs-cfaeaa4a-3c1e-4f18-934e-56302c3a250e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-cfaeaa4a-3c1e-4f18-934e-56302c3a250e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f982ea7e-a44f-4007-b948-1b4979238631' class='xr-var-data-in' type='checkbox'><label for='data-f982ea7e-a44f-4007-b948-1b4979238631' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([392494.585616, 392551.913387, 392609.241158, ..., 428152.459311,\n",
-       "       428209.787082, 428267.114853])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-766efe9a-2b56-444d-bd2b-57d69a0faa9d' class='xr-section-summary-in' type='checkbox'  ><label for='section-766efe9a-2b56-444d-bd2b-57d69a0faa9d' class='xr-section-summary' >Attributes: <span>(11)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>transform :</span></dt><dd>(57.32777121388223, 0.0, 392465.9217302375, 0.0, -57.44833434997156, 6639492.260164049)</dd><dt><span>crs :</span></dt><dd>+init=epsg:3857</dd><dt><span>res :</span></dt><dd>(57.32777121388223, 57.44833434997156)</dd><dt><span>is_tiled :</span></dt><dd>0</dd><dt><span>nodatavals :</span></dt><dd>(nan, nan, nan)</dd><dt><span>scales :</span></dt><dd>(1.0, 1.0, 1.0)</dd><dt><span>offsets :</span></dt><dd>(0.0, 0.0, 0.0)</dd><dt><span>AREA_OR_POINT :</span></dt><dd>Area</dd><dt><span>TIFFTAG_RESOLUTIONUNIT :</span></dt><dd>1 (unitless)</dd><dt><span>TIFFTAG_XRESOLUTION :</span></dt><dd>1</dd><dt><span>TIFFTAG_YRESOLUTION :</span></dt><dd>1</dd></dl></div></li></ul></div></div>"
-      ],
-      "text/plain": [
-       "<xarray.DataArray (band: 3, y: 317, x: 625)>\n",
-       "[594375 values with dtype=uint16]\n",
-       "Coordinates:\n",
-       "  * band     (band) int64 1 2 3\n",
-       "  * y        (y) float64 6.639e+06 6.639e+06 6.639e+06 ... 6.621e+06 6.621e+06\n",
-       "  * x        (x) float64 3.925e+05 3.926e+05 3.926e+05 ... 4.282e+05 4.283e+05\n",
-       "Attributes:\n",
-       "    transform:               (57.32777121388223, 0.0, 392465.9217302375, 0.0,...\n",
-       "    crs:                     +init=epsg:3857\n",
-       "    res:                     (57.32777121388223, 57.44833434997156)\n",
-       "    is_tiled:                0\n",
-       "    nodatavals:              (nan, nan, nan)\n",
-       "    scales:                  (1.0, 1.0, 1.0)\n",
-       "    offsets:                 (0.0, 0.0, 0.0)\n",
-       "    AREA_OR_POINT:           Area\n",
-       "    TIFFTAG_RESOLUTIONUNIT:  1 (unitless)\n",
-       "    TIFFTAG_XRESOLUTION:     1\n",
-       "    TIFFTAG_YRESOLUTION:     1"
-      ]
-     },
-     "execution_count": 88,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "gent"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The output of xarray is a bit different to what we've previous seen. Let's go through the different elements:\n",
-    "\n",
-    "- It is a `xarray.DataArray`, one of the main data types provided by xarray\n",
-    "- It has 3 __dimensions__:\n",
-    "    - `band`: 3 bands (RGB)\n",
-    "    - `y`: the y coordinates of the data set\n",
-    "    - `x`: the x coordinates of the data set\n",
-    "- Each of these dimensions are defined by a __coordinate__ (1D) array\n",
-    "- Other metadata provided by the `tiff` are stored in the __`Attributes`__\n",
-    "\n",
-    "Looking to the data itself (click on the icons on the right), we can see this is still a Numpy array"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 89,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#gent.values"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 90,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
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-       "\n",
-       ".xr-var-name span,\n",
-       ".xr-var-data,\n",
-       ".xr-attrs {\n",
-       "  padding-left: 25px !important;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs,\n",
-       ".xr-var-attrs,\n",
-       ".xr-var-data {\n",
-       "  grid-column: 1 / -1;\n",
-       "}\n",
-       "\n",
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-       "  grid-template-columns: 125px auto;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt, dd {\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  float: left;\n",
-       "  padding-right: 10px;\n",
-       "  width: auto;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt {\n",
-       "  font-weight: normal;\n",
-       "  grid-column: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt:hover span {\n",
-       "  display: inline-block;\n",
-       "  background: var(--xr-background-color);\n",
-       "  padding-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dd {\n",
-       "  grid-column: 2;\n",
-       "  white-space: pre-wrap;\n",
-       "  word-break: break-all;\n",
-       "}\n",
-       "\n",
-       ".xr-icon-database,\n",
-       ".xr-icon-file-text2 {\n",
-       "  display: inline-block;\n",
-       "  vertical-align: middle;\n",
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-       "  stroke: currentColor;\n",
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-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray (band: 3, y: 317, x: 625)&gt;\n",
-       "[594375 values with dtype=uint16]\n",
-       "Coordinates:\n",
-       "  * band     (band) &lt;U1 &#x27;R&#x27; &#x27;G&#x27; &#x27;B&#x27;\n",
-       "  * y        (y) float64 6.639e+06 6.639e+06 6.639e+06 ... 6.621e+06 6.621e+06\n",
-       "  * x        (x) float64 3.925e+05 3.926e+05 3.926e+05 ... 4.282e+05 4.283e+05\n",
-       "Attributes:\n",
-       "    transform:               (57.32777121388223, 0.0, 392465.9217302375, 0.0,...\n",
-       "    crs:                     +init=epsg:3857\n",
-       "    res:                     (57.32777121388223, 57.44833434997156)\n",
-       "    is_tiled:                0\n",
-       "    nodatavals:              (nan, nan, nan)\n",
-       "    scales:                  (1.0, 1.0, 1.0)\n",
-       "    offsets:                 (0.0, 0.0, 0.0)\n",
-       "    AREA_OR_POINT:           Area\n",
-       "    TIFFTAG_RESOLUTIONUNIT:  1 (unitless)\n",
-       "    TIFFTAG_XRESOLUTION:     1\n",
-       "    TIFFTAG_YRESOLUTION:     1</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>band</span>: 3</li><li><span class='xr-has-index'>y</span>: 317</li><li><span class='xr-has-index'>x</span>: 625</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-1447a1a2-cf73-4e61-bdd5-c75fd7b9784a' class='xr-array-in' type='checkbox' checked><label for='section-1447a1a2-cf73-4e61-bdd5-c75fd7b9784a' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>...</span></div><div class='xr-array-data'><pre>[594375 values with dtype=uint16]</pre></div></div></li><li class='xr-section-item'><input id='section-cd37ca2b-90e9-4235-8da8-5894e6576773' class='xr-section-summary-in' type='checkbox'  checked><label for='section-cd37ca2b-90e9-4235-8da8-5894e6576773' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>band</span></div><div class='xr-var-dims'>(band)</div><div class='xr-var-dtype'>&lt;U1</div><div class='xr-var-preview xr-preview'>&#x27;R&#x27; &#x27;G&#x27; &#x27;B&#x27;</div><input id='attrs-b9beaeb6-91b0-4115-bd9c-bb167b5f39d9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b9beaeb6-91b0-4115-bd9c-bb167b5f39d9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-546a8382-9a1f-4a64-99dc-edb258e14b3d' class='xr-var-data-in' type='checkbox'><label for='data-546a8382-9a1f-4a64-99dc-edb258e14b3d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;R&#x27;, &#x27;G&#x27;, &#x27;B&#x27;], dtype=&#x27;&lt;U1&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>6.639e+06 6.639e+06 ... 6.621e+06</div><input id='attrs-5c4f59d3-9b66-461e-af53-e7b98328b170' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5c4f59d3-9b66-461e-af53-e7b98328b170' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5e66b15f-9c76-46b3-8f48-51c2a73ec8e2' class='xr-var-data-in' type='checkbox'><label for='data-5e66b15f-9c76-46b3-8f48-51c2a73ec8e2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([6639463.535997, 6639406.087663, 6639348.639328, ..., 6621424.759011,\n",
-       "       6621367.310677, 6621309.862342])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>3.925e+05 3.926e+05 ... 4.283e+05</div><input id='attrs-1c89b72b-7bcf-4e09-8a86-1b5f1427d7a7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1c89b72b-7bcf-4e09-8a86-1b5f1427d7a7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b138c21b-1334-4195-a1bf-0ab276647bf8' class='xr-var-data-in' type='checkbox'><label for='data-b138c21b-1334-4195-a1bf-0ab276647bf8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([392494.585616, 392551.913387, 392609.241158, ..., 428152.459311,\n",
-       "       428209.787082, 428267.114853])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-24472e23-3848-4cbe-aaeb-b4f20fb1f5e2' class='xr-section-summary-in' type='checkbox'  ><label for='section-24472e23-3848-4cbe-aaeb-b4f20fb1f5e2' class='xr-section-summary' >Attributes: <span>(11)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>transform :</span></dt><dd>(57.32777121388223, 0.0, 392465.9217302375, 0.0, -57.44833434997156, 6639492.260164049)</dd><dt><span>crs :</span></dt><dd>+init=epsg:3857</dd><dt><span>res :</span></dt><dd>(57.32777121388223, 57.44833434997156)</dd><dt><span>is_tiled :</span></dt><dd>0</dd><dt><span>nodatavals :</span></dt><dd>(nan, nan, nan)</dd><dt><span>scales :</span></dt><dd>(1.0, 1.0, 1.0)</dd><dt><span>offsets :</span></dt><dd>(0.0, 0.0, 0.0)</dd><dt><span>AREA_OR_POINT :</span></dt><dd>Area</dd><dt><span>TIFFTAG_RESOLUTIONUNIT :</span></dt><dd>1 (unitless)</dd><dt><span>TIFFTAG_XRESOLUTION :</span></dt><dd>1</dd><dt><span>TIFFTAG_YRESOLUTION :</span></dt><dd>1</dd></dl></div></li></ul></div></div>"
-      ],
-      "text/plain": [
-       "<xarray.DataArray (band: 3, y: 317, x: 625)>\n",
-       "[594375 values with dtype=uint16]\n",
-       "Coordinates:\n",
-       "  * band     (band) <U1 'R' 'G' 'B'\n",
-       "  * y        (y) float64 6.639e+06 6.639e+06 6.639e+06 ... 6.621e+06 6.621e+06\n",
-       "  * x        (x) float64 3.925e+05 3.926e+05 3.926e+05 ... 4.282e+05 4.283e+05\n",
-       "Attributes:\n",
-       "    transform:               (57.32777121388223, 0.0, 392465.9217302375, 0.0,...\n",
-       "    crs:                     +init=epsg:3857\n",
-       "    res:                     (57.32777121388223, 57.44833434997156)\n",
-       "    is_tiled:                0\n",
-       "    nodatavals:              (nan, nan, nan)\n",
-       "    scales:                  (1.0, 1.0, 1.0)\n",
-       "    offsets:                 (0.0, 0.0, 0.0)\n",
-       "    AREA_OR_POINT:           Area\n",
-       "    TIFFTAG_RESOLUTIONUNIT:  1 (unitless)\n",
-       "    TIFFTAG_XRESOLUTION:     1\n",
-       "    TIFFTAG_YRESOLUTION:     1"
-      ]
-     },
-     "execution_count": 90,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "gent = gent.assign_coords(band=(\"band\", [\"R\", \"G\", \"B\"]))\n",
-    "gent"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Hence, we can __name dimensions__ and also extract (slice) data using these names..."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 91,
-   "metadata": {},
-   "outputs": [
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-       ".xr-attrs dt:hover span {\n",
-       "  display: inline-block;\n",
-       "  background: var(--xr-background-color);\n",
-       "  padding-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dd {\n",
-       "  grid-column: 2;\n",
-       "  white-space: pre-wrap;\n",
-       "  word-break: break-all;\n",
-       "}\n",
-       "\n",
-       ".xr-icon-database,\n",
-       ".xr-icon-file-text2 {\n",
-       "  display: inline-block;\n",
-       "  vertical-align: middle;\n",
-       "  width: 1em;\n",
-       "  height: 1.5em !important;\n",
-       "  stroke-width: 0;\n",
-       "  stroke: currentColor;\n",
-       "  fill: currentColor;\n",
-       "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray (y: 317, x: 625)&gt;\n",
-       "[198125 values with dtype=uint16]\n",
-       "Coordinates:\n",
-       "    band     &lt;U1 &#x27;R&#x27;\n",
-       "  * y        (y) float64 6.639e+06 6.639e+06 6.639e+06 ... 6.621e+06 6.621e+06\n",
-       "  * x        (x) float64 3.925e+05 3.926e+05 3.926e+05 ... 4.282e+05 4.283e+05\n",
-       "Attributes:\n",
-       "    transform:               (57.32777121388223, 0.0, 392465.9217302375, 0.0,...\n",
-       "    crs:                     +init=epsg:3857\n",
-       "    res:                     (57.32777121388223, 57.44833434997156)\n",
-       "    is_tiled:                0\n",
-       "    nodatavals:              (nan, nan, nan)\n",
-       "    scales:                  (1.0, 1.0, 1.0)\n",
-       "    offsets:                 (0.0, 0.0, 0.0)\n",
-       "    AREA_OR_POINT:           Area\n",
-       "    TIFFTAG_RESOLUTIONUNIT:  1 (unitless)\n",
-       "    TIFFTAG_XRESOLUTION:     1\n",
-       "    TIFFTAG_YRESOLUTION:     1</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>y</span>: 317</li><li><span class='xr-has-index'>x</span>: 625</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-6066f1a2-3c76-44b3-88af-1e6537a45e75' class='xr-array-in' type='checkbox' checked><label for='section-6066f1a2-3c76-44b3-88af-1e6537a45e75' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>...</span></div><div class='xr-array-data'><pre>[198125 values with dtype=uint16]</pre></div></div></li><li class='xr-section-item'><input id='section-5c4c540a-cf3e-477e-9396-29d4f2ea1507' class='xr-section-summary-in' type='checkbox'  checked><label for='section-5c4c540a-cf3e-477e-9396-29d4f2ea1507' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>band</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>&lt;U1</div><div class='xr-var-preview xr-preview'>&#x27;R&#x27;</div><input id='attrs-f9aa029c-d957-4f72-9b33-46f8d3b8b36f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f9aa029c-d957-4f72-9b33-46f8d3b8b36f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3fe345ec-ca86-4d13-8c88-1d631b2814b1' class='xr-var-data-in' type='checkbox'><label for='data-3fe345ec-ca86-4d13-8c88-1d631b2814b1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(&#x27;R&#x27;, dtype=&#x27;&lt;U1&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>6.639e+06 6.639e+06 ... 6.621e+06</div><input id='attrs-a6b53bc1-c3bc-4e00-bbde-567217c057d7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a6b53bc1-c3bc-4e00-bbde-567217c057d7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f21bbc78-7ca7-4681-b4e7-bd6b56002c9a' class='xr-var-data-in' type='checkbox'><label for='data-f21bbc78-7ca7-4681-b4e7-bd6b56002c9a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([6639463.535997, 6639406.087663, 6639348.639328, ..., 6621424.759011,\n",
-       "       6621367.310677, 6621309.862342])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>3.925e+05 3.926e+05 ... 4.283e+05</div><input id='attrs-209d54ea-192b-4287-ac59-61e280257b58' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-209d54ea-192b-4287-ac59-61e280257b58' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-03f88dde-acbe-475d-aec4-c54af84e9ede' class='xr-var-data-in' type='checkbox'><label for='data-03f88dde-acbe-475d-aec4-c54af84e9ede' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([392494.585616, 392551.913387, 392609.241158, ..., 428152.459311,\n",
-       "       428209.787082, 428267.114853])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-3a46249f-1089-4cb5-b060-55bd89dea7a1' class='xr-section-summary-in' type='checkbox'  ><label for='section-3a46249f-1089-4cb5-b060-55bd89dea7a1' class='xr-section-summary' >Attributes: <span>(11)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>transform :</span></dt><dd>(57.32777121388223, 0.0, 392465.9217302375, 0.0, -57.44833434997156, 6639492.260164049)</dd><dt><span>crs :</span></dt><dd>+init=epsg:3857</dd><dt><span>res :</span></dt><dd>(57.32777121388223, 57.44833434997156)</dd><dt><span>is_tiled :</span></dt><dd>0</dd><dt><span>nodatavals :</span></dt><dd>(nan, nan, nan)</dd><dt><span>scales :</span></dt><dd>(1.0, 1.0, 1.0)</dd><dt><span>offsets :</span></dt><dd>(0.0, 0.0, 0.0)</dd><dt><span>AREA_OR_POINT :</span></dt><dd>Area</dd><dt><span>TIFFTAG_RESOLUTIONUNIT :</span></dt><dd>1 (unitless)</dd><dt><span>TIFFTAG_XRESOLUTION :</span></dt><dd>1</dd><dt><span>TIFFTAG_YRESOLUTION :</span></dt><dd>1</dd></dl></div></li></ul></div></div>"
-      ],
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-       "[198125 values with dtype=uint16]\n",
-       "Coordinates:\n",
-       "    band     <U1 'R'\n",
-       "  * y        (y) float64 6.639e+06 6.639e+06 6.639e+06 ... 6.621e+06 6.621e+06\n",
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-       "Attributes:\n",
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-       "    TIFFTAG_XRESOLUTION:     1\n",
-       "    TIFFTAG_YRESOLUTION:     1"
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-     },
-     "execution_count": 91,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "gent.sel(band='R')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Using xarray:\n",
-    "\n",
-    "- Data stored as a Numpy arrays\n",
-    "- Dimensions do have a name\n",
-    "- The coordinates of each of the dimensions can represent coordinates, categories, dates,... instead of just an index\n",
-    "   "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<div class=\"alert alert-info\" style=\"font-size:120%\">\n",
-    "\n",
-    "**REMEMBER**: <br>\n",
-    "\n",
-    "The [`xarray` package](xarray.pydata.org/en/stable/) introduces __labels__ in the form of dimensions, coordinates and attributes on top of raw numPy-like arrays. Xarray is inspired by and borrows heavily from Pandas.    \n",
-    "\n",
-    "</div>"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Numpy with labels..."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Recap the NDVI exercise of the Numpy notebook, using a stacked version of the 4th and 8th Sentinel band:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 712,
-   "metadata": {},
-   "outputs": [
-    {
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-       "\n",
-       ".xr-var-dtype {\n",
-       "  grid-column: 3;\n",
-       "  text-align: right;\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-var-preview {\n",
-       "  grid-column: 4;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name,\n",
-       ".xr-var-dims,\n",
-       ".xr-var-dtype,\n",
-       ".xr-preview,\n",
-       ".xr-attrs dt {\n",
-       "  white-space: nowrap;\n",
-       "  overflow: hidden;\n",
-       "  text-overflow: ellipsis;\n",
-       "  padding-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name:hover,\n",
-       ".xr-var-dims:hover,\n",
-       ".xr-var-dtype:hover,\n",
-       ".xr-attrs dt:hover {\n",
-       "  overflow: visible;\n",
-       "  width: auto;\n",
-       "  z-index: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-var-attrs,\n",
-       ".xr-var-data {\n",
-       "  display: none;\n",
-       "  background-color: var(--xr-background-color) !important;\n",
-       "  padding-bottom: 5px !important;\n",
-       "}\n",
-       "\n",
-       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
-       ".xr-var-data-in:checked ~ .xr-var-data {\n",
-       "  display: block;\n",
-       "}\n",
-       "\n",
-       ".xr-var-data > table {\n",
-       "  float: right;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name span,\n",
-       ".xr-var-data,\n",
-       ".xr-attrs {\n",
-       "  padding-left: 25px !important;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs,\n",
-       ".xr-var-attrs,\n",
-       ".xr-var-data {\n",
-       "  grid-column: 1 / -1;\n",
-       "}\n",
-       "\n",
-       "dl.xr-attrs {\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 125px auto;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt, dd {\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  float: left;\n",
-       "  padding-right: 10px;\n",
-       "  width: auto;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt {\n",
-       "  font-weight: normal;\n",
-       "  grid-column: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt:hover span {\n",
-       "  display: inline-block;\n",
-       "  background: var(--xr-background-color);\n",
-       "  padding-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dd {\n",
-       "  grid-column: 2;\n",
-       "  white-space: pre-wrap;\n",
-       "  word-break: break-all;\n",
-       "}\n",
-       "\n",
-       ".xr-icon-database,\n",
-       ".xr-icon-file-text2 {\n",
-       "  display: inline-block;\n",
-       "  vertical-align: middle;\n",
-       "  width: 1em;\n",
-       "  height: 1.5em !important;\n",
-       "  stroke-width: 0;\n",
-       "  stroke: currentColor;\n",
-       "  fill: currentColor;\n",
-       "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray (band: 2, y: 317, x: 625)&gt;\n",
-       "[396250 values with dtype=float64]\n",
-       "Coordinates:\n",
-       "  * band     (band) int64 1 2\n",
-       "  * y        (y) float64 6.639e+06 6.639e+06 6.639e+06 ... 6.621e+06 6.621e+06\n",
-       "  * x        (x) float64 3.925e+05 3.926e+05 3.926e+05 ... 4.282e+05 4.283e+05\n",
-       "Attributes:\n",
-       "    transform:      (57.32777121388223, 0.0, 392465.9217302375, 0.0, -57.4483...\n",
-       "    crs:            +init=epsg:3857\n",
-       "    res:            (57.32777121388223, 57.44833434997156)\n",
-       "    is_tiled:       0\n",
-       "    nodatavals:     (nan, nan)\n",
-       "    scales:         (1.0, 1.0)\n",
-       "    offsets:        (0.0, 0.0)\n",
-       "    AREA_OR_POINT:  Area</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>band</span>: 2</li><li><span class='xr-has-index'>y</span>: 317</li><li><span class='xr-has-index'>x</span>: 625</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-1ed2c72f-45de-4c52-9c9e-d2997116ebae' class='xr-array-in' type='checkbox' checked><label for='section-1ed2c72f-45de-4c52-9c9e-d2997116ebae' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>...</span></div><div class='xr-array-data'><pre>[396250 values with dtype=float64]</pre></div></div></li><li class='xr-section-item'><input id='section-84389e70-8c67-46ff-b260-ad315b6bf124' class='xr-section-summary-in' type='checkbox'  checked><label for='section-84389e70-8c67-46ff-b260-ad315b6bf124' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>band</span></div><div class='xr-var-dims'>(band)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1 2</div><input id='attrs-581d92e9-c6a9-411c-aa8b-3801fd9d3e36' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-581d92e9-c6a9-411c-aa8b-3801fd9d3e36' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e6709070-3913-4efe-9327-8b7c7c9678a5' class='xr-var-data-in' type='checkbox'><label for='data-e6709070-3913-4efe-9327-8b7c7c9678a5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([1, 2])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>6.639e+06 6.639e+06 ... 6.621e+06</div><input id='attrs-ddbe1ee5-b257-4ad6-bbf8-87966e11abda' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ddbe1ee5-b257-4ad6-bbf8-87966e11abda' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-59272eab-23a1-4ac6-99c9-6af1e0a0d10e' class='xr-var-data-in' type='checkbox'><label for='data-59272eab-23a1-4ac6-99c9-6af1e0a0d10e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([6639463.535997, 6639406.087663, 6639348.639328, ..., 6621424.759011,\n",
-       "       6621367.310677, 6621309.862342])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>3.925e+05 3.926e+05 ... 4.283e+05</div><input id='attrs-373dfd33-203a-44c6-a091-be3a07c5aefc' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-373dfd33-203a-44c6-a091-be3a07c5aefc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-027dd133-06cc-4d78-8258-4ff2052ace70' class='xr-var-data-in' type='checkbox'><label for='data-027dd133-06cc-4d78-8258-4ff2052ace70' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([392494.585616, 392551.913387, 392609.241158, ..., 428152.459311,\n",
-       "       428209.787082, 428267.114853])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-5136aa49-51f4-4b63-b43b-7e156777c6fd' class='xr-section-summary-in' type='checkbox'  checked><label for='section-5136aa49-51f4-4b63-b43b-7e156777c6fd' class='xr-section-summary' >Attributes: <span>(8)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>transform :</span></dt><dd>(57.32777121388223, 0.0, 392465.9217302375, 0.0, -57.44833434997156, 6639492.260164049)</dd><dt><span>crs :</span></dt><dd>+init=epsg:3857</dd><dt><span>res :</span></dt><dd>(57.32777121388223, 57.44833434997156)</dd><dt><span>is_tiled :</span></dt><dd>0</dd><dt><span>nodatavals :</span></dt><dd>(nan, nan)</dd><dt><span>scales :</span></dt><dd>(1.0, 1.0)</dd><dt><span>offsets :</span></dt><dd>(0.0, 0.0)</dd><dt><span>AREA_OR_POINT :</span></dt><dd>Area</dd></dl></div></li></ul></div></div>"
-      ],
-      "text/plain": [
-       "<xarray.DataArray (band: 2, y: 317, x: 625)>\n",
-       "[396250 values with dtype=float64]\n",
-       "Coordinates:\n",
-       "  * band     (band) int64 1 2\n",
-       "  * y        (y) float64 6.639e+06 6.639e+06 6.639e+06 ... 6.621e+06 6.621e+06\n",
-       "  * x        (x) float64 3.925e+05 3.926e+05 3.926e+05 ... 4.282e+05 4.283e+05\n",
-       "Attributes:\n",
-       "    transform:      (57.32777121388223, 0.0, 392465.9217302375, 0.0, -57.4483...\n",
-       "    crs:            +init=epsg:3857\n",
-       "    res:            (57.32777121388223, 57.44833434997156)\n",
-       "    is_tiled:       0\n",
-       "    nodatavals:     (nan, nan)\n",
-       "    scales:         (1.0, 1.0)\n",
-       "    offsets:        (0.0, 0.0)\n",
-       "    AREA_OR_POINT:  Area"
-      ]
-     },
-     "execution_count": 712,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "xr_array = xr.open_rasterio(\"./data/gent/raster/2020-09-17_Sentinel_2_L1C_B0408.tiff\")\n",
-    "xr_array"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "In Numpy, we would do:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 95,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(2, 317, 625)"
-      ]
-     },
-     "execution_count": 95,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "b48_bands = xr_array.values  # 0 is band 4 and 1 is band 8\n",
-    "b48_bands.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 102,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "ndvi_np = (b48_bands[1] - b48_bands[0])/(b48_bands[0] + b48_bands[1]) # or was it b48_bands[0] -  b48_bands[1] ?"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 97,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.image.AxesImage at 0x7f1242398e90>"
-      ]
-     },
-     "execution_count": 97,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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W33cVQBQojseavu0paneI+JpPXDKMXKk72ALPaSFVY7h6HDuu3jgkkmQRXdvTlA3xJKLvDB/fbjiWHZfnCZ6vSWcJQeDRG0Nbd3haYsYDeDqP6TtDGPl0Tcex6EknAVESUlcN0+WEYRjYr46ks4Qk9Hn/7R27Y8fNpibwJHGoKYuWxy+XJzpOexohBX3nEHs90kjloXIVTdejPM0k8dhtCi6fzEeNBXdQ3x5OB+1xm9P39lRt/nR5Tux5TEcU7KlPa2GHgbo3/Pz6I/uiIpprPOkqrNUhZ3Oz5/LJwmkWWvHVzR1xEp5opMcvz7l759ZrGKCse85mIZHWXG/37FYHlJa8mM3J25bPXzzC/0yyqVzSBUh9l+DWdUPeNpRdyzQIyNuWx+nErd9g+Xaz5QeLMyKl8EdtBiDUitYamrY/HQJycIdXoDT7uqYxhnxXEE1CLh4tEOBoH6kwgyVvW+q65eObOyaz5PTepjO0VYcxliDyR3AlAAcA6rJhjeOv7wHX+npHOo1JpvFIcQ4nzSmIg1EzMXRNx+FQu3WwA74n+Xa55cV0xuHRCMqGgWPT0FQtf/r8Kcs4GtcNfn79ka++fAdCYHtDtJzQt58S7/LJAnBaRehpmqpFSMF652inMAl5spwDuHuThgx2oGndWlZFc6KX4jSkqTr8UccBSLKIpnIgZPNxR98Z0mnM+dMzjtucyTylLpvfmYe/L+TOMAz/CvhX/09e6xCgIooDvDSiqFuuv7tlcTWja3riNKTvjBMSjKVrOm5e7/Ajn7rpmU78E/rbHRrCQKE9RVt1KO04NyEEcvy3pwRdB9kkYPVhS1O2VE1P3RjmWUAQ+3RtT6Q1Ueg7hDbxEUKglGJ5nmKMpRu5eaWd6FMVDcMA6SyhqV0JCY67f/TUx/SG4yZHKImUgupY4wce6Sxhvy3o2p5Hz8+BO6rcLVxTtS7hxT5SCLQUzGbuQGmrjij49CAOw8A89p2o1bpNfvF0QXGomMwS3n1zw/xqRvd27aiaQNO0hmwW4fl6pAAEddO7pCaEE9aMRWtBPHHJe5c7ASrfFWRnEybzlPff3nF2McHznaBljHWVzTAQBYqyaJkFHm3VYo2lOLhStOstVWOYpR7Hscz0tCJKQ/zQ0TLaU1hr2dzsTnvhuM1p44Cut3TGoqTAWkdf2WFgd7snnkQ0dcvsPGN3dzgh9WEY2K5yut5yLNwBE4Xa8Z+ZN66zJC9brh5lzC+n7FdHbt6uSSbRyI0KlFY0puer7YZAaV5MM/KuIx3pkrLv+T8+fOAsjvlsvuDtYU/etmRBwGfLMxaThMsk4aYokELQW8s3Xzn2Mk7D0wGyObh9ZUahLQsC1rucR8/P8aSk7FoCrYi0R2cMl0nKuiopu46Xszm9Haj7nkkQYAdGqkWeKozY86majqLraKw5JXclBKHS1NLQqU9I/35fBEoxDUMObTMmVkNnDHXZoKXk+nqLH3kspinG10zmKX3bE0Q+8STibJnRGkNTtScBsi4avMDpD0opB5RGAbTv3N+WR4f0nbblKqj9vmZ1dyTLwlFv6SiqfhSkJcYMfPk3b7g9z7i4nLFa7enansFY/rOf/IBw5MLztuPn1x/58O0t2tcIIUgvMvarI23dsXw858XnVwTaVfLHqma7L35Dc7JmoNiXfF24JH9fzd6+XTOZJVR5NVK+7lBoqg6pHA3atz1mpG6SacRgB7KzlKpoUFpxPkldrtSas7Pod+bV7y25/78JIcXpFPJCj9X7DXVj2N0dsMYSZxHa0/iRQ9V959RzP/CIQs3+2DKd+CzOU47b4sRlNVXHZBajtKI/1ijPcXnPX52xvXXvfdhV1I3jqc9mIZ/96Anv3tzRtT2l1xF7Hp6vOe5rnn1+QRA5R8Jmn1Me3CJJrdjdHU7KflM5VT2InWCjlOPGj9uW7b7B9yVp4tPULXfvN6SzGN9TVMeaZu74Qa0VxrjktrnZc9yVPHq2oC4bzh8vyA8l2SKlrHckkVtGa4dRI9BURUMQ+tRlw9mjGXXdEiUBtjcEkc/Hm5zlLGC2SGjKBmscdTHYgfrg1sKMDiFjBtrOrcPsPCN6t+bj9ZHVrkEIwWSRokZhKowDotSVmF7g0RtHmxljTtyykILb6wNF1eNpVw3UraVuDcYMpPFAEPuYrmd1vWMydRXCYVeRTCKWj+fcvlsTxAHzecwwlEShomntSZeI0pCbj/vR/bDluK/Jqx5fC3ozYAdoW8NyHjAdhet7EFCX7rpefn7OdO5oi3SW0DadEzmN5bgr0b4TIN+st+zuDvBHn/P2sOefPn5CY3ra3vB4kvH1as0ijHg6ybgrC3bNgCcVZ1GMEpJAKTyl6IzBDz3KQ4VIQrqmc3QWjnfPMp90lhBqDzu6jDyliD2fN/sdkyDgPE5GNJkhEcwD363juJaelLw7HniWTU/Pn7GWKPB4fzyQ+v7pcLr3RYRaUXQdWRBS9T2x5yHHRHZPR6WzmLpoOKxzhmHgQ7Fmdp4xDANF2/LtLz+glUBrhQ40bd0RJoHbX11PeayYzFOSUXPK5gld25OdTbDG0ZZd2xNPIo5dfkK+QghXgQUO9ZdFy0Q7UKWVII09nn1xxVd/+wGtJbcfd9RVw2SWIAT8Rz/6jFiPzw8D/9Nf/jXbTcmLLy5OQuxhk3M41Dx5fsZgnYvoMgjZNTUXkxSbwu3heBLmg8ijqboR6Ah2dwfCJHTmhpHyC5OQ/eqIEJBMI+qioSlbpHJGgvJYcX41J9Sabe6AkLWWXeOqkbxu2B6K35lXfy+S+zCMNqWyQUhHxQC0TU/bW8JkAA/mF9OTol4cKpq6JTubEGf9WF4bZssMqQTb24PjVfOaZBKhfY3pelCS5XLKYZ1jjKUZ7YcCZ5nMq4blY3dTlZTs8pLdOidOfPqRKw6UYpYlVHlN3xk0rpxygqUrFa0doGzwIx/tWfrOsN+WNJ0hDBRCSo57d7AcdqXbiNOYb7/8SOgrrB3ozcBxX9G0hiRyCTueOE7vzTd3eNrx6XnpELwaxePjrmR6lhLE/mj5c0l1upxQ7Et2u4qut9xuarLWnJw3QjgaouucPnFP93iepDcDt29WPP/hYy6enlEXDet9g/IUQeSTTNz975qe+Syl7p0YpKTg7CymqZxwuF8fudvWGDMwy3xH+0hBNosZhoH84LQJrSriX+NXlafwPcn6esdhk3OzKkm2JbO502UW5xn79RFjB7p+4Kuv1uRVzyTWzmlkB7QUtK2j1KrGoJRkMo1ZXM14/9W1o1ukwIxWt6poEEoSjJRblTdIKdC+ZnaeOfQoXAUWpyFl1/Kzi0vWVUlnLJHWpL7PIo35drfl1WzOPIz4ZrflKnFurl/td7SNs/cmScjickY6S5xds+sp85o0dqJmksWcLTP2dc0XTy/ZVhVXacr7w4FiX9JFPU8nGYHW9NZ9vkPZfELbYyKr+w5PKpSUtNYQeR4fbjbU0ymdsfjK0Y8AgVLuIJGSqv9EzwzDQGcNprdYY8l3BdPlhDBxTp/Y8wi1x5dfvWMYXAULoLWi2Jcn/WV+kRH6Hk3fE2hN07vkB86okC3Sk31QKUeZdm3vgNUo6D/+7ILd3YFiVZwcLc+ez0+OqovLCY+fLnnz3Q1xGvLF4wueZVOiX6OgJILLZ0uev1TcXG9pm47F5QxrcmbzmMXCOePMSAOVXce+rDhs3IHm+Rpr7Qnxg6vqndCvnWi/Ojqx+cmCyTw5WayldNbsumzwQ9/RiNZS9z0X2QR/PuWbD3f0nSEJfaqhO33Gb4vfi+SutOS4zZFK0jY9g7UoTzFNw9EhYAmTEGss1gxUeU2UBERpSJZGrFb737jQe9ujs5UN3H3YusQ3WuvKxnH0926Q+1M/L3uKfUkyjdnm9QldhKHHdJHiha5yyCuHIqwZiCcBSiuKY8V+WzEMg3NZjEl4GAau328BiEPN9oA7UIqGourdQ5sGeL6mLhpCX9GO3viy7tHKfcfOdDSdszBG0YHQVyfXShQ4GsMh457F5ZTJPOG4LUhnMVJKsjhkvTtienuyOO6OHXVrePk4ZbqcjI4DcbI+TqTHJNbEkcfNumK9b/De3I0i9cB84tM1PTdvVuyOLb0ZmBROzb+6mrN8PGd3d2B+MWV7u+ftuz3GDDy+cAhGa0VxqDh/umCWJXTW8s3fviMcBVvtaZLRLvrddxsCT6KU4P3dkc9fztBa0feGwHecrOdraF0p3o1+46azeMp5rpUnmSQefW+x1rmN7hPM0x884m//zWu63hJHHkXVIcuOtm65eLZkGAYmM+fQiLOIV/MF0Viag6PipmHI32/WvH1zSzpL+PxiSRYEPE4nfMiPfMxzHk8m/HBxxl1ZcJPnJwdTU7Xs10fSaczHN2u63hL6itlywvMfzOjajqZsefPNDcvHc35ydcEijLDDwIvpjGWcsK0r+sESC0GPS8rgbI1CgJaSzhgOTUPetvxkeY4UgsTzeTpRKCHxpGJd1zxKPnHjAkHseRhrHef+6w6o8frDJODi+ZKXywXvDweGYXC2zLJ0CXkes9+WGDtwtkzRgRPik2nEMMBmfSCeRBRVQ1M2DsiMPLW1dqyEfQbrxNYwCkjGii7NYura+dcniUfTGoqqx9w5e+WzqyXnXzzlpih48uKCL+YLlnH8ycPOgMRd03/6/AWdtfwbKfnlX3/HenDPbjqNOZb1yXn3q/2NAzLnGdPlhLp0n9/WTodr7OCStIB4EmGNZTKLMb095TprLKYzKM/lj/lFRpSGzLOEeZZwHifUfcexbdmM9GyzOVL5Gi/wCOPgd+bV34vkLoSg7y1qgGFw5btztzjkaI1lMk8dwhlcA869J/Q+2rExQnvq5N4IIh+lFbZ3VEBZNDx6MTm9T146WkApQVU7BH/cFhw2Odtji68lz16ecfFsSRh41E3Hu6+uqVtHC0npmiTiLEJrh3yb1vG/99/d0RHw7NWSNIsp6zdOUKmdfSoKlROSk4D9tmB5NeP6/RaJIAw++dkPRUfXWa6WEdOzCVXRIJWg2DtRUHsKP/LZ3e7xAs1+dWR5OaOsGqzpKDrHNZd5jR2gbtx9HgbB25vyhP6nibPb9f0w2h81ddOzyHy63pIfarre0nSWNNLUrfs7OzZ65VXP0lrsMPD8bH5Knn1nuFg4q+Xiyvnr27pjcTnlbDZhl5d896trytqQJZpspEEm84TyWJ0soFnq4SnJZu2u58Vn5xz3Nc1IpTSdpW2dh7vFjpTQ2HAywPJiwnFbkJfO8XR3e6QpG7KzlN4MbA4153OYpD5JFvP6uzWrzVuWZzE//vEz5mHEdX5kX9d4ScyqKLm4nPFiOuN//+XXrG6PKCm4fHIGwKFpmIYh0yDkL/7Pr0h+9jmhp7k+HNndHUhHgTHOIqq8ZjpNEC8F+a4c95dgc+MEtcFYLl+c8+PLc/zR+cGIxpWUTHz/lHQ7Y5gGDikLBO3oHvOU4tVsznV+pOw6Ut/HDgNKSlLfp+g6uqYm830mvndC76mnaczo2jLmRMsMw0AS+pRNS3moeC1cMmzKhrugdLrVJHKgKQ3Ij26twiTADz3aqkNKSRAHVHlNvi8JIp/8UDGZJ/Sd4fr1ijD2mcxTykP1CfBpzbGsyXeOnrBjc2NZ9wS+whs99LHvOYoqjIg8zTKOST3tGg3tgBw4Ccv315s3zanvpm87jrsCIQTnTxZMImfX3NUHjLVkYYhWinp0821u9viBozjlSBHd2y4D38MLPWZpzGp7JJnGRFozSSNmYYgUrvFMCmc/VVJStC3b2z1SSpRy696NOex3xe9Fcu/a/iSsgSublRxQTUfXdCyfLEbhyLC93iGURFXO77rztWuU6A1pFgNONd/e7gki39EmgWazdj51/W7Do1cXALT9gNbQdY7vTWNNEPvst07YC3zngRZCIJXA9zVaS+rcdcZOYk2+Lwljl5jb1qFiYwf224Js5r7P2TJlPkv5+GFN3Tg7o5SCSaxJs4h4EnH3foPvKa7fb3n66pz3394Rh5o0DWBwr+86y8XzJfvVka7pkEoST0Imi5TDOkeqHiEl+a4kncXkecWjxYz3q60T5m727Pc1VeOQTRJpgkDRtpabdU2WeiSRJhsTvBCCZBqhq19paNYAACAASURBVI7pWcrHN2tnw5TCidKja6SoegJP0gr3gE2XE2ZhSN62BIFH1/YoLblbl0xiffLuB5GP6S3v369oqpa2tSc01ZQNOtCY3nB7fcCYYaw2WnzPUQJtP3DYFszPkpMlc34149tf3WCHAa2dyBr6EjO45qjV7dE5pcxA1XROgLUDm5s9A/DyccqjVxcs05Tr7R5PS7LU52c/fQHAbZETaM3t4UjVd/x46UTNb3db8l2JMQPTWcSL6dShViH5brOlazsuni74++tbAFbvt66Ck06oVlrx5MkSJSXPzxd8mdcopVwSjDzWH7bML6Ys0wRjByrb40l5QuOtNZiRUwfGRDWcRNP7pN+P/vjHkwmtsRya5iSwainZ1TW+khzallCrk99bCUnq+eS0NKZHSYmSwh0G1nLzZsXsYkoQOJebEILjNucmr5nMEqIkcGg88hDKJd0g8jHG9Ze4Bq6x2qpaLp8sKPaVA3FydM8UNdaOQK9u2e5yDpuctnaJzgucF3wZeuzWOb2xbFc57873PM4yNlXJT5JzpMBZQvuefdOQ+b5zLCFADJS9cT0cWtFWLW1nUPpTT0TsefSdczQd1jm5KlCeJsti15Xe9ESTkK7u2K+OZGcT5tNk7IVQtHFP3fdMs5jI81BCYgZL1fc0fY+WrjO8qNuT8+fegunAq8ELPj1Hvy1+L5K7EK693ZgB35cIIEkDJrOE4lhx93ZNkUWsb48cy47lzPG7wzAwGMvq/QYdaJRWJEnILIkwCyccHrc5gx1QakRBh5ZsX/L46Rzvek9RG7qx9VgrJ9YaOxAHinAswQ+bnPxQsricAQ6hxqHjgJvO0tSOkmh7R/GEvqN52qrFCz2Wj+ds94Vzx3gST8Mk8QjigOlywvp6i1SCrhmIAqcNvPjhFdpz3ORxW7A4cw/IvWjrWucF/fjvOItG66UlSh0llO8KbsfKJd917DYF1g4Odccei6lPXvUEvmQYFGb00A9jIrx3/rT1jt0o/nS9xYxdlH3vaC91T3uMjg8nFAkS3+csiiiLmq7umMQarST79fHkBGmrlmAsL50oamg7i7U957OEbJoQBVvq1uBr10h1voioqo667UiyiOJQUR5r/GgUI8cK6r6NX0rBLPWIAo0XaOqypWoMYvTud70hmYS8XGYsL5zQmHgeV/MpxSPHl0ohqPqed2+d2H714pzP5ws6Y7gpclI/4PmPHvP1L94RhD43RUFnDFng1iyLQ25ut+T7ivXaaS+PrybMzrNTctoeCtI0QgvJ8mpOfigpxn03O88QSvJhvUOdC2LPZ12VnMfx+Aw56qQxLjlc/BqtoqXgLArZ1k4ol0KMXvuWQGtMaym6DjNYlJCcRe497cCpqQmc7U8i8KU6JaGy60ZBNSGMfaZByMfNju2tMxjsthWer0kyR0l0raMN7wXS+6jymsEOVHWPsQPziynx6CrzAk2xr1BjC761I8e/L12ynUTEaXjqa2g7Q+Br8vJTpa+F5NFkwo/PnPU07zr+7c0NX379Ac/X/OjlIx6nE2LP4zrPCeOAy6s5q5Xz7xfHmmQSspimrLau2rPWUldO2L04n7EvqlNzkumN299NR5bFeEqxLysmUegqFaWYRxHGWq7znCp3QmnfGc6WGevVgck8oa3bU5K8T+zOtdb9xmiFfyh+L5L7vSNDKUE68s9JFjkLlJKkVzO6uqNu3MyKpjXo0YVixtO+2FWOT352Rqg1jxdT7AB9avACj3gSke9LZucZZ7PUCXVNR/HxSNu6970XTccREISxTxgHZJOYD29XjutPQ6bd6FQIPLwAgtDn1Y8e8au/eQ+AHzhbXVk4gbhre9dgoeTYfStQnubRsyVN23F9k6OlYDrxuXp5ge/rk4Mi8rwTtTEMA+XBJQetJRdX8al5KMki/DTi8eOULAh4u9mRnU1cqbsrEFLStNYJ1L48cfuhr+h71wSmlUArwfbYnTzbqw9b8tzpA/dzYYxxM0eWM9c41PWOiuqtmweTzhImo5c60vrUfi6r1nUb5y3TLGC7KUgTd4DevVvTjHSXVg653HzcobTk6uU53Vc3pFlIEDo/9GQYKL5eke9LttuKtrdMWsN0HrM8TxmGnEPZE4euy9TTEu3rUYuxLGecmsWyhaP8ikPJsydLzuOYv37zgfffrXj+xSXHbcFf/c23dG3PxbMznp8veDGdUXYdvbVkfoCWki8WC7yfSQ7HkiwIuC0K7sqStunG96+4WxUjjTXQNQ7Zmd5y9fSM2PO42x3pIyeGdiOtuF8dUVpyWB+ZLifkbUvqB0gETW/orMFXmrrr6QfrwIc3OkCG4UQ1SCE4NA29tbzZ73g5m7OtKwDytmUahESeE2Mb07OXkmUUufkvAwRKnkYQWEbb30jPZIuUZ9mUY+MOkHv76sWV46QP65y+Mywfz5FSks40Xdudms2kkjStG32RRPqU/CfzhLt3G84ezRDCOeS6uiOdJaeZRpO546ivr7cMAyQTZ4esmx5PKwLfPUMzP2RT15Rdzy/ubrm53RHGPtrX/NtffMvXs4Q4i9hc77j+eGBzd+DsYsrdbcFiHqKU4nk2JW8aLhYZt5sD+c6Bto8fN+7ZHx1nwzBwc3tw1lmlODYN8ySmt45z7xLnPMp3zkb55ps7Xv3wiqZqef31NXLs0k+m8cm91bc9XqCp8gprht84HP+h+L1I7uCQexo7F4L21KlECyIfBnchaawd1dL21E1/6ibcbt0GteOcFKUVXhjRmB5rLcW+5PzxghePz9HSbcjGGB4/P3ft6ocKYyx+4PH0s0tCrdnlJV3d0VQtf/LqBf/syVOObcuv1ismC+d0kOOwJakcUgr+zKOu2/H7D9RFTV223Ly+o6rdQgy45DW/yIg9z3Hio9gXxAG+r8n8gKJrXXLXHnEWsV8dAVhf75gkHuHoJBkG9/ld2zP1NavNgXaanMrcYeygLYsWpQTZOCIgCjW7Q0PgS6LAc30BjeOhpQDfV2glKfIGJQWLzGdzaD8N/hLQjxWP70nazuJrQZa6EjdQiiRJeLvZ8f7NmuWFcxqUVUcSaXb70dJVtJgPW3fQjJ21prccC9eU1jadc4xkTlz3fM3dhy1Pv7hyqLtxvQZPHmen7kVrB54885jtS6RWeL4mO0tPjoTNzc4l6quMdJacOgHPzjK2RUnsefih0xhW7zdEk5AwCfjZH73k1WzumnbGbs9fpz18qfh8vuAucJVI7Hmsj4WbDXTnRj0MuOonSzyXCAY3eOqwL7BZjPYUTycZ744HJlHI3lZEk5Dq6MS8fFcyyxKXqNuGX375lounCzdIq7cko9OsM5/4Y3DJeV2WVH2HlpIfnJ2hhWQ9OA98VTa8mM7IgoDXux3TMMQMzvbnSXmiyxLPY1NXaOGG3mVBgBKSVZ6zb2o2udNCwthHeZow9sdGMnXqgfB9zXbsJhXjoLwwdsYEsy+ZLicjcNIYY3n82QVx4LPd5Xihx+U0Y5XnhIkbHRCMriDnB3c0UN85DW35ZEFZNchJRmMMbw9HPhwPvP7mmscvLyiLmkeLGX3bM5kltG1/mqNU1a6fIvCd+Pn8xQWp7/P54ozXe9dzEcY+xb6kqTqMMbSNc7EEkX86uHZ5iZSSpu/57utrqrIhTgKyRUpTtUyXGecXE9q6PR1addHQ1K4xqjhUrku67Yn8EC/waOvuE6r/LfF7kdztiJR3ecdZbyhG33UQOSujm48iidKAIPRpxlrxfnKitQOBr1Cepjg67/kwDEzD0JVpjZsBs60rpBDMwxBPKrZ1yWSe8tnzK4QQvL1esbreEoQOTTZlQ713SOfn1x/55qsPHA61m2OT+pRlN4pRguOjGYf1kck85bjNSaYx51dzXryaIoRgVZSsi4Ld3QEv8JikkROmpOTFi7lbrKrluCuIzz0izztxpnoUUYpDhdaKZOqGjg12YLc6UBxrAl/TnzsO9+PbFUkWsVsdxzEKIcrrsb2hrNz4gPvpl21nORQ9SejcN9YOriq6r5C0pG0MUagIRhpKjzbJYRhYnqfstuWpkpJKUh1rbpXE8zRt1VI3hrubI2fLlNnFlNdf3Z5Q8zBA3Tj033bu+wB05tPYCM/XxJOI69d3PPn8iiDy+fbvPlA3hqcvl/yTyzNu8hxfa+6ut1RFw09//Bz7dKDpe2LPR4/Z6ctvP3Dz8cBP/uQ5n8/n/N3NHY+Xc252BzzlegsOTcMijZ1lVUlm5xk/Pb+g6Dq2dUWgnEum6dzBIgU0xpz47lkYsqlqrlfOtrlel84K6EvCQBCGHtrX5AdXxmeLlK7p2a2PKCV5pw8Ozfo+Ugo2Nwe2m4KqMfz0Hz3FDAPfbDfMw4j0R0/J/ICf//JbZucZ53FC3rbEnua2Ksl8n1BptBAsk5iydRx5Zyy5aWn6nmkQ4I1UQdP3hJ4+WSfb8bqcndJRPLMgpLPWAZDxd9P77k5r2d0dXeUtBVkY0nkeh7w6WUcP2xxjDLNFxvXrlTMDjCLpfDnhi6eXKCl5t9+PDjIPTymenC9YRCGB0iSex4f8iDfaNHvr7JtPX12y2xxZXe/QSo5aRUbZtcSez+v9juvXdxR5Q1m43PKmceMdjB1HGAhBEmn2hWMLfE8SJuHpM/O25frtinQan4CVEBAlIeWxIp5ECAHpqLn1neHu3S2zZcbViyW/+PkbtOpYf9yhfc1hc8QLPDfPKAnJFhPCOCCeRFRFPQIAN0+nKRvOl1PKqKMq6t+ZV38vkrvAdcNJCcWxGgdLOSQyDKMH1NcnH+l9226axJxdzjh/0rO9PZw6xMIkYBE53/RknjI7d8j6tijYb3M2/R5rLT946eZINMZwfbN1QoWvnXJfNKOFaWBf1/zqb99Qjui77y1JFiNVfeL8lJLUjeHVxZSrqznr7ZHVzY5f/c0blHTfaXo2YbJImaUxszCkbDu8VPFoOuGb97c0Vcd+k6N9zflsghaSxvTkByfw3lNG4MbBekqx/rhlOk9OQ8/COGB7s0fIGNMZVoeaKFCk05jZ0wWbm/1pvGo7+tkniRvh0HTOXeL70o0H6CyqdxRM3TohOPAVUejcO0II+lWB70lqMzBJtPP4Ste9mfkBr28/YKwTQ+cXGReTlNu3a4DTxMm2c8Pcus6itcTXgtB3B/R2U/Dk+Rmr9xu6fmD1YcN0OUEIOI8D0iym6LoTersvX4UQ3G0PeIEm0p9cH1JKAl/yLHO03YuzOXdFQdd0XKUpZdex3h25XEz5T/7DP8JXzh7YD5ZAK+quJxjdDH/38Yb1xy3/5I+/cFVY15G3LXfHnOM2Z3OXA7A8T9huSnxPMj1zbh0hHZU1mSccNjmzZXaanVQV9ck7bXq3J7MsxO4qotBn4ges8px1UbBMU94e9k7UXx1RF+c8yTI6a9lWNYHShMqJ4BLBN7st0yDA2IHWGhZRRKg9GmP4eruhs26SYey5RFz03UlYtYNz3qSex7v86Ggh47pSO2tJfZ9l5rpQ/bGze5uX7FdHgshVKfcTV6WU3LxZ4wfe2H3qrvPZ03PsMPDtxzsO6yNREroBcVqf1kII16UrhODNfoevlBsRLSU311s+vNvy7OUZP3n+mF98+54yr/n2g0vgx13hOuKVYPV+47qRPcX8Ykq+K/BDj8nc0TO7X17jacHicoof+twWhRtKWDVM5slpNPd0pD+3twfmFxnzxYSm7zluCq4/7JikPk3nOraVlqd+krNlchq7cj9+eHOzOz3fXdNjjCFbpOOMGcsfv3pK3rb83d+8phwdd78tfi+Su+cr4siNH4iS0DknxsaOe3uTH+iTvVAIQZyGKC1pmo6r6eTUBJGFIYe65u31ym2q2M38eF/ccMhb5nM3mGiwA1XvEH2kHbpt6u6EIMC1e/uRx13phEglBVHs8/SzSwKl2B3L09wIIQVl3fPn/9tXjl6auW7OpA75wRdP+Iu/+PuTfW8YXGOQFIIo1EwWKWfLjEUUMQ1C1pVL5lpKDm2D6Z0yX9Y9+2NHWbvRAtnZBDGWtNrTfPz2lrNHcy6fL9mvj3iBJh0n0t3dHomziDD22a+O7I+upJNS0PUt7ThqVSk34qAeUXs/DlyzFrLEo6wdyr5H8U1nR7um2+j3ir7pDb/87q3jzC9SwtjnyWzq/PHnGc2HHYeiww7gKWeFvViEJ8fUdOpE8/vmFTfnf2C7rejbnlc/cpMYjbUknkcz7o3l5Yzl5YxN5e51Xbbcja6efFdwd71nfpZS9x1Z4Hjr3TYn35f89dsPPFrMePIoo7OGbV1xHjthUiLG0bg+rTX40on3n//xD8iCgHfHA3nbst8cWV/vycse33caizUD02lENAptURrSlI2bqWNdm73zPQ9Of3i7pml7Lp8taexAmAQnfvUiScj84ARkzuOYRRTSZFMsrqnIU5Lr3I00qPueqe+75MzAWRTR9IZ5FNFbN/fFDg7BV03H0/mU2PNPVYhloOx7Us87zRZX0t2HYWyOqrqO6lhjMid0eoHHfnUgziLKQ+UG7yXBaQxzN2oQ9xHEAV3tWvBXuyNxEnLc5szOM5RWxJ5HpDXL+Ne890KQeJ5zrlhLME6CVVry2Y+u+Kcvn/Pzd+8pDq6TePXRJU07DASjw+7eSy+EoDy6QXfHvRPkNzd70sgJwcH9wTlaK8Mo4NGjBYHSfFhvTxXmky8uEULw/s2dm0wZeoS+dJbsccrlYZMzSTzWe9eROjufMJ3E7A4FVVFT5A1JGrBd5aPd2rkJi2NN3Ri62tGUZW2o2/8fJHelFK9+8gTTW8pjdXKDDHb4jYf73teuPcXZmZvXcO9iyAsnHB69gqZu3VxoKSgO5YjGfbym53CoT41DZhxf2pie0He++eO2cO6d3gI+WRaTV41rjOktXuv4yWZ0fTiU4njiLPG43dQcix6oKMdZM03fszyf8O13W+JQOS6575mmHk3b09/uAdiIA8WhGjt2lWtG+r+oe5Mfy9I8Tes583zuZNcGN/MpIjIyIzNroEFdQgipS0hsWcGWBVL/C/SaVf8L9I4NEmxasEIgEKCmKSiJBrIrxxh8tPGOZ54Pi993j0dKlVlNFy0lVwqFh8v9hpndc77zG973eT2X2dVaVBCWwTAcSPOWtpXW/bMvrtgeMqqkoGsF4DRfx4JPyGv6fqRWF8Hj+y0vfviM/WNC3Q7YpqgRHEstWJUSxjQ+taWubWAYGucXEYuLGfuHo2J9GJNEy3ItfM9hoXS6Jy309U9nOIYshx2Fjh1HGF5IGd02Hfd3CeOIQML8nqyQmbyruifTEoPW2dVC5Iqnh6NihncnJ6CmM3ddoGDmiFpoY+Xc3e3wQxdL18mQPcF8HfN6vsA1DbZlxc3FCi5WPIsijlXF24NotV/OF3I4q/GYZRgkdS0yS0tn5jjcZim3Wcr24cBxmwlSwdJpFFr68jxgeTmXoAVEwTT0A61CaHx8s8G2dHRDm5DFwcxnFTg4hkFnGjRVI+M1Rx5mWdPQjQOhZWMqjo9vWQyM/HqzwbcsvjvsuYli6q6bVDChZXMZRjJS6sRAN3djDlWFZ/WsgwBLKWFqPkHH2n6g1gWvrKGha8KnSeqGXVbgOhaJ6pa6ticOPVgJs/wUvlNkImP0I7HaF2lFNPfxQpeh67E9i3Sfo+s6h22K6zlcr+aYuk6kHsKPeUbetjyLIvnsx4EzZfT5uD9KdkDRCBVS1yYFznFfCObCs/ECh2Qn6pQsbxi6nmDms31M2R1r2n7k6qWJYehcvT4j8ByOqnPWdaGAJtuMx6cDzy/PuFrO2Vcli3XM2vfZlSVe4LC9P/Lss3Nmq4i6aggij2SfU5Yt66s5bXegrDvMo2AXkl1GW3e8+MEl2SEnS+vpvnW9gfXVnLsPe7b3B6qml4Ah8/cH6f1BHO4Az+MZ3TjwTVFL2olpoCmYTjjz0U0DL3AIbJt9kvP4cGAYBvpuwFAKjXgVkSiZ3ewswvXFQRkGcui4/nEKTrDMlrSoWEZyQWdNPUHJ+l4q0LpqWM1D0lbSV1xbDt3jVpxvhqFjKU56dsixHZOf/vEzHj/sqNsB17NJ05rvvr6DUcYWgwKXjeNpbyCzzGeXS9K65uHjQWBYSjb2MI6UZUsYu9y8POenn93w9cOTXAxVS1JUjL38HPZpo4I5CgEwmQZF2Qi/whNXW5lVvPjhM14gDsaslM7A8WxcZU3vR3n4yFJKw3dtAsum7Fq4mFEVDfmxoO96Lq9X1F1HkhTcvhN79PnNivs3j7JY8p1JOVEVDY5rsbyc8+pGllPfrXds7/f4nkFZ9YS+RejL/PHs2YJlFDB3XLKg4ZvfPNKqJK3y//iWth1YrXwuXpwxc92JNw7y0H/aHJV7sKboB6JFQLQI+DdfvCCyLfpBFr8XQaASp0Ye8oyl55M0Ar8yNf239OIA+6rkPktpVafy9HHHfl9M7PuqaQl9i+sXSxzfmWijRVqyOJ9xeErwlaKjzEUJ4bg2+06QGKZlCEBqHLFtk6aWhSQItnd7SBm6gfBa9gCPecaZmrUHtk3d9cwdF8c0+P7LMw0s3aUfB8ChaIWxXnUtse3gWoJL2OSi9gHpWIZxJGka5rYzQcVcw6TQO+aBNzFxHMPACuTBKo5rmQk3VUOSDFyp4BtdSRz7fsB21QJeBVmceE2r9YyHJONmMcPWPynHfv70SNV22I483HtddmvdbGSTZayu5jRVyz/55Td0bUfbynjRtnRsxxQciG1ODHaQmXjXD1iWjm3Lkne+jjEMnaISTtQwyGe7uz9MRqasqVn5Ptl9jhu41LaMp55fnrFcxRyPOYdNimUZ6AuDIPJoaxFpzBYBT48peVZTFfWEod4/HkmPJV0/SBdtnExQBuuLmKZuCWKDjx+PvwUN/OtefxCH+8jIU5HjmRa2ZzM7i/Ajj7Mw5DFJJ/zs9u7AnUJ5lrU4HheXcw6PRzEy9Z8g/q4pM+lMbyaehhs4vHgxlxg2JCJrGEf6caBqWtJ9LrD82Jqi/LaHTPTqjkngChBoeSEOy0pJHcdhpOsGyrwlnPd4oQNZraSUYtTZbXOpHtTiN0srgYVVHXU70vY9+8ejaht1cdfVLZZjUdU91VOO7ex5k9zixx7LiznrKKTuO+wo5LNn66mq7MeRs/PZVN0aukbWNLTDQFnUJLtMxjrLkOyQK+a0LP3qoibNm0+GMl0j9E3qRsYvrcIXgOjhP96lkza+Gz7Fw1V1T1q09E8FgWdiGhqzhSyY7t488V3ZEEQu5zcr/u5Pv+Db7Zbdw5G2bimKllc/vOL1SrCqtqEDNr5i6oeBUAVPKTuPH7bc9+M0qnjvmJNB6tROl3lNGPv8aHVG2XVsywJLNzjUFaFtoyNuwNhx2JYlXdtTduLgnExCXUfW1Lx584Dr2xyeEsqqm4xOpm1yPFaEvhjLDMtQBi4b37Io9Yq+67Edi2gZ4FkWH949UWYVTd3iOua0JBMDma8+F5Vw5VgkRcXFcoaOhqVLJ1G07dRt3MxEp38eBJRdh2ta3zMiiQx3GEX94hom/SgjjUNVUbQttm4QO870kIRPXJpuHLGRuTuME3PG0EXbbRkGm4Okke3ujzTqXr24mmEcBUMwKuXbiaGvG4L4rWsRFHTKMBQ7DgvPI7AskrqexjCegpqd5JiG6uzXvs8mk+o3DD2O+4z9JqNRCOzZSvDGVS7hNd04Yps6WdHi+A7zuUfU9vixx9ANUyhKXciI+PxmRX4sZCbvudi6QVpWvM13HJ5S4n5kbxk0Vcv7fSbqKc/mxQ+vuPv2ke3tnvXNUkEIC/zAkRExUBcNXuhO6VNp0X0y87UDXT9MyW+WbWJYBq8ckfR++D3n6h/E4c4o1WLZtcwC2bInu4zt/QFDucKKQtyElmUQRw7rc5uXLy8YRglseHi3Icsb5guf9fWSuv+UM5hUFZ4l8Ke26aaAgNM2e5cVbG53GKZs7R/fb2WOrsnCq1LdRJJUeI7J1z//yLPnS/zYm24+EDVEsk2lOjBlw247Fk3VyEyvE5OGO4Jl6iq2TxaWZdtSly2zubBUWiWpSlOBXjmWzu2Hg6hIdiX6uz22KXPvwDVYrEKKtJy+Fl1XBhDXwg9dPr7bTU/60/f28H47YRt0XWFVVRs9Kma9wMkgzVtZdlo6lqFjWfrkN0iLjmEUJ6hl6NRFLbm2usYslBZZAkwkQ7ZIK1xlPf/F//mO3eWBm+drXv7oFb+5f8RQD59xHKfAh7xtcR2DrGiZn8/EDTgME6nxmOaYhi5KpGVIpVKxXN9msYr58x9fomsab48H/pf/7Vf4vsWf/emXLN1P2NSTRHbleSyWnizSt5tpPHF/FPJf13YcN9I2z5fCqbddi2SXcXE1o2sV9lZRAftuIKNm/5iAkqgmu2xiHem6kA1PGOdhGPjix89ZBwH3uj7Nv0GxirZHzpdCXNQ1SLYZ5xdzbuIYxzDJ25asbdCRg6/p+wkEdspQNXXJZDU0sdh7lkXWKje2ptEMPUlTE6jRzylHWPJYZTTTDQN118m4apvhXMjPYf9wFJmepvGDn1zT1C1ffy2UyK7tcXyH67MF7+6eqPKa3f0By7UkTs61eH55RtsPLFyPom355nGDbui8XC0wNNlDyRLdmMJPPFPHsS1mvkfVdcJ+UubFqullmWno4o6tW6pmYDZz8UKx8l+9Pp+iGjVdw9QFGd53PYvzmYAEFyGWrrP2A37z+CTJUZ5D24lk0nIt1cFbk2hh6EWGWxUSnGPaJnVSE0Qa87OYdJ+LfyWtKNKKJGum7u1k7hxHaOtuKsBOXo1TN/e7Xn8Qh3vXdPzqZ++E0xI4dE0nll9dY3E+w3IsNL2cFlInbTeArRssr+bcvtlI7qoaPdRlw/pSYPlV0WDHspDN04rjocQ2dRzTpOo60r3A+01lbNJ1oSueKHZJUilq5IARGsSWGEuSrbAhTMsgXgY0VSsPgUMpVvC9gKFMPXDepQAAIABJREFUS9QqJ9StYUqlA+A2HVkmuNn5OpIDAPlQLbVZPxEb+17Ihv3IpMWNfJMgFC5HOPdFC640wlVe0ykD0eosnLCsddUIVE3tEeSdBgLdUrREjZcv5rx7f6Buh8lgtoztKVz4/PkZb399Rz+MxIH8vaoZMFyJ9BvTCtcWQmOwcAlilapVSvqMLN5MyBvevjvw9t2BV68WfPXZNf71FYXS+bd9T2A5rHyPv/dnP+Gf/LNfKbkaPLzbkuRy6DuWhIU4ns327sDFixUvn625CkNu05T/6+Eez7Io25aXX17yxfoMgGboxXauXjdRzDf7HY5p8vZxR3bIsWzhbieJ0CwdW2e+CpmvpVqv8lqCR2xTaIeKzlmm4r84xQ+6vj3RAU/LRdMycHyH9FjgumK2s11L0pLUP5N3YhDj0ziMUwarY5pYrsVlGOEYwn9x1G6gHST7tOzlXqr7gbSRRbpjGPim0AvrXpa1li5LyW/3uym/9M+ubyYNfzuckAYiXx6QdKad0vL7scfju82UazqO4O0yJS00hFZ6Fgl3Js1IDwXPXp8zmwXkpYxFZ5FP5DjYhggOfMviT26eAdAOPaFlS0feNL8FMHvMc7KsRAs93n19R5LKvukUfnGiyY59p4xyMg3wA1f09N2AoXIENE3jejXn/dNecftln5PUNYe0YH/I0HQ5nMPAnbTt2SHHcW3Cua847eILOSmeHt5usD1LGFbHSu5FtftK8nYa/UWBdcqVx0IKrWMqy9emks+/7/pPoeK/4/UHcbhrujbNUq1anJi2WqQZhrTUbd3ieJZUOgrpe2cZ2K5NEHpcv17/loPS8R0e73aS63gs6FuZIfe9tGPhzJfFUdlwf3tkuRLtuGWbNGGDZuiEkUdZ1MwXYp3uFHkwT+TwNnSN+9ujyAkjB9uxyNJahdd+0mg7nhg5do8ywsjzBluxNU6vru2JIn8yK4WxJ8YK22W3K1jMhYpZVJIDa9gGbTvgBAZnz8RSne4z5ssI2zB4eNxzeBJHY5GKYuHwJMvLoevJy5az84jl5Zzd/QE/EsxC3Q58+dUlry/O6PuBzVNG08lNcshabFOWrbffSq6pZUqTHgcWedVRN2IL7/qRouqIfKFzBpFNptraE81x6AVrHPomvmvy4f2B3VPG6x9d8cdXVyS1VGhZ07DwhIDoRx6/+uUjeSaB4dfXM2xX5rVh7HMdxdivdZ6Kgnd3T7wdn/jyxSVl16KjTYoQxzCoFWP+qSxZ+Z7qenQsw+Bjkiimj872MUXXkQWWCX7kqv2BPcUwnlg8ju+QJwVB5NH3vcraFB308VgReCaGZfKDz+TA+vbdPa5vs3nKMPSOm+szpVO3sA25PWeBVLCaBodNyuXLM/quZ1sWckiHrrBI2pb/+5v32J7FT2+u6IaRxzwjsKTi1TVRRInBSdRiwNThnnJSh36Ysjq7qdKX3YMkcmk0fU/bD6z9gM0hpVXL/KYVT4QfOKyvlyKjzStef3nB5uOeMPJoq5bH95vp+h9hCoReeB62oSvppYxMB0TGaWgSEXgKrTkpgBzTILIjDF3jZ3/1ht2+kuBvXcPSwFY5DsvL+RQ+LbLqkexYEC8CeRAFDvmx4PWzNQvXw7oU6aumCUPneTxjn4hXpWt71jdL0qyc5JyGJdnAuiHL28NTIsx/Q6fKa1l8hy6maZAdC6pmoFNjzlahS04qu7u3mwn1YVlS5N192HPz6oy27kRgov//YKGq6Rq2pU+hDF3bTfjfkzxxvo4l+UeFSOdJSZFVKsVE5IAnyaChWq9xkEi2pu0nl2vdDBR1j2lWVIXo019/eUGRlrJAScpp3JKrgs60DGGjqAABw9DpBxn7zGYum21Bep+xiBVhz9AZx4HDocSxDcZ9weo84tUPr0i2qUiukopjllOpSnl7t6dX+NC+7amKZnJPNp3Iy8K5j/GU4vr2pEzIk4LHD1viRcDjfUK8jMCB2TLCtE2asiVLS1AHo2HqwlapJPjA8WwWFzNsx+K4lYfPzdlC8K6Wge+a1GmjiIJS7RSKoBn6klV5PJbM5y7zpUjVirTE0JlGMrYjl1ldNtO+Iz1WErbdSwTa5iAXf1Z2/OyfvWf3cOTvfPWaiyDg17st7+42shhNK+aRRRDYvPxCfAovZjNmjizN/+mbtzy+2/C0l9n1518I1vbp6cjlxYKmbrk9JoRr9VlpBp4l1v2yaznWNY93O4ZenKNt1XL+bE7XykGY7PLJQZ2nMptnFAXOOIzkqZh1to9H1ldz6rIlz2o++/EzquKOourQqo6iFat9tAi5/fYBy9RZXy9FvljUjJ7sYWzDwDVF7mfaJl98eQ3A0+Y4Rc6doh73dcX7tzsMQ6ikP7q6mHjto8La9sNIUtf0Sm1jqQVp20sVeJ9nNHWLH3vMAm86TPVRslPLXpbseddOC2zDFKZ/VciiEGR00LU9oZI1Or6DH7m8+eVHbNdC06WyPzwlE0LgMgwJFG4DmNyx33/VvfDkfcuapLCnnUChMktPvHXL0PDdT6OxRp0lhqmTJ2I2slVXVRc1ry9WaJH4S451RdMPdOMwsfdPD8PZKiLZ52SHQhWdNppuYZsGu/sDwzCyvJgRL0MePu5xHXmQRHOfqpB7yXZttseM0DfVCFRnPvfk8wxdul7IrIZxckBL536SGoeWT5n/K4zZ0zTtDZACPdCN4/hvaJq2BP4L4BXwBvgPxlFBkX/HaxxGnr1aUxdi4vBUenqDtLrrywUf3z6KccDQaaqW+TpGP7FP1KEPslXuWlHb5Emp4EHS2gyjgMnmC5+zZwts0yRNC+LIZxEH1H3PYSNjkTIXFUlTdyJFcyV6L4g9lhcznj7uGEdptW98m8M2U1F/ok21LGn2k7xlEdmsLxfkeTUlyxR5jalgZr4rS5L9g0giLWVLP26qCcVbpKVoo3vpIFZXc/xYWvgiLcnTEsvUKbNShW6LxKtTREY/dDFtU5Z3ZcNiFQjat+2ZxwH7Q4ZpGjy/8WmHge/e3BPMfKJFSPurO8q6p1O+A8fSaboRrx+IV96EYGiqhjxvmC38CUtaFC27Tcbzz8+lWyobTNvED2wsUxE0M5Fc6o5BkrXoOny8TTjs/jnPPz/nz16/5C8y0R+3VcvqcsbzyzN+sl5T9/2kMX/7tJNEJYVj/umfPmcVBNRdx+c35ziGyfLKwzIkWejX7+5ZnsWUVSNM9aqhymuBkqmFctuP2J4oOrzAJV4GE0d76EcsW5O/k1VT52gYOpFpkO5zjkmN7xpsbvc8++xccmXfbthuE9Zns0nqWW5z3n7zRJFWXN6s2DwcWJ7PMDSNqmspqoaL5UxkmJrO9fmSW13lBCi999v9nucvl9x93PPh20eyQ86Xnz2j7cWRepqnLz2PpK6p+5667zF1DccQqepVGGFoGv044prCcB80DUOTB3vWNJia/PlK62gHFZGpdOyjUh3puj4lkp2iIqNFMOF/+66n73scla6WHXKs2RzHMKduwdA/jctOi9u57tKpA31UHYdrmtS9jKDOrpe0bzcKiGdgq1m+FwiI8O7dE7ZjEcRiHHr/najngsCe9P/7suRQVxIkEvqkdY2lG7zd76nyWi3yB+brBU3ZkOyFD7P5uCPNRJ2maQlt3SruUo9lGTKSS0vmZ4Li2Cv6bN30rGZyTfWdgNFGJYxo6gHfNYhDG8u15KEUunSt3Ne/7/X/ReX+5+M4br733/8A+O/HcfyHmqb9A/Xf//HvewPbkXFLbwmboW06zp4tVCq4haVm4EVWYZoGRSrzZT+SMUqRlDi+RMidAmwljNnHtGqyvJnSjOpWsk/zY0Hr2pMkquw6qVhCl7sPe4Zh5PLZnFnoTi7UcRwnXkS8DLFdG93QJtrdOAqaYLaQ2ffD3QHL0Cnrnndf3zFbRXiRPLjC2KPdFfiuPJGFa62J0UrN6B3XJsn2iqFjs90W2LYuAdaKNV2XDR/vUn7yx9eEsc/hKcFWAdOA8D08R/YBbT8Znk7KvqEf5CZXSUk//uJmYtZEoSwbn39+Lkx11+bu/Y62G0Ta2Igape2Ue1UlNnVtjxc4HPcFeSUjiyiUz+rwlNDUHfOziHEY2d5LTODZeYRhGNRvtnS9uFUbQ+PXP78D4F9/+VwwrYq0eJum/A+/+YbH91uJTvz8EtMyKfOEs+sFwaxiEfiUndjtX8/ndMPIfZYKo+SkNvjuQe1wNLpafa0zj3YrxrU4MCf8het9ugFNy2S3k5GcXGcSjRYsQwlbN3Q0XaeoOup2ICs66qJhGCT96+79TvTYquo9W2uyyLMEodB3A3Xd4itWzDhKxf3x63vOrpdczGOu10ve3T0R2TbtMPD0ccfFizN0QxPlUdOR1DWvlV5f1zQWjkPZ92Ja6rqpivctC0eNgW7imLss4+GY4FkSJXhabseOQ90LOO+pyGVk5nocSYWAuAzZ3O2nnYLlSIao7VoT/z2M5LoSdrlB1XU0TcfHNMHSdc6DUJa4CIcpaeRw1TWNfSmBJJdhiD6K96AfR/phYO64fHV9Kc5TwLHFFT10A1lS8OJyRXkW8/Buo9QptjqIBcz3lMp+YFcW7B+O1FVLFuSq4HSoy5Zw7rN/lGtYij9hvDxu8gmn4TmGcpOPUwC8heybdF1cskHk4TrGtBdwfFvCO7qBdJczm3n0bUeSyXjsmDYEylBoLgJsT+L6ft/rX8VY5t8D/p769X8G/I/8DYe7hgrZsAxClXbTtf10CDvPz/Ajj9s3T9iWQdP27BVN72S7L5JSpbqME5VtHEaWl3O0x4QirymUbG1/lPi7aBEyj3zypsE0DGaBx7bpmM09thsxQ0WLQLoBFVx7cT6n7DqOm1QtMQMcz+bi+RkrhSG2bJP941Eoi55C6aqvaVC/rosG09CIIg/HtajLhqddiWUIdKhR+ZnnF9GU1HP6WWVFx9e/uOfF6xW3dwmmYk3brsXmbk+6yXj5xfm0XJYKSdrmPCkJ5/50Ycwjn/v7vVRaTS/fW1Xh+jZpVrKIA84XMXUkyqJknysvgND7HN/BGQV0pGkangOj8gmsL2eskU7EMgwetjseH1Li0KbMKvWQ0ZgvfDnYq4Znz2Ykh5y6Gfjsh2Lo+PZX9+THgn/7j77EtUz+6dt3pPuM6+sztqY8/AztJM8c+fDtE/HcY5vmPLx94vzFGb/ZbkXFktfTXuOYNrT9wNncZRiGCXOhaaIvNm0NPxKJWl02VGWt5KMFx30xzUlPD/7YjoRYqTJZTcsgCiz6XhDR4dwnOxS8/GyNburEoacwAc1kwT/RSnVdp8wqVlFAUteTV0DTP+EVLoIA81pCKLpxmPwHALZr88UPrll5Pp0KoTA0DUuRSU3No+57MSM10t5XnbCSAssndhyavqdo1LKwqVkHoqUvNIiUVDFrGpKmxnYt0MQ4ZDuWCkkXlUrf9pRlS1Z2LGOH1dVcmCy1KMRsFWNoqCziN46F69tcXi0JbZusaejHkTwXoUQQe1wEAT0S6iNfX0NgWbRDz/JihhtUUwdh+RZVUUuo9SCS3rrJWdsmy5UETserkJvZjEP1ideiaRq7pxTT0Hh6zBRnqaUqGwxd481vHpjPJWjHyDtc2yCIvekMEAFHOQVrVGqMsr5eysJVyYuf38zou57driAKbIpxZHEeT+amcRjZKpm0jjb5cP6m19/2cB+B/1bTtBH4T8dx/EfAxTiOd+pGu9M07fyv+4uapv194O8DOGdCcTstWLYK9er44ibL84q2blmcRczOZGOsG7py83ns7g74kcs8Dnh8OAiGsxLFTaCSXcLYJVKVw34nN+fFzYq0VGHQ2sA2LdneHVhdzTEMQ7WZYDlyUfixx9PmyO37nXBBZh6aqojaVqRKRVahGxpe4E65l03VcHa9ZBnJiCAIA7nIVEUIEMT+xGpu6pYqr1lezDBMfYJ9maYcYHUraIBxGNEQKmNbtaS7TKU6DdNM/pTPmOxzZsuQo3LnRYsQxxEeSqHURces5Ze/eCdsjciTReww4BjiGai7DseTGeV54DD0A/EynEZZad4QBTZt2xPOLdU6GspYJvPqYRjJy1Y+x7rF9ix22xzfrScNfVZ0uI7B268fxIFc97x7f+D2VcomyykUHK5Q4K5xhO9+c8v+UKFp8OoHF0KP/LjDjz32j0easqVRrP4ka7FVxmayywT6FLo0ZYOrKqjTnL3KaxpdDiLbtScuzsX1nDKv2W5y0n3+KedTSddOB/TZeTR1WLquc/3FBfmxwFI8l2EYuLhZkSUSrlwkorAxTIN4GbLLZKdyMnT1NyvJDtWYdOzDOE4LRMkXLvjJj18R2jaOYcg1/L3ZtahQBL4V2TaL3qVoO/K2IVUqG8cwCZVx7eVszoXiw3fjMKmLbF3QxVkqPP1gJvTStpVcXk37JKk9v5zhHgtWzxZcr+bcHRLqUnABJ0lfmctoa+h6Ng8J6T6f0N+O7/DxuyeCwCbdZbRNx9VyTtW1bA+ZICmu1wwjvH625u3DlvSQ8/RhO43Lfv7tRy4uF3zQnjAMTcaajsX926dpX5fussmB7SuqpWWbeFmlHiTg+Q6OZxGrBfpxkxKH0pk0ZaNCZgZGQ76Xuh0IHYtDIioiL3SVk9fGd3v2u1ylt8n5ZNrmNIFo65a722SSJWumdIpFUk5/5ne9/raH+781juOtOsD/O03Tfvkv+hfVg+AfAUSfrcaTtKdrOubrmO39nqpoWD1boOmaOMCKWn5vHmKo7f3ckxYv3eegDu/ZWYS2z7HqluvPL6nyivRQ4Hgmi/MZddGQFS15VrJexOzSnGJfMg4jfuRSZoJXtRyTxw/byY1693bDQbHODUPHMmtu08cJhWuaOr4rpqGHhwzL1Hn91VLit6qWyuuE59FKJFldNmoZI3F5QSwPiyJtSPMOay9Ey6zsSIsO29QxHSUBNXVsz8IyxfB03GUKqNaTJpLiY7sWj7cH4rkn2FVVlW4fU54eUkJfcMKPm1ykoZZIFLu2x7UtOrfnmBQ0gcwzj0cxeVm2BKPs7g989+t7OdC7Ad812SW1LLJUpmVTtxyPFUlSyaFty9evmwZtKpRLkLzYOHKYn894+tktuqYxj4VfEgeWUiw0vPnVHW03MFff04svrqhq6fB0Q+SQpmWQ7iWhZ78vqVth4SzmLnne4DkGq5XP9fUZwcwn3WVTYTH0A52qIquiUcELAorL05KuH1mdx5JLq/T0yaEgiD0JaVfdZBj7FJnMbfu2Z3+oWCGxi1Ve44WeIigmRItQCJ+KSHh+uWBElCrjOLK+XvJyJUvuRAH1Ts7StGn4fLHkQ5owX8cMyvW58iX04ySvO2ndv//qR0m+8k1Tsg+Ggd4aBZQ2SDzdvionCWTVtfiWzWOZ85W3pmiEz7NrkwmfcML9DqPsPk4+jrpquP7ikt3DgSc1ookWUuQUyrUaxj7RPMB2bYq0pCpq6qqlqVr6p5TzqzmWa4nwIS15Vz/hhd7UhS48+Z6zpiGMffl7bU+nuDlVnbA8nxHFalTkWNx+90hVNngj3H4rDmjXkR3Y2bMFdVGzvT9MILRxHMmTinDmiTLqWKAbGrNVRJ4Uk7FOM6EuZTHddTljPzCPbbKi5d2vbnHUHscNHPRSeFEndPY4tjy824pSsJZoScPQWT1bMKoz7+Hdlj/+8Uv+199zxv6tDvdxHG/Vvx81TfvHwN8FHjRNu1JV+xXw+C/wPpO9t++HafZ94kFXuSSC257N3XePArgyjUnStH84UpYt+13BYikV8LPXsrw6PZGbqqGpGraPqaqCdfJjSRz5uCoA4kR5+371dLKCo1yXmkpsMg1JRlnfrPjKd6dN+v3bJ7bbQj4kINlnGJagY/fbhNkyEvfcVpQyYd0Rznzarhfzgi7/z3msfUKJOkLCiyIH17fJygPjOLK9lZHE4iycKkbbtfBPoQiaVJKWa1EXzcRmAdHJbw8Vm4PsCcpadhJZ0WK5rWhsHRNHLbtuv3kgK1rKWpY/kZKP9b28j8TVyed50hbXVUtZNLI4VaMJ15YZc6lm+AAvPj/n7dePsrfoBxaxjW0Z5GWH5xh88dMbMboYBlXdE4fSPRRphaXr2L7L/vFIOJMDePdwpOtH0qLD1DXOl64YyyyDcB5weEqoy4bb2y0HNaJpv0fclPcuOSaiX3d9m+2xELCdJ0aV2VmEgcHzL6/Ij5IhANC1Ha6S1J1a8aYdCH1TxbS1oGmTOutESTwZ9n7yk1dkbUPdtFjqQXoRBHz7sBGbvmfzw/MzGpV3MHd1irZhs08FI+E7DEOFqem4lskmL1h4HvUgh7uGRtF1OMonclKatGqOfnqdfv+U6OSZpgSIDD2x7UgC0ziwLUvKXMZVu/uDaNqVjDfNJYBd1zWiRSgZptey/Fz5AXe7g3SRy1Di7Op2YkoZhiAb4qWMJuqi4eN7CcR4drPAC13SfU6lRBht3fLz4IHId3nzzb3M9mc+0SIgO5ZAI52wJk7PMqs4PCUcjxXLlUidz67mkzyyLBrBc6swatsxSQ8i5T0F05/OjL4bxFmsfn66oVOk0kXqgUPdDviRPPjj2JjUbqdOLytk3+aoSjwrWuLYpcgl6+GUtXCSgRdtS5ZW05nzu17/0oe7pmkBoI/jmKpf/7vAfwL818B/CPxD9e//6m96r9MMWtPEMGDZJqNyYJ0UJNmx4BRQu17EpHUt8qa6Jc+bKTjiJKMqMmGuHLepkN08abdNcxRlStqQHPJJgRP7ohVuNY14EZKlkt/ouGJjjxYBq8s53/3ybmKXe6FL7LtYKjUJIJ8HHI8VF6FNMBO36fs3W1794BzTFvu87dl8+UfPOe5S0n2ueBjy/WuaROUNwzjp421Lx7FNwpkvPJfzgK7pOKaNLFjUgvS4EZ58q+ZxoW0TX695TOQAO9Eag1AWrCeX7DBAOwozehhEkdIoZGnXdmyeMpKsnaiRmgZP93KAdipFaxglizYOhQ0z9CI964eRNJeLVyBkOvt9QRzKzsPxbeqioahOh8GWvh8m38PpYXSiQjbdoFDF6mF6v2exjoXyl1XsdwVx5NCVLecrCRM/0SrrUuSlUSd7lLps1GdsKdefBIj3bSeOSUtnnzZ4Ta8McrJcPcW9GZZJU7WfzEVq4X7cZRMyw488soeUYYRA13FjR2WHDjRlQziTFnu2iji/mHO3O7C7O/DZD6/xLZuqa3m/O1CkJacQiNPB7pgGx6rFtyxmsc/XH7YEnRwk+6pkpflsy4KV7+PoBiMjZddzrGvqrmPhefgKzfH9Q97QtYn8WLVihW+HHkPTZUSnG5Rdy66sJi5KW7fMVsLx2T0cyctuYu1YhuwJTF1j5rp4psQBXi5m/PzhiGWbktTU9Zw/l2DxE5DvtMvxI1f8K57N9jHBDxz80KXveyzbJet6ylTGnD/+0QtC2+Yxz/BMi18c3/PZj28IbZsPd1vFt9GoCqHE6rqO4ci83wtdiryh66UDM2a+LD8D2bs4rkV8Cu5+88T8fMbckQIzL1vOL2V+Hi2CqXPzHIPjvpCwnHkw7Ql0XadSUELQMB2T65fnfHz7SK+YOABV1Yqju25hHDmWgmT5n/7n3z8o+dtU7hfAP1aHqQn85+M4/jeapv0l8F9qmvYfAe+Af/9veqNhEBqk7dpYjqhJTikkw2k5OsoTbBzGyZl2qvYNQ8N1TIKZh2EY9L3YgV3P4eLFGb5jc3e7lRQYFZRQqIoqUeMM0zKmZJ1uGKgLidI6DMm0wIo8l4trobMNTY+ul5Tnyt1nyPxxvgjJ05Iqrzm/WtINoqTpmg5TScV0XeRb+irGDdzp5k72+fQzOaWtnMhwddMR9IM4dnWd7SZB0zKOac3m/iBB0I5gFapGRZcpOmLf9dRNzxdfrdlsjpMpTNeRBU7VYRja5EQ1bZOPXz/wtBcWvGlIhF7sy0PkhAE2AM+QmL7AMzlmrYrKGyW+TwVuGIbO2dwhK6RKbXvBMDxbBsw9j1/ev1MkyYHF0mfzlNF1I89uZizOZzIScm0c45PKx1RRjLYreNoS6VpMQ+f+qWAZ2ywv55NqSLrDka7ppkX4SfNcl62Mw9R4oOsG9G4gCB1cx2R/rNU1Ke9Vl9IFLkwDTiaVopawBSWTPC3F02PBYuGRZTWNcpo6ns3mdj/l3ja1ZBicAi2++JEoltqhxzUttq2kDlV5RbLNeK/rXAQBnmlOh6VnWbz+6oYP39yz22Zcns2n67IbZIxjGboKh9bI2obQttn33W+NbwZkfu+pQ39A3LBnfkA/DJMM8TZNafqedJ8TLUPqshW54TLk4T6hqGQvpKsM23Dui1opOTIMI54rPoNwpnZNapyWHQq19xjpWqnmLdtkHEZWlzPqsqXaFQxDpTACPWHoEK8i6rLh3LWn/YBnWmyynNkq5DyQr3/oB47HksCTB3pTtTStYHZ105CRmWtSVmIy9CLpQhzPZhxGZmcx+bHgm282U3Sj49pohk7gWVPGa9t0E054dhaxud1PZ9bJG9HU8r6+MpVZtklkO/z5v/YV//vXbxj6gd1RJNOOJ4lPD/diqGs6UeL8vte/9OE+juO3wJ/8Nb+/Bf6d/zfv1fcDD5uCRaRMAmeRjEh6IT6etKWCtD3woDTmtlImeL5DEHus1zP6ceTh45YsEdnkIvCV627EC23iyOeYFCR5h2VoStZmSFs/D5SETSz0nim287xscXYZizhguYrpO3komGomH858Ls/mExbW9R3evT/ivnvi+sWaFy/POeblBLA6LZAMTVMfOqyuFnhNN1HkdircYVBMD9cxCecBpnoARYsAL3TZ/+wjVd1PF9p2W6BpcP/miWwutnDbs9F12GyOXF+ueNwnHPcF8dwjWoQse7kY377d4ahRwUHx3otKVAAgN2ngmRJgPUrFXrcDi0h4+JtDje+adN2I7moMncx0XVtcw54jio++HynrnsNTwvyFmlWFJYHgAAAgAElEQVTf55jKfFO3Mr8PYp+njzsOip4YOQ4X62CqtJermLkrHVdCyWGT8uqHVxyeEg67nOM2VZrgfrqhDk8JjmdP+46hGTFM+Z4tV/I5+0GC1w3blupRpcwfDiXzuYc+jIL2Vdm0x2NJ6H+S+jGOZIdCgjgOwp93bUM5GHWKrFKRgHL9h/Ngkg4eNymN0oyvAolcsx0ZBZmW7FQ2t3tWn3tyII8yMtHRcAyDL3/0XGLz2lbB0XS2ZYFnmrimgY5G04kBb6+UIb5loWuSRHTSuw+jROyVbcfKlyDnE3vnZBxKkmKKKGybboqDlAAYYf+HgUVRSpW7dQ7ouiZz6dlIO+2BZPF6kus2lTxsq0IheSOPYRA2/zAMnF9EU1erZRXhTLKE/dBltxOp6w+vL2VJaXzCE3yxWOIGDlFgYzrmlGDmW4JVnq0iylyAf44/MF/HeMrkpGsaedny7S9vuXm95vLMx3KtSUnluJZMF2pZEt+8vsA2DJKimpDNpmWwfUwm8N4wjvLAN3XiVcSffPZchZDAl88v+VnZYOWilW/qjvObJbttrsLitWn8+btefxAOVZBUoMddRV52dG03Qesdz8bxxEB03BeMSOWR5C0gqotT5Q0o1rYpxMWs4sPdltv3O5p24IVlcBzh3TePyogj8rGq+KSSOLFrNBWdphs6vmuqSkou8JOZab6OqcuGeCZyteyQTxZ/09R58/aguOxzYt8lLWuJ0tuLciL2XUzLYHYWEXoOXdvx7LNzyrSibjrmq1CpH0TmadumpMD4LoFtk401lqWT5i2GMfDiC3HF7R+OFGVL/ZhimSJP0zWNIqvohoGzecTGFcNP3w2iOR9H6lq+96YdVHcklfcpWq+oOiKFGTA0qeYj3yQvO7bHBMcy8F2ZlQN4jklZd1PakmXqzGIHx+owVfDBKWD5ZIGv8oq2HdA9+PoXd2TqvV69mNEO0sXkSUl2LEgPBXx+yfGY8+2vHzFNDW+fEc59Xr264Lvv7qWSUvrhIPapyppwHigCqBjdNA01orGp6l7i9XRRPtiePY3hZpHC4OoaF1cxRVJy2FeYpjaFe0eKSNkp7sds4eP6jljzS9GBjyqAQ1NBHrHvUnUdXS9ERF3Z1bUwZJNlysUplMGqqIVfYjtTGLZrGiw8jw/JkW4Y2D4e8WOPm1i6vHYY1KE8MjDSDcMETNtVJZauT7ryrKklfFsd8KaucawqZq7LfZYpBY4pXPlDLtVt6FJXDck2FRWboQuWQjNxbPlnf6gIvI5+GFmuJTDbjz2JpFRxcnUh9FXDNCaDzsnRbBhixDt18ZomahdJbOune7euWtbXS351+yBjyrmPYRmkh5xftKqDdmSXESrcyEm2OQwDuiowFs8EAJflQvJczEPOL2e8fbvjuBE4YKsMWhI+0mFZMk93AxdbEVl1VUDKdKIhjF2aUkaclmPAOKpwk5TyRTslxr3bHYQRpXTyx2NJmt7iuybPni/ou4Hvvtv+3jP1D+JwPy3ibBUMcXsv8rQwdqf58zhCEIpR6ex6yfxY8HAvVdjqTEIJjmlBXTQKBCYmm/pOljxxIDOrx/sjmqapanJklzT4roHtyI2SH8tpNp0ErlocngJzG3YPh2k/sLiYoRs6SVJM9MrdUQKlQ89k9ixmcT6bAkU8x2KIPXEubhJyq5hgXr7nMI8DenXh6oYYl5qqIT0WcpBY8hDL9WYCBw2D8D76YRSJlpr9P/3ygXkoELHTrB4gLStWQcDyasGH754Yx5bHDzsOmYwcmqbHj+WQO4V59P0Ipjhvt8eath0oTkajpp/yVG1bJy87zi8kMi7NREW0XokjeHd/EEWLZ2JZBn3bk1TVlMozjiPvHwosQ6OohQMEgjHwApd32z2zZcgwDNx/2JPmJcfjG7KiI/AMlqsATRPu0HIWcvNiTZKV6hCSzz9U0lTHE7ffbCWHrWWbvP8g+50TVvjseikxfYFDeiiUDlm6lKposFwLTx3qJ3rfOIziOTB0bM+eqtlBSRXrQh4YhimmnLprJ0BYXQrQrUhKirTkYj1XYgKpCoPYZ76O2dzusQy56b/ebCScXeF3j6moNxxHXKsn7frpIWqgMXddmqHH1HRBEavFXDf05K0oYpKmJqlrPNPkWMuvV77PwvXYFDn7RII1dMVyyhJZqvfDiO/LdbeORdGUHXI8V0QMmtoXdG1Hr3ISTgIKxxXl2Al6Bwr9UXdUZU20CDF0bYLr6YYmn6fCJazmEaauU7Ytm6SUcO66xVNsmdtvH6eHhu2IAuW0q7E9oVl6oUuZVzi+eDHSfa7AhRLA/blyFL/5lezeHDXm1VXWbhh6lFUj3hnXVZCvQZEih4nW2rY9vmdP2JTlxYzbJOE2SdjeHcT1XHfkpSz6HUtMi3/nTz/nOo45VjUf3/9e4/8fxuEOQpnTFK9EvvmB9x8StcTTuTwPpoXN0Iu8KvDEFQjCp7Fskyj0piWdFwlyd3u3lx+86+D6B3YPR/bpJ/ni6bV/OEqn4NuiDd9lXJzPcX0JKcjyiq7upvY72WY4vg3jOCU4gVS6y6Wgh+eOy64sRIoYeriuLFqjRUiyVZCwuU9RygFfN62qRKRl2z8mNO1AVfc07YHdsaHtZfn5/MKf5m6hZ2JoGllVs707TEtJR5P2rVYGqzKryGyLupBZXlEpSp6u0SDGhbzsKCpRzywV8XEcR+LIIUlrXNugqDqquievekJNk5SgXlQOT48pUWAJk1rXeHjK1cJRqt7HXYXjGFiGxv1fvp0Il4ahoavroKgkus91DKJAogFNdZPOwhBd1/nu6yfSvMWy5M8BU2X3zW9uWV7M8NTNU5eCFtg8iUR1dR59r+oT5OrUzdU9vjvw+H7LMAxq6a7s90VLoOIKo0WIZWfcfTyq701l26qADduRn3O8DBkXAaYlARadyknVVYqS7ouSwvFsosgnSQoZ32nygDiPI7bznKso4q9+84H5OhaAl1KN9SqX4PHhwPnFnH2fcdim9MuVzOzLAm+QfY9l6MwcUSnVKvu0HXQMTXjpAEUr4R3Csh94ynOqvMbQZaF6qKTKrvIKS+FnTxRTCZN2pujAvpPFc6Q+B0sXwYAfeZOTOVPGqxNBtWs7FuczmqolCCSERdtrDJ0gME7u874VhVmZ1yIp1DQsXef+mE/4XT90p+DquumxxxHHF+VUr7wg2bEgnPkC0Xs4MluGkzBjHGVZnuwEzzGOEMx8nn9xQboTp7OrdPj5sWAYRsGEdAM7TYQgnnpwWbZJ13Yq+SmhzCqO+4IodjFMg5fzBW8P+0nOa5UNbVtQ1t/bX2hyHXbD8Ftn11/3+oM53B3FYjmhfgdDDgzHNohmrspYlIDh00WjG+IOLdKSu49iY5+fxUJoUyafYRh4uJdIOvf1BZYjVePFmS8Sqk5i6I6JVApe3U3LtWEYJSFJLTrjZcjZ9ZLH91uRVakFkKnasZME7+RQix2HZujZ3R+xXYu3t3suX625eraSBcrVQmasdStZk5HMhZNdNmGP00IokI4aO9WtIsmZOrZr45n6hFY47DNxqOZqRpy2GEqpsL6R4Iu6aEiOuaT7GDqBJyahsu4VfU4eXLap49pqYaQegodjJVmq4afFcFX3XJ55WI4E+u6SmqZRJEFDuN+2ai1PUsNeSSgHpbRpexn7uLZB1fQ03YihQdeN+K7O9lAxj6TaPWwS/MClKmqV+3q6TkzqQrT1eVZT1j1FXvPs1dlk82/qliapmc1c9puUs8u5BBOP/bQIPnUrRdWxWAY0yhkMsFy4bHblFMc49OKLWJ+H1IVwuC3HUvI1Mb65gcPD+y0oGaNuyOE3dAP9KLsAZ2XSOgMzx+HDZi+pPN73+epwFUWq+oavri8V2tfkWSScElOXqD7PtCg8G9sVhYuQHIfpILeUBHIYJVDa0PQJL1B2neLQ9NS6zNbztuVZHDNfu+Rty32WUratgvCJ9LIuG3XvyiF/SI7EocX1/ILFxYwyrQSNrYsc2LBEDddULaYls3TDFLqlZuiM/UCyFYBZssvIk5KzqzmGZWDpcu/vHxPCmad46zHLWcjmkLKIA9C0KSjnm7/6yMUwYjkmy3UovoNuIN3nKkpTZv2FIXuQi+cr9o8JyccdQeSi6ypgpGqpB7m+pHjqWV7MWUUBtw87Xi0X/NWx4HSRN1VDVbV0/YheCNojTyuOWcsXP/CIZuKC7/uRQOGvm77DMU22iZg4o3lAVUhwfV52+K7BrizxLZtvN9vfYu/8da8/iMN9VBctSMblMAgRrVY5k1ZeszgXtGvoOOyTXFW27cRQX50F9K3AsiR+r1dJRibDIMuwRS6ZisurOftHGelYw4imNRwU+RDAOJb4kShNnvbJ1Bpauo4TevTPFixmAdttwjgKS8UNHCzHYjUbCWKf1dWcTZJNZo2TuWp7f6Ce+RPjO91nPH5TEfkm1kFQuVUtN6WhS06ioWn4nhzukW8KInUYGYeB9FizWHh8uE3Zpw+/9XPVNGi7EV0b8AOX3/zz9xi6JnrefqRqPgVzhGpR2g0iyzIM0fOXRSMLW0uXCjpt2BzkQei7BhqidpLD7NMBruuaMmSIo9axDTzXpG5EWtd1gzrk5cEVKiWOrmtYhuwsThV9P4x0vfBstpucru7YHCrqdsDQoGoGVmdSjZV5zWIV0j+lLNeRsFsci07djG0l9nFLWd39yCNPCopKMm011emYtkkw8+X7UHJHN3CI646HuyOuLQ/0/cOR1bMFbdDKDgAZOXihq0xlg/gY6pbDLmd9NWc5C2mHgSQpiGOPbZ6j6RrfKadvMPMp0pIkkeV4Utd4lsU3394JnsMwMHWNpK55zHM802RXynLU0DVeLZdsy4Jv9ntC2yapKqyZMS1Kv//StU/uVU3T2FfVlHtbDIOkVKn7YlPkbLaCsbVsEz9wJMYwr/Ejj7dvd9imThSYrG+WdK3E763PZuyOsjsQYJ7O6nJOtAg4blPOVeFxorjajsnmKcOxdF59dU04r7h9s2G5jqb7bX29VKTEAd9zaAeRPQ/9QBj7vP2VdG43n63JDgXJVvDXQeyzvdtTVVLcpPuc2Uq6uGSXMVtGLC9n7LaCTTh5anb7im4YefVqiW7oPN3lrC4Fe7A+m3Edx+Q3LXXf0fQ9f/kX36BpMp4dxxHTNEjyjqzoJHx75uOo0fBpXANiNnt8SJlF9pQUZ6hCdxzhlz97x290CS1aX854/3vO1T+Iwx2Fs2zaga4fWcY2O6X7Xi49FuexqFyOuRidup7HuwOObVA3PUXdw7GefpDz0J/Y1HnVTKlAEl4hZoBSsV1kseKS5JLOcjpQmrZndR5T5TXzdUw8k5BnXdM4X8aiGDAN0mMp4w/PJl4EbO9b9tsMP3LxI0+1/WIjXqwkiWZ7d6DtRMd6VLPuY9ZO0KGTNNENpAp0bH2iFHZKrui7JkUh2urjsZpm41UjyplBjVoqlZb08btH7jailT7N6Pt+nPTkw6BNy0sNoVrqOpOc8aSY0TTkPXUN27KUVn7Aqho2u1LGQf0g6UvtoMIINA5pQ9sNMnZTC9S+H4gCazJRfc9Dgyk9qBzs3SlOTfYzm0OldgIaXT/SND0P9ylnZ8K/6dqO88t4Qro+3e6k6zuLuPnBFek+Y/+YyPdRt/KzR+SLwzDihy6Zsq67gUNbS5U6qKzZqinJio5Z01HV3YRz6Fox7Oi6dEBVUU/GsqZqCUIH0zY5pAXpPhf3Yttz3KT80Q9fUPsB9/sjVV4J7yT22TwcKG2Lj++eSA4lL19dKPa6QWg73CeioY8d8S588+6BH7y8JG8alQDVsPR85o4rGQSapFqVnRQrC9eZFpQgyplsHKcF66bI8S2Lom3YJzlVUROoLtJXyIYfPL/gIc8p85o8q5mtItaLmMCyMHSdt9v9hKoIZzIjj+YBmUIm7x+OLC5muIFDss/VeMrhyx/esPYD/uL9b8SJrR6WQSwBOW3TsYwCVp7Pm8OeeBlyc7bgIclYnMdyXXcDy4sZR5Wt/PG7Ry6fryYme9P2dG2H7XksL+cM40i6k4r6mNRY5v9D3Zv82pan6VnP6vu1+9PdLuJGRGZkll1VYMvCDJARA5h5hAQjBkie8AcAI0aWGDFk4AGCCSDPYIaQEWIAqEqusstVWZmRGRG3Pd0+u1t9vxh8v71ulqnMxA1S+kwi89x7z7l3n7V/zfu97/PqBKFJFJh8eCw57VJuXl/K+yCv2N2XzNcx1arjq6VsUseq4vu5S5o3/OCvvuD1YoGORnb6E/JS1ALbtSaYW5o3+MecfeDimiaObWC51lRIFPmy7kRzX8KPjRz+OnUY+FUfvxWLu6mLVlc3YpM7c75fvZxx9WJNXbfcfdiJZUpJLUWtpv/DiO8YakEbVVpQJ3aF7+05Fl0/AsPEcymqjrLumYcWSVoTxy6rmcM+aaQLte5puoHk+x2WSspFc/HODqP4fo/bRCiVhoamyVXRUtzyrh+n5GM8kySnYRocdhIRn60j/uyP3lHWvdgdh3FCBdcqgDSOTD980ZHFH37+tXPFnSTbdJpOp6gUy17JFaOymI0jZOknINLTsZ7sWIYhRQB1O1A3MlS2LB2tk8IUy4TV2uXuNpGiEFVKEXgmXT+yWAbkSSkDQyWRmKbO46FiFlhyulbdmYFnUtUS6uhU2cl5gznPDs66vHwfFN1PxzSljeryKuLjbcIwMsk3vdocTifp62ybbmLdv/3FA1HkMqrk8YuvrgXf3Im8labyBj5vcq5vT1wj6QYwMMyeoZbTuGmZtKqw2nYsZorxf9wmeIGD4zvTHAg+dQGYlsFhlxEvQ+njncnGo+masGWahroTkmA4D4gdh2Ec+Suvn7MrC7bdgB/YbHx5Dsuuo+pafvf6GseQlqTV9QLPNNkWBYFtE0cOWSOQK8swpg7UZhDJpe2FfW5o0ngEn8JMUubRsj9lOMs52zIn2WfS85mLMyxahKxWMR8OJ3Z3B4ZOnudg5rNPc06GzioIhH2flOrfLgeWpuloyoYskxSmk1b4kad4Sgab9YyV5/PtfsfTNufiMiKcB6w8ga3dPuxJDjmbr0NC2+aHqzWnsGJfKjR00xEtQnlfWmLVbKoWz3ek5Pr5ivSQT61g4yDZmnOP7yx2yHNJV4+DIB1eXApmAIQvY1gGs1VEHHoTpvjbw57H3Yk0b/j6917wo/UGU9fp1OxmUXZSTXnI5aCmixMrnAcc9il+7OG5JoYhDV3RTBSIqpKboaaB74qxolK5i1+5rv6zL8X/8j80XWMe2TzsKmxbn3zUAL/4s/c8HWpWc4e//te+Yl8Wghe4jkmOBYekwbJG+qJTQZWEm5vVBJUaxpHlTEIO27sjVS0SgalrtN2obI/a5KAZTInRm6Y2nVbv3guAantIqBVvxPFt+k4GQudav7psmG9i2CbM19FUH1blNcftp112sYq5vBLaY1G2kzPEscTjbds6lqGxPzVyujR1WpVWCzxBEeyO8prYukZZibPF9OWkW9WyEdStODT8wObtR/EfG7rGIrYJA5vjSXgv5/RoFJgUlUg1z64Cbl5fSr3cPkPXZQG1bYPANeSEqiSMqumh6dF1waz6KnwWBRZX1zF+7PH9Nw983JZTRdu5+ENshiLvCEpYbg3nG9QwSJmH4zuMI6SnYirv1jUZqu+TBtM2Jj591/b0bcf2saEbRpqmIApEdipyoQVev1rTVi3v3+5p+4HLtU+e1VPTluPZ1IUEj84p5lyFzbzQJZj5bD/scJTzZfeUcWWbGG03NV9ZtkmvBtmaphHFLmgajmnS6oIX1oEkyehiT+h/twcuX23AkSq873cSuc+zmtlCJJmzRfGXJRNL1/nxesM3+930+fMCvvKkb7Y/c8KHkbRpKFppuLJ1qa1zTHP6PQC+Jf+2smsJbJvbUrj3pm2im0JSrPt+Sto+3R2VI6gW5IamsctzhmHg5asLnnYJdSX47DM9Mz8VkhpeBjy8fyJLKhbrkODG5u3pyP7+RBwKwrpIS+m3dT1qFQDM24aibfnp3cNkWTxvrpLqlfUljH36fiDZSXlPonhCkqUxQNN4ekgmK+zFixmuKgMSWVbolU3d4o0iuZ0DkE3V8v3Pb2nqjiRvqZsB0xS0wHkNqroW0zJFbhxHklTwv+cZ3ThKcn77cY/jO+iGRrJLFYpbMj1nu23bDdON/Nd9/FYs7qYlqMygkBLm83Dm/j6lbgYcx2C2CDhUAtvS1WnatsQ62Slole8K6+Hh8SBJzbzGcix00xBOipJxHNvAMjWKqueYtTwda15c+piGjuMZtJ1wNs72QF2D228fWD9bTiS5umzwY48iKfFCl4e7E4ulz2Yz4/Jizsx1+e5uy2n3F6mDeVLSNrIA6LrOScX6z3IJMJ2ku2FUi6jo22e6XdMMWJagWz3HoG4HIjUjaOuW/dhg9IO6scD9Y45taoyjpHnbVkJLs8iZbHx5JSfqRSQ3nYsXa0xd5+mQ8+79USrtLnyOacOo/o7CjJFTd6e6RYdhxDTkZO8GDsHMn/gpfT+gn/3Lv7RhncshAJWUVVRLlYStG7l1pYcMQ9dwLOGkm5bBUb1+IHLR40OCbRkcEtkYF5GNZelkRUfoS8dpkVZcvlrjBQ6fWwZZUnA6lvTKNWN7YlurlcTVNt2kFUvx8sDu9kDdDriBRnoqMA2Nx7sjy3Uoi5ctfb66oalgjwzIJodU5HH/XmoQdF0q2jaLGP2LS2VtFI7L1Szi7d2TnKSzij/85g0//Oya2HYYkGEpMC36a18kyY9JgmuaU8hLnmN1W9O1KTNwqiougpB+HCjUAHdAWpeKtmEeitT1tJfDyfrZckI1uKbJ3e2O4y5jHJkWxjPkT7PFjVIkJTerBetVTNmJU0iKxmVGJqfnkXgpLigvdHl6OqHrOn7kSuBL9dI+JBnEcBGEBHOL2yzlZw+PKjXcMpvJTWr3mPLsszXpMRc5rx/kQKbeF3IIkOKLpm55uDtNmAxdh9s3T3jKCde1TJ0NciAccVwbx7Am4mqeC8LXNDR6U6PrRt7//J7XqxXbIufbn9+SJBXWWUpsBzkIuTJrOm8yQz8CPeE8EpOFwqp03YCvEAjnWyf8K+CWMVUq9PIqpu8HDnsZTI3AxVK6GDVdmwIDoypX0JRWbxgai4WHaZlT2vB4kDZ5P/II5z7DeTLejaR5M20MlqFhm+LSsC2dvGwJPEs1OIl1rGp6HnYll68MGY7oOqMi2+Wnkr4b+PwHV1LEoCrAnoqCMhPbmx+5+LGHtgh4+4tHhkOJ7xokmSBATV3DsnQur+d8fL9nGMBQsK04tqd/p22J66RSjBcQy+AZ0XBGhn7SUD8VBLu2QaMIfW0/UlQ9IBV/s8imrDr2SYNt6VJ4rfzh332/n6xYwyhYAV2X2YBYJnM8x5C+x4HJJunaBlcv1mRJoYqC4WLpkhWd6oHV5HQDnCb2jM4isqVQ5czVUKcVgDytplKQshb54FyI0A0jliaBM9PUuVg6fNyWFFWHM8rGWFQ98VKf0LRBLGTGl59d8uHdVoHaNAKFXY7nkg8488l1XePhPuHV6w3v90+8/OKCoeunofkZODdKrHjatM8hqr7v6VtJ5lZ5RXKUZ3RzNWPoBk5FSXrIKZKS1zcbDF2n7Doebw9oiGV2HEf+6J98h+vbfPHqmkNVkp8K/u0ffYWh67x53HG1EGnq8Hhi58hAM1B+dt80MTWduuupOmG1J3WNqeuEtj3hg2/TdHLZFGmJGzhsFG4YYHd/4FH1Cju2KulWJRxdK7fotpFN3fFs3nx4nHALbS1VlXcfD1xez0iPgsqOlyKj5Cc5ALm+Pd0iXN9hFQWcqor7U0p2zJlvYgwFtgPwY4/9LpHyHluCS2fjRZGWOJUUvLfFJxzI7vFEELlSf6jeZ+eP+8ccXR2gzsNoXdNYXs55uZzzdictcOc+3zOSe7NwGQZJYf+f//CnFKW41hzrk/wXB9bUM9z3A/3HvSS1244gjohCj3dVN/Um5GWH48HmZiEJ7KSebre/cl39Z12I///4GGAKLZwToYaukVcdTSuFy9EiEHa6KlQo0hLdlDKEzWUkwyzjE5XNcwR5a1oGTdlOrGjX1hXQaMC2dG6uwon8qBs6tvqv5xiEvsUhketT6JuUWclyJYMayzBITjl932OaxlT2kJ8KKvX1wrkgAk67DD2vZedXJ/Lz4uxYuor1Wyw28rXvb4X6qLeyY8ehPUXgTUNcKIah0agCXdPUSZIK0ziDwETDt5W1UdOg6WTBtCxB+5oqKGSoE/Js4dOrB3KmwEgPdycqZWsMfVMcIraB5Vi0dct87nE4lFRNT9dJmXHbDniuwZe/80LKIRyLIAbzKeOYNPRKU297kQc0UFgC0R+ffymIZpFYOn76zROhL/yO46HglLaEvjkt1o4n7JuqEazv5csVH79/wnUMQSU0A00zoOsi8ey36bRZlFnF6mpO1Unz18df3BOvxJEhA9JuCqx1rRRqhL7FcZswm3l4gTNZ6x7f76ZmsHgVykwmrfBjD9d3yNMS0Om6Hk/F1pcbOO4ybM/GcS1uZjFvG/me7/dHxmHk5Xohs4/IYnk5Jz8VeIGDG0hadhxHnl0sJYfRNNJ2VVcEnoPvOWzvD2RVzb2R8Wo+pxsFIRDaNqFt45kmWdtQKXzv+XTvGCa6CX1di5e+G/B8h7pu0WHqOyj7gXgVTkwV0zanA1ijinRMSwaEj++euHi5FitoPxBHzvTr6anA9WUoXBUNz764ZOgHdncHbNcmjH12ac7+4cjiYobjSfbBdmVW4PqO2IerlnAeTJZe0zJIjwWzVSShs8Bhmdfcv3tiGAblYMl4dhPzUXHT5XWVA5ShSfo4DiyaTpxs3//0lu6LbmJOWaaGe7bAKuhh3Yjsdob46ZqGFzrMXQlWub7D4+2RuhHrd9MObGY++7IhVSXevlL3Dn0AACAASURBVGtOZM353CNJpERnGGQuZv+r4HNPk4pv3yVcLF05eSlYvWVqhDOfYOYTqqHFcZtgGCoZ1/ZsLiMWFzOyo/hWzzxl27VYXAih7Wd/fg9ApAJSIAuMrYZi4lN3GIeRtMnQdJ1o7pMeCwJP3DTRPCA95ixXMY/3B96/O+C7JuvLmKYWO6YXemRHCXysny3JE3lgx1FYI+8f8k+uE8BzDdGYTUmsbW/3NKrq7RwIEo1NSI55Vk9D5JcvZrz/cFKLXEdZyy3EtQ11Oh8YR23C4+4T0e4sSxZ9y5T6v35U2nxeE/oWri3N7JZlkKk5RhxaRL5qKLJNPvviisNRSj8sS3ztwzjSdQO2a/LqB9dkmZxi/UDImXfmDsvSoR2o2hHLkp9DP0LfCoNmBB7ebllczigyKVSx1EZrOaYaDo+T3JKW5zYpezrRbz/sqWp5vTZLnad9qTZDfSo8OAefwrnPbBHyzZ++kwq6TUxbt3QKE13m9UQeHLp+OqXrpsTKf/6nH7BNnR//7mfiuHCtqTs0mPnTc/j2p7cc0gbXFjyD6zt4ochol64s7HlS8pNtQqS6gLu253B/ZBEHWIpOqmsiWZ1vGIeg5MvlUlXQQdo01FXL1cqfAF/zTTwNS5O6JrQs2r7H1HWOVUU7fGLFdMPAriz4cDgReQ5JUZEeMmarCN+xeXw4ymvUymZ4llZM28QLPQnvtD22a5MnBeMggSHbs3l89yQSnqrL7NuO2UoKW4q0nFxFr3/0nIXrsnA9toXc+tzAIUtLCZWpjffMmjqnW4/bRA1RA+YbeU/eff9I0woArm06gRFqQj0NIpcqr7ncBNzepxRpxWrmTJboYRxpGjmICXJbbqVnqXB3e5DeCE2bwGZnK29VKTmv6xQ91sH1HOqqYfeYcPPZmq+uLsiTkv1JeEz9MFIVtaSf85rdY4Jl6qw38jycZ4KWWhs9mCr4ftXHb8XiHgQ2Lz6bT1e+4z4XN0skXBlJf2qsZiHd5ZwyE4KkpgkX+4//4A2z0OLZ55tpJz/Xs/muanlRcXpb7XhdN0hgqm6lhk7tqF03kBUl602EaeqEgTv98HRd53jIuLhacHhKZWJu6sRBSHrI1VVSeDht3RIvI+7fPPLuPpcTrfOpLMFRi/ygjdPXH0fRzH3XUM4XDdOQTWi/L2n7QWFz5QbjWAbLC4k4P+5FM/Zc+XW3FzyubYmX9mLhsj3WU/BhHMfJL+9YOosLGVCd3xBl3ajhrsH1pTxg5zfU48NRwiWl2EddW6esxfYYBypB13S0isB4db0kmnnThlLv5WQ+ap8cQU03UNc9T8eKtu25eX0hp+asFbeHY8kNYRgZFVhspptTUUqlQkieY+B7huq9dVkvPYZ+IFUOrH3SYCpbZd/JprRYR9x9PKI9JTz74mpyUNRlQ1XUE6vdsAzOP8EyE8dR2w28ffPAbC0aaVFVqhBGOgjyU0Feiof+4mZBOA+oSylrsdTNEkTK2T6mzNYygPZd0Xsft8ep+GUYBspUBsKvPrtk5rq8PZ2omxb3+TMug4C72BestG5Q9x2vF0vavpeBqUJTxI7DthDpU9c0LNPi3ekoRTJNM8mL2TEnnEuR+iktZMaUV8wiX4q1FdOpzCoGd5hcSoZlUJcSxCoywQJ3qpGoyMS1NfQjeSL+fsezefVqI3RIy2LmugyMzF2X/UJO4ZHvEsc+ZSWvXZ6WUk7S9Tx8PNC0Yqst04qjnkxyTjz36OqOvhd5rUhKOZSo0o9o7vPC1Hn77sSLZxFm0U4SqG3rtKUABkX7FgdZPwjVVD8WhDOxUJ5P8VKlKWG2Z1/EhJ5DkoncdpYQv//mgW/+/F4MBaaOacqho1JJ26IUuaxpB7RciKSOb08ZoM++uOLh/sD90ydX1l/28VuxuGu6xlq1jBRJSTz3cIqG1dWc5TJiO5wkOHCQoU5dtvLGHAaOp4quG9ifGpZZhWEanJ5STmnNMWtxbJfLTTCByKpS2O8izYAffFrsCmUXDDxrKm841/jVTYel5Jdg5nP9ao3tWLi2xfGQMd/EU9rUdgT9+fhhp5JlJoMtC4+uSyI0zVuurqU4OD3kMsk/5tMMQdflumfoGlkhfJCzM+UsOfXjOJVx+G47IXUj1VzUK57JYAjF0NQ1hlE2FtM2eRb7zNbRhF99+LD79KYI7Qk7UOTi137+WhoTk30m/u66x7UNvvzxM979/A5N07h6vqDKKyWZyen09sOTkBEDh2OaKgqghKU0ZDjrWPq0eC9jh8BzeP7VFUn2ThxOjoWpa+Jy6kVea1q4Wnn4vkU8N3A9h4e7oyBlHbmVhfOAMPI47VOSnWxIvRpSxcuQmeOiXy0mhtHHb+/ZPF8JGVBhe8+a81nzjyJXFuzIxlvISescQpmtInzPmdDChqlzdR0zW8eqrSgnOYiW+/zzS9q+xzVNzE1M23Q8X8z4+ft78brbJvmxZLMOePcxIfnFDs8x+OHv3Ci+SMWLeEbZtZyqmpkrUs0wjjiWQeQ4HKuKrKlpB8keCHVQYvqOaUxo34Xr0Q8ju520QbmuzY1KUp/Sgr7tmUU+/TiSFpVo13XL7jFlfRlLwlURFccR1jcLsfAV4kDSEunKHXupq8yOOeHMRzd0FvOQY5ITRT7ffndH++qCtR9QtA0z10VH493dE6Yt+IbdY6rkHFkwg9DBUW1Mmi5D3HEUibGoChUAGlVGZsD2bE4HcV3l5Z7L65nMwJKKxcKfUMJ1rTptO0nerlc+aaq6mA0dz+1VgNElDD1y5cQy1IbtOJZkYwxdsXT6aYZ0zq2YhrzHu36kqlqC2MdWBoB+GHFsg2dfXGJZJj/5R28ZesE3f/7iEk3T+KNfs67+Vizupor4Pt0eVAR4TZlXRJFM/k3LoC5qsSN+FGuYZZukhxzHNshLOeV+//2ely8GHM+aGDWua+GrpnMAw9Sxq/aT/3oYGJCAy6CuvEHsoWnicth+EKKkbemsny2IIjk9LGchu2MmCTd1PTtr/EVaSf9mKY6Ns4NE1zWWq4Bw7vPhu+00Z2jq7i+UjeiahmnA1cs1rm9z++2DGkIOoE7y27sjtupXjSxD+e010qJjvgyIV5E4Z+6PU7TescVhY3u2eLTXEb5lkTcNtmkyW0fi5ukGmrRWLhSNJJd6vLaWpGaRlswWPrZVUVQ9H7+95+JmQVw0eKFHmZUChfJt2lo1HGkaybEUnV3TaCpBLWi+ydANStMUDEFethwPGaddOm1mlhpodmpusk8abLVwng45VC1N2RKGDkXeyMZTycn7fFqzPQtNDbdcW8iPdd+x9n2ufhCyLXL+6A+/Q/u4Z3U1Z76JOTyepBSmbGk7+RmFM0+12wu1UDc7qrzCViGog1ogk33G5cs1lxcLPnzYcn2zIr4QG+Xh8URRShEFpslVGDJ7LeTFcYT5KuK4S3l4yNA1jVkomQHT0NjMI9p+wDIMfEvCMLuixDEMXsQzec51jT+/f2QThcxdl8c8p2hbHvKcfhim7oJzeGnleyS1WBgdz1Zl2gb3T0fMs+3PtbiII9K04P6NcH1sS4q8e1fi/uE8oFW1h7O1JIQ936HMhYh6HmS6vkglpmWQqR7jQhVXtMPAXZbSDQOWrrP0POJlyOkpldOtqsErswovdHn2+QXJKef+/Y6maDFycXSdZ1D9MNJWHY7vTGTJxSrkSvUBdypBfa6ZNQydYegmGdG1DWYzV9AgWUs3jPixOaWWx3EkOeXEs0A2a88RymXdEto2s8Bjrx+p6g4N0fNnoUWStQwDZGXHq1cLFhczqqJWheulkpZFYtrdH0myljgtJaTWtjy7Wf32L+6DOrFLgYQ2VZQ9PRyJV+G0s4/DiBcIta2pWyFCznzggB84lEUtIaW0nCLyhi7tO7rilHdqMHlmkPfdwGwV4lkW+vVi6m4EWFzOeLo94DrG9KAedglN1bKKhMH99HGPpmk4vj1VglV5Jfxy38H1bLa7gotNMF3dlrOQ7nnHw8cjXdcrANAnEJCmaZjjyOH+yGwdES1COGSfrn3D2REjmYC6bBRgTSPyzck1NPTiaBmUpdLSNELf4uL5Svopy4btxz1FWvH8yyth6I+i9+dVN1ktxW46KohZL+GLUoZ3y8DBcW3efCt2ve1jimHIgFhOWnJy6/t+GvBOU35Do2nkBnVImsmHn+Ydf/6Te5puIPQE3xw7jsIJ6xOgarkKpBKt6lX/pLBjANYrH9OUYZofSd+srut4rknfD2yeLwk8h6bvuUtTZq7Lw/2BOLQxFT9+s55hXy/58PZRTp8aqoZNneQTSSyegWbzOODp6cRxm5KXsvA9vHti75yYrUKZbzQNQz/w/PmG3THls4uVPD+GyX0mxc+6rvHwYcd+J0GXthNP8yK2CSKXY1mSNQ1FXvHl5YakqaVgpu85lOUkvTmOxWNyPuXKafKntzJ/cn2HpedjaJ9wv495Pt1APrx9ZL6OMW2TWSB6OoBl6JNk0A8js4VPqOYLlnKQXa0/oTfCQLp3QcwBrjqUzTcx24975XcPJ877xfPVVMydZBWH+yOnqzljP+BFLg93RzSgTGpmodTwlVXD3bsdddPz4rMV27sjdTPgBya+b1GW7RSSq9SGahhSj1cVNY/bnPXSw3IsjvsczzVZLH3YF1xdhASxJ3mPY4GuQ2CL2aJtxdnj+tLWVtUt24/76dZX5RVEAbomWQ7XkRCfbYszrvpegpmhZ/LyxQU/Xm/4o/s7smMxWUCbshVMdeyzmpd8+cPn6JrGL775SLwIfu26+luxuLdtJ/7jqmd56U3N8VP12dAQRT4zx6ENfI6laE2x6/Lxdoetymzv32wlXeiJpHBMG5pWCHS2bbJ7OOJFLvlJ/KNN2Ygnt2x4d3zAdmQImykv9Jkzch5+1SpO/uHjCdd3mKuHsm06omXA7u7A/V2CaQrPui4b1tdzirwRNo1jkp0K3r/bEsxETkizBs+R7zMMAjE7D32ruueU1vzwrz6nKmuGfpwWmdMhp1ZOljP4S9M0lhu5Ih8eTpIkLES/C32TxTKgLsS18/DuiaoZmM1cyrrn4e2WNBf9um57NJj0/TMCwTB1tK6nbQcey2pqYDKMYtoMmnbAdw3SvKH/sOflD28A8GqXt9/cyxC3H8HQJsyBpsmiMShNsu/VZmToUxHJoK7ZgWdycRkp4NfIbSo+9yJtlSNGZJ6xH9B0cQqVWTUB54ZhwDONqTHJNgzStODp9iA1fWnD1aW4KqquQ9c01tdiPxvUMPvM/h/GkboeuFyGRJFPXjWkh5yy7ui6EdOAjoF0X7B9yrnYBHz+xTWYJpFt460XPOQ5z6OYom2kUUohcau8Zr0J8ZKSj1uZp+xPjWy86e2UaH76uGd5veBmNec2TXj3RvhCX3xxQ2gr+mPdUuY1Viw5kM8vVnxMEh6TlA9lg2FJA1FTt1w8X9HULWVW43o1z25W7E4ZVdGQnUrmodAYN1czGbavQrJjQbrPKKuO1UXEJhKMsmMYVF1HkQpF8uK5WJoPjwnJXvz+fdcLo96zsQyDlSe35rqXEJgXuSLbBY6QGQ0JH56dXck+I1oEOLaCDC5CvNDj7Td3gPjHz7kRs6iVWcNQLW+CDbEtsTMPfU1WdswWPkVaEQX2lIoPZj6ub/PMsbh9s52GmcePJzlQGRof3++p24HPP1+Jp145ZUxNV13QIut13cBhm+Da4gycrSLuHvZ4pklZNdy8WPP0eJrgZl7oYrkWL7685JDkZE7N8nL2F5LQf9nHb8Xifv64uJRor2hmHYtNTFnUdE1Hss8YLuc83R+wHYuLzZzAssTDPgyESqN997Nb0LSpB/LyKubh7ZasaOXFjDxW13M1wBwnENH5+xaq2cVx7Wn4UyiqXZlVcsNoBTN6uZGr++PtgXEcef/hJCnTpicrpOgZwHNNNEOfmN7ZqZiquNpW0mZ9L8UXwGTt6wfxuIJM+DVNw1TXwGGQB3azcAU3mhSkuQwxHU/Ss2neEPoWjqUrJ4dIMqNyyJR1j6tCEcdEZJhWLazXFwF+5ClbJizXIbWCLYW+Rl6KB3ccpeD64/fbKZCVFR22bfDiszmWrssV2XNYX8Y87B8lBKV+5mfYmKGLh9g2dRqGyZVgWeJUGEam0pRoGVIXonNbKu8QxDZ51XG5CUgTGcr2x4JxlDYp45hjWCazVUh6zHn/zR1f/fgFjiESwekplfmIIwvdfB0zc1x2Rc4wjLx4ecGHD1spgnEtirTk6uVaeDO+gwa8/eZOblLtIC1fSoJazF0Ox4rTqSKrai6iENswcU2Np6LgPs+wDal5/Pr6kl1ZcHWx4GF7lO/7v/05usJErC4ifu+rV9iGjqUrUmgvz+9a91n9WEJHvmVxKEuqquFyFkuZdZrzfDXHMUyGXpxnZ/DXMAj7pVGSih9JAcchK2ROoF7LpKjI05LV1ZyZ7/G4T6SBSNeYLXwurhYUrcgvu11Crzpdl1dSfnHcJtiOHNryTG4DfSvOk1AhPj4eTximwe13DzJE7EeSXkBermtRHis5eOhSRLO4mFHVPZ5rcng8qfVD8MyeYxDPA7q2I15FHB9VJ/OxYHk5w/ElYY46yFmGTlu1eKGjODYeL67WJE3Nw4fdBIfzXbEE3z9K0f0ssrFMnTgU8ueg+p2zpmHhuhMnxrV18n6YMidJ1lJUB56/WvHth0fcwOF5PCPNSvq+Z7/NMOqWZRSw8QN+9vBIW0sIMlN/l1/18euNkoCmaf+NpmmPmqb96S99bqlp2v+qadrP1X8Xv/Rr/7mmab/QNO1nmqb9u7/p658/KmUBLLJqqu5Kj7kUR+saZV6TJQXPnq0ZhpGnfcLb7R7DNETDPMo18PLlWoh1vkUUWFR5NXmCZ6uIVm0UfuwRzsXPHS9DgsgTDXwcaWtpgwIU6KdnOQulR3FUrpamY5/mPL9c4boWb94e1YCm45gK6jOIXIqsYnE1Z6ZwwX7kYZo6ddFwv6s45S1pLlr2iNgBq0bcHVXdc/V8yTDIQLSs5Ic69AOWCkwsLmc8e7Fmvo6JAnM6jZSZ+N5915zcO+cHM/AcZnOPcRw5Zu1f4Mz0vQS3tvtyKgMoqp6m7ijyRqxtrjWFmqqm5/bNk+LZTMRTTF1juZTyBEMN8MJ5QBxYPLvwmKtwFshw9RzDLutPN5ezDKLrGv0oHmrDMrh/s6XvBra3B2aRzXoT4igPflt30n3qCz/GNHXRU1UhdlXUmKYULhyTXBbvfuBHP3zJi89W/N6//pqXP7yROU/fEdg2nmtzzAourpeSm6haXr+8mkI2t989cDxk2JYxOaJaFVCpm14K1hUErmt76Tw1Td6ejgS2Td20AoaybZK6YuX53IQRP355Q9V1LGObOLSm19YzTcXm0RVEDBaui6np7IpCcWpEa67yWgaqfc/zhRTHfEhO9KqPdxzEC+648m+xFa00U7yg3d1BWDCtPHPDMLC8nE/tXrZjcfVqLTkUTeOn/+Qd798+yiLu2URLQfCWacX2/U4S1pUgcC9u5vzNv/EDZuuIeBHiGAbvng7TUFTXJfFcZtKjYDki9QWeKRWPzcD2UPP+F/d4rsnv/+5rZqpVTNOkG6LtRlV04rG/O0hi3ZBD3jCMJLt0qj8EiAJT/t4LgZy9fnZBpPz/XuAQLwJ+53c/48UProUlZOkTvymM3AmBbFiSNpUilnEqRT9z7wWDovPsxRxN0zhuE05PKYYhHJpXmyXRIqRueg6HkqSq8EyT3392Mx0Mf1PN3m9c3IH/Fvj3/qnP/WfAPxjH8SvgH6j/j6ZpPwb+A+B31J/5rzVNM/gNH7quc3Ejhb6l2s1zdRoosoqhH1hdzXEDB9+SoaXlSI/muWIrT8rJb764mPGDr1/w8osNIBVqfS8e+PWNOCO6VgocvNCVSrW6FdaF0lHzpKTIa6LQpu9H3r55oEiryWt9Okky8CnLVWxaHB/9KIEf0aol6BHHPsdtQnbM0Q0JRTwdK7EB6p+6EHVNm04kUWCJZGF80qnHUVp+NF2THf7ZjGSf8XB/EP7zIsBxbR7ePdG0vfjnu57jUWSoqulJTyVl3fL81QVXFyFtKzwXxzZYb0KuLgJ8T1gvWdmxOzVcrT01kDWmDRA+ESPrplc2LVnU+lHYPGcA1dlDXeWVOHUU591V/mFx9sjmJs8RrOYO67mDeSZgti1D1+OHLqZtTjjZM4rCj1wurmIMFSADWG5EiglCZ/o9ui6yXZ6UfPz+idNTysO7Jw5VyWoVT6A2yzLZHVMBdCnefq4OCvNVxKGSk1Ornjcvcmm7fuLezEOLwDOn18e0DL740TWLOKAbB8qu4yoMCSxZ9JOq4i5NCW0HQ9ek4lHTSI85VzdzXn99g65rbB9StkUhw993Hyi7jrsso+q6T/AvNExdZ18WbDYz/vrNDSvPZ+a42IaJY5r86OqCry83PF/Npc7NtRiHkedRzOpyzjDIxhovw8mee9bjT08JT3dH7ncn2qaV7IlloisJb+gHuq7n8d0TRSLNZpquoZ3zKd3AcS+OId+Smr7DViSlPCk4PJzITwWOb+M6JouLGM04g+XkOe7HcXp+PN/h9Q+f8fZxz8oP8EOXIHQI5wG+b7G5WTD0I4ZlkhylPGMcJblqmMZkc7ZskUiGYeD4eJJ1xjA41RVNJ6jeupSy8HPYahwhrzp2JzVb6qQq8bzBGLpONw5Ensvf+jf+Cr/7+6/RNI3r65gXX15Jf66p8XSUsGRdNpi6jmvKwdQ0dV69XpOfCo51pRK5hdQDGv+CIaZxHP8PTdM++6c+/beBv6X+938H/O/Af6o+/z+O41gD32ua9gvgbwD/16/7HoNqUwlnwkG3TVMGRGWjyjcMilSqx5qqVeULMiBt63Zib2uavDhSpC0e3S++fk5RN0QqKHH/ZqtaYDoOj8mU4DxbJUE07HPSsm3kJDiOI33X4/g2NxcyHBIvtVihrtY+90+qiaUd8F1LtOCkJA0c5puY/cOJvFR2RV9+fX9qpkFqEDqsrueTl3l7f+L+3RPrq7nitshrddzJKVHztMmD3dQth6cMP3Comp7VOhRvcdFQDj2t4u+srubCkM8q0fMUoXG28DEtk1Rdv9crn9NJ/h5V3eGMMg9Iiw7X7qgakR4MQ2O9kSLoEWhVsKjvR7YPx4kb0vc9D3fiP25bmSfIEHSYbkO1SpKapsDQQt/kYiO+cEPTaFqJwuuGrqBQslFquo7jWZR5zeFQEgVSzlEkEu8/niqev1pNp+6mFoaR4wlCtqk77t9uef3lDUXbMg4j+530kOrq+Ti7Is5D56Xnk+wyyrSShb3uCCIXs2iwzEEB2WSmsLkSnO2zxQzXtGQmUpXMHZdRG7maRfzkm/csL6Vk/TZNpfRa09jdH8Xi+uHAi+czvnh9jalrxE7A3HXxLYubKGJflXwxX/BvPn9B0/cc65rLMOQqjBhG2BY5seOw8QMcU9Ksd2lKmdc4rsVpl/H8+YbYcTB0nY/hI0VeM0cso2Uui0+h2EiDOq5eruYUkS8bn+/g2AVtLQwWxxPAWH4q5PY8jhimQTj3mVsGfuzx5x/uVLvaSHqqpqF7oBxJhmXQtT3hzGf/cGJ5OZMhvamzuoiBhDyref/2kV98f6D+oRTQ+5GgDjbPV5yeUs4FOpFCB9uOJb2ve2lourie0/e9ePnVMzb0A9/eb2nKllR18xZJydvjgb4VWmMcWug65GXPYZfhOiar64Uyhcg8Ecsidhyypub+8aA2JekLHkf4/EfP+OM/eMMxqfn9310xcx3+0f09+4eTMLNij93dkfeHI7dpSlM2RMuQPE9/7dr9z6u5X47jeAcwjuOdpmkX6vPPgP/7l37fB/W5/9eHpml/B/g7AEbs8Pggskpbd2JXVA314yALQ3rIsWzRCk11SnB8RwUpTBrFnYlXQlvUDJ26qCmzEsMycVVSMl6GE7Rr7AfQNUZNww8c8aMqHfw86KuagbKusEydzfUcyzFJxpGn+wxdySmXL1bUTadOalIivVwJ6vdwqsnLLZ6alEtMX86ogsfVWEQiUaRpxTjsKQpxWgwj2LqURThKktANnTZreDoUOE7NauYwDMMEUuuajjCwiVeRDICHEUfV9Om6xuHxRN188tgGnrBYzlbRM2wMhLjoha4UUhcSWPJdYxp4dsPI2A0cD6VUHtb9VLdXtwM//7k4aAR0JF/XsnQuFg5VoykOjXDc2+4MDhOgmKZJm47tSn/q3HVp2oGmFbeDqU6JbTfgNB2OZ+G4FuuN9GJmx0KegVLmDuIPHicOiaGKl/u+x/Wlu3N/yiQRqutoK42557FLcxahT+8Ok+dbN3Q+piUXl/Ope/PyekkcehyOGffvpbh4GOQ2t7qY4VvWdCvQNA1T02kHGYKHtsMXX9zwx//wWx7ebglmPn/zR1/wkOdSGdn0rC9jvv78RqQYNIq2IXbUoWMY2RUFpqZP7V9V23GfpHw+X/CL/Y6ibtj4gaRQi5Kqk0PGGUes6/pEUo0dh69+8Ix//Mffid0w8pito6kW79pa8uH9ltlMvt7j3Z6qaDAtY1pYZ4Y+STWtKtSxXZuulbmQGwhS+UnNq2zX5uJmLrKjkpPSrCErWp6bOmHsKy6NmC20oReiZORguxZPjynrufPpRh7ITcoNxAZ9XqxPT6ncyh5PKu8g9mFrm2BZBve3UpnoKVDfqy+uuLqOeJ+c2H7co5uGdNjaQhmNZi5tJ3OWQ9LgOD3uNhHzgQa+qjTcFQW3759wPLGL7h4T5poExCzHZBZakqTtB/6XP/xT1tcLwSN3PT/54/e8eLUkT0rypKSsOqq7I57765fvf9kD1b9MBPpL6TbjOP494O8BuM/jcR4LYjWIPRzPxndkKHjcSujlvBP7sezo6SHHdiUwURc15xKP7FTgh670dxo6fdORnVJc355OELohydN2HZGdisl7ayn/rWmbYwWYMgAAIABJREFUHA8Fjm0CHduDTMYvblDYToFj2er2ELoOdTNMfPYXn68Zz/qqa1BWPYYub+ROOWL6XnT0KBD9OlPJuNsHua7GgZzw0rzjmJ0UF+eTe+Us1ZSV1PE9bWXRWsxlAzuHkdq6U6ched0d3+GUplOpxzyyJeCUlJPkZBgaZSHOGaMUi6LrGBOvxnA0TlnLLLBwbGOSi87I4lHxY8ZxpOukLm/sBiyVFs5KRWgsu+nvqWuKNGmIpzuKXUm6tr0K3RjEkUOaNegazBYBp0M+DZzrssV25GeTHnJOhwLfszBVrd2ZfeIoHARIila3LJzApipq1QVgcBFHeKbFsZa8wvsPWzZXC7pByH11UQuCQJN05fxiRlm3dG3HhzdPNM2AY4sv//rzCwxN4827B5pKDh+fX64IbbH7lV2HqbdsDwm2pbM71cSriG4U18wPv3zOzHVJ6hrXlCAXwKDeVndZxsr3mLsuD3lG2bU4psmf/Owd65sFP9/vsQ2dme/hmMb05wxdp0hKNF2bcB4rzxOXymgwd1z8QFDZjmNx2CaSkmx7cnXTergT2NUZ8gWfelDPgSGA1cWMoqzVvCuX/lcl8YRzfwod5Qq8FqomKsfSMSObLKkoM6lVzB9SJXd8SoOmh5yVGvi7vk3byAFRGzTaRgCAx22CF7qsbhY83R8m10rbj9OsLDZ0mk4S2wBB5JGkBZdBKGXmhcC6ilwKgNpuoMrFiGAryqltChpaegEMjkk+JemTY8HN3GccIZp5NLUcSg6PCbNFgKbBn/zJd+i6xtPdgbEfMCyTvCrY3p/463/tS+6co5TPqyrNX/fxz7u4P2iadq1O7dfAo/r8B+DFL/2+58Dtb/xqI9NDMigrmKELO0ZXUXvLFoSsYcrptKk7dncHIlWW4IUuji1Vd6ZlcnpKqMtGNgrF385PBfuHE33bk/jy+c0zqQPrGulO1Q0BkyXHkrxsaVVwRCQROfkFMx/XlgHqGdN71sQtJZP0/TANcrthnKrqbFMjVp9PcimZHoaRx31N4EmC1Va7/jgynfJ91+TZy6XCkyZTu1TXj+SVyEiBZ6DpomkeHk+4nkOaN1imcFU0TeO4lyLvRWxLwXXZsVh400PbdgNNqyyKyGZw2GWUdU9VS4I09ATsZipt0bENbNdmqTbFVoW+yhp0XZqSfMW40XWN1cyZWpmGQXIOi0gsnrPImYZihZKOkmPK9jqXWrakxrLl9TlTG01VxlCXDVneTLOMrpPr/GGXo+ufSk7Wm5BOReUrRQzUNE3cImXDzszZBAFXYcjdKH2o58BRW7dkp4Kvf/CCbZphmDrP1gsCy+Inb2+xTZ3AszgmNZ9/dUFTNtyfChzXFrzDW0nr/uDlFaY6LZ+xFevrBe37vcxwkCHyyhP3Sz8K/6XpRcYqmhZLN7iJIpK65iHLsJWUMyiL4saXP/tqNuf744FtUdD2PbHj8JRlGJbB1y+uaYeenW0R2up5HSXkFMQeoSelIReXc9phIEtLqrwjOeYs1tFkH97fHwHYPFsSeQ67w6e0NsgB7PCUAUxzFsuWn5tlm9zfnjANjXgu6VXDMuj6keevN5KEvT+hIYcLgPky+JSX0DS8SKiwRVax3xXEoY0XuRweBR3ctD1+5LG/P5LnDY6lM1/46LqkR3VVKLMCHN8WK7ZncdymbOOAn/3JOwZVCnPeYM83+2GQ25PrCHm1UeYCx7N58/NH6nZgEcnNOjsK+M0LHI7bFD90iRcBq1XMx/dPBLEnMLpWXFjHfU6oDjCHqlS8IpvqIJvdr/v4513c/2fgPwL+S/Xf/+mXPv/fa5r2XwE3wFfAH/x/+YKWaqWpioambrlPxXcdeKKve4FD3/YYpkF2KqVbUF0ny1TY7bqhT7v+MIw0dYemy6nEtk2Cyzl7S7oz7+5TfKdgczWbBhOmYeIHLkVecXEz51b5VnVd49l1yGwdTX7o1czm+4/5JGOMI1PK9OObJ4YRLq5ioplHXspJWdNUe5DxyYbpuqpCKzCnhX3yvLcDjZJQQk/mCk7b83iffBpCGqMqxjCFh+GYVKW0r9eV3Dh0XWOxjtQD207f+5imXF0E003g5rM1yS7lYZszc00MS9ghZ2QByNC3qntsW5/cG7qypBmmuHP0UKL/1qmYiJGGoUnbUy36f9302JbBi2eRJEWLBsuxiOY+i2XE4/1BPNdVy+JiZLs9qT5cCVnV6qo/DCNeqN5sus5iKQ6oxUXM7Zst794fqRpJvmqaSGFZUpLkgnhdz10OpxrfMzgqDLAbOOzKko3vM3Ndvr8XDohhClvn8uWah2MyDeNOdcXcdbm8kF7Qxw87fvBX1sS+y/39AV3XSY85pmUKVfC7J+arSAbN6tTqmibEHrYl6F1T13EMlczVNBaux30mYStT04kcB8cQfMChKvEsi6/XG5JanDHLSGyFjmHSDL18fhh4vVjgGCa+ZdP2PY5p0NQdmyAgtMUxk7etlHYM8rpfXsyJHYcPhxNe4PDdT56m922Riga/ebbk8Jio50UWINs2SQ4ZZSabp+uY5GUrfKEm5eb5gjwRB9cwjIyGRpZUE0O/6weePu5ZXMzE7bLL8R1zktsEoywLXK7sxeMwMotlmPrh7Q7XMagawepu709omuA/HNtg6AeethltP2IpiXJxMVMpbdl040WAY5p88TvP+Ok/fk/gGpPjJS87ekV1NHSNeSybo+mIpu94Nt+9OxF4JnU7MF9Ip4Pr21MyOD1kOL7D++xRugRcawLU7Z4yosjBMkUS1TSN9TJmNgso1I3h9GvW1N+4uGua9j8gw9O1pmkfgP8CWdT/vqZp/zHwDvj3ZYEb/0zTtL8P/ATogP9kHMf+N30P0zanAajuaLx/s5uQrlnRcXMpLeC2a5Edc0bFyThX7rmBMxU8/PJD13QDxaEgrFtBFfg2XuASzgNyBedJ9tlUpGEp6Niogg8vX28o0opMXdctXbTppm7xQhfbLgXLWda0qmZuvYloqgY0jexYYHvSM2qZukIQiBxzufIAWXyTvJ2ugnFoc0prsrrHsQw0x6BppP08PeR4oUscOQxDRduPhL5FWctgxg8cCWf0I1XZ4PQmrmNw89mGmUI5HJKcw2PC6STXXsYRlBb89HE/nawdVafWmDquLWUcvQIoObYhoSnE8ZLmkgCMfSmprsuWuqgnjobvGtMbqO/h7kmGnq4jSUnbs+nqI0Ve43gWv/jJewmdWAY/fvWMP/wnv2B5NZdZivK8D10/QcM0RfILXIP1ZcxxL4zttGhJc9XQZQowrqx7srJjGEUKun8qpCczcjFNmWk8vN/JQn61YB54rK4XZMec01PK5sWKMq+pi5pXLy7wTJOHPOd9cuIqCHkRW7yIZ1iGwYfkpIZhBzbPlhy3CaFv4kcehqZxE8X8vNzy9pt7DENjcz3n8uUaU9P5+X5HmpWEN87Ef3m9WMq/ZRxxLZO0rnl7OuKaFhdByE+2j8xdl9B2CG2b7+6fuFrNWHgeC0/wAqLXt6qAXcczLXZFycr5JFeZunS06oa41ZJC/PnnsvD15WyqIvz+Z3dEkcv2417cNY4z4YQPVUU4D7h7s8W0DHmPAstYvpftWQSxlHV4Ycf2McPxTRzfpswkMOiFrgx9PZsokoS37Zhij04rHN+lazpxy3WCfb56taGpGuZzD8aRsWyJQpeqarEtkREPacNN6ErwyTJoWinu6YejlFYPI4ulDHGf7g4sL2dcXsV0XS+Sa9NN8lDdDnhq0/ECZwphvrpc8WH+9Ikf03REqpCka/sJUCf6vMZ8HXHapZPjqqyFeuu4FjeriIXrYRvipLlbBP/iIaZxHP/DX/FL/86v+P1/F/i7v+nr/lN/RhjqoYuua6w3IX7o8ubbLSAnMlu5T5pa3AymaXA4lNw9ZMSBxWIdTnq5pmmUZsXuJDJD24/MQ4u2aqeavPVFxP3tif2pxjhIWCn0pYHn/KJ7gctsHWHZ8sCdkgLbs5kvIwxNSh00Q6fM5KZg2/o0MFpdzUmPhbTJuMbEJRlGWMY2lmvRJ7Vi3EgoyDR1HvfqlGOr8E4vHaIjQoy0nDNeVENT+qmuC9xIN3Th3asrrYMM9cZxJK8abNtkFvlsP+wVxlR00abt8QPB0La1nFhOh4IgdCaGytnCaJoGXScPX5qLn7/vR5YzkbnqUrjnghyQTWlzEQrG9FTz2U3IOML2ULE7NpTVgavrWIbjjkTTD2mLbWrcfThwebHg3/rXvuaPv3/PYhlx937PMMIpbTgmUnbiuwZNI9JU2x0pFI5gHEVe6pQr6HyLAEE12LZsqLYlAzuAh4/7CYWQHEsuny1wA3muNi9WpPsM0zLZXC0o25b73QnHtVhFolcDuJbJY54zd13yUuZIfT8Qq5SspmmTlmtaBs9fb0j2GR/e7vniR9fkbYNnWVxehpLaNQwCoFIafd11kh/QdXZliWM0OIbJ8ZDhbExix+E+y7hZzfFti34Y+HKx5C5L+bP7BzzXZuG6uKbFhyShaBtWSNHG+YDUDTI7OdcE3r95Ynk9J458DNOQtGXbTfz3xcWMIHDphoFv3t8zX0bEtkPZqVmIZZKe5KZ8fEp59fUND++e8COPaCHWwnEUvIhpm8zX8ZRH0TSNj99vhZDqiqZ+trtOLKd+xHNMwrlPnkh133Gb0nY9YSwW2L4bKKuOEXFz3d5J6th3R6LIIQhF9i2zirYbpoKWJOswdxl9P0xZma4Rd1S0CEl2KX3bk2c1RS4HEF3X+bNv3svcwDTUbKyhKFquXywneJpuGlPx96Be8zhyeNyVkttoOoLI4+n2wPbjni++uiFvW+abmNefX/Htr1lXfysSqme9U5ww1gTxWq3kDWeYUsNXpCWOa7O6WnB6SqRUdpsI47wfSY+5KjSWN+3ZudGe05i2LIRimWylm1G1EkWeOTlW4lhwA0VWMQyfrn2WYzGLfdphwDIlGr0JAr57/4hh6FxuAmzXYr6JhWVzf+R0koXQ0KXW79wUVBeN8vlKu1JdCz/kPH22LF29cXqqRqShu9uE+Az26j4NLl88n7N9THF9R0kXAzcvlliOxfbDju9+9sBi7jJbRQQzf6JFuoFDlpTCajlLHp3gSnW9J5x5VIXcQsZeXkNquU01ylopISPRspN9RhD7DIO4LsZRrshn3r5h6KxuJO/27v49AKFuTlz69Vxwyf2xpuz/H+repNe2LT3TemZdr3KvXZ763gjfiHA4E9LQpUMTiS4tOohspejQghY/AIkObYRo8QsSIdEhESLBGGM7Hb5xy1Pscu1VzrqeNL6x5onMdIQtAVJ4Sbezz93lmnPMMd7vfZ8XfE12maauU5XS8Qkyzzghfuv6c0fpMMjDxFILuwDCdPJ+kKCY2q0bhqb80jJPidOGb379hGPJQO2EJvbmJncfdwzDwJc/vx4pk7PIJy0rcV3c76nqjiff4hc/f0PRlnidiWMYZE3D1WzCD8ma4yYhnPmUec2bLy5pejlp2qbJ6izAPF/imtZYAG3pOk3f85frJ/phOv6OvmUJ9qDteNodqIuar15eiZ96NRtxvjPHJbQl0l/3nQo6eVxOI3U9ClPGNnRM3cHWjdHe6BgmdVfL6bcSuFcw9cVS2A+8u1pxp8E88Ikin+36iKZrxMcM0zYJZwF5UVHVDU+ftjRVw+pmwfWblZwG257smLO4nIkJQlXh2Y4l16S6jy3bRNd19uujuKJskSzitJaTX+AK9Muz6XORKSz7dB13qgNgIJz4ZKc+VXVKlSYkUQA0TeNwEMdXrWTYsupIH2JevpyR5g37Y0WcCy3SNnVuvrhgGnhkdc32fk+SiaV5uQoxDGNk1nf9gO8J0z5PFPakajhuE5JM8ChD95lU6dgGy6u51C7eLIi36WjXzrOaD++fMAydP/7ZO/6WDNPvz+JuOZakxUyxU4m1yqRtpCV9UPztk34ulV06Ye3LcG0ZsX3YU7cN+0dxfpimgK/yslXFE5I4nbc9s0VAUgirI/JMZhMBRtVlw/FY4tr65w7QombzcKBU3JembklPJcGLJbpaRDRdpJgTx+SUXNMUxH85c6nqdtxxLs8CwqlP3/U8fNrRdgO+a7A5VMSK/25b4m9v+2H0x54KO9pWmBeXVwtpxclKDs81T9uSlzeRhLy6ntA32ewKonnARnV/6ho0rdSR9b04FUbnSicDST/yqMuGIq9HK2XfDzSKK911veq8VTzqsiGYMAbLNE0jnAXc//CkSs+hv5XF8gQkOw2i87Ll7rnnZiXXQ9vK4PDTNw+8ns44v1rwvI8JQofdrhh35Z06OWiavN+hb4yhsL6XlqpT89QJqNX1EqwyVY9u3fT4MFowNU3mJ4+bAssSxIHvSYCuMgwOSS6pTkNKno8f93iuydMx5g8uzqXn1TApW3mg/OLtDT9ud+SxdHYufZ+562Eq4qGhHmJV1/GYpehoZE3DNs+ItylVUXO9mJLWNftjxnIWsvR8It8lU+heQ9NZej5dL0NYS9dZZxmuZeIYJseqJrQslp4/7rZ1TWNfFFj/ShjGUE6WrunwVPajKqW7+PAcc5xHnEchv/rmE45rc9wmbNdHLm4WFEmp2s9kdjZ0sgnJVJjJMA3OziMhaHZC24x3KdfvziVMWDayaNctGppQIH2H6aDCiL0Ur3uhS9t2RLNAms40Of1O+kGknLRksoykZLqUYu/JMsKrmvHf6lJwGUVa4gc2aVqNZUFtV9JUKv6va2xzcYcFnkVVt/JeujYfvr6XwnJTJwwddFX7tzmU+Or+Pe5zoqlUTE4je6xdPKGp+34QJ85wctJJqUnoONRBM3r0Na2Se/huz9cPTzRV8zvX1d+PxV3XcNRA1bJNKtU4HwYu3TBw9+OaupQ0qe1aTB0XQ9MoqoZXr8/lRtV0hQFOORwK0qIdAyjCKBGbXla0ytpX4YUOZtZg2zJ0baqWsu5wbV1slspv2ykZYhhkNx/NA3pVrXa5nFIVDW9+InzlQDHMH2+38vXNRqiO6vcsHw6yEBkafugSTnxcU5CqT5t8tCx6joFlCcWwbmpsU8NzTZqmG+Fbug7HbUqRVdiOxf1DIgnBYWC3y5lNXRxPLGp+O3D7YYdt6VxcTWX3sM9lh1K01LXQKT3HwLEH/MDBseUmel4nopnz2S3Q9+IgAvldFmehyDBJge1YeDOHMq9GX/kJcrarK2xTZzGxSfOWOG+JPJNJYJGVHetdydXKp1VpzySr+V/+j1+N/umz6zl3D6lotopDM8LClJsh8H5zOC2ViHXbqx5MqIoW04C6ETsc6vSmK9yBpmljIXld91y+nBBaNs9Jyjz0+eL8jKxp2JcSdvnJz6+wHJOrSHbFn45Hlr5PXEna2tR0fnZ5Tr5sWPmChZV+UtnZ5U1NUtfYuoFvWxhqwT7NmWzX4v39M14o3a/ZMWfy05dstzH+xKPuekydUcJxDIN26DkPhEjYDj1V20qJNgM62riAa5ow3k9pYpF7BDttuRav31xQtC1e4Eo13iIkq2s+fXzmeZ3h2AVJ3jIJLNI45+xqTug4MIHjDxLtf96X5KXIKI4tZfenYnnLMZmtIrqup1Ywrsk8pGlaqlLCQ5Xy0FuWgd71uIFL1/Xq9KuPXv+q7tivjyLfllKteTgUhL7YhdNDhh95TJaR1G52A9HMHx07SVKNlmj5G8hGwHMMHEu4MdOzCNM2efywwfFtZmcR07OIZJ8RTETaMkyd+3XONLDwI0+V1sjMwrBMeZhEIvnmSTEydvp+YLEKsR2RkDd7ea+bqmF/KNVGUVSO998+jQiS3/b6vVjch36gLGUoVleNpFDtmmA2oWfg6s2K3ZN0Sl7cLDlWJffv1+I5DiteXCxpevExG5bJJLTpe4kF68gCPw0sLMtgEpi4nkyrHVcWvkNcM50JayZopIcRJLVaFTV5KQ6Pl69Ef90/HYVHYeh8utvw4eMePu65vozoVz2vVgvs1+fjm1MqXvVhX4hHOxLu92QeMrEddkWOputcX0XjReBYOoahc4hrmk7Qt1UtsK5WWaAMQ+SUIPK4/bgbQ0aaJiiAtunkDdY0HFtKrIuyY/ccc/X6jMki5PHDhjRvlBYtv+eF42JaJo+3W9qmFfwpiN+46tB1eHHujXOC+SKgVUXS4dQf7aqn3Zrj2XT9wD6Wqjnb0mWIbBsEg8Hl1URQy5Xw3uO0Fl6M11HmtaRQtc+dmP0wUKjwk5A7ZbbQtjK/OC3qhqGrEwCfd+2dJGvHOYWuMShARt8P0MOgf55lNN1AldfUKnBkajppXY/Exfm1N17HVdvhmtIglNY1cVnyajojrYWl8pxlPGcZpq4z9zy2ec7EcYSjrutYho6h6VRdS9V2o65cZBWL5YQ0k9nQfpfxq+9uKbOK/Tom/IXDwvX4fr3hi/Mz0roSW6MORdPKzl4laye2w7EqaYeeTZazjhOWvvwOuiY0xrrv2JcFZVZRdVJl13c919dL2e2nudQK2oJ2sEyd8xcLifEbOnFeyv06DDyuM+X/NsbT87DLePnFOXVZU2QVQeTRlA2TeUjbdRR5Jb7wXSo2aMdCN2Q+1NatIqLKSbKsM0wFaWu7gdt1wf1G3Gxh6DCbeWJxNg0YhlFmNU0D3VHybeBKr6mtqzJ3eVhMAouul6rEs9nA2c1ccZrE19/WLbZnj9bj0wZjcTbh5Y0s2hcvlrRdxw+/upfNZVpRK1Ch6zs0VYsfOPLwsk0uLufSz7xLONzGYhZpe+Zzj8d1xuE5ZnWzYLjbU1b//1gh/z999b2wXkzLoMgqscYVzRi4qKtmJDc+3m6pqwbDlONOFhecLSc4hsnM87g95Cqc0NO0EirwVZKrVKUYgSmDxzwpxlg+SEHCbn0cB6n7tbAk6qbEtkRLPCGAj9uUYOKxfjiMQ8skKdENjQfboi5rKfZ9sRwvVk2TYWmaN0wiB9eUHULoOMSBI8Az5cmPt6lK0oJriP3qhDnte5EgdA3CmTDNDeNzh6pwW0zOXy45bGKqvKZVJDqAvOi4/eGZ+TKkbnvFupZ/0zVNpUKdMc1qGhrLyxle4BKEhXK12MzPp1iOiWUrl9EgXP7nu92oZwcTjyItx537IW0glWRv4A2EioRpGDrLmSMAsEB6S21XTnPNoZCig6NwQS4WLh8fha19soTK1xBe/UnuKVSGwDA0fMeQE0orkhKjtKSYNhIqAE00WNOUk94JXLY7pkI61OQEFtcVhqYxc11hApkWVdfynGdCK7VtDkXBNw9r5tOAbQ5ZUXEzn/KUpjR9z000wTJ0HMPE0GVRr7uWXVEQJzllXlFmFTdfXFC1LbPIx3Ytdn/2keM+Y3li2nQdVdeymkUYuo6rWaS1hJ4ANlnOWeDjGAaOaVBkMpRdxwlvzhbYii7ZDwNF23IoS97/+CgcpmFQDrWBuuu4iSY872N2uxzXMfjiJ9e4psXd8w7N0MeTmoR7pF8gU0gKy9KZTRypZ8yq8YR+CoaBbEhOsp7IhyLbOb6NZZuyaVDvo6YNGJrGsepGm+tJkhsGKPIaz7Ooc+VsMQSFUWYFhmWgdf1IhCyLWgWjYLPNpfxG5SmaqmVxMSWKRO4yXJflNKRsW1oF8bp4dUaZVRw2CW0jFl3L1Hm63aJpGrtjxXLmjH8L3ZRhqaHcO5Ztsjif4pgmX3/9abQBn7Djp9fhUKAbByU3/24j4u/F4m4YxpjG8kON5JBz8XKOoekcq3JkrRiGQV3WTJcRWVLguTIwSYqKxuk57lPSQ/Y5MWbq2CrVCeB5Fpud2Ie6Vpgk15cR+33Obptj2XsJRAXqhg3EEmiYBllcSGhqFsiEfCcBlgFUoEgGoF0rKNWTjHSSRaJ5ONqfnh5ijnHF49Oei/MZbd/TtbKYDR0Ets3sxYpvvrml6QayoiH0TDXAbGm6niiwsS1dGpksA8vUx77YAcZ+S8s2yZKSOGtZzUWnzIqWrOxo1+K/7wfGiyjw1K5b2azE2VJRNTtlfTRJ8paqFgxEkZX4KoBm2XI5nb9cUuX1mPg87VQkWCQzgGkoyAXPlzLlE4/eaHtpVoLR5XBKwfa92DXPXyxY70q6gc+65SDhKk0TJ8Qp/GUYGoErH6+bnqbp1e8lKcsTfnj4jU1Q6JuCHNZgElosr+fohs7qbEpSSAl0mdeEkSc7bt0gb2pC28HUdL57euZmOeN6MuFRF6gXwKvlnO/v1hiWwfX5Z0jZoSrp+p6553Ebxwz9wBeXK/5czW+WgcT486bmZjKh++UL6rJmPgtF5uk7qlb6R5u+4z5JWHoeP25lvvFiLu1MTd/jaxaWbrCuBOcxsT9X8wlNsuWDYsKfL6f88MMDjmtTNTVpWlD5Pq7vYBg6pqFTdepEY5ts7vf0bUdVd8wWgbjfVEnO+x82YmZQpNIsKaRmT9dHB9Lz/Y6Xr875/tt7ZitBEZdVS9P2DJl00pq2ydQyiJNK9QyIfAkK52HJonlIG+aR3HsnKmpZddSNDG87FXbLivbzAN0xVCmKhP+k5k6wIW7gkqYFUSinnDgv8ZRzx3EsdNcenVBFVqoQkkrZHwqmkUDQun5gMnGJZgFpnDNbTbh/v2HoxC1TTHxsR9Lpq5uFWHM/SvDt8jxgvcl5eExE2lz6PP6OdfX3YnGvq1r+SMrcH0y8MVAR71M6dQG1jaQKd0+CLJ0sAs6vFmzXRypL+k1Plsq2K4RQ2PUMgzhSmlq0wa6V45FhSA1fWcvwMt5Jr6OmQbxP1c/WjNYocmnRcRQ2ti6lUHuu9GM/sJkuQjYPewxDxzCNkaeR7FPZeTQdF1cTScU2Ld99fYuvUmnp3Y5g4nPMCs4mIaubBU/P2agVB6FQEvdJTVlLwnOyjMYB0nGTjFiEPKt4+rhB00+DXRX6UgnaKhbL1mJic78pRQ/UYDbzCCKP7fpIOPHQDQFgnUqsT6ldGZLlXL09p61b0qNA3fzIG4+whx+fAAAgAElEQVSrheKEm4bOY1JSpQ12YDGLbPzAoS7rkVh5YsWASCeGoZMcS9pO3hvLsbi9T3Acg8s3Noupw+5YMY0sLq+n44MlPRbs9znaIEd1SatK5dr1ylfwMxWkKTroBwZTEz6qphN6pmLeyE0feCb7p6N0BLzUWX/acPlmJacsyxqZ6qedd9G0/MHVxchuOUXkdUujbFp0U2y9nmnR9j2eabJSfaF53ZAecl5cLUlrabqyXIuH3YHZJMAz5fu9Wsxouo6nNCVrGiaOg2vKwn4oS15EE9mhty2BZZE1DVPHpWgbQtsSTb6Xod5puNoOPbuy4Ltv7qT0fRHS9T3v3l1xyAqZNYWenBKKmtVFxKuX51i6wSZN8T2H2VnE46ftOPDfPkhqdRdXuOoBfTyWCqxn4KiOgdX1AkPZQzf7hMk84OHDBte18HybJKlkgR9qsZWqkE/b9fSlKk1XQ3pN1yXwg2xwHNeibTvyUmZty7MJu+dEkuHqvpg4hsJmDNTNQFZWdGoD5Qay0J94VUlaiK1zn7JTSdLZuTw8k73ISJ0qCdc0jclZSF02HJIa09AIA5vX7y4p25ZKlX2fX89UNaFIYdOl2K/fzuY0Xcdt9SgZga5nMbEp1Wn63/1Hv+BX/A+/dV39vVjcQQapQz/gTzz26yP3jzsWquar7weqQibbpm2y28mT0AtdjvtU+BiWQZ02pEmFZcqN7Vg6xQkuZevqohIf/Gk3D7JTcyxhPOuGNkKQxDNuMlmEynJlMV9OaLqO+++fCCYem40MJQcYgx2ThbDf67LBtk0JNal/z9NSCJSBzWw1wfUdkkOmWqdU5dz6SFM2BFOf66sJd/dSDL7b5eONE3iyUF+cz0RL9WWQG3VSElBmFWXZUNU9eSna+PFYCsK4Hz7jaJteMgBtT92IplkWFU0rTVUgC3vTyg1TK6mi7weCiVDyTieSk84+W0XkcaEKoXMeNgV13bNceWRlx+ZQMe/Fl3ySxFzfJksr0TiVrj4MEiRplR3PUdbW3dOBF+9WHP78TlqkHmJef7GibTos22Q2lfrEpu1GIJlpSSLxtOAbuoZuavQtn6exbU+aN6RFK+XkhsYxa3Dqjpdvzsjigqu357imSdE0BJZF3tRKq+6wlLVQ1zRC26btB86DgMdY6H1pU2PZltQb5hlLzydvGlzLJLRtsiajLhvun3YEUx9/4jELfTa7mLnnYemGFHmEIekwMHHlPY+rSmiOms7MdTnV9l2rAa/bdlRdx9KTXefS95jnHvfZnroXhMbXD088326l2HwespxFLFyPom24mU0IVIm6qetcLWZ8rBoOWcEilJLr+Jipwo6Ap8eYIi1ZvVjwfLsj8uVaizNZYLuup661cQCfp+VIaWzKBsu1ZMdcNv/SsNxQrp5g4rHfF3J6VI64vh84JDUktWQ8LGkOs10LvdE5W0VkcU7bdFKobRsc4loanDybQM1rqrrHt3TaVk6y0tSk83y/5+rNiuNWCJOLi5nkSVQOoMorlpdzNvc7sqIhK1rOF9IRYTkWczUItT2LqhNgmsivCU0tQTtBRrdoesZsHnIbH8mKCi+Uyr2PP26w1Izr4sWSq+DvQc0eDKRxLvJML/JLVcgx8ESDHHpZVKJFCIOgBXYPBym9jYTP7Hg2nmsSp2JxnISWPLE1uSgMNVgxLSnt7dtOeN+eaLu6LuUDxyQhi4VLYrsKf6COmL5l8Wl9JM0bzm5smq6nrSTAFCiUsK7LYMayTfbPMcvzKZtTk5Il+vnlmxV12ZCc9MxSUq8nts7dxy3zRYnr2yqk09Mqjfj0XxyX/Pmf/YhpaFy9PpOjsmVge5IUzY4FlikX/glY1h7KUX90bX3ke0TzCfFOfMqDOjpqGmNju670yN98Pa8TbEsfUclu4JAdC4VYhaHvedqWmIbGP/yjSym8VnyaY9JIqk8NReuyIcnlIj/BwCQiPijCZ808snncFGw3GW/eXnJ55rHeyZD3x2/XI+vmZD89IYG7Thvff9PQ6NQADkBTTU66odNpA0PTE0SmyDWaJjbFUk6LwzAQzQMe7rfMVpNxsbOVZq5rGoamk9Zia3NMsSHezKbcx7E0fcUF19dL9qlQHJe+R9XKANPUdGarCReTkGNVEUWOWCbPZIiraxqbQ8LmkKjr1qE3hrEDdVvk44NlW+TiujHU39Iw2JflWAay8FzSyKfpevZlgeWYrF4IFjl0HbK65iIIOVQlliEl1p5r8+PjRrpOVzPivGSf5SN2WtM0pmcTLm+W3H5Y8/RxS6VksOVZyPaww3EMTFMfZbO2atEMfZypVbXIhiKR6GiqIORUmqMXNVVe0XXi7pF5F0SBlGEcUhWGcmXnvH08YrsWcVwS+tLxOj+fyoNMF9nOsk05GXs2ris/VzQPsByLZJ/hBw5posq7y0YCScUzeSnaumlorC6nrG+3giFvei5WAY5rCc/eMfEjFzdweL1a8FfffuLixZKyFLu3KAOC45gtPdqm4/bDmiqXEOZk5mO5lvT7OrI5/Wp5RtP/PRioarpOW7V0hhyNTinJk+YWRp813eMmoao7oqk7VuX1XY/WaxIqernEO2Qctilx2oz9m4vVhOlZhNAUe54+buW4VjScX81Gj21yyJlfTLEci/36SHpQC94gb3jb9+PA5OnjZpQSbBUhrvKarusEPaBwtSAX/n4ds4srGe4dctb3n22Rz/uKi6VwTfpu4OJqJlKKabC6kF7U7TrhaSfsjbYfRh256wesu734c+uWfVzx6vVipCHO5wZ5Jl71rpcBYtW0dJ1OmzaymzV0LEuOyqYlidxTYYpZthySetxBOY4xHrO7fuDtF1f8iz9/TxKXRBMpP2mKhrrpWM0dopnP+/c7DnGN7xqYukZatOziitAz6fqeMLDFe9/Jwm8qm9chqRVFUOPlyxmTUm787359y/mrM9L8gbLuyUupZjwB0qpGeDK+uslBTimn+r5ODVQBTFMXciWAKZ8rJxvpjg19wS2fXc9ZeD65WxAq+yBAWtcElsVTltINA5++feCPfvmWfVEwdV3WWcbEdVnnMbPVBFs3eLtc4KhFcRgG5q5HZNu0Q8/Hxy2Wa2EFsqhbKnDkWzaOIXLLensk6Xr8wOXlZMpjmjBzXSzd4DnPsBR3ZpvnVF3Hz89WrLNMdupDT9V2LBVYbJ/luLZFdsypSwkNNrUMVmeOy64seL7dUlctwcQjmHhsD6mgOEoZjr++WPJps5dciqnjuEJzBNHCF5dT7u+P48lzGtp4ipHfNSrS33ZM5wF11XA4SAbDtgzKuqdVzUumWiP6YWAxERnXMmU+5ocuQVpS5RWrF0u2j4dRZvVVqYpu6nRtJ01LM3H3BBNP5j+hq7AhgugwDF3on2ozkOyzMZ1dqUT0MWuwTZ2BI9cvF2wfDwSeSTj1+er1Ff/7n34jMo0hRTd31hHd0EmTQswZgUOZlXi+Q6dk57aWlPtp+HpC/ZqmSJXvfn7NwvPGXMpve/1eLO4oPXhQiNe6avFUIOC0YDXtgGnK0UXXhdCoaZAoH2g0D9B1natZwMY2cVybeJ+RZ3LUTw8Z/sSTJvdOosKuYxA4FrZnjWW0bd2yfTjgR0KMTA8Z6bEQ50JeMVzPWVzOMEyDh7uDaOsGIxphv0lwbFkY4rhEN3Qeb7dydKs72rbnUPe0H3cSoArkLbi+FI092ctNkx4L0qRkMvPwQ5e+G3AdldBUidZOQcZODhyzkkFpVXfcfdoznwlHpypq/GFQmNLPVXZtJxJF0/Y8PBdMQknXdp2gTB3PptwkJHmjvo88pEyFWw19i7btScuKcOKSxqXAsTLZiXT9wOO2pH/Kx4dCFFiCO1bcoGlg4QcO+30+ogx816RqOsEAaIIJaJqePC6YL3xxJRxLJouai6spD3cHdE2naXuxiqoYt2Hp1G1PnDZEgTX+/IErrBzUrrLpBnRLZ2gH6HpaxFY5AL5ncfPlJduHvUhlZxbB1CdQrUdtP7BJU1Lb4rBP+eLmnPkv30rZs2nRDj2OYfDdt3csLqVTNrRtbpOYIiv54zeveM5zpo7LN7st+20sVEAFzXI8m9X1gi/nC0xdZ1vkOIbMciLP4eVkSjf07HYJ/Xzg7WzO0vdpum58+OiaRt13zD2PfVFQq9OhZ1p8OB7I4gJrafLliwssXeoFPyRbmr4jayBvpCBn6AeuXp4BcNynXIQhn0qJxZ9kOMMyKfOKr95eczsL+PHre7Ki5fluB0hJzXR+wvzKyTCcBarByOC4SXB8eyywidN6PLGCLOy6rnO28Ebgn6aChbunI4ap4wYupcp+aJo8rNxAchez1YTIc3l82lMXDX4onvkTdqAqpfHrhDGJY0mse44hfRGLkKeHI1XT49oG80hmdrapU1cyc6qLGsPQCS2bi9dn42k8T4rx79A2HU3dcv5iSTgLpP7RFltzVdaUZUOtJMU+b5mEFmc3Elb8+eocU/scyvttr9+Lxd1QfIVaFXXouoZhGLx8tZJC3mNOt0koK/HJWqZG17RsDhWurWPorfhlpz79AGe+z14XffsUJx4GWH/csDENXry74MW71RhQ2j0cxMaVVjiWzurFknib8OmbB9xA2pHapqNICjlm2abwZ9TOz3MMXn95Tt/1hBOP53VKWUuYpswqqrpTuxCd5dShqDrqVi6O1YsldVFLGEVp3G3TsbyaMT/viXcpdx93zBc+buDw5RspLImTijRXtipdEqJ52dIqa2PXSwNRUTREU9Fai1IW3Hkk3ZJJLicbccrI8fL+43Z8XzRN2o/OF/L5wr2R73eyZ7XdwMOPa97+wQ15WfP+1w/MFgH1MR8ZLQCRb/LVL2+IfJf/+09/xLZ0pmrBrdTv3XUDjmOMeN4Tvva0217vSpZTmQ0Ensn28cDbn71g/RgzjSzKqiXJW+q8GQNNddND03OsK9A1TFu874au0dmGLPCaRl/3n+PEraRwTUMjyWruf3gizxvenE2IazmeV103SjBT5RO/WM1Gf/vM81j5Pn0rwaJ3X16PO637NCG0bfq+509+/Ijr21i6dGdenM95Wotr69WXVxinkJGyBYe2TdG0vJhPeUxTtkXO3PN4dbmUTUwjjhffssmbGsswWPkBd3HMdp/wD1+/4C9u7/nZ9SVN19E0rUpkdnIq7Xsc0+T6bM6uyFn6AS8nU/KkYHWzkBlCKmaDT5s9609btseKYZ3huwbLs5A//OlL8fXf7TimDbUqs1hMbM5uFhRJQV3WhOq6dFQ0//Acy/tFrTT6ZixhN83TNd4xnZhy2q9bDMvEMo1R2mvajjST4WVRdkSBDG0t16KuWg7PMbkrMpRU5VUYlkGZV/iRMIAGhQdp2p75Qhrehl4au8KZwdXNfOwGOAHELMeirVvchUvbtIShx7Eq8T2Hum3F7BAXBFOfYRj4t756yz/7k7/mWbm/rq4WtH3P/Schbnq+Q5q3I756shCHXtf2/OLi4l9LFf9Nr9+LxR3g8kYCEkXT8PRxy36bMltNJASUV4oEp4Z2RUNWVNStcGNqlTx1fYfnux2rmwWhI8c0bZOK/c2F2WrCcZPw8PGZq1crwlC5QmYKk9sNfNzmrHcVl2ceXigtLslOLua+H3i62zNbBESLUBjOtj4OdNqmw/gN7EBRdSMR8qTPnQZEnVo86kKcQkVSounaWNp9NhGmd1M1mIeCLDl1rup0Xc/l9YyqrMeL8Px6xuPtXnCpba80RPEYd/uci6sZs9WEx09bNocKz5GTUtcNvLiZkCclhqGrxqVuvHhkyNTKsLUd8Bydl2+W9P3A/ScBkBVlS1E1THyX1eWUu0973nyxYraseX5KaFvhuye7dGzesQyJ/2uaJtN/U8c0e1YLlehTD0BT/yyTeI7swIa0krKFbuD2+0fe/sElj++fObuYstI1/vJfPH4+sv5GsxQ6tFVHUnagePWDBhgajmdQVR20A6jiEEPX2CcyHJsEFk3dsEtzFqGPZ5ocSpEdJo7D94/PXCym5E1D1/bcP+1oVz2RbbNNMt4uF7RDT9f3OF0v4bwo4tfbBykm94aRv27aJi8vz7jfHvACh0JRHC8CuSbKtmFX5NiG4Jg3WT6GqrKmoRv6USo6VhWZroBjRU1zKhzppHPVtS3qqmEVBPiWLQEtyxZfvefhWSZpXXN5NmNiO/zVj3ejRBrvM5LsN1xBSia9O8Q8vl+zPVb4rsHMsrFMjekyYuh6vMijKuUBrBs6uycB12ZFyyS0SbKaNJe0OCB0VGUbXZ7JELEuGqniaz7H9W1H5LOmbDjEsmE4JZZPg9q27fAsQZfIfaiSrobO+v4wZjN2cU3b9qM08rM/eoHv2ux3iZg1fAfHteRU3fdj/WfXSkNUW7fsh3i89CaLcMSZTxYh3263BFOf7Tpm93REu5ox87yRw9Q2snacsMgnzo/tmNwmMeeBj/43diN9fv3eLO7bTUw0D7BNk8vXZ+NuTjdk6Ne20oauK5fLMZXSXsfSWSzlDX+621NUHeHEZ+Z5WK7FdOqx3mRcvlkx8V38iUS4n263qlQ6ZBp41F3Hi9fSLvN4f+TTU8bNqme2mgDyZD5uk5ET33c9y5lLmjcjg9nxbNJDxmLhEcclbStOncHQcW2xW1WNOAYuV74MhZ9jgsgdn+jz0KcfBuKqoq4a1vcHxT4XKUHXBtquJ09LHNeis02Wl6FcNDOP5FgSeCaHRG6QrGjxXWH0XF7OCSc+++eYsqi4fxC86G6bcX45oS4bLqYeyT4bH0xdL2nQwDOJApuilEViejZhNvPUkVN2L60Ko3muLMLvv38eAWNdL6UXri9hrUBVHZZVy/nlBMs2ef/9ht2+xFG0xtPf7LROB54lA2Czoas7otDmeVdwdgOrFwuSvWCjZ6FFVrRjmKnvhxEmBsoHX3UMls509rnBvveErb+LawWUgnkkQ7jrV+Ldv1hM0ZHSjPMgpBt6iqZlMQ3HgmrbMHAmEWldsS0KvrpYsc0LXFXtdxmGZE1D2cq1Mwk9pq7Lh+OBpecRRT5pU/P6fEHWNDiG/P3qXiUbTQvHMHEtk6UnEoxlGCOV8i+++0iZVfwbv3zHme/LoNZ1uQjEcjkPfT5s9/z0fEXdyXW78gVVcBrOThwJR6V1zY+fnnj5YsVznkm+RJXNdE07YnYlWCenwG//6k7yE/3ALHSYLiO6VmSIIivxI0HxahrkWUXd9J9nJBOPvBAZzXdFbx+GAVOdTqfLiPWnLW3XUxXNuHPu2h7TluFk3fR06nM0TT00Ji5FKsiR7f1eTp8KSGgqG21eikGg2mYjdtc0pR2syiuZ9+UVy+u5tHgZjrjfjrnKxzjcfTrIPGDqUCjkQj/A2aohmvncftjy8Jhim9rIjLLMEsc06YeBuqxHy/TyLKAsqvGBFU59dEPjcb3nn673nK9mv3NN/b1Z3A1DZ+q65E3D08cNr99dUnUdL66WVG1LWghDO1ODiJtzfwwJTeaCRhW728Dj7Y7kkI2OlavLiInv4hgmvTUQq10AiD1xH2dYjsnC8/mDt9e8e3XJ3XqHYRlMAw/D1Nnc7wEII5d3X16R1TXhLOCYiC7qTzxMw6AsG1zPZjJxybOavBQpyXNtHM9G10oG5GERzgLSQ4bryWCpzEqem5amakXvzkTL9tqe47FkPvdG6p2uizNhc7+jyCpevlpRdR2rrqNISsKZeGhPwLHLmyVZXWObJldXC+K8ZPOcoWmiwce7jH4YKHMZYC7PQrK4YB/XikHuqoVbUL6b+x22a1GXDTdfXEot4jBI+fFqwtT3+Okf3vB//ckHdL3jYhXQ9z1lLoNyKT4X6UxOKy1ZKZz1vJSFou8HzufOGDaJ01oG301HnDWEE5em6fn+rx/4yS+kqvfTD7/xQBkGbEsgYboui/pU9dXKrkyCK2nespjaYoG0dC7PXOq6H79O0/Y83+8xTZ07UyfZZ/z8Z6/Fm97IrljXBP716W6DplLM89Bn6fnUXY9liNa+zQvaQfztmzzj5fmC+90R35IKu8Jq0ZBNxo/Hgl/+8i0AkePw3U5CSZdhSNV2UrCtruOul8G6pmm8e3XJoSrH3a5jGTiGSVpXyrJp8Hy3Yxp4BLY98uLr/rM0883Dk3iuzyJMy+CQFSI3KAeZ49nkack8sqnbniRrxDm2TsYHsqYJfK7MqxFHHU59LEV/3K+PWKbBIRH5BVunzOux4cy1Dc5mDnHa4Cq57sN3azTFAHL7XpkKOoa6U+A9E9uSCjzT1KlV6bplm4RT2bE/JTuiqU+RluyealbXc7zIJVSWTdcxRyfZ5ZsVXdur2VtOnDXYnuyst48yc9M0jaxolFwJkWdh2iZd3oy6+DDA++83smlS9NFTgvp86XHYp7z//pkvf3YFgKVa6CwVaNKValFXDY7vCCTx7wM4rChq8ZGez8mLCtMyeXrcszifjpay3hUvt2mbIlWoQathSYlG9hteck95psuq43Lqc/3iDMsQZ4JlGFxcztkfFdCqqDGUZrfN5GNT32N1Nh1vnMcPGx7XGaEvOzvfEq9qesiYz32iuQD4q0rezKZqSPN29JN3/YClIv3TZUhyyBTDWayYeSrYguMmkem8rpHFBbYjOIJknzGbeUJ5VLRE3RCmdt/1aLYkDB3DGHePbdMxW02wXEEefPzhicXFFM+ySKuKD9880PYDkW+yPJ8Is9wyuf3uUar74oK752Jks5R1gu8a+K45FpvsnlOu30jjUJxLE9P7b5/ElnazoC5lB1xVHXFccjXxSHNJ7ib7lOlZRBB5lFnJw+1eav0Uf73rBhrFg08LKWl4fR2KVBY4uJ48uEPf5N/84y/Yx2KP7Np7siKlVl+jVzOa0wMxK0TyMQ0NDdjFNZdLl6vXZ+i6RrLPeHpMSNRx/Gkr6WjTFNdMfrvHtQ0ixxG4lmGwX285m0W0fc+Xry6o1DCz6TrKtuE5z7mJJqR1jWeZfDgcmDgOZ35AWst1uy8L8qSgaztWswjHlRxH1bX0w8DS93g7m/PDfkfkOESOBI/2eYGphrQ/7GXxfztfEDkOdddSd1KIPfc8XNPCNS2p77ucEdk2hq4zc1zZHGk6mDJAPbWedW0/2hSnkc++SyV1XDa8+eKS9dNBBoBVIgXm2jDSNaNAjAWaGvpbymhwokOeJIjNoWQSqr6GssZ1Ti4iRoRzVXdUjaqvsyS1nOcNx7TmJz89Jz3mMAw87wp89fka4HsGlmkoZlVNMPG5frPi/v0zfQ/TuUhhZ5MQ8ysD2zQJLRvXMrndH9k+HOj7nud1RhSYLFQh/fx8wu0Pz+g6eKEz/p62ZagFmbGAO/BMdEMjK6XPuOt6ZWfuaVr5meuqZRfXPLx/5uVPLmnqlu0mlc81DelKKGtcT9w1lm1SFX8PFndDhQXyphlRA03dsn06UM0kej3xXdJKeBWO76Bp2mgzPCZS1dU2HXUh+pmlFvmXr87pemmu7xmwdANT0/GWM+q+4/FpT5XXTM+i0QJ2/7gjmgeSZD3m4qyx9VFf3BaFJE5diyItVcrNIVXIgqZs8FwJ3LTdQBTaEtIoGoKpgPnLvMKyTXHzdAN5VokHu+sJpz5ZXMiDx9BZKqvm8+2OyzcriqQU+mVc0w0Dkd+MNMM3by7ou4HDJh6Lj4d+YH4pzfInq+n2UOE4huBM1zGvf3qFbmj4kYut+kVPw6zQF8qisM9lSKoVLZfXM5azkLwRb/Hjhw37RCrB9seH0a4pBeAtySHHdiziQzZytEtVOjyb+/LgaCR4tD2qj0e2AM8qeViFE8HaPvy4pm06zi8nvIgmYvsrCt59eYVhPvHp9ohhaDSKUXLKrJ2seLUahru2wWwR4Icu2TEnnPnSkevb/MWffqRRwTddh7KSSrUwdOiGnqRq8G2Ls1lEaNtUbYeh60xNi3WW4ls2T1nG3PX4YbMljwux1jomFy/PmLkuC88lsKSMe369oupaQtvmq5dXdMPJ1SKLoq5JG9OJBfMxPsrC7/kYms7NROiVVddhGzrbolYnh5a0lrYwxzQEVGYa3EQT1llG28sMIG/k+n7OMtxATgUnF8ss8KRLdSlzhbVy8piWwWS5oMwqHjYFvYK8BZ7BbCY75e0m5YufXdP3/Ti/urpYUKluZMMwJMV9yKXYourUdWeRFc0IhHM1kSb7XnbuWSkzrv36iO3aVGXDauFJZaemjU1Fad6wWFr03YBuaEwjn3QWSP/tzdkoP91++0hVd1y/WvLm6owyK4kP4vSyLF1JNR162dJWLY5tSBJVzcoW51MmacHTx61w31UGxAscmrpVmwGdQhFPA8/kbGby6t2K2/cb/vDn51i2GAWKtMRzDEL1c356L5WBRVphWQaWkoJ/1+v3YnE3LWNsT2+bdiw9LtKSeJfKoEKB/cOJz+0PT+i6xuJyxjTwMEOdum3leO87tE1LqcowTnpk03X4lj1+T03TsHVpOzpFyQ1dZ6MARrvHA3kiA7NCJVDrVnjrRVaqJ2s2Jl03sYClLl6eSejimHP/aS8JTE2jyCpmq0hapFT6tW07ZUUcVKISZfnKx+ESQHoUBsV0GRFvUykS7wfxgreQ5C1xFhO4Bl7gMJuH467Hc9TN8rAff2/TMnjzekaeSKDJMiVWvz+UojnWPVFgjp8bZw1vX4u+Z6TlGEx5fjwSTH1shUm9eXtOkVe4nk2vGDZljcITy4mmrVsms0C915346A19ZPX3VTdaLPNSvsZq7rJParYPB2xXZBU06R8t0pI/+/ETf/haZJkfPq25fiVOqOd9ialro/0yKdrPlEjj5H3vuLs74SxCuqYjmPrMHFcSzpXsws9XUiDT1i3z8wlxVWFqugweHZdWLcRHNWQFpITaNPhw/yxsFU3Q00NWY5oyWD4PAiaOwwSHomlHzRtAR6NnkBo83+MhTcdIOsgg19INGuVdPwWW8kaamRzDZFvkfLU844f9niryrLcAACAASURBVKJtuN8esGyTeehzrEoc08DUdXZlQdV2rONkLIE3DB0/9PDmEwxNJ29qNkc5dZ7fLAhtG2dhkJXSf2sa5Xjiyktws4rZ0mJ1EeE6Fq4pAbnTRq5pRLLM1Qbp7GpGlhRczYKx7GJ1HpEecxzPpipqFhdTnh+kCi9wDfJKIbD7irPLmXjVAcexWN/tMC2DM88e15fQkXnBT95dsytyItvm40bujTiVfMX9Rxl21lXDYhWN1se87EbzRN8PLJYBz+uUadtx8XoFQBT5PAyb8QHVdgP7vdBObVtnPnXQ4orlzObm7Tmbux27x4P8HeICx3dUqGlgGPoxH9N0A1Y3gAF50bCMXBYXM775Xevq7/i302Lw3wD/HrAehuEP1cf+C+A/Bp7V//afD8PwT9W//WfAfwR0wH8yDMP/+Hf4Hji+I9wKVXx8qveqm56mrek+bambjnAi0knXdOweD7RLoUk+f9piWIaCGhlK2zOp2pam77n99oFwFvDqxQod0SJ7ZGdalTXPm6N0edbtCPsaVOsRML6hq6UvONz7I003cKU0yTwuJCH3HHN2MWOIPC6v5agGsHk8EFQe6/sDq8vpWLPVNR15Ke00vnKw7JPm80zAEvmpyquRjnlyJvT9wGIhM4HjQXYp28cDurKTurY+wrIeM7nAwqTGtvQx3DVbhDiuLfqnpbPeyylFL2THXtaCAb69i8Xr2/Y4tkGlioGTvbiaqqIm3qXUTY/ni/XUVAvo2czhkNQym1Ap3nAWjLz858cj82WAYRnYliz487nHQpOBmOs7LC7kaB3vEqk9dC0Ou4xo6hHvM+LLknWSohsa+23M8nJGXm4o6880zBMqWWWaRitrP8DzOqVIS67fXVBXDR82CaFvSeAmsqnymtkqYrGcULUt3317h+1avHixIrQdDHTlGy/ZPh14/fKcfhhIUgmgVLUgMExD4+rVGXUpsyX3nQzSHo8Jb5cLdmVB1/c85rLDXXoybHUMYY2crtuuF9lnXxR8uZC5lKFBN/SK7tgwc0/EyI7LMORjfOR6OaNohSsPgin+68c1Z5MQzzRls6RQDU3dch6FNH1PUlekacHQ9Ux8l6yuKZqGqm0pUikhcaycou9wLA3fNVldzbBdm7qsieMcbSJ6/QjbMnRc0ySfeBL19x3SYz5qyZqmjTCuaBYwPROYGMiGaxJYUrLS9iR5S/F+y/WLmTiqhmG8h/zQJU9LDusjTd1y/e6Cy2nE9uHAcDlwrVxOjn2yBcMPv7od9Xfbkvvo+kzQDkXVkeYNF7a0qnVdT7xNeP36gqSqMHTZoTedzCJ81xRnXDuIAcPSsS2D779+kKxN5KKXrTjVKpED60ayKM+PR9KiZTGxR/de3w1UZc3h+bMb5296/V127v8t8F8D/92/8vH/ahiG//I3P6Bp2s+B/wD4BXAN/E+apv30byvJHvqBIilUQk4YMieswPp2R1FJItUNHDbPKY4tUW/TkYWvrVt0UxbG0zF/t5PC3MuVz/nLM5bXczZ3e75VVMlp5GMZ4m+ti5rSrDHTUmr8lh6pkjPWDwdstYMzDY3L14L2bDspcpAGpGpEd/YdxMdMNTFJJ2SRlFy+XIo8sgxGfGnXyg3UKMcBiIfc1DXaXlJwg/q+dx93TCYu4dRnsghJDxl5WpLGsqiHoUOaSin108et7Dav5zx8ktDEaRfR9gNa25OrnUWSH3j3xZnQ9lyLvJSgVRTYLK9m9N+uR1vYtpRd9PIs5NffbXEsGS7uNwmWabDeFXiOkDsXF1PcwOXjt4+sd6XcBE03lpzncUHddFy/WTGZeXiRx/7xQJI1TEJt5Aj5kYfj2RRpORYzGJZJ5Dv/UlPXn//FD1iuhetJ1WCZV3z1D17x9GnDTiFcs6IVZrqq2asa4eSDBJk2h4ry6weiyKGpGhzPZqYezrNVJCUSvYSpslRCXvf3W7Z+wnIWyQ7cdghfnjN3PfKmIQw9KdcILULfYn4xlTlLUnDx6ozLMOKvbx9AE5a6qetKGzdZuB6Omged/Oc90HSygE9dl5Uf0PX9OAs6ViUTxx2dMwBVJ70G20Myduzmbj1+zTKr8BazEREcKYlJCzWavie0bQxN5/IiZFsIg17TNDa7mN3jYQzRaRoEnhBKr18tOVsKouHF9QW7ouR+u8e0JCOiaxppVhHMbcKZT9f2hJ7D5Isrvvv6Vu6d0MWPJLUtISmd3XMy4nQbleloVEeu8GQMNnd76qYbNxfDMFAVNYdjSVn3RPsUx7GoculreEaortevlvyjd69wlf3zMU05ZAXrTxt2x5rtUbj8ri2y2HGbsrqI+P77HZqm8fC0IznkY39B0+qkvZwGJDQoKI+uG0jVTMd1lBMmcskzWbuqumc2sTkmNYY+cHUZ4noOuiEodNMyqPJ6fBD8ttffpSD7n2ma9uZv+//U698H/vthGCrgR03TvgP+beB/+12fpKmQUVM1Yh0E9LQcaYwnb2sWS8+hTJl7vBCp6qoailxgVzevFlRFjWmW1C08bXKO8R3XL+a8/eoG2zA4ZgWHOMMLXGarCZu7HV0jWmFV1uwPcpzW9M+VdpJC05h5Hr/+uKUfpLjaVvWAIDtM3dDFHaDrakhp4KuGlngrPaf7dcxsFeH69jg0aVXjelmrMgkN9nE9BjgEZyuD1t3Tket357iBy2QRjj5hQSDAdBmRHjLifcZsEXDYZXIyMGUOYJs6niPoVcuVWcB+XwgHxtS4WMjg2nIs5jNXAiW5OBp2cc0h2Y274NPnOJYERppGeY5dm4nv8uLdOf13T6KZBg5lVpFmwv5p2p6maqTbNXBlUORaIyLCss2xEzacBVR5RZFVmMr3nOzTz+XegSuFyqHLch6xbjuOm4QXr885Hj5IFmDuso9rbFPHdeT0sVj4VIU0HjEMpFlNmlZkRUvYSCfAQpWVbJ8Oo0skCJ0RVHZ4TqQAPHCwbItVJO6tyyDk608PlFnFT3/5gshxiMuSoR94/eUVZd3w4XjgbDEhsCw+rHfix355Tq0Y7a6lhpCGzEf0QRvdLf0gaVxd02j6jqMiHQL4liXtZLrO//qX3/Lv/IM/4GIx5XFzID1khK7D3POkzMOz2RUF15E1Onq+/uYTv/jqFd0g8pBjGvy4FYfNQ5KMoRzTNjkeS7KiHZ0516+Wsjs3TD4+bHg9nWHq2shtD2cBWVby9mI5/vy7JBv5NVfvzkW2bDriXYJhGHiqtez9+50E0PphfFDP5z59K61dhx82mOrklygssJWW7A8lZS2S32adsN+kcpItGvbrmCKX4X//9iVN1xNYNl/MF7TTnni54H/+53/FZl/R/MaQFGCyiAjuj2yPFbqeCLvmcsHKkgfA06ctaVoJd+g3CmKkc8Hg7HLG88OBVkmGrQIeVnXHzcs5tmuxvt1RF404jHYZniP8rJMH/7e9/t9o7v9E07T/EPg/gf90GIY9cAP889/4f27Vx/61l6Zp/xj4xwD2wlO2unqM2LuBQxYX8kRLSpaX05GUqB8y2S3EspvzQzkuPa8T7j/tuHoxl9IGXSMrO15/eY7r29jKMTMPZSgn9DyDdjXh8BzjeNaoA/e9+GYXy0AeMt2AhmiqraK4TUJ7pFbOziKSgzwwHFdsbSfomW4Iz6JtO4qsktqspyNJWuM5BmcKO7Df5XRdT99LyUHXi2zU9wO2JTsueZ0GtDVRKL70+JCP/HOQY10e17x4NefiZs7h6zW2LX2goS/1eX3f44euAn0pXbzpediUaNuSHz8dOZs53Lyc4a4THrfFmBbVNE2hfwfAYBh6JpakcOuq5bhNOGxiXM/BUGlB4yhUy7zsRvZ6mUsbT3rIBHng/z/UvTmvZVl6pvfseT7zneNmZORQmaxikU12s1sDIDTkydRfkAwZ+gEyZbQtuQJavgTIFGQIbcgjIBIsASSrilWRUww37nTmPc9bxrfOjmqAXWxQQCN1gERlVAZuxD337LW+4X2f1xl/DqZl0FQtTdVgOa1U5FlJXTVc3C5ZXM2JtwllXlPmMvbp2p6n5wNFVlIXMt569dUVeVyIYunxgKZrBBOfLM4Zevl+/NClawV94Xg2hzgm08Qg8/icMctr4qxhFtlkRYtj60RTH8OUpWCZi+pF5K7gmAbvj3Lpnt0u6bueMz9g6flUXctzluHYFuvHPZef3eCZ4qSsSjEabZ+PZEGJf3mulnAtliG0TEN1MHdJzJkfSAiHYYqUUSl0zvyAQ1XSdB2ff3ZF2/fkTaOonROavuftds+L+ZSu7Zi5LnFVYRsS8Xd+u6RoW67CkENZsslzGd29MCWMfp+xulnQtx27XU7gmUwm7mjnHwbR5buBLJ/P/IB1LtmznmkSTkWp8+EQYytmVN/3nN0s+GQ6kzQqpUhrmxbbs5grCuapUh8GmM4DwllAUzU8bp6oW5nFH48f92XElSzVDenAfVcudpCgoKETdk1Rdrzebfnp6kydUWDqOhPH4Yuf3XL8i+8ZED5RUUFZ97j3O2ZTlyStKcqWT7+64rCOP7rac0l28tXzfGI7LaYS3NFUDRc3c979sEHT4PJCqvSykDHsKfwkL1u2RxlJOZbONLJxbIPs9xzQ/9jD/X8C/hWSdfCvgP8B+K/g77VM/b10m2EY/jXwrwGiz5aDYegC5lIPdRaL1b3IK0xDJ5j65GlJnkp1p+s6SVbz/JTwkz+cYFgSVvz61w9sH4+cXc8J65ZJWUv8myNzuOf1AcsRne4pOagqVCKMZTIox5rt2cynAZOJT5aVzAs5PPpOULKOJRF3uqGzvJzRdb0sWPcFq7MQ05Ilo+3ZNGWjHHEyDpDFTataMKnoczVLPFXqJ2SCruZ3J73rrcqM3ccq8aZuuH+ImUc2k0UoI4OjqIdsW+ewSaR9tRSr3DFHFkehFDv+xMOfeAzDgbSQII62FsbKhoqi2uM5Bl99sWS3zej7gX1SU+UdvS7s89AXPbit3pfjPhc1TCYzyKYbSLKWbhi4OAuoy5qJqsbrRjTKZxcT9puEoHMF8+uYRGo2f1pCn5LqZ6tarOPKiOIpSBqIvLUuGtm5lDWrixnXswk/3K+5/PSMqqhpm471rhB7fyhsIUPldJqWSD4b9dBr2sDzrvydkPKPn+OuFVnc45v12JlYkwn7QsZltmdzMZ1g6hpF26js1I4v5guKtuXy8xBD12lU8tTiQpygN9fLMQTEx6bpO9Z5O4ZrWIbBi2gyHvZN1ylFjeAK6l4Wwa5p0XQ96zynrJoRnjd1HKaOQ9G2pMecd7rGl5fn/N27exbLCV8ul7T9gKFJJKBlGLgvTW6n09PzOx5ipqnz8ouL3yk+4Plux/P6gD/x+Ha344vFApAgGt+S+Lr7JEE3tBHvHM1EobYtch4fZcl52Ihu3thmPCk0htA/Rfn0cB/T3h3H/7/ve6q652wV4Po2Dx9krJqqNKaT6suxTHRdeDDhLCBO9zRdz7vf3vP5XKLuTqY3XdP4Yr5gfbvjt99uVYawCqmvW2V0TEd1XV1IZGiyS3Edk6atqZuO6dTl4SlVbu+equ5YPydMIoflKlAFRk+Rlez38vkJPJnrF1WHZWgqqcxhfj7l7run33tI/6MO92EYxq+qadr/DPwf6pd3wO3v/NYXwP2/x9eT7Xjb4QUuXuiRHPLxYTEtg8M6pmt7bEcCgwfVf+q6xuZ+L6hbx8J1JNvUn3jE21Rm3mlBXalZVSH28FMWYnLIhbd+BNu1MUzpBPzQ5ZjkuL5DEAjp8FSx970K+l1GNFXDYZNgOxZRIHOyDx+OLKYO0Tyka1os11LhHTrbxwPLpbSRumlQ5NXIoA886TYcWw6YVmlhbZWolCcFT497LNtk87AXvfixYDl1WF7PebGa867teX6SSlygZsKxX81kZNR3PV3T07TNx8Vi17N+TrAsnbllc4hral2kaGdzl7wQLrwsBqX1X04dVl8sOe4zNvuKq/NAFA1lg+3I7NbxbNKjHPLzyGJ7rHEsnfvHhKuLkKZsRslj0/QEXoFjS2XoRS7TZYRrWzzcbem6DjdwmV/O8CdyERqGYI1PstXnx6No5W1jDCZ+fkrpuwH7y2uZ4To255EkGAUTT2BODwdhvA86P/vZp0pmu+Hu7Za87DD0YaRNxmkj6U6eAMSqvOLDd0+jk3k6DQS+1XZchhGemmPrmjYuN5uu4z5NaLqO56fD6IEY+oGLSTgmKj1lmQriGEbVV6Y48iehgKFrYycQKkOSoensSzkcZo4rmaldxyqUav4iCHBNieLbZ7l0l7pO08t7vH46sPAkeHvpC32w6QQ8Zmo67dDzarkgm0RsUpE5rsJwnKn7rk049ZjMQ86DgLJpeb3eYFmCRo6rCssQFrqmQVlW4/efHXMcR5AI//xnn5O/rPmb37wlPhTEmYwhNKBUkkvb/Le73OEUTGPKWNSxDTzPYqa6X1GhyIzdULPr/U6SwKqmZxfXrPMc17QwVe5sPww8Z5LgNPEtTFMblWh52eIVAvoCIUt2raCd66qlKEW/DvDKF0PeauZgOyZf3C5xXIutUuaddkr7fcHmUGFburBxDFnEa6b8b9fK3me2DHn7e87Vf9Thrmna1TAMD+qX/yXwS/Xv/zvwv2ia9j8iC9Uvgb/8h75e30sbFyr5UXbMR633MECcVBSFoGn9tsP1ZWFxfjmRSsk2Oe4zjqkcWJOJK4CguGB+OaNrWvq2Y/14UDxkRqSwYeqqipd8wulEKvzN/Z4qr7h4ecZ+L5K/5Tziu9cfGAYJlGibDkvxldNjju2YTFTwxSnsW9M0yk3C7GyC4zu4gUORVkwWAftNSlmJXR+gqGTu1g/gaxKSnTY9Qejw4pMznp8OmJbB/vk4ziQNXePzr19g6NK666Y+qlqiifDhHWWc0E1DJRG1uLZgbn1l4pIAENEVf/GFzEzjg4CODvGethfNPgiPI057miZmsfD4ydmEvu/pO3m6irQUk1krM+nHdT7KJwuVd5kkFYtlQLPOJS7N+agX7tpulDkSSpCHF7pUqntazkLu3q+5vFmK1yAp2KwF1jaPbBYXU3ZPR5JMTGX7fc63rz+wup5j6Dq/+fYDvwHObhb89OUN9YsLdkVBVlTj4fcHn1zz8H4neOBe4tw07fT9DxSVpGt1Tcd0GaluoKVve7KiwnVtHNPAMgTbW3UacV7S9z0X0wm2ofPt0w7d0Bi6Htc0edrsmfoeaVNTtC3XUTSyZAAcw1QGulYMR0Bg2bw7Hpm73vj7AA5lial47sdS9lfLuejxf/vmgbOLmSxGdY3lxYwPPzzTVA13b7d8/gfXcsFZplJ/6EKGVPPxomm53+65WsxYhSGOadD1PReLKY5hoGsaly8j3h0P3McxfTcwCyTFaXtMmYQeZdty3MScXS+4nk54SBKe3gkSZPcslfjcdbkMA9w/tPjrb9+N6pdTB1xkFYUah0luslTTjqWz3+WqGIMsq7FM6bCzpOD+IaFuKjG4GdqId9Y1aJqe16/vuP7TSFKQkpjHpz2PH/Y0TT8CzFzbwHVkeZwccpbnU47bhB9ePzKZuFSlFJOuYzCPbLpOlroXFyGz1UTi99axLOZtY6RgeoHDq0WI9f2aycwjnPrsn47oukY4cdU+Lef+bj9SI/9dr38fKeT/CvxLYKVp2h3w3wP/UtO0f4KMXN4A/w3AMAy/0jTtfwN+DbTAf/sPKWV+94eVp+K47LsKNG1csIJUY6clnu3Z6LpOkRYKEdwonOvHr1nmNYbik6+3ifBfegkCcdSoxDAlYKOuGsq85vrFnM3TEdMWNOjq1SXv7tYKDVAxC31m51OaOzEpmHHOF1+94Ho1583dM17ojmkveVLSNIV0GQOj2uWUDZke8jEDsu0GSYaHEad7ipqr1JhqvT5iOSZ3bzaqyhVj0ad/cINlyMN1SHK293vmZxMa1S5qmiyi795uoRDjxSl5ybA1Qa5Ofb7+4xmHdUxVd3ihy9QRFr0EGOs83O3ph2G041dNT1a2aHtxwM7PJziezeZ+z+zcF3hS0xFMfFjnlFkDpo6uWuO8bAmLmvOFyyGRh8/xJJKwKhqOx4Jp0ykYk/zc3cAh2WVoC435xZSm75lPA8LIkyT5uiWY+nz47kmFKPecLzz2x4q3HxKSpOJf/NmX2I7Ft9+ueX5KeFj4TOYBn12f8Wo2p2fgQxwT2sMYOn7KmbXNj6MyXdMwTQPHtbE9C8u1OG4SurYnvttx+ULGEJdhOJqDZoFHUlWskxTTMpgvJ6x8n+8+PI8OascwSOuaQ1GwS3O+PFuNz8jJzHTaj7R9z1OWcuaLZn1XFERKx/2JGp8UbcvtZEobRQzDIKwa3xYEhmEw8zymjkt9I5fV+eUEx7Fouo7QdqjaduTbtEPP3VEKC9d32Jfys9c1pQTJKvqux594nAUBU9flcSNRe+u64WIxpS5rStcSWuX1AkvXqbt+XE7vtinXtwsOm4TnLOM6imj6blywn9j8p+pcU5kGp6rdMLRxd7Q/lFiW4KKruqd8uxXeUyPjNnnOZKfVDYwsmud1xm+2G4Zh4K//SmrjwJV90mIVjsquyTLk+f5AWXVETSujykrUMKuzgDQumK8ifvLTFVldU5cND2/WxIcnslLGaJ5jkBXiXDUs4e90bS8ZtIF026ubBT+8fuTsZoFjmnz/20eq5qPC7t95rv5DwPf/EC/vdjqc/df/dAy8Ptltj1uZt11czejUh8fxbOYXU7JjTnLIORxkxr1ZpzTKUWkZGtPI5uLlmdiCNY0yK3F8R5KWNsk403z3sB5RBn0nH5jTAtQwpTqXbFAXy7F4+OEZEMqbpLeH2CrYA2QUMfQD0SLg8c1aXTA2Tw9HDF3j8sWc5/u9Ch7Q8D2LohSLuaPgYFkh6ILPPluRJQVpWnGI61GDu5w66LrG1atzpoFUQW3XcffNI7Zr8fVPbjlUYgyJHIfn7ZHvX8vfe7EQPjygWC81fujy9HAcu57z2yV9J8jWrlFYYSUNLSvBAcxCi0MqqTO6LlFnr35yQXrIyeKcYCIuz67teftmS1Z2lJW49hxLZz6xFXNH9hJ1UcuYo6zRdZEKBhOfrpPKeBhkvmnaJtv7Pf7EI5j4tHVLNAso8or13VbMIKUQBauqw3WMMf3JtQ1uriO+/vKWX/w/34zqB9eW9tdzTF58eQnAF4vFiAb49bd3ZHHBIamlq1LAOtmP6EyWkXxGJh5D1/P2+zW2ZfDZT6+5UAtJkIBqXdNwTZOZ62JoOnXXyrJUjStmrkvbD5RtQ1JUvJhPxzzh+0TUGF8uliOvvem70chUdz22oWNoOnFdjdX9KSUqb2rePG/RdZ133z5xcTPn7EwuSUvXuXu3ZnEx5TwK2au/s2MYI7IgrWvliPV4PCaEnoNnWsR1xfp+hz/xxmdUN3Rhqruy+HcDR4JslEHR9iSlqEjlzxkGcHyb4z7HdUz2ccVq4Yns95jLCE3JYW0F8VvvJfvXd8Vwp2kSt3nK0wUpbE5Mf9PUMXVtVH6B4LpXM4eylnyBkxvVVMqWU+h8oHgx8/MJmqaxezpiOybv3h9o24HlzGa+imhUKAgIT+q4TznuUvKsHuWPTSNS1sg3sU6eFEeeA8PU5YBvuhGauNukHNOG2xcTFhcznt6uyYqWKLT57X/3b34xDMM/+/vO1R+FQxVEFtT3qmIdINlnBBOfAMbFp2GJPG//dOTtO8FzllVHdR9jGTpXl5HCfVbkZcv3v3mgrDuWU4fzT1a4rixzWEV0fc8xL2Q8k9eUWcX1Z+dUZcNpk6Lpyh7f9cxnIYdYVDrHpGZ5aZKn4vCscgEsPb3dyAfM1LFNk8XljN3jgTgX7PCxbIG9VMeupMy4gYNuypLuxasL9tuYH77bUDc968cjP/n5LW0nsr4iLSlLGU+FUx/HsZQu2uSbbx7YHitehC4PuwP7p6OAvkKXLC5GvrxhynJ6GQWsD4ksFh+P5KWwo5ttThyXOJbOMZWxhmFonF9O6NpetOROQ9t23N7O+PbbrVRPbU+uiIGub6Obwr5xA5evf34ji9Oy4bjP6Hu4+mRJnpQcdhm6aTBZiqt26AeqUnInT9mz2VGi3DZPsTBFypZwphMELpXVkCYFhqGYNGp+f7p0DF2jUxCrouq4+xBjGPdc3i7ZHR/Q+oG6Fc9CGBp896sPzBbBCJ1bhSH/2R99Rdk23MUxd+/WcqAoumiZ1zx9ENSyZZuYlsE//bPPyZuGvuuF/lg3GKbBdRRh6QZ/+8Md3o2A3kxNZ5/lFGnJq+szmUX3LY+Pe5bnU0xNyJx5UzP3PHzLEnqgIkT2A2N+6zDIQa9rGgvXG7ECZdOSdy1t/3GsNl+GXJzPaPueRDlSl1czGqWddgwDyxBUwSGR9/8sCmnUn+u7YvTbFTmHdSxy26pldT0njwvCmc/+OVaSZXfsVstK2D7T0BqzFjQNglNUpm3QtB0XFyF1IWOi0LcENtYN4j4vKsKZAMiqohaJ48yXbj6rJDZy5iu+u1T1Jwm1Zsi40/R05tFHY1B6LOg6Uf0Yhk7ddPTFIO7pXoJ2mrZjeDyMAdaWY3FzPcF2LBzf5mIx5bvvHyQfVWVBJ8dCchSGU3CKdBempuGqKMGq7tQZIDkWRVWOezrd0MkrccaaSkpq2iZeP2C7Hx33f9/rR3G49/1A3XRMZgHxIcPS5QbuOmnV4l1K2/VEvkNV1ORJwWrmUNXd6MDs+4HdVgBbF5cTKmVm8hRE6OGHZ5p2YDLzsB2L1DIU60TegqJqSQ85lmvRVCK9mk0Cykwg/r26YGq1BPInHsdtwsPbjTjSbHMkIjZ1Sxzn4xLYj0Tn3vYS4hv6JnnR4fky87Rsk9X1nDQtuH8vVf2JJrdfx0SLgCKreFznnDJG5aCv8S2LfZxRVi2ffrrg/HLOX/3l90S+SagqKV3XmEU2Qajcu5607pouPPmsFH77NLJpWlGuk2k1uwAAIABJREFUGIbOcuZiuRZ5Uo5KFICqbrHVclpX9v6zi8koI3U9Ry3LNKUckA+h7Vp89tMXqvXshFWeloRT4YGkBzGx6bqMi06BCl1b8uLLS+Jdyuwsons88u23G47bhKtX5+wUWqGsO9pWjFpl3Y0IBl3XuD7zxEdQdRz3GdE8YBJa7OMaDanWDFPHD4SXcj6fCIzracPb+zWL1YTIdviTr2XhWnctD57N07stgWXQ9z37dTwuwb9+ecWhLPn2t3f8yc8/UwCvnCQv5SDSNLq+xzR0lQLW8RgnglOeBgRTn9CyyZqGppK4u0aFWetqjNn2A2ldfTQ+KaTwU5ZyGUakTY2h6bw7Hnh6t+UPvrrlJ1cXvNntmEyEWPnbu0c0Q2fuegSWxa4s2OS5LDYVnrlrOzUnb3BNizM/wDMtHrOUumyI5uFYrUaOw+7pgK4Mf71ybe82Kbqu0Svncl6KychWCpu2amlUHoJjmyTHkmNaM4vUPmwdj8ZAx7Wpq4auaUd6ZDDx0XSN3TrGMg3KvFaLYo22EemkYci4xlI+h7Lu8DVZUPqhy9D3I17X82083yZNK5qmp1QjlGNcEUx8Jkvp2B3PJt6lpIdM8oP3olSbLkOO+5y0kG7Adw2iwKasJLLSVN9nUYqJMY5LVmehdOp5wypwxHsBXF1GYtZsO2olADnx6H/f60dxuGvAZBawuJxKOMZBFqrHTYIfutiuhWvoY6qP5QjXu97nKrVJG3Mxq6ImnPq0jbgRTwqZvhcZmvBURMfu+vb4ITANjWgRsH86st2k/OTnt8xcl7dFzeXVgrt3aw67jLYbOL8Qk1Ctqpy2G9AVPU8OI2mpHNeit03qqsEwdK4uQ1n0tAOdmp1OFhHvvnmgzEqGAS6upoTHnPW+JC9bnh4OPD0cKKpudOSVdY+ut2THnDyW7sPzLPzI4/0PT3iOwfJ8im5o8j5uU4ahJpgIf70bBrGOqwtQ4F7y3uqaRl13GLri3qgH7vSeFakkQM0incnEw9/nzOa+oIi7niItRwpgOAtUsINSN6mvUWYlTx8OvPjsjC/+4AXdMLDfxvLg+pKLeWq966bHVYf8T3/+KU9rmeF2vUgrw12KF7rstxl5KQjVrGixbQNd5dN6jkE0FdXHpB/YbiWacTb3OSYNUWASReK6nMwD4n1GWouDE+CTqxV13/H2fk2ZVyJX9F3++PIK5+YFj2nK95stdbEhK2rSY87r94+YtslkEeJbNr4FTd/hew6hZXN/iMXmPwxcXS0EmpdV2K6Nqeu0TctznDANpDDokZzVdujJGqWpNww8PyBrmo/hKrrOi4nQVCe2wzrP+HK55JPpjLf7PY5p4NrS8b3Z7bFci9UkBBhdrekhoy4bXlwsqbqW1rXZHzPCyMPQdX447LEMg7nrctyn2AqgV5cN1oXB4mJGsk+Ff17WJEd55mzL4Pw6GscrJ7rrMAxUeU3V9OhtTxi56Locise0oWkrAtfk7Dxgv8uJgpZDfFJMaXS6xmETi2/FNnlY57AtsC2dWSSHtGHqWFVLqbeSjGbpmKYUkHXVjClsq5sFmw872lqx2HvJAliENrPzKZOJT1U3xLuU52OObho8PMnFZcU1ty8XYoR8OpLk7UgjrZqeiVK9GIaIHqq6pe2ks6jqjiwu8EKXxdKUS2Of0bedSJVDQW7v1vFo2iuyjxyjv+/1ozjcdSVdrIqa5JCrh9whWsi83I+8kWioaRoXt0thkjwnCqspX+dYyKx1tuqxlRbetAz2m1QYJbbO5cszNF2jSEosR9roYYAXn18AjGnmj2/XxFGK6zukRcVmnVI1PfNIJHBFUozuVcPQsByTLJabO5pK5unTOkPXYD5xuHp1TnbMmc0GjseSru4oi5r985HdsWYxtanqnjjd06n22VBadzE2DQSKTujY4lZs6pbpMiKyDOJdyubDjqKUYALdOLl+W1xfZrbJLsWLXDRgv0v44bsNnmMwDaRC6DrhsDRdD6U4cH3PGh22fadxdj1nWjVqCV3R9xAfCspM4ucsFSZu2eaYFLW8no9o5axpyVMBlFm2SVpUZMcc3dB5+emFUDqLejzYHdvg5VfX5FnJ69+8pygairJDQ0ZBjw8xZ+chuo7yBkiFdWKKF4qWKWObbtQu37/f8dM/fsnjY8Kgxjbv746YZoIGxPFbplOP2VnE/fbA0ImbNjkWpHHB8nzKwvVY5xkvpzP+yc011eU5r9ebEeymGzq3lyteP6/5yfkZcSmHnOEIvG3qusoRKp/5m9WcuyfRcj+929B2A7f//Ct8y+ZRSSd9yx514RPHpGhbHtOEz+cLDJWxaukGM2X48S25LH3L4tPFgn1Z8O77J6bLENsVM5yl61iGwbEsyZuG1Wo6yiyLqqUu63EEULYtTddxTHK+uDjjbDUlsCweOlGY6YY++gjauqVupNv47OsrFaEp/5S5XGQnS/2gdk5l3ZEmJb1aXJ9EFFXTsX7OROnWdIS+uKCztCLOGhxLVHOaOkgj3yTOGkGX+Caff32lipNS8CEqwSnZ5eOSPC/FNLXbl1xcRgBcfyZYYss2SQ8Zx00iADyVi+oaOrcvpjiuLV1r4MjFltRjMXYKjSlUapq4U03SvBkxxk3TKyVdKYDAfUZZ1LiePRaNhmkwW4YM/cD93Z6z8+j3nqs/isO9a3ue328Jpz7BxMOPPHRDI5oH5HGBGwgRrqmlxY53KU8PR7HqW/qIB6hrUZGEM5FU7p8EBdq0EhE3CUUzuv6wU23YQHKQKq7rB6ZzqTTDiad4yTWfvDznV3/zhqbtcW2d1dWcRi3PyqyirFqCSB6kWpEMqzpTob49g66xPVbcWgaf3Z7zFKf4kaBfNV0XNrqab+elGBU0TSLouh6G5gS70kYZlqFr2I6JpwIPAPzIw4tc+r4Y0cWGYXzkahwKsqLjGsjnNbunI1UtX9syxXhUFBJ9dr7whNWj2tS26Tjuc+qm4+Z2QZFVRDOf3dNR6YM7+l5az9mpGkoadTG7tHWLFwoewFNI5OXljPvvn0eKX52W/JCVeIHLdBVxfbsaJYXbpwP3dwfF6+hHymTXDWDBZp2Slx3zic3TTqqZ038/zXTzROSZQy+SOejHyhwgSyuWU9EfP65z+qGneE55Wme4tnRLJw636xhcuha//P4O0zLYbmPqqhGd+s2SP725wTEMirYdzUxvjwe2DwfObhb0g6hoTmHUnmmNevhPr5Q65gvYP8dUbUfkaLyazflmt2XquoIxHmfuEr/XDwNN21A2LVhChjyhCKpWXKtvn7aCOph4zGchoS2xepZhcB8LhCqwbdJScmF/+LDm8cOeKHJZXc9HRUw48TFMg7iu+Pa3d8wvphw3guBID5kQO+t2fH7LSjJEbTVHbpsO15cUo6buMFQ3muYNy6kjl1cn48dKges0TSMpWmxTwzR1okh+Vm3dchU6ZGmFHwmUzHclflDTZAyU5nJBnRKXDMsgTiQBynNErVZUHYErYT/XL6Z4ocv2fs/z+w153mAaGpZlEC1Cnu+lkDmmDVosLvNJKHumpmqYn0/ZHZ/HZamh2ECnhWrfD2wOJa5t8PXPrnh8KxRJ05J9UvN0ZB/XTEOLcOrTdz1v3uxwLJ3L6yl5Kgyn/Tb9vefqj+JwHxBmSV62mKbO2UWH4zvjB+HEE+lamenuN8kYDt0r80LXyQ14tvAkyXybsD+UuI5Q2QDCmU+yTyUBKClAk3HO8nI2amhP0sWhH5ifhySFtH+2JbrwSOFqr8OIv3yWB6IuRFa5WIYMm1Tm5XWHpeb9jm2w9Dw806Lve+7f7wg8Cz+ymb1YYHzYcf+YYinQUdsPaB3jFr7rBoxuGKP2LKXoOfkA+rYjPeac3SyYn095vttSJ+KyrfKK7VGCS1zHIMlq3G1CriLAfjdBfToXxyhAW7ckWU3dSmck1S2URSUX4yAXin6Q3+85BnXbc4grlkuDNK2Yzjw5VCuJVzthGQCOu1SNYKAqG/Ksxvct2qYlPQhPflBy0PPLOV7o8sPrJ9KiHTsakQXKXH01dyRerZNOyjS08e9d1j1P22KM2mvbHk2T6nGqIvlmi0BAaV3P0K8pK6m+T6lUp8ttGGCx8Pn57RX3ScJvf/WOaB5Qq0zQt6/vRcnS9bycz/Etiz/75AXPWcbLn814/bzm7rhF0zW+urmk7Qd828LQpOrel80YuLK8muHbFm8PBy6CkBeTKd/udlxHEceyxDMt3sdHDE0O/6xpiOuKpe+RNQ37osAyDGHNdx2TacDumOIGDr5l45gmv/rNO4rbJY4pyVVt33MehVwEId99dy9xhXrF09s1fT8IFjmULuZ9vKEsG5qqlS7lJ1dYhsFuE5MnBQ8fDuKodEwmSxn9ZMdcIvEsQ2GgBVFQ1R1XFyGTeSCJR3GJaWiYnjn+HIZBiri+bzgkNZ99OkfXNXa7XObplSyxT+PbWWiNYgDLsdg/x5JuZIu6plOHumXqnM3dUa213xY090dmUxfbtdVFMHAxEwzy4izk4cMR29LpB0GBx2nNbOax32YslFS2aTsC18RSHUmveDiD9jFzdvd0ZHk5FWrlVs4OCaf5aO2Pd+k4IXDU3vHyUwnDufs95+qP4nDXNG2sjoxu4PlR8LKWY1Hk1RjmoRsf+SquLQsS4SYP4w2s6dIWapoiC6p5vGGZeKE7wotOLBLblbCFtK6pipogcImPmZobw9vXD1LNXk54vD/y+H5DOAvYGuIidWwDW0XiSWr7gOuYTOcqcEONB374sOb6ckEeFxxTSWpadT1VUTNZhPxk6tP3Aw93kgNb1Z3Ew2kabiCLG8M0RD/ednRNR5xKyntV9ywWnlpeSmVcNx150ZDksrCZBBZXNzORhRYVpULQrs4C2qolTiq0tCb0BXOwi2UebyjFiaYh6FJTvt/n99uRMWMYGpVClOZlR/2UMo9sWZK13ajLTuOS5fmExeVsJCPqus7QyeVTNx1+1UqyTS+o3KbtubiZE+9S5nNPHqC9mKJc21S5tD19r0LHTdE197pcgsDv0AFltnlSWXVNhx95ZIVUnX/7i7dYps4nr1YcFQyuKBqRty0DtpsUzzGZzAO+2+9Zf5Dko0L5MwRJLRTI4ybh/TePTJchk0XIF6sVoW2zjALu0pKhGfi7d/d0bc/5xYwkL3Fdm6XnscmFMRS6ErwdhB73aUKRV2RxzkUQ4Bgmlq7zcjojrSt6BuHTaBpF23K3P3JYx7i+zWwRcR4E3D/tVCfXUBc1X99ecfHJirbpuJxF7MsCXdM48wN+vVljGGLAma0i0cYP8Px+y/pRTDXTecDZ1YzPbs5HBELT9+w1hLGUVkRTj67t2dzvWV7K5WnZJmmcjylPTd2ymIrKqqkl6D5NKpGaGvL5sxA9e1mLjNC2dOJDPsbptW1PWTbYlvg4un7AMuWs8CNvdLpH82BEU59fCK472WdYrkW8S1lezjgeitHlfNwmY47rCTccLUJeBS66rvH0bkPTDuRly8NTimnqOMeCKDCVP0I6eNc2xpFn5FuEkXyPcVyKW93QcT17rNTzpGB/KLl7u+WYtUx8k8AT/tCJhvoPbVR/FIc7yFIy8GQOlf1OYr2tUkeypMRVxEDHNpnMRORflTXv3h3kUDV1irwaNai2JVt0WZ46RJGPocnNnaelLDkXUrWfFBy+ZWHMhPfx+tfvMHSNx23JPhYA0PCckiflaNLRTcEdLC9no+xL13W8yB3t94apU2Ql33//gB+6TEOLY9qwT2qCtidOttx+umKyCLi8mHN/vxUCZFKMsswyq2iaDsuSzE/PlZtceNM9k3kgXPr7vVTDauQi8z5RtPRqDNFWLZPAxDKF19I0UlVcXssFFnimcrUONN3AiRjiR+44s9R1jaruFFxJowV1oUpFkpctc88m3pecr0SP7gcOhqmrUG0J6+i7Bk0tA6umZzazqPKaRteolVEq+WaNqWtqzmpxc7sQmJRrkR4LhiElyRtqlbhUqTl73ciD71gGVSMXpgYS5mxoxPuMcOrR93DYZTi2Qdf1PLzfkRYtN9cTCW13bTYfdpiGxmQREu8zOmW8OfGQQtXRJftUuPutmMPOL+fMXJe/+rvvCWeBPLSbhLOr2ei6jdOCuqyZBt44Tujajs02Fuyxkj/6U4uf3VzSdL3KZRW54qEs0dGwTVnQVm3LzWzC29cP9H3P9cUCQ5O9lq7rVMVeyIyNGp2pebpnmmJc6jruf3iWIPOJN1rqTVvef6vrWV7PeXm2oB8GLENgYIeu4+37Z/K4EHzEPCCay/vy+HbN/vnIZBHy8G6LH4gSxzDFP2G51igvPCUonXZsJ5Oj75rKfCWd22kc26txnVwGwpEJfVMCs5VoABixGJZtEk1cDruMrh9YnUXc3K7QNY2biTjJ86Tg8xfn/HVaEnUDN6/O+PDDmmgRctwko6EqnPpUZSM7H03jmDXEac0nr5YcUglocWyReYe+KYHitsQMnrpniQ5tmSzCf6vYzP72PYekkXwFXROPiVLLDIPEU/6+14/icNd1jevrKavrOcdNwps3wgo/VzQ927VZXYn2/epKgEun9t5yLV50A+lR0ossU3jiVVEzXwbjrLdtWtYKRtTUEpNluRZlKwHHTdUwnQY0XYdjmqwPCR+ehQO+mjni6NSlU8jLlrbLuXm1wvWdMR0pO+ZEi5DlaiKOw9Dnu7iga3umi1Dp4jVuP13hPx542BTUTY2uafzqlw/87A+vePHikngWyMJsGSp9v8H2fi+O1boVtG7bE4YOQeTJokjpe0cFkClaXVPX0Cydtw8Zzqbg9oXkpRqWwfM6w8zq0f58UDO8RDE8TFOqcsuUcI/4UBCEzphqYxc1ul6NI5thANv8CFza73MsQ2O3K9B16NOGoe+ZnU8pM7kk+n7Aj1zmU0faViVfy7OKY1qTFR2GIfyQouqkSmta6kr+Gfqe1czFj1weHySxpuk+LudAxlx9LwWEZcj3ZJs6cVwyO4skQNnUcHSDi5criqSkfLtjt83w0pKzFwuquuWTL6+o8oquacdUqaqoqVUFOjubUOU1pmlw9mJJW7c8P+6pVpHI9J6OtLXsaG4ul3imya/Xd/zp1694TBMcwySwLJ6OMWfzCbsko61b1vuYKPJZP+7RbzWqtmXp+6R1zcx1mbku+7JgWxSc+T6GLsiDcOKOaWJN33EWBORNo9zaPWld4RgGZ37Ac5bimCZO1/Gb9w+8/PySompYhD7P+5jpNKAbhH564gW9Xe/44uKMuK7YFwWOIeogXdexHHPUzM8nAcMnA0/vNhzWMTevzsawZ0uXQ7LrOhZL4fPsn2MEbqoplZbwXVpVATfNx12JbWpkhaCZy7oj27TcnPvjItbzBbttWh7JLuXwLqZS0YkD8B//i6/4crH4t5yv/+mnL9E18RC8DuS9+OOLS4qXt/yff/7X4mx1hXF1miaYtcD9/Fbcr+mxIPRMjmmD2WqcnUdMVxOOm5j9NqOoMrKywzblsz6ZBVxdLcjqmnfvnymSkkPSULc9/SDafNsyyBNxvRumBBP9vteP4nDXNE2ML5qG49tcKgPDyaU48V3KtsW0DL775l6WO3U7phxNliHhzGd7vx8B9p4n+uwiLSXNSLFK0rxVSSg66SEbiYN5UorVdyZMiad3G0xDI81lD/DiZoppmfzmt8/oqprwAlE7sIT9s/Bn6lLSaQbANgziuJRNuC9RX/vnI/NzucjKulPM9tPYIufbpzU3yxmmrmMbJvZiwUOaEoYeVd2Q7DP6vv+I/FXyzaZqJInKc0Qlk5QjryJPZCHk2gbxoaAfhPEiAb1yKE8jm1IdnoBiRevoOiq3UuL2TENYLadXo/jwp38/WbhBFrVBYFOWUlW3XU+a1UwUWGmykDnsqUJrm26c+R+ShraTKEEZp4i78xDXozRMNzS2j0eedyWLusOxDa7OQvV1at4/ZcLqUTbt0xxT02A+dVjdLJiEUrnnZYdlDqwmIedXF7iBw+PbtQSmDDBfRfIznnh0CrS2WErV3mZiLW/KZnzwDuuYvpUDPz3klHktY4HZRKSxms79MebiZsmHJMY2DFrlAg08h/d3a7Ghn03YPx+5Xs7ZqDd27nnj+38XH8fw67I8stU0ItshtO0xNvBQFGNVrmsaxuWCQ1agW5pSxegsfdG9v95uuL1csS8LFqFPq7hPWSH4DWMacLmaCSdmn5A1H7k3U9el7oWZoumiCOr7nrSqVBcgrmXLFqmfVTZ8+OF5FDLomsZul/DwlHKxlKV+33b0vchypQKXuDrfNTkmEqUZ+iZ6qYiPpk5WtNx8sqBtujFd7RQqsrqaU+YV6THnP/9PfsZ1FI18+NNZZGonWTC8vD3Htyx+tX7mw4cNedmKk1w5o9umoykbzm+XbD7sBCNgKDXTzCcIu/H3HTexpH29XPKLX9yNxVDfn/ZHErdpGFLpXywFzVFUHfukGVHdp6S4kxjh3/X6URzuJ/nUtpbxSl00ZEUj45TAJc5L2qbl6cOerGg5HkumU5eu6ZidCcI02WfkRTMu3ILQ4ZMvhGO9fz6OphrT1McfZLwTPe/ZzUKqefUB++XfCk/ixIy+vAgJpj7JXjC7ui4t4XvV9n7y1TVN/RGcX5cNuq9TVhJcXTUdu3WK71s4vsNxm+BHHnUtkDBDg6++Ph8j3GauK1IwTdggF4FY2H3LEllk10vIr1r2nqpYQ1UrI3ukk/GI7AEMZhOb512p5FjaaLOeRjbTVUT9cOBxLeMq19bV1xJzkKls3s+7cnR7dk2PZmiczR3afsA+aeV1jcUyGDG8x7gSKaojfKDt44HZaoJh6mN0YK+khut9KbsTpHI6ydSGYWA+dWhbpdhQeNg0b3Ecg+ncF6ZPXJCXLavziE8tUS+cQg3SXH7ubTuQZDXl98/8yZ9+jm3rfPb1Na+WCzzTxDIM/tnLWw4XZyRVxcPugO2Ij2D7sGe6jGifYx4eE0njsmQhZ3sWq/Mpm+cjtmMxKNb/xfkM27WIQo/9TlzBH9Y7/Mhj83RguorIi4rId1mGPpahc/Q/dkjBxOPpGAvptKpYej5F27DOMxauN8LFTMsgPma4S5OhHrhYTHl+v5VDzpA4v4nj8JxlTHx5rs6ikLJtCG2b32zWFFVD1XV0bUdvD2QKwXDcJAwKcasvIuK0wI/E+GQZBmldkTc1SVEyXU0os5LpPOSwS8gbSVGqcukuZewhbu/pXPIS+n7gzXePKhReI5iKdyI5ZLQqj/eYCYkx8HTFjJexY11LRe/YBtHUZX4+lTPhfs/j/WHkt/zRP31FaNm8/u4Dr/7gBTeTCbomnon/69evSQ45/8V/9HN085TO1XMeBPzi9Vu++26Da+tc3cz44uUl379/xrAk+nFxNaM5QQLrU8ZBTRB5TK7nguD45d3Y4abfr7lYuuRlp9g48n4/rw9jiHcw9TAMg6o+KOOahMbP+0GwxsrM+fteP4rD/dS+ieHFxI9ceRPyRkIWVmq+NXHRtBI/cLh8ecbMcXlz98xxIzFXXT8Q+Za0b77Dw/sNyVGIbXUhlWLQCtTrNC82TIPjNqFTuZ4fHrekuVi1PcfgcmXz+efXlG1Lldd8+YVo7LtWdKlt0/Hdrz7QDwOHWGSNj0+ifLGVtGkYIC1akrzhbO5S1R37QzkymqPAYrGccIzFlPTkuli6QWBZo3LCMQ3ypud2MeNQljR9z/VsIlCnzy7Y7xLiXcp2k+E6BlMF/Er2GcMwsLiYkhzycbwxUdr2s+s5Z6spuqYRzUPSv3ojEi/HYD51MB2T/S5nNvPGEURViXbZsETDe2LAu7b82jT1Ue749G6DYYhe2TJ1TMdgsgjlAjI+Mu41NcM3TZ22k0ByXYNTAz4A+2PFauWTJhWQCTqilgSorhVd9WmJ/vwYjwnzt58KK+ewEQfpPq5H5+o2yQhDB8+xiGx7bM91TWPheSw8j+so4jfbDY5hEAYuA/Dh/X6MdquUQ/rESjmpQZK9HGB379eC1TANDMvgqy9eyGKzazkg/PnVLGKf5twP4kBdzQWRUSiMwWEd8/LTCwxdZ51nXIYhpqbLbmi3xVKse2Ac52i6xtnNgvSQsQ19JgoqtvJ9irbFdSye44SfXV7w53/7DV3bsbyaM3EcenugUzua9JCzvJpx3CRMFhI1+GI5Y1sUPGYptgoLcUyDy0lEPww89D1Jko8ihq6V4PGTyzTZixTYUdC1tpIiJJz6GEY5dnLC6/cB6Rb7Hpp2IAgdFrbM1S0Vx1ikJbt1PMYq1k1P6JtE84DLlz5Lz+NufySIPF7NJPD9tOMwbZPdLievG97sD2zSjOe7LW3dkpeSYWoYgjdI6xrbE0e1psFhLc7iVnGK8lLgYW7gfEz5Qi6isu5pmhPrSB8Lpd0uJ00qzi6n6tJSy2DPUvgBXRaybU+2zpiENs3/Vyrkf4jXwMDzU8IktMeIuPNPVhgPB0HyOhbxXmD481XE7YszzvyAb9Yb7u/2zBRSoFcMCICulS18VfccNrFKy6nGy8PtB/Xh0SnzmqtX5yT7lB++25CV8jXmkcXyes4uyUYYWN8NwgqvGuqyIU0+LjVOwRq6LiOKsm5GTK6JqDniVGbcIJ1BN4jU8OFuO6KPn3eicmhcF6cVQ4qpCViqHwYc08Toe3zLZuI4pHVNETRCc8wqFSSdyvdcd8zmokk/AZUGYLIIWFzMCG1bYVwbDuuYr39+w/tvn0jyluX1nDD0GPonXF+0xBqwmjvox3ocwwyDGp8gS9FCsTA2D3uyrKZRZiTh1Oikhxw/chl6U71vIussqk6FGPQYhobvGlimJO90vVQuRdUxjcTx+ryr1EhHuqWi6phMXGzA9WzSx5Q4bbAfBYrmeDZXlzOidUyVS4BC27R4gcPd22deTKZ4phiDfvHbHyjzmvn5hJ/eXHE7mZI3NUvP5ynLVPEh6OQvvjyWAmUvAAAgAElEQVTnuEvH+WvXSrqUG0gOcNt01HFBXYg8dZ2k8p5ZBuFMLmGQQ8ZSyFxT03Fsm/MgZGPbaGczPplMuUtilp5PaNvYuvDdv1gsGIaBd8cjaauqwPsDs0XAq08v+fC4Jc5LAhW955mWSB/7HjuM+O12Q3zI+fIPb5k6Lp4l/+3N8xbDNJiuIpZ+gHYmrKV5IHP9ueviWzbd0PP6/SPXFwt0NO6etjze7Xn5k0uqomboB8VwqpgsI3GeR54YiZT1X5+oEWIqWOTt85HF2YRw5tO3PYap064Trj5ZMp+FTByHdZ7T1C333z/x/t1eSQglg9iydFYzh+kqkkV2XGDpBi+Xc757XFN3PfdJwpvdjniXslsnGIbGv/nzv6FQgDvXkQWvrSS5p2f83ZsncchXDWVRY1uGGKlsA9+2uF5NRsNU34vyZTbzmJ9P2D0cKMqWY1oT+RaZKv7KulejwZjZaoJuaNSlROtZScXmUKPr0pXPIlsAab/j0/j7Xj+Kw90wJOS2qls+/PCMbUlm4u1nF7x9fU+RCXTqkDas2o5He8/f3H/P7lgr/WvBbNJTVjLr1nUNTdd48dkF87SgU1jgrpUKy7QMvMjhcjllm2QEkx7Xtvjm3YfxMDY0WF3NOZvLkmeXZMTbVOa9gUvaD9THYkxM0oDQlzbcNHVMk3Fmn5WdLAN1jazoRp5GrySctqmPao3l9Zy6qFnNI/Km4d37Z/7s61c4psFFEPDhEOM6FreTKU3XkTcthq7z6WLOU5aSHkRidvZiKR/Ih71Yw1VARxDYnF9JPFzf91Rty24bi2Gm7ng58fjyD2/55pfvBfyklC2bp5jrT1fousblfMqH5x3v32yUFFWja2TUoxsagz4oH0KB55i4jhD/8qRkeT0fI/VOI6a+l99/0gzrumiUXUcemn4QPb5x8g1YOvfPOXXbY5syfz1JHPNMjYBMg1lksz1W7OKa1czheZ2x28ky8OJqgtv2XC1mxGHFN798z1/88lsc1yZLCg4H+dmutznrux1/9EefcR1FFG3LsSqxLUMWt7a05idznVSZMDubkOxlQW27Qg398K7k/HLKly9F8dIzEFcV11FEXIlUT9c0Zo7LY5bSI5mm58qk9C4+sj0kTByHY1lRjJx3h0apZOqywYs8oqnM5X3L4vxsRpyXVArj23QfDVDDMEim6sTlzPexdJn998PA9n7P51+/4BBnOKaBbRhUmsY+y0ktUfHsy1i46+fzkVSpaRpnl9NRd54lBUUqhMjjJmEyD8Z9Wd/3Qnr0bLJjju0IymB5PlFIX6mq0zhnOg+4PV/wFKckeUkeFzzeH9gnDV33sccbBjncJ8tItOtFzePdjuQgKWJ5ViunaUucykxblvYioTwxb4ZhoMirkeVzin00TEEXGKb4TlzPYLX0SdMKwzLwIlHMbe53TBYhx23K4kJGRcHUx/aUYMGQi2hQtMqsbHneVTheieuLW7VQihrH1tXeasDzLOX2/f8JOMxzZLEAooCxXZuH9xvKqqWsOiahrZaaBs/3e5L89E0LZ7lpOvKqw9A0bFuX5Y1p4s8nJFXFzDAoK1nSnuBUu1T0yJcvlnzzq/ejUUBv4WLpMpvLwq/pOuqiZreOydOS6UIAP43iyJ9MFoauYaq203WkZZzOA7K44GFT0HS9zFNTmWMahj4uI/thYL0v0bS9zOWzAs+1WV3O5fdqAneKLpzxz2o6mQuKfK1lv0vouo5PXp6zcD16ZBRhWAaRbWJapmRath22I7LSt989cnefEAUWoW9RFzWR73L96Yo8LrBdWxnFBmzHYhHK4bVYRGMwd67Y1INC4IrKQX14bZMsrbh4OZFLpqhHVZREKkouahTIR/Hly/kYT7iIAv7i//6GthW1hGXqXJ4HHA6FVPamMY6XJjOf/S5jogJKmkr2LxI83hGnQgjsBwkHj3eyP7nfSKLV6mLK/GzC9ulAliojl2p7y7pjXxaK6qjzm795J8iKm6kgM/YZWdFSNT1eKEvQ4yYhiMTEFc58bj6/FIu9Y/FuvSMIPaqqYR764xwdIKkqnrdHrldzNE1jmxdUnYRNg8j5fMvC1g0cw2CdZ/QMWLqOZ1mY1wvmrss+8rgMQ9K6xjUttlVC6tSc+T6aplE1Dbqm8e3Tmk+uzvAUdCxrGuGnDwMvvrzi+elAXTX8Or1jsoi4Wc54v97j2sJ8j/cpxkIjKypq5SK/vFqQ5JKslh+FI1O3PbrKGHi82xGqvFV90HF8h+yYizDCcUYi4sObtToUxcU+XUb88GENSDTl7lgplrsEc09Ci6oWtHTfi4Luy9sLHrWUixuZfZdFxWwharQsrZj9v8y9ya9teZqe9ay+X7s/3T3n3ogbTWZlZZXLhQSWGPIHwARmCCRLniBZzEwzSQlZeIIlpiUxAInOEkgwQzICISQoF3aZKmdFZGTc/p5293uvvmXw/fa6WZCZLlwe5JZCeXUyzj0n9l7rW9/v+973eWOHdlfIKbrriUIby7EkS0IB8nRDJ00rdkdpJkehhabrjGYheVLSqZ1A3/fURc3mQYLU21rMhWVeDaIBW6lsqrwiVY7waSy5vIFrUtYtD08ptplRNaKRP5myfvDlVE6+e0FqF2rn9qtevxHFHWS2VFZSyF1PyHJdK3PaE2lvv03lwztpW9uevu+wQrkYxqE8FOJJwGwUcv+4IT2oxU/s4Sqda9d2w3xyvIg5HjOSrBlSgiQ+q2W9OnB+NiavpesfTQJcRXI87nPR6iJuTl3X2O5LXMfAc82hkyjzCsuxuJj1bI+1QIuUuaJuOln+LULmocvDx608KNTR+iII6RCOd+yIAsLSDdDFDKFpGlXXktUV3765Gxgtp0i2j4c9eVJSVOqY6QnGYbyIKQuh6mmaxnQks9iTTVw3dK7mEw6hx9PthudfXPD+1YOYYhyL0LJZrfYkmRRM25QH2gmNULc9oWcSRC5B5NFUDYd1wsWLBfbJq+DZ5MmBxfVsGF3slgc2y+MQXtB9dS57EUOjqZWWue344oeXrB+E92J79qC9j2OJQ9xtCnZJ/ecwBdtjJbZ0hYVIVd7r9nGPaZtslwcJY25aRhOf0SyiKqTDK8qW+zdP5EnBfi1uwfPLkYo+LIbs2LxsVTEPFE2xoyoq1nclP/7x59iGzp98+448Kfnyt28o0gIrjijbhn0hYdZ11fDifIah6zwkCedBQEfPw3ZPdpRwjHY+RzM0DmXBMk2lG+8k1m9b5BzKks/HYlj7uN8z9XyeLaZscplbm7qMZtZ5hh+I4uLEhHdMA7PTJLz6doUfebiBw+P7FZtlQvfVOa5vs3ra8/LmDGYxjmmyetoPfKgkL2mUb6TMKvzIHWTELA94nszYk1YQ0fvVgXgWSbj5fothCSNmsohlFn4vQorN5gFHab5PmQcg407HQjWIPWnRYvQ9q3XGH//pa2aXUtjrqsE0DUzL5LhNOKRyP15cRANlcaI67Mf3K6bn4hytcjk9nibcVd3hNKK+63oZtRqGzosvz/AClw/f3ZMmIvN1ux7LkjhI1xeCZF1J0xFFLrudfKaeigetGyHdjkYeba32dZk0o9Nz8dKsVwm2Z2F71q+tqb8xxb3ve6KRT71OB0lQPAnQVRhukRZ0bU9V1sNmOs2lMH7x8pKkEvztsSwZuS5l07BWs9bZ5YTt04HRPELXNRbnY3qgKGueT8f8wz99TeiL/bqqOtXhiUJgOo+py4auk4QZyxbOuGlo5FlJELkYlki2Tg60rGhEGdKWygavoes609jmmMpRWtdlRp0VLcd9ztn1DH+TsNzkZMuc6b7gbBKzSTK6rmOf5jybjIgdh7Jph/SlfZrz828+DE/4vut5uF1TVDXbp4NEE7oCSMuVHC9PCuqyZnoxxrBMXN/m7dsNoWcq2t6eMPZJkxzd0Hi63zCahnx8tyaaBGgBQ9ceeKZyCUrwgB84EvTbtsOy9PzFXPTKyvjz6ps7Qt9i/mzKw9sl++Mn9ISpdPW+K9wQ2zKEIWTIwvz2UfjvQeSxWx3o0lJOQCrwvC6EwHkKY5AsWU0B5kQ257vmQOvTTYOzOCKdZSS7jGPasD0eOeyEKhjEHuHYIEsKvvv2kb7vmY0koPjz8YT1ecbqkIgk8vUSL3Sgl1Qx+p7J+YjpKOT1+wfcwGF6PqadiZkqGEmCkqXr+K6LYxocs4JDKR3ZdSTBEMtMivLjveAuvo0eeT6f8HG752Y6pmylazyUJRulQsmDBh2NF+MJddeq4A6fsmkxdFnErncJZ9OYqeeyznIhRpoWrdbhmAZnFxPJai0bvvita775k/fslgcWz6aDcqcHYtshnoVsVYE/fc6gYHSlJIBZXc/8aiJy4aJSY1JZNC9vRRRx/nzGIgj4+dt7iqyCQ8bDKldqmE9L4xNSpChFqjoKbcpK5LqGhqJQ6mj6J1GB48lStO8FMT6JbHUv6oRjn83Djs2DXNeWyvLt2h7TNnFaQYs3nTScmq5R5hWe73DMUixTdjqmrqtM35bdJmW8iNSCVOf9G5FSzkYOnmcpdK9GmtcEnsX2UBL6JjefzbFci67tRFnXdXRZz+puM2T1yu/4lyzumqbdAP8FcIGIF/6g7/v/VNO0KfDfAp8hUXv/Rt/3W/U9/z7w14EW+Jt93/9Pv+5nnLqp0TySBUQillxN1xjHwo5+mz/huCaOZ/F0t8My5WmXHnLyuh7CgR3DoGxb1mtRSwjPWdyMnnLiWYZB0cgN8Wevb8mzcpi1C39ETDCjeUSaFhINlgnVMAi9wTbftC1N2aAZIs06WaXlohb9d6u0ubLlF2hVrbb4wpIRWP+7b2+xXVs0tI1Aqr799gMXL+aYlsXE9RTaVVyIP/3+loe73SBZPHWKfd9L+kwpubGebwvNMKkU9z4jOxZYlkGlch472+TFC+luDruMQ1Lx+HGtjDlCnrt7K1Kwpw9rto5gCEYji8WzKbevHimUzvz8+YyHdyvohUPi+A6x74pG+JCrRCmJSnz/6omm6dgeRWmgay3j2Cb0LcKxr/YbqWJi99xchiw3Ocdjwe/+6AX/9zGnbzuapsXVNdarVDjp7acIspM13XdNbFMb3l9NgzRvKfcl4WiF49kctymOrWO0cvPvk4rlVlDMtiUjodOC8eT2vQgkBLpuO7zAYbc8sL7fMbuaMBkFpEXFIZOl+/Zxz5c/uCara5K0YDYKaTspGHfHI5+PJxJW0sqYK61r2r6j7Tq2T5/UPw/vljRVw2eXc948rrieTyjbFs80B0TwKUDbtywCyyKpKkxd4y458vV0xq4sCCOPievxarvllNp1KEtuYmGzz3yftutI7Iq66/i933/J7d1awpknIY5pUquHuKFoj3XZyGlXPWzTQz6ExDuujRs4JPuM5CCS1xPSdjyPcAOH7eOexJFFaTjyGc8iNP2J41H2RjIGFWOaZWh0fc8ostA08WN4jsH5eYjrCZjM8YQOexJUaK7FcZtiW4LREN6RjHjDccD9x63c+7OQIi+5f0zUYl/Csg1DV6o9bXjQzMeuaPLbnkOWUWTl0Lnruk5ykP9ezzG4uJJR3uFQoCF+k2ksUY3trpDIQoVk2K2Ow0hG1zWelin7vTB33v78iZvPZr++dv/TYvY0TbsELvu+/0eapkXAPwT+NeDfBjZ93/8dTdP+PWDS9/3f0jTtR8B/DfyLSEj23we+/nVZqtHLWf/ib/41gTapX+dUcM8uRlw/m7PaHSXpJ3S5ffPJYGRZwkY+u5H/0Id3KyaLmDwtiMbBYDE/rBPOns+HLXfbtAShx7d/8n7QVOvqAun7ntC3ePaFio075sSTAMd3GAUe3393K526oiG6Skcbxy55VpIXLZalE6hRk7gjhYvRNN2AALVMnTiwKOtOgc8Um17TqOqWqpEormjkEUSeQuyKi3P1eBDXrEqWOUXSHXb5wFTxQmGUF2k5mIiyQh54Z3N/CMTQDTFlHI4lYxXK29QtTw9CfXz5xZyqqMUR2veDK9ayTXTT4LA+stvlA2o1y1tsW+erH18TOQ4PT1t+9u0jui7O0HHs0LZS1JOsGdyEICeB083049/7jLuPK7JjwXJbcDZ1mV1NSPcqm1ZpftdPR0YTn7aRS2y7zTmmEqtmGBrj0GI0i1g/HXCdU8fXDMEXo4nP+fWMn/7xu6EhOCEUBDsr10QUWHz19WIAy312faboi2LBjx1HVE1VKcqmrhOOPWBZojP3TFlEyknTpmwbFn7AMku5u1tzfjHhmBXURc3LqwXrPGf9tOfhbsdo5PL119e8fvPAZp0QBjZffP2MsyDk7W7L6m6L69tMZzFXUfznZvVt1wmbvesZuy6WrnOXHEWVo7AFp92Nob4GkFSVzNbLkrrrBPzVdjS1uMQDRSMt84oqr7E9yd4t84r0kLF8PNJ1PZOJh2mZ1JUQGi3HIhr72K5N27Tcv1/j2BLgLrxyh/n5GEPTaPued9/dsdwWA0pA1zVCzxxGM64tZNWuh9AzcR0DP/aolLEMxNfiq2WnpjEgwwNPTFW7nbCMPEdgZ6N5xOphR930w/3a9z3zcwnk0TSN7JjL2OpRlueT2KHrOo6pyHJnM39Y4o/HHtHYx7AMirRkszySFy2OrTOZhWzWCbp6YGgKC9z3cnJ4eEoH2JjnyDJ/Gjt8/x/+/X/2mL2+7++Be/Xno6Zp3wDPgH8VCc4G+M+B/xX4W+rr/03f9yXwRtO075FC/3/8qp/RteK4bOqWx4eDKnYicevudpLaE7oct+mgLT1p0bWmo7d1VrebodimhwxN1zEsg8lZTLoXXvZ+dUTTYDSPybJclorqQtF6KdJV3VI3/SDVKotqwP86vkPbdYQjn4fbLbapY1oaXuhS1anMGzMZyYSBLdRJTcMLben+leHl8iJi+ZQo9CySwHLIh5FNr4ukUHgSLX7Tsd8IzXK7yTi/HBGPZZZdV1Kk6kokfW2XYfQ9jm2yWUtsmGmbGIYUUN+Vorrb5Vxdi3LFtE0m5yPcoODx/kCRVTz74pxgnxGGjuLwGDiehFXPVOqOBrRKVWEaOnHksNkWhL7JxQsBUv3hH/98SEU6wdwc31H8lYJGMUF6IA5MsZe3PYaBuCxnkQDePEtQqg87iqyiXAkX23NNxtMAx5NZ/uPt9hev3QE7kCWFynAVfswp4q2jJzvmHHcpo5HL9uNhGCedCruui5PQtfXB37C+2/Inm9cAXHy2YBGFkmSUF8w8n7MgoFTQtKSq2OQZpmFIXqkh8tZDWQ4c9cCymJ+PSQsZnc1jUcgA5GlB6Fuc3cy4CiPCH9h8f/dEss845iW+ZfEsjqUz1TQ85Y3QNW0I4Gi7jqtIjvRN1/HhsOdYyvdWXYtjSBduqNxVHbl2A8viviwJbYdjJWoQU+nLT/eVbZq0VkenPsvlrey6UhXIXtcdu11OHLuMpiFuMJV7eZ/Tro5DhoKmaXiexWgWUpcNSZIPubmzqwl1vaJqOoqy/YXPV+pEoTKJq6bnkMoy/UJlI2cK+PX8q0tcx+L+w4qu62maXmG6S+y0Vo2XuNctR+BcQeSyWor01VOjoK4VH4OmaQM7ByAOLObP5PR1aZu8+kZGS4YhMLs8KwlHHlVeo+s6QejQdXKq67puyFCencVMz0a0Xcfj+5XUiLEjWcBtRxw5PK1z7pafnOK/7PX/a+auadpnwF8F/hA4V4Wfvu/vNU07U//aM+D//IVv+6i+9v/+u/4G8DcA7Kk3OBVBkKw9AqQq647jLmO/TVVXjTLhmMMc9fSPSPHEDOWoRZvjOxJZNpZczIvzCbtENvPJLmM08dmsU0wVGBKqIIFg5KmlkHSowom2BdvbSgee5g2+azKyTGZnI9JDRoyMAnRDH0J942nI+1dLNA0iXy64Wp0Q1vty6BRPy8NcJahb6kKLxtJJF1mJaxvqhNMPN1mVV3KSKGtMQ8fzpfOxXIvVMsGuO8JIlqa2ZzN/NuX2zZKnhz22qRPPJA/0/PmcwyZlNAvl/TQNvMBhcT5m+biThJ66JZrkmJaJbsipIMlqrl9MCccBi+sW37XZrA98/83DMHIxDQ297sCT0ZrjWez3xYBudmydrhMmfqtCwv/wj36O78uS/LDLeXhMhAaqfxp/aZpGesjYrIW10/fyd5w4NK4tY6+H9eEX5vDy806ntKrqsNZHoklAuM5Eb9+hRjuflu9xKGO9pmoGuVtV1vyTf/wBx5I9wcWLOfU4Yub5eKY5oCoOlYw7ntKEom44C6Qo2IbJvizwVaRe3ld0LYN5bfmwZb8v+L3f/5zfv7ik7jrWeUZ6yPnyiysAtkXBRRgSRaIQi22HrK643e55NhGD2quPTzRXPW3foaOxLwosFX4NcKxKikbcqbHtCKjLsnAMk/MgZJvn5EVFFHqYuk5SlOyWDX7osn7aqyxgne3Tge02p257fNdA1wVtMT0f8fxixsP+yPJ2M2TUnnYfVd1BI5LholgTRC7HrXgHQBadTdvTqOX1CUmhaQz/nNyhp6Sj5FhydjVm9bCjKCWIwz4Txd1meaRRP3sa27RdPyzyy7qjqgvCHkkzU8Egp9CSU3B1lhTYjsgSLy4iDEMXJ2+PNFrKIS6+FhEbbB73hMrMdUosazuJCNU0jRdfnuH6gonoNO3TqToR0u3iLELTIPRqAtfg15X3v3Bx1zQtBP474N/t+/5wuuB/2b/6S772/5n99H3/B8AfALjXo94wZGY3m2nUT+mngGPV7XXtJ3Y7iEmlqUTh4vg2ZVZhK9OBuNsalYqSkicFN19fEgbucEyNXZdluhFKXn0aVyQ8/+qS62dzPnxcDnjSvpdO3tR10lrkXqPIYX8ssUwJSy4zMa14oQT9uoErxgnbJN3nqsOR3MSqFMebpgnzeb2XnEbbNgBZHjaNyCMd2xg27H7k4UeuJMjsZVO/OI+xbJPl44FaRY+ZuZhvbNeiLmoc3xEDxeNeClXfM5mFOCqaL3QdjpEcYS0V2p0qmmW6z6jKWoh1nk3XtNy+WXL+bMLZYsz96yeyQkLGT5bxD28e2e1yWqXjF5Y1g8nMdm3WDzuKqh2OmIFncjYPBIHa9USn3YVSObiOiWMbQ9yg+s+gzUSO2jSdjI7qlqqSDtJ3DQJPmgBTLVWLqmUxcSkqAcDFgeSopvcJf/XZlIuLiMMuZzwVoqFhGty/eRIE7zhgszwMlvlg5A8RhFXTk+9L9n92zyv9gavrMT/8/ApbwcBuYunE1ruEZ/MJlmEw83zSWlhED8kRyxCTjaHpbIucru/58H6Lbel8/+1H3r195F/40UtefXxiu0lZL+TavjifEFo2iVUxcT1C2+bDYU/kuzwmCaZh8OJqwdRzSZUEclvkXAQilRy5LrEjihvHMIkdh8c0IW8aYsfBMcScE3oOsePgmhYj12UWBKR1xfvXjxR5yfnNnNwveD72RV02j5SOXkabP929x3YtZpcTVndb6lISmHLlAzmNJNq2pSglSH0c2+RFQ3+/k2WpoaGpwJS2lRxZX8kYgQFy59o6u6Tm1ffCoR+FNg93O/zYU0Hdcqq6uoxwPYeHux3zqcfDUk67RdXiuQ19pzM9H8k+qxP102geCUCu6ejtnuX9Tpg9hj7o9nMEU+K7pqhlcgnjmS9Cylyk0Ela0bY9T5sCXdeIQ0vi/XyHppPr3nEl07ffFeg6g/S6aTsWZ9FfnueuaZqFFPb/su/7/159+VHTtEvVtV8CT+rrH4GbX/j2a+Du1//9ohzo+55O3aQnPWhdNUSTUG7mrqcqam4/bNkp5vLiXBZ+p+76lF8ajQOaqqFIS7zQxbMsWbRmKXVRU/ui9ZajpWiy+x4C16btOhzV/VZFTVVUzOcj7u83g5swnoWSUlQ1vPvugXjsqa7BYL9N0dYJlilW+65tPrHFHZF5mbYpoCnL4Hxhs9nkVCpEAlAXmChvetVRRJOAMqsGre5p7rd+OgykPNcxlKRTYzQKWN5uaI85k7NYFsT7jLZpmZzJET075pR5Rdu03H3YEoU2+9URwzRYr1LpuMtmkH46nsXsLMb2bB4et6x2wqLJjmK9r5tOGPL1ibGBcvcJc72qO3UDaJ/+6WEaO9y8POf+w4rdJsV1TCULa9kfK+nsFY/dd81PsDGVjnTqqsv61L1pvyBvFbPI6XcxLYMmkxHYal8NBMn3P3/k5oszzq5n7FdHPN/Btyyuf/8rirrh56/vWFyOJUkocAeH82zkkGQ1fmQRxD7TC1ma/fTncutNzmJuJmNCy+Z3bi6HsU/VtUN3X3cdM0cMRklbkVQV50HAlz+8ECTFwwHtWPKz2wf6rifwLK7GMU+WzPrbviOrJehjuUsVDsCkbFuKomLkuDSqANZty1kQoGka8+CTvt7QdXQNLgOfZZbylCZEtryved0MiOHAsuh6E8cwZQm7yMiTAlPXubya4ZkWd2tJnuo6OfmEYwmCub/d8fr1Ru4FNcM+hZr7rkHgmoqB3lPWYv1vmk7CMCILV+1amqbFtQ2SrFYnq16xoCTj+PZ2T5o3g1v1mNW4tsHT+9XgyQg8EQYUuTRXeV4T+iZhYOOpz/hUV3Rd55hUWKZGmUlMX3IsBtNcWXfkiZipLFPn/NxiPHKpSjnl1XVLOJPmp6lb0qQkilzCkUdRLckK0co/fNwyU8TMpmyIZyHHbU5RSahInhTopoFp6ANf/le9/iJqGQ34z4Bv+r7/u7/wf/2PwL8F/B31v//DL3z9v9I07e8iC9WvgH/w635G3wu/ous6mqphej7C9mx8z6GsBL7luzaWYbDeHYW73Ql1zQ1c6qImPQhf3fFtxotYtOfoGIbO5GzEzPf55s0dfuxxeTbhYb3n6mrGLvKw3AOGYXDzfIFlGGzTDMOQXMnd8SDcdk3CnZ8+7gkDe9hig+Bis2OB49n0nWhT47E/0Nv6XjrWroO2y9GAPq3QUOHaeoPnGujV6e+TAlwmzoAAACAASURBVO/aDLuH8VRuxq7rcD1n4PAUWYnrmLiOSVk1ip6ncf92yc1XF7Kr2Oc8vl9x+fmZWiZp5ElBU7eKfwLxLGI2Dwcn3vrpiGMLG9sydQJPzEinzuGEF766jEkPOcttqZABJxZ8x1dfn3P3YUNetnSdxiE9BTDLA7VX4CfflTnu/YcVeVJICHjZkOQNrm0wn/ukScntQ4ZtS+jx6Th/mruWlZADzxfS/a9UQtRpfFNUAjpLsnowzHVq9BJ4Evpx6uZ8y+IxL7l/vxyAYW3TyqgvLXl6TOSo7pjE0wld22NYIs/cLg/UlaCIT6O5eRiyTFMOZknRNHw9leV/2/Wgd1yEYo7q6LkrCo55wSwIOJSlQiMXeK7c0G+/fyIObdxAum1d08QZalrMfZ99UfC03BGMfDzLItmljKcRU89lmWU0XUfVttzEIwxd41iWWLpEAsa2Q9V2/MlySat2A1XXss5ybldbirTAjzy+ujgjqSru1lscz+b5YopzYeJaJjoaD2mCH7rQi2eja3vaumGzVCMLx+DsPJKcg2PBeldgtD1nc9mdrJ6OQ7RekklsXuhbGJbEJILIF7tOYjUnZyMebrckWcPVRUiyz5RzmmGJD6JWq+qWsurwXAlNb9uO9FgwjiWMpG5asqwemqeuU3gEQx9C1B3fwagawqilKoRTlGSN4Hk7mMYGZVapDrun73qVKif3rBc4tHXDep2J6umzKa9erek6UWk93O3xXVloF1mF5VroekGrlvxZIZGQdf0rNSrAX6xz/5eBfxP4U03T/rH62n+AFPW/p2naXwfeA/+6KmQ/1TTt7wF/BjTAv/PrlDLqeyhSybjM85qvzyYcy5K8kGoX+zJOEZzokbbrubyeDEud8UJYDJvHPaZpkOxS3MDFi2TuXFcNWVWLzl0THnZbN6zWB9YPO+qm4/L5jO0hxVXLviItmcQBwUiCJk5xe13XcziW3MwiLp7NKCpxvR63qXDklQ0fJE8yTasBTmRZshewLX1g4JiGRqYAYnLER6XIaBj9CXMK+61Y9ptGAiJOY4m+Z1gQnt7LExFyebvh4sWCKr8ny2ruXj1iuRKGoesa8SxUPBuoS+lkD7uMaORx/mw8UDDrplcMcJGEWsqa/ahi1CaLGNcvqEvh0dRFTdu2XF1Mebrb0TQnrretELo9Tyt5qIxPHXdeUUryIZZlMLsYcft+I4EhZUOaN1ydeThKqnoq7G3XCwqg6ah62KfSoTnqd+w5waHkD3XTDylXJ83x5lBxNXcJYp/3r57IzwuuX5zx8Z0cRm/fPBGPA3RD47DLmc38gVXz9GFNVsjvZ64zdE2jqFpOHPK2bvmzfc6zz8/YJhmH9ZE0LViMI66jWJamekepFCmmrvNsLHNyo9EYL2IcV1DRz7++4uHDioeHIwvL5O545CqSk2vT9UMSkh95TD1fdhOLMavtkZ+VNYFrkxYVgSsh203TsVNxfV3fc6hKYlt2M5qmMXE92q7n6XDk9s1S0B7LhPXdlt/73ZdMRyFzX9yeQoasRPveyy4DTULcp+dj7t8u2Sc1l+chn31xwd3HFaZlYjomZwsZf/W96MoX54JF3qwz2lbCNDbrBK8/zddF7irIip6m3WGb4iPRdI3p+YhoEvD69QbXlji/SqFHqlp11s8m1FUjEZ6GuGQ1TcOqZV+naYLSaBS6ZLNMiEYu8TTEC1yWt3KK//y3rnn3szvKumM+kTl7oMQUVVGrpk7eD/HL5Oz3BbN5wFXs0bU9v/vlc7arI03bc0yl+XADBy8QgCLANBau0om/JNf/XzKJqe/7/51fPkcH+Fd+xff8beBv/9P+7tPrBNQyzUZ44UlG6Dnc3u8A8K9tjrkUT9OSUczmcc/Fiznzqwl5WjKdxfQ9A6mRXmKxRqOA0mo4VIIbHQUe20PK/cctVzdTMeAYGuv7HVnR8OWPrijSUpCtfc9ZHLE8JpRZSdfKcbgoGx4+btg87gXWNY8EaWDo2O7pYVWS5qekGxViYGhsnw6C1HU0FUHH0FmcRkNF1aJrskzVdW1g0Ti+Q58WAyK3qRol96oII4dWJUNZtsl6lfDwJCwNyxWomunIkfDy8zM+fv/AcZvihXLy0XSN0VRmibttRpHKTTqehUOSfdudEAM9ZVaphBlhh/Rth+UIoa9rWo7bhIenLVkhHU1dd2gFaq/wyU2oaQxH1ZOaZv5sShi4JPuc7TYjyWp6pMu2XZn7C1VSTnCif9bQdXA0HUuhhyWkox/m4pYnHeAotHhcF0SBxWpX4rsGP/zxC/7oD1/RdD1v3mzo2o6vvnzGd999lEByZSq5+mw+MFBuP2wGIqQsa3XKumUSWeRly8VZIA/XUsYlt2+eVIoOWI6JZ5oqANshUHr0xzRhXxQYus656t5r2yQcB1yEIfmsJBz5XM0nOKbBMstY+P6gitGAsyjE1HVcFbu3swwiT4qXYeqEthT3ROF886ZWmaoS2C1L1o627yiqWq59VVhFglxzvz/y+WxKZMsieJmlHAp5wPd9zyjy0UJtCDRxfZtxJL6I7S7huMskn/YspiwqOR0bGpX682nc0nQ9T49i3uos2YGhSeyeZcl1dcwkOHsUiYHOdgUhcD7zSPMay9Q5purvM3pOaWFlXtFUsps76ept18K0xD+j65owXtICy9QYzeRBmu4zgtjjbDZin+Wc3cw4u0GZknQ+vlvLCdA1BY1SSRZFltXY6qT54eOeq4uIrmn5oz/9nngcKAWZioxsOhzPZjGOWB0SxW4vVCi8Phifft3rN8KhqmnCiNE0yIuWD98/EEQueVIyuxhhGQal0XDYJBimQbLL2Bwq8u8eubqeEI59skIyUetCQiuyYyFP5qSQzl7NylrPoakaRmOPwzZlfj6iKmtc32Zum2LLbzqMUMxQmqYReg6tAkplRYOuSfzfifwGUGYltmcPXHdd8c9PLsjLyymrzYGm/TRvtkyDvGyGp7NqdrBtnawU7G0cuyo1XkBFbdtKcW8k2k3TNILYJxyLbt20TfKkIAptXLuhbzuKUuiUWi7vzXf/5KPIPssGTdcxHZMiq/BC6d6bpiPvZRlUpCWOZ9MAQeioi1/GQU3VkBxqDDXS0DWN7TZnMvGYXU0kRUoX3G/fdPSmzHR1XR9OHaZpyO9gy3syORsRBC66pjG7HHM4iFzSNiVj1w00TMse2OBB5DJWc9EiLWm7njD2yPIWqxMJmmXpWIbku1qGnGri0OJhVdDXHbkGr7+/ZzpxqauG2cWYwybh/cclP/z6hqpreXjcEs9C8qMsv9KDsD+6TuSWgWfyL/21r/if/5dvhBPimSyeTbkIQzJHlqY3L8958+2tylyNOVYVq82BeBQQWvYgSbyKIh7TlHf7PZvVgbZpObuYoKMN+NxDVRJiyzinlzm6rgnjZey6vH1aYzuSFPTjywt+tpaRl27otK7HKkuZeb5S6liCHdB0kkYK/q4oCCybu9WW4y7Dd00xlkWeulZ7iqYekswengSdEcQeri2M96yssByJ5mvbjvGsJ91nIi+2DMy+5/52pxAjHZOpP+yRHNtQaAtNXSdiCLR1nU75GVxbTnFGL6c4y7XYLY84vs1mnUrA/ExGek3XMw5N2bkYGrvlAT/yZLfnWLiBCCF2ywNFJs3ZaRw3u5goemxJ14jJMhj5lE0zYJ31IadXivohES2/7Vq0Ks94cTGS8c6klRFV12EoGNn58zlvvrklDm12h4okKXEPGZYjv/Nxn3NQXX1RtVSaRt/Xv7auGj/5yU/+OZbpf7bXf/Sf/Mc/sX/3gij2cF1TApl1jbJsRO0ROZR5zdPDAVc9req6pR5i03RJnFkdqRUcbLPJ8JSh4nIqR1NN13k+nRAFLpNJRE3H+dkYTB16hhsXIBz5OIYxHDlNXedwyMjzCkPXVIizRMQl+4xDUospyJJMSM3QmV9N0HSNtunYrPYctqnQ5CwDTdeolL3eUGRJXZeL+qS9z4qWKHLwfIdklyqKYq/QvZL2bjtiY85Vp13m9TCH1tXPblrZT+R5xTGpGMcOpiGUuTStaOsOyzYEG9B0nF1NsBU10gtdykLwxnle0zcd2bFgvc0ZjX3Ksv5zv6+mweXNlPt3a6JxwLPP5kSugaHL/NFzxQPQNy2uY8ocVVV6UToJOK4HPNui61vyTApO1/VYukakuhxdE4TDCVdBD4ekpiwafvRXnzOdBhRpTlnJ2MqxdZGl9QDaoOLpO6jqhih0SLN6kLJqusb+mOH6DlfTEVktnVXbtOx3uTzQ1AgsyRvSfUIcWESBzXgq6Tuu72BoOm/fPrK63/Lbv/MZk3kkMKqiIg49Hu83XM7HLI8pRVvjWha2YaCjsTuknF+IA7VV4yjdMrhQc/y5H8hDQhVrwzR43B8xDHl4G4bOtpQA7+vZGM3QGbny8KzajrQSDXtkO8KpX+/wXYfIcSjahnevHrAck/Eipm1kydg2LXHo8d3Pb0nKCtezGUU+valBD4FS2BzyAnrBbwtbRbhMp5Oa49lEI4+zZ1PaqmY0FQmu5VjEk4DdNmMy8YbZsueatGrJXtbt4FUIfUtOva2oqPYHcbNWTUeWN4MccTb1qeuWspR7Yq4UZYvZiKYXq392yOnajsnZCMPUMUyDPC3IjjldK14Z0zaFO7RJKLNK/Cy6TpYU7DYJhq7j+xazizGu75Adcly1f7NsA5CHwYmx1Pc9j4ordZJyZmVLWzVUuTSt8SQQ5lVaUdUd52chRVGT/G/v7n/yk5/8wS+rq78Rnbuua8wXoZDg7g5cXY9UeK4xqDmSvXQPru9QIJiAtmxFK52UjMYe5zczsmPO4+1Ojt8zUYfcrbdczEaDM/C7V7cSkzUJWG0OWI412KL3qyORokG2fc/qcadCnXuhKqoZnige4LjLiKcBmpaySySGTtc1nn22wLEtHIUyLbJqCCco85qj4sDblihnNJUY5DqyqGu7GsfSuX9ImOUyu/M1OeYapo4XOBRZyWGTMF5Eg2X5xEY3Lbm5i6rl/HLEmzcS8Ow5xoBb1TTVnVft8FA8kf1aFSg9vRiT7DJFV5SFTtt2uI6htP/mMPfvOlGt+JGHoQv7W5yKOXHkcPPZnO3TnrqQAmop52BdNMOyuFbHeE/lQ44XMQcVZKxpEiZ892GD55hqOdYOOnrXMfjh7zzj7XcP5EnBfBrTPJ/x7TcPYnxTiUxxYLFLaiaRzbIpBw69vCfyQASolGTNtAwyX0aCk1GAMYmYX0zEVr7PsGyT7erI7GzEdikMI8sxef9qyWQRY+k6hilQKMcQmmNa1xyKFN3QuL5eSNe9zzg7H5NUFfssZxJIuPtmc5TF3sJi5vvUbcuHzY6b6XhQAs38gLJthFPjOTKLrpvB6/HD84UiTLa0XcfYdcnVLuTpIGA6U9e5mIzQNTiUJZ5pyshhErPcSeEbL2KqEm4/rBjNxWC2O6TYjsVZJEiCVw9LgtDDtAzuP6yxXIt4GnL3dsloEiiG/SeA10lVtVsdRfbX96QHOdEu1xmubTAayVIzTwqOWTOEnTsqMKVqOkIV2lGUB1n6N5+Is63af3SdKHPi0OLxdjMU3SD0JP7PFySB5VhYjikgPV2XUdH6SFV34oFRo9GTACFPCwzTUEajHr3S8JMCL3SxPcmp8HyH5JjjhdbQ6ZvKMf+LjtrjocAyNDxXFsj3b5fi90lriqrlbOpy+dmCzT9692vr6m9Ecfc8my+/uOJxs0e7PxBPI+5eP5KXLTcvF8zG4uA8JBVVvVMcEjhbBPzeb3/Ovih4/f09u+WBspA3anY1EcOFZYq7zDTxTIttnpMdCx43BT+eRQO/QdM1XM9RIcdy5M+KnN3qKDAo9RJXWihP86Lh4mqMpoh3oSdH0LoUSVRo2VRGizaW0cxueSDZZQPq11bWfy9webvOBIPQ9Wh9j+fbkAkqNC+liEqeZE+VS4RXU7fcK9uzH7nDTXLK8UTTuPxswYdXT9RNp7TzoOtynDuZNgB2Sc16vyXwTKLIoaplrn37Zkk08vAjnceHg8wepz67XU6SlNKFq0XlKHLQDF0SbNqeh4cjlims/vYop4YolGzP9JjTd7KoNU2DMq9k9q+JpdtyJDDisE5EP9y36oEhJ6cTPsC2DEyzHzwQVS4z7T/8w/d8/dmI+bMpuq6xO9bDLP4UI1jWHaPIomnEwPLmw4EffjWjaVrxNzgypnMDl3HgcXu3pvTtoYszTJ3PPr+QBKeXGuss4+l+x3GT8LDKiAIL1zRJK5nF/vivfD6kaM08TwqcYZDV9SA5fHrcYXvWECBtmAaFypVdrQ881FtM5UVwDHmPfMvGMiQUm65j7vvcH48kuwzHs3EUDXV5TFjfbykWJaY6MZ2Y6998vOfmbEpWVwOnydR1otBjXwhfaXo5JjuIZyMYeXx+Piera2rb5vZ2hWtbeF3HOA5wTFMcuGqB79+4TBQ/5rSbObmjTwjuXgG2tmtZts8XIdtNNjhcT+Yzy9Co1Tz+hNvW5OYExB/SddI0uraBYWiUWcP2UEoEp2soXb2EvJwMkNEklBGQJd4SL5Cwj+MmHa4rOT1IUQ9if2hGdEOnbVq5B1Tz1NSipgFUGHzHYX0UKq2u9myWQd9JKtlpFGSYMvLtWsESp0VDWcqkwnPkAbJbHhjHzqA//2Wv34jiDvKBBKGHZencvRYQ1fMvFsSRz3K1Z7tJGUWitlicR0zPJUT6lC6znoUCHcpqLq8n2I7F4/1GdOfrI+5vWXz/8RHTNllcTej6jZIYaYQj+ZCCkU+6z/Bjb5ilzy/HktW5ExrhyW6cJkJ89GPpUL6MPI7bZDAo3L1+ov+s53wco9sa22wv/OhpIKOUsuawy/B8m7dvRQZlGbqYcOoOz5FFb7QIBc/gWqT7XF24kiJ1eT1D0ySDtipronFAshd5Vdf1uL7N+l5MMKFncsjk6wNStxfdeVq0Q7C12KSrIazAC10Ou4zJPBS2BpCl5WAWsV2Zf8fjgPnVBEvXSdKC3TZT6e4ds5GjCmzF/j7hB7FPGPsDyTJXel1NF3XFCRXxcLcnyRqiQBZTJwdiHLscjyVN03NxFQ8u1aKoWd0LTC0KLN4/pDyuhf1umUKbPC2pQ9/CqmQ0FIx8vv9+Rd32jBcxj+9XROMAP/J49+0tx20iRqm6we4tNsuE0UQydVe+w0Ypd8ZxwJc/vmZ5u+HlyxmmZTB1PRzDxDqXIr5fHQlGPj99914t4KXw/uztvdKDy2jFiuR9LLKS1TIhCmxMy6RtPwnPHpIjY/dTSHLZSMEygLMgwL4xCCybQ1WS1vWAhJaYyIyyqPkrv/0Z26IgyyU+bub75HXD249P/PbnzzgmogT74uUluzxn7HnEtqR/nRa/z+KY0Lb5uN6RGRWOYzGxJGhk9pXH3WbP7ZsnuqZlNI+IXZcmlvDqZJfiBQ7HXTYQS3UdWTAWFeOJJ0A4VfzrRjwMo8gS46GS5lqmnI7KQgHy1H1SNR2BKU7YILTEFKgQvQIL7JiORALcdB15Kvf96dR//2EziBrCkaikZEEqeIK+7zkeCoUssEjzGk9lORimTrLPVS6sTqFQyJqmsT/kzOayM8vScsiy8COpJ0XW0taCQG8a2RuJCEPqwqtXK86mn4LSf9nrN6K49wjF7uHDiqyQYAWAzf2OZJdRZuXAahmHLqNZxMzzWKYp3282uKaMcMJxQFnthYpY1Fi2yeXllMvLKbqmcf9xy9nFCDdw+O3f/YzNXuzNXdfTtd3QsWuahh95IrvStaFLk5vRFmNQ1zOZBkNXZDsG3sWE1dOeumyoagly3nmFSurRiWK5ER1fJFsLz6bIShYzn+U6w1BUSc+RZRJAsxez1vFQqMBoUWY83kuYuBd5OK5FepATyX6bUdUdZ+cRy8fjEHkHYCuSJsgy0zK0ITxa02RplZeyN8iLirbvh4tp+XjEd03R2eYtcfhplKXrsqzu+x5T1xlHPulZxDHd0PdyKjib+1zfiHPx8nLKu7ePuL6NaZvYCvFwwuQe9zm7YzW4kY9pw3RkDzfrap3RI8qQ064BpCCcggxM01BEx165nLXhZFSUDX7gEPtis8+SgulIOufDJqHverJjwfZxzzFtME3JBGjbjts3K0FCdx1nNzP26yP7XU4YOuxXRxbPpvz+b33OP/7ZO8JxwKEqef3mAT/2WN9tsRyTr19csH7YipMxKSjzSqmpdJ6PxmR1Rd6IxrouRO1he9YAw7s+n7EtchzTZJPnrJ72mJYhGaWaJtGJaLx//cjNy3NeTiZs85zF+Zinhy1N3eIFDlfXc9Z5zszzmPu+nGqrmo6ey8spb9YbOoXpXe6OvFhM8UxrIEGC8H/KpiV2HF4sptwdDjRtS2BZNF3Pts6ZxAHJLmW5z5kVNYVpDqlmEkvYMZqGlEWFrruMPCE37g/lAJHLy1aps3psS5fAjWVC03REsYtpiX48OYgK5xdZ75rGgPgwbZO6LanblnFocfP1JY5hsskz8rTk7HpGkZW0dYvtWjKOVPkRttfiKf69APxkdCPSTHnoOkrJ0mc1E+2TNLltO0GZKNe47wraQ9d19lvBelimTrIT+bHsc1D3ply/J7nzeOTCvhiu+1/1+o0o7nXV8H/9g9cUZasAQmIweXefYBk65zMXL7SFnKiLnHB1v8VxbeFKrxN2qwPhyOf5V5e8//k9oZqLvX3zwGgWEQQuE2VM2K2OVOdiW5+ej9k+7QfdtGkZ1JUUY8sxRdnRyTgknvjU6mntWMKAblXuqW6aaIgU07QMkkNGkZeUH0Vu5Xg2tisOxkkckPk1RVYOD5GTfLGsO0EaeBaaIcqAY1oLm7oX01PTCmXP8cVNChIyftoFLM5CoknA+w87FTzSDAjcQyqLSMljlPffNpV6x5AMz9NFM1FmIZDib5g6Ny8X3L1d4fjCxvYjV+b0dUt2yAnmNkXTsHw8Ypq6MD0OFUlWEwUWgWvifSkqIjeQ723bliKrCEf+n2OBG4aMUwToJpz9SSQGsoeVJL8fjwXXny+gPwVuy/y/rBo8xxiIk5ah0aql9Qk12xkaeVJimsLjOY0ouq7nsEtxbJPpRGa9m8c9dVkzmvhMz0X1kOxSQT4jCUkfb+XBXl83PH9xxtNyx4OSwem6xvVXFxi6zrulxK9VuWRkzqcxRSOa7W2RU7ctjmkyjgO6piNU70d6yOQBeC4wsdhxyIqKpm4ochmBmJbozQ1NwrFDS4pv2ba8/e6Oum6Zno+ETNg03L9d8l1aMT+PeX69wDHF0JSo2L/skFNmJReXUyzdwNJ1HMOgo8cx5Wc5hoGuibvWNk3KquY+SWi7joUf8P3jkvSQ8/zlAj9w2W8Tnt3M2e4SWTgei4EymqclNxdz7iyTJF3Kg33qkZXZ8Fk2bT8QSk/M/eMuw/EsacR0bZDOpnnLPqkHXtXTUuTBs5Ez+BEAXNMkmNgcsgIvcNk87lgrM5XEZ0I0Dnj4sKas5MR5fjmSZkA1XZqmyef6KEHso3nEu++f5N7VNFzfJt3nfHzKWIwdtScrB4lxptAaV9cT/K7j9evN0HRVKlf4fOaBpjGbB0Mm8q96/UYUd8My6HuBanUdHDKJnxIddM/uWBGOfSxDii205EnNfpcz76QYXL08V6oJ0Y536s1dKK3wfp/SdR1FWmJZYnTKspqbL2yubuZ4lsXtw1qewnWLE9uyWDMZlCd5Vor+W6FzP7xe4jmSunSay58+4NnFhGnok9U19x9W7LfCGrdsk9QVPntj2xxLYVScZnqWKcVHr1uKtCL0LWxFixPpl0Y89nj+2Tn7YzZo2w1TZ7MtmIxE6/vh1ZMcBauOi4WAip6WKZomc7uTxttUBg8ZBX3q6n3PwHIs1utsADOtNjn7Q8nzz+c4vsNhc6RKKuJZxPZpz/2HDcHI57hNFJ5V+BqnNCTpmltu1xJDZloGD2/3hGORqx0OBYHi3Xw+9jlsUqq64+YqUtF3+hBnpmnS0RdVS9s+MR57HI/FkH9ZqaBux9IHqeJ06rPf55RVSxRKDJppiqHMM+UhWNUp8/OYPC357IsLjlmB7zls1weSPbi+jWWZrB93PN7v8V0T37N4fJAl3uZQkr96wvotk69vLvijP/4540XM+1dPzM8ifvTFNWtN4+lhSzQJ2a+OtGPR6buOxT7L2S0lmehqHJP6FWPHYn/I0FNZpJdtw2ISs8ul8HqRR37McV2b0La5fZBg677rSR0L1zKZuB7Pvrxg87hjsRhxf7emzIQOWrc95urIDz67lAdB03Aehnz//pFkl2JYBm++v8f4+hmXYchdkhLaNkXdsM9yIs/FUcHih33KbnmgvhiT7jOyRcUkDpj/TsTT5sDmac/1sznffftBsL+TgOwoWvGqqLl5caZC5jWiyGW/Fz6TqYu6ybHkc+p7STCyLVGpzC7EKJWXrZgADUkE6/oeUxOU9umU6rsyNplGEiyS1adTakPsi5KoiDyKrELXhf9ydjkmOxay76lkv5buM9K8HrDBddOxXmfUbYdrG2wf9zyuCz5/JmOf3TrBsU1i32S6CNU+pcK1DTzHJC8F25HsUvoedVplOMU7Kk7UAkzXUrXwV79+I4q7qev84EcXMsNcJjjqSTcb2Vy8WHDcpkPm5WgWDguYwLOGjk/XNY5bmdvZauHUNh2vPzzxW59f8eHNo2J0t5xfBsMbs7rd0F9N2HcZx23K+Gw0uOBMZR7SNOkyDVMIbXkhRcU2dTRkfl2k5ZCaXmYVZzczDE2nUoqEeBzIwyWrCMcSnaZrmrA5+gLN0Dm/jKmKWi4QBTNrmn5wrWoaBJ7FeB6zXh8UxrahVBz2+VzctOuH3S88DODsZi7uyl8ICDAsk7O5wX5fYKMrqJZgdDv1YM3ybAgPPp1sQlvnsE2p7rdUdUc89rj/uCWOXSw1K8/TAscyaLpTBiR0nRwrHUvwzOcvPaKLhgAAIABJREFUFuRpieVISpRhaJxdynK6Vg5hw9D54vMpeVKQFkICTbJG5WaiqIMax7T+cxz2y3MH22oxDdFPN22H75hD9ur5uaihqrymbtpBRtd1PV5gkh1zgthnrIBah7JkPh9hK4hTVTXMzseAmE1WijCoKYDZsen4+U9vab5umZyP+O7bRz7/fCYLNYX5Pamo+r5nFHi0XUfddTx+WCuefkPeNGTHnHSficlsHuH6DudByIfDHtMwcHwHyzaZjUOaruNhvSdPS0aziIvJiKQqeb/eMgokZMZxbbb7VOb3TcuLlwvKoiKIfX729p7rZ3Ouopg3uy2TRUx6FIVMW7c8LXdEto2p6eyLgqbr+HI+x9J1Xu+2VE2DpmtstgXxtOHqes5FGPHN7QM3iwkXsxG7POft20f8SNQpu6UYlET1FXPMBIhXpCWub/O0Stlt0k8I7KrFd6UZ3CcVcWBx/eVExlt1N2Q8iPGv5fw6VgX3Ux5p4Jm0dcvLH94MnPpMqV1A8MgyC5dgGdcRRUueFOyTejjxNs0n9+npBGCaGsrqAsDL60ial32GqeS1L39wTpXXpIcc2zHJs5LxLEQ/FrzbJaz3FS8ufTzfYbyI+O6bB0xdI45d+rYTo6LCYvzauvqXK8v/fF5t3w/60tk8wDAMTEtm6IsopCoq2tahymspCEryBLJdr6uGu9dP+JGLGziEYxl97NOcp/sdZ+fjAXYfB2JYKNKCxfVs+FCSXQpKjjaaR0NwiMza5d/JDrnMK11zmIdVdYdpGkwvxwNLpsgqkl3Kz9ZH6kL2B5qusbieSWjwQUYKkWKDVEXNeCHc+bqUuDnPMZhMPD7cHYkDi8ATiaQfCaCsqVt0XefyxZxSSSLrpmcyC7i93ZPkMoqJA4v1w5bZxYTFeUzbdOjblKdVyuVFpPTpMhJI80alQ0GFyE11XQqWbQuFb3uo6Do4Kgfh/uNBUTUbXr6cs3rc43kWi4mDaeqsdqVY801xNzZtjx/LZ6dpcP5cIvi80Bs+B03XBsRqU4v084sv57iBy/d/9v9Q9ya/luRpmtZj83jszHdy93D3iJyquykKCpBa/UcgdrCABYhmAUJIrOgVUqt30IgVUiE2SCCEBAuEWkKwRAJa3VVdXVWZEZkx+HDnM9s8G4vvd8yjoDIqUSOUHCkVke7h914/x+xn3/C+z3vPAEJ+5Ixq0Ed/gucYJElJFLnEsVRaq6VPkddMAkuNXAqhTPo2Rt2iG6JnDtpuVMq8eXkhyVe6YrArlommy/ht6ri8+vFrDmVBXTVsdrn6OymlRDuQHjNuXq6Igp1cR4PG7piwnE0E96prvHlxyS7JeHq/wfZs8kzcnG3T8iEt2T0nvHi7ZjWfkBQyn3+yU67DCb96fMa0TELb5nkfM418uka07fMowNQ1WagmOV/92Qf1oBuYzX2CiTciJ7bPibiyVVLZn76/YzoPSZKcq9crulYW+ruHI/enGNe2mLkuVddxLEt0DRn/KG7Rq9cLdEMnyUv2+4RhGPiTP3vHyy+uWHg+3itxzj49H6mKmqISEUGZVQSRRz/tebiTndI5FzktZPxxzkA4jw7PELCqqEekB8jB77smeSa5xlXajMymJGv4/KdXhLaNoek8xwlh6GFoMpY85QVR6OHYku8QznyhrirxwSy0xrn+qzcr7j/sKKpWjYEEBz6/CmmVOMAwDcmFbTtBQ0xDjroYlKqixg/EM5OrLsBSTuxzhup8Irupi5dLavWZoaYUP/T6rTjcm6qhbTtFUOzHBaemQd6IHXlxOZOoqdu9qvQkjKPvB+J9SpqL/FA3dGbrCM80uT2kGIbGZnMaQyE0TezDTd1iOxXRPOS0T4ThPvHYPZ+wHYl507RPF1GRlNx/2DFfhlx8tpIlYlFzeDrh+A5lJvIyQx00s7UgdkXzbHH77fOY8F72A9v7A+06IvJd0mPOq8/WYwDA/ELAULohuvZjUjOfKw3sIZPwDFNndT3nYhISVxXPj7Fc3LtMLM9xj6mcsLZjcfv1o0QNBg6uY7I/1dzdx2MYxTki8PtJN10nyoTzzA/k5no+SGRYo2LPJr4Yz7KkYDL1hHapgrB1rRpvwHNwRtf2NFXDcRMzmQdM5iGuY1GqbNwyq0AT/X+V1xRVOx48n71dcdqn7PcFlZpFS/BCr3YOsI7EedgdCkLfGh2NfT+MHZd4F3osx6JIS4ahpmkHglDGWgvP5ZvDQZQOpkE1iE660VthDlkWh7Igbxpefn6J4+7ZblKVsylIWm/iSbB55MnIKnS4fLVk7Qc4hklSy2JvPQmpL2qqUmbf53bftQ2uXy2YRj6BZZE3Uj02XUdSVYShh6XreKZJFPlYhoEXeoSzgPv7nVT6psn2TgKm3/z4gkIxyAHJNvVs1pcRju9wsYh49+5J5LxdJwaepBy74MuXS077hOWVjDojR/T0uqaxqwqKTObVk0UgcYPvtkznQkKczAPyrOS0T3hzs6bpe96+WBNEHg/fPbM7VWiaOI3vvtuotDLwQnfssk0VS+m7Anp7/VKQI/E+4e7uhKFrXK899qeKpmfk99eNzKsDTzwZ86V00UXTcrc9sJyFTF2XQ1Go+E3IyprlzRzTEpLkWR+/iGxMQ8yGZ0niedGZZHI4u464Tk9JTeibDL3s8oKV4AvSssLzHdquG3+vUGlprm0wCWQ5bLsh2Skf93i7x8NYLHZdr2irv/71W3G4n3WhlXJjOb5wPM4uvrpqSA4pu+eEvOq4vtLxQhfXd9jeH8ZghqLqiAYhFnbuQJGULC+mJHuRstmWrg6wntOpJEkqSVdSlckwDKKiWIZMHEld2myFgX7aJUKpO4lxZXE5JZz5LK5n7B+OrF8tZWzTdJiWqfYFIvPTDZ3V5ZSyqDhtE+HMn3LqoiaNPIKpN8rk/NBVmFTUzE+jHyCOy9Ew1Lcds3XE1Pck+qyoRqBQP7T85GeXbA63Cqkqo4MzX0XXdYqyxVAcm/Ez0GTs4jjS8uqaJPmEvqmojjLSOnNUzvKzZeSMEYKOkvV1psHQ95xU5Xz+PudDfrsR48rQC3zs7sOem1cLgsijLCTtpykbmV0GDoapq4flkTQuMA2d1Uqs8Mk+5f45V3+3T2qguhSrdjjzxchjy0GhJaVghOMKz5HxSN/LbNYPbKqi5sXbC/pBvBF596lddwOXwJUQ5McsxbcsJrbgdtOJx+fLCb/4x7fMQot//vd/jGUYbLKMF5+tyZOPo+Lqq4cnqrzmd97ecCxL3t0+4/g2L16s2B0S2kb01bqhY9omt++fCX/8iqqQwIbIcdgVBc+3O6bLCfMLbzTo2bbJ891+HHl4oYvlmEw0WM8jDpY5Kpscz5IcUbVM/uM/+la6q0beh8uXS1lypqU4qguFvt4fxUx3MZefJc9JDilu4LL0RPro+g7rK8FUy4O9Z3t/wHHl57wKJ2xySVZbvVhgWDFN1fDwJA/ISSC7puQkCIvLlSzb66Zjc6iwLJ1yk+FYOkZS4tgGugZPu4K+l9FjmtSsVx6G8SmS7/J6RlXWFGlJt5DYQD90yeIY0zDY3IvCq6lbsrQimnkYlknfy+zdMjUZ97Uitzwl1bgTA1H+XV9HbJ4TyWNQSJIsLuj7nq7plJZdqvPjJsZ0TJJTOd4npkJox/tUcCWauPBN2xwBZ6ZljGPjX/f67Tjc0bh4uSQ5ZCRKCmQYxshLeffhpDjg0rKkcTFWeFmcE/oWbduR1C1V0bC6srl73vO8L5hfTknzBscyuLyS8AA/dPFD93uwKpmpZ2e34fOJOvLRdI1coXENy8BzB46xJCd9fLfDtg4EgU1ZtST7VEiSl1MGNYfLk4L1ywXrecT2mHDaJTw+payXPkUp1veySrCPuZodi5uzHwbcY06eSaupaYyHt694145r0amNZVM2hJ7JKRXT0+HpJOD/VubNVV7RtB22J/r6tGjHMYZhaNS1hDR/4q9rOI6pKiXGdBrXltl8o8vCKvTN0fXZdb2K+hMWTpnXZM/ZGEJwrqIMpYYCKEqJJEyynsPzSXTD/cDpVIrxTGVIZlnNz//44/igcGypwK9er4UN4tnstqnEuqnE+zKv1fy1wFBu3bNM0kMCIXRdbiJNA9e1yVLR71dVwzf7HcdNLJZ55SWwTRND1zE0HVs5TXVNw9It2vmE+/sdTdtzyhp+9f5REsCUW3S+jrAcUYPURY0feTym8oB4+WJF1wuPPTlmRItwvDf6rieNSxaeO6pTQtthk2Xouqh8+mFg6rjs05xVGNBeSbiE41g0Tctf/dln3O9PwuuZBPzy4UDf9axeLLiIJtxvD6SnHNcxsVyLQI08+2HgIgi4bzr2T0eJm9MFD9BUoqjRNY3jIaXvFJclK0lPhQDKZgHxPh0DoV1bDqT72y3lVUtg21iOFEKLyykfvn6S67iTCrimZz73RrXZcZ8RRi5p3jIgCqi66VlMnfEB33TSsfb9AAM0nWQkaKpYOUPuvMAhbxr5Z1IwnQZkhRRiTw8nbEs8EX0nIR2BZ6LrEsEZRg7xsUDXNBYLn+X1nOyU8/RwGrOQr26mkvXrmJiWBHac90jffLPFUulNZdWCkk6DjBXbrqeuWpq2JwhswllAUzU4nk2Zy6iyyiumqhP4da/fisN9GAb2j0dZEoQuddmQJ6VYuy2Dq6VLpwBNj7uCU9pQ1akc6t2AYQzYrs3Clgg3QxMw0PlJmBXt2LYvruactgm2ZzNfRsSxLKvausV2LPK0VPFfPdmhGPNd/YnL862EDJyj4GTMI+znruvJi4bNl0/MQovV9Ywg8rAdC88ULWycNliGxn4v8P0zhvRccQ4D2I4p+vHjpwAtUxc3nm0ZuL5NEpd89Se3XN1Mub5ZCr9GfR2ANG+YKou9aejkRYNjCy2yzCp0taAdlDRQ18HWNdpOLq75zCXN6pGFftYXd70c8raSfhVVR9XIzdQNIjNbDxBEHl4gFcbEt3g+CM3Odw3qWmSkjmsDxfgzHJKGND+NIRuzieSmLq9mgoz97pm+7zmmDVXdC9fdcfnDn9+KmuEqwvUd+k4olZ0awRzjivnMHROmLEf04n03kCU5cdbKDFXdXPO1JFvtHg4cdhnLC435LCS0bfKmIatrsOWgaPsBXRvG4OvTLhHOUSejQss20X1XwiuqBtMy8CaOXBOORZIWdF7PZ9MZ71XA9fxiysR3x2Dwqu4IJw4PacrC9YQk2bUUibpOh4FtnmNoGl+slnz18EShbO+6a4sh66cuhmkQV4KDOIPoZqFP2shoUTeNMTqwKmsMw2D3cEC/kQW36zlcvzB5/Ljjp3/tszFXONVrXlwsOJQFrmmSFhVZLNmnDMPouIxjMQeZloEXuhRpSalXLOcTkqKiazvWV1PlCSkIQgfDNGgbMTtVhVBIdV2676vrCNMyufu4l4pWdWzZqaUzNAalMOm6Ac1iVKLVZcM+ljSl0LJxL+bsk2ykX5a5zOqLqgN6rAE0XVLidF0QAH2f8dnbFbfvd/gTj8d3GzYHWei+eRnhKTKtjHalyvYjj6qoFalVIgIfHhPJduBTypznyGi2VzJPL3Ql3a0fyJQzeuhFEn5eRv+612/N4b55TliuQkU+FJOIpcKMZWkmF8V8GZArLosbOOjKEeqHLnXVMPQD7795ZHMomU2sUfs8AN7E46fLFV8OA2VeE8c5h8cjbuDQ99JChVOP9FTQqBSU7f1BVA5qtAPi4jzjan1daJbhLGBxOWN7vx9dqsHUJz3mfH3M2T6d1ChEDgPRY8tM+6xUMXWV0FRLZe06kkpfVpI6M1sE2K7F1es18T7lm683HLapIlTKk6zvB9K84Yvff020T2jqluRUymhFMVpAvvdZAeNYBrPIHoMMTnElyIBOTEzT0FKALkb7c9UMqioSyZlry01XlzVN1bC8mnF9GfLZ55c4X97x9ceENJdx0KvZhOM2VrF5wkJv2p5Wl/n+PLJl4anaVsezZemrbObu+UZuRD5aNz275wTLzLh4teTy1Yp51WB8+yyUyFlAvEuo1C7gsM+ZBIop4hrUbc96GeI2nYzxEskoBfEfRI7DwynBtk36rh8VNI5pYOkGtcLtJpkUEYFnsricUuYV1oXB3BNlSF015GmJH7oiMTR1phOf5ywl2WcSMKPr3H7Y8PSY0A0DoWcSRB6nrCDOS17MIm4PJ0zb5Ppizk0oBElT1yjbhtfrBazh/hSzfTpyiivivOSL1ZJTWfLVxy2LyxmWbfLLn38gzRtC32Loe/JYYGiObRBMxaUdHwQ3m525MmXDKZYONwo9iqbBtUxWuk/XD8SdGL4EIWFRF6I4ubyeyVjKNDg8x2Moj6ZpXE8nPKUph2fxfRz2OaeTWtROXOnSS3mo+5HHJLD4vZ++EeTx44kkLnn1asb794fRsCTGDOkU87IjLzspGCwd3zXYPUqgyTn1LTvlih8lKIzXP7ogOWSj9r5T8sam6Ql9i6qUBK+hFxRDXrZcLAR1sn+Sz+esoul7SZfrmm6M0DyfI2fuTeiZI1Y5DGzqqiUvO7y65bRN2DwnY9h91/aEM//PFYB/0eu34nDXNI3PvrjAcS11iIpaYroULej+6QR8itJbvZhj2RZV/ilcuu97JvOAze2e3Uk+jGgmi5287OTwMXR8W/ShXSMjnL4f0HQNHVHenI1M55tbU9r0eJ9yTBsMTarsrJDDxtA1bu9ODMNJ2d7t8eeMD3IDBxOPF2/XbG733D7nLKc2i6lDOAv47rsdrm3QqQP/7j7G0AUXrGmfjEZN07PfpYSBjabBbDnhZ47J7bcbWQ72n0iQbTvw/pcPshyceNI1HAUZ2vUDE9/CcyTIwnNMMgUmi9VCyLZ0FlNHxdsZKl9WGwN/Pz0UdBzLwDTl55Xvr5NkNfXHPa9/coWuabz6yTWnpCZTmaVd03FIGnRNnHi2bTANLeqmH3kxji/B3DPX5f3TTn0WjBRIgD/9R+8FeqYWWv0Ax01M30q19/pnN2iaxuZuT1l1ZKdK5XbC4VSNHZjniFln/WrJi0nEHz3sleROwFEParldK97Lc5ZxFYY4hsl9EkvK19cf6YeBxdTG9WwOz7EokZqGvDkRqmvRD110U8ezXYJAqvpv3j2IamLi8f6XD/T9wCyyiVNJ87Fdm64Vpr6xmJEcUl69XLP0fHZFLjP/sqHuOqauyzZOuYgmUhh9OBDvE3aBx+YQs7yaY9smDx82bI+SrNXGNVFocUxqXryYYlqiJHp+jJm0PfPAJ/MKkkMqAgHbkoInLVjPJuzyHMc0ed6dROLoyAOpVowl0zbxJxaarvH4fkuWVqwuI3TTYHtIWFxfsA4Cwh853N5uOEfv2bY+Kk76XrgyZxbMt9sd//TNNTevl3z91ZNU+N9TTdFLOpahSxLTLHIki1V97aRtefqwxfUdZhOfyjapK9HUT5ehOFQdi7aRk7huRZoceoI+Pu/ptk8nXn5+QRD5bG53nE4lWdFysXBHtV28z4izZtSrn/dxtq1jGKLVDycuQy8u1jxv0HW4eTkni3Oed8XodA3UzhBQjPdf//qtONxdxyaKfDaPh5G0ZpjyVAT4/EfXWIa0lfffPjEMA2/fXhEbGpqhU2Zi8e+7flTNDAMsrqb86k/vxKbvGHi+wzbLMXVZThRZxWw9ociqMbDifJh3bcfz/ZFwIhb1phVXW5I1MpqwDaLAVKG4csF0ikkdLUKOm4SnTSZLO9dmtphgfX5B1z1ySIR/4fgOnmPguyZeKG7NOK2xLUHTuoHLcZ+Nae9t23M4loLxVYuZH/21lwS2zXYf80s1EjqmDUXZ0fXlGOu13aQSkDHIDP1s5Xd8m77vxwPctQ2SrBn5M0XV4aqRUanm/55jYKmO6JxcH6cF04lNchTHXRSokY7KCLUsHaMWpsvTrmAR2ZJ0X0lQtWVYeCr/1fFtyqzku6/u+PHvvAJNtOyDAkedUa9J9gmAVjU9s1CgbfE+5eUXVxR5JYn3jsXnf+WGzd2eTHV95+4JoGo6sqLhp5OAd8eDqHmaHlxYXM+IfJel59MNPX/yyw8MndxcVSV+i6ysSY85F+tg5L8sr2bMo+CT23c6kdnuPGTqODwnKR++fuDm80vevrni492WIiuZLYKxDZ+tB57vDxyeT6xfLCSvdyc/X9f37Iqc50NMGAo+IG/k/dB1nZ//QoiBF5chl9cLtkfJxXUdi83jgd02G99vy9ZJc3FBR8uJCAHSgsCzmCwC9mmurumYKq/xApe+12gqRaH0fe6OJ8LIJ7Idpq5LWldsjRxdF233+eE1mfnM1hFN2VAkhTz0qhLPlExWyzZZLIPR1Fc3HW0p4Dw3kMVv3YhKhZtr/qnPbjjtxOm6XMq+AwBTrjlTqWQOp4puGFhEznjd6kpG/fWXt1x/fjHuq7zzFGAYOB0yTEPiJs9d92mXoqsu2zQ0urYjUcajn/7uKx7ebcTtrIrOfpAi0HQMHPuTRv/cdZqGxvFYMI0cHN9mfiGHdlO3ZFlNoBAMpqmPXdT54fJDr9+Kw13TwNJ1zhzy8ytPy1FyeHkxE3iXUn30w8DC8zloGg/vtxi6xGuJY1FUH59NZ/yKO9G2Owbvv7rn1jKYrSaUym16ZltEy5DH2wNN249BAZ5jUJeNaFPXIY5rE3+7IS/b8WGRJiUT3yIrVT5oXHLxcsnqRt7a523G3ccDnQrXiNUhVVQd370TCmM48ykSMSRNAovrtyJZc9VycvNwHDNY235gd5KOpa4aLl+t0DWN1SLiuIx52ooBqlWGD9cusF2L0Jd0oLPcUdPAtSUEO/BMplOBcfmuKZmpoUORixLAVKOjc67jJLA5JdWoSjCVG/B82IK46ja3O7wf2fLQVp9rVjRcrX1aRZ30XZNGmUsMQ2MeybzyTBN8990jXdONP69papR1T1t1ghPQBPt6HjOZpkEQ+eyejmMHZVom9988EUx9/NClyOSz38c1jq1hGhoXV1OuwhBT09kMezzXIFpOiCY+12FI3fUYmj52eo5hcIhjYYCXrUguFYelyEoJOVEmIgDXFBbPh68fxoBnL3RlfKFLaLdlC9L6uBEMtRc4uK7Fxaulav99haQOJAyjaVhMQ15OIg5lgW+JftyZGtLVmrpoqpMcP3AxNI28rLm/PaLrGm9+fEnf9wy9LEPjpOLhu2clx404JTXe44n5RcTMcfFvLLaHhK7teHy3wZu4PBo660nITy/WnMoS1xRgGEDT9zxmFd/+/Imbl1Mczx6RE03d4k9cwsBln2TEu5Q3by5FVpzXvPzxtQLxNcRHWcxbtjmy4JuqIa4q4egYGm4gbJ9zvKHvmUxDi8Uy4P4hkSmNphGnNZ5rcEya8ddf3ExHtG84CzAUiO/+9kTVdGPAR9tJB922PaapExgCC4v3KUlcEoTCsc+zirRox0JrFlrMZy6rFwv6c2Hw5aMIABxDnLR9zws1Ply/WJDHhaBPkOu+VUohR4k1hmEgPf0TjmU0TXsF/JfAFdADfzAMw3+qadp/CPybwEb9p39rGIa/p/7MfwD8G0AH/LvDMPxPP/Q9hgEswyBaiI29a3tsxxxHEmVe0apWJE0qfL/n4WHPqxcrQcW2PV4kIQK+a5IqFvpTJtXqeVZVFTVF2tK3Ytk3LYO+G4jmAatlhO1KJmZZSdC0rouW3lb0yNNe8LqWWpo9b7NPoRaOwWwu7Vq8S1Vsl1Qq777Zcthl465gEdmYpj6GNddlQ1G2WKYYnaa+xzzwuX3Y8XwvB/tqHXA8FPS12KvTvGHIahw3xlJuySyTNv7MJ+86MSZFrei5Zdkn3c3ZDLKIHPKyRVMGEMe3gZqqqNmfasWvl1n3mQppuRZBK8wOTTlavx+bZ6pWs65a3v/yHtMxWa4CnLjAtgyStJZDWVUx9iCJSUXZ4c5tTifR0U9mPg/3MYahcbUS9VKtPpumlVlq18t4yA0M1heTcaxmWPY45wTperZPMuefrSNWN3OiXcrm8YRjG6zVe/h0jMmTkoubuchD5XomsCyqruP61UqWmMohPF1OWClzma5pLD2PynHYpzmPhxPntK76oiPepaBpxGlFFAqb55wIlMY5b95e8fCwx1Ayt/liwhcvLvj69gk3cLmKAqqipu97Hp4PdF3P65s1Xx/2QmC0HXZFTtW2/PTlFR/jE/f3O9FYBy5N30tIs6XjWDq7x6O4ljVt5OLXjexYdo9HHFuRFouaL+/2vP3imtV8gqnrlOsIgN3Dgd3DgbdfXLP0fDzTpGwbqq7F0HSWsxD9dwTpUWYV9x92LFYh8wsJVtc1TQV3CHqhVhLMpedx/90zXuAwW4a4vkMWFzR1qw7flg+nI9eTCVVek51ynvefxhfnguTx8dPB3g0S9jJ8D8AnC0yRC3+8P/D6zZK8kLB7zzXGxeYksEWRl51NhiaaIYCm41Ekv33bcfdxO/ounnYljmOMsm2QkUzfShbqgDwszq5Z05KCokhLbM9GN9uRhxX69hjY7Sh12F/2+k0q9xb494dh+ENN0ybAP9Q07X9Wv/efDMPwH33/P9Y07a8A/zLwV4Eb4H/RNO0nPxSSXbctv/iz91y9WTNbRyMzuqlb8qQkWoScThm37wWNSw5oBbcPO7q2Y7We4E9cRfGTyjiKXHbbmGMiYw69bLFNHVe5znzbpKk64qPYmzVdYzoN0F6v2D2IjtefuMwvpzimOYYlu44xxqsZmnBZAFYzD9d3SPapAKXajmHwCCKP65uI9JRTViLhOkfvubbYpQ9Jw8XSY3ElodSziU/Z98LoaHtW64A3b6/IXtQSyKxCfYuq47RLJOO0aUkL6SiSXLoI09Somp66aginHvunk0IkGORlPeJvLVMbH2YC8uo5JPWoGT9Xxefqe7tJaboB3xH7//p6RhYXPDylo2QyikT15AbOSNZcXc2USS1VQdkQzTxOByH5uY4wcoZBlC5mWlLW3RgCsnE0AAAgAElEQVQw0qmKqWrEaXlesFqmznzu0SvOkKaLKqLvesqqpd+nTGY+h12mEMOlMPU9m4vrGcHUZ+GJ8/fwfCIvW/ZPJ4WQ0Hh3PDJ1HPEbnOQhHS1DBpUf6zQWb69WGLo+grQWoc9XX92OcCfpAN2xEJAMWNG965rkc95+3NC2nUgf1xZl3XBbijz2+nJB0bbjAvBMgHyOZWl+dR0SVyXbOGUW+vSIWeycxZllJUHgEoQen//sRvg/T2LAqeoe05QDcUDlEFeSLdp3Pc+3e2zX4uOHDa5vs15N0U2dpmqJ45LZ3CfOBf/bDj0zx8Uz5QCtOuHznw/n2SJguhKPRt8PHI4px23C4nIqBq26xTEMLMPAdqwxpN2yTTUD77A8KUD+6O+/44/VKLGo4jFwRYoMfRwrDgPUnQDAzvkDvjLg9b0QQLNCEBaaLg+bLJaH/jm/2JtIBONZ6XOWFh8PBXkpDKOBFsMSquw8sqmbnrYdOOxzzFPB6nIqOHGVR/yzn16IgKTt6bqO7CzxzivmF9Mxy/m8kLVdeT+G4ZO65odev0lA9gPwoP490TTtF8CLH/gj/yLw3wzDUAHfaZr2NfAvAP/br/sDXddjOvKjLCcBRVqS7jNMx2T1YsFyFvLuu0faduCY1jiWgWVKaHEYRlR1I/MptSAFmMx8do+i7bUtncnUF7u1ptEWtdz8/VnOCB+/23Knbbl6OWe6kqDoaBESOqKHbcqGqzcXBK7NP/6jd/T9MGpwz0AiSTuXf3dsU2F4Cw5Jo6RVcsFbhsZMXTRRaOP4Nq9eX6Arzf3Dg0gu+35gGjm8eH2BoetErguvljx8+yyV+zBwShvKWg7LrhvIOyHLndvTNG9wfWfEFRh6/z3SnYwYXNfiFFfY1sBk6lGr7NPQN8nLT5UvoEY9BoYm5ipXQdh0Qx9ZL30/sN8XLObycDQNg9tvHnE8i8f74/iwmAQmdVGPgR62qZMlpdovDCOLvazFdARykJuGxqnqxqUUyM0/DMPYtrdNS9f2vPrRpfBuUoGKVbWMfMq84ukxxrYMVhdTjmVJ1sh75Sq+/OFZYGBd0/LizUv6YeDFesEuEDjW+uVyVETsioKyajhtY/zIIwg9NttMdSfSRr9+c8n+UaBpXSP5AdEiZPt4ABAwlWVw82bFcZPgTzre/eqJm1dzTnmBa0uiUaiAc3leMQk9GtvkT375gfnllLZuifOSpJCADd3QiKYBTdex38bYrkV6zFlcTZnMRZ32/Z3T090B13P4+JzhOi2ea7I9VtxcWTieHK67Q8Lh6cT8csrqYjL6RXanlO0w8NPrSxzDwLckiLvvBpqqHRPNDPNTUMXT+w2mEix0bU/fdjzqB26bDWVWsricsns8khwygsinUMEl+5MqTtT97XkW+7geWS9npZdtyY5mElqUVUdRNON9WFTCOjorYTpPOs9oHrApavQza0Z1f1VeSxUeCM9H13WWKwNtl9L3A5OJOyIdFuuIumo47HPKusPQJHPAjzwZ80Y2h03M4nLK1dWcpefzD/70G+qyYX4Roesa97dHFgufvu3GqMGuH6jqnkqFwv/Q6//RzF3TtDfAPwP8H8DfAP4dTdP+NeAfINX9ATn4//fv/bFb/oKHgaZpfxP4mwD2wmP9YsHM8zgWoi3XDB3bsYiVdjg+FrT9MPJBVtdzVrMJaV3j2nLRNSo30TLPbkxNbaPl0DlLs8qqYzoxmV+EUhmoQ3Xoe1arKY1yiE48WWyctgl107G935P6DqFvKeekAb5DMB3GA6VpB5qmZ3uoaLqBq6Uro5e6o1XqGlO504qiIYgcFpdT+mEgTgs0pUQR+FNIHhd8+OaRm7ey8Hn6sFOmLF0QommFHzh89/F0fl/pugHT/B4+QKXdGJaB1fWkhSx0pxMbwzJ4eEyZT4SXcQ4p0TVZlJZ1RxRYahTSj9LNM7KgaTsxYiDd0qTraVvRv3dtz9MHSYLfH0r2h1Js+Y7MxpumIytkz7BcBZLoVLXUxlliKaOlrpd5vqNGbGeYWd0OhIrDk8XFGAX44jMHN3C5f7el73tcT5ACZV6Nlddhl5HmLYupwVU4oRt6vnnccHOz5LOXa778xQcBpU08nm53bPKMtR/gWSJFc1yb7756QAN8T9zNju+IbdyxJHzGE8VT2wlio2xbTNtUo0WLi2hC3Xd8+3xPPyAJWGVDEHqcVB6wqbI2hWZqYum64IE7QTikSmrn+g77xxNZnGOaojY6qQB41zRJkpyqqEf1h+i2Zdf1+ctrLF0nriue74883h/RdajqjlQt1m1HVGb+XFQwwdQfsSDnr3t+sMZVhWeZhJZN5Di8eXPJ0+YoD9xOdi1xWaoR0zD6LxzfoWxaLNeSQzDy6JqOt7/zkpOS9Rq6+CCi0BqRGGc/hq6hOFAapgrWPqcsZUWLaXz6p6uWm3UjI2DZAcl4ZnoxEdmqUswZpoABQZK+HN/5c0TG9WU0ZjbEsRQRp12C49ms1iFPj/EovX16v5HzZy6QP9MyuL/fwQ2sbhYcNzEvrpZ8+eVH+X5lrYyWUjw2rQghzsvmH3r9xoe7pmkh8N8B/94wDLGmaf8Z8LcRddrfBv5j4F9HMhT+r6//WwMxDMMfAH8AELyeD3XZ8O3jabRGZ0VLmooDrql2ZGVH1/V8/vmScBYQeg63d1vypGB5Pafve6W3NsZ2xTB0ZjMP3dDwQpcw8tnc76lq0bC/ulqxm3gcnqWiCWc+hq5jGQZWJFXB8+5EVYr12DANkkOG7ckDxlb2fqkSO/Z7idDrlMFnGCDJW1Yzh6bpqZqOeWSzvp6Rngr8wFGa55xKXWBB5Ek47z5l83AclSF3Xz8SLUO8QNQ7aVphVJK5+u42/kSPVMvJsyPUNmXUstum48J6Glr0PeRly3aTcbESkNRxG4+Vuq4z0he3x4rANTFNbcTontPos0LJG/uBPKvp+oHp1GVxKeaV/eNxVMREoUWoDrCilODiczWt641yCksMW6t47MIckc+zURjfwJNqchpIMPI5Fb5XiiLLsfB8h6uXc6qi5vnhyCRyma3F+FLmouNfzhxe/+Sabuhpuo7Dc8zD+y2f/fia5fWcl8sZcVURTH0OcSYgug9bdseS2cTGUelQp6RiPvdH6udsMUFD3j9LcXm2VcHVm45QVevzWcg2zXAdi4vrGbqhcbGe8ad//I4Pv7wf9eCGro2JRW+8FZs8F1lk04niS5nuTNvk+HFP2/V0eYujYHCOb1O1konr+jbDALOrCS8mEU3fUTYtTSeJRlUrUDzbs3l4iEcXaNcPI56jSMsxUi5PS+JdQqHEBOHE5c0XV6S13MNNJyEeS8/HvNDZJHINbu8PzNaRyne1yeJC0BAa49/VVjhgI3B5vtuP3xtgOrF52hWYps7lOhilhZapj9e9Y+njjsm2deXWFg5O4JlkRctyFdArtn/TChOoKmvefXlH0/Ys1hO2TzGeK7b/shQ5o8QM+uPoeBjEXFXlFZYpTJwka0myFt/7tPPrmpbNocQ2dY6HHNPQ+NVXT3zxozUvoojSa/nDpyPvPz6PiqxWLVvP/Jpa+SjCwCbLan5opfobHe6aplnIwf5fDcPw36vD+el7v/+fA/+j+r+3wKvv/fGXwP0Pff1eWd81Beoq1QhjoQ6z5JhxsdSJFiFvb9YcypL72618cK6N49kjLW0YRFFyrqRGKE/Z4C6Evb6+njFfRvTIgR1MPaKJzDB1TSOpKgJbbor79zsyFXYxomDXEW3TEu9TdtuMSWBhudYI0Nc0Dfv+RNP2vHw1U3/HAS+c4Po2x01CkonRyVH8cj9w6fqeIqvITicVBPKpxfQmrjhqFZc+mHh0XcfHj0dlef4ESvo+RuBi4Y4GrDhtCTyDNBdpWeDJx386lTRVI4TNVuh5Z8QqyNO6GwYsTRc6palTqpl56JnKRNVLUEHVUOU1ZV5z8VLogNBxuXTxVe5r0/ZyoCv7dt/1PO8KJr78PKYjN1Nd1OMDVNPkc22Vq3c9d8bDda6WwmcTnOPZQvhzrNHZOFtHLKYhTddRBy6OnbK8mLIIJVbO1DXe/uh6HI0Nw4BrWtwfY26WM+q+4/27J5KsHpOBQt9i4lgUuSygdaXYsg2D29vNqLhq+wFT19jeH/jJFy/45t0DddkwmQfUbauwDQ7bXcx8FWI71vgZdq2Yi8JZQD8MzByXbZPRVA26aXB5NScra5q6wXNNxdFXbkfXYB4F7E+SerTwfO6e91wEAj7rB3PE27b9QNN3FCoA3VBdr6kkr/unE17o4AUuwdTlaiVB3r/8k4/0gzDSp6sJRSO45sJt6NqOq/mUhediKLVLXTX85IsX7LIMx7XHOXZTS+WfJyXTZUiZV4SzQBAPhkbXDlR1x/WrhZiETFnCN1VLmjesLyYsIltkwCqk2jBkR6ZrksJVNS29BpZjMbNNsqSkaeV9LpWg4OkpxVK+kSIVaWqVV5wOOf0go5eh78euo287kqSUpDjADxy225ysbGVkZOs4tgRdD4MoX0LfommVqs/SWS4jdnnBoSxI44KrV0uqohZeVilSZOkAZZyZFWJw+kumMr+RWkYD/gvgF8Mw/N3v/fq1mscD/EvAn6p//x+A/1rTtL+LLFR/DPz9v+z7ZKeCLBY+RKt06nIQxoSzgCyWtvK7+w3JMVcyPYPrtxf4lkXXCopzc6hQSBSRWkU+hnZu2zQWl7MRlRlXFTPHZe56dENP0TQ8Ph+4+7BnvvBl6WbpVLWMeKJFSHrM+ez1Bcc0J40LjgoleuY8nC3Tji3Lyr4TQJOneDZ5Wo7ESSFaCjxp/XLB9u7AMMgI4uPtaWTJrFY+4SwgSfLRxLF5PAnCVc3C4RO3pWl6aAcmcwfHs0mTcnSHpmrZGnimjDE6md9FoU3TdOOMsm56QnX4e+qwkQNHKrm2ldGPZerMLyKe7w8UmWREXr1Z05TCwoiTisCTpVCZVdRNN1L1hmEgz6Q7m/giwWzVfPGUNSwih1q9B+eW+qy3t01dpI5tg+WYuL2wbeYXC/JEKsGqqMd8gPt3Gz50z7z4bEka57z+yTXLIMAydHZFQWQ7GLrOKRMji2MYbHPBRN9vRco6W0fML6Y8fPdM1/UjZwgYD/LH2wN5WopiR9c4qLhA05ZFcdFKETKZB7yYTfm4P+J4MubbPhy4/mxNWdYqR7NmGOTzcl2brh/YFzmzwCO3TUkPsixOSc5xk/DmJzcU//gDh7hGt2F5EbE7puN1uC9yGc3pogIBYa+Etk7VteOv1WrZmBUty6moPCzLII0l5u+4iSmDiuNGxpWGIcqvSSEL/5c/uqKpGyzbIqkrFp7gQyLH4Tmv2CQp+6cji8sZc9eiSESdomkQK9fl+X46JaKVx5TxV7xP/xzPPysa8rLl4+2Rz17NKOvkz6F/Xc/Gdiz2O1HO6bpw2s+daV7KtQSfuhRd15jPPXRDqvGy6gTgpWtkacXyYoI/8biaT6WLbmre//KeomwJLRPfM2R5O/XJ4lz46+rBtlo7uL6NYUl3un868Wd//H6M1bNMkVd2TSe+EvgE5lML4rPT/fpqwv4HztTfpHL/G8C/CvyJpmn/SP3a3wL+FU3Tfg8ZubwD/i2AYRj+TNO0/xb4OaK0+bd/SCkDMPQDXSdP20xxUKZTmXe3aivtTcTEkZ5yhl5IaWXV8Xy7Z3ixoO9FSSG8DzEAuCrv0DI+0dPO6Mz1TKoMQ1EX+2HAMSUs2TIP3N7FrGbCR3EdA9eRpc/dpkDTPqCbBo87kZF1ypl5VhBsH4/0PaMJYmlFBJFUrecU9DOus257DnFNmj+Nm/nQN2nTBl03mE7skV3vKiLcdOLjeDZ377aqyhAjzjnxXdM0Bh2u1qLrPp2KMZfVcyw0TeapjeLj2KbO7liOaIJFJFVI28v+oKzEmVfW/fh1zovMqhF65/Jiiu1ZoyxtfTUnPmWjImd3aogCU+WPaniudDhF2dINquVsVLCGa8pBuRXVVG8PeBNHDojIxbIlMzfep/KgUUvttht4fLchKxq++J0b1mshgrqBQ121HI+FGNdWEbqhs00z0mNGkVU8K55QW7fC1u+HsZp8+LjHtnSOyZabmymWYzKf+iz6gc3DkbRouVgFI3aiSEvCqc8XP7vi268eabtPyoaiaVhczWibToxIDwcOh0KBqTTef3VP03b4amlnmCLF7dXi9jIMqbpu7DLfPWxZr6akx5xTnBNOpIs5G1483+HtbEbd9RzKAtPzyZt6fNA4psGXD098tppziDNu3qx496snkb0OSEKS0qV3Xc/mTiICl7MJQegBz8SxSFePW4mli/cJ4SzgxSxik+f8/P4JwzRYhQHXixlPp5jrz9YYmsa3X96Rq7HO+nLC+npGXTZjAPX+SXT2mqaxfU5wLJ35MmDYipM9yZtxQf9wH3N1HZEVhzGys61lvHvOUq1rCY+pVHDMeS/X1D1F08sIte35eJeIBPciwGx7fMUt6kZSqcF1KIC3fhg4rVNytV9aXUm2w7lYdTxLTGmrCfE+xQs9vlgt2RU5+6cTh1jQwFFoj2cUyM9qmp9UapoG67k8bLOiVXymX//6TdQy/yt/8Rz97/3An/k7wN/5y772+NJg/WJBXTxRlB3LVYimySJzfiESqck8xHJELVBmFeFM5+FWVAbJQTbtWSHtddNJK2s7Fu++eiCaebieM75pi8sZlq5TIClQtcJqAkxdl5dfXGJ/2KJp2uh4XV5ElHmFqZckWYOui+TJ0GR2/Xi7581PrwWWNfVF/uXIhn77FLO+mo6sd8M0QROE6nkZVLcDZd0Q+iamabJeWqSpOGi90MO2TdKk4OO3G6ZTl1dvL3k292SqPdM0DcP4xLzujYH1yyWnbaLCNCTBxrFll3B28ukaYwTZgGjPk6xVba0oZipVyZm6zNx91yAMbA7HksCTVlrTzsEmOukp5/0v72XxpGkYlqFUCQL4ur890Q8tk9BWraWY0lzbQGsEPJY8xDRdzzSwcGzRE7u2IUHmpbz/558/L1tsy8APHA6HXBZlyvq+frWkbTp+8vmcx1PCehKKlj0uiA8Zu13OauWPYRq2I0qa2ToifzgS71P8wCZNJfRhu1FGma6XMJnrGYu2G12LlmNhmLq4CFX4THws8D2L+cWUw9OJm5crDlnFU5rieDZR03FKKgkycU3arseuWw6Hgrc/uZSH1yln7nq8f9qRHDOqvGZ1M8dyLfanFNMy2NzuqBsZU83WE6LQozuPd4aeyyCgHxi7h7knRUORlrSLfiw8sqJD00QVJcYgRoxFesqpqwbHEOZOdTPHdhJc36FWPP7FxVTkjLqMRA7PJ8EXWwZzV75OVTVcRBNe/OiSu6+fsD2Ly+sFpq5TtC1t05EcsnFBDbC6mHB5vaBWssGi6sZRpKZJ6Mxhl3J9EbDZF2OBdXZjA2PGwfl/ri24Dt81majD9ZgKKbRte5KkIi9b5sgOb3OoMIwDf/33fyZnT12zzTNBjRga4dSXHOaqoSrE6e0GHV3bMwwleVZRlzVxVfH4fOCQ1KNkWfDBco23rQgw2lp+/nPBYxj6SFO9V+ffr3v9VjhUhwGePuwwbZNJ0ONPJJTYC4Vmt1hF1ErjK8RGkactV6Ekh5f1iEltOpHUWSosYnuqlRJGqqe2brl8vaIf4BTn1Cq13LcsXNOiG/pRA33WfNuWwepyxn4bs5xVGLp8GOuZQ1a2GKqLOFus949HgshXIcsyj949n/ADB91U0jilBHFsad/P5p/FMqRtWuYXU5r3G46bRLngEu5vT1xchnzz7shul7M9VuOHfubZG7qMPC6WHukxo8olJ3U9c/B9i7pqBTtg6eOo5pz+UtZSmZyre8PQSDKZ058fHrous/1EOf2atud0EBRrl5bkSTkC3+Jjhm0ZktPZ9tQt1HcnXMfA9y2KohlHAF030HWiDnl8iIXcF1pMAms0rdiOKbjZuhUCpJJ66rrGdOFRFw2XVxHhLGA5DRWOV+fhGPNxc+C0TXhqNviRN7owr9+sVWZpyP7xRJlJ2lZdNpS5zD0NUyfwLAmHUYuusmxwKpFONlXDZObTth2GYdB1HVXdUVT5+PCM05rkywdC3yJOC15fLHhKUwzTkCD0XubWrmMQziJs12L/1dMITuvanq++viWcBbx8fUHVtrz78h5d05grQ5Gp2n/Hs1ksJbLu917ckNTSUVTtp0BoTdPQ0ai6jmDqc7s9UFcNHz4eMYwzDVQUWcJ6ks+hUs7ubSommtNWkpY0XbILLl8tybMSPfT4R1+95+LFApD7rswrTppG6Dk4piRIDcMAP7pk93Dk7sMG0zJGw09ykvGZa4vz1ricsXk68uH9nrYbCH2T2cSm7XoCz2IfV2xPsmPpuoGOYZQsVk2FoVQnhq6NC2/XMZhPRTW22+VKzWVI56ZMgItIwu1XN3NsT8J8fvX4LMvhuwNvf/aCIBI4XN/1aI5JfirJ0gpDdQ+GZVBmJXHWMnzYsX88sj9VsotzDKqmp2qGkYU0Is6VKmxu6+zjekxOi3wLy/r/QViHaRnYjsntnQKEbWJWLxaYlslE8bC7Vtp/TdeEwRwLGU03NExLWlpdg0odcIPScNumxmwR4EceySEdAzWGYRiT3QEKz2Y6FVv3xHMpJ5IoZJo6lqmRF6LaEJBWOx6ivVLGOKY+suhnF1P2DweiWUB6yiUQou04nST+rWs6DFMnmnn03cAXXwiD5PkxlqrItXj6sOV+K8uxfVxhmzqnrKFSs/isaJWcS+fFK2nzzw+js/nk7uORsu5UAs3A864gcIVs16ibYxikxTvf0FXTE6nlZZy3KphY5o1j6LByC+u6xnLq4E9cNF0jPeaYps7mcOaxG1im6ipU+9v3g0i4cvBcE2iJM6n4OqXVbzpZmK7W4tZrz2qGrsFp5WFguxZ5UvDjvyoq23nosznEGKbBF+sVdddi6QabPOPx3QY3cCSvsmmJ9xn9MHBzs8QxTNpZx7c/vycvWxZTh6LqSDJx8a5mDqYKm9Z0jTxpJMlHhcmYtomna3z3q2eyomU1d5ivJriOPHSkItdYrUNm64gqrzhuYzzfYekHFElJkddcqYAH2xILfHJIcWyDNKlI4pL5MuTq1Yq17zNx5PB7+9cX7IqcL//sPfOLqYyTlGEochxRbbWfxl5Z07D0PeqqpWwbAksq4uVEdPDbu4OCwsFsIvJXwWA4ZKec0yHDDxxMRzwcZ2ljGsvMvCxq9k/iym2qhmgpmvzl9ZzkkFKXwv2xHdkxhJc2dd8R2DbdOpJuKHC5DEIMXRdF0fbEEIoire9FItwPcLn0aFuJ58uKlsUywK9aJr4pJi9P7vHzw/g84nCUpd80JAAjDGXUudtJXnBZS/CPYWj01adxSByXXL8xCSbeGNNZZBW/+7ufczOZcApC/uHPvxVUguoiZlNXNPzHAj/QqJuO5dQRiWne0A/gqMznsu5HvEeZS0FqGYrJpMYyZ2Cf75oiBW0+7dv+wnP1n+xY/n/n1bUdjw+SURh4JtEiZOgHDFtXaScSXm2oOWKpclTD0CNU2ZuPtzsxCvWylDin50SBJW+4Orx0Q4iQdS/SsHGepWuUdUNtdDT1pwAKX82GH99tqBsxAJmGuDrTvFEoWjnIzu1jUzayBFY8etsx6S2DYFDb9azGMHQWy4DJzCOMfLK0IJoI6+a4z3jal9TqA/ccSUfPS9GEXyw8mrZjdTXjuIlJjzl1Ixe65xjsd+JiaxQuue8HYjXXrGoZrZxxAeIJENOQpS74VBEvh2Gg7aXy1DVYLv0Rxdq0PdOJw/xqJn+no8SBFWkpAC9lpBqGgZtXczYPR5USLyiHSlUzWt0y8S3iTA7D9nsMmzN/JE9K5kt/JH+2SmHRdz3TwCOra7799oE0LohmAfVihmvKnLrp+08MF9scZXsAT89HLi9mEjBRtGgabA7l+PvnUZblinPxbGbpul4ZoerRqNL1wjM6pQ1FeaRTFdn1ixmZSt368KtHPvvxFaZtctonOGuDy/VM9PcTD+eYjbjcYRCM8vXbC6pcNOBNI6NAWxcHZ28OBJZF8Hs/4t3zDsM0MMsaN3D4+v2joGFtm1NZ8vl8IZF4aNRdT9PL3N4xDIqm4XTKyEsJH+8HUWbYloFhicyv7TquX68k98C1SQ4ZXdfhhy6r67kyieniXm57LhFVyuaYMI8Cdg8H2qbD9R2e7g7iY6gafvrZNb5l8Sqa0jNwKkt6BnzD4NV0CsBmI0Xf0A9cfrZkugz50c0F3z5t+fDNs3SulujWTdskjktevl6yfTiwU9VxFFrMFz67bUZedqznLpZrkcYFYeQxixyaQ0nwPSPeiNwY4ObVQtzjywnf/eKWf+6f/TGGrvPN3TOBZWHoYtDan2rlAtdZrU0O+3zkyDftgGNrTKYup2NB3w80w0BXDuM9eJZRi1dFvvcksBgGRtheVrTCmvpLHKo/XNf/f/UaGIMrmlZs97ZrSXRYUY34TS90yU6fULxpWrB9OnLcJ7SNaKY1Jf9zfbEZa2pkkhwyZqtIVc0GjmFKnJuiSfatqC26VlKBHj7u6fuBY1zzvC/GQ30285jORaaoafLknU0sLq6mYt8vGxzfZrqakBcNtmfjqXnlZC6jo8Azx4eGF7rUtcgq+04A/nfPBaaucbl0Wc0d4VhocH0ZEqcNz/uCj485//AP73jYFsRpPV5UpqmP+mrL0Ak9UyW/D8rYheh+1Xvj2HKQmqY2Vu/nKL3QMzHVyGgAtttcQrdnHqYpXdTh8cj24Uih5GJNO4w2cNPQ1Mhkgutaqir11WgKjkdxwp5VP2UtP/fN9YQo+j+pe3NY2dI1TetZ8xxz7PFMOdzMmzdvVVHVTSMkhBDCwEPCQHgYSG3idmPhNg4eTns4CCQkRDsICaQWINGFqK6i7pTjyTPsIXbMw5pHjO+PdbKarlSDCukS0uB6lwgAACAASURBVFXePLn3PrEjVvzrG973eV1efHzJ5GrE+OJDAIKu69y/2/L0ThaJ6+2R1f2W0yHDMmUkcrc79JdWlhbCR6kEPWuZooBqWkENPD5uef9m0+MkGvVaOYq/sjuWNJXMTA21sylSCbOwbJPFUyyhJaX8DpFvMR6rWXbRsHiQdJ7hLMJXFTCAYZnsTyn7JOPnn9yKA/r5lJnK7Yz3Kc8+veIcjdg2MhNPq4qkqvrsUoCx6/G3X71gNgjJTjmh65AlBRe3E6aez/e/ued3Dws8NXZsupaxq4qiJKYDZhMZ7TRtx2xo992h44qD2o9cNg874oMY7RzPwrJNtk8HVvdbmrpl/mzCeOxh6Bqu5/S69PfvBN1Rl7XkrLom04tIIF2aziHPiasSWzcIbYfQsiUP1rSY+j6fvbjCciQM5biJmY4jXj+tAUFWW6bQHU3bJFd7LMsVUF5RNFiGSHcfHk8ck4rpUMaj6SnHtqQo8wcevmv0eA5BCDh89NklhqHz7ocNp22MYxh8+YcfcRGE/VhplSZ8t1qjGxqvXo4JfVOi95QyTDW6op46FOy2AvczDUFl+4oM6yg9fppLB3Fm1PiuJJ7tTiWDgct0eObS/3Rt/ntRuZ/dfLne9JK34yamGfr9lt6LXLKTsCeunl1i6Tqb3QmALJGEmdHIU/CjpocDpXmt9OI1p50wX86tmqZpvTVfUzpcECu7BOCKPOyMtQ08CcC+eTHn61+9w7Z0NK1jPA0lmSYvxcln6Hz9l+8pqpZuK6OaYOARH1K8yKVtOsqi6ls7gN02ZX4RUW8yXFt46rlKhbdMg+2hYHsQlQNNh+kYPdK0aUWWOBnYooKpReHiezLPT/Omv5hAWlTH1tUhLCAm3zWwTZ3jj8iOuroZSIh2h+tb/fL2/LNOKrEpGrjSIRyF5BkENpou0Knt8sDoYkie5Bw2sbTNoUOaCB64rFocx+DqMiQ+5pISNBtQ1JI4tH7ciypHg/VvHhSzI8XxNn2ouGnI4jY55SSnhYQvKFORpdCsRVpS1pKm41gGjSUJ8mcejmHITQ9gNHQZjTSW65Q4kQ9VnshSta5bscZfjTD1I1UHlqXhWELT1A2N2dQnS0UTb9omi7ud4qpEItkceBzTnCzOeARePbsgryv2p5SqqLl4PmUWhtyvtn0nWVcN+yxDR2Pk/lWWd9O2HIuCm08uKRqRbT4fSOX7r/+rX6CjscsykqqkaluuAik05r7PYxxjn0NnPJO0ODPyG4ynI9fPJwKLG/pE45DIc9g1rURQNh3jSPY7besyvhhKR+ha1GXN6k5Y/I0KI7cdS3TwScHq6Y78YynedEPGMHNfRkTvN2s+n864CALWSYoXuCoApeTr37xjdjPGjzxM2+TlyxlVXnH/TrIRvNDltBXpo8ypPyjmri4C4lOB7UmX3XUCyzNMuY6OWxndBrZJWzekp4zBJMQLBdr19o0kXU3/lS/FG3EzZ3EUvEVTNRRp2b+GZwDeWWIqEmC9h9F1laClPdfAtcSwd/Z0VFVH1kkFL6/pnsd1xnKdMP6Rgu6nHr8Xh3vX0VuJxT7fslocxNCUFmrWlpOccjzfEUNCIwvMruvUGKLrZYu2pWakCtVpq+XhdhNjKjt+Wilbr6FjdF3PdgbUmKPtXW9dJ+OL208ueXq/4Yev7nt0r2PpbDcxzlFa8/n1iCqvOMYVTSfUxdMhVVbtIfv1sa/wDUMnjDzeffNAXXcsHo9CQwxsCRsJLeqm5e1j3Eu5dF3H8aT6PDPVz5XoeQZnqlFLmjVUzfnPtB7OpevS0umatJ1nJ2ihbmC2mjkWVUvb0rfzw6HsC477TNADZY2pAk7apuUUl/1O4ajwwYahc9zGDCahcEUUSa9twfMdxhcDFm/XZEVDNAqwHQvTMrh7vVRVffch2LwT4qVp6ti2zurpyM3zCU3dYDkWrm9LtmzTYpg66T6nLmvJ4FTsmcNJMmTpKtzMZHY1QtegbeHm2ZD10xFNk73JfnUkcKXdF0mi7E5C5U48bmM81wBFpxyN5AMn77+M3jzPIIg8nn0sBclqdRA382IvfKKLAbqhsUkTurbjk4sZD/GJ+Jjyw/unXjPfJzkdM0LHYRGfsHSDqpUl6SIWYN0ntxd8/eZRbiJtwz4XVvqxKEjz8q/kJJzKElPX2R7EOVrX4m+4nHrsjkWPT7Bci1iNjMq8ZDoZsz8J5mB+PcK0TcJRoNKlDJ59fEl8kg77PEbNyxbblS4kHPqc9ilt2/H+9Qrb1CXe0DC4Ox3xLYvd8sBvqoarYYRvidmpKmvKXF7XM+72bPZyA9lD1E2rWPAdVy/nvFu8pW1hGFkMpiHvftiIpHakFFqOiRe4fdh4kZdUeYWufnZyENWT4wlaOD5K9OHrzYY8KdgtD1JI+SJ3tF0Lx7epNylVLWPVpu6IC3Fyh5HclPd74SadCzHHNnh2O+S4T4SBUzS9W/y0TySFTlXzJ7WjGs3/f5ChatsfnkZeir3dD2SuV9WN5IAmbX8HLOqaeC8HZjD0MY2YvGj7OZmMQly2i72ETdjCTM6KhotZwHgQcEzz3qqv2ybxPuW4T2iajrrpuL6VJeVmHZPmDR99dolrW0wuh/zln99Rqpny2VBzNkgcNzFJJss4zxHVSBC5GIZBesqIxsKLiUYSrLu837I/llTKqHA79xQxUaSId09ZbxYC+gM88EwJEa5byqwm8Ez2p6pXv9i+VKtVJm7Dc+BHqbTqRSW+gFQ56dpWZGKeY6BBP3d0AnlvsqJhs0kJPFPN0+XAvbwdsVsecQOH2e2E0zZmrYxLRSmdmB9J+LjjOwwmIVkiCzjHd3B9h+c/u6KuGr7/SjxxpqlzSmSZLek6cgMzVOr8mR3v2mL0CYc+ZVFJrqhr0TaCXbZci8MhJwqk7c7TUiBv3Ye82Xc/bPrQ8cXDntHI63Xouq4xVuqlc66AacrBYpryXAyFazUMXSiRSSm7maZDaxqeTSOuL8akVcX7t0sGk5DNg4CmNoeC7TZjPHQIhj6Ob/OUxKRJzmwyoGwapq/khvGUJLI0Lyq+//YBXTFnzge/oXgyoW2TqZwCZyzSwrYDz7LIioqXozFv9zv+4t09t7MxjmlQpKXi0je9S3w29XnxyZU4evOK4TTq91hxWZIcUyGdli3TWYhpGQynEYdtTHLMGE5DDusTz352xX51ZLMXkNloHglXf+DJHk2F3by8neOYBqYm/gPbsfpOOq1KPNdmVx6IxgHDScRudaTMq17tFI1Drp+NpSNYxmJ+G6SMI5soMBnNB+yWB+mmu46Twn+UecVX/+e7vqByPQfDMHrXvJAZ5SZX5VVPXP3utw989gfPpOtX4DzLM2X5n1dcXIoYwHItyn3GILBwPLtHhwgfXorG87XoeBavrm64+3YheA8VRv/27iiKHVN2kKarC7nzb4ot8//lQ9M1rm8G7LcJZdVydTPsxxW2JSMJXdNUzFbJ/XdPfaVeVw2DkU+3S/vlZp4U+C8cHrOSi4m0bsf0KAd3WZPVNaddLFb4XObiICRH27Opq4a2bbl6Nu2DAk67hNEgIAo9nt8O+P7t/q/8DnXb4Vni1APUElSWqV3bUVYltivyyun1CNsweP3VPcutGFjqVsN3DVY7cZOe39zA+8DKAVmuDAKTNGso1AEki0ulTrE+JL3XTYul0MBnRo1haNimYIurpuuDw9uuYxBIq5oVjWJoSFcl36OT5g2WpRMFot/XNJkdy3KzY/lu3aMQAOZXQ4q8ZLdNCX05YE1FYaxaaetD22aVHPndrx9xbZ2LywHLp2P/OzWKMd62gDo027brIWHH/QckrOVaCkBloxmCUp3NQ9FVZyX3jyLbQ5PX4eVt1C/B16tTv4jfPO4VwbCl0Zu++gfYH0sKNfaLAglB0QzpxFLFy9E0eS/ruuWwjfFUDmg4ChhGPjvzyHGfifV8FrBaxji+w+r1itEkIBh4PDxsRP1SVTTKfesruZ1vWYKqqCraputhYBeDiKyuefXJFaFt88N+R5YoEJyu8+mlHKBd23H3w4o33z7hOgbXL2fsFeLZPl/DaYX+wxPjCxnttG3HlS/spUUck51y/MijU4x10xJJ59m0o+katmeJ2qlS7PK6Y/u4F5RCKz6BVnFy7p42cvM9ymJ+dj0mTwvmUwmjmfsBthqtjFyXqqrZLY/slgfpanJxArfK3V7XkpP8/OWEppHkppOKkTQMUW8FA4/tU0kU2Fy9mpMcM4GEZbIoTo4StG40rcLytkS+ieuYxGnF268esB0TL3IpsorTPsV2TH72xXMcw+Tb1w/MbydY9p70mJEor4SuaT3yejwUVpRuGlRl3T9Py9KxLJ3Ak1Q2yxRZsmXpHJMSO/0wPv3rHr8Xh7tpGKJr9226pJRFjKZx++klI8/jd795y9Mmk3ln02F0iutdSjxapxayvQutahk6DkXVMpn64hB7fyAvG9b7nO7rB/zIo1WqBxAjx8XzKbNRxHff3pMcMp7ajSyHbIP4mBFnBZ5rc/FswvEoodOWIZRHx9J7p10UCA3uDBoD+rFPMBRueJzkEi+nNuJtK9LEVVr3+tbQN5X9WH6v8xjmPE8PfbOHdwmwSFQ8Vd2B2r4bhsYosoizuv9+EESBoaq/D5W9YAWSvOmDnqu6pa5kSRp5JoGyyidZzPX1hLZuiMYBRxVk0slfjWEZIj21RIc/vhzi+jYD1+WY5zKKSct+Jnu2759dfefn2asImqb/2VUlFXNRtsRZTttCEDqUuYDL2rjof2YQ2D0VU9Mg9K0e0zCchCpwuKF8OOC7giDeH+VD6KmlvO3ZVHmFZRm0nYzTPMfow1baSvJn2+4sV6N3SMZxwbuvH7l+NSeMPHb7mNU64ZOfXRAfU8YXQ8ntnEXEh5TRXHCvsRrlFXrNZRByTHM2jzt0NQbrWjm8dF2W8qZlYM91vl+saOuWZdVQ5CUvX13iWxabLOPrhyfyJKdVS2PT1JnMB9RlzcU8YDAJSU95D8RaLOXatV2ZlX8fL4nGAY9vVmz2OezlxmGXDWUuu408r/jlFy/UwlqCT06HVK6BqUiDDVOnq7p+fKLrciN2fVt8K1nFZrFnejVik6VcBCGOYfAsGvDVeoWuiUTz/uHAKLR4+fNb7r59RDd0TnHJdOqz3abQdcwuhnz7m/cSEN+KEmsy8YlUDmowEHXS8m4rCryRjxe47FcnkqzqP5+Ob3PYpbiO2ZuKsqLG9XWJElTsl8izeXzcohvCOAJBq5yD4AHCyEGLCxzbYHw1okgLppcj0iTn7vVKrj0FJxT1UkfXCeky8EwF2pPP7E89fj/UMkA4CuiaFj9QlbO6o1u6zsvPboh8s9eX67rY1n8c6Dyf+piGRuiZjKcBpi6V5sPDgW++XsocWde4nPm4gSO4A1WNVYU4yb7/asFvf/WG7TZF02C7lvCJM1FuvzoCshx69nKqQhdkC16qccE5ZNtSTkfDMkDTlLnEYhBJanmZl70BSdNgGFoMRx7DwGIY2VzNPFylZDEMjWFocXEZqupb70cyuZI1dh0qEk8n9C2mFxG2LSqBU1pT110vjZQtvaGQvTJnlfg9uUBBfpZt6kKDVH6BuhU8hKZrvPz0Qmaxh5Td8sjpkEnepWo1JQ1JDq3ZrQDEAtsW+uLTgTQpOaU1m0PJRhlP4rTmbilB3mXVUqrM1jSXIJJaPXfLkp3DOZtS9h8ydvMDB9u18EIHxzZkqZuWnJKK0Le4mn1Qshy2MZouppEO2J1KnjYZuvIwdI0Aova7FFMl7/iuqYBlIqebXo16hZJtSiHQNG0/Nz1jqA3TYPF+zfsf1nTqGrp9eYGmadzcTFm+33D1ckZVVBJ5t884bk8cjinfPa1IDqmkHV2N8AJXKVZsorF4JNzAYZtlPL5dEx9TdEMjPeV8/Zt3VG3L376+YTwMiMahZLzOAj76/Lofffxrf/w5Td2wWopI4U/+4GM++mjKZpNy3CZKlupR5hV0Xb9QB/FGxGnd76zeLTY8bvdEI3H+CliuY7OOOSVScRcKZVsWFYXaBZwhb37kYjtS9e/ynEV8oqhrXu93pMeMXZyiG3J9ji+GXAYBly/n5LngRPZ76Yb9yGO3Pcnosm6xFG6gqUSG+3i/ww+Fe3M8ym7DtEyJ8ItlGS78mVrcu51ADqXAEnhXXQoR9vzch7MBL57NMW2T+3dbtk97DEukzK6K7yyyknDg8snPn3FYn1g/HXFNk+SQyjhWvbddR48EGU5Drm/Hct3cDnn2cko09H/yTP29qNw7OvIkx1eJ6ablUOUVu+WBYeAxdBw++fKWu28XrPcFg8Dqwy9AOClNW4j8z9RxfYe73QFbIWrrpiNQsqE4KbkciPkpUzbyTmnBi6qlPkm03P5YYtu6MEvqDrvtYJuQnjJefH6DZZvMLyMWiv5oGhrziYcbOJiWID4dzyY5pP3Yp1XI0bqqKbKKvJAW/hCXonRJS1zHYBBKNxFFDmZa4nkWV6/m7J4OfbCFo+bmhvGBuz4eyIc9PWasn47UqhrOioZhZOG7gjrNi0YudkNaPtvU+ptSVohyRMhzMss3TbFoO5ZItxqFPS1SoW0CxFnNOLLBQCrasMMLHMbDgKJpOKxPvNmcOKpF0jke71zxGj+q1Fu1qBa7tUhCTdPg/imh6/Qf8bs/SDerouqXuQB24JCccloVw3gO+T7G50AHMaZ4QSqgpoNUwaPIYjgOyJOCWjk6Nejn245K8uo6CEeyWJ3ejCmrhiwXDXLbQtOBpebxly+m7JYHyqxkOHQZImzu5eOWtun4/MuPefS3lHnF6nGPpsHtRzMxvzWdstC37E8pQeASXbpUTcPqScZHj/d7rp7PWD3tubgZs18dyZSrtGtavn/9SGjb/Ms3t5zKkuTqgqbt+OrxieSUcftijqkJMsF3Dea3EwLLoiwqamXdNy1DGQZtYavEJUVVq52IvA9p3jCbh7iBQ5FXNHXLzc2U6mrM4m5DmZfs65zDqWTQdTiu3Qd0nPMXJP7SVJ2aznZ9pBz5FE1DZDs8xCsZt7yYwyfikfEtm6thRDxLeP9ux3jgYHsW795uGQRSNJVLWVTalk5eNGwedtw8n4h/oVE5C5ZBmZc4nsRgGmoHVdWt0p63gM5oPqBbHkmLM0VTZKuh6zDzA3KFdyiqlvXyRNN0TGeBYvnn6LpONAq4e7dSNFaN19899ONX17fJE4HSRb5gsg3TwHJMonFAcsgk33cc9rTGf97j9+JwL4uK0y6R4F/bZL86kmQ12S7nuP+BP/zjj7gMQwZ/8Iq//IvXxGlNq0D8Z9fo/iQW66bpuFIjgU59TdO27ONSQjY0jTwrMAyj1w+fdddd1xH5Fi8/u6ZtO1b3WzQkCm6zlnCHQ9NhfLdgOItwA5fRqOwPFCHbyUVa5RUjlcZS5RVF23HxfMoP3z2KkiMpcB1RcIwi+T7LsVhuBVgUejLLnt2M6dpOGDF51dPpyrolCm3Kou555o5tslmelMlKJy0aHHXA5IUsfpNM8MUDZYxolUoiyWpcW+/DMZTikjgTKaltygywbTs5BEIXN3Bxg4L0JMalcOR/UE0YcjEeTilP79aUVcshlufZFKJ66Tp6HXDbyd/r2h8keecIP8sxOaxPWIpP41hKt65uOmI+KogCuZk5vq2UVErmVtYKcdByTCSFilS6wGgc4kceSVxwcT1i+binLGoc3+FyPmD7tKeqBUlgWvI7jW2TYCA38mHgCVI3cDD0kmST06ib0/l3aesPREzHF1Ln8mFPVbc8fzVF0zQG05D33zz2KIY8KTisT4zmAzHqjUM+nk0BWMQndtsTg6no2j/+/EpckadMMnhnUZ9RYNg2wcDj3XbPs0i07EXd0HQt81GE5Zic4oxf5QsxhU0jPhmP+dX7R97fHVS+p4VhGgxcl7yWCt7dJQSeS9dBVtRUVatURZLNaloG21XM27dbbq4HDCYhT+8kUKU+ixsCB9uzyU5ZH6d5dqHTyD6nzErWDzuyyGM0DpndjDluYvanlOcXE94+rvnTX3/XIwu++MNnmJYwcdoO9nHFcBpKF6vc63XT8rQplepEmR51jc1iT9eJbFnXlCrLk6KraVoqTfYGZ4SIrT5jui7KMc1zKRVd87SLiXyT2eWQLMnJYoEMTi8i6VgUNTUcSWiHpgm+4eJ6RJGXJHHBYOAymkf9hMH1HVrFyHEci7Ks+anH78Xhrmkax1PB8VQQBTa5amerqmVftfzw/SNffP4CxzAYTUOOiSwzTVUxCMejkXGA2eH6Du++XzIeuRyOspk2dI3b52Mc3+a4FemXYep0JXRtS9PJSOX2owvGgS+xZKbe0wsdWyzCRdXIzLuUmDQ/dFk/SdapYYp13B94OKaJZYiCIDmkBEOf0y4hTaR9i4ZeD9xyLJ3hNKRtPsy5i7LtAzbOy+MkFlnoaOSSZyV5XuG6Fm7dqiVNje8rl2xZo0G/FHUdg2NSYZu6oAeU1PFs9HBVJ9AfuJ2OrsmM+3xINU2LaQmHw/bsfukYjXySY8azZ3OSsuTiekKWl2yf9tw/nFTlKUvnmUpSquqWJBeEr2vraJ3Wm566ruu5H2JcS7l/PMkIy9b659k05yR4TS3RWgoN2rbA9W0836Yua9b7nOnQUXp3ccKe9xVlLtFpP/uD56wfdlimzuxmrK5L8AKH/U5kd2VWqm7GYjob4Cn7/vVkxCDyefvdI8NIETVVatXkUvJG80LlZn56y+P+yHgqwcdFVqKjkR4z/uSPP+XNw0res7YTaahrkRcV8yjEM01WacrAcegmcDql8j4bOlle8vMvXlA1grY4x0iWijM/mwzYZCm/vnvkh2+E+hgEggb48vaKom4IPYe0KPnN/UIVRx2WoRNNQj67uSSpKjCh8mwuX8x6lIBXNZJDrMH4YshxI/z4/UYOuHDkYzlyQzRVQHpRypLTMA1c3yYY+mwXMsI4PEm8oWsnWJZBUdaSAqXIqqP5gKsw5PViTXLKSFQ1LBwZOTjLQgoJ0xAsxkefX/P6q0eKsqVqROl2nsOHvokb2BxjWSpHqnP78XsO9Du283jWUWPPOBN6ZZ4UxPtEOV9zphcDiryUhbzCdjhlzSef3LBPMlb3W7nxuLbsC/YyltnvUum6PTnI0zgnTwpFyBwyuRyRpQWeuqH9dY/fi8Pdtk0ur4c8PR5IM2lT5heBBMsaMlfruo7FMWbxcOiXbedHWXc4ltFrdc+xWqOxz2RqcNhnMj8e+pRZialav3OWY56WfRL6eBAAsN4ce5i/bho9psCxDIqyYfV06pUWvm8JY0TJk+qqYeC67JOshyoVqaTWzy4H1FXDYSdQrTitRC1TNbx7r5jUScF6n5OXHZ4j+Fg56OWQXm/Sno1hVg1h6FCXtVp+Nn3wgKkWh4dT2Y96NEV3fNrkWNYHe/M+rhgGJpZxzknVmY9d4rTquRejsa/i6kqe3q4IR/JadZ2ymq8PYs5KSx4WMrtt2w8wN5AKO44rXN/swzaaRip5GX+I7X489mkqoSGuFgcVQmzgufKBOi/PzxbtQWBhKbpeq3T3pTpQDV1GFG0rXZIXSirTw+snFu83DEYBmgYPDwcxmqiYvK7rGE4iDuuT0uZ3hEpZEdqicijbhryu2a2OjC6GzG6lIzxuT5iWyWQq1fLscsDq6UjVttiORThSo5+q5u1hT6rcnH7k8XI85s9++5p8XRDlAeOLIa5lElclpi55rb5lUTgWXiDc+uNGXNpVIZX15e2UNBO+vmmbRLbNYxxTZpWMCNQ1vX068Ou25Y+e3/Ll/IKiafhms5YxV5CR5I3M8/OMzV7oj00t7Jgg8lSohkgb98sDP/zunuefXjEMPI7jAMuxCEKP17+9wwsddU0IMG+9TjFUl3n1bIwbOKyeTn1q2TGuuJi47I4lWd6QJznTmzFhFJBXNev7LcttztVMIvmOm5McoErx9vzjuQSBLxNuTZ3ZPOTN2x2muhYNJWWu647hLGL+3Oa4iTlsTv2iVz7zojgbhRZF1bLdZniudPuvXk3I4pzTqaBrWgzLpEhLNA3u3u1IFcEx8EwmsxBN11jvT6zuthRlI8H1iz3DWYRh6sxvJ8LOinMh4p5ydttUdjltx/JBxnaO77C8/yma++/J4d52HZ++vMILJTXob/3iY/7JP/2aroPnn1zQti1xVfL97x76WbulxgSDgdtXZ1UlKgBDjUaauiVNCrEoqwDuc2KT5cjYo9sn7BWudTh0OSQZjmNx2MYC9ClKHFc0t1lx7GfzTduxWewZTiM0XcdxxeXWdZAeM2pVlXmhLIeWDzumF0NB0Spe93absjuJa/JsjV8+nZhOfSxDZFBl1bLbJHjq+Za5XOijkfehOlPJ6LYryyTflXFPkkm71+eOqg1720GoC5YA5MMWuEbfMuu6zNbLSr7nTMWTOaCSWho6D3c7nr2c9tTOcxA1iA5fDm4ZB53107quYVg6RdEAcsDUitTXQb8wLrKSfVz1OnyQyunMrQeRfgL9+9FWXZ/9alvyRZ5n4TpmH9AMcNilaq5q0Kkbbd10tJ0UCm++uufVz28Zuh73qy2Wa2EYBnmeSPhGu6S6nbBW8W/+wBMktS0Zp2++eWAwDXEDlzQv+exizmUQ8o2/JKvk/d8s9oxmEV7kcvduRZmVxGUpr2t8wgtcTodcIFumyQ9Pa0aDgIsgZOhI8MXTao8/8MQ449nout4D1Q7rI599fCOa9LzENS2+v1+SZwWmaVAWNZ16IdNjxv/x3VsmswEvh0N+Mb+gnc15e7XnhzcLTMvAM80+NlFCUYaiMnk6MSpqJpdDJlcjqqKShaQyI+p1038eAUzbJPCkALHUYrptO9aPe4KBx2wectqn1HEHtDxtcwnMUZREXdfZHGL26yOff/mC8WqP5VhsHnZcf3xBU7dsn/YMxgGOa+FcjwkHPnffLrj9ghASlQAAIABJREFU5JLxLqEopauqFIsp9GWPtFYYBT9yKTWZ/4/mkXhqdinBwKPYSIcSDoQPU2Qlk6sRU01j+X6DaUioTzgKeHj9hOcayjxosl6JouyZKziH9pBy2qe9Yg/A0EWOe/1iTtO2WNOQ/frI5YuZYDCaFsd3KFLpTn/q8XtxuFeVOPxupyNxrR0PogawdV5/vcCxddJxIDzvthNKoSZ2b0fZiB1Lp66l+o6TnKbpeFgIe/uLX86wbIuqrPplYFXUNHVC23SEA49wIFrpqqjUf1cp5hOpStKjHJqWKRfZeSnYtiKnKrKyV4+cKzPbtdk+7QkiD9PQSY6ShGNaJrtNzCERWV1ZtjwsxHjh2oLIjQKzl0C2rYQmHDYn8lwq/aqosB2Rnlmm0dvw27ZTC+YzB9pgOHCIEwkGL0rZ9ruOgQMKTyBtZ140hL4p1XbVUlUwmXi9oanrOlzf7mWdUSCHPsBpK6EXu32O50rw8CAwOXTn1CYdre6IUzEEdY3EKVqmyDFBlDpG3bIvhZ53Juh5SlXk2kafRGObeo85tlUsWlW3dOr3iNOai4lLo26cySmnSeXG7LsG9++2DIcunmNS5RWupm7uB+H1v/nqns+/fIEXujy+2/SBHIe4Ik4rVr9+oKrU/B6IxiLX22yOpFlF2MjYL09LXu8k2u7T6zl3hwN33y0kYk/NXU3LIKlbdvtYMNeaRp4WDEZeD/9K7JKx52EbOp5lskoTmkYYMW/ulli2yc+vL+Um2XWs0pRv3zxSZCJNNHVNogsLCcLQ1Q20UQEyRS6MpUylQA1dF0uXbnfkeayUicp2LXabBMtOBCdgKfPWMe1pq17gYjkdF89kkRwnIjM+x9LVZQ3KqOc7Bh2wOcgCMRz5eKHDPj7nDWj9otELXRZvV+SljC9PWcHPnl/xZrlBN6VjyouCrpWMU0PXGbsuK10j+aFmdb/l4mbM3duNeDYMre9y6ydxmopPRDoEXZcRTJ6WeK7JdiNwNccSPb9t6ixXCYtlQuAaAtzbZurzIuybX/7RM7773SPLLKNRGc9ZUlCkBX7kSaGnYgbDoc/mac/66dizdwAml0NqNQbO4pz33z2pPNifPld/Lw73pm64X24ZKffm8v2Gi2cTiqwk3gsj5FwRRoGNYUhuolFoJAoTXKvZ2ctP5mRxLrZfhY81TFn0NLVOFue9ZfkcruC49l8xRbWKgyEKCZP0KLAkkGgv25VZrmHqZKcczdD76t5TCTqnXcz66cghrri5rNkeS3zX6G9Qp7RWxiOpJpum66mYvmty8XzK+l70siNlJBnNhVW+edhh2jJWGrg28SGVODn7A9ddbn7imi2L+oMztRSPwPYgIQGGLnLI0Ld6OWRRCYDMcwxZFlsGjmsRRF4vdxPeRcv26dA/Ty90CYc+Td0wqFviQ6oAaXA4yU3TtsXclRo1hRrJ6HrH9VxiDR+WoolO87qv9JtGLPGHUykohI4ezfD8o0u2iz2LtRhOzlLOthO/gx/YpIksp7oOdrsUTb2PZyt+14GuQGiyCBUp5+OjVOZ3T6nkhDoGl/OAqhDuvOdZbHc520PBrG44nFIe32+ZzITb8s2v7iiqluurCO2ZxuZxx/xmwsuf35KeMjGs7BIml0N+8Yev8EyTqm3ZxmnPZddVYpdtm3zzbsEvXt7I9aIya1s6JhdDvphJpdeqccLzwZDwU5uv3j6S1TXLlYTPXFxPWD/Jzko39F4tdl7offT8kqptWO1PSiGjY2g6E8/HicQklcY58SFlOI24vBVXaJ4U6IbczKZXY8q85OH1E2XVMlJmu+QonJYsqwhDh/nthO3Tnrqo8RyZl58/TxfTD9earisJYy0jomgsn8mv/vKO1/YDH//8hutXc95/80g4EnJp8fUDk8shRy2WZbySWgYDj1c/u+ThzUrY6gp5nRWiNhOfTENT1WCJnDIaBZz2Ca7dEEUBSVywWJwEmQ2MQotDUlHXZ7dh13tv8kSY7m0p/Kzh0GUwDakjl67tOO4SeT2POdFQsN1h5LJ4ODA85ZiOSZmJNPP6dkg0DvF8W+isxk+f7r8Xh3vbwZvvV8AKgJ99ecPlcMAuF0xrcsrEbKOq7kjxXsofwbDqusUyxNCxedxT1yLhmgxstos90+sxhmnw/LNrikwCjc8mg9XdRs3MXbJEAEB5XuGpgO2yqHAsW7TjKk+0yErCkRg6jLbDCF3SY0YW5yRNy9Mm628uu31OWcqS+KhJRXJepBq6HLDbtCTwTCxD4/qjC5pa0AqHTcz6fsvzz65JjxmWa/H8s2uWdxscV4LB47Tunalt2zEMLU5JTdN1JCeRV5aqFbXU/PAcuuEpxxyIptYydT56NiHep4p7UROo8I3R3OciCjkUBZvlgc3yQNvCal/w4ipA1yVEvFQp8WIyU93Aj2y2TXNm2Eh+q6lctoZl9KoGR7kl60Yq8ixv+puWpmmcFNvntJcK1jR1uqpFPlgifd0eCkmxn/oYhqQotS0M1e5gtzz244K2bnpZZ5o3WHUn6fJFQ1t3aIbGQVWTZdUyHnuKD6RxezPCD1z+7E9fy8154PH07gzM6jjsJAfUMHW++817BqOA6fVItO9vVxyUASyNc4KBx2ETq4yDjqZt2GRZrzN/f5RixlDz4K+/vuPv/OGnEtmXpTiGhGA0bYuh6fzi5Q2v1xsMQ/7+sq4Fm1w1NHXDaS9Mm/bMT9FguT3KmOOjCyLfZXmUg/46ing+GPL8T4b8br1ivzn1r+FoPsDxHU67WAVaNwynAkkbziLSo1S0i4e9JCD5Nst3a9KsUlm+guWNU3lfLUvvozQ1Tf493eX9kteydCYT8SzkSc4ntxdYP7+lqGvCUUCZlXiRS+g4zGdDsrrG1HWGjkPVtnx+fcmf/e4H7h8Ogi3RNE5pzSAwsUzBWowvPFzf5rRNiE9Fn+0cKzOSoWv4Ck52Nv2JekiYToau8f7tlqxoGIQW88uI0y7h9VePfPLFDdunPUlccIhLLqeehN7XLcNpSDCQqt7xHTbZjuevpjy7nrJLUhzXQj8Vfcf71z3+RQKyXeB/Bhz19f9N13X/iaZpE+C/Bl4hGar/Xtd1O/U9/zHwHwIN8B91Xfc//NTfYehikAlck7lC54K0ZJuFzNTOgR61CreeX4ijb7UrKGtZ2F3dSPL8Yp3RdhL4oGmwWCYUWcnVyzmzMKB1HKpItLXv3y0pSnFkVkUtGt2qFPhY16k21qCuanabhIubkTxn0yAMXC6eT3uU6VbBgnQ1Y3Qsg1LJDDVNLP5nTW1VC972PAs/LwY15b6rVPscjcSokB5lOVsrq3dTtywf93TQKz9yJcs6xJXAxCrB9jq2QVEU/Xwz9C2aRoI3TEOnbZueD53mNelRoE+ObeAGH5Lpi7yi8MVYtnzcc0wqlQ/acTgVPXpBaJyy9Ezzuict2qauxkUfWOmhL7N527Nom475xON4KjANnX1c9Zp0qeIhL1si21S/C7x9d+iXsYZxplXKks51DPKiITnlDMcB17djkmMqIzPHwjDlUG1ruXFc3YwYzQe8/+aR3VFuDK5tEIZWH+KQ5LJsXiyTXtXUtmIv9xyD4UiYKWle9zmdlqmTJzmGZRIOPLI45+ndmmgUoGkSPuMGDvExJT3Je+94UkQ8v5qR1xVZktPWLbYno77Z9RjHtrh6OcMzTb7fbVkvD3z50S37Ihf1i21j6DqR7/aReufHIo5p6gYvdIn3CW4giITX75cct3FfBF0EIXkt1++37xfoL64JLZs/vLwkHo/5erAi3qckpw8YgrtvF0wuh0qSeZAc030iMZjKT3GG85WVmIuKqmW5zXsZcNvBeCZLZwnuMUiOmeQ8qMCds3nouEv4XXEvckhNY/Oww/FtTvuEh1JkrRJvabC3JHvXtE3mtxP8yOX9D2vyUnYDXQejeaTECxbvvl9Ql3Xf7YNw1cOhz3oVc1DFk2EYDEKbg9qhaWqPZBgaNxd+7+FJc3Eu54kofHzfUqE1KKqsKQoqXWe3PvHiZyFV3RDvE7aK9W9apijZ/gbGMgXwb3ZdF2uaZgH/q6Zp/z3w7wL/U9d1/0DTtL8P/H3g72ma9gvg3we+BG6A/1HTtM9+KiRb0+QO2CqzRlM3PGx2PLxZy50yzSRSazZQ4xLYLY+UyhlnGBqYMJhEfPub9/JzOtgqY0pWNJi6RrBLuBwNqNuW9ebIk4qzCjxLIUlNXry8YLU+SBB3J+zuStdIVB6irVKibMdi5LocT6kskXKR2FlKSz4Z2gSRLF16+L5S8YCMhM5KjkbNs8+Hqx95ZBo0StNqqW6hLCRmMDvlFFnZB1tYtsTPLZ9O/evR/kgzvthkojRxDALPIivq3tB0TKq+2j8/p/1R1DVF2TCa24zmA9pWjCyLpcTVHeKqX8i6tqH2Ax2DUEZctqWz3hV9BKBpaH1CTal4Nr5j4LkmXSc3Bde3JR2raiXHte0wNHrWtUjXoCjb/oZet9J9TIcOq50gaC8nrrquYDIUhUZZ1KxXMcOhStE5ZWi63o/jhLcf4FsWwVCoiEAfutEjCwJL8mbVDUwDltucY1rzyasRmqZxd3/AMvRehhqnNYPQoitrTMuRcdo06o1jIrW18EOxvZ9jId3ApelaVusD89kQQ9dJq0r8GW3H/dslZVYR7xMuL8a8enZB3bW8WW64GA/wTJO3h32v/PEtG8+ULiqvavzIIi5LMqtgHPrsk0zQEkfJSb2ZDDkUcq25voMbuLx+Wku0YFnz88s5f+vZLc1Nxz7Peb3esFYHqz/wWD/seFqcuLyK+uv+TFg9h6HXbUeVy+fTCyxm85Bg6NM2rfoMdrz+eiEO8Krt83xtU+f2OsJ2bbzAoWlatgsZN12+nHE7HlK3Hd98f8/9g4C3LMfi9qMLqrLm6fEgObS3Y774oxe8/u1dX4hlScHFsym2IWPJNK+V5nwgyinoM1wHocVg4EoxZpvkZdZ3xJoitjq+07/Pkmfc0t3tBBE9kBCaIPJETTcbsnjcUlcin4z3CWkm8MTzzSxPCsLI/Ss3nP9Xh3snPyFW/2qp/3XAvwP8G+rP/wvgHwN/T/35f9V1XQH8oGnad8DfAf63n/g7+o14WVT9Ida2YmwRBneN7eZEIx9bmYWq+gOk6vY6okgLltu8Tyj59NMZbuDw5//0jlNWEx5Siqbm7v2KN28lXDb0LapaXKwusD3EPQO8rhtMtWQ1DY2ri5DdLiU4ZQwHPu9WW6m2FU+k67reNWroktxTqbHDGevpu/J7yeZf6w/IcxJ723bcfbfg+tW819o2dYPpCeekrho0rVUZkhajecTT/R73PJrIP3QCjiU3mnFk07TnEI2q14lrmsSp6ZrG9lgq5YCF58gSp8hFvZHFGZZj8cN3T/0ozFZQo/MydjxwCIae0C/jHM8ysZO6l17q525FMfB1Xe08XAuvbilSMaHZnk29lsgzTQNDKSo0DbWnkNftrMCxDJ3ZyMX2LK4tg6d12vOFbEu00bquc9iciNOq57JvdzmTscSgAYSGzpuvH/F8mzQpmQydfhZblA2mqVGWLZu6wLUN5mOXpskkYMQyyPOaLM57aaeun2f/nTJqdTIqKSX8WW4uGpcvZv1BbZoGH//8lsMhIYtzrocRuzxn+bBDNw1uptI1egpve/8g4dVF2TCeDpi4HnFZ8rMrYcMfywLLMBg4DqskwdB1PNMUvTqgozF2PbJBxTdfvWc4jXADh59/8YLF5oBjmnzzzbteX14VFbqhM/U8RmOXu+MRXdO4iSI80+SPbq5J5jOORUFcFjR1Q9N13D8ccSy9z+6ti4bnr6asH/dYps70YtCPP9fLE2/f7anrtk9PGqoM3aJq6WJxzd5chSRxwXBsMJsOSErJLz4feKs0ZfF2xdMq6c+Y+JTz8GbJaD5gNCqVYVIUXp//8gW/+9VbdE3j7v6IaRn4z+ZMb8ZsfrdAOxXMb005Fyq5LjrViZ9OBXXdYpYtUSC5v5omQSKjeUTbdP3IzbESUoWnAIgMjSKtmH90xe6YcPduxXGfcvVswrOPPOg66rZjvS+4fEEPNVytE8aDvwGdu6ZpBvBnwKfAf9513Z9qmnbZdd2jeuEeNU27UF9+C/yTH337nfqzf/Zn/l3g7wKYymASDm0ub6cUpfA1dF0jycXqbRgaqUKZAv2BbKnN9fWrOd/9+k7mUF3H7dwTU1Ho9YdcnNbc36+FHKhGJoCSCMry6Dz+OBtJmkoq6aqs8UMXL3DI04LqR0teyzY5xqVEtSF3viSr8X90Y22VWefiaoDj2mpjX/eHu6VGFgDbY4l5t+H206sei/r+hzWjsagl8lTlufoCxbIVtOyjz694/dWjQgXIvPo8GjiHehuOLA1BiJu6pjG7HuGFMoop0lJaVMvAtwSutluKSSvOakahhWkapIWEXFimzmzq91X3Wa45nEbMqprJtRiC3n63ZDpy2R8LLAPVpXW0jWJgK6WR49kflqKt6N8HoSVmlKymquX7KlRXY+jCHleKq3Fkc0wqBoHFYHLWkjfkZdtLLF3fpm474qSkPuSUKmLQsnT2e3VgK0hTVbW0CqMsVafsAqq65XLqso8rNOBi7FDVZ/kp/LNF1blNP0cP7jYJ0VDi/7q2IxqHEsX3+omLZxPGo5C3Txu80OWXf/QR+zjlfrkV85hyYL94PiI+pEQjn9ViRz6umIQ+nmli6jpFU1PUNfeFzGe/e1z1O6MyK/n4Zs63i6XgKrYZh2PBcOAQfnbLZ9cXJFVFNAlFfrjYiSLIs3FME9+y+WQ84X/51Td8G79ncjViNhlg6jpz32fkugSf2Vj2I+kpl27RMBiMPCaXIwa+KyTEvGL5uJfdhnJCR76pZK8t+13GbC4GP00Da2Rz9XzKfn0SFVXgkNUS7uMGjiStWUZv5Dq/D6PZgNXigO1aDCOfKPSIExmBtW3HZnPk6sWMx7drfM1gfDHklOXkSc4gMBnPIpGrHlJJeevkWqubjiSXrGHfFf5V07R9+ln8dotlCubj22+WfXd/7mRNyyTJKt6+eeK4TwlCh6bt+MtfPTLwTa5vh0wGNsttzuLtSox4poHnfEgG++se/0KHuxqp/Euapo2A/1bTtF/+xJf/8yZB/7f+oeu6fwj8QwD/xaib3U4YBp4shCwbTdfYKocmiLEmCiySY8Zum/Qw+66Dl9eyIDrEUv35rsgCF3c7dsujusuC7xksF+Iue/ZizP17MRlEoY+mUATHfda7PMeXw76iNNWsrqyURv1pL2iDWtC1nisOz1yhPA9JTV7KQtfxbCwrxzZ13EAIfqEvY4+zlf7MwBE2vGBXf/cX78jLth+btI0svlq1rCmzkuD5jI2q+N99+8TN8wlP9zts16Y45SotvVFwMhvPNSnKmlMqCpphZHHcxhL2bJlMLoeC8G2FDCgHj/BqqrpVbaWMmWYjgXRVhYCVsrzGsGoGk5DkIMTDPMmZXY25upEZrL8+8XC3h06ojZtNymhgkycfwn6fXQYcTgXLbUFeNEoyeU6dksvrPM4qqoZa3QRCz8R2TEZqSbp6OvWRgXnZyI5Bl/GKq8Y88qEUBVMYOsRxQWjKvH8c2YyHDllesz+VH0LEy7ZPnh9Hdt/dnZK6d6bqGpx/o/MYwjA0Nb+VStIwDIqsomslbDsYSot+2iWE147IInVdIiENnaJtefvNgpuXU8kyiDyGM+lYx5OIX8wv+PPHBzxTPtaSN1Dx8HrJ51++4PIy4PXDit1SlrLbcch+der17kIrlaQjw9AZX434+GrGu82OQCX/lHnFb9f3DGcRtmHw+HCkbjueNo9o2gLfNfjosyumw5BTUWC7FtEkxPccyrqWZXtRUbs2j2/XEhbjmgSBmMskBCXBUuzyOK3ZrOM++KVumj4tyVTGtPSU4fjCcTrtpesLfUs8KJrMxVeLA23XCXp3UlJXNY4nUt4sztmvT4wvBrz6/JrV/ZbXv73vx6YvfnZFmZUqfNzmdMxJMpH0aurAq9uOrKj7jtw3dYryQ+DO4yplOnL46IsbFm9W7A85ug675QHHEmmxZepMr8cMJhXHXz8K0XU+YDCJyMsHwqHPR6+uOOS5jC/z8ieO4f+Hapmu6/aapv1j4N8GnjRNu1ZV+zWwVF92Bzz/0bc9Ax5+8klYJpHvUjQNhq5zzHPef/NIrF5AQ9P6zM+mFclgrtoi3zX44suX/Povf+i37L4jEXlJLhzzq6nI4PzAITLlrjzwXSzb5OHdRsKsj5lIE10TTdfRDa2fr1m2SRZLAovvWximznGb4Ecu201C4Fk9FuH8wT3/cx9XzFSwQ9N0PL5dc3k7RjN0As9ke5A3SFQg50OoZRhYfU6rYQje1srK/oNoGHKjWC92MtpoWqq64/FOxk1JXDAceWRPMlG7mHickrJXGdmmjqX04mVRYzsm4VwqmsX9huSYEacVUWD3gKxGLWCTUlLmO1V9J5kQF0eqUj6ofUC3PGBYJm++ume9L7jJSkbzgWBLq7ZnoQMYpi48cF1j/nwK7zdsj0IOPLNzQt+ijStMU6qkcyxZ23YMAyFBmqaYjboORmOvl9G9vzvQdYKZOPsBXNtA18B1JE+3a2TktDuWHGOp+uZjF9OQsVulLOTnR12rjqtq2B5LLEPvHZfnhXkHvefAsS0MyyAc+X2KmB+6Mt++mZJWFYNJRFVW1G3LYX0SDpEjHWIa50zmIad9wjlFLN4nfPHxLRPX4y8Wj5y2SV/Fur5DcswENlWWHNOc3fKAbugMpxGPdxvqsibJqr6rEJRyg2PL2OzxdGL9sMP1bT65mrE8niQj2HV5XO4IfVPyB3QD35NK0vFsRo6LqemUrqQGbZaHPuA+i3OyouoDaEbzAdunA/fv971voKxbwsBG1zT2sYzTBtOI3WKPpmtEo4AsltCX1d2GYOirm2OHHkunYCU5buDg+DabdcIgtInTivvvF3z25QsGjkPRiKN8OAlxfYciK7l8MePd1w/9KLBSo2Jd19krKe0gsIQc2nV9NGXaga2Q24au4bln74nsjaKhx8B1WWgyji3V2HIYWH0k43nxOxnaokS73zKYhIzGPpqu8f5+3bP9zzjxv/Zc/cn/CmiaNgcqdbB7wL8F/KfAPwL+A+AfqH/+d+pb/hHwX2qa9p8hC9WfAf/7T/0dbdtSNA15WrCJ96yejsRprSosg/HE57iXscH5g3N74XMOQHhc7mSUYeqYyukIMFXLNMe3+ySW0XwgLaGmMZ8NJSYrLYRLYWi4gbRfi4e9em6dmv9qDAKLqqzZ7XMCz+RxIek1h7jCORS9NvtscKgbISk+beS5u7bB7lgynokCp20l+1Rr6KuE8zjFMDQuLqNe7pQcJfUmTvN+uXfWwluu4APOlvxBYLHZF321OQgtkkzMT4tNTtMKj/pi6mE5llw8kU9W1+x3MfEhVdW+RlbUGInMFM/cFM81qRQsrWtahkOP+CTmn7O0cRBKoIfdCv7YtSueViIpsy2dKLSxXZs8kbSapm6xbOH4pMeMNKvUh0LyXc8slDSvewDaeVw3HToMpyF5WpIXEuwSZzWalkswt2vjuwbHuu3fz/HYx7QM1QXWeOo6FISwhKWMI6t3JBuGYt6oZfDZVm67lox6lMHq3ILXyqR1NlwNQpssl1mtzIolH/hsOd8eYnbLI+HQ55NnF7x92pCpFKC6ahhMQgzDIBjIjda0TO6/X6DrOne7A3ccePxhSdu2EsXo2+r/a6SnguytjPJ00xC2va4xuRqyeCOHStvKc5Xs4EaWooHL47sVp1MhlviqZOC7nLKC9/drmqZhOA0ZTuGj55dEjkPZ1LimxSI+8fWv31HWbe8Qj0YBF1GINRpyyHPGFwMe73Yct7Fk/zYt+7iSRaNnYrsygjIMuYFmKrpPPg8ZtiNd57kbTk+ZJLNZBhGiaNstD8yfTZlcjvi/2juTGEnSq47/vtgyIvel1u7q6emeHhsPi2zLWCAjC2EJsEHABckHbubmA4gDsmUJiYMPcEDckJABIbFYllkOPmGxiAsyjO0Ze/aemd67lqys3DMy1o/D+zK6Pe5pj3E31VmKv1TKrKisUrzKjBff997//f+DgyHzQUin2yQztey3rx0wGy/Y2usWA47LRcTOxU0ObvRp1VxG/QlZmhPU/cLYPkmywg3JthMp3+U5eS66RdO5ECyCwMVfyVZPQl757vVicKriWlx8ZpO7NwYkac5knpC8vk/Fs6k1fOIwoX80xzEL0iRO8Sqi1nnnWp8ovrfQeBDey8p9F/hrU3e3gK9orb+mlPov4CtKqc8AN4HfBNBav6yU+grwCpACn30YUwZEEuD47lCGSrQujCSyTKYtp+NQVlOmlhonOX7Np9rw6bTrvPita6wUHVOjY7J1oYdf9Rj1J7ieuCHVGgFHN4+pvu8cFVOX3L20JVOv8yVZmjMZTGWc37XobbUKb8Y7146I4rzQUPZrFSn3VCgapPZ9k5yWaWoaVqfU30IZpe8finJjveGTpAtyU4MGTJddUa0K+6Xi2bQ6VVq9Bv03DWXLttjuVsS8wbFNk+UecyVOJBGvpjarvmM456nQ9epu0aX3qx7xMmaQ5QzuDpmHyfe4qsdxXkzDohR5KjXNJMmKsf/ZSIxLWnVhHbXrFRzPQamMOMkI6jZb202O+9NiPHs+FkZGluVk8wi34oquvuGWJ2aStNvycM0QGkpW7CtrQ8sS2lyjJeJiy0jq7pa5mMIooxZkNDNhWriuVRiFp3FKlsjwzDJKxXjBu2e23TE3siiWLf7exR7T0ZzhMCyMYTa6AbNpZCiaspMMIymxrXovcapx05zxVJhbji07EdsOcY0rEkAaS8nC7lpMoojxYFaItVUCj3C2ZONcR6i5tk20FM37n/7IM9y40+fG9QFBxebCM1tMTmbkaU47CMgzTa/X5Ob1Q2qNoLiBxMtE/E0OW7NuAAAQlUlEQVQNt7/d8kGJ6FbgO3S329Q9D6/i8vSzTZHrcFxavs9gNCv02Lf3eizmS2xLMVyGnKs3sJQYeK9KF47n4FcrTIZzrhsHLsexxXC74TOdLgu1z9WuajSVv+9WRAYjSXOG44inLvXobTR5+7U7RIuIertWLJRAyqcLk6SnQ5mi7bRqLKKYZ6+cp9kdc67XoT+dMR7OuHJpl4PhWFbBShZNy3lEe7NJb0csJI8GIXmu2ZT8Rppl0q+yLBwXOg2X4VQS/P2yHwCLRVIspoJqhXARce5Cl1F/wmIpuj+WJTuVlZR1f7hkswNe4GLNYwbHM5bziM5O+57r1m6b+TQ0k0EPxnthy3wH+NADjg+AT7zL73wR+OIP+tsrrKR7tRZD2lbDEzlOVxqT+8cheS6rtJVq4HAwY2evx3i6oFHzCCqSbLZ3WzS7dSqeWyTM5ULkX+/eEupjezij222wTFOR5I3Swq4rDBP8isMzH9grVP/CJGFjpy31wjiVRLRMpE5uuvrLODdlFDm/1bZ8GUs5IzLsjVpg06h7KMviqcvbpK/dYTqPSVO5STTbAdEiNm+6bJGPDqd0OkExJJHmmmZbXG1Wjac01TRq0lzNzOAPGCMRk/wbNQ8vcMmSTKZpGzJ4NTqeUm34BHUfpWQU3HNkxRwlQg9zE3E1io0qpl8RNb9bd6Y4jjLCXeJzKmqLonej44z5JKTWDOj2alTrPrZhRqRGF92yFEmU4FZcw5uXz4XnilNQtSk35ZVaKIBlQcfYAh4ezrAt9T3DJWmuqRuLwnkojWvXVmCa68soo1mX/0cUC91NIQMoPbMlHs8StjZrzKYRF9qix33pmV06QcDV24cc7w+lTr3ICm9boDAfF1VO0bvxjQ6ObeYfXMfGdm3SNCsmVW3botWpEzguT13e5s1XbolpuWnoH90eFD2o8Sjk3IUuJ+GC40MRPNvYbjLqS/K+fG6TXGs26jX60xnnL25xfDhi60KPmbEmnI3mRj8pYzaXa6TV8untdnA9h6Oh2B1mWU7FspgnManOyZKUVk/kgsNFRBIlHE6kDv5mcs/+rdMJuPK+8+w1mhzMZ9zeH4gBj+fguE4xt+E5Fos0JU6FTrvZqzKfRRwOQs5t1wgCl2gsfa0kShgcT5gtEnN9SLN/93yHWrNKOJXJzcHRjDjJubzXZRHFkuhdh26rTn8qg1au7/LGW3fYubDBVqfJrTvHYtTtuSwmIZ1uQ7T+sz6jmUyJthoetlZmLkCmZx3HxlJJ0ZNJU2GDrXLAyrVpPpNdq1/zsYZzsixhsD+i0aoymoq/62qgazyN8QrChQwTToczhicL5mHKlSubVO7TpHkQnogJVa01/aMpvmfT6tXNBSNbfOG1KmybQlkQYGevI1rkcUpnW8bzr75+WHz4p1ORaT3pz+htNVguIlqtQD5Mgcfx8ZhwHgl9cRHT7NbJ0pzuTpt6PcC1bSykDhmlaZFsw9mS49GSjbZfsDnSVJqLs4XFcCq12txI0K7YPCt6ZBhlPHd5m7devs3h/oloapjJ0hWt0zZ1aMdWLEwpIU8zOp0ApUJxVYpTM/J8L9mtBn5Wk6a2rfADT5qENdF+7+10WExDlILDG8firKQU5zabTAYzjkeRWYFIcl0lvDDKqNUrOLbF8TQyK9qcTtOj06sxHYkImtay2rVtC8sS+79VPCI6ppiPQ6bjUGr0YYxjWVi2bSR5FWBRM6WY8WDKSX8iPHogMBoeG5ui7X33rUPDnxeqaZoaCqRv4xk2wXgWywq6KhPAYZThGbuyoO6TZjnVio1vRuBXzVrHlpXs9nmhIFYCj3kcM56HwphK5f+8sjtbXZgrzQ/XtahaCs+zjEKnlHM2t4yaZi6fPdsW96HWRpNwGbOMksIAO0lyjveHbO71ODoYkxpv24pn0+zUufrSLXpbTTOZPWQyjYRxlOccjKdc7HW42OvQn8853B9TC+bsPbvL8GhMtRHg+Z5RLRWtkzwX74DOVhPHdai3HRqB7F7rnse1u33Gx1OyLGdzryeSv3XhaI/2JyZx2wQNn85Wk2m4ZOEHbNVq7NtD4mVSrD5XWCxT5mFGrmWQcaVfrjUMRyKL7Rj9oOFgTuA7bO82OenPihv63DRVx4Mp55/ZBmTReLI/wq9VZILWtrl+7UBYb/WAOIyxbYv+3RPcvQ0unt9klsT3FpphRL3ms3dlh94sLHpwlunJJXFKHCYcj5ZFI3VVGg0qdvHZyHPpH+hchv2Obh0X17fOcxbGv7ZpKJQr7apqrYIfpUznxqvCvPd5Lv2X1kbjoXn1iUjuIAky1Bnp0ZRcyyrTjYRilBhx/9XkV6vu8tTuJsczEVoCOLx5jK0UN2+OaA5mwlv3bKo1j60doeP1D0cs8pybVw+YLlI6DRfbttjc67LdbWEZok+qcxwlF6zWmqbvk0Qpk8GU45EYWNuWNPMsJRdvFMuggWXu5sKGUIWPqVKSbLtNj3C+lJ3E7TGx8UOU+rtFGqXFDQekb2A5diE52+2JoUk4j3Btha1kZD8zvPd5mNBs3BvcWcxjLCUaOr3dDjVfyg3Do3FxI0ySnBeevwnA3m6djfNdrr9xQL3p0z+ak+cUNMFaIFK9idmNaA3RMmFsmkqNqmPsyLRJ8grPl6Q5HMypBC6WrWi2pfnl2BauL/TKNBWnnO3zLXa2O5JE50uO7o6wlCqYPrs7Ddl5uK6x8XO5fvVQ2C+GUioKk9IPmMwTLCU1Vq0xPHRZ6TvjhbkAYWq09lfTpauhL6UUg8ORMFRaVfL0XhnMti3iJCvYMKvdjnDwZcWujMmD5yo63SpZklFv16gEYioSLxP8qgw3jfoTkR9wLCOiJWWZOIzxHIt2R8Sjdi5ucnTnhGqtIk3nk1lRtlpMQl69erswgfA8hyiSnguAY4nG0ngwI4yEhdYfLoseBkCWyQrbr1YgqNDyfZaJGe03cyhZkpJaYiEZh8JASeJU/FRPZKq80alxYzg0jkc58TJGWdLQnU9kkaGUotP0CrLEqqS2OpdVkoySHIW4ae33Fzz3E7uM+hPyzNBiPdFkiqMEnWuCpsghD45n1Ns1JvmS8SgkS7LC4cqvVnh2b5swSak4tpSXtGYcJZwcjulutxgeTciynA/85EVOxqJVkxtDlek8NWJtEofrKqNd5BDUK0zH0uOyrFw472ZWpVL16G7URbTNlAodWxU9llozoNlrUA8qXH3lFiemLG1bisuXOtTbNQ5uPKwo84Qk99U23DZMhRUbotEKyIcLZoZZsHIDevr9u0xjUbLzqx7b9TqVKy7q7UPyNGMwWtKsucVQQxglJLG4PU3nMZ1OtTDL0Bq2u63iXGZJzGSyIKhVsC2LJEmxbMs0XuOitpxrzOi86J2ES5GjXfmpZpmm7jqAvseRNrouV189EANls2XLtCYyJZ7rd2fFkFDFtfCMifdK4Mr1HGkchnExqKSUYjJLsF2bC9stpqM5sTF0rjZk8rHXkybS6oPSrru4ro1jW5xMlubmIGqBtaDC3tMbDA/HhuKZ4TmrlWdGp+0TGk0Q21JUfNcYRoum/GrFrJSi2a5x3J9KP8JMbI4GM1HIq0hzyvUcaS7aIo8ahTEHh0PDU5bVca0ZUG/X6N8eyEU1DrEdGUlvbcjNY1WvbdVdavUKylL4QYVLVY9wHrF/OCv6CbalcCxlHJwcVuYNq4ZonssNajqP0Ydjnv6x8xzcOiZL0sJU26/YxnqNounrmxp+FIszkVJQ8Rxx7zFa7rmpte90WoyrS/Zv9mU8fZlQbQS0N5t4tk2cScmm2gg4uj2g1RNhvUrgUav5XHvjgI3NBovZ0iRbaW0twgQ3kd3TdDhjaFacAI2WcOKrjQBlRRyeTKhX3YKS2667zBap0TgJqNZ80yCd0QukPNTqNeS8mnJ99Y1wnGVbhdFEr1bh3FObTCYLlKVYziNqzUAYKKMFd98+orfbJlxKXT42swhpmuOZ3kea5mhHdo5ZrtnaaRKFCbZjcb5aYXg4ZuupjUIfaPVe5GleJFvHdbjy4+fZbTS4eTykaRJ+0PALMkW4ndINfO5Op8zjWBynzLyHX/O58oEmw9GM1166gV+rENR86u2qTPKm91RiV/02pRTDacx4ntCoOmJYDxzdHRmDbUgjoQx7vsv89pDcaNJ4viu0zkkocsDmWtrZqklJNhYZlJu3Rt83S/FOqB80wvr/AaVUH5gDx6d9Lo8YG5y9mOBsxnUWY4KzGddZjAn+b3Fd1FpvPugHT0RyB1BKPa+1/shpn8ejxFmMCc5mXGcxJjibcZ3FmODRx2X94JeUKFGiRIl1Q5ncS5QoUeIM4klK7n9+2ifwGHAWY4KzGddZjAnOZlxnMSZ4xHE9MTX3EiVKlCjx6PAkrdxLlChRosQjQpncS5QoUeIM4tSTu1Lql5VSryul3jR2fWsDpdRfKqWOlFIv3Xesq5T6ulLqqnns3Pezz5s4X1dK/dLpnPXDoZS6oJT6d6XUq0qpl5VSv2OOr21cSilfKfXfSqkXTUx/aI6vbUz3QyllK6W+rZT6mvl+7eNSSl1XSn1XKfWCUup5c2yt41JKtZVSX1VKvWaur599rDGJ9vjpfAE28BZwGfCAF4HnTvOcfsjz/zjwYeCl+479MfA58/xzwB+Z58+Z+CrAJRO3fdoxPCCmXeDD5nkDeMOc+9rGhWh41c1zF/gG8DPrHNM74vs94O+Ar52Fz6A51+vAxjuOrXVciB3pb5vnHtB+nDGd9sr9o8CbWuu3tdYx8GXEg3UtoLX+T+DkHYd/HXkTMY+/cd/xL2utI631NWDlLftEQWu9r7X+lnk+BV5FbBLXNi4teDcf4LWMaQWl1B7wK8CX7ju89nG9C9Y2LqVUE1kM/gWA1jrWWo94jDGddnI/D9y67/sH+q2uGb7HWxa431t2rWJVSj2NyD1/gzWPy5QuXkAcw76utV77mAz+FPh97rn6wdmISwP/opT6phK/ZVjvuC4DfeCvTAntS0qpGo8xptNO7u/Jb/WMYK1iVUrVgX8AfldrPXnYSx9w7ImLS2udaa0/iNg+flQ9Ah/g04ZS6leBI631N9/rrzzg2BMXl8HHtNYfBj4JfFYp9fGHvHYd4nKQEu6faa0/hGhpPazH+CPHdNrJ/Yf2W10DHCrxlEX9iN6ypwWllIsk9r/VWv+jObz2cYH4AAP/wX0+wLC2MX0M+DWl1HWkpPkLSqm/Yf3jQmt91zweAf+ElCTWOa7bwG2zYwT4KpLsH1tMp53c/wd4Vil1SSnlAZ9GPFjXGStvWfh+b9lPK6UqSqlLvAdv2dOAUkohdcFXtdZ/ct+P1jYupdSmUqptnq98gF9jjWMC0Fp/Xmu9p7V+Grl2/k1r/VuseVxKqZpSqrF6Dvwi8BJrHJfW+gC4pZR6vzn0CcSK9PHF9AR0kD+FMDLeAr5w2ufzQ5773wP7QILcaT8D9IB/Ba6ax+59r/+CifN14JOnff7vEtPPIdu/7wAvmK9PrXNcwE8B3zYxvQT8gTm+tjE9IMaf5x5bZq3jQurTL5qvl1d54QzE9UHgefM5/Geg8zhjKuUHSpQoUeIM4rTLMiVKlChR4jGgTO4lSpQocQZRJvcSJUqUOIMok3uJEiVKnEGUyb1EiRIlziDK5F6iRIkSZxBlci9RokSJM4j/Bbh5zZfpdXuaAAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.imshow(ndvi, cmap=\"YlGn\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "In __xarray__:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 354,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "xr_array = xr_array.assign_coords(band=(\"band\", [\"b4\", \"b8\"]))\n",
-    "xr_data = xr_array.to_dataset(dim=\"band\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 355,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "ndvi_xr = (xr_data[\"b8\"] - xr_data[\"b4\"])/(xr_data[\"b8\"] + xr_data[\"b4\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 356,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.image.AxesImage at 0x7f1237c61e10>"
-      ]
-     },
-     "execution_count": 356,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.imshow(ndvi_xr, cmap=\"YlGn\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The result is the same, but no more struggling on what index is representing which variable!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 357,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 357,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.allclose(ndvi_xr.data, ndvi_np)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can keep the result together with the other data variables by adding a new variable to the data, in a very similar way as we created a new column in Pandas:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 358,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
-       "<defs>\n",
-       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
-       "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
-       "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
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-       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
-       " *\n",
-       " */\n",
-       "\n",
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-       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
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-       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
-       "  --xr-background-color: var(--jp-layout-color0, white);\n",
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-       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
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-       "\n",
-       "html[theme=dark],\n",
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-       "  --xr-background-color: #111111;\n",
-       "  --xr-background-color-row-even: #111111;\n",
-       "  --xr-background-color-row-odd: #313131;\n",
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-       "\n",
-       ".xr-wrap {\n",
-       "  display: block;\n",
-       "  min-width: 300px;\n",
-       "  max-width: 700px;\n",
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-       "\n",
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-       "\n",
-       ".xr-header > div,\n",
-       ".xr-header > ul {\n",
-       "  display: inline;\n",
-       "  margin-top: 0;\n",
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-       "\n",
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-       ".xr-array-name {\n",
-       "  margin-left: 2px;\n",
-       "  margin-right: 10px;\n",
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-       "\n",
-       ".xr-obj-type {\n",
-       "  color: var(--xr-font-color2);\n",
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-       "\n",
-       ".xr-sections {\n",
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-       "  display: grid;\n",
-       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
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-       "\n",
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-       "\n",
-       ".xr-section-item input {\n",
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-       ".xr-section-item input + label {\n",
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-       "\n",
-       ".xr-section-item input:enabled + label {\n",
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-       "\n",
-       ".xr-section-summary > span {\n",
-       "  display: inline-block;\n",
-       "  padding-left: 0.5em;\n",
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-       "\n",
-       ".xr-section-summary-in:disabled + label {\n",
-       "  color: var(--xr-font-color2);\n",
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-       ".xr-section-summary-in + label:before {\n",
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-       "  content: '►';\n",
-       "  font-size: 11px;\n",
-       "  width: 15px;\n",
-       "  text-align: center;\n",
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-       "\n",
-       ".xr-section-summary-in:disabled + label:before {\n",
-       "  color: var(--xr-disabled-color);\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:checked + label:before {\n",
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-       "\n",
-       ".xr-section-summary-in:checked + label > span {\n",
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-       "\n",
-       ".xr-section-summary,\n",
-       ".xr-section-inline-details {\n",
-       "  padding-top: 4px;\n",
-       "  padding-bottom: 4px;\n",
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-       ".xr-section-inline-details {\n",
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-       "\n",
-       ".xr-section-details {\n",
-       "  display: none;\n",
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-       "  margin-bottom: 5px;\n",
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-       "\n",
-       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
-       "  display: contents;\n",
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-       "\n",
-       ".xr-array-wrap {\n",
-       "  grid-column: 1 / -1;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 20px auto;\n",
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-       "\n",
-       ".xr-array-wrap > label {\n",
-       "  grid-column: 1;\n",
-       "  vertical-align: top;\n",
-       "}\n",
-       "\n",
-       ".xr-preview {\n",
-       "  color: var(--xr-font-color3);\n",
-       "}\n",
-       "\n",
-       ".xr-array-preview,\n",
-       ".xr-array-data {\n",
-       "  padding: 0 5px !important;\n",
-       "  grid-column: 2;\n",
-       "}\n",
-       "\n",
-       ".xr-array-data,\n",
-       ".xr-array-in:checked ~ .xr-array-preview {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-array-in:checked ~ .xr-array-data,\n",
-       ".xr-array-preview {\n",
-       "  display: inline-block;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list {\n",
-       "  display: inline-block !important;\n",
-       "  list-style: none;\n",
-       "  padding: 0 !important;\n",
-       "  margin: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list li {\n",
-       "  display: inline-block;\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list:before {\n",
-       "  content: '(';\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list:after {\n",
-       "  content: ')';\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list li:not(:last-child):after {\n",
-       "  content: ',';\n",
-       "  padding-right: 5px;\n",
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-       "\n",
-       ".xr-has-index {\n",
-       "  font-weight: bold;\n",
-       "}\n",
-       "\n",
-       ".xr-var-list,\n",
-       ".xr-var-item {\n",
-       "  display: contents;\n",
-       "}\n",
-       "\n",
-       ".xr-var-item > div,\n",
-       ".xr-var-item label,\n",
-       ".xr-var-item > .xr-var-name span {\n",
-       "  background-color: var(--xr-background-color-row-even);\n",
-       "  margin-bottom: 0;\n",
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-       "\n",
-       ".xr-var-item > .xr-var-name:hover span {\n",
-       "  padding-right: 5px;\n",
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-       "\n",
-       ".xr-var-list > li:nth-child(odd) > div,\n",
-       ".xr-var-list > li:nth-child(odd) > label,\n",
-       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
-       "  background-color: var(--xr-background-color-row-odd);\n",
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-       "\n",
-       ".xr-var-name {\n",
-       "  grid-column: 1;\n",
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-       "\n",
-       ".xr-var-dims {\n",
-       "  grid-column: 2;\n",
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-       "\n",
-       ".xr-var-dtype {\n",
-       "  grid-column: 3;\n",
-       "  text-align: right;\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-var-preview {\n",
-       "  grid-column: 4;\n",
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-       "\n",
-       ".xr-var-name,\n",
-       ".xr-var-dims,\n",
-       ".xr-var-dtype,\n",
-       ".xr-preview,\n",
-       ".xr-attrs dt {\n",
-       "  white-space: nowrap;\n",
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-       "  padding-right: 10px;\n",
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-       "\n",
-       ".xr-var-name:hover,\n",
-       ".xr-var-dims:hover,\n",
-       ".xr-var-dtype:hover,\n",
-       ".xr-attrs dt:hover {\n",
-       "  overflow: visible;\n",
-       "  width: auto;\n",
-       "  z-index: 1;\n",
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-       "\n",
-       ".xr-var-attrs,\n",
-       ".xr-var-data {\n",
-       "  display: none;\n",
-       "  background-color: var(--xr-background-color) !important;\n",
-       "  padding-bottom: 5px !important;\n",
-       "}\n",
-       "\n",
-       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
-       ".xr-var-data-in:checked ~ .xr-var-data {\n",
-       "  display: block;\n",
-       "}\n",
-       "\n",
-       ".xr-var-data > table {\n",
-       "  float: right;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name span,\n",
-       ".xr-var-data,\n",
-       ".xr-attrs {\n",
-       "  padding-left: 25px !important;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs,\n",
-       ".xr-var-attrs,\n",
-       ".xr-var-data {\n",
-       "  grid-column: 1 / -1;\n",
-       "}\n",
-       "\n",
-       "dl.xr-attrs {\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 125px auto;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt, dd {\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  float: left;\n",
-       "  padding-right: 10px;\n",
-       "  width: auto;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt {\n",
-       "  font-weight: normal;\n",
-       "  grid-column: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt:hover span {\n",
-       "  display: inline-block;\n",
-       "  background: var(--xr-background-color);\n",
-       "  padding-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dd {\n",
-       "  grid-column: 2;\n",
-       "  white-space: pre-wrap;\n",
-       "  word-break: break-all;\n",
-       "}\n",
-       "\n",
-       ".xr-icon-database,\n",
-       ".xr-icon-file-text2 {\n",
-       "  display: inline-block;\n",
-       "  vertical-align: middle;\n",
-       "  width: 1em;\n",
-       "  height: 1.5em !important;\n",
-       "  stroke-width: 0;\n",
-       "  stroke: currentColor;\n",
-       "  fill: currentColor;\n",
-       "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
-       "Dimensions:  (x: 625, y: 317)\n",
-       "Coordinates:\n",
-       "  * y        (y) float64 6.639e+06 6.639e+06 6.639e+06 ... 6.621e+06 6.621e+06\n",
-       "  * x        (x) float64 3.925e+05 3.926e+05 3.926e+05 ... 4.282e+05 4.283e+05\n",
-       "Data variables:\n",
-       "    b4       (y, x) float64 ...\n",
-       "    b8       (y, x) float64 ...\n",
-       "    ndvi     (y, x) float64 0.5554 0.4881 0.1721 0.153 ... 0.7042 0.6673 0.6353\n",
-       "Attributes:\n",
-       "    transform:      (57.32777121388223, 0.0, 392465.9217302375, 0.0, -57.4483...\n",
-       "    crs:            +init=epsg:3857\n",
-       "    res:            (57.32777121388223, 57.44833434997156)\n",
-       "    is_tiled:       0\n",
-       "    nodatavals:     (nan, nan)\n",
-       "    scales:         (1.0, 1.0)\n",
-       "    offsets:        (0.0, 0.0)\n",
-       "    AREA_OR_POINT:  Area</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-3a9da4b2-7566-4a14-b42e-58e0e94b42ff' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-3a9da4b2-7566-4a14-b42e-58e0e94b42ff' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>x</span>: 625</li><li><span class='xr-has-index'>y</span>: 317</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-67d80795-8fae-40f8-b65e-db8e7f8dd305' class='xr-section-summary-in' type='checkbox'  checked><label for='section-67d80795-8fae-40f8-b65e-db8e7f8dd305' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>6.639e+06 6.639e+06 ... 6.621e+06</div><input id='attrs-2e6ea0de-7b34-4a0f-8ec5-40e9dc98be31' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2e6ea0de-7b34-4a0f-8ec5-40e9dc98be31' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6affb026-673c-4851-a856-65460ceb1021' class='xr-var-data-in' type='checkbox'><label for='data-6affb026-673c-4851-a856-65460ceb1021' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([6639463.535997, 6639406.087663, 6639348.639328, ..., 6621424.759011,\n",
-       "       6621367.310677, 6621309.862342])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>3.925e+05 3.926e+05 ... 4.283e+05</div><input id='attrs-53af2d32-648a-4685-b990-15913b09df81' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-53af2d32-648a-4685-b990-15913b09df81' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-39b0a333-9a16-4225-915a-a1b89f70f930' class='xr-var-data-in' type='checkbox'><label for='data-39b0a333-9a16-4225-915a-a1b89f70f930' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([392494.585616, 392551.913387, 392609.241158, ..., 428152.459311,\n",
-       "       428209.787082, 428267.114853])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-db4814b9-b6d2-45b4-88ce-5e3c5032a968' class='xr-section-summary-in' type='checkbox'  checked><label for='section-db4814b9-b6d2-45b4-88ce-5e3c5032a968' class='xr-section-summary' >Data variables: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>b4</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-916b5034-ec87-4332-ab78-1c617d66630d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-916b5034-ec87-4332-ab78-1c617d66630d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d5c350b6-00e0-4d89-8fae-063ced94bf22' class='xr-var-data-in' type='checkbox'><label for='data-d5c350b6-00e0-4d89-8fae-063ced94bf22' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>[198125 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>b8</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-725a3063-3205-4fec-94bc-ae203ad8aaf1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-725a3063-3205-4fec-94bc-ae203ad8aaf1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e5413517-682f-4648-94b9-593527d654fe' class='xr-var-data-in' type='checkbox'><label for='data-e5413517-682f-4648-94b9-593527d654fe' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>[198125 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ndvi</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.5554 0.4881 ... 0.6673 0.6353</div><input id='attrs-32e2c794-bad8-434f-81c4-af65b5648ec1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-32e2c794-bad8-434f-81c4-af65b5648ec1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-07909e60-ad5b-46d0-b23d-4c4d70cbf346' class='xr-var-data-in' type='checkbox'><label for='data-07909e60-ad5b-46d0-b23d-4c4d70cbf346' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[0.55541097, 0.48813784, 0.17210779, ..., 0.46578366, 0.40432598,\n",
-       "        0.45748096],\n",
-       "       [0.42886378, 0.50698781, 0.5046415 , ..., 0.55407855, 0.54530095,\n",
-       "        0.5581515 ],\n",
-       "       [0.38293909, 0.50371527, 0.5535106 , ..., 0.59598584, 0.58643251,\n",
-       "        0.62915396],\n",
-       "       ...,\n",
-       "       [0.57524964, 0.54699581, 0.62176363, ..., 0.27087528, 0.48637406,\n",
-       "        0.63795195],\n",
-       "       [0.60114281, 0.61600067, 0.62748996, ..., 0.48922162, 0.49313521,\n",
-       "        0.72751249],\n",
-       "       [0.60678168, 0.53669486, 0.56684468, ..., 0.70420497, 0.66733   ,\n",
-       "        0.63534578]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-7e79b61c-dcb1-4c31-97bf-d9018a666b77' class='xr-section-summary-in' type='checkbox'  checked><label for='section-7e79b61c-dcb1-4c31-97bf-d9018a666b77' class='xr-section-summary' >Attributes: <span>(8)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>transform :</span></dt><dd>(57.32777121388223, 0.0, 392465.9217302375, 0.0, -57.44833434997156, 6639492.260164049)</dd><dt><span>crs :</span></dt><dd>+init=epsg:3857</dd><dt><span>res :</span></dt><dd>(57.32777121388223, 57.44833434997156)</dd><dt><span>is_tiled :</span></dt><dd>0</dd><dt><span>nodatavals :</span></dt><dd>(nan, nan)</dd><dt><span>scales :</span></dt><dd>(1.0, 1.0)</dd><dt><span>offsets :</span></dt><dd>(0.0, 0.0)</dd><dt><span>AREA_OR_POINT :</span></dt><dd>Area</dd></dl></div></li></ul></div></div>"
-      ],
-      "text/plain": [
-       "<xarray.Dataset>\n",
-       "Dimensions:  (x: 625, y: 317)\n",
-       "Coordinates:\n",
-       "  * y        (y) float64 6.639e+06 6.639e+06 6.639e+06 ... 6.621e+06 6.621e+06\n",
-       "  * x        (x) float64 3.925e+05 3.926e+05 3.926e+05 ... 4.282e+05 4.283e+05\n",
-       "Data variables:\n",
-       "    b4       (y, x) float64 ...\n",
-       "    b8       (y, x) float64 ...\n",
-       "    ndvi     (y, x) float64 0.5554 0.4881 0.1721 0.153 ... 0.7042 0.6673 0.6353\n",
-       "Attributes:\n",
-       "    transform:      (57.32777121388223, 0.0, 392465.9217302375, 0.0, -57.4483...\n",
-       "    crs:            +init=epsg:3857\n",
-       "    res:            (57.32777121388223, 57.44833434997156)\n",
-       "    is_tiled:       0\n",
-       "    nodatavals:     (nan, nan)\n",
-       "    scales:         (1.0, 1.0)\n",
-       "    offsets:        (0.0, 0.0)\n",
-       "    AREA_OR_POINT:  Area"
-      ]
-     },
-     "execution_count": 358,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "xr_data[\"ndvi\"] = ndvi_xr\n",
-    "xr_data"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You already encountered `xarray.DataArray`, but now we created a `xarray.Dataset`:\n",
-    "\n",
-    "- A `xarray.Dataset` is the second main data type provided by xarray\n",
-    "- It has 2 __dimensions__:\n",
-    "    - `y`: the y coordinates of the data set\n",
-    "    - `x`: the x coordinates of the data set\n",
-    "- Each of these dimensions are defined by a __coordinate__ (1D) array\n",
-    "- It has 3 __Data variables__: `band_4`, `band_8` and `ndvi` that share the same coordinates\n",
-    "- Other metadata provided by the `tiff` are stored in the __`Attributes`__\n",
-    "\n",
-    "Looking to the data itself (click on the icons on the right), we can see each of the _Data variables_ is a Numpy ndarrays:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 360,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "numpy.ndarray"
-      ]
-     },
-     "execution_count": 360,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "type(xr_data[\"b4\"].data)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And also the coordinates that describe a dimension are Numpy ndarrays:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 361,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "numpy.ndarray"
-      ]
-     },
-     "execution_count": 361,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "type(xr_data.coords[\"x\"].values)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "__Selecting data__"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Xarray’s labels make working with multidimensional data much easier:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 362,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "xr_array = xr.open_rasterio(\"./data/gent/raster/2020-09-17_Sentinel_2_L1C_B0408.tiff\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Rename the coordinates of the band dimension:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 363,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "xr_array = xr_array.assign_coords(band=(\"band\", [\"b4\", \"b8\"]))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We could use the Numpy style of data slicing:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 682,
-   "metadata": {},
-   "outputs": [
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-       "  color: var(--xr-font-color3);\n",
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-       "\n",
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-       "\n",
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-       "\n",
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-       "\n",
-       ".xr-attrs dt {\n",
-       "  font-weight: normal;\n",
-       "  grid-column: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt:hover span {\n",
-       "  display: inline-block;\n",
-       "  background: var(--xr-background-color);\n",
-       "  padding-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dd {\n",
-       "  grid-column: 2;\n",
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-       "\n",
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-       ".xr-icon-file-text2 {\n",
-       "  display: inline-block;\n",
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-       "  stroke: currentColor;\n",
-       "  fill: currentColor;\n",
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-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray (y: 317, x: 625)&gt;\n",
-       "array([[0.078202, 0.088396, 0.175097, ..., 0.103395, 0.104005, 0.086397],\n",
-       "       [0.102205, 0.075898, 0.085496, ..., 0.073198, 0.065705, 0.065507],\n",
-       "       [0.101106, 0.080003, 0.076997, ..., 0.068803, 0.073304, 0.070497],\n",
-       "       ...,\n",
-       "       [0.072694, 0.074205, 0.068101, ..., 0.140902, 0.100801, 0.068299],\n",
-       "       [0.070298, 0.069505, 0.067201, ..., 0.073396, 0.089006, 0.059907],\n",
-       "       [0.070603, 0.0748  , 0.072404, ..., 0.057694, 0.067597, 0.082994]])\n",
-       "Coordinates:\n",
-       "    band     &lt;U2 &#x27;b4&#x27;\n",
-       "  * y        (y) float64 6.639e+06 6.639e+06 6.639e+06 ... 6.621e+06 6.621e+06\n",
-       "  * x        (x) float64 3.925e+05 3.926e+05 3.926e+05 ... 4.282e+05 4.283e+05\n",
-       "Attributes:\n",
-       "    transform:      (57.32777121388223, 0.0, 392465.9217302375, 0.0, -57.4483...\n",
-       "    crs:            +init=epsg:3857\n",
-       "    res:            (57.32777121388223, 57.44833434997156)\n",
-       "    is_tiled:       0\n",
-       "    nodatavals:     (nan, nan)\n",
-       "    scales:         (1.0, 1.0)\n",
-       "    offsets:        (0.0, 0.0)\n",
-       "    AREA_OR_POINT:  Area</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>y</span>: 317</li><li><span class='xr-has-index'>x</span>: 625</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-7578a904-0956-4db4-b510-0c16819832ab' class='xr-array-in' type='checkbox' checked><label for='section-7578a904-0956-4db4-b510-0c16819832ab' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>0.0782 0.0884 0.1751 0.1658 0.1104 ... 0.0708 0.05769 0.0676 0.08299</span></div><div class='xr-array-data'><pre>array([[0.078202, 0.088396, 0.175097, ..., 0.103395, 0.104005, 0.086397],\n",
-       "       [0.102205, 0.075898, 0.085496, ..., 0.073198, 0.065705, 0.065507],\n",
-       "       [0.101106, 0.080003, 0.076997, ..., 0.068803, 0.073304, 0.070497],\n",
-       "       ...,\n",
-       "       [0.072694, 0.074205, 0.068101, ..., 0.140902, 0.100801, 0.068299],\n",
-       "       [0.070298, 0.069505, 0.067201, ..., 0.073396, 0.089006, 0.059907],\n",
-       "       [0.070603, 0.0748  , 0.072404, ..., 0.057694, 0.067597, 0.082994]])</pre></div></div></li><li class='xr-section-item'><input id='section-cbf6c680-e189-4a2b-b30a-7aefc12c070f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-cbf6c680-e189-4a2b-b30a-7aefc12c070f' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>band</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>&lt;U2</div><div class='xr-var-preview xr-preview'>&#x27;b4&#x27;</div><input id='attrs-7f8723e1-60a3-453a-b2a5-9709237c502f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7f8723e1-60a3-453a-b2a5-9709237c502f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ffb77ddd-a666-499a-b0d1-286f435947ed' class='xr-var-data-in' type='checkbox'><label for='data-ffb77ddd-a666-499a-b0d1-286f435947ed' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(&#x27;b4&#x27;, dtype=&#x27;&lt;U2&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>6.639e+06 6.639e+06 ... 6.621e+06</div><input id='attrs-0634c69d-96c0-4555-a6fe-3d08ef374bdb' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0634c69d-96c0-4555-a6fe-3d08ef374bdb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-320edafe-13e2-43ea-9980-c543a3419219' class='xr-var-data-in' type='checkbox'><label for='data-320edafe-13e2-43ea-9980-c543a3419219' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([6639463.535997, 6639406.087663, 6639348.639328, ..., 6621424.759011,\n",
-       "       6621367.310677, 6621309.862342])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>3.925e+05 3.926e+05 ... 4.283e+05</div><input id='attrs-c55b84fd-fa65-4cde-821c-42c7405922bc' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c55b84fd-fa65-4cde-821c-42c7405922bc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3c167951-cd35-45f3-935f-92e978cd37ab' class='xr-var-data-in' type='checkbox'><label for='data-3c167951-cd35-45f3-935f-92e978cd37ab' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([392494.585616, 392551.913387, 392609.241158, ..., 428152.459311,\n",
-       "       428209.787082, 428267.114853])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-efb0efdb-da5c-4f10-8ad5-4687639c053a' class='xr-section-summary-in' type='checkbox'  checked><label for='section-efb0efdb-da5c-4f10-8ad5-4687639c053a' class='xr-section-summary' >Attributes: <span>(8)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>transform :</span></dt><dd>(57.32777121388223, 0.0, 392465.9217302375, 0.0, -57.44833434997156, 6639492.260164049)</dd><dt><span>crs :</span></dt><dd>+init=epsg:3857</dd><dt><span>res :</span></dt><dd>(57.32777121388223, 57.44833434997156)</dd><dt><span>is_tiled :</span></dt><dd>0</dd><dt><span>nodatavals :</span></dt><dd>(nan, nan)</dd><dt><span>scales :</span></dt><dd>(1.0, 1.0)</dd><dt><span>offsets :</span></dt><dd>(0.0, 0.0)</dd><dt><span>AREA_OR_POINT :</span></dt><dd>Area</dd></dl></div></li></ul></div></div>"
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-       "<xarray.DataArray (y: 317, x: 625)>\n",
-       "array([[0.078202, 0.088396, 0.175097, ..., 0.103395, 0.104005, 0.086397],\n",
-       "       [0.102205, 0.075898, 0.085496, ..., 0.073198, 0.065705, 0.065507],\n",
-       "       [0.101106, 0.080003, 0.076997, ..., 0.068803, 0.073304, 0.070497],\n",
-       "       ...,\n",
-       "       [0.072694, 0.074205, 0.068101, ..., 0.140902, 0.100801, 0.068299],\n",
-       "       [0.070298, 0.069505, 0.067201, ..., 0.073396, 0.089006, 0.059907],\n",
-       "       [0.070603, 0.0748  , 0.072404, ..., 0.057694, 0.067597, 0.082994]])\n",
-       "Coordinates:\n",
-       "    band     <U2 'b4'\n",
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-       "    AREA_OR_POINT:  Area"
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-     },
-     "execution_count": 682,
-     "metadata": {},
-     "output_type": "execute_result"
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-   "source": [
-    "xr_array[0]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "However, it is often much more powerful to use xarray’s `.sel()` method to use label-based indexing:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 683,
-   "metadata": {},
-   "outputs": [
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-       "  padding-left: 0 !important;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
-       "}\n",
-       "\n",
-       ".xr-section-item {\n",
-       "  display: contents;\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input + label {\n",
-       "  color: var(--xr-disabled-color);\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input:enabled + label {\n",
-       "  cursor: pointer;\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input:enabled + label:hover {\n",
-       "  color: var(--xr-font-color0);\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary {\n",
-       "  grid-column: 1;\n",
-       "  color: var(--xr-font-color2);\n",
-       "  font-weight: 500;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary > span {\n",
-       "  display: inline-block;\n",
-       "  padding-left: 0.5em;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:disabled + label {\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in + label:before {\n",
-       "  display: inline-block;\n",
-       "  content: '►';\n",
-       "  font-size: 11px;\n",
-       "  width: 15px;\n",
-       "  text-align: center;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:disabled + label:before {\n",
-       "  color: var(--xr-disabled-color);\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:checked + label:before {\n",
-       "  content: '▼';\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:checked + label > span {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary,\n",
-       ".xr-section-inline-details {\n",
-       "  padding-top: 4px;\n",
-       "  padding-bottom: 4px;\n",
-       "}\n",
-       "\n",
-       ".xr-section-inline-details {\n",
-       "  grid-column: 2 / -1;\n",
-       "}\n",
-       "\n",
-       ".xr-section-details {\n",
-       "  display: none;\n",
-       "  grid-column: 1 / -1;\n",
-       "  margin-bottom: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
-       "  display: contents;\n",
-       "}\n",
-       "\n",
-       ".xr-array-wrap {\n",
-       "  grid-column: 1 / -1;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 20px auto;\n",
-       "}\n",
-       "\n",
-       ".xr-array-wrap > label {\n",
-       "  grid-column: 1;\n",
-       "  vertical-align: top;\n",
-       "}\n",
-       "\n",
-       ".xr-preview {\n",
-       "  color: var(--xr-font-color3);\n",
-       "}\n",
-       "\n",
-       ".xr-array-preview,\n",
-       ".xr-array-data {\n",
-       "  padding: 0 5px !important;\n",
-       "  grid-column: 2;\n",
-       "}\n",
-       "\n",
-       ".xr-array-data,\n",
-       ".xr-array-in:checked ~ .xr-array-preview {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-array-in:checked ~ .xr-array-data,\n",
-       ".xr-array-preview {\n",
-       "  display: inline-block;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list {\n",
-       "  display: inline-block !important;\n",
-       "  list-style: none;\n",
-       "  padding: 0 !important;\n",
-       "  margin: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list li {\n",
-       "  display: inline-block;\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list:before {\n",
-       "  content: '(';\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list:after {\n",
-       "  content: ')';\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list li:not(:last-child):after {\n",
-       "  content: ',';\n",
-       "  padding-right: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-has-index {\n",
-       "  font-weight: bold;\n",
-       "}\n",
-       "\n",
-       ".xr-var-list,\n",
-       ".xr-var-item {\n",
-       "  display: contents;\n",
-       "}\n",
-       "\n",
-       ".xr-var-item > div,\n",
-       ".xr-var-item label,\n",
-       ".xr-var-item > .xr-var-name span {\n",
-       "  background-color: var(--xr-background-color-row-even);\n",
-       "  margin-bottom: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-var-item > .xr-var-name:hover span {\n",
-       "  padding-right: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-var-list > li:nth-child(odd) > div,\n",
-       ".xr-var-list > li:nth-child(odd) > label,\n",
-       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
-       "  background-color: var(--xr-background-color-row-odd);\n",
-       "}\n",
-       "\n",
-       ".xr-var-name {\n",
-       "  grid-column: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-var-dims {\n",
-       "  grid-column: 2;\n",
-       "}\n",
-       "\n",
-       ".xr-var-dtype {\n",
-       "  grid-column: 3;\n",
-       "  text-align: right;\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-var-preview {\n",
-       "  grid-column: 4;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name,\n",
-       ".xr-var-dims,\n",
-       ".xr-var-dtype,\n",
-       ".xr-preview,\n",
-       ".xr-attrs dt {\n",
-       "  white-space: nowrap;\n",
-       "  overflow: hidden;\n",
-       "  text-overflow: ellipsis;\n",
-       "  padding-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name:hover,\n",
-       ".xr-var-dims:hover,\n",
-       ".xr-var-dtype:hover,\n",
-       ".xr-attrs dt:hover {\n",
-       "  overflow: visible;\n",
-       "  width: auto;\n",
-       "  z-index: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-var-attrs,\n",
-       ".xr-var-data {\n",
-       "  display: none;\n",
-       "  background-color: var(--xr-background-color) !important;\n",
-       "  padding-bottom: 5px !important;\n",
-       "}\n",
-       "\n",
-       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
-       ".xr-var-data-in:checked ~ .xr-var-data {\n",
-       "  display: block;\n",
-       "}\n",
-       "\n",
-       ".xr-var-data > table {\n",
-       "  float: right;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name span,\n",
-       ".xr-var-data,\n",
-       ".xr-attrs {\n",
-       "  padding-left: 25px !important;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs,\n",
-       ".xr-var-attrs,\n",
-       ".xr-var-data {\n",
-       "  grid-column: 1 / -1;\n",
-       "}\n",
-       "\n",
-       "dl.xr-attrs {\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 125px auto;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt, dd {\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  float: left;\n",
-       "  padding-right: 10px;\n",
-       "  width: auto;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt {\n",
-       "  font-weight: normal;\n",
-       "  grid-column: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt:hover span {\n",
-       "  display: inline-block;\n",
-       "  background: var(--xr-background-color);\n",
-       "  padding-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dd {\n",
-       "  grid-column: 2;\n",
-       "  white-space: pre-wrap;\n",
-       "  word-break: break-all;\n",
-       "}\n",
-       "\n",
-       ".xr-icon-database,\n",
-       ".xr-icon-file-text2 {\n",
-       "  display: inline-block;\n",
-       "  vertical-align: middle;\n",
-       "  width: 1em;\n",
-       "  height: 1.5em !important;\n",
-       "  stroke-width: 0;\n",
-       "  stroke: currentColor;\n",
-       "  fill: currentColor;\n",
-       "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray (y: 317, x: 625)&gt;\n",
-       "array([[0.078202, 0.088396, 0.175097, ..., 0.103395, 0.104005, 0.086397],\n",
-       "       [0.102205, 0.075898, 0.085496, ..., 0.073198, 0.065705, 0.065507],\n",
-       "       [0.101106, 0.080003, 0.076997, ..., 0.068803, 0.073304, 0.070497],\n",
-       "       ...,\n",
-       "       [0.072694, 0.074205, 0.068101, ..., 0.140902, 0.100801, 0.068299],\n",
-       "       [0.070298, 0.069505, 0.067201, ..., 0.073396, 0.089006, 0.059907],\n",
-       "       [0.070603, 0.0748  , 0.072404, ..., 0.057694, 0.067597, 0.082994]])\n",
-       "Coordinates:\n",
-       "    band     &lt;U2 &#x27;b4&#x27;\n",
-       "  * y        (y) float64 6.639e+06 6.639e+06 6.639e+06 ... 6.621e+06 6.621e+06\n",
-       "  * x        (x) float64 3.925e+05 3.926e+05 3.926e+05 ... 4.282e+05 4.283e+05\n",
-       "Attributes:\n",
-       "    transform:      (57.32777121388223, 0.0, 392465.9217302375, 0.0, -57.4483...\n",
-       "    crs:            +init=epsg:3857\n",
-       "    res:            (57.32777121388223, 57.44833434997156)\n",
-       "    is_tiled:       0\n",
-       "    nodatavals:     (nan, nan)\n",
-       "    scales:         (1.0, 1.0)\n",
-       "    offsets:        (0.0, 0.0)\n",
-       "    AREA_OR_POINT:  Area</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>y</span>: 317</li><li><span class='xr-has-index'>x</span>: 625</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-72e30558-3ccd-45b1-b345-1fcdaf0e9545' class='xr-array-in' type='checkbox' checked><label for='section-72e30558-3ccd-45b1-b345-1fcdaf0e9545' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>0.0782 0.0884 0.1751 0.1658 0.1104 ... 0.0708 0.05769 0.0676 0.08299</span></div><div class='xr-array-data'><pre>array([[0.078202, 0.088396, 0.175097, ..., 0.103395, 0.104005, 0.086397],\n",
-       "       [0.102205, 0.075898, 0.085496, ..., 0.073198, 0.065705, 0.065507],\n",
-       "       [0.101106, 0.080003, 0.076997, ..., 0.068803, 0.073304, 0.070497],\n",
-       "       ...,\n",
-       "       [0.072694, 0.074205, 0.068101, ..., 0.140902, 0.100801, 0.068299],\n",
-       "       [0.070298, 0.069505, 0.067201, ..., 0.073396, 0.089006, 0.059907],\n",
-       "       [0.070603, 0.0748  , 0.072404, ..., 0.057694, 0.067597, 0.082994]])</pre></div></div></li><li class='xr-section-item'><input id='section-c0cf1b72-5cd6-424f-bdd7-ac232bb90493' class='xr-section-summary-in' type='checkbox'  checked><label for='section-c0cf1b72-5cd6-424f-bdd7-ac232bb90493' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>band</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>&lt;U2</div><div class='xr-var-preview xr-preview'>&#x27;b4&#x27;</div><input id='attrs-1fcf1870-c42f-4f20-91c5-87934dc94945' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1fcf1870-c42f-4f20-91c5-87934dc94945' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ebe4155d-f1f1-4a3c-9130-d027b24032d6' class='xr-var-data-in' type='checkbox'><label for='data-ebe4155d-f1f1-4a3c-9130-d027b24032d6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array(&#x27;b4&#x27;, dtype=&#x27;&lt;U2&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>6.639e+06 6.639e+06 ... 6.621e+06</div><input id='attrs-2d8c9979-df40-4077-901e-addd302df6b1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2d8c9979-df40-4077-901e-addd302df6b1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-296df2c7-eda3-47c1-9f44-26fb9d80ce7c' class='xr-var-data-in' type='checkbox'><label for='data-296df2c7-eda3-47c1-9f44-26fb9d80ce7c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([6639463.535997, 6639406.087663, 6639348.639328, ..., 6621424.759011,\n",
-       "       6621367.310677, 6621309.862342])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>3.925e+05 3.926e+05 ... 4.283e+05</div><input id='attrs-9893a5d4-466b-4b22-8ead-3db84acb7eae' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9893a5d4-466b-4b22-8ead-3db84acb7eae' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b0ebd0b1-d9f3-4a6d-8915-b182e1bb809d' class='xr-var-data-in' type='checkbox'><label for='data-b0ebd0b1-d9f3-4a6d-8915-b182e1bb809d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([392494.585616, 392551.913387, 392609.241158, ..., 428152.459311,\n",
-       "       428209.787082, 428267.114853])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-4fa5bf4c-5d0b-4244-8ff2-0a97b00299e0' class='xr-section-summary-in' type='checkbox'  checked><label for='section-4fa5bf4c-5d0b-4244-8ff2-0a97b00299e0' class='xr-section-summary' >Attributes: <span>(8)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>transform :</span></dt><dd>(57.32777121388223, 0.0, 392465.9217302375, 0.0, -57.44833434997156, 6639492.260164049)</dd><dt><span>crs :</span></dt><dd>+init=epsg:3857</dd><dt><span>res :</span></dt><dd>(57.32777121388223, 57.44833434997156)</dd><dt><span>is_tiled :</span></dt><dd>0</dd><dt><span>nodatavals :</span></dt><dd>(nan, nan)</dd><dt><span>scales :</span></dt><dd>(1.0, 1.0)</dd><dt><span>offsets :</span></dt><dd>(0.0, 0.0)</dd><dt><span>AREA_OR_POINT :</span></dt><dd>Area</dd></dl></div></li></ul></div></div>"
-      ],
-      "text/plain": [
-       "<xarray.DataArray (y: 317, x: 625)>\n",
-       "array([[0.078202, 0.088396, 0.175097, ..., 0.103395, 0.104005, 0.086397],\n",
-       "       [0.102205, 0.075898, 0.085496, ..., 0.073198, 0.065705, 0.065507],\n",
-       "       [0.101106, 0.080003, 0.076997, ..., 0.068803, 0.073304, 0.070497],\n",
-       "       ...,\n",
-       "       [0.072694, 0.074205, 0.068101, ..., 0.140902, 0.100801, 0.068299],\n",
-       "       [0.070298, 0.069505, 0.067201, ..., 0.073396, 0.089006, 0.059907],\n",
-       "       [0.070603, 0.0748  , 0.072404, ..., 0.057694, 0.067597, 0.082994]])\n",
-       "Coordinates:\n",
-       "    band     <U2 'b4'\n",
-       "  * y        (y) float64 6.639e+06 6.639e+06 6.639e+06 ... 6.621e+06 6.621e+06\n",
-       "  * x        (x) float64 3.925e+05 3.926e+05 3.926e+05 ... 4.282e+05 4.283e+05\n",
-       "Attributes:\n",
-       "    transform:      (57.32777121388223, 0.0, 392465.9217302375, 0.0, -57.4483...\n",
-       "    crs:            +init=epsg:3857\n",
-       "    res:            (57.32777121388223, 57.44833434997156)\n",
-       "    is_tiled:       0\n",
-       "    nodatavals:     (nan, nan)\n",
-       "    scales:         (1.0, 1.0)\n",
-       "    offsets:        (0.0, 0.0)\n",
-       "    AREA_OR_POINT:  Area"
-      ]
-     },
-     "execution_count": 683,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "xr_array.sel(band=\"b4\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can select a specific set of coordinate values as a __list__ and take the value that is most near to the given value:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 385,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       " *\n",
-       " */\n",
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-       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
-       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
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-       "\n",
-       "html[theme=dark],\n",
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-       "  --xr-background-color: #111111;\n",
-       "  --xr-background-color-row-even: #111111;\n",
-       "  --xr-background-color-row-odd: #313131;\n",
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-       "\n",
-       ".xr-wrap {\n",
-       "  display: block;\n",
-       "  min-width: 300px;\n",
-       "  max-width: 700px;\n",
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-       "\n",
-       ".xr-text-repr-fallback {\n",
-       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
-       "  display: none;\n",
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-       "  padding-top: 6px;\n",
-       "  padding-bottom: 6px;\n",
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-       "\n",
-       ".xr-header > div,\n",
-       ".xr-header > ul {\n",
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-       "  margin-top: 0;\n",
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-       "\n",
-       ".xr-obj-type,\n",
-       ".xr-array-name {\n",
-       "  margin-left: 2px;\n",
-       "  margin-right: 10px;\n",
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-       "\n",
-       ".xr-obj-type {\n",
-       "  color: var(--xr-font-color2);\n",
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-       "\n",
-       ".xr-sections {\n",
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-       "\n",
-       ".xr-section-item {\n",
-       "  display: contents;\n",
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-       "\n",
-       ".xr-section-item input {\n",
-       "  display: none;\n",
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-       "\n",
-       ".xr-section-item input + label {\n",
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-       "\n",
-       ".xr-section-item input:enabled + label {\n",
-       "  cursor: pointer;\n",
-       "  color: var(--xr-font-color2);\n",
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-       "\n",
-       ".xr-section-item input:enabled + label:hover {\n",
-       "  color: var(--xr-font-color0);\n",
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-       "\n",
-       ".xr-section-summary > span {\n",
-       "  display: inline-block;\n",
-       "  padding-left: 0.5em;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:disabled + label {\n",
-       "  color: var(--xr-font-color2);\n",
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-       "\n",
-       ".xr-section-summary-in + label:before {\n",
-       "  display: inline-block;\n",
-       "  content: '►';\n",
-       "  font-size: 11px;\n",
-       "  width: 15px;\n",
-       "  text-align: center;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:disabled + label:before {\n",
-       "  color: var(--xr-disabled-color);\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:checked + label:before {\n",
-       "  content: '▼';\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:checked + label > span {\n",
-       "  display: none;\n",
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-       "\n",
-       ".xr-section-summary,\n",
-       ".xr-section-inline-details {\n",
-       "  padding-top: 4px;\n",
-       "  padding-bottom: 4px;\n",
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-       "\n",
-       ".xr-section-inline-details {\n",
-       "  grid-column: 2 / -1;\n",
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-       "\n",
-       ".xr-section-details {\n",
-       "  display: none;\n",
-       "  grid-column: 1 / -1;\n",
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-       "\n",
-       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
-       "  display: contents;\n",
-       "}\n",
-       "\n",
-       ".xr-array-wrap {\n",
-       "  grid-column: 1 / -1;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 20px auto;\n",
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-       "\n",
-       ".xr-array-wrap > label {\n",
-       "  grid-column: 1;\n",
-       "  vertical-align: top;\n",
-       "}\n",
-       "\n",
-       ".xr-preview {\n",
-       "  color: var(--xr-font-color3);\n",
-       "}\n",
-       "\n",
-       ".xr-array-preview,\n",
-       ".xr-array-data {\n",
-       "  padding: 0 5px !important;\n",
-       "  grid-column: 2;\n",
-       "}\n",
-       "\n",
-       ".xr-array-data,\n",
-       ".xr-array-in:checked ~ .xr-array-preview {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-array-in:checked ~ .xr-array-data,\n",
-       ".xr-array-preview {\n",
-       "  display: inline-block;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list {\n",
-       "  display: inline-block !important;\n",
-       "  list-style: none;\n",
-       "  padding: 0 !important;\n",
-       "  margin: 0;\n",
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-       "\n",
-       ".xr-dim-list li {\n",
-       "  display: inline-block;\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list:before {\n",
-       "  content: '(';\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list:after {\n",
-       "  content: ')';\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list li:not(:last-child):after {\n",
-       "  content: ',';\n",
-       "  padding-right: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-has-index {\n",
-       "  font-weight: bold;\n",
-       "}\n",
-       "\n",
-       ".xr-var-list,\n",
-       ".xr-var-item {\n",
-       "  display: contents;\n",
-       "}\n",
-       "\n",
-       ".xr-var-item > div,\n",
-       ".xr-var-item label,\n",
-       ".xr-var-item > .xr-var-name span {\n",
-       "  background-color: var(--xr-background-color-row-even);\n",
-       "  margin-bottom: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-var-item > .xr-var-name:hover span {\n",
-       "  padding-right: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-var-list > li:nth-child(odd) > div,\n",
-       ".xr-var-list > li:nth-child(odd) > label,\n",
-       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
-       "  background-color: var(--xr-background-color-row-odd);\n",
-       "}\n",
-       "\n",
-       ".xr-var-name {\n",
-       "  grid-column: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-var-dims {\n",
-       "  grid-column: 2;\n",
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-       "\n",
-       ".xr-var-dtype {\n",
-       "  grid-column: 3;\n",
-       "  text-align: right;\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-var-preview {\n",
-       "  grid-column: 4;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name,\n",
-       ".xr-var-dims,\n",
-       ".xr-var-dtype,\n",
-       ".xr-preview,\n",
-       ".xr-attrs dt {\n",
-       "  white-space: nowrap;\n",
-       "  overflow: hidden;\n",
-       "  text-overflow: ellipsis;\n",
-       "  padding-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name:hover,\n",
-       ".xr-var-dims:hover,\n",
-       ".xr-var-dtype:hover,\n",
-       ".xr-attrs dt:hover {\n",
-       "  overflow: visible;\n",
-       "  width: auto;\n",
-       "  z-index: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-var-attrs,\n",
-       ".xr-var-data {\n",
-       "  display: none;\n",
-       "  background-color: var(--xr-background-color) !important;\n",
-       "  padding-bottom: 5px !important;\n",
-       "}\n",
-       "\n",
-       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
-       ".xr-var-data-in:checked ~ .xr-var-data {\n",
-       "  display: block;\n",
-       "}\n",
-       "\n",
-       ".xr-var-data > table {\n",
-       "  float: right;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name span,\n",
-       ".xr-var-data,\n",
-       ".xr-attrs {\n",
-       "  padding-left: 25px !important;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs,\n",
-       ".xr-var-attrs,\n",
-       ".xr-var-data {\n",
-       "  grid-column: 1 / -1;\n",
-       "}\n",
-       "\n",
-       "dl.xr-attrs {\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 125px auto;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt, dd {\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  float: left;\n",
-       "  padding-right: 10px;\n",
-       "  width: auto;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt {\n",
-       "  font-weight: normal;\n",
-       "  grid-column: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt:hover span {\n",
-       "  display: inline-block;\n",
-       "  background: var(--xr-background-color);\n",
-       "  padding-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dd {\n",
-       "  grid-column: 2;\n",
-       "  white-space: pre-wrap;\n",
-       "  word-break: break-all;\n",
-       "}\n",
-       "\n",
-       ".xr-icon-database,\n",
-       ".xr-icon-file-text2 {\n",
-       "  display: inline-block;\n",
-       "  vertical-align: middle;\n",
-       "  width: 1em;\n",
-       "  height: 1.5em !important;\n",
-       "  stroke-width: 0;\n",
-       "  stroke: currentColor;\n",
-       "  fill: currentColor;\n",
-       "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray (band: 2, y: 317, x: 3)&gt;\n",
-       "array([[[0.059602, 0.081895, 0.164004],\n",
-       "        [0.059998, 0.065293, 0.1355  ],\n",
-       "        ...,\n",
-       "        [0.136004, 0.058305, 0.108705],\n",
-       "        [0.067506, 0.082795, 0.092805]],\n",
-       "\n",
-       "       [[0.249195, 0.327794, 0.204807],\n",
-       "        [0.2374  , 0.348608, 0.167697],\n",
-       "        ...,\n",
-       "        [0.203403, 0.2374  , 0.147601],\n",
-       "        [0.2112  , 0.357198, 0.132799]]])\n",
-       "Coordinates:\n",
-       "  * band     (band) &lt;U2 &#x27;b4&#x27; &#x27;b8&#x27;\n",
-       "  * y        (y) float64 6.639e+06 6.639e+06 6.639e+06 ... 6.621e+06 6.621e+06\n",
-       "  * x        (x) float64 4.068e+05 4.104e+05 4.139e+05\n",
-       "Attributes:\n",
-       "    transform:      (57.32777121388223, 0.0, 392465.9217302375, 0.0, -57.4483...\n",
-       "    crs:            +init=epsg:3857\n",
-       "    res:            (57.32777121388223, 57.44833434997156)\n",
-       "    is_tiled:       0\n",
-       "    nodatavals:     (nan, nan)\n",
-       "    scales:         (1.0, 1.0)\n",
-       "    offsets:        (0.0, 0.0)\n",
-       "    AREA_OR_POINT:  Area</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>band</span>: 2</li><li><span class='xr-has-index'>y</span>: 317</li><li><span class='xr-has-index'>x</span>: 3</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-cc3e4332-5b4d-4bd1-84ad-e5cd667a4d1c' class='xr-array-in' type='checkbox' checked><label for='section-cc3e4332-5b4d-4bd1-84ad-e5cd667a4d1c' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>0.0596 0.0819 0.164 0.06 0.06529 ... 0.1476 0.2112 0.3572 0.1328</span></div><div class='xr-array-data'><pre>array([[[0.059602, 0.081895, 0.164004],\n",
-       "        [0.059998, 0.065293, 0.1355  ],\n",
-       "        ...,\n",
-       "        [0.136004, 0.058305, 0.108705],\n",
-       "        [0.067506, 0.082795, 0.092805]],\n",
-       "\n",
-       "       [[0.249195, 0.327794, 0.204807],\n",
-       "        [0.2374  , 0.348608, 0.167697],\n",
-       "        ...,\n",
-       "        [0.203403, 0.2374  , 0.147601],\n",
-       "        [0.2112  , 0.357198, 0.132799]]])</pre></div></div></li><li class='xr-section-item'><input id='section-bb319da7-7773-44fe-b0e9-47ec20746e01' class='xr-section-summary-in' type='checkbox'  checked><label for='section-bb319da7-7773-44fe-b0e9-47ec20746e01' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>band</span></div><div class='xr-var-dims'>(band)</div><div class='xr-var-dtype'>&lt;U2</div><div class='xr-var-preview xr-preview'>&#x27;b4&#x27; &#x27;b8&#x27;</div><input id='attrs-80923d8e-45e5-4823-a000-cc9995a8c84e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-80923d8e-45e5-4823-a000-cc9995a8c84e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5317faa8-c89b-41a8-93dd-a52b2687a8f6' class='xr-var-data-in' type='checkbox'><label for='data-5317faa8-c89b-41a8-93dd-a52b2687a8f6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;b4&#x27;, &#x27;b8&#x27;], dtype=&#x27;&lt;U2&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>6.639e+06 6.639e+06 ... 6.621e+06</div><input id='attrs-a22631dd-accf-469c-89af-53e5371cb128' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a22631dd-accf-469c-89af-53e5371cb128' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-217ec649-2d06-4b78-afa9-ebc6d3c088d1' class='xr-var-data-in' type='checkbox'><label for='data-217ec649-2d06-4b78-afa9-ebc6d3c088d1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([6639463.535997, 6639406.087663, 6639348.639328, ..., 6621424.759011,\n",
-       "       6621367.310677, 6621309.862342])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>4.068e+05 4.104e+05 4.139e+05</div><input id='attrs-bfc9b517-0836-4beb-9ae3-061ef973c856' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-bfc9b517-0836-4beb-9ae3-061ef973c856' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a456dcca-1bb2-4162-b9c5-3c1dba6170b3' class='xr-var-data-in' type='checkbox'><label for='data-a456dcca-1bb2-4162-b9c5-3c1dba6170b3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([406826.528419, 410380.850235, 413935.17205 ])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-49df5fb5-4b18-4228-aec8-e5b330760746' class='xr-section-summary-in' type='checkbox'  checked><label for='section-49df5fb5-4b18-4228-aec8-e5b330760746' class='xr-section-summary' >Attributes: <span>(8)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>transform :</span></dt><dd>(57.32777121388223, 0.0, 392465.9217302375, 0.0, -57.44833434997156, 6639492.260164049)</dd><dt><span>crs :</span></dt><dd>+init=epsg:3857</dd><dt><span>res :</span></dt><dd>(57.32777121388223, 57.44833434997156)</dd><dt><span>is_tiled :</span></dt><dd>0</dd><dt><span>nodatavals :</span></dt><dd>(nan, nan)</dd><dt><span>scales :</span></dt><dd>(1.0, 1.0)</dd><dt><span>offsets :</span></dt><dd>(0.0, 0.0)</dd><dt><span>AREA_OR_POINT :</span></dt><dd>Area</dd></dl></div></li></ul></div></div>"
-      ],
-      "text/plain": [
-       "<xarray.DataArray (band: 2, y: 317, x: 3)>\n",
-       "array([[[0.059602, 0.081895, 0.164004],\n",
-       "        [0.059998, 0.065293, 0.1355  ],\n",
-       "        ...,\n",
-       "        [0.136004, 0.058305, 0.108705],\n",
-       "        [0.067506, 0.082795, 0.092805]],\n",
-       "\n",
-       "       [[0.249195, 0.327794, 0.204807],\n",
-       "        [0.2374  , 0.348608, 0.167697],\n",
-       "        ...,\n",
-       "        [0.203403, 0.2374  , 0.147601],\n",
-       "        [0.2112  , 0.357198, 0.132799]]])\n",
-       "Coordinates:\n",
-       "  * band     (band) <U2 'b4' 'b8'\n",
-       "  * y        (y) float64 6.639e+06 6.639e+06 6.639e+06 ... 6.621e+06 6.621e+06\n",
-       "  * x        (x) float64 4.068e+05 4.104e+05 4.139e+05\n",
-       "Attributes:\n",
-       "    transform:      (57.32777121388223, 0.0, 392465.9217302375, 0.0, -57.4483...\n",
-       "    crs:            +init=epsg:3857\n",
-       "    res:            (57.32777121388223, 57.44833434997156)\n",
-       "    is_tiled:       0\n",
-       "    nodatavals:     (nan, nan)\n",
-       "    scales:         (1.0, 1.0)\n",
-       "    offsets:        (0.0, 0.0)\n",
-       "    AREA_OR_POINT:  Area"
-      ]
-     },
-     "execution_count": 385,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "xr_array.sel(x=[406803, 410380, 413958], method=\"nearest\")   # .sel(band=\"b4\").plot.line(hue=\"x\");"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Sometimes, a specific range is required. The `.sel()` method also supports __slicing__, so we can select band 4 and slice a subset of the data along the x direction:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 386,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.image.AxesImage at 0x7f1237733390>"
-      ]
-     },
-     "execution_count": 386,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "xr_array.sel(x=slice(400_000, 420_000), band=\"b4\").plot.imshow()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "__Note__ Switch in between `Array` and `Datasets` as you like, it won't hurt your computer memory:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 686,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "xr_data = xr_array.to_dataset(dim=\"band\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 688,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#xr_data.to_array()    # dim=\"band\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Reduction"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Just like in numpy, we can reduce xarray DataArrays along any number of axes:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 199,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "('x',)"
-      ]
-     },
-     "execution_count": 199,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "xr_data[\"b4\"].mean(axis=0).dims"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 200,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "('y',)"
-      ]
-     },
-     "execution_count": 200,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "xr_data[\"b4\"].mean(axis=1).dims"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "But we have __dimensions with labels__, so rather than performing reductions on axes (as in Numpy), we can perform them on __dimensions__. This turns out to be a huge convenience:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 202,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "('y',)"
-      ]
-     },
-     "execution_count": 202,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "xr_data[\"b4\"].mean(dim=\"x\").dims"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Calculate minimum or quantile values for each of the bands separately:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 681,
-   "metadata": {},
-   "outputs": [
-    {
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-       "\n",
-       ".xr-attrs dt, dd {\n",
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-       "}\n",
-       "\n",
-       ".xr-attrs dt {\n",
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-       "  grid-column: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt:hover span {\n",
-       "  display: inline-block;\n",
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-       "  padding-right: 10px;\n",
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-       "\n",
-       ".xr-attrs dd {\n",
-       "  grid-column: 2;\n",
-       "  white-space: pre-wrap;\n",
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-       "\n",
-       ".xr-icon-database,\n",
-       ".xr-icon-file-text2 {\n",
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-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray (band: 2)&gt;\n",
-       "array([1.e-06, 1.e-06])\n",
-       "Coordinates:\n",
-       "  * band     (band) &lt;U2 &#x27;b4&#x27; &#x27;b8&#x27;</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>band</span>: 2</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-807979eb-dea3-4261-b33d-13932548e629' class='xr-array-in' type='checkbox' checked><label for='section-807979eb-dea3-4261-b33d-13932548e629' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>1e-06 1e-06</span></div><div class='xr-array-data'><pre>array([1.e-06, 1.e-06])</pre></div></div></li><li class='xr-section-item'><input id='section-9365c9be-5f52-4734-9d51-b01b75c79dc8' class='xr-section-summary-in' type='checkbox'  checked><label for='section-9365c9be-5f52-4734-9d51-b01b75c79dc8' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>band</span></div><div class='xr-var-dims'>(band)</div><div class='xr-var-dtype'>&lt;U2</div><div class='xr-var-preview xr-preview'>&#x27;b4&#x27; &#x27;b8&#x27;</div><input id='attrs-7fb4ea71-2d7a-48bc-b227-c86ed00d08cc' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7fb4ea71-2d7a-48bc-b227-c86ed00d08cc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5a80b6d2-a9dd-4171-a900-d2de7402815a' class='xr-var-data-in' type='checkbox'><label for='data-5a80b6d2-a9dd-4171-a900-d2de7402815a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;b4&#x27;, &#x27;b8&#x27;], dtype=&#x27;&lt;U2&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ae118c5e-de55-413a-8b51-949cef108b73' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-ae118c5e-de55-413a-8b51-949cef108b73' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
-      ],
-      "text/plain": [
-       "<xarray.DataArray (band: 2)>\n",
-       "array([1.e-06, 1.e-06])\n",
-       "Coordinates:\n",
-       "  * band     (band) <U2 'b4' 'b8'"
-      ]
-     },
-     "execution_count": 681,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "xr_array.min(dim=[\"x\", \"y\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 676,
-   "metadata": {},
-   "outputs": [
-    {
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-       "\n",
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-       "  margin: 0;\n",
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-       "}\n",
-       "\n",
-       ".xr-attrs dt {\n",
-       "  font-weight: normal;\n",
-       "  grid-column: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt:hover span {\n",
-       "  display: inline-block;\n",
-       "  background: var(--xr-background-color);\n",
-       "  padding-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dd {\n",
-       "  grid-column: 2;\n",
-       "  white-space: pre-wrap;\n",
-       "  word-break: break-all;\n",
-       "}\n",
-       "\n",
-       ".xr-icon-database,\n",
-       ".xr-icon-file-text2 {\n",
-       "  display: inline-block;\n",
-       "  vertical-align: middle;\n",
-       "  width: 1em;\n",
-       "  height: 1.5em !important;\n",
-       "  stroke-width: 0;\n",
-       "  stroke: currentColor;\n",
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-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray (quantile: 3, band: 2)&gt;\n",
-       "array([[0.05749599, 0.13560693],\n",
-       "       [0.08310063, 0.23059434],\n",
-       "       [0.13060197, 0.32539864]])\n",
-       "Coordinates:\n",
-       "  * band      (band) &lt;U2 &#x27;b4&#x27; &#x27;b8&#x27;\n",
-       "  * quantile  (quantile) float64 0.1 0.5 0.9</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>quantile</span>: 3</li><li><span class='xr-has-index'>band</span>: 2</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-7fbf0937-c773-4734-88a5-a45b58019487' class='xr-array-in' type='checkbox' checked><label for='section-7fbf0937-c773-4734-88a5-a45b58019487' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>0.0575 0.1356 0.0831 0.2306 0.1306 0.3254</span></div><div class='xr-array-data'><pre>array([[0.05749599, 0.13560693],\n",
-       "       [0.08310063, 0.23059434],\n",
-       "       [0.13060197, 0.32539864]])</pre></div></div></li><li class='xr-section-item'><input id='section-3a0f3404-53b5-4f2e-b813-35209ccaaf03' class='xr-section-summary-in' type='checkbox'  checked><label for='section-3a0f3404-53b5-4f2e-b813-35209ccaaf03' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>band</span></div><div class='xr-var-dims'>(band)</div><div class='xr-var-dtype'>&lt;U2</div><div class='xr-var-preview xr-preview'>&#x27;b4&#x27; &#x27;b8&#x27;</div><input id='attrs-058311ff-29e8-40ea-9ecd-6b847a0e6b53' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-058311ff-29e8-40ea-9ecd-6b847a0e6b53' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-02f6765b-32ce-46bb-97d2-9b6ea5391b21' class='xr-var-data-in' type='checkbox'><label for='data-02f6765b-32ce-46bb-97d2-9b6ea5391b21' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;b4&#x27;, &#x27;b8&#x27;], dtype=&#x27;&lt;U2&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>quantile</span></div><div class='xr-var-dims'>(quantile)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.1 0.5 0.9</div><input id='attrs-dd219a25-56a3-4a00-9c75-022f28defdb0' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-dd219a25-56a3-4a00-9c75-022f28defdb0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-03dc8b2f-a2ac-49e8-af1e-b2da4698202e' class='xr-var-data-in' type='checkbox'><label for='data-03dc8b2f-a2ac-49e8-af1e-b2da4698202e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0.1, 0.5, 0.9])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-9ef59a5f-61ce-480c-980f-f0536c7c8c54' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-9ef59a5f-61ce-480c-980f-f0536c7c8c54' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
-      ],
-      "text/plain": [
-       "<xarray.DataArray (quantile: 3, band: 2)>\n",
-       "array([[0.05749599, 0.13560693],\n",
-       "       [0.08310063, 0.23059434],\n",
-       "       [0.13060197, 0.32539864]])\n",
-       "Coordinates:\n",
-       "  * band      (band) <U2 'b4' 'b8'\n",
-       "  * quantile  (quantile) float64 0.1 0.5 0.9"
-      ]
-     },
-     "execution_count": 676,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "xr_array.quantile([0.1, 0.5, 0.9], dim=[\"x\", \"y\"])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Element-wise computation"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Xarray dataarrays and datasets work seamlessly with arithmetic operators and numpy array functions."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 206,
-   "metadata": {},
-   "outputs": [
-    {
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-       " *\n",
-       " */\n",
-       "\n",
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-       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
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-       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
-       "  --xr-background-color: var(--jp-layout-color0, white);\n",
-       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
-       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
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-       "\n",
-       "html[theme=dark],\n",
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-       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
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-       "  --xr-background-color: #111111;\n",
-       "  --xr-background-color-row-even: #111111;\n",
-       "  --xr-background-color-row-odd: #313131;\n",
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-       "\n",
-       ".xr-wrap {\n",
-       "  display: block;\n",
-       "  min-width: 300px;\n",
-       "  max-width: 700px;\n",
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-       "\n",
-       ".xr-text-repr-fallback {\n",
-       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
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-       "  padding-bottom: 6px;\n",
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-       "\n",
-       ".xr-header > div,\n",
-       ".xr-header > ul {\n",
-       "  display: inline;\n",
-       "  margin-top: 0;\n",
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-       "\n",
-       ".xr-obj-type,\n",
-       ".xr-array-name {\n",
-       "  margin-left: 2px;\n",
-       "  margin-right: 10px;\n",
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-       "\n",
-       ".xr-obj-type {\n",
-       "  color: var(--xr-font-color2);\n",
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-       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
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-       "\n",
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-       "  grid-column: 1;\n",
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-       "  display: inline-block;\n",
-       "  padding-left: 0.5em;\n",
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-       "\n",
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-       "  color: var(--xr-font-color2);\n",
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-       "\n",
-       ".xr-section-summary-in + label:before {\n",
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-       "  width: 15px;\n",
-       "  text-align: center;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:disabled + label:before {\n",
-       "  color: var(--xr-disabled-color);\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:checked + label:before {\n",
-       "  content: '▼';\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:checked + label > span {\n",
-       "  display: none;\n",
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-       "\n",
-       ".xr-section-summary,\n",
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-       "  padding-top: 4px;\n",
-       "  padding-bottom: 4px;\n",
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-       "\n",
-       ".xr-section-inline-details {\n",
-       "  grid-column: 2 / -1;\n",
-       "}\n",
-       "\n",
-       ".xr-section-details {\n",
-       "  display: none;\n",
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-       "  margin-bottom: 5px;\n",
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-       "\n",
-       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
-       "  display: contents;\n",
-       "}\n",
-       "\n",
-       ".xr-array-wrap {\n",
-       "  grid-column: 1 / -1;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 20px auto;\n",
-       "}\n",
-       "\n",
-       ".xr-array-wrap > label {\n",
-       "  grid-column: 1;\n",
-       "  vertical-align: top;\n",
-       "}\n",
-       "\n",
-       ".xr-preview {\n",
-       "  color: var(--xr-font-color3);\n",
-       "}\n",
-       "\n",
-       ".xr-array-preview,\n",
-       ".xr-array-data {\n",
-       "  padding: 0 5px !important;\n",
-       "  grid-column: 2;\n",
-       "}\n",
-       "\n",
-       ".xr-array-data,\n",
-       ".xr-array-in:checked ~ .xr-array-preview {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-array-in:checked ~ .xr-array-data,\n",
-       ".xr-array-preview {\n",
-       "  display: inline-block;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list {\n",
-       "  display: inline-block !important;\n",
-       "  list-style: none;\n",
-       "  padding: 0 !important;\n",
-       "  margin: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list li {\n",
-       "  display: inline-block;\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list:before {\n",
-       "  content: '(';\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list:after {\n",
-       "  content: ')';\n",
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-       "\n",
-       ".xr-dim-list li:not(:last-child):after {\n",
-       "  content: ',';\n",
-       "  padding-right: 5px;\n",
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-       "\n",
-       ".xr-has-index {\n",
-       "  font-weight: bold;\n",
-       "}\n",
-       "\n",
-       ".xr-var-list,\n",
-       ".xr-var-item {\n",
-       "  display: contents;\n",
-       "}\n",
-       "\n",
-       ".xr-var-item > div,\n",
-       ".xr-var-item label,\n",
-       ".xr-var-item > .xr-var-name span {\n",
-       "  background-color: var(--xr-background-color-row-even);\n",
-       "  margin-bottom: 0;\n",
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-       "\n",
-       ".xr-var-item > .xr-var-name:hover span {\n",
-       "  padding-right: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-var-list > li:nth-child(odd) > div,\n",
-       ".xr-var-list > li:nth-child(odd) > label,\n",
-       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
-       "  background-color: var(--xr-background-color-row-odd);\n",
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-       "\n",
-       ".xr-var-name {\n",
-       "  grid-column: 1;\n",
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-       "\n",
-       ".xr-var-dims {\n",
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-       "\n",
-       ".xr-var-dtype {\n",
-       "  grid-column: 3;\n",
-       "  text-align: right;\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-var-preview {\n",
-       "  grid-column: 4;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name,\n",
-       ".xr-var-dims,\n",
-       ".xr-var-dtype,\n",
-       ".xr-preview,\n",
-       ".xr-attrs dt {\n",
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-       "  overflow: hidden;\n",
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-       "  padding-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name:hover,\n",
-       ".xr-var-dims:hover,\n",
-       ".xr-var-dtype:hover,\n",
-       ".xr-attrs dt:hover {\n",
-       "  overflow: visible;\n",
-       "  width: auto;\n",
-       "  z-index: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-var-attrs,\n",
-       ".xr-var-data {\n",
-       "  display: none;\n",
-       "  background-color: var(--xr-background-color) !important;\n",
-       "  padding-bottom: 5px !important;\n",
-       "}\n",
-       "\n",
-       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
-       ".xr-var-data-in:checked ~ .xr-var-data {\n",
-       "  display: block;\n",
-       "}\n",
-       "\n",
-       ".xr-var-data > table {\n",
-       "  float: right;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name span,\n",
-       ".xr-var-data,\n",
-       ".xr-attrs {\n",
-       "  padding-left: 25px !important;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs,\n",
-       ".xr-var-attrs,\n",
-       ".xr-var-data {\n",
-       "  grid-column: 1 / -1;\n",
-       "}\n",
-       "\n",
-       "dl.xr-attrs {\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 125px auto;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt, dd {\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  float: left;\n",
-       "  padding-right: 10px;\n",
-       "  width: auto;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt {\n",
-       "  font-weight: normal;\n",
-       "  grid-column: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt:hover span {\n",
-       "  display: inline-block;\n",
-       "  background: var(--xr-background-color);\n",
-       "  padding-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dd {\n",
-       "  grid-column: 2;\n",
-       "  white-space: pre-wrap;\n",
-       "  word-break: break-all;\n",
-       "}\n",
-       "\n",
-       ".xr-icon-database,\n",
-       ".xr-icon-file-text2 {\n",
-       "  display: inline-block;\n",
-       "  vertical-align: middle;\n",
-       "  width: 1em;\n",
-       "  height: 1.5em !important;\n",
-       "  stroke-width: 0;\n",
-       "  stroke: currentColor;\n",
-       "  fill: currentColor;\n",
-       "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;b4&#x27; (y: 317, x: 625)&gt;\n",
-       "array([[0.00782025, 0.00883955, 0.01750973, ..., 0.01033951, 0.01040055,\n",
-       "        0.00863966],\n",
-       "       [0.01022049, 0.00758984, 0.00854963, ..., 0.00731975, 0.00657053,\n",
-       "        0.0065507 ],\n",
-       "       [0.01011063, 0.00800031, 0.0076997 , ..., 0.00688029, 0.00733043,\n",
-       "        0.00704967],\n",
-       "       ...,\n",
-       "       [0.0072694 , 0.00742046, 0.0068101 , ..., 0.01409018, 0.01008011,\n",
-       "        0.00682994],\n",
-       "       [0.00702983, 0.00695048, 0.00672007, ..., 0.00733959, 0.00890059,\n",
-       "        0.00599069],\n",
-       "       [0.00706035, 0.00747997, 0.00724041, ..., 0.00576944, 0.00675975,\n",
-       "        0.00829938]])\n",
-       "Coordinates:\n",
-       "  * y        (y) float64 6.639e+06 6.639e+06 6.639e+06 ... 6.621e+06 6.621e+06\n",
-       "  * x        (x) float64 3.925e+05 3.926e+05 3.926e+05 ... 4.282e+05 4.283e+05</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'b4'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>y</span>: 317</li><li><span class='xr-has-index'>x</span>: 625</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-371c4b27-a41b-4c75-a2a6-d6e18a5e1a9a' class='xr-array-in' type='checkbox' checked><label for='section-371c4b27-a41b-4c75-a2a6-d6e18a5e1a9a' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>0.00782 0.00884 0.01751 0.01658 ... 0.00708 0.005769 0.00676 0.008299</span></div><div class='xr-array-data'><pre>array([[0.00782025, 0.00883955, 0.01750973, ..., 0.01033951, 0.01040055,\n",
-       "        0.00863966],\n",
-       "       [0.01022049, 0.00758984, 0.00854963, ..., 0.00731975, 0.00657053,\n",
-       "        0.0065507 ],\n",
-       "       [0.01011063, 0.00800031, 0.0076997 , ..., 0.00688029, 0.00733043,\n",
-       "        0.00704967],\n",
-       "       ...,\n",
-       "       [0.0072694 , 0.00742046, 0.0068101 , ..., 0.01409018, 0.01008011,\n",
-       "        0.00682994],\n",
-       "       [0.00702983, 0.00695048, 0.00672007, ..., 0.00733959, 0.00890059,\n",
-       "        0.00599069],\n",
-       "       [0.00706035, 0.00747997, 0.00724041, ..., 0.00576944, 0.00675975,\n",
-       "        0.00829938]])</pre></div></div></li><li class='xr-section-item'><input id='section-195e9b46-2126-44b6-8281-9d05c3480089' class='xr-section-summary-in' type='checkbox'  checked><label for='section-195e9b46-2126-44b6-8281-9d05c3480089' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>6.639e+06 6.639e+06 ... 6.621e+06</div><input id='attrs-93c15187-0ee8-4670-9d8c-8596386d3d14' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-93c15187-0ee8-4670-9d8c-8596386d3d14' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-19dc0ace-ce23-475d-bc0c-dcc21136a313' class='xr-var-data-in' type='checkbox'><label for='data-19dc0ace-ce23-475d-bc0c-dcc21136a313' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([6639463.535997, 6639406.087663, 6639348.639328, ..., 6621424.759011,\n",
-       "       6621367.310677, 6621309.862342])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>3.925e+05 3.926e+05 ... 4.283e+05</div><input id='attrs-2411aaf1-8dce-495c-a8ef-236eda8b7190' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2411aaf1-8dce-495c-a8ef-236eda8b7190' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3aabcc6d-0e26-4e6a-926c-202e9ce5b277' class='xr-var-data-in' type='checkbox'><label for='data-3aabcc6d-0e26-4e6a-926c-202e9ce5b277' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([392494.585616, 392551.913387, 392609.241158, ..., 428152.459311,\n",
-       "       428209.787082, 428267.114853])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-a9134971-72fd-4262-90a3-0292c60218bc' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-a9134971-72fd-4262-90a3-0292c60218bc' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
-      ],
-      "text/plain": [
-       "<xarray.DataArray 'b4' (y: 317, x: 625)>\n",
-       "array([[0.00782025, 0.00883955, 0.01750973, ..., 0.01033951, 0.01040055,\n",
-       "        0.00863966],\n",
-       "       [0.01022049, 0.00758984, 0.00854963, ..., 0.00731975, 0.00657053,\n",
-       "        0.0065507 ],\n",
-       "       [0.01011063, 0.00800031, 0.0076997 , ..., 0.00688029, 0.00733043,\n",
-       "        0.00704967],\n",
-       "       ...,\n",
-       "       [0.0072694 , 0.00742046, 0.0068101 , ..., 0.01409018, 0.01008011,\n",
-       "        0.00682994],\n",
-       "       [0.00702983, 0.00695048, 0.00672007, ..., 0.00733959, 0.00890059,\n",
-       "        0.00599069],\n",
-       "       [0.00706035, 0.00747997, 0.00724041, ..., 0.00576944, 0.00675975,\n",
-       "        0.00829938]])\n",
-       "Coordinates:\n",
-       "  * y        (y) float64 6.639e+06 6.639e+06 6.639e+06 ... 6.621e+06 6.621e+06\n",
-       "  * x        (x) float64 3.925e+05 3.926e+05 3.926e+05 ... 4.282e+05 4.283e+05"
-      ]
-     },
-     "execution_count": 206,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "xr_data[\"b4\"] /10."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 208,
-   "metadata": {},
-   "outputs": [
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-       "\n",
-       ".xr-attrs,\n",
-       ".xr-var-attrs,\n",
-       ".xr-var-data {\n",
-       "  grid-column: 1 / -1;\n",
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-       "\n",
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-       "  display: grid;\n",
-       "  grid-template-columns: 125px auto;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt, dd {\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  float: left;\n",
-       "  padding-right: 10px;\n",
-       "  width: auto;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt {\n",
-       "  font-weight: normal;\n",
-       "  grid-column: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt:hover span {\n",
-       "  display: inline-block;\n",
-       "  background: var(--xr-background-color);\n",
-       "  padding-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dd {\n",
-       "  grid-column: 2;\n",
-       "  white-space: pre-wrap;\n",
-       "  word-break: break-all;\n",
-       "}\n",
-       "\n",
-       ".xr-icon-database,\n",
-       ".xr-icon-file-text2 {\n",
-       "  display: inline-block;\n",
-       "  vertical-align: middle;\n",
-       "  width: 1em;\n",
-       "  height: 1.5em !important;\n",
-       "  stroke-width: 0;\n",
-       "  stroke: currentColor;\n",
-       "  fill: currentColor;\n",
-       "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;b8&#x27; (y: 317, x: 625)&gt;\n",
-       "array([[-1.29610906, -1.35870858, -1.39473763, ..., -1.25985297,\n",
-       "        -1.4056924 , -1.4605655 ],\n",
-       "       [-1.36376827, -1.4610258 , -1.34825398, ..., -1.36609837,\n",
-       "        -1.49923676, -1.46531016],\n",
-       "       [-1.48458449, -1.41714613, -1.31713162, ..., -1.30271196,\n",
-       "        -1.2687131 , -1.17216053],\n",
-       "       ...,\n",
-       "       [-1.31082926, -1.37276006, -1.23101297, ..., -1.40407569,\n",
-       "        -1.23200636, -1.17442956],\n",
-       "       [-1.265138  , -1.22928996, -1.22554093, ..., -1.5418142 ,\n",
-       "        -1.33866383, -0.96811823],\n",
-       "       [-1.24305213, -1.39393775, -1.33977018, ..., -1.1014086 ,\n",
-       "        -1.08235694, -0.98832914]])\n",
-       "Coordinates:\n",
-       "  * y        (y) float64 6.639e+06 6.639e+06 6.639e+06 ... 6.621e+06 6.621e+06\n",
-       "  * x        (x) float64 3.925e+05 3.926e+05 3.926e+05 ... 4.282e+05 4.283e+05</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'b8'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>y</span>: 317</li><li><span class='xr-has-index'>x</span>: 625</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-0396e51b-84ab-481e-ae82-32b27e323d94' class='xr-array-in' type='checkbox' checked><label for='section-0396e51b-84ab-481e-ae82-32b27e323d94' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>-1.296 -1.359 -1.395 -1.489 -1.527 ... -1.002 -1.101 -1.082 -0.9883</span></div><div class='xr-array-data'><pre>array([[-1.29610906, -1.35870858, -1.39473763, ..., -1.25985297,\n",
-       "        -1.4056924 , -1.4605655 ],\n",
-       "       [-1.36376827, -1.4610258 , -1.34825398, ..., -1.36609837,\n",
-       "        -1.49923676, -1.46531016],\n",
-       "       [-1.48458449, -1.41714613, -1.31713162, ..., -1.30271196,\n",
-       "        -1.2687131 , -1.17216053],\n",
-       "       ...,\n",
-       "       [-1.31082926, -1.37276006, -1.23101297, ..., -1.40407569,\n",
-       "        -1.23200636, -1.17442956],\n",
-       "       [-1.265138  , -1.22928996, -1.22554093, ..., -1.5418142 ,\n",
-       "        -1.33866383, -0.96811823],\n",
-       "       [-1.24305213, -1.39393775, -1.33977018, ..., -1.1014086 ,\n",
-       "        -1.08235694, -0.98832914]])</pre></div></div></li><li class='xr-section-item'><input id='section-8987f2fd-bdf1-4d01-872c-88591bdffc32' class='xr-section-summary-in' type='checkbox'  checked><label for='section-8987f2fd-bdf1-4d01-872c-88591bdffc32' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>6.639e+06 6.639e+06 ... 6.621e+06</div><input id='attrs-daebfa8c-512e-44b8-a2cb-af297e25a4b9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-daebfa8c-512e-44b8-a2cb-af297e25a4b9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7ae7af6e-fdac-4e4e-8f5e-cb0f654a3bfd' class='xr-var-data-in' type='checkbox'><label for='data-7ae7af6e-fdac-4e4e-8f5e-cb0f654a3bfd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([6639463.535997, 6639406.087663, 6639348.639328, ..., 6621424.759011,\n",
-       "       6621367.310677, 6621309.862342])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>3.925e+05 3.926e+05 ... 4.283e+05</div><input id='attrs-cba425ef-3557-43a5-a47c-c8924b0d7af9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-cba425ef-3557-43a5-a47c-c8924b0d7af9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-55b83fb3-7fc8-473a-93d6-34f3f1114c22' class='xr-var-data-in' type='checkbox'><label for='data-55b83fb3-7fc8-473a-93d6-34f3f1114c22' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([392494.585616, 392551.913387, 392609.241158, ..., 428152.459311,\n",
-       "       428209.787082, 428267.114853])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-2d5c5307-cffe-4574-86f6-5fe63c0b633c' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-2d5c5307-cffe-4574-86f6-5fe63c0b633c' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
-      ],
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-       "<xarray.DataArray 'b8' (y: 317, x: 625)>\n",
-       "array([[-1.29610906, -1.35870858, -1.39473763, ..., -1.25985297,\n",
-       "        -1.4056924 , -1.4605655 ],\n",
-       "       [-1.36376827, -1.4610258 , -1.34825398, ..., -1.36609837,\n",
-       "        -1.49923676, -1.46531016],\n",
-       "       [-1.48458449, -1.41714613, -1.31713162, ..., -1.30271196,\n",
-       "        -1.2687131 , -1.17216053],\n",
-       "       ...,\n",
-       "       [-1.31082926, -1.37276006, -1.23101297, ..., -1.40407569,\n",
-       "        -1.23200636, -1.17442956],\n",
-       "       [-1.265138  , -1.22928996, -1.22554093, ..., -1.5418142 ,\n",
-       "        -1.33866383, -0.96811823],\n",
-       "       [-1.24305213, -1.39393775, -1.33977018, ..., -1.1014086 ,\n",
-       "        -1.08235694, -0.98832914]])\n",
-       "Coordinates:\n",
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-      ]
-     },
-     "execution_count": 208,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.log(xr_data[\"b8\"])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "As we seen in the example of the NDVI, we can combine multiple xarray datasets in arithemetic operations:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 209,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
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-       "\n",
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-       "\n",
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-       "\n",
-       ".xr-attrs,\n",
-       ".xr-var-attrs,\n",
-       ".xr-var-data {\n",
-       "  grid-column: 1 / -1;\n",
-       "}\n",
-       "\n",
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-       "}\n",
-       "\n",
-       ".xr-attrs dt, dd {\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  float: left;\n",
-       "  padding-right: 10px;\n",
-       "  width: auto;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt {\n",
-       "  font-weight: normal;\n",
-       "  grid-column: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt:hover span {\n",
-       "  display: inline-block;\n",
-       "  background: var(--xr-background-color);\n",
-       "  padding-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dd {\n",
-       "  grid-column: 2;\n",
-       "  white-space: pre-wrap;\n",
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-       "}\n",
-       "\n",
-       ".xr-icon-database,\n",
-       ".xr-icon-file-text2 {\n",
-       "  display: inline-block;\n",
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-       "  stroke: currentColor;\n",
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-       "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray (y: 317, x: 625)&gt;\n",
-       "array([[0.35179675, 0.34538796, 0.42299535, ..., 0.38709087, 0.34920272,\n",
-       "        0.31850156],\n",
-       "       [0.35790036, 0.30789654, 0.34518959, ..., 0.32829786, 0.28900587,\n",
-       "        0.29651331],\n",
-       "       [0.32770275, 0.32240787, 0.34489967, ..., 0.34059663, 0.3544976 ,\n",
-       "        0.38019379],\n",
-       "       ...,\n",
-       "       [0.34229038, 0.3276112 , 0.36009766, ..., 0.38649577, 0.39250782,\n",
-       "        0.37729458],\n",
-       "       [0.35249866, 0.36200504, 0.36079957, ..., 0.28738842, 0.35120165,\n",
-       "        0.43970397],\n",
-       "       [0.35910582, 0.32289616, 0.33430991, ..., 0.39009689, 0.40639353,\n",
-       "        0.45519188]])\n",
-       "Coordinates:\n",
-       "  * y        (y) float64 6.639e+06 6.639e+06 6.639e+06 ... 6.621e+06 6.621e+06\n",
-       "  * x        (x) float64 3.925e+05 3.926e+05 3.926e+05 ... 4.282e+05 4.283e+05</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>y</span>: 317</li><li><span class='xr-has-index'>x</span>: 625</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-d92abb01-3381-44b0-93dd-6d1742622bea' class='xr-array-in' type='checkbox' checked><label for='section-d92abb01-3381-44b0-93dd-6d1742622bea' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>0.3518 0.3454 0.423 0.3915 0.3276 ... 0.4381 0.3901 0.4064 0.4552</span></div><div class='xr-array-data'><pre>array([[0.35179675, 0.34538796, 0.42299535, ..., 0.38709087, 0.34920272,\n",
-       "        0.31850156],\n",
-       "       [0.35790036, 0.30789654, 0.34518959, ..., 0.32829786, 0.28900587,\n",
-       "        0.29651331],\n",
-       "       [0.32770275, 0.32240787, 0.34489967, ..., 0.34059663, 0.3544976 ,\n",
-       "        0.38019379],\n",
-       "       ...,\n",
-       "       [0.34229038, 0.3276112 , 0.36009766, ..., 0.38649577, 0.39250782,\n",
-       "        0.37729458],\n",
-       "       [0.35249866, 0.36200504, 0.36079957, ..., 0.28738842, 0.35120165,\n",
-       "        0.43970397],\n",
-       "       [0.35910582, 0.32289616, 0.33430991, ..., 0.39009689, 0.40639353,\n",
-       "        0.45519188]])</pre></div></div></li><li class='xr-section-item'><input id='section-765e453a-82d9-4e75-af14-fc25e973d2da' class='xr-section-summary-in' type='checkbox'  checked><label for='section-765e453a-82d9-4e75-af14-fc25e973d2da' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>6.639e+06 6.639e+06 ... 6.621e+06</div><input id='attrs-96220c18-033a-4ebc-84e3-f28873a719b7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-96220c18-033a-4ebc-84e3-f28873a719b7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2dcb5fc1-73ce-45e3-8e53-318c6f878e57' class='xr-var-data-in' type='checkbox'><label for='data-2dcb5fc1-73ce-45e3-8e53-318c6f878e57' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([6639463.535997, 6639406.087663, 6639348.639328, ..., 6621424.759011,\n",
-       "       6621367.310677, 6621309.862342])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>3.925e+05 3.926e+05 ... 4.283e+05</div><input id='attrs-9a5bb4c5-7100-41d0-baac-21b81ab4861b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9a5bb4c5-7100-41d0-baac-21b81ab4861b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e05ca831-805c-4d5a-bb71-51afafa85e63' class='xr-var-data-in' type='checkbox'><label for='data-e05ca831-805c-4d5a-bb71-51afafa85e63' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([392494.585616, 392551.913387, 392609.241158, ..., 428152.459311,\n",
-       "       428209.787082, 428267.114853])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f47486fe-833f-42d5-b0b0-1d3e63a0ecd4' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-f47486fe-833f-42d5-b0b0-1d3e63a0ecd4' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
-      ],
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-       "        0.31850156],\n",
-       "       [0.35790036, 0.30789654, 0.34518959, ..., 0.32829786, 0.28900587,\n",
-       "        0.29651331],\n",
-       "       [0.32770275, 0.32240787, 0.34489967, ..., 0.34059663, 0.3544976 ,\n",
-       "        0.38019379],\n",
-       "       ...,\n",
-       "       [0.34229038, 0.3276112 , 0.36009766, ..., 0.38649577, 0.39250782,\n",
-       "        0.37729458],\n",
-       "       [0.35249866, 0.36200504, 0.36079957, ..., 0.28738842, 0.35120165,\n",
-       "        0.43970397],\n",
-       "       [0.35910582, 0.32289616, 0.33430991, ..., 0.39009689, 0.40639353,\n",
-       "        0.45519188]])\n",
-       "Coordinates:\n",
-       "  * y        (y) float64 6.639e+06 6.639e+06 6.639e+06 ... 6.621e+06 6.621e+06\n",
-       "  * x        (x) float64 3.925e+05 3.926e+05 3.926e+05 ... 4.282e+05 4.283e+05"
-      ]
-     },
-     "execution_count": 209,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "xr_data[\"b8\"] + xr_data[\"b4\"]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Broadcasting"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Perfoming an operation on arrays with differenty coordinates will result in automatic broadcasting:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 689,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "((625,), (317, 625))"
-      ]
-     },
-     "execution_count": 689,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "xr_data.x.shape, xr_data[\"b8\"].shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 690,
-   "metadata": {},
-   "outputs": [
-    {
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-       "\n",
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-       "       [392494.84131127, 392552.14538523, 392609.50085157, ...,\n",
-       "        428152.71441121, 428210.01038262, 428267.34585964],\n",
-       "       [392494.81221232, 392552.15579188, 392609.50906092, ...,\n",
-       "        428152.73110458, 428210.06827535, 428267.42455042],\n",
-       "       ...,\n",
-       "       [392494.85521224, 392552.16679363, 392609.53315492, ...,\n",
-       "        428152.70490484, 428210.07878881, 428267.4238485 ],\n",
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-       "Coordinates:\n",
-       "  * y        (y) float64 6.639e+06 6.639e+06 6.639e+06 ... 6.621e+06 6.621e+06\n",
-       "  * x        (x) float64 3.925e+05 3.926e+05 3.926e+05 ... 4.282e+05 4.283e+05</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>y</span>: 317</li><li><span class='xr-has-index'>x</span>: 625</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-68a8d4cb-162c-4108-9994-48e1f29db9c7' class='xr-array-in' type='checkbox' checked><label for='section-68a8d4cb-162c-4108-9994-48e1f29db9c7' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>3.925e+05 3.926e+05 3.926e+05 ... 4.282e+05 4.282e+05 4.283e+05</span></div><div class='xr-array-data'><pre>array([[392494.85921011, 392552.17037951, 392609.48905634, ...,\n",
-       "        428152.74300661, 428210.03227932, 428267.34695829],\n",
-       "       [392494.84131127, 392552.14538523, 392609.50085157, ...,\n",
-       "        428152.71441121, 428210.01038262, 428267.34585964],\n",
-       "       [392494.81221232, 392552.15579188, 392609.50906092, ...,\n",
-       "        428152.73110458, 428210.06827535, 428267.42455042],\n",
-       "       ...,\n",
-       "       [392494.85521224, 392552.16679363, 392609.53315492, ...,\n",
-       "        428152.70490484, 428210.07878881, 428267.4238485 ],\n",
-       "       [392494.8678162 , 392552.20588725, 392609.53475711, ...,\n",
-       "        428152.6733034 , 428210.04927787, 428267.49465036],\n",
-       "       [392494.87411817, 392552.1614835 , 392609.50306412, ...,\n",
-       "        428152.79171341, 428210.12587816, 428267.48705137]])</pre></div></div></li><li class='xr-section-item'><input id='section-b52440e8-962d-4f8e-96d2-5a0ab8b282be' class='xr-section-summary-in' type='checkbox'  checked><label for='section-b52440e8-962d-4f8e-96d2-5a0ab8b282be' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>6.639e+06 6.639e+06 ... 6.621e+06</div><input id='attrs-ad3d09cd-35a6-4927-948f-af3544d4d40a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ad3d09cd-35a6-4927-948f-af3544d4d40a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-02824a39-3d6b-44a1-a6f8-963c5804a78a' class='xr-var-data-in' type='checkbox'><label for='data-02824a39-3d6b-44a1-a6f8-963c5804a78a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([6639463.535997, 6639406.087663, 6639348.639328, ..., 6621424.759011,\n",
-       "       6621367.310677, 6621309.862342])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>3.925e+05 3.926e+05 ... 4.283e+05</div><input id='attrs-b83ac432-debc-4c76-a2db-ac75b3e94158' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b83ac432-debc-4c76-a2db-ac75b3e94158' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-51ea7c6f-e0ef-4cd5-bc78-c76dbabb8867' class='xr-var-data-in' type='checkbox'><label for='data-51ea7c6f-e0ef-4cd5-bc78-c76dbabb8867' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([392494.585616, 392551.913387, 392609.241158, ..., 428152.459311,\n",
-       "       428209.787082, 428267.114853])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-14b19a68-c667-4864-9a0d-4f270a87c7c5' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-14b19a68-c667-4864-9a0d-4f270a87c7c5' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
-      ],
-      "text/plain": [
-       "<xarray.DataArray (y: 317, x: 625)>\n",
-       "array([[392494.85921011, 392552.17037951, 392609.48905634, ...,\n",
-       "        428152.74300661, 428210.03227932, 428267.34695829],\n",
-       "       [392494.84131127, 392552.14538523, 392609.50085157, ...,\n",
-       "        428152.71441121, 428210.01038262, 428267.34585964],\n",
-       "       [392494.81221232, 392552.15579188, 392609.50906092, ...,\n",
-       "        428152.73110458, 428210.06827535, 428267.42455042],\n",
-       "       ...,\n",
-       "       [392494.85521224, 392552.16679363, 392609.53315492, ...,\n",
-       "        428152.70490484, 428210.07878881, 428267.4238485 ],\n",
-       "       [392494.8678162 , 392552.20588725, 392609.53475711, ...,\n",
-       "        428152.6733034 , 428210.04927787, 428267.49465036],\n",
-       "       [392494.87411817, 392552.1614835 , 392609.50306412, ...,\n",
-       "        428152.79171341, 428210.12587816, 428267.48705137]])\n",
-       "Coordinates:\n",
-       "  * y        (y) float64 6.639e+06 6.639e+06 6.639e+06 ... 6.621e+06 6.621e+06\n",
-       "  * x        (x) float64 3.925e+05 3.926e+05 3.926e+05 ... 4.282e+05 4.283e+05"
-      ]
-     },
-     "execution_count": 690,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "xr_data[\"b8\"] + xr_data.x  # Note, this calculaton does not make much sense, but illustrates broadcasting"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Plotting"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Similar to Pandas, there is a `plot` method, which can be used for different plot types:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 316,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "xr_array = xr.open_rasterio(\"./data/gent/raster/2020-09-17_Sentinel_2_L1C_B0408.tiff\")\n",
-    "xr_array = xr_array.assign_coords(band=(\"band\", [\"b4\", \"b8\"]))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "It supports both 2 dimensional (e.g. line) as 3 (e.g. imshow, pcolormesh) dimensional plots. When just using `plot`, xarray will do a _best guess_ on how to plot the data. However being explicit `plot.line`, `plot.imshow`, `plot.pcolormesh`, `plot.scatter`,...  gives you more control."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 701,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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UYynXNTpfyjEnU9D5DHTnFMe/egoqKgw/ck+8zNlCjE0SwVUMt6nk3NV5WLxt0Hre7e9bLtu10/Q7oLLGtV+do7w2DNc2isCj3taSFcehziPEqwbRyTl8P0fv9jbUrKLedX19X6LcxgsMst7ItYkjua5E8MMc3OuA7p2HPMdqLdePCDRbgQd5uFZ632nQb50CWhdyPfNOcBgsslZjbJ52/vwU5dOHGD1bo7g6AKpK9pn+RjqbfrZXbjAns5xbkkikMejCHwwBR6i+5Bp4KHuYj8YCG65Wavx1DRWFGIIkAU3maLajATXW6Hdlz+i+5P3hZ1/fL1Berwjh5SjzRPRDRPRD+pZ/DKmT+jSA/x3Af/Rqx3yscwgAg+Zr+ON9uMkcvNdFemcW4Iq6htvU8GkM38/g1hvF4GNg2BePR7FZxFFYwLppW1x/uhIlOuy3EQczw11M5bv2hmJkGo1AykoWdUSCfxqspBgnvA+eb13LBlwIlMD7Q8FpPeQ7s1QW9WIZMNmDsSgwO1fmNofg5hsN/fsCq5ihiWPxLKsmYPsKm8i/6kltnRfHEShL5TgH2pDUNpw9kgQ8UEPVNEHhrNZ6PfR4cSzKLMtQXz+Aqxq40yl8L0N07wzIUokygFaJuoulKjcCliv4q4dwk2X7vT5PEPV7cu0MvmJGdPM+/LUDwZstoZ135B5MZxKlnW6NQXByXnAEN1miA8h1jBVeYY/hv7wpOZWykftQVQqzVADHcJsSNFkgKnOw64GqBs1br4M1Wq3HPcQXU/lsN0f32QtguQaPeljf6KP7/BS+m4Z7otKMuoiWZTCkRBCcUxPjANDLQUUFmiyCMo/j4CwoPEMnp6C8E6C1RIw/FRWaQQ7HDFou1QnIZO1aotzgMgCYzkBJgtR7pHclj2F7BkSSyzHDbFBRJwOWK/CxGDdKYtC6kH3inOwxz0hemsja6ffE6PVyOYdOBhRViF43kshGlsGtizaybfNG641cCz0HOnt9OoowGM3rRPVn5u97ldcZ0qLloeXxjhCIxHNwgD8cIbp9FnBn9i0849YF3GwdEqnOBWW7WAm8QJoYtrCcGXy4F0JyDWmprMFEEqKyJJLbMLaXt14VKb5dX91rPTDOYsVNXdhAlg+wf6smMFaMKWUJUYMeLh5Y3E68xxYOaNSjLjWZqjgwGi/nbRGR4e7mBRq7Zi3eKs2X8tunM/FQWfInoiQZXAt7g+MYNFsJ3msRh23GqpLf0c31s4T43hTuQgxadHIeIgfbaHGseZw6JC87iqdvijb3EZ1coBl3JSG9N2gNHR+M4CZLuXdNI4qkacQYmHfvtrYOEaonx6ieGIPTBG5ZoD4coHr6CMgSuV6dTGAzQK6Jnaued7XflfWkCrC80gdt6nYdxPdnAAA/1oRq3cAf76G4NgTVjNOv2Uc0XaN+5sqlW7u5losCNYWm31sPM8mbRBFQVKDZEv5gBKQpOFcjvFi2eDuYBf6xKGq9EQhsUwDzJaKXTkEL9donU9BsKTkGQNbHdCaf345kzydi1GIHkOTCqGou5+sctREnH43BzsGdnIsxsHxb1YB7HUnEdzP4433Uh33QYo1mkGP5FdeA0wtJgKeJoALTmXx3FrcREDK5d8gycT4aDywliuXx4GUUyBcmHvxQj0chj7dBAAS7TCJlmDhZ4H0N0ZlFkcxX8v+miJNEPOKmEWVkCtM8qo5AHzSZK3tGmTyTGdCwbACTTMJSf2Usm2040FBeoo34/qw1RFQpQyhLwyYF5HidVJKzaSyLV7+Xu4rrZhnQyYQZdbwvytIrxbCqQg6ik8k5MMt5p4n87l4XPNDrYiG1Gi8u1PMjNR55JxgoZTHRYgOsNyifOVIlH4PIaRRWwh8MwIulQmI+RBJlGSCCRnM0640ogE52OalouLclccsyJAtXa2EYmZEEAM+ILgSyo+UGSGL4awfC7OpormjQB+d63a4dCZ1W/4VzEmlcO0A83aDqx5LkJkK0KkGV3Dfe6wNFKbDZchWMnVGUvUe8KOU3NQ2i+RrRskY9EiPmx/0WpnGrQq7BpkA9NIgN6J3U4DSGW20xcgD0PvQsyoMc1VuvhHtOhPhiBarrNi+Cqoabr4DZXJT5bBaiNWXB+VFX7kfTwB+N5T5lqRi7o7Hc/yQG9vfae85pImuPWe6p5WYs6m0awfprYSDRyctMEh2PZO0RwSnrivsdIV7MlmEPlBWorOGmS8Q3T4EkRjRdoffxe8BoIIlyZiE7JIk4fLOl5EKGXTR73daZkeuqMOhyJcbudRAG0IAf6vEo5PE2CJbIvVgFD9qYQAZVmKeSxCH8VaXC/VwwzEy9JqV5lk+OZaMRCV7vmxa6IGbEZwtZmA/WCxh7yBRGHIVcgOHghW741apVnjRdCgSkCqOFdepGchV6/pynUgMwW8rrowGQpqBuN0Q/m0LOufHgbiZKKhKlQN6DzicCQW1h4WSJ5bVumrwT8GOjbBYFeNhDeutCDFVRhFwDAJ9GchzjzFu+g5TdYkbSFDozOEtCjsHYMVaP0bJRtpLpdj2NT2/Xvark9+4NWhyb43C/6XwCKhthqDiH6jAXQ9M0AgnO1gIPpk4MRy5Rk+9E4FSjlVRoty012TxtVahuVSoFlVFcHyI+mSC9O0c97IBWpdxfotZ75/EATASfOVDD4JgE/38Zye4uEU83IbG6KeQzWaLRig+OTa8L5B1Qnsv13xRyXctSIqyqht8fwC3WAtdonovqWtZ5FIlXbXmFpcJxabp1f+JAQBgNAl1UI8dWEnFGOInkOLVE7bw/BF3MZQ8lCV78k4dybrZvOmnId23VD9WHEqHQ6XmAxhovkXnVwM3UcVquwD2hc8NqMej1U5W7COHNKszisVXqKdmCddQqMM41EbxYiQFIkxbDp2UhXsa6uKTc01sXCt/UQTExg68dwfcy+L7CF5dwXcAPOqIgat1YtVEzvWw2Kwgy3N5RONfRQDZLJ1EGTSXvU0MhNFUHKspAxWwaxbAVAujmorBIku00XYCmC/m9F1MJu70XKGG+CKG95TWcg98fCHZrBk4xb2QZaF3K+VWVKJx+T/InjVePTrw2VLV41d08RCqGJa838jvunILu3AcWSzT7vQBZWS5gIZ4jnZ5fZkMpA6Z451VR6lacFEegsymiuxNRznZtqirQSvU5JgJN5uCjMe5/9RDl9RHqp47Q/8gJls8MpNCKGcn9BWi+Dl5vR6O1WmGTbi6/a7ES+AQA9zJkt5QK6RzmT2fgbhpgvKoB5yk4iZDeX8DHhGShSm9vKB70A4WI5UEXHDvx8KtKouKIBHdvGmGSRWqEi1IUuiWC+z1ZS3kHNF2ifstVqY9JE6n/IMnP0FLrXojaSNKPe3Ivhz3dA7pe6hrVjX0g72D2rgMgS4XJtVpd3p+F0JnN0NFM82xOjALWG6As8fRP35EoXlljLZxV6v0rK2CxRPzCPc3lDVqHiff6wGIFKiu5zk7vi5PoQWp2upcjy9cgDKBifqjHo5DH2yAA6s0qo6iXwR+NRLEdDMUzWZfgLEZzdV8w4O3NxuptskIciu9zr6OeVRS8EEC8T2bx0leFYKqexRCYx1qUkniu6ks8bgCiSIxBxCyvbwpJenkvSldD6M8SZkmErjdtnoPni7YgqFVWyzXqJw7QDDoharIkZRyhecs1YVwZiwS4ZAzd/als4E4mUVUlmxHrdUhGd3MgjsTYLtdiXCwyUOPMSRQYYFazEMchH9PT5PyVA0QnF5fPxXjqLQ7tWvqnRR3xokLT1wKqqgo1FgpX0apo4UFuC7EkOdn59H2BvE7OcPTbM6S3J4hmayCJ4SpGM+q2vHdLYLcGutFEfb8rCtjYWczw+32NRAqgm4PWBZp0q0qd9T4TtWuoc2+DaL5B99lJWBdZ2sI8AJBM1mj6Uk/Dgy7K6yOhQ1eVeMSxOhmrtcKRek28B0+mLcON9/qIZmspAFwX8t5+N8BA7BWPXyjNGfDH+8o4y8Xweg90u0hOZli86whMQHM4kLqSBxPJ26w9gwSTBLRYB4cqTQUWrZuA+8dxiDDt9+S51sY0Ab4CJJGuxYu0KcWAdTPNZbBSpavLUNJrEH5IuGgHGT0KMYqi8vHpYh44/FtCtZfCnO0F67n1LFtP36h4VrJvyVddTO5sBnfzLtzJqSaSE3Aawd05g1sW4rF1MtCm1pyAhryWh1DYqLm+326O7cIz7iRCp7MNZdWXpoiJQmWy9yBLgFsiWfnn8fkS8d2pKEKLGvS3RHcnck7bvGyLBOxYkcI7ZdWG/XYdaLIIXn/DYYNafQERfE9Cf2FYubDBzSiYMIM1KgGReqIuwGvbkNzeEP7JK+3nopv3pVWDKZquslGyFDwQumz5zJEYayBAD91c1kctBV9uusL5113B5skRfD8Dx4TVtQ58JxVl2slCLsXuS9MAG3E0eNhrE7bubCZ5j1yuZX19jMPfmWH91DDki5IItKngFkIJbbqaFC1q1IeDYNy31+8Ld5CczBBNVqDaI707l2u0KcC9LFBF847cM6uPyTLw09r6xuBSosC8ahpgvpRrrkweaU0iRV+ISAhN87VAnQZNLQWyjDYeydqjGmWSX7O9tWXM2uvV6D3qpC1DivfkvtN8HUgUcdwWuaGqxVj1umgOh2IUhpIc9lfGEmV0MqBp0FwdS54gTUCrAvVYYFQ+3IPf66F82+Vk/RcsmsJ4mMejkMfbIFRKPWu8LNY4kqIpLQJy9yZteB/dn4EKjQY6qYTD7MG9bmD7rCVx6rupJvAeqE+wOgJAQvVIPD3kHWC+lARcVUlvmW02SykYNyKH+ulj8e7s/JmBvRE4jQVXPZ8F1o0ldgGhge71A9Mo0vYVVqDWyWQD5Z3Wkybv5TcVSqs12qpvAhbeXstaqZcuPJK4hXhCWwf9XXUj+YjtKlOjJzoIq2stWDTvK7PG8jj2fXEMd3Iq9NI0lSTmQGEmy0dYf6qLKdxk3kIWIEJ05zzkOharUCMyWQAsipPKGlRWwhaymhGt7m7eck0qhU9rLJ6IUY866P3BOaLSI7pzBooiifYMkoljia7yXJKosyXofIaLb/sS1Ff3wIMupl/zBGg6F0rsQupFun8QOqw3g44Y/k6C5vohshcn8GksrTyma1HieRoUKyTH04y6KJ6UdcKxAyeSe6HJQtbMYtUaYqExL6WI8O5FCymS5aes+CyKpIo3UtZdUQl01DSa4C7FyTEoL47DGpuKAejcWiA7WYQ1Ej2wTgCNDAtZh6u1GASiAE3GkRZ9+mC4pnP4/X5r+KL7E82Nydp2ZzNxAGOp86GqwZ3vuiHJ6boRqu6wD7p/AXc6Rfrs6zM+nSFb/mEej0Ieb4NgIabiibaJfC8LoTez1Bk0jdQqjPrw3VRC1iSR3jnGrqlrVeh6Ow1jN+/cFnsiyVBT7JyngmGvJZnrh9pDpvWWNIfQ+LbRVkurtOTkFpbPva2ktLU9AHTTKdXWQuk0Cb1nOhnqK6PWy8NkFqikwOWIx1palFWAgHpZKKKra8GUzeja9TBKLPuQM9ii6iKOpLrWDEg3D0nh2SJETAYDRZFi014S50kU8PbtCGErwmq/a4uphX5XlLflRizpaIrKYL9O0iZBmzwGd1LkN6c4ev+nkb54Dkxm6H3sbttnyk0V215v0FwZyfVfr4XimWXggxH2PnoukFPDyKb1Fo3ZB2LCpXUrv6vpy/WLJku4dQXOE/CwKxz7vcuFVNFzL8EVjUBHiRp7M47dHMhSqVJeb+T5fi/UG1ghZOt1J6FYcbUOxAFHQJaAD8YS7ZgDZE5HojUSWuwWbRpwFqE83mL1PYidWyGmfZ9V3G+Lfq4+HrXrng9GwpoDpBJ52MPmXU+G6mZyUkM0mQHrDdyiwNVfuZBr10mFdabGBy66bKBekxCah3w8Cnm8DQJ4K2np2zJ3dzoVD3VTXF4MJHUJVHtwHKM+GqC6NpJwdIubT7fviZLNUmHTaCIM9RZNNU2BswvQugAVdSjaAaSdgXHo7bNEknhrWLy2KGpZK9yRMNfYOZKf6F2qjOUrQqeEi4JitMprpTFisUR8MhFDtVjK89bLJ4pEWWxDQ1UF9HKwFmDRsmgL61A3IUloFFIA6OVoRrlGKd43jOsAACAASURBVC4YzLUWxFnxX1m296XtR5NpXsLyF4leSzNok6kYEytyMwVj0YLVcJixYZbzUBjBnc20BUWuXnQkXqQpz26O5VtH0ncJQPoHd9DkMaio4Z9QKmqvGxhgViSmBXtuXbXrxJ1JOxBabkD3L1BcHaA+7CHaNKiv7cPdm4AWG7hlIXRcleiTLwobiQjxvNRaAgeOnXi99y9QPLUvxu3B1R47xLfPZP06XQtJEvI82gMIQBvJ8nigVfbalNEirqnATn6ssEtP60QWes9tTwHBuM6X4e8kATFQDTNkn7jTJv75cP/ySTuJXi+++Rk1CF6S2MxibA0SnS8Q3zlvoz/fE2MLQKKx+Rqd338p5KOSWKKMwz2gm8P3M02w61qaLwSSGvTQHA/RXHvgvL5AkaQyPdTjUchjbhDUa/dNoEryA4nSrTATkVNWkeQYqPLyMMViMhqop8wg84KNi21QkrXcNY/d+9BSIsuk7YW2FRCPu4FPpQtq63Ebtm+FPmXZFvJwtNWWAxBa3YUmd9WItLx+zZtsvvJp2dSrtbadUEVoeRYNrw1fNoouTeeCCy+XoihMLMFnSt85YLkW6K3S3jNuq9+SXcPGB0U11wSli+BHvUAJJhIITGsv2qaC82VQ+EDwOLdZItsQ1WIJTtVI5h2NQipphDbRLpirtUBunQTdF5RZ0+8BSYzkVKqh3Xwj13O5ainG6KShBUoqrC2UpUBGkUJqTjqv+kQUW7SqwDEFTLyqA60XAv9wvxPgEgfpWLosWgNPfiuPZeIIyekS/mgvPFfXksfaGwQ4p67l+m+3jXBan2M9uparNjdCtp7X2hhv0JP9UFVt7ylTtsYgszWd3JsjsqaN2kCwvDoILT8AWA+r8QdflPvezcH7e/jHH/gHAnFZQSizsADVAXCrEnRyimbcb/snIUnAh3sSkTcNaDITenEcwV0sxVBqNN88c1Ug3cUK0dkC0enWun4NwsAuQnhTiykl46+Tkw1i0s2D924eZ9NIW4mmgStr+FQ90OEA2N+TZGyatD2BWspdFIVCIGMtzBch5LYNN50FCMj6v3iP5DMnUk1tYXRZCkxjHrMm0TgJFcr+eE82S0eVZq6enEe7gXw3A0ZDdD55IvRSw8lNKVhDtravkkJZjVdPXmsBtH1DG1EdjkOYDwBpAn80gu9p2+Islc9b63HFrbHSVh95Lnx1AFgu4e6dS/ifywan2gd6oN3Lbh68/liLpDTS4mEvRAl5R67HaCiArbXLjiNJzCs9E85Jx1oA3FFYpWrQHA3lfUCbjMd8EQxpksjxrKiwKAM5gH1LmcSmAC1X6D57IV7+rXtIXjyVezNfXGpD0QoR3P0pml6K9Y0Rmp4WKq43uPWn34F4UYJqD//U1c/6HG1KoPbSTiRLpZhrIfkZ3h8K6+1iIr990AvR63ZjRG0AhyQWownAj4cCn2q7k+aqRFptc0gz4odyP5obR6gP+uDYYf2u62huHGHz9iuAA+pnroCPxlh/1TPiXAGX9+Dd+wCAT/7ZPpZfeiBFaoO+gu/iwNBK2HfRiychNxI50P0L+b3rTYggLPk8makxq4V+fPdCoq88DR2BXwfxTA/1eBTyeBsEu+axeF1GIaVCIY9BTzakwSRbjezc6VRaC5S14L+bQgq5YieLyqCKvKNJxCbwpBWiuJT0fIA901IngZCgtUTu9uY07zGJheJnbZ1rHwwDIB5jT7B+nycyrOTOfXCvA3dyKklGrcxtO0cO+vK9M/GO+HBPNg8rNmzKyhg4xnay3ETLG6fW63fTleRBGlGgPBq0HmbrzVvOxWowLJLJMmC1koZ7jeY4rJLZOdTvvBGM6moNf3UfmC9RP30FzbUDgZ4M2tOKdD/MQYW2HbeIZzIPLUWyDMVh3rZD5iyRpPeqDOukbjTxri2qrd+/sbSMImz3G5DGa3Y/tSWIK5XGTBSq47drMOxeXsxRvvUYVDM691aBRjka4NqvLYQYsVqHnI0tK20HTetCEtM9rSgvhZxAd06Bfhfu+lX5/GIl+aQ4btk4raPSeGnhbnkqh7ZKnLsZotO5KGJLpw06WkQpEVZ0b4a6K7BNtJFq5WRSyP/P1i3Vdv32o8s1JEWJF/7slwEAvvRH5+jcWQsbDWp0NYHN3UwckvEI7qV7LazUFnpqxOqWG/hBV6IeQGAjJSb4aweS65stPzuP8wXKLkJ4MwujxSCpUKWWJMKNL0vZTDrYpd1YhoumqbBPAElEORIWxkyrhmcL8XjrOjS+Gw5EEVg0Yewk9Z4w6IuHaV7t9mO51j7/6r3nHfEiAXAUwR8MJXxXvLllkzgHHvbEm64bcBLDrcUrpCgCzddYvO8ZLeDaUjz2my2J28mkS2hRiOdrMBoQipmsJmJ/rLkTxWvNI2fG2TdckwZzhRTH0UR73FjVMpG00lBmSwtXaZ0I+r3LhYRJAqQpmieOEN8+kzbTGy3UI0Jz/VBpkkLhLd9+Fat3XxfvddSVKmDn4Mq6rT5ue9h0c9z9409g9pRAb+7+RFpJ9DJUB92wHparkEi3e2nY9qaQ4/a7oQtrN5f1NpMIgMcDkPfC8b9+1CZtq3c8oYOaSLx9uz/McKVH04ulSrphiQQb6wUkUe/Fu0eXkqHu1j0sv+I6/Kgn9TURCQynXjEG/XYOBjrS6qQlSFj/Lq/Gp5tLhX8c632HYPJKAuBcoymH4KCMtMgtioDVCunZCtR4JGfSeDG6dwEQhPW03oAYyD92K0STakDHn/T4Yz/wg6DFWtrEG/yq1xexDIzCZNZCVNW1kUBG5mAUpSSONfLwV8R40FSYX/UTB9gcSQdYtsLE10EYhAbuoR6PQh5vgwAOCvtBb9344yYWOva64X1Oh9MYS8naBatHzwcj2SBVJf2DDMc2apyxSCy5+iB7wrBww+8t0tCkF51OQJO5dMqsvRSeeQ8qpcqVGpnZwEnUtqwmlnGIKEpgbwRkCXrPad+aLNlKZAfvnPeHof30dvsIU55AqMOw1ggWTZkSi6VI7eCDLwm91oafdHPxQls4zksrDeByJOS9GF6D13Tegz8ay32JSJL+2i6DO6osqy0KL6SmpPuJe+A4EuJAROK9ei+5Ie3m6Y9G2Dw9RrpkxEXw7FFWQu9khCR4Nw/VzHq/WuPCLN7+YtUaRTJ6pLazoEKMDBXSj4iTWBL80w3qgbaqiCgkfCOH+Lk7iFY1moM+qmEKG7m6fLoP67B68MFb4Zyst9Fv34RbSuKeGhZPfNATB0PrH+CilkLdTmirG4FYrJDOhiutNy1VuD7sSZGbXe/t72CWgkWrT2k83Plc1uRi3XYM6Dx3Dj4ao3zmCJ3nL8CjAcq3HAfYadBDOm/QuT2Tiu08lT1mw5m00h7Wd0sZTslz9yQ3ZD2o9oaybhzBTeZwC3V04lgK8BYFFk/okJ3J7HJe6jXKDjJ6s4o2J2vH/Vn7ZWUhgAicyAblbtYmnLASDBKLpUzwAkL4b9O1NJnKHQm5aSV0NwtvMZsH2mWSwF/dlz44vU7LcAn5BzEWPJtp8Y3WBBg76HwCeul+MGpNI0aiqEBzqVIFs5x3VQfFXUiZPy03ErlYzcHeMLTLThLQnVOBgRQa4qoKcNF2zsAiHkuGp0ngn6+lEyYPu6BKpqhZMpxqL51Pt3nokQvwlVFTt7uEahdON11Kj5uGW7ZN+a6n4GbSydJ3E0SrUhqoJTGSu3M0V0a488cPpT/PfAW3KNr++9zNsPqyY7jZGtmdGdJ5AyaAViXKtx63ijW9eR4iQHMELH9RlmHkqrWL7nflnusMCBRVC0XWRwOU+8LWchPNh2h7ieS+cPSpaAJ9OJP7Ei0KUFkj//SpGJnlCoNfvymK3QYnHYwv1STw/hDFDWlFTou1KPDNFj5e1aEwc9CTNTEeBTKFUnA5icD9HPVTx8BsDreuEJ8vt9YrZGyoMqJouQnDoCIn1wNA+rzkA+Q7JLlPkznSm+contwDlRXST91BO1N6UyB//kLqJ+bSIZeqWowCFNLTOgQM+rBqc2Sp5IvUmeBuJqNTgctwpZeeVXQ+w/EHnhcixvZ0wNcoDELJ0UM9HoW8oQaBiPaI6GeI6BNE9PtE9K+8zHu+mYg+TEQfJ6J/ps91iOjXiegj+vxf3Hr/T+v7P0xEzxPRh/X5Z4hovfXajz3EGWpnzq2mWttRgTajAwBaFUIVXFh/k554heaRAIG3rTUCbFWVqXQd9X2ttLTv0QppiTB868nSWhuJmcHaHu1olNGyCopSG7GxMT6WK0mQnU2FXfLC/WAINoVsGu0vhMYDi6VQRi1qMcjGIhNH4lnVNZi9/G1wWrgxgRlknlqprRGqSq5X40UJbQpJRmoXS9/LgOvHgdUUOVH+F1O0lcoaRfFmE6im3sOPe6IMIQrIdxLEywrljbG0jX72jiiyJG6vWfT8Ca6//6XAcFpqC3PL49jIxdqj96kLNBmB0xjRqoIf9YS+a5izzYJ+sHWGVWvPF/KYLQJNFgjTxZhR92J0bk5QH3Th+7n8nlR+i88zafucaPEVIM7BqAfaVFLVXtcSPanxIYs2jTFklb+eQXfPkJ5sMWYMElIDz70wFrNtWX56Efov2WyIhkGLNeLJCnAyopPKRtqvO9c2b+SBtiHJt/ox2Zq3hnHb1eW+EYpnWSH71MmlaKOF5Iz+TDLXXIghaDuxtgljJWPAkZI3ilADNF/LZ7IsMOGsSHW9CQZoey+8DsIAPNxDPR6FxK/+ltckfx3AB5j5u4koBXAJjCOiPQA/CuA7mPlFIjrWlwoAf4yZF0SUAPhVIvo5Zv4QM3/v1uf/KoDt5v6fYeb3PPTZPQjRbE8Fq9UjqxuQdsNsIQxLGm6KMMRdk4FSzq9sE03skra7dgspqWfnBBYxiut6A+rKAqcLGVJO6zJUaL6cd0IU4Cn1YMg86rK6TBEE9DzFYLg7Z/BH4/Z8WuaTtpjm9QaUpaLE4q0oBQB1OtoDR+c3lCW4rluDEWYqe3mPKRBjqrRKPwKmcxAAOvcSlZgnax6Zs4yk0E5BFOAkvQYcRXKNmcFpjM1Rht6zU7gslh45zqiOWshmRma50sFE2vnVGrNFETq35Hc3/Q5cWSNZMSbvHmH/t07lWHMdLmS/magtzsNiJd7u2YUoG6v8NoXjlUpqrBUidF6cYPJVhxh97DzUCGQRyDOi6UqaF2qFMgFiUAExBEjD2iwricrWG/n/LJMhRVsRAgDpNRQ5IE51ylnTKjxaFQHqUuXekipsXyyWLRWWex1Ub7+GaFPDdzO4VSFdeKzVNABae7RDj0yS5DLcuFV70lI8yzLMKk+SMMHPKMLW6G7Ylcln1w8RTVcyiOjeueTprOWF5reQqUFxJHUhqzX4YCQRjF1HIDCPbD1vXp+kMoBHljB+GHnDzBARDQF8E4D/AwCYuWTmyQNv+1MA/m9mflHfc0//ZWbWXYdEH5eoFkREAL4HwN//ws9SPKl2YpNVwMpJSNi5XMlC1iQbOplQ6RRT557QLeunj9E8cRCKoBrxWqhptP1B4HjTdC6Rgs4pRiLFTbTZ2tBLLfmPNCE96IMyKwDamsFghVpEaA4HgfFjv8G8RVMSinWTVVaX1nxu0ypyUn48tHiJD8bwTx6HORFWNXoxFSVt8x5MKdkowqKUBnoLrdaNFOpSZkubeK4b6fYZR/DXjyU6MKjFRn5mCZB30Fzdl2MtVkBdI3rxpG0v4LMYvc9MwGmMcpggOrlA8dSeVg0noRYCkH+Npnu4h/KZA3ASS8HTQoa2VHsZfCfG/q++hP3fvC/rYLUO99jaWFsVr2LQKKsAf9kgpaaRHkmdLChZ9eKbvS72/oXi/ZtC8iF2/yZT8LAHt6mk4SAQ6jPsnuoccPS7QnmO47aVw+pte2i+5Ik2QpAdlYSePwCaY5nFQaYErc2Gtb4GhGJrTpB65/7aAejkFJzocJumEZhHjT5vOTI+T6S2xpGQJqxJIXOL5dtUvrZa3CTVSv65EiZGMk+Zc+kazJq3cEUFTiMU14eyXi3vpZ8zRpfcqyW8rilabkLjwW1GXzeXWRTWuPJ1EGZCw+6hHo9C3shvfSuA+wB+koh+h4h+goh6D7znHQDGRPTLRPRbRPT99gIRRQoH3QPwi8z8Lx/47DcCuMvMn9p67i36Xf+MiL7x5U6KiP5DIvpNIvrNkgvUR0NJLpoCLMuQIzBMuC1SK9skpx8PW0yXs1gK1Govz/V7oUc/EDYiSXtqf+1APEgi8WLaUX2Sg6jectzS5HjUA7qayN7um5Tn4XPKgw8ttV34bqt0bhPmGk2sS9loeUeOa3i/Dbfxvq0JoLMLuJdOxXBYO+G6EYiqbsJGM080jtrCKooiMHtwVYtiV0+LWZPHcayT4iQx7+7cl3Df8gUaIXEiEUV0qk3QHhyMsykQX6zAcYzyIEc6k1A/+9Rd2eiT2eW+SUmM+niE6sYBVs8Mkd68AM2XYCI0B334TorO8+fSzE8jRftcewynWHiqLce1JTl3szAP2poAZqkwi6yKu/EtBBTdn8kshq33M5FUXUMYY1Q1aHLFlbfZZ7O5rKXICeTogPXbDsQAjQdwFQustN16Ya69gzRqiaYr6Y2012uN/MW3vEWpt1poVzXwg47MQGCWY790Cr5ygHhRybkSocmCSiGtjfGjLtzt+2G9TzSo1/VAtZIrFJ65+Na3BeKCzf/Qehzu5zIbIY7bmgF3vmgjUFoWyF6aSaGgQXf9bshHGQU6jmW/2JyNrYI5o5CjbtrJfJcK+l6jeNBDPR6FvJEGIQbw1QD+FjN/FYAlgB95mff8UQD/OoBvB/AXiOgdAMDMjcI/TwL4WiJ69wOf/T5cjg7uAHhKv+s/BfBTGqVcEmb+cWZ+LzO/N6UM8Qt3xRtfrgJjYtsIqAGojwbSOsIR3Mk53GItHS2TCBxFcGUtIbb1RCor4al7yHGt3e58JYlD46OfnbfVmL4vLXqTm6GZGS1lYI0fdIR9Yh6zbwQGsV4xmshrIRlAirpibadgQ2FsfOZ8Ib95OpfnrHGb9YOP45DoS1Otro22jEgu3VK3O5BmadiEtrniGJRloJ50EEUuhUrU74FXq/BdR/stDEB37gcohkiStEWNW9/7VrRDWPb35HftDeXvsRQZuckcnY/fRvyZl0ILcfN4DRs/3NNJeQ7RskS8aGB979dP9aXzbFFtFZgtQ9RjozyHA/l+mwKmCXNsilDvoA0J27Ycq7Xi+U2gIuc5OEuFLbbcCOOqYaFsAtovq4HvpNjsJ5ex7O2K68YLDEWE7FRgkHq/h/yjyirKOyGXYF1Jt6BCWm5kHKlGkeNfek7Ot9uVhLI6MDSZCYx6NoW/fgiywTKrNWhVIF43mqeKsXzHPpbvOhZD1ZUeS76fh/5baQo+GIdrpfd//E8/ozBcV6jGTvNTg77shyQB56l0JF2u2gp/KmppIb6plHkWyevzpUTyNgEwlmR/aN6ocJBFLUki+TfvhfbbyUJfpNcoklSOH+rxKOSNNAi3ANza8ux/BmIgHnzPB5h5ycynAD4I4Cu336Aw0y8D+A57johiAP8GgJ/eel/BzGf6928B+AwkAnllcZIc5v2hMCqyNISqhpsuV7IQG2Wy2FAQxT+prGWSWFEFL0fnMnMaS/hvjKBOegmL5OVK4JaW+UOycM0gdbutJ+zO52EQj8EdvgmeMiB3c294ud/Q9tAf8yqB8Jm+zBRGnrf9ajjXDbuQhKHAWOvLs4ttupWymph9YB7Z80ThubIMVafabpoS7SBaVmKYvJfjABKBjAfi2TmHZtxFvFF4wznx+B0FRhazwE5WgZzn0olzu3HeFhw4ec8h3LoW7xloKZbd56aSdAdCt1aFX/hAk7uN19qLVTs8CZtCGDVGQeata27QXK0GKtV11skAVqzdSRKTqkY8YEAKxToZmkEHVDWoeiGX0/4m+9e6ds6WcMsCtC6RvHgaHASdDWy9gWTanFw3f7gXIkRAWnkALRWZ+zn8jSsSSScSxfjjfdCqRHU80KlwEfyoi8WTWr9QNuh95CUwQYwbCz7vrOAuckID1Z5IbR6DWe5hOysiCvtqvpCkfp6i3ssR35c1YwVkWK3hTsXhofsXUnAGhOrxupZCxev7bd6PrTWK15yX7b1E9o31S+K9/ivrkc9D3uxJ5TfsW5n5BMBNInqnPvWtAH7vgbf9IwDfSEQxEXUBvA/A7xPRkSacQUQ5gG8D8Imtz30bgE8wc0u01s9E+vdbAbwdwLOf+yS9ekeFKHqbZmVJT6KWO86RAyfuUk8jN19tKT6FFAb9NpFLtYc3jn1VicIyJo5z4jXbMBMiuJt3pZagbsJs5e3zcRTGSRKhbVRnIyM9JMRO4paa2PbEsYEhgCbrugLNRBH83gA87F42RoOeKAqlmPKVAzFCxrqoKqGiai1F22+n8YFhY9fPGrwZhbSuA95u0EWm7SiS4AXTyZl451kKKhpc+dUzcOIw/9obGhFxSxFscyDG5qnrUKlNJFXLeQdn3/oMOInQe6lA0xdGjk+oJQTQZCFV6GdTbeOhldk6IYymmpA1xsxsDpsF0BZIWV2JDag3lpS2HOdcI6nFUu6fKszyxn5ItGri049FEflugv2Par1ImmL9Vc9Iw0JrBmetUFwUhg4Z+WG1lqpdvRZtpe7FFCgruFt35e+1RBK0kCpnvzfQPAUkR6OVzX7Ugysq0GqNi3fmuP+1ezj/hutwdy+w/2t3lOxAaK6O0b25EKq1wbDmEFnzP+vBtQXlIY7brrItrKNwk1sVoMUGyclMIuaDcQthIU1RXz8QyCzLhJLclaibzmctbBq/cK+NqqnTCQ6b7WOrHh/kaA4Hci5b/aReqzRMD/V4FPJGm6EfBvD3iOijAN4D4C8T0Q8R0Q8BADP/PoAPAPgogF8H8BPM/LsArgH4Jf3cb0ByCO/fOu6/hc9OJn8TgI8S0Ucg0cgPMfPnjvOMTmZFQsYgaoRJ0baSrir4xIEqL+MBXSjMonUpeLyO+wvjLWUxx+fLwFYYj4LCtUE1xkyKnA5fUczURmt6O0crqBmFxBigxVl7wVs0RlQ7XrMMm9CMF7PMjU2MGbTV2XO5EuhrsRKDp5g2rUsZpJ4k4nmb528bWRuMST5AE511I8rJajuskMtaYFvxn3nR203N7Bpobxx3copqvyv3oWExmKt1GFqvDBIe6yhRpWXa0Bu3lJqL0adXcBdLxKeSL5BCMSjer3x1g4HiWLzLba/cFLwNlLHK6aIMSd6qVprtFvOs2w2tyLUHE/JcrtN6DaQp0tuTtkupPxwBcQx39wLRokA0W6Pcz9t7XPUjnL13H5sbQ0nEK4uK8ywwu9p740NiVSu+ySCpLNU+QB4YDiQCY7mW7nQC7nUFLjI6q9a4WFR2/E9u48rPvYDhZ1ZKA+6junEA1A2i0zncfIPVu65IDcB2g8E210VhXkRLOpBIR/IGoQNus98DdxKsvuxYYKvpXBh6mxo0X4HzDNFig/zZc4Gaiq1kMDN4b6iQaxl6clmNz+iBFtxVBTqdIDpfSgeC89nnVCUPK2/2SuU3FKhi5g8DeO8DT//YA+/5KwD+ygPPfRTAV32O4/7Ayzz3swB+9vM6wSQOzII0FYreulAPvR+8hW4uve4BNMdDRIbZtx0ZRQG3YxfLUqlupVDizqYCSc2XYciMsZZswW4K7TKaB1pjJwtJMPUYUTdCGd0oT9/wzbqW1hX9rkQJyyX42pHgymrsuJPoFDKNSO4qv1wnwvGoJ9HSXAuujDaqniNiJ0PlXzptIyKua2E/ZVHogWSjP9vK2ihARtpArm0NbSwpQIwwUaCxAsEbJ5J7UJZItxV0rtcrlc6idD6Ta2H3aDjQJoAdEIDJO7vYL2op2mNGeXWIzk0dLbrQnknjUUhm2ihUywfYjIckAV9MQMNhgJb2RqFYDQjJeTvfOAY2JYi9KHBHgaaqozzd3Q14ry/3VPMwTT8TllEnKInBp6a4+Z37qPMU7qkUe5/qIL11IUyhbYfAIl2LNgFwJxWjn3cUwkpEYdcNzr5ygONfvIAfD+EWa6Go9nvCwCqlr1d11Ec0L0Fq6P3xHuK7UyBNJP9y9wK8P5TZ08yY3YjR/bTbqj+gUPvT64XuulYPomvm4n1XMf7nt6TZZNWAHcF3U+S3F2jH3vZz0FTaalOh7WbYo3pyD+myaGdVo9eTa2PzOToZeDIFHeyjba9ikTiAdt6ItuGwNjWvh/hHxCB6GHnzntkXQww71P5AZIVExpEmkj4xWSILUDdUcziAH/clQaatElolqB4k93PwsAufxgK9AKGOAQjQgHWItI1LFKiJNqDDNo9ngT4iQjPI0Vh/G51eRRfz0I8+jqVJm/U30qRbO8ZwK59A3oPWuuAjF9oAWLfWNNQwtHMd1JBRV6An3ihkU5RtFSq6ueL5ignPl2gH/ix09KJhxi5qPVV6MDw3hVoUGgFEwfMutAurKV7n2rxDO/u5boRayCwziosKVDZI7i6QvjQN8xr6WplrPYb2h1h95ZNBkVUKX2gin7rd0Oa5m8u/xjzLU4Hkhv02l8JmIBsf8kmWQzLIsa7lPg16LSwVv3SBppdi8raQD6n3cjz1D25i9GyF2VuBOo/k+ArTFW+7EiJDo/ZqYt1dLMJ8b3NIlOZ89BuSPHbTZaBblpVMt5vOgckMyc0zzQvIfXGTZVDkaoxoMkczlNzL1Z+7KetyOyJv5x9HQv3N01CzsSmAskLvTikRSlkDSYT4vlREU1kHWLGoW+MnXUnlN7uiaQ03Dsaw+d585UCT2E1YW7YXt9lbzG1xJZXNZ40l/UKFgTd1hPB4G4RGC6LMG4hFKTWHQ6l2zKSrozubtpspOl8iulhJ187FOngc2y0wtI8QLTdw2rXR50moMdBELDYFmv2BLNSewA/N0Z6U4afa3dM8vkFPGE7TlRS2eY9oshTlz7zSYwAAIABJREFUa4rQhs549c5V4fhrRy2G6/cGwtsG5Bx6XdB8DVquwrzj0VB+j8EMqsjcfNPOfUbjQeTgj0ZibGx8Zi+X89UCJr5yEAqSslQUv3PgogTWa+HmL9dCb9wUoXLU2Ee2WXXYOZ3P5DtMuVixm/b2B6B9+KOWfcLjAT75H1/D5sufwJVfuA1arCUanM6lj5FFbJqottoPmq/Q/fidy7Rg63VluR8zVutN2zQNWQa6fU/yQRZJav99dNLQE2rQC/i41WmYUTqfhHta11JtTmgLpRZPZrj53TeQTgq87e+donNnKQQGTQxnL55vwTLKaNsftjCkVMJHEulkaVu1TmtV9Cu9zy6SepvDPTm34QA87OH0646weM91XLzvqvweo9Jq9Xz5lmMkL55ic30QchntdeS2YR13ZMgTnc9aeFaizALpyRycy7WiM2XHVfo9lpg3ynNVi0OkMGB8V99PJNdaWXS+Y/2OHChXh86KSp1r9zOPBqGfE3B5tshrEAah4uihHo9CHm+DEDlhW1g/oW4K7mVwa1EQNF22uYR2XoJ5oqYIDJO35Ot63SZeOU+FfQQIT9s8E+8x+aZngCxFNBdlSefCk3dF1fKsLZT1V8YCDyl0FKlRaL/bBsRb19FEmCFtMzVL2kba/I2oZRL5vV477rFtw2FVo4AYLP2d3EnaHv3GBvJZgmbURfPEEZobR/JeM5J1IxWjgPyW5UoUIjPIohSgbfLWNiSzjph6j9prYXMHJjOBn5xrmWF85UB68Fs7ZjMYOkDnnf/Ti8juzFq4ihSmcKfTwMQCQg5JjRH3ui0jJuRgvESHgCYqVdk1jc6pVs86icWoGizZzcH3TgXr70neY/mlh6E+pKxCVDjoh6JEhSaf+IfPtWtr/E8+jePfLoT55gF3MWs7k3orYDNjqpPcaKZV2R2tiYiFlNBGxtbqgShg/lkSvH+rZWDG4a/cQff2Cq4COI3b3MfmxghwTnoUkUMyr2TdDHqy1rahViLQYiOFoWkiEa1RUp3UVbilNYsUo0OF5hW2p991skC+sHu0XSBpCXdA6koi1xa/8WQqPcJuncje1T1GRgDZG0rUat2QX6Mw47EtTHvzixP4AIX2vmlYO4ZCFq/XQrNtmGe7HYRhjnHcjhmk/bE8dz4RJZoK9io0v7jdXMNPzOTvokJzbb/tKEpnU/Coj/rKCP5oTzzw2ofkZhwJLVDbOWO51krOOLSUiCOdDSthLpsHygy6mEsPnIu5Kj3hg2M2l2Ic42KrkYnuXbQb2asyh5foAFmKWNsMNN0Y0WwjStmSxf2tXk/WYMw8xX4PfLwvxVg2CGa1FnqfDm6B96Eh2nb7cWYxkFEkG35/D7Qu4RaboARsuM9qjfJQ2Fy0LoUVNF8ERWGtDZomRFtpEiCxugnfpcdDFGlrZ2V+9fLQIM2inO5W7YexvbaHrHgPzmL0Pnxb16Ky14bagnqu09VcJJPotP5j/dVvaQ+R/e4LiC4WmH7FvhS2JXI95m9VIx5HWP2RJ8QTz1OJxtI00HRNUTZKAsjSQEI4n8hDSQBUaEVwUUiubLGEe/EEo98+kT5fiwLod9H5jFBduSt9u6KJFH3W454w7rZHpwJAXYuzZDVAK6W29rpYPz2SfbPdNsJyNFZIpoWkPOgGcgYQalsAWUuFzBHnYa9lQXFRgPo90NEB6OgAuHYEvn4EvnoouSm9HpwmgY7+muXhitL+/1iY9odCOEvAvQzpc/cVq0xaJe6v7gcjYOMtN4V4wpY3MKqqVRCbkRj05TjrEtxNRSn7JsBKKn7ck2Erd8/aEYFNPzQxk3OM5By0SrYZdaWi9lhqDthgh/EoUC+rSqKISBuNbfckMoPmnFQG97LQqM4S3dbjSIuX/Kgr/YJsBvNoECICINAZPQQf7vWCJ2geWVFqZONayigiNRadDLw3CJ5YpjTZ9UYgC96qhgZgU938E0fSfK2q5LPm4Q41gV1V6Dx7FhSJeaiGnzPL9et15bwM1rDeS4tlMORZ1nallWKzqmVBCWc/awfWt32qrGJZryNlmSgv5zQqVPhQJ+px7MIQJWbJm1w9FC+bGflHb15ewLM5Rr/4SRRXlFWVJBh+4kKprQ6uYZRPjnWetpPzN4MQR8Jw8grDlFWIjtKkLZhsPXIPMVA6UKdtQWLkDGv1st6IIZ0vQZOZQKfrCvHJJDhXpe6fyAVIcbUWqEYNKccka6Sq5PqwFgpql9622V03l0g078g93BTy3vlSjHW/2zIJJ39kLNEPkTg1XnMImyLkJc6mcn7LFeobh0CyVeH+GoWxixDevMKQZKOJtv6lugZNF2CFa/y1A/jrh6hvHAF5Lk2xUq0nsOQUEJKqW1OoAOFwu+UmJIyBS9PMOFXFqTNoAYAaD3frHtzJuTT7KiuhItYN2CGU+0ehsyTWG6V7SpLSWgegagLFjrRX/1afGVpqz5o6YMBYaA//9aZNOFNZS52A1RWoRIsCIKAZd2VFWXGe0WYtiooikPdCjVQl5O5ewArtOInCBDRruaHhPyVJgI1MvJdiqdEgNJ1zTnDu8wtRBoN+YBsphNRcPww5nzgSJo15oXb/thlkXW2tsV5LSwRAoKS+UkltZKf1ZrJjWCIyjtAMOy0WT7Nlu+7aSWyDLuorI4mYLDltSshmblj/pweEogidDz8vRsCkYSBLQJ6RnsxA6wrF1b4kk432bNCnYf/mqFidyvWjgPfXjcBs3lp4JG3BHk1modGiHWMrEgQQKq+BdohT2110qysAd+IWtux97I4cZ28oxXD7A41kMiEXaHU6klgYXsrUkmp4F36b9Z9arNAkCFRlqxa3nk3bMOzW2qZzrXl4nWSXVH6zCstip4XCLo1AFLQu0VzblwlOqkSbPJbh576RDepZWD7Moqg1j9Bc3w/l9oD0h1FKZNtqghnRbZlf4G7fh3vxpPUGsVwj/vRtRM+fhAU6XwBNgyaXAidqGNQ0iO7PQuJX6aG+lwUIRDcpGfVOf7NM1VJqYpLoYByhnrZD6ptG4JtNAZrO4U7O23J+5B1guUR0tgB3M02ee/g0kpnPkROMfFOEBKrlWQDBgetGsFtrWeycwHfWQbQoQpLYOrraOEpT1kkSoK40FaWsjefIWkXrsBkAqmgTWfVZqk3nthgmpuysDoFIlMumCD2e1hvQdCH05MUq0Iins1BoZQbavPA4QvTCXfFK11qEtSkkh7EqwYMuaL5CNN+0HUJRVuLJ9rqgdSFGu2mkXci2bBlmKms0ez34jtAk64M+ktO1RGPTOTrPnWu+QWnNBnVaE8Etaio2hcCnSj1uDvXaVrXktBKdbZ13wnrr6EQ78/JtEJDNT8h1H9gwIesu2nhg2AcPetLzyOC+djyp5KTcmRYg9nK5juuNRE7WPNHWRFVJhfW+5kH299rc3eG/uNdCmPz/sfemsZZkyXnYFyczb979vv3V1j3TPQuHw204ooakRAmELJC0CVAQDBmyAAumAdMCJMM/Zf0QYMHWHxOCIEAmaZm2YFiGBZmQQMmmadGCSdGSTYo0h8tw9u7prupa3nb3JbcT/hER5+St6ZlpTler26hO4KGqXr13l7yZ50R88S1VFaNB61rylp9cS/b3+SH83dNQpHz+Lxz9QVaWr3kw3lo4zvsBOe/GYRdjohdrJgrJ4gPHOqxjsYwoKmQPpiJgcuq6mGdw2wJ8+zSyduoGyfVK2DpqR0HMgaHA437kPJPOAAyiAAKrpHn5jgS7H06iuAkIAqp0uhEqrOH9ZQXzqHHzTYCs5JcoergYLNSmANpXkshmYotxrxvVnZV2RIaBmwoZENZGlkqYy6N5OKe01Ezf7Q682Uhla13JrgDvduAXbsfF3gKF8g6al+/sf0ZVFZTW/s5Z6EBYk77ocgp/OBDTtEBr1PN7M5OFdbuLtNZKF357P6bfMNW3fpaCsyucoYppSxdDXQutdL0NXkYBrgKkKrXFdam/m6Vh2I4slVmQ4vMgJ6Evs2Uc3q43+/i56mXkvLS0GPp331M2mGZppNcr8WQqYqSoKyo0p+P4nux8OSfXhXV1ZnSorrrJG9eByeUeXEjHYMwiIFhHyOyjiloahe3QsAgKN5uQmCYZ3gM5921L8DQV8kHeCfkhnOjrqptIre50ZJ5h95B9dkPxzaKZOu3WCi1maYw7nc5FQ8IsDCpqLYVZBrfcBRdVpCnIPzvIqOL0LX29G8fzvSEYjuwcqgONUVxtkb9yIVmt3Rz13WMxsRvkSK9XckGrJoBWW9BsieTBVeRU72S4hqaRzaRqxG9nLLh/UBx3c1n8Tg+CHTFXVUwlyxRmsEU0SZC+fgnsClSnQ4GD8g7qD9+VG9uMywLMoB2J4eQ6bG5bZ/BYqKx+kMuNwozmpduifLYhnxm5aWWGjg58tethhXuyVy+ifQSRnCcAZmtB1sJ7GWJTmsI9voqfxXYbAuCTax0y74qIaW8lWIcNz/UsbCxdDN3NMgbIGAU0SWSW0Zr/YDQQyK8dgpIkUVFt4fQGXQ0Hcg4Hfd2AI3+drmYRarFwIiDoNXjQl3mO0Te3O9kcVBglC+QW25cOwYMcsx94IZ4P+ywHPQQHWYMFAfn3wSTCJkAIpq+OeuBOAs4zFHfHqD5wCj+WRdPnIh7D9TTSeotSILY2jdYgOCsszGKbXOwmSsP3tRNqGvV2Ejp0CMUhipvH4QQY9LD71jtiDXHQj7OLXUtTslD4by4bZLDTOD8MNhN+0pfOpShQflDnWWaNbjGf6nLqnkzFiiNLRbHc64qb8HiAJz9wFO0zzF9rV8T41arCt/wXX/gmF5mnD0LzFr/ejeP53hDIhaFY5+E8tqpZFqoNdhAPI48IA3gfB2FW5ZsVQ68bVa4A6Mk1qOGY62uYtN0kgFzkvS5wfipdBOS52KiKt05Qf/A8LjQEYWxkKXyqC3BRRYilaSL8AbTER1uBswBpka/ngEuEnZNlYKX6sWLM1T31v6mquCmYYIelikw25X4VC8SNw/jvyv5gsx92rU3LICg97zzux/a/rRy1DIL2YXGdoyFAYlexBykZTGWe/scT+bzmy/3FzRbaRCE9q8RVUBZouLbh69A+dDdWrdvmPR7KIuZ96Kx42A0QJVwSNlJkGTrzEjRbYfh6a55l56iQWE/On6J+qoIWdVSEU9mEbIL6UIuS0iPZVEjeuEbx4gFcqbMao2xmMkCmtjW4I1k0reuxvPCyguVgo6rjjMRYP2kq9thJIh3P1UztIpQuXBTYvSgiTWIGbQrJpy4rKU7Mk6sjJIOgXF5r12t2HHUdhuzJgyv40wN0vvQ4JqmlifhDqWrZIEs3W0r3uliFeRTNpJNp7p1EAaiSMepxF8lrjxCsV57BwQA8u7f09W4cz/eGoBUdtaECW0wMmyeSdhWQFnIg7qh8HAda/kA51so88ZNBhEFMaZmQfFk1udkKda+oBMLY7kCFGLXRrpbAEZYKkmZC7aw/cIb6hRN0XrlA9voVmkkPrhIPmiB4mi/2219mUdV6LxRW5yQ8RIdo/nAQq872NW9QgG5eYBa4xiphg7Eup3EhYaWw7oqwePNuJ90BkeDfjYrIzO44z+NN2GiMqOkCWl5A/vwQ9Uu3EMJjdPHm00PZbHp5FIK1q3X7d10LPjxfyiJli70pp0+PpAOwhC6reC1wSKEKnujCNR6F18nWDZlQaq2pXst1+CxoqVbf3VzhojTAc8mDK6H4Tjeh+i1eOlYGjmYS7yp57hCjqUWGk6Aa/+ItwHukFwtw6lD3U9C6QPb6FahqcPlDLyJdykbrFi3n0/lCKnoTj+nmRputsMgM+inUesQSx4j0fdYBAkOaBKdWrFUdXzdCQdVqPH+wgB92ka4qzD95rsyigdCgzUHXbKtNnKiEANM6YNgHp6lqSDSHuptHGrJ26/5UvMNYtQQyZG4iLKbX9dm/nOHqEyOY4ywA+OOxZDkbzNqGdt/m8X6H8F49HEm1uNmqklhhlc02MBCy16+QffmxMCzaYjSDMirLQRBMtDk9kLNq1hP2px3GSgGCwR1nidxs6hnkpnJTM5HclHUDVzYgz3Blg/rFU9R3jkRtW3kNGinRTKwyp+hMarirFwyXlq3Xwiz8cR3+phcLpBdzGaYDwTsGdSM3Y0IRi20ngzVNWFh52I0bUlHIZmBPZ5g6EL2cyjJSPCvxFwomc+2P6sEFks+9huTVR/INhXI4TaXadoAplnnQV0hsuH8jmymdg2DX3sOiM0MBYP+2OQtzhI8SJwu7DTurShZBo7GaY6bh732xL8FARYLbXcC/yRw1Tw7C4JU221D55q9NEXKrdQ5F83XcsBzJ++hk8HmKzd1+2MTTheYSZCn80Qi02KAYq8iraoRXfzCOJnx2tKM2tztht+U5/MEAfHQAy98OliQcXx86qmGw1LV+L3QYtN4Ak7FAgksReibXK0z+xWtyDjqq/C41gMoe24oSIw44F4ont94hmkzqnGk8DHMa7mZibd6ecVR1pJCrrUXx0dtoxjmSkiNVGAq/Zea3lX11F/xNHsz0zDoEIvoRIvo8EX2JiJ7OmgERTYjon7Sy6X/8Gz3m870hGNPGexHHrDexcrVOoaoDu8JUqnvD2OFAIhwvbqTKrxq4y3lkbNRNqHzcUoRQvpdFil7VCBxVN+pT5OBPD+A2JdIHV0ge3gBZKulUiTJxvEjzk+lGupbGo75zCDTqdWQQhr2PySi6aB4OZGCum5vv56hfvoMQtFOrpUEnQ+eNGeqxVPBuLnYdAOTnlqu4YNp5Mm692VyHgaVUYtTvR+GY93K+zQZC4xM5UcjKug6j9XqOcwiz1F4sxZGzk8ZOpdcFLVbwd08FGjB31cNJrOA9IpuqqiIziDnOiA7GCkWRUhpr2dAaL75NrNeJMVUMIrOKUhk0tFVGlRIP6hdPZSNfizPq7s4I5qnF4wH8UDaw4oVDYcnY60rkM2lGvXD50k6ur/ThDQZflghS7udws7VkM+epfGa+wb1/dB9+kEsXenUj1b3ZlCfaDR2Mo2bCZk3dHG6qrKqyjOIxExl6L5uKbQ72uxpBGajYFhcLCFvIHFmZUR7rfELhK/S6cu5zsdXgowNhsk1XQO0jq89MEk2joql4pvPgnmxWVFbCmKtrEQyOR4E9lRQNqnGGo9+eR3ZZXct1bJuAOfE+g0OGym/fukKt/v8rAP8mgI8D+HeJ6ONP/dhfBPD7zPxdAH4QwN/QbPuveTzfG4K1jk0j4iZbhIzqSW7PopmzRERJQMSLqzqawBGJ7xEzgshJsXy3KsINxDY4S1NQWUm1uFzBLXfwfbG7oE0RRUvK6qGyQTPqIp0JpkrzpfydSEQ/QPSPt5vUTPlai2HQLRABqdhZ1LcO4qzBuO56PvhwFDYw9/Aq4vpPi3VsYF6qhUYbz2+bgz3NrDJhnz4m2SL4FCcc0HmEKYyVwutmy/hcuri7xzexC2kUvulk2HNC1SAj+36wXzZX0OtpPB+djmghEqcQmHZ+9jos49rgMN8ED536dIxm2AVPhnC7SmAvz9h+xz30PqsdT12DnZPCxDOyhQ7UNYq1OugKMSCJ55yqGtzroHrhWASQxmYb9uB2FarjAYK1RpZh+vFhPOemvi3U2C8own0UorVYWP5wIO950IsGf5oexwa3OorsKiMElGXs9iwuNViiO2A0QPeLTxSGdLFrHsgsjjbbOItxDlSUcOsiGvkZY6mWmUa4P4G4aQAKSTkhJ1jnTE6Krk0jm6tBms5FCNl8uZ6eX33TxzPLVP4UgC8x8yvMXAL4+wD+1FM/wwBGmj8/BHAD4Ot6cDzfG0LIANZFq65jtOSRhH6Yz37AzW3YC0SIYixzBX9+GFkQG4UWtjvl+esF1TTSumvWgh+qyvIgVoNU1LpIulD1+tvH4NQhWRUCDS1krsAk9Evu64JL4lMUzLhUZOWma1BRw13NBGt/arE1CCNw0pMEnKXIrqTaFHOwWtg2RkO1RdCiPYH9nGeDk4giPjvsR0jJFiSrRstKcGHTTdjGYNGedqQp/NkB/L1zrepbrrDtYWktXUvzwqks9BYJqQsdGx/e8GUrEFR8RxYJ6pJoa7FXNSbx9y2a0V63nYtOB+n9SyT6mQf8HgA1jN1Hb4VhqVtupHNIHGhXivJYq1VXM177t0/ANvgGgPlCrEhqxvbDJ6CiRnXU16q4RrrUqr4swf0cJ7/6WAbRo2HUhhAF65LYteoiOxyExdNtNMjGnHnb5yJ1ytpqXQdADAJqPHjcR/mh80i4sM92uY6bh6mWl2tJn9N7wPc7kQKqBpG0LeK9m6bAwVgG094LfXexkSIszMdIrt001Q6CgG4Hze0j5Pen4udkXS0ReLGKjDJg7329nUOGym9Zh3Bi+e/69ROth7oLoC1bf6Dfax9/G8C3AngI4HcB/CfMLVzsTY7ne0MInGsXK6QgBlvj6J/fjxdJrwd3MVMPIDltftSX6tl70JNrqfyBeKHawmAwiYrYqChjhwFoyppUVtQoZt0STHHegc8ScEqoJ12hZWprz/1OMKxzerNR1UQMVgeqYYBYVTEutK7VUgMy2LRBbFUBqzWoboJhGiaj+HjtzqDt8WJKXbNdACI7RBdnsUjQ99/+XRssm/Ok/X6bb9/NpTpvOXju/UxRRiO2jdBU65MRkpt1HI5axToayHlrUy0NQhr0onDJzkd7cK6JeQIZil8TW7hRRwVRQLQAyTtohjlosZaNu5HOjR3QudmKfuJwIl2adoPcEY8ng0eSxRbpBkiWUc1u5zz94gN0ZgWaowGSdSXZxQOBjnjUB58egt64CKEveznCNiPp96XSHw1V4OdiRkevG3Q1wYLEIiWzDM2oG6yi/dlBYKJRw4GGTYsNspuNdBqT0V5gD3q9eL84hVq7HRkKdzsRUtps9+ZxZs1iUaq00846TaVD22wDlCnzDB2Yz9ehu3zyKXsfurEY0WQ4iBv8Mz7+AErlK8t/16+/03qYN5s6P93G/DCATwO4Awko+9tvljPfPp7vDQFohbgoPOS9LPJApB4qg4IPVdxzOZNqb70Te4ueVucOMlwG9sN3rOqqKrhH1zEVDQLf+FG0/aXFJm4G6j5Js4VYPjgVfy2W4INhlNPrAkpbUf0yUcw+0IFye2Hl1EnLnWVAohuJLcKbLdDrwZ+1lJnm12MWDr1uhKDs+c0W2nOIPwysDoNnNHei3crvLfit9xL+3rarsN9JXMjO3WMTJYny6lWs1M2RvvooejcppzzQJA26MGZT08hGkDpJjuur4VtVyQKm8E3oohovrKW8I7h8kshGasaB1rnUteT/Ein5wAFpivzJBrszwbv9IBe2m25GblvJfKqUDf2NP3ksFOjUfXW16ghuV4tSXFXIydUy/NzV9x7Dv3iuKW+9/fmMsZaWK7Gg0A4zXPvGAjMxotlcJNo1bbZiNW2ZG7WPmguLM9Xuzfc7As3YzMJ0ORY9aocNgAGgqtF90BIcmgWLE7jTPj8qG1Tnqqa2fGa7Lswrqqokm8Su3dUGi0/tsPvgkcx5Bt0Ibbaub2a/X7y8jeMZKpUfAGgJV3AP0gm0jx8H8A9Zji8BeBXAx77egz7nG4Je6EWhUNEWrD746AjsEvxh6gbVYS9Q85qhLIpuuRHM23skD29kYGwCm8NJ5O8b5NTvhYqZ1jtZxJ0DHx2guX0cFzMgcN39rSNQw0hXKtc/PgQtN6jO1JumrGSoWlWgtVhNBGaMbUzrdcvUTeiBPBkgeb3l2++9LACbjRjq2UJqLI6i0AWy3r9xbANT/QM9uUbw8u/m4j0zVjaLwQzWyttwMsBDuhAQgTW3mc07SV0qDa6g9mZjnjtEUuVWuugP+gJ9hKxo/T/73I8OBK4z2CvvyIJe10LznC8jOwmQz7SqQkXJB2MZXnc6cePqdZUVo1qWQT9AfLRYgRZr1LcOUNwaoP9br8kiDQiUuNpIkXIzk89pI5Davf/5Kzj+vQrVUe+rFyfPoEeXyD53XxY1QK4p3UCPPy1UZhkku9jBEoX5CHpdYDISKxLLRCiKKOrTqt9YTvTkWsRtdQ0/7An9N+/I95V4wYdCzW2GUvBQ1YiNivfyWPbYHRWwmW5AjQDd5Vzuv/lSzoO5yzYCt5L5gzmH2SdPMf+QmA7W904QQo3MIHGlCuXUBTsR9Lro/24X2bIMQrQA/YXccgIdTIQN9owOD/eWvr7B8a8AfISIXtJB8Z8F8I+f+pnXIVn2IKJzAN+Cb5Az/5xvCHpYVZomIa2LB7lUO16qQCxX6HzmdfW/EWyxORkJC6LFjXdVI5j1rogYpFXBVmWpwAye0UwE8/WDXARwSRK42WZTwEkCdzmTaMLDkeCnADoPpgKHWCBMrYI05+KwbaVWASa2I5Kg+ELEUF81GFabCLbFzfD7Nixjg25LdjP45Kh102RKS1VmEC0UIrAksMBOiiaAe38W5R5lFYBAOJ4Da4umy3h+zRLCoCcTWnmWjmoyjJ9BXcvCRiRV7HQui6Wj6GIKHUSqsKy8p4pyY5s1Xnyw2t1HlgXOPC3WsuCVpUQ3DgdxU+x0kD64QmdWYPPdH5AMjnUhivFuHllRSp8U2+Yhem8shWbcFvwBcb7iGVRUYolh7JpOCp+nGi/pY+iLKt2D+KwRnQVtxU4aFkdptFKDkCzAKE1FJd/vi5amqGPFb+lsgOhDGoFv3MMr2aims7jB2tC/TTc1e5fECQxr+RZtJb/5bemsrP9I7oknP/oBXH6iL+deCQ7BENBmRdYxVxXyKaJo1AR4jWpJ0lSV7qWwAZ/BwQxU3r2lr6//OFwD+EsA/ncAnwXwD5j5M+3MegD/OYA/QkS/C+CfAfjLzHz15o8ox7tjmPGeOQySSCITp9eFPxwLvrwrgFQ3gMlIKuaViGCcWVJ0UqExAsJuWQmMxEdjoa5Z9VVILKBVimbjS8yCfzYM2ih+vdpIbN9DOMggAAAgAElEQVSgB9zMkDy6Bo/FroJqLyK01SZa9pqaNE0A7yJ32m54O7bbWDX1elFta38arp8kSF9TEzCzGc47+zx1IFIWbZHc7sCrNShN5c88j2E5JgSaLeQ1mVUAs4YQ6WZgG5BuQmGDbhqBcIoiQgKlLlqmMC3L6GJaVfC3TuAup8B4pB5BLjJMLHAoI+m4Zos4GLUF3zaoNEHni4/VGI/EsXS2imwbIA5Ee90YPWnpZ70EnGaiDE+TAL/5zEmHoLblrJ+BZXG4ixmo34M/HIr4al2hvjtCRzeC9Q98BK5i9H7tSzF2dLkGjQZCLEgSuG0l1FOzs7bNOxXGXAiP16qc8xS0FQ0ODidSnPQ6oGkRFmQe90FFifQrwg568Kfv4t7f+0IsOlIxFKQlS5Gwq+HvncGtCnAnkc1Dlc1+0oe7WUlpWpb682WwWSGbWdm1bkPlso5zDUe4/0M99B+1bu0si4JB+3wSVYiXZWAPnv+zhxEyBKKRoek9NGOEbObxNg+BjJ5NHc7MvwDgF5763s+0/v4QwA/9QR7z+e4QrDKwFjxNxZPfqzlboUwPo7eFCjSBWxdiB80chrom6adSKiY+ORC2kVlKAwihKTof4FSsBlB7oU+utxG/DJVLLcI46zpWGzS3jtDcPd7LP2Zl6gThnNe2f72WL3ufNmg2fLzNpjDcvn3YgFordwAt9pAsYnw8EehK3yd1u/s/A0S3Unse5ggJmV22PWWz35mQUXWt6jdBldkiAMEbH1UFf+cEdP9RtCOxTGh7rwZzeS83+3gIpCmqD92K4j7bbAEZFGvXEzIb6kZw9DQBOh2x1S7KWGSUZRgwU1FHXn7i0Ay7SLbqPXQ6hO9lSB9eA1Ut11btw+DWPb6J7rFJPE+uYviMsP3eDyOkyun7ZQ3xCdbqRqHdFfo6VDOiLqBWgYf35pwUL2kiTqvGSCpFTc+Ho/De7v38w3Cd+NODSGNNU2w+dBgLGRbDuTBjKkqZBVl31+9HmFPfI5Wta9PgRYMr9ec4z/DC/7HD5jZAHrj19z4T9SuAdJpGiXaIGd56PprjYXyO4SB2l4AWAe6r74m3cTy3SmUiOiCinyOizxHRZ4no+9/kZ36QiD6tSrpf0e91iejXWwq7v9b6+f+MiN7Q3/k0Ef1brf/7K6ra+zwR/fA3fIF607OFcTcNUDWCqRcF6hdP1URs3FLjsiyu07m0491UlMYjxfNX68DSaQYd9VZRwY0alQXnT++RzDZw1wu45QbBj8c41rsyGN8FTr2arxGre2SvGyykqd+Pg9e8I6/T1MRpGp0yAXmOtjDMNqHmqdaYFb4wfL9u4o1Z14FqK1bJefydQkVMRREVopY9Peyj/vBdNC/dBuW5LvYsgi9AOoM0/apNAUUZfaPattVPB/FkGdzDq+jPY13ZaCgb590z+XeaxOGoKoizV57I41h34710GFez+D4220BNNT8iMCN5fBM/u7V8nrTR929zqk4H/mCAZLZWoVUDVwgThidDec7FSozVutqdponMiLIMPqWAZ6frGsPPXqP7cI3dt92Tc3N8qFx6YPfCgUBIRv80MzqjEHuvBobqZGsD5bwTZl086gO+QXP3VH5WI0Xpei5d0sFYDehk0XUPr+APx0E57BOIs6jGd/LhSGZ05jpqrDK7H60LLZSZt1iK+t+0DEoDproR2/D5CjRfofPKBT70Nz+PWz//KsigzHAJR5gtuVhoVOkmMJCS+5fynGUZnYOt8y3LKG58BscfkHb6r/14pzuEvwXgF5n5YwC+C4J1hYOIDgD8FIAfY+ZvA/Bn9L8KAH9CFXafAPAjRPR9rV/9m8z8Cf36BX2sj0MGK98G4EcA/JSq+b7+MRqqx466MtrQy3vJLiaS6EirWlq5srSrkXz5oQzsmIPK1HyC0iuxwd7Lkh2PQGcnsapdrsOQjIddqaQqUeGGm+r8MAbqGEPDQlO6mnZmWbSAVFoG47QqG15vIqvG7CHyTozgNOy37Zpqwibj7GsOLwYauG6wjy5cYUiY6gDQFvBBP1anqxbubkNkRxH2cASua4REq/ZCoece7OPrNOzZ8g5amcocLBbs89NLvi3MupnFToNIYJHNJlKQS1Vwb1uCKqPOWmqe0SFVN2LKazOh48lQHW5TgRqzFF758GJB0gjf/mgcxG1UVNG1dTICj4cYfu5aNgoArvRi0VDX6Ex3KD50HoanblOi7rvoO2QFj3VW250wazabEKzE/Z5AMWbd0vhAO06uFvsEgPCYXjYFVf7y8QRutsTND9wDNlsMvzANuofy3qFobjJVIw/6Uddin6d1tms9n8OBWKk0TexmvXQ1tLVuVT+3vHUPvBkryKBIU0M3jZxv09QYo6odAGXXUvKslspnZ13xThzv2AxB+a5/HMC/DwCqpiuf+rE/B6FFva4/c6F/MgAljiPTr2+0Rf8pAH+fmQsArxLRlyBqvv/7a/6G4anjkWDKKqXnozFoU4jlLrNgqsbQsKEae7ip4K+mlE2ezGUxtItJYQp3OYuVUJJIRWXVflECJ4dh8MrjAcgiHR1JBXkxCxdsc+sIblvAXc2i66Qtyvb463W0DyhK+V6WSrB9pUra66m8FyKBISrEFLiWbUGAncyaweCosgTSHtyulOGrcdRtSNnriSBPNzwAwcHSn4uLajIXryeuqtAlAECwqYBUd2QDNs+y4DmKsI7NfwBdoIv4XLsClCSyuWxZKkrXF3ZK08hCZQNWl8T5AevswjYP6wiAuNm1Mg1oXUSH1qIET2fCY5/L9UErPQfDPmhXoTkeAOgief0C6HXF1tx78AfPgdqLbqCW3Abu94TSmqagXhebbz1D//ceAZ6RrHag1Q487IJTh87FEjwagIoSnGcYfuZSspaJQJUG7hSKxQ8lXxl5Cqi9BkFV7XkudiDUl/PT70W77MkIoBwolrqIU7R2qGvQcgN/NMLRb1wKBJsQiIXR0/nyEzlHdRMp0VkWPlO+dSJQ7WoRK3bgzanFHZmDmCYizEJalFzqdoOCOTyvDpMxEP2IBBY5+OORBFXZdaXZDwFibFHF3+7xbuUlv5XjndyGXgZwCeDvEtFvEdHPEtHgqZ/5KIBDIvplIvpNIvrz9h9ElBDRpwFcAPglZv611u/9JSL6HSL674hIPZrfknIPRPQTpvwra61UTUCk1UUzyOWia3yEWaz6MGtpUxGniUA9Y/XNZ5ZKMlU+tOHvNkw0DN541pYolWciWqsahBxnG5oZ5FSWSOYb6UTSJArNgMjOaAXqBDijbSpnkFBf6Yut9x1+z4Zs7Y1tvYmVFRCGxUEEl8RFMii+zdO+zVIBwFkizp7bXRR/6XMz+z3dAR0fxcquXaWZKrkV1hPel/kTocXEIfXU3+lwNE0Fl1ftQfCoMkqk5iMHV1vrkOz16WdLax0gm5Np4mJaG5E4xE7GsunrDKjJWxoNg+yyVDYGQOAhPVe03mj2hFStvftaTDjC7u442Gonc6moabmWIfDNQv68nAocOZRBM9bbaCi4WAp8s1E7h10hmQGOZJOvFCK0RXHQB5ZrseI2/6ntTqM0Gf5Yoi7dtgrxq0J/dWIpwTr7CfYePt4DgLDfHMn50muaN5sw8A6HbtqbD4rdSugUANnwLAucSM6VzZ3aehlj3inM6OabuOlr0SP5CVn8rJ7BISyj5C19vRvHO7khpAA+CeCnmfm7AawBPO3IlwL4QwB+FKKq+6tE9FEAYOaGmT8BEVx8ioi+XX/npwF8CAIlPQLwN/T7b7btflVXwcx/x5R/naQvC1etrbRWEun9S6lulZrIvTxAAf7WkV6QejGPhvCTgWDFFhqed+JNZI9rUIMZgBkTqNcF1mJSZ0Z1fDBEc6QZwCY+68g8wg9V6Wodx7AfaayNB58fRyqf18Wrb2lgaRxomx5gtgj4cYAngPhvIN5cZsVhcw1bLOy5hn3BuV84l3Nnmw5zOB/+9rHkQ3RTqX7TFMG0zknwOVs1liTwwxxsIjkieVzPCmukEUayG7fX1fPak84gz+PjtzfYbg56fCX8ees4gNhVqS2J+eWHqrGsQngQd7PonLlRy2ujZurjuY1RHjl0fb3ffyRQjIa3BFZTKjRWPj0EtjvsXjiIuhXtSqmoJFTGMzqzQkOFtMJfbuIwfNiX57CkO/sse10x67P4zHYmBJG4iOp8gbuZCCBzNbxbb8DbrTzfTmci/V6o5N18E9XBylaj67l0O3P9WSMHMOv10YDPjuQzNvhvvgivh7rdqDK3mZIyuvpfvpF7upvFGdtgEESgSFzc1EyHYt20+WEt1Trb7tdEi4xeF+5yiuZE88fb87e3cTzPEZoPADxoVfY/B9kgnv6ZX2TmtfJj/zlk1hAOZp4B+GXIXADM/EQ3Cw/gv4HAQvZY30i5t3/YhWuLo2HDo4HcwMu1VnEuQjJ2KKTRHA/l/83BE4j8bRt2GhUPEMqo4a8D9fUpKyRXmrxWifWAW+vg+WYWQ0K0+k9eexS7kbq1iGdZzOw1uMN49XaztTUA9nf7t4WtGJ7e7izsZgJ0UfYtd1QXX8N6Izi0MYqMY256A10MQkZEm3duT2VU080WbrGVKhyIgz57PQb5AZHeez2Nm4sxnSw72jB/GxoSxWrcNjVb5IB4XWSZmB+OR9HGIXEyKLXFutJBZa8XKnA+FSgQs3nMjl4J28s9ug5DZrtumvOJ0CVzGUxnml8gaXX9AFuYRqTJE/BkGMWTel5oU8SNq5KoT+735PtmOe5FbMijXhQQKtuHRwInUa2+QEUt1bJusAGzN/iMFKpbb+IQ1qpxZik6dgU4TeD7eewiCyVNNJI+KI+jorjJODCfwr1nG6d9/pqVHfQoAPC0VY9zUUxpxAbbLBzFOZpv4sZgczMAbteK9XxGhwe9pa9343jHNgRmfgzgPhF9i37r3wDw+0/92M8D+GNElBJRH8D3AvgsEZ3qwBlE1APwJwF8Tv99u/X7fxrA7+nf/zGAP0tEORG9BOAjAH79675I0vZ0qIMtw0sNp25X2pqT4B7fSCs7yOGPx0iulnBPpnEYZYPcoohDsroR5SpzDAQPgfGJLLDtzIS6Fl688bIrpbDuCsmz9RzFaPZ8nSx6CaloKlSB7ShKMyAbj3RD0i6kKKLxnPkPyQcZb1K9weujQWQmKespQCuJRDdiIeK36FETLzW3KUGXU/k5AHzrRP7DKn6N+6RM/Hy4l8vrbRo1KNPNxxKyAFnc5stw/gLbq81GAsLwE95HWwVABrwWGG96gjDw1E3LUsOMHZNlsXq1TcTrbOFwJJuzaTxWLWt1ZjlfxkbSgWny+gXALAZ43iN5PJXsbMt71k6tGsp56nzugby1Tor6dBS7CVNta2VMRRXpqia80sWVtmWcK5SVbKRLzUGezuN1//BCNoCODu17PbluLU0u1wp9oow8KxQ6WejKaFsIfGWbpXZjZHOuPJOuaTKWTXSsLrv9fri25DNzUVmu1zr3JHObR5oLoRYr/tZRpI1a8WddArC3caJpItykjCwhlGRxNvU2j/c6y+idFqb9xwD+R5VWvwLgx01Fx8w/w8yfJaJfBPA7ADyAn2Xm3yOi7wTw3ytLyEFUeP+LPuZ/SUSfgJzbrwD4j/TxPkNE/wCy6dQA/iIzf33gL00Cw8IgGJ4MRUC0UuyyrGLVrRhjc/tIqtyiCThpsIgAWtWRDxU4XdxoNdKS7NvRePm+5R+bLN9zsFigWazmuWlAnW6o8gI7wkJ2rArUoVuAVWzxt0XSqIfM0mpvNnLzWTfRUZ1Ca1DXvHgGanQI20kiNVPfT33vBOnVMkIvlrAFgCcjYdPkGejsCLQpgYHCbW0vG+tcPMvHnFDclABlgXEYugPYt2TQhQ2j4R78I5Wfvm8LL2KO2LFdD8zC8rIhvfldVZVs0FuKecP9njzHrgAGPbl+Lm5EN6JCLT462Lds2BViY37BsWvr9+EnA7hNAT/M4RZL8KivdGSKVNuUwcq64ltHQnV2Tu6GYNfgxFWUJHfYrQsZuqr7apjFND5mSy81+Ga7U8qw+jVZ1sNcMwy6edTKpInMKUyzYEwyEzgaXGXMKysMhoOoXFfIFED8jM0zyqr7dqfqm1hoDfr6ORK42wGnXir6xAFOvJNCl2H079Cpcnydva7Cah5YySbI58eg9Q7J5UxniS2B59s83i0G0Vs53tENgZk/DeB7nvr2zzz1Mz8J4Cef+t7vAPjur/GY/97Xeb6/DuCvv+UX6JVRZBz6uhahmLGJVuvYPtZNwP2TVx7GKiNNI79eXsQ+Z9la3TSN1D87NtuITZbCsfcHA7jXHssQcrmW37VFRx+bdAgZMHFbKG0xM3YGOYDryA5ab8DHE2mx7XVkWRw8D9VWwrUe2+YciZPOgIH0WoZ/nHfkXK3WUlVahV83oerkYQ+kGwIxA3WE3siqy/a5AiJ0BYRFjicD0GXZgq44VsI6uKfjI4HYQh6xMlDm67gpJLKJ8dlR8CzCroB7dAn/wjncahPsO7ArZA5hm+NgAN/twK23YYZAM93cR0PB+KtWURD8cCAVtW9kI+nmovJtV6qJE5PBxEUxGbMsloeT2PU1XtLQAMlQ7qRwi+1+q19VaG4fIbl/CXezQnM2hktlMzTbE8wWaF66g+SNSyDryuObC29XNRijITjPQI8uoxtw40PkpaSSVUFVHCy1beMzrctwIO/bKK/WDXcyuccM37f7zbrflWZYNHWck9nMyswDnSz+7nop0bU2PDb7daNnt6FGO+d27xgka7BZkojJpBVVhhY8g4OZUL+HN4T37iv713Bw6mRRGA/Bg1yUoQ2jOTtUSEGxTsP9VX2KNAWfHoJPjmL1bEPeLFODrOjlw9Y2MwvbIk1j7rANK1UURGUNvnMq1U2WRnaTLXJ2oWeZZMXa98zMzhZMzxLfyCxMnkYWWVqs4+DMfjawS9y+rYRVUMzSOhPBlXVIX6NtEWEhAGi82DNbELvToPXTQ+Gqtw5a7eICoHz9trU26+bG7EFlI1i/GZVZh6M2BnAusGYwGcVug0g2gzabRz9H2umiPx4F+qq7/0T4+AYTdjKQpaZpx+dmS4HIRoOYDtfpyDVi8IP9jjN/KUTmkwmsbHMyX6JeR+AhZmHpQBb88lvvYfXRQ2C1lo2iqtG5fwO+fRIEZ9zrxAUvTQCXIHkkSXt+MkDy8EY27CwR+E0VyMkbl7GqN3zdAu6VRkxXs7hgq8GgOJqK0pnzNBry2Tk2O+vlWtX6YhqJ2UKgIOs+zA02U7JDr4fqw7eDRkBOTKu4so1mPIodnyWmOYKbC8Mq5FlY9kLe6lbtGlcIsH7plthZDPReOdWkuq7cdzySDIX1R1vuv2/zeC9DRs/1hgBCYK1wtwNabsTSOnP7VYXRQFW1irICp6nww60qt5+zRda6DPaguoE/PZSbaLVVKEhVt8tVMC+j2oNuFuIK2Uh2ApJEfsb0Bop7N4fDmKFs/GoAgQLazQVO6PdAB5PgqBlen9E99XtuugAWS9Rn4wg7AIGuSKXQHs2CQAzCmjiw1nPA1o2QzDX4eCIQ3GIVBWlAa9Og2L7b4Ug0E+ZlZIE57edhHym2VsXZuTJqoUFnnSxqNuoaPFF7c24RCiYjOZezhSajJbLAm4mfzoKQJAJ93WhX2Qr3CfDDfKFxlIn44LTPp2sthHWrw2sYrqjEIrrU6047q8FXZBOia7F54EFX6cnqp5WQZCk4iiQE3YxINQL+WC009JquX74dCARBHW/FzWodle1VJYviQnQHPBrIZ6nQDpuos2lks1muBHosStlcO2m0NHFxUwl6l7qW11yWgG+Q3b+R57LN2zZ8Y8nVdcTzzdfKNlb2YkJn10iSCOHDNjvrHpom2Hsn840UNrtCusyGQZsiGFZuX5wAqzVc8SZCt2/ieK/PEJ7rDYHKWqqW7U4oc2pbnKyFicInB0LjXK1lCBi8jEh+16rL7Q7N+UQCPUx34CLuzmkiN/tI8VL2kUqp4eV0dRMXvp3g3lQqBc+GeS2HyORmKQsNEMU3BksBQFVJDnM3i3g7EDH3bi6QklJTm9tHqF++I779psEgEu8arS7T6xVotRWhnG+ipgIIAT/uei7PtRZ7AnpyHWGhphFbhulS5jbOiTV0O1vBYDjTbgCRTWR4tDGI0lQquxCkruI2z3K+Ntu4yNlMoJuLd1G7G0pTOedN02KdtJ4fQLQAL+X3TRBnNE+z897JXCRg82Zv0YhYjxcL6SztNeuAnzZbYLGSvGF7/KZB57MPgm0Fj0VIR/NVyHJIrpZw8w2SpWosjg6kaq8qoZ5q4JJbauGyE5Fh+roIx7BYSQejcZ/czyM9Wf+kmbKhyIG2hQxqLeVtVwIDVTkb/m+eRLtCHE51s+Hbp2G4y9YxmdZnp8E2NsQF5P+N2ZXqPKPxCLbmNsg3AgGzbNTatRGzZHRb12DFS5JIF1CUqE6H4jk1GcnjT+eygc1lpjJ/WTqf3ucev/WF5Rsc728I79lDoQBjS2x3oKtZyDO2SoGPxlJtDMR5Er2u4N+rfQdE31VM1CpnQDD02UIWfc1t5ckw4vhFy0a6mweuOicJ8MaTAOlYAliw3y5KUUa3cXdTYZoLKbCPZRtLI8wefGDMJI+nSC/m0ffHLKLrJuQtNJM+QnyiPa8xOKwady1qoFX97cpMb1zOErFpDifPx0rPoAE7DJc2qq29PnMWNSaPLcI2x+h09qt8+xmbT6w3sftjUQgjy2R2oOypoOa219POabbN3yC5NNXqXCmZi1XsBHRBCqI1q4C9l2vGYBvrWGyTTJIQ/UiWCKc2JRaxibqWBWyzFS0LEAfF5mW1WIoK2LWuB6VKSwj9WDbLxTqaHtpnaPYj263ETg4FUvLjnly/pTKFykoKIdMBpCn4/Fi8u8pKbE7UO4l6PemiLN/CSB2pizOvQS9AV+jm8c9OhvpsEq8z06C0NQeOxGfMKNF23ehroF0FnB4hXRSi4jc2HQCkKeoXTlCfjXH7nz6JcNkzOJ5nHcJ7/3AkN7ypNxu/r3RNE7jLuWDYSklz841AOYulYMXOwd85Q/KVxzIo7OZozg4l7MY3wqFXZhGVApPQVh6b+7199W2eS0ewK+AeXsji4b3AI0YhNXEUkYh6urKAoSii104rbN447QFz1Yq2uXWI5vwgMklSHawlCYJ1c1GhfuE0zBOSh1eyeOlA2986QfHxu9FHqaOMoiTe1P7emcAxhpkDEUf3ALJkX11tC6ExoNppb3ZTO6dumsme0pqNedLvyTmxDcqyISwlbr2R8Jh2DvJmK5izUpGpk8lipnMYDPsh6yDYZJvHkXUbgHRbR6PIbGl3L8zAoC8Fgs5YwIzyY/fQfOAcxcdfwMWPfQS7b72DkFdgeH6awI/6WH/HbVkYDdc3HytVS9NyA3qklvc2JLaZkDF0DE5rn2tAPp9dgeBWap2dWawwA9dTgXWIpFM0kWPTRNM8+6xtw+l2gZNDsSK3wsAYcqZl6OaalaydmmVh2CbQeKGjKnwk4tFO3AwMdjw6CF1wMt0ABDGZtILLKLGrNbAr4Z5MZX4GtNhNhM2dHupBhvLOJOo2ntHxXOoQ/n9xPC186XWB7Q5eb+hm1Iv5uKYO9mo61s3BN1PJRnh4IRBQIh7zycMrUNWguXWoDIwW1dMwY2aBCYw9wozmSFkY42FkJDkXXTsViqo/eA7zNgrpVjaEK9UoDECwByjLqFnQn2PnpHpSamp9Ng6eR4HBwT4MOAHEAaA+n5sukP/efYHCrPp/yhzP3X8SWSN22nt6g1qwkFWhpg2wm9s0FkDcGIz5tNkGuA9JAn/vLFhvB32HGaV1Wji/sqrI+2DtwceHAvPoZyTK3L7QRx9fiS1JoS6hds3Y+QgeVU4Xmg2S+0/k51brEHQ0/cNnYlQ4Vp67aSEaj84XHsJtK+RfeIST35yh+6VLPS9J3MyqCm61xeCLN/C3TtSZsww6EGHIqEjMztmwH+mqtngmSnKoKjkvuonT9TRkfewJKm0hPJzIZzQail7A4CGjHNtnbIw7Dbyhm4VAg2aRbk6/SSJwYV2L8V+WtezDXbyG28I/68rahVRbbJmrD5FtZGUpFGmGvM7FMhJDNBYXdS2q+SzB7gOHgaDQWdSYfksHV9/ZFZ0MPZsFmhmovXtLX+/G8XxvCIC0rQY/aMVHpVS/bhcXW1LXyradNJ2dyA01Gko77j2akcb/bQpNwVJla78nN9x2JwuLQhPB4yhJ4Ba7aDOhOQHB66fXRXM2BvdypI9mKppSV1Tb0JyLuKq23D7PWrCIDqqzDOlsg/RKk8TyjjizdlqKTqUIuvka9QfOWurlRn7ncBKUpu7hVdgQkeeq5m3BKO1KtKV45iSR4Z1tMs5FZXFdx+hMIC5ytvmZLYaa7rnVDs1Lt5Wj7mXhMoFdkkQ9gSlpVyIIs8wBHnbhB90YIbndCh5/ehg7rLoW/6PhIHaUxgKzBXHQAw4nMogdDUPq3uRzC1z80VPx/1cn0fpDdyLLDED9wiloV6O6dxg7wMNRsGX2kz5wNcXV94ilBU+GsVMweMo3sUMC5HVNRvJv7WxosYr/bx2S0Z+Dj5ZuRCbOK3W4nKWCsxuryz7L82MpZGzO1iq0Zj/wAubfdRSfR2dhtC2AXg/0UDbAZLYW7yJm+XwM/jFaby0Kcaw34FEfN99/a582XZSRfBE6TsBVDf7X/+vng+Ejnx8Lk1AZYZZHsrmVhWKi+/nHIA+4Cs9sM7DjfcjovXxo1Y/pPATM07oQF08LCykrsHMiFgNQnA9DqLhRMI32SbVvXfCl0BStmhn0pWozuMCYP7oQUllFaMF7UJJEzNk5JIsd/Fg3rqQFBRjuDgRaJk8GykhCq8r2cCsbdgrzgqomCrVyrRjbxmOOpMpqG4zVXvBgw5qd+OPDOWGYzBfx9/Nc3qfNE8yWgkizqBG7AoMStCFyhyEAACAASURBVMMgSyhjL/47g96+SGw8in/3jOTVR+L0WlXBPpw1oIgNttL3FOxEGgZt1RPIexngmqWCzpQ4dYHZEnx8el15XXUtGLkNva0TBPYWak4SHH5+g+3Hbok54XIlkEaus437j5HevwQxI/viwwih7LQAmYzg3rgEJiOc/dLruP7UiZAOnIu8fu2eeNjTbkCVvMaxdy6w2gDI/1n+t4klA0SVRmaOKbqdk/sE8pnxkYbhHB3IdTSdx9mEem+hLHHwr57g4P98RT9/FzvVularmKFmfifCiAOC4R+AQFBAnstnkSYStEOIojY73+1rbNiLxAsg6g00T8RmKW62xubDBzj61QdxntF4uBJgJ/fNM+sQ3p8hvIePxoP7OZoP3Eb9LS+g+tgLwnJwFHyM6HqO5s6RQAwaQdh9MA/xiCHNKhNr4ORyFisZ48wH6KaSeUXj5QbMFIeuajGE000G251goVYx28252siNl6Xgy+sWD1ura+fQ3DuJi+Z2J4wpAOaZtOdtBEglZq0/kbTqZthmPktPZsHV0p8eAqkTHNxiOs1OwoagbW0GINW2vSZlrbjpAvTkGu7R5T51F9hXhZIY3gU4yfQdjZfFzd7HTO3LraI0hgw5YccsWzGdg0HQgdBiJYImQBbfLG1tOtq5XUfMnw0CdElMh0sSWQCHfXCeihLb/JdUiSsK7QS9zz0OCzldT4HEYfcdL4LU7oKzBMXHX4iwzWIpLJhE2TmDLpDnGH1lJ1qVXSEsn+NJ6LQ4V0jI8i9MbWyd33oTePcYDSOdWqMvo4ZGz62xm9TmXWCmrTixZllg5mDQVxhKef5mNKgMIirrOIsw99heF1ivBZYx2EpZQjzsRcdcIKS1+dMDFB+9jeN/+VgtLPQ+aIQY4idi2+17OXyWgFOHH/2+Hw2kBZqt4GZrCd5ZreEPBsivCoWu0jDsP/7tJW7/3Be/mZXl6x7M9Ja+3o3j+d4QANDFDZKHV0gfTpE9kpxdfzhEsFbudJC8cS0XdZbKAKqqBYftdUUJu9GKMUsEb7bFrdeN1VjbTwWI0AyRhKxfKuW0ruVmvpkJXGSt/EYG3+56IRuDefmb7kBvcKqEqUJFHTFcu7HbeQitDsAWbk4oumGGbAIfB3wQqi5VrSGwsUrSeCOhtXkGWqfZJAdjtTpCXS3oJtzYds5sMyTagygAxIGisZPMgnswiN1A3pFNzl6DU7aMcdoBOAttKUtZFJpGupEQCpQGnUGgVobgFO00huqvX3uxm6jraPKWJHAPL5B/SfIPuJMG5hDSFJ3LjVh41B50cYP80QJYb9C8fAfm5UOPr+X8L7fgPMX0Y7342qxD0AQwtxG6JhtsYxDOcCBBNR+7JxuL4f/Mcm7N5M54+8xxMzI4ERAtQou4EK5bs/BQ9TeM3msFiJ3/lmWEH6uA0bQXSjnmUU+6tabZ25z45ABuukL++/djNQ/Ea4RIIEztaMlbN5uHuYaY2NVCFNHNJv2y+mCaen44QDLfKJV1Ee/jZ3C8P1R+jx5srJM9I7dCzOrMMC5NVJ8gwibzlrGOgQrFLJdrgVKcA4/6chEab95seDudyKpQO2HUjVR0BjXYYLNpBDIx6mNgkySCCa824p2z20W6onNwl/PI+LAKvvGCLdsiqQsEW+W12cpjAnGjMhOzbo6Q68AeSEgWCIO2zK/JTMOMwWLURns+WzisowH2Bs1Bify0A6tZclsVq4I3WQzqOFMwoz4icfBM07ggqpVGiOQ0C/IAi/jACqK1Wlt3dPYxUIv0QR84mIATgu9lkZ5rG+uujCZpzskGoYuIPzsIlNf6aBBETyaQc6ttxOaHA7l2nENyuZBF3AbAAHiQozwd4uxfCJOIe7m48xoMk2j+QJ7K9WNzG7WVKD90js6rF9Eh1HIjzo7iAq8iLQDg3U50CFUVCAe0LeQcHozjZ2WdiIXlmO17mgYaKOpGlMteOnOUpTCVsizoKsx7i9Zqs974CPEBosHwLDCkDceNcmrXQpqCvIfTTYba19NwEN6z2yiTcCqdJi1ioqCpv4NeqO2n9DYOIUS9Dxm9Jw+qavBoIKrf8wmak1Fc1DSUxg8Es6f1JjJX1DyMO0lcCBMHqmq4TSGWwXUDHg/BVkkaq8FRHPBppUTbSmiqQKi0Q6i8hqFwV2EMz0EFiqIEjcexQjajOauWgQgR1c3+wG1XyHsyQ7Syirx2q+YM5irKMCykmQrLzFHSjjQJ7A4e9WN1b+wtM8qzzGHv42JvJnMty4nQGWlF7kf9ODS0yra1odiAnrfbMMAmw75tJjPox1nFfCGfyba18Fioi+lLZgv5ucFANs3VWuZCqyJCWLYx2etuByDVNZClInpUei81Hk0vgx+JloW7mSw+LIucPxhoseEjxl6LhbWZ8+UPZqB1AdpWAnkNVbls59vOoy3ARoRYb8Qh1bo3G7J3cwTTPyD6Fq23gfqMxKF+/YEWMqq1sfnI7dOolbEFdSCMPZSlXA/KUDNTO5ouAVKIs2kw+6MvyGJtBpB2jeRC2ohsI+2g1+uY5Qzs03+9fJa03iG5fymzJdsQTNNCmj9CLlJSM+30Gg9abDD/nttyXvT+eDYHofHuLX29G8dzvSHAEfwol0jKTSkuklY1AmIcttwEmbtZIvhuBzzIRQVqttl5HhdFq2bsInJuX29glYwu3lTVSGa6CLUrEYOtiKSLSJzcCFb1m1L6adGWPZ9VRlkGf3oIf/s42AwACFqLwMSxuYFWxNzVoe7JQYzTtIWbWRaFlpWyvX6aLuMGZa/JeO/23sNgs2nRTf3+LAEI7CVnbrFmeeCc3MD9nhrLcbDGoMfXkSFj2blVFf2ebF6yK9QRVQR/fDAUSm+vGz18jPKYpSLeshCkNivHAo+0++Mb5duvt1L1N16q+F2BZLFD5/4N3M0yvFc2umZdy+ZhGRee5fNIUzz44WP4Wyfgbory7gT+aCg2FMO+2GUrC4lTJ/YNjQxNaV1I92e6i7bgTWckyDIRltn5AvZzMZS++/Av/5HITqp0mF6U8nxKIbXPJsCKnU4kZ5gF+HoTruXk8RQYD3Hwq6/JtW40UoPbTO3dzs82zYdZWpgI0bQrRrG2f1uBY9dbmkjSXJLIzNBgUe+lCNAQnvEX5tEM0D27pfL9GcJ79Uic4LalLErpV560/O+VVeTcvn3Eag03V3fPRip4HslQshl2RXtA+jubLdgGf40XWKHlD480hT8/lCGYLbRtjyHWSm65grtZafusg+ayFNjCKzPEBq4G1bSdJ21ASRSZTZaRbJVsUUT6XpKIJsJDMp6XGw1z0YrSVLZtYzsbNtrGYtABEC2Vjw/j/MI88A1CstfdHgwDsvnm+jimgLX3Yti3KXgBqWgHPfAtsRyRIWUDy2gOpoKHymbRORFYlL9OIUB/diRzBK0e/ckk3i3TuSzwlmUBRKsMzXEOYiuvcN12FxfbTCxGOE9B87W4nBozbK1dSpbCnx2AVQtz95cXQOrgHlyg82CG7d0Blt9+CjZhHyDzpYbF48pBuP5jhS8rZZINBvL8q3Vku9lrsmG65j7wnj16Dy/+D6/K0FoZQqS6BlpvpFrXvHGeqN9RpyMFjG3Eh5P9dDJTmi8tu0K6UDavItZueLuLLCgTzI1H0aSuBU3y0VjuR+vEgbhB2MLfeGGVXc8V6q3VL6yKEFdRojrqyYYx6MdN6G0ejPcho/fuwVDTNh8x1ZCY5GMlrAubYbsWRG4XHW0FQkgupnERNxEXdAhXFIL3mo1CwMhb+OagH5/Tjk5Hnn+5ajmauihEAqKs32h6FpRjr7coQE0DJpJIQEDDcGifs73Zhsf1eSoLS8OxkjYKoqmRB33hyo8HcYEGZCGxTkAXj9Un70r1qhsXnxzsWwoYVdF45UTAeChB8tNlbNltk7NheJqCzfrBIKm+DiRtEWh3ZwpZ8cVVZHTZQDRN4GaixHZTGd7bvMVdzlCdDEWomGXC8OkrvZPcXseHTkecPg+GcZitUKTpQsp7B+LhfzCUmURZivEhIO674wHc1Rw0XYKmS7jLmYj8iMB5Clcyph9JQLWXggKQzmZbwG0V/gNAT66j5YUJ/ey8t1xmOUvES2u9jTi+2VUbFNUIwy0IJW0zN8HbRHLFaVdF40brPj1LkVSWMQbV/IvsGjDh4PlpvM9MoLjd6T1QRasSy0+2I0lAq53cj5Yn3oYVbfOzAqp9rQ3UYt427UEP6XQXo1HdM1qgOTbu3+jr3Tie7w2h8aDVTmMwEzR3T+X7bXzarBpWa6VaSlUfONqmfNRAlORiEbzksd3BPbgQeuF6Ky2+tb6qRqamgZutwf0c9YkOdo2Jwaw0wEQW8NEwmnRZoD0gN7IZwtkg22iGLYtlMpaMvncACBGILtJF6xdPpdrKHPwoj+ZjFjM4GsgNN5fq3PQZ9tqp19tnMgEYvLoQy+u6Bsbq5dS2A2AW9s9CfPbDoHG+jJvseivVqYmmVGVNpgQ36Op6GgfRgHYi+nnq94icMqrGcZBaVHK+LRfYNu5eF83tI3TemAnjpJOJJsDOo9ljtH1zmAV/tvO/2cKPe6Ba7MM7r17CPboG7SpQWaN8+UzOc78nG6cNSntdoJOh+NB5SJaj6RK9z1/gxX9yifpQISPVHPAgF4fTNqPGZiLKluK8s0dX5lEf9WEffHaE6oOn0bcpTeV8A9IVGbFBPYqCcjlNwMeH0mmb+n62UCtyhXx0FsDjQfwczCa8rQ+pazF6ZBYTQCsujg/jLCHPY6rdZhsH+MOBXEO+kfurnZUNRP2OEgJ4kEuRBsjrNAW82tm4XYnlJ+9Ge45ndLzPMnqvHi1rBH88FhqaXaCNMlPOj2XxzfPYmj++0W6h9bNh8XFR8GOLulVjiYvdQ1kBeQZabuBHfbE9Zoa/cxLbUw3tASCLg+HXiYsLeVm28OwmDC/DawombE+VHe1SxDfwx6PAyElffYzsyRLJopBFzdg2gLye2SKySpwLr4u3ap5mHkKeA+RA2zLCEmYrPh4KHPPCuegudJhJs1W0H8haVSgQoR8gLsZ2tAN3nNunzrbFRUoB5uUq+hI5ksXEqKsdgc/4cAQ0HsnlIoa5AxpNqu/TOZnPMEs32Jqd8GgQuysioaXefxLhvcSBtqUIGvWapOu5wHQG0ZUV0nWrg6hr1LcPwERIZtvYCTGHx/H9vNUJtKrwYV+G1gCwWMGfHYCWG2SvPAEt1khnW6CqBCKbLyPTzeAZjcuk67mytwTSoc1WOgATJ46GCo/6cM7D8NyIDjbXUbU1qzo/dK0Omk3B+wN/Zpn5DNR7qFHyw2od7526QXAcNvaTkSrUt4u2pbijmmNAKbYZvq+f8XaH0eduYuf3DA5+f6j8Hj68R3MykuGwc3BTvaCqWgzKel2h8J0cIPiu70yxqm3p4SReMMY4sgrMFtE8V0GQ3dBNHOqRg1vvkFyvkDyeCgUOCCwdPjsCH4wiJNO2pvZNZEhYBWw/Y86mSsmksoJbSseCpgGWK9l8csHH3euPhR7pm7gQQbqKIKCz57U5hA13h2KjQXkeYBykKZhbTCRTlNo8IVP4hEgq3MZL5XZ8KD9nG5lFYGaZPJ4dptK2w8XNPZyr9jmxP40VlCr0UahBnWVc22Kpw0pSyIz7uZjGrSTFjjuZwG+6gLk3LgVO2WwF356MQPMluN9BczDQQoHlGtDKmMeDYBWRXm9QHw/j8NPmKJ0O+HCE5GoJfzAIliLbW11UZ0MQM6rbk1A4cCpzseK8Hy1ZrNDp98I5lwrawb3+RN1xHfzhWBb6bi7QmcGShxOBvwyGcQqHhnmbnud2V6bpg+GauZmBiloUxufHMsswNlHdiHHd+alcQzbTmS7lMwKiWFI3aqw3oiI/P1YBaBM7IiukHEW6tRUEm9YGum11tnUDHB2g+Mgt+XcnC3O/N/6dl7/2GvJNHO9DRu/VI02QzLdw1+IpLxe4XDhUSWVC20rw1SwT5s3pYfjdoDw2eMaCyc2GuW3qZtV9UcrPWWqZwT+7QpSZdvGqJXMIpGmzj+wmMspoy3AupLyZ6IYohqgDQUnsb6n1QVHAPRLRk9tWkYJZ1TLEvFKWTN0IVMQs56iba0XtsHlJvXeSp2AZs90wnNZ87HWhco8uBRc2VgkgEFHbuMwes6pASRI3BXPxfNqW2PBuG9IblGavw/Bs03fYJmKW4RbRaFjyqmX7cKAU3+UK3EklgyBNsf7YcYyHtGpzuwNPRnAPLpA8voG/fQrupvDHk0AsoNUunBeqaglm6nUjLm6VPTP8wUCuQ4WlOrManSdLNMMuXNkEHN+q66Robca9GKTkpgs5xysNu79zGq4TN9vXJsgm1gC7Uro2uzYtt9s+J9brw9x4+6JYposbea+7IjKW+j1536W6mnbzoMmhNgza7yEE2VjSmUFEBs0ZJdZeFxD1IVYUaZHGTSO6D7s/TIuisCCPemAiFIf6/AaV+QageE0/i+N9ltF7+OA8C9Sz6uVzlB8QZ0O3FhVwc9CD7wg+W92eoBn3pJI2WqdBMybGaVelIZmL46JnOGbjNW+gDpxwiXtUGuPhJHQRdDVTl0ml1hlTiURkxVkiXG+rqO0Y9GK73+b+16qXuLiJgiAgbjCqG3CPb6Q7acMyxjcvKwl8GfWR3+zC8DVwwQ0WU4omT0Zx8xuPBOc3aMA0D8Z/N38dYym1OhLSQBn7HusGHgbobZaWRTPmnX1YzYaptlHahqvCNT46EOHUaBAgOrrR2VCaAJOx0JG9zHgG/+8DoCgllSzvxC7SuoW8AzdfI7leSUQqEao7B2KtDchnttnCp04FhboReIYfdXH1h0Uc9eRTQ3ldTYPsZov5dxyDmkaKF90MqGpQng2RXa0ikwpQ9tJWqnkT4jmSa8A2SmOeeS+bhvl8bQVG4tunsdPbFXJtGDXXZh79npyj1To+f96JUNBqLUWAKbkt54NIHtu1igrr2Gxzt47CNgffBBUxnx3B3zsTmrLTAbjNYJhB4zFovgzMQOQdeR3M8HdPpRvZbDH+zYfy2arimgd93P2fvvRMhWnvbwjv1YMgqlHFtJNFAfIsyWe6ULhS7SV6OVxRw2dOgm7aC6xdtG0IgyWtKSxAbVW0Yd3GA2eWG902CGbgairDPxtctzN5LVweANjD55lEGQb4SK0WzJp5I8PamGGsN3BPIyzthjObAINf6ho0X8mNn2VSVbYXXvagooYz354gMKOodk5TuXFnC4ElJoMwE+CTA/k9q75W6zivsFlOVUmVZrCcWU5o9kMQn+k5Dwu8RnCGQ88rhwGmi7iwbSgqtqNtId2TaQiUDRMGohuNQbUKV2nH7sk0MrHSNHY3tuknTgb73iNZFTHMZiAZHPmDWSwcdEFyiy1Of+UR3LrAwSu1XFPDAYgZo1dWoIalu/V6vXnG/MNd+J7ahLQT8tJURWU+bDiy6DdhQWbL1Ehc3OCV4kk3Gg3al46DNkXsUMNGsImw5XanXXAtvk+LpXwuQ6VxdjIdSqfK+EkjQ842duuWjCLdKGvMPoOuuOtSWUt2iWkeSCxhgh27zfuYZWMB1EdpC3e9wCv/4Qf0s2op+pNWcWcd4zM4nlvaKREdENHPEdHniOizRPT9b/IzP0hEnyaizxDRr+j3ukT060T02/r9v9b6+Z/Ux/sdIvpHRHSg3/8gEW31sT5NRD/zDV+gDfIUUnA7qa6bYR4WKZ+nSB9cgZZrJA9vkK5K1Md9ufnMj6a9uAwH8v0kEdYFoPDBULOTB3EBU6pkczSSm8tgk9VaqrflWlOqFMu1nGbD89MUzekBOHVoxnl8PaMBcKLe7hrigzSVVt4SxFITorUYMmWl4q86Do2ZQddzqWatdW8zlbZKCyWKC3njI7RGBFaozR+P4R5fyfCRWURTtni2K0FbVHWB51F/H94B4s/rIHxvMw450y2hm24cIXrTHgP4atB2uxV+up3PXlcIAIu1fI4GlzRNNHez3OZhP8IaCmuVLwks4wc5/EAEfu7JVKwTrLIOc480LkCOwsLIWYLea3MpRjyD5ivQzowLKcI3jnD6K4+RvP5YZmBd9Wjq5xLBaSy3NBWthuUXqA6AzCHVzqdBRMr04lznRnPxW0JRauSnEh0OJTyK2QsduNBK28RlJiRTRbAw1Yq4AQwHch77PbHxmM5ilwfEKNnJSM0EO8IOMxaQ+ToN+tJxp4k8/lJ1EWZ/vt0JKeP0CM2tQ7z8X78ayQt5Huy2iVleU9vF9m0ez/MM4W8B+EVm/hiA7wLw2fZ/6mL+UwB+jJm/DcCf0f8qAPwJZv4uAJ8A8CNE9H36f78E4NuZ+TsBfAHAX2k95JeZ+RP69Re+4atjFsWoMkXgGeSBdL4LlXD6cBpvjjSFz1MJ4q59WNjC0K7xkSaauBgGnmXY3RrYm9bgEhEU8cmRXHTtKtcWqrIUi+GdKkKdDqLN4bGq4MoanFIYjIPUa2dXSEDLdBbhHHMdTZM9TYAlx/nzQ2GCALGld8qEKaoYYN+OutRs5DBk1GqPlhsRCR0MgSwBFRXc45vIAbfhoME1dgeYxsALjt+8dDuya6xKs6rXoI72RtGGGazytxQ8W3TM4sM+MyBWkanMi/hATO54qLj1dgce9YVia0ltpmJuG7btSnm+shTjtl4XnQczwBGqiVpMKH5uHUKI7tzuoioe0E0wCdcX7SpQrYywqganaazSjeZKFNg8nGeix0gTGeiaMp0Z/taR2HBbyt3ec8rf+eQomP4FFtN8JZYt41EUfKkTL5g1fEagPbJ5DQA6OpTP3vQis4U8n1mZWIDPZivX2Uq6njBUBoS9ZPeQkSsA6QQsZ8GME624smKhPVy366gsZYO2x7Q5TFFE6He7A8/mz2yFZhC8d2/p69043rFnJaIxgD8O4L8FAGYumXn21I/9OQD/kJlf15+50D+ZmZVug0y/WP/vnzKzAXr/D4B73/SLzJJII6wq1CdDpDdrgQu4tUAYnp2l8JmeMs/iCgrEqs6qtGC0FQfCvc+LqIhKFQERyWB3sRKL3n4eIQ4bjBIJ1GLYdaILb+tmoGvxpw++7WkCeKHRclHEG6o98O31hO0xUHrqwQTwDdz1UhYsACGcZLOJOH+7CjfMeb2NUJNtVhqoQmUjSvD1TpSr42HcTCz2EYhdR6cjMJYN9JTuKbTCUhcJXcjbC6dcGPJnvxc3Avt+OwKxDRkBkR6bxoEtBr0wz5GwHFlcyFxMl2t5P7siWm6YNgII2DRtK+kk60Z0KJ6B+4+j9XatJm/toygQfKeKAubUajOMZtKHz4Tb76YLsavoybkg7Zbocgq+fSobKen7q+rg2AovMFPyxLIN0tAF+HPtLOtGOlQ7Ty0rDVrqwmyusHZ4jq6igFw/AzUR1GjYAFHa79vsZr2J+hNjx9l8yIblnY5cQwZrMoPqRjpxY6W1Fdd5J+pybLMw6Eo3RjddoTjuhplgczyU91DpcLyqxS+sbM3m3ubBb/Hr3TjeyW3oZQCXAP4uEf0WEf0sEQ2e+pmPAjgkol8mot8koj9v/0FECRF9GsAFgF9i5l97k+f4DwD8b61/v6TP9StE9Mfe7EUR0U8Q0W8Q0W+UhTon6kLgMxczDNIktrhFCR7k8KMuOl+5RPrG9b4y1Qzv6jqGwSzX+5YSxqCwRUZvaKSJQFLTZYSCrGI2Gl9LWcq9jsYyZuFmyb4sugF4loW4rvd47WgaMUczEZYOFAMnW0V1IXPXKj9A2uVVtOkAsNfBoKqiWAiQRcz+XweG3FNLi5YKVnj/LbaJ5TIb7l0rjmyVO1EU/Bndsa3INh1D26YDiBuH0Rzt/3yrM2gfRSmLvW+939bv8WQobLOjcRSujUcI0ZV1HWAPKiv5HNTiovPZ+6DzE6nejw81plQ0Gnw0jlGnvS5iiFEidFMAPBkieXSNdCpCMz4YSeBR+7CY1/kqXkdGZDAfKINajGJs1bSm3wVlrlOGWirYvj+diJOsHd7LdQ7I4hvmYrqsGDSjrDI25psVUGUl31M4iGYr6WKHMmzn5Wr/cUydHvRCjeRTqFYjHGYRY8P5NAWupvJ/NlA2yjQR+q9MgX4P5YtHqCZ5S+/g47zsWXkZPcOhMhH9CBF9noi+RET/6df4ma+C5L/e8U5uCCmATwL4aWb+bgBrAE+/6BTAHwLwowB+GMBfJaKPAgAzN8z8CUgH8P+x96axsmXXedi39plqrju/qQd2U006bdE2JYa2A9gIKAmSYyOOMwByHETJjwgMbCVA/iRBBiQBZCTWnwSIbcWQEQiwHEGRAtgxAiqCEVlAJFmDLTMSm5Oa3f3mO9Zcdaa98mOttfe5l91kS3xUN908wMV77766datOnbP3Wt/6hk8S0Xd2f5CI/gsADYCf0m89BvCC/q7/BMDf1S7l2sHMf4uZP8HMn8jTQWSa7Epkl5u4CNw8vOC4yHOJ3zOBDBDpimmKYAdtGL2xboo83mi2EHeFU4qpBlqlDW/DwNeLg+NsCWpZg1LyWE1ZVd00oNVW+OiHB4F+R3UrN69WYO3JvpiqGR5u76UsY4cDBAomb1UrYDeGzU5sOOnbuInaRum9eCFdCMbePN8R3Vnko71XIC6w3ke6YVnGTitNO+Zqavpni56dJ4PTAKU6trJYsw4jbTHqahWsajWc3EzbzHMpV1yZXJj1BNvzJOk4yoq+wU/6sihZ5V0rPXk0DBbXpCweupjLAn4xj1nBZpfQK/CVf+cummEWPws7x3YYQwfQmc42bFDN3cOYcbBaxyCdXESQ8h45UJEBwN8+kN8xGUshZF5XeQ739EqKGc+RemrnMvgGuU43QUHpzDoXAyDnZTIW91fLVVCxYDiaRuY9dkxUB6HeVFjKgk1eKa9hCJ6Ee+xLP3QsHQ8AjAbwt4+w+Pjt2BFqKh1K2Ux86tD7whN5LtMVdUkXz+p4Bi0CESUA/jqAPwPgVQB/4DLbXAAAIABJREFUkYhevfGYd4Lk3/H4Zm4IDwA86FT2PwvZIG4+5jPMvGbmcwC/BJk1hENhpl8E8AP2PSL6IQB/DsBfYmaDkkpmvtC//yaA34V0IF/jkDa0ujsFH+2JNa85eholTt1Eqaqlxd7ukFwJJBAgg+6iTU67Ab2Jh/04o0hcHHYCaO8ewB9NpToEZJBs1bmxNEbD63kNhsleSK6t3+soYQ3+YBahlLJteLcTzDZgzS2QEPwgk8WrKCILSqtBAIq/KiV2oE6YTROrdxuqOmVqdRcqEpdUscoW5kz61ln8eXs/JkQzRpHh8facBv+YK6tlGOvvINuYruZSLXYHzAYf2UJTdzovIOL+lsOsMB+lKfjeSTynbQs/lsEyD6KZIQA5V8N+EDQiS4VtVFUiapxq19IrZOEbDBAyobMU2489J6lddw4j4+xoD2ZD8tL/9hT566fAeChzoiQRAVuaguYrNHcPZJFuvaTGDQfBayp9dCGKYyDQdGm5VV+gtcynLLmvacQ59YnSUPVckgm07EicaCnqWjrHYNZ4QwQIyLkkpcPaTMFIA03sDDAaxtxnG2qbzbvdW1fzaDJndGDdjPytfSEeKKMNVQ1stnjlbz8RgZ2q+uuDPia/9GX5ncbk6hUCi253KE7X8bVbFogFQD3D4xl1CJ8E8GVmfp2ZKwA/DeDP33jM20LyX+v4pm0IzPwEwH0i+qh+63sAfO7Gw/4egD9FRCkRDQD8cQCvEdFxhz3UB/C9AD6v//4BAP8pZNcLgar6M4n+/WUArwB4/Wu+SMV+k10T8Var4JtWFmrDnO3PkfL9LUi+0IW/qqQK9a18zywdVptQXXCeBmUrmJE8ncE9vkA76klF2cXEzUmz1AHk3gjtXfGyCayPLBONBLP+bo54awdTJ4Ox7PnLCsn9M6RvPAVnCdp9dcS0DqALGRlsYwEtt48ivzyXzsePB3EYDAQYjZ6cx9nARDUGBg9pZV8fDSN8043SNMGadQae48DRKtDhILpntl6GpqYCzzPB75MkfnaWZxFER/56ZdqptOnJeehOmrsHcEtZQGi5FX2B+dvYecuUKWXCKDWTs9hIHhTggwnaW9PYBfUK9F+/BPp9gX4U76bZUjx2zIXWFuW6AQ97+NRP/HLIvnZVG2GhMNNKghOteP5TFEImLuD+tNmieUmC6v3zt+I8qCzlWgWkA+t+rgDcpozWG8yyaBKFQXDQwhjsNB5FTySj4K7X0TQRCHO1kIftSL2QHDAaiqq+yxqzOctyDXc6EwZYX/UmjmJEqGl1+gXe+v4C/oXbArnaZ900opK2OY3e+yFX29aDZyRMYwDe07v6AnBk8LZ+/XDnqe4BuN/59wP9Xvd4R0j+nY70G3x/X+/4EQA/RUQ5ZHH+94no0wDAzD/OzK8R0WcAfBbCkfgJZv5tIvojAH5SF3gH4GeY+R/oc/7PAAoAv0Dyof6qMor+NID/jogaAC2ATzPz5dd8dWpKJoNLxa21pUeWyjC09WjvHSM5vRL7Au/h94Zw6xI8HIhxnVax7fEeiIUPnsyVGWN0vFZggeZDt4S5VAvFk0faQWz18QbDdMVUmy2orJBUMsTjPJWs55OJWHbrAnhNYTqSi557mcADJu9njtkG3osZX5EBi5VU9Oud/O7NNs4wjJ7a7kDQBTzL4KdDuF0FN1coyn6/8crzDLtXbqH35TOBiIJyOQWmEzTHY2SvP420UyK1yagFUlgsowrVKkoieW+mqwiwjwu2EtYFkL1ns4GwDsZucOsQiICkwziy3IWBmPSlOnzl6Ujw/p5QUFGW0uEYlTdJwIdT0GKN5u6B+Ax5VtuHFtS0SE4vFY4po9cPCxxo7CSkKej0ApiOwUUufjtFrnOJBr/0F/4IkAHtyQTJ1QacZ7pJNZGyqeLB5K3TuCFqlrQ/2oPTLO30yQxYb+Bs8Koqe9rVYPaxqmeWYkgH+/72gcwbANmkbfEP0FwCrmvpLIkEvmSGn2oXMhwG+xHOpNsJ0KrRWnUzQFnBLTsL89U8FjijgVxb7ATGYwYGfdRHQ5mrGUOrqvEdf+dC2G+jfpw1Mct1Y52OQcbrbRRMAs9sQxA46OvPB/Q4Z+ZPvMP/vd2T3ASaDJL/HgB9AL9CRL/KzF98p1/4Td0QmPm3ANx8Qz9+4zE/BuDHbnzvswA+/g7P+R3v8P2fA/Bzv+cXadhg3Sjl0EzcYuWVnC8QogMVn65ujZE/VSfOwUAqnoSkgCGSCypNwcMB4ABqGGhbuF2N5s4e0lNhz9CuRlot5cI3Wqf+vD+aSsSgLeCK29K5kLWSB3pDmttm4tTLXfHsphGMtVvh2MJtUFddx0Fw4yMMNBxEgz45wfJ3TY7j4QBktsmpA51t4mJs3UILFE9Xski0HQGUYvTpehOrPqNw7iSsJiSZWRawMndsceK6Bu3vycbQpSYC8f2GwaEDmjpCZvaY7izBquiBWHqHiNPhQDejTCIWAeDiEjg5AsihnfSQXCoMmKUC3/R7aIYZdic9jH7zAWjtxSRvswUf7IEWsvhxQqCNLoqHU7SjQjYfs7/IUrFzKHKhvJZNR/+Qou1ncJs0wEy00A3S4Ls8A9Z1PI/bLTAcwK224MMpmmkP2VvnAbrhPAGZGn42l83Ahv1dim6WiobCOrUOfCOzNOkcyDb67S7MR9yVUZddEJ1RVUfzSJvjnByo9UUeYUJz0G1awCljyPzBWDtIFTZmj2bgYQ9URRiVSmFa0WUNPt7Xz1M3t/kmDqYN0hzpPbBtn+kM4RkxWB8AeL7z7+cAPHqbx5wz8xrAmogMkn/HDeG9Ibu+Xw6FXQIN0iXC8bZQ70YXBkc6gJMLlrMErvaoT8bCblEHyOTLD4VK2Flk6OJKqpK+2P+60xnS++fwo6jA9ONerHbtxksTUQBbiMhGvZY2Wxl6qkNkMLHLM8FRDb4InkTqAmleO7ao2gygA1PRYtWZM/iInRo90HDi1oPWG3lfeSoJZXYuzeXSC6xGp5fwhxP4e8fg6Uj9mtp4VziH9qU7CN73RX4txJ7vHGvWQxYez5dXQgVcruJGYlBc97O1IKHNJrKZuo+xuUEXYlM8GeuNQBXTAdqjiWx8Ojinw4OweCxfVvjBoCtlivUezDH6nCy2fDABzVbwt/YlTEahIFptQxdIqy3SrzxBMGgzM0SFa2i+FmqmQZSrDZJ1LS65q60mlLnIUrP3WqjPlimnidAejkCzJbLP3w9W35tXDkWX0MXt7ecNGtruIpHhchbgJbReICH73akOjQeDKFa8nIHPLqL1hc0FdqUw0Yo0FCCc6hzJZkbhtWQRJrRrxAb+5ktkZIZhD+2w0LAgCnnXr3/6Zem0tpVoTPq9qDAHog9ZWQLrLernDyPB41kdz2CoDODXAbxCRC8pAvODAP7+jce8LST/tZ70g70hWFVoN7Iu+O3tg4h9A7FltgD6loVTzizVuC4mfHIo1sYAmueP5WK2iML1LlbDVQV3MUf9/AHgPdx8IwO+EFFJKL/z+SAuCzRLZoSkqg48wwMZCreTfqiO2/2RhrsncTEdDa9f2KZ+Nn52YBp1aHxGhbSOwg4LKn9yKVjv4b4MG40FYkeaBlM2Or2UCro72FVHWO6EoXBHDU2PNXeg07oT6YY2GFy3GbDN3RZ5W0js30bhBcJ5vrYZWBWqlW17MIS7WiGZrYVdZPGqVaWV4xZ7v/JQhFqmVAZkUK8V6+aVI8GmRwPJvTD7CwuhryWxrb67F/j/aJpAaKhuT+S6y+W90eVCxFKHUyTLLajUjQBQ2rAu/ibGS1PZpAd9GVZvd0jOlwqHyHnhvQnSTYvt7X6cF5gOpfNZy/WgGL9V7jp4J3LxWqrrEJPpB6r6L3KQzTiaRjYHO+cALPITLPoMmquy2CxarDhR9hcPB/L/nqWIU+NIf7InJIAiQzLfIlnsIqsIwGv/wd+MCWwtXxd6hmQ1lkKvbZHdv4hEh2dyvLuB8tcbKqsW668A+HnIIv8zzPw7RPTpDiz/GgCD5H8NCsl/ref9YG8IIFlMzYV0V8JdriT5LDhutnHACchjdhWShxdwu1YskI3r38/ARwdIzySXtTmeSBRj0+gCsgtDLiQO2ZtnQdBECsVIJVSg+P/e0opfKz3j01vbbNzyshQmT90gPVuiORpJxZY6sTo2Va4NQi1c3bDSsJgotKRurDwdwt87Dopq2YzEMTIkgRnMpiwi9+QcIRZ0fxj1Dd2uwyiGNtytayQPL2STa1uh1l5cISTXqW/OtereMP4w8OO4MXQHxl3ctztEfKeePSz4Mg9I5jpH2ZUyCxgUUSPRthrnqN3kfCkbQ5pGbL2uMfinb8q51gwJ2sr7577AYn5PVMfZV07j7GilUJVLkH/lVBZy86nS90bbSp7T/JBWG7leLAbVKJjmNtp62Vxtge0wwqis0PYSjH7l9biJ9opY8TsXrdztXNrmqcw2Nrq1HdqFuvk6nnMzzAOEvZRnYqdR1/KZ97VTtuyDmWYuzNRB+GouMM5oqGFT2klfifss+j2BfsjBlTWefOpIZynStfFigT/7sU8FFT73UvCwB38wjvBpT8N3LmdAlqK5c6Aw1bMxtwPwrDoEMPP/xcwfYeYPM/OP6vd+nJl/vPOYH2PmV5n5O5n5f/x6z/kB3xBYKnci4PhAh48+Uh3t7zcOmq3CYkOdxc73MslQLiukD1VA5ADkOZoXTuSHTYjT7+lgl6PKNcAoJMwHQKpgW1R7hYjLbHPwrAumdgBE4IRQP7cPYkb6+DL+rEFMpJi6CaB0iInRMOKzzGKLULdCh+wqShMSqMMGubZJ1Y3SXz3YOdE4GEXWRHLOCQ/emB26yQCIUIRWauFx1g0A8abUoXSwWr4Zb9i11rBzavTS7tHVJNjzK9OMp2MZtBtfnb1UlGbkliTBmoIu5uC9iWDUJr5qWlSvPh/Vsr0iDPlRFFIBexZnTSs+JuKf07zynL52mYPQxVwes92pl08/QHS0q9Dc3RdqcNUGiqV0hrppjwbhuSRUSTfTYdxs+689judkMJCFWl9zt7ONOprk2r3Rtam4njOg10WhhZd9VkkieL5pY5hlEQaks7hzfM3PKhzWkauPEveECkzbKlpWVBU4S3Dn/3wLqBtxoa0qhHClLiRWt3BXylCajAWmY5brS0WU9UfuSXH0LA4G2NO7+novjg/2hmDsIFWCBoGOXTjUuYCLHLw3QfvirbBAkvdwD07RPh8vlrafXuOpu+UO7f4A1HjUH9bwjfVGMnkt+s9ENZY/q5z24HtjOGpVR+aDwV1lFboPPyhAXgbYyWwjN4hVeAblVJVgsdudVLwWrmNOqLogu6uFiNvqFu0Lt+MpW26u89KBUKmZqVvy8Ew8lowv3jIssc33UoHH5ouga/DHEUpAlslmYDdtqYNtg72s6rckLVNXd83vDDZiCYQPqVvdRcxcU3VD4476FXkOMtaVKXFTGfByQurXo3qDXSkL/HIdKaeKb+dfeCS/dzSIG5+P7zN4+5iDp3oZJfNtZEeFrAtVTptmYL0BHMH3C6RPZuAijz5UmQokHclrWqzi5xy8etKoAK5q8GotsJ1uTmQD/7q5Didud9fgE1bLcLl/nAjZgKg49z76e5lpnjnUWgKbCcHyHOgV4EFfuk0gztbs94ewJukuabGWAm1XXkvLcxeLwFpzy91Xa2TqGu7hmQztm0Y2FEtvU8ownxwgfeNp7N6e2UHv8usP/vhgbwgBawZQN2huTdEejdGeTCItzZw3XYJ22keyEPy2efmuMICcQ/LGEzQv3YHPHHyWSDvbcY1M5lv4ngyi/ckBylefhy/SmD+rTIn2eO96d6IKyuAIafhsUYjve93IZqFWCu5ijvTRlUBWQFSSKmsDaQJvLqRFAZqtJBmuV0h1alRXIhHMJYnATglFKmnXmsB8Z4CgrA12xLNloOgCEGgjFWNA7uuCsCuF9aIc/zCI35QydzCPJKtSbabT9eWplSdfllEXoLg8iEBFEfHtziA7sJB0YyUbyNoiVVVSxZs2Ik2EbrrcAHkuVaduTjxUO4Qb1h58qGl6C7Vg2GxlcF9qpoS9JutE60YomLbp1Y1UzaZ5qWqZrxSq60jTQIdGlshmtd5GyvByHb2FZgvwYiXvcTiMHaqmn5lwrL29j2uxl/vj60H2gHYIcn+QBdW4zvkb9CObzYbMRk21xd02YOuE+/0wYCb12wKzVO2H+6KTGI+iJ1Lrgy13YLV1iwhzvDXI6cZB5i6wWsvM0Aor3dA4TeIcI9EN5lkdzwgy+mYcH+wNQRdst5WFN1lX4DQBpwoHFFlscxMHYkY77YMWa0nL6giaxOee4RqP5kQvcls4iZCdrYBWGErZogze9SGAnDnYb4cFzRaM4Maq3Pmqku5hOJBqZrMFLTdoXjiOP2PBN/2eQAsbMU1zs6UstpXCGA7gIoPv5bFqSxO4B6dhYUoeX8bXmSaycTWtLBzrTbzJTf0JwO9P4kKYxopdMN4IG/CoL3bJh/tyg6sy2F0s4nsHokjLdAemUFYorH35bvT87+oMOlVvGGbb6007w3Lj0duGDMh7MThutQGtxcbcjxWrNvHZrg4DXJ7qXKH1QgMFok+RvR6DXFYbhCAXkrkK2YI01TjNvjJ/dFOj9SYM3rkQcRqrXsQtdzFxzT4XY5OlKZqPvYSQY2w0Zb0+7T0nT2exI9mVqo52YMu2cA4hDRAI16MdpFYevNvJXMGMCU1BbufWNnh1kUVZxg219SLMyzMZSteNnG+bRdh1yixzlCyV16ZOvjjcl/83JbLNOFzn54BwbSUPzzT0KgkzJLKCoWnkGn0b6Pj3fXx7Q3i/HrIwUCWwCmlKWjLfwPcy+EEOfzSValCdN5Mz1SSsdGCapeBbh/B5AibIDcSM+u4e2r2BYJyJ3AhuXcL3U3BCyB7P42IX6KasQ91GsGbz5tEWF14rJkCqQLVltos1fXAe/eABWEIZ9/N4c7Bk1Vrbnzw13NYqrCTMFNzpDK6s44C3FW8iTnVR6Dio8sEkVt0A3NkV2nvHQd/gzuew+En36DzOYJ5eSEdxfimLmOeo37CuxGYHKmxr7xzA7w+lsttJNcikxnMGsQGxkrXzm6Zx6AxIxQwobTeXfF77d8ckr767FzuaxosdQtPo+RgJrKFdHj0+izqKXMVkjVblXqtaTaSLv0sXoiKXc7DaAA+eyGscD8LnG/6uQitarIC6hh/mMZbS4JSuJbeyhbIvPIjQpItVfujOzITRbLJzsaNuJz1ZKSYjqdxrhSFNEWxDZrueWy+dmV0futGzeiKFz6JUX6iqjbYoWnTQTNhUbr0TivZOXWC9aFCC19DBnmyW640UGOOBdNSXM3kP8+X1GZMt7CQ+S+Wr98DH+3Bnczlvi6Wot5frTn56FTqHb/hgAEzv7us9OD7YG4LdAHUjPOgsRfroEtR4+EwGo0wE9+RS2ETG0AAiJKQVZrKs4CoZvvlM6ZNarfthIXx9ZmSnS1EHM8eW3RZHU+O2rYjEvFZYB3tyc40G0ZrCQuFtwTOM+mZSmLGQMmFUBKFR2yrE0qFpWtVoYSb9nnjf2MFSjaYz5ZJ3mSqNh799JF1DJR1OcnoFt63hdpV0DEDEwYsiDn9bH7FbrfipK1qzx+l5p0aour6XxPdorKLpJMI8RmNt26i6ts+OCDQZiekaILRM6x5qYRkhTdG8eIJkJboPf2tfFMHOhaAcUkom3zkS3/zJWOARAPAe9e1JMGITt1vtCKoqQit5HmYRVoHTZCJFymoHM+mjUmcPVSWzgboWnPvhRRALhvmOzZiMuqnGfv75WwheQR3FOoA423AOPOjHz/F8KWFGS50FBGdb6TT4YC8utEE/0LFz35XAcAg6OQodqGhOimiuaEymPBcbFyCYAXKWxGu0V0S4xzayTr4FlQ14bxLOv82PyPy+uoNwctgdZHEt0D/TN552oFvt4rv3/jd4WIP69b7ei+ODvSEQSaRj20p7bwt83SB/OEOy3CJ5fCGis7MZ2nEffjq4/hytlwExgHYo1b9r5NPkRC4+t2vQDnLxLLL5gA3jrPIgHQA6VWmawjPLou0DOTEl2xsJBc+yB2yIbWyfUl0aLbJzPAxeLZypB810HIfZQNwkwuZQSeUL6HxBFnB3PpPfvd3JQNcWh6u5MGbsStYK2vezGBVpdFdr+W1wWtfxPJiYzHBrS8hSqikf74vvz3aH9ErwfOQ5kvtnaPY1rWw4VNhGB49GnbXz3PFOIqvWy1JytG1B05lNsq5EawGIXmQsn38I7QFgbqVETinKPojlkk2t4e6ZdGs2pzHPnUyzAmweYouQMXqMBUYkVfBCcoH56CB0GTyW901b/dxXnbQ3G6Sr+tldLBHsJSzmFAiLt0E1psr3YyE6cL8IUFd7otbfhvM7yHu7dRhVyNapmS32RkkOtvkBkVRxJQE0za1p9Ksa9MPcIfnKo3ht2JzI8srni9gRrtaC/Z9fhvdERR7vhek4ss16koo2/cUvi2LZuhODnbqJgs/68PTuvt6D44O9ITCDTGHpkuuVdtNI5zAZSfU16keM3yofvaGTS6GXFp97CFd7sSRoGa6MNzh5D1fWUcVshy1OVtEyS1W+3gJZCn8wioujdhwSDagc6TyPC4kxikz3wCwq0FxDdYo82C4zkVSBWSaGbY0P8FRQEnsvi/6uiti392JuZzRD31loFTu/BoUBkrs8U18ig6C2O5g9NB/vS4U+1LCXulH76CzAE8axp/kq0BNpVyPabnvZIJRZEkRSXdio85rCYRuQ6U2sS9AFhDa6eFVi+0x1G1lZOjTlLpygbCDWDGx3sRCOvcJpZE6xpkspS4VUXDyXaRoX9DyP56RX6Ll30qn0e7F6Hg+BnlJ3R0N5XrOesIV4u9MFv2MFYQJEhZDIhqs6Y3JnCm1mCmstViL2Wq0DlEdVG/M5zKLctCYmarNK3uZjaSIFkuWK1LXAKf0esD8VMoJV6ZYzYSplS8qz+M/VWj6Da6QB7b5tdqEK8WusqK3mMpdlZOwxx+JkMtaO3ccO4hkcxO/u6704PtgbgvfxhmcvsBF7pWRKchdthRFC6xK0q8MAOuDaK7EUcIstMOgjeXiB7P4l2EHiNi8XoOUWydVGPIgcYmtvG0uviJiyehDxwQR+byx4tQ7tuJ9HW940AQgya7DqPlhuD8NQEHkuv1etJ3h/DExGoLKS96aB9TTXfALbUIAI05RlZMbYhqHwh9+fCFRkVXjTyOZaaQjQwzOlu2q1VWSqoHZBpEYrCVGh5Tpy+YtMraXFzpnmazW3c8I2sRAZg/3MrdJIAMY+IQqMFq5rzfb1cSZjG4AdzsmCoO6skuKli8F6HTfXXiH5Dp5Bk1Gstvs98GQEKorY+5tCvGlEvGZVa1HIfMo2ICJ5jjyLNGCnYjYjEFzNEazR2xZuJYNfztP4+ah1CWugEnZlGDZTd+EsxTeK7dpR2vPl934Y6+9+PpISAFBZB5W176uFhAkdFfIRVk4Wcyy8ly4gzyM0pqw31A1cpboBnRmlpqBOE9CmAmsWM7ctuKoFPtpu5eviMn5eeQ5Sei5dzjTT3HVIEkbV3Ua9hNFfzVa9quNmYD5eQITgnpUF9rsdKH97Q3gPDvVqN6w+CKjUkA5AZDZ0U6CsjQ8XllZFtlj3cmRPlzI4ttbT2nhAxGw3X4cZgengk1ZbsRm2BRaIF3FdA+SQPlTr7KaJbo1W8dq/jQFi7/Gyk2VrjpvOCZMCECGWQRT2O4GIbxNJJa3UWmFXaXdh3Y6FDtlia7/Hzp/HdSGaZSQM+/L/+9NojWx5tgpncV8hqqCrqK+HGml4Clv11+8H0R+lqSii7TC7AnXUDK6xg354Hnp6EQVdtugp7JAsLfGtI2DMM6mydU7BNnTdyaZKuzpqTSAwFAChrhpVU6v/YD3OLOdBoZXwZRqFqpas48kwDF6xK8G9VBby/bFuigoRXs2D7UOwQjGn080WxMDqdhqFecu1aFIa2Qip1plMX11Dl2sdihcyEDfDORMtGjHgan7tWqbzmfx/28qAfLHE7tV7wK4SD7Cgopdqn9tW6LGFBusMBnGOZo+zTtugwOEwensBsvmlSYAJRfVfx9c7Ggo0ebSnxZZ+tl37jm/oeJcD5W8Pld+Dw1GsAJgFzlCzNCob8KAv3umDfsQuLay7baVFN2HTwQTN3gDtwRCNsou6rJugBPY6W7AWGhCowAZ6hvmaAMduJkfCgtruFK6Rqjh9/RHgEulorMI3y4ebTqV1HRaRdtoXqMbUzvo4Wm8EfrBhbuvlRu/3pLIP1EAxNqPTSxladydh3SGlMV3sufIcpJ1FgC0MZricyXu4nGH9sTvaURRi3aAbDJ1fRmii1sVuV0Y2TdOA98eSATEeiQttvyeLB1Fg+dDeFMHvXitlALLRLFfSLVrkZNNET6dSZis8HgKPTqO54XAgC0tZ4en33Q1ZwjRfCg3Ziohl1CSgVZiqaUBnV7HargXya4/3ohBtMkb90edET2IeQyo0bE6maE/2Y+YzIDYOl0vRLbQcHVQrZRlZ+BOzqN+XOqdqGvTOa0zeFPGdHwoJgFOxJedhTxTso0I9hJIwaF79C4cRjlFzxfKV26KkTrQz2mxCtGo4bENlRu+LTySPwdTUBv3o/cpmWWHpcDazMOaUQp3trT3wZiOPsY2lVwB31I5lsYxdGRD/VLM+Op9JMbArr3ffz+L4dofwPj1MYWuQQCVBJia+oqYVGqni82xun0UePfCLHPAyPHWlwiIEtKNCn6uO2gDTK1iGrgXNaJgJALl4MzWEMy8ZIFI/TdFqSlMVNpFlFVtHc1NNrBROfzgJMwR6ehFaa6q0sjQxj4l9up2RR1x8gTDzkI5E3FyvYfXdgZzFEVYV6OmFKFHNk6nUxcqzLLTjEYb/+HWpcvdGIYP6msJWcWJarCPUoq5fdpd0AAAgAElEQVSuASID4kKRuBiZaKaBhuNbMAyAEDl6papjG2TaRmsdBYDm1RdR3R4HrUDzkeeAIsetXzyNNgxESM+W8TxMxvK7jYVTFLHQ0IKk+shdgAiJDs+RZcByhey+MOC4r5XvSmZJyXyL5FI2Zd4fR7gkSUCbraio6zpSlPMsdAibT7wk77+nSu9egd6jZbhm3K4CT0fSrSaJEARWGzRj7RSAkA3df7KNC7gOzUM3bIQFg8fSJBQ6AdaxYXuSCASUptcpo/5mwaGF1FKVymkqHUCeYXN3IKymJJHuq1SxoWlGzFfLTPraVjoei7k1Y0oT3j3DGQL8u/x6D44P9oagFTSP+oJ1DgcaR9gKJmuYoi5stN6AE+W7E0UM2jm4pQ3SWBYAhlRXeR5xbqVYNremYKfmc+b1boH1WpXSahcrayAMTmVBb+LADYiLr0EVZm5mFfpkJNXVoBAqbS+Dz12EgOw5EheZSwZfGawz7IO8F4aTplyFxd+Gds5pnoD/6iGuDfq+yr5AqaEHe5F/botvXYstwWKpvHWpxHk67mQ4aLfW8erHaChVr1WOtbqHdr1sTBxmG0agtRqE1dmIrZvToB0e9oCEkGxq5F98rNGYfYkI1dQz7E2B8RB8MEH1nIqebGhv1hIAgkkgs1TPWYrsVOc5BsUAUjyw+ETR1VI6n+FAICrvo4VG3UY1uW00RQ4+OcDmY/eigZwXhtfgN98Ql1o7mha0KdF7vBKIbLkBaokQrW+NA8SVv3EWCwonOoDkwXkc9GpHlH/hEdIvPYgkBdMhBOKCfhbG6DH7j+61aZ5fujlcGwzbY8Yj2UiOD4HFCsP7nThMj5i+tlhK4dG28frYbK9nbthnohRgPx3GWNlv9GB8GzJ63x5asdC6FKVuVUkVNVF62nwRla7K2KH5Oi5y5ikzGgQefLLagQup2txiF+GlQhkzTYv00SW4lwrNsSiim6cNi21OYbOJztDRXS5lsdlsUb98SyrFXN0/R0NZJIaaf2zsmpUyLFKH5Mkl4IH8d58CRYHmeIz2YBj84kOOgkFOxve+nAkb6WopFeZkrLRGEebBhqjmPTQcyuvRG4z3RtG3yeAzg6XKSqCgnlol243Zsdzm/bHc9Aa5mMjMXqdx7hcrWZSv5qJivdRuwXtZREvl+49HMjvZG6P5jnviR9Wl3ZoVsr5XPt6XhVSpi2RiuzQRfyvLqMiyUDFjV4HWO+G6K/wVLBvqRhZlHb6jKALFmDqbsT+coHp+X+CXl0+kEyxLuQaqOkZP9nvgNMHuebEgZ9tstzvZWFvG4HOP5bMdjwLsVn/0ObjTy9ixqB5EaNitVuwEdz6XdLuqhr99INfFaAgYg0phLG6a4JiLqo7akjxHcKC160BzPEJxY7Bd9/4CYhfRyQ3nukFQQtusoqqCRX3bT8N1T3PV4dimMx1HK267Bs1vyRTu9vvTVBxbjeb6DI5vs4zerwcjOBq6+ToqT7c7dfSUITGnUvnybck0pot5qKj5YBLYHWIJUYO2lcBNQa4vdL1QGZKL1VR3EbXDqldykROtXvFsgrQkQfbgSuMvl7HiWm+izw8A9HuycXRnJYkO0jVsxa3LeBOarbQGtYdoT1Vlo66lcjVL8KZRD511fO3mJ2PBLIAMJE3XYJukMoBYB3c8HsTXYa9FFwmBjdqAy2PQlwCbsTq2kosDQoMgzK9n0I/VKSCvbb6QheHxGaiV2U7z4bsRFurqFXZlsKEAswjYjILsNMjIyWLCo85GvBWsfPJPnlwf8IfzLN2lzHI6MFwtzC+s13D3nyI7lwF68fmHsRMrS5lTMMu1tZGs595rjyPebRAbINewdXuq+uVRD9nDK7FoZ4bfH0aKKxCHzJsyCNYAwN1/Kl1a1xJaCxcy0z6zkjDls1mIW6ypncvDaYBdOc8iEcEEi93DwoKyVL6SJAYTWbe33cHfPkL2pUcIoUBtC5xfyWuzzdBmdkYDVwcAf/coXsOA/Hxdy+byrI5v5RkCEf0VItr/g3gxf+CHI4GK7MM3QY6jiB22XroHtX9ubx+EC5pHfdDppdg5EIkC2Xzzra21uYOxftJUFuJBXzDj7U6qaXNZ7fcER7/JnNChNpldhQ3TdmWEI4zaCUR8X+cMzYsn4s5JBDq7QvPiLZR3p0hWpVA9jZnBLAtVngu2bFWbUSaJgkqaj/aA6URgCs9hQArnJNVNvXh4fxyZHGbzPR6FsBMy5fBsJZ5GWqXz8X7AhrHdhW6ND6fBrdXN1teDXMzHfziIbf5mK5+lagkClHJxBQBIvvJYZjv22mzBsi6kJ3MlPpIQI2q0uldNAc0VgqlqiX30LDz6vYluikkcTBrDyKwa0lRUwHUdCAU8UL3BcBhMCAOTzOZO2534Ky03qD5yRypYMxgc9qWTsMrb6J7MMX1uIJ5c4gw6l3N5tQatd2J4qMl77Z1DwDkRZNoi3+/Jpnxz8x7oIF/zn8Nia7M6RzIbMFNH5+S9qQaIZsqAs0o9Ta7ZalOaClnAOpnxKBIzrHNsGrhHp5FEoYUDpmO5JhZL6RyNlr3egCcjcJHC3z6CW+5kU2waKRDsuntWtNP3+fFuOoTbAH6diH6GiH6A6Jsh3XuPDlswrTrzWtV1Er9CSH1ZgqoWydks3GhGw+M8lYvZLhrnBK/PU9lMWCGAtVhHrz+im4pvY/u83QmGvFjh/E8eggeFhMx0DL+6mD0fTCKcZFL+rsjNWByJAxmzCQhzCc5ceC6sN/CHU/CgQP3iMdpb0+uLoonrdnVcMHW2wYX+//GBvNbBQIZ3vV6cazgHvz9CCFG3kJ+kc/mFChlhLkNLyzRu9MYeSJbzRjfH5TYueuylYrau61KcaIMy2o7ERR5/5/vuyWVcNDuvG4AsIkUhLC8bTNr5ZVYfJ/lZ882nssbmO+SctIcjqUATpwKpVphhe6OwUAZhZNvK+76ax/Pv9Tob9b/qOmjevI/88QIh4cxU30aUMDJDpSrp0TCycWygvd2BthXKl47ARQ6qWylKMlGZcy+D72Vi32JdgRUO3aPL2DH9R4Bf2vh925Bto7SsbYOUbIhvFFo7QoyqFCU86kemWJfRZ0WLVfqZ3ntzmR8EKriqkmkpiXhuUwYzQK5ruF1Hxd+lNn+Dx7c0ZMTM/yWAVwD8bQD/HoAvEdFfJaIPf5Nf2zf/8F6hBh2oFmrnPNbW2RF8L1d1ahrZPVY5ariHm62l1UwT1M8foB0V8L0c9X5fmEe5dhiTEdDLMfz8Bfx4EGmIRgkciE7h6DdmoNUuDPCau4eha+GpiKCi704TF+00Ce032lZCeYiQvnkqxmYs8En98i2wI+QXGxVzJWj2enGQ3mrLbu6fo0HkqVtlm2cyNAeE1WO4uddFoBOJSRfzuAGE6NEOfOL1940GmjOcxWG6DdbLCri4ElpsXctrKsu4CZsi1zbJYFPgIyxnEIIdtrAq7Tb5ymN5v1plh83W8qhtMbfBp0IbtNiE+YM7mwNFBtrVGPzOU/jDieQbbLYR2gCks1jtIgWzLNHe3heGESCMF4PoFAqk+Vo24OlEXnPbIr11IjqBvhYWm60ugPK8n/+Pb8n3ZnNQ00qlb91aN79hu0Px1iWoaeGenEvwfeuRzAUySt86Q/LoXIf4Ps5kLMfA4Cgbmtv7JZJKfn8qf9qXCR2BKByzbmKqnZVlMySJwD02fDbLjiy5Th1db2Tj3ZdwJX84lcJA9RthkGyzrckodms60OblSqjUaSr3n7m8PiuWEQPf8tYVzMwAnuhXA2AfwM8S0V/7Wj9HRHtE9LNE9Hkieo2I/uTbPOZfJqLfIqLfIaJ/pN/rEdGvEdE/0+//t53HHxDRLxDRl/TP/c7//edE9GUi+gIRff/Xf2cUcd2yCosVOycLk3OA0wUvGJPVsRKxhW29gXt0jvZ4D159e4gld5mJQvBGyAp2JL4/3UhN851xThZQG1jXtVhtGxtCK2k3V0zbfGYUPuIiD1VcsqnQ3tqLEM1QPG98JtkM7aiINyEBbr5B9mQR8WY9moOOjYBhwnWD5HItr9WGenpjsg64Q9ANS06uUHUruRFNBGTUQk3QkqGgD6wiP9KhsYWpO90cLmfCutqfCL4b3gfFz9MqUWOsGHvIBpPmpWQL01jiR4OLrH4eVhwAiCZ0xrQqS9GjaLXJ4yFwrhGgvpWM7eVaF59UZyVWvVLYgPydQ3Aqn0vYqBZLhcBaLP6lD0l3sljHKloN+NAr5BotlQm13obh6Ef/my/oBilutG6h19KulE1nNo/6ByL5d5YFmjDZkN4G7kCkZRplU7uyZl+osL6vIUfdhDS5QeOfXZZc121VFdZhDmMQFDN4s4lK89SJL5M9X5qqoLARQd14AHcxj9cjs3pa+bhJLdex4zVrF9v8maMmaDwUGO9ZHd/iM4T/iIh+E8BfA/D/AvgYM/+HAL4bwL/xdX78fwLwGWb+QwD+KCQMuvvcewD+BoB/lZn/MIB/S/+rBPApZv6jAP4YgB8goj+h//efAfiHzPwKgH+o/wYRvQrgBwH8YQA/AOBvENGNnvZtjm66VFlKlWrVDbMEkveKWNHWjTy+GwvYl2oteXSOdC1+KEyE7NFMsNyuLmC7kwXWzNeyTGhtxmayr34v2lVXrdzs46HAFYaPWsWkn6J/7kQ459oxcCKOq82LJ4o5r4FBH9nlVgepDDQN6pdvSboXICyot06Dv78MUQnNvUMRrJlmwl6DCcfMViLLxFlyVyoFssbu1XshZhK9Qio7q/wtDtMGwHUNHg9lONvvCwNGM5ux3cm58j6Y6rlHp3KelisZlAaxn974y1XURxhV2GjEpNXtwZ5slp7hP3RHsHv9rAwi8vujQCSwXIby1XuyqeyqmFu8WIFvHyJEbQ76sglezuRx/V7sOPXz40EBt6nADuJcWlYCXU7GsihNJ5j88huygWqITOh6TFRo17HRnPX302Ag8EqH8uonfamevbK0gutvjTf/l1saucmhIAmbT55F6qwVMmaomGdwVYPqI3fRjAspflR5XT1/gPL5/cCcq29Pgt4gqMStqxz0QTafOtiLwsI8lxmCdnh0dhWZeCOdwc0W2plo2qCaPFKSSEYCIJ9fmqjavhYYaVfK7zGIajoRFpdn2ZRXG5AluD2D41saMgJwBOBfZ+bvZ+b/nZlrAGBmD+DPvdMPEdEEwJ+GQE1g5oqZZzce9m8D+D+Y+S19zKn+ycysUyJk+mWn6M8D+En9+08C+Nc63/9pZi6Z+SsAvgzgk1/33ZlYyWh/XcohEDnQYcirlW5dS2VYNzokFrgneXiB9NGlzBpswbtmvJUgWZVo7x7Iv6tKFjWbA9jvLDsV4GodFawBM+dgvUCNVtQtx6qTGe5yifRyjfTRZaygtztQ04B2DdLTOZqX7oA8pBJWUzg+nEbsXc9DciXzjcB4MrgNEGx9uYr4/EaFTqsNMBig9/q5dD5atdP5LAqh7Lw6JwtaIZbS0lFU8RzqPMBtysggMjZKJ5uBSBeWrh2B5RBYVGUiuD2zvFY21k7ixEm1qmQB2ZsGKMHdfyoLj9F5tzvkT5Yov+vDwHYrfk47XaA94McKwdl7O9gL1Wu4FrRDpI1YS2dvnEW8usjBeRLDjLKOEDHLRLHsWUPp152CpRYhljmuVhXKF/fB/UwYWa3QZblIIlU4VOwOL/7li6CiDvkQwc7ERcgtSeTz6qjQ3fkc+ZceI//CQxEeBr0JpABpWmC1QTrbRjhvu4sdosJOnCj77OxCNvOONQubX9ZwIF2XWYHb81hmiBVgNoOwz545zmfsMPaZdu8GuWI2Dxbl4Vw8i+NbuUNg5v+amd98h/977e2+r8fLAM4A/K9E9E+J6CeIaHjjMR8BsE9Ev0hEv0lE/679BxElRPRbAE4B/AIz/2P9r1vM/Fh//2MAml6PewDud577gX7v2kFEP0xEv0FEv1GxLAb+uRPF4pOYZGa4YqqQjC2opmAEpCLdH0eGjfLoedAPQyhWkVW7Nwx8aD/IBa+2KrEo0HzoFswyOnDwnRMR1KAf2Q6LZfQFUoyWM9EuuHMZePPhVCEuhRZWmzi0bRrAQ9xHiZA+mUlV2iuEjpomcXisVtnp5Vq6JlsYbLFtGqmsDEZpGjlXhWyyfGC+9KpAtmD6iXrT64LY7g0DxTG8N+Ow55l49NgQ1+YY641dhJFF4yjmQXQjM+05jaFjLp0q4qPZIjKDPAtrx6i7k3HsMJZC7xS2kUc76aM4FdO96mQgg/XLGerjoeT1bqu40V9cxcV+tZHXN+jLwm45FIm6utqGR+LvHyI7bZGua5khWfQpoMEy8pnTSpXXCm0VX3gCty4lCGq7gz+aInkiDKuQrNcronZAIZjqcCD3xngUtQB23TXN9Q2im1tthYxCrPlbl8gfzqTDrCqp7omCt1ew8u4VosOYr8U6gpxU8DbnA0AH+9GHabuT12JJe4B8BtYZAnGTMEjM1Pj2d1P3T8aqj9EN0q6ZXQleLKIJ5rM4vpU3hG/gSAF8F4C/ycwfB7CGwjs3HvPdAP4sgO8H8F8R0UcAgJlbZv5jAJ4D8Eki+s6v8/vebgrzVaeVmf8WM3+CmT+RJwP4caFxjR5+1JOb2I6q0gWrkylr9sVVjXZvCN+Pkv/geOkQ7S/qJrb16txZj3O5WQ3ntosWiBe2BaVsq5hLbE6MRoXkzsLXKmNpV0YDu3CWtfpSjjhttkCeo74zjdWZ4exEUbFc1eA8lUpXRXXhxrLDRGHmMArIYmaq70xmBDSXKs6dzTW7NrpnJpo3gKKQYbuJm9R9M2gAxsMI39nAl1kqOVNvW/XfjVu019lhj4Xv2UZflsKvz7XjWq7AVzOwJrnx0UG80B6dAftTpG+e6uLr0XtrJhvzaIB0WUYFum1gezpP8SwWKVrx0rYStpGeN1ptYTbhtFLK8NEIblWCj/cFChn0ZXE3ozqtxLmXyXN1zepWa7kmLecbgFtsweMB6lvCfKpvjYNAjjXgyN8+QLJt4DZVdFEN3bJSQU1t3Z3dtD6yeCyUSM32rp1/LTjau5L9wX2F/cwyxTo/XbB5sdCiQ59DCxFW5h72pjFNcL7odOQdlpMVDVbYWNiPzQZVEc95JvDecAh/+0g2Jv9sVuh3Cxe9nyGj3+/xAMCDTmX/s5AN4uZjPsPMa2Y+B/BLkFlDOBRm+kXIXAAAnhLRHQDQP087z/V850efA/Doa75C75FcSGXf3D0QTnueydDPaJelMCwCPGFUNufEvuBsFofFzODxQHjlnlWNKoKs5GwmVW2/h96bVzEARZXA6dUaIWNgoPTJ1UYhpfP4eqrOUFtx9KAjMJ+h7szCFs6uZ0ySAPMFskezOGz1XhYZ4JowqT7QQCCjEJoFhy0Auuha6DwtVmEAyZdX8p4Ni7bn0QqNlhv1k8nk+8M+3OMz6XK2Wxn6AWhP9sEnh/KelBnDZakiNBdfD3OYrZDNOQCEjIYi61SGLm6EnYWKzmfIvvhQnqPXA2Up+HgfPBD/oOCWWdXRR2ckxnlUy2fpNlWkdI6GwlgyR1hdRI3FU9+ZKtSXyPs1TYV1pprzwHkCP+oJjXm1Fiq0wW19GVLT2ZWcUy1kbLNjjaCkJ+fBepueXIjgLUlEgaywJh3sA20L9+AU6ZsyEOejvQA/wbdRtGjdrFFMjbbbevDJgXSFeS7zENOHhMe0wGwh52q1EdtqQLri1TpCSpMxeDwUlpEjoWZDuwrvJQBnuZJNYLaQe0bJAdzZBCMLTldai+G03Aw7l1kqq6JaybiVdsbPyroC+NZnGf1+DmZ+AuA+EX1Uv/U9AD5342F/D8CfIqKUiAYA/jiA14joWAfOIKI+gO8F8Hn9mb8P4If07z+kz2Hf/0EiKojoJQhV9te+9otEqOJIhTghPNza3q7kvnv0e8IgIjHBCzYTQFx8zL4YiAEnncjMcEFOJx28mSJ90CovoigQS1OpBDv/DouzXeTWOdxMZOvANAZJ+ElfQ3QEavGFWjAvxE8nf+0+3Okssj70vYe0tSQBNlup/HWR5EL8aqgfXVQBxC6qV0TxnRkFFkUcXiZJdCPd7uC2JdYf2Y/vpWkQrJGBeD47LBbuqmjXG3nu5TpujrbBm++RnUtmgXE6r7vZ68umXNdimDYeaR6B/l7duLDTzmW5lv+fzYGy1AhVVe2aELGsAPbI7l/CjwtZ/LWTNOdami/hxwO00z6oZSQPLwTqA2ThM2JBkcn8ZzwKKmE/6Yfrj3STRZYJY2q7k5nGZhtnNDacNdGYDaa9WKbzeBAXdes2TUdi12dH1EeXC9WUuLgxG+3YPrNeIRuYHfpaKCzOYuoXEtAatdIYDYDURfjHPiszMLQcCUvbs3vDUuHqOliXCHyXBJ8sODV6tAyNPJMN0T27pfKD2iEAwI8A+Cki+iyELfRXiejTRPRpIMwgPgPgs5DF+yeY+bcB3AHw/+jP/TpkhvAP9Dn/ewDfR0RfAvB9+m8w8+8A+BnIpvMZAH+ZmW9gBDcOR8HxMlnsJPijbmRIqiZnzYkuFmFuIAsNDwqp0syFUxkTgQXUEYbxwSRm23ZzXfXCr29PQLtawtxbH9PCDGqyCjhNUL14CGrU3VRTuYIRlx22QOZ5jLjMsuj9Y4EprYe7Wgs7SamXbtNxM7Uq1LyAAHkdZuVs8JVhsdqN0LaM9tGAUgU5mtS1XiCkIKxzGhCvXYQNSk3duq0w+q1H8jvGo6+GfuywTcsohma9DcTNxPx5VF8x/65bctNbqpgqaDEeBeqmzxLUH74tG2eWxMhLg07qWmYElgNhHV6agldruMVWOidA5x/rQHXkYSHhSdtSLNPVgoLVs5+aRgqPupFNlJzSbztQ2HYXDe5mC+lSHp4hWE5bkWCLow3+y0rs3bNMOpTVOgq2Rh1lcqI0bPVF4slINiC7Hqzy7kKJ3Y7SHGO7orYsQ3P3QIsAJ4tuVccshY5VBgCBgwzeMxHmkTKHzA/JBsbbXdx4ZotAtGCNDg3UUpv12QyqqoXNp8I05BmoqsX6+wNif/2M1BZvfzDzbwH4xI1v//iNx/wYgB+78b3PAvj4OzznBaTbeLv/+1EAP/p7epFWVe8quOU6VtJq8Jae6s0/Hkmlabh12wLQKknTuXhQgM0faLUDOye5Aykh2wkeT6eX4ebkqVg7Z7/7BHw4RTNIkdlCY7S/TIzhbKiVf/kJ/NEeXAeaAQA+nEp4i90sJlgDpCIfFLJoWMtuHOzJCLSVwB2ZEzQK3wzgjcsdqumBdA47HcIZfmvdSl1Li9/PBffX2EZSS45g5NadVVi+w6Afgn8ARB0GoGEmAi2EjOVU4Rcb0NoRAoQoiuPssAXJTPMWK0x/WRhc5cc/jOK331S8vbPhDAcoHi+As0tgbxJnTHsT8NmFKLJz5eJbnnLiAFIG2r1bwHIbFbWWF6wZEDRbBSdVsWbW85MK64q2FVK1riCjoVYVeL6U33NyIM+x3qodSgl/OIF7cBq9hACUH72L4ivnoKtdHCD3xSG0OdbrXJlyKMu4MFvRY38fj0QcOOirc6qef6eso845pstFVCNbUTEahM7PckHqF46QvXY/xmNut7H7tM3GXGidE33K/gjtKENuIjm1tw6fszHhDNpykM5jNIwzqjTRrkLdBDRfAoB0qJVSrx0Q4k2/0eM9rP7fzfHN7hDe30cXh7WLySrWzVaqDQ3S4EFxDTaiXY3kTCwDuCcVSjvuwWcJfJZ0oBoge+s80gL3JoGmR0/OA7ZJizX6b82vawyGA62wWBSmqlugphFzMSC073S5EKw4WDrbvEMWX1pt4yIJRA/41Ubw7Eb1Fbr486gPt9qifukksq0Mg+/3YwVvUAEAPt6HH+tgXiu/AF11h8DjYZwpNAqlmfbCHEAtdCZL5TOwFDjn4I+VQ2+5ulaZ3uxkup+zwVZE8j5tM62lKi1++00dmnogS+Ig39hPQDynnsUs7fgQYJagGNvk0iQOMElV2oD8TufUUkEr9hCFSvH1qWMtXS0FprGZkZ1nNcWjkyPQQOdVaSJdaN1g9fF70h0QhU6QTw5QvH4qMKZtXDqEhvdi124MMRsUm8mh0XT7/dhB5blAizfPqXXR3fPtuQMxJYgmhBXIA/VHn5PMkdEgvjYrXJomWlyY7YTeq/VeIVW06g6CtYTNBCbjWNh1XwcQaeDmVwaAxwNxcdW85wDnBlju28K0f/4PBtp7x2KiZjelto/tvUPpCgYD0QwwC+XPbnzNGwgBLs4BDAk1IShLwiFZCKOHp8PY0tpCbRCHDbWWa2GJ6ALpJ314G2gqD5/7BWhdot3rS5vNLIslM2i2wvK77gpOTCQ3w3oTb87FMs4ykkRomMa2YI7h6kki1MDWI3vzLNhC0LkO/qzqM5YUoJj3WuYNm07b371RAXmu5Vq+v1ZH2brWgbFW2GaXbMNxc88ExLzsQjBtvz/SmMRBVD23nUrOoBIg8uWBQMMN509jM5kla5nOruDeeIygkPYsC/JqLedwqjTHsgYmIyEjdDZwPx0IZOa95DeM1BraqMDGKjPmjeUNty3KFyyBTeHCIheSw04cWs8+9Zw8ly7m/nAsn42yjoavz4HxSEwCTdi4KeEPxhFq2xdaMmeJDMI1ipQsjrQorn8+et7l9WRoD4aB6iwLd4fwUOTRnsMYcLbBO4qFwHgklioPr4S0oUP2eG/qZ7/eRpqrfY7M6L1xKRbu46F0JzYbs27WjlQFdUf78Vo1HZGZVBKBFmuZE223As9dXkU2nNmIP6OD/Lv7ei+OD/aGoNYUbAPgXoH2aAI/HoSkND8dwu0kSwCt6g6ctsitBy03ohVYrZFerECzlZhi6SLNZmpn1bHBTWUV7QzsgmOGH/XUHqKGe3whcwpb1JZrUN2Ax31QrRnQ3QWQGeVE8d5gf90PA7Ig7wdi+hsQW3LzpTFGhdlSmw4DkBtsvYE/SdkAACAASURBVIlsq1DF6QLY74GP9uDvHcvGZFnMmkQWNlMgajqsy7JNyeiB250KvTiKhZwME7nIxIbB8qrND8p+j80Pbloo9/thQaalJHzxdidZA13xYdrZPGzjNjtvmw8tV7HrsjjM6Vg2RefQvHgLAKRgqDuwiw2C99VC2sKRiFA8mMeFUA+6XIT3cfSrF4BvBZIL8Ix6B5mGBIgK+bLC5pUjeU2Wz71cS4UP6PyJ4rzKhJbWfbWtXKf2ma23oJbluuwqlu38WCHSFVACAkepu608z0Y+f51LALhmK3KNhm1WI9z5LOcLWejNYsQoyXYYE6qRuRnnHQKDaQzSVDbupEOZ1tdLJ0dyrehz+uNOiNA/x8cHe0MAJMhmvgmGWq5qwL00QEfu8RmoEmUvPBBEPF44+bw3DgZxFs7hzqQLoNkqKEJpU0rVbYponVsEzFwXY5+52C5bGMu2Bmzx34nAKHl8gfpYXTQ7FdHRL7wBkANPx2LzAAgl1OAK5YX7iSp9DWv1Uimz4fa2+G22Qgk07L9pY6SgbQqlzgbqWlghZ1cizHp6ITf+cBA7BAuScaZAlqqNavEV4jtH8nz2nJezqFg2b3vv4Z5ewY8K8K1Dyd81XLhXRNYMEBc1IHZmB3uRwqjWBsVn38DsUx8GFUXks9uxXgvN1Q7nIoRwcaUdBwPDIcqXDoFWUsfSx5dimJYLc4sWq+t+O8Zks4VTq+Lm+SMgTeW62u4ETpmOxWp8W8If78VAmtRFrLtuQiESxJUABp8/hT/ai8yhLIU7u4KbbwSKBGRTtbmNRbeOhDbKicKEZaQ1uyeX8nuH+n48R1W+mQMadKYCQC6rIDbEcBCiRJuX1dAvsO86cF9nYzDbijC4tw3R2EvdDb3IUb36vHT5i6XM7rrzDO0WWI0IuZ8H3Qu8VyGhzBGpbeFW3xamfTCOIHSRhZBWW1n8geCXj8uZZNPaTWsWwy6B72donjtSO4NtvLnTFKgqJA8vIjYPRPjDLm7zhgEA9sINdw4ocviBQj8OAgnYwl83wGiA7I0zSTvTG6l54QTVK3fgp0P4SQ9uU8qm0LJAEcZysgGZ5Sebj9JmG2+6Lj4MxCqwG7HZPZQ5Ekzq7DAvHBt2K7sJ7KORHXMIIKHZKnYz1ilYd6Wvhw8mYXbAidILrTMwDN82Xea4wNtiYL77SRKSt7htsfcLX8LyT7wYXU1VqQ3nZDDeetFHeC8B7soEEnhlDbBH8WWRxfBARFT+9pHQJo/243tQem1gdBmdUqvd9NElLOIydFfOyevyLdzZTDbK0RBusQMf7cnvK5WdZp+xQTqAuO4aTGKzC+cQcrLN+G2iIjWDGxfLaO54INeQm0VTOE6dLP7mh2TXgg3H7TppW9EM5LnApur9JC+uM9sCYsfa/bcd5tM1luE0Dwed53EIPlubLZgIu+84vs5A0g7T3i+bGWXjYyFhmQvaCVIdh83f8MHvb9rpN5Vl9L4/unBMT3jw/lBopsK8IWl10wR+3IPbVKIe3ZVS4SxXQDtB+vAUzUu3kT4UERCtyxDgzdOhxE7uSulC1pugR2iPJiBm2ZX1gmMiYXEUBdzjC3ltNwekZvPrvVgQWHsOIH80F9fMXQVOE9QHfRRvXSJLhfGULFbwt/aRXK5jJWgQidExlS2E+SLaKJQV2nuHYCJw5kS8ZRWZiZGmY1Fmd0VK9nz2PAazGKPF4BdVWQchU5LEEB0b7OmGSKudBL+fzjr21x1aYNedcziMOLejqHIGYudg/HT2GP2jL2L33R9Gsm3gswT5P3tdmET287UUBGRaBPtszEFUzwWpl4+br8UXab5UdXops6DJMDqXmlW3kQi6ecNdl1vTj6y34fzR5UwWsOUqQijMMQ9gvoBFczYvnCA9nYfzzsOBbBTDQUydAyJ8ZL5LRREYbwK5QCCa9VZEkQqBBchntY4Fgs2o1JZCZgkuehE1LdJHVxFqsu7AyB222Nv1o4FJtNwILde6QyB6Eik0Wbx1KfGjdljeeBqLluTxpTLglG2oDKjgPnBxBTKzw2d1vEeL/bs5PtgdgrWzWi21zx1Jqhgg3UC/B7AXnUDLUlmylxsQkMpRcdP00ZUMY1utog2LXG5ipWpVxk4iCclab7v4+j2hhuZZZHt0VNDyhCZC61RUNhi9aTWckLBL6hpUt6LKThNQ1YCLFPVz+5Eyut4GSmNgIClkYF711MgNS7VXczWdMUzH8n7UCjx0P11lNxBppECsVhMXRUKdijywZIDYUakuAJuNprT5OJBv2whZmI0Gs8BCZRV9+LsWHXzjzlTsuvdPvoLsiw9RfE6tsSz9jghkA+/tTs6P8tV5oV5WPbXGtuS39RrN/iBkFQu9VhfRzVauJfWp4p7YToc5lUE0Bt+Y11UqJoTNyVTnTXqemgb+aA+8PxbxYl/8jXgoXPv0ySw+x/4UdDkT7Y2KxLDdAmUpENFKFlx/5zDqK2xg291gO6SCAD0C4KMD0W5kGUKErDG07LG7KjLRrBsE4hzBrNH3RAWPPLseeORIrxmdoVzOEBLq1GnVp9o12GcPhM+JTVxoHYN2M7xR6/u6efv77Rs9vg0ZvU8PksElF1lcfIikO9CcWmQZ2l4q+D8gF7ctZsNBDDk3nrRR2ox3bvx7w3gB6Tj2hkDj4W/to53qBqPD5FCpGY3RsgAAhLSy7VaqNDOdA2RTcgSqavhhAT8QUzR/vCeQ02IpNhB5Ck4SZE+XMhA3zLduYhVmcZha0fqTPfhMzP/SJzOBs6xi33aCaqpa7Lbt6DJ9DEozBpH+3SwawCyOnOZ86Zycu7IC73ZxoyGSmz/MNRS3NkvxQT92DFp18kIEW0brlCH49btOglhSgZCaRsRJN9WwRgO1hUQXweC1b49tvUAsgz7SN09l4+xlwrAaDuR6soQzJSlwkQUXV94fBxEfAJlFHO/H5ydC+uBcozYH8XSXtcBAzoHOrqRrKxvwZAAUGeoXj2M1vjcRjyR7XUUulNbLRbCqIMtYANTEUPUVGhbFhRQ/fijahvbugQgd1xsk60quo21H02Bzq1bypzlPBe7qbha2EVhxsN3Je7Tfa5/tbCHf0/sUPZkpoSiwe+UYSFO4xkejSlNU1w14uxWo0Xthjtlm1nrQwV68Zu2ae0aJaYRvs4zev4fa99LjM/HRWZdCIz2/jHDHdofe5x5GLLSqIntlvQGnCaoP30I3yQlE8PtD1C8cyWJ/o7r3+xNRnzrAXSzgliXasXUdVnG569RL36mIuypQABbWHn62lBs5OV0IbHG1EqO6kwMJhAeARBYoWu9iNd5lHt3wSwIRyHvRVPgWVDVCzWUWWmQh4jG+KxGS/s7x9WqcKA6FDTbQ4BLuK9yw3kqHZlVxx/SPRsNOZdh5XjvC4uxlgVhvVH0slE6ysHTdYJCrp4+GtZMNuIki2wgQrUGRi4eRGbtttzrIbCNf/WCvg9HnUS1rjKfFEmefPADvjcAXl8BkhOrlE1n4dyXAXrrFsgQ8RL/Q78lmafMXWyR6ebxWmKNqOM/R7A/EPlx1J6SCSVpIIFN6Jelu3JPPt50OUB8pfVeFbcEWGxBIyDo76/as0vain/HjXtjgOXFiFum1++1CeUB43vV33gJdzEFPzgVeM1V1N9HOJVGT0FVRm9p8fypOAvr+mxeOQU8vsPvQPkiH3PlrD+J1YvGtgJAHgAgbWoeyXkcKrLHHTLfyLI5nOEPQSOMvaCjYTePQ7uP+RSJqiejf/HrP+QHfEDgsruyc3Ezmt2+pWCYc0kGvbRS82QS4hTyD98bXUpXcYov0YiM5AmZ0phc3F4lQ3QCBmRaruFADOoTWr65Nw01voO0udiJdIRCgAeokG08uwjq6WqI+HgIe0Z65lmFj+yGxZgA5mR/UtVgl7w9DFyQbTFyMqb0BYzGj2esDDPhBJjeq4bvKDgJR9ApKEvhbwg/n4/3AYWfTMdgC3O/JELHrKRX8cbQyr2s5V3umrUhjBWjBJ0TBhdbvD2PmQZrIgm/vpUtVvZQ5BSUdwZ/3guEb48zsQwA1K6wCjBXS1vIcJz//JuhyATqULIP8tQdAG98jBn1hpNWNDM7XG5knKQRC602otm1Bx2IJPD2Tvxc50seqFTFMfLtDvd+XTduzVP+7Mqiik0fnyO9fyiZjGhCDiGxO1lcVeZHLTAIQmFDNGalloQBPRjIQrxv5XN9uEdVrpTgv5foYDuV9T0bRh8gEY9ttdG61Aq31anGxAeoGbS+VjaNtkX75IZAk6H3pLF6btqHYNZM46Tatq1SXgUCh1iIQjqK2xQR1z+p4BpCRhn/9dQB/BsCrAP6ihoS93eP+BwA//25e2gd7Q7BF3olBXfaV07hwGWPI7BzUK6Z9/liw/pOjoD5OLzagTYl2kAsUxIx2fyT+QF3nRlXagmShdgvhwQvrw0X4wHJr7SawgVqaxE2hW9kYbNI0wGotnjeDPvz+CMlyCz/UzeClE2RXW1ng1SHTErbcrpEqt7ChmmwM7mwOPyzgzueRqlnVcMsd2n6K+uVb8HmK5vYe0LZIFuX1WUavuH5DOidMIjP5IxJKLZEqmBvQraOQkGasLpqtrt/clk1hzCVbsBfLkM0QNqr1Om6axrq5WgtMshKRHO3vxbnDSCEY25DssEwKIsHlO1TjkC98sCfwVC+Pr8EqbuPpz8Vfhw/2NGdCMjKsu+EsBdUtmpfvRLhEz41Fd9KuBo8H4FuHoNEQ7Ut31e3WDPT6AT7LX7svFGAgemppV9g+dwQA+N2/dBz1HKaNMZhqow6iHS0LVTXa44nMlhaqS1lvw/t0jy9itW8Fln1+tYQzIcvkuVsvM4u1OrXabKlXYPfqPViSWlioSckBqzWKLz6ORUeaovxD94CyRH5fHYVtQK7XDC9W0m1ahzAaSFFg2dZGTc4ygVOtG+2yoL7R49nMED4J4MvM/DozVwB+GhISdvP4EQA/h+gK/TWPD/aGYDerXQQ2OCukHcV8EeEZrUiTxU4WneUa1UfvCe1R3S6p8YKzZylcWYs61BYBIISuJA8vZHgMhAXH60LM/TwyLFRUA29q0irOJQyKULdRZJnkC0/HoPUGnBDaQRatucc3mBKOUN3RYZ2qVsPRqA/SUlgmycOLDkMjcr2pkc0ouf80wCNuvpY5w8MLudHNHbO7kNsg2DnQrgJ5Lzj32YX831qDcvanYoxn8BIg712rO94bx/AWm7X0+7KZd+cWQJwXWMdg0N9kpKZmCmMNBh2rCqmAQSQMMWNisVbaNuPxPi60s7lATjqw9IP8+nkfDmSRXazEJdcWIgCP/pU7cQFrxfYaWSZFhnaYXCi9c7sDzVbSCfZ7opC3UKa9SewsFeuXylqZbNNR0Askl5K29sL/ree8aa7ZqIf32TTRHVUFb8nFSnMXtnKvlGIUib3J9Q3Zrinr6mx219Pqv7u5e5bXT9Ld9N68EijSjOg8RxuYLItECINSgeg8XDfyPGZ9vVrLfMDOi+lqTJQ5HumMaRjngyy2Mc9SmPZ7gIyOLMxLv3648zT38HUCwYjoHoC/gBv+cV/r+GBvCMzwFjSvw8f2ZF+EP+rpjr6aePV7govvKrQv3pEugSFe8k5Sv5KrlQxcJzIsM4GatPxpvCHyDFS2sfrIUiSXS3FLvVpGawkgcuKteql06Ow7FD2tLKlp1Ba5AV0tBe9PZEjOCaEeZ6C6Db79+cNZaMPdxVJZMXrTmxgMkGpWZwShtd7ukJ4v4Srh2iebKgjQqNaNwMzFEl1oJ6N4Pj2jvXcs3ctc/OyJ9MZs1SHTsOuQG5zFG3lXSmW6XKnaVYVpvr1OhwUitANE5bYNiG2xM6x7uZKNajwSmGg0EBsIM+47mESYIVXLBFvMtjv4uydSTChrLLnqCP0qHdRaklpZBlqme3qFu3/3C8qScQI/1iL2cw9OI8FBhWT/P3tvFmvbmpWHfeOf3Zqr3d3Zp79NQVVB4UAVIBxkFRg7EdhEJolIgh1hWYqEUIxjJ35w8uAoUmQ/BNmOHwKIgP1Egi2MbEsmlcYyWBYOGHBRVNUtV3e7c0+z29WvNdec8x95GGP8/9z73tS9cM9VneTeKW2dfdZezVyz+ccY3/i+b/jjvUgAIJKEQr2L/FibwMZ2Gw1g7qt+fyCLfNNg+W/dlO+bJnKtKKUTmQrHjEFmDfuRjqzU6YDeMmi1Ow+w13IdK9osjcfL+j4Kw9K2vmp7oYuyzAVP5JqZzoAiF1GfCu+CYtmU7cMB4Bx233Qvnm/bJ0fYfsu9OAvcIMZSqcTLVRTTEUmyZ5MJW6ko3bpj0fI0tndeIZzZMC/9+dnOu7zVDl2vK/5HAH/lbV2fO9v7PCAodADIxZGmIBPb7HbgXq7NXJYm2aNT+MkAycUCzdFIcMqOPYIfl4K5E0lPIFA4Dcf24cYgg6ZCMHLRlrrLfunSI81J06AlAF3hFm12cTEEpMmXp/DjHnbHI/S/fAHfz+FOL9WfSJq6sh/6PQZlpO0ZZztNhe9t3kdGEfUeyaMLUNtic0fdYKdzpOfLsO88jt7/wghZd/B/QjLbdJrj1GnkCfRA5tffmcfAxwexitJ+QWhWm+eMLT7W6O/ixPZ8R8ImA7RKqGSx8f7KjIbg8aMDj/hwIu81nUcKqgZ3d3IhC+LlLNKMbca0eUYZpGNwk8KRfEvm9tJ8JaywqepAVHPQ3joQnBzSo+Islfe9nMnn6nxwN99eNWWbzuXcOSeiS22gvvFHXRy9acwuE156FndXs9Xe1XEGhKp/3XYnFW2/jEwn0wXYeEubMNiFXoi0ga2aEFPQl6Z69qHRDiAcd6PMUqFmiKn6cWn1kT+aC1289dFnq2nQ+91XYmVdVfGaN+LEdXsT7T25mcKAlzNhWz2NjfG0WEYP8PYDwb4TwC8S0SsAfhjATxHRv4+vsb2/A4IjBEWoyt/dk0spo1PBcYXBQ3JR9/syoMQ5pK+fimWwQUmnU1DL4DxF+lDxWi2twyIKSGY1LLWpV8OPSxlh6L3cGKt1uKF4IiMOURTxprGbwyx/DVa6zuBRnNUttkgenKF4XWizIdsxQ7lOQ9gadQAQ5hyYPfW2kkBZqerY+iN5DlpXKF9VFej+JC6Eo4H2C1xcKLT6aG9OkEzXUTWsvYwQ0LoZ2fkl+HACHst4SFpXEhSMeTJWlszhfjRmM1sIQOmaHQzYhFIGp3W8nMjort4Hym04VjqbgTY7tHdvqJBMqbqjgfRgLBO271JVglPvaslkVxsJcDasJ01x8qc+AgxKydy9Vi7WL7DA1bSSZNjAJudk8fMs3288DPRVMjGZnctMaaWJi0IuInz0b70OlD184b+4GVlqxrBJE+BiCn5yJp+3NxYoxxTESQJM55JwDAqpIK0qbNroW2Xn0YJ819PLtq0e58VSgvJWg/pae2zmp5SmMWDbe2y2se8xXyB/5RzBMNECi6mT0yRaXjgnASfLEBx2F0shFxgc23FApuny9728/L9uT6eH8K8AfJiIXiSiHMCPQIaExY9hfpGZX2DmFyATK/9zZv6HX+tN398BweiW1Q58OUWwKGYZuL69PYzPA6JH+2wuN8Z8IWyKNBGF8roSVo86l4pPfgLzPbriwqjvSdZQNXZDloZylyzD8q2Y0aWJsjs672MjKa9bSQCRgmnBxCwF7PPTBLt7+9ELJ9PM2WwgzPpYhU90MZdFzSyn9yeyz6u1mu4NJJDZEJ7pPIyXDA16IjR3D8GOpGcAiBbBehxdaMH6D0YpdZDPXq6kSZplsmhezgTaSUj21b6HbaZAtQltNl9BYZEwKOk6I8XESaaWtsZ9XSOZrdHcnKg4Tc0PL6bRmsQqBy9JgmhHtoHrH9lKJbZHiuvbNTboy/6sVhpU1M12tY5q5MSJtYr3krh0psmBWSamEUV83SASa9SbSd+2wkd/9iJm/yZwvBRFM/VLYDSUJOZiGr+XBl7x+dKF04JFmkTasBnPWe/HekHMUiVladTwKGxHmtxw28r1Y86r9je7/ltNkIaDjjmdVrsGERrV+3gvzF8I1zaAMFWw7GH7rc/JCFF/LegYQ/ApbU+DdsrMDYCfgLCHXgLw95n5c90BZH+Q7f1tXWFwi/eSGQZMuYWbb1A+PIsTxiyDZxZaprJT3IU01mghrpjp66eiZdAG8hVbAJKF1i3WodlM6wo0LCJHfgdZ5Ab9MIM5NDm72Lhd8EulItribKwYy/JMUToXqIYPJyBrpgFCOTRmx2QkFgu5HgtT5S4VVlP4qn3uljSS7TOKHM2NkVAemWXhPtpHsz9A+uoTtPdvoB4X6L16KRi4Z6QvP4qVk29lsbGbWrNMZi/QWuLEnAzo9GG0orBgPVuAJiNpCNrcZ30venKuC9AgnsskiRYLVilc0TV4cLUDDfoxSKw2sqCrPXl6KuMs/a0DwOtilWXAeoP1t91D+coMtKlk303VXDeyYBW5JBSjfdz/J+fY3h2jvJhHNs2wD06H6qYr2gLz6EHTSDAYDASCM5jMdRY6Y0s1rVx784Vct00D3urIWLVrIWs4e5braL6Qc2gVyq6Gu5wLpLPbxeYuID5HNojHmsPmJWVQYL2MgcDMEM35dDCAPxxJn6QLHe6U6mvZvi3kXVKCDebxDB4W0kPTpIcPxpLAqBDNXSxjoOjCVcxoXryFZpCCGkb1/D6Kl88DSQKDUoJj9hSXyrfP/t/Z2zD/CoBfufbYWzaQmfnPvZP3fF9XCOzoisozZKYsDcIri3GY+sQKE5SIU6Jawcqdgz/aQ3MwiDeONRN1doIf9WP2qzcAJxQNzqxxbK6gapHgB+LiScZ6sfm2piLtZn5AtCgY6QI5HolCdqMwE/uI7ebi9Mh5GnsZukjWt8YxM1Z82dmIT8PwN1sJBib6IgKqWqAz5+A2shi0+31w5pB84dVYCXQzL7PQBqTxTp1M0LQgRrtNtfK6nAkDyHtZJNcd6mK3qUwKD4aA2fEL6sIbFiAAYaQkCoOZcKlpBNqw5AECwxEzmhduhlkU65vad2EvwcAatmYTsYzMtN3NIU4+IRbsnCfg0QCcOvHE6pdyzs2bfzKKkFRVCQvLWETsAzvHnUwjTJIm0uw2H6BucOoVUXdgg6EM19/VMYDaqFDTluyuWXrbiEwLXAa3mI7Hc2TF2XFQ2qlbVR3DPcX6zVJlvlCig4vBzsaCmmDTKtY2XlN0uYjXWPf6snNsdhlpgrZMpGJNO8+1z/P85mTh3WzvFC56SkHj97u9rwMC7fRiMuFLl6rYtU0207LlWnj6AMJEKc0W6fQSXKTgIkF6tooXUViMvDCPtrvYHAbQHKtP0mggitimkfJ21FfWhUA0yXIrsIRl6/NlVG4axGCW0a6DgVqDrW1jcNtswMNSMPmOv5AvszBLV7JeRvZ4Ltj8eARMRmLKNl2GCsdPFEfv5aK6HZSgJ+fgUgzc4D1otYVrZH5D+sUH8QRYo9cWFut/AHLM9saRWjgciHhOfw+NwEEZZ1oTyd/sJu6XMbMzSM4yWfO22Z90/Pg1s91pRrpcXYX/gPi5y5UwYJT1RFWNZLEFnc/AhxMc/ctT0RSM+nG8o04qswE96Jfwn/8SknWD53/+i2JvfTEXbYIHeFQKBLkRR1O0HnR+Kb0KHeqD2zeAXQ1/6wD+SER29XNHci0V8t39QDyy2ntHsalr13eey7kNx1MqOKw3gCOsv/15OU7Dfmzab7aRCWbOtlY1WO9lOIiECWObORfhpA4UFqAs76NA0JIVayCbCHM8kuubSCfJieYkjKY1mLGuo/uuqtQ5z4DtDmznTT+LGganBCageG0a2XXW7+iSE97lRng6kNF7tb2vAwIgF2OYvKWLEQ/6kZ4GBKaNZFN1p3nWyP+bVqhxLSM5W8gNbRlF53d2Tqaijfvgfon29iHcZiemY2r+RTcO4Z5cSiZvAWpXCySyWEcDOKPz2cVrlY59ri2ENqjHsHMtv2lbC73PMv9dLU6mOmYx9EKYsbs5CgpR2nSGnCxWIlgjknnRi3VgkNB6Ixx53adkWYNeERIEdS2/u/tsVFBjEw00UGu2mEzXkQEDaKM3ZveswrwQWCyTv3LKOf6NOQbJ0RAY9sXMzZ6nLq+hWW+aBxsJacrl2UIy+s0uzrbebIHxEO2oJ1TVyVgWr0EpfaeJcO2Tm8cyUCnLogiybsQfynv5N3HynoMSmIwlMdBzQ5sdUPbgTqdoS9EoJL/5eXCeqe3EBtS2oJMLGflq+14UUZtgql1zuLXvtdmi/I0vy3POLyO05uTz2SooO3+JExhrPIrHrWmuzs+oO3oEqzDNR8w5UFnGsZZ1LUmGGfptttJLq+M1ElhDHZopALnWbeyqQlnWUKfRUIIqANQN8jemSLYtyn/zJF7bRDFZut4Ef5fb+zYgENEeEf0SEX2BiF4iou9+i+f8USL6NBF9joh+TR+7T0T/TF/zOSL6i53n/z19/qeJ6BUi+rQ+/gIRbTp/ewdiDIEA3KXyqo/2gKHgtdwvRIyi+oHAP7fsxpSxCqf4G3uSYRuriAjt4TAoNP2NfcVlB0JhyxLRHngg0ANNcJM4dTy1jEfUoZHCyXFRNN60ZcfBGz6JjUTv0U5KMTgDoq20952BPYT09dPALkpOLsXegQj543lQQWO3k+qh1IXObCIenUppb9BIN5hN50heewzq9xGmcxmM4H3I0PmwI/4xeMsWnOVKs3EXh7CPBhIcFbunsnzzzWv9ArvRTQgVrBBaOQYzmUpGW6EK8/GBHLueeglVirEPBnI+eoVk5IptV3fG4iG12kr1pZl2+sqTSFmsKmC+FI+f+UIUzW0roxt7hSxgSv3ksgBtdnIOjPmz2aI5lOYzlyKE476+riiQPbgUTcPtmzJ2VXtNVMki7PcGaI7UlmS90R6Bj1VY4kIm7O8cAZ4FtrMmuWH8qsGgs4tIxwWieM8a5oAEDWsmdz2oAuW6lrZJewAAIABJREFUjfBNlsVKbjwCDQfS/7HFvmmkl2Kv7eURZgUQJhHaBDq7H7R64V4WSRPzZaQh1zWKLzyS9zCIzPa9Fv1FsAp5GtszDBm9103lvw3gU8z8w0qN6nf/SER7AH4KwA8w82tEZDaZDYC/zMy/Q0QjAL9NRP8nM3+emf+Tzuv/BoBZ5y2/wswff+e7x4E10dw7QnoyAw960nRlFs62zRM2ryHz7bdxjcN+uMn9rYOgCOabhwIFsfLW0wiFNAcDpC8/hj8+kMe1OesPdD5vnssFa1m/YsHU+Ks6A+tz1E3sQXTZEwbP90uZ3mU3j3NykxhevNsFKmZzMEA2k8yejRnjKJqKpanYEjQNaLaQwS9LnSk8GSIMNrfMb9iPPHzdR9YKgtmDfKQm0oWORcyUrum0+rHFu2nRvHCE9CvW11EownvpHZi4qPUx1TGhWzeAGoYdfJFitUIXc3BVgZbatLQ5A3kmRnNPToP5nVtv1B6iL6ptO0YegKpcMR5KoDT2kTWpN1u5zowuvFiiffE2qG7hzudSGRQZ3IMT8NGB/H+3E4fTQSmV5sFYJn5tDJNPRT8zLKM3VqO2GkRwF0s4G4Zk4yuHA8AROCsBB9EdjAZwF4t4XHpFTFA0IWK1r7AkhqudMJLsfPVlRgNZlm3H3zQA3d6R2U0HV+A64PtoPdDP5fyaRqfsiTdXo+9hLrddqwxzGNApgxhLgz705fol/LgEvfpImureAy3F4OScwHMKTZFVMU9j+zot9u9ke88qBCIaA/geAD8PAMy8Y+bptaf9GQC/zMyv6XNO9N9HzPw7+vsCQqu6LssmAP8xgP/1Xe2o2VUT5CLYNcKr7uVo9/txBCQgTTuTygNRIRuEXF6anMs16PQStDa4JhOLhqqN1LfJCFwkUv5rg9fnqVAZgypZsx1r+hndEAheLwGHv8KQ0QXQhEAGxdgNt1pH3LxLzzP/JCBUCQAkg14sI69bPZn4+DBaI1j15CjSVa3CKTvK2TSRYGCisy422zRxqpsd725DEhDlr9FId7tY2ndZLd3NGFW6uPHNw6vHbDTUrFADY5ZKJWNNf2ZZNJdrgcGSRKAps/LQRnvxlSey+Gy2oMUKnDrsbo2CmR4AeR9Ask1TrltmDCB5fCkUUiM2aE+DVusIjdS1XDNlL6rOTWOh55daDlDZ7vnDSH9er8NijiSRCk/PabBSMZNArULX3/WhKDQzaw8i0HQOFIVahF+zjLaKDpCAMdR5EG0brwUg9gts6lySRMM5mxthPQcAYbofIPBcQuoL1ak8AHltr4h/y/NYZWnvi/MMi28Yg/YmcZ+LHGGec9Co6PX7lOyvn/WJae8lZPQhAKcA/i4R/Wsi+jkiuj566CMA9onoV4not4noz15/EyJ6AcAnAPzGtT99EsATZv5S57EX9bN+jYg++bZ7aKpMZiSLrZTTTYvdgVy06YMzyeJV+MLDUjD2ohDeuEFGKtN3s1UcQWjlrUvgllvQttZhJAAYaMc9uFUlE8+UMZSezJC+cS5QVb+MN0/rA9bN40E0S+veBIm7wjKRx5JghnZlloAt/gYd2MK22SD70sN4A3hGmIDVL+W7mXYjTcVOwGCV0SBaS2y2wsnXzDrADSQMpsC5t/fqNvHsu1hjmBntnYPADqG1eu5YJmeVlC1GiTJVAle9w0JxJA3v7rD37tQz47VbExRQ102tkrQfRGbUZluagEd9+MOxBIu6hputkT+aoz0Y4ez77qHdG0jgWq2D0yjKnkAz1txNU7FLsMVRNSR2rfHeWJrLxq3v5UDdgofGvErjd9Dn5A8uIzSj55jLDL5faAXLgTEVekCmDB8NUX5V2UoW+NnH47laCUznWcd7dlg9toBaJWoCyySR82WW5NbH2CmDabWWfTLNw3gUmUdWWWhzmnatXN+9QsR5eR6psaZXsCTBGvqq9aFqh9FXF7EyIbGD333jTeyeO4j6Gat6rtt4v5vtGYaM3suAkAL4dgA/zcyfALACcN2zOwXwHQB+EMD3A/irRPQR+yMRDSFOfX+JmefXXvuncbU6eATgOf2s/wrA/6JVypWNiH7MzKJ2Pg6XsWlgXOYgz3DTFfzRBG65icwJD/HPYTX9AiL7SJvMAWvsl9LIbZowQzadrgEHJOsd2DlxrByqzsEWoCwTNbGV69cmNXGWiLLZGmhdJpNRI8062B5r29gD0Spj+R334iAZs3K2bb2JDKaurfOVhVKyOt6q7/+ujkHrcCKaBttMfd2TG5bOpnHmgLmeOhcH2ZML319EbAb7aNZsrC+rJBbLcMyvQAV2fojAJr4zRe713oKZ41lTe7PVyWWEF//hWcxmDTax5qouGFQ18HkatQjbCuwc3KbC0a8+FD2FqmS565vUyHfffcNNrD4miCnnWiUNB2pm5+H3BqDFSsgAaxlqQ8yiSgaCjXn40cDkJ/14LFRk5k6moiswO4p+KYHVaJv6HXncj8OPTHWtYrbQK7BrwZTztj95rt5HdVTUG0NPgzS3rRxzC0I2E3rQj7RiQPZxV0c4MVSxkQ5LVROvp2oXGWV2bLqsO6PmPr4APzkNlZsla8QAzqQ6ZpsR8VbV5x9we78OyHkA4AEzW2b/S5AAcf05n2LmFTOfAfjnAL4NAIgogwSDX2DmX+6+iIhSAP8hgL9njzFzxczn+vtvA/gKpAK5sjHzz5pZVO76CD7nyzXSx1PQcgtqGc2NsQxrMd5604IWK23y6YLNHJtYXZ654uTcSyWzu7EfqHO+LOBzWQzboxHasWYgth+6GGG5loqiC6mkKdzlCtmZluNm4WuWFrYlytdfrjSbTuLsA23QDX9PhWXm0qkDdEJDUBXIQrnLIgxgjBFAoIaxxlyzGABkpsS2iorltntj72LlY4uqDkYJQcKrZUhRwOmN3j5/MzYB5SIQgzc7DgYdKezl7x0LnAFhq5AtwoN+/BxTeQNyjpwTfF6b3nLMGrz8Q4eg6374BkVpb4SLFMliAxzsobl/JCZ2uzr0ntwbOrNgMJBFtpXs1l3OgTRB/toFBi+dyqK/rtAejQP0RhdTuNk6Bk6zbNfkBB7SxFZr6qCtYJZ+z542/9cbaUYXebRwIKl8bAyqXNPCeqLzWbyWuh5TBqmppUmwhujStoGoW7DA2/poJte0ch4smdlsO5P79DrNMjmnNrzHJqYtV7KPRrO29zWX00YpsXbOSWdwmL5ht5MgQATqWJjQdI78wSWy02WAZOliKtfi0wwI70fIiJkfA3idiD6qD/1xAJ+/9rR/BOCTRJQSUR/AHwbwkvYHfh7AS8z8N9/i7f8dAF9g5kBqJ6IbOgwCRPQhAB8G8NW33VETKyV2oxFc1UaRirF22hbtnQMR/Oi8VpmxzKqObYCqAk2X8DcmaA77WD0/gh/1og9KIj4z5D1c3cJnHexfp4OxKT2JxHhPs6TlJ+5GZfBsEW8yawiHZmrnlDonFr7mh5OmKpLTiztx0bK4UNfRLI0jBzW4hWBlN5MJqxoxq2uPxoJtN03IOnm7lYUP6JimNVHYZSpoG17j6M39BMV73a658p3svNBOFzRbQGwfNdOl46MIQ1njcFvFc2oVVaojUtNEPYD0GC1XUu1oQ5HNytwouXkG38vRPH+MdqJjMT0jfTILgZbnc9ET3NjXnpNm7+aZZH0b30bbkrpG8vJD+Y4XqhBmJUDMF0IXViYSVpt4nAGhZXaqzWCFbY17dboNYyedA++NQU0LHvakKhkUkQiw3si+mTW0Gu2FCiFx4OUqivY6x07GXeax12X7oYkUN41AYECEQVcb+D2tLm3+hDXATXhZmvbERWiqKKIg0ZK4PBPx3mQgSYpNNdT9Ds8LppJK7igytM/dkiDrnFQ21jd8t9s7hYv+/xYQdPsLAH6BiD4D4OMA/nrXa4OZXwLwKQCfAfCbAH6OmT8L4I8A+FEAf6xDI/2Tnff9Eby5mfw9AD5DRL8LqUZ+nJkv3m4Hdx86lgu17Ek24BnpxUqG5RhrRbOI5JXHCHYUZRlFUqtVlMTXNdx8g+zhFKPPnsLN1gI9ANJwrFv4PIU7nSJ77Uxw0MDi8ILLm/eL2gm0tw/hGsbi4yrMmowkcFzvIVjWagtrloq1QKsLocEgNuXLmroGBwDyerPd6LqubraSKS3XMQBppp289lhu3Ns3wDpukHq96LhpVZPNCT7YC8pbgaUUU7eMrmPB4C47zqmHk/i5jkSMZBXDchXN5vJM5kGMpZEpzWuplGQ2s1723eahOoUGs7TJSOm0Z+EQkE7Q4uMDYfPc2Id7eIL0YoX0YiXiuf0+2oORaA2YQQf7oNlSzgMg4quRCPYwXyj0l0ifYFeDe5nYrw+1L1LkEToxw7Y8E5M9ZvDeUM7zRhxJbTaHKeqpVoiqadTqQgIBj/qBQUXqeUUbgVro5EICiQVEIPYa6jo2vG3kqE56233zfdULbGL1wdpfcE4gQ3XJhZNRpfSGzm2pKgksbQs3XUivzHorBikZ08lGl6o9eIDx1FSPLTnRJjQtNzJTwui1NhfaKmTrJSaJwHxnUySvP5HGudeZFl1I9d1uz3BAeE9pp8z8aYgFa3f7mWvP+UkAP3ntsX8BvKXft/39z73FY/8AAjG98/0zrBYAE6l3SoLq3gT5E4VZOg2q9cfvo/+ZNyQrKvugqgIXOWgrkAr3C9BiGbH0vTHo/BK8d1O+TL8UAVPjAysnma7kxtyfBHYHOwfarOMNBaA422Jzq0Sz30d6uhDs2BgqlmEbVQ9AmPlrkI85SgJxEQxmekVkHZmYCEBwg7V+RV3L+86XkUZqitHdDvT4XDLJWv1yylIWtqoGT2ey8JQ94PQiNn6J4lAbC0BGjdR9Za0K/KBAcqlB0BqnNkK0UthufwJUNZKXH6F98Tb8vWPh/QOyeCkbiVn9q7pKVA1KvDeMbB8N8uIZtbWLTd5OJ4UxkZyPbQX4kajK8zQocKuP3kHx+mVsSs+XEX4J77WSftKjU7i9iUIygsvz3lAwcmt+Frk016sdKHHyujIHJwTfy8RjabMFRgMZ4FQpK8oLT5+PD0Q02O/HY27NWg0epGpuzlNZ8GcL8Qc6uQAaZXmtNjFxyTLkX3p0dcyofb+uJYldf/Z3O895LtROYxFt68g42u2iqNJEjeZSavAQUYBlqWtFokmSu1wCyxX+yaf/L/zgd/970ezQFM1dS3nb51r2gdrOfr7LjfD1g4PeyfZeVwjP/OZq46IDyHMRk3mIF4rNylULiXTVYPuxO+KP/saJwCyqMEbdiIhmOAiYu2T7Q8FxdWi3O5kieeNUsrayJ9nT3jjePG0LPyjgD8cBykoenYN2DXonW+wmudj02vxmG1FporbdTiwVXFxQkWehxKfFRpwwjc4X6JZZzLCOldVjWZiZxXUts23SVJFHxWniQlM2iMTU7sN/5L6MH12tYzPcAlNl3kmyAPrjPYGpdNFMLhZI3jiXCs0YM7aZp47y6XExDQ315JXHEgxs8VUtALN66gfxnLwHH07k83t5qKhwOUNwAdX+CT0+17GLGhB6aRA6JSeXgo+fXobmd/HZV4HZXFTQthhaIAfCcaVVpaM8NU9TAgDNVsBqDb51JAvnfBmtF5hBLYNOL6XnZcwt0zpMVZFtFuH7E2G8HY5jA3qzFdLBWhf4yThYSbjXnwhECQh81aVj7o0jFGmZur2HZdTWpLc+gR3zbt+ho1bmVqGz9UaugcuZ/H2+iM6zVqmYSt1YZrWKRoMFetT+IEuB4QA/+Il/N1bM1vszV9ZeEQJtIB0Uhepjno51BQCQ53f08/XY3vcBgXWWMU2XAScsHuq810EJ5Dnq2xNp8AIoHi2ihYHObG2eP45ZinGtFWrifgHkuUAISYLtN99Ge/8Gts9NxARvoA26TjOUvIcv8ys0THe5AtUevZO1yPDXVew3mC03IDeJ2SAAsYFrizh7+FygIn/vWPexDIsbiGQxC/x9FxcYIFYixiqxoGk3vd1IVq30CgkKAOCB9v5N+T1NZZEaDiKdsFcAg74sbLYZ3m9MF6MBEmH3zfe0SkqjqV4Hyrrihmr9A0Aa4Xbc7dj0ckkCEofElMP22mFfsfZhPKbTmWoRWsw/Ooa/uQ+eDOLiZYu6winYm8gMbYPRun0Py5aD95RAaXxjX+YmK4Qo9uP96L9lSvTWh8Z6+uAsVoJpGm08amHANft9YSg1XiA8pYPSqurYZ7RBnb3+rg/Je42Hqs5X+nGWhgYxH+1dXeitQZznEdMPjDcfgiKr3byxuQBIoAbU5p3kHiSSHoz1J6wasErSkiGDxoKFuovnWO2zkWVyngA14qPYmLYAUeSBJcc6IvWpKZXf5z2EZ3sjwG2aiCMSyfxgHYaCzRY8kgXIbWq4hiV7NHO0pgHnCXym4/u8D01Vf1OaiHQ+k8pgqupfAtpSoIp6mIohXi+7KpnPxNK3ubUnC7y6OrpVJYtKlsEPSyDRzGcyluzbFrAsjQtPVYkPTcfTJ5kL28Q9Fh98mi9jWW/++R3K3pWRhR3zOTZ7AGalBGYxWBikZLg3kVxtDtHY7HIm+7enTCWbKd0tz52yh0wIZurgtkX+uddiY9aM1rqT4HT/w35bg3K9Bl9cRiVsmkazQFtwrJdQ5BLQ+mXMkB2J4d+uBo72MX5pDvfgRKDApWpRmlYCyLaKBnwGy4wGMYDa8UqTWPnotUcXc9BsJTYU20oSD1usN9tIt7Xv2Ssi/GdOvYMB/KinfYYd0rOlzDBYV/D9HP5oIoteo7bcttgCgPfo/+4bcq5TrQy614LaY4c+QNvGprkN8slSuYbTJIolDSbTa44enV1leKUpSAkIPNEeic4NYbOwNqfWWhv042Fk+lW72OuajGIVC2hPBlfhqtq0RGm8zmxOeqdh/7S29yXL6P8LG21r8fXv+q94L6MJ1cCLFhtkr50FfD85uYy4PDNo1yL/8mNkZ0vpEdzYB5Zr8Xc3PrU15BKx2W2GqVhvQ3BxWmopnSTCSwfkBvCM9ngctRLnl8GsDgmBswScJmhujMS+14bMqM8Sm2eQOXtqJuTLQrLhMFtY/YnM4kHtAaxRHQREoaErl0143CiLFoRMcRwsir0Irmyz0ZwGddnUNFuMyx7CSM/l6s26ArOAMIV04mRBsP2zgSltK/oD02J0GD5kGLKxnZwDnJNF16zFR0N530EZF3Bz3qxVlb1ci1ZFjQvNWVOapxB/LPMLst6MKdHDe+pxHPSDzUSgFPcK8TpSIVZzYxSqJbbKzOi81U5mLByOY9Ww2YRkROBQO48Jtscl3GKr7qqWjKSRDWXVZZoIbGWZNHNUMwNyTA72YmXoYkAJJnPW2O/Cm60SFwx6XG0CndVP+mqSWAlkdTGN15ypprvMH9OZTEZaeUgVxtNZhGM322hmaAGgEFt5LvOOl1gSEyMLtE9z+6BCeEY3QoRagHCzGK0vqnvlMKWvncYbmTmqKIEwFYt2TfStsaavZih+WCBdN2DSDIB0prA2awVWIhGm6f5Ry0LDM5dKp5lkKzeeHxSgphXrZWNgeLlh6WIa5f6WJTFLw9TUyLqIuzdO44AaR4EGygbV2DChLtXPNsOGbR+JZJE3jHo0lMALIHn5UQy+BjGFaVcsallbGEw0B8TjCYTGNx/tSeZtDeY8F+qkQUhNC+qwscggEQuqYZEwPr9H+pWHHUisikpmVduyDZ+/nIaZy2g9vOL76Jeq/m5BdSPDkICr1Vt3frEFBUDOU1/7O4U2y62qWG2QnE6RLLbhuXS4L0HEArGqid2jc+mjJC6yxnY13EYsoDkXRXP5cCXzLvTYo2nFVsSm8rVthMHMK8jOgcFeuu++n0dmmjGLkkSEdRYEjHkExErLUcT0i1zJBR5utg4wrTTEGf7ujWDD3iUdYKXEDCJgsYo6k40SG5TJZc8nC2YG8XYq+WDrbXBcXcOP1GjxKW0fVAjP6MamfAyYd0c9a1mpcsmNruf3dOpULplUczAQM7DHF5odKq5Z16pEbQT6AdAOC3DmkK5akGeUr07hh9EnxT08g3t0DjqfIXn1EdIvPoA7m0nTMlXvmW5W2shEMbfVzHOmNMbdTm4sZlXvevlbmoLHQ3mtKTnzPFoB79R3xnD6zVZ8fawBbt/PZtIWBdqbk6h4tTLclKt2TOeim7CqJywqQFwggJBZ+huTiO2abQEQKYiaTdLD0yCI45MzYTI9ORe4wL6TCc/qWoKb6iy4kalazD4GukEf/t4x6rtxRrE/GAnkVgm+T6roDZnq3lj0J8bOsooscQJLAToOU/2WbPGzHoBBWkUudFNz4jSbdbOEMPIAEdrnb0uD2SorY291ews2I8PmHpj4ynuBi8YlqG6RffVJtC/RJIHWG1l8AaFFb6s48U01OLyvSYo6prrHF4G9Ffo6vUJeb41ow+kLgWjZKtKOiZ4E+R24l8HNN+BBL1wP7sGJvNflLLrPGkHBkhZTkVs/yeA5G49b5BHaAqLQ0mBgSwIrtTsfj+BW2/j8p7F9UCE8oxsh+hHpRcT7I+FoG1adOPFPVwdOstGX21r8i3aabRgcoH79fOtI/9+IUAkQrrptDFS3x2hGKqTqWgsbZOAolOZPvvcYtBKbiPbFO7FZBsgcgyBUU1jEfG3s/czqOHWRWWHWC5fKShoMAHLwR5PIcrKF3VgPRvnUoBe0GZbtmuDJPg9AcPRUDJi7mSJz2Bdpcibxb8YOMX8bYxi5JC4iALhpQEkicxZs8bmYyv4PSmA4EM57vx+a7VSWoPOZZJMG2WiFl50uwUUGHg+l4uuamymHPsBHlahehbs/jPBjLfuEXR19cXSmMputx2odvabmC7jpSjLyQV9EZzqHgw/GwoAqe/BFhuRyKfMqhtIgxkRZar0instKFeEjtWBfb4Q4oV5NVGlvp19KP0T9gbjItBnLcXwqM3xfF04d4ETzVUw+rHfhKI69rGQgkDVlwyJsA5uyTOcjU6Qfr9ZgnTtCF/PQIHYXwnIK1dd4FKtKowyH+6bDBjLiQGAzJaHaCIQDu468lwRuPIzoQLA4eYrLJON9a13x7G8MuUH6JXgyAo8G8D0ZdHNlKLlRziYjYfeYjB6yGNtAcixXAeqgukX1sfuxj2BURECmMxnlmUjgActyzP9o2AffOgKPh0hO5rj5K6+qR8sGyWyN9ngM9/BE4B8A7tGp0EXbtsOrpphFpgl42JOgUuRo7h7KYqnwhthVbARzfnAijbdeEe21LYuyRdlYTFkWHC+t7wJmubnSDsQAwL32WBbi8bgDx1EMNsxojkV81h4LwybYQ5iHjVYR3Go2mriO5QVHZtREG7sqdgqwhy3MdY3dR+6Ez0WaCPtmXQVhHm0q8ZsaD7D5Q3evVjNd+GoqymSqGunrFBpkDvbkfF4qHKE8ezq/BKazqF4GZFGudmHyHl1MJShrokI7eX3y+hNgsYR7dCrf6eGJBJbNVq4PG/dp4jelOyNL5XxrL8b3ctBUF2yr5JZrgU6KDG61BRcpqo/dRfPiLbiTiwCxuMcXEpTVo0sSgTh5L/SmiIJNB1swMCt1ozx36ZXjEeqDEpxJEJReDuvkPQ1QNr6zqgKRIFQrQGQT6eQ8LtIIo6YpfL+APxhGhpJdE50ZDhgMgs8Xl7mgAd1z/y42wgeQ0TO7kWfBVIkkc846NgeN0uW0OYm6AbYdF9PCBGHX3rSuw/Oz6SZaNKtfDxPFHgKApGpFuDUexowzy0S85b1guuO+KCaP9q6oiv29Y7mBHWLT1y7c8SjitGodYDYWXOYAA+1eP06rAmJ2nWVw00XsBxCpCKiT2RtsBG30mU7BBukc7ncyemnYYm8S7C7CzWcLq5c5uG5TwxdpCHSiaG5ihbCTyszoifH1HejBvJesutMmJFdV6Gmg7CF/5TQ8T4KPD862tK1hCmGqW/QeLiTTz+Mx4e40tt0OWK5Aq6007NNUYB/77kBIDALLpvGxebzdKcumL0KyIodbCzRDF/M3T37T7x6CIVHUszSNsIKqnUAl2qfhVHsKnmWU62iA7X1tQJuJm/VVZnNQ1SC73MpEv/EwalfaVpIHO/5mR26b+RyVvVBNhWrASAfW07IqWL23ii89lkC8Fk8iYpbfJ6OoCaqb6JyaZ1KtMF+9lonEjfVSqgueDNDuD+EHOXyRgvdGkWLbvU6757NpQbsWtNjECv5pbHYtv93P12F7T5XKz/ymC64/moi3um2Jg+8PZPTgUqmnwxK03AC1B7YrGWU4GgQVbVgo+/1gxeDqGrw3ktcBsghdbkXZWreoj0rhjWsmxYM+MB6AtjXaWwdwmwo8LCU77JcBrpKGZSvD6714+vDxoZT2t45A3st+DXpinqaNZXc+BzyjPhzA1R5uXUdlMyA36mgon3k27ZTVLjKArLw3mEwbiVxVUSHaevDJWXT+NO8exdF5NJAs2Y6bNaQBuMUabraCPxxJNWPPMfYREAR0NB7HTBFSNZBabBuGHeYklz2Q2XOs1lEfUQi7hKpCLKHLHjhLIEOSMhlF6X1nepuLyuwil32ynsB6A78/xG7/CL0vPRGcvOzLPIN+P46rHAzifGJLPIzyWddyvRUpaLkR+qUudnwwBm224Bv7MUj0xYKCb4nojSq59qjxUiUYLbNpQbagJ06a93mO8stn8Rx05wqMR9JDKlJh/ACgVBqyvN4IPAdEuMZ8gmwxM4NEIqmSk0Sn+7VRyNhq43sow3RwsId2vy9EC0CuYQfQVhv8BgkC8v9gO9FES/Rmo+6tCjNZxe2B5HIZvyuz7Nd8EeG2bSWBeaOsMYWaRCneuU/e5faBUvmZ3WQRd7M13LaRrNRDpe5zyb5ZFgPD70PDtargR4VMIguiMCcXZcfMzZw/4YVpwWSfuULx5ZNISfSt2AUQiYsoszScgTBUhy4XURAGZTYVuQScRG5kt1jL93CAO1coS/19bLynazRDXXbfsqjsAAAgAElEQVSyPGvENY0EAyLROhgTA4g2AQYdmRYBAGWZNAkVFiCjhGrTXmbjyuvJXFRts2zIzMnqGrTeyQwBpUgGKGg8lMzdYClT1l7frH9hQWu1joJB63UUOTAegusa7X4f1DRiP7IVVo7ZVHPqUD2/LxoUqxo9A3tjVC8eXlGxurMZer/3mgi2xgPQfI3tx+7G7NsCg3Mx67cxj/Ml/M190OMzwfuNEqv7TNtaYMQ0lTkdgASSyQhU6aB5M2u8nAUCAPfEJ0uqkP5Vx1rrnzDHAT6ToXz3zVbcf7dNnPM96EswsIrMxlWWPalyrQKwzNs5OU9G/7TzvdmGJAbkAnyZPJmiVe0PbSrQqoI3IZnpGYCojDcNSlDUK/TqReRpPS8uMwmcF5fia2QkCOs5EMUxpkWuyvRcg/DuavXxbrZ32lD+ADL6OmwEXHxSBGW03MAtK3CumYzZNpuBVrWTm62KnkDUcsyCdCG3BYxv7Evw2O1kEVuvRQW72MBtKsC3YFvQ1kqzNPpb4kDrHZJXHouNRVkEe4Hm+WPwnRtwpzPQ4zN5fLEEPTkHTXW+8nYnjTgbNAPI/pucv+sZZMwXWyhNpAMIbmzGZIBium38SVPQ4QFw4yAGRb1h2ewFlBJKs2WsKgxSMK8a+2yjmCYOtFrD9xKtAHyw8cBiFUV8RS5K8NkcGA2lOnDaiNaSP4iNAPndFLSAfK/5EpQkSE7mMmvgfKaZfQ6qG5l+lqcovvQErqpF81EW0r+4mKJ46aHs22IJJAnm33EnNI7p5AI8KlE8Vnfajso66DmKHBgNZBHsFcIqu30DvDcUiM8CyVigL1pXcNOFQD5AbJputrLY2yyGg6h/oOU2LmhNE43dVmuEwUrLVeTh161UcKOhVEKLlcwE31bRZqTIY2BR5pHQnJtoHWG0Yvu+xkLTY0/nlzpzvA33Do8HSE6n0tzW59Lrj7RfsLuqLrcJgmVPGvWdaWh8+whuEStMdybnlZJElPC7WlhglpC1XunOWm2qhxUtIrnkaW0fNJWf4e3gXz6OC8ZyJXhpvx8XKrN0TqMRnmVXYRxml5WgA1xouZEsSx1C/e0beuNEj3zq92PGY0wezZxNQ5C8+kSw39EAvp+jGWRSKXRwRupS5SxzAmITz5SwCm9w5iQDsZJ7UEaM1AJbd+KVPW5TymwBTxPw+YVAL00TlKfwHBk/xj+vdlEAZr2IriWGaSg4HmNq+cox94Mi2l3YZsd+sQwWEzYB7IrpmUEAFvDs77YvYTiPDklqFHsflNLXOJpge3+C9JUnMufALMGVHWMT6Mafv8DZ992XecNpClpXEeNuGtFJbKtASd5+6EhorG2L9mgUritaiZI4YPbqXuoHvU5/QnpTpqYPbJokkT4XoMGzlecNjGIpmTQfjOXcBZGW6ChY53WIAE6ua2pioiDD6zU5sgZxIcOPwmB6o2latdwVFgJybE3HYwlJHSsmmto17iNzadBHGNFq8JY6ltLZhTxW9gA1kGQd2Srn95qOoFfIOaxr8GYjbqt3bgSYlFZrPZ5aPT1VL6MPAsKzudloShPU5B3DMWa5uPcnAnmU5hQa2TFu28RMT5usza094VMXuTAsVPTk1ipyShOEucGm3LTFSdkZPBbqYfXxF4AkQbLYoJ2USC5WKB7N4wwEIH6+ZmN+b4AwFSrvZP8q869fPEb2lcdIT+exorGsy7I4s4gYD5UJ4mOzcW8Shuyg9TKT1vDcJBH1LBAXpzwLdgtsnHAgWoKbh74Nq0lTWbQXS6SPlDqqVYJb7wT2ULM1pIk0YI3yu9MmbqYW1UA0v2OWm7+qgIupVDCmpDWPJD0etFXDt2GJdqLwRd0iP1uLotvUrrkOcTHfHeeA5RpHv3EW6JrcywSC2R9JhrrUcZzDPvjGPnpffKxjNRskJ3PpPZkyufHyPYaDyEYCNNBWYbGlh6cxCTAYysZ+mt+VSySRsOSGSBhzk4EcT4P7KqlAgoVDnit0tAH31XRRje8sqLMF4M1GFtKyFJ2HMYJMsa73TfgxwzqDlnqF9MuKXD7X6MZGzV1vtLGs/kLHB5HJlmUwyxVaiSKZzGerK6a0Y2dED1LB4ngkGpbAHvOBpCDW5E9zHsKz21R+fwcEa34BkaFisvc8F0xf8V2ygSGWsSWJeLjYRa+eOD5XAdBqLc0pa7qt1sIlN062LbxdczpdeIU55FC8ciH/n62kt7HbSXUxGsqCX/aujRRkcaf0rXwPK3UNJkoTybqB6P0PRAGYZd7KiMJ0HtkkxsJYb6S0ZpbvvVhGyKJpQJm4ibI1SYkEUipy0GwRvY+uM1MsizRxXa6q1eEgQlZbhe0MjrCssktd1EY2zVehmYw0iWMfoc3nJEH7wi35DhaoVFvB/UIW0OlCgn7rhT3UeO0zdfoTFnCAKDS7nAV/HS4yGRn66CzCI3r86VQEb3w4kWPTnTC22cKda+DTQS8A4JYbtJO+PM+GDZlP0MaSGx+PZapQldmYW6VL1Jk/oU1eO5ZWLVqVXPZk1sK2FmLCoK/2IlqNGOzp/Zs0IgBivyKcZx8rBqvWDEo8u5T9mC+kgmi92HtrVcp749CAp20tnztfyL1lkOBqHTQT8CzU4+VaelwWPEzEZoFis40w5q6O7EBAq8yn21T+gHb6DG7cy7D76N1ADeSJMGz8qA9/OJKSfbHE7s5EFMqahfOwlEzcSlezmPYe+aO5sDLMU8ey6TyX7MdM2LIsGpFZ47C7LcS/3d85AnqFOHACaG7vyfuvVjJdan8crRDs5reJWHWH672U56dvnIsRXpqg+ej92FhzTgaiHIzVxbQD59jQdZuYZpmTUU/TJAYAzyByUo4be2NX6w2rQq1eAZqv5bMM4+9aUzSa9duIxkEfYC+q6C4u3W30JUor9YqHX7dZADSz5bD/yVcfxorEmqvDAWi1BQ8K8KgvDXbv0R6MQIuNViRtaEay2SXbORyPZNznZgtM51LVmJitquCPJlJl2fctcgkyTaONd3Eh5cOJwEuAfN5kHBa55ORSFl7VD3Bdxwy2r26kXbwekO84Gsh53e3A/QKrD+9Hl9N+GQYz8eFER4pqFp+moiBuW7S3pULi5SqeK/OOGmhCoQE3wGrWuzLNSVeM1gnUqCqB/bIswlK7nRAzALmHpvMYgNfKKDIoy/tIhw76AgqQKlmvwCqHPJdr09xaTUtjTDAAIAeaLqLd/NPYPmgqP7tb/mQRbh7a7ECzFXwpnO1mVHQakk4azgBouhS/GCDe2N6DDyfYPr8P9+gcPOjFMtk5yd62QiMNg2bsJjaXUcucu/0BW1AA0QdUwjG3oe/u8VmEXcxaAoC/cywXfN7JwnUhTWYbsUkAImRkn7fr8K21auHlKg4psdcMBjIVzTmZWUwO6PclMHRnJs8Wws9fd2bdEkXmiN2IphY31odRWM2kbjxAdVBEtpEpYC0oMOs+lNHEDpDvZlUCAMrSK0K29s5RhMis6jBKpYngyhzJdCULtNlD6H6f/vDHJLANlcLoFdoaDSWQ2XeZjAVmfHCi/kt1aABbVdSOxUSPzqbSUCbpJWA2j9WcZfjsw/B5ShLpe+l1I2Islr7VZgvOUjQv3opW1WUPvpdi8MWLOIrS4Mo8QTMuBCIa9LU5rTDMai39lFsH4dwjy+R7m9ncRieyWXPWBHnhXqAYoO3e2dUxcSEnlan1d6z66BWxzxPICXqt1o1WhCn84fjqlDc7bmapYv5JFjR0+hwmI9FxzBc6WW4t0C2LRoOWHcjuXWyEDyqEZ3trWsF5c7Wh9h4ggLYN8geXgHNItg18nkTfIYOOFJc2fj4tt+i9fB6HajhhPIRse1sJ/1vdNHlYoj3eD68PC2Bno6ZFe6DVyWYrys000Ruywx9Xvj9vhafuLmWWbxCnAbFcX2/QlmnE1c33CIg3GQAUBfjWkRiEFWqLYOWzuXISyUI8EiYV2WAbAIEyajRP65ekqVx5TSdgKo4bGtZAzKzJgc5n6H/1UuEvH3sjtikkxr1Mjk1RxJGd6438a4HA8OEiRzu42jcCIKZqHmgHcq5NJEfLrfgamdr3zjGO/4/XJIFwGqQXS6ki1mu1b1hJxl/mkV1lQfvmvqiZsxQYDeMs5r0x3CuPQI/PxO02FyaSP9Ygbkwp56KTbZGF70YnMpHOnVzI/71H+qrAm3Qp0FRytkBwWN1speL1Mmwn+9JD+V3V2kgS+P2hQqFbuNNpsKkOA+uVuMDHByE4B9t0IAYC52ATCMP520mDnNnLdXWwJx5S5kaq1Wu0B3dRX2IKbe2LcOqCI4CwqbQv1WWWeZZr3votK1VZV3VIDrlp5Jyajc3T2vidDcf5YEDO12Uj1HdiuUibHfhgjPTJDO0oD1hkcr4EJ4TktZOo9AXiQqIzl8MMhWEvLpZ1g+Yb78Zh42YDwUJ1TR5fSHYJxCzLcNuyBOcJksul2DIDcKeXImZrW4EedEgLbzahyeuHPdTPHcnNb41aw+E7nv/CXhpKxmxQzDaqoKF01gATFXl8L1MmVyrYUtplF6YIc23t/yYiq3ZwJ9PYUzBtgzmk2nsbtgxEkRUQ6Y7mxmqLicFzvUKH0euNbpx4Y4tYhtg02O11+gxpAu4X2Nwdwo8KBDfcRhZ6yxbhPdq7NySLrxskZwvQ2UWESMzCROEMuphj8eGJTqaLYj8334Tz4yfCkGqPxgJV7k/ARwdI5tswmMctxZQuiNqSJPYcztSW3QYdmYV52ZOeT5ahua2N9moXLa69F/IAAHP+FAgoFYqvOr66xxfRHReIuo6iuDIQh86mcfBMd7P51VZxX+f1j0dy/FQ34C50YuFqHckItqhvld202UY34cUyOLpiPIqeXt17M9yrqpMxFX+pFi6FenDVNagoRNTZcqTbPq3tA8joWd1Y5hIkwlbhQjJIP+pL89UumkChk79T1YSSGEDkyetFT5eLCNVsK5Bn+MOJGr+lQXMQGslb5XYbNm59hiITyKBpkD44k1LYvIps0/GYlAiO7/cG8JmDz5LoumosEMvMmJG9fAImRB8my9Ys+y1z8F2dDWBN49EgLtZ74+DqGTJuy+5VI0DWvCMC26xbIDp39ktteutx6CqRbZ9q9eGp1RIjz2VhVpdQ6vUiU8Sz6B3MNM6gBaO/mp2CQT5piuFvvS4DZLZV6DsU5xWoZbhdK7RaVb02dw/leXtjJMutmMztjaORnCMdjtNEi2bVs+TzNjKaLDkwHylAePLrjQxBenyB6v4+8OQ0MqpqgbXcYhsrHefku5pvlUGLhtm3bayOfIv0ciXXiy3MZhsORM6+XVvzRSBPYDiIxAT7u1FgTf9g597+vU4YCPeKQn4G3xgt2HoSivsHV11mqVLs2igK1UfUQlbQ48vq10Tns5jxW6ITBv7wVc2NJUWJA++NBAHwrI1x1UBsqrfu8b2L7QPI6BndqGqQvyzTnlhHVlLdgtoW6aVOXFKb3ey1szh0pm6iCMosfdebwO8GIBescp+T2Qa+n0VhUuLg798MzeYwyKbTCOXDifDB7eYoe9Ef3gJU0sm4iIC7N4UFQgRXa1acJmjuH6G5fxT3S73+8688CeVxGH2pN0Iy30h1sNrIPlhDLs/kBlmsoj0AEOieYdEwIdzhvvDxdT/9nSO5YdX3X/x1GmnEGpWyy4ufjMS5tOrMMKibaEnQfdzw9SQR3N0y1aDLaK/y453MoHAzFd/tatBsifRS1N6006Z8UQBFjvQNmZnNhfoCZamIq3pFzFZ3u5g17+oQgHqfeTUE//Z4DH8wQnvvKOhceNRXhfYGfGMfxaM5aH8PzYu3xGJ9W8WMXitIfziJC1XXdiKIJV2kWbZexF7bKpAZeH8UhWrdgL7ZSmADRKNi2g1TyWuF6ZYboWWv1sL0KVXJrXoQSlOdQVFfrRhskFNn7Cl3R4qaqNGYQnYPcCQEoOwJfLc3lureKrl+KdehQU3dYKT9g80nXsD5n/gw6o/clYpmvRFG2eMLuSbNadY25thXercbI/ZL3u7n67C9pwGBiPaI6JeI6AtE9BIRffdbPOePEtGniehzRPRr+th9Ivpn+prPEdFf7Dz/vyOiN/Q1nyaiP9n5239DRF8mon9DRN//9jsIdQa15pRc6LubQ8mcbdoWS9bQHAxkCI01g6nDtumK1q5ziC9nguFatk0ETkjKf/Z48r1HER/txUZ2kMyPhwJNnKpzaaXcbpscZYs5EXjUR/bVJxLAANT3D0P56Y/3IlziOTT7gjldp+qhkwv1oDFBl1YZi6WOy9QbhFngmm6D13QSyzUwW4C3yolPUxnE08m2SF00k5cfBXYM72vJb9TJvXGwR8Bc329bRbZPUUQarLnT2nkwNs5kpDh3KkFOLcLd+Tw2lA2u03NO2xp04/DKQuj3BoJTL9YRW7bGpTHGtCrzo/7Vik6vi+ThBdwbp+LZo5URbXZBuU27Rr43M1zVIHs0k2Tk+OBKFdaOi6g2t+vU+ivB2qFF9U13pYrc2LQ9CRQ0XUbWTdNGOKttxQVVr7HtN96IcJ0pwQ2uupwFjyGqVdm/2sTzFWzdOVYwNoAKiFUAuSges78lDnxTqzI7rl2xmFWBFrwMcjXtQeceDVRbIpSffQN7X9Dxo2av0hWF2jEE4md17Obf9fY+hoz+NoBPMfM3Afg2AC91/0hEewB+CsCfYuZvAfAf6Z8aAH+Zmb8ZwL8N4M8T0cc6L/1bzPxx/fkVfa+PAfgRAN8C4AcA/BQRfW3ysF7wpBbRtN4AyzWSTRt95HVrj8bghNCOe/Gm0gycxmNZtLJOg9IyUy9URuQ5mn0pvZujEdg5WRybFrf+6ZNoNa3YL1UN2gOFaBYrbSh3YCrvEaZ/DQbCdOlkjza9LHtwCU4J6clcFr/JGM0LN0U8d+fGmyX5vnPh6wSuUCEAcmPqsBje1WCrloCwQLOJnxLNuslJpVAUgdHEWQo+OYuNVmsKbrdSmUxGkuHuamA6l6C72gRaq1mEB0Gf9RsM5zaIwfbL6KtdeMV8ibpsIGv+6+LGPf27WoS7BydwJ1PNvhMR4202AiEe7EU+fp5JRTdbSLWi9Ft/+xC7b7wZF5k0Vnh+f9w5jlJluNd03sTlTNTTNhhmvZEqVimj/t4x2hdvK7ssiwPjmwbFV9Ror6eLnw4sCmIuC55mBTEaqlLXg0d99D7/BrCtpAnetBF2Wayw+c4PAQDOfuAbZLAPIOfDYEFTCtv9YrobEyIGFXIZhZqZVqGrjbDTbGaJVmC8WErPrJtFT8ZRBDkexgRmV8dgZp+vDePs0SwkDf5476o9htM+jAXapyVMw9ODjIjoBzT5/TIR/ddv8ff/lIg+oz+/TkTf9nbv+Z4FBCIaA/geAD8PAMy8Y+bptaf9GQC/zMyv6XNO9N9HzPw7+vsCEkjuvs1H/hCAX2TmiplfBvBlAN/1tV7AWYL6G24J9Wyxgal709NFuJnQK9DePQQxw23bNzNbmAUqCtmN4Nt+f4zWqJ1ByXwNhzSM0zLbsGMMVBWSB2f6OZqBhwaZ6iYOJ0LxG0R7CbqYC7Y7nevF7JG9rhRZFXSlr58Jv/vk4s3VjO3v9aohFY+n4D2/2YLyDHjutuxyq8FguxWhki3OqhLlk7M41jFJQLOFVCaJHhtt1obgYtugBI72xe7a9A42ztNYIhYI7XeDkvr9+N6ALIZWxVAnYNuxNYrkthK7kYOh9IOsodgNyFUlLK48i7qL5ToygJpGGpxFHsz3+M4NuEfnyF96ICyoIg1+V5ylohAGhPO+FjsQHO4HBlWATFYrOVbrKizmnCVwmxputornbreLi2uayvnul0JGMBpnZ+axP5rE4+I6VapN9DudxSpERXnlV87BwxJHv34SYSXntHmfXD03dq1ZMMw7lZNBbGrnwiqI3Hz4RuxbKe5PRoSw82iQp33ObHGFzBAqXUugyhLpw3NwvxBDvsUSl3+oY5CoyvqQHPmnWB0AT4VlpMnu/wTgTwD4GIA/fS1pBoCXAXwvM38rgP8ewM++3b69lxXChwCcAvi7RPSviejniGhw7TkfAbBPRL9KRL9NRH/2+psQ0QsAPgHgNzoP/4RGvb9DRPv62F0Ar3ee8wBvEUSI6MeI6LeI6Lfq7RLJaieBIHGCHdpFVfbQ3DtCczyRbP58geRsLm6lXqd+Ge6qJbuf9NHe3FP8EqJbKHsylOPmPlzVoL21Lxn7VHFrtWjefuvzEau0JnCWXlWT2uB0SI+BnpzLwtyyBIIyCzevf+5mHAbfehmwHialkWSShju7t7gMslQy37qWBU2bsnQxF9jIjufZVKiZSRLYGUFUZtRArxYXQFSD6txptgV7s5Ub2Rw+l6sIERgv3bBir8epO1PBe6nqbFqc9RjU29/fOgDmyzitLc+FVjidRW+hsicQRZGD12up4IyVZVWSLbTjoXDzbVAS6yI5W8Af7YFvHgr0Y43fvlqKK60UZQ+0a8UPK8vEZoGcuK1OF+DDfTnu55dxYVdHT759IyYF4z78uISbb+GLLNqB26LcoUWHSqBtpWqxoUg6C8Ett9K3su9rQTKY3nXIFX2dLd2LTWgucll8TZNR12ovkb45kbKRrLavJqzUoEzKHuq9pqQHU7jbcRiKVkBmYm9i8z3P49yL3U5+ny+i3UahjqaeUd0agvs5MChx+E9fjdcsd4JLo8N93ipx+oNs7xQuevuP+y4AX2bmrzLzDsAvQpLi+FHMv87MysHF/w3g3tu96XsZEFIA3w7gp5n5EwBWAK6XNSmA7wDwgwC+H8BfJaKP2B+JaAjgHwD4S8ysADp+GsA3APg4gEcA/oY9/S324U2HlZl/lpm/k5m/M3cldodmZ72TebmmL7B3JIjvj2X3yv0P3v+WmTDH2QhlT0ZaAsqNTyWQmPirYckmFZ5ArwA7zYZW6zBzNmgMbCiLQVUm9+8wOfydI1ArQclNF+KD081sbR/zFOuP3YY7vYxNyO7FbgZ1W5lvQGV5rTGnQcxYVW0bs+4siyIs5jjsx2iQNmzIMkzvQft78TkGK9i/qkPYffSuLJAdnDf8u6eZnSm0rVltDUpHwMGeUDwnI1lo8lztOZR+mOfwd45FmbyShiyNx3Ef0uTqombsFKVuMmtlo/vrzqaiFymzCDWYJYfRaG2UZyvOqlyKSV3bz2U/zi/FjdNYZ6UKwcqezDxQyxKqarhlBVoLhfnKOTcFss1d6BXSWDbM3Pur2XNVy8Q2YyIZbj4axPezx7WqoM0uVJR0MY2wjVph0GOtTrsWMV1Dx+6/pmtwMUmhTSXW2ZZkmN226Vk6/YZwf3V6NtTvS6Jg8NigDBV476VHePTJsSj+j/biNW7n3ajjT9O2ApChP+/gB8CRJa/682Odt3pHCXBn+88A/G9vt3/vZUB4AOABM1tm/0uQAHH9OZ9i5hUznwH455BeA4gogwSDX2DmX7YXMPMTZm5ZVC//MyIs9ADA/c573wPw8GvuIQHZvIMNajNJREQt3LYWsZBaDDd3tBixctWoa0WK5rljOZrdhmnrpW/ADE4d3JNLJF99iPRkdhW731ZIKg/X+MiEKTrW0B01LgC5+eYr4afroBd3OpXPuJDsnc5ncjO0LfhgjOTRhTx+coH+S4/jQqPvZ8Zgga0DqIdNh8Wji5mV88HdNXHB1I4Xy6hcTVXAZougzvjlfhH7E4ulKLjHw7gfxuFXnDl/6XUJ1k6nlKneIzSylULJq3XQedBGbSz2JtL4vJxJL2Yv9lraYU9YMk0DN1uJoMlgqKYJs3t5qSI8y76Zpak86EfbbSBm4qMBaLGCW13lrvub+7GqVAsJsEwEo6oBLTZIZht57zQF7t6Mxx6QrD5LJRjZzOyqll6TWYIYlMYsx95ceJUJRxdzYW2ZZmNbyXGZjORcFIXoQ5iVGppIlWLU0yyLMF/Zw+pjNySIGr33Yqp0Wk2scmU22SJtCYddY71CSAJ6rQXtit0H63V05QXkuSZIG/RFD2LGfNtKsnsgVkj2PkRAvxSb8Y69x91f+KK4FJiBIxDhIrNZt4TmaW3+Hf4AZ5a86k8X8nlHCTAAENH3QQLCX3m7XXvPAgIzPwbwOhF9VB/64wA+f+1p/wjAJ4koJaI+gD8M4CUiIkjv4SVm/pvdFxDR7c5//wMAn9Xf/zGAHyGigoheBPBhAL/5dvuZnC9jKVz25CZQ1o67VG60jVLctUjfOBf5f5ca5gEQBIJwxtLQ37WyoEY8akJWbl4/Sq1rC4f8ZNXZMRdxY8OEu+rOLJNsD5Cbrm5k0fdttH1QJgYtN0BRSF/j+Zt2IONNavvkOtkoEBWgs0VouLLNzrUJagDY7BPqWvoKdnMCwHgEPtyHPxzFRelyESsNbTLTxTQ2Dy3QdBkiyuSirvgsTSMbSY31AIAvp+Gc8rkEwgD5LFTLkDikrz6JuHJdB7gozNK+nMnifuPwKgPG2EtXxo9SVP42rbiZpnq+VMnrHp7pNK+lQhfS8ORBT/o+7EGLVTAtpG0t2a2J6dIU9Y2hBIy2BQ974FEp530ykoBBFF8DxOrAPIeYowmfMbrMY8oCeJ5H00TfynEx+NGSASKg2qH/1akone/eiHRhg4Oc+kulSVyUTW+izDReLKUKVQoqJUnUjCi0h90uGCqGytxuk9ce62nRQEBOzffKyL4zh+I8h9vusLu/L8HPWGF2rCyoAVr1uEhdvd7/exfb76NC+FrbO0qAiehbAfwcgB9i5vO3e9P3mmX0FwD8AhF9BgLx/HUi+nEi+nEAYOaXAHwKwGcgi/fPMfNnAfwRAD8K4I+9Bb30fyCi39P3/D4A/6W+1+cA/H1I0PkUgD/PzJ3O0ltsmuHHclabV9ZDsguECGAvgzvGA8HubbG3MlWl/vIfXSh1LCc7BySEZq+Mi7DR93RzNcvAG+4EmqYDrzDHwAUIA8X8lCwDt9fZDbPbCcZb9sSeo2svEbsAACAASURBVJfKd1Pv/qAK5s7rmGUhNMaNqjy5o9QMGXEwDbvq/RMmTjkn0NN6IxzvPBcdgimzzQ7cMPJW2DW8XEU4wxYwe9/gHcWywBmUYDd1tZP9U1grjPG0YzIaAL1clL1XjAZzOf52HPbGKphay8I/Uitws2xYK7Rnozq9ZtSGzy/XAqcAUg1Y8/pyJt95vY6OpW0rA28MU29a8GwhwaxV23AvTp/ZVx5j/S230dw5AF0uQPMVmntHwHaH9vnbsu/adJYqM40Lq6mLXRKazbRYR+VyWUbV9lIVz06q0XB8LVO/sQ9T96PsRWVx997SBZztOutCPFkmY1cPVT1tzCSiCJHaNWL3S+LEZ0izfcwXUlF0NzNGTJNYHRmrbLmSqXMJXWWhBTjYRajTelEGb1kP7N1uT6+H8K8AfJiIXiSiHMKw/MfdJxDRcwB+GcCPMvMX38nuPSW1xVtvzPxpAN957eGfufacnwTwk9ce+xd465IIzPyjX+Pz/hqAv/aOd7DLjR6U8IMCbuV0vnEB8pKd8ETYJv7GRJgWXiCkMNHpYopkkelcV10kmYFOOGJHUjFYf0AFVdJsrlC8eh7dT+taMtnuTFyb+cocb85BP5bfJvLxiFlav5TKJE9BF3MkJ7VYPgNx8QFiJmWLllO6Xp6Fxi5pRhuG29vgIEDgIkcgTxGKMjvlrvq4biSIWUPY+POjYaRCti1oOIj8+H4Zld2psoQGuiiv1rJAmC+NOs7WH74jzKqmBR8fSKadZWhu7yG9WAkEkibS+J0pF9858NGBUI8LbcbauMtaPKJw75YMbmlbgRwWS1lEbfRit4G72wF5JtXko9OY2ZqDa2CYCW4fFkp12STrGexqwcR3OxCkGZ9f7tAOUqSjAbb3JnC7FthskMxScLWTz7Jz20K+2/5IAl46jLx+O88aaP2oD1fVEgzMgNEG6lRK29V51FQLFElT6bvx0R5oIYZwZgFDZ1PRuYzHkYhg57BfSmVgHldZGsWMBjFd6SdxDA7jkezHdfaP9RW67LFElzhlmM0+cYzeWR3mafNmA+r3wU5hI+/l9XZ8TJT31PyMno5PETM3RPQTAP53AAmAv8PMn+sk2z8D4L8FcAih4ANAw8zX1+Mr23tdITz7m/Kk/aCQTJ5IaJx6Y/PeSOTwbSslvwm1Ok2s4OFjGfK6U1YD4MzBZw7prIqlsNIy3cUi6h26yuO6FsaHqYGn82gTYXin3SjmE2OPmSdNpVO37PGyFMtnY03YotSFogzbtZkIVi6T+gVZU9izwBSahVvVwIf7CLztsidsIcvYqioOpQfiTdul3BrTY6cur9UujB+Vhatz/JQdEyiGmmVmD0Q9zIcTcQ7tZcBmI+LAxQqYjMHHB5IpGhNnWwlcAyBYXYQGcA0qSwkGgCxI84VkvisdPTkaxgx4fxKgPtq1sSmplQpbxWdWJswSVPM89FQC88YsMDZbVPf24Ed9pK8+QZs5YLvD9jBFsm6Cid+VBqj34ANx/6RdE7F3WzRtAE3Zk+vbvJ/UiZXHOpinX8oM58TJtbqtIrefSGDBNIXfH8s123rpVXShli7F1BKa6+IxIGoTTOzZqVLF5G8o98BkBBNYhu88GsRq2u6J/6e9d4+1NT3vg37Pd1n3y95r730uM2fGM2N3HGdC4hDTNEQuUVDbUIKlQiPRVjRgIRQRUFGRIFEEEgIBrSUEqAL3gpBQndKStqINNKkFJBDROokb240zY3tsz5yZObd9W/e1vrW+733443me933X9hnP2HPm7F3O90hbZ5+11+Vd3+V9br/n97NymdrqMEHji9/YBYdYD8HM7gEjx7PJ70dldj++0887vg3/78z8IjN/UINhMPOn1RmAmf9NZt6PZra+rTMAnnSHQPBRd7LcACmB1TmgEipgVJVi7zOhLei1A8onSqPdwSBs1J02UDGye2Nk9yfI3zpHui6BBCgPeuFkO8byIzcC7wwQ0CSNhoqaK9f80SggdSz1B+C5lgwDbrVpjbSSt47FocUwWXMazu0gkPzNowNiKArBwUOaeR77beu38kZVwesh3D8OpTibGraIyIaiXGgas1FPACHi00ibjYDND0LN5Tmm9WCT2u2WZA0mEt9XepHJPGg9WDlnuxUiOQDJ7fuinmaUEFrTtuEsZJnMenTaIVMxiGqWSdPejsNqHUjiVI+Ys1TI2WL+HkCyLJXF5H4HfG2EzQ9+EDzoofrQ06GExk76N46B4QDNN8eivNduofPqKZClGH7pDMmmRPX0QYjqAQ/vpfOZOLLxNPRevLCOBgSLVeA10mYtD7sCYyUCpnMkx+fgZi6aFO2WKAJWLOU/50CbEkmxhWsoUsh4kBzLZ5mmhpVooywZQCg3KSdRfI/swIsLzcS2JVY/8Cyo2Qi8YsdKwDdfaC/EyrNWPkpw4++8JufNymm5oKK8LO3Fa7ysdstL79UYtYTmlTVSuJ1zHsfMnQbS6To0dRRSx13V67UTZTV9nRdIzuZhQAaQhmIUvacnM+HMIYSpS1eh88oD3UA1O7DsInYSVp6JhUAsSi5lTL86UrK5otiRg/SbM3PQ/rXpYLvRjE+nqjxiw0oc/OBEcPKm0GZQxDSRtaoAiWUIpn/LpoCVZbJp6CbO5oCUU4kGvaipbdBNhTFag7+R496//KFQhzaabFt7QnJ+lPGSlhJhc7cjjvVsGjaWLAOnJAAAbUSSZUJVFM3reaWzacDlGwePOdSikLU4J9/HiN7aLZknmAk6iayZSyTHwnj+u23JIEqHbLkFsgRv/KFe4EQyxs6qAjczuE5TZir2e9qEDn2rZKZ8Rb4pXwbxGXPI1oOy2ZKLm3MpWQr32sLE+9ZxuMaUcTRRXQByTuhXFNhAK0ErZXfPBCTx3A0PafUNX/0sHvalZGMBCRDQRyaXafV/E9YB4DUwmg2URwO0v/D6TtmI2QXtBC23SSbv5No35Judv7IKPQKT1YxtE5XNLk70vxd7RBnC+2FPtkOwm6HdRvXMETghwXhXDlhvpPZcOtB0CeRCmEbbcjdiAeT52oRav3AYUuU4JVaUUPb1O7ohJxK5sG4mVo6xspNh+lOp/2JdRDTUQu+MUiekm02k9ychgh5PQ8nloji4SWoaygcIlMB5JpuXT/Mbwia63YZU3UpJgGyulh3YcQAE8dNqBQH284kvS5D1N0gbe9Yjabejxm8vqJ8BQJri2j8cKwVILopdQxWkvzZC+dSBYuAbPsPBdiv0C1ZyMyhvmiK9PwayJGz+joMes5X5GqkqryWhp3N8FjlEnU2ICf6IPLsoLYvQi7A6tFmnHcpcS6FgKA5bKK738NwvHWP7wnWBMesxxmQGmixQ7rWwffFpYUbVy2RzcwBsK/mugOel4qqS49XW89DrqrykUGFjPAlBxybwKKEoMH5pKGtWWK0EEysl+wNcpyEINyJxDJ2mZGVEKJ8aYfvhW0ina8+kSy397sqKSuNpaNoOFH222fj+Ag+6Iegx3WqDyGp5NXv9vmgWVFUAOajcrS9Bmka6Rv683xflQ0PuAeG+sns0nmy3a9494g360TSV3xd7sh2CbUDbLdIHUyRFCddqSKrfasDtdUExPYI1GW3THqmWgtE8FxuPLAo1/mq35h9TH8f8KJSE0oi9Pn4dIDeK1X3tR+khuKeQWcOJGw7bpjYvRj8WwVsk7QfHIqSUvdZxqONHER0XhTBa2s1lnzvoB/iqIUxsIyJt1GWp0EkDMHlJlKV8h/NJINzTm5M2utFrduNJBp2TuQ5A/t9pB1rpsgoU3RZ12zEtnYefegdo/Z31RjKDYT80gK2OrREj2yCTlWG67QCbVc0HN1SUVCMPusDFVtht+105JhDn03pzjsb5GpwS1odNjD/cCXoDaQL0OsjPV8i/+hbOf/SW8FLlGcpuBlqusHnhWvgepTpeFejxNNiAiPUMu+o8Kz9r4WvvnTb631z5+8I7UoPjZkpix0KXQeczmUNwDthskN0+Rn5n7KG3cq3o9WG1eDsHRFpClJKYBSlUlNKHSVN5j3URoKwAUFVYfX+EuDRwx9FBQCiVVdjI9TNpPBeFQcs0PSSYw7m3e9vAFX7A8REOpzn3rn4uw55sh2CRgnKzu1YuJ0ORD8nxGOX1Icobe0Isxk4uDCOim859NGrpZ/u18xChxNOUcV3d8M8WJSUkylgWyZgE4HYL7rYEhVKWgtrQOrncyFrvXK19FOfV2bqdXTTLBefiUUZ5BjpRrL7x6pgYud04FtHb+nRz8eWhWC4xy8J6bMCp05YbW2cfTLc5e+tU1mFzDqO9ILhjjKVKhkZFKcfNJB/PJ0CSqOaxfieLJs2sOWxZnTkZx0jWm2jAThvieyp7uVrJxmLntyxlqMyOpTXlBz2BX6YpeNhTZFhPyNcGPXCegm8cSj9jvpLjslhIeUX7DNhuQRuBndJ6IzoMJaNzX2rp3G6Cj/ZR7nVQDpqYfvyDGL4yBfIcVa+FfCLRfePebMfpe0rzLJVgQzdJOp+F7CzPBSIb9TdwNkb2hvaBTJvbuLaYPdSVGxloLb0L7+xsItiEjrYlqmdvCIzYGFANaWWgje1WnKkxzppmsv1/vdFMy6DV0pto/darPhDhshSk0Nm5nINMKV9cFeZVDC5spHtOuLe8uI+Vr+zaV5gqAIEpZ4/IITDwHQymPXZ7sh0CEFJTANnrD0ArYep0PamXZ6dzEMukMYBd5SSrPdsGVlaevoLiaD+OPBLyQzZoNIRjKEm1qS1RKM3XoskcoyzS1JeKeL8vF6sNtwFBHcz6FrEIujkjM4OGNnQKdziQOvyZ0J74iN+chzZveS60xZRl0nRW0jUvYu43njI4WkPTrNY+E6DxXHD+Fl0P+/K32UIiQ18eEOlI9LriAKy8ZOfNk/AJ0sXj2G3txsdjzgzw8NDN9b5g5C0r2VMIZaoNyPlyZ8YhUaEiAOBDkYnkRgbcPZaa+2IdIupbAu1NT+fgJJHyz1I3RCtHtFuyKSkYwPVaKPc7mH7vCNmikqZx5UBlBTggO5khm6yRbizDKZE+OEf+1beAbSkynpbNpkkoozSbHkzgru/L385nPqL3AUYsHkM6d9JuB3EY44WqqnDdZQnS+xNUnYYETHkm94eSD/LhHtK7pwGqbIR2dj5YBY5ss69k6t3td+Fpy60EGAcodo3quSaSsh4NBuFetOvcHJtdDz5jiIAVcb/QLEbtPUIJTcK7G0p7F4Np74u9r3MIV97sAqmcDgrlUovudkQ3FsD2Wh/JppLNwtLXTKGRXWWMdAy4wIHER/tSp44hpmUotYgoidy8yWqL6vpQ5DkBuTmSRJAhCclGb5uflkQ8bcJkpjTRmnrH05TxRGeSiGJZIw+SioAMT1GiRGcc8P9Wz7VmqSJ0yGsMECgRnDhP598CdfRlJ6N3VmgiJtNQxtpsdLOpQrmo00Zxc4DmegPuaWa0QIh0Ky0BGWmesbHaDd/r7jh4LwBvE9HWVGVG4+U35L3GUm5yvTaoqzxBOsPh+wPM4pD2DE5KMqxlzXmbZRjtyTF964E/FwkgQjKjPVnX6XkoVQC6yTokt2dIsgz5m0YOqHq+uoEtnztA83iNzjfGoNkS7mCA5P45+GgfrttE+vr9wFE0GgKTKaqnDpGeTJWMTmZSPP206R3YfQDIY/2e9MpaLWA2x/0//mFc/+XXlFRu61FbyWytQQkhna38dWvDhWjkYCKQ9sDoTKnIJrMQKGh5k8/HMmvRagLIkdw/l88ynYOyBNhKOdHmXVbY/lPPi9BTqwnOM3GgNjOx2QSHUpbiZGyeoSt9HPfUIThNkT44D0ir5SrMFDVy6ZE0o8zzvdolbfbvxuoMgSjw9djGbfV3IuR3xmDDwBvM0RgRjWwtQgPRqhDoXUwfYU1kS0WJPJy06reE+O6iGTWCRU+21jT1tf8dXiDTYoga2AB8KkwGwSs20gTOoilOQP5vfDKHIynr2HsYvLQsBQpqx8B442MrVBxHaYylWZkFbH1PkD/oKKlaobMZ3a7wFt2biTNYaxnOtAqsvDeZCistIBTUSo1QPXMEPj5VbVyNEJer0PyNdAfkuEjESSR9CmKV37SSkDbkvRiPc+AsFcpqO6daEuReW7K25Qq815PjqwNm2GykDGIDX4O+vHY2DyCAqgp0EQnJZtVqgkonIj3LAsmW4doZXFu1izcSVNB4Lg1co/Ro5MInxIz0eAyw8w1yWm/DrIsOZnk2U4N9rtYoP/g0TG/i+t/9Zpgh0YatSc2iI9+bKpbrcFsC/Z5AYM8nMuS2WMksjIIKYsCCZR2ib1HtNnu1nCbXnwssp8bLpMifxqv3PPSaNlvp/1mvyivoJSEjs96WZrfJvJDs3wviROVH1ZSgbSWBwqOyGmV0hS2XiVo+HIWGbBbx1KwLEbUvS5k1aDblRtbJWwChdAQArgrlJbvZDDpHBB7thSjcSlXj5e6amIOcpCGPqkoI4PIsoFdaDUn/Y2nI+IKyaWAgNGSVjI2tnmslgVZT6BwAEYw/Gfubh43RM8+FpsIyoRjhYWZMlRalAVJi2G7lOJ1PQpnNSNDUMfGgB5rOJQOaaFPXWEIt1bcsTo3SFFithRTOZg0sYzB450V+fKt3m1jOeiPZiA1uqbg7ttsg/fnMddBG6v202oQGe5Jge9iTJnWnDRrPpbk5m8uw2WIlG/xiEaa3h31xDIrgYcuY1oVE3ecTcSJ6/XG7gdarx8jfOEN6Ppem+2QRGDyn80DyZ+cAELW8dkt0uRu5Slwa15BsgmzT7vZe/S6yO0qJYvX1sgokdc2mvJ8eZ5osUA1aKIeCJOJWDpcnUm7alnC3rqPq5GF9MWPtRXVBG3qzx4ypNcuEgfWCspm/tvNcrrGyFBSg9S8O+6Gxbb0J6xHYjw2lWcbUaASBoagxT8tdosLv2uoewhU2mwZtNuVCauSCn44VlgwjX4lalkeqbLfSfNTNikcDGOtmdvtBmI60yLSSqVFiiUzMEWUPptIw/MD1oHcMhDTXrN8TuJ41pSmRAbqtDqEN+uGits0wy6IBIa3fTmdyExjM0yCHVuO3QS+nmPaqAjWb0jcwwjitVV+kBRbxmiQ0uxdL+U7LpazB6DVazUBl0O8Jemqzle9n+HvAT3ObI/ClM+Zd3hzHImhz4zB8d6s5a13aH8tEZBl5oCR0jahhaP8O+jqQ1PbfK3njvmzWKy2VeJbVLRrffCCwz8oJ75A6LH7haWBvIA1nPdY8X8Dt90I0XLldimaDAuv1x3s9cUCzuRxHPWbotkNmau/lhPLcBuHyr7wZzutmC7p7LO8zUQ6g5VKYTfNc1m1oJAMFNBqhl0NJIKlzVSDDUzhtOtugOpS+UH53Is9ZrZHMV2h8+XZoxCfaH+p1wr1kmaQFEVYGNa6r7XYnCPDQabtPbE2WZWxLgJ0IFG22unZtLGtTmff7XsEvNXCDWeXCtWHBV3y9vUerUUZX1NhghHaRkgwsVYf9CK9PIZ1UOmkAYWMzqJw16jw/EMuFCADM2D53JKnz2TjQM0SpITHLTR43tphlE7Zyjx9M03T37qlQRSxXsknp0A6AMHFpyBqbjrXvEvMiWabQbHq4pkX/rI1ELlUq0zGo04afPYgZXe17NZt+joAPhoK0yTOZtjbH0GpKXT1JZMNTHiL5fvIvzxeyiVr2kyR+s/Kfb2vYbARSGJ8nazrH1MuA1LaXEeGcOQKbSt2WstZCylxelEcpk/3GXUTRrPItkaFjrE4ekweWJfi5p5C8dlf1rKvgAKw2vliF/kJZaQ+JZQNNU4mUW/F1VMqEdJJ4bQh/HoBAUmfvqYgiMvpsY4q1jVOpKgAEbezF0m+4bNPYzOLIE0L6YCoQ1KoCLQspH9lsjqeh1n6Uqb/FJaK477Nay5pXa0Ev5XkoN9okvX23CAbN/U4oFelUNM0uyGxagNdqguYirOM6Cut9GL+QBRKNxiOcVn6X5aK6ZHQ5xg1NC9stYcq0zchuCkBTx0LSftsQ8jzQRVjkaIgF42G3RlRZIf/GfYlEel1t8q7kIl+ukM7XSO+eSUnKMhL73OtHcCNFFRkdtd2oSSKOxCCazWbQUcjCjVY9fRBuul7Axnsqhng6utuWujkg5QbVuaWBCtFYxNZq7W7KZuYcMr0p11tpsi5XsokRCS/9jX0PhXSDtvYROgFDDuyil+wmURpoc047fRPWTduI6UxTwQbrcsmYknsncH2lCLEp9Z70MLzojW2itvkcjUJ2BoTGPOA3Ex4NfKnRHe2L9nJRYDvq+EwneevY9wj8nIWVcLZbcZKm/8wcNipmyWRPzqQxbYOFOtfhru1h8eJIso12SxTzDGG0LoJcZpLI+bUhLish2fe+fiCOwcjlLHDQnonoUij/z0yzCVchPZ9Lo3mzBZ0rEo4Z3MzEiRyNAurLCO5sat3OnaeTl0yQNjpJbIFSnBElKbxEp2OdKC9x7w/dAA+62luLz5ELAVGayr3pnPA3OQ5AB+1z8KAHbjfgnrkuCnidKHN7L8aoHcKVN6tlZpnOGzBcXymNk1TphKNI0Ed81W4ECOgmv5QNd7YIUYmZRtxkw05VBTqdoLq2H1TWonSRmGUIqd0OEUtPeWaqKkAxt9vguKx2r9+LtlUY2bfNPy5pdVRFarMV1BAlwM0j8HIZJCcXi521mdNg4+TRJjsbpUOjIRvk+UQ2gk40ibzZID1f6vCQkgZGPRUkFGg0bI12bBoNnSqm8GOEf51O+H7sQlnKqJ+tEZwkQioYw4GZZZPQ4SmfsqtaGqcEd6TZTpaGrMKalq2m9CEAoNMRzqHNBm5/IKSGhtrKc+W9ija/5UohmcqwWWxCzd6OASBRum+K6/frdWUIrpWj85r0HXjYQzlohXkYo1+4uMmsCwlMNlsvQsNpCjfsAlkqPRBm6SONBt+6UWUGcJB/y6O+j9o5t1IpY/NUX5wYsDsEpyU3n7mNBuJIbC0tbSSbNkYMythuA+eRvpc7GIJKCfJoq5Pa7GRoMz7P05k0+e3YWl/HzlGSgkrpFSXHY9klH+UGXfcQrqZRsRX4YE95cNgJlC9JwEkiuG0zg08afLCqZJO5yMdufQkglGNsc0lIYG57Ax/xWcqcns+FBmCkjWvNALiR+eE1X1ddrZGcTsJUrk08rxT+t1CVqZ5Ei8m8CFQYgBDW2eYDqIhKGbh80gT04Ew4iqw8YlGwUVtDInci4arx/7bbnr+I5ivtl2i0uTeQNeVCP4F1EZxqJZmW8dt7fqRm0w+7lc9e80R+fhguLtHNdI7AhFqs2d9uBfU3zQrcXt/PYgDwOrt8sB8iV0Ayi402xB1Ap5NArQBoA1I3J+t9JCQZXZKASpkRQCHvD2ZxgKtVmOzOc9m0nAsTvqeq1zDswzN/Xuwx2HduNkTZr5Te1urZARqvvOmzKU8wqA1k6vfkeykPFRq55/JJ7h7LtbUtAzJttRbBp4MhUFXiMCybAgT1tdkgO5lLZt3rIjmewD17A3R6jsb9eYDymjZBLIOpGzydTgTppSVAL6QUEz/alLeR2DVCiZQ2JYiB1VNdYL2RqL7VDOqCNhtj0+uGdGo2ZAAumnPYYTh12A3q3qNd5TmEJ9ohsOq/cq7QRBW3T5XAi1PaTTv9CzmkoBE2H0mC9Uu3Qh3f6CH0YuKhEXdpzd6ac5qSc5bIWiLE0HbUhtvr7mrVmg7uciW/mxOKVcYipSzPywPIxnA2DvjsPJcIOKa3MFI6e8+FQjnTRJrLthFEJSNmF3iPylIck/UvbKArkzpwNeqq3KVEae6pQ4kqt1tFQOkGaJufagtU3bDJ+3KSTauyk/UURTQIl+wilCyTcE6O87Av56TbkX8TCndEqQ5/uQIO9+EaGThP4a7vy0YFBMffbEo2CEgmVylcVjNOztSZH42CI+mLLgE3ZS6FShfKhfNlkHk0gIMR1hmFgkFM89wjsWgl2WH79jSUujrtcD0Zcs3QTvZ5GizwyMqCWcgaDVAxVfnNvYFoRkc4f5qEjZsMVZcQym4OdDpwrUagLon5uixrtSlyPe4eDVUUYEPVWfAynckxsd6aTRkr7PToc2fovnoOrFYyJDpfgrsdQWXZbEHMkpuk0n+Iy8EumlZutx79LlmXjK6mmRf24/y22dgNH6dtdiMbfbNZnglktd8DH+7J/ExVhR6DzR+wTLZyStH7c7jQWw24Rurhe6ZV3HhT0RVro3RQSJzi573ASJYGbiUdbJIoJxGVKIV9Gk01Xz8AOh2RxCQVm48yGeQyjew3Wm0so3KgRh6mT4f9AD1NElmDY6lH99rhPQ2uOJ4i/fqdoLJWVkhu3/NRMLML6CWjX2hKvT0pqtATsLkLe02WgfaGgT4804arsZBWcnO7pw6BTlsE6fNUkDcTLbVkiZxz6y0UG+ELOhBHnmxKEYbZ64GvjUIGCIhIe0JYvnQ9lEI04k/WinTaKrWzOap2SxyTDklJSbAKzt4GJw1Pb3BlQHmY1OGmiWQKiwUwm8uMgjnTLEV1XXmXtPbv+129rjgmK61Z2a2lugztVri2pjPPa8WjgT9mKLUxvt0K1bdF2+0WGq9LJpSezz1HFc/m4kyshGgEiBGqjsZzyaI7bUFvWaBCSRAmsnLWbB7Ktqu13Fsa7Sd3ha2VTs9lzbMFykNBc1X7KvaTpYFmnkgzc3VUqiSXLDcyif4ozPox7+bnEuyJdgicUrjgmeEOhkCaSBOWIPS/FiXFg17Ajgcnrb9XfW1Ex9j87W5JicoI1WKka1kGzBbIXzuWzcNQP0kqqXMhMweG03b7A6mRW7nKKBbss7RB7v+2VFWofldKOmUFunsMHk8C/DSKtOEYPF/IJkuCsmGTpgSEXkJ1lFmjNyKdPbD6fqE3UbMRoX4owAWNUMyoDFToZAfKGmdfZSmoMH3cN77ts42OIMvEydnkrHNwN0ah30DaZE2FpI0joRehvUAB5QAAIABJREFUoq58PwUJobw5kscAQSfdP4UN/vFAhuyqw4H0DPaGKAZaBiJ1KusC6XgREEG28UXw4qrfBjczlE8dSEba68q5ni93h7l049185BklqMtDCcRfYCT18WYjwCrvnYdp8HURmtHWZGVBMdFaqDe8FrRliuaQFNBAJ+NwDhMK1PHKMMttHVKzHkapinNHIzlXG2UUnkd9HUNkRSVYD9e1e9A4lZrNsD7LbGzwzagz4t5LqymbfquJ7M4ZMJsjPVsEpTu9V9yNkTjixSIARgyNFetnv1erM4SraZwQNr/vJlBWmP3+Z0QEpN9BekfJ3jahdIMkQfX8TXjmSztpxkvEjLKfo/26YtDbDYksLWId9pFMV0hevxe8/0MEZ6g0h+PkZ1vKjTqZBcbKZhpuKkMTmWi4/3LsoyYrbVGsHtVugbod2TgMD+7Yb/qkmwxXsmlSnkm2UFW7jeK7D+T7HY1CCcJggtOZpOPNUIv2vQuDDJoz89q3HJwGc5BWBNC4O/XUH2SvIX1dmvpGMrXbAntV55KcTPwgVXL/PByb9UboOrIMePMeXK8p5bVI5zl7/T6S1Rbp63eR3D2Gu3nky040mYN7LaSqbb1+bh/7v/46vvknb8jr94dyLKYzcZyt5i4duTWwmUGl4uaNKdbmJ5T+2gbEuKrQuDMROKoNPhpZ4nCgDfSFotIUWttpy6ZpwjsRdYoQ+UXkcTZEadeknVNjcs0ymZnRDITjjVc5nJBLiZIP9oX8b74UlNp8Cb5xgLuf+EBwaD29Bg0CrAy0tCpU8lM3ZKMQj0nmjFLlZAzXa8tnEQGLpZwnO7fbUihKVmusPnw9nF8rq6aJgBtOJtI3bOpE9mggWW5ZhTLwo7DaIVxhIwDM6H/xvqSPD86ANEE6XoWUWXHIyWobTYNamYSA8wmKF28iXVc+kqBtJbTMmTSymEhQGNbo8021NESNSSLptd5sbtQPzqfXlcZYr4t0sgoNsrjZZdwtdkE18vB5ReEHpoSKQDMIu6EH/TClfbjvS1E+au/3dknvNMKnZkNeu1yF4bhB3ztT2lQBg27KYkaz7Zz2C1xAmrALWYHSSKPdDtQFJhpvkFPDhzNrVM6ik2uRrU3argt4CpF+L5TzskwYX5tN0FrOAZsOQC49C7L5BmZQPIdSOcBUw1ZrZLMtMOjh+b/8Dbij/fB5gMwnFFtUw07A5VOC5FzKNdzMherBdHxjsEJLhHHA2hC27++J/tSJbjaobozCNbE3lL+VlUfPeQZZ2yiZPS0LK7KJlkUoORmdBrMfYMzuKb31fiQ8X1bAthKEzlw1IYztttX0ME+aLXHzf3sjHEObIjcAQFFIkLVcBnnTZmOXwsSyK9Neriokb94H7jzwf0/uPJDNHAiBXVWh/cXbodzYyMPMhepMJxOl9Oi0BTI9mfte1yMxRrh23+nnEuyJdgjJSvDmmxdvwvPJDAdSi5zZoJemlbO51Js19eZDGapCU+l0E6BsKwzPNjVDaeQZiBnpbA1365r83bQCrERh5ZHtVsi0em3ZoDptuWmKQpzVdCYbuyGDbBDK5hAAKTVZ0/JwJOgQE0ixzcAyA0uFJyqxuD+UMf9uNzg9QKZbDYWUqAqW1W8nUz/dybkOMvW1zj6ehDJFoyH18kIpjR1LL8GOc6yP2+0KKsnq8dasrlzYHAwpYtGrQmPJtCUAzQQKr1/ASsi2s+kmknHR2Vggiw9OAymeZk7VB27Ke58q7bZKWdLJmR6vNsg5rG8NUT1zhOR8KrxLZQkM+nCjPrjXwuIDXZnDuDaQMqBtNJVQnnAzD5Qbeu3YND0oERTPtgzO2xy+ft9kUwbm1/FErtcsDdmZyVlasJOm/jGazEK5cVsKmme+3BW9N14jnaHxmVqagE61NGV9nu0WdDaVQGY8De+7LaVBXWwERWXlIuuJWLPZNMQXUgJkm52JMy11uIZ0i1UKab6S72DN935PrjsrL+m1ASA4Xa8yp3V8y6geWU2fQ/b/Tj+XYO+rQyCiPSL6JSJ6hYheJqIfechzfoyIvkBEXyaiX9fHniGi/0tf82Ui+jPR8z+l7/clIvrbRLSnjz9HRCt9ry8Q0affcYEK72p85Q6wLbG5pQMocTPNUrdYG9ix6MkCnjiLEwIxQtPoYTC10iE5VzSKRogCxUvC5gaA5iupZ09mYVp6tQ6fb2USi3wsmrPSASAlg1Yz4Olt8Ml0CUzO0nhsGg3hLHpwEkpQSRLgnXbDDftSKzYVrIjYD5CGIDpt2WBWmwAbVIJAHnRD6t/rRLDRqFYOyO+mA2yUFYCytS4DwqkoZJO3GQh7LeAdRPniLZkRSAg0iqDEdm7ZCdfP4Z7Qk9jrZwupffc6SNYyJ8FH+vpiE0pcWQbMl0hPZmh98TXBwGupxRhik+UGNFti8Pm7AjN+69SXLFwjBbcakm04J415Q4yVFcqbez7LKw86QZ/YkE3Rd6H7p6j2tEHa78l5Ma6jVjOUPjaK0tHBQ2+2QXY78jqDDbcaQdegLENPwpxVPBE+j7i5ytLX73G4H8pVnba892SmwYCeT0NHxX2YtsJVUxLnYn0VZv9ZREkoO9rr54twjPIMXqUwhipbppRE6zc+K0D2AytnPQpjPNFN5f8GwK8w8/cA+AEAL8d/1M38vwPwCWZ+CcBP6Z9KAP8+M38EwB8A8LNE9L36t88C+D5m/n4AXwXw89Fbfp2ZP6o/P/NuFtj4yh2ACJsP3RDnbcR0HtlAgUcnxoCbODsArNZofe0YYKB85gjVjRGYyMMs7eTS2Vg2PiPRY8Wd68i9O+j7jYCN30Uv6B28vVlZSsmlcrs31WolePrVGhhPQfdPwY0M7umjUE6yDbapCJHV2iOHvBMoit1pYf2uXo3L8P6mZlWWWqZKogEx9qRk3MpBd09C/Xa+DBmBvZ8d87Xy8Uc9D5kqFkglWYlpOvfpv1+jlZJ0sDC7fezx7m4QwTCJAsRwI9Gs7+vo8aUsg9vrS3ZGJFj3s6mcv+k84Oqt1k4kQ2+t6HwUynjKLKUQLQO6gwHc4R7SeYHkdAY6PRfMvDV1NYJNF6G8lx/Pw6bXbvnhPllvBfR7SO+dySY3X4ShRSJx8p22bLSm62EbuV0ThsU3/h5zIPOlzKRY4GLfN3YOa/03SwMVSp6Dmw0ZxjN+oFznMuwaNAoQg4LabIqhn1YrXyqk81nI7pSfy+tA2PoNohuTSKrT9lxZiQZIzdwLEXmuozzH8qO3pIQ8lsz4W+aN3otd4R7C+6aHQEQDAH8QwL8OAMy8AXCRIepPAvhbzHxbn/NA/70L4K7+PiOilwE8DeD3mPnvR6//hwD++HtYpGy+wz7y4wW2R11pQF40Yz+1gaWjfeD0XDaztaBLsFiieXeKatAGUoLrNqVZ+ODMT17C9AYAuSlt8jhNgY1DcjyWSU0HwZRvNuD1GtTphEjflxjUyegafN1cLyYqK2C0JzDXufYDKgQqgoN9EOAHkpid8NlUTqeoCeT0hjXobJYFJ5GlgkSKRHpE7L4lw1vrtazZGu/FZnfYJkmk+ehnNVhu9KZyBLVb6kikLr1DoWDTyWkiJacIsugzGWZV4oqG9NaFChGl4Rhut3A3DpHYJmP1ZfuMRIa1AIQygnNAmgVEVVmGyLenJHHzpZYmouhytfbZJ48GSM7mgf6b2TOwum4TScWgkzO4Z29I7X2xAN88QjloghzgMkKydciO1SGhxOp7bqD91WN/jfC1UchW5iq9aqXBbleOrfENFRv5/NVaApXKhaa4Xf9WvtMhPi9MZLrRm61sntsyZFAAsF4j6XVD9mHU0tafazUDu2sjD43jRg4+2getNlLKs/mH1TrIalZOy47qUCzzcU7Ov92/01no6bRbQV9aKeZpVYjj0XPQefkBXL+DhEjupYva5O/FLmmzfzf2fmYILwA4BvA/EtHvENFfIaLuhee8CGCfiH6NiD5PRH/64psQ0XMAfhDA5x7yGZ8E8Pei/z+vn/XrRPTxhy2KiP4tIvptIvrtjVuFjSlLAmrFUklr1l2UZjxT2UaDo5qk5FgbhDaIllKIFIlQ7fdCqgvITbPZhptHseU0nfvIhixttgaboZwu6BD46NggelZ/BbTc5Xb+zg9OwsRvpZtoq+kjbzj206wmZs7LlUSJejy8QlWSSCmlrECTOdyNkTgNo5+wOrHqP/sykJUjbPPdbMNkrdEiABEnlGYgthlZc9ueZ5O3ckDkX93E+FhKNJyngWQNkE0v0eeZQ42b9bGSGCAbap4Jnt7+7hvc7eBU+t0QyQ56Ya2ANG6NQjuVYTcwi0oeEdLbDzwLadXJpczYboGWBVyeYjPIMXuuhfVRE9VBD64vQ3CtN6bSl3ACSaalbHZMFPSxDaSwWEjzvNiERq3OTrCnMjGdkGiYLKIXQbcrCKr5Arxae2dMvS5o0AMdjKRZf3Qgx3bQR/mhp0P93mZf4vKIZeJOrgdOSdBfBkmdzYE0kWzXyPiyTK4xa4AD+j2dPGZ9No9G01KTBjru5oEGHnpstiW400Q1UO1z01J4JPYus4P/H6KMMgD/NID/npl/EKJ99XMPec4PAfgXAfwRAP8REb1ofySiHoC/CeDfY+Zp/EIi+gVIaekz+tBdAM/qZ/1ZAL+oWcqOMfNfYuaPMfPHGqnUJmk8Ay0LNL7xAF5r2PDMQGhoxmYbbiWDSoYx51RghOm9M8F/y2Il0tQ69A6vil3oloJb+cFEZTyltdt9bvaQ5M6YI5NEnBEAOEjzz+CsVtvuyGAO2bBaeuFSaDbAUymh0P6e3OhR49s2GHfzSJzDXNP61Vq4l3TQh4ytUknXBOGjfRNrjpqDs36JIYos2jQNhRjZFfPiJwQc7EsTGwi8NICHHpJSWtD90+CYAGA6F2RJQgEEEPHreBUxOy+6mezMS+xcJ5VsIsuVDFfZfIg19QG4a3vy3Nkc7mAofDmDnqzNMlHdNPPbJ9ILyXNUe10070yQrSp072zR/foY20ED5X4Lbq8rgURM290U3idiFrCECdcP5PjQ2RhIElnPoC+OoCylqW60IkCYMI6mm3mrm3O3GxxAW/sCdvw2G5kCb2RSvtpukay3sgETBbr3opBo3q6Nlc6vZCmSNx8IVNguTy2xJvMiDOmVUh5jm8a3GQulYUG7hfL5m9or07kfy0IilBEf7olT1mG1shNoLNiADO/VGKEn8U4/l2Dvp0N4E8CbzGyR/S9BHMTF5/wKMy+Y+QTA/w3pNYCIcogz+Awz/634RUT00wB+EsCfYhZXyswFM5/q758H8HVIBvLOpgpWnnQsqgf74RybLgX8xK6lzK7d9BtlutwGD2/QQI3KPTeLNaxjughZuNDyptJQ8/oCFtnYTxw9xH0NqwcTgU7G4CyVzWAbRcRDUULj0cBvcn6wi5Wkrit1diJNr6czuZFsAMoQTa2mOLmiCEysdqMvFiIGZBlLnkmUp9O/fnOPj69tsp5qIdY8eMgNYueASGYKzmfwAutWGrBmOlGgDvFi77oG20iYQ4RfVTBiO68TYce6ke+uw86hsdACAjxYrgTdVCiaS7OG5HQK3uvDYKw8ULLCmLa63/ONVb5xCDfswLVS6cVkhGIvw/L5PeSTAi5PkExXoTdlUNfxFCYABOeEo2skKDpfRweQTFeoDnqCrAPgVdsUdMA97e2YToYOJaKpqCVjHrWMDwgKaswyoDgcyEZ+MkHyxn3PWeWv2632Ijjq1XU72h9qwLMGpIkPPHwmqde27ystV+GaWgnNdbrchMl146aqHFwz1zkV+QA2BNpyhfZr554dmBYXRKzeiz2JGQIz3wPwBhF9WB/65wH83oWn/a8APk5EGRF1APwwgJeJiAD8DwBeZub/Kn4BEf0EgP8Q0oheRo8fEVGqv78A4PcB+MY7LrTTBh8M4Z65HqIOOxmlohms9snSrPRlFSKlGIDfFJJ7ZyK5mekUbp4FMi6Du223cmEPB1KXvzYKU50WZS4Wymtfqi5AlO5XFdYv3ULx0efgVb9sfYaZ7nZkQ5gvfQpOhTqrTlsao4Zoscah1UltLfbdnTRdMZtLdGnUDtutbPLWmJ6rzrA6TzpVFbJmQ7KUyUxw8s7tkvwBwUHEamdA6Jno8/ggwr4DwYEvlRspTQMsd74Ejs/CtTPX0oFuEn6a2eYCADlHFDtgdWKmJ9BsenSXp/UwiKT1Hwy9xSx1b3M8ljUoWRxGe6KwdjZFMllItLo3kGxqIRrf5Y09LD8wQNlrIL8zBjYbNF++g+E/uI3uF95C1c4F0LDQhul8KfDMrqK5Gg1guQS3G6DxFLTaSISeSr2dC5nPSB9Mw/ez42EkhuO5bOCKdKNmw2srC3X2KlyDWQY+3JNylZXBqgqcpdjeGPjGt0cFAUCqxHnNJrjfgTschvr+npQlyaC4do06JxksoM7LRZoPJOfMYKSNPCjiORcyzKoSXqamaHPQyVj0r3vdEAyu1vCEiY/E+IlGGf27AD5DRF8C8FEA/zkR/QwR/QwAMPPLAH4FwJcA/CaAv8LMvwvgRwH8awB+PIKR/lF9z78AoA/gsxfgpX8QwJeI6IuQbORnmDnsBg81QQJxKqUa19PIJB6X18lJvzG1bO4geo6LXsOM7PaxsGmq/J6Pzg15Y2P9CpOj1Uacgtal3eGeNv0ky/B9BIuObHP0ZSbemeDkYhNSZmuEG+ppXQQn4hh87xg8VHRTUXwLlvtbIpXVWhrfesPQZhs2YkCcoEX4nU5I3zUbSY/Hkn1sK3Ark80pnj41h2mIlTTdObZUlLtIKyP9q5xHG3mhlHUBD09lDllMnFVZBhAjj2xYMK6bm4Nq5B7B5BlZrYRn9XgTAapc+EzNjmglzdlkvACKrZQbgZDJFBtBvFQSFCyfboMTmdKuRn24gwG2H7whkWuWIV2Xof5t5cCTM2VTTYOutJYT3bAj14Few5SmsrHmclx4qY7FyqSme9zRkkmiMrBGmmcNbAsqtlvpjxTSvKdjEa8n56T8xSzOy7Iu56QEN5OaPo3nMs1vzkKFduhsGrJt7QmQZcvbUs5z3BtSCnUbuquePpLsKMtCNmPXSDMTkkG9V7htDKppyDgf1aAYh0DinX4uw943lBEAMPMXAHzswsOfvvCcTwH41IXHfgPAbtc0/O1Db/P434SUmN69pXJRUsXglETQJFdcMhF42JULNeZbOZ+EZrNRV791LLXPVkOi0m5b6CWAXUUsI3QzYi/HEfNiIzw3pW9pGvN6DWoNA3c9gMaxDIBhOgs3F+tw13wZ0BbrAqBEDmizIZvOsCuc78zA6blcgAdDGbwqSxW9lzSe2Qkk1TnPZVN8z9NovvJWKLe0FdETw0QNGpiQ1PWnc5/q0+kE1BOsOx8Mg+IcAM/eWgj0lNsqDEMk39UEXfS7yGsoOFHboJ0DRgNAxVN8BqXTyb7ZzhQchjW5I8lNT8+tNNze9DpghafuQFCNqqLZkOg8S8FJF1Rs4G6MpM+S56DZUs6tfVZVgdu5/N8xBr/9FpBlWH3oEO3ffQvu2h6yyVoi6DSBa6RIL+pyGADBUF7NphDepanAZovI+VVKWb5YyrriuQ9mCVKGij4aqgDQ/WNp6A77cgzKUrJRr+ehTeqeKKoJrn+76zD7PYnglT2X1gXK50ei57w3CJBRIzI0vWXbpFczlTrVQbdUB+b2h8CZaoNTEqagEwhSiVk5t6L7K+7drYtwbVjpyTGweUTkdnZ8rqi93xnC1TdmcJ5KY9FuqL6knjTRzc70YK0mDuzW/gGJ0uYy9l7uS8rpbozChsUcItfVOuDAtVZNJ2PVH17JcFQU6XhTSUkQofU73xRCNbvpH2Y2nGSYa6tNbkXVCsUmDJSlKejuiae29utzDDrQZu3+0NM+NN8499w2vnZtSKwkgVezsuwkpaCpbJvldC6pfyML8EdD+dj6Y9SIOQyvRdDYPT4JSQ1a6+gGWfR6AtttGKICdpum9r4e2noBVWOOzerQUQRrCC0/6KfKZJgvPPEbZgtp+jZy0UOwkkDlpPGu348LhcZan4kZ3Gmi/bVjcZylg2vIpDkPe8iP55LhKbLIuJ58prNRmOhaonZab+VaM/SUzXckiWQtG+3xTKbYPDXE/U98KMjLmr4AJbvO2Epipldg5yseprTegGV8pmJn/Yh+T+r8RKpAuNqhtgY77Wdoeajf89mBD64cA5MpKM/BVQX33E11yjpfQxSCLmvcAwLNBcQRWaO6mYfswJ7/qOwK9xDe1wzhyptjiZgm80AhsdkKIduwD1LedT7aB2eZaPZa4xQAGg1snjsQdFKSAk0p5xCzXHxZ4qNq7rWDWHqSABsXbhbj8wFCM3s2l8jcST+CSsgNN1+Cl0u5Kc/GoalqZQ2beSBpDHKeAu09mYeYaXRspaS9gUTc9nxoKazTDvXThCSS6/dkcne/L9/DMPVWhzcYpvVXDF9fVXD3zpAM+2ETXasjU5qE5N4ZdiirDfarGhXCty9IItfOJZNr5EHJLqFA22FkZbO5cPMDEkna9KndaBWHTM/Op72HHQ9DykSiKUb0ByDMZ6hQDbWV7luhkSDJjLiRgSxq35Zy3NfaiN9swAf7gnUfT2RznMz8d0da6QbuQFrPTrs6CX4qXFkEBDSaARAAicpt2r7VAFojKWEaCmnQweagg80wQ/f1GWYfGmD4G6/JNXbtAI2v3cX1203PjkqTefjeZamZUQpEQ4u+oV/o9axCQSj1e/vymV4fw74ci3UBstmCUpvZzYY8Bmg2LvQtfG0khI/aDwu9M+ejfUpT0JsPZKo+TZG+UYTMt5HL/Wj9ndUatBJamWRMoMVS+hiQ6yHZbB+dQzCI7xW1JztDYA4DLkCIZvM0EHOVFWhRCJpGHUaQo1zC5YkKfGeoRl2JTBOCkebx4Z40oudB8JuvjeCevYHy1iG8lrDRYD8MTsrsa+V+8CpNpI4cP8fw5oB3Hl545WLEkabg83H43o1G0Dawxq5u4Hy0L89tNiSasg3d+JQMmdNohDIVkdSjy1KGknQC1DfsrCRjkbAyTNpm4J2DCdFnGXB8JprEBkFNEj8zIAN9HDZ3g+AaQV6ShGjPommj7bDBJnMASm7ms56q8s6C0oBqYXNeZlamsU05zwO9yGIhGeNsHoj2EolY6WwsYu8kTg9VJa9rNf3xdbeueThi1W9LdtXvCjLOZFTte9vxYafEgILFL54WpA9WK4BFJCifrPHgYynKfhPZOprnKDa62WrJUbU1UKouBktvy89jAHLsTKrUWH4HvZBBaHkJ05kOKxbSF7m+L9TzMWWMc4LsKZSJ18p5mQ6ocXSugZAtammPTXDJxH6aTYEmd5vg2VzKxJY1UxJmUNSSiegwsPVmBv1vvS+/W7vCGcIT7hAQuEyiiyu5dxaaVxbh23BUxBRavPQsAMgNWlVIT2bAukB2tgDpRlH1lH5CyyrVC0+h6jb9vILnZbGBHKuZGs+/mab2OwNo5jwqF/hthoMQgTBLVJ9SKFfZhWb/mhMqCvB4EprmFnGO9kBnU9BoH27YkR6KTVh32ioSHw0y2XAZM6jXDZQbViYAxFF0lOTO0FcX2V8Xy7Cx6ev5KaXesFJCJJEpUXR1oUyhG5AXT4/mOBTnjnXhv5NHrcx0MNDUyaKavDX7uKp2z49lZkBobiuLq+DoEyQnE3GutomajnK3I2iXNBURmbIM1wQz0O3KBtWSZnP61rHnAUpOJtr3UsdjAk7xzIg23Juv3JX3VU2M5HSGZFEgKYB7P9xGunZK3KiMqYUKMbWaup4QJfO2FEI/ox2366+spCk+m8vnq5PnayM5B3ZPaf8hHS/AaSrlz/kC3G7IPESzoVDdXigXxvBfxwGkYdcx4Mt6lKYBhqrH22hpSDXJfSnUGrhaUuL5QgKZ6QrpnROU1waPcINmrxf+Tj+XYU+2QyB4vQAe9EJdUdNONsEXAHTvRATWmzKlu37pFlxDDl92bxyiKAAotkjHSxQHLaSzdYhGiw2S6RqcEVgFWtyta1JDrypJry2ybkbDU1a+sWgTEIRQR2+Mt4PEKaFX8tZxgAka6sdqtzqBDBXD8Uybq/VuPb2spBHa64ZswATQTawnTUMK31EhnjyX3kMUxVKey01qrwHku+4NPMzQC+dYtJSlwsf0zHVxFur0qmeO5L077VDiMsbNRh54d/JsF8qqrJuGkOKyFCRNpwN0u3BHe+B2E+OPfyCoeiWJ32R2nAEQNmET/bF1G0+PqfHNV6EBWurQ3WQW9J4HfeDmUYCv7g3BvZYgXyybBEKPxr5HWQVkmZaEvF5GuyX6A0oM6OcFdPjx+c/cwbN/4w00786w+cgzcIOWXFurtTx3uQoZ7EaEkowugvVaYh08Q7sV4MY2/9JuSZmxqsIcTDvQiaR3T+U7NHIJPjZbcLcjqn4b4bhy10ah52NZ+qAXnOYyzAl43W+lCqdeV2DcpfMAgWSyEL4wG5AkBZUYVxcgqmoAsrdOd5Fw78UYod9R019fMTP4Zk+oiwPHTSWb2QXtAzo+D00ztWxRysVokbXWa123ic4/viMiKkf7QJKgfPYIbtBCsnUAazOrYsFsJwncjUO5gDudUN6wujtRiE5ZoiW6fxoamTETKqBlES2XDAcBSQH472lRCDW0LNJueZgeRnsSqZY6ia0EeNyVqVherQI81DIsoyQoNgH5ZFw3/oDJZ1OWyeZm5HxJKnw203lYo9XoAaDZRPLGfSRv3Jdj3e1IFDfTEpnNSxhCKYaWAvDKYF6oJt9xEKQcSm6/CzfShmWWYO83bu86Yjsn9tPI5XzkmWDpyzIg1azUVFVS0tqWGm3rGuMJbZueHU+Us6ctzmkyBS0LGTRsN0KWs1zCEwoazJhI2FnLUnoGvY6wy67WcL3IyRNJFG+fW2wE+bUs0Lh9hs2wIVKuujYJGNJv3aSMDn2jpRnLAq1MA+w0Hoy5AAALO0lEQVRqXK8LyRz6PbmXbEbGbL4MpUiVmmXtMSTKxwVAS3wuXOvzxQ7Yw6uymVSoHevjM0FbaUmSpnNxnplea61mCHgAUVXT+4LzR+QQADyx9NdX3yQCpbHAIflgGAapPLqFgM0G7uaR6PFuJZ1vvXwH7S+8juzrd4BWE26/JzjnsgLOxiEqbzTAzRxu2AFtKmFB1c1kO+rIha4EaPTGXflMhbpZjdomlqnVCtjxm0dyE1vZJB55V7QOD3oBjQEAewO5uQsRradOJ0T7NqlsG/J6g9mPvSjNy2Ol4Gg2QfdOZFO+eSQNOqPTBmSDWiwjiuWNbAaKUhEEyVKj01zLQhrFWskhTWU9CifEZivH1WC+RousdBjjj452SwrmlG02wbIze9xKDdvSE8cB8M3s5M0HUka5eyqNeGAX3cQs6+uoaE+aSjO52AhW3pqiNsxkKK6FagjMhJacR4PQuNaoWcopunbrYfS6gbDNJpnbLfnsTlsoJ9hJr4qUbtuySev1ECGdrGS+pddFNezIPAQgZcB+N5TaEkL7q8fI3zyXay9JgFvXAUBKavp5flgxJgK0HgAgAcHBfijl2PECotmIxE/sc7sp2aFt1rOlQKMtG7R+kmk7FJuQFcVDXNb70gljADvDZZwkeh/rcKirAjMuEapRX2cstNeRZXAdgUk/CmMA7Phd/VyGPeEOgUODs9gIiqKhPDpxgxJAcjpB1cnDZmPTrBrl0noTeN+JpExSlkK3DOiFih2YZDZeY3tzqEpnVeAKAqQO2umIA4hH8u1CuX1XSktAGOYCJFpSpAudRVOy2iAj44WPb0iLjDYbyRb6PSxfuo7+V8dyU+R5wH/rd6FNJSIvQPgMYLdJHm/I1ig2uUQtvYFlBkTKWJudITafpsf1W4uq9fnrURJqy/Fz8kw2hVZTNlyDtRp30naL5LW7YV7BcYhW50ud5tX19iOxoCzb/b723Qb94PDsO5tmr802GC00sziPbkdKIQYntgHDNAmot6j5ThvlD7KszdT5UlUps8/S6weA9L7USSUKbEjHCy0XbaQ3YUp6RRGAAtbwns5BUxUearUEDXShXMaVgB08pFTPm5E8wuggrEmvDWd3MPAZCy2WchyUe6q6sS/QaPsu5mTt9SYOFV9/8T07X4Ygw+5J54Jm9reALOQ6ImZp8G+1qd/vSg/oUaKM6gzhqhqFxmXMkqlTwVyWQm1tpRRmbG7tK4dKxMdj6bHxpJRScpKygV54FYO2FdJ5AXKM7HiG5GSM/Gt35O+9jucK4uk0XLRWR09INJDNtOnsJxqTC5uUCt54aCUQznanI89ZroDNVqJEY41syQBQ58v3hVcpJnUrokjfomaD+nluokqciPHi21CRKdLZ46u1Dh6V0kMxqGVcuiMpWdAkwvOrrKVlNjf//n19XKdJLVtqNAR7b8NnF9lMgUAlYk3mWHM4Nhd6ADvHOd5YrBzTjUqKC9ErdkcqZdloiNDMoBuiUNM+sEwQkHmXeNZiswkZj5Ym6XwGdNtwe+KseDbXen4k3NNqSt9nMpX1GZ5fARPca/lGqjjpJjDsC9ndZBbKawbfNVSO//pOT1MS4LIDnXpvNUGTmUxV97tS4rJzYyg2ZoHG9nvwbLGFOMb0/iRE/0ZG2Oti8/Seai00AltwrJ5m94xRjhibsInyLKPSk2Vjpic9nkqGeDKWfqFjuG7kHB6RXeWmMvElwZuughHRMYDXH9HbHQI4eUTv9SjtKq6rXtO7t6u4rqu4JuD9X9cHmPnovbwBEf0KZJ3vxk6Y+Sfey+d9p/ZEO4RHaUT028x8kabj0u0qrqte07u3q7iuq7gm4Oqu658ke8JLRrXVVltttZnVDqG22mqrrTYAtUN4lPaXLnsBb2NXcV31mt69XcV1XcU1AVd3Xf/EWN1DqK222mqrDUCdIdRWW2211aZWO4TaaqutttoA1A4BRNQiot8koi8S0ZeJ6D/Rx3+AiP4BEf1jIvq7RDTQx58jolUk7fnp6L1+SJ//KhH9t6oNDSJqEtFf18c/R0TPRa/5aSL6mv789IW1pUT0O0T0y/r/ERF9Vp/7WSLaj5778/r+XyGiP3LZa3qcx+lt1vVTej4dEX3swnMv61g9dE2XfE19ioheIaIvEdHfJqK9x32cvpN1Pe7r6okzZn6ifyCcpz39PQfwOQB/AMBvAfjn9PFPAvhP9ffnAPzu27zXbwL4EX3PvwfgX9DH/20An9bf/1UAf11/HwH4hv67r7/vR+/3ZwH8IoBf1v//eQA/p7//HIA/p79/L4AvQhQ9ngfwdQDpJa/psR2nt1nXRwB8GMCvAfhY9LzLPFZvt6bHdqwesqY/DCDT3/8cLuGa+g7X9diO1ZP4c+kLuEo/ADoA/hGAHwYwRWi6PwPg9/T3h16QAG4CeCX6/58A8Bf1918F8CP6ewaZpqT4Ofq3vwjgT+jvtwD8HwB+PLpJvgLgZvR5X9Hffx7Az0fv86t6Y1zmmh7LcXq7dUV/+zXsbr6Xdqy+zZoey7H6dmvSv/8xAJ95nMfpu1jXY7uunsSfJ75kBPh09QsAHgD4LDN/DsDvAviEPuWnIE7B7HlNb3+diD6ujz0N4M3oOW/qY/a3NwCAmUsAEwAH8eMPec1/DeA/ABCzXF1n5rv6PncBXLv4/hfe5zLXBDye4/R263o7u8xj9e3ssq6p2D4Jiax33v/C+1zG+YvXBTy+6+qJs9ohAGDmipk/ColUfj8RfR/kIvxZIvo8gD4AZfjCXQDPMvMPQtNckv4CPeyt9d+3+9tDHyeinwTwgJk//y6/wnf0/o9pTe/7cQKA+lg9mjUR0S8AKAF85rt5/+/2Nd/Fuh7LdfWkWu0QImPmMSSd/wlmfoWZ/zAz/xCAvwapoYKZC2Y+1d8/r4+/CIkubkVvdwuAUpniTWiGQUQZgCGAs/jxC6/5UQCfIKLXAPzPAH6ciP4qgPtEdFPf5yYko8G3eZ9LW9NjOk74Nut6O7vMY/VQu+RrCtpM/UkAf4q1dvKYjhO+03U9xuvqybTLrlld9g+AIwB7+nsbwP8DuQiv6WMJgP8JwCej51tz7QUAbwEY6f9/C9KQtqbWH9XHfxa7Ta2/ob+PAHwT0tDa199HF9b3Ywj1+k9ht4H75/X3l7DbAPxGtMbLWtNjPU4X1xU99mvYrddf2rH6Nmu6zGvqJwD8HoCjC895rMfpO1jXY7+unqSfS1/AZf8A+H4AvwPgS5C+wX+sj/8ZAF/Vn/8SocH8rwD4st4s/wjAvxS918f0Pb4O4C9Er2kB+F8AvApBQrwQveaT+virAP6Nh6wvvkkOIM23r+m/o+h5v6Cf+xUouuIy1/S4j9ND1vXHIBFgAeA+gF+9AsfqoWt63MfqwppehdTRv6A/n76M4/Ru1/W4j9WT9lNTV9RWW2211Qag7iHUVltttdWmVjuE2mqrrbbaANQOobbaaqutNrXaIdRWW2211Qagdgi11VZbbbWp1Q6httpqq602ALVDqK222mqrTa12CLXVpkZE/4zy77eIqEuiXfB9l72u2mp7XFYPptVWW2RE9J9BJlvbAN5k5v/ikpdUW22PzWqHUFttkRFRA8KJswbwzzJzdclLqq22x2Z1yai22nZtBKAHoTxvXfJaaqvtsVqdIdRWW2RE9HcgNMzPQ5Tg/p1LXlJttT02yy57AbXVdlWMiP40gJKZf5GIUgD/LxH9ODP/n5e9ttpqexxWZwi11VZbbbUBqHsItdVWW221qdUOobbaaqutNgC1Q6itttpqq02tdgi11VZbbbUBqB1CbbXVVlttarVDqK222mqrDUDtEGqrrbbaalP7/wCG/0PuvE6UKwAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "xr_array.sel(band=\"b4\").plot();  # add .line() -> ValueError: For 2D inputs, please specify either hue, x or y."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 703,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "xr_array.sel(x=420000, method=\"nearest\").plot.line(hue=\"band\");"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`facetting` splits the data in subplots according to a dimension, e.g. `band`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 704,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 504x216 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "xr_array.sel(x=420000, method=\"nearest\").plot.line(col=\"band\");  # row=\"band\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Use the `robust` option when there is a lack of visual difference. This will use the 2nd and 98th percentiles of the data to compute the color limits. The arrows on the color bar indicate that the colors include data points outside the bounds."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 706,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 864x360 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "ax = xr_array.sel(band=\"b4\").plot(cmap=\"Reds\", robust=True, figsize=(12, 5))\n",
-    "ax.axes.set_aspect('equal')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Compare data variables within a `xarray Dataset`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 707,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.collections.PathCollection at 0x7f1232325ad0>"
-      ]
-     },
-     "execution_count": 707,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "xr_data = xr_array.to_dataset(dim=\"band\")\n",
-    "xr_data.plot.scatter(x=\"b4\", y=\"b8\", s=2)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Calculating and plotting the NDVI in three classes illustrates the options of the `imshow` method:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 364,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.image.AxesImage at 0x7f1237c5a950>"
-      ]
-     },
-     "execution_count": 364,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "xr_data[\"ndvi\"] = (xr_data[\"b8\"] - xr_data[\"b4\"])/(xr_data[\"b8\"] + xr_data[\"b4\"])\n",
-    "xr_data[\"ndvi\"].plot.imshow(levels=[-1, 0, 0.3, 1.], colors=[\"gray\", \"yellowgreen\", \"g\"])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Let's practice!"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The data set for the following exercises is from [Argo floats](https://argo.ucsd.edu/), an international collaboration that collects high-quality temperature and salinity profiles from the upper 2000m of the ice-free global ocean and currents from intermediate depths.\n",
-    "\n",
-    "These data do not represent full coverage image data (like remote sensing images), but measurements of salinity and temperature as a function of water `level` (related to the pressure). Each measurements happens at a given `date` on a given location (`lon`/`lat`)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 371,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "argo = xr.load_dataset(\"./data/argo_float.nc\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 372,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
-       "<defs>\n",
-       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
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-       "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
-       "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
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-       "</svg>\n",
-       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
-       " *\n",
-       " */\n",
-       "\n",
-       ":root {\n",
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-       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
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-       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
-       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
-       "}\n",
-       "\n",
-       "html[theme=dark],\n",
-       "body.vscode-dark {\n",
-       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
-       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
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-       "  --xr-border-color: #1F1F1F;\n",
-       "  --xr-disabled-color: #515151;\n",
-       "  --xr-background-color: #111111;\n",
-       "  --xr-background-color-row-even: #111111;\n",
-       "  --xr-background-color-row-odd: #313131;\n",
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-       "\n",
-       ".xr-wrap {\n",
-       "  display: block;\n",
-       "  min-width: 300px;\n",
-       "  max-width: 700px;\n",
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-       "\n",
-       ".xr-text-repr-fallback {\n",
-       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
-       "  display: none;\n",
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-       "  padding-bottom: 6px;\n",
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-       "\n",
-       ".xr-header > div,\n",
-       ".xr-header > ul {\n",
-       "  display: inline;\n",
-       "  margin-top: 0;\n",
-       "  margin-bottom: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-obj-type,\n",
-       ".xr-array-name {\n",
-       "  margin-left: 2px;\n",
-       "  margin-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-obj-type {\n",
-       "  color: var(--xr-font-color2);\n",
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-       "\n",
-       ".xr-sections {\n",
-       "  padding-left: 0 !important;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
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-       "\n",
-       ".xr-section-item {\n",
-       "  display: contents;\n",
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-       "\n",
-       ".xr-section-item input {\n",
-       "  display: none;\n",
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-       "\n",
-       ".xr-section-item input + label {\n",
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-       "\n",
-       ".xr-section-item input:enabled + label {\n",
-       "  cursor: pointer;\n",
-       "  color: var(--xr-font-color2);\n",
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-       "  color: var(--xr-font-color0);\n",
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-       "  grid-column: 1;\n",
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-       "  font-weight: 500;\n",
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-       "\n",
-       ".xr-section-summary > span {\n",
-       "  display: inline-block;\n",
-       "  padding-left: 0.5em;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:disabled + label {\n",
-       "  color: var(--xr-font-color2);\n",
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-       "\n",
-       ".xr-section-summary-in + label:before {\n",
-       "  display: inline-block;\n",
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-       "  font-size: 11px;\n",
-       "  width: 15px;\n",
-       "  text-align: center;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:disabled + label:before {\n",
-       "  color: var(--xr-disabled-color);\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:checked + label:before {\n",
-       "  content: '▼';\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:checked + label > span {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary,\n",
-       ".xr-section-inline-details {\n",
-       "  padding-top: 4px;\n",
-       "  padding-bottom: 4px;\n",
-       "}\n",
-       "\n",
-       ".xr-section-inline-details {\n",
-       "  grid-column: 2 / -1;\n",
-       "}\n",
-       "\n",
-       ".xr-section-details {\n",
-       "  display: none;\n",
-       "  grid-column: 1 / -1;\n",
-       "  margin-bottom: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
-       "  display: contents;\n",
-       "}\n",
-       "\n",
-       ".xr-array-wrap {\n",
-       "  grid-column: 1 / -1;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 20px auto;\n",
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-       "\n",
-       ".xr-array-wrap > label {\n",
-       "  grid-column: 1;\n",
-       "  vertical-align: top;\n",
-       "}\n",
-       "\n",
-       ".xr-preview {\n",
-       "  color: var(--xr-font-color3);\n",
-       "}\n",
-       "\n",
-       ".xr-array-preview,\n",
-       ".xr-array-data {\n",
-       "  padding: 0 5px !important;\n",
-       "  grid-column: 2;\n",
-       "}\n",
-       "\n",
-       ".xr-array-data,\n",
-       ".xr-array-in:checked ~ .xr-array-preview {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-array-in:checked ~ .xr-array-data,\n",
-       ".xr-array-preview {\n",
-       "  display: inline-block;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list {\n",
-       "  display: inline-block !important;\n",
-       "  list-style: none;\n",
-       "  padding: 0 !important;\n",
-       "  margin: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list li {\n",
-       "  display: inline-block;\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list:before {\n",
-       "  content: '(';\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list:after {\n",
-       "  content: ')';\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list li:not(:last-child):after {\n",
-       "  content: ',';\n",
-       "  padding-right: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-has-index {\n",
-       "  font-weight: bold;\n",
-       "}\n",
-       "\n",
-       ".xr-var-list,\n",
-       ".xr-var-item {\n",
-       "  display: contents;\n",
-       "}\n",
-       "\n",
-       ".xr-var-item > div,\n",
-       ".xr-var-item label,\n",
-       ".xr-var-item > .xr-var-name span {\n",
-       "  background-color: var(--xr-background-color-row-even);\n",
-       "  margin-bottom: 0;\n",
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-       "\n",
-       ".xr-var-item > .xr-var-name:hover span {\n",
-       "  padding-right: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-var-list > li:nth-child(odd) > div,\n",
-       ".xr-var-list > li:nth-child(odd) > label,\n",
-       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
-       "  background-color: var(--xr-background-color-row-odd);\n",
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-       "\n",
-       ".xr-var-name {\n",
-       "  grid-column: 1;\n",
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-       "\n",
-       ".xr-var-dims {\n",
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-       "\n",
-       ".xr-var-dtype {\n",
-       "  grid-column: 3;\n",
-       "  text-align: right;\n",
-       "  color: var(--xr-font-color2);\n",
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-       "\n",
-       ".xr-var-preview {\n",
-       "  grid-column: 4;\n",
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-       "\n",
-       ".xr-var-name,\n",
-       ".xr-var-dims,\n",
-       ".xr-var-dtype,\n",
-       ".xr-preview,\n",
-       ".xr-attrs dt {\n",
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-       "\n",
-       ".xr-var-name:hover,\n",
-       ".xr-var-dims:hover,\n",
-       ".xr-var-dtype:hover,\n",
-       ".xr-attrs dt:hover {\n",
-       "  overflow: visible;\n",
-       "  width: auto;\n",
-       "  z-index: 1;\n",
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-       "\n",
-       ".xr-var-attrs,\n",
-       ".xr-var-data {\n",
-       "  display: none;\n",
-       "  background-color: var(--xr-background-color) !important;\n",
-       "  padding-bottom: 5px !important;\n",
-       "}\n",
-       "\n",
-       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
-       ".xr-var-data-in:checked ~ .xr-var-data {\n",
-       "  display: block;\n",
-       "}\n",
-       "\n",
-       ".xr-var-data > table {\n",
-       "  float: right;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name span,\n",
-       ".xr-var-data,\n",
-       ".xr-attrs {\n",
-       "  padding-left: 25px !important;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs,\n",
-       ".xr-var-attrs,\n",
-       ".xr-var-data {\n",
-       "  grid-column: 1 / -1;\n",
-       "}\n",
-       "\n",
-       "dl.xr-attrs {\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 125px auto;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt, dd {\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  float: left;\n",
-       "  padding-right: 10px;\n",
-       "  width: auto;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt {\n",
-       "  font-weight: normal;\n",
-       "  grid-column: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt:hover span {\n",
-       "  display: inline-block;\n",
-       "  background: var(--xr-background-color);\n",
-       "  padding-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dd {\n",
-       "  grid-column: 2;\n",
-       "  white-space: pre-wrap;\n",
-       "  word-break: break-all;\n",
-       "}\n",
-       "\n",
-       ".xr-icon-database,\n",
-       ".xr-icon-file-text2 {\n",
-       "  display: inline-block;\n",
-       "  vertical-align: middle;\n",
-       "  width: 1em;\n",
-       "  height: 1.5em !important;\n",
-       "  stroke-width: 0;\n",
-       "  stroke: currentColor;\n",
-       "  fill: currentColor;\n",
-       "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
-       "Dimensions:      (date: 75, level: 78)\n",
-       "Coordinates:\n",
-       "  * level        (level) int32 0 1 2 3 4 5 6 7 8 ... 69 70 71 72 73 74 75 76 77\n",
-       "  * date         (date) datetime64[ns] 2012-07-13T22:33:06.019200 ... 2014-07...\n",
-       "    lon          (date) float64 -39.13 -37.28 -36.9 ... -33.83 -34.11 -34.38\n",
-       "    lat          (date) float64 47.19 46.72 46.45 46.23 ... 42.6 42.46 42.38\n",
-       "Data variables:\n",
-       "    salinity     (level, date) float64 35.64 35.51 35.57 35.4 ... nan 34.94 nan\n",
-       "    temperature  (level, date) float64 18.97 18.44 19.1 19.79 ... nan 3.714 nan\n",
-       "    pressure     (level, date) float64 6.8 6.1 6.5 5.0 ... 2e+03 nan 2e+03 nan</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-d1115e2d-c9c5-474d-a029-41d44ddaac1b' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-d1115e2d-c9c5-474d-a029-41d44ddaac1b' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>date</span>: 75</li><li><span class='xr-has-index'>level</span>: 78</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-5d2e800c-739b-4dc7-af93-3a22c800170c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-5d2e800c-739b-4dc7-af93-3a22c800170c' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>level</span></div><div class='xr-var-dims'>(level)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 ... 72 73 74 75 76 77</div><input id='attrs-a0b2dc88-ff92-4e18-b65d-cca50e1ec4e9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a0b2dc88-ff92-4e18-b65d-cca50e1ec4e9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-09542a21-d03b-4592-af55-d3f0f1aeb834' class='xr-var-data-in' type='checkbox'><label for='data-09542a21-d03b-4592-af55-d3f0f1aeb834' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
-       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
-       "       36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,\n",
-       "       54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71,\n",
-       "       72, 73, 74, 75, 76, 77], dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>date</span></div><div class='xr-var-dims'>(date)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2012-07-13T22:33:06.019200 ... 2...</div><input id='attrs-39098bdf-efbb-4ebd-a703-e209529fa2c1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-39098bdf-efbb-4ebd-a703-e209529fa2c1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3799dbaf-1b70-4f9f-9c96-30bd10f04c7b' class='xr-var-data-in' type='checkbox'><label for='data-3799dbaf-1b70-4f9f-9c96-30bd10f04c7b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2012-07-13T22:33:06.019200000&#x27;, &#x27;2012-07-23T22:54:59.990400000&#x27;,\n",
-       "       &#x27;2012-08-02T22:55:52.003200000&#x27;, &#x27;2012-08-12T23:08:59.971200000&#x27;,\n",
-       "       &#x27;2012-08-22T23:29:01.968000000&#x27;, &#x27;2012-09-01T23:17:38.976000000&#x27;,\n",
-       "       &#x27;2012-09-12T02:59:18.960000000&#x27;, &#x27;2012-09-21T23:18:37.036800000&#x27;,\n",
-       "       &#x27;2012-10-02T03:00:17.971200000&#x27;, &#x27;2012-10-11T23:13:27.984000000&#x27;,\n",
-       "       &#x27;2012-10-22T02:50:32.006399999&#x27;, &#x27;2012-10-31T23:36:39.974400000&#x27;,\n",
-       "       &#x27;2012-11-11T02:40:46.041600000&#x27;, &#x27;2012-11-20T23:08:29.990400000&#x27;,\n",
-       "       &#x27;2012-12-01T02:47:51.993600000&#x27;, &#x27;2012-12-10T23:23:16.972800000&#x27;,\n",
-       "       &#x27;2012-12-21T02:58:48.979200000&#x27;, &#x27;2012-12-30T23:07:23.030400000&#x27;,\n",
-       "       &#x27;2013-01-10T02:56:43.008000000&#x27;, &#x27;2013-01-19T23:24:26.956800000&#x27;,\n",
-       "       &#x27;2013-01-30T02:43:53.011199998&#x27;, &#x27;2013-02-08T23:15:27.043200000&#x27;,\n",
-       "       &#x27;2013-02-19T01:12:50.976000000&#x27;, &#x27;2013-02-28T23:07:13.008000000&#x27;,\n",
-       "       &#x27;2013-03-11T02:43:30.979200000&#x27;, &#x27;2013-03-20T23:17:22.992000000&#x27;,\n",
-       "       &#x27;2013-03-31T01:50:38.025600000&#x27;, &#x27;2013-04-09T23:19:07.968000000&#x27;,\n",
-       "       &#x27;2013-04-20T02:53:29.990400000&#x27;, &#x27;2013-04-29T23:28:33.024000000&#x27;,\n",
-       "       &#x27;2013-05-10T02:50:18.009600000&#x27;, &#x27;2013-05-19T23:21:05.990400000&#x27;,\n",
-       "       &#x27;2013-05-30T02:50:30.969600000&#x27;, &#x27;2013-06-08T23:32:49.027200000&#x27;,\n",
-       "       &#x27;2013-06-19T03:42:51.004800000&#x27;, &#x27;2013-06-28T23:32:16.022400000&#x27;,\n",
-       "       &#x27;2013-07-09T03:28:30.979200000&#x27;, &#x27;2013-07-18T23:33:57.974400000&#x27;,\n",
-       "       &#x27;2013-07-29T03:15:42.019200000&#x27;, &#x27;2013-08-07T23:25:02.035200004&#x27;,\n",
-       "       &#x27;2013-08-18T01:47:44.966400000&#x27;, &#x27;2013-08-28T03:02:59.020799996&#x27;,\n",
-       "       &#x27;2013-09-07T03:03:51.984000000&#x27;, &#x27;2013-09-16T23:32:07.987200000&#x27;,\n",
-       "       &#x27;2013-09-27T04:08:27.974400000&#x27;, &#x27;2013-10-06T23:25:39.964800000&#x27;,\n",
-       "       &#x27;2013-10-17T02:55:50.995200000&#x27;, &#x27;2013-10-27T03:45:47.001600000&#x27;,\n",
-       "       &#x27;2013-11-06T01:14:52.022400000&#x27;, &#x27;2013-11-16T03:29:54.009600000&#x27;,\n",
-       "       &#x27;2013-11-26T03:03:56.995200000&#x27;, &#x27;2013-12-05T23:33:59.011200000&#x27;,\n",
-       "       &#x27;2013-12-16T02:58:01.977600000&#x27;, &#x27;2013-12-25T23:22:43.017600000&#x27;,\n",
-       "       &#x27;2014-01-05T02:52:06.009600000&#x27;, &#x27;2014-01-14T23:41:18.009600000&#x27;,\n",
-       "       &#x27;2014-01-25T03:00:43.977600000&#x27;, &#x27;2014-02-03T23:29:13.977600000&#x27;,\n",
-       "       &#x27;2014-02-14T02:50:11.961600000&#x27;, &#x27;2014-02-23T23:03:21.974400000&#x27;,\n",
-       "       &#x27;2014-03-06T02:58:03.964800000&#x27;, &#x27;2014-03-15T23:10:28.012800000&#x27;,\n",
-       "       &#x27;2014-03-26T02:51:22.032000000&#x27;, &#x27;2014-04-04T23:25:58.972800000&#x27;,\n",
-       "       &#x27;2014-04-15T03:00:45.964800000&#x27;, &#x27;2014-04-24T23:24:40.003200000&#x27;,\n",
-       "       &#x27;2014-05-05T02:56:22.012800000&#x27;, &#x27;2014-05-15T00:10:06.009600000&#x27;,\n",
-       "       &#x27;2014-05-25T03:02:43.036800000&#x27;, &#x27;2014-06-03T23:34:53.961600000&#x27;,\n",
-       "       &#x27;2014-06-14T03:01:23.980800000&#x27;, &#x27;2014-06-23T23:24:31.968000000&#x27;,\n",
-       "       &#x27;2014-07-04T03:08:30.019200000&#x27;, &#x27;2014-07-13T23:47:43.008000000&#x27;,\n",
-       "       &#x27;2014-07-24T03:02:33.014400000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>(date)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-39.13 -37.28 ... -34.11 -34.38</div><input id='attrs-b4620e3c-12dd-49b7-a05a-b5520e3a9ce9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b4620e3c-12dd-49b7-a05a-b5520e3a9ce9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e69f2987-c2f2-4473-9f8f-ccfbb341ae87' class='xr-var-data-in' type='checkbox'><label for='data-e69f2987-c2f2-4473-9f8f-ccfbb341ae87' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([-39.13 , -37.282, -36.9  , -36.89 , -37.053, -36.658, -35.963,\n",
-       "       -35.184, -34.462, -33.784, -32.972, -32.546, -32.428, -32.292,\n",
-       "       -32.169, -31.998, -31.824, -31.624, -31.433, -31.312, -31.107,\n",
-       "       -31.147, -31.044, -31.14 , -31.417, -31.882, -32.145, -32.487,\n",
-       "       -32.537, -32.334, -32.042, -31.892, -31.861, -31.991, -31.883,\n",
-       "       -31.89 , -31.941, -31.889, -31.724, -31.412, -31.786, -31.561,\n",
-       "       -31.732, -31.553, -31.862, -32.389, -32.318, -32.19 , -32.224,\n",
-       "       -32.368, -32.306, -32.305, -32.65 , -33.093, -33.263, -33.199,\n",
-       "       -33.27 , -33.237, -33.221, -33.011, -32.844, -32.981, -32.784,\n",
-       "       -32.607, -32.87 , -33.196, -33.524, -33.956, -33.944, -33.71 ,\n",
-       "       -33.621, -33.552, -33.828, -34.11 , -34.38 ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>(date)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>47.19 46.72 46.45 ... 42.46 42.38</div><input id='attrs-44839768-d592-473c-b9b7-559b63a751dc' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-44839768-d592-473c-b9b7-559b63a751dc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-759bd92f-6652-40fa-bc2f-95440e7b2005' class='xr-var-data-in' type='checkbox'><label for='data-759bd92f-6652-40fa-bc2f-95440e7b2005' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([47.187, 46.716, 46.45 , 46.23 , 45.459, 44.833, 44.452, 44.839,\n",
-       "       44.956, 44.676, 44.13 , 43.644, 43.067, 42.662, 42.513, 42.454,\n",
-       "       42.396, 42.256, 42.089, 41.944, 41.712, 41.571, 41.596, 41.581,\n",
-       "       41.351, 41.032, 40.912, 40.792, 40.495, 40.383, 40.478, 40.672,\n",
-       "       41.032, 40.864, 40.651, 40.425, 40.228, 40.197, 40.483, 40.311,\n",
-       "       40.457, 40.463, 40.164, 40.047, 39.963, 40.122, 40.57 , 40.476,\n",
-       "       40.527, 40.589, 40.749, 40.993, 41.162, 41.237, 41.448, 41.65 ,\n",
-       "       42.053, 42.311, 42.096, 41.683, 41.661, 41.676, 42.018, 42.395,\n",
-       "       42.532, 42.558, 42.504, 42.63 , 42.934, 42.952, 42.777, 42.722,\n",
-       "       42.601, 42.457, 42.379])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f956f0f0-c330-4a1b-ad2d-69ed51b8cbff' class='xr-section-summary-in' type='checkbox'  checked><label for='section-f956f0f0-c330-4a1b-ad2d-69ed51b8cbff' class='xr-section-summary' >Data variables: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>salinity</span></div><div class='xr-var-dims'>(level, date)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>35.64 35.51 35.57 ... nan 34.94 nan</div><input id='attrs-7d7d892a-c04d-445b-92fd-a7c25bfdaf42' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7d7d892a-c04d-445b-92fd-a7c25bfdaf42' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3e8c3a50-3bde-4858-a8e1-c545d19c89ff' class='xr-var-data-in' type='checkbox'><label for='data-3e8c3a50-3bde-4858-a8e1-c545d19c89ff' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[35.6389389 , 35.51495743, 35.57297134, ..., 35.82093811,\n",
-       "        35.77793884, 35.66891098],\n",
-       "       [35.63393784, 35.5219574 , 35.57397079, ..., 35.81093216,\n",
-       "        35.58389664, 35.66791153],\n",
-       "       [35.6819458 , 35.52595901, 35.57297134, ..., 35.79592896,\n",
-       "        35.66290665, 35.66591263],\n",
-       "       ...,\n",
-       "       [34.91585922, 34.92390442, 34.92390442, ..., 34.93481064,\n",
-       "        34.94081116, 34.94680786],\n",
-       "       [34.91585922, 34.92390442, 34.92190552, ..., 34.93280792,\n",
-       "        34.93680954, 34.94380951],\n",
-       "       [34.91785812, 34.92390442, 34.92390442, ...,         nan,\n",
-       "        34.93680954,         nan]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>temperature</span></div><div class='xr-var-dims'>(level, date)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>18.97 18.44 19.1 ... nan 3.714 nan</div><input id='attrs-f7b0d543-1bd6-4adc-893d-f55d7c1fb324' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f7b0d543-1bd6-4adc-893d-f55d7c1fb324' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3a2551fb-eefa-4d54-8233-99d424298f07' class='xr-var-data-in' type='checkbox'><label for='data-3a2551fb-eefa-4d54-8233-99d424298f07' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[18.97400093, 18.43700027, 19.09900093, ..., 19.11300087,\n",
-       "        21.82299995, 20.13100052],\n",
-       "       [18.74099922, 18.39999962, 19.08200073, ..., 18.47200012,\n",
-       "        19.45999908, 20.125     ],\n",
-       "       [18.37000084, 18.37400055, 19.06500053, ..., 18.22999954,\n",
-       "        19.26199913, 20.07699966],\n",
-       "       ...,\n",
-       "       [ 3.79299998,  3.81399989,  3.80200005, ...,  3.80699992,\n",
-       "         3.81100011,  3.8599999 ],\n",
-       "       [ 3.76399994,  3.77800012,  3.75699997, ...,  3.75399995,\n",
-       "         3.74600005,  3.80599999],\n",
-       "       [ 3.74399996,  3.74600005,  3.7249999 , ...,         nan,\n",
-       "         3.71399999,         nan]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pressure</span></div><div class='xr-var-dims'>(level, date)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>6.8 6.1 6.5 5.0 ... nan 2e+03 nan</div><input id='attrs-36ab42ae-12a5-4718-b85e-74ae6f7b590b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-36ab42ae-12a5-4718-b85e-74ae6f7b590b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8b05f870-a01c-431f-8343-a5d30788e118' class='xr-var-data-in' type='checkbox'><label for='data-8b05f870-a01c-431f-8343-a5d30788e118' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[   6.80000019,    6.0999999 ,    6.5       , ...,    7.0999999 ,\n",
-       "           7.20000029,    6.5       ],\n",
-       "       [  10.69999981,   10.59999943,   10.39999962, ...,   10.79999924,\n",
-       "          11.09999943,   10.39999962],\n",
-       "       [  15.69999981,   14.09999943,   14.89999962, ...,   15.89999962,\n",
-       "          15.59999943,   15.89999962],\n",
-       "       ...,\n",
-       "       [1900.60009766, 1900.        , 1900.19995117, ..., 1899.70007324,\n",
-       "        1900.40002441, 1899.80004883],\n",
-       "       [1949.90002441, 1950.        , 1949.89990234, ..., 1950.59997559,\n",
-       "        1950.20007324, 1949.70007324],\n",
-       "       [1999.30004883, 1998.        , 1998.5       , ...,           nan,\n",
-       "        2000.40002441,           nan]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-6357ac3c-f593-44d9-8566-512236572d31' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-6357ac3c-f593-44d9-8566-512236572d31' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
-      ],
-      "text/plain": [
-       "<xarray.Dataset>\n",
-       "Dimensions:      (date: 75, level: 78)\n",
-       "Coordinates:\n",
-       "  * level        (level) int32 0 1 2 3 4 5 6 7 8 ... 69 70 71 72 73 74 75 76 77\n",
-       "  * date         (date) datetime64[ns] 2012-07-13T22:33:06.019200 ... 2014-07...\n",
-       "    lon          (date) float64 -39.13 -37.28 -36.9 ... -33.83 -34.11 -34.38\n",
-       "    lat          (date) float64 47.19 46.72 46.45 46.23 ... 42.6 42.46 42.38\n",
-       "Data variables:\n",
-       "    salinity     (level, date) float64 35.64 35.51 35.57 35.4 ... nan 34.94 nan\n",
-       "    temperature  (level, date) float64 18.97 18.44 19.1 19.79 ... nan 3.714 nan\n",
-       "    pressure     (level, date) float64 6.8 6.1 6.5 5.0 ... 2e+03 nan 2e+03 nan"
-      ]
-     },
-     "execution_count": 372,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "argo"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The bold font (or * symbol in plain text output version) in the coordinate representation above indicates that x and y are 'dimension coordinates' (they describe the coordinates associated with data variable axes) while band is a 'non-dimension coordinates'. We can make any variable a non-dimension coordinate."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let's plot the coordinates of the available measurements and add a background map using [contextly](https://contextily.readthedocs.io/en/latest/index.html):"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 605,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x432 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import contextily as cx\n",
-    "from pyproj import CRS\n",
-    "crs = CRS.from_epsg(4326)\n",
-    "\n",
-    "fig, ax = plt.subplots(figsize=(6, 6))\n",
-    "argo.plot.scatter(x='lon', y='lat', ax=ax, color='red', s=2)\n",
-    "\n",
-    "# Custom adjustments of the limits, as we are in the middle of the ocean\n",
-    "xmin, xmax = ax.get_xlim()\n",
-    "ymin, ymax = ax.get_ylim()\n",
-    "ax.set_xlim(xmin*1.5, xmax*0.5)\n",
-    "ax.set_ylim(ymin*0.5, ymax*1.5)\n",
-    "\n",
-    "cx.add_basemap(ax, crs=crs.to_string())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<div class=\"alert alert-success\">\n",
-    "\n",
-    "**EXERCISE**:\n",
-    "\n",
-    "Add a new variable to the `argo` data set, called `temperature_kelvin`, by converting the temperature to Kelvin. \n",
-    "    \n",
-    "Degrees Kelvin = degrees celsius + 273.\n",
-    "   \n",
-    "<details>\n",
-    "    \n",
-    "<summary>Hints</summary>\n",
-    "\n",
-    "* Remember that xarray works as Numpy and relies on the same broadcasting rules.\n",
-    "\n",
-    "</details>    \n",
-    "    \n",
-    "</div>"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 421,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "argo[\"temperature_kelvin\"] = argo[\"temperature\"] + 273.15"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<div class=\"alert alert-success\">\n",
-    "\n",
-    "**EXERCISE**:\n",
-    "\n",
-    "The water level classes define different water depths. The pressure is a proxy for the water depth. Verify the relationship between the pressure and the level using a scatter plot. Does a larger value for the level represent deeper water depths or not?\n",
-    "    \n",
-    "<details><summary>Hints</summary>\n",
-    "    \n",
-    "* If you get the error `ValueError: Dataset.plot cannot be called directly. Use an explicit plot method, e.g. ds.plot.scatter(...)`, read the message and do what it says.\n",
-    "\n",
-    "</details>    \n",
-    "    \n",
-    "</div>"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 457,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.collections.PathCollection at 0x7f1239bffad0>"
-      ]
-     },
-     "execution_count": 457,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX4AAAEICAYAAABYoZ8gAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjAsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8GearUAAAZOElEQVR4nO3df7TcdX3n8efLBCQgNKTc5FwiMWDT7FKpid4jsFgP24iAuCR2NwgHdq81x2x31TW6yxJWDtYePE2L9djd9rQGa00XiokoSY7aYkybeuRA9EKCgCEb0BhIrskVZEkVRdj3/jGfK5ObmXtn5s7nOz++r8c5OTPzmfnO9833Xl73M5/Pdz5fRQRmZlYer+h0AWZmViwHv5lZyTj4zcxKxsFvZlYyDn4zs5Jx8JuZlUzW4Jf0IUmPSnpE0p2STpI0R9I2SfvS7ek5azAzs2Mp13n8kuYD3wTOjYjnJW0CvgqcCzwTEeskrQVOj4gbJnuvM844IxYuXJilTjOzfvXAAw/8KCIGJrbPzLzfmcAsSb8ATgYOATcCF6fnNwA7gEmDf+HChYyMjOSr0sysD0n6Qa32bEM9EXEQ+ARwABgF/m9EfA2YFxGj6TWjwNxcNZiZ2fGyBX8au18OnA2cCZwi6bomtl8taUTSyNjYWK4yzcxKJ+fk7luB70fEWET8AvgS8K+Aw5IGAdLtkVobR8T6iBiKiKGBgeOGqMzMrEU5g/8AcIGkkyUJWAbsAbYCw+k1w8CWjDWYmdkE2SZ3I2KnpLuAB4EXgV3AeuBVwCZJq6j8cViZqwYzMzte1rN6IuKjwEcnNP+cSu/fzMxqWLj2K8e1fepdS1ixdH5b3t/f3DUz6yK1Qh9gzcbdbN51sC37cPCbmfWIW+/Z25b3yf0FLjMzq6FWz37/uism3ebQs8+3Zd/u8ZuZFazecE699nFnzp7Vlv27x29mlkErPfqpXH/p4mltP849fjOzNmu1Rw/1/zi086we9/jNzLrMdD8ZTMXBb2bWgnq999yh3Q4e6jEza9JkQzbTGc4p6o+Ge/xmZh3QyU8GDn4zszpaPTNn/7orspzV0y4OfjOzGqZzZg50T8jX4uA3s9Lq5l55Tp7cNbNSynGu/VTPdQv3+M3MWtALAV+Pg9/M+tZNmx/m9vsPHNfey6HdDh7qMbO+VC/0oTfOtc8pW49f0mJgY1XTOcDNwN+k9oXAfuCqiPhxrjrMrL/Vm6C9c+eT037vfgj5WrL1+CNib0QsiYglwBuBnwJ3A2uB7RGxCNieHpuZNW2yCdqXIibdtp979FMpaox/GfBERPxA0nLg4tS+AdgB3FBQHWZWEjOklsO/3xUV/FcDd6b78yJiFCAiRiXNrbWBpNXAaoAFCxYUUqSZdZ9WF0O75vyz6o7xl132yV1JJwJXAl9oZruIWB8RQxExNDAwkKc4M+tq01kM7ZYV53HdBbU7jWXt6Y8rosd/OfBgRBxOjw9LGky9/UHgSAE1mFkXy/UN2ltWnMctK86b9vv0myJO57yGl4d5ALYCw+n+MLClgBrMrEvl+AZt2Xv0U8na45d0MnAJ8B+rmtcBmyStAg4AK3PWYGb9zSHfvKzBHxE/BX51QtvTVM7yMbOSKOtiaN3K39w1s6zKvBhat/JaPWY2bdfedh/3PvHMce3tCGcHfPu5x29m01Iv9METtN3KPX4zm5Z6od8Mh3yxHPxm1hBP0PYPD/WY2ZR8rn1/cY/fzKblotfOmXK4xyHfXRz8Zga0vhjaHe+9MOtZPdZ+Dn4zm9ZiaFAJf+sdDn6zEvEErYEnd81KwxO0Ns49fjNriEO+fzj4zfqIh3KsER7qMesTXgzNGuUev1kP2bzrIGs27j6u3YuhWTPc4zfrEfVCHzxBa81xj9+sR9x6z95pv4dD3iD/pRdnA58BXgcE8B5gL7ARWAjsB66KiB/nrMOsl9SboD307PMdqMb6Ue6hnj8F/j4i/gXwemAPsBbYHhGLgO3psZkx+QTtmbNnTbqth3KsUdl6/JJOA94CvBsgIl4AXpC0HLg4vWwDsAO4IVcdZt2m1TVxrr90cd0x/kbfwwzy9vjPAcaAv5a0S9JnJJ0CzIuIUYB0O7fWxpJWSxqRNDI2NpaxTLPiTGdNnBVL5/Opdy2p+ZwD35qRc4x/JvAG4AMRsVPSn9LEsE5ErAfWAwwNDUWeEs16y4ql81mxdH6ny7AelzP4nwKeioid6fFdVIL/sKTBiBiVNAgcyViDWUf4G7TWzbIN9UTED4EnJS1OTcuA7wJbgeHUNgxsyVWDWSd4MTTrdrnP4/8AcIekE4HvAb9L5Y/NJkmrgAPAysw1mPUUh7zlljX4I2I3MFTjqWU592uWW6tDOfvXXdHyWT1m7eJv7po1abKhnEbD36yTHPxmdXiC1vqVF2kzq8ETtNbP3OM3y8Ahb93MwW+l5aEcKysP9VgpeSjHysw9futbOU+bdMhbL3OP3/rSdBZDA/fqrb+5x29Wh0Pe+pWD33qaJ2jNmuehHutZnqA1a417/NbVcvboHfJWVu7xW9fK0aOf6jmzMnCP3/qWA96sNge/ddQln9zBviM/Oa7doW2Wj4d6rGPqhT54gtYsp6w9fkn7gaPAS8CLETEkaQ6wEVgI7Aeuiogf56zDulO90G+GQ96seUX0+P91RCyJiPErca0FtkfEImB7emwlcdPmh1m49ivu0Zt1UCfG+JcDF6f7G4AdwA0dqMMKdtPmh7n9/gNNbeOQN2u/3MEfwNckBfDpiFgPzIuIUYCIGJU0N3MN1mGN9O7NrDi5g/+iiDiUwn2bpMca3VDSamA1wIIFC3LVZ5m1Gvru6ZvlkzX4I+JQuj0i6W7gTcBhSYOptz8IHKmz7XpgPcDQ0FDkrNO6g8PerBjZgl/SKcArIuJouv824A+ArcAwsC7dbslVg3WGh3bMulvOHv884G5J4/v524j4e0nfBjZJWgUcAFZmrMEK5qEds+6XLfgj4nvA62u0Pw0sy7VfK57D3qy3+Ju7Ni0OfbPe47V6rDAOe7Pu4OC3pm3edZA1G3d3ugwza5GHeqwprYa+e/tm3cM9fptSq2G/aO4pbPvwxe0vyMymxT1+m5RD36z/OPhtUrfes7fpbebPnuXQN+tiHuopqVqnYc479UR2fuSSY9oOPft8U+8764QZXH/p4mnVZmZ5ucdfQvXOvT989AXO//i2Y9rOnD2r4fedP3sWf/g757Fi6fxp1WdmebnHb8c4fPSFYx5ff+niScf4fbaOWe9x8JdEKxdBAX7Ze68V/g59s97k4C+BVkN/3Iql8z18Y9ZHHPx9rJV1dOademKGSsysm3hyt0+1GvoTz+oxs/7jHr95rN6sZBz8fcRXvjKzRkwa/JKOAuPXu1W6jXQ/IuK0jLVZE7wuvpk1atLgj4hTp7sDSTOAEeBgRLxD0hxgI7AQ2A9cFRE/nu5+yshhb2ataHhyV9KbJf1uun+GpLMb3PSDwJ6qx2uB7RGxCNieHluTHPpm1qqGxvglfRQYAhYDfw2cCNwOXDTFdq8GrgA+Dnw4NS8HLk73NwA7gBuaK9ua4bA3s2qNTu6+E1gKPAgQEYckNTIM9CngvwPVr50XEaPpfUYlza21oaTVwGqABQsWNFhmf/PkrZm1Q6NDPS9ERJAmeiWdMtUGkt4BHImIB1opLCLWR8RQRAwNDAy08hZ9xUM7ZtYujfb4N0n6NDBb0nuB9wC3TbHNRcCVkt4OnAScJul24LCkwdTbHwSOtFq81eawN7PJNNTjj4hPAHcBX6Qyzn9zRPyvKba5MSJeHRELgauBf4iI64CtwHB62TCwpcXaS6OZ3r5D38ym0ujk7oeAL0TEtilfPLV1VD5BrAIOACvb8J6l58A3s0Y1OtRzGnCPpGeAzwN3RcThRncSETuonL1DRDwNLGuuzPJqpLd/0WvnFFCJmfWLRod6PhYRvwG8DzgT+CdJX89amTUc+ne898ICqjGzftHs6pxHgB8CTwM1T8O0Yjn0zaxZDQW/pP8kaQeVb9qeAbw3In4zZ2Fl10hv3+P6ZtaKRsf4XwOsiYj6F1+1Qjn0zaxVjY7xrwVeVbVWz0ATa/VYk/wNXTPLqdGhno9SWU/nxtR0ApW1eqzNPMRjZrk1Orn7TuBK4CdQWauHY9ffMTOzHpFtrR5r3uZdB6d8jXv7ZjZdjQb/xLV6vs7Ua/VYEzbvOsiajZPPnTv0zawdGjqrJyI+IekS4DleXqunHcs3WDJV6JuZtUvDF1tPQe+wz8ATumZWpGYutn7MU/hi62ZmPSn7xdZtco1M6JqZtVOza/VYGzUyoQse5jGz9nLwd5BD38w6wcHfIZ7QNbNOcfCbmZVMtuCXdJKkb0l6SNKjkj6W2udI2iZpX7o9PVcN3coTumbWSTl7/D8HfjsiXg8sAS6TdAGwFtgeEYuorO+/NmMNXccTumbWadmCPyr+OT08If0LYDmwIbVvAFbkqqEbOfTNrNOyjvFLmiFpN5VLNm6LiJ3AvIgYBUi3NS/hKGm1pBFJI2NjYznLLIwndM2sG2QN/oh4KSKWAK8G3iTpdU1suz4ihiJiaGBgIF+RZmYlU8hZPRHxLLADuAw4LGkQIN0eKaKGTjv/417myMy6Q86zegYkzU73ZwFvBR4DtgLD6WXDwJZcNXSL8z++jcNHX5jydR7mMbMiNLw6ZwsGgQ2SZlD5A7MpIr4s6T4q6/uvAg4AKzPW0BUc+mbWTbIFf0R8B1hao/1pYFmu/fYih76ZFcnf3M2skTN5zMyK5ODP6Nrb7ut0CWZmx3HwZ3Ltbfdx7xPPTPk6D/OYWdEc/Jk49M2sWzn4zcxKxsGfgZdmMLNu5uBvM0/omlm3c/C3kSd0zawXOPjbyKFvZr3AwW9mVjIO/jbxhK6Z9QoHfxt4WQYz6yUO/mlqNPTd2zezbuHgL4BD38y6iYPfzKxkHPzT4AldM+tFOS+9eJakf5S0R9Kjkj6Y2udI2iZpX7o9PVcNZmZ2vJw9/heB/xoR/xK4AHifpHOBtcD2iFgEbE+Pe457+2bWq7IFf0SMRsSD6f5RYA8wH1gObEgv2wCsyFVDLg59M+tlhYzxS1pI5fq7O4F5ETEKlT8OwNwiajAzs4rswS/pVcAXgTUR8VwT262WNCJpZGxsLF+BTXJv38x6Xdbgl3QCldC/IyK+lJoPSxpMzw8CR2ptGxHrI2IoIoYGBgZylmlmVio5z+oR8FfAnoj4ZNVTW4HhdH8Y2JKrhnbz0gxm1g9mZnzvi4B/DzwsaXdq+x/AOmCTpFXAAWBlxhraxkszmFm/yBb8EfFNQHWeXpZrv53k0DezXuBv7jbAE7pm1k8c/GZmJePgn4IndM2s3zj4J+EJXTPrRw7+aXLom1mvcfDX4QldM+tXDn4zs5Jx8NfQSG//tFfOKKASM7P2c/BP0Gjof+djlxVQjZlZ+zn4W+DQN7Ne5uBvkid0zazXOfirTDXMM0P1lh4yM+sdDv6kkbH9a84/q4BKzMzycvDTWOhfd8ECbllxXgHVmJnl5eBvkEPfzPqFg9/MrGRKH/xemsHMyibnNXc/K+mIpEeq2uZI2iZpX7o9Pdf+G+HQN7Myytnj/xww8ZtOa4HtEbEI2J4ed4RD38zKKlvwR8Q3gGcmNC8HNqT7G4AVufZvZma1FT3GPy8iRgHS7dx6L5S0WtKIpJGxsbG2FnHT5ofb+n5mZr2kayd3I2J9RAxFxNDAwEDb3vemzQ9z+/0Hpnydh3nMrF8VHfyHJQ0CpNsjBe+fO3c+OeVrHPpm1s+KDv6twHC6PwxsKXj/vBQx6fMOfTPrdzlP57wTuA9YLOkpSauAdcAlkvYBl6THZmZWoJm53jgirqnz1LJc+zQzs6l17eRuDlOdu+9hHjMrg1IFv5mZOfh/yb19MyuL0gR/I0s0mJmVQWmC38zMKkoR/Jt3Hex0CWZmXaMUwX/rPXs7XYKZWdcoRfAfevb5TpdgZtY1ShH8ky/S4DN6zKxcShH8Zmb2sr4Pfn9b18zsWH0f/GZmdiwHv5lZyfR18HuYx8zseH0d/GZmdjwHv5lZyTj4zcxKpiPBL+kySXslPS5pbY59eH0eM7PaCg9+STOAPwcuB84FrpF0bjv3sXnXQdZs3D3pazyxa2Zl1Yke/5uAxyPiexHxAvB5YHk7d+BF2czM6utE8M8Hnqx6/FRqO4ak1ZJGJI2MjY01tYOpFmVzb9/MyqwTwa8abcetoxYR6yNiKCKGBgYGmtrBmbNntVqbmVnf60TwPwWcVfX41cChdu7g+ksXt/PtzMz6SieC/9vAIklnSzoRuBrY2s4drFg6n0+9a0nN5zzMY2ZlN7PoHUbEi5LeD9wDzAA+GxGPtns/K5bOZ8XS46YOzMxKr/DgB4iIrwJf7cS+zczKzt/cNTMrGQe/mVnJOPjNzErGwW9mVjKKOO67U11H0hjwgxY3PwP4URvLaRfX1RzX1RzX1Zx+res1EXHcN2B7IvinQ9JIRAx1uo6JXFdzXFdzXFdzylaXh3rMzErGwW9mVjJlCP71nS6gDtfVHNfVHNfVnFLV1fdj/GZmdqwy9PjNzKxKXwd/Edf2rbPfsyT9o6Q9kh6V9MHU/vuSDkranf69vWqbG1OdeyVdmrG2/ZIeTvsfSW1zJG2TtC/dnl5kXZIWVx2T3ZKek7SmU8dL0mclHZH0SFVb08dI0hvTsX5c0v+UVOtaFNOt61ZJj0n6jqS7Jc1O7QslPV917P6y4Lqa/tkVVNfGqpr2S9qd2gs5XpNkQ7G/XxHRl/+orPz5BHAOcCLwEHBuQfseBN6Q7p8K/B8q1xf+feC/1Xj9uam+VwJnp7pnZKptP3DGhLY/Btam+2uBPyq6rgk/tx8Cr+nU8QLeArwBeGQ6xwj4FnAhlYsP/R1weYa63gbMTPf/qKquhdWvm/A+RdTV9M+uiLomPP8nwM1FHi/qZ0Ohv1/93OPPfm3feiJiNCIeTPePAnuocXnJKsuBz0fEzyPi+8DjVOovynJgQ7q/AVjRwbqWAU9ExGRf2MtaV0R8A3imxj4bPkaSBoHTIuK+qPxf+jdV27Stroj4WkS8mB7eT+XCRnUVVdckOnq8xqXe8VXAnZO9R7vrmiQbCv396ufgb+javrlJWggsBXampvenj+Wfrfo4V2StAXxN0gOSVqe2eRExCpVfTGBuB+oadzXH/s/Y6eM1rtljND/dL7LG91Dp+Y07W9IuSf8k6bdSW5F1NfOzK/p4/RZwOCL2VbUVerwmZEOhv1/9HPwNXds3awHSq4AvAmsi4jngL4DXAkuAUSofNaHYWi+KiDcAlwPvk/SWSV5b6DFU5YpsVwJfSE3dcLymUq+Woo/dR4AXgTtS0yiwICKWAh8G/lbSaQXW1ezPruif6TUc28Eo9HjVyIa6L62z/2nV1c/Bn/3avpORdAKVH+wdEfElgIg4HBEvRcT/A27j5eGJwmqNiEPp9ghwd6rhcProOP7R9kjRdSWXAw9GxOFUY8ePV5Vmj9FTHDvskq1GScPAO4Br08d+0tDA0+n+A1TGhn+9qLpa+NkVebxmAr8DbKyqt7DjVSsbKPj3q5+DP/u1fetJ44d/BeyJiE9WtQ9WveydwPjZBluBqyW9UtLZwCIqEzftrusUSaeO36cyMfhI2v9wetkwsKXIuqoc0wvr9PGaoKljlD6uH5V0Qfp9+A9V27SNpMuAG4ArI+KnVe0Dkmak++ekur5XYF1N/eyKqit5K/BYRPxyqKSo41UvGyj696vV2ele+Ae8ncqs+RPARwrc75upfOz6DrA7/Xs78L+Bh1P7VmCwapuPpDr3Ms2zGSap6xwqZwg8BDw6fkyAXwW2A/vS7Zwi60r7ORl4GviVqraOHC8qf3xGgV9Q6VmtauUYAUNUAu8J4M9IX5hsc12PUxkDHv89+8v02n+bfsYPAQ8C/6bgupr+2RVRV2r/HPB7E15byPGifjYU+vvlb+6amZVMPw/1mJlZDQ5+M7OScfCbmZWMg9/MrGQc/GZmJePgNzMrGQe/2QTjX+Tpt32ZjXPwW6mosu76Y5I2pAXE7pJ0siprs98s6ZvASklvk3SfpAclfSGtrYKkdZK+m7b9RGpbKekRSQ9J+kZqe7ekP6va75clXZzu/7OkP5C0E7hQ0nWSvqXKOvCf9h8Dy83Bb2W0GFgfEb8JPAf859T+s4h4M/B14CbgrVFZ0G4E+LCkOVSWH/iNtO0tabubgUsj4vVUFpmbyilU1n4/n8q3ld9FZfG8JcBLwLXt+I80q2dmpwsw64AnI+LedP924L+k++OLdl1A5QIY91aWQeFE4D4qfyR+BnxG0leAL6fX3wt8TtImYHzRrcm8RGWRLqhcf+CNwLfTvmbx8gJdZlk4+K2MJq5TMv74J+lWwLaIuGbihpLeRCWsrwbeD/x2RPyepPOBK4DdkpZQWSK5+hP1SVX3fxYRL1Xta0NE3Did/yCzZniox8pogaQL0/1rgG9OeP5+4CJJvwaQ5gB+PY3z/0pEfBVYQ2WteSS9NiJ2RsTNwI+oLKO7H1gi6RWSzqL+FcK2A/9O0tz0XnMkvaZt/6VmNbjHb2W0BxiW9GkqqyH+BfCB8ScjYkzSu4E7Jb0yNd8EHAW2SDqJSk/9Q+m5WyUtSm3bqazwCPB9KitUPkJlxcfjRMR3Jd1E5apor6CykuT7gMkuPWk2LV6d00olXe7uyxHxug6XYtYxHuoxMysZ9/jNzErGPX4zs5Jx8JuZlYyD38ysZBz8ZmYl4+A3MysZB7+ZWcn8f1RohP+ul1BLAAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "argo.plot.scatter(x=\"pressure\", y=\"level\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<div class=\"alert alert-success\">\n",
-    "\n",
-    "**EXERCISE**:\n",
-    "\n",
-    "Assume that buoyancy is defined by the following formula:\n",
-    "    \n",
-    "$$g \\cdot ( 2\\times 10^{-4} \\cdot T - 7\\times 10^{-4} \\cdot P )$$\n",
-    "\n",
-    "With:\n",
-    "- $g$ = 9.8\n",
-    "- $T$ = temperature\n",
-    "- $P$ = pressure\n",
-    "\n",
-    "Calculate the buoyancy and add it as a new variable `buoyancy` to the `argo` data set. \n",
-    "\n",
-    "Make a 2D (image) plot with the x-axis the date, the y-axis the water level and the color intensity the buoyancy. As the level represents the depth of the water, it makes more sense to have 0 (surface) at the top of the y-axis: switch the y-axis direction.\n",
-    "    \n",
-    "<details><summary>Hints</summary>\n",
-    "\n",
-    "* Remember that xarray works as Numpy and relies on the same broadcasting rules.\n",
-    "* The `imshow` method does not work on irregular intervals. Matplotlib and xarray also have `pcolormesh`.    \n",
-    "* Look for options [in the xarray documentation](http://xarray.pydata.org/en/stable/plotting.html#other-axes-kwargs) to control the axis direction. (The `ax.invert_yaxis()` Matplotlib function is not supported for pcolormesh)\n",
-    "    \n",
-    "</details>    \n",
-    "    \n",
-    "</div>"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 427,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "argo[\"buoyancy\"] = 9.8 * (2e-4 * argo[\"temperature\"] - 7e-4 * argo[\"salinity\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 435,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.collections.QuadMesh at 0x7f123a69fad0>"
-      ]
-     },
-     "execution_count": 435,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "argo[\"buoyancy\"].plot(yincrease=False)  # xarray decides the plot type when no specific method is used"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 483,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.collections.QuadMesh at 0x7f12391c5590>"
-      ]
-     },
-     "execution_count": 483,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# More explicit\n",
-    "argo[\"buoyancy\"].plot.pcolormesh(x=\"date\", y=\"level\", yincrease=False)  # pcolormesh instead of imshow"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<div class=\"alert alert-success\">\n",
-    "\n",
-    "**EXERCISE**:\n",
-    "\n",
-    "Make a line plot of the salinity as a function of time at level 10\n",
-    "   \n",
-    "<details><summary>Hints</summary>\n",
-    "\n",
-    "Break it down into different steps and chain the individual steps:\n",
-    "    \n",
-    "* From the argo data set, select the variable `salinity`. This is similar to selecting a column in Pandas.\n",
-    "* Next, use the `sel` method to select the `level=10`\n",
-    "* Next, use the `plot.line()` method.\n",
-    "\n",
-    "</details>    \n",
-    "    \n",
-    "</div>"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 373,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7f12379b9410>]"
-      ]
-     },
-     "execution_count": 373,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "argo[\"salinity\"].sel(level=10).plot.line()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<div class=\"alert alert-success\">\n",
-    "\n",
-    "**EXERCISE**:\n",
-    "\n",
-    "- Make a line plot of the temperature as a function of time for the levels 10, 20 and 30 at the same graph \n",
-    "- Make a second line plot with each of the 3 levels (10, 20, 30) in a different subplot. \n",
-    "   \n",
-    "<details><summary>Hints</summary>\n",
-    "\n",
-    "Break it down into different steps and chain these individual steps:\n",
-    "    \n",
-    "* From the argo data set, select the variable `temperature`. This is similar to selecting a column in Pandas.\n",
-    "* Next, use the `sel` method to select the levels 10, 20 and 30.\n",
-    "* Next, use the `plot.line()` method, but make sure the `hue` changes for each level\n",
-    "    \n",
-    "For the subplots, check the [facetting documentation](http://xarray.pydata.org/en/stable/plotting.html#faceting) of xarray. \n",
-    "\n",
-    "</details>    \n",
-    "    \n",
-    "</div>"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 387,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "argo[\"temperature\"].sel(level=[10, 20, 30]).plot.line(hue=\"level\");"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 493,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 720x216 with 3 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "argo[\"temperature\"].sel(level=[10, 20, 30]).plot.line(col=\"level\");"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<div class=\"alert alert-success\">\n",
-    "\n",
-    "**EXERCISE**:\n",
-    "\n",
-    "You wonder how the temperature evolves with increasing latitude and what the effect is of the depth (level):\n",
-    "\n",
-    "- Create a scatter plot of the `level` as a function of the `temperature` colored by the `latitude`. \n",
-    "    \n",
-    "- As a further exploration step, pick a subset of levels 1, 10, 25, and 50 and create a second scatter plot with in the x-axis the latitude of the measurement and in the y-axis the temperature. To compare the effect of the different levels, give each level a separate subplot next to each other.\n",
-    "   \n",
-    "<details><summary>Hints</summary>\n",
-    "\n",
-    "* In a scatter plot, the color or hue can be linked to a variable.\n",
-    "* From the argo data set, use the `sel` method to select the levels 1, 10, 25, and 50.\n",
-    "* For the second scatter plot, but make sure the `col` changes for each `level` and define which variables need to go to which axis.\n",
-    "\n",
-    "</details>    \n",
-    "    \n",
-    "</div>"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 668,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.collections.PathCollection at 0x7f1233420c50>"
-      ]
-     },
-     "execution_count": 668,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "argo.plot.scatter(x=\"temperature\", y=\"level\", hue=\"lat\", s=2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 670,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<xarray.plot.facetgrid.FacetGrid at 0x7f123311a890>"
-      ]
-     },
-     "execution_count": 670,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 936x216 with 4 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "argo.sel(level=[1, 5, 25, 50]).plot.scatter(x=\"lat\", y=\"temperature\", col=\"level\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<div class=\"alert alert-success\">\n",
-    "\n",
-    "**EXERCISE**:\n",
-    "\n",
-    "Make an image plot of the temperature as a function of time. Divide the colormap in 3 discrete categories:\n",
-    "    \n",
-    "* x < 5\n",
-    "* 5 < x < 15\n",
-    "* x > 15\n",
-    "    \n",
-    "Choose a custom colormap and adjust the label of the colorbar to `'Temperature (°C)'`\n",
-    "   \n",
-    "<details>\n",
-    "    \n",
-    "<summary>Hints</summary>\n",
-    "\n",
-    "- Check the help of the `plot` function or the [xarray documentation](http://xarray.pydata.org/en/stable/plotting.html#discrete-colormaps) on discrete colormaps.\n",
-    "- Adjustments to the colorbar settings can be defined with the `cbar_kwargs` as a dict. Adjust the `label` of the colorbar.    \n",
-    "\n",
-    "</details>    \n",
-    "    \n",
-    "</div>"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 501,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "argo.temperature.plot.pcolormesh(yincrease=False, \n",
-    "                                 cbar_kwargs={'label': 'Temperature (°C)'},\n",
-    "                                 cmap='Reds',\n",
-    "                                 levels=[5, 15]\n",
-    "                                );"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<div class=\"alert alert-success\">\n",
-    "\n",
-    "**EXERCISE**:\n",
-    "\n",
-    "Calculate the average salinity and temperature as a function of level over the measurements taken between 2012-10-01 and 2012-12-01. \n",
-    "\n",
-    "Make a separate line plot for each of them. Define the figure and 2 subplots first with Matplotlib. \n",
-    "    \n",
-    "<details><summary>Hints</summary>\n",
-    "\n",
-    "* xarray supports to query dates using a string representation.\n",
-    "* Use the `slice` operator within the `sel` to select a range of the data.\n",
-    "* Whereas in Numpy we used `axis` in reduction functions, xarray uses the `dim` name.\n",
-    "* Also for line plots you can define which dimension should be on the x-axis and which on the y-axis by providing the name.  \n",
-    "* Use `fig, (ax0, ax1) = plt.subplots(1, 2)` to create subplots.\n",
-    "</details>    \n",
-    "    \n",
-    "</div>"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 453,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "subset_mean = argo.sel(date=slice('2012-10-01', '2012-12-01')).mean(dim=\"date\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 454,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig, (ax0, ax1) = plt.subplots(1, 2)\n",
-    "subset_mean[\"salinity\"].plot.line(y=\"level\", yincrease=False, ax=ax0)\n",
-    "subset_mean[\"temperature\"].plot.line(y=\"level\", yincrease=False, ax=ax1)\n",
-    "plt.tight_layout()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Pandas for multiple dimensions..."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 747,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "argo = xr.load_dataset(\"./data/argo_float.nc\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If we are interested in the _average over time_ for each of the levels, we can use a reducton function to get the averages of each of the variables at the same time:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 748,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
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-       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
-       "  --xr-border-color: #1F1F1F;\n",
-       "  --xr-disabled-color: #515151;\n",
-       "  --xr-background-color: #111111;\n",
-       "  --xr-background-color-row-even: #111111;\n",
-       "  --xr-background-color-row-odd: #313131;\n",
-       "}\n",
-       "\n",
-       ".xr-wrap {\n",
-       "  display: block;\n",
-       "  min-width: 300px;\n",
-       "  max-width: 700px;\n",
-       "}\n",
-       "\n",
-       ".xr-text-repr-fallback {\n",
-       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-header {\n",
-       "  padding-top: 6px;\n",
-       "  padding-bottom: 6px;\n",
-       "  margin-bottom: 4px;\n",
-       "  border-bottom: solid 1px var(--xr-border-color);\n",
-       "}\n",
-       "\n",
-       ".xr-header > div,\n",
-       ".xr-header > ul {\n",
-       "  display: inline;\n",
-       "  margin-top: 0;\n",
-       "  margin-bottom: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-obj-type,\n",
-       ".xr-array-name {\n",
-       "  margin-left: 2px;\n",
-       "  margin-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-obj-type {\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-sections {\n",
-       "  padding-left: 0 !important;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
-       "}\n",
-       "\n",
-       ".xr-section-item {\n",
-       "  display: contents;\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input + label {\n",
-       "  color: var(--xr-disabled-color);\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input:enabled + label {\n",
-       "  cursor: pointer;\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input:enabled + label:hover {\n",
-       "  color: var(--xr-font-color0);\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary {\n",
-       "  grid-column: 1;\n",
-       "  color: var(--xr-font-color2);\n",
-       "  font-weight: 500;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary > span {\n",
-       "  display: inline-block;\n",
-       "  padding-left: 0.5em;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:disabled + label {\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in + label:before {\n",
-       "  display: inline-block;\n",
-       "  content: '►';\n",
-       "  font-size: 11px;\n",
-       "  width: 15px;\n",
-       "  text-align: center;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:disabled + label:before {\n",
-       "  color: var(--xr-disabled-color);\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:checked + label:before {\n",
-       "  content: '▼';\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:checked + label > span {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary,\n",
-       ".xr-section-inline-details {\n",
-       "  padding-top: 4px;\n",
-       "  padding-bottom: 4px;\n",
-       "}\n",
-       "\n",
-       ".xr-section-inline-details {\n",
-       "  grid-column: 2 / -1;\n",
-       "}\n",
-       "\n",
-       ".xr-section-details {\n",
-       "  display: none;\n",
-       "  grid-column: 1 / -1;\n",
-       "  margin-bottom: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
-       "  display: contents;\n",
-       "}\n",
-       "\n",
-       ".xr-array-wrap {\n",
-       "  grid-column: 1 / -1;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 20px auto;\n",
-       "}\n",
-       "\n",
-       ".xr-array-wrap > label {\n",
-       "  grid-column: 1;\n",
-       "  vertical-align: top;\n",
-       "}\n",
-       "\n",
-       ".xr-preview {\n",
-       "  color: var(--xr-font-color3);\n",
-       "}\n",
-       "\n",
-       ".xr-array-preview,\n",
-       ".xr-array-data {\n",
-       "  padding: 0 5px !important;\n",
-       "  grid-column: 2;\n",
-       "}\n",
-       "\n",
-       ".xr-array-data,\n",
-       ".xr-array-in:checked ~ .xr-array-preview {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-array-in:checked ~ .xr-array-data,\n",
-       ".xr-array-preview {\n",
-       "  display: inline-block;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list {\n",
-       "  display: inline-block !important;\n",
-       "  list-style: none;\n",
-       "  padding: 0 !important;\n",
-       "  margin: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list li {\n",
-       "  display: inline-block;\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list:before {\n",
-       "  content: '(';\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list:after {\n",
-       "  content: ')';\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list li:not(:last-child):after {\n",
-       "  content: ',';\n",
-       "  padding-right: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-has-index {\n",
-       "  font-weight: bold;\n",
-       "}\n",
-       "\n",
-       ".xr-var-list,\n",
-       ".xr-var-item {\n",
-       "  display: contents;\n",
-       "}\n",
-       "\n",
-       ".xr-var-item > div,\n",
-       ".xr-var-item label,\n",
-       ".xr-var-item > .xr-var-name span {\n",
-       "  background-color: var(--xr-background-color-row-even);\n",
-       "  margin-bottom: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-var-item > .xr-var-name:hover span {\n",
-       "  padding-right: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-var-list > li:nth-child(odd) > div,\n",
-       ".xr-var-list > li:nth-child(odd) > label,\n",
-       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
-       "  background-color: var(--xr-background-color-row-odd);\n",
-       "}\n",
-       "\n",
-       ".xr-var-name {\n",
-       "  grid-column: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-var-dims {\n",
-       "  grid-column: 2;\n",
-       "}\n",
-       "\n",
-       ".xr-var-dtype {\n",
-       "  grid-column: 3;\n",
-       "  text-align: right;\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-var-preview {\n",
-       "  grid-column: 4;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name,\n",
-       ".xr-var-dims,\n",
-       ".xr-var-dtype,\n",
-       ".xr-preview,\n",
-       ".xr-attrs dt {\n",
-       "  white-space: nowrap;\n",
-       "  overflow: hidden;\n",
-       "  text-overflow: ellipsis;\n",
-       "  padding-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name:hover,\n",
-       ".xr-var-dims:hover,\n",
-       ".xr-var-dtype:hover,\n",
-       ".xr-attrs dt:hover {\n",
-       "  overflow: visible;\n",
-       "  width: auto;\n",
-       "  z-index: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-var-attrs,\n",
-       ".xr-var-data {\n",
-       "  display: none;\n",
-       "  background-color: var(--xr-background-color) !important;\n",
-       "  padding-bottom: 5px !important;\n",
-       "}\n",
-       "\n",
-       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
-       ".xr-var-data-in:checked ~ .xr-var-data {\n",
-       "  display: block;\n",
-       "}\n",
-       "\n",
-       ".xr-var-data > table {\n",
-       "  float: right;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name span,\n",
-       ".xr-var-data,\n",
-       ".xr-attrs {\n",
-       "  padding-left: 25px !important;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs,\n",
-       ".xr-var-attrs,\n",
-       ".xr-var-data {\n",
-       "  grid-column: 1 / -1;\n",
-       "}\n",
-       "\n",
-       "dl.xr-attrs {\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 125px auto;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt, dd {\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  float: left;\n",
-       "  padding-right: 10px;\n",
-       "  width: auto;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt {\n",
-       "  font-weight: normal;\n",
-       "  grid-column: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt:hover span {\n",
-       "  display: inline-block;\n",
-       "  background: var(--xr-background-color);\n",
-       "  padding-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dd {\n",
-       "  grid-column: 2;\n",
-       "  white-space: pre-wrap;\n",
-       "  word-break: break-all;\n",
-       "}\n",
-       "\n",
-       ".xr-icon-database,\n",
-       ".xr-icon-file-text2 {\n",
-       "  display: inline-block;\n",
-       "  vertical-align: middle;\n",
-       "  width: 1em;\n",
-       "  height: 1.5em !important;\n",
-       "  stroke-width: 0;\n",
-       "  stroke: currentColor;\n",
-       "  fill: currentColor;\n",
-       "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
-       "Dimensions:      (level: 78)\n",
-       "Coordinates:\n",
-       "  * level        (level) int32 0 1 2 3 4 5 6 7 8 ... 69 70 71 72 73 74 75 76 77\n",
-       "Data variables:\n",
-       "    salinity     (level) float64 35.91 35.9 35.9 35.9 ... 34.94 34.94 34.93\n",
-       "    temperature  (level) float64 17.6 17.57 17.51 17.42 ... 3.789 3.73 3.662\n",
-       "    pressure     (level) float64 6.435 10.57 15.54 ... 1.95e+03 1.999e+03</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-ba6c9298-a72c-4687-951c-6abf2aae4689' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-ba6c9298-a72c-4687-951c-6abf2aae4689' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>level</span>: 78</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-9277fadb-1929-4e91-ada7-18f5608b05b8' class='xr-section-summary-in' type='checkbox'  checked><label for='section-9277fadb-1929-4e91-ada7-18f5608b05b8' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>level</span></div><div class='xr-var-dims'>(level)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 ... 72 73 74 75 76 77</div><input id='attrs-d86f4cd6-7745-45fc-ae02-988a94456ec4' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d86f4cd6-7745-45fc-ae02-988a94456ec4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-59f8a498-b092-4c68-879f-78bf3ff49e1f' class='xr-var-data-in' type='checkbox'><label for='data-59f8a498-b092-4c68-879f-78bf3ff49e1f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
-       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
-       "       36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,\n",
-       "       54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71,\n",
-       "       72, 73, 74, 75, 76, 77], dtype=int32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ef40a63f-c504-4aa0-9f66-ad63644c155f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ef40a63f-c504-4aa0-9f66-ad63644c155f' class='xr-section-summary' >Data variables: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>salinity</span></div><div class='xr-var-dims'>(level)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>35.91 35.9 35.9 ... 34.94 34.93</div><input id='attrs-f2f1b58d-dc0c-4df3-9072-0a0ffd5691f7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f2f1b58d-dc0c-4df3-9072-0a0ffd5691f7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-54b89114-fbdd-456d-a69e-aa8ef77905ae' class='xr-var-data-in' type='checkbox'><label for='data-54b89114-fbdd-456d-a69e-aa8ef77905ae' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([35.9063218 , 35.90223138, 35.90313435, 35.90173139, 35.90544583,\n",
-       "       35.9100359 , 35.90946015, 35.91343146, 35.91967712, 35.92615988,\n",
-       "       35.93195456, 35.94055356, 35.94091596, 35.93905366, 35.93931069,\n",
-       "       35.93786745, 35.93525794, 35.93118039, 35.92534328, 35.91652257,\n",
-       "       35.90671895, 35.89617843, 35.88888019, 35.8789927 , 35.86946183,\n",
-       "       35.8598671 , 35.85061713, 35.84211978, 35.83150467, 35.81969395,\n",
-       "       35.80945061, 35.80092265, 35.79078674, 35.77886525, 35.76833627,\n",
-       "       35.75838795, 35.74923783, 35.73923559, 35.73000444, 35.71877237,\n",
-       "       35.68864513, 35.65607159, 35.62678265, 35.59231774, 35.56205662,\n",
-       "       35.45401408, 35.41392634, 35.3810557 , 35.34845245, 35.31531555,\n",
-       "       35.28392568, 35.26568334, 35.2389473 , 35.21583745, 35.19686081,\n",
-       "       35.18231257, 35.1648436 , 35.15073542, 35.12509338, 35.10155869,\n",
-       "       35.08199799, 35.06317012, 35.0490097 , 35.03678253, 35.02174266,\n",
-       "       35.01135579, 35.00212936, 34.99386297, 34.98810328, 34.98008094,\n",
-       "       34.97214884, 34.96517645, 34.95664983, 34.9507985 , 34.9465696 ,\n",
-       "       34.94198907, 34.93844852, 34.93290652])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>temperature</span></div><div class='xr-var-dims'>(level)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>17.6 17.57 17.51 ... 3.73 3.662</div><input id='attrs-0b333a13-716c-4c00-9f5c-c568a8613a64' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0b333a13-716c-4c00-9f5c-c568a8613a64' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0604eaa4-cf54-4b3e-854b-e1e0342185a2' class='xr-var-data-in' type='checkbox'><label for='data-0604eaa4-cf54-4b3e-854b-e1e0342185a2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([17.60172602, 17.57223609, 17.5145833 , 17.42326395, 17.24943838,\n",
-       "       17.03730134, 16.76787661, 16.44609588, 16.17439195, 16.04501356,\n",
-       "       15.65827023, 15.4607296 , 15.26114862, 15.12489191, 14.99133783,\n",
-       "       14.90160808, 14.81990544, 14.74535139, 14.66822971, 14.585027  ,\n",
-       "       14.49732434, 14.41904053, 14.35412163, 14.27102702, 14.19081082,\n",
-       "       14.11487838, 14.04347293, 13.98067566, 13.90994595, 13.83274319,\n",
-       "       13.76139196, 13.69836479, 13.62335132, 13.54185131, 13.46647295,\n",
-       "       13.39395946, 13.32541891, 13.25205403, 13.18131082, 13.10233782,\n",
-       "       12.89268916, 12.67795943, 12.4649189 , 12.2178513 , 11.98270268,\n",
-       "       11.1281081 , 10.80430666, 10.49702667, 10.1749066 ,  9.83453334,\n",
-       "        9.48625332,  9.19793334,  8.66010666,  8.12324001,  7.60221333,\n",
-       "        7.15289333,  6.74250667,  6.39543999,  6.04598667,  5.74538665,\n",
-       "        5.48913333,  5.26604001,  5.08768   ,  4.93479998,  4.77769334,\n",
-       "        4.65368   ,  4.54237334,  4.44274664,  4.35933333,  4.2653784 ,\n",
-       "        4.17290539,  4.08902703,  3.99864865,  3.92163514,  3.85617567,\n",
-       "        3.78916217,  3.72950001,  3.66207691])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pressure</span></div><div class='xr-var-dims'>(level)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>6.435 10.57 ... 1.95e+03 1.999e+03</div><input id='attrs-099882f1-c898-48c6-9cc9-828da227a166' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-099882f1-c898-48c6-9cc9-828da227a166' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4603ead3-3e9b-48fe-831e-64436928062a' class='xr-var-data-in' type='checkbox'><label for='data-4603ead3-3e9b-48fe-831e-64436928062a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([   6.43466671,   10.56891882,   15.54246568,   20.46301361,\n",
-       "         25.42567552,   30.44459441,   35.44324375,   40.4391894 ,\n",
-       "         45.40810832,   50.37837879,   60.47297323,   70.48378413,\n",
-       "         80.40270347,   90.48243311,  100.51216311,  110.46081151,\n",
-       "        120.52702795,  130.49459282,  140.51216064,  150.40540376,\n",
-       "        160.40810559,  170.36216035,  180.41080949,  190.4108097 ,\n",
-       "        200.39999761,  210.34729499,  220.32026858,  230.31351224,\n",
-       "        240.28918808,  250.41486297,  260.39999843,  270.36891752,\n",
-       "        280.42432136,  290.42297075,  300.4229691 ,  310.46351087,\n",
-       "        320.50675346,  330.5297266 ,  340.41891521,  350.49729383,\n",
-       "        375.41080867,  400.3797294 ,  425.29864626,  450.38378205,\n",
-       "        475.30675403,  550.47703016,  575.68400146,  600.42400716,\n",
-       "        625.30800456,  650.34533773,  675.33333984,  700.37067546,\n",
-       "        750.42400716,  800.36666992,  850.38534017,  900.4613387 ,\n",
-       "        950.45067383, 1000.38534261, 1050.38534668, 1100.45734212,\n",
-       "       1150.45201335, 1200.40534505, 1250.25067383, 1300.49467773,\n",
-       "       1350.40268392, 1400.41734538, 1450.25734212, 1500.40267253,\n",
-       "       1550.46401367, 1600.44055011, 1650.34595056, 1700.42163251,\n",
-       "       1750.42299013, 1800.35677358, 1850.33379467, 1900.20676401,\n",
-       "       1950.20946771, 1999.28975423])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-68f98522-0424-415f-b9db-b9987279dba1' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-68f98522-0424-415f-b9db-b9987279dba1' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
-      ],
-      "text/plain": [
-       "<xarray.Dataset>\n",
-       "Dimensions:      (level: 78)\n",
-       "Coordinates:\n",
-       "  * level        (level) int32 0 1 2 3 4 5 6 7 8 ... 69 70 71 72 73 74 75 76 77\n",
-       "Data variables:\n",
-       "    salinity     (level) float64 35.91 35.9 35.9 35.9 ... 34.94 34.94 34.93\n",
-       "    temperature  (level) float64 17.6 17.57 17.51 17.42 ... 3.789 3.73 3.662\n",
-       "    pressure     (level) float64 6.435 10.57 15.54 ... 1.95e+03 1.999e+03"
-      ]
-     },
-     "execution_count": 748,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "argo.mean(dim=[\"date\"])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "But if we wanted the _average for each month of the year_ per level, we would first have to __split__ the data set in a group for each month of the year, __apply__ the average function on each of the months and __combine__ the data again. \n",
-    "\n",
-    "We already learnt about the [split-apply-combine](https://pandas.pydata.org/pandas-docs/stable/user_guide/groupby.html) approach when using Pandas. The syntax of Xarray’s groupby is almost identical to Pandas! "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "First, extract the month of the year (1-> 12) from each of the date coordinates:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 760,
-   "metadata": {},
-   "outputs": [
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-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;month&#x27; (date: 75)&gt;\n",
-       "array([ 7,  7,  8,  8,  8,  9,  9,  9, 10, 10, 10, 10, 11, 11, 12, 12, 12,\n",
-       "       12,  1,  1,  1,  2,  2,  2,  3,  3,  3,  4,  4,  4,  5,  5,  5,  6,\n",
-       "        6,  6,  7,  7,  7,  8,  8,  8,  9,  9,  9, 10, 10, 10, 11, 11, 11,\n",
-       "       12, 12, 12,  1,  1,  1,  2,  2,  2,  3,  3,  3,  4,  4,  4,  5,  5,\n",
-       "        5,  6,  6,  6,  7,  7,  7])\n",
-       "Coordinates:\n",
-       "  * date     (date) datetime64[ns] 2012-07-13T22:33:06.019200 ... 2014-07-24T...\n",
-       "    lon      (date) float64 -39.13 -37.28 -36.9 -36.89 ... -33.83 -34.11 -34.38\n",
-       "    lat      (date) float64 47.19 46.72 46.45 46.23 ... 42.72 42.6 42.46 42.38</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'month'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>date</span>: 75</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-0d129c29-adba-462c-adc4-8c3d068096b7' class='xr-array-in' type='checkbox' checked><label for='section-0d129c29-adba-462c-adc4-8c3d068096b7' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>7 7 8 8 8 9 9 9 10 10 10 10 11 11 12 ... 3 3 3 4 4 4 5 5 5 6 6 6 7 7 7</span></div><div class='xr-array-data'><pre>array([ 7,  7,  8,  8,  8,  9,  9,  9, 10, 10, 10, 10, 11, 11, 12, 12, 12,\n",
-       "       12,  1,  1,  1,  2,  2,  2,  3,  3,  3,  4,  4,  4,  5,  5,  5,  6,\n",
-       "        6,  6,  7,  7,  7,  8,  8,  8,  9,  9,  9, 10, 10, 10, 11, 11, 11,\n",
-       "       12, 12, 12,  1,  1,  1,  2,  2,  2,  3,  3,  3,  4,  4,  4,  5,  5,\n",
-       "        5,  6,  6,  6,  7,  7,  7])</pre></div></div></li><li class='xr-section-item'><input id='section-389b546a-ba3a-4773-be58-f87623b5840d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-389b546a-ba3a-4773-be58-f87623b5840d' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>date</span></div><div class='xr-var-dims'>(date)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2012-07-13T22:33:06.019200 ... 2...</div><input id='attrs-fae8bbc5-cfdf-4ad1-b8ba-a6439594af2c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-fae8bbc5-cfdf-4ad1-b8ba-a6439594af2c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-109de923-1ca6-4d88-b526-e46198ba7993' class='xr-var-data-in' type='checkbox'><label for='data-109de923-1ca6-4d88-b526-e46198ba7993' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2012-07-13T22:33:06.019200000&#x27;, &#x27;2012-07-23T22:54:59.990400000&#x27;,\n",
-       "       &#x27;2012-08-02T22:55:52.003200000&#x27;, &#x27;2012-08-12T23:08:59.971200000&#x27;,\n",
-       "       &#x27;2012-08-22T23:29:01.968000000&#x27;, &#x27;2012-09-01T23:17:38.976000000&#x27;,\n",
-       "       &#x27;2012-09-12T02:59:18.960000000&#x27;, &#x27;2012-09-21T23:18:37.036800000&#x27;,\n",
-       "       &#x27;2012-10-02T03:00:17.971200000&#x27;, &#x27;2012-10-11T23:13:27.984000000&#x27;,\n",
-       "       &#x27;2012-10-22T02:50:32.006399999&#x27;, &#x27;2012-10-31T23:36:39.974400000&#x27;,\n",
-       "       &#x27;2012-11-11T02:40:46.041600000&#x27;, &#x27;2012-11-20T23:08:29.990400000&#x27;,\n",
-       "       &#x27;2012-12-01T02:47:51.993600000&#x27;, &#x27;2012-12-10T23:23:16.972800000&#x27;,\n",
-       "       &#x27;2012-12-21T02:58:48.979200000&#x27;, &#x27;2012-12-30T23:07:23.030400000&#x27;,\n",
-       "       &#x27;2013-01-10T02:56:43.008000000&#x27;, &#x27;2013-01-19T23:24:26.956800000&#x27;,\n",
-       "       &#x27;2013-01-30T02:43:53.011199998&#x27;, &#x27;2013-02-08T23:15:27.043200000&#x27;,\n",
-       "       &#x27;2013-02-19T01:12:50.976000000&#x27;, &#x27;2013-02-28T23:07:13.008000000&#x27;,\n",
-       "       &#x27;2013-03-11T02:43:30.979200000&#x27;, &#x27;2013-03-20T23:17:22.992000000&#x27;,\n",
-       "       &#x27;2013-03-31T01:50:38.025600000&#x27;, &#x27;2013-04-09T23:19:07.968000000&#x27;,\n",
-       "       &#x27;2013-04-20T02:53:29.990400000&#x27;, &#x27;2013-04-29T23:28:33.024000000&#x27;,\n",
-       "       &#x27;2013-05-10T02:50:18.009600000&#x27;, &#x27;2013-05-19T23:21:05.990400000&#x27;,\n",
-       "       &#x27;2013-05-30T02:50:30.969600000&#x27;, &#x27;2013-06-08T23:32:49.027200000&#x27;,\n",
-       "       &#x27;2013-06-19T03:42:51.004800000&#x27;, &#x27;2013-06-28T23:32:16.022400000&#x27;,\n",
-       "       &#x27;2013-07-09T03:28:30.979200000&#x27;, &#x27;2013-07-18T23:33:57.974400000&#x27;,\n",
-       "       &#x27;2013-07-29T03:15:42.019200000&#x27;, &#x27;2013-08-07T23:25:02.035200004&#x27;,\n",
-       "       &#x27;2013-08-18T01:47:44.966400000&#x27;, &#x27;2013-08-28T03:02:59.020799996&#x27;,\n",
-       "       &#x27;2013-09-07T03:03:51.984000000&#x27;, &#x27;2013-09-16T23:32:07.987200000&#x27;,\n",
-       "       &#x27;2013-09-27T04:08:27.974400000&#x27;, &#x27;2013-10-06T23:25:39.964800000&#x27;,\n",
-       "       &#x27;2013-10-17T02:55:50.995200000&#x27;, &#x27;2013-10-27T03:45:47.001600000&#x27;,\n",
-       "       &#x27;2013-11-06T01:14:52.022400000&#x27;, &#x27;2013-11-16T03:29:54.009600000&#x27;,\n",
-       "       &#x27;2013-11-26T03:03:56.995200000&#x27;, &#x27;2013-12-05T23:33:59.011200000&#x27;,\n",
-       "       &#x27;2013-12-16T02:58:01.977600000&#x27;, &#x27;2013-12-25T23:22:43.017600000&#x27;,\n",
-       "       &#x27;2014-01-05T02:52:06.009600000&#x27;, &#x27;2014-01-14T23:41:18.009600000&#x27;,\n",
-       "       &#x27;2014-01-25T03:00:43.977600000&#x27;, &#x27;2014-02-03T23:29:13.977600000&#x27;,\n",
-       "       &#x27;2014-02-14T02:50:11.961600000&#x27;, &#x27;2014-02-23T23:03:21.974400000&#x27;,\n",
-       "       &#x27;2014-03-06T02:58:03.964800000&#x27;, &#x27;2014-03-15T23:10:28.012800000&#x27;,\n",
-       "       &#x27;2014-03-26T02:51:22.032000000&#x27;, &#x27;2014-04-04T23:25:58.972800000&#x27;,\n",
-       "       &#x27;2014-04-15T03:00:45.964800000&#x27;, &#x27;2014-04-24T23:24:40.003200000&#x27;,\n",
-       "       &#x27;2014-05-05T02:56:22.012800000&#x27;, &#x27;2014-05-15T00:10:06.009600000&#x27;,\n",
-       "       &#x27;2014-05-25T03:02:43.036800000&#x27;, &#x27;2014-06-03T23:34:53.961600000&#x27;,\n",
-       "       &#x27;2014-06-14T03:01:23.980800000&#x27;, &#x27;2014-06-23T23:24:31.968000000&#x27;,\n",
-       "       &#x27;2014-07-04T03:08:30.019200000&#x27;, &#x27;2014-07-13T23:47:43.008000000&#x27;,\n",
-       "       &#x27;2014-07-24T03:02:33.014400000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>(date)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-39.13 -37.28 ... -34.11 -34.38</div><input id='attrs-5fc0d739-d2d6-4865-9fd2-91cb0aeb8cbd' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5fc0d739-d2d6-4865-9fd2-91cb0aeb8cbd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a50481ed-a015-48cb-8476-e40cabb863ad' class='xr-var-data-in' type='checkbox'><label for='data-a50481ed-a015-48cb-8476-e40cabb863ad' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([-39.13 , -37.282, -36.9  , -36.89 , -37.053, -36.658, -35.963,\n",
-       "       -35.184, -34.462, -33.784, -32.972, -32.546, -32.428, -32.292,\n",
-       "       -32.169, -31.998, -31.824, -31.624, -31.433, -31.312, -31.107,\n",
-       "       -31.147, -31.044, -31.14 , -31.417, -31.882, -32.145, -32.487,\n",
-       "       -32.537, -32.334, -32.042, -31.892, -31.861, -31.991, -31.883,\n",
-       "       -31.89 , -31.941, -31.889, -31.724, -31.412, -31.786, -31.561,\n",
-       "       -31.732, -31.553, -31.862, -32.389, -32.318, -32.19 , -32.224,\n",
-       "       -32.368, -32.306, -32.305, -32.65 , -33.093, -33.263, -33.199,\n",
-       "       -33.27 , -33.237, -33.221, -33.011, -32.844, -32.981, -32.784,\n",
-       "       -32.607, -32.87 , -33.196, -33.524, -33.956, -33.944, -33.71 ,\n",
-       "       -33.621, -33.552, -33.828, -34.11 , -34.38 ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>(date)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>47.19 46.72 46.45 ... 42.46 42.38</div><input id='attrs-fa74e4ad-5678-4912-9b2b-d5d45670a5a1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-fa74e4ad-5678-4912-9b2b-d5d45670a5a1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-073aa794-243a-4726-9298-e98942d2f412' class='xr-var-data-in' type='checkbox'><label for='data-073aa794-243a-4726-9298-e98942d2f412' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([47.187, 46.716, 46.45 , 46.23 , 45.459, 44.833, 44.452, 44.839,\n",
-       "       44.956, 44.676, 44.13 , 43.644, 43.067, 42.662, 42.513, 42.454,\n",
-       "       42.396, 42.256, 42.089, 41.944, 41.712, 41.571, 41.596, 41.581,\n",
-       "       41.351, 41.032, 40.912, 40.792, 40.495, 40.383, 40.478, 40.672,\n",
-       "       41.032, 40.864, 40.651, 40.425, 40.228, 40.197, 40.483, 40.311,\n",
-       "       40.457, 40.463, 40.164, 40.047, 39.963, 40.122, 40.57 , 40.476,\n",
-       "       40.527, 40.589, 40.749, 40.993, 41.162, 41.237, 41.448, 41.65 ,\n",
-       "       42.053, 42.311, 42.096, 41.683, 41.661, 41.676, 42.018, 42.395,\n",
-       "       42.532, 42.558, 42.504, 42.63 , 42.934, 42.952, 42.777, 42.722,\n",
-       "       42.601, 42.457, 42.379])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-219b0aca-61f4-4649-bac0-f8816e797e04' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-219b0aca-61f4-4649-bac0-f8816e797e04' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
-      ],
-      "text/plain": [
-       "<xarray.DataArray 'month' (date: 75)>\n",
-       "array([ 7,  7,  8,  8,  8,  9,  9,  9, 10, 10, 10, 10, 11, 11, 12, 12, 12,\n",
-       "       12,  1,  1,  1,  2,  2,  2,  3,  3,  3,  4,  4,  4,  5,  5,  5,  6,\n",
-       "        6,  6,  7,  7,  7,  8,  8,  8,  9,  9,  9, 10, 10, 10, 11, 11, 11,\n",
-       "       12, 12, 12,  1,  1,  1,  2,  2,  2,  3,  3,  3,  4,  4,  4,  5,  5,\n",
-       "        5,  6,  6,  6,  7,  7,  7])\n",
-       "Coordinates:\n",
-       "  * date     (date) datetime64[ns] 2012-07-13T22:33:06.019200 ... 2014-07-24T...\n",
-       "    lon      (date) float64 -39.13 -37.28 -36.9 -36.89 ... -33.83 -34.11 -34.38\n",
-       "    lat      (date) float64 47.19 46.72 46.45 46.23 ... 42.72 42.6 42.46 42.38"
-      ]
-     },
-     "execution_count": 760,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "argo.date.dt.month  # The coordinates is a Pandas datetime index"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can use these arrays in a groupby operation:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 761,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DatasetGroupBy, grouped over 'month' \n",
-       "12 groups with labels 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12."
-      ]
-     },
-     "execution_count": 761,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "argo.groupby(argo.date.dt.month)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Xarray also offers a more concise syntax when the variable you’re grouping on is already present in the dataset. This is identical to the previous line:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 762,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "DatasetGroupBy, grouped over 'month' \n",
-       "12 groups with labels 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12."
-      ]
-     },
-     "execution_count": 762,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "argo.groupby(\"date.month\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Next, we apply an aggregation function _for each of the months_ over the `date` dimension in order to end up with: _for each month of the year, the average (over time) for each of the levels_:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 794,
-   "metadata": {},
-   "outputs": [
-    {
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-       "\n",
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-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in + label:before {\n",
-       "  display: inline-block;\n",
-       "  content: '►';\n",
-       "  font-size: 11px;\n",
-       "  width: 15px;\n",
-       "  text-align: center;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:disabled + label:before {\n",
-       "  color: var(--xr-disabled-color);\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:checked + label:before {\n",
-       "  content: '▼';\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:checked + label > span {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary,\n",
-       ".xr-section-inline-details {\n",
-       "  padding-top: 4px;\n",
-       "  padding-bottom: 4px;\n",
-       "}\n",
-       "\n",
-       ".xr-section-inline-details {\n",
-       "  grid-column: 2 / -1;\n",
-       "}\n",
-       "\n",
-       ".xr-section-details {\n",
-       "  display: none;\n",
-       "  grid-column: 1 / -1;\n",
-       "  margin-bottom: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
-       "  display: contents;\n",
-       "}\n",
-       "\n",
-       ".xr-array-wrap {\n",
-       "  grid-column: 1 / -1;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 20px auto;\n",
-       "}\n",
-       "\n",
-       ".xr-array-wrap > label {\n",
-       "  grid-column: 1;\n",
-       "  vertical-align: top;\n",
-       "}\n",
-       "\n",
-       ".xr-preview {\n",
-       "  color: var(--xr-font-color3);\n",
-       "}\n",
-       "\n",
-       ".xr-array-preview,\n",
-       ".xr-array-data {\n",
-       "  padding: 0 5px !important;\n",
-       "  grid-column: 2;\n",
-       "}\n",
-       "\n",
-       ".xr-array-data,\n",
-       ".xr-array-in:checked ~ .xr-array-preview {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-array-in:checked ~ .xr-array-data,\n",
-       ".xr-array-preview {\n",
-       "  display: inline-block;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list {\n",
-       "  display: inline-block !important;\n",
-       "  list-style: none;\n",
-       "  padding: 0 !important;\n",
-       "  margin: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list li {\n",
-       "  display: inline-block;\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list:before {\n",
-       "  content: '(';\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list:after {\n",
-       "  content: ')';\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list li:not(:last-child):after {\n",
-       "  content: ',';\n",
-       "  padding-right: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-has-index {\n",
-       "  font-weight: bold;\n",
-       "}\n",
-       "\n",
-       ".xr-var-list,\n",
-       ".xr-var-item {\n",
-       "  display: contents;\n",
-       "}\n",
-       "\n",
-       ".xr-var-item > div,\n",
-       ".xr-var-item label,\n",
-       ".xr-var-item > .xr-var-name span {\n",
-       "  background-color: var(--xr-background-color-row-even);\n",
-       "  margin-bottom: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-var-item > .xr-var-name:hover span {\n",
-       "  padding-right: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-var-list > li:nth-child(odd) > div,\n",
-       ".xr-var-list > li:nth-child(odd) > label,\n",
-       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
-       "  background-color: var(--xr-background-color-row-odd);\n",
-       "}\n",
-       "\n",
-       ".xr-var-name {\n",
-       "  grid-column: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-var-dims {\n",
-       "  grid-column: 2;\n",
-       "}\n",
-       "\n",
-       ".xr-var-dtype {\n",
-       "  grid-column: 3;\n",
-       "  text-align: right;\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-var-preview {\n",
-       "  grid-column: 4;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name,\n",
-       ".xr-var-dims,\n",
-       ".xr-var-dtype,\n",
-       ".xr-preview,\n",
-       ".xr-attrs dt {\n",
-       "  white-space: nowrap;\n",
-       "  overflow: hidden;\n",
-       "  text-overflow: ellipsis;\n",
-       "  padding-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name:hover,\n",
-       ".xr-var-dims:hover,\n",
-       ".xr-var-dtype:hover,\n",
-       ".xr-attrs dt:hover {\n",
-       "  overflow: visible;\n",
-       "  width: auto;\n",
-       "  z-index: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-var-attrs,\n",
-       ".xr-var-data {\n",
-       "  display: none;\n",
-       "  background-color: var(--xr-background-color) !important;\n",
-       "  padding-bottom: 5px !important;\n",
-       "}\n",
-       "\n",
-       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
-       ".xr-var-data-in:checked ~ .xr-var-data {\n",
-       "  display: block;\n",
-       "}\n",
-       "\n",
-       ".xr-var-data > table {\n",
-       "  float: right;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name span,\n",
-       ".xr-var-data,\n",
-       ".xr-attrs {\n",
-       "  padding-left: 25px !important;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs,\n",
-       ".xr-var-attrs,\n",
-       ".xr-var-data {\n",
-       "  grid-column: 1 / -1;\n",
-       "}\n",
-       "\n",
-       "dl.xr-attrs {\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 125px auto;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt, dd {\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  float: left;\n",
-       "  padding-right: 10px;\n",
-       "  width: auto;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt {\n",
-       "  font-weight: normal;\n",
-       "  grid-column: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt:hover span {\n",
-       "  display: inline-block;\n",
-       "  background: var(--xr-background-color);\n",
-       "  padding-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dd {\n",
-       "  grid-column: 2;\n",
-       "  white-space: pre-wrap;\n",
-       "  word-break: break-all;\n",
-       "}\n",
-       "\n",
-       ".xr-icon-database,\n",
-       ".xr-icon-file-text2 {\n",
-       "  display: inline-block;\n",
-       "  vertical-align: middle;\n",
-       "  width: 1em;\n",
-       "  height: 1.5em !important;\n",
-       "  stroke-width: 0;\n",
-       "  stroke: currentColor;\n",
-       "  fill: currentColor;\n",
-       "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
-       "Dimensions:      (level: 78, month: 12)\n",
-       "Coordinates:\n",
-       "  * level        (level) int32 0 1 2 3 4 5 6 7 8 ... 69 70 71 72 73 74 75 76 77\n",
-       "  * month        (month) int64 1 2 3 4 5 6 7 8 9 10 11 12\n",
-       "Data variables:\n",
-       "    salinity     (month, level) float64 35.95 35.95 35.95 ... 34.94 34.94 34.93\n",
-       "    temperature  (month, level) float64 15.06 15.06 15.06 ... 3.779 3.733 3.663\n",
-       "    pressure     (month, level) float64 6.6 10.42 15.32 ... 1.95e+03 1.998e+03</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-8008d1ec-c252-4eae-9b36-190696d3ac9a' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-8008d1ec-c252-4eae-9b36-190696d3ac9a' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>level</span>: 78</li><li><span class='xr-has-index'>month</span>: 12</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-654580ab-3757-45b7-a8cd-3f11dd1956fb' class='xr-section-summary-in' type='checkbox'  checked><label for='section-654580ab-3757-45b7-a8cd-3f11dd1956fb' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>level</span></div><div class='xr-var-dims'>(level)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 ... 72 73 74 75 76 77</div><input id='attrs-ffec4c5d-05eb-4907-89aa-307c104022ab' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ffec4c5d-05eb-4907-89aa-307c104022ab' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9727b772-80f7-4f08-aec7-1aebacef084a' class='xr-var-data-in' type='checkbox'><label for='data-9727b772-80f7-4f08-aec7-1aebacef084a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
-       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
-       "       36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,\n",
-       "       54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71,\n",
-       "       72, 73, 74, 75, 76, 77], dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>month</span></div><div class='xr-var-dims'>(month)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1 2 3 4 5 6 7 8 9 10 11 12</div><input id='attrs-937cca4c-29bb-4dba-8c68-4c9ef8baae58' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-937cca4c-29bb-4dba-8c68-4c9ef8baae58' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-366e3e46-58e7-432b-934f-92d46321ece9' class='xr-var-data-in' type='checkbox'><label for='data-366e3e46-58e7-432b-934f-92d46321ece9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-792679c4-de50-43d4-a3bb-a0942428a98a' class='xr-section-summary-in' type='checkbox'  checked><label for='section-792679c4-de50-43d4-a3bb-a0942428a98a' class='xr-section-summary' >Data variables: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>salinity</span></div><div class='xr-var-dims'>(month, level)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>35.95 35.95 35.95 ... 34.94 34.93</div><input id='attrs-2db39c82-ffa2-4363-bdd0-3e0df37e7b8e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2db39c82-ffa2-4363-bdd0-3e0df37e7b8e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bef86fc7-790b-4fd4-8856-38f0a64e75cf' class='xr-var-data-in' type='checkbox'><label for='data-bef86fc7-790b-4fd4-8856-38f0a64e75cf' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[35.94653575, 35.94653511, 35.9463679 , 35.94636854, 35.944369  ,\n",
-       "        35.94387054, 35.94136937, 35.93703651, 35.93436941, 35.9337031 ,\n",
-       "        35.93253581, 35.92838605, 35.92570241, 35.92271932, 35.92321905,\n",
-       "        35.91988691, 35.90888468, 35.89755122, 35.89805158, 35.90155156,\n",
-       "        35.89838473, 35.89155134, 35.89071782, 35.88738505, 35.88840294,\n",
-       "        35.8868866 , 35.8815848 , 35.87410037, 35.86493174, 35.85942904,\n",
-       "        35.85442924, 35.84742546, 35.83709145, 35.82842255, 35.8182532 ,\n",
-       "        35.81408564, 35.80641619, 35.79924901, 35.79274686, 35.78724543,\n",
-       "        35.75673993, 35.71923192, 35.67922592, 35.63004875, 35.60821215,\n",
-       "        35.47102674, 35.42835172, 35.37917964, 35.34684308, 35.31467247,\n",
-       "        35.3026708 , 35.2681675 , 35.2131621 , 35.21466192, 35.22882652,\n",
-       "        35.21299235, 35.19999059, 35.19215457, 35.14898618, 35.12281736,\n",
-       "        35.09448306, 35.05331421, 35.02397982, 35.0021464 , 34.97881317,\n",
-       "        34.97431374, 34.96848106, 34.96398099, 34.96398163, 34.95931498,\n",
-       "        34.95614815, 34.95331637, 34.94731712, 34.94448344, 34.94148445,\n",
-       "        34.93915113, 34.93648402, 34.93235588],\n",
-       "       [35.89972496, 35.90005811, 35.89989217, 35.90039253, 35.89972496,\n",
-       "        35.90005938, 35.90122541, 35.90122604, 35.90105947, 35.90022596,\n",
-       "        35.90022723, 35.89639155, 35.89455986, 35.89272499, 35.89322599,\n",
-       "        35.89139175, 35.88905907, 35.88672574, 35.88305791, 35.88005829,\n",
-       "...\n",
-       "        35.10666733, 35.07386475, 35.06346512, 35.05146484, 35.0374649 ,\n",
-       "        35.02506638, 35.01266632, 35.00106583, 34.98906479, 34.98726425,\n",
-       "        34.97906494, 34.97006531, 34.95726624, 34.95106506, 34.94786606,\n",
-       "        34.94466705, 34.94266663, 34.92985725],\n",
-       "       [36.0026652 , 36.00316556, 36.00333277, 36.00283305, 36.01826804,\n",
-       "        36.01855414, 36.01869638, 36.0191258 , 36.01926858, 36.01898248,\n",
-       "        36.01998247, 36.0188397 , 36.01526751, 36.00183868, 36.00869533,\n",
-       "        36.01626587, 36.02469526, 36.01412255, 36.00483595, 35.9976932 ,\n",
-       "        35.98940768, 35.97297832, 35.96712058, 35.95684814, 35.93784768,\n",
-       "        35.92327554, 35.90417371, 35.89404188, 35.88547134, 35.87091119,\n",
-       "        35.86049543, 35.85292053, 35.84177617, 35.83077294, 35.81791251,\n",
-       "        35.80876814, 35.80062376, 35.79119274, 35.78061894, 35.77347401,\n",
-       "        35.74446869, 35.70917565, 35.66716712, 35.63859177, 35.61030143,\n",
-       "        35.51071439, 35.47527913, 35.4458471 , 35.41055679, 35.3728387 ,\n",
-       "        35.32597569, 35.30368696, 35.28382874, 35.24739511, 35.22581972,\n",
-       "        35.2198203 , 35.18624496, 35.20252991, 35.15709741, 35.11523819,\n",
-       "        35.09195164, 35.06366457, 35.03737804, 35.01394817, 34.99623544,\n",
-       "        34.98466219, 34.97823497, 34.97309276, 34.96837779, 34.96066448,\n",
-       "        34.95609283, 34.94966398, 34.94437844, 34.94252232, 34.94066674,\n",
-       "        34.93838228, 34.93666676, 34.93206024]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>temperature</span></div><div class='xr-var-dims'>(month, level)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>15.06 15.06 15.06 ... 3.733 3.663</div><input id='attrs-ad76ed77-8c59-434e-9c4a-e4bf7735f603' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ad76ed77-8c59-434e-9c4a-e4bf7735f603' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-23f45b20-cf03-47ee-b514-44b370455562' class='xr-var-data-in' type='checkbox'><label for='data-23f45b20-cf03-47ee-b514-44b370455562' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[15.05599976, 15.05833324, 15.05766662, 15.06166681, 15.05516672,\n",
-       "        15.04283333, 15.03349972, 15.01066653, 14.99283346, 14.99166663,\n",
-       "        14.98616664, 14.96616666, 14.95250003, 14.93933344, 14.94133313,\n",
-       "        14.92733335, 14.86250003, 14.7711668 , 14.75533326, 14.7420001 ,\n",
-       "        14.70083332, 14.66266648, 14.64016644, 14.54766687, 14.49650002,\n",
-       "        14.4278334 , 14.36633317, 14.30000003, 14.22200012, 14.16349983,\n",
-       "        14.08966668, 14.02549998, 13.92916663, 13.85066668, 13.77833335,\n",
-       "        13.74049981, 13.66950003, 13.61616659, 13.55666685, 13.51116673,\n",
-       "        13.3191665 , 13.10066652, 12.84433301, 12.52416658, 12.3496666 ,\n",
-       "        11.38833348, 11.02249988, 10.60700003, 10.29350011,  9.93483321,\n",
-       "         9.60100015,  9.24199994,  8.55033318,  8.09533326,  7.72949998,\n",
-       "         7.28616667,  6.88616673,  6.58066662,  6.13900002,  5.8378334 ,\n",
-       "         5.54400007,  5.21483334,  4.96049992,  4.75650001,  4.53549997,\n",
-       "         4.44733326,  4.34666665,  4.27249996,  4.21516657,  4.14200004,\n",
-       "         4.07133325,  4.00899998,  3.94000002,  3.87983338,  3.82100002,\n",
-       "         3.76816662,  3.72016664,  3.64350009],\n",
-       "       [14.43433348, 14.43516684, 14.43666681, 14.43650007, 14.43683322,\n",
-       "        14.4388334 , 14.43983332, 14.43833319, 14.43016656, 14.42850002,\n",
-       "        14.4236668 , 14.40166664, 14.39283339, 14.38700008, 14.38533322,\n",
-       "        14.37683344, 14.36866665, 14.35533333, 14.34200017, 14.32849995,\n",
-       "...\n",
-       "         5.66700001,  5.34379997,  5.17019997,  5.00860004,  4.86059999,\n",
-       "         4.7368001 ,  4.61699991,  4.50999994,  4.38580008,  4.32800002,\n",
-       "         4.227     ,  4.13199997,  4.01800003,  3.93739996,  3.88059998,\n",
-       "         3.81880002,  3.75939999,  3.64700007],\n",
-       "       [16.3378334 , 16.33916648, 16.34100008, 16.33999983, 16.44442872,\n",
-       "        16.44557149, 16.44699996, 16.44842843, 16.449143  , 16.44514302,\n",
-       "        16.45071398, 16.44457122, 16.43214308, 16.29371452, 15.99871458,\n",
-       "        15.77285712, 15.58657128, 15.44085721, 15.34957136, 15.26457119,\n",
-       "        15.15414279, 15.03057126, 14.97799982, 14.85214288, 14.69985717,\n",
-       "        14.59171418, 14.46257142, 14.38071428, 14.32242843, 14.21785709,\n",
-       "        14.14185715, 14.08314283, 14.00599984, 13.9140001 , 13.82471439,\n",
-       "        13.74700001, 13.67157146, 13.59100001, 13.51314286, 13.45285715,\n",
-       "        13.25885705, 13.04057135, 12.76942853, 12.54028566, 12.32628577,\n",
-       "        11.52057144, 11.22199999, 10.95385715, 10.64757116, 10.29900006,\n",
-       "         9.86400005,  9.55828571,  9.06914289,  8.46785722,  7.93571431,\n",
-       "         7.50214291,  6.95328569,  6.74199997,  6.24899994,  5.85328565,\n",
-       "         5.56214292,  5.30028575,  5.0560001 ,  4.83942849,  4.65957144,\n",
-       "         4.52271427,  4.42871428,  4.33642851,  4.26228571,  4.16785717,\n",
-       "         4.09142859,  4.01014277,  3.9365715 ,  3.883     ,  3.83199995,\n",
-       "         3.77928577,  3.73271431,  3.66299999]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pressure</span></div><div class='xr-var-dims'>(month, level)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>6.6 10.42 ... 1.95e+03 1.998e+03</div><input id='attrs-008bac6f-4801-4e3b-8ed1-32c78237a2eb' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-008bac6f-4801-4e3b-8ed1-32c78237a2eb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-485e46ee-ef67-4c7c-8014-14033784b463' class='xr-var-data-in' type='checkbox'><label for='data-485e46ee-ef67-4c7c-8014-14033784b463' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[   6.59999998,   10.41666651,   15.3166666 ,   20.29999955,\n",
-       "          25.29999987,   30.29999987,   35.30000051,   40.33333397,\n",
-       "          45.2833341 ,   50.31666629,   60.20000013,   70.21666845,\n",
-       "          80.36666743,   90.63333384,  100.5       ,  110.61666743,\n",
-       "         120.6166687 ,  130.53333028,  140.48332723,  150.51666514,\n",
-       "         160.53333282,  170.43333181,  180.53333028,  190.63332876,\n",
-       "         200.43333181,  210.16666412,  220.31666565,  230.36666361,\n",
-       "         240.28333282,  250.04999797,  259.7666626 ,  270.04999797,\n",
-       "         280.5333252 ,  290.61666361,  300.6499939 ,  310.66666158,\n",
-       "         320.58332316,  330.5333252 ,  340.44999186,  350.41666158,\n",
-       "         375.31667074,  400.51666768,  425.41665649,  450.5       ,\n",
-       "         475.3833313 ,  550.08333333,  575.68334961,  600.3666687 ,\n",
-       "         625.33334351,  650.41667684,  675.56667074,  700.45002238,\n",
-       "         750.58334351,  800.18332926,  850.65001424,  900.65000407,\n",
-       "         950.46668498, 1000.48332723, 1050.43332926, 1100.50002035,\n",
-       "        1150.5       , 1200.51668294, 1249.86667887, 1300.45001221,\n",
-       "        1350.51668294, 1400.38336182, 1450.45001221, 1500.        ,\n",
-       "        1550.63334147, 1600.45003255, 1650.5       , 1700.55000814,\n",
-       "        1750.56669108, 1800.48335775, 1850.28334554, 1900.11665853,\n",
-       "        1950.05000814, 1999.95001221],\n",
-       "...\n",
-       "       [   6.42857143,   10.55714308,   15.33333349,   20.35000006,\n",
-       "          25.34285709,   30.35714258,   35.35714177,   40.35714231,\n",
-       "          45.35714286,   50.4857145 ,   60.2714277 ,   70.17142923,\n",
-       "          80.20000131,   90.7285723 ,  100.81428637,  110.71428571,\n",
-       "         120.59999956,  130.62857274,  140.61428833,  150.51428877,\n",
-       "         160.51429095,  170.47143119,  180.51428659,  190.41429138,\n",
-       "         200.48571777,  210.62857274,  220.58571734,  230.32857186,\n",
-       "         239.81428746,  250.80000305,  260.55714199,  270.65714373,\n",
-       "         280.44285366,  290.2714277 ,  300.59999302,  310.18571254,\n",
-       "         320.57142857,  330.68571254,  340.67142596,  350.5857108 ,\n",
-       "         375.30000087,  400.28571865,  425.05713763,  450.54285104,\n",
-       "         475.0857108 ,  550.54284668,  575.49999128,  600.34284319,\n",
-       "         625.38570731,  650.41427176,  675.52856445,  700.49999128,\n",
-       "         750.60000174,  800.3999721 ,  850.62855748,  900.54284668,\n",
-       "         950.64285714, 1000.29998779, 1050.52858189, 1100.44285366,\n",
-       "        1150.48573521, 1200.54288156, 1249.97141811, 1300.47143555,\n",
-       "        1350.50001744, 1400.15713937, 1450.29999651, 1500.5       ,\n",
-       "        1550.64285714, 1600.35714286, 1650.21428571, 1700.0857108 ,\n",
-       "        1750.51428223, 1800.40002441, 1850.51428223, 1900.41430664,\n",
-       "        1950.30001395, 1998.1499939 ]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-c0a19913-4c2c-4692-ac45-f273959620f1' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-c0a19913-4c2c-4692-ac45-f273959620f1' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
-      ],
-      "text/plain": [
-       "<xarray.Dataset>\n",
-       "Dimensions:      (level: 78, month: 12)\n",
-       "Coordinates:\n",
-       "  * level        (level) int32 0 1 2 3 4 5 6 7 8 ... 69 70 71 72 73 74 75 76 77\n",
-       "  * month        (month) int64 1 2 3 4 5 6 7 8 9 10 11 12\n",
-       "Data variables:\n",
-       "    salinity     (month, level) float64 35.95 35.95 35.95 ... 34.94 34.94 34.93\n",
-       "    temperature  (month, level) float64 15.06 15.06 15.06 ... 3.779 3.733 3.663\n",
-       "    pressure     (month, level) float64 6.6 10.42 15.32 ... 1.95e+03 1.998e+03"
-      ]
-     },
-     "execution_count": 794,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "argo.groupby(\"date.month\").mean(dim=\"date\")        #[\"temperature\"].sel(level=1).to_series().plot.barh()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Another (alike) operation - specifically for time series data - is to `resample` the data to another time-aggregation. For example, resample to monthly (`1M`) or yearly (`1Y`) median values:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 821,
-   "metadata": {},
-   "outputs": [
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-       "  width: auto;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt {\n",
-       "  font-weight: normal;\n",
-       "  grid-column: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt:hover span {\n",
-       "  display: inline-block;\n",
-       "  background: var(--xr-background-color);\n",
-       "  padding-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dd {\n",
-       "  grid-column: 2;\n",
-       "  white-space: pre-wrap;\n",
-       "  word-break: break-all;\n",
-       "}\n",
-       "\n",
-       ".xr-icon-database,\n",
-       ".xr-icon-file-text2 {\n",
-       "  display: inline-block;\n",
-       "  vertical-align: middle;\n",
-       "  width: 1em;\n",
-       "  height: 1.5em !important;\n",
-       "  stroke-width: 0;\n",
-       "  stroke: currentColor;\n",
-       "  fill: currentColor;\n",
-       "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
-       "Dimensions:      (date: 25, level: 78)\n",
-       "Coordinates:\n",
-       "  * date         (date) datetime64[ns] 2012-07-31 2012-08-31 ... 2014-07-31\n",
-       "  * level        (level) int32 0 1 2 3 4 5 6 7 8 ... 69 70 71 72 73 74 75 76 77\n",
-       "Data variables:\n",
-       "    salinity     (date, level) float64 35.58 35.58 35.6 ... 34.94 34.94 34.94\n",
-       "    temperature  (date, level) float64 18.71 18.57 18.37 ... 3.811 3.754 3.714\n",
-       "    pressure     (date, level) float64 6.45 10.65 14.9 ... 1.95e+03 2e+03</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-89819bb8-b5a9-4d2d-bb7e-e666fed625b9' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-89819bb8-b5a9-4d2d-bb7e-e666fed625b9' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>date</span>: 25</li><li><span class='xr-has-index'>level</span>: 78</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-4947c156-75f4-45a2-9ae4-8ade5e06abe2' class='xr-section-summary-in' type='checkbox'  checked><label for='section-4947c156-75f4-45a2-9ae4-8ade5e06abe2' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>date</span></div><div class='xr-var-dims'>(date)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2012-07-31 ... 2014-07-31</div><input id='attrs-5041f9e9-4da0-43fd-a2fc-05eea8d951b8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5041f9e9-4da0-43fd-a2fc-05eea8d951b8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ba5698b5-f8fa-4043-8960-28ac72dc8912' class='xr-var-data-in' type='checkbox'><label for='data-ba5698b5-f8fa-4043-8960-28ac72dc8912' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2012-07-31T00:00:00.000000000&#x27;, &#x27;2012-08-31T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2012-09-30T00:00:00.000000000&#x27;, &#x27;2012-10-31T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2012-11-30T00:00:00.000000000&#x27;, &#x27;2012-12-31T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2013-01-31T00:00:00.000000000&#x27;, &#x27;2013-02-28T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2013-03-31T00:00:00.000000000&#x27;, &#x27;2013-04-30T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2013-05-31T00:00:00.000000000&#x27;, &#x27;2013-06-30T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2013-07-31T00:00:00.000000000&#x27;, &#x27;2013-08-31T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2013-09-30T00:00:00.000000000&#x27;, &#x27;2013-10-31T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2013-11-30T00:00:00.000000000&#x27;, &#x27;2013-12-31T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2014-01-31T00:00:00.000000000&#x27;, &#x27;2014-02-28T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2014-03-31T00:00:00.000000000&#x27;, &#x27;2014-04-30T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2014-05-31T00:00:00.000000000&#x27;, &#x27;2014-06-30T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2014-07-31T00:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>level</span></div><div class='xr-var-dims'>(level)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 ... 72 73 74 75 76 77</div><input id='attrs-6ebb2f78-a4a4-4b36-bd38-92fa3e744fb9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6ebb2f78-a4a4-4b36-bd38-92fa3e744fb9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3bab8bbe-6281-428e-ba0a-82235fa47ad5' class='xr-var-data-in' type='checkbox'><label for='data-3bab8bbe-6281-428e-ba0a-82235fa47ad5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
-       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
-       "       36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,\n",
-       "       54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71,\n",
-       "       72, 73, 74, 75, 76, 77], dtype=int32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-9e272452-928e-4bea-b6b7-049bcec0ca8f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-9e272452-928e-4bea-b6b7-049bcec0ca8f' class='xr-section-summary' >Data variables: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>salinity</span></div><div class='xr-var-dims'>(date, level)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>35.58 35.58 35.6 ... 34.94 34.94</div><input id='attrs-072797fa-d488-4a46-ab1c-5dbc833e0843' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-072797fa-d488-4a46-ab1c-5dbc833e0843' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0742afd1-f265-4ba5-b910-f07daccbaa12' class='xr-var-data-in' type='checkbox'><label for='data-0742afd1-f265-4ba5-b910-f07daccbaa12' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[35.57694817, 35.57794762, 35.60395241, ..., 34.91988182,\n",
-       "        34.91988182, 34.92088127],\n",
-       "       [35.45191574, 35.45191574, 35.45091629, ..., 34.92390442,\n",
-       "        34.92190552, 34.92290497],\n",
-       "       [35.51696014, 35.51796341, 35.51696014, ..., 34.92086029,\n",
-       "        34.92086029, 34.91938019],\n",
-       "       ...,\n",
-       "       [35.90583801, 35.90784073, 35.90884018, ..., 34.95571518,\n",
-       "        34.94576263, 34.93976212],\n",
-       "       [35.83697128, 35.83697128, 35.83496857, ..., 34.93985748,\n",
-       "        34.93885803, 34.93681145],\n",
-       "       [35.77793884, 35.66791153, 35.66591263, ..., 34.94081116,\n",
-       "        34.93680954, 34.93680954]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>temperature</span></div><div class='xr-var-dims'>(date, level)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>18.71 18.57 18.37 ... 3.754 3.714</div><input id='attrs-162b90dc-6b8e-47a4-9cfc-bed8376238af' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-162b90dc-6b8e-47a4-9cfc-bed8376238af' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-10aa5e47-2881-4690-9194-fce5df62bd3d' class='xr-var-data-in' type='checkbox'><label for='data-10aa5e47-2881-4690-9194-fce5df62bd3d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[18.7055006 , 18.57049942, 18.37200069, ...,  3.80349994,\n",
-       "         3.77100003,  3.745     ],\n",
-       "       [19.14699936, 19.14599991, 19.14299965, ...,  3.75      ,\n",
-       "         3.71799994,  3.69199991],\n",
-       "       [20.14500046, 20.14800072, 20.14500046, ...,  3.65700006,\n",
-       "         3.61199999,  3.56850004],\n",
-       "       ...,\n",
-       "       [15.48600006, 15.48799992, 15.48799992, ...,  3.91199994,\n",
-       "         3.81500006,  3.75399995],\n",
-       "       [16.84399986, 16.84600067, 16.84600067, ...,  3.8440001 ,\n",
-       "         3.79399991,  3.74299991],\n",
-       "       [20.13100052, 19.45999908, 19.26199913, ...,  3.81100011,\n",
-       "         3.75399995,  3.71399999]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pressure</span></div><div class='xr-var-dims'>(date, level)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>6.45 10.65 14.9 ... 1.95e+03 2e+03</div><input id='attrs-54b64de2-e4ef-4a37-9a41-233ae4a4ef1a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-54b64de2-e4ef-4a37-9a41-233ae4a4ef1a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6d761748-f873-4816-88f0-a343176c7cdb' class='xr-var-data-in' type='checkbox'><label for='data-6d761748-f873-4816-88f0-a343176c7cdb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[   6.45000005,   10.64999962,   14.89999962, ..., 1900.30004883,\n",
-       "        1949.95001221, 1998.65002441],\n",
-       "       [   5.9000001 ,   10.39999962,   15.5       , ..., 1900.19995117,\n",
-       "        1949.89990234, 1997.30004883],\n",
-       "       [   6.29999971,   10.5       ,   15.59999943, ..., 1899.89990234,\n",
-       "        1949.90002441, 1998.65002441],\n",
-       "       ...,\n",
-       "       [   6.5       ,   10.89999962,   15.80000019, ..., 1900.69995117,\n",
-       "        1950.09997559, 1998.5       ],\n",
-       "       [   6.80000019,   10.69999981,   15.90000057, ..., 1900.5       ,\n",
-       "        1950.        , 2000.35003662],\n",
-       "       [   7.0999999 ,   10.79999924,   15.89999962, ..., 1899.80004883,\n",
-       "        1950.20007324, 2000.40002441]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-15734416-659c-4071-91e8-a5f33cae63ba' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-15734416-659c-4071-91e8-a5f33cae63ba' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
-      ],
-      "text/plain": [
-       "<xarray.Dataset>\n",
-       "Dimensions:      (date: 25, level: 78)\n",
-       "Coordinates:\n",
-       "  * date         (date) datetime64[ns] 2012-07-31 2012-08-31 ... 2014-07-31\n",
-       "  * level        (level) int32 0 1 2 3 4 5 6 7 8 ... 69 70 71 72 73 74 75 76 77\n",
-       "Data variables:\n",
-       "    salinity     (date, level) float64 35.58 35.58 35.6 ... 34.94 34.94 34.94\n",
-       "    temperature  (date, level) float64 18.71 18.57 18.37 ... 3.811 3.754 3.714\n",
-       "    pressure     (date, level) float64 6.45 10.65 14.9 ... 1.95e+03 2e+03"
-      ]
-     },
-     "execution_count": 821,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "argo.resample(date=\"1M\").median()  # 1Y"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 827,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/stijnvh/miniconda3/envs/DS-geospatial/lib/python3.7/site-packages/xarray/core/common.py:1124: FutureWarning: 'base' in .resample() and in Grouper() is deprecated.\n",
-      "The new arguments that you should use are 'offset' or 'origin'.\n",
-      "\n",
-      ">>> df.resample(freq=\"3s\", base=2)\n",
-      "\n",
-      "becomes:\n",
-      "\n",
-      ">>> df.resample(freq=\"3s\", offset=\"2s\")\n",
-      "\n",
-      "  freq=freq, closed=closed, label=label, base=base, loffset=loffset\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "argo[\"salinity\"].sel(level=1).plot.line(x=\"date\");\n",
-    "argo[\"salinity\"].resample(date=\"1M\").median().sel(level=1).plot.line(x=\"date\");  # 1Y"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "A similar, but different functionality is `rolling` to calculate rolling window aggregates:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 866,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
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-       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
-       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
-       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
-       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
-       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
-       "  --xr-background-color: var(--jp-layout-color0, white);\n",
-       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
-       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
-       "}\n",
-       "\n",
-       "html[theme=dark],\n",
-       "body.vscode-dark {\n",
-       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
-       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
-       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
-       "  --xr-border-color: #1F1F1F;\n",
-       "  --xr-disabled-color: #515151;\n",
-       "  --xr-background-color: #111111;\n",
-       "  --xr-background-color-row-even: #111111;\n",
-       "  --xr-background-color-row-odd: #313131;\n",
-       "}\n",
-       "\n",
-       ".xr-wrap {\n",
-       "  display: block;\n",
-       "  min-width: 300px;\n",
-       "  max-width: 700px;\n",
-       "}\n",
-       "\n",
-       ".xr-text-repr-fallback {\n",
-       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-header {\n",
-       "  padding-top: 6px;\n",
-       "  padding-bottom: 6px;\n",
-       "  margin-bottom: 4px;\n",
-       "  border-bottom: solid 1px var(--xr-border-color);\n",
-       "}\n",
-       "\n",
-       ".xr-header > div,\n",
-       ".xr-header > ul {\n",
-       "  display: inline;\n",
-       "  margin-top: 0;\n",
-       "  margin-bottom: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-obj-type,\n",
-       ".xr-array-name {\n",
-       "  margin-left: 2px;\n",
-       "  margin-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-obj-type {\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-sections {\n",
-       "  padding-left: 0 !important;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
-       "}\n",
-       "\n",
-       ".xr-section-item {\n",
-       "  display: contents;\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input + label {\n",
-       "  color: var(--xr-disabled-color);\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input:enabled + label {\n",
-       "  cursor: pointer;\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input:enabled + label:hover {\n",
-       "  color: var(--xr-font-color0);\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary {\n",
-       "  grid-column: 1;\n",
-       "  color: var(--xr-font-color2);\n",
-       "  font-weight: 500;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary > span {\n",
-       "  display: inline-block;\n",
-       "  padding-left: 0.5em;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:disabled + label {\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in + label:before {\n",
-       "  display: inline-block;\n",
-       "  content: '►';\n",
-       "  font-size: 11px;\n",
-       "  width: 15px;\n",
-       "  text-align: center;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:disabled + label:before {\n",
-       "  color: var(--xr-disabled-color);\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:checked + label:before {\n",
-       "  content: '▼';\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:checked + label > span {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary,\n",
-       ".xr-section-inline-details {\n",
-       "  padding-top: 4px;\n",
-       "  padding-bottom: 4px;\n",
-       "}\n",
-       "\n",
-       ".xr-section-inline-details {\n",
-       "  grid-column: 2 / -1;\n",
-       "}\n",
-       "\n",
-       ".xr-section-details {\n",
-       "  display: none;\n",
-       "  grid-column: 1 / -1;\n",
-       "  margin-bottom: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
-       "  display: contents;\n",
-       "}\n",
-       "\n",
-       ".xr-array-wrap {\n",
-       "  grid-column: 1 / -1;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 20px auto;\n",
-       "}\n",
-       "\n",
-       ".xr-array-wrap > label {\n",
-       "  grid-column: 1;\n",
-       "  vertical-align: top;\n",
-       "}\n",
-       "\n",
-       ".xr-preview {\n",
-       "  color: var(--xr-font-color3);\n",
-       "}\n",
-       "\n",
-       ".xr-array-preview,\n",
-       ".xr-array-data {\n",
-       "  padding: 0 5px !important;\n",
-       "  grid-column: 2;\n",
-       "}\n",
-       "\n",
-       ".xr-array-data,\n",
-       ".xr-array-in:checked ~ .xr-array-preview {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-array-in:checked ~ .xr-array-data,\n",
-       ".xr-array-preview {\n",
-       "  display: inline-block;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list {\n",
-       "  display: inline-block !important;\n",
-       "  list-style: none;\n",
-       "  padding: 0 !important;\n",
-       "  margin: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list li {\n",
-       "  display: inline-block;\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list:before {\n",
-       "  content: '(';\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list:after {\n",
-       "  content: ')';\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list li:not(:last-child):after {\n",
-       "  content: ',';\n",
-       "  padding-right: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-has-index {\n",
-       "  font-weight: bold;\n",
-       "}\n",
-       "\n",
-       ".xr-var-list,\n",
-       ".xr-var-item {\n",
-       "  display: contents;\n",
-       "}\n",
-       "\n",
-       ".xr-var-item > div,\n",
-       ".xr-var-item label,\n",
-       ".xr-var-item > .xr-var-name span {\n",
-       "  background-color: var(--xr-background-color-row-even);\n",
-       "  margin-bottom: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-var-item > .xr-var-name:hover span {\n",
-       "  padding-right: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-var-list > li:nth-child(odd) > div,\n",
-       ".xr-var-list > li:nth-child(odd) > label,\n",
-       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
-       "  background-color: var(--xr-background-color-row-odd);\n",
-       "}\n",
-       "\n",
-       ".xr-var-name {\n",
-       "  grid-column: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-var-dims {\n",
-       "  grid-column: 2;\n",
-       "}\n",
-       "\n",
-       ".xr-var-dtype {\n",
-       "  grid-column: 3;\n",
-       "  text-align: right;\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-var-preview {\n",
-       "  grid-column: 4;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name,\n",
-       ".xr-var-dims,\n",
-       ".xr-var-dtype,\n",
-       ".xr-preview,\n",
-       ".xr-attrs dt {\n",
-       "  white-space: nowrap;\n",
-       "  overflow: hidden;\n",
-       "  text-overflow: ellipsis;\n",
-       "  padding-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name:hover,\n",
-       ".xr-var-dims:hover,\n",
-       ".xr-var-dtype:hover,\n",
-       ".xr-attrs dt:hover {\n",
-       "  overflow: visible;\n",
-       "  width: auto;\n",
-       "  z-index: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-var-attrs,\n",
-       ".xr-var-data {\n",
-       "  display: none;\n",
-       "  background-color: var(--xr-background-color) !important;\n",
-       "  padding-bottom: 5px !important;\n",
-       "}\n",
-       "\n",
-       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
-       ".xr-var-data-in:checked ~ .xr-var-data {\n",
-       "  display: block;\n",
-       "}\n",
-       "\n",
-       ".xr-var-data > table {\n",
-       "  float: right;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name span,\n",
-       ".xr-var-data,\n",
-       ".xr-attrs {\n",
-       "  padding-left: 25px !important;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs,\n",
-       ".xr-var-attrs,\n",
-       ".xr-var-data {\n",
-       "  grid-column: 1 / -1;\n",
-       "}\n",
-       "\n",
-       "dl.xr-attrs {\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 125px auto;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt, dd {\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  float: left;\n",
-       "  padding-right: 10px;\n",
-       "  width: auto;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt {\n",
-       "  font-weight: normal;\n",
-       "  grid-column: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt:hover span {\n",
-       "  display: inline-block;\n",
-       "  background: var(--xr-background-color);\n",
-       "  padding-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dd {\n",
-       "  grid-column: 2;\n",
-       "  white-space: pre-wrap;\n",
-       "  word-break: break-all;\n",
-       "}\n",
-       "\n",
-       ".xr-icon-database,\n",
-       ".xr-icon-file-text2 {\n",
-       "  display: inline-block;\n",
-       "  vertical-align: middle;\n",
-       "  width: 1em;\n",
-       "  height: 1.5em !important;\n",
-       "  stroke-width: 0;\n",
-       "  stroke: currentColor;\n",
-       "  fill: currentColor;\n",
-       "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
-       "Dimensions:      (date: 75, level: 78)\n",
-       "Coordinates:\n",
-       "  * level        (level) int32 0 1 2 3 4 5 6 7 8 ... 69 70 71 72 73 74 75 76 77\n",
-       "  * date         (date) datetime64[ns] 2012-07-13T22:33:06.019200 ... 2014-07...\n",
-       "    lon          (date) float64 -39.13 -37.28 -36.9 ... -33.83 -34.11 -34.38\n",
-       "    lat          (date) float64 47.19 46.72 46.45 46.23 ... 42.6 42.46 42.38\n",
-       "Data variables:\n",
-       "    salinity     (level, date) float64 nan nan nan nan nan ... nan nan nan nan\n",
-       "    temperature  (level, date) float64 nan nan nan nan nan ... nan nan nan nan\n",
-       "    pressure     (level, date) float64 nan nan nan nan nan ... nan nan nan nan</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-3a6fdaf7-e006-453a-8889-5a27dd9c9a9d' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-3a6fdaf7-e006-453a-8889-5a27dd9c9a9d' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>date</span>: 75</li><li><span class='xr-has-index'>level</span>: 78</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-83f30bf4-065c-4fcc-beda-cebec55433a1' class='xr-section-summary-in' type='checkbox'  checked><label for='section-83f30bf4-065c-4fcc-beda-cebec55433a1' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>level</span></div><div class='xr-var-dims'>(level)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 ... 72 73 74 75 76 77</div><input id='attrs-50479c8e-76e1-4797-bec4-4ad28f5096ae' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-50479c8e-76e1-4797-bec4-4ad28f5096ae' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-53197c8c-bf7e-46f4-a6e6-2c0c94a54ec5' class='xr-var-data-in' type='checkbox'><label for='data-53197c8c-bf7e-46f4-a6e6-2c0c94a54ec5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
-       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
-       "       36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,\n",
-       "       54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71,\n",
-       "       72, 73, 74, 75, 76, 77], dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>date</span></div><div class='xr-var-dims'>(date)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2012-07-13T22:33:06.019200 ... 2...</div><input id='attrs-ce8caa55-89e0-4757-9035-633bcc271cb6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ce8caa55-89e0-4757-9035-633bcc271cb6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d37ac0d5-7084-4f71-8d80-aca07fcc41a9' class='xr-var-data-in' type='checkbox'><label for='data-d37ac0d5-7084-4f71-8d80-aca07fcc41a9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2012-07-13T22:33:06.019200000&#x27;, &#x27;2012-07-23T22:54:59.990400000&#x27;,\n",
-       "       &#x27;2012-08-02T22:55:52.003200000&#x27;, &#x27;2012-08-12T23:08:59.971200000&#x27;,\n",
-       "       &#x27;2012-08-22T23:29:01.968000000&#x27;, &#x27;2012-09-01T23:17:38.976000000&#x27;,\n",
-       "       &#x27;2012-09-12T02:59:18.960000000&#x27;, &#x27;2012-09-21T23:18:37.036800000&#x27;,\n",
-       "       &#x27;2012-10-02T03:00:17.971200000&#x27;, &#x27;2012-10-11T23:13:27.984000000&#x27;,\n",
-       "       &#x27;2012-10-22T02:50:32.006399999&#x27;, &#x27;2012-10-31T23:36:39.974400000&#x27;,\n",
-       "       &#x27;2012-11-11T02:40:46.041600000&#x27;, &#x27;2012-11-20T23:08:29.990400000&#x27;,\n",
-       "       &#x27;2012-12-01T02:47:51.993600000&#x27;, &#x27;2012-12-10T23:23:16.972800000&#x27;,\n",
-       "       &#x27;2012-12-21T02:58:48.979200000&#x27;, &#x27;2012-12-30T23:07:23.030400000&#x27;,\n",
-       "       &#x27;2013-01-10T02:56:43.008000000&#x27;, &#x27;2013-01-19T23:24:26.956800000&#x27;,\n",
-       "       &#x27;2013-01-30T02:43:53.011199998&#x27;, &#x27;2013-02-08T23:15:27.043200000&#x27;,\n",
-       "       &#x27;2013-02-19T01:12:50.976000000&#x27;, &#x27;2013-02-28T23:07:13.008000000&#x27;,\n",
-       "       &#x27;2013-03-11T02:43:30.979200000&#x27;, &#x27;2013-03-20T23:17:22.992000000&#x27;,\n",
-       "       &#x27;2013-03-31T01:50:38.025600000&#x27;, &#x27;2013-04-09T23:19:07.968000000&#x27;,\n",
-       "       &#x27;2013-04-20T02:53:29.990400000&#x27;, &#x27;2013-04-29T23:28:33.024000000&#x27;,\n",
-       "       &#x27;2013-05-10T02:50:18.009600000&#x27;, &#x27;2013-05-19T23:21:05.990400000&#x27;,\n",
-       "       &#x27;2013-05-30T02:50:30.969600000&#x27;, &#x27;2013-06-08T23:32:49.027200000&#x27;,\n",
-       "       &#x27;2013-06-19T03:42:51.004800000&#x27;, &#x27;2013-06-28T23:32:16.022400000&#x27;,\n",
-       "       &#x27;2013-07-09T03:28:30.979200000&#x27;, &#x27;2013-07-18T23:33:57.974400000&#x27;,\n",
-       "       &#x27;2013-07-29T03:15:42.019200000&#x27;, &#x27;2013-08-07T23:25:02.035200004&#x27;,\n",
-       "       &#x27;2013-08-18T01:47:44.966400000&#x27;, &#x27;2013-08-28T03:02:59.020799996&#x27;,\n",
-       "       &#x27;2013-09-07T03:03:51.984000000&#x27;, &#x27;2013-09-16T23:32:07.987200000&#x27;,\n",
-       "       &#x27;2013-09-27T04:08:27.974400000&#x27;, &#x27;2013-10-06T23:25:39.964800000&#x27;,\n",
-       "       &#x27;2013-10-17T02:55:50.995200000&#x27;, &#x27;2013-10-27T03:45:47.001600000&#x27;,\n",
-       "       &#x27;2013-11-06T01:14:52.022400000&#x27;, &#x27;2013-11-16T03:29:54.009600000&#x27;,\n",
-       "       &#x27;2013-11-26T03:03:56.995200000&#x27;, &#x27;2013-12-05T23:33:59.011200000&#x27;,\n",
-       "       &#x27;2013-12-16T02:58:01.977600000&#x27;, &#x27;2013-12-25T23:22:43.017600000&#x27;,\n",
-       "       &#x27;2014-01-05T02:52:06.009600000&#x27;, &#x27;2014-01-14T23:41:18.009600000&#x27;,\n",
-       "       &#x27;2014-01-25T03:00:43.977600000&#x27;, &#x27;2014-02-03T23:29:13.977600000&#x27;,\n",
-       "       &#x27;2014-02-14T02:50:11.961600000&#x27;, &#x27;2014-02-23T23:03:21.974400000&#x27;,\n",
-       "       &#x27;2014-03-06T02:58:03.964800000&#x27;, &#x27;2014-03-15T23:10:28.012800000&#x27;,\n",
-       "       &#x27;2014-03-26T02:51:22.032000000&#x27;, &#x27;2014-04-04T23:25:58.972800000&#x27;,\n",
-       "       &#x27;2014-04-15T03:00:45.964800000&#x27;, &#x27;2014-04-24T23:24:40.003200000&#x27;,\n",
-       "       &#x27;2014-05-05T02:56:22.012800000&#x27;, &#x27;2014-05-15T00:10:06.009600000&#x27;,\n",
-       "       &#x27;2014-05-25T03:02:43.036800000&#x27;, &#x27;2014-06-03T23:34:53.961600000&#x27;,\n",
-       "       &#x27;2014-06-14T03:01:23.980800000&#x27;, &#x27;2014-06-23T23:24:31.968000000&#x27;,\n",
-       "       &#x27;2014-07-04T03:08:30.019200000&#x27;, &#x27;2014-07-13T23:47:43.008000000&#x27;,\n",
-       "       &#x27;2014-07-24T03:02:33.014400000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>(date)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-39.13 -37.28 ... -34.11 -34.38</div><input id='attrs-68e194dd-f2ef-4926-a2a3-68a923aaa532' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-68e194dd-f2ef-4926-a2a3-68a923aaa532' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0b109c71-3081-4f6a-ab2b-3d3379313df3' class='xr-var-data-in' type='checkbox'><label for='data-0b109c71-3081-4f6a-ab2b-3d3379313df3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([-39.13 , -37.282, -36.9  , -36.89 , -37.053, -36.658, -35.963,\n",
-       "       -35.184, -34.462, -33.784, -32.972, -32.546, -32.428, -32.292,\n",
-       "       -32.169, -31.998, -31.824, -31.624, -31.433, -31.312, -31.107,\n",
-       "       -31.147, -31.044, -31.14 , -31.417, -31.882, -32.145, -32.487,\n",
-       "       -32.537, -32.334, -32.042, -31.892, -31.861, -31.991, -31.883,\n",
-       "       -31.89 , -31.941, -31.889, -31.724, -31.412, -31.786, -31.561,\n",
-       "       -31.732, -31.553, -31.862, -32.389, -32.318, -32.19 , -32.224,\n",
-       "       -32.368, -32.306, -32.305, -32.65 , -33.093, -33.263, -33.199,\n",
-       "       -33.27 , -33.237, -33.221, -33.011, -32.844, -32.981, -32.784,\n",
-       "       -32.607, -32.87 , -33.196, -33.524, -33.956, -33.944, -33.71 ,\n",
-       "       -33.621, -33.552, -33.828, -34.11 , -34.38 ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>(date)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>47.19 46.72 46.45 ... 42.46 42.38</div><input id='attrs-5d69f25e-74bc-4c4a-a816-316a665306b1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5d69f25e-74bc-4c4a-a816-316a665306b1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-abd0c0cf-2ba5-45f1-8655-0362b19de36f' class='xr-var-data-in' type='checkbox'><label for='data-abd0c0cf-2ba5-45f1-8655-0362b19de36f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([47.187, 46.716, 46.45 , 46.23 , 45.459, 44.833, 44.452, 44.839,\n",
-       "       44.956, 44.676, 44.13 , 43.644, 43.067, 42.662, 42.513, 42.454,\n",
-       "       42.396, 42.256, 42.089, 41.944, 41.712, 41.571, 41.596, 41.581,\n",
-       "       41.351, 41.032, 40.912, 40.792, 40.495, 40.383, 40.478, 40.672,\n",
-       "       41.032, 40.864, 40.651, 40.425, 40.228, 40.197, 40.483, 40.311,\n",
-       "       40.457, 40.463, 40.164, 40.047, 39.963, 40.122, 40.57 , 40.476,\n",
-       "       40.527, 40.589, 40.749, 40.993, 41.162, 41.237, 41.448, 41.65 ,\n",
-       "       42.053, 42.311, 42.096, 41.683, 41.661, 41.676, 42.018, 42.395,\n",
-       "       42.532, 42.558, 42.504, 42.63 , 42.934, 42.952, 42.777, 42.722,\n",
-       "       42.601, 42.457, 42.379])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-a3abd2d5-3dbc-4830-af2b-03a31f142121' class='xr-section-summary-in' type='checkbox'  checked><label for='section-a3abd2d5-3dbc-4830-af2b-03a31f142121' class='xr-section-summary' >Data variables: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>salinity</span></div><div class='xr-var-dims'>(level, date)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan nan ... nan nan nan nan</div><input id='attrs-59c6e890-cdba-4b56-ac66-ee1b127e2caa' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-59c6e890-cdba-4b56-ac66-ee1b127e2caa' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2cb4c3dd-5c37-445c-bd85-6b76f2df39a6' class='xr-var-data-in' type='checkbox'><label for='data-2cb4c3dd-5c37-445c-bd85-6b76f2df39a6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[nan, nan, nan, ..., nan, nan, nan],\n",
-       "       [nan, nan, nan, ..., nan, nan, nan],\n",
-       "       [nan, nan, nan, ..., nan, nan, nan],\n",
-       "       ...,\n",
-       "       [nan, nan, nan, ..., nan, nan, nan],\n",
-       "       [nan, nan, nan, ..., nan, nan, nan],\n",
-       "       [nan, nan, nan, ..., nan, nan, nan]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>temperature</span></div><div class='xr-var-dims'>(level, date)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan nan ... nan nan nan nan</div><input id='attrs-1381a7f1-930c-4843-80a0-8efe1a5d26eb' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1381a7f1-930c-4843-80a0-8efe1a5d26eb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2126278e-94dd-4f08-8be0-0fcefbe3ebbb' class='xr-var-data-in' type='checkbox'><label for='data-2126278e-94dd-4f08-8be0-0fcefbe3ebbb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[nan, nan, nan, ..., nan, nan, nan],\n",
-       "       [nan, nan, nan, ..., nan, nan, nan],\n",
-       "       [nan, nan, nan, ..., nan, nan, nan],\n",
-       "       ...,\n",
-       "       [nan, nan, nan, ..., nan, nan, nan],\n",
-       "       [nan, nan, nan, ..., nan, nan, nan],\n",
-       "       [nan, nan, nan, ..., nan, nan, nan]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pressure</span></div><div class='xr-var-dims'>(level, date)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan nan ... nan nan nan nan</div><input id='attrs-710212e1-2f13-4f88-a1ab-f1138b7bda0c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-710212e1-2f13-4f88-a1ab-f1138b7bda0c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5a10941b-c069-4f12-b69b-d26362ef5ef5' class='xr-var-data-in' type='checkbox'><label for='data-5a10941b-c069-4f12-b69b-d26362ef5ef5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[nan, nan, nan, ..., nan, nan, nan],\n",
-       "       [nan, nan, nan, ..., nan, nan, nan],\n",
-       "       [nan, nan, nan, ..., nan, nan, nan],\n",
-       "       ...,\n",
-       "       [nan, nan, nan, ..., nan, nan, nan],\n",
-       "       [nan, nan, nan, ..., nan, nan, nan],\n",
-       "       [nan, nan, nan, ..., nan, nan, nan]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-59bc1084-323f-4d89-b05d-b0557168ce9e' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-59bc1084-323f-4d89-b05d-b0557168ce9e' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
-      ],
-      "text/plain": [
-       "<xarray.Dataset>\n",
-       "Dimensions:      (date: 75, level: 78)\n",
-       "Coordinates:\n",
-       "  * level        (level) int32 0 1 2 3 4 5 6 7 8 ... 69 70 71 72 73 74 75 76 77\n",
-       "  * date         (date) datetime64[ns] 2012-07-13T22:33:06.019200 ... 2014-07...\n",
-       "    lon          (date) float64 -39.13 -37.28 -36.9 ... -33.83 -34.11 -34.38\n",
-       "    lat          (date) float64 47.19 46.72 46.45 46.23 ... 42.6 42.46 42.38\n",
-       "Data variables:\n",
-       "    salinity     (level, date) float64 nan nan nan nan nan ... nan nan nan nan\n",
-       "    temperature  (level, date) float64 nan nan nan nan nan ... nan nan nan nan\n",
-       "    pressure     (level, date) float64 nan nan nan nan nan ... nan nan nan nan"
-      ]
-     },
-     "execution_count": 866,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "argo.rolling(level=10, center=True).std()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 880,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "No handles with labels found to put in legend.\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "argo[\"salinity\"].sel(date='2012-10-31').plot.line(y=\"level\", yincrease=False, color=\"grey\");\n",
-    "argo[\"salinity\"].sel(date='2012-10-31').rolling(level=10, center=True).median().plot.line(y=\"level\", yincrease=False, linewidth=3, color=\"crimson\");\n",
-    "plt.legend(), plt.title(\"\");"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<div class=\"alert alert-info\" style=\"font-size:120%\">\n",
-    "\n",
-    "**REMEMBER**: <br>\n",
-    "\n",
-    "The [xarray `groupby`](http://xarray.pydata.org/en/stable/groupby.html) with the same syntax as Pandas implements the __split-apply-combine__ strategy. Also [`resample`](http://xarray.pydata.org/en/stable/time-series.html#resampling-and-grouped-operations) and [`rolling`](http://xarray.pydata.org/en/stable/computation.html?highlight=rolling#rolling-window-operations) are available in xarray.\n",
-    "    \n",
-    "__Note:__ Xarray adds a [`groupby_bins`](http://xarray.pydata.org/en/stable/generated/xarray.Dataset.groupby_bins.html#xarray.Dataset.groupby_bins) convenience function for binned groups (instead of each value).\n",
-    "\n",
-    "</div>"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "---------------\n",
-    "\n",
-    "__Note:__ Values are only read from disk when needed. For example, the following statement only reads the coordinate information and the metadata. The data itself is not yet loaded:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 466,
-   "metadata": {},
-   "outputs": [
-    {
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-       ".xr-section-inline-details {\n",
-       "  grid-column: 2 / -1;\n",
-       "}\n",
-       "\n",
-       ".xr-section-details {\n",
-       "  display: none;\n",
-       "  grid-column: 1 / -1;\n",
-       "  margin-bottom: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
-       "  display: contents;\n",
-       "}\n",
-       "\n",
-       ".xr-array-wrap {\n",
-       "  grid-column: 1 / -1;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 20px auto;\n",
-       "}\n",
-       "\n",
-       ".xr-array-wrap > label {\n",
-       "  grid-column: 1;\n",
-       "  vertical-align: top;\n",
-       "}\n",
-       "\n",
-       ".xr-preview {\n",
-       "  color: var(--xr-font-color3);\n",
-       "}\n",
-       "\n",
-       ".xr-array-preview,\n",
-       ".xr-array-data {\n",
-       "  padding: 0 5px !important;\n",
-       "  grid-column: 2;\n",
-       "}\n",
-       "\n",
-       ".xr-array-data,\n",
-       ".xr-array-in:checked ~ .xr-array-preview {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-array-in:checked ~ .xr-array-data,\n",
-       ".xr-array-preview {\n",
-       "  display: inline-block;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list {\n",
-       "  display: inline-block !important;\n",
-       "  list-style: none;\n",
-       "  padding: 0 !important;\n",
-       "  margin: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list li {\n",
-       "  display: inline-block;\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list:before {\n",
-       "  content: '(';\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list:after {\n",
-       "  content: ')';\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list li:not(:last-child):after {\n",
-       "  content: ',';\n",
-       "  padding-right: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-has-index {\n",
-       "  font-weight: bold;\n",
-       "}\n",
-       "\n",
-       ".xr-var-list,\n",
-       ".xr-var-item {\n",
-       "  display: contents;\n",
-       "}\n",
-       "\n",
-       ".xr-var-item > div,\n",
-       ".xr-var-item label,\n",
-       ".xr-var-item > .xr-var-name span {\n",
-       "  background-color: var(--xr-background-color-row-even);\n",
-       "  margin-bottom: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-var-item > .xr-var-name:hover span {\n",
-       "  padding-right: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-var-list > li:nth-child(odd) > div,\n",
-       ".xr-var-list > li:nth-child(odd) > label,\n",
-       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
-       "  background-color: var(--xr-background-color-row-odd);\n",
-       "}\n",
-       "\n",
-       ".xr-var-name {\n",
-       "  grid-column: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-var-dims {\n",
-       "  grid-column: 2;\n",
-       "}\n",
-       "\n",
-       ".xr-var-dtype {\n",
-       "  grid-column: 3;\n",
-       "  text-align: right;\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-var-preview {\n",
-       "  grid-column: 4;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name,\n",
-       ".xr-var-dims,\n",
-       ".xr-var-dtype,\n",
-       ".xr-preview,\n",
-       ".xr-attrs dt {\n",
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-       "  overflow: hidden;\n",
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-       "\n",
-       ".xr-var-name:hover,\n",
-       ".xr-var-dims:hover,\n",
-       ".xr-var-dtype:hover,\n",
-       ".xr-attrs dt:hover {\n",
-       "  overflow: visible;\n",
-       "  width: auto;\n",
-       "  z-index: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-var-attrs,\n",
-       ".xr-var-data {\n",
-       "  display: none;\n",
-       "  background-color: var(--xr-background-color) !important;\n",
-       "  padding-bottom: 5px !important;\n",
-       "}\n",
-       "\n",
-       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
-       ".xr-var-data-in:checked ~ .xr-var-data {\n",
-       "  display: block;\n",
-       "}\n",
-       "\n",
-       ".xr-var-data > table {\n",
-       "  float: right;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name span,\n",
-       ".xr-var-data,\n",
-       ".xr-attrs {\n",
-       "  padding-left: 25px !important;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs,\n",
-       ".xr-var-attrs,\n",
-       ".xr-var-data {\n",
-       "  grid-column: 1 / -1;\n",
-       "}\n",
-       "\n",
-       "dl.xr-attrs {\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 125px auto;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt, dd {\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  float: left;\n",
-       "  padding-right: 10px;\n",
-       "  width: auto;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt {\n",
-       "  font-weight: normal;\n",
-       "  grid-column: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt:hover span {\n",
-       "  display: inline-block;\n",
-       "  background: var(--xr-background-color);\n",
-       "  padding-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dd {\n",
-       "  grid-column: 2;\n",
-       "  white-space: pre-wrap;\n",
-       "  word-break: break-all;\n",
-       "}\n",
-       "\n",
-       ".xr-icon-database,\n",
-       ".xr-icon-file-text2 {\n",
-       "  display: inline-block;\n",
-       "  vertical-align: middle;\n",
-       "  width: 1em;\n",
-       "  height: 1.5em !important;\n",
-       "  stroke-width: 0;\n",
-       "  stroke: currentColor;\n",
-       "  fill: currentColor;\n",
-       "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray (band: 3, y: 317, x: 625)&gt;\n",
-       "[594375 values with dtype=uint16]\n",
-       "Coordinates:\n",
-       "  * band     (band) int64 1 2 3\n",
-       "  * y        (y) float64 6.639e+06 6.639e+06 6.639e+06 ... 6.621e+06 6.621e+06\n",
-       "  * x        (x) float64 3.925e+05 3.926e+05 3.926e+05 ... 4.282e+05 4.283e+05\n",
-       "Attributes:\n",
-       "    transform:               (57.32777121388223, 0.0, 392465.9217302375, 0.0,...\n",
-       "    crs:                     +init=epsg:3857\n",
-       "    res:                     (57.32777121388223, 57.44833434997156)\n",
-       "    is_tiled:                0\n",
-       "    nodatavals:              (nan, nan, nan)\n",
-       "    scales:                  (1.0, 1.0, 1.0)\n",
-       "    offsets:                 (0.0, 0.0, 0.0)\n",
-       "    AREA_OR_POINT:           Area\n",
-       "    TIFFTAG_RESOLUTIONUNIT:  1 (unitless)\n",
-       "    TIFFTAG_XRESOLUTION:     1\n",
-       "    TIFFTAG_YRESOLUTION:     1</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>band</span>: 3</li><li><span class='xr-has-index'>y</span>: 317</li><li><span class='xr-has-index'>x</span>: 625</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-c2c0a85d-7984-40fc-8d93-e5405ed06b50' class='xr-array-in' type='checkbox' checked><label for='section-c2c0a85d-7984-40fc-8d93-e5405ed06b50' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>...</span></div><div class='xr-array-data'><pre>[594375 values with dtype=uint16]</pre></div></div></li><li class='xr-section-item'><input id='section-3276fb24-76e7-4b80-87db-c1d26c4a7982' class='xr-section-summary-in' type='checkbox'  checked><label for='section-3276fb24-76e7-4b80-87db-c1d26c4a7982' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>band</span></div><div class='xr-var-dims'>(band)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1 2 3</div><input id='attrs-5c2a48a3-4587-4c88-b5b4-3cfcda6c3603' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5c2a48a3-4587-4c88-b5b4-3cfcda6c3603' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d70c3132-0451-4c03-b910-6fd76b79dbad' class='xr-var-data-in' type='checkbox'><label for='data-d70c3132-0451-4c03-b910-6fd76b79dbad' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([1, 2, 3])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>6.639e+06 6.639e+06 ... 6.621e+06</div><input id='attrs-48ee1a8a-7cda-467c-8318-66b2318f5681' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-48ee1a8a-7cda-467c-8318-66b2318f5681' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0b0d7569-fab3-473c-8e88-bf4cd98de45d' class='xr-var-data-in' type='checkbox'><label for='data-0b0d7569-fab3-473c-8e88-bf4cd98de45d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([6639463.535997, 6639406.087663, 6639348.639328, ..., 6621424.759011,\n",
-       "       6621367.310677, 6621309.862342])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>3.925e+05 3.926e+05 ... 4.283e+05</div><input id='attrs-322cbb7c-dd17-479d-8a20-d938b7a310fa' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-322cbb7c-dd17-479d-8a20-d938b7a310fa' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-34653a38-2e29-42ee-8e64-473ea4f1be5d' class='xr-var-data-in' type='checkbox'><label for='data-34653a38-2e29-42ee-8e64-473ea4f1be5d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([392494.585616, 392551.913387, 392609.241158, ..., 428152.459311,\n",
-       "       428209.787082, 428267.114853])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-2fe23231-7348-45b2-8f4f-aac0161dd172' class='xr-section-summary-in' type='checkbox'  ><label for='section-2fe23231-7348-45b2-8f4f-aac0161dd172' class='xr-section-summary' >Attributes: <span>(11)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>transform :</span></dt><dd>(57.32777121388223, 0.0, 392465.9217302375, 0.0, -57.44833434997156, 6639492.260164049)</dd><dt><span>crs :</span></dt><dd>+init=epsg:3857</dd><dt><span>res :</span></dt><dd>(57.32777121388223, 57.44833434997156)</dd><dt><span>is_tiled :</span></dt><dd>0</dd><dt><span>nodatavals :</span></dt><dd>(nan, nan, nan)</dd><dt><span>scales :</span></dt><dd>(1.0, 1.0, 1.0)</dd><dt><span>offsets :</span></dt><dd>(0.0, 0.0, 0.0)</dd><dt><span>AREA_OR_POINT :</span></dt><dd>Area</dd><dt><span>TIFFTAG_RESOLUTIONUNIT :</span></dt><dd>1 (unitless)</dd><dt><span>TIFFTAG_XRESOLUTION :</span></dt><dd>1</dd><dt><span>TIFFTAG_YRESOLUTION :</span></dt><dd>1</dd></dl></div></li></ul></div></div>"
-      ],
-      "text/plain": [
-       "<xarray.DataArray (band: 3, y: 317, x: 625)>\n",
-       "[594375 values with dtype=uint16]\n",
-       "Coordinates:\n",
-       "  * band     (band) int64 1 2 3\n",
-       "  * y        (y) float64 6.639e+06 6.639e+06 6.639e+06 ... 6.621e+06 6.621e+06\n",
-       "  * x        (x) float64 3.925e+05 3.926e+05 3.926e+05 ... 4.282e+05 4.283e+05\n",
-       "Attributes:\n",
-       "    transform:               (57.32777121388223, 0.0, 392465.9217302375, 0.0,...\n",
-       "    crs:                     +init=epsg:3857\n",
-       "    res:                     (57.32777121388223, 57.44833434997156)\n",
-       "    is_tiled:                0\n",
-       "    nodatavals:              (nan, nan, nan)\n",
-       "    scales:                  (1.0, 1.0, 1.0)\n",
-       "    offsets:                 (0.0, 0.0, 0.0)\n",
-       "    AREA_OR_POINT:           Area\n",
-       "    TIFFTAG_RESOLUTIONUNIT:  1 (unitless)\n",
-       "    TIFFTAG_XRESOLUTION:     1\n",
-       "    TIFFTAG_YRESOLUTION:     1"
-      ]
-     },
-     "execution_count": 466,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "gent = xr.open_rasterio(data_file)\n",
-    "gent"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`load()` will explicitly load the data into memory:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 467,
-   "metadata": {},
-   "outputs": [
-    {
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-       ".xr-section-item input:enabled + label {\n",
-       "  cursor: pointer;\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input:enabled + label:hover {\n",
-       "  color: var(--xr-font-color0);\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary {\n",
-       "  grid-column: 1;\n",
-       "  color: var(--xr-font-color2);\n",
-       "  font-weight: 500;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary > span {\n",
-       "  display: inline-block;\n",
-       "  padding-left: 0.5em;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:disabled + label {\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in + label:before {\n",
-       "  display: inline-block;\n",
-       "  content: '►';\n",
-       "  font-size: 11px;\n",
-       "  width: 15px;\n",
-       "  text-align: center;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:disabled + label:before {\n",
-       "  color: var(--xr-disabled-color);\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:checked + label:before {\n",
-       "  content: '▼';\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:checked + label > span {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary,\n",
-       ".xr-section-inline-details {\n",
-       "  padding-top: 4px;\n",
-       "  padding-bottom: 4px;\n",
-       "}\n",
-       "\n",
-       ".xr-section-inline-details {\n",
-       "  grid-column: 2 / -1;\n",
-       "}\n",
-       "\n",
-       ".xr-section-details {\n",
-       "  display: none;\n",
-       "  grid-column: 1 / -1;\n",
-       "  margin-bottom: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
-       "  display: contents;\n",
-       "}\n",
-       "\n",
-       ".xr-array-wrap {\n",
-       "  grid-column: 1 / -1;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 20px auto;\n",
-       "}\n",
-       "\n",
-       ".xr-array-wrap > label {\n",
-       "  grid-column: 1;\n",
-       "  vertical-align: top;\n",
-       "}\n",
-       "\n",
-       ".xr-preview {\n",
-       "  color: var(--xr-font-color3);\n",
-       "}\n",
-       "\n",
-       ".xr-array-preview,\n",
-       ".xr-array-data {\n",
-       "  padding: 0 5px !important;\n",
-       "  grid-column: 2;\n",
-       "}\n",
-       "\n",
-       ".xr-array-data,\n",
-       ".xr-array-in:checked ~ .xr-array-preview {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-array-in:checked ~ .xr-array-data,\n",
-       ".xr-array-preview {\n",
-       "  display: inline-block;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list {\n",
-       "  display: inline-block !important;\n",
-       "  list-style: none;\n",
-       "  padding: 0 !important;\n",
-       "  margin: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list li {\n",
-       "  display: inline-block;\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list:before {\n",
-       "  content: '(';\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list:after {\n",
-       "  content: ')';\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list li:not(:last-child):after {\n",
-       "  content: ',';\n",
-       "  padding-right: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-has-index {\n",
-       "  font-weight: bold;\n",
-       "}\n",
-       "\n",
-       ".xr-var-list,\n",
-       ".xr-var-item {\n",
-       "  display: contents;\n",
-       "}\n",
-       "\n",
-       ".xr-var-item > div,\n",
-       ".xr-var-item label,\n",
-       ".xr-var-item > .xr-var-name span {\n",
-       "  background-color: var(--xr-background-color-row-even);\n",
-       "  margin-bottom: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-var-item > .xr-var-name:hover span {\n",
-       "  padding-right: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-var-list > li:nth-child(odd) > div,\n",
-       ".xr-var-list > li:nth-child(odd) > label,\n",
-       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
-       "  background-color: var(--xr-background-color-row-odd);\n",
-       "}\n",
-       "\n",
-       ".xr-var-name {\n",
-       "  grid-column: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-var-dims {\n",
-       "  grid-column: 2;\n",
-       "}\n",
-       "\n",
-       ".xr-var-dtype {\n",
-       "  grid-column: 3;\n",
-       "  text-align: right;\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-var-preview {\n",
-       "  grid-column: 4;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name,\n",
-       ".xr-var-dims,\n",
-       ".xr-var-dtype,\n",
-       ".xr-preview,\n",
-       ".xr-attrs dt {\n",
-       "  white-space: nowrap;\n",
-       "  overflow: hidden;\n",
-       "  text-overflow: ellipsis;\n",
-       "  padding-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name:hover,\n",
-       ".xr-var-dims:hover,\n",
-       ".xr-var-dtype:hover,\n",
-       ".xr-attrs dt:hover {\n",
-       "  overflow: visible;\n",
-       "  width: auto;\n",
-       "  z-index: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-var-attrs,\n",
-       ".xr-var-data {\n",
-       "  display: none;\n",
-       "  background-color: var(--xr-background-color) !important;\n",
-       "  padding-bottom: 5px !important;\n",
-       "}\n",
-       "\n",
-       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
-       ".xr-var-data-in:checked ~ .xr-var-data {\n",
-       "  display: block;\n",
-       "}\n",
-       "\n",
-       ".xr-var-data > table {\n",
-       "  float: right;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name span,\n",
-       ".xr-var-data,\n",
-       ".xr-attrs {\n",
-       "  padding-left: 25px !important;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs,\n",
-       ".xr-var-attrs,\n",
-       ".xr-var-data {\n",
-       "  grid-column: 1 / -1;\n",
-       "}\n",
-       "\n",
-       "dl.xr-attrs {\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 125px auto;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt, dd {\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  float: left;\n",
-       "  padding-right: 10px;\n",
-       "  width: auto;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt {\n",
-       "  font-weight: normal;\n",
-       "  grid-column: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt:hover span {\n",
-       "  display: inline-block;\n",
-       "  background: var(--xr-background-color);\n",
-       "  padding-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dd {\n",
-       "  grid-column: 2;\n",
-       "  white-space: pre-wrap;\n",
-       "  word-break: break-all;\n",
-       "}\n",
-       "\n",
-       ".xr-icon-database,\n",
-       ".xr-icon-file-text2 {\n",
-       "  display: inline-block;\n",
-       "  vertical-align: middle;\n",
-       "  width: 1em;\n",
-       "  height: 1.5em !important;\n",
-       "  stroke-width: 0;\n",
-       "  stroke: currentColor;\n",
-       "  fill: currentColor;\n",
-       "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray (band: 3, y: 317, x: 625)&gt;\n",
-       "array([[[12812, 14483, 28688, ..., 16941, 17039, 14156],\n",
-       "        [16744, 12435, 14008, ..., 11993, 10764, 10731],\n",
-       "        [16564, 13107, 12615, ..., 11272, 12009, 11551],\n",
-       "        ...,\n",
-       "        [11911, 12157, 11157, ..., 23085, 16515, 11190],\n",
-       "        [11518, 11387, 11010, ..., 12026, 14582,  9814],\n",
-       "        [11567, 12255, 11862, ...,  9453, 11075, 13599]],\n",
-       "\n",
-       "       [[15663, 16777, 27115, ..., 17826, 18186, 16482],\n",
-       "        [16040, 15188, 16433, ..., 15663, 14221, 13779],\n",
-       "        [16400, 15548, 15221, ..., 14631, 15778, 15974],\n",
-       "        ...,\n",
-       "        [15319, 15450, 15188, ..., 21610, 18841, 15810],\n",
-       "        [15401, 15532, 15401, ..., 14516, 16859, 15859],\n",
-       "        [15696, 15483, 15352, ..., 13844, 15810, 17334]],\n",
-       "\n",
-       "       [[17776, 18645, 28475, ..., 19153, 19497, 18235],\n",
-       "        [18563, 17842, 18596, ..., 17613, 17023, 17154],\n",
-       "        [18628, 17563, 18088, ..., 17400, 18137, 17776],\n",
-       "        ...,\n",
-       "        [17645, 17744, 17432, ..., 22380, 20152, 17727],\n",
-       "        [17580, 17383, 17252, ..., 17776, 18284, 17350],\n",
-       "        [17891, 17678, 17662, ..., 16335, 17301, 18415]]], dtype=uint16)\n",
-       "Coordinates:\n",
-       "  * band     (band) int64 1 2 3\n",
-       "  * y        (y) float64 6.639e+06 6.639e+06 6.639e+06 ... 6.621e+06 6.621e+06\n",
-       "  * x        (x) float64 3.925e+05 3.926e+05 3.926e+05 ... 4.282e+05 4.283e+05\n",
-       "Attributes:\n",
-       "    transform:               (57.32777121388223, 0.0, 392465.9217302375, 0.0,...\n",
-       "    crs:                     +init=epsg:3857\n",
-       "    res:                     (57.32777121388223, 57.44833434997156)\n",
-       "    is_tiled:                0\n",
-       "    nodatavals:              (nan, nan, nan)\n",
-       "    scales:                  (1.0, 1.0, 1.0)\n",
-       "    offsets:                 (0.0, 0.0, 0.0)\n",
-       "    AREA_OR_POINT:           Area\n",
-       "    TIFFTAG_RESOLUTIONUNIT:  1 (unitless)\n",
-       "    TIFFTAG_XRESOLUTION:     1\n",
-       "    TIFFTAG_YRESOLUTION:     1</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>band</span>: 3</li><li><span class='xr-has-index'>y</span>: 317</li><li><span class='xr-has-index'>x</span>: 625</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-50f7c3be-eae6-4bd8-b122-58fa465b0293' class='xr-array-in' type='checkbox' checked><label for='section-50f7c3be-eae6-4bd8-b122-58fa465b0293' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>12812 14483 28688 27164 18088 14221 ... 16072 17203 16335 17301 18415</span></div><div class='xr-array-data'><pre>array([[[12812, 14483, 28688, ..., 16941, 17039, 14156],\n",
-       "        [16744, 12435, 14008, ..., 11993, 10764, 10731],\n",
-       "        [16564, 13107, 12615, ..., 11272, 12009, 11551],\n",
-       "        ...,\n",
-       "        [11911, 12157, 11157, ..., 23085, 16515, 11190],\n",
-       "        [11518, 11387, 11010, ..., 12026, 14582,  9814],\n",
-       "        [11567, 12255, 11862, ...,  9453, 11075, 13599]],\n",
-       "\n",
-       "       [[15663, 16777, 27115, ..., 17826, 18186, 16482],\n",
-       "        [16040, 15188, 16433, ..., 15663, 14221, 13779],\n",
-       "        [16400, 15548, 15221, ..., 14631, 15778, 15974],\n",
-       "        ...,\n",
-       "        [15319, 15450, 15188, ..., 21610, 18841, 15810],\n",
-       "        [15401, 15532, 15401, ..., 14516, 16859, 15859],\n",
-       "        [15696, 15483, 15352, ..., 13844, 15810, 17334]],\n",
-       "\n",
-       "       [[17776, 18645, 28475, ..., 19153, 19497, 18235],\n",
-       "        [18563, 17842, 18596, ..., 17613, 17023, 17154],\n",
-       "        [18628, 17563, 18088, ..., 17400, 18137, 17776],\n",
-       "        ...,\n",
-       "        [17645, 17744, 17432, ..., 22380, 20152, 17727],\n",
-       "        [17580, 17383, 17252, ..., 17776, 18284, 17350],\n",
-       "        [17891, 17678, 17662, ..., 16335, 17301, 18415]]], dtype=uint16)</pre></div></div></li><li class='xr-section-item'><input id='section-d9c51772-20cd-4c61-88c9-b32fcede782f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d9c51772-20cd-4c61-88c9-b32fcede782f' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>band</span></div><div class='xr-var-dims'>(band)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1 2 3</div><input id='attrs-4932e0e7-d245-461f-847a-a2c8fc304ec4' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4932e0e7-d245-461f-847a-a2c8fc304ec4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4de567c5-2ddf-484a-87ce-f79bd0367717' class='xr-var-data-in' type='checkbox'><label for='data-4de567c5-2ddf-484a-87ce-f79bd0367717' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([1, 2, 3])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>6.639e+06 6.639e+06 ... 6.621e+06</div><input id='attrs-313ddd7b-b81b-4b0d-87fb-66dd9db720de' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-313ddd7b-b81b-4b0d-87fb-66dd9db720de' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-53354902-b533-4009-9704-6d4f5def338b' class='xr-var-data-in' type='checkbox'><label for='data-53354902-b533-4009-9704-6d4f5def338b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([6639463.535997, 6639406.087663, 6639348.639328, ..., 6621424.759011,\n",
-       "       6621367.310677, 6621309.862342])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>3.925e+05 3.926e+05 ... 4.283e+05</div><input id='attrs-820f8246-f4b6-478c-befc-bd67f3689b13' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-820f8246-f4b6-478c-befc-bd67f3689b13' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e3f731e8-5dc3-4aad-89ab-2c78ce50ec8d' class='xr-var-data-in' type='checkbox'><label for='data-e3f731e8-5dc3-4aad-89ab-2c78ce50ec8d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([392494.585616, 392551.913387, 392609.241158, ..., 428152.459311,\n",
-       "       428209.787082, 428267.114853])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-3f302940-139f-4614-90db-ec3e9b5c64bc' class='xr-section-summary-in' type='checkbox'  ><label for='section-3f302940-139f-4614-90db-ec3e9b5c64bc' class='xr-section-summary' >Attributes: <span>(11)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>transform :</span></dt><dd>(57.32777121388223, 0.0, 392465.9217302375, 0.0, -57.44833434997156, 6639492.260164049)</dd><dt><span>crs :</span></dt><dd>+init=epsg:3857</dd><dt><span>res :</span></dt><dd>(57.32777121388223, 57.44833434997156)</dd><dt><span>is_tiled :</span></dt><dd>0</dd><dt><span>nodatavals :</span></dt><dd>(nan, nan, nan)</dd><dt><span>scales :</span></dt><dd>(1.0, 1.0, 1.0)</dd><dt><span>offsets :</span></dt><dd>(0.0, 0.0, 0.0)</dd><dt><span>AREA_OR_POINT :</span></dt><dd>Area</dd><dt><span>TIFFTAG_RESOLUTIONUNIT :</span></dt><dd>1 (unitless)</dd><dt><span>TIFFTAG_XRESOLUTION :</span></dt><dd>1</dd><dt><span>TIFFTAG_YRESOLUTION :</span></dt><dd>1</dd></dl></div></li></ul></div></div>"
-      ],
-      "text/plain": [
-       "<xarray.DataArray (band: 3, y: 317, x: 625)>\n",
-       "array([[[12812, 14483, 28688, ..., 16941, 17039, 14156],\n",
-       "        [16744, 12435, 14008, ..., 11993, 10764, 10731],\n",
-       "        [16564, 13107, 12615, ..., 11272, 12009, 11551],\n",
-       "        ...,\n",
-       "        [11911, 12157, 11157, ..., 23085, 16515, 11190],\n",
-       "        [11518, 11387, 11010, ..., 12026, 14582,  9814],\n",
-       "        [11567, 12255, 11862, ...,  9453, 11075, 13599]],\n",
-       "\n",
-       "       [[15663, 16777, 27115, ..., 17826, 18186, 16482],\n",
-       "        [16040, 15188, 16433, ..., 15663, 14221, 13779],\n",
-       "        [16400, 15548, 15221, ..., 14631, 15778, 15974],\n",
-       "        ...,\n",
-       "        [15319, 15450, 15188, ..., 21610, 18841, 15810],\n",
-       "        [15401, 15532, 15401, ..., 14516, 16859, 15859],\n",
-       "        [15696, 15483, 15352, ..., 13844, 15810, 17334]],\n",
-       "\n",
-       "       [[17776, 18645, 28475, ..., 19153, 19497, 18235],\n",
-       "        [18563, 17842, 18596, ..., 17613, 17023, 17154],\n",
-       "        [18628, 17563, 18088, ..., 17400, 18137, 17776],\n",
-       "        ...,\n",
-       "        [17645, 17744, 17432, ..., 22380, 20152, 17727],\n",
-       "        [17580, 17383, 17252, ..., 17776, 18284, 17350],\n",
-       "        [17891, 17678, 17662, ..., 16335, 17301, 18415]]], dtype=uint16)\n",
-       "Coordinates:\n",
-       "  * band     (band) int64 1 2 3\n",
-       "  * y        (y) float64 6.639e+06 6.639e+06 6.639e+06 ... 6.621e+06 6.621e+06\n",
-       "  * x        (x) float64 3.925e+05 3.926e+05 3.926e+05 ... 4.282e+05 4.283e+05\n",
-       "Attributes:\n",
-       "    transform:               (57.32777121388223, 0.0, 392465.9217302375, 0.0,...\n",
-       "    crs:                     +init=epsg:3857\n",
-       "    res:                     (57.32777121388223, 57.44833434997156)\n",
-       "    is_tiled:                0\n",
-       "    nodatavals:              (nan, nan, nan)\n",
-       "    scales:                  (1.0, 1.0, 1.0)\n",
-       "    offsets:                 (0.0, 0.0, 0.0)\n",
-       "    AREA_OR_POINT:           Area\n",
-       "    TIFFTAG_RESOLUTIONUNIT:  1 (unitless)\n",
-       "    TIFFTAG_XRESOLUTION:     1\n",
-       "    TIFFTAG_YRESOLUTION:     1"
-      ]
-     },
-     "execution_count": 467,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "xr.open_rasterio(data_file).load()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Acknowledgements and great thanks to https://earth-env-data-science.github.io for the inspiration, data and examples."
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python [conda env:DS-geospatial]",
-   "language": "python",
-   "name": "conda-env-DS-geospatial-py"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.6"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/_solved/14-combine-data.ipynb b/_solved/14-combine-data.ipynb
new file mode 100644
index 0000000..303b8bf
--- /dev/null
+++ b/_solved/14-combine-data.ipynb
@@ -0,0 +1,624 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "b32d4d6e-6b28-4ace-ad01-0f8c4465794c",
+   "metadata": {},
+   "source": [
+    "<p><font size=\"6\"><b>Stacking of raster data</b></font></p>\n",
+    "\n",
+    "\n",
+    "> *DS Python for GIS and Geoscience*  \n",
+    "> *October, 2025*\n",
+    ">\n",
+    "> *© 2025, Joris Van den Bossche and Stijn Van Hoey. Licensed under [CC BY 4.0 Creative Commons](http://creativecommons.org/licenses/by/4.0/)*\n",
+    "\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "40325d7c-ccb2-4c18-8a5f-98fb25383399",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import shutil\n",
+    "from pathlib import Path\n",
+    "\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "import xarray as xr"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a11a4867-cc75-43c3-971e-df1e9991761e",
+   "metadata": {},
+   "source": [
+    "## Introduction"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1563d475-a2c7-4914-8e4c-2bb5fe08d312",
+   "metadata": {},
+   "source": [
+    "Geospatial time series data is often stored as multiple individual files. For example, remote sensing data or geoscience model output are typically organized with each time step (or band) in a separate file. Handling all these indidivudal files is culbersome and workflows to combine these files into a single `xarray.Dataset` or `xarray.DataArray` prior to the analysis are required. \n",
+    "\n",
+    "In this notebook, we will explore some ways to combine indidivual files into a single data product ready for analysis."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c094477e-1610-4477-a6f9-25934dcb23b0",
+   "metadata": {},
+   "source": [
+    "## Load multiple files into a single `xarray.Dataset/xarray.DataArray`"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "404fe62e-1aa6-4d0f-85bc-14ab660d248a",
+   "metadata": {},
+   "source": [
+    "In some of the previous notebooks, we used the band 4 and band 8 Sentinel image data from Ghent, which are both stored as a separate data file in the `data` directory.\n",
+    "\n",
+    "One way to handle this is to load each of the data sets into memory and concatenate these afterwards:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3a765a96-ef2e-47a9-b115-62012edad698",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "arr_b4 = xr.open_dataarray(\"data/gent/raster/2020-09-17_Sentinel_2_L1C_B04.tiff\", engine=\"rasterio\")\n",
+    "arr_b8 = xr.open_dataarray(\"data/gent/raster/2020-09-17_Sentinel_2_L1C_B08.tiff\", engine=\"rasterio\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6954ed80-d853-4786-9ed9-c72d2253c6be",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "band_var = xr.Variable('band', [\"B4\", \"B8\"])\n",
+    "arr = xr.concat([arr_b4, arr_b8], dim=band_var)\n",
+    "arr"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b5345cdb-ca88-457b-9604-4b993995f5e3",
+   "metadata": {},
+   "source": [
+    "From now on, the data is contained in a single `DataArray` to do further analysis. This approach works just fine for this limited set of data.\n",
+    "\n",
+    "However, when more files need to be processed, this becomes labor/code intensive and additional automation is required. Consider the following example data in the data folder `./data/herstappe/raster/sentinel_moisture/`:\n",
+    "\n",
+    "```\n",
+    "./data/herstappe/raster/sentinel_moisture/\n",
+    "├── 2016-05-01, Sentinel-2A L1C, Moisture index.tiff\n",
+    "├── 2019-02-15, Sentinel-2A L1C, Moisture index.tiff\n",
+    "└── 2020-02-07, Sentinel-2A L1C, Moisture index.tiff\n",
+    "```\n",
+    "\n",
+    "It is a (small) extract of a time series of moisture index data derived from sentinel-2A, made available by [Sentinel-Hub](https://apps.sentinel-hub.com/eo-browser), a time series of remote sensing images. \n",
+    "\n",
+    "Instead of manually loading the data, we rather automate the data load from these files to a single xarray object:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f11f2f34-e7ce-4796-b2d9-15352c825ca8",
+   "metadata": {},
+   "source": [
+    "1. Identify all files in the data folder and make a list of them:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e849f0bc-040f-4e46-b48e-5ff4cce481ad",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pathlib import Path\n",
+    "moisture_index_files = list(sorted(Path(\"./data/herstappe/raster/sentinel_moisture\").rglob(\"*.tiff\")))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a97ef2f0-1b1e-440d-a4ef-108eed4911c6",
+   "metadata": {},
+   "source": [
+    "2. Extract the time-dimension from each individual file name"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6bbe7b26-993c-493e-b2ea-d566a6c5e9fa",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "moisture_index_dates = [pd.to_datetime(file_name.stem.split(\",\")[0]) for file_name in moisture_index_files]\n",
+    "moisture_index_dates"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5dfd11d6-4543-4072-93ed-6cf647270071",
+   "metadata": {},
+   "source": [
+    "__Note__ we use `pathlib` instead of `glob.glob` as it returns `Path` objects instead to represent the file names which are more powerful than regular strings returned by `glob.glob`, e.g. usage of `stem` attribute."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3a1d1094-9593-4ae4-be5d-2b06b5d3d578",
+   "metadata": {},
+   "source": [
+    "3.  Prepare an xarray variable which can be used as the additional date dimension/coordinate"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "13e68fe3-9378-4b11-a56f-ebe3168fdb95",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "date_var = xr.Variable('date', moisture_index_dates)\n",
+    "date_var"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a99d11cc-7d15-4bfa-8e6e-7b6aeafa157e",
+   "metadata": {},
+   "source": [
+    "4. Load in and concatenate all individual GeoTIFFs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "569d758c-bae3-4e7d-8308-fc5d16b37c03",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "moisture_index = xr.concat(\n",
+    "    [xr.open_dataarray(file_name, engine=\"rasterio\", mask_and_scale=False) for file_name in moisture_index_files],\n",
+    "    dim=date_var\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "58ea6fae-e682-4694-ae2d-5f26ff267599",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "moisture_index.sortby(\"date\").plot.imshow(col=\"date\", figsize=(15, 4), aspect=1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5cabf74a-b9b5-472c-88b9-bf5e2e40deb2",
+   "metadata": {},
+   "source": [
+    "## Lazy load multiple files into a single `xarray.Dataset`"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8b9a3cbb-1ae5-4fec-94d8-ebc4d7604eab",
+   "metadata": {},
+   "source": [
+    "In the previous example, all data is read into memory. Xarray provides a separate function [`open_mfdataset`](http://xarray.pydata.org/en/stable/generated/xarray.open_mfdataset.html#xarray-open-mfdataset) to read data lazy from disk (so not loading the data itself in memory) from multiple files. \n",
+    "\n",
+    "A usefule feature is the ability to preprocess the files:\n",
+    "\n",
+    "> __preprocess (callable(), optional)__ – If provided, call this function on each dataset prior to concatenation. You can find the file-name from which each dataset was loaded in ds.encoding[\"source\"].\n",
+    "\n",
+    "Applied to the previous moisture index files example:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a1ecb7b7-4a42-41ca-b79a-899cb9bf34e4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def add_date_dimension(ds):\n",
+    "    \"\"\"Add the date dimension derived from the file_name and rename to moisture_index\"\"\"\n",
+    "    ds_date = pd.to_datetime(Path(ds.encoding[\"source\"]).stem.split(\",\")[0])\n",
+    "    ds = ds.assign_coords(date=(\"date\", [ds_date])).rename({\"band_data\": \"moisture_index\"})\n",
+    "    return ds"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cf285fc6-e005-47b3-b1fa-8caa9b8eec9b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "moisture_index_lazy = xr.open_mfdataset(sorted(Path(\"./data/herstappe/raster/sentinel_moisture\").rglob(\"*.tiff\")), \n",
+    "                                        preprocess=add_date_dimension, engine=\"rasterio\", mask_and_scale=False,\n",
+    "                                        chunks={\"date\": 1, \"band\": -1, \"x\": -1, \"y\": -1}, data_vars=\"all\") # parallel=True\n",
+    "moisture_index_lazy[\"moisture_index\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3fb4684d-a762-4d90-a900-d97c77e3d775",
+   "metadata": {},
+   "source": [
+    "The data itself is not loaded directly and is divided into 3 chunks, i.e. we chunked for each date. See the notebook [15-xarray-dask-big-data](./15-xarray-dask-big-data.ipynb) notebook for more information on the processing of (out of memory) lazy data with Dask."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "761309bb-ef60-4141-97ae-aa697c160001",
+   "metadata": {},
+   "source": [
+    "Further reading:\n",
+    "\n",
+    "- See http://xarray.pydata.org/en/stable/user-guide/io.html#reading-multi-file-datasets for more examples.\n",
+    "- https://medium.com/@bonnefond.virginie/handling-multi-temporal-satellite-images-with-xarray-30d142d3391\n",
+    "- https://docs.dea.ga.gov.au/notebooks/Frequently_used_code/Opening_GeoTIFFs_NetCDFs.html#Loading-multiple-files-into-a-single-xarray.Dataset"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9e1046d9-8a09-46ec-967a-e0584d1113b7",
+   "metadata": {},
+   "source": [
+    "## Save concatenated data to a single file"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "85d3559b-c79e-4e8b-971b-cb8074c6cc2d",
+   "metadata": {},
+   "source": [
+    "After processing multiple files, it is convenient to save the data in a preferred format afterwards. Convenient choices are [NetCDF](https://www.unidata.ucar.edu/software/netcdf/) and [Zarr](https://zarr.readthedocs.io/en/stable/). Zarr is a newer format providing some advantages when working in cloud environments, but can be used on a local machine as well."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b010efc3-fea9-4363-997a-39bff87cfef4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "moisture_index.to_netcdf(\"moisture_index_stacked.nc\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0fde5d0f-56ee-4282-b28f-c27d5aa26ac1",
+   "metadata": {},
+   "source": [
+    "Hence, the next the data set can be loaded directly from disk:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3bc44dbd-5234-430e-9599-147d67321d40",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "xr.open_dataset(\"moisture_index_stacked.nc\", engine=\"netcdf4\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8165b475-b856-4c84-8464-8d9bf2f82315",
+   "metadata": {},
+   "source": [
+    "Storing to zarr files works on the `xarray.DataSet` level:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c28bedfa-74cf-40c0-a295-c818e1675976",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "moisture_index_lazy.to_zarr(\"moisture_index_stacked.zarr\", consolidated=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "11e3b613-6d09-484a-8cc7-f0a7d8dba939",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "xr.open_dataset(\"moisture_index_stacked.zarr\", engine=\"zarr\", consolidated=False)    "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1b918bb6-1ff9-4503-9438-8cd81532e4b6",
+   "metadata": {},
+   "source": [
+    "_Clean up of these example files_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5362f004-462b-4b4b-815e-d4bf12efd33d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import shutil\n",
+    "\n",
+    "if Path(\"moisture_index_stacked.zarr\").exists():\n",
+    "    shutil.rmtree(\"moisture_index_stacked.zarr\")\n",
+    "if Path(\"moisture_index_stacked.nc\").exists():    \n",
+    "    Path(\"moisture_index_stacked.nc\").unlink()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "afb8d675-e257-4af4-b2e3-cdcd4f070238",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-success\">\n",
+    "\n",
+    "**EXERCISE**:\n",
+    "    \n",
+    "The [NOAA's NCEP Reanalysis data](https://psl.noaa.gov/data/gridded/data.ncep.reanalysis.html) files are stored on a remote server and can be accessed over OpenDAP.\n",
+    "    \n",
+    "> The NCEP/NCAR Reanalysis data set is a continually updated (1948–present) globally gridded data set that represents the state of the Earth's atmosphere, incorporating observations and numerical weather prediction (NWP) model output from 1948 to present.\n",
+    "    \n",
+    "An example can be found in NCEP Reanalysis catalog:\n",
+    "\n",
+    "https://www.esrl.noaa.gov/psd/thredds/catalog/Datasets/ncep.reanalysis/surface/catalog.html\n",
+    "\n",
+    "The dataset is split into different files for each variable and year. For example, a single file download link for surface air temperature looks like:\n",
+    "\n",
+    "https://psl.noaa.gov/thredds/fileServer/Datasets/ncep.reanalysis/surface/air.sig995.1948.nc   \n",
+    " \n",
+    "The structure is `'https://psl.noaa.gov/thredds/fileServer/Datasets/ncep.reanalysis/surface/air.sig995.`' + `'YYYY'` + `'.nc'`\n",
+    "    \n",
+    "We want to download the surface temperature data from 1990 till 2000 and combine them all in a single xarray DataSet. To do so:\n",
+    "    \n",
+    "- Prepare all the links by composing the base_url ('http://www.esrl.noaa.gov/psd/thredds/dodsC/Datasets/ncep.reanalysis/surface/air.sig995') with the required years\n",
+    "- Use the list of file links as the inputfor the `xr.open_mfdataset` to create a single `xarray.DataSet`.\n",
+    "- Whereas this is 600MB of data, the initial loading is not actually reading in the data.\n",
+    "   \n",
+    "<details>\n",
+    "    \n",
+    "<summary>Hints</summary>\n",
+    "\n",
+    "* Python works with string formatting, e.g. f'{base_url}.{year}.nc' will nicely create the required links.\n",
+    "* Xarray can both work with file names on a computer as a compatible network link.\n",
+    "* As the netcdf data provided by NOAA is already well structured and confomr, no further adjustments are required as input to the \n",
+    "`open_mfdataset` function, :-)\n",
+    "\n",
+    "</details>    \n",
+    "    \n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2de8367c-644c-40eb-ad97-132183352142",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "base_url = 'https://psl.noaa.gov/thredds/fileServer/Datasets/ncep.reanalysis/surface/air.sig995'\n",
+    "\n",
+    "files = [f'{base_url}.{year}.nc' for year in range(1990, 2001)]\n",
+    "files"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5d26bd7e-4dbb-4e11-b721-30de58b38e5f",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "ds = xr.open_mfdataset(files, parallel=True)\n",
+    "ds"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2ee0d4d0-1c5d-47f9-9b51-cc458cc8c221",
+   "metadata": {},
+   "source": [
+    "## (Optional) Online data Catalogs: STAC"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b93e187b-9702-4d3b-9135-810cbe10e5f6",
+   "metadata": {},
+   "source": [
+    "__Note__ _These dependencies are not included in the environment, to run this section, install the required packages first in your conda environment: `conda install stackstac pystac-client`._"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4acc3a9c-9f5c-4fea-9037-c61593d9cb8f",
+   "metadata": {},
+   "source": [
+    "Multiple initiatives do exist which publish data online and enable (lazy) loading of the data directly in xarray, such as [OpenDAP](https://www.opendap.org/) and [THREDDS](https://www.unidata.ucar.edu/software/tds/current/) which are well-known and used in the oceanographic and climate studies communities (see exercise). See for example the [ROMS Ocean Model Example](http://xarray.pydata.org/en/stable/examples/ROMS_ocean_model.html) tutorial of xarray. \n",
+    "\n",
+    "Another initiative that interacts well with xarray is the [SpatioTemporal Asset Catalogs](https://stacspec.org/) specification, which is increasingly used to publish remote sensing products.\n",
+    "\n",
+    "Let's explore some Sentinel data hosted on AWS (https://registry.opendata.aws/sentinel-2-l2a-cogs/):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "684349c7-1133-44a2-9164-2db4a0b5a91c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import stackstac\n",
+    "import pystac_client"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "83c6fdfd-03e0-4379-8dec-99a914969037",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lon, lat = -105.78, 35.79"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "21b7f65b-8c92-4b99-8298-979a28d6ad59",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "URL = \"https://earth-search.aws.element84.com/v1\"\n",
+    "catalog = pystac_client.Client.open(URL)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "eb94265d-3f79-4ca8-a8ef-d1082355f967",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results = catalog.search(\n",
+    "    intersects=dict(type=\"Point\", coordinates=[lon, lat]),\n",
+    "    collections=[\"sentinel-2-l2a\"],\n",
+    "    datetime=\"2020-04-01/2020-05-01\"\n",
+    ")\n",
+    "results.matched()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5c694b2a-6fc5-4501-927e-a3b725b08750",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "list(results.items())[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "be235744-2534-45c2-a1c6-b544ff42f00c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results.item_collection()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "51bcab24-e300-4392-ac0f-f5ed64273026",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stacked = stackstac.stack(results.item_collection(), epsg=32613)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c394c52d-c3bd-4fa3-a9eb-b2a5e6fdd646",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stacked"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "eea49d3d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stacked.sel(band=\"nir\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1640d68a-ecb7-4f21-b0b7-aea920ebdca6",
+   "metadata": {},
+   "source": [
+    "See also https://github.com/stac-utils/pystac-client and https://stackstac.readthedocs.io/en/latest/."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5fab0dbd-09cf-4eb0-be57-41a3b7d384cc",
+   "metadata": {},
+   "source": [
+    "__Acknowledgements__ Thanks to [@rabernat](https://rabernat.github.io/research_computing_2018/xarray-tips-and-tricks.html) for the example case of NCEP reanalysis data load and https://stackstac.readthedocs.io/en/latest/basic.html#Basic-example for the stackteac example."
+   ]
+  }
+ ],
+ "metadata": {
+  "jupytext": {
+   "default_lexer": "ipython3"
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.11"
+  },
+  "widgets": {
+   "application/vnd.jupyter.widget-state+json": {
+    "state": {},
+    "version_major": 2,
+    "version_minor": 0
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/_solved/15-xarray-dask-big-data.ipynb b/_solved/15-xarray-dask-big-data.ipynb
new file mode 100644
index 0000000..5cec5f1
--- /dev/null
+++ b/_solved/15-xarray-dask-big-data.ipynb
@@ -0,0 +1,234 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<p><font size=\"6\"><b>Working with big data: xarray and dask (DEMO)</b></font></p>\n",
+    "\n",
+    "\n",
+    "> *DS Python for GIS and Geoscience*  \n",
+    "> *October, 2025*\n",
+    ">\n",
+    "> *© 2025, Joris Van den Bossche and Stijn Van Hoey. Licensed under [CC BY 4.0 Creative Commons](http://creativecommons.org/licenses/by/4.0/)*\n",
+    "\n",
+    "---\n",
+    "\n",
+    "Throughout the course, we worked with small, often simplified or subsampled data. In practice, the tools we have seen still work well with data that fit easily in memory. But also for data larger than memory (e.g. large or high resolution climate data), we can still use many of the familiar tools.\n",
+    "\n",
+    "This notebooks includes a brief showcase of using xarray with dask, a package to scale Python workflows (https://dask.org/). Dask integrates very well with xarray, providing a familiar xarray workflow for working with large datasets in parallel or on clusters."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from dask.distributed import Client, LocalCluster\n",
+    "client = Client(LocalCluster(processes=False))  # set up local cluster on your laptop\n",
+    "client"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The *Multi-Scale Ultra High Resolution (MUR) Sea Surface Temperature (SST)* dataset (https://registry.opendata.aws/mur/) provides freely available, global, gap-free, gridded, daily, 1 km data on sea surface temperate for the last 20 years. I downloaded a tiny part this dataset (8 days of 2020) to my local laptop, and stored a subset of the variables (only the \"sst\" itself) in the zarr format (https://zarr.readthedocs.io/en/stable/), so we can efficiently read it with xarray and dask:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import xarray as xr"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ds = xr.open_zarr(\"data/mur_sst_zarr/\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ds"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Looking at the actual sea surface temperature DataArray:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ds.analysed_sst"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The representation already indicated that this DataArray (although being a tiny part of the actual full dataset) is quite big: 20.7 GB if loaded fully into memory at once (which would not fit in the memory of my laptop).\n",
+    "\n",
+    "The xarray.DataArray is now backed by a dask array instead of a numpy array. This allows us to do computations on the large data in *chunked* way."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For example, let's compute the overall average temperature for the full globe for each timestep:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "overall_mean = ds.analysed_sst.mean(dim=(\"lon\", \"lat\"))\n",
+    "overall_mean"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This returned a lazy object, and not yet computed the actual average. Let's explicitly compute it:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%time \n",
+    "overall_mean.compute()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This takes some time, but it *did* run on my laptop even while the dataset did not fit in the memory of my laptop."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Integrating with hvplot and datashader, we can also still interactively plot and explore the large dataset:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import hvplot.xarray"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ds.analysed_sst.isel(time=-1).hvplot.quadmesh(\n",
+    "    'lon', 'lat', rasterize=True, dynamic=True,\n",
+    "    width=800, height=450, cmap='RdBu_r')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Zooming in on this figure we re-read and rasterize the subset we are viewing to provide a higher resolution image."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**As a summary**, using dask with xarray allows:\n",
+    "\n",
+    "- to use the familiar xarray workflows for larger data as well\n",
+    "- use the same code to work on our laptop or on a big cluster"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "\n",
+    "# PANGEO: A community platform for Big Data geoscience\n",
+    "\n",
+    "\n",
+    "<center><img src=\"https://pangeo.io/Pangeo-assets/pangeo_logo.png\" width=\"500px\"></center>\n",
+    "\n",
+    "Website: https://pangeo.io/\n",
+    "\n",
+    "They have a gallery with many interesting examples, many of them using this combination of xarray and dask.\n",
+    "\n",
+    "Pangeo focuses primarily on *cloud computing* (storing the big datasets in cloud-native file formats and also doing the computations in the cloud), but all the tools like xarray and dask developed by this community and shown in the examples also work on your laptop or university's cluster."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "jupytext": {
+   "default_lexer": "ipython3"
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.11"
+  },
+  "widgets": {
+   "application/vnd.jupyter.widget-state+json": {
+    "state": {},
+    "version_major": 2,
+    "version_minor": 0
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/_solved/90_package_numpy.ipynb b/_solved/90_package_numpy.ipynb
new file mode 100644
index 0000000..6f3ea2a
--- /dev/null
+++ b/_solved/90_package_numpy.ipynb
@@ -0,0 +1,2274 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<p><font size=\"6\"><b>Numpy</b></font></p>\n",
+    "\n",
+    "\n",
+    "> *DS Python for GIS and Geoscience*  \n",
+    "> *September, 2024*\n",
+    ">\n",
+    "> *© 2024, Joris Van den Bossche and Stijn Van Hoey. Licensed under [CC BY 4.0 Creative Commons](http://creativecommons.org/licenses/by/4.0/)*\n",
+    "\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "from matplotlib.colors import ListedColormap\n",
+    "from matplotlib.lines import Line2D\n",
+    "\n",
+    "import xarray as xr\n",
+    "import rasterio\n",
+    "from rasterio.plot import plotting_extent, show"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Introduction"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "> __In this notebook, the usage of Numpy when working with spatial data is explained and some exercises from xarray are solved using Numpy directly.__\n",
+    "\n",
+    "On of the most fundamental parts of the scientific python 'ecosystem' is [numpy](https://numpy.org/). A lot of other packages - you use Pandas, GeoPandas and xarray in this course - are built on top of Numpy and the `ndarray`  (n-dimensional array) data type it provides."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's start from reading in a GeoTiff data set from file, this time a Sentinal Band 4 of the City of Ghent:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "xr_b4_data = xr.open_dataarray(\"./data/gent/raster/2020-09-17_Sentinel_2_L1C_B04.tiff\", \n",
+    "                               engine=\"rasterio\", mask_and_scale=False)\n",
+    "ax = xr_b4_data.plot(robust=True, figsize=(10, 4))\n",
+    "ax.axes.set_aspect('equal')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "When reading in data, xarray contains a Numpy `ndarray` inside the xarray DataArray:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "type(xr_b4_data), type(xr_b4_data.data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We'll continu with the Numpy ndarray (ignore the additional coordinate and metadata information from xarray):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "b4_data = xr_b4_data.data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "b4_data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Numpy supports different `dtype`s (`float`, `int`,...), but all elements of an array do have the same dtype. Note that NumPy auto-detects the data-type from the input."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "b4_data.dtype"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The data type of this specific array `b4_data.data` is 16bit unsigned integer. More information on the data types Numpy supports is available in the [documentation](https://numpy.org/devdocs/user/basics.types.html#array-types-and-conversions-between-types). Detailed info on data types is out of scope of this course, but remember that using 16bit unsigned integer, it can contain `2**16` different (all positive) integer values:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "2**16"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's check this by calculating the minimum and maximum value in the array:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "b4_data.min(), b4_data.max()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Converting to another data type is supported by `astype` method. When floats are preferred during calculation:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "b4_data.astype(float)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "b4_data.max()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Just as any other object in Python, the `ndarray` has a number of attributes. We already checkes the `dtype` attribute. The `shape` and `ndim` of the array are other relevant attribute:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "b4_data.shape, b4_data.ndim"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Hence, we have a single band with dimensions (317, 625) and data type `uint16`. Compare this to the metadata stored in the geotiff file:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#!gdalinfo ./data/gent/raster/2020-09-17_Sentinel_2_L1C_B04.tiff"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The metadata on the dimensions and the datatype correspond, but the spatial information is lost when we only store the Numpy array."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Numpy works very well together with the other fundamental scientific Python package [Matplotlib](https://matplotlib.org/). An useful plot function to know when working with raster data is `imshow`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "ax.imshow(b4_data.squeeze());"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "__Note:__ Numpy function `squeeze` used to get rid of the single-value dimension of the numpy array."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As the Numpy array does not contain any spatial information, the x and y axis labels are defined by the indices of the array. Remark that the xarray plot returned this plot with the coordinate information in the axis labels. \n",
+    "\n",
+    "With a small trick, the same result can be achieved with Matplotlib:\n",
+    "\n",
+    "1. When reading in a data set using xarray, get the spatial extent from the geographical boundaries stored in the xarray `rio` accessor and convert them to an extent:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "xr_b4_data = xr.open_dataarray(\"./data/gent/raster/2020-09-17_Sentinel_2_L1C_B04.tiff\", engine=\"rasterio\", mask_and_scale=False)\n",
+    "\n",
+    "(left, bottom, right, top) = xr_b4_data.rio.bounds()  # get boundary information\n",
+    "b4_data_extent = (left, right, bottom, top)   # convert boundaries to extent info\n",
+    "b4_data_extent"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "2. Add the `extent` argument to the `imshow` plot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "ax.imshow(b4_data.squeeze(), extent=b4_data_extent)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-info\" style=\"font-size:120%\">\n",
+    "\n",
+    "**REMEMBER**: <br>\n",
+    "\n",
+    "The [`numpy` package](https://numpy.org/) is the backbone of the scientific Python ecosystem. The `ndarray` provides an efficient data type to store and manipulate raster data, but it does NOT contain any spatial information.\n",
+    "    \n",
+    "Use the spatial `extent` trick to add coordinate information to imshow plot axis. Convert to the preferred datatype using `astype()` method.\n",
+    "\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Reshape, slice and index"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "b4_data.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We already used `squeeze` to remove the single-value dimension. We could also select the data we needed, similar to slicing in lists or Pandas DataFrames:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "b4_data[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "b4 = b4_data[0]\n",
+    "b4.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If you do not like the order of dimensions of the data, you can switch these using `transpose`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "b4.transpose(1, 0).shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Getting rid of the dimensions and flattening all values into a single 1-D array can be done using `flatten` method:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "b4.flatten().shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Flattening an arrya is useful to create a histogram with Matplotlib:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "plt.hist(b4.flatten(), bins=100);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# slice, subsample, reverse\n",
+    "# slice + assign\n",
+    "# fancy indexing\n",
+    "# fancy indexing + assign"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "b4 = b4_data[0]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Select a specific row/column:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "b4[10].shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "b4[:, -2:].shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Select every nth element in a given dimension:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "b4[100:200:10, :].shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Reversing an array:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "b4[:, ::-1].shape  # Note you can also np.flip an array"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "b4[0, :4]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "b4_rev = b4[:, ::-1]\n",
+    "b4_rev[0, -4:]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can also combine assignment and slicing:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "b4[0, :3] = 10\n",
+    "b4"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Use a __condition__ to select data, also called fancy indexing or boolean indexing:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "b4 < 1000"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Onle keep the data which are True for the given condition"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "b4[b4 < 1000]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Or combine assignment and fancy indexing, e.g. a reclassification of the raster data:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "b4[b4 < 5000] = 0  # assign the value 0 to all elements with a value lower than 5000"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "b4"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A powerfull shortcut to handle this kind or reclassifications is the `np.where` function:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "np.where(b4 < 5000, 10, b4)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-success\">\n",
+    "\n",
+    "**EXERCISE**:\n",
+    "\n",
+    "* Read in the file `./data/gent/raster/2020-09-17_Sentinel_2_L1C_True_color.tiff` with xarray, extract the data as a Numpy ndarray and assign the data to a new variable `tc_data`.  \n",
+    "* Select only the *second* layer of `tc_data` and assign the output to a new variable `tc_g`.\n",
+    "* Assign to each of the elements in the `tc_g` array with a value above 15000 the new value 65535.\n",
+    "    \n",
+    "<details><summary>Hints</summary>\n",
+    "\n",
+    "* You can combine the assignment of new values together with fancy indexing of a numpy array.\n",
+    "* Python (and also Numpy) uses 0 as the first-element index\n",
+    "\n",
+    "</details>\n",
+    "    \n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "xr_tc_data = xr.open_dataarray(\"./data/gent/raster/2020-09-17_Sentinel_2_L1C_True_color.tiff\", \n",
+    "                            engine=\"rasterio\", mask_and_scale=False)\n",
+    "tc_data = xr_tc_data.data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# Get the green channel\n",
+    "tc_g = tc_data[1]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# Convert all values above 15000\n",
+    "tc_g[tc_g > 15000] = 65535\n",
+    "tc_g"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-success\">\n",
+    "\n",
+    "**EXERCISE**:\n",
+    "\n",
+    "Subsample the ndarray `tc_data` by taking only the one out of each 5 data points for all layers at the same time (Be aware that this is a naive resampling implementation for educational purposes only). \n",
+    "    \n",
+    "<details><summary>Hints</summary>\n",
+    "\n",
+    "* The result should still be a 3-D array with 3 elements in the first dimension.\n",
+    "\n",
+    "</details>   \n",
+    "    \n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "xr_tc_data = xr.open_dataarray(\"./data/gent/raster/2020-09-17_Sentinel_2_L1C_True_color.tiff\", \n",
+    "                               engine=\"rasterio\", mask_and_scale=False)\n",
+    "tc_data = xr_tc_data.data    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# subsample the data\n",
+    "tc_data[:, ::5, ::5].shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-success\">\n",
+    "\n",
+    "**EXERCISE**:\n",
+    "\n",
+    "Elements with the value `65535` do represent 'Not a Number' (NaN) values. However, Numpy does not support NaN values for integer data, so we'll convert to float first as data type. After reading in the data set `./data/gent/raster/2020-09-17_Sentinel_2_L1C_B04.tiff` (assign data to variable `b4_data`):\n",
+    "    \n",
+    "* Count the number of elements that are equal to `65535`\n",
+    "* Convert the data type to `float`, assign the result to  a new variable `b4_data_f`\n",
+    "* Assign Nan (`np.nan`) value to each of the elements of `b4_data_f` equal to `65535`\n",
+    "* Count the number of Nan values in the `b4_data_f` data\n",
+    "* Make a histogram of both the `b4_data` and `b4_data_f` data. Can you spot the difference?\n",
+    "    \n",
+    "    \n",
+    "<details><summary>Hints</summary>\n",
+    "\n",
+    "* `np.nan` represents _Not a Number (NaN)_ in Numpy. You can assign an element to it, e.g. `dummy[2] = np.nan`\n",
+    "* `np.sum` will by default sum all of the elements of the input array and can also count boolean values (True = 1 and False = 0), resulting from a conditional expression. \n",
+    "* To test if a value is a nan, Numpy provides `np.isnan(...)` which results in an element-wise check returning boolean values.\n",
+    "* Check the help of the `plt.hist` command to find out more about the `bins` and the `log` arguments.\n",
+    "\n",
+    "</details>    \n",
+    "    \n",
+    "   \n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "xr_b4_data = xr.open_dataarray(\"./data/gent/raster/2020-09-17_Sentinel_2_L1C_B04.tiff\", \n",
+    "                               engine=\"rasterio\", mask_and_scale=False)\n",
+    "b4_data = xr_b4_data.data    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# Count the number of cells with value 65535\n",
+    "np.sum(b4_data == 65535)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# Convert to float and make 65535 equal to Nan\n",
+    "b4_data_f = b4_data.astype(float)\n",
+    "b4_data_f[b4_data == 65535] = np.nan"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# Count the number of cells with value 0\n",
+    "np.sum(np.isnan(b4_data_f))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# Create the histogram plots\n",
+    "fig, (ax0, ax1) = plt.subplots(1, 2, sharey=True)\n",
+    "ax0.hist(b4_data.flatten(), bins=30, log=True);\n",
+    "ax1.hist(b4_data_f.flatten(), bins=30, log=True);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Reductions, element-wise calculations and broadcasting"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Up until now, we worked with the 16bit integer values. For specific applications we might want to rescale this data. A (fake) example is the linear transformation to the range 0-1 after log conversion of the data. To do so, we need to calculate _for each element_ in the original $b$ array the following:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "$$x_i= \\log(b_i)$$\n",
+    "$$z_i=\\frac{x_i-\\min(x)}{\\max(x)-\\min(x)}$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "__1. reductions__\n",
+    "\n",
+    "As part of it, we need the minimum `min(x)` and the maximum `max(x)` of the array. These __reductions__ (aggregations) are provided by Numpy and can be applied along one or more of the data dimensions, called the __axis__:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "dummy = np.arange(1, 10).reshape(3, 3)\n",
+    "dummy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "np.min(dummy), np.min(dummy, axis=0), np.min(dummy, axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "dummy = np.arange(1, 25).reshape(2, 3, 4)\n",
+    "dummy.shape, dummy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "np.min(dummy), np.min(dummy, axis=0), np.min(dummy, axis=(0, 1)), np.min(dummy, axis=(0, 2))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In some applications, the usage of the `keepdims=True` is useful to keep the number of dimensions after reduction:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "np.min(dummy, axis=(0, 2), keepdims=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "When working with Nan values, the result will be Nan as well:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "np.min(np.array([1., 2., np.nan]))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Use the `nanmin`, `nan...` version of the function instead, if available:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "np.nanmin(np.array([1., 2., np.nan]))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "__2. Element-wise__"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The __for each element__ is crucial for Numpy. The typical answer in programming would be a `for`-loop, but Numpy is optimized to do these calculations __element-wise__ (i.e. for all elements together):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "dummy = np.arange(1, 10)\n",
+    "dummy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "dummy*10"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Instead of:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "[el*20 for el in dummy]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Numpy provides most of the familiar arithmetic operators to apply on an element-by-element basis:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "np.exp(dummy), np.sin(dummy), dummy**2, np.log(dummy)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For some function, you can either use the `np.min(my_array)` or the `my_array.min()` approach:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "dummy.min() == np.min(dummy)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "__3. Broadcasting__"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "When we combine arrays with different shapes during arithmetic operations, Numpy applies a set of __broadcoasting__ rules and the smaller array is _broadcast_ across the larger array so that they have compatible shapes. An important consequence for out application is:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "np.array([1, 2, 3]) + 4. , np.array([1, 2, 3]) + np.array([4.]), np.array([1, 2, 3]) + np.array([4., 4., 4.])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The smallest array is broadcasted to make both compatible. It starts with the trailing (i.e. rightmost) dimensions. Exploring all the rules are out of scope in this lesson and are well explained in the [broadcasting Numpy documentation](https://numpy.org/devdocs/user/basics.broadcasting.html#general-broadcasting-rules)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "__Back to our function__"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "By combining these three elements, we know enough to translate our conversion into Numpy code on the example data set:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "xr_b4_data = xr.open_dataarray(\"./data/gent/raster/2020-09-17_Sentinel_2_L1C_B04.tiff\", \n",
+    "                               engine=\"rasterio\", mask_and_scale=False)\n",
+    "b4_data = xr_b4_data.data\n",
+    "\n",
+    "b4_data = b4_data.squeeze().astype(float)    # squeeze and convert to float\n",
+    "b4_data[b4_data == 0.0] = 0.00001  # to overcome zero-division error"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Take the log of al the values __element-wise__:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "b4_data_log = np.log(b4_data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Get the min and max __reductions__:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "b4_min, b4_max = b4_data_log.min(), b4_data_log.max()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "__Broadcast__ our single value `b4_min` and `b4_max` to all elements of `b4_data_log`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "b4_rescaled = ((b4_data_log - b4_min)/(b4_max - b4_min))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "plt.hist(b4_rescaled.flatten(), bins=100);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "__Remark 1:__ One-dimensional linear interpolation towards a new value range can be calculated using the `np.interp` function as well. For the range 0 -> 1: \n",
+    "\n",
+    "```\n",
+    "np.interp(b4_data, (b4_data.min(), b4_data.max()), (0, 1))\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "__Remark 2: Why not iterate over the values of a list?__"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's use the  rescaling example to compare the calculation with Numpy versus a list comprehension (for-loop in Python):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "b4_min, b4_max = b4_data.min(), b4_data.max()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "With Numpy:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "%%time\n",
+    "rescaled_values_1 = ((b4_data - b4_min)/(b4_max - b4_min))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Using a list with a for loop:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "b4_as_list = b4_data.flatten().tolist()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "%%time\n",
+    "rescaled_values_2 = [((data_point - b4_min)/(b4_max - b4_min)) for data_point in b4_as_list]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "np.allclose(rescaled_values_1.flatten(), rescaled_values_2)  # np.allclose also works element wise"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-info\" style=\"font-size:120%\">\n",
+    "\n",
+    "**REMEMBER**: <br>\n",
+    "\n",
+    "The combination of element-wise calculations, efficient reductions and broadcasting provides Numpy a lot of power. In general, it is a good advice to __avoid for loops__ when working with Numpy arrays.\n",
+    "\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Let's practice!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-success\">\n",
+    "\n",
+    "**EXERCISE**:\n",
+    "\n",
+    "The data set `./data/herstappe/raster/2020-09-17_Sentinel_2_L1C_True_color.tiff` (assign to variable `herstappe_data`) contains 3 bands. The `imshow` function of Matplotlib can plot 3-D (RGB) data sets, but when running `plt.imshow(herstappe_data)`, we got the following error:\n",
+    "    \n",
+    "    ```\n",
+    "    ...\n",
+    "    TypeError: Invalid shape (3, 227, 447) for image data\n",
+    "    ```\n",
+    "\n",
+    "- Check in the help op `plt.imshow` why the `herstappe_data` can not be plot as such\n",
+    "- Adjust the data to fix the behavior of `plt.imshow(herstappe_data)`\n",
+    "    \n",
+    "Next, plot a greyscale version of the data as well. Instead of using a custom function just rely on the sum of the 3 bands as a proxy.\n",
+    "    \n",
+    "<details><summary>Hints</summary>\n",
+    "\n",
+    "* In a Jupyter Notebook, us the SHIFT-TAB combination when the cursor is on the `imshow` function or type in a new cell `?plt.imshow` to see the documentation of a function.\n",
+    "* The `imshow` function requires the different color bands as last dimension, so we will need to transpose the image array.\n",
+    "* Add the extent to see the coordinates in the axis labels.\n",
+    "* A greyscale image requires a greyscale `cmap`, checkt he available names in [the documentation online](https://matplotlib.org/tutorials/colors/colormaps.html)\n",
+    "\n",
+    "</details>    \n",
+    "    \n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "xr_herstappe_data = xr.open_dataarray(\"./data/herstappe/raster/2020-09-17_Sentinel_2_L1C_True_color.tiff\", \n",
+    "                                      engine=\"rasterio\", mask_and_scale=False)\n",
+    "herstappe_data = xr_herstappe_data.data\n",
+    "\n",
+    "(left, bottom, right, top) = xr_herstappe_data.rio.bounds()\n",
+    "herstappe_extent = (left, right, bottom, top)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# Make a RGB plot\n",
+    "fig, ax = plt.subplots(figsize=(12, 5))\n",
+    "ax.imshow(herstappe_data.transpose(1, 2, 0), extent=herstappe_extent);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# Make a Grey scale plot\n",
+    "greyscale_data = herstappe_data.sum(axis=0)\n",
+    "fig, ax = plt.subplots(figsize=(12, 5))\n",
+    "ax.imshow(greyscale_data, extent=herstappe_extent, cmap=\"Greys\");"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-success\">\n",
+    "\n",
+    "**EXERCISE**:\n",
+    "\n",
+    "The data set `./data/herstappe/raster/2020-09-17_Sentinel_2_L1C_True_color.tiff` (assign to variable `herstappe_data`) has values ranging in between 0.11325, 0.8575. To improve the quality of the visualization, stretch __each of the layers individually__ to the values to the range 0. to 1. with a linear transformation: \n",
+    "    \n",
+    "$$z_i=\\frac{x_i-\\min(x)}{\\max(x)-\\min(x)}$$\n",
+    "\n",
+    "Make a plot of the end result and compare with the plots of the previous exercise. \n",
+    "   \n",
+    "<details><summary>Hints</summary>\n",
+    "\n",
+    "* Keep into account that the data set is 3-dimensional. Have a look at the optional arguments for the reduction/aggregation functions in terms of `axis` and `keepdims`. \n",
+    "* You need the minimal/maximal value over 2 axis to end up with a min/max for each of the layers.\n",
+    "* Broadcasting starts comparison of the alignment on the last dimension.\n",
+    "\n",
+    "</details>    \n",
+    "    \n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "clear_cell": false
+   },
+   "outputs": [],
+   "source": [
+    "xr_herstappe_data = xr.open_dataarray(\"./data/herstappe/raster/2020-09-17_Sentinel_2_L1C_True_color.tiff\", \n",
+    "                                      engine=\"rasterio\", mask_and_scale=False)\n",
+    "herstappe_data = xr_herstappe_data.data\n",
+    "\n",
+    "# Get extent\n",
+    "(left, bottom, right, top) = xr_herstappe_data.rio.bounds()\n",
+    "herstappe_extent = (left, right, bottom, top)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# Calculate the min and max for each channel\n",
+    "h_min = herstappe_data.min(axis=(1, 2), keepdims=True)\n",
+    "h_max = herstappe_data.max(axis=(1, 2), keepdims=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# Rescale the data\n",
+    "herstappe_rescaled = ((herstappe_data - h_min)/(h_max - h_min))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# Make a plot\n",
+    "fig, ax = plt.subplots(figsize=(12, 5))\n",
+    "ax.imshow(herstappe_rescaled.transpose(1, 2, 0), extent=herstappe_extent);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-success\">\n",
+    "\n",
+    "**EXERCISE**:\n",
+    "    \n",
+    "You want to reclassify the values of the 4th band data to a fixed set of classes:\n",
+    "    \n",
+    "* x < 0.05 need to be 10\n",
+    "* 0.05 < x < 0.1 need to be 20\n",
+    "* x > 0.1 need to be 30\n",
+    "       \n",
+    "Use the data set `./data/gent/raster/2020-09-17_Sentinel_2_L1C_B04.tiff` (assign data to variable `b4_data`):\n",
+    "    \n",
+    "* Read the data set and exclude the single-value dimension to end up with a 2D array. \n",
+    "* Convert to float data type. and normalize the values to the range [0., 1.].\n",
+    "* Create a new variable `b4_data_classified` with the same shape as `b4_data` but datatype int.\n",
+    "* Assign the new values (10, 20, 30) to the elements for which each of the conditions apply. \n",
+    "* Make a image plot of the reclassified variable `b4_data_classified`.\n",
+    "    \n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "clear_cell": false
+   },
+   "outputs": [],
+   "source": [
+    "xr_b4_data = xr.open_dataarray(\"./data/gent/raster/2020-09-17_Sentinel_2_L1C_B04.tiff\", \n",
+    "                               engine=\"rasterio\", mask_and_scale=False)\n",
+    "b4_data = xr_b4_data.data\n",
+    "\n",
+    "# Get extent\n",
+    "(left, bottom, right, top) = xr_b4_data.rio.bounds()\n",
+    "b4_data_extent = (left, right, bottom, top)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# Squeeze to 2D float array \n",
+    "b4_data = b4_data.squeeze().astype(float)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# Rescale the data\n",
+    "b4_data = (b4_data - b4_data.min())/(b4_data.max() - b4_data.min())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# Create a new array with the same shape as the original b4_data\n",
+    "b4_data_classified = np.empty_like(b4_data).astype(int)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# Assign the new values according to the classes\n",
+    "b4_data_classified[b4_data < 0.05] = 10\n",
+    "b4_data_classified[(0.05 <= b4_data) & (b4_data < 0.1)] = 20\n",
+    "b4_data_classified[0.1 <= b4_data] = 30"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# Create an image plot\n",
+    "fig, ax = plt.subplots(figsize=(10, 4))\n",
+    "img = ax.imshow(b4_data_classified, extent=b4_data_extent)\n",
+    "fig.colorbar(img, values=[10, 20, 30], ticks=[10, 20, 30])\n",
+    "ax.set_aspect(\"equal\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-success\">\n",
+    "\n",
+    "**EXERCISE**:\n",
+    "\n",
+    "The data sets `./data/gent/raster/2020-09-17_Sentinel_2_L1C_B04.tiff` and `./data/gent/raster/2020-09-17_Sentinel_2_L1C_B08.tiff` contain respectively the 4th and the 8th band of a sentinel satellite image. To derive the [Normalized Difference Vegetation Index) (NDVI)](https://nl.wikipedia.org/wiki/Normalized_Difference_Vegetation_Index), the two bands need to be combined as follows:\n",
+    "    \n",
+    "$$\\frac{band_8 - band_4}{band_8 + band_4} $$\n",
+    "    \n",
+    "Process the images and create a plot of the NDVI:\n",
+    "    \n",
+    "- Read both data sets using Rasterio and store them in resp. `b4_data` and `b8_data`. \n",
+    "- Combine both data sets using the `np.vstack` function and assign it to the variable `b48_bands`\n",
+    "- Transform the data range of each of the layers to the range .0 - 1.\n",
+    "- For the values equal to zero in the `b48_bands` data set, assign a new (very small) value 1e-6\n",
+    "- Calculate the NDVI\n",
+    "- Plot the NDVI and select an appropriate colormap.\n",
+    "    \n",
+    "<details><summary>Hints</summary>\n",
+    "\n",
+    "* For more specific adjustments to the colormap, have a check on the [Matplotlib documentation on colormap normalization](https://matplotlib.org/3.3.2/tutorials/colors/colormapnorms.html)\n",
+    "\n",
+    "</details>   \n",
+    "           \n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "xr_b4_data = xr.open_dataarray(\"./data/gent/raster/2020-09-17_Sentinel_2_L1C_B04.tiff\", \n",
+    "                               engine=\"rasterio\", mask_and_scale=False)\n",
+    "b4_data = xr_b4_data.data\n",
+    "xr_b8_data = xr.open_dataarray(\"./data/gent/raster/2020-09-17_Sentinel_2_L1C_B08.tiff\", \n",
+    "                               engine=\"rasterio\", mask_and_scale=False)\n",
+    "b8_data = xr_b8_data.data\n",
+    "\n",
+    "# Get extent\n",
+    "(left, bottom, right, top) = xr_b4_data.rio.bounds()\n",
+    "b4_data_extent = (left, right, bottom, top)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# Combine both arrays by stacking them together\n",
+    "b48_bands = np.vstack((b4_data, b8_data))  # 0 is b4 and 1 is b8"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "b48_bands.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# Rescale the data to 0-1\n",
+    "b48_min = b48_bands.min(axis=(1, 2), keepdims=True)\n",
+    "b48_max = b48_bands.max(axis=(1, 2), keepdims=True)\n",
+    "b48_bands = ((b48_bands - b48_min)/(b48_max - b48_min))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# Assign very small value to 0-values\n",
+    "b48_bands[b48_bands == 0] = 1e-6"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# Calculate the ndvi using the stacked data\n",
+    "ndvi = (b48_bands[1] - b48_bands[0])/(b48_bands[0] + b48_bands[1])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "clear_cell": false
+   },
+   "source": [
+    "Using a Matplotlib norm to adjust colormap influence on image https://matplotlib.org/api/_as_gen/matplotlib.colors.TwoSlopeNorm.html"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# A Sequential colormap `YlGn` with a normalization on the color limits\n",
+    "import matplotlib.colors as mcolors\n",
+    "div_norm = mcolors.Normalize(0.1, 0.8)\n",
+    "fig, ax = plt.subplots(figsize=(10, 4))\n",
+    "ll = ax.imshow(ndvi, cmap=\"YlGn\", extent=b4_data_extent, norm=div_norm)\n",
+    "fig.colorbar(ll);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# A Diverging colormap `RdYlGn` with a normalization on the color limits in two directions of the central point:\n",
+    "div_norm = mcolors.TwoSlopeNorm(vmin=-0.1, vcenter=0.4, vmax=0.8)\n",
+    "fig, ax = plt.subplots(figsize=(10, 4))\n",
+    "ll = ax.imshow(ndvi, cmap=\"RdYlGn\", extent=b4_data_extent, norm=div_norm)\n",
+    "fig.colorbar(ll);\n",
+    "plt.axis('off');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "\n",
+    "## For the curious: Some more building blocks"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Numpy provides lower-level building blocks used by other packages and you will once in a also need to rely on these functions to do some custom implementation. Some other useful building blocks with repect to reclassification could potentially help you:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "- Remember the `np.where` function?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "dummy = np.arange(1, 10).reshape(3, 3)\n",
+    "dummy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "np.where(dummy > 4, 0, dummy)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "- Clip the values in yanour array to defined limits can be done using `np.clip`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "dummy = np.arange(1, 10).reshape(3, 3)\n",
+    "dummy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "np.clip(dummy, 2, 6)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "- Numpy provides also a `np.histogram` function, which is really useful to get the bincounts over a custom bin-set:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "np.histogram(b4_data_classified, bins=[5, 15, 25, 35])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "np.histogram(b4_data, bins=[0.001, 0.1, 0.2, 0.5])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "- The `np.digitize` function return the indices of the bins to which each value in input array belongs. As such, it can be used to select and manipulate values containing to a specific bin:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "dummy = np.arange(9).reshape(3, 3)\n",
+    "np.random.shuffle(dummy)\n",
+    "dummy"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Define the bin to which each of the values belong to, using the bins x<2, 2<=x<4, x>=4:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "id_mask = np.digitize(dummy, bins=[2, 4])\n",
+    "id_mask"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "dummy[id_mask == 1] = 20\n",
+    "dummy"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Besides, it is also a practical method to create discrete classified maps:\n",
+    "\n",
+    "  1. Apply digitize to create classes:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ndvi_class_bins = [-np.inf, 0, 0.3, np.inf]  # These limits are for demo purposes only \n",
+    "ndvi_landsat_class = np.digitize(ndvi, ndvi_class_bins)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "  2. Define custom colors and names:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "nbr_colors = [\"gray\", \"yellowgreen\", \"g\"]\n",
+    "ndvi_names = [\"No Vegetation\", \"Bare Area\", \"Vegetation\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "  3. Prepare Matplotlib elements:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "nbr_cmap = ListedColormap(nbr_colors)\n",
+    "# fake entries required for each class to create the legend\n",
+    "dummy_data = [Line2D([0], [0], color=color, lw=4) for color in nbr_colors]  "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "  4. Make the plot and add a legend:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(12, 12))\n",
+    "im = ax.imshow(ndvi_landsat_class, cmap=nbr_cmap, extent=b4_data_extent)\n",
+    "ax.legend(dummy_data, ndvi_names, loc='upper left', framealpha=1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "- Find the modal (most common) value in an array is not provided by Numpy itself, but is available in the Scipy package:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from scipy.stats import mode"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "mode(b4_data.flatten()), mode(b4_data_classified.flatten())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Side-note on convolution"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In case you need custom convolutions for your 2D array, check the `scipy.signal.convolve` function as the Numpy function only works for 1-D arrays."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from scipy import signal"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "xr_b4_data = xr.open_dataarray(\"./data/gent/raster/2020-09-17_Sentinel_2_L1C_B04.tiff\", \n",
+    "                               engine=\"rasterio\", mask_and_scale=False)\n",
+    "b4_data = xr_b4_data.data\n",
+    "\n",
+    "# Get extent\n",
+    "(left, bottom, right, top) = xr_b4_data.rio.bounds()\n",
+    "b4_data_extent = (left, right, bottom, top)\n",
+    "\n",
+    "b4_data = b4_data.squeeze().astype(float)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As an example, apply a low pass filter example as window, smoothing the image:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "window = np.ones((5, 5), dtype=int)\n",
+    "window[1:-1, 1:-1] = 4\n",
+    "window[2, 2] = 12\n",
+    "window"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "grad = signal.convolve(b4_data, window, mode='same')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "fig, (ax0, ax1) = plt.subplots(1, 2, figsize=(16, 6))\n",
+    "ax0.imshow(b4_data, extent=b4_data_extent)\n",
+    "ax1.imshow(grad, extent=b4_data_extent)"
+   ]
+  }
+ ],
+ "metadata": {
+  "celltoolbar": "Nbtutor - export exercises",
+  "jupytext": {
+   "cell_metadata_filter": "-run_control,-deletable,-editable,-jupyter,-slideshow",
+   "notebook_metadata_filter": "-jupytext.cell_metadata_filter,-jupytext.notebook_metadata_filter"
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/_solved/91_package_rasterio.ipynb b/_solved/91_package_rasterio.ipynb
new file mode 100644
index 0000000..7d2364a
--- /dev/null
+++ b/_solved/91_package_rasterio.ipynb
@@ -0,0 +1,1228 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<p><font size=\"6\"><b>Rasterio</b></font></p>\n",
+    "\n",
+    "\n",
+    "> *DS Python for GIS and Geoscience*  \n",
+    "> *September, 2024*\n",
+    ">\n",
+    "> *© 2024, Joris Van den Bossche and Stijn Van Hoey. Licensed under [CC BY 4.0 Creative Commons](http://creativecommons.org/licenses/by/4.0/)*\n",
+    "\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "> __In this notebook, the tupical workflow when doing operations with Rasterio to handle spatial data is explained .__\n",
+    "\n",
+    "Both when working with Numpy as in xarray, [Rasterio](https://rasterio.readthedocs.io/en/latest/) provides an interface to read the data from disk. \n",
+    "\n",
+    "Whereas Numpy is a powerful package for calculation, it does not provide any spatial information so lacking support for reprojection, warping,... Apart from reading in a broad set of GIS raster formats, Rasterio also provides some of these GIS raster operations."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "import geopandas\n",
+    "\n",
+    "import rasterio\n",
+    "from rasterio.plot import plotting_extent, show, show_hist, reshape_as_image"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's recap with what we did so far with Rasterio:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_file = \"./data/herstappe/raster/2020-09-17_Sentinel_2_L1C_True_color.tiff\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "xarray provides the `open_dataset` and `open_dataarray` functions to read data. When reading with `engine=\"rasterio\"`, the underlying rasterio method is used under the hood. It is also possible to use the Rasterio read data methods as well:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with rasterio.open(data_file) as src:    \n",
+    "    herstappe_data = src.read([1, 2, 3], out_dtype=float, masked=False) # Note the float as output\n",
+    "    herstappe_profile = src.profile\n",
+    "    herstappe_ext = plotting_extent(src)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "herstappe_data.shape, herstappe_data.dtype"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Convenient plot functions for data exploration"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Rasterio also provides dedicated plot functions `show` and `show_hist` for quick data exploration:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "with rasterio.open(data_file) as src:\n",
+    "    \n",
+    "    # convenience plot functions from rasterio\n",
+    "    fig, (axr, axg, axb) = plt.subplots(1,3, figsize=(20, 10))\n",
+    "    show((src, 1), ax=axr, cmap='Reds', title='Red channel')\n",
+    "    show((src, 2), ax=axg, cmap='Greens', title='Green channel')\n",
+    "    show((src, 3), ax=axb, cmap='Blues', title='Blue channel')\n",
+    "    \n",
+    "    fig, ax = plt.subplots(1, 1, figsize=(10, 5))\n",
+    "    show_hist(src, bins=50, lw=0.0, stacked=False, \n",
+    "              alpha=0.3, histtype='stepfilled', title=\"Histogram\",\n",
+    "              label=[1, 2, 3])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "__Note:__ Rasterio directly interacts with Matplotlib objects, so adjustments starting from the figure can be done using the Matplotlib syntax. For example, change the title and the legend labels:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "_, current_labels = ax.get_legend_handles_labels()\n",
+    "current_labels"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "ax.set_title(\"Histogram of RGB bands of Herstappe\")\n",
+    "ax.legend([\"Blue channel\", \"Green channel\", \"Blue channel\"])\n",
+    "fig"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "__Reminder on opening and Closing File Connections__\n",
+    "\n",
+    "The Rasterio library is efficient as it establishes a connection with the raster file rather than directly reading it into memory. Because it creates a connection, it is important that you close the connection after it is opened AND after you’ve finished working with the data! That is why we use the context manager to work with raster data in rasterio. This will handle opening and closing the raster file for you."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "__Utility functions for reshaping__"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#plt.imshow(herstappe_data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Python image processing software packages and Matplotlib organize arrays differently than Rasterio. The interpretation of a 3-dimension array read from rasterio is:\n",
+    "\n",
+    "`(bands, rows, columns)`\n",
+    "\n",
+    "while Matplotlib and image processing software like scikit-image, pillow are generally ordered:\n",
+    "\n",
+    "`(rows, columns, bands)`\n",
+    "\n",
+    "We can rely on Numpy to transpose the data :"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "plt.imshow(herstappe_data.transpose(1, 2, 0))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "But Rasterio also provides the utility functions `reshape_as_image` and `reshape_as_raster` to support the conversion:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from rasterio.plot import reshape_as_image, reshape_as_raster"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "plt.imshow(reshape_as_image(herstappe_data))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Extract the data you need"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In many applications, a specific research area is used. Extracting the data you need from a given raster data set by a vector (polygon) file is a common operation in GIS analysis. We use the clipping example to explain the typical workflow in Rasterio.\n",
+    "\n",
+    "For our Herstappe example, the study area is available as vector data `./data/herstappe/vector/herstappe.geojson`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "herstappe_vect = geopandas.read_file(\"./data/herstappe/vector/herstappe.geojson\")\n",
+    "herstappe_vect"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "herstappe_vect.plot()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# herstappe_vect.crs  # vs herstappe_profile[\"crs\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Make sure both data sets are defined in the same CRS and extracting the geometry can be used as input for the masking:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "herstappe_vect = herstappe_vect.to_crs(epsg=3857)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[Masking a raster](https://rasterio.readthedocs.io/en/latest/quickstart.html#dataset-georeferencing) with a geometry in Rasterio is supported by the `rasterio.mask` module:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import rasterio.mask"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#?rasterio.mask.mask"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The `mask` function by default just masks the image, but it can also be used to `crop` the data by setting the `crop` argument to True:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As the required input for the `shape` to mask with, the documentation defines: \n",
+    "    \n",
+    "    The values must be a GeoJSON-like dict or an object that implements\n",
+    "    the Python geo interface protocol (such as a Shapely Polygon).\n",
+    "    \n",
+    "We learned earlier that GeoPandas relies on Shapely as spatial entities:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "type(herstappe_vect.geometry), type(herstappe_vect.geometry[0])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can do the data extraction (masking) within the context manager:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_file = \"./data/herstappe/raster/2020-09-17_Sentinel_2_L1C_True_color.tiff\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with rasterio.open(data_file) as src:\n",
+    "    out_image, out_transform = rasterio.mask.mask(src, herstappe_vect.geometry, crop=True)\n",
+    "    herstappe_profile = src.profile\n",
+    "    herstappe_ext = plotting_extent(src)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "type(out_image)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can work with `out_image` as a numpy array, but we are lacking the spatial information by doing so, which makes it hard to combine it with other spatial data sets for further analysis:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "plt.imshow(reshape_as_image(out_image)) # extent ?!? extent=herstappe_ext would not be correct."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Remember the `meta` and the `profile` attributes of the Rasterio object? The latter contains the spatial information and the settings for the GeoTiff file:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "herstappe_profile"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "out_image.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To contain the spatial information or save the file for later usage keeping the spatial information, make sure to __update the profile metadata__ according to the transformations you did. In this case:\n",
+    "\n",
+    "- width/height of the cropped image\n",
+    "- affine transform function"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "herstappe_profile.update({\"height\": out_image.shape[1],\n",
+    "                          \"width\": out_image.shape[2],\n",
+    "                          \"transform\": out_transform})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "herstappe_profile"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Just as before, we can use the `plotting_extent` function to extract the extent of our data, using the outputs of the masking:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "new_extent = plotting_extent(out_image[0], out_transform)  # see docstring of plotting_extent on [0] requirement"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "plt.imshow(reshape_as_image(out_image), extent=new_extent)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The updated profile information also allows us to store the output as a Geotiff file:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with rasterio.open(\"./test.tiff\", \"w\", **herstappe_profile) as dest:\n",
+    "    dest.write(out_image)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's read the data we just saved to a file:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "with rasterio.open(\"./test.tiff\") as clipped:\n",
+    "    fig, ax = plt.subplots(figsize=(12, 5))\n",
+    "    show(clipped, (1, 2, 3), cmap='Greys')\n",
+    "\n",
+    "    clipped_ext = plotting_extent(clipped)\n",
+    "    clipped_array = clipped.read([1, 2, 3], \n",
+    "                                 out_dtype=float, masked=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Quick check if the extent in both situations are the same:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "new_extent == clipped_ext"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Rasterio workflow"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The workflow applied to clip the data set is typical when working with Rasterio. It consists of the following main steps:\n",
+    "\n",
+    "```\n",
+    "# 1\n",
+    "with rasterio.open(data_file) as src:  \n",
+    "\n",
+    "    # 2\n",
+    "    out_image, out_transform = rasterio.mask.mask(src, \n",
+    "        herstappe_vect.geometry, crop=True)    \n",
+    "    \n",
+    "    # 3\n",
+    "    herstappe_profile = src.profile\n",
+    "    herstappe_profile.update({\"height\": out_image.shape[1],\n",
+    "                              \"width\": out_image.shape[2],\n",
+    "                              \"transform\": out_transform})  \n",
+    "    # 4                              \n",
+    "    with rasterio.open(\"./test.tiff\", \"w\", **herstappe_profile) as dest: \n",
+    "        dest.write(out_image)\n",
+    "```\n",
+    "\n",
+    "- 1. Read in a data set using the context manager\n",
+    "- 2. Read and transform the data set by clipping, resampling,...\n",
+    "- 3. Update the spatial metadata/profile of the data set\n",
+    "- 4. Save the new data set with the updated metadata/profile"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Similar operations are:\n",
+    "\n",
+    "- Reprojecting a data set, see https://rasterio.readthedocs.io/en/latest/topics/reproject.html\n",
+    "- Resampling a data set, see https://rasterio.readthedocs.io/en/latest/topics/resampling.html\n",
+    "- Create a raster mosaic, see https://rasterio.readthedocs.io/en/latest/api/rasterio.merge.html and https://automating-gis-processes.github.io/CSC/notebooks/L5/raster-mosaic.html"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's apply this workflow to resample our data set with a factor 2, i.e. doubling the pixel size:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_file = \"./data/herstappe/raster/2020-09-17_Sentinel_2_L1C_True_color.tiff\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import rasterio\n",
+    "from rasterio.enums import Resampling\n",
+    "\n",
+    "scaling_factor = 2 \n",
+    "\n",
+    "with rasterio.open(data_file) as src:  # 1\n",
+    "\n",
+    "    # resample data to target shape  # 2\n",
+    "    new_width = int(src.width / scaling_factor)\n",
+    "    new_height = int(src.height / scaling_factor)\n",
+    "    out_image = src.read(\n",
+    "        out_shape=(src.count, new_height, new_width),\n",
+    "        resampling=Resampling.bilinear\n",
+    "    )\n",
+    "\n",
+    "    # scale image transform and update metadata  # 3\n",
+    "    data_profile = src.profile\n",
+    "    out_transform = src.transform * src.transform.scale(scaling_factor)\n",
+    "    data_profile.update({\"height\": new_height,\n",
+    "                         \"width\": new_width,\n",
+    "                         \"transform\": out_transform})      \n",
+    "\n",
+    "    # save the output to disk # 4\n",
+    "    with rasterio.open(\"./test_resample.tiff\", \"w\", **data_profile) as dst:\n",
+    "        dst.write(out_image)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "__Note:__ Other resampling algorithms are available in the `Resampling` module of rasterio."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#!gdalinfo ./data/herstappe/raster/2020-09-17_Sentinel_2_L1C_True_color.tiff\n",
+    "#!gdalinfo ./test_resample.tiff"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-info\" style=\"font-size:120%\">\n",
+    "\n",
+    "**REMEMBER**: <br>\n",
+    "\n",
+    "Many of the typical GIS operations provided by Rasterio have a common workflow. In order to preserve the spatial information, make sure to update the attributes such as transform, width and height accordingly before saving the file.\n",
+    "\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-success\">\n",
+    "\n",
+    "**EXERCISE**:\n",
+    "\n",
+    "You received remote sensing data from Ghent. You're wondering if the remote sensing image covers the entire administrative area of Ghent and decide to make a plot to make the comparison. The vector file of Ghent is available as well, see `./data/herstappe/vector/herstappe.geojson`.\n",
+    "    \n",
+    "Make a plot of `gent_data` (band 4 `./data/gent/raster/2020-09-17_Sentinel_2_L1C_B04.tiff`) together with `gent_vect` (read from `./data/herstappe/vector/herstappe.geojson`) using Matplotlib.\n",
+    "    \n",
+    "    \n",
+    "<details><summary>Hints</summary>\n",
+    "\n",
+    "* Make sure to align the CRS of both the vector as the raster data set.\n",
+    "* Remember the `extent` trick to use the coordinates as Matplotlib axis?\n",
+    "* The Matplotlib `plot` function provides `color`, `edgecolor`,... to adjust the style of the plot.\n",
+    "    \n",
+    "</details>\n",
+    "    \n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# Read the geojson vector data\n",
+    "gent_vect = geopandas.read_file(\"./data/gent/vector/gent.geojson\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# Read the tiff raster data\n",
+    "with rasterio.open(\"./data/gent/raster/2020-09-17_Sentinel_2_L1C_B04.tiff\") as src:\n",
+    "    gent_data = src.read(out_dtype=float, masked=False)\n",
+    "    gent_extent = plotting_extent(src)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# Combine both in a single plot\n",
+    "fig, ax = plt.subplots(figsize=(12, 5))\n",
+    "ax.imshow(gent_data.squeeze(), extent=gent_extent, cmap=\"Reds\")  # Extent trick to plot numpy\n",
+    "gent_vect.to_crs(epsg=3857).plot(color='None', edgecolor='grey', linewidth=2, ax=ax)\n",
+    "plt.axis(\"off\");"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-success\">\n",
+    "\n",
+    "**EXERCISE**:\n",
+    "\n",
+    "The files `./data/gent/raster/2020-09-17_Sentinel_2_L1C_B04.tiff` and `./data/gent/raster/2020-09-17_Sentinel_2_L1C_B08.tiff` are respectively band 4 and 8. Combine them in a single Geotiff file:\n",
+    "    \n",
+    "- Read in each of the data sets with rasterio\n",
+    "- Stack the two data sets with Numpy\n",
+    "- Write the resulting `(2, 317, 625)` array to a new geotiff file `./B0408.tiff` with data type `uint32`\n",
+    "    \n",
+    "    \n",
+    "<details><summary>Hints</summary>\n",
+    "\n",
+    "* The workflow is similar to before: you want to read, update the metadata/profile and write the output\n",
+    "* Stacking arrays in Numpy is done by `np.vstack`, converting the dtype by `astype(...)`\n",
+    "* The metadata/profile to update is not the transform, neither the widht/height. Check the `count` and the `dtype` keys of the profile\n",
+    "    \n",
+    "</details>\n",
+    "    \n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# Read the vector data sets #1\n",
+    "with rasterio.open(\"./data/gent/raster/2020-09-17_Sentinel_2_L1C_B04.tiff\") as src:\n",
+    "    b4_data = src.read()\n",
+    "    data_profile = src.profile\n",
+    "with rasterio.open(\"./data/gent/raster/2020-09-17_Sentinel_2_L1C_B08.tiff\") as src:\n",
+    "    b8_data = src.read() "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# Stack both data sets and convert to uint32 data (#2)\n",
+    "b48_bands = np.vstack((b4_data, b8_data))  # 0 is b4 and 1 is b8  \n",
+    "b48_bands = b48_bands.astype(\"uint32\")\n",
+    "b48_bands.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# Update profile metadata (#3) and save to a new tiff file (#4)\n",
+    "data_profile.update({\"count\": 2, \n",
+    "                     \"dtype\": \"uint32\"})  #3\n",
+    "\n",
+    "with rasterio.open(\"./B0408.tiff\", \"w\", **data_profile) as dst: #4\n",
+    "    dst.write(b48_bands)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-success\">\n",
+    "    \n",
+    "**ADVANCED EXERCISE**:    \n",
+    "\n",
+    "You received multiple remote sensing layers of Ghent that need to be resampled. Instead of copy pasting the same code over and over again, you decide to write your own function to resample a file with Rasterio. The inputs of your function are:\n",
+    "    \n",
+    "- input_file : reference to a file on disk\n",
+    "- output_file :  where t write the output \n",
+    "- resampling_method : method from rasterio.Resampling to use when resampling\n",
+    "- scaling_factor : factor to which the resampling should be applied, default is 2\n",
+    "    \n",
+    "Apply the function on each of the Geotiff files in the folder `./data/gent/raster` (make sure to not overwrite the original files!)\n",
+    "    \n",
+    "<details><summary>Hints</summary>\n",
+    "\n",
+    "* Start from the code from the example\n",
+    "* Try to make the function as reusable as possible\n",
+    "* Make sure to document your own function using [numpy docstring](https://numpydoc.readthedocs.io/en/latest/format.html)    \n",
+    "* The [pathlib](https://docs.python.org/3/library/pathlib.html) module is very powerful for working with file systems. Use the `glob` method to find all files with a given extension.\n",
+    "    \n",
+    "</details>\n",
+    "    \n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# Create a custom function\n",
+    "def resample_raster_file(input_file, output_file, resampling_method, scaling_factor=2):\n",
+    "    \"\"\"Resample a raster file with a given scaling factor using rasterio\n",
+    "    \n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    input_file : str | Path\n",
+    "        Input raster file to resample\n",
+    "    output_file : str | Path\n",
+    "        Output raster file\n",
+    "    resampling_method : rasterio.Resampling\n",
+    "        Method available in the Resampling module of rasterio, e.g. Resampling.bilinear\n",
+    "    scaling_factor : float, default 2\n",
+    "        Scaling factor to use for resampling the data\n",
+    "    \n",
+    "    Examples\n",
+    "    --------\n",
+    "    >>> resample_raster_file(\"./input_file.tiff\", \"output_file.tiff\", \n",
+    "    ...                      Resampling.bilinear, scaling_factor=3)\n",
+    "    \"\"\"\n",
+    "    with rasterio.open(input_file) as src:  \n",
+    "        \n",
+    "        # resample data to target shape\n",
+    "        new_width = int(src.width / scaling_factor)\n",
+    "        new_height = int(src.height / scaling_factor)\n",
+    "        out_image = src.read(\n",
+    "            out_shape=(src.count, new_height, new_width),\n",
+    "            resampling=resampling_method  #Resampling.bilinear\n",
+    "        )\n",
+    "\n",
+    "        # scale image transform and update metadata  # 3\n",
+    "        data_profile = src.profile\n",
+    "        out_transform = src.transform * src.transform.scale(scaling_factor)\n",
+    "        data_profile.update({\"height\": new_height,\n",
+    "                             \"width\": new_width,\n",
+    "                             \"transform\": out_transform})      \n",
+    "\n",
+    "        # save the output to disk # 4\n",
+    "        with rasterio.open(output_file, \"w\", **data_profile) as dst:\n",
+    "            dst.write(out_image)    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# Apply function to a single file \n",
+    "resample_raster_file(\"./data/gent/raster/2020-09-17_Sentinel_2_L1C_B04.tiff\", \n",
+    "                     \"b4_out.tiff\", Resampling.bilinear, scaling_factor=2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# Apply function to all files in the directory\n",
+    "from pathlib import Path\n",
+    "for file in Path(\"./data/gent/raster\").glob(\"*.tiff\"):\n",
+    "    print(f\"Converting {file.name}...\")\n",
+    "    resample_raster_file(file, Path(f\"./{file.name}_resampled.tiff\"), Resampling.bilinear, scaling_factor=2)    "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-info\" style=\"font-size:120%\">\n",
+    "\n",
+    "**REMEMBER**: <br>\n",
+    "\n",
+    "When you need to do a particular operation a lot on different files, convert the functionality to a function that you can reuse!\n",
+    "\n",
+    "If you are familiar to the command line, using the power of [GDAL](https://gdal.org/index.html) is a valid alternative to programming these conversions with Python/Rasterio. Have a look at [gdalwarp](https://gdal.org/programs/gdalwarp.html) in particular. If interested, the [Using GDAL for preprocessing](https://ocw.un-ihe.org/course/view.php?id=11&section=3) course provides a good introduction to GDAL.\n",
+    "\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## BONUS: only download what you need\n",
+    "\n",
+    "Rasterio only reads the data from disk that is requested to overcome loading entire data sets into memory. The same applies to downloading data, overcoming entire downloads when only a fraction is required (when the online resource supports this). An example is https://zenodo.org/record/2654620, which is available as [Cloud Optimized Geotiff (COG)](https://www.cogeo.org/). Also cloud provides (AWS, google,...) do support COG files, e.g. [Landstat images](https://docs.opendata.aws/landsat-pds/readme.html).\n",
+    "\n",
+    "These files are typically very large to download, whereas we might only need a small subset of the data. COG files do support to downloading a subset of the data you need using the masking approach.\n",
+    "\n",
+    "Let's use the Averbode nature reserve data as an example, available at the URL: http://s3-eu-west-1.amazonaws.com/lw-remote-sensing/cogeo/20160401_ABH_1_Ortho.tif"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "averbode_cog_rgb = 'http://s3-eu-west-1.amazonaws.com/lw-remote-sensing/cogeo/20160401_ABH_1_Ortho.tif'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Check the metadata, without downloading the data itself:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "with rasterio.open(averbode_cog_rgb) as averbode:\n",
+    "    print(averbode.count, averbode.width, averbode.height)\n",
+    "    print(averbode.meta)\n",
+    "    averbode_extent = plotting_extent(averbode)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Downloading the entire data set would be 37645*35405 pixels of 8 bit, so more or less 1.3 GByte"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "37645*35405 / 1e9  # Gb"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Assume that we have a study area which is much smaller than the total extent of the available image:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "left, right, bottom, top = averbode_extent"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "averbode_study_area = geopandas.read_file(\"./data/averbode/study_area.geojson\")\n",
+    "ax = averbode_study_area.plot();\n",
+    "ax.set_xlim(left, right);\n",
+    "ax.set_ylim(bottom, top);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In the case of COG data, the data can sometimes be requested on different resolution levels when stored as such. So, to get a very broad overview of the data, we can request the coarsest resolution by resampling and download the data at the resampled resolution:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "with rasterio.open(averbode_cog_rgb) as src:\n",
+    "    print(f\"Available resolutions are {src.overviews(1)}\")\n",
+    "    oview = src.overviews(1)[-1] # list of overviews, selecting the latest (most coarse)\n",
+    "    print(f\"Resampling factor= {oview}\")\n",
+    "    # Download a resampled version of the data (http://rasterio.readthedocs.io/en/latest/topics/resampling.html), aka thumbnail\n",
+    "    thumbnail = src.read(1, out_shape=(1, int(src.height // oview), int(src.width // oview)))    "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Compare the thumbnail version of the data with our study area:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "ax.imshow(thumbnail, extent=averbode_extent);\n",
+    "averbode_study_area.plot(ax=ax, color='None', edgecolor='red', linewidth=2);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Downloading the entire data file would be overkill. Instead, we only want to download the data of the study area:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "output_file = \"./averbode_orthophoto.tiff\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The resulting data set will still be around 120MB and will take a bit of time, but this is only a fraction of the original data file (GBs):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Only run this cell when sufficient band width ;-)\n",
+    "with rasterio.open(averbode_cog_rgb) as averbode_rgb:\n",
+    "    averbode_rgb_image, averbode_rgb_transform = rasterio.mask.mask(averbode_rgb, averbode_study_area.geometry, crop=True)\n",
+    "    averbode_rgb_profile = averbode_rgb.profile  \n",
+    "\n",
+    "    averbode_rgb_profile.update({\"driver\": \"GTiff\",\n",
+    "                                 \"height\": averbode_rgb_image.shape[1],\n",
+    "                                 \"width\": averbode_rgb_image.shape[2],\n",
+    "                                 \"transform\": averbode_rgb_transform\n",
+    "                                })\n",
+    "    \n",
+    "    with rasterio.open(output_file, \"w\", **averbode_rgb_profile) as dest:\n",
+    "        dest.write(averbode_rgb_image)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Thanks to https://geohackweek.github.io/raster/04-workingwithrasters/ for the inspiration"
+   ]
+  }
+ ],
+ "metadata": {
+  "celltoolbar": "Nbtutor - export exercises",
+  "jupytext": {
+   "cell_metadata_filter": "-run_control,-deletable,-editable,-jupyter,-slideshow",
+   "notebook_metadata_filter": "-jupytext.cell_metadata_filter,-jupytext.notebook_metadata_filter"
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/_solved/case-argo-sea-floats.ipynb b/_solved/case-argo-sea-floats.ipynb
new file mode 100644
index 0000000..e27c9fd
--- /dev/null
+++ b/_solved/case-argo-sea-floats.ipynb
@@ -0,0 +1,635 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<p><font size=\"6\"><b>CASE - Argo Sea Floats</b></font></p>\n",
+    "\n",
+    "\n",
+    "> *DS Python for GIS and Geoscience*  \n",
+    "> *September, 2025*\n",
+    ">\n",
+    "> *© 2025, Joris Van den Bossche and Stijn Van Hoey. Licensed under [CC BY 4.0 Creative Commons](http://creativecommons.org/licenses/by/4.0/)*\n",
+    "\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "import xarray as xr"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For this use case, we do a data exploration of the [Argo floats](https://argo.ucsd.edu/) data set, an international collaboration that collects high-quality temperature and salinity profiles from the upper 2000m of the ice-free global ocean and currents from intermediate depths.\n",
+    "\n",
+    "> Argo is an international program that collects information from inside the ocean using a fleet of robotic instruments that drift with the ocean currents and move up and down between the surface and a mid-water level. Each instrument (float) spends almost all its life below the surface.\n",
+    "\n",
+    "These data do __NOT represent full spatial coverage data__ (like remote sensing images), but measurements of salinity and temperature as a function of water `level` (related to the pressure). Each measurements happens at a given `date` on a given location (`lon`/`lat`)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import xarray as xr\n",
+    "argo = xr.load_dataset(\"./data/argo_float.nc\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "argo"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "__Note:__\n",
+    "\n",
+    "The bold font (or * symbol in plain text output version) in the coordinate representation above indicates that level and date are 'dimension coordinates' (they describe the coordinates associated with data variable axes) while 'lon' and 'lat' are 'non-dimension coordinates'. We can make any variable a non-dimension coordinate.\n",
+    "\n",
+    "The 'lon' and 'lat' coordinates are both linked to the `date` dimension, i.e. a measurement at a given `date` is done at a given location (`lon`/`lat`). As such, all data is contained in a single `xarray.DataSet`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To get an overview, let's plot the coordinates of the available measurements and add a background map using [contextly](https://contextily.readthedocs.io/en/latest/index.html):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import contextily as cx\n",
+    "from pyproj import CRS\n",
+    "crs = CRS.from_epsg(4326)\n",
+    "\n",
+    "fig, ax = plt.subplots(figsize=(6, 6))\n",
+    "argo.plot.scatter(x='lon', y='lat', ax=ax, color='red', s=2)\n",
+    "\n",
+    "# Custom adjustments of the limits, as we are in the middle of the ocean\n",
+    "xmin, xmax = ax.get_xlim()\n",
+    "ymin, ymax = ax.get_ylim()\n",
+    "ax.set_xlim(xmin*1.5, xmax*0.5)\n",
+    "ax.set_ylim(ymin*0.5, ymax*1.5)\n",
+    "\n",
+    "cx.add_basemap(ax, crs=crs.to_string())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-success\">\n",
+    "\n",
+    "**EXERCISE**:\n",
+    "\n",
+    "Add a new variable to the `argo` data set, called `temperature_kelvin`, by converting the temperature to Kelvin. \n",
+    "    \n",
+    "$T_{K} = T_{^{\\circ}C} + 273.15$\n",
+    "   \n",
+    "<details>\n",
+    "    \n",
+    "<summary>Hints</summary>\n",
+    "\n",
+    "* Remember that xarray works as Numpy and relies on the same broadcasting rules.\n",
+    "\n",
+    "</details>    \n",
+    "    \n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "argo[\"temperature_kelvin\"] = argo[\"temperature\"] + 273.15"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-success\">\n",
+    "\n",
+    "**EXERCISE**:\n",
+    "\n",
+    "The water level classes define different water depth (level). The pressure is a proxy for the water depth. Verify the relationship between the pressure and the level using a scatter plot. Does a larger value for the level represent deeper water depths or not?\n",
+    "    \n",
+    "<details><summary>Hints</summary>\n",
+    "\n",
+    "* If you get the error `ValueError: Dataset.plot cannot be called directly. Use an explicit plot method, e.g. ds.plot.scatter(...)`. When encountering an Error, always check the output message at the end of the error statement.\n",
+    "\n",
+    "</details>    \n",
+    "    \n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "argo.plot.scatter(x=\"pressure\", y=\"level\", edgecolor=\"None\", s=10)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-success\">\n",
+    "\n",
+    "**EXERCISE**:\n",
+    "\n",
+    "Assume that buoyancy (in dutch _'drijfvermogen'_, it is the upward force exerted by a fluid that opposes the weight of a partially or fully immersed object) is defined by the following formula:\n",
+    "    \n",
+    "$$g \\cdot ( 2\\times 10^{-4} \\cdot T - 7\\times 10^{-4} \\cdot P )$$\n",
+    "\n",
+    "With:\n",
+    "- $g$ = 9.8\n",
+    "- $T$ = temperature\n",
+    "- $P$ = pressure\n",
+    "\n",
+    "Calculate the buoyancy and add it as a new variable `buoyancy` to the `argo` data set. \n",
+    "\n",
+    "Make a 2D (image) plot with the x-axis the date, the y-axis the water level and the color intensity the buoyancy. As the level represents the depth of the water, it makes more sense to have 0 (surface) at the top of the y-axis: switch the y-axis direction.\n",
+    "    \n",
+    "<details><summary>Hints</summary>\n",
+    "\n",
+    "* $2\\times 10^{-4}$ can be written in Python as `2e-4`\n",
+    "* Remember that xarray works as Numpy and relies on the same broadcasting rules.\n",
+    "* The `imshow` method does not work on irregular intervals. Matplotlib and xarray also have `pcolormesh`.    \n",
+    "* Look for options [in the xarray documentation](http://xarray.pydata.org/en/stable/plotting.html#other-axes-kwargs) to control the axis direction. (The `ax.invert_yaxis()` Matplotlib function is not supported for pcolormesh)\n",
+    "    \n",
+    "</details>    \n",
+    "    \n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "argo[\"buoyancy\"] = 9.8 * (2e-4 * argo[\"temperature\"] - 7e-4 * argo[\"pressure\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "argo[\"buoyancy\"].plot(yincrease=False)  # xarray decides the plot type when no specific method is used"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# More explicit version defining the x and y axis\n",
+    "argo[\"buoyancy\"].plot.pcolormesh(x=\"date\", y=\"level\", yincrease=False)  # pcolormesh instead of imshow"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-success\">\n",
+    "\n",
+    "**EXERCISE**:\n",
+    "\n",
+    "Make a line plot of the salinity as a function of time at level 10\n",
+    "   \n",
+    "<details><summary>Hints</summary>\n",
+    "\n",
+    "Break it down into different steps and chain the individual steps:\n",
+    "    \n",
+    "* From the argo data set, select the variable `salinity`. This is similar to selecting a column in Pandas.\n",
+    "* Next, use the `sel` method to select the `level=10`\n",
+    "* Next, use the `plot.line()` method.\n",
+    "\n",
+    "</details>    \n",
+    "    \n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "argo[\"salinity\"].sel(level=10).plot.line()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-success\">\n",
+    "\n",
+    "**EXERCISE**:\n",
+    "\n",
+    "- Make a line plot of the temperature as a function of time for the levels 10, 20 and 30 at the same graph \n",
+    "- Make a second line plot with each of the 3 levels (10, 20, 30) in a different subplot. \n",
+    "   \n",
+    "<details><summary>Hints</summary>\n",
+    "\n",
+    "Break it down into different steps and chain these individual steps:\n",
+    "    \n",
+    "* From the argo data set, select the variable `temperature`. This is similar to selecting a column in Pandas.\n",
+    "* Next, use the `sel` method to select the levels 10, 20 and 30.\n",
+    "* Next, use the `plot.line()` method, but make sure the `hue` changes for each level\n",
+    "    \n",
+    "For the subplots, check the [facetting documentation](http://xarray.pydata.org/en/stable/plotting.html#faceting) of xarray. \n",
+    "\n",
+    "</details>    \n",
+    "    \n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "argo[\"temperature\"].sel(level=[10, 20, 30]).plot.line(hue=\"level\");"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "argo[\"temperature\"].sel(level=[10, 20, 30]).plot.line(col=\"level\");"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-success\">\n",
+    "\n",
+    "**EXERCISE**:\n",
+    "\n",
+    "You wonder how the temperature evolves with increasing latitude and what the effect is of the depth (level):\n",
+    "\n",
+    "- Create a scatter plot of the `level` as a function of the `temperature` colored by the `latitude`. \n",
+    "    \n",
+    "- As a further exploration step, pick a subset of levels 1, 10, 25, and 50 and create a second scatter plot with in the x-axis the latitude of the measurement and in the y-axis the temperature. To compare the effect of the different levels, give each level a separate subplot next to each other.\n",
+    "   \n",
+    "<details><summary>Hints</summary>\n",
+    "\n",
+    "* In a scatter plot, the color or `hue` can be linked to a variable (instead of the deafult `date`).\n",
+    "* From the argo data set, use the `sel` method to select the levels 1, 10, 25, and 50.\n",
+    "* For the second scatter plot, but make sure the `col` changes for each `level` and define which variables need to go to which axis.\n",
+    "\n",
+    "</details>    \n",
+    "    \n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "argo.plot.scatter(x=\"temperature\", y=\"level\", s=5, hue=\"lat\", edgecolor=\"None\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "argo.sel(level=[1, 5, 25, 50]).plot.scatter(x=\"lat\", y=\"temperature\", col=\"level\", edgecolor=\"None\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-success\">\n",
+    "\n",
+    "**EXERCISE**:\n",
+    "\n",
+    "Make an image plot of the temperature as a function of time. Divide the colormap in 3 discrete categories using xarray plot methods:\n",
+    "    \n",
+    "* x < 5\n",
+    "* 5 < x < 15\n",
+    "* x > 15\n",
+    "    \n",
+    "Choose a custom colormap and adjust the label of the colorbar to `'Temperature (°C)'`\n",
+    "   \n",
+    "<details>\n",
+    "    \n",
+    "<summary>Hints</summary>\n",
+    "\n",
+    "- Check the help of the `plot` function or the [xarray documentation](http://xarray.pydata.org/en/stable/plotting.html#discrete-colormaps) on discrete colormaps.\n",
+    "- Adjustments to the colorbar settings can be defined with the `cbar_kwargs` as a dict. Adjust the `label` of the colorbar.    \n",
+    "\n",
+    "</details>    \n",
+    "    \n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "argo.temperature.plot.pcolormesh(yincrease=False, \n",
+    "                                 cbar_kwargs={'label': 'Temperature (°C)'},\n",
+    "                                 cmap='Reds',\n",
+    "                                 levels=[5, 15]\n",
+    "                                );"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-success\">\n",
+    "\n",
+    "**EXERCISE**:\n",
+    "\n",
+    "Plot a salinity profile, i.e. how the salinity evolves with the depth/level, for the measurement done at 2012-10-31. Add two lines to the plot:\n",
+    "    \n",
+    "- The salinity data of 2012-10-31 with the salinity in the x-axis and the level in the y-axis.\n",
+    "- The salinity data of 2012-10-31 with the level in the y-axis, but using a rolling median value for the level coordinate using 10 levels (with `center=True` to make the two plots comparable) to smooth the data. \n",
+    "    \n",
+    "Feel free to adjust the settings of both lines (color, width,...)\n",
+    "    \n",
+    "<details><summary>Hints</summary>\n",
+    "    \n",
+    "* Use `fig, ax = plt.subplots()` to prepare a Figure/axes fo both lines.\n",
+    "* The `rolling` method need to be applied to the `level` coordinate.\n",
+    "* You can pass configuration options such as `linewidth`, `color`,... to the line plot method.\n",
+    "* By adding `label` to the plots and remove the addition of a legend for each of the plots (`add_legend=False`), calling the `ax.legend()` method at the end will collect these labels into a single legend. \n",
+    "\n",
+    "</details>    \n",
+    "    \n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "salinity_20121031 = argo[\"salinity\"].sel(date='2012-10-31')\n",
+    "\n",
+    "fig, ax = plt.subplots(figsize=(6, 6))\n",
+    "salinity_20121031.plot.line(y=\"level\", yincrease=False, color=\"grey\", add_legend=False, label=\"raw\");\n",
+    "salinity_20121031.rolling(level=10, center=True).median().plot.line(y=\"level\", yincrease=False, linewidth=3, color=\"orange\", add_legend=False, label=\"smooth\");\n",
+    "ax.legend()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-success\">\n",
+    "\n",
+    "**EXERCISE**:\n",
+    "\n",
+    "An improved version of the salinity profile of the previous exercise uses the `pressure` data instead of the levels, plotting the pressure-salinity relationship. In the current data representation the pressure is a data variable. However, it can also be interpreted as a coordinate, i.e. the depth of the measurement (see exercise on the relation between pressure and level. \n",
+    "    \n",
+    "To create a salinity and temperature profile for each of the measurements in December 2012, convert the `pressure` variable to a coordinate instead of a data variable first. Prepare two subplots with Matplotlib to plot respectively the salinity and the temperature profile for each of the measurements in December 2012.  Use the pressure in the y-axis and make sure pressure increases from top to bottom of the graph.   \n",
+    "    \n",
+    "    \n",
+    "<details><summary>Hints</summary>\n",
+    "    \n",
+    "* Use `assign_coords` to use the pressure as coordinate data.\n",
+    "* Do not forget to select all measurements of December 2012, e.g. using the string representation `.sel(date='2012-12')`\n",
+    "* You can directly create two axes objects with matplotlib: `fig, (ax0, ax1) = plt.subplots(1, 2)`\n",
+    "</details>    \n",
+    "    \n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "argo_pressure = argo.assign_coords(pressure=argo[\"pressure\"]).sel(date='2012-12')\n",
+    "\n",
+    "fig, (ax0, ax1) = plt.subplots(1, 2, figsize=(12, 6))\n",
+    "argo_pressure[\"salinity\"].plot.line(y=\"pressure\", yincrease=False, ax=ax0, hue='date');\n",
+    "argo_pressure[\"temperature\"].plot.line(y=\"pressure\", yincrease=False, ax=ax1, hue='date');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-success\">\n",
+    "\n",
+    "**EXERCISE**:\n",
+    "\n",
+    "Calculate the average salinity and temperature as a function of level over the measurements taken between 2012-10-01 and 2012-12-01. \n",
+    "\n",
+    "Make a separate line plot for each of them. Define the Figure and 2 subplots with Matplotlib first. \n",
+    "    \n",
+    "<details><summary>Hints</summary>\n",
+    "\n",
+    "* xarray supports to query dates using a string representation.\n",
+    "* Use the `slice` operator within the `sel` to select a range of the data.\n",
+    "* Whereas in Numpy we used `axis` in reduction functions, xarray uses the `dim` name.\n",
+    "* Also for line plots you can define which dimension should be on the x-axis and which on the y-axis by providing the name.  \n",
+    "* Use `fig, (ax0, ax1) = plt.subplots(1, 2)` to create subplots.\n",
+    "</details>    \n",
+    "    \n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "subset_mean = argo.sel(date=slice('2012-10-01', '2012-12-01')).mean(dim=\"date\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "fig, (ax0, ax1) = plt.subplots(1, 2)\n",
+    "subset_mean[\"salinity\"].plot.line(y=\"level\", yincrease=False, ax=ax0)\n",
+    "subset_mean[\"temperature\"].plot.line(y=\"level\", yincrease=False, ax=ax1)\n",
+    "plt.tight_layout()"
+   ]
+  }
+ ],
+ "metadata": {
+  "celltoolbar": "Nbtutor - export exercises",
+  "jupytext": {
+   "default_lexer": "ipython3"
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.11"
+  },
+  "widgets": {
+   "application/vnd.jupyter.widget-state+json": {
+    "state": {},
+    "version_major": 2,
+    "version_minor": 0
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/_solved/case-curieuzeneuzen-air-quality.ipynb b/_solved/case-curieuzeneuzen-air-quality.ipynb
index cc0cecd..d43bad0 100644
--- a/_solved/case-curieuzeneuzen-air-quality.ipynb
+++ b/_solved/case-curieuzeneuzen-air-quality.ipynb
@@ -2,25 +2,25 @@
  "cells": [
   {
    "cell_type": "markdown",
+   "id": "0d39c8d2",
    "metadata": {},
    "source": [
     "<p><font size=\"6\"><b> CASE - CurieuzeNeuzen citizen science air quality data</b></font></p>\n",
     "\n",
     "\n",
     "> *DS Python for GIS and Geoscience*  \n",
-    "> *October, 2020*\n",
+    "> *October, 2025*\n",
     ">\n",
-    "> *© 2020, Joris Van den Bossche and Stijn Van Hoey. Licensed under [CC BY 4.0 Creative Commons](http://creativecommons.org/licenses/by/4.0/)*\n",
+    "> *© 2025, Joris Van den Bossche and Stijn Van Hoey. Licensed under [CC BY 4.0 Creative Commons](http://creativecommons.org/licenses/by/4.0/)*\n",
     "\n",
     "---"
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "86ea794e-be6e-4e7b-a591-add03506d728",
    "metadata": {},
    "source": [
-    "\n",
-    "\n",
     "Air pollution remains a key environmental problem in an increasingly urbanized world. While concentrations of traffic-related pollutants like nitrogen dioxide (NO2) are known to vary over short distances, official monitoring networks remain inherently sparse, as reference stations are costly to construct and operate.\n",
     "\n",
     "The [**CurieuzeNeuzen**](https://curieuzeneuzen.be/curieuzeneuzen-vlaanderen-2018/) citizen science project collected a large, spatially distributed dataset that can complement official monitoring. In a first edition in 2016, in Antwerp, 2000 citizens were involved. This success was followed by a second edition in 2018 engaging 20.000 citizens across Flanders, a highly urbanized, industrialized and densely populated region in Europe. The participants measured the NO2 concentrations in front of their house using a low-cost sampler design (see picture below, where passive sampling tubes are attached using a panel to a window at the facade). \n",
@@ -29,15 +29,17 @@
     "\n",
     "In this case study, we are going to make use of the data collected across Flanders in 2018: explore the data and investigate relationships with other variables.\n",
     "\n",
-    "<img src=\"../img/CN_measurement_setup.png\" alt=\"Measurement panel\" style=\"width:800px\">\n"
+    "<img src=\"../img/CN_measurement_setup.png\" alt=\"Measurement panel\" style=\"width:800px\">"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
+   "id": "b7b5841c",
    "metadata": {},
    "outputs": [],
    "source": [
+    "import numpy as np\n",
     "import pandas as pd\n",
     "import geopandas\n",
     "\n",
@@ -46,6 +48,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "8819dfb3",
    "metadata": {},
    "source": [
     "## Importing and exploring the data"
@@ -53,6 +56,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "8af710eb",
    "metadata": {},
    "source": [
     "<div class=\"alert alert-success\">\n",
@@ -67,153 +71,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>code</th>\n",
-       "      <th>campaign</th>\n",
-       "      <th>no2</th>\n",
-       "      <th>lat</th>\n",
-       "      <th>lon</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1387656</td>\n",
-       "      <td>facade</td>\n",
-       "      <td>23.0</td>\n",
-       "      <td>50.82594</td>\n",
-       "      <td>4.704003</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>192852</td>\n",
-       "      <td>facade</td>\n",
-       "      <td>47.3</td>\n",
-       "      <td>51.21328</td>\n",
-       "      <td>4.444702</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>192873</td>\n",
-       "      <td>facade</td>\n",
-       "      <td>21.2</td>\n",
-       "      <td>51.30897</td>\n",
-       "      <td>4.188672</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>192876</td>\n",
-       "      <td>facade</td>\n",
-       "      <td>26.6</td>\n",
-       "      <td>51.09620</td>\n",
-       "      <td>3.991424</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>192879</td>\n",
-       "      <td>facade</td>\n",
-       "      <td>25.6</td>\n",
-       "      <td>51.19922</td>\n",
-       "      <td>4.461859</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>17881</th>\n",
-       "      <td>STAT_R811</td>\n",
-       "      <td>reference station</td>\n",
-       "      <td>18.7</td>\n",
-       "      <td>51.25159</td>\n",
-       "      <td>4.491521</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>17882</th>\n",
-       "      <td>STAT_R817</td>\n",
-       "      <td>reference station</td>\n",
-       "      <td>24.8</td>\n",
-       "      <td>51.17741</td>\n",
-       "      <td>4.417936</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>17883</th>\n",
-       "      <td>STAT_R831</td>\n",
-       "      <td>reference station</td>\n",
-       "      <td>23.8</td>\n",
-       "      <td>51.34881</td>\n",
-       "      <td>4.339557</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>17884</th>\n",
-       "      <td>STAT_R833</td>\n",
-       "      <td>reference station</td>\n",
-       "      <td>22.0</td>\n",
-       "      <td>51.32855</td>\n",
-       "      <td>4.363960</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>17885</th>\n",
-       "      <td>STAT_SZ02</td>\n",
-       "      <td>reference station</td>\n",
-       "      <td>19.0</td>\n",
-       "      <td>50.91298</td>\n",
-       "      <td>4.512118</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>17886 rows × 5 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "            code           campaign   no2       lat       lon\n",
-       "0        1387656             facade  23.0  50.82594  4.704003\n",
-       "1         192852             facade  47.3  51.21328  4.444702\n",
-       "2         192873             facade  21.2  51.30897  4.188672\n",
-       "3         192876             facade  26.6  51.09620  3.991424\n",
-       "4         192879             facade  25.6  51.19922  4.461859\n",
-       "...          ...                ...   ...       ...       ...\n",
-       "17881  STAT_R811  reference station  18.7  51.25159  4.491521\n",
-       "17882  STAT_R817  reference station  24.8  51.17741  4.417936\n",
-       "17883  STAT_R831  reference station  23.8  51.34881  4.339557\n",
-       "17884  STAT_R833  reference station  22.0  51.32855  4.363960\n",
-       "17885  STAT_SZ02  reference station  19.0  50.91298  4.512118\n",
-       "\n",
-       "[17886 rows x 5 columns]"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "id": "bb48a8bf",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
    "source": [
     "df = pd.read_csv(\"./data/CN_Flanders_open_dataset.csv\")\n",
     "df"
@@ -221,26 +86,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "17886"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "id": "3fcb9fcc",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
    "source": [
     "len(df)"
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "a6d39dd0",
    "metadata": {},
    "source": [
     "The dataset contains longitude/latitude columns of the measurement locations, the measured NO2 concentration, and in addition also a \"campaign\" column indicating the type of measurement location (and an internal \"code\", which we will ignore here).\n",
@@ -263,30 +123,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "facade               17842\n",
-       "background              23\n",
-       "reference station       21\n",
-       "Name: campaign, dtype: int64"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "id": "729f9d28",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
    "source": [
     "df[\"campaign\"].value_counts()"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "id": "da01da11",
+   "metadata": {
+    "clear_cell": false
+   },
    "source": [
     "Most of the measurements are performed at the facade of the house or building of a participant. In addition, some measurement tubes were also placed next to reference monitoring stations of the VMM and in background locations (e.g. nature reserve or park).\n",
     "\n",
@@ -295,6 +149,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "10c764cf",
    "metadata": {},
    "source": [
     "<div class=\"alert alert-success\">\n",
@@ -318,54 +173,35 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "22.782818964553282"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "id": "9815290d",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
    "source": [
     "df[\"no2\"].mean()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "count    17886.000000\n",
-       "mean        22.782819\n",
-       "std          6.422454\n",
-       "min         10.900000\n",
-       "25%         18.300000\n",
-       "50%         21.100000\n",
-       "75%         25.500000\n",
-       "max         75.300000\n",
-       "Name: no2, dtype: float64"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "id": "39f28379",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
    "source": [
     "df[\"no2\"].describe()"
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "64db0a98",
    "metadata": {},
    "source": [
     "A histogram:"
@@ -373,86 +209,53 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<AxesSubplot:>"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "df['no2'].hist()  # plot.hist()"
+   "execution_count": null,
+   "id": "e1d73663",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "df['no2'].plot.hist()"
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "bf65012e",
    "metadata": {},
    "source": [
-    "A more expanded histogram (not asked in the exercise, but uncomment to check the code!)"
+    "A more expanded histogram (not asked in the exercise, but uncomment to check the code)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.lines.Line2D at 0x7fdc7970d8b0>"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": null,
+   "id": "04eba02d",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
    "source": [
     "fig, ax = plt.subplots()\n",
     "# using predefined bins\n",
-    "df['no2'].hist(ax=ax, bins=np.arange(0, 80, 5))\n",
+    "df['no2'].plot.hist(ax=ax, bins=np.arange(0, 80, 5))\n",
     "# adding x/y axis labels, and setting the range for the x axis\n",
     "ax.set(xlabel=\"NO2 concentration (µg/m³)\",\n",
     "       ylabel=\"Number of measurements\", xlim=(0,80))\n",
     "# adding a line with the mean\n",
     "ax.axvline(x=df['no2'].mean(), color='C2', linewidth=3)\n",
     "# adding a line with the EU yearly limit value of 40 µg/m³\n",
-    "ax.axvline(x=40, color='C3', linewidth=3)"
+    "ax.axvline(x=40, color='C3', linewidth=3)\n",
+    "ax.grid()"
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "7717db12",
    "metadata": {},
    "source": [
     "<div class=\"alert alert-success\">\n",
@@ -461,7 +264,7 @@
     "\n",
     "* Determine the percentage of locations that exceed the EU and WHO yearly limit value of 40 µg/m³.\n",
     "\n",
-    "Tip: first create a boolean mask determining whether the NO2 concentration is above 40 or not. Using this boolean mask, you can determine the percentage of values that follow the condition.\n",
+    "__Tip__: first create a boolean mask determining whether the NO2 concentration is above 40 or not. Using this boolean mask, you can determine the percentage of values that follow the condition.\n",
     "    \n",
     "<details><summary>Hints</summary>\n",
     "\n",
@@ -474,8 +277,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
+   "execution_count": null,
+   "id": "74f4c694",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
    "outputs": [],
    "source": [
     "exceedances = df['no2'] > 40"
@@ -483,26 +291,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "2.264340825226434"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "id": "f3860339",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
    "source": [
     "exceedances.mean() * 100  # the mean is equivalent here to counting the True values and dividing by the total number of elements"
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "0b0c62ac",
    "metadata": {},
    "source": [
     "So overall in Flanders, around 2.3% of the measurement locations exceeded the limit value. This might not seem much, but assuming that the dataset is representative for the population of Flanders (and effort was done to ensure this), around 150,000 inhabitants live in a place where the annual NO2 concentration at the front door exceeds the EU legal threshold value.  \n",
@@ -511,6 +314,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "bb1a86f3",
    "metadata": {},
    "source": [
     "<div class=\"alert alert-success\">\n",
@@ -532,50 +336,35 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "14.756521739130434"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "id": "19d0939b",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
    "source": [
     "df[df['campaign'] == \"background\"][\"no2\"].mean()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "campaign\n",
-       "background           14.756522\n",
-       "facade               22.789710\n",
-       "reference station    25.719048\n",
-       "Name: no2, dtype: float64"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "id": "046a78d6",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
    "source": [
     "df.groupby('campaign')[\"no2\"].mean()"
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "9d1f7f1e",
    "metadata": {},
    "source": [
     "The background locations (parks, nature reserves) clearly show a lower concentration than the average location. Note that the number of observations in each class are very skewed, so those averages are not necessarily representative!"
@@ -583,6 +372,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "ec0396a8",
    "metadata": {},
    "source": [
     "## Converting to a geospatial dataset"
@@ -590,6 +380,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "3f10de2c",
    "metadata": {},
    "source": [
     "The provided data was a CSV file, and we explored it above as a pandas DataFrame. To further explore the dataset using the spatial aspects (point data), we will first convert it to a geopandas GeoDataFrame.\n",
@@ -598,7 +389,7 @@
     "\n",
     "**EXERCISE**:\n",
     "\n",
-    "* Convert `df` into a GeoDataFrame, using the 'lat'/'lon' columns to create a Point geometry column. Also specify the correct Coordinate Reference System with the `crs` keyword. Call the result `gdf`.\n",
+    "* Convert `df` into a GeoDataFrame, using the 'lat'/'lon' columns to create a Point geometry column (tip: check the `geopandas.points_from_xy()` function). Also specify the correct Coordinate Reference System with the `crs` keyword. Call the result `gdf`.\n",
     "* Do a quick check to see the result is correct: look at the first rows, and make a simple plot of the GeoDataFrame with `.plot()` (you should recognize the shape of Flanders, if not, something went wrong)\n",
     "\n",
     "    \n",
@@ -616,8 +407,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
+   "execution_count": null,
+   "id": "5f2c23c4",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
    "outputs": [],
    "source": [
     "gdf = geopandas.GeoDataFrame(df, geometry=geopandas.points_from_xy(df['lon'], df['lat']), crs=\"EPSG:4326\")"
@@ -625,216 +421,35 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>code</th>\n",
-       "      <th>campaign</th>\n",
-       "      <th>no2</th>\n",
-       "      <th>lat</th>\n",
-       "      <th>lon</th>\n",
-       "      <th>geometry</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1387656</td>\n",
-       "      <td>facade</td>\n",
-       "      <td>23.0</td>\n",
-       "      <td>50.82594</td>\n",
-       "      <td>4.704003</td>\n",
-       "      <td>POINT (4.70400 50.82594)</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>192852</td>\n",
-       "      <td>facade</td>\n",
-       "      <td>47.3</td>\n",
-       "      <td>51.21328</td>\n",
-       "      <td>4.444702</td>\n",
-       "      <td>POINT (4.44470 51.21328)</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>192873</td>\n",
-       "      <td>facade</td>\n",
-       "      <td>21.2</td>\n",
-       "      <td>51.30897</td>\n",
-       "      <td>4.188672</td>\n",
-       "      <td>POINT (4.18867 51.30897)</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>192876</td>\n",
-       "      <td>facade</td>\n",
-       "      <td>26.6</td>\n",
-       "      <td>51.09620</td>\n",
-       "      <td>3.991424</td>\n",
-       "      <td>POINT (3.99142 51.09620)</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>192879</td>\n",
-       "      <td>facade</td>\n",
-       "      <td>25.6</td>\n",
-       "      <td>51.19922</td>\n",
-       "      <td>4.461859</td>\n",
-       "      <td>POINT (4.46186 51.19922)</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>17881</th>\n",
-       "      <td>STAT_R811</td>\n",
-       "      <td>reference station</td>\n",
-       "      <td>18.7</td>\n",
-       "      <td>51.25159</td>\n",
-       "      <td>4.491521</td>\n",
-       "      <td>POINT (4.49152 51.25159)</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>17882</th>\n",
-       "      <td>STAT_R817</td>\n",
-       "      <td>reference station</td>\n",
-       "      <td>24.8</td>\n",
-       "      <td>51.17741</td>\n",
-       "      <td>4.417936</td>\n",
-       "      <td>POINT (4.41794 51.17741)</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>17883</th>\n",
-       "      <td>STAT_R831</td>\n",
-       "      <td>reference station</td>\n",
-       "      <td>23.8</td>\n",
-       "      <td>51.34881</td>\n",
-       "      <td>4.339557</td>\n",
-       "      <td>POINT (4.33956 51.34881)</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>17884</th>\n",
-       "      <td>STAT_R833</td>\n",
-       "      <td>reference station</td>\n",
-       "      <td>22.0</td>\n",
-       "      <td>51.32855</td>\n",
-       "      <td>4.363960</td>\n",
-       "      <td>POINT (4.36396 51.32855)</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>17885</th>\n",
-       "      <td>STAT_SZ02</td>\n",
-       "      <td>reference station</td>\n",
-       "      <td>19.0</td>\n",
-       "      <td>50.91298</td>\n",
-       "      <td>4.512118</td>\n",
-       "      <td>POINT (4.51212 50.91298)</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>17886 rows × 6 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "            code           campaign   no2       lat       lon  \\\n",
-       "0        1387656             facade  23.0  50.82594  4.704003   \n",
-       "1         192852             facade  47.3  51.21328  4.444702   \n",
-       "2         192873             facade  21.2  51.30897  4.188672   \n",
-       "3         192876             facade  26.6  51.09620  3.991424   \n",
-       "4         192879             facade  25.6  51.19922  4.461859   \n",
-       "...          ...                ...   ...       ...       ...   \n",
-       "17881  STAT_R811  reference station  18.7  51.25159  4.491521   \n",
-       "17882  STAT_R817  reference station  24.8  51.17741  4.417936   \n",
-       "17883  STAT_R831  reference station  23.8  51.34881  4.339557   \n",
-       "17884  STAT_R833  reference station  22.0  51.32855  4.363960   \n",
-       "17885  STAT_SZ02  reference station  19.0  50.91298  4.512118   \n",
-       "\n",
-       "                       geometry  \n",
-       "0      POINT (4.70400 50.82594)  \n",
-       "1      POINT (4.44470 51.21328)  \n",
-       "2      POINT (4.18867 51.30897)  \n",
-       "3      POINT (3.99142 51.09620)  \n",
-       "4      POINT (4.46186 51.19922)  \n",
-       "...                         ...  \n",
-       "17881  POINT (4.49152 51.25159)  \n",
-       "17882  POINT (4.41794 51.17741)  \n",
-       "17883  POINT (4.33956 51.34881)  \n",
-       "17884  POINT (4.36396 51.32855)  \n",
-       "17885  POINT (4.51212 50.91298)  \n",
-       "\n",
-       "[17886 rows x 6 columns]"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "id": "9bafbc1f",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
    "source": [
     "gdf"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<AxesSubplot:>"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": null,
+   "id": "d5e858d2",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
    "source": [
     "gdf.plot()"
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "372ea534",
    "metadata": {},
    "source": [
     "Let's make that last plot a bit more informative:\n",
@@ -846,43 +461,33 @@
     "* Make a plot of the point locations of `gdf` and use the \"no2\" column to color the points.\n",
     "* Make the figure a bit larger by specifying the `figsize=` keyword.\n",
     "\n",
+    "<details><summary>Hints</summary>\n",
+    "\n",
+    "* The figure size can be specified with the `figsize=` keyword of the `plot()` method.\n",
+    "* For using one of the columns of the GeoDataFrame to determine the fill color, use the `column=` keyword.\n",
+    "\n",
+    "</details>\n",
+    "\n",
     "</div>"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<AxesSubplot:>"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1440x720 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": null,
+   "id": "531e70ce",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
    "source": [
     "gdf.plot(column=\"no2\", figsize=(20, 10))"
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "165aaa9c",
    "metadata": {},
    "source": [
     "We can already notice some spatial patterns: higher concentrations (and also more measurement locations) in urban areas. But, the visualization above is not really a good way to visualize many points. There are many alternatives (e.g. heatmaps, hexbins, etc), but in this case study we are going to make a [*choropleth* map](https://en.wikipedia.org/wiki/Choropleth_map): choropleths are maps onto which an attribute, a non-spatial variable, is displayed by coloring a certain area. \n",
@@ -892,6 +497,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "32c124b6",
    "metadata": {},
    "source": [
     "## Combining with municipalities\n",
@@ -901,6 +507,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "d455b2d3",
    "metadata": {},
    "source": [
     "<div class=\"alert alert-success\">\n",
@@ -915,8 +522,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
+   "execution_count": null,
+   "id": "fcc92d99",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
    "outputs": [],
    "source": [
     "muni = geopandas.read_file(\"./data/VRBG/Refgem.shp\")"
@@ -924,184 +536,35 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>OIDN</th>\n",
-       "      <th>UIDN</th>\n",
-       "      <th>VERSDATUM</th>\n",
-       "      <th>TERRID</th>\n",
-       "      <th>DATPUBLBS</th>\n",
-       "      <th>NUMAC</th>\n",
-       "      <th>NISCODE</th>\n",
-       "      <th>NAAM</th>\n",
-       "      <th>LENGTE</th>\n",
-       "      <th>OPPERVL</th>\n",
-       "      <th>geometry</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>278.0</td>\n",
-       "      <td>278.0</td>\n",
-       "      <td>1900-01-01</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1976-01-23</td>\n",
-       "      <td>1975123003</td>\n",
-       "      <td>13001</td>\n",
-       "      <td>Arendonk</td>\n",
-       "      <td>32278.608018</td>\n",
-       "      <td>5.501111e+07</td>\n",
-       "      <td>POLYGON ((198892.290 231761.690, 202087.220 22...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>209.0</td>\n",
-       "      <td>209.0</td>\n",
-       "      <td>1900-01-01</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1982-12-29</td>\n",
-       "      <td>1982001920</td>\n",
-       "      <td>13031</td>\n",
-       "      <td>Oud-Turnhout</td>\n",
-       "      <td>33317.260657</td>\n",
-       "      <td>3.917257e+07</td>\n",
-       "      <td>POLYGON ((191910.672 228249.047, 192000.239 22...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>67.0</td>\n",
-       "      <td>67.0</td>\n",
-       "      <td>1900-01-01</td>\n",
-       "      <td>3</td>\n",
-       "      <td>1831-02-07</td>\n",
-       "      <td>None</td>\n",
-       "      <td>13025</td>\n",
-       "      <td>Mol</td>\n",
-       "      <td>77705.294504</td>\n",
-       "      <td>1.144999e+08</td>\n",
-       "      <td>POLYGON ((203378.850 223128.860, 205356.450 22...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>53.0</td>\n",
-       "      <td>53.0</td>\n",
-       "      <td>1900-01-01</td>\n",
-       "      <td>4</td>\n",
-       "      <td>1976-01-23</td>\n",
-       "      <td>1975123003</td>\n",
-       "      <td>72037</td>\n",
-       "      <td>Hamont-Achel</td>\n",
-       "      <td>31929.641885</td>\n",
-       "      <td>4.373537e+07</td>\n",
-       "      <td>POLYGON ((230005.370 221212.320, 230240.580 22...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>102.0</td>\n",
-       "      <td>102.0</td>\n",
-       "      <td>1900-01-01</td>\n",
-       "      <td>5</td>\n",
-       "      <td>1982-12-29</td>\n",
-       "      <td>1982001920</td>\n",
-       "      <td>13036</td>\n",
-       "      <td>Retie</td>\n",
-       "      <td>35339.133575</td>\n",
-       "      <td>4.865207e+07</td>\n",
-       "      <td>POLYGON ((200827.109 220823.391, 201389.922 22...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "    OIDN   UIDN   VERSDATUM  TERRID   DATPUBLBS       NUMAC NISCODE  \\\n",
-       "0  278.0  278.0  1900-01-01       1  1976-01-23  1975123003   13001   \n",
-       "1  209.0  209.0  1900-01-01       2  1982-12-29  1982001920   13031   \n",
-       "2   67.0   67.0  1900-01-01       3  1831-02-07        None   13025   \n",
-       "3   53.0   53.0  1900-01-01       4  1976-01-23  1975123003   72037   \n",
-       "4  102.0  102.0  1900-01-01       5  1982-12-29  1982001920   13036   \n",
-       "\n",
-       "           NAAM        LENGTE       OPPERVL  \\\n",
-       "0      Arendonk  32278.608018  5.501111e+07   \n",
-       "1  Oud-Turnhout  33317.260657  3.917257e+07   \n",
-       "2           Mol  77705.294504  1.144999e+08   \n",
-       "3  Hamont-Achel  31929.641885  4.373537e+07   \n",
-       "4         Retie  35339.133575  4.865207e+07   \n",
-       "\n",
-       "                                            geometry  \n",
-       "0  POLYGON ((198892.290 231761.690, 202087.220 22...  \n",
-       "1  POLYGON ((191910.672 228249.047, 192000.239 22...  \n",
-       "2  POLYGON ((203378.850 223128.860, 205356.450 22...  \n",
-       "3  POLYGON ((230005.370 221212.320, 230240.580 22...  \n",
-       "4  POLYGON ((200827.109 220823.391, 201389.922 22...  "
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "id": "355ea6bf",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
    "source": [
     "muni.head()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<AxesSubplot:>"
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": null,
+   "id": "442bd40a",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
    "source": [
     "muni.plot()"
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "483b0170",
    "metadata": {},
    "source": [
     "Now we have a dataset with the municipalities, we want to know for each of the measurement locations in which municipality it is located. This is a \"point-in-polygon\" spatial join.\n",
@@ -1127,187 +590,58 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<Geographic 2D CRS: EPSG:4326>\n",
-       "Name: WGS 84\n",
-       "Axis Info [ellipsoidal]:\n",
-       "- Lat[north]: Geodetic latitude (degree)\n",
-       "- Lon[east]: Geodetic longitude (degree)\n",
-       "Area of Use:\n",
-       "- name: World\n",
-       "- bounds: (-180.0, -90.0, 180.0, 90.0)\n",
-       "Datum: World Geodetic System 1984\n",
-       "- Ellipsoid: WGS 84\n",
-       "- Prime Meridian: Greenwich"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "id": "23ec916b",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
    "source": [
     "gdf.crs"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<Projected CRS: EPSG:31370>\n",
-       "Name: Belge 1972 / Belgian Lambert 72\n",
-       "Axis Info [cartesian]:\n",
-       "- X[east]: Easting (metre)\n",
-       "- Y[north]: Northing (metre)\n",
-       "Area of Use:\n",
-       "- name: Belgium - onshore\n",
-       "- bounds: (2.5, 49.5, 6.4, 51.51)\n",
-       "Coordinate Operation:\n",
-       "- name: Belgian Lambert 72\n",
-       "- method: Lambert Conic Conformal (2SP)\n",
-       "Datum: Reseau National Belge 1972\n",
-       "- Ellipsoid: International 1924\n",
-       "- Prime Meridian: Greenwich"
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "id": "7d62b7e9",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
    "source": [
     "muni.crs"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
+   "execution_count": null,
+   "id": "eff83813",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
    "outputs": [],
    "source": [
-    "gdf_lambert = gdf.to_crs(\"EPSG:31370\")  # or .to_crs(muni.crs)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>code</th>\n",
-       "      <th>campaign</th>\n",
-       "      <th>no2</th>\n",
-       "      <th>lat</th>\n",
-       "      <th>lon</th>\n",
-       "      <th>geometry</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1387656</td>\n",
-       "      <td>facade</td>\n",
-       "      <td>23.0</td>\n",
-       "      <td>50.82594</td>\n",
-       "      <td>4.704003</td>\n",
-       "      <td>POINT (173620.694 168443.713)</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>192852</td>\n",
-       "      <td>facade</td>\n",
-       "      <td>47.3</td>\n",
-       "      <td>51.21328</td>\n",
-       "      <td>4.444702</td>\n",
-       "      <td>POINT (155306.762 211483.262)</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>192873</td>\n",
-       "      <td>facade</td>\n",
-       "      <td>21.2</td>\n",
-       "      <td>51.30897</td>\n",
-       "      <td>4.188672</td>\n",
-       "      <td>POINT (137441.996 222141.743)</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>192876</td>\n",
-       "      <td>facade</td>\n",
-       "      <td>26.6</td>\n",
-       "      <td>51.09620</td>\n",
-       "      <td>3.991424</td>\n",
-       "      <td>POINT (123567.283 198522.057)</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>192879</td>\n",
-       "      <td>facade</td>\n",
-       "      <td>25.6</td>\n",
-       "      <td>51.19922</td>\n",
-       "      <td>4.461859</td>\n",
-       "      <td>POINT (156507.609 209920.408)</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "      code campaign   no2       lat       lon                       geometry\n",
-       "0  1387656   facade  23.0  50.82594  4.704003  POINT (173620.694 168443.713)\n",
-       "1   192852   facade  47.3  51.21328  4.444702  POINT (155306.762 211483.262)\n",
-       "2   192873   facade  21.2  51.30897  4.188672  POINT (137441.996 222141.743)\n",
-       "3   192876   facade  26.6  51.09620  3.991424  POINT (123567.283 198522.057)\n",
-       "4   192879   facade  25.6  51.19922  4.461859  POINT (156507.609 209920.408)"
-      ]
-     },
-     "execution_count": 23,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
+    "gdf_lambert = gdf.to_crs(\"EPSG:31370\")  # or .to_crs(muni.crs)\n",
     "gdf_lambert.head()"
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "d8f99188",
    "metadata": {},
    "source": [
-    "The EPSG:31370 or \"Belgian Lambert 72\" (https://epsg.io/31370) is the local, projected CRS most often used in Belgium. "
+    "The EPSG:31370 or \"Belgian Lambert 72\" (https://epsg.io/31370) is the local, projected CRS most often used in Belgium."
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "e3e163e8",
    "metadata": {},
    "source": [
     "<div class=\"alert alert-success\">\n",
@@ -1315,13 +649,11 @@
     "**EXERCISE**:\n",
     "\n",
     "* Add the municipality information to the measurements dataframe. We are mostly interested in the \"NAAM\" column of the municipalities dataframe (the name of the municipality). Call the result `gdf_combined`.\n",
-    "\n",
-    "TODO hints\n",
     "    \n",
     "<details><summary>Hints</summary>\n",
     "\n",
     "* Joining the measurement locations with the municipality information can be done with the `geopandas.sjoin()` function. The first argument is the dataframe to which we want to add information, the second argument the dataframe with the additional information.\n",
-    "* You can select a subset of columns to pass the the `sjoin()` function. \n",
+    "* You can select a subset of columns when passing a GeoDataFrame to the `sjoin()` function.\n",
     "\n",
     "</details>\n",
     "\n",
@@ -1330,8 +662,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
+   "execution_count": null,
+   "id": "69e238f7",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
    "outputs": [],
    "source": [
     "gdf_combined = geopandas.sjoin(gdf_lambert, muni[[\"NAAM\", \"geometry\"]])"
@@ -1339,127 +676,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>code</th>\n",
-       "      <th>campaign</th>\n",
-       "      <th>no2</th>\n",
-       "      <th>lat</th>\n",
-       "      <th>lon</th>\n",
-       "      <th>geometry</th>\n",
-       "      <th>index_right</th>\n",
-       "      <th>NAAM</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1387656</td>\n",
-       "      <td>facade</td>\n",
-       "      <td>23.0</td>\n",
-       "      <td>50.825940</td>\n",
-       "      <td>4.704003</td>\n",
-       "      <td>POINT (173620.694 168443.713)</td>\n",
-       "      <td>258</td>\n",
-       "      <td>Oud-Heverlee</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>79</th>\n",
-       "      <td>194382</td>\n",
-       "      <td>facade</td>\n",
-       "      <td>21.2</td>\n",
-       "      <td>50.832260</td>\n",
-       "      <td>4.726776</td>\n",
-       "      <td>POINT (175221.796 169154.247)</td>\n",
-       "      <td>258</td>\n",
-       "      <td>Oud-Heverlee</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>544</th>\n",
-       "      <td>199500</td>\n",
-       "      <td>facade</td>\n",
-       "      <td>19.3</td>\n",
-       "      <td>50.838730</td>\n",
-       "      <td>4.663526</td>\n",
-       "      <td>POINT (170763.174 169854.340)</td>\n",
-       "      <td>258</td>\n",
-       "      <td>Oud-Heverlee</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1047</th>\n",
-       "      <td>204192</td>\n",
-       "      <td>facade</td>\n",
-       "      <td>13.7</td>\n",
-       "      <td>50.810110</td>\n",
-       "      <td>4.649417</td>\n",
-       "      <td>POINT (169781.438 166666.813)</td>\n",
-       "      <td>258</td>\n",
-       "      <td>Oud-Heverlee</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1207</th>\n",
-       "      <td>205932</td>\n",
-       "      <td>facade</td>\n",
-       "      <td>18.5</td>\n",
-       "      <td>50.830579</td>\n",
-       "      <td>4.731757</td>\n",
-       "      <td>POINT (175573.602 168968.919)</td>\n",
-       "      <td>258</td>\n",
-       "      <td>Oud-Heverlee</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "         code campaign   no2        lat       lon  \\\n",
-       "0     1387656   facade  23.0  50.825940  4.704003   \n",
-       "79     194382   facade  21.2  50.832260  4.726776   \n",
-       "544    199500   facade  19.3  50.838730  4.663526   \n",
-       "1047   204192   facade  13.7  50.810110  4.649417   \n",
-       "1207   205932   facade  18.5  50.830579  4.731757   \n",
-       "\n",
-       "                           geometry  index_right          NAAM  \n",
-       "0     POINT (173620.694 168443.713)          258  Oud-Heverlee  \n",
-       "79    POINT (175221.796 169154.247)          258  Oud-Heverlee  \n",
-       "544   POINT (170763.174 169854.340)          258  Oud-Heverlee  \n",
-       "1047  POINT (169781.438 166666.813)          258  Oud-Heverlee  \n",
-       "1207  POINT (175573.602 168968.919)          258  Oud-Heverlee  "
-      ]
-     },
-     "execution_count": 25,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "id": "e31ff39e",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
    "source": [
     "gdf_combined.head()"
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "ac11e257",
    "metadata": {},
    "source": [
     "<div class=\"alert alert-success\">\n",
@@ -1482,26 +713,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "NAAM\n",
-       "Aalst         25.170196\n",
-       "Aalter        20.734951\n",
-       "Aarschot      19.738710\n",
-       "Aartselaar    28.256604\n",
-       "Affligem      24.745455\n",
-       "Name: no2, dtype: float64"
-      ]
-     },
-     "execution_count": 26,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "id": "6d3b2c02",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
    "source": [
     "muni_mean = gdf_combined.groupby(\"NAAM\")[\"no2\"].mean()\n",
     "muni_mean.head()"
@@ -1509,78 +728,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>NAAM</th>\n",
-       "      <th>no2</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Aalst</td>\n",
-       "      <td>25.170196</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Aalter</td>\n",
-       "      <td>20.734951</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Aarschot</td>\n",
-       "      <td>19.738710</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Aartselaar</td>\n",
-       "      <td>28.256604</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Affligem</td>\n",
-       "      <td>24.745455</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "         NAAM        no2\n",
-       "0       Aalst  25.170196\n",
-       "1      Aalter  20.734951\n",
-       "2    Aarschot  19.738710\n",
-       "3  Aartselaar  28.256604\n",
-       "4    Affligem  24.745455"
-      ]
-     },
-     "execution_count": 27,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "id": "9f4c881e",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
    "source": [
     "muni_mean = muni_mean.reset_index()\n",
     "muni_mean.head()"
@@ -1588,152 +743,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>OIDN</th>\n",
-       "      <th>UIDN</th>\n",
-       "      <th>VERSDATUM</th>\n",
-       "      <th>TERRID</th>\n",
-       "      <th>DATPUBLBS</th>\n",
-       "      <th>NUMAC</th>\n",
-       "      <th>NISCODE</th>\n",
-       "      <th>NAAM</th>\n",
-       "      <th>LENGTE</th>\n",
-       "      <th>OPPERVL</th>\n",
-       "      <th>geometry</th>\n",
-       "      <th>no2</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>278.0</td>\n",
-       "      <td>278.0</td>\n",
-       "      <td>1900-01-01</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1976-01-23</td>\n",
-       "      <td>1975123003</td>\n",
-       "      <td>13001</td>\n",
-       "      <td>Arendonk</td>\n",
-       "      <td>32278.608018</td>\n",
-       "      <td>5.501111e+07</td>\n",
-       "      <td>POLYGON ((198892.290 231761.690, 202087.220 22...</td>\n",
-       "      <td>18.921053</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>209.0</td>\n",
-       "      <td>209.0</td>\n",
-       "      <td>1900-01-01</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1982-12-29</td>\n",
-       "      <td>1982001920</td>\n",
-       "      <td>13031</td>\n",
-       "      <td>Oud-Turnhout</td>\n",
-       "      <td>33317.260657</td>\n",
-       "      <td>3.917257e+07</td>\n",
-       "      <td>POLYGON ((191910.672 228249.047, 192000.239 22...</td>\n",
-       "      <td>17.872973</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>67.0</td>\n",
-       "      <td>67.0</td>\n",
-       "      <td>1900-01-01</td>\n",
-       "      <td>3</td>\n",
-       "      <td>1831-02-07</td>\n",
-       "      <td>None</td>\n",
-       "      <td>13025</td>\n",
-       "      <td>Mol</td>\n",
-       "      <td>77705.294504</td>\n",
-       "      <td>1.144999e+08</td>\n",
-       "      <td>POLYGON ((203378.850 223128.860, 205356.450 22...</td>\n",
-       "      <td>19.103061</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>53.0</td>\n",
-       "      <td>53.0</td>\n",
-       "      <td>1900-01-01</td>\n",
-       "      <td>4</td>\n",
-       "      <td>1976-01-23</td>\n",
-       "      <td>1975123003</td>\n",
-       "      <td>72037</td>\n",
-       "      <td>Hamont-Achel</td>\n",
-       "      <td>31929.641885</td>\n",
-       "      <td>4.373537e+07</td>\n",
-       "      <td>POLYGON ((230005.370 221212.320, 230240.580 22...</td>\n",
-       "      <td>17.855172</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>102.0</td>\n",
-       "      <td>102.0</td>\n",
-       "      <td>1900-01-01</td>\n",
-       "      <td>5</td>\n",
-       "      <td>1982-12-29</td>\n",
-       "      <td>1982001920</td>\n",
-       "      <td>13036</td>\n",
-       "      <td>Retie</td>\n",
-       "      <td>35339.133575</td>\n",
-       "      <td>4.865207e+07</td>\n",
-       "      <td>POLYGON ((200827.109 220823.391, 201389.922 22...</td>\n",
-       "      <td>18.064286</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "    OIDN   UIDN   VERSDATUM  TERRID   DATPUBLBS       NUMAC NISCODE  \\\n",
-       "0  278.0  278.0  1900-01-01       1  1976-01-23  1975123003   13001   \n",
-       "1  209.0  209.0  1900-01-01       2  1982-12-29  1982001920   13031   \n",
-       "2   67.0   67.0  1900-01-01       3  1831-02-07        None   13025   \n",
-       "3   53.0   53.0  1900-01-01       4  1976-01-23  1975123003   72037   \n",
-       "4  102.0  102.0  1900-01-01       5  1982-12-29  1982001920   13036   \n",
-       "\n",
-       "           NAAM        LENGTE       OPPERVL  \\\n",
-       "0      Arendonk  32278.608018  5.501111e+07   \n",
-       "1  Oud-Turnhout  33317.260657  3.917257e+07   \n",
-       "2           Mol  77705.294504  1.144999e+08   \n",
-       "3  Hamont-Achel  31929.641885  4.373537e+07   \n",
-       "4         Retie  35339.133575  4.865207e+07   \n",
-       "\n",
-       "                                            geometry        no2  \n",
-       "0  POLYGON ((198892.290 231761.690, 202087.220 22...  18.921053  \n",
-       "1  POLYGON ((191910.672 228249.047, 192000.239 22...  17.872973  \n",
-       "2  POLYGON ((203378.850 223128.860, 205356.450 22...  19.103061  \n",
-       "3  POLYGON ((230005.370 221212.320, 230240.580 22...  17.855172  \n",
-       "4  POLYGON ((200827.109 220823.391, 201389.922 22...  18.064286  "
-      ]
-     },
-     "execution_count": 28,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "id": "dafd1262",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
    "source": [
     "muni_no2 = pd.merge(muni, muni_mean, on=\"NAAM\")\n",
     "muni_no2.head()"
@@ -1741,6 +758,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "c7799e37",
    "metadata": {},
    "source": [
     "<div class=\"alert alert-success\">\n",
@@ -1763,38 +781,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<AxesSubplot:>"
-      ]
-     },
-     "execution_count": 29,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1152x360 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": null,
+   "id": "2ea01630",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
    "source": [
     "muni_no2.plot(column=\"no2\", figsize=(16, 5), legend=True)"
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "92e1067e",
    "metadata": {},
    "source": [
     "When specifying a numerical column to color the polygons, by default this results in a continuous color scale, as you can see above.\n",
@@ -1802,11 +803,12 @@
     "However, it is very difficult for the human eye to process small differences in color in a continuous scale. \n",
     "Therefore, to create effective choropleths, we typically classify the values into a set of discrete groups.\n",
     "\n",
-    "With GeoPandas' `plot()` method, you can control this with the `scheme` keyword (indicating which classification scheme to use, i.e how to divide the continuous range into a set of discrete classes) and the `k` keyword to indicate how many classes to use. This uses the [mapclassify](https://pysal.org/mapclassify/) package under the hood. "
+    "With GeoPandas' `plot()` method, you can control this with the `scheme` keyword (indicating which classification scheme to use, i.e how to divide the continuous range into a set of discrete classes) and the `k` keyword to indicate how many classes to use. This uses the [mapclassify](https://pysal.org/mapclassify/) package under the hood."
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "cafed763",
    "metadata": {},
    "source": [
     "<div class=\"alert alert-success\">\n",
@@ -1820,22 +822,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1152x360 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": null,
+   "id": "905b2ac4",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
    "source": [
     "ax = muni_no2.plot(column=\"no2\", figsize=(16, 5), legend=True, scheme=\"FisherJenks\", k=7, cmap=\"plasma\")\n",
     "ax.set_axis_off()"
@@ -1843,13 +837,14 @@
   },
   {
    "cell_type": "markdown",
+   "id": "e31aa82f",
    "metadata": {},
    "source": [
     "<div class=\"alert alert-success\">\n",
     "\n",
     "**EXERCISE**:\n",
     "\n",
-    "* What is the percentage of exceedance for each municipality? Repeat the same calculating we did earlier on the original dataset `df`, but now using `gdf_combined` and grouped per municipality.\n",
+    "* What is the percentage of exceedance for each municipality? Repeat the same calculation we did earlier on the original dataset `df`, but now using `gdf_combined` and grouped per municipality.\n",
     "* Show the 10 municipalities with the highest percentage of exceedances.\n",
     "\n",
     "<details><summary>Hints</summary>\n",
@@ -1862,8 +857,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
+   "execution_count": null,
+   "id": "a0910fad",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
    "outputs": [],
    "source": [
     "gdf_combined[\"exceedance\"] = gdf_combined[\"no2\"] > 40"
@@ -1871,8 +871,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
+   "execution_count": null,
+   "id": "e767ca8a",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
    "outputs": [],
    "source": [
     "exceedances_muni = gdf_combined.groupby(\"NAAM\")[\"exceedance\"].mean() * 100"
@@ -1880,551 +885,90 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "NAAM\n",
-       "Antwerpen      17.131474\n",
-       "Vilvoorde       7.894737\n",
-       "Asse            6.493506\n",
-       "Melle           6.122449\n",
-       "Mechelen        6.008584\n",
-       "Berlare         5.882353\n",
-       "Zwijndrecht     5.714286\n",
-       "Dilbeek         5.600000\n",
-       "Gent            5.321782\n",
-       "Kaprijke        5.263158\n",
-       "Name: exceedance, dtype: float64"
-      ]
-     },
-     "execution_count": 33,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "id": "d885a787",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
    "source": [
     "exceedances_muni.sort_values(ascending=False).head(10)"
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "9618ca92",
    "metadata": {},
    "source": [
     "## Combining with Land Use data\n",
     "\n",
     "The CORINE Land Cover (https://land.copernicus.eu/pan-european/corine-land-cover) is a program by the European Environment Agency (EEA) to provide an inventory of land cover in 44 classes of the European Union. The data is provided in both raster as vector format and with a resolution of 100m.\n",
     "\n",
-    "The data for the whole of Europe can be downloaded from the website (latest version: https://land.copernicus.eu/pan-european/corine-land-cover/clc2018?tab=download). This is however a large dataset, so we downloaded the raster file and cropped it to cover Flanders, and this subset is included in the repo as `data/CLC2018_V2020_20u1_flanders.tif` (the code to do this cropping can be see at [INCLUDE LINK]).\n",
+    "The data for the whole of Europe can be downloaded from the website (latest version: https://land.copernicus.eu/pan-european/corine-land-cover/clc2018?tab=download). This is however a large dataset, so we downloaded the raster file and cropped it to cover Flanders, and this subset is included in the repo as `data/CLC2018_V2020_20u1_flanders.tif` (the code to do this cropping can be see at [data/preprocess_data.ipynb#CORINE-Land-Cover](data/preprocess_data.ipynb#CORINE-Land-Cover)).\n",
     "\n",
-    "The air quality is indirectly linked to land use, as the presence of pollution sources will depend on the land use. Therefore, we will determine here the land use for each of the measurement locations based on the CORINE dataset and explore the relationship of the NO2 concentration and land use."
+    "The air quality is indirectly linked to land use, as the presence of pollution sources will depend on the land use. Therefore, in the following section we will determine the land use for each of the measurement locations based on the CORINE dataset and explore the relationship of the NO2 concentration and land use."
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "8da0be0d",
    "metadata": {},
    "source": [
     "<div class=\"alert alert-success\">\n",
     "\n",
     "**EXERCISE**:\n",
     "\n",
-    "* Open the land cover raster file (`data/CLC2018_V2020_20u1_flanders.tif`) with rasterio or xarray, inspect the metadata, and do a quick visualization.\n",
+    "* Open the land cover raster file (`data/CLC2018_V2020_20u1_flanders.tif`) with xarray, inspect the metadata, and do a quick visualization.\n",
+    "\n",
+    "<details><summary>Hints</summary>\n",
+    "\n",
+    "* The tif file represents a single layer (with one band), so you can use `xarray.open_dataarray()` to read it into an xarray.DataArray.\n",
+    "* With `mask_and_scale=False` the `int8` data type will be kept when reading in the data.\n",
+    "\n",
+    "</details>\n",
     "\n",
     "</div>"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "With rasterio:"
-   ]
-  },
   {
    "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'driver': 'GTiff', 'dtype': 'int8', 'nodata': -128.0, 'width': 2530, 'height': 1133, 'count': 1, 'crs': CRS.from_epsg(3035), 'transform': Affine(100.0, 0.0, 3789500.0,\n",
-      "       0.0, -100.0, 3177900.0)}\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import rasterio\n",
-    "import rasterio.plot\n",
-    "\n",
-    "with rasterio.open(\"data/CLC2018_V2020_20u1_flanders.tif\") as src:\n",
-    "    print(src.meta)\n",
-    "    rasterio.plot.show(src)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "With xarray:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "<defs>\n",
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-       "</symbol>\n",
-       "</defs>\n",
-       "</svg>\n",
-       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
-       " *\n",
-       " */\n",
-       "\n",
-       ":root {\n",
-       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
-       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
-       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
-       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
-       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
-       "  --xr-background-color: var(--jp-layout-color0, white);\n",
-       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
-       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
-       "}\n",
-       "\n",
-       "html[theme=dark],\n",
-       "body.vscode-dark {\n",
-       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
-       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
-       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
-       "  --xr-border-color: #1F1F1F;\n",
-       "  --xr-disabled-color: #515151;\n",
-       "  --xr-background-color: #111111;\n",
-       "  --xr-background-color-row-even: #111111;\n",
-       "  --xr-background-color-row-odd: #313131;\n",
-       "}\n",
-       "\n",
-       ".xr-wrap {\n",
-       "  display: block;\n",
-       "  min-width: 300px;\n",
-       "  max-width: 700px;\n",
-       "}\n",
-       "\n",
-       ".xr-text-repr-fallback {\n",
-       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-header {\n",
-       "  padding-top: 6px;\n",
-       "  padding-bottom: 6px;\n",
-       "  margin-bottom: 4px;\n",
-       "  border-bottom: solid 1px var(--xr-border-color);\n",
-       "}\n",
-       "\n",
-       ".xr-header > div,\n",
-       ".xr-header > ul {\n",
-       "  display: inline;\n",
-       "  margin-top: 0;\n",
-       "  margin-bottom: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-obj-type,\n",
-       ".xr-array-name {\n",
-       "  margin-left: 2px;\n",
-       "  margin-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-obj-type {\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-sections {\n",
-       "  padding-left: 0 !important;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
-       "}\n",
-       "\n",
-       ".xr-section-item {\n",
-       "  display: contents;\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input + label {\n",
-       "  color: var(--xr-disabled-color);\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input:enabled + label {\n",
-       "  cursor: pointer;\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input:enabled + label:hover {\n",
-       "  color: var(--xr-font-color0);\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary {\n",
-       "  grid-column: 1;\n",
-       "  color: var(--xr-font-color2);\n",
-       "  font-weight: 500;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary > span {\n",
-       "  display: inline-block;\n",
-       "  padding-left: 0.5em;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:disabled + label {\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in + label:before {\n",
-       "  display: inline-block;\n",
-       "  content: '►';\n",
-       "  font-size: 11px;\n",
-       "  width: 15px;\n",
-       "  text-align: center;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:disabled + label:before {\n",
-       "  color: var(--xr-disabled-color);\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:checked + label:before {\n",
-       "  content: '▼';\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:checked + label > span {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary,\n",
-       ".xr-section-inline-details {\n",
-       "  padding-top: 4px;\n",
-       "  padding-bottom: 4px;\n",
-       "}\n",
-       "\n",
-       ".xr-section-inline-details {\n",
-       "  grid-column: 2 / -1;\n",
-       "}\n",
-       "\n",
-       ".xr-section-details {\n",
-       "  display: none;\n",
-       "  grid-column: 1 / -1;\n",
-       "  margin-bottom: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
-       "  display: contents;\n",
-       "}\n",
-       "\n",
-       ".xr-array-wrap {\n",
-       "  grid-column: 1 / -1;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 20px auto;\n",
-       "}\n",
-       "\n",
-       ".xr-array-wrap > label {\n",
-       "  grid-column: 1;\n",
-       "  vertical-align: top;\n",
-       "}\n",
-       "\n",
-       ".xr-preview {\n",
-       "  color: var(--xr-font-color3);\n",
-       "}\n",
-       "\n",
-       ".xr-array-preview,\n",
-       ".xr-array-data {\n",
-       "  padding: 0 5px !important;\n",
-       "  grid-column: 2;\n",
-       "}\n",
-       "\n",
-       ".xr-array-data,\n",
-       ".xr-array-in:checked ~ .xr-array-preview {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-array-in:checked ~ .xr-array-data,\n",
-       ".xr-array-preview {\n",
-       "  display: inline-block;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list {\n",
-       "  display: inline-block !important;\n",
-       "  list-style: none;\n",
-       "  padding: 0 !important;\n",
-       "  margin: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list li {\n",
-       "  display: inline-block;\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list:before {\n",
-       "  content: '(';\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list:after {\n",
-       "  content: ')';\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list li:not(:last-child):after {\n",
-       "  content: ',';\n",
-       "  padding-right: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-has-index {\n",
-       "  font-weight: bold;\n",
-       "}\n",
-       "\n",
-       ".xr-var-list,\n",
-       ".xr-var-item {\n",
-       "  display: contents;\n",
-       "}\n",
-       "\n",
-       ".xr-var-item > div,\n",
-       ".xr-var-item label,\n",
-       ".xr-var-item > .xr-var-name span {\n",
-       "  background-color: var(--xr-background-color-row-even);\n",
-       "  margin-bottom: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-var-item > .xr-var-name:hover span {\n",
-       "  padding-right: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-var-list > li:nth-child(odd) > div,\n",
-       ".xr-var-list > li:nth-child(odd) > label,\n",
-       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
-       "  background-color: var(--xr-background-color-row-odd);\n",
-       "}\n",
-       "\n",
-       ".xr-var-name {\n",
-       "  grid-column: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-var-dims {\n",
-       "  grid-column: 2;\n",
-       "}\n",
-       "\n",
-       ".xr-var-dtype {\n",
-       "  grid-column: 3;\n",
-       "  text-align: right;\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-var-preview {\n",
-       "  grid-column: 4;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name,\n",
-       ".xr-var-dims,\n",
-       ".xr-var-dtype,\n",
-       ".xr-preview,\n",
-       ".xr-attrs dt {\n",
-       "  white-space: nowrap;\n",
-       "  overflow: hidden;\n",
-       "  text-overflow: ellipsis;\n",
-       "  padding-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name:hover,\n",
-       ".xr-var-dims:hover,\n",
-       ".xr-var-dtype:hover,\n",
-       ".xr-attrs dt:hover {\n",
-       "  overflow: visible;\n",
-       "  width: auto;\n",
-       "  z-index: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-var-attrs,\n",
-       ".xr-var-data {\n",
-       "  display: none;\n",
-       "  background-color: var(--xr-background-color) !important;\n",
-       "  padding-bottom: 5px !important;\n",
-       "}\n",
-       "\n",
-       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
-       ".xr-var-data-in:checked ~ .xr-var-data {\n",
-       "  display: block;\n",
-       "}\n",
-       "\n",
-       ".xr-var-data > table {\n",
-       "  float: right;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name span,\n",
-       ".xr-var-data,\n",
-       ".xr-attrs {\n",
-       "  padding-left: 25px !important;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs,\n",
-       ".xr-var-attrs,\n",
-       ".xr-var-data {\n",
-       "  grid-column: 1 / -1;\n",
-       "}\n",
-       "\n",
-       "dl.xr-attrs {\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 125px auto;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt, dd {\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  float: left;\n",
-       "  padding-right: 10px;\n",
-       "  width: auto;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt {\n",
-       "  font-weight: normal;\n",
-       "  grid-column: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt:hover span {\n",
-       "  display: inline-block;\n",
-       "  background: var(--xr-background-color);\n",
-       "  padding-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dd {\n",
-       "  grid-column: 2;\n",
-       "  white-space: pre-wrap;\n",
-       "  word-break: break-all;\n",
-       "}\n",
-       "\n",
-       ".xr-icon-database,\n",
-       ".xr-icon-file-text2 {\n",
-       "  display: inline-block;\n",
-       "  vertical-align: middle;\n",
-       "  width: 1em;\n",
-       "  height: 1.5em !important;\n",
-       "  stroke-width: 0;\n",
-       "  stroke: currentColor;\n",
-       "  fill: currentColor;\n",
-       "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray (band: 1, y: 1133, x: 2530)&gt;\n",
-       "[2866490 values with dtype=int8]\n",
-       "Coordinates:\n",
-       "  * band     (band) int64 1\n",
-       "  * y        (y) float64 3.178e+06 3.178e+06 3.178e+06 ... 3.065e+06 3.065e+06\n",
-       "  * x        (x) float64 3.79e+06 3.79e+06 3.79e+06 ... 4.042e+06 4.042e+06\n",
-       "Attributes:\n",
-       "    transform:      (100.0, 0.0, 3789500.0, 0.0, -100.0, 3177900.0)\n",
-       "    crs:            +init=epsg:3035\n",
-       "    res:            (100.0, 100.0)\n",
-       "    is_tiled:       0\n",
-       "    nodatavals:     (-128.0,)\n",
-       "    scales:         (1.0,)\n",
-       "    offsets:        (0.0,)\n",
-       "    AREA_OR_POINT:  Area</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>band</span>: 1</li><li><span class='xr-has-index'>y</span>: 1133</li><li><span class='xr-has-index'>x</span>: 2530</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-cb28d75a-7a24-4bb1-bd5b-4b58789ef72a' class='xr-array-in' type='checkbox' checked><label for='section-cb28d75a-7a24-4bb1-bd5b-4b58789ef72a' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>...</span></div><div class='xr-array-data'><pre>[2866490 values with dtype=int8]</pre></div></div></li><li class='xr-section-item'><input id='section-340d3bee-1cfe-4a4a-99ff-d9be0c420239' class='xr-section-summary-in' type='checkbox'  checked><label for='section-340d3bee-1cfe-4a4a-99ff-d9be0c420239' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>band</span></div><div class='xr-var-dims'>(band)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1</div><input id='attrs-2c217651-fb04-4dd5-8a3b-37acca525c40' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2c217651-fb04-4dd5-8a3b-37acca525c40' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-67fb0bb5-4782-4b4e-9c0b-7a196db47c9c' class='xr-var-data-in' type='checkbox'><label for='data-67fb0bb5-4782-4b4e-9c0b-7a196db47c9c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([1])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>3.178e+06 3.178e+06 ... 3.065e+06</div><input id='attrs-755bb6db-607f-4a8d-a344-31b17951eda2' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-755bb6db-607f-4a8d-a344-31b17951eda2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0f7cc257-8508-4169-9781-5c9247878f1d' class='xr-var-data-in' type='checkbox'><label for='data-0f7cc257-8508-4169-9781-5c9247878f1d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([3177850., 3177750., 3177650., ..., 3064850., 3064750., 3064650.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>3.79e+06 3.79e+06 ... 4.042e+06</div><input id='attrs-4f283af1-5ea3-4965-b8d1-f45854965395' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4f283af1-5ea3-4965-b8d1-f45854965395' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-78913e80-fcc0-403a-93f9-df11ff9567e9' class='xr-var-data-in' type='checkbox'><label for='data-78913e80-fcc0-403a-93f9-df11ff9567e9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([3789550., 3789650., 3789750., ..., 4042250., 4042350., 4042450.])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-0dec658b-ac35-451a-97b0-ed30b5a97cde' class='xr-section-summary-in' type='checkbox'  checked><label for='section-0dec658b-ac35-451a-97b0-ed30b5a97cde' class='xr-section-summary' >Attributes: <span>(8)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>transform :</span></dt><dd>(100.0, 0.0, 3789500.0, 0.0, -100.0, 3177900.0)</dd><dt><span>crs :</span></dt><dd>+init=epsg:3035</dd><dt><span>res :</span></dt><dd>(100.0, 100.0)</dd><dt><span>is_tiled :</span></dt><dd>0</dd><dt><span>nodatavals :</span></dt><dd>(-128.0,)</dd><dt><span>scales :</span></dt><dd>(1.0,)</dd><dt><span>offsets :</span></dt><dd>(0.0,)</dd><dt><span>AREA_OR_POINT :</span></dt><dd>Area</dd></dl></div></li></ul></div></div>"
-      ],
-      "text/plain": [
-       "<xarray.DataArray (band: 1, y: 1133, x: 2530)>\n",
-       "[2866490 values with dtype=int8]\n",
-       "Coordinates:\n",
-       "  * band     (band) int64 1\n",
-       "  * y        (y) float64 3.178e+06 3.178e+06 3.178e+06 ... 3.065e+06 3.065e+06\n",
-       "  * x        (x) float64 3.79e+06 3.79e+06 3.79e+06 ... 4.042e+06 4.042e+06\n",
-       "Attributes:\n",
-       "    transform:      (100.0, 0.0, 3789500.0, 0.0, -100.0, 3177900.0)\n",
-       "    crs:            +init=epsg:3035\n",
-       "    res:            (100.0, 100.0)\n",
-       "    is_tiled:       0\n",
-       "    nodatavals:     (-128.0,)\n",
-       "    scales:         (1.0,)\n",
-       "    offsets:        (0.0,)\n",
-       "    AREA_OR_POINT:  Area"
-      ]
-     },
-     "execution_count": 35,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "id": "4dc2391f",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
    "source": [
     "import xarray\n",
     "\n",
-    "raster = xarray.open_rasterio(\"data/CLC2018_V2020_20u1_flanders.tif\")\n",
+    "raster = xarray.open_dataarray(\"data/CLC2018_V2020_20u1_flanders.tif\", engine=\"rasterio\", mask_and_scale=False)\n",
     "raster"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.collections.QuadMesh at 0x7fdc5a81fe80>"
-      ]
-     },
-     "execution_count": 36,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": null,
+   "id": "345df0dd",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
    "source": [
     "raster.plot(vmin=1, vmax=44)"
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "cd1a353e",
    "metadata": {},
    "source": [
-    "The goal is now to to query from the raster file the value of the land cover class for each of the measurement locations. This can be done with the `rasterstats` package and with the point locations of our GeoDataFrame. But first, we need to ensure that both our datasets are using the same CRS. In this case, it's easiest to reproject the point locations to the CRS of the raster file.\n",
+    "The goal is now to query from the raster file the value of the land cover class for each of the measurement locations. This can be done with the `rasterstats` package and with the point locations of our GeoDataFrame. But first, we need to ensure that both our datasets are using the same CRS. In this case, it's easiest to reproject the point locations to the CRS of the raster file.\n",
     "\n",
     "<div class=\"alert alert-success\">\n",
     "\n",
@@ -2444,102 +988,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>code</th>\n",
-       "      <th>campaign</th>\n",
-       "      <th>no2</th>\n",
-       "      <th>lat</th>\n",
-       "      <th>lon</th>\n",
-       "      <th>geometry</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1387656</td>\n",
-       "      <td>facade</td>\n",
-       "      <td>23.0</td>\n",
-       "      <td>50.82594</td>\n",
-       "      <td>4.704003</td>\n",
-       "      <td>POINT (3948208.320 3092876.582)</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>192852</td>\n",
-       "      <td>facade</td>\n",
-       "      <td>47.3</td>\n",
-       "      <td>51.21328</td>\n",
-       "      <td>4.444702</td>\n",
-       "      <td>POINT (3933249.515 3137216.482)</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>192873</td>\n",
-       "      <td>facade</td>\n",
-       "      <td>21.2</td>\n",
-       "      <td>51.30897</td>\n",
-       "      <td>4.188672</td>\n",
-       "      <td>POINT (3916264.675 3149224.100)</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>192876</td>\n",
-       "      <td>facade</td>\n",
-       "      <td>26.6</td>\n",
-       "      <td>51.09620</td>\n",
-       "      <td>3.991424</td>\n",
-       "      <td>POINT (3900625.411 3126731.909)</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>192879</td>\n",
-       "      <td>facade</td>\n",
-       "      <td>25.6</td>\n",
-       "      <td>51.19922</td>\n",
-       "      <td>4.461859</td>\n",
-       "      <td>POINT (3934326.401 3135565.137)</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "      code campaign   no2       lat       lon                         geometry\n",
-       "0  1387656   facade  23.0  50.82594  4.704003  POINT (3948208.320 3092876.582)\n",
-       "1   192852   facade  47.3  51.21328  4.444702  POINT (3933249.515 3137216.482)\n",
-       "2   192873   facade  21.2  51.30897  4.188672  POINT (3916264.675 3149224.100)\n",
-       "3   192876   facade  26.6  51.09620  3.991424  POINT (3900625.411 3126731.909)\n",
-       "4   192879   facade  25.6  51.19922  4.461859  POINT (3934326.401 3135565.137)"
-      ]
-     },
-     "execution_count": 37,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "id": "1343edd8",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "raster.rio.crs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4e851777",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
    "source": [
     "gdf_raster = gdf.to_crs(\"EPSG:3035\")\n",
     "gdf_raster.head()"
@@ -2547,6 +1017,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "7580a7d8",
    "metadata": {},
    "source": [
     "<div class=\"alert alert-success\">\n",
@@ -2573,8 +1044,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {},
+   "execution_count": null,
+   "id": "e946c529",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
    "outputs": [],
    "source": [
     "import rasterstats"
@@ -2582,8 +1058,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
-   "metadata": {},
+   "execution_count": null,
+   "id": "06cfb1ef",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
    "outputs": [],
    "source": [
     "gdf['land_use'] = rasterstats.point_query(gdf_raster.geometry, \"data/CLC2018_V2020_20u1_flanders.tif\", interpolate='nearest')"
@@ -2591,54 +1072,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "2     14629\n",
-       "20      937\n",
-       "1       604\n",
-       "12      504\n",
-       "21      348\n",
-       "3       277\n",
-       "18      234\n",
-       "11       69\n",
-       "25       61\n",
-       "24       56\n",
-       "23       46\n",
-       "4        44\n",
-       "16       18\n",
-       "5        15\n",
-       "10       15\n",
-       "27        7\n",
-       "40        5\n",
-       "29        4\n",
-       "43        4\n",
-       "41        3\n",
-       "26        2\n",
-       "37        1\n",
-       "9         1\n",
-       "35        1\n",
-       "30        1\n",
-       "Name: land_use, dtype: int64"
-      ]
-     },
-     "execution_count": 40,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "id": "4c32300e",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
    "source": [
     "gdf['land_use'].value_counts()"
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "de438086",
    "metadata": {},
    "source": [
-    "As you can see, we have obtained a large variety of land cover classes. To make this more practical to work with, we a) want to convert the numbers into a class name, and b) reduce the number of classes.\n",
+    "As you can see, we have obtained a large variety of land cover classes. To make this more practical to work with, we \n",
+    "\n",
+    " 1. want to convert the numbers into a class name, and \n",
+    " 2. reduce the number of classes.\n",
     "\n",
     "The full hierarchy (with 3 levels) of the 44 classes can be seen at https://wiki.openstreetmap.org/wiki/Corine_Land_Cover. For keeping the exercise a bit practical here, we prepared a simplified set of classes and provided a csv file with this information.  \n",
     "\n",
@@ -2663,463 +1117,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
-   "metadata": {},
+   "execution_count": null,
+   "id": "e7dc0bec",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
    "outputs": [],
    "source": [
-    "legend = pd.read_csv(\"data/CLC2018_V2018_legend_grouped.csv\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>code</th>\n",
-       "      <th>value</th>\n",
-       "      <th>class</th>\n",
-       "      <th>group</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>111</td>\n",
-       "      <td>1</td>\n",
-       "      <td>Continuous urban fabric</td>\n",
-       "      <td>Continuous urban fabric</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>112</td>\n",
-       "      <td>2</td>\n",
-       "      <td>Discontinuous urban fabric</td>\n",
-       "      <td>Discontinuous urban fabric</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>121</td>\n",
-       "      <td>3</td>\n",
-       "      <td>Industrial or commercial units</td>\n",
-       "      <td>Industrial or commercial units</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>122</td>\n",
-       "      <td>4</td>\n",
-       "      <td>Road and rail networks and associated land</td>\n",
-       "      <td>Transport units</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>123</td>\n",
-       "      <td>5</td>\n",
-       "      <td>Port areas</td>\n",
-       "      <td>Transport units</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>124</td>\n",
-       "      <td>6</td>\n",
-       "      <td>Airports</td>\n",
-       "      <td>Transport units</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>131</td>\n",
-       "      <td>7</td>\n",
-       "      <td>Mineral extraction sites</td>\n",
-       "      <td>Mine, dump and construction sites</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>132</td>\n",
-       "      <td>8</td>\n",
-       "      <td>Dump sites</td>\n",
-       "      <td>Mine, dump and construction sites</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>133</td>\n",
-       "      <td>9</td>\n",
-       "      <td>Construction sites</td>\n",
-       "      <td>Mine, dump and construction sites</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>141</td>\n",
-       "      <td>10</td>\n",
-       "      <td>Green urban areas</td>\n",
-       "      <td>Green urban areas</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>142</td>\n",
-       "      <td>11</td>\n",
-       "      <td>Sport and leisure facilities</td>\n",
-       "      <td>Green urban areas</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11</th>\n",
-       "      <td>211</td>\n",
-       "      <td>12</td>\n",
-       "      <td>Non-irrigated arable land</td>\n",
-       "      <td>Agricultural areas</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12</th>\n",
-       "      <td>212</td>\n",
-       "      <td>13</td>\n",
-       "      <td>Permanently irrigated land</td>\n",
-       "      <td>Agricultural areas</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>13</th>\n",
-       "      <td>213</td>\n",
-       "      <td>14</td>\n",
-       "      <td>Rice fields</td>\n",
-       "      <td>Agricultural areas</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>14</th>\n",
-       "      <td>221</td>\n",
-       "      <td>15</td>\n",
-       "      <td>Vineyards</td>\n",
-       "      <td>Agricultural areas</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>15</th>\n",
-       "      <td>222</td>\n",
-       "      <td>16</td>\n",
-       "      <td>Fruit trees and berry plantations</td>\n",
-       "      <td>Agricultural areas</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>16</th>\n",
-       "      <td>223</td>\n",
-       "      <td>17</td>\n",
-       "      <td>Olive groves</td>\n",
-       "      <td>Agricultural areas</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>17</th>\n",
-       "      <td>231</td>\n",
-       "      <td>18</td>\n",
-       "      <td>Pastures</td>\n",
-       "      <td>Agricultural areas</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>18</th>\n",
-       "      <td>241</td>\n",
-       "      <td>19</td>\n",
-       "      <td>Annual crops associated with permanent crops</td>\n",
-       "      <td>Agricultural areas</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19</th>\n",
-       "      <td>242</td>\n",
-       "      <td>20</td>\n",
-       "      <td>Complex cultivation patterns</td>\n",
-       "      <td>Agricultural areas</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>20</th>\n",
-       "      <td>243</td>\n",
-       "      <td>21</td>\n",
-       "      <td>Land principally occupied by agriculture with ...</td>\n",
-       "      <td>Agricultural areas</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>21</th>\n",
-       "      <td>244</td>\n",
-       "      <td>22</td>\n",
-       "      <td>Agro-forestry areas</td>\n",
-       "      <td>Agricultural areas</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>22</th>\n",
-       "      <td>311</td>\n",
-       "      <td>23</td>\n",
-       "      <td>Broad-leaved forest</td>\n",
-       "      <td>Forest and seminatural areas</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>23</th>\n",
-       "      <td>312</td>\n",
-       "      <td>24</td>\n",
-       "      <td>Coniferous forest</td>\n",
-       "      <td>Forest and seminatural areas</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>24</th>\n",
-       "      <td>313</td>\n",
-       "      <td>25</td>\n",
-       "      <td>Mixed forest</td>\n",
-       "      <td>Forest and seminatural areas</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>25</th>\n",
-       "      <td>321</td>\n",
-       "      <td>26</td>\n",
-       "      <td>Natural grasslands</td>\n",
-       "      <td>Forest and seminatural areas</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>26</th>\n",
-       "      <td>322</td>\n",
-       "      <td>27</td>\n",
-       "      <td>Moors and heathland</td>\n",
-       "      <td>Forest and seminatural areas</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>27</th>\n",
-       "      <td>323</td>\n",
-       "      <td>28</td>\n",
-       "      <td>Sclerophyllous vegetation</td>\n",
-       "      <td>Forest and seminatural areas</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>28</th>\n",
-       "      <td>324</td>\n",
-       "      <td>29</td>\n",
-       "      <td>Transitional woodland-shrub</td>\n",
-       "      <td>Forest and seminatural areas</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>29</th>\n",
-       "      <td>331</td>\n",
-       "      <td>30</td>\n",
-       "      <td>Beaches dunes sands</td>\n",
-       "      <td>Forest and seminatural areas</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>30</th>\n",
-       "      <td>332</td>\n",
-       "      <td>31</td>\n",
-       "      <td>Bare rocks</td>\n",
-       "      <td>Forest and seminatural areas</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>31</th>\n",
-       "      <td>333</td>\n",
-       "      <td>32</td>\n",
-       "      <td>Sparsely vegetated areas</td>\n",
-       "      <td>Forest and seminatural areas</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>32</th>\n",
-       "      <td>334</td>\n",
-       "      <td>33</td>\n",
-       "      <td>Burnt areas</td>\n",
-       "      <td>Forest and seminatural areas</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>33</th>\n",
-       "      <td>335</td>\n",
-       "      <td>34</td>\n",
-       "      <td>Glaciers and perpetual snow</td>\n",
-       "      <td>Forest and seminatural areas</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>34</th>\n",
-       "      <td>411</td>\n",
-       "      <td>35</td>\n",
-       "      <td>Inland marshes</td>\n",
-       "      <td>Wetlands</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>35</th>\n",
-       "      <td>412</td>\n",
-       "      <td>36</td>\n",
-       "      <td>Peat bogs</td>\n",
-       "      <td>Wetlands</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>36</th>\n",
-       "      <td>421</td>\n",
-       "      <td>37</td>\n",
-       "      <td>Salt marshes</td>\n",
-       "      <td>Wetlands</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>37</th>\n",
-       "      <td>422</td>\n",
-       "      <td>38</td>\n",
-       "      <td>Salines</td>\n",
-       "      <td>Wetlands</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>38</th>\n",
-       "      <td>423</td>\n",
-       "      <td>39</td>\n",
-       "      <td>Intertidal flats</td>\n",
-       "      <td>Wetlands</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>39</th>\n",
-       "      <td>511</td>\n",
-       "      <td>40</td>\n",
-       "      <td>Water courses</td>\n",
-       "      <td>Water bodies</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>40</th>\n",
-       "      <td>512</td>\n",
-       "      <td>41</td>\n",
-       "      <td>Water bodies</td>\n",
-       "      <td>Water bodies</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>41</th>\n",
-       "      <td>521</td>\n",
-       "      <td>42</td>\n",
-       "      <td>Coastal lagoons</td>\n",
-       "      <td>Water bodies</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>42</th>\n",
-       "      <td>522</td>\n",
-       "      <td>43</td>\n",
-       "      <td>Estuaries</td>\n",
-       "      <td>Water bodies</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>43</th>\n",
-       "      <td>523</td>\n",
-       "      <td>44</td>\n",
-       "      <td>Sea and ocean</td>\n",
-       "      <td>Water bodies</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "    code  value                                              class  \\\n",
-       "0    111      1                            Continuous urban fabric   \n",
-       "1    112      2                         Discontinuous urban fabric   \n",
-       "2    121      3                     Industrial or commercial units   \n",
-       "3    122      4         Road and rail networks and associated land   \n",
-       "4    123      5                                         Port areas   \n",
-       "5    124      6                                           Airports   \n",
-       "6    131      7                           Mineral extraction sites   \n",
-       "7    132      8                                         Dump sites   \n",
-       "8    133      9                                 Construction sites   \n",
-       "9    141     10                                  Green urban areas   \n",
-       "10   142     11                       Sport and leisure facilities   \n",
-       "11   211     12                          Non-irrigated arable land   \n",
-       "12   212     13                         Permanently irrigated land   \n",
-       "13   213     14                                        Rice fields   \n",
-       "14   221     15                                          Vineyards   \n",
-       "15   222     16                  Fruit trees and berry plantations   \n",
-       "16   223     17                                       Olive groves   \n",
-       "17   231     18                                           Pastures   \n",
-       "18   241     19       Annual crops associated with permanent crops   \n",
-       "19   242     20                       Complex cultivation patterns   \n",
-       "20   243     21  Land principally occupied by agriculture with ...   \n",
-       "21   244     22                                Agro-forestry areas   \n",
-       "22   311     23                                Broad-leaved forest   \n",
-       "23   312     24                                  Coniferous forest   \n",
-       "24   313     25                                       Mixed forest   \n",
-       "25   321     26                                 Natural grasslands   \n",
-       "26   322     27                                Moors and heathland   \n",
-       "27   323     28                          Sclerophyllous vegetation   \n",
-       "28   324     29                        Transitional woodland-shrub   \n",
-       "29   331     30                                Beaches dunes sands   \n",
-       "30   332     31                                         Bare rocks   \n",
-       "31   333     32                           Sparsely vegetated areas   \n",
-       "32   334     33                                        Burnt areas   \n",
-       "33   335     34                        Glaciers and perpetual snow   \n",
-       "34   411     35                                     Inland marshes   \n",
-       "35   412     36                                          Peat bogs   \n",
-       "36   421     37                                       Salt marshes   \n",
-       "37   422     38                                            Salines   \n",
-       "38   423     39                                   Intertidal flats   \n",
-       "39   511     40                                      Water courses   \n",
-       "40   512     41                                       Water bodies   \n",
-       "41   521     42                                    Coastal lagoons   \n",
-       "42   522     43                                          Estuaries   \n",
-       "43   523     44                                      Sea and ocean   \n",
-       "\n",
-       "                                group  \n",
-       "0             Continuous urban fabric  \n",
-       "1          Discontinuous urban fabric  \n",
-       "2      Industrial or commercial units  \n",
-       "3                     Transport units  \n",
-       "4                     Transport units  \n",
-       "5                     Transport units  \n",
-       "6   Mine, dump and construction sites  \n",
-       "7   Mine, dump and construction sites  \n",
-       "8   Mine, dump and construction sites  \n",
-       "9                   Green urban areas  \n",
-       "10                  Green urban areas  \n",
-       "11                Agricultural areas   \n",
-       "12                Agricultural areas   \n",
-       "13                Agricultural areas   \n",
-       "14                Agricultural areas   \n",
-       "15                Agricultural areas   \n",
-       "16                Agricultural areas   \n",
-       "17                Agricultural areas   \n",
-       "18                Agricultural areas   \n",
-       "19                Agricultural areas   \n",
-       "20                Agricultural areas   \n",
-       "21                Agricultural areas   \n",
-       "22       Forest and seminatural areas  \n",
-       "23       Forest and seminatural areas  \n",
-       "24       Forest and seminatural areas  \n",
-       "25       Forest and seminatural areas  \n",
-       "26       Forest and seminatural areas  \n",
-       "27       Forest and seminatural areas  \n",
-       "28       Forest and seminatural areas  \n",
-       "29       Forest and seminatural areas  \n",
-       "30       Forest and seminatural areas  \n",
-       "31       Forest and seminatural areas  \n",
-       "32       Forest and seminatural areas  \n",
-       "33       Forest and seminatural areas  \n",
-       "34                           Wetlands  \n",
-       "35                           Wetlands  \n",
-       "36                           Wetlands  \n",
-       "37                           Wetlands  \n",
-       "38                           Wetlands  \n",
-       "39                       Water bodies  \n",
-       "40                       Water bodies  \n",
-       "41                       Water bodies  \n",
-       "42                       Water bodies  \n",
-       "43                       Water bodies  "
-      ]
-     },
-     "execution_count": 42,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
+    "legend = pd.read_csv(\"data/CLC2018_V2018_legend_grouped.csv\")\n",
     "legend"
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "072cac6b",
    "metadata": {},
    "source": [
     "Convert the \"land_use\" integer values to a column with land use class names:"
@@ -3127,7 +1140,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": null,
+   "id": "2e5ea7ef",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3136,7 +1150,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": null,
+   "id": "c278f2af",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3145,41 +1160,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": null,
+   "id": "f2e24409",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Discontinuous urban fabric           14629\n",
-       "Agricultural areas                    2041\n",
-       "Continuous urban fabric                604\n",
-       "Industrial or commercial units         277\n",
-       "Forest and seminatural areas           177\n",
-       "Green urban areas                       84\n",
-       "Transport units                         59\n",
-       "Water bodies                            12\n",
-       "Wetlands                                 2\n",
-       "Mine, dump and construction sites        1\n",
-       "Name: land_use_class, dtype: int64"
-      ]
-     },
-     "execution_count": 45,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "gdf['land_use_class'].value_counts()"
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "e04d9682",
    "metadata": {},
    "source": [
     "Now we have the land use data, let's explore the air quality in relation to this land use.\n",
     "\n",
-    "We can see in the `value_counts` above that we have a few classes with only very few observations, though (<50). Calculating statistics for those small groups is not very reliable, so lets leave them out for this exercises (note, this is not necessarily the best strategy in real life! Amongst others, we could also inspect those points and re-assign to a dominant land use class in the surrounding region).\n",
+    "We can see in the `value_counts()` result  above that we have a few classes with only very few observations, though (<50). Calculating statistics for those small groups is not very reliable, so lets leave them out for this exercises (note, this is not necessarily the best strategy in real life! Amongst others, we could also inspect those points and re-assign to a dominant land use class in the surrounding region).\n",
     "\n",
     "<div class=\"alert alert-success\">\n",
     "\n",
@@ -3190,21 +1186,19 @@
     "* Using those frequent classes, filter the `gdf` to only include observations from those classes, and call this `subset`.\n",
     "* Based on this subset, calculate the average NO2 concentration per land use class.\n",
     "\n",
-    "TODO hints\n",
-    "\n",
-    "<details><summary>Hints</summary>\n",
-    "\n",
-    "* \n",
-    "\n",
-    "</details>\n",
     "\n",
     "</div>"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
-   "metadata": {},
+   "execution_count": null,
+   "id": "0b70af82",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
    "outputs": [],
    "source": [
     "counts = gdf['land_use_class'].value_counts()"
@@ -3212,17 +1206,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
-   "metadata": {},
+   "execution_count": null,
+   "id": "6d7a6705",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
    "outputs": [],
    "source": [
-    "frequent_categories = counts[counts > 50].index"
+    "frequent_categories = counts.index[counts > 50]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
-   "metadata": {},
+   "execution_count": null,
+   "id": "37324027",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
    "outputs": [],
    "source": [
     "subset = gdf[gdf[\"land_use_class\"].isin(frequent_categories)]"
@@ -3230,34 +1234,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "land_use_class\n",
-       "Agricultural areas                19.145811\n",
-       "Continuous urban fabric           34.367715\n",
-       "Discontinuous urban fabric        22.779602\n",
-       "Forest and seminatural areas      17.427684\n",
-       "Green urban areas                 22.376190\n",
-       "Industrial or commercial units    25.736462\n",
-       "Transport units                   32.996610\n",
-       "Name: no2, dtype: float64"
-      ]
-     },
-     "execution_count": 49,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "id": "c8804025",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
    "source": [
     "subset.groupby(\"land_use_class\")['no2'].mean()"
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "af945b86",
    "metadata": {},
    "source": [
     "<div class=\"alert alert-success\">\n",
@@ -3279,8 +1270,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
-   "metadata": {},
+   "execution_count": null,
+   "id": "c76a341a",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
    "outputs": [],
    "source": [
     "import seaborn"
@@ -3288,38 +1284,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<AxesSubplot:xlabel='no2', ylabel='land_use_class'>"
-      ]
-     },
-     "execution_count": 51,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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gbdu2bdvayra0tLSV76pOLu23r7906dKc27OdDZ//zYnMAb4z/0JyEDoYOA74mqS9022HkIwWzAJmAEdGRDXwD+DYiDi2k/ZmAt+KiDcBLwIn9xDbpwEi4iDgQ0BNmjR0ZQ4wJSIOTOtc10P77R0BXJiOogC8Abg2ImYDLwOfStdfHRGHR8SBJEnCiVltjI6II0j2zcXtO4iIk4DmiJgTETeSJExviYhDgB8D52cVnw2cALwVWCRpny7iBEBSJfBe4M0RcTBJcteBpDMk1UmqW79+fY87xcwGzooVK2hpaQGSBGD58uU7bcscuAEiguXLl3dbJ5f2269raGjIuT3b2XAbQejOUcCPImI78Kyk3wOHkxwsV0ZEI4Ck+4AykoNddx7PjE6QJCVlOfR/FUBEPCzpCaC7uRGPATMkXQXcBuT7W70yIh7PWn4qIu5KX/8QmE8yUnFs+k19PPBa4AHg/9JyP09/5vL+AKYCN6aJ1y5Adv+/jIhmoFnS7SSJwYudxJlxHHBdRGwBiIjnO+swIq4FrgWoqKiIzsoMVZknEQ6X68jnz5/PKw1/HewwCmav8a2MLZs5bD6f/tDT6Mrxxx/PsmXLaGlpobS0lLlz5+607ZZbbmlLEiQxd+5cIqLLOrm0377+lClTWLduXU7t2c6G2wjCA8BhXWxTN/W2Zr3eTm6JU1d1trFjv2aPEHTVf3b5tjoR8QLJaMcdJKMP3+2ubnoKZZesbZvblW1/8Ix0BOO/gPenoxTfaRdz5j3muk+uIhmROIhkbkJ2Wx367yLODHVSx8yKSFVVFcmfJigpKaGqqmqnbaNH7/izUlpaSlVVVbd1cmm//bqFCxfm3J7tbLglCL8Dxkj698wKSYdLejtwJ3CKpFGS9gSOBlb20N5GYLc8Y2hgR5Ly/qz1dwKnpjEdAEwHHknLz5FUImkayTdr0vkLJRHxM2AhcGgPfb0HKO0mrumS3pq+/hDJCEnmAN4kaUK7eHtjD5L5GADt/xe+R9JYSZOAY4CevmouBz6euYpE0mv7GJuZDbDJkydTWVmJJCorK5k0adJO2+bNm9e2PG/ePCZNmtRtnVzab79u5syZObdnOxtWpxjSyXXvA74h6QLgFZKD6LkkB+i3An8j+WZ6fkQ8I+mN3TR5LVAr6eku5iF05nLgJ5JOI0lYMv4LuEbSGpJv/qdHxFZJd5EMxa8B7gfuSctPAa6TlEnivtBJX98BfilpJfBbuv42DvAQUCXpv4F64NvpJMXvpH030PNBuyeLgZ9KWgfcDeyXtW0lyamS6cDSiPhHd5efRsSvJM0B6iS9CiwjuSLFzIpIVVUVDQ0NnX5zr6qqor6+vu11LnVyab/9unzasx2UPUnErJhVVFREXV3dYIeRs8z52+FyjjszB+Giik393teldRMA+rWvS+smMLbs8GHz+fSH4fY7PBJJWhURFZ1tG1YjCGbFpLy8fLBDMOsT/w4Pb04QzAaJr6+3Yuff4eFtuE1SNDMzswJwgmBmZmYdOEEwMzOzDjwHwcwK5slNo9quMOhPT2xMnkHWn309uWlUt7c6NRvunCCYWUEM5Iz2XRsbARib3q66PxyAZ+nbyOYEwcwKwjPazYYXz0EwMzOzDpwgmJmZWQdOEMzMzKwDJwhmZmbWgScpmg2w6upq1q5d2+X2xnSG/tQcZ+iXl5d7gqCZFZwTBLMBtnbtWu594F6Y2EWBl5If67W+58ZeLFBQZmbtOEEwGwwTofWY1k43ldyRnPnrantnZc3MCs1/XczMzKwDJwhmZmbWgRMEMzMz68AJglmW6upqqqurBzuMIcv7x2zk8CRFsyzdXX5o3j9mI4lHEMzMzKwDjyCYWV7Wr1/P0UcfzahRo2htbWX06NGMGjWK17zmNTz99NMA7LLLLkgiInj11VcZM2YMU6ZMYdSoUUhi9OjRvOtd7+LKK69k6tSpjB8/nm3btvGPf/yDffbZh7Fjx/LZz36WK664gubmZp555hmmTZvG5z//eS677DIee+wxpk+fztixYxk9ejSf/exn+cpXvsK6deu4+uqraW1t5ZxzzuGqq66ivLycpqYmLrnkEhYvXkxEtL2eNGkSQNv2+fPnU11dvdM2s5Eq5wRB0leBS4Fm4FfAwcC5EfHDfoqtaEh6H/Bz4J8i4uFe1F8C3BkRv+lF3QagAtgGfDgi/ivfNrppezGwKSIuL1SbVvzWrVsHwPbt2wFoaWmhpaWlLTkAePXVV3eqs3XrVh577LGd1j300EPAjjtHZmTKLV26lIaGhrb1jz766E7rnnzyybZt2euXLFkCwObNm1myZAk/+MEPqKmpYfXq1dTU1BARba8XLFgA0LZ96dKlPPHEEzttMxup8jnFMDciXgZOBBqBA4DP9UtUxedDwB+BD+ZbUdKoiFjUm+SgnYnAp3rTfx/77apdj04NQ+vX53B3xxxFRLfbs5OD7ta1X9/Q0NC23NDQQF1dHbW1tUQEy5YtY9myZUQEtbW1bNiwgaamprbtDQ0NO20zG8ny+SNemv6cB/woIp6X1A8hFRdJE4AjgWOBW4DF6foS4Grg7cDjJMnY9yPipvRb//eBucDVkt4F3JpuOxz4JrArsBV4J3AyUBERZ6Vt3wpcHhF3ZIXyFWB/SfcBK4DbgPMi4sS0ztVAXURc30n/uwFnALsAa4HTImJLN+/53cBFafkNwKkR8Ww64rAPUAY0SToHuAaYnlY9NyLuknQE8A1gHMmI1Mci4hFJbwKuS9stAU6OiPoePoKCamxspLm5uV+fbVBfXw893yQxN5uS9gbqWQyZ0YNismjRorZkpKWlpW19a2tr24hC+2Qls82jCDaS5ZMg/J+kh0n+oH9K0p7AK/0TVlF5L/CriHhU0vOSDo2Ie4B/ITlQHgS8DniI5KCc8UpEHAWQJghI2gW4ETglIv4qaXeS/Z2LC4ADI2JO2tYxPZTP7n9SRHwnfX0p8Angqm7q/hF4S0SEpH8Dzgc+m247DDgqIpol/S/w9Yj4o6TpwK+BfwIeBo6OiG2SjgO+RJIEnQl8MyJuSPdFj6Mbks4gSW6YPn16D6VtJNq0aVPb6+xEoKWlheXLl7e9zpbZ5gTBRrKcE4SIuEDSfwIvR8R2SZuB9/RfaEXjQyTfhgF+nC7fAxwF/DQiWoFnJN3ert6NnbT1BuDpiPgrQHpKh34aqcnu/8A0MZgITCA5kHdnKnCjpL1Jvu0/nrXtlojIJDXHAbOy4t89Ha3YA6iRNBMIdoxO/Rm4UNJU4Oe5jB5ExLXAtQAVFRXdj1nnIPMExf681n/+/Pncu+7ewjQ2AWZOmTlg9yY4+uijB6SfQpowYQJbt26lpaWl7f9SRFBaWsrcuXPbTj1kJwmZbWYjWc5zECR9ANiWJgcXAT8kGU4esSRNAt4BfDcdtv8ccIqSv0I9HdU3d9YkyQGzvW3s/FmNzSG8nupk9389cFZEHARckkP7VwFXp+X/o1357HZLgLdGxJz035SI2AgsBW6PiAOBd2fqR8T/AieRjJr8WtI7en6bNpCmTJky2CHkbcmSJW2JQWlpKaNHJ9+LSkpKqKqqoqqqqkMSntlmNpLlM0lxYURslHQU8P+AGuDb/RNW0Xg/8IOI2DciyiJiGsm36aNIhuFPllQiaS/gmBzaexjYJ52HgKTd0sl+DcCctK1pwBGd1N0I7Ja1/ATJt/cxkvYgmcvQld2ApyWVAqfmEOceQOZkdHd/RZcDZ2UWJM3ppP7pWdtnAI9FRDXJfI7ZOcRiA2jPPfcsWFs9jYyVlZXltK79+rKysrblsrIyKioqqKysRBLz5s1j3rx5SKKyspJJkyYxefLktu1lZWU7bTMbyfJJELanP08Avh0RvyQZXh7JPgTc3G7dz4APpz8bgfuB/wb+ArzUXWMR8SpwCnCVpL+RTDYcC9xFknisAS4nOYXRvu4G4C5J90v6WkQ8BfwEWA3cAHQ3pr0wjW8FSZLSk8XATyX9AWjqptx8oELSakkPkswxAPgq8GVJd7HzPINTgPvTiZZvBH6QQyw2wDKjCJl7GpSWljJ27Fj23nvvtjK77LILY8aMYZddkj8RY8aMYcaMGcycOZMDDjiAWbNm8ZnPfAZITusccMABzJgxg7FjxzJjxgxmzZrFwoULmTVrFvvttx/jxo3jgAMOYOHChcyYMQNI5pxk2lq4cCEzZ85k/PjxLFq0iIsuuohdd92VRYsWAVBVVcXs2bPbRgwyrzMy6xYuXNhhm9lIpZ4uNWormMycX0dyXvkwkmHglRFxcP+FV9wkTYiITempiJXAkRHxzGDHNVxVVFREXV1dn9rIXA0wEHMQWo/p/FKGkjuSvL2r7e3LHjLlkAGbgzAQ+8fMBo6kVRFR0dm2fK5i+FfgXSSX172YTlDzfRC6d6ukiSQjLUudHAx95eXlgx3CkOb9YzZy5HMVwxbg55Jel16yBrkNR49YEXHMYMdg+Rmo+wkUK+8fs5Ejn6sYTpJUT3Iu/Pfpz9r+CszMzMwGTz6TFJcCbwEejYj9SOYi3NUvUZmZmdmgyidBaElnypdIKomI24E5/ROWmZmZDaZ8Jim+mD534E7gBknPkdyMx8zy9eKOqxU62wbdbG9ftvjuXWRmRSCfBOE9JM9e+AzJzXT2AJb0R1Bmw1lPVwI0RvL446lTpvbc2BRfWWBm/SPn+yCYDXWFuA+CmdlI0qf7IEjaSPJ8gPbPCRAQEbF7QaI0MzOzIaPHBCEiduupjJmZmQ0v+dwH4S3po3ozyxMkvbl/wjIzM7PBlM9ljt8GNmUtb8FPczQzMxuW8rmKQZE1ozEiWtNHEZtZqrq6mrVr13a5vbExvUJhag5XKKTKy8t9i2MzG3D5HOAfkzSfHaMGnwIeK3xIZsVr7dq1PHzffby+i+0b058vNnX3lOwd/HQvMxss+SQIZwLVwEUkVzP8FjijP4IyK2avBz6BOt32vfRCoK62d1XezGyg5fM0x+eAD3a1XdIXIuLLBYnKzMzMBlU+kxR78oECtmVmZmaDqJAJQm5jpmZmZjbkFTJB8MlSK1rV1dVUV1cPdhiDyvvAzLIV8jJFjyBY0eru0sSRwvvAzLIVcgThpwVsy8zMzAZRPrdaPkDSbyXdny7PlnRRZntEfKk/AjSzwdHU1MTZZ5/Nhg0bePTRR6msrGTt2rU7rc+1fiHKmdnAymcE4TvAF4AWgIhYTTeXPXZG0nZJ92X9K8unfh79HCPpbf3U9umSru6Ptjvp6yRJF/Sy7kRJnypwPIslnVfINm3oqqmpYfXq1dTU1HDppZeyefNmlixZstP6XOsXopyZDax8EoTxEbGy3bptefbXHBFzsv415FKpF7d0PgbolwRhIEXELRHxlV5Wn0hyt8u8SBrVy/56ate35S4iTU1N1NbWEhHcdtttNDQ0ANDQ0MBtt91GRFBbW9vlt/7s+oUoZ2YDL58/2k2S9ie9WkHS+4Gn+xqApDnANcB44O/AxyPiBUl3AH8CjgRuSZevBCYATcDpEfF0evvnM0mSlQeBC9Ll7ZI+ApwdEX/I6u8I4BvAOKAZ+FhEPCLpdOCkNI79gZsj4vy0zsdIRk+eBh4FtnbyPt4OfDNdDODoiNgo6XPAvwJj0jYvTkdOfgX8EXgL8DfgOuAS4HXAqRGxMo2pIiLOknQ98DJQQXKzvvMj4iZJE4BfAq8BSoGLIuKXwFeA/SXdB6wAbgPOi4gT03ivBuoi4npJDcD3gbnA1elTO88AdgHWAqdFxJZuPsN3k9xhcxdgQxr/s5IWA/sAZSS/P+eQfNbT06rnRsRd3Xwmb0r3yy4kyezJEVHfVRx90djYSHNzc5+feVBfX1/QiT0bgPX19QPyLIb6+nrGjRsHJN/qM49eaWlp2anctm3J94LW1lZqampYsGBBh7ay6xeinJkNvHz+ln0a+G/gjZLWAecCn8yzv3FZpxduTtf9APh8RMwG1gAXZ5WfGBFvJ7nF81XA+yPiMJKD2WVpmQuAQ9L6Z6ajEtcAX09HKf7Azh4mOXgfAiwCsudOzAFOAQ4CTpE0TdLeJAfuI4HjgVldvLfzgE9HxBzgn4FmSXOBmcARaduHSTo6LV9OklDMBt4IfBg4Km3ni130sXda5kSSBADgFeB9EXEocCxwhSSl++Xv6T74XBftZXslIo6KiB8DP4+IwyPiYOAh4BM91P0j8JZ0n/4YOD9r22HAeyLiw+n7/XpEHA6cDHw3LdPVZ3Im8M10n1YAje07lnSGpDpJdevXr8/hbVouVqxY0SExaK+lpYXly5f3WL8Q5cxs4OVzq+XHgOMk7QqURMTGnup0ojn9Yw+ApD1IkoDfp6tq2PlqiBvTn28ADgRWJMc+RrFj9GI1cIOkXwC/yCGGPYAaSTNJvumXZm37bUS8lMb2ILAvMBm4IyLWp+tvBA7opN27gCsl3UBygG1ME4S5wL1pmQkkCcOTwOMRsSZt84G075C0huQbd2d+ERGtwIOS9krXCfhSmni0AlOAvbqo350bs14fKOlSktMUE4Bf91B3KnBjmkztAjyete2WiGhOXx8HzEo/Q4Dd09GKrj6TPwMXSppKsk87jB5ExLXAtQAVFRW9vhdH5umKfb0PwPz583nxvvv61Ea2ScDEmTMH5P4E2aMUxx9/PMuWLes2SSgtLWXu3LmdbsuuX4hyZjbw8rmK4RxJuwNbgK9Luic9APanzZnugQey5i4cFBGZvk8AvkXyTXVVDue6lwK3R8SBwLuBsVnbsk8dbGdHAtXjgSedK/BvJMPkd0t6Yxr3l7PiLo+I73XSV2vWcitdJ27ZdTJH2VOBPYHD0uTr2XbvKWMbO3/e7ctsznp9PXBWRBxEMnrSWXvZrgKuTsv/R7vy2e2WAG/N2h9T0kSz088kIv6X5LRPM/BrSe/oIQ4rkKqqKjKJXGlp6U7bRo9Ofj1LSkqoqqrqsX4hypnZwMvnFMPHI+Jlkm/ErwM+xo5h7l5Jv62/IOmf01WnAb/vpOgjwJ6S3gogqVTSmySVANMi4naSYe2JJN94NwK7ddHtHsC69PXpOYT5F+AYSZMkldLFMyck7R8RayLiP4E6ktMGvwY+ns4TQNIUSa/Loc987AE8FxEtko4lGfWAjvvgCZJv72PSkZt3dtPmbsDT6fs9NccYMvu0u7/wy4GzMgvp/JP29U/P2j4DeCwiqoFbSE7H2ACYPHkylZWVSOKEE06grKwMgLKyMk444QQkUVlZyaRJk3qsX4hyZjbw8pmkmPnGOg+4LiL+pqyx4j6oAq6RNB54jCTx2ElEvJpOiqxOD26jSSa1PQr8MF0nkvPbL0r6P+AmSe+h3SRF4Kskw9kLgN/1FFw6EXIxyXD308A9JKc42js3PUBvJ5ksWRsRWyX9E/DndFdtAj6SlimUG4D/k1QH3EdyPp+I2CDprvS+FbUR8TlJPyE5JVPPjtMenVlIkhg9QTIvpKtkK2Mx8NN0bsrdwH5dlJsPfEvSapLP8E6SeQZdfSanAB+R1AI8AyzpIQ4roKqqKhoaGqiqquLEE0/knHPOYdGiRUycOLFtfa71C1HOzAaWMjOIeywoXUdyfns/4GCSg+Qd6aRBs0FXUVERdXV1vaqbOf9eqDkIn+jizuPfS89WdbW9s/IT58wZ0DkIfh6D2cghaVVEVHS2LZ8RhE+QzMR/LCK2SJpEJ9/2zYpReXn5YIcw6LwPzCxbPgnCUenP2YU5s2A2dAzEfQaGOu8DM8uWT4KQfS39WJJr+1cBnlluZmY2zORzH4R3Zy9LmkYyuczMzMyGmb7cFbaR5OZFZmZmNszkPIIg6Sp23DCohGTC4t/6ISYzMzMbZPnMQci+fmwb8KOIuKvA8ZgVvWfYcTlje5n7g3e1vbO2JhYiKDOzPOUzB6Hbh7VL+llEnNz3kMyKV0+XCm5qTJ43NTF99kNPJubQpplZf8hnBKEnMwrYlllR8qWCZjZcFPLR9b1+kp6ZmZkNLYVMEMzMzGyYKGSC4NsrmpmZDROFnIPw+QK2ZWY5qq6uZu3atb2q25hOmpya46TJfJWXl3tehlmR6jFBkLSGbuYXRMTs9OfyAsZlZjlau3YtD6x5iInjX5d33Ze2bARAWzcUOixe3PJcwds0s4GTywjCienPT6c//yf9eSqwpeARmVneJo5/Hce+8YN517v94R8D9Kpurm2bWXHqMUGIiCcAJB0ZEUdmbbpA0l3Akv4KzszMzAZHPpMUd5WUeeQzkt4G7Fr4kMzMzGyw5TNJ8RPA9yXtkS6/CHy84BGZmZnZoMvnVsurgIMl7Q4oIl7qv7DMBld1dTXgOyNa1/w7YsNdPk9zHAOcDJQBo6XktgcR4TkINuz09rJBGzn8O2LDXT6nGH4JvASsArb2TzhmZmY2FOQzSXFqRJwSEV+NiCsy//otMjOzItPU1MTZZ5/Nhg2Fu69Ed21mttXX1+fUb3/EN1x5X+WXIPxJ0kH5NC5pU57lj5F0az51suqeK2l8N9u/K2lWD23cIamiN/0PF5IqJFX3UKbXn1MnbZ0p6aPp69Ml7VOIds0GQ01NDatXr6ampmZA2sxsW7p0aU799kd8w5X3VX4JwlHAKkmPSFotaY2k1f0VWC+cC3SaIEgaFRH/FhEP9lfnkkb1V9v9QVKnp5cioi4iBmzWVURcExE/SBdPB5wgWFFqamqitraWiKC2trYg3zy7azN7W0NDQ4/99kd8w5X3VSKfOQiVve1E0jHAYqAJOJBkHsNHIiIkvQv4Rrrtnqw6i4FNEXF5unw/yV0d1wM/AaYCo4ClwF4kB5bbJTVFxLHp6MWVwP8DPivpUuC8iKiT9G3gcGAccFNEXNxD/O8ELifZX38FPhkRWyU1AN8H5gJXAz/OqrMXcA0wI131yYj4k6QF7Lg89LsR8Q1JZcCvgD8CbwH+BlwHXAK8Djg1Ilam+2Q/YG/gAGBBWr4SWAe8OyJaJB2WvvcJ6X49PSKelnQH8CfgSOAWSXcC3yS5n8VW4J3AYel+OlHSEelnMw5oBj4WEY90s59OByoi4qx0+Vbg8oi4I/08vknyGTYD74mIZzOfM9AAVAA3SGoG3gpcDJwEbAOWR8R5XfVdaI2NjTQ3NxfFDPX6+npaXx16z0rb9MoL1Nc/XxT7sDfq6+sZN25c23JNTQ0RyV3pW1tbqampYcGCBX3qo7s2s7dldNdvf8Q3XHlfJXIeQYiIJ9K7KjaTPJsh8y9Xh5B8y59FctA8UtJY4DvAu4F/Bl6fQzvvAv4REQdHxIHAryKiGvgHcGxEHJuW2xW4PyLeHBF/bNfGhRFRAcwG3i5pdledpTFeD5wSEQeRJAmfzCrySkQcFRHt7ytbDfw+Ig4GDgUeSA/cHwPeTHJg/3dJh6Tly0kOoLOBNwIfJhm1OQ/4Yla7+wMnAO8BfgjcnsbVDJwgqRS4Cnh/RBxGksBcllV/YkS8PS1zI3BOGuNxaRvZHgaOjohDgEXAl7raTznYFbg77etO4N+zN0bETUAdSTI0hyQpeR/wpvR5H5d21qikMyTVSapbv359H8Iz65sVK1bQ0tICQEtLC8uX9/3xNN21mb0to7t++yO+4cr7KpHPZY4nAVeQfFN/DtgXeAh4U45NrIyIxrSt+0gul9wEPB4R9en6HwJn9NDOGuBySf8J3BoRf+ii3HbgZ11s+1dJZ5C8/71JkpauTpe8IY3x0XS5huS5FN9Il2/sot47gI8CRMR24KX0TpQ3R8RmAEk/J0mMbkn7WJOufwD4bTrCsoZkX2XUpqMEa0hGUH6Vrs+UewPJKM2K9FLUUcDTWfUz8b4BeDoi/prG+HLad/Z72AOokTSTJBks7eK95uJVIDNvYRVwfA/lXwZeAb4r6basujuJiGuBawEqKirySVi7lXm6YeZa96Fs/vz5rPv70BsCnTD2NUzZf1JR7MPeaD8ycvzxx7Ns2TJaWlooLS1l7ty5fe6juzazt2V0129/xDdceV8l8pmDsJTkW++jEbEfyXD0XXnUz740cjs7kpOu/qhvaxffWID0QH0YyQHxy5IWdVH/lfTAvBNJ+5F8K39n+s30tkzbXehp7HZzD9tzbSt7/7RmLbeycyK3FSAiWoGW2DHGmCkn4IGImJP+Oygisn+7M/GKnkeAlpKMUBxIMsrT3X6CLj6zVHas2Z9/pyJiG3AESZL3XnYkQmZDUlVVVVuCXVJSQlVVVb+2mb0to7t++yO+4cr7KpFPgtASERuAEkklEXE7MKeP/T8M7Cdp/3T5Q1nbGkiG5pF0KMm5d9JZ7lsi4ock8wIOTctvBHbLoc/dSQ6SL6XzBHqaW/EwUCapPF0+Dfh9Dv38lvRUhKRR6R0o7wTeK2m8pF1JhtC7GgHprUeAPSW9Ne27VFJnozwPA/tIOjwtt1snExf3IJnbAMkEwp40AHMklUiaRnKAz0fbZyhpArBHRCwjOTU1J8+2zAbU5MmTqaysRBKVlZVMmjSpX9vM3lZWVtZjv/0R33DlfZXIZ5Lii+kf7TtJJpI9R/KNsdci4pV0qP82SU0kk/QOTDf/DPhoejrir0BmiP8g4GuSWoEWdswHuBaolfR01jyEzvr8m6R7gQeAx+hhFCSN8WPAT9MD6F9JJh/25BzgWkmfIPnG/MmI+LOk64GVaZnvRsS96STFgoiIVyW9H6hOn5sxmuR0yAOdlDsFuEpSZhLice2a+yrJKYYFwO9y6P4u4HGS0Z37yZp0mqPrgWvSSYqVwC/TOSACPpNnW2YDrqqqioaGhoJ+4+yuzcy2+fPnU11d3WO//RHfcOV9lTxTIbeCyTfeV0j+WJ9K8u3yhnRUwWzQVVRURF1dXUHaypxfLobz55k5CMe+8YN517394WRubW/q5tL2SJiDMFzfn40Mklalk/Y7yOdhTdnn2kfunSNsRCgvL++5kI1o/h2x4a7HBEHSRjqfzCYgImL3gkdlNsiG67X7Vjj+HbHhrscEISJymfhnZmZmw0g+VzGYmZnZCOEEwczMzDpwgmBmZmYd5HMfBDMbol7c8lzbJYv51gN6VTeXtqcwMm8wYzYcOEEwK3J9udwuGpPnc02ZWvgD+RQm+VJAsyLmBMGsyPlyOzPrD56DYGZmZh04QTAzM7MOnCCYmZlZB56DYDbEVFdXs3bt2l7Xb2xsBGDq1Km9ql9eXu55DWbmBMFsqFm7di33/+1v7LZL7/57bnw1eQr79o0v9bqumZkTBLMhaLddRnPEXq/pVd2Vz74A0Kv6mbpmZp6DYGZmZh04QTAzM7MOnCCYmZlZB04QzHpQXV1NdXX1YIdhKX8eZgPDkxTNetCXSw6t8Px5mA0MjyCYmZlZB04QzMzMrAMnCH0gaS9J/yvpMUmrJP1Z0vsGO67OSDpG0q2DHYeZmRUHJwi9JEnAL4A7I2JGRBwGfBDocH9bSYM612MI9D9qMPs3M7P8eZJi770DeDUirsmsiIgngKsAJJ0OnACMBXaV9O5020Ek+31xRPwyPXh+BTgGGAN8KyL+W9IxwGKgCTgQWAV8JCIiOwhJdwDnRUSdpMlAXUSUte8fWALsLulm4A3AncCnIqJV0reBw4FxwE0RcXHadgNQA7wbKAU+EBEPt+u/DPiftA+AsyLiT2n8FwNPA3MkHdTF+5wA/BJ4TdrHRel+2RX4CUnCNQpYGhE39vCZ9IvGxkaam5sH7PkE9fX1bNu2fUD6am/Ltu3U19cP6Wcx1NfXM27cuMEOw2zYc4LQe28C7umhzFuB2RHxvKQvAb+LiI9LmgislPQb4FTgpYg4XNIY4C5Jy9P6h6T9/AO4CzgS+GMeMWb3fwxwBDALeAL4FfAvwE3AhWmZUcBvJc2OiNVpG00RcaikTwHnAf/Wro/ngOMj4hVJM4EfARXptiOAAyPicUlndPE+nwLeFxEvpwnO3ZJuAd4F/CMiTgCQtEdnbzBt9wyA6dOn57FrzMysO04QCkTSt4CjSEYVDk9Xr4iI59PXc4GTJJ2XLo8FpqfrZ0t6f7p+D2Am8CqwMiIa0/bvA8rIL0HI7p+0vcfS9n6UxnsT8K/pgXY0sDdJEpFJEH6e/lxFklC0VwpcLWkOsB04oF1/j2e9/87eZyPwJUlHA63AFGAvYA1wuaT/BG6NiD909gYj4lrgWoCKiororExfZZ6KOFDX3s+fP58nHnpgQPpqb/zoUew7c+aQvs/AUB7dMBtOnCD03gPAyZmFiPh0Zog/q8zmrNcCTo6IR7IbSecynB0Rv263/hhga9aq7XT+eW1jx1ySse22bW633P4AGpL2IxkZODwiXpB0fbt2MjF01f9ngGeBg9M4Xumi/67e5+nAnsBhEdGSntYYGxGPSjoMmAd8WdLyiFjSSf9mZtYPPEmx934HjJX0yax147sp/2vg7DQhQNIhWes/Kak0XX9Aev49Vw3AYenr93dTDuAISftJKgFOIRmN2J3kQP6SpL2Ayjz6hmQk4OmIaAVOI5kv0Jmu3ucewHNpcnAssG+6fR9gS0T8ELgcODTPuMzMrA88gtBLERGS3gt8XdL5wHqSA+3nu6iyFPgGsDpNEhqAE4Hvkpw6uCddvx54bx6hXA78RNJpJElLd/5MMlHwIJJJijenkxTvJRkReYxkrkM+/gv4maQPALfTcdQio6v3eQPwf5LqgPuAzCTIg4CvSWoFWoBPYmZmA8YJQh9ExNMklzZ2tu164Pqs5WbgPzop1wp8Mf2X7Y70X6bcWV308zAwO2vVRV30v1N77do4vYv1ZVmv60iuQGhfpr5d/1/orL9u3ickkynbayAZdTAzs0HgBMGsB+Xl5YMdgmXx52E2MJwgmPXAs+aHFn8eZgPDkxTNzMysAycIZmZm1oETBDMzM+vACYKZmZl14EmKZkPQxle3sfLZF3pdF+hV/UxdMzMnCGZDTF8v42tsbAR2PENioPs3s+HBCYLZEOPL+MxsKPAcBDMzM+vACYKZmZl14ATBzMzMOvAcBDMrqOrqatauXZt3vb5OruxJeXm553eY5cEJgpkV1Nq1a3nk/oeYttvr86q3eeNGALZs793lnd15auMzBW/TbLhzgmBmBTdtt9fz2SM+lledK1ZeB5B3vXzaNrPceQ6CmZmZdeAEwczMzDpwgmBmZmYdOEEwGyaqq6uprq4e7DCsl/z52VDjSYpmw0RvLi20ocOfnw01HkEwMzOzDpwgmJkNM01NTZx99tls2LBhp9dm+XCCMARImiTpvvTfM5LWZS3vMtjxZZM0R9K8ArZ3pqSPpq9Pl7RPodo2G6lqampYvXo1NTU1O702y4fnIAwBEbEBmAMgaTGwKSIuz2yXNDoitg1OdDtIGk0SZwWwrBBtRsQ1WYunA/cD/yhE22YjUVNTE7W1tUQEy5YtIyKICGpra6mqqmLSpEmDHaIVCScIQ5Sk64HngUOAeyTdCHwDGAc0Ax+LiEcknQ6cBIwH9gdujojzJY0CvkdyMA/g+xHxdUl3APcBRwC7Ax+PiJWSXgt8H5gBbAHOiIjVacKyD1AGNAFHAeMkHQV8OSJuzIr5dKAiIs5Kl28FLo+IOyRtAr4JnJjG/56IeDaTEAENaaw3SGoG3gpcnL63bcDyiDivEPt2uGpsbKS5uXnQnzdQX19P6bahNTj53JbnaalvGvR90536+nrGjRvX53ZqamqICABaWlra1re2tlJTU8OCBQv63IeNDEPrf7G1dwBwXER8FngYODoiDgEWAV/KKjcHOAU4CDhF0rR03ZSIODAiDgKy7zW7a0S8DfgUSVIAcAlwb0TMBr4I/CCr/GEkB/QPp33fGBFzspODHOwK3B0RBwN3Av+evTEibgLqgFMjYg5JIvQ+4E1pTJd21qikMyTVSapbv359HuGYDU8rVqxoSwwyoweQJAvLly8fzNCsyHgEYWj7aURsT1/vAdRImkkyIlCaVe63EfESgKQHgX2BB4AZkq4CbgOy/zL8CCAi7pS0u6SJJCMDJ6frf5fOi9gjLX9LRDT38b28Ctyavl4FHN9D+ZeBV4DvSrotq+5OIuJa4FqAioqK6GOMRS3zFMTBvpZ+/vz5bHmi8A9c6ovXjX8t4/d9zaDvm+4UanTj+OOPZ9myZbS0tCAJSBKF0tJS5s6dW5A+bGTwCMLQtjnr9VLg9og4EHg3MDZr29as19uB0RHxAnAwcAfwaeC7WWXaH0gDUCf9Z8pt7mRbZ7ax8+9Udowtkfkqk8bYXUPpnIsjgJ8B7wV+lWMMZiNaVVVVW2JQWlrK6NHJf7WSkhKqqqoGMzQrMk4QiscewLr09ek9FZY0GSiJiJ8BC4FDszafkpY5CngpHX24Ezg1XX8M0BQRL3fS9EZgty66bQDmSCpJT3Mc0VOcXbUtaQKwR0QsA84lncRpZt2bPHkylZWVSGLevHnMmzcPSVRWVnqCouXFpxiKx1dJTjEsAH6XQ/kpwHWSMkngF7K2vSDpT6STFNN1i9Pyq0kmKXb1VeN24AJJ99FukiJwF/A4sIbkaoR7cogz2/XANekkxUrgl5LGkoxufCbPtsxGrKqqKhoaGqiqqiIi2l6b5cMJwhATEYu7WP9nkkmLGQvT9deTHFgz5U7MKpM9apDtZxGRnTAQEc8D7+kpnrTc4V3EGKSjEJ1sm5D1+ibgpvbtp6MdP8uqlu8IhJmRjCJcddVVbcvZr81y5QTBbJgoLy8f7BCsD/z52VDjBGGEiYhjBjsG6x9D+Rp/65k/PxtqPEnRzMzMOnCCYGZmZh04QTAzM7MOnCCYmZlZB56kaGYF99TGZ7hi5XU9F2xXB8i7Xq5tv4HXFLxds+HMCYKZFVRvL9fbtTG5o/f4qYU/kL+B1/gyQrM8OUEws4Ly5Xpmw4PnIJiZmVkH2vGAPbPiJmk98ESOxScDTf0YTn9y7IOjmGOH4o7fsfeffSNiz842OEGwEUlSXURUDHYcveHYB0cxxw7FHb9jHxw+xWBmZmYdOEEwMzOzDpwg2Eh17WAH0AeOfXAUc+xQ3PE79kHgOQhmZmbWgUcQzMzMrAMnCGZmZtaBEwQb1iR9X9Jzku7PWvdaSSsk1ac/h+RN+iVNk3S7pIckPSDpnHT9kI9f0lhJKyX9LY39knT9kI89Q9IoSfdKujVdLqbYGyStkXSfpLp0XVHEL2mipJskPZz+7r+1iGJ/Q7rPM/9elnRuscTfnhMEG+6uB97Vbt0FwG8jYibw23R5KNoGfDYi/gl4C/BpSbMojvi3Au+IiIOBOcC7JL2F4og94xzgoazlYood4NiImJN1DX6xxP9N4FcR8UbgYJLPoChij4hH0n0+BzgM2ALcTJHE30FE+J//Det/QBlwf9byI8De6eu9gUcGO8Yc38cvgeOLLX5gPHAP8OZiiR2YSvKH/B3ArcX2ewM0AJPbrRvy8QO7A4+TTqAvptg7eS9zgbuKNf6I8AiCjUh7RcTTAOnP1w1yPD2SVAYcAvyFIok/HaK/D3gOWBERRRM78A3gfKA1a12xxA4QwHJJqySdka4rhvhnAOuB69LTO9+VtCvFEXt7HwR+lL4uxvidIJgNdZImAD8Dzo2Ilwc7nlxFxPZIhlqnAkdIOnCQQ8qJpBOB5yJi1WDH0gdHRsShQCXJqamjBzugHI0GDgW+HRGHAJspluH4LJJ2AU4CfjrYsfSFEwQbiZ6VtDdA+vO5QY6nS5JKSZKDGyLi5+nqookfICJeBO4gmQtSDLEfCZwkqQH4MfAOST+kOGIHICL+kf58juQc+BEUR/yNQGM62gRwE0nCUAyxZ6sE7omIZ9PlYosfcIJgI9MtQFX6uork3P6QI0nA94CHIuLKrE1DPn5Je0qamL4eBxwHPEwRxB4RX4iIqRFRRjJM/LuI+AhFEDuApF0l7ZZ5TXIu/H6KIP6IeAZ4StIb0lXvBB6kCGJv50PsOL0AxRc/4Dsp2jAn6UfAMSSPXH0WuBj4BfATYDrwJPCBiHh+kELskqSjgD8Aa9hxLvyLJPMQhnT8kmYDNcAoki8iP4mIJZImMcRjzybpGOC8iDixWGKXNINk1ACSIfv/jYjLiij+OcB3gV2Ax4CPkf4OMcRjB5A0HngKmBERL6XrimLft+cEwczMzDrwKQYzMzPrwAmCmZmZdeAEwczMzDpwgmBmZmYdOEEwMzOzDpwgmJkNMElfS59WuFrSzZl7RpgNJU4QzMwG3grgwIiYDTwKfGGQ4zHrwAmCmVkfSSqT9JCk70h6QNJySeMkzZF0d9ZIwWsAImJ5RGxLq99N8rwKsyHFCYKZWWHMBL4VEW8CXgROBn4AfD4dKVhDcifP9j4O1A5UkGa5coJgZlYYj0fEfenrVcD+wMSI+H26rgbY6amKki4EtgE3DFSQZrkaPdgBmJkNE1uzXm8HJnZXWFIVcCLwzvA9720I8giCmVn/eAl4QdI/p8unAb8HkPQu4PPASRGxZZDiM+uWRxDMzPpPFXBN+oS/zJMJAa4GxgArkqd6c3dEnDk4IZp1zk9zNDMzsw58isHMzMw6cIJgZmZmHThBMDMzsw6cIJiZmVkHThDMzMysAycIZmZm1oETBDMzM+vg/wO3aA96dlKFEAAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": null,
+   "id": "05a26072",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
    "source": [
     "seaborn.boxplot(y=\"land_use_class\", x=\"no2\", data=subset)"
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "81e1ee0a",
    "metadata": {},
    "source": [
     "Tweaking the figure a bit more (not asked in the exercise, but uncomment and run to see):"
@@ -3327,22 +1306,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 720x720 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": null,
+   "id": "06dc86e1",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
    "source": [
     "# defining custom order of the classes (following the order in the CORINE hierarchy as defined in the legend csv)\n",
     "classes = legend[\"group\"].unique()\n",
@@ -3351,13 +1322,14 @@
     "frequent_categories = counts[counts > 50].index\n",
     "classes = [value for value in classes if value in frequent_categories]\n",
     "\n",
-    "fig, ax = plt.subplots(figsize=(10, 10))\n",
+    "fig, ax = plt.subplots(figsize=(8, 6))\n",
     "seaborn.boxplot(y=\"land_use_class\", x=\"no2\", data=subset, ax=ax, color=\"C0\", order=classes)\n",
     "ax.set(xlabel=\"NO$_2$ concentration (µg/m³)\", ylabel=\"CORINE Land Cover classes\");"
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "cd7f57b7",
    "metadata": {},
    "source": [
     "The dense urban areas and areas close to large roads clearly have higher concentrations on average. On the countryside (indicated by \"agricultural\" areas) much lower concentrations are observed."
@@ -3365,6 +1337,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "6c55c158",
    "metadata": {},
    "source": [
     "## A focus on Gent\n",
@@ -3374,7 +1347,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": null,
+   "id": "b77bc3a7",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3383,29 +1357,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
+   "execution_count": null,
+   "id": "986d9e1c",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
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-      ],
-      "text/plain": [
-       "<shapely.geometry.polygon.Polygon at 0x7fdc7820ef40>"
-      ]
-     },
-     "execution_count": 54,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "gent"
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "c8984bbf",
    "metadata": {},
    "source": [
     "<div class=\"alert alert-success\">\n",
@@ -3423,23 +1385,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/svg+xml": [
-       "<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" width=\"300\" height=\"300\" viewBox=\"93723.95040798881 184751.4811668892 20859.760364005197 25096.581864038948\" preserveAspectRatio=\"xMinYMin meet\"><g transform=\"matrix(1,0,0,-1,0,394599.54419781733)\"><path fill-rule=\"evenodd\" fill=\"#66cc99\" stroke=\"#555555\" stroke-width=\"167.31054576025966\" opacity=\"0.6\" d=\"M 94653.45343999026,185680.98419889063 L 113654.20773999256,185680.98419889063 L 113654.20773999256,208918.5599989267 L 94653.45343999026,208918.5599989267 L 94653.45343999026,185680.98419889063 z\" /></g></svg>"
-      ],
-      "text/plain": [
-       "<shapely.geometry.polygon.Polygon at 0x7fdcbb661d60>"
-      ]
-     },
-     "execution_count": 55,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "id": "2415936a",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
    "source": [
     "gent_region = gent.envelope\n",
     "gent_region"
@@ -3447,6 +1400,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "90cdfb96",
    "metadata": {},
    "source": [
     "<div class=\"alert alert-success\">\n",
@@ -3469,35 +1423,35 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
-   "metadata": {},
+   "execution_count": null,
+   "id": "61c5e656",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
    "outputs": [],
    "source": [
-    "gdf_gent = gdf_lambert[gdf_lambert.within(gent_region)]"
+    "gdf_gent = gdf_lambert[gdf_lambert.within(gent_region)].copy()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "1291"
-      ]
-     },
-     "execution_count": 57,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "id": "90ca5add",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
    "source": [
     "len(gdf_gent)"
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "97a3a59f",
    "metadata": {},
    "source": [
     "Alternatively, we can also use the `geopandas.clip()` function. For points there is not much difference with the method above, but for lines or polygons, it will actually \"clip\" the geometries, i.e. removing the parts that fall outside of the specified region (in addition, this method also uses a spatial index under the hood and will typically be faster for large datasets):"
@@ -3505,40 +1459,33 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 58,
+   "execution_count": null,
+   "id": "53cfc86e",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "1291"
-      ]
-     },
-     "execution_count": 58,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "gdf_gent = geopandas.clip(gdf_lambert, gent_region)\n",
+   "outputs": [],
+   "source": [
+    "gdf_gent = geopandas.clip(gdf_lambert, gent_region).copy()\n",
     "len(gdf_gent)"
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "e59ab782",
    "metadata": {},
    "source": [
     "<div class=\"alert alert-success\">\n",
     "\n",
     "**EXERCISE**:\n",
     "\n",
-    "* Make a visualization of the measurements in Gent. Use contextily to add a background basemap.\n",
+    "* Make a visualization of the measurements in Gent. Use contextily to add a background basemap. Color the points based on the NO2 concentration.\n",
+    "\n",
     "\n",
-    "TODO hints\n",
-    "    \n",
     "<details><summary>Hints</summary>\n",
     "\n",
-    "* \n",
+    "* For using one of the columns of the GeoDataFrame to determine the fill color, use the `column=` keyword.\n",
+    "* To add a background map, use the `contextily.add_basemap()` function. It takes the matplotlib `ax` to which to add a map as the first argument.\n",
+    "* Remember that, to use a contextily background, the data needs to be in Web Mercator (\"EPSG:3857\").\n",
+    "\n",
     "</details>\n",
     "\n",
     "</div>"
@@ -3546,8 +1493,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 59,
-   "metadata": {},
+   "execution_count": null,
+   "id": "f5a6846f",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
    "outputs": [],
    "source": [
     "import contextily"
@@ -3555,22 +1507,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 60,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1080x1080 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": null,
+   "id": "4b19cc48",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
    "source": [
     "fig, ax = plt.subplots(figsize=(15, 15))\n",
     "ax = gdf_gent.to_crs(3857).plot(column=\"no2\", ax=ax, legend=True, vmax=50)\n",
@@ -3580,50 +1524,47 @@
   },
   {
    "cell_type": "markdown",
+   "id": "2cf1174b",
    "metadata": {},
    "source": [
-    "Further zooming in on the city center (not asked in exercise, but uncomment and run to see):"
+    "The solution above includes a `vmax=50` to indicate to use a max value of 50 for the colorbar range (there are few measurements with much higher values, that would otherwise make most other points look dark blue-ish). A better option can be to use one of the classification schemes from `mapclassify` through the `scheme` keyword.\n",
+    "\n",
+    "Further zooming in on the city center, using a discrete color scheme and a different tile provider (not asked in exercise, but uncomment and run to see):"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 61,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 864x864 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": null,
+   "id": "de6a968e",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
    "source": [
     "fig, ax = plt.subplots(figsize=(12, 12))\n",
     "ax = gdf_gent.to_crs(3857).plot(column=\"no2\", ax=ax, scheme=\"NaturalBreaks\", k=6, legend=True)\n",
     "ax.set(xlim=(408_000, 420_000), ylim=(6_625_000, 6_638_000))\n",
-    "contextily.add_basemap(ax, source=contextily.providers.Stamen.TonerLite)\n",
+    "contextily.add_basemap(ax, source=contextily.providers.CartoDB.PositronNoLabels)\n",
     "ax.set_axis_off()"
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "25ea2219",
    "metadata": {},
    "source": [
     "## Combining with OpenStreetMap information\n",
     "\n",
-    "We downloaded and filtered OpenStreetMap data for the area of Gent, focusing on the street network information, and provided this as a GeoPackage file (`data/osm_network_gent.gpkg`, see this [notebook](./data/preprocess_data.ipynb) to check the code to download and preprocess the raw OSM data).\n",
+    "We downloaded and filtered OpenStreetMap data for the area of Gent, focusing on the street network information, and provided this as a GeoPackage file (`data/osm_network_gent.gpkg`, see this [notebook](./data/preprocess_data.ipynb#OpenStreetMap) to check the code to download and preprocess the raw OSM data).\n",
     "\n",
     "The OpenStreetMap street network data includes information about the type of street in the \"highway\" tag. We can use this as a proxy for traffic intensity of the street, and relate that to the measured NO2 concentration."
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "6ddbb026",
    "metadata": {},
    "source": [
     "<div class=\"alert alert-success\">\n",
@@ -3639,114 +1580,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 62,
-   "metadata": {},
+   "execution_count": null,
+   "id": "feae0179",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
    "outputs": [],
    "source": [
-    "streets = geopandas.read_file(\"data/osm_network_gent.gpkg\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 63,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>highway</th>\n",
-       "      <th>name</th>\n",
-       "      <th>osm_type</th>\n",
-       "      <th>geometry</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>primary</td>\n",
-       "      <td>Oudenaardsesteenweg</td>\n",
-       "      <td>way</td>\n",
-       "      <td>LINESTRING (3.70951 51.01845, 3.70951 51.01863...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>primary</td>\n",
-       "      <td>Krijgslaan</td>\n",
-       "      <td>way</td>\n",
-       "      <td>LINESTRING (3.71157 51.02641, 3.71165 51.02642)</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>residential</td>\n",
-       "      <td>Paul Fredericqstraat</td>\n",
-       "      <td>way</td>\n",
-       "      <td>LINESTRING (3.71340 51.04028, 3.71338 51.04032...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>residential</td>\n",
-       "      <td>Meersstraat</td>\n",
-       "      <td>way</td>\n",
-       "      <td>LINESTRING (3.71036 51.03886, 3.71041 51.03888)</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>residential</td>\n",
-       "      <td>Jakob Heremansstraat</td>\n",
-       "      <td>way</td>\n",
-       "      <td>LINESTRING (3.71154 51.04173, 3.71149 51.04166...</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "       highway                  name osm_type  \\\n",
-       "0      primary   Oudenaardsesteenweg      way   \n",
-       "1      primary            Krijgslaan      way   \n",
-       "2  residential  Paul Fredericqstraat      way   \n",
-       "3  residential           Meersstraat      way   \n",
-       "4  residential  Jakob Heremansstraat      way   \n",
-       "\n",
-       "                                            geometry  \n",
-       "0  LINESTRING (3.70951 51.01845, 3.70951 51.01863...  \n",
-       "1    LINESTRING (3.71157 51.02641, 3.71165 51.02642)  \n",
-       "2  LINESTRING (3.71340 51.04028, 3.71338 51.04032...  \n",
-       "3    LINESTRING (3.71036 51.03886, 3.71041 51.03888)  \n",
-       "4  LINESTRING (3.71154 51.04173, 3.71149 51.04166...  "
-      ]
-     },
-     "execution_count": 63,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
+    "streets = geopandas.read_file(\"data/osm_network_gent.gpkg\")\n",
     "streets.head()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 64,
-   "metadata": {},
+   "execution_count": null,
+   "id": "373bf787",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
    "outputs": [],
    "source": [
     "streets = streets.to_crs(\"EPSG:31370\")"
@@ -3754,79 +1609,45 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 65,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<AxesSubplot:>"
-      ]
-     },
-     "execution_count": 65,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": null,
+   "id": "b5f41caa",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
    "source": [
     "streets.plot()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 66,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<AxesSubplot:>"
-      ]
-     },
-     "execution_count": 66,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1440x1440 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": null,
+   "id": "62ce070c",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
    "source": [
     "streets.plot(column=\"highway\", figsize=(20, 20), legend=True, cmap=\"tab20\")"
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "f25d21c8",
    "metadata": {},
    "source": [
-    "(Note: for interactively exploring such data, there are better solutions as the GeoPandas `.plot()` method, such as opening the data in QGIS, or using an interactive visualization library)\n",
+    "(Note: for interactively exploring such data, there are better solutions as the GeoPandas `.plot()` method, such as opening the data in QGIS, or using an interactive visualization library, see notebook [visualization-04-interactive](./visualization-04-interactive.ipynb)).\n",
     "\n",
     "To relate the measured concentration with the road type, we want to determine for each location at what type of street it is located. Since the measurements are not exactly located on one of the lines, we are going to look at the closest street for each location."
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "77abeaec",
    "metadata": {},
    "source": [
     "<div class=\"alert alert-success\">\n",
@@ -3848,6 +1669,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "60c64971",
    "metadata": {},
    "source": [
     "We take the first point geometry in the Gent dataset:"
@@ -3855,7 +1677,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 67,
+   "execution_count": null,
+   "id": "de97f06f",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -3864,94 +1687,52 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 68,
+   "execution_count": null,
+   "id": "b56de5c2",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/svg+xml": [
-       "<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" width=\"100.0\" height=\"100.0\" viewBox=\"100685.45574927583 191420.39818577655 2.0 2.0\" preserveAspectRatio=\"xMinYMin meet\"><g transform=\"matrix(1,0,0,-1,0,382842.7963715531)\"><circle cx=\"100686.45574927583\" cy=\"191421.39818577655\" r=\"0.06\" stroke=\"#555555\" stroke-width=\"0.02\" fill=\"#66cc99\" opacity=\"0.6\" /></g></svg>"
-      ],
-      "text/plain": [
-       "<shapely.geometry.point.Point at 0x7fdc7802ff40>"
-      ]
-     },
-     "execution_count": 68,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "point"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 69,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0        3349.763366\n",
-       "1        3251.414227\n",
-       "2        3402.320300\n",
-       "3        3251.133464\n",
-       "4        3376.044304\n",
-       "            ...     \n",
-       "27198    5040.478847\n",
-       "27199    5004.141518\n",
-       "27200    8586.518821\n",
-       "27201    8419.520072\n",
-       "27202    8426.402834\n",
-       "Length: 27203, dtype: float64"
-      ]
-     },
-     "execution_count": 69,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "id": "5416854a",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
    "source": [
     "streets.distance(point)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 70,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.18067199114036214"
-      ]
-     },
-     "execution_count": 70,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "id": "05e21ec8",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
    "source": [
     "streets.distance(point).min()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 71,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "5831"
-      ]
-     },
-     "execution_count": 71,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "id": "0922941d",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
    "source": [
     "idx_closest = streets.distance(point).idxmin()\n",
     "idx_closest"
@@ -3959,70 +1740,49 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 72,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "highway                                           residential\n",
-       "name                                   Kleine Goedingenstraat\n",
-       "osm_type                                                  way\n",
-       "geometry    LINESTRING (100713.4011653297 191402.809063442...\n",
-       "Name: 5831, dtype: object"
-      ]
-     },
-     "execution_count": 72,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "id": "f2a5eae1",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
    "source": [
     "streets.loc[idx_closest]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 73,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'residential'"
-      ]
-     },
-     "execution_count": 73,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "id": "6f279c41",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
    "source": [
     "streets[\"highway\"][idx_closest]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 74,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'residential'"
-      ]
-     },
-     "execution_count": 74,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "id": "7b34181b",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
    "source": [
     "streets.loc[idx_closest, \"highway\"]"
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "6772c723",
    "metadata": {},
    "source": [
     "We now want to repeat the above analysis for each measurement location. So let's start with writing a reusable function.\n",
@@ -4049,8 +1809,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 75,
-   "metadata": {},
+   "execution_count": null,
+   "id": "8e0c6d13",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
    "outputs": [],
    "source": [
     "def closest_road_type(point, streets):\n",
@@ -4062,26 +1827,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 76,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'residential'"
-      ]
-     },
-     "execution_count": 76,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "id": "fda877c2",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
    "source": [
     "closest_road_type(point, streets)"
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "e4e9bda6",
    "metadata": {},
    "source": [
     "Now we can apply this function to each of the point locations. However, with this brute force method, applying it as is using `gdf_gent` and `streets` takes quite some time. We can speed up the distance calculation by reducing the number of linestrings in the `streets` dataframe to compare with.\n",
@@ -4100,6 +1860,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "68bd6b12",
    "metadata": {},
    "source": [
     "When running this, you can see it already takes a bit of time, even for the first 20 rows:"
@@ -4107,56 +1868,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 77,
+   "execution_count": null,
+   "id": "13226d3f",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CPU times: user 3.22 s, sys: 352 µs, total: 3.22 s\n",
-      "Wall time: 3.22 s\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "22     residential\n",
-       "35     residential\n",
-       "46       secondary\n",
-       "53     residential\n",
-       "54        tertiary\n",
-       "55     residential\n",
-       "61     residential\n",
-       "62     residential\n",
-       "65        tertiary\n",
-       "66     residential\n",
-       "67     residential\n",
-       "68     residential\n",
-       "75     residential\n",
-       "87     residential\n",
-       "91     residential\n",
-       "92        tertiary\n",
-       "95     residential\n",
-       "110    residential\n",
-       "152       tertiary\n",
-       "165    residential\n",
-       "Name: geometry, dtype: object"
-      ]
-     },
-     "execution_count": 77,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "%time gdf_gent.geometry.head(20).apply(lambda point: closest_road_type(point, streets))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 78,
-   "metadata": {},
+   "execution_count": null,
+   "id": "55f89dbd",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
    "outputs": [],
    "source": [
     "streets_unioned = streets.dissolve(\"highway\").reset_index()"
@@ -4164,8 +1892,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 79,
-   "metadata": {},
+   "execution_count": null,
+   "id": "9cd3a699",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
    "outputs": [],
    "source": [
     "gdf_gent[\"road_type\"] = gdf_gent.geometry.apply(lambda point: closest_road_type(point, streets_unioned))"
@@ -4173,160 +1906,43 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 80,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>code</th>\n",
-       "      <th>campaign</th>\n",
-       "      <th>no2</th>\n",
-       "      <th>lat</th>\n",
-       "      <th>lon</th>\n",
-       "      <th>geometry</th>\n",
-       "      <th>road_type</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>22</th>\n",
-       "      <td>193077</td>\n",
-       "      <td>facade</td>\n",
-       "      <td>19.1</td>\n",
-       "      <td>51.03088</td>\n",
-       "      <td>3.665770</td>\n",
-       "      <td>POINT (100686.456 191421.398)</td>\n",
-       "      <td>residential</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>35</th>\n",
-       "      <td>193479</td>\n",
-       "      <td>facade</td>\n",
-       "      <td>28.6</td>\n",
-       "      <td>51.05893</td>\n",
-       "      <td>3.700956</td>\n",
-       "      <td>POINT (103182.680 194519.003)</td>\n",
-       "      <td>residential</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>46</th>\n",
-       "      <td>193677</td>\n",
-       "      <td>facade</td>\n",
-       "      <td>43.1</td>\n",
-       "      <td>51.02716</td>\n",
-       "      <td>3.747452</td>\n",
-       "      <td>POINT (106412.751 190956.529)</td>\n",
-       "      <td>secondary</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>53</th>\n",
-       "      <td>193797</td>\n",
-       "      <td>facade</td>\n",
-       "      <td>31.6</td>\n",
-       "      <td>51.01916</td>\n",
-       "      <td>3.763333</td>\n",
-       "      <td>POINT (107519.603 190057.392)</td>\n",
-       "      <td>residential</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>54</th>\n",
-       "      <td>193836</td>\n",
-       "      <td>facade</td>\n",
-       "      <td>30.1</td>\n",
-       "      <td>51.07007</td>\n",
-       "      <td>3.703455</td>\n",
-       "      <td>POINT (103368.966 195756.687)</td>\n",
-       "      <td>tertiary</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "      code campaign   no2       lat       lon                       geometry  \\\n",
-       "22  193077   facade  19.1  51.03088  3.665770  POINT (100686.456 191421.398)   \n",
-       "35  193479   facade  28.6  51.05893  3.700956  POINT (103182.680 194519.003)   \n",
-       "46  193677   facade  43.1  51.02716  3.747452  POINT (106412.751 190956.529)   \n",
-       "53  193797   facade  31.6  51.01916  3.763333  POINT (107519.603 190057.392)   \n",
-       "54  193836   facade  30.1  51.07007  3.703455  POINT (103368.966 195756.687)   \n",
-       "\n",
-       "      road_type  \n",
-       "22  residential  \n",
-       "35  residential  \n",
-       "46    secondary  \n",
-       "53  residential  \n",
-       "54     tertiary  "
-      ]
-     },
-     "execution_count": 80,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "id": "79904b18",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
    "source": [
     "gdf_gent.head()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 81,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "residential      886\n",
-       "tertiary         208\n",
-       "secondary         61\n",
-       "primary           50\n",
-       "unclassified      39\n",
-       "living_street     22\n",
-       "service            7\n",
-       "track              5\n",
-       "path               4\n",
-       "pedestrian         3\n",
-       "footway            3\n",
-       "cycleway           3\n",
-       "Name: road_type, dtype: int64"
-      ]
-     },
-     "execution_count": 81,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "id": "e43e2410",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
    "source": [
     "gdf_gent[\"road_type\"].value_counts()"
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "1f5b8eb6",
    "metadata": {},
    "source": [
-    "**Note!** We have been using a brute-force search for the closest street by calculating for each point the distance to all streets. This is a good exercise to learn the syntax, but there are however better methods for such \"nearest\" queries. See eg https://automating-gis-processes.github.io/site/notebooks/L3/nearest-neighbor-faster.html"
+    "**Note!** We have been using a brute-force search for the closest street by calculating for each point the distance to all streets. This is a good exercise to learn the syntax, but there are better methods for such \"nearest\" queries. See eg https://geopandas.org/en/latest/docs/user_guide/mergingdata.html#nearest-joins"
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "3b738e6d",
    "metadata": {},
    "source": [
     "<div class=\"alert alert-success\">\n",
@@ -4348,9 +1964,18 @@
     "</div>"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "7c47a51f",
+   "metadata": {},
+   "source": [
+    "Replace categories:"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 82,
+   "execution_count": null,
+   "id": "4435673c",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -4363,16 +1988,30 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 83,
-   "metadata": {},
+   "execution_count": null,
+   "id": "1f953fc5",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
    "outputs": [],
    "source": [
     "gdf_gent[\"road_type\"] = gdf_gent[\"road_type\"].replace(mapping)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "8b353618",
+   "metadata": {},
+   "source": [
+    "Filter categories:"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 84,
+   "execution_count": null,
+   "id": "ba577bd7",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -4381,8 +2020,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 85,
-   "metadata": {},
+   "execution_count": null,
+   "id": "8a482897",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
    "outputs": [],
    "source": [
     "subset = gdf_gent[gdf_gent[\"road_type\"].isin(categories)]"
@@ -4390,32 +2034,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 86,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "road_type\n",
-       "pedestrian      32\n",
-       "primary         50\n",
-       "residential    886\n",
-       "secondary       61\n",
-       "tertiary       208\n",
-       "dtype: int64"
-      ]
-     },
-     "execution_count": 86,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "id": "5bf99b99",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
    "source": [
     "subset.groupby(\"road_type\").size()  # subset[\"road_type\"].value_counts()"
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "dda7e1b5",
    "metadata": {},
    "source": [
     "<div class=\"alert alert-success\">\n",
@@ -4429,32 +2062,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 87,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "road_type\n",
-       "pedestrian     24.850000\n",
-       "primary        37.318000\n",
-       "residential    24.719413\n",
-       "secondary      30.209836\n",
-       "tertiary       28.471154\n",
-       "Name: no2, dtype: float64"
-      ]
-     },
-     "execution_count": 87,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "id": "972f0de1",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
    "source": [
     "subset.groupby(\"road_type\")['no2'].mean()"
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "56d5a61a",
    "metadata": {},
    "source": [
     "<div class=\"alert alert-success\">\n",
@@ -4468,8 +2090,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 88,
-   "metadata": {},
+   "execution_count": null,
+   "id": "996c7a01",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
    "outputs": [],
    "source": [
     "import seaborn"
@@ -4477,47 +2104,33 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 89,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<AxesSubplot:xlabel='road_type', ylabel='no2'>"
-      ]
-     },
-     "execution_count": 89,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": null,
+   "id": "754bc51a",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
    "source": [
     "seaborn.boxplot(x=\"road_type\", y=\"no2\", data=subset, order=categories)"
    ]
   },
   {
    "cell_type": "markdown",
+   "id": "ba789dca",
    "metadata": {},
    "source": [
-    "This analysis confirms that the NO2 concentration is clearly related to traffic!"
+    "This analysis confirms that the NO2 concentration is clearly related to traffic."
    ]
   }
  ],
  "metadata": {
+  "jupytext": {
+   "default_lexer": "ipython3"
+  },
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -4531,9 +2144,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.5"
+   "version": "3.12.11"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 4
+ "nbformat_minor": 5
 }
diff --git a/_solved/case-sea-surface-temperature.ipynb b/_solved/case-sea-surface-temperature.ipynb
index b5a9c43..db7f137 100644
--- a/_solved/case-sea-surface-temperature.ipynb
+++ b/_solved/case-sea-surface-temperature.ipynb
@@ -4,12 +4,20 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Use case: Sea surface temperature"
+    "<p><font size=\"6\"><b>CASE - Sea Surface Temperature data</b></font></p>\n",
+    "\n",
+    "\n",
+    "> *DS Python for GIS and Geoscience*  \n",
+    "> *September, 2025*\n",
+    ">\n",
+    "> *© 2025, Joris Van den Bossche and Stijn Van Hoey. Licensed under [CC BY 4.0 Creative Commons](http://creativecommons.org/licenses/by/4.0/)*\n",
+    "\n",
+    "---"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 130,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -30,24 +38,32 @@
     "\n",
     "\n",
     "\n",
-    "First we download the dataset. We will use the [NOAA Extended Reconstructed Sea Surface Temperature (ERSST)](https://psl.noaa.gov/thredds/catalog/Datasets/noaa.ersst/catalog.html?dataset=Datasets/noaa.ersst/sst.mnmean.v4.nc) v4 product. Download the data from this link: https://psl.noaa.gov/thredds/fileServer/Datasets/noaa.ersst/sst.mnmean.v4.nc and store it in the same folder as the notebook as `sst.mnmean.v4.nc`."
+    "First we download the dataset. \n",
+    "\n",
+    "<div class=\"alert alert-warning\">\n",
+    "\n",
+    "**! Manual download needed !** <br>\n",
+    "\n",
+    "We will use the [NOAA Extended Reconstructed Sea Surface Temperature (ERSST)](https://psl.noaa.gov/thredds/catalog/Datasets/noaa.ersst/catalog.html?dataset=Datasets/noaa.ersst/sst.mnmean.v4.nc) v4 product. Download the data from this link: https://psl.noaa.gov/thredds/fileServer/Datasets/noaa.ersst/sst.mnmean.v4.nc and store it in a subfolder `data/` from the notebook as `sst.mnmean.v4.nc`.\n",
+    "\n",
+    "</div>"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Reading in the data set, ignoring the `time_bnds` variable: "
+    "Reading in the data set, ignoring the `time_bnds` variable:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 194,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
-    "data = './sst.mnmean.v4.nc'\n",
-    "ds = xr.open_dataset(data, drop_variables=['time_bnds'], engine=\"h5netcdf\")"
+    "data = './data/sst.mnmean.v4.nc'\n",
+    "ds = xr.open_dataset(data, drop_variables=['time_bnds'])"
    ]
   },
   {
@@ -59,514 +75,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 195,
-   "metadata": {},
-   "outputs": [
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-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
-       "Dimensions:  (lat: 89, lon: 180, time: 708)\n",
-       "Coordinates:\n",
-       "  * lat      (lat) float32 88.0 86.0 84.0 82.0 80.0 ... -82.0 -84.0 -86.0 -88.0\n",
-       "  * lon      (lon) float32 0.0 2.0 4.0 6.0 8.0 ... 350.0 352.0 354.0 356.0 358.0\n",
-       "  * time     (time) datetime64[ns] 1960-01-01 1960-02-01 ... 2018-12-01\n",
-       "Data variables:\n",
-       "    sst      (time, lat, lon) float32 -1.8 -1.8 -1.8 -1.8 ... nan nan nan nan\n",
-       "Attributes:\n",
-       "    history:                 created 10/2014 by CAS using NCDC&#x27;s ERSST V4 asc...\n",
-       "    title:                   NOAA Extended Reconstructed Sea Surface Temperat...\n",
-       "    climatology:             Climatology is based on 1971-2000 SST, Xue, Y., ...\n",
-       "    description:             In situ data: ICOADS2.5 before 2007 and NCEP in ...\n",
-       "    citation:                Huang et al, 2014: Extended Reconstructed Sea Su...\n",
-       "    comment:                 SSTs were observed by conventional thermometers ...\n",
-       "    Conventions:             CF-1.2\n",
-       "    institution:             This version written at NOAA/ESRL PSD: obtained ...\n",
-       "    keywords_vocabulary:     NASA Global Change Master Directory (GCMD) Scien...\n",
-       "    keywords:                Earth Science &gt; Oceans &gt; Ocean Temperature &gt; Sea...\n",
-       "    platform:                Ship and Buoy SSTs from ICOADS R2.5 and NCEP GTS\n",
-       "    instrument:              Conventional thermometers\n",
-       "    source:                  ICOADS R2.5 SST, NCEP GTS SST, HadISST ice, NCEP...\n",
-       "    source_comment:          SSTs were observed by conventional thermometers ...\n",
-       "    geospatial_lon_min:      [-1.]\n",
-       "    geospatial_lon_max:      [359.]\n",
-       "    geospatial_laty_max:     [89.]\n",
-       "    geospatial_laty_min:     [-89.]\n",
-       "    geospatial_lat_max:      [89.]\n",
-       "    geospatial_lat_min:      [-89.]\n",
-       "    geospatial_lat_units:    degrees_north\n",
-       "    geospatial_lon_units:    degrees_east\n",
-       "    cdm_data_type:           Grid\n",
-       "    project:                 NOAA Extended Reconstructed Sea Surface Temperat...\n",
-       "    license:                 No constraints on data access or use\n",
-       "    original_publisher_url:  http://www.ncdc.noaa.gov\n",
-       "    dataset_title:           Extended Reconstructed Sea Surface Temperature (...\n",
-       "    Source:                  http://www.ncdc.noaa.gov/data-access/marineocean...\n",
-       "    data_modified:           2020-03-03\n",
-       "    References:              http://www.psl.noaa.gov/data/gridded/data.noaa.e...</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-51c25a2b-47dc-47d1-a86a-f3a08d26973a' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-51c25a2b-47dc-47d1-a86a-f3a08d26973a' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>lat</span>: 89</li><li><span class='xr-has-index'>lon</span>: 180</li><li><span class='xr-has-index'>time</span>: 708</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-9a407649-b064-483d-9d2a-7bad8b261ca2' class='xr-section-summary-in' type='checkbox'  checked><label for='section-9a407649-b064-483d-9d2a-7bad8b261ca2' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>88.0 86.0 84.0 ... -86.0 -88.0</div><input id='attrs-9141cfb8-9c5c-48d7-9c7a-c9fe291284f1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9141cfb8-9c5c-48d7-9c7a-c9fe291284f1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e40fdfee-ca67-470f-a948-994c343f78c4' class='xr-var-data-in' type='checkbox'><label for='data-e40fdfee-ca67-470f-a948-994c343f78c4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>long_name :</span></dt><dd>Latitude</dd><dt><span>actual_range :</span></dt><dd>[ 88. -88.]</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>coordinate_defines :</span></dt><dd>center</dd></dl></div><div class='xr-var-data'><pre>array([ 88.,  86.,  84.,  82.,  80.,  78.,  76.,  74.,  72.,  70.,  68.,  66.,\n",
-       "        64.,  62.,  60.,  58.,  56.,  54.,  52.,  50.,  48.,  46.,  44.,  42.,\n",
-       "        40.,  38.,  36.,  34.,  32.,  30.,  28.,  26.,  24.,  22.,  20.,  18.,\n",
-       "        16.,  14.,  12.,  10.,   8.,   6.,   4.,   2.,   0.,  -2.,  -4.,  -6.,\n",
-       "        -8., -10., -12., -14., -16., -18., -20., -22., -24., -26., -28., -30.,\n",
-       "       -32., -34., -36., -38., -40., -42., -44., -46., -48., -50., -52., -54.,\n",
-       "       -56., -58., -60., -62., -64., -66., -68., -70., -72., -74., -76., -78.,\n",
-       "       -80., -82., -84., -86., -88.], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 2.0 4.0 ... 354.0 356.0 358.0</div><input id='attrs-726f898a-1073-4c45-9142-69f7487f9836' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-726f898a-1073-4c45-9142-69f7487f9836' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-44f095f5-2eff-4884-8d8d-c6fc51234a2f' class='xr-var-data-in' type='checkbox'><label for='data-44f095f5-2eff-4884-8d8d-c6fc51234a2f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>long_name :</span></dt><dd>Longitude</dd><dt><span>actual_range :</span></dt><dd>[  0. 358.]</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>coordinate_defines :</span></dt><dd>center</dd></dl></div><div class='xr-var-data'><pre>array([  0.,   2.,   4.,   6.,   8.,  10.,  12.,  14.,  16.,  18.,  20.,  22.,\n",
-       "        24.,  26.,  28.,  30.,  32.,  34.,  36.,  38.,  40.,  42.,  44.,  46.,\n",
-       "        48.,  50.,  52.,  54.,  56.,  58.,  60.,  62.,  64.,  66.,  68.,  70.,\n",
-       "        72.,  74.,  76.,  78.,  80.,  82.,  84.,  86.,  88.,  90.,  92.,  94.,\n",
-       "        96.,  98., 100., 102., 104., 106., 108., 110., 112., 114., 116., 118.,\n",
-       "       120., 122., 124., 126., 128., 130., 132., 134., 136., 138., 140., 142.,\n",
-       "       144., 146., 148., 150., 152., 154., 156., 158., 160., 162., 164., 166.,\n",
-       "       168., 170., 172., 174., 176., 178., 180., 182., 184., 186., 188., 190.,\n",
-       "       192., 194., 196., 198., 200., 202., 204., 206., 208., 210., 212., 214.,\n",
-       "       216., 218., 220., 222., 224., 226., 228., 230., 232., 234., 236., 238.,\n",
-       "       240., 242., 244., 246., 248., 250., 252., 254., 256., 258., 260., 262.,\n",
-       "       264., 266., 268., 270., 272., 274., 276., 278., 280., 282., 284., 286.,\n",
-       "       288., 290., 292., 294., 296., 298., 300., 302., 304., 306., 308., 310.,\n",
-       "       312., 314., 316., 318., 320., 322., 324., 326., 328., 330., 332., 334.,\n",
-       "       336., 338., 340., 342., 344., 346., 348., 350., 352., 354., 356., 358.],\n",
-       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1960-01-01 ... 2018-12-01</div><input id='attrs-2806d5e2-bca2-4b50-a5fe-138a9cfeca61' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2806d5e2-bca2-4b50-a5fe-138a9cfeca61' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a8f80e5e-f7cb-4f96-aba1-c62c99df6987' class='xr-var-data-in' type='checkbox'><label for='data-a8f80e5e-f7cb-4f96-aba1-c62c99df6987' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Time</dd><dt><span>delta_t :</span></dt><dd>0000-01-00 00:00:00</dd><dt><span>avg_period :</span></dt><dd>0000-01-00 00:00:00</dd><dt><span>prev_avg_period :</span></dt><dd>0000-00-07 00:00:00</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>axis :</span></dt><dd>T</dd><dt><span>actual_range :</span></dt><dd>[19723. 80384.]</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;1960-01-01T00:00:00.000000000&#x27;, &#x27;1960-02-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;1960-03-01T00:00:00.000000000&#x27;, ..., &#x27;2018-10-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2018-11-01T00:00:00.000000000&#x27;, &#x27;2018-12-01T00:00:00.000000000&#x27;],\n",
-       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-03c027c0-32be-488e-85a5-060af8e06361' class='xr-section-summary-in' type='checkbox'  checked><label for='section-03c027c0-32be-488e-85a5-060af8e06361' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>sst</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-1.8 -1.8 -1.8 -1.8 ... nan nan nan</div><input id='attrs-a6f1ae8c-f535-49a9-8bcc-8cd04ecf7b76' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a6f1ae8c-f535-49a9-8bcc-8cd04ecf7b76' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ec44c902-e4f2-4a2e-bdac-43de9a18e5a7' class='xr-var-data-in' type='checkbox'><label for='data-ec44c902-e4f2-4a2e-bdac-43de9a18e5a7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Monthly Means of Sea Surface Temperature</dd><dt><span>units :</span></dt><dd>degC</dd><dt><span>var_desc :</span></dt><dd>Sea Surface Temperature</dd><dt><span>level_desc :</span></dt><dd>Surface</dd><dt><span>statistic :</span></dt><dd>Mean</dd><dt><span>actual_range :</span></dt><dd>[-1.8  33.95]</dd><dt><span>valid_range :</span></dt><dd>[-5. 40.]</dd><dt><span>dataset :</span></dt><dd>NOAA Extended Reconstructed SST V4</dd><dt><span>parent_stat :</span></dt><dd>Individual Values</dd></dl></div><div class='xr-var-data'><pre>array([[[-1.8 , -1.8 , -1.8 , ..., -1.8 , -1.8 , -1.8 ],\n",
-       "        [-1.8 , -1.8 , -1.8 , ..., -1.8 , -1.8 , -1.8 ],\n",
-       "        [-1.8 , -1.8 , -1.8 , ..., -1.8 , -1.8 , -1.8 ],\n",
-       "        ...,\n",
-       "        [  nan,   nan,   nan, ...,   nan,   nan,   nan],\n",
-       "        [  nan,   nan,   nan, ...,   nan,   nan,   nan],\n",
-       "        [  nan,   nan,   nan, ...,   nan,   nan,   nan]],\n",
-       "\n",
-       "       [[-1.8 , -1.8 , -1.8 , ..., -1.8 , -1.8 , -1.8 ],\n",
-       "        [-1.8 , -1.8 , -1.8 , ..., -1.8 , -1.8 , -1.8 ],\n",
-       "        [-1.8 , -1.8 , -1.8 , ..., -1.8 , -1.8 , -1.8 ],\n",
-       "        ...,\n",
-       "        [  nan,   nan,   nan, ...,   nan,   nan,   nan],\n",
-       "        [  nan,   nan,   nan, ...,   nan,   nan,   nan],\n",
-       "        [  nan,   nan,   nan, ...,   nan,   nan,   nan]],\n",
-       "\n",
-       "       [[-1.8 , -1.8 , -1.8 , ..., -1.8 , -1.8 , -1.8 ],\n",
-       "        [-1.8 , -1.8 , -1.8 , ..., -1.8 , -1.8 , -1.8 ],\n",
-       "        [-1.8 , -1.8 , -1.8 , ..., -1.8 , -1.8 , -1.8 ],\n",
-       "        ...,\n",
-       "...\n",
-       "        ...,\n",
-       "        [  nan,   nan,   nan, ...,   nan,   nan,   nan],\n",
-       "        [  nan,   nan,   nan, ...,   nan,   nan,   nan],\n",
-       "        [  nan,   nan,   nan, ...,   nan,   nan,   nan]],\n",
-       "\n",
-       "       [[-1.77, -1.77, -1.76, ..., -1.78, -1.77, -1.77],\n",
-       "        [-1.8 , -1.8 , -1.8 , ..., -1.8 , -1.8 , -1.8 ],\n",
-       "        [-1.7 , -1.74, -1.67, ..., -1.8 , -1.76, -1.69],\n",
-       "        ...,\n",
-       "        [  nan,   nan,   nan, ...,   nan,   nan,   nan],\n",
-       "        [  nan,   nan,   nan, ...,   nan,   nan,   nan],\n",
-       "        [  nan,   nan,   nan, ...,   nan,   nan,   nan]],\n",
-       "\n",
-       "       [[-1.8 , -1.8 , -1.8 , ..., -1.8 , -1.8 , -1.8 ],\n",
-       "        [-1.8 , -1.8 , -1.8 , ..., -1.8 , -1.8 , -1.8 ],\n",
-       "        [-1.8 , -1.8 , -1.8 , ..., -1.8 , -1.8 , -1.8 ],\n",
-       "        ...,\n",
-       "        [  nan,   nan,   nan, ...,   nan,   nan,   nan],\n",
-       "        [  nan,   nan,   nan, ...,   nan,   nan,   nan],\n",
-       "        [  nan,   nan,   nan, ...,   nan,   nan,   nan]]], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-b272b4b3-c343-425e-86bb-1e6e2908eab0' class='xr-section-summary-in' type='checkbox'  ><label for='section-b272b4b3-c343-425e-86bb-1e6e2908eab0' class='xr-section-summary' >Attributes: <span>(30)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>history :</span></dt><dd>created 10/2014 by CAS using NCDC&#x27;s ERSST V4 ascii values</dd><dt><span>title :</span></dt><dd>NOAA Extended Reconstructed Sea Surface Temperature (ERSST), Version 4 (in situ only)</dd><dt><span>climatology :</span></dt><dd>Climatology is based on 1971-2000 SST, Xue, Y., T. M. Smith, and R. W. Reynolds, 2003: Interdecadal changes of 30-yr SST normals during 1871.2000. Journal of Climate, 16, 1601-1612.</dd><dt><span>description :</span></dt><dd>In situ data: ICOADS2.5 before 2007 and NCEP in situ data from 2008 to present. Ice data: HadISST ice before 2010 and NCEP ice after 2010.</dd><dt><span>citation :</span></dt><dd>Huang et al, 2014: Extended Reconstructed Sea Surface Temperatures Version 4 (ERSST.v4), Part I. Upgrades and Intercomparisons. Journal of Climate.</dd><dt><span>comment :</span></dt><dd>SSTs were observed by conventional thermometers in Buckets (insulated or un-insulated canvas and wooded buckets) or Engine Room Intaker</dd><dt><span>Conventions :</span></dt><dd>CF-1.2</dd><dt><span>institution :</span></dt><dd>This version written at NOAA/ESRL PSD: obtained from NOAA/NESDIS/National Climatic Data Center</dd><dt><span>keywords_vocabulary :</span></dt><dd>NASA Global Change Master Directory (GCMD) Science Keywords</dd><dt><span>keywords :</span></dt><dd>Earth Science &gt; Oceans &gt; Ocean Temperature &gt; Sea Surface Temperature &gt;</dd><dt><span>platform :</span></dt><dd>Ship and Buoy SSTs from ICOADS R2.5 and NCEP GTS</dd><dt><span>instrument :</span></dt><dd>Conventional thermometers</dd><dt><span>source :</span></dt><dd>ICOADS R2.5 SST, NCEP GTS SST, HadISST ice, NCEP ice</dd><dt><span>source_comment :</span></dt><dd>SSTs were observed by conventional thermometers in Buckets (insulated or un-insulated canvas and wooded buckets) or Engine Room Intaker</dd><dt><span>geospatial_lon_min :</span></dt><dd>[-1.]</dd><dt><span>geospatial_lon_max :</span></dt><dd>[359.]</dd><dt><span>geospatial_laty_max :</span></dt><dd>[89.]</dd><dt><span>geospatial_laty_min :</span></dt><dd>[-89.]</dd><dt><span>geospatial_lat_max :</span></dt><dd>[89.]</dd><dt><span>geospatial_lat_min :</span></dt><dd>[-89.]</dd><dt><span>geospatial_lat_units :</span></dt><dd>degrees_north</dd><dt><span>geospatial_lon_units :</span></dt><dd>degrees_east</dd><dt><span>cdm_data_type :</span></dt><dd>Grid</dd><dt><span>project :</span></dt><dd>NOAA Extended Reconstructed Sea Surface Temperature (ERSST)</dd><dt><span>license :</span></dt><dd>No constraints on data access or use</dd><dt><span>original_publisher_url :</span></dt><dd>http://www.ncdc.noaa.gov</dd><dt><span>dataset_title :</span></dt><dd>Extended Reconstructed Sea Surface Temperature (ERSST) v4</dd><dt><span>Source :</span></dt><dd>http://www.ncdc.noaa.gov/data-access/marineocean-data/extended-reconstructed-sea-surface-temperature-ersst-v4</dd><dt><span>data_modified :</span></dt><dd>2020-03-03</dd><dt><span>References :</span></dt><dd>http://www.psl.noaa.gov/data/gridded/data.noaa.ersst.v4.html</dd></dl></div></li></ul></div></div>"
-      ],
-      "text/plain": [
-       "<xarray.Dataset>\n",
-       "Dimensions:  (lat: 89, lon: 180, time: 708)\n",
-       "Coordinates:\n",
-       "  * lat      (lat) float32 88.0 86.0 84.0 82.0 80.0 ... -82.0 -84.0 -86.0 -88.0\n",
-       "  * lon      (lon) float32 0.0 2.0 4.0 6.0 8.0 ... 350.0 352.0 354.0 356.0 358.0\n",
-       "  * time     (time) datetime64[ns] 1960-01-01 1960-02-01 ... 2018-12-01\n",
-       "Data variables:\n",
-       "    sst      (time, lat, lon) float32 -1.8 -1.8 -1.8 -1.8 ... nan nan nan nan\n",
-       "Attributes:\n",
-       "    history:                 created 10/2014 by CAS using NCDC's ERSST V4 asc...\n",
-       "    title:                   NOAA Extended Reconstructed Sea Surface Temperat...\n",
-       "    climatology:             Climatology is based on 1971-2000 SST, Xue, Y., ...\n",
-       "    description:             In situ data: ICOADS2.5 before 2007 and NCEP in ...\n",
-       "    citation:                Huang et al, 2014: Extended Reconstructed Sea Su...\n",
-       "    comment:                 SSTs were observed by conventional thermometers ...\n",
-       "    Conventions:             CF-1.2\n",
-       "    institution:             This version written at NOAA/ESRL PSD: obtained ...\n",
-       "    keywords_vocabulary:     NASA Global Change Master Directory (GCMD) Scien...\n",
-       "    keywords:                Earth Science > Oceans > Ocean Temperature > Sea...\n",
-       "    platform:                Ship and Buoy SSTs from ICOADS R2.5 and NCEP GTS\n",
-       "    instrument:              Conventional thermometers\n",
-       "    source:                  ICOADS R2.5 SST, NCEP GTS SST, HadISST ice, NCEP...\n",
-       "    source_comment:          SSTs were observed by conventional thermometers ...\n",
-       "    geospatial_lon_min:      [-1.]\n",
-       "    geospatial_lon_max:      [359.]\n",
-       "    geospatial_laty_max:     [89.]\n",
-       "    geospatial_laty_min:     [-89.]\n",
-       "    geospatial_lat_max:      [89.]\n",
-       "    geospatial_lat_min:      [-89.]\n",
-       "    geospatial_lat_units:    degrees_north\n",
-       "    geospatial_lon_units:    degrees_east\n",
-       "    cdm_data_type:           Grid\n",
-       "    project:                 NOAA Extended Reconstructed Sea Surface Temperat...\n",
-       "    license:                 No constraints on data access or use\n",
-       "    original_publisher_url:  http://www.ncdc.noaa.gov\n",
-       "    dataset_title:           Extended Reconstructed Sea Surface Temperature (...\n",
-       "    Source:                  http://www.ncdc.noaa.gov/data-access/marineocean...\n",
-       "    data_modified:           2020-03-03\n",
-       "    References:              http://www.psl.noaa.gov/data/gridded/data.noaa.e..."
-      ]
-     },
-     "execution_count": 195,
-     "metadata": {},
-     "output_type": "execute_result"
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
     }
-   ],
+   },
+   "outputs": [],
    "source": [
     "ds = ds.sel(time=slice('1960', '2018')).load()  # load into memory\n",
     "ds"
@@ -576,7 +91,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The data with the extension `nc` is a NetCDF format. NetCDF (Network Common Data Format) is the most widely used format for distributing geoscience data. NetCDF is maintained by the [Unidata](https://www.unidata.ucar.edu/) organization. Check the [netcdf website](https://www.unidata.ucar.edu/software/netcdf/docs/faq.html#whatisit) for more information. Xarray was designed to make reading netCDF files in python as easy, powerful, and flexible as possible. "
+    "The data with the extension `nc` is a NetCDF format. NetCDF (Network Common Data Format) is the most widely used format for distributing geoscience data. NetCDF is maintained by the [Unidata](https://www.unidata.ucar.edu/) organization. Check the [netcdf website](https://www.unidata.ucar.edu/software/netcdf/docs/faq.html#whatisit) for more information. Xarray was designed to make reading netCDF files in python as easy, powerful, and flexible as possible."
    ]
   },
   {
@@ -626,138 +141,86 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 196,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "11342160"
-      ]
-     },
-     "execution_count": 196,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
    "source": [
+    "# size attribute of array object\n",
     "ds[\"sst\"].size"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 197,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(708, 89, 180)"
-      ]
-     },
-     "execution_count": 197,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
    "source": [
+    "# shape attribute of array object\n",
     "ds[\"sst\"].shape"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 198,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Frozen(SortedKeysDict({'lat': 89, 'lon': 180, 'time': 708}))"
-      ]
-     },
-     "execution_count": 198,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
    "source": [
+    "# dims attribute of dataset object\n",
     "ds.dims"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 199,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'long_name': 'Monthly Means of Sea Surface Temperature',\n",
-       " 'units': 'degC',\n",
-       " 'var_desc': 'Sea Surface Temperature',\n",
-       " 'level_desc': 'Surface',\n",
-       " 'statistic': 'Mean',\n",
-       " 'actual_range': array([-1.8 , 33.95], dtype=float32),\n",
-       " 'valid_range': array([-5., 40.], dtype=float32),\n",
-       " 'dataset': 'NOAA Extended Reconstructed SST V4',\n",
-       " 'parent_stat': 'Individual Values'}"
-      ]
-     },
-     "execution_count": 199,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
    "source": [
+    "# attributes of array\n",
     "ds[\"sst\"].attrs"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 200,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'history': \"created 10/2014 by CAS using NCDC's ERSST V4 ascii values\",\n",
-       " 'title': 'NOAA Extended Reconstructed Sea Surface Temperature (ERSST), Version 4 (in situ only)',\n",
-       " 'climatology': 'Climatology is based on 1971-2000 SST, Xue, Y., T. M. Smith, and R. W. Reynolds, 2003: Interdecadal changes of 30-yr SST normals during 1871.2000. Journal of Climate, 16, 1601-1612.',\n",
-       " 'description': 'In situ data: ICOADS2.5 before 2007 and NCEP in situ data from 2008 to present. Ice data: HadISST ice before 2010 and NCEP ice after 2010.',\n",
-       " 'citation': 'Huang et al, 2014: Extended Reconstructed Sea Surface Temperatures Version 4 (ERSST.v4), Part I. Upgrades and Intercomparisons. Journal of Climate.',\n",
-       " 'comment': 'SSTs were observed by conventional thermometers in Buckets (insulated or un-insulated canvas and wooded buckets) or Engine Room Intaker',\n",
-       " 'Conventions': 'CF-1.2',\n",
-       " 'institution': 'This version written at NOAA/ESRL PSD: obtained from NOAA/NESDIS/National Climatic Data Center',\n",
-       " 'keywords_vocabulary': 'NASA Global Change Master Directory (GCMD) Science Keywords',\n",
-       " 'keywords': 'Earth Science > Oceans > Ocean Temperature > Sea Surface Temperature >',\n",
-       " 'platform': 'Ship and Buoy SSTs from ICOADS R2.5 and NCEP GTS',\n",
-       " 'instrument': 'Conventional thermometers',\n",
-       " 'source': 'ICOADS R2.5 SST, NCEP GTS SST, HadISST ice, NCEP ice',\n",
-       " 'source_comment': 'SSTs were observed by conventional thermometers in Buckets (insulated or un-insulated canvas and wooded buckets) or Engine Room Intaker',\n",
-       " 'geospatial_lon_min': array([-1.], dtype=float32),\n",
-       " 'geospatial_lon_max': array([359.], dtype=float32),\n",
-       " 'geospatial_laty_max': array([89.], dtype=float32),\n",
-       " 'geospatial_laty_min': array([-89.], dtype=float32),\n",
-       " 'geospatial_lat_max': array([89.], dtype=float32),\n",
-       " 'geospatial_lat_min': array([-89.], dtype=float32),\n",
-       " 'geospatial_lat_units': 'degrees_north',\n",
-       " 'geospatial_lon_units': 'degrees_east',\n",
-       " 'cdm_data_type': 'Grid',\n",
-       " 'project': 'NOAA Extended Reconstructed Sea Surface Temperature (ERSST)',\n",
-       " 'license': 'No constraints on data access or use',\n",
-       " 'original_publisher_url': 'http://www.ncdc.noaa.gov',\n",
-       " 'dataset_title': 'Extended Reconstructed Sea Surface Temperature (ERSST) v4',\n",
-       " 'Source': 'http://www.ncdc.noaa.gov/data-access/marineocean-data/extended-reconstructed-sea-surface-temperature-ersst-v4',\n",
-       " 'data_modified': '2020-03-03',\n",
-       " 'References': 'http://www.psl.noaa.gov/data/gridded/data.noaa.ersst.v4.html'}"
-      ]
-     },
-     "execution_count": 200,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
    "source": [
+    "# attributes of data set\n",
     "ds.attrs"
    ]
   },
@@ -777,7 +240,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 201,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -786,548 +249,13 @@
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-       "...\n",
-       "        ...,\n",
-       "        [  nan,   nan,   nan, ...,   nan,   nan,   nan],\n",
-       "        [  nan,   nan,   nan, ...,   nan,   nan,   nan],\n",
-       "        [  nan,   nan,   nan, ...,   nan,   nan,   nan]],\n",
-       "\n",
-       "       [[-1.77, -1.77, -1.76, ..., -1.78, -1.77, -1.77],\n",
-       "        [-1.8 , -1.8 , -1.8 , ..., -1.8 , -1.8 , -1.8 ],\n",
-       "        [-1.7 , -1.74, -1.67, ..., -1.8 , -1.76, -1.69],\n",
-       "        ...,\n",
-       "        [  nan,   nan,   nan, ...,   nan,   nan,   nan],\n",
-       "        [  nan,   nan,   nan, ...,   nan,   nan,   nan],\n",
-       "        [  nan,   nan,   nan, ...,   nan,   nan,   nan]],\n",
-       "\n",
-       "       [[-1.8 , -1.8 , -1.8 , ..., -1.8 , -1.8 , -1.8 ],\n",
-       "        [-1.8 , -1.8 , -1.8 , ..., -1.8 , -1.8 , -1.8 ],\n",
-       "        [-1.8 , -1.8 , -1.8 , ..., -1.8 , -1.8 , -1.8 ],\n",
-       "        ...,\n",
-       "        [  nan,   nan,   nan, ...,   nan,   nan,   nan],\n",
-       "        [  nan,   nan,   nan, ...,   nan,   nan,   nan],\n",
-       "        [  nan,   nan,   nan, ...,   nan,   nan,   nan]]], dtype=float32)\n",
-       "Coordinates:\n",
-       "  * lat      (lat) float32 88.0 86.0 84.0 82.0 80.0 ... -82.0 -84.0 -86.0 -88.0\n",
-       "  * lon      (lon) float32 0.0 2.0 4.0 6.0 8.0 ... 350.0 352.0 354.0 356.0 358.0\n",
-       "  * time     (time) datetime64[ns] 1960-01-01 1960-02-01 ... 2018-12-01\n",
-       "Attributes:\n",
-       "    long_name:     Monthly Means of Sea Surface Temperature\n",
-       "    units:         degC\n",
-       "    var_desc:      Sea Surface Temperature\n",
-       "    level_desc:    Surface\n",
-       "    statistic:     Mean\n",
-       "    actual_range:  [-1.8  33.95]\n",
-       "    valid_range:   [-5. 40.]\n",
-       "    dataset:       NOAA Extended Reconstructed SST V4\n",
-       "    parent_stat:   Individual Values</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'sst'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 708</li><li><span class='xr-has-index'>lat</span>: 89</li><li><span class='xr-has-index'>lon</span>: 180</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-f7579ee5-04bb-4c1a-8256-5d858845af82' class='xr-array-in' type='checkbox' checked><label for='section-f7579ee5-04bb-4c1a-8256-5d858845af82' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>-1.8 -1.8 -1.8 -1.8 -1.8 -1.8 -1.8 ... nan nan nan nan nan nan nan</span></div><div class='xr-array-data'><pre>array([[[-1.8 , -1.8 , -1.8 , ..., -1.8 , -1.8 , -1.8 ],\n",
-       "        [-1.8 , -1.8 , -1.8 , ..., -1.8 , -1.8 , -1.8 ],\n",
-       "        [-1.8 , -1.8 , -1.8 , ..., -1.8 , -1.8 , -1.8 ],\n",
-       "        ...,\n",
-       "        [  nan,   nan,   nan, ...,   nan,   nan,   nan],\n",
-       "        [  nan,   nan,   nan, ...,   nan,   nan,   nan],\n",
-       "        [  nan,   nan,   nan, ...,   nan,   nan,   nan]],\n",
-       "\n",
-       "       [[-1.8 , -1.8 , -1.8 , ..., -1.8 , -1.8 , -1.8 ],\n",
-       "        [-1.8 , -1.8 , -1.8 , ..., -1.8 , -1.8 , -1.8 ],\n",
-       "        [-1.8 , -1.8 , -1.8 , ..., -1.8 , -1.8 , -1.8 ],\n",
-       "        ...,\n",
-       "        [  nan,   nan,   nan, ...,   nan,   nan,   nan],\n",
-       "        [  nan,   nan,   nan, ...,   nan,   nan,   nan],\n",
-       "        [  nan,   nan,   nan, ...,   nan,   nan,   nan]],\n",
-       "\n",
-       "       [[-1.8 , -1.8 , -1.8 , ..., -1.8 , -1.8 , -1.8 ],\n",
-       "        [-1.8 , -1.8 , -1.8 , ..., -1.8 , -1.8 , -1.8 ],\n",
-       "        [-1.8 , -1.8 , -1.8 , ..., -1.8 , -1.8 , -1.8 ],\n",
-       "        ...,\n",
-       "...\n",
-       "        ...,\n",
-       "        [  nan,   nan,   nan, ...,   nan,   nan,   nan],\n",
-       "        [  nan,   nan,   nan, ...,   nan,   nan,   nan],\n",
-       "        [  nan,   nan,   nan, ...,   nan,   nan,   nan]],\n",
-       "\n",
-       "       [[-1.77, -1.77, -1.76, ..., -1.78, -1.77, -1.77],\n",
-       "        [-1.8 , -1.8 , -1.8 , ..., -1.8 , -1.8 , -1.8 ],\n",
-       "        [-1.7 , -1.74, -1.67, ..., -1.8 , -1.76, -1.69],\n",
-       "        ...,\n",
-       "        [  nan,   nan,   nan, ...,   nan,   nan,   nan],\n",
-       "        [  nan,   nan,   nan, ...,   nan,   nan,   nan],\n",
-       "        [  nan,   nan,   nan, ...,   nan,   nan,   nan]],\n",
-       "\n",
-       "       [[-1.8 , -1.8 , -1.8 , ..., -1.8 , -1.8 , -1.8 ],\n",
-       "        [-1.8 , -1.8 , -1.8 , ..., -1.8 , -1.8 , -1.8 ],\n",
-       "        [-1.8 , -1.8 , -1.8 , ..., -1.8 , -1.8 , -1.8 ],\n",
-       "        ...,\n",
-       "        [  nan,   nan,   nan, ...,   nan,   nan,   nan],\n",
-       "        [  nan,   nan,   nan, ...,   nan,   nan,   nan],\n",
-       "        [  nan,   nan,   nan, ...,   nan,   nan,   nan]]], dtype=float32)</pre></div></div></li><li class='xr-section-item'><input id='section-82ef9e19-038c-4a89-aa5f-9fd517bfaf61' class='xr-section-summary-in' type='checkbox'  checked><label for='section-82ef9e19-038c-4a89-aa5f-9fd517bfaf61' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>88.0 86.0 84.0 ... -86.0 -88.0</div><input id='attrs-406763ae-e4a3-4dae-b768-577b687379b8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-406763ae-e4a3-4dae-b768-577b687379b8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-614cbf96-29d5-4701-aea3-32ae90bdd20e' class='xr-var-data-in' type='checkbox'><label for='data-614cbf96-29d5-4701-aea3-32ae90bdd20e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>long_name :</span></dt><dd>Latitude</dd><dt><span>actual_range :</span></dt><dd>[ 88. -88.]</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>coordinate_defines :</span></dt><dd>center</dd></dl></div><div class='xr-var-data'><pre>array([ 88.,  86.,  84.,  82.,  80.,  78.,  76.,  74.,  72.,  70.,  68.,  66.,\n",
-       "        64.,  62.,  60.,  58.,  56.,  54.,  52.,  50.,  48.,  46.,  44.,  42.,\n",
-       "        40.,  38.,  36.,  34.,  32.,  30.,  28.,  26.,  24.,  22.,  20.,  18.,\n",
-       "        16.,  14.,  12.,  10.,   8.,   6.,   4.,   2.,   0.,  -2.,  -4.,  -6.,\n",
-       "        -8., -10., -12., -14., -16., -18., -20., -22., -24., -26., -28., -30.,\n",
-       "       -32., -34., -36., -38., -40., -42., -44., -46., -48., -50., -52., -54.,\n",
-       "       -56., -58., -60., -62., -64., -66., -68., -70., -72., -74., -76., -78.,\n",
-       "       -80., -82., -84., -86., -88.], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 2.0 4.0 ... 354.0 356.0 358.0</div><input id='attrs-bff7998d-13c2-4c8f-8d74-93e36a938c0d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bff7998d-13c2-4c8f-8d74-93e36a938c0d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7cf6795b-9d31-495c-aee5-d1349c9a2904' class='xr-var-data-in' type='checkbox'><label for='data-7cf6795b-9d31-495c-aee5-d1349c9a2904' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>long_name :</span></dt><dd>Longitude</dd><dt><span>actual_range :</span></dt><dd>[  0. 358.]</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>coordinate_defines :</span></dt><dd>center</dd></dl></div><div class='xr-var-data'><pre>array([  0.,   2.,   4.,   6.,   8.,  10.,  12.,  14.,  16.,  18.,  20.,  22.,\n",
-       "        24.,  26.,  28.,  30.,  32.,  34.,  36.,  38.,  40.,  42.,  44.,  46.,\n",
-       "        48.,  50.,  52.,  54.,  56.,  58.,  60.,  62.,  64.,  66.,  68.,  70.,\n",
-       "        72.,  74.,  76.,  78.,  80.,  82.,  84.,  86.,  88.,  90.,  92.,  94.,\n",
-       "        96.,  98., 100., 102., 104., 106., 108., 110., 112., 114., 116., 118.,\n",
-       "       120., 122., 124., 126., 128., 130., 132., 134., 136., 138., 140., 142.,\n",
-       "       144., 146., 148., 150., 152., 154., 156., 158., 160., 162., 164., 166.,\n",
-       "       168., 170., 172., 174., 176., 178., 180., 182., 184., 186., 188., 190.,\n",
-       "       192., 194., 196., 198., 200., 202., 204., 206., 208., 210., 212., 214.,\n",
-       "       216., 218., 220., 222., 224., 226., 228., 230., 232., 234., 236., 238.,\n",
-       "       240., 242., 244., 246., 248., 250., 252., 254., 256., 258., 260., 262.,\n",
-       "       264., 266., 268., 270., 272., 274., 276., 278., 280., 282., 284., 286.,\n",
-       "       288., 290., 292., 294., 296., 298., 300., 302., 304., 306., 308., 310.,\n",
-       "       312., 314., 316., 318., 320., 322., 324., 326., 328., 330., 332., 334.,\n",
-       "       336., 338., 340., 342., 344., 346., 348., 350., 352., 354., 356., 358.],\n",
-       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1960-01-01 ... 2018-12-01</div><input id='attrs-4dba8332-cc3e-4207-bdfa-817bc5259e15' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4dba8332-cc3e-4207-bdfa-817bc5259e15' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bcde614f-6bda-4952-9eb3-30e1360fdb3c' class='xr-var-data-in' type='checkbox'><label for='data-bcde614f-6bda-4952-9eb3-30e1360fdb3c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Time</dd><dt><span>delta_t :</span></dt><dd>0000-01-00 00:00:00</dd><dt><span>avg_period :</span></dt><dd>0000-01-00 00:00:00</dd><dt><span>prev_avg_period :</span></dt><dd>0000-00-07 00:00:00</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>axis :</span></dt><dd>T</dd><dt><span>actual_range :</span></dt><dd>[19723. 80384.]</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;1960-01-01T00:00:00.000000000&#x27;, &#x27;1960-02-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;1960-03-01T00:00:00.000000000&#x27;, ..., &#x27;2018-10-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2018-11-01T00:00:00.000000000&#x27;, &#x27;2018-12-01T00:00:00.000000000&#x27;],\n",
-       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e7738610-8656-49b1-938b-96ebd1d18f02' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e7738610-8656-49b1-938b-96ebd1d18f02' class='xr-section-summary' >Attributes: <span>(9)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Monthly Means of Sea Surface Temperature</dd><dt><span>units :</span></dt><dd>degC</dd><dt><span>var_desc :</span></dt><dd>Sea Surface Temperature</dd><dt><span>level_desc :</span></dt><dd>Surface</dd><dt><span>statistic :</span></dt><dd>Mean</dd><dt><span>actual_range :</span></dt><dd>[-1.8  33.95]</dd><dt><span>valid_range :</span></dt><dd>[-5. 40.]</dd><dt><span>dataset :</span></dt><dd>NOAA Extended Reconstructed SST V4</dd><dt><span>parent_stat :</span></dt><dd>Individual Values</dd></dl></div></li></ul></div></div>"
-      ],
-      "text/plain": [
-       "<xarray.DataArray 'sst' (time: 708, lat: 89, lon: 180)>\n",
-       "array([[[-1.8 , -1.8 , -1.8 , ..., -1.8 , -1.8 , -1.8 ],\n",
-       "        [-1.8 , -1.8 , -1.8 , ..., -1.8 , -1.8 , -1.8 ],\n",
-       "        [-1.8 , -1.8 , -1.8 , ..., -1.8 , -1.8 , -1.8 ],\n",
-       "        ...,\n",
-       "        [  nan,   nan,   nan, ...,   nan,   nan,   nan],\n",
-       "        [  nan,   nan,   nan, ...,   nan,   nan,   nan],\n",
-       "        [  nan,   nan,   nan, ...,   nan,   nan,   nan]],\n",
-       "\n",
-       "       [[-1.8 , -1.8 , -1.8 , ..., -1.8 , -1.8 , -1.8 ],\n",
-       "        [-1.8 , -1.8 , -1.8 , ..., -1.8 , -1.8 , -1.8 ],\n",
-       "        [-1.8 , -1.8 , -1.8 , ..., -1.8 , -1.8 , -1.8 ],\n",
-       "        ...,\n",
-       "        [  nan,   nan,   nan, ...,   nan,   nan,   nan],\n",
-       "        [  nan,   nan,   nan, ...,   nan,   nan,   nan],\n",
-       "        [  nan,   nan,   nan, ...,   nan,   nan,   nan]],\n",
-       "\n",
-       "       [[-1.8 , -1.8 , -1.8 , ..., -1.8 , -1.8 , -1.8 ],\n",
-       "        [-1.8 , -1.8 , -1.8 , ..., -1.8 , -1.8 , -1.8 ],\n",
-       "        [-1.8 , -1.8 , -1.8 , ..., -1.8 , -1.8 , -1.8 ],\n",
-       "        ...,\n",
-       "...\n",
-       "        ...,\n",
-       "        [  nan,   nan,   nan, ...,   nan,   nan,   nan],\n",
-       "        [  nan,   nan,   nan, ...,   nan,   nan,   nan],\n",
-       "        [  nan,   nan,   nan, ...,   nan,   nan,   nan]],\n",
-       "\n",
-       "       [[-1.77, -1.77, -1.76, ..., -1.78, -1.77, -1.77],\n",
-       "        [-1.8 , -1.8 , -1.8 , ..., -1.8 , -1.8 , -1.8 ],\n",
-       "        [-1.7 , -1.74, -1.67, ..., -1.8 , -1.76, -1.69],\n",
-       "        ...,\n",
-       "        [  nan,   nan,   nan, ...,   nan,   nan,   nan],\n",
-       "        [  nan,   nan,   nan, ...,   nan,   nan,   nan],\n",
-       "        [  nan,   nan,   nan, ...,   nan,   nan,   nan]],\n",
-       "\n",
-       "       [[-1.8 , -1.8 , -1.8 , ..., -1.8 , -1.8 , -1.8 ],\n",
-       "        [-1.8 , -1.8 , -1.8 , ..., -1.8 , -1.8 , -1.8 ],\n",
-       "        [-1.8 , -1.8 , -1.8 , ..., -1.8 , -1.8 , -1.8 ],\n",
-       "        ...,\n",
-       "        [  nan,   nan,   nan, ...,   nan,   nan,   nan],\n",
-       "        [  nan,   nan,   nan, ...,   nan,   nan,   nan],\n",
-       "        [  nan,   nan,   nan, ...,   nan,   nan,   nan]]], dtype=float32)\n",
-       "Coordinates:\n",
-       "  * lat      (lat) float32 88.0 86.0 84.0 82.0 80.0 ... -82.0 -84.0 -86.0 -88.0\n",
-       "  * lon      (lon) float32 0.0 2.0 4.0 6.0 8.0 ... 350.0 352.0 354.0 356.0 358.0\n",
-       "  * time     (time) datetime64[ns] 1960-01-01 1960-02-01 ... 2018-12-01\n",
-       "Attributes:\n",
-       "    long_name:     Monthly Means of Sea Surface Temperature\n",
-       "    units:         degC\n",
-       "    var_desc:      Sea Surface Temperature\n",
-       "    level_desc:    Surface\n",
-       "    statistic:     Mean\n",
-       "    actual_range:  [-1.8  33.95]\n",
-       "    valid_range:   [-5. 40.]\n",
-       "    dataset:       NOAA Extended Reconstructed SST V4\n",
-       "    parent_stat:   Individual Values"
-      ]
-     },
-     "execution_count": 202,
-     "metadata": {},
-     "output_type": "execute_result"
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
     }
-   ],
+   },
+   "outputs": [],
    "source": [
     "sst"
    ]
@@ -1356,32 +284,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 203,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.collections.QuadMesh at 0x7f0e75d65bd0>"
-      ]
-     },
-     "execution_count": 203,
-     "metadata": {},
-     "output_type": "execute_result"
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
     },
-    {
-     "data": {
-      "image/png": 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GDO+5m8Bgwv720aGmYl8kLPikkZQ5/AywN6DA84Crga8AuwB/A45R1dvmeo6XPXr5zfae+cOrH5dOxPpCUidhofiEhfPCR4BzVPVJItIEJoE3Auep6ntE5ETgROD1gzoSSVcRUpA2epHmpHGJ2hFPf8M/AIiTFWo/l0HP7HnYsWuzFVoQBGy4w2YCXrPNVgB859P34rCjLsoM0elz0FhZs81WiBEe96KrGBsfK/zRZoV3GpL9sY6vGM+kSJu12PYVtaMscn/qzg3ZOYJGg8e+8PdZn989da/C2N/79dSVs/raxEjhs1ccubA/GvP5o9+csC6ycRxnunu31LKIFCR4EeH4997GxIrc+C0m1/u7kkMQONtGCYM0CzUEkksaKe5vd/47YCWOdAiRytCyQKRj3xzVUwsqO4nIauBBwGcBVLWlqrcDRwGnJ81OBx6/MCP0LCa8BDB6LjrrsOzlKSEGMQNey1DSWGiF267Av4DTROQ3IvIZEVkBbKuqNwIk/29TdbCIHCcia0Vk7frb/zW6UXs8ns2eNGFh39eIEhaKyKSI/D8ROTV5v7uIPHY+zrXQ6qkQ2B94uapeLCIfwaqiaqGqpwCnAOy65wEaZ8YvG58BsGFKefPp1ml740brW/6BF9sa3uOTbmK1vIjSxMoxpu70SQeHiasmMUGAiawKIw6CLNU1gDGGo158NVvuYNcJd/zr1kI/zYkmYaK2EhECxxCZrurEiKPyqNY7W1VVmuxuq7wPI1lCvOZ4g1d87E4AVqxoMD4RZNkG4pL6M1PPoMTJOBohfPwH9vNUZXX8f/a4QUuQdhL0FDYazEzbbRMEWWbXMMwLoLkxDUEP3Z6RPJuDSK7ZcdVWoauqolrXZNvbzwIROnGaMh36pIibNbWC+0annjoNuARIg4quB74GfHfYJ1roSeN64HpVvTh5/3XspHGTiGyvqjeKyPbAzaMe2BnvvtuoT+mpYLkE1i0Uw0xGWFZRHf6kXwyl3yVLneC+0amndlPVp4jIsQCqOiXzdPIFnTRU9Z8i8ncR+Q9VvRo4Avh98no28J7k/7MG95W71kYRdJJI0FYrZnrKfjAz08GIcMJH1mMCyQqyxI77ZdhoMrXBF0waFlU/WGKERuJPGQRB9odnjMlcFMt/jE878XoAxifHszZBYLJVa78/XneFW5BGpPh5eTsMTfYdESPEkaIVK1UxEGRfIcE4XpjuajkI4DPn2fcvOKLncJccG9fdmW3HnThzKphYMc7YpH3OY43AKYxmSobr/H+3pIFLwehNahTX/P6WpA4lkQhxskPEwy2JUMt7anSSRktEJrAeqIjIbsC8qEsWWtIAeDlwRuI59VfguVhby1dF5PnAdcCTRzmgNJeNx7Mc2NQ08b1IpcAHP+Hnm9TPqDnmYMNThtBPnTiNQeVgAURkJ+ALwHbYee4UVf2IiLwFeCHW7gvwRlX9fo9uTgbOAXYSkTOAQ4HnDL6K2bPgk4aqXgYcUPHRnNdiqhAGwkxLabeVlasa3Hbb4qnA54HW1DTNiXHECBorQSO0LpqxYkKTuc8CPOOkG+wfqJGClFGUOIqZjd3gvUySkbySmnuMiGAyCSQfo4gU+rUFvDT7DKwLqCBEsRIG1t02jpQwtHaVWG1wWhp4aoPT4AsXwrMevMm3cdGxcf0GNq7fwNjkOGIM01MtVm0xSTNxw52YDGk281LM6W9qGAphaF3lw8Td1t3uREIzVAKjRCrWtmFSu0VR/IvUOOKEZFLH0JU1iYdUP2rW0+gAr1bVS0VkFXCJiPwo+ey/VfUDgzpQ1R+JyKXAQYAAJ6jqv+ucfLYs+KQxLLZaVXw/08p/NNIJIwwN//2ylaMclqcHzYm8uFUajwG9y2O6OXxcQ2ovo+oocH8vwsBVaznbzkJ0sXlf9grgm6177YXfPCTbfsQzf8PYZP5sV20xmW1PTObPuXCPetwvd7sZOipkJ6q7PGGUaQQx053hZ7UGukPSe7UZQOIhmnqLrheRq4BZGVVF5Gjg/1T1e8n7LUTk8ar67dn0U4eFdrn1eDyeJYlI7hXW+wXA3mloQPI6rnefsguwH5A6B71MRC4Xkc+JyF36DOdkVc3Scyfxbidv6jVWsWwkjds2FKNJJyeEjVObT36hpURZdZSqosJGmEkaqTvt0068nrARZKqosBFk0oVxVUyOJNJrJWQN4fm2q5LK1U2OUdxZJGqsxI7x3BiyDKdhIDSS1bKbfttVS4VBnvfIiD3+fxNTwFMPWWQiyBx4yDH2N84YoTVtr2d6Y4OJFXlhpfFx+2RWrjA003xTjaLxO71HYUDmWuu62TaDmMAkUfmiGEfSyNRQGmdutp1YaAROGxXOXtsjGd0sse7Fg2waAcAVqnrCoP5EZCXwDeAVqrpORD4JvB2rE3078EFsmqXK4VTsm5ff92UzaVRxwuMEe4leJbVQuKoOn8docTKfNcKXM3XiNOp6vYpIAzthnKGq3wRQ1Zucz0+lf8zFWhH5EPBx7CTzcmzcxtBZ1pOGZ3GR5iqCXDIIG40sWC9oBNlKv1DIR/KAO9dttpc3o3H+UMVIvpI1eU6qIJDCH3Qh/1FFx0YoHBsEkuniA0MuXYSSZWq1q+V0O18tu4FrscLXfplLXk8+aHFrjN3JJbVFNcaamQu1KzlqrLRbNqB2ZqZDu21vTBgIa1baa145HhGaNLDWsVsZxWQ5pjTLN2UkdnJPVWsSYjWF4L7UBhKrZNlvh0KtIkyDz5fEU3wWuEpVP+Ts3z7NjAEcDVzRp5uXA/8Pm+hVgHOBlw48+RxYNpPGXVbAcx+y0KPwLHeCAUV3lio+t9TsGWKW20OBZwK/E5HLkn1vBI4VkX2xksPfgBf16kBVNzCLbBqbwrKZNDwej2ekpMapQW0GoKo/Baoa9orJ6EJEtgZeB9wbyNzXVPWhdfuoS99JQ0SeUKOP6T4BJx4PYOuwpzmmOu12pn5qjjdpjFm1hbtyc1VJYSPI0m2boBRNnMVg5MeKUMg9laqrTCCVaigjFGI28r7z8xiRJKo7V0PlMQa5y22zkaukGqEWjLlpe7cQUBzXS+M/DJ7ymmuzVOVBUIwx+PJ7dpxVX0EjJGzY5+aqpxrNRv4cjGTZFmamO3SSxE+NBqyZsGqrLcanaJgk9T1C5Kioqn6ORbQQ/a3Ob22m3pKY9JbGxmT7IxViHZ76z5igoEatYmBuquFxBlY19VjgeGwmjXnJ4jpI0jgVm8Kj33T5IGYxI3o8nqVLqsba7PNOUUzC2K/NiNhKVT8rIieo6oXAhSJy4XycaNCk8QNV7eXiBYCIfGmI45l3LvljnjX1fntsObD9qT+2/6erxOc91EbzpqvFZxzW/aU48VQbTDg93eHaq60da4ddtgbg46/ZYs5jX4oc/15bcHH1lisZn7SG8I3rp7I/puZ4s5A/KpUKAse1tjGW5y0yzuo1fQ82cDN3xXXySolkGgS735E0pNhHur8yJ5Xk34EgsJJFmPz1BKYoXaSBaDZq2a55wyA37EIesBwZKRiAYZ4C0Uo0x5uZ1GECw/PemucETUuy9mOLbfLMwGOTY9mzdQubqWr2TFavGWfru9rPtrtLh60nk+zB4UZMIhfEmEwSKEgQpTWre7/i5DOlfB+7j43VVLaZM1JDPVUvInwYtJP/bxSRxwA3ALMTH2vSd9JQ1WcM6qBOG49nuTBAG7Go+coH7p5tP/vN/1zAkSwTFpek8Q4RWQO8GvgYsBp45XycqLYhXEQOwdbszo5R1S/Mw5g8Ho9n0SPGpMF7fdvM6xhE3quqrwcmkojwO4B59SOtNWmIyBeB3YDLgDScUrGZGRcdl//p5ixSVChGjTbEishGYq748z/Z+57b9e0rsdFiEkPmmT9LjZ3KWBjznSR85nH3y2/lxIT9Iq1a1WDbbXfN9l9/3fpNvralxMs+dAcrV1u1xdhYkCT8A9iCf99s1RNu4kEj0BgLs/aNJC15IyzGVKRqFTehYDn5nfu37Ho9VrlAVqXpTrfTv/lUPZWeo5Ekz4MkHiPZbgRxrpIyefRyus+OPzf4WpWJO5ruuvXzQXqfW9NWq+Ear1/4rluydqe+cSve+iXbJoo0KzOw0x7bZSrDyckGK1akz81kEf/tjmaR8ltuYdj5rjZT9w6Tt7AC+7dg4ohYkhIFEqDJA1DJ1U2KydRMiuRtcNtIwfJaVkvZ/qVL1bUpSI2EhSMowvRoEXkT8AZs0aV5p66kcQCwl+owy7Jvfmxu9gzP4ucLb9+e57zlpsENPV0MMU5jUzgH+DewQkTWuacGVFVXD/uEdSeNK7C53m8c1HAxEEqnIGmkLnqCEppO1s5IxF/++ldMUlnnHrvds6uvRphH8YI1YDZDm5ZZ1ea1CYxy7m9bPOK+zcKxO2wbMDFmj59sxpy9NuLIA5awUrwPbzvD3tdWO85K526x5QSrV9mv2MS4ZCV4N07FNJv5d7mRpMpuNgxjTXujx5q5obn8d1fI/5RtOwV5XCO3KHHB+Fl/3WNE81xVxr7PpAvXyF2SKFIXUiNx9j0MyCWNjgZ04jDZNkN1A50rqXRg09Tn39EPnaWsWhWiau/B2Lb2O75iYoJVE/Y6V0+0mQg3AtAwUSZFRXGQRWavDKfYAivBTE7fjlF7bCwBsbHSVWyCvHiSCTIJRJFsG0AlN5Zr8tBdA7crmcQYEGjFxb/NoWD9tge0md9nq6qvBV4rImep6lHzerKEQXEa38H+la0Cfi8iv8KpBqWqRw5jECISAGuBf6jqY0VkS6zP8S7YSMhjVPW2YZxrUzn2UOGbv8pVEVWc/Az7R3Da+SMblsczK/oVGjv1jbln1IfO0qFVultI7rP7YI+w2SJBgAyhCNMmj8P+fq6Y9xMlDJI0Bhb/GBInAFdhLf5gw+HPU9X3iMiJyfvXj2gsHo/HMxgZrH4aRY1wVY1EZKOIrHHTo88Xg1xuL4SChT5DRN4LbHLwiIjsCDwGeCfwqmT3UcDhyfbpwAUMmDSmZ1r86S/XAjBmIsRZHmXqKafAs6BIHBc+B7juT1eVet6z5znbkekpbQBsu6adfZ4bQif6Xcai5jWftCqIZjNgYjKg2chXUTtsZ79KrorXGBhvJimtEzXfdf8U7rGjIUpSV4cBjIVJpHAY00zul1UNuXENTjK7ROVjTK56LBhF+6yMM7/+Gv76IprVo7bxG3myvMBEhKnqyUSEkqikiAmc7Vw1GhMnMRgdDekkbVpxY1Gop1I+d/I2nPQ5q0zYbtsmO22rrBxLan43OqxqTgGwOljPqraNeWq2NxBM2dgk0ShTycSmkRmtZToiiFr2JBqjSSH12DRQY/ermGy/VT25BvL8HmVGcTFFo3hFG4CNplShbVgYUyNOY2Qut9PY3FU/AjakO1X1v4Z9oro2jYfT/aP9qIp9c+HD2Jwp7pPdNs3uqKo3ikilbJkUMzkOYIcdZlXoqjbHHtr90J/wAPeLsjxtFPPFK44UzvzZMtB3LCM+/5ZtF3oI88qeu81LjFstQ/gIvKdSvpe85p1BNo0XAy8BdhORy52PVgE/29STi8hjgZtV9RIROXy2x6vqKcApAPvss4+GSVBkELULK1BX0si3NZM8UqPcTVddQlNMtj+MZrjlip+z1d55OcvZsHKslRlFc5aupDGZlOucmAiYnDCFMp17bGcXN6HJ73GshsC5/lXhRq78M9xrG6fsp0SFlXkVZTdJ99m6bVLpIXYMoYCzP3e57CVpFL4vTp4jk7hup2MMJMq36WTfIaMRJs6v2R1rasztmCZtsYZZIzFtDbMxXfrHW9h/j9ymsBDsvqt1k97lrlPsOGGDAFe1bqXR3khjnX3OwcZ1mBkreeJcL6r56lpMFg2pJsgkB8JGti1Bp9Bene3M+O1KEOJIFJLfU7WVtOw2eT+iys2/X8s2ex2wiXelgsAMjvYcUe4pVT19JCdisKTxZeAHwLsppt1dr6q3Vh8yKw4FjhSRR2MzM65O0pLclOaSF5HtgZv79uLxeOaNVa1h/KkvPwSDDPCOGvT50MYicg0VroGquqvT5vLy5xX8S1WP6NdgkE3jDhFZD+yjqtfWOOGsUNU3YINSSCSN16jqM0Tk/dgsje9J/j9rGOdrRA6hxtoAACAASURBVDPZihBH0oCiXUOSlZOJ2gRRi/Vrz0E6LUx7JjsWYPJBxwxjWEuO7bYOEluFsnI8X2XebeImwthKe9l9Bv4R3Y09WpdDotIu1lEtfs9dPXYvfbAm+U8Lq05yyaPsrllw0XS2e1EtmcaZBGE0LkipJu50tS+O1zmvBHSCNuNsoB2MYWQ8k7QWi23jITteDcCqjTcz8ffrAZDpDZDYANEYOp3uA8u5mERsRqQgRExg77gIGrURY396xLSKz9kUpYqkUfE5pxKLcYMBAzTp0/1eqAmITJObf7+WdjhGJEOsBmEYrH4anU3DFaXGgScD5eR6AfDoPn0IcPagEw28g6oai8hvRWRnVb1uUPsh8R7gqyLyfOA67A3oy9jYWGWchctNV81L9cOePOjeI/OCGwlve7ZVqaSuxOUJI8WdMAAO2nMNt9ZZ4ywBjLPQcK/TXYC4FNRkTqxBOxirar4okekN+Zse19nzxzEIB7dZoogMjggfVe4pVb2ltOvDIvJT4M3OvhcNWvyLyEsGnavutLs9cGUSp+Fa5ocSp5H0dQHWSyq9AX1FJI/H41lQ3CjSfm1GMhTZ33lrsJJH2W3sahHZS1V/Xzr23ljb8r+SglB9qTtpvLVmu0VNELUyVYKJ2vkHrt5RY0yiYpE4ylRVEnf6+3JuRmy1KmLVWIutxm3+oK30JlasswsdE80gUX6PpTXNxhsuZyIuOQRIrmLK7n+QG0tdw2aZTFXhqCfcPq1qK3X7zNVcZVWWVuibyyqp9JmLxvb7kKy0RR237vLqu+LaYgmIgyTvWdwmSL5jQdCmHYwnYzUYjbnmL38mjK0ub6fd96q8B/PF9Fc/wLZjSeG3KIJOO38OrtTgXnOxWlXxfZoGwOTPUyR3VZWK59eFCBrkRvGCeioztIdoMj417vNvENCi1VxZ4+pnySIyhAMfdLY7JEHRpTYfAz5ZceyOwEnA0+qcqNakoaoXisi2wP2TXb9SVW+c9ng8S4J58Z6qo54akSFcVetktt0njb0rHftDEflg1QFV1M1yewzwfqz6SICPichrVfXrdU+02DBxO1sppqvVONEzpwbvdHUJ2BVTuroaXWGVRcmaiRnu0rRZarfSm1h9x98J11tJQ1ozkEgaXZJZakgtrzpTgnyF2FOsN0W3zEqJxTHIqgkqJYpeWOkikSaiKL8GjXtLFulY0nFkK2GTSRdigkyCjU0jl3jjTuY84OZXMhrR6ExxyxU/J2xZ19Y1+z+s9nXMljs/kThHapxdixlrIk1rx5KwUTB+a+keSFnayD4oGcazNqb7cxdTfJ5ZCvLEqG63AzS5v2o6aJKsTINGtj+II6JwnHmhVu6p+VVPicjjgMtTW4WIvBl4InAtcIKqXuM075dCuXZ65brqqZOA+6fSRVLE/MfAkpo07rr3wdn2ukt+2PX5FvseDsCdvxzgQKAxkw9+6jCHtiTZSjfD7Ki9DMHLGAlHk659vkj/roeOkcHeU/NvCH8ncBBkcW/PAI4F9gM+Bfyn0/ZPIvJoVS2U5xaRRwF/rXvCupOGKamjbqG67rvH4/FsFogJBhZhGoFNQ1U1ibLkCcBnVfUS4JIKT6hXAt9NNEepK+kBwMHAY+uesO6kcY6I/BA4M3n/FOD7fdp7gOnvnwLA+KOPW+CRDJetx25nTXwLq2+3HtiNW2+E9UmetDiqVBPZz2axSi/4+xdVH+LuT9VcjsoLx9Dal6rxOMbvgkoyLqnaSuqT1CAqQZgZjDUICNLYgSBEkxW7Ma1cfWJmsvgC12iPahYXoiakMXU7G37xbQBWHPz4wdeWMPV/X8zGKZ02tPP8T5mRGoinbe6oaKaFSTO3aox0ImBq8Inceuqmh4qxHMPhtrVv8s0gcPLeB7kRPuiQ5cyPwyx/HK40VIrrmDcWQWp0QERkJbAR63H6Ceezgl5OVf8oIvtgDd57J7svxLriTtc9YV1D+GtF5InYCG4BTlHVb9U9yWJk9f3+s+dnKw8amiexx+NZrgzJ5VZEdsJWQd0OiLG/rx+pWSLiw9iKquuAq1R1bdLnflTUP1LVGeC0gYPqQ+3wSFX9BvCNTTnZUueOD5xgN5LV55rXfqz/AZ02TG1g+ivvA2D8Ka+bz+GNjO2m/sr4upswt9i8RHrnOrSduzBLuip09bmxoq6B3F3lpw4JpZV/zxWri9NGBhlXe+HYKdRZfRM7Ukc6jtRVNAgc6SLIjd+NRrbqFRPkK+AwzFyR1QQQtJLhB4Xo5apoedOZQRIJwcxMMX3uaYw/4rl9L2n6ax9MxmCye63tFjrtLCiT8ccbNtK+02o4opkWQSORfFqdnvUipJex2/3MmKJLbXrvSm65mYrH5M9QRZBGIxtn7pAS5M/LzWRtcldcaU3DxAjsMGVJulebwXSAV6vqpSKyCqta+hHwHAaUiFDVzyVaoG2A3zof/ROo/JIkWT7K8QN3YGsavVpV+9o36npPPQF4bzIwSV7zUkrQ4/F4NpVVD3jM/J/EBEOJ00gyeqdZvdeLyFXA3ahZIkJV/wH8o6LPXnwIuAGbW1CAp2KlnKuBzznnrKTukux9wJGqukZVV6vqKj9heDyezZrUpjHoNasuZRes59PFlEpEYBftw+CRqvppVV2vquuSbOGPVtWvAHcZdHBd9dRNqlquTrTZ0tlQwzCYEE/ZtvGGDZl6S6OoqIopGVnvclJV0ObCs/GirwEwOXUn3HIz0Z02IlyjqOCnnxbVca9LoyhT/WgUoZ00cWTs1KeuVk+V8/ek7csqElct0oteldTUVUNVGcgTVUumrgkCTKI+0SDI3FIljiBV1TUaWdpw6TjqLBPkcSSF+BJTiHDPshG0W5Amy5yZQac3MvXldwMw8bQ3VF5PdKvNTKuddnav43aHzsap7Bqz8WucfaejVocoeX6mpJqSUoUt914WnpWznX0vHNUT5CqpQvvS/ZXk+yKuyq8BRI4aMtmv8xwPUUl9m8beIrLW2XtK8kNdaiorsSaAV6jqunms+hcnHlRpyMSTnM8Gpr2oO2msFZGvAN+mWCP8m3VHuRxY85qPAHDLW15Qq/34kS9l4+eXRQYWj8dTpn7lvitU9YT+zaSBnTDOcH5XZ10iIilYl3lN9Ugy+3TgI1hPKwV+CTxDRCaAlw06R91JYzXWpesRzj4FNqtJI6W9YZqo1eYfJzyFu33kK3PqI1t1l105gVveZl10t3pz12JkwZj+9kcxyVjj22+ls259toLtJQkUpIhORJyswONOVLj+qntQ1W/fNj1Wvj37qek/nx4rYpAw98s3jZA4XQmHjcx9VaMwM+AWpI4gzB0Dyq7C2cmclbmbfnxmGm0lJVFbM8TTdt0Wt1q0P/wqVr/iQ13j7qxbl7TpZOPUTkQ7kSg6U9narzAGV9pzn0v5HrouzuJIEWIkk1C6pIgKacQ4rrUmDJAoObbRKErg6bM1UogIz1xxTYiojlbiEJM7QPRi0OdYn1ngs1jvJ/dhnk3NEhEiciQ2/9QO2Mnl7sBVwL3LbRND9+N6dDWchIWq2tdVQ0TeoKrvrtPX5sbkc06u3H/rO1404pF4PHNjVOm9h8FIMzUMr0b4ocAzsTW+L0v2vZHZlYh4OzYy/Mequp+IPAQbGV4xJNkDm7hwW1XdW0Tug7VZv6POYIcVeTKw3oXH4/EsK4TcrtHvNQBV/amqiqreR1X3TV7fV9VbVPUIVd09+b9fCcV2UlLCiIhR1fOBfXu0PRVb/K6dnP9yrAdVLYZVxmpOS5FNDGpZUFzxfi6kqh2rnnFUAqlBNo65+Q3PYpt3f6F2nze+2mY2DprFx1qnjzSWxJ47VSm1s/gLbXeINthSKu07N6KdqGh4TmMBnOuxBn/bV9TqEHeirv0ax5XqqV6rWzGm8Jkby1G1v1KtUtlv9/nKahSJAqtOsQPPVSmx5tudKFMraeCkmRDTMw9RVSZU6yyQ3PuZVhaxHbc7mZqvEFNSIv1+aifK1FPEMdGMPXb6jqnseUBu9DaBEEdFxwTJ7oF7f3N1kwlN4X5n24HpqbaSLKV5VIgFye6E+5xjLcbLpM/QBGhjIYtZ1fCOGp267PbEkH4RcIaI3IyN/6hiUlV/VTK092rbxbAkjbkWmkiDWu6FFa1eKiJ7YYNYzlPV3YHzKNYnX3C2e/+XFnoIHo+nxMThldqY+SOtp9HvVcOmMSSOwtqdXwGcA/yF3naLf4vIbiS/2yLyJCqix3uxoJLGpga1LCStDdYw+cdjHwnAHmeeU/vYm096TuF95hLZifLVeLK6uv5lVvO34/98rW+f1x3/hGwlF3eclXwU8feXPJGdPtE/mD9OpAhUcymo0yZKVqydjVMFt8wy6kgaboR3nF2bsx1psX0PKiUK8hWvlN04Tf/95X7yndKzbeZ2GsY2z1HouOOatJZ85EQ1m6I7qeNy2pOSa3L6fyrlxa1cuojanez5VD2HvE87zthxb+5Mt5DAoFFM685pona3a7EYKTyToFGUIIr3ODfm5+0MJnS3U0lLCq7SJky2wyBbuaoR0hEZMVmBJXGup4CTxl7DJhsv+hqTh41OU25rkQ9yuR3RWFQ3iMjdgd1V9XQRmcTWBK/ipcApwJ4i8g/gGmx23FoMS9Lo/4tWgxEFtQyVclyBx7MUGLQAKbOUDOGjpUZg34hq74jIC7FxF59Odt0NGyLRhar+VVUfBmwN7KmqD1TVv9U9V900Iu8D3oFNd3kOcF9sAMqXkkG8q+4Je/Q/p6AWETkOOA5g55133pQhzBkxBhMIf3nWY9jtC98b2P6m1z+zsJpzbQBRq0M008q2+63CU/7yrMck4yi5NCa4eut+ROttUaW41SJOVrBxFGXj6WycyfTk4cQYM3ds6Oqj7K7p6sY1VuJOXHifjTFp5+rM3euw15W798bOajdvW5REisdS0b63e26W/8iVAOIYEypx2+ZjclfNmtozSnaLcr9Vz1PjOJM04ijKVtRRu5M/B1cCjfLtznSbmfXT3H70EUyvs89m//P6e0zecd2/uePoI+jMdCqfgXvdYoS447jDGjs29zml+6OWY9uYsRKKGJN9J0wYZFKEe+8NeWCedqKCy7ErdaV2D2238zZBYPNMLRSLoAiTw0uBB2AX3ajqn5KYDWco8qqqA9Pf25K7b0/qqqceoaqvE5Gjgeux3lLnA5us3N+UoJYkqvIUgAMOOGCkBbx3P+P7/Onpj57VMTe9/pnzNJrRssN/n8k1zytmAu7l37/cpDHXaFuYeBZZNcet3vKZyv13HH3ErPtyI8PLE0a+nV9/qqpa9tRQT+mo9FMwo6qtdAIQkZBuW/Oq5P//wJbuTqvNPQ74Sd0T1Z000pSRjwbOVNVbhxHiPoygFo/H41kQpIahe3SG8AtF5I3AhIg8HHgJ8B23gaq+FUBEzgX2V9X1yfu3MAsTQ91J4zsi8geseuolSbnXYciFwwhqWTDSFVU5R4/LDa+0Hh2u2qNgqI5jNErVU20609bg2Z5qZ4bOqB1z+aMP5z7fv6DQtyvpaLtaDRV3YqJWhyuPemhm+Cz34zJz+/pMLRK12sRtu93eOEN7yo7tqic+PLum7Pw9pAvNVC9auGb387SfuFOtTjJBdWRy0f02zrY1dg3hcZerqHu8/V+d/QbIXU1TVZAJA7udqonCID+mXUoBXuq/65qd1PBWbZc7QqTfBVcl5X5fonaUqfmiVid7JtFMh/Z0xM8PuD9Bw9CYTAo+Jde+77kXdY2vF/l9T5+Loxrs5NfmqhU1Tlx2w/xZuc8NHAOqGlz5M3v+kLvTdgST1V1vE6d128NGHomvMZKq0tLiUqPEccnuyejUUycCzwd+B7wIWySvWtyEnQH3hrWw4Q21qBsRfqKIvBdYp6qRiGzEejhtEqr6U3r7F8xejvbMmVUn2PoLUyXPLs/yYq9vnde177JHHLYAI1n61PGeGlVaE1WNgVNF5HRs6pB/aCELZ4EvAr8SkW9hVVhHY71Ua1HXED6JNbTsjDU874DVi3237omWO+HEGNe//JjCvuJKHFJjrn2fu8TGjgtlZ6bTvV3hGglliaW40ndX+AU33gG01m3MpIvOdCtfybY6hXFkq9BIa9kwXMN4F4UVq5vPyq7iIkcaIVJHcoihwgHQSi/umLqli/K5CpJJ2C2BRFFUCCSMOxFVjgf9qJIue7kluxJFIWAy1uw5dGY6BTfm9L6YQArPp72xzcUHH4jGSjieS8UHXPiLbLtKGuslKZWvJ5F7Kj7vFaCp5Hc7J2hCnK5/HQcBdVyaJewg08n4Wi1MWlRqUF2L+WARGMJF5FPAx1T1ShFZA/wC+zC2FJHXqOqZ5WNU9Z0i8gMgXS08V1V/U/ecddVTp2ELkR+SvL8eqwPbrCeNXT+fX355wtgc6DkJLGcWIgX3ArPcnBmGhUqQVQvs12aeOUxVj0+2nwv8UVUfLyLbAT8AuiYNAFW9FLh0Lies6+awm6q+jzxXyRQjC1vxeDyexYdVT5m+rxEsMlzbxMNJYjNU9Z/lhiIycJKo06aupNFKcq2nYee74dTV8BQpqKUi1+jbrW5wVRKdmU5mCO/M5Oqg9PO1Dz4YyNUKcScu9JnHRVSrM1Iuecih2RjLfv3RTCuLdu9MtzMVWTlPkav2itrV+aPK9FqxZg4Czj4DlaqqQrtIu4ytafuqyG/bT3JPSzEbrtrKNfyWVVCu6qZXrEc/VU76uRuzkj4fd7+reiw7ERRTltvzBk1nu5GPzQDheJjtd502UueKsnrNzUNlz1/9w+eqRvMrdr8nhqjCQcOEZAb13O3A9mOSiPu43UESNakJHKeDckxNmsMqMY5PVo50nqhfhGk+uV1EHost93oo1hieutxOlNreS0Qu79OXAGsGnbDupHEyNqhvJxE5Ixncc2oe6/F4PMuPOhHf8z9pvAj4KDbp6yscCeMIoBxtvGeN/gZGA9f1nvpRIrYchJ2NTlDVf9c5dnOnym3SXV1a18puw2bUjukVzX3lUQ/N+3ekC7d9ldQBxRVxY6LB7x77EABW72AXGFGrk7n6tqfaBUNr6mKc9uleg9awb1TZQEwg2SpRHGOsxsUVZZVEAUWJoXiN1QWi3PMOyklVzK9UlF56ufHasfcYa1QtLRRW7IX8XL3K4NrzBYXxN7o+T2muyF3D0z7bU1aKvPSIB9q+Gq6klLj3xlorUK/7+tPvRR70Z689co7Jr82V5qKZ9HODBHkOq9yduihFp9+dqNVh+o4pbn6yrRO359fOHTjuTUbMgts0VPWPwCMr9v8Q+GFp37XDOGdd7ykBHgXsqqpvE5GdReQBqvqrYQxiObDjx75auf+6458w4pF4PMPj3mf938A26cJjc0ORgRHfI4wIHxl1DeGfAA4mrwS1Hvj4vIzI4/F4lgCDjOD2tfwmjbo2jQNVdX8R+Q2Aqt4mIs15HNeywM3PFHeiTD0Rd6JCtHeVQdn68ifRroEUjJyu4bTKWJp+BnSpjVJxvjHZIByzj7+5ollQkUVuojxHzdWZSdULrppB0EgrE96VqVJPRW1H1dN2VR15JLcEeSRymeqEhEV1TjkRYnoFddRTvQoPFc/Xm55JCge2KRq7C0n+Kq6n13jK6cxddWmUODkEY2GXwV9j7Zs5oIybrt4l/V7ZPvPYEfe4fgW3yn3GnaigFnUdR9LvZ9yO+PUDD+L+P/1l7fHPiVpxGvObh0tETkgK1x2qqj+bxXFpGvUfJ05OYZpWZBB1r6gtIgG599TWlP8yPZslg7KqepYms5kw9vnu+fM3kEVMLIbYBH1fOv+JLJ+b/P+xugdUpFHfkR5p1KuoK2l8FPgWsI2IvBN4EvCmuicZBRuv/gO/fuBBgF2xR618NXzoby5ZqGHlac+dCN+4E+WrImfl5EoXvYjacWH1757HPbaqDeSrtpn1LcbXTGRjS10jO9PtgotuIX12OzfSgl0l/vqBB3VJGYX3UXVfLnG7ODbAFguKcwmkirKBXCpW4GIMUWHVXkzL7YyicEx2LWnbQBIjbG5g1x7Lpl7ut/nK2ZTad/dRzmc2SKrp5/KcSaPu9y5STMOeI2gYwrE0d9TwDLfutUet2LnuoqRar69uJ5JiBoWi1A3w8wPuD8Aha38994vox+Jwub1KRP4GbF1ypxVAVfU+FccMTKPej4GThticz9cAr8O6cQnweFW9qu5JPB7P8qbKYP77OaRhX1KkAXx9mG+bhqoem0R//xA4clD7hDpp1HsycNJQ1VhEPqiqBwN/qNuxx+PxLGcWi/dUEptx38TOvEey+2pV7SGjD06j3o+66qlzReSJwDf7ZE5cUFRzsTSONPPxhlxMhXkUVR1SA7hrqLaqjTS9deyorZyo7pJBOTUuapT7o5fpZYDO7kUpIjdVSQCOwVsKY0tFfkjVMEpnuruSoKudqTTmRt0qg/R6qhAn0Z7GERoV1VXZNQS5sTYu7cvH3SsiPG8/qLpflZF2UJLC7vbd6p7y2KoM27NV20AxDsT93DVGp/02Jhp5gsMwIGjOTzxBMRvB3Myg5dgM16Gk6rvk3s+gYbJMCsMmTSMyqM0oEJEHA18A/obVBu0kIs9W1ariSq8HXkC9NOpd1J00XgWsADoiMk2uL1td90Sepcv9zv8ZFx984EIPw7PE2Otb5y3rGA4lqBG8N7Lsux/CVli9GkBE9sAmK7yf2ygxN1yuqnsDp87lRHUjwlcNbrWwuBN6L5fEUXDN844srPSqIrbL+aB6rbyrXFS7VvtVbUorfLf/qBXTWNGgMR5kac/L9avLeaaqzpUahqs+y9N+O/mwXCN9p/p63TxE6XvnSu1Yg2LxnyrKLqrumKtyR/WTOurUFR9U5KncV6+a5HZHtetqL7JnoKVI6YoiT2UnBddN1n2WVfmiNhX3O1UlBZbp55acp4DPf77qZlyukyOtLiqDJYk6koaIfA54LHBz8mOeVtN7IfCvpNkbVfX7fbpppBMG2EjxpJR2cTzW3PBbEdlZVa8bOLgKavmDicj+Fa/dEgPKvCAijxSRq0XkzyJy4nydZ3NkNiq61KX2wF9cPF/D8Swy9jjznKH1tc93z1++LrnDC+77PBWpQID/VtV9k1e/CQNgrYh8VkQOT16nYstZVLE9cKWInCciZ6evOgOF+uqpTwD7Y3VgAPsAvwW2EpHjVXWoiV6SmJCPY1P9Xg/8WkTOVtXf9zzGSFbispxvaVT85VmPybZtsF2eydYtYlTOQ+Uek9JvRdRLunCPrWoTdxQTChfea18AVjr2jbI7bdAsrid+tl8u5dqcP/0zn5bHKYFk0oYJpVLaiDsKzn4N8+A+N8eta9+oQ1ESyG0S7kq7ylbRLWn0ly66M98W+3WLOFVKPG4JWcgyupZLyeYuwb1LyFZJjnEnssa/ZAzOwVmb9sb5SV4dtd2yu9W50Fx6uSWXn9ugfoCC1NWroNlcGJYhXFV/IiK7bOJwXox1pf0vrPngJ9jf7Sreuiknqjtp/A14vqpeCSAiewGvBd4OfBMYdnawBwB/VtW/Juf7X2x52Z6TxkJTnjCy7c7oJ69h4FZ18wymjsplKdCYHFvoISwZYmOD+/qRGMr3FpG1zu5TVPWUGqd4mYg8C1gLvFpVb+t5HtUZrF3jQ4M6VdULa5y7J3W/6XumE0Zy0t8D+6U/6vPA3YC/O++vT/YVEJHjRGStiKy9rdXLu8zj8XiGTyppDHoBV6jqAc6rzoTxSWA3YF/gRuCDwxq3iKwXkXXJa1pEIhFZV/f4upLG1SLySeB/k/dPAf4oImP0jNfdJKpkui59RnLzTwHYe8vVOr7arpL6pRWfD/7w5EdkorJbGMnNJVXO49QrN1RKv3Tbrmok7VMCk6moxOTqnLKaqlIt1M7H1kt8L6udtBVnEdgmkL4utWk7N2I7cNyJexnG3f4y11o7guRfk9UX11iQKB1PUS2W5q0SUzSixwUVUZzt723MdlSJPVRX3cb2VCUXZOMWY7Jo8p4em8YU1FLV6rM81bngGF1NUSniqr/S750JA8R1cmj1SO61gLiOCu6+1Dmj7FzQK/+V6xQy3N+F+XO5VdWbsrNY+8TQSmuXHZtE5PFY7U4t6koazwH+DLwCeCXw12RfG5gPn7rrgZ2c9zsCN8zDeTZr7nf+z7jf+bVznM2ZB11Ru2a9Z5my/3k/Zd9zL2Lfcy9a6KEMjXpSxtwmDRHZ3nl7NHDFLI41IlI7HEJVvw08dGDDhLout1Mi8gngu65bV8KddU82C34N7C4i98CWMXwq8LR+B4gI4bg1hIuJiLNSlVpYgfz+6CPY61vnDW2glz3isKJbqiNRwOxc/Pq6CjvXUzjG2Y5L79LP44q2ABfea18efNVlQC5hpO66P73v/j3dGNVxo5VEykj3DZIa+qFtR0JoFI3lmeTkbKd5oOxYqgMBXVwpoLB6DaoN4b0y3Jb76DZ4d+83QaewQk63NYqykqVxJ3LadBvVbUd91nk9DOGuIbh4P5LcU2NNmISZ22slOR0JVRmG7f9FxwH3Gbj31yW97gDQeHgOn7EExAPiNOIaCQtF5EzgcOCuInI9tlLq4SKyL1bD8jdsEF6/Pr4MHI9N7HUJsEZEPqSq769o6xb5McABDDONSHKSI4H3A03gHsnFvE1V6+Y6mRWq2hGRl2HzqQTA51ybymLhskccttBDGAr3/+kvC1HzdalKENiLsrTxk733m/X5FgN14yeWItu9/0sjOc99z6kKUrY1y5cUtSLCa8SkqB5bsfuzsxzNXqq6TkSejo3wfj128uiaNIDHOdsd7KR0VN0TzaZG+AOACwBU9bIhuIj1JfFLHuSb7PF4PAtCHZfbuaqn5kAjCeZ7PPA/qtoWkV7Sw2fKtTdE5FDg5jonqjtpdFT1jrK/+GJCjNCYaCbbHaJWIrbOMhJ1NmQ5mmY6ffMpVZ0/356dS27XFbhqOGd37Ch0+p3pyne8wQAAIABJREFUJ3vvl0fYNoJafuziGLGz87nxFe3i/h/vuA8AD7v+d5SJO5q1r1JH5deT74+c4lQZ7v0vXUN6fbHjslFOw17V3m1nAimosbo+6xFpXlRJRVmboGG3TRhgQs3aZIbtXjEexD2lnUEqqUJMUCfCNBIVZjMkmJzgzo+/DoCVL31fZf8LRUHNF/a7v0FX+zLDjN+yuac2PSJ8SHwaKzH8FvhJUmSpl0fUx7Bxd4P2VVJ30rhCRJ4GBCKyOzaA5Oc1j/UsEQ5Z++uRqY1SddX5u993JOfzLH7u8/0LlpSKKlZDrANsGjqa+B1V/Si27lHKtSJScFISkYOBQ7C1N17lfLSaWSTJqjtpvBw4CZjBJsH6ITawb9EgQUBjxTgAQTMvWdqeag0130yZqNW9cull0O61yulVtGc2mEAyqcP2mRirA8kKUgWlHEMpqcH7J3vvN7AIVHZMDwnBxZU4AH607d48/KYr+L9dinVhekaHO1RJS3X/HKMB1yRB73teduNMpZJeRnXTCEorYdt30DDEHdf1NSnf6xit7WrZkTQCd+Wc70+vvDujbiIBxupIKfl3qpxxN82cHHei0aXVmwPlkrVBIy8YZUKTbWfXHJgeUtrQR7YoUqMDJOEPTwR2ofi7/jZnuwmsTD533W7XYQvr1aKu99RG7KRxUt2ONweqJgyPx7N5UM+mMTLOAu7AGr8rc8EkkeAXisjnVfXauZ6o76QhIt+hjyvWfHlPeTwez2JHGSxJjHBS2VFVq5IeVrFRRN4P3BsYT3eqaq1YjUGSxgeS/58AbAekPnnHYo0uiwYxhrE1KwCI27l6Khxv0Jm2FlCNbZ3itEjSPT5XO7FjJQf+4mIuPvjA7gJAm5CavUqULtSZ7pMOvDiGPOI8aCRx1JEWomPTCPJydIcbc1E2amdjaueR3FoyPEvDUbc5n0nD8MOt7k0wYTBpMsKGVKqZCkbxcHb3002O2I/sHH1iS1I3ChNaQ3hqZC8Yv90CUe04v9+BYLKI9VxlYlVSSZxGrFmboGGy52NCk98XdY3vfWqHpyqZvhftPKukfdzuELft30swMcGGT5/Eihe9s18v80K59oarkkrVUEEjyAzeJjQEzSQ2KzDO/sB5NgFVMTjDYLFU7kv4uYjso6rd3ibdnAF8BZuO/Xjg2eQp2AfSd9JIE1uJyNtV9UHOR98RkWpn682MA39xMb9+4EELPQyPxzNiVM1AQ7fqyCaNBwLPEZFrsOopwRbKu09F261U9bMicoKjsqqdxLCuIXxrEdnVyTp7D2DruicZBWKE5pqVQBKVnaycopkW4XTLbrfyFdVQz11zZZuPtXd5UZc8Irv3F7PqeLsvX8mmdJeWNVmb1FjuurQCjjShRFNR175KpmLC1farFU05kkZyXDBhkIaTh2rCSXyetHGv2G3rSh39JDr3s17PJu2rThR73LEuzVLp4uy4sjox+OJEqWusBI3B5wkaaXvJ8iSZENJU5tbI7V5Enj69sKLu8R1zx+N+rqlruhHMxCRTX3oXABPPeOPAMW8qVx6Va0WKbsz59bjG76AZZtum4Wyn+4M8r1bRoaC6ONdcWWSSxqNm0TZ1Pr9RRB6DTdG0Y92D604arwQuEJE0q+0uwHF1T+LZ/PjPW2wA//ca/7HAI/F45ofFZNNIDdsisg2OnaIH7xCRNcCrsfEZq7G/8bWo6z11ThKfsWey6w9J/naPx+PZLFGVgeqnUamnklRPHwR2wEZ23x24CmvsdtsFwO6q+l2st9WsE84O8p7aX1UvhazIx2/7tVlIxBiCyQkATOxUMVPNRO+4baWy9df+c97GEUdaiE4Ox7tvca+qcen7FFNR3a+sVsi3q2teuxRqdjvp2aN2TJREt0ftGDFRtp2qoKKSKqqnaqoP6TEzN7VYec+JfOxunEO6kRqeS3Q5HdRQN6iTKr0qCWNdL/7COGs6O+SqsZg4Sp95TFSjoECW1DDWPO4mjtE47Sc3ihdiGcIASXRY4lYD7JMHKUuTHitZ3vYR050CPY/BSO9F0Axzg3cjzFVVDXd/I1dPBUWjeN+Ej7MkxhAN+PbEo1NPvR04CPixqu6XBPZ15bRS1SiZYP57ricaJGmcJiKH098h47PAkso+t/OnvrnQQ1jWPKZdToTs8Sw/lMUjaQBtVb0lSYtuVPV8EXlvj7Y/F5H/wXpQbUh31l38D5o01mCDRfpdeW1XrXlFBGkmuadwjM1BkBnC+q205sKlRzywa1+5IFGvQjKZ+2WjnAK7v8ttHUmjV3EaF7cgTdzJi1Z1Zjq0p+wyuLWhU0x5PpUfnxqn5yJxQL5q75KKGlVte+eGmg1xpBin/6yuuruvT/aArghs19W2Il9VL7qM2fOB+10InFV3OPhvQdu5GLTh1DcBsOKF75ingVbfr3I99kIbqf6eZ9fmXnMYFK6foRrC69gsRjZp3C4iK4GLgDNE5GZsBtsqDkn+d6PFlZo1NQa53O5SpxNPkQN/cXG2XTWxLCcGSRXp594g7ll21LFpjG7SOAq7rHsF8HTsgv9tVQ1VddZ2DJfhVSRZaESQRiPfTt31mk0YS5wJmoOcCupz2SMOK0gVEkhXWpHfPvJBWaBXL5tDIRBpgMQBeYBimXKm1VQfbLfT1WXpC6x5KVo3V5cJUx+H6Ww1HneUaCrRMZPv21RMI+jK75RdTw2JopdkNijLqZWgujMu9So81Ysqqacqs3HZjlU5plhK2912DMTdr0jaXqQr8C8fpMn/d6WOdL/7vVDNclJpFFkbB8xrBtzULhF38r8fN5ttXbIcXqXbkBewyiWQYRDXiNOIR6SeUtUNSWbb3VX1dBGZpEcSQhHZFngXsIOqPkpE9gIOVtVaNTwWbNJIwtgfB7SAvwDPVdXbk8/eADwfG5D7X6r6w4UaZz/cH5jNMQ/Vozf+oXbbXhLJXIo/LRbmqiZbciyQYXyxEzO4sMH8pUotIiIvxIZBbAnsBtwN+BRwREXzzwOnkecS/CPWvlFr0hhN3t5qfgTsnUQs/hF4A0Ay6z0V6yr2SOATiZuYx+PxLB4S9dSg14h4KXAoSQ0NVf0TsE2PtndV1a+SzHmq2iHPmDOQuuVeBasn21VV3yYiOwPbqeqv6p6ojKqe67z9JXlq3qOA/01cfK8RkT9jqwb+osZA7X9BmIuhJoDQqq10fIJ4fAUbfm69p1Yc8oTKbgbxu8c+BBMGxFH/6PJeKqmi+iiPdu3nflvst9pwWDAKBt3uitYVM2+Tq2oiJ1fXTGEMqQQVtWMmtm8ydWOr7zXPhUyFF1Q7CLjX1stQPRdVRq/a2XWpdN/t4xLcS1U1X7mR8oEm6+Gg6KLrqqoKhvFOYgiPFa3/WzInrnnekbn61KH8d1HAUatm31UTEaTfkShCnXvaS221qSym4D5gRlVb2d+3SEhvQWeDiGyVfi4iB2FjNmpRVz31Ceys9FCscWU98A1gWLqF52HFI7Bi1S+dz65P9nkWAWmk97A4ZO2vfe4uz5Kkjk1jhJLGhSLyRmBCRB4OvAT4To+2rwLOBnYTkZ9hU0INt54GcKCq7i8ivwFQ1dtEpDnoIBH5MTY7bpmTVPWspM1JWNewM9LDKtpXzpgichxJOpOdtlqTf2Ck2rXOhMTh2KBhDyTNRho0mkTtmA7VBll3tVw2eBdX1M4KNKxeXRX6F3f12r0aL0gyQVAd9OT0r7FmgY/BeDPL59OYaLBqu9XcdOX8BUPaceVjSqUOEwaF7Tqr8Nms1NUpfpS+ny2zkVR6Pc+i22hZ6nSf7TxoktPvUcktPd2ORZCOlUCHWSa1MAQjhGOJIbyH23L52rOxiOM40IkymSgANJGc1MnDJUaG6navZH4CPRmhNehErB34d8CLgO8Dn6lqqKqXisiDgf/A/t5erao1wk0tdSeNdmJXSMWZralxP1T1Yf0+F5FnY9PzHqGq6e2/HtjJabYjNqFWVf+nAKcA7L/LDqOyOWXUqae9HHBdiD2bH/M1YSx1FlPCQlWNgVOTV19EZBwriTwQ+5t+kYh8SlWn65yr7qTxUeBbwDYi8k6sKPOmmsdWIiKPBF4PPDipDJhyNvBlEfkQNo/K7sCcbScej8czH6jWUD/Ns3pKRC7ve/rq1OhfwJoYPpa8Pxb4IvDkOuesm7DwDBG5BOu+JcDjVfWqOsf24X+AMeBHifHml6p6vKpeKSJfBX6PVVu9VFVrWON6CBrG5IbAIbkONibH0DgmSPzKw7Gihe1PT3900i7X4IkxlTEbZUOo6zs/KG6j69hU9SRSqZIyjTCLZSn4q2uc5ecyjQbBmB13Y2WeH2pUpKoKEwaE43asZRXeIDVUP1VO1ap5rmoqVz1VZRQvn8utBV6OeC7vd1WVJgwyVVJXXI/Jv1N5mvTe6qw0J1UhlklMrrYJG5njiAlD6HTQTm3NRV+uO/4J+bgTbL30pLBVWCokNuA7D1TGrMSdKI9NaeUuoirDVU/FKkQDJoU6YT8i8jmsxuVmVd072bcl1s67C7bg3TGqelvVMLA/fl/G2jCmKtqU+Q9Vva/z/nwR6cor2ItBCQu3dN7eDJzpfqaqt9Y9URlVvWefz94JjL50mGdBuN/5P8u23doKHs9ippZLbT1J4/PYRfQXnH0nAuep6ntE5MTk/eu7x6D7isieWGnhy9jF9peBcxNX2ip+IyIHqeovAUTkQOBnPdp2MUjSuAQ7iwmwM3Bbsr0FcB1wj7onmneUzBWPWIuxkMl+6bQJ2rXUdpWkK6XmilyCKBtUe62Ey4bqbL+Tv6jsbjvI/baw0nSkCzffjmk0MsO2NBpIul3O+JlEActYk2DGutWmkcGjJI0ODpohjYlGtp0X0qnnojooItxt00siGETBEK5u9Hb/7MTdY612ZnBzLFWVMsUYjJtjyY18rsjs2lUaNl11G8ndcYMg345DaEQIE9DeNGnj2uMeX/rep+6+vV3Ie1GWVLJ9rtTlkD0PVWSIgYqaP/bebWr1oz8RkV1Ku48CDk+2TwcuoGLSSI7/A3AycLKIPAU7+bwXeH+PUx4IPEtErkve7wxcJSK/s91VqrQyBuWeugeAiHwKOFtVv5+8fxTQ18i9OTBX/36PZ0mxiRPGcmUWhvC9RWSts/uUxImnH9uq6o0AqnpjUlypEhG5GzYg+mjswv6VWBt0Lx454Nx9qWsIv7+qHp++UdUfiMjbN+XEHo/Hs5Sp5XJrP79CVU+YjzEktb1XAV8FngOkJoP/3965h8tSVIf+t7p7Zu/zAhTQgKAiARMkXjXo9RENvlAxV27Um4v5NJrrDbnGtzFGriafmmui8RFjzENQI0GFYBTFJ4LhoaICKuJBRYmgIkRyiMIBzt4z3bXuH1XdXd27e6b33rP3zJlTv++bb2q6a7pXV/dM1XrUqn6bC0FVfygid8NGqSbe9omkRs/ZJSKvBT6AbatnA7d2/O50aHJ4DQfIwJqntL/IXV/6CFsf9YzOh0y2jJ/j0ea8rm4XmhaDkXpCwYbjrDiX7yCOPRNGrv57Jqmo14PEM1X55oncSZtl6GI+I3jzQy1z81RvS49k0ZoB44Ve66x231TThD8juGlfE501SM88VZiqvEW/2madt4WwrpjV35TevMNiSxWTlLcIEZXU6N5cJt8RHsc2iwJAr2/tL4vA7s4ThhuJ+73mLAU1E2uT6bFzuvqG34LNjlAGiEzUEW4EY8ZoGmP2j+CnInKI0zIOwfqUm7gP9j/596kuwS1u+/3qX3AD/udhc/7ljTuZ1Ogez8LazHKV51IaVoUKBALzyeL/+MNpizBzKONX5lvHsOs84LnAm9z7xxtlWNvyFb8FHKmqa8oJ1DXk9j+BDVGvJkZtsZkCicpQW5OVmkYUo71+5zxUt77hFPo7tnqHrY5wKpqEP5ppGDmOwg/LXNVMY4mqS1wWmoYXZtvvIy6csuLwFM/pajIkt2GbzXeE+87veMGVF/uFwzfynOK+RtWqabRob/XvrDUNNwDGkM9N1TQrU4sb45W1ormN1X58jcJLY+7PavZH1JVnMI6rWmR+7IpTPKqE35bPgldOemhbuvWO3HLq7wDQ27ZYPJ/xQr/ZgS9VzTG/d6rVtvZpaseK9lLXtCaYGr2LI7wLInIW1ul9kIjciB2gvwk4R0Sejw066jSHoiM7scFMbdrLSLomLLyIhkAAVQ3xkYFAYJ9kUsu9qmqb1aYprfkk+Ats2O1OIF88B1V9WpcvdzVPvdIrLwLPoH0pwUAgEJh7jIFsjP1pRjOwnIENyf0Wa7CgdTVPfa226UvOaz8ziETFGuFE3hwEP0mZMUXa52iwVFV7W7j9Ha8AoLd9a9WcIR0SoUVSMROMW//bD8XQtnjyujqemzAkKsue6Ul6vdIklZSOcJJeo3kK1TI1tntfOu9vAVh82gsrp1763D86mbLy1+HNLwBYfNLzm6+jhdz5HfUS4sIRXiZRjBf6pbmtl1Qcnk1zMPDuUwXxZ9/X9q82RblRNE+Tn2XlnI00K++j0cJMusJU1fAcrni+CmduVHNmr5zh3XY9/jOCRMXMb+K4+ly47Sbpl3XWwM/e+ALiLYvFNeSZBqJ+v5qZoMWB30Tl96JeO9bb0DdLes/IJB3hs5R7SkTeCvyjqnZJQ71LVd+51nN1NU/5M8Mj4Fdpzl4bCAQC+wRGO4fcbgbfBU5z62j8I3CWqraFvH1NRP4C62z3zVMTDbn1Z4anwPXYNLyzQxTBth3l5yYHqO+5Gg6QfHSYDln68NtsleVldLBc1E92bC+PVx/ViTcqykdw/lMSSRm+WB/lFCNQU8qkzVpH9RJawjX9hXR8J7e3CBVJD81HlFFSOjn9azOmaBfJhpUR8V2XnlOez2Q2JLMud409/3pmUd7yuOe01gPrNO3vsPmu/BxY0UKfqDJKdWHD/YXKiNRvG/Hbwn8W/NF44XSNq/ubnp2mEaqWo1zxR7z5THo11XKTJulpIG3nqzxfElU1RF8rapKxfi2+BlZpIzc7PklQt3yA9vpFivHVsPtv/sgedtsWYtcu0usROUuALJQBGdJLyvaPk+rMdPGuOb8cqLR7pX1z6r/B4suT0zLsOSfjCJ8Eqvoe4D0icn/gd4Gr3VoZp6vqRbXqD3bv/kI2Ew+5/eV62lwRWf/CFIFAILCXkqmQjZmHMW7/JHHLV/ySe+0Cvgm8QkR+X1VPzuup6mPXc56uncZlwENq277csG1qaNLD7Heg++CNOFXB5CPnDIYuNNl45XRY5qeKY2SLC631NYXYayrfh+GHKBYnzf0YUXW01Kr9NIw0W8IMxc/am58jl8PJqnEMkZVXo7jQLrSuXRQL1VTll8z6MsRkVTna5M93FyNAN7r0bM4ax9x52UdHhjb3D9ivDDP1c2b1+4W/KuovlPb3OKmGlOYjzCwt71fij16T8ho8v1cl+6lE9rMrj7x2//or15yuuP5K+LJ6lu6Kjb72HDQ9R77c/n3rOIpWaRl5F8ePMLFbHjlxmkHaPZx/6aw3EzvtXL30I9Lv23sH0Pd8JUlS1YTzc3vPrb0nDZNh1dhnNGdcrjQ1k/VpdMk9tUmaiFtK4mnA54E/95bifrOIXFure0/gz4FDVfUpInIM8AhVfW+Xc43LcvsL2KVWt4jIg6F41vcDtrZ+cV9h1ByAfYGmDmNfYlZsExvM9od3isTc95gh8xR27sVra2sT5Tys9vn9WL/Ha9zn72HTsK+/0wCehJ1ufhjwdm/7buD/djlBIBAIzCOz4AgXkdzacxXwS/XJrKr69dwhLiKJS5d+kKqeIyKnujqpiHSeyTsuy+0ZwBki8gxV/cgqrmXTMXGPpQMOAZza6pkNIuPCbIfLRMu2I46W94CbHV5RWxe3VEMRfUe2h4oUJqCV20fPUta6hpKbiXyHn9bMFg1qtV9fo9JxqFGMilvnOS5n9RqJy++IoHjmmeK8irjQbTEZ4uZ0NmoSWu7z21sKU01WbnfsvvKzACR32PVkZPkuuM2W+/c8uJQjKXNm+WGg1tyUmy1qphZfxjvvsO9btllzHVjnv/uu+s7fqNYuLWafevik+PNdffNUxUFe/hbr5rsm8vbyTWbqmTYr5ShufC5G0hZI4d2n/HkwUfdw2z0XfdAe58B7EOW/q7vuLM+3sAh9G35L0kNdEIXGZdnEveL+mKhXmsmiGCOjZ3KLmur9aKkzSYxuaO6prrxt1OmpOrcvx7oU7hSRA91+ROThQOfkYuPMU89W1Q8A9xWRV6yQSPXtDV9bFSLySmze94NVdZfbdio2OisDXqKq56/3PIF9i8Wn26w3Sxe8f7qCBOYW7aJpbLgMq3Jq5z3YK7Dhtke6CKuDsUt4d2KceWqbe9/esG/dipeIHA48EZtbJd92DDY3/AOwa4RfKCJHj1vyNYt6/HzboeWxvRFy7Baw6mXL9Ad2BNpb3k08cCsjestZaq+PcSGHRHEx6vRHmeplBa1rDf6oSCUicmJH2bAYdfojHpWoPEd9kl3ZKFWtgoZRJxHGk9V42kUhD+VxjH+8WqoDEXvuyNcaWLmATd7GkadRiJrymjUrrtkvx9kAs7ClPMd+B9jy1m3l9Se9Zsd+W6ZSkUKj0CgpcorJ1h2lphWXxzRRgkb5qLbU0tq0MZ/8nvuj81w7w1voZ0WbtRnAW3xjuRz+fWvTELuGxoqvEXkU2rh6wQ9jfHZ7LrYLeWqvT7rfQe47EfHSbgDihduLAABd2Eq2aP9ONOmTxU7TiOJCozBRQua0myxKiue2/gybhnuSP7M5+TNdv38ywb/xWXKEA4jII7HLw/rpzv/Jq3KwN/g/F/g0tiNZxq6PNHK98Zxx5ql3u+KFqlpZDlBEHtXlBGP4K+BVVDM4ngScrarLwPUich3WkfPlCZxvoqwwMwXmnnDPAzlKh05h86KnzgSOxPo28gG2Ul1CNsYqAPWHeFVBTV1Dbv+GleG1Tds6IyJPA36iqt+sOW/uBXzF+3yj29Z0jFNwOeQPPfTQpiqBQCCwIXQyT22epnEccIyOzo10s6q+Yb0nGufTeATwSKpqDdiQ27E5hkXkQprTjbwGG311QtPXGrY1NoRbMvE0gF8+9sG6a3hQoxyRWJW0Hw/pb7Mx5wvblkiMLcemzL1oJCpUZKsWO3OOxoVZyBCtyHCZq8ypxhgtVeJeZI+9IMv03fzInlkuzumbkjJpvh0qUXluiYvRrqpnqvDy4BiiQrZc1uJYnmMua1DzwTM7ec0eiamYAAQtzViUTkhBiVwgRmzSwlQVa1pcc5wNiBPnFN12IAt3lut5NZrq/LkTeKYVk5XmubhXOG5VpBq/n9/DuDR5aBQ332epmhe74puhmoIH2py0ded6kxnS317d5pVrJsYms5pQ3sOqaU2JnXkqMcOinNe55dtXco9jjqsca/cVn4Zt+wMwXNjBoL/dfcewkLdrslDcn7S/ragzSLYwjKwJ2BBh3F9JRvnbMURkrqwqGLPSnKoImXfdeQ3/Oc20+jutm7HWQ5aNnxqyiQkLd2L/a28eUWciavI4TaOPVWcS7JKCObfTwXGiqo3riIvIrwBHALmWcRjwdRF5GFazONyrfhhw07hzBQKBwGYzbZ+GiHwCO6jeAXxbRC6nPd35RFKtj/NpXAJcIiLvV9UfTuKE7rjfAoqF0kXkBuA4Vd0lIucBH3IzHA8FjsKGio0kNRG33LWt+FxEgKoQRfZDLEovNq5sKttzjHqOZm+EknnhdamJqhO2EdIs3yeV0UUvthW39DO29+0IbmuyRC9xs4ZRUo3dd5Pi3HbEnjtXS5mMRo2jK+PJa7TULrKWfP5tI99Kqh5Pm7D7THFOES1H1LVy7JyNkRjiyGkaZCSxvf44SQstL8kGZPv1vZOudGCuCAmuxUQs3nUrt28/pBiNjwqtbHKuZiSFpljR0rR51J+3TRP+9opm1uKorR+7LStqMbqu3U/T4bs++b2p3FfK+9SLByRO08g1jn66RJ3BlgNIY6stLPW2kzrtopctl0ERvS2Fw3s52cqSWNP5sllgkNr6qUZkxt0HFTKnUWQaYdy1ZkYa/3wN5W/ST1HuW7tHJb9dLzPiCH9r14pN64Wvha4+jbtE5C3YiKZFT4iJL8KkqteIyDnAt7HJEV84LnIqsG+z46Enctv3dk5bjLmlbpoKWLpM7tvoPsMN7BGRN6vqH/v7ROTNwCWTPmdXo+0Hsal3jwBeD9wAXDEpIVT1vvkcDff5jap6pKreX1U/M6nzBAKBwKRQo5gOr03iiQ3bnrIRJ+qqaRyoqu8VkZd6JquJ92DrYZgK//6z8nKa8uxFUptE3DAlwv8c1TJP5ypwZlZ+J3eIpZkW+4yBOLYHWOjHbHFrXm9d2FKYyex3StOWf+7cbBZHpTlBVSqjm9gzsZnCrFZm1zQqhdyVDOHiOWzFyxIeaXHeqmmqWj8WU/qpPfNULJ6DXAyJGK/sTFWSkcTWPBfFpnCcj1oas8mBm5GQkrDr336CiQ4sTUNSNdXkZjXfdGQ0Kp2uGhVBAUar5qn8LrXNZaG2v2r2KeniD227/i5mpy7LioLXFp6csSiJC9jIJKbnAjLihgWYfvT979jCwt0YRNbosEe3kmX2avuyyGBhsThHqvYYQ+2xnFlT1VLaZymz5xhmEUP33WEWkbrf0SAtTb7+Cnl181L5u6Ncp12rDuhiyfNYJmqeqq2l1cgm+DReAPwBcD8R8edZ7MAmmp04XTuNfPbbzSLyVKxj+rCNECgQCAT2BmbEp/Eh4DPYdb9f7W3fPSkfRp2uncb/E5H9gT/Ezs/YD3jZRgi0VoYZ3LJrda6PijM7H6V4aZ7iSApNIY5K+2XmaRN2/SIp1FD/QTKqRG5okyRCr2fLvUSI8hnOqsVoqb7ecOyG/3FclvNz5u9Feqa4qjnlsqZeWKAx1e/m+Jm348i+7Hb1NBCIcg3CaUGVep4Wko9iY9FeSJlLAAAdQElEQVQi2CCJTKHBRGKKEW/SEMprjzPe0WyDAlx4rJbhl344tIg2nstqbLnTtSwrpSbnH6e4p6uMWowarNoV53U9VLbDn8yYDPUr9vvPaixmRb1IDD2XJy2LYjIX9hw35LC7M9qvKC8bpzlkfQZZ+VeSROU58nZNTcQgs8ddGsYsDe3Jl4cRQxfxPhhaTT0vD4f2OP7vzf9NGe+i7XpX9nOaGtK0lCFJouK9359savRpz9NwyQhvA57l1tO4J/Z/fbuIbFfVH408wBroukb4J13xNuCxACIyU53GNAiTgwOBfRdjFJON7hV0k3waIvIi4HXATymtoQo8cNLn6qppNPEK4B2TEiQQCAT2JmZB0/B4GXB/Vb11bM11sp5OY6bG2enQcMstNgFh1fRSNR01oUY985QWq8fFcVSotnEs5OlOVLWSYbo+cTivl5u2bB0pl3kWqWRNryze5uSwS0dr8d3ieqQ8bpJEJElp/srNRSLltWae2p6NGRUV54hWHjOOIXEfosiaxfJrSGKtnDsqHOmlqcvO+Sjr5yaMJPJnlmvFZOJfc9FW3iX45qNKjH8loEBJPBOZb/YqYvy1nHfjm6eAkeap1dirxy38l5+r6U8mknZzXdM5VJvPZ9u3fPDyIAohIsvNU3FM6kxKiVTtpZd/9zZEthWy5vWW04SltHRsp97ciTwgY5gKS24BwOWBctcedeWUdOieT2/kng4N2Rgvsx+ZZJcLt/WNZ54yRr3nOSLpjU1k0ZkuPo2uMbdurtpubN6oVFVXG+f8Y1aR3nw9rKfT2Lw+NBAIBGYMo+NDas1qRhbwWH/qwSr5AXCxiHyK6ozwdS9fUWdc7qndNHcOAmyZtDDrIUszfn7rncXnyBtq5TfO1D3Nle+X++LCcRYTu5FJFAlRPtKWlWqnrxXkI+OkFxM5rSCKSk2lLp9PofF4D6Q/ivI1IevYs/L1+nHFWZ5rAVmmFQ2juvT46Ac6jqV0IvakOH6SSEULSRKpOszzdb5rM3NLTQP6iT13L/Ec6qKV0N+oMru6Wcbc+W2MFCPcSkiyKEmcaz7lKNMPS1atzpwvswmUxzHaPCu5K3UNrAlr7li5M4608ux0IWrSNGoh1EPn544iLcJeU2NIoqjYDnDRt/a4z71KVoTU5N8RllNbHgyFZefkHgxh4LSIPUuGPXvsCffsyVhett7vwVLqacKm+I1mmSl+U/Z3lMvkWw604jPIy1lmSIeZV8eFGUdRcZxJoGZ8yO0m5p76kXv13WvDGJdGZMeo/YFAILCvYlTHahKjk85WqwKfE2uHfLdLxtoZVX09gIjssB/1jtV8fzWsxzw1U2SZ4Y6flZqG7wcYF8Fg1LefR0TF6DohGpQjnHyUEsdRcUyjWtFgRIQ4HxUlkeffiAqZIqnK10Y5clJSN+vJeBpRlEQsLCRO1rj1mP71j3vIIxHPp1NqGvEwKrSmOCp9MkkSFeUostqHeJpGuQqsFhpAJBS+mF4vInEKQC+hKNcH1rn9vZdoZV8+kstMOYlRVcqw30iQMolxxdeTTx6zWke53x8d5mU7kUsr2yrt5vl0mrQC/5rjqKzf5q/x9/mTTNvyKrUpIismdErDvkyKcOjUROVESv+7Tuvb0ku5banPMCsn3/ltOUythgHWd7G0ZBvrrj0Ze+6yO5aXUpb22PJwOS19EZkhdfG3JjXF7zKO48IXEcVVn4T/p5xrFL6WokbJvFS0XX53XfFX9h1VBzhWRK70Np/W0Ck8SlVvEpF7ABeIyHdV9dKusojIscCZwN3d513A76jqNV2P0ZW56TSmwSrtlXNH3qFAaZKadzbR3DAz+GbC25Y21PKxV2Ed4eM0DQB2qupLR9fTm9z7LSJyLnbhuc6dBnaJiFeo6kUAInI8cDp2aYuJMjkDXyAQCOxD5D6NUa9ukzVlmzMrITY87QTs+hirYVveYQCo6sWUy3VPlLnRNNQoy0s2aCCKmvvCTs7EBCQPGUyzQuU1UVR4DiNnnipy3XjDT4ki0nwN6Ug8p3BptpJIPBOONI7SjXd8k5pChfdV7ziJCnNV3CGUsO44bJIhiqMipFdNVJhkoshUQonjOGKYun2eScbOLs+vv1TfrWPfljOjlfDjxM2U7/eiigmnas6y5YV+VMyCj6RqVsrL9jylPDlZVtZJUyWrBBqMLlsTYRnKaWcp52YcqYVBl6HJOVEs9Ht2Q68n9F1ap3iEhiZeG/nmr+KYI0xSTfUq8kj9O6VJ0XfY+8dJM2GY5jO5YTjMt2vxLAyHSurKy8sZg4Ftr6WllOWl3Pk9ZDiw5XSYkeWm18wUpqRsmGFyc1OUkQ5z03Dcanr2zcyF+dgzHZsJq4mZls9QGx0TFt4TONfdgwT4kKp+dpXi/EBE/gRrogJ4NnD9Ko/RibnpNAKBQGBTMTrWX9rFEa6qPwD+yzql+V/YDOQfxY6YLgV+d53HbGSuOo3CcWyaHV9dNZBilIIpcwP5zu5UPC2jNgEvM96oW/Cz94g7v3jO5noobiGDH3LbotX4deIR4cR+/abrtmHCXtlz+Dc57+PYOsULh79I4STPrwlWjrLKa6CYuLVi8lVSamPF8QR6LmfQQj+i38vlqOXbashCrEY9DcTTdrxQ5Mz78aepqeQwyjW8dFiGcQ4H6Yrw7bzNev2kCIOOkqjMPdaL6DltsN+PvHxIZeZV+yx4x8y3x7JiG+R5z8pyub1av8grFlcngDZhtzc9j/UQWmXZaRHDgSk0ijQ1DAe2ndJhxmBQag55MIevXWTDrNAufKe1feZzB3mGuJDeNEqJ4zzfmKdlGNP4B641T7WsYgnfccxIwkJ3Hv0Z8JLNONdUOw0ReTHwIuxiS59S1Ve57acCz8fOjnyJqp4/PSnb2ay8MoHZITer7Evs4/EerXSZ3LfR/xFupdP281eXe50IU+s0ROSxwEnAA1V12YWaISLHACdjVwk8FLhQRI4Oq/cFAoFZQjskLNyEaLtHYFOInAV8lU1I7zRNTeMFwJtUdRlsqJnbfhJwttt+vYhchw0/+/JqDr7WeGw1inF5kSITdR4p+GYqJXfC1WtlK2Rrc4T7+E7xiinLlI56NaWDsDJLtsUkBeXMd820skhQbmrJPDOaL3McRxXnr2+GqTsp/bBkP6V17sCv59jKj1k3meXbe72YxDmU/fki/nVlWemgNJk/s14rs4+L/ESpKZzu1hnr5zAqTSrpMPXkNkRSmtLyNhskMcvFnIIy15Gfx8zPFNBLqlkDirIXXODnMLPnq7anvX6/nraauXreL77JRCVSml7zwIHUc3LvWbLtsbQnLWZ1+6a7NjOU8QI4jBqyvH6aFSYk9cyEpmZW8tvaeCasnLbfaeW3FskKc9V6sMu9jpsDtuFq2i9gV+17FvDbwKeAszZifkbONENujwYeLSJfFZFLROShbvu9sD1nzo1u2wpE5BQRuVJErhwsbch6I4FAINCIqo59bbRtT1UzVf2sqj4XeDhwHTYH1Ys36pwbqmmIyIXYnrDOa9y574a90IcC54jI/WhWrxpb3s2qPA3ggIOPbb07bQ5w7zjlB1OOWjTqfsNLR/h4O2ZdC4o6OOcKLaLWFNkwH6VXnf/+jNj6jPe8jh9y6xMnZfhuW5hy7M12BwqnOPgLWlU1pKZQSa3ZhaM2mbxZ9kXZCw/2NRMf44UZW0drqWn4eY7ysvFHu1lWzgJ34aD+sfLnQ1S875SzmkWkaEv/+uM4aryeSoBELfCgfk11/Jn89WbI2zSOoopW49M009zXxnJtotQuMk+7SMtcTyuc2VVndbl9pXZRvZ7ymuvagl9uygTd9Nle42THyPka4aPYjAnAIrIAPBWrbdwXeCc2impD2NBOQ1Wf0LbPrW37UbVP1eUiYoCDsJrF4V7Vw7DLywYCgSmQDvfBafAd6LYI08bKICJnAMdil3x9vaqudlLgqpmmeepjwOMARORobGbGXcB5wMkisiAiRwBHAZdPTcpAIBBoQLVMWjjqtcE8B2vqfylwmYjc7l67ReT2jTjhNB3h7wPeJyI7gQHwXKd1XCMi5wDfxobivrBr5FSTSjrONFWnkgCtZRSxYl6HajGL3JpbVg4vKnJk1WMY8tTNIxzirgV8lb8em17O/fBMRcYUjkOTmrJOHDeaoXyzVd100OQUB2tKKI9bntufgS7efJS29NajzHr+uf3j+HNfcsd+3RRWtEVmihT46TCtzLWpmFQaAgl800qTbH6bS8WBH5dy+9kBWsxWlWM2BBe0PSOVuRktdaxj3rs/YxzJdi5LabobDlKGy3YaeDrMivJwMChMciuPW6Yl99ExYUV+ffHTxdf+CZpMu6PMVpOknmGhsc4Gh9yq6qYP/KfWaajqADvVvWnfG4E3bq5EgUAg0J02f0y10vxNcpnLGeE+2cq419HHaMhf03TsUanXc8dzPgpKFnqV/elwWDjlKiOhDqKqUYYDu26mSbNmTSOJy1mztdTQ2dKwKG+/2/4ALN21p9iWuJjMha2LANy1+053HEPS6xXnqY7Oy30A+x20f1Hec0d5bH90XYR1pllxPW346erF12pqo3tfG/FH+Dm+zH6KeV879Be5qjtsW8nKW7fCIdtwn0fJ7V9zozO3Fq5dHlOqo3Nvn689V7IAeDP3m7QrNbXw42FaaBTDwYB02d63wZ5iobhWeUUioqRc0KyUJ6ppUg2/C48uQSM+/v2cNMZUFzhrokOihr2Oueo0ZpVPvPuXK5+f+r833FfViY///f0BeNJzr1qx76Pv/EUATnjON1Z93H9+632K8m/83rfXKF1gVvA72EBJEVY7utLmCLOJhE4jEAgE1oBNfz6RLLd7FXPTaahq4fCF5pTJdVOOr+rmdUzafAxV4yUQrCVBcyr14la7bHok0YoZrSvlNSPjxv1rgdLMoWpIeknF8diVz535YB5/sg1E+/zZD1v19+vEcVyYe9LhsGKiyvnk6ccU2kaWZo0mquU7rQnLZFmlTUqzTeSlbo8rJozmeSdRoznIDwrw7dG+47ROlq3OvOlTN4uMm7XcNjehrX6bqW5FfWeSql+Lb8KrzpspEwX67ZQOh0V5uDxY8Yw2URyXrKhvgxn8+1aujil+4ETDNeXZGkYRmdrv2hNzteat0YzXNFax3Otew9x0GrPAZ97/wE71PvWeY8fWGWfCOv+MB3U6V52mzmLUsT535oPXdB6fT55+zMj9xz9zVRliAoGZoIsjPHQaM85weaVDtU1byMm1g9ypl6XV0VPT6Dmw+fj3Nh+lJv2E2N2fmDJ8WMkqYZqZp5VlTXmLhs2aI5SaZ1OK7S55jPznTRtmRNeP2eQU9qkv+NUWXutrxe2zpssRfpO8PotbtxQO797C+pZ8VaNoPvz3tYA4Lp3lWmodVZmb28dvF9MSUVLXZNdL5gUKtBHMUzPMUffdsqbv5dpBbrapc+FZx61ZpvXQRRuZFy7+l0eMrfOY3/ziJkgSaOMz738gv/70y4CVv4lHn/SFaYg0fUy7yTFnHpdPmJtOI7Bv4/s02qhkUfV9V/VRf4OtfuWIsQw3zo/ddKzV/GmoH7tL1XdRyLEOH8uokO6KD6jwdURFO21Gx1C5tiQmcv4L//qr7VkNj6/7bJLexv69heipOeCCD/7qmr87CcdwYOO49Nxfa9y+lpDgSTFuVvO8sK6OaoqM+j+Qs9d//C6LMM2hojFfnUYgEAhsFuqlWGmtM4cDi9BpBPYqTnjONyr5jCq5nbwwzkp4tfORG89BXslPVHNqNzmvbbnZgZ3vWw1NC2bVyc1Efo6wwZ7NH/VHcTwz2kal/c3o2eMbL4uO7RSCTyMQCOwVfOHjj562CJvGeszS68F0WU8jdBqBwOygxlRyPkk5IG/OeEy5JK6pTcD0R4SR59zOiv0NTln1w1/bJwjOK3lEW9eRfpf2KXN9CUTxmNpTposjvHn9uL2a0GkEAhNiXxrdT4Nphb+3YTIdm5dr3CJNeyPTXIQpEAgE9lqMmk6vLojIk0XkWhG5TkRevcGir4ugaQQCgcBa6LAIU5eYWxGJgb8Fnohd7voKETlPVWcyRfTUOg0ReRDwD8AidoW+P1DVy92+U4HnY6cjvURVz5+WnIHZYhK5sAKToW3uzL6CdpkR3m1y38OA61T1BwAicjZwEnb10pljmprGX2IXQv+MiJzoPh8vIscAJwMPAA4FLhSRo7su+RoIBAKbQWaysVmQXcbgY0XkSm/zaap6mvf5XsCPvc83Av91UnJOmml2Ggrs58r7Aze58knA2aq6DFwvItdhe+KQCjUQCMwOXbLc2v07VfWlI6o1hZ/NrAd9mp3Gy4DzReStWIf8I932ewFf8erd6LatQEROAU4BuPe9771xkgYCgUANpX2yZ1Gnm3nqRuBw7/NhlIPomWNDOw0RuRD4hYZdrwEeD7xcVT8iIr8FvBd4AqvodZ2KdxrAcccdN7M9cyAQmD8muJ7GFcBRInIE8BOsef631y3gBrGhnYaqPqFtn4j8E5CrbB8G3uPKe1WvGwgE9k2MMeN9GmNyUwGoaioiLwLOB2Lgfap6zUSE3ACmOU/jJuDXXflxwPdd+TzgZBFZcD3vUUDzYheBQCAwJXJNY+SrY2p0Vf20qh6tqkeq6hs3WPR1MU2fxu8Bfy0iCbCE802o6jUicg423CwFXhgipwKBwKxhV0Uco0nMYWqZqXUaqvpFoDHTmOtpZ7q3DQQC+zaqE5unsVcRZoQHAoHAWlDTIXoqrKcRCAQCASDLDFm6fkf43kboNAKBQGANdAq5DT6NQCAQCFg6mKdmd2L3mgmdRiAQCKwevfn6j3PrzZeNrLS856cwwylB1oLMi3dfRP4DuBPYNW1ZahxEkKkrsyhXkKkbe5tM91HVg9d6YBE5FOiacvlrqvrvaz3XrDE3nQaAiFypqjO1vFeQqTuzKFeQqRtBpn2HsHJfIBAIBDoTOo1AIBAIdGbeOo3TxlfZdIJM3ZlFuYJM3Qgy7SPMlU8jEAgEAhvLvGkagUAgENhAQqcRCAQCgc7MRachIk8WkWtF5DoRefWUZblBRL4lIlfli8mLyN1F5AIR+b57v9sGy/A+EblFRHZ621plEJFTXdtdKyJP2kSZXiciP3FtdZWInLjJMh0uIheJyHdE5BoReanbPrW2GiHT1NpKRBZF5HIR+aaT6fVu+7SfqTa5pvpczT2qule/sCtd/RtwP6APfBM4Zory3AAcVNv2l8CrXfnVwJs3WIbHAA/BLmg/UgbgGNdmC8ARri3jTZLpdcArG+pulkyHAA9x5R3A99y5p9ZWI2SaWlthl2De7so94KvAw2fgmWqTa6rP1by/5kHTeBhwnar+QFUHwNnASVOWqc5JwBmufAbw3zfyZKp6KfCfHWU4CThbVZdV9XrgOmybboZMbWyWTDer6tddeTfwHeBeTLGtRsjUxmbIpKp6h/vYcy9l+s9Um1xtbIpc8848dBr3An7sfb6R0T+yjUaBz4nI10TkFLftnqp6M9g/BeAeU5CrTYZpt9+LRORqZ77KzRubLpOI3BebFuKrzEhb1WSCKbaViMQichVwC3CBqs5EO7XIBTPyXM0j89BpSMO2acYRP0pVHwI8BXihiDxmirJ0YZrt9/fAkcCDgJuBt01DJhHZDnwEeJmq3j6qasO2DZGrQaaptpWqZqr6IOAw4GEicuyI6pvWTi1yzcRzNa/MQ6dxI3C49/kw4KYpyYKq3uTebwHOxaq/PxWRQwDc+y1TEK1Nhqm1n6r+1P3oDXA6palg02QSkR72z/mDqvpRt3mqbdUk0yy0lZPj58DFwJOZoWfKl2tW2mpemYdO4wrgKBE5QkT6wMnAedMQRES2iciOvAycAOx08jzXVXsu8PEpiNcmw3nAySKyICJHAEcBl2+GQPkfjuM3sW21aTKJiADvBb6jqm/3dk2trdpkmmZbicjBInKAK28BngB8lyk/U21yTfu5mnum7YmfxAs4ERtl8m/Aa6Yox/2w0RnfBK7JZQEOBD4PfN+9332D5TgLq5YPsaOr54+SAXiNa7trgadsokxnAt8Crsb+oA/ZZJl+DWueuBq4yr1OnGZbjZBpam0FPBD4hjv3TuBPxz3Xm3T/2uSa6nM176+QRiQQCAQCnZkH81QgEAgENonQaQQCgUCgM6HTCAQCgUBnQqcRCAQCgc6ETiMQCAQCnQmdRiAQCAQ6EzqNfQARuWN8rXUd/9MicoB7/cEavn+8iHxylfVvE5FPt+x/v4g8c7Vy7I24tnik9/nlIvIjEXnXNOUKzC+h0wisG1U9UW0ahwOAVXcaa+QLqnri+GprR0SSjTz+hDgeKDoNVf0r4E+nJk1g7gmdxj6KiDxIRL7iMoGem2cCFZGLReTNbnGb74nIo932rSJyjqv/zyLyVRE5zu27QUQOAt4EHOkWvnlLXYMQkXeJyPNc+cki8l0R+SLwdK/ONpeZ9AoR+YaIjE1zL5Z3ici3ReRTeFmEReRXReQSl3X4fC9X0kPdtXzZybrTbX+eiHxYRD6BzVbcKI/LrvoWt/1qEfl9t/0QEbnUtcHOvP1a5D7Bnf/r7pzb3fY/dcfdKSKnudQiiMhL3DVeLSJni82C+3+Al7vztZ4rEJgY056SHl4b/wLuaNh2NfDrrvwG4B2ufDHwNlc+EbjQlV8JvNuVjwVS4Dj3+QbgIOC+VBdZOh74pPf5XcDzgEVsiuqjsJlHz8nrAX8OPNuVD8Cmh9lWk71+3KcDF2AX5DoU+DnwTOz6CpcBB7t6/xN4nyvvBB7pym/K5Xby3YhLidEmD3AK8Fq3fQG4Eruwzx9Spo+JgR0t9+Qg4NL82oA/pkyD4afjOBP4b658E7CQy+LeX0dtwSF3De+a9nMXXvP52hvU78CEEZH9sX86l7hNZwAf9qrk2V6/hu0IwOZE+msAVd0pIlevQ4RfAq5X1e87eT6A/RMGm+TxaSLySvd5Ebg3djGiNh4DnKWqGXCTiPyr235/bAd3gRusx8DNLsndDlW9zNX7EPAb3vEuUNV8wag2eU4AHuj5TvbHdoJXAO8Tm6n2Y6p6VYvMD8euJPclJ1sf+LLb91gReRWwFbg7No/ZJ7Ad/QdF5GPAx0a0RyCwYYROI9DEsnvPKJ+RprUIxpFSNYEueuW2pGcCPENVr13luZqOJ8A1qvqIysbxa7TfOU4eZzJ6saqev+Kkdg2VpwJnishbVPWfWmS7QFWfVfvuIvB3WC3uxyLyOsp2eyq2g3wa8Cci8oAx1xEITJzg09gHUdXbgJ95NvDnAJeM+ArAF4HfAhCRY4BfaaizG7uudc4PgWPEpqLeH3i82/5d4AgROdJ99v84zwde7NnxH9zhki7FpryOnc/isW77tcDBIvIId6yeiDxAVX8G7BaRh7t6J484dps85wMvcBoFInK083/cB7hFVU/Hpjh/SMtxvwI8SkR+0X1/q4gcTdlB7HI+jme6/RFwuKpeBLwKayrbzso2DwQ2lKBp7BtsFZEbvc9vx65/8A8ishX4AfC7Y47xd8AZziyVp6O+za+gqreKyJecU/kzqvpHInKOq/t99z1UdUnsUrifEpFd2A4pXwnuz4B3AFe7P+obqJqOmjgXeBw2Hfb3cB2gqg6c+eidrtNK3LGvwaZmP11E7sT6cW5rOO4oed6DNd193W3/D+wa2ccDfyQiQ+AO4HeaDqqq/yE2KOAsEVlwm1+rqt8TkdPdtdyANXeBNa19wF2HAH+lqj93Dvt/cQ76F6vqF8a0VSCwLkJq9EAnRCQGeu4P/0js+glHq+pgCrIcj3X+jutMRh1ju6re4cqvxq658NIJiThVXGd0nKq+aNqyBOaPoGkEurIVuMiZYwR4wTQ6DMcAOFZEPq1rn6vxVBE5Ffsb+CE24mivR0Rejg3D/ci0ZQnMJ0HTCAQ2GBH5KjYs1+c5qvqtacgTCKyH0GkEAoFAoDMheioQCAQCnQmdRiAQCAQ6EzqNQCAQCHQmdBqBQCAQ6Mz/B2sdrSZYp7BjAAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
    "source": [
     "sst.sel(time=\"1960-01-01\").plot.pcolormesh(vmin=-2, vmax=30, \n",
     "                                           cmap=\"coolwarm\")"
@@ -1403,7 +315,7 @@
     "\n",
     "**EXERCISE**:\n",
     "    \n",
-    "How did the SST evolve in time for a specific location on the earth? Make a line plot of the SST at lon=300, lat=50 as a function of time.\n",
+    "How did the SST evolve in time for a specific location on the earth? Make a line plot of the SST at `lon=300`, `lat=50` as a function of time.\n",
     "    \n",
     "Do you recognize the seasonality of the data?\n",
     "   \n",
@@ -1420,22 +332,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
    "source": [
     "sst.sel(lon=300, lat=50).plot.line();"
    ]
@@ -1468,40 +374,32 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/stijnvh/miniconda3/envs/DS-geospatial/lib/python3.7/site-packages/xarray/core/nanops.py:142: RuntimeWarning: Mean of empty slice\n",
-      "  return np.nanmean(a, axis=axis, dtype=dtype)\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
    "source": [
     "ds_mm = sst.groupby(sst.time.dt.month).mean(dim='time')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
    "source": [
     "ds_mm.sel(lon=164, lat=[-23.4, 23.4], method=\"nearest\").plot.line(hue=\"lat\");"
    ]
@@ -1533,84 +431,50 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/stijnvh/miniconda3/envs/DS-geospatial/lib/python3.7/site-packages/xarray/core/nanops.py:142: RuntimeWarning: Mean of empty slice\n",
-      "  return np.nanmean(a, axis=axis, dtype=dtype)\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
    "source": [
     "ds_mm = sst.groupby(sst.time.dt.month).mean(dim='time')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.image.AxesImage at 0x7f0e96db9490>"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
     },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
    "source": [
     "ds_mm.mean(dim='lon').plot.imshow(x=\"month\", y=\"lat\", vmin=-2, vmax=30, cmap=\"coolwarm\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.image.AxesImage at 0x7f0e96e78590>"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
     },
-    {
-     "data": {
-      "image/png": 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IlMBFZnZ5erpt9VRZFcE1kq4FLk7v/y4FG1ivRh05WATlut/Oz8si0JhP+mjDsY1H9RmfgOPEZp/AuadFoJKPFVpxei8Gnaw5gNKIXyA6F0q5ZA0J+DJJ7PW8KQ9N1lOdg3M9VSZFYGZ/IekdJJXAIklvusJLqCAIgsKTn0VwOPD7wE8l3Zme+zgt1FO1Sub8RTP7OonpUkisXscc0thU9RvP6IWN+lgEdaddO8DERp9dZXWbV4yg+z4X5UGfdOUBpxRdgNKwX6+vXMjBqjezW2DGxmNtqafKmjX0dkn3SdooaZOkzZJ8Er+DIAi6hX7oPjqFzwG/Y2b3eAoTBEHQPahvJpRN8ljhlUC9Tn1T/kZKacKvstjMXNZtbPNxDXm2Xh536t1T3ebjwqmNO6YVOzGxxSeteNApCA1QGfFzO80XE1iPDKbJqgjWSPp34Bs8f2bx5TO/JAiCoJfpv6ZzS4BtwFumnDOgMIrA6nWXoqTShGNQsOFTOFRzsgiqW/3663gFX83pPbaGjzXnuXZ9wseK8bI0ACojfl1ec6GfFIGZ/eFsj0s608z+Oh+RgiAIugPrkRhBXurMLb81CIKgkEx2H+2jrKFmtKQW8+zDbQ1zcYmUqn4VpFbzaTfs5cIZd8r1B7+gbnXU5+9Xr/q4nAAaTp8LLypOtRrg647MhbAInkerTs3JPtyvAF4HnCLpAJK+2zea2f7AjTx/XnIQBEEBEFYuZzqKTkctgrS16mSb1c2SpvbhPiJ92mqSyWWzDmSwRoPqVgeLwPGP2PAaQOK0Q/PatYNfOqZX4NVz1+5nbfi8x559l7wqw3NBdIXbJwt5KYL/mO8Cs/XhluTWhzsIgqBVrEcUQdYWE5+TtETSgKQbJW2QdOLk42b2V/MRYvs+3HN43crJQQ9Pbiu4LzEIgh4jrSzOchScrBbBW8zsdElvA9aRZAl9B/jafAWYTx9uM1sFrAI4aJelVnXIZ/Zq1AV+7oVubLTm9V54uVms3oV1BE7vhVdAHnwD0XnQVxYBMJD+PAa42MyeyuPiGfpwg3Mf7iAIgpaQknkEWY6Ck3W7+01J9wKjwAfSUZV5bL9z68NtDXPZCXtVY4LfLtgreDextQuDxU4797rTOEmA+kR3pY+WB/x2xZ6B6Pli9E5BWdbK4jMkfRbYZGZ1SdtIMnvmRRH6cAdBELRMP7mGJC0ATgEuSE+9GJh1EHMQBEGvYyjTUXSyuoa+AtwOvD69v44kZfRbHkK1gjXMxY3j2VzMLVg82n2tl92Cuk5/P6v7uYY8A9EeeFZZF7vdt/ouWPwSM/scUAUws1FaLCILgiDoGfqs19CEpBHSVhKSXsKUuQSFwLrPIvDa7VRHnSpIx/2CmJ47bA8aXZg+6kWj2o1V1vPHJBpdkBGUhayK4BPANcBeki4iyfZ5n5dQQRAEXUGfZQ1dL+kOksZwAk4zsw2ukgVBEBScXokRZFIEaeHXbwP7mdmnJe0t6VAzu81XvOyYmYurRY5mr5erxcvlVHNyOXmicm/s2IqMp5us2C25uyMjKAtZ1dkXgcOA96T3NwNfcJEoCIKgSzCVMh1FJ2uM4NfN7NWSfgRgZk9LGnSUa86YFTuwNB11r537mM+6nu9vqct27p6Whko+a/ul0vpZBIX+nxY9EyPIqqqqkso8lzW0jGSiWBAEQV9iiIbKmY5mSLpQ0uOS1k4590lJj0i6Mz2O8fpdsloEfw9cAewi6WzgncBfegnVEmYu/kTP3Y6bL3/Mx6/qWujktMP22rm7WjBOa9cnfPZuntZR0VNpc3T7fBX4B5LRvVP5WzM7N6+LzERTRSCpBDwAnE7S/0fA8WZ2j7NsQRAEhSavYLGZfTcdztURmioCM2tI+hszOwy4tw0yBUEQdAFzajGxs6Q1U+6vSuepNOODkv4AWEMy3/3puUqZhayuoeskvQO43MyKaauZj+ui4dlryMnV0m29asAvQFoe9Kn8LFf8MkG8PnMqFz97ZXu8Phd5MYc21BvMbK6NOi8APkMSm/0M8DfA++e4RiayKoI/AxYCNUljJO4hM7MlHkIFQRAUHZMyBYJbXt/sscnbkv6ZJk0+Je1rZg80OzcdWSuLF2d5XkeRKA04/FEcC8q8Ao7dGCCtjPiMBB0YGWj+pBbwHMbiZil6pY82+jeB0LOgbHJcb3r3bcDa2Z5PMvL31duduwx4TbNrZa0s3n5xgI3AL83MJfVF0tHA+UAZ+JKZneNxnSAIglbJK2tI0sXAESSxhHUk/d2OkHQwiWvoQeCPZ3jty4FXAjtIevuUh5YAw1mun3Ub9kUSTfPT9P6rgB8DL5L0J2Z2XcZ1MpHWLHwBeDPJ7IMfSrrKzO7O8zpBEATzIcesofdMc/rLGV/+MuBYYEfgd6ac3wz8UZYFsiqCB4GTzewuAEkHAH9BEsC4HMhVEQCHAveb2S/S611CMhpzZkUgn8BSN/aq8XJbeL4XlSEfX6vXuqWKn2/Ya2Wvvj3W6L4gdB5YQQbTmNmVwJWSDjOz77eyRtbf4uWTSiC98N3AIZNf1A7sATw85f669NzzkLRS0hpJa56ZKO6Q6yAIepOCjap8m6QlkgYk3Shpg6QTs7wwq0XwM0kXAJek938X+LmkIdKpZTkz3Tv3gkhXmoe7CuCAHRa7RMI8UzG7LSjo21/HZ2fltXOvDPkEt6H7gq+eHUKLXlncyLyXbgtvMbPTJb2NZPP8LuA7wNeavTDrp/l9wAeAD5N8Sd8C/DmJEnhjCwI3Yx2w15T7ewKPOlwnCIKgRYQVSxFMpsgdA1xsZk8pY51D1vTRUUlfBL5lZj/b7uEtmcXMzg+B/SXtCzwCnAC81+E6QRAELWH4po+2wFWS7gVGgQ+kzUHHsrwwa/roccDngUFg3zSl6dNmdlyLAs+KmdUkfRC4liR2duHUGMX0L/IxIz0ri73cTl4up5JPSn5X4lvx6rXL7C6XE/gOvcmDgimCO0iqkR8BzgTeQFIM3JS5zCw+FLgJwMzu9G6QZGbfBr7teY0gCIL5UDBF8L/N7CBJvwH8FnAucB7w681emFUR1MxsY1Z/U2cwlyCbZ7Cq6IGwXsCtervgPXCmwyutuNjjJD0p3KjKyT/EW4ELzOxKSZ/M8sKsimCtpPcCZUn7A6cC/z1nMYMgCHoEAxpWqGDxI5L+CTgK+Gya1ZlJwKy/xYdISpjHgYuBTSQZREEQBH1LweoI3k0SVz3azJ4BdiIp/G1K1qyhbcBZ6VFMnNpQe+LlXvDao3g2nfNyW3jVEXjVPSRr+6zrVl/ShW6yvCiSayj9nr58yv31wPqZX/EcsyoCSd9kmkKuKRdyyRoKgiAoPsKsOIpgPjSzCCZnZb4d2I3nKtTeQ9J/qDjIqfLVZ6ww4Fep67WuZzVtedBnba91K8N+ubTdlkTgGSxu1Iqb8mpAo0AWwXyY9b/EzG4GkPQZM3vDlIe+Kem7rpIFQRAUGStcsLhlsm6Xlknab0o30H2BZX5itYDk4g8ud1ncAfx8tgML/HbBXtaG187dy9LoRlwtgoL//xUpRjAfsn6aPwLcJGmy2+g+wEoXiYIgCLqC/okRAGBm16T1Ay9PT91rZuN+YgVBEBSbAvYaaplmWUOvNrM7ANIv/h/P9pxOIvmkIFrDbwCJFy6zm/F1h3SbC6cyPOiybjfi2Ta76IHzfrEIviLpCKafDzDJl4FDcpMoCIKgSyhuTtPcaKYIdgBuZ3ZF8ER+4rSOSmJgpLvaY5pbEZXPuoML/XbBAwuGfNZdOOKybtkxfdQLlbvPui2yRWCoP7KGzGyfNskRBEHQdfSLaygIgiCYgb4IFnsi6fPA7wATwP8D/jBtlISkM4GTSdqqnmpm12ZYzyUw6GmaeuVIe+Xke7lvPNeuOK1bHuq+YHEpXEP5YlBk8eZCJx1c1wMHmtlBwM9JJuog6QCS0ZSvBI4Gviip+z7BQRD0NJPpowXqPtoyWUdVCvg9YD8z+7SkvYHdzOy2Vi9sZtdNufsD4J3p7RXAJWm66gOS7ieZjvb9JkK6pPR57ki8tJtXyqSnRVBZOOyz7gKfYHGpGy2CAZ/PhZmj1Vytuq2dB70SI8hqEXwROIyk2RzAZuALOcrxfuDq9PYewMNTHluXnguCICgQom7ZjqYrSRdKelzS2inndpJ0vaT70p9LvX6TrIrg183sFGAMwMyeJhlkPyuSbpC0dppjxZTnnEXS4/OiyVPTLDXtlkPSSklrJK15amwi468SBEEwf4zEIshyZOCrJK7wqZwB3Ghm+wM3pvddyGorVlM/vQFIWkaGWgozO2q2xyWdBBwLHGnP2ZfrgL2mPG1P4NEZ1l8FrAI4aJel5hMs7r6SEa+q18qIo2vIyYVTDtfQs1jVxzXk2XSuXPANXl5eMTP7rqR9tju9Ajgivb0auAn4WD5XfD5ZLYK/B64AdpF0NnAL8FfzubCko0l+qePSyTqTXAWcIGko7XK6P9ByLCIIgsCLOQSLd570XqRHlqadu6ZTxianje3i9XtkbTp3kaTbgSNJXDfHm9k987z2PwBDwPVJLJofmNmfmNldki4F7iZxGZ1iZk23HJJc+tV4WgRe4w7dUjGdqnTBMajrte5gF1oEFae+S55tqMcLbBHMLX10g5ktd5RmXjRrOrfTlLuPkwyuf/YxM3uq1Qub2a/O8tjZwNmtrh0EQeCNAY2Ga9bQY5J2N7P1knYn+Q52odkW4XaS31fA3sDT6e0dgYeAfb0EC4IgKDrOoyqvAk4Czkl/Xul1oWa9hvYFkPSPwFVm9u30/m8DswaC205JLtWennUEXpPEvIK6nq4hNxfOgoUu66oLXUNM+LhZSnXPYHGxx57kFSyWdDFJYHhnSeuAT5AogEslnUyy8X5XPld7IVmdhq81sz+ZvGNmV0v6jJNMQRAEhcdynFBmZu+Z4aEjc7lAE7Iqgg2S/hL4Gomr6ETgSTepWkAquaY3euAxYxmgsmiBy7rlhT7rApQWLXZZV04WgQ36VEJ7ogGf3XXJ/BIqCm0R9GGvofeQDKu/AvgGSRrTTBosCIKgLzDLdhSdrOmjTwGnOcsSBEHQNRhkah/RDWRtOvcdpmnzYGZvyl2iFlG5RGVx/m4AOblvwK+OoLLIxx1SdnLfAGjhIpd1bYGPzDbgFyw2OU2umxhzWVeOW96yU4A7L7pht5+FrDGCP59yexh4B0mxVxAEQd/SV4rAzG7f7tT3JN3sIE/LJBZB/rvKklM1JvhZG6VFPrtrOa0LYAt3cFm3PuIjc2Og+4LFXjJ7TrfSRHGDxWbQ6DPX0NQK4xLwGmA3F4mCIAi6hL6yCHh+hXENeIBklGRxKJUpLV6S+7JytAhwGh1YWuyzu7aF+b+/k9QW+KxdG3ayCMpdGCOo+fjb5Zk+Wi12jKDefc2JpyXrt9wrzOx5kSZJ3ZW0HwRBkCOT8wh6gaxbj/+e5tzsoyODIAh6mYw1BN3gPmrWfXQ3kjGRI5IO4bnpYUsAvzLTFlC5jJbk7xJx7Snj5BrySpmsL9zRZV2A6rCPa2hiyMc1VC/l3/J8Ei/XUKVcbDfLdAzViz2zuFcqi5u5hn4LeB/JlLDzppzfDHzcSaYgCILCk7iGOi1FPjTrProaWC3pHWb29TbJ1BJWrlBfvFPzJ86R0pBPEQ4ATjs/r5TJiRE/i2B82CfAPVbxKa6ryc8i8KJS8tlde1kwACUrdrlSXygCSSea2deAfST92faPm9l507xsTkj6c+DzwDIz25CeO5MkK6kOnGpm1873OkEQBLli/ZM1NLmdmm6LOW9dKGkv4M0kvbYnzx0AnAC8EngxcIOkl2YZVxkEQdAukgllnZYiH5q5hv4pvXmDmX1v6mOSDs/h+n8LnM7zJ++sAC4xs3HgAUn3A4fSJEvJShUmFu+cg0jPp1ztPtdQddDHHTI25FdHMFrxCXCPmU81ba3uV19iTlOvKvJxs9QHPGuLi02vuIayfhP934znMiPpOOARM/vxdg/tATw85f669Nx0a6yUtEbSmg3PbJyPOEEQBHOmX9JHDwNeDyzbLkawBGia+yjpBqZvRXEWSdbRW6Z72TTnpn0rzWwVsArgoANfaVsW7NJMpDkzUPfrdeIVZBuv+GT2jkuNUeIAAA3sSURBVOJjaQCM1n127uN1n6ButeHXldaLinz8GNWKX+C87tj9d75YDw2maWbTDZLEByrAVNt9E/DOZoub2bRzjSW9imTw/Y8lQZKeeoekQ0ksgL2mPH1P4NFm1wqCIGg31g3b/Qw0ixHcDNws6atm9su8LmpmPyWZcgaApAeB5Wa2QdJVwL9JOo8kWLw/cFte1w6CIMiLeo+ksGSN8myT9HmSTJ5nbXiPwTRmdpekS4G7SRrcnZIlY6iuCk8r/2Dx4IBfNWajuXetJSYaPqb6aN2vvdRozUfm8ZrPe1yr++XOeyWiVEo+u9dhx8aMEwUORHeL/z8LWT/NFwH3krhzPgU8CPwwLyHMbJ/JGoL0/tlm9hIze5mZXZ3XdYIgCPKkYdmOopNV3b7IzL4s6bQp7qJCDaapNco8PZ5/CuJAyc/289r5TTilNo5W/XZnY1WfHfbYhM+6tS50CZSdjJjhQT/raGKouMFi6B2LIOt/9mRt+npJbyUJ3u7pI1IQBEF3YN2w3c9AVkXwfyTtAHyUpH5gCfBhN6mCIAgKjvVRiwkAzOxb6c2NwBsBJBVKEdQb4unR/FtGewXYwG+oxXjNZ10vNwvAuFO3Ya91a4690LzcDV4p+WOOrqFxJ5dhXjRysgjSzMnNJP3Vama2PJeFMzKfd/kFTeiCIAj6hck21DlWFr/RzA5utxKA7K6h6SjUjLZaQzy1Of8tz6Bj9pqXWVl12q2O+hVZU636bIPHJ3zWrdc9LUWfdZ3mIDHoGSx2sm5zoQ/TR6ejR96CIAiCVjAalu0Adp7si5YeK1+wGFwn6fZpHnOnWa+hzUz/hS9gxEWiFmk0YPPW/Nf1bHXilXDgtQuemPCLjE04yVyt+shcd4wSerU2Lpd9dteeFkHNqSAwLyz732pDE5fP4Wb2qKRdgOsl3Wtm3523gBlp1mLCpzdwEARBl2OWn4vQzB5Nfz4u6QqS1vttUwTFDskHQRAUGDPLdMyGpIWSFk/eJunKvLYN4j9LcRt5zJF63di8Jf9yTy9zGvw6F3q5WcbH/cppqxM+a9dqXq4hz2Cxz9ppp9/cGRj0c9/UasUNRRq5uXd3Ba5I/z4V4N/M7JpcVs5IzyiCIAiCtmL5VBab2S+AX5u/QK3TM4qg0TC2bMm/eqhS8fOeeaWeTTjtrsfH/aqoqk7WRs2pKZCrReAULVbJ57Ps+T/SqOdfJJonvZI+2jOKIAiCoN3kVVncaUIRBEEQtICZ0XC0DNtJRxWBpA8BHyQZQPOfZnZ6ev5M4GSSvhunmtm1zdZqNIxt2zxcQ36BsIZTLvr4mI8Lpzrh5xqacFq7XnVyOTmt240MOJbfF30UZKPg8mWlY4pA0huBFcBBZjaeFlIg6QDgBJJpaC8GbpD00ixTyoIgCNpJ0RVVVjppEfwpcI6ZjUNSSJGeXwFckp5/QNL9JMUV359tsUbdGN2a/1jJstc0D/yqU6tOQd3xMadWnkB1wmftqlP7Uc8+9N2WPlp1HCdZ5C9as96JEXSyoOylwP+SdKukmyW9Nj2/B/DwlOetS8+9AEkrJ3t3jG190lncIAiC55Nz99GO4WoRSLoB2G2ah85Kr70UeB3wWuBSSfsxfVfTad9KM1sFrAJYtsfBXfB2B0HQK5iZa8+pduKqCMzsqJkek/SnwOWW2H63SWoAO5NYAHtNeeqeJKMxZ6XRMEa35N8nuTLgFyz2+hBNOLlwJsbyd71NUh33Wbvm1JO7V0YU5kHN0TWkUoHbUNM7n4NOuoa+AbwJQNJLgUFgA3AVcIKkIUn7AvsDt3VMyiAIghmwhmU6ik4ng8UXAhdKWgtMACel1sFdki4F7iZJKz0lS8ZQo9FgzGFySqXm9xZ57VYnRn121xNjfpNpvN6LhlNlsVf1bzdSrg+4rV0qskVgfq3k203HFIGZTQAnzvDY2cDZ7ZUoCIIgO0bvuIaisjgIgqAlmreY7hZ6RhFYwxjflr/roj7gV8dWd3JbjG8bc1nXK6AL0Kh3lwunV/LH88Dzy3DCqfYhF8x3Ul076RlFEARB0E7CNVRAzMxlx+oZFPSyCGpVn/TRutO64LfDjqCuP3W/FlSuVui8yWkeQRHoGUUQBEHQXiyazgVBEPQ7YREUDGs0nFxDfn9oLxdOzcmc9nJlBd2Np/vNq74kD4xiN8WbCz2jCIIgCNqKQb3WGzGonlEEhk8VadXRIvBKmYzUxiBoB1FHEARB0NeY9U5WWiiCDuL1IeqVD2cQFLrXEL1jfYciCIIgaJFwDQVBEPQxZkYjgsXFQkCp4jdEJgiC6VHJb6yJ59p50LB8FIGko4HzgTLwJTM7J5eFM9IziiAIgqCt5NRiQlIZ+ALwZpIJjT+UdJWZ3T3vxTPSM4rgpfst5MaLX9tpMYIg6ALyaGpq5DZ97FDgfjP7BYCkS4AVJMO52kLH7C5JB0v6gaQ7Ja2RdOiUx86UdL+kn0n6rU7JGARBMBtmlukAdk6/5yaPlVOW2QN4eMr9dem5ttFJi+BzwKfM7GpJx6T3j5B0AHAC8ErgxcANkl6aZVxlEARB27BkRG5GNpjZ8hkem84+aWs6UicVgQFL0ts7AI+mt1cAl5jZOPCApPtJTKfvt1/EIAiC6TEsr+4A64C9ptzfk+e+D9tCJxXBh4FrJZ1L4qJ6fXp+D+AHU543o5mUmlcrAfbee28/SYMgCLYnv3kEPwT2l7Qv8AiJR+S9eSycFVdFIOkGYLdpHjoLOBL4iJl9XdK7gS8DRzEHM8nMVgGrAJYvX94blR1BEHQNeSgCM6tJ+iBwLUn66IVmdte8F54DrorAzI6a6TFJ/wKclt79D+BL6e2Om0lBEATNsdzqCMzs28C3c1msBTpZrfEo8Jvp7TcB96W3rwJOkDSUmkr7A7d1QL4gCIIZsdQ1lOUoOp2MEfwRcL6kCjBG6us3s7skXUqSQ1sDTomMoSAIikivNHjsmCIws1uA18zw2NnA2e2VKAiCYA5YbllDHadnKouDIAjaiRFtqIMgCPqbGEwTBEHQ73RHIDgLoQiCIAhaxHJKH+00oQiCIAhaIBlM0xvBYvXKqDVJTwC/7LQcwM7Ahk4LMQe6TV4ImdtBt8kLc5P5V8xs2XwuJuma9JpZ2GBmR8/nep70jCIoCpLWzNJlsHB0m7wQMreDbpMXulPmolDsOXBBEASBO6EIgiAI+pxQBPmzqtMCzJFukxdC5nbQbfJCd8pcCCJGEARB0OeERRAEQdDnhCIIgiDoc0IR5ICkvSR9R9I9ku6SdFrzVxUDSWVJP5L0rU7LkgVJO0q6TNK96ft9WKdlmg1JH0k/E2slXSxpuNMybY+kCyU9LmntlHM7Sbpe0n3pz6WdlHF7ZpD58+nn4ieSrpC0Yydl7CZCEeRDDfiomb0CeB1wiqQDOixTVk4D7um0EHPgfOAaM3s58GsUWHZJewCnAsvN7ECSMYQndFaqafkqsH2x0xnAjWa2P3Bjer9IfJUXynw9cKCZHQT8HDiz3UJ1K6EIcsDM1pvZHentzSRfTnt0VqrmSNoTeCvPjQktNJKWAG8gmW+NmU2Y2TOdlaopFWAkHcC0gAKOXTWz7wJPbXd6BbA6vb0aOL6tQjVhOpnN7Dozq6V3f0Ay5jbIQCiCnJG0D3AIcGtnJcnE3wGnA93SOWs/4AngK6k760uSFnZaqJkws0eAc4GHgPXARjO7rrNSZWZXM1sPyUYH2KXD8syV9wNXd1qIbiEUQY5IWgR8HfiwmW3qtDyzIelY4HEzu73TssyBCvBq4AIzOwTYSvFcFs+S+tVXAPsCLwYWSjqxs1L1PpLOInHXXtRpWbqFUAQ5IWmARAlcZGaXd1qeDBwOHCfpQeAS4E2SvtZZkZqyDlhnZpPW1mUkiqGoHAU8YGZPmFkVuBx4fYdlyspjknYHSH8+3mF5MiHpJOBY4PcsiqQyE4ogBySJxG99j5md12l5smBmZ5rZnma2D0kA87/MrNC7VTP7H+BhSS9LTx0J3N1BkZrxEPA6SQvSz8iRFDi4vR1XASelt08CruygLJmQdDTwMeA4M9vWaXm6iVAE+XA48Psku+o70+OYTgvVo3wIuEjST4CDgb/qsDwzkloulwF3AD8l+X8rXBsESRcD3wdeJmmdpJOBc4A3S7oPeHN6vzDMIPM/AIuB69P/wX/sqJBdRLSYCIIg6HPCIgiCIOhzQhEEQRD0OaEIgiAI+pxQBEEQBH1OKIIgCII+JxRB0JekXUw/MOX+Ed3SgTUI8iYUQdCv7Ah8oOmzgqAPCEUQFB5J+6R95r+U9vW/SNJRkr6X9ss/NO2f/420F/0PJB2UvvaTae/6myT9QtKp6bLnAC9JC48+n55bNGXWwUVpNXAQ9DyVTgsQBBn5VeBdwErgh8B7gd8AjgM+DjwM/MjMjpf0JuBfSCqPAV4OvJGk6vRnki4gaVZ3oJkdDIlriKRr7CtJWkV/j6Ri/JZ2/HJB0EnCIgi6hQfM7Kdm1gDuIhmaYiStG/YhUQr/CmBm/wW8SNIO6Wv/08zGzWwDSfO0XWe4xm1mti69xp3pukHQ84QiCLqF8Sm3G1PuN0gs2+ncOJP9U6a+ts7MlnDW5wVBTxGKIOgVvgv8Hjzr5tnQZCbEZhJXURD0PbHjCXqFT5JMLvsJsI3nWihPi5k9mQab15JMsvpPfxGDoJhE99EgCII+J1xDQRAEfU4ogiAIgj4nFEEQBEGfE4ogCIKgzwlFEARB0OeEIgiCIOhzQhEEQRD0Of8f587mZi9nHRAAAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
    "source": [
-    "# alternative using transpose instead of deinging the x and y in the plot function\n",
+    "# alternative using transpose instead of defining the x and y in the plot function\n",
     "ds_mm.mean(dim='lon').transpose().plot.imshow(vmin=-2, vmax=30, cmap=\"coolwarm\")"
    ]
   },
@@ -1641,32 +505,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.image.AxesImage at 0x7f0e96c994d0>"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
     },
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
    "source": [
     "(ds_mm.sel(month=1) - ds_mm.sel(month=7)).plot.imshow(vmax=10)"
    ]
@@ -1698,7 +546,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 204,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1716,536 +564,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/stijnvh/miniconda3/envs/DS-geospatial/lib/python3.7/site-packages/xarray/core/nanops.py:142: RuntimeWarning: Mean of empty slice\n",
-      "  return np.nanmean(a, axis=axis, dtype=dtype)\n"
-     ]
-    },
-    {
-     "data": {
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-       "</symbol>\n",
-       "</defs>\n",
-       "</svg>\n",
-       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
-       " *\n",
-       " */\n",
-       "\n",
-       ":root {\n",
-       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
-       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
-       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
-       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
-       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
-       "  --xr-background-color: var(--jp-layout-color0, white);\n",
-       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
-       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
-       "}\n",
-       "\n",
-       "html[theme=dark],\n",
-       "body.vscode-dark {\n",
-       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
-       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
-       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
-       "  --xr-border-color: #1F1F1F;\n",
-       "  --xr-disabled-color: #515151;\n",
-       "  --xr-background-color: #111111;\n",
-       "  --xr-background-color-row-even: #111111;\n",
-       "  --xr-background-color-row-odd: #313131;\n",
-       "}\n",
-       "\n",
-       ".xr-wrap {\n",
-       "  display: block;\n",
-       "  min-width: 300px;\n",
-       "  max-width: 700px;\n",
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-       "\n",
-       ".xr-text-repr-fallback {\n",
-       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
-       "  display: none;\n",
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-       "\n",
-       ".xr-header {\n",
-       "  padding-top: 6px;\n",
-       "  padding-bottom: 6px;\n",
-       "  margin-bottom: 4px;\n",
-       "  border-bottom: solid 1px var(--xr-border-color);\n",
-       "}\n",
-       "\n",
-       ".xr-header > div,\n",
-       ".xr-header > ul {\n",
-       "  display: inline;\n",
-       "  margin-top: 0;\n",
-       "  margin-bottom: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-obj-type,\n",
-       ".xr-array-name {\n",
-       "  margin-left: 2px;\n",
-       "  margin-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-obj-type {\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-sections {\n",
-       "  padding-left: 0 !important;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
-       "}\n",
-       "\n",
-       ".xr-section-item {\n",
-       "  display: contents;\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input + label {\n",
-       "  color: var(--xr-disabled-color);\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input:enabled + label {\n",
-       "  cursor: pointer;\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input:enabled + label:hover {\n",
-       "  color: var(--xr-font-color0);\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary {\n",
-       "  grid-column: 1;\n",
-       "  color: var(--xr-font-color2);\n",
-       "  font-weight: 500;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary > span {\n",
-       "  display: inline-block;\n",
-       "  padding-left: 0.5em;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:disabled + label {\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in + label:before {\n",
-       "  display: inline-block;\n",
-       "  content: '►';\n",
-       "  font-size: 11px;\n",
-       "  width: 15px;\n",
-       "  text-align: center;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:disabled + label:before {\n",
-       "  color: var(--xr-disabled-color);\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:checked + label:before {\n",
-       "  content: '▼';\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:checked + label > span {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary,\n",
-       ".xr-section-inline-details {\n",
-       "  padding-top: 4px;\n",
-       "  padding-bottom: 4px;\n",
-       "}\n",
-       "\n",
-       ".xr-section-inline-details {\n",
-       "  grid-column: 2 / -1;\n",
-       "}\n",
-       "\n",
-       ".xr-section-details {\n",
-       "  display: none;\n",
-       "  grid-column: 1 / -1;\n",
-       "  margin-bottom: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
-       "  display: contents;\n",
-       "}\n",
-       "\n",
-       ".xr-array-wrap {\n",
-       "  grid-column: 1 / -1;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 20px auto;\n",
-       "}\n",
-       "\n",
-       ".xr-array-wrap > label {\n",
-       "  grid-column: 1;\n",
-       "  vertical-align: top;\n",
-       "}\n",
-       "\n",
-       ".xr-preview {\n",
-       "  color: var(--xr-font-color3);\n",
-       "}\n",
-       "\n",
-       ".xr-array-preview,\n",
-       ".xr-array-data {\n",
-       "  padding: 0 5px !important;\n",
-       "  grid-column: 2;\n",
-       "}\n",
-       "\n",
-       ".xr-array-data,\n",
-       ".xr-array-in:checked ~ .xr-array-preview {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-array-in:checked ~ .xr-array-data,\n",
-       ".xr-array-preview {\n",
-       "  display: inline-block;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list {\n",
-       "  display: inline-block !important;\n",
-       "  list-style: none;\n",
-       "  padding: 0 !important;\n",
-       "  margin: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list li {\n",
-       "  display: inline-block;\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list:before {\n",
-       "  content: '(';\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list:after {\n",
-       "  content: ')';\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list li:not(:last-child):after {\n",
-       "  content: ',';\n",
-       "  padding-right: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-has-index {\n",
-       "  font-weight: bold;\n",
-       "}\n",
-       "\n",
-       ".xr-var-list,\n",
-       ".xr-var-item {\n",
-       "  display: contents;\n",
-       "}\n",
-       "\n",
-       ".xr-var-item > div,\n",
-       ".xr-var-item label,\n",
-       ".xr-var-item > .xr-var-name span {\n",
-       "  background-color: var(--xr-background-color-row-even);\n",
-       "  margin-bottom: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-var-item > .xr-var-name:hover span {\n",
-       "  padding-right: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-var-list > li:nth-child(odd) > div,\n",
-       ".xr-var-list > li:nth-child(odd) > label,\n",
-       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
-       "  background-color: var(--xr-background-color-row-odd);\n",
-       "}\n",
-       "\n",
-       ".xr-var-name {\n",
-       "  grid-column: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-var-dims {\n",
-       "  grid-column: 2;\n",
-       "}\n",
-       "\n",
-       ".xr-var-dtype {\n",
-       "  grid-column: 3;\n",
-       "  text-align: right;\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-var-preview {\n",
-       "  grid-column: 4;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name,\n",
-       ".xr-var-dims,\n",
-       ".xr-var-dtype,\n",
-       ".xr-preview,\n",
-       ".xr-attrs dt {\n",
-       "  white-space: nowrap;\n",
-       "  overflow: hidden;\n",
-       "  text-overflow: ellipsis;\n",
-       "  padding-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name:hover,\n",
-       ".xr-var-dims:hover,\n",
-       ".xr-var-dtype:hover,\n",
-       ".xr-attrs dt:hover {\n",
-       "  overflow: visible;\n",
-       "  width: auto;\n",
-       "  z-index: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-var-attrs,\n",
-       ".xr-var-data {\n",
-       "  display: none;\n",
-       "  background-color: var(--xr-background-color) !important;\n",
-       "  padding-bottom: 5px !important;\n",
-       "}\n",
-       "\n",
-       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
-       ".xr-var-data-in:checked ~ .xr-var-data {\n",
-       "  display: block;\n",
-       "}\n",
-       "\n",
-       ".xr-var-data > table {\n",
-       "  float: right;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name span,\n",
-       ".xr-var-data,\n",
-       ".xr-attrs {\n",
-       "  padding-left: 25px !important;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs,\n",
-       ".xr-var-attrs,\n",
-       ".xr-var-data {\n",
-       "  grid-column: 1 / -1;\n",
-       "}\n",
-       "\n",
-       "dl.xr-attrs {\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 125px auto;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt, dd {\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  float: left;\n",
-       "  padding-right: 10px;\n",
-       "  width: auto;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt {\n",
-       "  font-weight: normal;\n",
-       "  grid-column: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt:hover span {\n",
-       "  display: inline-block;\n",
-       "  background: var(--xr-background-color);\n",
-       "  padding-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dd {\n",
-       "  grid-column: 2;\n",
-       "  white-space: pre-wrap;\n",
-       "  word-break: break-all;\n",
-       "}\n",
-       "\n",
-       ".xr-icon-database,\n",
-       ".xr-icon-file-text2 {\n",
-       "  display: inline-block;\n",
-       "  vertical-align: middle;\n",
-       "  width: 1em;\n",
-       "  height: 1.5em !important;\n",
-       "  stroke-width: 0;\n",
-       "  stroke: currentColor;\n",
-       "  fill: currentColor;\n",
-       "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;sst&#x27; (time: 708, lat: 89, lon: 180)&gt;\n",
-       "array([[[-3.55851650e-03, -3.55851650e-03, -3.38900089e-03, ...,\n",
-       "         -2.88057327e-03, -3.55851650e-03, -3.72791290e-03],\n",
-       "        [-7.79581070e-03, -8.30423832e-03, -8.98241997e-03, ...,\n",
-       "         -3.72803211e-03, -4.57561016e-03, -5.08403778e-03],\n",
-       "        [-8.81290436e-03, -9.66036320e-03, -1.23721361e-02, ...,\n",
-       "         -5.08403778e-03, -6.77907467e-03, -8.98241997e-03],\n",
-       "        ...,\n",
-       "        [            nan,             nan,             nan, ...,\n",
-       "                     nan,             nan,             nan],\n",
-       "        [            nan,             nan,             nan, ...,\n",
-       "                     nan,             nan,             nan],\n",
-       "        [            nan,             nan,             nan, ...,\n",
-       "                     nan,             nan,             nan]],\n",
-       "\n",
-       "       [[-1.18553638e-03,  9.53674316e-07,  9.53674316e-07, ...,\n",
-       "         -1.01613998e-03, -1.35505199e-03, -1.35505199e-03],\n",
-       "        [-1.49145126e-02, -1.93213224e-02, -2.13551521e-02, ...,\n",
-       "         -5.08391857e-03, -8.13484192e-03, -1.28805637e-02],\n",
-       "        [-1.50841475e-02, -1.83044672e-02, -2.23722458e-02, ...,\n",
-       "         -4.74500656e-03, -7.79581070e-03, -1.05077028e-02],\n",
-       "...\n",
-       "                     nan,             nan,             nan],\n",
-       "        [            nan,             nan,             nan, ...,\n",
-       "                     nan,             nan,             nan],\n",
-       "        [            nan,             nan,             nan, ...,\n",
-       "                     nan,             nan,             nan]],\n",
-       "\n",
-       "       [[-1.96602345e-02, -2.11856365e-02, -2.20330954e-02, ...,\n",
-       "         -1.25415325e-02, -1.55924559e-02, -1.81348324e-02],\n",
-       "        [-2.18636990e-02, -2.45755911e-02, -2.66094208e-02, ...,\n",
-       "         -1.25415325e-02, -1.61008835e-02, -1.94908381e-02],\n",
-       "        [-2.79653072e-02, -3.10163498e-02, -3.33890915e-02, ...,\n",
-       "         -1.40670538e-02, -2.05078125e-02, -2.59313583e-02],\n",
-       "        ...,\n",
-       "        [            nan,             nan,             nan, ...,\n",
-       "                     nan,             nan,             nan],\n",
-       "        [            nan,             nan,             nan, ...,\n",
-       "                     nan,             nan,             nan],\n",
-       "        [            nan,             nan,             nan, ...,\n",
-       "                     nan,             nan,             nan]]],\n",
-       "      dtype=float32)\n",
-       "Coordinates:\n",
-       "  * lat      (lat) float32 88.0 86.0 84.0 82.0 80.0 ... -82.0 -84.0 -86.0 -88.0\n",
-       "  * lon      (lon) float32 0.0 2.0 4.0 6.0 8.0 ... 350.0 352.0 354.0 356.0 358.0\n",
-       "  * time     (time) datetime64[ns] 1960-01-01 1960-02-01 ... 2018-12-01</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'sst'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 708</li><li><span class='xr-has-index'>lat</span>: 89</li><li><span class='xr-has-index'>lon</span>: 180</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-1d96006a-59a5-426a-bb4c-4ce2cedbf6f2' class='xr-array-in' type='checkbox' checked><label for='section-1d96006a-59a5-426a-bb4c-4ce2cedbf6f2' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>-0.0035585165 -0.0035585165 -0.003389001 -0.0030499697 ... nan nan nan</span></div><div class='xr-array-data'><pre>array([[[-3.55851650e-03, -3.55851650e-03, -3.38900089e-03, ...,\n",
-       "         -2.88057327e-03, -3.55851650e-03, -3.72791290e-03],\n",
-       "        [-7.79581070e-03, -8.30423832e-03, -8.98241997e-03, ...,\n",
-       "         -3.72803211e-03, -4.57561016e-03, -5.08403778e-03],\n",
-       "        [-8.81290436e-03, -9.66036320e-03, -1.23721361e-02, ...,\n",
-       "         -5.08403778e-03, -6.77907467e-03, -8.98241997e-03],\n",
-       "        ...,\n",
-       "        [            nan,             nan,             nan, ...,\n",
-       "                     nan,             nan,             nan],\n",
-       "        [            nan,             nan,             nan, ...,\n",
-       "                     nan,             nan,             nan],\n",
-       "        [            nan,             nan,             nan, ...,\n",
-       "                     nan,             nan,             nan]],\n",
-       "\n",
-       "       [[-1.18553638e-03,  9.53674316e-07,  9.53674316e-07, ...,\n",
-       "         -1.01613998e-03, -1.35505199e-03, -1.35505199e-03],\n",
-       "        [-1.49145126e-02, -1.93213224e-02, -2.13551521e-02, ...,\n",
-       "         -5.08391857e-03, -8.13484192e-03, -1.28805637e-02],\n",
-       "        [-1.50841475e-02, -1.83044672e-02, -2.23722458e-02, ...,\n",
-       "         -4.74500656e-03, -7.79581070e-03, -1.05077028e-02],\n",
-       "...\n",
-       "                     nan,             nan,             nan],\n",
-       "        [            nan,             nan,             nan, ...,\n",
-       "                     nan,             nan,             nan],\n",
-       "        [            nan,             nan,             nan, ...,\n",
-       "                     nan,             nan,             nan]],\n",
-       "\n",
-       "       [[-1.96602345e-02, -2.11856365e-02, -2.20330954e-02, ...,\n",
-       "         -1.25415325e-02, -1.55924559e-02, -1.81348324e-02],\n",
-       "        [-2.18636990e-02, -2.45755911e-02, -2.66094208e-02, ...,\n",
-       "         -1.25415325e-02, -1.61008835e-02, -1.94908381e-02],\n",
-       "        [-2.79653072e-02, -3.10163498e-02, -3.33890915e-02, ...,\n",
-       "         -1.40670538e-02, -2.05078125e-02, -2.59313583e-02],\n",
-       "        ...,\n",
-       "        [            nan,             nan,             nan, ...,\n",
-       "                     nan,             nan,             nan],\n",
-       "        [            nan,             nan,             nan, ...,\n",
-       "                     nan,             nan,             nan],\n",
-       "        [            nan,             nan,             nan, ...,\n",
-       "                     nan,             nan,             nan]]],\n",
-       "      dtype=float32)</pre></div></div></li><li class='xr-section-item'><input id='section-16e99d5e-1c15-4788-a216-9ae3a929c479' class='xr-section-summary-in' type='checkbox'  checked><label for='section-16e99d5e-1c15-4788-a216-9ae3a929c479' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>88.0 86.0 84.0 ... -86.0 -88.0</div><input id='attrs-ea406925-f20f-474e-86a2-8a5c6c93f1d7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ea406925-f20f-474e-86a2-8a5c6c93f1d7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-03b5aefe-1113-4377-9659-86f91868e444' class='xr-var-data-in' type='checkbox'><label for='data-03b5aefe-1113-4377-9659-86f91868e444' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>long_name :</span></dt><dd>Latitude</dd><dt><span>actual_range :</span></dt><dd>[ 88. -88.]</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>coordinate_defines :</span></dt><dd>center</dd></dl></div><div class='xr-var-data'><pre>array([ 88.,  86.,  84.,  82.,  80.,  78.,  76.,  74.,  72.,  70.,  68.,  66.,\n",
-       "        64.,  62.,  60.,  58.,  56.,  54.,  52.,  50.,  48.,  46.,  44.,  42.,\n",
-       "        40.,  38.,  36.,  34.,  32.,  30.,  28.,  26.,  24.,  22.,  20.,  18.,\n",
-       "        16.,  14.,  12.,  10.,   8.,   6.,   4.,   2.,   0.,  -2.,  -4.,  -6.,\n",
-       "        -8., -10., -12., -14., -16., -18., -20., -22., -24., -26., -28., -30.,\n",
-       "       -32., -34., -36., -38., -40., -42., -44., -46., -48., -50., -52., -54.,\n",
-       "       -56., -58., -60., -62., -64., -66., -68., -70., -72., -74., -76., -78.,\n",
-       "       -80., -82., -84., -86., -88.], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 2.0 4.0 ... 354.0 356.0 358.0</div><input id='attrs-2c6af2ff-49e2-4061-9936-a25af729025d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2c6af2ff-49e2-4061-9936-a25af729025d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-958f0e50-e1f4-42a1-abdc-94c9a6e6c5ea' class='xr-var-data-in' type='checkbox'><label for='data-958f0e50-e1f4-42a1-abdc-94c9a6e6c5ea' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>long_name :</span></dt><dd>Longitude</dd><dt><span>actual_range :</span></dt><dd>[  0. 358.]</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>coordinate_defines :</span></dt><dd>center</dd></dl></div><div class='xr-var-data'><pre>array([  0.,   2.,   4.,   6.,   8.,  10.,  12.,  14.,  16.,  18.,  20.,  22.,\n",
-       "        24.,  26.,  28.,  30.,  32.,  34.,  36.,  38.,  40.,  42.,  44.,  46.,\n",
-       "        48.,  50.,  52.,  54.,  56.,  58.,  60.,  62.,  64.,  66.,  68.,  70.,\n",
-       "        72.,  74.,  76.,  78.,  80.,  82.,  84.,  86.,  88.,  90.,  92.,  94.,\n",
-       "        96.,  98., 100., 102., 104., 106., 108., 110., 112., 114., 116., 118.,\n",
-       "       120., 122., 124., 126., 128., 130., 132., 134., 136., 138., 140., 142.,\n",
-       "       144., 146., 148., 150., 152., 154., 156., 158., 160., 162., 164., 166.,\n",
-       "       168., 170., 172., 174., 176., 178., 180., 182., 184., 186., 188., 190.,\n",
-       "       192., 194., 196., 198., 200., 202., 204., 206., 208., 210., 212., 214.,\n",
-       "       216., 218., 220., 222., 224., 226., 228., 230., 232., 234., 236., 238.,\n",
-       "       240., 242., 244., 246., 248., 250., 252., 254., 256., 258., 260., 262.,\n",
-       "       264., 266., 268., 270., 272., 274., 276., 278., 280., 282., 284., 286.,\n",
-       "       288., 290., 292., 294., 296., 298., 300., 302., 304., 306., 308., 310.,\n",
-       "       312., 314., 316., 318., 320., 322., 324., 326., 328., 330., 332., 334.,\n",
-       "       336., 338., 340., 342., 344., 346., 348., 350., 352., 354., 356., 358.],\n",
-       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1960-01-01 ... 2018-12-01</div><input id='attrs-d8076b1e-dc09-48a3-899c-d31c53eb315f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d8076b1e-dc09-48a3-899c-d31c53eb315f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3882ac32-0a49-47b5-a195-5ffbbc03aed3' class='xr-var-data-in' type='checkbox'><label for='data-3882ac32-0a49-47b5-a195-5ffbbc03aed3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Time</dd><dt><span>delta_t :</span></dt><dd>0000-01-00 00:00:00</dd><dt><span>avg_period :</span></dt><dd>0000-01-00 00:00:00</dd><dt><span>prev_avg_period :</span></dt><dd>0000-00-07 00:00:00</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>axis :</span></dt><dd>T</dd><dt><span>actual_range :</span></dt><dd>[19723. 80384.]</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;1960-01-01T00:00:00.000000000&#x27;, &#x27;1960-02-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;1960-03-01T00:00:00.000000000&#x27;, ..., &#x27;2018-10-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2018-11-01T00:00:00.000000000&#x27;, &#x27;2018-12-01T00:00:00.000000000&#x27;],\n",
-       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-a0213188-ea19-4898-be86-e35cb7438e9c' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-a0213188-ea19-4898-be86-e35cb7438e9c' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
-      ],
-      "text/plain": [
-       "<xarray.DataArray 'sst' (time: 708, lat: 89, lon: 180)>\n",
-       "array([[[-3.55851650e-03, -3.55851650e-03, -3.38900089e-03, ...,\n",
-       "         -2.88057327e-03, -3.55851650e-03, -3.72791290e-03],\n",
-       "        [-7.79581070e-03, -8.30423832e-03, -8.98241997e-03, ...,\n",
-       "         -3.72803211e-03, -4.57561016e-03, -5.08403778e-03],\n",
-       "        [-8.81290436e-03, -9.66036320e-03, -1.23721361e-02, ...,\n",
-       "         -5.08403778e-03, -6.77907467e-03, -8.98241997e-03],\n",
-       "        ...,\n",
-       "        [            nan,             nan,             nan, ...,\n",
-       "                     nan,             nan,             nan],\n",
-       "        [            nan,             nan,             nan, ...,\n",
-       "                     nan,             nan,             nan],\n",
-       "        [            nan,             nan,             nan, ...,\n",
-       "                     nan,             nan,             nan]],\n",
-       "\n",
-       "       [[-1.18553638e-03,  9.53674316e-07,  9.53674316e-07, ...,\n",
-       "         -1.01613998e-03, -1.35505199e-03, -1.35505199e-03],\n",
-       "        [-1.49145126e-02, -1.93213224e-02, -2.13551521e-02, ...,\n",
-       "         -5.08391857e-03, -8.13484192e-03, -1.28805637e-02],\n",
-       "        [-1.50841475e-02, -1.83044672e-02, -2.23722458e-02, ...,\n",
-       "         -4.74500656e-03, -7.79581070e-03, -1.05077028e-02],\n",
-       "...\n",
-       "                     nan,             nan,             nan],\n",
-       "        [            nan,             nan,             nan, ...,\n",
-       "                     nan,             nan,             nan],\n",
-       "        [            nan,             nan,             nan, ...,\n",
-       "                     nan,             nan,             nan]],\n",
-       "\n",
-       "       [[-1.96602345e-02, -2.11856365e-02, -2.20330954e-02, ...,\n",
-       "         -1.25415325e-02, -1.55924559e-02, -1.81348324e-02],\n",
-       "        [-2.18636990e-02, -2.45755911e-02, -2.66094208e-02, ...,\n",
-       "         -1.25415325e-02, -1.61008835e-02, -1.94908381e-02],\n",
-       "        [-2.79653072e-02, -3.10163498e-02, -3.33890915e-02, ...,\n",
-       "         -1.40670538e-02, -2.05078125e-02, -2.59313583e-02],\n",
-       "        ...,\n",
-       "        [            nan,             nan,             nan, ...,\n",
-       "                     nan,             nan,             nan],\n",
-       "        [            nan,             nan,             nan, ...,\n",
-       "                     nan,             nan,             nan],\n",
-       "        [            nan,             nan,             nan, ...,\n",
-       "                     nan,             nan,             nan]]],\n",
-       "      dtype=float32)\n",
-       "Coordinates:\n",
-       "  * lat      (lat) float32 88.0 86.0 84.0 82.0 80.0 ... -82.0 -84.0 -86.0 -88.0\n",
-       "  * lon      (lon) float32 0.0 2.0 4.0 6.0 8.0 ... 350.0 352.0 354.0 356.0 358.0\n",
-       "  * time     (time) datetime64[ns] 1960-01-01 1960-02-01 ... 2018-12-01"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
     }
-   ],
+   },
+   "outputs": [],
    "source": [
     "sst = ds[\"sst\"]\n",
     "ds_anom = sst.groupby('time.month').apply(remove_time_mean)\n",
@@ -2270,570 +595,13 @@
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   {
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-       "\n",
-       ".xr-dim-list li:not(:last-child):after {\n",
-       "  content: ',';\n",
-       "  padding-right: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-has-index {\n",
-       "  font-weight: bold;\n",
-       "}\n",
-       "\n",
-       ".xr-var-list,\n",
-       ".xr-var-item {\n",
-       "  display: contents;\n",
-       "}\n",
-       "\n",
-       ".xr-var-item > div,\n",
-       ".xr-var-item label,\n",
-       ".xr-var-item > .xr-var-name span {\n",
-       "  background-color: var(--xr-background-color-row-even);\n",
-       "  margin-bottom: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-var-item > .xr-var-name:hover span {\n",
-       "  padding-right: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-var-list > li:nth-child(odd) > div,\n",
-       ".xr-var-list > li:nth-child(odd) > label,\n",
-       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
-       "  background-color: var(--xr-background-color-row-odd);\n",
-       "}\n",
-       "\n",
-       ".xr-var-name {\n",
-       "  grid-column: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-var-dims {\n",
-       "  grid-column: 2;\n",
-       "}\n",
-       "\n",
-       ".xr-var-dtype {\n",
-       "  grid-column: 3;\n",
-       "  text-align: right;\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-var-preview {\n",
-       "  grid-column: 4;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name,\n",
-       ".xr-var-dims,\n",
-       ".xr-var-dtype,\n",
-       ".xr-preview,\n",
-       ".xr-attrs dt {\n",
-       "  white-space: nowrap;\n",
-       "  overflow: hidden;\n",
-       "  text-overflow: ellipsis;\n",
-       "  padding-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name:hover,\n",
-       ".xr-var-dims:hover,\n",
-       ".xr-var-dtype:hover,\n",
-       ".xr-attrs dt:hover {\n",
-       "  overflow: visible;\n",
-       "  width: auto;\n",
-       "  z-index: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-var-attrs,\n",
-       ".xr-var-data {\n",
-       "  display: none;\n",
-       "  background-color: var(--xr-background-color) !important;\n",
-       "  padding-bottom: 5px !important;\n",
-       "}\n",
-       "\n",
-       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
-       ".xr-var-data-in:checked ~ .xr-var-data {\n",
-       "  display: block;\n",
-       "}\n",
-       "\n",
-       ".xr-var-data > table {\n",
-       "  float: right;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name span,\n",
-       ".xr-var-data,\n",
-       ".xr-attrs {\n",
-       "  padding-left: 25px !important;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs,\n",
-       ".xr-var-attrs,\n",
-       ".xr-var-data {\n",
-       "  grid-column: 1 / -1;\n",
-       "}\n",
-       "\n",
-       "dl.xr-attrs {\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 125px auto;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt, dd {\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  float: left;\n",
-       "  padding-right: 10px;\n",
-       "  width: auto;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt {\n",
-       "  font-weight: normal;\n",
-       "  grid-column: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt:hover span {\n",
-       "  display: inline-block;\n",
-       "  background: var(--xr-background-color);\n",
-       "  padding-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dd {\n",
-       "  grid-column: 2;\n",
-       "  white-space: pre-wrap;\n",
-       "  word-break: break-all;\n",
-       "}\n",
-       "\n",
-       ".xr-icon-database,\n",
-       ".xr-icon-file-text2 {\n",
-       "  display: inline-block;\n",
-       "  vertical-align: middle;\n",
-       "  width: 1em;\n",
-       "  height: 1.5em !important;\n",
-       "  stroke-width: 0;\n",
-       "  stroke: currentColor;\n",
-       "  fill: currentColor;\n",
-       "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;sst&#x27; (time: 708, lat: 89, lon: 180)&gt;\n",
-       "array([[[-3.55851650e-03, -3.55851650e-03, -3.38900089e-03, ...,\n",
-       "         -2.88057327e-03, -3.55851650e-03, -3.72791290e-03],\n",
-       "        [-7.79581070e-03, -8.30423832e-03, -8.98241997e-03, ...,\n",
-       "         -3.72803211e-03, -4.57561016e-03, -5.08403778e-03],\n",
-       "        [-8.81290436e-03, -9.66036320e-03, -1.23721361e-02, ...,\n",
-       "         -5.08403778e-03, -6.77907467e-03, -8.98241997e-03],\n",
-       "        ...,\n",
-       "        [            nan,             nan,             nan, ...,\n",
-       "                     nan,             nan,             nan],\n",
-       "        [            nan,             nan,             nan, ...,\n",
-       "                     nan,             nan,             nan],\n",
-       "        [            nan,             nan,             nan, ...,\n",
-       "                     nan,             nan,             nan]],\n",
-       "\n",
-       "       [[-1.18553638e-03,  9.53674316e-07,  9.53674316e-07, ...,\n",
-       "         -1.01613998e-03, -1.35505199e-03, -1.35505199e-03],\n",
-       "        [-1.49145126e-02, -1.93213224e-02, -2.13551521e-02, ...,\n",
-       "         -5.08391857e-03, -8.13484192e-03, -1.28805637e-02],\n",
-       "        [-1.50841475e-02, -1.83044672e-02, -2.23722458e-02, ...,\n",
-       "         -4.74500656e-03, -7.79581070e-03, -1.05077028e-02],\n",
-       "...\n",
-       "                     nan,             nan,             nan],\n",
-       "        [            nan,             nan,             nan, ...,\n",
-       "                     nan,             nan,             nan],\n",
-       "        [            nan,             nan,             nan, ...,\n",
-       "                     nan,             nan,             nan]],\n",
-       "\n",
-       "       [[-1.96602345e-02, -2.11856365e-02, -2.20330954e-02, ...,\n",
-       "         -1.25415325e-02, -1.55924559e-02, -1.81348324e-02],\n",
-       "        [-2.18636990e-02, -2.45755911e-02, -2.66094208e-02, ...,\n",
-       "         -1.25415325e-02, -1.61008835e-02, -1.94908381e-02],\n",
-       "        [-2.79653072e-02, -3.10163498e-02, -3.33890915e-02, ...,\n",
-       "         -1.40670538e-02, -2.05078125e-02, -2.59313583e-02],\n",
-       "        ...,\n",
-       "        [            nan,             nan,             nan, ...,\n",
-       "                     nan,             nan,             nan],\n",
-       "        [            nan,             nan,             nan, ...,\n",
-       "                     nan,             nan,             nan],\n",
-       "        [            nan,             nan,             nan, ...,\n",
-       "                     nan,             nan,             nan]]],\n",
-       "      dtype=float32)\n",
-       "Coordinates:\n",
-       "  * lat      (lat) float32 88.0 86.0 84.0 82.0 80.0 ... -82.0 -84.0 -86.0 -88.0\n",
-       "  * lon      (lon) float32 0.0 2.0 4.0 6.0 8.0 ... 350.0 352.0 354.0 356.0 358.0\n",
-       "  * time     (time) datetime64[ns] 1960-01-01 1960-02-01 ... 2018-12-01\n",
-       "    month    (time) int64 1 2 3 4 5 6 7 8 9 10 11 ... 2 3 4 5 6 7 8 9 10 11 12</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'sst'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 708</li><li><span class='xr-has-index'>lat</span>: 89</li><li><span class='xr-has-index'>lon</span>: 180</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-8f299120-2e14-496b-a322-b07185e97988' class='xr-array-in' type='checkbox' checked><label for='section-8f299120-2e14-496b-a322-b07185e97988' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>-0.0035585165 -0.0035585165 -0.003389001 -0.0030499697 ... nan nan nan</span></div><div class='xr-array-data'><pre>array([[[-3.55851650e-03, -3.55851650e-03, -3.38900089e-03, ...,\n",
-       "         -2.88057327e-03, -3.55851650e-03, -3.72791290e-03],\n",
-       "        [-7.79581070e-03, -8.30423832e-03, -8.98241997e-03, ...,\n",
-       "         -3.72803211e-03, -4.57561016e-03, -5.08403778e-03],\n",
-       "        [-8.81290436e-03, -9.66036320e-03, -1.23721361e-02, ...,\n",
-       "         -5.08403778e-03, -6.77907467e-03, -8.98241997e-03],\n",
-       "        ...,\n",
-       "        [            nan,             nan,             nan, ...,\n",
-       "                     nan,             nan,             nan],\n",
-       "        [            nan,             nan,             nan, ...,\n",
-       "                     nan,             nan,             nan],\n",
-       "        [            nan,             nan,             nan, ...,\n",
-       "                     nan,             nan,             nan]],\n",
-       "\n",
-       "       [[-1.18553638e-03,  9.53674316e-07,  9.53674316e-07, ...,\n",
-       "         -1.01613998e-03, -1.35505199e-03, -1.35505199e-03],\n",
-       "        [-1.49145126e-02, -1.93213224e-02, -2.13551521e-02, ...,\n",
-       "         -5.08391857e-03, -8.13484192e-03, -1.28805637e-02],\n",
-       "        [-1.50841475e-02, -1.83044672e-02, -2.23722458e-02, ...,\n",
-       "         -4.74500656e-03, -7.79581070e-03, -1.05077028e-02],\n",
-       "...\n",
-       "                     nan,             nan,             nan],\n",
-       "        [            nan,             nan,             nan, ...,\n",
-       "                     nan,             nan,             nan],\n",
-       "        [            nan,             nan,             nan, ...,\n",
-       "                     nan,             nan,             nan]],\n",
-       "\n",
-       "       [[-1.96602345e-02, -2.11856365e-02, -2.20330954e-02, ...,\n",
-       "         -1.25415325e-02, -1.55924559e-02, -1.81348324e-02],\n",
-       "        [-2.18636990e-02, -2.45755911e-02, -2.66094208e-02, ...,\n",
-       "         -1.25415325e-02, -1.61008835e-02, -1.94908381e-02],\n",
-       "        [-2.79653072e-02, -3.10163498e-02, -3.33890915e-02, ...,\n",
-       "         -1.40670538e-02, -2.05078125e-02, -2.59313583e-02],\n",
-       "        ...,\n",
-       "        [            nan,             nan,             nan, ...,\n",
-       "                     nan,             nan,             nan],\n",
-       "        [            nan,             nan,             nan, ...,\n",
-       "                     nan,             nan,             nan],\n",
-       "        [            nan,             nan,             nan, ...,\n",
-       "                     nan,             nan,             nan]]],\n",
-       "      dtype=float32)</pre></div></div></li><li class='xr-section-item'><input id='section-0684a3f1-d54f-4c32-99f0-2e73865a5805' class='xr-section-summary-in' type='checkbox'  checked><label for='section-0684a3f1-d54f-4c32-99f0-2e73865a5805' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>88.0 86.0 84.0 ... -86.0 -88.0</div><input id='attrs-1226c5f1-fe6f-4cfb-b0d7-e8ab2d0c0b9e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1226c5f1-fe6f-4cfb-b0d7-e8ab2d0c0b9e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-63f06b9a-b352-40ad-a7f1-3250a25a8115' class='xr-var-data-in' type='checkbox'><label for='data-63f06b9a-b352-40ad-a7f1-3250a25a8115' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>long_name :</span></dt><dd>Latitude</dd><dt><span>actual_range :</span></dt><dd>[ 88. -88.]</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>coordinate_defines :</span></dt><dd>center</dd></dl></div><div class='xr-var-data'><pre>array([ 88.,  86.,  84.,  82.,  80.,  78.,  76.,  74.,  72.,  70.,  68.,  66.,\n",
-       "        64.,  62.,  60.,  58.,  56.,  54.,  52.,  50.,  48.,  46.,  44.,  42.,\n",
-       "        40.,  38.,  36.,  34.,  32.,  30.,  28.,  26.,  24.,  22.,  20.,  18.,\n",
-       "        16.,  14.,  12.,  10.,   8.,   6.,   4.,   2.,   0.,  -2.,  -4.,  -6.,\n",
-       "        -8., -10., -12., -14., -16., -18., -20., -22., -24., -26., -28., -30.,\n",
-       "       -32., -34., -36., -38., -40., -42., -44., -46., -48., -50., -52., -54.,\n",
-       "       -56., -58., -60., -62., -64., -66., -68., -70., -72., -74., -76., -78.,\n",
-       "       -80., -82., -84., -86., -88.], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 2.0 4.0 ... 354.0 356.0 358.0</div><input id='attrs-f552b684-90a2-4837-a57b-513f8ffad0b7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f552b684-90a2-4837-a57b-513f8ffad0b7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-70fa0efe-d574-4aec-9232-20ab3dfc3d5c' class='xr-var-data-in' type='checkbox'><label for='data-70fa0efe-d574-4aec-9232-20ab3dfc3d5c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>long_name :</span></dt><dd>Longitude</dd><dt><span>actual_range :</span></dt><dd>[  0. 358.]</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>coordinate_defines :</span></dt><dd>center</dd></dl></div><div class='xr-var-data'><pre>array([  0.,   2.,   4.,   6.,   8.,  10.,  12.,  14.,  16.,  18.,  20.,  22.,\n",
-       "        24.,  26.,  28.,  30.,  32.,  34.,  36.,  38.,  40.,  42.,  44.,  46.,\n",
-       "        48.,  50.,  52.,  54.,  56.,  58.,  60.,  62.,  64.,  66.,  68.,  70.,\n",
-       "        72.,  74.,  76.,  78.,  80.,  82.,  84.,  86.,  88.,  90.,  92.,  94.,\n",
-       "        96.,  98., 100., 102., 104., 106., 108., 110., 112., 114., 116., 118.,\n",
-       "       120., 122., 124., 126., 128., 130., 132., 134., 136., 138., 140., 142.,\n",
-       "       144., 146., 148., 150., 152., 154., 156., 158., 160., 162., 164., 166.,\n",
-       "       168., 170., 172., 174., 176., 178., 180., 182., 184., 186., 188., 190.,\n",
-       "       192., 194., 196., 198., 200., 202., 204., 206., 208., 210., 212., 214.,\n",
-       "       216., 218., 220., 222., 224., 226., 228., 230., 232., 234., 236., 238.,\n",
-       "       240., 242., 244., 246., 248., 250., 252., 254., 256., 258., 260., 262.,\n",
-       "       264., 266., 268., 270., 272., 274., 276., 278., 280., 282., 284., 286.,\n",
-       "       288., 290., 292., 294., 296., 298., 300., 302., 304., 306., 308., 310.,\n",
-       "       312., 314., 316., 318., 320., 322., 324., 326., 328., 330., 332., 334.,\n",
-       "       336., 338., 340., 342., 344., 346., 348., 350., 352., 354., 356., 358.],\n",
-       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1960-01-01 ... 2018-12-01</div><input id='attrs-b96cd5be-76aa-49d2-a40b-b1705a94b427' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b96cd5be-76aa-49d2-a40b-b1705a94b427' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9e98cac5-6057-4f46-b0f2-15188dea6571' class='xr-var-data-in' type='checkbox'><label for='data-9e98cac5-6057-4f46-b0f2-15188dea6571' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Time</dd><dt><span>delta_t :</span></dt><dd>0000-01-00 00:00:00</dd><dt><span>avg_period :</span></dt><dd>0000-01-00 00:00:00</dd><dt><span>prev_avg_period :</span></dt><dd>0000-00-07 00:00:00</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>axis :</span></dt><dd>T</dd><dt><span>actual_range :</span></dt><dd>[19723. 80384.]</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;1960-01-01T00:00:00.000000000&#x27;, &#x27;1960-02-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;1960-03-01T00:00:00.000000000&#x27;, ..., &#x27;2018-10-01T00:00:00.000000000&#x27;,\n",
-       "       &#x27;2018-11-01T00:00:00.000000000&#x27;, &#x27;2018-12-01T00:00:00.000000000&#x27;],\n",
-       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>month</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1 2 3 4 5 6 7 ... 6 7 8 9 10 11 12</div><input id='attrs-9388166e-149a-431f-af1d-a43c5a86c65c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9388166e-149a-431f-af1d-a43c5a86c65c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6ce1f81b-9203-4ed8-9e43-cf199c8f2c86' class='xr-var-data-in' type='checkbox'><label for='data-6ce1f81b-9203-4ed8-9e43-cf199c8f2c86' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,\n",
-       "        6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10,\n",
-       "       11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,\n",
-       "        4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,\n",
-       "        9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,\n",
-       "        2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,\n",
-       "        7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11,\n",
-       "       12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,\n",
-       "        5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9,\n",
-       "       10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,\n",
-       "        3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,\n",
-       "        8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,\n",
-       "        1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,\n",
-       "        6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10,\n",
-       "       11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,\n",
-       "        4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,\n",
-       "        9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,\n",
-       "        2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,\n",
-       "        7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11,\n",
-       "       12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,\n",
-       "...\n",
-       "        3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,\n",
-       "        8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,\n",
-       "        1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,\n",
-       "        6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10,\n",
-       "       11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,\n",
-       "        4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,\n",
-       "        9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,\n",
-       "        2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,\n",
-       "        7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11,\n",
-       "       12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,\n",
-       "        5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9,\n",
-       "       10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,\n",
-       "        3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,\n",
-       "        8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,\n",
-       "        1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,\n",
-       "        6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10,\n",
-       "       11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,\n",
-       "        4,  5,  6,  7,  8,  9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,\n",
-       "        9, 10, 11, 12,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12,  1,\n",
-       "        2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-196b781d-6ab5-49ed-8eb1-7805cc9bd158' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-196b781d-6ab5-49ed-8eb1-7805cc9bd158' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
-      ],
-      "text/plain": [
-       "<xarray.DataArray 'sst' (time: 708, lat: 89, lon: 180)>\n",
-       "array([[[-3.55851650e-03, -3.55851650e-03, -3.38900089e-03, ...,\n",
-       "         -2.88057327e-03, -3.55851650e-03, -3.72791290e-03],\n",
-       "        [-7.79581070e-03, -8.30423832e-03, -8.98241997e-03, ...,\n",
-       "         -3.72803211e-03, -4.57561016e-03, -5.08403778e-03],\n",
-       "        [-8.81290436e-03, -9.66036320e-03, -1.23721361e-02, ...,\n",
-       "         -5.08403778e-03, -6.77907467e-03, -8.98241997e-03],\n",
-       "        ...,\n",
-       "        [            nan,             nan,             nan, ...,\n",
-       "                     nan,             nan,             nan],\n",
-       "        [            nan,             nan,             nan, ...,\n",
-       "                     nan,             nan,             nan],\n",
-       "        [            nan,             nan,             nan, ...,\n",
-       "                     nan,             nan,             nan]],\n",
-       "\n",
-       "       [[-1.18553638e-03,  9.53674316e-07,  9.53674316e-07, ...,\n",
-       "         -1.01613998e-03, -1.35505199e-03, -1.35505199e-03],\n",
-       "        [-1.49145126e-02, -1.93213224e-02, -2.13551521e-02, ...,\n",
-       "         -5.08391857e-03, -8.13484192e-03, -1.28805637e-02],\n",
-       "        [-1.50841475e-02, -1.83044672e-02, -2.23722458e-02, ...,\n",
-       "         -4.74500656e-03, -7.79581070e-03, -1.05077028e-02],\n",
-       "...\n",
-       "                     nan,             nan,             nan],\n",
-       "        [            nan,             nan,             nan, ...,\n",
-       "                     nan,             nan,             nan],\n",
-       "        [            nan,             nan,             nan, ...,\n",
-       "                     nan,             nan,             nan]],\n",
-       "\n",
-       "       [[-1.96602345e-02, -2.11856365e-02, -2.20330954e-02, ...,\n",
-       "         -1.25415325e-02, -1.55924559e-02, -1.81348324e-02],\n",
-       "        [-2.18636990e-02, -2.45755911e-02, -2.66094208e-02, ...,\n",
-       "         -1.25415325e-02, -1.61008835e-02, -1.94908381e-02],\n",
-       "        [-2.79653072e-02, -3.10163498e-02, -3.33890915e-02, ...,\n",
-       "         -1.40670538e-02, -2.05078125e-02, -2.59313583e-02],\n",
-       "        ...,\n",
-       "        [            nan,             nan,             nan, ...,\n",
-       "                     nan,             nan,             nan],\n",
-       "        [            nan,             nan,             nan, ...,\n",
-       "                     nan,             nan,             nan],\n",
-       "        [            nan,             nan,             nan, ...,\n",
-       "                     nan,             nan,             nan]]],\n",
-       "      dtype=float32)\n",
-       "Coordinates:\n",
-       "  * lat      (lat) float32 88.0 86.0 84.0 82.0 80.0 ... -82.0 -84.0 -86.0 -88.0\n",
-       "  * lon      (lon) float32 0.0 2.0 4.0 6.0 8.0 ... 350.0 352.0 354.0 356.0 358.0\n",
-       "  * time     (time) datetime64[ns] 1960-01-01 1960-02-01 ... 2018-12-01\n",
-       "    month    (time) int64 1 2 3 4 5 6 7 8 9 10 11 ... 2 3 4 5 6 7 8 9 10 11 12"
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
     }
-   ],
+   },
+   "outputs": [],
    "source": [
     "gb = sst.groupby('time.month')  # make groups (in this example each month of the year is a group) \n",
     "ds_anom = gb - gb.mean(dim='time')  # subtract each element of the group/month by the mean of that group/month over time \n",
@@ -2849,32 +617,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7f0e96b93990>]"
-      ]
-     },
-     "execution_count": 22,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
     }
-   ],
+   },
+   "outputs": [],
    "source": [
     "ds_anom.sel(lon=300, lat=50).plot.line()"
    ]
@@ -2888,32 +637,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.collections.QuadMesh at 0x7f0e96b29050>"
-      ]
-     },
-     "execution_count": 23,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
     }
-   ],
+   },
+   "outputs": [],
    "source": [
     "(ds_anom.sel(time='2018-01-01') - ds_anom.sel(time='1960-01-01')).plot()"
    ]
@@ -2945,59 +675,50 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 205,
-   "metadata": {},
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
    "outputs": [],
    "source": [
+    "# slice the point of interest\n",
     "ds_anom_loc = ds_anom.sel(lon=300, lat=50)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 206,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/stijnvh/miniconda3/envs/DS-geospatial/lib/python3.7/site-packages/xarray/core/common.py:1124: FutureWarning: 'base' in .resample() and in Grouper() is deprecated.\n",
-      "The new arguments that you should use are 'offset' or 'origin'.\n",
-      "\n",
-      ">>> df.resample(freq=\"3s\", base=2)\n",
-      "\n",
-      "becomes:\n",
-      "\n",
-      ">>> df.resample(freq=\"3s\", offset=\"2s\")\n",
-      "\n",
-      "  freq=freq, closed=closed, label=label, base=base, loffset=loffset\n"
-     ]
-    }
-   ],
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
    "source": [
-    "ds_anom_resample = ds_anom_loc.resample(time='5Y').median(dim='time')\n",
+    "# compute the resampling and rolling\n",
+    "ds_anom_resample = ds_anom_loc.resample(time='5YE').median(dim='time')\n",
     "ds_anom_rolling = ds_anom_loc.rolling(time=12, center=True).median()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
    "source": [
+    "# make a combined plot\n",
     "fig, ax = plt.subplots()\n",
     "ds_anom_loc.plot.line(ax=ax, label=\"monthly anom\")\n",
     "ds_anom_resample.plot.line(marker='o', label=\"5 year resample\")\n",
@@ -3010,46 +731,27 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Make projection aware maps"
+    "### Make projection aware maps"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The previous maps were the default outputs of xarray without specification of the spatial context. For reporting these plots are not appropriate. We can use the [cartopy](https://scitools.org.uk/cartopy/docs/latest/) package to adjust our Matplotlib axis to make them spatially aware.\n",
+    "The previous maps were the default outputs of xarray without specification of the spatial context. For reporting these plots are not appropriate. We can use the [cartopy](https://scitools.org.uk/cartopy/docs/latest/) package to adjust our Matplotlib axis to make them spatially aware. \n",
     "\n",
-    "To make sure the data of xarray can be integrated in a cartopy plot, the crucial element is to define the `transform` argument to to control which coordinate system that the given data is in. You can add the transform keyword with an appropriate `cartopy.crs.CRS` instance from the `import cartopy.crs` module:"
+    "For more in-depth information on cartopy, see the [visualization-03-cartopy](./visualization-03-cartopy.ipynb) notebook. As a short recap, to make sure the data of xarray can be integrated in a cartopy plot, the crucial element is to define the `transform` argument to to control which coordinate system that the given data is in. You can add the transform keyword with an appropriate `cartopy.crs.CRS` instance from the `import cartopy.crs` module:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 136,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.collections.QuadMesh at 0x7f0e76cda750>"
-      ]
-     },
-     "execution_count": 136,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1152x648 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
     }
-   ],
+   },
+   "outputs": [],
    "source": [
     "import cartopy.crs as ccrs\n",
     "import cartopy\n",
@@ -3084,32 +786,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 135,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.collections.QuadMesh at 0x7f0e7d1afdd0>"
-      ]
-     },
-     "execution_count": 135,
-     "metadata": {},
-     "output_type": "execute_result"
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
     },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 864x432 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
    "source": [
     "map_proj = ccrs.Orthographic(-20, 5)\n",
     "\n",
@@ -3138,481 +824,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 65,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
-       "<defs>\n",
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-       "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
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-       "</symbol>\n",
-       "</defs>\n",
-       "</svg>\n",
-       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
-       " *\n",
-       " */\n",
-       "\n",
-       ":root {\n",
-       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
-       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
-       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
-       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
-       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
-       "  --xr-background-color: var(--jp-layout-color0, white);\n",
-       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
-       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
-       "}\n",
-       "\n",
-       "html[theme=dark],\n",
-       "body.vscode-dark {\n",
-       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
-       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
-       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
-       "  --xr-border-color: #1F1F1F;\n",
-       "  --xr-disabled-color: #515151;\n",
-       "  --xr-background-color: #111111;\n",
-       "  --xr-background-color-row-even: #111111;\n",
-       "  --xr-background-color-row-odd: #313131;\n",
-       "}\n",
-       "\n",
-       ".xr-wrap {\n",
-       "  display: block;\n",
-       "  min-width: 300px;\n",
-       "  max-width: 700px;\n",
-       "}\n",
-       "\n",
-       ".xr-text-repr-fallback {\n",
-       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-header {\n",
-       "  padding-top: 6px;\n",
-       "  padding-bottom: 6px;\n",
-       "  margin-bottom: 4px;\n",
-       "  border-bottom: solid 1px var(--xr-border-color);\n",
-       "}\n",
-       "\n",
-       ".xr-header > div,\n",
-       ".xr-header > ul {\n",
-       "  display: inline;\n",
-       "  margin-top: 0;\n",
-       "  margin-bottom: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-obj-type,\n",
-       ".xr-array-name {\n",
-       "  margin-left: 2px;\n",
-       "  margin-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-obj-type {\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-sections {\n",
-       "  padding-left: 0 !important;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
-       "}\n",
-       "\n",
-       ".xr-section-item {\n",
-       "  display: contents;\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input + label {\n",
-       "  color: var(--xr-disabled-color);\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input:enabled + label {\n",
-       "  cursor: pointer;\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-section-item input:enabled + label:hover {\n",
-       "  color: var(--xr-font-color0);\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary {\n",
-       "  grid-column: 1;\n",
-       "  color: var(--xr-font-color2);\n",
-       "  font-weight: 500;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary > span {\n",
-       "  display: inline-block;\n",
-       "  padding-left: 0.5em;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:disabled + label {\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in + label:before {\n",
-       "  display: inline-block;\n",
-       "  content: '►';\n",
-       "  font-size: 11px;\n",
-       "  width: 15px;\n",
-       "  text-align: center;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:disabled + label:before {\n",
-       "  color: var(--xr-disabled-color);\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:checked + label:before {\n",
-       "  content: '▼';\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:checked + label > span {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary,\n",
-       ".xr-section-inline-details {\n",
-       "  padding-top: 4px;\n",
-       "  padding-bottom: 4px;\n",
-       "}\n",
-       "\n",
-       ".xr-section-inline-details {\n",
-       "  grid-column: 2 / -1;\n",
-       "}\n",
-       "\n",
-       ".xr-section-details {\n",
-       "  display: none;\n",
-       "  grid-column: 1 / -1;\n",
-       "  margin-bottom: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
-       "  display: contents;\n",
-       "}\n",
-       "\n",
-       ".xr-array-wrap {\n",
-       "  grid-column: 1 / -1;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 20px auto;\n",
-       "}\n",
-       "\n",
-       ".xr-array-wrap > label {\n",
-       "  grid-column: 1;\n",
-       "  vertical-align: top;\n",
-       "}\n",
-       "\n",
-       ".xr-preview {\n",
-       "  color: var(--xr-font-color3);\n",
-       "}\n",
-       "\n",
-       ".xr-array-preview,\n",
-       ".xr-array-data {\n",
-       "  padding: 0 5px !important;\n",
-       "  grid-column: 2;\n",
-       "}\n",
-       "\n",
-       ".xr-array-data,\n",
-       ".xr-array-in:checked ~ .xr-array-preview {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       ".xr-array-in:checked ~ .xr-array-data,\n",
-       ".xr-array-preview {\n",
-       "  display: inline-block;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list {\n",
-       "  display: inline-block !important;\n",
-       "  list-style: none;\n",
-       "  padding: 0 !important;\n",
-       "  margin: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list li {\n",
-       "  display: inline-block;\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list:before {\n",
-       "  content: '(';\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list:after {\n",
-       "  content: ')';\n",
-       "}\n",
-       "\n",
-       ".xr-dim-list li:not(:last-child):after {\n",
-       "  content: ',';\n",
-       "  padding-right: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-has-index {\n",
-       "  font-weight: bold;\n",
-       "}\n",
-       "\n",
-       ".xr-var-list,\n",
-       ".xr-var-item {\n",
-       "  display: contents;\n",
-       "}\n",
-       "\n",
-       ".xr-var-item > div,\n",
-       ".xr-var-item label,\n",
-       ".xr-var-item > .xr-var-name span {\n",
-       "  background-color: var(--xr-background-color-row-even);\n",
-       "  margin-bottom: 0;\n",
-       "}\n",
-       "\n",
-       ".xr-var-item > .xr-var-name:hover span {\n",
-       "  padding-right: 5px;\n",
-       "}\n",
-       "\n",
-       ".xr-var-list > li:nth-child(odd) > div,\n",
-       ".xr-var-list > li:nth-child(odd) > label,\n",
-       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
-       "  background-color: var(--xr-background-color-row-odd);\n",
-       "}\n",
-       "\n",
-       ".xr-var-name {\n",
-       "  grid-column: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-var-dims {\n",
-       "  grid-column: 2;\n",
-       "}\n",
-       "\n",
-       ".xr-var-dtype {\n",
-       "  grid-column: 3;\n",
-       "  text-align: right;\n",
-       "  color: var(--xr-font-color2);\n",
-       "}\n",
-       "\n",
-       ".xr-var-preview {\n",
-       "  grid-column: 4;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name,\n",
-       ".xr-var-dims,\n",
-       ".xr-var-dtype,\n",
-       ".xr-preview,\n",
-       ".xr-attrs dt {\n",
-       "  white-space: nowrap;\n",
-       "  overflow: hidden;\n",
-       "  text-overflow: ellipsis;\n",
-       "  padding-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name:hover,\n",
-       ".xr-var-dims:hover,\n",
-       ".xr-var-dtype:hover,\n",
-       ".xr-attrs dt:hover {\n",
-       "  overflow: visible;\n",
-       "  width: auto;\n",
-       "  z-index: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-var-attrs,\n",
-       ".xr-var-data {\n",
-       "  display: none;\n",
-       "  background-color: var(--xr-background-color) !important;\n",
-       "  padding-bottom: 5px !important;\n",
-       "}\n",
-       "\n",
-       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
-       ".xr-var-data-in:checked ~ .xr-var-data {\n",
-       "  display: block;\n",
-       "}\n",
-       "\n",
-       ".xr-var-data > table {\n",
-       "  float: right;\n",
-       "}\n",
-       "\n",
-       ".xr-var-name span,\n",
-       ".xr-var-data,\n",
-       ".xr-attrs {\n",
-       "  padding-left: 25px !important;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs,\n",
-       ".xr-var-attrs,\n",
-       ".xr-var-data {\n",
-       "  grid-column: 1 / -1;\n",
-       "}\n",
-       "\n",
-       "dl.xr-attrs {\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  display: grid;\n",
-       "  grid-template-columns: 125px auto;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt, dd {\n",
-       "  padding: 0;\n",
-       "  margin: 0;\n",
-       "  float: left;\n",
-       "  padding-right: 10px;\n",
-       "  width: auto;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt {\n",
-       "  font-weight: normal;\n",
-       "  grid-column: 1;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dt:hover span {\n",
-       "  display: inline-block;\n",
-       "  background: var(--xr-background-color);\n",
-       "  padding-right: 10px;\n",
-       "}\n",
-       "\n",
-       ".xr-attrs dd {\n",
-       "  grid-column: 2;\n",
-       "  white-space: pre-wrap;\n",
-       "  word-break: break-all;\n",
-       "}\n",
-       "\n",
-       ".xr-icon-database,\n",
-       ".xr-icon-file-text2 {\n",
-       "  display: inline-block;\n",
-       "  vertical-align: middle;\n",
-       "  width: 1em;\n",
-       "  height: 1.5em !important;\n",
-       "  stroke-width: 0;\n",
-       "  stroke: currentColor;\n",
-       "  fill: currentColor;\n",
-       "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;basin&#x27; (Z: 33, lat: 180, lon: 360)&gt;\n",
-       "[2138400 values with dtype=float32]\n",
-       "Coordinates:\n",
-       "  * lat      (lat) float32 -89.5 -88.5 -87.5 -86.5 -85.5 ... 86.5 87.5 88.5 89.5\n",
-       "  * Z        (Z) float32 0.0 10.0 20.0 30.0 50.0 ... 4000.0 4500.0 5000.0 5500.0\n",
-       "  * lon      (lon) float32 0.5 1.5 2.5 3.5 4.5 ... 355.5 356.5 357.5 358.5 359.5\n",
-       "Attributes:\n",
-       "    long_name:  basin code\n",
-       "    scale_max:  58\n",
-       "    CLIST:      Atlantic Ocean\\nPacific Ocean \\nIndian Ocean\\nMediterranean S...\n",
-       "    valid_min:  1\n",
-       "    valid_max:  58\n",
-       "    scale_min:  1\n",
-       "    units:      ids</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'basin'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>Z</span>: 33</li><li><span class='xr-has-index'>lat</span>: 180</li><li><span class='xr-has-index'>lon</span>: 360</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-722318ab-11f9-4fbd-9f7d-fb272dadc95a' class='xr-array-in' type='checkbox' checked><label for='section-722318ab-11f9-4fbd-9f7d-fb272dadc95a' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>...</span></div><div class='xr-array-data'><pre>[2138400 values with dtype=float32]</pre></div></div></li><li class='xr-section-item'><input id='section-f75b4361-4f0f-47f1-8749-2f9e6de6898d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-f75b4361-4f0f-47f1-8749-2f9e6de6898d' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-89.5 -88.5 -87.5 ... 88.5 89.5</div><input id='attrs-4209b89e-6cb8-4ed7-9124-6874a3c68da8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4209b89e-6cb8-4ed7-9124-6874a3c68da8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a0ceb523-f5cd-4762-bae7-7dc1a133aec1' class='xr-var-data-in' type='checkbox'><label for='data-a0ceb523-f5cd-4762-bae7-7dc1a133aec1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>pointwidth :</span></dt><dd>1.0</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>gridtype :</span></dt><dd>0</dd><dt><span>units :</span></dt><dd>degree_north</dd></dl></div><div class='xr-var-data'><pre>array([-89.5, -88.5, -87.5, -86.5, -85.5, -84.5, -83.5, -82.5, -81.5, -80.5,\n",
-       "       -79.5, -78.5, -77.5, -76.5, -75.5, -74.5, -73.5, -72.5, -71.5, -70.5,\n",
-       "       -69.5, -68.5, -67.5, -66.5, -65.5, -64.5, -63.5, -62.5, -61.5, -60.5,\n",
-       "       -59.5, -58.5, -57.5, -56.5, -55.5, -54.5, -53.5, -52.5, -51.5, -50.5,\n",
-       "       -49.5, -48.5, -47.5, -46.5, -45.5, -44.5, -43.5, -42.5, -41.5, -40.5,\n",
-       "       -39.5, -38.5, -37.5, -36.5, -35.5, -34.5, -33.5, -32.5, -31.5, -30.5,\n",
-       "       -29.5, -28.5, -27.5, -26.5, -25.5, -24.5, -23.5, -22.5, -21.5, -20.5,\n",
-       "       -19.5, -18.5, -17.5, -16.5, -15.5, -14.5, -13.5, -12.5, -11.5, -10.5,\n",
-       "        -9.5,  -8.5,  -7.5,  -6.5,  -5.5,  -4.5,  -3.5,  -2.5,  -1.5,  -0.5,\n",
-       "         0.5,   1.5,   2.5,   3.5,   4.5,   5.5,   6.5,   7.5,   8.5,   9.5,\n",
-       "        10.5,  11.5,  12.5,  13.5,  14.5,  15.5,  16.5,  17.5,  18.5,  19.5,\n",
-       "        20.5,  21.5,  22.5,  23.5,  24.5,  25.5,  26.5,  27.5,  28.5,  29.5,\n",
-       "        30.5,  31.5,  32.5,  33.5,  34.5,  35.5,  36.5,  37.5,  38.5,  39.5,\n",
-       "        40.5,  41.5,  42.5,  43.5,  44.5,  45.5,  46.5,  47.5,  48.5,  49.5,\n",
-       "        50.5,  51.5,  52.5,  53.5,  54.5,  55.5,  56.5,  57.5,  58.5,  59.5,\n",
-       "        60.5,  61.5,  62.5,  63.5,  64.5,  65.5,  66.5,  67.5,  68.5,  69.5,\n",
-       "        70.5,  71.5,  72.5,  73.5,  74.5,  75.5,  76.5,  77.5,  78.5,  79.5,\n",
-       "        80.5,  81.5,  82.5,  83.5,  84.5,  85.5,  86.5,  87.5,  88.5,  89.5],\n",
-       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>Z</span></div><div class='xr-var-dims'>(Z)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 10.0 20.0 ... 5000.0 5500.0</div><input id='attrs-02597609-321e-433c-a7e9-c485c968aba2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-02597609-321e-433c-a7e9-c485c968aba2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3ff7dca7-7dd0-4a43-97c0-7ffac57ee93f' class='xr-var-data-in' type='checkbox'><label for='data-3ff7dca7-7dd0-4a43-97c0-7ffac57ee93f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>gridtype :</span></dt><dd>0</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>array([   0.,   10.,   20.,   30.,   50.,   75.,  100.,  125.,  150.,  200.,\n",
-       "        250.,  300.,  400.,  500.,  600.,  700.,  800.,  900., 1000., 1100.,\n",
-       "       1200., 1300., 1400., 1500., 1750., 2000., 2500., 3000., 3500., 4000.,\n",
-       "       4500., 5000., 5500.], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.5 1.5 2.5 ... 357.5 358.5 359.5</div><input id='attrs-0c317e00-3809-4733-bd3b-b6ec4441a529' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0c317e00-3809-4733-bd3b-b6ec4441a529' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-98cba856-334d-4c2e-93fb-757c4c8c1dfc' class='xr-var-data-in' type='checkbox'><label for='data-98cba856-334d-4c2e-93fb-757c4c8c1dfc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>pointwidth :</span></dt><dd>1.0</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>gridtype :</span></dt><dd>1</dd><dt><span>units :</span></dt><dd>degree_east</dd></dl></div><div class='xr-var-data'><pre>array([  0.5,   1.5,   2.5, ..., 357.5, 358.5, 359.5], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-a5d79291-41be-4fb4-ae0f-99c8624be139' class='xr-section-summary-in' type='checkbox'  checked><label for='section-a5d79291-41be-4fb4-ae0f-99c8624be139' class='xr-section-summary' >Attributes: <span>(7)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>basin code</dd><dt><span>scale_max :</span></dt><dd>58</dd><dt><span>CLIST :</span></dt><dd>Atlantic Ocean\n",
-       "Pacific Ocean \n",
-       "Indian Ocean\n",
-       "Mediterranean Sea\n",
-       "Baltic Sea\n",
-       "Black Sea\n",
-       "Red Sea\n",
-       "Persian Gulf\n",
-       "Hudson Bay\n",
-       "Southern Ocean\n",
-       "Arctic Ocean\n",
-       "Sea of Japan\n",
-       "Kara Sea\n",
-       "Sulu Sea\n",
-       "Baffin Bay\n",
-       "East Mediterranean\n",
-       "West Mediterranean\n",
-       "Sea of Okhotsk\n",
-       "Banda Sea\n",
-       "Caribbean Sea\n",
-       "Andaman Basin\n",
-       "North Caribbean\n",
-       "Gulf of Mexico\n",
-       "Beaufort Sea\n",
-       "South China Sea\n",
-       "Barents Sea\n",
-       "Celebes Sea\n",
-       "Aleutian Basin\n",
-       "Fiji Basin\n",
-       "North American Basin\n",
-       "West European Basin\n",
-       "Southeast Indian Basin\n",
-       "Coral Sea\n",
-       "East Indian Basin\n",
-       "Central Indian Basin\n",
-       "Southwest Atlantic Basin\n",
-       "Southeast Atlantic Basin\n",
-       "Southeast Pacific Basin\n",
-       "Guatemala Basin\n",
-       "East Caroline Basin\n",
-       "Marianas Basin\n",
-       "Philippine Sea\n",
-       "Arabian Sea\n",
-       "Chile Basin\n",
-       "Somali Basin\n",
-       "Mascarene Basin\n",
-       "Crozet Basin\n",
-       "Guinea Basin\n",
-       "Brazil Basin\n",
-       "Argentine Basin\n",
-       "Tasman Sea\n",
-       "Atlantic Indian Basin\n",
-       "Caspian Sea\n",
-       "Sulu Sea II\n",
-       "Venezuela Basin\n",
-       "Bay of Bengal\n",
-       "Java Sea\n",
-       "East Indian Atlantic Basin</dd><dt><span>valid_min :</span></dt><dd>1</dd><dt><span>valid_max :</span></dt><dd>58</dd><dt><span>scale_min :</span></dt><dd>1</dd><dt><span>units :</span></dt><dd>ids</dd></dl></div></li></ul></div></div>"
-      ],
-      "text/plain": [
-       "<xarray.DataArray 'basin' (Z: 33, lat: 180, lon: 360)>\n",
-       "[2138400 values with dtype=float32]\n",
-       "Coordinates:\n",
-       "  * lat      (lat) float32 -89.5 -88.5 -87.5 -86.5 -85.5 ... 86.5 87.5 88.5 89.5\n",
-       "  * Z        (Z) float32 0.0 10.0 20.0 30.0 50.0 ... 4000.0 4500.0 5000.0 5500.0\n",
-       "  * lon      (lon) float32 0.5 1.5 2.5 3.5 4.5 ... 355.5 356.5 357.5 358.5 359.5\n",
-       "Attributes:\n",
-       "    long_name:  basin code\n",
-       "    scale_max:  58\n",
-       "    CLIST:      Atlantic Ocean\\nPacific Ocean \\nIndian Ocean\\nMediterranean S...\n",
-       "    valid_min:  1\n",
-       "    valid_max:  58\n",
-       "    scale_min:  1\n",
-       "    units:      ids"
-      ]
-     },
-     "execution_count": 65,
-     "metadata": {},
-     "output_type": "execute_result"
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
     }
-   ],
+   },
+   "outputs": [],
    "source": [
     "basin = xr.open_dataset(\"./data/basin.nc\")\n",
     "basin = basin.rename({'X': 'lon', 'Y': 'lat'})\n",
-    "basin.basin"
+    "basin[\"basin\"]"
    ]
   },
   {
@@ -3624,27 +846,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 207,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "1       Atlantic Ocean\n",
-       "2       Pacific Ocean \n",
-       "3         Indian Ocean\n",
-       "4    Mediterranean Sea\n",
-       "5           Baltic Sea\n",
-       "Name: basin, dtype: object"
-      ]
-     },
-     "execution_count": 207,
-     "metadata": {},
-     "output_type": "execute_result"
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
     }
-   ],
+   },
+   "outputs": [],
    "source": [
-    "basin_names = basin_surface.attrs['CLIST'].split('\\n')\n",
+    "basin_names = basin[\"basin\"].attrs['CLIST'].split('\\n')\n",
     "basin_s = pd.Series(basin_names, index=np.arange(1, len(basin_names)+1))\n",
     "basin_s = basin_s.rename('basin')\n",
     "basin_s.head()"
@@ -3666,32 +876,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 208,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.collections.QuadMesh at 0x7f0e74bfa7d0>"
-      ]
-     },
-     "execution_count": 208,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
     }
-   ],
+   },
+   "outputs": [],
    "source": [
     "basin_surface = basin[\"basin\"].sel(Z=0.0).drop_vars(\"Z\")\n",
     "basin_surface.plot(vmax=10, cmap='tab10')"
@@ -3706,32 +897,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 209,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.collections.QuadMesh at 0x7f0e74a1cb90>"
-      ]
-     },
-     "execution_count": 209,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
     }
-   ],
+   },
+   "outputs": [],
    "source": [
     "basin_surface_interp = basin_surface.interp_like(sst, method='nearest')\n",
     "basin_surface_interp.plot(vmax=10, cmap='tab10')"
@@ -3767,8 +939,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 221,
-   "metadata": {},
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
    "outputs": [],
    "source": [
     "basin_mean_sst = sst.groupby(basin_surface_interp).mean()\n",
@@ -3777,563 +953,68 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 222,
-   "metadata": {},
-   "outputs": [
-    {
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-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;sst&#x27; (basin: 14)&gt;\n",
-       "array([19.367807  , 21.26458   , 21.203249  , 19.93899   ,  8.15021   ,\n",
-       "       15.153743  , 28.530907  , 26.736147  ,  0.44065136,  1.6160094 ,\n",
-       "       -0.7505018 , 12.300379  , 14.34969   , 28.52797   ], dtype=float32)\n",
-       "Coordinates:\n",
-       "  * basin    (basin) float64 1.0 2.0 3.0 4.0 5.0 ... 10.0 11.0 12.0 53.0 56.0</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'sst'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>basin</span>: 14</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-5cfefc7c-a2b1-45d3-bf78-f208a59c405f' class='xr-array-in' type='checkbox' checked><label for='section-5cfefc7c-a2b1-45d3-bf78-f208a59c405f' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>19.367807 21.26458 21.203249 19.93899 ... 12.300379 14.34969 28.52797</span></div><div class='xr-array-data'><pre>array([19.367807  , 21.26458   , 21.203249  , 19.93899   ,  8.15021   ,\n",
-       "       15.153743  , 28.530907  , 26.736147  ,  0.44065136,  1.6160094 ,\n",
-       "       -0.7505018 , 12.300379  , 14.34969   , 28.52797   ], dtype=float32)</pre></div></div></li><li class='xr-section-item'><input id='section-1e2d74cc-dc5e-4445-b923-7a2b88dd98a5' class='xr-section-summary-in' type='checkbox'  checked><label for='section-1e2d74cc-dc5e-4445-b923-7a2b88dd98a5' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>basin</span></div><div class='xr-var-dims'>(basin)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.0 2.0 3.0 4.0 ... 12.0 53.0 56.0</div><input id='attrs-dec5c074-49b5-4cf1-aed5-e7636c46a8ef' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-dec5c074-49b5-4cf1-aed5-e7636c46a8ef' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-99e76eaf-74b6-49d3-87ee-85455197ed0a' class='xr-var-data-in' type='checkbox'><label for='data-99e76eaf-74b6-49d3-87ee-85455197ed0a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  9., 10., 11., 12., 53., 56.])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-d6177dfc-e9cd-454a-a5a0-7a0ad346dcfd' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-d6177dfc-e9cd-454a-a5a0-7a0ad346dcfd' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
-      ],
-      "text/plain": [
-       "<xarray.DataArray 'sst' (basin: 14)>\n",
-       "array([19.367807  , 21.26458   , 21.203249  , 19.93899   ,  8.15021   ,\n",
-       "       15.153743  , 28.530907  , 26.736147  ,  0.44065136,  1.6160094 ,\n",
-       "       -0.7505018 , 12.300379  , 14.34969   , 28.52797   ], dtype=float32)\n",
-       "Coordinates:\n",
-       "  * basin    (basin) float64 1.0 2.0 3.0 4.0 5.0 ... 10.0 11.0 12.0 53.0 56.0"
-      ]
-     },
-     "execution_count": 222,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
    "source": [
     "# In such a situation, the ELLIPSIS can be used to aggregate over all dimensions\n",
     "basin_mean_sst = sst.groupby(basin_surface_interp).mean(dim=...) # ellipsis is shortcut for all dimensions\n",
     "basin_mean_sst"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Convert to a Pandas DataFrame:"
-   ]
-  },
   {
    "cell_type": "code",
-   "execution_count": 223,
-   "metadata": {},
-   "outputs": [
-    {
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-       "      <th>10.0</th>\n",
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-       "      <th>11.0</th>\n",
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-       "      <th>53.0</th>\n",
-       "      <td>14.349690</td>\n",
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-       "      <th>56.0</th>\n",
-       "      <td>28.527969</td>\n",
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-      "text/plain": [
-       "             sst\n",
-       "basin           \n",
-       "1.0    19.367807\n",
-       "2.0    21.264580\n",
-       "3.0    21.203249\n",
-       "4.0    19.938990\n",
-       "5.0     8.150210\n",
-       "6.0    15.153743\n",
-       "7.0    28.530907\n",
-       "8.0    26.736147\n",
-       "9.0     0.440651\n",
-       "10.0    1.616009\n",
-       "11.0   -0.750502\n",
-       "12.0   12.300379\n",
-       "53.0   14.349690\n",
-       "56.0   28.527969"
-      ]
-     },
-     "execution_count": 223,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
    "source": [
+    "# Convert to a Pandas DataFrame:\n",
     "basin_mean_df = basin_mean_sst.to_dataframe()\n",
     "basin_mean_df"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Merge the data with the `basin_s` data on the index:"
-   ]
-  },
   {
    "cell_type": "code",
-   "execution_count": 226,
-   "metadata": {},
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
    "outputs": [],
    "source": [
+    "# Merge the data with the `basin_s` data on the index:\n",
     "basin_mean_df_merged = pd.merge(basin_mean_df, basin_s, left_index=True, right_index=True)"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Create a bar chart of the SST per basin data:"
-   ]
-  },
   {
    "cell_type": "code",
-   "execution_count": 228,
-   "metadata": {},
-   "outputs": [
-    {
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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
    "source": [
+    "# Create a bar chart of the SST per basin data:\n",
     "basin_mean_df_merged.sort_values(by=\"sst\").plot.barh(x=\"basin\");"
    ]
   },
@@ -4345,13 +1026,24 @@
     "\n",
     "Acknowledgements to https://earth-env-data-science.github.io/lectures/xarray/xarray-part2.html"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
+  "celltoolbar": "Nbtutor - export exercises",
+  "jupytext": {
+   "default_lexer": "ipython3"
+  },
   "kernelspec": {
-   "display_name": "Python [conda env:DS-geospatial]",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
-   "name": "conda-env-DS-geospatial-py"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
@@ -4363,7 +1055,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.6"
+   "version": "3.12.11"
   }
  },
  "nbformat": 4,
diff --git a/_solved/visualization-01-matplotlib.ipynb b/_solved/visualization-01-matplotlib.ipynb
index 99ebf78..9b82d09 100644
--- a/_solved/visualization-01-matplotlib.ipynb
+++ b/_solved/visualization-01-matplotlib.ipynb
@@ -4,15 +4,14 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "<p><font size=\"6\"><b>Matplotlib: Introduction </b></font></p>\n",
+    "<p><font size=\"6\"><b>Visualization - Matplotlib</b></font></p>\n",
     "\n",
+    "> *DS Python for GIS and Geoscience*  \n",
+    "> *September, 2025*\n",
+    ">\n",
+    "> *© 2025, Joris Van den Bossche and Stijn Van Hoey. Licensed under [CC BY 4.0 Creative Commons](http://creativecommons.org/licenses/by/4.0/)*\n",
     "\n",
-    "> *DS Data manipulation, analysis and visualisation in Python*  \n",
-    "> *December, 2019*\n",
-    "\n",
-    "> *© 2016-2020, Joris Van den Bossche and Stijn Van Hoey  (<mailto:jorisvandenbossche@gmail.com>, <mailto:stijnvanhoey@gmail.com>). Licensed under [CC BY 4.0 Creative Commons](http://creativecommons.org/licenses/by/4.0/)*\n",
-    "\n",
-    "---\n"
+    "---"
    ]
   },
   {
@@ -43,13 +42,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "run_control": {
-     "frozen": false,
-     "read_only": false
-    }
-   },
+   "execution_count": null,
+   "metadata": {},
    "outputs": [],
    "source": [
     "import numpy as np\n",
@@ -60,7 +54,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## - dry stuff - The matplotlib `Figure`, `axes` and `axis`\n",
+    "## - dry stuff - The matplotlib `Figure`, `Axes` and `Axis`\n",
     "\n",
     "At the heart of **every** plot is the figure object. The \"Figure\" object is the top level concept which can be drawn to one of the many output formats, or simply just to screen. Any object which can be drawn in this way is known as an \"Artist\" in matplotlib.\n",
     "\n",
@@ -69,24 +63,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "run_control": {
-     "frozen": false,
-     "read_only": false
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<Figure size 432x288 with 0 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "fig = plt.figure()\n",
     "plt.show()"
@@ -100,32 +79,16 @@
     "\n",
     "By far the most useful artist in matplotlib is the **Axes** artist. The Axes artist represents the \"data space\" of a typical plot, a rectangular axes (the most common, but not always the case, e.g. polar plots) will have 2 (confusingly named) **Axis** artists with tick labels and tick marks.\n",
     "\n",
+    "![](../img/matplotlib_fundamentals.png)\n",
+    "\n",
     "There is no limit on the number of Axes artists which can exist on a Figure artist. Let's go ahead and create a figure with a single Axes artist, and show it using pyplot:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "run_control": {
-     "frozen": false,
-     "read_only": false
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "ax = plt.axes()"
    ]
@@ -136,174 +99,94 @@
    "source": [
     "Matplotlib's ``pyplot`` module makes the process of creating graphics easier by allowing us to skip some of the tedious Artist construction. For example, we did not need to manually create the Figure artist with ``plt.figure`` because it was implicit that we needed a figure when we created the Axes artist.\n",
     "\n",
-    "Under the hood matplotlib still had to create a Figure artist, its just we didn't need to capture it into a variable. We can access the created object with the \"state\" functions found in pyplot called **``gcf``** and **``gca``**."
+    "Under the hood matplotlib still had to create a Figure artist, its just we didn't need to capture it into a variable."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## - essential stuff - `pyplot` versus Object based"
+    "## - essential stuff - Matplotlib works 'Object Oriented'"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Some example data:"
+    "Objects have characteristics (attributes) and can do things (methods)."
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "run_control": {
-     "frozen": false,
-     "read_only": false
-    }
-   },
-   "outputs": [],
+   "cell_type": "markdown",
+   "metadata": {},
    "source": [
-    "x = np.linspace(0, 5, 10)\n",
-    "y = x ** 2"
+    "Some example data:"
    ]
   },
   {
-   "cell_type": "markdown",
+   "cell_type": "code",
+   "execution_count": null,
    "metadata": {},
+   "outputs": [],
    "source": [
-    "Observe the following difference:"
+    "x = np.linspace(0, 5, 10)\n",
+    "y = x ** 10"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**1. pyplot style: plt...** (you will see this a lot for code online!)"
+    "Create a `Figure` and `Axes` object to plot data. Use the `Axes` object to plot x and y data."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "run_control": {
-     "frozen": false,
-     "read_only": false
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7f2733a84b20>]"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
-    "plt.plot(x, y, '-')"
+    "fig, ax = plt.subplots()\n",
+    "ax.plot(x, y, '-')"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**2. creating objects**"
+    "Using the existing objects, further adjustment can be done using the existing methods, e.g."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "run_control": {
-     "frozen": false,
-     "read_only": false
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7f2733a69d90>]"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
+    "from matplotlib import ticker\n",
+    "\n",
     "fig, ax = plt.subplots()\n",
-    "ax.plot(x, y, '-')"
+    "ax.plot(x, y, '-')\n",
+    "\n",
+    "# Add a title\n",
+    "ax.set_title(\"My data\")\n",
+    "\n",
+    "# Add custom formatting instead of the default scientific formatting:\n",
+    "ax.yaxis.set_major_formatter(ticker.FormatStrFormatter(\"%.1f\"))"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Although a little bit more code is involved, the advantage is that we now have **full control** of where the plot axes are placed, and we can easily add more than one axis to the figure:"
+    "By using the Matplotlib objects, we now have **full control** of where the plot axes are placed, and we can add more than one axis to the Figure:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "run_control": {
-     "frozen": false,
-     "read_only": false
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7f2733bf1fa0>]"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "fig, ax1 = plt.subplots()\n",
     "ax1.plot(x, y, '-')\n",
@@ -331,37 +214,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "run_control": {
-     "frozen": false,
-     "read_only": false
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7f273398cd90>]"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "fig, ax = plt.subplots()\n",
     "ax.plot(x, y, '-')\n",
@@ -377,31 +232,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "run_control": {
-     "frozen": false,
-     "read_only": false
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 720x504 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
-    "import matplotlib.pyplot as plt\n",
-    "import numpy as np\n",
-    "\n",
     "x = np.linspace(-1, 0, 100)\n",
     "\n",
     "fig, ax  = plt.subplots(figsize=(10, 7))\n",
@@ -412,9 +246,11 @@
     "ax.plot(x, x**2, color='0.4', label='power 2')\n",
     "ax.plot(x, x**3, color='0.8', linestyle='--', label='power 3')\n",
     "\n",
-    "ax.vlines(x=-0.75, ymin=0., ymax=0.8, color='0.4', linestyle='-.') \n",
-    "ax.axhline(y=0.1, color='0.4', linestyle='-.')\n",
-    "ax.fill_between(x=[-1, 1.1], y1=[0.65], y2=[0.75], color='0.85')\n",
+    "ax.vlines(x=-0.75, ymin=0., ymax=0.8, color='0.4', linestyle='-.')  # vertical dotted line within range [0, 0.8]\n",
+    "ax.fill_between(x=x, y1=x**2, y2=1.1*x**2, color='0.85')   # color fill between the two exponential functions\n",
+    "\n",
+    "ax.axhline(y=0.1, color='0.4', linestyle='-.')   # ax-wide horizontal dotted line \n",
+    "ax.axhspan(ymin=0.65, ymax=0.75, color='0.95')   # ax-wide horizontal filled space\n",
     "\n",
     "fig.suptitle('Figure title', fontsize=18, \n",
     "             fontweight='bold')\n",
@@ -429,11 +265,25 @@
     "ax.text(0.5, 0.2, 'Text centered at (0.5, 0.2)\\nin data coordinates.',\n",
     "        horizontalalignment='center', fontsize=14)\n",
     "\n",
-    "ax.text(0.5, 0.5, 'Text centered at (0.5, 0.5)\\nin Figure coordinates.',\n",
+    "ax.text(0.5, 0.5, 'Text centered at (0.5, 0.5)\\nin relative Axes coordinates.',\n",
     "        horizontalalignment='center', fontsize=14, \n",
     "        transform=ax.transAxes, color='grey')\n",
     "\n",
-    "ax.legend(loc='upper right', frameon=True, ncol=2, fontsize=14);"
+    "ax.annotate('Text pointing at (0.0, 0.75)', xy=(0.0, 0.75), xycoords=\"data\",\n",
+    "            xytext=(20, 40), textcoords=\"offset points\",\n",
+    "            horizontalalignment='left', fontsize=14,\n",
+    "            arrowprops=dict(facecolor='black', shrink=0.05, width=1))\n",
+    "\n",
+    "ax.legend(loc='lower right', frameon=True, ncol=2, fontsize=14)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Adjusting specific parts of a plot is a matter of accessing the correct element of the plot:\n",
+    "\n",
+    "<img src=\"https://matplotlib.org/stable/_images/anatomy.png\" width=\"800\"/>"
    ]
   },
   {
@@ -475,79 +325,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "run_control": {
-     "frozen": false,
-     "read_only": false
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['Solarize_Light2',\n",
-       " '_classic_test_patch',\n",
-       " 'bmh',\n",
-       " 'classic',\n",
-       " 'dark_background',\n",
-       " 'fast',\n",
-       " 'fivethirtyeight',\n",
-       " 'ggplot',\n",
-       " 'grayscale',\n",
-       " 'seaborn',\n",
-       " 'seaborn-bright',\n",
-       " 'seaborn-colorblind',\n",
-       " 'seaborn-dark',\n",
-       " 'seaborn-dark-palette',\n",
-       " 'seaborn-darkgrid',\n",
-       " 'seaborn-deep',\n",
-       " 'seaborn-muted',\n",
-       " 'seaborn-notebook',\n",
-       " 'seaborn-paper',\n",
-       " 'seaborn-pastel',\n",
-       " 'seaborn-poster',\n",
-       " 'seaborn-talk',\n",
-       " 'seaborn-ticks',\n",
-       " 'seaborn-white',\n",
-       " 'seaborn-whitegrid',\n",
-       " 'tableau-colorblind10']"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "plt.style.available"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "run_control": {
-     "frozen": false,
-     "read_only": false
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 576x396 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "x = np.linspace(0, 10)\n",
     "\n",
-    "with plt.style.context('seaborn'):  # 'seaborn', ggplot', 'bmh', 'grayscale', 'seaborn-whitegrid', 'seaborn-muted'\n",
+    "with plt.style.context('seaborn-v0_8-colorblind'): \n",
     "    fig, ax = plt.subplots()\n",
     "    ax.plot(x, np.sin(x) + x + np.random.randn(50))\n",
     "    ax.plot(x, np.sin(x) + 0.5 * x + np.random.randn(50))\n",
@@ -563,16 +356,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {
-    "run_control": {
-     "frozen": false,
-     "read_only": false
-    }
-   },
+   "execution_count": null,
+   "metadata": {},
    "outputs": [],
    "source": [
-    "plt.style.use('seaborn-whitegrid')"
+    "plt.style.use('seaborn-v0_8-whitegrid')"
    ]
   },
   {
@@ -601,30 +389,20 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Interaction with Pandas"
+    "## Interaction with Pandas/xarray"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "run_control": {
-     "frozen": false,
-     "read_only": false
-    }
-   },
+   "metadata": {},
    "source": [
-    "What we have been doing while plotting with Pandas:"
+    "The default plotting of Pandas/xarray relies on Matplotlib. Let's use a Pandas DataFrame as an example"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {
-    "run_control": {
-     "frozen": false,
-     "read_only": false
-    }
-   },
+   "execution_count": null,
+   "metadata": {},
    "outputs": [],
    "source": [
     "import pandas as pd"
@@ -632,353 +410,118 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {
-    "run_control": {
-     "frozen": false,
-     "read_only": false
-    }
-   },
+   "execution_count": null,
+   "metadata": {},
    "outputs": [],
    "source": [
-    "df = pd.DataFrame(np.random.randn(100, 3), columns=[\"A\", \"B\", \"C\"], index=pd.date_range(\"2020-01-01\", periods=100))"
+    "flowdata = pd.read_csv('data/vmm_flowdata.csv', \n",
+    "                       index_col='Time', \n",
+    "                       parse_dates=True)"
    ]
   },
   {
-   "cell_type": "markdown",
+   "cell_type": "code",
+   "execution_count": null,
    "metadata": {},
+   "outputs": [],
    "source": [
-    "### Pandas versus matplotlib"
+    "flowdata.head()"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "#### Comparison 1: single plot"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {
-    "run_control": {
-     "frozen": false,
-     "read_only": false
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<AxesSubplot:>"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1152x432 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "df.plot(figsize=(16, 6)) # shift tab this!"
+    "### From Pandas/xarray to Matplotlib"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Making this with matplotlib..."
+    "Under the hood, Pandas creates an Matplotlib Figure with an Axes object. The `Axis` object(s) returned can be further adjusted:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {
-    "run_control": {
-     "frozen": false,
-     "read_only": false
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.legend.Legend at 0x7f27295afca0>"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1152x432 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig, ax = plt.subplots(figsize=(16, 6))\n",
-    "ax.plot(df)\n",
-    "ax.legend([\"L06_347\", \"LS06_347\", \"LS06_348\"])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
+   "execution_count": null,
    "metadata": {},
+   "outputs": [],
    "source": [
-    "is still ok!"
+    "ax = flowdata.plot()\n",
+    "ax.set_title('discharge [$m^3/s$]');"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "#### Comparison 2: with subplots"
+    "Pandas/xarray provide a number of convenience options as keyword parameters:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {
-    "run_control": {
-     "frozen": false,
-     "read_only": false
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1152x576 with 3 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "axs = df.plot(subplots=True, sharex=True,\n",
-    "              figsize=(16, 8), colormap='viridis', # Dark2\n",
-    "              fontsize=15, rot=0)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
+   "execution_count": null,
    "metadata": {},
+   "outputs": [],
    "source": [
-    "Mimicking this in matplotlib (just as a reference):"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {
-    "run_control": {
-     "frozen": false,
-     "read_only": false
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1152x576 with 3 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "from matplotlib import cm\n",
-    "import matplotlib.dates as mdates\n",
-    "\n",
-    "colors = [cm.viridis(x) for x in np.linspace(0.0, 1.0, len(df.columns))] # list comprehension to set up the colors\n",
-    "\n",
-    "fig, axs = plt.subplots(3, 1, figsize=(16, 8))\n",
-    "\n",
-    "for ax, col, station in zip(axs, colors, df.columns):\n",
-    "    ax.plot(df.index, df[station], label=station, color=col)\n",
-    "    ax.legend()\n",
-    "    if not ax.is_last_row():\n",
-    "        ax.xaxis.set_ticklabels([])\n",
-    "        ax.xaxis.set_major_locator(mdates.YearLocator())\n",
-    "    else:\n",
-    "        ax.xaxis.set_major_locator(mdates.YearLocator())\n",
-    "        ax.xaxis.set_major_formatter(mdates.DateFormatter('%Y'))\n",
-    "        ax.set_xlabel('Time')\n",
-    "    ax.tick_params(labelsize=15)"
+    "axs = flowdata.plot(subplots=True, sharex=True,\n",
+    "                    figsize=(16, 8), colormap='viridis', # Dark2\n",
+    "                    fontsize=15, rot=0)\n",
+    "axs[0].set_title('discharge [$m^3/s$]');"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Is already a bit harder ;-)"
+    "### From Matplotlib to Pandas/xarray"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Best of both worlds..."
+    "Starting from 'empty' Matplotlib objects, we can tell Pandas/xarray to use these to plot new data on:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<AxesSubplot:>"
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig, ax = plt.subplots() #prepare a matplotlib figure\n",
-    "\n",
-    "df.plot(ax=ax) # use pandas for the plotting"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {
-    "run_control": {
-     "frozen": false,
-     "read_only": false
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Text(0.5, 0.98, 'Flow time series')"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1080x360 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "fig, ax = plt.subplots(figsize=(15, 5)) #prepare a matplotlib figure\n",
-    "\n",
-    "df.plot(ax=ax) # use pandas for the plotting\n",
+    "fig, (ax0, ax1) = plt.subplots(2, 1) #prepare a Matplotlib figure\n",
     "\n",
-    "# Provide further adaptations with matplotlib:\n",
-    "ax.set_xlabel(\"\")\n",
-    "ax.grid(which=\"major\", linewidth='0.5', color='0.8')\n",
-    "fig.suptitle('Flow time series', fontsize=15)"
+    "flowdata.plot(ax=ax0) # use Pandas for the plotting"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {
-    "run_control": {
-     "frozen": false,
-     "read_only": false
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.legend.Legend at 0x7f272958aeb0>"
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1152x432 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
-    "fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(16, 6)) #provide with matplotlib 2 axis\n",
+    "fig, (ax0, ax1) = plt.subplots(2, 1, figsize=(16, 6)) #provide with matplotlib 2 axis\n",
     "\n",
-    "df[[\"A\", \"B\"]].plot(ax=ax1) # plot the two timeseries of the same location on the first plot\n",
-    "df[\"C\"].plot(ax=ax2, color='0.2') # plot the other station on the second plot\n",
+    "flowdata[[\"L06_347\", \"LS06_347\"]].plot(ax=ax0) # plot the two timeseries of the same location on the first plot\n",
+    "flowdata[\"LS06_348\"].plot(ax=ax1, color='0.2') # plot the other station on the second plot\n",
     "\n",
     "# further adapt with matplotlib\n",
-    "ax1.set_ylabel(\"Y label 1\")\n",
-    "ax2.set_ylabel(\"Y label 2\")\n",
-    "ax2.legend()"
+    "ax0.set_ylabel(\"L06_347\")\n",
+    "ax1.set_ylabel(\"LS06_348\")\n",
+    "ax1.legend()"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "run_control": {
-     "frozen": false,
-     "read_only": false
-    }
-   },
+   "metadata": {},
    "source": [
-    "<div class=\"alert alert-info\">\n",
+    "<div class=\"alert alert-info\" style=\"font-size:18px\">\n",
     "\n",
-    " <b>Remember</b>: \n",
+    "**Remember** \n",
     "\n",
-    " <ul>\n",
-    "  <li>You can do anything with matplotlib, but at a cost... <a href=\"http://stackoverflow.com/questions/tagged/matplotlib\">stackoverflow</a></li>\n",
-    "      \n",
-    "  <li>The preformatting of Pandas provides mostly enough flexibility for quick analysis and draft reporting. It is not for paper-proof figures or customization</li>\n",
-    "</ul>\n",
-    "<br>\n",
-    "\n",
-    "If you take the time to make your perfect/spot-on/greatest-ever matplotlib-figure: Make it a <b>reusable function</b>!\n",
+    "`fig.savefig()` to save your Figure object!\n",
     "\n",
     "</div>"
    ]
@@ -987,20 +530,23 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "<div class=\"alert alert-info\" style=\"font-size:18px\">\n",
-    "\n",
-    " <b>Remember</b>: \n",
-    "\n",
-    "`fig.savefig()` to save your Figure object!\n",
-    "\n",
-    "</div>"
+    "# Need more matplotlib inspiration?"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Need more matplotlib inspiration? "
+    "<div class=\"alert alert-info\">\n",
+    "\n",
+    " <b>Remember</b>: \n",
+    "\n",
+    "* You can do anything with matplotlib, but at a cost... <a href=\"http://stackoverflow.com/questions/tagged/matplotlib\">stackoverflow</a>\n",
+    "* The preformatting of Pandas provides mostly enough flexibility for quick analysis and draft reporting. It is not for paper-proof figures or customization\n",
+    "\n",
+    "If you take the time to make your perfect/spot-on/greatest-ever matplotlib-figure: Make it a <b>reusable function</b>!\n",
+    "\n",
+    "</div>"
    ]
   },
   {
@@ -1023,7 +569,7 @@
     "\n",
     "Galleries are great to get inspiration, see the plot you want, and check the code how it is created:\n",
     "    \n",
-    "* [matplotlib gallery](http://matplotlib.org/gallery.html\") is an important resource to start from\n",
+    "* [matplotlib gallery](http://matplotlib.org/gallery.html) is an important resource to start from\n",
     "* [seaborn gallery](https://seaborn.pydata.org/examples/index.html)\n",
     "* The Python Graph Gallery (https://python-graph-gallery.com/)\n",
     "\n",
@@ -1032,8 +578,11 @@
   }
  ],
  "metadata": {
+  "jupytext": {
+   "default_lexer": "ipython3"
+  },
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -1047,7 +596,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.5"
+   "version": "3.12.11"
   },
   "nav_menu": {},
   "toc": {
@@ -1065,6 +614,13 @@
    "right": "1657px",
    "top": "106px",
    "width": "212px"
+  },
+  "widgets": {
+   "application/vnd.jupyter.widget-state+json": {
+    "state": {},
+    "version_major": 2,
+    "version_minor": 0
+   }
   }
  },
  "nbformat": 4,
diff --git a/_solved/visualization-02-geopandas.ipynb b/_solved/visualization-02-geopandas.ipynb
new file mode 100644
index 0000000..51ba139
--- /dev/null
+++ b/_solved/visualization-02-geopandas.ipynb
@@ -0,0 +1,415 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<p><font size=\"6\"><b> Visualizing spatial data with Python: GeoPandas</b></font></p>\n",
+    "\n",
+    "\n",
+    "> *DS Python for GIS and Geoscience*  \n",
+    "> *October, 2025*\n",
+    ">\n",
+    "> *© 2025, Joris Van den Bossche and Stijn Van Hoey. Licensed under [CC BY 4.0 Creative Commons](https://creativecommons.org/licenses/by/4.0/)*\n",
+    "\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import geopandas\n",
+    "\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "countries = geopandas.read_file(\"data/ne_110m_admin_0_countries.zip\")\n",
+    "countries = countries[countries['continent'] != \"Antarctica\"]\n",
+    "cities = geopandas.read_file(\"data/ne_110m_populated_places.zip\")\n",
+    "rivers = geopandas.read_file(\"data/ne_50m_rivers_lake_centerlines.zip\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## GeoPandas visualization functionality"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "GeoPandas itself provides some visualization functionality, and together with matplotlib for further customization, you can already get decent results for visualizing vector data."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Basic plot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "countries.plot()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-info\" style=\"font-size:120%\">\n",
+    "\n",
+    "**NOTE**: <br>\n",
+    "\n",
+    "Making a quick interactive plot is also available as the `.explore()` method on a GeoDataFrame or GeoSeries, using the Folium package. See the [visualization-04-interactive notebook](./visualization-04-interactive.ipynb) for more information on interactive plotting packages or check the [GeoPandas documentation](https://geopandas.org/en/stable/docs/user_guide/interactive_mapping.html) for more examples.\n",
+    "\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "countries.explore()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Adjusting the figure size"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "countries.plot(figsize=(15, 6))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Removing the box / x and y coordinate labels"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ax = countries.plot(figsize=(15, 6))\n",
+    "ax.set_axis_off()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Coloring based on column values\n",
+    "\n",
+    "Let's first create a new column with the GDP per capita:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "countries = countries[(countries['pop_est'] >0 ) & (countries['name'] != \"Antarctica\")]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "countries['gdp_per_cap'] = countries['gdp_md_est'] / countries['pop_est'] * 100"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "and now we can use this column to color the polygons:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ax = countries.plot(figsize=(15, 6), column='gdp_per_cap', legend=True)\n",
+    "ax.set_axis_off()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Using a classification scheme to bin the values (using [`mapclassify`](https://pysal.org/mapclassify/)):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ax = countries.plot(figsize=(15, 6), column='gdp_per_cap', scheme='quantiles', legend=True)\n",
+    "ax.set_axis_off()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Combining different dataframes on a single plot\n",
+    "\n",
+    "The `.plot` method returns a matplotlib Axes object, which can then be re-used to add additional layers to that plot with the `ax=` keyword:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ax = countries.plot(figsize=(15, 6))\n",
+    "cities.plot(ax=ax, color='red', markersize=10)\n",
+    "ax.set_axis_off()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ax = countries.plot(edgecolor='k', facecolor='none', figsize=(8, 8))\n",
+    "rivers.plot(ax=ax)\n",
+    "cities.plot(ax=ax, color='C1')\n",
+    "ax.set(xlim=(-20, 60), ylim=(-40, 40))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Adding a background map with contextily"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The contextily package allow to easily add a web-tile based background (basemap) to your GeoPandas plots.\n",
+    "\n",
+    "Currently, the only requirement is that your data is already in the WebMercator projection (EPSG:3857)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# selecting the cities in Europe\n",
+    "cities_europe = cities[cities.within(countries[countries['continent'] == 'Europe'].union_all())]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# converting to WebMercator\n",
+    "cities_europe2 = cities_europe.to_crs(epsg=3857)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ax = cities_europe2.plot()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import contextily"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# import matplotlib\n",
+    "# matplotlib.rcParams['figure.dpi'] = 300"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ax = cities_europe2.plot(figsize=(10, 6))\n",
+    "contextily.add_basemap(ax)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ax = cities_europe2.plot(figsize=(10, 6))\n",
+    "contextily.add_basemap(ax, source=contextily.providers.NASAGIBS.BlueMarble)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Projection-aware maps with Cartopy\n",
+    "\n",
+    "Cartopy is the base matplotlib cartographic library, and it is used by `geoplot` under the hood to provide projection-awareness (http://scitools.org.uk/cartopy/docs/latest/index.html).\n",
+    "\n",
+    "The following example is taken from the docs: http://geopandas.readthedocs.io/en/latest/gallery/cartopy_convert.html#sphx-glr-gallery-cartopy-convert-py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from cartopy import crs as ccrs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Define the CartoPy CRS object.\n",
+    "crs = ccrs.AlbersEqualArea()\n",
+    "\n",
+    "# This can be converted into a `proj4` string/dict compatible with GeoPandas\n",
+    "crs_proj4 = crs.proj4_init\n",
+    "countries_ae = countries.to_crs(crs_proj4)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Here's what the plot looks like in GeoPandas\n",
+    "countries_ae.plot()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Here's what the plot looks like when plotting with cartopy\n",
+    "fig, ax = plt.subplots(subplot_kw={'projection': crs})\n",
+    "ax.add_geometries(countries_ae['geometry'], crs=crs)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Here's what the plot looks like when plotting with cartopy and geopandas combined\n",
+    "fig, ax = plt.subplots(subplot_kw={'projection': crs})\n",
+    "countries_ae['geometry'].plot(ax=ax)\n",
+    "ax.set_global()\n",
+    "ax.gridlines()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**For more on cartopy**, see the [visualization-03-cartopy.ipynb](visualization-03-cartopy.ipynb) notebook."
+   ]
+  }
+ ],
+ "metadata": {
+  "jupytext": {
+   "default_lexer": "ipython3"
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.11"
+  },
+  "toc-autonumbering": false,
+  "toc-showcode": false,
+  "toc-showmarkdowntxt": false,
+  "toc-showtags": false,
+  "widgets": {
+   "application/vnd.jupyter.widget-state+json": {
+    "state": {},
+    "version_major": 2,
+    "version_minor": 0
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/_solved/visualization-03-cartopy.ipynb b/_solved/visualization-03-cartopy.ipynb
new file mode 100644
index 0000000..44955c0
--- /dev/null
+++ b/_solved/visualization-03-cartopy.ipynb
@@ -0,0 +1,797 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<p><font size=\"6\"><b>Creating maps with Cartopy</b></font></p>\n",
+    "\n",
+    "\n",
+    "> *DS Python for GIS and Geoscience*  \n",
+    "> *October, 2025*\n",
+    ">\n",
+    "> *© 2025, Joris Van den Bossche and Stijn Van Hoey. Licensed under [CC BY-SA 4.0 Creative Commons](https://creativecommons.org/licenses/by-sa/4.0/)* Adapted from material from Phil Elson and Ryan Abernathey (see below).\n",
+    "\n",
+    "---\n",
+    "\n",
+    "# Maps in Scientific Python\n",
+    "\n",
+    "Making maps is a fundamental part of GIS and geoscience research.\n",
+    "Maps differ from regular figures in the following principle ways:\n",
+    "\n",
+    "- Maps require a *projection* of geographic coordinates on the 3D Earth to the 2D space of your figure.\n",
+    "- Maps often include extra decorations besides just our data (e.g. continents, country borders, etc.)\n",
+    "\n",
+    "The maps we have made up to now, for example using the GeoPandas `plot()` method, assume the data can be plotted as is on the figure. This works fine if your data is already in projected coordinates, and has a limited extent (small study area). When mapping data of a larger area of the full globe, properly taking into account the projection becomes much more important!\n",
+    "\n",
+    "In this notebook, we will learn about [Cartopy](https://scitools.org.uk/cartopy/docs/latest/), one of the most common packages for making maps within python.\n",
+    "\n",
+    "### Credit: Phil Elson and Ryan Abernathey\n",
+    "\n",
+    "Lots of the material in this notebook was adopted from https://earth-env-data-science.github.io/intro.html by Ryan Abernathey, which itself was adopted from [Phil Elson](https://pelson.github.io/)'s excellent [Cartopy Tutorial](https://github.com/SciTools/cartopy-tutorial). Phil is the creator of Cartopy and published his tutorial under an [open license](http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/), meaning that we can copy, adapt, and redistribute it as long as we give proper attribution. **THANKS PHIL AND RYAN!** 👏👏👏"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Background: Projections"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Most of our media for visualization *are* flat\n",
+    "\n",
+    "Our two most common media are flat:\n",
+    "\n",
+    " * Paper\n",
+    " * Screen\n",
+    "\n",
+    "![](https://raw.githubusercontent.com/SciTools/cartopy-tutorial/master/static/flat_medium.jpg)\n",
+    "\n",
+    "### [Map] Projections: Taking us from spherical to flat\n",
+    "\n",
+    "A map projection (or more commonly refered to as just \"projection\") is:\n",
+    "\n",
+    "> a systematic transformation of the latitudes and longitudes of locations from the surface of a sphere or an ellipsoid into locations on a plane. [[Wikipedia: Map projection](https://en.wikipedia.org/wiki/Map_projection)].\n",
+    "\n",
+    "### The major problem with map projections\n",
+    "\n",
+    "![orange peel](https://raw.githubusercontent.com/SciTools/cartopy-tutorial/master/static/orange_peel.jpg)\n",
+    "\n",
+    " * The surface of a sphere is topologically different to a 2D surface, therefore we *have* to cut the sphere *somewhere*\n",
+    " * A sphere's surface cannot be represented on a plane without distortion.\n",
+    " \n",
+    "There are many different ways to make a projection, and we will not attempt to explain all of the choices and tradeoffs here. Instead, you can read Phil's [original tutorial](https://github.com/SciTools/cartopy-tutorial/blob/master/tutorial/projections_crs_and_terms.ipynb) for a great overview of this topic.\n",
+    "Instead, we will dive into the more practical sides of Caropy usage."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import xarray as xr"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Introducing Cartopy\n",
+    "\n",
+    "https://scitools.org.uk/cartopy/docs/latest/\n",
+    "\n",
+    "Cartopy makes use of the powerful [PROJ.4](https://proj4.org/), numpy and shapely libraries and includes a programatic interface built on top of Matplotlib for the creation of publication quality maps.\n",
+    "\n",
+    "Key features of cartopy are its object oriented projection definitions, and its ability to transform points, lines, vectors, polygons and images between those projections.\n",
+    "\n",
+    "### Cartopy Projections and other reference systems\n",
+    "\n",
+    "In Cartopy, each projection is a class.\n",
+    "Most classes of projection can be configured in projection-specific ways, although Cartopy takes an opinionated stance on sensible defaults.\n",
+    "\n",
+    "Let's create a Plate Carree projection instance.\n",
+    "\n",
+    "To do so, we need cartopy's crs module. This is typically imported as ``ccrs`` (Cartopy Coordinate Reference Systems)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import cartopy.crs as ccrs\n",
+    "import cartopy"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Cartopy's projection list tells us that the Plate Carree projection is available with the ``ccrs.PlateCarree`` class: https://scitools.org.uk/cartopy/docs/latest/reference/projections.html#cartopy-projections \n",
+    "\n",
+    "**Note:** we need to *instantiate* the class in order to do anything projection-y with it!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ccrs.PlateCarree()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Drawing a map\n",
+    "\n",
+    "Cartopy optionally depends upon matplotlib, and each projection knows how to create a matplotlib Axes (or AxesSubplot) that can represent itself.\n",
+    "\n",
+    "The Axes that the projection creates is a [cartopy.mpl.geoaxes.GeoAxes](https://scitools.org.uk/cartopy/docs/latest/matplotlib/geoaxes.html#cartopy.mpl.geoaxes.GeoAxes). This Axes subclass overrides some of matplotlib's existing methods, and adds a number of extremely useful ones for drawing maps:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "fig, ax = plt.subplots(subplot_kw={'projection': ccrs.PlateCarree()})\n",
+    "ax"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "That was a little underwhelming, but we can see that the Axes created is indeed one of those GeoAxes[Subplot] instances.\n",
+    "\n",
+    "One of the most useful methods that this class adds on top of the standard matplotlib Axes class is the ``coastlines`` method. With no arguments, it will add the Natural Earth ``1:110,000,000`` scale coastline data to the map."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig, ax = plt.subplots(subplot_kw={'projection': ccrs.PlateCarree()})\n",
+    "ax.coastlines()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Projection classes have options we can use to customize the map"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ccrs.PlateCarree?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig, ax = plt.subplots(subplot_kw={'projection': ccrs.PlateCarree(central_longitude=180)})\n",
+    "ax.coastlines()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Useful methods of a GeoAxes\n",
+    "\n",
+    "The [cartopy.mpl.geoaxes.GeoAxes](https://scitools.org.uk/cartopy/docs/latest/matplotlib/geoaxes.html#cartopy.mpl.geoaxes.GeoAxes) class adds a number of useful methods.\n",
+    "\n",
+    "Let's take a look at:\n",
+    "\n",
+    " * [set_global](https://scitools.org.uk/cartopy/docs/latest/matplotlib/geoaxes.html#cartopy.mpl.geoaxes.GeoAxes.set_global) - zoom the map out as much as possible\n",
+    " * [set_extent](https://scitools.org.uk/cartopy/docs/latest/matplotlib/geoaxes.html#cartopy.mpl.geoaxes.GeoAxes.set_extent) - zoom the map to the given bounding box\n",
+    " \n",
+    "\n",
+    " * [gridlines](https://scitools.org.uk/cartopy/docs/latest/matplotlib/geoaxes.html#cartopy.mpl.geoaxes.GeoAxes.graticule) - add a graticule (and optionally labels) to the axes\n",
+    " * [coastlines](https://scitools.org.uk/cartopy/docs/latest/matplotlib/geoaxes.html#cartopy.mpl.geoaxes.GeoAxes.coastlines) - add Natural Earth coastlines to the axes\n",
+    " * [stock_img](https://scitools.org.uk/cartopy/docs/latest/matplotlib/geoaxes.html#cartopy.mpl.geoaxes.GeoAxes.stock_img) - add a low-resolution Natural Earth background image to the axes\n",
+    " \n",
+    " \n",
+    " * [imshow](https://matplotlib.org/api/_as_gen/matplotlib.axes.Axes.imshow.html#matplotlib.axes.Axes.imshow) - add an image (numpy array) to the axes\n",
+    " * [add_geometries](https://scitools.org.uk/cartopy/docs/latest/matplotlib/geoaxes.html#cartopy.mpl.geoaxes.GeoAxes.add_geometries) - add a collection of geometries (Shapely / GeoPandas) to the axes\n",
+    " \n",
+    "### Some More Examples of Different Global Projections"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "projections = [ccrs.PlateCarree(),\n",
+    "               ccrs.Robinson(),\n",
+    "               ccrs.Mercator(),\n",
+    "               ccrs.Orthographic(),\n",
+    "               ccrs.InterruptedGoodeHomolosine()\n",
+    "              ]\n",
+    "\n",
+    "\n",
+    "for proj in projections:\n",
+    "    fig, ax = plt.subplots(subplot_kw={'projection': proj})\n",
+    "    ax.stock_img()\n",
+    "    ax.coastlines()\n",
+    "    ax.set_title(f'{type(proj)}')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Regional Maps\n",
+    "\n",
+    "To create a regional map, we use the `set_extent` method of GeoAxis to limit the size of the region."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ax.set_extent?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "central_lon, central_lat = -10, 45\n",
+    "extent = [-40, 20, 30, 60]\n",
+    "fig, ax = plt.subplots(subplot_kw={'projection': ccrs.Orthographic(central_lon, central_lat)})\n",
+    "ax.set_extent(extent)\n",
+    "ax.gridlines()\n",
+    "ax.coastlines(resolution='50m')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Adding features to the map\n",
+    "\n",
+    "To give our map more styles and details, we add `cartopy.feature` objects.\n",
+    "Many useful features are built in. These \"default features\" are at coarse (110m) resolution.\n",
+    "\n",
+    "Name | Description\n",
+    "-----|------------\n",
+    "`cartopy.feature.BORDERS` | Country boundaries\n",
+    "`cartopy.feature.COASTLINE` | Coastline, including major islands\n",
+    "`cartopy.feature.LAKES` | Natural and artificial lakes\n",
+    "`cartopy.feature.LAND` | Land polygons, including major islands\n",
+    "`cartopy.feature.OCEAN` | Ocean polygons\n",
+    "`cartopy.feature.RIVERS` | Single-line drainages, including lake centerlines\n",
+    "`cartopy.feature.STATES` | (limited to the United States at this scale)\n",
+    "\n",
+    "Below we illustrate these features in a customized map of North America."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import cartopy.feature as cfeature\n",
+    "import numpy as np\n",
+    "\n",
+    "central_lat = 37.5\n",
+    "central_lon = -96\n",
+    "extent = [-120, -70, 24, 50.5]\n",
+    "central_lon = np.mean(extent[:2])\n",
+    "central_lat = np.mean(extent[2:])\n",
+    "\n",
+    "fig, ax = plt.subplots(subplot_kw={'projection': ccrs.AlbersEqualArea(central_lon, central_lat)}, \n",
+    "                       figsize=(12, 6))\n",
+    "ax.set_extent(extent)\n",
+    "\n",
+    "ax.add_feature(cartopy.feature.OCEAN)\n",
+    "ax.add_feature(cartopy.feature.LAND, edgecolor='black')\n",
+    "ax.add_feature(cartopy.feature.LAKES, edgecolor='black')\n",
+    "ax.add_feature(cartopy.feature.RIVERS)\n",
+    "ax.gridlines()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If we want higher-resolution features, Cartopy can automatically download and create them from the [Natural Earth Data](http://www.naturalearthdata.com/) database or the [GSHHS dataset](https://www.ngdc.noaa.gov/mgg/shorelines/gshhs.html) database."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Adding vector data to the map\n",
+    "\n",
+    "You will typically have your own data to add to a map, of course. To add vector data, we can add single Shapely geometries with `ax.add_geometries()`, or combine with GeoPandas.\n",
+    "\n",
+    "The GeoPandas `plot()` method works *if*:\n",
+    "\n",
+    "* you pass the GeoAxes object to the `ax=` keyword\n",
+    "* the data is in the same CRS as the cartopy projection OR you specify the corresponding `transform=` in the `plot()` method."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import geopandas\n",
+    "cities = geopandas.read_file(\"data/ne_110m_populated_places.zip\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "proj = ccrs.AlbersEqualArea()\n",
+    "\n",
+    "fig, ax = plt.subplots(figsize=(10, 10), subplot_kw={'projection': proj})\n",
+    "# Reproject the cities dataset to the corresponding CRS of the cartopy projection\n",
+    "cities_albers = cities.to_crs(proj.proj4_init)\n",
+    "cities_albers.plot(ax=ax)\n",
+    "ax.coastlines()\n",
+    "ax.set_global()\n",
+    "ax.gridlines(draw_labels=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "proj = ccrs.AlbersEqualArea()\n",
+    "\n",
+    "fig, ax = plt.subplots(figsize=(10, 10), subplot_kw={'projection': proj})\n",
+    "# cities is in geographic lat/lon data, so we can specify PlateCarree as the cartopy equivalent\n",
+    "cities.plot(ax=ax, transform=ccrs.PlateCarree())\n",
+    "ax.coastlines()\n",
+    "ax.set_global()\n",
+    "ax.gridlines(draw_labels=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Or we can also plot specific points:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "proj = ccrs.AlbersEqualArea()\n",
+    "\n",
+    "fig, ax = plt.subplots(figsize=(10, 10), subplot_kw={'projection': proj})\n",
+    "ax.plot(0, 0, marker='o', color='red', markersize=12, transform=ccrs.PlateCarree())\n",
+    "ax.coastlines()\n",
+    "ax.set_global()\n",
+    "ax.gridlines()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Because our map is a matplotlib axis, we can use all the familiar matplotlib commands to make plots.\n",
+    "By default, the map extent will be adjusted to match the data. We can override this with the `.set_global` or `.set_extent` commands.\n",
+    "\n",
+    "Another example to show the difference of adding a transform or not:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# create some test data\n",
+    "new_york = dict(lon=-74.0060, lat=40.7128)\n",
+    "honolulu = dict(lon=-157.8583, lat=21.3069)\n",
+    "lons = [new_york['lon'], honolulu['lon']]\n",
+    "lats = [new_york['lat'], honolulu['lat']]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Key point: **the data also have to be transformed to the projection space**.\n",
+    "This is done via the `transform=` keyword in the plotting method. The argument is another `cartopy.crs` object.\n",
+    "If you don't specify a transform, Cartopy assume that the data is using the same projection as the underlying GeoAxis.\n",
+    "\n",
+    "From the [Cartopy Documentation](https://scitools.org.uk/cartopy/docs/latest/tutorials/understanding_transform.html)\n",
+    "\n",
+    "> The core concept is that the projection of your axes is independent of the coordinate system your data is defined in. The `projection` argument is used when creating plots and determines the projection of the resulting plot (i.e. what the plot looks like). The `transform` argument to plotting functions tells Cartopy what coordinate system your data are defined in."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig, ax = plt.subplots(subplot_kw={'projection': ccrs.PlateCarree()})\n",
+    "ax.plot(lons, lats, label='Equirectangular straight line')\n",
+    "ax.plot(lons, lats, label='Great Circle', transform=ccrs.Geodetic())\n",
+    "ax.coastlines()\n",
+    "ax.legend()\n",
+    "ax.set_global()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Adding raster data to the map\n",
+    "\n",
+    "The same principles apply to 2D data. Below we create some example data defined in regular lat / lon coordinates."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "lon = np.linspace(-80, 80, 25)\n",
+    "lat = np.linspace(30, 70, 25)\n",
+    "lon2d, lat2d = np.meshgrid(lon, lat)\n",
+    "data = np.cos(np.deg2rad(lat2d) * 4) + np.sin(np.deg2rad(lon2d) * 4)\n",
+    "plt.contourf(lon2d, lat2d, data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we create a `PlateCarree` projection and plot the data on it without any `transform` keyword.\n",
+    "This happens to work because `PlateCarree` is the simplest projection of lat / lon data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig, ax = plt.subplots(subplot_kw={'projection': ccrs.PlateCarree()})\n",
+    "ax.set_global()\n",
+    "ax.coastlines()\n",
+    "ax.contourf(lon, lat, data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "However, if we try the same thing with a different projection, we get the wrong result."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "projection = ccrs.RotatedPole(pole_longitude=-177.5, pole_latitude=37.5)\n",
+    "fig, ax = plt.subplots(subplot_kw={'projection': projection})\n",
+    "ax.set_global()\n",
+    "ax.coastlines()\n",
+    "ax.contourf(lon, lat, data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To fix this, we need to pass the correct transform argument to `contourf`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "projection = ccrs.RotatedPole(pole_longitude=-177.5, pole_latitude=37.5)\n",
+    "fig, ax = plt.subplots(subplot_kw={'projection': projection})\n",
+    "ax.set_global()\n",
+    "ax.coastlines()\n",
+    "ax.contourf(lon, lat, data, transform=ccrs.PlateCarree())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Xarray Integration\n",
+    "\n",
+    "Cartopy transforms can be passed to xarray! This creates a very quick path for creating professional looking maps from netCDF data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from urllib.request import urlretrieve\n",
+    "\n",
+    "file_path_data_nc = \"./data/20190101120000-REMSS-L4_GHRSST-SSTfnd-MW_IR_OI-GLOB-v02.0-fv05.0.nc\"\n",
+    "urlretrieve(\"https://data.remss.com/SST/daily/mw_ir/v05.0/netcdf/2019/20190101120000-REMSS-L4_GHRSST-SSTfnd-MW_IR_OI-GLOB-v02.0-fv05.0.nc\", file_path_data_nc)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import xarray as xr\n",
+    "ds = xr.open_dataset(file_path_data_nc)\n",
+    "ds"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sst = ds[\"analysed_sst\"]\n",
+    "\n",
+    "fig, ax = plt.subplots(subplot_kw={'projection': ccrs.Robinson()}, figsize=(9,6))\n",
+    "ax.coastlines()\n",
+    "ax.gridlines()\n",
+    "sst.plot(ax=ax, transform=ccrs.PlateCarree(), \n",
+    "         cbar_kwargs={'shrink': 0.4}, cmap=\"RdBu_r\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Add OGC WMS and WMTS services to cartopy"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A lot of online map services are available, which are capable of providing background layers to a map similar as these are used in online applications. The [OGC WMS/WMTS](https://www.ogc.org/standards/wms) (Web Map Service) are important standards by which a lot of map layers are made available.\n",
+    "\n",
+    "A (non-official, but very comprehensive) list of such services for Belgium is made available by Michel Stuyts, see https://wms.michelstuyts.be. It is always a matter of finding the correct URL and the `layer_name` for a given service.\n",
+    "\n",
+    "As an example, consider the Orthophoto WMS (provided by the National Geographic Institute):\n",
+    "\n",
+    "- WMS: https://wms.ngi.be/inspire/ortho/service\n",
+    "- GetCapabilities: https://wms.ngi.be/inspire/ortho/service?REQUEST=GetCapabilities&SERVICE=WMS\n",
+    "\n",
+    "With the following layers available (see website Stuyts or GetCapabilities overview):\n",
+    "\n",
+    "- orthoimage_coverage: Orthoimage coverage\n",
+    "- orthoimage_coverage_2016: Orthoimage coverage - 2016\n",
+    "- orthoimage_coverage_2018: Orthoimage coverage - 2018\n",
+    "\n",
+    "Let's use the `orthoimage_coverage` in combination with our city of Ghent contours data set (__Note__ the WMS service might sometimes return server errors so retry if required):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "base_uri = 'https://wms.ngi.be/inspire/ortho/service'\n",
+    "layer_name = 'orthoimage_coverage'\n",
+    "\n",
+    "plain_crs = ccrs.Mercator()\n",
+    "fig, ax = plt.subplots(subplot_kw={\"projection\": plain_crs}, figsize=(10, 10))\n",
+    "ax.set_extent([3.57, 3.85, 50.98, 51.19], crs=ccrs.PlateCarree())\n",
+    "\n",
+    "# Add WMS imaging.\n",
+    "ax.add_wms(base_uri, layers=layer_name)\n",
+    "\n",
+    "# Add city of Ghent boundary from file\n",
+    "gent = geopandas.read_file(\"./data/gent/vector/gent.geojson\")\n",
+    "gent = gent.to_crs(plain_crs.proj4_init)  # adjust to projection\n",
+    "gent.plot(ax=ax, facecolor=\"none\", edgecolor=\"white\", linewidth=2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Someone interested in flood control, might want to know the sensitive areas for inundation in this area. Looking in the available services, https://inspirepub.waterinfo.be/arcgis/rest/services/watertoets/watertoets_pluviaal/MapServer/WMTS/1.0.0/WMTSCapabilities.xml has the available layer `watertoets_watertoets_pluviaal` (dutch for rain-induced zones).\n",
+    "\n",
+    "Hence, we can combine this WMTS layer into the map as well:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plain_crs = ccrs.Mercator()\n",
+    "fig, ax = plt.subplots(subplot_kw={\"projection\": plain_crs}, figsize=(10, 10))\n",
+    "ax.set_extent([3.57, 3.85, 50.98, 51.19], crs=ccrs.PlateCarree())\n",
+    "\n",
+    "# Add WMS imaging of ortophoto.\n",
+    "base_uri = 'https://wms.ngi.be/inspire/ortho/service'\n",
+    "layer_name = 'orthoimage_coverage'\n",
+    "ax.add_wms(base_uri, layers=layer_name)\n",
+    "\n",
+    "# Add WMTS of sensitive areas for inundation.\n",
+    "base_uri = \"https://inspirepub.waterinfo.be/arcgis/rest/services/watertoets/watertoets_fluviaal/MapServer/WMTS\"\n",
+    "layer_name = 'watertoets_watertoets_fluviaal'\n",
+    "ax.add_wmts(base_uri, layer_name=layer_name, alpha=0.8)\n",
+    "\n",
+    "# Add city of Ghent boundary from file\n",
+    "gent = geopandas.read_file(\"./data/gent/vector/gent.geojson\")\n",
+    "gent = gent.to_crs(plain_crs.proj4_init)  # adjust to projection\n",
+    "gent.plot(ax=ax, facecolor=\"none\", edgecolor=\"#433d78\", linewidth=2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### (optional) How the add a satellite image manually\n",
+    "\n",
+    "When a (satellite) image does not contain the spatial information, we can still plot the image on a map if we know its extent"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from urllib.request import urlretrieve"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "file_path_data_rgb = \"./data/RGB.byte.tif\"\n",
+    "urlretrieve(\"https://github.com/rasterio/rasterio/raw/main/tests/data/RGB.byte.tif\", file_path_data_rgb)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Read a tiff file as an image without spatial context to illustrate the concept:\n",
+    "da = xr.open_dataarray(file_path_data_rgb, engine=\"rasterio\", mask_and_scale=False)\n",
+    "\n",
+    "# DataArray as a 3-D array/image as used for viz-libraries (channel dim at the end)\n",
+    "img = np.transpose(da.data, [1, 2, 0])\n",
+    "\n",
+    "# extract the extent from the bounds\n",
+    "(left, bottom, right, top) = da.rio.bounds()\n",
+    "img_extent = (left, right, bottom, top)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "proj = ccrs.UTM(zone=18, southern_hemisphere=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "By providing the `extent` and the `proj` information to Matplotlib/Cartopy, the image can be visualized correctly:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(8, 12), subplot_kw={'projection': proj})\n",
+    "\n",
+    "ax.imshow(img, extent=img_extent, transform=proj)\n",
+    "ax.coastlines(resolution='10m', color='red', linewidth=1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "__Note__ This is what xarray is doing under the hood to pass rio/spatial arrays to Matplotlib;"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Doing More\n",
+    "\n",
+    "Browse the [Cartopy Gallery](https://cartopy.readthedocs.io/stable/gallery/index.html) to learn about all the different types of data and plotting methods available!"
+   ]
+  }
+ ],
+ "metadata": {
+  "jupytext": {
+   "default_lexer": "ipython3"
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.11"
+  },
+  "widgets": {
+   "application/vnd.jupyter.widget-state+json": {
+    "state": {},
+    "version_major": 2,
+    "version_minor": 0
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/_solved/visualization-04-interactive.ipynb b/_solved/visualization-04-interactive.ipynb
new file mode 100644
index 0000000..70835a0
--- /dev/null
+++ b/_solved/visualization-04-interactive.ipynb
@@ -0,0 +1,390 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<p><font size=\"6\"><b>Interactive or web-based visualization</b></font></p>\n",
+    "\n",
+    "\n",
+    "> *DS Python for GIS and Geoscience*  \n",
+    "> *October, 2025*\n",
+    ">\n",
+    "> *© 2025, Joris Van den Bossche and Stijn Van Hoey. Licensed under [CC BY 4.0 Creative Commons](https://creativecommons.org/licenses/by/4.0/)*\n",
+    "\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In the course, we used the Matplotlib library for all visualizations. This is the library that is most widely used and integrated into the other packages (e.g. the plotting functionality of pandas, geopandas, xarray, rasterio, etc all use matplotlib), and is still the \"go to\" library for making high-quality, publication graphics. \n",
+    "\n",
+    "There are, however, a bunch of alternatives to matplotlib, mainly focusing on providing more interactivity and based on web (javascript) tools. To many such packages exist to cover in this course, but we list here a few that can handle (to some extent) geospatial data:\n",
+    "\n",
+    "- Bokeh: https://bokeh.pydata.org/en/latest/docs/gallery/texas.html\n",
+    "- GeoViews (other interface to Bokeh/matplotlib): http://geo.holoviews.org\n",
+    "- Altair: https://altair-viz.github.io/gallery/index.html#maps\n",
+    "- Plotly: https://plot.ly/python/#maps\n",
+    "- ...\n",
+    "\n",
+    "\n",
+    "Altair and Plotly are mostly useful for vector data. Using bokeh through holoviews with raster data is shown below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import geopandas\n",
+    "\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "countries = geopandas.read_file(\"data/ne_110m_admin_0_countries.zip\")\n",
+    "countries = countries[(countries['pop_est'] >0 ) & (countries['name'] != \"Antarctica\")]\n",
+    "countries['gdp_per_cap'] = countries['gdp_md_est'] / countries['pop_est'] * 100\n",
+    "cities = geopandas.read_file(\"data/ne_110m_populated_places.zip\")\n",
+    "rivers = geopandas.read_file(\"data/ne_50m_rivers_lake_centerlines.zip\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Vector data with Leaflet"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Another popular javascript library for online maps is [Leaflet.js](https://leafletjs.com/), and this has python bindings in the [folium](https://github.com/python-visualization/folium) and [ipyleaflet](https://github.com/jupyter-widgets/ipyleaflet) packages."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "An example with ipyleaflet:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import ipyleaflet"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "m = ipyleaflet.Map(center=[48.8566, 2.3429], zoom=6)\n",
+    "\n",
+    "layer = ipyleaflet.GeoJSON(data=cities.__geo_interface__)\n",
+    "m.add_layer(layer)\n",
+    "m"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "m = ipyleaflet.Map(center=[48.8566, 2.3429], zoom=3)\n",
+    "geo_data = ipyleaflet.GeoData(\n",
+    "    geo_dataframe = countries,\n",
+    "    style={'color': 'black', 'fillColor': '#3366cc', 'opacity':0.05, 'weight':1.9, 'dashArray':'2', 'fillOpacity':0.6},\n",
+    "    hover_style={'fillColor': 'red' , 'fillOpacity': 0.2},\n",
+    "    name = 'Countries')\n",
+    "m.add_layer(geo_data)\n",
+    "m"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "More: https://ipyleaflet.readthedocs.io/en/latest/api_reference/geodata.html"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "An example with folium:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import folium"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "m = folium.Map([48.8566, 2.3429], zoom_start=6, tiles=\"OpenStreetMap\")\n",
+    "folium.GeoJson(countries).add_to(m)\n",
+    "folium.GeoJson(cities).add_to(m)\n",
+    "m"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "m = folium.Map([0, 0], zoom_start=1)\n",
+    "folium.Choropleth(geo_data=countries, data=countries, columns=['iso_a3', 'gdp_per_cap'],\n",
+    "             key_on='feature.properties.iso_a3', fill_color='BuGn', highlight=True).add_to(m)\n",
+    "m"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-info\" style=\"font-size:120%\">\n",
+    "\n",
+    "**NOTE**: <br>\n",
+    "\n",
+    "Making a quick interactive plot is also available as the `.explore()` method on a GeoDataFrame or GeoSeries, using the Folium package. See the [visualization-04-interactive notebook](./visualization-04-interactive.ipynb) for more information on interactive plotting packages or check the [GeoPandas documentation](https://geopandas.org/en/stable/docs/user_guide/interactive_mapping.html) for more examples.\n",
+    "\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Vector data with Lonboard"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Lonboard is a Python library for fast, interactive geospatial vector data visualization in Jupyter. Compared to Leaflet (shown above), it is especially useful when you want to explore larger datasets.\n",
+    "\n",
+    "https://developmentseed.org/lonboard/latest/"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import lonboard"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lonboard.viz(cities)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Using Holoviews for raster data with xarray"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The Holoviews project (http://holoviews.org/) is a set of packages built on top of Bokeh, and which integrate with many of the other packages (pandas, GeoPandas, xarray) through the `hvplot` package (https://hvplot.holoviz.org/) and [GeoViews](https://geoviews.org/).\n",
+    "\n",
+    "Using the Gent example Sentinel data to showcase hvplot integration with xarray:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import xarray as xr\n",
+    "\n",
+    "import hvplot.xarray\n",
+    "import cartopy.crs as ccrs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gent = xr.open_dataarray(\"./data/gent/raster/2020-09-17_Sentinel_2_L1C_B0408.tiff\", engine=\"rasterio\", mask_and_scale=False)\n",
+    "gent = gent.assign_coords(band=(\"band\", [\"b4\", \"b8\"]))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Using the default xarray Matplotlib integration with the `.plot.imshow` method:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gent.sel(band=\"b4\").plot.imshow(robust=True, cmap=\"summer\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The equivalent method in hvplot is `.hvplot.image` (and linking the coordinate names):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gent.sel(band=\"b4\").hvplot.image(x=\"x\", y=\"y\", cmap=\"summer\", \n",
+    "                                 clim=(0.05, 0.2))  # 'robust' option is not available"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "hvplot/geoviews translates multiple dimensions (in this case the bands) automatically in a drop-down menu:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gent.hvplot.image(x=\"x\", y=\"y\", cmap=\"summer\", # groupby=\"band\" # adding groupby makes this explicit instead of 'automagically'\n",
+    "                  clim=(0.05, 0.2))  # 'robust' option is not available"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Similar to matplotlib plots using cartopy (see notebook `visualization-03-cartopy.ipynb`), the CRS information can be used.\n",
+    "\n",
+    "From https://hvplot.holoviz.org/user_guide/Geographic_Data.html#declaring-an-output-projection:\n",
+    "\n",
+    "> The `crs=` argument specifies the input projection, i.e. it declares how to interpret the incoming data values. You can independently choose any output projection, i.e. how you want to map the data points onto the screen for display, using the `projection=` argument."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gent.rio._crs = False  # small bug in hvplot's handling of rioxarray crs\n",
+    "gent.hvplot.image(x=\"x\", y=\"y\", cmap=\"summer\", \n",
+    "                  frame_height=400, \n",
+    "                  crs=ccrs.epsg(3857),\n",
+    "                  projection=ccrs.UTM(zone=31), \n",
+    "                  project=True, geo=True,\n",
+    "                  clim=(0.05, 0.2))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "From https://hvplot.holoviz.org/user_guide/Geographic_Data.html#declaring-an-output-projection:\n",
+    "\n",
+    "> Note that when displaying raster data in a projection other than the one in which the data is stored, it is more accurate to render it as a quadmesh rather than an image. As you can see above, a QuadMesh will project each original bin or pixel into the correct non-rectangular shape determined by the projection, accurately showing the geographic extent covered by each sample. An Image, on the other hand, will always be rectangularly aligned in the 2D plane, which requires warping and resampling the data in a way that allows efficient display but loses accuracy at the pixel level. Unfortunately, rendering a large QuadMesh using Bokeh can be very slow"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gent.hvplot.quadmesh(x=\"x\", y=\"y\", cmap=\"summer\", \n",
+    "                     frame_height=400, \n",
+    "                     crs=ccrs.epsg(3857), \n",
+    "                     projection=ccrs.UTM(zone=31), \n",
+    "                     project=True, geo=True,\n",
+    "                     clim=(0.05, 0.2))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "See https://hvplot.holoviz.org/user_guide/Geographic_Data.html#declaring-an-output-projection for more options."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "jupytext": {
+   "default_lexer": "ipython3"
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/check_environment.py b/check_environment.py
index cd471ac..e6818e5 100644
--- a/check_environment.py
+++ b/check_environment.py
@@ -2,11 +2,13 @@
 # https://github.com/amueller/scipy-2018-sklearn/blob/master/check_env.ipynb
 # and glemaitre: https://github.com/glemaitre/pyparis-2018-sklearn/blob/master/check_environment.py
 
-from __future__ import print_function
-from distutils.version import LooseVersion as Version
 import sys
 
 
+# packaging is not in the stdlib, but should be available as dependency of
+# some other package (eg jupyterlab, geopandas, ..)
+from packaging import version
+
 try:
     import curses
     curses.setupterm()
@@ -28,17 +30,15 @@ def import_version(pkg, min_ver, fail_msg=""):
     mod = None
     try:
         mod = importlib.import_module(pkg)
-        if pkg in {'PIL'}:
-            ver = mod.VERSION
-        elif pkg in {'xlrd'}:
-            ver = mod.__VERSION__
-        else:
+        if min_ver:
             ver = mod.__version__
-        if Version(ver) < min_ver:
-            print(FAIL, "%s version %s or higher required, but %s installed."
-                  % (lib, min_ver, ver))
+            if version.parse(ver) < version.parse(min_ver):
+                print(FAIL, "%s version %s or higher required, but %s installed."
+                    % (lib, min_ver, ver))
+            else:
+                print(OK, '%s version %s' % (pkg, ver))
         else:
-            print(OK, '%s version %s' % (pkg, ver))
+            print(OK, f"{pkg}")
     except ImportError:
         print(FAIL, '%s not installed. %s' % (pkg, fail_msg))
     return mod
@@ -47,20 +47,21 @@ def import_version(pkg, min_ver, fail_msg=""):
 # first check the python version
 print('Using python in', sys.prefix)
 print(sys.version)
-pyversion = Version(sys.version)
-if pyversion >= "3":
-    if pyversion < "3.6":
-        print(FAIL, "Python version 3.6 is required,"
+pyversion = version.parse(sys.version.split(" ")[0])
+if pyversion >= version.parse("3"):
+    if pyversion < version.parse("3.10"):
+        print(FAIL, "Python version 3.10 is required,"
                     " but %s is installed." % sys.version)
 else:
     print(FAIL, "Python 3 is required, but %s is installed." % sys.version)
 
 print()
 requirements = {'numpy': "1.9", 'matplotlib': "2.0",
-                'pandas': "1.1", 'xarray': '0.16',
-                'geopandas': '0.8', 'rasterio': '1.1',
+                'pandas': "2.0", 'xarray': '0.16',
+                'geopandas': '1.0', 'rasterio': '1.1',
                 'owslib': '0.19', 'fsspec': '0.8',
-                's3fs': '0.3', 'pyproj': '2.4'}
+                's3fs': '0.3', 'pyproj': '2.4',
+                'hvplot.xarray': None}
 
 # now the dependencies
 for lib, required_version in list(requirements.items()):
diff --git a/docs/_config.yml b/docs/_config.yml
new file mode 100644
index 0000000..ba7bb43
--- /dev/null
+++ b/docs/_config.yml
@@ -0,0 +1,5 @@
+title: Python for GIS and Geoscience
+logo:
+description: Doctoral schools of Ghent University
+show_downloads: true
+theme: jekyll-theme-minimal
\ No newline at end of file
diff --git a/CONTRIBUTING.md b/docs/contributing.md
old mode 100755
new mode 100644
similarity index 50%
rename from CONTRIBUTING.md
rename to docs/contributing.md
index 75b1233..cf50d20
--- a/CONTRIBUTING.md
+++ b/docs/contributing.md
@@ -16,17 +16,17 @@ Think this course is useful? Let others discover it, by telling them in person,
 
 ### Ask a question ⁉️
 
-Trying out the material and got stuck? Post your question as an [issue on GitHub](https://github.com/jorisvandenbossche/DS-python-geospatial/issues/new). While we cannot offer user support, we'll try to do our best to address it, as questions often lead to the discovery of bugs.
+Trying out the material and got stuck? Post your question as an [issue on GitHub](https://github.com/plovercode/course-python-geospatial/issues). While we cannot offer user support, we'll try to do our best to address it, as questions often lead to the discovery of bugs.
 
-Want to ask a question in private? Contact the package maintainer by [email](jorisvandenbossche@gmail.com).
+Want to ask a question in private? Contact the course maintainer by [email](jorisvandenbossche@gmail.com).
 
 ### Propose an idea 💡
 
-Have an idea for to improve the course? Take a look at the [issue list](https://github.com/jorisvandenbossche/DS-python-geospatial/issues) to see if it isn't included or suggested yet. If not, suggest your idea as an [issue on GitHub](https://github.com/jorisvandenbossche/DS-python-geospatial/issues/new).
+Have an idea for to improve the course? Take a look at the [issue list](https://github.com/plovercode/course-python-geospatial/issues) to see if it isn't included or suggested yet. If not, suggest your idea as an [issue on GitHub](https://github.com/plovercode/course-python-geospatial/issues/new).
 
 ### Report a bug 🐛
 
-Using the course and discovered a bug or a typo? That's annoying! Don't let others have the same experience and report it as an [issue on GitHub](https://github.com/jorisvandenbossche/DS-python-geospatial/issues/new) so we can fix it. A good bug report makes it easier for us to do so, so please include:
+Using the course and discovered a bug or a typo? That's annoying! Don't let others have the same experience and report it as an [issue on GitHub](https://github.com/plovercode/DS-python-geospatial/issues) so we can fix it. A good bug report makes it easier for us to do so, so please include:
 
 * Your operating system name and version (e.g. Mac OS 10.13.6).
 * Any details about your local setup that might be helpful in troubleshooting.
@@ -38,9 +38,8 @@ Care to fix issues or typo's? Awesome! 👏
 
 Some notes to take into account:
 
-- When updating course material, **edit the 'solved' notebooks**, the other ones (the ones used in the tutorial) are generated automatically using the `convert_notebooks.sh` script.
+- The course material is developed in the [course-python-geospatial](https://github.com/plovercode/course-python-geospatial) repository. When updating course material, edit the notebooks in the [course-python-geospatial](https://github.com/plovercode/course-python-geospatial) repository, the other ones (the ones used in the tutorial) are generated automatically.
 - the exercises are cleared using the `nbtutor` notebook extension: <https://github.com/jorisvandenbossche/nbtutor>
 
 
 
-
diff --git a/docs/index.md b/docs/index.md
new file mode 100644
index 0000000..f753d84
--- /dev/null
+++ b/docs/index.md
@@ -0,0 +1,52 @@
+# Python for GIS and Geoscience
+
+## Introduction
+
+
+An important aspect of daily work in geographic information science and earth sciences is the handling of potentially large amounts of data. Reading in spatial data, exploring the data, creating visualisations and preparing the data for further analysis may become tedious tasks. Hence, increasing efficiency and reproducibility in this process without the need of a GUI interface is beneficial for many scientists. The usage of high-level scripting languages such as R and Python are increasingly popular for these tasks thanks to the development of GIS oriented packages.
+
+This course trains students to use Python effectively to do these tasks, with a focus on geospatial data. It covers both vector and raster data. The course focuses on introducing the main Python packages for handling such data (GeoPandas and Xarray) and how to use those packages for importing, exploring, visualizing and manipulating geospatial data. It is the aim to give the students an understanding of the data structures used in Python to represent geospatial data (geospatial dataframes, (multi-dimensional) arrays and composite netCDF-like multi-dimensional datasets), while also providing pointers to the broader ecosystem of Python packages for GIS and geosciences.
+
+The course has been developed as a course for the Doctoral schools of Ghent University, but can be taught to others upon request.
+
+## Course info
+
+### Aim & scope
+
+This course targets researchers that want to enhance their general data manipulation, explorative visualisation and analysis skills in Python specifically for handling geospatial data.
+
+The course does not aim to provide a course in specific spatial analysis and statistics, cartography, remote sensing, OGC web services or general Geographical Information Management (GIS). It aims to provide researchers the means to effectively tackle commonly encountered spatial data handling tasks in order to increase the overall efficiency. The course does not tackle desktop GIS Python extensions such as arcpy or pyqgis.
+
+### Program
+
+After setting up the programming environment with the required packages using the conda package manager and an introduction of the Jupyter notebook environment, the vector data analysis package GeoPandas is introduced. Next, more advanced features of GeoPandas for spatial joins and overlays are explained.
+
+For working with raster data, the Xarray package is introduced. Next, the combination of raster and vector data and more advanced plotting are taught. The acquired skills will immediately be brought into practice to handle real-world data sets.
+
+In the last part, the use of xarray for spatial data cubes (netcdf-like), out-of-memory raster data  and interactive visualisation of spatial data is tackled. Participants work on case studies to apply the learned skills of the full workshop. The course closes with an overview of other packages that are being used in the geospatial Python ecosystem (other visualization frameworks, specialized GIS oriented packages).
+
+## Getting started
+
+The course uses Python 3 and some data analysis packages such as Pandas, Numpy and Matplotlib and geospatial packages such as GeoPandas and Xarray. To install the required libraries, we recommend Miniconda or another Python distribution that
+includes the scientific libraries (this recommendation applies to all platforms, so for both Window, Linux and Mac).
+
+For detailed instructions to get started on your local machine, see the [setup instructions](./setup.html).
+
+In case you do not want to install everything and just want to try out the course material, use the environment setup by
+Binder [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/plovercode/DS-python-geospatial/HEAD) and open de notebooks
+rightaway (inside the `notebooks` directory).
+
+## Slides
+
+For the course slides, click [here](https://plovercode.github.io/DS-python-geospatial/slides.html).
+
+## Contributing
+
+Found any typo or have a suggestion, see [how to contribute](./contributing.html).
+
+## Meta
+Authors: Joris Van den Bossche, Stijn Van Hoey
+
+<img src="./static/img/logo_flanders+richtingmorgen.png" width="79%">
+<img src="./static/img/doctoralschoolsprofiel_hq_rgb_web.png" width="20%">
+
diff --git a/docs/setup.md b/docs/setup.md
new file mode 100644
index 0000000..9eb0e3c
--- /dev/null
+++ b/docs/setup.md
@@ -0,0 +1,206 @@
+---
+layout: default
+---
+
+# Course setup
+
+To get started, you should have the following three elements setup:
+
+1. Download the course material to your computer
+2. Install Python and the required Python packages using `conda`
+3. Test your configuration and installation
+4. Start Jupyter lab
+
+In the following sections, more details are provided for each of these steps. When all three are done, you are ready to start coding!
+
+## 1. Getting the course materials
+
+### Option 1: You are already a git user
+
+As the course has been set up as a [git](https://git-scm.com/) repository managed on [Github](https://github.com/plovercode/DS-python-geospatial),
+you can clone the entire course to your local machine. Use the command line to clone the repository and go into the course folder:
+
+```
+git clone https://github.com/plovercode/DS-python-geospatial.git
+cd DS-python-geospatial
+```
+
+In case you would prefer using Github Desktop,
+see [this tutorial](https://help.github.com/desktop/guides/contributing-to-projects/cloning-a-repository-from-github-to-github-desktop/).
+
+### Option 2: You are not a git user
+
+To download the repository to your local machine as a zip-file, click the  `download ZIP` on the
+repository page <https://github.com/plovercode/DS-python-geospatial> (green button "Code"):
+
+![Download button](./static/img/download-button.png)
+
+After the download, unzip on the location you prefer within your user account (e.g. `My Documents`, not `C:\`). Watch out for a nested 'DS-python-geospatial/DS-python-geospatial' folder structure after unzipping and move the inner DS-python-geospatial folder to your preferred location.
+
+__Note:__ Make sure you know where you stored the course material, e.g. `C:/Users/yourusername/Documents/DS-python-geospatial`.
+
+## 2. Install Python and the required Python packages using `conda`
+
+For scientific and data analysis, we recommend to use `conda`, a command line
+tool for package and environment management (<https://docs.conda.io/projects/conda/>).
+`conda` allows us to install a Python distribution with the the scientific libraries
+we will use in this course (this recommendation applies to all
+platforms, so for both Windows, Linux and Mac).
+
+### 2.1 Install `conda`
+
+#### Option 1: I do not have `conda` installed
+
+We recommend to use the installer provided by the conda-forge community: <https://conda-forge.org/download/>.
+
+Follow the instructions on that page, i.e. first download the appropriate
+installed (depending on your operating system), and then run that installer.
+
+On Windows, this will mean double-clicking the downloaded `.exe` file, and
+following the instructions. During installation, choose the options (click checkbox):
+
+- '_Register Miniforge3 as my default Python 3.12_' (in case this returns an error about an existing Python 3.12 installation, remove the existing Python installation using [windows Control Panel](https://support.microsoft.com/en-us/windows/uninstall-or-remove-apps-and-programs-in-windows-4b55f974-2cc6-2d2b-d092-5905080eaf98)).
+- '_Clear the package cache upon completion_'.
+
+On MacOS or Linux, you have to open a terminal,
+and run `bash Miniforge3-$(uname)-$(uname -m).sh`
+
+#### Option 2: I already have `conda`, Anaconda or Miniconda installed
+
+When you already have an installation of `conda` or Anaconda, you have to make
+sure you are working with a recent version. If you installed it only a few
+months ago, this step is probably not needed, otherwise follow the next steps:
+
+1. Open a terminal window (on Windows, use the dedicated "Anaconda Prompt" or "Miniforge Prompt", via Start Menu)
+2. Run `conda update conda`, by typing that command, hit the ENTER-button
+   (make sure you have an internet connection), and respond with *Yes* by typing `y`.
+3. Run `conda config --add channels conda-forge`, by typing that command, hit the ENTER-button
+4. Run `conda config --set channel_priority strict`, by typing that command, hit the ENTER-button
+
+If you are using Anaconda on Windows, replace each time "Miniforge Prompt" by "Anaconda Prompt" in the following sections.
+
+### 2.2 Setup after `conda` installation
+
+Now we will use `conda` to install the Python packages we are going to use
+throughout this course.
+As a good practice, we will create a new _conda environment_ to work with.
+
+The packages used in the course are enlisted in
+an [`environment.yml` file](https://raw.githubusercontent.com/plovercode/DS-python-geospatial/main/environment.yml). The file looks as follows:
+
+```
+name: DS-geospatial
+channels:
+- conda-forge
+dependencies:
+- python=3.12
+- geopandas
+- ...
+```
+
+The file contains information on:
+- `name` is the name used for the environment
+- `channels` to define where to download the packages from
+- `dependencies` contains each of the packages
+
+The environment.yml file for this course is included in the course material you
+downloaded.
+
+Now we can create the environment:
+
+1. Open the terminal window (on Windows use "Miniforge Prompt", open it via Start Menu > 'Miniforge Prompt')
+2. Navigate to the directory where you downloaded the course materials (that
+   directory should contain a `environment.yml` file, double check in your file explorer):
+
+   ```
+   cd FOLDER_PATH_TO_COURSE_MATERIAL
+   ```
+
+3. Create the environment by typing the following commands line by line +
+   hitting the ENTER-button (make sure you have an internet connection):
+
+   ```
+   conda env create -f environment.yml
+   ```
+
+__!__ `FOLDER_PATH_TO_COURSE_MATERIAL` should be replaced by the path to the
+folder containing the downloaded course materials (e.g. in the example it is `C:/Users/yourusername/Documents/DS-python-geospatial`)
+
+__!__ You can safely ignore the warning `FutureWarning: 'remote_definition'...`.
+
+Respond with *Yes* by typing `y` when asked. Output will be printed and if no error occurs, you should have the environment configured with all packages installed.
+
+When finished, keep the terminal window (or "Miniforge Prompt") open (or reopen it). Execute the following commands to check your installation:
+
+```
+conda activate DS-geospatial
+ipython
+```
+
+Within the terminal, a Python session will be started in which you can start writing Python! Type the following command:
+
+```
+import pandas
+import matplotlib
+```
+
+If no message is returned, you're all set! If a message (probably an error) returned, contact the instructors. Copy paste the message returned.
+
+To get out of the Python session, type:
+
+```
+quit
+```
+
+## 3. Test your configuration
+
+To check if your packages are properly installed, open the Conda Terminal again (see above) and navigate to the course directory:
+
+```
+cd FOLDER_PATH_TO_COURSE_MATERIAL
+```
+
+With `FOLDER_PATH_TO_COURSE_MATERIAL` replaced by the path to the folder with the downloaded
+course material (e.g. in the example it is `C:/Users/yourusername/Documents/DS-python-geospatial`).
+
+Activate the newly created conda environment:
+
+```
+conda activate DS-geospatial
+```
+
+Then, run the `check_environment.py` script:
+
+```
+python check_environment.py
+```
+
+When all checkmarks are ok, you're ready to go!
+
+
+## 4.(_start of day during course_)  Starting Jupyter Notebook with Jupyter Lab
+
+Each of the course modules is set up as a [Jupyter notebook](http://jupyter.org/), an interactive  environment to write and run code. It is no problem if you never used jupyter notebooks before as an introduction to notebooks is part of the course.
+
+
+* In the terminal (or "Miniforge Prompt"), navigate to the `DS-python-geospatial` directory (downloaded or cloned in the previous section)
+
+  ```
+  cd FOLDER_PATH_TO_COURSE_MATERIAL
+  ```
+
+* Ensure that the correct environment is activated.
+
+  ```
+  conda activate DS-geospatial
+  ```
+
+* Start a jupyter notebook server by typing
+
+  ```
+  jupyter lab
+  ```
+
+## Next?
+
+This will open a browser window automatically. Navigate to the course directory (if not already there) and choose the `notebooks` folder to access the individual notebooks containing the course material.
\ No newline at end of file
diff --git a/docs/slides.html b/docs/slides.html
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+++ b/docs/slides.html
@@ -0,0 +1,395 @@
+<!DOCTYPE html>
+<html>
+  <head>
+    <title>Python for GIS and Geoscience</title>
+    <meta charset="utf-8">
+    <link rel="stylesheet" type="text/css" href="static/slides.css">
+  </head>
+  <body>
+    <textarea id="source">
+
+class: center, middle
+
+# Python for GIS and Geoscience
+
+Doctoral schools of Ghent University<br>
+Ooctober 13, 16 and 19, 2025
+
+Joris Van den Bossche, Stijn Van Hoey
+
+https://github.com/plovercode/DS-python-geospatial
+
+---
+class: center, middle
+
+# Who are you?
+
+Go to https://hackmd.io/1bcxgBFIRP28vgyMSZMaBg?both
+
+<iframe src="https://hackmd.io/1bcxgBFIRP28vgyMSZMaBg?both" height="400px" width="800px"></iframe>
+
+---
+
+### Joris Van den Bossche
+
+<a href="https://github.com/jorisvandenbossche"><img src="./static/img/icon_github.svg" alt="Github logo"  class="icon"> jorisvandenbossche</a>
+
+* Open source software developer and teacher
+* Pandas, GeoPandas, scikit-learn, Apache Arrow
+
+---
+
+### Stijn Van Hoey
+
+<a href="https://github.com/stijnvanhoey"><img src="./static/img/icon_github.svg" alt="Github logo"> stijnvanhoey</a>
+
+* Freelance developer and teacher
+* IT Team Lead at [Fluves](https://www.fluves.com/)
+
+.center[
+![:scale 90%](./static/img/work_stijn_1.png)]
+
+---
+class: middle, section_background
+
+# Setting up a working environment
+
+---
+
+## Setting up a working environment
+
+For the setup instructions, see the [setup page](https://plovercode.github.io/DS-python-geospatial/setup.html).
+
+---
+class: left, middle
+
+0. If not already downloaded the course material, installed conda and setup the environment, see [setup instructions section 1 and 2](https://plovercode.github.io/DS-python-geospatial/setup.html)
+1. Next, make sure to complete 3 and 4 of the [setup instructions](https://plovercode.github.io/DS-python-geospatial/setup.html).
+
+> If you succesfully done all steps, put up your `green sticky note` on your laptop screen...
+
+Next:
+
+- Surf to and fill in [the questionnaire](https://hackmd.io/1bcxgBFIRP28vgyMSZMaBg?both)
+- In Jupyter Lab, start with the 'notebooks/00-jupyter_introduction.ipynb'.
+
+> Installation or setup issues? Put up your `red sticky note` on your laptop screen.
+
+---
+class: center, middle
+
+When you see something like this...
+
+
+![:scale 100%](./static/img/startup.png)
+
+...relax, you're ready to start!
+
+---
+class: center, middle
+
+![:scale 100%](https://i.ytimg.com/vi/PlaYMh-u-2w/maxresdefault.jpg)
+
+---
+class: middle, left
+
+### Time is divided between
+
+- group sessions: we explain new concepts (aka 'theory')
+- practise sessions: you work on exercises or case studies
+
+In case of questions, remarks, suggestions, you can always interrupt us and just ask.
+
+During practise sessions, use the `red sticky note` on top of your laptop screen to let us know you have a question.
+
+### Status check
+
+We will regularly ask for a check (ready with exercise, installation succesfull...). Use the `green sticky note` on top of your laptop screen to say 👍.
+
+### Feel lost?
+
+Just ask either one of us, we are here to help you.
+
+---
+class: middle, center
+
+![:scale 80%](./static/img/issuetracker.png)
+
+Report bugs, typo's, suggestions... as issues ([New issue](https://github.com/plovercode/DS-python-geospatial/issues/new))
+
+or see the [contributing guidelines](https://plovercode.github.io/DS-python-geospatial/contributing.html)
+
+---
+class: middle, section_background
+
+# Working with Python
+
+---
+
+# Conda
+
+### Why using conda?
+
+- Consistent package manager across Windows, Mac and Linux
+- Many precompiled packages available
+- Less problems with installation
+
+--
+
+### Why different environments?
+
+- Manage the dependencies of a specific project/paper/group/...
+- You can install different version of Python and other packages alongside on your computer
+- Easily share environments with other
+
+---
+## Small overview of conda commands
+
+Creating a new environment:
+
+```
+conda create -n my_env python=3.10 pandas
+
+# or from environment file
+conda env create -f environment.yml
+```
+
+Activating an environment:
+
+```
+conda activate my_env
+```
+
+Install a new package:
+
+```
+conda install matplotlib     # if not working, try: pip install ...
+```
+
+List all installed packages: `conda list`
+
+List all your environments: `conda info -e`
+
+See the docs: https://docs.conda.io/projects/conda/en/latest/user-guide/index.html
+
+---
+class: center, middle
+
+### Keep track of your python ecosystem <br>with an `environment.yml`
+<br><br>
+
+```
+conda env export > environment.yml
+```
+
+---
+
+# Writing Python code
+
+## IPython console
+
+<br>
+.center[![:scale 75%](./static/img/ipython.png)]
+
+---
+
+## Interactive Development Environment (IDE)
+
+* [**Spyder**](https://pythonhosted.org/spyder/) is shipped with Anaconda. The familiar environment for Matlab/Rstudio-users.
+* [**VS Code**](https://code.visualstudio.com/) is also shipped with Anaconda. General purpose editor with powerful plugin ecosystem.
+* [**PyCharm**](https://www.jetbrains.com/pycharm/): Popular for web-development and Django applications, powerful when doing 'real' development (packages, libraries, software)
+* [Eclipse + **pyDev plugin**](http://www.pydev.org/): If you already work in Eclipse, add the python environment
+* ...
+
+---
+
+## Jupyter Lab/Notebook
+<small>(*previously called IPython notebook*)</small>
+
+**Jupyter notebook** provides an **interactive** scripting environment,<br> ideal for exploration, prototyping,...
+
+.center[![:scale 70%](./static/img/notebook.png)]
+
+--
+
+ ...the stuff we're dealing with in this course!
+
+---
+class: center, middle
+
+# How are you feeling?
+
+![:scale 100%](http://esq.h-cdn.co/assets/15/51/980x490/landscape-1450137389-john-cleese.JPG)
+
+### [Feedback Form](https://docs.google.com/forms/d/e/1FAIpQLSfiVYj-S0Z8BvCgWIbRORYKwCxiJ6Gcl3KuR1_XFPhgat_0Wg/viewform?usp=header)
+
+Please fill in the questionnaire.
+
+---
+class: center, middle
+
+# Closing notes
+
+---
+class: center, middle
+
+# Python's scientific/GIS ecosystem
+
+#### ## Inspired by figure by Jake VanderPlas
+
+---
+class: center, middle, bgheader
+background-image: url(./static/img/ecosystem-gis-overview_6.svg)
+background-size: cover
+
+---
+class: center, middle, bgheader
+background-image: url(./static/img/ecosystem-gis-overview_5.svg)
+background-size: cover
+
+---
+class: center, middle, bgheader
+background-image: url(./static/img/ecosystem-gis-overview_3.svg)
+background-size: cover
+
+---
+class: center, middle, bgheader
+background-image: url(./static/img/ecosystem-gis-overview_2.svg)
+background-size: cover
+
+---
+class: center, middle, bgheader
+background-image: url(./static/img/ecosystem-gis-overview_1.svg)
+background-size: cover
+
+---
+class: center, middle, bgheader
+background-image: url(./static/img/ecosystem-gis-overview.svg)
+background-size: cover
+
+---
+
+# A rich ecosystem of packages
+
+<br>
+
+![:scale 60%](https://pysal.org/pysal1.png)
+
+Python Spatial Analysis Library (https://pysal.org). Collection of packages for:
+
+* detection of spatial clusters, hot-spots, and outliers
+* construction of graphs from spatial data
+* spatial regression and statistical modeling on geographically embedded networks
+* spatial econometrics
+* exploratory spatio-temporal data analysis
+* ...
+
+
+---
+
+# A rich ecosystem of packages
+
+<br>
+
+Building up on / integrating with GeoPandas:
+
+**MovingPandas** (https://anitagraser.github.io/movingpandas/): working with movement data
+
+**Momepy** (http://docs.momepy.org/en/latest/): quantitative analysis of urban morphology
+
+**geoplot** (https://residentmario.github.io/geoplot/):  high-level Python geospatial plotting library (combining GeoPandas, matplotlib and cartopy)
+
+**...**
+
+Check out the [GeoPandas ecosystem](https://geopandas.org/en/stable/community/ecosystem.html#geopandas-ecosystem).
+
+---
+
+# A rich ecosystem of packages
+
+<br>
+
+Building up on / integrating with xarray:
+
+**Satpy** (https://satpy.readthedocs.io/en/stable/): package for earth-observing satellite data processing
+
+**xesmf** (https://xesmf.readthedocs.io/): Universal Regridder for Geospatial Data
+
+**verde** (https://www.fatiando.org/verde/): interpolating spatial data on regular grids
+
+**...**
+
+Check out the [Xarray ecosystem](https://docs.xarray.dev/en/stable/ecosystem.html).
+
+---
+
+# A rich ecosystem of packages
+
+<br>
+
+Several more domain/application specific packages:
+
+**MetPy** (https://unidata.github.io/MetPy): working with weather data
+
+**xgcm** (https://xgcm.readthedocs.io/en/latest/index.html): working with output from General Circulation Models
+
+**osmnx** (https://github.com/gboeing/osmnx): street networks
+
+**...**
+
+
+---
+
+# A rich ecosystem of packages
+
+<br>
+
+**Machine learning**: scikit-learn, tensorflow, pytorch, keras, chainer, ...
+
+**Performance**: Numba, Cython, Numexpr, Pythran, C/Fortran wrappers, ...
+
+**Visualisation**: Bokeh, Seaborn, Plotnine, Altair, Plotly, Mayavi, HoloViews, datashader, vaex ...
+
+**Data structures and parallel/distributed computation**: Xarray, Dask, Distributed, Cupy, ...
+
+Specialized packages in many **scientific fields**: astronomy, natural language processing, image processing, geospatial, ...
+
+**Packaging and distribution**: pip/wheels, conda, Anaconda, Canopy, ...
+
+
+---
+class: center, middle
+
+### Reading advice
+
+[Good Enough Practices in Scientific Computing](https://arxiv.org/pdf/1609.00037v1.pdf)
+
+> "*However, while most scientists are careful to validate their laboratory and field equipment, most do not know how reliable their software is*"
+
+---
+class: center, middle
+
+# Thanks
+
+### Joris Van den Bossche
+
+<a href="https://github.com/jorisvandenbossche"><img src="./static/img/icon_github.svg" alt="Github logo"> jorisvandenbossche</a>
+
+### Stijn Van Hoey
+
+<a href="https://github.com/stijnvanhoey"><img src="./static/img/icon_github.svg" alt="Github logo"> stijnvanhoey</a>
+
+</textarea>
+    <script src="./static/remark-latest.min.js"></script>
+    <script>
+      var slideshow = remark.create();
+    </script>
+    <script>
+        remark.macros.scale = function (percentage) {
+          var url = this;
+          return '<img src="' + url + '" style="width: ' + percentage + '" />';
+      };
+      var slideshow = remark.create();
+    </script>
+  </body>
+</html>
\ No newline at end of file
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PTESyvh4Zwyx5NgWlXMY+JlUAW5lyHrXQabtoO5IrVaX2vBt9v53S/KoE/gc9Zg2RPYek6DebOtgTss9sBC1rlhGUlNQjbUw+JAz2aa9V86ePw2gycOApPPgqOUyb5GmOYysAzb8H/e0yPO8BPp+DZE3CuG3oURHwB3rcGeuPBccSROJ2BOQPn68fodwLzjy+s3ShpfhmdhWzeVVCdZRz1yl9+L0sTpzOaiWjlC5Wv7ZtxsxY56qg1s17FjvmsxvICiASqsmXriDrK1eKcdYZCNo4UiK8BVrzcUHtm4v7GJOgzBE2XgGV4mzo3PPog+Ry2tgVysnDRKge+tr/oPr/E6RHhpAi/9034zAeV2e2OwTVr1XwchStWwzkp4GT0daUwR/bSt0o0xt4nuZaqlv1j9P/tf5ZSLEvCnUVweFGnYiz9hH95TdSKWux1ZvxTgce1CIP7R0nmMxHRrGg9vZoPoIUpPeuBxoaPBmfsqqoBCFx3UUuxe6VmXAMN1zsDoigWA5qvgra7IJ7z5VW+HUho2rzhbWqH+e5ByGoQv58oBRHhVFLY+XXYdwNctbK6IVjA1Wvga8dqG7qIcMlV8Iub4YnHYfV5wsnXCSXEfpW5/xmCnmcZy1jGIoKTJE+BszlkaJf+2ziaDxzc6InF9/02ysiH5luOyuyVcwq3JJsU/7yMFhDglbMwqyn2tPxbPzhZTZOXeq8SJMuCvgsxp15CBm/HHCpwqaVEGODbb8DeJ+FTN0BXlWaDq9fAOldFHRVv7F/MsSRc+yG47V44dRwe+XN49K9hxSph6lQxIS4dY2lbYc+zLAUvowix5Tq0HYE/i9fCk0Vxw5JOw9xFqnYLqyt9FkH37msKB0KKlTTUh5eLW6zgsFGh45X4WoGmE2Az/ilk8y5k8w4lvsemYN0q6H4fvP4TvWj1RXD8RS2UEFB7tVRyPJGByaPwsSfhD66LNlw7aJ7483uhJ1HZ/uo/f94l8Aub9d8r18L/+jswkIYv/Ak88Wi5WroS8Q16nnYSYUnfDlPdLucqurAFVV2JmwjCAjAw+526crLKwAB0X1toLz8lbponry8xEE+EFp1oFjRN4PpCiIiXbSr/3O7fnr5N3AOOgUN7m/JuZPNulaQ8xKO4Riu4AlBo45oBykw2dx61iIKjH5D41goUz5sFxMB8uQUZw9oESafBrHefUyB1LTK00/fcF+N/2ILEmIlMxF9V37fendf+lZwJPCSbd9WYkEMwE01+N+veBf509WEOax2DFL5v7+9AfbpT2AsWCVriBR2Wzi9PsN77qxBfDQkHc7DcmzCIaL4yA3/5HFy6Av71O8rbzhq95o0FDUd6xypY2QVMlztEBdl0AVathQ2Xg51RaRjgwrfB7/w5/NnvwoHPB6udS9ttBWRoV+jCKf3g5KY7oKsbTHfB0UQH6f52/5dfpwLd7iZ0MDoZfb6pW++GrO0SXym0V3yVj/gLZHLI0ChIruGNrGwsOdt9lg3qYVkyjPLfUnyBpXY12bwT4vHGGAXjFLcf+cHXuBt4KSejWqyh3JxsuQ2cbvUO9zNQEjB+b73k0OpFtln0OcNLoTm/L3afxZdGMei587t1XveqcysGc6BO/4GMXdIHEUtACt9ntWjFHjTtYyBtq8j+W16OMD8QHYuh/HsEzVmeiOerB3llbiPXbkkpXK2H3ZXvrhhBcxYrvHenwTCqJqFtkeRF0uKRz2EeG8PsL4/RjZIqf3oa7n0SHvc54DnAC9Pw5Juaxe+qleoFfdVKuHJV9Jg8L2djDH2rVfq1c/D0f4dXf1oQYt56DX7wDTjtJfeow9u5ZV7RpV7aw7sh0VX7h6ueZ7BpZz63builw6OQyRXuq6kPC0xSM+E0CNk8qoxJ1vZ9gI3MrUuEsjlkeJdKSmcSSpnG4VGwU9SVThDAktqk9w5DpcmkSxjqJVQCRrUWsnlPU8fXFLTC5ySRKvzbssOv88MIOKLFJPpecvM/O/oTE3j0E3XX5NWc+7vAqjOeXwQsR9d/h1HEusiW22ChS1OsZYwWum5yNY7IUl8BhK2U4D13En7/W7C+Fy7shedOQU8crl4L3SXsxLXnwJefrzwmEeGiy2Hgw3DJuyEzC0efg6f/ES5+J/zpbvjRd8FeqDzGKEm41U5Zee6xkT4EsFPITXcExjqqqs5qmM5hgdw4inm8gfVlGhxHKNxGpzYgW27LFws/kyDDOxufP1eTIJt31m3CaAckfTtMdxOspQm9q/J5YyNDuzGTBUlK0mmYPQeku2Ar7XvVJzVm0W13EelBK+Hlk1qfGvIVHivC93hmfBzZuqtwwM7UrS2UdBqGdpb1UXtDqtmQzbtaU4ClSuQJsGwaBbqhe0Y9lkVgeCdyy66G7T21Ep3Sl+MnXFngqTdh2z/A71wDV58D54QwQu9Zo0S6Glz2Hnjbe/XfyR542/vg5Kvw2T+EbxyE6ZKQpqgxVoNmq6xl085gVU9djQkku5GBgWLnlKFRmpZ+TwS69INatCENIuD0IBtHKhZEWErQ4glNZF48O+oidDKUgR2Qcolv8BWNtA7iIEM7QGxw4q562wLmNflHMg4n1qtwIzaYnibNe8g8ixQ8uiOGXUsYknniIS0h6GYOq+s953L6PdkWPLqPfIhqrZja0FzexSjt61TZx3jeXiiJIseOvJ10656mfVjVSr9h93rXzNrw9dfUJvyZ88PvuWatek1XGv/aCwrOV348cQS+/gicLKnPXk02rHZKwTK4y5dSz98ZvsXqOthYuGrgKhruurbQx3AI8TVQcMDy9YFVhcODwNTFVQykRuRNe64NyrNRG5/9TaS6OQBI1JpWLaDtqHloI82KZNSMcefFE3MsfZdVjU9g0+LyUpWBj0BqDcWTH0ZlvJs89XSA70RwL+73ZGlp0r7nyxhKjdu9U23tcVXNFg2jrrURYe6qUNyjrjAk/6nNvx3ZflmzAx+BlGsPjJcnN6oKxjUhBL0Lg+49nkU173TpORJW2G/FQoZ2lvlHtANxkl060IP3AAGOJ9mcLp460aj0W3rOa28mC4degj95Ev6vEM/o9b1wUS88cyJ6fJe9G37lnxUff/YJ+KuPwavP1ze+atAUxsZV55aMxF2E2VBnJxkYgK73u7VQQ8ZsuWaJWH9QJ7pu4oLZH7xwNefujgIBDLzIFOLDG4L7zEYAp2qOVquymOgxIjV9oGVOcYM7gxkkaK8jkzHhxNeJnjPZ5GPAQqfJQoZ3Nd8Lt170rCE6WYXonGRMqClE1/CoS5yCdhmfN70JLvJhjFs32XVmMhMljnCeB3b5jR0hCoHofRGmNwAO2P213duzDkw+60l9/etnHbD2DFjRTnGqETDBc+y1TRNMYnUgrhPqhBOCFRS7oLcA1RKt0uuOzcJf/ggu7Yd/887ge65ZC185Gt5mMgXX/IqGHXk4+QZ85nfg+R8Fj6EqL+G2qeNK7VoGTLximIqnWpaNOyARFiYj6qRjZ0ukXw3bqUTk8loUz2M6rA+nTmd8T2LDAFnMZO1E3CN+Wp4uFfmRytYRzP46VNGODbE2Fh4LQ+Am70D/0YpmAO9dFxHiwAuV2IRloGoXQomaH1YW88gnIi8pJBRKuwQoQpqW8HAzM/4pV/KcCxpt9DgXAcz4OHLLLi1CJRlkYEdVGclEBAa3F+jubJVOXOUtlUy9SrelzEwQPI1A9JqQiuUlJZ2GqcvAymqMuG3g8Cca2uctbAtiFnJjiEfYdA6y9ZRZK0bYIEWEtSth/TrXNaHGPMvPT8H9T8HhgNSkszZcthJWd+v9fkcp72fNumL1s52DT+6EH35bna6C+qzW27mRa+qGbdUUI2oe26slpcIgBGy4sZpsJuZLH3dVRCGoqQxdCSbv17yyDUrQ5pFPu9JG2DgF7HoqfCxiiMFMjtVkgzcHx9x3GW6DZKqzZeZkYCCEpvnCjowb+10lzPi4u9l76yNYhyybIrzCxcCBP626z8UG8+X7NN4ToEs0pCsKlg0336HaH9v9xtc1lkPaHQkgVRHforsm74/2fheQLRHvb+oyeOOkStsrXlaCNbS7pjGUwqI7rraLeMiE2gk4/Gd1NV6tZPueS+GTO+CfDcDlGyAhxQUPwjyPPfzoBPzeN+EHrqr5dBaePgFPvwVvWwGrkuF9rzwHVp8Lc6f07y/+CXzrcLHTVVCf1Txbs66pDgZi9cW2mUP3EW5sKuH4jY2ZuKf2Pg6MRS5+Gbq79jYn7286M2MmIhiLxeVf1BiMgzlQn3pT32XEBWKp1qNT6P45CuvW+H5ciOOqlDXJhgxtR7z0k5UwuTd6E7fCi7WQiy06J7VaYR7bB5msak7shDqZlS0Gd74lpirGib2oT4hpmsNlJTt3+H0V1rwTIgAO7lY1+mq3nfFx1QbYdkN7eNx86eNucHoMbKvIHie37AKshhdN1P1r+uHXPwi/dgPc8svwlSfg8wfhmz+A7riwYIe349lcc8BTx2HXf4c/vl4Z2w19cFVK18nabvjx6cL1Xjv9a4Sf3wwmBT94En78PZj4PLx1rHKflZ63GdfUBIe6CGNRA5XqqxrK66LWgsjHXUQVZ8KKw4sgm3ZgDi712r8RtuBqW5i8P9yZByJMDu2A/92Vj69exgPc73ZwF8R80nQpNgVrExdD4odmwBz+pKpjFy6Dhay7loK8qh3XP6SRfam0TQNvNGgTtcVNARtCbIN8UjJvQnIlGF8e7GRSifL7P1r3UDQR4cQDcGgfEAMriWza6RLlXEsXjYiwdhX82gfdwQjcdD188ffgs3fDvxqCd2yIMFFSIJDztpYuvPcpeNdqOD/lJpKz9O+gvvvXwDU3wzuu1Ln8b5+CV492l7UdNf5qnrHlMMCKBjnrRISawOsj2aAdc3KMcNVlY003E+ZglIS3iAbaABohQoVGOklkgyFbwzPGgYG5Jow5WclkcmaskSiY8XE1LdkeIQv4cVqQ50mk4epU5tC9FZzzyvc5c+RzmEP7inMj2DkVfBoYT76nTqlGrrkcLj63+JglcMM1+jP5TfjPX4F//D70pYSZ+fCxzmbhqy/Dx74Lf+TzjL72XPgvPy2//tJ3wbpL4PRx+OwfwNHnu6gm0rwjEm4UxMGMN+a9Z7708WgnBTEN53E2xrixxEHOQIts0/KHNfjRrFjrTsFQXIigkaYm92lGohDI5j2YA02UfqpBLsLbFamplGEonKyqsE2IZCBmcce3L2X0vVjXberNvkfXftyA5IAExaaK/NWV2xveBlYCsjN1jcdDy1wzqyE+q1fAL10dft5B6zg8OAo/fhG+eBAOfhNW9AlTM8GE+LU5+KsfwiW98JtX6bF3rYRzuouvW3sB3HCLOn/dOwLPfAvIOnDVr8LTh2p6zobLgzXaRjuIV9P4szACv8gIWwj9XfLSjYA5FO35WxNMBMGzF5mEbDdrEUuF70Hg5IXqkBUzBUnQGLCnz6ikLu1G3UzN0J3KNB0cg8Ed+i7y4Yd+c4IBI5HMo3p271Av+gbfZctzQUd5P69IwXVXht87PaPq554kXH053HcH/N0fwx2/Du99G6zpl0AHrRem4f7vwaMv6fFrz4GUrzKSiLBijcb+/qf74JuHYPY0cOUNcH5hQI16O1dC07QOuSbFiUXRlqZpSJYIAQv1zF7aTjSRyRbqQS5CY9TsvipApDQkz48mxlsHVHArQzyGOXi/m/jGRiMH7l8mvo3AaaB0p5sa1BgDiRnNl1+0v/v+LYbI0JCh3bBgmlJQpqPBib0puC6gspGHN0/DqpLIj3deAv/ho/DRLfDFR+HhI/DS63DKpUGe6ve5E/B734L1/fDuVfD2FfD9t/SaZAqu/SA8eQQe+awWW2D9lfD2d2G+vA/Y11T1cUtjgo1p/UdtgDcbU7Xk0Qb6q/F661Uz4FcZG5fDXWzSdjvRbKE0912IXxs8p+2e55vuInSBNfPzMxZe/gQtMhDRXyarMeANqirPRMjARyC5pvhgDLwwoyIYGoqlL5Jmp3ugx3tBpb8hH6YWABneBnOxvClDNu+B3ufrlsyFGdApAAAgAElEQVTbVg0pCBedC10Rvj/Ts7A6JPTy0gth1/+iUvHd/1Il6XNXFfKUOsB3jsPOr8OxObhqNcTdp121Fi56B3z+j+HY88C5V8DVGzFffrCsn0YIcVvs6i2srFR0qkHHh3ZANrnVkaY3qFe/5VX5cX+Exudrsdmqa4XJNLe5I0fCmap2KwsSEdtZM1/bSlvjWqPWkhdKGUvqtWteb+IAziDEHFhpCj9A4MsSmuJPICLQnYTelyhEXvidx1wErF0Z3gZZC/pmka0jyNYRdVRuwNbfUQn44og8zgBzGejtCT9//BRcfhEMXg//xzD8zWH428fhgrU6kbYDX3sVtn0NBi6E83vg6DSsOAeOjMPzTwPnXg7v3AR9ryJXXAF9l2GefLS9TlSLBREOWM3rw3EliBI00IVsHnWlXa/dFr63NqtVmwpDeErMRttdFJ9KxCBqSLpRCWZ8DNm0w4shCe7XGLUTusOK3KQlKCRmUUxoW+B3IG15ecuhO6GvkDI0tL+S6Zf0qDrdJQC7J/S6WtExAmwB50XU613IqXYnar84PQPnuRqMdavhrg/Db2yCzz8Kf/0o/OAozMzG+crLObpj2me8C7pT8MPvgm0suPx6eOUf4NlZyHXB8YD8kyHwiHRHq8C0K91loy1s2gELQE9zNxb9gNqoyFnKErDQFLtVLR0uhrSUgJvzvHkwB/ciN90ByS6CxSVUIp+dLxSND4DcerePhpckDxE6WqlnccGXBGloBxBT27oDxGL5koLKjBu8al/+cDtVH09hJj7nq/Lnc8LKvwdTaGsezONjDUeZhKFjBLi7G1ZEZPbLZCFeIS/E/AL0pYqPJZLwkc3wm8Pw+UPwV4dyPPMSHHwRLuyHUyn46dOwMAsk1sI/fQf6r4ELboTcafhpOzeoswOe16A5Mqa1YwPFXUGGa6ymY6TEi7HQ1jIC0CpmTSJE4PlU8PFWwEQ4YYWFDDXS3eFPRkpsXhlXSafdKlRocgo3mY2IwOYoBlLg0BiwTID9n7iZ3KvE1CQxB4szYpkDY4H1qeWmOyCRgORKZHA7xGMq1XoLJujTcJyK+aEbRccIcDwWndfBNlq5Kwo5B5KJ4mOzWVjnEvaN18EvXA2PfQP+8xF46kd6j0m9HdZeBFYX9FwOsz+DY/8VejeA1YIpicWQwbvUISguahOaHFvyaemqxtCdIN68utxpAMxE9TGaMlxFsn2vO+8yMSVh3hGEo0giOUPQsseJsoU2n/DVM4xCicUmwxCyDn3f9qkLgAz0vQan1xcyLd30US3UIULoN+GZwvwxrCbXZk3GIkDpFNsSnhBFTBHxBYoTaHiXDe2gUOErwAlLYlTMDtggWk6Aw+yofSlVIX/te8H3ebnSw84D5Gz4uu98JguvHodX34Jjx+HHR+GFV+HocXhjXiWl/gTMzf2E3OxP3LseLTTwRvCYG854ZdvwqPvB+Aj8WWNjduJw6F6alZLO434rEhSjKSU99VRNfXh25TMJLeP3IhiZdmbLirJFR1VwagQVvmEt59kFiW44fhF0GcBNSpDo0tzDYgWP22PQg2JYzzb4HlkZku2QKZ8HSY9qMpZq4MR9KUXzLXidtKWsZssJcJiU15sSurvgl98bfN/UHPzkFbjm7eXnHAMvvwGHn4DZBXj+FXj2RQ1HmlvQ9KSvHYd5UrCmF9Z0w9HXkBVZZmfhytXw6hwcn4seo0cgoyTVStf4z8vg7Zrz0sliDnyy6PyZCtlyG1jx5kr7JlGB+DrRRRWq6uMMfC+tWmtRqt/uoPJ7LUKUKlxaxQhU8EDLdUPc0kxzt26HbAzirjTeF4cpQ3HWtSB7ciGGVbaOgKmxHu+ZAP8Ue/XpV75Qft1MHFZUl+nNHLo3PJ95k5kc2ToCdlfBAdVYmINjnVNBz8/D6ZD8EbNZeGtW/ReeewN+9rIS2RePwbMvwU9fgbdOQyYDx04BsTUw91ahga4YXHgOnLcOXnldb3AczFpVbb8yo2FJPxOtKdw22K46I6o035mGhS5Y3YwSZApJh6SyBDe7DZHFuauGsZa2x3MQDMGJ5lvY4aJwwILQKjeNIzzuWNJpsNZD1mXCv7Sv+JLxsQJRDlRPGOS6keIQwKIY1rMJJfNsxYI9y50sZryGdKOG4O+8ictF66H3gj2DeewhLZfZ/T4kne4cAXaA13wl/xwDR1+DH72kku+PjsKTP4I3T6pEu5CFY29AJoCRNea4SpJ9fXD+Oli/Cubm4aUTcPx0IYPK+cAxOHESfnIKrl0Ha6e1/GGO2uN2q5GQi64dvkvtv31v1tTPUoUMb9OMQM3MiTslEZKc1KVuDm7KpsNh8q1B09Ix+rBolAUR0mgjNadDIFtuA0LiJEVg5lIgq5mXQmC+tE9j10PWtJ/45m3BvT+rf9BnApw4od+mVb3NVgsOJQlkfpr0meQd7YzkEyaZI0eQjTtg5tLOxgE/9Rx87K/g2RfUXvvWlNp1T5xWuulHEJHLp5ZMdcHb18MvXq3u1V/7Lrx0XKn25R+AZ74K3XG43CE272C/ojmjf3Ia0m+Dr3fDk8cpCidqumr4w7fBnMB882piLnrEkpAqJE+XrSPg9JY7PARArhuBc3rVhhi3IJ7UYhCh78U0LyUnRKtVFxF0Tvsgl9N5ylnhfiMCxBLqEOif00b6HxiA1PsbaqNpsCX82VthNzURSQoAcnOavWnmmeb0VxLDelbBUCgFagkwX3aJDG+DTA3aRRMr+cZ9f0Ts/7Jxe+WKWAsZdfzyHO1WlmgBkxaYDifieOZ59UzOhNT8jZIwRQT6e2HNChi4FjZ/AP7T4/DV74NZAQtHIZuFKz4AG66Gp/4C6+encY4Dz4LJGZIiHH4Z7nyP5o0+/HIxEQ7ru1oUEfHTSQ3iTjY3E9FihWwdgWwvZr9vs7C9uDN/6FC51CLpNFxwMTCF2f9QPoxJ0mng4tA+m5WSU/vZ0JS2Wg67D8hiDj2g6vnpsCoSvjV96BNFc9rQhh6/pv57mwQRgVv2VLABt4CbskPqRoNL8GNarq7EI7ceBMewnkUQwHG5K9uGTDGnlQ91PFxufpKte5TIWHHMpI/hjHLMizBZmMf2hZ4rg+toF/iNmUz7CbDcdIeqEDbdxfTffxauuRH6z8McqW7zFBFY2aNJoQd/Hv7HG+HxJ+CPvwA/OQozBc6oSJo9H+LDkPs2mF49nzGG/i7hb38M26+Gd6yGLzzbmPQrIrDltzSpx/W/Dk8/DjeNqISCE2r/Vc/eFFhZlb5iAsem4G0nYO5SinUiS0A0Y2W5M4TjaIFmA8VEWKEOEQ5YGyCbg1iPxgZv2qmb6+mLVMLJM0W+eWimhDN1cXOnOKouaqN2ZseBhH7GZnwMGdoeqaIverBmlFaMDNZvva+DDO6Em7Zr5p5EXFVoIc8vgzubV5ABotXaxug3bCrU2daLCV5wroYvIIb1rIT32LEk+HJISDoNW3YDp4Lvs+cxh8L8HkLUz13Z8uP1wHW08zO6kh7V79ZJtCcMyRh1JmBVF6SSMJfBTH4SkU/As4fhl34LGdythZIj2sjjHZfAF39XY5n+4C/g4Dc07siHUsnVWgdcCawDfN/EdAb+8TXY8DP4366Ad66C//Bt+P5JyEQ40wURaRn4CGzcrvlCV7wEB/8LEFevMkHVfj77b76Ny39Of/dn8xlXZGg7nNcD013QNwPT/nqKnc39J+k0zK3G7A9mmjxmojR7jDm4V8N7ysbuPo+YAnG24qq6AbSCgKXXhdGrZko4UZJUPYiHOdlAYFrOGmAOFhxORARuHnVDK/zjL2F0Bu+C4d06na1SZxog2XpnIY+gyuadbkxtxMVNpFsaQxqq79Y0w11obGTd0PkLimE9WyHpNEw+DEO7NbmJyYF1Gcy/jjnyueCbEqky50MZ3kYRISjqxClzmKsXeUe76Q3IplFlEucykMqCI60jwP5cyjIwAOdeq4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DCZ/2WZ2OJFwMoJe910S7Bd5f+QQrg+H3vksuaO75KmHHzjncE74nigVZyDD31RzKQidrurgUAiP/rzTAA0MMt2/IA/OFKAPgsufNxMW4WmxIYbABnqbD8EgB454EOcrnmntMAANTDB1LZ/UXiIGkmPs6A0TQ7SE5aVdGv2Zj/4vpnR4rIBVXDPCcYBGS1j09ufMHUyBTslZaQk0FfE/eBDfnAybV3M1FToSc3kXfSXHu/Fpi68FbGW4f+5wZSRPNV1u306A/rvvJkgnvEyP1nMC3xBypWyfQTYHMjgMfXMfxF3zevYGTWbzVCt0Rx7jXkOPXlu6POSW+e/Rdypn2EHD1hR2b1YBDogzHuuUQVAey59SoVMPeNaxj+4rML7iMnut8yKbL65uVl3MeJaD3p61LOytvK/YTxol9/ZR3xMsg/483R9rMeDeYBgFRx+XU3q65ra73IClCN7x9oax9740xmIKc/Z1Qhet8Knjc5sSlhOtrFqK3fuRCwbIqbAmG5hmgYbAZ/Z9mj3Nmm49Y/AwCMV/M8Kx1+/4ME6+CHm8k6tun2NHdJHiBH+/cTtNzAHaQeWP0f5BhKuY6XjKkR1OmYc1F9kE62e4RveDnh6rd3k+OG9eRYOdBxrKCKc4uow1+uAwCOsdxsYrypzskX3fPkeXJAvPHDWVjbW/6sVUNRXjWX8/cnfK8dbVisUqCu/40L262Y/eB8RksYkTLK6ZECc9J7+K5nWbg+2AEA0ADhm/sTMzrIyzTWJyZ+xGc7uYd4EF3Jk5UG237Cutz/fXK8UZv6ts1EfYr80rbijJVxS8oPmXn/d0k3Gs7WoXa4m1pcQU7rwsf63EmW6QffY10+9go53gEAUOtVCFgLOF1Y5D7mb9ABxjl2bep6GB3qqmnbgPp7z1ykWmIbBmBtL3nBXFI+c+Kk3EDT3mHDSX/VMjyzod0KYNjGzODpo4zG4u+pTXqNX/ayr2NmnSc5gSR/JYdlSRSSQLSiluTGlAZBrjr1V9IjtWnayi3k8NwzO3fu3pfgSPgzwpK6cwAA2Vtq7fsHmUi4gV7aluQaOPgwJ+HHZL10dSMAhCkJEKA4EykR5g5balsaxZzamBnbUM7KQdD2k5Lbnr+FPM/aLh8u7neCjlQDUJ0zYZsoPHEGoz9/tpzxGTtRzUxvLEXcWjqf5iS8x3TK0B84GXuI4bAZadFJRu6z0pdk4/mkAId7yGEzN52FRaTTun7OzXBtJccrsq1IFGELUyNe15+fcU2TtofIcTMA+PfYAY2PU0YEREYOHDnOXQwpYhj8pZAPAO0Y0Kx49OocAJh0d+4opQCqAcAKiV9a9YzPw2i63l6GG8EJRou3JPWql7j+Wm4SSYZ3uc5z9DEAwGKkxzYAwJLUhZJAP5eUtI+oB60pLVKbyOGNpDFW/TY5njcfw1pa2LeyLqn6Vc2vEpFxUTRNbuxn3yNkqtRoGUeVtVGnCXmHd8tzNxiBMxx/7Z0sAlzLAWyc9jXswV7HGUypw83MEDDADPYTOjWH9MdJD2NUrk8EMvL5q8gCqW19mlJc9B5LTnuNHFKu3JxNDq8kZUgmiCVZ85WWFStXtszH9pICb7LHZKNzt09DRv1Kpj1SNf3cVS0BBpvtZO5LLWO6tjIU8Aw4r3z26031H0+8coTKg6tch8LmsRrr5MHjH9cznkROH8OOUUY0+6c5MjrCtscDALOEsy8pg8iFNAoPgKitINsx+D2eNGYKzKf9R0ERrWAmeYYdBDPnMh4Zl7mOLHgJkY132ZdiTcSiJmkUU92qsaQwSwHDaxHTgU16JkRI6wTgPan48o3vmoetz2nXzHlQjTXBfPmJj4NW7FzFiI9+ZusLl0ZCTBwWM1unsiW/NVkRoZMD/kgGD4Nlra31VaY8BWfunklfT0QHT1rvnBwAFqW4rVQjdLdLw8zQG6BFlkuP/FAsB0DmTjRH3kwAMjdTiUXtAFBin0HhJLufCzDYzLhILgoyGpCGcNw/qm594uPX+v+gX9avvTT4VX/Z7tcDo0Dvai2ZtI0wig49nUMa03gZizB4WjUZBbjaAaIM5sp4nHUddz2JgYo3VRh88W2hnmYAEDIjJ+TxyPcJM+N1VyEy0FnOggVAHYx+oOkkf32q4CSTVgEA+aKACirO3EpAROcxXb0iammUM7Mm/dSQZd2ffpz7y9HTxebcioaTz7ttulrQPYxhyx71+nQyU/ZJlhsowPPaZILGw3mSwZnYrXjv9a8IWJqyWr0B6JN8qSSRZ6EzBmbA46ozAHwnI4wTbRVmmS+90E0yfD4f6WSDSVnFwwHXTKxKyprSZcuWcbuDwOOjdq18ZnYsjjAnqmkEhiWr1h8bzssr91Mnj5TXLFn6Ui9Gmol6EQ7KyejtZhgcYAXbJK55Ik2GmTc84rt3CS/2ZOliGeSL8mfMRzKNsEtubmJBNsYDXh0AmGYcjDjREuujKHeap3hWcAEV+TKffQjNuvB0D5A/5TdOkY5X3Dw9xt2PgkCAwcwAihzms0uin5+sIY1Fmg3HTUaN8+ULvxoI+4zAVHSJ16Mhn22OYWxovi2Yyu1kjCSJGu0tF86THA/4WcjwTSHMYAWrIBv2qUIAkEzB3aKcGARVR5qpdOEC2w0MhswHAXTUDYSAvJoIHLPM6p6iTGDFQYjBDEMVjJwRRy2E4hAA8RxcrgJRC3Z9zxgIw1MB+KIyrZTs8AJtsVcpofdG5s1gox2MepYQynhTjL/0TN+ozT0bIhVs/Y95PIE/+Jz5Yin2VpKyCvLeLgPq5roWz07qFMxTIlRdnruHV2ZkCB8AA2knoRAA++cAKsiQIOMaqmIQ+vxbyIerMZEClIiRuJIWGiKpTAaRjAqLNL5JKgSEvwNK9R1SQL05xDyyiJLQqyGe75zfxBOFX/IT5mTPy9tYXtFXzYfBSt5URrSmiCILsgry3h5Ajv5ovlI3KWO01DkYnb1VlQvZ11IZC51Rxpuml/AnILixgACDQy+RHp+bT/RweTEJLJQ3umiamu7tW77OrjjYItu24xixRqj+43ly52Ufkx68TLq3tMQyhOJ5LAEAQI6Tx1R/B5GOjm+8z8lI0rQzAb+qxszKUkT1+VkFeW+fQ5HfrQo74RFHFQOxZsLSwHtLhtACWtEMAOjj8t5GnscnjFQVEoxOCzGYSXcQcSSWyrVKlSIsCk4f3t81Fjjnqmoce+LHtk+ubL2BFDRGdYiociKfgxau+GieLQp59sFREhPx7KakPENS2QwglfNJTPLFAinCnqYTC3JBJJ1dZrDAagV8A8QhRyWdAGQLcmzXATp/AAAUCRMmbWKVEqFtOOGULPMIMDjIDEGTFEDJ1WpRnn+q4/jwmGWxaqf2wi3acHfPxJeCke7+2j/e/wkNoJQxJIulzEfTTlJa2gXIMgnp4HkbH08UB+Mue38y7+fFYPBKTDLLTbUSxHzeGr68HMLPidzofNBKBJo3CFAFvPOyTFER67oJsk2Xm1ilBFB1406EPLkCDLbnEwVnUFsZEc0NdfU3bu2jAaDv+vJL1erpdyR7990153Ve92jVT+qfngNVx9hw86LxdXqXWuQJA/nzC7grGgBUBbFLablCYrMQN5kgj9t4kuOIIFQlMglTJWqDRGLjrnBIiEGbsz8RC0we7yIIs9gyaOBRsouHFxQenmseJ0OwLkG6MC/OtJzYjmgWu05hBRBWCzB4nDHrdh3/2/DYeBCY0dEAYLzoCveIon+r6WVtQ0WnITTyk1+rr6x5wFY7xbxPKaOtU7mOfLgQX3fMDqpQAKiP6amyqmEvtdJKOtfRZJOIeHlSkhDqRyeVyxnpaj5GrbRyJEUtkV3C2pCoUO7WAgKNilnQ6tQn746TP+U3LsR8SS0ep4MAKFMfANTm20/EWhlT3YS9C2TBAAAIMdjSDAB5Rvl7g/1TADCw7gBgvmRj+Mgi89+2b/yH1arSWdxl2P/H20TLfv/gkqhnX/U2ACJznpxSauECUHRUXiGT+PojvC60QigeBRBduz5DXGTNA9UgJ0KRZ4eN5TxKVnSCx0mmZPBFjS/n8NhGzieH5CURBqUamffgWgDIWcXjz9dGDsclMT0ozFQjryYCx4Ls0xs/mMqfgayc9CHfLgDYCACgk6vrEcsCUCrcAsaf8Tw5pI3VU0e75srVADCp0gGLr+zrK3a/+dHGditQ5fe6K4C/vx2WaB8vYtwB9cpJQLHE2O3trdDRLkCRO1M93HFI1lQ/nsFUX8xMPeQiH4CDDNEQXD6fT2+dJZXl0W9v4KEVnclzkg+OQRTVE+oWF0xyh3qix9GCm/6NdPko4mSEy5JzK4hLuGsgbkUJMtWwf75nD+8GihnjYvHJSMviyhEi2CIlAPJJZENfcmyjb6CpqVbVpxfowR63eXSx4rgTQ4t7FLMA6CPLtmP75sbI28+3XfmSFUD1DNxlklDkkbJag/dcPOIGgKUdNKSN4iOBqiOBSk8YKHSZToQAn2nSE29LOcUs+3JsqFXWB8jkl70T2f7ivJFGt/Fvc8NLAb41I9OXeV6B8THjUb+KpqMRPXc8fpiTt5IsIx1MGQ36NnHdv/TepDb7NXJ4XxOPI3MJVSNr5LW9Ebd/m3wV1iCuJh/1oCx5dLdaAeCwkPnWUlSlmHQCI0U5pMF01qoReNR+76NqzfhSKYCmSfioQmD2QaU4Mr7ud5UAVLUdQKV8NCAtHCObVxRNOUMA1LAnbGbiONaRiOjHpPSWHAUASBg7YdSUIBU7dvY6TcwGCcuSGuWDfGYnP9EKuF6GMCwxxjSoNgNnUYFiXFFfE/gsBD2kWeju5mZU30yOL+viPZjo4yWnYiuCH8Xt+lqpw6FEPnG3o18TXigVeu54WR7GAaVpVkwm0YEDq4HDt+4vaNv6iuuaOkpUNwHaZQZVt+4Zp61kovDhtRTWHAxCmxexob4/bCAMHhoFIFEHMtgWmp6YDJoBwMysRjNKFqVzAbqwHGSxVdvGue+Ki8AH0tpXcpNnjnYtib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84eoLJIoCqFAV8OHlUDS4/6yRcMGM8SgoLkm9kwq4Dx/C1367RnEatLBfad4uhq/Hiw8aIFUO6uR9Ib08Xtr77BiLhkGGlpIShDLSQ5gtWAJ48ERhmUKr+ZrrbRRdLrhFiw32i3S4KsGZprksjQA7ERsH4uQiGI2AreI/Phykz5qCotJwpFrvICYWE1gkrq0jyfUiShHtuuhwXnzHJ7ulE6ewJ4rEXP8Dq1z5GW3ef0ZnPIRXFASy+4BR84aLpKC1yLjpeeHcnfrT8eW6OFWouFJKOTuEtF3a6Ydh9G9cdP3IAe7Z9wByDqpeotQG0qwn9ngXLACmQ9Z6SlNFbgmuW5LokJy5CbGQQ7T3TFwSeFJaOXOJBFjtwiybMQSgSC9ulDkuQMGriNJRXDY22odUPib4HKSXGdLO06IfbniA+nXB5tG0XXXAqvnTZTBAHJm9KKZ7duBPf/tOzeP2jfegNRdTyEXDnw58b/4mt7w1FsGnnYTzzzk5UlRVhwvBKR2UaP7wSrZ192Lq3Kf66GY7H/KckvpyUX+byUGLME71f7LbUsC5QXIa+7k709XTpD4H6N1AGdBy2fY55yeIVmuNnrgviAAL1dzhlPnDKNcC2x3NdohOPqQvs5922LjfH7bdoVm6PB5hyDbBdPN+5wtlMYWkwaPRUhBUCJTrnifpGLqusQeXQ0ZAphawAMrXxgfahgEyJMZ3Pq7D5tLwKs6z9HzG4Al+99hxHlXp3bwg/fvTfuOvRl3CkrRsySOxDnX6kuLTG1h78cPlLuPsvL6Onz153jHppCW5dMAdTxg4xXh/Dhzl/hb0W2kfh8sFkW0A7Vz4ftbgPFAoFRkyYCp8/YLBCUeIBhpxq+xzzizqtlch3//UjCAEUCVjwaK5LIhC4j9awxeIVuS7JCUvWxEbQXxUXT8Pj82PExFNBQaBQQIFaGRk/BAqYZap2w8XnM0snUPR9m+5P/VBC8K3Fc1FWHLB9PoebO3HzfeuxfsMuRBSmbGblj/tYlN8kb0ShWPvmJ/jq/zyB5o4e2+UrCvjws5suQ3GhP3ZMmF2PRGWxsV4xSTM7BnPNPf5CDB13MqLdXrro8BcBHl+i04ox4gzmM8v2dXGd2jpg0djcHd9NCAEkCVgkBIdgAEI0K8fCFbkuyQlJVsRGxcQzEZIVY8VCJAwZcxI8BQGm9au3qIlJq5hrWSt8uknrHHxLnE9T0888eSQumDHB9vkcOt6Bm+9bjw/3HjNp8fMfm9Yay4+6n/d3H8XXfv8Umjt6bZdz1OAK/Pj6i0GIZCwLuGsFMNYYmKQRxF9n3mqkWTTM7ktcGkV5zXCUDKpBzFlXexEMnW7z7BLFSskSF9QBytj8GWniBgQAJGDRidCfLzghkYiwcOSArIiNPupHRGaGuRIJRWWDUF4zUm0ZK4CiMK1hhW8xW1gzoi1oixY1b3FQ4tMACV+95izb59Le3YfbH3gOe4902LAEmFk1Uvmo+/t4XzO+9eBz6HbQpXLxzPH44sWnGcuj8Psm5lYh3moRd70TWUNI/H1UiOEeDBk7BcTjgUFwSF6gbETikxoxO/Ys5ZKqMQNLaOjopyQsHIIBCwEWCkGdTTIuNkomnqNaNQDoZnMiSagZMwUK2+IG18+PBD4GbIuZb0Gb5iGWabNPHoHpE4bZOpeIrOCuFa9g6/7jjG+GBDN/C6OVIMWPibXm3Z1N+Pljb0KJi2lhDiEE31xwFk6fOMza10Ixub6WaazFibN6cNaLuHsI4/3xFhZj0JDRMauGWuDkYsN4hsiJ6NDjZ7gCjXnUal+haGlEBkIRdYgqeRIgzUCkFSB9QES7sPooIe2fK+ijwBYud2mHAkGeIVFg4UO5LsUJgzeTO5cCpQhRDxRFBjsstaxmBPzF5VBsvxj54axMnAzKDZOlTB79hUkRPySWqhXxFy8+zfbkavWvbcdzmxq0wZpW25g4CaZVATA7o+qf1W/sxPTxg/G586fa2kNhgQ/3fuUS1N69Ci2dfczOiFZRcQWOVnpswdm87LZsOal2/Zl8hnOnzD/1mBXDx6Oj+Qgiob7YPiUJKBtuPjpl5JncfgAcynJQsGuXA5InzZ3Q2GUiCrDKbtjl+sSrr1oGBHwA9UB7yFMvou5UV3sfUH9H6vsRJMbN4ayZJNPlrK0Fjk1Tl8tGAVQBAgDCPkBSYl180VdMuo0Moo4eE2SFjIqN8rGno73POJur5PWjYvgksLPJA4i9eKPf+cBbXB4zYRGt2EzEiYngGFZZgjnTRtk6l6Nt3fjN6o2QGXFDKYUshyFHZCiKDEXWPooMWY5El9X/CihVZzylFKDR6KlqcSRJAkC0/+p3QiRIHg8kSf3v8XghebzweL24a8XLOGVsNaaOGWyr/KNqynH39RfgGw88DyV68SlzDSnMBQPi42iw15kVHHFCg3Dbsd/VypZ4ClA+bCya9+3QdkkAKgFlIxMMhdX2w+0+a3jTsGjoVoiwAqy/0bUiRWFjCdQuA5QCpDdKhgDKIBcKJhAkob4eScW0Tm0tIF+hWRfTeL6J5jAq4sxknIyKje6w5hcRdQolKB86FpLXz1g1uEqNVasJLRdsov7fzGmQMc/rLWrN4nHxjHEoDvhtnctv/vESPv30U0TCIUTCYciRiGqxoVSzdHBqicQtpAjXimf44vdb8Oqf7kRFaZGtPV06awJuvHw6lj39QbzFgRUKNJHwYK0biF1Tq3KyYobv+tG+llSPQnvTAUT6utUEQlTLwaBxQOte4zaEeVZ0wZFN5j+SxjG1vqR1S10sUAL0cORX/AEoLke8orcLAWofBupvcrFwAkEasMJk/gqtJkvxh0mgOnuLmZIzSsZ8Nsonz0VEZioXQuDxF6G4epRp/71xpIiJz0bUx4DC6MfAjYbg9wVm22i6Ovz1wtPH2jqXQ8fa8OdV/0ZHazN6ujoRCgYhy7IWNEyKmZsNH4/2sUq3+ljkl9h1qqXow92N+K//WcU43yaGEOD2BWfirJOGR69D/OiTBNcveh/MRvxY+dgw98v0HlNQ4kHZkHEwzCFCJKCEs9qMOtt4Mrnw1/AyDq2OoMCqhtxMFPXMbaoJXEa84LMDIYCS0XaJQJA665YAxxuguqynACFA1WgXCyQwI2NiI0i9MSdGIgGQUFIzGvD4zEeXxI1cMPmuj6AAN+oBZusIFCoZRnVEIjJ6u7vQ3tyEUFsTzjxpuK1z+cdz76K9J2QhBLRlyaP6GkgSJwwkizxWHy1Pwu1j+//Ls+/i4fVv2b4vAb8Xv/nKhfB0NaHlyH50tjUj1NcXGyliGAWjXz99pI/ZKBSTkS0UUCi1GA1kPgKmsHIYysoqwHa5QfIA5aOA0eeoH/Vhip2Mnm/kmcDI2cg4i1YgJaFBKbBqCXI+2+raJYAiI6W+J0KARcJZVJCnvFIHrFqKlPtVXXP2FliRkeZK+cSz0BXRVKbWp+bxFaCwarjWfWLRFWLS1RFbz/hdGPKA8e8g0WVKKeRIEKGeToT6ehDu60YkFALVJqCafeZUFBYkdw6KyDL++eLm2MNoOvcK32doUiGana8pND6L2VwlWs+FQinu+MNanHHyaJwxxZ46H1FTjmXf/TyuvuOP6Go7DgDweDzwF5bAX1iCgsISePwFajRVg08HrLtb9Puj5fVIEnweCT6PB8UBL6pKCzGsuhSDy4sxqDSAIYNKMay6DOXFflSXF2N4dQX8XgnbdjXg1h/dhw2bt6r7qxgduwYEMDq0Uu2ea2kjZxvfNdl2HDWDAli9JNeliLH2Rk00pDITrXghC/KcVUu0GBpOn20CLHhE/X0IMkJGxEaIFECmSlRogBAUVo8CJB83AoX3xzBZR7mKnPXroLFWMAUgB/sQ7uvSBEY35HAoZm7X/2uOmLOmjrV1LnsON2PrniPWYsNKfJgsOvsBMD4QxCQdscq3NyTjP+7+K9548HbUVJTY2vslZ07F95d+FnUPrdccXWX0draht7MVoBQen18VHsXl8BeWQNJn42UmUOOFR2mhH1+9ehbmTBuNsiI/yksCqC4vRoHP/mM2Y9okPHTvdzH9iqWQ5UjseLrIjAoOIPrQsIKD0Mw4jV5Q5/z9RSnQ05GBwqTJ6hs0K41ThO+GoB+wNwiM0xyjnUDSHWEmSITrYqN0xGT0UaY/nRAQjweByuFGoREnLpgV0XX6MjFdR0ER7ulEqLsdoZ52depyXYxE/SgYscH0808dby+Ww9tb9yEkU+O++LJGT4hP487LMSYOkLyVQ7seOw8042u/WYXHfvJl+DzJfzSEAN/5z89gw9a9ePatrWolTXQnTgo5EkZvRwt6O5pBiARfoBiB0gr4iytU4cFoDv3L/7tuHpZePiOF8zQydeIYXHrubDz76jsAUeL9DA68rf7nfThYwXHQZatG1Sg4f3khf6dvX73EeQuQQPhuCPKfTbcAY/U4OA6eb2G4yyiuX15aMhThiHG4a0H5UMBTwET+tJo3g/W/MIlOCTWwVm9nC9obP8Xx3R+g7dBO9LQdRSQUQtRyEefrEO9sOX5Eja3z2bzzEOL8M/TvUiJHUBNH0ahPRrJPMidTzmdEy7/mta34/b9et32vAn4fHr3reoweXg2Dk6rEnp8ESilCvZ3oaNqP5j0fou3gTvR0NCMSiaj3S/PnmHuKO05WhBDcfsNibVI87RFt3gXsfwvYvyGW8cDbwIF32A31BVfKYcRhq4dC89PIY8KqsHTEQIyYKhh4rL4hBQsnUWPoCDKC62IjrMS6TtQjSPBXDFcdBk0cAy0nKENMeMiKgmB3Gzoa96B17xZ0Nu5BsKsNlOqiRkogMsxHegytKrN1Pp8earbef5zzZhKhAF1pS7EPkYzf9fWWgoURJVweCgk/ePg5vPrBHtv3a2hVGVb+aAkCgYJ4EWMidCgIQr2d6GpqQOu+j9B5ZC+C3R2QKcUHu5tsHzcZF549A9OnTlS/EAnwJBiifOCd2OfgRtfKYMBxHZuJvhyXeXxpasW8oM7tkggE7kNl59v4hHkjU7h6ZSvHz0BERkxoEAJPoAxSoNQoJsCOHmHSYLRqREJB9Bw/gPZ9H6GzcRdCXS2ag6dJZc9W0IksDpoYqCovTno+CqU41t4Tb82QmNZ/IotD1JrBfrgRKXF5TEasJBI5rDVC8iAUoVhybz0am+37Cpw/YxJ+dOOVIJajaMzPjyoUwa5WdDbuQseB7XjqtU3OHxoLCvw+fPU/rkW066rU3sghAJkTHI5IcRhe1klBbVSOc78YAoHbrLnR+VDvftBG6K+4KjaC3nLI2mgPaCZwX9lg41BV8BN5cUNXKUW4pwNdhz9Bx/6P0Nd2BIocRkxMEPOK3VAxW4sM/UNsmoMVEM2K4IkXNZaVMeMzYrBiJOoS4c6Jt36YxfKwEAcNR9pxwy/XIBiO2DpHQoD/vu5iLL5ohoWw4MSTYVkVA3K4D70drQ6fmMR88eqLUVNVoR7D40duTfgOjy33ZKYYbiPtc/6CJeKNLBigiG7CjOGq2Agzo0MAtTKSSqqNXSOGuA3sDKEKQp3H0XVwG7obP0GkpwPQu0gSVdJxIsMTv96QpuWxi50KPlG3h6UgSfIx6SaJ2y/MBIj6/9l3d+EXf3/V9mn6vB48+sMv4dp50y3Ka3XeHgQCBbjqvJm45xufs39dbVBcFEBBQYH6TEkSUJ7DwDtO69cD/WTOhfo6iOacYODi9NkWv4VM4ZpreeXE2WiPWo4lgABSoBTEWwiqD1/VRy6wsTIARLqOI9zWCCXcB+MoEsZKYLoca1kblwm3rFXMzLpwJHl/nkQIKsuKte0R2z4KYf6ZKWIuzYlqpjBpQbJDPpn1zIiQ2HYUP/v7a5hzyhhcdsZEW4csDvjx2N3Xo+6RZ/C7f76CcCSC2AgVbSgzs1xdXoz/vPIc3DT/PJw8dqhta5Fdnvj3BjQebUH0/pYMBtr2uXoM25C4MciJGRUG3OtVyixOzy0fW3/z7wc8+m+V+Z2woWCiMWpIvHldAlC/JDtlFWQPhTrz7RZaI2O4JjbC3mLIIQpdaIBlsCmEAAAgAElEQVRI8BRXQYkKjZgfh76s9LUj3HoASqjXKB5MBYVkksdKmHAig4+1AYLj7T22YlKMHjKIESv6P5NRD2YihFt0BC9gDD8CXViwAcD0ZRq9vhGF4j/vXYMNf/wKxg2zN5lWYYEP9379Gnzhkpn4xV9fxPMbd6CjqwdUUY/n9RKMHVaJry6Yh5vnz0NpcSDFE0zMi+/uwFd+8DvI7Ix93oKMHMsWfLyXZJDCjBXFdWQF8DgwcqYS8jwTXPso4DP73Zn8/vjfEy+YKIDFK2N9vdkMK794pf28/WWG2HzBG4Y6A7KLOB0ynk/3bNFDALE3HxgA9bfuUlBC98RGdEprRCtnEqiINSj0HzsFaKQPkbaDkLtbOXFhR2zYsXyYWDq4/4eOd2CKjRlTp40dbLRs8LE1DM+cDetGNNki3epFbrByaE01flIy/R/RhzQSNLX1YOmvH8dTP7/O9qRzBMCMySPxr58uQXNHN7buaUTjsXYECnwYP7wKU8cNhddGLI9UWffGNtz0m3Vo6ezTrr2iVfb9KOgOcVB55xov6T8tuquWAX6tay0TBhaJqFaORSsByEBDWI3bIOifBL2Ag7rV/WeKqrMv1+fJM0ScdO9SQHLP0d0VsVE6dJwayIvQWIUueQB/iTbrK6LRJpWuY4i0HVQnyjBYHxJYMqLxMxJZM7j8plYORL/vOtiCS2YlP7czp4xk9smu4VpRfMvJDDvmZzYP5QUGD2WEHGPZ0M3H2kyrr364Hz9Y/hJ+97XLHVvAq8qKcf7p9rph3OCxf3+Ir/3hafQGZcBXCES0+ClEs5oNPQ048mHWyhNlIHQ1WFF/Q65LkJyrlgEFfkQtp5mGAIAHGOsBxi0HVvWDaySI51AEGOekmrPxcEUAeO2+DwhAc2iRNVDnLDuFq8+9K80vT+lQhGXd3K1W+sRXCEokdWZUSqBEggg3fYJIyz5NaPBOjlZdJoyzomFEBzdhGesIapXOzJ760V57MSGmjanB0KpSzvoSPwNr/LLJR7eM2P1YOpAmOSYrwrRr8Ye17+LBJ95z43ZnBEoplj35Hm687yn0BCPqVCi+QkSvgy4a/fbCsWeghA7zE2DhwxkpyQnH4keBaByYLB9bFx2LVwALHszywQVpM95hfjtdhOuW9B9LIMuiMXD0A3K5u9QVsREkfhgqBRA1YijUWVjl3jZEjnwMGuyM5dMr74TBuMzS2QqXr/z5ClobQspuA9Ucv/GTRlvnVl5cgPNPG2sihEwqeENgrkRiIY2PLugMw2L5a6L/90TLSwnBnX9+Ef/3yjY3brmrUArc96838M0HXkBIVqKjlajkY+6Z/lzlKFy24rCWIwCICO2dFlct0+Zw0Z/7XEIAqQhYICJM9itkp6PCbD5njuphAtQ+4rAcmcBhdR9xN1aQK2JDhtfY+iRqlwmlgNLRCOX4p9rU1rzQ4K0YumDR1vECwlScJIpdoXXnMCJD7475eN9xNHf02jq//7joFBD2uNGKnhiPxX8A8/RUP6b7Y4UUL0DYPB70hRR85X+ewhNv73LjtruColDc86d/4Xu/uB+RruOgFKAgJj1I+rl4gIIcWDeUoPNtCHHm/CeIsfBRQB/2nC8QAB6P5s8h6BdIDqs4u49bKOhMcNAcNzxmLYOjAlMKrHd3BlxXxIbHp82wx/hJ0FA3lKMfg7YdQNSxgBUPUaFhZdkwERMSW5maWT5Mgk9FRYYxKFdfRMFLHzTYOr/LZo3DhOGV8aICVhW/SXnc+JiGKTcrCyM+uHydvREsvnsN/vLCR2o01hwSicj4zn1/xY//tx6RcBi0ZQ+U1n2gigxKKWiwK3YPWWtRxZjsF3Zdig5elKY4w+oJzOLljI9WnrJ4Ra5LILCD4wiiNvM/eQscV965ZJzP2e8pA8VNW2wMnjQTvcGwVhcwFQIAhHq4Ss+sIjTrbkggRMzymnY3aIKEt4Qw/h/r3tpp6xwLfB7cdu2seFFhVfGzlog4SAoffhcmAkfi/EgMjrHG6x2SFdz4u6fxo5WvoS9kL8qoEzq6+/CP5zbigfqX0d5lbj0KhsK4/Vcr8duVT4KyI2u6joIe+Qj02HYg1G08X/3ZypnfRgpmRb3ci1cAtfe5XaCBx+LlAHWlDZQ59J/k4nwwjQsSQhw+S05EgZO8hADzVzgri5s4+U1RAJ4G14uQtm2nhxbFxAHf0ucquWjXg6nQYLtLCGPFMOliiUsjJstca5g1xWtvi+c2N+BYew9qyouSnudNnzkNv1//PnY3tpus5QSB6y0ys/1RRn0ywYr0/ERfz/VHUAoQiohC8bN/bsCb2w5h2e1XYNIIe3E4ErHvSAv+vO5N/PWZt3HgSAtAZWzavg8P33U9JOaa9AXDuPmnD+NvT76m3hsKgChQnyEFkMNAJBi7V+yIG4rEk7Jlkkg74BlkfjsSoT97SiWwcAXgaQHq73C/fP2d+Y+oMRHy2KARg6hlXbgcWHNDrgsjMOOqZdr7zsED5SRv53GgrMb+NrnqSbn01863qa9zvRhpNyG8hcUwigy2Jc1W/BLXdWJhkYjrLtE/JiNNEn3A7MviWM0dQax+w551o7DAi/tuugAejwcxAWNl0XDwSeijkejDHpc9b37frNiDQbRRELz84X7MvHU5fvzX13G83fl8HhFZwZsf7cXn7lqBSZ+/Bz//y/M4cLQ9epyVT23Aulfej+bv6g2i9jt/xF+fehM0GgDO4prAYp2Uo1/tum8hrXlBCFF/A7RSG90gRqtEqX1Qdf7tF0JDg2iNotraXJdEYIY/hfmUQmH7eV/4trPnlebo4S6tdnYdlBRmy7VB2m/trr4IQPxGoRHXZUKSCA2TClNi92ViHTG1bHCVMEz+R5dV/vT0Fiy97FQU+JIHjLrqzPFYcsk0PPL8VhiesoT3MY0HLOGm3DhvLpI5dCtGtIuCaJloTO1r23T1hXH3397Ag09sxpcunobPnz8Fp08YggKf+eOhUIqGxlY8u/ET/P35zXh7614oigI1zKgEED0Ilzr/zdK7V+DdbQ0YWlmGvz/9Ft79eDdTHj2/9h+SOjW0fi+pomVlykwkwFcEhHMw2Vl3B1BU5vwlZkDb1uNTRQfVgufUu+uQ1X+oA2ihO0Ijas3TnikaBLwR4NgxoGakmkf2qc8akZy3fOMgAP0sgPq0iy5wkdpa55U7pcATX3G+jd3nhwBYtBxYnWVLmFNfjbX7M1KMtMVGoLBIFRz6m8LKSdGqJW4lNIiF2OAFDT/CJZHQ4FvLBNjS0IwX3t+Hq84cn/RcJYngvpvOw8f7W/D2J0ccXqkMq1p29wbhoYsO5rsuOsCsJxTH2nvwuzXv4g/r3sXwymJMHz8EJ42sRHVZIbweCc3t3dh3tA1bPj2MhsMt6OkLagKDEXqICQ21n1BBR3cQ9658Rj2OHswNfH5o/zUBQpVY+aLnoW1GJCBQkRux8cxtaleI0yBfljDXSh+5QhVA6gHqb3Vh//2AhWOR9rWkAKQIsMqhYKtdBihpjnqhUOdmWfeN1PchcBd6JRw/U6k4cTaEgLEB+4fKdhTkq5bB0XWgFI6Df9kkLbFBvAXoCUbiRYPB1J9IZPBp/H54McJbMqxEBpPHVGSwF5/gh399C5+ZNRY+G/NDlBX5sf6uq3Fl3Xq89+nRBBcn0yYzbf+GH4g+6ic+KbrMWzqoVvnrFgNQyArFgWNdOHCsA0++Da3i1ywi0WWtZQh9vW6Z4ISE3lOn+2VERQjU9axIQUygxIsMRoAQSR3+2pnO9UuDNUuARY9m7h4TCVBKNPGhCbTeLlXoDDTm35/6ddQf/Z721K+NHkb6gjqgamxqZSEEkHLltCyIo/YRNS6O01vpNJYOoIayH/cobHskUKhWl/osWcL8Bc6uA3F/wICOzStkzpCxUyDpcSziHDIJbAkK20KD/xiHslrvXyuDxHyHcf2HDc1Y+e/tts+7prwQz959Lb7ymVNQWlQA0/N04reRzsf02rHrOdEVXQ9mG26doUsKXDpnWSLcdY0TmHwZpPg0vqymZYHxv7/U9v3KCKuXZnY4G2EWJI/adbN4hTqMduGjAyc6qaeEOVcHUAooEWD19e6IsFfq1AmnFCW1YX8SAa4Ro1NyzoIH1ZgWjkUjTX3yPUejUqB1u2UJJzU8pcDqm/KiKHGE4Iv5VvAOoYkcKFMSGhYVnZkDqFmXDS+EmMqTgqDuH+/gSKt9s3xVaQB//saF2Hr/F1F33VkYUZWkZZNJzcEfgL3myUSHwUKE+OuXdM4Z/r5q27CBz/jj8PlNhQdM0ph83hyNSGGRnsre+Hn2OkoSIPlU4aF/rn0UmTJ/ZowFD6ZQKUC95mEZWJsBH5c1SwHZYcAmHRt+X4IMUrsM8CQfWeg6Tn0wshVyo3aZs/wZtsanJTbaukOIyApXeZgJDCvrBF/hSYyAsGkJiRtaa2HZiNuXsSI71NKD/370TSiKsydhdE0pfvyF2dj95y/jsW9fhjlThsLn1S0CFhWp69YN/aMlAdoCJxTMxELUyqHfDzDXMpFA4cVNEmsG+DIwwtFMjJiKDMTy52pECkt9PbC6QZ2WPBew19knAYvGxsRHXoRHToJU6HwbSgGpwfXohgbW3awOv3YqJDP7rhYkYuFKpDzhGaWq43c6KE5i8BCgti6949lBCcDRQ5mhUSg6ab2xB1XXoDliYbpnHUMNYbS5yt5sKKuhAuO3MbN08JWurqHYddp3QzqbBjz22i5cOWsMrjt/suNrUeDz4AvzJuFzcyfinZ1N+NNz27B2wx509joYSpU2BKpPBhCVz7pHtn7+UWdRGnPMBIn5bkRHgBBthIju2a9tAz2diYtBtX0QSXM21Q8oxcoRfT4kpgwkzpnU8PzoPh+UCXWvly16j3NNHbAG6mRhVMpthcO2TKg3FuWSApC688zhtM75JpQCzQ1ql0emWXeTek+d3tDaB4H6r2WkSAITFq8wvsdSgSD9rjgPtW+xIACULERBdurEnuF4MWmJDa8/AETY1m0iAcC1Xq0mYbMUGnqLlutKiWsdc6326ENoJjrYRXWY5q3LXsfJowZh5vialK6JJBGcc/JQnHPyUPzsS2fitY8b8db2I3h9WyM+3t8COVkrOFVTFmUrdf0/IzxY0cFnpZQTCswyZcoUFRmIiQPCiASDIydriWBmBAYjUlgBwn43G51iEDVQ/RjyiVVLgfnLAE8+zefB3vMSdU4PQgESAeoz1zdriwWjnV0nStX5abIhNHTI01r/ut1yEkBJwVojcEAdsGAUE86e2L89plBgVUP6xaq/Qft92d0gw++IRSucHSML3cFpiQ1ZoeZCwrLLhPnwE5slExoGywaJXzZ0ATBiI667QC99vGUDULuGrvzJ03jyritxxsTUBIfOyKoSXHfeJFx33iRt30G8taMJb2xrxJs7jmDrvha0dofcmaOEf3FTVtXqlbZuzdBUBDsklnL/waZxIkNf1gVGVPCZCAjKfCfsd+YeRq0qrPVDt66w56AfkyLrQ8jsoM+fEp03I19Eh0b0+vm0kS4RYNUB5MTXw8bILwOEAGtTnJ8mVerrgcUOh1Dm2S3vV9TWAjgHQDEQ8gFeBSBek3eSixc5EoJ7zz8z+s4OVy3T5ljJAE4uEaXOgpmlSFpio7m9GwgMgqHySPjhLBjRSoixUNgSGib9/Lw1xVRkMN9ZuIe3qb0Xn/3p0/jLty7CZ2aMSucSGagoLsCVs0bjylmjAQCt3UFs3deiCpDtjfhwXwsOHu+Gkq744APNsMKDtXTwVg7HgoPt6tC7QPQfGy88ZEbo6IKC604x3B8tb/RYhDmPdFszGWbVEjVEcFk1oiN68hKv6ueB5UDz/uxZDa74Axy+Dd1pfabCqqXOZu7NVZTIbJDpGYzZ154PALQGBXtJ3by8ClT/HLegEQB+e2UkBPD73Dt2XFlg/1oROA9mlgJpiQ2qm995J8Q4Pw1WQPDCgxcVNoSGQZxwQgfMOiBeZFipYi79aEcf5v/iOdz1uVn47/nT4fe67yMwqLgA86YOw7ypw/BdnI6eYAQHm7vxzs4mvL79CF7d2ohdje0OLFxM5R1NYoQHux/CWznYeBvgBAeNCYJoGisKGBGgWzoM94HvOknUnYJYmqE7hZjsI4954dvq/9oHALlYK24eVkSEAPAA1ePUSdBWZbbfFgAQKE5hozq3S2EfqyiRUaGuLSiKc4uNIDdQqsbLcZM1X3EmyDLld7ZouXU9Z0Y0gGJmSdOln7MuOB2JYjXCJKHQYLtROCuH4b9WvmiaXmT7N6EvLOMHf9+I9e824Pc3zsWZkwZntDu+qMCLycPLMXl4Ob58geqkerilBy9sOYhn3z+IV7Y2oqmtN0G3C2uqgLocFRq6YOCsHPp31mcjTnDo3RoSDIG14gJxMVaIaDAvVkwQGLpTWAdR1jrCdrFAcw41iBvi6D7mFNYpc8Fy5vnNXZGs8ah9vTSsvjgzheTgJUsp0BDMXFnsEKFqS5sNihcBEOwYmIHWBjwU6Elz9Inlrplgh8kgRB2eWu9yV4pTX6ieLnePb4EL4we5it6suyRRd0qcQDETGpxVw8whNGrpAPPdTGQQY9lt8M7O4zjvB0/gi/Mm4jsLp2PqyIp0L5pthlcW4foLJ+P6Cyejuy+C17cfweoNe/H05gM43GIWF0QXFCwmLTND94kuOBAvNOIEByNeDNdYT+fTOKHBdqdQRuQAxntJ9YOz4oQy6/Kytk7MWt1qUAcsGg0gD2c4JQSANmfLqiUZPIbtzGqUxlzy+NLcHl/gIhQIyZkTiX29QKDI/jMupzhc14pZDsOTA1kTzOmJDYJ4i4JZcK+k3SlmAsSB0IimAdEWtaH1y/1PoVUckilWvrIL/3prD66YOQpfv3wqLjhlGDxS9mqL4oAXl88YictnjERnbxhPbz6AZc/vwKtbG41+Hux58+HMdesGa+Ug2jrWj8Pwn8auNeWOYejq0I9pJjY060ecIylg6B7h/Tqic6cwooQXkv2OOmA183Xhw9roGslZX2um0K/t4keB4/vc9eW4oM5hf3KOYpgIBh6UAlILsP6OzB3jqa8zDuI2cLsnZZwXjl4gWfQxcrEbhRUJBHHCwjAhW6JuE0Zo8MNcCffdtPuEFRRMheZS5dQbkrHm7QasebsBwyuLcO2ZY3D5jJE456QhqC4NZK0OLC304fNzx+Pzc8djS0ML7l2zBes2NqAvxAVmiYoE/aVtITgAGGJvGAQH040C3UmUEXeGLhb9PgAxwcCIBIMjKCdK9GeFMttE7yMzBDbntbHLrGGGodbWAuHPAh7NkuO2970jJHW+EDe95mtG23/BUQAdx9w5ruDERX/3rV6SrQPC0Tuqdrk6dNaVQzuwllIK9GaoO8mEtMSGx+OBbBYZMm60CCdCDGKEX88KE4vtDcvgjs9UZNF1DIle3A5HgRxu6cGDz27Hg89uR3mRH7Mn1uDymSNxyWnDMXXUIFsTu7nB9LGV+McdF2Lz7uP48T834+n3D4Dy8Tx058/od2bZUnDoPhy6CGAUSFRc6NYRxknU4H/B5DUIQMZywXad6A6rcUNnOWGSrTDh2aa+HobpymtrgcjlmuOhLsKyKD4IAQoC7gkOJxNkEcQcbQUCx2i/FTkYG5aeDUgroFTabCQQ++I7GVctc94wyaLPUVpio6K0EM0yW8lzvhem08qzgoOreKQE25gKDQtrRnS/MKYnI1rR2YSp8Np7Qnjxw0N48cND8HkljKouwaXTR+CKGSNx4anDUVaYwWFOUM9u1oRqPP7/LsXqDXvxrUffQWNrj7FS5gUH70AavUascEBMgOgHMgx11bfXBQovPEisdW6IuQEYYmmYOYvyabqz6ECzbCSCFx8AMP/PgLcARsebDEIABAJQR4TUpbkvJ2UdoIJSkGF0364wsDrzQzrjqL9DC6plF5d+v06H0mZ5moW0xIZPIlpLhan8+eVE08zzXSpm1hBeXJiNPjFYM1jhARhvpNlN5S+4nTx6VvOHJCxT7GnqxLLnd2DZ8zvg90qYPbEGZ08ejDknDcbsiTUYUlGYkeG0Hongc3PHY97Uobj5T2/iyff2mwgOTljEOX1qy+zoE4BJZyr8qAiRACozokLbIM5HQxN0/LBWM2dRdh3fjUIzG8c/r+FjA8z/M+DRJ6ZzYDlwyqKxRl+TVHBimBFaQ+AEfbSQBKD++tyWxelvcOFD6Y8AcxLokFLA+3R6x3NIWmIj3NcNeMtgrOhZEcA7elo5h5oIETMfjURCgx1uZLBkEMO/eExa+2Z50iAUoXhzx1G8ueMofgvA75UwYWgZZk2owrknD8FZkwfjpOHlKPS7FxVz2KAirPnOxfjB3zfht+s/gsJOFGQmOPh01l8g6t/Bb8uJE8PIEajr2EBcvK+GYfisbhGxchZl7yfJ+KRB/QpWfNTWAvJnob50M2AFWrgivfgEwrAhcINoo4eqLfSIktnJ+ZxyvAGoGmfzeSfqLM7pUPuwZqmw+QOj0Kym2SMtsdFytBEYMRzmVg3uv5WfRqIhsLaEhr5/xIsMvcJLhu7xTtn8mXvThSIKth9sw/aDbfjbq7vh9UioKinAGROrcfGpw3HRqcNw2pjKtP0CfR4J937pDAwdVIjv/vVdRCJMBR0VDdz3OMGhWxwURC0RgPHaGgKH6dYIfT17HzjhwIcuZydji6ax2zDDbZVIehdnoMJ3uyx8GCBakIh0HyjigtWEOtiHuMcnFtE4JjA+JwZfMajdI82HsjtPjlNeqXM+P0k6yF6tcWGT7MTxMpCW2Kgs8aHD60VYhrHijxMKFmKBFQ28IOGFBtvC1UepGLpNdMuGiciwesnyk5exw+zinHYyJz4isoKm9l48tekAntp0AADBqOpiXDlzJBafMxZzThqMooLUbpUkEdxx9SkAgO/8ZSNkmbdw6F9YoQHEiy72eiJemETzMmJDv2eGMOMExm4WEzGR8AN1+0iOAz31F6IjXeqAxaOdeatbsWglsDpFM7WTY2fHv1pgl1U57prob8gRwGvXYkFU60SqEyQ6DcGwbklqx0mDtH7Ocm+7FrLcxNIQrUSk+DTdX0N3CLXKw4oUPY0XGryQMevKsaq4ovl1mAqNWTQmmFR+rhHb54Hj3Vj2/Ce49CfP4bQ71+Guf27G7iOdKe/5v646BbdeMTVeeMV1U8CYRxduhLkubFkN94xdZs/HLB9/bLsfbb/h7pSvxYlJnRqKfPX1ACJpjuah2qRZqW1qm2AotWMIBPnAOofCQUmxG33hQ/2iyzEtsdF2ZB9I1DxrJRaYSt/U8ZMXDbxYYbc32XecqR7cukQw+c3S9cWEu3FDeCTYngC7j3TinlVbcNqd67Dk/tex/WCb8yMQ4N4vzcaFpwxLnjH6n6vgefEQTYfx+hvECZ83geiz+sTlAdDT7PgaCDRW3QhIDUjZlkoI1KnXM0w+zuwrEDjBiagnKVbHxOus+snSXCg86RkqKUWRjxUCrDXCxNHTIBzMPnwFww9vJbEbYtqaZo/FYNgvD78fLp3/6rrwSJCXW9UTjGDlK7sx/b/X46YH38ThVrNw5dYU+j340y1zUVHMhcg1s24kKiJhFuKEHSc0ohuYCQn+2ImEDfNRFCCYnXj+A5b6OnVG01QFR6otKSc/jcIXUzyIQJAnyA6tc4secn4MJ75YlAKrlzo/hguk3Sva190B80qfEwy85QIm+eNGr2jr2UiiAAzdJlYig+9isRQV0Q0sztAk3ZaesPMAOHnzxghHFDzy71044ztP4O+v7YHiYLz05OHluPPaU5MUy0J0WVqLWKHBb8eKCi6dFyjsvbGyboAANCK6Udxi1dLUu1RqH3C+jZNDHZvmfP8CQT7heAp7f/IsLAsehqN6JLUqxxXSFhuFUtik0jfrKjFJM+sy4cWKqdBAfAWkwwqMuK6cZKLDoRBJW3Ck19HW2NqL6+9/HV/98wbIDgTHt66ahtE1JckzsteCRP/AcA2j+fh7wYoMXlyAuy/cvvkuGl5YRoJi6KubrG5wvg0hQKQwhYM5eOZ9qUxFLxDkGY66Uhzu2+Ng4AClajTVHJH2rK+0+zhIwQhQMydN1lJh2o1iIk4s5z0BjBYNGCs3Pq8V+noa/WOeJ+4BIeb5LZKzhaxQPPTiTgDAgzefY2tiuJKAD9+Zfxq+8dBbJmuTnJB+baLZtAX2e/Qas6KBieUBYhwaaxAx+jEIYkNv9YiA2vqQs+6jlFm0IvnzxNLX594cIlmlDqCPxoS8XTI9CWHFoMzuXyDIBkoQkArsv0ucxLJxEiSPAFibu/dT2paNSPNekLiWLW/VYIQE341iZnUwdRJlLRqAodvEVGjwLWqCogIvygp9kOIsLOC2syITL9cEFbvpKvP8j/x7J/73uR22j/of503EkAqmZWpmIYp9sbHHJNafqJWDSTR0mTD/44QmMe6jt8VGeXKAL23tnjtWL4Vj1ZyKQ5ujn1A/cLEXCJKx9hZnz73dvAtXOGsM5Xg+qbTfjt0tR1DkBXpkXkDwVg2LrhWDwOCdRNltgMRdKTrqst8rYe5JNbjstGE4c1IVJg4pQWnAB0KAsKzgUEsvPtrfhte2N+HpzQfR2NJjfLWZWjf0/dOkSc5If3IthVL88LFNuPCUoThlVPIWYUWxH188dwL+58mtiTNGr4N+krwogdHSoVsl2Gljo2naBrqgYCOTGoQhG9uD+1AKdDUlPT9XcPJDBpyZNPMRx49hCg+9k2PImZ1PSCDISwhgax4iJ5ZFCkDK7VByV96OJNgB+Kpg7rjJWS4MQgTWeeK6ZHjBwQsN9X91WQFuuWQSbrlkIkZVFVmWuaYsgNPHDsKXzxuHcETBk5sP4rdPbMOGT45p89MkepE6URdpKhHTlzOfqO6/oyeMby1/B8/f9RlINh7E686bgPuf2YaIbHNEQsJTIUaBZtWVYhBxJo5xYVAAAB/iSURBVOKCtWbx87CAAHI4e2LDae3b32eidap33Zqt0gopN0P0BALXIUFACdj8jRFg/ihgXYIsF9Q570Kpz20Xrysx+kjPcUQrCLMuFatuESvn0Gi3idk+YSo0PBLBjRdNwPu/vAL3fP60hEKDx+eVsODM0Xil7jP4223nYkRlEXOcbOG0oqLMJ8ZLWxvx3JZDtvYwfWwVJgwpdXhcMPchmmDxn0+Ot0AZhSW3D4OQ1JZDndkbJ+5YO2TzeckA4Tyr3DMtZgSCbFF/Cxy9UDxJqubqsc5eN3nQEHJFbCjNeyBJXKwNM0HBO4DqXSxmMTYATpxYi5nKkgL84/a5+PPNZ2JkpX2RweP1EHzx3HF45xdXJg9+xZMn70VKgfvWf2wrr98r4cJTh9vcs9kJ6veCT+asEdE0dhsT8QiTPHxaz3Gb5XUDhzeVUGD+/ZkpSjbw9zrcIIUXmJNLmlWxLxBkGEfiOUleJz89CqC5wcEGmcEVsRFqPQQvDcHSsmEmIgy+HYjf1mwZXD4A55xUg7d/dhk+d/Zo1fHTBUZUFuHJ712E684dn+CFl8UXocMH6/XtTfjkcLut7BeYiipeIHDpvN8Gf1+jyWaWK3a/nFgxPBcmaaBAy15b5+UKxOlEYASQUhe7uScLw+KcGE9y3xgTCNyj1947GYD6vlv0iPm62vvguP7Jg0nrXPHZiAR7UBRqRyhQZC0UWHFh5vhn8MngW8zMOm1/Xong1stPws++cBqKU5ykLBFFBV48/LU5aO7sw3Mf2OuWyBeCYRlPvncAJ11TnjTv6WOr4JEIE6dDq9RZDL4Y3Orod32B/6+t44ev8mmmjqLsvggQ7suivwaAvggQ8MDRD7s/h9gOVQBOfDKzobcv/TXwwrezcCAbLHwYIF5En12Fql163gNqRFaBIBHP3OZwOL1FvaZUOvvt0fyISeTavIpS+36QpJYJptVqaMHqeTjnT95PQ1uuKQvgX/81D7/7zxkZERo6hX4PVnzjXEwaVpbmnrJvDn7po0Zb+SYMLXMwoyxntYimkfgfkMGSxW5L4tdbdaEYulqQ5S4UpBgzgwLXWLRI8h2fw9dBKj4VvR2wbbIgBCitcn6MTBHt7gVUK5akjkBSxgGLV6gVyaJH1eXaFbksqSBfcVTxu2Hao4DnGRf2kz6uiY1w0w54oJgIBRPhED0sl4+vXOK6ZIDzpgzBxp99BgvOHKmJm8wytKIQK79xrj1Rk31NYcnHB1rRE0zeDeD1EIyu1qKJml1PO10prAXK0JVivHcJu9hY8WnlNNyyJ+n55BxCAH8/tW44fbel8rw/c5uz46Q6OVUmsCoL22DSLbOiC0hghudZ+86ahAALuIZL7TI4+uFRAPX19vNnENd+ycH2o/CFWrVvfIUDroIB99+slWtM83k9+O+rp+KZ71+IsYOzG8b4nJMG457rZqa5l2QPiLtKpam9F+099sZVD49zqmWvvZ5EjKv5NDbdzKJBrNLM7rv2nRUhkT6gfb+t83GVVEa+UABXpzChUq5x+ghmxcOdpD6dvdsIASFIl/p6Z78zD9dwUfzOtped+p1lDnebDc27mZYtMS6bda9YdJPExIn6f0hFIVbdMQ+/+o8ZKEqx1RiRFRzvDOJ4ZxAR2flb45tXTMGis8ekdOwYVk+J+yaRvpCM9p6wrbzlRczkPwYBQSzSrIRhgvtuZsWytG4wQlQ/TtdRdU6UbLN6fwotfgIU9LOAVPMdTugEAA7m4zFAHM4VIV+R2nHcZNHyeHGdCCFMBFbYjWtkisMqe91NaRzLXVwVG8F9G+HTnf4AGCsg7ntcpYW4bQgBLjplKN79xRW45oyRKY2Ea2zrxW0rNmP0N55Azc3rUPOVtRj3zfW4feVmNLbaH+rnkQj+dPM5OGl4cqfLxJhV1jY2SeE4drpRzDe1KFci64bBWsVkMHSj8D45/LEsxAsocHxnaueSNnWpa8EFK1wsR4bxpuD71LovtWP19jqzikj50JXisAxiokCBFWtvsP/8U2ijTwAsWObsOHkQW4PF1V+xEuqB1HmIsUyQ2DJvtTBUVqzpXF3yeSR8f+FpeOp7FzkK0MXy4kdNOPuH/8Yfn9uFxra+aPrBlh784ZlPMOeu5/FBQ2uCPRipLgtg5TfPRUnADadU960ZPGkNBTb4TQCspclSQLLfTX03ADUOC8ytH6b+GxIQ6gZaszjklYf0pBbgSyLqCIZ8Z9GjcPw8Upr6cLqnvu5wAwIsWpnasdygdgUcXR9KgdYDmSqNYCBg991MCCBXqMueAmc/Uym/BK/rTQbS+IFWybGVCRJ/5yqmoYOK8Ph3L8RPP386Ail0m0Rkip+s/hhX/foN7G+2niG04Vg3Ft33Oo6291nm4TlrUg1++eUzsiAVNJweSMsvEYLKEn/ivBqylQK2IzjivrPC0iyoG5/XZHve8tW2D1By2PdY/zU4CxChQQBIPs2pK0+55hGk9BpIt9GUSqurNkfCTYHD3yHJi7gGgjzGkeWLHVBhEwqg/kYHx8g8rouNYOM2eEMdRnO6YZik/p+/cASEEFw+YwQ2/uKzuGLGiJS6TQ629OKzv3oddau3IRhOfkP3NHXipmXvOPLj+NplJ+ML5453XjinWJ6/SYXMrR5U4kdJoT2/gWMdfZzgY/dlITj0A+nWDMKt5+85P0uvmcgws25QGTi0ydZ5ZBRJTr2CVQo060Gecemv1ZEzTn9nlAKep9I7tqfVmeAgABSvOidENln8qP1WqE6exDUQ5DFrHHSlSHD+/nDiF5UlXBcbVA6DHNsOY0XIWzT0xVi63yuh7vMzsO47F2FUdWqjTZ7/sAlzf/wSnv/IWeCnJzcfxO+e2m47PyHAH288C1NHVjgton0SCg2zNGP6qOpio+NnAg4c74FRCHDHMBMcfLcICOJ9MnTRwCjz6DouvD3hnxXt03EY6HMQeS9T1N+ElNWGfp4L80hw1C4HymvgXGlopDucrv4OOJ/SnqhzQmRrdErtcoA6fUXmT1wDQZ5jWx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DCZ/2WZ2OJFwMoJe910S7Bd5f+QQrg+H3vksuaO75KmHHzjncE74nigVZyDD31RzKQidrurgUAiP/rzTAA0MMt2/IA/OFKAPgsufNxMW4WmxIYbABnqbD8EgB454EOcrnmntMAANTDB1LZ/UXiIGkmPs6A0TQ7SE5aVdGv2Zj/4vpnR4rIBVXDPCcYBGS1j09ufMHUyBTslZaQk0FfE/eBDfnAybV3M1FToSc3kXfSXHu/Fpi68FbGW4f+5wZSRPNV1u306A/rvvJkgnvEyP1nMC3xBypWyfQTYHMjgMfXMfxF3zevYGTWbzVCt0Rx7jXkOPXlu6POSW+e/Rdypn2EHD1hR2b1YBDogzHuuUQVAey59SoVMPeNaxj+4rML7iMnut8yKbL65uVl3MeJaD3p61LOytvK/YTxol9/ZR3xMsg/483R9rMeDeYBgFRx+XU3q65ra73IClCN7x9oax9740xmIKc/Z1Qhet8Knjc5sSlhOtrFqK3fuRCwbIqbAmG5hmgYbAZ/Z9mj3Nmm49Y/AwCMV/M8Kx1+/4ME6+CHm8k6tun2NHdJHiBH+/cTtNzAHaQeWP0f5BhKuY6XjKkR1OmYc1F9kE62e4RveDnh6rd3k+OG9eRYOdBxrKCKc4uow1+uAwCOsdxsYrypzskX3fPkeXJAvPHDWVjbW/6sVUNRXjWX8/cnfK8dbVisUqCu/40L262Y/eB8RksYkTLK6ZECc9J7+K5nWbg+2AEA0ADhm/sTMzrIyzTWJyZ+xGc7uYd4EF3Jk5UG237Cutz/fXK8UZv6ts1EfYr80rbijJVxS8oPmXn/d0k3Gs7WoXa4m1pcQU7rwsf63EmW6QffY10+9go53gEAUOtVCFgLOF1Y5D7mb9ABxjl2bep6GB3qqmnbgPp7z1ykWmIbBmBtL3nBXFI+c+Kk3EDT3mHDSX/VMjyzod0KYNjGzODpo4zG4u+pTXqNX/ayr2NmnSc5gSR/JYdlSRSSQLSiluTGlAZBrjr1V9IjtWnayi3k8NwzO3fu3pfgSPgzwpK6cwAA2Vtq7fsHmUi4gV7aluQaOPgwJ+HHZL10dSMAhCkJEKA4EykR5g5balsaxZzamBnbUM7KQdD2k5Lbnr+FPM/aLh8u7neCjlQDUJ0zYZsoPHEGoz9/tpzxGTtRzUxvLEXcWjqf5iS8x3TK0B84GXuI4bAZadFJRu6z0pdk4/mkAId7yGEzN52FRaTTun7OzXBtJccrsq1IFGELUyNe15+fcU2TtofIcTMA+PfYAY2PU0YEREYOHDnOXQwpYhj8pZAPAO0Y0Kx49OocAJh0d+4opQCqAcAKiV9a9YzPw2i63l6GG8EJRou3JPWql7j+Wm4SSYZ3uc5z9DEAwGKkxzYAwJLUhZJAP5eUtI+oB60pLVKbyOGNpDFW/TY5njcfw1pa2LeyLqn6Vc2vEpFxUTRNbuxn3yNkqtRoGUeVtVGnCXmHd8tzNxiBMxx/7Z0sAlzLAWyc9jXswV7HGUypw83MEDDADPYTOjWH9MdJD2NUrk8EMvL5q8gCqW19mlJc9B5LTnuNHFKu3JxNDq8kZUgmiCVZ85WWFStXtszH9pICb7LHZKNzt09DRv1Kpj1SNf3cVS0BBpvtZO5LLWO6tjIU8Aw4r3z26031H0+8coTKg6tch8LmsRrr5MHjH9cznkROH8OOUUY0+6c5MjrCtscDALOEsy8pg8iFNAoPgKitINsx+D2eNGYKzKf9R0ERrWAmeYYdBDPnMh4Zl7mOLHgJkY132ZdiTcSiJmkUU92qsaQwSwHDaxHTgU16JkRI6wTgPan48o3vmoetz2nXzHlQjTXBfPmJj4NW7FzFiI9+ZusLl0ZCTBwWM1unsiW/NVkRoZMD/kgGD4Nlra31VaY8BWfunklfT0QHT1rvnBwAFqW4rVQjdLdLw8zQG6BFlkuP/FAsB0DmTjRH3kwAMjdTiUXtAFBin0HhJLufCzDYzLhILgoyGpCGcNw/qm594uPX+v+gX9avvTT4VX/Z7tcDo0Dvai2ZtI0wig49nUMa03gZizB4WjUZBbjaAaIM5sp4nHUddz2JgYo3VRh88W2hnmYAEDIjJ+TxyPcJM+N1VyEy0FnOggVAHYx+oOkkf32q4CSTVgEA+aKACirO3EpAROcxXb0iammUM7Mm/dSQZd2ffpz7y9HTxebcioaTz7ttulrQPYxhyx71+nQyU/ZJlhsowPPaZILGw3mSwZnYrXjv9a8IWJqyWr0B6JN8qSSRZ6EzBmbA46ozAHwnI4wTbRVmmS+90E0yfD4f6WSDSVnFwwHXTKxKyprSZcuWcbuDwOOjdq18ZnYsjjAnqmkEhiWr1h8bzssr91Mnj5TXLFn6Ui9Gmol6EQ7KyejtZhgcYAXbJK55Ik2GmTc84rt3CS/2ZOliGeSL8mfMRzKNsEtubmJBNsYDXh0AmGYcjDjREuujKHeap3hWcAEV+TKffQjNuvB0D5A/5TdOkY5X3Dw9xt2PgkCAwcwAihzms0uin5+sIY1Fmg3HTUaN8+ULvxoI+4zAVHSJ16Mhn22OYWxovi2Yyu1kjCSJGu0tF86THA/4WcjwTSHMYAWrIBv2qUIAkEzB3aKcGARVR5qpdOEC2w0MhswHAXTUDYSAvJoIHLPM6p6iTGDFQYjBDEMVjJwRRy2E4hAA8RxcrgJRC3Z9zxgIw1MB+KIyrZTs8AJtsVcpofdG5s1gox2MepYQynhTjL/0TN+ozT0bIhVs/Y95PIE/+Jz5Yin2VpKyCvLeLgPq5roWz07qFMxTIlRdnruHV2ZkCB8AA2knoRAA++cAKsiQIOMaqmIQ+vxbyIerMZEClIiRuJIWGiKpTAaRjAqLNL5JKgSEvwNK9R1SQL05xDyyiJLQqyGe75zfxBOFX/IT5mTPy9tYXtFXzYfBSt5URrSmiCILsgry3h5Ajv5ovlI3KWO01DkYnb1VlQvZ11IZC51Rxpuml/AnILixgACDQy+RHp+bT/RweTEJLJQ3umiamu7tW77OrjjYItu24xixRqj+43ly52Ufkx68TLq3tMQyhOJ5LAEAQI6Tx1R/B5GOjm+8z8lI0rQzAb+qxszKUkT1+VkFeW+fQ5HfrQo74RFHFQOxZsLSwHtLhtACWtEMAOjj8t5GnscnjFQVEoxOCzGYSXcQcSSWyrVKlSIsCk4f3t81Fjjnqmoce+LHtk+ubL2BFDRGdYiociKfgxau+GieLQp59sFREhPx7KakPENS2QwglfNJTPLFAinCnqYTC3JBJJ1dZrDAagV8A8QhRyWdAGQLcmzXATp/AAAUCRMmbWKVEqFtOOGULPMIMDjIDEGTFEDJ1WpRnn+q4/jwmGWxaqf2wi3acHfPxJeCke7+2j/e/wkNoJQxJIulzEfTTlJa2gXIMgnp4HkbH08UB+Mue38y7+fFYPBKTDLLTbUSxHzeGr68HMLPidzofNBKBJo3CFAFvPOyTFER67oJsk2Xm1ilBFB1406EPLkCDLbnEwVnUFsZEc0NdfU3bu2jAaDv+vJL1erpdyR7990153Ve92jVT+qfngNVx9hw86LxdXqXWuQJA/nzC7grGgBUBbFLablCYrMQN5kgj9t4kuOIIFQlMglTJWqDRGLjrnBIiEGbsz8RC0we7yIIs9gyaOBRsouHFxQenmseJ0OwLkG6MC/OtJzYjmgWu05hBRBWCzB4nDHrdh3/2/DYeBCY0dEAYLzoCveIon+r6WVtQ0WnITTyk1+rr6x5wFY7xbxPKaOtU7mOfLgQX3fMDqpQAKiP6amyqmEvtdJKOtfRZJOIeHlSkhDqRyeVyxnpaj5GrbRyJEUtkV3C2pCoUO7WAgKNilnQ6tQn746TP+U3LsR8SS0ep4MAKFMfANTm20/EWhlT3YS9C2TBAAAIMdjSDAB5Rvl7g/1TADCw7gBgvmRj+Mgi89+2b/yH1arSWdxl2P/H20TLfv/gkqhnX/U2ACJznpxSauECUHRUXiGT+PojvC60QigeBRBduz5DXGTNA9UgJ0KRZ4eN5TxKVnSCx0mmZPBFjS/n8NhGzieH5CURBqUamffgWgDIWcXjz9dGDsclMT0ozFQjryYCx4Ls0xs/mMqfgayc9CHfLgDYCACgk6vrEcsCUCrcAsaf8Tw5pI3VU0e75srVADCp0gGLr+zrK3a/+dHGditQ5fe6K4C/vx2WaB8vYtwB9cpJQLHE2O3trdDRLkCRO1M93HFI1lQ/nsFUX8xMPeQiH4CDDNEQXD6fT2+dJZXl0W9v4KEVnclzkg+OQRTVE+oWF0xyh3qix9GCm/6NdPko4mSEy5JzK4hLuGsgbkUJMtWwf75nD+8GihnjYvHJSMviyhEi2CIlAPJJZENfcmyjb6CpqVbVpxfowR63eXSx4rgTQ4t7FLMA6CPLtmP75sbI28+3XfmSFUD1DNxlklDkkbJag/dcPOIGgKUdNKSN4iOBqiOBSk8YKHSZToQAn2nSE29LOcUs+3JsqFXWB8jkl70T2f7ivJFGt/Fvc8NLAb41I9OXeV6B8THjUb+KpqMRPXc8fpiTt5IsIx1MGQ36NnHdv/TepDb7NXJ4XxOPI3MJVSNr5LW9Ebd/m3wV1iCuJh/1oCx5dLdaAeCwkPnWUlSlmHQCI0U5pMF01qoReNR+76NqzfhSKYCmSfioQmD2QaU4Mr7ud5UAVLUdQKV8NCAtHCObVxRNOUMA1LAnbGbiONaRiOjHpPSWHAUASBg7YdSUIBU7dvY6TcwGCcuSGuWDfGYnP9EKuF6GMCwxxjSoNgNnUYFiXFFfE/gsBD2kWeju5mZU30yOL+viPZjo4yWnYiuCH8Xt+lqpw6FEPnG3o18TXigVeu54WR7GAaVpVkwm0YEDq4HDt+4vaNv6iuuaOkpUNwHaZQZVt+4Zp61kovDhtRTWHAxCmxexob4/bCAMHhoFIFEHMtgWmp6YDJoBwMysRjNKFqVzAbqwHGSxVdvGue+Ki8AH0tpXcpNnjnYtib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DCZ/2WZ2OJFwMoJe910S7Bd5f+QQrg+H3vksuaO75KmHHzjncE74nigVZyDD31RzKQidrurgUAiP/rzTAA0MMt2/IA/OFKAPgsufNxMW4WmxIYbABnqbD8EgB454EOcrnmntMAANTDB1LZ/UXiIGkmPs6A0TQ7SE5aVdGv2Zj/4vpnR4rIBVXDPCcYBGS1j09ufMHUyBTslZaQk0FfE/eBDfnAybV3M1FToSc3kXfSXHu/Fpi68FbGW4f+5wZSRPNV1u306A/rvvJkgnvEyP1nMC3xBypWyfQTYHMjgMfXMfxF3zevYGTWbzVCt0Rx7jXkOPXlu6POSW+e/Rdypn2EHD1hR2b1YBDogzHuuUQVAey59SoVMPeNaxj+4rML7iMnut8yKbL65uVl3MeJaD3p61LOytvK/YTxol9/ZR3xMsg/483R9rMeDeYBgFRx+XU3q65ra73IClCN7x9oax9740xmIKc/Z1Qhet8Knjc5sSlhOtrFqK3fuRCwbIqbAmG5hmgYbAZ/Z9mj3Nmm49Y/AwCMV/M8Kx1+/4ME6+CHm8k6tun2NHdJHiBH+/cTtNzAHaQeWP0f5BhKuY6XjKkR1OmYc1F9kE62e4RveDnh6rd3k+OG9eRYOdBxrKCKc4uow1+uAwCOsdxsYrypzskX3fPkeXJAvPHDWVjbW/6sVUNRXjWX8/cnfK8dbVisUqCu/40L262Y/eB8RksYkTLK6ZECc9J7+K5nWbg+2AEA0ADhm/sTMzrIyzTWJyZ+xGc7uYd4EF3Jk5UG237Cutz/fXK8UZv6ts1EfYr80rbijJVxS8oPmXn/d0k3Gs7WoXa4m1pcQU7rwsf63EmW6QffY10+9go53gEAUOtVCFgLOF1Y5D7mb9ABxjl2bep6GB3qqmnbgPp7z1ykWmIbBmBtL3nBXFI+c+Kk3EDT3mHDSX/VMjyzod0KYNjGzODpo4zG4u+pTXqNX/ayr2NmnSc5gSR/JYdlSRSSQLSiluTGlAZBrjr1V9IjtWnayi3k8NwzO3fu3pfgSPgzwpK6cwAA2Vtq7fsHmUi4gV7aluQaOPgwJ+HHZL10dSMAhCkJEKA4EykR5g5balsaxZzamBnbUM7KQdD2k5Lbnr+FPM/aLh8u7neCjlQDUJ0zYZsoPHEGoz9/tpzxGTtRzUxvLEXcWjqf5iS8x3TK0B84GXuI4bAZadFJRu6z0pdk4/mkAId7yGEzN52FRaTTun7OzXBtJccrsq1IFGELUyNe15+fcU2TtofIcTMA+PfYAY2PU0YEREYOHDnOXQwpYhj8pZAPAO0Y0Kx49OocAJh0d+4opQCqAcAKiV9a9YzPw2i63l6GG8EJRou3JPWql7j+Wm4SSYZ3uc5z9DEAwGKkxzYAwJLUhZJAP5eUtI+oB60pLVKbyOGNpDFW/TY5njcfw1pa2LeyLqn6Vc2vEpFxUTRNbuxn3yNkqtRoGUeVtVGnCXmHd8tzNxiBMxx/7Z0sAlzLAWyc9jXswV7HGUypw83MEDDADPYTOjWH9MdJD2NUrk8EMvL5q8gCqW19mlJc9B5LTnuNHFKu3JxNDq8kZUgmiCVZ85WWFStXtszH9pICb7LHZKNzt09DRv1Kpj1SNf3cVS0BBpvtZO5LLWO6tjIU8Aw4r3z26031H0+8coTKg6tch8LmsRrr5MHjH9cznkROH8OOUUY0+6c5MjrCtscDALOEsy8pg8iFNAoPgKitINsx+D2eNGYKzKf9R0ERrWAmeYYdBDPnMh4Zl7mOLHgJkY132ZdiTcSiJmkUU92qsaQwSwHDaxHTgU16JkRI6wTgPan48o3vmoetz2nXzHlQjTXBfPmJj4NW7FzFiI9+ZusLl0ZCTBwWM1unsiW/NVkRoZMD/kgGD4Nlra31VaY8BWfunklfT0QHT1rvnBwAFqW4rVQjdLdLw8zQG6BFlkuP/FAsB0DmTjRH3kwAMjdTiUXtAFBin0HhJLufCzDYzLhILgoyGpCGcNw/qm594uPX+v+gX9avvTT4VX/Z7tcDo0Dvai2ZtI0wig49nUMa03gZizB4WjUZBbjaAaIM5sp4nHUddz2JgYo3VRh88W2hnmYAEDIjJ+TxyPcJM+N1VyEy0FnOggVAHYx+oOkkf32q4CSTVgEA+aKACirO3EpAROcxXb0iammUM7Mm/dSQZd2ffpz7y9HTxebcioaTz7ttulrQPYxhyx71+nQyU/ZJlhsowPPaZILGw3mSwZnYrXjv9a8IWJqyWr0B6JN8qSSRZ6EzBmbA46ozAHwnI4wTbRVmmS+90E0yfD4f6WSDSVnFwwHXTKxKyprSZcuWcbuDwOOjdq18ZnYsjjAnqmkEhiWr1h8bzssr91Mnj5TXLFn6Ui9Gmol6EQ7KyejtZhgcYAXbJK55Ik2GmTc84rt3CS/2ZOliGeSL8mfMRzKNsEtubmJBNsYDXh0AmGYcjDjREuujKHeap3hWcAEV+TKffQjNuvB0D5A/5TdOkY5X3Dw9xt2PgkCAwcwAihzms0uin5+sIY1Fmg3HTUaN8+ULvxoI+4zAVHSJ16Mhn22OYWxovi2Yyu1kjCSJGu0tF86THA/4WcjwTSHMYAWrIBv2qUIAkEzB3aKcGARVR5qpdOEC2w0MhswHAXTUDYSAvJoIHLPM6p6iTGDFQYjBDEMVjJwRRy2E4hAA8RxcrgJRC3Z9zxgIw1MB+KIyrZTs8AJtsVcpofdG5s1gox2MepYQynhTjL/0TN+ozT0bIhVs/Y95PIE/+Jz5Yin2VpKyCvLeLgPq5roWz07qFMxTIlRdnruHV2ZkCB8AA2knoRAA++cAKsiQIOMaqmIQ+vxbyIerMZEClIiRuJIWGiKpTAaRjAqLNL5JKgSEvwNK9R1SQL05xDyyiJLQqyGe75zfxBOFX/IT5mTPy9tYXtFXzYfBSt5URrSmiCILsgry3h5Ajv5ovlI3KWO01DkYnb1VlQvZ11IZC51Rxpuml/AnILixgACDQy+RHp+bT/RweTEJLJQ3umiamu7tW77OrjjYItu24xixRqj+43ly52Ufkx68TLq3tMQyhOJ5LAEAQI6Tx1R/B5GOjm+8z8lI0rQzAb+qxszKUkT1+VkFeW+fQ5HfrQo74RFHFQOxZsLSwHtLhtACWtEMAOjj8t5GnscnjFQVEoxOCzGYSXcQcSSWyrVKlSIsCk4f3t81Fjjnqmoce+LHtk+ubL2BFDRGdYiociKfgxau+GieLQp59sFREhPx7KakPENS2QwglfNJTPLFAinCnqYTC3JBJJ1dZrDAagV8A8QhRyWdAGQLcmzXATp/AAAUCRMmbWKVEqFtOOGULPMIMDjIDEGTFEDJ1WpRnn+q4/jwmGWxaqf2wi3acHfPxJeCke7+2j/e/wkNoJQxJIulzEfTTlJa2gXIMgnp4HkbH08UB+Mue38y7+fFYPBKTDLLTbUSxHzeGr68HMLPidzofNBKBJo3CFAFvPOyTFER67oJsk2Xm1ilBFB1406EPLkCDLbnEwVnUFsZEc0NdfU3bu2jAaDv+vJL1erpdyR7990153Ve92jVT+qfngNVx9hw86LxdXqXWuQJA/nzC7grGgBUBbFLablCYrMQN5kgj9t4kuOIIFQlMglTJWqDRGLjrnBIiEGbsz8RC0we7yIIs9gyaOBRsouHFxQenmseJ0OwLkG6MC/OtJzYjmgWu05hBRBWCzB4nDHrdh3/2/DYeBCY0dEAYLzoCveIon+r6WVtQ0WnITTyk1+rr6x5wFY7xbxPKaOtU7mOfLgQX3fMDqpQAKiP6amyqmEvtdJKOtfRZJOIeHlSkhDqRyeVyxnpaj5GrbRyJEUtkV3C2pCoUO7WAgKNilnQ6tQn746TP+U3LsR8SS0ep4MAKFMfANTm20/EWhlT3YS9C2TBAAAIMdjSDAB5Rvl7g/1TADCw7gBgvmRj+Mgi89+2b/yH1arSWdxl2P/H20TLfv/gkqhnX/U2ACJznpxSauECUHRUXiGT+PojvC60QigeBRBduz5DXGTNA9UgJ0KRZ4eN5TxKVnSCx0mmZPBFjS/n8NhGzieH5CURBqUamffgWgDIWcXjz9dGDsclMT0ozFQjryYCx4Ls0xs/mMqfgayc9CHfLgDYCACgk6vrEcsCUCrcAsaf8Tw5pI3VU0e75srVADCp0gGLr+zrK3a/+dHGditQ5fe6K4C/vx2WaB8vYtwB9cpJQLHE2O3trdDRLkCRO1M93HFI1lQ/nsFUX8xMPeQiH4CDDNEQXD6fT2+dJZXl0W9v4KEVnclzkg+OQRTVE+oWF0xyh3qix9GCm/6NdPko4mSEy5JzK4hLuGsgbkUJMtWwf75nD+8GihnjYvHJSMviyhEi2CIlAPJJZENfcmyjb6CpqVbVpxfowR63eXSx4rgTQ4t7FLMA6CPLtmP75sbI28+3XfmSFUD1DNxlklDkkbJag/dcPOIGgKUdNKSN4iOBqiOBSk8YKHSZToQAn2nSE29LOcUs+3JsqFXWB8jkl70T2f7ivJFGt/Fvc8NLAb41I9OXeV6B8THjUb+KpqMRPXc8fpiTt5IsIx1MGQ36NnHdv/TepDb7NXJ4XxOPI3MJVSNr5LW9Ebd/m3wV1iCuJh/1oCx5dLdaAeCwkPnWUlSlmHQCI0U5pMF01qoReNR+76NqzfhSKYCmSfioQmD2QaU4Mr7ud5UAVLUdQKV8NCAtHCObVxRNOUMA1LAnbGbiONaRiOjHpPSWHAUASBg7YdSUIBU7dvY6TcwGCcuSGuWDfGYnP9EKuF6GMCwxxjSoNgNnUYFiXFFfE/gsBD2kWeju5mZU30yOL+viPZjo4yWnYiuCH8Xt+lqpw6FEPnG3o18TXigVeu54WR7GAaVpVkwm0YEDq4HDt+4vaNv6iuuaOkpUNwHaZQZVt+4Zp61kovDhtRTWHAxCmxexob4/bCAMHhoFIFEHMtgWmp6YDJoBwMysRjNKFqVzAbqwHGSxVdvGue+Ki8AH0tpXcpNnjnYtib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cA8A1Tp+2ZWa6+9Noeggts28Zt0waSJA+y3UPr592zZOWAkW1rzAAAIJIekgDgYtxdc0Qy7elxd82a1D2M6o42+/YleO6PIfFxYAFpLFfHwGWfDYK1FnuN+2BVVj1sfQdoyV+r5p6K2eVVn8Ibv4+DTLwnYViy85zEltReVwFo9oEBDADQ/yI2dSTgagkoSLnooY0bU4Y3Scxe5C6SUWJ15IvHX3ELAGYGSj8pd6z7Iqbl6ZFdFQrhZ3mDD/ha08bYOfF2bHW3YxERHtTTkk7roLFoNf0BgCADIgRg6OF4du8dcCO+U5jsuntAeMC9NWafBbNejwTYijgImeMzGHQLvAvwBse5pSkGrS8Rjn08mr3b8Hnt0yb1x5W66A0hyIiUho2C94HwrSqAutxPAKcVrbMcgIhAjvCdnsKI6j8ujB0xeULkA7MI2S82wEH5vWLqPgOmdcwbX/kMLq5/miIyVdeDuVtwwrYR3BxAIgL1DC8SkYeMe3iYbSvdEJR8TeUTBwAOuBDz0klNc/501ElfWtajvY437QdnYO4zoVoTDIJkuBPO+gmcDwVm5wY1tKbopyyueBeL9frPYtF6eQJWfxtSsHQ6KQoVySwkcRC+9hp0DyUD4jzoalsQoJKDyIcsvG+D6ABkv6nnImZwpD1YxkFw9SU9+rXs7Sw/MSxf3jK2W0wzyxaV5YuGdDdCYmsAbQtAMIM3WbkFMYR0G78wnaEWtmI9SbAFiCHPxgv1u+Mn+Myi/+nZFt5dPWu8nM6vQzACx15Kz/eeag0LI2XM6JXlqAq977oZmJm9dWDx7dJbR8e46f0kijzBMhUDCzIQ1uHKPhBgBjEDB+7s4cABFEfwtfw99Oh2ENTnhbcTKclJDpJDAo/L5AYfD3izPHMiFnTa8V2fQ+xeE3Hk8KPuNqgSM/p4AIriqMOP3qe5a9NZgUgEIOqZWnSSIIu2i0kAEXBif0xvGbfovj32f6AH1iChF65pmFCetcAXBUSwggOyoFIZMYGIUdBA5LPbIdC6vC0+OV+5eMCBCJdACicFHCfCx5GDZKOj74ARAAEGzoKpouAEQhGIaP3szdSgDj9BYkFB2kCBnbfDju5RonXJir7DF7WtHwlUQjtJ+6q2GgecPqYrYgYC+ONoIFH/bz87e/rTf3/lvV+PBlP3t3CQcQ9UZLHQibqqSlBQkIeU7bgMbvmI6Cd6v9q/bybI8rWdE7EsW0EiIQI8a28FQnAEZyWcYbC1NWBgrnn3eLxTXvAp4KmXhSl6IHgdJ5ojH2TBhYt6dwiuJI7rPw12AcIDNsBbWbOJXCQMZkyBFQCq2AuISOPyjSaNsKwzn4S2VYMJAJFASBa9A8oNvr8h5kjdYcftBPDSed87eFjRQbSc++7F3fOBBAIEm43l4vqapWiuXd71MyNKeMR+WKL2WfSrmnHLXt+kfca61evHYN3KpUsKlog5FlQgKuQh1laC0VEFCEoCGFi0atGQ8cvWzNxrKOhr5QLFMQJJJQpBBEFWGnmtCQAEA2c2pgKBBAGGIJk32zAQoF5CrWD2msDQKuGDd+hRYPX6NaPRvq6+PwAWFGzT+hVrq9mHE8dUzCwJ2tY+DoAQleMOBNxzS/Vra4Iy3ussuv2ngBACIMCDeOMiQjX9ivMwOOqaMoIhgr0MWKHG3m52HDXTbMbBrF09AOtK+Y0diXHauVCAs7HM44IgAMuKLXgtjirnBxaoZWdicZubjKi0QqqAkMDFLICEQ0QULpq0jgNBapyNOEDEUiDUOUBWTgZ3kw9SQNIaCUEBspo5gvS4cONOF+3ta/pjfZsYDjCDiOuLM+egig2+31WKKIC3FXEWXNfCzMziwbNWPTVnGcXSBUQKX765KoQACIJFqO5OusiaqviTC+f0Hb981gYmgvc4NWBxIl5BY985W7DoWN/U3LquecU3ZMFXSS04IHK1lEphSK57v0/vv1w3zFcMwvzWGROa3+2cuZ+0+zWghn0MktblJBwTwznxRW/BiH2/Cd5LgSwPCBFQwzSwobmpqamxqampqampN3sBplWZNAHeB0JS0A6f7ZF94/b19fH6kutXkYNlqX31+rUJonDiyArEQAjb3GlE8APOzuakbV/5tG7dpVm1AoHiSOj48sOeePz1DEaKgDhd36uOSCDY1ebEQy7nsQffHYAuNzsWezV4sXqQWoqmZMkWWl/qkP1aU+79i+uaXRDFICK2QK21Ekx3dxwy/dTqEekaAKBsaV93+u3r3v/kIS9/5qU/359oUOIcJYQIUiOy4pOdGUDOXoTcyAQJGFHGeAvzRzXFG3NO0ZwMQKEmoYKJvLMFdhTBtpx038bOpe3xkAVl09KFp7Rz6hb37iNNLK7+HAAOUgDbtIERslWD92vV/fbKzJA4jqVFwQHOB+yyy1eXvPrUu4V6i+bbnil6ArmsDcfdOfe3YlLj77sUEA5SBiasbszXoJ9fvEX0QrfFA2eXyv3x9TuLOlBgGYQQ5KgOmH/VsbvuHoYOXtHl3c0d8/bRt88csr7/uP2OeeyqPQVCoIbUVFkJLV2QqvaEvwtBeZ8zEMVaJ4EiAEOX3TRiWd0g2uD4kqhufh8UR8bXkI3ToqaClgUVf/6+Hv0qh/4LdN4bqDhcDQsaR7av7F9md8bXVxEDUtDmlnUrABKt+5qvSU7ZSzQCSOCliURILKSmZOjQU8P0Qu+4kSclNjh2XDv5EnVG5/ivPvZmBUCBACGsTZANaHVJg2vfQgNNxr2Q69743aXjSNrE6kh4+GK2Pn74/HD9lBlx3U53d5Vds6xh0mHPpnPbz5j3Im77LbEE54UQiIVA5BHZ2j4M8MU7KpJJSFQhwNfE3/rgS580rhIVqmx35U5vA2QLmU5gq0ICJ0RI+MAx723oV172jUjTyml0ChypOZMW1Pet1qF45VcBJiBs242JGM6hHVNR1ADAkjgEyIgDOyTOVu2Jn5y4uro+hDigWOxV//iVcwb8VF6xoQWC4Ly2AN2nHdW1umMLDXSai7C2CvrqR4WWLLyhhDnBN+5dm/xs50NRNajvhqMzC3eYc8qzSxsGnHVrqWVvFLRwECYyMmYwyUA+HhSIklMCeyvIxzAJ8M7v+h/f1EaEAGIQMQFKA4ijiECCOQguGCZTc/6XN2xLneYGOF0AmAgM0bjsg9rS+yN9whd+t1zR5RC2A0AAwFtn3NIHqRQSjgTYsSgAQBBKcrIfuPz8k+2FpMp0zF3Y1njxyMfmoXtT6sAqSZhEUSGq2TTvDgDwJiSwTgIz3xyfF4NP0ogcpTVHvrjb2Wf99LmqZPKyV7sLvz9uzjGD3thhbPIgRp70lfNHlkO1J8gY0kkyUhCP2nWWqCkYQuDIxHkiHds+63fRm7MliAJzsTVuUJxYVaVtrQHOuqaz26oF4+vhTW2lMCKZYMGw1a5ftaHaC38CFsS0bT9wwyLzx6e/c1mNATyRZUGRvOX1/cbuMBhwXruCqJ2c/+IFAMA5E1uX/+jQi37RFU9Fjpo8iWjOa0evRVIwWwgCOMTwiAC+f9cGBamKwUYMf0RL70G/uDKiltNv2LDRcYsHd559/erznp5VfXrbTcv+VDQOMsBGPnEh8uRRfcMpwVtnqyhKhYkSomIkUZtE3Z4/AyARSg07q8BiwbSLBJyEjmQgtr2Pv7fbxATnIgQfo2vXIQlzd11eN3NfC3zpVgNBlY3+dgKI9V/67bXHAxHAMAneuRx/QP9JE3ae2IBCRoJOPfWNe1+mGv3q0n0GvzPt0pkvAQARmfFjDBOuWNVrCeKizbdgwRCMQBLYT7w/VqpEsIsokIp3X3HMY6hvPQ93bSw9Z8w7R/z4jaF3YOTp1+GhGbuSRhR5ipwuWI6NCHbyGesEInYcpPDCxMKJzubzM08gcNfelcLQqb2HcgBq/zZxd0WaEq7ykmx83r0bQPGQYBcDlcQHAfmCfnb+LrUKg078I4jAIP4YOZHpnzrl4alTVniiAv55EAhY+eevnHDXipmqukrqDux9613j+u156sBfPjZy6dwvCwAg5EOvdgWH154YDgdBfivcROwhYfHNiztKVkZaxJGyEZNB22PUv/WQK25duWGHRaWla6o/tfKZl+mT6jew54FIcJCkA4yTPvIh4MJgZFzwLhTiRHpUG0T50ohCCIG5cnzB8aA9vtjmmVHseB5EReHYOs9wB+60YWCDcAjEXQpIgoA57SuSNwUkvgRdiUbTFjZ9U+rhJwqEP/8ZaOrdp3HIyr91jQrLaROvKt24X01t2imSve6+73F3xFGnr/rUSxdPfpwYBHeFfJjqcRGquXsJ3Iw4ig2omDZe93AxK9VFsD6Bi0g6vfNDn1657x8XX7exLGHe6HdOffabK4add2876M1fnxex80WNSFpJxDF7tftBjqxPTHCIRQFLiMCt4/esRDcQBAHFd/Tpg1b2S4KM6jIUNEkTaSmlTRsuvLJ7sEAODAsQSFSi9+uX9N5pxt51Jp6w20yiyr57uzUwVHS7bf6Ux//nb12hW0bc2KRn/PzJEtUWhU/DWT8bMrX+N3Of5cWfAhNE5PtZq2qeeBvOFkBhK1ufKKIMEanbsVbExViGYkTVETET+IRLT3yw5YR04ygzVixfNGLXD+qPrP8lAFy6PvYiCnHCXkZKsOEIOFFkwSOmJOhQeP6+RkIIfWuVtdZqZXSeG7y1uh+k9MzFqHDvuoIKiYkkrCGc1X2kV0AohMigErSqqNp7nR9UT4Vy+HLkSYTtcaS3QSRYIul7y5vfH5fk3lDLt3//xslHPrXDrH37rWIQx8hQs/YSwJoquLSmYc3mIxZV8TrExU+NQCAuzu9drxNFRU3MMMmty7Hf9E3Lv9snNGDXT/9lIQCRX3y/CJKBIL2IrUzKtVL3DVkvkRXJyyjC10vVgVGouuiVno3stn/aAECguS555p+/TkgEMKIA1XLoQ12SyagdVLBdulKZsUtXNOz91r4FF51+e0dXRPqjp3DPaMzWASQ4xH1e+9t+N6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BoxRatios BoxRotation BoxRotationPoint BoxStyle BoxWhiskerChart Bra BracketingBar BraKet BrayCurtisDistance BreadthFirstScan Break Brown BrownForsytheTest BrownianBridgeProcess BrowserCategory BSplineBasis BSplineCurve BSplineCurve3DBox BSplineCurveBox BSplineCurveBoxOptions BSplineFunction BSplineSurface BSplineSurface3DBox BubbleChart BubbleChart3D BubbleScale BubbleSizes BulletGauge BusinessDayQ ButterflyGraph ButterworthFilterModel Button ButtonBar ButtonBox ButtonBoxOptions ButtonCell ButtonContents ButtonData ButtonEvaluator ButtonExpandable ButtonFrame ButtonFunction ButtonMargins ButtonMinHeight ButtonNote ButtonNotebook ButtonSource ButtonStyle ButtonStyleMenuListing Byte ByteCount ByteOrdering C CachedValue CacheGraphics CalendarData CalendarType CallPacket CanberraDistance Cancel CancelButton CandlestickChart Cap CapForm CapitalDifferentialD CardinalBSplineBasis CarmichaelLambda Cases Cashflow Casoratian Catalan CatalanNumber Catch CauchyDistribution CauchyWindow CayleyGraph CDF CDFDeploy CDFInformation CDFWavelet Ceiling Cell CellAutoOverwrite CellBaseline CellBoundingBox CellBracketOptions CellChangeTimes CellContents CellContext CellDingbat CellDynamicExpression CellEditDuplicate CellElementsBoundingBox CellElementSpacings CellEpilog CellEvaluationDuplicate CellEvaluationFunction CellEventActions CellFrame CellFrameColor CellFrameLabelMargins CellFrameLabels CellFrameMargins CellGroup CellGroupData CellGrouping CellGroupingRules CellHorizontalScrolling CellID CellLabel CellLabelAutoDelete CellLabelMargins CellLabelPositioning CellMargins CellObject CellOpen CellPrint CellProlog Cells CellSize CellStyle CellTags CellularAutomaton CensoredDistribution Censoring Center CenterDot CentralMoment CentralMomentGeneratingFunction CForm ChampernowneNumber ChanVeseBinarize Character CharacterEncoding CharacterEncodingsPath CharacteristicFunction CharacteristicPolynomial CharacterRange Characters ChartBaseStyle ChartElementData ChartElementDataFunction ChartElementFunction ChartElements ChartLabels ChartLayout ChartLegends ChartStyle Chebyshev1FilterModel Chebyshev2FilterModel ChebyshevDistance ChebyshevT ChebyshevU Check CheckAbort CheckAll Checkbox CheckboxBar CheckboxBox CheckboxBoxOptions ChemicalData ChessboardDistance ChiDistribution ChineseRemainder ChiSquareDistribution ChoiceButtons ChoiceDialog CholeskyDecomposition Chop Circle CircleBox CircleDot CircleMinus CirclePlus CircleTimes CirculantGraph CityData Clear ClearAll ClearAttributes ClearSystemCache ClebschGordan ClickPane Clip ClipboardNotebook ClipFill ClippingStyle ClipPlanes ClipRange Clock ClockGauge ClockwiseContourIntegral Close Closed CloseKernels ClosenessCentrality Closing ClosingAutoSave ClosingEvent ClusteringComponents CMYKColor Coarse Coefficient CoefficientArrays CoefficientDomain CoefficientList CoefficientRules CoifletWavelet Collect Colon ColonForm ColorCombine ColorConvert ColorData ColorDataFunction ColorFunction ColorFunctionScaling Colorize ColorNegate ColorOutput ColorProfileData ColorQuantize ColorReplace ColorRules ColorSelectorSettings ColorSeparate ColorSetter ColorSetterBox ColorSetterBoxOptions ColorSlider ColorSpace Column ColumnAlignments ColumnBackgrounds ColumnForm ColumnLines ColumnsEqual ColumnSpacings ColumnWidths CommonDefaultFormatTypes Commonest CommonestFilter CommonUnits CommunityBoundaryStyle CommunityGraphPlot CommunityLabels CommunityRegionStyle CompatibleUnitQ CompilationOptions CompilationTarget Compile Compiled CompiledFunction Complement CompleteGraph CompleteGraphQ CompleteKaryTree CompletionsListPacket Complex Complexes ComplexExpand ComplexInfinity ComplexityFunction ComponentMeasurements ComponentwiseContextMenu Compose ComposeList ComposeSeries Composition CompoundExpression CompoundPoissonDistribution CompoundPoissonProcess CompoundRenewalProcess Compress CompressedData Condition ConditionalExpression Conditioned Cone ConeBox ConfidenceLevel ConfidenceRange ConfidenceTransform ConfigurationPath Congruent Conjugate ConjugateTranspose Conjunction Connect ConnectedComponents ConnectedGraphQ ConnesWindow ConoverTest ConsoleMessage ConsoleMessagePacket ConsolePrint Constant ConstantArray Constants ConstrainedMax ConstrainedMin ContentPadding ContentsBoundingBox ContentSelectable ContentSize Context ContextMenu Contexts ContextToFilename ContextToFileName Continuation Continue ContinuedFraction ContinuedFractionK ContinuousAction ContinuousMarkovProcess ContinuousTimeModelQ ContinuousWaveletData ContinuousWaveletTransform ContourDetect ContourGraphics ContourIntegral ContourLabels ContourLines ContourPlot ContourPlot3D Contours ContourShading ContourSmoothing ContourStyle ContraharmonicMean Control ControlActive ControlAlignment ControllabilityGramian ControllabilityMatrix ControllableDecomposition ControllableModelQ ControllerDuration ControllerInformation ControllerInformationData ControllerLinking ControllerManipulate ControllerMethod ControllerPath ControllerState ControlPlacement ControlsRendering ControlType Convergents ConversionOptions ConversionRules ConvertToBitmapPacket ConvertToPostScript ConvertToPostScriptPacket Convolve ConwayGroupCo1 ConwayGroupCo2 ConwayGroupCo3 CoordinateChartData CoordinatesToolOptions CoordinateTransform CoordinateTransformData CoprimeQ Coproduct CopulaDistribution Copyable CopyDirectory CopyFile CopyTag CopyToClipboard CornerFilter CornerNeighbors Correlation CorrelationDistance CorrelationFunction CorrelationTest Cos Cosh CoshIntegral CosineDistance CosineWindow CosIntegral Cot Coth Count CounterAssignments CounterBox CounterBoxOptions CounterClockwiseContourIntegral CounterEvaluator CounterFunction CounterIncrements CounterStyle CounterStyleMenuListing CountRoots CountryData Covariance CovarianceEstimatorFunction CovarianceFunction CoxianDistribution CoxIngersollRossProcess CoxModel CoxModelFit CramerVonMisesTest CreateArchive CreateDialog CreateDirectory CreateDocument CreateIntermediateDirectories CreatePalette CreatePalettePacket CreateScheduledTask CreateTemporary CreateWindow CriticalityFailureImportance CriticalitySuccessImportance CriticalSection Cross CrossingDetect CrossMatrix Csc Csch CubeRoot Cubics Cuboid CuboidBox Cumulant CumulantGeneratingFunction Cup CupCap Curl CurlyDoubleQuote CurlyQuote CurrentImage CurrentlySpeakingPacket CurrentValue CurvatureFlowFilter CurveClosed Cyan CycleGraph CycleIndexPolynomial Cycles CyclicGroup Cyclotomic Cylinder CylinderBox CylindricalDecomposition D DagumDistribution DamerauLevenshteinDistance DampingFactor Darker Dashed Dashing DataCompression DataDistribution DataRange DataReversed Date DateDelimiters DateDifference DateFunction DateList DateListLogPlot DateListPlot DatePattern DatePlus DateRange DateString DateTicksFormat DaubechiesWavelet DavisDistribution DawsonF DayCount DayCountConvention DayMatchQ DayName DayPlus DayRange DayRound DeBruijnGraph Debug DebugTag Decimal DeclareKnownSymbols DeclarePackage Decompose Decrement DedekindEta Default DefaultAxesStyle DefaultBaseStyle DefaultBoxStyle DefaultButton DefaultColor DefaultControlPlacement DefaultDuplicateCellStyle DefaultDuration DefaultElement DefaultFaceGridsStyle DefaultFieldHintStyle DefaultFont DefaultFontProperties DefaultFormatType DefaultFormatTypeForStyle DefaultFrameStyle DefaultFrameTicksStyle DefaultGridLinesStyle DefaultInlineFormatType DefaultInputFormatType DefaultLabelStyle DefaultMenuStyle DefaultNaturalLanguage DefaultNewCellStyle DefaultNewInlineCellStyle DefaultNotebook DefaultOptions DefaultOutputFormatType DefaultStyle DefaultStyleDefinitions DefaultTextFormatType DefaultTextInlineFormatType DefaultTicksStyle DefaultTooltipStyle DefaultValues Defer DefineExternal DefineInputStreamMethod DefineOutputStreamMethod Definition Degree DegreeCentrality DegreeGraphDistribution DegreeLexicographic DegreeReverseLexicographic Deinitialization Del Deletable Delete DeleteBorderComponents DeleteCases DeleteContents DeleteDirectory DeleteDuplicates DeleteFile DeleteSmallComponents DeleteWithContents DeletionWarning Delimiter DelimiterFlashTime DelimiterMatching Delimiters Denominator DensityGraphics DensityHistogram DensityPlot DependentVariables Deploy Deployed Depth DepthFirstScan Derivative DerivativeFilter DescriptorStateSpace DesignMatrix Det DGaussianWavelet DiacriticalPositioning Diagonal DiagonalMatrix Dialog DialogIndent DialogInput DialogLevel DialogNotebook DialogProlog DialogReturn DialogSymbols Diamond DiamondMatrix DiceDissimilarity DictionaryLookup DifferenceDelta DifferenceOrder DifferenceRoot DifferenceRootReduce Differences DifferentialD DifferentialRoot DifferentialRootReduce DifferentiatorFilter DigitBlock DigitBlockMinimum DigitCharacter DigitCount DigitQ DihedralGroup Dilation Dimensions DiracComb DiracDelta DirectedEdge DirectedEdges DirectedGraph DirectedGraphQ DirectedInfinity Direction Directive Directory DirectoryName DirectoryQ DirectoryStack DirichletCharacter DirichletConvolve DirichletDistribution DirichletL DirichletTransform DirichletWindow DisableConsolePrintPacket DiscreteChirpZTransform DiscreteConvolve DiscreteDelta DiscreteHadamardTransform DiscreteIndicator DiscreteLQEstimatorGains DiscreteLQRegulatorGains DiscreteLyapunovSolve DiscreteMarkovProcess DiscretePlot DiscretePlot3D DiscreteRatio DiscreteRiccatiSolve DiscreteShift DiscreteTimeModelQ DiscreteUniformDistribution DiscreteVariables DiscreteWaveletData DiscreteWaveletPacketTransform DiscreteWaveletTransform Discriminant Disjunction Disk DiskBox DiskMatrix Dispatch DispersionEstimatorFunction Display DisplayAllSteps DisplayEndPacket DisplayFlushImagePacket DisplayForm DisplayFunction DisplayPacket DisplayRules DisplaySetSizePacket DisplayString DisplayTemporary DisplayWith DisplayWithRef DisplayWithVariable DistanceFunction DistanceTransform Distribute Distributed DistributedContexts DistributeDefinitions DistributionChart DistributionDomain DistributionFitTest DistributionParameterAssumptions DistributionParameterQ Dithering Div Divergence Divide DivideBy Dividers Divisible Divisors DivisorSigma DivisorSum DMSList DMSString Do DockedCells DocumentNotebook DominantColors DOSTextFormat Dot DotDashed DotEqual Dotted DoubleBracketingBar DoubleContourIntegral DoubleDownArrow DoubleLeftArrow DoubleLeftRightArrow DoubleLeftTee DoubleLongLeftArrow DoubleLongLeftRightArrow DoubleLongRightArrow DoubleRightArrow DoubleRightTee DoubleUpArrow DoubleUpDownArrow DoubleVerticalBar DoublyInfinite Down DownArrow DownArrowBar DownArrowUpArrow DownLeftRightVector DownLeftTeeVector DownLeftVector DownLeftVectorBar DownRightTeeVector DownRightVector DownRightVectorBar Downsample DownTee DownTeeArrow DownValues DragAndDrop DrawEdges DrawFrontFaces DrawHighlighted Drop DSolve Dt DualLinearProgramming DualSystemsModel DumpGet DumpSave DuplicateFreeQ Dynamic DynamicBox DynamicBoxOptions DynamicEvaluationTimeout DynamicLocation DynamicModule DynamicModuleBox DynamicModuleBoxOptions DynamicModuleParent DynamicModuleValues DynamicName DynamicNamespace DynamicReference DynamicSetting DynamicUpdating DynamicWrapper DynamicWrapperBox DynamicWrapperBoxOptions E EccentricityCentrality EdgeAdd EdgeBetweennessCentrality EdgeCapacity EdgeCapForm EdgeColor EdgeConnectivity EdgeCost EdgeCount EdgeCoverQ EdgeDashing EdgeDelete EdgeDetect EdgeForm EdgeIndex EdgeJoinForm EdgeLabeling EdgeLabels EdgeLabelStyle EdgeList EdgeOpacity EdgeQ EdgeRenderingFunction EdgeRules EdgeShapeFunction EdgeStyle EdgeThickness EdgeWeight Editable EditButtonSettings EditCellTagsSettings EditDistance EffectiveInterest Eigensystem Eigenvalues EigenvectorCentrality Eigenvectors Element ElementData Eliminate EliminationOrder EllipticE EllipticExp EllipticExpPrime EllipticF EllipticFilterModel EllipticK EllipticLog EllipticNomeQ EllipticPi EllipticReducedHalfPeriods EllipticTheta EllipticThetaPrime EmitSound EmphasizeSyntaxErrors EmpiricalDistribution Empty EmptyGraphQ EnableConsolePrintPacket Enabled Encode End EndAdd EndDialogPacket EndFrontEndInteractionPacket EndOfFile EndOfLine EndOfString EndPackage EngineeringForm Enter EnterExpressionPacket EnterTextPacket Entropy EntropyFilter Environment Epilog Equal EqualColumns EqualRows EqualTilde EquatedTo Equilibrium EquirippleFilterKernel Equivalent Erf Erfc Erfi ErlangB ErlangC ErlangDistribution Erosion ErrorBox ErrorBoxOptions ErrorNorm ErrorPacket ErrorsDialogSettings EstimatedDistribution EstimatedProcess EstimatorGains EstimatorRegulator EuclideanDistance EulerE EulerGamma EulerianGraphQ EulerPhi Evaluatable Evaluate Evaluated EvaluatePacket EvaluationCell EvaluationCompletionAction EvaluationElements EvaluationMode EvaluationMonitor EvaluationNotebook EvaluationObject EvaluationOrder Evaluator EvaluatorNames EvenQ EventData EventEvaluator EventHandler EventHandlerTag EventLabels ExactBlackmanWindow ExactNumberQ ExactRootIsolation ExampleData Except ExcludedForms ExcludePods Exclusions ExclusionsStyle Exists Exit ExitDialog Exp Expand ExpandAll ExpandDenominator ExpandFileName ExpandNumerator Expectation ExpectationE ExpectedValue ExpGammaDistribution ExpIntegralE ExpIntegralEi Exponent ExponentFunction ExponentialDistribution ExponentialFamily ExponentialGeneratingFunction ExponentialMovingAverage ExponentialPowerDistribution ExponentPosition ExponentStep Export ExportAutoReplacements ExportPacket ExportString Expression ExpressionCell ExpressionPacket ExpToTrig ExtendedGCD Extension ExtentElementFunction ExtentMarkers ExtentSize ExternalCall ExternalDataCharacterEncoding Extract ExtractArchive ExtremeValueDistribution FaceForm FaceGrids FaceGridsStyle Factor FactorComplete Factorial Factorial2 FactorialMoment FactorialMomentGeneratingFunction FactorialPower FactorInteger FactorList FactorSquareFree FactorSquareFreeList FactorTerms FactorTermsList Fail FailureDistribution False FARIMAProcess FEDisableConsolePrintPacket FeedbackSector FeedbackSectorStyle FeedbackType FEEnableConsolePrintPacket Fibonacci FieldHint FieldHintStyle FieldMasked FieldSize File FileBaseName FileByteCount FileDate FileExistsQ FileExtension FileFormat FileHash FileInformation FileName FileNameDepth FileNameDialogSettings FileNameDrop FileNameJoin FileNames FileNameSetter FileNameSplit FileNameTake FilePrint FileType FilledCurve FilledCurveBox Filling FillingStyle FillingTransform FilterRules FinancialBond FinancialData FinancialDerivative FinancialIndicator Find FindArgMax FindArgMin FindClique FindClusters FindCurvePath FindDistributionParameters FindDivisions FindEdgeCover FindEdgeCut FindEulerianCycle FindFaces FindFile FindFit FindGeneratingFunction FindGeoLocation FindGeometricTransform FindGraphCommunities FindGraphIsomorphism FindGraphPartition FindHamiltonianCycle FindIndependentEdgeSet FindIndependentVertexSet FindInstance FindIntegerNullVector FindKClan FindKClique FindKClub FindKPlex FindLibrary FindLinearRecurrence FindList FindMaximum FindMaximumFlow FindMaxValue FindMinimum FindMinimumCostFlow FindMinimumCut FindMinValue FindPermutation FindPostmanTour FindProcessParameters FindRoot FindSequenceFunction FindSettings FindShortestPath FindShortestTour FindThreshold FindVertexCover FindVertexCut Fine FinishDynamic FiniteAbelianGroupCount FiniteGroupCount FiniteGroupData First FirstPassageTimeDistribution FischerGroupFi22 FischerGroupFi23 FischerGroupFi24Prime FisherHypergeometricDistribution FisherRatioTest FisherZDistribution Fit FitAll FittedModel FixedPoint FixedPointList FlashSelection Flat Flatten FlattenAt FlatTopWindow FlipView Floor FlushPrintOutputPacket Fold FoldList Font FontColor FontFamily FontForm FontName FontOpacity FontPostScriptName FontProperties FontReencoding FontSize FontSlant FontSubstitutions FontTracking FontVariations FontWeight For ForAll Format FormatRules FormatType FormatTypeAutoConvert FormatValues FormBox FormBoxOptions FortranForm Forward ForwardBackward Fourier FourierCoefficient FourierCosCoefficient FourierCosSeries FourierCosTransform FourierDCT FourierDCTFilter FourierDCTMatrix FourierDST FourierDSTMatrix FourierMatrix FourierParameters FourierSequenceTransform FourierSeries FourierSinCoefficient FourierSinSeries FourierSinTransform FourierTransform FourierTrigSeries FractionalBrownianMotionProcess FractionalPart FractionBox FractionBoxOptions FractionLine Frame FrameBox FrameBoxOptions Framed FrameInset FrameLabel Frameless FrameMargins FrameStyle FrameTicks FrameTicksStyle FRatioDistribution FrechetDistribution FreeQ FrequencySamplingFilterKernel FresnelC FresnelS Friday FrobeniusNumber FrobeniusSolve FromCharacterCode FromCoefficientRules FromContinuedFraction FromDate FromDigits FromDMS Front FrontEndDynamicExpression FrontEndEventActions FrontEndExecute FrontEndObject FrontEndResource FrontEndResourceString FrontEndStackSize FrontEndToken FrontEndTokenExecute FrontEndValueCache FrontEndVersion FrontFaceColor FrontFaceOpacity Full FullAxes FullDefinition FullForm FullGraphics FullOptions FullSimplify Function FunctionExpand FunctionInterpolation FunctionSpace FussellVeselyImportance GaborFilter GaborMatrix GaborWavelet GainMargins GainPhaseMargins Gamma GammaDistribution GammaRegularized GapPenalty Gather GatherBy GaugeFaceElementFunction GaugeFaceStyle GaugeFrameElementFunction GaugeFrameSize GaugeFrameStyle GaugeLabels GaugeMarkers GaugeStyle GaussianFilter GaussianIntegers GaussianMatrix GaussianWindow GCD GegenbauerC General GeneralizedLinearModelFit GenerateConditions GeneratedCell GeneratedParameters GeneratingFunction Generic GenericCylindricalDecomposition GenomeData GenomeLookup GeodesicClosing GeodesicDilation GeodesicErosion GeodesicOpening GeoDestination GeodesyData GeoDirection GeoDistance GeoGridPosition GeometricBrownianMotionProcess GeometricDistribution GeometricMean GeometricMeanFilter GeometricTransformation GeometricTransformation3DBox GeometricTransformation3DBoxOptions GeometricTransformationBox GeometricTransformationBoxOptions GeoPosition GeoPositionENU GeoPositionXYZ GeoProjectionData GestureHandler GestureHandlerTag Get GetBoundingBoxSizePacket GetContext GetEnvironment GetFileName GetFrontEndOptionsDataPacket GetLinebreakInformationPacket GetMenusPacket GetPageBreakInformationPacket Glaisher GlobalClusteringCoefficient GlobalPreferences GlobalSession Glow GoldenRatio GompertzMakehamDistribution GoodmanKruskalGamma GoodmanKruskalGammaTest Goto Grad Gradient GradientFilter GradientOrientationFilter Graph GraphAssortativity GraphCenter GraphComplement GraphData GraphDensity GraphDiameter GraphDifference GraphDisjointUnion GraphDistance GraphDistanceMatrix GraphElementData GraphEmbedding GraphHighlight GraphHighlightStyle GraphHub Graphics Graphics3D Graphics3DBox Graphics3DBoxOptions GraphicsArray GraphicsBaseline GraphicsBox GraphicsBoxOptions GraphicsColor GraphicsColumn GraphicsComplex GraphicsComplex3DBox GraphicsComplex3DBoxOptions GraphicsComplexBox GraphicsComplexBoxOptions GraphicsContents GraphicsData GraphicsGrid GraphicsGridBox GraphicsGroup GraphicsGroup3DBox GraphicsGroup3DBoxOptions GraphicsGroupBox GraphicsGroupBoxOptions GraphicsGrouping GraphicsHighlightColor GraphicsRow GraphicsSpacing GraphicsStyle GraphIntersection GraphLayout GraphLinkEfficiency GraphPeriphery GraphPlot GraphPlot3D GraphPower GraphPropertyDistribution GraphQ GraphRadius GraphReciprocity GraphRoot GraphStyle GraphUnion Gray GrayLevel GreatCircleDistance Greater GreaterEqual GreaterEqualLess GreaterFullEqual GreaterGreater GreaterLess GreaterSlantEqual GreaterTilde Green Grid GridBaseline GridBox GridBoxAlignment GridBoxBackground GridBoxDividers GridBoxFrame GridBoxItemSize GridBoxItemStyle GridBoxOptions GridBoxSpacings GridCreationSettings GridDefaultElement GridElementStyleOptions GridFrame GridFrameMargins GridGraph GridLines GridLinesStyle GroebnerBasis GroupActionBase GroupCentralizer GroupElementFromWord GroupElementPosition GroupElementQ GroupElements GroupElementToWord GroupGenerators GroupMultiplicationTable GroupOrbits GroupOrder GroupPageBreakWithin GroupSetwiseStabilizer GroupStabilizer GroupStabilizerChain Gudermannian GumbelDistribution HaarWavelet HadamardMatrix HalfNormalDistribution HamiltonianGraphQ HammingDistance HammingWindow HankelH1 HankelH2 HankelMatrix HannPoissonWindow HannWindow HaradaNortonGroupHN HararyGraph HarmonicMean HarmonicMeanFilter HarmonicNumber Hash HashTable Haversine HazardFunction Head HeadCompose Heads HeavisideLambda HeavisidePi HeavisideTheta HeldGroupHe HeldPart HelpBrowserLookup HelpBrowserNotebook HelpBrowserSettings HermiteDecomposition HermiteH HermitianMatrixQ HessenbergDecomposition Hessian HexadecimalCharacter Hexahedron HexahedronBox HexahedronBoxOptions HiddenSurface HighlightGraph HighlightImage HighpassFilter HigmanSimsGroupHS HilbertFilter HilbertMatrix Histogram Histogram3D HistogramDistribution HistogramList HistogramTransform HistogramTransformInterpolation HitMissTransform HITSCentrality HodgeDual HoeffdingD HoeffdingDTest Hold HoldAll HoldAllComplete HoldComplete HoldFirst HoldForm HoldPattern HoldRest HolidayCalendar HomeDirectory HomePage Horizontal HorizontalForm HorizontalGauge HorizontalScrollPosition HornerForm HotellingTSquareDistribution HoytDistribution HTMLSave Hue HumpDownHump HumpEqual HurwitzLerchPhi HurwitzZeta HyperbolicDistribution HypercubeGraph HyperexponentialDistribution Hyperfactorial Hypergeometric0F1 Hypergeometric0F1Regularized Hypergeometric1F1 Hypergeometric1F1Regularized Hypergeometric2F1 Hypergeometric2F1Regularized HypergeometricDistribution HypergeometricPFQ HypergeometricPFQRegularized HypergeometricU Hyperlink HyperlinkCreationSettings Hyphenation HyphenationOptions HypoexponentialDistribution HypothesisTestData I Identity IdentityMatrix If IgnoreCase Im Image Image3D Image3DSlices ImageAccumulate ImageAdd ImageAdjust ImageAlign ImageApply ImageAspectRatio ImageAssemble ImageCache ImageCacheValid ImageCapture ImageChannels ImageClip ImageColorSpace ImageCompose ImageConvolve ImageCooccurrence ImageCorners ImageCorrelate ImageCorrespondingPoints ImageCrop ImageData ImageDataPacket ImageDeconvolve ImageDemosaic ImageDifference ImageDimensions ImageDistance ImageEffect ImageFeatureTrack ImageFileApply ImageFileFilter ImageFileScan ImageFilter ImageForestingComponents ImageForwardTransformation ImageHistogram ImageKeypoints ImageLevels ImageLines ImageMargins ImageMarkers ImageMeasurements ImageMultiply ImageOffset ImagePad ImagePadding ImagePartition ImagePeriodogram ImagePerspectiveTransformation ImageQ ImageRangeCache ImageReflect ImageRegion ImageResize ImageResolution ImageRotate ImageRotated ImageScaled ImageScan ImageSize ImageSizeAction ImageSizeCache ImageSizeMultipliers ImageSizeRaw ImageSubtract ImageTake ImageTransformation ImageTrim ImageType ImageValue ImageValuePositions Implies Import ImportAutoReplacements ImportString ImprovementImportance In IncidenceGraph IncidenceList IncidenceMatrix IncludeConstantBasis IncludeFileExtension IncludePods IncludeSingularTerm Increment Indent IndentingNewlineSpacings IndentMaxFraction IndependenceTest IndependentEdgeSetQ IndependentUnit IndependentVertexSetQ Indeterminate IndexCreationOptions Indexed IndexGraph IndexTag Inequality InexactNumberQ InexactNumbers Infinity Infix Information Inherited InheritScope Initialization InitializationCell InitializationCellEvaluation InitializationCellWarning InlineCounterAssignments InlineCounterIncrements InlineRules Inner Inpaint Input InputAliases InputAssumptions InputAutoReplacements InputField InputFieldBox InputFieldBoxOptions InputForm InputGrouping InputNamePacket InputNotebook InputPacket InputSettings InputStream InputString InputStringPacket InputToBoxFormPacket Insert InsertionPointObject InsertResults Inset Inset3DBox Inset3DBoxOptions InsetBox InsetBoxOptions Install InstallService InString Integer IntegerDigits IntegerExponent IntegerLength IntegerPart IntegerPartitions IntegerQ Integers IntegerString Integral Integrate Interactive InteractiveTradingChart Interlaced Interleaving InternallyBalancedDecomposition InterpolatingFunction InterpolatingPolynomial Interpolation InterpolationOrder InterpolationPoints InterpolationPrecision Interpretation InterpretationBox InterpretationBoxOptions InterpretationFunction InterpretTemplate InterquartileRange Interrupt InterruptSettings Intersection Interval IntervalIntersection IntervalMemberQ IntervalUnion Inverse InverseBetaRegularized InverseCDF InverseChiSquareDistribution InverseContinuousWaveletTransform InverseDistanceTransform InverseEllipticNomeQ InverseErf InverseErfc InverseFourier InverseFourierCosTransform InverseFourierSequenceTransform InverseFourierSinTransform InverseFourierTransform InverseFunction InverseFunctions InverseGammaDistribution InverseGammaRegularized InverseGaussianDistribution InverseGudermannian InverseHaversine InverseJacobiCD InverseJacobiCN InverseJacobiCS InverseJacobiDC InverseJacobiDN InverseJacobiDS InverseJacobiNC InverseJacobiND InverseJacobiNS InverseJacobiSC InverseJacobiSD InverseJacobiSN InverseLaplaceTransform InversePermutation InverseRadon InverseSeries InverseSurvivalFunction InverseWaveletTransform InverseWeierstrassP InverseZTransform Invisible InvisibleApplication InvisibleTimes IrreduciblePolynomialQ IsolatingInterval IsomorphicGraphQ IsotopeData Italic Item ItemBox ItemBoxOptions ItemSize ItemStyle ItoProcess JaccardDissimilarity JacobiAmplitude Jacobian JacobiCD JacobiCN JacobiCS JacobiDC JacobiDN JacobiDS JacobiNC JacobiND JacobiNS JacobiP JacobiSC JacobiSD JacobiSN JacobiSymbol JacobiZeta JankoGroupJ1 JankoGroupJ2 JankoGroupJ3 JankoGroupJ4 JarqueBeraALMTest JohnsonDistribution Join Joined JoinedCurve JoinedCurveBox JoinForm JordanDecomposition JordanModelDecomposition K KagiChart KaiserBesselWindow KaiserWindow KalmanEstimator KalmanFilter KarhunenLoeveDecomposition KaryTree KatzCentrality KCoreComponents KDistribution KelvinBei KelvinBer KelvinKei KelvinKer KendallTau KendallTauTest KernelExecute KernelMixtureDistribution KernelObject Kernels Ket Khinchin KirchhoffGraph KirchhoffMatrix KleinInvariantJ KnightTourGraph KnotData KnownUnitQ KolmogorovSmirnovTest KroneckerDelta KroneckerModelDecomposition KroneckerProduct KroneckerSymbol KuiperTest KumaraswamyDistribution Kurtosis KuwaharaFilter Label Labeled LabeledSlider LabelingFunction LabelStyle LaguerreL LambdaComponents LambertW LanczosWindow LandauDistribution Language LanguageCategory LaplaceDistribution LaplaceTransform Laplacian LaplacianFilter LaplacianGaussianFilter Large Larger Last Latitude LatitudeLongitude LatticeData LatticeReduce Launch LaunchKernels LayeredGraphPlot LayerSizeFunction LayoutInformation LCM LeafCount LeapYearQ LeastSquares LeastSquaresFilterKernel Left LeftArrow LeftArrowBar LeftArrowRightArrow LeftDownTeeVector LeftDownVector LeftDownVectorBar LeftRightArrow LeftRightVector LeftTee LeftTeeArrow LeftTeeVector LeftTriangle LeftTriangleBar LeftTriangleEqual LeftUpDownVector LeftUpTeeVector LeftUpVector LeftUpVectorBar LeftVector LeftVectorBar LegendAppearance Legended LegendFunction LegendLabel LegendLayout LegendMargins LegendMarkers LegendMarkerSize LegendreP LegendreQ LegendreType Length LengthWhile LerchPhi Less LessEqual LessEqualGreater LessFullEqual LessGreater LessLess LessSlantEqual LessTilde LetterCharacter LetterQ Level LeveneTest LeviCivitaTensor LevyDistribution Lexicographic LibraryFunction LibraryFunctionError LibraryFunctionInformation LibraryFunctionLoad LibraryFunctionUnload LibraryLoad LibraryUnload LicenseID LiftingFilterData LiftingWaveletTransform LightBlue LightBrown LightCyan Lighter LightGray LightGreen Lighting LightingAngle LightMagenta LightOrange LightPink LightPurple LightRed LightSources LightYellow Likelihood Limit LimitsPositioning LimitsPositioningTokens LindleyDistribution Line Line3DBox LinearFilter LinearFractionalTransform LinearModelFit LinearOffsetFunction LinearProgramming LinearRecurrence LinearSolve LinearSolveFunction LineBox LineBreak LinebreakAdjustments LineBreakChart LineBreakWithin LineColor LineForm LineGraph LineIndent LineIndentMaxFraction LineIntegralConvolutionPlot LineIntegralConvolutionScale LineLegend LineOpacity LineSpacing LineWrapParts LinkActivate LinkClose LinkConnect LinkConnectedQ LinkCreate LinkError LinkFlush LinkFunction LinkHost LinkInterrupt LinkLaunch LinkMode LinkObject LinkOpen LinkOptions LinkPatterns LinkProtocol LinkRead LinkReadHeld LinkReadyQ Links LinkWrite LinkWriteHeld LiouvilleLambda List Listable ListAnimate ListContourPlot ListContourPlot3D ListConvolve ListCorrelate ListCurvePathPlot ListDeconvolve ListDensityPlot Listen ListFourierSequenceTransform ListInterpolation ListLineIntegralConvolutionPlot ListLinePlot ListLogLinearPlot ListLogLogPlot ListLogPlot ListPicker ListPickerBox ListPickerBoxBackground ListPickerBoxOptions ListPlay ListPlot ListPlot3D ListPointPlot3D ListPolarPlot ListQ ListStreamDensityPlot ListStreamPlot ListSurfacePlot3D ListVectorDensityPlot ListVectorPlot ListVectorPlot3D ListZTransform Literal LiteralSearch LocalClusteringCoefficient LocalizeVariables LocationEquivalenceTest LocationTest Locator LocatorAutoCreate LocatorBox LocatorBoxOptions LocatorCentering LocatorPane LocatorPaneBox LocatorPaneBoxOptions LocatorRegion Locked Log Log10 Log2 LogBarnesG LogGamma LogGammaDistribution LogicalExpand LogIntegral LogisticDistribution LogitModelFit LogLikelihood LogLinearPlot LogLogisticDistribution LogLogPlot LogMultinormalDistribution LogNormalDistribution LogPlot LogRankTest LogSeriesDistribution LongEqual Longest LongestAscendingSequence LongestCommonSequence LongestCommonSequencePositions LongestCommonSubsequence LongestCommonSubsequencePositions LongestMatch LongForm Longitude LongLeftArrow LongLeftRightArrow LongRightArrow Loopback LoopFreeGraphQ LowerCaseQ LowerLeftArrow LowerRightArrow LowerTriangularize LowpassFilter LQEstimatorGains LQGRegulator LQOutputRegulatorGains LQRegulatorGains LUBackSubstitution LucasL LuccioSamiComponents LUDecomposition LyapunovSolve LyonsGroupLy MachineID MachineName MachineNumberQ MachinePrecision MacintoshSystemPageSetup Magenta Magnification Magnify MainSolve MaintainDynamicCaches Majority MakeBoxes MakeExpression MakeRules MangoldtLambda ManhattanDistance Manipulate Manipulator MannWhitneyTest MantissaExponent Manual Map MapAll MapAt MapIndexed MAProcess MapThread MarcumQ MardiaCombinedTest MardiaKurtosisTest MardiaSkewnessTest MarginalDistribution MarkovProcessProperties Masking MatchingDissimilarity MatchLocalNameQ MatchLocalNames MatchQ Material MathematicaNotation MathieuC MathieuCharacteristicA MathieuCharacteristicB MathieuCharacteristicExponent MathieuCPrime MathieuGroupM11 MathieuGroupM12 MathieuGroupM22 MathieuGroupM23 MathieuGroupM24 MathieuS MathieuSPrime MathMLForm MathMLText Matrices MatrixExp MatrixForm MatrixFunction MatrixLog MatrixPlot MatrixPower MatrixQ MatrixRank Max MaxBend MaxDetect MaxExtraBandwidths MaxExtraConditions MaxFeatures MaxFilter Maximize MaxIterations MaxMemoryUsed MaxMixtureKernels MaxPlotPoints MaxPoints MaxRecursion MaxStableDistribution MaxStepFraction MaxSteps MaxStepSize MaxValue MaxwellDistribution McLaughlinGroupMcL Mean MeanClusteringCoefficient MeanDegreeConnectivity MeanDeviation MeanFilter MeanGraphDistance MeanNeighborDegree MeanShift MeanShiftFilter Median MedianDeviation MedianFilter Medium MeijerG MeixnerDistribution MemberQ MemoryConstrained MemoryInUse Menu MenuAppearance MenuCommandKey MenuEvaluator MenuItem MenuPacket MenuSortingValue MenuStyle MenuView MergeDifferences Mesh MeshFunctions MeshRange MeshShading MeshStyle Message MessageDialog MessageList MessageName MessageOptions MessagePacket Messages MessagesNotebook MetaCharacters MetaInformation Method MethodOptions MexicanHatWavelet MeyerWavelet Min MinDetect MinFilter MinimalPolynomial MinimalStateSpaceModel Minimize Minors MinRecursion MinSize MinStableDistribution Minus MinusPlus MinValue Missing MissingDataMethod MittagLefflerE MixedRadix MixedRadixQuantity MixtureDistribution Mod Modal Mode Modular ModularLambda Module Modulus MoebiusMu Moment Momentary MomentConvert MomentEvaluate MomentGeneratingFunction Monday Monitor MonomialList MonomialOrder MonsterGroupM MorletWavelet MorphologicalBinarize MorphologicalBranchPoints MorphologicalComponents MorphologicalEulerNumber MorphologicalGraph MorphologicalPerimeter MorphologicalTransform Most MouseAnnotation MouseAppearance MouseAppearanceTag MouseButtons Mouseover MousePointerNote MousePosition MovingAverage MovingMedian MoyalDistribution MultiedgeStyle MultilaunchWarning MultiLetterItalics MultiLetterStyle MultilineFunction Multinomial MultinomialDistribution MultinormalDistribution MultiplicativeOrder Multiplicity Multiselection MultivariateHypergeometricDistribution MultivariatePoissonDistribution MultivariateTDistribution N NakagamiDistribution NameQ Names NamespaceBox Nand NArgMax NArgMin NBernoulliB NCache NDSolve NDSolveValue Nearest NearestFunction NeedCurrentFrontEndPackagePacket NeedCurrentFrontEndSymbolsPacket NeedlemanWunschSimilarity Needs Negative NegativeBinomialDistribution NegativeMultinomialDistribution NeighborhoodGraph Nest NestedGreaterGreater NestedLessLess NestedScriptRules NestList NestWhile NestWhileList NevilleThetaC NevilleThetaD NevilleThetaN NevilleThetaS NewPrimitiveStyle NExpectation Next NextPrime NHoldAll NHoldFirst NHoldRest NicholsGridLines NicholsPlot NIntegrate NMaximize NMaxValue NMinimize NMinValue NominalVariables NonAssociative NoncentralBetaDistribution NoncentralChiSquareDistribution NoncentralFRatioDistribution NoncentralStudentTDistribution NonCommutativeMultiply NonConstants None NonlinearModelFit NonlocalMeansFilter NonNegative NonPositive Nor NorlundB Norm Normal NormalDistribution NormalGrouping Normalize NormalizedSquaredEuclideanDistance NormalsFunction NormFunction Not NotCongruent NotCupCap NotDoubleVerticalBar Notebook NotebookApply NotebookAutoSave NotebookClose NotebookConvertSettings NotebookCreate NotebookCreateReturnObject NotebookDefault NotebookDelete NotebookDirectory NotebookDynamicExpression NotebookEvaluate NotebookEventActions NotebookFileName NotebookFind NotebookFindReturnObject NotebookGet NotebookGetLayoutInformationPacket NotebookGetMisspellingsPacket NotebookInformation NotebookInterfaceObject NotebookLocate NotebookObject NotebookOpen NotebookOpenReturnObject NotebookPath NotebookPrint NotebookPut NotebookPutReturnObject NotebookRead NotebookResetGeneratedCells Notebooks NotebookSave NotebookSaveAs NotebookSelection NotebookSetupLayoutInformationPacket NotebooksMenu NotebookWrite NotElement NotEqualTilde NotExists NotGreater NotGreaterEqual NotGreaterFullEqual NotGreaterGreater NotGreaterLess NotGreaterSlantEqual NotGreaterTilde NotHumpDownHump NotHumpEqual NotLeftTriangle NotLeftTriangleBar NotLeftTriangleEqual NotLess NotLessEqual NotLessFullEqual NotLessGreater NotLessLess NotLessSlantEqual NotLessTilde NotNestedGreaterGreater NotNestedLessLess NotPrecedes NotPrecedesEqual NotPrecedesSlantEqual NotPrecedesTilde NotReverseElement NotRightTriangle NotRightTriangleBar NotRightTriangleEqual NotSquareSubset NotSquareSubsetEqual NotSquareSuperset NotSquareSupersetEqual NotSubset NotSubsetEqual NotSucceeds NotSucceedsEqual NotSucceedsSlantEqual NotSucceedsTilde NotSuperset NotSupersetEqual NotTilde NotTildeEqual NotTildeFullEqual NotTildeTilde NotVerticalBar NProbability NProduct NProductFactors NRoots NSolve NSum NSumTerms Null NullRecords NullSpace NullWords Number NumberFieldClassNumber NumberFieldDiscriminant NumberFieldFundamentalUnits NumberFieldIntegralBasis NumberFieldNormRepresentatives NumberFieldRegulator NumberFieldRootsOfUnity NumberFieldSignature NumberForm NumberFormat NumberMarks NumberMultiplier NumberPadding NumberPoint NumberQ NumberSeparator NumberSigns NumberString Numerator NumericFunction NumericQ NuttallWindow NValues NyquistGridLines NyquistPlot O ObservabilityGramian ObservabilityMatrix ObservableDecomposition ObservableModelQ OddQ Off Offset OLEData On ONanGroupON OneIdentity Opacity Open OpenAppend Opener OpenerBox OpenerBoxOptions OpenerView OpenFunctionInspectorPacket Opening OpenRead OpenSpecialOptions OpenTemporary OpenWrite Operate OperatingSystem OptimumFlowData Optional OptionInspectorSettings OptionQ Options OptionsPacket OptionsPattern OptionValue OptionValueBox OptionValueBoxOptions Or Orange Order OrderDistribution OrderedQ Ordering Orderless OrnsteinUhlenbeckProcess Orthogonalize Out Outer OutputAutoOverwrite OutputControllabilityMatrix OutputControllableModelQ OutputForm OutputFormData OutputGrouping OutputMathEditExpression OutputNamePacket OutputResponse OutputSizeLimit OutputStream Over OverBar OverDot Overflow OverHat Overlaps Overlay OverlayBox OverlayBoxOptions Overscript OverscriptBox OverscriptBoxOptions OverTilde OverVector OwenT OwnValues PackingMethod PaddedForm Padding PadeApproximant PadLeft PadRight PageBreakAbove PageBreakBelow PageBreakWithin PageFooterLines PageFooters PageHeaderLines PageHeaders PageHeight PageRankCentrality PageWidth PairedBarChart PairedHistogram PairedSmoothHistogram PairedTTest PairedZTest PaletteNotebook PalettePath Pane PaneBox PaneBoxOptions Panel PanelBox PanelBoxOptions Paneled PaneSelector PaneSelectorBox PaneSelectorBoxOptions PaperWidth ParabolicCylinderD ParagraphIndent ParagraphSpacing ParallelArray ParallelCombine ParallelDo ParallelEvaluate Parallelization Parallelize ParallelMap ParallelNeeds ParallelProduct ParallelSubmit ParallelSum ParallelTable ParallelTry Parameter ParameterEstimator ParameterMixtureDistribution ParameterVariables ParametricFunction ParametricNDSolve ParametricNDSolveValue ParametricPlot ParametricPlot3D ParentConnect ParentDirectory ParentForm Parenthesize ParentList ParetoDistribution Part PartialCorrelationFunction PartialD ParticleData Partition PartitionsP PartitionsQ ParzenWindow PascalDistribution PassEventsDown PassEventsUp Paste PasteBoxFormInlineCells PasteButton Path PathGraph PathGraphQ Pattern PatternSequence PatternTest PauliMatrix PaulWavelet Pause PausedTime PDF PearsonChiSquareTest PearsonCorrelationTest PearsonDistribution PerformanceGoal PeriodicInterpolation Periodogram PeriodogramArray PermutationCycles PermutationCyclesQ PermutationGroup PermutationLength PermutationList PermutationListQ PermutationMax PermutationMin PermutationOrder PermutationPower PermutationProduct PermutationReplace Permutations PermutationSupport Permute PeronaMalikFilter Perpendicular PERTDistribution PetersenGraph PhaseMargins Pi Pick PIDData PIDDerivativeFilter PIDFeedforward PIDTune Piecewise PiecewiseExpand PieChart PieChart3D PillaiTrace PillaiTraceTest Pink Pivoting PixelConstrained PixelValue PixelValuePositions Placed Placeholder PlaceholderReplace Plain PlanarGraphQ Play PlayRange Plot Plot3D Plot3Matrix PlotDivision PlotJoined PlotLabel PlotLayout PlotLegends PlotMarkers PlotPoints PlotRange PlotRangeClipping PlotRangePadding PlotRegion PlotStyle Plus PlusMinus Pochhammer PodStates PodWidth Point Point3DBox PointBox PointFigureChart PointForm PointLegend PointSize PoissonConsulDistribution PoissonDistribution PoissonProcess PoissonWindow PolarAxes PolarAxesOrigin PolarGridLines PolarPlot PolarTicks PoleZeroMarkers PolyaAeppliDistribution PolyGamma Polygon Polygon3DBox Polygon3DBoxOptions PolygonBox PolygonBoxOptions PolygonHoleScale PolygonIntersections PolygonScale PolyhedronData PolyLog PolynomialExtendedGCD PolynomialForm PolynomialGCD PolynomialLCM PolynomialMod PolynomialQ PolynomialQuotient PolynomialQuotientRemainder PolynomialReduce PolynomialRemainder Polynomials PopupMenu PopupMenuBox PopupMenuBoxOptions PopupView PopupWindow Position Positive PositiveDefiniteMatrixQ PossibleZeroQ Postfix PostScript Power PowerDistribution PowerExpand PowerMod PowerModList PowerSpectralDensity PowersRepresentations PowerSymmetricPolynomial Precedence PrecedenceForm Precedes PrecedesEqual PrecedesSlantEqual PrecedesTilde Precision PrecisionGoal PreDecrement PredictionRoot PreemptProtect PreferencesPath Prefix PreIncrement Prepend PrependTo PreserveImageOptions Previous PriceGraphDistribution PrimaryPlaceholder Prime PrimeNu PrimeOmega PrimePi PrimePowerQ PrimeQ Primes PrimeZetaP PrimitiveRoot PrincipalComponents PrincipalValue Print PrintAction PrintForm PrintingCopies PrintingOptions PrintingPageRange PrintingStartingPageNumber PrintingStyleEnvironment PrintPrecision PrintTemporary Prism PrismBox PrismBoxOptions PrivateCellOptions PrivateEvaluationOptions PrivateFontOptions PrivateFrontEndOptions PrivateNotebookOptions PrivatePaths Probability ProbabilityDistribution ProbabilityPlot ProbabilityPr ProbabilityScalePlot ProbitModelFit ProcessEstimator ProcessParameterAssumptions ProcessParameterQ ProcessStateDomain ProcessTimeDomain Product ProductDistribution ProductLog ProgressIndicator ProgressIndicatorBox ProgressIndicatorBoxOptions Projection Prolog PromptForm Properties Property PropertyList PropertyValue Proportion Proportional Protect Protected ProteinData Pruning PseudoInverse Purple Put PutAppend Pyramid PyramidBox PyramidBoxOptions QBinomial QFactorial QGamma QHypergeometricPFQ QPochhammer QPolyGamma QRDecomposition QuadraticIrrationalQ Quantile QuantilePlot Quantity QuantityForm QuantityMagnitude QuantityQ QuantityUnit Quartics QuartileDeviation Quartiles QuartileSkewness QueueingNetworkProcess QueueingProcess QueueProperties Quiet Quit Quotient QuotientRemainder RadialityCentrality RadicalBox RadicalBoxOptions RadioButton RadioButtonBar RadioButtonBox RadioButtonBoxOptions Radon RamanujanTau RamanujanTauL RamanujanTauTheta RamanujanTauZ Random RandomChoice RandomComplex RandomFunction RandomGraph RandomImage RandomInteger RandomPermutation RandomPrime RandomReal RandomSample RandomSeed RandomVariate RandomWalkProcess Range RangeFilter RangeSpecification RankedMax RankedMin Raster Raster3D Raster3DBox Raster3DBoxOptions RasterArray RasterBox RasterBoxOptions Rasterize RasterSize Rational RationalFunctions Rationalize Rationals Ratios Raw RawArray RawBoxes RawData RawMedium RayleighDistribution Re Read ReadList ReadProtected Real RealBlockDiagonalForm RealDigits RealExponent Reals Reap Record RecordLists RecordSeparators Rectangle RectangleBox RectangleBoxOptions RectangleChart RectangleChart3D RecurrenceFilter RecurrenceTable RecurringDigitsForm Red Reduce RefBox ReferenceLineStyle ReferenceMarkers ReferenceMarkerStyle Refine ReflectionMatrix ReflectionTransform Refresh RefreshRate RegionBinarize RegionFunction RegionPlot RegionPlot3D RegularExpression Regularization Reinstall Release ReleaseHold ReliabilityDistribution ReliefImage ReliefPlot Remove RemoveAlphaChannel RemoveAsynchronousTask Removed RemoveInputStreamMethod RemoveOutputStreamMethod RemoveProperty RemoveScheduledTask RenameDirectory RenameFile RenderAll RenderingOptions RenewalProcess RenkoChart Repeated RepeatedNull RepeatedString Replace ReplaceAll ReplaceHeldPart ReplaceImageValue ReplaceList ReplacePart ReplacePixelValue ReplaceRepeated Resampling Rescale RescalingTransform ResetDirectory ResetMenusPacket ResetScheduledTask Residue Resolve Rest Resultant ResumePacket Return ReturnExpressionPacket ReturnInputFormPacket ReturnPacket ReturnTextPacket Reverse ReverseBiorthogonalSplineWavelet ReverseElement ReverseEquilibrium ReverseGraph ReverseUpEquilibrium RevolutionAxis RevolutionPlot3D RGBColor RiccatiSolve RiceDistribution RidgeFilter RiemannR RiemannSiegelTheta RiemannSiegelZ Riffle Right RightArrow RightArrowBar RightArrowLeftArrow RightCosetRepresentative RightDownTeeVector RightDownVector RightDownVectorBar RightTee RightTeeArrow RightTeeVector RightTriangle RightTriangleBar RightTriangleEqual RightUpDownVector RightUpTeeVector RightUpVector RightUpVectorBar RightVector RightVectorBar RiskAchievementImportance RiskReductionImportance RogersTanimotoDissimilarity Root RootApproximant RootIntervals RootLocusPlot RootMeanSquare RootOfUnityQ RootReduce Roots RootSum Rotate RotateLabel RotateLeft RotateRight RotationAction RotationBox RotationBoxOptions RotationMatrix RotationTransform Round RoundImplies RoundingRadius Row RowAlignments RowBackgrounds RowBox RowHeights RowLines RowMinHeight RowReduce RowsEqual RowSpacings RSolve RudvalisGroupRu Rule RuleCondition RuleDelayed RuleForm RulerUnits Run RunScheduledTask RunThrough RuntimeAttributes RuntimeOptions RussellRaoDissimilarity SameQ SameTest SampleDepth SampledSoundFunction SampledSoundList SampleRate SamplingPeriod SARIMAProcess SARMAProcess SatisfiabilityCount SatisfiabilityInstances SatisfiableQ Saturday Save Saveable SaveAutoDelete SaveDefinitions SawtoothWave Scale Scaled ScaleDivisions ScaledMousePosition ScaleOrigin ScalePadding ScaleRanges ScaleRangeStyle ScalingFunctions ScalingMatrix ScalingTransform Scan ScheduledTaskActiveQ ScheduledTaskData ScheduledTaskObject ScheduledTasks SchurDecomposition ScientificForm ScreenRectangle ScreenStyleEnvironment ScriptBaselineShifts ScriptLevel ScriptMinSize ScriptRules ScriptSizeMultipliers Scrollbars ScrollingOptions ScrollPosition Sec Sech SechDistribution SectionGrouping SectorChart SectorChart3D SectorOrigin SectorSpacing SeedRandom Select Selectable SelectComponents SelectedCells SelectedNotebook Selection SelectionAnimate SelectionCell SelectionCellCreateCell SelectionCellDefaultStyle SelectionCellParentStyle SelectionCreateCell SelectionDebuggerTag SelectionDuplicateCell SelectionEvaluate SelectionEvaluateCreateCell SelectionMove SelectionPlaceholder SelectionSetStyle SelectWithContents SelfLoops SelfLoopStyle SemialgebraicComponentInstances SendMail Sequence SequenceAlignment SequenceForm SequenceHold SequenceLimit Series SeriesCoefficient SeriesData SessionTime Set SetAccuracy SetAlphaChannel SetAttributes Setbacks SetBoxFormNamesPacket SetDelayed SetDirectory SetEnvironment SetEvaluationNotebook SetFileDate SetFileLoadingContext SetNotebookStatusLine SetOptions SetOptionsPacket SetPrecision SetProperty SetSelectedNotebook SetSharedFunction SetSharedVariable SetSpeechParametersPacket SetStreamPosition SetSystemOptions Setter SetterBar SetterBox SetterBoxOptions Setting SetValue Shading Shallow ShannonWavelet ShapiroWilkTest Share Sharpen ShearingMatrix ShearingTransform ShenCastanMatrix Short ShortDownArrow Shortest ShortestMatch ShortestPathFunction ShortLeftArrow ShortRightArrow ShortUpArrow Show ShowAutoStyles ShowCellBracket ShowCellLabel ShowCellTags ShowClosedCellArea ShowContents ShowControls ShowCursorTracker ShowGroupOpenCloseIcon ShowGroupOpener ShowInvisibleCharacters ShowPageBreaks ShowPredictiveInterface ShowSelection ShowShortBoxForm ShowSpecialCharacters ShowStringCharacters ShowSyntaxStyles ShrinkingDelay ShrinkWrapBoundingBox SiegelTheta SiegelTukeyTest Sign Signature SignedRankTest SignificanceLevel SignPadding SignTest SimilarityRules SimpleGraph SimpleGraphQ Simplify Sin Sinc SinghMaddalaDistribution SingleEvaluation SingleLetterItalics SingleLetterStyle SingularValueDecomposition SingularValueList SingularValuePlot SingularValues Sinh SinhIntegral SinIntegral SixJSymbol Skeleton SkeletonTransform SkellamDistribution Skewness SkewNormalDistribution Skip SliceDistribution Slider Slider2D Slider2DBox Slider2DBoxOptions SliderBox SliderBoxOptions SlideView Slot SlotSequence Small SmallCircle Smaller SmithDelayCompensator SmithWatermanSimilarity SmoothDensityHistogram SmoothHistogram SmoothHistogram3D SmoothKernelDistribution SocialMediaData Socket SokalSneathDissimilarity Solve SolveAlways SolveDelayed Sort SortBy Sound SoundAndGraphics SoundNote SoundVolume Sow Space SpaceForm Spacer Spacings Span SpanAdjustments SpanCharacterRounding SpanFromAbove SpanFromBoth SpanFromLeft SpanLineThickness SpanMaxSize SpanMinSize SpanningCharacters SpanSymmetric SparseArray SpatialGraphDistribution Speak SpeakTextPacket SpearmanRankTest SpearmanRho Spectrogram SpectrogramArray Specularity SpellingCorrection SpellingDictionaries SpellingDictionariesPath SpellingOptions SpellingSuggestionsPacket Sphere SphereBox SphericalBesselJ SphericalBesselY SphericalHankelH1 SphericalHankelH2 SphericalHarmonicY SphericalPlot3D SphericalRegion SpheroidalEigenvalue SpheroidalJoiningFactor SpheroidalPS SpheroidalPSPrime SpheroidalQS SpheroidalQSPrime SpheroidalRadialFactor SpheroidalS1 SpheroidalS1Prime SpheroidalS2 SpheroidalS2Prime Splice SplicedDistribution SplineClosed SplineDegree SplineKnots SplineWeights Split SplitBy SpokenString Sqrt SqrtBox SqrtBoxOptions Square SquaredEuclideanDistance SquareFreeQ SquareIntersection SquaresR SquareSubset SquareSubsetEqual SquareSuperset SquareSupersetEqual SquareUnion SquareWave StabilityMargins StabilityMarginsStyle StableDistribution Stack StackBegin StackComplete StackInhibit StandardDeviation StandardDeviationFilter StandardForm Standardize StandbyDistribution Star StarGraph StartAsynchronousTask StartingStepSize StartOfLine StartOfString StartScheduledTask StartupSound StateDimensions StateFeedbackGains StateOutputEstimator StateResponse StateSpaceModel StateSpaceRealization StateSpaceTransform StationaryDistribution StationaryWaveletPacketTransform StationaryWaveletTransform StatusArea StatusCentrality StepMonitor StieltjesGamma StirlingS1 StirlingS2 StopAsynchronousTask StopScheduledTask StrataVariables StratonovichProcess StreamColorFunction StreamColorFunctionScaling StreamDensityPlot StreamPlot StreamPoints StreamPosition Streams StreamScale StreamStyle String StringBreak StringByteCount StringCases StringCount StringDrop StringExpression StringForm StringFormat StringFreeQ StringInsert StringJoin StringLength StringMatchQ StringPosition StringQ StringReplace StringReplaceList StringReplacePart StringReverse StringRotateLeft StringRotateRight StringSkeleton StringSplit StringTake StringToStream StringTrim StripBoxes 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Symbol SymbolName SymletWavelet Symmetric SymmetricGroup SymmetricMatrixQ SymmetricPolynomial SymmetricReduction Symmetrize SymmetrizedArray SymmetrizedArrayRules SymmetrizedDependentComponents SymmetrizedIndependentComponents SymmetrizedReplacePart SynchronousInitialization SynchronousUpdating Syntax SyntaxForm SyntaxInformation SyntaxLength SyntaxPacket SyntaxQ SystemDialogInput SystemException SystemHelpPath SystemInformation SystemInformationData SystemOpen SystemOptions SystemsModelDelay SystemsModelDelayApproximate SystemsModelDelete SystemsModelDimensions SystemsModelExtract SystemsModelFeedbackConnect SystemsModelLabels SystemsModelOrder SystemsModelParallelConnect SystemsModelSeriesConnect SystemsModelStateFeedbackConnect SystemStub Tab TabFilling Table TableAlignments TableDepth TableDirections TableForm TableHeadings TableSpacing TableView TableViewBox TabSpacings TabView TabViewBox TabViewBoxOptions TagBox TagBoxNote TagBoxOptions TaggingRules TagSet TagSetDelayed TagStyle 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VerifySolutions VerifyTestAssumptions Version VersionNumber VertexAdd VertexCapacity VertexColors VertexComponent VertexConnectivity VertexCoordinateRules VertexCoordinates VertexCorrelationSimilarity VertexCosineSimilarity VertexCount VertexCoverQ VertexDataCoordinates VertexDegree VertexDelete VertexDiceSimilarity VertexEccentricity VertexInComponent VertexInDegree VertexIndex VertexJaccardSimilarity VertexLabeling VertexLabels VertexLabelStyle VertexList VertexNormals VertexOutComponent VertexOutDegree VertexQ VertexRenderingFunction VertexReplace VertexShape VertexShapeFunction VertexSize VertexStyle VertexTextureCoordinates VertexWeight Vertical VerticalBar VerticalForm VerticalGauge VerticalSeparator VerticalSlider VerticalTilde ViewAngle ViewCenter ViewMatrix ViewPoint ViewPointSelectorSettings ViewPort ViewRange ViewVector ViewVertical VirtualGroupData Visible VisibleCell VoigtDistribution VonMisesDistribution WaitAll WaitAsynchronousTask WaitNext WaitUntil WakebyDistribution WalleniusHypergeometricDistribution WaringYuleDistribution WatershedComponents WatsonUSquareTest WattsStrogatzGraphDistribution WaveletBestBasis WaveletFilterCoefficients WaveletImagePlot WaveletListPlot WaveletMapIndexed WaveletMatrixPlot WaveletPhi WaveletPsi WaveletScale WaveletScalogram WaveletThreshold WeaklyConnectedComponents WeaklyConnectedGraphQ WeakStationarity WeatherData WeberE Wedge Wednesday WeibullDistribution WeierstrassHalfPeriods WeierstrassInvariants WeierstrassP WeierstrassPPrime WeierstrassSigma WeierstrassZeta WeightedAdjacencyGraph WeightedAdjacencyMatrix WeightedData WeightedGraphQ Weights WelchWindow WheelGraph WhenEvent Which While White Whitespace WhitespaceCharacter WhittakerM WhittakerW WienerFilter WienerProcess WignerD WignerSemicircleDistribution WilksW WilksWTest WindowClickSelect WindowElements WindowFloating WindowFrame WindowFrameElements WindowMargins WindowMovable WindowOpacity WindowSelected WindowSize WindowStatusArea WindowTitle WindowToolbars 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eliminate_using ellipse elliptic_e elliptic_ec elliptic_eu elliptic_f elliptic_kc elliptic_pi ematrix empty_graph emptyp endcons entermatrix entertensor entier equal equalp equiv_classes erf erfc erf_generalized erfi errcatch error errormsg errors euler ev eval_string evenp every evolution evolution2d evundiff example exp expand expandwrt expandwrt_factored expint expintegral_chi expintegral_ci expintegral_e expintegral_e1 expintegral_ei expintegral_e_simplify expintegral_li expintegral_shi expintegral_si explicit explose exponentialize express expt exsec extdiff extract_linear_equations extremal_subset ezgcd %f f90 facsum factcomb factor factorfacsum factorial factorout factorsum facts fast_central_elements fast_linsolve fasttimes featurep fernfale fft fib fibtophi fifth filename_merge file_search file_type fillarray findde find_root find_root_abs find_root_error find_root_rel first fix flatten flength float floatnump floor flower_snark flush flush1deriv flushd flushnd flush_output fmin_cobyla forget fortran fourcos fourexpand fourier fourier_elim fourint fourintcos fourintsin foursimp foursin fourth fposition frame_bracket freeof freshline fresnel_c fresnel_s from_adjacency_matrix frucht_graph full_listify fullmap fullmapl fullratsimp fullratsubst fullsetify funcsolve fundamental_dimensions fundamental_units fundef funmake funp fv g0 g1 gamma gamma_greek gamma_incomplete gamma_incomplete_generalized gamma_incomplete_regularized gauss gauss_a gauss_b gaussprob gcd gcdex gcdivide gcfac gcfactor gd generalized_lambert_w genfact gen_laguerre genmatrix gensym geo_amortization geo_annuity_fv geo_annuity_pv geomap geometric geometric_mean geosum get getcurrentdirectory get_edge_weight getenv get_lu_factors get_output_stream_string get_pixel get_plot_option get_tex_environment get_tex_environment_default get_vertex_label gfactor gfactorsum ggf girth global_variances gn gnuplot_close gnuplot_replot gnuplot_reset gnuplot_restart gnuplot_start go Gosper GosperSum gr2d gr3d gradef gramschmidt graph6_decode graph6_encode graph6_export graph6_import graph_center graph_charpoly graph_eigenvalues graph_flow graph_order graph_periphery graph_product graph_size graph_union great_rhombicosidodecahedron_graph great_rhombicuboctahedron_graph grid_graph grind grobner_basis grotzch_graph hamilton_cycle hamilton_path hankel hankel_1 hankel_2 harmonic harmonic_mean hav heawood_graph hermite hessian hgfred hilbertmap hilbert_matrix hipow histogram histogram_description hodge horner hypergeometric i0 i1 %ibes ic1 ic2 ic_convert ichr1 ichr2 icosahedron_graph icosidodecahedron_graph icurvature ident identfor identity idiff idim idummy ieqn %if ifactors iframes ifs igcdex igeodesic_coords ilt image imagpart imetric implicit implicit_derivative implicit_plot indexed_tensor indices induced_subgraph inferencep inference_result infix info_display init_atensor init_ctensor in_neighbors innerproduct inpart inprod inrt integerp integer_partitions integrate intersect intersection intervalp intopois intosum invariant1 invariant2 inverse_fft inverse_jacobi_cd inverse_jacobi_cn inverse_jacobi_cs inverse_jacobi_dc inverse_jacobi_dn inverse_jacobi_ds inverse_jacobi_nc inverse_jacobi_nd inverse_jacobi_ns inverse_jacobi_sc inverse_jacobi_sd inverse_jacobi_sn invert invert_by_adjoint invert_by_lu inv_mod irr is is_biconnected is_bipartite is_connected is_digraph is_edge_in_graph is_graph is_graph_or_digraph ishow is_isomorphic isolate isomorphism is_planar isqrt isreal_p is_sconnected is_tree is_vertex_in_graph items_inference %j j0 j1 jacobi jacobian jacobi_cd jacobi_cn jacobi_cs jacobi_dc jacobi_dn jacobi_ds jacobi_nc jacobi_nd jacobi_ns jacobi_p jacobi_sc jacobi_sd jacobi_sn JF jn join jordan julia julia_set julia_sin %k kdels kdelta kill killcontext kostka kron_delta kronecker_product kummer_m kummer_u kurtosis kurtosis_bernoulli kurtosis_beta kurtosis_binomial kurtosis_chi2 kurtosis_continuous_uniform kurtosis_discrete_uniform kurtosis_exp kurtosis_f kurtosis_gamma kurtosis_general_finite_discrete kurtosis_geometric kurtosis_gumbel kurtosis_hypergeometric kurtosis_laplace kurtosis_logistic kurtosis_lognormal kurtosis_negative_binomial kurtosis_noncentral_chi2 kurtosis_noncentral_student_t kurtosis_normal kurtosis_pareto kurtosis_poisson kurtosis_rayleigh kurtosis_student_t kurtosis_weibull label labels lagrange laguerre lambda lambert_w laplace laplacian_matrix last lbfgs lc2kdt lcharp lc_l lcm lc_u ldefint ldisp ldisplay legendre_p legendre_q leinstein length let letrules letsimp levi_civita lfreeof lgtreillis lhs li liediff limit Lindstedt linear linearinterpol linear_program linear_regression line_graph linsolve listarray list_correlations listify list_matrix_entries list_nc_monomials listoftens listofvars listp lmax lmin load loadfile local locate_matrix_entry log logcontract log_gamma lopow lorentz_gauge lowercasep lpart lratsubst lreduce lriemann lsquares_estimates lsquares_estimates_approximate lsquares_estimates_exact lsquares_mse lsquares_residual_mse lsquares_residuals lsum ltreillis lu_backsub lucas lu_factor %m macroexpand macroexpand1 make_array makebox makefact makegamma make_graph make_level_picture makelist makeOrders make_poly_continent make_poly_country make_polygon make_random_state make_rgb_picture makeset make_string_input_stream make_string_output_stream make_transform mandelbrot mandelbrot_set map mapatom maplist matchdeclare matchfix mat_cond mat_fullunblocker mat_function mathml_display mat_norm matrix matrixmap matrixp matrix_size mattrace mat_trace mat_unblocker max max_clique max_degree max_flow maximize_lp max_independent_set max_matching maybe md5sum mean mean_bernoulli mean_beta mean_binomial mean_chi2 mean_continuous_uniform mean_deviation mean_discrete_uniform mean_exp mean_f mean_gamma mean_general_finite_discrete mean_geometric mean_gumbel mean_hypergeometric mean_laplace mean_logistic mean_lognormal mean_negative_binomial mean_noncentral_chi2 mean_noncentral_student_t mean_normal mean_pareto mean_poisson mean_rayleigh mean_student_t mean_weibull median median_deviation member mesh metricexpandall mgf1_sha1 min min_degree min_edge_cut minfactorial minimalPoly minimize_lp minimum_spanning_tree minor minpack_lsquares minpack_solve min_vertex_cover min_vertex_cut mkdir mnewton mod mode_declare mode_identity ModeMatrix moebius mon2schur mono monomial_dimensions multibernstein_poly multi_display_for_texinfo multi_elem multinomial multinomial_coeff multi_orbit multiplot_mode multi_pui multsym multthru mycielski_graph nary natural_unit nc_degree ncexpt ncharpoly negative_picture neighbors new newcontext newdet new_graph newline newton new_variable next_prime nicedummies niceindices ninth nofix nonarray noncentral_moment nonmetricity nonnegintegerp nonscalarp nonzeroandfreeof notequal nounify nptetrad npv nroots nterms ntermst nthroot nullity nullspace num numbered_boundaries numberp number_to_octets num_distinct_partitions numerval numfactor num_partitions nusum nzeta nzetai nzetar octets_to_number octets_to_oid odd_girth oddp ode2 ode_check odelin oid_to_octets op opena opena_binary openr openr_binary openw openw_binary operatorp opsubst optimize %or orbit orbits ordergreat ordergreatp orderless orderlessp orthogonal_complement orthopoly_recur orthopoly_weight outermap out_neighbors outofpois pade parabolic_cylinder_d parametric parametric_surface parg parGosper parse_string parse_timedate part part2cont partfrac partition partition_set partpol path_digraph path_graph pathname_directory pathname_name pathname_type pdf_bernoulli pdf_beta pdf_binomial pdf_cauchy pdf_chi2 pdf_continuous_uniform pdf_discrete_uniform pdf_exp pdf_f pdf_gamma pdf_general_finite_discrete pdf_geometric pdf_gumbel pdf_hypergeometric pdf_laplace pdf_logistic pdf_lognormal pdf_negative_binomial pdf_noncentral_chi2 pdf_noncentral_student_t pdf_normal pdf_pareto pdf_poisson pdf_rank_sum pdf_rayleigh pdf_signed_rank pdf_student_t pdf_weibull pearson_skewness permanent permut permutation permutations petersen_graph petrov pickapart picture_equalp picturep piechart piechart_description planar_embedding playback plog plot2d plot3d plotdf ploteq plsquares pochhammer points poisdiff poisexpt poisint poismap poisplus poissimp poissubst poistimes poistrim polar polarform polartorect polar_to_xy poly_add poly_buchberger poly_buchberger_criterion poly_colon_ideal poly_content polydecomp poly_depends_p poly_elimination_ideal poly_exact_divide poly_expand poly_expt poly_gcd polygon poly_grobner poly_grobner_equal poly_grobner_member poly_grobner_subsetp poly_ideal_intersection poly_ideal_polysaturation poly_ideal_polysaturation1 poly_ideal_saturation poly_ideal_saturation1 poly_lcm poly_minimization polymod poly_multiply polynome2ele polynomialp poly_normal_form poly_normalize poly_normalize_list poly_polysaturation_extension poly_primitive_part poly_pseudo_divide poly_reduced_grobner poly_reduction poly_saturation_extension poly_s_polynomial poly_subtract polytocompanion pop postfix potential power_mod powerseries powerset prefix prev_prime primep primes principal_components print printf printfile print_graph printpois printprops prodrac product properties propvars psi psubst ptriangularize pui pui2comp pui2ele pui2polynome pui_direct puireduc push put pv qput qrange qty quad_control quad_qag quad_qagi quad_qagp quad_qags quad_qawc quad_qawf quad_qawo quad_qaws quadrilateral quantile quantile_bernoulli quantile_beta quantile_binomial quantile_cauchy quantile_chi2 quantile_continuous_uniform quantile_discrete_uniform quantile_exp quantile_f quantile_gamma quantile_general_finite_discrete quantile_geometric quantile_gumbel quantile_hypergeometric quantile_laplace quantile_logistic quantile_lognormal quantile_negative_binomial quantile_noncentral_chi2 quantile_noncentral_student_t quantile_normal quantile_pareto quantile_poisson quantile_rayleigh quantile_student_t quantile_weibull quartile_skewness quit qunit quotient racah_v racah_w radcan radius random random_bernoulli random_beta random_binomial random_bipartite_graph random_cauchy random_chi2 random_continuous_uniform random_digraph random_discrete_uniform random_exp random_f random_gamma random_general_finite_discrete random_geometric random_graph random_graph1 random_gumbel random_hypergeometric random_laplace random_logistic random_lognormal random_negative_binomial random_network random_noncentral_chi2 random_noncentral_student_t random_normal random_pareto random_permutation random_poisson random_rayleigh random_regular_graph random_student_t random_tournament random_tree random_weibull range rank rat ratcoef ratdenom ratdiff ratdisrep ratexpand ratinterpol rational rationalize ratnumer ratnump ratp ratsimp ratsubst ratvars ratweight read read_array read_binary_array read_binary_list read_binary_matrix readbyte readchar read_hashed_array readline read_list read_matrix read_nested_list readonly read_xpm real_imagpart_to_conjugate realpart realroots rearray rectangle rectform rectform_log_if_constant recttopolar rediff reduce_consts reduce_order region region_boundaries region_boundaries_plus rem remainder remarray rembox remcomps remcon remcoord remfun remfunction remlet remove remove_constvalue remove_dimensions remove_edge remove_fundamental_dimensions remove_fundamental_units remove_plot_option remove_vertex rempart remrule remsym remvalue rename rename_file reset reset_displays residue resolvante resolvante_alternee1 resolvante_bipartite resolvante_diedrale resolvante_klein resolvante_klein3 resolvante_produit_sym resolvante_unitaire resolvante_vierer rest resultant return reveal reverse revert revert2 rgb2level rhs ricci riemann rinvariant risch rk rmdir rncombine romberg room rootscontract round row rowop rowswap rreduce run_testsuite %s save saving scalarp scaled_bessel_i scaled_bessel_i0 scaled_bessel_i1 scalefactors scanmap scatterplot scatterplot_description scene schur2comp sconcat scopy scsimp scurvature sdowncase sec sech second sequal sequalignore set_alt_display setdifference set_draw_defaults set_edge_weight setelmx setequalp setify setp set_partitions set_plot_option set_prompt set_random_state set_tex_environment set_tex_environment_default setunits setup_autoload set_up_dot_simplifications set_vertex_label seventh sexplode sf sha1sum sha256sum shortest_path shortest_weighted_path show showcomps showratvars sierpinskiale sierpinskimap sign signum similaritytransform simp_inequality simplify_sum simplode simpmetderiv simtran sin sinh sinsert sinvertcase sixth skewness skewness_bernoulli skewness_beta skewness_binomial skewness_chi2 skewness_continuous_uniform skewness_discrete_uniform skewness_exp skewness_f skewness_gamma skewness_general_finite_discrete skewness_geometric skewness_gumbel skewness_hypergeometric skewness_laplace skewness_logistic skewness_lognormal skewness_negative_binomial skewness_noncentral_chi2 skewness_noncentral_student_t skewness_normal skewness_pareto skewness_poisson skewness_rayleigh skewness_student_t skewness_weibull slength smake small_rhombicosidodecahedron_graph small_rhombicuboctahedron_graph smax smin smismatch snowmap snub_cube_graph snub_dodecahedron_graph solve solve_rec solve_rec_rat some somrac sort sparse6_decode sparse6_encode sparse6_export sparse6_import specint spherical spherical_bessel_j spherical_bessel_y spherical_hankel1 spherical_hankel2 spherical_harmonic spherical_to_xyz splice split sposition sprint sqfr sqrt sqrtdenest sremove sremovefirst sreverse ssearch ssort sstatus ssubst ssubstfirst staircase standardize standardize_inverse_trig starplot starplot_description status std std1 std_bernoulli std_beta std_binomial std_chi2 std_continuous_uniform std_discrete_uniform std_exp std_f std_gamma std_general_finite_discrete std_geometric std_gumbel std_hypergeometric std_laplace std_logistic std_lognormal std_negative_binomial std_noncentral_chi2 std_noncentral_student_t std_normal std_pareto std_poisson std_rayleigh std_student_t std_weibull stemplot stirling stirling1 stirling2 strim striml strimr string stringout stringp strong_components struve_h struve_l sublis sublist sublist_indices submatrix subsample subset subsetp subst substinpart subst_parallel substpart substring subvar subvarp sum sumcontract summand_to_rec supcase supcontext symbolp symmdifference symmetricp system take_channel take_inference tan tanh taylor taylorinfo taylorp taylor_simplifier taytorat tcl_output tcontract tellrat tellsimp tellsimpafter tentex tenth test_mean test_means_difference test_normality test_proportion test_proportions_difference test_rank_sum test_sign test_signed_rank test_variance test_variance_ratio tex tex1 tex_display texput %th third throw time timedate timer timer_info tldefint tlimit todd_coxeter toeplitz tokens to_lisp topological_sort to_poly to_poly_solve totaldisrep totalfourier totient tpartpol trace tracematrix trace_options transform_sample translate translate_file transpose treefale tree_reduce treillis treinat triangle triangularize trigexpand trigrat trigreduce trigsimp trunc truncate truncated_cube_graph truncated_dodecahedron_graph truncated_icosahedron_graph truncated_tetrahedron_graph tr_warnings_get tube tutte_graph ueivects uforget ultraspherical underlying_graph undiff union unique uniteigenvectors unitp units unit_step unitvector unorder unsum untellrat untimer untrace uppercasep uricci uriemann uvect vandermonde_matrix var var1 var_bernoulli var_beta var_binomial var_chi2 var_continuous_uniform var_discrete_uniform var_exp var_f var_gamma var_general_finite_discrete var_geometric var_gumbel var_hypergeometric var_laplace var_logistic var_lognormal var_negative_binomial var_noncentral_chi2 var_noncentral_student_t var_normal var_pareto var_poisson var_rayleigh var_student_t var_weibull vector vectorpotential vectorsimp verbify vers vertex_coloring vertex_connectivity vertex_degree vertex_distance vertex_eccentricity vertex_in_degree vertex_out_degree vertices vertices_to_cycle vertices_to_path %w weyl wheel_graph wiener_index wigner_3j wigner_6j wigner_9j with_stdout write_binary_data writebyte write_data writefile wronskian xreduce xthru %y Zeilberger zeroequiv zerofor zeromatrix zeromatrixp zeta zgeev zheev zlange zn_add_table zn_carmichael_lambda zn_characteristic_factors zn_determinant zn_factor_generators zn_invert_by_lu zn_log zn_mult_table absboxchar activecontexts adapt_depth additive adim aform algebraic algepsilon algexact aliases allbut all_dotsimp_denoms allocation allsym alphabetic animation antisymmetric arrays askexp assume_pos assume_pos_pred assumescalar asymbol atomgrad atrig1 axes axis_3d axis_bottom axis_left axis_right axis_top azimuth background background_color backsubst berlefact bernstein_explicit besselexpand beta_args_sum_to_integer beta_expand bftorat bftrunc bindtest border boundaries_array box boxchar breakup %c capping cauchysum cbrange cbtics center cflength cframe_flag cnonmet_flag color color_bar color_bar_tics colorbox columns commutative complex cone context contexts contour contour_levels cosnpiflag ctaypov ctaypt ctayswitch ctayvar ct_coords ctorsion_flag ctrgsimp cube current_let_rule_package cylinder data_file_name debugmode decreasing default_let_rule_package delay dependencies derivabbrev derivsubst detout diagmetric diff dim dimensions dispflag display2d|10 display_format_internal distribute_over doallmxops domain domxexpt domxmxops domxnctimes dontfactor doscmxops doscmxplus dot0nscsimp dot0simp dot1simp dotassoc dotconstrules dotdistrib dotexptsimp dotident dotscrules draw_graph_program draw_realpart edge_color edge_coloring edge_partition edge_type edge_width %edispflag elevation %emode endphi endtheta engineering_format_floats enhanced3d %enumer epsilon_lp erfflag erf_representation errormsg error_size error_syms error_type %e_to_numlog eval even evenfun evflag evfun ev_point expandwrt_denom expintexpand expintrep expon expop exptdispflag exptisolate exptsubst facexpand facsum_combine factlim factorflag factorial_expand factors_only fb feature features file_name file_output_append file_search_demo file_search_lisp file_search_maxima|10 file_search_tests file_search_usage file_type_lisp file_type_maxima|10 fill_color fill_density filled_func fixed_vertices flipflag float2bf font font_size fortindent fortspaces fpprec fpprintprec functions gamma_expand gammalim gdet genindex gensumnum GGFCFMAX GGFINFINITY globalsolve gnuplot_command gnuplot_curve_styles gnuplot_curve_titles gnuplot_default_term_command gnuplot_dumb_term_command gnuplot_file_args gnuplot_file_name gnuplot_out_file gnuplot_pdf_term_command gnuplot_pm3d gnuplot_png_term_command gnuplot_postamble gnuplot_preamble gnuplot_ps_term_command gnuplot_svg_term_command gnuplot_term gnuplot_view_args Gosper_in_Zeilberger gradefs grid grid2d grind halfangles head_angle head_both head_length head_type height hypergeometric_representation %iargs ibase icc1 icc2 icounter idummyx ieqnprint ifb ifc1 ifc2 ifg ifgi ifr iframe_bracket_form ifri igeowedge_flag ikt1 ikt2 imaginary inchar increasing infeval infinity inflag infolists inm inmc1 inmc2 intanalysis integer integervalued integrate_use_rootsof integration_constant integration_constant_counter interpolate_color intfaclim ip_grid ip_grid_in irrational isolate_wrt_times iterations itr julia_parameter %k1 %k2 keepfloat key key_pos kinvariant kt label label_alignment label_orientation labels lassociative lbfgs_ncorrections lbfgs_nfeval_max leftjust legend letrat let_rule_packages lfg lg lhospitallim limsubst linear linear_solver linechar linel|10 linenum line_type linewidth line_width linsolve_params linsolvewarn lispdisp listarith listconstvars listdummyvars lmxchar load_pathname loadprint logabs logarc logcb logconcoeffp logexpand lognegint logsimp logx logx_secondary logy logy_secondary logz lriem m1pbranch macroexpansion macros mainvar manual_demo maperror mapprint matrix_element_add matrix_element_mult 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attackEnabled backpack backpackCargo backpackContainer backpackItems backpackMagazines backpackSpaceFor behaviour benchmark binocular blufor boundingBox boundingBoxReal boundingCenter breakOut breakTo briefingName buildingExit buildingPos buttonAction buttonSetAction cadetMode call callExtension camCommand camCommit camCommitPrepared camCommitted camConstuctionSetParams camCreate camDestroy cameraEffect cameraEffectEnableHUD cameraInterest cameraOn cameraView campaignConfigFile camPreload camPreloaded camPrepareBank camPrepareDir camPrepareDive camPrepareFocus camPrepareFov camPrepareFovRange camPreparePos camPrepareRelPos camPrepareTarget camSetBank camSetDir camSetDive camSetFocus camSetFov camSetFovRange camSetPos camSetRelPos camSetTarget camTarget camUseNVG canAdd canAddItemToBackpack canAddItemToUniform canAddItemToVest cancelSimpleTaskDestination canFire canMove canSlingLoad canStand canUnloadInCombat captive captiveNum cbChecked cbSetChecked ceil cheatsEnabled checkAIFeature civilian className clearAllItemsFromBackpack clearBackpackCargo clearBackpackCargoGlobal clearGroupIcons clearItemCargo clearItemCargoGlobal clearItemPool clearMagazineCargo clearMagazineCargoGlobal clearMagazinePool clearOverlay clearRadio clearWeaponCargo clearWeaponCargoGlobal clearWeaponPool closeDialog closeDisplay closeOverlay collapseObjectTree combatMode commandArtilleryFire commandChat commander commandFire commandFollow commandFSM commandGetOut commandingMenu commandMove commandRadio commandStop commandTarget commandWatch comment commitOverlay compile compileFinal completedFSM composeText configClasses configFile configHierarchy configName configProperties configSourceMod configSourceModList connectTerminalToUAV controlNull controlsGroupCtrl copyFromClipboard copyToClipboard copyWaypoints cos count countEnemy countFriendly countSide countType countUnknown createAgent createCenter createDialog createDiaryLink createDiaryRecord createDiarySubject createDisplay createGearDialog createGroup createGuardedPoint createLocation createMarker createMarkerLocal createMenu createMine createMissionDisplay createSimpleTask createSite createSoundSource createTask createTeam createTrigger createUnit createUnit array createVehicle createVehicle array createVehicleCrew createVehicleLocal crew ctrlActivate ctrlAddEventHandler ctrlAutoScrollDelay ctrlAutoScrollRewind ctrlAutoScrollSpeed ctrlChecked ctrlClassName ctrlCommit ctrlCommitted ctrlCreate ctrlDelete ctrlEnable ctrlEnabled ctrlFade ctrlHTMLLoaded ctrlIDC ctrlIDD ctrlMapAnimAdd ctrlMapAnimClear ctrlMapAnimCommit ctrlMapAnimDone ctrlMapCursor ctrlMapMouseOver ctrlMapScale ctrlMapScreenToWorld ctrlMapWorldToScreen ctrlModel ctrlModelDirAndUp ctrlModelScale ctrlParent ctrlPosition ctrlRemoveAllEventHandlers ctrlRemoveEventHandler ctrlScale ctrlSetActiveColor ctrlSetAutoScrollDelay ctrlSetAutoScrollRewind ctrlSetAutoScrollSpeed ctrlSetBackgroundColor ctrlSetChecked ctrlSetEventHandler ctrlSetFade ctrlSetFocus ctrlSetFont ctrlSetFontH1 ctrlSetFontH1B ctrlSetFontH2 ctrlSetFontH2B ctrlSetFontH3 ctrlSetFontH3B ctrlSetFontH4 ctrlSetFontH4B ctrlSetFontH5 ctrlSetFontH5B ctrlSetFontH6 ctrlSetFontH6B ctrlSetFontHeight ctrlSetFontHeightH1 ctrlSetFontHeightH2 ctrlSetFontHeightH3 ctrlSetFontHeightH4 ctrlSetFontHeightH5 ctrlSetFontHeightH6 ctrlSetFontP ctrlSetFontPB ctrlSetForegroundColor ctrlSetModel ctrlSetModelDirAndUp ctrlSetModelScale ctrlSetPosition ctrlSetScale ctrlSetStructuredText ctrlSetText ctrlSetTextColor ctrlSetTooltip ctrlSetTooltipColorBox ctrlSetTooltipColorShade ctrlSetTooltipColorText ctrlShow ctrlShown ctrlText ctrlTextHeight ctrlType ctrlVisible curatorAddons curatorCamera curatorCameraArea curatorCameraAreaCeiling curatorCoef curatorEditableObjects curatorEditingArea curatorEditingAreaType curatorMouseOver curatorPoints curatorRegisteredObjects curatorSelected curatorWaypointCost currentChannel currentCommand currentMagazine currentMagazineDetail currentMagazineDetailTurret currentMagazineTurret currentMuzzle currentNamespace currentTask currentTasks currentThrowable currentVisionMode currentWaypoint currentWeapon currentWeaponMode currentWeaponTurret currentZeroing cursorTarget customChat customRadio cutFadeOut cutObj cutRsc cutText damage date dateToNumber daytime deActivateKey debriefingText debugFSM debugLog deg deleteAt deleteCenter deleteCollection deleteEditorObject deleteGroup deleteIdentity deleteLocation deleteMarker deleteMarkerLocal deleteRange deleteResources deleteSite deleteStatus deleteTeam deleteVehicle deleteVehicleCrew deleteWaypoint detach detectedMines diag activeMissionFSMs diag activeSQFScripts diag activeSQSScripts diag captureFrame diag captureSlowFrame diag fps diag fpsMin diag frameNo diag log diag logSlowFrame diag tickTime dialog diarySubjectExists didJIP didJIPOwner difficulty difficultyEnabled difficultyEnabledRTD direction directSay disableAI disableCollisionWith disableConversation disableDebriefingStats disableSerialization disableTIEquipment disableUAVConnectability disableUserInput displayAddEventHandler displayCtrl displayNull displayRemoveAllEventHandlers displayRemoveEventHandler displaySetEventHandler dissolveTeam distance distance2D distanceSqr distributionRegion doArtilleryFire doFire doFollow doFSM doGetOut doMove doorPhase doStop doTarget doWatch drawArrow drawEllipse drawIcon drawIcon3D drawLine drawLine3D drawLink drawLocation drawRectangle driver drop east echo editObject editorSetEventHandler effectiveCommander emptyPositions enableAI enableAIFeature enableAttack enableCamShake enableCaustics enableCollisionWith enableCopilot enableDebriefingStats enableDiagLegend enableEndDialog enableEngineArtillery enableEnvironment enableFatigue enableGunLights enableIRLasers enableMimics enablePersonTurret enableRadio enableReload enableRopeAttach enableSatNormalOnDetail enableSaving enableSentences enableSimulation enableSimulationGlobal enableTeamSwitch enableUAVConnectability enableUAVWaypoints endLoadingScreen endMission engineOn enginesIsOnRTD enginesRpmRTD enginesTorqueRTD entities estimatedEndServerTime estimatedTimeLeft evalObjectArgument everyBackpack everyContainer exec execEditorScript execFSM execVM exp expectedDestination eyeDirection eyePos face faction fadeMusic fadeRadio fadeSound fadeSpeech failMission fillWeaponsFromPool find findCover findDisplay findEditorObject findEmptyPosition findEmptyPositionReady findNearestEnemy finishMissionInit finite fire fireAtTarget firstBackpack flag flagOwner fleeing floor flyInHeight fog fogForecast fogParams forceAddUniform forceEnd forceMap forceRespawn forceSpeed forceWalk forceWeaponFire forceWeatherChange forEachMember forEachMemberAgent forEachMemberTeam format formation formationDirection formationLeader formationMembers formationPosition formationTask formatText formLeader freeLook fromEditor fuel fullCrew gearSlotAmmoCount gearSlotData getAllHitPointsDamage getAmmoCargo getArray getArtilleryAmmo getArtilleryComputerSettings getArtilleryETA getAssignedCuratorLogic getAssignedCuratorUnit getBackpackCargo getBleedingRemaining getBurningValue getCargoIndex getCenterOfMass getClientState getConnectedUAV getDammage getDescription getDir getDirVisual getDLCs getEditorCamera getEditorMode getEditorObjectScope getElevationOffset getFatigue getFriend getFSMVariable getFuelCargo getGroupIcon getGroupIconParams getGroupIcons getHideFrom getHit getHitIndex getHitPointDamage getItemCargo getMagazineCargo getMarkerColor getMarkerPos getMarkerSize getMarkerType getMass getModelInfo getNumber getObjectArgument getObjectChildren getObjectDLC getObjectMaterials getObjectProxy getObjectTextures getObjectType getObjectViewDistance getOxygenRemaining getPersonUsedDLCs getPlayerChannel getPlayerUID getPos getPosASL getPosASLVisual getPosASLW getPosATL getPosATLVisual getPosVisual getPosWorld getRepairCargo getResolution getShadowDistance getSlingLoad getSpeed getSuppression getTerrainHeightASL getText getVariable getWeaponCargo getWPPos glanceAt globalChat globalRadio goggles goto group groupChat groupFromNetId groupIconSelectable groupIconsVisible groupId groupOwner groupRadio groupSelectedUnits groupSelectUnit grpNull gunner gusts halt handgunItems handgunMagazine handgunWeapon handsHit hasInterface hasWeapon hcAllGroups hcGroupParams hcLeader hcRemoveAllGroups hcRemoveGroup hcSelected hcSelectGroup hcSetGroup hcShowBar hcShownBar headgear hideBody hideObject hideObjectGlobal hint hintC hintCadet hintSilent hmd hostMission htmlLoad HUDMovementLevels humidity image importAllGroups importance in incapacitatedState independent inflame inflamed inGameUISetEventHandler inheritsFrom initAmbientLife inputAction inRangeOfArtillery insertEditorObject intersect isAbleToBreathe isAgent isArray isAutoHoverOn isAutonomous isAutotest isBleeding isBurning isClass isCollisionLightOn isCopilotEnabled isDedicated isDLCAvailable isEngineOn isEqualTo isFlashlightOn isFlatEmpty isForcedWalk 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lbSetValue lbSize lbSort lbSortByValue lbText lbValue leader leaderboardDeInit leaderboardGetRows leaderboardInit leaveVehicle libraryCredits libraryDisclaimers lifeState lightAttachObject lightDetachObject lightIsOn lightnings limitSpeed linearConversion lineBreak lineIntersects lineIntersectsObjs lineIntersectsSurfaces lineIntersectsWith linkItem list listObjects ln lnbAddArray lnbAddColumn lnbAddRow lnbClear lnbColor lnbCurSelRow lnbData lnbDeleteColumn lnbDeleteRow lnbGetColumnsPosition lnbPicture lnbSetColor lnbSetColumnsPos lnbSetCurSelRow lnbSetData lnbSetPicture lnbSetText lnbSetValue lnbSize lnbText lnbValue load loadAbs loadBackpack loadFile loadGame loadIdentity loadMagazine loadOverlay loadStatus loadUniform loadVest local localize locationNull locationPosition lock lockCameraTo lockCargo lockDriver locked lockedCargo lockedDriver lockedTurret lockTurret lockWP log logEntities lookAt lookAtPos magazineCargo magazines magazinesAllTurrets magazinesAmmo magazinesAmmoCargo magazinesAmmoFull magazinesDetail magazinesDetailBackpack magazinesDetailUniform magazinesDetailVest magazinesTurret magazineTurretAmmo mapAnimAdd mapAnimClear mapAnimCommit mapAnimDone mapCenterOnCamera mapGridPosition markAsFinishedOnSteam markerAlpha markerBrush markerColor markerDir markerPos markerShape markerSize markerText markerType max members min mineActive mineDetectedBy missionConfigFile missionName missionNamespace missionStart mod modelToWorld modelToWorldVisual moonIntensity morale move moveInAny moveInCargo moveInCommander moveInDriver moveInGunner moveInTurret moveObjectToEnd moveOut moveTime moveTo moveToCompleted moveToFailed musicVolume name name location nameSound nearEntities nearestBuilding nearestLocation nearestLocations nearestLocationWithDubbing nearestObject nearestObjects nearObjects nearObjectsReady nearRoads nearSupplies nearTargets needReload netId netObjNull newOverlay nextMenuItemIndex nextWeatherChange nMenuItems not numberToDate objectCurators objectFromNetId objectParent objNull objStatus onBriefingGroup onBriefingNotes onBriefingPlan onBriefingTeamSwitch onCommandModeChanged onDoubleClick onEachFrame onGroupIconClick onGroupIconOverEnter onGroupIconOverLeave onHCGroupSelectionChanged onMapSingleClick onPlayerConnected onPlayerDisconnected onPreloadFinished onPreloadStarted onShowNewObject onTeamSwitch openCuratorInterface openMap openYoutubeVideo opfor or orderGetIn overcast overcastForecast owner param params parseNumber parseText parsingNamespace particlesQuality pi pickWeaponPool pitch playableSlotsNumber playableUnits playAction playActionNow player playerRespawnTime playerSide playersNumber playGesture playMission playMove playMoveNow playMusic playScriptedMission playSound playSound3D position positionCameraToWorld posScreenToWorld posWorldToScreen ppEffectAdjust ppEffectCommit ppEffectCommitted ppEffectCreate ppEffectDestroy ppEffectEnable ppEffectForceInNVG precision preloadCamera preloadObject preloadSound preloadTitleObj preloadTitleRsc preprocessFile preprocessFileLineNumbers primaryWeapon primaryWeaponItems primaryWeaponMagazine priority private processDiaryLink productVersion profileName profileNamespace profileNameSteam progressLoadingScreen progressPosition progressSetPosition publicVariable publicVariableClient publicVariableServer pushBack putWeaponPool queryItemsPool queryMagazinePool queryWeaponPool rad radioChannelAdd radioChannelCreate radioChannelRemove radioChannelSetCallSign radioChannelSetLabel radioVolume rain rainbow random rank rankId rating rectangular registeredTasks registerTask reload reloadEnabled remoteControl remoteExec remoteExecCall removeAction removeAllActions removeAllAssignedItems removeAllContainers removeAllCuratorAddons removeAllCuratorCameraAreas removeAllCuratorEditingAreas removeAllEventHandlers removeAllHandgunItems removeAllItems removeAllItemsWithMagazines removeAllMissionEventHandlers removeAllMPEventHandlers removeAllMusicEventHandlers removeAllPrimaryWeaponItems removeAllWeapons removeBackpack removeBackpackGlobal removeCuratorAddons removeCuratorCameraArea removeCuratorEditableObjects removeCuratorEditingArea removeDrawIcon removeDrawLinks removeEventHandler removeFromRemainsCollector removeGoggles removeGroupIcon removeHandgunItem removeHeadgear removeItem removeItemFromBackpack removeItemFromUniform removeItemFromVest removeItems removeMagazine removeMagazineGlobal removeMagazines removeMagazinesTurret removeMagazineTurret removeMenuItem removeMissionEventHandler removeMPEventHandler removeMusicEventHandler removePrimaryWeaponItem removeSecondaryWeaponItem removeSimpleTask removeSwitchableUnit removeTeamMember removeUniform removeVest removeWeapon removeWeaponGlobal removeWeaponTurret requiredVersion resetCamShake resetSubgroupDirection resistance resize resources respawnVehicle restartEditorCamera reveal revealMine reverse reversedMouseY roadsConnectedTo roleDescription ropeAttachedObjects ropeAttachedTo ropeAttachEnabled ropeAttachTo ropeCreate ropeCut ropeEndPosition ropeLength ropes ropeUnwind ropeUnwound rotorsForcesRTD rotorsRpmRTD round runInitScript safeZoneH safeZoneW safeZoneWAbs safeZoneX safeZoneXAbs safeZoneY saveGame saveIdentity saveJoysticks saveOverlay saveProfileNamespace saveStatus saveVar savingEnabled say say2D say3D scopeName score scoreSide screenToWorld scriptDone scriptName scriptNull scudState secondaryWeapon secondaryWeaponItems secondaryWeaponMagazine select selectBestPlaces selectDiarySubject selectedEditorObjects selectEditorObject selectionPosition selectLeader selectNoPlayer selectPlayer selectWeapon selectWeaponTurret sendAUMessage sendSimpleCommand sendTask sendTaskResult sendUDPMessage serverCommand serverCommandAvailable serverCommandExecutable serverName serverTime set setAccTime setAirportSide setAmmo setAmmoCargo setAperture setApertureNew setArmoryPoints setAttributes setAutonomous setBehaviour setBleedingRemaining setCameraInterest setCamShakeDefParams setCamShakeParams setCamUseTi setCaptive setCenterOfMass setCollisionLight setCombatMode setCompassOscillation setCuratorCameraAreaCeiling setCuratorCoef setCuratorEditingAreaType setCuratorWaypointCost setCurrentChannel setCurrentTask setCurrentWaypoint setDamage setDammage setDate setDebriefingText setDefaultCamera setDestination setDetailMapBlendPars setDir setDirection setDrawIcon setDropInterval setEditorMode setEditorObjectScope setEffectCondition setFace setFaceAnimation setFatigue setFlagOwner setFlagSide setFlagTexture setFog setFog array setFormation setFormationTask setFormDir setFriend setFromEditor setFSMVariable setFuel setFuelCargo setGroupIcon setGroupIconParams setGroupIconsSelectable setGroupIconsVisible setGroupId setGroupIdGlobal setGroupOwner setGusts setHideBehind setHit setHitIndex setHitPointDamage setHorizonParallaxCoef setHUDMovementLevels setIdentity setImportance setLeader setLightAmbient setLightAttenuation setLightBrightness setLightColor setLightDayLight setLightFlareMaxDistance setLightFlareSize setLightIntensity setLightnings setLightUseFlare setLocalWindParams setMagazineTurretAmmo setMarkerAlpha setMarkerAlphaLocal setMarkerBrush setMarkerBrushLocal setMarkerColor setMarkerColorLocal setMarkerDir setMarkerDirLocal setMarkerPos setMarkerPosLocal setMarkerShape setMarkerShapeLocal setMarkerSize setMarkerSizeLocal setMarkerText setMarkerTextLocal setMarkerType setMarkerTypeLocal setMass setMimic setMousePosition setMusicEffect setMusicEventHandler setName setNameSound setObjectArguments setObjectMaterial setObjectProxy setObjectTexture setObjectTextureGlobal setObjectViewDistance setOvercast setOwner setOxygenRemaining setParticleCircle setParticleClass setParticleFire setParticleParams setParticleRandom setPilotLight setPiPEffect setPitch setPlayable setPlayerRespawnTime setPos setPosASL setPosASL2 setPosASLW setPosATL setPosition setPosWorld setRadioMsg setRain setRainbow setRandomLip setRank setRectangular setRepairCargo setShadowDistance setSide setSimpleTaskDescription setSimpleTaskDestination setSimpleTaskTarget setSimulWeatherLayers setSize setSkill setSkill array setSlingLoad setSoundEffect setSpeaker setSpeech setSpeedMode setStatValue setSuppression setSystemOfUnits setTargetAge setTaskResult setTaskState setTerrainGrid setText setTimeMultiplier setTitleEffect setTriggerActivation setTriggerArea setTriggerStatements setTriggerText setTriggerTimeout setTriggerType setType setUnconscious setUnitAbility setUnitPos setUnitPosWeak setUnitRank setUnitRecoilCoefficient setUnloadInCombat setUserActionText setVariable setVectorDir setVectorDirAndUp setVectorUp setVehicleAmmo setVehicleAmmoDef setVehicleArmor setVehicleId setVehicleLock setVehiclePosition setVehicleTiPars setVehicleVarName setVelocity setVelocityTransformation setViewDistance setVisibleIfTreeCollapsed setWaves setWaypointBehaviour setWaypointCombatMode setWaypointCompletionRadius setWaypointDescription setWaypointFormation setWaypointHousePosition setWaypointLoiterRadius setWaypointLoiterType setWaypointName setWaypointPosition setWaypointScript setWaypointSpeed setWaypointStatements setWaypointTimeout setWaypointType setWaypointVisible setWeaponReloadingTime setWind setWindDir setWindForce setWindStr setWPPos show3DIcons showChat showCinemaBorder showCommandingMenu showCompass showCuratorCompass showGPS showHUD showLegend showMap shownArtilleryComputer shownChat shownCompass shownCuratorCompass showNewEditorObject shownGPS shownHUD shownMap shownPad shownRadio shownUAVFeed shownWarrant shownWatch showPad showRadio showSubtitles showUAVFeed showWarrant showWatch showWaypoint side sideChat sideEnemy sideFriendly sideLogic sideRadio sideUnknown simpleTasks simulationEnabled simulCloudDensity simulCloudOcclusion simulInClouds simulWeatherSync sin size sizeOf skill skillFinal skipTime sleep sliderPosition sliderRange sliderSetPosition sliderSetRange sliderSetSpeed sliderSpeed slingLoadAssistantShown soldierMagazines someAmmo sort soundVolume spawn speaker speed speedMode splitString sqrt squadParams stance startLoadingScreen step stop stopped str sunOrMoon supportInfo suppressFor surfaceIsWater surfaceNormal surfaceType swimInDepth switchableUnits switchAction switchCamera switchGesture switchLight switchMove synchronizedObjects synchronizedTriggers synchronizedWaypoints synchronizeObjectsAdd synchronizeObjectsRemove synchronizeTrigger synchronizeWaypoint synchronizeWaypoint trigger systemChat systemOfUnits tan targetKnowledge targetsAggregate targetsQuery taskChildren taskCompleted taskDescription taskDestination taskHint taskNull taskParent taskResult taskState teamMember teamMemberNull teamName teams teamSwitch teamSwitchEnabled teamType terminate terrainIntersect terrainIntersectASL text text location textLog textLogFormat tg time timeMultiplier titleCut titleFadeOut titleObj titleRsc titleText toArray toLower toString toUpper triggerActivated triggerActivation triggerArea triggerAttachedVehicle triggerAttachObject triggerAttachVehicle triggerStatements triggerText triggerTimeout triggerTimeoutCurrent triggerType turretLocal turretOwner turretUnit tvAdd tvClear tvCollapse tvCount tvCurSel tvData tvDelete tvExpand tvPicture tvSetCurSel tvSetData tvSetPicture tvSetPictureColor tvSetTooltip tvSetValue tvSort tvSortByValue tvText tvValue type typeName typeOf UAVControl uiNamespace uiSleep unassignCurator unassignItem unassignTeam unassignVehicle underwater uniform uniformContainer uniformItems uniformMagazines unitAddons unitBackpack unitPos unitReady unitRecoilCoefficient units unitsBelowHeight unlinkItem unlockAchievement unregisterTask updateDrawIcon updateMenuItem updateObjectTree useAudioTimeForMoves vectorAdd vectorCos vectorCrossProduct vectorDiff vectorDir vectorDirVisual vectorDistance vectorDistanceSqr vectorDotProduct vectorFromTo vectorMagnitude vectorMagnitudeSqr vectorMultiply vectorNormalized vectorUp vectorUpVisual vehicle vehicleChat vehicleRadio vehicles vehicleVarName velocity velocityModelSpace verifySignature vest vestContainer vestItems vestMagazines viewDistance visibleCompass visibleGPS visibleMap visiblePosition visiblePositionASL visibleWatch waitUntil waves waypointAttachedObject waypointAttachedVehicle waypointAttachObject waypointAttachVehicle waypointBehaviour waypointCombatMode waypointCompletionRadius waypointDescription waypointFormation waypointHousePosition waypointLoiterRadius waypointLoiterType waypointName waypointPosition waypoints waypointScript waypointsEnabledUAV waypointShow waypointSpeed waypointStatements waypointTimeout waypointTimeoutCurrent waypointType waypointVisible weaponAccessories weaponCargo weaponDirection weaponLowered weapons weaponsItems weaponsItemsCargo weaponState weaponsTurret weightRTD west WFSideText wind windDir windStr wingsForcesRTD worldName worldSize worldToModel worldToModelVisual worldToScreen _forEachIndex _this _x",
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decompose decrement decrypt deduplicate def defa defau defaul default defaults deferred defi defin define degrees delayed delegate delete delete_all delimited demand dense_rank depth dequeue des_decrypt des_encrypt des_key_file desc descr descri describ describe descriptor deterministic diagnostics difference dimension direct_load directory disable disable_all disallow disassociate discardfile disconnect diskgroup distinct distinctrow distribute distributed div do document domain dotnet double downgrade drop dumpfile duplicate duration each edition editionable editions element ellipsis else elsif elt empty enable enable_all enclosed encode encoding encrypt end end-exec endian enforced engine engines enqueue enterprise entityescaping eomonth error errors escaped evalname evaluate event eventdata events except exception exceptions exchange exclude excluding execu execut execute exempt exists exit exp expire explain export export_set extended extent external external_1 external_2 externally extract failed failed_login_attempts failover failure far fast feature_set feature_value fetch field fields file file_name_convert filesystem_like_logging final finish first first_value fixed flash_cache flashback floor flush following follows for forall force form forma format found found_rows freelist freelists freepools fresh from from_base64 from_days ftp full function general generated get get_format get_lock getdate getutcdate global global_name globally go goto grant grants greatest group group_concat group_id grouping grouping_id groups gtid_subtract guarantee guard handler hash hashkeys having hea head headi headin heading heap help hex hierarchy high high_priority hosts hour http id ident_current ident_incr ident_seed identified identity idle_time if ifnull ignore iif ilike ilm immediate import in include including increment index indexes indexing indextype indicator indices inet6_aton inet6_ntoa inet_aton inet_ntoa infile initial initialized initially initrans 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merge microsecond mid migration min minextents minimum mining minus minute minvalue missing mod mode model modification modify module monitoring month months mount move movement multiset mutex name name_const names nan national native natural nav nchar nclob nested never new newline next nextval no no_write_to_binlog noarchivelog noaudit nobadfile nocheck nocompress nocopy nocycle nodelay nodiscardfile noentityescaping noguarantee nokeep nologfile nomapping nomaxvalue nominimize nominvalue nomonitoring none noneditionable nonschema noorder nopr nopro noprom nopromp noprompt norely noresetlogs noreverse normal norowdependencies noschemacheck noswitch not nothing notice notrim novalidate now nowait nth_value nullif nulls num numb numbe nvarchar nvarchar2 object ocicoll ocidate ocidatetime ociduration ociinterval ociloblocator ocinumber ociref ocirefcursor ocirowid ocistring ocitype oct octet_length of off offline offset oid oidindex old on online only opaque open operations operator 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publishingservername purge quarter query quick quiesce quota quotename radians raise rand range rank raw read reads readsize rebuild record records recover recovery recursive recycle redo reduced ref reference referenced references referencing refresh regexp_like register regr_avgx regr_avgy regr_count regr_intercept regr_r2 regr_slope regr_sxx regr_sxy reject rekey relational relative relaylog release release_lock relies_on relocate rely rem remainder rename repair repeat replace replicate replication required reset resetlogs resize resource respect restore restricted result result_cache resumable resume retention return returning returns reuse reverse revoke right rlike role roles rollback rolling rollup round row row_count rowdependencies rowid rownum rows rtrim rules safe salt sample save savepoint sb1 sb2 sb4 scan schema schemacheck scn scope scroll sdo_georaster sdo_topo_geometry search sec_to_time second section securefile security seed segment select self sequence sequential serializable server servererror session session_user sessions_per_user set sets settings sha sha1 sha2 share shared shared_pool short show shrink shutdown si_averagecolor si_colorhistogram si_featurelist si_positionalcolor si_stillimage si_texture siblings sid sign sin size size_t sizes skip slave sleep smalldatetimefromparts smallfile snapshot some soname sort soundex source space sparse spfile split sql sql_big_result sql_buffer_result sql_cache sql_calc_found_rows sql_small_result sql_variant_property sqlcode sqldata sqlerror sqlname sqlstate sqrt square standalone standby start starting startup statement static statistics stats_binomial_test stats_crosstab stats_ks_test stats_mode stats_mw_test stats_one_way_anova stats_t_test_ stats_t_test_indep stats_t_test_one stats_t_test_paired stats_wsr_test status std stddev stddev_pop stddev_samp stdev stop storage store stored str str_to_date straight_join strcmp strict string struct stuff style subdate subpartition subpartitions substitutable substr substring subtime subtring_index subtype success sum suspend switch switchoffset switchover sync synchronous synonym sys sys_xmlagg sysasm sysaux sysdate sysdatetimeoffset sysdba sysoper system system_user sysutcdatetime table tables tablespace tan tdo template temporary terminated tertiary_weights test than then thread through tier ties time time_format time_zone timediff timefromparts timeout timestamp timestampadd timestampdiff timezone_abbr timezone_minute timezone_region to to_base64 to_date to_days to_seconds todatetimeoffset trace tracking transaction transactional translate translation treat trigger trigger_nestlevel triggers trim truncate try_cast try_convert try_parse type ub1 ub2 ub4 ucase unarchived unbounded uncompress under undo unhex unicode uniform uninstall union unique unix_timestamp unknown unlimited unlock unpivot unrecoverable unsafe unsigned until untrusted unusable unused update updated upgrade upped upper upsert url urowid usable usage use use_stored_outlines user user_data user_resources users using utc_date utc_timestamp uuid uuid_short validate validate_password_strength validation valist value values var var_samp varcharc vari varia variab variabl variable variables variance varp varraw varrawc varray verify version versions view virtual visible void wait wallet warning warnings week weekday weekofyear wellformed when whene whenev wheneve whenever where while whitespace with within without work wrapped xdb xml xmlagg xmlattributes xmlcast xmlcolattval xmlelement xmlexists xmlforest xmlindex xmlnamespaces xmlpi xmlquery xmlroot xmlschema xmlserialize xmltable xmltype xor year year_to_month years yearweek",literal:"true false null",built_in:"array bigint binary bit blob boolean char character date dec decimal float int int8 integer interval number numeric real record serial serial8 smallint text varchar varying 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checksum chelp ci cii cl class classutil clear cli clis clist clo clog clog_lf clog_p clogi clogi_sw clogit clogit_lf clogit_p clogitp clogl_sw cloglog clonevar clslistarray cluster cluster_measures cluster_stop cluster_tree cluster_tree_8 clustermat cmdlog cnr cnre cnreg cnreg_p cnreg_sw cnsreg codebook collaps4 collapse colormult_nb colormult_nw compare compress conf confi confir confirm conren cons const constr constra constrai constrain constraint continue contract copy copyright copysource cor corc corr corr2data corr_anti corr_kmo corr_smc corre correl correla correlat correlate corrgram cou coun count cox cox_p cox_sw coxbase coxhaz coxvar cprplot cprplot_7 crc cret cretu cretur creturn cross cs cscript cscript_log csi ct ct_is ctset ctst_5 ctst_st cttost cumsp cumsp_7 cumul cusum cusum_7 cutil d|0 datasig datasign datasigna datasignat datasignatu datasignatur datasignature datetof db dbeta de dec deco decod decode deff des desc descr descri describ describe destring dfbeta dfgls dfuller di di_g dir dirstats dis discard disp disp_res disp_s displ displa display distinct do doe doed doedi doedit dotplot dotplot_7 dprobit drawnorm drop ds ds_util dstdize duplicates durbina dwstat dydx e|0 ed edi edit egen eivreg emdef en enc enco encod encode eq erase ereg ereg_lf ereg_p ereg_sw ereghet ereghet_glf ereghet_glf_sh ereghet_gp ereghet_ilf ereghet_ilf_sh ereghet_ip eret eretu eretur ereturn err erro error est est_cfexist est_cfname est_clickable est_expand est_hold est_table est_unhold est_unholdok estat estat_default estat_summ estat_vce_only esti estimates etodow etof etomdy ex exi exit expand expandcl fac fact facto factor factor_estat factor_p factor_pca_rotated factor_rotate factormat fcast fcast_compute fcast_graph fdades fdadesc fdadescr fdadescri fdadescrib fdadescribe fdasav fdasave fdause fh_st file open file read file close file filefilter fillin find_hlp_file findfile findit findit_7 fit fl fli flis flist for5_0 form forma format fpredict frac_154 frac_adj frac_chk frac_cox frac_ddp frac_dis frac_dv frac_in frac_mun frac_pp frac_pq frac_pv frac_wgt frac_xo fracgen fracplot fracplot_7 fracpoly fracpred fron_ex fron_hn fron_p fron_tn fron_tn2 frontier ftodate ftoe ftomdy ftowdate g|0 gamhet_glf gamhet_gp gamhet_ilf gamhet_ip gamma gamma_d2 gamma_p gamma_sw gammahet gdi_hexagon gdi_spokes ge gen gene gener genera generat generate genrank genstd genvmean gettoken gl gladder gladder_7 glim_l01 glim_l02 glim_l03 glim_l04 glim_l05 glim_l06 glim_l07 glim_l08 glim_l09 glim_l10 glim_l11 glim_l12 glim_lf glim_mu glim_nw1 glim_nw2 glim_nw3 glim_p glim_v1 glim_v2 glim_v3 glim_v4 glim_v5 glim_v6 glim_v7 glm glm_6 glm_p glm_sw glmpred glo glob globa global glogit glogit_8 glogit_p gmeans gnbre_lf gnbreg gnbreg_5 gnbreg_p gomp_lf gompe_sw gomper_p gompertz gompertzhet gomphet_glf gomphet_glf_sh gomphet_gp gomphet_ilf gomphet_ilf_sh gomphet_ip gphdot gphpen gphprint gprefs gprobi_p gprobit gprobit_8 gr gr7 gr_copy gr_current gr_db gr_describe gr_dir gr_draw gr_draw_replay gr_drop gr_edit gr_editviewopts gr_example gr_example2 gr_export gr_print gr_qscheme gr_query gr_read gr_rename gr_replay gr_save gr_set gr_setscheme gr_table gr_undo gr_use graph graph7 grebar greigen greigen_7 greigen_8 grmeanby grmeanby_7 gs_fileinfo gs_filetype gs_graphinfo gs_stat gsort gwood h|0 hadimvo hareg hausman haver he heck_d2 heckma_p heckman heckp_lf heckpr_p heckprob hel help hereg hetpr_lf hetpr_p hetprob hettest hexdump hilite hist hist_7 histogram hlogit hlu hmeans hotel hotelling hprobit hreg hsearch icd9 icd9_ff icd9p iis impute imtest inbase include inf infi infil infile infix inp inpu input ins insheet insp inspe inspec inspect integ inten intreg intreg_7 intreg_p intrg2_ll intrg_ll intrg_ll2 ipolate iqreg ir irf irf_create irfm iri is_svy is_svysum isid istdize ivprob_1_lf ivprob_lf ivprobit ivprobit_p ivreg ivreg_footnote ivtob_1_lf ivtob_lf ivtobit ivtobit_p jackknife jacknife jknife jknife_6 jknife_8 jkstat joinby kalarma1 kap kap_3 kapmeier kappa kapwgt kdensity kdensity_7 keep ksm ksmirnov ktau kwallis l|0 la lab labe label labelbook ladder levels levelsof leverage lfit lfit_p li lincom line linktest lis list lloghet_glf lloghet_glf_sh lloghet_gp lloghet_ilf lloghet_ilf_sh lloghet_ip llogi_sw llogis_p llogist llogistic llogistichet lnorm_lf lnorm_sw lnorma_p lnormal lnormalhet lnormhet_glf lnormhet_glf_sh lnormhet_gp lnormhet_ilf lnormhet_ilf_sh lnormhet_ip lnskew0 loadingplot loc loca local log logi logis_lf logistic logistic_p logit logit_estat logit_p loglogs logrank loneway lookfor lookup lowess lowess_7 lpredict lrecomp lroc lroc_7 lrtest ls lsens lsens_7 lsens_x lstat ltable ltable_7 ltriang lv lvr2plot lvr2plot_7 m|0 ma mac macr macro makecns man manova manova_estat manova_p manovatest mantel mark markin markout marksample mat mat_capp mat_order mat_put_rr mat_rapp mata mata_clear mata_describe mata_drop mata_matdescribe mata_matsave mata_matuse mata_memory mata_mlib mata_mosave mata_rename mata_which matalabel matcproc matlist matname matr matri matrix matrix_input__dlg matstrik mcc mcci md0_ md1_ md1debug_ md2_ md2debug_ mds mds_estat mds_p mdsconfig mdslong mdsmat mdsshepard mdytoe mdytof me_derd mean means median memory memsize meqparse mer merg merge mfp mfx mhelp mhodds minbound mixed_ll mixed_ll_reparm mkassert mkdir mkmat mkspline ml ml_5 ml_adjs ml_bhhhs ml_c_d ml_check ml_clear ml_cnt ml_debug ml_defd ml_e0 ml_e0_bfgs ml_e0_cycle ml_e0_dfp ml_e0i ml_e1 ml_e1_bfgs ml_e1_bhhh ml_e1_cycle ml_e1_dfp ml_e2 ml_e2_cycle ml_ebfg0 ml_ebfr0 ml_ebfr1 ml_ebh0q ml_ebhh0 ml_ebhr0 ml_ebr0i ml_ecr0i ml_edfp0 ml_edfr0 ml_edfr1 ml_edr0i ml_eds ml_eer0i ml_egr0i ml_elf ml_elf_bfgs ml_elf_bhhh ml_elf_cycle ml_elf_dfp ml_elfi ml_elfs ml_enr0i ml_enrr0 ml_erdu0 ml_erdu0_bfgs ml_erdu0_bhhh ml_erdu0_bhhhq ml_erdu0_cycle ml_erdu0_dfp ml_erdu0_nrbfgs ml_exde ml_footnote ml_geqnr ml_grad0 ml_graph ml_hbhhh ml_hd0 ml_hold ml_init ml_inv ml_log ml_max ml_mlout ml_mlout_8 ml_model ml_nb0 ml_opt ml_p ml_plot ml_query ml_rdgrd ml_repor ml_s_e ml_score ml_searc ml_technique ml_unhold mleval mlf_ mlmatbysum mlmatsum mlog mlogi mlogit mlogit_footnote mlogit_p mlopts mlsum mlvecsum mnl0_ mor more mov move mprobit mprobit_lf mprobit_p mrdu0_ mrdu1_ mvdecode mvencode mvreg mvreg_estat n|0 nbreg nbreg_al nbreg_lf nbreg_p nbreg_sw nestreg net newey newey_7 newey_p news nl nl_7 nl_9 nl_9_p nl_p nl_p_7 nlcom nlcom_p nlexp2 nlexp2_7 nlexp2a nlexp2a_7 nlexp3 nlexp3_7 nlgom3 nlgom3_7 nlgom4 nlgom4_7 nlinit nllog3 nllog3_7 nllog4 nllog4_7 nlog_rd nlogit nlogit_p nlogitgen nlogittree nlpred no nobreak noi nois noisi noisil noisily note notes notes_dlg nptrend numlabel numlist odbc old_ver olo olog ologi ologi_sw ologit ologit_p ologitp on one onew onewa oneway op_colnm op_comp op_diff op_inv op_str opr opro oprob oprob_sw oprobi oprobi_p oprobit oprobitp opts_exclusive order orthog orthpoly ou out outf outfi outfil outfile outs outsh outshe outshee outsheet ovtest pac pac_7 palette parse parse_dissim pause pca pca_8 pca_display pca_estat pca_p pca_rotate pcamat pchart pchart_7 pchi pchi_7 pcorr pctile pentium pergram pergram_7 permute permute_8 personal peto_st pkcollapse pkcross pkequiv pkexamine pkexamine_7 pkshape pksumm pksumm_7 pl plo plot plugin pnorm pnorm_7 poisgof poiss_lf poiss_sw poisso_p poisson poisson_estat post postclose postfile postutil pperron pr prais prais_e prais_e2 prais_p predict predictnl preserve print pro prob probi probit probit_estat probit_p proc_time procoverlay procrustes procrustes_estat procrustes_p profiler prog progr progra program prop proportion prtest prtesti pwcorr pwd q\\s qby qbys qchi qchi_7 qladder qladder_7 qnorm qnorm_7 qqplot qqplot_7 qreg qreg_c qreg_p qreg_sw qu quadchk quantile quantile_7 que quer query range ranksum ratio rchart rchart_7 rcof recast reclink recode reg reg3 reg3_p regdw regr regre regre_p2 regres regres_p regress regress_estat regriv_p remap ren rena renam rename renpfix repeat replace report reshape restore ret retu retur return rm rmdir robvar roccomp roccomp_7 roccomp_8 rocf_lf rocfit rocfit_8 rocgold rocplot rocplot_7 roctab roctab_7 rolling rologit rologit_p rot rota rotat rotate rotatemat rreg rreg_p ru run runtest rvfplot rvfplot_7 rvpplot rvpplot_7 sa safesum sample sampsi sav save savedresults saveold sc sca scal scala scalar scatter scm_mine sco scob_lf scob_p scobi_sw scobit scor score scoreplot scoreplot_help scree screeplot screeplot_help sdtest sdtesti se search separate seperate serrbar serrbar_7 serset set set_defaults sfrancia sh she shel shell shewhart shewhart_7 signestimationsample signrank signtest simul simul_7 simulate simulate_8 sktest sleep slogit slogit_d2 slogit_p smooth snapspan so sor sort spearman spikeplot spikeplot_7 spikeplt spline_x split sqreg sqreg_p sret sretu sretur sreturn ssc st st_ct st_hc st_hcd st_hcd_sh st_is st_issys st_note st_promo st_set st_show st_smpl st_subid stack statsby statsby_8 stbase stci stci_7 stcox stcox_estat stcox_fr stcox_fr_ll stcox_p stcox_sw stcoxkm stcoxkm_7 stcstat stcurv stcurve stcurve_7 stdes stem stepwise stereg stfill stgen stir stjoin stmc stmh stphplot stphplot_7 stphtest stphtest_7 stptime strate strate_7 streg streg_sw streset sts sts_7 stset stsplit stsum sttocc sttoct stvary stweib su suest suest_8 sum summ summa summar summari summariz summarize sunflower sureg survcurv survsum svar svar_p svmat svy svy_disp svy_dreg svy_est svy_est_7 svy_estat svy_get svy_gnbreg_p svy_head svy_header svy_heckman_p svy_heckprob_p svy_intreg_p svy_ivreg_p svy_logistic_p svy_logit_p svy_mlogit_p svy_nbreg_p svy_ologit_p svy_oprobit_p svy_poisson_p svy_probit_p svy_regress_p svy_sub svy_sub_7 svy_x svy_x_7 svy_x_p svydes svydes_8 svygen svygnbreg svyheckman svyheckprob svyintreg svyintreg_7 svyintrg svyivreg svylc svylog_p svylogit svymarkout svymarkout_8 svymean svymlog svymlogit svynbreg svyolog svyologit svyoprob svyoprobit svyopts svypois svypois_7 svypoisson svyprobit svyprobt svyprop 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t=this;t.containerElement=e,m.addClass(e,"remark-container"),e===t.dom.getBodyElement()?(m.addClass(t.dom.getHTMLElement(),"remark-container"),n(t.events,window,["hashchange","resize","keydown","keypress","mousewheel","message","DOMMouseScroll"]),n(t.events,t.containerElement,["touchstart","touchmove","touchend","click","contextmenu"])):(e.style.position="absolute",e.tabIndex=-1,n(t.events,window,["resize"]),n(t.events,e,["keydown","keypress","mousewheel","touchstart","touchmove","touchend"])),t.events.on("tap",function(e){e<t.containerElement.clientWidth/2?t.slideshow.gotoPreviousSlide():t.slideshow.gotoNextSlide()})},r.prototype.configureChildElements=function(){function e(){a.scaleElements()}function t(e){var t;t=e.isPortrait?.4*e.pageHeight:e.pageHeight,a.slideViews.forEach(function(a){a.scale({clientWidth:e.pageWidth,clientHeight:t}),e.isPortrait&&(a.scalingElement.style.top="20px",a.notesElement.style.top=t+40+"px")})}var 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e=this;e.slideViews.forEach(function(t){e.elementArea.removeChild(t.containerElement)}),e.slideViews=e.slideshow.getSlides().map(function(t){return new i(e.events,e.slideshow,e.scaler,t)}),e.slideViews.forEach(function(t){e.elementArea.appendChild(t.containerElement)}),e.updateDimensions(),e.slideshow.getCurrentSlideIndex()>-1&&e.showSlide(e.slideshow.getCurrentSlideIndex())},r.prototype.scaleSlideBackgroundImages=function(e){var t=this;t.slideViews.forEach(function(t){t.scaleBackgroundImage(e)})},r.prototype.showSlide=function(e){var t=this,a=t.slideViews[e],r=t.slideViews[e+1];t.events.emit("beforeShowSlide",e),a.show(),r?t.previewArea.innerHTML=r.containerElement.outerHTML:t.previewArea.innerHTML="",t.events.emit("afterShowSlide",e)},r.prototype.hideSlide=function(e){var t=this,a=t.slideViews[e];t.events.emit("beforeHideSlide",e),a.hide(),t.events.emit("afterHideSlide",e)},r.prototype.updateDimensions=function(){var 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diff --git a/img/slides.css b/docs/static/slides.css
similarity index 53%
rename from img/slides.css
rename to docs/static/slides.css
index aaa9748..d90aa7d 100755
--- a/img/slides.css
+++ b/docs/static/slides.css
@@ -1,15 +1,12 @@
-@import url(webfont-ubuntu-400-300-100.css);
-@import url(webfont-ubuntu-mono-400-700-400italic.css);
-
 body {
-  font-family: 'Ubuntu';
+  font-family: -apple-system, BlinkMacSystemFont, avenir next, avenir, segoe ui, helvetica neue, helvetica, Cantarell, Ubuntu, roboto, noto, arial, sans-serif;
   font-weight: normal;
 }
 
 h1, h2, h3, h4, h5, h6 {
-  font-family: 'Ubuntu';
   font-weight: 300;
-  margin-top: 0;
+  margin-top: 5px;
+  margin-bottom: 10px;
 }
 h1 {
   margin-top: 0.5em;
@@ -26,26 +23,16 @@ h2, h3, h4, h5, h6 {
   font-weight: normal;
 }
 
-
 li {
   font-size: 120%;
-  line-height: 160%;
+  line-height: 130%;
 }
 
 p {
-font-size: 120%;
-  line-height: 140%;
-}
-
-.singleimg .middlebelowheader {
-  text-align: center;
+  font-size: 100%;
+  line-height: 120%;
 }
 
-.singleimg img {
-  max-width: 90%;
-  max-height: 600px;
-  /*border: 2px solid #ddd;*/
-}
 table {
   margin: 0 auto 0.8em;
   border-collapse: collapse;
@@ -62,6 +49,13 @@ td, th {
   color: white;
   border-radius: .5em;
 }
+
+.section_background {
+  background-color:#c2c444;
+  color: #fff;
+  font-weight: normal;
+}
+
 .middlebelowheader {
   /* This fixed size height was found to work well with the slide
      scaling mechanism of remark.js:
@@ -70,28 +64,6 @@ td, th {
   display: table-cell;
   vertical-align: middle;
 }
-.widespace h2 {
-  line-height: 200%;
-}
-.big .remark-code  {
-  font-size: 200%;
-}
-.remark-code, .remark-inline-code {
-   font-family: 'Ubuntu Mono';
-}
-
-.medium .remark-code  {
-  font-size: 120%;
-}
-
-.mmedium .remark-code  {
-  font-size: 99%;
-}
-
-.affiliations img {
-  height: 100px;
-  margin: 2em;
-}
 
 .hidden {
   visibility: hidden;
@@ -101,21 +73,6 @@ td, th {
   font-size: 90%;
 }
 
-.credits {
-  font-style: italic;
-  font-size: 70%;
-}
-
-.bunchoflogos img {
-  max-height: 100px;
-  padding: 1em;
-}
-
-.bunchoflogos p {
-  text-align: center;
-  width: 750px;
-}
-
 a:visited {
   color: #356196;
 }
@@ -128,16 +85,16 @@ a:link {
   color: #808080;
   background-color: rgba(256, 256, 256, 0.9);
   font-size: 60%;
-  position: absolute; 
-  bottom: 30px; 
+  position: absolute;
+  bottom: 30px;
   left: 20px;
   text-align: left;
   line-height: 100%;
   padding: 5px;
 }
 
-.remark-slide-content { 
-  background-size: contain; 
+.remark-slide-content {
+  background-size: contain;
 }
 
 .emphasize  {
@@ -153,4 +110,35 @@ a:link {
 
 .fadetext {
   opacity: 0.3;
-]
+}
+
+blockquote {
+	padding: 0px 20px;
+  font-weight: lighter;
+	border-left: 5px solid #eee;
+}
+
+code {
+  font-family: Menlo, Consolas, Monaco, Liberation Mono, Lucida Console, monospace;
+  background-color: #f5f5f5;
+	border: 1px solid #ccc;
+  border-radius: 4px;
+  font-size: 0.95;
+}
+
+.remark-code {
+  font-family: Menlo, Consolas, Monaco, Liberation Mono, Lucida Console, monospace;
+	display: block;
+	padding: 9.5px;
+	margin: 0 0 10px;
+	font-size: 13px;
+	line-height: 1.42857143;
+	color: #333;
+	word-break: break-all;
+	word-wrap: break-word;
+	background-color: #f5f5f5;
+	border: 1px solid #ccc;
+	border-radius: 4px;
+}
+
+
diff --git a/environment.yml b/environment.yml
index df343be..da4f038 100644
--- a/environment.yml
+++ b/environment.yml
@@ -2,38 +2,44 @@ name: DS-geospatial
 channels:
   - conda-forge
 dependencies:
-  - python=3.8
-  - jupyter
-  - jupyterlab
+  - python=3.12
   - ipython
+  - jupyter
+  - jupyterlab>3
   - numpy
   - pandas
   - xarray
   - geopandas
-  - pygeos
   - rasterio
   - owslib
   - rasterstats
   - matplotlib
-  - descartes
   - mapclassify
   - cartopy
   - contextily
-  - geoplot
   - fsspec
   - s3fs
   - dask
   - distributed
+  - dask-labextension
   - bokeh
+  - jupyter_bokeh
   - intake
   - intake-xarray
+  - ipympl
   - ipyleaflet
   - folium
+  - lonboard
   - seaborn
   - hvplot
   - geoviews
   - datashader
-  - pygeoprocessing
+  - cmocean 
   - zarr
   - h5netcdf
   - h5py
+  - cfgrib
+  - pooch
+  - rioxarray
+  - xarray-spatial
+  - pyepsg
diff --git a/img/download-button.png b/img/download-button.png
deleted file mode 100644
index 6e4f874..0000000
Binary files a/img/download-button.png and /dev/null differ
diff --git a/img/environment_save.png b/img/environment_save.png
deleted file mode 100644
index 6f4c314..0000000
Binary files a/img/environment_save.png and /dev/null differ
diff --git a/img/matplotlib_fundamentals.png b/img/matplotlib_fundamentals.png
new file mode 100644
index 0000000..d37f280
Binary files /dev/null and b/img/matplotlib_fundamentals.png differ
diff --git a/img/matplotlib_fundamentals.svg b/img/matplotlib_fundamentals.svg
new file mode 100644
index 0000000..dc52644
--- /dev/null
+++ b/img/matplotlib_fundamentals.svg
@@ -0,0 +1,246 @@
+<?xml version="1.0" encoding="UTF-8" standalone="no"?>
+<svg
+   xmlns:dc="http://purl.org/dc/elements/1.1/"
+   xmlns:cc="http://creativecommons.org/ns#"
+   xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#"
+   xmlns:svg="http://www.w3.org/2000/svg"
+   xmlns="http://www.w3.org/2000/svg"
+   xmlns:sodipodi="http://sodipodi.sourceforge.net/DTD/sodipodi-0.dtd"
+   xmlns:inkscape="http://www.inkscape.org/namespaces/inkscape"
+   width="210mm"
+   height="297mm"
+   viewBox="0 0 210 297"
+   version="1.1"
+   id="svg8"
+   inkscape:version="1.0.2 (394de47547, 2021-03-26)"
+   sodipodi:docname="matplotlib_fundamentals.svg">
+  <defs
+     id="defs2">
+    <marker
+       style="overflow:visible;"
+       id="Arrow2Mend"
+       refX="0.0"
+       refY="0.0"
+       orient="auto"
+       inkscape:stockid="Arrow2Mend"
+       inkscape:isstock="true">
+      <path
+         transform="scale(0.6) rotate(180) translate(0,0)"
+         d="M 8.7185878,4.0337352 L -2.2072895,0.016013256 L 8.7185884,-4.0017078 C 6.9730900,-1.6296469 6.9831476,1.6157441 8.7185878,4.0337352 z "
+         style="fill-rule:evenodd;stroke-width:0.625;stroke-linejoin:round;stroke:#333333;stroke-opacity:1;fill:#333333;fill-opacity:1"
+         id="path954" />
+    </marker>
+    <marker
+       style="overflow:visible"
+       id="Arrow2Mend-7"
+       refX="0"
+       refY="0"
+       orient="auto"
+       inkscape:stockid="Arrow2Mend"
+       inkscape:isstock="true">
+      <path
+         transform="scale(-0.6)"
+         d="M 8.7185878,4.0337352 -2.2072895,0.01601326 8.7185884,-4.0017078 c -1.7454984,2.3720609 -1.7354408,5.6174519 -6e-7,8.035443 z"
+         style="fill:#333333;fill-opacity:1;fill-rule:evenodd;stroke:#333333;stroke-width:0.625;stroke-linejoin:round;stroke-opacity:1"
+         id="path954-3" />
+    </marker>
+    <marker
+       style="overflow:visible"
+       id="Arrow2Mend-9"
+       refX="0"
+       refY="0"
+       orient="auto"
+       inkscape:stockid="Arrow2Mend"
+       inkscape:isstock="true">
+      <path
+         transform="scale(-0.6)"
+         d="M 8.7185878,4.0337352 -2.2072895,0.01601326 8.7185884,-4.0017078 c -1.7454984,2.3720609 -1.7354408,5.6174519 -6e-7,8.035443 z"
+         style="fill:#333333;fill-opacity:1;fill-rule:evenodd;stroke:#333333;stroke-width:0.625;stroke-linejoin:round;stroke-opacity:1"
+         id="path954-7" />
+    </marker>
+    <marker
+       style="overflow:visible"
+       id="Arrow2Mend-7-2"
+       refX="0"
+       refY="0"
+       orient="auto"
+       inkscape:stockid="Arrow2Mend"
+       inkscape:isstock="true">
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GDIPlus_EncodersGetParamListSize GDIPlus_EncodersGetSize GDIPlus_FontCreate GDIPlus_FontDispose GDIPlus_FontFamilyCreate GDIPlus_FontFamilyCreateFromCollection GDIPlus_FontFamilyDispose GDIPlus_FontFamilyGetCellAscent GDIPlus_FontFamilyGetCellDescent GDIPlus_FontFamilyGetEmHeight GDIPlus_FontFamilyGetLineSpacing GDIPlus_FontGetHeight GDIPlus_FontPrivateAddFont GDIPlus_FontPrivateAddMemoryFont GDIPlus_FontPrivateCollectionDispose GDIPlus_FontPrivateCreateCollection GDIPlus_GraphicsClear GDIPlus_GraphicsCreateFromHDC GDIPlus_GraphicsCreateFromHWND GDIPlus_GraphicsDispose GDIPlus_GraphicsDrawArc GDIPlus_GraphicsDrawBezier GDIPlus_GraphicsDrawClosedCurve GDIPlus_GraphicsDrawClosedCurve2 GDIPlus_GraphicsDrawCurve GDIPlus_GraphicsDrawCurve2 GDIPlus_GraphicsDrawEllipse GDIPlus_GraphicsDrawImage GDIPlus_GraphicsDrawImagePointsRect GDIPlus_GraphicsDrawImageRect GDIPlus_GraphicsDrawImageRectRect GDIPlus_GraphicsDrawLine GDIPlus_GraphicsDrawPath GDIPlus_GraphicsDrawPie GDIPlus_GraphicsDrawPolygon GDIPlus_GraphicsDrawRect GDIPlus_GraphicsDrawString GDIPlus_GraphicsDrawStringEx GDIPlus_GraphicsFillClosedCurve GDIPlus_GraphicsFillClosedCurve2 GDIPlus_GraphicsFillEllipse GDIPlus_GraphicsFillPath GDIPlus_GraphicsFillPie GDIPlus_GraphicsFillPolygon GDIPlus_GraphicsFillRect GDIPlus_GraphicsFillRegion GDIPlus_GraphicsGetCompositingMode GDIPlus_GraphicsGetCompositingQuality GDIPlus_GraphicsGetDC GDIPlus_GraphicsGetInterpolationMode GDIPlus_GraphicsGetSmoothingMode GDIPlus_GraphicsGetTransform GDIPlus_GraphicsMeasureCharacterRanges GDIPlus_GraphicsMeasureString GDIPlus_GraphicsReleaseDC GDIPlus_GraphicsResetClip GDIPlus_GraphicsResetTransform GDIPlus_GraphicsRestore GDIPlus_GraphicsRotateTransform GDIPlus_GraphicsSave GDIPlus_GraphicsScaleTransform GDIPlus_GraphicsSetClipPath GDIPlus_GraphicsSetClipRect GDIPlus_GraphicsSetClipRegion GDIPlus_GraphicsSetCompositingMode GDIPlus_GraphicsSetCompositingQuality GDIPlus_GraphicsSetInterpolationMode GDIPlus_GraphicsSetPixelOffsetMode GDIPlus_GraphicsSetSmoothingMode GDIPlus_GraphicsSetTextRenderingHint GDIPlus_GraphicsSetTransform GDIPlus_GraphicsTransformPoints GDIPlus_GraphicsTranslateTransform GDIPlus_HatchBrushCreate GDIPlus_HICONCreateFromBitmap GDIPlus_ImageAttributesCreate GDIPlus_ImageAttributesDispose GDIPlus_ImageAttributesSetColorKeys GDIPlus_ImageAttributesSetColorMatrix GDIPlus_ImageDispose GDIPlus_ImageGetDimension GDIPlus_ImageGetFlags GDIPlus_ImageGetGraphicsContext GDIPlus_ImageGetHeight GDIPlus_ImageGetHorizontalResolution GDIPlus_ImageGetPixelFormat GDIPlus_ImageGetRawFormat GDIPlus_ImageGetThumbnail GDIPlus_ImageGetType GDIPlus_ImageGetVerticalResolution GDIPlus_ImageGetWidth GDIPlus_ImageLoadFromFile GDIPlus_ImageLoadFromStream GDIPlus_ImageResize GDIPlus_ImageRotateFlip GDIPlus_ImageSaveToFile GDIPlus_ImageSaveToFileEx GDIPlus_ImageSaveToStream GDIPlus_ImageScale GDIPlus_LineBrushCreate GDIPlus_LineBrushCreateFromRect GDIPlus_LineBrushCreateFromRectWithAngle GDIPlus_LineBrushGetColors GDIPlus_LineBrushGetRect GDIPlus_LineBrushMultiplyTransform GDIPlus_LineBrushResetTransform GDIPlus_LineBrushSetBlend GDIPlus_LineBrushSetColors GDIPlus_LineBrushSetGammaCorrection GDIPlus_LineBrushSetLinearBlend GDIPlus_LineBrushSetPresetBlend GDIPlus_LineBrushSetSigmaBlend GDIPlus_LineBrushSetTransform GDIPlus_MatrixClone GDIPlus_MatrixCreate GDIPlus_MatrixDispose GDIPlus_MatrixGetElements GDIPlus_MatrixInvert GDIPlus_MatrixMultiply GDIPlus_MatrixRotate GDIPlus_MatrixScale GDIPlus_MatrixSetElements GDIPlus_MatrixShear GDIPlus_MatrixTransformPoints GDIPlus_MatrixTranslate GDIPlus_PaletteInitialize GDIPlus_ParamAdd GDIPlus_ParamInit GDIPlus_ParamSize GDIPlus_PathAddArc GDIPlus_PathAddBezier GDIPlus_PathAddClosedCurve GDIPlus_PathAddClosedCurve2 GDIPlus_PathAddCurve GDIPlus_PathAddCurve2 GDIPlus_PathAddCurve3 GDIPlus_PathAddEllipse GDIPlus_PathAddLine GDIPlus_PathAddLine2 GDIPlus_PathAddPath GDIPlus_PathAddPie GDIPlus_PathAddPolygon GDIPlus_PathAddRectangle GDIPlus_PathAddString GDIPlus_PathBrushCreate GDIPlus_PathBrushCreateFromPath GDIPlus_PathBrushGetCenterPoint GDIPlus_PathBrushGetFocusScales GDIPlus_PathBrushGetPointCount GDIPlus_PathBrushGetRect GDIPlus_PathBrushGetWrapMode GDIPlus_PathBrushMultiplyTransform GDIPlus_PathBrushResetTransform GDIPlus_PathBrushSetBlend GDIPlus_PathBrushSetCenterColor GDIPlus_PathBrushSetCenterPoint GDIPlus_PathBrushSetFocusScales GDIPlus_PathBrushSetGammaCorrection GDIPlus_PathBrushSetLinearBlend GDIPlus_PathBrushSetPresetBlend GDIPlus_PathBrushSetSigmaBlend GDIPlus_PathBrushSetSurroundColor GDIPlus_PathBrushSetSurroundColorsWithCount GDIPlus_PathBrushSetTransform GDIPlus_PathBrushSetWrapMode GDIPlus_PathClone GDIPlus_PathCloseFigure GDIPlus_PathCreate GDIPlus_PathCreate2 GDIPlus_PathDispose GDIPlus_PathFlatten GDIPlus_PathGetData GDIPlus_PathGetFillMode GDIPlus_PathGetLastPoint GDIPlus_PathGetPointCount GDIPlus_PathGetPoints GDIPlus_PathGetWorldBounds GDIPlus_PathIsOutlineVisiblePoint GDIPlus_PathIsVisiblePoint GDIPlus_PathIterCreate GDIPlus_PathIterDispose GDIPlus_PathIterGetSubpathCount GDIPlus_PathIterNextMarkerPath GDIPlus_PathIterNextSubpathPath GDIPlus_PathIterRewind GDIPlus_PathReset GDIPlus_PathReverse GDIPlus_PathSetFillMode GDIPlus_PathSetMarker GDIPlus_PathStartFigure GDIPlus_PathTransform GDIPlus_PathWarp GDIPlus_PathWiden GDIPlus_PathWindingModeOutline GDIPlus_PenCreate GDIPlus_PenCreate2 GDIPlus_PenDispose GDIPlus_PenGetAlignment GDIPlus_PenGetColor GDIPlus_PenGetCustomEndCap GDIPlus_PenGetDashCap GDIPlus_PenGetDashStyle GDIPlus_PenGetEndCap GDIPlus_PenGetMiterLimit GDIPlus_PenGetWidth GDIPlus_PenSetAlignment GDIPlus_PenSetColor GDIPlus_PenSetCustomEndCap GDIPlus_PenSetDashCap GDIPlus_PenSetDashStyle GDIPlus_PenSetEndCap GDIPlus_PenSetLineCap GDIPlus_PenSetLineJoin GDIPlus_PenSetMiterLimit GDIPlus_PenSetStartCap GDIPlus_PenSetWidth GDIPlus_RectFCreate GDIPlus_RegionClone GDIPlus_RegionCombinePath GDIPlus_RegionCombineRect GDIPlus_RegionCombineRegion GDIPlus_RegionCreate GDIPlus_RegionCreateFromPath GDIPlus_RegionCreateFromRect GDIPlus_RegionDispose GDIPlus_RegionGetBounds GDIPlus_RegionGetHRgn GDIPlus_RegionTransform GDIPlus_RegionTranslate GDIPlus_Shutdown GDIPlus_Startup GDIPlus_StringFormatCreate GDIPlus_StringFormatDispose GDIPlus_StringFormatGetMeasurableCharacterRangeCount GDIPlus_StringFormatSetAlign GDIPlus_StringFormatSetLineAlign GDIPlus_StringFormatSetMeasurableCharacterRanges GDIPlus_TextureCreate GDIPlus_TextureCreate2 GDIPlus_TextureCreateIA GetIP GUICtrlAVI_Close GUICtrlAVI_Create GUICtrlAVI_Destroy GUICtrlAVI_IsPlaying GUICtrlAVI_Open GUICtrlAVI_OpenEx GUICtrlAVI_Play GUICtrlAVI_Seek GUICtrlAVI_Show GUICtrlAVI_Stop GUICtrlButton_Click GUICtrlButton_Create GUICtrlButton_Destroy GUICtrlButton_Enable GUICtrlButton_GetCheck GUICtrlButton_GetFocus GUICtrlButton_GetIdealSize GUICtrlButton_GetImage GUICtrlButton_GetImageList GUICtrlButton_GetNote GUICtrlButton_GetNoteLength GUICtrlButton_GetSplitInfo GUICtrlButton_GetState GUICtrlButton_GetText GUICtrlButton_GetTextMargin GUICtrlButton_SetCheck GUICtrlButton_SetDontClick GUICtrlButton_SetFocus GUICtrlButton_SetImage GUICtrlButton_SetImageList GUICtrlButton_SetNote GUICtrlButton_SetShield GUICtrlButton_SetSize GUICtrlButton_SetSplitInfo GUICtrlButton_SetState GUICtrlButton_SetStyle GUICtrlButton_SetText GUICtrlButton_SetTextMargin GUICtrlButton_Show GUICtrlComboBoxEx_AddDir GUICtrlComboBoxEx_AddString GUICtrlComboBoxEx_BeginUpdate GUICtrlComboBoxEx_Create GUICtrlComboBoxEx_CreateSolidBitMap GUICtrlComboBoxEx_DeleteString GUICtrlComboBoxEx_Destroy GUICtrlComboBoxEx_EndUpdate GUICtrlComboBoxEx_FindStringExact GUICtrlComboBoxEx_GetComboBoxInfo GUICtrlComboBoxEx_GetComboControl GUICtrlComboBoxEx_GetCount GUICtrlComboBoxEx_GetCurSel GUICtrlComboBoxEx_GetDroppedControlRect GUICtrlComboBoxEx_GetDroppedControlRectEx GUICtrlComboBoxEx_GetDroppedState GUICtrlComboBoxEx_GetDroppedWidth GUICtrlComboBoxEx_GetEditControl GUICtrlComboBoxEx_GetEditSel GUICtrlComboBoxEx_GetEditText GUICtrlComboBoxEx_GetExtendedStyle GUICtrlComboBoxEx_GetExtendedUI GUICtrlComboBoxEx_GetImageList GUICtrlComboBoxEx_GetItem GUICtrlComboBoxEx_GetItemEx GUICtrlComboBoxEx_GetItemHeight GUICtrlComboBoxEx_GetItemImage GUICtrlComboBoxEx_GetItemIndent GUICtrlComboBoxEx_GetItemOverlayImage GUICtrlComboBoxEx_GetItemParam GUICtrlComboBoxEx_GetItemSelectedImage GUICtrlComboBoxEx_GetItemText GUICtrlComboBoxEx_GetItemTextLen GUICtrlComboBoxEx_GetList GUICtrlComboBoxEx_GetListArray GUICtrlComboBoxEx_GetLocale GUICtrlComboBoxEx_GetLocaleCountry GUICtrlComboBoxEx_GetLocaleLang GUICtrlComboBoxEx_GetLocalePrimLang GUICtrlComboBoxEx_GetLocaleSubLang GUICtrlComboBoxEx_GetMinVisible GUICtrlComboBoxEx_GetTopIndex GUICtrlComboBoxEx_GetUnicode GUICtrlComboBoxEx_InitStorage GUICtrlComboBoxEx_InsertString GUICtrlComboBoxEx_LimitText GUICtrlComboBoxEx_ReplaceEditSel GUICtrlComboBoxEx_ResetContent GUICtrlComboBoxEx_SetCurSel GUICtrlComboBoxEx_SetDroppedWidth GUICtrlComboBoxEx_SetEditSel GUICtrlComboBoxEx_SetEditText GUICtrlComboBoxEx_SetExtendedStyle GUICtrlComboBoxEx_SetExtendedUI GUICtrlComboBoxEx_SetImageList GUICtrlComboBoxEx_SetItem GUICtrlComboBoxEx_SetItemEx GUICtrlComboBoxEx_SetItemHeight GUICtrlComboBoxEx_SetItemImage GUICtrlComboBoxEx_SetItemIndent GUICtrlComboBoxEx_SetItemOverlayImage GUICtrlComboBoxEx_SetItemParam GUICtrlComboBoxEx_SetItemSelectedImage GUICtrlComboBoxEx_SetMinVisible GUICtrlComboBoxEx_SetTopIndex GUICtrlComboBoxEx_SetUnicode GUICtrlComboBoxEx_ShowDropDown GUICtrlComboBox_AddDir GUICtrlComboBox_AddString GUICtrlComboBox_AutoComplete GUICtrlComboBox_BeginUpdate GUICtrlComboBox_Create GUICtrlComboBox_DeleteString GUICtrlComboBox_Destroy GUICtrlComboBox_EndUpdate GUICtrlComboBox_FindString GUICtrlComboBox_FindStringExact GUICtrlComboBox_GetComboBoxInfo GUICtrlComboBox_GetCount GUICtrlComboBox_GetCueBanner GUICtrlComboBox_GetCurSel GUICtrlComboBox_GetDroppedControlRect GUICtrlComboBox_GetDroppedControlRectEx GUICtrlComboBox_GetDroppedState GUICtrlComboBox_GetDroppedWidth GUICtrlComboBox_GetEditSel GUICtrlComboBox_GetEditText GUICtrlComboBox_GetExtendedUI GUICtrlComboBox_GetHorizontalExtent GUICtrlComboBox_GetItemHeight GUICtrlComboBox_GetLBText GUICtrlComboBox_GetLBTextLen GUICtrlComboBox_GetList GUICtrlComboBox_GetListArray GUICtrlComboBox_GetLocale GUICtrlComboBox_GetLocaleCountry GUICtrlComboBox_GetLocaleLang GUICtrlComboBox_GetLocalePrimLang GUICtrlComboBox_GetLocaleSubLang GUICtrlComboBox_GetMinVisible GUICtrlComboBox_GetTopIndex GUICtrlComboBox_InitStorage GUICtrlComboBox_InsertString GUICtrlComboBox_LimitText GUICtrlComboBox_ReplaceEditSel GUICtrlComboBox_ResetContent GUICtrlComboBox_SelectString GUICtrlComboBox_SetCueBanner GUICtrlComboBox_SetCurSel GUICtrlComboBox_SetDroppedWidth GUICtrlComboBox_SetEditSel GUICtrlComboBox_SetEditText GUICtrlComboBox_SetExtendedUI GUICtrlComboBox_SetHorizontalExtent GUICtrlComboBox_SetItemHeight GUICtrlComboBox_SetMinVisible GUICtrlComboBox_SetTopIndex GUICtrlComboBox_ShowDropDown GUICtrlDTP_Create GUICtrlDTP_Destroy GUICtrlDTP_GetMCColor GUICtrlDTP_GetMCFont GUICtrlDTP_GetMonthCal GUICtrlDTP_GetRange GUICtrlDTP_GetRangeEx GUICtrlDTP_GetSystemTime GUICtrlDTP_GetSystemTimeEx GUICtrlDTP_SetFormat GUICtrlDTP_SetMCColor GUICtrlDTP_SetMCFont GUICtrlDTP_SetRange GUICtrlDTP_SetRangeEx GUICtrlDTP_SetSystemTime GUICtrlDTP_SetSystemTimeEx GUICtrlEdit_AppendText GUICtrlEdit_BeginUpdate GUICtrlEdit_CanUndo GUICtrlEdit_CharFromPos GUICtrlEdit_Create GUICtrlEdit_Destroy GUICtrlEdit_EmptyUndoBuffer GUICtrlEdit_EndUpdate GUICtrlEdit_Find GUICtrlEdit_FmtLines GUICtrlEdit_GetCueBanner GUICtrlEdit_GetFirstVisibleLine GUICtrlEdit_GetLimitText GUICtrlEdit_GetLine GUICtrlEdit_GetLineCount GUICtrlEdit_GetMargins GUICtrlEdit_GetModify GUICtrlEdit_GetPasswordChar GUICtrlEdit_GetRECT GUICtrlEdit_GetRECTEx GUICtrlEdit_GetSel GUICtrlEdit_GetText GUICtrlEdit_GetTextLen GUICtrlEdit_HideBalloonTip GUICtrlEdit_InsertText GUICtrlEdit_LineFromChar GUICtrlEdit_LineIndex GUICtrlEdit_LineLength GUICtrlEdit_LineScroll GUICtrlEdit_PosFromChar GUICtrlEdit_ReplaceSel GUICtrlEdit_Scroll GUICtrlEdit_SetCueBanner GUICtrlEdit_SetLimitText GUICtrlEdit_SetMargins GUICtrlEdit_SetModify GUICtrlEdit_SetPasswordChar GUICtrlEdit_SetReadOnly GUICtrlEdit_SetRECT GUICtrlEdit_SetRECTEx GUICtrlEdit_SetRECTNP GUICtrlEdit_SetRectNPEx GUICtrlEdit_SetSel GUICtrlEdit_SetTabStops GUICtrlEdit_SetText GUICtrlEdit_ShowBalloonTip GUICtrlEdit_Undo GUICtrlHeader_AddItem GUICtrlHeader_ClearFilter GUICtrlHeader_ClearFilterAll GUICtrlHeader_Create GUICtrlHeader_CreateDragImage GUICtrlHeader_DeleteItem GUICtrlHeader_Destroy GUICtrlHeader_EditFilter GUICtrlHeader_GetBitmapMargin GUICtrlHeader_GetImageList GUICtrlHeader_GetItem GUICtrlHeader_GetItemAlign GUICtrlHeader_GetItemBitmap GUICtrlHeader_GetItemCount GUICtrlHeader_GetItemDisplay GUICtrlHeader_GetItemFlags GUICtrlHeader_GetItemFormat GUICtrlHeader_GetItemImage GUICtrlHeader_GetItemOrder GUICtrlHeader_GetItemParam GUICtrlHeader_GetItemRect GUICtrlHeader_GetItemRectEx GUICtrlHeader_GetItemText GUICtrlHeader_GetItemWidth GUICtrlHeader_GetOrderArray GUICtrlHeader_GetUnicodeFormat GUICtrlHeader_HitTest GUICtrlHeader_InsertItem GUICtrlHeader_Layout GUICtrlHeader_OrderToIndex GUICtrlHeader_SetBitmapMargin GUICtrlHeader_SetFilterChangeTimeout GUICtrlHeader_SetHotDivider GUICtrlHeader_SetImageList GUICtrlHeader_SetItem GUICtrlHeader_SetItemAlign GUICtrlHeader_SetItemBitmap GUICtrlHeader_SetItemDisplay GUICtrlHeader_SetItemFlags GUICtrlHeader_SetItemFormat GUICtrlHeader_SetItemImage GUICtrlHeader_SetItemOrder GUICtrlHeader_SetItemParam GUICtrlHeader_SetItemText GUICtrlHeader_SetItemWidth GUICtrlHeader_SetOrderArray GUICtrlHeader_SetUnicodeFormat GUICtrlIpAddress_ClearAddress GUICtrlIpAddress_Create GUICtrlIpAddress_Destroy GUICtrlIpAddress_Get GUICtrlIpAddress_GetArray GUICtrlIpAddress_GetEx GUICtrlIpAddress_IsBlank GUICtrlIpAddress_Set GUICtrlIpAddress_SetArray GUICtrlIpAddress_SetEx GUICtrlIpAddress_SetFocus GUICtrlIpAddress_SetFont GUICtrlIpAddress_SetRange GUICtrlIpAddress_ShowHide GUICtrlListBox_AddFile GUICtrlListBox_AddString GUICtrlListBox_BeginUpdate GUICtrlListBox_ClickItem GUICtrlListBox_Create GUICtrlListBox_DeleteString GUICtrlListBox_Destroy GUICtrlListBox_Dir GUICtrlListBox_EndUpdate GUICtrlListBox_FindInText GUICtrlListBox_FindString GUICtrlListBox_GetAnchorIndex GUICtrlListBox_GetCaretIndex GUICtrlListBox_GetCount GUICtrlListBox_GetCurSel GUICtrlListBox_GetHorizontalExtent GUICtrlListBox_GetItemData GUICtrlListBox_GetItemHeight GUICtrlListBox_GetItemRect GUICtrlListBox_GetItemRectEx GUICtrlListBox_GetListBoxInfo GUICtrlListBox_GetLocale GUICtrlListBox_GetLocaleCountry GUICtrlListBox_GetLocaleLang GUICtrlListBox_GetLocalePrimLang GUICtrlListBox_GetLocaleSubLang GUICtrlListBox_GetSel GUICtrlListBox_GetSelCount GUICtrlListBox_GetSelItems GUICtrlListBox_GetSelItemsText GUICtrlListBox_GetText GUICtrlListBox_GetTextLen GUICtrlListBox_GetTopIndex GUICtrlListBox_InitStorage GUICtrlListBox_InsertString GUICtrlListBox_ItemFromPoint GUICtrlListBox_ReplaceString GUICtrlListBox_ResetContent GUICtrlListBox_SelectString GUICtrlListBox_SelItemRange GUICtrlListBox_SelItemRangeEx GUICtrlListBox_SetAnchorIndex GUICtrlListBox_SetCaretIndex GUICtrlListBox_SetColumnWidth GUICtrlListBox_SetCurSel GUICtrlListBox_SetHorizontalExtent GUICtrlListBox_SetItemData GUICtrlListBox_SetItemHeight GUICtrlListBox_SetLocale GUICtrlListBox_SetSel GUICtrlListBox_SetTabStops GUICtrlListBox_SetTopIndex GUICtrlListBox_Sort GUICtrlListBox_SwapString GUICtrlListBox_UpdateHScroll GUICtrlListView_AddArray GUICtrlListView_AddColumn GUICtrlListView_AddItem GUICtrlListView_AddSubItem GUICtrlListView_ApproximateViewHeight GUICtrlListView_ApproximateViewRect GUICtrlListView_ApproximateViewWidth GUICtrlListView_Arrange GUICtrlListView_BeginUpdate GUICtrlListView_CancelEditLabel GUICtrlListView_ClickItem GUICtrlListView_CopyItems GUICtrlListView_Create GUICtrlListView_CreateDragImage GUICtrlListView_CreateSolidBitMap GUICtrlListView_DeleteAllItems GUICtrlListView_DeleteColumn GUICtrlListView_DeleteItem GUICtrlListView_DeleteItemsSelected GUICtrlListView_Destroy GUICtrlListView_DrawDragImage GUICtrlListView_EditLabel GUICtrlListView_EnableGroupView GUICtrlListView_EndUpdate GUICtrlListView_EnsureVisible GUICtrlListView_FindInText GUICtrlListView_FindItem GUICtrlListView_FindNearest GUICtrlListView_FindParam GUICtrlListView_FindText GUICtrlListView_GetBkColor GUICtrlListView_GetBkImage GUICtrlListView_GetCallbackMask GUICtrlListView_GetColumn GUICtrlListView_GetColumnCount GUICtrlListView_GetColumnOrder GUICtrlListView_GetColumnOrderArray GUICtrlListView_GetColumnWidth GUICtrlListView_GetCounterPage GUICtrlListView_GetEditControl GUICtrlListView_GetExtendedListViewStyle GUICtrlListView_GetFocusedGroup GUICtrlListView_GetGroupCount GUICtrlListView_GetGroupInfo GUICtrlListView_GetGroupInfoByIndex GUICtrlListView_GetGroupRect GUICtrlListView_GetGroupViewEnabled GUICtrlListView_GetHeader GUICtrlListView_GetHotCursor GUICtrlListView_GetHotItem GUICtrlListView_GetHoverTime GUICtrlListView_GetImageList GUICtrlListView_GetISearchString GUICtrlListView_GetItem GUICtrlListView_GetItemChecked GUICtrlListView_GetItemCount GUICtrlListView_GetItemCut GUICtrlListView_GetItemDropHilited GUICtrlListView_GetItemEx GUICtrlListView_GetItemFocused GUICtrlListView_GetItemGroupID GUICtrlListView_GetItemImage GUICtrlListView_GetItemIndent GUICtrlListView_GetItemParam GUICtrlListView_GetItemPosition GUICtrlListView_GetItemPositionX GUICtrlListView_GetItemPositionY GUICtrlListView_GetItemRect GUICtrlListView_GetItemRectEx GUICtrlListView_GetItemSelected GUICtrlListView_GetItemSpacing GUICtrlListView_GetItemSpacingX GUICtrlListView_GetItemSpacingY GUICtrlListView_GetItemState GUICtrlListView_GetItemStateImage GUICtrlListView_GetItemText GUICtrlListView_GetItemTextArray GUICtrlListView_GetItemTextString GUICtrlListView_GetNextItem GUICtrlListView_GetNumberOfWorkAreas GUICtrlListView_GetOrigin GUICtrlListView_GetOriginX GUICtrlListView_GetOriginY GUICtrlListView_GetOutlineColor GUICtrlListView_GetSelectedColumn GUICtrlListView_GetSelectedCount GUICtrlListView_GetSelectedIndices GUICtrlListView_GetSelectionMark GUICtrlListView_GetStringWidth GUICtrlListView_GetSubItemRect GUICtrlListView_GetTextBkColor GUICtrlListView_GetTextColor GUICtrlListView_GetToolTips GUICtrlListView_GetTopIndex GUICtrlListView_GetUnicodeFormat GUICtrlListView_GetView GUICtrlListView_GetViewDetails GUICtrlListView_GetViewLarge GUICtrlListView_GetViewList GUICtrlListView_GetViewRect GUICtrlListView_GetViewSmall GUICtrlListView_GetViewTile GUICtrlListView_HideColumn GUICtrlListView_HitTest GUICtrlListView_InsertColumn GUICtrlListView_InsertGroup GUICtrlListView_InsertItem GUICtrlListView_JustifyColumn GUICtrlListView_MapIDToIndex GUICtrlListView_MapIndexToID GUICtrlListView_RedrawItems GUICtrlListView_RegisterSortCallBack GUICtrlListView_RemoveAllGroups GUICtrlListView_RemoveGroup GUICtrlListView_Scroll GUICtrlListView_SetBkColor GUICtrlListView_SetBkImage GUICtrlListView_SetCallBackMask GUICtrlListView_SetColumn GUICtrlListView_SetColumnOrder GUICtrlListView_SetColumnOrderArray GUICtrlListView_SetColumnWidth GUICtrlListView_SetExtendedListViewStyle GUICtrlListView_SetGroupInfo GUICtrlListView_SetHotItem GUICtrlListView_SetHoverTime GUICtrlListView_SetIconSpacing GUICtrlListView_SetImageList GUICtrlListView_SetItem GUICtrlListView_SetItemChecked GUICtrlListView_SetItemCount GUICtrlListView_SetItemCut GUICtrlListView_SetItemDropHilited GUICtrlListView_SetItemEx GUICtrlListView_SetItemFocused GUICtrlListView_SetItemGroupID GUICtrlListView_SetItemImage GUICtrlListView_SetItemIndent GUICtrlListView_SetItemParam GUICtrlListView_SetItemPosition GUICtrlListView_SetItemPosition32 GUICtrlListView_SetItemSelected GUICtrlListView_SetItemState GUICtrlListView_SetItemStateImage GUICtrlListView_SetItemText GUICtrlListView_SetOutlineColor GUICtrlListView_SetSelectedColumn GUICtrlListView_SetSelectionMark GUICtrlListView_SetTextBkColor GUICtrlListView_SetTextColor GUICtrlListView_SetToolTips GUICtrlListView_SetUnicodeFormat GUICtrlListView_SetView GUICtrlListView_SetWorkAreas GUICtrlListView_SimpleSort GUICtrlListView_SortItems GUICtrlListView_SubItemHitTest GUICtrlListView_UnRegisterSortCallBack GUICtrlMenu_AddMenuItem GUICtrlMenu_AppendMenu GUICtrlMenu_CalculatePopupWindowPosition GUICtrlMenu_CheckMenuItem GUICtrlMenu_CheckRadioItem GUICtrlMenu_CreateMenu GUICtrlMenu_CreatePopup GUICtrlMenu_DeleteMenu GUICtrlMenu_DestroyMenu GUICtrlMenu_DrawMenuBar GUICtrlMenu_EnableMenuItem GUICtrlMenu_FindItem GUICtrlMenu_FindParent GUICtrlMenu_GetItemBmp GUICtrlMenu_GetItemBmpChecked GUICtrlMenu_GetItemBmpUnchecked GUICtrlMenu_GetItemChecked GUICtrlMenu_GetItemCount GUICtrlMenu_GetItemData GUICtrlMenu_GetItemDefault GUICtrlMenu_GetItemDisabled GUICtrlMenu_GetItemEnabled GUICtrlMenu_GetItemGrayed GUICtrlMenu_GetItemHighlighted GUICtrlMenu_GetItemID GUICtrlMenu_GetItemInfo GUICtrlMenu_GetItemRect GUICtrlMenu_GetItemRectEx GUICtrlMenu_GetItemState GUICtrlMenu_GetItemStateEx GUICtrlMenu_GetItemSubMenu GUICtrlMenu_GetItemText GUICtrlMenu_GetItemType GUICtrlMenu_GetMenu GUICtrlMenu_GetMenuBackground GUICtrlMenu_GetMenuBarInfo GUICtrlMenu_GetMenuContextHelpID GUICtrlMenu_GetMenuData GUICtrlMenu_GetMenuDefaultItem GUICtrlMenu_GetMenuHeight GUICtrlMenu_GetMenuInfo GUICtrlMenu_GetMenuStyle GUICtrlMenu_GetSystemMenu GUICtrlMenu_InsertMenuItem GUICtrlMenu_InsertMenuItemEx GUICtrlMenu_IsMenu GUICtrlMenu_LoadMenu GUICtrlMenu_MapAccelerator GUICtrlMenu_MenuItemFromPoint GUICtrlMenu_RemoveMenu GUICtrlMenu_SetItemBitmaps GUICtrlMenu_SetItemBmp GUICtrlMenu_SetItemBmpChecked GUICtrlMenu_SetItemBmpUnchecked GUICtrlMenu_SetItemChecked GUICtrlMenu_SetItemData GUICtrlMenu_SetItemDefault GUICtrlMenu_SetItemDisabled GUICtrlMenu_SetItemEnabled GUICtrlMenu_SetItemGrayed GUICtrlMenu_SetItemHighlighted GUICtrlMenu_SetItemID GUICtrlMenu_SetItemInfo GUICtrlMenu_SetItemState GUICtrlMenu_SetItemSubMenu GUICtrlMenu_SetItemText GUICtrlMenu_SetItemType GUICtrlMenu_SetMenu GUICtrlMenu_SetMenuBackground GUICtrlMenu_SetMenuContextHelpID GUICtrlMenu_SetMenuData GUICtrlMenu_SetMenuDefaultItem GUICtrlMenu_SetMenuHeight GUICtrlMenu_SetMenuInfo GUICtrlMenu_SetMenuStyle GUICtrlMenu_TrackPopupMenu GUICtrlMonthCal_Create GUICtrlMonthCal_Destroy GUICtrlMonthCal_GetCalendarBorder GUICtrlMonthCal_GetCalendarCount GUICtrlMonthCal_GetColor GUICtrlMonthCal_GetColorArray GUICtrlMonthCal_GetCurSel GUICtrlMonthCal_GetCurSelStr GUICtrlMonthCal_GetFirstDOW GUICtrlMonthCal_GetFirstDOWStr GUICtrlMonthCal_GetMaxSelCount GUICtrlMonthCal_GetMaxTodayWidth GUICtrlMonthCal_GetMinReqHeight GUICtrlMonthCal_GetMinReqRect GUICtrlMonthCal_GetMinReqRectArray GUICtrlMonthCal_GetMinReqWidth GUICtrlMonthCal_GetMonthDelta GUICtrlMonthCal_GetMonthRange GUICtrlMonthCal_GetMonthRangeMax GUICtrlMonthCal_GetMonthRangeMaxStr GUICtrlMonthCal_GetMonthRangeMin GUICtrlMonthCal_GetMonthRangeMinStr GUICtrlMonthCal_GetMonthRangeSpan GUICtrlMonthCal_GetRange GUICtrlMonthCal_GetRangeMax GUICtrlMonthCal_GetRangeMaxStr GUICtrlMonthCal_GetRangeMin GUICtrlMonthCal_GetRangeMinStr GUICtrlMonthCal_GetSelRange GUICtrlMonthCal_GetSelRangeMax GUICtrlMonthCal_GetSelRangeMaxStr GUICtrlMonthCal_GetSelRangeMin GUICtrlMonthCal_GetSelRangeMinStr GUICtrlMonthCal_GetToday GUICtrlMonthCal_GetTodayStr GUICtrlMonthCal_GetUnicodeFormat GUICtrlMonthCal_HitTest GUICtrlMonthCal_SetCalendarBorder GUICtrlMonthCal_SetColor GUICtrlMonthCal_SetCurSel GUICtrlMonthCal_SetDayState GUICtrlMonthCal_SetFirstDOW GUICtrlMonthCal_SetMaxSelCount GUICtrlMonthCal_SetMonthDelta GUICtrlMonthCal_SetRange GUICtrlMonthCal_SetSelRange GUICtrlMonthCal_SetToday GUICtrlMonthCal_SetUnicodeFormat GUICtrlRebar_AddBand GUICtrlRebar_AddToolBarBand GUICtrlRebar_BeginDrag GUICtrlRebar_Create GUICtrlRebar_DeleteBand GUICtrlRebar_Destroy GUICtrlRebar_DragMove GUICtrlRebar_EndDrag GUICtrlRebar_GetBandBackColor GUICtrlRebar_GetBandBorders GUICtrlRebar_GetBandBordersEx GUICtrlRebar_GetBandChildHandle GUICtrlRebar_GetBandChildSize GUICtrlRebar_GetBandCount GUICtrlRebar_GetBandForeColor GUICtrlRebar_GetBandHeaderSize GUICtrlRebar_GetBandID GUICtrlRebar_GetBandIdealSize GUICtrlRebar_GetBandLength GUICtrlRebar_GetBandLParam GUICtrlRebar_GetBandMargins GUICtrlRebar_GetBandMarginsEx GUICtrlRebar_GetBandRect GUICtrlRebar_GetBandRectEx GUICtrlRebar_GetBandStyle GUICtrlRebar_GetBandStyleBreak GUICtrlRebar_GetBandStyleChildEdge GUICtrlRebar_GetBandStyleFixedBMP GUICtrlRebar_GetBandStyleFixedSize GUICtrlRebar_GetBandStyleGripperAlways GUICtrlRebar_GetBandStyleHidden GUICtrlRebar_GetBandStyleHideTitle GUICtrlRebar_GetBandStyleNoGripper GUICtrlRebar_GetBandStyleTopAlign GUICtrlRebar_GetBandStyleUseChevron GUICtrlRebar_GetBandStyleVariableHeight GUICtrlRebar_GetBandText GUICtrlRebar_GetBarHeight GUICtrlRebar_GetBarInfo GUICtrlRebar_GetBKColor GUICtrlRebar_GetColorScheme GUICtrlRebar_GetRowCount GUICtrlRebar_GetRowHeight GUICtrlRebar_GetTextColor GUICtrlRebar_GetToolTips GUICtrlRebar_GetUnicodeFormat GUICtrlRebar_HitTest GUICtrlRebar_IDToIndex GUICtrlRebar_MaximizeBand GUICtrlRebar_MinimizeBand GUICtrlRebar_MoveBand GUICtrlRebar_SetBandBackColor GUICtrlRebar_SetBandForeColor GUICtrlRebar_SetBandHeaderSize GUICtrlRebar_SetBandID GUICtrlRebar_SetBandIdealSize GUICtrlRebar_SetBandLength GUICtrlRebar_SetBandLParam GUICtrlRebar_SetBandStyle GUICtrlRebar_SetBandStyleBreak GUICtrlRebar_SetBandStyleChildEdge GUICtrlRebar_SetBandStyleFixedBMP GUICtrlRebar_SetBandStyleFixedSize GUICtrlRebar_SetBandStyleGripperAlways GUICtrlRebar_SetBandStyleHidden GUICtrlRebar_SetBandStyleHideTitle GUICtrlRebar_SetBandStyleNoGripper GUICtrlRebar_SetBandStyleTopAlign GUICtrlRebar_SetBandStyleUseChevron GUICtrlRebar_SetBandStyleVariableHeight GUICtrlRebar_SetBandText GUICtrlRebar_SetBarInfo GUICtrlRebar_SetBKColor GUICtrlRebar_SetColorScheme GUICtrlRebar_SetTextColor GUICtrlRebar_SetToolTips GUICtrlRebar_SetUnicodeFormat GUICtrlRebar_ShowBand GUICtrlRichEdit_AppendText GUICtrlRichEdit_AutoDetectURL GUICtrlRichEdit_CanPaste GUICtrlRichEdit_CanPasteSpecial GUICtrlRichEdit_CanRedo GUICtrlRichEdit_CanUndo GUICtrlRichEdit_ChangeFontSize GUICtrlRichEdit_Copy GUICtrlRichEdit_Create GUICtrlRichEdit_Cut GUICtrlRichEdit_Deselect GUICtrlRichEdit_Destroy GUICtrlRichEdit_EmptyUndoBuffer GUICtrlRichEdit_FindText GUICtrlRichEdit_FindTextInRange GUICtrlRichEdit_GetBkColor GUICtrlRichEdit_GetCharAttributes GUICtrlRichEdit_GetCharBkColor GUICtrlRichEdit_GetCharColor GUICtrlRichEdit_GetCharPosFromXY GUICtrlRichEdit_GetCharPosOfNextWord GUICtrlRichEdit_GetCharPosOfPreviousWord GUICtrlRichEdit_GetCharWordBreakInfo GUICtrlRichEdit_GetFirstCharPosOnLine GUICtrlRichEdit_GetFont GUICtrlRichEdit_GetLineCount GUICtrlRichEdit_GetLineLength GUICtrlRichEdit_GetLineNumberFromCharPos GUICtrlRichEdit_GetNextRedo GUICtrlRichEdit_GetNextUndo GUICtrlRichEdit_GetNumberOfFirstVisibleLine GUICtrlRichEdit_GetParaAlignment GUICtrlRichEdit_GetParaAttributes GUICtrlRichEdit_GetParaBorder GUICtrlRichEdit_GetParaIndents GUICtrlRichEdit_GetParaNumbering GUICtrlRichEdit_GetParaShading GUICtrlRichEdit_GetParaSpacing GUICtrlRichEdit_GetParaTabStops GUICtrlRichEdit_GetPasswordChar GUICtrlRichEdit_GetRECT GUICtrlRichEdit_GetScrollPos GUICtrlRichEdit_GetSel GUICtrlRichEdit_GetSelAA GUICtrlRichEdit_GetSelText GUICtrlRichEdit_GetSpaceUnit GUICtrlRichEdit_GetText GUICtrlRichEdit_GetTextInLine GUICtrlRichEdit_GetTextInRange GUICtrlRichEdit_GetTextLength GUICtrlRichEdit_GetVersion GUICtrlRichEdit_GetXYFromCharPos GUICtrlRichEdit_GetZoom GUICtrlRichEdit_GotoCharPos GUICtrlRichEdit_HideSelection GUICtrlRichEdit_InsertText GUICtrlRichEdit_IsModified GUICtrlRichEdit_IsTextSelected GUICtrlRichEdit_Paste GUICtrlRichEdit_PasteSpecial GUICtrlRichEdit_PauseRedraw GUICtrlRichEdit_Redo GUICtrlRichEdit_ReplaceText GUICtrlRichEdit_ResumeRedraw GUICtrlRichEdit_ScrollLineOrPage GUICtrlRichEdit_ScrollLines GUICtrlRichEdit_ScrollToCaret GUICtrlRichEdit_SetBkColor GUICtrlRichEdit_SetCharAttributes GUICtrlRichEdit_SetCharBkColor GUICtrlRichEdit_SetCharColor GUICtrlRichEdit_SetEventMask GUICtrlRichEdit_SetFont GUICtrlRichEdit_SetLimitOnText GUICtrlRichEdit_SetModified GUICtrlRichEdit_SetParaAlignment GUICtrlRichEdit_SetParaAttributes GUICtrlRichEdit_SetParaBorder GUICtrlRichEdit_SetParaIndents GUICtrlRichEdit_SetParaNumbering GUICtrlRichEdit_SetParaShading GUICtrlRichEdit_SetParaSpacing GUICtrlRichEdit_SetParaTabStops GUICtrlRichEdit_SetPasswordChar GUICtrlRichEdit_SetReadOnly GUICtrlRichEdit_SetRECT GUICtrlRichEdit_SetScrollPos GUICtrlRichEdit_SetSel GUICtrlRichEdit_SetSpaceUnit GUICtrlRichEdit_SetTabStops GUICtrlRichEdit_SetText GUICtrlRichEdit_SetUndoLimit GUICtrlRichEdit_SetZoom GUICtrlRichEdit_StreamFromFile GUICtrlRichEdit_StreamFromVar GUICtrlRichEdit_StreamToFile GUICtrlRichEdit_StreamToVar GUICtrlRichEdit_Undo GUICtrlSlider_ClearSel GUICtrlSlider_ClearTics GUICtrlSlider_Create GUICtrlSlider_Destroy GUICtrlSlider_GetBuddy GUICtrlSlider_GetChannelRect GUICtrlSlider_GetChannelRectEx GUICtrlSlider_GetLineSize GUICtrlSlider_GetLogicalTics GUICtrlSlider_GetNumTics GUICtrlSlider_GetPageSize GUICtrlSlider_GetPos GUICtrlSlider_GetRange GUICtrlSlider_GetRangeMax GUICtrlSlider_GetRangeMin GUICtrlSlider_GetSel GUICtrlSlider_GetSelEnd GUICtrlSlider_GetSelStart GUICtrlSlider_GetThumbLength GUICtrlSlider_GetThumbRect GUICtrlSlider_GetThumbRectEx GUICtrlSlider_GetTic GUICtrlSlider_GetTicPos GUICtrlSlider_GetToolTips GUICtrlSlider_GetUnicodeFormat GUICtrlSlider_SetBuddy GUICtrlSlider_SetLineSize GUICtrlSlider_SetPageSize GUICtrlSlider_SetPos GUICtrlSlider_SetRange GUICtrlSlider_SetRangeMax GUICtrlSlider_SetRangeMin GUICtrlSlider_SetSel GUICtrlSlider_SetSelEnd GUICtrlSlider_SetSelStart GUICtrlSlider_SetThumbLength GUICtrlSlider_SetTic GUICtrlSlider_SetTicFreq GUICtrlSlider_SetTipSide GUICtrlSlider_SetToolTips GUICtrlSlider_SetUnicodeFormat GUICtrlStatusBar_Create GUICtrlStatusBar_Destroy GUICtrlStatusBar_EmbedControl GUICtrlStatusBar_GetBorders GUICtrlStatusBar_GetBordersHorz GUICtrlStatusBar_GetBordersRect GUICtrlStatusBar_GetBordersVert GUICtrlStatusBar_GetCount GUICtrlStatusBar_GetHeight GUICtrlStatusBar_GetIcon GUICtrlStatusBar_GetParts GUICtrlStatusBar_GetRect GUICtrlStatusBar_GetRectEx GUICtrlStatusBar_GetText GUICtrlStatusBar_GetTextFlags GUICtrlStatusBar_GetTextLength GUICtrlStatusBar_GetTextLengthEx GUICtrlStatusBar_GetTipText GUICtrlStatusBar_GetUnicodeFormat GUICtrlStatusBar_GetWidth GUICtrlStatusBar_IsSimple GUICtrlStatusBar_Resize GUICtrlStatusBar_SetBkColor GUICtrlStatusBar_SetIcon GUICtrlStatusBar_SetMinHeight GUICtrlStatusBar_SetParts GUICtrlStatusBar_SetSimple GUICtrlStatusBar_SetText GUICtrlStatusBar_SetTipText GUICtrlStatusBar_SetUnicodeFormat GUICtrlStatusBar_ShowHide GUICtrlTab_ActivateTab GUICtrlTab_ClickTab GUICtrlTab_Create GUICtrlTab_DeleteAllItems GUICtrlTab_DeleteItem GUICtrlTab_DeselectAll GUICtrlTab_Destroy GUICtrlTab_FindTab GUICtrlTab_GetCurFocus GUICtrlTab_GetCurSel GUICtrlTab_GetDisplayRect GUICtrlTab_GetDisplayRectEx GUICtrlTab_GetExtendedStyle GUICtrlTab_GetImageList GUICtrlTab_GetItem GUICtrlTab_GetItemCount GUICtrlTab_GetItemImage GUICtrlTab_GetItemParam GUICtrlTab_GetItemRect GUICtrlTab_GetItemRectEx GUICtrlTab_GetItemState GUICtrlTab_GetItemText GUICtrlTab_GetRowCount GUICtrlTab_GetToolTips GUICtrlTab_GetUnicodeFormat GUICtrlTab_HighlightItem GUICtrlTab_HitTest GUICtrlTab_InsertItem GUICtrlTab_RemoveImage GUICtrlTab_SetCurFocus GUICtrlTab_SetCurSel GUICtrlTab_SetExtendedStyle GUICtrlTab_SetImageList GUICtrlTab_SetItem GUICtrlTab_SetItemImage GUICtrlTab_SetItemParam GUICtrlTab_SetItemSize GUICtrlTab_SetItemState GUICtrlTab_SetItemText GUICtrlTab_SetMinTabWidth GUICtrlTab_SetPadding GUICtrlTab_SetToolTips GUICtrlTab_SetUnicodeFormat GUICtrlToolbar_AddBitmap GUICtrlToolbar_AddButton GUICtrlToolbar_AddButtonSep GUICtrlToolbar_AddString GUICtrlToolbar_ButtonCount GUICtrlToolbar_CheckButton GUICtrlToolbar_ClickAccel GUICtrlToolbar_ClickButton GUICtrlToolbar_ClickIndex GUICtrlToolbar_CommandToIndex GUICtrlToolbar_Create GUICtrlToolbar_Customize GUICtrlToolbar_DeleteButton GUICtrlToolbar_Destroy GUICtrlToolbar_EnableButton GUICtrlToolbar_FindToolbar GUICtrlToolbar_GetAnchorHighlight GUICtrlToolbar_GetBitmapFlags GUICtrlToolbar_GetButtonBitmap GUICtrlToolbar_GetButtonInfo GUICtrlToolbar_GetButtonInfoEx GUICtrlToolbar_GetButtonParam GUICtrlToolbar_GetButtonRect GUICtrlToolbar_GetButtonRectEx GUICtrlToolbar_GetButtonSize GUICtrlToolbar_GetButtonState GUICtrlToolbar_GetButtonStyle GUICtrlToolbar_GetButtonText GUICtrlToolbar_GetColorScheme GUICtrlToolbar_GetDisabledImageList GUICtrlToolbar_GetExtendedStyle GUICtrlToolbar_GetHotImageList GUICtrlToolbar_GetHotItem GUICtrlToolbar_GetImageList GUICtrlToolbar_GetInsertMark GUICtrlToolbar_GetInsertMarkColor GUICtrlToolbar_GetMaxSize GUICtrlToolbar_GetMetrics GUICtrlToolbar_GetPadding GUICtrlToolbar_GetRows GUICtrlToolbar_GetString GUICtrlToolbar_GetStyle GUICtrlToolbar_GetStyleAltDrag GUICtrlToolbar_GetStyleCustomErase GUICtrlToolbar_GetStyleFlat GUICtrlToolbar_GetStyleList GUICtrlToolbar_GetStyleRegisterDrop GUICtrlToolbar_GetStyleToolTips GUICtrlToolbar_GetStyleTransparent GUICtrlToolbar_GetStyleWrapable GUICtrlToolbar_GetTextRows GUICtrlToolbar_GetToolTips GUICtrlToolbar_GetUnicodeFormat GUICtrlToolbar_HideButton GUICtrlToolbar_HighlightButton GUICtrlToolbar_HitTest GUICtrlToolbar_IndexToCommand GUICtrlToolbar_InsertButton GUICtrlToolbar_InsertMarkHitTest GUICtrlToolbar_IsButtonChecked GUICtrlToolbar_IsButtonEnabled GUICtrlToolbar_IsButtonHidden GUICtrlToolbar_IsButtonHighlighted GUICtrlToolbar_IsButtonIndeterminate GUICtrlToolbar_IsButtonPressed GUICtrlToolbar_LoadBitmap GUICtrlToolbar_LoadImages GUICtrlToolbar_MapAccelerator GUICtrlToolbar_MoveButton GUICtrlToolbar_PressButton GUICtrlToolbar_SetAnchorHighlight GUICtrlToolbar_SetBitmapSize GUICtrlToolbar_SetButtonBitMap GUICtrlToolbar_SetButtonInfo GUICtrlToolbar_SetButtonInfoEx GUICtrlToolbar_SetButtonParam GUICtrlToolbar_SetButtonSize GUICtrlToolbar_SetButtonState GUICtrlToolbar_SetButtonStyle GUICtrlToolbar_SetButtonText GUICtrlToolbar_SetButtonWidth GUICtrlToolbar_SetCmdID GUICtrlToolbar_SetColorScheme GUICtrlToolbar_SetDisabledImageList GUICtrlToolbar_SetDrawTextFlags GUICtrlToolbar_SetExtendedStyle GUICtrlToolbar_SetHotImageList GUICtrlToolbar_SetHotItem GUICtrlToolbar_SetImageList GUICtrlToolbar_SetIndent GUICtrlToolbar_SetIndeterminate GUICtrlToolbar_SetInsertMark GUICtrlToolbar_SetInsertMarkColor GUICtrlToolbar_SetMaxTextRows GUICtrlToolbar_SetMetrics GUICtrlToolbar_SetPadding GUICtrlToolbar_SetParent GUICtrlToolbar_SetRows GUICtrlToolbar_SetStyle GUICtrlToolbar_SetStyleAltDrag GUICtrlToolbar_SetStyleCustomErase GUICtrlToolbar_SetStyleFlat GUICtrlToolbar_SetStyleList GUICtrlToolbar_SetStyleRegisterDrop GUICtrlToolbar_SetStyleToolTips GUICtrlToolbar_SetStyleTransparent GUICtrlToolbar_SetStyleWrapable GUICtrlToolbar_SetToolTips GUICtrlToolbar_SetUnicodeFormat GUICtrlToolbar_SetWindowTheme GUICtrlTreeView_Add GUICtrlTreeView_AddChild GUICtrlTreeView_AddChildFirst GUICtrlTreeView_AddFirst GUICtrlTreeView_BeginUpdate GUICtrlTreeView_ClickItem GUICtrlTreeView_Create GUICtrlTreeView_CreateDragImage GUICtrlTreeView_CreateSolidBitMap GUICtrlTreeView_Delete GUICtrlTreeView_DeleteAll GUICtrlTreeView_DeleteChildren GUICtrlTreeView_Destroy GUICtrlTreeView_DisplayRect GUICtrlTreeView_DisplayRectEx GUICtrlTreeView_EditText GUICtrlTreeView_EndEdit GUICtrlTreeView_EndUpdate GUICtrlTreeView_EnsureVisible GUICtrlTreeView_Expand GUICtrlTreeView_ExpandedOnce GUICtrlTreeView_FindItem GUICtrlTreeView_FindItemEx GUICtrlTreeView_GetBkColor GUICtrlTreeView_GetBold GUICtrlTreeView_GetChecked GUICtrlTreeView_GetChildCount GUICtrlTreeView_GetChildren GUICtrlTreeView_GetCount GUICtrlTreeView_GetCut GUICtrlTreeView_GetDropTarget GUICtrlTreeView_GetEditControl GUICtrlTreeView_GetExpanded GUICtrlTreeView_GetFirstChild GUICtrlTreeView_GetFirstItem GUICtrlTreeView_GetFirstVisible GUICtrlTreeView_GetFocused GUICtrlTreeView_GetHeight GUICtrlTreeView_GetImageIndex GUICtrlTreeView_GetImageListIconHandle GUICtrlTreeView_GetIndent GUICtrlTreeView_GetInsertMarkColor GUICtrlTreeView_GetISearchString GUICtrlTreeView_GetItemByIndex GUICtrlTreeView_GetItemHandle GUICtrlTreeView_GetItemParam GUICtrlTreeView_GetLastChild GUICtrlTreeView_GetLineColor GUICtrlTreeView_GetNext GUICtrlTreeView_GetNextChild GUICtrlTreeView_GetNextSibling GUICtrlTreeView_GetNextVisible GUICtrlTreeView_GetNormalImageList GUICtrlTreeView_GetParentHandle GUICtrlTreeView_GetParentParam GUICtrlTreeView_GetPrev GUICtrlTreeView_GetPrevChild GUICtrlTreeView_GetPrevSibling GUICtrlTreeView_GetPrevVisible GUICtrlTreeView_GetScrollTime GUICtrlTreeView_GetSelected GUICtrlTreeView_GetSelectedImageIndex GUICtrlTreeView_GetSelection GUICtrlTreeView_GetSiblingCount GUICtrlTreeView_GetState GUICtrlTreeView_GetStateImageIndex GUICtrlTreeView_GetStateImageList GUICtrlTreeView_GetText GUICtrlTreeView_GetTextColor GUICtrlTreeView_GetToolTips GUICtrlTreeView_GetTree GUICtrlTreeView_GetUnicodeFormat GUICtrlTreeView_GetVisible GUICtrlTreeView_GetVisibleCount GUICtrlTreeView_HitTest GUICtrlTreeView_HitTestEx GUICtrlTreeView_HitTestItem GUICtrlTreeView_Index GUICtrlTreeView_InsertItem GUICtrlTreeView_IsFirstItem GUICtrlTreeView_IsParent GUICtrlTreeView_Level GUICtrlTreeView_SelectItem GUICtrlTreeView_SelectItemByIndex GUICtrlTreeView_SetBkColor GUICtrlTreeView_SetBold GUICtrlTreeView_SetChecked GUICtrlTreeView_SetCheckedByIndex GUICtrlTreeView_SetChildren GUICtrlTreeView_SetCut GUICtrlTreeView_SetDropTarget GUICtrlTreeView_SetFocused GUICtrlTreeView_SetHeight GUICtrlTreeView_SetIcon GUICtrlTreeView_SetImageIndex GUICtrlTreeView_SetIndent GUICtrlTreeView_SetInsertMark GUICtrlTreeView_SetInsertMarkColor GUICtrlTreeView_SetItemHeight GUICtrlTreeView_SetItemParam GUICtrlTreeView_SetLineColor GUICtrlTreeView_SetNormalImageList GUICtrlTreeView_SetScrollTime GUICtrlTreeView_SetSelected GUICtrlTreeView_SetSelectedImageIndex GUICtrlTreeView_SetState GUICtrlTreeView_SetStateImageIndex GUICtrlTreeView_SetStateImageList GUICtrlTreeView_SetText GUICtrlTreeView_SetTextColor GUICtrlTreeView_SetToolTips GUICtrlTreeView_SetUnicodeFormat GUICtrlTreeView_Sort GUIImageList_Add GUIImageList_AddBitmap GUIImageList_AddIcon GUIImageList_AddMasked GUIImageList_BeginDrag GUIImageList_Copy GUIImageList_Create GUIImageList_Destroy GUIImageList_DestroyIcon GUIImageList_DragEnter GUIImageList_DragLeave GUIImageList_DragMove GUIImageList_Draw GUIImageList_DrawEx GUIImageList_Duplicate GUIImageList_EndDrag GUIImageList_GetBkColor GUIImageList_GetIcon GUIImageList_GetIconHeight GUIImageList_GetIconSize GUIImageList_GetIconSizeEx GUIImageList_GetIconWidth GUIImageList_GetImageCount GUIImageList_GetImageInfoEx GUIImageList_Remove GUIImageList_ReplaceIcon GUIImageList_SetBkColor GUIImageList_SetIconSize GUIImageList_SetImageCount GUIImageList_Swap GUIScrollBars_EnableScrollBar GUIScrollBars_GetScrollBarInfoEx GUIScrollBars_GetScrollBarRect GUIScrollBars_GetScrollBarRGState GUIScrollBars_GetScrollBarXYLineButton GUIScrollBars_GetScrollBarXYThumbBottom GUIScrollBars_GetScrollBarXYThumbTop GUIScrollBars_GetScrollInfo GUIScrollBars_GetScrollInfoEx GUIScrollBars_GetScrollInfoMax GUIScrollBars_GetScrollInfoMin GUIScrollBars_GetScrollInfoPage GUIScrollBars_GetScrollInfoPos GUIScrollBars_GetScrollInfoTrackPos GUIScrollBars_GetScrollPos GUIScrollBars_GetScrollRange GUIScrollBars_Init GUIScrollBars_ScrollWindow GUIScrollBars_SetScrollInfo GUIScrollBars_SetScrollInfoMax GUIScrollBars_SetScrollInfoMin GUIScrollBars_SetScrollInfoPage GUIScrollBars_SetScrollInfoPos GUIScrollBars_SetScrollRange GUIScrollBars_ShowScrollBar GUIToolTip_Activate GUIToolTip_AddTool GUIToolTip_AdjustRect GUIToolTip_BitsToTTF GUIToolTip_Create GUIToolTip_Deactivate GUIToolTip_DelTool GUIToolTip_Destroy GUIToolTip_EnumTools GUIToolTip_GetBubbleHeight GUIToolTip_GetBubbleSize GUIToolTip_GetBubbleWidth GUIToolTip_GetCurrentTool GUIToolTip_GetDelayTime GUIToolTip_GetMargin GUIToolTip_GetMarginEx GUIToolTip_GetMaxTipWidth GUIToolTip_GetText GUIToolTip_GetTipBkColor GUIToolTip_GetTipTextColor GUIToolTip_GetTitleBitMap GUIToolTip_GetTitleText GUIToolTip_GetToolCount GUIToolTip_GetToolInfo GUIToolTip_HitTest GUIToolTip_NewToolRect GUIToolTip_Pop GUIToolTip_PopUp GUIToolTip_SetDelayTime GUIToolTip_SetMargin GUIToolTip_SetMaxTipWidth GUIToolTip_SetTipBkColor GUIToolTip_SetTipTextColor GUIToolTip_SetTitle GUIToolTip_SetToolInfo GUIToolTip_SetWindowTheme GUIToolTip_ToolExists GUIToolTip_ToolToArray GUIToolTip_TrackActivate GUIToolTip_TrackPosition GUIToolTip_Update GUIToolTip_UpdateTipText HexToString IEAction IEAttach IEBodyReadHTML IEBodyReadText IEBodyWriteHTML IECreate IECreateEmbedded IEDocGetObj IEDocInsertHTML IEDocInsertText IEDocReadHTML IEDocWriteHTML IEErrorNotify IEFormElementCheckBoxSelect IEFormElementGetCollection IEFormElementGetObjByName IEFormElementGetValue IEFormElementOptionSelect IEFormElementRadioSelect IEFormElementSetValue IEFormGetCollection IEFormGetObjByName IEFormImageClick IEFormReset IEFormSubmit IEFrameGetCollection IEFrameGetObjByName IEGetObjById IEGetObjByName IEHeadInsertEventScript IEImgClick IEImgGetCollection IEIsFrameSet IELinkClickByIndex IELinkClickByText IELinkGetCollection IELoadWait IELoadWaitTimeout IENavigate IEPropertyGet IEPropertySet IEQuit IETableGetCollection IETableWriteToArray IETagNameAllGetCollection IETagNameGetCollection IE_Example IE_Introduction IE_VersionInfo INetExplorerCapable INetGetSource INetMail INetSmtpMail IsPressed MathCheckDiv Max MemGlobalAlloc MemGlobalFree MemGlobalLock MemGlobalSize MemGlobalUnlock MemMoveMemory MemVirtualAlloc MemVirtualAllocEx MemVirtualFree MemVirtualFreeEx Min MouseTrap NamedPipes_CallNamedPipe NamedPipes_ConnectNamedPipe NamedPipes_CreateNamedPipe NamedPipes_CreatePipe NamedPipes_DisconnectNamedPipe NamedPipes_GetNamedPipeHandleState NamedPipes_GetNamedPipeInfo NamedPipes_PeekNamedPipe NamedPipes_SetNamedPipeHandleState NamedPipes_TransactNamedPipe NamedPipes_WaitNamedPipe Net_Share_ConnectionEnum Net_Share_FileClose Net_Share_FileEnum Net_Share_FileGetInfo Net_Share_PermStr Net_Share_ResourceStr Net_Share_SessionDel Net_Share_SessionEnum Net_Share_SessionGetInfo Net_Share_ShareAdd Net_Share_ShareCheck Net_Share_ShareDel Net_Share_ShareEnum Net_Share_ShareGetInfo Net_Share_ShareSetInfo Net_Share_StatisticsGetSvr Net_Share_StatisticsGetWrk Now NowCalc NowCalcDate NowDate NowTime PathFull PathGetRelative PathMake PathSplit ProcessGetName ProcessGetPriority Radian ReplaceStringInFile RunDos ScreenCapture_Capture ScreenCapture_CaptureWnd ScreenCapture_SaveImage ScreenCapture_SetBMPFormat ScreenCapture_SetJPGQuality ScreenCapture_SetTIFColorDepth ScreenCapture_SetTIFCompression Security__AdjustTokenPrivileges Security__CreateProcessWithToken Security__DuplicateTokenEx Security__GetAccountSid Security__GetLengthSid Security__GetTokenInformation Security__ImpersonateSelf Security__IsValidSid Security__LookupAccountName Security__LookupAccountSid Security__LookupPrivilegeValue Security__OpenProcessToken Security__OpenThreadToken Security__OpenThreadTokenEx Security__SetPrivilege Security__SetTokenInformation Security__SidToStringSid Security__SidTypeStr Security__StringSidToSid SendMessage SendMessageA SetDate SetTime Singleton SoundClose SoundLength SoundOpen SoundPause SoundPlay SoundPos SoundResume SoundSeek SoundStatus SoundStop SQLite_Changes SQLite_Close SQLite_Display2DResult SQLite_Encode SQLite_ErrCode SQLite_ErrMsg SQLite_Escape SQLite_Exec SQLite_FastEncode SQLite_FastEscape SQLite_FetchData SQLite_FetchNames SQLite_GetTable SQLite_GetTable2d SQLite_LastInsertRowID SQLite_LibVersion SQLite_Open SQLite_Query SQLite_QueryFinalize SQLite_QueryReset SQLite_QuerySingleRow SQLite_SafeMode SQLite_SetTimeout SQLite_Shutdown SQLite_SQLiteExe SQLite_Startup SQLite_TotalChanges StringBetween StringExplode StringInsert StringProper StringRepeat StringTitleCase StringToHex TCPIpToName TempFile TicksToTime Timer_Diff Timer_GetIdleTime Timer_GetTimerID Timer_Init Timer_KillAllTimers Timer_KillTimer Timer_SetTimer TimeToTicks VersionCompare viClose viExecCommand viFindGpib viGpibBusReset viGTL viInteractiveControl viOpen viSetAttribute viSetTimeout WeekNumberISO WinAPI_AbortPath WinAPI_ActivateKeyboardLayout WinAPI_AddClipboardFormatListener WinAPI_AddFontMemResourceEx WinAPI_AddFontResourceEx WinAPI_AddIconOverlay WinAPI_AddIconTransparency WinAPI_AddMRUString WinAPI_AdjustBitmap WinAPI_AdjustTokenPrivileges WinAPI_AdjustWindowRectEx WinAPI_AlphaBlend WinAPI_AngleArc WinAPI_AnimateWindow WinAPI_Arc WinAPI_ArcTo WinAPI_ArrayToStruct WinAPI_AssignProcessToJobObject WinAPI_AssocGetPerceivedType WinAPI_AssocQueryString WinAPI_AttachConsole WinAPI_AttachThreadInput WinAPI_BackupRead WinAPI_BackupReadAbort WinAPI_BackupSeek WinAPI_BackupWrite WinAPI_BackupWriteAbort WinAPI_Beep WinAPI_BeginBufferedPaint WinAPI_BeginDeferWindowPos WinAPI_BeginPaint WinAPI_BeginPath WinAPI_BeginUpdateResource WinAPI_BitBlt WinAPI_BringWindowToTop WinAPI_BroadcastSystemMessage WinAPI_BrowseForFolderDlg WinAPI_BufferedPaintClear WinAPI_BufferedPaintInit WinAPI_BufferedPaintSetAlpha WinAPI_BufferedPaintUnInit WinAPI_CallNextHookEx WinAPI_CallWindowProc WinAPI_CallWindowProcW WinAPI_CascadeWindows WinAPI_ChangeWindowMessageFilterEx WinAPI_CharToOem WinAPI_ChildWindowFromPointEx WinAPI_ClientToScreen WinAPI_ClipCursor WinAPI_CloseDesktop WinAPI_CloseEnhMetaFile WinAPI_CloseFigure WinAPI_CloseHandle WinAPI_CloseThemeData WinAPI_CloseWindow WinAPI_CloseWindowStation WinAPI_CLSIDFromProgID WinAPI_CoInitialize WinAPI_ColorAdjustLuma WinAPI_ColorHLSToRGB WinAPI_ColorRGBToHLS WinAPI_CombineRgn WinAPI_CombineTransform WinAPI_CommandLineToArgv WinAPI_CommDlgExtendedError WinAPI_CommDlgExtendedErrorEx WinAPI_CompareString WinAPI_CompressBitmapBits WinAPI_CompressBuffer WinAPI_ComputeCrc32 WinAPI_ConfirmCredentials WinAPI_CopyBitmap WinAPI_CopyCursor WinAPI_CopyEnhMetaFile WinAPI_CopyFileEx WinAPI_CopyIcon WinAPI_CopyImage WinAPI_CopyRect WinAPI_CopyStruct WinAPI_CoTaskMemAlloc WinAPI_CoTaskMemFree WinAPI_CoTaskMemRealloc WinAPI_CoUninitialize WinAPI_Create32BitHBITMAP WinAPI_Create32BitHICON WinAPI_CreateANDBitmap WinAPI_CreateBitmap WinAPI_CreateBitmapIndirect WinAPI_CreateBrushIndirect WinAPI_CreateBuffer WinAPI_CreateBufferFromStruct WinAPI_CreateCaret WinAPI_CreateColorAdjustment WinAPI_CreateCompatibleBitmap WinAPI_CreateCompatibleBitmapEx WinAPI_CreateCompatibleDC WinAPI_CreateDesktop WinAPI_CreateDIB WinAPI_CreateDIBColorTable WinAPI_CreateDIBitmap WinAPI_CreateDIBSection WinAPI_CreateDirectory WinAPI_CreateDirectoryEx WinAPI_CreateEllipticRgn 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BabyMonsterGroupB Back Background BackgroundTasksSettings Backslash Backsubstitution Backward Band BandpassFilter BandstopFilter BarabasiAlbertGraphDistribution BarChart BarChart3D BarLegend BarlowProschanImportance BarnesG BarOrigin BarSpacing BartlettHannWindow BartlettWindow BaseForm Baseline BaselinePosition BaseStyle BatesDistribution BattleLemarieWavelet Because BeckmannDistribution Beep Before Begin BeginDialogPacket BeginFrontEndInteractionPacket BeginPackage BellB BellY Below BenfordDistribution BeniniDistribution BenktanderGibratDistribution BenktanderWeibullDistribution BernoulliB BernoulliDistribution BernoulliGraphDistribution BernoulliProcess BernsteinBasis BesselFilterModel BesselI BesselJ BesselJZero BesselK BesselY BesselYZero Beta BetaBinomialDistribution BetaDistribution BetaNegativeBinomialDistribution BetaPrimeDistribution BetaRegularized BetweennessCentrality BezierCurve BezierCurve3DBox BezierCurve3DBoxOptions BezierCurveBox BezierCurveBoxOptions BezierFunction BilateralFilter Binarize BinaryFormat BinaryImageQ BinaryRead BinaryReadList BinaryWrite BinCounts BinLists Binomial BinomialDistribution BinomialProcess BinormalDistribution BiorthogonalSplineWavelet BipartiteGraphQ BirnbaumImportance BirnbaumSaundersDistribution BitAnd BitClear BitGet BitLength BitNot BitOr BitSet BitShiftLeft BitShiftRight BitXor Black BlackmanHarrisWindow BlackmanNuttallWindow BlackmanWindow Blank BlankForm BlankNullSequence BlankSequence Blend Block BlockRandom BlomqvistBeta BlomqvistBetaTest Blue Blur BodePlot BohmanWindow Bold Bookmarks Boole BooleanConsecutiveFunction BooleanConvert BooleanCountingFunction BooleanFunction BooleanGraph BooleanMaxterms BooleanMinimize BooleanMinterms Booleans BooleanTable BooleanVariables BorderDimensions BorelTannerDistribution Bottom BottomHatTransform BoundaryStyle Bounds Box BoxBaselineShift BoxData BoxDimensions Boxed Boxes BoxForm BoxFormFormatTypes BoxFrame BoxID BoxMargins BoxMatrix BoxRatios BoxRotation BoxRotationPoint BoxStyle BoxWhiskerChart Bra BracketingBar BraKet BrayCurtisDistance BreadthFirstScan Break Brown BrownForsytheTest BrownianBridgeProcess BrowserCategory BSplineBasis BSplineCurve BSplineCurve3DBox BSplineCurveBox BSplineCurveBoxOptions BSplineFunction BSplineSurface BSplineSurface3DBox BubbleChart BubbleChart3D BubbleScale BubbleSizes BulletGauge BusinessDayQ ButterflyGraph ButterworthFilterModel Button ButtonBar ButtonBox ButtonBoxOptions ButtonCell ButtonContents ButtonData ButtonEvaluator ButtonExpandable ButtonFrame ButtonFunction ButtonMargins ButtonMinHeight ButtonNote ButtonNotebook ButtonSource ButtonStyle ButtonStyleMenuListing Byte ByteCount ByteOrdering C CachedValue CacheGraphics CalendarData CalendarType CallPacket CanberraDistance Cancel CancelButton CandlestickChart Cap CapForm CapitalDifferentialD CardinalBSplineBasis CarmichaelLambda Cases Cashflow Casoratian Catalan CatalanNumber Catch CauchyDistribution CauchyWindow CayleyGraph CDF CDFDeploy CDFInformation CDFWavelet Ceiling Cell CellAutoOverwrite CellBaseline CellBoundingBox CellBracketOptions CellChangeTimes CellContents CellContext CellDingbat CellDynamicExpression CellEditDuplicate CellElementsBoundingBox CellElementSpacings CellEpilog CellEvaluationDuplicate CellEvaluationFunction CellEventActions CellFrame CellFrameColor CellFrameLabelMargins CellFrameLabels CellFrameMargins CellGroup CellGroupData CellGrouping CellGroupingRules CellHorizontalScrolling CellID CellLabel CellLabelAutoDelete CellLabelMargins CellLabelPositioning CellMargins CellObject CellOpen CellPrint CellProlog Cells CellSize CellStyle CellTags CellularAutomaton CensoredDistribution Censoring Center CenterDot CentralMoment CentralMomentGeneratingFunction CForm ChampernowneNumber ChanVeseBinarize Character CharacterEncoding CharacterEncodingsPath CharacteristicFunction CharacteristicPolynomial CharacterRange Characters ChartBaseStyle ChartElementData ChartElementDataFunction ChartElementFunction ChartElements ChartLabels ChartLayout ChartLegends ChartStyle Chebyshev1FilterModel Chebyshev2FilterModel ChebyshevDistance ChebyshevT ChebyshevU Check CheckAbort CheckAll Checkbox CheckboxBar CheckboxBox CheckboxBoxOptions ChemicalData ChessboardDistance ChiDistribution ChineseRemainder ChiSquareDistribution ChoiceButtons ChoiceDialog CholeskyDecomposition Chop Circle CircleBox CircleDot CircleMinus CirclePlus CircleTimes CirculantGraph CityData Clear ClearAll ClearAttributes ClearSystemCache ClebschGordan ClickPane Clip ClipboardNotebook ClipFill ClippingStyle ClipPlanes ClipRange Clock ClockGauge ClockwiseContourIntegral Close Closed CloseKernels ClosenessCentrality Closing ClosingAutoSave ClosingEvent ClusteringComponents CMYKColor Coarse Coefficient CoefficientArrays CoefficientDomain CoefficientList CoefficientRules CoifletWavelet Collect Colon ColonForm ColorCombine ColorConvert ColorData ColorDataFunction ColorFunction ColorFunctionScaling Colorize ColorNegate ColorOutput ColorProfileData ColorQuantize ColorReplace ColorRules ColorSelectorSettings ColorSeparate ColorSetter ColorSetterBox ColorSetterBoxOptions ColorSlider ColorSpace Column ColumnAlignments ColumnBackgrounds ColumnForm ColumnLines ColumnsEqual ColumnSpacings ColumnWidths CommonDefaultFormatTypes Commonest CommonestFilter CommonUnits CommunityBoundaryStyle CommunityGraphPlot CommunityLabels CommunityRegionStyle CompatibleUnitQ CompilationOptions CompilationTarget Compile Compiled CompiledFunction Complement CompleteGraph CompleteGraphQ CompleteKaryTree CompletionsListPacket Complex Complexes ComplexExpand ComplexInfinity ComplexityFunction ComponentMeasurements ComponentwiseContextMenu Compose ComposeList ComposeSeries Composition CompoundExpression CompoundPoissonDistribution CompoundPoissonProcess CompoundRenewalProcess Compress CompressedData Condition ConditionalExpression Conditioned Cone ConeBox ConfidenceLevel ConfidenceRange ConfidenceTransform ConfigurationPath Congruent Conjugate ConjugateTranspose Conjunction Connect ConnectedComponents ConnectedGraphQ ConnesWindow ConoverTest ConsoleMessage ConsoleMessagePacket ConsolePrint Constant ConstantArray Constants ConstrainedMax ConstrainedMin ContentPadding ContentsBoundingBox ContentSelectable ContentSize Context ContextMenu Contexts ContextToFilename ContextToFileName Continuation Continue ContinuedFraction ContinuedFractionK ContinuousAction ContinuousMarkovProcess ContinuousTimeModelQ ContinuousWaveletData ContinuousWaveletTransform ContourDetect ContourGraphics ContourIntegral ContourLabels ContourLines ContourPlot ContourPlot3D Contours ContourShading ContourSmoothing ContourStyle ContraharmonicMean Control ControlActive ControlAlignment ControllabilityGramian ControllabilityMatrix ControllableDecomposition ControllableModelQ ControllerDuration ControllerInformation ControllerInformationData ControllerLinking ControllerManipulate ControllerMethod ControllerPath ControllerState ControlPlacement ControlsRendering ControlType Convergents ConversionOptions ConversionRules ConvertToBitmapPacket ConvertToPostScript ConvertToPostScriptPacket Convolve ConwayGroupCo1 ConwayGroupCo2 ConwayGroupCo3 CoordinateChartData CoordinatesToolOptions CoordinateTransform CoordinateTransformData CoprimeQ Coproduct CopulaDistribution Copyable CopyDirectory CopyFile CopyTag CopyToClipboard CornerFilter CornerNeighbors Correlation CorrelationDistance CorrelationFunction CorrelationTest Cos Cosh CoshIntegral CosineDistance CosineWindow CosIntegral Cot Coth Count CounterAssignments CounterBox CounterBoxOptions CounterClockwiseContourIntegral CounterEvaluator CounterFunction CounterIncrements CounterStyle CounterStyleMenuListing CountRoots CountryData Covariance CovarianceEstimatorFunction CovarianceFunction CoxianDistribution CoxIngersollRossProcess CoxModel CoxModelFit CramerVonMisesTest CreateArchive CreateDialog CreateDirectory CreateDocument CreateIntermediateDirectories CreatePalette CreatePalettePacket CreateScheduledTask CreateTemporary CreateWindow CriticalityFailureImportance CriticalitySuccessImportance CriticalSection Cross CrossingDetect CrossMatrix Csc Csch CubeRoot Cubics Cuboid CuboidBox Cumulant CumulantGeneratingFunction Cup CupCap Curl CurlyDoubleQuote CurlyQuote CurrentImage CurrentlySpeakingPacket CurrentValue CurvatureFlowFilter CurveClosed Cyan CycleGraph CycleIndexPolynomial Cycles CyclicGroup Cyclotomic Cylinder CylinderBox CylindricalDecomposition D DagumDistribution DamerauLevenshteinDistance DampingFactor Darker Dashed Dashing DataCompression DataDistribution DataRange DataReversed Date DateDelimiters DateDifference DateFunction DateList DateListLogPlot DateListPlot DatePattern DatePlus DateRange DateString DateTicksFormat DaubechiesWavelet DavisDistribution DawsonF DayCount DayCountConvention DayMatchQ DayName DayPlus DayRange DayRound DeBruijnGraph Debug DebugTag Decimal DeclareKnownSymbols DeclarePackage Decompose Decrement DedekindEta Default DefaultAxesStyle DefaultBaseStyle DefaultBoxStyle DefaultButton DefaultColor DefaultControlPlacement DefaultDuplicateCellStyle DefaultDuration DefaultElement DefaultFaceGridsStyle DefaultFieldHintStyle DefaultFont DefaultFontProperties DefaultFormatType DefaultFormatTypeForStyle DefaultFrameStyle DefaultFrameTicksStyle DefaultGridLinesStyle DefaultInlineFormatType DefaultInputFormatType DefaultLabelStyle DefaultMenuStyle DefaultNaturalLanguage DefaultNewCellStyle DefaultNewInlineCellStyle DefaultNotebook DefaultOptions DefaultOutputFormatType DefaultStyle DefaultStyleDefinitions DefaultTextFormatType DefaultTextInlineFormatType DefaultTicksStyle DefaultTooltipStyle DefaultValues Defer DefineExternal DefineInputStreamMethod DefineOutputStreamMethod Definition Degree DegreeCentrality DegreeGraphDistribution DegreeLexicographic DegreeReverseLexicographic Deinitialization Del Deletable Delete DeleteBorderComponents DeleteCases DeleteContents DeleteDirectory DeleteDuplicates DeleteFile DeleteSmallComponents DeleteWithContents DeletionWarning Delimiter DelimiterFlashTime DelimiterMatching Delimiters Denominator DensityGraphics DensityHistogram DensityPlot DependentVariables Deploy Deployed Depth DepthFirstScan Derivative DerivativeFilter DescriptorStateSpace DesignMatrix Det DGaussianWavelet DiacriticalPositioning Diagonal DiagonalMatrix Dialog DialogIndent DialogInput DialogLevel DialogNotebook DialogProlog DialogReturn DialogSymbols Diamond DiamondMatrix DiceDissimilarity DictionaryLookup DifferenceDelta DifferenceOrder DifferenceRoot DifferenceRootReduce Differences DifferentialD DifferentialRoot DifferentialRootReduce DifferentiatorFilter DigitBlock DigitBlockMinimum DigitCharacter DigitCount DigitQ DihedralGroup Dilation Dimensions DiracComb DiracDelta DirectedEdge DirectedEdges DirectedGraph DirectedGraphQ DirectedInfinity Direction Directive Directory DirectoryName DirectoryQ DirectoryStack DirichletCharacter DirichletConvolve DirichletDistribution DirichletL DirichletTransform DirichletWindow DisableConsolePrintPacket DiscreteChirpZTransform DiscreteConvolve DiscreteDelta DiscreteHadamardTransform DiscreteIndicator DiscreteLQEstimatorGains DiscreteLQRegulatorGains DiscreteLyapunovSolve DiscreteMarkovProcess DiscretePlot DiscretePlot3D DiscreteRatio DiscreteRiccatiSolve DiscreteShift DiscreteTimeModelQ DiscreteUniformDistribution DiscreteVariables DiscreteWaveletData DiscreteWaveletPacketTransform DiscreteWaveletTransform Discriminant Disjunction Disk DiskBox DiskMatrix Dispatch DispersionEstimatorFunction Display DisplayAllSteps DisplayEndPacket DisplayFlushImagePacket DisplayForm DisplayFunction DisplayPacket DisplayRules DisplaySetSizePacket DisplayString DisplayTemporary DisplayWith DisplayWithRef DisplayWithVariable DistanceFunction DistanceTransform Distribute Distributed DistributedContexts DistributeDefinitions DistributionChart DistributionDomain DistributionFitTest DistributionParameterAssumptions DistributionParameterQ Dithering Div Divergence Divide DivideBy Dividers Divisible Divisors DivisorSigma DivisorSum DMSList DMSString Do DockedCells DocumentNotebook DominantColors DOSTextFormat Dot DotDashed DotEqual Dotted DoubleBracketingBar DoubleContourIntegral DoubleDownArrow DoubleLeftArrow DoubleLeftRightArrow DoubleLeftTee DoubleLongLeftArrow DoubleLongLeftRightArrow DoubleLongRightArrow DoubleRightArrow DoubleRightTee DoubleUpArrow DoubleUpDownArrow DoubleVerticalBar DoublyInfinite Down DownArrow DownArrowBar DownArrowUpArrow DownLeftRightVector DownLeftTeeVector DownLeftVector DownLeftVectorBar DownRightTeeVector DownRightVector DownRightVectorBar Downsample DownTee DownTeeArrow DownValues DragAndDrop DrawEdges DrawFrontFaces DrawHighlighted Drop DSolve Dt DualLinearProgramming DualSystemsModel DumpGet DumpSave DuplicateFreeQ Dynamic DynamicBox DynamicBoxOptions DynamicEvaluationTimeout DynamicLocation DynamicModule DynamicModuleBox DynamicModuleBoxOptions DynamicModuleParent DynamicModuleValues DynamicName DynamicNamespace DynamicReference DynamicSetting DynamicUpdating DynamicWrapper DynamicWrapperBox DynamicWrapperBoxOptions E EccentricityCentrality EdgeAdd EdgeBetweennessCentrality EdgeCapacity EdgeCapForm EdgeColor EdgeConnectivity EdgeCost EdgeCount EdgeCoverQ EdgeDashing EdgeDelete EdgeDetect EdgeForm EdgeIndex EdgeJoinForm EdgeLabeling EdgeLabels EdgeLabelStyle EdgeList EdgeOpacity EdgeQ EdgeRenderingFunction EdgeRules EdgeShapeFunction EdgeStyle EdgeThickness EdgeWeight Editable EditButtonSettings EditCellTagsSettings EditDistance EffectiveInterest Eigensystem Eigenvalues EigenvectorCentrality Eigenvectors Element ElementData Eliminate EliminationOrder EllipticE EllipticExp EllipticExpPrime EllipticF EllipticFilterModel EllipticK EllipticLog EllipticNomeQ EllipticPi EllipticReducedHalfPeriods EllipticTheta EllipticThetaPrime EmitSound EmphasizeSyntaxErrors EmpiricalDistribution Empty EmptyGraphQ EnableConsolePrintPacket Enabled Encode End EndAdd EndDialogPacket EndFrontEndInteractionPacket EndOfFile EndOfLine EndOfString EndPackage EngineeringForm Enter EnterExpressionPacket EnterTextPacket Entropy EntropyFilter Environment Epilog Equal EqualColumns EqualRows EqualTilde EquatedTo Equilibrium EquirippleFilterKernel Equivalent Erf Erfc Erfi ErlangB ErlangC ErlangDistribution Erosion ErrorBox ErrorBoxOptions ErrorNorm ErrorPacket ErrorsDialogSettings EstimatedDistribution EstimatedProcess EstimatorGains EstimatorRegulator EuclideanDistance EulerE EulerGamma EulerianGraphQ EulerPhi Evaluatable Evaluate Evaluated EvaluatePacket EvaluationCell EvaluationCompletionAction EvaluationElements EvaluationMode EvaluationMonitor EvaluationNotebook EvaluationObject EvaluationOrder Evaluator EvaluatorNames EvenQ EventData EventEvaluator EventHandler EventHandlerTag EventLabels ExactBlackmanWindow ExactNumberQ ExactRootIsolation ExampleData Except ExcludedForms ExcludePods Exclusions ExclusionsStyle Exists Exit ExitDialog Exp Expand ExpandAll ExpandDenominator ExpandFileName ExpandNumerator Expectation ExpectationE ExpectedValue ExpGammaDistribution ExpIntegralE ExpIntegralEi Exponent ExponentFunction ExponentialDistribution ExponentialFamily ExponentialGeneratingFunction ExponentialMovingAverage ExponentialPowerDistribution ExponentPosition ExponentStep Export ExportAutoReplacements ExportPacket ExportString Expression ExpressionCell ExpressionPacket ExpToTrig ExtendedGCD Extension ExtentElementFunction ExtentMarkers ExtentSize ExternalCall ExternalDataCharacterEncoding Extract ExtractArchive ExtremeValueDistribution FaceForm FaceGrids FaceGridsStyle Factor FactorComplete Factorial Factorial2 FactorialMoment FactorialMomentGeneratingFunction FactorialPower FactorInteger FactorList FactorSquareFree FactorSquareFreeList FactorTerms FactorTermsList Fail FailureDistribution False FARIMAProcess FEDisableConsolePrintPacket FeedbackSector FeedbackSectorStyle FeedbackType FEEnableConsolePrintPacket Fibonacci FieldHint FieldHintStyle FieldMasked FieldSize File FileBaseName FileByteCount FileDate FileExistsQ FileExtension FileFormat FileHash FileInformation FileName FileNameDepth FileNameDialogSettings FileNameDrop FileNameJoin FileNames FileNameSetter FileNameSplit FileNameTake FilePrint FileType FilledCurve FilledCurveBox Filling FillingStyle FillingTransform FilterRules FinancialBond FinancialData FinancialDerivative FinancialIndicator Find FindArgMax FindArgMin FindClique FindClusters FindCurvePath FindDistributionParameters FindDivisions FindEdgeCover FindEdgeCut FindEulerianCycle FindFaces FindFile FindFit FindGeneratingFunction FindGeoLocation FindGeometricTransform FindGraphCommunities FindGraphIsomorphism FindGraphPartition FindHamiltonianCycle FindIndependentEdgeSet FindIndependentVertexSet FindInstance FindIntegerNullVector FindKClan FindKClique FindKClub FindKPlex FindLibrary FindLinearRecurrence FindList FindMaximum FindMaximumFlow FindMaxValue FindMinimum FindMinimumCostFlow FindMinimumCut FindMinValue FindPermutation FindPostmanTour FindProcessParameters FindRoot FindSequenceFunction FindSettings FindShortestPath FindShortestTour FindThreshold FindVertexCover FindVertexCut Fine FinishDynamic FiniteAbelianGroupCount FiniteGroupCount FiniteGroupData First FirstPassageTimeDistribution FischerGroupFi22 FischerGroupFi23 FischerGroupFi24Prime FisherHypergeometricDistribution FisherRatioTest FisherZDistribution Fit FitAll FittedModel FixedPoint FixedPointList FlashSelection Flat Flatten FlattenAt FlatTopWindow FlipView Floor FlushPrintOutputPacket Fold FoldList Font FontColor FontFamily FontForm FontName FontOpacity FontPostScriptName FontProperties FontReencoding FontSize FontSlant FontSubstitutions FontTracking FontVariations FontWeight For ForAll Format FormatRules FormatType FormatTypeAutoConvert FormatValues FormBox FormBoxOptions FortranForm Forward ForwardBackward Fourier FourierCoefficient FourierCosCoefficient FourierCosSeries FourierCosTransform FourierDCT FourierDCTFilter FourierDCTMatrix FourierDST FourierDSTMatrix FourierMatrix FourierParameters FourierSequenceTransform FourierSeries FourierSinCoefficient FourierSinSeries FourierSinTransform FourierTransform FourierTrigSeries FractionalBrownianMotionProcess FractionalPart FractionBox FractionBoxOptions FractionLine Frame FrameBox FrameBoxOptions Framed FrameInset FrameLabel Frameless FrameMargins FrameStyle FrameTicks FrameTicksStyle FRatioDistribution FrechetDistribution FreeQ FrequencySamplingFilterKernel FresnelC FresnelS Friday FrobeniusNumber FrobeniusSolve FromCharacterCode FromCoefficientRules FromContinuedFraction FromDate FromDigits FromDMS Front FrontEndDynamicExpression FrontEndEventActions FrontEndExecute FrontEndObject FrontEndResource FrontEndResourceString FrontEndStackSize FrontEndToken FrontEndTokenExecute FrontEndValueCache FrontEndVersion FrontFaceColor FrontFaceOpacity Full FullAxes FullDefinition FullForm FullGraphics FullOptions FullSimplify Function FunctionExpand FunctionInterpolation FunctionSpace FussellVeselyImportance GaborFilter GaborMatrix GaborWavelet GainMargins GainPhaseMargins Gamma GammaDistribution GammaRegularized GapPenalty Gather GatherBy GaugeFaceElementFunction GaugeFaceStyle GaugeFrameElementFunction GaugeFrameSize GaugeFrameStyle GaugeLabels GaugeMarkers GaugeStyle GaussianFilter GaussianIntegers GaussianMatrix GaussianWindow GCD GegenbauerC General GeneralizedLinearModelFit GenerateConditions GeneratedCell GeneratedParameters GeneratingFunction Generic GenericCylindricalDecomposition GenomeData GenomeLookup GeodesicClosing GeodesicDilation GeodesicErosion GeodesicOpening GeoDestination GeodesyData GeoDirection GeoDistance GeoGridPosition GeometricBrownianMotionProcess GeometricDistribution GeometricMean GeometricMeanFilter GeometricTransformation GeometricTransformation3DBox GeometricTransformation3DBoxOptions GeometricTransformationBox GeometricTransformationBoxOptions GeoPosition GeoPositionENU GeoPositionXYZ GeoProjectionData GestureHandler GestureHandlerTag Get GetBoundingBoxSizePacket GetContext GetEnvironment GetFileName GetFrontEndOptionsDataPacket GetLinebreakInformationPacket GetMenusPacket GetPageBreakInformationPacket Glaisher GlobalClusteringCoefficient GlobalPreferences GlobalSession Glow GoldenRatio GompertzMakehamDistribution GoodmanKruskalGamma GoodmanKruskalGammaTest Goto Grad Gradient GradientFilter GradientOrientationFilter Graph GraphAssortativity GraphCenter GraphComplement GraphData GraphDensity GraphDiameter GraphDifference GraphDisjointUnion GraphDistance GraphDistanceMatrix GraphElementData GraphEmbedding GraphHighlight GraphHighlightStyle GraphHub Graphics Graphics3D Graphics3DBox Graphics3DBoxOptions GraphicsArray GraphicsBaseline GraphicsBox GraphicsBoxOptions GraphicsColor GraphicsColumn GraphicsComplex GraphicsComplex3DBox GraphicsComplex3DBoxOptions GraphicsComplexBox GraphicsComplexBoxOptions GraphicsContents GraphicsData GraphicsGrid GraphicsGridBox GraphicsGroup GraphicsGroup3DBox GraphicsGroup3DBoxOptions GraphicsGroupBox GraphicsGroupBoxOptions GraphicsGrouping GraphicsHighlightColor GraphicsRow GraphicsSpacing GraphicsStyle GraphIntersection GraphLayout GraphLinkEfficiency GraphPeriphery GraphPlot GraphPlot3D GraphPower GraphPropertyDistribution GraphQ GraphRadius GraphReciprocity GraphRoot GraphStyle GraphUnion Gray GrayLevel GreatCircleDistance Greater GreaterEqual GreaterEqualLess GreaterFullEqual GreaterGreater GreaterLess GreaterSlantEqual GreaterTilde Green Grid GridBaseline GridBox GridBoxAlignment GridBoxBackground GridBoxDividers GridBoxFrame GridBoxItemSize GridBoxItemStyle GridBoxOptions GridBoxSpacings GridCreationSettings GridDefaultElement GridElementStyleOptions GridFrame GridFrameMargins GridGraph GridLines GridLinesStyle GroebnerBasis GroupActionBase GroupCentralizer GroupElementFromWord GroupElementPosition GroupElementQ GroupElements GroupElementToWord GroupGenerators GroupMultiplicationTable GroupOrbits GroupOrder GroupPageBreakWithin GroupSetwiseStabilizer GroupStabilizer GroupStabilizerChain Gudermannian GumbelDistribution HaarWavelet HadamardMatrix HalfNormalDistribution HamiltonianGraphQ HammingDistance HammingWindow HankelH1 HankelH2 HankelMatrix HannPoissonWindow HannWindow HaradaNortonGroupHN HararyGraph HarmonicMean HarmonicMeanFilter HarmonicNumber Hash HashTable Haversine HazardFunction Head HeadCompose Heads HeavisideLambda HeavisidePi HeavisideTheta HeldGroupHe HeldPart HelpBrowserLookup HelpBrowserNotebook HelpBrowserSettings HermiteDecomposition HermiteH HermitianMatrixQ HessenbergDecomposition Hessian HexadecimalCharacter Hexahedron HexahedronBox HexahedronBoxOptions HiddenSurface HighlightGraph HighlightImage HighpassFilter HigmanSimsGroupHS HilbertFilter HilbertMatrix Histogram Histogram3D HistogramDistribution HistogramList HistogramTransform HistogramTransformInterpolation HitMissTransform HITSCentrality HodgeDual HoeffdingD HoeffdingDTest Hold HoldAll HoldAllComplete HoldComplete HoldFirst HoldForm HoldPattern HoldRest HolidayCalendar HomeDirectory HomePage Horizontal HorizontalForm HorizontalGauge HorizontalScrollPosition HornerForm HotellingTSquareDistribution HoytDistribution HTMLSave Hue HumpDownHump HumpEqual HurwitzLerchPhi HurwitzZeta HyperbolicDistribution HypercubeGraph HyperexponentialDistribution Hyperfactorial Hypergeometric0F1 Hypergeometric0F1Regularized Hypergeometric1F1 Hypergeometric1F1Regularized Hypergeometric2F1 Hypergeometric2F1Regularized HypergeometricDistribution HypergeometricPFQ HypergeometricPFQRegularized HypergeometricU Hyperlink HyperlinkCreationSettings Hyphenation HyphenationOptions HypoexponentialDistribution HypothesisTestData I Identity IdentityMatrix If IgnoreCase Im Image Image3D Image3DSlices ImageAccumulate ImageAdd ImageAdjust ImageAlign ImageApply ImageAspectRatio ImageAssemble ImageCache ImageCacheValid ImageCapture ImageChannels ImageClip ImageColorSpace ImageCompose ImageConvolve ImageCooccurrence ImageCorners ImageCorrelate ImageCorrespondingPoints ImageCrop ImageData ImageDataPacket ImageDeconvolve ImageDemosaic ImageDifference ImageDimensions ImageDistance ImageEffect ImageFeatureTrack ImageFileApply ImageFileFilter ImageFileScan ImageFilter ImageForestingComponents ImageForwardTransformation ImageHistogram ImageKeypoints ImageLevels ImageLines ImageMargins ImageMarkers ImageMeasurements ImageMultiply ImageOffset ImagePad ImagePadding ImagePartition ImagePeriodogram ImagePerspectiveTransformation ImageQ ImageRangeCache ImageReflect ImageRegion ImageResize ImageResolution ImageRotate ImageRotated ImageScaled ImageScan ImageSize ImageSizeAction ImageSizeCache ImageSizeMultipliers ImageSizeRaw ImageSubtract ImageTake ImageTransformation ImageTrim ImageType ImageValue ImageValuePositions Implies Import ImportAutoReplacements ImportString ImprovementImportance In IncidenceGraph IncidenceList IncidenceMatrix IncludeConstantBasis IncludeFileExtension IncludePods IncludeSingularTerm Increment Indent IndentingNewlineSpacings IndentMaxFraction IndependenceTest IndependentEdgeSetQ IndependentUnit IndependentVertexSetQ Indeterminate IndexCreationOptions Indexed IndexGraph IndexTag Inequality InexactNumberQ InexactNumbers Infinity Infix Information Inherited InheritScope Initialization InitializationCell InitializationCellEvaluation InitializationCellWarning InlineCounterAssignments InlineCounterIncrements InlineRules Inner Inpaint Input InputAliases InputAssumptions InputAutoReplacements InputField InputFieldBox InputFieldBoxOptions InputForm InputGrouping InputNamePacket InputNotebook InputPacket InputSettings InputStream InputString InputStringPacket InputToBoxFormPacket Insert InsertionPointObject InsertResults Inset Inset3DBox Inset3DBoxOptions InsetBox InsetBoxOptions Install InstallService InString Integer IntegerDigits IntegerExponent IntegerLength IntegerPart IntegerPartitions IntegerQ Integers IntegerString Integral Integrate Interactive InteractiveTradingChart Interlaced Interleaving InternallyBalancedDecomposition InterpolatingFunction InterpolatingPolynomial Interpolation InterpolationOrder InterpolationPoints InterpolationPrecision Interpretation InterpretationBox InterpretationBoxOptions InterpretationFunction InterpretTemplate InterquartileRange Interrupt InterruptSettings Intersection Interval IntervalIntersection IntervalMemberQ IntervalUnion Inverse InverseBetaRegularized InverseCDF InverseChiSquareDistribution InverseContinuousWaveletTransform InverseDistanceTransform InverseEllipticNomeQ InverseErf InverseErfc InverseFourier InverseFourierCosTransform InverseFourierSequenceTransform InverseFourierSinTransform InverseFourierTransform InverseFunction InverseFunctions InverseGammaDistribution InverseGammaRegularized InverseGaussianDistribution InverseGudermannian InverseHaversine InverseJacobiCD InverseJacobiCN InverseJacobiCS InverseJacobiDC InverseJacobiDN InverseJacobiDS InverseJacobiNC InverseJacobiND InverseJacobiNS InverseJacobiSC InverseJacobiSD InverseJacobiSN InverseLaplaceTransform InversePermutation InverseRadon InverseSeries InverseSurvivalFunction InverseWaveletTransform InverseWeierstrassP InverseZTransform Invisible InvisibleApplication InvisibleTimes IrreduciblePolynomialQ IsolatingInterval IsomorphicGraphQ IsotopeData Italic Item ItemBox ItemBoxOptions ItemSize ItemStyle ItoProcess JaccardDissimilarity JacobiAmplitude Jacobian JacobiCD JacobiCN JacobiCS JacobiDC JacobiDN JacobiDS JacobiNC JacobiND JacobiNS JacobiP JacobiSC JacobiSD JacobiSN JacobiSymbol JacobiZeta JankoGroupJ1 JankoGroupJ2 JankoGroupJ3 JankoGroupJ4 JarqueBeraALMTest JohnsonDistribution Join Joined JoinedCurve JoinedCurveBox JoinForm JordanDecomposition JordanModelDecomposition K KagiChart KaiserBesselWindow KaiserWindow KalmanEstimator KalmanFilter KarhunenLoeveDecomposition KaryTree KatzCentrality KCoreComponents KDistribution KelvinBei KelvinBer KelvinKei KelvinKer KendallTau KendallTauTest KernelExecute KernelMixtureDistribution KernelObject Kernels Ket Khinchin KirchhoffGraph KirchhoffMatrix KleinInvariantJ KnightTourGraph KnotData KnownUnitQ KolmogorovSmirnovTest KroneckerDelta KroneckerModelDecomposition KroneckerProduct KroneckerSymbol KuiperTest KumaraswamyDistribution Kurtosis KuwaharaFilter Label Labeled LabeledSlider LabelingFunction LabelStyle LaguerreL LambdaComponents LambertW LanczosWindow LandauDistribution Language LanguageCategory LaplaceDistribution LaplaceTransform Laplacian LaplacianFilter LaplacianGaussianFilter Large Larger Last Latitude LatitudeLongitude LatticeData LatticeReduce Launch LaunchKernels LayeredGraphPlot LayerSizeFunction LayoutInformation LCM LeafCount LeapYearQ LeastSquares LeastSquaresFilterKernel Left LeftArrow LeftArrowBar LeftArrowRightArrow LeftDownTeeVector LeftDownVector LeftDownVectorBar LeftRightArrow LeftRightVector LeftTee LeftTeeArrow LeftTeeVector LeftTriangle LeftTriangleBar LeftTriangleEqual LeftUpDownVector LeftUpTeeVector LeftUpVector LeftUpVectorBar LeftVector LeftVectorBar LegendAppearance Legended LegendFunction LegendLabel LegendLayout LegendMargins LegendMarkers LegendMarkerSize LegendreP LegendreQ LegendreType Length LengthWhile LerchPhi Less LessEqual LessEqualGreater LessFullEqual LessGreater LessLess LessSlantEqual LessTilde LetterCharacter LetterQ Level LeveneTest LeviCivitaTensor LevyDistribution Lexicographic LibraryFunction LibraryFunctionError LibraryFunctionInformation LibraryFunctionLoad LibraryFunctionUnload LibraryLoad LibraryUnload LicenseID LiftingFilterData LiftingWaveletTransform LightBlue LightBrown LightCyan Lighter LightGray LightGreen Lighting LightingAngle LightMagenta LightOrange LightPink LightPurple LightRed LightSources LightYellow Likelihood Limit LimitsPositioning LimitsPositioningTokens LindleyDistribution Line Line3DBox LinearFilter LinearFractionalTransform LinearModelFit LinearOffsetFunction LinearProgramming LinearRecurrence LinearSolve LinearSolveFunction LineBox LineBreak LinebreakAdjustments LineBreakChart LineBreakWithin LineColor LineForm LineGraph LineIndent LineIndentMaxFraction LineIntegralConvolutionPlot LineIntegralConvolutionScale LineLegend LineOpacity LineSpacing LineWrapParts LinkActivate LinkClose LinkConnect LinkConnectedQ LinkCreate LinkError LinkFlush LinkFunction LinkHost LinkInterrupt LinkLaunch LinkMode LinkObject LinkOpen LinkOptions LinkPatterns LinkProtocol LinkRead LinkReadHeld LinkReadyQ Links LinkWrite LinkWriteHeld LiouvilleLambda List Listable ListAnimate ListContourPlot ListContourPlot3D ListConvolve ListCorrelate ListCurvePathPlot ListDeconvolve ListDensityPlot Listen ListFourierSequenceTransform ListInterpolation ListLineIntegralConvolutionPlot ListLinePlot ListLogLinearPlot ListLogLogPlot ListLogPlot ListPicker ListPickerBox ListPickerBoxBackground ListPickerBoxOptions ListPlay ListPlot ListPlot3D ListPointPlot3D ListPolarPlot ListQ ListStreamDensityPlot ListStreamPlot ListSurfacePlot3D ListVectorDensityPlot ListVectorPlot ListVectorPlot3D ListZTransform Literal LiteralSearch LocalClusteringCoefficient LocalizeVariables LocationEquivalenceTest LocationTest Locator LocatorAutoCreate LocatorBox LocatorBoxOptions LocatorCentering LocatorPane LocatorPaneBox LocatorPaneBoxOptions LocatorRegion Locked Log Log10 Log2 LogBarnesG LogGamma LogGammaDistribution LogicalExpand LogIntegral LogisticDistribution LogitModelFit LogLikelihood LogLinearPlot LogLogisticDistribution LogLogPlot LogMultinormalDistribution LogNormalDistribution LogPlot LogRankTest LogSeriesDistribution LongEqual Longest LongestAscendingSequence LongestCommonSequence LongestCommonSequencePositions LongestCommonSubsequence LongestCommonSubsequencePositions LongestMatch LongForm Longitude LongLeftArrow LongLeftRightArrow LongRightArrow Loopback LoopFreeGraphQ LowerCaseQ LowerLeftArrow LowerRightArrow LowerTriangularize LowpassFilter LQEstimatorGains LQGRegulator LQOutputRegulatorGains LQRegulatorGains LUBackSubstitution LucasL LuccioSamiComponents LUDecomposition LyapunovSolve LyonsGroupLy MachineID MachineName MachineNumberQ MachinePrecision MacintoshSystemPageSetup Magenta Magnification Magnify MainSolve MaintainDynamicCaches Majority MakeBoxes MakeExpression MakeRules MangoldtLambda ManhattanDistance Manipulate Manipulator MannWhitneyTest MantissaExponent Manual Map MapAll MapAt MapIndexed MAProcess MapThread MarcumQ MardiaCombinedTest MardiaKurtosisTest MardiaSkewnessTest MarginalDistribution MarkovProcessProperties Masking MatchingDissimilarity MatchLocalNameQ MatchLocalNames MatchQ Material MathematicaNotation MathieuC MathieuCharacteristicA MathieuCharacteristicB MathieuCharacteristicExponent MathieuCPrime MathieuGroupM11 MathieuGroupM12 MathieuGroupM22 MathieuGroupM23 MathieuGroupM24 MathieuS MathieuSPrime MathMLForm MathMLText Matrices MatrixExp MatrixForm MatrixFunction MatrixLog MatrixPlot MatrixPower MatrixQ MatrixRank Max MaxBend MaxDetect MaxExtraBandwidths MaxExtraConditions MaxFeatures MaxFilter Maximize MaxIterations MaxMemoryUsed MaxMixtureKernels MaxPlotPoints MaxPoints MaxRecursion MaxStableDistribution MaxStepFraction MaxSteps MaxStepSize MaxValue MaxwellDistribution McLaughlinGroupMcL Mean MeanClusteringCoefficient MeanDegreeConnectivity MeanDeviation MeanFilter MeanGraphDistance MeanNeighborDegree MeanShift MeanShiftFilter Median MedianDeviation MedianFilter Medium MeijerG MeixnerDistribution MemberQ MemoryConstrained MemoryInUse Menu MenuAppearance MenuCommandKey MenuEvaluator MenuItem MenuPacket MenuSortingValue MenuStyle MenuView MergeDifferences Mesh MeshFunctions MeshRange MeshShading MeshStyle Message MessageDialog MessageList MessageName MessageOptions MessagePacket Messages MessagesNotebook MetaCharacters MetaInformation Method MethodOptions MexicanHatWavelet MeyerWavelet Min MinDetect MinFilter MinimalPolynomial MinimalStateSpaceModel Minimize Minors MinRecursion MinSize MinStableDistribution Minus MinusPlus MinValue Missing MissingDataMethod MittagLefflerE MixedRadix MixedRadixQuantity MixtureDistribution Mod Modal Mode Modular ModularLambda Module Modulus MoebiusMu Moment Momentary MomentConvert MomentEvaluate MomentGeneratingFunction Monday Monitor MonomialList MonomialOrder MonsterGroupM MorletWavelet MorphologicalBinarize MorphologicalBranchPoints MorphologicalComponents MorphologicalEulerNumber MorphologicalGraph MorphologicalPerimeter MorphologicalTransform Most MouseAnnotation MouseAppearance MouseAppearanceTag MouseButtons Mouseover MousePointerNote MousePosition MovingAverage MovingMedian MoyalDistribution MultiedgeStyle MultilaunchWarning MultiLetterItalics MultiLetterStyle MultilineFunction Multinomial MultinomialDistribution MultinormalDistribution MultiplicativeOrder Multiplicity Multiselection MultivariateHypergeometricDistribution MultivariatePoissonDistribution MultivariateTDistribution N NakagamiDistribution NameQ Names NamespaceBox Nand NArgMax NArgMin NBernoulliB NCache NDSolve NDSolveValue Nearest NearestFunction NeedCurrentFrontEndPackagePacket NeedCurrentFrontEndSymbolsPacket NeedlemanWunschSimilarity Needs Negative NegativeBinomialDistribution NegativeMultinomialDistribution NeighborhoodGraph Nest NestedGreaterGreater NestedLessLess NestedScriptRules NestList NestWhile NestWhileList NevilleThetaC NevilleThetaD NevilleThetaN NevilleThetaS NewPrimitiveStyle NExpectation Next NextPrime NHoldAll NHoldFirst NHoldRest NicholsGridLines NicholsPlot NIntegrate NMaximize NMaxValue NMinimize NMinValue NominalVariables NonAssociative NoncentralBetaDistribution NoncentralChiSquareDistribution NoncentralFRatioDistribution NoncentralStudentTDistribution NonCommutativeMultiply NonConstants None NonlinearModelFit NonlocalMeansFilter NonNegative NonPositive Nor NorlundB Norm Normal NormalDistribution NormalGrouping Normalize NormalizedSquaredEuclideanDistance NormalsFunction NormFunction Not NotCongruent NotCupCap NotDoubleVerticalBar Notebook NotebookApply NotebookAutoSave NotebookClose NotebookConvertSettings NotebookCreate NotebookCreateReturnObject NotebookDefault NotebookDelete NotebookDirectory NotebookDynamicExpression NotebookEvaluate NotebookEventActions NotebookFileName NotebookFind NotebookFindReturnObject NotebookGet NotebookGetLayoutInformationPacket NotebookGetMisspellingsPacket NotebookInformation NotebookInterfaceObject NotebookLocate NotebookObject NotebookOpen NotebookOpenReturnObject NotebookPath NotebookPrint NotebookPut NotebookPutReturnObject NotebookRead NotebookResetGeneratedCells Notebooks NotebookSave NotebookSaveAs NotebookSelection NotebookSetupLayoutInformationPacket NotebooksMenu NotebookWrite NotElement NotEqualTilde NotExists NotGreater NotGreaterEqual NotGreaterFullEqual NotGreaterGreater NotGreaterLess NotGreaterSlantEqual NotGreaterTilde NotHumpDownHump NotHumpEqual NotLeftTriangle NotLeftTriangleBar NotLeftTriangleEqual NotLess NotLessEqual NotLessFullEqual NotLessGreater NotLessLess 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NyquistPlot O ObservabilityGramian ObservabilityMatrix ObservableDecomposition ObservableModelQ OddQ Off Offset OLEData On ONanGroupON OneIdentity Opacity Open OpenAppend Opener OpenerBox OpenerBoxOptions OpenerView OpenFunctionInspectorPacket Opening OpenRead OpenSpecialOptions OpenTemporary OpenWrite Operate OperatingSystem OptimumFlowData Optional OptionInspectorSettings OptionQ Options OptionsPacket OptionsPattern OptionValue OptionValueBox OptionValueBoxOptions Or Orange Order OrderDistribution OrderedQ Ordering Orderless OrnsteinUhlenbeckProcess Orthogonalize Out Outer OutputAutoOverwrite OutputControllabilityMatrix OutputControllableModelQ OutputForm OutputFormData OutputGrouping OutputMathEditExpression OutputNamePacket OutputResponse OutputSizeLimit OutputStream Over OverBar OverDot Overflow OverHat Overlaps Overlay OverlayBox OverlayBoxOptions Overscript OverscriptBox OverscriptBoxOptions OverTilde OverVector OwenT OwnValues PackingMethod PaddedForm Padding PadeApproximant PadLeft PadRight PageBreakAbove PageBreakBelow PageBreakWithin PageFooterLines PageFooters PageHeaderLines PageHeaders PageHeight PageRankCentrality PageWidth PairedBarChart PairedHistogram PairedSmoothHistogram PairedTTest PairedZTest PaletteNotebook PalettePath Pane PaneBox PaneBoxOptions Panel PanelBox PanelBoxOptions Paneled PaneSelector PaneSelectorBox PaneSelectorBoxOptions PaperWidth ParabolicCylinderD ParagraphIndent ParagraphSpacing ParallelArray ParallelCombine ParallelDo ParallelEvaluate Parallelization Parallelize ParallelMap ParallelNeeds ParallelProduct ParallelSubmit ParallelSum ParallelTable ParallelTry Parameter ParameterEstimator ParameterMixtureDistribution ParameterVariables ParametricFunction ParametricNDSolve ParametricNDSolveValue ParametricPlot ParametricPlot3D ParentConnect ParentDirectory ParentForm Parenthesize ParentList ParetoDistribution Part PartialCorrelationFunction PartialD ParticleData Partition PartitionsP PartitionsQ ParzenWindow PascalDistribution PassEventsDown PassEventsUp Paste PasteBoxFormInlineCells PasteButton Path PathGraph PathGraphQ Pattern PatternSequence PatternTest PauliMatrix PaulWavelet Pause PausedTime PDF PearsonChiSquareTest PearsonCorrelationTest PearsonDistribution PerformanceGoal PeriodicInterpolation Periodogram PeriodogramArray PermutationCycles PermutationCyclesQ PermutationGroup PermutationLength PermutationList PermutationListQ PermutationMax PermutationMin PermutationOrder PermutationPower PermutationProduct PermutationReplace Permutations PermutationSupport Permute PeronaMalikFilter Perpendicular PERTDistribution PetersenGraph PhaseMargins Pi Pick PIDData PIDDerivativeFilter PIDFeedforward PIDTune Piecewise PiecewiseExpand PieChart PieChart3D PillaiTrace PillaiTraceTest Pink Pivoting PixelConstrained PixelValue PixelValuePositions Placed Placeholder PlaceholderReplace Plain PlanarGraphQ Play PlayRange Plot Plot3D Plot3Matrix PlotDivision PlotJoined PlotLabel PlotLayout PlotLegends PlotMarkers PlotPoints PlotRange PlotRangeClipping PlotRangePadding PlotRegion PlotStyle Plus PlusMinus Pochhammer PodStates PodWidth Point Point3DBox PointBox PointFigureChart PointForm PointLegend PointSize PoissonConsulDistribution PoissonDistribution PoissonProcess PoissonWindow PolarAxes PolarAxesOrigin PolarGridLines PolarPlot PolarTicks PoleZeroMarkers PolyaAeppliDistribution PolyGamma Polygon Polygon3DBox Polygon3DBoxOptions PolygonBox PolygonBoxOptions PolygonHoleScale PolygonIntersections PolygonScale PolyhedronData PolyLog PolynomialExtendedGCD PolynomialForm PolynomialGCD PolynomialLCM PolynomialMod PolynomialQ PolynomialQuotient PolynomialQuotientRemainder PolynomialReduce PolynomialRemainder Polynomials PopupMenu PopupMenuBox PopupMenuBoxOptions PopupView PopupWindow Position Positive PositiveDefiniteMatrixQ PossibleZeroQ Postfix PostScript Power PowerDistribution PowerExpand PowerMod PowerModList PowerSpectralDensity PowersRepresentations PowerSymmetricPolynomial Precedence 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ProgressIndicatorBoxOptions Projection Prolog PromptForm Properties Property PropertyList PropertyValue Proportion Proportional Protect Protected ProteinData Pruning PseudoInverse Purple Put PutAppend Pyramid PyramidBox PyramidBoxOptions QBinomial QFactorial QGamma QHypergeometricPFQ QPochhammer QPolyGamma QRDecomposition QuadraticIrrationalQ Quantile QuantilePlot Quantity QuantityForm QuantityMagnitude QuantityQ QuantityUnit Quartics QuartileDeviation Quartiles QuartileSkewness QueueingNetworkProcess QueueingProcess QueueProperties Quiet Quit Quotient QuotientRemainder RadialityCentrality RadicalBox RadicalBoxOptions RadioButton RadioButtonBar RadioButtonBox RadioButtonBoxOptions Radon RamanujanTau RamanujanTauL RamanujanTauTheta RamanujanTauZ Random RandomChoice RandomComplex RandomFunction RandomGraph RandomImage RandomInteger RandomPermutation RandomPrime RandomReal RandomSample RandomSeed RandomVariate RandomWalkProcess Range RangeFilter RangeSpecification RankedMax RankedMin Raster Raster3D Raster3DBox Raster3DBoxOptions RasterArray RasterBox RasterBoxOptions Rasterize RasterSize Rational RationalFunctions Rationalize Rationals Ratios Raw RawArray RawBoxes RawData RawMedium RayleighDistribution Re Read ReadList ReadProtected Real RealBlockDiagonalForm RealDigits RealExponent Reals Reap Record RecordLists RecordSeparators Rectangle RectangleBox RectangleBoxOptions RectangleChart RectangleChart3D RecurrenceFilter RecurrenceTable RecurringDigitsForm Red Reduce RefBox ReferenceLineStyle ReferenceMarkers ReferenceMarkerStyle Refine ReflectionMatrix ReflectionTransform Refresh RefreshRate RegionBinarize RegionFunction RegionPlot RegionPlot3D RegularExpression Regularization Reinstall Release ReleaseHold ReliabilityDistribution ReliefImage ReliefPlot Remove RemoveAlphaChannel RemoveAsynchronousTask Removed RemoveInputStreamMethod RemoveOutputStreamMethod RemoveProperty RemoveScheduledTask RenameDirectory RenameFile RenderAll RenderingOptions RenewalProcess 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RogersTanimotoDissimilarity Root RootApproximant RootIntervals RootLocusPlot RootMeanSquare RootOfUnityQ RootReduce Roots RootSum Rotate RotateLabel RotateLeft RotateRight RotationAction RotationBox RotationBoxOptions RotationMatrix RotationTransform Round RoundImplies RoundingRadius Row RowAlignments RowBackgrounds RowBox RowHeights RowLines RowMinHeight RowReduce RowsEqual RowSpacings RSolve RudvalisGroupRu Rule RuleCondition RuleDelayed RuleForm RulerUnits Run RunScheduledTask RunThrough RuntimeAttributes RuntimeOptions RussellRaoDissimilarity SameQ SameTest SampleDepth SampledSoundFunction SampledSoundList SampleRate SamplingPeriod SARIMAProcess SARMAProcess SatisfiabilityCount SatisfiabilityInstances SatisfiableQ Saturday Save Saveable SaveAutoDelete SaveDefinitions SawtoothWave Scale Scaled ScaleDivisions ScaledMousePosition ScaleOrigin ScalePadding ScaleRanges ScaleRangeStyle ScalingFunctions ScalingMatrix ScalingTransform Scan ScheduledTaskActiveQ ScheduledTaskData ScheduledTaskObject ScheduledTasks SchurDecomposition ScientificForm ScreenRectangle ScreenStyleEnvironment ScriptBaselineShifts ScriptLevel ScriptMinSize ScriptRules ScriptSizeMultipliers Scrollbars ScrollingOptions ScrollPosition Sec Sech SechDistribution SectionGrouping SectorChart SectorChart3D SectorOrigin SectorSpacing SeedRandom Select Selectable SelectComponents SelectedCells SelectedNotebook Selection SelectionAnimate SelectionCell SelectionCellCreateCell SelectionCellDefaultStyle SelectionCellParentStyle SelectionCreateCell SelectionDebuggerTag SelectionDuplicateCell SelectionEvaluate SelectionEvaluateCreateCell SelectionMove SelectionPlaceholder SelectionSetStyle SelectWithContents SelfLoops SelfLoopStyle SemialgebraicComponentInstances SendMail Sequence SequenceAlignment SequenceForm SequenceHold SequenceLimit Series SeriesCoefficient SeriesData SessionTime Set SetAccuracy SetAlphaChannel SetAttributes Setbacks SetBoxFormNamesPacket SetDelayed SetDirectory SetEnvironment 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SpearmanRankTest SpearmanRho Spectrogram SpectrogramArray Specularity SpellingCorrection SpellingDictionaries SpellingDictionariesPath SpellingOptions SpellingSuggestionsPacket Sphere SphereBox SphericalBesselJ SphericalBesselY SphericalHankelH1 SphericalHankelH2 SphericalHarmonicY SphericalPlot3D SphericalRegion SpheroidalEigenvalue SpheroidalJoiningFactor SpheroidalPS SpheroidalPSPrime SpheroidalQS SpheroidalQSPrime SpheroidalRadialFactor SpheroidalS1 SpheroidalS1Prime SpheroidalS2 SpheroidalS2Prime Splice SplicedDistribution SplineClosed SplineDegree SplineKnots SplineWeights Split SplitBy SpokenString Sqrt SqrtBox SqrtBoxOptions Square SquaredEuclideanDistance SquareFreeQ SquareIntersection SquaresR SquareSubset SquareSubsetEqual SquareSuperset SquareSupersetEqual SquareUnion SquareWave StabilityMargins StabilityMarginsStyle StableDistribution Stack StackBegin StackComplete StackInhibit StandardDeviation StandardDeviationFilter StandardForm Standardize StandbyDistribution Star 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t=this,r=t.slideViews[e],a=t.slideViews[e+1];t.events.emit("beforeShowSlide",e),r.show(),a?t.previewArea.innerHTML=a.containerElement.outerHTML:t.previewArea.innerHTML="",t.events.emit("afterShowSlide",e)},a.prototype.hideSlide=function(e){var t=this,r=t.slideViews[e];t.events.emit("beforeHideSlide",e),r.hide(),t.events.emit("afterHideSlide",e)},a.prototype.updateDimensions=function(){var e=this,t=e.scaler.dimensions;e.helpElement.style.width=t.width+"px",e.helpElement.style.height=t.height+"px",e.scaleSlideBackgroundImages(t),e.scaleElements()},a.prototype.scaleElements=function(){var 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r=this;r.events=e,r.element=t,r.startTime=null,r.pauseStart=null,r.pauseLength=0,t.innerHTML="0:00:00",setInterval(function(){r.updateTimer()},100),e.on("start",function(){r.startTime=new Date}),e.on("resetTimer",function(){r.startTime=null,r.pauseStart=null,r.pauseLength=0,r.element.innerHTML="0:00:00"}),e.on("pause",function(){r.pauseStart=new Date}),e.on("resume",function(){r.pauseLength+=new Date-r.pauseStart,r.pauseStart=null})}e("../../utils");t.exports=a,a.prototype.updateTimer=function(){var e=this;if(e.startTime){var t;t=e.pauseStart?e.pauseStart-e.startTime-e.pauseLength:new Date-e.startTime-e.pauseLength;var r=Math.floor(t/1e3)%60,a=Math.floor(t/6e4)%60,i=Math.floor(t/36e5);e.element.innerHTML=i+(a>9?":":":0")+a+(r>9?":":":0")+r}}},{"../../utils":24}]},{},[4]);
\ No newline at end of file
diff --git a/img/startup.png b/img/startup.png
deleted file mode 100644
index 8467f95..0000000
Binary files a/img/startup.png and /dev/null differ
diff --git a/img/work_stijn_1.png b/img/work_stijn_1.png
deleted file mode 100644
index b94aea6..0000000
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diff --git a/index.md b/index.md
deleted file mode 100644
index 603acac..0000000
--- a/index.md
+++ /dev/null
@@ -1,52 +0,0 @@
-# Python for GIS and Geoscience
-
-## Introduction
-
-An important aspect of daily work in geographic information science and earth sciences is the handling of potentially large amounts of data. Reading in spatial data, exploring the data, creating visualisations and preparing the data for further analysis may become tedious tasks. Hence, increasing efficiency and reproducibility in this process without the need of a GUI interface is beneficial for many scientists. The usage of high-level scripting languages such as R and Python are increasingly popular for these tasks thanks to the development of GIS oriented packages.
-
-This course trains students to use Python effectively to do these tasks, with a focus on geospatial data. It covers both vector and raster data. The course focuses on introducing the main Python packages for handling such data (GeoPandas, Numpy and Rasterio, Xarray) and how to use those packages for importing, exploring, visualizing and manipulating geospatial data. It is the aim to give the students an understanding of the data structures used in Python to represent geospatial data (geospatial dataframes, (multi-dimensional) arrays and composite netCDF-like multi-dimensional datasets), while also providing pointers to the broader ecosystem of Python packages for GIS and geosciences.
-
-The course has been developed as a specialist course for the Doctoral schools of Ghent University, but can be taught to others upon request.
-
-## Course info
-
-### Aim & scope
-
-This course targets researchers that want to enhance their general data manipulation and analysis skills in Python specifically for handling geospatial data.
-
-The course does not aim to provide a course in specific spatial analysis and statistics, cartography, remote sensing, OGC web services, ... or general Geographical Information Management (GIS). It aims to provide researchers the means to effectively tackle commonly encountered spatial data handling tasks in order to increase the overall efficiency of the research. The course does not tackle desktop GIS Python extensions such as arcpy or pyqgis.
-
-### Program
-
-The course is organized as a three day course with following program:
-
-- **Day 1** - Setting up the programming environment with the required packages using the conda package manager, a recap of Python concepts and introduction to GeoPandas and related packages to work with geospatial vector data.
-- **Day 2** - An introduction to numpy and rasterio for working with raster data. More advanced features of GeoPandas for spatial joins and overlays. Combining raster and vector data and more advanced plotting. The acquired skills will immediately be brought into practice to handle real-world data sets.
-- **Day 3** - An introduction to xarray for multi-dimensional netCDF-like datasets. Further case studies to apply the learned skills of the full workshop. The day will end with an overview of other packages that are being used in the geospatial Python ecosystem (to scale to bigger datasets, other visualization frameworks, specialized packages).
-
-## Getting started
-
-The course uses Python 3, data analysis packages such as Pandas, Numpy and Matplotlib and geospatial packages such as GeoPandas, Rasterio and Xarray. To install the required libraries, we highly recommend Anaconda or miniconda (<https://www.anaconda.com/download/>) or another Python distribution that includes the scientific libraries (this recommendation applies to all platforms, so for both Window, Linux and Mac).
-
-For detailed instructions to get started on your local machine , see the [setup instructions](./setup.md).
-
-In case you do not want to install everything and just want to try out the course material, use the environment setup by Binder [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/jorisvandenbossche/DS-python-geospatial/master) and open de notebooks rightaway.
-
-## Slides
-
-For the course slides, click [here](https://jorisvandenbossche.github.io/DS-python-geospatial/slides.html).
-
-
-## Contributing
-
-Found any typo or have a suggestion, see [how to contribute](./CONTRIBUTING.html).
-
-
-## Meta
-Authors: Joris Van den Bossche, Stijn Van Hoey
-
-<img src="img/logo_flanders+richtingmorgen.png" width="79%">
-<img src="img/doctoralschoolsprofiel_hq_rgb_web.png" width="20%">
-
-
-
diff --git a/notebooks/00-jupyter_introduction.ipynb b/notebooks/00-jupyter_introduction.ipynb
index cac5fbc..094d29b 100644
--- a/notebooks/00-jupyter_introduction.ipynb
+++ b/notebooks/00-jupyter_introduction.ipynb
@@ -2,30 +2,22 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
+   "metadata": {},
    "source": [
     "<p><font size=\"6\"><b>Jupyter notebook INTRODUCTION </b></font></p>\n",
     "\n",
     "\n",
     "> *DS Python for GIS and Geoscience*  \n",
-    "> *October, 2020*\n",
+    "> *October, 2025*\n",
     ">\n",
-    "> *© 2016-2020, Joris Van den Bossche and Stijn Van Hoey  (<mailto:jorisvandenbossche@gmail.com>, <mailto:stijnvanhoey@gmail.com>). Licensed under [CC BY 4.0 Creative Commons](http://creativecommons.org/licenses/by/4.0/)*\n",
+    "> *© 2025, Joris Van den Bossche and Stijn Van Hoey  (<mailto:jorisvandenbossche@gmail.com>, <mailto:stijnvanhoey@gmail.com>). Licensed under [CC BY 4.0 Creative Commons](http://creativecommons.org/licenses/by/4.0/)*\n",
     "\n",
     "---"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
+   "metadata": {},
    "source": [
     "<big><center>To run a cell: push the start triangle in the menu or type **SHIFT + ENTER/RETURN** <br>\n",
     "![](../img/notebook/shiftenter.jpg)\n",
@@ -41,22 +33,14 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
+   "metadata": {},
    "source": [
     "We will work in **Jupyter notebooks** during this course. A notebook is a collection of `cells`, that can contain different content:"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
+   "metadata": {},
    "source": [
     "## Code"
    ]
@@ -64,15 +48,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "run_control": {
-     "frozen": false,
-     "read_only": false
-    },
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# Code cell, then we are using python\n",
@@ -82,15 +58,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "run_control": {
-     "frozen": false,
-     "read_only": false
-    },
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "DS = 10\n",
@@ -106,33 +74,21 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
+   "metadata": {},
    "source": [
     "## Markdown"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
+   "metadata": {},
    "source": [
     "Text cells, using Markdown syntax. With the syntax, you can make text **bold** or *italic*, amongst many other things..."
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
+   "metadata": {},
    "source": [
     "* list\n",
     "* with\n",
@@ -146,24 +102,16 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
+   "metadata": {},
    "source": [
     "Mathematical formulas can also be incorporated (LaTeX it is...)\n",
     "$$\\frac{dBZV}{dt}=BZV_{in} - k_1 .BZV$$\n",
-    "$$\\frac{dOZ}{dt}=k_2 .(OZ_{sat}-OZ) - k_1 .BZV$$\n"
+    "$$\\frac{dOZ}{dt}=k_2 .(OZ_{sat}-OZ) - k_1 .BZV$$"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "subslide"
-    }
-   },
+   "metadata": {},
    "source": [
     "Or tables:\n",
     "\n",
@@ -186,22 +134,14 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "subslide"
-    }
-   },
+   "metadata": {},
    "source": [
     "Code can also be incorporated, but than just to illustrate:"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
+   "metadata": {},
    "source": [
     "```python\n",
     "BOT = 12\n",
@@ -211,11 +151,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
+   "metadata": {},
    "source": [
     "See also: https://github.com/adam-p/markdown-here/wiki/Markdown-Cheatsheet"
    ]
@@ -229,11 +165,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
+   "metadata": {},
    "source": [
     "You can also use HTML commands, just check this cell:\n",
     "<h3> html-adapted titel with &#60;h3&#62; </h3> <p></p>\n",
@@ -242,11 +174,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
+   "metadata": {},
    "source": [
     "## Headings of different sizes: section\n",
     "### subsection\n",
@@ -255,76 +183,49 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
+   "metadata": {},
    "source": [
     "## Raw Text"
    ]
   },
   {
    "cell_type": "raw",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
+   "metadata": {},
    "source": [
     "Cfr. any text editor"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
+   "metadata": {},
    "source": [
     "# Notebook handling ESSENTIALS"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
+   "metadata": {},
    "source": [
-    "## Completion: TAB\n",
-    "![](../../img/notebook/tabbutton.jpg)"
+    "## Execution: SHIFT + ENTER\n",
+    "\n",
+    "**REMEMBER**: To run a cell: <strike>push the start triangle in the menu or</strike> type **SHIFT + ENTER**\n",
+    "    \n",
+    "![](../img/notebook/shiftenter.jpg)"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
+   "metadata": {},
    "source": [
-    "* The **TAB** button is essential: It provides you all **possible actions** you can do after loading in a library *AND* it is used for **automatic autocompletion**:"
+    "## Completion: TAB\n",
+    "![](../../img/notebook/tabbutton.jpg)"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "run_control": {
-     "frozen": false,
-     "read_only": false
-    },
-    "slideshow": {
-     "slide_type": "subslide"
-    }
-   },
-   "outputs": [],
+   "cell_type": "markdown",
+   "metadata": {},
    "source": [
-    "import os\n",
-    "os.mkdir"
+    "* The **TAB** button is essential: It provides you all **possible actions** you can do after loading in a library *AND* it is used for **automatic autocompletion**:"
    ]
   },
   {
@@ -333,53 +234,29 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "import os\n",
     "os.mkdir"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "run_control": {
-     "frozen": false,
-     "read_only": false
-    },
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "my_very_long_variable_name = 3"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
-    "my_very_long_variable_name"
+    "my_very_long_variable_name = 3"
    ]
   },
   {
    "cell_type": "raw",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
+   "metadata": {},
    "source": [
     "my_ + TAB"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
+   "metadata": {},
    "source": [
     "## Help: SHIFT + TAB\n",
     "![](../../img/notebook/shift-tab.png)"
@@ -387,27 +264,15 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "subslide"
-    }
-   },
+   "metadata": {},
    "source": [
-    "* The  **SHIFT-TAB** combination is ultra essential to get information/help about the current operation "
+    "* The  **SHIFT-TAB** combination is ultra essential to get information/help about the current operation"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "run_control": {
-     "frozen": false,
-     "read_only": false
-    },
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "round(3.2)"
@@ -416,15 +281,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "run_control": {
-     "frozen": false,
-     "read_only": false
-    },
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "os.mkdir"
@@ -433,15 +290,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "run_control": {
-     "frozen": false,
-     "read_only": false
-    },
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# An alternative is to put a question mark behind the command\n",
@@ -450,29 +299,17 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "subslide"
-    }
-   },
+   "metadata": {},
    "source": [
     "<div class=\"alert alert-success\">\n",
-    "    <b>EXERCISE</b>: What happens if you put two question marks behind the command?\n",
+    "    <b>EXERCISE 1</b>: What happens if you put two question marks behind the command?\n",
     "</div>"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "run_control": {
-     "frozen": false,
-     "read_only": false
-    },
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "import glob\n",
@@ -481,16 +318,12 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
+   "metadata": {},
    "source": [
-    "## *edit* mode to *command* mode\n",
+    "## *edit* mode vs *command* mode\n",
     "\n",
-    "* *edit* mode means you're editing a cell, i.e. with your cursor inside a cell to type content --> <font color=\"green\">green colored side</font>\n",
-    "* *command* mode means you're NOT editing(!), i.e. NOT with your cursor inside a cell to type content --> <font color=\"blue\">blue colored side</font>\n",
+    "* *edit* mode means you're editing a cell, i.e. with your cursor inside a cell to type content -->  <font color=\"blue\">blue colored border</font>\n",
+    "* *command* mode means you're NOT editing(!), i.e. NOT with your cursor inside a cell to type content --> selected cell has a grey border\n",
     "\n",
     "To start editing, click inside a cell or \n",
     "<img src=\"../img/notebook/enterbutton.png\" alt=\"Key enter\" style=\"width:150px\">\n",
@@ -501,11 +334,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
+   "metadata": {},
    "source": [
     "## new cell A-bove\n",
     "<img src=\"../img/notebook/keya.png\" alt=\"Key A\" style=\"width:150px\">\n",
@@ -515,11 +344,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
+   "metadata": {},
    "source": [
     "## new cell B-elow\n",
     "<img src=\"../img/notebook/keyb.png\" alt=\"Key B\" style=\"width:150px\">\n",
@@ -529,44 +354,28 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
+   "metadata": {},
    "source": [
-    "## CTRL + SHIFT + P"
+    "## CTRL + SHIFT + C"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
+   "metadata": {},
    "source": [
     "Just do it!"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
+   "metadata": {},
    "source": [
     "## Trouble..."
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
+   "metadata": {},
    "source": [
     "<div class=\"alert alert-danger\">\n",
     "\n",
@@ -580,37 +389,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "source": [
-    "## Overload?!?"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "subslide"
-    }
-   },
-   "source": [
-    "<img src=\"../img/notebook/toomuch.jpg\" alt=\"Key A\" style=\"width:500px\">\n",
-    "<br><br>\n",
-    "<center>No stress, just go to </center>\n",
-    "<br>\n",
-    "<center><p style=\"font-size: 200%;text-align: center;margin:500\">`Help` > `Keyboard shortcuts`</p></center>"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
+   "metadata": {},
    "source": [
     "* **Stackoverflow** is really, really, really nice!\n",
     "\n",
@@ -619,45 +398,30 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
+   "metadata": {},
    "source": [
     "* Google search is with you!"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
+   "metadata": {},
    "source": [
     "<big><center>**REMEMBER**: To run a cell: <strike>push the start triangle in the menu or</strike> type **SHIFT + ENTER**\n",
-    "![](../../img/shiftenter.jpg)"
+    "    \n",
+    "![](../img/notebook/shiftenter.jpg)"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
+   "metadata": {},
    "source": [
     "# some MAGIC..."
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
+   "metadata": {},
    "source": [
     "## `%psearch`"
    ]
@@ -665,15 +429,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "run_control": {
-     "frozen": false,
-     "read_only": false
-    },
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "%psearch os.*dir"
@@ -681,11 +437,7 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
+   "metadata": {},
    "source": [
     "## `%%timeit`"
    ]
@@ -693,15 +445,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "run_control": {
-     "frozen": false,
-     "read_only": false
-    },
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "%%timeit\n",
@@ -714,15 +458,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "run_control": {
-     "frozen": false,
-     "read_only": false
-    },
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "import numpy as np"
@@ -731,15 +467,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "run_control": {
-     "frozen": false,
-     "read_only": false
-    },
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "%%timeit\n",
@@ -749,37 +477,25 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "subslide"
-    }
-   },
+   "metadata": {},
    "source": [
-    "## `%lsmagic`"
+    "## `%whos`"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "run_control": {
-     "frozen": false,
-     "read_only": false
-    },
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
-    "%lsmagic"
+    "%whos"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## `%whos`"
+    "## `%lsmagic`"
    ]
   },
   {
@@ -788,16 +504,12 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "%whos"
+    "%lsmagic"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
+   "metadata": {},
    "source": [
     "# Let's get started!"
    ]
@@ -805,15 +517,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "run_control": {
-     "frozen": false,
-     "read_only": false
-    },
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "from IPython.display import FileLink, FileLinks"
@@ -822,24 +526,26 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "run_control": {
-     "frozen": false,
-     "read_only": false
-    },
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "FileLinks('.', recursive=False)"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
+  "jupytext": {
+   "default_lexer": "ipython3"
+  },
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -853,7 +559,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.5"
+   "version": "3.12.11"
   },
   "nav_menu": {},
   "toc": {
diff --git a/notebooks/01-introduction-tabular-data.ipynb b/notebooks/01-introduction-tabular-data.ipynb
new file mode 100644
index 0000000..c6c44f4
--- /dev/null
+++ b/notebooks/01-introduction-tabular-data.ipynb
@@ -0,0 +1,1824 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "edceaf1b",
+   "metadata": {},
+   "source": [
+    "<p><font size=\"6\"><b> Introduction to working with pandas and tabular data</b></font></p>\n",
+    "\n",
+    "\n",
+    "> *DS Python for GIS and Geoscience*  \n",
+    "> *October, 2025*\n",
+    ">\n",
+    "> *© 2025, Joris Van den Bossche and Stijn Van Hoey  (<mailto:jorisvandenbossche@gmail.com>, <mailto:stijnvanhoey@gmail.com>). Licensed under [CC BY 4.0 Creative Commons](http://creativecommons.org/licenses/by/4.0/)*\n",
+    "\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d93bb4dc",
+   "metadata": {},
+   "source": [
+    "## Working with tabular data using Pandas"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ebdb30ec",
+   "metadata": {},
+   "source": [
+    "To load the pandas package and start working with it, we first import the package. The community agreed alias for pandas is `pd`,  which we will also use here:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2de0f6b4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fb180e5d",
+   "metadata": {},
+   "source": [
+    "Let's start with importing some actual data. Pandas provides functions for many different formats, but here we will read a simple CSV file with information about the countries of the world:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a983927b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "countries = pd.read_csv(\"data/countries.csv\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e2fcb925",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "countries"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8a8ba82f",
+   "metadata": {},
+   "source": [
+    "The object created here is a **DataFrame**:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0cc8e201",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "type(countries)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5d6c6d8b",
+   "metadata": {},
+   "source": [
+    "A `DataFrame` is a 2-dimensional, **tablular data structure** comprised of rows and columns. It is similar to a spreadsheet, a database (SQL) table or the data.frame in R.\n",
+    "\n",
+    "<img align=\"center\" width=50% src=\"../img/pandas/01_table_dataframe1.svg\">"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8773d8a5",
+   "metadata": {},
+   "source": [
+    "A DataFrame can store data of different types (including characters, integers, floating point values, categorical data and more) in columns. In pandas, we can check the data types of the columns with the `dtypes` attribute:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0a103cee",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "countries.dtypes"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0243387e",
+   "metadata": {},
+   "source": [
+    "### Each column in a `DataFrame` is a `Series`\n",
+    "\n",
+    "When selecting a single column of a pandas `DataFrame`, the result is a pandas `Series`, a 1-dimensional data structure. \n",
+    "\n",
+    "To select the column, use the column label in between square brackets `[]`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "91ee1fb0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "countries['pop_est']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d2084877",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "s = countries['pop_est']\n",
+    "type(s)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8b08091e",
+   "metadata": {},
+   "source": [
+    "### Pandas objects have attributes and methods\n",
+    "\n",
+    "Pandas provides a lot of functionalities for the DataFrame and Series. The `.dtypes` shown above is an *attribute* of the DataFrame. In addition, there are also functions that can be called on a DataFrame or Series, i.e. *methods*. As methods are functions, do not forget to use parentheses `()` to call them.\n",
+    "\n",
+    "A few examples that can help exploring the data:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "932715b3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "countries.head() # Top rows"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "75f9ef89",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "countries.tail() # Bottom rows"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "89feaca7",
+   "metadata": {},
+   "source": [
+    "The ``describe`` method computes summary statistics for each column:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6e2c4031",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "countries['pop_est'].describe()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b9e42167",
+   "metadata": {},
+   "source": [
+    "**Sort**ing your data **by** a specific column is another important first-check:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "37e4591f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "countries.sort_values(by='pop_est')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3b971568",
+   "metadata": {},
+   "source": [
+    "<div style=\"border: 5px solid #3776ab; border-radius: 2px; padding: 2em;\">\n",
+    "\n",
+    "## Python recap\n",
+    "\n",
+    "Python objects have **attributes** and **methods**:\n",
+    "\n",
+    "* Attribute: `obj.attribute` (no parentheses!) -> property of the object (pandas examples: `dtypes`, `columns`, `shape`, ..)\n",
+    "* Method: `obj.method()` (function call with parentheses) -> action (pandas examples: `mean()`, `sort_values()`, ...)\n",
+    "\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "19889faf",
+   "metadata": {},
+   "source": [
+    "## Basic operations on Series and DataFrames"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "75e0a020",
+   "metadata": {},
+   "source": [
+    "### Elementwise-operations"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f20b1b98",
+   "metadata": {},
+   "source": [
+    "The typical arithmetic (+, -, \\*, /) and comparison (==, >, <, ...) operations work *element-wise*.\n",
+    "\n",
+    "With as scalar:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "dedd8497",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "population = countries['pop_est'].head()\n",
+    "population"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f6ad75e4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "population / 1000"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e76f9989",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "population > 1_000_000"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "724590ec",
+   "metadata": {},
+   "source": [
+    "With two Series objects:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3a6bdc55",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "countries['gdp_md_est'] / countries['pop_est']"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "173ce59c",
+   "metadata": {},
+   "source": [
+    "### Aggregations (reductions)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8d120745",
+   "metadata": {},
+   "source": [
+    "Pandas provides a large set of **summary** functions that operate on different kinds of pandas objects (DataFrames, Series, Index) and produce a single value. When applied to a DataFrame, the result is returned as a pandas Series (one value for each column)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ea50c351",
+   "metadata": {},
+   "source": [
+    "The average population number:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2e967cb7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "population.mean()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6e133940",
+   "metadata": {},
+   "source": [
+    "The maximum GDP:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f024df06",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "countries['gdp_md_est'].max()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "766e35b8",
+   "metadata": {},
+   "source": [
+    "For dataframes, only the numeric columns are included in the result:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "04b4e05c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "countries.median(numeric_only=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "874cdb01",
+   "metadata": {},
+   "source": [
+    "### Adding new columns\n",
+    "\n",
+    "We can add a new column to a DataFrame with similar syntax as selecting a columns: create a new column by assigning the output to the DataFrame with a new column name in between the `[]`.\n",
+    "\n",
+    "For example, to add the GDP per capita calculated above, we can do:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7e68c4ca",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "countries['gdp_capita'] = countries['gdp_md_est'] / countries['pop_est']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "65913a92",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "countries.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "132a2771",
+   "metadata": {},
+   "source": [
+    "## Indexing: selecting a subset of the data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e6e629bb",
+   "metadata": {},
+   "source": [
+    "### Subset variables (columns)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e13082c8",
+   "metadata": {},
+   "source": [
+    "For a DataFrame, basic indexing selects the columns (cfr. the dictionaries of pure python)\n",
+    "\n",
+    "Selecting a **single column**:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c799e9ef",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "countries['pop_est'] # single []"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d1f92bd3",
+   "metadata": {},
+   "source": [
+    "Remember that the same syntax can also be used to *add* a new columns: `df['new'] = ...`.\n",
+    "\n",
+    "We can also select **multiple columns** by passing a list of column names into `[]`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2ca18f9d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "countries[['name', 'pop_est']] # double [[]]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "161d2f8f",
+   "metadata": {},
+   "source": [
+    "### Subset observations (rows)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "df93af63",
+   "metadata": {},
+   "source": [
+    "Using `[]`, slicing or boolean indexing accesses the **rows**:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5264cfab",
+   "metadata": {},
+   "source": [
+    "### Slicing"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6625555a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "countries[0:4]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2e3aeca6",
+   "metadata": {},
+   "source": [
+    "### Boolean indexing (filtering)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5b7ba9ce",
+   "metadata": {},
+   "source": [
+    "Often, you want to select rows based on a certain condition. This can be done with *'boolean indexing'* (like a WHERE clause in SQL).\n",
+    "\n",
+    "The indexer (or boolean mask) should be 1-dimensional and the same length as the thing being indexed."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "aeef0c9c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# taking the first 5 rows to illustrate\n",
+    "df = countries.head()\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1b197017",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mask = df['pop_est'] > 1_000_000\n",
+    "mask"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1ba591b2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df[mask]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0cfc3c25",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# or in one go\n",
+    "df[df['pop_est'] > 1_000_000]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8f518f67",
+   "metadata": {},
+   "source": [
+    "With the full dataset:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d700f288",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "countries[countries['gdp_md_est'] > 5_000_000]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7b56e218",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "countries[countries['continent'] == \"Oceania\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "521c959f",
+   "metadata": {},
+   "source": [
+    "An overview of the possible comparison operations:\n",
+    "\n",
+    "Operator   |  Description\n",
+    "------ | --------\n",
+    "==       | Equal\n",
+    "!=       | Not equal\n",
+    "\\>       | Greater than\n",
+    "\\>=       | Greater than or equal\n",
+    "<       | Lesser than\n",
+    "<=       | Lesser than or equal\n",
+    "\n",
+    "and to combine multiple conditions:\n",
+    "\n",
+    "Operator   |  Description\n",
+    "------ | --------\n",
+    "&       | And (`cond1 & cond2`)\n",
+    "\\|       | Or (`cond1 \\| cond2`)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "495be455",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-info\" style=\"font-size:120%\">\n",
+    "<b>REMEMBER</b>: <br><br>\n",
+    "\n",
+    "So as a summary, `[]` provides the following convenience shortcuts:\n",
+    "\n",
+    "* **Series**: selecting a **label**: `s[label]`\n",
+    "* **DataFrame**: selecting a single or multiple **columns**:`df['col']` or `df[['col1', 'col2']]`\n",
+    "* **DataFrame**: slicing or filtering the **rows**: `df['row_label1':'row_label2']` or `df[mask]`\n",
+    "\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1154d8f2",
+   "metadata": {},
+   "source": [
+    "## Let's practice! (part I)\n",
+    "\n",
+    "Throughout the exercises in this course on vector data, we will work with several datasets about the city of Paris.\n",
+    "\n",
+    "Here, we start with the following datasets:\n",
+    "\n",
+    "- Information about the administrative districts of Paris: `paris_districts.csv`\n",
+    "- Real-time (at the moment I downloaded them ..) information about the public bicycle sharing system in Paris (vélib, https://opendata.paris.fr/explore/dataset/stations-velib-disponibilites-en-temps-reel/information/): `paris_bike_stations.csv`"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "90fd2f8e",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-success\">\n",
+    "\n",
+    "**EXERCISE 1**:\n",
+    "\n",
+    "* Read the `data/paris_districts.csv` file, and assign the DataFrame to a variable called `districts`.\n",
+    "* Check the first rows of the DataFrame. \n",
+    "\n",
+    "<details>\n",
+    "  <summary>Hints</summary>\n",
+    "\n",
+    "* For reading a CSV file, we can use the `pd.read_csv()` function. The first argument it takes is the path to the file.\n",
+    "\n",
+    "</details>\n",
+    "    \n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "54df6e11",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/01-introduction-tabular-data1.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b46e32b1",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/01-introduction-tabular-data2.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9893d670",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/01-introduction-tabular-data3.py"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3a4dfeb9",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-success\">\n",
+    "\n",
+    "**EXERCISE 2**:\n",
+    "\n",
+    "* What is the average population number of the districts?\n",
+    "* And what is the median?\n",
+    "\n",
+    "<details>\n",
+    "  <summary>Hints</summary>\n",
+    "\n",
+    "* The average of a column can be calculated with the `mean()` method.\n",
+    "* Selecting a single column uses the \"square bracket\" notation `df['colname']`.\n",
+    "\n",
+    "</details>\n",
+    "    \n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ed02425b",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/01-introduction-tabular-data4.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9bd61a2e",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/01-introduction-tabular-data5.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "723d10e1",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/01-introduction-tabular-data6.py"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a5098bf3",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-success\">\n",
+    "\n",
+    "**EXERCISE 3**:\n",
+    "\n",
+    "* What is the maximum area of the districts?\n",
+    "* The area column is expressed in m². What is the maximum area in km² ?\n",
+    "* And can you also calculate in the area in km² for the full column?\n",
+    "\n",
+    "<details>\n",
+    "  <summary>Hints</summary>\n",
+    "\n",
+    "* The maximum of a column can be calculated with the `max()` method.\n",
+    "* The division operator is `/`, and if we want to take the power of 2 we can use `10**2` (so not `10^2` as you might expect from other languages!).\n",
+    "* Operations on a Series are *element-wise*. For example, to add a number to each element of the Series `s`, we can do `s + 2`.\n",
+    "    \n",
+    "</details>\n",
+    "    \n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9cef74ee",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/01-introduction-tabular-data7.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bac960b5",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/01-introduction-tabular-data8.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fc050db0",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/01-introduction-tabular-data9.py"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c9e59ff8",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-success\">\n",
+    "\n",
+    "**EXERCISE 4**:\n",
+    "\n",
+    "* Calculate the population density for all districts, expressed in \"inhabitant / km²\". Call the resulting Series `population_density`.\n",
+    "* What is the maximum population density?\n",
+    "\n",
+    "<details>\n",
+    "  <summary>Hints</summary>\n",
+    "\n",
+    "* Dividing two Series objects also works element-wise: the first element of Series 1 divided by the first element of Series 2, the second by the second, etc.\n",
+    "\n",
+    "</details>\n",
+    "    \n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "851c95e1",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/01-introduction-tabular-data10.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a9ae6dec",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/01-introduction-tabular-data11.py"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e944bd4a",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-success\">\n",
+    "\n",
+    "**EXERCISE 5**:\n",
+    "\n",
+    "* Add the population density as a new column to the `districts` dataframe.\n",
+    "\n",
+    "<details>\n",
+    "  <summary>Hints</summary>\n",
+    "\n",
+    "* Adding a column to a DataFrame uses similar syntax as selecting one with `[]`. But now we assign values to that column: `df[\"new_colum\"] = values`\n",
+    "\n",
+    "</details>\n",
+    "    \n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "521d4731",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/01-introduction-tabular-data12.py"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "92dc8b47",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-success\">\n",
+    "\n",
+    "**EXERCISE 6**:\n",
+    "\n",
+    "* Sort the `districts` DataFrame by the population number. Sort in such a way that the districts with the largest population are at the top of the DataFrame (Tip: check the help of the function!)\n",
+    "\n",
+    "<details>\n",
+    "  <summary>Hints</summary>\n",
+    "\n",
+    "* Sorting values of a Series, of the rows of a DataFrame, can be done with the `sort_values` method.\n",
+    "* For sorting a DataFrame by a certain column, specify the column name with the `by=` keyword.\n",
+    "* The `ascending` keyword determines if the largest values are sorted at the bottom or at the top of the DataFrame.\n",
+    "</details>\n",
+    "    \n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "726585c0",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/01-introduction-tabular-data13.py"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "da37b2f8",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-success\">\n",
+    "\n",
+    "**EXERCISE 7**:\n",
+    "\n",
+    "* Select all rows for the districts of the 3rd arrondissement.\n",
+    "* For this subset, what is the total population of the arrondissement?\n",
+    "    \n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f8b95875",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/01-introduction-tabular-data14.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5b54d98c",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/01-introduction-tabular-data15.py"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5c3c1523",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-success\">\n",
+    "\n",
+    "**EXERCISE 8**:\n",
+    "\n",
+    "* Select the districts with a population of more than 50.000 inhabitants.\n",
+    "\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1efa8314",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/01-introduction-tabular-data16.py"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "515bc15d",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-success\">\n",
+    "\n",
+    "**EXERCISE 9**:\n",
+    "\n",
+    "* How many districts have a population of more than 50.000 inhabitants?\n",
+    "\n",
+    "<details>\n",
+    "  <summary>Hints</summary>\n",
+    "\n",
+    "* The number of rows of a DataFrame can be obtained with the `len()` function (not a method!). Or alternatively with the `shape` attribute.\n",
+    "* Alternatively, we can also get this number based on the *condition*: counting the number of True values is the same as taking the sum of this condition.\n",
+    "</details>\n",
+    "    \n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "19d263a0",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/01-introduction-tabular-data17.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7a3141d6",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/01-introduction-tabular-data18.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8a30d981",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/01-introduction-tabular-data19.py"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "44a2903c",
+   "metadata": {},
+   "source": [
+    "## Plotting: visual exploration of data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3c17f0c6",
+   "metadata": {},
+   "source": [
+    "The **`plot`** method can be used to quickly visualize the data in different ways:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7772d637",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "countries['pop_est'].plot();"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7f42df91",
+   "metadata": {},
+   "source": [
+    "The default is a *line* plot. However, for this dataset, that's a not very useful plot type. With the `.plot.<kind>` accessor (or the `kind` keyword), you can specify other plot types:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b567739a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "countries['pop_est'].plot.hist()  # or: .plot(kind='hist')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a750bfc1",
+   "metadata": {},
+   "source": [
+    "This is still not the best plot. This is due to a few countries with a very large population."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "24abb880",
+   "metadata": {},
+   "source": [
+    "### A `matplotlib` primer\n",
+    "\n",
+    "The plot produced by pandas with the `.plot()` method, is actually created using the `matplotlib` package.\n",
+    "\n",
+    "[Matplotlib](http://matplotlib.org/) is a Python package used widely throughout the scientific Python community to produce high quality 2D publication graphics. It transparently supports a wide range of output formats including PNG (and other raster formats), PostScript/EPS, PDF and SVG and has interfaces for all of the major desktop GUI (graphical user interface) toolkits. It is a great package with lots of options.\n",
+    "\n",
+    "However, matplotlib is is also a *huge* library, with which you can plot anything you want and customize every detail of the plot ... if you know how to do this. \n",
+    "\n",
+    "So in this course, we are mostly going to use matplotlib through a convenience layer, such as the pandas `.plot()` method, or a helper library like `seaborn`. But you'll some basic matplotlib techniques as well."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "77d11b9c",
+   "metadata": {},
+   "source": [
+    "Matplotlib comes with a convenience sub-package called ``pyplot`` which, for consistency with the wider matplotlib community, should always be imported as ``plt``:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "78c4d72e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a13a07e5",
+   "metadata": {},
+   "source": [
+    "The object returned by the pandas `plot()` method is called an **Axes**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3b8d31ca",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ax = countries['pop_est'].plot.hist()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c7cd9fe9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "type(ax)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1689986f",
+   "metadata": {},
+   "source": [
+    "The Axes represents the \"data space\" of a typical plot: where data is being plotted, and typically having an x and y *axis*. The Axes is part of a **Figure** (in the above, the Figure has one Axes, or subplot, but you can also create a Figure with multiple Axes or subplots). \n",
+    "\n",
+    "We can also create this Figure and Axes manually:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bcc89b8b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig, axs = plt.subplots()  # ncols="
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "132be32a",
+   "metadata": {},
+   "source": [
+    "and then specify the Axes to plot on with pandas:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "23889ae8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig, axs = plt.subplots()\n",
+    "countries['pop_est'].plot.hist(ax=axs)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2f8d97aa",
+   "metadata": {},
+   "source": [
+    "The Axes object can then be used for further customization:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a46197ea",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig, axs = plt.subplots()\n",
+    "countries['pop_est'].plot.hist(ax=axs, bins=50)\n",
+    "axs.set_title(\"Countries of the world\")\n",
+    "axs.set_xlabel(\"Population\")\n",
+    "axs.set_xlim(0, 5e8)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "22a58dcb",
+   "metadata": {},
+   "source": [
+    "An example of another package built on top of matplotlib is **`seaborn`** (https://seaborn.pydata.org/). It provides a high-level interface for a set of statistical graphics.\n",
+    "\n",
+    "For example, we can make a boxplot of the GDP per capita for each continent:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "db436501",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import seaborn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "10bb43df",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(8, 8))\n",
+    "seaborn.boxplot(y=\"continent\", x=\"gdp_capita\", color=\"C0\", data=countries, ax=ax)  # violinplot"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a7178dfe",
+   "metadata": {},
+   "source": [
+    "This is only an ultra short intro to matplotlib. In the course materials, you can find another notebook with some more details ([visualization-01-matplotlib.ipynb](visualization-01-matplotlib.ipynb#An-small-cheat-sheet-reference-for-some-common-elements))."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "512961a9",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-info\" style=\"font-size:18px\">\n",
+    "\n",
+    "**Galleries!**\n",
+    "\n",
+    "Galleries are great to get inspiration, see the plot you want, and check the code how it is created:\n",
+    "    \n",
+    "* [matplotlib gallery](https://matplotlib.org/stable/gallery/) is an important resource to start from\n",
+    "* [seaborn gallery](https://seaborn.pydata.org/examples/index.html)\n",
+    "* The Python Graph Gallery (https://python-graph-gallery.com/)\n",
+    "\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b848eca8",
+   "metadata": {},
+   "source": [
+    "## Aggregating statistics grouped by category\n",
+    "\n",
+    "<img align=\"center\" src=\"../img/pandas/06_groupby1.svg\">"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7f4d2824",
+   "metadata": {},
+   "source": [
+    "Assume we want to know the total population number of a certain continent. Using the boolean indexing and aggregation we learned above, we can do:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "123152df",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "africa = countries[countries['continent'] == \"Africa\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "67392102",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "africa['pop_est'].sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "648ec166",
+   "metadata": {},
+   "source": [
+    "To calculate this for all continents, we could repeat the above for each continent. However, this quickly becomes tedious, and can directly be performed by pandas:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "decddd9d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "countries.groupby('continent')['pop_est'].sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "56ca34b8",
+   "metadata": {},
+   "source": [
+    "Calculating a given statistic (e.g. sum of the population) for each category in a column (e.g. the different continents) is a common pattern. The `groupby()` method is used to support this type of operations. More general, this fits in the more general split-apply-combine pattern:\n",
+    "\n",
+    "* **Split** the data into groups\n",
+    "* **Apply** a function to each group independently\n",
+    "* **Combine** the results into a data structure\n",
+    "\n",
+    "The apply and combine steps are typically done together in pandas."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0f7ae4f9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "countries.groupby('continent')['pop_est'].sum().plot.barh()  # or plot(kind=\"barh\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a95e39ca",
+   "metadata": {},
+   "source": [
+    "## Let's practice! (part II)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8caca287",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "districts = pd.read_csv(\"data/paris_districts.csv\")\n",
+    "stations = pd.read_csv(\"data/paris_bike_stations.csv\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c705486d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "districts.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a0f53ece",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stations.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e229a0f9",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-success\">\n",
+    "<b>EXERCISE 10</b>:\n",
+    "\n",
+    "* Plot the population distribution of the Paris districts\n",
+    "\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "556ba277",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/01-introduction-tabular-data20.py"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0ffbdbbd",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-success\">\n",
+    "\n",
+    "<b>EXERCISE 11</b>:\n",
+    "\n",
+    "* Using groupby(), calculate the total population of each of the arrondissements.\n",
+    "\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4d24b6a6",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/01-introduction-tabular-data21.py"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ef016c60",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-success\">\n",
+    "\n",
+    "<b>EXERCISE 12</b>:\n",
+    "\n",
+    "* Using the bike stations dataset (`stations`), make a figure with a histogram of both the total bike stands and available bikes.\n",
+    "\n",
+    "<details>\n",
+    "<summary>Hints</summary>\n",
+    "\n",
+    "* You can plot both histograms on top of each other using transparancy (`alpha` keyword) to see both.\n",
+    "* Use the `plt.subplots()` function to create a figure and axes object. You can then use this axes object to pass to `.plot.hist(..)` to plot two histograms on the same axes.\n",
+    "</details>\n",
+    "\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1cc89119",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/01-introduction-tabular-data22.py"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "57a486ad",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-success\">\n",
+    "\n",
+    "<b>EXERCISE 13</b>:\n",
+    "\n",
+    "* Using `seaborn`, make a boxplot of the number of bike stands (in the `stations` dataframe) per arrondissement.\n",
+    "\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f4172121",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/01-introduction-tabular-data23.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4e10223b",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/01-introduction-tabular-data24.py"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8124483d",
+   "metadata": {},
+   "source": [
+    "## Other useful things to know!\n",
+    "\n",
+    "This notebook is only an intro. The pandas library provides a lot more functionality for working with tabular data, which we won't be cover in this course."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5e3b784a",
+   "metadata": {},
+   "source": [
+    "### Counting values"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6000cab9",
+   "metadata": {},
+   "source": [
+    "Do you want to know what the unique values are of a column, and how many times each value occurs? Use the `value_counts()` method:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "19c8cbd5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "countries['continent'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2def17b3",
+   "metadata": {},
+   "source": [
+    "### Selecting a specific value of the DataFrame"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "809f2c7a",
+   "metadata": {},
+   "source": [
+    "To access a specific value of a DataFrame, we can use the `.loc` method passing the (row label, column name):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cd1291c4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "countries.loc[0, \"name\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6e994287",
+   "metadata": {},
+   "source": [
+    "### Merging two dataframes"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5a07b612",
+   "metadata": {},
+   "source": [
+    "Pandas provides several ways to combine different DataFrames. If there is a common column on which you want to match both DataFrames, we can use the `pd.merge()` function:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d6d515df",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cities = pd.read_csv(\"data/cities.csv\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e3aa23d6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cities.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "18d769c9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "countries.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e7419654",
+   "metadata": {},
+   "source": [
+    "Both DataFrames have the `'iso_a3'` column with a 3-character code of the country. Based on this, we can add information about the country to the cities dataset:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b08032c8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pd.merge(cities, countries, on=\"iso_a3\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9b05d64a",
+   "metadata": {},
+   "source": [
+    "## Towards geospatial data\n",
+    "\n",
+    "The datasets used in this notebook contain spatial information: data about areas (countries, districts) or point locations (cities, bike stations). But the data itself didn't always explicitly include the the spatial component. For example, we don't know the exact extent of the countries with the dataset used here.\n",
+    "\n",
+    "With point locations, such as the cities DataFrame, the location is included as two columns:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "66335f4e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cities.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "01f5b54e",
+   "metadata": {},
+   "source": [
+    "Which allows us to, for example, plot the locations manually:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "300fe025",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cities.plot.scatter(x=\"longitude\", y=\"latitude\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c9243515",
+   "metadata": {},
+   "source": [
+    "However, this doesn't enable to *easily* work with those locations and do spatial analyses on them. For that, we are going to introduce a new package: **`geopandas`**.\n",
+    "\n",
+    "As illustration, we convert the pandas DataFrame with cities into a geopandas GeoDataFrame:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "185fd8f7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import geopandas"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c35ee0e5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cities_geo = geopandas.GeoDataFrame(cities, geometry=geopandas.points_from_xy(cities[\"longitude\"], cities[\"latitude\"]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f24bee75",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cities_geo"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7cbac42e",
+   "metadata": {},
+   "source": [
+    "Now, we have a single column with the location information (the \"geometry\" column). This is the topic of the next notebook."
+   ]
+  }
+ ],
+ "metadata": {
+  "jupytext": {
+   "default_lexer": "ipython3",
+   "formats": "ipynb,md:myst"
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/notebooks/02-introduction-geospatial-data.ipynb b/notebooks/02-introduction-geospatial-data.ipynb
new file mode 100644
index 0000000..2cc754f
--- /dev/null
+++ b/notebooks/02-introduction-geospatial-data.ipynb
@@ -0,0 +1,1026 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "60ebdf8b",
+   "metadata": {},
+   "source": [
+    "<p><font size=\"6\"><b> Introduction to geospatial vector data in Python</b></font></p>\n",
+    "\n",
+    "\n",
+    "> *DS Python for GIS and Geoscience*  \n",
+    "> *October, 2025*\n",
+    ">\n",
+    "> *© 2025, Joris Van den Bossche and Stijn Van Hoey  (<mailto:jorisvandenbossche@gmail.com>, <mailto:stijnvanhoey@gmail.com>). Licensed under [CC BY 4.0 Creative Commons](http://creativecommons.org/licenses/by/4.0/)*\n",
+    "\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4cb8b8e1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import geopandas"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b59f6882",
+   "metadata": {},
+   "source": [
+    "## Importing geospatial data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5c35bcd4",
+   "metadata": {},
+   "source": [
+    "Geospatial data is often available from specific GIS file formats or data stores, like ESRI shapefiles, GeoJSON files, geopackage files, PostGIS (PostgreSQL) database, ...\n",
+    "\n",
+    "We can use the GeoPandas library to read many of those GIS file formats (relying on the `pyogrio` library under the hood, which is an interface to GDAL/OGR), using the `geopandas.read_file` function.\n",
+    "\n",
+    "For example, let's start by reading a shapefile with all the countries of the world (adapted from http://www.naturalearthdata.com/downloads/110m-cultural-vectors/110m-admin-0-countries/, zip file is available in the `/data` directory), and inspect the data:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3c51c022",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "countries = geopandas.read_file(\"data/ne_110m_admin_0_countries.zip\")\n",
+    "# or if the archive is unpacked:\n",
+    "# countries = geopandas.read_file(\"data/ne_110m_admin_0_countries/ne_110m_admin_0_countries.shp\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4a08e7c6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "countries.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ccf47b00",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "countries.plot()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6c0ac4fa",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "countries.explore()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "69191cf2",
+   "metadata": {},
+   "source": [
+    "What do we observe:\n",
+    "\n",
+    "- Using `.head()` we can see the first rows of the dataset, just like we can do with Pandas.\n",
+    "- There is a `geometry` column and the different countries are represented as polygons\n",
+    "- We can use the `.plot()` (matplotlib) or `explore()` (Folium / Leaflet.js) method to quickly get a *basic* visualization of the data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "508d3f1d",
+   "metadata": {},
+   "source": [
+    "## What's a GeoDataFrame?\n",
+    "\n",
+    "We used the GeoPandas library to read in the geospatial data, and this returned us a `GeoDataFrame`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ea4363f9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "type(countries)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7c1f1160",
+   "metadata": {},
+   "source": [
+    "A GeoDataFrame contains a tabular, geospatial dataset:\n",
+    "\n",
+    "* It has a **'geometry' column** that holds the geometry information (or features in GeoJSON).\n",
+    "* The other columns are the **attributes** (or properties in GeoJSON) that describe each of the geometries\n",
+    "\n",
+    "Such a `GeoDataFrame` is just like a pandas `DataFrame`, but with some additional functionality for working with geospatial data:\n",
+    "\n",
+    "* A `.geometry` attribute that always returns the column with the geometry information (returning a GeoSeries). The column name itself does not necessarily need to be 'geometry', but it will always be accessible as the `.geometry` attribute.\n",
+    "* It has some extra methods for working with spatial data (area, distance, buffer, intersection, ...), which we will learn in later notebooks"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9c3f719b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "countries.geometry"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "34dbbaba",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "type(countries.geometry)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f8af4ea8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "countries.geometry.area"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "33b554b4",
+   "metadata": {},
+   "source": [
+    "**It's still a DataFrame**, so we have all the Pandas functionality available to use on the geospatial dataset, and to do data manipulations with the attributes and geometry information together.\n",
+    "\n",
+    "For example, we can calculate average population number over all countries (by accessing the 'pop_est' column, and calling the `mean` method on it):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "373067e4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "countries['pop_est'].mean()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c7042158",
+   "metadata": {},
+   "source": [
+    "Or, we can use boolean filtering to select a subset of the dataframe based on a condition:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f22cd53f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "africa = countries[countries['continent'] == 'Africa']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6e56375b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "africa.plot();"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7a0ea171",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-info\" style=\"font-size:120%\">\n",
+    "\n",
+    "**REMEMBER:** <br>\n",
+    "\n",
+    "* A `GeoDataFrame` allows to perform typical tabular data analysis together with spatial operations\n",
+    "* A `GeoDataFrame` (or *Feature Collection*) consists of:\n",
+    "    * **Geometries** or **features**: the spatial objects\n",
+    "    * **Attributes** or **properties**: columns with information about each spatial object\n",
+    "\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "03114e5e",
+   "metadata": {},
+   "source": [
+    "## Geometries: Points, Linestrings and Polygons\n",
+    "\n",
+    "Spatial **vector** data can consist of different types, and the 3 fundamental types are:\n",
+    "\n",
+    "![](../img/simple_features_3_text.svg)\n",
+    "\n",
+    "* **Point** data: represents a single point in space.\n",
+    "* **Line** data (\"LineString\"): represents a sequence of points that form a line.\n",
+    "* **Polygon** data: represents a filled area.\n",
+    "\n",
+    "And each of them can also be combined in multi-part geometries (See https://shapely.readthedocs.io/en/stable/manual.html#geometric-objects for extensive overview)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4719339c",
+   "metadata": {},
+   "source": [
+    "For the example we have seen up to now, the individual geometry objects are Polygons:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "995a0901",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(countries.geometry[2])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "65ddf8cf",
+   "metadata": {},
+   "source": [
+    "Let's import some other datasets with different types of geometry objects.\n",
+    "\n",
+    "A dateset about cities in the world (adapted from http://www.naturalearthdata.com/downloads/110m-cultural-vectors/110m-populated-places/, zip file is available in the `/data` directory), consisting of Point data:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c3295faf",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cities = geopandas.read_file(\"data/ne_110m_populated_places.zip\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a5045be0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(cities.geometry[0])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b47470e3",
+   "metadata": {},
+   "source": [
+    "And a dataset of rivers in the world (from http://www.naturalearthdata.com/downloads/50m-physical-vectors/50m-rivers-lake-centerlines/, zip file is available in the `/data` directory) where each river is a (multi-)line:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1c533d17",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "rivers = geopandas.read_file(\"data/ne_50m_rivers_lake_centerlines.zip\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "84582bad",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(rivers.geometry[0])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6c0e225e",
+   "metadata": {},
+   "source": [
+    "### The `shapely` library\n",
+    "\n",
+    "The individual geometry objects are provided by the [`shapely`](https://shapely.readthedocs.io/en/stable/) library"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1d1a86ed",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "type(countries.geometry[0])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7ac32d9c",
+   "metadata": {},
+   "source": [
+    "To construct one ourselves:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0af29507",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from shapely import Point, Polygon, LineString"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "10a5ecb1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "p = Point(0, 0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4f197ebd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(p)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "98d6ede1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "polygon = Polygon([(1, 1), (2,2), (2, 1)])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2d6f595f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "polygon.area"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9d4185e1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "polygon.distance(p)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "aa0ae3df",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-info\" style=\"font-size:120%\">\n",
+    "\n",
+    "**REMEMBER**: <br>\n",
+    "\n",
+    "Single geometries are represented by `shapely` objects:\n",
+    "\n",
+    "* If you access a single geometry of a GeoDataFrame, you get a shapely geometry object\n",
+    "* Those objects have similar functionality as geopandas objects (GeoDataFrame/GeoSeries). For example:\n",
+    "    * `single_shapely_object.distance(other_point)` -> distance between two points\n",
+    "    * `geodataframe.distance(other_point)` ->  distance for each point in the geodataframe to the other point\n",
+    "\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0315991d",
+   "metadata": {},
+   "source": [
+    "## Plotting our different layers together"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d7e672f4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# import matplotlib.pyplot as plt\n",
+    "# fig, ax = plt.subplots(figsize=(10, 8))\n",
+    "ax = countries.plot(edgecolor='k', facecolor='none', figsize=(10, 8))\n",
+    "rivers.plot(ax=ax)\n",
+    "cities.plot(ax=ax, color='red')\n",
+    "ax.set(xlim=(-20, 60), ylim=(-40, 40))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "747aeba8",
+   "metadata": {},
+   "source": [
+    "See the [visualization-02-geopandas.ipynb](visualization-02-geopandas.ipynb) notebook for more details on visualizing geospatial datasets."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d049bee7",
+   "metadata": {},
+   "source": [
+    "## Let's practice!\n",
+    "\n",
+    "Throughout the exercises in this course, we will work with several datasets about the city of Paris.\n",
+    "\n",
+    "Here, we start with the following datasets:\n",
+    "\n",
+    "- The administrative districts of Paris (https://opendata.paris.fr/explore/dataset/quartier_paris/): `paris_districts_utm.geojson`\n",
+    "- Real-time (at the moment I downloaded them ..) information about the public bicycle sharing system in Paris (vélib, https://opendata.paris.fr/explore/dataset/stations-velib-disponibilites-en-temps-reel/information/): `data/paris_bike_stations_mercator.gpkg`\n",
+    "\n",
+    "Both datasets are provided as spatial datasets using a GIS file format.\n",
+    "\n",
+    "Let's explore further those datasets, now using the spatial aspect as well."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7e43492e",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-success\">\n",
+    "\n",
+    "**EXERCISE 1**:\n",
+    "\n",
+    "We will start with exploring the bicycle station dataset (available as a GeoPackage file: `data/paris_bike_stations_mercator.gpkg`)\n",
+    "    \n",
+    "* Read the stations datasets into a GeoDataFrame called `stations`.\n",
+    "* Check the type of the returned object\n",
+    "* Check the first rows of the dataframes. What kind of geometries does this datasets contain?\n",
+    "* How many features are there in the dataset? \n",
+    "    \n",
+    "<details><summary>Hints</summary>\n",
+    "\n",
+    "* Use `type(..)` to check any Python object type\n",
+    "* The `geopandas.read_file()` function can read different geospatial file formats. You pass the file name as first argument.\n",
+    "* Use the `.shape` attribute to get the number of features\n",
+    "\n",
+    "</details>\n",
+    "    \n",
+    "    \n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3f339748",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/02-introduction-geospatial-data1.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fd87d352",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/02-introduction-geospatial-data2.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6f46af55",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/02-introduction-geospatial-data3.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ca3f144f",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/02-introduction-geospatial-data4.py"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ba47565a",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-success\">\n",
+    "\n",
+    "**EXERCISE 2**:\n",
+    "\n",
+    "* Make a quick plot of the `stations` dataset.\n",
+    "* Make the plot a bit larger by setting the figure size to (12, 6) (hint: the `plot` method accepts a `figsize` keyword).\n",
+    " \n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9bb788bd",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/02-introduction-geospatial-data5.py"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d3a51c45",
+   "metadata": {},
+   "source": [
+    "A plot with just some points can be hard to interpret without any spatial context. We have seen that we can use the `explore()` method to easily get an interactive figure that by default includes a background map. But also for the static matplotlib-based plot, it can be useful to add such a base map, and that's what we will learn in the next excercise.\n",
+    "\n",
+    "We are going to make use of the [contextily](https://github.com/darribas/contextily) package. The `add_basemap()` function of this package makes it easy to add a background web map to our plot. We begin by plotting our data first, and then pass the matplotlib axes object (returned by dataframe's `plot()` method) to the `add_basemap()` function. `contextily` will then download the web tiles needed for the geographical extent of your plot.\n",
+    "\n",
+    "\n",
+    "<div class=\"alert alert-success\">\n",
+    "\n",
+    "**EXERCISE 3**:\n",
+    "\n",
+    "* Import `contextily`.\n",
+    "* Re-do the figure of the previous exercise: make a plot of all the points in `stations`, but assign the result to an `ax` variable.\n",
+    "* Set the marker size equal to 5 to reduce the size of the points (use the `markersize` keyword of the `plot()` method for this).\n",
+    "* Use the `add_basemap()` function of `contextily` to add a background map: the first argument is the matplotlib axes object `ax`.\n",
+    "\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ca984bfe",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/02-introduction-geospatial-data6.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f98afb71",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/02-introduction-geospatial-data7.py"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1295be7c",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-success\">\n",
+    "\n",
+    "**EXERCISE 4**:\n",
+    "\n",
+    "* Make a histogram showing the distribution of the number of bike stands in the stations.\n",
+    "\n",
+    "<details>\n",
+    "  <summary>Hints</summary>\n",
+    "\n",
+    "* Selecting a column can be done with the square brackets: `df['col_name']`\n",
+    "* Single columns have a `hist()` method to plot a histogram of its values.\n",
+    "    \n",
+    "</details>\n",
+    "    \n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "57c87858",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/02-introduction-geospatial-data8.py"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e233a8e5",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-success\">\n",
+    "\n",
+    "**EXERCISE 5**:\n",
+    "\n",
+    "Let's now visualize where the available bikes are actually stationed:\n",
+    "    \n",
+    "* Make a plot of the `stations` dataset (also with a (12, 6) figsize).\n",
+    "* Use the `'available_bikes'` columns to determine the color of the points. For this, use the `column=` keyword.\n",
+    "* Use the `legend=True` keyword to show a color bar.\n",
+    " \n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7040b996",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/02-introduction-geospatial-data9.py"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f74d86fd",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-success\">\n",
+    "\n",
+    "**EXERCISE 6**:\n",
+    "\n",
+    "Next, we will explore the dataset on the administrative districts of Paris (available as a GeoJSON file: \"data/paris_districts_utm.geojson\")\n",
+    "\n",
+    "* Read the dataset into a GeoDataFrame called `districts`.\n",
+    "* Check the first rows of the dataframe. What kind of geometries does this dataset contain?\n",
+    "* How many features are there in the dataset? (hint: use the `.shape` attribute)\n",
+    "* Make a quick plot of the `districts` dataset (set the figure size to (12, 6)).\n",
+    "    \n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "317c8ff3",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/02-introduction-geospatial-data10.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d310726c",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/02-introduction-geospatial-data11.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a4f4d738",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/02-introduction-geospatial-data12.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6212655a",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/02-introduction-geospatial-data13.py"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cae2de14",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-success\">\n",
+    "\n",
+    "**EXERCISE 7**:\n",
+    "    \n",
+    "What are the largest districts (biggest area)?\n",
+    "\n",
+    "* Calculate the area of each district.\n",
+    "* Add this area as a new column to the `districts` dataframe.\n",
+    "* Sort the dataframe by this area column for largest to smallest values (descending).\n",
+    "\n",
+    "<details><summary>Hints</summary>\n",
+    "\n",
+    "* Adding a column can be done by assigning values to a column using the same square brackets syntax: `df['new_col'] = values`\n",
+    "* To sort the rows of a DataFrame, use the `sort_values()` method, specifying the colum to sort on with the `by='col_name'` keyword. Check the help of this method to see how to sort ascending or descending.\n",
+    "\n",
+    "</details>\n",
+    "\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d00905ab",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/02-introduction-geospatial-data14.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fcac6e86",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/02-introduction-geospatial-data15.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2b0f6b0c",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/02-introduction-geospatial-data16.py"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c2dbb64f",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-success\">\n",
+    "\n",
+    "**EXERCISE 8**:\n",
+    "\n",
+    "* Add a column `'population_density'` representing the number of inhabitants per squared kilometer (Note: The area is given in squared meter, so you will need to multiply the result with `10**6`).\n",
+    "* Plot the districts using the `'population_density'` to color the polygons. For this, use the `column=` keyword.\n",
+    "* Use the `legend=True` keyword to show a color bar.\n",
+    "\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "709ad19d",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/02-introduction-geospatial-data17.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "246781fc",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/02-introduction-geospatial-data18.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "643af16c",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/02-introduction-geospatial-data19.py"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ba22cba9",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "\n",
+    "## For the curious: A bit more on importing and creating GeoDataFrames"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "00092e1d",
+   "metadata": {},
+   "source": [
+    "### Constructing a GeoDataFrame manually"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "dbbca0cf",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "geopandas.GeoDataFrame({\n",
+    "    'geometry': [Point(1, 1), Point(2, 2)],\n",
+    "    'attribute1': [1, 2],\n",
+    "    'attribute2': [0.1, 0.2]})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9ddbb420",
+   "metadata": {},
+   "source": [
+    "### Creating a GeoDataFrame from an existing dataframe\n",
+    "\n",
+    "For example, if you have lat/lon coordinates in two columns:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1182f116",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.DataFrame(\n",
+    "    {'City': ['Buenos Aires', 'Brasilia', 'Santiago', 'Bogota', 'Caracas'],\n",
+    "     'Country': ['Argentina', 'Brazil', 'Chile', 'Colombia', 'Venezuela'],\n",
+    "     'Latitude': [-34.58, -15.78, -33.45, 4.60, 10.48],\n",
+    "     'Longitude': [-58.66, -47.91, -70.66, -74.08, -66.86]})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "69eb3c12",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gdf = geopandas.GeoDataFrame(\n",
+    "    df, geometry=geopandas.points_from_xy(df.Longitude, df.Latitude))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b63b1508",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gdf"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ea7a3211",
+   "metadata": {},
+   "source": [
+    "See https://geopandas.org/en/latest/gallery/create_geopandas_from_pandas.html for full example"
+   ]
+  }
+ ],
+ "metadata": {
+  "jupytext": {
+   "default_lexer": "ipython3"
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/notebooks/03-coordinate-reference-systems.ipynb b/notebooks/03-coordinate-reference-systems.ipynb
new file mode 100644
index 0000000..3a33f16
--- /dev/null
+++ b/notebooks/03-coordinate-reference-systems.ipynb
@@ -0,0 +1,529 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "fb148624",
+   "metadata": {},
+   "source": [
+    "<p><font size=\"6\"><b>Coordinate reference systems</b></font></p>\n",
+    "\n",
+    "\n",
+    "> *DS Python for GIS and Geoscience*  \n",
+    "> *October, 2025*\n",
+    ">\n",
+    "> *© 2025, Joris Van den Bossche and Stijn Van Hoey  (<mailto:jorisvandenbossche@gmail.com>, <mailto:stijnvanhoey@gmail.com>). Licensed under [CC BY 4.0 Creative Commons](http://creativecommons.org/licenses/by/4.0/)*\n",
+    "\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9f6f168a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import geopandas"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8b49d24c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "countries = geopandas.read_file(\"data/ne_110m_admin_0_countries.zip\")\n",
+    "cities = geopandas.read_file(\"data/ne_110m_populated_places.zip\")\n",
+    "rivers = geopandas.read_file(\"data/ne_50m_rivers_lake_centerlines.zip\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0e2b3c1c",
+   "metadata": {},
+   "source": [
+    "## Coordinate reference systems\n",
+    "\n",
+    "Up to now, we have used the geometry data with certain coordinates without further wondering what those coordinates mean or how they are expressed.\n",
+    "\n",
+    "> The **Coordinate Reference System (CRS)** relates the coordinates to a specific location on earth.\n",
+    "\n",
+    "For an in-depth explanation, see https://docs.qgis.org/2.8/en/docs/gentle_gis_introduction/coordinate_reference_systems.html"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "525679f2",
+   "metadata": {},
+   "source": [
+    "### Geographic coordinates\n",
+    "\n",
+    "> Degrees of latitude and longitude.\n",
+    ">\n",
+    "> E.g. 48°51′N, 2°17′E\n",
+    "\n",
+    "The most known type of coordinates are geographic coordinates: we define a position on the globe in degrees of latitude and longitude, relative to the equator and the prime meridian. \n",
+    "With this system, we can easily specify any location on earth. It is used widely, for example in GPS. If you inspect the coordinates of a location in Google Maps, you will also see latitude and longitude.\n",
+    "\n",
+    "**Attention!**\n",
+    "\n",
+    "in Python we use (lon, lat) and not (lat, lon)\n",
+    "\n",
+    "- Longitude: [-180, 180]{{1}}\n",
+    "- Latitude: [-90, 90]{{1}}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cb62ebd7",
+   "metadata": {},
+   "source": [
+    "### Projected coordinates\n",
+    "\n",
+    "> `(x, y)` coordinates are usually in meters or feet\n",
+    "\n",
+    "Although the earth is a globe, in practice we usually represent it on a flat surface: think about a physical map, or the figures we have made with Python on our computer screen.\n",
+    "Going from the globe to a flat map is what we call a *projection*.\n",
+    "\n",
+    "![](../img/projection.png)\n",
+    "\n",
+    "We project the surface of the earth onto a 2D plane so we can express locations in cartesian x and y coordinates, on a flat surface. In this plane, we then typically work with a length unit such as meters instead of degrees, which makes the analysis more convenient and effective.\n",
+    "\n",
+    "However, there is an important remark: the 3 dimensional earth can never be represented perfectly on a 2 dimensional map, so projections inevitably introduce distortions. To minimize such errors, there are different approaches to project, each with specific advantages and disadvantages.\n",
+    "\n",
+    "Some projection systems will try to preserve the area size of geometries, such as the Albers Equal Area projection. Other projection systems try to preserve angles, such as the Mercator projection, but will see big distortions in the area. Every projection system will always have some distortion of area, angle or distance.\n",
+    "\n",
+    "<table><tr>\n",
+    "<td> <img src=\"../img/projections-AlbersEqualArea.png\"/> </td>\n",
+    "<td> <img src=\"../img/projections-Mercator.png\"/> </td>\n",
+    "</tr>\n",
+    "<tr>\n",
+    "<td> <img src=\"../img/projections-Robinson.png\"/> </td>\n",
+    "</tr></table>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9bf3f673",
+   "metadata": {},
+   "source": [
+    "**Projected size vs actual size (Mercator projection)**:\n",
+    "\n",
+    "![](../img/mercator_projection_area.gif)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8f0dac79",
+   "metadata": {},
+   "source": [
+    "## Coordinate Reference Systems in Python / GeoPandas"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0d95b51c",
+   "metadata": {},
+   "source": [
+    "A GeoDataFrame or GeoSeries has a `.crs` attribute which holds (optionally) a description of the coordinate reference system of the geometries:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3fbe677b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "countries.crs"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "04f95c5c",
+   "metadata": {},
+   "source": [
+    "For the `countries` dataframe, it indicates that it uses the EPSG 4326 / WGS84 lon/lat reference system, which is one of the most used for geographic coordinates.\n",
+    "\n",
+    "\n",
+    "It uses coordinates as latitude and longitude in degrees, as can you be seen from the x/y labels on the plot:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "736cc1b5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "countries.plot()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ec639d0c",
+   "metadata": {},
+   "source": [
+    "The `.crs` attribute returns a `pyproj.CRS` object. To specify a CRS, we typically use some string representation:\n",
+    "\n",
+    "\n",
+    "- **EPSG code**\n",
+    "  \n",
+    "  Example: `EPSG:4326` = WGS84 geographic CRS (longitude, latitude)\n",
+    "  \n",
+    "- **Well-Know-Text (WKT)** representation\n",
+    "\n",
+    "- In older software and datasets, you might also encounter a \"`proj4` string\" representation:\n",
+    "  \n",
+    "  Example: `+proj=longlat +datum=WGS84 +no_defs`\n",
+    "\n",
+    "  This is however no longer recommended.\n",
+    "\n",
+    "\n",
+    "See eg https://epsg.io/4326\n",
+    "\n",
+    "Under the hood, GeoPandas uses the `pyproj` / `PROJ` libraries to deal with the re-projections.\n",
+    "\n",
+    "For more information, see also http://geopandas.readthedocs.io/en/latest/projections.html."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7bdfb050",
+   "metadata": {},
+   "source": [
+    "### Transforming to another CRS\n",
+    "\n",
+    "We can convert a GeoDataFrame to another reference system using the `to_crs` function. \n",
+    "\n",
+    "For example, let's convert the countries to the World Mercator projection (http://epsg.io/3395):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "75acd02f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# remove Antartica, as the Mercator projection cannot deal with the poles\n",
+    "countries = countries[(countries['name'] != \"Antarctica\")]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cc49ad60",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "countries_mercator = countries.to_crs(epsg=3395)  # or .to_crs(\"EPSG:3395\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "326bc22b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "countries_mercator.plot()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2242f826",
+   "metadata": {},
+   "source": [
+    "Note the different scale of x and y."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "97870295",
+   "metadata": {},
+   "source": [
+    "### Why using a different CRS?\n",
+    "\n",
+    "There are sometimes good reasons you want to change the coordinate references system of your dataset, for example:\n",
+    "\n",
+    "- Different sources with different CRS -> need to convert to the same crs\n",
+    "\n",
+    "    ```python\n",
+    "    df1 = geopandas.read_file(...)\n",
+    "    df2 = geopandas.read_file(...)\n",
+    "\n",
+    "    df2 = df2.to_crs(df1.crs)\n",
+    "    ```\n",
+    "\n",
+    "- Mapping (distortion of shape and distances)\n",
+    "\n",
+    "- Distance / area based calculations -> ensure you use an appropriate projected coordinate system expressed in a meaningful unit such as meters or feet (not degrees).\n",
+    "\n",
+    "<div class=\"alert alert-info\" style=\"font-size:120%\">\n",
+    "\n",
+    "**ATTENTION:**\n",
+    "\n",
+    "All the calculations that happen in GeoPandas and Shapely assume that your data is in a 2D cartesian plane, and thus the result of those calculations will only be correct if your data is properly projected.\n",
+    "\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5db42e9b",
+   "metadata": {},
+   "source": [
+    "## Let's practice!\n",
+    "\n",
+    "Again, we will go back to the Paris datasets. Up to now, we provided the datasets in an appropriate projected CRS for the exercises. But the original data were actually using geographic coordinates. In the following exercises, we will start from there.\n",
+    "\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8504045d",
+   "metadata": {},
+   "source": [
+    "Going back to the Paris districts dataset, this is now provided as a GeoJSON file (`\"data/paris_districts.geojson\"`) in geographic coordinates.\n",
+    "\n",
+    "For converting to projected coordinates, we will use the standard projected CRS for France is the RGF93 / Lambert-93 reference system, referenced by the `EPSG:2154` number (in Belgium this would be Lambert 72, EPSG:31370).\n",
+    "\n",
+    "<div class=\"alert alert-success\">\n",
+    "\n",
+    "**EXERCISE 1: Projecting a GeoDataFrame**\n",
+    "\n",
+    "* Read the districts datasets (`\"data/paris_districts.geojson\"`) into a GeoDataFrame called `districts`.\n",
+    "* Look at the CRS attribute of the GeoDataFrame. Do you recognize the EPSG number?\n",
+    "* Make a plot of the `districts` dataset.\n",
+    "* Calculate the area of all districts.\n",
+    "* Convert the `districts` to a projected CRS (using the `EPSG:2154` for France). Call the new dataset `districts_RGF93`.\n",
+    "* Make a similar plot of `districts_RGF93`.\n",
+    "* Calculate the area of all districts again with `districts_RGF93` (the result will now be expressed in m²).\n",
+    "    \n",
+    "    \n",
+    "<details><summary>Hints</summary>\n",
+    "\n",
+    "* The CRS information is stored in the `.crs` attribute of a GeoDataFrame.\n",
+    "* Making a simple plot of a GeoDataFrame can be done with the `.plot()` method.\n",
+    "* Converting to a different CRS can be done with the `.to_crs()` method, and the CRS can be specified as an EPSG number using the `epsg` keyword.\n",
+    "\n",
+    "</details>\n",
+    "\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "47b85c58",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/03-coordinate-reference-systems1.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a7f886e3",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/03-coordinate-reference-systems2.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "31a9176b",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/03-coordinate-reference-systems3.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c423340e",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/03-coordinate-reference-systems4.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9971503b",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/03-coordinate-reference-systems5.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5109ab88",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/03-coordinate-reference-systems6.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9b7eb762",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/03-coordinate-reference-systems7.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3a2b00db",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/03-coordinate-reference-systems8.py"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1e28cd04",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-success\">\n",
+    "\n",
+    "**EXERCISE 2:**\n",
+    "\n",
+    "In the previous notebook, we did an exercise on plotting the bike stations locations in Paris and adding a background map to it using the `contextily` package.\n",
+    "\n",
+    "Currently, `contextily` assumes that your data is in the Web Mercator projection, the system used by most web tile services. And in that first exercise, we provided the data in the appropriate CRS so you didn't need to care about this aspect.\n",
+    "\n",
+    "However, typically, your data will not come in Web Mercator (`EPSG:3857`) and you will have to align them with web tiles on your own.\n",
+    "    \n",
+    "* Read the bike stations datasets (`\"data/paris_bike_stations.geojson\"`) into a GeoDataFrame called `stations`.\n",
+    "* Convert the `stations` dataset to the Web Mercator projection (`EPSG:3857`). Call the result `stations_webmercator`, and inspect the result.\n",
+    "* Make a plot of this projected dataset (specify the marker size to be 5) and add a background map using `contextily`.\n",
+    "\n",
+    "    \n",
+    "<details><summary>Hints</summary>\n",
+    "\n",
+    "* Making a simple plot of a GeoDataFrame can be done with the `.plot()` method. This returns a matplotlib axes object.\n",
+    "* The marker size can be specified with the `markersize` keyword if the `.plot()` method.\n",
+    "* To add a background map, use the `contextily.add_basemap()` function. It takes the matplotlib `ax` to which to add a map as the first argument.\n",
+    "\n",
+    "</details>\n",
+    "\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8a26c794",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/03-coordinate-reference-systems9.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "00d60562",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/03-coordinate-reference-systems10.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5d5a4488",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/03-coordinate-reference-systems11.py"
+   ]
+  }
+ ],
+ "metadata": {
+  "jupytext": {
+   "default_lexer": "ipython3"
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/notebooks/04-spatial-relationships-joins.ipynb b/notebooks/04-spatial-relationships-joins.ipynb
new file mode 100644
index 0000000..ff866ce
--- /dev/null
+++ b/notebooks/04-spatial-relationships-joins.ipynb
@@ -0,0 +1,1205 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "6ec40f36",
+   "metadata": {},
+   "source": [
+    "<p><font size=\"6\"><b>Spatial relationships and joins</b></font></p>\n",
+    "\n",
+    "\n",
+    "> *DS Python for GIS and Geoscience*  \n",
+    "> *October, 2025*\n",
+    ">\n",
+    "> *© 2025, Joris Van den Bossche and Stijn Van Hoey  (<mailto:jorisvandenbossche@gmail.com>, <mailto:stijnvanhoey@gmail.com>). Licensed under [CC BY 4.0 Creative Commons](http://creativecommons.org/licenses/by/4.0/)*\n",
+    "\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "91f5eb4a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import geopandas"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "030222e2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "countries = geopandas.read_file(\"data/ne_110m_admin_0_countries.zip\")\n",
+    "cities = geopandas.read_file(\"data/ne_110m_populated_places.zip\")\n",
+    "rivers = geopandas.read_file(\"data/ne_50m_rivers_lake_centerlines.zip\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "82e05049",
+   "metadata": {},
+   "source": [
+    "## Spatial relationships\n",
+    "\n",
+    "An important aspect of geospatial data is that we can look at *spatial relationships*: how two spatial objects relate to each other (whether they overlap, intersect, contain, .. one another).\n",
+    "\n",
+    "The topological, set-theoretic relationships in GIS are typically based on the DE-9IM model. See https://en.wikipedia.org/wiki/Spatial_relation for more information.\n",
+    "\n",
+    "![](../img/TopologicSpatialRelarions2.png)\n",
+    "(Image by [Krauss, CC BY-SA 3.0](https://en.wikipedia.org/wiki/Spatial_relation#/media/File:TopologicSpatialRelarions2.png))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "77bb5a86",
+   "metadata": {},
+   "source": [
+    "### Relationships between individual objects"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4ec1f00e",
+   "metadata": {},
+   "source": [
+    "Let's first create some small toy spatial objects:\n",
+    "\n",
+    "A polygon <small>(note: we use `.item()` here to to extract the scalar geometry object from the GeoSeries of length 1)</small>:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e943800a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "belgium = countries.loc[countries['name'] == 'Belgium', 'geometry'].item()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "173a2f18",
+   "metadata": {},
+   "source": [
+    "Two points:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2bb3cf6b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "paris = cities.loc[cities['name'] == 'Paris', 'geometry'].item()\n",
+    "brussels = cities.loc[cities['name'] == 'Brussels', 'geometry'].item()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5a8b6eab",
+   "metadata": {},
+   "source": [
+    "And a linestring:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8752404b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from shapely import LineString\n",
+    "line = LineString([paris, brussels])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f027692e",
+   "metadata": {},
+   "source": [
+    "Let's visualize those 4 geometry objects together (I only put them in a GeoSeries to easily display them together with the geopandas `.plot()` method):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "421d8325",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "geopandas.GeoSeries([belgium, paris, brussels, line]).plot(cmap='tab10')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6fe336aa",
+   "metadata": {},
+   "source": [
+    "You can recognize the abstract shape of Belgium.\n",
+    "\n",
+    "Brussels, the capital of Belgium, is thus located within Belgium. This is a spatial relationship, and we can test this using the individual shapely geometry objects as follow:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cd6864f7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "brussels.within(belgium)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "646b1c29",
+   "metadata": {},
+   "source": [
+    "And using the reverse, Belgium contains Brussels:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b1962d41",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "belgium.contains(brussels)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5de0cdad",
+   "metadata": {},
+   "source": [
+    "On the other hand, Paris is not located in Belgium:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "dd091664",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "belgium.contains(paris)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4a2c48f8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "paris.within(belgium)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "544ca814",
+   "metadata": {},
+   "source": [
+    "The straight line we draw from Paris to Brussels is not fully located within Belgium, but it does intersect with it:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b4d8af11",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "belgium.contains(line)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4a28b3d1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "line.intersects(belgium)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "31e3c4ba",
+   "metadata": {},
+   "source": [
+    "### Spatial relationships with GeoDataFrames\n",
+    "\n",
+    "The same methods that are available on individual `shapely` geometries as we have seen above, are also available as methods on `GeoSeries` / `GeoDataFrame` objects.\n",
+    "\n",
+    "For example, if we call the `contains` method on the world dataset with the `paris` point, it will do this spatial check for each country in the `world` dataframe:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "669486fb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "countries.contains(paris)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "06eb23d2",
+   "metadata": {},
+   "source": [
+    "Because the above gives us a boolean result, we can use that to filter the dataframe:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a0ea2703",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "countries[countries.contains(paris)]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4ece2631",
+   "metadata": {},
+   "source": [
+    "And indeed, France is the only country in the world in which Paris is located."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0446872f",
+   "metadata": {},
+   "source": [
+    "Another example, extracting the linestring of the Amazon river in South America, we can query through which countries the river flows:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0dbe59fc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "amazon = rivers[rivers['name'] == 'Amazonas'].geometry.item()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "85a70d36",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "countries[countries.crosses(amazon)]  # or .intersects"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b1ca2507",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-info\" style=\"font-size:120%\">\n",
+    "\n",
+    "**REFERENCE**:\n",
+    "\n",
+    "Overview of the different functions to check spatial relationships (*spatial predicate functions*):\n",
+    "\n",
+    "* `equals`\n",
+    "* `contains`\n",
+    "* `crosses`\n",
+    "* `disjoint`\n",
+    "* `intersects`\n",
+    "* `overlaps`\n",
+    "* `touches`\n",
+    "* `within`\n",
+    "* `covers`\n",
+    "* `covered_by`\n",
+    "\n",
+    "\n",
+    "See https://shapely.readthedocs.io/en/stable/manual.html#predicates-and-relationships for an overview of those methods.\n",
+    "\n",
+    "See https://en.wikipedia.org/wiki/DE-9IM for all details on the semantics of those operations.\n",
+    "\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c12ebdf9",
+   "metadata": {},
+   "source": [
+    "## Let's practice!\n",
+    "\n",
+    "We will again use the Paris datasets to exercise. Let's start importing them again, and directly converting both to the local projected CRS:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "54bf64ff",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "districts = geopandas.read_file(\"data/paris_districts.geojson\").to_crs(epsg=2154)\n",
+    "stations = geopandas.read_file(\"data/paris_bike_stations.geojson\").to_crs(epsg=2154)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "76901eee",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-success\">\n",
+    "\n",
+    "**EXERCISE 1: The Eiffel Tower**\n",
+    "\n",
+    "The Eiffel Tower is an iron lattice tower built in the 19th century, and is probably the most iconic view of Paris.\n",
+    "\n",
+    "The location of the Eiffel Tower is: x of 648237.3 and y of 6862271.9\n",
+    "\n",
+    "* Create a Shapely point object with the coordinates of the Eiffel Tower and assign it to a variable called `eiffel_tower`. Print the result.\n",
+    "* Check if the Eiffel Tower is located within the Montparnasse district (provided).\n",
+    "* Check if the Montparnasse district contains the bike station location.\n",
+    "* Calculate the distance between the Eiffel Tower and the bike station (note: in this case, the distance is returned in meters).\n",
+    "\n",
+    "\n",
+    "<details><summary>Hints</summary>\n",
+    "\n",
+    "* The `Point` class is available in the `shapely.geometry` submodule\n",
+    "* Creating a point can be done by passing the x and y coordinates to the `Point()` constructor.\n",
+    "* The `within()` method checks if the object is located within the passed geometry (used as `geometry1.within(geometry2)`).\n",
+    "* The `contains()` method checks if the object contains the passed geometry (used as `geometry1.contains(geometry2)`).\n",
+    "* To calculate the distance between two geometries, the `distance()` method of one of the geometries can be used.\n",
+    "\n",
+    "</details>\n",
+    "\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "17abfdd8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Import the Point geometry\n",
+    "from shapely import Point"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8f0ef088",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/04-spatial-relationships-joins1.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "88ee3bf9",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/04-spatial-relationships-joins2.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7764a34e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Accessing the Montparnasse geometry (Polygon)\n",
+    "district_montparnasse = districts.loc[districts['district_name'] == 'Montparnasse', 'geometry'].item()\n",
+    "bike_station = stations.loc[stations['name'] == '14033 - DAGUERRE GASSENDI', 'geometry'].item()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a4026fb9",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/04-spatial-relationships-joins3.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ab518184",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/04-spatial-relationships-joins4.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "47b390af",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/04-spatial-relationships-joins5.py"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6fe734dc",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-success\">\n",
+    "\n",
+    "**EXERCISE 2: In which district in the Eiffel Tower located?**\n",
+    "\n",
+    "In previous exercise, we constructed a `Point` geometry for its location, and we checked that it was not located in the Montparnasse district. Let's now determine in which of the districts of Paris it *is* located.\n",
+    "\n",
+    "* Create a boolean mask (or filter) indicating whether each district contains the Eiffel Tower or not. Call the result `mask`.\n",
+    "* Filter the `districts` dataframe with the boolean mask and print the result.\n",
+    "\n",
+    "\n",
+    "<details><summary>Hints</summary>\n",
+    "\n",
+    "* To check for each polygon in the districts dataset if it contains a single point, we can use the `contains()` method of the `districts` GeoDataFrame.\n",
+    "* Filtering the rows of a DataFrame based on a condition can be done by passing the boolean mask into `df[..]`.\n",
+    "\n",
+    "</details>\n",
+    "\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4c2f0cd4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Construct a point object for the Eiffel Tower\n",
+    "eiffel_tower = Point(648237.3, 6862271.9)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e22452ad",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/04-spatial-relationships-joins6.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "312ce404",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/04-spatial-relationships-joins7.py"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4143054a",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-success\">\n",
+    "\n",
+    "**EXERCISE 3: How far is the closest bike station?**\n",
+    "\n",
+    "Now, we might be interested in the bike stations nearby the Eiffel Tower. To explore them, let's visualize the Eiffel Tower itself as well as the bikes stations within 1km.\n",
+    "\n",
+    "To do this, we can calculate the distance to the Eiffel Tower for each of the stations. Based on this result, we can then create a mask that takes `True` if the station is within 1km, and `False` otherwise, and use it to filter the stations GeoDataFrame. Finally, we make a visualization of this subset.\n",
+    "\n",
+    "* Calculate the distance to the Eiffel Tower for each station, and call the result `dist_eiffel`.\n",
+    "* Print the distance to the closest station (which is the minimum of `dist_eiffel`).\n",
+    "* Select the rows the `stations` GeoDataFrame where the distance to the Eiffel Tower is less than 1 km (note that the distance is in meters). Call the result `stations_eiffel`.\n",
+    "\n",
+    "<details><summary>Hints</summary>\n",
+    "\n",
+    "* The `.distance()` method of a GeoDataFrame works element-wise: it calculates the distance between each geometry in the GeoDataFrame and the geometry passed to the method.\n",
+    "* A Series has a `.min()` method to calculate the minimum value.\n",
+    "* To create a boolean mask based on a condition, we can do e.g. `s < 100`.\n",
+    "\n",
+    "</details>\n",
+    "\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9b5d2f80",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/04-spatial-relationships-joins8.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8e62a28d",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/04-spatial-relationships-joins9.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "33632bc2",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/04-spatial-relationships-joins10.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0e0dbe53",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Make a plot of the close-by bike stations using matplotlib and contextily\n",
+    "import matplotlib.pyplot as plt\n",
+    "import contextily\n",
+    "\n",
+    "fig, ax = plt.subplots(figsize=(8, 8))\n",
+    "stations_eiffel.plot(ax=ax)\n",
+    "geopandas.GeoSeries([eiffel_tower], crs='EPSG:2154').plot(ax=ax, color='red')\n",
+    "contextily.add_basemap(ax, crs=stations_eiffel.crs)\n",
+    "ax.set_axis_off()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "de64844f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Make a plot of the close-by bike stations using matplotlib and contextily\n",
+    "m = stations_eiffel.explore(marker_kwds=dict(radius=5))\n",
+    "geopandas.GeoSeries([eiffel_tower], crs='EPSG:2154').explore(m=m, color='red', marker_kwds=dict(radius=5))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4ae35c11",
+   "metadata": {},
+   "source": [
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "67ce618b",
+   "metadata": {},
+   "source": [
+    "## Spatial joins"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ae1c467f",
+   "metadata": {},
+   "source": [
+    "In the previous section of this notebook, we could use the spatial relationship methods to check in which country a certain city was located. But what if we wanted to perform this same operation for every city and country? For example, we might want to know for each city in which country it is located.  \n",
+    "\n",
+    "In tabular jargon, this would imply adding a column to our cities dataframe with the name of the country in which it is located. Since country name is contained in the countries dataset, we need to combine - or \"join\" - information from both datasets. Joining on location (rather than on a shared column) is called a \"spatial join\".\n",
+    "\n",
+    "So here we will do:\n",
+    "\n",
+    "- Based on the `countries` and `cities` dataframes, determine for each city the country in which it is located.\n",
+    "- To solve this problem, we will use the the concept of a \"spatial join\" operation: combining information of geospatial datasets based on their spatial relationship."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9115eeb7",
+   "metadata": {},
+   "source": [
+    "### Recap - joining dataframes\n",
+    "\n",
+    "Pandas provides functionality to join or merge dataframes in different ways, see https://chrisalbon.com/python/data_wrangling/pandas_join_merge_dataframe/ for an overview and https://pandas.pydata.org/pandas-docs/stable/merging.html for the full documentation."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "64ecdd74",
+   "metadata": {},
+   "source": [
+    "To illustrate the concept of joining the information of two dataframes with pandas, let's take a small subset of our `cities` and `countries` datasets:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0089f168",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cities2 = cities[cities['name'].isin(['Bern', 'Brussels', 'London', 'Paris'])].copy()\n",
+    "cities2['iso_a3'] = ['CHE', 'BEL', 'GBR', 'FRA']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "df2b8a1e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cities2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ea259c55",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "countries2 = countries[['iso_a3', 'name', 'continent']]\n",
+    "countries2.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "db42db7d",
+   "metadata": {},
+   "source": [
+    "We added a 'iso_a3' column to the `cities` dataset, indicating a code of the country of the city. This country code is also present in the `countries` dataset, which allows us to merge those two dataframes based on the common column.\n",
+    "\n",
+    "Joining the `cities` dataframe with `countries` will transfer extra information about the countries (the full name, the continent) to the `cities` dataframe, based on a common key:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d42d1dfa",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cities2.merge(countries2, on='iso_a3')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2f7b8b2f",
+   "metadata": {},
+   "source": [
+    "**But** for this illustrative example we added the common column manually, it is not present in the original dataset. However, we can still know how to join those two datasets based on their spatial coordinates."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "99b140bb",
+   "metadata": {},
+   "source": [
+    "### Recap - spatial relationships between objects\n",
+    "\n",
+    "In the previous section, we have seen the notion of spatial relationships between geometry objects: within, contains, intersects, ...\n",
+    "\n",
+    "In this case, we know that each of the cities is located *within* one of the countries, or the other way around that each country can *contain* multiple cities.\n",
+    "\n",
+    "We can test such relationships using the methods we have seen in the previous notebook:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "42d1d955",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "france = countries.loc[countries['name'] == 'France', 'geometry'].item()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "82e25307",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cities.within(france)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "16806fc5",
+   "metadata": {},
+   "source": [
+    "The above gives us a boolean series, indicating for each point in our `cities` dataframe whether it is located within the area of France or not.  \n",
+    "Because this is a boolean series as result, we can use it to filter the original dataframe to only show those cities that are actually within France:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "50f09e4a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cities[cities.within(france)]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e311e469",
+   "metadata": {},
+   "source": [
+    "We could now repeat the above analysis for each of the countries, and add a column to the `cities` dataframe indicating this country. However, that would be tedious to do manually, and is also exactly what the spatial join operation provides us.\n",
+    "\n",
+    "*(note: the above result is incorrect, but this is just because of the coarse-ness of the countries dataset)*"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0ca11f96",
+   "metadata": {},
+   "source": [
+    "## Spatial join operation\n",
+    "\n",
+    "<div class=\"alert alert-info\" style=\"font-size:120%\">\n",
+    "\n",
+    "**SPATIAL JOIN** = *transferring attributes from one layer to another based on their spatial relationship* <br>\n",
+    "\n",
+    "\n",
+    "Different parts of this operations:\n",
+    "\n",
+    "* The GeoDataFrame to which we want add information\n",
+    "* The GeoDataFrame that contains the information we want to add\n",
+    "* The spatial relationship (\"predicate\") we want to use to match both datasets ('intersects', 'contains', 'within')\n",
+    "* The type of join: left or inner join\n",
+    "\n",
+    "\n",
+    "![](../img/illustration-spatial-join.svg)\n",
+    "\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6117d1c5",
+   "metadata": {},
+   "source": [
+    "In this case, we want to join the `cities` dataframe with the information of the `countries` dataframe, based on the spatial relationship between both datasets.\n",
+    "\n",
+    "We use the [`geopandas.sjoin`](http://geopandas.readthedocs.io/en/latest/reference/geopandas.sjoin.html) function:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9ed6570a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "joined = geopandas.sjoin(cities, countries, predicate='within', how='left')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b0684ca5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "joined"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7a1f5964",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "joined[joined[\"name_right\"] == \"France\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2e574f9b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "joined['continent'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a5aff0ca",
+   "metadata": {},
+   "source": [
+    "## Let's practice!\n",
+    "\n",
+    "We will again use the Paris datasets to do some exercises. Let's start importing them:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7756034f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "districts = geopandas.read_file(\"data/paris_districts.geojson\").to_crs(epsg=2154)\n",
+    "stations = geopandas.read_file(\"data/paris_bike_stations.geojson\").to_crs(epsg=2154)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0b340745",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-success\">\n",
+    "\n",
+    "**EXERCISE 4:**\n",
+    "\n",
+    "* Determine for each bike station in which district it is located (using a spatial join!). Call the result `joined`.\n",
+    "\n",
+    "<details><summary>Hints</summary>\n",
+    "\n",
+    "- The `geopandas.sjoin()` function takes as first argument the dataframe to which we want to add information, and as second argument the dataframe that contains this additional information.\n",
+    "\n",
+    "</details>\n",
+    "\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fbdbc1c7",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/04-spatial-relationships-joins11.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8656ae41",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/04-spatial-relationships-joins12.py"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6e5a00cb",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-success\">\n",
+    "\n",
+    "**EXERCISE 5: Map of tree density by district (I)**\n",
+    "\n",
+    "Using a dataset of all trees in public spaces in Paris, the goal is to make a map of the tree density by district. We first need to find out how many trees each district contains, which we will do in this exercise. In the following exercise, we will use this result to calculate the density and create a map.\n",
+    "\n",
+    "To obtain the tree count by district, we first need to know in which district each tree is located, which we can do with a spatial join. Then, using the result of the spatial join, we will calculate the number of trees located in each district using the pandas 'group-by' functionality.\n",
+    "\n",
+    "- Import the trees dataset `\"paris_trees.gpkg\"` and call the result `trees`. Also read the districts dataset we have seen previously (`\"paris_districts.geojson\"`), and call this `districts`. Convert the districts dataset to the same CRS as the trees dataset.\n",
+    "- Add a column with the `'district_name'` to the trees dataset using a spatial join. Call the result `joined`.\n",
+    "\n",
+    "<details><summary>Hints</summary>\n",
+    "\n",
+    "- Remember, we can perform a spatial join with the `geopandas.sjoin()` function.\n",
+    "- `geopandas.sjoin()` takes as first argument the dataframe to which we want to add information, and as second argument the dataframe that contains this additional information.\n",
+    "- The `op` argument is used to specify which spatial relationship between both dataframes we want to use for joining (options are `'intersects'`, `'contains'`, `'within'`).\n",
+    "\n",
+    "</details>\n",
+    "\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bcc8a5e9",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/04-spatial-relationships-joins13.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1ac4e1a9",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/04-spatial-relationships-joins14.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5fb48650",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/04-spatial-relationships-joins15.py"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "288272ba",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-success\">\n",
+    "\n",
+    "**EXERCISE 6: Map of tree density by district (II)**\n",
+    "\n",
+    "Calculate the number of trees located in each district: group the `joined` DataFrame by the `'district_name'` column, and calculate the size of each group. Call the resulting Series `trees_by_district`.  <br>\n",
+    "\n",
+    "We then convert `trees_by_district` to a DataFrame for the next exercise.\n",
+    "\n",
+    "<details><summary>Hints</summary>\n",
+    "\n",
+    "- The general group-by syntax in pandas is: `df.groupby('key').aggregation_method()`, substituting 'key' and 'aggregation_method' with the appropriate column name and method. \n",
+    "- To know the size of groups, we can use the `.size()` method.\n",
+    "\n",
+    "</details>\n",
+    "\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f3650cad",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/04-spatial-relationships-joins16.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2170f77a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Convert the series to a DataFrame and specify column name\n",
+    "trees_by_district = trees_by_district.to_frame(name='n_trees')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7fcdcc21",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Inspect the result\n",
+    "trees_by_district.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "63c3beea",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-success\">\n",
+    "\n",
+    "**EXERCISE 7: Map of tree density by district (III)**\n",
+    "\n",
+    "Now we have obtained the number of trees by district, we can make the map of the districts colored by the tree density.\n",
+    "\n",
+    "For this, we first need to merge the number of trees in each district we calculated in the previous step (`trees_by_district`) back to the districts dataset. We will use the [`pd.merge()`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.merge.html) function to join two dataframes based on a common column.\n",
+    "\n",
+    "Since not all districts have the same size, we should compare the tree density for the visualisation: the number of trees relative to the area.\n",
+    "\n",
+    "- Use the `pd.merge()` function to merge `districts` and `trees_by_district` dataframes on the `'district_name'` column. Call the result `districts_trees`.\n",
+    "- Add a column `'n_trees_per_area'` to the `districts_trees` dataframe, based on the `'n_trees'` column divided by the area.\n",
+    "- Make a plot of the `districts_trees` dataframe, using the `'n_trees_per_area'` column to determine the color of the polygons.\n",
+    "\n",
+    "\n",
+    "<details><summary>Hints</summary>\n",
+    "\n",
+    "- The pandas `pd.merge()` function takes the two dataframes you want to merge as the first two arguments.\n",
+    "- The column name on which you want to merge both datasets can be specified with the `on` keyword.\n",
+    "- Accessing a column of a DataFrame can be done with `df['col']`, while adding a column to a DataFrame can be done with `df['new_col'] = values` where `values` can be the result of a computation.\n",
+    "- Remember, the area of each geometry in a GeoSeries or GeoDataFrame can be retrieved using the `area` attribute. So considering a GeoDataFrame `gdf`, then `gdf.geometry.area` will return a Series with the area of each geometry.\n",
+    "- We can use the `.plot()` method of a GeoDataFrame to make a visualization of the geometries. \n",
+    "- For using one of the columns of the GeoDataFrame to determine the fill color, use the `column=` keyword.\n",
+    "\n",
+    "\n",
+    "</details>\n",
+    "\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4feedaa2",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/04-spatial-relationships-joins17.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7152250d",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/04-spatial-relationships-joins18.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "53d6038c",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/04-spatial-relationships-joins19.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3a9d5170",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "jupytext": {
+   "default_lexer": "ipython3"
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/notebooks/05-spatial-operations-overlays.ipynb b/notebooks/05-spatial-operations-overlays.ipynb
new file mode 100644
index 0000000..1f898c3
--- /dev/null
+++ b/notebooks/05-spatial-operations-overlays.ipynb
@@ -0,0 +1,1367 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "b24c03ef",
+   "metadata": {},
+   "source": [
+    "<p><font size=\"6\"><b>Spatial operations and overlays: creating new geometries</b></font></p>\n",
+    "\n",
+    "\n",
+    "> *DS Python for GIS and Geoscience*  \n",
+    "> *October, 2025*\n",
+    ">\n",
+    "> *© 2025, Joris Van den Bossche and Stijn Van Hoey  (<mailto:jorisvandenbossche@gmail.com>, <mailto:stijnvanhoey@gmail.com>). Licensed under [CC BY 4.0 Creative Commons](http://creativecommons.org/licenses/by/4.0/)*\n",
+    "\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2245b3f3",
+   "metadata": {},
+   "source": [
+    "In the previous notebook we have seen how to identify and use the spatial relationships between geometries. In this notebook, we will see how to create new geometries based on those relationships."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8fb8f8cd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import geopandas\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bcded20e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "countries = geopandas.read_file(\"data/ne_110m_admin_0_countries.zip\")\n",
+    "cities = geopandas.read_file(\"data/ne_110m_populated_places.zip\")\n",
+    "rivers = geopandas.read_file(\"data/ne_50m_rivers_lake_centerlines.zip\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "68f625b2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# defining the same example geometries as in the previous notebook\n",
+    "belgium = countries.loc[countries['name'] == 'Belgium', 'geometry'].item()\n",
+    "brussels = cities.loc[cities['name'] == 'Brussels', 'geometry'].item()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b7a42c28",
+   "metadata": {},
+   "source": [
+    "## Spatial operations\n",
+    "\n",
+    "Next to the spatial predicates that return boolean values, Shapely and GeoPandas also provide operations that return new geometric objects.\n",
+    "\n",
+    "**Binary operations:**\n",
+    "\n",
+    "<table><tr>\n",
+    "<td> <img src=\"../img/spatial-operations-base.png\"/> </td>\n",
+    "<td> <img src=\"../img/spatial-operations-intersection.png\"/> </td>\n",
+    "</tr>\n",
+    "<tr>\n",
+    "<td> <img src=\"../img/spatial-operations-union.png\"/> </td>\n",
+    "<td> <img src=\"../img/spatial-operations-difference.png\"/> </td>\n",
+    "</tr></table>\n",
+    "\n",
+    "**Buffer:**\n",
+    "\n",
+    "<table><tr>\n",
+    "<td> <img src=\"../img/spatial-operations-buffer-point1.png\"/> </td>\n",
+    "<td> <img src=\"../img/spatial-operations-buffer-point2.png\"/> </td>\n",
+    "</tr>\n",
+    "<tr>\n",
+    "<td> <img src=\"../img/spatial-operations-buffer-line.png\"/> </td>\n",
+    "<td> <img src=\"../img/spatial-operations-buffer-polygon.png\"/> </td>\n",
+    "</tr></table>\n",
+    "\n",
+    "\n",
+    "See https://shapely.readthedocs.io/en/stable/manual.html#spatial-analysis-methods for more details."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2ac691cd",
+   "metadata": {},
+   "source": [
+    "For example, using the toy data from above, let's construct a buffer around Brussels (which returns a Polygon):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "73a6711e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "geopandas.GeoSeries([belgium, brussels.buffer(1)]).plot(alpha=0.5, cmap='tab10')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fd7d7159",
+   "metadata": {},
+   "source": [
+    "and now take the intersection, union or difference of those two polygons:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "401744bc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "brussels.buffer(1).intersection(belgium)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4519bf91",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "brussels.buffer(1).union(belgium)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "02cbfaf6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "brussels.buffer(1).difference(belgium)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "109cc881",
+   "metadata": {},
+   "source": [
+    "### Spatial operations with GeoPandas"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bad10271",
+   "metadata": {},
+   "source": [
+    "Above we showed how to create a new geometry based on two individual shapely geometries. The same operations can be extended to GeoPandas. Given a GeoDataFrame, we can calculate the intersection, union or difference of each of the geometries with another geometry.\n",
+    "\n",
+    "Let's look at an example with a subset of the countries. We have a GeoDataFrame with the country polygons of Africa, and  now consider a rectangular polygon, representing an area around the equator:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1462dffb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "africa = countries[countries.continent == 'Africa']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5b596b4c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from shapely import LineString\n",
+    "box = LineString([(-10, 0), (50, 0)]).buffer(10, cap_style=3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bf41ef29",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(6, 6))\n",
+    "africa.plot(ax=ax, facecolor='none', edgecolor='k')\n",
+    "geopandas.GeoSeries([box]).plot(ax=ax, facecolor='C0', edgecolor='k', alpha=0.5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "246079f7",
+   "metadata": {},
+   "source": [
+    "The intersection method of the GeoDataFrame will now calculate the intersection with the rectangle for each of the geometries of the africa GeoDataFrame element-wise. Note that for many of the countries, those that do not overlap with the rectangle, this will be an empty geometry:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8ff545ab",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "africa_intersection = africa.intersection(box)\n",
+    "africa_intersection.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "92761ae8",
+   "metadata": {},
+   "source": [
+    "What is returned is a new GeoSeries of the same length as the original dataframe, containing one row per country, but now containing only the intersection. In this example, the last element shown is an empty polygon, as that country was not overlapping with the box."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0562f5e9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# plot the intersection\n",
+    "africa_intersection.plot()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2a797b7d",
+   "metadata": {},
+   "source": [
+    "## Unary union and dissolve\n",
+    "\n",
+    "Another useful method is `union_all()` (in older GeoPandas versions this is available as the `unary_union` attribute), which converts the set of geometry objects in a GeoDataFrame into a single geometry object by taking the union of all those geometries.\n",
+    "\n",
+    "For example, we can construct a single Shapely geometry object for the Africa continent:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "27802095",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "africa_countries = countries[countries['continent'] == 'Africa']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "64bf79e6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "africa = africa_countries.union_all()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "269f065f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "africa"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f62cf729",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(str(africa)[:100])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9cf02954",
+   "metadata": {},
+   "source": [
+    "Alternatively, you might want to take the unary union of a set of geometries but *grouped* by one of the attributes of the GeoDataFrame (so basically doing \"groupby\" + \"unary_union\"). For this operation, GeoPandas provides the `dissolve()` method:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "13c9fde0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "continents = countries.dissolve(by=\"continent\")   # , aggfunc=\"sum\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "03a499d5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "continents"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "76a174f0",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-info\" style=\"font-size:120%\">\n",
+    "\n",
+    "**REMEMBER**:\n",
+    "\n",
+    "GeoPandas (and Shapely for the individual objects) provide a whole lot of basic methods to analyze the geospatial data (distance, length, centroid, boundary, convex_hull, simplify, transform, ....), much more than what we can touch in this tutorial.\n",
+    "\n",
+    "An overview of all methods provided by GeoPandas can be found here: https://geopandas.org/en/latest/docs/reference.html\n",
+    "\n",
+    "\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e1b4fcd4",
+   "metadata": {},
+   "source": [
+    "## Let's practice!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "addc8e3b",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-success\">\n",
+    "\n",
+    "**EXERCISE 1: What are the districts close to the Seine?**\n",
+    "\n",
+    "Below, the coordinates for the Seine river in the neighborhood of Paris are provided as a GeoJSON-like feature dictionary (created at http://geojson.io). \n",
+    "\n",
+    "Based on this `seine` object, we want to know which districts are located close (maximum 150 m) to the Seine. \n",
+    "\n",
+    "* Create a buffer of 150 m around the Seine.\n",
+    "* Check which districts intersect with this buffered object.\n",
+    "* Make a visualization of the districts indicating which districts are located close to the Seine.\n",
+    " \n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e4e8a1ff",
+   "metadata": {
+    "clear_cell": false
+   },
+   "outputs": [],
+   "source": [
+    "districts = geopandas.read_file(\"data/paris_districts.geojson\").to_crs(epsg=2154)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "90b165c2",
+   "metadata": {
+    "clear_cell": false
+   },
+   "outputs": [],
+   "source": [
+    "# created a line with http://geojson.io\n",
+    "s_seine = geopandas.GeoDataFrame.from_features({\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"properties\":{},\"geometry\":{\"type\":\"LineString\",\"coordinates\":[[2.408924102783203,48.805619828930226],[2.4092674255371094,48.81703747481909],[2.3927879333496094,48.82325391133874],[2.360687255859375,48.84912860497674],[2.338714599609375,48.85827758964043],[2.318115234375,48.8641501307046],[2.298717498779297,48.863246707697],[2.2913360595703125,48.859519915404825],[2.2594070434570312,48.8311646245967],[2.2436141967773438,48.82325391133874],[2.236919403076172,48.82347994904826],[2.227306365966797,48.828339513221444],[2.2224998474121094,48.83862215329593],[2.2254180908203125,48.84856379804802],[2.2240447998046875,48.85409863123821],[2.230224609375,48.867989496547864],[2.260265350341797,48.89192242750887],[2.300262451171875,48.910203080780285]]}}]},\n",
+    "                                               crs='EPSG:4326')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8ab944e5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# convert to local UTM zone\n",
+    "s_seine_utm = s_seine.to_crs(epsg=2154)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "45a9a4f1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "fig, ax = plt.subplots(figsize=(20, 10))\n",
+    "districts.plot(ax=ax, color='grey', alpha=0.4, edgecolor='k')\n",
+    "s_seine_utm.plot(ax=ax)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9c288b21",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# access the single geometry object\n",
+    "seine = s_seine_utm.geometry.item()\n",
+    "seine"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c766fb27",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/05-spatial-operations-overlays1.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9fdc30eb",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/05-spatial-operations-overlays2.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d9d18b39",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/05-spatial-operations-overlays3.py"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3d71f1aa",
+   "metadata": {},
+   "source": [
+    "------"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4ed17cfb",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-success\">\n",
+    "\n",
+    "**EXERCISE 2: Exploring a Land Use dataset**\n",
+    "\n",
+    "For the following exercises, we first introduce a new dataset: a dataset about the land use of Paris (a simplified version based on the open European [Urban Atlas](https://land.copernicus.eu/local/urban-atlas)). The land use indicates for what kind of activity a certain area is used, such as residential area or for recreation. It is a polygon dataset, with a label representing the land use class for different areas in Paris.\n",
+    "\n",
+    "In this exercise, we will read the data, explore it visually, and calculate the total area of the different classes of land use in the area of Paris.\n",
+    "\n",
+    "* Read in the `'data/paris_land_use.zip'` shape file and assign the result to a variable `land_use`.\n",
+    "* Make a plot of `land_use`, using the `'class'` column to color the polygons. Add a legend with `legend=True`, and make the figure size a bit larger.\n",
+    "* Add a new column `'area'` to the dataframe with the area of each polygon.\n",
+    "* Calculate the total area in km² for each `'class'` using the `groupby()` method, and print the result.\n",
+    "\n",
+    "<details><summary>Hints</summary>\n",
+    "\n",
+    "* Reading a file can be done with the `geopandas.read_file()` function.\n",
+    "* To use a column to color the geometries, use the `column` keyword to indicate the column name.\n",
+    "* The area of each geometry can be accessed with the `area` attribute of the `geometry` of the GeoDataFrame.\n",
+    "* The `groupby()` method takes the column name on which you want to group as the first argument.\n",
+    "\n",
+    "</details>\n",
+    "\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "488d84c2",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/05-spatial-operations-overlays4.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b44ba7aa",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/05-spatial-operations-overlays5.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8aea730c",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/05-spatial-operations-overlays6.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "afdb9885",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/05-spatial-operations-overlays7.py"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "73e3a926",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-success\">\n",
+    "\n",
+    "**EXERCISE 3: Intersection of two polygons**\n",
+    "\n",
+    "For this exercise, we are going to use 2 individual polygons: the district of Muette extracted from the `districts` dataset, and the green urban area of Boulogne, a large public park in the west of Paris, extracted from the `land_use` dataset. The two polygons have already been assigned to the `muette` and `park_boulogne` variables.\n",
+    "\n",
+    "We first visualize the two polygons. You will see that they overlap, but the park is not fully located in the district of Muette. Let's determine the overlapping part:\n",
+    "\n",
+    "* Plot the two polygons in a single map to examine visually the degree of overlap\n",
+    "* Calculate the intersection of the `park_boulogne` and `muette` polygons.\n",
+    "* Plot the intersection.\n",
+    "* Print the proportion of the area of the district that is occupied by the park.\n",
+    "\n",
+    "<details><summary>Hints</summary>\n",
+    "\n",
+    "* To plot single Shapely objects, you can put those in a `GeoSeries([..])` to use the GeoPandas `plot()` method.\n",
+    "* The intersection of to scalar polygons can be calculated with the `intersection()` method of one of the polygons, and passing the other polygon as the argument to that method.\n",
+    "\n",
+    "</details>\n",
+    "\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "247f0b9a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "land_use = geopandas.read_file(\"data/paris_land_use.zip\")\n",
+    "districts = geopandas.read_file(\"data/paris_districts.geojson\").to_crs(land_use.crs)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ad05ef0e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# extract polygons\n",
+    "land_use['area'] = land_use.geometry.area\n",
+    "park_boulogne = land_use[land_use['class'] == \"Green urban areas\"].sort_values('area').geometry.iloc[-1]\n",
+    "muette = districts[districts.district_name == 'Muette'].geometry.item()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c5ea4872",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Plot the two polygons\n",
+    "geopandas.GeoSeries([park_boulogne, muette]).plot(alpha=0.5, color=['green', 'blue'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ea28fa16",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/05-spatial-operations-overlays8.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0e52f8ed",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/05-spatial-operations-overlays9.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a87d87af",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/05-spatial-operations-overlays10.py"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "eead4835",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-success\">\n",
+    "\n",
+    "**EXERCISE 4: Intersecting a GeoDataFrame with a Polygon**\n",
+    "\n",
+    "Combining the land use dataset and the districts dataset, we can now investigate what the land use is in a certain district.\n",
+    "\n",
+    "For that, we first need to determine the intersection of the land use dataset with a given district. Let's take again the *Muette* district as example case.\n",
+    "\n",
+    "* Calculate the intersection of the `land_use` polygons with the single `muette` polygon. Call the result `land_use_muette`.\n",
+    "* Remove the empty geometries from `land_use_muette`.\n",
+    "* Make a quick plot of this intersection, and pass `edgecolor='black'` to more clearly see the boundaries of the different polygons.\n",
+    "* Print the first five rows of `land_use_muette`.\n",
+    "\n",
+    "<details><summary>Hints</summary>\n",
+    "\n",
+    "* The intersection of each geometry of a GeoSeries with another single geometry can be performed with the `intersection()` method of a GeoSeries.\n",
+    "* The `intersection()` method takes as argument the geometry for which to calculate the intersection.\n",
+    "* We can check which geometries are empty with the `is_empty` attribute of a GeoSeries.\n",
+    "    \n",
+    "</details>\n",
+    "\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c666328b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "land_use = geopandas.read_file(\"data/paris_land_use.zip\")\n",
+    "districts = geopandas.read_file(\"data/paris_districts.geojson\").to_crs(land_use.crs)\n",
+    "muette = districts[districts.district_name == 'Muette'].geometry.item()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8019d083",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/05-spatial-operations-overlays11.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6d39781d",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/05-spatial-operations-overlays12.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "748cd904",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/05-spatial-operations-overlays13.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d38d537a",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/05-spatial-operations-overlays14.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "23cb581d",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/05-spatial-operations-overlays15.py"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6f0d770f",
+   "metadata": {},
+   "source": [
+    "You can see in the plot that we now only have a subset of the full land use dataset. The original `land_use_muette` (before removing the empty geometries) still has the same number of rows as the original `land_use`, though. But many of the rows, as you could see by printing the first rows, consist now of empty polygons when it did not intersect with the Muette district.\n",
+    "\n",
+    "The `intersection()` method also returned only geometries. If we want to combine those intersections with the attributes of the original land use, we can take a copy of this and replace the geometries with the intersections (you can uncomment and run to see the code):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "242946d4",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/05-spatial-operations-overlays16.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "927f846d",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/05-spatial-operations-overlays17.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5946bf4f",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/05-spatial-operations-overlays18.py"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2f0c8161",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-success\">\n",
+    "\n",
+    "**EXERCISE 5: The land use of the Muette district**\n",
+    "\n",
+    "Based on the `land_use_muette` dataframe with the land use for the Muette districts as calculated above, we can now determine the total area of the different land use classes in the Muette district.\n",
+    "\n",
+    "* Calculate the total area per land use class.\n",
+    "* Calculate the fraction (in percentage) for the different land use classes.\n",
+    "\n",
+    "<details><summary>Hints</summary>\n",
+    "\n",
+    "* The intersection of each geometry of a GeoSeries with another single geometry can be performed with the `intersection()` method of a GeoSeries.\n",
+    "* The `intersection()` method takes as argument the geometry for which to calculate the intersection.\n",
+    "* We can check which geometries are empty with the `is_empty` attribute of a GeoSeries.\n",
+    "    \n",
+    "</details>\n",
+    "\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "aa2af958",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/05-spatial-operations-overlays19.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "577cd57c",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/05-spatial-operations-overlays20.py"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a57ae832",
+   "metadata": {},
+   "source": [
+    "The above was only for a single district. If we want to do this more easily for all districts, we can do this with the overlay operation."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d74fd1ac",
+   "metadata": {},
+   "source": [
+    "## The overlay operation\n",
+    "\n",
+    "In a spatial join operation, we are not changing the geometries itself. We are not joining geometries, but joining attributes based on a spatial relationship between the geometries. This also means that the geometries need to at least overlap partially.\n",
+    "\n",
+    "If you want to create new geometries based on joining (combining) geometries of different dataframes into one new dataframe (eg by taking the intersection of the geometries), you want an **overlay** operation."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bbff8735",
+   "metadata": {},
+   "source": [
+    "### How does it differ compared to the intersection method?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "534a6667",
+   "metadata": {},
+   "source": [
+    "With the `intersection()` method introduced in the previous section, we could for example determine the intersection of a set of countries with another polygon, a circle in the example below:\n",
+    "\n",
+    "<img width=\"70%\" src=\"../img/geopandas/chapter3-overlay-countries-circle-intersection-new.png\"/>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "11d6849e",
+   "metadata": {},
+   "source": [
+    "However, this method (`countries.intersection(circle)`) also has some limitations.\n",
+    "\n",
+    "* Mostly useful when intersecting a GeoSeries with a single polygon.\n",
+    "* Does not preserve attribute information of the intersecting polygons.\n",
+    "\n",
+    "For cases where we require a bit more complexity, it is preferable to use the \"overlay\" operation, instead of the intersection method."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0aaaa8aa",
+   "metadata": {},
+   "source": [
+    "Consider the following simplified example. On the left we see again the 3 countries. On the right we have the plot of a GeoDataFrame with some simplified geologic regions for the same area:\n",
+    "\n",
+    "<table width=\"80%\"><tr>\n",
+    "<td> <img src=\"../img/geopandas/chapter3-overlay-countries.png\"/> </td>\n",
+    "<td> <img src=\"../img/geopandas/chapter3-overlay-regions.png\"/> </td>\n",
+    "</tr></table>\n",
+    "\n",
+    "By simply plotting them on top of each other, as shown below, you can see that the polygons of both layers intersect. \n",
+    "\n",
+    "But now, by \"overlaying\" the two layers, we can create a third layer that contains the result of intersecting both layers: all the intersections of each country with each geologic region. It keeps only those areas that were included in both layers.\n",
+    "\n",
+    "<table width=\"80%\"><tr>\n",
+    "<td> <img src=\"../img/geopandas/chapter3-overlay-both.png\"/> </td>\n",
+    "<td> <img src=\"../img/geopandas/chapter3-overlay-overlayed.png\"/> </td>\n",
+    "</tr></table>\n",
+    "\n",
+    "This operation is called an intersection overlay, and in GeoPandas we can perform this operation with the `geopandas.overlay()` function."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "19573384",
+   "metadata": {},
+   "source": [
+    "Another code example:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8bcd4a28",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "africa = countries[countries['continent'] == 'Africa']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "01aee38c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "africa.plot()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "891b75a1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cities['geometry'] = cities.buffer(2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5866c061",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "intersection = geopandas.overlay(africa, cities, how='intersection')\n",
+    "intersection.plot()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9afc38eb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "intersection.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d6b2fbef",
+   "metadata": {},
+   "source": [
+    "With the overlay method, we pass the full GeoDataFrame with all regions to intersect the countries with. The result contains all non-empty intersections of all combinations of countries and city regions.\n",
+    "\n",
+    "Note that the result of the overlay function also keeps the attribute information of both the countries as the city regions. That can be very useful for further analysis."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f8608488",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "geopandas.overlay(africa, cities, how='intersection').plot()  # how=\"difference\"/\"union\"/\"symmetric_difference\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "84928cc9",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-info\" style=\"font-size:120%\">\n",
+    "<b>REMEMBER</b> <br>\n",
+    "\n",
+    "* **Spatial join**: transfer attributes from one dataframe to another based on the spatial relationship\n",
+    "* **Spatial overlay**: construct new geometries based on spatial operation between both dataframes (and combining attributes of both dataframes)\n",
+    "\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "53ad68d6",
+   "metadata": {},
+   "source": [
+    "## Let's practice!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6af711a3",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-success\">\n",
+    "\n",
+    "**EXERCISE 6: Overlaying spatial datasets I**\n",
+    "\n",
+    "We will now combine both datasets in an overlay operation. Create a new `GeoDataFrame` consisting of the intersection of the land use polygons which each of the districts, but make sure to bring the attribute data from both source layers.\n",
+    "\n",
+    "* Create a new GeoDataFrame from the intersections of `land_use` and `districts`. Assign the result to a variable `combined`.\n",
+    "* Print the first rows the resulting GeoDataFrame (`combined`).\n",
+    "\n",
+    "<details><summary>Hints</summary>\n",
+    "\n",
+    "* The intersection of two GeoDataFrames can be calculated with the `geopandas.overlay()` function.\n",
+    "* The `overlay()` functions takes first the two GeoDataFrames to combine, and a third `how` keyword indicating how to combine the two layers.\n",
+    "* For making an overlay based on the intersection, you can pass `how='intersection'`.\n",
+    "* The default `overlay()` call will generate a warning because some of the intersections result in a point or linestring, and thus not in a new polygon. For this exercise, we are only interested in the resulting polygons, and therefore we set `keep_geom_type=True` to suppress the warning and say to `overlay()` we only need the resulting geometries of the same type as the original geometries (thus, only polygons).\n",
+    "\n",
+    "</details>\n",
+    "\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f15c6be8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "land_use = geopandas.read_file(\"data/paris_land_use.zip\")\n",
+    "districts = geopandas.read_file(\"data/paris_districts.geojson\").to_crs(land_use.crs)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "43b9d6ae",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/05-spatial-operations-overlays21.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4e698a9e",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/05-spatial-operations-overlays22.py"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "98b05bae",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-success\">\n",
+    "\n",
+    "**EXERCISE 7: Overlaying spatial datasets II**\n",
+    "\n",
+    "Now that we created the overlay of the land use and districts datasets, we can more easily inspect the land use for the different districts. Let's get back to the example district of Muette, and inspect the land use of that district.\n",
+    "\n",
+    "* Add a new column `'area'` with the area of each polygon to the `combined` GeoDataFrame.\n",
+    "* Create a subset called `land_use_muette` where the `'district_name'` is equal to \"Muette\".\n",
+    "* Make a plot of `land_use_muette`, using the `'class'` column to color the polygons.\n",
+    "* Calculate the total area for each `'class'` of `land_use_muette` using the `groupby()` method, and print the result.\n",
+    "\n",
+    "<details><summary>Hints</summary>\n",
+    "\n",
+    "* The area of each geometry can be accessed with the `area` attribute of the `geometry` of the GeoDataFrame.\n",
+    "* To use a column to color the geometries, pass its name to the `column` keyword.\n",
+    "* The `groupby()` method takes the column name on which you want to group as the first argument.\n",
+    "* The total area for each class can be calculated by taking the `sum()` of the area.\n",
+    "\n",
+    "</details>\n",
+    "\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2b82c0f8",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/05-spatial-operations-overlays23.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c4d1bc02",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/05-spatial-operations-overlays24.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1c9c407f",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/05-spatial-operations-overlays25.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "16d7def6",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/05-spatial-operations-overlays26.py"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9e3eb4b9",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-success\">\n",
+    "\n",
+    "**EXERCISE 8: Overlaying spatial datasets III**\n",
+    "\n",
+    "Thanks to the result of the overlay operation, we can now more easily perform a similar analysis for *all* districts. Let's investigate the fraction of green urban area in each of the districts.\n",
+    "\n",
+    "* Based on the `combined` dataset, calculate the total area per district using `groupby()`.\n",
+    "* Select the subset of \"Green urban areas\" from `combined` and call this `urban_green`.\n",
+    "* Now calculate the total area per district for this `urban_green` subset, and call this `urban_green_area`.\n",
+    "* Determine the fraction of urban green area in each district.\n",
+    "* Check the districts with the largest and smallest fraction of urban green (you can sort the result, or use `nlargest()` / `nsmallest()`)\n",
+    "\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d72d47ab",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/05-spatial-operations-overlays27.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9e2d3dcc",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/05-spatial-operations-overlays28.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d2655198",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/05-spatial-operations-overlays29.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b42b2dfb",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/05-spatial-operations-overlays30.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "386b4184",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/05-spatial-operations-overlays31.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "56729761",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/05-spatial-operations-overlays32.py"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6a752bcc",
+   "metadata": {},
+   "source": [
+    "An alternative to calculate the area per land use class in each district:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9b67c046",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "combined.groupby([\"district_name\", \"class\"])[\"area\"].sum().reset_index()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b5db3143",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "jupytext": {
+   "default_lexer": "ipython3"
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/notebooks/10-introduction-raster.ipynb b/notebooks/10-introduction-raster.ipynb
new file mode 100644
index 0000000..e36cc79
--- /dev/null
+++ b/notebooks/10-introduction-raster.ipynb
@@ -0,0 +1,536 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<p><font size=\"6\"><b>Introduction to geospatial raster data in Python</b></font></p>\n",
+    "\n",
+    "\n",
+    "> *DS Python for GIS and Geoscience*  \n",
+    "> *October, 2025*\n",
+    ">\n",
+    "> *© 2025, Joris Van den Bossche and Stijn Van Hoey. Licensed under [CC BY 4.0 Creative Commons](http://creativecommons.org/licenses/by/4.0/)*\n",
+    "\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The two primary types of geospatial data are raster and vector data. Vector data structures represent specific features on the Earth’s surface, and assign attributes to those features. \n",
+    "\n",
+    "__Raster data__ is stored as a grid of values which are rendered on a map as pixels. Raster files are different from photographs in that they are spatially referenced. Each pixel represents an area of land on the ground. That area is defined by the spatial resolution of the raster."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![](../img/raster-concept.png)\n",
+    "\n",
+    "<small>_Source: Colin Williams, NEON._</small>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Raster data contains at least 3 dimensions:\n",
+    "\n",
+    "- longitude of the pixel \n",
+    "- latitude of the pixel \n",
+    "- data value(s) of the pixel\n",
+    "\n",
+    "The data value can be a single data point for each pixel, also called a single band (or layer) raster data set. A well-known single-band raster data set is a Digital Elevation Model (DEM): each pixel data value in a DEM represents the surface elevation of the area represented by the pixel. \n",
+    "\n",
+    "Each pixel can also contain multiple data values, i.e. multiple bands (or layers). Remote sensing measurement are typically raster data sets that consist out of multiple bands. For example, bands 2, 3, and 4 of the [Landsat 8](https://landsat.gsfc.nasa.gov/landsat-8/landsat-8-bands) measurements capture respectively visible blue, green, and red (each with a resolution of 30m).\n",
+    "\n",
+    "Whereas we could actually store raster data as a tabular data set using an x, y and value column, the representation as a N-d array/grid is more convenient for raster data handling. \n",
+    "\n",
+    "Similar to the GeoPandas library for feature based data, meet [xarray](http://xarray.pydata.org/en/stable/) for raster based data."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "## Importing raster data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Just like geospatial feature based (vector) data, raster data is available from specific GIS file formats or data stores, like Geotiff files, Esri grid, NetCDF files, PostGIS (PostgreSQL) database, ...\n",
+    "\n",
+    "We can use the xarray library to read many of those GIS file formats (relying on the [Rasterio library](https://rasterio.readthedocs.io/en/latest/) under the hood, which is an interface to raster data). An important data format is GeoTiff. \n",
+    "\n",
+    "Let's start with reading the Sentinel RGB bands for Herstappe:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import xarray as xr"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "file_herstappe = \"./data/herstappe/raster/2020-09-17_Sentinel_2_L1C_True_color.tiff\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "herstappe = xr.open_dataarray(file_herstappe, engine=\"rasterio\")\n",
+    "herstappe"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Xarray brings its own plotting methods, but relies on Matplotlib for the actual plotting:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ax = herstappe.plot.imshow(figsize=(12, 5))\n",
+    "#ax.axes.set_aspect('equal')  # adjust the ratio of y-unit to x-unit, using 'esual' uses the same scaling for x and y, "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A raster is an __image__ in local pixel coordinates until we specify what part of the earth the image covers. This is done through the usage of raster file metadata. It depends on the file type how these are stored on disk. When reading in a file, the metadata need to be interpreted as well in order to know the __spatial information__. The translation from the data and metadata to a Python Nd-array with geographical context is done by the rasterio/xarray packages.\n",
+    "\n",
+    "As an quick introduction (we will learn the specific syntax later).\n",
+    "\n",
+    "> Plot of the Red band only, using a Red color scale_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ax = herstappe.sel(band=1).plot.imshow(cmap=\"Reds\", figsize=(12, 5))  #, robust=True)\n",
+    "ax.axes.set_aspect('equal')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "> _plot the intersection of the data at x coordinate closest to 600000 for each band:_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "herstappe.sel(x=600_000, method='nearest').plot.line(col='band')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## What's a xarray.DataArray?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's have a look at the data again:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "herstappe"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The output of xarray is a bit different to what we've previous seen. Let's go through the different elements:\n",
+    "\n",
+    "- It is a `xarray.DataArray`, one of the main data types provided by xarray\n",
+    "- It has 3 __dimensions__:\n",
+    "    - `band`: 3 bands (in this case RGB)\n",
+    "    - `y`: the y coordinates of the data set\n",
+    "    - `x`: the x coordinates of the data set\n",
+    "- Each of these dimensions are defined by a __coordinate__ (1D) array\n",
+    "- It contains 304407 (227\\*447\\*3) data values (stored as float32)\n",
+    "- Other metadata, such as some of the metadata provided by the `tiff` file are stored in the __`Attributes`__. The attributes is a dictionary.\n",
+    "\n",
+    "Looking to the data itself (click on the icons on the right), we can see these are Numpy arrays..."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Xarray adds (spatial) context to NumPy arrays"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Both the data values as the values of the coordinates are..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "type(herstappe.values), type(herstappe.x.values), type(herstappe.y.values), type(herstappe.band.values)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "... all Numpy arrays! \n",
+    "\n",
+    "Numpy is on of the most fundamental parts of the scientific python 'ecosystem'. A lot of other packages - you already used Pandas and GeoPandas in this course, and now also xarray - are built on top of NumPy and the ndarray (n-dimensional array) object it provides."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "When you read raster data using `xarray.open_dataarray` you are actually creating a set of Numpy arrays combined in a `xarray.DataArray` object. A Numpy array is an efficient data type to calculate with arrays in general. Let's extract the data of herstappe as an Numpy `ndarray`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "herstappe_array = herstappe.values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "herstappe_array"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "type(herstappe_array), herstappe_array.dtype"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "type(herstappe.x.values), type(herstappe.y.values)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "NumPy supports different dtypes (float, int,...), but __all elements of an array do have the same dtype__. Note that NumPy auto-detects the data-type from the input. The data type of this specific array `herstappe_array` is float32. More information on the data types NumPy supports is available in the [documentation](https://numpy.org/devdocs/user/basics.types.html#array-types-and-conversions-between-types)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Just as any other object in Python, the `ndarray` has a number of attributes. We already checked the `dtype` attribute. The `shape` and `ndim` of the array are other relevant attributes:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "herstappe_array.shape, herstappe_array.ndim"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Hence, we have three bands with dimensions (227, 447) and data type float32. But with this single Numpy array, we do not have any spatial information on which area of the earth this data represents. We also need the `herstappe.x` and `herstappe.y` arrays to link the indidivual elements in the array with the corresponding earth coordinates and band. \n",
+    "\n",
+    "By using the `xarray.DataArray` to wrap and combine these NumPy arrays, we can keep the data and the spatial context together."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-info\" style=\"font-size:120%\">\n",
+    "\n",
+    "**REMEMBER**:\n",
+    "\n",
+    "The [`numpy` package](https://numpy.org/) is the backbone of the scientific Python ecosystem. The `ndarray` provides an efficient data type to store and manipulate raster data, but it does __NOT contain any spatial information__. \n",
+    "    \n",
+    "If we use `xarray.DataArray` to wrap this NumPy array, we can keep the data and the spatial context together.\n",
+    "\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "## Raster data attributes - spatial context"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To quickly scan the spatial metadata of a Raster data file, let's use the [`gdalinfo` command](https://gdal.org/programs/gdalinfo.html#gdalinfo). \n",
+    "\n",
+    "> The `gdalinfo` command lists information about a raster dataset.\n",
+    "\n",
+    "It is not a Python command, but a program that need to be run from the terminal (aka command line). Using a small Jupyter notebook - `!` trick, we can use it within the notebook as well:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "!gdalinfo -mm ./data/herstappe/raster/2020-09-17_Sentinel_2_L1C_True_color.tiff"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Important information we get from the `gdalinfo` command are \n",
+    "\n",
+    "* Coordinate reference system (CRS), see also [02-coordinate-reference-systems notebook](./02-coordinate-reference-systems.ipynb).\n",
+    "* Number of bands in the data set: A data set can contain one (single) or more (multi) layers (bands).\n",
+    "* Spatial resolution: This resolution represents the area on the ground that each pixel covers. The units for your data are determined by the CRS above (e.g. meters).\n",
+    "* Spatial extent (bounding box) of the data: The geographic area that the raster data covers.\n",
+    "\n",
+    "<img src=\"../img/raster-spatial-extent-coordinates.png\" alt=\"spatial-extnet\" style=\"width:500px;\"/>\n",
+    "\n",
+    "<small>_Image Source: National Ecological Observatory Network (NEON)._</small>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's see how `xarray.open_dataarray` interprets this information:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "herstappe = xr.open_dataarray(file_herstappe, engine=\"rasterio\")\n",
+    "herstappe"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "- Coordinate reference system (CRS) stored as attribute to the `xr.DataArray` `rio` accessor:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "herstappe.rio.crs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "type(herstappe.rio.crs) # herstappe.rio.crs.to_string(), # herstappe.rio.crs.to_wkt()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "- Number of bands in the data set. Each band in the original data set is represented in the `band` dimension:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "herstappe.band"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "- Spatial resolution: This resolution represents the area on the ground that each pixel covers. It is available with the `rio`  accessor, but also translated to the coordinates dimensions x and y as the distance between two elements:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "herstappe.rio.resolution()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "herstappe.x[1] - herstappe.x[0], herstappe.y[1] - herstappe.y[0]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "- Spatial extent (bounding box) of the data. [Xarray translates](https://corteva.github.io/rioxarray/stable/getting_started/getting_started.html) the the coordinates info and spatial extent:\n",
+    "\n",
+    "    >  The x and y coordinates are generated automatically from the file’s geoinformation, shifted to the center of each pixel.\n",
+    "    \n",
+    "Hence, the minimal and maximum values of the coordinates are still the same as the boundaries defined by the metadata:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "herstappe.rio.bounds()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "herstappe.x.min(), herstappe.x.max(), herstappe.y.min(), herstappe.y.max()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-info\" style=\"font-size:120%\">\n",
+    "\n",
+    "**TIP**: <br>\n",
+    "\n",
+    "The [`gdal` library](https://gdal.org/) is a very powerful set of functions. It is the\n",
+    "open source Swiss Army knife for raster and vector geospatial data handling. GDAL provides Python bindings to run it from Python code, but these are not very 'Pythonic'. When familiar with the command line the [command line raster functions](https://gdal.org/programs/index.html#raster-programs) (CLI) are certainly worthwhile to check out!\n",
+    "    \n",
+    "You can run a CLI command inside a Jupyter Notebook by prefixing it with the `!` character.\n",
+    "\n",
+    "</div>"
+   ]
+  }
+ ],
+ "metadata": {
+  "celltoolbar": "Nbtutor - export exercises",
+  "jupytext": {
+   "default_lexer": "ipython3"
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/notebooks/11-xarray-intro.ipynb b/notebooks/11-xarray-intro.ipynb
new file mode 100644
index 0000000..f2bbfa7
--- /dev/null
+++ b/notebooks/11-xarray-intro.ipynb
@@ -0,0 +1,1740 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<p><font size=\"6\"><b>Xarray intro</b></font></p>\n",
+    "\n",
+    "\n",
+    "> *DS Python for GIS and Geoscience*  \n",
+    "> *October, 2025*\n",
+    ">\n",
+    "> *© 2025, Joris Van den Bossche and Stijn Van Hoey. Licensed under [CC BY 4.0 Creative Commons](http://creativecommons.org/licenses/by/4.0/)*\n",
+    "\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import matplotlib.colors as mcolors"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Introduction"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import xarray as xr"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's start with reading the Sentinel RGB bands for Herstappe again:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "herstappe_file = \"./data/herstappe/raster/2020-09-17_Sentinel_2_L1C_True_color.tiff\"\n",
+    "herstappe = xr.open_dataarray(herstappe_file, engine=\"rasterio\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "herstappe"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ax = herstappe.plot.imshow(figsize=(12, 5))\n",
+    "ax.axes.set_aspect('equal')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can make the dimension information more useful by renaming the labels of the 'band' dimension:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "herstappe = herstappe.assign_coords(band=(\"band\", [\"red\", \"green\", \"blue\"]))\n",
+    "herstappe"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Or just rename the coordinate names itself:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "herstappe.rename({\"x\": \"easting\", \"y\": \"northing\"})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "These __dimension coordinate names__ can be used to extract (slice) data making data selections very declarative:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "herstappe.sel(band='red')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The data type of this `xarray.DataArray` `herstappe` is 'float32'. Xarray uses the data types provided by NumPy. More information on the data types NumPy supports is available in the [documentation](https://numpy.org/devdocs/user/basics.types.html#array-types-and-conversions-between-types).\n",
+    "\n",
+    "Converting to another data type is supported by `astype()` method:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "herstappe.astype('float64')   # .nbytes"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Using xarray:\n",
+    "\n",
+    "- Data stored as a NumPy arrays.\n",
+    "- Dimensions do have a name.\n",
+    "- The coordinates of each of the dimensions can represent geographical coordinates, categories, dates, ... instead of just a positional index."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-info\" style=\"font-size:120%\">\n",
+    "\n",
+    "**REMEMBER**: <br>\n",
+    "\n",
+    "The [`xarray` package](xarray.pydata.org/en/stable/) introduces __labels__ in the form of dimensions, coordinates and attributes on top of raw NumPy-like arrays. Xarray is inspired by and borrows heavily from Pandas.    \n",
+    "\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Selecting data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Xarray’s labels make working with multidimensional data much easier:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "herstappe = xr.open_dataarray(herstappe_file, engine=\"rasterio\")\n",
+    "herstappe = herstappe.assign_coords(band=(\"band\", [\"red\", \"green\", \"blue\"]))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We could use the Numpy style of data slicing:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "herstappe[0]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "However, it is often much more powerful to use xarray’s `.sel()` method to use label-based indexing, making the selection more declarative:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "herstappe.sel(band=\"red\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can select a specific set of coordinate values as a __list__ and take the value that is most near to the given value:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "herstappe.sel(x=[600_000, 610_000, 620_000], method=\"nearest\").sel(band=\"red\").plot.line(hue=\"x\");"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Sometimes, a specific range is required. The `.sel()` method also supports __slicing__, so we can select the green band and slice a subset of the data along the x direction:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "herstappe.sel(x=slice(580_000, 620_000), band=\"green\").plot.imshow(cmap=\"Greens\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The __positional indexing__ as you would do with the underlying Numpy array is still possible as well:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "herstappe[0, 100:200:10, 100:200:10]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Use a __condition__ to select data, also called fancy indexing or boolean indexing:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "herstappe > 0.3"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "However, with xarray we cannot use a mask like this to directly filter the array or assign new values. \n",
+    "\n",
+    "One typical use case for raster data is where you want to apply a mask to the data and set those values to some \"NODATA\" value. For plotting, this can for example be `np.nan`, and for this we can use the `where()` method:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "herstappe.where(herstappe > 0.3).sel(band=\"red\").plot.imshow()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Reading NODATA values"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "GeoTiff raster files should encode the presence of NODATA pixels as part of the metadata. Check the metadata using the CLI tool GDAL, we see for each band the value that is used to represent NODATA pixels:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#!gdalinfo ./data/gent/raster/2020-09-17_Sentinel_2_L1C_True_color.tiff"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In this example, the data is stored as _Unsigned Int16_ values (i.e. range from 0 to 65535, 16 bits are used to represent the magnitude of the number and none are reserved for a sign) and the value `65535` is used to represent NODATA.\n",
+    "\n",
+    "Rioxarray will use this metadata by default to replace array values equal to `_FillValue` with NaN (and scale if a scale factor and/or offset are in the metadata as well). This is because the `mask_and_scale` parameter is by default `True`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tc_data = xr.open_dataarray(\"./data/gent/raster/2020-09-17_Sentinel_2_L1C_True_color.tiff\", \n",
+    "                            engine=\"rasterio\", mask_and_scale=True)\n",
+    "tc_data.dtype"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Whereas the data us stored as UInt16, rioxarray returns data as float32 with the NODATA values converted to NaN. Let's count the number of NaN values in the data set:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "np.isnan(tc_data).sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To explicitly ignore the default behavior and read the data as stored without masking the NODATA values, put the `mask_and_scale` on False:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tc_data = xr.open_dataarray(\"./data/gent/raster/2020-09-17_Sentinel_2_L1C_True_color.tiff\", \n",
+    "                            engine=\"rasterio\", mask_and_scale=False)\n",
+    "tc_data.dtype"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Whereas the NODATA value is not applied, the nodata value stored in the geotiff is still available as attribute of the DataArray or accessible by the `.rio` accessor:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tc_data.rio.nodata, tc_data.attrs[\"_FillValue\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The data is read as UInt16 and the number of values equal to 65535 in the data set:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "(tc_data == 65535).sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-info\" style=\"font-size:100%\">\n",
+    "\n",
+    "**Remember**: <br>\n",
+    "\n",
+    "The `mask_and_scale` parameter is [by default `True`](https://docs.xarray.dev/en/stable/generated/xarray.open_dataarray.html) in xarray for the different engines. This might lead to unwanted data type conversions when the 'NODATA' is not properly included in the raw data file. Make sure to check the data types.\n",
+    "\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Let's practice!\n",
+    "\n",
+    "We'll look at some Sentinel GeoTiff data, this time from the region of the City of Ghent:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-success\">\n",
+    "\n",
+    "**EXERCISE 1**:\n",
+    "\n",
+    "* Read in the file `./data/gent/raster/2020-09-17_Sentinel_2_L1C_True_color.tiff` with xarray and assign the data to a new variable `tc_data`.  \n",
+    "* Use the `mask_and_scale` parameter explicitly in the reader function to mask (and scale) the dataset with the NODATA metadata encoded in the geotiff file.     \n",
+    "* Check which value the `tc_data.rio.nodata` attribute specifies as NODATA. Why is this not 65535?\n",
+    "    \n",
+    "<details><summary>Hints</summary>\n",
+    "\n",
+    "* Whereas `mask_and_scale` is by default `True`, adding it makes the conversion explicit.\n",
+    "* After masking, the NaN value in the float data is np.nan as `tc_data.rio.nodata` attribute also specifies.\n",
+    "\n",
+    "</details>\n",
+    "    \n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/11-xarray-intro1.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/11-xarray-intro2.py"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-success\">\n",
+    "\n",
+    "**EXERCISE 2**:\n",
+    "\n",
+    "* Read in the file `./data/gent/raster/2020-09-17_Sentinel_2_L1C_True_color.tiff` with xarray in the __original raw data__ dtype and assign the data to a new variable `tc_data`.  \n",
+    "* Inspect the display of `tc_data`. What are the different dimensions of the array? \n",
+    "* Select only the *second* layer of `tc_data` and assign the output to a new variable `tc_g`.\n",
+    "* Plot the second (green) layer.\n",
+    "* Create a new array with each of the elements in the `tc_g` array with a value above 15000 set to a new value of 65535.\n",
+    "    \n",
+    "<details><summary>Hints</summary>\n",
+    "\n",
+    "* Use the `mask_and_scale` parameter in the reader function to make sure the data type of tc_data is the same as the raw data.\n",
+    "* To select a specific subset for a certain dimension of a DataArray, use the `.sel()` method. The argument name to use is the name of the dimension.\n",
+    "* You can mask an array and set the masked values to another value with the `.where()` method. Check the help of the method for information on the keyword arguments.    \n",
+    "\n",
+    "</details>\n",
+    "    \n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/11-xarray-intro3.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/11-xarray-intro4.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/11-xarray-intro5.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/11-xarray-intro6.py"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-success\">\n",
+    "\n",
+    "**EXERCISE 3**:\n",
+    "\n",
+    "Subsample the array `tc_data` by taking only one out of each 5 data points for all layers at the same time. Use _positional indexing_ to subsample the data.\n",
+    "    \n",
+    "(Be aware that this is a naive resampling implementation for educational purposes only). \n",
+    "    \n",
+    "<details><summary>Hints</summary>\n",
+    "\n",
+    "* Slicing in Numpy is similar to Python lists, but in multiple dimensions, e.g. for 2 dimensions `my_array[start:end:step-size, start:end:step-size]`.\n",
+    "* When selecting all elements in a given dimension, just use a single colon, e.g. `my_array[:]`. When slicing from `start` to `end` of an array, leave these values empty, e.g. `my_array[::step-size]`.\n",
+    "* The result should still be a 3-D array with 3 elements in the first dimension.\n",
+    "\n",
+    "</details>   \n",
+    "    \n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/11-xarray-intro7.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/11-xarray-intro8.py"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-success\">\n",
+    "\n",
+    "**EXERCISE 4**:\n",
+    "\n",
+    "For the file `2020-09-17_Sentinel_2_L1C_B04.tiff`, the NODATA is not stored in the metadata of the file. Still, the elements with the value `65535` do represent 'Not a Number' (NaN) values. The conversion need to be done manually and use `mask_and_scale=False`. \n",
+    "\n",
+    "NumPy does not support NaN values for integer data, so we'll convert to float first as data type. After reading in the data set `./data/gent/raster/2020-09-17_Sentinel_2_L1C_B04.tiff` (assign data to variable `b4_data`):\n",
+    "    \n",
+    "* Count the number of elements that are equal to `65535`\n",
+    "* Convert the data type to `float`, assign the result to  a new variable `b4_data_f` (numpy does not support NaN for integers).\n",
+    "* Assign NaN (`np.nan`) value to each of the elements of `b4_data_f` equal to `65535`\n",
+    "* Count the number of NaN values in the `b4_data_f` data\n",
+    "* Make a histogram of both the `b4_data` and `b4_data_f` data. Can you spot the difference?\n",
+    "\n",
+    "<details><summary>Hints</summary>    \n",
+    "\n",
+    "* `np.nan` represents _Not a Number (NaN)_ in Numpy. You can mask an array with np.nan values using the `where()`method\n",
+    "* `np.sum` will by default sum all of the elements of the input array and can also count boolean values (True = 1 and False = 0), resulting from a conditional expression. \n",
+    "* To test if a value is a NaN, numpy provides `np.isnan(...)` which results in an element-wise check returning boolean values.\n",
+    "* Check the help of the `plt.hist` command to find out more about the `bins` and the `log` arguments.\n",
+    "\n",
+    "</details>\n",
+    "    \n",
+    "   \n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/11-xarray-intro9.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/11-xarray-intro10.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/11-xarray-intro11.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/11-xarray-intro12.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/11-xarray-intro13.py"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This is what the `mask_and_scale` option also does: it reads the nodata value from the metadata in the tiff file and uses this to mask the no-data value with a NaN value."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-info\" style=\"font-size:100%\">\n",
+    "\n",
+    "**Remember**: <br>\n",
+    "\n",
+    "Whereas the `mask_and_scale` parameter is a convenient functionality, it only works when the NODATA metadata is stored in the GeoTiff file. Make sure to check the value distribution with `mask_and_scale=False` to check for missing NODATA metadata.\n",
+    "\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Plotting"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We already used `.plot.imshow` and `.plot.line` in the previous section and exercise. Similar to Pandas, `xarray` has a `plot` method, which can be used for different plot types."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "xr_array = xr.open_dataarray(\"./data/gent/raster/2020-09-17_Sentinel_2_L1C_B0408.tiff\", \n",
+    "                             engine=\"rasterio\")\n",
+    "xr_array = xr_array.assign_coords(band=(\"band\", [\"b4\", \"b8\"]))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "It supports both 2 dimensional (e.g. line) as 3 (e.g. imshow, pcolormesh) dimensional plots. When just using `plot`, xarray will do a _best guess_ on how to plot the data. However being explicit `plot.line`, `plot.imshow`, `plot.pcolormesh`, `plot.scatter`,...  gives you more control."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "xr_array.sel(band=\"b4\").plot();  # add .line() -> ValueError: For 2D inputs, please specify either hue, x or y."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "xr_array.sel(x=420000, method=\"nearest\").plot.line(hue=\"band\");"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\"facetting\" splits the data in subplots according to a dimension, e.g. `band`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "xr_array.plot.imshow(col=\"band\", cmap=\"Reds\");"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Facetting also works for line plots:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "xr_array.sel(x=420000, method=\"nearest\").plot.line(col=\"band\");  # row=\"band\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Use the `robust` option when there is a lack of visual difference. This will use the 2nd and 98th percentiles of the data to compute the color limits. The arrows on the color bar indicate that the colors include data points outside the bounds."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "ax = xr_array.sel(band=\"b4\").plot(cmap=\"Reds\", robust=True, figsize=(12, 5))  # use False as well\n",
+    "ax.axes.set_aspect('equal')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In case you want to show the output with a __discrete colormap__, one can define a [set of levels to split the colormap](http://xarray.pydata.org/en/stable/user-guide/plotting.html#discrete-colormaps) on:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ax = xr_array.sel(band=\"b8\").plot(cmap=\"Reds\", levels=[0, 0.04, 0.08, 0.1], figsize=(12, 5))  # plot without these levels\n",
+    "ax.axes.set_aspect('equal')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For more control, defining the `Figure` and `Axes` Matplotlib object first provides more flexibility in terms of further adjustments. One can pass an `axes` object to an xarray `.plot` method in order to link the output:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig, (ax0, ax1, ax2) = plt.subplots(1, 3, figsize=(20, 4))\n",
+    "\n",
+    "# first subplot a histogram of band 4\n",
+    "xr_array.sel(band=\"b4\").plot.hist(bins=30, ax=ax0);\n",
+    "ax0.set_title(\"Histogram of b4\");\n",
+    "\n",
+    "# second subplot a line plot at given x coordinate\n",
+    "xr_array.sel(x=420000, method=\"nearest\").plot.line(hue=\"band\", ax=ax1);\n",
+    "\n",
+    "# third subplot a histogram of band 8\n",
+    "xr_array.sel(band=\"b8\").plot.hist(bins=30, ax=ax2);\n",
+    "ax2.set_title(\"Histogram of b8\");"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Reductions, element-wise calculations and broadcasting"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "herstappe = xr.open_dataarray(herstappe_file, engine=\"rasterio\")\n",
+    "herstappe = herstappe.assign_coords(band=(\"band\", [\"red\", \"green\", \"blue\"]))\n",
+    "herstappe_red = herstappe.sel(band=\"red\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Reductions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The __reductions__ (aggregations) are provided as methods and can be applied along one or more of the data dimensions."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "By default, the array is reduced over all dimensions, returning a single value as a DataArray:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "herstappe_red.mean()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In NumPy, the dimensions are called the __axis__:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "herstappe_red.mean(axis=1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "But we have __dimensions with labels__, so rather than performing reductions on axes (as in NumPy), we can perform them on __dimensions__. This turns out to be convenient and declarative:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "herstappe_red.mean(dim=\"x\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Calculate the mean values for each of the bands separately:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "herstappe.mean(dim=[\"x\", \"y\"])  # read as: 'take the mean over the dimensions x and y combined'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Or some quantiles:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "herstappe.quantile([0.1, 0.5, 0.9], dim=[\"x\", \"y\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Element-wise computations"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The __for each element__ is crucial for NumPy and Xarray. The typical answer in programming would be a `for`-loop, but Numpy is optimized to do these calculations __element-wise__ (i.e. for all elements together):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "dummy = np.arange(1, 10)\n",
+    "dummy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "dummy*10"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Instead of:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "[el*20 for el in dummy]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Numpy provides most of the familiar arithmetic operators to apply on an element-by-element basis:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "np.exp(dummy), np.sin(dummy), dummy**2, np.log(dummy)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Xarray works seamlessly with those arithmetic operators and numpy array functions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "herstappe_red * 10."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "np.log(herstappe_red)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can combine multiple xarray arrays in arithemetic operations:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "herstappe.sel(band=\"red\") - herstappe.sel(band=\"green\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Broadcasting"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "When we combine arrays with different shapes during arithmetic operations, NumPy and Xarray apply a set of __broadcoasting__ rules and the smaller array is _broadcast_ across the larger array so that they have compatible shapes. \n",
+    "\n",
+    "Perfoming an operation on arrays with different coordinates will result in automatic broadcasting:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "herstappe.x.shape, herstappe_red.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "herstappe_red + herstappe.x  # Note, this calculaton does not make much sense, but illustrates broadcasting"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-info\" style=\"font-size:120%\">\n",
+    "\n",
+    "**REMEMBER**: <br>\n",
+    "\n",
+    "The combination of element-wise calculations, efficient reductions and broadcasting provides NumPy and Xarray a lot of power. In general, it is a good advice to __avoid for loops__ when working with arrays.\n",
+    "\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Let's practice!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-success\">\n",
+    "\n",
+    "**EXERCISE 5**:\n",
+    "\n",
+    "The data set `./data/herstappe/raster/2020-09-17_Sentinel_2_L1C_True_color.tiff` (assign to variable `herstappe_data`) has values ranging in between 0.11325, 0.8575. To improve the quality of the visualization, stretch __each of the layers individually__ to the values to the range 0. to 1. with a linear transformation: \n",
+    "    \n",
+    "$$z_{i,scaled}=\\frac{z_i-\\min(z)}{\\max(z)-\\min(z)}$$\n",
+    "\n",
+    "Make a plot of the end result and compare with a plot of the original data. \n",
+    "\n",
+    "<details><summary>Hints</summary>\n",
+    "\n",
+    "* We ask to do it *\"for each of the bands\"*, but you don't need to do it separately for each band! Using broadcasting and element-wise operations, we can do this in one go.\n",
+    "* First calculate the minimum and maximum value for the bands (i.e. reducing over the other (x, y) dimensions). This should result in an 1D array of length 3.\n",
+    "* With those min/max arrays, you can add and subtract those from the original `herstappe_data` array to obtain the scaled data.\n",
+    "\n",
+    "</details>    \n",
+    "    \n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "herstappe_data = xr.open_dataarray(\"./data/herstappe/raster/2020-09-17_Sentinel_2_L1C_True_color.tiff\", \n",
+    "                                   engine=\"rasterio\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/11-xarray-intro14.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/11-xarray-intro15.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/11-xarray-intro16.py"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-success\">\n",
+    "\n",
+    "**EXERCISE 6**:\n",
+    "\n",
+    "The true color data set for Ghent `./data/gent/raster/2020-09-17_Sentinel_2_L1C_True_color.tiff` contains 3 bands. Plotting with the `imshow` function can plot 3-D (RGB) data sets, but when running `gent.plot.imshow()`, we get an error. This is because matplotlib expects data in the range of [0..1] for floats or [0..255] for integers. \n",
+    "    \n",
+    "When working with the original raw data of the file, the data type of this specific array `gent` is 16bit unsigned integer. Remember that using 16bit unsigned integer, it can contain `2**16` different (all positive) integer values to represent the data range (in this case 0 to 1):\n",
+    "\n",
+    "```\n",
+    ">>> 2**16\n",
+    "65536\n",
+    "```\n",
+    "\n",
+    "In this excercise, we will convert the data to floats within the data range 0 -> 1 so we can plot it as RGB values.\n",
+    "\n",
+    "- Read the data file and assign to a variable `gent`. Read the original raw data without masking. \n",
+    "- Try to plot it with the `imshow()` method.\n",
+    "- Convert the array to a float array, switch values equal to 65535 into NaN-values and call the result `gent_f`. \n",
+    "- Now divide the array by 65535 to get our data in a [0-1] range.\n",
+    "- Plot the result with the `imshow()` method.\n",
+    "\n",
+    "<details><summary>Hints</summary>\n",
+    "\n",
+    "* To convert the data type of an array, you can use the `astype()` method.\n",
+    "* The `.where()` method is very useful to convert all values not according to the condition into NaN values\n",
+    "* Masking out part of the data based on a condition can be done with the `where()` method.\n",
+    "\n",
+    "</details>    \n",
+    "    \n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/11-xarray-intro17.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/11-xarray-intro18.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/11-xarray-intro19.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/11-xarray-intro20.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/11-xarray-intro21.py"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-success\">\n",
+    "\n",
+    "**EXERCISE 7**:\n",
+    "\n",
+    "Next, plot a greyscale version of the data as well. Instead of using a custom function just rely on the sum of the 3 bands as a quick proxy.\n",
+    "    \n",
+    "<details><summary>Hints</summary>\n",
+    "\n",
+    "* A greyscale image requires a greyscale `cmap`, check the available names in [the documentation online](https://matplotlib.org/tutorials/colors/colormaps.html)\n",
+    "* Xarray provides the common aggregation functions as methods to a DataArray, e.g. `my_array.sum(...)`\n",
+    "* To do a reduction over a given dimension, use the `dim`-ension name as parameter of the method.\n",
+    "\n",
+    "</details>    \n",
+    "    \n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/11-xarray-intro22.py"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-success\">\n",
+    "\n",
+    "**EXERCISE 8**:\n",
+    "\n",
+    "The data sets `./data/gent/raster/2020-09-17_Sentinel_2_L1C_B04.tiff` and `./data/gent/raster/2020-09-17_Sentinel_2_L1C_B08.tiff` contain respectively the 4th and the 8th band of a sentinel satellite image. To derive the [Normalized Difference Vegetation Index) (NDVI)](https://nl.wikipedia.org/wiki/Normalized_Difference_Vegetation_Index), the two bands need to be combined as follows:\n",
+    "    \n",
+    "$$\\frac{band_8 - band_4}{band_8 + band_4} $$\n",
+    "    \n",
+    "Process the images and create a plot of the NDVI:\n",
+    "    \n",
+    "- Read both data sets and store them in resp. `b4_data` and `b8_data`. \n",
+    "- Transform the data range of each of the layers to the range .0 - 1.\n",
+    "- Calculate the NDVI\n",
+    "- Make a plot of the NDVI plot the data as such using the \"Greens\" colormap. \n",
+    "\n",
+    "__IMPROVE THE COLORMAP__\n",
+    "    \n",
+    "Using these default plot settings, the NDVI visualisation is not very informative. Actually, whereas the range is [-1, 1], values of around 0.1 and below are not considered living plants. Consider two alternative methods to improve the colormap taking into account this information:\n",
+    "    \n",
+    "1. Normalize the data linearly into the range 0.1 to 0.8 and apply this normalization to the `imshow` while using the colormap `YlGn` (so values just above 0.1 are still yellow, whereas only higher values are green). Use the Matplotlib function `mcolors.Normalize` for the data normalization.\n",
+    "2. To improve the contrast between vegetation versus no-vegetation, use a diverging colormap (`RdYlGn`) in combination with a [`TwoSlopeNorm`](https://matplotlib.org/stable/api/_as_gen/matplotlib.colors.TwoSlopeNorm.html) to normalize the data. Let the range vary from -0.1 till 0.8 with the center on 0.4.    \n",
+    "\n",
+    "<details><summary>Hints</summary>\n",
+    "\n",
+    "* For more specific adjustments to the colormap, have a check on the [Matplotlib documentation on colormap normalization](https://matplotlib.org/3.3.2/tutorials/colors/colormapnorms.html). \n",
+    "* Check out the available normalization by exploring the `mcolors.` module of Matplotlib. \n",
+    "* The `imshow` method can accept a `norm` parameter to normalize the data.  \n",
+    "* It is a two step process: 1/ define a normalization; 2/ pass the normalization to the `norm` parameter of the plot function.\n",
+    "\n",
+    "\n",
+    "</details>   \n",
+    "           \n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/11-xarray-intro23.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/11-xarray-intro24.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/11-xarray-intro25.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/11-xarray-intro26.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/11-xarray-intro27.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/11-xarray-intro28.py"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-success\">\n",
+    "\n",
+    "**(ADVANCED) EXERCISE 9**:\n",
+    "    \n",
+    "You want to reclassify the values of the 4th band data to a fixed set of classes:\n",
+    "    \n",
+    "* x < 0.05 need to be 0\n",
+    "* 0.05 < x < 0.1 need to be 1\n",
+    "* x > 0.1 need to be 2\n",
+    "       \n",
+    "Use the data set `./data/gent/raster/2020-09-17_Sentinel_2_L1C_B04.tiff` (assign data to variable `b4_data`):\n",
+    "    \n",
+    "* Read the data set and exclude the single-value dimension to end up with a 2D array. \n",
+    "* Convert to float data type and normalize the values to the range `[0., 1.]`.\n",
+    "\n",
+    "To reclassify the values, we can use the `np.digitize` function. This function return the indices of the bins to which each value in input array belongs. As such, it can be used to select and manipulate values containing to a specific bin.\n",
+    "    \n",
+    "* Apply the `np.digitize` function to `b4_data` using the `xr.apply_ufunc()` function. The first argument is the function to apply, the following arguments are the arguments that would be passed to the function (`np.digitize`). Call the result `b4_data_classified`.\n",
+    "* Make a image plot of the reclassified variable `b4_data_classified`.\n",
+    "    \n",
+    "\n",
+    "<details><summary>Hints</summary>\n",
+    "\n",
+    "* The `apply_ufunc` function requires as input another function (in this case `np.digitize`) and is a way to use [external functionalities within xarray](http://xarray.pydata.org/en/stable/user-guide/computation.html#wrapping-custom-computation).   \n",
+    "* The `np.digitize` function would have as inputs the data `b4_data` and the bins to split the data, `[0.05, 0.1]`.\n",
+    "\n",
+    "</details>   \n",
+    "    \n",
+    "    \n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "clear_cell": false
+   },
+   "outputs": [],
+   "source": [
+    "b4_data = xr.open_dataarray(\"./data/gent/raster/2020-09-17_Sentinel_2_L1C_B04.tiff\", \n",
+    "                            engine=\"rasterio\", mask_and_scale=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/11-xarray-intro29.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/11-xarray-intro30.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/11-xarray-intro31.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/11-xarray-intro32.py"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-info\">\n",
+    "    \n",
+    "__Note:__\n",
+    "    \n",
+    "When only interested in a discrete colormap for plotting instead of the data manipulation, xarray provides the `levels` parameter whe plotting, see http://xarray.pydata.org/en/stable/user-guide/plotting.html#discrete-colormaps.    \n",
+    "    \n",
+    "</div>"
+   ]
+  }
+ ],
+ "metadata": {
+  "celltoolbar": "Nbtutor - export exercises",
+  "jupytext": {
+   "default_lexer": "ipython3"
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.11"
+  },
+  "widgets": {
+   "application/vnd.jupyter.widget-state+json": {
+    "state": {},
+    "version_major": 2,
+    "version_minor": 0
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/notebooks/12-xarray-advanced.ipynb b/notebooks/12-xarray-advanced.ipynb
new file mode 100644
index 0000000..775184b
--- /dev/null
+++ b/notebooks/12-xarray-advanced.ipynb
@@ -0,0 +1,1494 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "37c046a3-39ce-4b51-9fb5-07a2d93d3fa2",
+   "metadata": {},
+   "source": [
+    "<p><font size=\"6\"><b>Xarray advanced</b></font></p>\n",
+    "\n",
+    "\n",
+    "> *DS Python for GIS and Geoscience*  \n",
+    "> *October, 2025*\n",
+    ">\n",
+    "> *© 2025, Joris Van den Bossche and Stijn Van Hoey. Licensed under [CC BY 4.0 Creative Commons](http://creativecommons.org/licenses/by/4.0/)*\n",
+    "\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c4b0d759-b8e0-4950-b6b1-706edad1b1e2",
+   "metadata": {},
+   "source": [
+    "Acknowledgments to the data service: https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-land-monthly-means?tab=overview"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7c2ad45b-1592-4128-a126-e984bfba1a4b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import xarray as xr\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "import cmocean"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1015efcc-929c-4bc5-9dfb-7805fd888a45",
+   "metadata": {},
+   "source": [
+    "## `xarray.Dataset` for multiple variables"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e6ce0cd6-3a30-4085-ae16-0fd6083d6959",
+   "metadata": {},
+   "source": [
+    "We already know `xarray.DataArray`, it is a single multi-dimensional array and each dimension can have a name and coordinate values. Next to the `DataArray`, `xarray` has a second main data structure to store arrays, i.e. `xarray.DataSet`. \n",
+    "\n",
+    "Let's read an xarray data set (global rain/temperature coverage stored in the file `2016-2017_global_rain-temperature.nc`), using the function `open_dataset`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "47aa01dd-cf62-4312-9596-1f5a4ba7ff25",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ds = xr.open_dataset(\"./data/2016-2017_global_rain-temperature.nc\", engine=\"netcdf4\")\n",
+    "ds"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d62db744-7f64-42e6-8e45-638b88421c53",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-info\" style=\"font-size:120%\">\n",
+    "\n",
+    "**REMEMBER**: <br>\n",
+    "\n",
+    "xarray provides reading function for different formats with the `open_dataset` and `open_dataarray` functions. For GIS formats such as geotiff and other GDAL readable raster data, the rasterio engine (`engine=\"rasterio\"`) is available after the installation of [rioxarray](https://corteva.github.io/rioxarray/stable/). NetCDF-alike data formats can also be loaded using the `open_dataset()` function using a NetCDF-compatible engine, e.g. `netcdf` or `h5netcdf`. The netcdf-engine will be _guessed_ by default.\n",
+    "\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e586c171-c6d7-4f90-8f5b-df54089c7cfa",
+   "metadata": {},
+   "source": [
+    "Let's take a closer look at this `xarray.Dataset`:\n",
+    "\n",
+    "- A `xarray.Dataset` is the second main data type provided by xarray\n",
+    "- This example has 3 __dimensions__:\n",
+    "    - `y`: the y coordinates of the data set\n",
+    "    - `x`: the x coordinates of the data set\n",
+    "    - `year`: the year coordinate of the data set\n",
+    "- Each of these dimensions are defined by a __coordinate__ (1D) array\n",
+    "- It has 2 __Data variables__: `precipitation` and `temperature` that both share the same coordinates\n",
+    "\n",
+    "Hence, a `Dataset` object stores *multiple* arrays that have shared dimensions (__Note:__ not all dimensions need to be shared).  It is designed as an in-memory representation of the data model from the netCDF file format.\n",
+    "\n",
+    "![](http://xarray.pydata.org/en/stable/_images/dataset-diagram.png)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c0e48ee0-c828-4f80-a6c8-5428155bef01",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ds[\"temperature\"].shape, ds[\"temperature\"].dims"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "72266de5-77e4-4b48-81fe-7388b7dff5b4",
+   "metadata": {},
+   "source": [
+    "The data and coordinate variables are also contained separately in the `data_vars` and `coords` dictionary-like attributes of a `xarray.DataSet` to access them directly:\n",
+    "\n",
+    "- The data variables:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2460b4e6-a5f1-40da-967d-325aa0287d4b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ds.data_vars"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ddc9f07c-0d81-4aab-a753-da40f57aa735",
+   "metadata": {},
+   "source": [
+    "- The data coordinates:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a67e0be0-204b-4164-a6ed-9bc6eb34eaed",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ds.coords"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ccc8e94c-17ca-420a-a3aa-c7c71fa76636",
+   "metadata": {},
+   "source": [
+    "If you rather use an alternative name for a given variable, use the `rename` method:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bbfc46d8-e4b3-4cc4-bd83-769258548716",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ds.rename({\"precipitation\": \"rain\"})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8efa9dcb-128e-4b01-987f-a970c7abe40f",
+   "metadata": {},
+   "source": [
+    "__Note:__ _the renaming is not entirely correct as rain is just a part of all precipitation (see https://en.wikipedia.org/wiki/Precipitation)._"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d8e17106-8575-4f55-bb92-ab6c2d90a6c1",
+   "metadata": {},
+   "source": [
+    "Adding new variables to the data set is very similar to Pandas/GeoPandas:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6ff98dc0-737f-4e55-8c73-2f9b62f89749",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ds[\"precipitation_m\"] = ds[\"precipitation\"]/1000."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1c8aa29e-6d87-4ad8-80e7-a265bb683ca6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ds"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "035e068b-d776-4f13-93c6-c91929d8ce26",
+   "metadata": {},
+   "source": [
+    "### Selecting `DataSet` data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "14cd95ad-d6fc-4685-a899-16084de71edb",
+   "metadata": {},
+   "source": [
+    "Each of the data variables can be accessed as a single `xarray.DataArray` similar to selecting from dictionaries or columns from DataFrames:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "400bcda7-b4d8-4aa9-a4ef-7ab0158c1f28",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "type(ds[\"precipitation\"]), type(ds[\"temperature\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "38951a5e-7515-4665-ad08-82cce47a75ac",
+   "metadata": {},
+   "source": [
+    "One can select multiple variables at the same time as well by passing a list of variable names:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e0e2ee9e-6882-4ddb-a01e-c7a64fae2c17",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ds[[\"temperature\", \"precipitation\"]]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6c3db5d4-6fe9-4c54-9180-c90e75b4ed80",
+   "metadata": {},
+   "source": [
+    "Or the other way around, use the `drop_vars` to drop variables from the data set:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a114abfe-0950-4262-9e13-9ecd78ff6f49",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ds.drop_vars(\"temperature\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "148ca2f1-14fb-4825-9c77-c761b275de66",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-info\" style=\"font-size:120%\">\n",
+    "\n",
+    "**NOTE**: <br>\n",
+    "\n",
+    "Selecting a single variable using `[]` results into a `xarray.DataArray`, selecting multiple variables using a list `[[..., ...]]` results into a `xarray.DataSet`. Using `drop_vars` always returns a `xarray.DataSet`.\n",
+    "\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "449e461c-b584-4e51-905a-eda5fd95e80a",
+   "metadata": {},
+   "source": [
+    "The selection with `sel` works as well with `xarray.DataSet`, selecting the data _for all variables in the DataSet_ and returning a DataSet:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "07b43c50-8b78-4ee2-9b03-85526b98f5f7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ds.sel(year=2016)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7db298ec-66a7-46d8-ac7f-0cc6389e598e",
+   "metadata": {},
+   "source": [
+    "The inverse of the `sel` method is the `drop_sel` which returns a DataSet with the enlisted indices removed:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2e0c699f-da00-41e4-811c-a5efbe72f046",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ds.drop_sel(year=[2016])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c4c20f29-fc0d-4743-9be7-a88ec04c8a96",
+   "metadata": {},
+   "source": [
+    "### DataSet plotting"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8fa04333-e701-43cb-aaa3-217fda033ace",
+   "metadata": {},
+   "source": [
+    "Plotting for data set level is rather limited. A typical use case that is supported to compare two data variables are scatter plots:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c0a5372f-c674-42a2-bbbd-6162a17cd125",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ds.plot.scatter(x=\"temperature\", y=\"precipitation\", s=1, alpha=0.1, edgecolor=\"none\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "79bf5a3c-d95e-4f35-8535-f7245c175cf0",
+   "metadata": {},
+   "source": [
+    "`facetting` is also supported here, by linking the `col` or `row` parameter to a data variable."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c4423911-7fad-48b0-8790-3b4ea5566b70",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ds.plot.scatter(x=\"temperature\", y=\"precipitation\", s=1, alpha=0.1, col=\"year\", edgecolor=\"none\")  # try also hue=\"year\" instead of col; requires hue_style=\"discrete\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a1be8b3c-2eee-42d8-9085-0affa62098f3",
+   "metadata": {},
+   "source": [
+    "### DataSet reductions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "68c5401b-bf33-4d18-86f4-e0d20b5d2927",
+   "metadata": {},
+   "source": [
+    "Datasets support arithmetic operations by automatically looping over all data variables and supports most of the same methods found on `xarray.DataArray`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0573e3b5-1260-44bf-ab12-245fbf2d12d9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ds.mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7012d2f1-094d-4493-8995-4d0f86d75e4d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ds.max(dim=[\"x\", \"y\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "da29365a-b560-49e6-a357-5f0e78955363",
+   "metadata": {},
+   "source": [
+    "__Note__ Using the names of the data variables (which is actually element-wise operations with DataArrays) makes a calculation very self-describing, e.g."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "797f8990-f31f-4745-8d91-e120317de979",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "(ds[\"temperature\"] * ds[\"precipitation\"]).sel(year=2016).plot.imshow()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1382ea01-2c2c-43c6-819a-ba6e54e1640b",
+   "metadata": {},
+   "source": [
+    "### Let's practice"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ef13ef35-d144-4cd7-ad8b-02763d44dd78",
+   "metadata": {},
+   "source": [
+    "For the next set of exercises, we use the [ERA5-Land monthly averaged data](https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-land-monthly-means?tab=overview), provided by the ECMWF (European Centre for Medium-Range Weather Forecasts).\n",
+    "\n",
+    "> ERA5-Land is a reanalysis dataset providing a consistent view of the evolution of land variables over several decades. Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset using the laws of physics. \n",
+    "\n",
+    "For these exercises, a subset of the data set focusing on Belgium has been prepared, containing the following variables:\n",
+    "\n",
+    "- `sf`: Snowfall (_m of water equivalent_)\n",
+    "- `sp`: Surface pressure (_Pa_)\n",
+    "- `t2m`: 2 metre temperature (_K_)\n",
+    "- `tp`: Total precipitation (_m_)\n",
+    "- `u10`: 10 metre U wind component (_m/s_)\n",
+    "\n",
+    "The dimensions are the `longitude`, `latitude` and `time`, which are each represented by a corresponding coordinate."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9c01ab8b-0c75-4992-8f7d-68759c1fb6b2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "era5 = xr.open_dataset(\"./data/era5-land-monthly-means_example.nc\") # engine=\"netcdf4\" is here optional\n",
+    "era5"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "724d816c-4f6b-4a8b-b9ba-34b13bff41b0",
+   "metadata": {},
+   "source": [
+    "To get a feeling on the spatial subset of the data set for these exercises, the following code creates a cartopy map of the average temperature with country borders added:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "86c9a88b-a63c-4903-a207-284f100aeeef",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import cartopy\n",
+    "import cartopy.crs as ccrs\n",
+    "\n",
+    "fig = plt.figure(figsize=(9,6))\n",
+    "ax = plt.axes(projection=ccrs.PlateCarree())\n",
+    "\n",
+    "ax.add_feature(cartopy.feature.BORDERS, linestyle=':')\n",
+    "era5_mean_temp = era5[\"t2m\"].mean(dim=\"time\") - 273.15\n",
+    "era5_mean_temp.plot.imshow(ax=ax, cmap=\"coolwarm\", \n",
+    "                           transform=ccrs.PlateCarree(),\n",
+    "                           cbar_kwargs={'shrink': 0.65});"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d52c2c00-fb94-4fbf-8442-defce3f55178",
+   "metadata": {},
+   "source": [
+    "(See notebook [visualization-03-cartopy](./visualization-03-cartopy.ipynb) for more information on the usage of Cartopy)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "050db69c-c7e0-4210-a6c4-530cba7f6cc5",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-success\">\n",
+    "\n",
+    "**EXERCISE 1**:\n",
+    "\n",
+    "The [short names used by ECMWF](https://confluence.ecmwf.int/display/CKB/ERA5-Land%3A+data+documentation) are not very convenient to understand. Rename the variables of the  data set according to the following mapping:\n",
+    "    \n",
+    "- `sf`: snowfall_m\n",
+    "- `sp`: pressure_pa\n",
+    "- `t2m`: temperature_k\n",
+    "- `tp`: precipitation_m\n",
+    "- `u10`: wind_ms   \n",
+    "    \n",
+    "Save the result of the mapping as the variable `era5_renamed`.\n",
+    "\n",
+    "<details><summary>Hints</summary>\n",
+    "\n",
+    "* Both `rename` and `rename_vars` can be used to rename the DataSet variables\n",
+    "* The `rename` function requires a `dict-like` input with the current names as the keys and the new names as the values. \n",
+    "\n",
+    "</details>    \n",
+    "    \n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1403ca2d-3bcb-41e3-898f-578ce6ec1971",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mapping = {\n",
+    "    \"sf\": \"snowfall_m\",\n",
+    "    \"sp\": \"pressure_pa\",\n",
+    "    \"t2m\": \"temperature_k\",\n",
+    "    \"tp\": \"precipitation_m\",\n",
+    "    \"u10\": \"wind_ms\"\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "597e057c-1f36-48cb-9b5a-22b4af6a8a59",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/12-xarray-advanced1.py"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0cd469ac-818c-4eb9-bc4c-91ada227339d",
+   "metadata": {},
+   "source": [
+    "__Note:__ _Make sure you have the variable `era5_renamed` correctly loaded for the following exercises. If not, load the solution of the previous exercise._"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "988e64dc-ef51-435e-b24c-d362f20ea912",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-success\">\n",
+    "\n",
+    "**EXERCISE 2**:\n",
+    "\n",
+    "Start from the `era5_renamed` variable. You are used to work with temperatures defined in degrees celsius instead of Kelvin. Add a new data variable to `era5_renamed`, named `temperature_c`, by converting the `temperature_k` into degrees celsius:\n",
+    "    \n",
+    "$T_{^{\\circ}C} = T_{K} - 273.15$\n",
+    "    \n",
+    "Create a histogram of the `temperature_c` to check the distribution of all the temperature valus in the data set. Use an appropriate number of bins to draw the histogram.\n",
+    "\n",
+    "<details><summary>Hints</summary>\n",
+    "\n",
+    "* Xarray - similar to Numpy - applies the mathematical operation element-wise, so no need for loops.\n",
+    "* Most plot functions work on `DataArray`, so make sure to select the variable to apply the `.plot.hist()`.\n",
+    "* One can define the number of bins using the `bins` parameter in the `hist` method.\n",
+    "\n",
+    "</details>    \n",
+    "    \n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0f5095b1-5e3d-4ba2-afa8-33e22e61d331",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/12-xarray-advanced2.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a2230a30-b3a8-4298-9666-30ecc58e4849",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/12-xarray-advanced3.py"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4d372680-c12e-41bf-8bef-9e8c998dabde",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-success\">\n",
+    "\n",
+    "**EXERCISE 3**:\n",
+    "\n",
+    "Calculate the total snowfall of the entire region of the dataset in function of time and create a line plot showing total snowfall in the y-axis and time in the x-axis.\n",
+    "\n",
+    "<details><summary>Hints</summary>\n",
+    "\n",
+    "* You need to calculate the total (`sum`) snowfall (`snowfall_m`) in function of time, i.e. aggregate over both the `longitude` and `latitude` dimensions (`dim=[\"latitude\", \"longitude\"]`).\n",
+    "* As the result is a DataArray with a single dimension, the default `plot` will show a line, but you can be more explicit by saying `.plot.line()`.\n",
+    "\n",
+    "</details>    \n",
+    "    \n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "733a1027-f906-40e5-8958-af99391dedd4",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/12-xarray-advanced4.py"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "633ece8e-0fcf-4822-a14d-d45e6677adb5",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-success\">\n",
+    "\n",
+    "**EXERCISE 4**:\n",
+    "\n",
+    "The speed to sound is linearly dependent on temperature:\n",
+    "    \n",
+    "$v = 331.5 + (0.6 \\cdot T)$\n",
+    "    \n",
+    "with $v$ the speed of sound and $T$ the temperature in degrees celsius.    \n",
+    "    \n",
+    "Add a new variable to the `era5_renamed` data set, called `speed_of_sound_m_s`, that calculates for each location and each time stamp in the data set the temperature corrected speed of sound.\n",
+    "    \n",
+    "Create a scatter plot to control the (linear) relationship you just calculated by comparing all the `speed_of_sound_m_s` and `temperature_c` data points in the data set.\n",
+    "\n",
+    "<details><summary>Hints</summary>\n",
+    "\n",
+    "* Creating a new variable is similar to GeoPandas/Pandas/dictionaries, `ds[\"MY_NEW_VAR\"] = ...`.\n",
+    "* Remember, calculations are __element-wise__ just as in Numpy/Pandas; so no need for loops. The numbers (331.5, 0.6) are broadcasted to all elements in the data set to do the calculation.\n",
+    "* To compare two variables in a data set visually, `plot.scatter()` it is.\n",
+    "\n",
+    "</details>    \n",
+    "    \n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "75b9ea92-7563-45b8-918d-0ee24092f1eb",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/12-xarray-advanced5.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "dc82bca4-bd8a-4ff7-89ef-2e007cc058ee",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/12-xarray-advanced6.py"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4bb8d66a-c910-4108-920d-d780838c9717",
+   "metadata": {},
+   "source": [
+    "## Working with time series"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "775b5de9-0cd2-4e73-993a-a4b2ed741ba2",
+   "metadata": {},
+   "source": [
+    "Let's start again from the ERA5 data set we worked with in the previous exercises, and rename the variables for convenience:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2053ddc5-1a08-48b5-b3be-3267441bbaa5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "era5 = xr.open_dataset(\"./data/era5-land-monthly-means_example.nc\")\n",
+    "mapping = {\n",
+    "    \"sf\": \"snowfall_m\",\n",
+    "    \"sp\": \"pressure_pa\",\n",
+    "    \"t2m\": \"temperature_k\",\n",
+    "    \"tp\": \"precipitation_m\",\n",
+    "    \"u10\": \"wind_ms\"\n",
+    "}\n",
+    "era5_renamed = era5.rename(mapping)\n",
+    "era5_renamed"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f09ee5e8-6a10-4831-95a0-6c9577437992",
+   "metadata": {},
+   "source": [
+    "Apart from the different coordinates, the data set also contains a `time` dimension. xarray borrows the indexing machinery from Pandas, also for datetime coordinates:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0bbf03b0-2b2b-4c69-90a3-90c91b783688",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "era5_renamed.time"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c964676f-5baf-4ea6-9e23-c38883cfe4fe",
+   "metadata": {},
+   "source": [
+    "With a DateTime-aware index (see `datetime64[ns]` as dtype), selecting dates can be done using the string representation, e.g."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a5577359-e2c4-4fc8-a157-8a580682d5ea",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "era5_renamed.sel(time=\"2002\").time "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "01dd349e-6fc3-42be-ae2d-96c1f4ed66ec",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "era5_renamed.sel(time=slice(\"2001-05\", \"2002-08\")).time"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0d6edf5a-506d-4217-82c9-1cfafbbe34ff",
+   "metadata": {},
+   "source": [
+    "And you can access the datetime components, e.g. \"year\", \"month\",..., \"dayofyear\", \"week\", \"dayofweek\",... but also \"season\". Do not forget to use the `.dt`-accessor to access these components:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0a6d3d01-763c-4197-97c0-1b6df3aa80eb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "era5_renamed[\"time\"].dt.season"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2775d2ed-eb40-4a7b-8ecc-1535e2e9594f",
+   "metadata": {},
+   "source": [
+    "Xarray contains some more powerful functionalities to work with time series, e.g. `groupby`, `resample` and `rolling`. All have a similar syntax as Pandas/GeoPandas, but applied on an N-dimensional array instead of a DataFrame."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c346962c-7ce8-4f55-a728-043b1f94463a",
+   "metadata": {},
+   "source": [
+    "### split-apply-combine, aka `groupby`"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "514eaf9e-1127-42cf-ab61-12551b73ec86",
+   "metadata": {},
+   "source": [
+    "If we are interested in the _average over time_ for each of the levels, we can use a reducton function to get the averages of each of the variables at the same time:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a397d3e5-991b-44de-a3df-70ad02523df8",
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "era5_renamed.mean(dim=[\"time\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "be63a859-d1d0-4dc6-a4d9-d92a328eb884",
+   "metadata": {},
+   "source": [
+    "But if we wanted the _average for each month of the year_ per level, we would first have to __split__ the data set in a group for each month of the year, __apply__ the average function on each of the months and __combine__ the data again. \n",
+    "\n",
+    "We already learned about the [split-apply-combine](https://pandas.pydata.org/pandas-docs/stable/user_guide/groupby.html) approach when using Pandas. The syntax of Xarray’s groupby is almost identical to Pandas!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7b2f35df-5bd3-46a9-85c1-0800bccd4b0a",
+   "metadata": {},
+   "source": [
+    "First, extract the month of the year (1 -> 12) from each of the time coordinate/dimension:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cdaf9650-9c43-4c10-b1b9-d9fb157409fa",
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "era5_renamed[\"time\"].dt.month  # The coordinates is a Pandas datetime index"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "71fa721e-685e-43ef-83f4-8a6ce44f7192",
+   "metadata": {},
+   "source": [
+    "We can use this array in a `groupby` operation:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8efd054d-939c-488e-9504-0a710e4a1e03",
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "era5_renamed.groupby(era5_renamed[\"time\"].dt.month)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9711cc34-8f5e-40f6-8ba8-88312a29b98b",
+   "metadata": {},
+   "source": [
+    "_split the data in (12) groups where each element is grouped according to the month it belongs to._"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c778cca9-a129-48bd-9064-9ef2bdc9fa87",
+   "metadata": {},
+   "source": [
+    "__Note:__ Xarray also offers a more concise syntax when the variable you're grouping on is already present in the dataset. The following statement is identical to the previous line:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fe0045b6-420e-473d-b5f4-0f0a46c9aafd",
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "era5_renamed.groupby(\"time.month\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b59f7bc2-9fee-4cfd-8843-87e9331796d1",
+   "metadata": {},
+   "source": [
+    "Next, we apply an aggregation function _for each of the months_ over the `time` dimension in order to end up with: _for each month of the year, the average (over time) for each of the levels_:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "353ce952-5452-4c1a-b64f-49109e45865c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "era5_renamed.groupby(\"time.month\").mean()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "49043296-6e04-4f36-ac97-9edc2568c2fa",
+   "metadata": {},
+   "source": [
+    "The resulting dimension month contains the 12 groups on which the data set was split up."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0fbab008-bd82-47c4-b9fd-f1075811b7e3",
+   "metadata": {},
+   "source": [
+    "<img align=\"center\" src=\"../img/pandas/06_groupby1.svg\">"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "797ae1ed-2940-4199-b297-7d6e9acab715",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-info\" style=\"font-size:120%\">\n",
+    "\n",
+    "**REMEMBER**: <br>\n",
+    "\n",
+    "The `groupby` method splits the data set in groups, applies some function _on each of the groups_ and combines again the results of each of the groups. It is not limited to time series data, but can be used in any situation where the data can be split up by a categorical variable. \n",
+    "\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6d51b68b-9a92-450b-b500-59b371a3eef5",
+   "metadata": {},
+   "source": [
+    "### resample/rolling"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fb9cdebc-c4bc-491d-81c0-3d6b2cce3b79",
+   "metadata": {},
+   "source": [
+    "Another (alike) operation - specifically for time series data - is to `resample` the data to another time-aggregation. For example, resample to monthly (`4M`) or yearly (`1Y`) median values:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6e8df3de-72ea-4281-b355-6216117c2e83",
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "era5_renamed.resample(time=\"1YE\").median()  # 4M"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6b846207-a635-42f7-8b31-0c38e3fa8b17",
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "era5_renamed[\"temperature_k\"].sel(latitude=51., longitude=4., method=\"nearest\").plot.line(x=\"time\");\n",
+    "era5_renamed[\"temperature_k\"].sel(latitude=51., longitude=4., method=\"nearest\").resample(time=\"10YE\").median().plot.line(x=\"time\");"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "da0d5e1a-3752-480f-a7e3-6e5fc92fa7f2",
+   "metadata": {},
+   "source": [
+    "A similar, but different functionality is `rolling` to calculate rolling window aggregates:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "44c7ffc1-fa4c-4e38-a57a-b2128cb93040",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "era5_renamed.rolling(time=12, center=True).median()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "eb325086-089c-45e6-84c5-131db680cd19",
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "era5_renamed[\"temperature_k\"].sel(latitude=51., longitude=4., method=\"nearest\").plot.line()\n",
+    "era5_renamed[\"temperature_k\"].sel(latitude=51., longitude=4., method=\"nearest\").rolling(time=12, center=True).min().plot.line()\n",
+    "era5_renamed[\"temperature_k\"].sel(latitude=51., longitude=4., method=\"nearest\").rolling(time=12, center=True).max().plot.line()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "71085f04-5124-4a10-9e5c-cbaa2674d564",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-info\" style=\"font-size:120%\">\n",
+    "\n",
+    "**REMEMBER**: <br>\n",
+    "\n",
+    "The [xarray `groupby`](http://xarray.pydata.org/en/stable/groupby.html) with the same syntax as Pandas implements the __split-apply-combine__ strategy. Also [`resample`](http://xarray.pydata.org/en/stable/time-series.html#resampling-and-grouped-operations) and [`rolling`](http://xarray.pydata.org/en/stable/computation.html?highlight=rolling#rolling-window-operations) are available in xarray.\n",
+    "    \n",
+    "__Note:__ Xarray adds a [`groupby_bins`](http://xarray.pydata.org/en/stable/generated/xarray.Dataset.groupby_bins.html#xarray.Dataset.groupby_bins) convenience function for binned groups (instead of each value).\n",
+    "\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ed0ed970-502e-428a-8306-5af774aad525",
+   "metadata": {},
+   "source": [
+    "### Let's practice"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "19baaf1d-8013-4e0e-8acc-93baefab799e",
+   "metadata": {},
+   "source": [
+    "Run this cell before doing the exercises, so the starting point is again the `era5_renamed` data set:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9b8f3607-8fea-484e-b899-cb8f35302105",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "era5 = xr.open_dataset(\"./data/era5-land-monthly-means_example.nc\")\n",
+    "mapping = {\n",
+    "    \"sf\": \"snowfall_m\",\n",
+    "    \"sp\": \"pressure_pa\",\n",
+    "    \"t2m\": \"temperature_k\",\n",
+    "    \"tp\": \"precipitation_m\",\n",
+    "    \"u10\": \"wind_ms\"\n",
+    "}\n",
+    "era5_renamed = era5.rename(mapping)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ce2fd02d-6d03-46cf-8fa6-8f3235cb29ca",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-success\">\n",
+    "\n",
+    "**EXERCISE 5**:\n",
+    "\n",
+    "Select the pressure data for the pixel closest to the center of Ghent (lat: -51.05, lon: 3.71) and assign the outcome to a new variable `ghent_pressure`.\n",
+    "\n",
+    "Define a Matplotlib `Figure` and `Axes` (respectively named `fig, ax`) and use it to create a plot that combines the yearly average of the pressure data in Gent with the monthly pressure data as function of time for that same pixel as line plots. Change the name of the y-label to `'Pressure (Pa)'` and the title of the plot to `'Pressure (Pa) in Ghent (at -51.05, 3.71)'` (see notebook [visualization-01-matplotlib.ipynb](./visualization-01-matplotlib.ipynb#An-small-cheat-sheet-reference-for-some-common-elements) for more information)\n",
+    "\n",
+    "<details><summary>Hints</summary>\n",
+    "    \n",
+    "* For the yearly average, actually both `resample(time=\"Y\")` as `groupby(\"time.year\")` can be used. The main difference is the returned dimension after the grouping: `resample` returns a DateTimeIndex, whereas `groupby` returns a dimension called `year` and no longer contains the DateTimeIndex. To combine the data with other time series data, using the DateTimeIndex is preferred (i.e. `resample`). \n",
+    "* `fig, ax = plt.subplots()` is a useful shortcut to prepare a Matplotlib Figure. You can add a label `set_label()` and title `set_title()` to the `axes` object.\n",
+    "\n",
+    "</details>    \n",
+    "    \n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3042fd53-7a4e-4ef0-8fe5-9e9885231b0a",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/12-xarray-advanced7.py"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8867e7b1-949f-4df4-860c-8c2947452502",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-success\">\n",
+    "\n",
+    "**EXERCISE 6**:\n",
+    "    \n",
+    "Select the precipitation data for the pixel closest to the center of Ghent (lat: -51.05, lon: 3.71) and assign the outcome to a new variable `ghent_precipitation`.\n",
+    "    \n",
+    "For the Ghent pixel, calculate the maximal precipitation _for each month of the year_ (1 -> 12) and convert it to mm precipitation.\n",
+    "    \n",
+    "To make a bar-chart (not supported in xarray) of the maximal precipitation for each month of the year, convert the output to a Pandas DataFrame using the `to_dataframe()` method and create a horizontal bar chart with the month in the y-axis and the maximal precipitation in the x-axis. Feel free to improve the axis labels.\n",
+    "\n",
+    "<details><summary>Hints</summary>\n",
+    "    \n",
+    "* You need to group based on the month of the data point, hence `groupby(\"...\")` with `time` as an existing dimension, the `time.month` shortcut to get the corresponding month will work.\n",
+    "* Plotting in Pandas is very similar to xarray, use `.plot.barh()` to create a horizontal bar chart.\n",
+    "\n",
+    "</details>    \n",
+    "    \n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "781a3ec8-6080-4871-950b-d2b3f9112421",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/12-xarray-advanced8.py"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f56b741b-d6bb-42f0-b1bb-106468ef1ef7",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-success\">\n",
+    "\n",
+    "**EXERCISE 7**:\n",
+    "    \n",
+    "Calculate the pixel-based average temperature _for each season_. Make a plot (`imshow`) with each of the seasons in a separate subplot next to each other. \n",
+    "\n",
+    "<details><summary>Hints</summary>\n",
+    "    \n",
+    "* You need to group based on the season of the data point, hence `groupby(\"...\")` with `time` as an existing dimension, the `time.season` shortcut to get the corresponding season will work.\n",
+    "* The labels of the season groups are sorted on the strings in the description instead of the seasons. The workaround is to `reindex` and provide a correctly sorted (i.e. `['DJF','MAM','JJA', 'SON']` version to sort the `season` with.\n",
+    "* Use facetting to plot each of the seasons next to each other, with `col=\"season\"`.\n",
+    "\n",
+    "\n",
+    "</details>    \n",
+    "    \n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3cfd5a68-7ea5-4078-b244-3913e1f75142",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/12-xarray-advanced9.py"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f437e339-2074-4aef-8cf2-0e3cd3ac2a3b",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-success\">\n",
+    "\n",
+    "**EXERCISE 8**:\n",
+    "\n",
+    "Calculate the average temperature of the entire region of the dataset in function of time. From this time series, use only those years for which all 12 months of the year are included in the data set and calculate the yearly average temperature.\n",
+    "    \n",
+    "Create a line plot showing the yearly average temperature in the y-axis and time in the x-axis.\n",
+    "\n",
+    "<details><summary>Hints</summary>\n",
+    "\n",
+    "* You need to calculate the average (`mean`) temperature in function of time, i.e. aggregate over both the `longitude` and `latitude` dimensions.\n",
+    "* Year 2021 is only available till June, so exclude the year (e.g. slice till 2020).\n",
+    "* Resample to yearly values.\n",
+    "* As the result is a DataArray with a single dimension, the default `plot` will show a line, but you can be more explicit by saying `.plot.line()`.\n",
+    "\n",
+    "</details>    \n",
+    "    \n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5bfd0ba8-4580-44ce-970d-b18fc0b75623",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/12-xarray-advanced10.py"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e87b7994-ccc2-473b-8317-5e86c7f84fbf",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-success\">\n",
+    "\n",
+    "**EXERCISE 9**:\n",
+    "    \n",
+    "Create the yearly total snowfall from 1991 up to 2005 and convert the snowfall into cm.  Make a plot (`imshow`) with each of the individual years in a separate subplot divided into 3 rows and 5 columns.\n",
+    "    \n",
+    "Make sure to update the name of the snowfall variable and/or colorbar label to make sure it defines the unit in cm.\n",
+    " \n",
+    "\n",
+    "<details><summary>Hints</summary>\n",
+    "\n",
+    "* When selecting time series data from a coordinate with datetime-aware data, one can use strings to define a date. In combination with `slice`, the selection of the required years becomes `slice(\"1991\", \"2005\")`.\n",
+    "* From montly to yearly data is a `resample` of the data.\n",
+    "* Use `.rename(NEW_NAME)` to update the name of a `DataArray`\n",
+    "* xarray supports _facetting_ directly, check out the `col` and `col_wrap` parameters in the plot functions of xarray or check http://xarray.pydata.org/en/stable/user-guide/plotting.html#faceting.\n",
+    "* To update the colorbar unit use, the `cbar_kwargs` option.\n",
+    "\n",
+    "</details>    \n",
+    "    \n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fa90d488-c4c2-40d1-a72d-f44178c6d643",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/12-xarray-advanced11.py"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "06a4eaf2-0c04-4586-8280-5ff7808d0178",
+   "metadata": {},
+   "source": [
+    "## xarray lazy data loading\n",
+    "\n",
+    "Values are only read from disk when needed. For example, the following statement only reads the coordinate information and the metadata. The data itself is not yet loaded:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "531a290d-2721-4201-89fc-b7109d0cb087",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_file = \"./data/2016-2017_global_rain-temperature.nc\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "36901805-8511-41ab-95ac-e4863358a2c1",
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "ds = xr.open_dataset(data_file)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "56ea00c4-2fbc-405a-bc04-1678ec50ef20",
+   "metadata": {},
+   "source": [
+    "`load()` will explicitly load the data into memory:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6d0d1e85-838e-4993-b504-a7d88d526f86",
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "xr.open_dataset(data_file).load()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "98492efe-ff37-41e9-ac78-a37007e5fd07",
+   "metadata": {},
+   "source": [
+    "## (Optional) From DataSet to DataArray and back"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ba05f4a7-1916-4c10-b307-2483bd4a3fad",
+   "metadata": {},
+   "source": [
+    "Consider the sentinel images of Gent used in a previous notebook:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "69c45250-c760-4b2f-9769-e8cdd22a0aa8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gent_file = \"./data/gent/raster/2020-09-17_Sentinel_2_L1C_B0408.tiff\"\n",
+    "gent_array = xr.open_dataarray(gent_file, engine=\"rasterio\")\n",
+    "gent_array.plot.imshow(col=\"band\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b447c79c-1e7f-4c25-bf2c-823b87c96ff5",
+   "metadata": {},
+   "source": [
+    "The data is a `xarray.DataArray` with 3 dimensions and corresponding coordinates. Assigning the names `b4` and `b8` to the individual bands, makes the data more self-describing:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bc9f62fb-2df7-490b-8a69-5570cf68eb0a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gent_array = gent_array.assign_coords(band=(\"band\", [\"b4\", \"b8\"]))\n",
+    "gent_array"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c927b116-bd41-4ecf-b94d-0a12e31f7e87",
+   "metadata": {},
+   "source": [
+    "In case one wants to work with the different bands as DataSet variables instead of a dimension of a single DataArray, the conversion `to_dataset` can be done, defining the dimension to convert to data set variables, e.g."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5a6b5f39-4861-4fed-b92a-963b1bd508b3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gent_ds = gent_array.to_dataset(dim=\"band\")\n",
+    "gent_ds"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b2324c6d-d5e0-4143-9b31-abd08795f13a",
+   "metadata": {},
+   "source": [
+    "Consider the calculation of the NDVI for Gent using a xarray DataArray:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "12ece407-f5ba-4978-97dd-3b1e45a3b55c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ndvi_array = (gent_array.sel(band=\"b8\") - gent_array.sel(band=\"b4\"))/(gent_array.sel(band=\"b8\") + gent_array.sel(band=\"b4\"))\n",
+    "ndvi_array.plot.imshow(cmap=\"YlGn\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "628c0255-eb78-4b64-a55b-b3ef56376de4",
+   "metadata": {},
+   "source": [
+    "One might find the usage of data variable in this case more convenient, as it provides the ability to:\n",
+    "\n",
+    "1. work with the variable names directly\n",
+    "2. Add the output tot the data set as a new variable\n",
+    "3. The output can be converted to an Array again using `to_array`\n",
+    "\n",
+    "For example:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "131a85e6-a7d2-40dc-99f8-e98616a1ab6e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gent_ds[\"ndvi\"] = (gent_ds[\"b8\"] - gent_ds[\"b4\"])/(gent_ds[\"b8\"] + gent_ds[\"b4\"])\n",
+    "gent_ds.to_array(dim=\"layer\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "jupytext": {
+   "default_lexer": "ipython3"
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.11"
+  },
+  "widgets": {
+   "application/vnd.jupyter.widget-state+json": {
+    "state": {},
+    "version_major": 2,
+    "version_minor": 0
+   }
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+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/notebooks/13-raster-processing.ipynb b/notebooks/13-raster-processing.ipynb
new file mode 100644
index 0000000..1e30f78
--- /dev/null
+++ b/notebooks/13-raster-processing.ipynb
@@ -0,0 +1,2544 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<p><font size=\"6\"><b> Raster operations and raster-vector tools</b></font></p>\n",
+    "\n",
+    "\n",
+    "> *DS Python for GIS and Geoscience*  \n",
+    "> *September, 2025*\n",
+    ">\n",
+    "> *© 2025, Joris Van den Bossche and Stijn Van Hoey. Licensed under [CC BY 4.0 Creative Commons](http://creativecommons.org/licenses/by/4.0/)*\n",
+    "\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "In the previous notebooks, we focused either on vector data or raster data. Often you encounter both types of data and want to combine them. In this notebook, we show *some* examples of typical raster/vector interactions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import geopandas\n",
+    "import rasterio\n",
+    "\n",
+    "import xarray as xr\n",
+    "\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# `rioxarray`: xarray extension based on rasterio"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In the previous notebooks, we already used `rasterio` (https://rasterio.readthedocs.io/en/latest/) to read raster files such as GeoTIFFs (through the `xarray.open_dataarray(...,engine=\"rasterio\")` function). Rasterio provides support for reading and writing geospatial raster data as numpy N-D arrays, mainly through bindings to the GDAL library. \n",
+    "\n",
+    "In addition, rasterio provides a Python API to perform some GIS raster operations (clip, mask, warp, merge, transformation,...) and can be used to only load a subset of a large dataset into memory. However, the main complexity in using `rasterio`, is that the spatial information is decoupled from the data itself (i.e. the numpy array). This means that you need to keep track and organize the extent and metadata throughout the operations (e.g. the \"transform\") and you need to keep track of what each dimension represents (y-first, as arrays are organized along rows first). Notebook [91_package_rasterio](./91_package_rasterio.ipynb) goes into more depth on the rasterio package itself. \n",
+    "\n",
+    "Enter `rioxarray` (https://corteva.github.io/rioxarray/stable/index.html), which extends xarray with geospatial functionalities powered by rasterio."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import rioxarray"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_file = \"./data/herstappe/raster/2020-09-17_Sentinel_2_L1C_True_color.tiff\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_raw = xr.open_dataarray(data_file, engine=\"rasterio\")\n",
+    "data_raw"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Once `rioxarray` is imported, it provides a `.rio` accessor on the xarray.DataArray object, which gives access to some properties of the raster data:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_raw.rio.crs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_raw.rio.bounds()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_raw.rio.resolution()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_raw.rio.nodata"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_raw.rio.transform()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For the remainder of this section, we work with the integer converted version:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = (data_raw * 255).astype(np.uint8)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Reprojecting rasters"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`rioxarray` gives access to a set of raster processing functions from rasterio/GDAL. \n",
+    "\n",
+    "One of those is to reproject (transform and resample) rasters, for example to reproject to different coordinate reference system, up/downsample to a different resolution, etc. In all those case, in the transformation of a source raster to a destination raster, pixel values need to be recalculated. There are different \"resampling\" methods this can be done: taking the nearest pixel value, calculating the average, a (non-)linear interpolation, etc.\n",
+    "\n",
+    "The functionality is available through the `reproject()` method. Let's start with reprojecting the Herstappe tiff to a different CRS:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data.rio.crs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data.rio.reproject(\"EPSG:31370\").plot.imshow(figsize=(10,6))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The default resampling method is \"nearest\", which is often not a suitable method (especially for continuous data). We can change the method using the `rasterio.enums.Resampling` enumeration (see [docs](https://rasterio.readthedocs.io/en/latest/api/rasterio.enums.html#rasterio.enums.Resampling) for a overview of all methods):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from rasterio.enums import Resampling"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data.rio.reproject(\"EPSG:31370\", resampling=Resampling.bilinear).plot.imshow(figsize=(10,6))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The method can also be used to downsample at the same time:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data.rio.reproject(data.rio.crs, resolution=120, resampling=Resampling.cubic).plot.imshow(figsize=(10,6))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Extract the data you need"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In many applications, a specific research area is used. Extracting the data you need from a given raster data set by a vector (polygon) file is a common operation in GIS analysis. We use the clipping example to explain the typical workflow with rioxarray / rasterio.\n",
+    "\n",
+    "For our Herstappe example, the study area is available as vector data `./data/herstappe/vector/herstappe.geojson`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "herstappe_vect = geopandas.read_file(\"./data/herstappe/vector/herstappe.geojson\")\n",
+    "herstappe_vect"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "herstappe_vect.plot()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "herstappe_vect.crs"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Make sure both data sets are defined in the same CRS and extracting the geometry can be used as input for the masking:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "herstappe_vect = herstappe_vect.to_crs(epsg=3857)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "clipped = data.rio.clip(herstappe_vect.geometry)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig, (ax0, ax1) = plt.subplots(ncols=2, figsize=(12,4))\n",
+    "data.plot.imshow(ax=ax0)\n",
+    "herstappe_vect.plot(ax=ax0, facecolor=\"none\", edgecolor=\"red\")\n",
+    "\n",
+    "clipped.plot.imshow(ax=ax1)\n",
+    "herstappe_vect.plot(ax=ax1, facecolor=\"none\", edgecolor=\"red\")\n",
+    "\n",
+    "fig.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The above uses the `rasterio` package (with the `mask` and `geometry_mask` / `rasterize` functionality) under the hood. This simplifies the operation compared to directly using `rasterio`.\n",
+    "\n",
+    "\n",
+    "```python\n",
+    "# cfr. The Rasterio workflow\n",
+    "\n",
+    "from rasterio.mask import mask\n",
+    "\n",
+    "# 1 - Open a data set using the context manager\n",
+    "with rasterio.open(data_file) as src: \n",
+    "\n",
+    "    # 2 - Read and transform the data set by clipping\n",
+    "    out_image, out_transform = mask(src, herstappe_vect.geometry, crop=True)\n",
+    "    \n",
+    "    # 3 - Update the spatial metadata/profile of the data set\n",
+    "    herstappe_profile = src.profile\n",
+    "    herstappe_profile.update({\"height\": out_image.shape[1],\n",
+    "                              \"width\": out_image.shape[2],\n",
+    "                              \"transform\": out_transform})\n",
+    "    # 4 - Save the new data set with the updated metadata/profile                   \n",
+    "    with rasterio.open(\"./herstappe_masked.tiff\", \"w\", **herstappe_profile) as dest: \n",
+    "        dest.write(out_image)\n",
+    "```\n",
+    "\n",
+    "The [91_package_rasterio](./91_package_rasterio.ipynb) notebook explains this workflow in more detail.\n",
+    "\n",
+    "One important difference, though, is that the above `rasterio` workflow will not load the full raster into memory when only loading (clipping) a small part of it. This can also be achieved in `rioxarray` with the `from_disk` keyword."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Convert vector to raster"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### [Intermezzo] - data preparation Zwalm catchment"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The following section provides the data preparation and data downloading steps to prepare the 'burning' of the shape of a river into a Digital Elevation Model (DEM) of the river catchment (all locations around the river from which rain ends up in the river). The 'burning' itself is a subtraction of the DEM pixels at locations of the river with a chosen value, i.e. decrease the elevation at locations where a river flows. To do so, we need:\n",
+    "\n",
+    "- A Digital Elevation Model (DEM) data set (raster data)\n",
+    "- The shape of the catchment (vector data)\n",
+    "- The river and tributaries (vector data)\n",
+    "\n",
+    "To be able to subtract the corresponding river pixels, the river vector data need to be converted into a raster with the same resolution and bounds as the (clipped) DEM."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "__Load DEM raster and river vector data__"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As example, we are using data from the Zwalm river area in Flanders. \n",
+    "\n",
+    "<div class=\"alert alert-warning\">\n",
+    "\n",
+    "**! Manual download needed !** <br>\n",
+    "\n",
+    "The digital elevation model (DEM) can be downloaded via the [governmental website](https://download.vlaanderen.be/product/936/configureer): choose number 30 on the map and then click \"Downloaden\". Finally extract the zip file in `/data` directory. Inside the `data`-directory, the file will be available in the subfolder `DHMVIIDSMRAS5m_k30/GeoTIFF/DHMVIIDSMRAS5m_k30.tif`.\n",
+    "\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dem_zwalm_file = \"data/DHMVIIDSMRAS5m_k30/GeoTIFF/DHMVIIDSMRAS5m_k30.tif\" "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dem_zwalm = xr.open_dataarray(dem_zwalm_file, engine=\"rasterio\").sel(band=1).squeeze()\n",
+    "dem_zwalm"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "img = dem_zwalm.plot.imshow(\n",
+    "    cmap=\"terrain\", figsize=(10, 4), interpolation='antialiased')\n",
+    "img.axes.set_aspect(\"equal\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Next, we download the shapes of the rivers in the area through a WFS (Web Feature Service):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import json\n",
+    "import requests\n",
+    "\n",
+    "wfs_rivers = \"https://geo.api.vlaanderen.be/VHAWaterlopen/wfs\"\n",
+    "params = dict(service='WFS', version='1.1.0', request='GetFeature', \n",
+    "              typeName='VHAWaterlopen:Wlas', outputFormat='json', \n",
+    "              cql_filter=\"(VHAZONENR=460)OR(VHAZONENR=461)\", srs=\"31370\")\n",
+    "\n",
+    "# Fetch data from WFS using requests\n",
+    "r = requests.get(wfs_rivers, params=params)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "__Note__: A WFS is a standardized way to share vector GIS data sets on the internet, typically also used by web application, see ['A bit more about WFS'](#A-bit-more-about-WFS) section for more info."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And convert the output of the wfs call to a GeoDataFrame:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create GeoDataFrame from geojson - this is vector data\n",
+    "segments = geopandas.GeoDataFrame.from_features(json.loads(r.content), crs=\"epsg:31370\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "segments.plot(figsize=(8, 7))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "__Clip raster with vector__\n",
+    "\n",
+    "The catchment extent is much smaller than the DEM file, so clipping the data first will make the computation less heavy and focuses exclusively on the region from which rainfall is collected."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's first download the catchment area of the Zwalm river from the Flemish government (using WFS again):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import json\n",
+    "import requests\n",
+    "\n",
+    "wfs_bekkens = \"https://geo.api.vlaanderen.be/Watersystemen/wfs\"\n",
+    "params = dict(service='WFS', version='1.1.0', request='GetFeature',\n",
+    "              typeName='Watersystemen:WsDeelbek', outputFormat='json',\n",
+    "              cql_filter=\"DEELBEKNM='Zwalm'\", srs=\"31370\")\n",
+    "\n",
+    "# Fetch data from WFS using requests\n",
+    "r = requests.get(wfs_bekkens, params=params)\n",
+    "catchment = geopandas.GeoDataFrame.from_features(json.loads(r.content), crs=\"epsg:31370\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Save to a file for later reuse:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# save to file\n",
+    "catchment = catchment.to_crs('epsg:4326') # geojson is default 4326\n",
+    "catchment.to_file(\"./data/zwalmbekken.geojson\", driver=\"GeoJSON\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "geopandas.read_file(\"./data/zwalmbekken.geojson\").plot()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "1. Clip using rioxarray (rasterio)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As shown above, we can use rioxarray to clip the raster file:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dem_zwalm = xr.open_dataarray(dem_zwalm_file, engine=\"rasterio\").sel(band=1)\n",
+    "dem_zwalm.spatial_ref"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "clipped = dem_zwalm.rio.clip(catchment.to_crs('epsg:31370').geometry)\n",
+    "clipped = clipped.squeeze().drop_vars(['band', 'spatial_ref']) # remove redundant coordinates"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Using rioxarray's `to_raster()` method, we can also save the result to a new GeoTIFF file:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "clipped.rio.to_raster(\"./dem_masked_rio.tiff\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This DEM raster file used -9999 as the NODATA value, which are read as NaN values:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dem_zwalm.rio.nodata, clipped.rio.nodata"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "img = clipped.plot.imshow(\n",
+    "    cmap='terrain', figsize=(10, 6), interpolation='antialiased')\n",
+    "img.axes.set_aspect(\"equal\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "2. (optional) Using GDAL CLI"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If we have the raster and vector files on disk, the [`gdal CLI`](https://gdal.org/programs/index.html) will be very fast to work with (note that GDAL automatically handles the CRS difference of the raster and vector)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "rm ./dem_masked_gdal.tiff"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!gdalwarp -cutline ./data/zwalmbekken.geojson -crop_to_cutline data/DHMVIIDSMRAS5m_k30/GeoTIFF/DHMVIIDSMRAS5m_k30.tif ./dem_masked_gdal.tiff"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "clipped_gdal = xr.open_dataarray(\"./dem_masked_gdal.tiff\", mask_and_scale=True).sel(band=1)\n",
+    "img = clipped_gdal.plot.imshow(\n",
+    "    cmap=\"terrain\", figsize=(10, 6), interpolation='antialiased')\n",
+    "img.axes.set_aspect(\"equal\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-info\" style=\"font-size:120%\">\n",
+    "\n",
+    "**TIP**: <br>\n",
+    "    \n",
+    "In the GIS world, also other libraries do provide a large set of functionalities as command line instructions with a `FILE IN` -> `RUN COMMAND` -> `FILE OUT` approach, with some of them providing a Python interface as well. Some important once are:\n",
+    "    \n",
+    "- The [`gdal` library](https://gdal.org/programs/index.html#raster-programs) is the open source Swiss Army knife for raster and vector geospatial data handling. \n",
+    "- The [SAGA GIS](http://www.saga-gis.org/en/index.html) has a [huge set](http://www.saga-gis.org/saga_tool_doc/8.0.0/a2z.html) of CLI commands, going from flow accumulation to classification algorithms.\n",
+    "- The [WhiteboxTools](https://www.whiteboxgeo.com/geospatial-software/) is another example of a library with a lot of functionalities, e.g. hydrological, agricultural and terrain analysis tools.\n",
+    "    \n",
+    "Other important initiatives like [Grass](https://grasswiki.osgeo.org/wiki/GRASS-Wiki) and [PCRaster](https://pcraster.geo.uu.nl/) are worthwhile to check out. Most of these libraries of tools can also be used with QGIS.\n",
+    "    \n",
+    "__NOTE:__ You can run a CLI command inside a Jupyter Notebook by prefixing it with the `!` character.\n",
+    "\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Convert vector to raster"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To create a raster with the vector converted into pixels, we can use the `rasterio.features.rasterize` function. This expects a list of (shape, value) tuples, and an output image shape and transform. Here, we will create a new raster image with the same shape and extent as the DEM above. And we first take a buffer of the river lines:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "clipped.rio.to_raster(\"zwalm_clip_test.tif\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import rasterio.features"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "segments_buffered = segments.geometry.buffer(100)\n",
+    "river_raster = rasterio.features.rasterize(\n",
+    "    segments_buffered, \n",
+    "    out_shape=clipped.shape, \n",
+    "    transform=clipped.rio.transform())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "type(river_raster)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The output of the rasterio function is a Numpy array. Let's convert to xarray objects"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ds_zwalm = clipped.to_dataset(name=\"dem\").squeeze()\n",
+    "ds_zwalm[\"river\"] = xr.DataArray(data=river_raster, coords=clipped.coords)\n",
+    "ds_zwalm[\"dem_burned\"] = ds_zwalm[\"dem\"] - ds_zwalm[\"river\"]*20"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As the river is now converted in a raster with the same pixels and boundaries as our DEM, we can burn the river into the DEM:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig, (ax0, ax1) = plt.subplots(1, 2, figsize=(12, 6))\n",
+    "\n",
+    "ds_zwalm[\"river\"].plot.imshow(ax=ax0)\n",
+    "ax0.axes.set_aspect('equal')\n",
+    "\n",
+    "ds_zwalm[\"dem_burned\"].plot.imshow(ax=ax1, cmap=\"terrain\", vmin=0)\n",
+    "ax1.axes.set_aspect('equal')\n",
+    "\n",
+    "fig.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Let's practice!\n",
+    "\n",
+    "For these exercises, a set of raster and vector datasets for the region of Ghent is available. Throughout the exercises, the goal is to map preferential locations to live given a set of conditions (certain level above sea-level, quiet and green neighbourhood, ..).\n",
+    "\n",
+    "We start with the Digital Elevation Model (DEM) for Flanders. The data is available at https://overheid.vlaanderen.be/informatie-vlaanderen/producten-diensten/digitaal-hoogtemodel-dhmv, We downloaded the 25m resolution raster image, and provided a subset of this dataset as a zipped Tiff file in the course material."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-success\">\n",
+    "\n",
+    "**EXERCISE 1**:\n",
+    "\n",
+    "* Read the DEM using `xarray`. The zip file is available at `data/gent/DHMVIIDTMRAS25m.zip`. You can either unzip the file, and use the path to the unzipped file, or prepend `zip://` to the path. \n",
+    "* What is the CRS of this dataset?\n",
+    "* The dataset has a third dimension with a single band. This doesn't work for plotting, so create a new `DataArray` by selecting the single band. Assign the result to a variable `dem`.\n",
+    "* Make a quick plot of the dataset.\n",
+    "\n",
+    "<details><summary>Hints</summary>\n",
+    "\n",
+    "* Rasterio can directly read from a zip-file. If one wants read the file `./data/gent/FILENAME.zip`, add the `zip://.data/gent/FILENAME.zip` to read the zip file directly.\n",
+    "* The `crs` is an attribute, not a function, so no `()` required.\n",
+    "* Selecting in xarray is done with `sel`\n",
+    "* We will improve the plot in the following exercises, but as a quick solution, we already learnt about the `robust=True` plot option of xarray. \n",
+    "* Just pick a color map that you like.\n",
+    "\n",
+    "</details>   \n",
+    "    \n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/13-raster-processing1.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/13-raster-processing2.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/13-raster-processing3.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/13-raster-processing4.py"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-success\">\n",
+    "\n",
+    "**EXERCISE 2**:\n",
+    "\n",
+    "The dataset uses a large negative value to denote the \"nodata\" value (in this case meaning \"outside of Flanders\"). The NODATA value stored in the file metadata is - by default - read by xarray as `np.nan` (see notebook 11-xarray-intro.ipynb), but this behaviour can be excluded:\n",
+    "    \n",
+    "* Read the `zip://./data/gent/DHMVIIDTMRAS25m.zip\"` file (band=1) without converting the NODATA value to `np.nan` (keep -9999.) and keeping the original data type.\n",
+    "* Check the value -9999. is used as \"nodata\" value.\n",
+    "* Repeat the plot from the previous exercise, but now set a fixed minimum value of 0 for the colorbar, to ignore the negative \"nodata\" in the color scheme.\n",
+    "* Replace the \"nodata\" value with `np.nan` using the `where()` method. \n",
+    "    \n",
+    "<details><summary>Hints</summary>\n",
+    "\n",
+    "* The `nodata` attribute is provided by the rioxarray package. Rioxarray loads this information from the geotiff file metadata and makes it available as `.rio.nodata`. \n",
+    "* The `.rio.nodata` is a class attribute, not a function, so no `()` required.\n",
+    "* `vmin` and `vmax` define the colormap limits.\n",
+    "* `ẁhere` expects a condition (i.e. boolean values), e.g. `... != dem.rio.nodata`.\n",
+    "</details>   \n",
+    "    \n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/13-raster-processing5.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/13-raster-processing6.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/13-raster-processing7.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/13-raster-processing8.py"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "__Remember__ Alternatively to masking the nodata value manually, you can do this explicitly when loading the data, using the `mask_and_scale=True` keyword (see also notebook [11-xarray-intro.ipynb](11-xarray-intro.ipynb)):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dem_masked = xr.open_dataarray(\"zip://./data/gent/DHMVIIDTMRAS25m.zip\", engine=\"rasterio\", mask_and_scale=True).sel(band=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dem_masked.plot.imshow(robust=True, cmap=\"terrain\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-success\">\n",
+    "\n",
+    "**EXERCISE 3**:\n",
+    "\n",
+    "We want to limit our search for locations to the surroundings of the centre of Ghent.\n",
+    "\n",
+    "* Create a `Point` object for the centre of Ghent. Latitude/longitude coordinates for the Korenmarkt are: 51.05393, 3.72174\n",
+    "* We need our point in the same Coordinate Reference System as the DEM raster (i.e. EPSG:31370, or Belgian Lambert 72). Use GeoPandas to reproject the point:\n",
+    "    * Create a GeoSeries with this single point and specify its CRS with the `crs` keyword.\n",
+    "    * Reproject this series with the `to_crs` method. Assign the resulting GeoSeries to a variable `gent_centre_31370`.\n",
+    "* Calculcate a buffer of 10km radius around the point.\n",
+    "* Get the bounding box coordinates of this buffer. Assign this to `gent_bounds`.\n",
+    "    \n",
+    "<details><summary>Hints</summary>\n",
+    "\n",
+    "* Remember the introduction on geospatial data and the shapely objects, e.g. `shapely.geometry.Point`?\n",
+    "* The Latitude/Longitude order is a typical gotcha. The `Point` expects `x` (longitude) as  first argument and `y` (latitude) as second argument.\n",
+    "* Use `geopandas.GeoSeries` to create a new GeoSeries and add the `crs` parameter. The lat/lon of the Kornmarkt are provided as EPSG:4326. \n",
+    "* In EPSG:31370, the unit is meter, so make sure to use meter to define the buffer size.\n",
+    "* `.total_bounds` is a class attribute.\n",
+    "\n",
+    "</details>   \n",
+    "    \n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/13-raster-processing9.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/13-raster-processing10.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/13-raster-processing11.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/13-raster-processing12.py"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-success\">\n",
+    "\n",
+    "**EXERCISE 4**:\n",
+    "\n",
+    "With this bounding box, we can now clip a subset of the DEM raster for the area of interest. \n",
+    "    \n",
+    "* Clip the `dem` raster layer. To clip with a bounding box instead of a geometry, you can use the `rio.clip_box()` method instead of `rio.clip()`.\n",
+    "* Make a plot. Use the \"terrain\" color map, and set the bounds of the color scale to 0 - 30.\n",
+    "\n",
+    "<details><summary>Hints</summary>\n",
+    "\n",
+    "* The `gent_bounds` is an array of 4 elements, whereas `clip_box` requires these as seperate input parameters... Did you know that in Python you can unpack these 4 values with the `*`: `*gent_bounds` will unpack to 4 individual input.\n",
+    "\n",
+    "</details>   \n",
+    "    \n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/13-raster-processing13.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/13-raster-processing14.py"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The CORINE Land Cover (https://land.copernicus.eu/pan-european/corine-land-cover) is a program by the European Environment Agency (EEA) to provide an inventory of land cover in 44 classes of the European Union. The data is provided in both raster as vector format and with a resolution of 100m.\n",
+    "\n",
+    "The data for the whole of Europe can be downloaded from the website (latest version: https://land.copernicus.eu/pan-european/corine-land-cover/clc2018?tab=download). This is however a large dataset, so we downloaded the raster file and cropped it to cover Flanders, and this subset is included in the repo as `data/CLC2018_V2020_20u1_flanders.tif` (the code to do this cropping can be see at [data/preprocess_data.ipynb#CORINE-Land-Cover](data/preprocess_data.ipynb#CORINE-Land-Cover))."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-success\">\n",
+    "\n",
+    "**EXERCISE 5**:\n",
+    "\n",
+    "* Read the land use data provided as a tif (`data/CLC2018_V2020_20u1_flanders.tif`). Directly select the single band.\n",
+    "* Make a quick plot. The raster is using a negative value as \"nodata\", consider using the `robust=True` option.\n",
+    "* What is the resolution of this raster?\n",
+    "* What is the CRS?\n",
+    "\n",
+    "<details><summary>Hints</summary>\n",
+    "\n",
+    "* `rio.resolution()` is a method, so it requires the `()`.\n",
+    "* `rio.crs` is an attribute, so it does not require the `()`.\n",
+    "\n",
+    "</details>   \n",
+    "    \n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/13-raster-processing15.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/13-raster-processing16.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/13-raster-processing17.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/13-raster-processing18.py"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-success\">\n",
+    "\n",
+    "**EXERCISE 6**:\n",
+    "\n",
+    "The land use dataset is a European dataset and uses a Europe-wide projected CRS (https://epsg.io/3035).\n",
+    "\n",
+    "* Reproject the land use raster to the same CRS as the DEM raster (EPSG:31370), and plot the result.\n",
+    "\n",
+    "<details><summary>Hints</summary>\n",
+    "\n",
+    "* For the sake of the exercise, pick any resampling algorithm or just the default option.\n",
+    "\n",
+    "</details>   \n",
+    "    \n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/13-raster-processing19.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/13-raster-processing20.py"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-success\">\n",
+    "\n",
+    "**EXERCISE 7**:\n",
+    "\n",
+    "In addition to reprojecting to a new CRS, we also want to upsample the land use dataset to the same resolution as the DEM raster, and to use the exact same grid layout, so we can compare and combine those rasters pixel-by-pixel.\n",
+    "\n",
+    "Such a reprojection can be done with the `reproject()` method by providing the target geospatial \"transform\" (which has the information for the bounds and resolution) and shape for the result. `rioxarray` provides a short-cut for this operation with the `reproject_match()` method, to reproject one raster to the CRS, extent and resolution of another raster.  \n",
+    "\n",
+    "* Reproject the land use raster to the same CRS and extent as the DEM subset for Ghent (`dem_gent`). Call the result `land_use_gent`.\n",
+    "* Make a plot of the result.\n",
+    "\n",
+    "<details><summary>Hints</summary>\n",
+    "\n",
+    "* Check the help of the `.rio.reproject_match` method (SHIFT + TAB) to know which input ou need. For the sake of the exercise, pick any resampling algorithm or just the default (nearest) option.\n",
+    "* The data calling the method is being transformed, and the input parameter is the target to match.\n",
+    "\n",
+    "</details>   \n",
+    "    \n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/13-raster-processing21.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/13-raster-processing22.py"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The land use dataset is a raster with discrete values (i.e. different land use classes). The [CurieuzeNeuzen case study](case-curieuzeneuzen-air-quality.ipynb#Combining-with-Land-Use-data) goes into more depth on those values, but for this exercise it is sufficient to know that values 1 and 2 are the Continuous and Discontinuous urban fabric (residential areas)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-success\">\n",
+    "\n",
+    "**EXERCISE 8**:\n",
+    "\n",
+    "Let's find the preferential locations to live, assuming we want to be future-proof and live at least 10m above sea level in a residential area.\n",
+    "\n",
+    "* Create a new array denoting the residential areas (where `land_use_gent` is equal to 1 or 2). Call this `land_use_residential`, and make a quick plot.\n",
+    "* Plot those locations that are 10m above sea-level.\n",
+    "* Combine the residential areas and areas > 10m in a single array called `suitable_locations`, and plot the result.\n",
+    "    \n",
+    "<details><summary>Hints</summary>\n",
+    "\n",
+    "* To select for multiple options, one can either combine multiple conditions using `|` (or) or us the `isin([...,...])` option, both will do.\n",
+    "* The output of a condition is a Boolean map that can be plot just like other maps, e.g. `(dem_gent > 10).plot.imshow()`.\n",
+    "* Fo the `suitable_locations`, both boolean conditions need to be True, so combine them with either `&` or just multiply them with `*`.\n",
+    "\n",
+    "</details>   \n",
+    "    \n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/13-raster-processing23.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/13-raster-processing24.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/13-raster-processing25.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/13-raster-processing26.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/13-raster-processing27.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/13-raster-processing28.py"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-success\">\n",
+    "\n",
+    "**EXERCISE 9**:\n",
+    "\n",
+    "In addition to the previous conditions, assume we also don't want to live close to major roads.\n",
+    "\n",
+    "We downloaded the road segments open data from Ghent (https://data.stad.gent/explore/dataset/wegsegmenten-gent/) as a GeoJSON file, and provided this in the course materials: `/data/gent/vector/wegsegmenten-gent.geojson.zip`\n",
+    "    \n",
+    "* Read the GeoJSON road segments file into a variable `roads` and check the first few rows.\n",
+    "* The column \"frc_omschrijving\" contains a description of the type of road for each segment. Get an overview of the available segments and types by doing a \"value counts\" of this column.\n",
+    "\n",
+    "<details><summary>Hints</summary>\n",
+    "\n",
+    "* GeoPandas does NOT need the `zip://...` to read in zip files.\n",
+    "* The first few rows are also the `head` of a data set.\n",
+    "* The `value_counts` provides the number of records for each of the different values in a column.\n",
+    "\n",
+    "</details>   \n",
+    "    \n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/13-raster-processing29.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/13-raster-processing30.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "roads[\"frc_omschrijving\"].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-success\">\n",
+    "\n",
+    "**EXERCISE 10**:\n",
+    "\n",
+    "We are interested in the big roads, as these are the ones we want to avoid: \"Motorway or Freeway\", \"Major Road\" and \"Other Major Road\".\n",
+    "\n",
+    "* Filter the `roads` table based on the provided list of road types: select those rows where the \"frc_omschrijving\" column is equal to one of those values, and call this `roads_subset`.\n",
+    "* Make a quick plot of this subset and use the \"frc_omschrijving\" colum to color the lines.\n",
+    "\n",
+    "<details><summary>Hints</summary>\n",
+    "\n",
+    "* Selecting multiple options at the same time is most convenient with the `isin()` method.\n",
+    "* Use the GeoPandas `.plot` method and specifh the `frc_omschrijving` column to the `column` parameter.\n",
+    "\n",
+    "</details>   \n",
+    "    \n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "road_types = [\n",
+    "    \"Motorway, Freeway or Other Major Road\",\n",
+    "    \"Major Road Less Important than a Motorway\",\n",
+    "    \"Other Major Road\",\n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/13-raster-processing31.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/13-raster-processing32.py"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-success\">\n",
+    "\n",
+    "**EXERCISE 11**:\n",
+    "\n",
+    "Before we convert the vector data to a raster, we want to buffer the roads. We will use a larger buffer radius for the larger roads.\n",
+    "\n",
+    "* Using the defined `buffer_per_roadtype` dictionary, create a new Series by mapping the values in the \"frc_omschrijving\" column to the matching buffer radius.\n",
+    "* Convert the `roads_subset` GeoDataFrame to CRS `EPSG:31370`, and create buffered lines (polygons) with the calculated buffer radius distances. Call the result `roads_buffer`.\n",
+    "    \n",
+    "<details><summary>Hints</summary>\n",
+    "\n",
+    "* Use the `map()` method to map the values using the provided dictionary `buffer_per_roadtype`.\n",
+    "* The conversion to EPSG:31370 is important to be able to work with the meters to define the buffer size.\n",
+    "* The `buffer` method can take a single value to apply to all values, but also a Series of values, with a buffer size defined for each element.\n",
+    "\n",
+    "</details>   \n",
+    "    \n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "buffer_per_roadtype = {\n",
+    "    \"Motorway, Freeway or Other Major Road\": 750,\n",
+    "    \"Major Road Less Important than a Motorway\": 500,\n",
+    "    \"Other Major Road\": 150,\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/13-raster-processing33.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/13-raster-processing34.py"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-success\">\n",
+    "\n",
+    "**EXERCISE 12**:\n",
+    "\n",
+    "Convert the buffered road segments to a raster:\n",
+    "    \n",
+    "* Use `features.features.rasterize()` to convert the `roads_buffer` GeoDataFrame to a raster:\n",
+    "    * Pass the geometry column as the first argument.\n",
+    "    * Pass the shape and transform of the `dem_gent` to specify the desired spatial extent and resolution of the output raster.\n",
+    "* Invert the values of the raster by doing `1 - arr`, and plot the array with `plt.imshow(..)`.\n",
+    "* Recalculate the `suitable_locations` variable, using 1/ land_use_residential, 2/ dem > 10 and 3/ outside the road buffers.  \n",
+    "    \n",
+    "<details><summary>Hints</summary>\n",
+    "\n",
+    "* Access the geometry column using the `.geometry` attribute.\n",
+    "* `shape` is also an attribute.\n",
+    "* `.rio.transform()` is a method, so it requires the `()`.\n",
+    "* Previously, suitable locations were `land_use_residential * (dem_gent > 10)`. Combine this with the `(1 - roads_buffer_arr)` output.\n",
+    "\n",
+    "</details>   \n",
+    "    \n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import rasterio.features"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/13-raster-processing35.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/13-raster-processing36.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/13-raster-processing37.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/13-raster-processing38.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/13-raster-processing39.py"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-success\">\n",
+    "\n",
+    "**EXERCISE 13**:\n",
+    "\n",
+    "Make a plot with a background map of the selected locations. \n",
+    "    \n",
+    "* Plot the provided `gent` GeoDataFrame (a single row table with the are of the Ghent municipality). Use a low \"alpha\" to give it a light color.\n",
+    "* Add a background map using contextily.\n",
+    "* Plot the `suitable_locations` raster on top of this figure: first mask the array to select only those values larger than zero (so the other values becomes NaN, and are not plotted). Then plot the result, adding it to the existing figure using the `ax` keyword.\n",
+    "\n",
+    "<details><summary>Hints</summary>\n",
+    "\n",
+    "* The `fig, ax = plt.subplots(figsize=(15, 15))` is a convenient shortcut to prepare a Matplotlib Figure and Axes. \n",
+    "* Make sure to define the `crs=\"EPSG:31370\"` for contextily.\n",
+    "* `where(...)` is a powerfull way to exclude data as it - by default - adds NaN values for piels where the condition is not True.\n",
+    "</details>   \n",
+    "    \n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import contextily"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gent = geopandas.read_file(\"data/gent/vector/gent.geojson\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/13-raster-processing40.py"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Advanced (optional) exercises:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-success\">\n",
+    "\n",
+    "**EXERCISE 14**:\n",
+    "\n",
+    "We downloaded the data about urban green areas in Ghent (https://data.stad.gent/explore/dataset/parken-gent).\n",
+    "\n",
+    "* Read in the data at `data/gent/vector/parken-gent.geojson` into a variable `green`.\n",
+    "* Check the content (first rows, quick plot)\n",
+    "* Remove the rows with an empty geometry (`None`)    \n",
+    "* Convert this vector layer to a raster using the spatial extent and resolution of `dem_gent` as the targer raster.\n",
+    "* The `rasterio.features.rasterize` results in a numpy array. Convert this to a DataArray using the `xr.DataArray(..)` constructor, specifying the coordinates of `dem_gent` (`dem_gent.coords`) for the coordinates of the new array.\n",
+    "    \n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/13-raster-processing41.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/13-raster-processing42.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/13-raster-processing43.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/13-raster-processing44.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/13-raster-processing45.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/13-raster-processing46.py"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-success\">\n",
+    "\n",
+    "**EXERCISE 15**:\n",
+    "\n",
+    "For the urban green areas, we want to calculate a statistic for a neighbourhood around each pixel (\"focal\" statistics). This can be expressed as a convolution with a defined kernel.    \n",
+    "\n",
+    "The [xarray-spatial](https://xarray-spatial.org/index.html) package includes functionality for such focal statistics and convolutions.\n",
+    "\n",
+    "* Use the `focal.focal_stats()` function from xarray-spatial to calculate the sum of green are in a neighborhood of 500m around each point. Check the help of this function to see which arguments to specify.\n",
+    "* Make a plot of the resulting `green_area` array.\n",
+    "    \n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from xrspatial import focal, convolution"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x, y = convolution.calc_cellsize(green_arr)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "kernel = convolution.circle_kernel(x, y, 500)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/13-raster-processing47.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/13-raster-processing48.py"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The `scipy` package also provides optimized convolution algorithms. In case of the \"sum\" statistic, this is equivalent:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from scipy import signal"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/13-raster-processing49.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/13-raster-processing50.py"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-success\">\n",
+    "\n",
+    "**EXERCISE 16**:\n",
+    "\n",
+    "Make a plot with a background map of the selected locations, i.e. land_use_residential, dem > 10, outside road buffers and 'sufficient'as green area. \n",
+    "    \n",
+    "Define sufficient green area as the `green_area` pixels where the sum of the convolution (previous exercise) is larger as 10 (keep those values and convert pixels with values lower than 10 to 0 values). Multiply the different conditions/categories, so the green area score is included in the final result.\n",
+    "    \n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/13-raster-processing51.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/13-raster-processing52.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import contextily"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gent = geopandas.read_file(\"data/gent/vector/gent.geojson\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(10, 10))\n",
+    "gent.to_crs(\"EPSG:31370\").plot(ax=ax, alpha=0.1)\n",
+    "ax.set(ylim=(190_000, 200_000), xlim=(100_000, 110_000))\n",
+    "contextily.add_basemap(ax, crs=\"EPSG:31370\")\n",
+    "\n",
+    "suitable_locations.where(suitable_locations>0).plot.imshow(ax=ax, alpha=0.5, add_colorbar=False)\n",
+    "ax.set_aspect(\"equal\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Extracting values from rasters based on vector data\n",
+    "\n",
+    "The **rasterstats** package provides methods to calculate summary statistics of geospatial raster datasets based on vector geometries (https://github.com/perrygeo/python-rasterstats)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-warning\">\n",
+    "\n",
+    "**! Manual download needed !** <br>\n",
+    "\n",
+    "The data can be downloaded from the EEA at [download link](https://www.eea.europa.eu/data-and-maps/data/world-digital-elevation-model-etopo5/zipped-dem-geotiff-raster-geographic-tag-image-file-format-raster-data/zipped-dem-geotiff-raster-geographic-tag-image-file-format-raster-data/at_download/file), then extract the zip file and save the file in the `data` subdirectory. After unzipping, the file will be available in the subdirectory `data/dem_geotiff/alwdgg.tif`.\n",
+    "\n",
+    "</div>\n",
+    "\n",
+    "To illustrate this, we are reading a raster file with elevation data of the full world (the file contains a single band for the elevation:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "countries = geopandas.read_file(\"./data/ne_110m_admin_0_countries.zip\")\n",
+    "cities = geopandas.read_file(\"./data/ne_110m_populated_places.zip\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dem_geotiff = \"data/DEM_geotiff/alwdgg.tif\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "img = xr.open_dataarray(dem_geotiff).sel(band=1).plot.imshow(cmap=\"terrain\", figsize=(10, 4), )\n",
+    "img.axes.set_aspect(\"equal\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Given this raster of the elevation, we might want to know the elevation at a certain location or for each country.\n",
+    "For the countries example, we want to extract the pixel values that fall within a country polygon, and calculate a statistic for it, such as the mean or the maximum.\n",
+    "\n",
+    "Such functionality to extract information from a raster for given vector data is provided by the rasterstats package."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import rasterstats"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For extracting the pixel values for polygons, we use the `zonal_stats` function, passing it the GeoSeries, the path to the raster file, and the method to compute the statistics."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "result = rasterstats.zonal_stats(countries.geometry, dem_geotiff,\n",
+    "                                 stats=['min', 'mean', 'max'], \n",
+    "                                 nodata=-999)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The results can be assigned to new columns:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "countries[['min', 'max', 'mean']] = pd.DataFrame(result)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "countries.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And then we can sort by the average elevation of the country:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "countries.sort_values('mean', ascending=False).head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For points, a similar function called `point_query` can be used (specifying the interpolation method):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cities[\"elevation\"] = rasterstats.point_query(cities.geometry, \n",
+    "                                              dem_geotiff, interpolate='bilinear',\n",
+    "                                              nodata=-999)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cities.sort_values(by=\"elevation\", ascending=False).head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "-----------\n",
+    "\n",
+    "# A bit more about WFS\n",
+    "\n",
+    "> The Web Feature Service (WFS) represents a change in the way geographic information is created, modified and exchanged on the Internet. Rather than sharing geographic information at the file level using File Transfer Protocol (FTP), for example, the WFS offers direct fine-grained...\n",
+    "\n",
+    "(https://www.ogc.org/standards/wfs)\n",
+    "\n",
+    "In brief, the WFS is the specification to __access and download vector datasets__.\n",
+    "\n",
+    "To access WFS data, you need the following information:\n",
+    "- URL of the service, e.g. `https://geoservices.informatievlaanderen.be/overdrachtdiensten/VHAWaterlopen/wfs`. Looking for these URLS, check [WFS page of Michel Stuyts](https://wfs.michelstuyts.be/?lang=en)\n",
+    "- The available projections and layers, also check [WFS page of Michel Stuyts](https://wfs.michelstuyts.be/?lang=en) or start looking into the `GetCapabilities`, e.g. [vha waterlopen](https://geoservices.informatievlaanderen.be/overdrachtdiensten/VHAWaterlopen/wfs?REQUEST=GetCapabilities&SERVICE=WFS)\n",
+    "\n",
+    "Instead of downloading the entire data set, filtering the request itself (only downloading what you need) is a good idea, using the `cql_filter` filter. Finding out these is sometimes a bit of hazzle... E.g. quickly [preview the data in QGIS](https://docs.qgis.org/3.10/en/docs/training_manual/online_resources/wfs.html?highlight=wfs).\n",
+    "\n",
+    "You can also use the [`OWSLib` library](https://geopython.github.io/OWSLib/#wfs). But as WFS is a webservice, the `requests` package will be sufficient for simple queries."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As an example - municipalities in Belgium, see https://wfs.michelstuyts.be/service.php?id=140&lang=en, _WFS Voorlopig referentiebestand gemeentegrenzen 2019_\n",
+    "\n",
+    "- URL of the service: https://geoservices.informatievlaanderen.be/overdrachtdiensten/VRBG2019/wfs\n",
+    "- Available projections:  EPSG:4258, EPSG:3812,...\n",
+    "- Available layers: VRBG2019:Refgem:,  VRBG2019:Refarr:,...\n",
+    "- Column `Naam` contains the municipatility, e.g. `Gent`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "wfs_municipality = \"https://geo.api.vlaanderen.be/VRBG2019/wfs\"\n",
+    "params = dict(service='WFS', version='1.1.0', request='GetFeature',\n",
+    "              typeName='VRBG2019:Refgem', outputFormat='json',\n",
+    "              cql_filter=\"NAAM='Gent'\", srs=\"31370\")\n",
+    "\n",
+    "# Fetch data from WFS using requests\n",
+    "r = requests.get(wfs_municipality, params=params)\n",
+    "gent = geopandas.GeoDataFrame.from_features(json.loads(r.content), crs=\"epsg:31370\")\n",
+    "gent.plot()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Cloud: only download what you need\n",
+    "\n",
+    "Rasterio/rioxarray only reads the data from disk that is requested to overcome loading entire data sets into memory. The same applies to downloading data, overcoming entire downloads when only a fraction is required (when the online resource supports this). An example is https://zenodo.org/record/2654620, which is available as [Cloud Optimized Geotiff (COG)](https://www.cogeo.org/). Also cloud providers (AWS, google,...) do support COG files, e.g. [Landstat images](https://docs.opendata.aws/landsat-pds/readme.html).\n",
+    "\n",
+    "These files are typically very large to download, whereas we might only need a small subset of the data. COG files support downloading a subset of the data you need using a masking approach.\n",
+    "\n",
+    "Let's use the Averbode nature reserve data as an example, available at the URL: http://s3-eu-west-1.amazonaws.com/lw-remote-sensing/cogeo/20160401_ABH_1_Ortho.tif"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "averbode_cog_rgb = 'http://s3-eu-west-1.amazonaws.com/lw-remote-sensing/cogeo/20160401_ABH_1_Ortho.tif'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Check the metadata, without downloading the data itself:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "averbode_data = xr.open_dataarray(averbode_cog_rgb, engine=\"rasterio\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "averbode_data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Downloading the entire data set would be 37645*35405\\*4 pixels of 4 byte, so more or less 5.3 GByte"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "37645*35405*4 / 1e9  # Gb"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "averbode_data.size  / 1e9  # Gb"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Assume that we have a study area which is much smaller than the total extent of the available image:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "left, bottom, right, top = averbode_data.rio.bounds()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "averbode_study_area = geopandas.read_file(\"./data/averbode/study_area.geojson\")\n",
+    "ax = averbode_study_area.plot();\n",
+    "ax.set_xlim(left, right);\n",
+    "ax.set_ylim(bottom, top);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In the case of COG data, the data can sometimes be requested on different resolution levels when stored as such. So, to get a very broad overview of the data, we can request the coarsest resolution by resampling and download the data at the resampled resolution:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with rasterio.open(averbode_cog_rgb) as src:\n",
+    "    # check available overviews for band 1\n",
+    "    print(f\"Available resolutions are {src.overviews(1)}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "averbode_64 = rioxarray.open_rasterio(averbode_cog_rgb, overview_level=5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-info\">\n",
+    "\n",
+    "**REMEMBER**:\n",
+    "\n",
+    "`rioxarray.open_rasterio` is used here instead of `xr.open_dataarray(..., engine=\"rasterio\")` as the latter does not support the `overview_level` argument. In general, both should be comparable in behaviour.\n",
+    "    \n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "averbode_64.size / 1e6  # Mb"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "averbode_64.rio.resolution()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Compare the thumbnail version of the data with our study area:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "averbode_64.sel(band=[1, 2, 3]).plot.imshow(ax=ax)\n",
+    "averbode_study_area.plot(ax=ax, color='None', edgecolor='red', linewidth=2);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Downloading the entire data file would be overkill. Instead, we only want to download the data of the study area. This can be done with the `clip()` method using the `from_disk` option. \n",
+    "The resulting data set will still be around 100MB and will take a bit of time to download, but this is only a fraction of the original data file:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%time\n",
+    "# Only run this cell when sufficient band width ;-)\n",
+    "averbode_subset = averbode_data.rio.clip(averbode_study_area.geometry, from_disk=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "averbode_subset.size / 1e6  # Mb"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "averbode_subset.sel(band=[1, 2, 3]).plot.imshow(figsize=(10, 10))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The rasterio way:\n",
+    "\n",
+    "```python\n",
+    "output_file = \"./averbode_orthophoto.tiff\"\n",
+    "\n",
+    "# Only run this cell when sufficient band width ;-)\n",
+    "with rasterio.open(averbode_cog_rgb) as averbode_rgb:\n",
+    "    averbode_rgb_image, averbode_rgb_transform = rasterio.mask.mask(averbode_rgb, averbode_study_area.geometry, crop=True)\n",
+    "    averbode_rgb_profile = averbode_rgb.profile  \n",
+    "\n",
+    "    averbode_rgb_profile.update({\"driver\": \"GTiff\",\n",
+    "                                 \"height\": averbode_rgb_image.shape[1],\n",
+    "                                 \"width\": averbode_rgb_image.shape[2],\n",
+    "                                 \"transform\": averbode_rgb_transform\n",
+    "                                })\n",
+    "    \n",
+    "    with rasterio.open(output_file, \"w\", **averbode_rgb_profile) as dest:\n",
+    "        dest.write(averbode_rgb_image)\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Thanks to https://geohackweek.github.io/raster/04-workingwithrasters/ for the inspiration"
+   ]
+  }
+ ],
+ "metadata": {
+  "jupytext": {
+   "default_lexer": "ipython3"
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.11"
+  },
+  "widgets": {
+   "application/vnd.jupyter.widget-state+json": {
+    "state": {},
+    "version_major": 2,
+    "version_minor": 0
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/notebooks/14-combine-data.ipynb b/notebooks/14-combine-data.ipynb
new file mode 100644
index 0000000..6cf8e9f
--- /dev/null
+++ b/notebooks/14-combine-data.ipynb
@@ -0,0 +1,620 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "b32d4d6e-6b28-4ace-ad01-0f8c4465794c",
+   "metadata": {},
+   "source": [
+    "<p><font size=\"6\"><b>Stacking of raster data</b></font></p>\n",
+    "\n",
+    "\n",
+    "> *DS Python for GIS and Geoscience*  \n",
+    "> *October, 2025*\n",
+    ">\n",
+    "> *© 2025, Joris Van den Bossche and Stijn Van Hoey. Licensed under [CC BY 4.0 Creative Commons](http://creativecommons.org/licenses/by/4.0/)*\n",
+    "\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "40325d7c-ccb2-4c18-8a5f-98fb25383399",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import shutil\n",
+    "from pathlib import Path\n",
+    "\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "import xarray as xr"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a11a4867-cc75-43c3-971e-df1e9991761e",
+   "metadata": {},
+   "source": [
+    "## Introduction"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1563d475-a2c7-4914-8e4c-2bb5fe08d312",
+   "metadata": {},
+   "source": [
+    "Geospatial time series data is often stored as multiple individual files. For example, remote sensing data or geoscience model output are typically organized with each time step (or band) in a separate file. Handling all these indidivudal files is culbersome and workflows to combine these files into a single `xarray.Dataset` or `xarray.DataArray` prior to the analysis are required. \n",
+    "\n",
+    "In this notebook, we will explore some ways to combine indidivual files into a single data product ready for analysis."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c094477e-1610-4477-a6f9-25934dcb23b0",
+   "metadata": {},
+   "source": [
+    "## Load multiple files into a single `xarray.Dataset/xarray.DataArray`"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "404fe62e-1aa6-4d0f-85bc-14ab660d248a",
+   "metadata": {},
+   "source": [
+    "In some of the previous notebooks, we used the band 4 and band 8 Sentinel image data from Ghent, which are both stored as a separate data file in the `data` directory.\n",
+    "\n",
+    "One way to handle this is to load each of the data sets into memory and concatenate these afterwards:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3a765a96-ef2e-47a9-b115-62012edad698",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "arr_b4 = xr.open_dataarray(\"data/gent/raster/2020-09-17_Sentinel_2_L1C_B04.tiff\", engine=\"rasterio\")\n",
+    "arr_b8 = xr.open_dataarray(\"data/gent/raster/2020-09-17_Sentinel_2_L1C_B08.tiff\", engine=\"rasterio\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6954ed80-d853-4786-9ed9-c72d2253c6be",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "band_var = xr.Variable('band', [\"B4\", \"B8\"])\n",
+    "arr = xr.concat([arr_b4, arr_b8], dim=band_var)\n",
+    "arr"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b5345cdb-ca88-457b-9604-4b993995f5e3",
+   "metadata": {},
+   "source": [
+    "From now on, the data is contained in a single `DataArray` to do further analysis. This approach works just fine for this limited set of data.\n",
+    "\n",
+    "However, when more files need to be processed, this becomes labor/code intensive and additional automation is required. Consider the following example data in the data folder `./data/herstappe/raster/sentinel_moisture/`:\n",
+    "\n",
+    "```\n",
+    "./data/herstappe/raster/sentinel_moisture/\n",
+    "├── 2016-05-01, Sentinel-2A L1C, Moisture index.tiff\n",
+    "├── 2019-02-15, Sentinel-2A L1C, Moisture index.tiff\n",
+    "└── 2020-02-07, Sentinel-2A L1C, Moisture index.tiff\n",
+    "```\n",
+    "\n",
+    "It is a (small) extract of a time series of moisture index data derived from sentinel-2A, made available by [Sentinel-Hub](https://apps.sentinel-hub.com/eo-browser), a time series of remote sensing images. \n",
+    "\n",
+    "Instead of manually loading the data, we rather automate the data load from these files to a single xarray object:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f11f2f34-e7ce-4796-b2d9-15352c825ca8",
+   "metadata": {},
+   "source": [
+    "1. Identify all files in the data folder and make a list of them:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e849f0bc-040f-4e46-b48e-5ff4cce481ad",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pathlib import Path\n",
+    "moisture_index_files = list(sorted(Path(\"./data/herstappe/raster/sentinel_moisture\").rglob(\"*.tiff\")))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a97ef2f0-1b1e-440d-a4ef-108eed4911c6",
+   "metadata": {},
+   "source": [
+    "2. Extract the time-dimension from each individual file name"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6bbe7b26-993c-493e-b2ea-d566a6c5e9fa",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "moisture_index_dates = [pd.to_datetime(file_name.stem.split(\",\")[0]) for file_name in moisture_index_files]\n",
+    "moisture_index_dates"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5dfd11d6-4543-4072-93ed-6cf647270071",
+   "metadata": {},
+   "source": [
+    "__Note__ we use `pathlib` instead of `glob.glob` as it returns `Path` objects instead to represent the file names which are more powerful than regular strings returned by `glob.glob`, e.g. usage of `stem` attribute."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3a1d1094-9593-4ae4-be5d-2b06b5d3d578",
+   "metadata": {},
+   "source": [
+    "3.  Prepare an xarray variable which can be used as the additional date dimension/coordinate"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "13e68fe3-9378-4b11-a56f-ebe3168fdb95",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "date_var = xr.Variable('date', moisture_index_dates)\n",
+    "date_var"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a99d11cc-7d15-4bfa-8e6e-7b6aeafa157e",
+   "metadata": {},
+   "source": [
+    "4. Load in and concatenate all individual GeoTIFFs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "569d758c-bae3-4e7d-8308-fc5d16b37c03",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "moisture_index = xr.concat(\n",
+    "    [xr.open_dataarray(file_name, engine=\"rasterio\", mask_and_scale=False) for file_name in moisture_index_files],\n",
+    "    dim=date_var\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "58ea6fae-e682-4694-ae2d-5f26ff267599",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "moisture_index.sortby(\"date\").plot.imshow(col=\"date\", figsize=(15, 4), aspect=1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5cabf74a-b9b5-472c-88b9-bf5e2e40deb2",
+   "metadata": {},
+   "source": [
+    "## Lazy load multiple files into a single `xarray.Dataset`"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8b9a3cbb-1ae5-4fec-94d8-ebc4d7604eab",
+   "metadata": {},
+   "source": [
+    "In the previous example, all data is read into memory. Xarray provides a separate function [`open_mfdataset`](http://xarray.pydata.org/en/stable/generated/xarray.open_mfdataset.html#xarray-open-mfdataset) to read data lazy from disk (so not loading the data itself in memory) from multiple files. \n",
+    "\n",
+    "A usefule feature is the ability to preprocess the files:\n",
+    "\n",
+    "> __preprocess (callable(), optional)__ – If provided, call this function on each dataset prior to concatenation. You can find the file-name from which each dataset was loaded in ds.encoding[\"source\"].\n",
+    "\n",
+    "Applied to the previous moisture index files example:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a1ecb7b7-4a42-41ca-b79a-899cb9bf34e4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def add_date_dimension(ds):\n",
+    "    \"\"\"Add the date dimension derived from the file_name and rename to moisture_index\"\"\"\n",
+    "    ds_date = pd.to_datetime(Path(ds.encoding[\"source\"]).stem.split(\",\")[0])\n",
+    "    ds = ds.assign_coords(date=(\"date\", [ds_date])).rename({\"band_data\": \"moisture_index\"})\n",
+    "    return ds"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cf285fc6-e005-47b3-b1fa-8caa9b8eec9b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "moisture_index_lazy = xr.open_mfdataset(sorted(Path(\"./data/herstappe/raster/sentinel_moisture\").rglob(\"*.tiff\")), \n",
+    "                                        preprocess=add_date_dimension, engine=\"rasterio\", mask_and_scale=False,\n",
+    "                                        chunks={\"date\": 1, \"band\": -1, \"x\": -1, \"y\": -1}, data_vars=\"all\") # parallel=True\n",
+    "moisture_index_lazy[\"moisture_index\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3fb4684d-a762-4d90-a900-d97c77e3d775",
+   "metadata": {},
+   "source": [
+    "The data itself is not loaded directly and is divided into 3 chunks, i.e. we chunked for each date. See the notebook [15-xarray-dask-big-data](./15-xarray-dask-big-data.ipynb) notebook for more information on the processing of (out of memory) lazy data with Dask."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "761309bb-ef60-4141-97ae-aa697c160001",
+   "metadata": {},
+   "source": [
+    "Further reading:\n",
+    "\n",
+    "- See http://xarray.pydata.org/en/stable/user-guide/io.html#reading-multi-file-datasets for more examples.\n",
+    "- https://medium.com/@bonnefond.virginie/handling-multi-temporal-satellite-images-with-xarray-30d142d3391\n",
+    "- https://docs.dea.ga.gov.au/notebooks/Frequently_used_code/Opening_GeoTIFFs_NetCDFs.html#Loading-multiple-files-into-a-single-xarray.Dataset"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9e1046d9-8a09-46ec-967a-e0584d1113b7",
+   "metadata": {},
+   "source": [
+    "## Save concatenated data to a single file"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "85d3559b-c79e-4e8b-971b-cb8074c6cc2d",
+   "metadata": {},
+   "source": [
+    "After processing multiple files, it is convenient to save the data in a preferred format afterwards. Convenient choices are [NetCDF](https://www.unidata.ucar.edu/software/netcdf/) and [Zarr](https://zarr.readthedocs.io/en/stable/). Zarr is a newer format providing some advantages when working in cloud environments, but can be used on a local machine as well."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b010efc3-fea9-4363-997a-39bff87cfef4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "moisture_index.to_netcdf(\"moisture_index_stacked.nc\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0fde5d0f-56ee-4282-b28f-c27d5aa26ac1",
+   "metadata": {},
+   "source": [
+    "Hence, the next the data set can be loaded directly from disk:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3bc44dbd-5234-430e-9599-147d67321d40",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "xr.open_dataset(\"moisture_index_stacked.nc\", engine=\"netcdf4\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8165b475-b856-4c84-8464-8d9bf2f82315",
+   "metadata": {},
+   "source": [
+    "Storing to zarr files works on the `xarray.DataSet` level:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c28bedfa-74cf-40c0-a295-c818e1675976",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "moisture_index_lazy.to_zarr(\"moisture_index_stacked.zarr\", consolidated=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "11e3b613-6d09-484a-8cc7-f0a7d8dba939",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "xr.open_dataset(\"moisture_index_stacked.zarr\", engine=\"zarr\", consolidated=False)    "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1b918bb6-1ff9-4503-9438-8cd81532e4b6",
+   "metadata": {},
+   "source": [
+    "_Clean up of these example files_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5362f004-462b-4b4b-815e-d4bf12efd33d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import shutil\n",
+    "\n",
+    "if Path(\"moisture_index_stacked.zarr\").exists():\n",
+    "    shutil.rmtree(\"moisture_index_stacked.zarr\")\n",
+    "if Path(\"moisture_index_stacked.nc\").exists():    \n",
+    "    Path(\"moisture_index_stacked.nc\").unlink()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "afb8d675-e257-4af4-b2e3-cdcd4f070238",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-success\">\n",
+    "\n",
+    "**EXERCISE 1**:\n",
+    "    \n",
+    "The [NOAA's NCEP Reanalysis data](https://psl.noaa.gov/data/gridded/data.ncep.reanalysis.html) files are stored on a remote server and can be accessed over OpenDAP.\n",
+    "    \n",
+    "> The NCEP/NCAR Reanalysis data set is a continually updated (1948–present) globally gridded data set that represents the state of the Earth's atmosphere, incorporating observations and numerical weather prediction (NWP) model output from 1948 to present.\n",
+    "    \n",
+    "An example can be found in NCEP Reanalysis catalog:\n",
+    "\n",
+    "https://www.esrl.noaa.gov/psd/thredds/catalog/Datasets/ncep.reanalysis/surface/catalog.html\n",
+    "\n",
+    "The dataset is split into different files for each variable and year. For example, a single file download link for surface air temperature looks like:\n",
+    "\n",
+    "https://psl.noaa.gov/thredds/fileServer/Datasets/ncep.reanalysis/surface/air.sig995.1948.nc   \n",
+    " \n",
+    "The structure is `'https://psl.noaa.gov/thredds/fileServer/Datasets/ncep.reanalysis/surface/air.sig995.`' + `'YYYY'` + `'.nc'`\n",
+    "    \n",
+    "We want to download the surface temperature data from 1990 till 2000 and combine them all in a single xarray DataSet. To do so:\n",
+    "    \n",
+    "- Prepare all the links by composing the base_url ('http://www.esrl.noaa.gov/psd/thredds/dodsC/Datasets/ncep.reanalysis/surface/air.sig995') with the required years\n",
+    "- Use the list of file links as the inputfor the `xr.open_mfdataset` to create a single `xarray.DataSet`.\n",
+    "- Whereas this is 600MB of data, the initial loading is not actually reading in the data.\n",
+    "   \n",
+    "<details>\n",
+    "    \n",
+    "<summary>Hints</summary>\n",
+    "\n",
+    "* Python works with string formatting, e.g. f'{base_url}.{year}.nc' will nicely create the required links.\n",
+    "* Xarray can both work with file names on a computer as a compatible network link.\n",
+    "* As the netcdf data provided by NOAA is already well structured and confomr, no further adjustments are required as input to the \n",
+    "`open_mfdataset` function, :-)\n",
+    "\n",
+    "</details>    \n",
+    "    \n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2de8367c-644c-40eb-ad97-132183352142",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/14-combine-data1.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5d26bd7e-4dbb-4e11-b721-30de58b38e5f",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/14-combine-data2.py"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2ee0d4d0-1c5d-47f9-9b51-cc458cc8c221",
+   "metadata": {},
+   "source": [
+    "## (Optional) Online data Catalogs: STAC"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b93e187b-9702-4d3b-9135-810cbe10e5f6",
+   "metadata": {},
+   "source": [
+    "__Note__ _These dependencies are not included in the environment, to run this section, install the required packages first in your conda environment: `conda install stackstac pystac-client`._"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4acc3a9c-9f5c-4fea-9037-c61593d9cb8f",
+   "metadata": {},
+   "source": [
+    "Multiple initiatives do exist which publish data online and enable (lazy) loading of the data directly in xarray, such as [OpenDAP](https://www.opendap.org/) and [THREDDS](https://www.unidata.ucar.edu/software/tds/current/) which are well-known and used in the oceanographic and climate studies communities (see exercise). See for example the [ROMS Ocean Model Example](http://xarray.pydata.org/en/stable/examples/ROMS_ocean_model.html) tutorial of xarray. \n",
+    "\n",
+    "Another initiative that interacts well with xarray is the [SpatioTemporal Asset Catalogs](https://stacspec.org/) specification, which is increasingly used to publish remote sensing products.\n",
+    "\n",
+    "Let's explore some Sentinel data hosted on AWS (https://registry.opendata.aws/sentinel-2-l2a-cogs/):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "684349c7-1133-44a2-9164-2db4a0b5a91c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import stackstac\n",
+    "import pystac_client"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "83c6fdfd-03e0-4379-8dec-99a914969037",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lon, lat = -105.78, 35.79"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "21b7f65b-8c92-4b99-8298-979a28d6ad59",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "URL = \"https://earth-search.aws.element84.com/v1\"\n",
+    "catalog = pystac_client.Client.open(URL)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "eb94265d-3f79-4ca8-a8ef-d1082355f967",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results = catalog.search(\n",
+    "    intersects=dict(type=\"Point\", coordinates=[lon, lat]),\n",
+    "    collections=[\"sentinel-2-l2a\"],\n",
+    "    datetime=\"2020-04-01/2020-05-01\"\n",
+    ")\n",
+    "results.matched()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5c694b2a-6fc5-4501-927e-a3b725b08750",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "list(results.items())[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "be235744-2534-45c2-a1c6-b544ff42f00c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results.item_collection()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "51bcab24-e300-4392-ac0f-f5ed64273026",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stacked = stackstac.stack(results.item_collection(), epsg=32613)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c394c52d-c3bd-4fa3-a9eb-b2a5e6fdd646",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stacked"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "eea49d3d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stacked.sel(band=\"nir\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1640d68a-ecb7-4f21-b0b7-aea920ebdca6",
+   "metadata": {},
+   "source": [
+    "See also https://github.com/stac-utils/pystac-client and https://stackstac.readthedocs.io/en/latest/."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5fab0dbd-09cf-4eb0-be57-41a3b7d384cc",
+   "metadata": {},
+   "source": [
+    "__Acknowledgements__ Thanks to [@rabernat](https://rabernat.github.io/research_computing_2018/xarray-tips-and-tricks.html) for the example case of NCEP reanalysis data load and https://stackstac.readthedocs.io/en/latest/basic.html#Basic-example for the stackteac example."
+   ]
+  }
+ ],
+ "metadata": {
+  "jupytext": {
+   "default_lexer": "ipython3"
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.11"
+  },
+  "widgets": {
+   "application/vnd.jupyter.widget-state+json": {
+    "state": {},
+    "version_major": 2,
+    "version_minor": 0
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/notebooks/15-xarray-dask-big-data.ipynb b/notebooks/15-xarray-dask-big-data.ipynb
new file mode 100644
index 0000000..5cec5f1
--- /dev/null
+++ b/notebooks/15-xarray-dask-big-data.ipynb
@@ -0,0 +1,234 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<p><font size=\"6\"><b>Working with big data: xarray and dask (DEMO)</b></font></p>\n",
+    "\n",
+    "\n",
+    "> *DS Python for GIS and Geoscience*  \n",
+    "> *October, 2025*\n",
+    ">\n",
+    "> *© 2025, Joris Van den Bossche and Stijn Van Hoey. Licensed under [CC BY 4.0 Creative Commons](http://creativecommons.org/licenses/by/4.0/)*\n",
+    "\n",
+    "---\n",
+    "\n",
+    "Throughout the course, we worked with small, often simplified or subsampled data. In practice, the tools we have seen still work well with data that fit easily in memory. But also for data larger than memory (e.g. large or high resolution climate data), we can still use many of the familiar tools.\n",
+    "\n",
+    "This notebooks includes a brief showcase of using xarray with dask, a package to scale Python workflows (https://dask.org/). Dask integrates very well with xarray, providing a familiar xarray workflow for working with large datasets in parallel or on clusters."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from dask.distributed import Client, LocalCluster\n",
+    "client = Client(LocalCluster(processes=False))  # set up local cluster on your laptop\n",
+    "client"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The *Multi-Scale Ultra High Resolution (MUR) Sea Surface Temperature (SST)* dataset (https://registry.opendata.aws/mur/) provides freely available, global, gap-free, gridded, daily, 1 km data on sea surface temperate for the last 20 years. I downloaded a tiny part this dataset (8 days of 2020) to my local laptop, and stored a subset of the variables (only the \"sst\" itself) in the zarr format (https://zarr.readthedocs.io/en/stable/), so we can efficiently read it with xarray and dask:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import xarray as xr"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ds = xr.open_zarr(\"data/mur_sst_zarr/\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ds"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Looking at the actual sea surface temperature DataArray:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ds.analysed_sst"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The representation already indicated that this DataArray (although being a tiny part of the actual full dataset) is quite big: 20.7 GB if loaded fully into memory at once (which would not fit in the memory of my laptop).\n",
+    "\n",
+    "The xarray.DataArray is now backed by a dask array instead of a numpy array. This allows us to do computations on the large data in *chunked* way."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For example, let's compute the overall average temperature for the full globe for each timestep:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "overall_mean = ds.analysed_sst.mean(dim=(\"lon\", \"lat\"))\n",
+    "overall_mean"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This returned a lazy object, and not yet computed the actual average. Let's explicitly compute it:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%time \n",
+    "overall_mean.compute()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This takes some time, but it *did* run on my laptop even while the dataset did not fit in the memory of my laptop."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Integrating with hvplot and datashader, we can also still interactively plot and explore the large dataset:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import hvplot.xarray"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ds.analysed_sst.isel(time=-1).hvplot.quadmesh(\n",
+    "    'lon', 'lat', rasterize=True, dynamic=True,\n",
+    "    width=800, height=450, cmap='RdBu_r')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Zooming in on this figure we re-read and rasterize the subset we are viewing to provide a higher resolution image."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**As a summary**, using dask with xarray allows:\n",
+    "\n",
+    "- to use the familiar xarray workflows for larger data as well\n",
+    "- use the same code to work on our laptop or on a big cluster"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "\n",
+    "# PANGEO: A community platform for Big Data geoscience\n",
+    "\n",
+    "\n",
+    "<center><img src=\"https://pangeo.io/Pangeo-assets/pangeo_logo.png\" width=\"500px\"></center>\n",
+    "\n",
+    "Website: https://pangeo.io/\n",
+    "\n",
+    "They have a gallery with many interesting examples, many of them using this combination of xarray and dask.\n",
+    "\n",
+    "Pangeo focuses primarily on *cloud computing* (storing the big datasets in cloud-native file formats and also doing the computations in the cloud), but all the tools like xarray and dask developed by this community and shown in the examples also work on your laptop or university's cluster."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "jupytext": {
+   "default_lexer": "ipython3"
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.11"
+  },
+  "widgets": {
+   "application/vnd.jupyter.widget-state+json": {
+    "state": {},
+    "version_major": 2,
+    "version_minor": 0
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/notebooks/90_package_numpy.ipynb b/notebooks/90_package_numpy.ipynb
new file mode 100644
index 0000000..4cb5987
--- /dev/null
+++ b/notebooks/90_package_numpy.ipynb
@@ -0,0 +1,2207 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<p><font size=\"6\"><b>Numpy</b></font></p>\n",
+    "\n",
+    "\n",
+    "> *DS Python for GIS and Geoscience*  \n",
+    "> *September, 2024*\n",
+    ">\n",
+    "> *© 2024, Joris Van den Bossche and Stijn Van Hoey. Licensed under [CC BY 4.0 Creative Commons](http://creativecommons.org/licenses/by/4.0/)*\n",
+    "\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "from matplotlib.colors import ListedColormap\n",
+    "from matplotlib.lines import Line2D\n",
+    "\n",
+    "import xarray as xr\n",
+    "import rasterio\n",
+    "from rasterio.plot import plotting_extent, show"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Introduction"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "> __In this notebook, the usage of Numpy when working with spatial data is explained and some exercises from xarray are solved using Numpy directly.__\n",
+    "\n",
+    "On of the most fundamental parts of the scientific python 'ecosystem' is [numpy](https://numpy.org/). A lot of other packages - you use Pandas, GeoPandas and xarray in this course - are built on top of Numpy and the `ndarray`  (n-dimensional array) data type it provides."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's start from reading in a GeoTiff data set from file, this time a Sentinal Band 4 of the City of Ghent:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "xr_b4_data = xr.open_dataarray(\"./data/gent/raster/2020-09-17_Sentinel_2_L1C_B04.tiff\", \n",
+    "                               engine=\"rasterio\", mask_and_scale=False)\n",
+    "ax = xr_b4_data.plot(robust=True, figsize=(10, 4))\n",
+    "ax.axes.set_aspect('equal')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "When reading in data, xarray contains a Numpy `ndarray` inside the xarray DataArray:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "type(xr_b4_data), type(xr_b4_data.data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We'll continu with the Numpy ndarray (ignore the additional coordinate and metadata information from xarray):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "b4_data = xr_b4_data.data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "b4_data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Numpy supports different `dtype`s (`float`, `int`,...), but all elements of an array do have the same dtype. Note that NumPy auto-detects the data-type from the input."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "b4_data.dtype"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The data type of this specific array `b4_data.data` is 16bit unsigned integer. More information on the data types Numpy supports is available in the [documentation](https://numpy.org/devdocs/user/basics.types.html#array-types-and-conversions-between-types). Detailed info on data types is out of scope of this course, but remember that using 16bit unsigned integer, it can contain `2**16` different (all positive) integer values:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "2**16"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's check this by calculating the minimum and maximum value in the array:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "b4_data.min(), b4_data.max()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Converting to another data type is supported by `astype` method. When floats are preferred during calculation:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "b4_data.astype(float)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "b4_data.max()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Just as any other object in Python, the `ndarray` has a number of attributes. We already checkes the `dtype` attribute. The `shape` and `ndim` of the array are other relevant attribute:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "b4_data.shape, b4_data.ndim"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Hence, we have a single band with dimensions (317, 625) and data type `uint16`. Compare this to the metadata stored in the geotiff file:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#!gdalinfo ./data/gent/raster/2020-09-17_Sentinel_2_L1C_B04.tiff"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The metadata on the dimensions and the datatype correspond, but the spatial information is lost when we only store the Numpy array."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Numpy works very well together with the other fundamental scientific Python package [Matplotlib](https://matplotlib.org/). An useful plot function to know when working with raster data is `imshow`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "ax.imshow(b4_data.squeeze());"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "__Note:__ Numpy function `squeeze` used to get rid of the single-value dimension of the numpy array."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As the Numpy array does not contain any spatial information, the x and y axis labels are defined by the indices of the array. Remark that the xarray plot returned this plot with the coordinate information in the axis labels. \n",
+    "\n",
+    "With a small trick, the same result can be achieved with Matplotlib:\n",
+    "\n",
+    "1. When reading in a data set using xarray, get the spatial extent from the geographical boundaries stored in the xarray `rio` accessor and convert them to an extent:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "xr_b4_data = xr.open_dataarray(\"./data/gent/raster/2020-09-17_Sentinel_2_L1C_B04.tiff\", engine=\"rasterio\", mask_and_scale=False)\n",
+    "\n",
+    "(left, bottom, right, top) = xr_b4_data.rio.bounds()  # get boundary information\n",
+    "b4_data_extent = (left, right, bottom, top)   # convert boundaries to extent info\n",
+    "b4_data_extent"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "2. Add the `extent` argument to the `imshow` plot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "ax.imshow(b4_data.squeeze(), extent=b4_data_extent)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-info\" style=\"font-size:120%\">\n",
+    "\n",
+    "**REMEMBER**: <br>\n",
+    "\n",
+    "The [`numpy` package](https://numpy.org/) is the backbone of the scientific Python ecosystem. The `ndarray` provides an efficient data type to store and manipulate raster data, but it does NOT contain any spatial information.\n",
+    "    \n",
+    "Use the spatial `extent` trick to add coordinate information to imshow plot axis. Convert to the preferred datatype using `astype()` method.\n",
+    "\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Reshape, slice and index"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "b4_data.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We already used `squeeze` to remove the single-value dimension. We could also select the data we needed, similar to slicing in lists or Pandas DataFrames:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "b4_data[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "b4 = b4_data[0]\n",
+    "b4.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If you do not like the order of dimensions of the data, you can switch these using `transpose`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "b4.transpose(1, 0).shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Getting rid of the dimensions and flattening all values into a single 1-D array can be done using `flatten` method:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "b4.flatten().shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Flattening an arrya is useful to create a histogram with Matplotlib:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "plt.hist(b4.flatten(), bins=100);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# slice, subsample, reverse\n",
+    "# slice + assign\n",
+    "# fancy indexing\n",
+    "# fancy indexing + assign"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "b4 = b4_data[0]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Select a specific row/column:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "b4[10].shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "b4[:, -2:].shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Select every nth element in a given dimension:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "b4[100:200:10, :].shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Reversing an array:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "b4[:, ::-1].shape  # Note you can also np.flip an array"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "b4[0, :4]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "b4_rev = b4[:, ::-1]\n",
+    "b4_rev[0, -4:]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can also combine assignment and slicing:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "b4[0, :3] = 10\n",
+    "b4"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Use a __condition__ to select data, also called fancy indexing or boolean indexing:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "b4 < 1000"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Onle keep the data which are True for the given condition"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "b4[b4 < 1000]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Or combine assignment and fancy indexing, e.g. a reclassification of the raster data:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "b4[b4 < 5000] = 0  # assign the value 0 to all elements with a value lower than 5000"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "b4"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A powerfull shortcut to handle this kind or reclassifications is the `np.where` function:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "np.where(b4 < 5000, 10, b4)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-success\">\n",
+    "\n",
+    "**EXERCISE 1**:\n",
+    "\n",
+    "* Read in the file `./data/gent/raster/2020-09-17_Sentinel_2_L1C_True_color.tiff` with xarray, extract the data as a Numpy ndarray and assign the data to a new variable `tc_data`.  \n",
+    "* Select only the *second* layer of `tc_data` and assign the output to a new variable `tc_g`.\n",
+    "* Assign to each of the elements in the `tc_g` array with a value above 15000 the new value 65535.\n",
+    "    \n",
+    "<details><summary>Hints</summary>\n",
+    "\n",
+    "* You can combine the assignment of new values together with fancy indexing of a numpy array.\n",
+    "* Python (and also Numpy) uses 0 as the first-element index\n",
+    "\n",
+    "</details>\n",
+    "    \n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/90_package_numpy1.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/90_package_numpy2.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/90_package_numpy3.py"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-success\">\n",
+    "\n",
+    "**EXERCISE 2**:\n",
+    "\n",
+    "Subsample the ndarray `tc_data` by taking only the one out of each 5 data points for all layers at the same time (Be aware that this is a naive resampling implementation for educational purposes only). \n",
+    "    \n",
+    "<details><summary>Hints</summary>\n",
+    "\n",
+    "* The result should still be a 3-D array with 3 elements in the first dimension.\n",
+    "\n",
+    "</details>   \n",
+    "    \n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/90_package_numpy4.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/90_package_numpy5.py"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-success\">\n",
+    "\n",
+    "**EXERCISE 3**:\n",
+    "\n",
+    "Elements with the value `65535` do represent 'Not a Number' (NaN) values. However, Numpy does not support NaN values for integer data, so we'll convert to float first as data type. After reading in the data set `./data/gent/raster/2020-09-17_Sentinel_2_L1C_B04.tiff` (assign data to variable `b4_data`):\n",
+    "    \n",
+    "* Count the number of elements that are equal to `65535`\n",
+    "* Convert the data type to `float`, assign the result to  a new variable `b4_data_f`\n",
+    "* Assign Nan (`np.nan`) value to each of the elements of `b4_data_f` equal to `65535`\n",
+    "* Count the number of Nan values in the `b4_data_f` data\n",
+    "* Make a histogram of both the `b4_data` and `b4_data_f` data. Can you spot the difference?\n",
+    "    \n",
+    "    \n",
+    "<details><summary>Hints</summary>\n",
+    "\n",
+    "* `np.nan` represents _Not a Number (NaN)_ in Numpy. You can assign an element to it, e.g. `dummy[2] = np.nan`\n",
+    "* `np.sum` will by default sum all of the elements of the input array and can also count boolean values (True = 1 and False = 0), resulting from a conditional expression. \n",
+    "* To test if a value is a nan, Numpy provides `np.isnan(...)` which results in an element-wise check returning boolean values.\n",
+    "* Check the help of the `plt.hist` command to find out more about the `bins` and the `log` arguments.\n",
+    "\n",
+    "</details>    \n",
+    "    \n",
+    "   \n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/90_package_numpy6.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/90_package_numpy7.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/90_package_numpy8.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/90_package_numpy9.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/90_package_numpy10.py"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Reductions, element-wise calculations and broadcasting"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Up until now, we worked with the 16bit integer values. For specific applications we might want to rescale this data. A (fake) example is the linear transformation to the range 0-1 after log conversion of the data. To do so, we need to calculate _for each element_ in the original $b$ array the following:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "$$x_i= \\log(b_i)$$\n",
+    "$$z_i=\\frac{x_i-\\min(x)}{\\max(x)-\\min(x)}$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "__1. reductions__\n",
+    "\n",
+    "As part of it, we need the minimum `min(x)` and the maximum `max(x)` of the array. These __reductions__ (aggregations) are provided by Numpy and can be applied along one or more of the data dimensions, called the __axis__:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "dummy = np.arange(1, 10).reshape(3, 3)\n",
+    "dummy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "np.min(dummy), np.min(dummy, axis=0), np.min(dummy, axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "dummy = np.arange(1, 25).reshape(2, 3, 4)\n",
+    "dummy.shape, dummy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "np.min(dummy), np.min(dummy, axis=0), np.min(dummy, axis=(0, 1)), np.min(dummy, axis=(0, 2))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In some applications, the usage of the `keepdims=True` is useful to keep the number of dimensions after reduction:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "np.min(dummy, axis=(0, 2), keepdims=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "When working with Nan values, the result will be Nan as well:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "np.min(np.array([1., 2., np.nan]))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Use the `nanmin`, `nan...` version of the function instead, if available:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "np.nanmin(np.array([1., 2., np.nan]))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "__2. Element-wise__"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The __for each element__ is crucial for Numpy. The typical answer in programming would be a `for`-loop, but Numpy is optimized to do these calculations __element-wise__ (i.e. for all elements together):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "dummy = np.arange(1, 10)\n",
+    "dummy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "dummy*10"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Instead of:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "[el*20 for el in dummy]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Numpy provides most of the familiar arithmetic operators to apply on an element-by-element basis:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "np.exp(dummy), np.sin(dummy), dummy**2, np.log(dummy)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For some function, you can either use the `np.min(my_array)` or the `my_array.min()` approach:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "dummy.min() == np.min(dummy)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "__3. Broadcasting__"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "When we combine arrays with different shapes during arithmetic operations, Numpy applies a set of __broadcoasting__ rules and the smaller array is _broadcast_ across the larger array so that they have compatible shapes. An important consequence for out application is:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "np.array([1, 2, 3]) + 4. , np.array([1, 2, 3]) + np.array([4.]), np.array([1, 2, 3]) + np.array([4., 4., 4.])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The smallest array is broadcasted to make both compatible. It starts with the trailing (i.e. rightmost) dimensions. Exploring all the rules are out of scope in this lesson and are well explained in the [broadcasting Numpy documentation](https://numpy.org/devdocs/user/basics.broadcasting.html#general-broadcasting-rules)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "__Back to our function__"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "By combining these three elements, we know enough to translate our conversion into Numpy code on the example data set:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "xr_b4_data = xr.open_dataarray(\"./data/gent/raster/2020-09-17_Sentinel_2_L1C_B04.tiff\", \n",
+    "                               engine=\"rasterio\", mask_and_scale=False)\n",
+    "b4_data = xr_b4_data.data\n",
+    "\n",
+    "b4_data = b4_data.squeeze().astype(float)    # squeeze and convert to float\n",
+    "b4_data[b4_data == 0.0] = 0.00001  # to overcome zero-division error"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Take the log of al the values __element-wise__:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "b4_data_log = np.log(b4_data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Get the min and max __reductions__:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "b4_min, b4_max = b4_data_log.min(), b4_data_log.max()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "__Broadcast__ our single value `b4_min` and `b4_max` to all elements of `b4_data_log`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "b4_rescaled = ((b4_data_log - b4_min)/(b4_max - b4_min))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "plt.hist(b4_rescaled.flatten(), bins=100);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "__Remark 1:__ One-dimensional linear interpolation towards a new value range can be calculated using the `np.interp` function as well. For the range 0 -> 1: \n",
+    "\n",
+    "```\n",
+    "np.interp(b4_data, (b4_data.min(), b4_data.max()), (0, 1))\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "__Remark 2: Why not iterate over the values of a list?__"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's use the  rescaling example to compare the calculation with Numpy versus a list comprehension (for-loop in Python):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "b4_min, b4_max = b4_data.min(), b4_data.max()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "With Numpy:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "%%time\n",
+    "rescaled_values_1 = ((b4_data - b4_min)/(b4_max - b4_min))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Using a list with a for loop:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "b4_as_list = b4_data.flatten().tolist()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "%%time\n",
+    "rescaled_values_2 = [((data_point - b4_min)/(b4_max - b4_min)) for data_point in b4_as_list]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "np.allclose(rescaled_values_1.flatten(), rescaled_values_2)  # np.allclose also works element wise"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-info\" style=\"font-size:120%\">\n",
+    "\n",
+    "**REMEMBER**: <br>\n",
+    "\n",
+    "The combination of element-wise calculations, efficient reductions and broadcasting provides Numpy a lot of power. In general, it is a good advice to __avoid for loops__ when working with Numpy arrays.\n",
+    "\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Let's practice!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-success\">\n",
+    "\n",
+    "**EXERCISE 4**:\n",
+    "\n",
+    "The data set `./data/herstappe/raster/2020-09-17_Sentinel_2_L1C_True_color.tiff` (assign to variable `herstappe_data`) contains 3 bands. The `imshow` function of Matplotlib can plot 3-D (RGB) data sets, but when running `plt.imshow(herstappe_data)`, we got the following error:\n",
+    "    \n",
+    "    ```\n",
+    "    ...\n",
+    "    TypeError: Invalid shape (3, 227, 447) for image data\n",
+    "    ```\n",
+    "\n",
+    "- Check in the help op `plt.imshow` why the `herstappe_data` can not be plot as such\n",
+    "- Adjust the data to fix the behavior of `plt.imshow(herstappe_data)`\n",
+    "    \n",
+    "Next, plot a greyscale version of the data as well. Instead of using a custom function just rely on the sum of the 3 bands as a proxy.\n",
+    "    \n",
+    "<details><summary>Hints</summary>\n",
+    "\n",
+    "* In a Jupyter Notebook, us the SHIFT-TAB combination when the cursor is on the `imshow` function or type in a new cell `?plt.imshow` to see the documentation of a function.\n",
+    "* The `imshow` function requires the different color bands as last dimension, so we will need to transpose the image array.\n",
+    "* Add the extent to see the coordinates in the axis labels.\n",
+    "* A greyscale image requires a greyscale `cmap`, checkt he available names in [the documentation online](https://matplotlib.org/tutorials/colors/colormaps.html)\n",
+    "\n",
+    "</details>    \n",
+    "    \n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/90_package_numpy11.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/90_package_numpy12.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/90_package_numpy13.py"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-success\">\n",
+    "\n",
+    "**EXERCISE 5**:\n",
+    "\n",
+    "The data set `./data/herstappe/raster/2020-09-17_Sentinel_2_L1C_True_color.tiff` (assign to variable `herstappe_data`) has values ranging in between 0.11325, 0.8575. To improve the quality of the visualization, stretch __each of the layers individually__ to the values to the range 0. to 1. with a linear transformation: \n",
+    "    \n",
+    "$$z_i=\\frac{x_i-\\min(x)}{\\max(x)-\\min(x)}$$\n",
+    "\n",
+    "Make a plot of the end result and compare with the plots of the previous exercise. \n",
+    "   \n",
+    "<details><summary>Hints</summary>\n",
+    "\n",
+    "* Keep into account that the data set is 3-dimensional. Have a look at the optional arguments for the reduction/aggregation functions in terms of `axis` and `keepdims`. \n",
+    "* You need the minimal/maximal value over 2 axis to end up with a min/max for each of the layers.\n",
+    "* Broadcasting starts comparison of the alignment on the last dimension.\n",
+    "\n",
+    "</details>    \n",
+    "    \n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "clear_cell": false
+   },
+   "outputs": [],
+   "source": [
+    "xr_herstappe_data = xr.open_dataarray(\"./data/herstappe/raster/2020-09-17_Sentinel_2_L1C_True_color.tiff\", \n",
+    "                                      engine=\"rasterio\", mask_and_scale=False)\n",
+    "herstappe_data = xr_herstappe_data.data\n",
+    "\n",
+    "# Get extent\n",
+    "(left, bottom, right, top) = xr_herstappe_data.rio.bounds()\n",
+    "herstappe_extent = (left, right, bottom, top)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/90_package_numpy14.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/90_package_numpy15.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/90_package_numpy16.py"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-success\">\n",
+    "\n",
+    "**EXERCISE 6**:\n",
+    "    \n",
+    "You want to reclassify the values of the 4th band data to a fixed set of classes:\n",
+    "    \n",
+    "* x < 0.05 need to be 10\n",
+    "* 0.05 < x < 0.1 need to be 20\n",
+    "* x > 0.1 need to be 30\n",
+    "       \n",
+    "Use the data set `./data/gent/raster/2020-09-17_Sentinel_2_L1C_B04.tiff` (assign data to variable `b4_data`):\n",
+    "    \n",
+    "* Read the data set and exclude the single-value dimension to end up with a 2D array. \n",
+    "* Convert to float data type. and normalize the values to the range [0., 1.].\n",
+    "* Create a new variable `b4_data_classified` with the same shape as `b4_data` but datatype int.\n",
+    "* Assign the new values (10, 20, 30) to the elements for which each of the conditions apply. \n",
+    "* Make a image plot of the reclassified variable `b4_data_classified`.\n",
+    "    \n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "clear_cell": false
+   },
+   "outputs": [],
+   "source": [
+    "xr_b4_data = xr.open_dataarray(\"./data/gent/raster/2020-09-17_Sentinel_2_L1C_B04.tiff\", \n",
+    "                               engine=\"rasterio\", mask_and_scale=False)\n",
+    "b4_data = xr_b4_data.data\n",
+    "\n",
+    "# Get extent\n",
+    "(left, bottom, right, top) = xr_b4_data.rio.bounds()\n",
+    "b4_data_extent = (left, right, bottom, top)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/90_package_numpy17.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/90_package_numpy18.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/90_package_numpy19.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/90_package_numpy20.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/90_package_numpy21.py"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-success\">\n",
+    "\n",
+    "**EXERCISE 7**:\n",
+    "\n",
+    "The data sets `./data/gent/raster/2020-09-17_Sentinel_2_L1C_B04.tiff` and `./data/gent/raster/2020-09-17_Sentinel_2_L1C_B08.tiff` contain respectively the 4th and the 8th band of a sentinel satellite image. To derive the [Normalized Difference Vegetation Index) (NDVI)](https://nl.wikipedia.org/wiki/Normalized_Difference_Vegetation_Index), the two bands need to be combined as follows:\n",
+    "    \n",
+    "$$\\frac{band_8 - band_4}{band_8 + band_4} $$\n",
+    "    \n",
+    "Process the images and create a plot of the NDVI:\n",
+    "    \n",
+    "- Read both data sets using Rasterio and store them in resp. `b4_data` and `b8_data`. \n",
+    "- Combine both data sets using the `np.vstack` function and assign it to the variable `b48_bands`\n",
+    "- Transform the data range of each of the layers to the range .0 - 1.\n",
+    "- For the values equal to zero in the `b48_bands` data set, assign a new (very small) value 1e-6\n",
+    "- Calculate the NDVI\n",
+    "- Plot the NDVI and select an appropriate colormap.\n",
+    "    \n",
+    "<details><summary>Hints</summary>\n",
+    "\n",
+    "* For more specific adjustments to the colormap, have a check on the [Matplotlib documentation on colormap normalization](https://matplotlib.org/3.3.2/tutorials/colors/colormapnorms.html)\n",
+    "\n",
+    "</details>   \n",
+    "           \n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/90_package_numpy22.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/90_package_numpy23.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "b48_bands.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/90_package_numpy24.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/90_package_numpy25.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/90_package_numpy26.py"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "clear_cell": false
+   },
+   "source": [
+    "Using a Matplotlib norm to adjust colormap influence on image https://matplotlib.org/api/_as_gen/matplotlib.colors.TwoSlopeNorm.html"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/90_package_numpy27.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/90_package_numpy28.py"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "\n",
+    "## For the curious: Some more building blocks"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Numpy provides lower-level building blocks used by other packages and you will once in a also need to rely on these functions to do some custom implementation. Some other useful building blocks with repect to reclassification could potentially help you:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "- Remember the `np.where` function?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "dummy = np.arange(1, 10).reshape(3, 3)\n",
+    "dummy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "np.where(dummy > 4, 0, dummy)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "- Clip the values in yanour array to defined limits can be done using `np.clip`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "dummy = np.arange(1, 10).reshape(3, 3)\n",
+    "dummy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "np.clip(dummy, 2, 6)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "- Numpy provides also a `np.histogram` function, which is really useful to get the bincounts over a custom bin-set:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "np.histogram(b4_data_classified, bins=[5, 15, 25, 35])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "np.histogram(b4_data, bins=[0.001, 0.1, 0.2, 0.5])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "- The `np.digitize` function return the indices of the bins to which each value in input array belongs. As such, it can be used to select and manipulate values containing to a specific bin:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "dummy = np.arange(9).reshape(3, 3)\n",
+    "np.random.shuffle(dummy)\n",
+    "dummy"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Define the bin to which each of the values belong to, using the bins x<2, 2<=x<4, x>=4:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "id_mask = np.digitize(dummy, bins=[2, 4])\n",
+    "id_mask"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "dummy[id_mask == 1] = 20\n",
+    "dummy"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Besides, it is also a practical method to create discrete classified maps:\n",
+    "\n",
+    "  1. Apply digitize to create classes:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ndvi_class_bins = [-np.inf, 0, 0.3, np.inf]  # These limits are for demo purposes only \n",
+    "ndvi_landsat_class = np.digitize(ndvi, ndvi_class_bins)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "  2. Define custom colors and names:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "nbr_colors = [\"gray\", \"yellowgreen\", \"g\"]\n",
+    "ndvi_names = [\"No Vegetation\", \"Bare Area\", \"Vegetation\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "  3. Prepare Matplotlib elements:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "nbr_cmap = ListedColormap(nbr_colors)\n",
+    "# fake entries required for each class to create the legend\n",
+    "dummy_data = [Line2D([0], [0], color=color, lw=4) for color in nbr_colors]  "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "  4. Make the plot and add a legend:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(12, 12))\n",
+    "im = ax.imshow(ndvi_landsat_class, cmap=nbr_cmap, extent=b4_data_extent)\n",
+    "ax.legend(dummy_data, ndvi_names, loc='upper left', framealpha=1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "- Find the modal (most common) value in an array is not provided by Numpy itself, but is available in the Scipy package:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from scipy.stats import mode"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "mode(b4_data.flatten()), mode(b4_data_classified.flatten())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Side-note on convolution"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In case you need custom convolutions for your 2D array, check the `scipy.signal.convolve` function as the Numpy function only works for 1-D arrays."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from scipy import signal"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "xr_b4_data = xr.open_dataarray(\"./data/gent/raster/2020-09-17_Sentinel_2_L1C_B04.tiff\", \n",
+    "                               engine=\"rasterio\", mask_and_scale=False)\n",
+    "b4_data = xr_b4_data.data\n",
+    "\n",
+    "# Get extent\n",
+    "(left, bottom, right, top) = xr_b4_data.rio.bounds()\n",
+    "b4_data_extent = (left, right, bottom, top)\n",
+    "\n",
+    "b4_data = b4_data.squeeze().astype(float)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As an example, apply a low pass filter example as window, smoothing the image:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "window = np.ones((5, 5), dtype=int)\n",
+    "window[1:-1, 1:-1] = 4\n",
+    "window[2, 2] = 12\n",
+    "window"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "grad = signal.convolve(b4_data, window, mode='same')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "fig, (ax0, ax1) = plt.subplots(1, 2, figsize=(16, 6))\n",
+    "ax0.imshow(b4_data, extent=b4_data_extent)\n",
+    "ax1.imshow(grad, extent=b4_data_extent)"
+   ]
+  }
+ ],
+ "metadata": {
+  "celltoolbar": "Nbtutor - export exercises",
+  "jupytext": {
+   "cell_metadata_filter": "-run_control,-deletable,-editable,-jupyter,-slideshow",
+   "notebook_metadata_filter": "-jupytext.cell_metadata_filter,-jupytext.notebook_metadata_filter"
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/notebooks/91_package_rasterio.ipynb b/notebooks/91_package_rasterio.ipynb
new file mode 100644
index 0000000..641272e
--- /dev/null
+++ b/notebooks/91_package_rasterio.ipynb
@@ -0,0 +1,1162 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<p><font size=\"6\"><b>Rasterio</b></font></p>\n",
+    "\n",
+    "\n",
+    "> *DS Python for GIS and Geoscience*  \n",
+    "> *September, 2024*\n",
+    ">\n",
+    "> *© 2024, Joris Van den Bossche and Stijn Van Hoey. Licensed under [CC BY 4.0 Creative Commons](http://creativecommons.org/licenses/by/4.0/)*\n",
+    "\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "> __In this notebook, the tupical workflow when doing operations with Rasterio to handle spatial data is explained .__\n",
+    "\n",
+    "Both when working with Numpy as in xarray, [Rasterio](https://rasterio.readthedocs.io/en/latest/) provides an interface to read the data from disk. \n",
+    "\n",
+    "Whereas Numpy is a powerful package for calculation, it does not provide any spatial information so lacking support for reprojection, warping,... Apart from reading in a broad set of GIS raster formats, Rasterio also provides some of these GIS raster operations."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "import geopandas\n",
+    "\n",
+    "import rasterio\n",
+    "from rasterio.plot import plotting_extent, show, show_hist, reshape_as_image"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's recap with what we did so far with Rasterio:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_file = \"./data/herstappe/raster/2020-09-17_Sentinel_2_L1C_True_color.tiff\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "xarray provides the `open_dataset` and `open_dataarray` functions to read data. When reading with `engine=\"rasterio\"`, the underlying rasterio method is used under the hood. It is also possible to use the Rasterio read data methods as well:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with rasterio.open(data_file) as src:    \n",
+    "    herstappe_data = src.read([1, 2, 3], out_dtype=float, masked=False) # Note the float as output\n",
+    "    herstappe_profile = src.profile\n",
+    "    herstappe_ext = plotting_extent(src)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "herstappe_data.shape, herstappe_data.dtype"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Convenient plot functions for data exploration"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Rasterio also provides dedicated plot functions `show` and `show_hist` for quick data exploration:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "with rasterio.open(data_file) as src:\n",
+    "    \n",
+    "    # convenience plot functions from rasterio\n",
+    "    fig, (axr, axg, axb) = plt.subplots(1,3, figsize=(20, 10))\n",
+    "    show((src, 1), ax=axr, cmap='Reds', title='Red channel')\n",
+    "    show((src, 2), ax=axg, cmap='Greens', title='Green channel')\n",
+    "    show((src, 3), ax=axb, cmap='Blues', title='Blue channel')\n",
+    "    \n",
+    "    fig, ax = plt.subplots(1, 1, figsize=(10, 5))\n",
+    "    show_hist(src, bins=50, lw=0.0, stacked=False, \n",
+    "              alpha=0.3, histtype='stepfilled', title=\"Histogram\",\n",
+    "              label=[1, 2, 3])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "__Note:__ Rasterio directly interacts with Matplotlib objects, so adjustments starting from the figure can be done using the Matplotlib syntax. For example, change the title and the legend labels:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "_, current_labels = ax.get_legend_handles_labels()\n",
+    "current_labels"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "ax.set_title(\"Histogram of RGB bands of Herstappe\")\n",
+    "ax.legend([\"Blue channel\", \"Green channel\", \"Blue channel\"])\n",
+    "fig"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "__Reminder on opening and Closing File Connections__\n",
+    "\n",
+    "The Rasterio library is efficient as it establishes a connection with the raster file rather than directly reading it into memory. Because it creates a connection, it is important that you close the connection after it is opened AND after you’ve finished working with the data! That is why we use the context manager to work with raster data in rasterio. This will handle opening and closing the raster file for you."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "__Utility functions for reshaping__"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#plt.imshow(herstappe_data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Python image processing software packages and Matplotlib organize arrays differently than Rasterio. The interpretation of a 3-dimension array read from rasterio is:\n",
+    "\n",
+    "`(bands, rows, columns)`\n",
+    "\n",
+    "while Matplotlib and image processing software like scikit-image, pillow are generally ordered:\n",
+    "\n",
+    "`(rows, columns, bands)`\n",
+    "\n",
+    "We can rely on Numpy to transpose the data :"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "plt.imshow(herstappe_data.transpose(1, 2, 0))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "But Rasterio also provides the utility functions `reshape_as_image` and `reshape_as_raster` to support the conversion:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from rasterio.plot import reshape_as_image, reshape_as_raster"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "plt.imshow(reshape_as_image(herstappe_data))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Extract the data you need"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In many applications, a specific research area is used. Extracting the data you need from a given raster data set by a vector (polygon) file is a common operation in GIS analysis. We use the clipping example to explain the typical workflow in Rasterio.\n",
+    "\n",
+    "For our Herstappe example, the study area is available as vector data `./data/herstappe/vector/herstappe.geojson`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "herstappe_vect = geopandas.read_file(\"./data/herstappe/vector/herstappe.geojson\")\n",
+    "herstappe_vect"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "herstappe_vect.plot()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# herstappe_vect.crs  # vs herstappe_profile[\"crs\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Make sure both data sets are defined in the same CRS and extracting the geometry can be used as input for the masking:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "herstappe_vect = herstappe_vect.to_crs(epsg=3857)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[Masking a raster](https://rasterio.readthedocs.io/en/latest/quickstart.html#dataset-georeferencing) with a geometry in Rasterio is supported by the `rasterio.mask` module:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import rasterio.mask"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#?rasterio.mask.mask"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The `mask` function by default just masks the image, but it can also be used to `crop` the data by setting the `crop` argument to True:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As the required input for the `shape` to mask with, the documentation defines: \n",
+    "    \n",
+    "    The values must be a GeoJSON-like dict or an object that implements\n",
+    "    the Python geo interface protocol (such as a Shapely Polygon).\n",
+    "    \n",
+    "We learned earlier that GeoPandas relies on Shapely as spatial entities:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "type(herstappe_vect.geometry), type(herstappe_vect.geometry[0])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can do the data extraction (masking) within the context manager:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_file = \"./data/herstappe/raster/2020-09-17_Sentinel_2_L1C_True_color.tiff\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with rasterio.open(data_file) as src:\n",
+    "    out_image, out_transform = rasterio.mask.mask(src, herstappe_vect.geometry, crop=True)\n",
+    "    herstappe_profile = src.profile\n",
+    "    herstappe_ext = plotting_extent(src)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "type(out_image)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can work with `out_image` as a numpy array, but we are lacking the spatial information by doing so, which makes it hard to combine it with other spatial data sets for further analysis:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "plt.imshow(reshape_as_image(out_image)) # extent ?!? extent=herstappe_ext would not be correct."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Remember the `meta` and the `profile` attributes of the Rasterio object? The latter contains the spatial information and the settings for the GeoTiff file:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "herstappe_profile"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "out_image.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To contain the spatial information or save the file for later usage keeping the spatial information, make sure to __update the profile metadata__ according to the transformations you did. In this case:\n",
+    "\n",
+    "- width/height of the cropped image\n",
+    "- affine transform function"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "herstappe_profile.update({\"height\": out_image.shape[1],\n",
+    "                          \"width\": out_image.shape[2],\n",
+    "                          \"transform\": out_transform})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "herstappe_profile"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Just as before, we can use the `plotting_extent` function to extract the extent of our data, using the outputs of the masking:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "new_extent = plotting_extent(out_image[0], out_transform)  # see docstring of plotting_extent on [0] requirement"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "plt.imshow(reshape_as_image(out_image), extent=new_extent)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The updated profile information also allows us to store the output as a Geotiff file:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with rasterio.open(\"./test.tiff\", \"w\", **herstappe_profile) as dest:\n",
+    "    dest.write(out_image)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's read the data we just saved to a file:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "with rasterio.open(\"./test.tiff\") as clipped:\n",
+    "    fig, ax = plt.subplots(figsize=(12, 5))\n",
+    "    show(clipped, (1, 2, 3), cmap='Greys')\n",
+    "\n",
+    "    clipped_ext = plotting_extent(clipped)\n",
+    "    clipped_array = clipped.read([1, 2, 3], \n",
+    "                                 out_dtype=float, masked=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Quick check if the extent in both situations are the same:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "new_extent == clipped_ext"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Rasterio workflow"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The workflow applied to clip the data set is typical when working with Rasterio. It consists of the following main steps:\n",
+    "\n",
+    "```\n",
+    "# 1\n",
+    "with rasterio.open(data_file) as src:  \n",
+    "\n",
+    "    # 2\n",
+    "    out_image, out_transform = rasterio.mask.mask(src, \n",
+    "        herstappe_vect.geometry, crop=True)    \n",
+    "    \n",
+    "    # 3\n",
+    "    herstappe_profile = src.profile\n",
+    "    herstappe_profile.update({\"height\": out_image.shape[1],\n",
+    "                              \"width\": out_image.shape[2],\n",
+    "                              \"transform\": out_transform})  \n",
+    "    # 4                              \n",
+    "    with rasterio.open(\"./test.tiff\", \"w\", **herstappe_profile) as dest: \n",
+    "        dest.write(out_image)\n",
+    "```\n",
+    "\n",
+    "- 1. Read in a data set using the context manager\n",
+    "- 2. Read and transform the data set by clipping, resampling,...\n",
+    "- 3. Update the spatial metadata/profile of the data set\n",
+    "- 4. Save the new data set with the updated metadata/profile"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Similar operations are:\n",
+    "\n",
+    "- Reprojecting a data set, see https://rasterio.readthedocs.io/en/latest/topics/reproject.html\n",
+    "- Resampling a data set, see https://rasterio.readthedocs.io/en/latest/topics/resampling.html\n",
+    "- Create a raster mosaic, see https://rasterio.readthedocs.io/en/latest/api/rasterio.merge.html and https://automating-gis-processes.github.io/CSC/notebooks/L5/raster-mosaic.html"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's apply this workflow to resample our data set with a factor 2, i.e. doubling the pixel size:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_file = \"./data/herstappe/raster/2020-09-17_Sentinel_2_L1C_True_color.tiff\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import rasterio\n",
+    "from rasterio.enums import Resampling\n",
+    "\n",
+    "scaling_factor = 2 \n",
+    "\n",
+    "with rasterio.open(data_file) as src:  # 1\n",
+    "\n",
+    "    # resample data to target shape  # 2\n",
+    "    new_width = int(src.width / scaling_factor)\n",
+    "    new_height = int(src.height / scaling_factor)\n",
+    "    out_image = src.read(\n",
+    "        out_shape=(src.count, new_height, new_width),\n",
+    "        resampling=Resampling.bilinear\n",
+    "    )\n",
+    "\n",
+    "    # scale image transform and update metadata  # 3\n",
+    "    data_profile = src.profile\n",
+    "    out_transform = src.transform * src.transform.scale(scaling_factor)\n",
+    "    data_profile.update({\"height\": new_height,\n",
+    "                         \"width\": new_width,\n",
+    "                         \"transform\": out_transform})      \n",
+    "\n",
+    "    # save the output to disk # 4\n",
+    "    with rasterio.open(\"./test_resample.tiff\", \"w\", **data_profile) as dst:\n",
+    "        dst.write(out_image)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "__Note:__ Other resampling algorithms are available in the `Resampling` module of rasterio."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#!gdalinfo ./data/herstappe/raster/2020-09-17_Sentinel_2_L1C_True_color.tiff\n",
+    "#!gdalinfo ./test_resample.tiff"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-info\" style=\"font-size:120%\">\n",
+    "\n",
+    "**REMEMBER**: <br>\n",
+    "\n",
+    "Many of the typical GIS operations provided by Rasterio have a common workflow. In order to preserve the spatial information, make sure to update the attributes such as transform, width and height accordingly before saving the file.\n",
+    "\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-success\">\n",
+    "\n",
+    "**EXERCISE 1**:\n",
+    "\n",
+    "You received remote sensing data from Ghent. You're wondering if the remote sensing image covers the entire administrative area of Ghent and decide to make a plot to make the comparison. The vector file of Ghent is available as well, see `./data/herstappe/vector/herstappe.geojson`.\n",
+    "    \n",
+    "Make a plot of `gent_data` (band 4 `./data/gent/raster/2020-09-17_Sentinel_2_L1C_B04.tiff`) together with `gent_vect` (read from `./data/herstappe/vector/herstappe.geojson`) using Matplotlib.\n",
+    "    \n",
+    "    \n",
+    "<details><summary>Hints</summary>\n",
+    "\n",
+    "* Make sure to align the CRS of both the vector as the raster data set.\n",
+    "* Remember the `extent` trick to use the coordinates as Matplotlib axis?\n",
+    "* The Matplotlib `plot` function provides `color`, `edgecolor`,... to adjust the style of the plot.\n",
+    "    \n",
+    "</details>\n",
+    "    \n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/91_package_rasterio1.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/91_package_rasterio2.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/91_package_rasterio3.py"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-success\">\n",
+    "\n",
+    "**EXERCISE 2**:\n",
+    "\n",
+    "The files `./data/gent/raster/2020-09-17_Sentinel_2_L1C_B04.tiff` and `./data/gent/raster/2020-09-17_Sentinel_2_L1C_B08.tiff` are respectively band 4 and 8. Combine them in a single Geotiff file:\n",
+    "    \n",
+    "- Read in each of the data sets with rasterio\n",
+    "- Stack the two data sets with Numpy\n",
+    "- Write the resulting `(2, 317, 625)` array to a new geotiff file `./B0408.tiff` with data type `uint32`\n",
+    "    \n",
+    "    \n",
+    "<details><summary>Hints</summary>\n",
+    "\n",
+    "* The workflow is similar to before: you want to read, update the metadata/profile and write the output\n",
+    "* Stacking arrays in Numpy is done by `np.vstack`, converting the dtype by `astype(...)`\n",
+    "* The metadata/profile to update is not the transform, neither the widht/height. Check the `count` and the `dtype` keys of the profile\n",
+    "    \n",
+    "</details>\n",
+    "    \n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/91_package_rasterio4.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/91_package_rasterio5.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/91_package_rasterio6.py"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-success\">\n",
+    "    \n",
+    "**ADVANCED EXERCISE 3**:    \n",
+    "\n",
+    "You received multiple remote sensing layers of Ghent that need to be resampled. Instead of copy pasting the same code over and over again, you decide to write your own function to resample a file with Rasterio. The inputs of your function are:\n",
+    "    \n",
+    "- input_file : reference to a file on disk\n",
+    "- output_file :  where t write the output \n",
+    "- resampling_method : method from rasterio.Resampling to use when resampling\n",
+    "- scaling_factor : factor to which the resampling should be applied, default is 2\n",
+    "    \n",
+    "Apply the function on each of the Geotiff files in the folder `./data/gent/raster` (make sure to not overwrite the original files!)\n",
+    "    \n",
+    "<details><summary>Hints</summary>\n",
+    "\n",
+    "* Start from the code from the example\n",
+    "* Try to make the function as reusable as possible\n",
+    "* Make sure to document your own function using [numpy docstring](https://numpydoc.readthedocs.io/en/latest/format.html)    \n",
+    "* The [pathlib](https://docs.python.org/3/library/pathlib.html) module is very powerful for working with file systems. Use the `glob` method to find all files with a given extension.\n",
+    "    \n",
+    "</details>\n",
+    "    \n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/91_package_rasterio7.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/91_package_rasterio8.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/91_package_rasterio9.py"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-info\" style=\"font-size:120%\">\n",
+    "\n",
+    "**REMEMBER**: <br>\n",
+    "\n",
+    "When you need to do a particular operation a lot on different files, convert the functionality to a function that you can reuse!\n",
+    "\n",
+    "If you are familiar to the command line, using the power of [GDAL](https://gdal.org/index.html) is a valid alternative to programming these conversions with Python/Rasterio. Have a look at [gdalwarp](https://gdal.org/programs/gdalwarp.html) in particular. If interested, the [Using GDAL for preprocessing](https://ocw.un-ihe.org/course/view.php?id=11&section=3) course provides a good introduction to GDAL.\n",
+    "\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## BONUS: only download what you need\n",
+    "\n",
+    "Rasterio only reads the data from disk that is requested to overcome loading entire data sets into memory. The same applies to downloading data, overcoming entire downloads when only a fraction is required (when the online resource supports this). An example is https://zenodo.org/record/2654620, which is available as [Cloud Optimized Geotiff (COG)](https://www.cogeo.org/). Also cloud provides (AWS, google,...) do support COG files, e.g. [Landstat images](https://docs.opendata.aws/landsat-pds/readme.html).\n",
+    "\n",
+    "These files are typically very large to download, whereas we might only need a small subset of the data. COG files do support to downloading a subset of the data you need using the masking approach.\n",
+    "\n",
+    "Let's use the Averbode nature reserve data as an example, available at the URL: http://s3-eu-west-1.amazonaws.com/lw-remote-sensing/cogeo/20160401_ABH_1_Ortho.tif"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "averbode_cog_rgb = 'http://s3-eu-west-1.amazonaws.com/lw-remote-sensing/cogeo/20160401_ABH_1_Ortho.tif'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Check the metadata, without downloading the data itself:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "with rasterio.open(averbode_cog_rgb) as averbode:\n",
+    "    print(averbode.count, averbode.width, averbode.height)\n",
+    "    print(averbode.meta)\n",
+    "    averbode_extent = plotting_extent(averbode)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Downloading the entire data set would be 37645*35405 pixels of 8 bit, so more or less 1.3 GByte"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "37645*35405 / 1e9  # Gb"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Assume that we have a study area which is much smaller than the total extent of the available image:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "left, right, bottom, top = averbode_extent"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "averbode_study_area = geopandas.read_file(\"./data/averbode/study_area.geojson\")\n",
+    "ax = averbode_study_area.plot();\n",
+    "ax.set_xlim(left, right);\n",
+    "ax.set_ylim(bottom, top);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In the case of COG data, the data can sometimes be requested on different resolution levels when stored as such. So, to get a very broad overview of the data, we can request the coarsest resolution by resampling and download the data at the resampled resolution:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "with rasterio.open(averbode_cog_rgb) as src:\n",
+    "    print(f\"Available resolutions are {src.overviews(1)}\")\n",
+    "    oview = src.overviews(1)[-1] # list of overviews, selecting the latest (most coarse)\n",
+    "    print(f\"Resampling factor= {oview}\")\n",
+    "    # Download a resampled version of the data (http://rasterio.readthedocs.io/en/latest/topics/resampling.html), aka thumbnail\n",
+    "    thumbnail = src.read(1, out_shape=(1, int(src.height // oview), int(src.width // oview)))    "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Compare the thumbnail version of the data with our study area:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "ax.imshow(thumbnail, extent=averbode_extent);\n",
+    "averbode_study_area.plot(ax=ax, color='None', edgecolor='red', linewidth=2);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Downloading the entire data file would be overkill. Instead, we only want to download the data of the study area:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "output_file = \"./averbode_orthophoto.tiff\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The resulting data set will still be around 120MB and will take a bit of time, but this is only a fraction of the original data file (GBs):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Only run this cell when sufficient band width ;-)\n",
+    "with rasterio.open(averbode_cog_rgb) as averbode_rgb:\n",
+    "    averbode_rgb_image, averbode_rgb_transform = rasterio.mask.mask(averbode_rgb, averbode_study_area.geometry, crop=True)\n",
+    "    averbode_rgb_profile = averbode_rgb.profile  \n",
+    "\n",
+    "    averbode_rgb_profile.update({\"driver\": \"GTiff\",\n",
+    "                                 \"height\": averbode_rgb_image.shape[1],\n",
+    "                                 \"width\": averbode_rgb_image.shape[2],\n",
+    "                                 \"transform\": averbode_rgb_transform\n",
+    "                                })\n",
+    "    \n",
+    "    with rasterio.open(output_file, \"w\", **averbode_rgb_profile) as dest:\n",
+    "        dest.write(averbode_rgb_image)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Thanks to https://geohackweek.github.io/raster/04-workingwithrasters/ for the inspiration"
+   ]
+  }
+ ],
+ "metadata": {
+  "celltoolbar": "Nbtutor - export exercises",
+  "jupytext": {
+   "cell_metadata_filter": "-run_control,-deletable,-editable,-jupyter,-slideshow",
+   "notebook_metadata_filter": "-jupytext.cell_metadata_filter,-jupytext.notebook_metadata_filter"
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/notebooks/_solutions/01-introduction-tabular-data1.py b/notebooks/_solutions/01-introduction-tabular-data1.py
new file mode 100644
index 0000000..eb37662
--- /dev/null
+++ b/notebooks/_solutions/01-introduction-tabular-data1.py
@@ -0,0 +1 @@
+districts = pd.read_csv("data/paris_districts.csv")
\ No newline at end of file
diff --git a/notebooks/_solutions/01-introduction-tabular-data10.py b/notebooks/_solutions/01-introduction-tabular-data10.py
new file mode 100644
index 0000000..15dacda
--- /dev/null
+++ b/notebooks/_solutions/01-introduction-tabular-data10.py
@@ -0,0 +1,2 @@
+population_density = districts["population"] / (districts["area"] / 1000**2)
+population_density
\ No newline at end of file
diff --git a/notebooks/_solutions/01-introduction-tabular-data11.py b/notebooks/_solutions/01-introduction-tabular-data11.py
new file mode 100644
index 0000000..8bd1ac9
--- /dev/null
+++ b/notebooks/_solutions/01-introduction-tabular-data11.py
@@ -0,0 +1 @@
+population_density.max()
\ No newline at end of file
diff --git a/notebooks/_solutions/01-introduction-tabular-data12.py b/notebooks/_solutions/01-introduction-tabular-data12.py
new file mode 100644
index 0000000..a6b4809
--- /dev/null
+++ b/notebooks/_solutions/01-introduction-tabular-data12.py
@@ -0,0 +1,2 @@
+districts['population_density'] = districts["population"] / (districts["area"] / 1000**2)
+districts.head()
\ No newline at end of file
diff --git a/notebooks/_solutions/01-introduction-tabular-data13.py b/notebooks/_solutions/01-introduction-tabular-data13.py
new file mode 100644
index 0000000..011c0a0
--- /dev/null
+++ b/notebooks/_solutions/01-introduction-tabular-data13.py
@@ -0,0 +1 @@
+districts.sort_values(by="population", ascending=False)
\ No newline at end of file
diff --git a/notebooks/_solutions/01-introduction-tabular-data14.py b/notebooks/_solutions/01-introduction-tabular-data14.py
new file mode 100644
index 0000000..99f6bf7
--- /dev/null
+++ b/notebooks/_solutions/01-introduction-tabular-data14.py
@@ -0,0 +1,2 @@
+arr3 = districts[districts["arrondissement"] == 3]
+arr3
\ No newline at end of file
diff --git a/notebooks/_solutions/01-introduction-tabular-data15.py b/notebooks/_solutions/01-introduction-tabular-data15.py
new file mode 100644
index 0000000..86abf12
--- /dev/null
+++ b/notebooks/_solutions/01-introduction-tabular-data15.py
@@ -0,0 +1 @@
+arr3["population"].sum()
\ No newline at end of file
diff --git a/notebooks/_solutions/01-introduction-tabular-data16.py b/notebooks/_solutions/01-introduction-tabular-data16.py
new file mode 100644
index 0000000..09d209f
--- /dev/null
+++ b/notebooks/_solutions/01-introduction-tabular-data16.py
@@ -0,0 +1 @@
+districts[districts['population'] > 50000]
\ No newline at end of file
diff --git a/notebooks/_solutions/01-introduction-tabular-data17.py b/notebooks/_solutions/01-introduction-tabular-data17.py
new file mode 100644
index 0000000..8b87f1f
--- /dev/null
+++ b/notebooks/_solutions/01-introduction-tabular-data17.py
@@ -0,0 +1,2 @@
+subset = districts[districts['population'] > 50000]
+len(subset)
\ No newline at end of file
diff --git a/notebooks/_solutions/01-introduction-tabular-data18.py b/notebooks/_solutions/01-introduction-tabular-data18.py
new file mode 100644
index 0000000..8143c36
--- /dev/null
+++ b/notebooks/_solutions/01-introduction-tabular-data18.py
@@ -0,0 +1 @@
+subset.shape
\ No newline at end of file
diff --git a/notebooks/_solutions/01-introduction-tabular-data19.py b/notebooks/_solutions/01-introduction-tabular-data19.py
new file mode 100644
index 0000000..911faaf
--- /dev/null
+++ b/notebooks/_solutions/01-introduction-tabular-data19.py
@@ -0,0 +1 @@
+(districts['population'] > 50000).sum()
\ No newline at end of file
diff --git a/notebooks/_solutions/01-introduction-tabular-data2.py b/notebooks/_solutions/01-introduction-tabular-data2.py
new file mode 100644
index 0000000..afe00ba
--- /dev/null
+++ b/notebooks/_solutions/01-introduction-tabular-data2.py
@@ -0,0 +1 @@
+districts
\ No newline at end of file
diff --git a/notebooks/_solutions/01-introduction-tabular-data20.py b/notebooks/_solutions/01-introduction-tabular-data20.py
new file mode 100644
index 0000000..7dc6396
--- /dev/null
+++ b/notebooks/_solutions/01-introduction-tabular-data20.py
@@ -0,0 +1 @@
+districts['population'].plot.hist()  # or .plot(kind="hist")
\ No newline at end of file
diff --git a/notebooks/_solutions/01-introduction-tabular-data21.py b/notebooks/_solutions/01-introduction-tabular-data21.py
new file mode 100644
index 0000000..82c94ac
--- /dev/null
+++ b/notebooks/_solutions/01-introduction-tabular-data21.py
@@ -0,0 +1 @@
+districts.groupby('arrondissement')['population'].sum()
\ No newline at end of file
diff --git a/notebooks/_solutions/01-introduction-tabular-data22.py b/notebooks/_solutions/01-introduction-tabular-data22.py
new file mode 100644
index 0000000..2940b5d
--- /dev/null
+++ b/notebooks/_solutions/01-introduction-tabular-data22.py
@@ -0,0 +1,5 @@
+fig, ax = plt.subplots()
+stations["bike_stands"].plot.hist(ax=ax, alpha=.5, label="Total bike stands")
+stations["available_bikes"].plot.hist(ax=ax, alpha=.5, label="Available bikes")
+ax.legend()
+ax.grid()
\ No newline at end of file
diff --git a/notebooks/_solutions/01-introduction-tabular-data23.py b/notebooks/_solutions/01-introduction-tabular-data23.py
new file mode 100644
index 0000000..556db3e
--- /dev/null
+++ b/notebooks/_solutions/01-introduction-tabular-data23.py
@@ -0,0 +1 @@
+import seaborn
\ No newline at end of file
diff --git a/notebooks/_solutions/01-introduction-tabular-data24.py b/notebooks/_solutions/01-introduction-tabular-data24.py
new file mode 100644
index 0000000..056c649
--- /dev/null
+++ b/notebooks/_solutions/01-introduction-tabular-data24.py
@@ -0,0 +1 @@
+seaborn.boxplot(x="arrondissement", y="bike_stands", data=stations)
\ No newline at end of file
diff --git a/notebooks/_solutions/01-introduction-tabular-data3.py b/notebooks/_solutions/01-introduction-tabular-data3.py
new file mode 100644
index 0000000..187631c
--- /dev/null
+++ b/notebooks/_solutions/01-introduction-tabular-data3.py
@@ -0,0 +1 @@
+districts.head()
\ No newline at end of file
diff --git a/notebooks/_solutions/01-introduction-tabular-data4.py b/notebooks/_solutions/01-introduction-tabular-data4.py
new file mode 100644
index 0000000..afd5cfc
--- /dev/null
+++ b/notebooks/_solutions/01-introduction-tabular-data4.py
@@ -0,0 +1 @@
+districts['population']
\ No newline at end of file
diff --git a/notebooks/_solutions/01-introduction-tabular-data5.py b/notebooks/_solutions/01-introduction-tabular-data5.py
new file mode 100644
index 0000000..3cbb245
--- /dev/null
+++ b/notebooks/_solutions/01-introduction-tabular-data5.py
@@ -0,0 +1 @@
+districts['population'].mean()
\ No newline at end of file
diff --git a/notebooks/_solutions/01-introduction-tabular-data6.py b/notebooks/_solutions/01-introduction-tabular-data6.py
new file mode 100644
index 0000000..0eba740
--- /dev/null
+++ b/notebooks/_solutions/01-introduction-tabular-data6.py
@@ -0,0 +1 @@
+districts['population'].median()
\ No newline at end of file
diff --git a/notebooks/_solutions/01-introduction-tabular-data7.py b/notebooks/_solutions/01-introduction-tabular-data7.py
new file mode 100644
index 0000000..fd4dd6f
--- /dev/null
+++ b/notebooks/_solutions/01-introduction-tabular-data7.py
@@ -0,0 +1 @@
+districts['area'].max()
\ No newline at end of file
diff --git a/notebooks/_solutions/01-introduction-tabular-data8.py b/notebooks/_solutions/01-introduction-tabular-data8.py
new file mode 100644
index 0000000..8cda6e3
--- /dev/null
+++ b/notebooks/_solutions/01-introduction-tabular-data8.py
@@ -0,0 +1 @@
+districts['area'].max() / 1000**2  # or / 10**6
\ No newline at end of file
diff --git a/notebooks/_solutions/01-introduction-tabular-data9.py b/notebooks/_solutions/01-introduction-tabular-data9.py
new file mode 100644
index 0000000..5926034
--- /dev/null
+++ b/notebooks/_solutions/01-introduction-tabular-data9.py
@@ -0,0 +1 @@
+districts['area'] / 1000**2
\ No newline at end of file
diff --git a/notebooks/_solutions/02-introduction-geospatial-data1.py b/notebooks/_solutions/02-introduction-geospatial-data1.py
new file mode 100644
index 0000000..30eb219
--- /dev/null
+++ b/notebooks/_solutions/02-introduction-geospatial-data1.py
@@ -0,0 +1 @@
+stations = geopandas.read_file("data/paris_bike_stations_mercator.gpkg")
\ No newline at end of file
diff --git a/notebooks/_solutions/02-introduction-geospatial-data10.py b/notebooks/_solutions/02-introduction-geospatial-data10.py
new file mode 100644
index 0000000..a9044ca
--- /dev/null
+++ b/notebooks/_solutions/02-introduction-geospatial-data10.py
@@ -0,0 +1 @@
+districts = geopandas.read_file("data/paris_districts_utm.geojson")
\ No newline at end of file
diff --git a/notebooks/_solutions/02-introduction-geospatial-data11.py b/notebooks/_solutions/02-introduction-geospatial-data11.py
new file mode 100644
index 0000000..187631c
--- /dev/null
+++ b/notebooks/_solutions/02-introduction-geospatial-data11.py
@@ -0,0 +1 @@
+districts.head()
\ No newline at end of file
diff --git a/notebooks/_solutions/02-introduction-geospatial-data12.py b/notebooks/_solutions/02-introduction-geospatial-data12.py
new file mode 100644
index 0000000..39488c9
--- /dev/null
+++ b/notebooks/_solutions/02-introduction-geospatial-data12.py
@@ -0,0 +1 @@
+districts.shape
\ No newline at end of file
diff --git a/notebooks/_solutions/02-introduction-geospatial-data13.py b/notebooks/_solutions/02-introduction-geospatial-data13.py
new file mode 100644
index 0000000..f6df2fa
--- /dev/null
+++ b/notebooks/_solutions/02-introduction-geospatial-data13.py
@@ -0,0 +1 @@
+districts.plot(figsize=(12, 6))
\ No newline at end of file
diff --git a/notebooks/_solutions/02-introduction-geospatial-data14.py b/notebooks/_solutions/02-introduction-geospatial-data14.py
new file mode 100644
index 0000000..8e80477
--- /dev/null
+++ b/notebooks/_solutions/02-introduction-geospatial-data14.py
@@ -0,0 +1 @@
+districts.geometry.area
\ No newline at end of file
diff --git a/notebooks/_solutions/02-introduction-geospatial-data15.py b/notebooks/_solutions/02-introduction-geospatial-data15.py
new file mode 100644
index 0000000..7abef29
--- /dev/null
+++ b/notebooks/_solutions/02-introduction-geospatial-data15.py
@@ -0,0 +1,2 @@
+# dividing by 10^6 for showing km²
+districts['area'] = districts.geometry.area / 1e6
\ No newline at end of file
diff --git a/notebooks/_solutions/02-introduction-geospatial-data16.py b/notebooks/_solutions/02-introduction-geospatial-data16.py
new file mode 100644
index 0000000..c04ca90
--- /dev/null
+++ b/notebooks/_solutions/02-introduction-geospatial-data16.py
@@ -0,0 +1 @@
+districts.sort_values(by='area', ascending=False)
\ No newline at end of file
diff --git a/notebooks/_solutions/02-introduction-geospatial-data17.py b/notebooks/_solutions/02-introduction-geospatial-data17.py
new file mode 100644
index 0000000..896c65a
--- /dev/null
+++ b/notebooks/_solutions/02-introduction-geospatial-data17.py
@@ -0,0 +1,2 @@
+# Add a population density column
+districts['population_density'] = districts['population'] / districts.geometry.area * 10**6
\ No newline at end of file
diff --git a/notebooks/_solutions/02-introduction-geospatial-data18.py b/notebooks/_solutions/02-introduction-geospatial-data18.py
new file mode 100644
index 0000000..223ee88
--- /dev/null
+++ b/notebooks/_solutions/02-introduction-geospatial-data18.py
@@ -0,0 +1,2 @@
+# Make a plot of the districts colored by the population density
+districts.plot(column='population_density', figsize=(12, 6), legend=True)
\ No newline at end of file
diff --git a/notebooks/_solutions/02-introduction-geospatial-data19.py b/notebooks/_solutions/02-introduction-geospatial-data19.py
new file mode 100644
index 0000000..09bff90
--- /dev/null
+++ b/notebooks/_solutions/02-introduction-geospatial-data19.py
@@ -0,0 +1,2 @@
+# As comparison, the misleading plot when not turning the population number into a density
+districts.plot(column='population', figsize=(12, 6), legend=True)
\ No newline at end of file
diff --git a/notebooks/_solutions/02-introduction-geospatial-data2.py b/notebooks/_solutions/02-introduction-geospatial-data2.py
new file mode 100644
index 0000000..7fcfffe
--- /dev/null
+++ b/notebooks/_solutions/02-introduction-geospatial-data2.py
@@ -0,0 +1 @@
+type(stations)
\ No newline at end of file
diff --git a/notebooks/_solutions/02-introduction-geospatial-data3.py b/notebooks/_solutions/02-introduction-geospatial-data3.py
new file mode 100644
index 0000000..b934d16
--- /dev/null
+++ b/notebooks/_solutions/02-introduction-geospatial-data3.py
@@ -0,0 +1 @@
+stations.head()
\ No newline at end of file
diff --git a/notebooks/_solutions/02-introduction-geospatial-data4.py b/notebooks/_solutions/02-introduction-geospatial-data4.py
new file mode 100644
index 0000000..14a992e
--- /dev/null
+++ b/notebooks/_solutions/02-introduction-geospatial-data4.py
@@ -0,0 +1 @@
+stations.shape
\ No newline at end of file
diff --git a/notebooks/_solutions/02-introduction-geospatial-data5.py b/notebooks/_solutions/02-introduction-geospatial-data5.py
new file mode 100644
index 0000000..4e591db
--- /dev/null
+++ b/notebooks/_solutions/02-introduction-geospatial-data5.py
@@ -0,0 +1 @@
+stations.plot(figsize=(12,6))  # or .explore()
\ No newline at end of file
diff --git a/notebooks/_solutions/02-introduction-geospatial-data6.py b/notebooks/_solutions/02-introduction-geospatial-data6.py
new file mode 100644
index 0000000..b4be0bf
--- /dev/null
+++ b/notebooks/_solutions/02-introduction-geospatial-data6.py
@@ -0,0 +1 @@
+import contextily
\ No newline at end of file
diff --git a/notebooks/_solutions/02-introduction-geospatial-data7.py b/notebooks/_solutions/02-introduction-geospatial-data7.py
new file mode 100644
index 0000000..2b6bb2d
--- /dev/null
+++ b/notebooks/_solutions/02-introduction-geospatial-data7.py
@@ -0,0 +1,2 @@
+ax = stations.plot(figsize=(12,6), markersize=5)
+contextily.add_basemap(ax)
\ No newline at end of file
diff --git a/notebooks/_solutions/02-introduction-geospatial-data8.py b/notebooks/_solutions/02-introduction-geospatial-data8.py
new file mode 100644
index 0000000..d9f8b25
--- /dev/null
+++ b/notebooks/_solutions/02-introduction-geospatial-data8.py
@@ -0,0 +1 @@
+stations['bike_stands'].plot.hist()
\ No newline at end of file
diff --git a/notebooks/_solutions/02-introduction-geospatial-data9.py b/notebooks/_solutions/02-introduction-geospatial-data9.py
new file mode 100644
index 0000000..e0c82dd
--- /dev/null
+++ b/notebooks/_solutions/02-introduction-geospatial-data9.py
@@ -0,0 +1 @@
+stations.plot(figsize=(12, 6), column='available_bikes', legend=True)
\ No newline at end of file
diff --git a/notebooks/_solutions/03-coordinate-reference-systems1.py b/notebooks/_solutions/03-coordinate-reference-systems1.py
new file mode 100644
index 0000000..20e8f4f
--- /dev/null
+++ b/notebooks/_solutions/03-coordinate-reference-systems1.py
@@ -0,0 +1,2 @@
+# Import the districts dataset
+districts = geopandas.read_file("data/paris_districts.geojson")
\ No newline at end of file
diff --git a/notebooks/_solutions/03-coordinate-reference-systems10.py b/notebooks/_solutions/03-coordinate-reference-systems10.py
new file mode 100644
index 0000000..0ec5aab
--- /dev/null
+++ b/notebooks/_solutions/03-coordinate-reference-systems10.py
@@ -0,0 +1,3 @@
+# Convert to the Web Mercator projection
+stations_webmercator = stations.to_crs("EPSG:3857")
+stations.head()
\ No newline at end of file
diff --git a/notebooks/_solutions/03-coordinate-reference-systems11.py b/notebooks/_solutions/03-coordinate-reference-systems11.py
new file mode 100644
index 0000000..9ba786e
--- /dev/null
+++ b/notebooks/_solutions/03-coordinate-reference-systems11.py
@@ -0,0 +1,4 @@
+# Plot the stations with a background map
+import contextily
+ax = stations_webmercator.plot(markersize=5)
+contextily.add_basemap(ax)
\ No newline at end of file
diff --git a/notebooks/_solutions/03-coordinate-reference-systems2.py b/notebooks/_solutions/03-coordinate-reference-systems2.py
new file mode 100644
index 0000000..87f4a2c
--- /dev/null
+++ b/notebooks/_solutions/03-coordinate-reference-systems2.py
@@ -0,0 +1,2 @@
+# Check the CRS information
+districts.crs
\ No newline at end of file
diff --git a/notebooks/_solutions/03-coordinate-reference-systems3.py b/notebooks/_solutions/03-coordinate-reference-systems3.py
new file mode 100644
index 0000000..4dbed29
--- /dev/null
+++ b/notebooks/_solutions/03-coordinate-reference-systems3.py
@@ -0,0 +1,2 @@
+# Show the first rows of the GeoDataFrame
+districts.head()
\ No newline at end of file
diff --git a/notebooks/_solutions/03-coordinate-reference-systems4.py b/notebooks/_solutions/03-coordinate-reference-systems4.py
new file mode 100644
index 0000000..6b718cb
--- /dev/null
+++ b/notebooks/_solutions/03-coordinate-reference-systems4.py
@@ -0,0 +1,2 @@
+# Plot the districts dataset
+districts.plot()
\ No newline at end of file
diff --git a/notebooks/_solutions/03-coordinate-reference-systems5.py b/notebooks/_solutions/03-coordinate-reference-systems5.py
new file mode 100644
index 0000000..be365da
--- /dev/null
+++ b/notebooks/_solutions/03-coordinate-reference-systems5.py
@@ -0,0 +1,2 @@
+# Calculate the area of all districts
+districts.geometry.area
\ No newline at end of file
diff --git a/notebooks/_solutions/03-coordinate-reference-systems6.py b/notebooks/_solutions/03-coordinate-reference-systems6.py
new file mode 100644
index 0000000..c8d0c24
--- /dev/null
+++ b/notebooks/_solutions/03-coordinate-reference-systems6.py
@@ -0,0 +1,2 @@
+# Convert the districts to the RGF93 reference system
+districts_RGF93 = districts.to_crs(epsg=2154)  # or to_crs("EPSG:2154")
\ No newline at end of file
diff --git a/notebooks/_solutions/03-coordinate-reference-systems7.py b/notebooks/_solutions/03-coordinate-reference-systems7.py
new file mode 100644
index 0000000..24f9bc5
--- /dev/null
+++ b/notebooks/_solutions/03-coordinate-reference-systems7.py
@@ -0,0 +1,2 @@
+# Plot the districts dataset again
+districts_RGF93.plot()
\ No newline at end of file
diff --git a/notebooks/_solutions/03-coordinate-reference-systems8.py b/notebooks/_solutions/03-coordinate-reference-systems8.py
new file mode 100644
index 0000000..0964ddb
--- /dev/null
+++ b/notebooks/_solutions/03-coordinate-reference-systems8.py
@@ -0,0 +1,2 @@
+# Calculate the area of all districts (the result is now expressed in m²)
+districts_RGF93.geometry.area
\ No newline at end of file
diff --git a/notebooks/_solutions/03-coordinate-reference-systems9.py b/notebooks/_solutions/03-coordinate-reference-systems9.py
new file mode 100644
index 0000000..62cda0a
--- /dev/null
+++ b/notebooks/_solutions/03-coordinate-reference-systems9.py
@@ -0,0 +1,2 @@
+stations = geopandas.read_file("data/paris_bike_stations.geojson")
+stations.head()
\ No newline at end of file
diff --git a/notebooks/_solutions/04-spatial-relationships-joins1.py b/notebooks/_solutions/04-spatial-relationships-joins1.py
new file mode 100644
index 0000000..f80f31a
--- /dev/null
+++ b/notebooks/_solutions/04-spatial-relationships-joins1.py
@@ -0,0 +1,2 @@
+# Construct a point object for the Eiffel Tower
+eiffel_tower = Point(648237.3, 6862271.9)
\ No newline at end of file
diff --git a/notebooks/_solutions/04-spatial-relationships-joins10.py b/notebooks/_solutions/04-spatial-relationships-joins10.py
new file mode 100644
index 0000000..9c55722
--- /dev/null
+++ b/notebooks/_solutions/04-spatial-relationships-joins10.py
@@ -0,0 +1,2 @@
+# Filter the bike stations closer than 1 km
+stations_eiffel = stations[dist_eiffel < 1000]
\ No newline at end of file
diff --git a/notebooks/_solutions/04-spatial-relationships-joins11.py b/notebooks/_solutions/04-spatial-relationships-joins11.py
new file mode 100644
index 0000000..4ac6100
--- /dev/null
+++ b/notebooks/_solutions/04-spatial-relationships-joins11.py
@@ -0,0 +1 @@
+joined = geopandas.sjoin(stations, districts[['district_name', 'geometry']], predicate='within')
\ No newline at end of file
diff --git a/notebooks/_solutions/04-spatial-relationships-joins12.py b/notebooks/_solutions/04-spatial-relationships-joins12.py
new file mode 100644
index 0000000..65f0807
--- /dev/null
+++ b/notebooks/_solutions/04-spatial-relationships-joins12.py
@@ -0,0 +1 @@
+joined.head()
\ No newline at end of file
diff --git a/notebooks/_solutions/04-spatial-relationships-joins13.py b/notebooks/_solutions/04-spatial-relationships-joins13.py
new file mode 100644
index 0000000..bf1f736
--- /dev/null
+++ b/notebooks/_solutions/04-spatial-relationships-joins13.py
@@ -0,0 +1,3 @@
+# Read the trees and districts data
+trees = geopandas.read_file("data/paris_trees.gpkg")
+districts = geopandas.read_file("data/paris_districts.geojson").to_crs(trees.crs)
\ No newline at end of file
diff --git a/notebooks/_solutions/04-spatial-relationships-joins14.py b/notebooks/_solutions/04-spatial-relationships-joins14.py
new file mode 100644
index 0000000..470e543
--- /dev/null
+++ b/notebooks/_solutions/04-spatial-relationships-joins14.py
@@ -0,0 +1,2 @@
+# The trees dataset with point locations of trees
+trees.head()
\ No newline at end of file
diff --git a/notebooks/_solutions/04-spatial-relationships-joins15.py b/notebooks/_solutions/04-spatial-relationships-joins15.py
new file mode 100644
index 0000000..7d1edda
--- /dev/null
+++ b/notebooks/_solutions/04-spatial-relationships-joins15.py
@@ -0,0 +1,3 @@
+# Spatial join of the trees and districts datasets
+joined = geopandas.sjoin(trees, districts, predicate='within')
+joined.head()
\ No newline at end of file
diff --git a/notebooks/_solutions/04-spatial-relationships-joins16.py b/notebooks/_solutions/04-spatial-relationships-joins16.py
new file mode 100644
index 0000000..ff90645
--- /dev/null
+++ b/notebooks/_solutions/04-spatial-relationships-joins16.py
@@ -0,0 +1,2 @@
+# Calculate the number of trees in each district
+trees_by_district = joined.groupby('district_name').size()
\ No newline at end of file
diff --git a/notebooks/_solutions/04-spatial-relationships-joins17.py b/notebooks/_solutions/04-spatial-relationships-joins17.py
new file mode 100644
index 0000000..b2f4840
--- /dev/null
+++ b/notebooks/_solutions/04-spatial-relationships-joins17.py
@@ -0,0 +1,3 @@
+# Merge the 'districts' and 'trees_by_district' dataframes
+districts_trees = pd.merge(districts, trees_by_district, on='district_name')
+districts_trees.head()
\ No newline at end of file
diff --git a/notebooks/_solutions/04-spatial-relationships-joins18.py b/notebooks/_solutions/04-spatial-relationships-joins18.py
new file mode 100644
index 0000000..578510e
--- /dev/null
+++ b/notebooks/_solutions/04-spatial-relationships-joins18.py
@@ -0,0 +1,2 @@
+# Add a column with the tree density
+districts_trees['n_trees_per_area'] = districts_trees['n_trees'] / districts_trees.geometry.area
\ No newline at end of file
diff --git a/notebooks/_solutions/04-spatial-relationships-joins19.py b/notebooks/_solutions/04-spatial-relationships-joins19.py
new file mode 100644
index 0000000..09c6314
--- /dev/null
+++ b/notebooks/_solutions/04-spatial-relationships-joins19.py
@@ -0,0 +1,3 @@
+# Make of map of the districts colored by 'n_trees_per_area'
+ax = districts_trees.plot(column='n_trees_per_area', figsize=(12, 6))
+ax.set_axis_off()
\ No newline at end of file
diff --git a/notebooks/_solutions/04-spatial-relationships-joins2.py b/notebooks/_solutions/04-spatial-relationships-joins2.py
new file mode 100644
index 0000000..a65a32a
--- /dev/null
+++ b/notebooks/_solutions/04-spatial-relationships-joins2.py
@@ -0,0 +1,2 @@
+# Print the result
+print(eiffel_tower)
\ No newline at end of file
diff --git a/notebooks/_solutions/04-spatial-relationships-joins3.py b/notebooks/_solutions/04-spatial-relationships-joins3.py
new file mode 100644
index 0000000..5a6fc3d
--- /dev/null
+++ b/notebooks/_solutions/04-spatial-relationships-joins3.py
@@ -0,0 +1,2 @@
+# Is the Eiffel Tower located within the Montparnasse district?
+print(eiffel_tower.within(district_montparnasse))
\ No newline at end of file
diff --git a/notebooks/_solutions/04-spatial-relationships-joins4.py b/notebooks/_solutions/04-spatial-relationships-joins4.py
new file mode 100644
index 0000000..150dd2f
--- /dev/null
+++ b/notebooks/_solutions/04-spatial-relationships-joins4.py
@@ -0,0 +1,2 @@
+# Does the Montparnasse district contains the bike station?
+print(district_montparnasse.contains(bike_station))
\ No newline at end of file
diff --git a/notebooks/_solutions/04-spatial-relationships-joins5.py b/notebooks/_solutions/04-spatial-relationships-joins5.py
new file mode 100644
index 0000000..35f393a
--- /dev/null
+++ b/notebooks/_solutions/04-spatial-relationships-joins5.py
@@ -0,0 +1,2 @@
+# The distance between the Eiffel Tower and the bike station?
+print(eiffel_tower.distance(bike_station))
\ No newline at end of file
diff --git a/notebooks/_solutions/04-spatial-relationships-joins6.py b/notebooks/_solutions/04-spatial-relationships-joins6.py
new file mode 100644
index 0000000..5e48d68
--- /dev/null
+++ b/notebooks/_solutions/04-spatial-relationships-joins6.py
@@ -0,0 +1,3 @@
+# Create a boolean Series
+mask = districts.contains(eiffel_tower)
+mask
\ No newline at end of file
diff --git a/notebooks/_solutions/04-spatial-relationships-joins7.py b/notebooks/_solutions/04-spatial-relationships-joins7.py
new file mode 100644
index 0000000..398cb90
--- /dev/null
+++ b/notebooks/_solutions/04-spatial-relationships-joins7.py
@@ -0,0 +1,2 @@
+# Filter the districts with the boolean mask
+districts[mask]
\ No newline at end of file
diff --git a/notebooks/_solutions/04-spatial-relationships-joins8.py b/notebooks/_solutions/04-spatial-relationships-joins8.py
new file mode 100644
index 0000000..51f3458
--- /dev/null
+++ b/notebooks/_solutions/04-spatial-relationships-joins8.py
@@ -0,0 +1,2 @@
+# The distance from each stations to the Eiffel Tower
+dist_eiffel = stations.distance(eiffel_tower)
\ No newline at end of file
diff --git a/notebooks/_solutions/04-spatial-relationships-joins9.py b/notebooks/_solutions/04-spatial-relationships-joins9.py
new file mode 100644
index 0000000..cf67ebd
--- /dev/null
+++ b/notebooks/_solutions/04-spatial-relationships-joins9.py
@@ -0,0 +1,2 @@
+# The distance to the closest station
+dist_eiffel.min()
\ No newline at end of file
diff --git a/notebooks/_solutions/05-spatial-operations-overlays1.py b/notebooks/_solutions/05-spatial-operations-overlays1.py
new file mode 100644
index 0000000..99d2151
--- /dev/null
+++ b/notebooks/_solutions/05-spatial-operations-overlays1.py
@@ -0,0 +1,3 @@
+# Take a buffer
+seine_buffer = seine.buffer(150)
+seine_buffer
\ No newline at end of file
diff --git a/notebooks/_solutions/05-spatial-operations-overlays10.py b/notebooks/_solutions/05-spatial-operations-overlays10.py
new file mode 100644
index 0000000..62c3d2e
--- /dev/null
+++ b/notebooks/_solutions/05-spatial-operations-overlays10.py
@@ -0,0 +1,2 @@
+# Print proportion of district area that occupied park
+print(intersection.area / muette.area)
\ No newline at end of file
diff --git a/notebooks/_solutions/05-spatial-operations-overlays11.py b/notebooks/_solutions/05-spatial-operations-overlays11.py
new file mode 100644
index 0000000..e27585c
--- /dev/null
+++ b/notebooks/_solutions/05-spatial-operations-overlays11.py
@@ -0,0 +1,2 @@
+# Calculate the intersection of the land use polygons with Muette
+land_use_muette = land_use.geometry.intersection(muette)
\ No newline at end of file
diff --git a/notebooks/_solutions/05-spatial-operations-overlays12.py b/notebooks/_solutions/05-spatial-operations-overlays12.py
new file mode 100644
index 0000000..5b616e2
--- /dev/null
+++ b/notebooks/_solutions/05-spatial-operations-overlays12.py
@@ -0,0 +1,2 @@
+# Print the first five rows of the intersection
+land_use_muette.head()
\ No newline at end of file
diff --git a/notebooks/_solutions/05-spatial-operations-overlays13.py b/notebooks/_solutions/05-spatial-operations-overlays13.py
new file mode 100644
index 0000000..5b931d7
--- /dev/null
+++ b/notebooks/_solutions/05-spatial-operations-overlays13.py
@@ -0,0 +1,2 @@
+# Remove the empty geometries
+land_use_muette = land_use_muette[~land_use_muette.is_empty]
\ No newline at end of file
diff --git a/notebooks/_solutions/05-spatial-operations-overlays14.py b/notebooks/_solutions/05-spatial-operations-overlays14.py
new file mode 100644
index 0000000..5b616e2
--- /dev/null
+++ b/notebooks/_solutions/05-spatial-operations-overlays14.py
@@ -0,0 +1,2 @@
+# Print the first five rows of the intersection
+land_use_muette.head()
\ No newline at end of file
diff --git a/notebooks/_solutions/05-spatial-operations-overlays15.py b/notebooks/_solutions/05-spatial-operations-overlays15.py
new file mode 100644
index 0000000..1b2a475
--- /dev/null
+++ b/notebooks/_solutions/05-spatial-operations-overlays15.py
@@ -0,0 +1,2 @@
+# Plot the intersection
+land_use_muette.plot(edgecolor='black')
\ No newline at end of file
diff --git a/notebooks/_solutions/05-spatial-operations-overlays16.py b/notebooks/_solutions/05-spatial-operations-overlays16.py
new file mode 100644
index 0000000..b89f2ff
--- /dev/null
+++ b/notebooks/_solutions/05-spatial-operations-overlays16.py
@@ -0,0 +1,4 @@
+land_use_muette = land_use.copy()
+land_use_muette['geometry'] = land_use.geometry.intersection(muette)
+land_use_muette = land_use_muette[~land_use_muette.is_empty]
+land_use_muette.head()
\ No newline at end of file
diff --git a/notebooks/_solutions/05-spatial-operations-overlays17.py b/notebooks/_solutions/05-spatial-operations-overlays17.py
new file mode 100644
index 0000000..c1c4b8d
--- /dev/null
+++ b/notebooks/_solutions/05-spatial-operations-overlays17.py
@@ -0,0 +1 @@
+land_use_muette.plot(column="class") #edgecolor="black")
\ No newline at end of file
diff --git a/notebooks/_solutions/05-spatial-operations-overlays18.py b/notebooks/_solutions/05-spatial-operations-overlays18.py
new file mode 100644
index 0000000..9eea360
--- /dev/null
+++ b/notebooks/_solutions/05-spatial-operations-overlays18.py
@@ -0,0 +1 @@
+land_use_muette.dissolve(by='class')
\ No newline at end of file
diff --git a/notebooks/_solutions/05-spatial-operations-overlays19.py b/notebooks/_solutions/05-spatial-operations-overlays19.py
new file mode 100644
index 0000000..f1f8195
--- /dev/null
+++ b/notebooks/_solutions/05-spatial-operations-overlays19.py
@@ -0,0 +1,3 @@
+land_use_muette['area'] = land_use_muette.geometry.area
+# Total land use per class
+land_use_muette.groupby("class")["area"].sum()
\ No newline at end of file
diff --git a/notebooks/_solutions/05-spatial-operations-overlays2.py b/notebooks/_solutions/05-spatial-operations-overlays2.py
new file mode 100644
index 0000000..57d0795
--- /dev/null
+++ b/notebooks/_solutions/05-spatial-operations-overlays2.py
@@ -0,0 +1,2 @@
+# Use the intersection
+districts_seine = districts[districts.intersects(seine_buffer)]
\ No newline at end of file
diff --git a/notebooks/_solutions/05-spatial-operations-overlays20.py b/notebooks/_solutions/05-spatial-operations-overlays20.py
new file mode 100644
index 0000000..6e60c28
--- /dev/null
+++ b/notebooks/_solutions/05-spatial-operations-overlays20.py
@@ -0,0 +1,2 @@
+# Relative percentage of land use classes
+land_use_muette.groupby("class")["area"].sum() / land_use_muette.geometry.area.sum() * 100
\ No newline at end of file
diff --git a/notebooks/_solutions/05-spatial-operations-overlays21.py b/notebooks/_solutions/05-spatial-operations-overlays21.py
new file mode 100644
index 0000000..4126026
--- /dev/null
+++ b/notebooks/_solutions/05-spatial-operations-overlays21.py
@@ -0,0 +1,2 @@
+# Overlay both datasets based on the intersection
+combined = geopandas.overlay(land_use, districts, how='intersection', keep_geom_type=True)
\ No newline at end of file
diff --git a/notebooks/_solutions/05-spatial-operations-overlays22.py b/notebooks/_solutions/05-spatial-operations-overlays22.py
new file mode 100644
index 0000000..8c968f8
--- /dev/null
+++ b/notebooks/_solutions/05-spatial-operations-overlays22.py
@@ -0,0 +1,2 @@
+# Print the first five rows of the result
+combined.head()
\ No newline at end of file
diff --git a/notebooks/_solutions/05-spatial-operations-overlays23.py b/notebooks/_solutions/05-spatial-operations-overlays23.py
new file mode 100644
index 0000000..d6d5277
--- /dev/null
+++ b/notebooks/_solutions/05-spatial-operations-overlays23.py
@@ -0,0 +1,2 @@
+# Add the area as a column
+combined['area'] = combined.geometry.area
\ No newline at end of file
diff --git a/notebooks/_solutions/05-spatial-operations-overlays24.py b/notebooks/_solutions/05-spatial-operations-overlays24.py
new file mode 100644
index 0000000..8c2ed4f
--- /dev/null
+++ b/notebooks/_solutions/05-spatial-operations-overlays24.py
@@ -0,0 +1,2 @@
+# Take a subset for the Muette district
+land_use_muette = combined[combined['district_name'] == 'Muette']
\ No newline at end of file
diff --git a/notebooks/_solutions/05-spatial-operations-overlays25.py b/notebooks/_solutions/05-spatial-operations-overlays25.py
new file mode 100644
index 0000000..0ec2514
--- /dev/null
+++ b/notebooks/_solutions/05-spatial-operations-overlays25.py
@@ -0,0 +1,2 @@
+# Visualize the land use of the Muette district
+land_use_muette.plot(column='class')
\ No newline at end of file
diff --git a/notebooks/_solutions/05-spatial-operations-overlays26.py b/notebooks/_solutions/05-spatial-operations-overlays26.py
new file mode 100644
index 0000000..db1f80d
--- /dev/null
+++ b/notebooks/_solutions/05-spatial-operations-overlays26.py
@@ -0,0 +1,2 @@
+# Calculate the total area for each land use class
+print(land_use_muette.groupby('class')['area'].sum() / 1000**2)
\ No newline at end of file
diff --git a/notebooks/_solutions/05-spatial-operations-overlays27.py b/notebooks/_solutions/05-spatial-operations-overlays27.py
new file mode 100644
index 0000000..329ef1d
--- /dev/null
+++ b/notebooks/_solutions/05-spatial-operations-overlays27.py
@@ -0,0 +1,2 @@
+districts_area = combined.groupby("district_name")["area"].sum()
+districts_area.head()
\ No newline at end of file
diff --git a/notebooks/_solutions/05-spatial-operations-overlays28.py b/notebooks/_solutions/05-spatial-operations-overlays28.py
new file mode 100644
index 0000000..bfbd0c2
--- /dev/null
+++ b/notebooks/_solutions/05-spatial-operations-overlays28.py
@@ -0,0 +1 @@
+urban_green = combined[combined["class"] == "Green urban areas"]
\ No newline at end of file
diff --git a/notebooks/_solutions/05-spatial-operations-overlays29.py b/notebooks/_solutions/05-spatial-operations-overlays29.py
new file mode 100644
index 0000000..94c3339
--- /dev/null
+++ b/notebooks/_solutions/05-spatial-operations-overlays29.py
@@ -0,0 +1,2 @@
+urban_green_area = urban_green.groupby("district_name")["area"].sum()
+urban_green_area.head()
\ No newline at end of file
diff --git a/notebooks/_solutions/05-spatial-operations-overlays3.py b/notebooks/_solutions/05-spatial-operations-overlays3.py
new file mode 100644
index 0000000..99a6217
--- /dev/null
+++ b/notebooks/_solutions/05-spatial-operations-overlays3.py
@@ -0,0 +1,5 @@
+# Make a plot
+fig, ax = plt.subplots(figsize=(20, 10))
+districts.plot(ax=ax, color='grey', alpha=0.4, edgecolor='k')
+districts_seine.plot(ax=ax, color='blue', alpha=0.4, edgecolor='k')
+s_seine_utm.plot(ax=ax)
\ No newline at end of file
diff --git a/notebooks/_solutions/05-spatial-operations-overlays30.py b/notebooks/_solutions/05-spatial-operations-overlays30.py
new file mode 100644
index 0000000..aac52a6
--- /dev/null
+++ b/notebooks/_solutions/05-spatial-operations-overlays30.py
@@ -0,0 +1,4 @@
+urban_green_fraction = urban_green_area / districts_area * 100
+# some district are missing from urban_green_area (no urban green in that district)
+# this gives NaNs in the result, which we fill with 0
+urban_green_fraction = urban_green_fraction.fillna(0)
\ No newline at end of file
diff --git a/notebooks/_solutions/05-spatial-operations-overlays31.py b/notebooks/_solutions/05-spatial-operations-overlays31.py
new file mode 100644
index 0000000..7be7346
--- /dev/null
+++ b/notebooks/_solutions/05-spatial-operations-overlays31.py
@@ -0,0 +1 @@
+urban_green_fraction.nlargest()
\ No newline at end of file
diff --git a/notebooks/_solutions/05-spatial-operations-overlays32.py b/notebooks/_solutions/05-spatial-operations-overlays32.py
new file mode 100644
index 0000000..1fdef3f
--- /dev/null
+++ b/notebooks/_solutions/05-spatial-operations-overlays32.py
@@ -0,0 +1 @@
+urban_green_fraction.nsmallest(20)
\ No newline at end of file
diff --git a/notebooks/_solutions/05-spatial-operations-overlays4.py b/notebooks/_solutions/05-spatial-operations-overlays4.py
new file mode 100644
index 0000000..4c7f819
--- /dev/null
+++ b/notebooks/_solutions/05-spatial-operations-overlays4.py
@@ -0,0 +1,3 @@
+# Import the land use dataset
+land_use = geopandas.read_file("data/paris_land_use.zip")
+land_use.head()
\ No newline at end of file
diff --git a/notebooks/_solutions/05-spatial-operations-overlays5.py b/notebooks/_solutions/05-spatial-operations-overlays5.py
new file mode 100644
index 0000000..036be0c
--- /dev/null
+++ b/notebooks/_solutions/05-spatial-operations-overlays5.py
@@ -0,0 +1,2 @@
+# Make a plot of the land use with 'class' as the color
+land_use.plot(column='class', legend=True, figsize=(15, 10))
\ No newline at end of file
diff --git a/notebooks/_solutions/05-spatial-operations-overlays6.py b/notebooks/_solutions/05-spatial-operations-overlays6.py
new file mode 100644
index 0000000..1063dcb
--- /dev/null
+++ b/notebooks/_solutions/05-spatial-operations-overlays6.py
@@ -0,0 +1,2 @@
+# Add the area as a new column
+land_use['area'] = land_use.geometry.area
\ No newline at end of file
diff --git a/notebooks/_solutions/05-spatial-operations-overlays7.py b/notebooks/_solutions/05-spatial-operations-overlays7.py
new file mode 100644
index 0000000..dfa7143
--- /dev/null
+++ b/notebooks/_solutions/05-spatial-operations-overlays7.py
@@ -0,0 +1,3 @@
+# Calculate the total area for each land use class
+total_area = land_use.groupby('class')['area'].sum() / 1000**2
+total_area
\ No newline at end of file
diff --git a/notebooks/_solutions/05-spatial-operations-overlays8.py b/notebooks/_solutions/05-spatial-operations-overlays8.py
new file mode 100644
index 0000000..5883250
--- /dev/null
+++ b/notebooks/_solutions/05-spatial-operations-overlays8.py
@@ -0,0 +1,2 @@
+# Calculate the intersection of both polygons
+intersection = park_boulogne.intersection(muette)
\ No newline at end of file
diff --git a/notebooks/_solutions/05-spatial-operations-overlays9.py b/notebooks/_solutions/05-spatial-operations-overlays9.py
new file mode 100644
index 0000000..40a0b42
--- /dev/null
+++ b/notebooks/_solutions/05-spatial-operations-overlays9.py
@@ -0,0 +1,2 @@
+# Plot the intersection
+geopandas.GeoSeries([intersection]).plot()
\ No newline at end of file
diff --git a/notebooks/_solutions/11-xarray-intro1.py b/notebooks/_solutions/11-xarray-intro1.py
new file mode 100644
index 0000000..03565de
--- /dev/null
+++ b/notebooks/_solutions/11-xarray-intro1.py
@@ -0,0 +1,2 @@
+tc_data = xr.open_dataarray("./data/gent/raster/2020-09-17_Sentinel_2_L1C_True_color.tiff", 
+                            engine="rasterio", mask_and_scale=True)
\ No newline at end of file
diff --git a/notebooks/_solutions/11-xarray-intro10.py b/notebooks/_solutions/11-xarray-intro10.py
new file mode 100644
index 0000000..fa7f7c7
--- /dev/null
+++ b/notebooks/_solutions/11-xarray-intro10.py
@@ -0,0 +1,2 @@
+# Count the number of cells with value 65535
+np.sum(b4_data == 65535).values
\ No newline at end of file
diff --git a/notebooks/_solutions/11-xarray-intro11.py b/notebooks/_solutions/11-xarray-intro11.py
new file mode 100644
index 0000000..9a408f0
--- /dev/null
+++ b/notebooks/_solutions/11-xarray-intro11.py
@@ -0,0 +1,3 @@
+# Convert to float and make 65535 equal to Nan
+b4_data_f = b4_data.astype(float)
+b4_data_f = b4_data_f.where(b4_data != 65535)
\ No newline at end of file
diff --git a/notebooks/_solutions/11-xarray-intro12.py b/notebooks/_solutions/11-xarray-intro12.py
new file mode 100644
index 0000000..7d426f5
--- /dev/null
+++ b/notebooks/_solutions/11-xarray-intro12.py
@@ -0,0 +1,2 @@
+# Count the number of cells with value 0
+np.sum(np.isnan(b4_data_f)).values
\ No newline at end of file
diff --git a/notebooks/_solutions/11-xarray-intro13.py b/notebooks/_solutions/11-xarray-intro13.py
new file mode 100644
index 0000000..fcc59c1
--- /dev/null
+++ b/notebooks/_solutions/11-xarray-intro13.py
@@ -0,0 +1,4 @@
+# Create the histogram plots
+fig, (ax0, ax1) = plt.subplots(1, 2, sharey=True, figsize=(12, 4))
+b4_data.plot.hist(bins=30, log=True, ax=ax0)
+b4_data_f.plot.hist(bins=30, log=True, ax=ax1);
\ No newline at end of file
diff --git a/notebooks/_solutions/11-xarray-intro14.py b/notebooks/_solutions/11-xarray-intro14.py
new file mode 100644
index 0000000..492bfd2
--- /dev/null
+++ b/notebooks/_solutions/11-xarray-intro14.py
@@ -0,0 +1,3 @@
+# Calculate the min and max for each channel
+h_min = herstappe_data.min(dim=["x", "y"])
+h_max = herstappe_data.max(dim=["x", "y"])
\ No newline at end of file
diff --git a/notebooks/_solutions/11-xarray-intro15.py b/notebooks/_solutions/11-xarray-intro15.py
new file mode 100644
index 0000000..46ef2a9
--- /dev/null
+++ b/notebooks/_solutions/11-xarray-intro15.py
@@ -0,0 +1,2 @@
+# Rescale the data
+herstappe_rescaled = ((herstappe_data - h_min)/(h_max - h_min))
\ No newline at end of file
diff --git a/notebooks/_solutions/11-xarray-intro16.py b/notebooks/_solutions/11-xarray-intro16.py
new file mode 100644
index 0000000..33158bd
--- /dev/null
+++ b/notebooks/_solutions/11-xarray-intro16.py
@@ -0,0 +1,2 @@
+# Make a plot
+herstappe_rescaled.plot.imshow(figsize=(9, 5))
\ No newline at end of file
diff --git a/notebooks/_solutions/11-xarray-intro17.py b/notebooks/_solutions/11-xarray-intro17.py
new file mode 100644
index 0000000..82e2b82
--- /dev/null
+++ b/notebooks/_solutions/11-xarray-intro17.py
@@ -0,0 +1,3 @@
+gent = xr.open_dataarray("./data/gent/raster/2020-09-17_Sentinel_2_L1C_True_color.tiff", 
+                         engine="rasterio", mask_and_scale=False)
+gent
\ No newline at end of file
diff --git a/notebooks/_solutions/11-xarray-intro18.py b/notebooks/_solutions/11-xarray-intro18.py
new file mode 100644
index 0000000..d0c9321
--- /dev/null
+++ b/notebooks/_solutions/11-xarray-intro18.py
@@ -0,0 +1 @@
+gent.plot.imshow()
\ No newline at end of file
diff --git a/notebooks/_solutions/11-xarray-intro19.py b/notebooks/_solutions/11-xarray-intro19.py
new file mode 100644
index 0000000..4fa5eb8
--- /dev/null
+++ b/notebooks/_solutions/11-xarray-intro19.py
@@ -0,0 +1,3 @@
+# Convert to float and convert values equal to 65535 into NaN
+gent_f = gent.astype(float)
+gent_f = gent_f.where(gent_f != 65535)
\ No newline at end of file
diff --git a/notebooks/_solutions/11-xarray-intro2.py b/notebooks/_solutions/11-xarray-intro2.py
new file mode 100644
index 0000000..0d7bc1c
--- /dev/null
+++ b/notebooks/_solutions/11-xarray-intro2.py
@@ -0,0 +1 @@
+tc_data.rio.nodata # after masking NODATA value are represented as np.nan
\ No newline at end of file
diff --git a/notebooks/_solutions/11-xarray-intro20.py b/notebooks/_solutions/11-xarray-intro20.py
new file mode 100644
index 0000000..0ca6dff
--- /dev/null
+++ b/notebooks/_solutions/11-xarray-intro20.py
@@ -0,0 +1,2 @@
+# Divide by the maximum of the int16 range to get [0-1] data
+gent_f = gent_f / 65535
\ No newline at end of file
diff --git a/notebooks/_solutions/11-xarray-intro21.py b/notebooks/_solutions/11-xarray-intro21.py
new file mode 100644
index 0000000..881d3e4
--- /dev/null
+++ b/notebooks/_solutions/11-xarray-intro21.py
@@ -0,0 +1,2 @@
+# Make a RGB plot
+gent_f.plot.imshow(figsize=(9, 5))
\ No newline at end of file
diff --git a/notebooks/_solutions/11-xarray-intro22.py b/notebooks/_solutions/11-xarray-intro22.py
new file mode 100644
index 0000000..b08b993
--- /dev/null
+++ b/notebooks/_solutions/11-xarray-intro22.py
@@ -0,0 +1,2 @@
+# Make a Grey scale plot
+gent_f.sum(dim="band").plot.imshow(cmap="Greys", figsize=(9, 5))
\ No newline at end of file
diff --git a/notebooks/_solutions/11-xarray-intro23.py b/notebooks/_solutions/11-xarray-intro23.py
new file mode 100644
index 0000000..caac1bc
--- /dev/null
+++ b/notebooks/_solutions/11-xarray-intro23.py
@@ -0,0 +1,4 @@
+b4_data = xr.open_dataarray("./data/gent/raster/2020-09-17_Sentinel_2_L1C_B04.tiff", 
+                            engine="rasterio", mask_and_scale=False).sel(band=1)
+b8_data = xr.open_dataarray("./data/gent/raster/2020-09-17_Sentinel_2_L1C_B08.tiff", 
+                            engine="rasterio", mask_and_scale=False).sel(band=1)
\ No newline at end of file
diff --git a/notebooks/_solutions/11-xarray-intro24.py b/notebooks/_solutions/11-xarray-intro24.py
new file mode 100644
index 0000000..98c0f39
--- /dev/null
+++ b/notebooks/_solutions/11-xarray-intro24.py
@@ -0,0 +1,3 @@
+# Rescale the data to 0-1
+b4_data = (b4_data - b4_data.min())/(b4_data.max() - b4_data.min())
+b8_data = (b8_data - b8_data.min())/(b8_data.max() - b8_data.min())
\ No newline at end of file
diff --git a/notebooks/_solutions/11-xarray-intro25.py b/notebooks/_solutions/11-xarray-intro25.py
new file mode 100644
index 0000000..c18995a
--- /dev/null
+++ b/notebooks/_solutions/11-xarray-intro25.py
@@ -0,0 +1,2 @@
+# Calculate the ndvi
+ndvi = (b8_data - b4_data)/(b8_data + b4_data)
\ No newline at end of file
diff --git a/notebooks/_solutions/11-xarray-intro26.py b/notebooks/_solutions/11-xarray-intro26.py
new file mode 100644
index 0000000..b8b3c45
--- /dev/null
+++ b/notebooks/_solutions/11-xarray-intro26.py
@@ -0,0 +1,4 @@
+# As a quick reference, plot using the `"Greens"` colormap as such:
+fig, ax = plt.subplots(figsize=(14, 5))
+ll = ndvi.plot.imshow(ax=ax, cmap="Greens")
+ax.set_aspect("equal")
\ No newline at end of file
diff --git a/notebooks/_solutions/11-xarray-intro27.py b/notebooks/_solutions/11-xarray-intro27.py
new file mode 100644
index 0000000..afebd64
--- /dev/null
+++ b/notebooks/_solutions/11-xarray-intro27.py
@@ -0,0 +1,9 @@
+# A sequential colormap `YlGn` using a Matplotlib norm to adjust colormap influence on image 
+# https://matplotlib.org/stable/tutorials/colors/colormapnorms.html
+import matplotlib.colors as mcolors
+
+fig, ax = plt.subplots(figsize=(14, 5))
+
+div_norm = mcolors.Normalize(0.1, 0.8)
+ll = ndvi.plot.imshow(ax=ax, cmap="YlGn", norm=div_norm)
+ax.set_aspect("equal")
\ No newline at end of file
diff --git a/notebooks/_solutions/11-xarray-intro28.py b/notebooks/_solutions/11-xarray-intro28.py
new file mode 100644
index 0000000..4c1bd3c
--- /dev/null
+++ b/notebooks/_solutions/11-xarray-intro28.py
@@ -0,0 +1,9 @@
+# A Diverging colormap `RdYlGn` with a normalization on the color limits in two directions of the central point - 
+# https://matplotlib.org/stable/api/_as_gen/matplotlib.colors.TwoSlopeNorm.html
+fig, ax = plt.subplots(figsize=(14, 5))
+
+div_norm = mcolors.TwoSlopeNorm(vmin=-0.1, vcenter=0.4, vmax=0.8)
+
+ll = ax.imshow(ndvi.values, cmap="RdYlGn", norm=div_norm)
+fig.colorbar(ll);
+ax.set_axis_off();
\ No newline at end of file
diff --git a/notebooks/_solutions/11-xarray-intro29.py b/notebooks/_solutions/11-xarray-intro29.py
new file mode 100644
index 0000000..43a0be3
--- /dev/null
+++ b/notebooks/_solutions/11-xarray-intro29.py
@@ -0,0 +1,2 @@
+# Convert to 2D float array 
+b4_data = b4_data.sel(band=1).astype(float)
\ No newline at end of file
diff --git a/notebooks/_solutions/11-xarray-intro3.py b/notebooks/_solutions/11-xarray-intro3.py
new file mode 100644
index 0000000..804cc90
--- /dev/null
+++ b/notebooks/_solutions/11-xarray-intro3.py
@@ -0,0 +1,2 @@
+tc_data = xr.open_dataarray("./data/gent/raster/2020-09-17_Sentinel_2_L1C_True_color.tiff", 
+                            engine="rasterio", mask_and_scale=False)
\ No newline at end of file
diff --git a/notebooks/_solutions/11-xarray-intro30.py b/notebooks/_solutions/11-xarray-intro30.py
new file mode 100644
index 0000000..474a475
--- /dev/null
+++ b/notebooks/_solutions/11-xarray-intro30.py
@@ -0,0 +1,2 @@
+# Rescale the data
+b4_data = (b4_data - b4_data.min())/(b4_data.max() - b4_data.min())
\ No newline at end of file
diff --git a/notebooks/_solutions/11-xarray-intro31.py b/notebooks/_solutions/11-xarray-intro31.py
new file mode 100644
index 0000000..ccb59e3
--- /dev/null
+++ b/notebooks/_solutions/11-xarray-intro31.py
@@ -0,0 +1,2 @@
+# Classify the array into 3 bins
+b4_data_classified = xr.apply_ufunc(np.digitize, b4_data, [0.05, 0.1])
\ No newline at end of file
diff --git a/notebooks/_solutions/11-xarray-intro32.py b/notebooks/_solutions/11-xarray-intro32.py
new file mode 100644
index 0000000..13b3828
--- /dev/null
+++ b/notebooks/_solutions/11-xarray-intro32.py
@@ -0,0 +1,4 @@
+# Create an image plot
+fig, ax = plt.subplots(figsize=(12, 5))
+img = b4_data_classified.plot.imshow(ax=ax, add_colorbar=False, interpolation="antialiased")
+fig.colorbar(img, values=[0, 1, 2], ticks=[0, 1, 2])
\ No newline at end of file
diff --git a/notebooks/_solutions/11-xarray-intro33.py b/notebooks/_solutions/11-xarray-intro33.py
new file mode 100644
index 0000000..13b3828
--- /dev/null
+++ b/notebooks/_solutions/11-xarray-intro33.py
@@ -0,0 +1,4 @@
+# Create an image plot
+fig, ax = plt.subplots(figsize=(12, 5))
+img = b4_data_classified.plot.imshow(ax=ax, add_colorbar=False, interpolation="antialiased")
+fig.colorbar(img, values=[0, 1, 2], ticks=[0, 1, 2])
\ No newline at end of file
diff --git a/notebooks/_solutions/11-xarray-intro4.py b/notebooks/_solutions/11-xarray-intro4.py
new file mode 100644
index 0000000..671db05
--- /dev/null
+++ b/notebooks/_solutions/11-xarray-intro4.py
@@ -0,0 +1,3 @@
+# Get the green channel
+tc_g = tc_data.sel(band=2)
+tc_g
\ No newline at end of file
diff --git a/notebooks/_solutions/11-xarray-intro5.py b/notebooks/_solutions/11-xarray-intro5.py
new file mode 100644
index 0000000..ad26379
--- /dev/null
+++ b/notebooks/_solutions/11-xarray-intro5.py
@@ -0,0 +1,2 @@
+# Plot the green channel
+tc_g.plot.imshow(cmap="Greens")
\ No newline at end of file
diff --git a/notebooks/_solutions/11-xarray-intro6.py b/notebooks/_solutions/11-xarray-intro6.py
new file mode 100644
index 0000000..a2a9749
--- /dev/null
+++ b/notebooks/_solutions/11-xarray-intro6.py
@@ -0,0 +1,2 @@
+# Convert all values above 15000
+tc_g.where(tc_g < 15000, 65535)
\ No newline at end of file
diff --git a/notebooks/_solutions/11-xarray-intro7.py b/notebooks/_solutions/11-xarray-intro7.py
new file mode 100644
index 0000000..804cc90
--- /dev/null
+++ b/notebooks/_solutions/11-xarray-intro7.py
@@ -0,0 +1,2 @@
+tc_data = xr.open_dataarray("./data/gent/raster/2020-09-17_Sentinel_2_L1C_True_color.tiff", 
+                            engine="rasterio", mask_and_scale=False)
\ No newline at end of file
diff --git a/notebooks/_solutions/11-xarray-intro8.py b/notebooks/_solutions/11-xarray-intro8.py
new file mode 100644
index 0000000..805ca9c
--- /dev/null
+++ b/notebooks/_solutions/11-xarray-intro8.py
@@ -0,0 +1,2 @@
+# subsample the data
+tc_data[:, ::5, ::5].shape
\ No newline at end of file
diff --git a/notebooks/_solutions/11-xarray-intro9.py b/notebooks/_solutions/11-xarray-intro9.py
new file mode 100644
index 0000000..e247fa7
--- /dev/null
+++ b/notebooks/_solutions/11-xarray-intro9.py
@@ -0,0 +1,2 @@
+b4_data = xr.open_dataarray("./data/gent/raster/2020-09-17_Sentinel_2_L1C_B04.tiff",
+                            engine="rasterio", mask_and_scale=False)
\ No newline at end of file
diff --git a/notebooks/_solutions/12-xarray-advanced1.py b/notebooks/_solutions/12-xarray-advanced1.py
new file mode 100644
index 0000000..439d8c3
--- /dev/null
+++ b/notebooks/_solutions/12-xarray-advanced1.py
@@ -0,0 +1,2 @@
+era5_renamed = era5.rename(mapping)
+era5_renamed
\ No newline at end of file
diff --git a/notebooks/_solutions/12-xarray-advanced10.py b/notebooks/_solutions/12-xarray-advanced10.py
new file mode 100644
index 0000000..501a378
--- /dev/null
+++ b/notebooks/_solutions/12-xarray-advanced10.py
@@ -0,0 +1,2 @@
+temp_mean = era5_renamed["temperature_k"].mean(dim=["latitude", "longitude"]).sel(time=slice("1981", "2020"))
+temp_mean.resample(time="YE").mean().plot.line(figsize=(12, 5))
\ No newline at end of file
diff --git a/notebooks/_solutions/12-xarray-advanced11.py b/notebooks/_solutions/12-xarray-advanced11.py
new file mode 100644
index 0000000..e53bd9e
--- /dev/null
+++ b/notebooks/_solutions/12-xarray-advanced11.py
@@ -0,0 +1,5 @@
+snowfall_1991_2005 = era5_renamed.sel(time=slice("1991", "2005"))["snowfall_m"]
+snowfall_yearly = snowfall_1991_2005.resample(time="YE").sum()*100
+snowfall_yearly = snowfall_yearly.rename("snowfall_cm")
+snowfall_yearly.plot.imshow(col="time", col_wrap=5, cmap=cmocean.cm.ice, 
+                            cbar_kwargs={"label": "snowfall (cm)"})
\ No newline at end of file
diff --git a/notebooks/_solutions/12-xarray-advanced2.py b/notebooks/_solutions/12-xarray-advanced2.py
new file mode 100644
index 0000000..d5d9bd4
--- /dev/null
+++ b/notebooks/_solutions/12-xarray-advanced2.py
@@ -0,0 +1 @@
+era5_renamed["temperature_c"] = era5_renamed["temperature_k"] - 273.15
\ No newline at end of file
diff --git a/notebooks/_solutions/12-xarray-advanced3.py b/notebooks/_solutions/12-xarray-advanced3.py
new file mode 100644
index 0000000..d62424d
--- /dev/null
+++ b/notebooks/_solutions/12-xarray-advanced3.py
@@ -0,0 +1 @@
+era5_renamed["temperature_c"].plot.hist(bins=50);
\ No newline at end of file
diff --git a/notebooks/_solutions/12-xarray-advanced4.py b/notebooks/_solutions/12-xarray-advanced4.py
new file mode 100644
index 0000000..f50a1a1
--- /dev/null
+++ b/notebooks/_solutions/12-xarray-advanced4.py
@@ -0,0 +1 @@
+era5_renamed["snowfall_m"].sum(dim=["latitude", "longitude"]).plot.line(figsize=(18, 5))
\ No newline at end of file
diff --git a/notebooks/_solutions/12-xarray-advanced5.py b/notebooks/_solutions/12-xarray-advanced5.py
new file mode 100644
index 0000000..f1e8ac5
--- /dev/null
+++ b/notebooks/_solutions/12-xarray-advanced5.py
@@ -0,0 +1 @@
+era5_renamed["speed_of_sound_m_s"] = 331.5 + (0.6*era5_renamed["temperature_c"])
\ No newline at end of file
diff --git a/notebooks/_solutions/12-xarray-advanced6.py b/notebooks/_solutions/12-xarray-advanced6.py
new file mode 100644
index 0000000..d7677fa
--- /dev/null
+++ b/notebooks/_solutions/12-xarray-advanced6.py
@@ -0,0 +1 @@
+era5_renamed.plot.scatter(x="temperature_c", y="speed_of_sound_m_s")
\ No newline at end of file
diff --git a/notebooks/_solutions/12-xarray-advanced7.py b/notebooks/_solutions/12-xarray-advanced7.py
new file mode 100644
index 0000000..e23f078
--- /dev/null
+++ b/notebooks/_solutions/12-xarray-advanced7.py
@@ -0,0 +1,8 @@
+ghent_pressure = era5_renamed.sel(latitude=51.05, longitude=3.71, method="nearest")["pressure_pa"]
+
+fig, ax = plt.subplots(figsize=(18, 6))
+ghent_pressure.plot.line(ax=ax)
+ghent_pressure.resample(time="YE").mean().plot.line(ax=ax)
+
+ax.set_ylabel('Pressure (Pa)')
+ax.set_title('Pressure (Pa) in Ghent (-51.05, 3.71)')
\ No newline at end of file
diff --git a/notebooks/_solutions/12-xarray-advanced8.py b/notebooks/_solutions/12-xarray-advanced8.py
new file mode 100644
index 0000000..cae9d52
--- /dev/null
+++ b/notebooks/_solutions/12-xarray-advanced8.py
@@ -0,0 +1,7 @@
+ghent_precipitation = era5_renamed.sel(latitude=51.05, longitude=3.71, method="nearest")["precipitation_m"]
+ghent_precipitation_month_max = ghent_precipitation.groupby("time.month").max()*1000
+
+fig, ax = plt.subplots()
+ghent_precipitation_month_max.to_dataframe()["precipitation_m"].plot.barh(ax=ax)
+ax.set_xlabel("Maximal monthly precipitation (mm)")
+ax.set_ylabel("Month of the year")
\ No newline at end of file
diff --git a/notebooks/_solutions/12-xarray-advanced9.py b/notebooks/_solutions/12-xarray-advanced9.py
new file mode 100644
index 0000000..d1d3b06
--- /dev/null
+++ b/notebooks/_solutions/12-xarray-advanced9.py
@@ -0,0 +1,4 @@
+seaons_temp = era5_renamed["temperature_k"].groupby("time.season").mean()
+# See https://github.com/pydata/xarray/issues/757 for getting well-sorted groups for plotting
+seaons_temp = seaons_temp.reindex(season=['DJF','MAM','JJA', 'SON'])
+seaons_temp.plot.imshow(col="season", cmap="Reds")
\ No newline at end of file
diff --git a/notebooks/_solutions/13-raster-processing1.py b/notebooks/_solutions/13-raster-processing1.py
new file mode 100644
index 0000000..7edc0dd
--- /dev/null
+++ b/notebooks/_solutions/13-raster-processing1.py
@@ -0,0 +1,2 @@
+dem = xr.open_dataarray("zip://./data/gent/DHMVIIDTMRAS25m.zip", engine="rasterio")
+dem
\ No newline at end of file
diff --git a/notebooks/_solutions/13-raster-processing10.py b/notebooks/_solutions/13-raster-processing10.py
new file mode 100644
index 0000000..290dbe2
--- /dev/null
+++ b/notebooks/_solutions/13-raster-processing10.py
@@ -0,0 +1 @@
+gent_centre = Point(3.72174, 51.05393)
\ No newline at end of file
diff --git a/notebooks/_solutions/13-raster-processing11.py b/notebooks/_solutions/13-raster-processing11.py
new file mode 100644
index 0000000..d5249e8
--- /dev/null
+++ b/notebooks/_solutions/13-raster-processing11.py
@@ -0,0 +1 @@
+gent_centre_31370 = geopandas.GeoSeries([gent_centre], crs="EPSG:4326").to_crs("EPSG:31370")
\ No newline at end of file
diff --git a/notebooks/_solutions/13-raster-processing12.py b/notebooks/_solutions/13-raster-processing12.py
new file mode 100644
index 0000000..13dc756
--- /dev/null
+++ b/notebooks/_solutions/13-raster-processing12.py
@@ -0,0 +1,4 @@
+gent_bounds = gent_centre_31370.buffer(10*1000).total_bounds
+# or first extracting the Point again:
+# gent_bounds = gent_centre_31370[0].buffer(10*1000).bounds
+gent_bounds
\ No newline at end of file
diff --git a/notebooks/_solutions/13-raster-processing13.py b/notebooks/_solutions/13-raster-processing13.py
new file mode 100644
index 0000000..a2316c4
--- /dev/null
+++ b/notebooks/_solutions/13-raster-processing13.py
@@ -0,0 +1,2 @@
+dem_gent = dem.rio.clip_box(*gent_bounds)
+dem_gent
\ No newline at end of file
diff --git a/notebooks/_solutions/13-raster-processing14.py b/notebooks/_solutions/13-raster-processing14.py
new file mode 100644
index 0000000..350fcc9
--- /dev/null
+++ b/notebooks/_solutions/13-raster-processing14.py
@@ -0,0 +1,3 @@
+img = dem_gent.plot.imshow(
+    cmap="terrain", figsize=(10, 10), interpolation='antialiased', vmin=0, vmax=30)
+img.axes.set_aspect("equal")
\ No newline at end of file
diff --git a/notebooks/_solutions/13-raster-processing15.py b/notebooks/_solutions/13-raster-processing15.py
new file mode 100644
index 0000000..b07e543
--- /dev/null
+++ b/notebooks/_solutions/13-raster-processing15.py
@@ -0,0 +1,2 @@
+land_use = xr.open_dataarray("data/CLC2018_V2020_20u1_flanders.tif").sel(band=1)
+land_use
\ No newline at end of file
diff --git a/notebooks/_solutions/13-raster-processing16.py b/notebooks/_solutions/13-raster-processing16.py
new file mode 100644
index 0000000..3438981
--- /dev/null
+++ b/notebooks/_solutions/13-raster-processing16.py
@@ -0,0 +1 @@
+land_use.plot.imshow(robust=True)
\ No newline at end of file
diff --git a/notebooks/_solutions/13-raster-processing17.py b/notebooks/_solutions/13-raster-processing17.py
new file mode 100644
index 0000000..dab4c42
--- /dev/null
+++ b/notebooks/_solutions/13-raster-processing17.py
@@ -0,0 +1 @@
+land_use.rio.resolution()
\ No newline at end of file
diff --git a/notebooks/_solutions/13-raster-processing18.py b/notebooks/_solutions/13-raster-processing18.py
new file mode 100644
index 0000000..df1f38e
--- /dev/null
+++ b/notebooks/_solutions/13-raster-processing18.py
@@ -0,0 +1 @@
+land_use.rio.crs
\ No newline at end of file
diff --git a/notebooks/_solutions/13-raster-processing19.py b/notebooks/_solutions/13-raster-processing19.py
new file mode 100644
index 0000000..16f8837
--- /dev/null
+++ b/notebooks/_solutions/13-raster-processing19.py
@@ -0,0 +1,2 @@
+land_use_reprojected = land_use.rio.reproject(dem_gent.rio.crs)
+land_use_reprojected
\ No newline at end of file
diff --git a/notebooks/_solutions/13-raster-processing2.py b/notebooks/_solutions/13-raster-processing2.py
new file mode 100644
index 0000000..c976b2a
--- /dev/null
+++ b/notebooks/_solutions/13-raster-processing2.py
@@ -0,0 +1 @@
+dem.rio.crs
\ No newline at end of file
diff --git a/notebooks/_solutions/13-raster-processing20.py b/notebooks/_solutions/13-raster-processing20.py
new file mode 100644
index 0000000..f1944f0
--- /dev/null
+++ b/notebooks/_solutions/13-raster-processing20.py
@@ -0,0 +1 @@
+land_use_reprojected.plot.imshow(robust=True)
\ No newline at end of file
diff --git a/notebooks/_solutions/13-raster-processing21.py b/notebooks/_solutions/13-raster-processing21.py
new file mode 100644
index 0000000..7f38349
--- /dev/null
+++ b/notebooks/_solutions/13-raster-processing21.py
@@ -0,0 +1 @@
+land_use_gent = land_use.rio.reproject_match(dem_gent)
\ No newline at end of file
diff --git a/notebooks/_solutions/13-raster-processing22.py b/notebooks/_solutions/13-raster-processing22.py
new file mode 100644
index 0000000..c5991f0
--- /dev/null
+++ b/notebooks/_solutions/13-raster-processing22.py
@@ -0,0 +1 @@
+land_use_gent.plot.imshow()
\ No newline at end of file
diff --git a/notebooks/_solutions/13-raster-processing23.py b/notebooks/_solutions/13-raster-processing23.py
new file mode 100644
index 0000000..32088f4
--- /dev/null
+++ b/notebooks/_solutions/13-raster-processing23.py
@@ -0,0 +1,2 @@
+land_use_residential = (land_use_gent == 1) | (land_use_gent == 2)
+land_use_residential
\ No newline at end of file
diff --git a/notebooks/_solutions/13-raster-processing24.py b/notebooks/_solutions/13-raster-processing24.py
new file mode 100644
index 0000000..047a80f
--- /dev/null
+++ b/notebooks/_solutions/13-raster-processing24.py
@@ -0,0 +1,3 @@
+# Alternative solution using `isin`
+land_use_residential = land_use_gent.isin([1, 2])
+land_use_residential
\ No newline at end of file
diff --git a/notebooks/_solutions/13-raster-processing25.py b/notebooks/_solutions/13-raster-processing25.py
new file mode 100644
index 0000000..299d178
--- /dev/null
+++ b/notebooks/_solutions/13-raster-processing25.py
@@ -0,0 +1 @@
+land_use_residential.plot.imshow()
\ No newline at end of file
diff --git a/notebooks/_solutions/13-raster-processing26.py b/notebooks/_solutions/13-raster-processing26.py
new file mode 100644
index 0000000..62a9390
--- /dev/null
+++ b/notebooks/_solutions/13-raster-processing26.py
@@ -0,0 +1 @@
+(dem_gent > 10).plot.imshow()
\ No newline at end of file
diff --git a/notebooks/_solutions/13-raster-processing27.py b/notebooks/_solutions/13-raster-processing27.py
new file mode 100644
index 0000000..e3cf6d9
--- /dev/null
+++ b/notebooks/_solutions/13-raster-processing27.py
@@ -0,0 +1 @@
+suitable_locations = land_use_residential * (dem_gent > 10)
\ No newline at end of file
diff --git a/notebooks/_solutions/13-raster-processing28.py b/notebooks/_solutions/13-raster-processing28.py
new file mode 100644
index 0000000..c132a5b
--- /dev/null
+++ b/notebooks/_solutions/13-raster-processing28.py
@@ -0,0 +1 @@
+suitable_locations.plot.imshow()
\ No newline at end of file
diff --git a/notebooks/_solutions/13-raster-processing29.py b/notebooks/_solutions/13-raster-processing29.py
new file mode 100644
index 0000000..8e80c15
--- /dev/null
+++ b/notebooks/_solutions/13-raster-processing29.py
@@ -0,0 +1 @@
+roads = geopandas.read_file("./data/gent/vector/wegsegmenten-gent.geojson.zip")
\ No newline at end of file
diff --git a/notebooks/_solutions/13-raster-processing3.py b/notebooks/_solutions/13-raster-processing3.py
new file mode 100644
index 0000000..54d332c
--- /dev/null
+++ b/notebooks/_solutions/13-raster-processing3.py
@@ -0,0 +1,2 @@
+dem = xr.open_dataarray("zip://./data/gent/DHMVIIDTMRAS25m.zip", engine="rasterio").sel(band=1)
+dem
\ No newline at end of file
diff --git a/notebooks/_solutions/13-raster-processing30.py b/notebooks/_solutions/13-raster-processing30.py
new file mode 100644
index 0000000..aea92ed
--- /dev/null
+++ b/notebooks/_solutions/13-raster-processing30.py
@@ -0,0 +1 @@
+roads.head()
\ No newline at end of file
diff --git a/notebooks/_solutions/13-raster-processing31.py b/notebooks/_solutions/13-raster-processing31.py
new file mode 100644
index 0000000..a124c13
--- /dev/null
+++ b/notebooks/_solutions/13-raster-processing31.py
@@ -0,0 +1 @@
+roads_subset = roads[roads["frc_omschrijving"].isin(road_types)]
\ No newline at end of file
diff --git a/notebooks/_solutions/13-raster-processing32.py b/notebooks/_solutions/13-raster-processing32.py
new file mode 100644
index 0000000..5fd5279
--- /dev/null
+++ b/notebooks/_solutions/13-raster-processing32.py
@@ -0,0 +1 @@
+roads_subset.plot(column="frc_omschrijving", figsize=(10, 10), legend=True)
\ No newline at end of file
diff --git a/notebooks/_solutions/13-raster-processing33.py b/notebooks/_solutions/13-raster-processing33.py
new file mode 100644
index 0000000..dc334e1
--- /dev/null
+++ b/notebooks/_solutions/13-raster-processing33.py
@@ -0,0 +1,2 @@
+buffers = roads_subset["frc_omschrijving"].map(buffer_per_roadtype)
+buffers
\ No newline at end of file
diff --git a/notebooks/_solutions/13-raster-processing34.py b/notebooks/_solutions/13-raster-processing34.py
new file mode 100644
index 0000000..610dee3
--- /dev/null
+++ b/notebooks/_solutions/13-raster-processing34.py
@@ -0,0 +1,2 @@
+roads_buffer = roads_subset.to_crs("EPSG:31370").buffer(buffers)
+roads_buffer.plot()
\ No newline at end of file
diff --git a/notebooks/_solutions/13-raster-processing35.py b/notebooks/_solutions/13-raster-processing35.py
new file mode 100644
index 0000000..fa32ca9
--- /dev/null
+++ b/notebooks/_solutions/13-raster-processing35.py
@@ -0,0 +1,4 @@
+roads_buffer_arr = rasterio.features.rasterize(
+    roads_buffer.geometry, 
+    out_shape=dem_gent.shape, 
+    transform=dem_gent.rio.transform())
\ No newline at end of file
diff --git a/notebooks/_solutions/13-raster-processing36.py b/notebooks/_solutions/13-raster-processing36.py
new file mode 100644
index 0000000..dedff0c
--- /dev/null
+++ b/notebooks/_solutions/13-raster-processing36.py
@@ -0,0 +1 @@
+1 - roads_buffer_arr
\ No newline at end of file
diff --git a/notebooks/_solutions/13-raster-processing37.py b/notebooks/_solutions/13-raster-processing37.py
new file mode 100644
index 0000000..c66fce8
--- /dev/null
+++ b/notebooks/_solutions/13-raster-processing37.py
@@ -0,0 +1 @@
+plt.imshow(1 - roads_buffer_arr)
\ No newline at end of file
diff --git a/notebooks/_solutions/13-raster-processing38.py b/notebooks/_solutions/13-raster-processing38.py
new file mode 100644
index 0000000..ea1da0d
--- /dev/null
+++ b/notebooks/_solutions/13-raster-processing38.py
@@ -0,0 +1 @@
+suitable_locations = land_use_residential * (dem_gent > 10) * (1 - roads_buffer_arr)
\ No newline at end of file
diff --git a/notebooks/_solutions/13-raster-processing39.py b/notebooks/_solutions/13-raster-processing39.py
new file mode 100644
index 0000000..c132a5b
--- /dev/null
+++ b/notebooks/_solutions/13-raster-processing39.py
@@ -0,0 +1 @@
+suitable_locations.plot.imshow()
\ No newline at end of file
diff --git a/notebooks/_solutions/13-raster-processing4.py b/notebooks/_solutions/13-raster-processing4.py
new file mode 100644
index 0000000..25ebefe
--- /dev/null
+++ b/notebooks/_solutions/13-raster-processing4.py
@@ -0,0 +1 @@
+dem.plot.imshow(robust=True, cmap="terrain")
\ No newline at end of file
diff --git a/notebooks/_solutions/13-raster-processing40.py b/notebooks/_solutions/13-raster-processing40.py
new file mode 100644
index 0000000..b942ca2
--- /dev/null
+++ b/notebooks/_solutions/13-raster-processing40.py
@@ -0,0 +1,7 @@
+fig, ax = plt.subplots(figsize=(15, 15))
+
+gent.to_crs("EPSG:31370").plot(ax=ax, alpha=0.2, color="grey")
+contextily.add_basemap(ax, crs="EPSG:31370")
+
+suitable_locations.where(suitable_locations > 0).plot.imshow(ax=ax, alpha=0.5, add_colorbar=False)
+ax.set_aspect("equal")
\ No newline at end of file
diff --git a/notebooks/_solutions/13-raster-processing41.py b/notebooks/_solutions/13-raster-processing41.py
new file mode 100644
index 0000000..f7199b6
--- /dev/null
+++ b/notebooks/_solutions/13-raster-processing41.py
@@ -0,0 +1,2 @@
+green = geopandas.read_file("data/gent/vector/parken-gent.geojson")
+green.head()
\ No newline at end of file
diff --git a/notebooks/_solutions/13-raster-processing42.py b/notebooks/_solutions/13-raster-processing42.py
new file mode 100644
index 0000000..fe8c331
--- /dev/null
+++ b/notebooks/_solutions/13-raster-processing42.py
@@ -0,0 +1 @@
+green.plot()
\ No newline at end of file
diff --git a/notebooks/_solutions/13-raster-processing43.py b/notebooks/_solutions/13-raster-processing43.py
new file mode 100644
index 0000000..2c439f9
--- /dev/null
+++ b/notebooks/_solutions/13-raster-processing43.py
@@ -0,0 +1 @@
+green = green.dropna(subset="geometry")
\ No newline at end of file
diff --git a/notebooks/_solutions/13-raster-processing44.py b/notebooks/_solutions/13-raster-processing44.py
new file mode 100644
index 0000000..d2c30cc
--- /dev/null
+++ b/notebooks/_solutions/13-raster-processing44.py
@@ -0,0 +1,6 @@
+import rasterio.features
+
+green_arr = rasterio.features.rasterize(
+    green.to_crs("EPSG:31370").geometry, 
+    out_shape=dem_gent.shape, 
+    transform=dem_gent.rio.transform())
\ No newline at end of file
diff --git a/notebooks/_solutions/13-raster-processing45.py b/notebooks/_solutions/13-raster-processing45.py
new file mode 100644
index 0000000..6be1cb7
--- /dev/null
+++ b/notebooks/_solutions/13-raster-processing45.py
@@ -0,0 +1 @@
+green_arr = xr.DataArray(green_arr, coords=dem_gent.coords)
\ No newline at end of file
diff --git a/notebooks/_solutions/13-raster-processing46.py b/notebooks/_solutions/13-raster-processing46.py
new file mode 100644
index 0000000..8c63786
--- /dev/null
+++ b/notebooks/_solutions/13-raster-processing46.py
@@ -0,0 +1 @@
+green_arr.plot.imshow(figsize=(8, 8))
\ No newline at end of file
diff --git a/notebooks/_solutions/13-raster-processing47.py b/notebooks/_solutions/13-raster-processing47.py
new file mode 100644
index 0000000..2fb3bf2
--- /dev/null
+++ b/notebooks/_solutions/13-raster-processing47.py
@@ -0,0 +1 @@
+green_area = focal.focal_stats(green_arr, kernel, stats_funcs=["sum"])
\ No newline at end of file
diff --git a/notebooks/_solutions/13-raster-processing48.py b/notebooks/_solutions/13-raster-processing48.py
new file mode 100644
index 0000000..e380b52
--- /dev/null
+++ b/notebooks/_solutions/13-raster-processing48.py
@@ -0,0 +1 @@
+green_area.sel(stats="sum").plot.imshow()
\ No newline at end of file
diff --git a/notebooks/_solutions/13-raster-processing49.py b/notebooks/_solutions/13-raster-processing49.py
new file mode 100644
index 0000000..b88a1fe
--- /dev/null
+++ b/notebooks/_solutions/13-raster-processing49.py
@@ -0,0 +1 @@
+green_area_arr = signal.convolve(green_arr, kernel, mode='same')
\ No newline at end of file
diff --git a/notebooks/_solutions/13-raster-processing5.py b/notebooks/_solutions/13-raster-processing5.py
new file mode 100644
index 0000000..499f83a
--- /dev/null
+++ b/notebooks/_solutions/13-raster-processing5.py
@@ -0,0 +1,2 @@
+dem = xr.open_dataarray("zip://./data/gent/DHMVIIDTMRAS25m.zip", engine="rasterio", mask_and_scale=False).sel(band=1)
+dem.rio.nodata
\ No newline at end of file
diff --git a/notebooks/_solutions/13-raster-processing50.py b/notebooks/_solutions/13-raster-processing50.py
new file mode 100644
index 0000000..b9be891
--- /dev/null
+++ b/notebooks/_solutions/13-raster-processing50.py
@@ -0,0 +1 @@
+xr.DataArray(green_area_arr, coords=dem_gent.coords).plot.imshow(vmin=0)
\ No newline at end of file
diff --git a/notebooks/_solutions/13-raster-processing51.py b/notebooks/_solutions/13-raster-processing51.py
new file mode 100644
index 0000000..cab2b76
--- /dev/null
+++ b/notebooks/_solutions/13-raster-processing51.py
@@ -0,0 +1 @@
+green_area10 = green_area.sel(stats="sum").where(green_area.sel(stats="sum") > 10, 0)
\ No newline at end of file
diff --git a/notebooks/_solutions/13-raster-processing52.py b/notebooks/_solutions/13-raster-processing52.py
new file mode 100644
index 0000000..5e4ee21
--- /dev/null
+++ b/notebooks/_solutions/13-raster-processing52.py
@@ -0,0 +1 @@
+suitable_locations = land_use_residential * (dem_gent > 10) * (1 - roads_buffer_arr) * green_area10
\ No newline at end of file
diff --git a/notebooks/_solutions/13-raster-processing53.py b/notebooks/_solutions/13-raster-processing53.py
new file mode 100644
index 0000000..5e4ee21
--- /dev/null
+++ b/notebooks/_solutions/13-raster-processing53.py
@@ -0,0 +1 @@
+suitable_locations = land_use_residential * (dem_gent > 10) * (1 - roads_buffer_arr) * green_area10
\ No newline at end of file
diff --git a/notebooks/_solutions/13-raster-processing6.py b/notebooks/_solutions/13-raster-processing6.py
new file mode 100644
index 0000000..6112416
--- /dev/null
+++ b/notebooks/_solutions/13-raster-processing6.py
@@ -0,0 +1 @@
+dem.plot.imshow(robust=True, vmin=0, cmap="terrain")
\ No newline at end of file
diff --git a/notebooks/_solutions/13-raster-processing7.py b/notebooks/_solutions/13-raster-processing7.py
new file mode 100644
index 0000000..ad10589
--- /dev/null
+++ b/notebooks/_solutions/13-raster-processing7.py
@@ -0,0 +1 @@
+dem_masked = dem.where(dem != dem.rio.nodata)
\ No newline at end of file
diff --git a/notebooks/_solutions/13-raster-processing8.py b/notebooks/_solutions/13-raster-processing8.py
new file mode 100644
index 0000000..ad96213
--- /dev/null
+++ b/notebooks/_solutions/13-raster-processing8.py
@@ -0,0 +1 @@
+dem_masked.plot.imshow(robust=True, cmap="terrain")
\ No newline at end of file
diff --git a/notebooks/_solutions/13-raster-processing9.py b/notebooks/_solutions/13-raster-processing9.py
new file mode 100644
index 0000000..72adba8
--- /dev/null
+++ b/notebooks/_solutions/13-raster-processing9.py
@@ -0,0 +1 @@
+from shapely import Point
\ No newline at end of file
diff --git a/notebooks/_solutions/14-combine-data1.py b/notebooks/_solutions/14-combine-data1.py
new file mode 100644
index 0000000..85b6b3e
--- /dev/null
+++ b/notebooks/_solutions/14-combine-data1.py
@@ -0,0 +1,4 @@
+base_url = 'https://psl.noaa.gov/thredds/fileServer/Datasets/ncep.reanalysis/surface/air.sig995'
+
+files = [f'{base_url}.{year}.nc' for year in range(1990, 2001)]
+files
\ No newline at end of file
diff --git a/notebooks/_solutions/14-combine-data2.py b/notebooks/_solutions/14-combine-data2.py
new file mode 100644
index 0000000..846c285
--- /dev/null
+++ b/notebooks/_solutions/14-combine-data2.py
@@ -0,0 +1,2 @@
+ds = xr.open_mfdataset(files, parallel=True)
+ds
\ No newline at end of file
diff --git a/notebooks/_solutions/90_package_numpy1.py b/notebooks/_solutions/90_package_numpy1.py
new file mode 100644
index 0000000..4373061
--- /dev/null
+++ b/notebooks/_solutions/90_package_numpy1.py
@@ -0,0 +1,3 @@
+xr_tc_data = xr.open_dataarray("./data/gent/raster/2020-09-17_Sentinel_2_L1C_True_color.tiff", 
+                            engine="rasterio", mask_and_scale=False)
+tc_data = xr_tc_data.data
\ No newline at end of file
diff --git a/notebooks/_solutions/90_package_numpy10.py b/notebooks/_solutions/90_package_numpy10.py
new file mode 100644
index 0000000..65b4b29
--- /dev/null
+++ b/notebooks/_solutions/90_package_numpy10.py
@@ -0,0 +1,4 @@
+# Create the histogram plots
+fig, (ax0, ax1) = plt.subplots(1, 2, sharey=True)
+ax0.hist(b4_data.flatten(), bins=30, log=True);
+ax1.hist(b4_data_f.flatten(), bins=30, log=True);
\ No newline at end of file
diff --git a/notebooks/_solutions/90_package_numpy11.py b/notebooks/_solutions/90_package_numpy11.py
new file mode 100644
index 0000000..2d2848f
--- /dev/null
+++ b/notebooks/_solutions/90_package_numpy11.py
@@ -0,0 +1,6 @@
+xr_herstappe_data = xr.open_dataarray("./data/herstappe/raster/2020-09-17_Sentinel_2_L1C_True_color.tiff", 
+                                      engine="rasterio", mask_and_scale=False)
+herstappe_data = xr_herstappe_data.data
+
+(left, bottom, right, top) = xr_herstappe_data.rio.bounds()
+herstappe_extent = (left, right, bottom, top)
\ No newline at end of file
diff --git a/notebooks/_solutions/90_package_numpy12.py b/notebooks/_solutions/90_package_numpy12.py
new file mode 100644
index 0000000..bea2ad7
--- /dev/null
+++ b/notebooks/_solutions/90_package_numpy12.py
@@ -0,0 +1,3 @@
+# Make a RGB plot
+fig, ax = plt.subplots(figsize=(12, 5))
+ax.imshow(herstappe_data.transpose(1, 2, 0), extent=herstappe_extent);
\ No newline at end of file
diff --git a/notebooks/_solutions/90_package_numpy13.py b/notebooks/_solutions/90_package_numpy13.py
new file mode 100644
index 0000000..398bef7
--- /dev/null
+++ b/notebooks/_solutions/90_package_numpy13.py
@@ -0,0 +1,4 @@
+# Make a Grey scale plot
+greyscale_data = herstappe_data.sum(axis=0)
+fig, ax = plt.subplots(figsize=(12, 5))
+ax.imshow(greyscale_data, extent=herstappe_extent, cmap="Greys");
\ No newline at end of file
diff --git a/notebooks/_solutions/90_package_numpy14.py b/notebooks/_solutions/90_package_numpy14.py
new file mode 100644
index 0000000..9033209
--- /dev/null
+++ b/notebooks/_solutions/90_package_numpy14.py
@@ -0,0 +1,3 @@
+# Calculate the min and max for each channel
+h_min = herstappe_data.min(axis=(1, 2), keepdims=True)
+h_max = herstappe_data.max(axis=(1, 2), keepdims=True)
\ No newline at end of file
diff --git a/notebooks/_solutions/90_package_numpy15.py b/notebooks/_solutions/90_package_numpy15.py
new file mode 100644
index 0000000..46ef2a9
--- /dev/null
+++ b/notebooks/_solutions/90_package_numpy15.py
@@ -0,0 +1,2 @@
+# Rescale the data
+herstappe_rescaled = ((herstappe_data - h_min)/(h_max - h_min))
\ No newline at end of file
diff --git a/notebooks/_solutions/90_package_numpy16.py b/notebooks/_solutions/90_package_numpy16.py
new file mode 100644
index 0000000..4718bf7
--- /dev/null
+++ b/notebooks/_solutions/90_package_numpy16.py
@@ -0,0 +1,3 @@
+# Make a plot
+fig, ax = plt.subplots(figsize=(12, 5))
+ax.imshow(herstappe_rescaled.transpose(1, 2, 0), extent=herstappe_extent);
\ No newline at end of file
diff --git a/notebooks/_solutions/90_package_numpy17.py b/notebooks/_solutions/90_package_numpy17.py
new file mode 100644
index 0000000..4aa57e2
--- /dev/null
+++ b/notebooks/_solutions/90_package_numpy17.py
@@ -0,0 +1,2 @@
+# Squeeze to 2D float array 
+b4_data = b4_data.squeeze().astype(float)
\ No newline at end of file
diff --git a/notebooks/_solutions/90_package_numpy18.py b/notebooks/_solutions/90_package_numpy18.py
new file mode 100644
index 0000000..474a475
--- /dev/null
+++ b/notebooks/_solutions/90_package_numpy18.py
@@ -0,0 +1,2 @@
+# Rescale the data
+b4_data = (b4_data - b4_data.min())/(b4_data.max() - b4_data.min())
\ No newline at end of file
diff --git a/notebooks/_solutions/90_package_numpy19.py b/notebooks/_solutions/90_package_numpy19.py
new file mode 100644
index 0000000..76575cd
--- /dev/null
+++ b/notebooks/_solutions/90_package_numpy19.py
@@ -0,0 +1,2 @@
+# Create a new array with the same shape as the original b4_data
+b4_data_classified = np.empty_like(b4_data).astype(int)
\ No newline at end of file
diff --git a/notebooks/_solutions/90_package_numpy2.py b/notebooks/_solutions/90_package_numpy2.py
new file mode 100644
index 0000000..dfd3e0b
--- /dev/null
+++ b/notebooks/_solutions/90_package_numpy2.py
@@ -0,0 +1,2 @@
+# Get the green channel
+tc_g = tc_data[1]
\ No newline at end of file
diff --git a/notebooks/_solutions/90_package_numpy20.py b/notebooks/_solutions/90_package_numpy20.py
new file mode 100644
index 0000000..314ffe3
--- /dev/null
+++ b/notebooks/_solutions/90_package_numpy20.py
@@ -0,0 +1,4 @@
+# Assign the new values according to the classes
+b4_data_classified[b4_data < 0.05] = 10
+b4_data_classified[(0.05 <= b4_data) & (b4_data < 0.1)] = 20
+b4_data_classified[0.1 <= b4_data] = 30
\ No newline at end of file
diff --git a/notebooks/_solutions/90_package_numpy21.py b/notebooks/_solutions/90_package_numpy21.py
new file mode 100644
index 0000000..0d3e46d
--- /dev/null
+++ b/notebooks/_solutions/90_package_numpy21.py
@@ -0,0 +1,5 @@
+# Create an image plot
+fig, ax = plt.subplots(figsize=(10, 4))
+img = ax.imshow(b4_data_classified, extent=b4_data_extent)
+fig.colorbar(img, values=[10, 20, 30], ticks=[10, 20, 30])
+ax.set_aspect("equal")
\ No newline at end of file
diff --git a/notebooks/_solutions/90_package_numpy22.py b/notebooks/_solutions/90_package_numpy22.py
new file mode 100644
index 0000000..90da7be
--- /dev/null
+++ b/notebooks/_solutions/90_package_numpy22.py
@@ -0,0 +1,10 @@
+xr_b4_data = xr.open_dataarray("./data/gent/raster/2020-09-17_Sentinel_2_L1C_B04.tiff", 
+                               engine="rasterio", mask_and_scale=False)
+b4_data = xr_b4_data.data
+xr_b8_data = xr.open_dataarray("./data/gent/raster/2020-09-17_Sentinel_2_L1C_B08.tiff", 
+                               engine="rasterio", mask_and_scale=False)
+b8_data = xr_b8_data.data
+
+# Get extent
+(left, bottom, right, top) = xr_b4_data.rio.bounds()
+b4_data_extent = (left, right, bottom, top)
\ No newline at end of file
diff --git a/notebooks/_solutions/90_package_numpy23.py b/notebooks/_solutions/90_package_numpy23.py
new file mode 100644
index 0000000..1e220bb
--- /dev/null
+++ b/notebooks/_solutions/90_package_numpy23.py
@@ -0,0 +1,2 @@
+# Combine both arrays by stacking them together
+b48_bands = np.vstack((b4_data, b8_data))  # 0 is b4 and 1 is b8
\ No newline at end of file
diff --git a/notebooks/_solutions/90_package_numpy24.py b/notebooks/_solutions/90_package_numpy24.py
new file mode 100644
index 0000000..0c7a9b9
--- /dev/null
+++ b/notebooks/_solutions/90_package_numpy24.py
@@ -0,0 +1,4 @@
+# Rescale the data to 0-1
+b48_min = b48_bands.min(axis=(1, 2), keepdims=True)
+b48_max = b48_bands.max(axis=(1, 2), keepdims=True)
+b48_bands = ((b48_bands - b48_min)/(b48_max - b48_min))
\ No newline at end of file
diff --git a/notebooks/_solutions/90_package_numpy25.py b/notebooks/_solutions/90_package_numpy25.py
new file mode 100644
index 0000000..6282356
--- /dev/null
+++ b/notebooks/_solutions/90_package_numpy25.py
@@ -0,0 +1,2 @@
+# Assign very small value to 0-values
+b48_bands[b48_bands == 0] = 1e-6
\ No newline at end of file
diff --git a/notebooks/_solutions/90_package_numpy26.py b/notebooks/_solutions/90_package_numpy26.py
new file mode 100644
index 0000000..f9a2fde
--- /dev/null
+++ b/notebooks/_solutions/90_package_numpy26.py
@@ -0,0 +1,2 @@
+# Calculate the ndvi using the stacked data
+ndvi = (b48_bands[1] - b48_bands[0])/(b48_bands[0] + b48_bands[1])
\ No newline at end of file
diff --git a/notebooks/_solutions/90_package_numpy27.py b/notebooks/_solutions/90_package_numpy27.py
new file mode 100644
index 0000000..397b68b
--- /dev/null
+++ b/notebooks/_solutions/90_package_numpy27.py
@@ -0,0 +1,6 @@
+# A Sequential colormap `YlGn` with a normalization on the color limits
+import matplotlib.colors as mcolors
+div_norm = mcolors.Normalize(0.1, 0.8)
+fig, ax = plt.subplots(figsize=(10, 4))
+ll = ax.imshow(ndvi, cmap="YlGn", extent=b4_data_extent, norm=div_norm)
+fig.colorbar(ll);
\ No newline at end of file
diff --git a/notebooks/_solutions/90_package_numpy28.py b/notebooks/_solutions/90_package_numpy28.py
new file mode 100644
index 0000000..2f3ae9c
--- /dev/null
+++ b/notebooks/_solutions/90_package_numpy28.py
@@ -0,0 +1,6 @@
+# A Diverging colormap `RdYlGn` with a normalization on the color limits in two directions of the central point:
+div_norm = mcolors.TwoSlopeNorm(vmin=-0.1, vcenter=0.4, vmax=0.8)
+fig, ax = plt.subplots(figsize=(10, 4))
+ll = ax.imshow(ndvi, cmap="RdYlGn", extent=b4_data_extent, norm=div_norm)
+fig.colorbar(ll);
+plt.axis('off');
\ No newline at end of file
diff --git a/notebooks/_solutions/90_package_numpy3.py b/notebooks/_solutions/90_package_numpy3.py
new file mode 100644
index 0000000..0365782
--- /dev/null
+++ b/notebooks/_solutions/90_package_numpy3.py
@@ -0,0 +1,3 @@
+# Convert all values above 15000
+tc_g[tc_g > 15000] = 65535
+tc_g
\ No newline at end of file
diff --git a/notebooks/_solutions/90_package_numpy4.py b/notebooks/_solutions/90_package_numpy4.py
new file mode 100644
index 0000000..2832685
--- /dev/null
+++ b/notebooks/_solutions/90_package_numpy4.py
@@ -0,0 +1,3 @@
+xr_tc_data = xr.open_dataarray("./data/gent/raster/2020-09-17_Sentinel_2_L1C_True_color.tiff", 
+                               engine="rasterio", mask_and_scale=False)
+tc_data = xr_tc_data.data    
\ No newline at end of file
diff --git a/notebooks/_solutions/90_package_numpy5.py b/notebooks/_solutions/90_package_numpy5.py
new file mode 100644
index 0000000..805ca9c
--- /dev/null
+++ b/notebooks/_solutions/90_package_numpy5.py
@@ -0,0 +1,2 @@
+# subsample the data
+tc_data[:, ::5, ::5].shape
\ No newline at end of file
diff --git a/notebooks/_solutions/90_package_numpy6.py b/notebooks/_solutions/90_package_numpy6.py
new file mode 100644
index 0000000..8a67c99
--- /dev/null
+++ b/notebooks/_solutions/90_package_numpy6.py
@@ -0,0 +1,3 @@
+xr_b4_data = xr.open_dataarray("./data/gent/raster/2020-09-17_Sentinel_2_L1C_B04.tiff", 
+                               engine="rasterio", mask_and_scale=False)
+b4_data = xr_b4_data.data    
\ No newline at end of file
diff --git a/notebooks/_solutions/90_package_numpy7.py b/notebooks/_solutions/90_package_numpy7.py
new file mode 100644
index 0000000..223b3e0
--- /dev/null
+++ b/notebooks/_solutions/90_package_numpy7.py
@@ -0,0 +1,2 @@
+# Count the number of cells with value 65535
+np.sum(b4_data == 65535)
\ No newline at end of file
diff --git a/notebooks/_solutions/90_package_numpy8.py b/notebooks/_solutions/90_package_numpy8.py
new file mode 100644
index 0000000..a1b1475
--- /dev/null
+++ b/notebooks/_solutions/90_package_numpy8.py
@@ -0,0 +1,3 @@
+# Convert to float and make 65535 equal to Nan
+b4_data_f = b4_data.astype(float)
+b4_data_f[b4_data == 65535] = np.nan
\ No newline at end of file
diff --git a/notebooks/_solutions/90_package_numpy9.py b/notebooks/_solutions/90_package_numpy9.py
new file mode 100644
index 0000000..c65ea83
--- /dev/null
+++ b/notebooks/_solutions/90_package_numpy9.py
@@ -0,0 +1,2 @@
+# Count the number of cells with value 0
+np.sum(np.isnan(b4_data_f))
\ No newline at end of file
diff --git a/notebooks/_solutions/91_package_rasterio1.py b/notebooks/_solutions/91_package_rasterio1.py
new file mode 100644
index 0000000..38a5cdb
--- /dev/null
+++ b/notebooks/_solutions/91_package_rasterio1.py
@@ -0,0 +1,2 @@
+# Read the geojson vector data
+gent_vect = geopandas.read_file("./data/gent/vector/gent.geojson")
\ No newline at end of file
diff --git a/notebooks/_solutions/91_package_rasterio2.py b/notebooks/_solutions/91_package_rasterio2.py
new file mode 100644
index 0000000..6611d90
--- /dev/null
+++ b/notebooks/_solutions/91_package_rasterio2.py
@@ -0,0 +1,4 @@
+# Read the tiff raster data
+with rasterio.open("./data/gent/raster/2020-09-17_Sentinel_2_L1C_B04.tiff") as src:
+    gent_data = src.read(out_dtype=float, masked=False)
+    gent_extent = plotting_extent(src)
\ No newline at end of file
diff --git a/notebooks/_solutions/91_package_rasterio3.py b/notebooks/_solutions/91_package_rasterio3.py
new file mode 100644
index 0000000..1604fb9
--- /dev/null
+++ b/notebooks/_solutions/91_package_rasterio3.py
@@ -0,0 +1,5 @@
+# Combine both in a single plot
+fig, ax = plt.subplots(figsize=(12, 5))
+ax.imshow(gent_data.squeeze(), extent=gent_extent, cmap="Reds")  # Extent trick to plot numpy
+gent_vect.to_crs(epsg=3857).plot(color='None', edgecolor='grey', linewidth=2, ax=ax)
+plt.axis("off");
\ No newline at end of file
diff --git a/notebooks/_solutions/91_package_rasterio4.py b/notebooks/_solutions/91_package_rasterio4.py
new file mode 100644
index 0000000..11ffb7a
--- /dev/null
+++ b/notebooks/_solutions/91_package_rasterio4.py
@@ -0,0 +1,6 @@
+# Read the vector data sets #1
+with rasterio.open("./data/gent/raster/2020-09-17_Sentinel_2_L1C_B04.tiff") as src:
+    b4_data = src.read()
+    data_profile = src.profile
+with rasterio.open("./data/gent/raster/2020-09-17_Sentinel_2_L1C_B08.tiff") as src:
+    b8_data = src.read() 
\ No newline at end of file
diff --git a/notebooks/_solutions/91_package_rasterio5.py b/notebooks/_solutions/91_package_rasterio5.py
new file mode 100644
index 0000000..764eeac
--- /dev/null
+++ b/notebooks/_solutions/91_package_rasterio5.py
@@ -0,0 +1,4 @@
+# Stack both data sets and convert to uint32 data (#2)
+b48_bands = np.vstack((b4_data, b8_data))  # 0 is b4 and 1 is b8  
+b48_bands = b48_bands.astype("uint32")
+b48_bands.shape
\ No newline at end of file
diff --git a/notebooks/_solutions/91_package_rasterio6.py b/notebooks/_solutions/91_package_rasterio6.py
new file mode 100644
index 0000000..ac55ae6
--- /dev/null
+++ b/notebooks/_solutions/91_package_rasterio6.py
@@ -0,0 +1,6 @@
+# Update profile metadata (#3) and save to a new tiff file (#4)
+data_profile.update({"count": 2, 
+                     "dtype": "uint32"})  #3
+
+with rasterio.open("./B0408.tiff", "w", **data_profile) as dst: #4
+    dst.write(b48_bands)
\ No newline at end of file
diff --git a/notebooks/_solutions/91_package_rasterio7.py b/notebooks/_solutions/91_package_rasterio7.py
new file mode 100644
index 0000000..2a9e412
--- /dev/null
+++ b/notebooks/_solutions/91_package_rasterio7.py
@@ -0,0 +1,40 @@
+# Create a custom function
+def resample_raster_file(input_file, output_file, resampling_method, scaling_factor=2):
+    """Resample a raster file with a given scaling factor using rasterio
+    
+    Parameters
+    ----------
+    input_file : str | Path
+        Input raster file to resample
+    output_file : str | Path
+        Output raster file
+    resampling_method : rasterio.Resampling
+        Method available in the Resampling module of rasterio, e.g. Resampling.bilinear
+    scaling_factor : float, default 2
+        Scaling factor to use for resampling the data
+    
+    Examples
+    --------
+    >>> resample_raster_file("./input_file.tiff", "output_file.tiff", 
+    ...                      Resampling.bilinear, scaling_factor=3)
+    """
+    with rasterio.open(input_file) as src:  
+        
+        # resample data to target shape
+        new_width = int(src.width / scaling_factor)
+        new_height = int(src.height / scaling_factor)
+        out_image = src.read(
+            out_shape=(src.count, new_height, new_width),
+            resampling=resampling_method  #Resampling.bilinear
+        )
+
+        # scale image transform and update metadata  # 3
+        data_profile = src.profile
+        out_transform = src.transform * src.transform.scale(scaling_factor)
+        data_profile.update({"height": new_height,
+                             "width": new_width,
+                             "transform": out_transform})      
+
+        # save the output to disk # 4
+        with rasterio.open(output_file, "w", **data_profile) as dst:
+            dst.write(out_image)    
\ No newline at end of file
diff --git a/notebooks/_solutions/91_package_rasterio8.py b/notebooks/_solutions/91_package_rasterio8.py
new file mode 100644
index 0000000..cd042c5
--- /dev/null
+++ b/notebooks/_solutions/91_package_rasterio8.py
@@ -0,0 +1,3 @@
+# Apply function to a single file 
+resample_raster_file("./data/gent/raster/2020-09-17_Sentinel_2_L1C_B04.tiff", 
+                     "b4_out.tiff", Resampling.bilinear, scaling_factor=2)
\ No newline at end of file
diff --git a/notebooks/_solutions/91_package_rasterio9.py b/notebooks/_solutions/91_package_rasterio9.py
new file mode 100644
index 0000000..c9422d4
--- /dev/null
+++ b/notebooks/_solutions/91_package_rasterio9.py
@@ -0,0 +1,5 @@
+# Apply function to all files in the directory
+from pathlib import Path
+for file in Path("./data/gent/raster").glob("*.tiff"):
+    print(f"Converting {file.name}...")
+    resample_raster_file(file, Path(f"./{file.name}_resampled.tiff"), Resampling.bilinear, scaling_factor=2)    
\ No newline at end of file
diff --git a/notebooks/_solutions/case-argo-sea-floats1.py b/notebooks/_solutions/case-argo-sea-floats1.py
new file mode 100644
index 0000000..1613793
--- /dev/null
+++ b/notebooks/_solutions/case-argo-sea-floats1.py
@@ -0,0 +1 @@
+argo["temperature_kelvin"] = argo["temperature"] + 273.15
\ No newline at end of file
diff --git a/notebooks/_solutions/case-argo-sea-floats10.py b/notebooks/_solutions/case-argo-sea-floats10.py
new file mode 100644
index 0000000..427203e
--- /dev/null
+++ b/notebooks/_solutions/case-argo-sea-floats10.py
@@ -0,0 +1 @@
+argo.sel(level=[1, 5, 25, 50]).plot.scatter(x="lat", y="temperature", col="level", edgecolor="None")
\ No newline at end of file
diff --git a/notebooks/_solutions/case-argo-sea-floats11.py b/notebooks/_solutions/case-argo-sea-floats11.py
new file mode 100644
index 0000000..8df0693
--- /dev/null
+++ b/notebooks/_solutions/case-argo-sea-floats11.py
@@ -0,0 +1,5 @@
+argo.temperature.plot.pcolormesh(yincrease=False, 
+                                 cbar_kwargs={'label': 'Temperature (°C)'},
+                                 cmap='Reds',
+                                 levels=[5, 15]
+                                );
\ No newline at end of file
diff --git a/notebooks/_solutions/case-argo-sea-floats12.py b/notebooks/_solutions/case-argo-sea-floats12.py
new file mode 100644
index 0000000..7617444
--- /dev/null
+++ b/notebooks/_solutions/case-argo-sea-floats12.py
@@ -0,0 +1,6 @@
+salinity_20121031 = argo["salinity"].sel(date='2012-10-31')
+
+fig, ax = plt.subplots(figsize=(6, 6))
+salinity_20121031.plot.line(y="level", yincrease=False, color="grey", add_legend=False, label="raw");
+salinity_20121031.rolling(level=10, center=True).median().plot.line(y="level", yincrease=False, linewidth=3, color="orange", add_legend=False, label="smooth");
+ax.legend()
\ No newline at end of file
diff --git a/notebooks/_solutions/case-argo-sea-floats13.py b/notebooks/_solutions/case-argo-sea-floats13.py
new file mode 100644
index 0000000..4dd2fed
--- /dev/null
+++ b/notebooks/_solutions/case-argo-sea-floats13.py
@@ -0,0 +1,5 @@
+argo_pressure = argo.assign_coords(pressure=argo["pressure"]).sel(date='2012-12')
+
+fig, (ax0, ax1) = plt.subplots(1, 2, figsize=(12, 6))
+argo_pressure["salinity"].plot.line(y="pressure", yincrease=False, ax=ax0, hue='date');
+argo_pressure["temperature"].plot.line(y="pressure", yincrease=False, ax=ax1, hue='date');
\ No newline at end of file
diff --git a/notebooks/_solutions/case-argo-sea-floats14.py b/notebooks/_solutions/case-argo-sea-floats14.py
new file mode 100644
index 0000000..9aba0c0
--- /dev/null
+++ b/notebooks/_solutions/case-argo-sea-floats14.py
@@ -0,0 +1 @@
+subset_mean = argo.sel(date=slice('2012-10-01', '2012-12-01')).mean(dim="date")
\ No newline at end of file
diff --git a/notebooks/_solutions/case-argo-sea-floats15.py b/notebooks/_solutions/case-argo-sea-floats15.py
new file mode 100644
index 0000000..b6ea7c9
--- /dev/null
+++ b/notebooks/_solutions/case-argo-sea-floats15.py
@@ -0,0 +1,4 @@
+fig, (ax0, ax1) = plt.subplots(1, 2)
+subset_mean["salinity"].plot.line(y="level", yincrease=False, ax=ax0)
+subset_mean["temperature"].plot.line(y="level", yincrease=False, ax=ax1)
+plt.tight_layout()
\ No newline at end of file
diff --git a/notebooks/_solutions/case-argo-sea-floats2.py b/notebooks/_solutions/case-argo-sea-floats2.py
new file mode 100644
index 0000000..856caac
--- /dev/null
+++ b/notebooks/_solutions/case-argo-sea-floats2.py
@@ -0,0 +1 @@
+argo.plot.scatter(x="pressure", y="level", edgecolor="None", s=10)
\ No newline at end of file
diff --git a/notebooks/_solutions/case-argo-sea-floats3.py b/notebooks/_solutions/case-argo-sea-floats3.py
new file mode 100644
index 0000000..db58aba
--- /dev/null
+++ b/notebooks/_solutions/case-argo-sea-floats3.py
@@ -0,0 +1 @@
+argo["buoyancy"] = 9.8 * (2e-4 * argo["temperature"] - 7e-4 * argo["pressure"])
\ No newline at end of file
diff --git a/notebooks/_solutions/case-argo-sea-floats4.py b/notebooks/_solutions/case-argo-sea-floats4.py
new file mode 100644
index 0000000..852399b
--- /dev/null
+++ b/notebooks/_solutions/case-argo-sea-floats4.py
@@ -0,0 +1 @@
+argo["buoyancy"].plot(yincrease=False)  # xarray decides the plot type when no specific method is used
\ No newline at end of file
diff --git a/notebooks/_solutions/case-argo-sea-floats5.py b/notebooks/_solutions/case-argo-sea-floats5.py
new file mode 100644
index 0000000..df6b5ea
--- /dev/null
+++ b/notebooks/_solutions/case-argo-sea-floats5.py
@@ -0,0 +1,2 @@
+# More explicit version defining the x and y axis
+argo["buoyancy"].plot.pcolormesh(x="date", y="level", yincrease=False)  # pcolormesh instead of imshow
\ No newline at end of file
diff --git a/notebooks/_solutions/case-argo-sea-floats6.py b/notebooks/_solutions/case-argo-sea-floats6.py
new file mode 100644
index 0000000..aac94c4
--- /dev/null
+++ b/notebooks/_solutions/case-argo-sea-floats6.py
@@ -0,0 +1 @@
+argo["salinity"].sel(level=10).plot.line()
\ No newline at end of file
diff --git a/notebooks/_solutions/case-argo-sea-floats7.py b/notebooks/_solutions/case-argo-sea-floats7.py
new file mode 100644
index 0000000..a017ef6
--- /dev/null
+++ b/notebooks/_solutions/case-argo-sea-floats7.py
@@ -0,0 +1 @@
+argo["temperature"].sel(level=[10, 20, 30]).plot.line(hue="level");
\ No newline at end of file
diff --git a/notebooks/_solutions/case-argo-sea-floats8.py b/notebooks/_solutions/case-argo-sea-floats8.py
new file mode 100644
index 0000000..0a4fe4f
--- /dev/null
+++ b/notebooks/_solutions/case-argo-sea-floats8.py
@@ -0,0 +1 @@
+argo["temperature"].sel(level=[10, 20, 30]).plot.line(col="level");
\ No newline at end of file
diff --git a/notebooks/_solutions/case-argo-sea-floats9.py b/notebooks/_solutions/case-argo-sea-floats9.py
new file mode 100644
index 0000000..40f6f3d
--- /dev/null
+++ b/notebooks/_solutions/case-argo-sea-floats9.py
@@ -0,0 +1 @@
+argo.plot.scatter(x="temperature", y="level", s=5, hue="lat", edgecolor="None")
\ No newline at end of file
diff --git a/notebooks/_solutions/case-curieuzeneuzen-air-quality1.py b/notebooks/_solutions/case-curieuzeneuzen-air-quality1.py
new file mode 100644
index 0000000..ec1d82d
--- /dev/null
+++ b/notebooks/_solutions/case-curieuzeneuzen-air-quality1.py
@@ -0,0 +1,2 @@
+df = pd.read_csv("./data/CN_Flanders_open_dataset.csv")
+df
\ No newline at end of file
diff --git a/notebooks/_solutions/case-curieuzeneuzen-air-quality10.py b/notebooks/_solutions/case-curieuzeneuzen-air-quality10.py
new file mode 100644
index 0000000..294656e
--- /dev/null
+++ b/notebooks/_solutions/case-curieuzeneuzen-air-quality10.py
@@ -0,0 +1 @@
+df[df['campaign'] == "background"]["no2"].mean()
\ No newline at end of file
diff --git a/notebooks/_solutions/case-curieuzeneuzen-air-quality11.py b/notebooks/_solutions/case-curieuzeneuzen-air-quality11.py
new file mode 100644
index 0000000..dc3cab4
--- /dev/null
+++ b/notebooks/_solutions/case-curieuzeneuzen-air-quality11.py
@@ -0,0 +1 @@
+df.groupby('campaign')["no2"].mean()
\ No newline at end of file
diff --git a/notebooks/_solutions/case-curieuzeneuzen-air-quality12.py b/notebooks/_solutions/case-curieuzeneuzen-air-quality12.py
new file mode 100644
index 0000000..144f1e4
--- /dev/null
+++ b/notebooks/_solutions/case-curieuzeneuzen-air-quality12.py
@@ -0,0 +1 @@
+gdf = geopandas.GeoDataFrame(df, geometry=geopandas.points_from_xy(df['lon'], df['lat']), crs="EPSG:4326")
\ No newline at end of file
diff --git a/notebooks/_solutions/case-curieuzeneuzen-air-quality13.py b/notebooks/_solutions/case-curieuzeneuzen-air-quality13.py
new file mode 100644
index 0000000..5ddbf72
--- /dev/null
+++ b/notebooks/_solutions/case-curieuzeneuzen-air-quality13.py
@@ -0,0 +1 @@
+gdf
\ No newline at end of file
diff --git a/notebooks/_solutions/case-curieuzeneuzen-air-quality14.py b/notebooks/_solutions/case-curieuzeneuzen-air-quality14.py
new file mode 100644
index 0000000..91c9033
--- /dev/null
+++ b/notebooks/_solutions/case-curieuzeneuzen-air-quality14.py
@@ -0,0 +1 @@
+gdf.plot()
\ No newline at end of file
diff --git a/notebooks/_solutions/case-curieuzeneuzen-air-quality15.py b/notebooks/_solutions/case-curieuzeneuzen-air-quality15.py
new file mode 100644
index 0000000..1dc3793
--- /dev/null
+++ b/notebooks/_solutions/case-curieuzeneuzen-air-quality15.py
@@ -0,0 +1 @@
+gdf.plot(column="no2", figsize=(20, 10))
\ No newline at end of file
diff --git a/notebooks/_solutions/case-curieuzeneuzen-air-quality16.py b/notebooks/_solutions/case-curieuzeneuzen-air-quality16.py
new file mode 100644
index 0000000..4800510
--- /dev/null
+++ b/notebooks/_solutions/case-curieuzeneuzen-air-quality16.py
@@ -0,0 +1 @@
+muni = geopandas.read_file("./data/VRBG/Refgem.shp")
\ No newline at end of file
diff --git a/notebooks/_solutions/case-curieuzeneuzen-air-quality17.py b/notebooks/_solutions/case-curieuzeneuzen-air-quality17.py
new file mode 100644
index 0000000..333cafa
--- /dev/null
+++ b/notebooks/_solutions/case-curieuzeneuzen-air-quality17.py
@@ -0,0 +1 @@
+muni.head()
\ No newline at end of file
diff --git a/notebooks/_solutions/case-curieuzeneuzen-air-quality18.py b/notebooks/_solutions/case-curieuzeneuzen-air-quality18.py
new file mode 100644
index 0000000..3a7ad18
--- /dev/null
+++ b/notebooks/_solutions/case-curieuzeneuzen-air-quality18.py
@@ -0,0 +1 @@
+muni.plot()
\ No newline at end of file
diff --git a/notebooks/_solutions/case-curieuzeneuzen-air-quality19.py b/notebooks/_solutions/case-curieuzeneuzen-air-quality19.py
new file mode 100644
index 0000000..fae15ee
--- /dev/null
+++ b/notebooks/_solutions/case-curieuzeneuzen-air-quality19.py
@@ -0,0 +1 @@
+gdf.crs
\ No newline at end of file
diff --git a/notebooks/_solutions/case-curieuzeneuzen-air-quality2.py b/notebooks/_solutions/case-curieuzeneuzen-air-quality2.py
new file mode 100644
index 0000000..68e759a
--- /dev/null
+++ b/notebooks/_solutions/case-curieuzeneuzen-air-quality2.py
@@ -0,0 +1 @@
+len(df)
\ No newline at end of file
diff --git a/notebooks/_solutions/case-curieuzeneuzen-air-quality20.py b/notebooks/_solutions/case-curieuzeneuzen-air-quality20.py
new file mode 100644
index 0000000..4539030
--- /dev/null
+++ b/notebooks/_solutions/case-curieuzeneuzen-air-quality20.py
@@ -0,0 +1 @@
+muni.crs
\ No newline at end of file
diff --git a/notebooks/_solutions/case-curieuzeneuzen-air-quality21.py b/notebooks/_solutions/case-curieuzeneuzen-air-quality21.py
new file mode 100644
index 0000000..553ebe4
--- /dev/null
+++ b/notebooks/_solutions/case-curieuzeneuzen-air-quality21.py
@@ -0,0 +1,2 @@
+gdf_lambert = gdf.to_crs("EPSG:31370")  # or .to_crs(muni.crs)
+gdf_lambert.head()
\ No newline at end of file
diff --git a/notebooks/_solutions/case-curieuzeneuzen-air-quality22.py b/notebooks/_solutions/case-curieuzeneuzen-air-quality22.py
new file mode 100644
index 0000000..2f3c63c
--- /dev/null
+++ b/notebooks/_solutions/case-curieuzeneuzen-air-quality22.py
@@ -0,0 +1 @@
+gdf_combined = geopandas.sjoin(gdf_lambert, muni[["NAAM", "geometry"]])
\ No newline at end of file
diff --git a/notebooks/_solutions/case-curieuzeneuzen-air-quality23.py b/notebooks/_solutions/case-curieuzeneuzen-air-quality23.py
new file mode 100644
index 0000000..7964f3a
--- /dev/null
+++ b/notebooks/_solutions/case-curieuzeneuzen-air-quality23.py
@@ -0,0 +1 @@
+gdf_combined.head()
\ No newline at end of file
diff --git a/notebooks/_solutions/case-curieuzeneuzen-air-quality24.py b/notebooks/_solutions/case-curieuzeneuzen-air-quality24.py
new file mode 100644
index 0000000..0de8b4d
--- /dev/null
+++ b/notebooks/_solutions/case-curieuzeneuzen-air-quality24.py
@@ -0,0 +1,2 @@
+muni_mean = gdf_combined.groupby("NAAM")["no2"].mean()
+muni_mean.head()
\ No newline at end of file
diff --git a/notebooks/_solutions/case-curieuzeneuzen-air-quality25.py b/notebooks/_solutions/case-curieuzeneuzen-air-quality25.py
new file mode 100644
index 0000000..3beedd6
--- /dev/null
+++ b/notebooks/_solutions/case-curieuzeneuzen-air-quality25.py
@@ -0,0 +1,2 @@
+muni_mean = muni_mean.reset_index()
+muni_mean.head()
\ No newline at end of file
diff --git a/notebooks/_solutions/case-curieuzeneuzen-air-quality26.py b/notebooks/_solutions/case-curieuzeneuzen-air-quality26.py
new file mode 100644
index 0000000..8e2eb78
--- /dev/null
+++ b/notebooks/_solutions/case-curieuzeneuzen-air-quality26.py
@@ -0,0 +1,2 @@
+muni_no2 = pd.merge(muni, muni_mean, on="NAAM")
+muni_no2.head()
\ No newline at end of file
diff --git a/notebooks/_solutions/case-curieuzeneuzen-air-quality27.py b/notebooks/_solutions/case-curieuzeneuzen-air-quality27.py
new file mode 100644
index 0000000..d4bd386
--- /dev/null
+++ b/notebooks/_solutions/case-curieuzeneuzen-air-quality27.py
@@ -0,0 +1 @@
+muni_no2.plot(column="no2", figsize=(16, 5), legend=True)
\ No newline at end of file
diff --git a/notebooks/_solutions/case-curieuzeneuzen-air-quality28.py b/notebooks/_solutions/case-curieuzeneuzen-air-quality28.py
new file mode 100644
index 0000000..b2dd389
--- /dev/null
+++ b/notebooks/_solutions/case-curieuzeneuzen-air-quality28.py
@@ -0,0 +1,2 @@
+ax = muni_no2.plot(column="no2", figsize=(16, 5), legend=True, scheme="FisherJenks", k=7, cmap="plasma")
+ax.set_axis_off()
\ No newline at end of file
diff --git a/notebooks/_solutions/case-curieuzeneuzen-air-quality29.py b/notebooks/_solutions/case-curieuzeneuzen-air-quality29.py
new file mode 100644
index 0000000..55b9604
--- /dev/null
+++ b/notebooks/_solutions/case-curieuzeneuzen-air-quality29.py
@@ -0,0 +1 @@
+gdf_combined["exceedance"] = gdf_combined["no2"] > 40
\ No newline at end of file
diff --git a/notebooks/_solutions/case-curieuzeneuzen-air-quality3.py b/notebooks/_solutions/case-curieuzeneuzen-air-quality3.py
new file mode 100644
index 0000000..386efd4
--- /dev/null
+++ b/notebooks/_solutions/case-curieuzeneuzen-air-quality3.py
@@ -0,0 +1 @@
+df["campaign"].value_counts()
\ No newline at end of file
diff --git a/notebooks/_solutions/case-curieuzeneuzen-air-quality30.py b/notebooks/_solutions/case-curieuzeneuzen-air-quality30.py
new file mode 100644
index 0000000..7e05c56
--- /dev/null
+++ b/notebooks/_solutions/case-curieuzeneuzen-air-quality30.py
@@ -0,0 +1 @@
+exceedances_muni = gdf_combined.groupby("NAAM")["exceedance"].mean() * 100
\ No newline at end of file
diff --git a/notebooks/_solutions/case-curieuzeneuzen-air-quality31.py b/notebooks/_solutions/case-curieuzeneuzen-air-quality31.py
new file mode 100644
index 0000000..457dfd1
--- /dev/null
+++ b/notebooks/_solutions/case-curieuzeneuzen-air-quality31.py
@@ -0,0 +1 @@
+exceedances_muni.sort_values(ascending=False).head(10)
\ No newline at end of file
diff --git a/notebooks/_solutions/case-curieuzeneuzen-air-quality32.py b/notebooks/_solutions/case-curieuzeneuzen-air-quality32.py
new file mode 100644
index 0000000..8d6dd95
--- /dev/null
+++ b/notebooks/_solutions/case-curieuzeneuzen-air-quality32.py
@@ -0,0 +1,4 @@
+import xarray
+
+raster = xarray.open_dataarray("data/CLC2018_V2020_20u1_flanders.tif", engine="rasterio", mask_and_scale=False)
+raster
\ No newline at end of file
diff --git a/notebooks/_solutions/case-curieuzeneuzen-air-quality33.py b/notebooks/_solutions/case-curieuzeneuzen-air-quality33.py
new file mode 100644
index 0000000..5b61e50
--- /dev/null
+++ b/notebooks/_solutions/case-curieuzeneuzen-air-quality33.py
@@ -0,0 +1 @@
+raster.plot(vmin=1, vmax=44)
\ No newline at end of file
diff --git a/notebooks/_solutions/case-curieuzeneuzen-air-quality34.py b/notebooks/_solutions/case-curieuzeneuzen-air-quality34.py
new file mode 100644
index 0000000..89e5f9f
--- /dev/null
+++ b/notebooks/_solutions/case-curieuzeneuzen-air-quality34.py
@@ -0,0 +1 @@
+raster.rio.crs
\ No newline at end of file
diff --git a/notebooks/_solutions/case-curieuzeneuzen-air-quality35.py b/notebooks/_solutions/case-curieuzeneuzen-air-quality35.py
new file mode 100644
index 0000000..93bb75b
--- /dev/null
+++ b/notebooks/_solutions/case-curieuzeneuzen-air-quality35.py
@@ -0,0 +1,2 @@
+gdf_raster = gdf.to_crs("EPSG:3035")
+gdf_raster.head()
\ No newline at end of file
diff --git a/notebooks/_solutions/case-curieuzeneuzen-air-quality36.py b/notebooks/_solutions/case-curieuzeneuzen-air-quality36.py
new file mode 100644
index 0000000..745b071
--- /dev/null
+++ b/notebooks/_solutions/case-curieuzeneuzen-air-quality36.py
@@ -0,0 +1 @@
+import rasterstats
\ No newline at end of file
diff --git a/notebooks/_solutions/case-curieuzeneuzen-air-quality37.py b/notebooks/_solutions/case-curieuzeneuzen-air-quality37.py
new file mode 100644
index 0000000..56320aa
--- /dev/null
+++ b/notebooks/_solutions/case-curieuzeneuzen-air-quality37.py
@@ -0,0 +1 @@
+gdf['land_use'] = rasterstats.point_query(gdf_raster.geometry, "data/CLC2018_V2020_20u1_flanders.tif", interpolate='nearest')
\ No newline at end of file
diff --git a/notebooks/_solutions/case-curieuzeneuzen-air-quality38.py b/notebooks/_solutions/case-curieuzeneuzen-air-quality38.py
new file mode 100644
index 0000000..9761fee
--- /dev/null
+++ b/notebooks/_solutions/case-curieuzeneuzen-air-quality38.py
@@ -0,0 +1 @@
+gdf['land_use'].value_counts()
\ No newline at end of file
diff --git a/notebooks/_solutions/case-curieuzeneuzen-air-quality39.py b/notebooks/_solutions/case-curieuzeneuzen-air-quality39.py
new file mode 100644
index 0000000..451ed31
--- /dev/null
+++ b/notebooks/_solutions/case-curieuzeneuzen-air-quality39.py
@@ -0,0 +1,2 @@
+legend = pd.read_csv("data/CLC2018_V2018_legend_grouped.csv")
+legend
\ No newline at end of file
diff --git a/notebooks/_solutions/case-curieuzeneuzen-air-quality4.py b/notebooks/_solutions/case-curieuzeneuzen-air-quality4.py
new file mode 100644
index 0000000..7614457
--- /dev/null
+++ b/notebooks/_solutions/case-curieuzeneuzen-air-quality4.py
@@ -0,0 +1 @@
+df["no2"].mean()
\ No newline at end of file
diff --git a/notebooks/_solutions/case-curieuzeneuzen-air-quality40.py b/notebooks/_solutions/case-curieuzeneuzen-air-quality40.py
new file mode 100644
index 0000000..8fd9499
--- /dev/null
+++ b/notebooks/_solutions/case-curieuzeneuzen-air-quality40.py
@@ -0,0 +1 @@
+counts = gdf['land_use_class'].value_counts()
\ No newline at end of file
diff --git a/notebooks/_solutions/case-curieuzeneuzen-air-quality41.py b/notebooks/_solutions/case-curieuzeneuzen-air-quality41.py
new file mode 100644
index 0000000..c8929ac
--- /dev/null
+++ b/notebooks/_solutions/case-curieuzeneuzen-air-quality41.py
@@ -0,0 +1 @@
+frequent_categories = counts.index[counts > 50]
\ No newline at end of file
diff --git a/notebooks/_solutions/case-curieuzeneuzen-air-quality42.py b/notebooks/_solutions/case-curieuzeneuzen-air-quality42.py
new file mode 100644
index 0000000..f7765bc
--- /dev/null
+++ b/notebooks/_solutions/case-curieuzeneuzen-air-quality42.py
@@ -0,0 +1 @@
+subset = gdf[gdf["land_use_class"].isin(frequent_categories)]
\ No newline at end of file
diff --git a/notebooks/_solutions/case-curieuzeneuzen-air-quality43.py b/notebooks/_solutions/case-curieuzeneuzen-air-quality43.py
new file mode 100644
index 0000000..810dc23
--- /dev/null
+++ b/notebooks/_solutions/case-curieuzeneuzen-air-quality43.py
@@ -0,0 +1 @@
+subset.groupby("land_use_class")['no2'].mean()
\ No newline at end of file
diff --git a/notebooks/_solutions/case-curieuzeneuzen-air-quality44.py b/notebooks/_solutions/case-curieuzeneuzen-air-quality44.py
new file mode 100644
index 0000000..556db3e
--- /dev/null
+++ b/notebooks/_solutions/case-curieuzeneuzen-air-quality44.py
@@ -0,0 +1 @@
+import seaborn
\ No newline at end of file
diff --git a/notebooks/_solutions/case-curieuzeneuzen-air-quality45.py b/notebooks/_solutions/case-curieuzeneuzen-air-quality45.py
new file mode 100644
index 0000000..1dc439b
--- /dev/null
+++ b/notebooks/_solutions/case-curieuzeneuzen-air-quality45.py
@@ -0,0 +1 @@
+seaborn.boxplot(y="land_use_class", x="no2", data=subset)
\ No newline at end of file
diff --git a/notebooks/_solutions/case-curieuzeneuzen-air-quality46.py b/notebooks/_solutions/case-curieuzeneuzen-air-quality46.py
new file mode 100644
index 0000000..dc46ae5
--- /dev/null
+++ b/notebooks/_solutions/case-curieuzeneuzen-air-quality46.py
@@ -0,0 +1,10 @@
+# defining custom order of the classes (following the order in the CORINE hierarchy as defined in the legend csv)
+classes = legend["group"].unique()
+# but removing the infrequent ones
+counts = gdf['land_use_class'].value_counts()
+frequent_categories = counts[counts > 50].index
+classes = [value for value in classes if value in frequent_categories]
+
+fig, ax = plt.subplots(figsize=(8, 6))
+seaborn.boxplot(y="land_use_class", x="no2", data=subset, ax=ax, color="C0", order=classes)
+ax.set(xlabel="NO$_2$ concentration (µg/m³)", ylabel="CORINE Land Cover classes");
\ No newline at end of file
diff --git a/notebooks/_solutions/case-curieuzeneuzen-air-quality47.py b/notebooks/_solutions/case-curieuzeneuzen-air-quality47.py
new file mode 100644
index 0000000..a707a81
--- /dev/null
+++ b/notebooks/_solutions/case-curieuzeneuzen-air-quality47.py
@@ -0,0 +1,2 @@
+gent_region = gent.envelope
+gent_region
\ No newline at end of file
diff --git a/notebooks/_solutions/case-curieuzeneuzen-air-quality48.py b/notebooks/_solutions/case-curieuzeneuzen-air-quality48.py
new file mode 100644
index 0000000..4416710
--- /dev/null
+++ b/notebooks/_solutions/case-curieuzeneuzen-air-quality48.py
@@ -0,0 +1 @@
+gdf_gent = gdf_lambert[gdf_lambert.within(gent_region)].copy()
\ No newline at end of file
diff --git a/notebooks/_solutions/case-curieuzeneuzen-air-quality49.py b/notebooks/_solutions/case-curieuzeneuzen-air-quality49.py
new file mode 100644
index 0000000..9ad1df9
--- /dev/null
+++ b/notebooks/_solutions/case-curieuzeneuzen-air-quality49.py
@@ -0,0 +1 @@
+len(gdf_gent)
\ No newline at end of file
diff --git a/notebooks/_solutions/case-curieuzeneuzen-air-quality5.py b/notebooks/_solutions/case-curieuzeneuzen-air-quality5.py
new file mode 100644
index 0000000..f158f61
--- /dev/null
+++ b/notebooks/_solutions/case-curieuzeneuzen-air-quality5.py
@@ -0,0 +1 @@
+df["no2"].describe()
\ No newline at end of file
diff --git a/notebooks/_solutions/case-curieuzeneuzen-air-quality50.py b/notebooks/_solutions/case-curieuzeneuzen-air-quality50.py
new file mode 100644
index 0000000..b4be0bf
--- /dev/null
+++ b/notebooks/_solutions/case-curieuzeneuzen-air-quality50.py
@@ -0,0 +1 @@
+import contextily
\ No newline at end of file
diff --git a/notebooks/_solutions/case-curieuzeneuzen-air-quality51.py b/notebooks/_solutions/case-curieuzeneuzen-air-quality51.py
new file mode 100644
index 0000000..526d1e5
--- /dev/null
+++ b/notebooks/_solutions/case-curieuzeneuzen-air-quality51.py
@@ -0,0 +1,4 @@
+fig, ax = plt.subplots(figsize=(15, 15))
+ax = gdf_gent.to_crs(3857).plot(column="no2", ax=ax, legend=True, vmax=50)
+contextily.add_basemap(ax)
+ax.set_axis_off()
\ No newline at end of file
diff --git a/notebooks/_solutions/case-curieuzeneuzen-air-quality52.py b/notebooks/_solutions/case-curieuzeneuzen-air-quality52.py
new file mode 100644
index 0000000..8d3ffb1
--- /dev/null
+++ b/notebooks/_solutions/case-curieuzeneuzen-air-quality52.py
@@ -0,0 +1,5 @@
+fig, ax = plt.subplots(figsize=(12, 12))
+ax = gdf_gent.to_crs(3857).plot(column="no2", ax=ax, scheme="NaturalBreaks", k=6, legend=True)
+ax.set(xlim=(408_000, 420_000), ylim=(6_625_000, 6_638_000))
+contextily.add_basemap(ax, source=contextily.providers.CartoDB.PositronNoLabels)
+ax.set_axis_off()
\ No newline at end of file
diff --git a/notebooks/_solutions/case-curieuzeneuzen-air-quality53.py b/notebooks/_solutions/case-curieuzeneuzen-air-quality53.py
new file mode 100644
index 0000000..7467f9e
--- /dev/null
+++ b/notebooks/_solutions/case-curieuzeneuzen-air-quality53.py
@@ -0,0 +1,2 @@
+streets = geopandas.read_file("data/osm_network_gent.gpkg")
+streets.head()
\ No newline at end of file
diff --git a/notebooks/_solutions/case-curieuzeneuzen-air-quality54.py b/notebooks/_solutions/case-curieuzeneuzen-air-quality54.py
new file mode 100644
index 0000000..35d6924
--- /dev/null
+++ b/notebooks/_solutions/case-curieuzeneuzen-air-quality54.py
@@ -0,0 +1 @@
+streets = streets.to_crs("EPSG:31370")
\ No newline at end of file
diff --git a/notebooks/_solutions/case-curieuzeneuzen-air-quality55.py b/notebooks/_solutions/case-curieuzeneuzen-air-quality55.py
new file mode 100644
index 0000000..675eb32
--- /dev/null
+++ b/notebooks/_solutions/case-curieuzeneuzen-air-quality55.py
@@ -0,0 +1 @@
+streets.plot()
\ No newline at end of file
diff --git a/notebooks/_solutions/case-curieuzeneuzen-air-quality56.py b/notebooks/_solutions/case-curieuzeneuzen-air-quality56.py
new file mode 100644
index 0000000..8f3bbeb
--- /dev/null
+++ b/notebooks/_solutions/case-curieuzeneuzen-air-quality56.py
@@ -0,0 +1 @@
+streets.plot(column="highway", figsize=(20, 20), legend=True, cmap="tab20")
\ No newline at end of file
diff --git a/notebooks/_solutions/case-curieuzeneuzen-air-quality57.py b/notebooks/_solutions/case-curieuzeneuzen-air-quality57.py
new file mode 100644
index 0000000..1b21c03
--- /dev/null
+++ b/notebooks/_solutions/case-curieuzeneuzen-air-quality57.py
@@ -0,0 +1 @@
+streets.distance(point)
\ No newline at end of file
diff --git a/notebooks/_solutions/case-curieuzeneuzen-air-quality58.py b/notebooks/_solutions/case-curieuzeneuzen-air-quality58.py
new file mode 100644
index 0000000..8ef27dd
--- /dev/null
+++ b/notebooks/_solutions/case-curieuzeneuzen-air-quality58.py
@@ -0,0 +1 @@
+streets.distance(point).min()
\ No newline at end of file
diff --git a/notebooks/_solutions/case-curieuzeneuzen-air-quality59.py b/notebooks/_solutions/case-curieuzeneuzen-air-quality59.py
new file mode 100644
index 0000000..1fcde26
--- /dev/null
+++ b/notebooks/_solutions/case-curieuzeneuzen-air-quality59.py
@@ -0,0 +1,2 @@
+idx_closest = streets.distance(point).idxmin()
+idx_closest
\ No newline at end of file
diff --git a/notebooks/_solutions/case-curieuzeneuzen-air-quality6.py b/notebooks/_solutions/case-curieuzeneuzen-air-quality6.py
new file mode 100644
index 0000000..f9a9529
--- /dev/null
+++ b/notebooks/_solutions/case-curieuzeneuzen-air-quality6.py
@@ -0,0 +1 @@
+df['no2'].plot.hist()
\ No newline at end of file
diff --git a/notebooks/_solutions/case-curieuzeneuzen-air-quality60.py b/notebooks/_solutions/case-curieuzeneuzen-air-quality60.py
new file mode 100644
index 0000000..c7a56e3
--- /dev/null
+++ b/notebooks/_solutions/case-curieuzeneuzen-air-quality60.py
@@ -0,0 +1 @@
+streets.loc[idx_closest]
\ No newline at end of file
diff --git a/notebooks/_solutions/case-curieuzeneuzen-air-quality61.py b/notebooks/_solutions/case-curieuzeneuzen-air-quality61.py
new file mode 100644
index 0000000..533a2ca
--- /dev/null
+++ b/notebooks/_solutions/case-curieuzeneuzen-air-quality61.py
@@ -0,0 +1 @@
+streets["highway"][idx_closest]
\ No newline at end of file
diff --git a/notebooks/_solutions/case-curieuzeneuzen-air-quality62.py b/notebooks/_solutions/case-curieuzeneuzen-air-quality62.py
new file mode 100644
index 0000000..1b88f6d
--- /dev/null
+++ b/notebooks/_solutions/case-curieuzeneuzen-air-quality62.py
@@ -0,0 +1 @@
+streets.loc[idx_closest, "highway"]
\ No newline at end of file
diff --git a/notebooks/_solutions/case-curieuzeneuzen-air-quality63.py b/notebooks/_solutions/case-curieuzeneuzen-air-quality63.py
new file mode 100644
index 0000000..455f744
--- /dev/null
+++ b/notebooks/_solutions/case-curieuzeneuzen-air-quality63.py
@@ -0,0 +1,5 @@
+def closest_road_type(point, streets):
+    """Type to the nearest road (OpenStreetMap)"""
+    dist = streets.distance(point)
+    idx_closest = dist.idxmin()
+    return streets.loc[idx_closest, "highway"]
\ No newline at end of file
diff --git a/notebooks/_solutions/case-curieuzeneuzen-air-quality64.py b/notebooks/_solutions/case-curieuzeneuzen-air-quality64.py
new file mode 100644
index 0000000..cfd539f
--- /dev/null
+++ b/notebooks/_solutions/case-curieuzeneuzen-air-quality64.py
@@ -0,0 +1 @@
+closest_road_type(point, streets)
\ No newline at end of file
diff --git a/notebooks/_solutions/case-curieuzeneuzen-air-quality65.py b/notebooks/_solutions/case-curieuzeneuzen-air-quality65.py
new file mode 100644
index 0000000..88501df
--- /dev/null
+++ b/notebooks/_solutions/case-curieuzeneuzen-air-quality65.py
@@ -0,0 +1 @@
+streets_unioned = streets.dissolve("highway").reset_index()
\ No newline at end of file
diff --git a/notebooks/_solutions/case-curieuzeneuzen-air-quality66.py b/notebooks/_solutions/case-curieuzeneuzen-air-quality66.py
new file mode 100644
index 0000000..1bd12a9
--- /dev/null
+++ b/notebooks/_solutions/case-curieuzeneuzen-air-quality66.py
@@ -0,0 +1 @@
+gdf_gent["road_type"] = gdf_gent.geometry.apply(lambda point: closest_road_type(point, streets_unioned))
\ No newline at end of file
diff --git a/notebooks/_solutions/case-curieuzeneuzen-air-quality67.py b/notebooks/_solutions/case-curieuzeneuzen-air-quality67.py
new file mode 100644
index 0000000..b62a490
--- /dev/null
+++ b/notebooks/_solutions/case-curieuzeneuzen-air-quality67.py
@@ -0,0 +1 @@
+gdf_gent.head()
\ No newline at end of file
diff --git a/notebooks/_solutions/case-curieuzeneuzen-air-quality68.py b/notebooks/_solutions/case-curieuzeneuzen-air-quality68.py
new file mode 100644
index 0000000..dfa4815
--- /dev/null
+++ b/notebooks/_solutions/case-curieuzeneuzen-air-quality68.py
@@ -0,0 +1 @@
+gdf_gent["road_type"].value_counts()
\ No newline at end of file
diff --git a/notebooks/_solutions/case-curieuzeneuzen-air-quality69.py b/notebooks/_solutions/case-curieuzeneuzen-air-quality69.py
new file mode 100644
index 0000000..7f29f2d
--- /dev/null
+++ b/notebooks/_solutions/case-curieuzeneuzen-air-quality69.py
@@ -0,0 +1 @@
+gdf_gent["road_type"] = gdf_gent["road_type"].replace(mapping)
\ No newline at end of file
diff --git a/notebooks/_solutions/case-curieuzeneuzen-air-quality7.py b/notebooks/_solutions/case-curieuzeneuzen-air-quality7.py
new file mode 100644
index 0000000..40c7204
--- /dev/null
+++ b/notebooks/_solutions/case-curieuzeneuzen-air-quality7.py
@@ -0,0 +1,11 @@
+fig, ax = plt.subplots()
+# using predefined bins
+df['no2'].plot.hist(ax=ax, bins=np.arange(0, 80, 5))
+# adding x/y axis labels, and setting the range for the x axis
+ax.set(xlabel="NO2 concentration (µg/m³)",
+       ylabel="Number of measurements", xlim=(0,80))
+# adding a line with the mean
+ax.axvline(x=df['no2'].mean(), color='C2', linewidth=3)
+# adding a line with the EU yearly limit value of 40 µg/m³
+ax.axvline(x=40, color='C3', linewidth=3)
+ax.grid()
\ No newline at end of file
diff --git a/notebooks/_solutions/case-curieuzeneuzen-air-quality70.py b/notebooks/_solutions/case-curieuzeneuzen-air-quality70.py
new file mode 100644
index 0000000..c7c71d3
--- /dev/null
+++ b/notebooks/_solutions/case-curieuzeneuzen-air-quality70.py
@@ -0,0 +1 @@
+subset = gdf_gent[gdf_gent["road_type"].isin(categories)]
\ No newline at end of file
diff --git a/notebooks/_solutions/case-curieuzeneuzen-air-quality71.py b/notebooks/_solutions/case-curieuzeneuzen-air-quality71.py
new file mode 100644
index 0000000..0313e1e
--- /dev/null
+++ b/notebooks/_solutions/case-curieuzeneuzen-air-quality71.py
@@ -0,0 +1 @@
+subset.groupby("road_type").size()  # subset["road_type"].value_counts()
\ No newline at end of file
diff --git a/notebooks/_solutions/case-curieuzeneuzen-air-quality72.py b/notebooks/_solutions/case-curieuzeneuzen-air-quality72.py
new file mode 100644
index 0000000..97c3ec2
--- /dev/null
+++ b/notebooks/_solutions/case-curieuzeneuzen-air-quality72.py
@@ -0,0 +1 @@
+subset.groupby("road_type")['no2'].mean()
\ No newline at end of file
diff --git a/notebooks/_solutions/case-curieuzeneuzen-air-quality73.py b/notebooks/_solutions/case-curieuzeneuzen-air-quality73.py
new file mode 100644
index 0000000..556db3e
--- /dev/null
+++ b/notebooks/_solutions/case-curieuzeneuzen-air-quality73.py
@@ -0,0 +1 @@
+import seaborn
\ No newline at end of file
diff --git a/notebooks/_solutions/case-curieuzeneuzen-air-quality74.py b/notebooks/_solutions/case-curieuzeneuzen-air-quality74.py
new file mode 100644
index 0000000..9d94dea
--- /dev/null
+++ b/notebooks/_solutions/case-curieuzeneuzen-air-quality74.py
@@ -0,0 +1 @@
+seaborn.boxplot(x="road_type", y="no2", data=subset, order=categories)
\ No newline at end of file
diff --git a/notebooks/_solutions/case-curieuzeneuzen-air-quality8.py b/notebooks/_solutions/case-curieuzeneuzen-air-quality8.py
new file mode 100644
index 0000000..1566f9c
--- /dev/null
+++ b/notebooks/_solutions/case-curieuzeneuzen-air-quality8.py
@@ -0,0 +1 @@
+exceedances = df['no2'] > 40
\ No newline at end of file
diff --git a/notebooks/_solutions/case-curieuzeneuzen-air-quality9.py b/notebooks/_solutions/case-curieuzeneuzen-air-quality9.py
new file mode 100644
index 0000000..db49fcf
--- /dev/null
+++ b/notebooks/_solutions/case-curieuzeneuzen-air-quality9.py
@@ -0,0 +1 @@
+exceedances.mean() * 100  # the mean is equivalent here to counting the True values and dividing by the total number of elements
\ No newline at end of file
diff --git a/notebooks/_solutions/case-sea-surface-temperature1.py b/notebooks/_solutions/case-sea-surface-temperature1.py
new file mode 100644
index 0000000..2c13233
--- /dev/null
+++ b/notebooks/_solutions/case-sea-surface-temperature1.py
@@ -0,0 +1,2 @@
+# size attribute of array object
+ds["sst"].size
\ No newline at end of file
diff --git a/notebooks/_solutions/case-sea-surface-temperature10.py b/notebooks/_solutions/case-sea-surface-temperature10.py
new file mode 100644
index 0000000..442076d
--- /dev/null
+++ b/notebooks/_solutions/case-sea-surface-temperature10.py
@@ -0,0 +1 @@
+ds_mm = sst.groupby(sst.time.dt.month).mean(dim='time')
\ No newline at end of file
diff --git a/notebooks/_solutions/case-sea-surface-temperature11.py b/notebooks/_solutions/case-sea-surface-temperature11.py
new file mode 100644
index 0000000..8def38d
--- /dev/null
+++ b/notebooks/_solutions/case-sea-surface-temperature11.py
@@ -0,0 +1 @@
+ds_mm.mean(dim='lon').plot.imshow(x="month", y="lat", vmin=-2, vmax=30, cmap="coolwarm")
\ No newline at end of file
diff --git a/notebooks/_solutions/case-sea-surface-temperature12.py b/notebooks/_solutions/case-sea-surface-temperature12.py
new file mode 100644
index 0000000..df62c3b
--- /dev/null
+++ b/notebooks/_solutions/case-sea-surface-temperature12.py
@@ -0,0 +1,2 @@
+# alternative using transpose instead of defining the x and y in the plot function
+ds_mm.mean(dim='lon').transpose().plot.imshow(vmin=-2, vmax=30, cmap="coolwarm")
\ No newline at end of file
diff --git a/notebooks/_solutions/case-sea-surface-temperature13.py b/notebooks/_solutions/case-sea-surface-temperature13.py
new file mode 100644
index 0000000..bce2bb4
--- /dev/null
+++ b/notebooks/_solutions/case-sea-surface-temperature13.py
@@ -0,0 +1 @@
+(ds_mm.sel(month=1) - ds_mm.sel(month=7)).plot.imshow(vmax=10)
\ No newline at end of file
diff --git a/notebooks/_solutions/case-sea-surface-temperature14.py b/notebooks/_solutions/case-sea-surface-temperature14.py
new file mode 100644
index 0000000..26baa93
--- /dev/null
+++ b/notebooks/_solutions/case-sea-surface-temperature14.py
@@ -0,0 +1,2 @@
+# slice the point of interest
+ds_anom_loc = ds_anom.sel(lon=300, lat=50)
\ No newline at end of file
diff --git a/notebooks/_solutions/case-sea-surface-temperature15.py b/notebooks/_solutions/case-sea-surface-temperature15.py
new file mode 100644
index 0000000..97387a5
--- /dev/null
+++ b/notebooks/_solutions/case-sea-surface-temperature15.py
@@ -0,0 +1,3 @@
+# compute the resampling and rolling
+ds_anom_resample = ds_anom_loc.resample(time='5YE').median(dim='time')
+ds_anom_rolling = ds_anom_loc.rolling(time=12, center=True).median()
\ No newline at end of file
diff --git a/notebooks/_solutions/case-sea-surface-temperature16.py b/notebooks/_solutions/case-sea-surface-temperature16.py
new file mode 100644
index 0000000..7deccc1
--- /dev/null
+++ b/notebooks/_solutions/case-sea-surface-temperature16.py
@@ -0,0 +1,7 @@
+# make a combined plot
+fig, ax = plt.subplots()
+ds_anom_loc.plot.line(ax=ax, label="monthly anom")
+ds_anom_resample.plot.line(marker='o', label="5 year resample")
+ds_anom_rolling.plot.line(label='12 month rolling mean')
+fig.legend(loc="upper center", ncol=3)
+ax.set_title("");
\ No newline at end of file
diff --git a/notebooks/_solutions/case-sea-surface-temperature17.py b/notebooks/_solutions/case-sea-surface-temperature17.py
new file mode 100644
index 0000000..72fa1b2
--- /dev/null
+++ b/notebooks/_solutions/case-sea-surface-temperature17.py
@@ -0,0 +1,9 @@
+map_proj = ccrs.Orthographic(-20, 5)
+
+fig, ax = plt.subplots(figsize = (12, 6), subplot_kw={"projection": map_proj})
+
+ax.gridlines()
+ax.coastlines()
+ds_anom.sel(time='2018-01-01').plot(ax=ax, vmin=-1.5, vmax=1.5,
+                                   cmap='coolwarm', transform = ccrs.PlateCarree(),
+                                   cbar_kwargs={'shrink':0.5, 'label': 'anomaly'})
\ No newline at end of file
diff --git a/notebooks/_solutions/case-sea-surface-temperature18.py b/notebooks/_solutions/case-sea-surface-temperature18.py
new file mode 100644
index 0000000..d55d4a6
--- /dev/null
+++ b/notebooks/_solutions/case-sea-surface-temperature18.py
@@ -0,0 +1,2 @@
+basin_mean_sst = sst.groupby(basin_surface_interp).mean()
+basin_mean_sst = basin_mean_sst.mean(dim="time")
\ No newline at end of file
diff --git a/notebooks/_solutions/case-sea-surface-temperature19.py b/notebooks/_solutions/case-sea-surface-temperature19.py
new file mode 100644
index 0000000..80b26d7
--- /dev/null
+++ b/notebooks/_solutions/case-sea-surface-temperature19.py
@@ -0,0 +1,3 @@
+# In such a situation, the ELLIPSIS can be used to aggregate over all dimensions
+basin_mean_sst = sst.groupby(basin_surface_interp).mean(dim=...) # ellipsis is shortcut for all dimensions
+basin_mean_sst
\ No newline at end of file
diff --git a/notebooks/_solutions/case-sea-surface-temperature2.py b/notebooks/_solutions/case-sea-surface-temperature2.py
new file mode 100644
index 0000000..86e3797
--- /dev/null
+++ b/notebooks/_solutions/case-sea-surface-temperature2.py
@@ -0,0 +1,2 @@
+# shape attribute of array object
+ds["sst"].shape
\ No newline at end of file
diff --git a/notebooks/_solutions/case-sea-surface-temperature20.py b/notebooks/_solutions/case-sea-surface-temperature20.py
new file mode 100644
index 0000000..c921207
--- /dev/null
+++ b/notebooks/_solutions/case-sea-surface-temperature20.py
@@ -0,0 +1,3 @@
+# Convert to a Pandas DataFrame:
+basin_mean_df = basin_mean_sst.to_dataframe()
+basin_mean_df
\ No newline at end of file
diff --git a/notebooks/_solutions/case-sea-surface-temperature21.py b/notebooks/_solutions/case-sea-surface-temperature21.py
new file mode 100644
index 0000000..a38a980
--- /dev/null
+++ b/notebooks/_solutions/case-sea-surface-temperature21.py
@@ -0,0 +1,2 @@
+# Merge the data with the `basin_s` data on the index:
+basin_mean_df_merged = pd.merge(basin_mean_df, basin_s, left_index=True, right_index=True)
\ No newline at end of file
diff --git a/notebooks/_solutions/case-sea-surface-temperature22.py b/notebooks/_solutions/case-sea-surface-temperature22.py
new file mode 100644
index 0000000..6f37759
--- /dev/null
+++ b/notebooks/_solutions/case-sea-surface-temperature22.py
@@ -0,0 +1,2 @@
+# Create a bar chart of the SST per basin data:
+basin_mean_df_merged.sort_values(by="sst").plot.barh(x="basin");
\ No newline at end of file
diff --git a/notebooks/_solutions/case-sea-surface-temperature3.py b/notebooks/_solutions/case-sea-surface-temperature3.py
new file mode 100644
index 0000000..fe585ef
--- /dev/null
+++ b/notebooks/_solutions/case-sea-surface-temperature3.py
@@ -0,0 +1,2 @@
+# dims attribute of dataset object
+ds.dims
\ No newline at end of file
diff --git a/notebooks/_solutions/case-sea-surface-temperature4.py b/notebooks/_solutions/case-sea-surface-temperature4.py
new file mode 100644
index 0000000..548537e
--- /dev/null
+++ b/notebooks/_solutions/case-sea-surface-temperature4.py
@@ -0,0 +1,2 @@
+# attributes of array
+ds["sst"].attrs
\ No newline at end of file
diff --git a/notebooks/_solutions/case-sea-surface-temperature5.py b/notebooks/_solutions/case-sea-surface-temperature5.py
new file mode 100644
index 0000000..c3bcc6f
--- /dev/null
+++ b/notebooks/_solutions/case-sea-surface-temperature5.py
@@ -0,0 +1,2 @@
+# attributes of data set
+ds.attrs
\ No newline at end of file
diff --git a/notebooks/_solutions/case-sea-surface-temperature6.py b/notebooks/_solutions/case-sea-surface-temperature6.py
new file mode 100644
index 0000000..39dd3e8
--- /dev/null
+++ b/notebooks/_solutions/case-sea-surface-temperature6.py
@@ -0,0 +1,2 @@
+sst.sel(time="1960-01-01").plot.pcolormesh(vmin=-2, vmax=30, 
+                                           cmap="coolwarm")
\ No newline at end of file
diff --git a/notebooks/_solutions/case-sea-surface-temperature7.py b/notebooks/_solutions/case-sea-surface-temperature7.py
new file mode 100644
index 0000000..1eca4f0
--- /dev/null
+++ b/notebooks/_solutions/case-sea-surface-temperature7.py
@@ -0,0 +1 @@
+sst.sel(lon=300, lat=50).plot.line();
\ No newline at end of file
diff --git a/notebooks/_solutions/case-sea-surface-temperature8.py b/notebooks/_solutions/case-sea-surface-temperature8.py
new file mode 100644
index 0000000..442076d
--- /dev/null
+++ b/notebooks/_solutions/case-sea-surface-temperature8.py
@@ -0,0 +1 @@
+ds_mm = sst.groupby(sst.time.dt.month).mean(dim='time')
\ No newline at end of file
diff --git a/notebooks/_solutions/case-sea-surface-temperature9.py b/notebooks/_solutions/case-sea-surface-temperature9.py
new file mode 100644
index 0000000..9971308
--- /dev/null
+++ b/notebooks/_solutions/case-sea-surface-temperature9.py
@@ -0,0 +1 @@
+ds_mm.sel(lon=164, lat=[-23.4, 23.4], method="nearest").plot.line(hue="lat");
\ No newline at end of file
diff --git a/notebooks/case-argo-sea-floats.ipynb b/notebooks/case-argo-sea-floats.ipynb
new file mode 100644
index 0000000..c10f61a
--- /dev/null
+++ b/notebooks/case-argo-sea-floats.ipynb
@@ -0,0 +1,618 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<p><font size=\"6\"><b>CASE - Argo Sea Floats</b></font></p>\n",
+    "\n",
+    "\n",
+    "> *DS Python for GIS and Geoscience*  \n",
+    "> *September, 2025*\n",
+    ">\n",
+    "> *© 2025, Joris Van den Bossche and Stijn Van Hoey. Licensed under [CC BY 4.0 Creative Commons](http://creativecommons.org/licenses/by/4.0/)*\n",
+    "\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "import xarray as xr"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For this use case, we do a data exploration of the [Argo floats](https://argo.ucsd.edu/) data set, an international collaboration that collects high-quality temperature and salinity profiles from the upper 2000m of the ice-free global ocean and currents from intermediate depths.\n",
+    "\n",
+    "> Argo is an international program that collects information from inside the ocean using a fleet of robotic instruments that drift with the ocean currents and move up and down between the surface and a mid-water level. Each instrument (float) spends almost all its life below the surface.\n",
+    "\n",
+    "These data do __NOT represent full spatial coverage data__ (like remote sensing images), but measurements of salinity and temperature as a function of water `level` (related to the pressure). Each measurements happens at a given `date` on a given location (`lon`/`lat`)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import xarray as xr\n",
+    "argo = xr.load_dataset(\"./data/argo_float.nc\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "argo"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "__Note:__\n",
+    "\n",
+    "The bold font (or * symbol in plain text output version) in the coordinate representation above indicates that level and date are 'dimension coordinates' (they describe the coordinates associated with data variable axes) while 'lon' and 'lat' are 'non-dimension coordinates'. We can make any variable a non-dimension coordinate.\n",
+    "\n",
+    "The 'lon' and 'lat' coordinates are both linked to the `date` dimension, i.e. a measurement at a given `date` is done at a given location (`lon`/`lat`). As such, all data is contained in a single `xarray.DataSet`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To get an overview, let's plot the coordinates of the available measurements and add a background map using [contextly](https://contextily.readthedocs.io/en/latest/index.html):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import contextily as cx\n",
+    "from pyproj import CRS\n",
+    "crs = CRS.from_epsg(4326)\n",
+    "\n",
+    "fig, ax = plt.subplots(figsize=(6, 6))\n",
+    "argo.plot.scatter(x='lon', y='lat', ax=ax, color='red', s=2)\n",
+    "\n",
+    "# Custom adjustments of the limits, as we are in the middle of the ocean\n",
+    "xmin, xmax = ax.get_xlim()\n",
+    "ymin, ymax = ax.get_ylim()\n",
+    "ax.set_xlim(xmin*1.5, xmax*0.5)\n",
+    "ax.set_ylim(ymin*0.5, ymax*1.5)\n",
+    "\n",
+    "cx.add_basemap(ax, crs=crs.to_string())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-success\">\n",
+    "\n",
+    "**EXERCISE 1**:\n",
+    "\n",
+    "Add a new variable to the `argo` data set, called `temperature_kelvin`, by converting the temperature to Kelvin. \n",
+    "    \n",
+    "$T_{K} = T_{^{\\circ}C} + 273.15$\n",
+    "   \n",
+    "<details>\n",
+    "    \n",
+    "<summary>Hints</summary>\n",
+    "\n",
+    "* Remember that xarray works as Numpy and relies on the same broadcasting rules.\n",
+    "\n",
+    "</details>    \n",
+    "    \n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/case-argo-sea-floats1.py"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-success\">\n",
+    "\n",
+    "**EXERCISE 2**:\n",
+    "\n",
+    "The water level classes define different water depth (level). The pressure is a proxy for the water depth. Verify the relationship between the pressure and the level using a scatter plot. Does a larger value for the level represent deeper water depths or not?\n",
+    "    \n",
+    "<details><summary>Hints</summary>\n",
+    "\n",
+    "* If you get the error `ValueError: Dataset.plot cannot be called directly. Use an explicit plot method, e.g. ds.plot.scatter(...)`. When encountering an Error, always check the output message at the end of the error statement.\n",
+    "\n",
+    "</details>    \n",
+    "    \n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/case-argo-sea-floats2.py"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-success\">\n",
+    "\n",
+    "**EXERCISE 3**:\n",
+    "\n",
+    "Assume that buoyancy (in dutch _'drijfvermogen'_, it is the upward force exerted by a fluid that opposes the weight of a partially or fully immersed object) is defined by the following formula:\n",
+    "    \n",
+    "$$g \\cdot ( 2\\times 10^{-4} \\cdot T - 7\\times 10^{-4} \\cdot P )$$\n",
+    "\n",
+    "With:\n",
+    "- $g$ = 9.8\n",
+    "- $T$ = temperature\n",
+    "- $P$ = pressure\n",
+    "\n",
+    "Calculate the buoyancy and add it as a new variable `buoyancy` to the `argo` data set. \n",
+    "\n",
+    "Make a 2D (image) plot with the x-axis the date, the y-axis the water level and the color intensity the buoyancy. As the level represents the depth of the water, it makes more sense to have 0 (surface) at the top of the y-axis: switch the y-axis direction.\n",
+    "    \n",
+    "<details><summary>Hints</summary>\n",
+    "\n",
+    "* $2\\times 10^{-4}$ can be written in Python as `2e-4`\n",
+    "* Remember that xarray works as Numpy and relies on the same broadcasting rules.\n",
+    "* The `imshow` method does not work on irregular intervals. Matplotlib and xarray also have `pcolormesh`.    \n",
+    "* Look for options [in the xarray documentation](http://xarray.pydata.org/en/stable/plotting.html#other-axes-kwargs) to control the axis direction. (The `ax.invert_yaxis()` Matplotlib function is not supported for pcolormesh)\n",
+    "    \n",
+    "</details>    \n",
+    "    \n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/case-argo-sea-floats3.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/case-argo-sea-floats4.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/case-argo-sea-floats5.py"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-success\">\n",
+    "\n",
+    "**EXERCISE 4**:\n",
+    "\n",
+    "Make a line plot of the salinity as a function of time at level 10\n",
+    "   \n",
+    "<details><summary>Hints</summary>\n",
+    "\n",
+    "Break it down into different steps and chain the individual steps:\n",
+    "    \n",
+    "* From the argo data set, select the variable `salinity`. This is similar to selecting a column in Pandas.\n",
+    "* Next, use the `sel` method to select the `level=10`\n",
+    "* Next, use the `plot.line()` method.\n",
+    "\n",
+    "</details>    \n",
+    "    \n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/case-argo-sea-floats6.py"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-success\">\n",
+    "\n",
+    "**EXERCISE 5**:\n",
+    "\n",
+    "- Make a line plot of the temperature as a function of time for the levels 10, 20 and 30 at the same graph \n",
+    "- Make a second line plot with each of the 3 levels (10, 20, 30) in a different subplot. \n",
+    "   \n",
+    "<details><summary>Hints</summary>\n",
+    "\n",
+    "Break it down into different steps and chain these individual steps:\n",
+    "    \n",
+    "* From the argo data set, select the variable `temperature`. This is similar to selecting a column in Pandas.\n",
+    "* Next, use the `sel` method to select the levels 10, 20 and 30.\n",
+    "* Next, use the `plot.line()` method, but make sure the `hue` changes for each level\n",
+    "    \n",
+    "For the subplots, check the [facetting documentation](http://xarray.pydata.org/en/stable/plotting.html#faceting) of xarray. \n",
+    "\n",
+    "</details>    \n",
+    "    \n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/case-argo-sea-floats7.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/case-argo-sea-floats8.py"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-success\">\n",
+    "\n",
+    "**EXERCISE 6**:\n",
+    "\n",
+    "You wonder how the temperature evolves with increasing latitude and what the effect is of the depth (level):\n",
+    "\n",
+    "- Create a scatter plot of the `level` as a function of the `temperature` colored by the `latitude`. \n",
+    "    \n",
+    "- As a further exploration step, pick a subset of levels 1, 10, 25, and 50 and create a second scatter plot with in the x-axis the latitude of the measurement and in the y-axis the temperature. To compare the effect of the different levels, give each level a separate subplot next to each other.\n",
+    "   \n",
+    "<details><summary>Hints</summary>\n",
+    "\n",
+    "* In a scatter plot, the color or `hue` can be linked to a variable (instead of the deafult `date`).\n",
+    "* From the argo data set, use the `sel` method to select the levels 1, 10, 25, and 50.\n",
+    "* For the second scatter plot, but make sure the `col` changes for each `level` and define which variables need to go to which axis.\n",
+    "\n",
+    "</details>    \n",
+    "    \n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/case-argo-sea-floats9.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/case-argo-sea-floats10.py"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-success\">\n",
+    "\n",
+    "**EXERCISE 7**:\n",
+    "\n",
+    "Make an image plot of the temperature as a function of time. Divide the colormap in 3 discrete categories using xarray plot methods:\n",
+    "    \n",
+    "* x < 5\n",
+    "* 5 < x < 15\n",
+    "* x > 15\n",
+    "    \n",
+    "Choose a custom colormap and adjust the label of the colorbar to `'Temperature (°C)'`\n",
+    "   \n",
+    "<details>\n",
+    "    \n",
+    "<summary>Hints</summary>\n",
+    "\n",
+    "- Check the help of the `plot` function or the [xarray documentation](http://xarray.pydata.org/en/stable/plotting.html#discrete-colormaps) on discrete colormaps.\n",
+    "- Adjustments to the colorbar settings can be defined with the `cbar_kwargs` as a dict. Adjust the `label` of the colorbar.    \n",
+    "\n",
+    "</details>    \n",
+    "    \n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/case-argo-sea-floats11.py"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-success\">\n",
+    "\n",
+    "**EXERCISE 8**:\n",
+    "\n",
+    "Plot a salinity profile, i.e. how the salinity evolves with the depth/level, for the measurement done at 2012-10-31. Add two lines to the plot:\n",
+    "    \n",
+    "- The salinity data of 2012-10-31 with the salinity in the x-axis and the level in the y-axis.\n",
+    "- The salinity data of 2012-10-31 with the level in the y-axis, but using a rolling median value for the level coordinate using 10 levels (with `center=True` to make the two plots comparable) to smooth the data. \n",
+    "    \n",
+    "Feel free to adjust the settings of both lines (color, width,...)\n",
+    "    \n",
+    "<details><summary>Hints</summary>\n",
+    "    \n",
+    "* Use `fig, ax = plt.subplots()` to prepare a Figure/axes fo both lines.\n",
+    "* The `rolling` method need to be applied to the `level` coordinate.\n",
+    "* You can pass configuration options such as `linewidth`, `color`,... to the line plot method.\n",
+    "* By adding `label` to the plots and remove the addition of a legend for each of the plots (`add_legend=False`), calling the `ax.legend()` method at the end will collect these labels into a single legend. \n",
+    "\n",
+    "</details>    \n",
+    "    \n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/case-argo-sea-floats12.py"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-success\">\n",
+    "\n",
+    "**EXERCISE 9**:\n",
+    "\n",
+    "An improved version of the salinity profile of the previous exercise uses the `pressure` data instead of the levels, plotting the pressure-salinity relationship. In the current data representation the pressure is a data variable. However, it can also be interpreted as a coordinate, i.e. the depth of the measurement (see exercise on the relation between pressure and level. \n",
+    "    \n",
+    "To create a salinity and temperature profile for each of the measurements in December 2012, convert the `pressure` variable to a coordinate instead of a data variable first. Prepare two subplots with Matplotlib to plot respectively the salinity and the temperature profile for each of the measurements in December 2012.  Use the pressure in the y-axis and make sure pressure increases from top to bottom of the graph.   \n",
+    "    \n",
+    "    \n",
+    "<details><summary>Hints</summary>\n",
+    "    \n",
+    "* Use `assign_coords` to use the pressure as coordinate data.\n",
+    "* Do not forget to select all measurements of December 2012, e.g. using the string representation `.sel(date='2012-12')`\n",
+    "* You can directly create two axes objects with matplotlib: `fig, (ax0, ax1) = plt.subplots(1, 2)`\n",
+    "</details>    \n",
+    "    \n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/case-argo-sea-floats13.py"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-success\">\n",
+    "\n",
+    "**EXERCISE 10**:\n",
+    "\n",
+    "Calculate the average salinity and temperature as a function of level over the measurements taken between 2012-10-01 and 2012-12-01. \n",
+    "\n",
+    "Make a separate line plot for each of them. Define the Figure and 2 subplots with Matplotlib first. \n",
+    "    \n",
+    "<details><summary>Hints</summary>\n",
+    "\n",
+    "* xarray supports to query dates using a string representation.\n",
+    "* Use the `slice` operator within the `sel` to select a range of the data.\n",
+    "* Whereas in Numpy we used `axis` in reduction functions, xarray uses the `dim` name.\n",
+    "* Also for line plots you can define which dimension should be on the x-axis and which on the y-axis by providing the name.  \n",
+    "* Use `fig, (ax0, ax1) = plt.subplots(1, 2)` to create subplots.\n",
+    "</details>    \n",
+    "    \n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/case-argo-sea-floats14.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/case-argo-sea-floats15.py"
+   ]
+  }
+ ],
+ "metadata": {
+  "celltoolbar": "Nbtutor - export exercises",
+  "jupytext": {
+   "default_lexer": "ipython3"
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.11"
+  },
+  "widgets": {
+   "application/vnd.jupyter.widget-state+json": {
+    "state": {},
+    "version_major": 2,
+    "version_minor": 0
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/notebooks/case-curieuzeneuzen-air-quality.ipynb b/notebooks/case-curieuzeneuzen-air-quality.ipynb
new file mode 100644
index 0000000..4ecdfe2
--- /dev/null
+++ b/notebooks/case-curieuzeneuzen-air-quality.ipynb
@@ -0,0 +1,2108 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "0d39c8d2",
+   "metadata": {},
+   "source": [
+    "<p><font size=\"6\"><b> CASE - CurieuzeNeuzen citizen science air quality data</b></font></p>\n",
+    "\n",
+    "\n",
+    "> *DS Python for GIS and Geoscience*  \n",
+    "> *October, 2025*\n",
+    ">\n",
+    "> *© 2025, Joris Van den Bossche and Stijn Van Hoey. Licensed under [CC BY 4.0 Creative Commons](http://creativecommons.org/licenses/by/4.0/)*\n",
+    "\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "86ea794e-be6e-4e7b-a591-add03506d728",
+   "metadata": {},
+   "source": [
+    "Air pollution remains a key environmental problem in an increasingly urbanized world. While concentrations of traffic-related pollutants like nitrogen dioxide (NO2) are known to vary over short distances, official monitoring networks remain inherently sparse, as reference stations are costly to construct and operate.\n",
+    "\n",
+    "The [**CurieuzeNeuzen**](https://curieuzeneuzen.be/curieuzeneuzen-vlaanderen-2018/) citizen science project collected a large, spatially distributed dataset that can complement official monitoring. In a first edition in 2016, in Antwerp, 2000 citizens were involved. This success was followed by a second edition in 2018 engaging 20.000 citizens across Flanders, a highly urbanized, industrialized and densely populated region in Europe. The participants measured the NO2 concentrations in front of their house using a low-cost sampler design (see picture below, where passive sampling tubes are attached using a panel to a window at the facade). \n",
+    "\n",
+    "Source: preprint paper at https://eartharxiv.org/repository/view/19/\n",
+    "\n",
+    "In this case study, we are going to make use of the data collected across Flanders in 2018: explore the data and investigate relationships with other variables.\n",
+    "\n",
+    "<img src=\"../img/CN_measurement_setup.png\" alt=\"Measurement panel\" style=\"width:800px\">"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b7b5841c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import geopandas\n",
+    "\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8819dfb3",
+   "metadata": {},
+   "source": [
+    "## Importing and exploring the data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8af710eb",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-success\">\n",
+    "\n",
+    "**EXERCISE 1**:\n",
+    "\n",
+    "* Read the csv file from `data/CN_Flanders_open_dataset.csv` into a DataFrame `df` and inspect the data.\n",
+    "* How many measurements do we have?\n",
+    "\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bb48a8bf",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/case-curieuzeneuzen-air-quality1.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3fcb9fcc",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/case-curieuzeneuzen-air-quality2.py"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a6d39dd0",
+   "metadata": {},
+   "source": [
+    "The dataset contains longitude/latitude columns of the measurement locations, the measured NO2 concentration, and in addition also a \"campaign\" column indicating the type of measurement location (and an internal \"code\", which we will ignore here).\n",
+    "\n",
+    "<div class=\"alert alert-success\">\n",
+    "\n",
+    "**EXERCISE 2**:\n",
+    "\n",
+    "* Check the unique values of the \"campaign\" columns and how many occurrences those have.\n",
+    "\n",
+    "    \n",
+    "<details><summary>Hints</summary>\n",
+    "\n",
+    "* A pandas Series has a `value_counts()` method that counts the unique values of the column.\n",
+    "\n",
+    "</details>\n",
+    "\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "729f9d28",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/case-curieuzeneuzen-air-quality3.py"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "da01da11",
+   "metadata": {
+    "clear_cell": false
+   },
+   "source": [
+    "Most of the measurements are performed at the facade of the house or building of a participant. In addition, some measurement tubes were also placed next to reference monitoring stations of the VMM and in background locations (e.g. nature reserve or park).\n",
+    "\n",
+    "Let's now explore the measured NO2 concentrations."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "10c764cf",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-success\">\n",
+    "\n",
+    "**EXERCISE 3**:\n",
+    "\n",
+    "* Calculate the overall average NO2 concentration of all locations (i.e. the mean of the \"no2\" column). \n",
+    "* Calculate a combination of descriptive statistics of the NO2 concentration using the `describe()` method.\n",
+    "* Make a histogram of the NO2 concentrations to get a visual idea of the distribution of the concentrations.\n",
+    "    \n",
+    "<details><summary>Hints</summary>\n",
+    "\n",
+    "* To calculate the mean of a column, we first need to select it: using the square bracket notation `df[colname]`\n",
+    "* The average can be calculate with the `mean()` method\n",
+    "* A histogram of a column can be plotted with the `hist()` method\n",
+    "\n",
+    "</details>\n",
+    "\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9815290d",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/case-curieuzeneuzen-air-quality4.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "39f28379",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/case-curieuzeneuzen-air-quality5.py"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "64db0a98",
+   "metadata": {},
+   "source": [
+    "A histogram:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e1d73663",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/case-curieuzeneuzen-air-quality6.py"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bf65012e",
+   "metadata": {},
+   "source": [
+    "A more expanded histogram (not asked in the exercise, but uncomment to check the code)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "04eba02d",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/case-curieuzeneuzen-air-quality7.py"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7717db12",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-success\">\n",
+    "\n",
+    "**EXERCISE 4**:\n",
+    "\n",
+    "* Determine the percentage of locations that exceed the EU and WHO yearly limit value of 40 µg/m³.\n",
+    "\n",
+    "__Tip__: first create a boolean mask determining whether the NO2 concentration is above 40 or not. Using this boolean mask, you can determine the percentage of values that follow the condition.\n",
+    "    \n",
+    "<details><summary>Hints</summary>\n",
+    "\n",
+    "* To know the fraction of `True` values in a boolean Series, we can use the `sum()` method, which is equivalent as counting the True values (True=1, False=0, so the sum is a count of the True values) and dividing by the total number of values. \n",
+    "\n",
+    "</details>\n",
+    "\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "74f4c694",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/case-curieuzeneuzen-air-quality8.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f3860339",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/case-curieuzeneuzen-air-quality9.py"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0b0c62ac",
+   "metadata": {},
+   "source": [
+    "So overall in Flanders, around 2.3% of the measurement locations exceeded the limit value. This might not seem much, but assuming that the dataset is representative for the population of Flanders (and effort was done to ensure this), around 150,000 inhabitants live in a place where the annual NO2 concentration at the front door exceeds the EU legal threshold value.  \n",
+    "We will also later see that this exceedance has a large spatial variation."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bb1a86f3",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-success\">\n",
+    "\n",
+    "**EXERCISE 5**:\n",
+    "\n",
+    "* What is the average measured concentration at the background location? Calculate this by first selecting the appropriate subset of the dataframe.\n",
+    "* More generally, what is the average measured concentration grouped on the \"Campaign\" type? \n",
+    "\n",
+    "<details><summary>Hints</summary>\n",
+    "\n",
+    "* To calculate a grouped statistic, use the `groupby()` method. Pass as argument the name of the column on which you want to group.\n",
+    "* After the `groupby()` operation, we can (similarly as for a normal DataFrame) select a column and call the aggregation column (`df.groupby(\"class\")[\"variable\"].method()`).\n",
+    "\n",
+    "</details>\n",
+    "\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "19d0939b",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/case-curieuzeneuzen-air-quality10.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "046a78d6",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/case-curieuzeneuzen-air-quality11.py"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9d1f7f1e",
+   "metadata": {},
+   "source": [
+    "The background locations (parks, nature reserves) clearly show a lower concentration than the average location. Note that the number of observations in each class are very skewed, so those averages are not necessarily representative!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ec0396a8",
+   "metadata": {},
+   "source": [
+    "## Converting to a geospatial dataset"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3f10de2c",
+   "metadata": {},
+   "source": [
+    "The provided data was a CSV file, and we explored it above as a pandas DataFrame. To further explore the dataset using the spatial aspects (point data), we will first convert it to a geopandas GeoDataFrame.\n",
+    "\n",
+    "<div class=\"alert alert-success\">\n",
+    "\n",
+    "**EXERCISE 6**:\n",
+    "\n",
+    "* Convert `df` into a GeoDataFrame, using the 'lat'/'lon' columns to create a Point geometry column (tip: check the `geopandas.points_from_xy()` function). Also specify the correct Coordinate Reference System with the `crs` keyword. Call the result `gdf`.\n",
+    "* Do a quick check to see the result is correct: look at the first rows, and make a simple plot of the GeoDataFrame with `.plot()` (you should recognize the shape of Flanders, if not, something went wrong)\n",
+    "\n",
+    "    \n",
+    "<details><summary>Hints</summary>\n",
+    "\n",
+    "* A GeoDataFrame can be created from an existing pandas.DataFrame by using the `geopandas.GeoDataFrame(...)` constructor. This constructor needs a `geometry=` keyword specifying either the name of the column that holds the geometries or either the geometry values.\n",
+    "* GeoPandas provides a helper function to create Point geometry values from a array or column of x and y coordinates: `geopandas.points_from_xy(x_values, y_values)`.\n",
+    "* Remember! The order of coordinates is (x, y), and for geographical coordinates this means the (lon, lat) order.\n",
+    "* Remember! The Coordinate Reference System typically used for geographical lon/lat coordinates is \"EPSG:4326\" (WGS84).\n",
+    "\n",
+    "</details>\n",
+    "\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5f2c23c4",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/case-curieuzeneuzen-air-quality12.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9bafbc1f",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/case-curieuzeneuzen-air-quality13.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d5e858d2",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/case-curieuzeneuzen-air-quality14.py"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "372ea534",
+   "metadata": {},
+   "source": [
+    "Let's make that last plot a bit more informative:\n",
+    "\n",
+    "<div class=\"alert alert-success\">\n",
+    "\n",
+    "**EXERCISE 7**:\n",
+    "\n",
+    "* Make a plot of the point locations of `gdf` and use the \"no2\" column to color the points.\n",
+    "* Make the figure a bit larger by specifying the `figsize=` keyword.\n",
+    "\n",
+    "<details><summary>Hints</summary>\n",
+    "\n",
+    "* The figure size can be specified with the `figsize=` keyword of the `plot()` method.\n",
+    "* For using one of the columns of the GeoDataFrame to determine the fill color, use the `column=` keyword.\n",
+    "\n",
+    "</details>\n",
+    "\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "531e70ce",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/case-curieuzeneuzen-air-quality15.py"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "165aaa9c",
+   "metadata": {},
+   "source": [
+    "We can already notice some spatial patterns: higher concentrations (and also more measurement locations) in urban areas. But, the visualization above is not really a good way to visualize many points. There are many alternatives (e.g. heatmaps, hexbins, etc), but in this case study we are going to make a [*choropleth* map](https://en.wikipedia.org/wiki/Choropleth_map): choropleths are maps onto which an attribute, a non-spatial variable, is displayed by coloring a certain area. \n",
+    "\n",
+    "As the unit of area, we will use the municipalities of Flanders."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "32c124b6",
+   "metadata": {},
+   "source": [
+    "## Combining with municipalities\n",
+    "\n",
+    "We downloaded the publicly available municipality reference from geopunt.be ([Voorlopig referentiebestand gemeentegrenzen, toestand 16/05/2018](https://www.geopunt.be/catalogus/datasetfolder/9ff44cc4-5f16-4507-81a6-6810958b14df)), and added the Shapefile with the borders to the course repo: `data/VRBG/Refgem.shp`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d455b2d3",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-success\">\n",
+    "\n",
+    "**EXERCISE 8**:\n",
+    "\n",
+    "* Read the Shapefile with the municipalities into a variable called `muni`.\n",
+    "* Inspect the data and do a quick plot.\n",
+    "    \n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fcc92d99",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/case-curieuzeneuzen-air-quality16.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "355ea6bf",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/case-curieuzeneuzen-air-quality17.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "442bd40a",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/case-curieuzeneuzen-air-quality18.py"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "483b0170",
+   "metadata": {},
+   "source": [
+    "Now we have a dataset with the municipalities, we want to know for each of the measurement locations in which municipality it is located. This is a \"point-in-polygon\" spatial join.\n",
+    "\n",
+    "<div class=\"alert alert-success\">\n",
+    "\n",
+    "**EXERCISE 9**:\n",
+    "\n",
+    "Before performing the spatial join, we need to ensure the two datasets are using the same Coordinate Reference System (CRS).\n",
+    "\n",
+    "* Check the CRS of both `gdf` and `muni`. What kind of CRS are they using? Are they the same?\n",
+    "* Reproject the measurements to the Lambert 72 (EPSG:31370) reference system. Call the result `gdf_lambert`.\n",
+    "\n",
+    "<details><summary>Hints</summary>\n",
+    "\n",
+    "* The CRS of a GeoDataFrame can be checked by looking at the `crs` attribute.\n",
+    "* To reproject a GeoDataFrame to another CRS, we can use the `to_crs()` method.\n",
+    "\n",
+    "</details>\n",
+    "\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "23ec916b",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/case-curieuzeneuzen-air-quality19.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7d62b7e9",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/case-curieuzeneuzen-air-quality20.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "eff83813",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/case-curieuzeneuzen-air-quality21.py"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d8f99188",
+   "metadata": {},
+   "source": [
+    "The EPSG:31370 or \"Belgian Lambert 72\" (https://epsg.io/31370) is the local, projected CRS most often used in Belgium."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e3e163e8",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-success\">\n",
+    "\n",
+    "**EXERCISE 10**:\n",
+    "\n",
+    "* Add the municipality information to the measurements dataframe. We are mostly interested in the \"NAAM\" column of the municipalities dataframe (the name of the municipality). Call the result `gdf_combined`.\n",
+    "    \n",
+    "<details><summary>Hints</summary>\n",
+    "\n",
+    "* Joining the measurement locations with the municipality information can be done with the `geopandas.sjoin()` function. The first argument is the dataframe to which we want to add information, the second argument the dataframe with the additional information.\n",
+    "* You can select a subset of columns when passing a GeoDataFrame to the `sjoin()` function.\n",
+    "\n",
+    "</details>\n",
+    "\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "69e238f7",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/case-curieuzeneuzen-air-quality22.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e31ff39e",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/case-curieuzeneuzen-air-quality23.py"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ac11e257",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-success\">\n",
+    "\n",
+    "**EXERCISE 11**:\n",
+    "\n",
+    "* What is the average measured concentration in each municipality?\n",
+    "* Call the result `muni_mean`. Ensure we have a DataFrame with a NO2 columns and a column with the municipality name by calling the `reset_index()` method after the groupby operation.\n",
+    "* Merge those average concentrations with the municipalities GeoDataFrame (note: those have a common column \"NAAM\"). Call the merged dataframe `muni_no2`, and check the first rows.\n",
+    "    \n",
+    "<details><summary>Hints</summary>\n",
+    "\n",
+    "* Something like `df.groupby(\"class\")[\"variable\"].mean()` returns a Series, with the group variable as the index. Calling `reset_index()` on the result then converts this into a DataFrame with 2 columns: the group variable and the calculated statistic.\n",
+    "* Merging two dataframe that have a common column can be done with the `pd.merge()` function. \n",
+    "\n",
+    "</details>\n",
+    "\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6d3b2c02",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/case-curieuzeneuzen-air-quality24.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9f4c881e",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/case-curieuzeneuzen-air-quality25.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "dafd1262",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/case-curieuzeneuzen-air-quality26.py"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c7799e37",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-success\">\n",
+    "\n",
+    "**EXERCISE 12**:\n",
+    "\n",
+    "* Make a choropleth of the municipalities using the average NO2 concentration as variable to color the municipality polygons.\n",
+    "* Set the figure size to be (16, 5), and add a legend.\n",
+    "    \n",
+    "<details><summary>Hints</summary>\n",
+    "\n",
+    "* To specify which column to use to color the geometries in the plot, use the `column=` keyword of the `plot()` method.\n",
+    "* The figure size can be specified with the `figsize=` keyword of the `plot()` method.\n",
+    "* Pass `legend=True` to add a legend to the plot. The type of legend (continuous color bar, discrete legend) will be inferred from the plotted values.\n",
+    "\n",
+    "</details>\n",
+    "\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2ea01630",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/case-curieuzeneuzen-air-quality27.py"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "92e1067e",
+   "metadata": {},
+   "source": [
+    "When specifying a numerical column to color the polygons, by default this results in a continuous color scale, as you can see above.\n",
+    "\n",
+    "However, it is very difficult for the human eye to process small differences in color in a continuous scale. \n",
+    "Therefore, to create effective choropleths, we typically classify the values into a set of discrete groups.\n",
+    "\n",
+    "With GeoPandas' `plot()` method, you can control this with the `scheme` keyword (indicating which classification scheme to use, i.e how to divide the continuous range into a set of discrete classes) and the `k` keyword to indicate how many classes to use. This uses the [mapclassify](https://pysal.org/mapclassify/) package under the hood."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cafed763",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-success\">\n",
+    "\n",
+    "**EXERCISE 13**:\n",
+    "\n",
+    "* Starting from the previous figure, specify a classification scheme and a number of classes to use. Check the help of the `plot()` method to see the different options, and experiment with those.\n",
+    "    \n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "905b2ac4",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/case-curieuzeneuzen-air-quality28.py"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e31aa82f",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-success\">\n",
+    "\n",
+    "**EXERCISE 14**:\n",
+    "\n",
+    "* What is the percentage of exceedance for each municipality? Repeat the same calculation we did earlier on the original dataset `df`, but now using `gdf_combined` and grouped per municipality.\n",
+    "* Show the 10 municipalities with the highest percentage of exceedances.\n",
+    "\n",
+    "<details><summary>Hints</summary>\n",
+    "\n",
+    "* For showing the 10 highest values, we can either use the `sort_values()` sorting the highest values on top and showing the first 10 rows, or either use the `nlargest()` method as a short cut for this operation.\n",
+    "</details>\n",
+    "\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a0910fad",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/case-curieuzeneuzen-air-quality29.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e767ca8a",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/case-curieuzeneuzen-air-quality30.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d885a787",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/case-curieuzeneuzen-air-quality31.py"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9618ca92",
+   "metadata": {},
+   "source": [
+    "## Combining with Land Use data\n",
+    "\n",
+    "The CORINE Land Cover (https://land.copernicus.eu/pan-european/corine-land-cover) is a program by the European Environment Agency (EEA) to provide an inventory of land cover in 44 classes of the European Union. The data is provided in both raster as vector format and with a resolution of 100m.\n",
+    "\n",
+    "The data for the whole of Europe can be downloaded from the website (latest version: https://land.copernicus.eu/pan-european/corine-land-cover/clc2018?tab=download). This is however a large dataset, so we downloaded the raster file and cropped it to cover Flanders, and this subset is included in the repo as `data/CLC2018_V2020_20u1_flanders.tif` (the code to do this cropping can be see at [data/preprocess_data.ipynb#CORINE-Land-Cover](data/preprocess_data.ipynb#CORINE-Land-Cover)).\n",
+    "\n",
+    "The air quality is indirectly linked to land use, as the presence of pollution sources will depend on the land use. Therefore, in the following section we will determine the land use for each of the measurement locations based on the CORINE dataset and explore the relationship of the NO2 concentration and land use."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8da0be0d",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-success\">\n",
+    "\n",
+    "**EXERCISE 15**:\n",
+    "\n",
+    "* Open the land cover raster file (`data/CLC2018_V2020_20u1_flanders.tif`) with xarray, inspect the metadata, and do a quick visualization.\n",
+    "\n",
+    "<details><summary>Hints</summary>\n",
+    "\n",
+    "* The tif file represents a single layer (with one band), so you can use `xarray.open_dataarray()` to read it into an xarray.DataArray.\n",
+    "* With `mask_and_scale=False` the `int8` data type will be kept when reading in the data.\n",
+    "\n",
+    "</details>\n",
+    "\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4dc2391f",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/case-curieuzeneuzen-air-quality32.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "345df0dd",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/case-curieuzeneuzen-air-quality33.py"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cd1a353e",
+   "metadata": {},
+   "source": [
+    "The goal is now to query from the raster file the value of the land cover class for each of the measurement locations. This can be done with the `rasterstats` package and with the point locations of our GeoDataFrame. But first, we need to ensure that both our datasets are using the same CRS. In this case, it's easiest to reproject the point locations to the CRS of the raster file.\n",
+    "\n",
+    "<div class=\"alert alert-success\">\n",
+    "\n",
+    "**EXERCISE 16**:\n",
+    "\n",
+    "* What is the EPSG code of the Coordinate Reference System (CRS) of the raster file? You can find this in the metadata inspected with rasterio or xarray above.\n",
+    "* Reproject the point dataframe (`gdf`) to the CRS of the raster and assign this to a temporary variable `gdf_raster`.\n",
+    "\n",
+    "<details><summary>Hints</summary>\n",
+    "\n",
+    "* Reprojecting can be done with the `to_crs()` method of a GeoDataFrame, and the CRS can be specified in the form of \"EPSG:xxxx\".\n",
+    "\n",
+    "</details>\n",
+    "\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1343edd8",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/case-curieuzeneuzen-air-quality34.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4e851777",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/case-curieuzeneuzen-air-quality35.py"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7580a7d8",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-success\">\n",
+    "\n",
+    "**EXERCISE 17**:\n",
+    "\n",
+    "* Use the `rasterstats.point_query()` function to determine the value of the raster for each of the points in the dataframe. Remember to use `gdf_raster` for passing the geometries.\n",
+    "* Because we have a raster file with discrete classes, ensure to pass `interpolate=\"nearest\"` (the default \"bilinear\" will result in floats with decimals, not preserving the integers representing discrete classes).\n",
+    "* Assign the result to a new column \"land_use\" in `gdf`.\n",
+    "* Perform a `value_counts()` on this new column do get a quick idea of the new values obtained from the raster file.\n",
+    "\n",
+    "Note that the query operation can take a while. Don't worry if it runs for around 20 seconds!\n",
+    "    \n",
+    "    \n",
+    "<details><summary>Hints</summary>\n",
+    "\n",
+    "* Don't forget to first import the `rasterstats` package.\n",
+    "* The `point_query()` function takes as first argument the point geometries (this can be passed as a GeoSeries), and as second argument the path to the raster file (this will be opened by `rasterio` under the hood).\n",
+    "\n",
+    "</details>\n",
+    "\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e946c529",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/case-curieuzeneuzen-air-quality36.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "06cfb1ef",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/case-curieuzeneuzen-air-quality37.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4c32300e",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/case-curieuzeneuzen-air-quality38.py"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "de438086",
+   "metadata": {},
+   "source": [
+    "As you can see, we have obtained a large variety of land cover classes. To make this more practical to work with, we \n",
+    "\n",
+    " 1. want to convert the numbers into a class name, and \n",
+    " 2. reduce the number of classes.\n",
+    "\n",
+    "The full hierarchy (with 3 levels) of the 44 classes can be seen at https://wiki.openstreetmap.org/wiki/Corine_Land_Cover. For keeping the exercise a bit practical here, we prepared a simplified set of classes and provided a csv file with this information.  \n",
+    "\n",
+    "This has a column \"value\" corresponding to the values used in the raster file, and a \"group\" column with the simplified classes that we will use for this exercise.\n",
+    "\n",
+    "<div class=\"alert alert-success\">\n",
+    "\n",
+    "**EXERCISE 18**:\n",
+    "\n",
+    "* Read the `\"data/CLC2018_V2018_legend_grouped.csv\"` as a dataframe and call it `legend`.\n",
+    "\n",
+    "The additional steps, provided for you, use this information to convert the column of integer land use classes to a column with the simplified names. After that, we again use `value_counts()` on this new column, and we can see that we now have less classes.\n",
+    "\n",
+    "<details><summary>Hints</summary>\n",
+    "\n",
+    "* Reading a csv file can be done with the `pandas.read_csv()` function.\n",
+    "\n",
+    "</details>\n",
+    "\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e7dc0bec",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/case-curieuzeneuzen-air-quality39.py"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "072cac6b",
+   "metadata": {},
+   "source": [
+    "Convert the \"land_use\" integer values to a column with land use class names:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2e5ea7ef",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "value_to_group = dict(zip(legend['value'], legend['group']))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c278f2af",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gdf['land_use_class'] = gdf['land_use'].replace(value_to_group)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f2e24409",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gdf['land_use_class'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e04d9682",
+   "metadata": {},
+   "source": [
+    "Now we have the land use data, let's explore the air quality in relation to this land use.\n",
+    "\n",
+    "We can see in the `value_counts()` result  above that we have a few classes with only very few observations, though (<50). Calculating statistics for those small groups is not very reliable, so lets leave them out for this exercises (note, this is not necessarily the best strategy in real life! Amongst others, we could also inspect those points and re-assign to a dominant land use class in the surrounding region).\n",
+    "\n",
+    "<div class=\"alert alert-success\">\n",
+    "\n",
+    "**EXERCISE 19**:\n",
+    "\n",
+    "* Assign the value counts of the \"land_use_class\" column to a variable `counts`. \n",
+    "* Using \"counts\", we can determine which classes occur more then 50 times. \n",
+    "* Using those frequent classes, filter the `gdf` to only include observations from those classes, and call this `subset`.\n",
+    "* Based on this subset, calculate the average NO2 concentration per land use class.\n",
+    "\n",
+    "\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0b70af82",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/case-curieuzeneuzen-air-quality40.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6d7a6705",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/case-curieuzeneuzen-air-quality41.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "37324027",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/case-curieuzeneuzen-air-quality42.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c8804025",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/case-curieuzeneuzen-air-quality43.py"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "af945b86",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-success\">\n",
+    "\n",
+    "**EXERCISE 20**:\n",
+    "\n",
+    "* Using the `seaborn` package and the `subset` DataFrame, make a boxplot of the NO2 concentration, splitted per land use class.\n",
+    "\n",
+    "Don't forget to first import `seaborn`. We can use the `seaborn.boxplot()` function (check the help of this function to see which keywords to specify).\n",
+    "    \n",
+    "<details><summary>Hints</summary>\n",
+    "\n",
+    "* With the `seaborn.boxplot()`, we can specify the `data=` keyword to pass the DataFrame from which to plot values, and the `x=` and `y=` keywords to specify which columns of the DataFrame to use.\n",
+    "\n",
+    "</details>\n",
+    "\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c76a341a",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/case-curieuzeneuzen-air-quality44.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "05a26072",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/case-curieuzeneuzen-air-quality45.py"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "81e1ee0a",
+   "metadata": {},
+   "source": [
+    "Tweaking the figure a bit more (not asked in the exercise, but uncomment and run to see):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "06dc86e1",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/case-curieuzeneuzen-air-quality46.py"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cd7f57b7",
+   "metadata": {},
+   "source": [
+    "The dense urban areas and areas close to large roads clearly have higher concentrations on average. On the countryside (indicated by \"agricultural\" areas) much lower concentrations are observed."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6c55c158",
+   "metadata": {},
+   "source": [
+    "## A focus on Gent\n",
+    "\n",
+    "Let's now focus on the measurements in Ghent. We first get the geometry of Gent from the municipalities dataframe, so we can use this to filter the measurements:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b77bc3a7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gent = muni[muni[\"NAAM\"] == \"Gent\"].geometry.item()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "986d9e1c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gent"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c8984bbf",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-success\">\n",
+    "\n",
+    "**EXERCISE 21**:\n",
+    "\n",
+    "For the exercise here, we don't want to select just the measurements in the municipality of Gent, but those in the region. For this, we will create a bounding box of Gent:\n",
+    "    \n",
+    "* Create a new Shapely geometry, `gent_region`, that defines the bounding box of the Gent municipality.\n",
+    "\n",
+    "Check this section of the Shapely docs (https://shapely.readthedocs.io/en/latest/manual.html#constructive-methods) or experiment yourself to see which attribute is appropriate to use here (`convex_hull`, `envolope`, and `minimum_rotated_rectangle` all create a new polygon encompassing the original).\n",
+    "\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2415936a",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/case-curieuzeneuzen-air-quality47.py"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "90cdfb96",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-success\">\n",
+    "\n",
+    "**EXERCISE 22**:\n",
+    "\n",
+    "* Using the `gent_region` shape, create a subset the measurements dataframe with those measurements located within Gent. Call the result `gdf_gent`.\n",
+    "* How many measurements are left in the subset?\n",
+    "    \n",
+    "<details><summary>Hints</summary>\n",
+    "\n",
+    "* Ensure to use `gdf_lambert` and not `gdf`, since the `gent` shape is extracted from the `muni` dataframe with EPSG:31370 CRS.\n",
+    "* To check for a series of points whether they are located in a given polygon, use the `within()` method.\n",
+    "* Use the resulting boolean Series to mask the original `gdf` GeoDataFrame using boolean indexing (`[]` notation).\n",
+    "    \n",
+    "</details>\n",
+    "\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "61c5e656",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/case-curieuzeneuzen-air-quality48.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "90ca5add",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/case-curieuzeneuzen-air-quality49.py"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "97a3a59f",
+   "metadata": {},
+   "source": [
+    "Alternatively, we can also use the `geopandas.clip()` function. For points there is not much difference with the method above, but for lines or polygons, it will actually \"clip\" the geometries, i.e. removing the parts that fall outside of the specified region (in addition, this method also uses a spatial index under the hood and will typically be faster for large datasets):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "53cfc86e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gdf_gent = geopandas.clip(gdf_lambert, gent_region).copy()\n",
+    "len(gdf_gent)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e59ab782",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-success\">\n",
+    "\n",
+    "**EXERCISE 23**:\n",
+    "\n",
+    "* Make a visualization of the measurements in Gent. Use contextily to add a background basemap. Color the points based on the NO2 concentration.\n",
+    "\n",
+    "\n",
+    "<details><summary>Hints</summary>\n",
+    "\n",
+    "* For using one of the columns of the GeoDataFrame to determine the fill color, use the `column=` keyword.\n",
+    "* To add a background map, use the `contextily.add_basemap()` function. It takes the matplotlib `ax` to which to add a map as the first argument.\n",
+    "* Remember that, to use a contextily background, the data needs to be in Web Mercator (\"EPSG:3857\").\n",
+    "\n",
+    "</details>\n",
+    "\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f5a6846f",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/case-curieuzeneuzen-air-quality50.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4b19cc48",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/case-curieuzeneuzen-air-quality51.py"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2cf1174b",
+   "metadata": {},
+   "source": [
+    "The solution above includes a `vmax=50` to indicate to use a max value of 50 for the colorbar range (there are few measurements with much higher values, that would otherwise make most other points look dark blue-ish). A better option can be to use one of the classification schemes from `mapclassify` through the `scheme` keyword.\n",
+    "\n",
+    "Further zooming in on the city center, using a discrete color scheme and a different tile provider (not asked in exercise, but uncomment and run to see):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "de6a968e",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/case-curieuzeneuzen-air-quality52.py"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "25ea2219",
+   "metadata": {},
+   "source": [
+    "## Combining with OpenStreetMap information\n",
+    "\n",
+    "We downloaded and filtered OpenStreetMap data for the area of Gent, focusing on the street network information, and provided this as a GeoPackage file (`data/osm_network_gent.gpkg`, see this [notebook](./data/preprocess_data.ipynb#OpenStreetMap) to check the code to download and preprocess the raw OSM data).\n",
+    "\n",
+    "The OpenStreetMap street network data includes information about the type of street in the \"highway\" tag. We can use this as a proxy for traffic intensity of the street, and relate that to the measured NO2 concentration."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6ddbb026",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-success\">\n",
+    "\n",
+    "**EXERCISE 24**:\n",
+    "\n",
+    "* Read the `\"data/osm_network_gent.gpkg\"` file into a `streets` variable, and check the first rows.\n",
+    "* Convert the data to the appropriate CRS to combine with the `gdf_gent` data, if needed.\n",
+    "* Make a quick plot to explore the data.\n",
+    "    \n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "feae0179",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/case-curieuzeneuzen-air-quality53.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "373bf787",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/case-curieuzeneuzen-air-quality54.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b5f41caa",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/case-curieuzeneuzen-air-quality55.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "62ce070c",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/case-curieuzeneuzen-air-quality56.py"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f25d21c8",
+   "metadata": {},
+   "source": [
+    "(Note: for interactively exploring such data, there are better solutions as the GeoPandas `.plot()` method, such as opening the data in QGIS, or using an interactive visualization library, see notebook [visualization-04-interactive](./visualization-04-interactive.ipynb)).\n",
+    "\n",
+    "To relate the measured concentration with the road type, we want to determine for each location at what type of street it is located. Since the measurements are not exactly located on one of the lines, we are going to look at the closest street for each location."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "77abeaec",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-success\">\n",
+    "\n",
+    "**EXERCISE 25**:\n",
+    "\n",
+    "* Calculate the distance between the point (defined below) and all streets. And what is the minimum distance?\n",
+    "* Use the `idxmin()` method to know get the label of which row contains the minimum distance.\n",
+    "* Using the result of `idxmin()`, we can get the row or the value in the \"highway\" column that corresponds to the street that is closest to `point`.\n",
+    "\n",
+    "<details><summary>Hints</summary>\n",
+    "\n",
+    "* The distance method of a Shapely geometry does not accept a GeoSeries of geometries, only a single other geometry (so `point.distance(series)` does not work). However, the distance method is commutative, so you can always switch the order to use the distance method of the GeoSeries (`series.distance(point)` does work).\n",
+    "* Given a row label, you can get the value of a Series/column with `s[label]`, or the row of a DataFrame with `df.loc[label]`. With both row label and column name, you can get the corresponding value of a DataFrame with `df.loc[label, column_name]`.\n",
+    "</details>\n",
+    "\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "60c64971",
+   "metadata": {},
+   "source": [
+    "We take the first point geometry in the Gent dataset:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "de97f06f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "point = gdf_gent[\"geometry\"].iloc[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b56de5c2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "point"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5416854a",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/case-curieuzeneuzen-air-quality57.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "05e21ec8",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/case-curieuzeneuzen-air-quality58.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0922941d",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/case-curieuzeneuzen-air-quality59.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f2a5eae1",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/case-curieuzeneuzen-air-quality60.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6f279c41",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/case-curieuzeneuzen-air-quality61.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7b34181b",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/case-curieuzeneuzen-air-quality62.py"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6772c723",
+   "metadata": {},
+   "source": [
+    "We now want to repeat the above analysis for each measurement location. So let's start with writing a reusable function.\n",
+    "\n",
+    "<div class=\"alert alert-success\">\n",
+    "\n",
+    "**EXERCISE 26**:\n",
+    "\n",
+    "* Define a function `closest_road_type` that accepts a single point and the streets dataframe, and returns the class of the closest street.\n",
+    "* Check that the function works by using it on the `point` defined above.\n",
+    "\n",
+    "As help, you can start from this skeleton:\n",
+    "    \n",
+    "```python\n",
+    "def closest_road_type(point, streets):\n",
+    "    # determine \"highway\" tag of the closest street\n",
+    "    idx_closest = ...\n",
+    "    ...\n",
+    "    return ... \n",
+    "```\n",
+    "\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8e0c6d13",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/case-curieuzeneuzen-air-quality63.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fda877c2",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/case-curieuzeneuzen-air-quality64.py"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e4e9bda6",
+   "metadata": {},
+   "source": [
+    "Now we can apply this function to each of the point locations. However, with this brute force method, applying it as is using `gdf_gent` and `streets` takes quite some time. We can speed up the distance calculation by reducing the number of linestrings in the `streets` dataframe to compare with.\n",
+    "\n",
+    "<div class=\"alert alert-success\">\n",
+    "\n",
+    "**EXERCISE 27**:\n",
+    "\n",
+    "* Create a `streets_unioned` dataframe with a single line per road type (a union of all lines per road type). Check the `dissolve()` method for this.\n",
+    "* Repeat the `apply` call, but now on all points and using `streets_unioned` instead of `streets`.\n",
+    "* Assign the result of the apply to a new columns \"road_type\".\n",
+    "* Do a value counts of this new column.\n",
+    "    \n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "68bd6b12",
+   "metadata": {},
+   "source": [
+    "When running this, you can see it already takes a bit of time, even for the first 20 rows:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "13226d3f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%time gdf_gent.geometry.head(20).apply(lambda point: closest_road_type(point, streets))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "55f89dbd",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/case-curieuzeneuzen-air-quality65.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9cd3a699",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/case-curieuzeneuzen-air-quality66.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "79904b18",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/case-curieuzeneuzen-air-quality67.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e43e2410",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/case-curieuzeneuzen-air-quality68.py"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1f5b8eb6",
+   "metadata": {},
+   "source": [
+    "**Note!** We have been using a brute-force search for the closest street by calculating for each point the distance to all streets. This is a good exercise to learn the syntax, but there are better methods for such \"nearest\" queries. See eg https://geopandas.org/en/latest/docs/user_guide/mergingdata.html#nearest-joins"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3b738e6d",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-success\">\n",
+    "\n",
+    "**EXERCISE 28**:\n",
+    "\n",
+    "There are some uncommon categories. For the remainder of this demo, let's group some related categories, and filter out some others.\n",
+    "    \n",
+    "* Using the defined mapping, replace some values with \"pedestrian\" in the `\"road_type\"` column.\n",
+    "* Using the defined subset of categories, create a subset of `gdf_gent` where the road type \"is in\" this subset of categories (look at the pandas `isin()` ([doc link](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.isin.html)) method). Call the result `subset`, and do again a value counts to check the result.\n",
+    "    \n",
+    "<details><summary>Hints</summary>\n",
+    "\n",
+    "* The `replace()` method can be called on a column. Pass a dictionary to this method, and the keys of the dictionary present in the column will be replaces with the corresponding values defined in the dictionary.\n",
+    "* The `Series.isin()` method accepts a list of values, and will return a boolean Series indicating for each element of the original Series whether it is present in the list of values or not. \n",
+    "* The boolean Series can then be used to filter the original `gdf_gent` dataframe using boolean indexing.\n",
+    "</details>\n",
+    "\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7c47a51f",
+   "metadata": {},
+   "source": [
+    "Replace categories:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4435673c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mapping = {\n",
+    "    \"footway\": \"pedestrian\",\n",
+    "    \"living_street\": \"pedestrian\",\n",
+    "    \"path\": \"pedestrian\",\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1f953fc5",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/case-curieuzeneuzen-air-quality69.py"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8b353618",
+   "metadata": {},
+   "source": [
+    "Filter categories:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ba577bd7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "categories = [\"primary\", \"secondary\", \"tertiary\", \"residential\", \"pedestrian\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8a482897",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/case-curieuzeneuzen-air-quality70.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5bf99b99",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/case-curieuzeneuzen-air-quality71.py"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dda7e1b5",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-success\">\n",
+    "\n",
+    "**EXERCISE 29**:\n",
+    "\n",
+    "* Calculate the average measured concentration depending on the type of the road next to the measurement location.\n",
+    "\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "972f0de1",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/case-curieuzeneuzen-air-quality72.py"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "56d5a61a",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-success\">\n",
+    "\n",
+    "**EXERCISE 30**:\n",
+    "\n",
+    "* Similarly, make a plot with `seaborn` of those results. Specify the `categories` as the order to use for the plot.\n",
+    "\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "996c7a01",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/case-curieuzeneuzen-air-quality73.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "754bc51a",
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/case-curieuzeneuzen-air-quality74.py"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ba789dca",
+   "metadata": {},
+   "source": [
+    "This analysis confirms that the NO2 concentration is clearly related to traffic."
+   ]
+  }
+ ],
+ "metadata": {
+  "jupytext": {
+   "default_lexer": "ipython3"
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/notebooks/case-sea-surface-temperature.ipynb b/notebooks/case-sea-surface-temperature.ipynb
new file mode 100644
index 0000000..4a6dc9a
--- /dev/null
+++ b/notebooks/case-sea-surface-temperature.ipynb
@@ -0,0 +1,1032 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<p><font size=\"6\"><b>CASE - Sea Surface Temperature data</b></font></p>\n",
+    "\n",
+    "\n",
+    "> *DS Python for GIS and Geoscience*  \n",
+    "> *September, 2025*\n",
+    ">\n",
+    "> *© 2025, Joris Van den Bossche and Stijn Van Hoey. Licensed under [CC BY 4.0 Creative Commons](http://creativecommons.org/licenses/by/4.0/)*\n",
+    "\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "import xarray as xr"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For this use case, we focus on the [Extended Reconstructed Sea Surface Temperature (ERSST)](https://www.ncdc.noaa.gov/data-access/marineocean-data/extended-reconstructed-sea-surface-temperature-ersst-v4), a widely used and trusted gridded compilation of historical Sea Surface Temperature (SST).\n",
+    "\n",
+    "> The Extended Reconstructed Sea Surface Temperature (ERSST) dataset is a global monthly sea surface temperature dataset derived from the International Comprehensive Ocean–Atmosphere Dataset (ICOADS). It is produced on a 2° × 2° grid with spatial completeness enhanced using statistical methods. This monthly analysis begins in January 1854 continuing to the present and includes anomalies computed with respect to a 1971–2000 monthly climatology. \n",
+    "\n",
+    "\n",
+    "\n",
+    "First we download the dataset. \n",
+    "\n",
+    "<div class=\"alert alert-warning\">\n",
+    "\n",
+    "**! Manual download needed !** <br>\n",
+    "\n",
+    "We will use the [NOAA Extended Reconstructed Sea Surface Temperature (ERSST)](https://psl.noaa.gov/thredds/catalog/Datasets/noaa.ersst/catalog.html?dataset=Datasets/noaa.ersst/sst.mnmean.v4.nc) v4 product. Download the data from this link: https://psl.noaa.gov/thredds/fileServer/Datasets/noaa.ersst/sst.mnmean.v4.nc and store it in a subfolder `data/` from the notebook as `sst.mnmean.v4.nc`.\n",
+    "\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Reading in the data set, ignoring the `time_bnds` variable:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = './data/sst.mnmean.v4.nc'\n",
+    "ds = xr.open_dataset(data, drop_variables=['time_bnds'])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For this use case, we will focus on the years after 1960, so we slice the data from 1960 and load the data into our computer memory. By only loading the data after the initial slice, we make sure to only load into memory the data we specifically need:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "ds = ds.sel(time=slice('1960', '2018')).load()  # load into memory\n",
+    "ds"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The data with the extension `nc` is a NetCDF format. NetCDF (Network Common Data Format) is the most widely used format for distributing geoscience data. NetCDF is maintained by the [Unidata](https://www.unidata.ucar.edu/) organization. Check the [netcdf website](https://www.unidata.ucar.edu/software/netcdf/docs/faq.html#whatisit) for more information. Xarray was designed to make reading netCDF files in python as easy, powerful, and flexible as possible."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "__Note:__ As the data is in a [OPeNDAP server](https://en.wikipedia.org/wiki/OPeNDAP), we could also load the NETCDF data directly without downloading anything. This would require us to add the `netcdf4` package in our conda environment"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Exploratory data analysis"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The data contains a single data variable `sst` and has 3 dimensions: lon, lat and time each described by a coordinate. Let's first get some insight in the structure and content of the data."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-success\">\n",
+    "\n",
+    "**EXERCISE 1**:\n",
+    "\n",
+    "- What is the total amount of elements/values in the xarray data set?\n",
+    "- How many elements are there in the different dimensions   \n",
+    "- The metadata of a netcdf file is also interpreted by xarray. Are the attributes on the xarray.Dataset `ds` the same as the attributes of the `sst` data itself?\n",
+    "   \n",
+    "<details>\n",
+    "\n",
+    "<summary>Hints</summary>\n",
+    "    \n",
+    "- The number of elements or `size` of an array is an attribute of an xarray.DataArray and not of a xarray.Dataset\n",
+    "- Also the `shape` of an array is an attribute of an xarray.DataArray. A xarray.Dataset has the `dims` attribute to query dimension sizes\n",
+    "\n",
+    "</details>    \n",
+    "    \n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/case-sea-surface-temperature1.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/case-sea-surface-temperature2.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/case-sea-surface-temperature3.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/case-sea-surface-temperature4.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/case-sea-surface-temperature5.py"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "---------"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As we work with a single data variable, we will introduce a new variable `sst` which is the `xarray.DataArray` of the SST values. Note that we only keep the attributes on the xarray.DataArray level."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sst = ds[\"sst\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "sst"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-success\">\n",
+    "\n",
+    "**EXERCISE 2**:\n",
+    "\n",
+    "Make an image plot of the SST in the first month of the data set, January 1960. Adjust the range of the colorbar and switch to the `coolwarm` colormap.\n",
+    "   \n",
+    "<details>\n",
+    "\n",
+    "<summary>Hints</summary>\n",
+    "    \n",
+    "- xarray can interpret a date string in the [ISO 8601](https://nl.wikipedia.org/wiki/ISO_8601) format as a date, e.g. `2020-01-01`.\n",
+    "- adjust ranges of the colorbar with `vmin` and `vmax`.\n",
+    "\n",
+    "</details>    \n",
+    "    \n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/case-sea-surface-temperature6.py"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "__Note__ \n",
+    "xaray uses xarray.plot.pcolormesh() as the default two-dimensional plot method because it is more flexible than xarray.plot.imshow(). However, for large arrays, imshow can be much faster than pcolormesh. If speed is important to you and you are plotting a regular mesh, consider using imshow."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-success\">\n",
+    "\n",
+    "**EXERCISE 3**:\n",
+    "    \n",
+    "How did the SST evolve in time for a specific location on the earth? Make a line plot of the SST at `lon=300`, `lat=50` as a function of time.\n",
+    "    \n",
+    "Do you recognize the seasonality of the data?\n",
+    "   \n",
+    "<details>\n",
+    "\n",
+    "<summary>Hints</summary>\n",
+    "    \n",
+    "- Use the `sel` for both the lon/lat selection.\n",
+    "\n",
+    "</details>    \n",
+    "    \n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/case-sea-surface-temperature7.py"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-success\">\n",
+    "\n",
+    "**EXERCISE 4**:\n",
+    "\n",
+    "What is the evolution of the SST as function of the month of the year?\n",
+    "    \n",
+    "Calculate the average SST with respect to the _month of the year_ for all positions in the data set and store the result as a variable `ds_mm`.\n",
+    "\n",
+    "Use the `ds_mm` variable to make a plot: For longitude `164`, make a comparison in between the monthly average at latitude `-23.4` versus latitude `23.4`. Use a line plot with in the x-axis the month of the year and in the y-axis the average SST.\n",
+    "   \n",
+    "<details>\n",
+    "\n",
+    "<summary>Hints</summary>\n",
+    "    \n",
+    "- Use the `sel` for both the lon/lat selection.\n",
+    "- If the exact values are not in the coordinate, you can use the `method=\"nearest\"` inside a selection.\n",
+    "\n",
+    "</details>    \n",
+    "    \n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/case-sea-surface-temperature8.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/case-sea-surface-temperature9.py"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-success\">\n",
+    "\n",
+    "**EXERCISE 5**:\n",
+    "    \n",
+    "How does the zonal mean climatology for each month of the year changes with the latitude? \n",
+    "\n",
+    "Reuse the `ds_mm` from the previous exercise or recalculate the average SST with respect to the _month of the year_ for all positions in the data set and store the result as a variable `ds_mm`.\n",
+    "    \n",
+    "To check the mean climatology (aggregating over the longitudes) as a function of the latitude for each month in the year, calculate the average SST over the `lon` dimension from `ds_mm`. Plot the result as an image with the month of the year in the x-axis and the latitude in the y-axis. \n",
+    "   \n",
+    "<details>\n",
+    "\n",
+    "<summary>Hints</summary>\n",
+    "\n",
+    "- You do not need another `groupby`, but need to calculate a reduction along a dimension.\n",
+    "\n",
+    "</details>    \n",
+    "    \n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/case-sea-surface-temperature10.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/case-sea-surface-temperature11.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/case-sea-surface-temperature12.py"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-success\">\n",
+    "\n",
+    "**EXERCISE 6**:\n",
+    "    \n",
+    "How different is the mean climatology in between January and July?\n",
+    "\n",
+    "Reuse the `ds_mm` variable from the previous exercises or recalculate the average SST with respect to the _month of the year_ for all positions in the data set and store the result as a variable `ds_mm`.\n",
+    "    \n",
+    "Calculate the difference of the mean climatology between January an July and plot the result as an image (map) with the longitude of the year in the x-axis and the latitude in the y-axis. \n",
+    "   \n",
+    "<details>\n",
+    "\n",
+    "<summary>Hints</summary>\n",
+    "\n",
+    "- You can subtract xarray just as Numpy arrays. You do not need another `groupby`, but only selections from the `ds_mm` variable. \n",
+    "\n",
+    "</details>    \n",
+    "    \n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/case-sea-surface-temperature13.py"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Calculate the residual by removing climatology"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To understand how the SST temperature evolved as a function of time during the last decades, we want to remove this climatology from the dataset and examine the residual, called the anomaly, which is the interesting part from a climate perspective. \n",
+    "\n",
+    "We will do this by subtracting the monthly average from the values of that specific month. Hence, subtract the average January value over the years from the January data, subtract the average February value over the years from the February data,...\n",
+    "\n",
+    "Removing the seasonal climatology is an example of a transformation: it operates over a group, but does not change the size of the dataset as we do the operation on each element (`x - x.mean()`) \n",
+    "\n",
+    "This is not the same as the aggregations (e.g. `average`) we applied on each of the groups earlier. When using `groupby`, a calculation to the group can be applied and just like in Pandas, these calculations can either be:\n",
+    "\n",
+    "- _aggregation_: reduces the size of the group\n",
+    "- _transformation_: preserves the groups full size\n",
+    "\n",
+    "One way to consider is that we `apply` a function to each of the groups. For our anomaly calculation we want to do a _transformation_ and apply the following function:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def remove_time_mean(x):\n",
+    "    \"\"\"Subtract each value by the mean over time\"\"\"\n",
+    "    return x - x.mean(dim='time')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can `apply` this function to each of the groups:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "sst = ds[\"sst\"]\n",
+    "ds_anom = sst.groupby('time.month').apply(remove_time_mean)\n",
+    "ds_anom"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In other words: \n",
+    "\n",
+    "> subtract each element by the average over time of all elements of the month the element belongs to"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Xarray makes these sorts of transformations easy by supporting groupby arithmetic. This concept is easiest explained by applying it for our application:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "gb = sst.groupby('time.month')  # make groups (in this example each month of the year is a group) \n",
+    "ds_anom = gb - gb.mean(dim='time')  # subtract each element of the group/month by the mean of that group/month over time \n",
+    "ds_anom"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we can view the climate signal without the overwhelming influence of the seasonal cycle:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "ds_anom.sel(lon=300, lat=50).plot.line()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Check the difference between Jan. 1 2018 and Jan. 1 1960 to see where the evolution in time is the largest:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "(ds_anom.sel(time='2018-01-01') - ds_anom.sel(time='1960-01-01')).plot()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-success\">\n",
+    "\n",
+    "**EXERCISE 7**:\n",
+    "    \n",
+    "Compute the _five-year median_ of the `ds_anom` variable for the location `lon=300`, `lat=50` as well as the 12 month rolling median of the same data set. Store the output as respectively `ds_anom_resample` and `ds_anom_rolling`.\n",
+    "    \n",
+    "Make a line plot as a function of time for the location `lon=300`, `lat=50` of the original `ds_anom` data, the resampled data and the rolling median data.\n",
+    "   \n",
+    "<details>\n",
+    "\n",
+    "<summary>Hints</summary>\n",
+    "\n",
+    "- If you only need a single location, do the slicing (selecting) first instead of calculating them for all positions.\n",
+    "- Use the `resample` and the `rolling` functions.\n",
+    "\n",
+    "\n",
+    "</details>    \n",
+    "    \n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/case-sea-surface-temperature14.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/case-sea-surface-temperature15.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/case-sea-surface-temperature16.py"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Make projection aware maps"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The previous maps were the default outputs of xarray without specification of the spatial context. For reporting these plots are not appropriate. We can use the [cartopy](https://scitools.org.uk/cartopy/docs/latest/) package to adjust our Matplotlib axis to make them spatially aware. \n",
+    "\n",
+    "For more in-depth information on cartopy, see the [visualization-03-cartopy](./visualization-03-cartopy.ipynb) notebook. As a short recap, to make sure the data of xarray can be integrated in a cartopy plot, the crucial element is to define the `transform` argument to to control which coordinate system that the given data is in. You can add the transform keyword with an appropriate `cartopy.crs.CRS` instance from the `import cartopy.crs` module:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import cartopy.crs as ccrs\n",
+    "import cartopy\n",
+    "\n",
+    "map_proj = ccrs.Robinson()  # Define the projection\n",
+    "\n",
+    "fig, ax = plt.subplots(figsize = (16,9), subplot_kw={\"projection\": map_proj})\n",
+    "ax.gridlines()\n",
+    "ax.coastlines()\n",
+    "\n",
+    "sst.sel(time=\"1960-01-01\").plot(ax=ax, vmin=-2, vmax=30,  center=5,\n",
+    "                                cmap='coolwarm', transform = ccrs.PlateCarree(), # tranform argument\n",
+    "                                cbar_kwargs={'shrink':0.75})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-success\">\n",
+    "\n",
+    "**EXERCISE 8**:\n",
+    "    \n",
+    "Make a plot of the `ds_anom` variable of 2018-01-01 with cartopy.\n",
+    "    \n",
+    "- Use the `ccrs.Orthographic` with the central lon/lat on -20, 5\n",
+    "- Add coastlines and gridlines to the plot    \n",
+    "   \n",
+    "   \n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/case-sea-surface-temperature17.py"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Spatial aggregate per basin"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Apart from aggregations as a function of time, also spatial aggregations using other (spatial) data sets can be achieved. In the next section, we want to compute the average SST over different ocean basins. The http://iridl.ldeo.columbia.edu/SOURCES/.NOAA/.NODC/.WOA09/.Masks/.basin/ is a data set that contains the main ocean basins in lon/lat:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "basin = xr.open_dataset(\"./data/basin.nc\")\n",
+    "basin = basin.rename({'X': 'lon', 'Y': 'lat'})\n",
+    "basin[\"basin\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The name of the basins are included in the attributes of the data set. Using Pandas, we can create a mapping in between the basin names and the index used in the basin data set:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "basin_names = basin[\"basin\"].attrs['CLIST'].split('\\n')\n",
+    "basin_s = pd.Series(basin_names, index=np.arange(1, len(basin_names)+1))\n",
+    "basin_s = basin_s.rename('basin')\n",
+    "basin_s.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We will use this mapping from identifier to label later in the analysis."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The basin data set provides multiple Z levels. We are interested in the division on surface level (0.0):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "basin_surface = basin[\"basin\"].sel(Z=0.0).drop_vars(\"Z\")\n",
+    "basin_surface.plot(vmax=10, cmap='tab10')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The next step is to align both data sets. For this application, using the 'nearest' available data point will work to map both data sets with each other. Xarray provides the function `interp_like` to interpolate the `basin_surface` to the `sst` variable:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [],
+   "source": [
+    "basin_surface_interp = basin_surface.interp_like(sst, method='nearest')\n",
+    "basin_surface_interp.plot(vmax=10, cmap='tab10')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-success\">\n",
+    "\n",
+    "**EXERCISE 9**:\n",
+    "\n",
+    "Compute the mean SST (over all dimensions) for each of the basins in the `basin_surface` variable starting from the `sst` variable. \n",
+    "\n",
+    "Next, we want to plot a horizontal bar chart with the SST for each bar chart. To do so:\n",
+    "    \n",
+    "- Convert the output to Pandas DataFrame.\n",
+    "- Combine the output with the `basin_s` variable by merging on the index (identifiers of the basin names).\n",
+    "- Create a horizontal barplot of the average temperature for each of the basins using the resulting dataframe.\n",
+    "   \n",
+    "<details>\n",
+    "\n",
+    "<summary>Hints</summary>\n",
+    "\n",
+    "- Use a `groupby` with the `basin_surface_interp` as input.\n",
+    "- Joining and merging of tables? See the [Pandas documentation](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.merge.html). \n",
+    "\n",
+    "</details>    \n",
+    "    \n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/case-sea-surface-temperature18.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/case-sea-surface-temperature19.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/case-sea-surface-temperature20.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/case-sea-surface-temperature21.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false
+    },
+    "tags": [
+     "nbtutor-solution"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# %load _solutions/case-sea-surface-temperature22.py"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "-------\n",
+    "\n",
+    "Acknowledgements to https://earth-env-data-science.github.io/lectures/xarray/xarray-part2.html"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "celltoolbar": "Nbtutor - export exercises",
+  "jupytext": {
+   "default_lexer": "ipython3"
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/notebooks/data/2016-2017_global_rain-temperature.nc b/notebooks/data/2016-2017_global_rain-temperature.nc
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diff --git a/notebooks/data/averbode/study_area.geojson b/notebooks/data/averbode/study_area.geojson
new file mode 100644
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+++ b/notebooks/data/averbode/study_area.geojson
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+{
+"type": "FeatureCollection",
+"name": "study_area",
+"crs": { "type": "name", "properties": { "name": "urn:ogc:def:crs:EPSG::31370" } },
+"features": [
+{ "type": "Feature", "properties": { "fid": 0 }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 191594.636644636746496, 191489.229103483259678 ], [ 191745.672989219106967, 191546.990878340788186 ], [ 191823.972283980780048, 191401.944643757306039 ], [ 191657.960664440237451, 191340.759948905557394 ], [ 191594.636644636746496, 191489.229103483259678 ] ] ] } }
+]
+}
diff --git a/notebooks/data/era5-land-monthly-means_example.nc b/notebooks/data/era5-land-monthly-means_example.nc
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diff --git a/notebooks/data/gent/DHMVIIDTMRAS25m.zip b/notebooks/data/gent/DHMVIIDTMRAS25m.zip
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diff --git a/notebooks/data/gent/raster/2020-09-17_Sentinel_2_L1C_True_color.tiff b/notebooks/data/gent/raster/2020-09-17_Sentinel_2_L1C_True_color.tiff
index b3a58bf..481c9fd 100644
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diff --git a/notebooks/data/gent/vector/parken-gent.geojson b/notebooks/data/gent/vector/parken-gent.geojson
new file mode 100644
index 0000000..8d9a245
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+{ "type": "Feature", "properties": { "gml_id": "Refgem.202", "UIDN": 642.0, "OIDN": 37.0, "TERRID": 59.0, "NISCODE": "73028", "NAAM": "Herstappe", "DATPUBLBS": "1831-02-07Z", "NUMAC": "" }, "geometry": { "type": "MultiPolygon", "coordinates": [ [ [ [ 5.415436863002157, 50.721484471060869 ], [ 5.415684921018761, 50.721397353030518 ], [ 5.416765312908869, 50.721017907834408 ], [ 5.41641583256236, 50.720695847128717 ], [ 5.41748876026266, 50.720293061786066 ], [ 5.41763799634565, 50.720234092250166 ], [ 5.418530615216082, 50.719863123823757 ], [ 5.419577843835636, 50.719433667497022 ], [ 5.419660210366953, 50.719494513315347 ], [ 5.420565307259117, 50.719024069456687 ], [ 5.421840163309114, 50.719896736837207 ], [ 5.421979818775962, 50.71999230847041 ], [ 5.421602791905216, 50.720232250877189 ], [ 5.421940974079237, 50.720471953381761 ], [ 5.422970881898959, 50.719927451592149 ], [ 5.423004333327011, 50.719909707886622 ], [ 5.424564279136889, 50.721000046813835 ], [ 5.424802119769315, 50.721160000793844 ], [ 5.425157739267839, 50.721380476289767 ], [ 5.427503498643832, 50.722726111001286 ], [ 5.427787648184318, 50.722922771216098 ], [ 5.428093851440412, 50.723180457672498 ], [ 5.428332954119909, 50.722998835809328 ], [ 5.429884113166358, 50.721821904785301 ], [ 5.431247821982302, 50.720594779745873 ], [ 5.431408212649313, 50.720440838916446 ], [ 5.431531274775989, 50.720283371257139 ], [ 5.431628956514007, 50.720105815204086 ], [ 5.431684456214469, 50.719935565526768 ], [ 5.431735759934012, 50.719716524415858 ], [ 5.431891915788171, 50.719049799911495 ], [ 5.432022616498704, 50.718685115663845 ], [ 5.432174587184167, 50.71835934739223 ], [ 5.43228649068341, 50.71818471810262 ], [ 5.432345087173366, 50.718101649944288 ], [ 5.432365144347798, 50.718124393797609 ], [ 5.435241775958008, 50.721178407132854 ], [ 5.434997027043306, 50.721315500994123 ], [ 5.435196316638367, 50.721641485021166 ], [ 5.435505808212241, 50.72199289578365 ], [ 5.435669479064017, 50.722184253179698 ], [ 5.435810913486113, 50.722103407641363 ], [ 5.435817273752574, 50.722109889438343 ], [ 5.436271199990894, 50.722568918508642 ], [ 5.436067221977497, 50.722670823765043 ], [ 5.436067112727302, 50.722670877807928 ], [ 5.435242129072173, 50.723083020257342 ], [ 5.435240836567022, 50.723073602572768 ], [ 5.435240076754162, 50.7230680694176 ], [ 5.435177411952173, 50.722611491294508 ], [ 5.433926396211791, 50.723354549010416 ], [ 5.43368146902552, 50.723500022688839 ], [ 5.433689980537303, 50.723504996135539 ], [ 5.433705249605449, 50.72351391877968 ], [ 5.43273178140335, 50.724116351116159 ], [ 5.432630108967349, 50.724178932183229 ], [ 5.432319785663099, 50.724369937293595 ], [ 5.432668354978003, 50.724591050554757 ], [ 5.432768190069423, 50.724654379675194 ], [ 5.432099983537983, 50.725178470211624 ], [ 5.431980012456847, 50.725272565061317 ], [ 5.431979277585033, 50.725273140857801 ], [ 5.431953825286691, 50.725293756113011 ], [ 5.431956141347045, 50.725294016457418 ], [ 5.432116796022801, 50.725312080299645 ], [ 5.432186085553443, 50.725318585913399 ], [ 5.432183190684196, 50.725329263553739 ], [ 5.432179674621204, 50.725342226891485 ], [ 5.432162270306296, 50.725413522486946 ], [ 5.43216226128636, 50.725413562128182 ], [ 5.432105825189311, 50.725644744089749 ], [ 5.431996980677827, 50.726090586763867 ], [ 5.431940678208563, 50.726190282638846 ], [ 5.431165249595917, 50.727563308826177 ], [ 5.43116467752332, 50.727564321884081 ], [ 5.431133354307374, 50.727609704859603 ], [ 5.430672411275812, 50.728414635266688 ], [ 5.430595026694275, 50.728654487455195 ], [ 5.430602566522146, 50.728763412440259 ], [ 5.430757219616327, 50.730762963242896 ], [ 5.430760266958655, 50.730802360701595 ], [ 5.430764808342163, 50.730852730330362 ], [ 5.430776132956526, 50.730978315711674 ], [ 5.430807270533228, 50.731323639121776 ], [ 5.430381927501319, 50.73155414350169 ], [ 5.430357152479895, 50.731567530858719 ], [ 5.431000307012001, 50.73202408179214 ], [ 5.430977284751433, 50.732034412697509 ], [ 5.430969529332772, 50.732037893333036 ], [ 5.429749913796111, 50.732580435019429 ], [ 5.429741810107178, 50.732631530778136 ], [ 5.427987709405691, 50.733011675926171 ], [ 5.426594468691856, 50.733317271615682 ], [ 5.426731045942708, 50.733646661319057 ], [ 5.426731066903019, 50.733646711477263 ], [ 5.426877169337019, 50.73399906514274 ], [ 5.426957718332627, 50.734197488937774 ], [ 5.426952875096359, 50.734198579289128 ], [ 5.426179836368832, 50.73437255467595 ], [ 5.426202288621401, 50.734416359239432 ], [ 5.426309433145578, 50.734625404553569 ], [ 5.426339077361481, 50.73469004918752 ], [ 5.426333879749901, 50.734692016612939 ], [ 5.425794266525052, 50.734896251366933 ], [ 5.424577834228318, 50.735356990646977 ], [ 5.424434220068956, 50.735411288404954 ], [ 5.416435132701666, 50.722800118276503 ], [ 5.416259897200522, 50.72283107038097 ], [ 5.416280565994294, 50.722818465406412 ], [ 5.415436863002157, 50.721484471060869 ] ] ] ] } }
+]
+}
diff --git a/notebooks/data/preprocess_data.ipynb b/notebooks/data/preprocess_data.ipynb
new file mode 100644
index 0000000..ecf8db4
--- /dev/null
+++ b/notebooks/data/preprocess_data.ipynb
@@ -0,0 +1,1019 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Preprocessing of raw data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Notebook containing the code to obtain and preprocess the raw data into a smaller or simplified dataset usable for the workshop context."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### OpenStreetMap"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Downloaded the .pbf file for Belgium at https://download.geofabrik.de/europe/belgium.html (around 400 MB)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import geopandas\n",
+    "import pyrosm"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "muni = geopandas.read_file(\"raw/Voorlopig_referentiebestand_gemeentegrenzen_toestand_17_08_2017_GewVLA_Shape/Shapefile/Refgem.shp\")\n",
+    "muniWGS84 = muni.to_crs(\"EPSG:4326\")\n",
+    "gent = muniWGS84[muniWGS84[\"NAAM\"] == \"Gent\"].geometry.item()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(3.5797745580829194, 50.97953856271044, 3.849340764693696, 51.18888027930442)"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "gent.bounds"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Initialize the OSM object\n",
+    "osm = pyrosm.OSM(\"raw/belgium-latest.osm.pbf\", bounding_box=list(gent.bounds))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Actually load the data while filtering for Gent, and extract the street network (note: this doesn't take *that* long, around a minute, but requires a lot of available RAM, around 12 GB):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "streets = osm.get_network()\n",
+    "streets.to_file(\"raw/streets.gpkg\", driver=\"GPKG\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now read this again, and trim the file a bit:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import geopandas"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "streets = geopandas.read_file(\"raw/streets.gpkg\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:>"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "streets.plot()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>highway</th>\n",
+       "      <th>name</th>\n",
+       "      <th>osm_type</th>\n",
+       "      <th>geometry</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>primary</td>\n",
+       "      <td>Oudenaardsesteenweg</td>\n",
+       "      <td>way</td>\n",
+       "      <td>LINESTRING (3.70951 51.01845, 3.70951 51.01863...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>primary</td>\n",
+       "      <td>Krijgslaan</td>\n",
+       "      <td>way</td>\n",
+       "      <td>LINESTRING (3.71157 51.02641, 3.71165 51.02642)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>residential</td>\n",
+       "      <td>Paul Fredericqstraat</td>\n",
+       "      <td>way</td>\n",
+       "      <td>LINESTRING (3.71340 51.04028, 3.71338 51.04032...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>residential</td>\n",
+       "      <td>Meersstraat</td>\n",
+       "      <td>way</td>\n",
+       "      <td>LINESTRING (3.71036 51.03886, 3.71041 51.03888)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>residential</td>\n",
+       "      <td>Jakob Heremansstraat</td>\n",
+       "      <td>way</td>\n",
+       "      <td>LINESTRING (3.71154 51.04173, 3.71149 51.04166...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27198</th>\n",
+       "      <td>pedestrian</td>\n",
+       "      <td>Pieter Vanderdoncktdoorgang</td>\n",
+       "      <td>way</td>\n",
+       "      <td>LINESTRING (3.73172 51.04893, 3.73140 51.04935)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27199</th>\n",
+       "      <td>residential</td>\n",
+       "      <td>Schepenenvijverstraat</td>\n",
+       "      <td>way</td>\n",
+       "      <td>LINESTRING (3.73172 51.04893, 3.73141 51.04874...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27200</th>\n",
+       "      <td>residential</td>\n",
+       "      <td>Spoorwegstraat</td>\n",
+       "      <td>way</td>\n",
+       "      <td>LINESTRING (3.70383 51.10477, 3.70323 51.10458...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27201</th>\n",
+       "      <td>service</td>\n",
+       "      <td>None</td>\n",
+       "      <td>way</td>\n",
+       "      <td>LINESTRING (3.68146 51.10663, 3.68146 51.10657...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27202</th>\n",
+       "      <td>service</td>\n",
+       "      <td>None</td>\n",
+       "      <td>way</td>\n",
+       "      <td>LINESTRING (3.67949 51.10613, 3.67939 51.10625...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>27203 rows × 4 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           highway                         name osm_type  \\\n",
+       "0          primary          Oudenaardsesteenweg      way   \n",
+       "1          primary                   Krijgslaan      way   \n",
+       "2      residential         Paul Fredericqstraat      way   \n",
+       "3      residential                  Meersstraat      way   \n",
+       "4      residential         Jakob Heremansstraat      way   \n",
+       "...            ...                          ...      ...   \n",
+       "27198   pedestrian  Pieter Vanderdoncktdoorgang      way   \n",
+       "27199  residential        Schepenenvijverstraat      way   \n",
+       "27200  residential               Spoorwegstraat      way   \n",
+       "27201      service                         None      way   \n",
+       "27202      service                         None      way   \n",
+       "\n",
+       "                                                geometry  \n",
+       "0      LINESTRING (3.70951 51.01845, 3.70951 51.01863...  \n",
+       "1        LINESTRING (3.71157 51.02641, 3.71165 51.02642)  \n",
+       "2      LINESTRING (3.71340 51.04028, 3.71338 51.04032...  \n",
+       "3        LINESTRING (3.71036 51.03886, 3.71041 51.03888)  \n",
+       "4      LINESTRING (3.71154 51.04173, 3.71149 51.04166...  \n",
+       "...                                                  ...  \n",
+       "27198    LINESTRING (3.73172 51.04893, 3.73140 51.04935)  \n",
+       "27199  LINESTRING (3.73172 51.04893, 3.73141 51.04874...  \n",
+       "27200  LINESTRING (3.70383 51.10477, 3.70323 51.10458...  \n",
+       "27201  LINESTRING (3.68146 51.10663, 3.68146 51.10657...  \n",
+       "27202  LINESTRING (3.67949 51.10613, 3.67939 51.10625...  \n",
+       "\n",
+       "[27203 rows x 4 columns]"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#subset = streets[[\"highway\", \"access\", \"bicycle\", \"name\", \"osm_type\", \"geometry\"]]\n",
+    "subset = streets[[\"highway\", \"name\", \"osm_type\", \"geometry\"]]\n",
+    "subset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "subset.to_file(\"osm_network_gent.gpkg\", driver=\"GPKG\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## CORINE Land Cover"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Downloaded the raster file from https://land.copernicus.eu/pan-european/corine-land-cover/clc2018?tab=download"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import rasterio\n",
+    "import rasterio.windows"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'driver': 'GTiff', 'dtype': 'int8', 'nodata': -128.0, 'width': 65000, 'height': 46000, 'count': 1, 'crs': CRS.from_epsg(3035), 'transform': Affine(100.0, 0.0, 900000.0,\n",
+      "       0.0, -100.0, 5500000.0)}\n",
+      "BoundingBox(left=900000.0, bottom=900000.0, right=7400000.0, top=5500000.0)\n"
+     ]
+    }
+   ],
+   "source": [
+    "with rasterio.open(\"raw/u2018_clc2018_v2020_20u1_raster100m/DATA/U2018_CLC2018_V2020_20u1.tif\") as src:\n",
+    "    print(src.meta)\n",
+    "    print(src.bounds)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Determine the bounding box of flanders:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import geopandas\n",
+    "flanders = geopandas.read_file(\"VRBG/RefgemG10.shp\")\n",
+    "# converting to same CRS as the raster file\n",
+    "flanders_3035 = flanders.to_crs(\"EPSG:3035\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([3799512.94480779, 3074640.31790764, 4032500.83220125,\n",
+       "       3167919.6130592 ])"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "flanders_3035.total_bounds"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Adding a small buffer around it:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(3789512.944807788, 3064640.317907639, 4042500.832201246, 3177919.613059201)"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from shapely.geometry import box\n",
+    "\n",
+    "# 10 km\n",
+    "bounds = box(*flanders_3035.total_bounds).buffer(10000).bounds\n",
+    "bounds"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Rounding it to 100 m (the resolution of the raster file):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([3789500., 3064600., 4042500., 3177900.])"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bounds = np.round(bounds, -2)  # negative number to specify the number of positions to the *left* of the decimal point.\n",
+    "bounds"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with rasterio.open(\"raw/u2018_clc2018_v2020_20u1_raster100m/DATA/U2018_CLC2018_V2020_20u1.tif\") as src:\n",
+    "    arr = src.read(1, window=rasterio.windows.from_bounds(*bounds, src.transform))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1133, 2530)"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "arr.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Reading a window with rasterio, and writing the cropped raster to a new file:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with rasterio.open(\"raw/u2018_clc2018_v2020_20u1_raster100m/DATA/U2018_CLC2018_V2020_20u1.tif\") as src:\n",
+    "    window = rasterio.windows.from_bounds(*bounds, src.transform)\n",
+    "\n",
+    "    kwargs = src.meta.copy()\n",
+    "    kwargs.update({\n",
+    "        'height': window.height,\n",
+    "        'width': window.width,\n",
+    "        'transform': rasterio.windows.transform(window, src.transform)})\n",
+    "\n",
+    "    with rasterio.open('CLC2018_V2020_20u1_flanders.tif', 'w', **kwargs) as dst:\n",
+    "        dst.write(src.read(window=window))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Something similar with xarray (only exploring, not saving):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import xarray"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = xarray.open_rasterio(\"raw/u2018_clc2018_v2020_20u1_raster100m/DATA/U2018_CLC2018_V2020_20u1.tif\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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+       ".xr-attrs dt, dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2 {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray (band: 1, y: 46000, x: 65000)&gt;\n",
+       "[2990000000 values with dtype=int8]\n",
+       "Coordinates:\n",
+       "  * band     (band) int64 1\n",
+       "  * y        (y) float64 5.5e+06 5.5e+06 5.5e+06 ... 9.002e+05 9.000e+05\n",
+       "  * x        (x) float64 9.000e+05 9.002e+05 9.002e+05 ... 7.4e+06 7.4e+06\n",
+       "Attributes:\n",
+       "    transform:      (100.0, 0.0, 900000.0, 0.0, -100.0, 5500000.0)\n",
+       "    crs:            +init=epsg:3035\n",
+       "    res:            (100.0, 100.0)\n",
+       "    is_tiled:       1\n",
+       "    nodatavals:     (-128.0,)\n",
+       "    scales:         (1.0,)\n",
+       "    offsets:        (0.0,)\n",
+       "    AREA_OR_POINT:  Area\n",
+       "    DataType:       Thematic</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>band</span>: 1</li><li><span class='xr-has-index'>y</span>: 46000</li><li><span class='xr-has-index'>x</span>: 65000</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-a0eaff24-7f37-429a-9942-685ed1866ade' class='xr-array-in' type='checkbox' checked><label for='section-a0eaff24-7f37-429a-9942-685ed1866ade' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>...</span></div><div class='xr-array-data'><pre>[2990000000 values with dtype=int8]</pre></div></div></li><li class='xr-section-item'><input id='section-db3e6aa9-608a-4166-8b51-49d16a146d50' class='xr-section-summary-in' type='checkbox'  checked><label for='section-db3e6aa9-608a-4166-8b51-49d16a146d50' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>band</span></div><div class='xr-var-dims'>(band)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1</div><input id='attrs-9de9fb90-c108-4ce3-827f-d10e096f52d6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9de9fb90-c108-4ce3-827f-d10e096f52d6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4a770cff-0380-463f-aa35-c87236f2b8be' class='xr-var-data-in' type='checkbox'><label for='data-4a770cff-0380-463f-aa35-c87236f2b8be' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([1])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>5.5e+06 5.5e+06 ... 9.000e+05</div><input id='attrs-373b2eed-2dbe-4710-88e9-ba1b44bf42e7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-373b2eed-2dbe-4710-88e9-ba1b44bf42e7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c25bae24-587c-4446-bd69-e9ab3ec2ce70' class='xr-var-data-in' type='checkbox'><label for='data-c25bae24-587c-4446-bd69-e9ab3ec2ce70' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([5499950., 5499850., 5499750., ...,  900250.,  900150.,  900050.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>9.000e+05 9.002e+05 ... 7.4e+06</div><input id='attrs-35ecc769-45ab-4165-aa47-859689a4897d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-35ecc769-45ab-4165-aa47-859689a4897d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dcee76a0-d24e-421a-bfd6-40286b84be36' class='xr-var-data-in' type='checkbox'><label for='data-dcee76a0-d24e-421a-bfd6-40286b84be36' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 900050.,  900150.,  900250., ..., 7399750., 7399850., 7399950.])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-6c449349-67d2-444d-90f4-5cee5b584e20' class='xr-section-summary-in' type='checkbox'  checked><label for='section-6c449349-67d2-444d-90f4-5cee5b584e20' class='xr-section-summary' >Attributes: <span>(9)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>transform :</span></dt><dd>(100.0, 0.0, 900000.0, 0.0, -100.0, 5500000.0)</dd><dt><span>crs :</span></dt><dd>+init=epsg:3035</dd><dt><span>res :</span></dt><dd>(100.0, 100.0)</dd><dt><span>is_tiled :</span></dt><dd>1</dd><dt><span>nodatavals :</span></dt><dd>(-128.0,)</dd><dt><span>scales :</span></dt><dd>(1.0,)</dd><dt><span>offsets :</span></dt><dd>(0.0,)</dd><dt><span>AREA_OR_POINT :</span></dt><dd>Area</dd><dt><span>DataType :</span></dt><dd>Thematic</dd></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.DataArray (band: 1, y: 46000, x: 65000)>\n",
+       "[2990000000 values with dtype=int8]\n",
+       "Coordinates:\n",
+       "  * band     (band) int64 1\n",
+       "  * y        (y) float64 5.5e+06 5.5e+06 5.5e+06 ... 9.002e+05 9.000e+05\n",
+       "  * x        (x) float64 9.000e+05 9.002e+05 9.002e+05 ... 7.4e+06 7.4e+06\n",
+       "Attributes:\n",
+       "    transform:      (100.0, 0.0, 900000.0, 0.0, -100.0, 5500000.0)\n",
+       "    crs:            +init=epsg:3035\n",
+       "    res:            (100.0, 100.0)\n",
+       "    is_tiled:       1\n",
+       "    nodatavals:     (-128.0,)\n",
+       "    scales:         (1.0,)\n",
+       "    offsets:        (0.0,)\n",
+       "    AREA_OR_POINT:  Area\n",
+       "    DataType:       Thematic"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "subset = data.sel(x=slice(bounds[0], bounds[2]), y=slice(bounds[3], bounds[1]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.QuadMesh at 0x7f0d104789a0>"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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gflbrr63OW4LuUFsCfe1JqLpaqyvm98NnR0sm8Ehve3Y6F0ptFMvEfGDlvZZrvnLBJQH5orSukVp0/fwxALDlCOLRMVz217/EaY9347THu7GvIxmQBQIeYJRKgeegxu/H5+QwPsdQQ3u+0Br6GwneBGfX85TvGWgFgBfaFF5oUyVpAwv4hcEpLKGG1+vZvznXt64wD3h8Ts7MGGJ5qGpvWSp6SnnG8txVSkEBwdkZz6kEY3vOOatyBmNeC9rJ0KfnoZCr4PVa2jSgn4C9UcAcAH528Xfs30zIOZyI2r971y9B7/oleOVSjxaofzGHmamXyq6bDyVljTLhRD7UYYoGCeqlrGukFhuWXw/AA3UA+FL6h6HLEwS4b5WqJSrlZifnTNh9dwOHgKE2aD4QjOWh6sO90L72pJ09DCyrwgvXmvNZMxgN3efxZYsw//EsqjJRVGWiPs+VfzMYyc9VdQ7Rg971keDf155ENh6eWAQEg72B/RFUV9gAxH1xzzE5/Zr+arufVcPm5R7TyTAN46WXe72WFcqnAf047Y0E5rS+91ahoSttp8+U+klOnXba492oPZzFN598BD+8557Q9bmlBKQNtSUw1JawxZeobilmP+z9ZWjhLmmr//3fsOEt1/o++0TiSgCGGgG8wWTxV0Zx7g+GQtcjwb2vPYnRFRdb/t0Cyqzi+5FdMIaa/mqrd5fHVSy4N940YQtLSZ18qQFEgqKqq8XkJRdg8pILMLriYgBmZnHgCjNaTMZzFizd82zBPKsQ660NePBU4QD+5CIORDw3i/90d8l9LpWWT+nn+Z/qtvVrOABEu+tR019tt8f/J+vDt0XaZqoCoxqo0EN/7bz0aUA/Dnsjgjlg5Iu0Yjy59NpjLxwBAHz4bdcVXWdDV9qCOuWOx64Ipylo5QpiuUaQb5sxhtX/sd1+vvKMp0I99PM/1Y29H6/D/msaAt/RyIvnYl4aPGAApGrWmA1oupZrHYae8GfZSqsZjOLbH7/b95kMbMrl5Dbt305A1x0Y9v9xjZWCEmilXLR1XbootfShd/yi6HaY8s9KizwuKWcEUFYCWc5k/EIOABLMWV2xahiYt+1Vba4iy2uFCR0p+5rm0F+H9kYFc8BLMnJtKgKZrnbdrYYIGBCVYB7rrcXc71ajrz1ZVt0iQb1v4gwAwNXb1+Dq7f4yBWFqleM1Ceayvkh2wRhyWf9xhYHn+36wLrjSbKSkhruUt8mkpqpfPgvADKIE87k/84KrVJEE1u2Cd8yAKWcNADDjoIGQ6EgEuRisGsWlZMLUPHI9lZgEch532LmJ9+RwcAV8y47PyRWVZJ6oVeqhVwroSqmoUur/KqW+X3h/hlJqm1LqucL/s8qtYxrQK7A3MpjT9n3o7+3fqq42VM/93p/vwyPP/Ag68zKu/Wjp5B0CSLwn5yspUEk1xOP11GkuLfOl9A+thh6AfeArSYKhlfI8q4aNZx7rrYWeiAaohXLlY6vqs4Y/Pg7QK2aLNzwNwBwbPXMJemHG/ZVldekRVw1WIzoSwcg8Q4PkZuRLnjd+56p03HNSrKyCNHLkdh1HPe+8nNUMRn2D7asxExSNlH0dB+WyFsCz4v3tAH6stT4PwI8L70vaNKCXsWkw9+yHvb8EAKj46fjmk4/YzznlvmFmj/2semevbxlaqSqGrkmuPaxCIoCyDS7CPPjHlt9rpYxhGvpKjF6oC8LFApEnYtR9u6AXFuQL46FdIHTff++qzb51ZReM2XIOpUxSQEz3l2n/rjX9eNR494NRvNBmwG28KSinrKTqI/dvvGnCeN5NE77ywaVKO9cMRis6vkpNQyGvy78q8dCVUk0A2gA8ID5+PwDynV8FcE259UwDegmbBvNw05mXA5+l/3gLAOC6C98NAHjkmR/hw2+7LlBhMczcJCSXp5+dzoXy69JTDwPucklHty2+HHftNXVdGDCkooJ2IuDs6yDUW2+pikpMAg7pjVL7UoxTBwpUT+twoFwtC5+R4ikVpPb/zvvf9yoB5gAs5SP3t6a/OgDW2XdceHzVKQtBUkn9uRQelToSzMOaXpyI5aGQ1dGyrwopl80ANsCfXNqotX4RAAr/zym3kmlAL2LTYB5u39q3HY888yMAwA/vuQc/vOceC+YA7HcMiI7NjmHmU163LwJHKQCppOxtMY8dKA3urt22+HLs/YcltpQBQZHUQBi3XA7kJ+vNy5Z1rZDCcb1Hl2qR++Jy0mFcejTmfdbXnrTLvNCmMLCsKuDpx3pry4Jd2LGUOj6eK5lAVUy/HvvpM5j7ZOmTJc8rTYI4PfR527x7zFW2TFXWqIZCHpGyL0BBKXWzUuq/xMuWD1VKvRfAoNb6v1/tPk0DeohNg/mrM4I6AMxMvYT9qxYGkolomUVRTBRA5LTHuwP9MTloSAmkOxiE0S7FepBKu2vvE9ZDH2pLBJQRxSRuBFZqnuX+8DOqXio1ggz7X1YNA8hGinr25O65j2cvOGwBOtc6XDRY6vbtpDz00DUTPr348VZLDEvOcveBoJ6bN1Y0szOMAuMx+q6Hk0hEID+4wnsBJ7fhRR6VB0W11vdrrf9AvGT1uMsAXK2U6gPwDQBXKKX+GcCAUupsACj8P1hun6YB3bFpMJ8ay55zBrLnnAGdebnsdJ5e4YGbl6C64KXevvFroTr2RMx4cGEUTNdIrQ+0ZcPpUjr1ud8NguYLbaooGEgPnV105m3zg0d9bxToNQdWie5ZHk99d3XRSomAAVs3aebF3tmh24p21xelMWSj5RnP1JatB1/M3BmLBF5Z8Ku5M4Vo1swEJA9/wjGHAqgPt06EVvqUoF4zaKqENm1Jle2tWqlprZDTkbKvcpSL1vpTWusmrXUzgOsBPK61/p8AHgPwscJiHwPwr+X2aRrQhU2DeWkbylfucVbv7EX1zl689FCwGBbgBUflFD/ekyupcvnhPfdYKqfSptCAH+iZNSp7nWYWRa1X5yogXFDn4BRWTIwgNp4cxsiFY6g5arTk5bzEBZtM6eHhBYbLnqwPcu7vv/rngd/VDEatJ+0D+N56qPoJTB6tDQymchYiqbCRC/0B0bAs1rDjBiqrZhjmrUtbsGl3kFN3Mk9L1WLhAHjBliO4YMsRu/xUl8yVZiiX8q9XkVh0J4AVSqnnAKwovC9p04BesGkwn1p75Jkfofuehah/8PSKVS2AFyBdXpspuZzLs5dTu7i28oynwre/wE+fSHNpCKmuIJUxWW/UKXoiivFZQWAOyw4FjKdf32sGOVa2bF2XtvsgpYPy9+5Mgtw0SwjQS+W2pFQxfyQTepy+bYRIJiUHzUxO37HF8vbVui5tX25dd+6rLF0gQV1mn/pkioNRtG495ltXVX0W8Z4cnuuYAcAAu9z3qmFz/bo3JypuyVfOTFC0quzreFL/tdY/0Vq/t/D3b7TW79Ran1f4/0i5308DOqbB/GQZqQzXS3T5dPm99NC3j8UD6wyTQtIqAfWLHn0eK3feWHY5ILxBBGcGbiVFCb4AQmuRh2WHzk7nLJhxtsIOTsUKWw0sqwp4nj4aKO5VT6zKRFE3WDpzshS945oEX7mPgKjFk43g/Fufw/m3PmeLtzHhSB6LLDsQdhxh1rhjEs3fyQQ+j3bXY+buIzivYwQAfI0/aC+0KbSum7pnvdKgqJ5O/X/tbBrMi5ukKo6HbgmzE0kGKualS249LLPUteezswOfXb19DR46cqmPegGcKXu/f0AiDeFmrtIIyPxdmPpDgrQEMpuwM+xPyXcVHTSXeuB6w7ZZ/2KubBLRjGdqbUCWFuadu9mdst/oyNsW45wuje9dtdny1K5kshIrVcVxYFkVRuc3oO8DcQCFwlzOLIjNPuq7q61ccbIeFsxPpAVfmOW1Qq6C12tpb1hAf6M0pjhRu2znB/Afx1rxH8da8Yc3/2ng++1j8VAPWlqp4ly0SukYd3u3b/waAL9H39CVtk2lafTaw0D9W7uS+NauJOI9hi4hkD+79gzfcmH72NCVtt5kGPCVAnOa9JprjnqNtjOLohhqS3hFqJx18fwV45PnbTMvN3uylJc+We9RL+WSb6iPJ5jLEsqAaRDeunoXej5pPPne9Ut8AO2CdVhZXBb+Gm/yYhD05jmwBurVfORMC+Y1/dX2mGScRjY2f7WmK8wUna7lcpJtGsjD7bKdH8BlOz+AP7z5T/H2xn2+7z7Usjz0N5WAehiwl/LYf3jPPWXXK713eukDNyXR+EAKf73pRjyfnW3B/OE7rsLDd1yF57Oz8b7T/y8AQwfxRXt27RkBMAf8NMBQWwLNnSn0r02if20SCzbtDk2SKWUS0JjkQ+06ILzybCQUtOUAw++L0VqNOzzv3OXTuc81/dXhSUuO98sBZrxpAlX13ihDb/zQZVU4dFmV1fS3dKQsT+8mJRUznhuWKpDbb12XtsdrB1OHeuJ5oHxUDlTHc40qsTwUJnRV2dc05XKSbNorD9rb/+JmtPz7jbgv04TqL78Jkftn48B1k/jZ316Cn/2tqTXOKn3FqicSfEuB8A/vuQeX/fUvQ78L84DLBUVpsplG/Ys5DNxkHtqH77gqsCw/q3/w9NB1UUVx/qe6rUKCvUwJFA1daeM9izJJs9O540opl56z7M1Kq8qYxKl52zwwOl5p3/gcUyPHPbfFtPEEu/rucN27L8BYn8WZ8SGcGffKDPeuX4JFX9xtyxFLa+5MhTbEcAerXMzfeUgGXOU+yr99AeHWYXu88Z6cLaPrJktNFbBPUy6/RZsGcr91jdRaKmXhAxqPbLjSfie9VoL66IqLkX9xwAfaksbYPhbH8tpMSVC/40w/oFeSNUpbXpsJgPwP77kHjQ/4ga7+xeLSx2JlCEifuKDZui5t9c2HE16RrIauNGqOGtA4cHPpVPWw7E8ZhOxrT1rA47LRLAJa6XIgRCDjuZy3zS+lPJ5ysvRqKxlEFq163udFn0gDbbcWu0xSerUVEk9mYhGmg6K/HZsGc7+1/PuN+NK6ldbzHpsdQ/UrE/Y9TWq9czGFobaEpTGW12Zwbuwwns/OtsBejiYJMxfMSc9UwtEDU1PCd6gtEchQBfxgMjudKyl3k16gnPaHWdVgtS/wSaNH7SYuhZlUh4QNiIeWqwAgE9TppRcD7LD9lqDNomG/zpi68VLGSC6dtXHCrKUjOHjSws5xpVpy2f+0XGOUqbA8Kkssei3tpG1NKVWrlPpPpVRaKfUrpdRnQ5Y5Xyn1lFJqXCl1m/NdXCn1baXUHqXUs0qpS93fV2LTYO63rpFazH+kKgDeY7ODsoJiYJn4l7XWI79hZg9umNnjA/ZSVixQGmb0yBOP/zmuvOWWouv7yOd+UNH6wo4xzKQsb7LeA0xXqphZFEXTlpT1jt9/9c/LzjZq+j0wp/ff155E4w4TDJ2dzmFfRxJVg9UYWBYtur6awahVbbi0yuFE1Gq4XZne5NFaW7ddKm58csICn+9LGHICv0d+2Vi0gXT3fRdhxpN7A58X489Zjz2MruH+y+YfJzNZ6HjMNLiIln3p15B2OZnDxziAK7TWCQBLAbxHKfUWZ5kjAG4BcFfI77cA+H+11ucDSMBfJ7giO9XBvO2tV1e0XNdILS7b+QHcl2kKgO6ha/wPaiUZmHO3a9y54QafB33DzB6cGztc0f64dvvGrxVN9Qc8CujKW24JBfYbZvZUDOq331a8Tjs9clkLZXxOzgfYTC5q2pLygd7sdA5dD5vkn8yiqFWsAP6EJAInteb0hhu60mL5gorlaHjDZG4PCC805RYWm7fNeOwMwFZKQ9hz0DRhuXU3UDrytsVQZ52JXAzo+eQSSxWdSLMQ+RupmpGDUhiYFwP4qQ6CuvaGUrloY4yaVBde2llmUGu9A4APVZRSMwG8A8A/FpbLaq0zx7P9NwKYs6N9MesaqcWVCy7B+v+6ztb6AODzzud+t9oH6sdDYdy54QZ87tdek+YwnjvM6FVTARP2G37m0i5hNTsA4NzYYStldO32jV9DZlE0MHhJY6OG8Tk5n+fJ4CgVIi+0KYyuuDgAFplFUdspRzbtmKwHfrMkmEZP0I73eIHXzKKoBfowSsYFczfJx11WevdsxO2uC/B7zuT35YBW311dlPphG77zvvpr+9mCbwQhjLx4ORpESiAlqH/wK7eGLt/cmQr2YX2NPHjTJLp8PfTX0k4qwVNoqfQ0TJWwbVrrcJlD0BYC+DWABwstmR5QSoXeUrIs5QTGAZz6YA4AXT9/rKiH3jVSi3fPTeDLv2fAuSZVb2uUPHzHVSWB7XjtZ397Cd676i/x3lV/WZHahSabYQCVq1rCjNtbXpvxefkf+dwPcPvGr9l1P7b83qJKG7ZSo0k1SNWsMVTNGsO/9Z8PALZJg7TJem+wySyK2he/A7ykocwik+LvKmWatpjiVYcTUTtASJO1TMIShQjguZgpB0A9O40DOcsUjM/JWW++ahh40+5JnPOo8h2/bX5dqP4obeHDv/YBOa33+uD5Ydo/B0LOhlwZIoDQQapY0+cwc3XrJ8sq7Vh0ygRFtdY5rfVSAE0A3qyUqjRFqwpAEsDfaa1/H8AwirRfkmUpf+/i3BsCzIGgh86szisXXGKBnNa0JYV4Ty5UygcEqw0WA71ilosp5GKqbOr9iQRNXYv35CztwsCpnBlsH4vjh/fcg9s3fg3nxg7bz5+6ZZPtKVrOxufkLM883jQRyhW7Ch1JsVAyx8+lJppAPTud8/HJgcYMswwtI7ltgtPhRDRQeEr+LQeSzKKopdHcQaAqE8UL12rLj/9mSZWvrID0fMMyVpnEQ5N9SqXt60j6lSv3XWSbUwCmoxGNyUjSOAjI0r+VBDyrZo2d5PK5kco49JO2B0F7TUKwBbrkJwDeU+FP+gH0C4/+2zAAP23C2t56tX19+fcS6OwJAnb3fRcF6IFyZVJ/9reXnLAXX8xLPx7vXVrYICF5fqmwcbfhev3LazM2sci1eE8uvM5HNmIzIoeGawD4gU7qnvnd8I0v289kAS/y5fTQq4a9v12bjOcs7UJQrxmMhuq4G3dMBmSJzZ0pO9jQS6/v9cr8js/JmZov2YjP82arOMBL65cziTCPV7/0a+R7D6Bu206c86gKKFUWfXE39nUkLQi3rt6Fcx5VdnbQ/846q4rhIBdWrKvY9sOsuTNVNGg7VaY1kNeRsq9XUW3xuO1kqlzOVErFC3/XAXgXgD2V/FZr/RKAA0opFol+J4CpaTNyCljXSC0yW6vQ9fPHkH9xABg3VNPM9x9E/9ok9nUkEakxoN26eldAKVGJ7tv12istV3vnhhtKcuLHY8XAnwBF6eTXXlkU4O+LbS+Tn1Fym0ybl9ZQP+5739zpKVuoIQcMODfumMRo6gwL3v1rkxYQpQfPMrkuNy0B+9A1E6FcujSmwsterTJYG+/JYWBZFAfbjEdcrKbL8IKczwuPdteHAqKkohY+/GtbBIv1UcISpfTomPXQczFzTsi9E9RnPLkX+zqSPsVNzyeXYMGm3YHAL1UxJ2pTRcPoCppbsMHFa2Un00M/G8ATSqmdAHbAcOjfV0qtUkqtAgCl1FlKqX4AfwXgDqVUfyEgCgB/CeD/FH6/FEDnSdzX3wkjrXLb/7kRR37ZiLa3Xo3IGXFs/MWjALxelo07chhqS9gXrdK6KWHe+fEESwmwLiAfD6gXA/OwgeXc2GGfVy634+rZl9dmcOddW0PXzWOUoEcAIwUjvXPZXg3wg3PTlhQyi7x679JTp1fOSpNVw/7vAeNN62EjXWSdl6rh8t3t5TU+nDB13ht35DCvK2IHFAloNYNRfO+qzfZYJ+f4OxeRjglLTNr/kTNR018NPTpm66McTzGuF9qUjUfo0TFfeYBcLChxlDGE5s5UoIaMa67jMtWcumkSXYmH/trZyVS57NRa/77W+mKt9YVa6/9V+Hyr1npr4e+XCp06Zmqt44W/Xyl893SBG79Ya32N1rrMrXxqW9dILTp7rsKXfy+B2ekcFmzajY3bv2HBvK89abnbwwnDm8rpPAGkEgvr4HM89vAdV1lu2zUJ9MVAe/tY3FeLpdgyXB9fxWge9+9P3/JnFRyFscmjtcj0nx5IxpmdzgUGy9npXMBDrRr2gMkNdLpBU0CoTYS3TE9+sj68q33VcDD7UrbGO9iWx8CyqI+nn7cNlsf+4FdutcdXVZ+FnhC1YvqrUXPU7GvNYBSTR2vLgqILsmHAKzXnkg8P06nLICmXpcKnmMqH10Y2yagZjOKcLu3j71+NvdF06NM2BUYN+T2LWjHz/QcDnjI78NDo9am6WjRtSdk2YydSvvbV2PPZ2fjaK4tCQZtAHwb4V95yC+7ccEPRAC6NOnjX+6ZxUOC+SLtu4w9x511bi0ogAZgHX9jk0VpLvXC676bbc30EHYKwpAyk1y7BWfYppRRPVkwcn2M8eXeWVTNoBgnWQGcJAztIxPKY1xVB4w5PSjlvm5mFSD25pFdYy116tE1bUv4gbMFzryQ4SX25qqtFNGs4eak5lxm52XjeNzgRsOmZV0K18PeHlit86B2/sJ8fb5/UcqYrzBQtR7kUS8JUSnUopQ4qpZ4uvEo/FDBqkml7nVrXSC2+tG4lZm7bCRQ4cRaHOnZFKz6RAEyUAnhg5b34XOelU5IKz228Gnv4jqtw2uPdAFpwX/x06IwJFMoG0q4VywaVJvfLevAh+nMXxJ/PzgYKgP/9t7bg+2jBOXX70f6zH2LV1jU27Z6gFSZNpDV3pux+sHBXc2cK3ZsTAUUHYHqluuVpWSaXn8+/f7d98Pd1JH2eelUmvPUaVR+nPd6NC3afiXzvAfS1J33eeX13NTKLYDXyQAHMQ9ZHUB9vmkC0v9rWg5msL4BuLG9K587JFW1eLYGaUsWWjhT06Bj2dZhs0LCko772pM+bnoznEC2hLy+mXrnhAz8GADyId+KmK36MBx6/HIjnEctEvLK7/aE/PW4zQdFKvO+yyzAJc0gpVQ3gSaXUDwvfbdJahyVehtq0h/46ta6RWtx+v6nt3b/WSzkPqzkCAJ1XXAPA8+DYdJjeeSX8eTSrj6tXZznrvmdh4LPrLnx36LKVgDkQzu/LmuehQO7Ye39uSgPr0THc+J8fxzkP7QcQDJZxik7wyPR7VRobutKBWU/NYBQtHV5pXQK4W5ebYE6bf/9uHLh5Cfrak6afaIEzJwBHs/76KuNNE5idzqG+19Qr2fMFc63dsgSAGRDGZ8EGRd2eqa6H3dyZsh66jCXUDEYtiEswLVc8yx3cSmWQxjIR6Ji2sw1pUkpJlU4p6ic3I2/A3LGwImwnapXr0EtbJUmYldo0oL8OrWuk1mrJ3SxDOU3vXb8Ez90Zt7TLUFvCgszhRPS4vWwW4JoKL18CL71z2offdp2PKkk8/ucVr/dDF4U/jHduuAHnxg7b8gPu/2Gm4qfjvNsz2L9qIWY+9bwFKhkAdNu9EcD62pO2ANTBFQgMuN9YczeatqSQax1GrnXY56GTS6fahYOvHCCojGHHHcCAs8uV7+tI4oItR2wd99npnC9TFYAnT3SsuTMVoDDke1frLikLAqoEd8mTq7paq0vPvuNC663LvqKUidb3mu1QJlmsGXQlmnKC+E1XPGH/l71J3Xo8r8ZMULSyTFGZAFl43SzXVSIJ8y+UUjuVUv+klJqFMjYN6K8z6xqpxV9uu6EoqLqe9rn3eZdQ8sJM1uD/EuinwgsPW4f7mZ6I4kvpH1rAAryZw5d/L1FxRUXAawTxrV3FH0aXM2c1yGKmMy9j4/ZvoLnTNGIgLywbQcjGx2x2zO8AAxAEXXrhwwtyeN+/32ISaGAydbkegq3MJo1mNZq2pOz5m6wveOUZ71rLwCk95fFZCGRqSvki9xsINlnmvpey8aYJtK5LW9ULKQt3PdIiZ8R99y7PAb1ueux7Ny71tpMc9skhK1GilAP3rz/0Tsw4GPF56VPNoVccFIXyJUAWXvfLdRVJwvw7AC0wKr8XAdyNMjYN6K8j6xqpxV8/+37fzSqzDuWDwoe//511yJ5zBjZu/wY6r7jGLvvSW+p8/xM8igVHp6IcgDsIfe9d92DV+nUADJBLYAcMqLtZrcXs3nu34NA1E3hs+b0nvH9fe2URAFN2QMVPtx4lgY0JMQRSgnfNYNTn4VJzLeudALANFeJNL6OqPouq+ixyA+b803N3lS1v2j2JgWVVoQ2hSb1wvTRew8YdOTz3MX+2pjQCu/S0i9VkkUbZIKmXsES0cs2WJbVS+4xHWtNDj45EfN46ANswo5R3vmDT7tCWfzR65iPz8vjoyh+X3MdXa5V66Po4yBOZhKm1HigAfR7APwB4c7nfTwP668RIs8z8+5nWk3DTxwnIhxNRH0f7/Oo81q1cjf03zgMQlMcBxQcGGqWKh66ZsC9pfM/vytEypYC3/fHvhm67lK1avw5zv1ttB4gwk8oYl09nGzraN598BLlz5vh+39eeRFUmarzJWN56l2ENi1vXpXHdW35pFSlNWww3W98bxR817cEHWtOYHI75imLxmrAaIwt38fr2tSctiI/PMYk+TVtS5vtMFPW9UVvr5eAKJjLl8MoSQ7cQtJs7U3Yb0uZtK14TRfLKYRpw10g9jc/JWRqjd/0S7F+1EAdXmO/rtu1E6+pd2PPFuUbjXvDS93UkMTlnArkZeZ9nzvrqxSoqUi3kcvmlOHECPBCcYbxaM8W5Xr0OvVgSplLqbLHYtQDKNkOdBvTXgUnOnHVRaDJ4JkHaBez+dxpPkByq+1t+V87mfrca8x+p8gE8P+f/5QD40DUTWLNmbUnwpXc8FXy9tDCKhUD+kc/9wHrpABAdMNy+q4Oet82ABj32aBY+7TKTaL7TXQC1gsfY155E20d+bpczCpOolS+611LWf2nakvINHPW9Ho/MgZi12auGg8FNwPOa+9eanARZObFUx6IwXpn7UoqWGZ+TC8g75X7Q9HC1PSbAtJmr6a9GdCQIPwT4sAC1Hh0rqpAJs7Cg6FRa5ZmiZVUuoUmYADYqpXYVPr8cwPpyK5oG9N+yUZpIb1Dyn/Ti3CCXuxzlZrPTuaIDwPHYges8rvFEkoxmPFOLe+/dUvT7UjXJw+x4OP9y+vVHV70r9PN9HUlfnMEd/Nz6JPu+6tFHYRK+R1LLbGBT6tA5U3JnUfTOAX8CUfd9F6G5M4XGHcGA6PisYEyludMUYqOqBfBTGG5P0TD6olwjZ4LtOV060MhCDo5SAcNMW4I5t2O/L3RBevTS8CzeYvvhAv/XvvNOC+RuBU2571Nhea0wmY+WfZWzEkmYK7XWFxU+v1pr/WK5dU0D+m/RCOacmrKjPI0qiGI2We+vVCezQlmCFfB7hpWm/79aK+WdA0ZhAgTBOgy8j9eLJ6hLmoXHfe3Wf/cWHB/HhuXXY99XW23NFAnq5HhzMQOs3fddZItITR6ttSA03jSB/rVJ1CWP2FXX9FcjmjXXUI+OobkzFUjvB7xEI3ndJVVy/q3PATDnhaV3XR6c1ReBoLfKxhQHV5iXW5/FHYzKSfoad0xar7xu204rmaQVVc3E8r5eqq5V1Rt0v/YpM9i7PLrMzHWppHINuqlNp01lLZd8Ba9TpZbLtJWwrpFarP+v61C3bacPsNwHQoI04PGvBJ7+d9bZ91Lr7A4MEuClnUgwtNygUOrBpenMy6F1OKaKggnz1P/3t96Hh++4yqeFf+7OOBZu0piZegkzUy8B8Djllg6TQDQ5Z8J66C9cqw2oFygYzqwA4I+aTO05di6S+n/mEsgBi9dD6tXl/wBsQg5gyun61C6iGIb0tA+2mQQggvl400RRyiUMwItptTnTGFhWhdZ1aY9Hb5oIlXZKI2C724h216MmZQ7q0Uu3Wk/eXZbnsRLJIWWKbFoibSrL6WoN5LQq+zolqi1OW3GjZ97ysW4AnmZcNrblDSzTvQkGYVSKC7LkUSmRY3U/1+MPo1TkZ+56ZWC2nG3dtLnod59OmWDVUFui5HJTZXImI+282zMAYOribP9G4PuGrnSgA88L12q0rt5lvefW1btC64UD5voNtSVscFPV1WJ2OodvrLnbXh9eT5myT8qEWaP9a5M+imJknufxNu6YRNVgNfZ1JDHeZCgNXufh1gkgG/FJMSuxvvakr5mzBHhfHZtC9miYLJLbYw0Yt31dX3vSBn+BcO+c25W0Fe9jlgN2jRmnMw5G8IPP/GHg+zDe/0Ss0uJcx6NyebU2DeivsckA6L6OpE0GKiYp5M3LNmXSeJPLpBXXyNWy/naxZSo1OZiUA/VStMvnk5dbGePV29dMaRelYsbz68on737k77Fh+fU2QYsSw6G2BPrakzYISi9dAnzVYLX1SLsefqvPO+d5behKo689ifn370bv+iXYumkzPpG4MjCQ9a/1aoaf86jC7HTOVz5XFgabcTBiPfADK8xj3NKRQn13tckybcsXjaHIaopyvbyfeJ/1tScxeckFdhCQhbBYPoClAAB/ISxp/J6JQ3IbNvB7tNZ66mF6cV4zd+ZQ7N6NZSJ2cPzad97p25dSZR2Ox/JaYVJHyr6mKZdT1CSYA17fyIautK2SSDCR4C6BkzdpGNfM73IxYOsqIxskmPNvaeTaWeq1mIUBNweZMJvXVfq2mvnU87735ZQzU+HBU48vE2/afvwr/0KFuvLxnpzNBHUHwQXf0LbFGut8t65LW09xfJa5DvSIJ+sLNdGbJnDgZgOI7I/5icSVGFhm9kdmms5OmxrnDfuO2e1yPTwGwE8pMGGHXru8Bud0aZ9X6urKpaPAGIE01h9v7kyhbttOU9I3pM4KqY5SswD5HcGZmaFhs5xiFEvYM0GTUkVp5P+nknapLFN0mnI55aztrVfjS+tW2veyKcFQW8ICh+sBSw064PGy7o0uNesAcNNDa2zd7zDglfW4h9oSvuzFSswdUIp562EDzyuXnhvwkkvZQ0cuDf08LOhbzHhewwaiTySuhM68jNw5c+xgw4JbsltPqdrbmUWmR2hLh6lK2LoujZF5eYw3TQQAp2rYFOzq/uz5WPTF3faaNW1J2cHg2x+/G/3vjmNgmXcPEKxzMf/gHctEMNRmCoOx7jnPSU1/ta07Lntt0uR9tOcLrT6tdrEAKamfYsDdui5tBxDq54GCF16kVIA014Pm4Ore98WSpL7+kOeRh92XYXkFJ2Jav4HqoU+bZ++eW2gB9syAvSHZIsyVGtIIuJSwkXqRQAwYr4MBUdebvOmhNYF1uyVfpYXJI4t54W7wsm6w2NGH2513bS1UYyxvK894KnAcmUVRjMzL49BydVzyzMOJqA1+Al6dGapu3GQjWVyKjRxaV++ywVDp0XLGJQOGQIHv7a72AdBkPfDYNZvQu34J5t/vgXpzZwqI5bFy5422BADgDzRzO7FMBNGsoTo4cLp1z2enc74a4JxNsKxBOVVL9c5eb7/gHwCqMl7MhzMUvq/bttPq0YcXmOxZ13rXLwmtoFiJBz0+J+fLuXDNVbYAxomayuSiijNFp2yL5W0a0E+yXbngEgAwreJgPC/A8zoAfxan23EG8Acii3nbXM/4LEffW+9XylAx4KpnwoxURymPhr+vf7EQhDvbv76pUK1cvX1N4DNKAGUmZiUmg6M3zOzB+DKTaLRx+zcQfWEQdz/y9/a4SykqyKcfuqwK3fddhOEFOd9spHVd2uqgDye8xtCyqTTtwM1LEO/JWTVL6z3DaLwjYsGxaUsKrat3oaa/OsBTN3emsHjD0wDM4DM5Z8LWkwEMwL/QplDTX+0r+ztvG2whNlfPTQ57qC3h6+0pz4dcPizJCSheGVRa2KzBNc6WsvE8GndMonHHJGoGzT181Wd/4tsnXy34kPVOlXcOmGqLFXHo0w0uTg27csElyI+PIVJTa+t/fCJxpf1egnM5La27PODvkE4eXErZfF4fvOp6YV5ZGMAzqEmPMGwZd58I7OXs88nKs/jmfrfa54kROEuBeUNXumRCEqWL//qgV6Jg4y8exYa3XIsNb7kWQDjlEKC65gSDud2bE9ZDZ5AyjB66/t5bfRUYWzpSiCyYj+5Vp0E9fyiwD+NzctAxjWhWB7xrgnNY/EJ60q7DcDjhNY6mDSyrQuOOSQxcV7zUrTQZp5HAP//+3YZSykRt6eGwWUGpOueuourACq+ueZgXzm0/+P13Br6bamM99EqqLb5WNg3oJ8m6Rjx+MD9e6LlYCLqputpAFxugOL3hTisbutIYWGYA4PP3/D0A2FraNK5LrvOOZ5+yywJBgA7z1vnZwLKglOxEk5RItbg8eqUZodLrl/tVygauG8PWTZsRe+EIvvnkIwA8UH/kmR/h2qdW+bo/zXzqeQy1JXy16AHPw3MLS0kj1SBbxwFe8Fvy8k1bUpbq6l+bRL73AGoGo6Gt1VjtkYCWH/cAt6ErjcOJKAaW+UvecoCXx3BoeRBkxueY+jWkMshj51qHMT4nF1CeEHjDNOfs99nzySXo+eSSQH1zd1BsXZeuyFMvZjd84Me44QM/9g0GDRd6SV68X6fSOwdYy2Ua0E95k2qWSE0tIoVuQ24dCqoYCNjlWsUxaMmpLINK0uvh1J6dZgiSs9M5m3I/O53DAyvvtdx7MZNA37jDowvKJXdIYCbISCOQE9gv++tfHld6P38neWO5PcoEaZf99S+t3hww9djJmSefNGWpF63ylDf7V5nGHDOfej40uxPwuHW3QqM02aEI8Nerl0YwJvDL0rxhtAWBifcVjfcAB5SDK2ADtaRbsvG8pWi4neEbC/x2SM10muSeSdE07sgFuHPuN1Cg/haUn3py+TAwl55/LBPx1TYvZaMpU7CMCh27P1NougIwn+bQf8eta6QW6791IyIL5qOvPRkqAytnMnDJoKks8gR4bb3YqchtWCyDWJKv5/pcFYzkdwmK8iHl9FwCL5OeZG1vqUQYWBYNaKkB2O0S2H/2t5fY+tiVKFb06Bi6NxuKwe2Iw3MhgXPXtedi4/Zv+Dxwt+mGtZoaLHzwIACjxpE1W4BgB57RFRejdfUuX9NjGsveMrDNY2vpSGH+/bt9My8GtfvXJn2DQ9iAMjlnwqcQkbMFO9AIKSUtbNCcnc75OjEB/k5FYZmb0hOWA6j04sNmGBJcAcPVv9o0/KtnPh34jOss5qxIWvLVmAYqSv1/LW0a0KfQCOZsOjA7nUN9j7l7+tqTOHZFKw7cbDqVH7h5CfrfHff9vlTAE/CniPOBZrBVtppjKdawdcn13fC9P6+oPrZ8MNhDkwMM95mgJcsSFLPbb1uFY1e0ovawcZmiWY352/K+pgfHazw2ue2+9iQu+2vT+GXD8ut9maA9W88F4Hnmqq7Wny1aU4Otmzaj5WPdoVw6B+oXrtXovu+iQE0TwE9jUNbozl5o1JS7AB4m7WOGKq0UQLV0pHy89oyDEai6Whxc4RXqCsu2DFOluHZwRbDrUbnWcNJceWLNYBRVs8Yq+j3588deWYqrZz7tSx6S1R7lvZuLmXNVyT1fiWmtMJmPlH1NB0V/B43p/KxPzRrV+qVfWxDktFp2hScQSZWLvAkpzSp3E8rAp8u5uyDL72ccjKBq2JvyS4BxVQ2Sk5eqDZdHZ4BsYFk04JmH2aHlCgdWRGy2Y6USRIJQsYJNcp+luen9BHbU1ACA58WPj9tU9DAjkEvj+7DBVCbNuDQKl2/akvJ9t2DTbuz5QmuAp95z93mB9Q/c5F9n/9okBpZFbcE3DvLxnhx61y/xgTg9cvtZLI9M/+lofCQ4mIw3TaC5M2X5fO57sW5AYR5+MRtvmvAKnpUBdemZP/bK0op+A/jjF6/WjqcF3Wtl04A+BUbOXPKMslmzBEMm8hCgGRyVenR6vqQwpCYd8POVbmBMbpv2pt2TvoYHcn1cZzGvuljBL5orw+MxuMoY2mV//UvrmY/NNg0g5KucDSwzVRAbutK23knNUX9lSjk4fac7YUFcUi70zFNvux8fftt1lpKRHrrk3QOBPFFO95xHvYd2dMXFVlrYui5tar58qts2kJY23jSB7s1GNihLvdYcNeDU88klqMqYRtUcYPvak7jgjgPo2XouVF0tImfEDZ1zob80xPgscw1IgUlHomrYnMcwwJ63DUA2Yj10VwEzrysS6PQEwCYvuVZKU17su2I9RaURxAF/ar/cL/e+jWbhiwW9WqtU5TLNof8OmQyA0mOll+2CWZjcjzwyvXQJjNIrl7w4ULp7utQqy+5HkiJxeXa5T9LkPgy1JXyaeB7PZL1HFbh1ZeQ6T3u8Gz/720swNvvE5QYuKJY6T4cTUVsADfC8843bvwEVPx2PPPMj37pcnp3m8uj0xFtX70Lr6l04dFkVVNaA2QvXmsdXarMlHdO/1lMjta7eZRN+pAfP76NZ04vzhTaFhVv3Ixcz1/a5TY1YtOp52yFo66bNtq8pYwvSeA0IxPJ8Sb78YFveDpKZ/tORWRT1fQ+Yc6pHx+w26BUfTzo9aZnZaX/jDbd4VylzQTxsHxZs2o3h1gnfwHPa490VJ7SVM9MrtPyrnCmlapVS/6mUSiulfqWU+mzh8zOUUtuUUs8V/i87v1D6tSwFdpLtDxK1+j9/dM5rtr3Ldn4ArzzeiHhPDtGsRi6mitY9AYIqloautK2ySI/8nIf2AzCAzcBSGAgDprclC0dJc2V20SxsFT5puZgfICXYcLthgxCDYPTeJY8qk1ekNXSloepqi6b8k6KhYoV/S9rGVctQtVCs7g2PEQuGfd42AOy/cR6aO1P4i1+lcd+7/ghAoaRv3B8gxPg4ntvUaOuIL9i0G+PLFqH3eoWqwWq0dKQwuuJiHLqsyvLVHFCpJlF1tXYA9nnRLHlbUJfU9xbqmcTyQDaCmsEoGndM4tBlVcjN8Abp1s/swbErWn3HOj4nZ39fMxjFwq378cLKhfb7yfoChePQNfO6vEqMtiuTuz8wnrp7L7jn2gYj2cYPgKqfgB6uNi3ohmO+Yl5cJ+8fVmYMu1flTLFSG2+a8GXo8tkZakvg5//yif/WWv/B8a3RM6XU8rl//D9+smDVFWWXfXr11zC096WiyK6UUgDqtdZDSqlqAE8CWAvgAwCOaK3vVErdDmCW1vqTpbY17aGfoF228wOo/vKbLOfrphTL1H4qAaRnK0viSn59/6qFGP4f51hvanY6F0iSYUAu9tNnbMIS4MncyHPK6nzcJ7l/7u/CGicAfo/bfYglmM9O53zNkI9Hikhr3GFUMYeWKwvq1N3LJJ7cjHwga5KqGtrsdA6LvrjbJ0kEgFeSZ6G50wDx8toM2n7gnYtAGV1BvTTumPTNjDgY1m3bCcB47gTpGQcjAa5cvreFqQRw8fzzs/E5ORy6rMont4tlIgEFyex0oURtvdcQev+qhVbjTtvzhVbUd3t10gHPQ+eMgolJ8aaXUf32I4HlaNI54X0eVkI3GsvZ4KvbCzTM+ZGNt91uSsVmfsVMHifgDaYncl+GWSXeudYK5TgXbWyo8La68NIA3g/gq4XPvwrgmnL7NA3ox2ldI7W4bOcHMPP9B30gHpbII82t3kcu3W16AJjBIX8kg4Vb9/tuepfDHl1xMWp29Pi+Z80P8q1sKlzTX+0DXta75u8kmFNCNzuds4WgcjG/h8xgrQQpl/7g9jKLolB1tbYGumthAdToSMRur3+taSrMLvQ03RBsy8bf8DpMXLwAgGlk8dydcWB8HIcTUdy19wkc+p8m0euGmT1Y/e//FvDOCez5FwfQ3JnCwLIq3wAqraUjZSmYxRue9qlVOAiwkbTlsgerzf7H8rhgiwHP1nVpn6fb0pGyBbjmbte2gbSUd5IGG2+asDSGVDrFe3K2gTXfW7ATlI+MBdQ/eDrqHzTnQ/Y3lSbvTQn4HIBqBqPIZYvz5NJkop3cL2nl4j3FrJIs7BOx40ksUkrdrJT6L/G6Wa5LKRVVSj0NYBCmp+gvATSy7Vzhf3+hoRCbBvTjsK6RWnT2XIXqL7/JKleAcE9DVogDwm8qmTQB+JUqAJA/kvEBFvXG9Aj5v/zO5SHDZISumoVGsCEYsas84HnOsi57sUJfrMHO7vPxnhyOXdGK7738+4FzEJbpyc9aOkwvzfFZCIB5dCSC1tW7MDlnwg6I3EfXjl3RisZHanHe+gG8cum5dn8/0JrGh992HZJP3ozltRm7fDG9Oq8LC3SVszBFheu152bkccGXMnj2E3EP6DNRq0WXGnM5YMZ7csjG8754Ab1r2a/ULTPLz30DuNC9R7Ma9b3+Al9v2h2uYHFVJaRMpMmyyLnWYVsTPSwRqRIrB+bSq5fF5qqGi9OBJ2wVeugagNb6fq31H4jX/b5VaZ3TWi8F0ATgzUqpcM+hjJ00QC9G9DvLnK+UekopNa6Uui3k+6hS6v8qpb5/svazUusaqcUtP/8Iqr/8JgD+tlg0mcLvAjgDksX6hEowp+cub3R629JGV1yMkbctxr4O02C4f23SB+CSd3RnEKRKZqdzthRB/9ok9nWY7bKUK3/jyhplQFT+Lb9n1yTAyBN/9reX+Pbf5ctpBOV9HUlb4oDGv/m/nHmwtrg8vzIA+8ql59q/Wa9FZ17GolXP47oL343V//5vFszdAGkxz9y1fR1Jb3B19OHdmxMYnwWrt49mEch85G8n47lQfXlzZwpVmahN32fpY9dkq0KZ7FXMwajKmCJeuZgK1CWneiVM5VSuDgsAoLce0e56Xz9TF8Rt4Fh45tIxKddfF/DiRONNExhvmggkqcnGJVNhWgO5vCr7Op566FrrDICfAHgPgAGl1NkAUPi/bE3Tk+mhjwO4QmudALAUwHuUUm9xljkC4BYAdxVZx1oAz560PazQqGS5560PY2BZlfWMGcjkjSazAXkDkr6QDwN136wiR3B1szoBLzEEKMjc7rvI6pLrtu1E/X+/YIKesxDIPKTiJEx5I7lucrKyXKuqq7UetgRJN4vVBQbKIbk8fx/LRDAz9ZJN9AGCHrXrrfN7tmGLZlHISDSzBFlHnstzH2XZAjducTgRxf5VC/Gvj73Vt73ltRl888lH8M0nH8GaNWsBAF9K/xAAbPBZxi362pNW8cL/5eBDXTlBXmWVLw7Q3JnCwod/jWc/EbczEP6+KhO1Hn5Lh9GRx3ty6N6csFmpsUwEhxNGicIuRW5GcWZR1N5jvA+jWe1RQtkIJuO5QL2V4QXBss5hnnTNYFAJw/uC+zYZN6qWXOuwD9Rdjty33gKYywxmmixnwfVUDfsHADlbodn2fs7+nqhNVZNopdSZSql44e86AO8CsAfAYwA+VljsYwD+tdw+nTRAL0H0y2UGtdY7AASurFKqCUAbgAdO1j6Ws66RWp8s8UvrVtq0cMlPut4rAB+wx3tyVs0i+XL+HUZ7SAtLmJBT8ebOFGqOetJHOVOQU06a1LTzfzfQxvfkffk7/i0DtfJzCf6c4lsK6cUBH4gyAUl66jQOLIcTXgXJXCxYQZLb5/7IfQ2rjUNVBZc7dkWrTS56PpdH4l/W4r2r/tIuL710VVcLVVeLmh09FtRJvUgKxpVWtnSkAt42A9HPrj3DfkbgrzlqQDCaDc8C7f7s+b73VZlCQLS/GsOtE15DjAXhCWmM/fh6gcbyVkLI/+mpVyJJrBmMIhcz531gmaF83Ps22l3vu24SgM//1PFLCV06ifcGr8Vw60SgYflUgTmAApXy6oOiAM4G8IRSaieAHTAc+vcB3AlghVLqOQArCu9L2knl0IsQ/ZXaZgAbAIQ3KvS2YYMNv/7N1CQMAJ5X3tlzFV7513kAPM7a9QwlaBBQw6bBLlCz5Kn7neRZZeGiqsFqVA1Wo3Vd2jYOlsvLfXILc0k+kVrjsH2SjTLkQMNZhdw3JkhlFnktyoDw6pGu4sYNBDfuyGHrps2hv+Vsp6UjFUiNj/f465DzfLoSz2LHezgRRbS73ssYhbnOvNYbt38DkbMbfQlcAAyoF+mhCXgeO/d3eIEpfQsY/t8N5nIGUnPUlJ31rWuzd0znf6obusEkIjV3pgKgWd9d7QNiXkvSX7xOVOS40kmZNSo14nI7dckjyMbzRYG+cYe4JqRQCiUQwrz8SjI8w64xzz8HBoK3pVRCio2FlTk4Uau0OFf59eidWuvf11pfrLW+UGv9vwqf/0Zr/U6t9XmF/4+UW9dJBfQTJfqVUu8FMKi1/u8KtmGDDWe+6cTKubp22c4P+Hp/vjR4ur1JdOZlbN20OdA4IkyXK+1wwig9yI+zRja5Tj5ofe1Jn1dRNezdhO60eGReHq9cei7iPTkf0EnKo6ErbUHSZlY6ndfdgYDAH1AewPuOMw7A8+K5LhmQc6ftfDBZQKtxR856tOy1CfjpF/6Gs5LTHu+22aI8Bq4TKK5LJ5jL+ihcNtpdj6u/u963r5N9z+Ovzr0Uk31G9qhHx4wGvTB7WbzhaR/tQtvXkQSyEd8sigoTUis1Rz0PuL672gL8yLw89OgYWtelfTEQ6v316Bjic47Z467vDWag0lztdkOXWeddewtqIwJeAXQPrjD32sEVfmAPA9zTF2a87WSivgbQNAJ+VSYa0JW7A6tMvqrpry5JtdDczGJ3G+5MY6pNA8jnVdnXKZcp6hD9ldhlAK5WSvUB+AaAK5RS/3xSds6xrpFa/DrTYN/PfP9BzH+kygKTHh3DbYsvx7c/fnfghpNcOuDncmenc6EV6FyZ3+y08cQXbNqNbDxvKZ2qTNRXdGjh1v2IZSIWsKWXLgNhTPvOLIr6dLnurILmetJ97UkfALIgk3y4uB1SORJE4z25QIEp6so5oPW1m+AnJZIH2/Jo3JGzoEJgdIPCjDew/d3w2f7zXUw9wevA4yAFc/6dfYg2mo5G39q33febqmbjwcd++gwad0z61uEqcGiT8ZyvDgvBN5o1XnjNoOm3ydgFB1Ye7/isQkGpQTPb4DWe+I8zMD4rfMCdrPcDGcGYfP5kfWHgLID5vK6I5Zul91rKk61/8HRbnlaapMnseRusto4IAb5U27tKM0XldQ4bTKwVjlOeiylrEq0rpFxOhSbRJYj+sqa1/pTWuklr3QzgegCPa63/58naV9qVCy7xeeav/Os87P2HJThw3aTlwQHj4a1av84CtmzrJoOHfECll+zWZQE8j5pa78YdOZvRR+B0wWz/qoV2/eOzvAbDBFRZi9vdnlunW3ro9LwPJ7xgqcxYrRmMmsBsYcruFu1yzSpgLrkAezcuxb6OpA/kxmcFFRc1/dXWW+SxDyzzQE2Pjvn49voXjR6+8YGUT5Ej1T4u505zQf/RS7cGjsHs6Lj9s27bTgumoysu9nnIBGPrkWeiPoDe12FmYj2fXGK9cmrLXZkjwV82ke5rT2L+/bttcJUzMxn4PNiWt15pVcYM9v1rkz4dvAS1g20ef+5yzKzKWN9rslZdqqi5M2WvhQ1SCmqF+1lpk4li33N2C/iT8dyuSrPTOd8MA/Bqw7Bt3VRZxcW5XkMX/WR66KFEv1JqlVJqFQAopc5SSvUD+CsAdyil+pVSM0/iPgWMgU/2/pQ28/0HMeOZWsx4ptanGWf6epiH7kvHLjygrHLnTiNdKRZBO7MoivNvfQ5zt2sfN13M43Sn3O5Uu9RNzHrddYPmYeLAQ3DgA6bqan3roScl+XieA9eDB4COr/wTWj+zxyo0WjpMFmNLRwrjs8yDT08uFwtvGAF4ANbQlbb7xpiFlKMxUMr4gaQ/pIyPtW5UXS2e29SIDW+5Ft/atx1/3BS8H7LvuBDZd1xo5aL9a5M4dFmVXTfliJNzTDCOoF5z1Ow3rxNVO7kZedv/s77XBH9nHIwErie5b26HiUqSpuAsiXRdfa+Z1bFc7GS9d/+495FsWyfP+8EVJmP00Uu3erz8UT9Ykm7KDdTZrF5ac2fK53EbhU3gtNpMUm7bLY9x2uPdvpIRQ20JmxXsyiHdypHndOmSappXZdpIF8u9Xks7mSqXYkT/Vq311sLfLxU88Zla63jh71ec9fxEa/3ek7WfANDZcxUArwgTCzoRiIuVigW8jE9X501+WIK47CspSwJITTtv+AM3L7GSM8Dz0ijdI1Cpulrs3bjU8tfS25VeqZQxurQIAIwWctBIF3F6zxT73vVL7HGoITMDqO/2kpj4IMtuQdy+TJfnzIPgJIF23rbCLOCoOU7Jv7d0pLCvI+kLBDfuyAWm7/K4+tcaSua0x7ut/NPVIM94ci/62pN2H5/b1Oj7nl2B8kcyVroYHc/bc8VgZv9a01GHGaM0GfdgLkE06wX0WIPFVR3xOMO0133tyUDAjzOxmn5/aj8HQDZQpvHvgyu8lnQDy0wNlqqMX4r4vh+sQ+u6tK+aolzf8I0vY+52bQei+l7vvpXdl9xjWPjwr7Hw4V/bz3huqFgqlqLP+AppOmluMHdgWVWAxjneTNNiVqnK5ZTj0F+vRiWL5MwBPycczfovh5uOH2bkhoEgcLKvZDGrGvZXLnRLgXIZrrtY1UXWVZHBUnkjVw17qgDKASl9pDVtSRVt+SVVM7mYF9QlEElQ72tP2qbQF9xxwHpifGA5LSc1xZkNSw407pi0Xi3XzfPBxBopCaV3TtOjY6GzG/ez1NvuR+pt9+Paj5o66G6LN8Bw6AeuiKFu206c9ni3pTC4PxygqgaN2kRllb0GzOxs7kzhQ+/4hV2nDNqNz8nZBCTAfy+xlZwEZtJX7iA9vCBnB/9iqe8yI1X2GZWDUKb/dDvTaF2Xtsfyvas2m//fdQ+GhmswsMyL2fB4+tcmUTNomlDLxDl53qVskyYH3bAGH27+AkHdDd7K+0G+psqmSuUylfaGBHTWY5F8OW3fV1ux8OFf2wsv0+sBr9+kmzUpvcliTRpcyV0YlTJZD1+GJWcAgD8Ixu1zms+HlooXatK5HX7GZe2+xPKYjOdM0EhwuJP13oNFUGe8wAV5CbRyHVT0cL/33H0e9q9aiGjWS5iKZv2BuaYt3rS8udPQFLJmjlsCWP5P8LIDRUEZQpCV2nT5cDd3pvCtfdvx4bddh+sufDdiz71ot5cfH0PV2Wf5jrelw2TzcjCljLVqGJZSqjlqAJNSRTmz2NeRxLd++hbohlwg2E2Qbekwg+Nw6wSatph6Lt2bE774A5srs1IhS9Laio0w90y8J4fvXbXZl318194nkJuRx6MfvRunL8zgsWs2ITcjj+9dtRnfu2oz4k0vF9WGt65L430/WBf43OXnud9uuVre9/s/cqalW85ecBi51mF7TViATo+OhfbiPbjCn7cwPscMnoeWK9/g7s7gpqowF20qyudOpb2hAJ18eWfPVdYr37txKVo+1m0yEB+pxXkdI0V/747wbi0TSanIrFEG6eSU/3AiarvK80HgzSfBl5QBJWuAkbY17phE445JREdM41wZEHUpD7d+CMGcU1GVVbhgyxEfkMpj5mdSAcMMUEAU8nLqdAxcN4YHVt5ri3KxdRrXyZkBk4uoy2eCCgGIHpjkzJmNyHPsBj7lQCJ5W/f6jbxtMVRdLT7Usty285t88aVQ71yaHNB5vYdbJ9DzySXoa09i66p7kZuRt2AtB/OWjhQWb3jalHaNe4FEfjcZ94KK9d2mtIErWW1dl7alHqQe/nCiEAAsXNueredaQM21DmPBpt1o/k4GAPDYNZsAAC/vj2PlzhvtewB46OIHfUXFXONxfSJxpe9zSaH1r/X22+Xvx5sm0PydDM6MD+HM+JB9Hlmv3HWkAK+peveq0wIqnKpMFNGRCKIjBtJkjkdgPVMF6vo4Xq+RvWEAvWvEPKBfWrcSv8404Mz4kOXM+T8f0v0fOTO0bgktbPomU60J5m7QU/LwpAVYsZCZg3L5bDzvmy7L8qx123ZiYJm/tGpYshPpCZe6YWsywCS67P/Imfa3BAqpFpFUiqSNZqfNjGDgujGjEFkwbEC5AO43/ufHLd0ip89M4Y/35HxTaLeiYzaeR26GkTFGs55ip6ErbZU9Esil6kOPjvkAhslRgHdNczFlG1240/vIGXFEamqRP5Ix7xfMt9+52yRlcd49+43cNOp5ldIzlrM3Vx89vMAAuVSgsNE07Zwubdcp18eKiDKOUjUM1KTMhfpE4kosWvU89OgY+t8d93nYj12zCQ9d/CCu/fqtWLV+HVatX4fbFl+O7s0J3+xBWv/apK3zft76ATsIkRba12Huu3jTyzhwc1CuS5v5yRr7N8scu8sTmIdaTiu6HmkyKN9930W+xh2lgP54rfIGF6eAbPH1YjJ9f/1/XWdLptIjWLzhaUS7630ALQM1bgd5mpswI9Psy3Wudwsm0VxunZ43PSUG20h5MNOPnig5cUk7nHHJAN5/9c99wdWwkr00ql5IbRQNysEDjqG2hFUaSMWBtJ6t51pgpKwy7DyFgbukeJiI1b05YcGaxcRcG11xsU82555vOcASnHiuR1dcjLELm+zfBHMJzrKI2uINT6N19S68sHIh5m/L4+rvrkd0JII7nn3Kdhpi3oAcZGit69JGmSLAuGlLKgDmhy6r8s24DieimHEwEgiqMkYy3Dph1TB6dMwmFdX3RrFmzVpbs+a2xZf7zjnN7WcK+BuhqDoz4LWuS/sGOanU4bLSS29dvQvq+UMAgF//30bbe5T7SsoFMPy+5Pjd5CdpdHCG2hJGjtl1EiFOv0ETi35bRq98/X9dF/gu2l1veypKymT/R860DZ6lVcqTM1goa6qErQswIHPa491o2pIK8MJh1teetFl0oysu9qbVn1ziadKbJuwD1H3fRTj9+t8g/RZznK6ckQ+YpFroHUoJI2cbXF7WapG2Zs3agBqo5WPdVscuuex4Tw5v2j2JaFYHAs802ZFIZqXGe3I+eZ3Lk9LTfuFabQdBBm1lLRgpfZQWqan1Tfnrtu1EvvcAAMNJy6QlOSviwDKwrAqn7zWP1k0PrTHqjZGI3U/SEARxCfD0yJu2pHxget1bfokXrtWY+7NJn1pJngNSWMOtE4H7c88XWtG/Nonr773Vnkf2ASWoSxtqS+D8T3UX9dBLJQdxxtncmUKm//TAefr2x+9G930X4UvpH+Kux/4RWDCMmsEo9nyhFTWDpktSLqZ8nrSbFSoDuq7JgfvgCgBZU2qZvWxJ3bx6U4Cu4PUa2ikH6PTIL9v5Aaz/r+t8gc+Wj3Wj8ZHa0Oa4gAfSrpTQ/R7w1AfuFF6qEqi+AEo3wJBt6IBwvTm55FzM7F/dtp1W5xvNwhZnOudRhdlpI2es6a9G7/ol6F+btHVXyD0D5qGc12XqirgerKxcSG5eFuDi72UA6jdLvOBltLsec/+5Bnc8+xQWPnjQ6IWzXqOMzKIoDqyI2N9lFnmFuvhq7kxh7nbty+bk9lkrvWrYm/6753ReV8Q3CFH1w+xUebxhaiG3vV++9wB61y+xHL6kniQl1dyZwugcz0tl8bQ7nn0K3ZsTFohIh7H0MTNDAb9nrLIKj6SWQQ1F7Tlu+8jPfQlHLOoFeIlZ9d3VQDaCqozJSG3akvIpUM7p0pi3LRj4H7huzGq/ZSJY/1rz9/CCXIA+c437xg5KAOz9e+1Tq1BVn8W1T63CbVf/Ceb+c41dlv/zHhtqS1gwl166bMknjc/Qwba8dXhIU7GUcv+746G/PV6rRIOupzn0E7eX8+ZwZNATABofqQ1wZ7PTORy6ZiIQMAwDXLdqnyy+VaxuiHwvNecu1XHg5iX2M7fuCNdNvpvFm/rak1bdIq25M4UXrtU42GZ04ywrOj7L356NCpb+tUlkFnm1QEodixsLkDI0fsZCWaSABpZVofOKa8xyhUAcO6/LwXFkXr4Q6PVqu8iywUNtCatBp5xUXsumLSbzUhoHW+lJcpbB2Aa/u+7Cd9tl5FTfpUb4nTszae70Wr3J7THAy3vsxm+uscoR974DYLzveM56xfTgAaP9l9TTt376Fuupj6642HdOmztTPoXKZNxf66dpSwrz79+NF9qUrysUYFQ6594X8RUro0nOf88XWn19b12T26sZjGK4dcLev42P1GJy2IDrvk9V49D/HLdZqFKtwxiOO9M9nPCfC+rX2Z2LxoEkLMA6ZfY6CogCpxigv3TgDHz59xKYM2MIi1Y9b4FcWmaRSRceuG4MM56pDUxNefOw6QMQbPDA34TRLpP14aqA5k6vW4o7XeXD1NyZwnjThNV101xtOGC4f7eErjRXkyslXpLCIMhJT5aelFwvVTnS+Bsm8ADB5gHP3RmHzryMfdfHA8cTy0SsJJHqBHms3Ad+RkkkMxIZVHY9RD06ZpctFgA7kRRwzoqkzl3SLws27Q7VTdOiWTOAL/ribsy/fzeaO1NYsGm3HdAOXVbllQoQjR5mHDTqDdIsbElHL5jnUB4TuWhKG2kcLPTomJ0pUB4pa7UDntyQg0oxGaNUU+3rSPquCQEa8NQ4fMai3fWYPFqLyaO1eKFN+VrXuaVxDyei0A3+e5ogHrYv9M7nbfMCodzuN9bcHXocx20a0HlV9vVa2ikF6JFXTGGlsRUvh34/cN0YxpPmKYh21xflwWnS++CDzBtNetpSOhdmkpaR4Mkpt1RqyCCOvKnlb8KaStBaV++yda5r+r2O55IakfVkwmYfVJRIxYhrEihdj5Xe8gMr77WfkQ7gukijEJyYIRhWupdZt+yvKb13pvez6QVrird0eDOcYvEJDohUufSvTWLGk3tLgjIDyryOzFZk7RvKFmenTV3wRz96t73GLD7W88kl0KNj6N6cwJ4vtOJr7/s76IacKX9QANXW1bssRTQ+y1AM8+/fbc+Py/3LGMO+jqSP1nGvn+yK1NJhaJjvvese+310PIeFm7S9bk1bUvacENRb16UDA6m811jPhfLCqsFqREcidqBlNqukWeT/YVbTX43DiWjJssW0sPIB8+/fXVawcDymKy7O9dqZ0q91sYGTaLXz5utzV/1VgDaR4CuTdsKM03RVV2vT3fmZ6+1RrfGNNXfbYl00dx9kI2QXWLhuKiAkYMn1Se7aBTzZh5OAcjjhpUZHs/40c84+5DG6enqZmORmftJLlWV0AU+d4YIipWgcvOQ+ynPA7bvt72QhMh4njbU9WjpMnW/q3Y9d0RqoTe/SWvs6klj0xd3Qo2MYXXExZjy5F4ABXW5jdMXFiI7nEfvpMz6A5LG7AUqeG1klslQQUcoDqbbJj49ZLTyPSY+OGW96Qc56zYBXSVGeHwKxa7y/+D/5cbl92oGbl1iahUBOz19llU9d454DeuXzthlPn005pEKLpqpziPX675ew/qSA8fJZtjjMmju9Y6IaZt42c09tXXWvlZNefM7B/9Za/0HoSiowpdTy09592U/O+Gj5qiQv/s3/xnjfwaLIrpSaD+BrAM6C6f9wv9Z6i1KqA8CfAqDsrl1r/YNS2zqlPPSaF0dC5XASiMt55Vye3jmBLgzMyRuvWr8OQPG2bIB5qA+25QOgT3Cj98507WIZbhJcyZFznTVHTaJU1bC5kQnQrB2isgrjszyNtmukMWSnIXcg47YlBcPjYEYqAHw69QRU/HT0rl9ityXBnOtwj+lwImrXIY+N141ZpnJ/ZI318299zoLiaY93Fy2zMDttCnZxkO1fm0Qu5n/muE9123baAGlzZwqjKy4OzFCk3LChK4279j6ByTkTVpddzEZXXAzE8ui+7yI7ANzx7FO+xKaqQRPctslGvUGg4zmTXY7CePqao/46Oq5aRI+OoeeTS3xgTiOY1wwaDnt4QbCWDs+RrLEui4hRiTOvK4KqwWrM64rgQxelKi6gFctEioI5EFJ4bJtXQG7V1jVW5TM1po7jVdImAdyqtb4AwFsArFFKkR7YpLVeWniVBHPgFAP03Okz8I01d4eCdrGEAtdb5oN5PDUfXKVLWPErmkxKIohn4/lAoJE9PWlhmXbSGBSbcTAS+Jzp+jMORmxQlWVoSRfQZK9RBiFlcNetJcLBk/u1ryOJyNlegasFm3bbQCQAXxyB50GCOXXys9PBcrLxHk/V0n3fRXa57vsuQkuHUdwcu6IV+SMZn6fJhCI5C2noSiM3I4+BZaYejik5oO1AXnPUA30JErKEq8vzckC7a+8TuPbrt6KqPhsIXvP87uswyiMG7M6/9Tks+uJu9K5fgs9dcCnuePYpe15duorWtMXfwUmWReD3bmC3aUsq0EOU98Toiosx8O1mNFx4BE1bUpi3zRs8JCgDwRiNLddLZyYbQc2gKWvQui5tYyTRrKf+qjlq7h82D5f39FTVLG/oSvuKpQGYOlDXMP50uVe51Wj9otY6Vfj7GEwf5XknskunFKADsN7yiVrYYFAs2s7vaEyJd/l2/u/eWPwNYCRZVLP0tSetRyb5QvlA08Nx9zHek8PIPPMbBrVY62N4gQF9yb+GlWoF/HQLTVJX0nsmJcUZRvvj30XnFddAZ0wsY8Gm3YEBTnLg8lyEKUiaO71m1fy+dfUuLyOwUHlQ1sbhzMBV45QLhnL2IgdlOeNq6EpbuSWPTX5/194ncPV315vz2hs+PWBKPOWT9d3VlpIiFUZQdwc2AnjTFm/mJIPqQEHLPsvvidtm1psTtrQu1UTjs/wATe24vN/DGl7U93qNVUjJMCvZBWQZvJS1dibrDR05rysS2tWo1OymnB1cAVsLHjDU6Pz7dwda/L0qq1CHLltlFl43h61OKdUM4PcBsF3nXyildiql/kkpNSvsN9JOOUA/XiuV0CMzJWUQUf4urASApCLk8rKOiwv27OAO+G/k+dvytkofPVJy5qynDXi1TKQyoeeTS6ye22zEpEDLgCctLJMS8Lxl+V7+Rq6nuTOFhQ8eRDw6Bp152craDty8JFAfhgMCg7xyMATMQ0/PVHrbDCzKtm8sUCXX786wZAzADj4jEax7//d8y8ltNe6YDD1XgAHJ2ekc9OiYVawAwLVfvxWLNzxtl5txMGLVH4CfZ5ZUzWS9F2dgwPVzF1yKO+/a6uPg3ZkaENy/XMxTgLA2DAd/VkyUAzn/fqFNIdN/OuZt88ph0FOXWZn0pOmV52Jm36uGYZuejM/J2dkWLZaJ2OsiKSrSXlTEZOP5igKfYcb69qSRmD0tlS3FKpQet1WoQzdldr1WmYXX/e7qlFINAP4FwLpCGfG/A9ACYCmAFwGUleeccoB+ooV3igE7k0gAfwNlaQRomVzkrjdsWzR5o7ugOrCsKpQ3lIkVrmfKkgGAVz8dgM8Lkt6dPCYJqgQcSVVQUjk+y6NupFyPXrl8aCbrvT6WciYRRt2wWBngARVliM2dJplovGkitO2b7LYUln3KIKikIW6Y2QPAALSs6tjcmcKBFREs3LrffkagZe1vetg81r0bl2LRF3fb8+3OfvZ1JL269Jmol94vygXL7lCAyTR9YOW9dr9dOal7XzCvwK1/Tu6cNJr8XV970tO8x0ychyUcujcngin2oha7bFICmJlTmBbczaiVx2D3sdBVKJaJ2Hu+FF8uTa7re1dtxmQ8Zyij7uqpkymG2RQV51JKVcOA+f/RWn8HALTWA4W+zHkA/wDgzeXWc8oBurTjBXcJXBJ4mP3JtH55w0rAd+V/EuRdK1YnRd6YxaabmUXeTQ948sewKSq17a3r0ja5yE2ln6z3yvC6um7ysmHHkWsdxtZVRppIUPt06glsWH69b/vN38ngg1+51e4fM0y5HWr3uS9y+6MrLoaqq7UD0+FEFOc8qqxXR3Am/SLPQeTsRlOqdt92ZON57N24FKmVm5BauQk7/sT8z6SisE46bplgzoIGlhmtuMvxA0D3Z8/Htz9+d2hAkgBfNeyvOU45pkwms+c4Bnw+eTkWfXE3Fn1xt80KZlKYNHeb3ZtN8lh9r0mnrzkKOzDKGZlMfqKckINNIL1e6ONrBk1GLykkOhHjTRO4YMsRu5y8P/mMSWUOs4Qbd0wW9c7Dnhd64/L9vfduAWBmIplFUXxjzd1Ys2YtNiy/HhuWXz913jlQGd1SgWxRKaUA/COAZ7XW/3/x+dlisWsBPOP+1rVTGtArqarmUh+AB9K8gYrJ3qRHLtPpXXMBUQK9m4Hpek7yO5rcH+5n2HapzQbMAxvNwtaLllaKp7Q8dcGYvt/SkcKiVc/jpofW+B4SZobSVF0t+j4QL3pepKcZpu6p27YTFz+R8QH1ocuqcDjh8be5GAKe98CyKnzzyUcAAMmH1iP9x1uQ/uMteMf//RgS/7IWy/5xPQDPIy5VfpfGGUNLR8qCq7TFG55GLBOxQTdKZH1yvlkeDSBVJI07cj6qjue8pcNw+nu+0IpPp54w5W9DBn15rxPECeRyOwyA0mS/U5qsl+/OMhjslNsG/MqbC76UwbOfiNv3Jk9gEqqu1lc4TjZyAeCbIRHUvazjoHMkl+9rT+Lee7fgff9+iy00tnXVvbj267eevExRDah8Ba/ya7oMwEoAVyilni68rgKwUSm1q9DG83IA68ut6JQD9HIg7nrtxbhwIFicCvBoF+mZA+bmdNctKRrp+RPImOzj0j2s4icfGHfgIe/sJh/JsrjcFpOV3GQUl9+UgU5ppGXYyIHesgw6AqbcbNj6XaCUFSzdZYpNse/a+4RNpaexDgoA26OT7fgAA+S0Zf+4Hol/WYuX98ftuXg+l/fonR9lsK/D9IvlQNG7fomtEOnWLuldv8S3L3KmMD7LA8hDyxWy8bwFUu4v4Ck56G3LOkDSqKv+3AWX2trybAzOfT2ciNrt8mV7lYoAJUFUUlvcJwnqDCbaDkxC3kiQpZZdBuJlieHhBaaMweFEFIcuq0Lv+iW+2jeyWBsdBc6KdEwjF4O9x8JmUNyXhVv3B7x14sCjH70bfe1JtD/+XVvvfkptCjx0rfWTWmtVaNdpJYpa65Va64sKn1+ttX6x3LpOOUAvZzIwKf+XxpuuWFkAaaRhKtW6y/os0ijZI9AD4Xp0Gh9M6c25VIYE/Mn6cFqh+76LsHfjUmTjeZ8nKQGWagQm/BTj+7Pnne1739yZsh6w3Cd34AtLlJLvux5+Kz6RuBKZRVHbbxQwADA+y0vckRRJLgY8+GHTZEIeN71owBSJou27Po6WjpTV4Td3pqxEkib3iQMbjZ4slT78jtQYzy29c9Yu39fhlf7lveRKQSXtcfttq3DHs0/ZBDJSVBxcpCRR/o7BUMnD89iatpjiYXKwadqSwnhyGA0XHkG86WVggUeC7/lCq01E4j7OOOiVInhlyRmo7662XnsuZvaLThO369byj2bN//I6lgt2A6aL2MCyKqxZszZUSdbcmcLnLrg08PmU2BRx6FNlbzhAp8kMUJfrljeNlMq5kj53MKAXJ38jy+hKIJMJSzJ5Rk67geIeK9chywm4gOMeL+B/WKqGTTr13O0auiHn0wG725WZgEy/dgeb6p29gX2UYN3XbjxRdzCTswrAO0/8XdOWlPXuZAEqAqf8bSwTsc01bvjen9uytc2dKcw4GMHoiovR3OlXedy19wn7np4rtfNNW1K+wKcEeNa8oTcrA8xhXDjPd9VgtallPyt4rnmv7OtI2oCiK/n7fPJy3PHsU9YB4LYm455HTlBn8Lmv3fOIuT4Ohn3tSRu/oPE4h4ZrMDRcY9cfbzJB7+7NCV/SFKWSz33sTBt7ok3GTdbnwRXe8bFAmlToUME03jSBedv8x80qnbJeu3QYaJzJAUA8OubTnHPmO2X2OgNz4A0M6ARUKQXk5+4yrixRSq4A7ybVo2OWTmAWqVyvTNKR8jw3UEneOizY6tIsYR6LbCIdZmzxJgG6vrs6oAN2tyHjBCxp6gbi2h//Lp77My8nQlIqfGCZlMMgJtUiso4Lt6nqagNJQkA4tSZ5dqqApBrIaJ6rbJLZg2/+CvZ9tRVXf9ejZt60e9KX6EQaSe4frXFHDo9+1FNQSEDkbEbuEwOSNUdN8FFmdUpngSBHD7e5M2X/5r59Pnk55t+/21d0qnVd2t6f1JnzXFHKSBCW0kXORNzj+0CrQyHOGsPQM2egdV0aVRnPkZBlbdlT1W2UMT7HqE7kwMGaOKSLSLvUd1f7Avh7Ny41VRoLNYZ4TtykLcmp33nXVlx/762hCpep6lgErYB8Ba/X0N6wgB4GhK7KxfXUZbo/g11SCVPuRpH8O9fJ/wngTKlXdbWBpBsuWyqIyf0rlvKei5kHQyZA8Tf8vXywOYORD6Kb0GLXnckEtiepm5qjfo+dEjq5r9y2PPZjV7Ta9cj2eNxfed73dSRtQs/iDU/7NOELNu3Ggk27bbXGG7+5Bg+++StYvOFpO2gwgEYgfuXSc0PP92Q9sHXTZnzwK7f6ShU0bTFUjSvLJKXB4x2f5c+MdAdfAj2loZP1HpcuW7Txdz4FTb13LljOQTby9qX+i2td/XajTCHd8p3u4P3ccOERX/B5fE7OV/nx2BVeRimTm2oGjRySfWOZc+AeLzXp8R5TqIxKsVjGK+pFZ2SoLRFQITV3ppDJ1eLOu7bixm+usb9RdV7V1WIF9E7EFACly79eS3vDAjotzPsNA/Mw/s59X8wjlg83BwP+XnqfYby+5NRprGVeqkyB9O758MjgEpNz3BT2sGNo7kz5HgieozDZ3v9a+Ps+OmNfR9Lnpcopr6weKAdKPrCz0znbUIJ6d2nuw0mgSa3cFNiWjyqZkTdc8ow8bvzmGlv7RRrPMddDsG7akrKzDUoxwySKDV1pqLpaS8VwQGSf2GjW0C8MJgL+jFvAKydAr1t2D+pdv8RHN0iQ5XkJK/ImG2jwOk3WmwDm0DNnhCZRSWMWKQcc8vRNW0yOgHx2LLXTNGEqgBZ+k1nk1evhNZXtGyUFSSeIv6UzAphzmYuZDkhzn/T4GdnPNZOr9Q0ywHST6N8pcwHqeKwUsIV9x5tXVgcstk73weJUkzebpGoIZNLzdPdHbi9MAskgq7ss4A/uSnrDcrEOlyoDmm5JgGJp1GF9M2nkfgmicpAAvOm63E8pcZP8K683B8bGHTkkH1qPaOOoBS/uC891LGNq2jBBCPDiH+TYG/YdCzStkMBN6sLV7PevTWL+/butEkaqOly5oQzk8r3MKC02EwsLui/64m77W9IpDKpzQOL5qDnqT9vndQ8bAAIWy/sGbM66OBugA0KPW4K6DKxLk/ft+CyveBeBn9eeKiD+Xs6+7nngf+OuvU/g88nLsWH59Tjv7w+ipcMLhrJA16lupxSg5wrOLm8QdyS+7K9/6f7Et5wEVNdzpxHE3azQUlM5/kZyh3KdUmbofuZy/BxA6NG6mnkJjC7o8wEmIJIzDwvaSuPxuok7LR0pjLxtsa/LD40PPf9nIo4Ex7DAK8FAenE8DimTJP8KGKDqXb/Erzr5mCmBMNSWQM1RLzjNdXAw2LtxqS3tShv4djP2faoawze+bEsEu4WwaLmYxwtXDZvuSxyoeF24/33tScQyEbueMBVTzVEPeOXxAd5gw/vJvefYOIPHTW6cXj5rrVPyOFlvVDdy4LQxiIE65AbqsGjV81i06nlvI4UsUdZ3l1Ue3XXI7FFkI75AMK13/RKc9ni3BWquZ2BZ1GYj0+TstqErjclLLrAxjEyuFpmcmWm9kjwL+RcHfAX5KN11FWmvyjSg8qrsqyIl+hTZKQXo+UJMr1jgjFXd5DLucmG0h0vBSAuT+hH8wqgacuRyO2EJJbLPqEsDyWxV3uCHE1EfuMuHXQILQZPJUJI/d+u0yEGD30v+mwNEscQNer39a5M47579dh3yoWYQUHK/bmVGaX3tSR+Yu/ssrxsHE1edxN/t3bgUizc8bSsBqrpavPJnr2BouAaLVj2Ps1a+hH0dXmlj9i6lJ0yJIj3gyXpPRVRsgG/uTNk6NDIYKY9DBv54HG/a7ddZSxqtaYuZ7ai6Wuz5gukD6ta0BwpZoAX5oexZSpOftX5mD1o/s8f3/eRRjx5kcFcOzJKbl7SiLD1RTE9OJ4Pnrdhycp2bH7rPnItcLVZtXeNLagu7d+ilT2WTi9cT3QKcYoAOwDZEPlGT3rf0dsNoDcDwdzTSKJIDprmUSZiCRS4btnwpKkkCs6sX5zq5DMGHAwMBnusJ2yeZtl9uWk4gZ4Pqcx7y6qG4kkEgGFyld+7OCORgIo9bPvzcd5k5ygCwTATiOiU19NJDZ+Hl/XG0fMw0SJaUEL3cL6V/aEGc6fpAcLDg/nPb9GS777vIptcDnqdMLl4GWLmuvvYkDl1WhbptO31qG+rKeQzP/s25qO816haWZOA67ABTCBhXZcx+yX2RFRXHly1C92fPR8/Wc9Gz9VwL5pLHl+eSPLc8n7LsL8sEyH2iXNWt+0OjLp0Zt7xX3edg1dY1AIBXkmf5PndplnldETR0pW0uwJTYNKCfPKs+VvmyYYk9/D8M1NwUeLdQV/XbjwTkghIogdJNmMP2q9jyhxNR23QZ8KtmqLoJo2Bo0quXXrpUj/DBlJp6mQ3JdUjvPFJTi0iNF2AkCD53y0I8d8tCe3xhGaRuXW9uj9NmyUFLfrmhK2314H3tSdsZqqUjZZsGT9bDNrOgykPV1SI64nmMenQMZ618CdGRiF0PA6fjs4y++dsfvxu3Lb7cNzDY5B/HI6dHLy0XM4FQeaxymXhPznecct3UlZPmaNqSsnI+mRA0vMDI/D6fvDxQ2ZEWzZr1MamJpQEautJ2FkJrqB/HmfEh+56zi30dycD6SZnIAVYmulGWyIqMPH46Qny+xmd5ahaeF2mz00Zk8MGv3Irr770Vb9o9GVBgAf52jtKKfX68VonC5XWnclFK/UUldXhDflerlPpPpVRaKfUrpdRnQ5Y5Xyn1lFJqXCl1m/h8vlLqCaXUs4Xfrq10u5V0JHJN0hnFlC5uo1maTFz4xpq7Q7l3d/2leG96pTTpOfN7ABhqOe24jpm/L5ZeLpc7nIjaHp+0fR2GXmDDB2ZU8jvX3OqAfNC5bZcfBfyzAJqs4UE9NWu3uLOFHX+yCY07cji0XOFb+7bb9dCLlwObHh3Doi/u9ilnej65BI9+9G7f9knHXPv1W3Hb4svteQCCdU7oqbrgTnqKAURppJ/615pBhBy47OdKj5Kp9oBXTE0mJ6msgsoqe3zNnaYJteztapOBCsFN2YKO53LP3eeh772epltaVX0WA99uxmTclLqVxdzCcjaYHMX7oHFHzgeoMq4iLZo1135kXt7q3OUz0L82aY+rbtvOwDk/2Jb3XqJaZBjVdMKmUVmDi9eZyuUsADuUUo8opd5TqAxWiY0DuEJrnYCp5/sepdRbnGWOALgFwF3O56VaMr0qCwv8SWDl+zBKpFTqf/2Dp+ODX7k14Ek3bQlW0KtEESPlXHIQKNV1qZR8EfCOXXrw1NLTk5XrkgoYwE+XEFDDGiqrulq0dHjp5AQRPrjuw0fqgN67TA1nYlCxc1Y17MkUl/3j+tCZDxs5S9vXkbTSPx7Toi/uxvv+/RZ033eRLTHAoBsHM4I5rw9liMwodfdVBiClZy719y6gdW9O+DTiTGdv6TBeef/apOXqpTdsPxvxyi33rl+Czycvx4JNu7F11b22ExKNHYXoeQ+3Tpikp8JAluk/HS/2zkbVrDFUzRqzn5kN5n3PRNi5rxk0DaOZ+i9pE1Ii3fddZAdnnotsPI/cjDzmbteYcTBi6Ro+A5P1hmph6QNJMQFmJlTTX43W1btsPXf2F51K+53z0LXWdwA4D6a848cBPKeU6lRKtZT5ndZac65WXXhpZ5lBrfUOABPO51PWkqlSKxX4JBVRDFSKWZjaxuXkpbcuA0huSjN/4z5A0qOnjpnJJGx2wf3gg0CtL6euMn08bJuz06b0KtUsLg8upX/7Orw0eVnrm1wwwc3VqgOeXFEmkcggI2cOrrEIlyxH8KGW5fZ3gKceae5MWUqGx8zj0qNjvlog1Jq7xyy9eqpWCEYSpOM9OTsQuCZBnOeFqiHuL6kiet+AaXjiGvXtbKDs9jE9cPMSHLuiFau2rsHnLrgUezcutcdpaZ3BalOeYLAa47MMJaNjQTQil86CXzYz1cmalsbEIkupNE2Y9P4uIx9tXb3LJ7GMZs15nbtd4+AK+LT6NJm/8O2P3215dN7zrkkgnzIJ4xSVz51Kq4hM0lprAC8VXpMAZgH4tlJqY6nfKaWiSqmnAQwC2Ka1DtcNll5HM/wtmdzvbWunifGhsEV8VixgWgzQaJJrdm/asABmsYQdd1oqZViufJK/42AyO53D/Pt3W0/HTbQplUEql5E1zqVHHKbMkJmiL1wbfMDlwEMaQ5pbj4PrJFdMOoQ8cC5mvPK79j7h+y1lctIjd2uSR0ciGGpL4PSFGftZvCdnmmI4dVPI2zZ3pnxxgO77LrI8N2cH8phcT5+/keum8T4h1eLOZsJmLHXbdvoCj7kZBqhnHIxg78aleOFabQHusWvMeeD31GW7NX3GZ5n7i8Hpx67ZZJtdVA0b+obt6PgCgHjTy6Z2SyyPyaO1yA3UmXUWkonmdUVsViyN16nYuQKM90zaZbh1wtIgbi4GADuQScqsoSttU/q3btqM6++9FZP1BszdJCL3mhxOmHr6U2a/a0FRpdQtSqn/BrARwM8AXKS1/nMA/wPAH5f6baHbxlIATQDerJS68Hh2LqQlU9g2bGun6pqGouuasuwwx0r1GZ2dNpmgbs0JwAPKYolH0uQN3rt+if0N+45ykHIHK2ZZuvvlmixh4Jb8la3e6NWRH6fSg9t1y+fS2K6NQUkXVAEDOrKS4rVfv9U2zwb8SSqX3mN6dqZWbvLVJpdev3su+L0svUBA5bKqrja0Wh+/o7FfKY+D/U3lOmlS4XLg5iW2YQcAy6sD/mAj6RhZErZpSwqLNzyN1tW7EMtEkI3n8b4frPOBphyYGfw1za/N+p/93HwAwCcSV9pBanhBzg4E/H335gSqMlFk+k9Hpv90xOccQ31vFKcvzFjahUb1CaWngLmOA8uiAapNlj/mbIznW/Y0dZ9Vyid99GeuFt9YY4LUbpMV19z7XtZ8eVWmK6yH/jrj0GcD+IDW+t1a629prScAoNAW6b2VbERrnQHwEwDvqXTHwloyVWJTAdyyDroERPle0i+lkpB61y9B62f2BLxpLuvODEgtuHQJ6RXXg3G3JysvykBoGKUk1zE7nbMPGWchhxNRqKFowHNntyCpNAnj0gnwMuWb2xyf5dWNoZfOAFksY9Qnizc87bueBEd626RVpDV0pfHy/rgv67ShK207Fsla5VRYNHSlA9xyX3vSVmrksUnqRP4tB2M3EYbGDNIZT+61sxoqQ2gE9VzMeN1yRrSvI4nuzQnrzcYyEbSuS6OlwwB99duPoPrtRzDw7WYMtxpKg4lE2XjegHJ3tb0mn049Yeul7924FHs3LsXIvLxdf0uH6VYUb3oZQ8+c4ZP6feA9P8fsdM5SF+NNE6EOiRxUa/pNwS1Wx6TH3X3fRb4uSLSGrrS9HwnUnMX2tSdtJcX+tUl8Pnl5YCB17/XDiSjmbfMPHK/WpqqWSzERiFLqDKXUNqXUc4X/y4pTKuHQ/0Zr/XyR754tsZNnKqXihb/rALwLwJ5iyzu/VQhpyVSJvRoNumw1BwDjyWFfyVZpEuQIGhIg6ZXOTufQ80k/Hx6WXCQDnmHcO719yY+H6cyptFiwabf1uul5Ax7gyFkEB4toNpjKLwsgcf8augyQ0MPcu3Ep9OiYffC+tW97wFunF0eqSNYeb+hK+86n2z1IGjsvySJTenTMN2C61MO+DgPOsUwEk3MmrKcuMzg/n7zcznjkNaDp0TEfHeF6n241RrltGReRhbX4+/FZXhCP24hlIlBD5hrNOGj2mxUXq4YNSEsQHU2dgYn/MC9WziStsXjD06h/8HQ0bUnZhh2fT16O8z/Vjfn374aOaV8afes6oyJpuPAIMv2nmybT9VlbRhfw7s3MIlOpk4lrgBf8begywVY3uC7NKl4KvUylzBEwNFpY2QRp8pxyv8J48sOJqA3STolVQrdU5p0XE4HcDuDHWuvzAPy48L6knUwd+tkAnii0T9oBw6F/Xym1Sim1CgCUUmcppfoB/BWAO5RS/UqpmSjekumErRzQy56htMZHau13gN+bkhUX6YFKCoUeyMCyqI+DlZ4yQVdmOMZ7gtmikmKhfDIsaMrfxHtyPi6RwO6aVA7wIdKjY76GGVLB4dJL9DAJBqQiPtSyHPkjGexftdB61Qx4ubwq38tOOlS9AF4Cz2Q9bAo+9002laCxsp4sUCWNAEKul9fCTdfnoAUAe25v9q2DtUvIQwP+0sKkrRjE5Tl0jXJGGcSUunTy4vwt9eK8D0mtkAuXzaYBD9xUXW0o3daz9VxDM60zM6zTF2asfLH67Ucw9MwZdlmZJSqrMHJbMmmIg660w4mondnRfPdCNoLxOeFJbe55uePZp6xceOuqe63SBTD3SKmg51R66ACmBNBLiEDeD+CrhcW+CuCacus6aYCutd6ptf79QvukC7XW/6vw+Vat9dbC3y9prZu01jO11vHC368Ua8l0svbVNalAkVmVgPe3S2P0rw33QqJZo292u7kDpliUBFOZXOECTKksUcA/YwgLxoYtT8DiMm4NEaC4HI+qBDZ4kL8pVpiLx+7OWPjbmqNme9/at933fWrlJt95k6ny3BYHO8YY5CDSuMPw9y511NKRsgOqa0xm6mtP+srrur8nd8xkH1n3heAO+GuqMCgsj19Wz+xrT/pK6wLw1aqXXX0kRy8bT7vAJY9xz93noWfruf4aLQAaP9hnZxqjqTMCgyGDojw/Nf3Vdr3y/FIW29eeNA09hv2Zzn3tpnlH444CzVfoU0olCgdMuf3hBTnrzFDRAsD3N+ANYmG1/VmTfcqki5UmFmm/eKPwujlslY4IpJFt5wr/zym3S6dUpuiJWlhg0y1YVCyDFAg2bXb56sYdxT0PmkuJkF6R3Lm0xh2TAbCWKhn5P1BcZSOXm/uzSXu85cqoyqQeN129pSMV4Jv3dSStF+dW6wO8mcVQW8Jy41zHh1qWI7MoavnTMPlomKKCgw5pKsmdA16xK5k+L9VJ5PSH2hLQo2PIj3tdi1iZsPu+i2zdb66XA4Fs8cYZhjQCP73r8VlBxQjPw/gc0/WnvtffDUseD2CAz/X2WYJB1t8hmJOCoQNRlzxiB52+9qRV3FRlTFA0N1CHyaO1NkO3rz1pHRyqp2TBOu6LTO6hHVzhdTOS0luCOTN1gWDJAZ5TdjiS17wYmBfbj1dlFXroUrxReN3vrqoSEUg5e8MDOnlcF5QBc9O4VIUEUXnzynR6N9hYzCTI0qshFyzT58MCTocuqwrIH8P6pcp9kfSNW3VuwabdPvmerAVOG5/jqVRkOjdpEbeSngRZqUaRKheet0PLlc+Dk91pCOIuIBJY5flkHIGeppyKU33Bcz2wrMqCRzQb7GXauMOk4nOgjNTUBipL1vRX2/0cmZe3AwcBSjZyZs6Aez1ZS7ylI2Vrv9R3V9tl2VpONtCgdW9OYHhBztIxPC8S+O31KwwalO1xpsTBjPsZpthpuPAIAEDHNKoy0cAsTII5lS/uwOOa6ykfbMvbJCQGiHVDzndfcb197V6mKAGc55XP0Ox0LjDTAaZO8TaVqf9FRCADSqmzC9+fDSP/LmmnPKCXSnNn44Swzwl47ETker0EolJp9/QM3VorEsh5c8lEIADWG5QWNjhwfXIbcnorQZ0UQFi5XMnJE0Bd4Jm3zaujwYeGDz0zQmnRLKxMUSbXuBI2ghxb2smYAoFLJq3Ic0CQdxOd3JkD+X/JLTd0pW1wFoAvPV5el6YtKR+I1//3Cxif5VEozZ0pZOMmjX7GQe9x2teRxMg8f+CS5yCsNACDgbJkMH9b3xu1YEztfdWw53VKKSEDp9I42PIa1W3bCT06ZmdH/L9pSwqNH+zz/XZfR9Kus/EOc3xSr85YAfu4WkHBnBxyrcPovu8iO7C7Cq2DKxCqcKnKRFE1aEC6atAMmFIw0PTjUQDATQ+tCaXL3PjS7HTODh5TnSk6FRx6CRHIYwA+Vvj7YwD+tdy6TnlAL1fnRHrWBEFJN9DbcNvGhRlBiMuS86POmzezLPRF2Rzgb98VqTGcraqrtcFTuZ8MunKdLkUSBv6yHjr3Dwg2ZQ77vVTDAN4MIizQKVO4mTxUc9TTzTd3pqyETapV3GqIEtxYa9xNvmroSvu8RRnQlR69THihsQdsX3vS1xXe9eBmPLnX/k2KguAKeEFhfkbNOz+XNAhBuxylVTXsZVRyICIg22JcsTzmbQMmh2MGGGN5DLdOmJfw2GuOerXQpQPDRhw00kZydsY4QeMH+9B9S70PgDm4uHz9wHVemQC3ENbAMi/lH7G8ra8OwKu1XthXDrZygHUtLLYhTW6fYD6V9dCnSOVSTARyJ4AVSqnnAKwovC9ppzygA8W9dFmzuRQ9IiVu0vstVduFg4BbZ4XvqWAhLSBVJq4duHmJzxNx91V6+dyGW9TL3bdiA1OpBCfuA+uuSCNASXXKwDKjceaMxy28BXg9TuUDy/Mhi3FJFYy7v7I7D23vxqW2qJP83PXk5Xlw6+hzsJDXhMdHYHX3hzprKVeU35F37mtP+vTf7nmh7rt19S7fQEQQlYDN5awVgJF0Q1irQJqrwJHLyu0yOa6+u9rGHAi8BO+B68YwcJ3//j20XNlg7cEVBqzpfdttNk34BuXvveseu24qY9xzGZbXIROxAKBh3zE07DuOEqwnYJUkFpUD9WIiEK31b7TW79Ran1f4/0i5/TnlAD1sJHe99HKp++7NQ871cCKK4bOjvs8liErQlpX+pPE9u5q7pW6lsdelrCfurqtYOzn3mMMaThdbZ1hJAQYk6YFyu7lYeNVBWiwTsTMUVVdrwYy1VCbnTAQ6xFM9w8/JUS/e8LTP03aDxXJgJIUj10ngD9Oay4xbvqfRC+Rn1OADBgC777vIps+T9+Y5ycX8lQz5GROoYpmITwPO/SMvLDN1m7Z43nlVxqhDGrrSFsDru00xqprBqKUteL2uS+7AyLy8D7w5UPWvTdpYQkuHl7glr2vrZ/agdfUujMzL20Bl1WC1T1kTZrohZ0sFIJa3WvvWdWl7jIxD0IO+9uu3Bq4B7XAiiv53emqb3vWesyMzQDmYyaqkU50tXimH/lpWcznlAL2S6VRYfeUwk5mhBNv6F71lh8/2Jwi565Oaaa6DXCk9D37G3wy1JWy5AD5w+SMZq3qR1tCVttNm9+Yn3+zSRa6kkfsjeeovpX9o+Xw3iCcbC0sjwLV0pGzMQQLC7HTOx232r/V07aReGJjkZ1LLLI+RSUlSG89Zi2w6EctE7P4PtSWsF8gkLcArLNXcmQp4lwRmJq4ww3F0xcWW045mDZDKBCxJN/EccJAiaLpUh+TXSbcAZt2ufG8ynrOgCPgHDHkdAEO3ZON5PJJahuhIJDTDdXwW8OCH7w2NJ+3rSAZKV3AAYn6F1KjT2MJuXlfE3uuuZ859z8b9PPpk3GuPyIzRb3/8btQezqLpx6OWQy9m7Ekqwfyk2dRQLlNmpxygu5UG+VklvwP8nrZMNHKzNw8noj5wD5MISjWHqziRST3ugNK7fonPa6NsTtXVWm9ZJhtJyoifS96eAV65jMwq5bFyPz74lVtx7IpWH5hLAGLDYQJUX7sJAO7duBRDbQmbOct66ORAWQ2RlQgBrw4KuXaeFwJg4w4/VUM6g8FQN3jrSibda8NrKPuIcl8efPNXkGsd9g16jIHQcjFTQEsGBeV+AUEdvquMkQHiXMzf2Ud60a2rd6Fpi2lZNxnP2RortIFvN1uwldSJVPfQ257XFcFkPGfBVdaqpzVtSfnqhfe1e7LB7s+ebzNuAXOe93zBxHzmbTOgPnm0Fg314wiz8z/lOQh23YXtxzIRe43mbTPLhsaA3lnn885pezcutdUmq4bhK1NQrNXklNjUcehTZqccoAPmwskAYyUXUk7Dyb1KOx45IrcpOwBJIGVQU65bLhMmbSP9EmYuhSSDr7Qwnh0INpLme3ruLncvk4BcTzOWifiOjeDBbbIaolShuJI5afTE5W+kNJMzCxlk5u/cWRM7GbnbkrTZDd/788A5pEXOiFtagunsPIZi+0+jHDRssOE6in3XvTnhCxzSM+++7yL8UdMenL4wY3/Xfd9FGG6dwMg8A26c5egGc4zztnnnjd8R8D+fvNy3DcAMXnQsoiMR5Gbk/fRaIUC6ddNmG6Ctf/B0E6QVJp0rORvheZudzvkaUgDBzlnSOr7yT/ZZoHfPuulNW1JFOxJNeYE+PTUc+lTaKQfoJzoKlwNofu8G0gD4vGYJrtTmumAetj3pdfe1J9H8nYwFjbAaz1IKFqaYOJzwmk9nFkWtmoYPkAvUxYKk8nzygZDNGiS9wf2anc7ZRsUy3TusljmljazpQnNppAWbdttjluoHdsxxrzvPO6Wn1KDbfRmJ+I55X0cS87fl0fKx7kBsorkzheduWYiao6YV3ffedU/gOFhud6gtEUhxlyYpGNkww1XM2M8KoFmVMXVIZAITALy8P263L43XY3Y6Z+uN8/xydiXvgZ5PLjFNNTIekMrYBc+ZVJW0rt6F1nVpvO/fb4EaiuLgCnMN53WZWuaMY+jRMRy7ohWLvrjbzmxysUIrvIIDMa8rgqpBU38mzHHJ5Grt/n4+eTlGV1yMBZt22zgEEMytAPz375R76MC0h/56Nj7EYU0EimVaSokiAabU+t3fE+jdm63vA3H7t2xYTKNHJz1u7qPrjctklLB67tIkDSOPmyYTdyToyuYRA8uitkGDlETKQCfggRh/G+aNycQeyZ3LDFBSOsU8MFZoJNDKa+QWgwo7Zlq8J4fbFl+ONWvWBmSIezcuRSwTsS3zipU05nEAfkUMYBQgDIJavjkbsVw5B7TxWYAaiqLr4bdixsGIpWxaV+9C6+pdFoCl5h/wzjeXl6DN/WHOAEE3bL+l2QSgmLa1y8MKZB1sy2PPF1p9lE5zZwrjTRN2ucYdXgVHSbNFs8EUf856Fmza7auF9Fra72RP0TeCyezJaDb8CrggTAAJy9CkyRtNgr3bzILeuQv2YUBPk0oKlxqRy/B/mWkpE27c/Wb2pk1acZJgZFlfua+NO3KWP2/ckQvIGMmHkxPncvyfy8nMUxblAoK6d9IwcsYkqRmapBjkgJebkbdJQRyIZKaspKIIcDOfeh77OpKhjT4Ao8cmSEtQ39dhSgSEDVi8jryW59/6HAAPPJmVKZuAALBFuzhYSa/dzBI8CR/T9N3EL8ADfQJ4LhbUgVOiSL5+78alvmNh5inrlgMGwOd1RTB848vmPIiBSUpbwwKlpLRkMpGMlfBe6P7s+b5AO0UMJ6v3QahNe+ivHwujP3IxhbptO0NHfLk8k4bk57IxRDFqhUZ1DDli+RvKBmUij7zBJYBL0Ja0kLtdqbShsWhSMWPwUi7j1qaW50YmPPHYZbYof+eW4QVgU9/lvvJvN3lE6tbljME9t1I+CsA2zCCgDLUlMHe7xlBbwqdB53JhwBBW4REwACtrkkgarnFHzgIXByGpuAEMyBHMacy2JZBLOWH35oRvhjTcarz77vsuQjSr7eDE2ijyfMpEJZ+cknVTnCYfuRl5VA0aJU/PJ5fg/Dv7TBPnbzdjXlcEuZhC1SxTPnl0xcW4a+8TiM85huEbXwbg1U/p3pywoC5nKfO6Ih5/no2YyosFlU80azj6b3/cNO9+YOW9duAm1cJjoIjhpFArYTbNob++zAUzBkNlqr/ktuUyQPHmzMVoFH5HyR4HACldbOhK+5QrNBfAuZ/S5DqZUs//mV5Oo9cOAAu37vcFfIFgMSS5TVci53Lycj8PLVfWE5Zeq1wfgVMCj/RapUklTC7meeA8dzKw7O5TLBOx25q7XVt6xN3nudu1L+7A/dy/aiEA45GyHrks7pWLhTfBYFZs2PEMLIv6qA93JiID1Twn+zqSdvBgJycOGOff+hxe+bNXfMcjaRW5f9T0s/m0XIYzCjlz4v8sizw0XONLJpKa+bNWvoTGD/bh7E+IZyOW91Eu43PMTNAOnCL427p6V2BA7WtP4vbbVtn3VLaEJRnRUz/p3vq0h/76MvlAu8koBCCCRDae99UccY3NIWSikOS2CTSsFBfmTbj0CbXWklqQHqkEfgYrF2wy2nTZ6ozlXN1tnPZ4N/avWuhT8QBGasdtknN3AS7ek7M8K7M6pQTxcCKKxRueRl97EnO364D0k0aqJixoCnggx4qNFtSOekFYeqKSfuG5lfsEeCqYYtsD/Dx74w6jfOKx1m3bibnbNbLxvC0QJgFbZpPSpOcuk86sRltU29y7cWlglsLvJNXG/WzuNAFZNsQ4a+VL2NdhrrFMy5claeW56L7vIrR+Zg+iIxGP6hmsxuQc0/VIN+QC+9+9OWH15w3141bZ8sK1Gp9IXGmv2YtfigKxPFRW2SCqO7BZkC8EgM//VLe9d5s7U7ht8eW2WTctG8/7AqIuqPPZ4j0QplybClMVvF5Le8MDOm/ssOxRlwOXgE8glZ1bwtbtZoGSOnGVJVxOgjW/Jx8tk49cPbo7S8jFjHJB1dX6OvPQ+tcmsWDTbl+ddgm0uZjyDRSuSa5yYJkBffLfbsalGwyV3LLcJgNckh+nqbraQFIOE3sOJ6IYb5oIgBXpHabhs9Ba2PniuQfMIMeqhxzU5m/zJHtcR3QkYoGDgCLBau/Gpb4YBD3evRuXIjcjb5NmAHONCYKLNzyNfR1JG2+Q50HeJ/J8EtzGly2yhbc4sIzPydl+rQT6gWVRO7sgxfLgh02BMgJ51WC1aUEYwnOf/6luqKxCbqDOKm1oMoDPYl/nfX0kIFm0RcWcAl0vPXSWPa5icRGpBqrE5m7XJZ/VE7JpHfrryySguqAlPZIwb5zBU3qtbiU/afIhdIOe/L9YfRVJ3bgqFsmVSw+755NLLPgxNZpJSRKki3U9crdPSsWlMOidNu7I+SSa3EcuT806j7GhK+3ThFPeqEfHLMi7wVhJRZBmYADSpaMIki4QyKJk3D+pBBldcTEautL4dOoJu28E6lxMBbw9cvncHr1KCeoyXT83w0ublyaXd7MmOZtiEpKcgfA8y1hB7KfP2N/WHDXUWc2gafjBoG33fRf5Ap997Un0rl/i06KHNcuW11+PjmHxhqdx+sIMTl+YsZ+7cQAAiM85hr031/goI5y/EC1fCJa2VVmFhy5+0IL64UQUuVgw3iNnmWx/GM3qkhQLz1m5gn2VmsJ06v9vzcpxaa7H5tYKJxjJcgDkbF2PVPLC0qTnHSYHLBaglBUEpZfO7+S62OdTgpn8O2ywccGT5mZMhgFkMY/X9bAl/eHa+CxYOSIBhg8d1yGn4CPz/KA31JawfS2ppgH8DTe4XVcF4VacBIDbb1uF+ffvNv02R8d8gzoDqfLcNO7IYc/d59lqkuSSSTeNzDP6dLaU4//ctzCT3ZR4LqnMofWv9WZ7PA43gCyvQc2gAUfWfJHfMwi5r8NTntQcDdHFO3bWypdw1sqXABgwz7ee41tu4NvNeNOX6tC6elegqBfgL2f7vXfdg9MXZnDtU6sw9MwZdtAtpusvpu46tFydNIrFZxpAvoLXdFB06i0sQBkWzHSNDwgBl5H0gWVVPjBo7kz5FCBhQCG3GyaDBPxet0vLuGn4rucvZwqSp+W+sca6+xDwN+5MwNMBa18hLKmYkTMSV0Hjpvm7241mzaDgVkskgDZ3prBw634AZtaRfceFAExw022XR5CXNXKkdLKvPWmbW0iT5/7QZaa4k0zk4rQ/F/MDRTae95XundcVsftPLptJXOR5uT9hlSn5ouqG12LgJs9Dj2Ui9njlfSUpBA4qMtEoG8/brkeAV+ZX5hBUDVZjz93n4cEP32sHGSpnHv3o3bhr7xOhg48eHUPP1nMBmNZ2gDdIdd93ERo/2IeqXz5ryxcQzAc+l4d6/lAAeBs/2IeWj3X7zos8Z4D/uZUDWN22nfa86Zi26pqTFhid1qG/Pk2qTORngEnqOXZFq++h4XdhDSnIAdOoLpGKFtfoYckbr6Er7VOtuNuWCU1SdVI1HJxSchnWWOd+EoDcphfuLIB0g+Sgud/7OpKBWiWHE16DEALpoeXKeq7yWOmZA0EPsK89aZUlLR0pxH76jOWA5TGQ8pDgzO3IlH+XA6fRc6SWnsaOPgzYMvvx0HIV8LA5iDd3mmYdVI2Qiw+77pNzJjBwk1fvhscg4yKZ5IQNlHJwJuDLJiscAHm/EOiy8bylRKoy0UDRrmw8bymheV0R3PjNNfZ8kEO/+rvrA0FJLtPXnsTkcMxH0Ui1i7VsxCQeFZQsjXdE7EBAe98P1pnz/e1m3+fFckPCrKErjbnbTZJTsWYWUwrw0xz6b9dmPvV84DOZnu9qzWluhuXstKkeyM8iNbUW1AnGrAMOGG8nzDN3PXAGO1kUStZh5//0eqXWmh5p0xZ/8wzSRMVkhZlF0UC9bFedw/VJGkRmgeZifi+Lx8D9bdyRMzrt/mqrVDG/8/8mN8Pf4YefE5yo0iA1I73MxRue9h2jq2ghdTGwLIqBZVGMrrgYoysu9lVAlMeqR8fQ0mEGi0Vf3B0IGksvmu95XA1daVvqN4yv5cDVunoXRueYmYpbQtg1l+YBvEFY0kHxHo9CO+eh/ZhxMGIbPrPs76MfvRsAMONgxHZZqjnqZXba9PwC3y9nRLLULmMYEswj3S9477N+eNExD91YCTHaGKyc2PjBPl+A+/P3/L397tByhdixYF6ENDdT2p0tT5VOvVIOvaJ1KfVPSqlBpdQz4rMOpdRBp+lFSXvDAforl55b9LswHls+RG7lRdn4AgDG3/tmy6ETXKNZHQh08cZiqVS5fdcDdvdPSiAB/4yAnKr02kkTucWjGndMerrrn00GaBd5LuQD4YJamDSP2ye/L8FdgqfsL7qvIxlaglYap+uypktzZ8quR07RwxRE5rjN33XbdqJu206b+CTt06kn7ODc0pGynrrZZx2Qt5KSks0YmKB0aLkXTN27calV66i6WjvbYCGtsOCnPN7Z6ZwdpA8nzEDMwUp2tYr35HDsilbkj2R8jaTv2vsE2j7yc1x/7602w9Se23rDrdPuePYpPHbNJvv+0Y/ejX0dSRvk5TlL/GIMiV+Yc9W6epcPYIvlMgDwlyouAL+7PM/D7betgqqrxWmPd6P1M3sQ++kzmPtkUHMqyy2QuqKd9nh32e5Gx216ShOLvgLgPSGfb5JNL8qt5JQF9LAp7lQsHwbwvLl/s8QrsM/O7wPLqhDLRFDTX+3rYETvXQ4I7lSQ71VdbYDXBvwZpdS3S9Clp9zQlUbTlpQP6AeWVdnBhI0BiiltuC/0iF09Ob1eZmgC/sFp4KakpWxoEkQJyjQ3gEr5JRCsP8L9Ydo692t2OofTHu/2ZYWSnglryEEaqK/dtKOL1NRa0M3NyFsPum7bThxarux5HWpL4MAK8xjx+Ljfizc8bQcpqcgYakvYQaKv3ZSkZTkDwEviOZyIWm+X0lAAvmMl7aVHx0IpQ8YyRubl8dCRS9H18FvtMqVq29/0kFc7JRcDblt8uZ0dcfnGHZN4JLUMj6SW2WUjZ8R9lAupmXO6/Kgma6hXzRoLePPSIQm7J2M/fcb3LMjrLI08OushTWlpgCmULWqtfwrgyKvdpVMK0CdnlF/GBsBKRMGLeXfue5d3B8wDtHXTZt+yVBC4XjCXl9sLy6YEgh6rTKSQWmWXCpDAmVkUxYJNuy2/O//+3XjT7snAcYU9RC6QurGCsIAZgXXm85M249ENxklgkr/jvpNekZ6fm0F7OBG1FADXdzgR9XnWzCiV9cD72pOWgmPGZnOnaQqt6mrR88kldnlWUgQMUA+1JbB341I0dKUtaFPNQnNnRdJrrDnq8di5GXlLu0jtei5mvPpo1my/aUvK6u3lTCdMBcLrQ6cjlon4wFwa69tLj71xxySu/fqthiorcOy8blXDwLc/bmgbqZgBTDOW1tW7fE3OWzpSXv0boTmfHI6h5QsToQ0yZEzhgZX3Wmki4A+EurMrwO8YqazCwba8ryF8qVLHx20VArpS6mal1H+J180VbuEvlFI7C5TMrHILn1KAXjXieUlhEkBe6LBytGH6cFqYh+CCuXyQV61f5/N62H1FBiLJyctaKTLhiNmh7jbpZcsbnOuWJlUuQ20J9K9N2kxWwBtkmEDElnjyd4Bf5sf9lxzlUFvCpywh10yAPJyIom7bTp/kUp4HOUhIasrlpwG/NpoDlyzTwJKs3Ebjjpx979JpjTtyWLh1v236zAGhrz3pawoNeNU3edwcECX14l4rFm5zB6qGrjQOLVcYmZcPeMbRrBksZPYnz8viDU8bSWq/VxMm7BwNn+11oXJtst4MOvPv320Trqge4eDQ0mFmermYQjRrqBcus2DTbow3eQ2oX7hWWyUM4AFl930X2QB8yRZ12dLw0/KNDADY2QKD72HUiRzU5HmZu11bKml4QQ7DC3Ile6wejx1PtUWt9f1a6z8Qr/sr2MTfAWgBsBTAiwDuLveDUwrQaXxoXOOFPnZFa4AH5e/CrJhCRXLD0iTPTeAEPBpGcp3uOgmKsqnyyNsWA/AoFCuPC7mxCRp8oE97vNt6x73rl9hjr+mv9iUWNe7Ihc4O5CAgOUpJ34R584Dfa1d1tYEMvcYdfg18dMRrGccBhuvntllCV75Ih5BT5/bDrqdU87gBtQWbdtuZD4OiM596HnXbdmLxhqcRWTAfgB/IyZVn43kbYOQAGmZhMRIetxtDAUxzCurZmbzlmlT50KRyidbcmcLiDU9Dj47Z0gfU/MuyAHL5z11wqbcvPF+xPG5bbJKQWlfvQtVgNe7a+0QAKAn03ZsTqBqstn1QL/hSxuzjrDF031JvvHYnW5SFveTgX//fLwSOHSgf5OQspWbQnI9S9eqPy7SGylfwOkGli9Z6QGud01rnAfwDgDeX+80pBehUFVDPG2alLn6x71zgJejK5SnVm6w33m8uZv53u6e4GW+z0zkftx72wMqyrtLcKagMDPIhIGjzvZuIxG1LuWKYLJO/LXX+mjtTNuszrHIi4H+YMovMQCP7i/I3suuQPCeyzRsHSFkBkc2a+9qTlkLhcXB7gOF6pe3rSPooGi7D1n8E81I2PsvTqk/W+88tjY7G4g1PWzVMNOu/9w4tV75iWS6f3NeeRN2g+U4GY+sGgdE5hTrjs7w2hJSzlrP+tUn7IhXFQC7vsdbVu2yDj9bVu2y2MGBmGG4DDBqXuWDLEft/yxcm0HrPMKrqgwHO+gdPx95ba3HHs0/Z46eElfe9K5fl+QgzzjJYs33K7CTKFpVSZ4u31wJ4ptiytKpyC/yuWakiT8drYRw5ECyPK/Xis9M5HEY1xpsmCtNjTxEjpYa0MN04tw2Yh3zmU89bagAIZsjxBv/2x+/GtV+/1aceAcwDXsxjnKz3OHgeF4+ZYCa3y2Pkes2+evx9WN0XIw80D5IJoHoV/NgLs39t0gIbPU63TV9DF6xCBDAAypol0azxsLs/e36h2QTs+hq6TI/Zhq4xSzW0dBiuvP6/X0D+SMbzTgvgRUpLnXWm71jcWVFDVxqLu7gdfwkCmQzD/aB+fe/GpdAN/mbIXG7+tjzqtu0012S7BpCzgymvUeMDjJt4A8HoHPP/wE1JO+PiNeLgB3gVFvvXJn3Xi406ZhyM4JVLz8Vpj4+h9TN77H1wx7NP4fPJy3Ht129FC7xksMQvxvDQkUvNtupqcf6tz6EnxOMn/fLsJ+K+z8/5Z3NvdN93ES644wD2r1qI2ekcvva2fzTVFRPmnLoFuBjMlxbmcLggH0a5nohRtjgl61LqYQB/CGC2UqofwGcA/KFSainMsNAH4M/KreeU8tCrhoN9LMtZqWm51B673qsL9BKUs3FT+Y4Pn6RsmGrPB0328AwLxvKzyBlx61W6Xi89qNsWXx6obyKXlYHMMIVAWNEiPTpmvTDSR9wn8rhuYhT5f6moIafMJhcEazbeIMUEeLVfZAYsrX9t0lerhqDJYOjc7do2MpY6dx7HoeXKdqsHgLELm2wGKs9l/kjG55W6NnO38TJdXbOscS5ncGEgEzaDpErowIpIYNAmVUfKbeAmj1YixUi1D2C4dO6DlKJy9iMDnIcTUZsDEMtEbNbtgZv9M5a/+bM/tfRfX3sSD6y8F3c8+xQA4JFfXGLPF2vhyPrnbtJYfM4x+3cupvDCtRqtq3dZLxyALZX7wMp7fTPOYr11i1nkjLhtJD3jYCQwSJ+wTa3K5SNa67O11tVa6yat9T9qrVdqrS/SWl+stb5aa/1iufWcUoBOK1YTJczkw0bPkODsJm4A/rR3euduS7P520wQi8uwXC6NWZRhgMWAqNze/lUL8cql51ovXdXV+lQEgOelVw17U2dpTVu8yoGAv+u6bM3mFkDibwET3JXBRVIsrsnu84ABVtlKLqz4FgBf+VtZaEtmh7Khcl970uruCe56dAwNXWk7qLnlbM95aL/dVvYdFwaoLIITz6ceHUO+94BvGXXWmXhlyRn2vZvhy+NzqS3KPrvvuwh7Ny7FwDIjSZTBYq5n7naNXCycUpADpQR8Nuhu6Uih/sUc6l8sJLSd7acKB25K2nIC1WJ2KGu2LN7wtL2uB25egvy4PwDPwfhzF1yKG7735/hOdwLztnlA/pe/+ogdNH3xlFgez33sTMuh0w6u8OiZ5s4Umr/jfX/nXVt9PL48D7RySVn5Ixmcf2dfyWVOyKZWhz4ldtIAXSlVq5T6T6VUWin1K6XUZ0OWOV8p9ZRSalwpdZvz3XuUUt1KqR6l1O3Hu/3jyQY7tFz5UsRpBF6Zpemu1wVzoxipssvTW5VeOrlVCQZyG2GVHw8nopbH7V2/xKoI+LDRO2J2qVyH9MYkYNMLJq/rZodSmubuh1yv25yYTR6oiwdgqzFyG/yd1I2zkXNzZ8oG/5iODwQDjTLYLI2NJrh/DP7R8279zB6MvG2xrUrI/+949il0XnYlxi5s8nnmqq7WgvorS86wYN7QlbbJLnwvE4t4vvgdrXX1Llt3XPLk3HcAPs27O3Pj7Gt0jhczkNtjQhe3OTrHD3j1L+bsb3k95GyODkG8J2eT46jJj/30GdRt22mrH/a1J7F4w9No+Vg3GrrSuPGbRo1y1sqX8OnUEzj/U90+lUvr6l1o6UjhuY8JDzkbQdWsMR9Aq+cP2f2/6aE1gUFM/k1llMzSdUtoAMCe25ux+J9eQdOPMnh2rTcgv1p7I9VyGQdwhdY6ASO7eY9S6i3OMkcA3ALgLvmhUioK4F4AVwJYAuAjSqnytV4rNNernLtdF418H06YOiRuhqar1ZagCARlbK6HLpeV5nrrtAWbdkO/9Gv0r03aglWAKTkQOSOOY1e0YqgtYWuKAx7FUkxLT6VKLBOxnjeVNP1rk/hE4srAVNldV5jmnTVRZLGvsMxPV5Ui+0byGjEmMrAsWKKA51TuIwOy8qGX5QP06FjAM6/Z0YPOy65E/kgGsZ8+YwfIY1e02t+9suSMgCd47IpWy9kTnN3rLs/Peffst/u4d+NSn1dM+SfPE+9HSZ8xEMqBjU0eaLJ428BN/g5VTVs88ON6+DfpG55TxnrkYCHLF0ezJn5BJ0V64QwssxRvGOXS0pFCZvA0IBvBdW/5JRrqx+26+9cmceyKVgDB5yOMaomORGxZYmblkkJzZ07q+UPY+/+bGVjHCdsUUi5TZScN0LWxocLb6sJLO8sMaq13AHDdrTcD6NFa79daZwF8A8D7p2rfKqmHzOxHAPahkQFDqkJ408kHizy5fM/PXIWLTJOXGnD5HvDzme6NnT+S8akpwrhfyt7o8RXT3buJSQRHV/csTT44bjNmcsEMRnJbbrMDAgTPk+TbuR/yd5yiSx7blQTSyw87Z65NvviS/bsYd8765zI5jRr1xRuetrVQ2J4OCA/AsYAU4O8C1dCVtgFNGsFWeuWT9X5QlvcJu0Y1PuANBAyAcptUwjQ+kLKBVPn7zCL//eFrSlEoV9HSkbJVG22N84IR1D+desJXI56UkzlJ5nl55BeX4I+a9tiA52S9oYl4LR9Yea9db5i2HjCgLgdHWROexgbWvF5TYVNZy2Wq7KRy6EqpqFLqaQCDALZprX9Z4U/nAZDkZX/hs7Bt2AysifGhsEVO2GRSCrnOw4moz1PjZ5ILLZaI5HLzxZYtZgduXuLrbCOldzK1XHpT0tykoTAVDyvbubSPy/VzfQPL/L03CShyGZnSzk45tKG2hD3PpGh4niQPTeAIq5go1RQSHMkpA+EgnR83nnguk/FVopT23C0mSHfa490WnJklOne7acYcy0Qw1JZATX+1pYLc+j0AfEolSbXJYxmd4y+29fLiPAZuSvqkigRjOhH02OsGzbkgT07lkrwPZBYut8/39L65XCxj9PHkw6mKcWk2wK9I4vKyYYZpXm3+3teRNJr0WN52LdrzhVaMN02Y+6XJOwertq7xzTjde7pxRw4Lvztk7qORiC1B4d6renQM3Z89H8AUShc1AK0re71GdlIBvSCKXwqgCcCblVIXlvkJLeyMh54VmYFVXdNwgnsatMYduUCBKgKgTAOn8aFyvfCmLSmfxwr4wdLNYAQ8lYgMbhbrQUrzcZBOdyL5EIcpd1zjcR64eQmG2hK+vqFyfUNtCdQc9csYCaBy0Mss8gZB1+MG/I2KWSclsyjq8+jp4UlPUHrgEgTl//xeqicA+JQt7nu2cWvoSvtkklyvlB9yPzhjm9dlWsHNOBjxXav8+JhvAJbHwfPp0lsEVXrUQ03mXEmvnUYPfuCmJBofSNmgKJ2N8VlBqpExhtE5/mtIwOb6D9y8JDAgUv+/6Iu7fVTPZDyHPV9oRe/6JZi4eAE+nXrCgDdM3IM8+mQ8h/icY5g8WotHfnEJXAt7LgC/cqmvPYn6g6MYnldnB1smngGwtXi677sI3fddBN0wRQlFwioJir4GrTasvSYqF611BsBPEF5NLMz6AchMjiYAh6Zqf8p5xW5ghUaJl6RX+DnX63q2MiDq8rq8CQ8nogGZHH8T5g1xXfTEXZ6cfDqNihry4y6Y+Kb8hYCuVO+QRpGt3dzByVXVtHR4NAu571wMlp93KzUOtXkdfUgrtHSYc8Sm17KGCfdH8uVyXe5MwOXwq7b9l/07UlPrm6a7U3by6TzfVKrISorF9M92NlVT6/PQeR7lb8mhs3mGPC56zw39QOMD5twS2Bl7IKgD/nu8r910sXJLMbsmPWB5PSfrDagzWC0llXp0LKBYmrfNxHyqd/Zi5Y9W4cEP32u7IEnaJdN/us0QPadL22Xoyd9511arzlr48K9Dk7uev8pz4s794WiA+mnpSJnEpWwEyEZQd2AIi//pldDjP15TlapcXkM7mSqXM5VS8cLfdQDeBWBPhT/fAeA8pdQCpVQMwPUAHpuqfQsL0Elz+VhZ21x6DvKhcSV/DCy59Uskp0ov0H3A2P4sDMxJcQAIBI8kz02uXA5M5OulF8PPZP2P2ekcRublfeUTeM6Yks/qiS7XyvUR9FkjJZr1gqU8pxLI5D7KQCDPk8vzSz21XNZVhdD62pMBrxwAcpmMTwLKgVUOsPkjGTR0pZF9x4Vo6Er7sn/lOeKAV+z+kh56S0dQssrfs4hXsQGJ14teOOkd6SlTscKXBOMwZ4XXX+ZHsIAZlUdUObnHNz7Lo9pa1xmqS8YsVv7I6MmRjWAybmYxiOV95XJ/s6QqMDiv2rrGpwTKzmkITRocWGa29/yVdT4nIaxgXPct9VDPT5FvWGlQ9FSQLQI4G8ATSqmdMAC9TWv9faXUKqXUKgBQSp1VyIr6KwB3KKX6lVIztdaTAP4CwI8APAvgEa31r6Zqx8I4xVJWLJOTICObX4QFG/l7VVdrU/FdrfiBm5f42sQRrF3v87x79gcAz90vbtsFQcBfs1wuz+/4GwbQZJkDGaQ8nIj6BpzJes+Tl7JBV+d/aLmybfzk7ECuj2n7qq4WB25egv61STuQ0fuWmY+SdpEUjfy/uTMVGiwLM7fwWfYdFyL7jgsR++kzvoqLizc87UsQOu+e/b7yCDwGOTDQ+tpNktO+jqSVzbolAIbaEqgb9HvFQ03efjJpbfGGp+05l9y9K191+XMZU3EHjLD38n9+LvMAXEpMj45ZvvrBD99rqReWtDU7HEF9r3ftH1h5Lx5Yea+d7XI2vP8jZ6L/nXVwTVIwgEed2cH7fD/V5pbieLX2eguKnrTUf631TgC/H/L5VvH3SzB0StjvfwCgbEH3E7EwoCtnMqDpeuM0+TCHBRwP3LwECzbtxoG1nn6boC4HjWNXtBY8zWBQ65VLz0VDV8a3/8UUK+7xcf8kiAJBkJcmg5quueUC3MxJeT7kesKBJWH3bWCZoQj06FjoLKWhy9t+2MA2sMwDljAvvpSFqWE4ELjnwT23eXFOSBmhUEaAy/PYuT/ufnrHYLI3527336tMmuJv3fuDJnMPxmeZwbZ/rR+YARNIHT47iuGbTNDVpc5YwXKoLWG153L9vE8ZB5F5ALSawSj+8lcf8X/oNLVgkBl4Cp+79v/BA4/eixu+9+eIZSJY+PCvke89EHqcjTtyNhDauCOH7GlRxGCkqBpAds4MTB6NonXrMVMIbAp9WFUozvV6slMyU1Qa63hMhRHMpSadLzdg6QIrk2n06JitYDjUZjobyfeRmlpLwxAsKemiRytpEn5Ok/vkzj4Y2JWp4As27bZUiTQZK5DrCWuywOVdDTk/l6DDYKdMk6cHTn695ih8DTkkr0teHggORPxfqmFklmkpC1PBMM2dJs+DbOIgBzJ53fduXOoroMZrx+Nn4hRLFchBsXFHznr/Mh7A9bMIGSkfUhHMMgW8OAzrmVeHADJglDWUMFYPwwJ7zVH4VC/jswr122Pe/9ynaNavROI1YiXJs1Z6slDAq3xIk3GpfdfH8bkLLrWB5/0fObOoZJHnyqUWgUIXrJ8+g9atpszAvK6IL67xqu11RrcApxigR18e8b2vtJ5LseVLDQQEKgm8gMe3u0ZQHmpLBGpkSHO9PsCrmBhGk0hvu5R3zmWZrk8lCeD13XS5Z3rV8mEKK/nLKbvkXwnwbsBMzkQoewQ8Hn18lgFzF7RlVqpsG8dj7GtP2jIBtGg26PkC4eAd9pC7CUjGQ/aeUHm/hElEZxyM+IB/qC2B0x7vtvu16IumbRxjCzLIKtffuMOoeSjF3NeRtCoaCeAAfFLNpi0pWwG0udNTvkiKJXNhDnWDsLLIxgeMJJJZou6gyUSeGQcjFuwPLVe2jpJ7Hrgv3Z8937QQdLhz2oJNuzGwLIqbHloTKC7nWtgAHTaLtBLXltOAPfuPGw8qsYool1OEQ3/NLXd6eMui4x2R6QmTxwbC67lII6iTH5YlccNMApGsSy4HEU6NCdTuACM1xPIzGbTlsUgvksqB+ffvRs8nl/im2dKzl7MAAr4cBOgZsRsM1yP32aVMpJyR0+R4T873uQRleWyyzrkM0tKYJSpnHFb9IeqiSECP1NQGNOjkzMOMVSNlaVjSLLSGrjT2blxqj737vouw5+7zAtfv2BWtaO5M2YSkudu19bhJI0hjGdyao36tPoHLpToA2EqgUqsu91tqshsf8GhA6cln43lbmiGaNaBO5RI5ezfOJGdIfe2mREDr6l0+DpvbYHXL3DzznLpVFeV25O94/GHW/dnz7XV2qUBXHvqq7HWkQQdOMUAvZsUkZcWWC1t+dtoDaqnUCDMZIM0sivoSUgD/ze8GL+UgQqrClaBJD0vuqwRfqZ/n78KOm3VW3ExWtzUc95Nd5fvavRrmPB4Z1JRUi9wfBlZl1uWh5co3TZffA17NDnqEnDVwQOAyroc+O52zRa7YHKFYglGx71wrVivENdlgGYBtKEIQd++xgWV+B8AtRcH3bqG3MLrMDV4CXnC08QFP4TQ7nbM6d8CjtPgd4DkvvLYy6JyLwUfLuWUveC2y8bwvcM3vF2zabSWhYSa5flcqXM6Op4HNiVqlssVTTof+2zCZXBDmKVcSDD3t8W4fhSIBnOAGeE0NaPTS+fexK1qL3rTuPp/2eLedmksrxQPTI5bgS0kheVv+nt5Xc6e/T2dDV9pSJfIYvYJj3jmQxyYDfRIMSO24NJDUvEs+XIK5zA6lyQd6YFnUV9hL1k+nPJKcfEtHCjOe3Os7Vj06hskVf2A987As0TBFDKWLd+19wkpbXRUPzzE7+tDCg7dm+XldEVvXho0vuD3SY2FgRGUMv5MBU9JdpF24vIxfHE5EkY3n7TWarPfuA8Ars7B4w9PY15H0lEn9JoGJ+xzLRHzBUFdpFB2J2AFJ0pR6dAyfTj1h7/nFf+p55mGzjTBzn+No1pvluM9csVnXCVsFdMsplfr/Wluu1tMF80K7GuZKjb8nl+iWyZVGb4DTQg4ErlXiIejRMcx86nkLQOSmmUjE6aikScIGJ3dfGVycrIeP1+b56mv3qxPc5hJuej4HDSbA8GGnPt2lR5gtygF2X4fhvGuOwlYfdL1SeoFy32RVTJ5PevcyniG7GPG8VuKBAx6Yy6Jb9jy+twpXf3e97zNukwMJ6Qjy562rd9lBVYK1GwuQg9vWTZsx1JawHbiA4rQG94H3udSey/tEdhYKU6NIWkOeW/4GMOeTmajsGkXjgFJs8HFtqC2B229bZYHXVdjIWYa8Z92yCtIJycWUL4tXWqWy1YpNayBfwasCUC80gR5USj0jPjtDKbVNKfVc4f9ZpdYBnGKAHnWocteDCrNSPHfv+iV2HQwGhnXkYYq2tDC5XViQMuzmJ5jL72Rm3mmPdxfNIC23TQC2h6QbeIpm/YWlAL9a59By5ftOdrnPLIpa8GPN8vFZQVlm7/olVgoXzZrPCRocXKqG4QuWzk4bCoYARMCWdBI9VamDJ+d87IrWgD5aPtz03CTgS29u78alloOn/lxSDIDnUUazsM2daW7GJ+BdF1k5k1UYWzpS+OBXbi3rAOzduNTmCMh7ifeoBEE5Q+D5bNxhKJeGrjTqBoHT90Z83LkccHIxc94lBw+YIDaTvHKxglLLSaAq18OzoSuNaFZj6ypTiMutRUQA5zEs3vC0/TtM6cX3xZ7tuT+bLLk/x2VTp3L5CoKZ9LcD+LHW+jwAPy68L2mnFKAD4dyZNHmRJSUQ9vC4nr1876pCqoa9bTMZppyFgW0xUHG/r1Q/T+MDIANOspAUHxoOTNLT4izBHbQYxJNgIROAGPCURr0yZY9hwT/+RvL+nPFQgkcrpoYgRdC0JeULHJYyNzUfQGj9j70blwYyOSWXHx2JYO/Gpb4AoEz4kjTVK5eea5dp6ErbLFFpUvYplU683zhLIp2Wi/m9efYo3btxKXIz8r7a4WEmS1mwvr2kc2juOXXfy1mHC+pusL5u20587oJLfdUVJX8+Mi9fUW/UcmU9RldcjBlP7i29kkpNo6Im0RWtSuufwpQTl/Z+AF8t/P1VANeUW4/Sr3EU9mRa3dnzdfKytSd1G648kKnQ1HiTigAqK9PrJvtUYmGBJJnEU2rf6R03d3oNFqS6IpaJIBvPW96Usj85zZXLS5u7XQf2g9pyBpRJR8iqicx4nKw3IE/qonFH8FyGnSeZ4NS4Y9IqUbg8YwayJnopY1aoS7cQ3KlndtUtgDezMElBGgPLopicM2F7h7JA1fEOyDyvcmDgPrA5tEwi4n0Y78nh0HJDQ5heqxFTU6ffrHd0jqd8YZ/SbNzsu5ztRLPeLEQem6S/eL8AXkCT93ax441mtZWHyuszuuJiX89Qeb8CsDV/ylnjjkmf/JSA/qOjD/y31voPyq6giCmlljed+7afLGp9b9ll/+up/42hYwf/DMDN4uP7tdb3O+tsBvB9rfWFhfcZrXVcfH9Ua12SdjmlPHSXcqEVU6OciIXx8bxp33/1zwH4g6LlLExZ47aXo/GGZyYpTSYLAeFKDA420tOau11brzuWiRTtcwkYMOMykg9dvOFpzDgokkIKemkGJfd1JG2wjVQM6RJXheHWyJEDgAxOS6qFfzNQ6jYOdrNEK5GwSjAvVqGPSUTyPLvn5WBbHou+uBvn3/ocAGB8jt8R4HUJSwIDwmkDVxVVNQxMCBpQzmDGZxWaWBfAj7XACeZDTbCDAZUupF5Iw/B6NXd6NXl4n8jKmrJJBverlGpMmpSUFrs+lD4W48dLiQbkfVa3bWcgYeyErRK6pfBIyaqwhdf9oet8lXZKATrNfTjcQlMEhqmQMfHhSvxiDD/720twOBEt2tSAFjYtlN9HakzPUAnsQ20Jn1fuUiJyPW7sQGYwcvruytMkOFG1wGqHuZgJ7EnpIZdjwg97WgIep811uA+bfMCowaaShpy51FrTyP0zqCrVN+SEyfUSJOUUnWDhet7uZxLM1VBxZ0B6ng1daZM4U7B9HUmcf+tz2HP3edhz93mG3ipUFuT1kNfALYtQbHvu925Mh8lA8lp4PLhCNp63WaGLvrjbas/ZuYjA7qqPGETl+aRihqqX+he9Oj8cxMP2P+w46EFLMHeTungsQ20JzN+Wx96NS1E1bOhCWZLANTnA0yEKW/eJmTbp/2VeryKzaEApdTYAFP4fLLP8qQnoxTwCygvZ1bxSK+Y9AZ6nlX6LuVkoxyr3e3cdYSbldKc93u2bdhO0qYt3pYNh2+VARtWJK4kj0ORiXpMCPTpme3LKcr+LNzwdmtjE6T672nC9Q20JC5Qso0uTg5M7m8rNyNsiU/I8cd0SxLl9bkMGQnle2Ppt78algRiF+5kEcybGyHMrKxd233eRlR+y3veeL7T6B4Rs8HFzB+N9HUnMfOp53zLFZpiszcLZDQdAN3GmuTNlaRUZB9GjYxhqS/gyRIHC36Ju/cBNSeudj8zL+8617IM6fHYB5C88Yo/Hvb/4P/8uBa51g/5EKC9Hwjhnk/XAjIORUIFAKSdiqkzlAZXT5V8nzmo/BuBjhb8/BuBfy/3glAR0aWFcL61SHrPUcmykLINV5dYrk36iWW3fy8p8EszdzDh5szLox1ohcjkCPs3NyKQkrn+tPyO0pcOvUW/uTKF3/RJb7ldmHgKeCoFATu6WiSqSUmD9EvLPVYPVPi/SrZXDgYOeoUwuOpyIWi22NPLwLR2eBptePW3xhqeRqyl/+7vT/AXf0IEAJ88pq0ByO8zCVENRqKEo5m3zBgn+Rt4rPDcMlLq1YQDjRZ/2eDe2btoMwKOU5PoIxtGsAcShtoQFa5kwJAPw7HBEnnz4bNP4Yl9H0v6W50OqWlo6vJICHAiGnjnD8ufFnoVSM5HJSy7wvQ+TKTLWAwTljjwv0qQYoFL5akU2RSoXpdTDAJ4C0FqoOvsnAO4EsEIp9RyAFYX3Je2UAvToyyOh3kw55Us5C1snveI1a/xB2GIce5jJgFCxZVRdrWkQXfCm5LLuQ8F6IHIZAj6t5iisDJP7K0HPzTAE/DpwTqc5OFDVQS9Zxg9ktyZ6524bOnK+lNI1dKWtSkNSETSZzcqYgNSocz3u/kvJWynre2+V5c2bv+/J2wgCh94WQ3Nnylf2tqUjFSjL2rou7ePf+TfvxWJSPleBwntP1dVaCu7AzUtw/b23+maa7vqkxnz4bG+AdO/l4bOjGGoyYFw3CFtHRQZLJT8uywIAXsyEVkpRVMxbl/+rulpU7+zF/9fe10bXdZVnPltXH9aHbSU4CFsKsS3HSjyJ5dFgmhQmGUxd6KjTDxbTaacFJoXJSp1pXQYmUDddiA71TDLTFdzVplkZhlDoAMPMgg6NOqVehCaUMaAgLDs4lpFlJZYcbBxHifVhy7p3z49zn33e/Z597r2WZcu+nGctLUn3nHvO3ufs/e53P+8XEPH6K16Yj0rhfXnSXUfOsZCLZhpIWVYS5FcRLBYt9N9a+2vW2tXW2jprbYe19r9ba1+21r7dWntz8bf2gkmgqgR6fmWTF70JxB4ZnEhrnrYXLeDTDJxTvd1o3HsApnGZoy/SkCawZ7dvjvzYz0b30HSNzs0NxBPDNC5z1+Ug1UbLUrsFzWXSkKVzXgNxgigg3s7KiSvzpDOPDTXMVUN5NE3UOK2KRlEaSoG4KLf0KSdObs25lAPnr4MzuMqtP7Xy3FysQbPvUhutJI+HpElkgJppXOYJE2ZJlClwAeAr//qPMfzJ2Jisr5lGsdEOwPe3Yt8LboG0s+dQ07DMJc5qHck7+kS/YwaCrd096KgUUisheoKeTBTiFORTHVGbqIHrPDCkYeRn5LnlziUUfyEhhfrF5F2S9FElcRmMSF1MZJGiVwAU5lqoLybIaQPwUuJeDPL1xvGBc8tzicHMIsYEaRROHJlnm/SDPJeQg1guTtLTRHqYSGEu88aw+hDzszBSkBWQ5LmA75ky015wuUPGd/Yk0ujKBFNMqcv2UGi0juS9iEWev3Z3zPnyeZLmofHu+L2bEqldpXtjCPXPPOc9c1JOBO8pteOOPYP41T/7EIDIkNy14yBsSx4bH9jvFptSkcs0DE/1dns+6gQXcbaDmikXZ0kHHe3rcQWjp1fnnFsix9SPfn2TZxTlu+NnMnUuhbn0RoqeYbygSQ+c0HzT6RLks+T53d/2g4pObq2NXBZ/cwWA9IpUaXEf7NP5rRuCxy8NFWjnWXKuS4MeSDrPisbFerpwMI18xE+BW2loOQ2LaQKXSHNdJDQdIakGCoS0vumcNPysdSTvlS6ThRToT868MUyXSq6cybqmeqNsjprKaZqocZQIoaMIpYZKIygFfGgis89MnUvNXT7XDQ8dQm7OT8kr+xTS2OneR0z0FhJGNtlu3pvX6tgziPa98XuVxtRyCFExNMACyeRtcoHmAuyC5YruidSweY1jH9zk1eekdh6CVBJYrJoUTuMp7oridkslqlwwF7X4k1ujIKm0dNVju3o8LyqNUk4OpNtOvLV+cblzVG4UzdLnLhAXll/+e9C9T4aeA2GhHPIxPrutK+gjLlFOmGtIdzAACX9ttkWeoyGjM2XhZ5kXhZ8xX4mstcprTG7IJTIxUsCQLyeNQK1KcuCAT+VQS6Sgktq41EaBpDDkcVlkmkJt/vUXMNXbHRQ6x7crPrwokHVuF2rTNMDSbZF5avi39OiQ7qH63ctc6xKSs04Tatrf/nR3XHMUiIS6pFNe23S9p50zJzrbrGk1nitdG3kf6WXEfgFhjys9/vQ7e8/1+9zfMoJZQi+u5TIxsl+j961f1JgUAFmBiyuJSrjyi+HUOGgldy2/r40tOghEI61YgkR+ctLdkxqGvKZ2W0yD9KwpF/TECUON/cjDWzC+MzaGUoixeALpFnkvyWmybfxMLnIt/UPO/Y1GURphQxn3JEVEzX1yQ85zkWwbmMeqobwzJrMy/dG+HpzvuIC2gTy6dhxMfWZaQ3fP5cl5z6gqKQkgrlcpKybJEml6rOlI0xAFpD1q1n/hx66fhCzcLTG9OhLq06uj5y0FPADUrLvRGTybX/I1e0d5FCkbQgYgtX0qznsvg9UkKqE6n/hXUbj/4T++OXHsYtLmlptvgF//95JRgQ/6JfqhXzSqSqDXnY3/Zhh6KaEeCtTQkBF9HLRacEt/ZH197cZ4ujuXCHDgddO2hPJzXRGJwlny4ZVGqcp2sv0UotI7Zs3T1qv6ztzdUpCzTfPNcAUvgDgARWf+4zMZ3xkJNR0yDkSCMY1ukZD9jXjz2sT7mO6KBHnDeJ1HYZSC1MZD5zOwibYAXvdoX49XqYjGWj4PHdSVBt2HUMZCZnkMtU26E47t6nECvvEUsOLQGbz09hsSQp5Ro9wNzb4+EvZSqK8aymOqI96JcOcjXVP5vvT8CylQH/3wfQB8w3GlkZ88lxlR2b4QpKKyaKiEQ88ol4WDXiwhASAHV2hbGAL9aUMaEKEHkL63DiCiMZMCbnb7ZpjGZZh560aYxmVeFR3pj25nz7mEU6HrLxTUHlv601O6zrUWPNdDtl9SK5MbokVPVm2SlIaeSDJgSBfF4Pf4maQqpEcRjYKyEAjfRymPCV2kWWrepFzWPjmPubtu83hYbdTLNxWCaX+BOAoW8LXvFYfOYMUh3wNNG7SpCMjqT519gygcO57aJ/l9DQr46dU5rP56pOWTRpFaPG0WQFhwMghM8uakaOg2q9Mby4jatHae3JrDZ//Fn+Pdn/mQu3el2rlMDQ3EVZrSXBkvdb4QxlYQVJTPjKKLjjRXxXKDLC1IKDQgtIESiH3I6YUiYRqXOcFMTb1x7wFPCFGY60IUMoiJ99Ztkpy5vPf0Pa+m1j2VmiMFZG7ON3TRq0QWT+D9GahEt0X2r7NvEGu/POkZqJnpkpNOb5cltXJyq99eqe3LLbRsNxEq5ixRSRZGCn8KM3rP8JnI6zB/uyy3psvkvbbpetcetkkuCjKgSPt5a15a55PR59CA6e5fFOJsA5+V0+ZFgRFJezW/FCs0chdF+wHbUz9Zk9hJaK+WcgKVJRlD/HkloELRsWcQ5zsuJBaoRXNdXEQ/9MVC1Qp0rTVz0msjpSwRpwdauS0xoQ1CkxtyTnBRGEujngQTE8nq8JVWVuFkppDP1yOx7aRwYN+an1iZSB6mUwpTUHBCy8Uw3xTlQJdClRo2hTR9xt3EuWV96nNL8x+WOyIKu9ntm127ZBUe2d91jxxypc2A2KDJ3xRQUtATOuho7Of9RF8yhYHM2wLAUS2yLSFhDcBp59pfW0O6MJZDyCgp2z29OgrNn+rtdlq+jkTVVJBsNxeGUPSqxoaH/Hcgd8Mhob5YGrOGfm6rhhaRPwdQsdtiRrksDhj6Tk8M6f2heW8GHVTi2hQaKLpuKIUgBXbaoNW5LHTxBQlpjA21p20gSccQleayaB2JiixQmNAzJTcTa16M9GSVIi5WFLJ83iG3wPrJGs+QJg2DcjdBnl5SOse31yQ447aBvMvICMQLoyySQNoE8DVyCtSQ5mZb8i5SVC+wa5626NpxEMOP3u7aS2NrvqngfPSlAEsT2mkg/SUhC0dorV0qCnJsU/Nufinm1PUOT15PGjFlGzTXrjlzYq61UDYiU/qjtxw9i/GdPUE7QDnKhRp8vj56/uc7LiTKRTYPR2mLF1+YIxLUhQp+riCqTqBTgEueVbrVyYEvPT7oTngxkWpAPBHk4JXab9M/HAlqJfo+pnGZE/6hbIBp7dJ0jpxcITtCaFDT8NjSP+SiavNNhcRC2Nk36PyL5fZb328+kM51/B2tnvcIqRrZLv5PGqZ2OqZU0voihY4M9GK71u4eRP0zzyV2RtI3nMfk+6GBrv6Z54ILrHzOJ7fmcMuHfoiJ3kKCgmGf5lvzjlMn3cHvSmHG9xBauHWgUSm32dD3Nf2icXJrLpgaIcqX46uZstA1+3rk4S1YuX4S0/e8iuGP3+LOTVMmWo6e9f7/wOfu9/6XOYJCqJ+scUVa3viVeCEK1QAmFtfLBTCFQvmfzMtlYcidPefoBCnEQ1j/hR+7rS85u5BrWTmknc/FIRRwpDU+qXkBcUi5Rqg0HeAP+LQcITxHatH6+PjOHi9FgszJIiNEpbcKha+s/kRvGNfvw6NoHcmjce8BL7UrDZ6nu3PO/ZGav0yfSw8bXlsu1PL50zim0xYAMcUytivyQJE5uEOgdu4Vqj4bU1ydfYPo2nEQ6x45hDXfmsfZbV1o769xu5ep3m5M9BY8vl1CCnXJpcuiEGko5fHDa4RA46c8T6bZBSI3xRN3G29RaRvI48RbkvSTBItJNz+xEs1PrEzNmeMtQp3LgcOjXnv4A1TmstgwXofcHPDiL1unjUtIAb6o3i1AhXRLRrksHIVYA0zTckIhwiHD0kIgJ1qphaF277OeQGHpM5bGkrQAaRa52ISMrLy/dLPUx8ppJjKy88Tdxp3PZ8aJRlpH2iVC+a+PfXCTo0PIs1NwyBB4elYwDwiTfLEPpGYoJPSugHQXqR9XWadY45I7niMPb8H5jgtOm9PvfO6u25yv+dzyXEIzl8WjgWSO+hAYvNT1u+HxJXc7esyG3vGKfS9E7oQpC1KaPYiQmRNDFZeaX4rSM4TC89Ncc1kH1TQuS/idl0p1QJfP1pF8apRoiCqhEiHjFLQwn+6KbRyLqpVrZEbRy4f8yib3d5pAlRzz6K/dACAOrQ5NoIsR9PTIIEJ8/Nxdt+H8z78ZDU9+1/tcavOmcZlLa5tGtehJUklgkSzjlmYUBeBKkPFZMZRfFyXmTkhq6UBcnHnt7kE0vBJ7RUijsKxbqdsvPV+kzzh5aeZzB6J7SKGqXSUBwNZbmKkcbH2kxb34y3GWSyZF8/ovdlDyWnN33Ya5u25zCbcmeqP6nI17DyQ47Zb+IUfrTPV2ux0CaS1pKEwLPJIaNPHanTdhqrfbs72EqDxeS95LQ16fyc2A0s4AerfBHQwXN12GMO1a3OUAkecVADz4/D5v0ed8ksJZ0nGVgmNlYnsUzLeQvEtBZBz60kLytIA/2KQLnUQofFlDTjpJeUgBTeHeMDCSEOZAHAlKwS5dH+W9SwluCkx6Isy1FrwJqLMQyutJDSY3U+OSZ9F1c3JDzuXTYOAQuW1tmwDiXOsypcBEb8EtCtTAZT4WgkFNbBv7lW/yOXj9Plv6hzDfHBsJ+S6YlxyI6Jv2/hpnDA3x4yfeWp+IU5jq7fb49OFHb0fXjoOYf/0F1/aW/rhq0diuHs8g3jBeh9pTdZjq7fbqYZJD565KjiWtQTPviX6HEvrzNU/bRLpbea70aKF2LceYzLYZReH6ArptIO9RI+V4enl92ZZjH9yU4NAnN0TPpr2/xnsOjF/QBcNdG4oLwHTXBW9cd/3uEPL1Bsfv3RT+4sXC2oo49CuppV82gW6MWWaM+a4xZsgY8wNjzMcD5xhjzJ8YY0aMMQeMMT3i2AeL33vOGPMFY8yCM+vQlVBqpdIXmJOgHMcWmkSSO5ZYse8FJ6QLZyad5sRQfiDJnYe0cWqfFKqhrTCPS+Hb0j/kcfH0RpFCg9fTofoM62ekpnaFpI+vzNMCxLsfKaylgGoYr3McvdyK8xlqF7rZ7ZtdemEKwpajZ4NGNh0/cOThLThxt/HuLwV7CPXPPIe55XFEMDVt7QsuDaq1p+q84gk8Jg2tcoGZ6C0kBCnBZ5Cbs+6cUD4gVjTSue/TIBdb3o9ujHKHFKK0SmnrIeWCqXp5nJWMuBjRA8hl2fziJJqfWIlPvefPgkoVx97JrbmgwkW6TmrxIS79dYfi3PaVpNqtCNYChQp+KoAxZswYc9AYs98Y8+xCm3Q5NfTzALZZa7sBbAHwTmPMHeqcnwNwc/HnXgB/DgDGmHYAvwPgTcUK2DkAv3oxN5d1Q6U3C+Bv89MEZLlr8/ocHCGu0c6eQ+HMZEJLJ7QAn92+2W33T9xtEgNPc4GhNtO3m9yt1qik4NSpdLnw8flIewPbQs8D+dx0MjBG6snFbt0jhzzhII2bsj+aR87XG9e2qd5uTHUudxM7tADLyEpqjhRUtiXqO42Voao6Wnvkc9To2nHQ1UwNLcRju3qC5dV0IQxCtkFSOHJxYQ4deruEguX4nOTCHbK5NL+UdyH+oTZUgiMPb3GKAhDm/Dv7BjHV4RebZj9CBnzSe6uGojxBDOkPjWNq6aH7UqhT0L+8qTZxzqJgcTn0t1lrt1hr37TQ5lw2gW4jTBX/rSv+6N79IoDPFs/9NoBWFkUFUAug0RhTC6AJwIlK7y3d7dKMoBqVDmYKL+n7DCS3soUzk55fuy5UUbv6DQkPmMa9B1D/zHOY3b7Zm6yhATvV2+36xu1628C8504mBSw1d/6vg4mAsMGYXPlUbxTWTeHIa4zv7HH0im6r1NS506CgkjSFpGXke2j+3ovuXiEbh6y9KdsuFwpXqejTr7mEXF07DroFVhuQuTPgdUK+9EBcABvwq9YPP3o7WvqHcL7jgtcXcugyHYAEtVlNi1FT15Tbin0vBBNf6eRlhPxbpkjQXi7ymZWDpnP092T/af/Qi8v4O1oBAJ+49U5HJ+nI3449g5joLWC664JnswEi6mWutRDUyjVmt29O0HSXhEq9XK4gLiuHbozJGWP2I6pWvdda+x11SjsAmZxiHEC7tXYCwH8F8CKAlwC8aq39u5R73GuMedYY8+yF89H6IV0WtRa3GIUuSEcAflAPkNTUvVD+61tRu/oNAID5l34EIObZdcXzE3cbHHl4i9N25b2BaFIyDzS51sa9B5w2y8khPVCkG6Cc4PJzTaNQUMrtPSsI8bvaZZGUjPycXgyyMIVclAAkjKvURNkmXuuNnxv1jss+yAVyrrUQCxzhHmcal2H447e4Z16JAKNAJvcuhTld+rSgaOkfwvCjt2Oit+A0c6mhy7FytK/Hq5eqoRfp1+68yS2uUkDKmII0aEGvfcw1pSXtGTrvvNbwQ/7wWrvm8z65NYeOr026e2546JAX9CQXm+bhOjQP13k7b3ktSbl07BnE6w7NVyTkLxkVui1KOVX8uVdfCcDfGWO+FzhWMS6rQLfW5q21WwB0AHizMUbHtIekqzXGXIdIe18HYA2AZmPMb6Tc43Fr7ZustW+qa2gJtoMZAplIn7hYukWGqocmLz/Xpa5k9GLhzKRLviUNoZqSYf6ZmfYCzl/nt5FJsNgXum9R2AKxd8b6x0a9SUchG8rnUsojQf7NfB+SutG0i5yYkxtyOLuty30nlC1SQnrjyHZLQS+FFvs8tqvHxRQAcUFjLaCGP35LQruUbQaANd+ax9G+Hk9487hcABr3HvAoma4dB10ul3KaoDxOOiKNvghlnGT71z826h3TedHTBDwNmC9vqk14aIUgg6AWquXKdnT2DQKHR914BqL3TAVE7zJoDJ1pL3j5ZDY+sB8N43WOopHo2nEQ010XPIG/aNXLrAXy+fI/sJ6cKv48rq72FmttDyIa+n5jzF0LadIV8XKx1k4C+HsA71SHxgHcKP7vQESt/AyAY9baH1trLwD4MoCfrvR+ocAbcnalklKlfR8Ie4ikXYP0gqtj+Re+r7KkYUJ+zHKhaZqoSVjztcCTuwMpXE5uzWH0viiPiswvQzDTIf3O6eImAzFm2iPPlDVPW+efnJuzbjJRWFCoSzqH7bzx8UOenzFLyYXOl/7yUnuXuy23mNyy3ltgKBBN4zK32HH3Qu0xlMNFPnMg4u0pqLkgUNsrJfSk1srUsrWn6tDeX+N4+xBCbot6cdXCTZ5TODOZqinze9q/XXujyJQKIXqG+ep16oW05yB3iTKaV7aNLooUwnb2nPtb91VSnE0TUYTo8KO3O/feudYCaqfjRe7lTbUu31DDeKTdv7yptmR1o4vGIlIu1toTxd+nAHwFwJsX0qTL6eVygzGmtfh3IyIhfVid9lUA7y16u9yBiFp5CRHVcocxpskYYwC8HcDzldxXutSFcClBBmkarNac5BYVADr/04XUhFvLnxp2qXMBOF5XImSVlxN0+VPDnsDkgsFCwUAkvKWHCfsj+6SNbHRVZDpiyeWS02bhCAr189fF/uPyOZ/d1pXY6lNIyx0DP5d8v7wOKaiQcCTdJOmrON1r1DczFe/S+Gxmt2/2aAdZbQiI8oRwMdHaegjMEc6fid4CGsbrglTG6e6c544ZQshlVfu9a0j7QhqYnyW0SyKtwt1fOSF+sQi9P+ZclxQjz+vsG3SxDfPNsdGzdjpONUEt/GhfD85fB5cRVNIytiUPW79IvLZFZV4uZW5njGk2xizn3wB+FkA4oVMZXE4NfTWAbxhjDgAYQMShP2mMuc8Yc1/xnL8BMApgBMB/A7ADAIpc+/8GMAjgYLGdeouSQO7VGXzx/j/GTHvBC3HXEyHNPVH7HsvPFgpqmfWnZmBnz2Gqt9szhtrZc06Yh0CPFT1p6aIIxAI85M4XKr6hOeO51gKaJqKhQI8g7eZZSmOkj7IUIrK9kgYhQv7vUgPn/6U+A+AyOco+kbeWvLe+t/OaUUE6EhTeDeM+F8sFbPjR210aAZank1GkbEvXjoNBnp7ujOTCW/qHPKNpyDW1Uui8L7zW9OqcK1whtWguwvxM+p1XQk3qBUZy56USw2lXxKnebheHwOhiIHqO56+LrnW+40IiMrl+sgYN43VYu3vQ2SJ4PhALe1lE49JRqYZedgFpA/APxpghAN8F0G+t/duFtOhyerkcsNb+Y2vtZmvtbdbaPyx+/pi19rHi39Zae7+1ttNae7u19lnx/Y9Za28pfvc91trz5e7ZcVuU7EdqmjrfB+C7NEqkGaNO3G1K8sv6GL1bnCA7PBrlrRDHpRaZrzfOoyOtHRLUlilASatoMC/G0b4e50bYsWfQeaZwMjdN1CQWubRFL+Lp44pA8jz2idodja90dZTaNjV8Gk9n2gseJaTvL7l37sKO/OYKVw2ez+xoX48TwAwgOt2dQ77eYMW+F7DmaesZY2nvkAUo2gbyiapDIdSeiu7z4i9bL+e+1Li7dhzE7PbNONrXk/DLZ3vl+6bRVLtv6u+ljZG0dAHSu0RG3vJekvYJGfh5z9DCpNuq26FTazCNLxC95wef34f5n7oVUx3w2kWlIeR/zqRcevfKvjW8ArTcdsajYYDIdTUtDcNFw9oo3Ui5n7KXsaPW2u7izz+y1v7RQptUVZGiLxxvw7s/86GERq4HJlO7lgPP0VSFPJY2sUY+sskzVvK3vi+Di1678yac3dblceohzVyCWhjD3+myaGfPoXD+nLs2A4W4uJGr5sQtlQaA4eF6gspJwmecn5xEZ1+cyIvg85PaNQXDkYe3eLymDHqS/Lp0ReU1pBBl7pncXGwkJo0SMvjJLJsyLkGPFy3UpV+6tFfwO6SomKcEiBbs0LlRGxGEfu9SadDGYHk9Ck/ZJ6mtr/7yqAhgiqmUzr44LoCKQqU7grTz5O5LavnMJyP7VPsdn1HV1+Qi7crIDdahYbzOLcwU+iy+AgCzg9d73yFC2UwXBIvKBPoV9FysKoFOyMEwvrMn6F+tzyNCQUf8XLp4VTLYNzx0COevC088onBm0hMi0iNB5+GQAkqel5aHpnBmMrHd5SSbay14QpNCgJOMHLj0aw4h5NdL42SpKMPQ82MgkMw7U2nODtY9ZU4S6bLG/C4UbDKve6hv0sc8dFyia8dB59euKxO1DUT5b2R0qzRQyt0dx5t2IQTiXaJWIpgyIOQqyOvqY3wGDKyS35cukcSKfS94VJoea3LcyrnDOp/8Xy8Scsx/4HP348Hn9yUCuEiXuuuMR99vGK/DheaYP9e54OnWymcwvrPHvafFdWVcNMpl0VB1Al1PPvpWE3TtC2nLgB+ZJzUMfd2QAAXiAZ5vKsDOnkuNiCMtkxZF2tKfLHAtIwilYAh5MrB8Xb7eN5B1fG0SLUfPon4yplnoE679z6X/txSCrDJ/cmvOc6nkPeWz1eXj2Df9/JomahxFFMoMKbXzkB1Eevyc3BpROXxerSN5b1HnOaEI0KN9PZh/fRQURCEgr0+EEq9teOhQMLc6UD6Yjc8sX2+CY0uOBfkuyikWsh26FgADuXjO8qeGnWBmGwpnJj0BL9Pbasi5o/unOX3dvk/ceqc7v7Nv0NFxR/tihWyqA86bCYjpFtYT5eej9613Qp1cu3z/aempLxoWP1mBRUsFPYi1YZSoRMvWaT3Jta575FDimLwHJ6D0UAgZWyXFsvypYU+r0G1MM47qNnCxSGsbkMyPzmuFckfL73KBiooeRJSBbNfRPt/DiHSP5MfpSrhqKO8oCt5vfGfsXywDniTlon3cec21uwc9D5XxnTE/zWtRA6YrqDREsnIO85rLCNHxnb5mSgM3KRmd5ZAIheeHwPeW5ski782FiBp66FqEfFYaobB/6Qsf6k/a/1qhKDe3dISpjBymcjG2Kwq20lHZdHUc29WTuoN78T3ri7aT6Psyc2gldWQrgi3A5vNlfzLK5RLQ0j/kckxIw934ztjbQ05mDTkQtRYoJwX5bsm7hga79ETRAUdnt3Ulqr/ze6USCGnOVH4uXffI9xbOTLqycoyYTKtcpPlsehpwErjiyN+aDybU6uwbdJ45EhRsdE3ktfP1SBRU0O+A7mf8nzSRvDeNZHy3XEjSct+wjeM7e5whUgoOLlykO0IZJYE4p8vRvp70kmvCIC7B8SQ9lqThUQvjShSQtAUhtEPQmRvPbuvyqKHQtVbse6GicoZ6bi1/ahgt/UPOTVe258Hn92GuteDNLy+p2yvR+214JebI2QbplgjEuYb050BsbC1n7K4YFhUm58oolwUh9+qM+5vCdO6u25x3BwDHyWnhmgY5SUOGKS2E+Fsb4bQmPtXb7YS5ecMNCYOoXIA00tpdSkPa+OnX0PnFKL8MDo9ipr2A6XteRUv/EHJz8QSSOwtOiHWPxCHZ5fyRp3q73cRhhK4EDbJrdw86vpueJ9LgKRfTkAeO3GGw7cOP3u6qHclzpeDiDovunFGQkw32jZ8z/zn7RKMaMycCUdZAHue9SkHy51LwpNEomi7Rf5dSTrh46h1iyCCbZi/g83rtzptK2hQqaRcQ73y5k1x5JKLcGEOyaihaQOmiyEhQWWyFmr1cYJomapzApyKSmysGGL0Szle0cFTKoV85VJVAz69scgOJW/yGgREAYc4zVN1FD8BKvGF4z+VPDTuNUAoHfQ2tyclyZDx/pr2QyvMTaSHdcuIeeXhLIlJVQhafkNy2HPg6sdbkhpzz3S7VvrW7B4PeHXOtBeceJ78vYwdkNSS6KaZ5L1ErI1UiDdGl2lbJgk46Rt5bllejYJRauH4mIbpDelqUawdtHJcK+czoRlkK8p4yNJ/Q7r+VKCBnt3XBNC7DyEfi2IT3/vVvYaojplKIsV09zrecCkCoMhYAtA3MY2xXj7fD1MVUmAJg0QyjFpmXy5XG2W1daB3JB1OcykT3zJ0dcmkqJzTJ0Y98ZFOwGgqFUWhLrmtLAn4mQgneh7850Vv6w3Ur08C2vzramqBMtMeAhHw2kuPX9I0M89Y8tBTULf1D7jpS6OmcHNLlEoD3vdycdRr05IYcGl5Jatrk/EPPAIjsIpqaaekfcilXWZgCgNMiZb/kM5M1QdMM56e7c6ncul4AK32vfFda8HMnMNdawNG+HvcOFxL5SZdXggFA7K/MOkmKhZB0zuh969HZN4gHn9+H3x/8BjY+sB/zr7+AVUOx8Vq+D3qqMRXDfDNcyL/n+jgdaegde2LXxbFdPZ4gbx6uW7x86IXKOHSbUS4LBzXzjQ/sTxiHpECys+ecx8TYrh40TdQ4TU+i3NZRntc2kPeEtuTw+b80pBaOHU+ce7o750rAycHN4/Q+4f2AaNCGKscDEYWSP9kYtbFzecKQK7fi2g5AzV27qYWyWLKdGtoAxe9RSw9dhwYxma1RBxVxUp/cWuui/2baC25BlbRLbs56/1eS+mGqt9vj1PXz7+wbdIFL8jgQ7Q5C9TrLge9C0nltA/myqQGAdM6b4AKSVrz5YrRs+RnbW3N9a+qubcW+F7zrr9096HbM93z33wCI/cw79gy6BV171HBB4Gfnr4sVAS6+010XMLt9sxsftdPFNAFzNS5adFFpl6xI9OXFmqetG7SaM2yaqPGoF20kk4EqaQgN+JBHiBSOaQVweZwCpvvb5xKLCgff+M5oO1n3T8+478i80TrbXqktekv/EOona1A/GZf20sL8/HXJSSxLjUn6hTy2LAit7wf4z+nGvXH0KPsgtfcQZFEROdltSx4N43Xu/eq0Bfl643m5SN6d0K6tQKzFyvB3cvL62TAZFw3DUhBVKth5ni6CnVbIQkPacULvn/0Iaf3aBlTOLVejcGYSNde3lqXgJD5x653ofN+wd4xaN8c9nwXdEmmAZog/87sQTMIlA4w69gyi+VguUfTikrGIFYsWC1Ul0HOvzjgvF3q6yDzeHXvi6jJSsK/dPei097TUmiEOnhkKCb0j4P/M3TLVG64ST437/3z1p50bHxBpnOQEW0ci42HzEyud14VEKVezjQ/sBw6PoqV/yNFMHPDS7U9CavyMvKydTk582Q4a2UKFDqQQWfO0dRpVyPgpw73p0aL7O74zMnrNtUaJr2qn/Tz4csGrJFWwjEjle9PFKIYfvT0oDLlDkoE+pXZ2WpsmWOBi1VDeef6k+XxP9SarEKWdp6HpFsmtA76vt+yDXPQrSf519C+6MLt9MwpnJhPHZD4jWailY0/sgx5qO11lpaDXY5cujfozQpYQvDRYwBbK/2SUy8IhaZUjD29B18fiBI+6hicHFNPDvuUP4vob0/e86n6kINY5y4lyGpMW5Hp7zHwxafmw5XdYWZ15UHQFIoKTTlaLmW+Gy/Otg64I6U4H+ME6pUBBNb6zx3nwOC8QxQ2H7iufiTR+SY8W7QVjW/LI18easyyKQfB7vH4o2IlgH09359AwHqW+ld46zKAIwIvqLOW6GupnmsE7zZslhIuldCpF2nVlm5k/iHlipnq7E3aqti8tczQMNWtd/Slks6LiwHulGUKBSNlgVCqpGk2plPt/obAWsAVb9iejXBaI/MomT8iuedpi5LGb3P8sCwf4ucj5naE7lnnfb3v3GJqfWAkAOPkr5xx1QoNPS78fzam3ulrrIfQuYKq322U7lMY0XpuCW16bgzJNINPFTLYhFFbOe8po0FCBhLaBeBsstVSd52VsV4/XJi3YeQ4hPSX4ueTOO/YMYtnpOSw7PRcsilx7qs4LtGH7ZCCSbC/znOvFqXYaCdqKz4R1MNv7a4Ll6C7WCyVkuGT7eazSgKSLQSl+vFT7Q2l/uXhT++bckPOrpd8v3ycX9ZrVbbCz5zxOn8Zm5umX96UrovR44TVpIJVtZeHuUKTo4hlFbWWBRVcQVSXQay5EW+SbPz/jcnhvuO+FVO05JOB4LgW5/K6sPMRrlDJEyW13KCOjhE5Vy/NpHNUufaRpJB1BuoAcul446IOdm4vvIYWY9E7h1l+2O7R4kLrheeTC6b0z+ms3uHPZdpnPnNcc29WDtV+O0hJQEHPbXXfgGH50R6P3nLUXhKxH2fBKXLyD/dLvJqSl5ev94tAyjSwQ5zkf/mQ3JnoL7nmmVfHR9wwt5CGtXlN3pa4ZQhoVo2maUpRNWvCQrPtas+5G1Ky70QuOa56YBRAXddFG8c6+QW9nzPtwx0jjdSjFQMjvXB8nVXf83k2pgnuRNPSz5zFTlm6ZL8who1wWiEJdTWRxH34RLf1DbpXmAKT7oN2ysej2F02wtAkkBXLbl3wOXWvi5bQcII6I1NpXaGLpzygMJO2g/YDJHWotT2qunCj8DqvryL4AMW/OcyTPKjnYkNshoaNg2XbJj9LPPLTNZzALF1Cds33VUN4Jb5k5snUkomHmWiMbhKZXjvb14MbHD3nvvXUkWYmeCwHdBzv7iu2uj2uEytqtmovWz7QU0twT5XOl0VUWk5Y0En+nFQABfDqlVC3VNKOuNKITP3zfDa4Iutl/BACcsZOujBTsdFXc9dRfAYjGWceeaDe0/rFRzBeTbtFVUYb2c5zWTvteTNKgCvgaeKXJ3RaA75/HOczYsyVPGscIVuOmkucsJqpKoBNSk5a8HrXa6fZGTPV247FHPgnA57pbjsYvKKQ9nd3WFdR+FwIdGCSNT6GqP4AvxDn4p3q7PYMljzMfCRNq5ZsKXl+JtoG888OXn0vBwYWC/1OAdOwZdBNSFqEAgMO/9brUwg4STvM/PIqxd7V6Rah579BCxu/KxUdizdPW5Xqvub7VaXal6mJKaos00PAnu53r4MR2//xQ2Tbdz1ILvfao0edz8dWZHNk+7k74WW4O3u6N58nf8j5pgr+S8H6CC12IjmrcewCF8/Ec/KOet+GPet6Ge/7n/cjN1DhBT4oQ8FMwcIzf+PghrwKXDO0f39njxiHbLnO3ECxIvRiw1tp1uBXHEkXYRNvtPH6E4ziCoaZFu3EZVJVAJ+UiUWoyfXjj2xLHx9/Rmviu/E3Plhv3RgUZKtHMAQT5X0LuJPQOYaa94FwqvXYKDv10t19Yl8c5yfNNBc+XOW2ySk1c+7Xrc4B4wZP+wdKfnjVINUgNne7OOe6a96DWLRdWZmAkmKwrtGOQkHRUWrY/CW34neqNkkSZuejdDX8y0s4xF542cncUwsXy4nLnp3cPMhmWFPDyHbBPABKLtexjGkJpcklT1qy70R0LVcXSoFCnkiWNy/oa2sbBvzlPCJ0aW1MpIc+0xcT38c2aWUynaunjGMEarIW1dvayNkSgqgQ6AC9abaq323F6QNKzJJSRMMS7ye9tfGC/m5iVTlA5abRwnOrtxsxbN3p8fmKREQOVgpuay4m7jRcApKmPtoG8ly6U50tXPk0DyfuRR9dukWu+Ne/6I4/JgCCtnUuvBRq4pKsiv6uLYABAY09M36wayru8HmmGZ9lfuVikCTAuYhQsfDYT26P3zDqUpFpCKEVhXAxY8Smk8Wpo/3mpFOgkX/L6UuNPc62U71XOq9PdOfzwfTfgh++7AYVjx9190rIY1jQsc6mVNXQf5T2lxm1nz3mR3Ryj0gOGEaGkbHiei94+PLp4BlGU1tKXQjsHqkygm5lzXikxAI7TA8p7IlTCgQPRBDi+vaYiblSDgUNSyz2+3Q/YoTA5cbfxUsvKc6QwpOvXXGvBZa3jsel7XgUQCSUGBsnJCcQaO3ccRGffoEcthVz9JEfJyDx65UiXtMkNfjpaGTxC6HwhTuO9ZT1mB6/HRG8h6KIpg5P0/zJ4qlQ/dLv0M2/fG/5boxSXXgqaVqFWTshjpQqmSMh3ma+PF7zOvkF3ba25l3KXpd1JCvBKqJkHn98HAIlC6fq7UkvXSbi0F4uEFtL8Xy4AvCbpmcVCmpa+FNo5UGUCPb+yKchtp0VqhlBKg6OL1fxP3eoyBIbOS/NuoEBNyxnD+8sSaxpay2Qgkr4HNfDmJ1YmKInj22uccZLXBGKj4xs/N+om+sYH9nvc+OSGHE7cbfDyploc7etxwpvXmW+O+8DvSkpI3k9q57LvrSN54PCoExxj74pyzjA8nNo/jauhbIwsqZamNeu4ACkovAlfH1NVrEVZSpCGdgH6/NPduaDHi4xM1d+TmREXks+7bSDvqCtZS5QUjd696iIaUlGa6u12wrzcrmR2+2Z84tY7AQD1z/iF7BntyRz4jT1nvLS4hOTE1+4exBs/N+rG07pHDjlhz1wtuGW9O//Gxw/h+L1+jqXFDP0PaelLpZ0DVSbQNZonZktq3dJg2tI/hJO/ci7VxVH+nFtVH/RK0JNCLiRrnrZu8K8ayjthPdXbHeVbqU8uRAAShi3ZJl5LQhoImTyJ39UWf06ek1sjAcNJwBSpNLqFPBtm2gvOpc8Ps/fbyKjPVUPhyk26n7JtmptfNVQMEDp61gVHtfTH0cBpC/eKfS8kBGi5yFHSEu39NV7EqH4PpXy709p0qdSMtOmUy0sjPZwYMt82kHcLtrbbkPrSi0tIc5eFKTTm7rotoZFLcFEJuSbqcwC4/D3jO3tQODPpknQBSQ19/B2t7nyppUvD6WJCa+lLpZ0DVSzQpacCC1FokHrgsQ33lQ9nlucTkqeX92ZwRW4mTM+UC93W/HSptmgXRQ5cWVWexlXuNGqn4SoPATH/TYS2p/zf1ltMbsglhJ3ULrXLXSVgf2gXoNeJ1KqmOpc7jXNsV09iJyONiWO7evDanTe56FogWZZQQxohQ++tEm5bQ9MkafYXHaVLaMMwEcpLE0LbQN55o6QpLUTamGM6DS4O0occiIyQhfNRSH/DwAjqn3kOjXsPJEos8vnxM46pC9+83l2rFMWibV9MA0FIbZ5CXCpvugLSpUJq6UupnQNVKNBJGwDAdHtjiTPhwpIvJsqP6OwbdBGj8j70hJFpBqSb3Fd/6ZGS19VGU/2ZPE/yn2kaLr1bZMHnNEgO+8TdxuWRIa8/titOv2rmouNMeKQ1xanebmd8TROe9EFnsBEhtWl+N6RZpb03KbS4KFDIc5GsJOMiF5P2/hpntE4T5qECErI9TJ+gNWK5gyq1gHOnVCl/HgJ92LlzChnRJWic5b1I55HyIb+dtuOg4P39wW94uVvaBuIiJFI409DPcSqDjJici+OAPurUwF0irq9NegZR+UMsWgk6AWrpRzC0ZNo5UIUCPS2pD5CcZFKLB4CRx24q6ZsrUXN9ayJ3Ba/X9bHDOPzRte5zKaB+4a8+6P4upX3riab7pPO+aOFP6iM3U+MJVQo2RmJSO+dklYIzN1PjafdrdyfdJ2VuldBzJ+2Tm4tpDAoKHm8dibh3+n2zv3q3EHo2IWESWugkRVWJMGef9JiRBkUZnUphq2k3jpHJDbkENy5pD7ZtenWcfkE+T74XLgyhxVlr9yGBrasUhcag7jMh6TRJn8lFLtfa6v7+/cFv4MHn92H3W34ORx7e4s2X+WZg5CObvM9oUJclBwlpGD32wWQU6HTXhSiPyztag7lf5PmLW7UoArX0l/GjJdPOAcDYK1wi6XLCGPNjAJXxJsAqAKcvY3OWAlmfrn5UW3+Aa7NPN1lrbyh/2rWFqhLoFwNjzLPW2jctdTsWE1mfrn5UW3+A6uzTtYqqo1wyZMiQ4ScVmUDPkCFDhirBT7JAf3ypG3AZkPXp6ke19Qeozj5dk/iJ5dAzZMiQodrwk6yhZ8iQIUNVIRPoGTJkyFAlqCqBboxZZoz5rjFmyBjzA2PMxwPnrDTG/LU45x5x7J3GmGFjzIgx5qNXtvVhLEKfxowxB40x+40xz17Z1odRYZ+uM8Z8xRhzoHjubeLYtfqeSvXpqntPhDEmZ4z5vjHmycAxY4z5k+K7OGCM6RHHrrr3VPWw1lbNDwADoKX4dx2A7wC4Q52zC8BDxb9vAHAGQD2AHICjANYX/x8CsOla7lPx/zEAq5a6Hwvo038B8LHi37cA+Hrx72v5PQX7dLW+J9G2fw/g8wCeDBz75wD+b7H/dwD4ztX8nqr9p6o0dBthqvhvXfFHW30tgOXGGAOgBZHwmwfwZgAj1tpRa+0cgC8C+MUr0/J0XGKfrkpU2KdNAL5ePP8wgLXGmDZc2+8prU9XLYwxHQB6AXwq5ZRfBPDZYv+/DaDVGLMaV+l7qnZUlUAH3PZwP4BTAPZaa7+jTvlTALcCOAHgIICd1toCgHYAx8V548XPlhyX0CcgEip/Z4z5njHm3ivV5nKooE9DAN5VPPfNAG4C0IFr+z2l9Qm4St8TgE8CeABAsr5jhLT3cdW+p2pG1Ql0a23eWrsF0UR5s+Qpi3gHgP0A1gDYAuBPjTErEG0ZE5e7fC2tHJfQJwB4i7W2B8DPAbjfGHPXFWl0GVTQp/8M4LqigPxtAN9HtOu4lt9TWp+Aq/A9GWN+HsApa+33Sp0W+MyW+DzDZUTVCXTCWjsJ4O8BvFMdugfAl4tbxBEAxxDxmeMAbhTndSDSeK8aLKBPsNaeKP4+BeAriLbCVw3S+mStfc1ae09RQL4XkW3gGK7h91SiT1fre3oLgF8wxowhoky2GWP+Up2T9j6u+vdUjagqgW6MucEY01r8uxHAzwCJCq4vAnh78Zw2AF0ARgEMALjZGLPOGFMP4FcBfPUKNT0Vl9InY0yzMWZ58fNmAD8L4DksMSrpkzGmtfgeAOADAJ6x1r6Ga/g9pfXpan1P1trfs9Z2WGvXInrOT1lrf0Od9lUA7y16u9wB4FVr7Uu4St9TtaN2qRuwyFgN4C+MMTlEi9WXrLVPGmPuAwBr7WMA/iOAzxhjDiLaFn7EWnsaAIwx/w7A1xBZ6D9trf3BUnRCYcF9MsasB/CVyFaKWgCft9b+7ZL0wkclfboVwGeNMXkAhwC8v3hs/hp+T8E+AWjD1fmeglB9+htEni4jAGYQ7Rav5vdU1chC/zNkyJChSlBVlEuGDBky/CQjE+gZMmTIUCXIBHqGDBkyVAkygZ4hQ4YMVYJMoGfIkOGagjHm08aYU8aYilw7jTG/Yow5ZKKkaZ+/3O1bSmReLhkyZLimUIyinUKUQ0ZH4+pzbwbwJQDbrLWvGGNeXwzeqkpkGnqGDBmuKVhrn0GUgM7BGNNpjPnbYi6cbxpjbike+rcA/sxa+0rxu1UrzIFMoGeoUhhjthbzcy8rRmL+IJBbJUP14HEAv22t/ScAPgzg0eLnGwFsNMZ8yxjzbWOMTptRVai2SNEMGQAA1toBY8xXAXwCQCOAv7TWLnk4fYbFhzGmBcBPA/hfxWhbAGgo/q4FcDOAf4Yon8w3jTG3FfPtVB0ygZ6hmvGHiHKKnAPwO0vclgyXDzUAJotJzzTGAXzbWnsBwDFjzDAiAT9wBdt3xZBRLhmqGdcjKvixHMCyJW5LhsuEYtK2Y8aYfwm4snjdxcN/BeBtxc9XIaJgRpeinVcCmUDPUM14HMAfAPgfAB5a4rZkWCQYY74AYB+ALmPMuDHm/QB+HcD7jTFDAH6AuDrS1wC8bIw5BOAbAP6DtfblpWj3lUDmtpihKmGMeS+AX7LWvquYAfH/Afg9a+1TS9y0DBkuGzKBniFDhgxVgoxyyZAhQ4YqQSbQM2TIkKFKkAn0DBkyZKgSZAI9Q4YMGaoEmUDPkCFDhipBJtAzZMiQoUqQCfQMGTJkqBL8f+yFDxe6jeKRAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "subset.plot(vmin=1, vmax=44)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/notebooks/data/vmm_flowdata.csv b/notebooks/data/vmm_flowdata.csv
new file mode 100644
index 0000000..e69febc
--- /dev/null
+++ b/notebooks/data/vmm_flowdata.csv
@@ -0,0 +1,11698 @@
+Time,L06_347,LS06_347,LS06_348
+2009-01-01 00:00:00,0.1374166666666667,0.09749999999999999,0.01683333333333334
+2009-01-01 03:00:00,0.13125,0.08883333333333332,0.016416666666666673
+2009-01-01 06:00:00,0.11350000000000003,0.09125,0.016750000000000004
+2009-01-01 09:00:00,0.13575,0.09149999999999998,0.016250000000000004
+2009-01-01 12:00:00,0.1409166666666667,0.09616666666666668,0.017000000000000005
+2009-01-01 15:00:00,0.09916666666666667,0.09166666666666667,0.01758333333333334
+2009-01-01 18:00:00,0.13266666666666668,0.09016666666666666,0.016250000000000004
+2009-01-01 21:00:00,0.10941666666666666,0.09116666666666666,0.016000000000000004
+2009-01-02 00:00:00,0.13383333333333336,0.09041666666666666,0.01608333333333334
+2009-01-02 03:00:00,0.09208333333333334,0.08866666666666666,0.016000000000000004
+2009-01-02 06:00:00,0.11291666666666667,0.09141666666666665,0.01633333333333334
+2009-01-02 09:00:00,0.14191666666666666,0.09708333333333331,0.016416666666666666
+2009-01-02 12:00:00,0.14783333333333334,0.10191666666666667,0.016416666666666673
+2009-01-02 15:00:00,0.10791666666666667,0.10025,0.016416666666666673
+2009-01-02 18:00:00,0.14358333333333334,0.09841666666666665,0.016750000000000004
+2009-01-02 21:00:00,0.11308333333333331,0.09808333333333334,0.01683333333333334
+2009-01-03 00:00:00,0.13583333333333333,0.09216666666666666,0.01683333333333334
+2009-01-03 03:00:00,0.08324999999999999,0.07999999999999997,0.01608333333333334
+2009-01-03 06:00:00,0.11941666666666666,0.08024999999999999,0.01541666666666667
+2009-01-03 09:00:00,0.12458333333333334,0.08441666666666664,0.015833333333333338
+2009-01-03 12:00:00,0.09166666666666666,0.08825,0.016250000000000004
+2009-01-03 15:00:00,0.125,0.08466666666666665,0.016500000000000004
+2009-01-03 18:00:00,0.12158333333333332,0.08208333333333331,0.015833333333333338
+2009-01-03 21:00:00,0.10716666666666667,0.09249999999999999,0.016000000000000004
+2009-01-04 00:00:00,0.13525,0.09116666666666666,0.01633333333333334
+2009-01-04 03:00:00,0.13558333333333336,0.09158333333333331,0.01608333333333334
+2009-01-04 06:00:00,0.11716666666666668,0.09516666666666666,0.016000000000000004
+2009-01-04 09:00:00,0.10899999999999999,0.10516666666666667,0.018000000000000002
+2009-01-04 12:00:00,0.15741666666666662,0.11075000000000002,0.01841666666666667
+2009-01-04 15:00:00,0.16041666666666665,0.11375000000000002,0.018416666666666675
+2009-01-04 18:00:00,0.15558333333333332,0.10908333333333332,0.018000000000000006
+2009-01-04 21:00:00,0.15116666666666664,0.105,0.01733333333333334
+2009-01-05 00:00:00,0.14875,0.10283333333333335,0.017000000000000005
+2009-01-05 03:00:00,0.10908333333333332,0.10525000000000001,0.017750000000000005
+2009-01-05 06:00:00,0.1465,0.11516666666666665,0.01766666666666667
+2009-01-05 09:00:00,0.1615,0.11458333333333333,0.02158333333333333
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+2012-12-30 21:00:00,1.06975,1.06975,0.10141666666666667
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+2013-01-01 00:00:00,1.6883333333333332,1.6883333333333332,0.20733333333333337
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+2013-01-01 12:00:00,1.7100000000000002,1.7100000000000002,0.12958333333333333
+2013-01-01 15:00:00,1.4200000000000002,1.4200000000000002,0.09633333333333333
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+2013-01-01 21:00:00,0.89825,0.89825,0.07716666666666666
+2013-01-02 00:00:00,0.86,0.86,0.075
diff --git a/notebooks/visualization-01-matplotlib.ipynb b/notebooks/visualization-01-matplotlib.ipynb
new file mode 100644
index 0000000..9b82d09
--- /dev/null
+++ b/notebooks/visualization-01-matplotlib.ipynb
@@ -0,0 +1,628 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<p><font size=\"6\"><b>Visualization - Matplotlib</b></font></p>\n",
+    "\n",
+    "> *DS Python for GIS and Geoscience*  \n",
+    "> *September, 2025*\n",
+    ">\n",
+    "> *© 2025, Joris Van den Bossche and Stijn Van Hoey. Licensed under [CC BY 4.0 Creative Commons](http://creativecommons.org/licenses/by/4.0/)*\n",
+    "\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Matplotlib"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[Matplotlib](http://matplotlib.org/) is a Python package used widely throughout the scientific Python community to produce high quality 2D publication graphics. It transparently supports a wide range of output formats including PNG (and other raster formats), PostScript/EPS, PDF and SVG and has interfaces for all of the major desktop GUI (graphical user interface) toolkits. It is a great package with lots of options.\n",
+    "\n",
+    "However, matplotlib is...\n",
+    "\n",
+    "> The 800-pound gorilla — and like most 800-pound gorillas, this one should probably be avoided unless you genuinely need its power, e.g., to make a **custom plot** or produce a **publication-ready** graphic.\n",
+    "\n",
+    "> (As we’ll see, when it comes to statistical visualization, the preferred tack might be: “do as much as you easily can in your convenience layer of choice [nvdr e.g. directly from Pandas, or with seaborn], and then use matplotlib for the rest.”)\n",
+    "\n",
+    "(quote used from [this](https://dansaber.wordpress.com/2016/10/02/a-dramatic-tour-through-pythons-data-visualization-landscape-including-ggplot-and-altair/) blogpost)\n",
+    "\n",
+    "And that's we mostly did, just use the `.plot` function of Pandas. So, why do we learn matplotlib? Well, for the *...then use matplotlib for the rest.*; at some point, somehow!\n",
+    "\n",
+    "Matplotlib comes with a convenience sub-package called ``pyplot`` which, for consistency with the wider matplotlib community, should always be imported as ``plt``:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## - dry stuff - The matplotlib `Figure`, `Axes` and `Axis`\n",
+    "\n",
+    "At the heart of **every** plot is the figure object. The \"Figure\" object is the top level concept which can be drawn to one of the many output formats, or simply just to screen. Any object which can be drawn in this way is known as an \"Artist\" in matplotlib.\n",
+    "\n",
+    "Lets create our first artist using pyplot, and then show it:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig = plt.figure()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "On its own, drawing the figure artist is uninteresting and will result in an empty piece of paper (that's why we didn't see anything above).\n",
+    "\n",
+    "By far the most useful artist in matplotlib is the **Axes** artist. The Axes artist represents the \"data space\" of a typical plot, a rectangular axes (the most common, but not always the case, e.g. polar plots) will have 2 (confusingly named) **Axis** artists with tick labels and tick marks.\n",
+    "\n",
+    "![](../img/matplotlib_fundamentals.png)\n",
+    "\n",
+    "There is no limit on the number of Axes artists which can exist on a Figure artist. Let's go ahead and create a figure with a single Axes artist, and show it using pyplot:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ax = plt.axes()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Matplotlib's ``pyplot`` module makes the process of creating graphics easier by allowing us to skip some of the tedious Artist construction. For example, we did not need to manually create the Figure artist with ``plt.figure`` because it was implicit that we needed a figure when we created the Axes artist.\n",
+    "\n",
+    "Under the hood matplotlib still had to create a Figure artist, its just we didn't need to capture it into a variable."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## - essential stuff - Matplotlib works 'Object Oriented'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Objects have characteristics (attributes) and can do things (methods)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Some example data:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x = np.linspace(0, 5, 10)\n",
+    "y = x ** 10"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Create a `Figure` and `Axes` object to plot data. Use the `Axes` object to plot x and y data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "ax.plot(x, y, '-')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Using the existing objects, further adjustment can be done using the existing methods, e.g."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from matplotlib import ticker\n",
+    "\n",
+    "fig, ax = plt.subplots()\n",
+    "ax.plot(x, y, '-')\n",
+    "\n",
+    "# Add a title\n",
+    "ax.set_title(\"My data\")\n",
+    "\n",
+    "# Add custom formatting instead of the default scientific formatting:\n",
+    "ax.yaxis.set_major_formatter(ticker.FormatStrFormatter(\"%.1f\"))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "By using the Matplotlib objects, we now have **full control** of where the plot axes are placed, and we can add more than one axis to the Figure:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig, ax1 = plt.subplots()\n",
+    "ax1.plot(x, y, '-')\n",
+    "ax1.set_ylabel('y')\n",
+    "\n",
+    "ax2 = fig.add_axes([0.2, 0.5, 0.4, 0.3]) # inset axes\n",
+    "ax2.set_xlabel('x')\n",
+    "ax2.plot(x, y*2, 'r-')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-info\" style=\"font-size:18px\">\n",
+    "\n",
+    "<b>REMEMBER</b>:\n",
+    "\n",
+    " <ul>\n",
+    "  <li>Use the <b>object oriented</b> power of Matplotlib!</li>\n",
+    "  <li>Get yourself used to writing <code>fig, ax = plt.subplots()</code></li>\n",
+    "</ul>\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig, ax = plt.subplots()\n",
+    "ax.plot(x, y, '-')\n",
+    "# ..."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## An small cheat-sheet reference for some common elements"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x = np.linspace(-1, 0, 100)\n",
+    "\n",
+    "fig, ax  = plt.subplots(figsize=(10, 7))\n",
+    "\n",
+    "# Adjust the created axes so that its topmost extent is 0.8 of the figure.\n",
+    "fig.subplots_adjust(top=0.9)\n",
+    "\n",
+    "ax.plot(x, x**2, color='0.4', label='power 2')\n",
+    "ax.plot(x, x**3, color='0.8', linestyle='--', label='power 3')\n",
+    "\n",
+    "ax.vlines(x=-0.75, ymin=0., ymax=0.8, color='0.4', linestyle='-.')  # vertical dotted line within range [0, 0.8]\n",
+    "ax.fill_between(x=x, y1=x**2, y2=1.1*x**2, color='0.85')   # color fill between the two exponential functions\n",
+    "\n",
+    "ax.axhline(y=0.1, color='0.4', linestyle='-.')   # ax-wide horizontal dotted line \n",
+    "ax.axhspan(ymin=0.65, ymax=0.75, color='0.95')   # ax-wide horizontal filled space\n",
+    "\n",
+    "fig.suptitle('Figure title', fontsize=18, \n",
+    "             fontweight='bold')\n",
+    "ax.set_title('Axes title', fontsize=16)\n",
+    "\n",
+    "ax.set_xlabel('The X axis')\n",
+    "ax.set_ylabel('The Y axis $y=f(x)$', fontsize=16)\n",
+    "\n",
+    "ax.set_xlim(-1.0, 1.1)\n",
+    "ax.set_ylim(-0.1, 1.)\n",
+    "\n",
+    "ax.text(0.5, 0.2, 'Text centered at (0.5, 0.2)\\nin data coordinates.',\n",
+    "        horizontalalignment='center', fontsize=14)\n",
+    "\n",
+    "ax.text(0.5, 0.5, 'Text centered at (0.5, 0.5)\\nin relative Axes coordinates.',\n",
+    "        horizontalalignment='center', fontsize=14, \n",
+    "        transform=ax.transAxes, color='grey')\n",
+    "\n",
+    "ax.annotate('Text pointing at (0.0, 0.75)', xy=(0.0, 0.75), xycoords=\"data\",\n",
+    "            xytext=(20, 40), textcoords=\"offset points\",\n",
+    "            horizontalalignment='left', fontsize=14,\n",
+    "            arrowprops=dict(facecolor='black', shrink=0.05, width=1))\n",
+    "\n",
+    "ax.legend(loc='lower right', frameon=True, ncol=2, fontsize=14)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Adjusting specific parts of a plot is a matter of accessing the correct element of the plot:\n",
+    "\n",
+    "<img src=\"https://matplotlib.org/stable/_images/anatomy.png\" width=\"800\"/>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For more information on legend positioning, check [this post](http://stackoverflow.com/questions/4700614/how-to-put-the-legend-out-of-the-plot) on stackoverflow!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## I do not like the style..."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**...understandable**"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Matplotlib had a bad reputation in terms of its default styling as figures created with earlier versions of Matplotlib were very Matlab-lookalike and mostly not really catchy. \n",
+    "\n",
+    "Since Matplotlib 2.0, this has changed: https://matplotlib.org/users/dflt_style_changes.html!\n",
+    "\n",
+    "However...\n",
+    "> *Des goûts et des couleurs, on ne discute pas...*\n",
+    "\n",
+    "(check [this link](https://fr.wiktionary.org/wiki/des_go%C3%BBts_et_des_couleurs,_on_ne_discute_pas) if you're not french-speaking)\n",
+    "\n",
+    "To account different tastes, Matplotlib provides a number of styles that can be used to quickly change a number of settings:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.style.available"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x = np.linspace(0, 10)\n",
+    "\n",
+    "with plt.style.context('seaborn-v0_8-colorblind'): \n",
+    "    fig, ax = plt.subplots()\n",
+    "    ax.plot(x, np.sin(x) + x + np.random.randn(50))\n",
+    "    ax.plot(x, np.sin(x) + 0.5 * x + np.random.randn(50))\n",
+    "    ax.plot(x, np.sin(x) + 2 * x + np.random.randn(50))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We should not start discussing about colors and styles, just pick **your favorite style**!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.style.use('seaborn-v0_8-whitegrid')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "or go all the way and define your own custom style, see the [official documentation](https://matplotlib.org/3.1.1/tutorials/introductory/customizing.html) or [this tutorial](https://colcarroll.github.io/yourplotlib/#/)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-info\">\n",
+    "\n",
+    "<b>REMEMBER</b>:\n",
+    "\n",
+    " <ul>\n",
+    "  <li>If you just want <b>quickly a good-looking plot</b>, use one of the available styles (<code>plt.style.use('...')</code>)</li>\n",
+    "  <li>Otherwise, the object-oriented way of working makes it possible to change everything!</li>\n",
+    "</ul>\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Interaction with Pandas/xarray"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The default plotting of Pandas/xarray relies on Matplotlib. Let's use a Pandas DataFrame as an example"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "flowdata = pd.read_csv('data/vmm_flowdata.csv', \n",
+    "                       index_col='Time', \n",
+    "                       parse_dates=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "flowdata.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### From Pandas/xarray to Matplotlib"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Under the hood, Pandas creates an Matplotlib Figure with an Axes object. The `Axis` object(s) returned can be further adjusted:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ax = flowdata.plot()\n",
+    "ax.set_title('discharge [$m^3/s$]');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Pandas/xarray provide a number of convenience options as keyword parameters:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "axs = flowdata.plot(subplots=True, sharex=True,\n",
+    "                    figsize=(16, 8), colormap='viridis', # Dark2\n",
+    "                    fontsize=15, rot=0)\n",
+    "axs[0].set_title('discharge [$m^3/s$]');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### From Matplotlib to Pandas/xarray"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Starting from 'empty' Matplotlib objects, we can tell Pandas/xarray to use these to plot new data on:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig, (ax0, ax1) = plt.subplots(2, 1) #prepare a Matplotlib figure\n",
+    "\n",
+    "flowdata.plot(ax=ax0) # use Pandas for the plotting"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig, (ax0, ax1) = plt.subplots(2, 1, figsize=(16, 6)) #provide with matplotlib 2 axis\n",
+    "\n",
+    "flowdata[[\"L06_347\", \"LS06_347\"]].plot(ax=ax0) # plot the two timeseries of the same location on the first plot\n",
+    "flowdata[\"LS06_348\"].plot(ax=ax1, color='0.2') # plot the other station on the second plot\n",
+    "\n",
+    "# further adapt with matplotlib\n",
+    "ax0.set_ylabel(\"L06_347\")\n",
+    "ax1.set_ylabel(\"LS06_348\")\n",
+    "ax1.legend()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-info\" style=\"font-size:18px\">\n",
+    "\n",
+    "**Remember** \n",
+    "\n",
+    "`fig.savefig()` to save your Figure object!\n",
+    "\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Need more matplotlib inspiration?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-info\">\n",
+    "\n",
+    " <b>Remember</b>: \n",
+    "\n",
+    "* You can do anything with matplotlib, but at a cost... <a href=\"http://stackoverflow.com/questions/tagged/matplotlib\">stackoverflow</a>\n",
+    "* The preformatting of Pandas provides mostly enough flexibility for quick analysis and draft reporting. It is not for paper-proof figures or customization\n",
+    "\n",
+    "If you take the time to make your perfect/spot-on/greatest-ever matplotlib-figure: Make it a <b>reusable function</b>!\n",
+    "\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For more in-depth material:\n",
+    "* http://www.labri.fr/perso/nrougier/teaching/matplotlib/\n",
+    "* notebooks in matplotlib section: http://nbviewer.jupyter.org/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/Index.ipynb#4.-Visualization-with-Matplotlib\n",
+    "* main reference: [matplotlib homepage](http://matplotlib.org/)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-info\" style=\"font-size:18px\">\n",
+    "\n",
+    "**Galleries!**\n",
+    "\n",
+    "Galleries are great to get inspiration, see the plot you want, and check the code how it is created:\n",
+    "    \n",
+    "* [matplotlib gallery](http://matplotlib.org/gallery.html) is an important resource to start from\n",
+    "* [seaborn gallery](https://seaborn.pydata.org/examples/index.html)\n",
+    "* The Python Graph Gallery (https://python-graph-gallery.com/)\n",
+    "\n",
+    "</div>"
+   ]
+  }
+ ],
+ "metadata": {
+  "jupytext": {
+   "default_lexer": "ipython3"
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.11"
+  },
+  "nav_menu": {},
+  "toc": {
+   "navigate_menu": true,
+   "number_sections": true,
+   "sideBar": true,
+   "threshold": 6,
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+   "toc_window_display": true
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+  "toc_position": {
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+}
diff --git a/notebooks/visualization-02-geopandas.ipynb b/notebooks/visualization-02-geopandas.ipynb
new file mode 100644
index 0000000..51ba139
--- /dev/null
+++ b/notebooks/visualization-02-geopandas.ipynb
@@ -0,0 +1,415 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<p><font size=\"6\"><b> Visualizing spatial data with Python: GeoPandas</b></font></p>\n",
+    "\n",
+    "\n",
+    "> *DS Python for GIS and Geoscience*  \n",
+    "> *October, 2025*\n",
+    ">\n",
+    "> *© 2025, Joris Van den Bossche and Stijn Van Hoey. Licensed under [CC BY 4.0 Creative Commons](https://creativecommons.org/licenses/by/4.0/)*\n",
+    "\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import geopandas\n",
+    "\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "countries = geopandas.read_file(\"data/ne_110m_admin_0_countries.zip\")\n",
+    "countries = countries[countries['continent'] != \"Antarctica\"]\n",
+    "cities = geopandas.read_file(\"data/ne_110m_populated_places.zip\")\n",
+    "rivers = geopandas.read_file(\"data/ne_50m_rivers_lake_centerlines.zip\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## GeoPandas visualization functionality"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "GeoPandas itself provides some visualization functionality, and together with matplotlib for further customization, you can already get decent results for visualizing vector data."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Basic plot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "countries.plot()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-info\" style=\"font-size:120%\">\n",
+    "\n",
+    "**NOTE**: <br>\n",
+    "\n",
+    "Making a quick interactive plot is also available as the `.explore()` method on a GeoDataFrame or GeoSeries, using the Folium package. See the [visualization-04-interactive notebook](./visualization-04-interactive.ipynb) for more information on interactive plotting packages or check the [GeoPandas documentation](https://geopandas.org/en/stable/docs/user_guide/interactive_mapping.html) for more examples.\n",
+    "\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "countries.explore()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Adjusting the figure size"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "countries.plot(figsize=(15, 6))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Removing the box / x and y coordinate labels"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ax = countries.plot(figsize=(15, 6))\n",
+    "ax.set_axis_off()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Coloring based on column values\n",
+    "\n",
+    "Let's first create a new column with the GDP per capita:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "countries = countries[(countries['pop_est'] >0 ) & (countries['name'] != \"Antarctica\")]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "countries['gdp_per_cap'] = countries['gdp_md_est'] / countries['pop_est'] * 100"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "and now we can use this column to color the polygons:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ax = countries.plot(figsize=(15, 6), column='gdp_per_cap', legend=True)\n",
+    "ax.set_axis_off()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Using a classification scheme to bin the values (using [`mapclassify`](https://pysal.org/mapclassify/)):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ax = countries.plot(figsize=(15, 6), column='gdp_per_cap', scheme='quantiles', legend=True)\n",
+    "ax.set_axis_off()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Combining different dataframes on a single plot\n",
+    "\n",
+    "The `.plot` method returns a matplotlib Axes object, which can then be re-used to add additional layers to that plot with the `ax=` keyword:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ax = countries.plot(figsize=(15, 6))\n",
+    "cities.plot(ax=ax, color='red', markersize=10)\n",
+    "ax.set_axis_off()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ax = countries.plot(edgecolor='k', facecolor='none', figsize=(8, 8))\n",
+    "rivers.plot(ax=ax)\n",
+    "cities.plot(ax=ax, color='C1')\n",
+    "ax.set(xlim=(-20, 60), ylim=(-40, 40))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Adding a background map with contextily"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The contextily package allow to easily add a web-tile based background (basemap) to your GeoPandas plots.\n",
+    "\n",
+    "Currently, the only requirement is that your data is already in the WebMercator projection (EPSG:3857)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# selecting the cities in Europe\n",
+    "cities_europe = cities[cities.within(countries[countries['continent'] == 'Europe'].union_all())]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# converting to WebMercator\n",
+    "cities_europe2 = cities_europe.to_crs(epsg=3857)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ax = cities_europe2.plot()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import contextily"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# import matplotlib\n",
+    "# matplotlib.rcParams['figure.dpi'] = 300"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ax = cities_europe2.plot(figsize=(10, 6))\n",
+    "contextily.add_basemap(ax)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ax = cities_europe2.plot(figsize=(10, 6))\n",
+    "contextily.add_basemap(ax, source=contextily.providers.NASAGIBS.BlueMarble)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Projection-aware maps with Cartopy\n",
+    "\n",
+    "Cartopy is the base matplotlib cartographic library, and it is used by `geoplot` under the hood to provide projection-awareness (http://scitools.org.uk/cartopy/docs/latest/index.html).\n",
+    "\n",
+    "The following example is taken from the docs: http://geopandas.readthedocs.io/en/latest/gallery/cartopy_convert.html#sphx-glr-gallery-cartopy-convert-py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from cartopy import crs as ccrs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Define the CartoPy CRS object.\n",
+    "crs = ccrs.AlbersEqualArea()\n",
+    "\n",
+    "# This can be converted into a `proj4` string/dict compatible with GeoPandas\n",
+    "crs_proj4 = crs.proj4_init\n",
+    "countries_ae = countries.to_crs(crs_proj4)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Here's what the plot looks like in GeoPandas\n",
+    "countries_ae.plot()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Here's what the plot looks like when plotting with cartopy\n",
+    "fig, ax = plt.subplots(subplot_kw={'projection': crs})\n",
+    "ax.add_geometries(countries_ae['geometry'], crs=crs)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Here's what the plot looks like when plotting with cartopy and geopandas combined\n",
+    "fig, ax = plt.subplots(subplot_kw={'projection': crs})\n",
+    "countries_ae['geometry'].plot(ax=ax)\n",
+    "ax.set_global()\n",
+    "ax.gridlines()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**For more on cartopy**, see the [visualization-03-cartopy.ipynb](visualization-03-cartopy.ipynb) notebook."
+   ]
+  }
+ ],
+ "metadata": {
+  "jupytext": {
+   "default_lexer": "ipython3"
+  },
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+   "name": "python3"
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+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.11"
+  },
+  "toc-autonumbering": false,
+  "toc-showcode": false,
+  "toc-showmarkdowntxt": false,
+  "toc-showtags": false,
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+ "nbformat_minor": 4
+}
diff --git a/notebooks/visualization-03-cartopy.ipynb b/notebooks/visualization-03-cartopy.ipynb
new file mode 100644
index 0000000..44955c0
--- /dev/null
+++ b/notebooks/visualization-03-cartopy.ipynb
@@ -0,0 +1,797 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<p><font size=\"6\"><b>Creating maps with Cartopy</b></font></p>\n",
+    "\n",
+    "\n",
+    "> *DS Python for GIS and Geoscience*  \n",
+    "> *October, 2025*\n",
+    ">\n",
+    "> *© 2025, Joris Van den Bossche and Stijn Van Hoey. Licensed under [CC BY-SA 4.0 Creative Commons](https://creativecommons.org/licenses/by-sa/4.0/)* Adapted from material from Phil Elson and Ryan Abernathey (see below).\n",
+    "\n",
+    "---\n",
+    "\n",
+    "# Maps in Scientific Python\n",
+    "\n",
+    "Making maps is a fundamental part of GIS and geoscience research.\n",
+    "Maps differ from regular figures in the following principle ways:\n",
+    "\n",
+    "- Maps require a *projection* of geographic coordinates on the 3D Earth to the 2D space of your figure.\n",
+    "- Maps often include extra decorations besides just our data (e.g. continents, country borders, etc.)\n",
+    "\n",
+    "The maps we have made up to now, for example using the GeoPandas `plot()` method, assume the data can be plotted as is on the figure. This works fine if your data is already in projected coordinates, and has a limited extent (small study area). When mapping data of a larger area of the full globe, properly taking into account the projection becomes much more important!\n",
+    "\n",
+    "In this notebook, we will learn about [Cartopy](https://scitools.org.uk/cartopy/docs/latest/), one of the most common packages for making maps within python.\n",
+    "\n",
+    "### Credit: Phil Elson and Ryan Abernathey\n",
+    "\n",
+    "Lots of the material in this notebook was adopted from https://earth-env-data-science.github.io/intro.html by Ryan Abernathey, which itself was adopted from [Phil Elson](https://pelson.github.io/)'s excellent [Cartopy Tutorial](https://github.com/SciTools/cartopy-tutorial). Phil is the creator of Cartopy and published his tutorial under an [open license](http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/), meaning that we can copy, adapt, and redistribute it as long as we give proper attribution. **THANKS PHIL AND RYAN!** 👏👏👏"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Background: Projections"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Most of our media for visualization *are* flat\n",
+    "\n",
+    "Our two most common media are flat:\n",
+    "\n",
+    " * Paper\n",
+    " * Screen\n",
+    "\n",
+    "![](https://raw.githubusercontent.com/SciTools/cartopy-tutorial/master/static/flat_medium.jpg)\n",
+    "\n",
+    "### [Map] Projections: Taking us from spherical to flat\n",
+    "\n",
+    "A map projection (or more commonly refered to as just \"projection\") is:\n",
+    "\n",
+    "> a systematic transformation of the latitudes and longitudes of locations from the surface of a sphere or an ellipsoid into locations on a plane. [[Wikipedia: Map projection](https://en.wikipedia.org/wiki/Map_projection)].\n",
+    "\n",
+    "### The major problem with map projections\n",
+    "\n",
+    "![orange peel](https://raw.githubusercontent.com/SciTools/cartopy-tutorial/master/static/orange_peel.jpg)\n",
+    "\n",
+    " * The surface of a sphere is topologically different to a 2D surface, therefore we *have* to cut the sphere *somewhere*\n",
+    " * A sphere's surface cannot be represented on a plane without distortion.\n",
+    " \n",
+    "There are many different ways to make a projection, and we will not attempt to explain all of the choices and tradeoffs here. Instead, you can read Phil's [original tutorial](https://github.com/SciTools/cartopy-tutorial/blob/master/tutorial/projections_crs_and_terms.ipynb) for a great overview of this topic.\n",
+    "Instead, we will dive into the more practical sides of Caropy usage."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import xarray as xr"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Introducing Cartopy\n",
+    "\n",
+    "https://scitools.org.uk/cartopy/docs/latest/\n",
+    "\n",
+    "Cartopy makes use of the powerful [PROJ.4](https://proj4.org/), numpy and shapely libraries and includes a programatic interface built on top of Matplotlib for the creation of publication quality maps.\n",
+    "\n",
+    "Key features of cartopy are its object oriented projection definitions, and its ability to transform points, lines, vectors, polygons and images between those projections.\n",
+    "\n",
+    "### Cartopy Projections and other reference systems\n",
+    "\n",
+    "In Cartopy, each projection is a class.\n",
+    "Most classes of projection can be configured in projection-specific ways, although Cartopy takes an opinionated stance on sensible defaults.\n",
+    "\n",
+    "Let's create a Plate Carree projection instance.\n",
+    "\n",
+    "To do so, we need cartopy's crs module. This is typically imported as ``ccrs`` (Cartopy Coordinate Reference Systems)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import cartopy.crs as ccrs\n",
+    "import cartopy"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Cartopy's projection list tells us that the Plate Carree projection is available with the ``ccrs.PlateCarree`` class: https://scitools.org.uk/cartopy/docs/latest/reference/projections.html#cartopy-projections \n",
+    "\n",
+    "**Note:** we need to *instantiate* the class in order to do anything projection-y with it!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ccrs.PlateCarree()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Drawing a map\n",
+    "\n",
+    "Cartopy optionally depends upon matplotlib, and each projection knows how to create a matplotlib Axes (or AxesSubplot) that can represent itself.\n",
+    "\n",
+    "The Axes that the projection creates is a [cartopy.mpl.geoaxes.GeoAxes](https://scitools.org.uk/cartopy/docs/latest/matplotlib/geoaxes.html#cartopy.mpl.geoaxes.GeoAxes). This Axes subclass overrides some of matplotlib's existing methods, and adds a number of extremely useful ones for drawing maps:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "fig, ax = plt.subplots(subplot_kw={'projection': ccrs.PlateCarree()})\n",
+    "ax"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "That was a little underwhelming, but we can see that the Axes created is indeed one of those GeoAxes[Subplot] instances.\n",
+    "\n",
+    "One of the most useful methods that this class adds on top of the standard matplotlib Axes class is the ``coastlines`` method. With no arguments, it will add the Natural Earth ``1:110,000,000`` scale coastline data to the map."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig, ax = plt.subplots(subplot_kw={'projection': ccrs.PlateCarree()})\n",
+    "ax.coastlines()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Projection classes have options we can use to customize the map"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ccrs.PlateCarree?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig, ax = plt.subplots(subplot_kw={'projection': ccrs.PlateCarree(central_longitude=180)})\n",
+    "ax.coastlines()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Useful methods of a GeoAxes\n",
+    "\n",
+    "The [cartopy.mpl.geoaxes.GeoAxes](https://scitools.org.uk/cartopy/docs/latest/matplotlib/geoaxes.html#cartopy.mpl.geoaxes.GeoAxes) class adds a number of useful methods.\n",
+    "\n",
+    "Let's take a look at:\n",
+    "\n",
+    " * [set_global](https://scitools.org.uk/cartopy/docs/latest/matplotlib/geoaxes.html#cartopy.mpl.geoaxes.GeoAxes.set_global) - zoom the map out as much as possible\n",
+    " * [set_extent](https://scitools.org.uk/cartopy/docs/latest/matplotlib/geoaxes.html#cartopy.mpl.geoaxes.GeoAxes.set_extent) - zoom the map to the given bounding box\n",
+    " \n",
+    "\n",
+    " * [gridlines](https://scitools.org.uk/cartopy/docs/latest/matplotlib/geoaxes.html#cartopy.mpl.geoaxes.GeoAxes.graticule) - add a graticule (and optionally labels) to the axes\n",
+    " * [coastlines](https://scitools.org.uk/cartopy/docs/latest/matplotlib/geoaxes.html#cartopy.mpl.geoaxes.GeoAxes.coastlines) - add Natural Earth coastlines to the axes\n",
+    " * [stock_img](https://scitools.org.uk/cartopy/docs/latest/matplotlib/geoaxes.html#cartopy.mpl.geoaxes.GeoAxes.stock_img) - add a low-resolution Natural Earth background image to the axes\n",
+    " \n",
+    " \n",
+    " * [imshow](https://matplotlib.org/api/_as_gen/matplotlib.axes.Axes.imshow.html#matplotlib.axes.Axes.imshow) - add an image (numpy array) to the axes\n",
+    " * [add_geometries](https://scitools.org.uk/cartopy/docs/latest/matplotlib/geoaxes.html#cartopy.mpl.geoaxes.GeoAxes.add_geometries) - add a collection of geometries (Shapely / GeoPandas) to the axes\n",
+    " \n",
+    "### Some More Examples of Different Global Projections"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "projections = [ccrs.PlateCarree(),\n",
+    "               ccrs.Robinson(),\n",
+    "               ccrs.Mercator(),\n",
+    "               ccrs.Orthographic(),\n",
+    "               ccrs.InterruptedGoodeHomolosine()\n",
+    "              ]\n",
+    "\n",
+    "\n",
+    "for proj in projections:\n",
+    "    fig, ax = plt.subplots(subplot_kw={'projection': proj})\n",
+    "    ax.stock_img()\n",
+    "    ax.coastlines()\n",
+    "    ax.set_title(f'{type(proj)}')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Regional Maps\n",
+    "\n",
+    "To create a regional map, we use the `set_extent` method of GeoAxis to limit the size of the region."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ax.set_extent?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "central_lon, central_lat = -10, 45\n",
+    "extent = [-40, 20, 30, 60]\n",
+    "fig, ax = plt.subplots(subplot_kw={'projection': ccrs.Orthographic(central_lon, central_lat)})\n",
+    "ax.set_extent(extent)\n",
+    "ax.gridlines()\n",
+    "ax.coastlines(resolution='50m')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Adding features to the map\n",
+    "\n",
+    "To give our map more styles and details, we add `cartopy.feature` objects.\n",
+    "Many useful features are built in. These \"default features\" are at coarse (110m) resolution.\n",
+    "\n",
+    "Name | Description\n",
+    "-----|------------\n",
+    "`cartopy.feature.BORDERS` | Country boundaries\n",
+    "`cartopy.feature.COASTLINE` | Coastline, including major islands\n",
+    "`cartopy.feature.LAKES` | Natural and artificial lakes\n",
+    "`cartopy.feature.LAND` | Land polygons, including major islands\n",
+    "`cartopy.feature.OCEAN` | Ocean polygons\n",
+    "`cartopy.feature.RIVERS` | Single-line drainages, including lake centerlines\n",
+    "`cartopy.feature.STATES` | (limited to the United States at this scale)\n",
+    "\n",
+    "Below we illustrate these features in a customized map of North America."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import cartopy.feature as cfeature\n",
+    "import numpy as np\n",
+    "\n",
+    "central_lat = 37.5\n",
+    "central_lon = -96\n",
+    "extent = [-120, -70, 24, 50.5]\n",
+    "central_lon = np.mean(extent[:2])\n",
+    "central_lat = np.mean(extent[2:])\n",
+    "\n",
+    "fig, ax = plt.subplots(subplot_kw={'projection': ccrs.AlbersEqualArea(central_lon, central_lat)}, \n",
+    "                       figsize=(12, 6))\n",
+    "ax.set_extent(extent)\n",
+    "\n",
+    "ax.add_feature(cartopy.feature.OCEAN)\n",
+    "ax.add_feature(cartopy.feature.LAND, edgecolor='black')\n",
+    "ax.add_feature(cartopy.feature.LAKES, edgecolor='black')\n",
+    "ax.add_feature(cartopy.feature.RIVERS)\n",
+    "ax.gridlines()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If we want higher-resolution features, Cartopy can automatically download and create them from the [Natural Earth Data](http://www.naturalearthdata.com/) database or the [GSHHS dataset](https://www.ngdc.noaa.gov/mgg/shorelines/gshhs.html) database."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Adding vector data to the map\n",
+    "\n",
+    "You will typically have your own data to add to a map, of course. To add vector data, we can add single Shapely geometries with `ax.add_geometries()`, or combine with GeoPandas.\n",
+    "\n",
+    "The GeoPandas `plot()` method works *if*:\n",
+    "\n",
+    "* you pass the GeoAxes object to the `ax=` keyword\n",
+    "* the data is in the same CRS as the cartopy projection OR you specify the corresponding `transform=` in the `plot()` method."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import geopandas\n",
+    "cities = geopandas.read_file(\"data/ne_110m_populated_places.zip\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "proj = ccrs.AlbersEqualArea()\n",
+    "\n",
+    "fig, ax = plt.subplots(figsize=(10, 10), subplot_kw={'projection': proj})\n",
+    "# Reproject the cities dataset to the corresponding CRS of the cartopy projection\n",
+    "cities_albers = cities.to_crs(proj.proj4_init)\n",
+    "cities_albers.plot(ax=ax)\n",
+    "ax.coastlines()\n",
+    "ax.set_global()\n",
+    "ax.gridlines(draw_labels=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "proj = ccrs.AlbersEqualArea()\n",
+    "\n",
+    "fig, ax = plt.subplots(figsize=(10, 10), subplot_kw={'projection': proj})\n",
+    "# cities is in geographic lat/lon data, so we can specify PlateCarree as the cartopy equivalent\n",
+    "cities.plot(ax=ax, transform=ccrs.PlateCarree())\n",
+    "ax.coastlines()\n",
+    "ax.set_global()\n",
+    "ax.gridlines(draw_labels=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Or we can also plot specific points:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "proj = ccrs.AlbersEqualArea()\n",
+    "\n",
+    "fig, ax = plt.subplots(figsize=(10, 10), subplot_kw={'projection': proj})\n",
+    "ax.plot(0, 0, marker='o', color='red', markersize=12, transform=ccrs.PlateCarree())\n",
+    "ax.coastlines()\n",
+    "ax.set_global()\n",
+    "ax.gridlines()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Because our map is a matplotlib axis, we can use all the familiar matplotlib commands to make plots.\n",
+    "By default, the map extent will be adjusted to match the data. We can override this with the `.set_global` or `.set_extent` commands.\n",
+    "\n",
+    "Another example to show the difference of adding a transform or not:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# create some test data\n",
+    "new_york = dict(lon=-74.0060, lat=40.7128)\n",
+    "honolulu = dict(lon=-157.8583, lat=21.3069)\n",
+    "lons = [new_york['lon'], honolulu['lon']]\n",
+    "lats = [new_york['lat'], honolulu['lat']]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Key point: **the data also have to be transformed to the projection space**.\n",
+    "This is done via the `transform=` keyword in the plotting method. The argument is another `cartopy.crs` object.\n",
+    "If you don't specify a transform, Cartopy assume that the data is using the same projection as the underlying GeoAxis.\n",
+    "\n",
+    "From the [Cartopy Documentation](https://scitools.org.uk/cartopy/docs/latest/tutorials/understanding_transform.html)\n",
+    "\n",
+    "> The core concept is that the projection of your axes is independent of the coordinate system your data is defined in. The `projection` argument is used when creating plots and determines the projection of the resulting plot (i.e. what the plot looks like). The `transform` argument to plotting functions tells Cartopy what coordinate system your data are defined in."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig, ax = plt.subplots(subplot_kw={'projection': ccrs.PlateCarree()})\n",
+    "ax.plot(lons, lats, label='Equirectangular straight line')\n",
+    "ax.plot(lons, lats, label='Great Circle', transform=ccrs.Geodetic())\n",
+    "ax.coastlines()\n",
+    "ax.legend()\n",
+    "ax.set_global()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Adding raster data to the map\n",
+    "\n",
+    "The same principles apply to 2D data. Below we create some example data defined in regular lat / lon coordinates."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "lon = np.linspace(-80, 80, 25)\n",
+    "lat = np.linspace(30, 70, 25)\n",
+    "lon2d, lat2d = np.meshgrid(lon, lat)\n",
+    "data = np.cos(np.deg2rad(lat2d) * 4) + np.sin(np.deg2rad(lon2d) * 4)\n",
+    "plt.contourf(lon2d, lat2d, data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we create a `PlateCarree` projection and plot the data on it without any `transform` keyword.\n",
+    "This happens to work because `PlateCarree` is the simplest projection of lat / lon data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig, ax = plt.subplots(subplot_kw={'projection': ccrs.PlateCarree()})\n",
+    "ax.set_global()\n",
+    "ax.coastlines()\n",
+    "ax.contourf(lon, lat, data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "However, if we try the same thing with a different projection, we get the wrong result."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "projection = ccrs.RotatedPole(pole_longitude=-177.5, pole_latitude=37.5)\n",
+    "fig, ax = plt.subplots(subplot_kw={'projection': projection})\n",
+    "ax.set_global()\n",
+    "ax.coastlines()\n",
+    "ax.contourf(lon, lat, data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To fix this, we need to pass the correct transform argument to `contourf`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "projection = ccrs.RotatedPole(pole_longitude=-177.5, pole_latitude=37.5)\n",
+    "fig, ax = plt.subplots(subplot_kw={'projection': projection})\n",
+    "ax.set_global()\n",
+    "ax.coastlines()\n",
+    "ax.contourf(lon, lat, data, transform=ccrs.PlateCarree())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Xarray Integration\n",
+    "\n",
+    "Cartopy transforms can be passed to xarray! This creates a very quick path for creating professional looking maps from netCDF data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from urllib.request import urlretrieve\n",
+    "\n",
+    "file_path_data_nc = \"./data/20190101120000-REMSS-L4_GHRSST-SSTfnd-MW_IR_OI-GLOB-v02.0-fv05.0.nc\"\n",
+    "urlretrieve(\"https://data.remss.com/SST/daily/mw_ir/v05.0/netcdf/2019/20190101120000-REMSS-L4_GHRSST-SSTfnd-MW_IR_OI-GLOB-v02.0-fv05.0.nc\", file_path_data_nc)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import xarray as xr\n",
+    "ds = xr.open_dataset(file_path_data_nc)\n",
+    "ds"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sst = ds[\"analysed_sst\"]\n",
+    "\n",
+    "fig, ax = plt.subplots(subplot_kw={'projection': ccrs.Robinson()}, figsize=(9,6))\n",
+    "ax.coastlines()\n",
+    "ax.gridlines()\n",
+    "sst.plot(ax=ax, transform=ccrs.PlateCarree(), \n",
+    "         cbar_kwargs={'shrink': 0.4}, cmap=\"RdBu_r\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Add OGC WMS and WMTS services to cartopy"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A lot of online map services are available, which are capable of providing background layers to a map similar as these are used in online applications. The [OGC WMS/WMTS](https://www.ogc.org/standards/wms) (Web Map Service) are important standards by which a lot of map layers are made available.\n",
+    "\n",
+    "A (non-official, but very comprehensive) list of such services for Belgium is made available by Michel Stuyts, see https://wms.michelstuyts.be. It is always a matter of finding the correct URL and the `layer_name` for a given service.\n",
+    "\n",
+    "As an example, consider the Orthophoto WMS (provided by the National Geographic Institute):\n",
+    "\n",
+    "- WMS: https://wms.ngi.be/inspire/ortho/service\n",
+    "- GetCapabilities: https://wms.ngi.be/inspire/ortho/service?REQUEST=GetCapabilities&SERVICE=WMS\n",
+    "\n",
+    "With the following layers available (see website Stuyts or GetCapabilities overview):\n",
+    "\n",
+    "- orthoimage_coverage: Orthoimage coverage\n",
+    "- orthoimage_coverage_2016: Orthoimage coverage - 2016\n",
+    "- orthoimage_coverage_2018: Orthoimage coverage - 2018\n",
+    "\n",
+    "Let's use the `orthoimage_coverage` in combination with our city of Ghent contours data set (__Note__ the WMS service might sometimes return server errors so retry if required):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "base_uri = 'https://wms.ngi.be/inspire/ortho/service'\n",
+    "layer_name = 'orthoimage_coverage'\n",
+    "\n",
+    "plain_crs = ccrs.Mercator()\n",
+    "fig, ax = plt.subplots(subplot_kw={\"projection\": plain_crs}, figsize=(10, 10))\n",
+    "ax.set_extent([3.57, 3.85, 50.98, 51.19], crs=ccrs.PlateCarree())\n",
+    "\n",
+    "# Add WMS imaging.\n",
+    "ax.add_wms(base_uri, layers=layer_name)\n",
+    "\n",
+    "# Add city of Ghent boundary from file\n",
+    "gent = geopandas.read_file(\"./data/gent/vector/gent.geojson\")\n",
+    "gent = gent.to_crs(plain_crs.proj4_init)  # adjust to projection\n",
+    "gent.plot(ax=ax, facecolor=\"none\", edgecolor=\"white\", linewidth=2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Someone interested in flood control, might want to know the sensitive areas for inundation in this area. Looking in the available services, https://inspirepub.waterinfo.be/arcgis/rest/services/watertoets/watertoets_pluviaal/MapServer/WMTS/1.0.0/WMTSCapabilities.xml has the available layer `watertoets_watertoets_pluviaal` (dutch for rain-induced zones).\n",
+    "\n",
+    "Hence, we can combine this WMTS layer into the map as well:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plain_crs = ccrs.Mercator()\n",
+    "fig, ax = plt.subplots(subplot_kw={\"projection\": plain_crs}, figsize=(10, 10))\n",
+    "ax.set_extent([3.57, 3.85, 50.98, 51.19], crs=ccrs.PlateCarree())\n",
+    "\n",
+    "# Add WMS imaging of ortophoto.\n",
+    "base_uri = 'https://wms.ngi.be/inspire/ortho/service'\n",
+    "layer_name = 'orthoimage_coverage'\n",
+    "ax.add_wms(base_uri, layers=layer_name)\n",
+    "\n",
+    "# Add WMTS of sensitive areas for inundation.\n",
+    "base_uri = \"https://inspirepub.waterinfo.be/arcgis/rest/services/watertoets/watertoets_fluviaal/MapServer/WMTS\"\n",
+    "layer_name = 'watertoets_watertoets_fluviaal'\n",
+    "ax.add_wmts(base_uri, layer_name=layer_name, alpha=0.8)\n",
+    "\n",
+    "# Add city of Ghent boundary from file\n",
+    "gent = geopandas.read_file(\"./data/gent/vector/gent.geojson\")\n",
+    "gent = gent.to_crs(plain_crs.proj4_init)  # adjust to projection\n",
+    "gent.plot(ax=ax, facecolor=\"none\", edgecolor=\"#433d78\", linewidth=2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### (optional) How the add a satellite image manually\n",
+    "\n",
+    "When a (satellite) image does not contain the spatial information, we can still plot the image on a map if we know its extent"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from urllib.request import urlretrieve"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "file_path_data_rgb = \"./data/RGB.byte.tif\"\n",
+    "urlretrieve(\"https://github.com/rasterio/rasterio/raw/main/tests/data/RGB.byte.tif\", file_path_data_rgb)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Read a tiff file as an image without spatial context to illustrate the concept:\n",
+    "da = xr.open_dataarray(file_path_data_rgb, engine=\"rasterio\", mask_and_scale=False)\n",
+    "\n",
+    "# DataArray as a 3-D array/image as used for viz-libraries (channel dim at the end)\n",
+    "img = np.transpose(da.data, [1, 2, 0])\n",
+    "\n",
+    "# extract the extent from the bounds\n",
+    "(left, bottom, right, top) = da.rio.bounds()\n",
+    "img_extent = (left, right, bottom, top)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "proj = ccrs.UTM(zone=18, southern_hemisphere=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "By providing the `extent` and the `proj` information to Matplotlib/Cartopy, the image can be visualized correctly:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(8, 12), subplot_kw={'projection': proj})\n",
+    "\n",
+    "ax.imshow(img, extent=img_extent, transform=proj)\n",
+    "ax.coastlines(resolution='10m', color='red', linewidth=1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "__Note__ This is what xarray is doing under the hood to pass rio/spatial arrays to Matplotlib;"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Doing More\n",
+    "\n",
+    "Browse the [Cartopy Gallery](https://cartopy.readthedocs.io/stable/gallery/index.html) to learn about all the different types of data and plotting methods available!"
+   ]
+  }
+ ],
+ "metadata": {
+  "jupytext": {
+   "default_lexer": "ipython3"
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.11"
+  },
+  "widgets": {
+   "application/vnd.jupyter.widget-state+json": {
+    "state": {},
+    "version_major": 2,
+    "version_minor": 0
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/notebooks/visualization-04-interactive.ipynb b/notebooks/visualization-04-interactive.ipynb
new file mode 100644
index 0000000..70835a0
--- /dev/null
+++ b/notebooks/visualization-04-interactive.ipynb
@@ -0,0 +1,390 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<p><font size=\"6\"><b>Interactive or web-based visualization</b></font></p>\n",
+    "\n",
+    "\n",
+    "> *DS Python for GIS and Geoscience*  \n",
+    "> *October, 2025*\n",
+    ">\n",
+    "> *© 2025, Joris Van den Bossche and Stijn Van Hoey. Licensed under [CC BY 4.0 Creative Commons](https://creativecommons.org/licenses/by/4.0/)*\n",
+    "\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In the course, we used the Matplotlib library for all visualizations. This is the library that is most widely used and integrated into the other packages (e.g. the plotting functionality of pandas, geopandas, xarray, rasterio, etc all use matplotlib), and is still the \"go to\" library for making high-quality, publication graphics. \n",
+    "\n",
+    "There are, however, a bunch of alternatives to matplotlib, mainly focusing on providing more interactivity and based on web (javascript) tools. To many such packages exist to cover in this course, but we list here a few that can handle (to some extent) geospatial data:\n",
+    "\n",
+    "- Bokeh: https://bokeh.pydata.org/en/latest/docs/gallery/texas.html\n",
+    "- GeoViews (other interface to Bokeh/matplotlib): http://geo.holoviews.org\n",
+    "- Altair: https://altair-viz.github.io/gallery/index.html#maps\n",
+    "- Plotly: https://plot.ly/python/#maps\n",
+    "- ...\n",
+    "\n",
+    "\n",
+    "Altair and Plotly are mostly useful for vector data. Using bokeh through holoviews with raster data is shown below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import geopandas\n",
+    "\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "countries = geopandas.read_file(\"data/ne_110m_admin_0_countries.zip\")\n",
+    "countries = countries[(countries['pop_est'] >0 ) & (countries['name'] != \"Antarctica\")]\n",
+    "countries['gdp_per_cap'] = countries['gdp_md_est'] / countries['pop_est'] * 100\n",
+    "cities = geopandas.read_file(\"data/ne_110m_populated_places.zip\")\n",
+    "rivers = geopandas.read_file(\"data/ne_50m_rivers_lake_centerlines.zip\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Vector data with Leaflet"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Another popular javascript library for online maps is [Leaflet.js](https://leafletjs.com/), and this has python bindings in the [folium](https://github.com/python-visualization/folium) and [ipyleaflet](https://github.com/jupyter-widgets/ipyleaflet) packages."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "An example with ipyleaflet:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import ipyleaflet"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "m = ipyleaflet.Map(center=[48.8566, 2.3429], zoom=6)\n",
+    "\n",
+    "layer = ipyleaflet.GeoJSON(data=cities.__geo_interface__)\n",
+    "m.add_layer(layer)\n",
+    "m"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "m = ipyleaflet.Map(center=[48.8566, 2.3429], zoom=3)\n",
+    "geo_data = ipyleaflet.GeoData(\n",
+    "    geo_dataframe = countries,\n",
+    "    style={'color': 'black', 'fillColor': '#3366cc', 'opacity':0.05, 'weight':1.9, 'dashArray':'2', 'fillOpacity':0.6},\n",
+    "    hover_style={'fillColor': 'red' , 'fillOpacity': 0.2},\n",
+    "    name = 'Countries')\n",
+    "m.add_layer(geo_data)\n",
+    "m"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "More: https://ipyleaflet.readthedocs.io/en/latest/api_reference/geodata.html"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "An example with folium:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import folium"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "m = folium.Map([48.8566, 2.3429], zoom_start=6, tiles=\"OpenStreetMap\")\n",
+    "folium.GeoJson(countries).add_to(m)\n",
+    "folium.GeoJson(cities).add_to(m)\n",
+    "m"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "m = folium.Map([0, 0], zoom_start=1)\n",
+    "folium.Choropleth(geo_data=countries, data=countries, columns=['iso_a3', 'gdp_per_cap'],\n",
+    "             key_on='feature.properties.iso_a3', fill_color='BuGn', highlight=True).add_to(m)\n",
+    "m"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-info\" style=\"font-size:120%\">\n",
+    "\n",
+    "**NOTE**: <br>\n",
+    "\n",
+    "Making a quick interactive plot is also available as the `.explore()` method on a GeoDataFrame or GeoSeries, using the Folium package. See the [visualization-04-interactive notebook](./visualization-04-interactive.ipynb) for more information on interactive plotting packages or check the [GeoPandas documentation](https://geopandas.org/en/stable/docs/user_guide/interactive_mapping.html) for more examples.\n",
+    "\n",
+    "</div>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Vector data with Lonboard"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Lonboard is a Python library for fast, interactive geospatial vector data visualization in Jupyter. Compared to Leaflet (shown above), it is especially useful when you want to explore larger datasets.\n",
+    "\n",
+    "https://developmentseed.org/lonboard/latest/"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import lonboard"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lonboard.viz(cities)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Using Holoviews for raster data with xarray"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The Holoviews project (http://holoviews.org/) is a set of packages built on top of Bokeh, and which integrate with many of the other packages (pandas, GeoPandas, xarray) through the `hvplot` package (https://hvplot.holoviz.org/) and [GeoViews](https://geoviews.org/).\n",
+    "\n",
+    "Using the Gent example Sentinel data to showcase hvplot integration with xarray:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import xarray as xr\n",
+    "\n",
+    "import hvplot.xarray\n",
+    "import cartopy.crs as ccrs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gent = xr.open_dataarray(\"./data/gent/raster/2020-09-17_Sentinel_2_L1C_B0408.tiff\", engine=\"rasterio\", mask_and_scale=False)\n",
+    "gent = gent.assign_coords(band=(\"band\", [\"b4\", \"b8\"]))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Using the default xarray Matplotlib integration with the `.plot.imshow` method:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gent.sel(band=\"b4\").plot.imshow(robust=True, cmap=\"summer\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The equivalent method in hvplot is `.hvplot.image` (and linking the coordinate names):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gent.sel(band=\"b4\").hvplot.image(x=\"x\", y=\"y\", cmap=\"summer\", \n",
+    "                                 clim=(0.05, 0.2))  # 'robust' option is not available"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "hvplot/geoviews translates multiple dimensions (in this case the bands) automatically in a drop-down menu:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gent.hvplot.image(x=\"x\", y=\"y\", cmap=\"summer\", # groupby=\"band\" # adding groupby makes this explicit instead of 'automagically'\n",
+    "                  clim=(0.05, 0.2))  # 'robust' option is not available"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Similar to matplotlib plots using cartopy (see notebook `visualization-03-cartopy.ipynb`), the CRS information can be used.\n",
+    "\n",
+    "From https://hvplot.holoviz.org/user_guide/Geographic_Data.html#declaring-an-output-projection:\n",
+    "\n",
+    "> The `crs=` argument specifies the input projection, i.e. it declares how to interpret the incoming data values. You can independently choose any output projection, i.e. how you want to map the data points onto the screen for display, using the `projection=` argument."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gent.rio._crs = False  # small bug in hvplot's handling of rioxarray crs\n",
+    "gent.hvplot.image(x=\"x\", y=\"y\", cmap=\"summer\", \n",
+    "                  frame_height=400, \n",
+    "                  crs=ccrs.epsg(3857),\n",
+    "                  projection=ccrs.UTM(zone=31), \n",
+    "                  project=True, geo=True,\n",
+    "                  clim=(0.05, 0.2))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "From https://hvplot.holoviz.org/user_guide/Geographic_Data.html#declaring-an-output-projection:\n",
+    "\n",
+    "> Note that when displaying raster data in a projection other than the one in which the data is stored, it is more accurate to render it as a quadmesh rather than an image. As you can see above, a QuadMesh will project each original bin or pixel into the correct non-rectangular shape determined by the projection, accurately showing the geographic extent covered by each sample. An Image, on the other hand, will always be rectangularly aligned in the 2D plane, which requires warping and resampling the data in a way that allows efficient display but loses accuracy at the pixel level. Unfortunately, rendering a large QuadMesh using Bokeh can be very slow"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gent.hvplot.quadmesh(x=\"x\", y=\"y\", cmap=\"summer\", \n",
+    "                     frame_height=400, \n",
+    "                     crs=ccrs.epsg(3857), \n",
+    "                     projection=ccrs.UTM(zone=31), \n",
+    "                     project=True, geo=True,\n",
+    "                     clim=(0.05, 0.2))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "See https://hvplot.holoviz.org/user_guide/Geographic_Data.html#declaring-an-output-projection for more options."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "jupytext": {
+   "default_lexer": "ipython3"
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/setup.md b/setup.md
deleted file mode 100644
index c184689..0000000
--- a/setup.md
+++ /dev/null
@@ -1,193 +0,0 @@
----
-layout: default
----
-
-# Course setup
-
-To get started, you should have the following three elements setup:
-
-1. Install Python and the required Python packages
-2. Download the course material to your computer
-3. Test your configuration and installation
-4. Start Jupyter lab
-
-In the following sections, more details are provided for each of these steps. When all three are done, you are ready to start coding!
-
-## 1. Install Python and the required Python packages
-
-For scientific and data analysis, we recommend to use Anaconda (or Miniconda) (<https://www.anaconda.com/download/>), which provides a Python distribution that includes the scientific libraries (this recommendation applies to all platforms, so for both Windows, Linux and Mac), instead of installing Python as such. After installation, proceed with the setup.
-
-### Install Anaconda
-
-#### Option 1: I do not have Anaconda installed
-
-For first time users and people not fully confident with using the command line, we advise to install Anaconda, by downloading and installing the Python 3.x version from <https://www.anaconda.com/download/>. Recent computers will require the 64-Bit installer.
-
-For more detailed instructions to install Anaconda, check the [Windows](https://docs.anaconda.com/anaconda/install/windows/), [Mac](https://docs.anaconda.com/anaconda/install/mac-os/) or [linux](https://docs.anaconda.com/anaconda/install/linux/) installation tutorial.
-
-**Note:** When you are already familiar with the command line and Python environments you could opt to use Miniconda instead of Anaconda and download it from <https://conda.io/miniconda.html>. The main difference is that Anaconda provides a graphical user interface (Anaconda navigator) and a whole lot of scientific packages (e.g <https://docs.anaconda.com/anaconda/packages/py3.8_win-64/>) when installing, whereas for Miniconda the user needs to install all packages using the command line. On the other hand, Miniconda requires less disk space.
-
-#### Option 2: I have installed Anaconda earlier
-
-When you already have an installation of Anaconda, you have to make sure you are working with the most recent versions. As the course is developed for Python 3, make sure you have Anaconda3 (on Windows, check Start > Programs > Anaconda3). If not, reinstall Anaconda according to the previous section.
-
-Start the Anaconda Navigator program (for Windows users: Start > Anaconda Navigator) and go to the Environments tab. You should see the *base (root) environment*, click the arrow next to it and click `Open terminal`, as shown in the following figure:
-
-![Navigator terminal](./img/navigator_terminal.png)
-
-Type following command + ENTER-button (make sure you have an internet connection):
-
-```
-conda update -n root conda
-```
-
-when finished, type (+ ENTER-button):
-
-```
-conda update --all
-```
-
-and respond with *Yes* by typing `y`. Packages should be updated after the completion of the command.
-
-### Setup after installation
-
-As not all packages we will use in the course are provided by default as part of Anaconda, we have to add the package to Anaconda to get started. As a good practice, we will create a new _conda environment_ to work with. This environment will contain the required packages on which this course depends.
-
-The packages used in the course are enlisted in an [`environment.yml` file](https://raw.githubusercontent.com/jorisvandenbossche/DS-python-geospatial/master/environment.yml). The file looks as follows:
-
-```
-name: DS-geospatial
-channels:
-  - conda-forge
-  - defaults
-dependencies:
-  - python=3.7
-  - geopandas
-  - ...
-```
-
-The file contains information on:
-- `name` is the name used for the environment
-- `channels` to define where to download the packages from
-- `dependencies` contains each of the packages
-
-To download the environment file, click to go to the [environment.yml](https://raw.githubusercontent.com/jorisvandenbossche/DS-python-geospatial/master/environment.yml) online. Once opened in the browser, right-click and save the file/page on your computer. The specific text depends on your browser (`Save page as...`, `Save as...`).
-
-![](./img/environment_save.png)
-
-You will need the folder/directory containing the `environment.yml` file in the next step. Make sure you know where you stored the file on your computer, e.g. when stored in the folder `C:/Users/yourusername/Documents` you should see the file `environment.yml` in File Explorer in that directory.
-
-Next, start the Anaconda Navigator program (for windows users: Start > Anaconda Navigator) and go to the Environments tab. You should see the *base (root) environment*, click the arrow next to it and click `Open terminal`, as shown in the following figure:
-
-![Navigator terminal](./img/navigator_terminal.png)
-
-Type following commands line by line + ENTER-button (make sure you have an internet connection):
-
-```
-conda config --add channels conda-forge
-conda config --set channel_priority strict
-cd FOLDER_PATH_TO_ENVIRONMENT_FILE
-conda env create -f environment.yml
-```
-
-__!__ `FOLDER_PATH_TO_ENVIRONMENT_FILE` should be replaced by the path to the folder containing the downloaded environment file. In the example earlier, this was `C:/Users/yourusername/Documents`, but make sure you use your specific folder (as seen in File Explorer).
-
-Respond with *Yes* by typing `y` when asked. Output will be printed and if no error occurs, you should have the environment configured with all packages installed.
-
-**Note:** If you did use Miniconda instead, create the environment using the same commands/instructions in the terminal (make sure to do the `conda config ...` steps.).
-
-When finished, keep the terminal window open (or reopen it). Execute the following commands to check your installation:
-
-```
-conda activate DS-geospatial
-ipython
-```
-
-Within the terminal, a Python session will be started in which you can start writing Python! Type the following command:
-
-```
-import geopandas
-```
-
-If no message is returned, you're all set! If a message (probably an error) returned, contact the instructors. Copy paste the message returned.
-
-
-## 2. Getting the course materials
-
-### Option 1: You are a git user?
-
-As the course has been setup as a [git](https://git-scm.com/) repository managed on [Github](https://github.com/jorisvandenbossche/DS-python-geospatial), you can clone the entire course to your local machine:
-
-Use the command line to clone the repository and go into the course folder:
-
-```
-> git clone https://github.com/jorisvandenbossche/DS-python-geospatial.git
-> cd DS-python-geospatial
-```
-
-In case you would prefer using Github Desktop, see [this tutorial](https://help.github.com/desktop/guides/contributing-to-projects/cloning-a-repository-from-github-to-github-desktop/).
-
-### Option 2: You are not a git user?
-
-To download the repository to your local machine as a zip-file, click the  `download ZIP` on the repository page <https://github.com/jorisvandenbossche/DS-python-geospatial> (green button "Code"):
-
-![Download button](./img/download-button.png)
-
-After the download, unzip on the location you prefer within your user account (e.g. `My Documents`, not `C:\`).
-
-__Note:__ Make sure you know where you stored the course material, e.g. `C:/Users/yourusername/Documents/DS-python-geospatial`
-
-### 3. Test your configuration
-
-To check if your packages are properly installed, open the Conda Terminal again (see above) and navigate to the course directory:
-
-```
-cd FOLDER_PATH_TO_COURSE_MATERIAL
-```
-
-With `FOLDER_PATH_TO_COURSE_MATERIAL` replaced by the path to the folder with the downloaded course material (e.g. in the example it is `C:/Users/yourusername/Documents/DS-python-geospatial`).
-
-Activate the newly created conda environment:
-
-```
-conda activate DS-geospatial
-```
-
-Then, run the `check_environment.py` script:
-
-```
-python check_environment.py
-```
-
-When all is green, you're ready to go!
-
-
-## 4. Starting Jupyter Notebook with Jupyter Lab
-
-Each of the course modules is set up as a [Jupyter notebook](http://jupyter.org/), an interactive  environment to write and run code. It it no problem if you never used jupyter notebooks before as an introduction to notebooks is part of the course.
-
-### Option 1: Using the command line
-
-* In the terminal, navigate to the `DS-python-geospatial` directory (downloaded or cloned in the previous section)
-
-* Ensure that the correct environment is activated.
-
-  ```
-  conda activate DS-geospatial
-  ```
-
-* Start a jupyter notebook server by typing
-
-  ```
-  $ jupyter lab
-  ```
-
-### Option 2: Using Anaconda Navigator
-
-In the Anaconda Navigator *Home* tab, first switch to the course environment, called `DS-geospatial` in the selection bar. Next, select the Launch button under the Jupyter Lab icon:
-
-![Navigator terminal](./img/navigator_notebook.png)
-
-## Next?
-
-This will open a browser window automatically. Navigate to the course directory (if not already there) and choose the `notebooks` folder to access the individual notebooks containing the course material.
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-    <textarea id="source">
-
-class: center, middle
-
-# Python for GIS and Geoscience
-
-Specialist course Doctoral schools of Ghent University,<br>
-5-6-8 October 2020
-
-Joris Van den Bossche, Stijn Van Hoey
-
-https://github.com/jorisvandenbossche/DS-python-geospatial
-
----
-
-### Joris Van den Bossche
-
-<a href="https://twitter.com/jorisvdbossche"><i class="fa fa-twitter" aria-hidden="true"></i> jorisvdbossche</a>,
-<a href="https://github.com/jorisvandenbossche"><i class="fa fa-github" aria-hidden="true"></i> jorisvandenbossche</a><br>
-
-* Open source software developer and teacher
-* Pandas, GeoPandas, scikit-learn, Apache Arrow
-
----
-
-### Stijn Van Hoey
-
-<a href="https://twitter.com/svanhoey"><i class="fa fa-twitter" aria-hidden="true"></i> SVanHoey</a>,
-<a href="https://github.com/stijnvanhoey"><i class="fa fa-github" aria-hidden="true"></i> stijnvanhoey</a>
-
-* Research software engineer
-
-.center[
-![:scale 90%](./img/work_stijn_1.png)]
-
----
-class: middle, section_background
-
-# Setting up a working environment
-
----
-
-## Setting up a working environment
-
-For the setup instructions, see the [setup page](https://jorisvandenbossche.github.io/DS-python-geospatial/setup.html).
-
----
-class: center, middle
-
-When you see something like this...
-
-
-![:scale 100%](./img/startup.png)
-
-...relax, you're ready to start!
-
-
----
-class: center, middle
-
-![:scale 100%](https://i.ytimg.com/vi/PlaYMh-u-2w/maxresdefault.jpg)
-
-
----
-class: center, middle
-
-# Why python?
-
----
-class: center, middle
-
-# Python is...
-
----
-
-## …WIDESPREAD
-* Large world-wide community ensures **fast development**
-* Used in **scientific** and non-scientific institutes <br> (Google, NASA, ESRI, VMM,...)
-* Python is a standard in **GIS** <br> (commercial and non-commercial tools)
-* **Multiplatform** (linux, windows,…)
-
----
-
-## …REPRODUCIBLE
-
-.center[
-
-instead of
-
-> *what did you/I clicked a few months ago?’-style WYSISYG*
-
-]
-
---
-
-## …MULTI-FUNCTIONAL
-
-* Research and development: the python *scientific ecosystem*
-* Webdevelopment (Django, Flask)
-* Daily use: great as ducttape/glue
-* **Automation** of time-consuming returning tasks
-
---
-
-## …OPEN SOURCE
-* No license costs!
-
----
-
-## ...SO WHAT? The Holy Grail ? No!
-* Commercial packages give specific support  and more direct help-functions (cfr. MATLAB)
-* Commercial packages can be more client-friendly
-* For hard number-crunching stuff, NOT the fastest solution
-* R, Fortran, C++ etc are also ‘widespread’, so python is just a language amongst the others
-* Risk of getting lost in the forest/flood of ‘useful packages’
-
----
-
-# Conda
-
-### Why using conda?
-
-- Consistent package manager across Windows, Mac and Linux
-- Many precompiled packages available
-- Less problems with installation
-
---
-
-### Why different environments?
-
-- Manage the dependencies of a specific project/paper/group/...
-- You can install different version of Python and other packages alongside on your computer
-- Easily share environments with other
-
-
----
-## Small overview of conda commands
-
-Creating a new environment:
-
-```
-conda create -n my_env python=3.7 pandas
-
-# or from environment file
-conda env create -f environment.yml
-```
-
-Activating an environment:
-
-```
-conda activate my_env
-```
-
-Install a new package:
-
-```
-conda install matplotlib     # if not working, try: pip install ...
-```
-
-
-List all installed packages: `conda list`
-
-List all your environments: `conda info -e`
-
-See the docs: http://conda.pydata.org/docs/using/index.html
-
----
-class: center, middle
-
-### Keep track of your python ecosystem <br>with an  `environment.yml`
-<br><br>
-
-```
-conda env export > environment.yml
-```
-
----
-class: middle, section_background
-
-# Working with Python
-
----
-
-## IPython console
-
-<br>
-.center[![:scale 75%](./img/ipython.png)]
-
----
-
-## Interactive Development Environment (IDE)
-
-* [**Spyder**](https://pythonhosted.org/spyder/) is shipped with Anaconda. The familiar environment for Matlab/Rstudio-users...
-* [**PyCharm**](https://www.jetbrains.com/pycharm/): Popular for web-development and Django applications, powerful when doing 'real' development (packages, libraries, software)
-* [Eclipse + **pyDev plugin**](http://www.pydev.org/): If you like working in Eclipse, just add the python environment
-* [**VS Code**](https://code.visualstudio.com/), [**Atom**](https://atom.io/), ...
-
----
-
-## Jupyter Notebook
-<small>(*previously called IPython notebook*)</small>
-
-**Jupyter notebook** provides an **interactive** scripting environment,<br> ideal for exploration, prototyping,...
-
-.center[![:scale 70%](./img/notebook.png)]
-
---
-
- ...the stuff we're dealing with in this course!
-
-
----
-class: middle, day_background
-
-# Overview of the first day
-
-...
-
----
-class: middle, center
-
-ask for help
-
-__TODO__
-
----
-class: middle, center
-
-INTRO TO COURSE/CHAT/... SETUP
-
-__TODO__
-
-
----
-class: middle, center
-
-![:scale 80%](./img/issuetracker.png)
-
-Report bugs, typo's, suggestions... as issues ([New issue](https://github.com/jorisvandenbossche/DS-python-geospatial/issues/new))
-
-or see the [contributing guidelines](https://github.com/jorisvandenbossche/DS-python-geospatial/blob/master/CONTRIBUTING.md)
-
-
----
-class: middle, day_background
-
-# Overview of the second day
-
-__TODO__
-
----
-class: middle
-
-## Continue with *case study XX:* TODO
-
----
-class: middle, day_background
-
-# Overview of the third day
-
-__TODO__
-
----
-class: center, middle
-
-# How are you feeling?
-
-![:scale 100%](http://esq.h-cdn.co/assets/15/51/980x490/landscape-1450137389-john-cleese.JPG)
-
-
-### https://forms.gle/S6ZaJrabvRGpsnsRA
-
-__TODO__ ADJUST URL
-
-
-Please fill in the questionnaire!
-
----
-class: center, middle
-
-# Closing notes
-
----
-class: center, middle
-
-# Python's scientific/GIS ecosystem
-
-
-#### ## Adjusted from figure by Jake VanderPlas
-
-__TODO__ Adjust with https://github.com/stijnvanhoey/course_gis_scripting/tree/master/docs/img
-
----
-class: center, middle, bgheader
-background-image: url(./img/ecosystem-gis-overview.svg)
-background-size: cover
-
----
-
-# A rich ecosystem of packages:
-
-<br>
-
-**Machine learning**: scikit-learn, tensorflow, pytorch, keras, chainer, ...
-
-**Performance**: Numba, Cython, Numexpr, Pythran, C/Fortran wrappers, ...
-
-**Visualisation**: Bokeh, Seaborn, Plotnine, Altair, Plotly, Mayavi, HoloViews, datashader, vaex ...
-
-**Data structures and parallel/distributed computation**: Xarray, Dask, Distributed, Cupy, ...
-
-Specialized packages in many **scientific fields**: astronomy, natural language processing, image processing, geospatial, ...
-
-**Packaging and distribution**: pip/wheels, conda, Anaconda, Canopy, ...
-
----
-class: center, middle
-
-### Reading advice
-
-[Good Enough Practices in Scientific Computing](https://arxiv.org/pdf/1609.00037v1.pdf)
-
-> "*However, while most scientists are careful to validate their laboratory and field equipment, most do not know how reliable their software is*"
-
-
----
-class: center, middle
-
-## Good luck!
-
-![](http://www.backwaterreptiles.com/images/pythons/angolan-python-for-sale.jpg)
-
-
-    </textarea>
-<!--    <script src="https://gnab.github.io/remark/downloads/remark-latest.min.js">
-</script>-->
-    <script src="img/remark.min.js" type="text/javascript">
-    </script>
-    <script>
-      var slideshow = remark.create();
-    </script>
-    <script>
-        remark.macros.scale = function (percentage) {
-          var url = this;
-          return '<img src="' + url + '" style="width: ' + percentage + '" />';
-      };
-      var slideshow = remark.create();
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